type 2 diabetes research singapore

• Open access
• Published: 08 February 2021

War on Diabetes in Singapore: a policy analysis

• Lai Meng Ow Yong   ORCID: orcid.org/0000-0002-4035-5848 1 &
• Ling Wan Pearline Koe 1  

Health Research Policy and Systems volume  19 , Article number:  15 ( 2021 ) Cite this article

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In April 2016, the Singapore Ministry of Health (MOH) declared War on Diabetes (WoD) to rally a whole-of-nation effort to reduce diabetes burden in the population. This study aimed to explore how this policy has been positioned to bring about changes to address the growing prevalence of diabetes, and to analyse the policy response and the associated challenges involved.

This qualitative study, using Walt and Gilson's policy triangle framework, comprised analysis of 171 organizational documents on the WoD, including government press releases, organizational archives, YouTube videos, newspaper reports and opinion editorials. It also involved interviews with 31 policy actors, who were policy elites and societal policy actors.

Findings showed that the WoD policy generated a sense of unity and purpose across most policy actors. Policy actors were cognisant of the thrusts of the policy and have begun to make shifts to align their interests with the government policy. Addressing those with diabetes directly is essential to understanding their needs. Being clear on who the intended targets are and articulating how the policy seeks to support the identified groups will be imperative. Issues of fake news, unclear messaging and lack of regulation of uncertified health providers were other identified problem areas. High innovation, production and marketing costs were major concerns among food and beverage enterprises.

While there was greater public awareness of the need to combat diabetes, continuing dialogues with the various clusters of policy actors on the above issues will be necessary. Addressing the various segments of the policy actors and their challenges in response to the WoD would be critical.

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Diabetes is a condition that affects more than 400 million adults globally, and this number is expected to increase to above 640 million, which equates to one in ten adults, by 2040 [ 1 ]. The global prevalence of diabetes among adults over 18 years of age rose from 4.7% in 1980 to 8.5% in 2014 [ 2 ]. It was estimated to be the seventh leading cause of death in 2016, where 1.6 million deaths were attributed to the condition [ 2 ]. In Singapore, over 400,000 Singaporeans live with the disease. The lifetime risk of developing diabetes is one in three among Singaporeans, and the number of those with diabetes is projected to surpass one million by 2050 [ 1 ]. An estimated 430,000 (or 14% of) Singaporeans aged 18 to 19 years are also diagnosed with pre-diabetes, where their normal blood sugar levels are higher than normal but not high enough to be diagnosed as diabetes [ 3 ].

In response to this, on 13 April 2016, the Singapore Health Minister declared War on Diabetes (WoD), citing the psychosocial burden on individuals and families and economic reasons for the thrusts of this policy [ 4 ]. This fight against diabetes is not new, as Singapore has previously explored measures to combat the rising prevalence of diabetes. For example, the annual National Healthy Lifestyle Campaign, introduced in 1992, aims to raise awareness of how Singaporeans can eat healthier foods and incorporate physical activity into their lives; the campaign concomitantly addresses other concerns such as smoking and mental well-being [ 5 ]. Unlike this campaign, the WoD policy specifically addresses the concerns of diabetes and is positioned to encourage a whole-of-society effort to reduce the burden of diabetes in the population and to keep people healthy as they age [ 1 , 3 ].

Diabetes poses a significant public health concern. It can lead to complications in many parts of the body, including kidney failure, leg amputation, nerve damage, heart attack, stroke, vision loss and severe disabilities [ 6 , 7 , 8 ]. It can also bring about substantial economic loss to people and their families and to health systems and national economies as a result of direct medical costs and loss of work and wages [ 8 ]. The World Health Organization (WHO) [ 8 ], in their 2016 Global Report on Diabetes, calls for a whole-of-government and whole-of-society approach, where all sectors are to systematically consider the health impact of policies in trade, agriculture, finance, transport, education and urban planning. It states that effective approaches, including policies and practices across whole populations and within specific settings, will be needed to contribute to good health for everyone.

This means adopting a life-course perspective and multisectoral and population-based approaches to reduce the prevalence of modifiable diabetes risk factors—such as overweight, obesity, physical inactivity and unhealthy diet—in the general population. It also means addressing the commercial determinants of health, involving multinational or transnational corporations, who are major drivers of noncommunicable disease epidemics, including diabetes, as their strategies and approaches used to promote products and choices could be detrimental to health [ 9 , 10 , 11 , 12 ].

Since the introduction of the WoD policy, there have been no studies exploring how the policy has been positioned to bring about changes and what the policy actors’ perceived challenges are. Not very much is known about the political, economic, infrastructural and ideational constructivist context in facilitating or hindering the policy at the national and subnational levels [ 13 ]. This study thus aims to contribute to addressing this knowledge gap by using the policy triangle framework, articulated by Walt and Gilson [ 14 ], to analyse the WoD policy response. The policy triangle framework has been widely applied to a variety of health policy concerns, including health sector reforms and public health, and in many countries [ 15 , 16 ]. It focuses on the content of the policy, the actors involved in the policy change, the processes in developing and implementing change, and the context within which the policy is developed [ 14 ]. The framework is built on the understanding that policy is a product of and constructed through political and social processes [ 15 ]. This study will identify the contextual factors that shaped the WoD policy, the actors involved, the content of the policy and organizational provisions, and analyse the strategies and policy processes. Results drawn from this study will be used to inform change agents, such as the relevant government authorities, and will contribute to the body of knowledge on diabetes policy, thereby enhancing the links between science and policy, based on the model of strategic science [ 17 ].

This study adopted a qualitative approach as the primary method to address the research questions. Qualitative approaches, as opposed to the natural scientific models used in quantitative research, are interpretive and offer an inductive view of the relationship between theory and research [ 18 , 19 ]. This study comprised interviews with 31 relevant policy actors and members of the general public and the analysis of 171 organizational documents on WoD, including government press releases, organizational archives, YouTube videos, newspaper reports and opinion editorials.

Participants

We conducted purposive sampling of prospective respondents from five distinct clusters of policy actors, including government officials, healthcare providers, food and beverage (F&B) manufacturers/producers/retailers (small and medium enterprises, or SMEs, to multinational corporations, or MNCs), professional associations, academic institutions/think tanks, and the general public (see Table 1 ). Non-general public respondents were senior officials within their agencies (for example, president, chief executive officer, general manager, director, deputy director, associate professor) and were actors in or close observers of the WoD policy.

