research about sleeping disorders

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

• Heart-Healthy Living
• High Blood Pressure
• Sickle Cell Disease
• Sleep Apnea
• Information & Resources on COVID-19
• The Heart Truth®
• Learn More Breathe Better®
• Blood Diseases and Disorders Education Program
• Publications and Resources
• Blood Disorders and Blood Safety

Sleep Science and Sleep Disorders

• Lung Diseases
• Health Disparities and Inequities
• Heart and Vascular Diseases
• Precision Medicine Activities
• Obesity, Nutrition, and Physical Activity
• Population and Epidemiology Studies
• Women’s Health
• Research Topics
• Clinical Trials
• All Science A-Z
• Grants and Training Home
• Policies and Guidelines
• Funding Opportunities and Contacts
• Training and Career Development
• Email Alerts
• NHLBI in the Press
• Research Features
• Ask a Scientist
• Past Events
• Upcoming Events
• Mission and Strategic Vision
• Divisions, Offices and Centers
• Advisory Committees
• Budget and Legislative Information
• Jobs and Working at the NHLBI
• Contact and FAQs
• NIH Sleep Research Plan
• < Back To The Science

Research Making a Difference

• Why It's Important
• Key Accomplishments
• Opportunities & Challenges

Advancing the Research

• Related Reading

WHY IT'S IMPORTANT

Getting the right amount of uninterrupted sleep at the right time of day is key to one’s overall health. About 50 to 70 million Americans have sleep or wakefulness disorders. Sleep deficiency and untreated sleep disorders are associated with a growing number of health problems, including heart disease, high blood pressure, stroke, diabetes, obesity, and certain cancers. Sleep disorders can also be costly. Each year sleep disorders, sleep deprivation, and sleepiness add to the national health care bill. Undiagnosed sleep apnea alone is estimated to cost the Nation $150 billion annually. Additional costs to society for related health problems, lost worker productivity, and accidents make sleep disorders a serious public health concern. The NHLBI funds research to discover better ways to promote and achieve sleep health, inform public policy, and increase community awareness to enhance human health.

KEY ACCOMPLISHMENTS

• NHLBI-funded research helped determine that adults who report regularly sleeping 7-8 hours a night have a lower risk of obesity and high blood pressure.
• Our research found that untreated sleep apnea increases the risk for new onset atrial fibrillation.
• NHLBI-research found that untreated sleep apnea also increases the risk for high blood pressure and diabetes during pregnancy.

OPPORTUNITIES & CHALLENGES

Although researchers have learned a lot about sleep and sleep disorders in recent years, important questions remain, such as how sleep and circadian disturbances affect human health and how to best prevent, diagnose, and treat these disorders. In 2016, the NHLBI released its Strategic Vision , which will guide the Institute’s research activities for the coming decade. Many of the objectives, compelling questions, and critical challenges identified in the plan focus on sleep. For example, researchers will be looking at whether changing the time of day (circadian rhythm) when one sleeps, eats, and takes medicines can help improve existing treatments for other diseases, such as high blood pressure, diabetes, and asthma. Training the next generation of sleep scientists is also a high priority for NHLBI.

Partnering on Sleep Research and Health - Sleep Science and Sleep Disorders

NHLBI will continue to work with its partners to translate scientific sleep research discoveries into improved strategies to prevent and treat sleep disorders. NHLBI is committed to working with researchers, health care providers, and public and private organizations to implement the research opportunities outlined in the NIH Sleep Research Plan. Recommended research initiatives include looking at the connection between sleep and the body’s natural circadian rhythm, studying the influence of genetic and environmental factors that could influence a person's sleep health, and conducting more clinical trials to improve treatments for sleep and circadian disorders.

Improving the Sleep Health of our Nation - Sleep Science and Sleep Disorders

Sleep health continues to be a nationwide health improvement priority in Healthy People 2030 . Healthy People provides science-based, national objectives for improving the health of all Americans over a 10-year period. The sleep health goal calls for an increase in public knowledge about how adequate sleep and treatment of sleep disorders can improve health, productivity, wellness, quality of life, and safety on the roads and in the workplace. This will be accomplished by focusing on four objectives:

• Increase the proportion of people with symptoms of obstructive sleep apnea who seek medical evaluation.
• Reduce the rate of vehicular crashes per 100 million miles traveled that are due to drowsy driving.
• Increase the proportion of students in grades 9-12 who get sufficient sleep.
• Increase the proportion of adults who get sufficient sleep.

NHLBI is advancing research and clinical care for people with sleep disorders. Learn more about some of our key efforts related to sleep science and sleep disorders.

We Perform Research

The NHLBI Division of Intramural Research and its Systems Biology Center are studying how genes and the environment influence sleep.

We Fund Research

The NHLBI Division of Lung Diseases is home to the National Center on Sleep Disorders Research , which supports research on sleep and the circadian biology of sleep disorders, including how the body regulates breathing during sleep, how sleep deficiencies affect the whole body, and what biomarkers can help assess sleep health. Other NHLBI Divisions support sleep research, including why sleep deficiency is a risk factor for obesity and some cardiovascular diseases, and how sleep and biology affect blood clotting, the immune system, and blood cell production.

The Promise of Precision Medicine

Through NHLBI’s Trans-Omics for Precision Medicine (TOPMed) program , researchers will use data from studies focused on heart, lung, blood and sleep disorders to better predict, prevent, diagnose, and treat sleep disorders based on a patient’s unique genes, environment, and molecular signatures. Learn more about the NHLBI precision medicine activities .

Coordinating Efforts to Address the Nation's Health Challenges

For more than 25 years, NHLBI’s National Center on Sleep Disorders Research (NCSDR) has supported and coordinated sleep science and disorders research, training, and awareness across NIH, other federal agencies and outside organizations. The center also participates in the translation of new sleep research findings for dissemination to healthcare professionals and the public. Read Celebrating 25 Years of Research to Promote Healthy Sleep to learn more about the Center's legacy in research and initiatives.

Facilitating Discussions on Sleep Disorders

The NHLBI supports the Sleep Disorders Research Advisory Board. Board members, including medical professionals, federal partners, and members of the public, meet regularly to provide feedback on sleep-related research and discuss how to move sleep research forward.

Partnering on Sleep Apnea and Pregnancy Outcomes Research

The NHLBI partnered with the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) in the NuMoM2B study , which found that sleep deficiency and mild sleep apnea in pregnant women increases the risk of pregnancy complications such as preeclampsia and diabetes.  The NHLBI and the NICHD are now studying whether the treatment of sleep apnea during pregnancy reduces these risks.

Collaborating on Sleep Apnea and Diabetes Research

The NHLBI partnered with the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) for the Sleep Ahead study, an offshoot of the Look Ahead study. Sleep Ahead found sleep apnea in over 80 percent of participants who had obesity and type 2 diabetes. It also found that weight loss reduced sleep apnea better than a diabetes education program. These findings have led to new co-sponsored studies with NIDDK, such as TODAY2, to see if sleep apnea affects diabetes medicines.

Providing Sleep Data and Resources for Researchers

The National Sleep Research Resource is an NHLBI resource for the sleep science community. It offers researchers free access to large collections of well-characterized data from completed studies that the NHLBI has funded. These data can be used in new research studies to advance sleep research.

Providing Access to NHLBI Biologic Specimens and Data

The Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) centralizes and integrates biospecimens and clinical data that were once stored in separate repositories. Researchers can find and request available resources on BioLINCC's secure website, which maximizes the value of these resources and advances heart, lung, blood, and sleep research.

Related Health Topics

Related news.

RELATED EVENT

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals

Sleep disorders articles from across Nature Portfolio

Sleep disorders are a group of conditions in which the normal sleep pattern or sleep behaviours are disturbed. Primary sleep disorders include insomnia, hypersomnia, obstructive sleep apnoea and parasomnias (abnormal sleep behaviours, such as sleepwalking and rapid eye movement sleep behaviour disorder).

Latest Research and Reviews

A randomized controlled trial of alpha phase-locked auditory stimulation to treat symptoms of sleep onset insomnia

• Scott Bressler

Genome-wide meta-analyses of restless legs syndrome yield insights into genetic architecture, disease biology and risk prediction

Pooled and sex-specific genome-wide association analyses identify new risk loci for restless legs syndrome and candidates for drug repurposing. Machine learning models combining genetic and other information show improved risk prediction performance.

• Barbara Schormair
• Juliane Winkelmann

Association of obstructive sleep apnea with risk of lung cancer: a nationwide cohort study in Korea

• Jaeyoung Cho

FDA-cleared home sleep apnea testing devices

• Ji Hyeun Park
• Changwon Wang
• Hangsik Shin

Quantum mechanics insights into melatonin and analogs binding to melatonin MT 1 and MT 2 receptors

• Gabriela de Lima Menezes
• Katyanna Sales Bezerra
• Umberto Laino Fulco

Generalized sleep decoding with basal ganglia signals in multiple movement disorders

• Jianguo Zhang

News and Comment

Pineal gland denervation derails sleep in heart disease.

• Felicity Tournant

Heart disease causes sleep disturbances via neuroimmune mechanisms

A new study reveals that the disrupted sleep patterns that are frequently observed in patients with cardiac disease are driven by immune-mediated sympathetic denervation and dysfunction of the pineal gland, which leads to a decrease in the circulating levels of melatonin and subsequent sleep disruption.

• Irene Fernández-Ruiz

Sweet dreams are made of these?

A novel orexin receptor antagonist improved sleep quality in people with insomnia, without residual effects on daytime functioning.

• Karen O’Leary

Cortical blood flow associated with cognitive decline in REM sleep behaviour disorder

Prodromal parkinson disease — time is brain.

Biomarkers that predict conversion from isolated REM sleep behaviour disorder to Parkinson disease are urgently needed. A new study finds that detection of misfolded α-synuclein in the cerebrospinal fluid is a good marker of conversion risk, but an inability to predict the timeline of progression might limit its utility.

• Sandrina Weber
• Brit Mollenhauer

Decreased NREM sleep linked with tauopathy

• Rebecca Kelsey

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Follow us on social media!

• See us on facebook
• See us on instagram

Research in the Division of Sleep Medicine

Research labs.

The Giardino Laboratory

The Giardino Lab, led by Dr​. William Giardino, aims to discover the neural mechanisms underlying psychiatric conditions of anxiety, addiction, and sleep disturbances. Their team uses advanced technologies to precisely map, monitor, and manipulate the emotional brain circuits that drive motivated behaviors, with the primary goal of identifying novel therapeutic approaches for treating mental health challenges.

For more information

Erin Gibson Lab

How do glia sculpt neural circuits and how does dysregulation of glia contribute to disease? Glia make up more than half of the cells in the human brain, but we are just beginning to understand the complex and multifactorial role glia play in health and disease. Glia are decidedly dynamic in form and function. Understanding the mechanisms underlying the dynamic nature of glia is imperative to developing novel therapeutic strategies for diseases of the nervous system that involve aberrant gliogenesis.

The Gibson Lab studies the cellular and molecular mechanisms modulating glia. One molecular mechanism that affords cells a dynamical nature is the circadian clock. While much is known about how the circadian clock influences neurons and peripheral cells throughout the body, little is known about how this core molecular mechanism regulates glia. We study how the circadian clock system regulates glial function to better understand diseases of the nervous system in which both circadian/sleep and glial dysfunction are prominent, such as autism, multiple sclerosis, and chemotherapy-related cognitive impairment.

Seiji Nishino Lab

Our research focuses on understanding the etiology and pathophysiology of human sleep and circadian disorders using various animal models. We are especially interested in hypersomnia with various etiology. We also research and develop new sleep sensing technologies for humans and animals.

Jamie Zeitzer Lab

Dr. Zeitzer studies the development of human centric lighting as a countermeasure to the ills of the 24-hour society, and the biological underpinnings of sleep quality -- in essence, why we sleep.

Computational Psychiatry, Neuroimaging, and Sleep Lab

The CoPsyN Sleep lab, led by Dr. Andrea Goldstein-Piekarski, utilizes human neuroimaging, high density EEG, computational methods, and clinical psychology to examine the role of sleep physiology in the development, maintenance, and treatment of psychopathology across the lifespan. A primary goal of this research is to identify novel sleep and neuroimaging related biomarkers of treatment response that could be used to better match patients to effective treatments.

Emmanuel Mignot Lab

The Mignot lab uses proteomics and genetics to further the understanding of human sleep and sleep disorders, notably narcolepsy. In one part of the lab, we are focusing on the generation and analysis of data generated from a study called the Stanford Technology Analytics and Genomics of Sleep (STAGES).  In the STAGES study we are collecting actigraphy, 3D facial morphometry, neurocognitive testing, subjective sleep questionnaire data and objective sleep EEG studies in 30,000 participants together with genetic and protein biomarker data. Analyses involve classic statistics and machine learning of polysomnography, clinical and biological data.

In the second part of the lab, we are focusing on more targeted clinical, pathophysiological and genetic studies of sleep disorders involving excessive daytime sleepiness, such as narcolepsy, hypersomnia and Kleine Levin syndrome. Narcolepsy studies in particular are the most advanced and are focusing on the autoimmune basis of type 1 narcolepsy through the study of T cell biology.

Sergiu Pasca Lab

The Pasca lab is interested in understanding the molecular and cellular mechanisms of neuropsychiatric disorders. We are using pluripotent stem cells (iPS cells) derived non-invasively from patients to generate in a dish specific regions of the human brain in a functional 3D structures known as organoids or assembloids, and employ live imaging, electrophysiology and other state-of-the-art technologies to identify disease phenotypes.

Researchers

Dr. Emmanuel During

Dr. During is conducting research on REM sleep behavior disorder (RBD), a condition that precedes Parkinson’s disease by several years and leads to abnormal dream-enactment during REM. He is the PI of a trial investigating a new drug in  treatment-refractory RBD  (NCT04006925), and serves as site PI for a NIH grant supporting a nation-wide consortium, the  North American Prodromal Synucleinopathy Consortium (NAPS) , laying the groundwork for a first neuroprotective trial in RBD. His most recent research interest pertains to wearable devices that can potentially monitor RBD activity in patients home environment and facilitate early diagnosis.

Dr. Clete Kushida

The Stanford Center for Human Sleep Research conducts clinical trials that improve ways to treat and manage sleep disorders.  These studies aim to increase the safety and effectiveness of current and novel applications of sleep medicine, and to improve the quality of life of the greater populations of individuals with sleep disorders.

Dr. Makoto Kawai

Dr. Kawai is a physician scientist in the field of sleep medicine in aging and brain function. Using combined polysomnogram and novel neuroimaging technology, he aims to identify potential sleep biomarkers to investigate the mechanism of progression from normal aging to Mild Cognitive Impairment (MCI) or dementia. Additionally, he investigates the impact of sleep on cognitive/affective function or behavior abnormality in various neurodevelopmental and neurodegenerative disorders.

Dr. Mitchell Miglis

Dr. Miglis is a member of the International REM sleep behavior disorder (RBD) study group and is currently the Principal Investigator of an international NIH funded trial evaluating the risk of neurodegenerative disease in patients with RBD and autonomic failure . He is also lead investigator of a study evaluating the presence of autonomic dysfunction in Idiopathic Hypersomnia and the presence of sleep and autonomic disorders in patients with Ehlers-Danlos syndrome. 

Sleep and sleep disorders

Sleep is essential for health and well-being. But millions of people don’t get enough, resulting in such problems as daytime sleepiness, poor decision-making, interference with learning and accidents. Cognitive-behavioral therapy, which helps people identify and change their thoughts and behaviors, can help.

Adapted from Why sleep is important and what happens when you don’t get enough

Resources from APA

Spotlight: Relationship of nightmares with waking variables

A special section in this APA Journals Article Spotlight in Dreaming, explores the complex relationship between waking variables and nightmares and what these disturbing dreams can reveal about dreamers’ waking lives.

Sleep deprivation and racial bias in the decision to shoot: A diffusion model analysis

An experimental study of sleep deprivation and racial bias with implications for policing

In brief: Depression and heart disease, fake news, and more research

The latest peer-reviewed studies within psychology and related fields

Diagnosing and treating sleep disorders

Psychologists have a leading role to play in treating insomnia and other common sleep disturbances

More resources about sleep

Pediatric Sleep Problems

Mindfulness-Based Therapy for Insomnia

Sleepwalking, Criminal Behavior, and Reliable Scientific Evidence

Magination Press children’s books

What to Do When You Dread Your Bed

Max Archer: The Case of the Wet Bed

Sleepy Time

Good Night Giants

Journals and special issues

Save 45% on a Home Sleep Test –  Shop Now

Sleep Disorders

What they are, their causes and symptoms, and how people with sleep disorders can get relief

Danielle Pacheco

Staff Writer

Danielle is originally from Vancouver, BC, where she has spent many hours staring at her ceiling trying to fall asleep. Danielle studied the science of sleep with a degree in psychology at the University of British Columbia

Want to read more about all our experts in the field?

Medically Reviewed by

Dr. Anis Rehman

Internal Medicine Physician

Dr. Rehman, M.D., is a board-certified physician in Internal Medicine as well as Endocrinology, Diabetes, and Metabolism.

Sleep Foundation

Fact-Checking: Our Process

The Sleep Foundation editorial team is dedicated to providing content that meets the highest standards for accuracy and objectivity. Our editors and medical experts rigorously evaluate every article and guide to ensure the information is factual, up-to-date, and free of bias.

The Sleep Foundation fact-checking guidelines are as follows:

• We only cite reputable sources when researching our guides and articles. These include peer-reviewed journals, government reports, academic and medical associations, and interviews with credentialed medical experts and practitioners.
• All scientific data and information must be backed up by at least one reputable source. Each guide and article includes a comprehensive bibliography with full citations and links to the original sources.
• Some guides and articles feature links to other relevant Sleep Foundation pages. These internal links are intended to improve ease of navigation across the site, and are never used as original sources for scientific data or information.
• A member of our medical expert team provides a final review of the content and sources cited for every guide, article, and product review concerning medical- and health-related topics. Inaccurate or unverifiable information will be removed prior to publication.
• Plagiarism is never tolerated. Writers and editors caught stealing content or improperly citing sources are immediately terminated, and we will work to rectify the situation with the original publisher(s)
• Although Sleep Foundation maintains affiliate partnerships with brands and e-commerce portals, these relationships never have any bearing on our product reviews or recommendations. Read our full Advertising Disclosure for more information.

The collective term sleep disorder refers to conditions that affect sleep quality, timing, or duration and impact a person’s ability to properly function while they are awake. These disorders can contribute to other medical problems, and some may also be symptoms for underlying mental health issues.

In 1979, the American Sleep Disorders Association published the first classification system dedicated to sleep disorders. Our knowledge and understanding of sleep health has evolved over the past four decades. More than 100 specific sleep disorders have been identified and today’s classifications use complex methodologies to categorize these disorders based on causes, symptoms, physiological and psychological effects, and other criteria. However, most sleep disorders can be characterized by one or more of the following four signs:

• You have trouble falling or remaining asleep
• You find it difficult to stay awake during the day
• There are imbalances in your circadian rhythm that interfere with a healthy sleep schedule
• You are prone to unusual behaviors that disrupt your sleep

Any of these signs could indicate a sleep disorder. People who experience issues with sleep or daytime energy should consult with their doctor.

Better Sleep for a

Trouble sleeping? Let us help.

Insomnia is characterized by an ongoing difficulty to fall or remain asleep despite wanting to sleep and having enough time to sleep. People with insomnia also experience daytime sleepiness and may have difficulty functioning while they are awake. Chronic insomnia is diagnosed when someone has these symptoms at least three times per week for at least three months. 

• Up to two-thirds of adults periodically experience some form of insomnia. 
• Insomnia is more likely to occur with older age, lower socioeconomic status, and anxiety or depression. 
• Therapy, sleep aids, and other approaches can reduce or resolve insomnia symptoms.

What Causes Insomnia?

Trying to find out why you have sleeping problems? Learn about the general causes of insomnia and how it can arise in the elderly, teens, and pregnant women.

Want to learn more about the symptoms of insomnia? Our guide covers short-term & chronic insomnia symptoms, including sleep issues and daytime impairments.

Want to learn more about how to treat insomnia? Our insomnia treatment guide covers medications, cognitive behavioral therapy for insomnia, and more.

Not all insomnia is the same. Learn about short-term and chronic types of insomnia along with other terms used to describe this serious sleeping problem.

Sleep Apnea

Sleep apnea is a common sleep-related breathing disorder that disrupts breathing at night. People with this condition often snore heavily and may wake up choking or gasping for air. There are two types of sleep apnea. Obstructive sleep apnea occurs when tissues in the mouth and throat relax, frequently blocking the upper airway. Central sleep apnea occurs when the brain temporarily stops sending signals to the muscles that control breathing.

• Obstructive sleep apnea affects at least 30 million Americans, but many cases go undiagnosed.
• People with sleep apnea often experience daytime sleepiness and fatigue, as well as morning headaches and dry mouth.
• Treatment options for sleep apnea include CPAP therapy, oral appliances, and, in some cases, surgery.

Obstructive Sleep Apnea

Obstructive sleep apnea is a condition marked by abnormal nighttime breathing. Learn more about the symptoms, causes, and treatments of obstructive sleep apnea.

Though CPAP therapy is the most common sleep apnea treatment, other options exist. We look at PAP devices, surgeries, and lifestyle changes for sleep apnea.

Sleep apnea is a relatively common disorder in which people experience disrupted breathing while they are sleeping.

In central sleep apnea, a lack of signals from the brain interrupts breathing during sleep. Learn more about this uncommon condition.

Narcolepsy is a sleep disorder that makes people feel excessively tired during the day despite getting an adequate amount of sleep. This can lead to an irrepressible urge to sleep, culminating in “sleep attacks” that typically last for a few minutes. These sleep attacks and other symptoms of narcolepsy are caused by disruptions in the brain’s ability to regulate the sleep-wake cycle.

• Narcolepsy affects roughly 1 in 2,000 people in the United States.
• Sleep attacks can be accompanied by cataplexy, a sudden loss of muscle tone that causes people to slump over as they nod off.
• People with narcolepsy are at a high risk for accident or injury, but treatment with medication and lifestyle changes can help.

Narcolepsy Treatment

Can narcolepsy be cured? Can its symptoms be improved? Learn about the different types of treatment for narcolepsy and their benefits and downsides.

Concerned that you have symptoms of narcolepsy? Learn about the tests and criteria used to diagnose narcolepsy and how to discuss them with your doctor.

Our guide to the symptoms of narcolepsy explores the causes and impact of each symptom of this complex, chronic sleep disorder.

Hypersomnia and narcolepsy both make people excessively sleepy during the day. Learn more about these disorders and what makes them different.

Restless Legs Syndrome

People with restless legs syndrome (RLS) experience tingling or crawling sensations that create an irresistible urge to move their legs. The sensations and urge to move tend to get worse when sitting or lying down, making it difficult to sleep. RLS is linked with pregnancy, Parkinson’s disease, iron deficiency, and other factors, but the cause of most RLS cases is unknown. 

• Up to 15% of people have RLS, but only around 2% to 3% experience significant symptoms.
• RLS symptoms are not only triggered by rest, they may also worsen with caffeine intake and use of certain medications. 
• Healthy sleep habits, dietary changes, exercise, medical devices, and medications are effective treatment strategies for RLS.

What Causes Restless Sleep?

Are you tossing and turning all night and struggling to wake up refreshed? Read more about restless sleep, what causes it, and steps to take to overcome it.

Diagnosing restless legs syndrome can be complex because there is no single test to confirm it. Learn how doctors determine whether someone has this condition.

Learn about common restless legs syndrome symptoms and how to know if you might have this condition.

Learn about different restless legs syndrome treatments to minimize uncomfortable symptoms and restore healthy sleep.

Parasomnias

Parasomnias are a group of unusual sleep behaviors that can occur before falling asleep, during sleep, or in the transition between sleep and wakefulness. Parasomnias are most common in children, but they affect adults as well. They include sleepwalking, bedwetting, night terrors, and more unique ones like exploding head syndrome. 

• Parasomnias occur in up to 20% of children.
• Parasomnias are categorized based on when in a person’s sleep cycle they arise. 
• Managing parasomnias typically involves maintaining the safety of the sleeper and any bed partners and promoting sufficient healthy sleep.

Sleep Paralysis: Symptoms, Causes, and Treatment

Have you experienced episodes of sleep paralysis? We explain symptoms, causes, and treatments of sleep paralysis.

Learn more about sexsomnia, a parasomnia that can cause involuntary sexual behaviors during sleep. It has a wide range of causes, triggers, and treatments.

Learn the key facts and figures to know about the causes, symptoms, dangers, and treatments of sleepwalking.

While painless and non-threatening, exploding head syndrome can cause anxiety and sleep problems. Learn more about this sleep disorder.

Excessive Sleepiness

It is normal to feel sleepy after a night of sleep loss. But excessive daytime sleepiness (EDS) is a medical term that describes extreme grogginess occurring almost every day for at least three months. EDS makes it difficult or impossible to stay awake during the day. A wide range of medical and psychological conditions can lead to EDS, including sleep apnea, narcolepsy, hypothyroidism, chronic pain, depression, and anxiety. 

• EDS is believed to occur in up to 25% of the population.
• EDS is not a sleep disorder itself but rather a symptom of many sleep disorders and other health conditions. 
• To determine the cause of EDS, a doctor may recommend a sleep study or other tests.

What Causes Excessive Sleepiness?

Learn what may be causing your excessive sleepiness. It could be an underlying cause like a sleep disorder, psychiatric illness, or lifestyle factor.

Are you constantly sleepy? Our guide explains the potential causes of excessive sleepiness including sleep deprivation and other medical conditions.

Excessive sleepiness can lead to workplace accidents. Learn how sleepiness impacts safety in the workplace and other aspects of working life.

Do you find it hard to get out of bed or have a strong desire to stay in bed? We discuss the causes, effects, and management of dysania.

Shift Work Disorder

Shift work disorder develops in some people whose jobs require them to work late at night or early in the morning. Sleeping during the day and working at night can cause misalignment between a person’s daily schedule and the circadian rhythms that guide their body to feel alert or sleepy in response to light or darkness. People with this condition often feel excessively tired at work and struggle to get enough sleep during their allotted daytime rest period. 

• At least one-third of shift workers meet the criteria for a shift work disorder diagnosis.
• People with shift work disorder get, on average, 90 minutes less sleep compared with people who work day shifts. 
• Treatment for shift work disorder focuses on strategies that encourage alertness while at work and quality sleep between shifts.

Shift Work Disorder Symptoms

Want to learn about shift work sleep disorder symptoms? Our guide also covers risk factors for workers and differences between shift work disorder and insomnia.

Do you have trouble sleeping during the day? Our guide explains how to fall asleep in the day and get enough rest for the night ahead.

Shift workers have an arsenal of tactics available to them that may help improve sleep and manage symptoms of shift work disorder.

People with shift work disorder struggle to get enough sleep and stay alert on the job. Our guide includes expert tips for coping with shift work disorder.

Non 24-Hour Sleep Wake Disorder

For most adults, the circadian rhythms that guide the sleep-wake cycle reset approximately every 24 hours. This is why many people start to get sleepy around the same time each night. In contrast, people with non-24-hour sleep wake disorder have circadian rhythms that are either shorter or longer than 24 hours. Affected individuals progressively shift their sleep and wake times one to two hours earlier or later each day. 

• Non-24-hour sleep wake disorder primarily affects people who are blind and unable to see light.
• People with this condition cycle through days or weeks of sleeping during the day and days or weeks of sleeping at night. 
• Non-24-hour sleep wake disorder is one of the six circadian sleep-wake rhythm disorders.

Living With and Managing Non-24 Hour Sleep-Wake Disorder

Get advice on how to manage non-24-hour sleep-wake disorder in the workplace, at school, or with friends and family.

Learn about the causes of non-24-hour sleep-wake disorder and who is most likely to suffer from this rare disorder.

Symptoms of Non-24 Hour Sleep-Wake Disorder include excessive daytime sleepiness and insomnia. Learn when to talk to your doctor about your symptoms.

Non-24-hour sleep-wake disorder treatment involves establishing a 24-hour circadian rhythm using light therapy, melatonin supplements, and other tactics.

All Sleep Disorders

Change Password

Your password must have 6 characters or more:.

• a lower case character, 
• an upper case character, 
• a special character 

Password Changed Successfully

Your password has been changed

Create your account

Forget yout password.

Enter your email address below and we will send you the reset instructions

If the address matches an existing account you will receive an email with instructions to reset your password

Forgot your Username?

Enter your email address below and we will send you your username

If the address matches an existing account you will receive an email with instructions to retrieve your username

• July 01, 2024 | VOL. 181, NO. 7 CURRENT ISSUE pp.565-686
• June 01, 2024 | VOL. 181, NO. 6 pp.461-564
• May 01, 2024 | VOL. 181, NO. 5 pp.347-460
• April 01, 2024 | VOL. 181, NO. 4 pp.255-346
• March 01, 2024 | VOL. 181, NO. 3 pp.171-254
• February 01, 2024 | VOL. 181, NO. 2 pp.83-170
• January 01, 2024 | VOL. 181, NO. 1 pp.1-82

The American Psychiatric Association (APA) has updated its Privacy Policy and Terms of Use , including with new information specifically addressed to individuals in the European Economic Area. As described in the Privacy Policy and Terms of Use, this website utilizes cookies, including for the purpose of offering an optimal online experience and services tailored to your preferences.

Please read the entire Privacy Policy and Terms of Use. By closing this message, browsing this website, continuing the navigation, or otherwise continuing to use the APA's websites, you confirm that you understand and accept the terms of the Privacy Policy and Terms of Use, including the utilization of cookies.

The Evolving Nexus of Sleep and Depression

• David T. Plante , M.D., Ph.D.

Search for more papers by this author

Sleep disturbances and depression are closely linked and share a bidirectional relationship. These interconnections can inform the pathophysiology underlying each condition. Insomnia is an established and modifiable risk factor for depression, the treatment of which offers the critical opportunity to prevent major depressive episodes, a paradigm-shifting model for psychiatry. Identification of occult sleep disorders may also improve outcomes in treatment-resistant depression. Sleep alterations and manipulations may additionally clarify the mechanisms that underlie rapid-acting antidepressant therapies. Both sleep disturbance and depression are heterogeneous processes, and evolving standards in psychiatric research that consider the transdiagnostic components of each are more likely to lead to translational progress at their nexus. Emerging tools to objectively quantify sleep and its disturbances in the home environment offer great potential to advance clinical care and research, but nascent technologies require further advances and validation prior to widespread application at the interface of sleep and depression.

It has long been appreciated that sleep disturbance and depression frequently co-occur. Recent reviews have detailed the history of sleep and depression research, from changes in sleep architecture observed in depression, including alterations in slow-wave and rapid eye movement (REM) sleep, to effects of antidepressant compounds and chronotherapeutics on sleep staging and mood ( 1 ). While great scientific progress has been made at the interface of sleep and depression, even robust findings have at times resulted in conclusions of limited translational utility. However, the potential to refine research strategies and clinical paradigms at the nexus of sleep and depression offers new and exciting trajectories that may dramatically alter the landscape of psychiatric practice.

Interconnections of Insomnia and Depression

Insomnia and depression share a bidirectional relationship, in which each separate entity can influence the course of the other ( 2 ). It is rare for patients experiencing a major depressive episode not to experience some form of sleep disturbance. Insomnia is the most common sleep complaint among those with comorbid depression, occurring in approximately 80%−90% of patients ( 3 ). Importantly, meta-analysis has demonstrated that insomnia increases the odds of developing depression roughly twofold, which is on par with the risk associated with having a first-degree relative with recurrent major depressive disorder ( 4 , 5 ). Additionally, twin and genome-wide association studies have demonstrated sizable genetic overlap between insomnia and major depression ( 6 , 7 ).

Given the connections between insomnia and depression both cross-sectionally and longitudinally, consideration of insomnia as a contributing factor in genetics and neuroimaging results could further advance our understanding of these heterogeneous processes ( 8 ). While relatively few studies have examined the impact of insomnia on neuroimaging findings in major depression, overlapping structural and functional abnormalities in the medial prefrontal cortex and posterior cingulate cortex within the default mode network, as well as the insula, anterior cingulate cortex, and amygdala within the salience network have separately been observed in each condition ( 9 ). In terms of structural findings, the ENIGMA (Enhancing Neuroimaging Genetics Through Meta-Analysis) depression working group recently demonstrated in 1,053 persons with major depressive disorder that more severe insomnia symptoms were associated with reduced cortical surface area, primarily in the right insula, left inferior frontal gyrus pars triangularis, left frontal pole, right superior parietal cortex, right medial orbitofrontal cortex, and right supramarginal gyrus ( 10 ). Notably, these associations were not observed among healthy or clinical control subjects with bipolar disorder, suggesting that these associations between surface area in frontoparietal cortical areas and insomnia are unique to depression ( 10 ). In terms of functional neuroimaging, Cheng et al. ( 12 ) analyzed data from the Human Connectome project, demonstrating that increased functional connectivity in multiple brain areas (lateral orbitofrontal cortex, dorsolateral prefrontal cortex, anterior and posterior cingulate cortices, insula, parahippocampal gyrus, hippocampus, amygdala, temporal cortex, and precuneus) was associated with both sleep quality (measured using the Pittsburgh Sleep Quality Index [ 11 ]) and depressive symptoms, suggesting a neural basis for their overlap. Drysdale et al. ( 13 ) successfully segregated treatment-resistant depression along two dimensions—one predicting anhedonia symptoms based on frontostriatal and orbitofrontal connectivity, the other predicting anxiety and insomnia based on connectivity within the amygdala, ventral hippocampus, ventral striatum, and lateral prefrontal cortex. While patterns of clinical symptoms prior to treatment were only modestly associated with therapeutic effects of transcranial magnetic stimulation applied at the dorsomedial prefrontal cortex, subgroups that accounted for these connectivity patterns were better able to predict treatment response ( 13 ).

An important limitation of nearly all neuroimaging approaches that are used to study insomnia and depression is that they evaluate the waking brain. Thus, quantitative measures acquired during the sleep period, such as EEG, are more likely to provide insights into brain changes that may co-occur in insomnia and depression. While increased high-frequency EEG activity during sleep has been generally reported in insomnia disorder ( 14 ), such sleep EEG changes have typically not been described using sleep spectral analysis in persons with major depression and comorbid insomnia ( 15 , 16 ). In this instance, the use of standard EEG montages that have limited spatial resolution may be a significant barrier in this line of inquiry, particularly since preliminary evidence has demonstrated that local alpha activity persists in sensory and sensorimotor cortical areas in persons with insomnia, even in the deepest stages of non-REM sleep ( 17 ). Thus, the use of high-density EEG arrays during sleep has significant promise to advance research on sleep and depression, as has been the case for other psychiatric disorders ( 18 , 19 ).

From a more pragmatic standpoint, it is important to know whether treatment of insomnia occurring in the context of a depressive episode enables therapeutic effects beyond the improvement of sleep symptoms. Two earlier randomized controlled trials focused on nonbenzodiazepine Z-drugs (placebo versus active compound) with open-label use of specific serotonin reuptake inhibitors (SSRIs) ( 20 , 21 ). While both studies demonstrated a significant benefit of the sedative-hypnotic on insomnia measures, results on depressive symptoms were mixed. One study demonstrated that the coadministration of eszopiclone and fluoxetine resulted in improvement of depressive symptoms, even beyond the impact of sleep-related changes ( 20 ). However, similar benefits from sedative-hypnotics were not observed in a study of coadministered extended-release zolpidem and escitalopram ( 21 ).

Studies that clarify the effect of sedative-hypnotic coadministration in patients with a major depressive episode are particularly relevant in patients at risk for suicide. Insomnia is independently associated with increased suicidal behaviors and ideation ( 22 ). However, suicidal ideation has also been associated with sedative-hypnotics ( 23 ), and many psychiatrists fear that prescription of sleep medications may increase the risk of overdose. To advance this area of inquiry, the Reducing Suicidal Ideation Through Insomnia Treatment (REST-IT) study applied a similar coadministration paradigm of extended-release zolpidem versus placebo with open-label SSRI in patients with major depression, insomnia, and co-occurring suicidal ideation ( 24 ). This landmark study demonstrated that coprescription of a sedative-hypnotic and an SSRI does not worsen suicidal ideation, and in fact may reduce suicidal ideation, particularly in patients with more severe insomnia ( 24 ).

The direct effects of cognitive-behavioral therapy for insomnia (CBT-I), a highly effective nonpharmacological treatment for insomnia that is generally considered a first-line therapeutic strategy ( 25 , 26 ), have not been thoroughly evaluated in depressed patients with suicidal ideation. A previous study that evaluated the effects of CBT-I on veterans with insomnia demonstrated that CBT-I may improve depressive symptoms and suicidal ideation, and that the observed relationship between insomnia severity and change in suicidal ideation remained significant even when adjusted for changes in depression severity ( 27 ). A smaller pilot trial of brief CBT-I in depressed veterans with and without suicidal ideation suggested that this modality may improve sleep, but effects on depression and suicidal ideation required larger follow-up studies ( 28 ). A subsequent larger randomized controlled trial in patients with major depression and/or posttraumatic stress disorder reporting suicidal ideation demonstrated large effects of brief CBT-I on depression and insomnia, with relatively small effects of the therapy on suicidal ideation, which were moderated by insomnia, although this effect fell short of statistical significance ( 29 ).

Beyond patients with suicidal ideation, the effects of concomitant thymoleptics and CBT-I in persons with depression and comorbid insomnia have also yielded mixed results. Initial randomized controlled pilot studies examining the use of CBT-I with coadministration of escitalopram in patients with depression and insomnia improved both insomnia symptoms and rates of depression remission compared with escitalopram and active control therapy ( 30 ). However, the larger follow-up Treatment of Insomnia in Depression (TRIAD) study, which employed a similar research paradigm, did not replicate the preliminary findings ( 31 ). Post hoc analyses suggested that childhood onset of depression and insomnia, as well as evening circadian preference, were potential moderators of depression outcomes in TRIAD ( 32 , 33 ). Change in depression symptoms also were not significantly different across treatment arms in a randomized controlled trial in depression and comorbid insomnia that compared three treatment conditions: escitalopram plus CBT-I, CBT-I plus placebo, and escitalopram plus sleep hygiene control ( 34 ).

Thus, taken in aggregate, studies that have examined the effects of concomitant use of insomnia treatments (i.e., medications or CBT-I) and antidepressants in persons with depression and insomnia have reliably demonstrated improvement in insomnia measures resulting from sleep-targeted therapy. However, improvement in depressive symptoms in these study paradigms has not been as consistent. While differences among study findings may be related to granular dissimilarities between investigations, from a pragmatic standpoint, specifically addressing insomnia complaints in the context of depression is likely to have a positive therapeutic effect, at least in some instances, and may improve sequelae of depression (e.g., suicidal ideation). However, from a public health perspective, it is possible that a more critical and fruitful time to target insomnia in the treatment of depression may be prior to the onset of a depressive episode.

Can Treatment of Insomnia Prevent Depression?

The ability to effectively prevent the onset of mental illness is the holy grail of psychiatry. Unlike the majority of fields of medicine, which have successfully developed and championed preventive strategies for a variety of chronic conditions, psychiatry still largely relies on diagnosis and management after symptoms are well established. Given the large societal and economic costs of mood disorders ( 35 ), the development of effective strategies to prevent the onset of depressive episodes should be a high priority for the field. The robust longitudinal connections between insomnia and risk of subsequent depression ( 4 , 25 , 26 ), as well as effective pharmacological and nonpharmacological methods to treat insomnia, place insomnia as a key target for depression prevention. While the possibility that insomnia may represent a unique opportunity for prevention of mental illness has been proposed for several decades ( 36 ), the development of Internet-based CBT-I therapies that are both effective and can be widely disseminated has begun to advance this critical area of inquiry.

Large-scale studies of digital CBT-I (dCBT-I) in persons with insomnia have generally demonstrated improvements in both insomnia and depressive symptoms. The GoodNight Study was a randomized controlled trial that examined the use of dCBT-I in adults with insomnia and subthreshold depression symptoms ( 37 ). The study demonstrated that dCBT-I significantly reduced depressive symptoms but did not significantly reduce the number of participants with major depressive disorder at 6 months. Freeman et al. ( 38 ) examined the effects of dCBT-I in a large study of university students with insomnia and demonstrated large reductions in depression scores at 10- and 22-week follow-ups in dCBT-I compared with usual care, with significant reductions in the number of persons developing a major depressive episode. Large-scale randomized controlled trials of the effects of dCBT-I have replicated findings of reductions in depressive rating scales resulting from the therapy ( 39 , 40 ).

More recently, Cheng et al. ( 41 ) conducted a randomized controlled trial of dCBT-I in a large group of persons with insomnia disorder, approximately half of whom had depressive symptoms rated as moderate or worse at baseline. dCBT-I was associated with significantly increased rates of depression remission at 1-year follow-up compared with the control condition (treatment as usual plus online sleep education). Baseline severity of insomnia, lack of insomnia response or remission to dCBT-I, and decreased durability of insomnia improvement with dCBT-I were conversely all associated with increased moderate to severe depression at 1-year follow-up. Notably, among individuals with minimal or no depression at baseline, the risk of developing moderate to severe depression was reduced by half in the dCBT-I relative to the control condition. When considering participants without depression at baseline, the number needed to treat to prevent depression at 1 year was 11 ( 41 ). Because of the high prevalence of insomnia in the general population ( 42 ), this finding suggests that treatment of insomnia with dCBT-I has the potential to dramatically decrease the incidence of depression if broadly available and applied. Clearly, such promising results demonstrate the need for further large-scale longitudinal research trials that focus on the ability to prevent major depressive episodes through the treatment of insomnia as a modifiable risk factor.

The Role of Sleep in Treatment-Resistant Depression

While the definition of treatment-resistant depression may vary across the literature and is not without some controversy, generally it is operationally defined as depression that fails to respond to at least two adequate therapeutic trials of an antidepressant medication ( 43 , 44 ). Treatment-resistant depression has been estimated to occur in up to half of cases of major depressive disorder ( 43 ). It is a significant cause of morbidity and mortality, and its optimal treatment relies on careful history taking and a tailored approach to treatment that balances comorbidities with the risks and benefits of available treatment options. Sleep disorders, particularly obstructive sleep apnea, may play an underappreciated role in treatment-resistant depression. The overarching pathophysiology of obstructive sleep apnea consists of cycles of apneas and hypopneas—intermittent upper airway obstruction due to pharyngeal collapse during sleep, resultant drops in blood oxygenation, and arousals from sleep to temporarily resolve upper airway obstruction. Obstructive sleep apnea is associated with several negative health outcomes, including hypertension, cardiovascular disease, arrhythmia, stroke, impaired glucose regulation, cognitive impairment, and mortality ( 45 ).

The prevalence of obstructive sleep apnea among patients with major depression is higher than in the general population, with approximately 20% of persons with major depression having obstructive sleep apnea and vice versa ( 46 ). Recent data suggest that such elevated rates of obstructive sleep apnea occur in persons with major depression even in the absence of symptoms suggestive of sleep-disordered breathing ( 47 ). In addition, persons with major depression and comorbid obstructive sleep apnea may have higher rates of suicidal ideation and acts compared with those without sleep apnea ( 48 , 49 ). Obstructive sleep apnea has been associated with nonresponse to antidepressant pharmacotherapy in adolescents and adults ( 50 , 51 ). Randomized controlled trials have also demonstrated that positive airway pressure therapy decreases both symptoms and cases of depression in persons with obstructive sleep apnea ( 52 ). Thus, as supported by the APA practice guidelines, “Clinicians should be alert to the possibility of sleep apnea in patients with depression, particularly those who present with daytime sleepiness, fatigue, or treatment-resistant symptoms” ( 53 ).

Unfortunately, screening for sleep-disordered breathing as a contributing factor for both depression and treatment resistance is limited among practicing psychiatrists. This is likely due to several factors, including limited education about sleep disorders among mental health providers while in training ( 54 ), absence of simple blood-based laboratory testing to evaluate for the presence or absence of obstructive sleep apnea, and the high cost associated with in-laboratory polysomnography, which has long been the gold standard for the diagnosis of sleep-disordered breathing. However, with the advent of widely accessible and lower-cost methods to assess for obstructive sleep apnea in the home environment ( 55 ), barriers to sleep testing are substantially lower than in previous decades. In the case of depression, unique opportunities exist to develop models of care that more deliberately consider sleep-disordered breathing in the diagnostic and therapeutic algorithms of care provision in ambulatory and inpatient treatment settings. While the identification of obstructive sleep apnea will not likely cure depression in most patients, identifying and treating this key comorbidity may improve the efficacy of existing treatments and decrease the likelihood of treatment resistance. Clearly, further research that clarifies the impact of unidentified sleep-disordered breathing on the presentation and treatment of treatment-resistant depression is warranted.

In addition to the role of occult sleep disorders such as sleep apnea contributing to treatment resistance, data suggest that sleep itself may be related to rapid antidepressant effects of emerging therapies. Sleep deprivation has long been known to rapidly improve depressive symptoms, but its effects are generally short-lived, with return of depression after a subsequent bout of sleep ( 56 ). While sleep deprivation is thus unlikely to be a clinically useful treatment strategy for patients, it does afford the opportunity to compare its effects with other rapidly acting antidepressants to understand potential underlying mechanisms of action.

The synaptic homeostasis hypothesis posits that extended wakefulness increases net synaptic strength, with subsequent renormalization during sleep reflected by increases in sleep slow-wave activity ( 57 ). Recently, this hypothesis has been combined with the synaptic plasticity hypothesis of major depression ( 58 , 59 ), positing that the enhanced homeostatic plasticity of extended wakefulness during sleep deprivation allows persons with major depression to temporarily move into a window of long-term potentiation inducibility/associative plasticity, which under normal circumstances would be unattainable in depression ( 60 ). Notably, increases in slow-wave activity and slope during non-REM sleep are observed after a single infusion of ketamine ( 61 ). Thus, hypothetically, these findings during sleep suggest that ketamine may acutely increase synaptic strength in persons with depression, which could contribute to its antidepressant properties.

However, just as the potential mechanisms underlying ketamine’s rapid antidepressant activity may be many, the effects of ketamine on sleep may extend beyond changes in sleep slow waves and include changes in sleep efficiency, continuity, and REM sleep ( 62 ). Notably, decreased nighttime wakefulness and other aspects of improved sleep have been associated with reductions in suicidal ideation resulting from ketamine treatment ( 63 , 64 ). Additionally, the effects of ketamine have been hypothesized also to have an impact on circadian rhythms, which along with sleep homeostatic processes, govern the overt sleep-wake rhythm ( 65 , 66 ). Further research that clarifies the connections between sleep, circadian rhythms, and rapid responses to ketamine may both elucidate underlying mechanisms of action and lead to new areas of inquiry that leverage alterations in sleep and chronobiology to optimize the antidepressant response for patients with treatment-resistant depression.

Embracing Heterogeneity to Unlock Connections Between Sleep and Depression

The fact that depression is a heterogeneous construct is evidenced by the myriad combinations of symptoms allowable under current diagnostic criteria ( 67 ). The challenge of understanding the underlying biology of depression under such circumstances has been highlighted by the National Institute of Mental Health’s Research Domain Criteria initiative (RDoC), which emphasizes the study of measurable traits that occur across psychiatric disorders rather than nosological distinctions that may have limited validity ( 68 , 69 ). Just as depression is heterogeneous, so too is sleep, and too often the consideration of sleep complaints in major depression in both clinical and research contexts occurs at a superficial level. Sleep disturbances are not all the same, and reliance on brief self-report measures alone is unlikely to meaningfully advance research or clinical care at the interface between sleep and depression. Within the RDoC framework, sleep falls under “Arousal and Regulatory Systems,” with three separate constructs (arousal, circadian rhythms, and sleep-wakefulness) available to more fully characterize sleep disturbances. The need for such an expansive tool kit to study sleep highlights the fact that sleep is a complex behavior generated by the brain and affects the entire organism and its functioning during wakefulness. During our lifespan, sleep plays a key role in development, and we spend more time sleeping than any other behavior during the course of our lives. In this context, it is crucial to more carefully phenotype the nature of a given sleep complaint in depressive illness to advance scientific progress at their nexus ( 70 ).

While the preponderance of research on the connections between sleep and depression have focused on insomnia, the relationships between hypersomnolence and depression highlight the importance of multiple levels of investigation and objective phenotyping of specific sleep complaints in clarifying relationships between sleep and psychiatric disorders. Hypersomnolence, broadly defined as excessive daytime sleepiness and/or excessive sleep duration, occurs in a minority of patients with major depression but, like insomnia, is associated with treatment resistance, symptomatic relapse, increased risk of suicide, and functional impairment ( 71 , 72 ). Like insomnia, hypersomnolence is also associated with longitudinal risk of developing depression ( 73 ). From a physiological standpoint, the presence or absence of hypersomnolence in major depression also has key effects on differences in regional slow-wave activity during sleep when quantified using high-density EEG ( 74 ). Like all sleep complaints, hypersomnolence has multiple facets and quantifiable aspects that must be considered to fully understand their relationships with mood disorders. In fact, these different measurable aspects of hypersomnolence are particularly critical to consider in the context of depression because different objective tests used to quantify somnolence and daytime vigilance can exhibit divergent relationships with depressive symptoms ( 75 , 76 ). These more discrete measurable aspects of hypersomnolence have also proven to be critical to identifying epigenetic changes associated with sleep disturbances, which would otherwise be missed by nonspecific self-report measures ( 77 ). Thus, more thorough consideration and delineation of the sleep phenotype is just as important to understanding the connections between sleep and depression as careful consideration of the multiple symptom domains that constitute depressive illness. From a broader transdiagnostic perspective, these discrete and measurable sleep-related traits may be particularly relevant for furthering scientific inquiry across the full spectrum of psychiatric disorders.

The ability to more accurately and objectively capture key sleep measures and phenotypes has long been hindered by low accessibility and high cost. Even beyond the use of home sleep testing for obstructive sleep apnea, emerging technologies offer the opportunity to dramatically lower barriers to the objective measurement of sleep in the ambulatory environment. Such methods have great promise to be adapted to tailor personalized sleep-related therapies early in the course of treatment and potentially reduce the burden of psychiatric illness. However, consumer-grade wearables are not yet accurate enough to define sleep stages, particularly REM sleep that may occur earlier than typical, as is often observed in depression ( 78 – 81 ). The development, production, and release of wearable products into the marketplace far outpaces their validation in persons with mood disorders, as these devices are generally designed to estimate sleep in healthy populations. In the coming years, advances in these technologies will likely lead to tools that can be leveraged at the interface of sleep and depression, but it will be critical to fully validate these products in patients with depression before they can be applied broadly in research or clinical care settings.

Conclusions

Sleep and depression are deeply intertwined on numerous levels. Careful consideration, evaluation, and treatment of sleep disturbances will inform research and clinical care in depressive disorders. Recent advances and scalability in the evaluation and treatment of sleep disorders has great promise for future advances in this area. The close longitudinal relationship between insomnia and depression offers the tantalizing opportunity to prevent depressive episodes using evidence-based treatments for insomnia, which would represent a paradigm shift for psychiatry. Recognition and clarification of the role of disordered sleep in treatment-resistant depression may improve the efficacy and effectiveness of rapid-acting antidepressants and lead to other novel therapeutics. Careful consideration and measurement of the type and nature of sleep disturbance will also lead to transdiagnostic discoveries that can be applied and studied across the full spectrum of psychiatric disorders to continue to advance our understanding of the biological bases of complex psychopathologies and their connections with sleep.

Dr. Plante has received research support from the American Sleep Medicine Foundation, the Brain and Behavior Research Foundation, the Madison Education Partnership, the National Institute of Nursing Research, the National Institute on Aging, NIMH, and the University of Illinois at Chicago Occupational and Environmental Health and Safety Education and Research Center (funded by the National Institute for Occupational Safety and Health); he has served as a consultant and/or advisory board member for Harmony Biosciences, Jazz Pharmaceuticals, and Teva Pharmaceuticals Australia; and he has received honoraria from the American Academy of Sleep Medicine and royalties from Cambridge University Press.

1. Riemann D , Krone LB , Wulff K , et al. : Sleep, insomnia, and depression . Neuropsychopharmacology 2020 ; 45 : 74 – 89 Crossref , Medline ,  Google Scholar

2. Krystal AD : Psychiatric disorders and sleep . Neurol Clin 2012 ; 30 : 1389 – 1413 Crossref , Medline ,  Google Scholar

3. Stewart R , Besset A , Bebbington P , et al. : Insomnia comorbidity and impact and hypnotic use by age group in a national survey population aged 16 to 74 years . Sleep 2006 ; 29 : 1391 – 1397 Crossref , Medline ,  Google Scholar

4. Baglioni C , Battagliese G , Feige B , et al. : Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies . J Affect Disord 2011 ; 135 : 10 – 19 Crossref , Medline ,  Google Scholar

5. Lohoff FW : Overview of the genetics of major depressive disorder . Curr Psychiatry Rep 2010 ; 12 : 539 – 546 Crossref , Medline ,  Google Scholar

6. Lind MJ , Hawn SE , Sheerin CM , et al. : An examination of the etiologic overlap between the genetic and environmental influences on insomnia and common psychopathology . Depress Anxiety 2017 ; 34 : 453 – 462 Crossref , Medline ,  Google Scholar

7. Hammerschlag AR , Stringer S , de Leeuw CA , et al. : Genome-wide association analysis of insomnia complaints identifies risk genes and genetic overlap with psychiatric and metabolic traits . Nat Genet 2017 ; 49 : 1584 – 1592 Crossref , Medline ,  Google Scholar

8. Buch AM , Liston C : Dissecting diagnostic heterogeneity in depression by integrating neuroimaging and genetics . Neuropsychopharmacology 2021 ; 46 : 156 – 175 Crossref , Medline ,  Google Scholar

9. Bagherzadeh-Azbari S , Khazaie H , Zarei M , et al. : Neuroimaging insights into the link between depression and insomnia: a systematic review . J Affect Disord 2019 ; 258 : 133 – 143 Crossref , Medline ,  Google Scholar

10. Leerssen J , Blanken TF , Pozzi E , et al. : Brain structural correlates of insomnia severity in 1053 individuals with major depressive disorder: results from the ENIGMA MDD Working Group . Transl Psychiatry 2020 ; 10 : 425 Crossref , Medline ,  Google Scholar

11. Buysse DJ , Reynolds CF 3rd , Monk TH , et al. : The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research . Psychiatry Res 1989 ; 28 : 193 – 213 Crossref , Medline ,  Google Scholar

12. Cheng W , Rolls ET , Ruan H , et al. : Functional connectivities in the brain that mediate the association between depressive problems and sleep quality . JAMA Psychiatry 2018 ; 75 : 1052 – 1061 Crossref , Medline ,  Google Scholar

13. Drysdale AT , Grosenick L , Downar J , et al. : Resting-state connectivity biomarkers define neurophysiological subtypes of depression . Nat Med 2017 ; 23 : 28 – 38 Crossref , Medline ,  Google Scholar

14. Zhao W , Van Someren EJW , Li C , et al. : EEG spectral analysis in insomnia disorder: a systematic review and meta-analysis . Sleep Med Rev 2021 ; 59 : 101457 Crossref , Medline ,  Google Scholar

15. Perlis ML , Smith MT , Andrews PJ , et al. : Beta/gamma EEG activity in patients with primary and secondary insomnia and good sleeper controls . Sleep 2001 ; 24 : 110 – 117 Crossref , Medline ,  Google Scholar

16. Kang SG , Mariani S , Marvin SA , et al. : Sleep EEG spectral power is correlated with subjective-objective discrepancy of sleep onset latency in major depressive disorder . Prog Neuropsychopharmacol Biol Psychiatry 2018 ; 85 : 122 – 127 Crossref , Medline ,  Google Scholar

17. Riedner BA , Goldstein MR , Plante DT , et al. : Regional patterns of elevated alpha and high-frequency electroencephalographic activity during nonrapid eye movement sleep in chronic insomnia: a pilot study . Sleep 2016 ; 39 : 801 – 812 Crossref , Medline ,  Google Scholar

18. Ferrarelli F , Huber R , Peterson MJ , et al. : Reduced sleep spindle activity in schizophrenia patients . Am J Psychiatry 2007 ; 164 : 483 – 492 Link ,  Google Scholar

19. Ferrarelli F , Peterson MJ , Sarasso S , et al. : Thalamic dysfunction in schizophrenia suggested by whole-night deficits in slow and fast spindles . Am J Psychiatry 2010 ; 167 : 1339 – 1348 Link ,  Google Scholar

20. Fava M , McCall WV , Krystal A , et al. : Eszopiclone co-administered with fluoxetine in patients with insomnia coexisting with major depressive disorder . Biol Psychiatry 2006 ; 59 : 1052 – 1060 Crossref , Medline ,  Google Scholar

21. Fava M , Asnis GM , Shrivastava RK , et al. : Improved insomnia symptoms and sleep-related next-day functioning in patients with comorbid major depressive disorder and insomnia following concomitant zolpidem extended-release 12.5 mg and escitalopram treatment: a randomized controlled trial . J Clin Psychiatry 2011 ; 72 : 914 – 928 Crossref , Medline ,  Google Scholar

22. Pigeon WR , Pinquart M , Conner K : Meta-analysis of sleep disturbance and suicidal thoughts and behaviors . J Clin Psychiatry 2012 ; 73 : e1160 – e1167 Crossref , Medline ,  Google Scholar

23. Tubbs AS , Fernandez FX , Ghani SB , et al. : Prescription medications for insomnia are associated with suicidal thoughts and behaviors in two nationally representative samples . J Clin Sleep Med 2021 ; 17 : 1025 – 1030 Crossref , Medline ,  Google Scholar

24. McCall WV , Benca RM , Rosenquist PB , et al. : Reducing Suicidal Ideation Through Insomnia Treatment (REST-IT): a randomized clinical trial . Am J Psychiatry 2019 ; 176 : 957 – 965 Link ,  Google Scholar

25. Buysse DJ : Insomnia . JAMA 2013 ; 309 : 706 – 716 Crossref , Medline ,  Google Scholar

26. Winkelman JW : Clinical practice: insomnia disorder . N Engl J Med 2015 ; 373 : 1437 – 1444 Crossref , Medline ,  Google Scholar

27. Trockel M , Karlin BE , Taylor CB , et al. : Effects of cognitive behavioral therapy for insomnia on suicidal ideation in veterans . Sleep 2015 ; 38 : 259 – 265 Crossref , Medline ,  Google Scholar

28. Pigeon WR , Funderburk J , Bishop TM , et al. : Brief cognitive behavioral therapy for insomnia delivered to depressed veterans receiving primary care services: a pilot study . J Affect Disord 2017 ; 217 : 105 – 111 Crossref , Medline ,  Google Scholar

29. Pigeon WR , Funderburk JS , Cross W , et al. : Brief CBT for insomnia delivered in primary care to patients endorsing suicidal ideation: a proof-of-concept randomized clinical trial . Transl Behav Med 2019 ; 9 : 1169 – 1177 Crossref , Medline ,  Google Scholar

30. Manber R , Edinger JD , Gress JL , et al. : Cognitive behavioral therapy for insomnia enhances depression outcome in patients with comorbid major depressive disorder and insomnia . Sleep 2008 ; 31 : 489 – 495 Crossref , Medline ,  Google Scholar

31. Manber R , Buysse DJ , Edinger J , et al. : Efficacy of cognitive-behavioral therapy for insomnia combined with antidepressant pharmacotherapy in patients with comorbid depression and insomnia: a randomized controlled trial . J Clin Psychiatry 2016 ; 77 : e1316 – e1323 Crossref , Medline ,  Google Scholar

32. Asarnow LD , Bei B , Krystal A , et al. : Circadian preference as a moderator of depression outcome following cognitive behavioral therapy for insomnia plus antidepressant medications: a report from the TRIAD study . J Clin Sleep Med 2019 ; 15 : 573 – 580 Crossref , Medline ,  Google Scholar