This approach is consistent with the policy triangle analysis framework, where it considers the political institutions and public bureaucracies in policy-making to be important aspects of the analysis. The framework also acknowledges and considers the influence of non-state actors, such as the private sector, the civil society organizations and the public [ 14 , 15 ]. This is consistent and aligned with WHO’s assertion that non-state actors, such as food producers and manufacturers, healthcare providers and people with diabetes, should be considered collectively in the multicomponent intervention in addressing diabetes [ 8 ]. The inclusion of the general public is also relevant because they are driven mostly by their cultural beliefs or personal experiences, which are often the most difficult to identify in terms of their policy goals; their views will therefore be relevant in this policy analysis [ 20 ].

All respondents who fulfilled the criteria were invited via letter or email to participate in a semi-structured interview. The interviews were conducted face-to-face in English. Three sets of topic guides comprising semi-structured questions were used for the interviews. They were designed specifically for (a) government officials; (b) healthcare providers, service providers (businesses, food manufacturers, and so on), and professional associations and academic institutions/think tanks; and (c) the general public (with and without diabetes, and caregivers of people with diabetes). The topic guides and interview questions were developed based on the policy triangle framework, articulated by Walt and Gilson [ 14 ]. The themes of the topic guides explored participants’ understanding of the following:

The WoD in terms of its policy goals, impetus, aims and problem definition. Includes who the policy addresses and what the concerns are (context)

Who the primary players in the policy are (actors)

The instruments that have been used and parameters that have been put in place, following the introduction of the policy in support of this endeavour (content)

The key challenges and areas needing to be addressed to better manage the issue of diabetes in Singapore (processes).

As policy and organizational documents constitute the socio-materiality of the policy itself, they were sampled for relevance [ 21 ]. All relevant documents within the period 1 January 2016 to 31 December 2019 were reviewed. The documents were obtained directly from the respondents if they were not accessible in the public domain. Documentary analysis was conducted in tandem with face-to-face interviews with the policy actors.

Data analyses

Data analysis consisted of thematic analysis and analysis of documents, including organizational annual reports, meeting minutes, government press releases (such as government statements; Committee of Supply Speech; speeches for conferences, opening ceremonies, and visits and events by ministers), YouTube videos, newspaper reports and opinion editorials. Thematic analysis was used to analyse data derived from the interviews and documents. The data were read for familiarization and then again in an iterative manner to identify emerging themes. Key categories of codes were analysed and grouped based on the predetermined codes and themes articulated by Walt and Gilson, including context, actors, content and processes [ 14 ]. Thereafter, the data derived from both the interviews and documentary analyses were triangulated to enhance the trustworthiness, reliability and validity of the findings [ 22 , 23 , 24 ].

Based on Walt and Gilson’s policy analysis triangle framework, we present the findings below.

All respondents in this study stated that the reasons for the development and introduction of the WoD policy were numerous. They include the rising prevalence of diabetes, an ageing population, an extended life expectancy, increasing comorbidities of diabetes and rising healthcare costs. In addition, the respondents attributed the introduction of the policy to an increasing economic burden of diabetes on the working population and the associated potential adverse impact on society. These factors together created the moral impetus for the government to introduce the policy to nudge its people into living a healthy lifestyle, respondents stated.

The causes of diabetes were many. Respondents pointed to a complex interaction of economic, social, cultural, individual, national and environmental factors, leading to the formulation of the policy [ 25 , 26 ]. For example, they highlighted that access to unhealthy food (exacerbated by food delivery service, technology and ready-to-eat meals), affluence of society, expansion of eating-out places, and roles of the F&B industry (manufacturers and retailers) led to the growing diabetes situation in Singapore. This was seen to be made worse by Singaporeans’ obesogenic lifestyle, characterized by work stress, poor sleep patterns and poor overall eating and living habits. The low health screening uptake and lack of prevention measures at the individual level were other reasons. Genetics, invincibility syndrome, culture, family and personal choice, health literacy, and prevailing treatment models of diabetes were seen to have exacerbated the diabetes situation.

The actors in the WoD comprised policy elites within the government and societal actors, including the F&B business community (SMEs and MNCs), professional associations, healthcare providers, academic think tanks, civil society and the general public. This policy-led implementation, which is inherently cross-sectoral, saw the Diabetes Prevention and Care Taskforce, set up by the Ministry of Health (MOH), facilitating and coordinating the involvement of the various policy actors. Policy actors such as the F&B business community were quick to acknowledge their corporate and social roles to fellow citizens, and promptly moved to align their business and corporate goals with the policy. Respondent 11, who was from a large MNC fast-food chain, stated:

[A]s cliché as it sounds, it is really a social responsibility on the business part to really care for the customers’ well-being.

The role of the civil society was seen in the involvement of professional associations and voluntary welfare organizations to promote healthier eating and living in the community. Funds were directed to academic and healthcare institutions to encourage and foster diabetes-related research to inform policy and practice. Healthcare institutions were seen to expand their ability to offer better diabetes treatment with increased drug subsidies. Schools, workplaces and organizations implemented policies promoting healthier eating on their premises. The general public were engaged through programmes and schemes, although their level of receptivity and engagement towards the policy varied.

In operationalizing the policy, a total of 171 WoD-related organizational documents were analysed. The government, in working with the various policy actors and through public forums and engagements, delivered a slew of measures at different time points following the declaration of the policy. The policy core of WoD, highlighted in the documents, centred primarily on increasing the population’s level of physical activity, improving the quality and quantity of dietary intake, increasing early screening uptake and improving intervention to better control diabetes and its associated complications [ 27 ].

Notably, in the first 2 years of the policy launch, the government actively used words, images and symbols to form winning coalitions with different policy actors, such as the F&B industry and people with diabetes and their caregivers, and through various languages, including dialects and vernacular languages, to address older adults in the public. The modes of the images included posters, health screening booths and media programmes. Some common symbols and schemes, such as the Healthier Choice Symbol (HCS), Healthier Ingredient Development Scheme (HIDS), Healthier Dining Innovation (HDI), Healthier Dining Grant (HDG) and National Steps Challenges™, targeted consumers, F&B enterprises and the general public.