33. Edinger JD , Manber R , Buysse DJ , et al. : Are patients with childhood onset of insomnia and depression more difficult to treat than are those with adult onsets of these disorders? A report from the TRIAD study . J Clin Sleep Med 2017 ; 13 : 205 – 213 Crossref , Medline ,  Google Scholar

34. Carney CE , Edinger JD , Kuchibhatla M , et al. : Cognitive behavioral insomnia therapy for those with insomnia and depression: a randomized controlled clinical trial . Sleep 2017 ; 40 : zsx019 Crossref ,  Google Scholar

35. GBD 2019 Diseases and Injuries Collaborators : Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019 . Lancet 2020 ; 396 : 1204 – 1222 Crossref , Medline ,  Google Scholar

36. Ford DE , Kamerow DB : Epidemiologic study of sleep disturbances and psychiatric disorders: an opportunity for prevention? JAMA 1989 ; 262 : 1479 – 1484 Crossref , Medline ,  Google Scholar

37. Christensen H , Batterham PJ , Gosling JA , et al. : Effectiveness of an online insomnia program (SHUTi) for prevention of depressive episodes (the GoodNight Study): a randomised controlled trial . Lancet Psychiatry 2016 ; 3 : 333 – 341 Crossref , Medline ,  Google Scholar

38. Freeman D , Sheaves B , Goodwin GM , et al. : The effects of improving sleep on mental health (OASIS): a randomised controlled trial with mediation analysis . Lancet Psychiatry 2017 ; 4 : 749 – 758 Crossref , Medline ,  Google Scholar

39. Espie CA , Emsley R , Kyle SD , et al. : Effect of digital cognitive behavioral therapy for insomnia on health, psychological well-being, and sleep-related quality of life: a randomized clinical trial . JAMA Psychiatry 2019 ; 76 : 21 – 30 Crossref , Medline ,  Google Scholar

40. Vedaa Ø , Kallestad H , Scott J , et al. : Effects of digital cognitive behavioural therapy for insomnia on insomnia severity: a large-scale randomised controlled trial . Lancet Digit Health 2020 ; 2 : e397 – e406 Crossref , Medline ,  Google Scholar

41. Cheng P , Kalmbach DA , Tallent G , et al. : Depression prevention via digital cognitive behavioral therapy for insomnia: a randomized controlled trial . Sleep 2019 ; 42 : zsz150 Crossref , Medline ,  Google Scholar

42. Ohayon MM : Epidemiology of insomnia: what we know and what we still need to learn . Sleep Med Rev 2002 ; 6 : 97 – 111 Crossref , Medline ,  Google Scholar

43. Berlim MT , Turecki G : What is the meaning of treatment resistant/refractory major depression (TRD)? A systematic review of current randomized trials . Eur Neuropsychopharmacol 2007 ; 17 : 696 – 707 Crossref , Medline ,  Google Scholar

44. Rush AJ , Thase ME , Dubé S : Research issues in the study of difficult-to-treat depression . Biol Psychiatry 2003 ; 53 : 743 – 753 Crossref , Medline ,  Google Scholar

45. Veasey SC , Rosen IM : Obstructive sleep apnea in adults . N Engl J Med 2019 ; 380 : 1442 – 1449 Crossref , Medline ,  Google Scholar

46. Ohayon MM : The effects of breathing-related sleep disorders on mood disturbances in the general population . J Clin Psychiatry 2003 ; 64 : 1195 – 1200 Crossref , Medline ,  Google Scholar

47. McCall WV , Benca RM , Rumble ME , et al. : Prevalence of obstructive sleep apnea in suicidal patients with major depressive disorder . J Psychiatr Res 2019 ; 116 : 147 – 150 Crossref , Medline ,  Google Scholar

48. Reddy A , Mansuri Z , Vadukapuram R , et al. : Increased suicidality and worse outcomes in MDD patients with OSA: a nationwide inpatient analysis of 11 years from 2006 to 2017 . J Acad Consult Liaison Psychiatry (Online ahead of print, June 7, 2021) Google Scholar

49. Bishop TM , Ashrafioun L , Pigeon WR : The association between sleep apnea and suicidal thought and behavior: an analysis of national survey data . J Clin Psychiatry 2018 ; 79 : 17m11480 Crossref , Medline ,  Google Scholar

50. Waterman L , Stahl ST , Buysse DJ , et al. : Self-reported obstructive sleep apnea is associated with nonresponse to antidepressant pharmacotherapy in late-life depression . Depress Anxiety 2016 ; 33 : 1107 – 1113 Crossref , Medline ,  Google Scholar

51. Robillard R , Chase T , Courtney D , et al. : Sleep-related breathing disturbances in adolescents with treatment resistant depression . Sleep Med 2019 ; 56 : 47 – 51 Crossref , Medline ,  Google Scholar

52. Jackson ML , Tolson J , Schembri R , et al. : Does continuous positive airways pressure treatment improve clinical depression in obstructive sleep apnea? A randomized wait-list controlled study . Depress Anxiety 2021 ; 38 : 498 – 507 Crossref , Medline ,  Google Scholar

53. American Psychiatric Association : Practice Guideline for the Treatment of Patients With Major Depressive Disorder, 3rd ed . Washington, DC , American Psychiatric Association , 2010 Google Scholar

54. Krahn LE , Hansen MR , Tinsley JA : Psychiatric residents’ exposure to the field of sleep medicine: a survey of program directors . Acad Psychiatry 2002 ; 26 : 253 – 256 Crossref , Medline ,  Google Scholar

55. Rosen CL , Auckley D , Benca R , et al. : A multisite randomized trial of portable sleep studies and positive airway pressure autotitration versus laboratory-based polysomnography for the diagnosis and treatment of obstructive sleep apnea: the HomePAP study . Sleep 2012 ; 35 : 757 – 767 Crossref , Medline ,  Google Scholar

56. Wu JC , Bunney WE : The biological basis of an antidepressant response to sleep deprivation and relapse: review and hypothesis . Am J Psychiatry 1990 ; 147 : 14 – 21 Link ,  Google Scholar

57. Tononi G , Cirelli C : Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration . Neuron 2014 ; 81 : 12 – 34 Crossref , Medline ,  Google Scholar

58. Castrén E : Is mood chemistry? Nat Rev Neurosci 2005 ; 6 : 241 – 246 Crossref , Medline ,  Google Scholar

59. Kuhn M , Mainberger F , Feige B , et al. : State-dependent partial occlusion of cortical LTP-like plasticity in major depression . Neuropsychopharmacology 2016 ; 41 : 2794 Crossref , Medline ,  Google Scholar

60. Wolf E , Kuhn M , Normann C , et al. : Synaptic plasticity model of therapeutic sleep deprivation in major depression . Sleep Med Rev 2016 ; 30 : 53 – 62 Crossref , Medline ,  Google Scholar

61. Duncan WC Jr , Zarate CA Jr : Ketamine, sleep, and depression: current status and new questions . Curr Psychiatry Rep 2013 ; 15 : 394 Crossref , Medline ,  Google Scholar

62. Duncan WC Jr , Ballard ED , Zarate CA : Ketamine-induced glutamatergic mechanisms of sleep and wakefulness: insights for developing novel treatments for disturbed sleep and mood . Handb Exp Pharmacol 2019 ; 253 : 337 – 358 Crossref , Medline ,  Google Scholar

63. Vande Voort JL , Ballard ED , Luckenbaugh DA , et al. : Antisuicidal response following ketamine infusion is associated with decreased nighttime wakefulness in major depressive disorder and bipolar disorder . J Clin Psychiatry 2017 ; 78 : 1068 – 1074 Crossref , Medline ,  Google Scholar

64. Rodrigues NB , McIntyre RS , Lipsitz O , et al. : Do sleep changes mediate the anti-depressive and anti-suicidal response of intravenous ketamine in treatment-resistant depression? J Sleep Res (Online ahead of print, June 16, 2021) Google Scholar

65. Duncan WC Jr , Slonena E , Hejazi NS , et al. : Motor-activity markers of circadian timekeeping are related to ketamine’s rapid antidepressant properties . Biol Psychiatry 2017 ; 82 : 361 – 369 Crossref , Medline ,  Google Scholar

66. Kohtala S , Alitalo O , Rosenholm M , et al. : Time is of the essence: coupling sleep-wake and circadian neurobiology to the antidepressant effects of ketamine . Pharmacol Ther 2021 ; 221 : 107741 Crossref , Medline ,  Google Scholar

67. American Psychiatric Association : Diagnostic and Statistical Manual of Mental Disorders , 5th ed . Washington, DC , American Psychiatric Association , 2013 Crossref ,  Google Scholar

68. Insel TR : The NIMH Research Domain Criteria (RDoC) project: precision medicine for psychiatry . Am J Psychiatry 2014 ; 171 : 395 – 397 Link ,  Google Scholar

69. Cuthbert BN , Insel TR : Toward the future of psychiatric diagnosis: the seven pillars of RDoC . BMC Med 2013 ; 11 : 126 Crossref , Medline ,  Google Scholar

70. Geiser T , Hertenstein E , Fehér K , et al. : Targeting arousal and sleep through noninvasive brain stimulation to improve mental health . Neuropsychobiology 2020 ; 79 : 284 – 292 Crossref , Medline ,  Google Scholar

71. Kaplan KA , Harvey AG : Hypersomnia across mood disorders: a review and synthesis . Sleep Med Rev 2009 ; 13 : 275 – 285 Crossref , Medline ,  Google Scholar

72. Plante DT : Hypersomnia in mood disorders: a rapidly changing landscape . Curr Sleep Med Rep 2015 ; 1 : 122 – 130 Crossref , Medline ,  Google Scholar

73. Plante DT , Finn LA , Hagen EW , et al. : Longitudinal associations of hypersomnolence and depression in the Wisconsin Sleep Cohort Study . J Affect Disord 2017 ; 207 : 197 – 202 Crossref , Medline ,  Google Scholar

74. Plante DT , Cook JD , Barbosa LS , et al. : Establishing the objective sleep phenotype in hypersomnolence disorder with and without comorbid major depression . Sleep 2019 ; 42 : zsz060 Crossref , Medline ,  Google Scholar

75. Plante DT , Finn LA , Hagen EW , et al. : Subjective and objective measures of hypersomnolence demonstrate divergent associations with depression among participants in the Wisconsin Sleep Cohort Study . J Clin Sleep Med 2016 ; 12 : 571 – 578 Crossref , Medline ,  Google Scholar

76. Plante DT , Hagen EW , Ravelo LA , et al. : Impaired neurobehavioral alertness quantified by the psychomotor vigilance task is associated with depression in the Wisconsin Sleep Cohort study . Sleep Med 2020 ; 67 : 66 – 70 Crossref , Medline ,  Google Scholar

77. Plante DT , Papale LA , Madrid A , et al. : PAX8/PAX8-AS1 DNA methylation levels are associated with objective sleep duration in persons with unexplained hypersomnolence using a deep phenotyping approach . Sleep (Online ahead of print, June 18, 2021) Google Scholar

78. Cook JD , Eftekari SC , Dallmann E , et al. : Ability of the Fitbit Alta HR to quantify and classify sleep in patients with suspected central disorders of hypersomnolence: a comparison against polysomnography . J Sleep Res 2019 ; 28 : e12789 Crossref , Medline ,  Google Scholar

79. Cook JD , Prairie ML , Plante DT : Ability of the Multisensory Jawbone UP3 to quantify and classify sleep in patients with suspected central disorders of hypersomnolence: a comparison against polysomnography and actigraphy . J Clin Sleep Med 2018 ; 14 : 841 – 848 Crossref , Medline ,  Google Scholar

80. Cook JD , Prairie ML , Plante DT : Utility of the Fitbit Flex to evaluate sleep in major depressive disorder: a comparison against polysomnography and wrist-worn actigraphy . J Affect Disord 2017 ; 217 : 299 – 305 Crossref , Medline ,  Google Scholar

81. Benca RM , Obermeyer WH , Thisted RA , et al. : Sleep and psychiatric disorders: a meta-analysis . Arch Gen Psychiatry 1992 ; 49 : 651 – 668 Crossref , Medline ,  Google Scholar

• Hypnotic Medications as an Adjunct Treatment to Cognitive Behavioral Therapy for Insomnia Sleep Medicine Clinics, Vol. 18, No. 1
• Association between psychotropic medication and sleep microstructure: evidence from large population studies Journal of Clinical Sleep Medicine, Vol. 19, No. 3
• Factors associated with poor sleep quality in patients with pre‐dialysis chronic kidney disease: A systematic review 21 February 2023 | Journal of Advanced Nursing
• Trends and disparities in sleep quality and duration in older adults in China from 2008 to 2018: A national observational study 17 February 2023 | Frontiers in Public Health, Vol. 11
• Downregulation of microRNA ‐330‐5p induces manic‐like behaviors in REM sleep‐deprived rats by enhancing tyrosine hydroxylase expression 16 February 2023 | CNS Neuroscience & Therapeutics, Vol. 8
• Recent advances in sleep and depression 16 November 2022 | Current Opinion in Psychiatry, Vol. 36, No. 1
• Association of depression symptoms and sleep quality with state-trait anxiety in medical university students in Anhui Province, China: a mediation analysis 19 August 2022 | BMC Medical Education, Vol. 22, No. 1
• Caffeine-Induced Sleep Restriction Alters the Gut Microbiome and Fecal Metabolic Profiles in Mice 27 November 2022 | International Journal of Molecular Sciences, Vol. 23, No. 23
• Correlation Between Sleep Electroencephalogram, Brain-Derived Neurotrophic Factor, AVPR1B Gene Polymorphism, and Suicidal Behavior in Patients with Depression 11 November 2022 | Applied Biochemistry and Biotechnology, Vol. 85
• The individual and joint associations of depression and sleep duration with cardiometabolic diseases and mortality: A prospective cohort study Atherosclerosis, Vol. 361
• Neural mechanism of the relationship between sleep efficiency and clinical improvement in major depressive disorder: A longitudinal functional magnetic resonance imaging study 3 October 2022 | Frontiers in Psychiatry, Vol. 13
• The DBST Index, the Discrepancy Between Desired Time in Bed and Desired Total Sleep Time: The Possible New Sleep Index Predicting Severity of Insomnia Sleep Medicine Research, Vol. 13, No. 2
• Sex Differences in Responses to Antidepressant Augmentations in Treatment-Resistant Depression 15 February 2022 | International Journal of Neuropsychopharmacology, Vol. 25, No. 6
• COVID-19, Critical Illness, and Sleep* 28 February 2022 | Critical Care Medicine, Vol. 50, No. 6
• Relationship of depression and sleep quality, diseases and general characteristics World Journal of Psychiatry, Vol. 12, No. 5
• Discrepancy Between Desired Time in Bed and Desired Total Sleep Time, Insomnia, Depression, and Dysfunctional Beliefs About Sleep Among the General Population Psychiatry Investigation, Vol. 19, No. 4
• Sleep Efficiency May Predict Depression in a Large Population-Based Study 13 April 2022 | Frontiers in Psychiatry, Vol. 13
• A Scoping Review and Conceptual Framework Examining the Role of Sleep Disturbance in Financial Exploitation in Older Adults 4 August 2022 | Gerontology and Geriatric Medicine, Vol. 8
• Improved Functional Organization in Patients With Primary Insomnia After Individually-Targeted Transcranial Magnetic Stimulation 10 March 2022 | Frontiers in Neuroscience, Vol. 16
• Ned H. Kalin , M.D.

• Depressive Disorders
• Sleep Disorders

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Overview of sleep & sleep disorders

Affiliation.

• 1 Department of Neuroscience, Divisions of Sleep Medicine & Clinical Neurophysiology at the NJ Neuroscience Institute at JFK Medical Center, Edison, NJ, USA. [email protected]
• PMID: 20308738

Sleep is defined on the basis of behavioural and physiological criteria dividing it into two states: non rapid eye movement (NREM) sleep which is subdivided into three stages (N1, N2, N3); and rapid eye movement (REM) sleep characterized by rapid eye movements, muscle atonia and desynchronized EEG. Circadian rhythm of sleep-wakefulness is controlled by the master clock located in the suprachiasmatic nuclei of the hypothalamus. The neuroanatomical substrates of the NREM sleep are located principally in the ventrolateral preoptic nucleus of the hypothalamus and those of REM sleep are located in pons. A variety of significant physiological changes occur in all body systems and organs during sleep as a result of functional alterations in the autonomic and somatic nervous systems. The international classification of sleep disorders (ICSD, ed 2) lists eight categories of sleep disorders along with appendix A and appendix B. The four major sleep complaints include excessive daytime sleepiness, insomnia, abnormal movements or behaviour during sleep and inability to sleep at the desired time. The most important step in assessing a patient with a sleep complaint is obtaining a detailed history including family and previous histories, medical, psychiatric, neurological, drug, alcohol and substance abuse disorders. Some important laboratory tests for investigating sleep disorders consist of an overnight polysomnography, multiple sleep latency and maintenance of wakefulness tests as well as actigraphy. General physicians should have a basic knowledge of the salient clinical features of common sleep disorders, such as insomnia, obstructive sleep apnoea syndrome, narcolepsy-cataplexy syndrome, circadian rhythm sleep disorders (e.g., jet leg, shift work disorder, etc.) and parasomnias (e.g., partial arousal disorders, REM behaviour disorder, etc.) and these are briefly described in this chapter. The principle of treatment of sleep disorders is first to find cause of the sleep disturbance and vigorously treat the co-morbid conditions causing the sleep disturbance. If a satisfactory treatment is not available for the primary condition or does not resolve the problem, the treatment should be directed at the specific sleep disturbance. Most sleep disorders, once diagnosed, can be managed with limited consultations. The treatment of primary sleep disorders, however, is best handled by a sleep specialist. An overview of sleep and sleep disorders viz., Basic science; international classification and approach; and phenomenology of common sleep disorders are presented.

PubMed Disclaimer

Similar articles

• Management of common sleep disorders. Ramar K, Olson EJ. Ramar K, et al. Am Fam Physician. 2013 Aug 15;88(4):231-8. Am Fam Physician. 2013. PMID: 23944726 Review.
• [Selective stimulations and lesions of the rat brain nuclei as the models for research of the human sleep pathology mechanisms]. Šaponjić J. Šaponjić J. Glas Srp Akad Nauka Med. 2011;(51):85-97. Glas Srp Akad Nauka Med. 2011. PMID: 22165729 Review. Serbian.
• [Movement disorders in sleep]. Etzioni T, Pillar G. Etzioni T, et al. Harefuah. 2007 Jul;146(7):544-8, 573. Harefuah. 2007. PMID: 17803170 Review. Hebrew.
• Sleep disorders. Krahn LE. Krahn LE. Semin Neurol. 2003 Sep;23(3):307-14. doi: 10.1055/s-2003-814742. Semin Neurol. 2003. PMID: 14722826
• Periodic leg movements (PLM): their relationship to sleep stages. Pollmächer T, Schulz H. Pollmächer T, et al. Sleep. 1993 Sep;16(6):572-7. Sleep. 1993. PMID: 8235243
• The role of arts therapies in mitigating Sleep Initiation and Maintenance Disorders: a systematic review. Luo X, Zhang A, Li H, Li Y, Ying F, Wang X, Yang Q, Zhang Z, Huang G. Luo X, et al. Front Psychiatry. 2024 May 16;15:1386529. doi: 10.3389/fpsyt.2024.1386529. eCollection 2024. Front Psychiatry. 2024. PMID: 38818021 Free PMC article.
• Prevalence and Characteristics of Sleep Disorders in Children Aged 7-17: Insights from Parental Observations at the Dental Office. Diéguez-Pérez M, Burgueño-Torres L, Reichard-Monefeldt G, Tapia-Sierra FE, Ticona-Flores JM. Diéguez-Pérez M, et al. Children (Basel). 2024 May 20;11(5):609. doi: 10.3390/children11050609. Children (Basel). 2024. PMID: 38790604 Free PMC article.
• FDA-cleared home sleep apnea testing devices. Park JH, Wang C, Shin H. Park JH, et al. NPJ Digit Med. 2024 May 13;7(1):123. doi: 10.1038/s41746-024-01112-w. NPJ Digit Med. 2024. PMID: 38740907 Free PMC article.
• Association between Exposure to Volatile Organic Compounds and the Prevalence of Sleep Problems in US Adults. Sun J, Gui C, Xiao Y, Ma R, Liu C, He L, Zhao H, Luo B. Sun J, et al. Toxics. 2024 Mar 18;12(3):222. doi: 10.3390/toxics12030222. Toxics. 2024. PMID: 38535955 Free PMC article.
• Comparison of the usability of an automatic sleep staging program via portable 1-channel electroencephalograph and manual sleep staging with traditional polysomnography. Kawamura A, Yoshiike T, Matsuo M, Kadotani H, Oike Y, Kawasaki M, Kurumai Y, Nagao K, Takami M, Yamada N, Kuriyama K. Kawamura A, et al. Sleep Biol Rhythms. 2022 Aug 26;21(1):85-95. doi: 10.1007/s41105-022-00421-5. eCollection 2023 Jan. Sleep Biol Rhythms. 2022. PMID: 38468906 Free PMC article.

Publication types

• Search in MeSH

Related information

• PubChem Compound
• PubChem Substance

LinkOut - more resources

Full text sources.

• Ovid Technologies, Inc.
• Wolters Kluwer

Other Literature Sources

• The Lens - Patent Citations
• MedlinePlus Health Information

• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

• Patient Care & Health Information
• Diseases & Conditions
• Sleep disorders

Sleep disorders are conditions that result in changes in the way that you sleep.

A sleep disorder can affect your overall health, safety and quality of life. Sleep deprivation can affect your ability to drive safely and increase your risk of other health problems.

Some of the signs and symptoms of sleep disorders include excessive daytime sleepiness, irregular breathing or increased movement during sleep. Other signs and symptoms include an irregular sleep and wake cycle and difficulty falling asleep.

There are many different types of sleep disorders. They're often grouped into categories that explain why they happen or how they affect you. Sleep disorders can also be grouped according to behaviors, problems with your natural sleep-wake cycles, breathing problems, difficulty sleeping or how sleepy you feel during the day.

Some common types of sleep disorders include:

• Insomnia, in which you have difficulty falling asleep or staying asleep throughout the night.
• Sleep apnea, in which you experience abnormal patterns in breathing while you are asleep. There are several types of sleep apnea.
• Restless legs syndrome (RLS), a type of sleep movement disorder. Restless legs syndrome, also called Willis-Ekbom disease, causes an uncomfortable sensation and an urge to move the legs while you try to fall asleep.
• Narcolepsy, a condition characterized by extreme sleepiness during the day and falling asleep suddenly during the day.

There are many ways to help diagnose sleep disorders. Doctors can usually treat most sleep disorders effectively once they're correctly diagnosed.

Products & Services

• A Book: Mayo Clinic Family Health Book
• Newsletter: Mayo Clinic Health Letter — Digital Edition

Symptoms of sleep disorders include being very sleepy during the daytime and having trouble falling asleep at night. Some people may fall asleep at inappropriate times, such as while driving. Other symptoms include breathing in an unusual pattern or feeling an uncomfortable urge to move while you are trying to fall asleep. Unusual or bothersome movements or experiences during sleep are also possible. Having an irregular sleep and wake cycle is another symptom of sleep disorders.

Clinical trials

Explore Mayo Clinic studies testing new treatments, interventions and tests as a means to prevent, detect, treat or manage this condition.

Sleep disorders care at Mayo Clinic

• Judd BG, et al. Classification of sleep disorders. http://www.uptodate.com/home. Accessed July 25, 2016.
• Chervin RD. Approach to the patient with excessive daytime sleepiness. http://www.uptodate.com/home. Accessed July 25, 2016.
• Sleep disorders and problems. National Sleep Foundation. https://sleepfoundation.org/sleep-disorders-problems. July 25, 2016.
• Olson EJ (expert opinion). Mayo Clinic, Rochester, Minn. July 8, 2019.
• Sleep disorders. American Academy of Sleep Medicine. http://yoursleep.aasmnet.org/Disorders.aspx. Accessed July 25, 2016.
• Riggin EA. Allscripts EPSi. Mayo Clinic, Rochester, Minn. June 24, 2019.
• American Academy of Sleep Medicine. International Classification of Sleep Disorders. 3rd ed. Darien, IL: American Academy of Sleep Medicine; 2014.
• Symptoms & causes
• Doctors & departments
• Care at Mayo Clinic

Mayo Clinic does not endorse companies or products. Advertising revenue supports our not-for-profit mission.

• Opportunities

Mayo Clinic Press

Check out these best-sellers and special offers on books and newsletters from Mayo Clinic Press .

• Mayo Clinic on Incontinence - Mayo Clinic Press Mayo Clinic on Incontinence
• The Essential Diabetes Book - Mayo Clinic Press The Essential Diabetes Book
• Mayo Clinic on Hearing and Balance - Mayo Clinic Press Mayo Clinic on Hearing and Balance
• FREE Mayo Clinic Diet Assessment - Mayo Clinic Press FREE Mayo Clinic Diet Assessment
• Mayo Clinic Health Letter - FREE book - Mayo Clinic Press Mayo Clinic Health Letter - FREE book

Your gift holds great power – donate today!

Make your tax-deductible gift and be part of the cutting-edge research and care that's changing medicine.

• Sleep Disorders
• Good Sleep Habits
• Other Sleep Problems
• What Affects Sleep
• Tests & Treatment
• View Full Guide

Understanding Sleep Problems -- The Basics

During normal sleep, you cycle through REM and four stages of non-REM (NREM) sleep numerous times a night. Stage 1 of NREM sleep is the lightest, while stage 4 is the deepest. 

When you're repeatedly interrupted and can't cycle normally through these types and stages of sleep , you may feel tired, fatigued, and have trouble concentrating and paying attention while you're awake. Sleepiness puts you at greater risk for car wrecks and other accidents.

What Are Sleep Disorders?

Circadian Rhythm Disorders

Typically, people sleep at night -- thanks not only to the conventions of the 9-to-5 workday, but also to the close interaction between our natural sleep and alertness rhythms, which are driven by an internal "clock."

This clock is a small part of the brain called the suprachiasmatic nucleus of the hypothalamus. It sits just above the nerves leaving the back of our eyes . Light and exercise "reset" the clock and can move it forward or backward. Abnormalities related to this clock are called circadian rhythm disorders ("circa" means "about," and "dies" means "day").

Circadian rhythm disorders include jet lag , adjustments to shift work, delayed sleep phase syndrome (you fall asleep and wake up too late), and advanced sleep phase syndrome (you fall asleep and wake up too early).

People who have insomnia don't feel as if they get enough sleep at night. They may have trouble falling asleep or may wake up frequently during the night or early in the morning. Insomnia is a problem if it affects your daytime activities. Insomnia has many possible causes, including stress , anxiety , depression, poor sleep habits, circadian rhythm disorders (such as jet lag ), and taking certain medications .

Many adults snore. The noise is produced when the air you inhale rattles over the relaxed tissues of the throat. Snoring can be a problem simply because of the noise it causes. It may also be a marker of a more serious sleep problem called sleep apnea .

• Sleep Apnea

Sleep apnea occurs when the upper airway becomes completely or partially blocked, interrupting regular breathing for short periods of time -- which then wakes you up. It can cause severe daytime sleepiness. If left untreated, severe sleep apnea may be associated with high blood pressure and the risk of stroke and heart attack .

Pregnancy and Sleep

Women often experience sleepless nights and daytime fatigue in the first and third trimesters of their pregnancy. During the first trimester , frequent trips to the bathroom and morning sickness may disrupt sleep. Later in pregnancy, vivid dreams and physical discomfort may prevent deep sleep. After delivery, the new baby's care or the mother's postpartum depression may interrupt sleep.

Narcolepsy is a brain disorder that causes excessive daytime sleepiness. There is sometimes a genetic component, but most patients have no family history of the problem. Though dramatic and uncontrolled "sleep attacks" have been the best-known feature of narcolepsy, in reality many patients do not have sleep attacks. Instead, they experience constant sleepiness during the day.

• Restless Legs Syndrome

In people who have restless legs syndrome, discomfort in the legs and feet peaks during the evening and night. They feel an urge to move their legs and feet to get temporary relief, often with excessive, rhythmic, or cyclic leg movements during sleep. This can delay sleep onset and cause brief awakening during sleep. Restless legs syndrome is a common problem among middle-aged and older adults.

Nightmares are frightening dreams that arise during REM sleep. They can be caused by stress, anxiety, and some drugs. Often, there is no clear cause.

Night Terrors and Sleepwalking

Both night terrors and sleepwalking arise during NREM sleep and occur most often in children between the ages of 3 and 5 years old. A night terror can be dramatic: Your child may wake up screaming, but unable to explain the fear. Sometimes children who have night terrors remember a frightening image, but often they remember nothing. Night terrors are often more frightening for parents than for their child. Sleepwalkers can perform a range of activities -- some potentially dangerous, like leaving the house -- while they continue to sleep.

What Causes Sleep Disorders?

Insomnia may be temporary and stem from a simple cause, such as jet lag. Short-term insomnia may also be caused by an illness, a stressful event, or drinking too much coffee, for example. Many medications have insomnia as a side effect.

Long-term insomnia may be caused by stress, depression , or anxiety . People can also become conditioned to insomnia: They associate bedtime with difficulty, expect to have trouble sleeping (and thus do), and become irritable (which can cause more insomnia). This cycle can be maintained for several years.

Circadian rhythm disorders are an important but less common cause of insomnia. People who abuse alcohol or drugs often suffer from insomnia.

Snoring and Sleep Apnea

When you fall asleep, many muscles in your body relax. If muscles in the throat relax too much, your breathing may be blocked and you may snore. Sometimes, snoring is caused by allergies , asthma , or nasal deformities that make breathing difficult.

Apnea means "no airflow." Obstructive sleep apnea was thought to be a disorder primarily of overweight , older men. But abnormal breathing during sleep can affect people of any age, any weight , and either sex. Researchers now know that in many cases of sleep apnea, the obstruction in the airways is only partial. Most people with sleep apnea have a smaller-than-normal inner throat and other subtle bone and soft-tissue differences.