As part of its overall strategy, the government collaborated with the primary care networks (PCNs) to provide more supportive services for people with diabetes [ 1 ]. It subsidized basic screening tests for the public to encourage early detection and treatment. It also put in place systems to foster healthier lifestyles, promote good health by employers in the workplace, and facilitate adjustment of lifestyle habits and better decision-making by individuals [ 28 , 29 ]. Nonstandard drugs in the treatment of diabetes were subsidized, which helped open up options for primary care physicians to offer newer treatments at lower rates to the general public. According to respondent 5, a physician, older generations of drugs were found to have “potential side-effects and less of non-glucose reducing properties”, whereas “newer drugs have heart failure protection, cardiovascular protection”. This could only benefit patients with diabetes.

The health ministry also partnered with the F&B industry to support major beverage companies and companies undertaking innovation to lower sugar content in their products, by fostering a supportive regulatory environment to encourage innovation and experimentation [ 30 , 31 ]. This is illustrated in the 2017 industry pact, where seven beverage companies pledged to reduce the sugar level in their beverages to 12% or less by 2020 [ 32 ]. This incremental decrease signalled the government’s recognition that innovation and (re)formulation of F&B products would need time, and that immediate introduction of any measures or regulation may backfire. Consumers’ taste acceptance of newer and healthier products would also need time to develop. The MOH further supported and enabled the industry to use Singapore as a regional headquarters and launch pad through which to access other Asian markets to sell their healthier products, to provide the economic conditions for the business community to thrive.

Legal parameters were also explored. A public consultation was carried out from 4 December 2018 to 25 January 2019, where a wide range of stakeholders were engaged for their inputs on introducing mandatory front-of-pack nutrient summary labelling, advertising regulations for the least healthy sugar-sweetened beverages (SSBs), excise duty on manufacturers and importers, and banning of higher-sugar prepackaged SSBs [ 33 ]. The proposed measures, which were scheduled to be rolled out later in 2020, came nearly three years after the declaration of the WoD, as the government set the stage to create an environment for its people to lead a healthier lifestyle. In November 2019, the MOH went on to introduce the Patient Empowerment for Self-Care Framework, which constituted the first tranche of materials for people with diabetes to more directly effect change in the lives of those with the condition [ 34 ].

Several critical factors enabled or constrained the context in the implementation of the WoD. The following discusses the support for and resistance to the WoD policy, and the potential resources that are further needed for its implementation.

Why war? Why diabetes?

While the WoD served as a useful “policy frame to galvanize government action, and whole-of-society action and attention”, as stated by a government official (P13), there were considerable competing views among non-policy elites. Many non-policy elite actors, for example, questioned the rationale of the WoD. A member of the general public with diabetes (P19) stated: “I am not sure what the logic is behind using diabetes as the condition, because diabetes is so innocent!” Some respondents, such as P12, a diabetes nurse educator, opined that waging a War on Diabetes was unnecessary, and it might risk perpetuating stigma among those with diabetes. She explained that some of her diabetes patients were upset with the policy and were relatively more withdrawn and “shut off” since its introduction due to their perceived stigma. One of her patients told her, “Then I am not going to tell people I have got diabetes,” because people will relate diabetes to medical complications, she said. Others, including P20, a member of the general public, suggested waging a war against sedentary lifestyle or promoting healthier living might be more appropriate.

Policy actors, particularly professional dieticians and the general public, were unclear whether looking solely at individual nutrients, such as sugar, which was seen to be the primary focus of the WoD, was the best approach to stem diabetes. Respondent 18, a representative from the national nutrition and dietetics association, said: “So I think in a sense we cannot look at individual nutrients; we need to look at diet as a whole. This probably has got to be a very consistent message to the public!” Along the same lines, respondents opined that the policy had focused too heavily on packaged SSBs, rather than on freshly cooked or prepared food. Respondent 3, an MNC F&B manufacturer, highlighted: “The beverage may not be the biggest culprit. In fact, the biggest culprit is food.”

Who is the policy for?

Many respondents were unclear of the intended target of the policy. For example, a respondent (P20), a member of the public, reported: “I am not sure who they are targeting, I always thought it is the general public from all age groups.” Another respondent [ 19 ], a medical social worker who works with diabetes patients, said: “It is more for the general public, not for those who already have diabetes.” Respondent 29, who has type 1 diabetes, explained: “Type 1 (diabetics) will switch off because it’s like it is too late for them, they already have diabetes.” This sentiment was echoed by respondents with type 2 diabetes and their caregivers, who highlighted that WoD should more directly address their immediate concerns, which would include helping them with their immediate treatment costs and costs of consumables and related devices. For type 1 diabetes, the causal factors were also unclear and it would not be possible to wage war against type 1 diabetes, stated respondent 29. Some respondents observed, and as a government official acknowledged (P13), that pre-diabetic programmes, whilst carefully designed to reduce diabetes incidence, were more accessible to retirees who were available to attend the programmes during workdays, rather than the “supposed” at-risk and younger diabetic groups, who may hold full-time jobs. Others, such as general public respondents P15 and P17, who were both aver 60 years of age, felt that any programme following the policy is good, as it signals a step forward in the fight against diabetes.

Messaging quality: unclear images, fake news and diet fads

The barrage of messages pertaining to diabetes was found to be at best overwhelming, at worse conflicting and confusing. Messages such as “white rice is bad” and “too much meat will increase diabetes risk” were confusing to the general public respondents. A respondent (P10), an academic, explained: “Everything you [can't eat] eat also cannot. That’s the flip side of pushing things too hard.” The HCS, which had made significant inroads in encouraging healthier F&B consumption, was found to be unclear in its representation. For example, respondent P10 explained: “If we take drinks with the Healthier Choice Symbol (beverages with lower sugar levels), does it mean drinking five bottles of it will be fine?” Rather than emphasizing a particular nutrient such as sugar, some respondents suggested focusing on individual needs, which might be more appropriate. Fake news and popular commercial “diet fads”, such as the ketogenic and Atkins diets, and intermittent fasting were other concerns reported by respondents. Academic and dietician respondents asserted that consistent advice was lacking, and relevant authorities needed to actively clarify unclear images and fake news, and provide consistent messaging on “diet fads” to the public.

With the proliferation of technology, some professionals and general public respondents highlighted the need to regulate healthcare services provided via online apps and virtual coaching programmes. Respondent 18, a dietician, explained that nutrition coaches on these platforms may not have the necessary qualifications and training, and could in fact, do more harm than good to service users or patients. She asserted that necessary regulation of online healthcare services is crucial to mitigate any potential risks of unregulated online healthcare services.

High innovation, production and marketing costs

High innovation, production and marketing costs in the (re)formulation of F&B products were major challenges for the F&B industry respondents. Respondents in this sector explained that taste acceptance for newer and healthier F&B products may not come immediately. F&B retailers, driven by profits, may not be quick to support the sale of healthier products, as the demand for them may not be there at the start. A general manager of an MNC F&B (P3), which produces aerated drinks among other F&B products, highlighted that government support to assist them in engaging in research and development (R&D), marketing, and diversifying and (re)formulating their products would be important and useful. They reported seeing double-digit negative profit margins since the introduction of the policy, and proposed a collaboration that would be beneficial, not just for their corporation, but also for the government and the general public:

We can actually kind of co-create product that we know that is good. Maybe there are certain health concerns, and can do this. Or it could be even at the launch, they [government] could endorse it, or they [government] could give us some promotional funds—how can we jointly, I mean with the help and the support, we can fund it.

Healthier F&B products must also have reach beyond the local market to offset the R&D costs of F&B manufacturers. F&B manufacturer/producer respondents explained that it would mean having to harmonize accreditation of healthier products across countries in order for it to make business sense for them, particularly for a country with a relatively small domestic market like Singapore. To this end, F&B respondents suggested government-to-government and business-to-business collaborations, expressed in forms of shared policies and practices, to give F&B manufacturers the legitimacy to market their (re)formulated healthier products worldwide.

Smaller F&B manufacturers and outlets, such as SMEs, reported acute cash flow issues and were less able to engage in innovation to (re)formulate healthier products. They had to contend with issues such as rising utility costs, rental footprints, high labour costs and limited physical space for stock-keeping units (SKUs) to offer healthier F&B options to their customers. Many respondents questioned the sustainability of rewards, vouchers and subsidies programmes that encourage healthier cooking, eating and living: “Once you finish, then what? I will go back to my own same old way of cooking. I think it’s about sustainability that we need to consider as well before we start on something” (respondent 12, a diabetes nurse educator).

In contrast, F&B retailers, such as larger supermarkets, were least hit by this policy. They were better resourced and better able to offer wider-ranging F&B products with both high and low/no sugar content to their consumers. Larger food establishments, such as restaurants, similarly reported no impact on their profit margins. They were better resourced and were able to offer a wider variety of F&B choices, whether healthier or otherwise, using better-quality and sometimes more expensive ingredients, to meet the needs of consumers who were more willing and able to pay higher prices in these establishments.

This study has explored how the WoD policy has been positioned to bring about changes in its population and the challenges that have arisen as a result. The findings showed that the WoD has generated, to varying extent, a sense of unity and purpose across most policy actors. Policy actors were cognisant of the thrusts of the policy and were quick to make shifts to align their interests with the policy. Legal parameters and economic conditions were debated at public consultations and would be set in place over time. Different policy actors were engaged at various time points. The findings also showed that most respondents demonstrated comprehension and acceptance of the arguments of the policy, and were able to appreciate the implications of diabetes for individuals, institutions and society.

Words, images and symbols were used to strategically shape the policy to produce “winning coalitions” with the policy actors. However, findings showed that there were competing perspectives or views across the policy actors. For example, some non-policy elites wondered whether a war should be waged against diabetes. Specifying diabetes as the target in the WoD could be seen as labelling or blaming those with diabetes and perpetuating stigma via the causal mechanism or action–consequences typology [ 35 ]. This causal mechanism has been observed elsewhere and among those with poorer diabetes control or advanced diabetic complications [ 36 , 37 ]. Sontag [ 38 ] cautions that describing disease in terms of siege and war or in the form of “militarized rhetoric” could backfire and may have unintended consequences. There is a need to foster and encourage a positive view towards prevention and treatment of diabetes.

Respondents with diabetes generally did not feel engaged by the policy. Many of them felt that the policy was directed at some “other groups”, but not them. Those with type 1 diabetes, for example, were unsure of who or what the war was being waged against, as the causal factors for type 1 diabetes are unclear. Those with type 2 diabetes reported that the policy should more directly address their underlying concerns regarding treatment costs. Being clear on who the intended targets are and articulating how the policy seeks to help them is important, as it will have implications for the end beneficiaries (winners) and target groups (or losers) [ 39 , 40 ]. It may also influence the distribution of costs and benefits, as it determines who gets what, when and how, and would have direct implications for practice and implementation [ 39 , 40 , 41 ]. Concerns over quality of messaging, information fatigue, diet fads and fake news, and the varying interpretations of the symbols (such as HCS) will need to be addressed.

Mitigating the high innovation, production and marketing costs for policy actors in the F&B industry would be crucial. Larger F&B businesses, including manufacturers, producers, retailers and F&B outlets, which were better resourced and better able to innovate and offer diverse and finer products, reported fewer issues in delivering on the policy. Smaller F&B enterprises—which generally have fewer resources—faced acute cash flow issues related to the necessary innovation and (re)formulation of healthier F&B products. Concerns over sustainability, linkages to marketing agencies, and physical space and costs highlighted the varying interests, paradigms, operational concerns and decision-making processes within the F&B business community and their associated implementation challenges, which will need to be addressed.

It will be crucial to continue to explore the concerns of the F&B industry and to support them in ways specific to their challenges. The individual F&B enterprises may differ in their challenges, depending on where they are situated in the larger business ecosystem and environment. They are also influenced by the nature and range of F&B products they produce or offer, their operational size, and their physical capacity and resources. As many of these business enterprises were quick to acknowledge their corporate and social roles to fellow citizens at the start, it would be imperative that they be supported in this endeavour as the challenges they face are real. Rather than describing their relationship with the government or policy-makers in adversarial terms, and masking them as “conflicts of interest”, it will be important, and perhaps more meaningful, to address their operational challenges head-on, and help them problem-solve to facilitate the implementation of the policy.

Additionally, the role of harmonizing accreditation for healthier products across countries will be critical for the F&B manufacturers, considering the relatively small domestic market in Singapore, to encourage them to engage in R&D for healthier products. A political commitment demonstrated as shared policies by governments to foster innovation and strengthen international partnerships to tackle diabetes and develop healthier F&B products will be crucial [ 42 ]. This could be achieved through epistemic communities, policy transfer and policy translation, and collaboration and coordination at the global level.