Drops in blood oxygen during sleep -- once thought to be the cause of waking up due to obstructive sleep apnea -- may or may not be present. Most likely, awakening occurs with the body's increased effort required to overcome the obstruction of the airway.

Drinking alcohol can make obstructive sleep apnea worse because it relaxes muscles that maintain an open airway.

A rare form of sleep apnea called central sleep apnea occurs when signals from the brain to your muscles decrease or stop for a short time. You may not snore if you have central sleep apnea .

You may need to consult an ear, nose, and throat specialist or have a sleep study to find out why you snore and whether you have sleep apnea.

Fatigue during the first trimester of pregnancy is likely caused by changing levels of hormones, such as progesterone . Toward the end of pregnancy, some women find it difficult to sleep because of the uncomfortable size of their abdomen . Some women are too excited, anxious, or worried about becoming mothers to sleep well. Other women who are pregnant complain that vivid dreams prevent them from getting restful sleep. Sleep apnea, especially if it's severe and causes your blood oxygen level to drop during sleep, is a risk to the fetus .

The cause of narcolepsy is not clear. Genetic and environmental factors likely play a role, although the data on genetic factors is still speculative and not well studied. There are some rare nerve disorders that may be linked to narcolepsy.

There are many possible causes of restless legs syndrome, including kidney failure, nerve disorders, vitamin and iron deficiencies, pregnancy, and some medications (such as antidepressants ). Recent studies have shown a strong genetic link and researchers have been able to isolate a gene that may be responsible for at least 40% of all cases of the disorder.

Nightmares and Night Terrors

Nightmares can be triggered by a frightening or stressful event, a fever or illness, or use of some medications or alcohol. Night terrors are most common in pre-school children, but they also can affect adults who are experiencing emotional or psychological problems.

Other Things that Impact Sleep

Young age . Infants may sleep up to 16 hours a day. But most won't sleep through the night without a feeding until 4 months of age. School-aged children may sleep 10 hours a day. Their sleep may be disturbed by an illness or fever. Call your doctor if your child has a fever and is sluggish when waking up.

Old age . People over age 60 may not sleep as deeply as younger people. Sleep apnea is also more common among older people.

Lifestyle . People who drink coffee, smoke cigarettes, or drink alcohol are more likely to have sleep problems than people who do not.

Medication . Many drugs can cause sleeplessness. Others can cause daytime fatigue .

Depression and anxiety . Insomnia is a common symptom of depression and anxiety.

Heart failure and lung problems . Some people find it difficult to sleep at night because they become breathless when they lie down. This can be a symptom of heart failure or a problem with the lungs .

• Can't Sleep at Night?
• Sleep Disorders Diagnosis and Treatment
• Sleeping Medications
• Sleep Specialist
• When to See a Doctor for Sleep Problems
• Sleep Journal

Top doctors in ,

Find more top doctors on, related links.

• Healthy Sleep News
• Healthy Sleep Reference
• Healthy Sleep Slideshows
• Healthy Sleep Quizzes
• Healthy Sleep Blogs
• Healthy Sleep Videos
• Sleep Medications
• Find a Neurologist
• Find a Sleep Specialist
• Circadian Rhythm
• Drug Interaction Checker
• Teeth Grinding
• Track Your Sleep
• More Related Topics

• Open access
• Published: 02 July 2024

Interaction effects between sleep-related disorders and depression on hypertension among adults: a cross-sectional study

• Chunhua Liu 1   na1 ,
• Zegen Ye 1   na1 ,
• Liping Chen 1 ,
• Huaqiang Wang 1 ,
• Binbin Wu 1 ,
• Sisi Pan 1 ,
• Weiwen Qiu 1 , 3 &
• Haiqin Ye 2 , 4  

BMC Psychiatry volume  24 , Article number:  482 ( 2024 ) Cite this article

Metrics details

Hypertension, sleep disorders, and depression represent notable public health issues, and their interconnected nature has long been acknowledged. The objective of this study is to explore the interplay between sleep disorders and depression in the context of hypertension.

This cross-sectional study involved 42,143 participants aged 18 and above from the NHANES database across seven survey cycles between 2005 and 2018. After excluding those with missing data on depression, sleep disorders, and hypertension, as well as incomplete main variables, 33,383 participants remained. We used weighted logistic regression to examine the relationship between sleep disorders, depression, and hypertension. Additionally, we assessed the interaction between sleep disorders and depression on hypertension using both multiplicative and additive approaches to quantify their combined effect.

Compared to individuals without sleep disorders, those with sleep disorders have an increased risk of hypertension (OR = 1.51, 95% CI: 1.37–1.67). Furthermore, individuals with depression experience a significantly higher risk of hypertension compared to those with sleep disorders alone (OR = 2.34, 95% CI: 1.95–2.80). Our study reveals a positive interaction between sleep disorders and depression in relation to hypertension risk (OR = 1.07, 95% CI: 1.02–1.13). In addition, we observed the quantitative additive interaction indicators (RERI = 0.73, 95% CI: 0.56 ~ 0.92; API = 0.31, 95% CI: 0.11 ~ 0.46; SI = 2.19, 95% CI: 1.08–3.46) influencing hypertension risk. Furthermore, our research also identified that individuals with less than 7 h of sleep, a sleep latency period between 5 and 30 min, or a latency period exceeding 30 min experience a significantly increased risk of hypertension.

Conclusions

Our research uncovered separate links between sleep disorders, depression, and hypertension prevalence. Moreover, we identified an interaction between depression and sleep disorders in hypertension prevalence. Enhancing mental well-being and tackling sleep disorders could help prevent and manage hypertension. Yet, more investigation is required to establish causation and clarify mechanisms.

Peer Review reports

Hypertension, a prevalent risk factor for cardiovascular disease, affects more than 1.2 billion people worldwide. It has emerged as a grave and costly public health dilemma, garnering significant attention [ 1 ]. Meta-analyses have consistently revealed a substantial association between hypertension and an elevated risk of neurological diseases such as Parkinson’s disease and stroke [ 2 , 3 , 4 ]. In addition, hypertension stands as a paramount predictor of mortality, exerting its influence as a global risk factor for death, disability, and years of life lost [ 5 ]. Furthermore, hypertension imposes a substantial economic burden. Despite improvements in hypertension awareness and treatment, the control rate among the hypertensive population remains low, plunging below 20–30% in several Western countries [ 6 , 7 ]. Given the increasingly younger age of hypertension onset, it becomes imperative to prioritize managing the overall risk profile of patients afflicted with underlying hypertension rather than solely focusing on blood pressure (BP) measurement. Therefore, it becomes crucial to delve into the potential risk factors of hypertension and establish efficacious prevention and risk management strategies.

When examining the factors contributing to hypertension, dietary habits (specifically, high sodium intake) and unhealthy lifestyles have consistently garnered attention. More recently, studies have uncovered a significant connection between sleep disorders and various chronic conditions, including cardiovascular disease [ 8 ], chronic kidney disease [ 9 ], and cognitive impairment [ 10 ]. Both insufficient and excessive sleep duration, as well as prolonged sleep onset latency, have been associated with an elevated risk of chronic ailments [ 11 ]. Sleep patterns influence blood pressure through alterations in autonomic nervous system function and other physiological mechanisms. Unfavorable sleep conditions, such as sleep apnea, insomnia, and abnormal sleep duration, heighten the susceptibility to hypertension [ 12 ]. Nevertheless, further research is required to investigate the relationship between sleep-related issues and blood pressure across different age and gender groups.

Depression, a mood disorder classified within the psychiatric domain, imposes a significant global disease burden [ 13 ]. Notably, studies have identified cardiovascular disease as the leading cause of mortality among individuals with mental illness [ 14 , 15 ]. This may be attributed to the pronounced BP fluctuations in individuals with psychiatric disorders, leading to increased cardiovascular risk [ 16 ]. Research has demonstrated a higher incidence of hypertension among patients with depression [ 17 , 18 , 19 ]. Furthermore, a systematic review has revealed a bidirectional relationship between sleep disorders and depression [ 20 ]. The interplay between depression and sleep disturbance augments the risk of stroke, and the risk of high blood pressure in people aged 60 years and above [ 21 , 22 ]. Consequently, there may exist shared pathways between depression and sleep disturbances that exert a mutual influence on cardiovascular disease, thereby substantially increasing the risk of cardiovascular disease in people affected by both conditions. However, prior investigations have primarily focused on individuals aged 60 years and above, with limited studies encompassing diverse adult age groups. In the context of mounting societal and economic pressures, the prevalence of sleep-related disorders and depression is escalating higher among younger. Therefore, it remains imperative for clinicians to elucidate the intricate relationship among sleep disorders, depression, and hypertension. Such insights will serve to enhance the management of hypertension by effectively addressing its underlying risk factors [ 23 , 24 ]. In this study, we utilized data from the National Health and Nutrition Examination Survey (NHANES), conducted by the National Center for Health Statistics (NCHS) in the United States, to explore the interaction between sleep disorders, depression, and hypertension risk. We also examined the independent and bidirectional associations between sleep disorder and depression. Furthermore, we performed a stratified analysis based on demographic factors such as age, sex, and body mass index to gain insight into potential variations within these associations.

Study design and population

The NHANES assesses the health and nutritional status of both adults and children in the United States. This research project employs questionnaires and physical examinations to target various population groups and health issues. Its findings help determine disease prevalence and risk factors, assess nutritional status, and understand the relationship between nutrition and health outcomes for disease prevention and health promotion. We conducted an analysis using data from the NHANES, an integral project of the National Center for Health Statistics under the Centers for Disease Control and Prevention [ 25 ]. The data was collected through a meticulous multistage probabilistic design that encompassed geographically stratified areas and proportional representation of minority populations. The NHANES database is typically managed by expert investigators affiliated with the NCHS or associated organizations. Trained extensively, these investigators oversee various survey phases, including demographic and dietary data, questionnaire survey data, laboratory examination data, and health check data. Their expertise guarantees the precision and dependability of NHANES surveys. For our analysis, we integrated seven consecutive NHANES survey cycles spanning from 2005 to 2018. Ethical approval was obtained from the National Center for Health Statistics Ethical Review Board, and all participants provided written informed consent [ 26 ].

This study implemented a cross-sectional design to retrieve data from the NHANES database, specifically targeting 42,143 participants aged 18 years and above across the seven survey cycles between 2005 and 2018. Exclusion criteria were applied by the study design, which included the following: [ 1 ] age < 18 years; [ 2 ] missing depression questionnaire data; [ 3 ] missing hypertension data; [ 4 ] duplicate respondents; [ 5 ] missing sleep disorder data; [ 5 ] respondents with incomplete main covariates. The selection process, as visually shown in Fig.  1 , resulted in the inclusion of 33,383 participants for our study.

The screening process of participants in this study

Assessment of hypertension

The primary outcome variable in this study was the presence of hypertension among the participants. Hypertension was defined as previously described [ 27 , 28 ]: [ 1 ] a mean systolic blood pressure ≥ 130 mmHg or a diastolic blood pressure ≥ 80 mmHg; [ 2 ] self-reported diagnosis of hypertension; [ 3 ] self-reported use of antihypertensive medication. Any of the above three conditions was indicative of a diagnosis of hypertension [ 29 ]. However, due to substantial data gaps in the other two methods, we chose questionnaire surveys as the hypertension diagnostic method. Hence, self-reporting was employed in this study to assess hypertension. Participants were asked a specific question, to which they responded affirmatively: “ Have you ever received a diagnosis of high blood pressure from a physician or other healthcare professional? ” While this question does not serve as a conclusive diagnosis of hypertension, it has been employed in epidemiological studies and has demonstrated utility in screening for hypertension [ 30 ].

Assessment of sleep-related disorders

We evaluated several sleep-related issues, including sleep duration, sleep onset latency, sleep disorders, and sleep difficulties. These outcomes were defined based on the NHANES sleep questionnaire [ 31 , 32 , 33 ]: sleep duration was categorized as relatively insufficient (< 7 h/night), normal (7–8 h/night), or relatively excessive (> 8 h/night). Sleep onset latency was classified into short (< 5 min/night), normal (5–30 min/night), or long (> 30 min/night). Regarding sleep disorders, participants were identified as experiencing a sleep disorder or having difficulty sleeping if they responded affirmatively to the question, “ Have you ever reported to a physician or other healthcare professional that you encounter challenges with sleep or have a diagnosed sleep disorder? “.

Assessment of depression

In the NHANES database, the assessment of depressive symptoms was conducted employing the PHQ-9 screening tool, which is encompassed in the diagnostic criteria for depression outlined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV). This screening tool has established its reliability and efficacy for both clinical and research purposes [ 34 ]. The questionnaire consists of nine questions, with each item rated on a scale from 0 to 3, yielding a total score ranging from 0 to 27. Rating options include 0 (no symptoms present), 1 (symptoms occurring over a few days), 2 (symptoms present more than half the time), and 3 (symptoms present almost daily). These questions were administered by trained interviewers at the Mobile Examination Center (MEC), a mobile medical facility utilized in the NHANES study for conducting on-site examinations and data collection. The PHQ-9 primarily captures the frequency of self-reported depressive symptoms experienced within the preceding two-week period [ 35 ]. Following DSM-IV, a PHQ-9 score of ≥ 10 was deemed indicative of a depressive symptom [ 36 ]. The severity of depression was further categorized as follows: absence of depression (PHQ-9 score: 0–9), moderate depression (PHQ-9 score: 10–14), moderate to severe depression (PHQ-9 score: 15–19), and severe depression (PHQ-9 score: 20–27) [ 37 ].

Data collection

Demographic information was collected through questionnaires, encompassing variables such as age (categorized into 18–44 years, 45–64 years, and 65 years and older), sex, race (including non-Hispanic white, non-Hispanic black, Hispanic, etc.), educational attainment (college or lower), and the ratio of family income to poverty (classified as below 1.33, 1.33–3.50, and above 3.5), representing the proportion of household income relative to the federal poverty line, adjusted for family size. Additional covariates comprised smoking status (defined as non-smoker [less than 100 cigarettes in a lifetime], former smoker [more than 100 cigarettes in one’s lifetime, now not smoking at all], current smoker [more than 100 cigarettes in a lifetime, now smoking sometimes or daily]), and drinking status (categorized as non-drinker, 1–5 drinks/month, 5–10 drinks/month, and 10 + drinks/month) [ 38 ], body mass index (low weight [< 18.5 kg/m 2 ], normal weight [18.5–25 kg/m 2 ], overweight [25–30 kg/m 2 ], obesity [≥ 30 kg/m 2 ]), and diabetes data. The diagnostic criteria for diabetes in this study included: [ 1 ] physician-diagnosed diabetes; [ 2 ] glycosylated hemoglobin (HbA1c) level exceeding 6.5%; [ 3 ] fasting blood glucose (FBG) level of ≥ 7.0 mmol/l. Blood samples for HbA1c measurement and FBG analysis were collected by trained medical personnel according to standardized procedures during the NHANES survey. Subsequent laboratory analysis of blood samples was conducted by qualified technicians to determine the HbA1c and FBG levels. The presence of any of these three conditions signified a diabetes diagnosis.

Key study variables encompassed hypertension, sleep disorder, depression, and sleep-related problems (sleep duration: 7–8 h/night, < 7 h/night, > 8 h/night; sleep onset time: <5 min, 5–30 min, > 30 min). All participants were divided into two groups: those with hypertension ( n  = 11,760) and those without hypertension ( n  = 21,623).

Statistical analysis

Statistical analysis in this study incorporated the complex sampling design of the NHANES database by applying weighted analysis using interview weights (WTMEC2YR) and sampling weights for study design variables (SDMVPSU and SDMVSTRA). Continuous variables were expressed as mean ± standard deviation (SD) and compared using Student’s t-test between groups. Categorical data were presented as counts and percentages [n (%)] and analyzed using the Rao-Scott chi-square test. Statistical software packages utilized for analysis included SPSS (version 23.0) and R (version 4.1.3). Multivariate logistic regression analysis was used to examine the relationship between sleep disorder, depression, and their interaction with hypertension. Model 1 was unadjusted (crude). Model 2 adjusted for sex, age, race, education level, and the ratio of family income to poverty. Model 3 added further adjustments for BMI, drinking status, smoking status, and diabetes [ 39 , 40 ]. In this study, all analyses were conducted using a two-sided approach, with statistical significance set at P < 0.05. We utilized several commonly used statistical packages, including ‘stats,’ ‘gtsummary,’ ‘glm,’ and ‘survey.’ Additionally, we employed the ‘interactionR’ tool to explore relevant indicators of additive interactions. It’s worth noting that we customized these R software packages to meet the specific needs of our study, allowing us to compute additional metrics and results.

Initially, we investigate whether there is a multiplicative interaction between sleep disorders and depression concerning hypertension risk by examining their product. This evaluation aims to determine the nature of this interaction, whether it is positive or negative. To further quantify their interaction in terms of hypertension risk, we utilize the relative excess risk due to interaction (RERI), the attributable proportion (AP), and the synergy index (SI). It’s noteworthy that when the 95% confidence interval (CI) for RERI or AP excludes 0, or the 95% CI for SI excludes 1, larger absolute values of these statistics indicate a higher degree of interaction [ 41 ]. RERI quantifies the excess risk attributed to the interaction between sleep disorders and depression. AP indicates the proportion of the combined risk attributable to this interaction. SI represents the increase in risk resulting from the combined impact of both factors. An SI value greater than 1 indicates a significant synergistic effect, where the combined impact exceeds the sum of individual effects. Conversely, an SI equal to or less than 1 suggests that the joint effect does not amplify the risk as much as the individual effects combined.

The characteristics of all participants

In this study, we utilized data from seven NHANES dataset periods, namely 2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2018. According to the study’s inclusion and exclusion criteria, a total of 33,383 participants aged 18 years and above were finally included in this analysis. The surveyed population encompassed individuals between the ages of 18 and 85 years. Among them, 14,550 (68%) participants were non-Hispanic whites, and 17,338 (52%) participants were women. Baseline characteristics were compared based on the presence or absence of hypertension, and the specific results are shown in Table  1 . The average age of hypertensive patients was 57 ± 15 years, with 6,216 (53%) being males and 5,345 (70%) being non-Hispanic whites. Additionally, individuals with hypertension displayed variations in depression, sleep duration, sleep onset latency, and symptoms of sleep disorder or difficulty compared to those without hypertension. Age, sex, race, education level, ratio of family income to poverty, smoking status, drinking status, diabetes, BMI, and waist circumference also exhibited significant differences between the hypertension and non-hypertension groups (all P  < 0.05).

Among the participants, 11,584 (36%) reported a nightly sleep duration of less than seven hours, whereas only 2,529 (7.5%) participants reported a sleep duration exceeding eight hours. Moreover, a total of 1,215 (11%) participants indicated a rapid sleep onset, falling asleep within just five minutes or less. In contrast, 2,396 (18%) participants reported a prolonged sleep onset period, requiring more than 30 min to drift into slumber. Furthermore, 10,631 (35%) individuals reported experiencing sleep disorders or encountering difficulties in their sleep patterns. Additionally, a total of 2,875 (8%) participants reported depression, among whom 2,562 had moderate to severe depression, while 313 had severe depression.

Comparisons of the characteristics between patients with and without hypertension

The median age of hypertensive patients is 58 years, significantly higher than that of non-hypertensive individuals ( P  < 0.001). Additionally, in the hypertension group, the proportion of individuals aged 65 and above is significantly higher compared to the non-hypertension group (42% vs. 13%, P  < 0.001). Similarly, patients with hypertension had a median BMI of 30 kg/m², which was higher than the median BMI of 27 kg/m² observed in non-hypertensive patients ( P  < 0.001). Additionally, there are notable disparities in the distribution of BMI categories between the hypertensive and non-hypertensive groups. Specifically, in the hypertensive group, the proportion of individuals with a BMI of 30 or higher is significantly greater compared to the non-hypertensive group (50% vs. 30%, P  < 0.001). Among hypertensive patients, the proportion of women was 53%, slightly higher than 51% of non-hypertensive patients ( P  = 0.03). The family poverty index of hypertensive patients was significantly lower than that of non-hypertensive patients (median 2.71 vs. 3.01, P  = 0.03). Moreover, in the hypertension group, the proportion of individuals with a household income-to-poverty ratio exceeding 3.5 is lower compared to the non-hypertension group (28% vs. 31%, P  < 0.001). The prevalence of smoking history among hypertensive patients reached 50%, significantly surpassing the 40% found in non-hypertensive patients ( P  < 0.001). Moreover, the proportion of hypertensive patients in individuals with diabetes was higher compared to non-hypertensive patients (31% vs. 8%, P  < 0.001). Meanwhile, the prevalence of sleep disorders among hypertensive patients was 46%, significantly higher than the 25% reported among non-hypertensive patients ( P  < 0.001). Furthermore, the proportion of individuals diagnosed with hypertension among patients with depression was 12%, significantly exceeding the 7% observed in patients without hypertension ( P  < 0.001). However, the prevalence of alcohol consumption among individuals with hypertension was lower than that in the non-hypertensive population (68% vs. 73%, P  < 0.001). Furthermore, in the hypertension group, the proportion of individuals with a higher education level is lower compared to the non-hypertension group (71% vs. 76%, P  < 0.001) (Table  1 ).

Associations of sleep disorders or depression with hypertension

Compared to individuals without sleep disorders, those with sleep disorders had a higher risk of hypertension in Model 1, after adjusting for age and sex (OR = 1.94, 95% CI: 1.77–2.11). This risk increased in Model 2 with additional adjustments for race, education level, and family income-to-poverty ratio (OR = 1.96, 95% CI: 1.79–2.15). In Model 3, which included comprehensive adjustments for various factors such as age, sex, race, education level, family income-to-poverty ratio, BMI, alcohol consumption, smoking history, and diabetes, patients with sleep disorders still showed an elevated risk of hypertension (OR = 1.62, 95% CI: 1.48–1.77).

The study also examined the relationship between sleep duration and sleep onset latency with hypertension. In Model 3, individuals sleeping less than 7 h per night had a higher risk of hypertension compared to those with 7–8 h of sleep (OR = 1.21, 95% CI: 1.11–1.32), after adjusting for various factors. Additionally, participants with a sleep onset latency of 5 to 30 min and those with a latency of more than 30 min had significantly increased risks of hypertension (OR = 1.47, 95% CI: 1.14–1.91) and (OR = 1.72, 95% CI: 1.33–2.24), respectively, compared to those with a latency of less than 5 min, after adjusting for the same variables. In summary, Table  2 provides robust statistical evidence supporting a strong association between sleep disorders, sleep duration, and sleep onset latency with the risk of hypertension.

Our investigation focused on the impact of depression on hypertension through the utilization of multiple models. In Model 1, wherein adjustments were made for age and sex, depression was associated with a higher risk of hypertension (OR = 2.14, 95% CI: 1.89–2.41). This increased risk of hypertension persisted in Model 2 (OR = 1.98, 95% CI: 1.74–2.26) and Model 3 (OR = 1.61, 95% CI: 1.39–1.87). Furthermore, our study delved into the connection between depression severity and hypertension within the confines of Model 3. Following adjustments for various factors, individuals experiencing moderate depression showcased an augmented risk of hypertension (OR = 1.57, 95% CI: 1.35–1.82), while those grappling with severe depression exhibited a significantly higher risk (OR = 1.99, 95% CI: 1.34–2.96). These findings conclusively indicated a positive correlation between depression and hypertension, with increasing depression severity associated with a higher risk of developing hypertension (Table  2 ).

Interaction effects between sleep disorders and depression on hypertension

Initially, we assessed the multiple interactions between sleep disorders and depression on hypertension risk (Table  3 ). After incorporating sleep disorders, depression, and their product into a multivariate logistic regression model, we observed a statistically significant contribution of the product of sleep disorders and depression to hypertension risk (OR = 1.07, 95% CI: 1.02–1.13, P  < 0.05), indicating a potential positive synergistic effect between sleep disorders and depression.

To further quantify the degree of interaction related to hypertension risk, we employed measures of RERI, AP, and SI to assess the extent of this interaction. We categorized participants into four groups based on their sleep disorders and depression status: no depression, but presence of sleep disorders; depression without sleep disorders; depression with coexisting sleep disorders; neither depression nor sleep disorders. Patients with both depression and sleep disorders had an increased risk of hypertension, with an OR of 3.30 (95% CI: 2.82–3.88) in Model 1, which remained significant even after adjusting for race, education level, and ratio of family income to poverty in Model 2 (OR = 3.11, 95% CI: 2.63–3.76) and in Model 3 (OR = 2.34, 95% CI: 1.95–2.80) (Table  4 ). The results in Table  4 indicate a significant synergy between sleep disorders and depression in relation to hypertension in Model 3 (RERI = 0.73, 95% CI = 0.56–0.92; AP = 0.31, 95% CI = 0.11–0.46; SI = 2.19, 95% CI = 1.08–3.46). In this context, the AP value of 0.31 in Model 3 suggests that 31% of hypertension cases in the study sample can be attributed to the interaction between sleep disorders and depression. The SI value of 2.19 indicates a significant increase in hypertension risk due to the combined effect of these two factors. The RERI value of 0.73 reveals an additional risk of 0.73 associated with the interaction between sleep disorders and depression in causing hypertension.

Subgroup analyses and sensitivity analyses

Subgroup analyses by age, BMI, and sex consistently demonstrated statistically significant risk ratios for individuals with both sleep disorders and depression ( P  < 0.05) (Tables  5 and 6 ). In the subgroup analysis presented in Table  5 , a multiplicative interaction effect between sleep disorders and depression on hypertension was observed, aligning with the overall analysis findings. Likewise, additional subgroup analysis for additive interaction (Table  6 ) affirmed this observation, signaling a notable increase in hypertension risk among those concurrently affected by sleep disorders and depression. These subgroup analysis results corroborate the overarching analysis outcomes, further fortifying the credibility of the study’s findings. The findings suggest a significant interaction among sleep disorders, depression, and hypertension.

Finally, we extensively examined sleep-related factors, including sleep duration and onset latency, among different demographic subgroups (Additional files 1 and 2). Among adults under 44 years, sleeping less than 7 h was significantly associated with a higher risk of hypertension compared to those who slept for 7 to 8 h (OR = 1.05, 95% CI: 1.04–1.06). Interestingly, individuals aged 65 years and older who slept for over eight hours also had a significantly increased risk of hypertension in comparison to those with 7 to 8 h of sleep (OR = 1.06, 95% CI: 1.01–1.12). Subgroup analyses consistently showed that individuals with a sleep onset latency over 30 min had a higher risk of hypertension compared to those with a latency of under 5 min ( P  < 0.05) (Additional file 1). Additionally, an investigation into the relationship between depression severity and hypertension risk across subgroups revealed a consistent trend: individuals with depression had an increased risk of hypertension, and this risk increased with the severity of depression (Additional file 2).

Our study, involving 33,383 participants, aimed to investigate the relationship between sleep disorders, depression, and the prevalence of hypertension. The results indicate a correlation between depression, sleep disorders, and an increased prevalence of hypertension. Moreover, we identified a potential interaction between depression and sleep disorders in the development of hypertension. Additionally, subgroup analyses based on gender, age, and BMI consistently revealed an interaction between sleep disorders and depression in the prevalence of hypertension, underscoring the robustness of our findings. These results provide a foundation for further research into the association between depression, sleep disorders, and hypertension.

The typical diagnostic criteria for hypertension are a systolic blood pressure (SBP) exceeding 130mmHg and/or a diastolic blood pressure (DBP) exceeding 80mmHg. It’s worth noting that this clinical standard may sometimes be influenced by the white coat effect, where anxiety in a medical setting can elevate blood pressure. In our study, 11,760 individuals were diagnosed with hypertension out of the total population, indicating a prevalence of approximately 35%. Our findings are in line with recent trends in hypertension prevalence among U.S. adults, which have risen significantly from 33.53 to 40.58% over the past decade [ 44 ]. These results suggest that while self-reported questionnaires were used for hypertension diagnosis in our study, the consistency between our observed hypertension prevalence and previous epidemiological survey results underscores the viability of using such questionnaires for hypertension diagnosis in our study. Additionally, around 8% of participants reported experiencing depression, aligning with the pre-COVID-19 prevalence of depression among U.S. adults as found in a study investigating depression rates and associated risk factors [ 45 ]. Furthermore, in this study, approximately 35% reported experiencing sleep disorders or difficulties with their sleep patterns, consistent with findings from an assessment of sleep habits and sleep disorders among U.S. adults from 2017 to 2020 [ 46 ]. The above results indicate that the epidemiological data on hypertension, sleep disorders, and depression in this study are consistent with previous research findings, demonstrating a high level of alignment with real-world situations. This underscores the reliability and applicability of our study results.

Globalization and rapid social and cultural changes have brought about significant alterations and new challenges, leading to substantial social and psychological pressures. In addition to inherent biological or genetic factors, factors such as job stress, financial limitations, stress from racial discrimination, depression, and anxiety can all play crucial roles in the development of cardiovascular diseases [ 47 ]. Psychological stress has been linked to an augmented susceptibility to hypertension, with research elucidating an elevated hypertension risk among individuals afflicted with depression [ 17 , 42 , 48 ]. Our study found a strong connection between depression and a heightened risk of hypertension, which increased alongside the severity of depression. These results support previous findings, reinforcing the notion that depression contributes to the likelihood of developing hypertension [ 17 ]. Plausible explanations for this association may be traced to the dysfunction of the hypothalamic-pituitary-adrenal (HPA) axis and heightened sympathetic activation observed in individuals manifesting depressive symptoms [ 49 ]. The HPA axis, a pivotal regulatory mechanism governing stress response and stress coping, facilitates the production and release of adrenal cortex hormones using the corticotropin-releasing factor (CRF) and adrenocorticotropic hormone (ACTH), consequently engendering the occurrence of hypertension. Moreover, multiple reviews have substantiated that the sympathetic nervous system assumes a crucial role in the pathophysiological response to stress-related hypertension [ 50 ].