The role of the F&B enterprises is paramount, and the above discussion has highlighted the importance of making the commercial determinants of health visible. Rather than obscuring the commercial sector responsibility for and contributions to population harms, this study underscores the need to work with these partners to find meaningful ways to work together and ensure policy coherence in tackling the issue of diabetes [ 43 ]. Importantly, it also suggests how it may be possible, and in fact necessary, to make certain  that the commercial determinants are consistent with the public interests to positively influence population health. This may mean shifting away from the dominant emphasis in research and policy on clinical management and behavioural change, and towards prevention based on societal and behavioural change [ 44 , 45 ]. The findings suggest that diabetes should be conceptualized beyond individual-level risk factors, and be reframed as the product of a complex system, in part shaped by the F&B industry [ 46 ]. Addressing the various segments of the policy actors and their challenges in response to the WoD is critical. A continued gathering of constant feedback from the various policy actors and exploring ways to support them in this agenda will also be important [ 47 ].

Study strengths and limitations

Current frameworks looking at diabetes prevention and management generally examine the wider determinants of population health, and the commercial or private sector often does not appear to be prominently included [ 43 , 48 ]. This study explicitly considers their roles and explores how they could be better supported in this WoD to mediate the negative impacts on health arising from their commercial activities. The findings gathered may add to the body of knowledge surrounding commercial determinants of health, where it is still a growing field [ 12 ]. The study’s inclusion of those with diabetes, their caregivers and the general public also means that their myriad views are considered and added to the diverse insights into this policy.

All studies have limitations. As with any qualitative research study, the findings cannot be generalized due to its inductive nature. The respective voice of the various policy actors from the five different clusters cannot be generalized, as they each constitute a small number of respondents. Potential respondents who viewed the WoD negatively or were not informed about the policy might not have participated in this study, and their views and experience would not have been reflected. A deep dive to explore the role of social determinants of health on diabetes in the context of the WoD would be useful.

This study has shown that the WoD policy has generated a general sense of unity and purpose across most policy actors. It has also illustrated the highly complex environment in “doing” policy analysis [ 49 ]. The findings showed that the WoD policy needs to segment and engage the clusters of policy actors separately, and to explore their concerns and listen to their voices. In this instance, addressing those with diabetes directly will be critical to understanding their needs, and being clear on who the intended targets are and articulating how the policy seeks to support them is imperative. Issues of fake news, unclear messaging and lack of regulation of uncertified online health providers need to be addressed. High innovation, production and marketing costs should be looked into in greater detail with the F&B enterprises. The policy also needs to be situated at the global stage and environment, to nurture the economic conditions necessary for the F&B industry (manufacturers and innovators in particular) to engage in innovation and venture into (re)formulation of healthier F&B products. Diabetes is a global issue, and efforts to foster and enhance collaboration and coordination across countries on diabetes prevention and management policy is essential and crucial.

Availability of data and materials

Data can be obtained from the corresponding author on reasonable request.

Abbreviations

Centralised Institutional Review Board

Food and beverage

Healthier Choice Symbol

Healthier Ingredient Development Scheme

Multinational corporations

Ministry of Health

Primary care network

Singapore Health Services

Stock-keeping units

Small and medium enterprises

Sugar-sweetened beverages

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Acknowledgements

We would like to acknowledge Dr. Carol Soon, Institute of Policy Studies, Lee Kuan Yew School of Public Policy, National University of Singapore, for her initial advice and guidance in this research. We are also appreciative of the sharing by our respondents in this research study.

This research was funded by the National Medical Research Council Health Services Research—New Investigator Grant (NMRC HSR-NIG) awarded to LM. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Ow Yong, L., Koe, L.W.P. War on Diabetes in Singapore: a policy analysis. Health Res Policy Sys 19 , 15 (2021). https://doi.org/10.1186/s12961-021-00678-1

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Research design and methods, conclusions, article information, differential health care use, diabetes-related complications, and mortality among five unique classes of patients with type 2 diabetes in singapore: a latent class analysis of 71,125 patients.

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Jun Jie Benjamin Seng , Yu Heng Kwan , Vivian Shu Yi Lee , Chuen Seng Tan , Sueziani Binte Zainudin , Julian Thumboo , Lian Leng Low; Differential Health Care Use, Diabetes-Related Complications, and Mortality Among Five Unique Classes of Patients With Type 2 Diabetes in Singapore: A Latent Class Analysis of 71,125 Patients. Diabetes Care 1 May 2020; 43 (5): 1048–1056. https://doi.org/10.2337/dc19-2519

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With rising health care costs and finite health care resources, understanding the population needs of different type 2 diabetes mellitus (T2DM) patient subgroups is important. Sparse data exist for the application of population segmentation on health care needs among Asian T2DM patients. We aimed to segment T2DM patients into distinct classes and evaluate their differential health care use, diabetes-related complications, and mortality patterns.

Latent class analysis was conducted on a retrospective cohort of 71,125 T2DM patients. Latent class indicators included patient’s age, ethnicity, comorbidities, and duration of T2DM. Outcomes evaluated included health care use, diabetes-related complications, and 4-year all-cause mortality. The relationship between class membership and outcomes was evaluated with the appropriate regression models.

Five classes of T2DM patients were identified. The prevalence of depression was high among patients in class 3 (younger females with short-to-moderate T2DM duration and high psychiatric and neurological disease burden) and class 5 (older patients with moderate-to-long T2DM duration and high disease burden with end-organ complications). They were the highest tertiary health care users. Class 5 patients had the highest risk of myocardial infarction (hazard ratio [HR] 12.05, 95% CI 10.82–13.42]), end-stage renal disease requiring dialysis initiation (HR 25.81, 95% CI 21.75–30.63), stroke (HR 19.37, 95% CI 16.92–22.17), lower-extremity amputation (HR 12.94, 95% CI 10.90–15.36), and mortality (HR 3.47, 95% CI 3.17–3.80).

T2DM patients can be segmented into classes with differential health care use and outcomes. Depression screening should be considered for the two identified classes of patients.

Type 2 diabetes mellitus (T2DM) is a growing global health problem that afflicts more than 425 million (8.8%) adults in 2017 ( 1 ). It has been projected by the International Diabetes Federation to affect 693 million adults worldwide by year 2045 ( 1 ). The global cost of T2DM is high, costing governments USD 1.3 trillion in 2015, and this is estimated to increase to USD 2.3 trillion by 2030 ( 2 ).

With aging populations, rising complexity of medical care, and mounting economic costs from T2DM, the administration and delivery of health care at the population level have become more challenging ( 3 , 4 ). Population health, which is defined as “the health outcomes of a group of individuals, including the distribution of such outcomes within the group,” has been gaining traction in recent years due to finite health care resources and the impracticability of designing patient-specific care delivery models tailored for every individual ( 5 ). Consequently, population segmentation has been suggested as an avenue for policymakers and health care administrators to develop more cost-efficient and targeted population health-related policies and care models. This process segregates a heterogeneous patient population into unique subgroups with relatively similar anthropological clinical or psychosocial characteristics and/or health care requirements ( 6 ). For example, a recent study involving primary care users identified six distinct classes of patients, and patients in the “metabolic disease and multi-organ complications” group were found to have the highest level of health care use and risk of mortality ( 7 ).

Among both patients with type 1 diabetes and T2DM patients, a landmark cluster analysis of 5,795 Swedish patients identified five subtypes of patients with diabetes with a varying likelihood of developing diabetes-related complications and differing disease progression ( 8 ). Specifically, in T2DM patients, another study in North America identified four distinct subclasses of patients with differing clinical trajectories and health care use. Of note, patients in the high comorbidity and high insulin use group had the highest risk of diabetic nephropathy progression and annual frequency of health care visits ( 9 ).

The prevalence of diabetes in Singapore is among the highest within Southeast Asia (13.7% vs. regional prevalence of 8.5%) ( 1 ). This multiethnic Asian country also has one of the highest diabetes-related lower-extremity amputation (LEA) rates among all Organization for Economic Cooperation and Development (OECD) countries ( 10 ). Currently, there remains sparse data pertaining to the usage of population segmentation methodologies for evaluating health care use, risk of diabetes-related complications, and mortality among different subgroups of T2DM patients in Asia, as the majority of studies were conducted in Europe and America. It is also important to note that Asians have a markedly increased predisposition for the development T2DM compared with their white counterparts, which arises from a complex interplay between genetics, environmental, lifestyle, and dietary-related factors ( 11 ). Therefore, we aim to segment T2DM patients into unique and relatively homogenous classes and evaluate whether health care use, diabetes-related complications, and mortality vary among the different patient subgroups.

Study Setting

Singapore is a multiethnic Asian country, which is comprised of a Chinese majority and minority ethnic groups such as Malay and Indians. The delivery of public health care is achieved through three integrated, regional health care clusters, namely, SingHealth Regional Health System (SRHS), National Healthcare Group, and National University Health Systems. Each health care cluster is supported by a network of general hospitals, tertiary care specialist centers, community hospitals, and polyclinics. Among the three clusters, SRHS forms the largest cluster, and its primary and tertiary health care facilities aid in catering to the health care needs of residents in both south-central and eastern regions of Singapore ( 12 ). While health care in the public sector is heavily subsidized by the government, the Community Health Assist Scheme (CHAS) enables residents from lower- to middle-income families to seek subsidized medical treatment for chronic conditions at accredited private general practitioner (GP) clinics near their home. Citizens will qualify for CHAS orange and CHAS blue subsidies if their monthly household income falls between USD 870 and 1,450 and less than USD 870, respectively ( 13 ).

Study Design

A retrospective cohort study was conducted involving all patients who were diagnosed with T2DM in or before 2012, were Singapore residents aged ≥21 years (the age of majority in Singapore), and had at least one visit in an SHRS health care institute or accredited CHAS GP clinic in year 2012. Patients who were noncitizens or did not have a visit in any SHRS institute were excluded, as they were unlikely to be on long-term medical follow-up in the SHRS. We excluded patients who were diagnosed with T2DM after 2012, as the analyses were to be conducted using patients’ baseline characteristics in 2012.

De-identified patient data from an administrative database (Ministry of Health, Singapore) containing details of 71,125 T2DM patients were extracted. These included details on patients’ baseline sociodemographic characteristics (age, ethnicity, sex), their comorbidities, CHAS subsidy class (orange, blue, or none) in year 2012, and data pertaining to their health care use, development of diabetes-related complications, and all-cause mortality from 2013 to 2016. CHAS subsidy class was used as a surrogate marker of the financial status of patients, while the comorbidities in the database were coded based on ICD-10.

The details of health care use included the total number of primary outpatient clinic, private general practitioner clinic, specialist outpatient clinic (SOC), and accident & emergency (A&E) visits and the number of inpatient admissions in year 2012. Diabetes complications examined included myocardial infarction, stroke, end-stage kidney disease requiring initiation of dialysis, and LEA. Myocardial infarction and stroke were coded based on ICD-10, while end-stage disease requiring initiation of dialysis and LEA were coded based on Ministry of Health, Singapore, Table of Surgical Procedures (TOSP) codes. The TOSP comprises a list of procedures claimable under MediSave (mandatory national medical savings scheme) or MediShield (a public health insurance for low-income Singapore citizens) ( 14 ). Details pertaining to clinical comorbidities, health care use, and diabetes-related complications were aggregated from data across public health care institutions from all three regional health systems in Singapore.

Ethics Approval and Consent to Participate

The study was approved by the Centralized Institutional Review Board in SingHealth (reference number: CIRB 2016/2294). Waiver of consent was obtained and approved by the committee for this study. Permission was also obtained from the hospitals and polyclinics for access to de-identified data from patient medical records.

Statistical Analysis

Latent class analysis (LCA) is a statistical approach used to derive groups of relatively homogenous individuals within a heterogenous population ( 7 , 15 ). The latent class indicators used included patients’ age (<65 years old, ≥65 years old), ethnicity (Chinese, Malay, Indians, and others), duration of diabetes (<5 years, 5–10 years, and >10 years), and clinical comorbidities of patients ( Supplementary File 1 ). We fitted a series of latent class models starting from k = 1 (where k is the number of classes) onward. We stopped fitting a model with an additional class when the previous model’s smallest class size was <1.5% of the study population ( 7 ). The rationale for selecting this cutoff was to ensure that the size of each class consists of a proportion of the study population sufficient to ensure practicality in the design of class-specific health care policies. The selected optimal value for k in the LCA model was determined using both model-fit indices that assess the fit and clinical interpretability of the classes. Indices used included the Akaike information criterion, Bayesian information criterion (BIC), sample size–adjusted BIC, and entropy, for which higher entropy and smaller values of Akaike information criterion, BIC, and sample size–adjusted BIC indicate better model fit ( 16 ). The clinical relevance and interpretability of the classes were evaluated by an endocrinologist within the research team and in relation to current clinical guidelines ( 17 ).