Our study revealed an association between sleep disorders and an augmented vulnerability to hypertension [ 51 ]. Sleep disorders are characterized by disruptions to normal sleep patterns, including sleep onset, maintenance, or duration difficulties. Among these, difficulties with sleep onset and inadequate sleep duration are commonly observed issues and represent primary manifestations of sleep disorders. Insufficient or excessive sleep, prolonged sleep latency, sleep disorders, and difficulties may be associated with hypertension. Our findings indicate a significant rise in hypertension risk among those experiencing sleep deprivation, aligning with prior research. Additionally, individuals with a sleep onset latency exceeding 5 min show an increased risk of hypertension, consistent with a meta-analysis by Itani Osamu et al. [ 52 ]. Subgroup analysis, based on age, sex, and BMI, shows that sleeping less than 7 h raises hypertension risk among individuals aged 18–44 years. Conversely, those aged 65 and above with 8 or more hours of sleep face a heightened risk of hypertension. This underscores how both inadequate and excessive sleep increases high blood pressure risk across age groups, aligning with Guo et al.‘s meta-analysis showing a positive association between excessive sleep duration and hypertension risk [ 53 ].

While a robust correlation between sleep duration and hypertension exists, causation remains elusive in existing studies. Epidemiological data presents inconsistent associations between sleep duration and adverse health outcomes across different age and sex groups. Most studies rely on self-reported data, potentially affected by misreporting, particularly among individuals with chronic illnesses. Additionally, a prospective cohort study observed a higher incidence of hypertension among workers with a history of shift work [ 54 , 55 ]. Changes in sleep patterns may disrupt the nocturnal blood pressure drop, leading to heightened sympathetic activity and hypertension. Subgroup analyses by sex revealed that sleep-deprived women had a higher hypertension risk, while men with insufficient or excessive sleep also faced increased risk. Results suggest that sleeping over 7 h may protect against hypertension in women but pose a risk for men. This study’s value lies in its ability to stratify the relationship between hypertension and sleep by sex, aligning with epidemiological findings showing a stronger association between sleep deprivation and hypertension in women [ 56 , 57 ]. Subgroup analyses showed a weakening of the inverse relationship observed in the overall adult population with age. Additionally, sleep-deprived women faced an elevated risk of developing high blood pressure across their lifespan.

Depression and sleep disorders often coexist with other physical or mental health issues rather than occurring alone. Declining sleep quality can lead to elevated blood pressure, weakened immunity, and psychological problems. Depression may also contribute to sleep difficulties and anxiety [ 43 ]. Our study found a strong link between depression and hypertension. It’s been widely observed that individuals with hypertension often have concurrent sleep disorders and depression [ 58 , 59 ]. Depression can incite high blood pressure, while high blood pressure can exacerbate depressive symptoms. Presently, research unequivocally underscores a connection between high blood pressure and depression [ 60 , 61 ]. The link between hypertension and depression may stem from shared physiological mechanisms risk factors, or both. However, the precise mechanisms remain unclear. Research on immune system inflammation may offer insights into a common underlying mechanism, alongside potential interconnected pathways. Emotional distress, such as anxiety and depression, can exacerbate blood pressure fluctuations in hypertensive patients, creating a harmful cycle that disrupts blood pressure regulation. A study indicates that individuals with uncontrolled hypertension are significantly more susceptible to depression, highlighting the intricate interplay between these conditions [ 58 ]. This may be attributed to depressed patients’ suboptimal adherence to medication, leading to inadequate blood pressure management.

This study revealed a synergistic interaction between depression and sleep disorders, significantly influencing hypertension development. Prior research has confirmed a robust correlation between sleep disorders and various mental and psychosomatic disorders [ 62 , 63 ]. Prospective cohorts have demonstrated a close association between both isolated sleep disorders and sleep disorders accompanied by depression and the risk of hypertension [ 64 ]. Positive factors like well-being, emotional stability, and life satisfaction typically enhance sleep quality, while negative factors such as poor well-being, anxiety, depression, and anger can diminish sleep quality [ 65 ]. A discontented mood can easily lead to sleep disorders, which can significantly impact both physical and mental well-being. The combined influence of depression and sleep disorders outweighs that of either condition alone. Inflammation is a key factor in depression, sleep disorders, and cardiovascular disease, all sharing common mechanisms and risk factors. Thus, physicians should prioritize enhancing patients’ sleep quality and mental health, intervening actively in psychological disorders, particularly depressive symptoms, alongside routine pharmacotherapy for hypertension.

Limitations

This study has several limitations. Firstly, being a cross-sectional study, it only allows us to determine the association between depression or sleep disorders and hypertension, without establishing causality definitively. Secondly, the definitions of sleep disorders and hypertension relied on self-reported data from NHANES participants, possibly introducing bias into the analysis. Thirdly, subgroup analysis was limited to gender, race, and age, warranting further investigation into hypertension subgroups to understand the interaction effects of depression and sleep disorders on hypertension across diverse populations. Fourth, although we employed the method of multiple testing in data analysis, we must be cautious about its potential impact, such as the increased likelihood of discovering statistical significance, thus possibly leading to false positive results. In conclusion, our study only examined sleep disorders as a general category without distinguishing specific types like sleep apnea or insomnia, which may have varying associations with hypertension. Furthermore, the dataset lacked data on potential confounding factors such as medication usage or comorbidities, which might have impacted the observed associations. Hence, although our study offers valuable insights into the link between sleep disorders and hypertension, further longitudinal research is required to understand the underlying mechanisms and causal relationships more thoroughly.

Our study investigated the impact of depression and sleep disorders on the prevalence of hypertension, utilizing data from 33,383 NHANES participants. The results unmistakably indicate that both depression and sleep disorders independently increase the prevalence of hypertension. Furthermore, our analysis reveals an interaction between depression and sleep disorders regarding hypertension prevalence, suggesting a synergistic effect. This underscores the significance of concurrent sleep disorders and depression in the development of hypertension.

Data availability

The datasets generated and/or analyzed during the current study are available in the NHANES repository, https://www.cdc.gov/nchs/nhanes/ . The dataset supporting the conclusions of this article is included in Additional file 3.

Abbreviations

National Health and Nutritional Examination Survey

Relative excess risk due to interaction

Attributable proportion of interaction

Synergy index

Cotropin-releasing factor

Adrenocorticotropic hormone

Blood pressure

Patient Health Questionnaire

Hypothalamic-pituitary-adrenal-axis

National Center for Health Statistics

Centers for Disease Control and Prevention

Body mass index

Diagnostic and statistical manual of mental disorders

Fasting blood glucose

Standard deviation

Confidence interval

Systolic blood pressure

Diastolic blood pressure

Rahimi K, Emdin CA, MacMahon S. The epidemiology of blood pressure and its worldwide management. Circul Res. 2015;116(6):925–36.

Article   CAS   Google Scholar  

Chen J, Zhang C, Wu Y, Zhang D. Association between Hypertension and the risk of Parkinson’s Disease: a Meta-analysis of Analytical studies. Neuroepidemiology. 2019;52(3–4):181–92.

Article   PubMed   Google Scholar  

Han H, Guo W, Shi W, Yu Y, Zhang Y, Ye X, et al. Hypertension and breast cancer risk: a systematic review and meta-analysis. Sci Rep. 2017;7:44877.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Katsi V, Papakonstantinou I, Solomou E, Antonopoulos AS, Vlachopoulos C, Tsioufis K. Management of hypertension and blood pressure dysregulation in patients with Parkinson’s Disease-a systematic review. Curr Hypertens Rep. 2021;23(5):26.

Angeli F, Reboldi G, Trapasso M, Gentile G, Pinzagli MG, Aita A, et al. European and US guidelines for arterial hypertension: similarities and differences. Eur J Intern Med. 2019;63:3–8.

Guo F, He D, Zhang W, Walton RG. Trends in prevalence, awareness, management, and control of hypertension among United States adults, 1999 to 2010. J Am Coll Cardiol. 2012;60(7):599–606.

Lazaridis AA, Sarafidis PA, Ruilope LM. Ambulatory blood pressure monitoring in the diagnosis, prognosis, and management of resistant hypertension: still a matter of our resistance? Curr Hypertens Rep. 2015;17(10):78.

Wang C, Hu J. Influence of the Interaction between depressive symptoms and Sleep disorders on Cardiovascular diseases occurrence. Int J Gen Med. 2021;14:10327–35.

Article   PubMed   PubMed Central   Google Scholar  

Huang CH, Cheng CS, Yen M. Factors associated with poor sleep quality in patients with pre-dialysis chronic kidney disease: a systematic review. J Adv Nurs. 2023;79(6):2043–57.

Pérez-Carbonell L, Iranzo A. Sleep-related changes prior to cognitive dysfunction. Curr Neurol Neurosci Rep. 2023;23(4):177–83.

Zhong X, Gou F, Jiao H, Zhao D, Teng J. Association between night sleep latency and hypertension: a cross-sectional study. Medicine. 2022;101(42):e31250.

Scott H, Lechat B, Guyett A, Reynolds AC, Lovato N, Naik G, Sleep Irregularity Is Associated With Hypertension: Findings From Over 2 Million Nights With a Large Global Population Sample., Hypertension et al. Dallas, Tex: (1979). 2023;80(5):1117-26.

Walker ER, McGee RE. Druss BGJJp. Mortality in mental disorders and global disease burden implications: a systematic review and meta-analysis. 2015;72(4):334 – 41.

Robson D, Gray RJI. Serious mental illness and physical health problems: a discussion paper. 2007;44(3):457 – 66.

De Hert M, Cohen D, Bobes J, Cetkovich-Bakmas M, Leucht S, Ndetei DM et al. Physical illness in patients with severe mental disorders. II. Barriers to care, monitoring and treatment guidelines, plus recommendations at the system and individual level. 2011;10(2):138.

Shahimi NH, Lim R, Mat S, Goh CH, Tan MP, Lim E. Association between mental illness and blood pressure variability: a systematic review. Biomed Eng Online. 2022;21(1):19.

Fang J, Zhang Z, Greenlund KJ. Association of depressive symptoms and hypertension prevalence, awareness, treatment and control among USA adults. J Hypertens. 2022;40(9):1658–65.

Rosas C, Oliveira HC, Neri AL, Ceolim MF. Depressive symptoms, symptoms of insomnia and stressful events in hypertensive older adults: cross-sectional study. Enfermeria Clin (English Edition). 2022;32(3):195–202.

Article   Google Scholar  

Chunnan L, Shaomei S, Wannian L. The association between sleep and depressive symptoms in US adults: data from the NHANES (2007–2014). Epidemiol Psychiatric Sci. 2022;31:e63.

Wang Y, Mei H, Jiang Y-R, Sun W-Q, Song Y-J, Liu S-J et al. Relationship between duration of sleep and hypertension in adults: a meta-analysis. 2015;11(9):1047–56.

Cai Y, Chen M, Zhai W, Wang C. Interaction between trouble sleeping and depression on hypertension in the NHANES 2005–2018. BMC Public Health. 2022;22(1):481.

Li J, Li L, Lv Y, Kang Y, Zhu M, Wang W. Effect of the Interaction between Depression and Sleep disorders on the Stroke occurrence: An Analysis Based on National Health and Nutritional Examination Survey. Behav Neurol. 2021;2021:6333618.

PubMed   PubMed Central   Google Scholar  

Karimi R, Mallah N, Scherer R, Rodríguez-Cano R, Takkouche B. Sleep quality as a mediator of the relation between depression and chronic pain: a systematic review and meta-analysis. Br J Anaesth. 2023;130(6):747–62.

Maddox PA, Elahi A, Khuram H, Issani A, Hirani R. Sleep quality and physical activity in the management of depression and anxiety. Prev Med. 2023;171:107514.

Johnson CL, Paulose-Ram R, Ogden CL, Carroll MD, Kruszon-Moran D, Dohrmann SM et al. National health and nutrition examination survey: analytic guidelines, 1999–2010. Vital and health statistics Series 2, Data evaluation and methods research. 2013(161):1–24.

Fain JA. NHANES Diabetes Educ. 2017;43(2):151.

Bakris G, Ali W, Parati, GJJotACoC. ACC/AHA versus ESC/ESH on hypertension guidelines: JACC guideline comparison. 2019;73(23):3018–26.

Whelton PK, Carey RM, Aronow WS, Jr. Casey DE, Collins KJ, Dennison Himmelfarb C, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, detection, evaluation, and management of high blood pressure in adults: executive summary: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice guidelines. Circulation. 2018;138(17):e426–83.

PubMed   Google Scholar  

James PA, Oparil S, Carter BL, Cushman WC, Dennison-Himmelfarb C, Handler J, et al. 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). JAMA. 2014;311(5):507–20.

Article   CAS   PubMed   Google Scholar  

Martin LM, Leff M, Calonge N, Garrett C, Nelson DE. Validation of self-reported chronic conditions and health services in a managed care population. Am J Prev Med. 2000;18(3):215–8.

Xi Y, Deng YQ, Chen SM, Kong YG, Xu Y, Li F et al. Allergy-related outcomes and sleep-related disorders in adults: a cross-sectional study based on NHANES 2005–2006. Allergy, asthma, and clinical immunology: official journal of the Canadian Society of Allergy and Clinical Immunology. 2022;18(1):27.

Du W, Liu J, Zhou J, Ye D, OuYang Y, Deng Q. Obstructive sleep apnea, COPD, the overlap syndrome, and mortality: results from the 2005–2008 National Health and Nutrition Examination Survey. Int J Chronic Obstr Pulm Dis. 2018;13:665–74.

Scinicariello F, Buser MC, Feroe AG, Attanasio R. Antimony and sleep-related disorders: NHANES 2005–2008. Environ Res. 2017;156:247–52.

Spitzer RL, Kroenke K, Williams JB. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary care evaluation of Mental disorders. Patient Health Questionnaire Jama. 1999;282(18):1737–44.

CAS   PubMed   Google Scholar  

Levis B, Sun Y, He C, Wu Y, Krishnan A, Bhandari PM, et al. Accuracy of the PHQ-2 alone and in Combination with the PHQ-9 for screening to detect Major Depression: systematic review and Meta-analysis. JAMA. 2020;323(22):2290–300.

Park LT, Zarate CA Jr. Depression in the primary care setting. N Engl J Med. 2019;380(6):559–68.

Brinkmann B, Payne CF, Kohler I, Harling G, Davies J, Witham M, et al. Depressive symptoms and cardiovascular disease: a population-based study of older adults in rural Burkina Faso. BMJ open. 2020;10(12):e038199.

Ni S, Zhong Z, Wei J, Zhou J, Cai L, Yang M, et al. Association between dietary intake of polyunsaturated fatty acid and prevalence of hypertension in U.S. adults: a cross-sectional study using data from NHANES 2009–2016. Hypertens Research: Official J Japanese Soc Hypertens. 2022;45(3):516–26.

Zhang HZ, Wang YH, Ge YL, Wang SY, Sun JY, Chen LL, et al. Obesity, malnutrition, and the prevalence and outcome of hypertension: evidence from the National Health and Nutrition Examination Survey. Front Cardiovasc Med. 2023;10:1043491.

Su Y, Ding N, Zhou Y, Yang G, Chai X. The association between bedtime at night and hypertension in adults. Postgrad Med. 2023;135(4):370–8.

Zhan Y, Yang Z, Liu Y, Zhan F, Lin S. Interaction between rheumatoid arthritis and mediterranean diet on the risk of cardiovascular disease for the middle aged and elderly from National Health and Nutrition Examination Survey (NHANES). BMC Public Health. 2023;23(1):620.

Stein DJ, Aguilar-Gaxiola S, Alonso J, Bruffaerts R, De Jonge P, Liu Z, et al. Associations between Mental Disorders Subsequent Onset Hypertens. 2014;36(2):142–9.

Google Scholar  

Li M, Zou X, Lu H, Li F, Xin Y, Zhang W, et al. Association of sleep apnea and depressive symptoms among US adults: a cross-sectional study. BMC Public Health. 2023;23(1):427.

Fan Z, Yang C, Zhang J, Huang Y, Yang Y, Zeng P, et al. Trends and influence factors in the prevalence, awareness, treatment, and control of hypertension among US adults from 1999 to 2018. PLoS ONE. 2023;18(9):e0292159.

Ettman CK, Abdalla SM, Cohen GH, Sampson L, Vivier PM, Galea S. Prevalence of depression symptoms in US adults before and during the COVID-19 pandemic. JAMA Netw open. 2020;3(9):e2019686.

Di H, Guo Y, Daghlas I, Wang L, Liu G, Pan A, Adults US, et al. JAMA Netw open. 2022;5(11):2017–20.

Liu MY, Li N, Li WA, Khan H. Association between psychosocial stress and hypertension: a systematic review and meta-analysis. Neurol Res. 2017;39(6):573–80.

Bacon SL, Campbell TS, Arsenault A, Lavoie KLJIJH. The impact of mood and anxiety disorders on incident hypertension at one year. 2014;2014.

Shi S, Liang J, Liu T, Yuan X, Ruan B, Sun L, et al. Depression increases sympathetic activity and exacerbates myocardial remodeling after myocardial infarction: evidence from an animal experiment. PLoS ONE. 2014;9(7):e101734.

Lambert EA, Lambert GWJC. Stress and its role in sympathetic nervous system activation in hypertension and the metabolic syndrome. 2011;13:244–8.

Kadier K, Qin L, Ainiwaer A, Rehemuding R, Dilixiati D, Du YY et al. Association of sleep-related disorders with cardiovascular disease among adults in the United States: a cross-sectional study based on national health and nutrition examination survey 2005–2008. Frontiers in cardiovascular medicine. 2022;9:954238.

Itani O, Jike M, Watanabe N, Kaneita Y. Short sleep duration and health outcomes: a systematic review, meta-analysis, and meta-regression. Sleep Med. 2017;32:246–56.

Guo X, Zheng L, Wang J, Zhang X, Zhang X, Li J et al. Epidemiological evidence for the link between sleep duration and high blood pressure: a systematic review and meta-analysis. 2013;14(4):324–32.

Rahim A, McIsaac MA, Aronson KJ, Smith PM, Tranmer JEJCJoC. The associations of shift work, sleep quality, and incidence of hypertension in ontario adults: a population-based study. 2021;37(3):513–8.

Han L, Wang Q. Association between Organophosphorus insecticides exposure and the prevalence of in the US adults: an analysis based on the NHANES 2007–2018. Ecotoxicol Environ Saf. 2023;255:114803.

Paciência I, Barros H, Araújo J, Ramos E. Association between sleep duration and blood pressure in adolescents. Hypertens Research: Official J Japanese Soc Hypertens. 2013;36(8):747–52.

Choi JK, Kim MY, Kim JK, Park JK, Oh SS, Koh SB, et al. Association between short sleep duration and high incidence of metabolic syndrome in midlife women. Tohoku J Exp Med. 2011;225(3):187–93.

Asmare Y, Ali A, Belachew A. Magnitude and associated factors of depression among people with hypertension in Addis Ababa, Ethiopia: a hospital based cross-sectional study. BMC Psychiatry. 2022;22(1):327.

Hitij JB. Association of depressive symptoms and hypertension prevalence, awareness, treatment. J Hypertens. 2022;40(9):1655–7.

Chen S, Conwell Y, Xue J, Li LW, Tang W, Bogner HR et al. Protocol of an ongoing randomized controlled trial of care management for comorbid depression and hypertension: the Chinese Older Adult Collaborations in Health (COACH) study. 2018;18(1):1–9.

Kiełbasa G, Stolarz-Skrzypek K, Pawlik A, Łątka M, Drożdż T, Olszewska M et al. Assessment of sleep disorders among patients with hypertension and coexisting metabolic syndrome. 2016;61(2):261–8.

Buysse DJ, Angst J, Gamma A, Ajdacic V, Eich D, Rössler WJS. Prevalence, course, and comorbidity of insomnia and depression in young adults. 2008;31(4):473–80.

Fan T, Su D. Interaction effects between sleep disorders and depression on heart failure. BMC Cardiovasc Disord. 2023;23(1):132.

Dong Y, Yang FMJPM. Insomnia symptoms predict both future hypertension and depression. 2019;123:41 – 7.

Ma L, Li YJJCN. The effect of depression on sleep quality and the circadian rhythm of ambulatory blood pressure in older patients with hypertension. 2017;39:49–52.

Download references

Acknowledgements

We thank Professor Qiu and Ye for their criticism and guidelines and the participants included in our study for their contributions.

Not applicable.

Author information

Chunhua Liu and Zegen Ye contributed equally to this work.

Authors and Affiliations

Department of Rehabilitation, Lishui Hospital of Traditional Chinese Medicine, Affiliated to Zhejiang University of Chinese Medicine, No. 800 Zhongshan Street, Liandu District, Lishui City, Zhejiang Province, China

Chunhua Liu, Zegen Ye, Liping Chen, Huaqiang Wang, Binbin Wu, Di Li, Sisi Pan & Weiwen Qiu

Lishui Central Hospital, No. 289 Kuocang Road, Liandu District, Lishui City, Zhejiang Province, China

Department of Neurology, Lishui Hospital of Traditional Chinese Medicine, Zhejiang University of Chinese Medicine, 800 Zhongshan Street, Lishui City, Zhejiang, 323000, China

Department of Clinical Training, Lishui Municipal Central Hospital, Lishui, Zhejiang Province, 323000, China

You can also search for this author in PubMed   Google Scholar

Contributions

C.L. and W.Q. designed the study. C.L. wrote the manuscript. C.L., H.Y., L.C., and H.W., contributed to the acquisition of data. C.L., Z.Y., D.L., and S.P. analyzed the data. L.C., B.W., and S.P. interpreted the data. H.Y. and W.Q. reviewed and edited the manuscript. All authors read and approved the manuscript.

Corresponding authors

Correspondence to Weiwen Qiu or Haiqin Ye .

Ethics declarations

Ethics approval and consent to participate.

This study used anonymous data from the National Health and Nutrition Examination Survey and complied with the ethical guidelines and regulations of the Declaration of Helsinki. The study was approved by the National Center for Health Statistics Ethics Review Board, and all participants provided written informed consent before the study.

Consent for publication

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary material 2, supplementary material 3, supplementary material 4, supplementary material 5, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Liu, C., Ye, Z., Chen, L. et al. Interaction effects between sleep-related disorders and depression on hypertension among adults: a cross-sectional study. BMC Psychiatry 24 , 482 (2024). https://doi.org/10.1186/s12888-024-05931-9

Download citation

Received : 04 July 2023

Accepted : 25 June 2024

Published : 02 July 2024

DOI : https://doi.org/10.1186/s12888-024-05931-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Hypertension
• Sleep disorders
• Interaction

BMC Psychiatry

ISSN: 1471-244X

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• Introduction
• Conclusions
• Article Information

The PCL tertile was based on PCL scores of less than 2 for the first tertile, 2 to 9 for the second tertile, and greater than 9 for the third tertile (n = 181). The diagonal dotted line indicates the trend slope, and the whiskers indicate the SE.

The PCL tertile was based on PCL scores of less than 2 for the first tertile, 2 to 9 for the second tertile, and greater than 9 for the third tertile (n = 181).

eTable 1. Association of PCL-5 Score With AHI

eTable 2. Association of PTSD Status With AHI

eTable 3. Association of Standardized PCL and BMI With AHI

eTable 4. Association of PCL-5 Score With AHI, Examining for Interaction With Zygosity

eTable 5. Association of PTSD With AHI

eFigure. Directed Acyclic Graph for Multivariable Models

Data Sharing Statement

• Obstructive Sleep Apnea and Psychiatric Disorders JAMA Network Open Invited Commentary June 24, 2024 Steven H. Woodward, PhD; Ruth M. Benca, MD, PhD

See More About

Sign up for emails based on your interests, select your interests.

Customize your JAMA Network experience by selecting one or more topics from the list below.

• Academic Medicine
• Acid Base, Electrolytes, Fluids
• Allergy and Clinical Immunology
• American Indian or Alaska Natives
• Anesthesiology
• Anticoagulation
• Art and Images in Psychiatry
• Artificial Intelligence
• Assisted Reproduction
• Bleeding and Transfusion
• Caring for the Critically Ill Patient
• Challenges in Clinical Electrocardiography
• Climate and Health
• Climate Change
• Clinical Challenge
• Clinical Decision Support
• Clinical Implications of Basic Neuroscience
• Clinical Pharmacy and Pharmacology
• Complementary and Alternative Medicine
• Consensus Statements
• Coronavirus (COVID-19)
• Critical Care Medicine
• Cultural Competency
• Dental Medicine
• Dermatology
• Diabetes and Endocrinology
• Diagnostic Test Interpretation
• Drug Development
• Electronic Health Records
• Emergency Medicine
• End of Life, Hospice, Palliative Care
• Environmental Health
• Equity, Diversity, and Inclusion
• Facial Plastic Surgery
• Gastroenterology and Hepatology
• Genetics and Genomics
• Genomics and Precision Health
• Global Health
• Guide to Statistics and Methods
• Hair Disorders
• Health Care Delivery Models
• Health Care Economics, Insurance, Payment
• Health Care Quality
• Health Care Reform
• Health Care Safety
• Health Care Workforce
• Health Disparities
• Health Inequities
• Health Policy
• Health Systems Science
• History of Medicine
• Hypertension
• Images in Neurology
• Implementation Science
• Infectious Diseases
• Innovations in Health Care Delivery
• JAMA Infographic
• Law and Medicine
• Leading Change
• Less is More
• LGBTQIA Medicine
• Lifestyle Behaviors
• Medical Coding
• Medical Devices and Equipment
• Medical Education
• Medical Education and Training
• Medical Journals and Publishing
• Mobile Health and Telemedicine
• Narrative Medicine
• Neuroscience and Psychiatry
• Notable Notes
• Nutrition, Obesity, Exercise
• Obstetrics and Gynecology
• Occupational Health
• Ophthalmology
• Orthopedics
• Otolaryngology
• Pain Medicine
• Palliative Care
• Pathology and Laboratory Medicine
• Patient Care
• Patient Information
• Performance Improvement
• Performance Measures
• Perioperative Care and Consultation
• Pharmacoeconomics
• Pharmacoepidemiology
• Pharmacogenetics
• Pharmacy and Clinical Pharmacology
• Physical Medicine and Rehabilitation
• Physical Therapy
• Physician Leadership
• Population Health
• Primary Care
• Professional Well-being
• Professionalism
• Psychiatry and Behavioral Health
• Public Health
• Pulmonary Medicine
• Regulatory Agencies
• Reproductive Health
• Research, Methods, Statistics
• Resuscitation
• Rheumatology
• Risk Management
• Scientific Discovery and the Future of Medicine
• Shared Decision Making and Communication
• Sleep Medicine
• Sports Medicine
• Stem Cell Transplantation
• Substance Use and Addiction Medicine
• Surgical Innovation
• Surgical Pearls
• Teachable Moment
• Technology and Finance
• The Art of JAMA
• The Arts and Medicine
• The Rational Clinical Examination
• Tobacco and e-Cigarettes
• Translational Medicine
• Trauma and Injury
• Treatment Adherence
• Ultrasonography
• Users' Guide to the Medical Literature
• Vaccination
• Venous Thromboembolism
• Veterans Health
• Women's Health
• Workflow and Process
• Wound Care, Infection, Healing

Get the latest research based on your areas of interest.

Others also liked.

• Download PDF
• X Facebook More LinkedIn

Shah AJ , Vaccarino V , Goldberg J, et al. Posttraumatic Stress Disorder and Obstructive Sleep Apnea in Twins. JAMA Netw Open. 2024;7(6):e2416352. doi:10.1001/jamanetworkopen.2024.16352

Manage citations:

© 2024

• Permissions

Posttraumatic Stress Disorder and Obstructive Sleep Apnea in Twins

• 1 Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia
• 2 Department of Medicine, Emory University School of Medicine, Atlanta, Georgia
• 3 Atlanta Veterans Affairs Healthcare System, Decatur, Georgia
• 4 Seattle Epidemiologic Research and Information Center, Office of Research and Development, Department of Veterans Affairs, Seattle, Washington
• 5 Department of Epidemiology, University of Washington, Seattle
• 6 Department of Pediatrics, Stanford University, California
• 7 Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia
• 8 Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York
• 9 Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia
• 10 Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia
• 11 Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, Georgia
• 12 Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
• 13 Department of Neurology, Emory University School of Medicine, Atlanta, Georgia
• Invited Commentary Obstructive Sleep Apnea and Psychiatric Disorders Steven H. Woodward, PhD; Ruth M. Benca, MD, PhD JAMA Network Open

Question   What is the association of posttraumatic stress disorder (PTSD) with obstructive sleep apnea (OSA) after accounting for acknowledged medical risk factors for OSA?

Findings   In a cross-sectional study of 132 older male veteran twins discordant for PTSD undergoing in-laboratory polysomnography, current PTSD diagnosis and symptoms were associated independently with OSA, even after controlling for demographics, behavioral factors, cardiovascular risk factors, and familial factors.

Meaning   These findings suggest that PTSD may be an independent, novel, and heretofore unrecognized risk factor for OSA in older men.

Importance   Obstructive sleep apnea (OSA) is a common condition in older adult (aged >65 years) populations, but more mechanistic research is needed to individualize treatments. Previous evidence has suggested an association between OSA and posttraumatic stress disorder (PTSD) but is limited by possible selection bias. High-quality research on this association with a careful evaluation of possible confounders may yield important mechanistic insight into both conditions and improve treatment efforts.

Objective   To investigate the association of current PTSD symptoms and PTSD diagnosis with OSA.

Design, Setting, and Participants   This cross-sectional study of twin pairs discordant for PTSD, which allows for adjustment for familial factors, was conducted using in-laboratory polysomnography from March 20, 2017, to June 3, 2019. The study sample comprised male veteran twins recruited from the Vietnam Era Twin Registry. The data analysis was performed between June 11, 2022, and January 30, 2023.

Exposure   Symptoms of PTSD in twins who served in the Vietnam War. Diagnosis of PTSD was a secondary exposure.

Main Outcomes and Measures   Obstructive sleep apnea was assessed using the apnea-hypopnea index (AHI) (≥4% oxygen saturation criterion as measured by events per hour) with overnight polysomnography. Symptoms of PTSD were assessed using the PTSD Checklist (PCL) and structured clinical interview for PTSD diagnosis.

Results   A total of 181 male twins (mean [SD] age, 68.4 [2.0] years) including 66 pairs discordant for PTSD symptoms and 15 pairs discordant for a current PTSD diagnosis were evaluated. In models examining the PCL and OSA within pairs and adjusted for body mass index (BMI) and other sociodemographic, cardiovascular, and psychiatric risk factors (including depression), each 15-point increase in PCL was associated with a 4.6 (95% CI, 0.1-9.1) events-per-hour higher AHI. Current PTSD diagnosis was associated with an adjusted 10.5 (95% CI, 5.7-15.3) events-per-hour higher AHI per sleep-hour. Comparable standardized estimates of the association of PTSD symptoms and BMI with AHI per SD increase (1.9 events per hour; 95% CI, 0.5-3.3 events per hour) were found.

Conclusions and Relevance   This cross-sectional study found an association between PTSD and sleep-disordered breathing. The findings have important public health implications and may also enhance understanding of the many factors that potentially affect OSA pathophysiology.

Obstructive sleep apnea (OSA) is typically considered to be a medical condition with multiple, often overlapping risk factors, including advanced age, obesity, male sex, upper airway narrowing, genetics, and cardiovascular disease. 1 Despite the wealth of evidence that has uncovered these OSA risk factors, the role of psychiatric disorders that might disrupt sleep, such as posttraumatic stress disorder (PTSD), is debated and has important clinical and public health implications given that both are increasingly prevalent conditions. 2 , 3 In addition, both PTSD and OSA are associated with increased cardiovascular disease risk, which underscores the public health importance of this research. 4 , 5

Previous studies of the association between PTSD and OSA have yielded inconclusive results primarily due to methodological constraints. 6 - 14 Many have examined symptomatic patients who were referred for clinical evaluation of possible OSA, which could bias the sample; individuals with and without PTSD could differ in the likelihood of reporting symptoms and seeking care. Furthermore, several investigations relied on PTSD assessments based on medical record review or self-report, which may not be as accurate as a formal clinical assessment. Genetic and familial factors (eg, early life socioeconomic status) may play an important role in each condition and, as such, may lead to attenuated estimates when not fully considered in models. 15 , 16 Finally, many studies lacked an adequate control sample without PTSD. 6

To overcome these limitations, we evaluated the association of PTSD with OSA in a controlled sample of male veteran twins who underwent a formal psychiatric and polysomnography (PSG) evaluation as part of the Emory Twin Study Follow-Up. 17 This sample was not selected based on referral for either PTSD or sleep disturbance. 18 We were also able to estimate the influence of PTSD with high internal validity by comparing PTSD-discordant brothers who shared genetic and familial characterstics. 15 , 16 , 19 We tested the hypothesis that veteran twin males with current PTSD symptoms or a clinical diagnosis of PTSD may be more likely to have obstructive apneic episodes and hypoxia compared with their brothers with fewer PTSD symptoms or without a PTSD diagnosis. We also assessed self-reported sleep disturbance to confirm the well-established association between PTSD and poor-quality sleep.

This cross-sectional study is a substudy based on a follow-up of the Emory Twin Study that was conducted from March 20, 2017, to June 3, 2019. 4 , 17 Twin participants at baseline were selected from the Vietnam Era Twin Registry, a large national sample of adult male twins aged 61 to 71 years who served on active duty during the Vietnam War era (1964-1975). 20 Twin pairs participated together on the same day to minimize measurement error. All twins signed a written informed consent, and the Emory University institutional review board approved the study. We followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

We obtained a clinical diagnosis of PTSD using the Structured Clinical Interview for DSM (SCID) for the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition). 21 Following the diagnostic algorithm, PTSD was classified as either current (met criteria in previous month) or remitted (did not meet criteria in previous month). We also examined current PTSD symptom severity using the self-administered PTSD checklist (PCL) for the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition), which has strong internal consistency, test-retest reliability, and convergent and discriminant validity. 22 A PCL score of greater than 30 (on a scale of 0-80) was found to have a 94% sensitivity and 94% specificity for classifying PTSD diagnosis in a previous validation study. 23

Twins underwent overnight full PSG in the Emory Sleep Center to derive measures of sleep-disordered breathing in a controlled environment. They slept in private rooms and elected bedtimes and wake-up times of their own choosing that were consistent with their home schedule. The day prior to the overnight study, twins were supervised by staff continuously from morning to bedtime. Napping was not permitted. The PSG procedures followed the guidelines of the American Academy of Sleep Medicine. 24 We recorded respiration with both airflow pressure transducers and thermocouples placed adjacent to the mouth (for oral breathing) and measured finger pulse oximetry throughout the recording. The PSG readings were scored in 30-second epochs by a trained registered polysomnographic technologist masked to all clinical information about the participant. The PSG was performed using the Embla N7000 digital recording system using RemLogic software (Natus). We scored OSA with current American Academy of Sleep Medicine guidelines for apnea (≥90% drop in oronasal sensor signal excursion for ≥10 seconds) and hypopnea (≥30% drop in oronasal sensor signal for ≥10 seconds accompanied by ≥4% drop in oxygen saturation [Sao 2 ]). 24 Apneas and hypopneas were divided by total sleep time to yield the apnea-hypopnea index (AHI), which constituted our primary measure of OSA severity. 25 The diagnosis of moderate or severe OSA was defined by an AHI of 15 or higher. 26 The proportion of central apneas (relative to obstructive and mixed apneas) was also tallied for each PSG.

Other indicators of OSA included the respiratory disturbance index, which includes all apneas, hypopneas, and respiratory effort–related arousals per hour of sleep, as well as several measures of hypoxic burden, including the oxygen desaturation index (ODI), defined as the number of drops in Sao 2 of at least 4% per hour, and the cumulative proportion of sleep spent with Sao 2 <90%, expressed as a percentage of total sleep time. A small number of participants in the sample had been previously diagnosed with OSA and had been prescribed continuous positive airway pressure (CPAP), and a subset of these reported regular use of CPAP in their homes. With approval of their personal physicians, CPAP was not used for the single night of PSG in this study.

At each visit, in addition to the sleep measures implemented specifically in the current protocol as described earlier, we performed a thorough assessment that included medical history, sociodemographic information, health behaviors, blood pressure, selected blood chemistries, anthropometric measures, and current medications, as previously described. 27 We collected self-identified race and ethnicity (non-Hispanic Black, non-Hispanic White) via self-report as part of the assessment of the sample demographic. Physical activity was measured using the Baecke Questionnaire of Habitual Physical Activity. 28 Employment was classified as working full-time or part-time by self-report. History of coronary artery disease that might have occurred from the time of the initial screen was defined as a previous diagnosis of myocardial infarction or coronary revascularization procedures. The SCID administration allowed us to assess lifetime history of major depression and substance abuse in addition to PTSD. Service in Southeast Asia was determined from military records. Zygosity information was assessed by DNA typing as previously described. 29 Participants completed the Pittsburgh Sleep Quality Index (PSQI), a self-rating scale (0-21, with higher scores meaning worse sleep quality) of general sleep disturbance. 30

The data were analyzed between June 11, 2022, and January 30, 2023. Our primary exposure for the study was the PCL score because of the greater number of pairs discordant for PCL symptoms (n = 132, defined by >0 difference in score between brothers) and increased statistical power compared with the 30 twins discordant for PTSD diagnosis. We first compared twin brothers within pairs discordant for PCL symptoms (defined by PCL score difference >0) by calculating prevalence (for binary data only) or mean (SD) values of baseline characteristics in the brother with above (higher PCL twin) vs below (lower PCL twin) the pair mean. These characteristics included sociodemographic factors, health factors, and medications. We then evaluated OSA continuous and categorical measures in both subgroups.

We examined the association between PTSD and OSA within twin pairs discordant for PTSD symptoms or diagnosis, which, by design, controlled for demographic, shared familial, and early environmental influences. This design also reduced the effect of shared exposures during the examination day since twin pairs were examined together. 31 We used generalized estimating equations, which offer robust methods for SE estimation. In each model, we examined the within-pair differences in sleep outcomes as the dependent variables as a function of the within-pair differences in PTSD symptoms or diagnoses as the independent variables. Within-pair difference terms were calculated by subtracting the twin pair average from the individual value. 32 We also compared OSA prevalence by current PTSD diagnosis and tested for significant differences using the Fisher exact test. More in-depth information regarding the statistical methods, as well as full outputs of the main models, are provided in the eMethods in Supplement 1 .

For our within-pair analyses, we excluded participants who were singletons (the other brother deceased or did not participate) or had concordant or exact same PCL scores (n = 49). Our primary exposure, within-pair PCL difference, was analyzed as a continuous variable. To enhance the applicability of our findings within a clinical setting, we present the estimated OSA outcomes associated with a 15-point within-pair PCL difference (one-half of the 30-point cutoff for PTSD). 23 Obstructive sleep apnea outcome measures included AHI as the primary outcome and respiratory disturbance index, ODI, and hypoxic burden as secondary outcomes. Our secondary exposure was current PTSD diagnosis based on clinical interview. We examined the prevalence of moderate or severe OSA by PTSD diagnosis group.

We examined 2 multivariable models. The first model included body mass index (BMI, as measured by weight in kilograms divided by height in meters squared) only, given its importance in OSA pathogenesis and relationship with other cardiovascular risk factors. Our full model included traditional cardiovascular disease risk factors as possible mediators in the association between PTSD and OSA; it also included possible confounders such as years of education, employment status, and psychological or behavioral factors, including depressive symptoms, past PTSD, antidepressant use, and alcohol use. The eFigure in Supplement 1 shows a graphical representation of the potential mechanistic and causal pathways involved. We examined both raw PCL score and BMI as well the standardized values to compare their strengths of association with each other in unadjusted and fully adjusted models. For the secondary analysis examining PTSD-discordant pairs only, we did not include all covariates (confounders and mediators) together in a single model to avoid model overfitting due to the limited sample size. Instead, we divided the covariates into 2 separate models. One model included only cardiovascular mediators, while the other included sociodemographic and psychiatric confounders. In addition, we examined for linear dose-response associations of OSA with PCL symptoms among individual twins, including an examination of the association by tertile of individual PCL scores with mean AHI and moderate to severe OSA prevalence. We also evaluated the potential effects of CPAP use and OSA history in additional sensitivity models.

Missing covariate data were rare (<5%); thus, we used all available data without imputation. A 2-sided P  < .05 was used for statistical significance, and 95% CIs were calculated from model parameters. Statistical analyses were performed using SAS, version 9.4 software (SAS Institute, Inc).

There were 181 male twins in the total sample (mean [SD] age, 68.4 [2.0] years; 10 Black [6%] and 171 White [94%] race) who underwent overnight sleep testing with PSG, including 74 complete twin pairs. Of these 74 pairs, 66 (42 [63%] monozygotic) were discordant for PCL symptom level, and 15 pairs were discordant for current PTSD. Table 1 compares twins within PCL-discordant pairs (n = 132) separated by whether the twin had the higher or lower PCL score compared with the mean pair score. The mean (SD) PCL score in the lower PCL group was 4.5 (7.0) and 15.4 (14.3) in the higher PCL group. In addition, the SD of the within-pair PCL score difference was 8.1. The brothers who had the higher PCL score showed expected differences compared with the brothers with the lower PCL score, including a higher likelihood of Vietnam combat exposure, smoking or alcohol history, depression, and use of antidepressants. They also had higher PSQI scores and sleep-disordered breathing outcomes.

The mean (SD) AHI was 17.7 (14.9) events per hour, and the mean proportion of the night with Sao 2 less than 90% was 8.9% (16 men). In addition, 74% of the sample (98 men) had at least mild OSA (AHI≥5), 40% of the sample (72 men) had moderate to severe OSA (AHI≥15), and 18% of the sample (33 men) had severe OSA (AHI≥30). Figure 1 shows an increasing mean AHI rate per PCL tertile across twins treated as individuals (tertile 1 [lowest], 14.3 [95% CI, 10.6-18.0]; tertile 2, 16.4 [95% CI, 12.2-20.5]; tertile 3 [highest], 22.9 [95% CI, 16.3-29.4]; P for trend = .02), and Figure 2 shows the trends in moderate to severe OSA prevalence by PCL tertile (tertile 1 [lowest], 36%; tertile 2, 36%; tertile 3 [highest], 50%; P for trend = .12). Both figures show increased OSA severity and frequency for each incremental step up in PCL tertile, although this trend was significant only for AHI. Within 15 twin pairs discordant for current PTSD, OSA was prevalent in 8 brothers (53%) with current PTSD and 4 brothers (27%) without current PTSD (Fisher exact P  = .10).

Table 2 and Table 3 present the multivariable analysis for the associations between within-pair differences in PCL scores and current PTSD with OSA outcomes. In fully adjusted models, each 15-point within-pair difference in PCL score was associated with a 4.6 (95% CI, 0.1-9.1) events-per-hour higher AHI, 6.4 (95% CI, 2.1-10.7) events-per-hour higher ODI, and a 4.8% (95% CI, 0.6%-9.0%) greater sleep duration with Sao 2 less than 90% ( Table 2 ). When examining current PTSD as an exposure variable, the results were consistent with PCL. Most notably, current PTSD associated with an approximately 10-unit higher adjusted higher AHI in separate models involving potential cardiovascular moderators (model 2) (10.5; 95% CI, 5.7-15.3) and sociodemographic and psychiatric confounders (model 3) (10.7; 95% CI, 4.0-17.4) ( Table 3 ). Other OSA-related outcomes showed similar associations with current PTSD, and the PTSD-zygosity interactions were not statistically significant in any of the models. We also evaluated the potential effects of CPAP therapy and OSA history in additional sensitivity models, but no meaningful differences in estimates were found. More detailed information on the results with full model outputs is available in eTables 1 to 5 in Supplement 1 .

When comparing standardized estimates of PCL and BMI together in mutually adjusted models, we found that each 1-SD increase in PCL score was associated with a 1.9 (95% CI, 0.5-3.3) events-per-hour increase in AHI, while each SD increase in BMI was associated with a 2.6 (95% CI, 1.0-4.2) events-per-hour increase in AHI. However, in fully adjusted models, the estimates changed to 2.1 (95% CI, 0.1-4.2) and 1.7 (95% CI, 0.2-3.2) for PCL and BMI, respectively.

In this cross-sectional study of Vietnam War era veteran twin pairs, we found that within brothers discordant for PTSD symptoms, increased symptoms were associated with statistically and clinically higher AHI. This analysis is, to our knowledge, the most rigorously controlled to date for examining the association between a psychiatric anxiety disorder such as PTSD and OSA, as the twin brothers are matched for demographic, familial, partial genetic, and other early life factors that can otherwise influence both PTSD and OSA. 15 Studies of discordant twins benefit from high levels of internal validity compared with individual-level analyses, and the focus on within-pair differences may be applicable to clinical settings in which within-person differences in PTSD symptoms before and after treatment are measured. 18 , 33 The statistical models were also adjusted for several possible confounders and moderators to estimate the association of PTSD symptoms specifically with OSA severity. The associations were stronger in the fully adjusted models that examined PTSD-discordant pairs ( Table 2 ) than models that examined twins as individuals ( Figure 1 ), which emphasizes the importance of the twin design. With our discordant twin models, we also found that the standardized effect size for PTSD symptoms was remarkably similar to BMI, which is one of the most established risk factors for OSA. 34 , 35

Our study is supported by previous work suggesting similar associations, although many previous studies found lower effect sizes that may have been due to methodological limitations. 6 Most studies have included clinical samples of individuals who were referred for sleep center evaluation because of suspected OSA, and in these studies, individuals with a lower propensity for clinical evaluation could be underrepresented. Such individuals may include, for example, those with mental health conditions, including PTSD, or those with increased barriers to accessing health care, such as financial or transportation. 36 From this underrepresentation, OSA rates may be underestimated in the PTSD group by restricting the cases to only participants with mild symptoms. Our work also suggests that PTSD may be more strongly associated with OSA than depression, which our group has previously examined with no association being found. 37 In addition, depression was not associated with OSA in our models (eTables 1-3 in Supplement 1 ). These negative findings do, however, contrast with previous studies showing positive associations in different populations 38 , 39 ; therefore, more research is needed on the effects of depression and other psychiatric conditions. In light of previous work that included depression, we view our findings as likely to be more specific to the altered breathing pathophysiology accompanying an anxiety disorder such as PTSD rather than accompanying a mood disorder. Despite the modestly small sample size, our use of twin models and the Vietnam Era Twin Registry offers advantages, as the results may be less prone to bias due to clinical symptoms or help-seeking engagement.

Our findings emphasize the need for more studies to examine mechanisms underlying endotypes of OSA that incorporate psychological stress pathways. Possible mechanisms include pharyngeal collapsibility and exaggerated loop gain, which describe the centrally mediated respiratory response to the mild carbon dioxide retention at the onset of sleep. 40 Posttraumatic stress disorder may cause a lower sleep arousal threshold and decreased autonomic and respiratory reflexes. 40 Nighttime PTSD symptoms, such as nightmares, may increase sleep fragmentation, which in turn may increase airway collapsibility. 6 We speculate that specific brain pathways that may be altered in PTSD may also be involved. 41 This speculation is supported by studies of OSA using functional brain imaging that have shown alterations in regions of the brain involved in stress regulation, such as the thalamus and anterior cingulate cortex. 42 Neurologic substrates between the brain and the visceral organs that regulate respiration and pharyngeal patency may also be involved. 43

The clinical relevance of our findings from a psychiatric treatment perspective is supported by studies suggesting that OSA may impair PTSD recovery. For example, previous studies have shown that OSA treatment may help to reduce PTSD symptoms, 44 although adherence rates are low. 45 Previous studies have also shown improvement in depressive symptoms with CPAP, further supporting the adjunct benefit that psychiatric interventions may offer. 46 Symptoms of OSA may also increase anxiety (due to choking sensation) and, therefore, may worsen PTSD symptoms in individuals who already have the condition. 6 Furthermore, OSA may have global cognitive consequences, which may result in decreased resiliency toward and coping with PTSD symptoms. 9 As such, OSA may enhance the risk of PTSD in individuals exposed to trauma and may be worsened by low adherence to therapies such as CPAP. Thus, sleep disturbance and PTSD symptoms may accelerate each other in a feed-forward fashion. Our study also suggests a need to examine the effects of PTSD treatment on OSA severity.

Our findings are subject to several limitations. The cross-sectional study design limited our ability to evaluate the directionality of our results and potential causality. Nonetheless, because the PTSD in our sample occurred subsequent to a prior traumatic event (elucidated via SCID), reverse causality (ie, OSA causing PTSD) may be less likely; in other words, OSA may not be a causal factor in incident PTSD within the sample of individuals studied here. Our sample consisted of older, mostly White men; therefore, the results cannot be generalized to other groups. Nonetheless, the high level of consistency has the distinct advantage of increasing the internal validity of the analysis, which is a critical first step for such studies. 18 Previous research also has suggested that these outcomes are not restricted to men and that they may be similar in women. 47 The small sample size of participants discordant for current PTSD limited our ability to adjust for all possible confounders in the same models, but the lack of confounding in our larger analysis of PTSD symptoms in which all covariates were included suggests that this association is not otherwise explained by any of the covariates. In addition, the smaller sample size is offset by the strength of using gold standard metrics, such as the structured clinical interview for psychiatric disorders and overnight in-laboratory PSG, which may decrease the risk of misclassification and improve the accuracy of the estimates.

In this cross-sectional study of veteran twins, we found a strong dose-response association between PTSD and OSA. The twin design allowed close control of familial influences, and the sampling strategy using the Vietnam Era Twins Registry minimized bias. The results suggest that functional neurobiologic and stress pathways modulating respiratory regulation and airway collapse are important in the etiology of OSA. More research is needed to examine possible biobehavioral and psychophysiologic interventions that could ameliorate both sleep-disordered breathing and PTSD.

Accepted for Publication: March 13, 2024.

Published: June 24, 2024. doi:10.1001/jamanetworkopen.2024.16352

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Shah AJ et al. JAMA Network Open .

Corresponding Author: Amit J. Shah, MD, MSCR, Department of Epidemiology, Rollins School of Public Health, Emory University, 1518 Clifton Rd, Claudia Nance Rollins Bldg 4045, Atlanta, GA 30322 ( [email protected] ).

Author Contributions: Dr Shah had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Shah, Vaccarino, Huang, Smith, Clifford, Bremner, Bliwise.

Acquisition, analysis, or interpretation of data: Shah, Vaccarino, Goldberg, Huang, Ko, Ma, Levantsevych, Alagar, Mousselli, Johnson, Clifford, Bremner, Bliwise.

Drafting of the manuscript: Shah, Huang, Ma.

Critical review of the manuscript for important intellectual content: Shah, Vaccarino, Goldberg, Huang, Ko, Levantsevych, Smith, Alagar, Mousselli, Johnson, Clifford, Bremner, Bliwise.

Statistical analysis: Shah, Huang, Ma, Alagar.

Obtained funding: Vaccarino, Smith, Clifford, Bremner.

Administrative, technical, or material support: Shah, Vaccarino, Goldberg, Levantsevych, Mousselli, Johnson, Clifford, Bremner, Bliwise.

Supervision: Shah, Vaccarino, Clifford, Bremner, Bliwise.

Conflict of Interest Disclosures: Dr Johnson reported receiving personal fees from Idorisa outside the submitted work. Dr Bliwise reported receiving personal fees from CliniLabs, Eisai, Ferring, Huxley, Idorsia, and Merck outside the submitted work. No other disclosures were reported.

Funding/Support: This research was supported by grants R01 HL136205, R01 HL155711, K23 HL127251, R01 HL686630, R03 HL146879, R01 AG026255, K24 HL077506, T32 HL130025, R01 HL088726, K24 MH076955, and UL1TR002378 from the National Institutes of Health.

Role of the Funding/Sponsor: The funder was not involved in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: Numerous organizations have provided invaluable assistance, including the VA Cooperative Study Program; Department of Defense; National Personnel Records Center, National Archives and Records Administration; Internal Revenue Service; National Institutes of Health; National Opinion Research Center; National Research Council, National Academy of Sciences; and Institute for Survey Research, Temple University. The authors acknowledge the continued cooperation and participation of the members of the Vietnam Era Twin Registry and their families and thank the tireless staff at Emory University. The authors honor and express their profound gratitude to the late Martica “Tica” Hall, PhD (University of Pittsburgh), who made invaluable contributions to the work. Her legacy and impact endure through the insights she made in her work in the conduct of this study and development of this article.

• Register for email alerts with links to free full-text articles
• Access PDFs of free articles
• Manage your interests
• Save searches and receive search alerts

Celebrating 150 years of Harvard Summer School. Learn about our history.

Why You Should Make a Good Night’s Sleep a Priority

Poor sleep habits and sleep deprivation are serious problems for most high school and college students. This guide offers important tips on how—and why—to improve your sleep hygiene.

The time you spend in high school and college can be both fun and rewarding. At the same time, these can be some of the busiest years of your life.

Balancing all the demands on your time—a full course load, extracurricular activities, and socializing with friends—can be challenging. And if you also work or have family commitments, it can feel like there just aren’t enough hours in the day. 

With so many competing priorities, sacrificing sleep may feel like the only way to get everything done. 

Despite the sleepiness you might feel the next day, one late night probably won’t have a major impact on your well-being. But regularly short-changing yourself on quality sleep can have serious implications for school, work, and your physical and mental health.

Alternatively, prioritizing a regular sleep schedule can make these years healthier, less stressful, and more successful long-term.

The sleep you need versus the sleep you get

According to the National Sleep Foundation , high school students (ages 14-17) need about eight to 10 hours of sleep each night. For young adults (ages 18 to 25), the range is need between seven and nine hours.

How do you know how much sleep you need within this range? 

According to Dr. Edward Pace-Schott, Harvard Summer School and Harvard Medical School faculty member and sleep expert, you can answer that question simply by observing how much you sleep when you don’t need to get up.

“When you’ve been on vacation for two weeks, how are you sleeping during that second week? How long are you sleeping? If you’re sleeping eight or nine hours when you don’t have any reason to get up, then chances are you need that amount or close to that amount of sleep,” says Pace-Schott. 

Most students, however, get far less sleep than the recommended amount. 

Seventy to 96 percent of college students get less than eight hours of sleep each week night. And over half of college students sleep less than seven hours per night. The numbers are similar for high school students; 73 percent of high school students get between seven and seven and a half hours of sleep .

Of course, many students attempt to catch up on lost sleep by sleeping late on the weekends. Unfortunately, this pattern is neither healthy nor a true long-term solution to sleep deprivation. 

And what about those students who say that they function perfectly well on just a couple hours of sleep?

“There are very few individuals who are so-called short sleepers, people who really don’t need more than six hours of sleep. But, there are a lot more people who claim to be short sleepers than there are real short sleepers,” says Pace-Schott.

Consequences of sleep deprivation

The consequences of sleep deprivation are fairly well established but may still be surprising.

For example, did you know that sleep deprivation can create the same level of cognitive impairment as drinking alcohol? 

According to the CDC , staying awake for 18 hours can have the same effect as a blood alcohol content (BAC) of 0.05 percent. Staying awake for 24 hours can equate to a BAC of 0.10 percent (higher than the legal limit of 0.08 percent). 

And according to research by AAA , drowsy driving causes an average of 328,000 motor vehicle accidents each year in the US. Drivers who sleep less than five hours per night are more than five times as likely to have a crash as drivers who sleep for seven hours or more.  

Other signs of chronic sleep deprivation include:

• Daytime sleepiness and fatigue
• Irritability and short temper
• Mood changes
• Trouble coping with stress
• Difficulty focusing, concentrating, and remembering

Over the long term, chronic sleep deprivation can have a serious impact on your physical and mental health. Insufficient sleep has been linked, for example, to weight gain and obesity, cardiovascular disease, and type 2 diabetes.

The impact on your mental health can be just as serious. Harvard Medical School has conducted numerous studies, including research by Pace-Schott, demonstrating a link between sleep deprivation and mental health disorders such as anxiety and depression.

Earn college credits with a summer course at Harvard Summer School.

Importance of sleep for high school and college students

As difficult as it is to prioritize sleep, the advantages of going to bed early and getting quality sleep every night are very real.

College students who prioritize sleep are likely to see an improvement in their academic performance.

If you are well rested, you will experience less daytime sleepiness and fatigue. You may need less caffeine to stay awake during those long lectures. And you will also find you are more productive, more attentive to detail, and able to concentrate better while studying.

But the connection between sleep and academic performance goes well beyond concentration and attentiveness.

“Sleep is very important for consolidating memories. In any sort of experimental setting, study results show better performance if you learn material and then sleep on it, instead of remaining awake. So there’s lots and lots of evidence now indicating that sleep promotes memory strengthening and memory consolidation,” says Pace-Schott. 

There is also a strong connection between sleep quality and stress.

Students who prioritize sleep are better able to cope with the stress that comes with being an active student. 

“It’s a vicious circle where the more stressed you get, the less you sleep, and the less you sleep, the more stressed you get. And in the long term, that can lead to serious psychiatric problems,” says Pace-Schott.

In the worst case scenario, the combination of lack of sleep and stress can lead to mental health disorders such as depression, general anxiety disorder, and potentially even post-traumatic stress disorder.

But prioritizing sleep can create a positive feedback loop as well. 

Establishing a sleep schedule and adequate sleep duration can improve your ability to cope with stress. Being active and productive will help you get more done throughout the day, which also reduces feelings of stress.

And the less stressed you feel during the day, the better you will sleep at night. 

Tips for getting more sleep as a student

The key to getting a good night’s sleep is establishing healthy sleep habits, also known as sleep hygiene.

The first step is deciding to make sleep a priority. 

Staying ahead of coursework and avoiding distractions and procrastination while you study is key to avoiding the need for late night study sessions. And prioritizing sleep may mean leaving a party early or choosing your social engagements carefully. 

Yet the reward—feeling awake and alert the next morning—will reinforce that positive choice. 

The next step is establishing healthy bedtime and daytime patterns to promote good quality sleep.

Pace-Schott offers the following tips on steps you can take to create healthy sleep hygiene:

• Limit caffeine in close proximity to bed time. College students should also avoid alcohol intake, which disrupts quality sleep.
• Avoid electronic screens (phone, laptop, tablet, desktop) within an hour of bedtime. 
• Engage in daily physical exercise, but avoid intense exercise within two hours of bedtime.
• Establish a sleep schedule. Be as consistent as possible in your bedtime and rise time, and get exposure to morning sunlight.
• Establish a “wind-down” routine prior to bedtime.
• Limit use of bed for daily activities other than sleep (e.g., TV, work, eating)

Of course, college students living in dorms or other communal settings may find their sleep disturbed by circumstances beyond their control: a poor-quality mattress, inability to control the temperature of your bedroom, or noisy roommates, for example. 

But taking these active steps to promote healthy sleep will, barring these other uncontrollable circumstances, help you fall asleep faster, stay asleep, and get a more restorative sleep.

And for students who are still not convinced of the importance of sleep, Pace-Schott says that personal observation is the best way to see the impact of healthy sleep habits. 

“Keep a sleep diary for a week. Pay attention to your sleep in a structured way. And be sure to record how you felt during the day. This can really help you make the link between how you slept the night before and how you feel during the day. It’s amazing how much you will learn about your sleep and its impact on your life.” 

Interested in summer at Harvard? Learn more about our summer programs.

Request Information

What You Need to Know About Pre-College Program Activities

Rigorous, fast-paced—and a lot of fun.

Harvard Division of Continuing Education

The Division of Continuing Education (DCE) at Harvard University is dedicated to bringing rigorous academics and innovative teaching capabilities to those seeking to improve their lives through education. We make Harvard education accessible to lifelong learners from high school to retirement.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Neuropsychopharmacology
• v.45(1); 2020 Jan

Sleep, insomnia, and depression

Dieter riemann.