In this study, the mean ± SD and number (percentage) were used to summarize continuous and categorical variables respectively. To profile the classes obtained from LCA, we compared the classes with the patients’ sociodemographic and clinical characteristics and their health care use using one-way ANOVA or χ 2 tests for continuous and categorical variables, respectively.

To evaluate the discriminative properties of the derived classes on total health care use and the risk of diabetes related-complications from years 2013–2016, we excluded deceased patients in year 2012 from these analyses. For assessment of the relationship between health care use and class membership with adjustment for confounders, multivariable negative binomial regression was performed where appropriate, and the incidence rate ratios (IRRs) are reported with 95% CIs. To assess the relationship of class membership with 4-year risk of developing diabetes-related complications and all-cause mortality, we performed Cox proportional hazards regression analyses, and the hazard ratios (HRs) are reported with 95% CIs. The regression analyses were adjusted for the patients’ age, sex, ethnicity, CHAS status, and duration of T2DM.

R, version 3.60, software (Foundation for Statistical Computing, Austria) was used for the latent class analyses, while Stata 15.0 software (2016) (StataCorp, College Station, TX) was used for all other statistical analyses.

Data and Resource Availability

The data sets generated and/or analyzed during the current study are not publicly available due to institutional restrictions but are available from the corresponding author on reasonable request.

A total of 71,125 T2DM patients were included in the analyses ( Supplementary File 2 ). Table 1 depicts the baseline demographic and clinical characteristics of patients in the year 2012, segregated by the latent classes. The majority of patients were of Chinese ethnicity ( n = 49,951 [70.2%]) and had a moderate (5–10 years) duration of diabetes ( n = 48,911 [68.8%]). The proportions of male and female patients (48.7% vs. 51.3%, respectively) and of elderly and younger patients (48.3% vs. 51.7%) were similar. Pertaining to socioeconomic status, one-half of the patient population was receiving CHAS subsidies (50.3%).

Baseline sociodemographic and clinical characteristics of patients in year 2012, segregated by latent classes

Data are means ± SD or n (%).

For the model selection, LCA analyses were performed for k = 1 to k = 7. A five-class model was selected in view of the better statistical fit and predetermined minimum class sizes. When compared with the model-fit indices and class sizes of other class models, it had the highest entropy, and the smallest class size was 1.57% ( Supplementary File 3 ).

The five derived classes were as follows, and their terminologies are defined in Supplementary File 4 .

Class 1: Younger patients with short T2DM duration and “relatively healthy” ( n = 11,133)

Class 2: Younger patients with short-to-moderate T2DM duration and moderate disease burden without end-organ complications ( n = 24,566)

Class 3: Younger females with short-to-moderate T2DM duration and high psychiatric and neurological disease burden ( n = 1,211)

Class 4: Older patients with moderate T2DM duration and moderate disease burden ( n = 26,254)

Class 5: Older patients with moderate-to-long T2DM duration, with depression, dementia, and high disease burden with end-organ complications ( n = 8,051)

Characteristics of the Classes

Supplementary File 5 depicts the heat map display of the sociodemographic and clinical characteristics of patients across the five classes relative to the overall population. The majority of patients in classes 1, 2, and 3 were ≤65 years old, while the majority of patients in classes 4 and 5 were >65 years old ( Table 1 ). The proportion of females was the highest among patients in class 3 (69.1%). With regard to ethnicity, the proportion of minority ethnic groups (Indians, Malays, and others) was the highest in class 5 (47.0%). ( Table 1 ) Pertaining to the duration of T2DM, patients in class 1 had the shortest mean ± SD T2DM duration (3.21 ± 2.90 years), while patients in class 5 had the longest mean T2DM duration (8.10 ± 3.10 years). Patients in class 1 had the lowest prevalence of cardiovascular, cerebrovascular, and renal-related comorbidities. Conversely, the prevalence of cardiovascular, cerebrovascular, and renal diseases was highest in patients in class 5. Patients in classes 3 and 5 also had a significantly higher prevalence of depression (13.1%–78.2% vs. 0.6%–1.7%; P < 0.001) compared with other classes. Patients in class 3 had the highest overall prevalence of psychiatric diseases, which included general anxiety disorder, major depression, schizophrenia, and bipolar disorder, and the prevalence of neurological diseases was high.

Health Care Use and Incidence of Diabetes-Related Complications From Years 2013–2016

A total of 1,777 (2.5%) patients were excluded from the analyses, as they died in year 2012. Patients in classes 3 and 5 had the highest tertiary health care use with regard to the average number of inpatient admissions and SOC and A&E visits from 2013 to 2016 ( Table 2 ). For primary health care use, classes 2, 3, and 4 had the highest number of polyclinic visits. Overall, class 1 has the lowest overall health care use.

Health care use (yearly), incidence of diabetes-related complications from 2013 to 2016, and 4-year all-cause mortality among patients, segregated by latent classes

For diabetes-related complications, class 5 had the highest proportion of patients who developed myocardial infarction, end-stage renal disease requiring dialysis initiation, stroke, and LEA ( P < 0.001). Conversely, class 1 had the lowest proportion of patients who developed diabetes-related complications in 2013.

Multivariable Analyses of Latent Classes With Total Health Care Use, Diabetes-Related Complications From Years 2013–2016, and 4-Year All-Cause Mortality

Class 1 was set as the reference group for analyses. Overall, class membership was predictive of health care use, diabetes-related complications and mortality ( P < 0.001) ( Table 3 ). With regard to primary health care use pattern across the five classes, patients in classes 2 and 4 had the highest total polyclinic visits (class 2 adjusted IRR 1.53, 95% CI 1.49–1.57; class 4 1.53, 95% CI 1.47–1.59). Pertaining to tertiary health care use, patients in class 3 had the highest total SOC visits (IRR 2.12, 95% CI 1.97–2.29) and total A&E visits (IRR 3.31, 95% CI 3.04–3.59). Class 5 had the highest total inpatient admissions (IRR 2.82, 95% CI 2.67–2.98).