1 Department of Psychiatry and Psychotherapy, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany

Lukas B. Krone

2 Sleep and Circadian Neuroscience Institute, University of Oxford, Oxford, UK

3 Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK

Katharina Wulff

4 Departments of Radiation Sciences & Molecular Biology, Umea University, Umeå, Sweden

5 Wallenberg Centre for Molecular Medicine (WCMM), Umea University, Umeå, Sweden

Christoph Nissen

6 University Hospital of Psychiatry and Psychotherapy, Bern, Switzerland

Since ancient times it is known that melancholia and sleep disturbances co-occur. The introduction of polysomnography into psychiatric research confirmed a disturbance of sleep continuity in patients with depression, revealing not only a decrease in Slow Wave Sleep, but also a disinhibition of REM (rapid eye movement) sleep, demonstrated as a shortening of REM latency, an increase of REM density, as well as total REM sleep time. Initial hopes that these abnormalities of REM sleep may serve as differential-diagnostic markers for subtypes of depression were not fulfilled. Almost all antidepressant agents suppress REM sleep and a time-and-dose–response relationship between total REM sleep suppression and therapeutic response to treatment seemed apparent. The so-called Cholinergic REM Induction Test revealed that REM sleep abnormalities can be mimicked by administration of cholinomimetic agents. Another important research avenue is the study of chrono-medical timing of sleep deprivation and light exposure for their positive effects on mood in depression. Present day research takes the view on insomnia, i.e., prolonged sleep latency, problems to maintain sleep, and early morning awakening, as a transdiagnostic symptom for many mental disorders, being most closely related to depression. Studying insomnia from different angles as a transdiagnostic phenotype has opened many new perspectives for research into mechanisms but also for clinical practice. Thus, the question is: can the early and adequate treatment of insomnia prevent depression? This article will link current understanding about sleep regulatory mechanisms with knowledge about changes in physiology due to depression. The review aims to draw the attention to current and future strategies in research and clinical practice to the benefits of sleep and depression therapeutics.

Introduction—the phenomenology of sleep in depression

A brief historical overview.

Robert Burton, in his Anatomy of Melancholia [ 1 ] remarked that ancient Greek physicians were well aware of the fact that melancholic individuals complained of difficulties falling asleep, maintaining sleep or of waking up too early in the morning. Treatment of difficulties with sleep in antique times consisted of listening to calm music, reading, or the use of opium or alcohol [ 2 ]. Emil Kraepelin, the founder of modern psychiatry, observed that mental symptoms fall mainly into two groups and he created the illness categories [ 3 ] ‘manic-depression’’ and ‘dementia praecox’’, today clinically similar to ‘affective’’ and ‘psychotic’’ phenotypes. He postulated that a certain type of sleep difficulty co-occurred with a certain depressive subtype, i.e., sleep onset problems with “neurotic” and sleep maintenance/early morning awakenings with “endogenous” depression. Thus, the idea that the type of sleep disturbance in depressed individuals might support differential-diagnostic considerations was already born over a 100 years ago and experienced a renaissance in the 1970s with the discovery of shortened rapid eye movement (REM) sleep latencies in depressed individuals. Before then, pharmacological agents like bromides, paraldehyde, scopolamine, or barbiturates were used to treat insomnia accompanying depression until these were replaced by benzodiazepine hypnotics and sedating antidepressants in the 1950/60s. The incidence of depression is steadily rising globally and present-day epidemiological data reveal that depression is now the third leading contribution to global disease burden [ 4 ] and an estimated 25% of the population in industrialized countries will suffer from depression once in a lifetime [ 5 ]. This review seamlessly ties in with and extends an earlier line of research of using sleep as a window into the brain’s neurobiological links between sleep processes and depression. By telling the story of an early success, a subsequent stagnation phase and new enthusiasm based on the integration of emerging neurobiological knowledge into the mechanistic overlap of sleep and depression disturbances, this review is paving a way for the integration of this knowledge into clinical practice.

REM sleep and depression—early hopes

The discovery of phases of REM during sleep in 1953 [ 6 ] and the ensuing interest in sleep research led to the establishment of psychiatric sleep research utilizing polysomnography (PSG). Kupfer et al. from Pittsburgh [ 7 , 8 ] were among the first to suggest that changes of REM sleep, i.e., shortened REM sleep latency (shorter interval between sleep onset and the first occurrence of REM sleep), increased total REM sleep duration and increased REM density (higher phasic eyes movements during REM sleep), are typical sleep characteristics of patients with primary vs. secondary depression (see Fig.  1 for illustration). Furthermore, polysomnographically measured sleep continuity was disturbed (i.e., long sleep latency and frequent awakenings throughout the sleep period) and Slow Wave Sleep (SWS) reduced. These findings were met with enthusiasm at the time and promoted the idea, later known as biomarkers [ 9 ], to identify functional subtypes within and across diagnostic categories by which treatment could be stratified and response predicted for each patient in order to achieve remission.

Comparison of the polysomnographic (PSG) profile of a good sleeper (upper panel) and a patient hospitalized for severe depression according to DSM-IV criteria (lower panel). Both subjects have been free from intake of any psychotropic drug for at least 14 days. The y -axis lists arousal (micro-arousals), wake and sleep stages (REM, stage N1 to N3) and eye movements). The x -axis is the time axis. Sleep in depression is characterized by alterations of sleep continuity (prolonged sleep onset and sleep maintenance problems), a decrement of SWS (Slow Wave Sleep, also measurable as a decline in Delta-Power) and a disinhibition of REM sleep: this encompasses shortening of REM latency, prolongation of the first REM period and increase of REM density. Original data from Freiburg sleep lab, hitherto unpublished

REM sleep and depression—disappointed expectations

The question of the differential-diagnostic value of REM sleep abnormalities puzzled psychiatric sleep research for almost two decades [ 10 ]. It was thought that REM sleep abnormalities might be more typical for the ‘primary/melancholic/endogenous’’ subtype compared to the ‘secondary/neurotic/non-melancholic’’ subtype. With increasing research efforts, however, it turned out that especially age, and to a certain extent sex, has a very strong effect on REM sleep latency, whereby REM sleep latency decreases with age. Revisiting the primary/secondary dichotomy and REM latency, carefully controlling for age and severity of depression, Thase et al. [ 11 ] concluded that initial positive findings may have represented epiphenomena of sample differences with respect to age or severity of depression. Additional research into other mental disorders (especially schizophrenia, borderline personality disorder and alcohol dependency) revealed that patients with these diagnostic entities also display some degree of REM sleep alterations [ 12 , 13 ], further weakening the assumptions of a high specificity of REM sleep abnormalities for depression.

Antidepressants and sleep in depression

An important impetus for the field of sleep research in depression was the observation that almost all antidepressants influence sleep, notably by strongly suppressing REM sleep [ 14 ], whereby the extent of REM sleep suppression covaried with clinical ratings indicating therapeutic efficacy of antidepressants response [ 10 ]. Thus, REM suppression appeared as a promising early predictor of subsequent treatment response. Identifying such early predictor that could function as a biomarker would be of high clinical value given the long latency to response and the limited response rate of ~60% of depressed patients. Insofar, the suppression of REM sleep seemed to constitute a window to the brain reflecting therapeutic efficacy of antidepressant substances. Table  1 summarizes the effects of antidepressants on different aspects of polysomnographically recorded sleep [ 14 ].

Impact of antidepressants on PSG recorded sleep (according to Riemann and Nissen, 2012 [ 14 ])

a Reported effects are based on preponderance of evidence from published studies (see text for details). Many effects are inconsistent between individual studies. “↑” Indicates increase from pre-treatment baseline; “↓” indicates decrease from pre-treatment baseline; “ ↔ ” indicates no change from pre-treatment baseline

Data in the table demonstrate that most antidepressants suppress REM sleep, with only a few exceptions such as trimipramine, trazodone and mirtazapine. Many of these substances lead to an enhanced SWS and improved sleep continuity but it is important to note that certain drug classes, especially SSRIs (Selective Serotonin Reuptake Inhibitors), may induce a deterioration of sleep continuity, which is clinically expressed as increased insomnia complaints. This heterogeneity posed a challenge to the initial hope of using PSG to predict treatment response and it has been given up by and large [ 14 ]—partly due to failures to replicate the predictive value of REM sleep suppression for later therapeutic response, partly due to the inherent costs and clinical impracticability of using PSG as a drug monitoring tool. Today, miniaturized EEG monitors and refined semi-automatic algorithms for sleep analyses might allow the introduction of sleep EEG into the clinics [ 15 ]. An intrinsic value of this line of research, however, is that it is now widely acknowledged that drug wash-out periods of at least 7–14 days (for fluoxetine even longer) are needed to obtain valid sleep data. This, however, renders PSG research into depression complicated, because today most patients with severe depression who come into contact with a research facility are pre-medicated and drug-withdrawal would not be consistent with standard guidelines and considered unethical.

The cholinergic REM induction test

An experimental avenue towards a mechanistic understanding of REM sleep abnormalities in depression constituted the Cholinergic REM Induction Test (CRIT). This strategy, developed by Gillin and Sitaram [ 16 ], was based on the reciprocal interaction model of Non-REM/REM sleep regulation [ 17 , 18 ] positing that REM sleep onset can be advanced in animals and healthy volunteers by cholinergic stimulation [ 19 ] because REM sleep is largely governed by cholinergic neurons in the brain stem (see Fig.  2 ).

a , b The Hobson/McCarley Model and the regulation of Non-REM and REM sleep in good sleepers and patients with depression. Experiments by Hobson and McCarley in cats especially in the brain stem were able to show that manipulation of cholinergic or aminergic cell groups is able to change sleep. In the case of good sleepers ( a ) the aminergic systems (dorsal raphe/locus coeruleus) are active during Non-REM sleep and cease their activity around the onset of REM sleep, where cholinergic activity becomes dominant. The reciprocal interaction between the aminergic and the cholinergic system determines the Non-REM-REM cycle. In depression ( b ), an overactive cholinergic system or a weakened aminergic system leads to an earlier onset of REM sleep. Based on refs [ 10 , 18 ], permission granted

Berger et al. [ 20 ] showed that such a cholinergic stimulus elicited an even more-pronounced response in patients with depression compared to good sleepers and patients with other mental disorders. Other studies showed that this early onset of REM sleep may also be provoked by cholinomimetic agents in individuals at high risk for depression [ 21 ], who later displayed a higher rate of episodes of depression over a follow-up period of >10 years [ 22 ]. Thus, it was demonstrated that early onset of REM sleep after cholinergic stimulation is not only a state and trait marker, but also a vulnerability marker for depression. Given that drug wash-out periods of at least 7–14 days are necessary to obtain valid CRIT data, it is understandable that, apart from its scientific value, the CRIT never saw widespread clinical application for determining risk profiles.

Biological timing and sleep deprivation in depression

Wehr et al. [ 23 ] published ground-breaking work on the likely involvement of the biological time keeping system in the pathogenesis of affective disorders, high-lighting that an advance of the sleep period by 6 h normalized the REM sleep phases and induced a longer-lasting remission of depressive symptoms. The authors inferred from their longitudinal observations that REM sleep rhythmicity and their underlying biochemical rhythms must have altered internal phase-relationships that are associated with certain psychopathological phenotypes. Similar remissions of depressive symptoms were also achieved in patients undergoing complete wake therapy (full night of sleep deprivation), that was first tested by Pflug and Toelle [ 24 ]. These results were better understood with the two-process model of sleep regulation proposed by Borbély et al. [ 25 – 27 ], in which a homeostatic sleep process (S) and a circadian process (C), interact in a threshold- and time-dependent manner (see Fig.  3 ), thereby incorporating that appropriate timing of sleep with respect to the internal clock is crucial for stable mood as described in the ‘internal coincidence’’ model [ 28 , 29 ].

The Two-Process-Model is based on two components or factors, i.e., process C and process S. Process C reflects circadian rhythmicity, which is under control of the light–dark cycle. Process S reflects sleep or sleep pressure and can be measured as delta waves during night sleep. The interaction between C and S describes or determines the sleep-wake cycle in good sleepers (upper left panel). In contrast, in depression (lower right panel), process S is deficient, as reflected by low levels of slow wave sleep/delta power in the spectral analysis of the sleep EEG. Sleep deprivation may act antidepressive because it enhances process S, thus leading to an increased amount of slow wave sleep after sleep deprivation. Personal permission by A. Borbely

Germain and Kupfer [ 30 ] summarized further chronobiologically inspired experiments based on circadian/ sleep hypotheses of depression, including the circadian phase-shift effects of light [ 31 ] or the social rhythm model [ 32 ]. At present, the most comprehensive, decisive review on sleep deprivation therapy explaining its antidepressant effects on the level of molecular circadian regulation was compiled by Bunney and Bunney [ 33 ]. Clinically, therapeutic sleep deprivation (SD) is a rapid acting treatment for a subset of patients with major depression. Within 24 h it provokes a transient, but strong decrease in depressive symptoms in those who respond [ 34 , 35 ]. Unfortunately, the effect is not long lasting and a relapse into depression occurs after the next night of sleep. Even brief daytime sleep periods after SD can reverse the therapeutic effect [ 36 ]. Strategies to enhance or prolong the antidepressive effect are concomitant pharmacotherapy, light therapy or sleep phase advance, if correctly timed and any deviation from the schedule avoided to prevent a rebound into depressive symptomatology. Paradigms of SD in depression include total sleep deprivation, partial sleep deprivation (mostly concerning the second half of the night), selective REM sleep deprivation and selective slow wave sleep (SWS) deprivation [ 37 ]. Clinical predictors for positive SD response include the melancholic subtype of depression, a high level of vigilance, the propensity to produce diurnal mood variations and short REM latency [ 38 , 39 ]. From a scientific point of view, SD presents a unique paradigm to study the neurobiology of major depression within a short period of time and without pharmacological interference because patients can be investigated in a depressed and non-depressed/euthymic state. Unfortunately, up to now, the neurobiological basis of the immediate antidepressant response to SD and its rapid drop with more sleep is not sufficiently understood. The rapid antidepressive mechanisms probably is quite different from conventional treatments (pharmacological/psychotherapy). Therefore, elucidating the neuronal mechanisms of SD has become crucially important for novel perspectives on antidepressive treatment.

We recently integrated the synaptic plasticity hypothesis of major depression [ 40 , 41 ] and the synaptic homeostasis hypothesis of sleep-wake regulation [ 42 , 43 ] into a synaptic plasticity model of sleep wake regulation. The model posits, in brief, that sleep deprivation, through an increase of overall synaptic strength, shifts patients with major depression into a more favorable window of synaptic plasticity and network function [ 44 ]. While certainly oversimplified, the model allows for testing specific predictions, such as proposed antidepressive properties of selective slow wave sleep suppression, for instance through auditory closed-loop stimulation [ 45 , 46 ] and translation into animal models of depression for further deciphering the neural mechanisms of rapid treatment response.

Sleep and depression: neuroimaging results

Advances in human brain imaging techniques, particularly [18F]2-fluoro-2-deoxy-d-glucose PET (Positron Emission Tomography) studies, have led to new insights into changes in brain metabolism during the sleep-wake cycle in healthy humans and those with depression, which are not accessible to surface electrophysiology. The integration of these studies suggests that functional neuroanatomic correlates can be assigned to characteristic PSG alterations in depression, of which three main components can be distinguished. First, persistent hyperactivity in the basic ascending arousal system throughout the sleep-wake cycle in depression might be implicated in the experience of hyperarousal during waking and disturbed sleep continuity [ 47 ]. Second, persistent hyperactivity in the ventral emotional system that includes the amygdala and the ventral anterior cingulate cortex (ACC) might relate to disturbances in affect, including depressed mood, and to the characteristic enhancement of REM sleep in depression [ 48 ]. Third, hypoactivity in the dorsal executive system throughout the sleep-wake cycle that includes the dorsolateral prefrontal cortex (DLPFC) might be implicated in the attenuation of executive functions and reduced slow-wave sleep in depression [ 49 ]. It is of note that these observations do not directly inform about the direction of causality. Interventional studies, such as non-invasive brain stimulation studies, would be needed to test whether the described alterations of sleep in depression can be corrected and whether this kind of non-invasive stimulation might exert therapeutic effects.

Interim summary

Sleep and circadian rhythm research in psychiatry had temporarily lost much of its momentum over the disappointment of the unspecificity of REM sleep alterations for depression [ 12 , 13 ], as well as due to the introduction of more easy-to-apply neuroimaging methods in psychiatry, and the rise of SSRIs as mainstay treatment for affective disorders. Promising strategies such as the cholinergic REM induction test or the application of PET to study sleep in depression provided highly interesting and exciting results, but were not followed-up because of their immense economic and time-consuming costs that precluded the introduction of these techniques into clinical routine. The fact that practically all available antidepressive medications have more or less strong effects on the physiology of sleep created another roadblock. Today, researchers are confronted with mainly pre-medicated patients for whom drug discontinuation (with a wash-out periods of at least 7–14 days) for research purposes would be considered unethical. A further big hurdle for sleep research (but indeed probably for all types of biological psychiatric research) constituted the fact that a categorical approach to nosology prevailed, hampering any biologically meaningful investigation into psychophysiological underlying mechanisms across diagnostic boundaries. This obstacle has finally been overcome by overwhelming evidence of transdiagnostic and dimensional constellations in pathophysiological traits. The transdiagnostic role of insomnia for many mental disorders, and especially considering its strong association with depression, either as a symptom, syndrome or a distinct diagnostic entity in itself, has become a viable strategy to investigate the sleep—depression relationship. It is our hope that this paradigm change in research may also be clinically more fruitful with respect to aspects of detection, diagnosis and treatment of depression.

The relationship between insomnia and depression

The previous chapters on the phenomenology of sleep and depression have exemplified that disturbances of sleep continuity ubiquitously accompany affective disorders. Whereas the low specificity of insomnia symptoms rendered the study of insomnia unattractive for years, today symptoms of insomnia are seen as a critical feature of depression and a better understanding of the processes involved should contribute to the refinement of pathophysiological concepts and therapeutic approaches for depressive disorders.

What is insomnia?

DSM-5 has given up the distinction between primary and secondary insomnia and now includes the diagnostic category “insomnia disorder” [ 50 ]. According to these criteria insomnia is defined as the experience of problems to fall asleep, to maintain sleep or to suffer from early morning awakening. These sleep symptoms have to be coupled with daytime impairments, like decreased attention or problems in concentration. These symptoms have to occur at least three times a week over a period of at least 3 months in order to be diagnosed as insomnia disorder. If the insomniac symptoms are clearly due to another medical/ mental disorder/sleep disorder or substance use, they are not diagnosed separately. However, in most cases, insomnia may have occurred before the medical or mental disorder, or may persist beyond the medical or mental disorder even when the other disorder has been treated successfully. In these cases, diagnostic comorbidity is preferred to single diagnoses. Insomnia as a symptom occurs very frequently (>50% of the population in a year) and in many cases will disappear, for example after the cessation of an acute stressor [ 51 , 52 ]. It is assumed that ~10% of the population in industrialized countries suffer from chronic insomnia [ 53 ]. Recent treatment guidelines [ 54 – 56 ] have summarized that insomnia is associated with high costs for the health care system and also for a high degree of suffering for afflicted individuals. Treatment constitutes of benzodiazepines, benzodiazepine receptor agonists, ramelteon, suvorexant and other substances, which are administered off-label like sedating antidepressants—an interesting development further underlining the close relationships between insomnia and depression. As of now, the first-line treatment of insomnia according to current guidelines is cognitive-behavioral therapy for insomnia (CBT-I) [ 54 – 56 ]. Importantly, the diagnosis of insomnia—as with all other mental disorders—is based on the subjective experience of the afflicted individual and not on PSG defined criteria.

Insomnia and hyperarousal

The idea that insomnia might be due to “hyperarousal” is a very old one and can frequently be found already in the medical literature of the nineteenth century (“overexcitation of the nerves”) or even earlier. We have documented [ 51 , 52 , 57 , 58 ] that the hyperarousal hypothesis offers an explanation into the origin of insomnia. The literature looking at psychophysiological variables of insomnia can be summarized as follows: parameters of the autonomous nervous system, the endocrine system, the neuroinflammatory system and neurophysiological indices clearly show that patients who suffer from insomnia show increased indices of hyperarousal, as reflected by altered heart rate, increased cortisol output, and an increase in fast waves as documented by increased amounts of beta waves measured by the sleep EEG [ 59 ]. On the psychological level numerous studies have demonstrated that hyperarousal and hyperarousability, as measured by questionnaires, is something very typical for patients with insomnia, which they also experience directly on a subjective level (e.g., racing thoughts whenever trying to sleep). The hyperarousal model of insomnia can be easily linked to neurobiological models of sleep and sleep regulation such as the flip-flop switch model of sleep regulation formulated by Saper et al. [ 60 ] (see also the first article in this volume and section “The flip-flop switch model and the role of the orexinergic system”).

Insomnia as a predictor of depression and suicidality

The renowned interest into the relationships between sleep and circadian rhythms and depression is mainly due to studies that have shown that insomnia/insomniac symptoms are independent predictors for depressive disorders [ 61 – 64 ] and suicidal ideation/ suicide and suicide attempts [ 65 – 68 ]. These epidemiologically based data have led to great interest in sleep continuity changes in the context of depression and other mental disorders. Thus, the quest for specific biological markers was given up in lieu of focussing on transdiagnostic overarching markers/mechanisms, which can be elucidated by subjective self-report, rendering research and clinical applicability easier and economic.

Sleep and insomnia: bi-directional relationships with depression

Given the fact, that on the one hand, almost all depressed patients display some kind of sleep alteration, and that on the other hand especially insomnia alone conveys an increased risk for depression and suicidality, the sleep-depression relationship needs to be conceptualized as bi-directional. Several observations converge with this view: When comparing etiological and pathophysiological explanations for insomnia and depression, a clear overlap is present—both conditions have been shown to be triggered by psychosocial stressors. Hyperarousal being regarded a psychological and a biological factor is present in both conditions. When considering treatment modalities, sedating antidepressants can be used for both conditions and CBT-I, the most effective insomnia treatment, has—to some extent—been derived from classical CBT for depression. On the other hand treating insomnia early has the potential to prevent or reduce depressive symptoms as promising preliminary data indicate. We suggest to acknowledge that both disorders can occur independently of each other, as evidenced by the fact that many patients with chronic insomnia never develop depression. Equally, insomnia might indicate a first step towards onset of psychopathology, sharing some underlying (epi-) genetic, personality and neurobiological changes typical for depression. Whether or not depression develops might depend on additional environmental triggers, such as psychosocial stress load, lifestyle, coping mechanisms and early preventative treatments. Given the fact that by and large only a minority of patients with insomnia is sufficiently treated (with CBT-I), there seems to be ample room to test the hypothesis whether stringent and early insomnia treatment might reduce the risk for becoming depressed in the long run.

Treatments of insomnia—a chance for prevention of depression?

As mentioned before, presently Cognitive-Behavioral Treatment for Insomnia (CBT-I) is acknowledged to be the first-line treatment for insomnia [ 54 – 56 , 69 ]—thus opening the possibility for large-scale studies to test whether early and adequate treatment of insomnia may prevent psychiatric sequelae, i.e., depression or psychosis. First studies targeting this issue showed that online delivered CBT-I seems to be able to reduce insomnia and depression scores in subclinically depressed patients with insomnia [ 70 ]. In a similar vein, advances in the field of chronobiology have led to a renaissance of chronotherapeutic approaches for mental disorders [ 71 – 73 ]. These strategies are useful non-pharmacological preventive implementations into everyday lifestyle—regular sleep-wake rhythmicity, season-adapted daily food and physical activity, day structure, correct light exposure at the “right” time in indoor lighting—all strategies showing depression-preventive properties, which started to be empirically tested and confirmed but need replication.

Insomnia as a transdiagnostic symptom/ syndrome for psychopathology

A further boost for the field came from DSM-5 [ 50 ] with the establishment of the category of insomnia disorder, thus ascribing independent value to this symptom complex instead of considering it mainly a symptom of any kind of mental disorder. Still, in DSM-5, separation of condition by categories is current practise but clinical research inspired by RDoC (Research Domain oriented Criteria; www.nimh.nih.gov ) is supporting a dimensional approach using constructs and domains to understand pathophysiology. RDoC has suggested a major domain named “arousal and regulatory systems” with the constructs “arousal”, “circadian rhythms” and “sleep-wakefulness” with a detailed listing of areas of interest for research from the level of molecules to circuits, behavior and paradigms. Two recently published meta-analysis on PSG derived sleep variables in insomnia and all different types of mental disorders [ 13 , 74 ] support this concept and stress a transdiagnostic approach of sleep continuity disturbances/ insomnia towards mental illness. Instead of adhering to an approach, which sought to identify disease-relevant mechanisms through identifying biological markers for specific mental disorders, we postulate that (i) sleep and circadian rhythm disturbances occur independently of and predict/coincide with affective disorders, (ii) clinical psychopathological syndromes do not necessarily reflect homogenous pathophysiological origin, (iii) neuropsychiatric syndromes like depression and sleep/circadian disturbances are linked through common mechanistic origins [ 58 , 75 ].

Neurobiology of sleep and circadian rhythms—relevance for depression

This chapter highlights recent developments in the field with emphasis on the so-called “flip-flop” switch model, the neuroplasticity hypothesis of sleep and depression, and neural biological timekeeping being pivotal in generating rhythmic behavior.

The flip-flop switch model and the role of the orexinergic system

Saper et al. [ 60 , 76 ] have developed neurobiological models of sleep-wake and REM-NREM (non-rapid eye movement sleep) regulation (details see Fig.  4 ; see also the first article in this issue of NPPR) proposing bistable switch mechanisms between wake and sleep-promoting cell populations, as well as REM and NREM promoting clusters. Wakefulness is governed by a network of cell groups in the hypothalamus (including orexinergic neurons), basal forebrain, and brain stem, which activate the thalamus and the cerebral cortex. These wake-promoting centers include but extend beyond the cell groups in the reticular formation of the brain stem originally described as the ARAS (ascending reticular activating system) by Moruzzi and Magoun [ 77 ]. As the main sleep-promoting center, the VLPO (ventrolateral preoptic nucleus) is primarily active during sleep, gives output to all major wake-promoting centers in the hypothalamus and brain stem that participate in arousal and reduces their activity using the inhibitory neurotransmitters galanin and GABA (gamma-aminobutyric acid). The VLPO also receives afferent signals from each of the major monoaminergic systems and its neurons are inhibited by noradrenaline and serotonin. Saper and colleagues postulate that this mutual inhibitory circuit constitutes a “flip-flop switch” with sharp state transitions between wake and sleep. The state of this intricate network is governed by circadian and homeostatic processes and exerts sleep-wake regulation in animals and in humans. It may be speculated that a dysfunctional “key switch” plays a major role in the pathogenesis of primary insomnia. An imbalance between sleep-promoting areas in the Central Nervous System (CNS) (i.e., the VLPO, neurotransmitter: GABA) and arousal-inducing neurons (among others orexin neurons in the lateral hypothalamus) with a relative overactivity of the orexin system or a hypofunction of the VLPO might create an unphysiological hybrid state between sleep and wakefulness. This offers a possible neurobiological model for insomnia (see section “Circuit-based interrogation of a shared neurobiological pathogenesis of insomnia and depression using animal models”). We speculatively assume that an overactivation of the arousal system is present both in insomnia and in depression with hyperarousal explaining the close association between both conditions. Considering the role of the orexin system in regulating both the arousal and the affective system, it could be possible that enhanced production of orexin related to psychophysiological hyperarousal increases the risk of reacting with enhanced emotional activation to stressful situations.

This figure displays the relationships between normal and insomniac sleep, spectral analysis of sleep EEG and neuroanatomical structures as delineated from the “flip-flop” switch model. a Upper panel—healthy sleeper; lower panel—insomniac sleeper: It is shown that there is no grossly disturbed macrostructure of Non-REM-REM sleep cycling in insomniac patients, but an altered microstructure with many brief awakenings and arousals in insomnia especially in REM sleep (see arrows). b Here shown are spectral analytic data from healthy good sleepers (HGS) and people with insomnia, reflecting a significant increase in fast waves especially in the sigma and beta range. c Anatomical structures and pathways involved in the regulation of wake (left), Non-REM (middle), and REM sleep (right) according to “flip-flop” switch model (see text). Panels a and b of the figure are taken from ref. [ 53 ]—permission granted. Panel c is based on refs [ 60 , 76 , 193 ]—permission granted

The orexinergic system [ 78 – 80 ] also orchestrates other depression-related factors such as behavioral and neuroendocrine responses to stress, reward-seeking behaviors, energy homeostasis, learning and memory. The orexin system directly innervates and excites noradrenergic, dopaminergic, serotonergic, histaminergic and cholinergic neurons. It also has a major role in modulating the release of glutamate and other amino acid transmitters and in enhancing hippocampal neurogenesis. Contradicting our line of reasoning, it has to be mentioned that narcolepsy, a debilitating disorder of excessive hypersomnolence (coupled with the frequent occurrence of shortened REM latencies) has been shown to be related to orexin deficiency [ 81 ].

Sleep, depression, and neuroplasticity

The synaptic plasticity hypothesis of major depression posits that alterations of synaptic plasticity represent a final common pathway for the clinical manifestations of the disorder. The hypothesis is based on animal models of depression [ 40 ], postmortem studies in humans and on non-invasive indices of plasticity in humans [ 41 ]. Of particular interest, sleep has been identified as a potential modulator of synaptic plasticity [ 82 ]. Current models emphasize that sleep might promote the strengthening of newly induced information-bearing synaptic connections, while—through down selection of less relevant synapses—keeping overall network function stable [ 42 , 83 ].

In particular, two links between sleep and depression are discussed. First, chronic sleep disruptions in the form of insomnia might disrupt synaptic plasticity and neural network function. Particularly, a dorsal executive network that includes the hippocampus and the prefrontal cortex appear to be particularly sensitivity to sleep loss, resulting in deficits such as attenuated hippocampus-dependent declarative memory formation in chronic insomnia. A ventral emotional network, which includes the amygdala, might be more resilient (for instance indexed by emotional fear conditioning) and might only decompensate with chronic severe sleep disruptions [ 84 ], relating to the clinical trajectory from cognitive to emotional complaints with chronic sleep disruptions. Second, it was recently proposed that therapeutic sleep deprivation—through a homeostatic, wake-related increase in overall synaptic strength—transiently shifts initially deficient net synaptic strength in patients with depression into a more favorable window of associative synaptic plasticity, related network function and behavior [ 44 ] (see also section “Biological timing and sleep deprivation in depression”). Proposed, but not sufficiently confirmed neural mechanisms include a BNDF, p11, Homer1a, and AMPA receptor cascade of rapid antidepressant treatment, potentially shared with the rapid antidepressant mechanism of ketamine [ 85 ]. While the level of evidence remains moderate and numerous questions open, such as specificity, these lines of research allow for deriving testable hypotheses, for instance on the effects of selective modulation of sleep slow waves through auditory or current stimulation, and potentially for the development of new, rapid acting antidepressive treatments.

Chronobiological timekeeping approaches

Compelling evidence suggests that mood disorders arise in part from alterations in the biological timekeeping system, triggered from within the body or from the body’s response to changes in the external environment. Each part of the human body has developed timekeeping mechanisms on every level, from molecular, cellular, neuronal, endocrine up to organ level, affecting the entire body. The timekeeping system is not hierarchical but has a multi-oscillatory structure [ 86 ]. Individuals with depression are likely to have internal oscillatory systems out of sync or dampened, but it is difficult to pinpoint the rhythms’ phase-relationships at every level. A classical psychopathological feature in this context is the diurnal mood variation [e.g., ref. [ 38 ]], observed in many patients with depression, which is indicated as a “morning low” in mood and a spontaneous alleviation of mood in the afternoon/ evening. Other well-known rhythmic features include seasonal modulation of onset of depression or rapid-cycling phenomena with strict periodicities [ 87 ]. Disrupting the body’s stable time patterns by shift work and jet lag can worsen or cause changes in mood. Various hypotheses have been suggested to explain these altered biological rhythms in affective disorders. One concerns disruption within the master pacemaker, the suprachiasmatic nuclei (SCN), causing mood disturbances [ 88 ], whereas another suggests that light directly from the retina and bypassing the SCN, targets brain regions to control mood [ 89 ]. Endogenous processes oscillate with an approximate cycle of 24 h, in adaption to the solar day-night cycle, and hence rhythms with this length are named ‘circadian’’ rhythms. However, many other functions have a more frequent activity pattern, called ‘ultradian’’ rhythms, for example the NREM-REM sleep cycles. At the behavioral level, human sleep-wake pattern displays circadian rhythmicity, while our food-intake displays an ultradian rhythmicity. Both are under the influence of the light–dark cycle whereby day light plays a crucial role in setting the activity of the SCN to the local environment. At the cellular level, circadian rhythms are generated by a molecular clockwork that consists of multiple transcriptional/translational feedback loops [ 86 ]. Nearly all tissues express circadian genes (e.g., Bmal1, Clock, Per , and Cry ), which heterodimerize in specific combinations, and regulate the expression of many clock-controlled genes, as well as their own transcription in a feedback fashion. These oscillatory processes ensure cellular timekeeping within tissues, while the SCN generates self-sustaining circadian oscillations, which ensure the synchronization of bodily rhythms. Some studies from individuals with depression found abnormal signaling in SCN cells to correlate with disease duration [ 90 , 91 ], while others reported altered nitric oxide signaling in the SCN [ 92 ], as well as in response to changes in the light–dark cycle [ 93 ] and nitric oxide has also been implicated in mood regulation [ 94 ]. Hence this substance may affect mood and circadian regulation independently and synergistically. While receiving and integrating information, the SCN maintains a functional circuit with the paraventricular nucleus (PVN), which is important for the control of pituitary hormones and melatonin secretion from the pineal gland [ 95 ]. Disturbances in the functioning of this circuit can potentially explain hormonal alterations associated with depression. Another structure with a direct connection to the SCN is the lateral habenula, which couples dopaminergic and serotonergic systems [ 96 ] and which is receptive to deep brain stimulation to treat depression [ 97 – 99 ]. The lateral habenula not only exhibits intrinsic neuronal oscillates but also responds to retinal light exposure directly [ 100 ] and by bringing it all together, it could be an important interface between retinal light, the pacemaker’s rhythms and multiple monoaminergic brain regions that control mood and motivational behaviors, stress and inflammatory systems, reward circuits, arousal and sleep. Melatonin secretion in the pineal is indirectly regulated by retinal light exposure involving the SCN and the PVN in a multi-synaptic pathway, and its secretion span feedbacks time-of-day information to MT1 and MT2 receptors, which are widespread in the brain, including the SCN, and in peripheral tissue [ 101 ]. Sleep deprivation (SD) with “lights on” suppresses melatonin synthesis in the pineal, dampens the neuronal firing rate in the SCN and attenuates light-induced phase-shifting. Moreover, SD changes circadian gene expression in brain regions involved in mood regulation, but not the SCN, while arousal due to SD when supposed to sleep, leads to an increased level of serotonin in the SCN [ 102 ].

Given these diverse relationships, it seems reasonable to test combinations of chronotherapeutic interventions on a larger level to derive an estimate of their clinical usefulness.

Synthesis—a model of the bi-directional relationships between sleep and depression

A comprehensive neuropsychobiological model linking the fields of insomnia and depression research is summarized in Fig.  5 .

Comprehensive insomnia model (see text)—authors´ own conception

This neuropsychobiological model integrates different strands of research from the fields of genetics, neurobiology, personality research, psychophysiology and cognitive-behavioral research to provide a comprehensive understanding of the mechanisms involved in the etiology and pathophysiology of insomnia and its role for the development of psychopathology, especially depression. The basic structure of this model is taken from the 3P model of insomnia formulated by Spielman et al. [ 103 ]. The 3P´s stand for: P redisposing, P recipitating, and P erpetuating factors.

Predisposing factors stem from the fields of genetics, neurobiology and personality.

In short [ 104 ] genetic and epigenetic factors [ 105 ] have been proven to be involved in the etiology and pathophysiology of insomnia by family and twin studies. Two GWAS studies [ 106 , 107 ] pointed to an involvement of MEIS1, which is also associated with the restless legs syndrome. Lane et al. [ 108 ] applied Mendelian randomization analysis and concluded that reliable evidence for a possible causal link between insomnia symptoms, coronary artery disease, depressive symptoms and well-being has been established.

Neurobiological mechanisms, like the flip-flop switch model (see section “The flip-flop switch model and the role of the orexinergic system”), the neuroplasticity hypothesis of sleep and depression (see section “Sleep, depression, and neuroplasticity”) and biological timekeeping mechanisms (see section “Chronobiological timekeeping approaches”) have to be taken into account when constructing a bridge between sleep, insomnia and depression on the cellular level.

With respect to personality variables as predisposing factors, sleep reactivity, the tendency to exhibit pronounced albeit transient sleep disturbance in response to stressful events, has been shown to be a major risk factor for chronic insomnia [ 109 ]. Moreover, several personality traits have been linked to insomnia, including neuroticism, perfectionism, sensitivity to anxiety symptoms and the tendency to internalize problems [ 110 ], some of which also seem to be depression-related.

Precipitating factors are significant life events, which facilitate the onset of acute episodes of insomnia. The most frequently reported triggers of acute episodes of insomnia are stressful life events that are related to threat of security to family, health, and work/school living, that lead to a negative emotional valence [ 111 , 112 ]. Additionally, experimental work using stress-induction paradigms clearly showed that acute stress has a negative effect on sleep initiation and sleep maintenance. Needless to say that life events have also been linked with depression.

Perpetuating factors: With respect to hyperarousal, we extended the hyperarousal concept (see section “Insomnia and hyperarousal”) to a more specific hypothesis of REM sleep instability as a possible pathway to insomnia and depression [ 57 ]. The concept is based on PSG findings showing that primary insomnia is characterized by a decreased amount of REM sleep and increased EEG arousals during REM sleep [ 113 ]. REM sleep regulation is governed by a unique neuronal pattern, which requires a delicate balance between arousing and de-arousing brain activities to sustain this highly aroused sleep brain-state accompanied by muscle atonia. This activity is dominated by cholinergic input to higher brain areas and an inhibition of noradrenergic, serotonergic and orexinergic output. The hypothesis of “REM instability” suggests that a generally increased arousal level leads to a modest REM sleep reduction and fragmentation in primary insomnia. This in turn results in altered perception of mental processes taking place during the REM sleep state: instead of experiencing dreams, patients with insomnia, due to the nature of their pre-sleep concerns, emotions and cognitions, might experience their REM sleep mentation as more wake-like and centered on their main concern, i.e., insomnia [ 114 ]. With respect to the relationship with depression, we propose that a chronic fragmentation of REM sleep in insomnia might interfere with basal processes of emotion regulation. With persistence of the disorder and hence REM sleep reduction and fragmentation, at some point a REM sleep rebound (leading to shortened REM latency and increased REM density) and a more stable pattern of emotional reactivity characterized by enhanced responses to negative stimuli (both subjectively and objectively measured) may occur, which may facilitate the development of a depressive episode.

Behavioral perpetuating factors include prolonged time in bed, an irregular sleep-wake schedule and daytime napping [ 103 ]. Many patients with insomnia use excessive time in bed and daytime napping as compensatory strategies against their perceived sleep loss. However, these strategies lead to more-pronounced difficulties in sleep-onset and sleep-maintenance [ 103 ]. Classical conditioning has also been suggested as an important perpetuating factor in insomnia [ 115 ]. In particular, it has been suggested that the bed and the bedroom environment of patients with insomnia become conditioned to arousal and anxiety during an acute episode of insomnia leading to sleep initiation and maintenance problems even after the removal of the initial stressor. In addition to this, a large body of evidence shows that worry and rumination are involved in the maintenance of insomnia [ 116 ]. These unproductive thought processes are believed to be associated with a level of physiological arousal that is incompatible with sleep initiation and maintenance. Moreover, self-report studies on emotions in insomnia showed an increased experience of negative emotions in general and specifically at bedtime [ 117 ].

What is known about the relationships between sleep, insomnia and the regulation of emotion? Experimental data suggest that people with insomnia report more negative emotions both in general and close to sleep time as compared to good sleepers [ 118 ]. Very recent results from negative emotional stimulation show that this response is correlated with a reduced activity of the amygdala and with a moderate level of arousal when compared to good sleepers [ 119 ]. In summary, altered emotional responses, both subjective and physiological, have been shown for insomnia. The sensitivity of emotion regulation in insomnia may explain the close association between insomnia and depression. Considering possible biological mechanisms involved in the link between sleep alterations, insomnia, emotion regulation, and mood symptoms, orexin neurons have been found to be related to both sleep-wake regulation as orexin is inhibited during sleep by GABA and galaninergic neurons from the ventrolateral preoptic nucleus (VLPO) (see section “The flip-flop switch model and the role of the orexinergic system”) and affective processes. Consistently, efferent pathways from the amygdala to the lateral hypothalamic area (LHA) have been identified and orexin production has been shown to be activated by emotional events, especially related to emotional stress [ 120 , 121 ].

Present management of sleep/ circadian disorders in depression

Subjectively reported sleep disturbances typically manifest in various forms, such as difficulties falling asleep and maintaining sleep, waking up too early and feeling worn, fatigued and sleepy during the day. Although these symptoms are described by DSM-5 as insomnia disorder, they are rarely investigated properly by general practitioners or mental health professionals—unfortunately at present it is far more probable that both insomnia and symptoms of circadian dysfunction are still more or less viewed as symptoms of an underlying mental disorder, which will, at best, remit when the “primary” (=underlying) disorder is properly treated or, at worst, treated with the wrong drugs. A main reason for this is that there is a serious lack in the knowledge about basic sleep and chronomedicine across fields in the medical professions, maybe because these disturbances are transdiagnostic. Insofar, there still is a strong need for further education of health professionals, in particular in psychiatry (hospital and community doctors, nurses, health advisors), in the domain of sleep and chronomedicine to enable them to recognize, properly diagnose and treat individuals with sleep problems [ 122 ]. Specific evidence-based treatment strategies incorporating chronomedicine (right drug, right dose, right time) for these disorders are available and might offer not only to improve the underlying sleep/circadian disorder but concomitantly ameliorate the outcome and course of mental disorders compared to standard psychiatric treatment.

Cognitive behavioral therapy for insomnia (CBT-I)

For non-pharmacological insomnia treatment the most extensive data basis exists now for CBT-I [ 69 ]. CBT-I has been primarily investigated in primary insomnia and studies are just gathering momentum to evaluate its efficacy and effectiveness in co-morbid insomnia. Most recent guidelines for insomnia treatment from the US and Europe [ 54 – 56 ] conclude that CBT-I is the first-line treatment, whereas recommendations for all investigated hypnotic drugs are not stronger than “weak”, and mostly for the short-term treatment (3–4 weeks; AASM, 2017) [ 123 ]. The AASM stresses that these rather low rankings for all existing hypnotics, especially benzodiazepines, benzodiazepine receptor agonists etc. reflect the fact that the Grade system (used to make the recommendations) downgrades the quality of evidence if the funding source is a pharmacological company. Furthermore, the AASM states that a weak recommendation should not be construed as an indication of ineffectiveness. The ultimate decision to prescribe sleep medication should be based on the clinician´s decision reflecting all individual circumstances, the patient´s shared consent and the availability of alternative treatment options in a given clinical setting. All of these guidelines [ 54 – 56 , 123 ] were based on thorough analyses of the relevant literature. Thus, either published meta-analyses (based on randomized clinical trials/ RCT)) or single RCTs were considered to evaluate the evidence. Insofar, the above-mentioned guidelines can be considered as based on the highest levels of presently available evidence. These new guidelines also indicate that CBT-I has significant long-term effects beyond the acute treatment phase, in that respect being superior to pharmacological treatment for primary insomnia. CBT-I encompasses relaxation techniques, sleep hygiene, stimulus control, sleep restriction and cognitive techniques to reduce nocturnal ruminations [ 124 ]. In psychological models of insomnia like for example the 3P Model [ 103 ], the model of Morin [ 52 ] or the cognitive models of Harvey and Espie [ 125 , 126 ] components of CBT-I can be easily matched to the psychological components of the pathophysiology of insomnia. It is also of interest that the 2-process model of sleep-wake regulation and the concept of sleep propensity/sleep pressure [ 25 ] have been at the core of informing behavioral strategies for insomnia, i.e., sleep restriction: this strategy is derived from the clinical observation that patients with chronic insomnia prolong time in bed to achieve more sleep—a behavior, which unfortunately perpetuates insomnia instead of relieving it. By shortening time in bed (as suggested by sleep restriction), in contrast, the homeostatic sleep drive is stimulated and subsequent sleep quality improved [ 127 , 128 ]. Considering sleep restriction as a kind of partial sleep deprivation, it is interesting to note that total sleep deprivation on its own is a rapid, albeit transient, treatment modality in depression (see section “Biological timing and sleep deprivation in depression”).

Hypnotics, chronobiotics, antidepressants

Regarding prescription drugs such as benzodiazepines (BZ)/benzodiazepine receptor agonists (BZRA), melatoninergic agents, suvorexant, sedating antidepressants and atypical antipsychotics are used in the wider field of insomnia and for sleep problems within the context of depressive disorders. Evidence from clinical studies is strong for effective short-term administration (3-4 weeks) of BZ/BZRA for insomnia [ 55 , 56 , 123 ]. Doubtlessly, hypnotic agents like BZ, BZRA, or suvorexant will produce stable positive effects on patient´s sleep over a window of 3–4 weeks; however, given the fact that practically all performed studies were sponsored by the pharmaceutical industry, overall recommendations for example by the AASM [ 123 ] could not be beyond “weak”. Many patients afflicted with insomnia suffer chronically and there is doubt for effective long-term treatment beyond four weeks with these drugs. A major concern with long-term BZ/BZRA treatment is the issue of adverse events (e.g., traffic safety, mnestic effects, paradoxical behavior during the night, intoxication, falls in the elderly etc [ 129 – 131 ]) and the development of tolerance/rebound and abuse/dependency [ 132 – 134 ]. These and related issues concerning the risks of BZ/ BZRA for increased mortality [ 135 , 136 ] are discussed controversially, because other studies [ 137 , 138 ] highlighted the likely role of confounding factors when relating risk of cancer or mortality to BZ/BZRA intake. Nevertheless, especially the issue of potential risks concerning abuse or dependency probably are responsible for the decision that treatment of insomnia with these agents beyond 3–4 weeks is not endorsed in Europe by drug regulating authorities. A different stance has been taken by the FDA (US American Food and drug administration) in recent years granting long-term use for all newly admitted hypnotics since 2004 (e.g., eszopiclone).

Melatoninergic agents like ramelteon [ 139 ] have been admitted to the US market. In Europe, a prolonged release formulation of melatonin Circadin, can be prescribed for long-term (3 months) treatment of insomnia in the over 55-years-old [EMEA: http://www.emea.europa.eu/humandocs/PDFs/EPAR/circadin/H-695-de1.pdf ]. The benefit of melatonin treatments with respect to circadian rhythm disturbances, like jet lag and phase delay [ 140 ] has been proven. However, the evidence for melatonin being beneficial in the treatment of insomnia is not very convincing [ 141 – 145 ].

Sedating antidepressants (SED; e.g., amitriptyline, doxepin, trimipramine, trazodone, mirtazapine, agomelatine or atypical antipsychotics (AP; quetiapine, olanzapine) are frequently used for symptoms of sleep disturbances in patients with mental disorders, i.e., major depression or bipolar disorder or schizophrenia [ 146 ]. They are not indicated for the treatment of insomnia without mental comorbidity. However, recent pharmaco-epidemiological studies revealed that in the US (for example trazodone) or in Europe (for example trimipramine, mirtazapine) sedating antidepressants are increasingly used in that field [ 147 , 148 ]. The efficacy and effectiveness of SED and AP to treat major mental disorders is undisputed—however, there still is a lack of data from large controlled trials, apart from a few pilot studies [ 149 – 151 ] and a recent meta-analysis [ 152 ], demonstrating clearly that these compounds are superior to placebo when addressing insomnia alone and sleep disturbances in mental disorders. Interestingly, doxepin, one of the “oldest” available SEDs, has been shown to be effective at very low doses in primary insomnia [ 153 ] and thus gained approval by US Food and Drug Administration. With respect to AP even less evidence is available—studies are available investigating the effects of AP on sleep in schizophrenia [ 154 ] and in insomnia [ 155 , 156 ]. At present, it is safe to conclude that most AP are clinically useful to combat sleep problems in a subset of patients with schizophrenia, but not for insomnia without mental comorbidity.

Behavioral chronotherapy and chronopharmacology

Behavioral chronotherapeutic approaches encompass bright light therapy, dark therapy, dawn simulation, sleep-wake manipulations (sleep deprivation, sleep phase advance/delay), often in combination with precisely personalized timed low-dose fast-release melatonin agents, as well as social rhythm therapy [ 72 , 73 ]. Application of bright light, usually for periods of 30–60 min at an intensity of 5000–10.000 lux in the morning (dependent on indication) or the use of a dawn simulator with a slow increase in light over 30 min around wake-time, is used therapeutically in circadian sleep-wake rhythm disorders, such as phase advance/delay, seasonal affective disorders (SAD), major depression and geriatric disorders (e.g., dementia). Dark therapy is successfully used to prevent manic phases in patients with bipolar disorder [ 157 ]. Sleep-wake manipulations include total and partial sleep deprivation (SD) and phase shifts of the sleep phase (sleep phase advance therapy). This type of manipulation is dominantly used in patients with major depression and especially those suffering from melancholia displaying clinical features like diurnal mood variations. Timed light exposure and type of light should be considered as important during sleep deprivation since light affects the circadian timing system. Wu and Bunney [ 34 ] published a systematic review on SD demonstrating that total SD produces a clinically significant, albeit short-lived, remission in mood in as many as 60–70% of patients with severe depression. Social rhythm therapy (SRT) aims at affective disorders, especially bipolar depression. In combination with specific psychotherapy (interpersonal therapy) and the administration of mood stabilizers (e.g., lithium), SRT aims at reducing the impact of stressful life events on the clinical course of the disorder by addressing issues of lifestyle regularity [ 158 , 159 ]. With respect to circadian disturbances, studies have assessed melatonin’s efficacy in the visually impaired (diminished light input to the pacemaker), jet lag syndrome, shift work, phase advance/ delay, depression and neurodevelopmental disorders like autism with promising results [ 140 , 160 – 163 ].

Another very relevant aspect of chronomedicine is Chronopharmacology, the timing of drug intake. Antidepressants in particular involve a number of neurotransmitters that are acting directly on the SCN or in circuits, which are regulated by the timekeeping system, for example the SCN has serotonin receptors and lithium is known to prolong the circadian period.

Unfortunately, up to now no data are available how frequent chronotherapeutic approaches are used for the treatment of circadian and other disorders—this is in contrast to prescribed medications and psychotherapeutic treatments, where usually sales figures or statistics derived from national health care systems can give at least a rough indication how frequently a certain intervention is applied. This is in part due to the fact that for example an intervention like sleep deprivation or light therapy in a depressed patient applied in a hospital will produce no tangible costs to figure in a statistic—thus apart from enhancing awareness for circadian/chronobiological disorders it will also be a challenge to collect data on the specific treatment modalities in clinical care to get an impression of their effectiveness in routine clinical care.

Non-invasive brain stimulation

Recent work suggests that arousal and sleep can be modulated by targeting a cortico-thalamic top-down pathway of sleep-wake regulation through different types of non-invasive brain stimulation (NIBS), including transcranial magnetic stimulation (TMS), transcranial current stimulation (tCS) or sensory stimulation, such as auditory stimulation [ 164 ]. Of note, patients in the diagnostic category of major depression might suffer from different sleep complaints along the domain of hyper- (insomnia) and hypoarousal (prolonged sleep duration). This domain may be suitable for structuring future NIBS research and treatment development, because it can be characterized on different levels, including the genetic, molecular, neurocircuitry and behavioral level. Of particular interest, it might be possible to identify more fine-graded alterations in individual patients, such as alterations of sleep slow waves, sleep spindles or local aspects of sleep that might be targeted with specific NIBS techniques. To date, several NIBS approaches have been used to modify sleep: Transcranial magnetic stimulation (TMS) uses a magnetic field, which modulates cortical activity. For instance, specific EEG patterns such as sleep slow waves and spindles can be triggered using this technique [ 45 , 46 ]. However, the TMS setting during sleep is demanding and limited to specific research questions. Transcranial current stimulation (tCS) can induce local shifts in cortical excitability [ 165 ] and has the potential to affect distinct aspects of sleep. For instance, anodal tDCS during slow wave sleep improved declarative memory [ 166 ]. Moreover, anodal tDCS can reduce total sleep time by ~25 min in healthy humans [ 167 ], but does not modify sleep in patients with insomnia, indicative of brain-state-dependent effects of the stimulation protocol [ 168 ]. While tDSC is easily applicable, the clinical relevance of the observed effects remains unclear. Auditory stimulation applied in a brain-state-informed protocol (auditory closed-loop stimulation) can enhance sleep slow waves and sleep-related memory consolidation [ 45 ] or selectively suppress slow waves and modulate neurophysiological measures of neuroplasticity [ 46 ]. These approaches bear the potential to target distinct aspects of sleep, to increase our mechanistic understanding and, potentially, to develop new sleep-based treatments for depression. Yet currently, no clinically relevant sleep-based interventions in this area are known.

Key directions for future research and clinical practice

Establish cbt-i as preventive strategy for depression.

Unfortunately, apart from research studies, CBT-I up to now has not been established in routine clinical practice or care in the US or European countries. In order to overcome this problem a stepped-care approach has been suggested by Espie [ 169 ]. According to this model, in a first step self-help approaches in bookform or internet-based programs can be used by afflicted individuals with mild to moderate symptoms [ 170 ]. In a next step, community-based approaches involving group courses given by trained nurses would reach larger numbers of patients face-to-face [ 171 ]. Contact with a qualified clinical psychologist/ psychiatrist or sleep specialist would be reserved for the most severe cases to administer state of the art CBT-I or abbreviated forms of this treatment [ 172 , 173 ]. Very recently, a large- scale study with digital cognitive-behavioral therapy for insomnia found sustained effects of improved psychological well-being and sleep-related quality of life resulting from an 8-week sleep-hygiene education program [ 174 ]. This provides first evidence that psychoeducation via web and/or mobile channels in addition to usual treatment is an easy-to-use and effective element in a stepped-care approach for insomnia.

Furthermore, the central question to address will be the efficacy and effectiveness of CBT-I for insomnia co-morbid with mental disorders—there is promising evidence now even in the form of meta-analysis [ 175 , 176 ]. Thus, larger-scale longitudinal studies are needed to prove that CBT-I adjuvant to standard psycho-and pharmacotherapy in different populations of psychiatric patients not only positively influences insomniac symptoms but beyond improves the general outcome of the disorder. In the same vein, the often-quoted assumption that insomnia treatment may prevent psychiatric sequelae in chronic insomniacs [ 62 ] needs to be proven—data from the “GoodNight” study [ 177 ] point to a first empirical confirmation of this assumption [ 70 ].

Integrate chronobiological interventions into the armamentarium of antidepressant treatments

It represents a timely and urgently needed step to translate the impressive basic science knowledge on circadian rhythmicity to the clinic. This translation needs to address the following points: (i) provide guidance for a clinically useful estimation of the individual circadian phase (e.g., through the wake-up time or mid sleep phase), (ii) provide recommendations for the application of melatonin and light with regard to the individual phase response curve, (iii) provide recommendations on the duration, monitoring and optimization of the treatment (e.g., phase-shift during therapy), (iv) test for effects on circadian rhythms, sleep, mental disorders and broader health outcomes. Although the points outlined above appear trivial referred to the deciphering of the molecular mechanisms of circadian rhythms, there is to date, to our knowledge, no practical recommendation for implementing circadian interventions into broader clinical practice,

Novel molecules for depression and sleep disturbances

As can be deduced from the section on the regulation of sleep and its neuropharmacology novel pharmaceutics may target noradrenergic, serotonergic, histaminergic, adenosinergic, melatoninergic, or orexinergic neurotransmission/ pathways to go beyond an influence on gaba-ergic neurotransmission. An already available new line of treatment consists in the application of orexin receptor agonists like suvorexant, which has just been recently admitted to the market in the US. This type of medication is based on orexin receptor antagonism and has been proven to produce clinically relevant effects on sleep in insomnia [ 178 , 179 ].

Potential for non-invasive brain stimulation approaches in the treatment of insomnia and depression

In addition to psycho-/pharmacotherapy and chronotherapeutic approaches, non-invasive brain stimulation techniques, including thermo-stimulation and transcranial direct current stimulation (tDCS), seem to have the potential to improve sleep. These techniques are able to induce local activity changes in specific cortical areas, which might modulate arousal and sleep via cortico-thalamo-cortical feedback loops. First proof-of-concept studies exist for the short-term induction of EEG slow waves by tDCS in healthy subjects [ 166 ] and a dose-dependent improvement of sleep latency and efficiency by frontal cerebral thermo-stimulation [ 180 ].

Another evolving strategy transforms recordings of brain activity patterns, such as EEG or real-time fMRI signals, into insomnia treatments by neurofeedback. These techniques aim at characterizing insomnia-related brain activity in order to allow patients to learn to control/ suppress specific patterns of brain activity with biofeedback [ 181 , 182 ]. Further work is necessary to evaluate whether neurofeedback can be helpful to treat insomnia and depression.

Circuit-based interrogation of a shared neurobiological pathogenesis of insomnia and depression using animal models

Rodent models of depression are widely used in drug discovery studies and a variety of tests has been developed to assess depressive behavior in rodents [ 183 , 184 ]. Surprisingly, sleep is rarely considered as outcome parameter in drug studies using animal models of depression. However, a cage exchange paradigm has been developed to investigate neural circuitry of stress-induced insomnia in rats inducing sleep disruption through mild stress and novelty of a depression [ 185 ]. This paradigm reproduces several of the polysomnographic features, which often accompany acute insomnia, namely sleep fragmentation, increased sleep onset latency, decreased NREM and REM sleep and an increase in high-frequency EEG activity during NREM sleep Interestingly, the regional assessment of neural activity using FOS expression, indicates a simultaneous activation of the sleep-promoting VLPO and arousal systems, leaving the sleep-wake regulatory switch in the brain in a hybrid state [ 185 ]. While these findings were acquired before the broad application of optogenetics to the neurobiological sleep research, the model provides a valuable basis for further delineation of neurophysiological mechanisms of insomnia in a rodent model

An intriguing opportunity to further disentangle the presumed hybrid state of sleep regulatory systems in insomnia arises from recent findings that many important aspects of sleep are regulated on a microscale [ 186 , 187 ]. Sleep homeostasis, the neuronal recovery process during NREM sleep, which is directly reflected by slow wave activity (0.5–4.0 Hz) during NREM, can strongly vary between individual regions depending on the previous use of cortical areas during wakefulness [ 188 – 190 ]. A hybrid pattern of signals characteristic for wakefulness and sleep can appear in the EEG of a wake and behaving animal after extensive training or prolonged wakefulness. Hybrid states of sleep microarchitecture have now also been reported in humans using intracerebral recordings acquired from patients undergoing diagnostic assessment for epilepsy [ 191 , 192 ]. Taken together, evidence from intracerebral recordings indicates a complex microarchitecture of vigilance states, which is not always observable on EEG recordings. Hybrid states on the microscale of either local cortical activation during sleep [ 191 ] or local cortical OFF states during wakefulness [ 190 , 192 ] can have remarkable behavioral consequences. These findings from basic research provide a putative explanation for the above-mentioned notion that subjective complaints of disrupted or non-restorative sleep are necessarily accompanied by polysomnographic findings (see section “What is insomnia?”). Further highly relevant basic research strategies encompass the modulation of neuroplasticity by sleep and the role of sleep as a key modulator of the neuro-immune axis.

Funding and disclosure

None of the authors declares any competing financial interest in relation to the work described. Concerning the pharmaceutical industry, DR received honoraria from HEEL pharmaceuticals in 2017/2018 for one congress presentation each. DR is a member of the executive board of Freiburg Institute for Behavioral Therapy and thus receives honoraria for regular meetings, examinations, and presentations in this context. DR frequently lectures at hospitals, universities, and other institutions of education about sleep, insomnia, and mental health—frequently, he receives a honorarium from these institutions for his work. DR receives royalties from several publishing houses for his books on sleep and insomnia. DR receives payment from the European Sleep Research Society for his duty as Editor in Chief of the Journal of Sleep Research. The other authors declare no competing interests.

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.