Adjusted effect of the five latent classes on health care use, diabetes-related complications from years 2013–2016, and 4-year all-cause mortality

Data are IRR (95% CI) unless otherwise specified. Models were adjusted for age, sex, ethnicity, CHAS status, and duration of T2DM.

Reference group.

Negative binomial regression analyses were performed.

Cox proportional hazards regression analyses were performed. HRs reported with 95% CI.

P < 0.05;

P < 0.001.

For diabetes-related complication patterns across the five classes, class 5 patients had the highest hazard for myocardial infarction (HR 12.05, 95% CI 10.82–13.42), end-stage renal dialysis requiring dialysis initiation (HR 25.81, 95% CI 21.75–30.63), stroke (HR 19.37, 95% CI 16.92–22.17), and LEA (HR 12.94, 95% CI 10.90–15.36).

The two classes with the highest hazard for 4 years all-cause mortality were class 3 (HR 2.31, 95% CI 1.94–2.74) and class 5 (HR 3.47, 95% CI 3.17–3.80).

Overall, our study identified five distinct classes of T2DM patients with unique health profiles, with differential health care use, diabetes complication risk, and mortality patterns. Given the association between predictive ability of the classes and future health care use and health outcomes in 2013 among T2DM patients, our findings support the usage of data-driven population segmentation methods among T2DM patients and have significant implications on diabetes care, health policy planning, and resource allocation.

Among class 3 and 5 patients who had the highest tertiary health care use, diabetes-related complications, and mortality, an important unifying characteristic noted was the high prevalence of depression (13.1%–88.7%). This was significantly higher than in the general population (5.8%) ( 18 ) and concurred with a systematic review by Roy et al. ( 19 ) that showed that the prevalence of depression in the population with diabetes was twice that in the population without diabetes (19.1% vs. 10.7%, respectively). Comorbid depression has significant repercussions on the outcomes of T2DM and has been associated with poorer health-related quality of life, premature mortality, and increased risk of diabetes-related complications ( 20 ). While our study findings support American Diabetes Association guidelines for consideration of depression screening among elderly T2DM patients, there are no recommendations on the specific subgroups of elderly T2DM patients who should be screened ( 17 ). With the aging population worldwide and limited health care resources, it is impractical to perform universal screening for all elderly T2DM patients. Importantly, our findings highlight the need for routine screening for depressive symptoms among elderly patients with moderate-to-long duration of T2DM (>5 years), multiple comorbidities, and end-organ complications.

Additionally, we have identified another subgroup of T2DM patients—class 3 (younger females with short-to-moderate T2DM duration and high psychiatric and neurological disease burden)—who may benefit from depression screening. Studies have shown that depressive symptoms and episodes among T2DM patients tend to be persistent, and the rates of relapses are exceptionally high, with a 5-year recurrence rate of 79% ( 20 ). Furthermore, another study by Ke et al. ( 21 ) also showed that high psychiatric disease burden among young-onset T2DM patients contributed to a significant 36.8% of inpatient admission bed-days. The early identification of these high-need patients, using validated instruments such as the Patient Health Questionnaire-9, may permit implementation of psychological interventions and treatment, which in turn promote disease remission and reduce the financial burden of disease.

For patients in class 5, the prevalence of dementia was also noted to be high (7.5%). Dementia is the most severe stage on the continuum of diabetes-related cognitive deficits and has been associated with poor glycemic control and increased risk of severe hypoglycemia ( 22 ). While guidelines have recommended dementia screening among elderly patients, the subtypes of elderly patients to be screened have not been defined ( 23 ). Our study suggests that there is a need for routine screening for dementia among elderly patients, especially with moderate–to–long-standing T2DM and multimorbidities.

For the patients in class 3 with high neurological disease burden, secondary and tertiary disease prevention plays an important role. As these conditions often culminate in significant physical, cognitive, behavioral, and psychosocial problems and limitations, neuro-rehabilitation involving a multidisciplinary team should be considered and incorporated in patient care following diagnosis. There is also little controversy on the benefits of risk factor modification for these patients, in particular tobacco use, lifestyle and dietary modifications, and aggressive treatment of concomitant metabolic diseases such as hyperlipidemia. While trials evaluating the impact of multiple risk factor interventions have shown promising results for health outcomes, the actualization and long-term sustainability of these interventions in real life are often confounded by factors such as treatment compliance ( 24 ). As such, more studies are needed on developing sustainable models of care for the optimization of outcomes for these patients.

Pertaining to the study population, it was important to note that the prevalence of concomitant metabolic diseases such as hypertension (85.5%), hyperlipidemia (87.8%), and chronic kidney disease (CKD) (80.1%) was exceptionally high. For hypertension, specifically, the prevalence in this study was among the highest within Asia (40.4%–85.8%) and in the world ( 25 ). Likewise, the prevalence of CKD was more than three times that in the U.S. (25%) ( 26 ). T2DM patients with CKD have been shown to have poorer glycemic control and higher risk of diabetes-related complications such as neuropathies and cardiovascular disease ( 27 ). Consequently, for patients in class 1 who were deemed to be “relatively healthy” and have the lowest health care use, there is still a significant proportion of patients with these diseases. Hence, targeted interventions for these patients as well as patients in classes 2 and 4 with moderate disease burden should encompass themes for disease maintenance, early intervention, and disease prevention. Potential strategies are the use of intensive glycemic control among T2DM patients and education programs. For example, Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation (ADVANCE) showed that intensive glycemic control was able to reduce risk of end-stage kidney disease by 65% and macroalbuminemia by 30% ( 28 ). Likewise, a review by Li et al. ( 29 ) showed that education programs for diabetic kidney disease patients have a positive influence on their self-management behaviors and knowledge of diabetes.

With regard to interethnic disparities in T2DM outcomes, our findings correlated with findings reported in literature, where class 5, which comprised the highest proportion of Indians and Malays, had the highest risk of diabetes-related complications. A study by Chew et al. ( 30 ) noted that Indians with T2DM suffered from increased diabetes-related complications such as LEA and nephropathy compared with Chinese patients. Both Malay and Indian T2DM patients have also been shown to have poorer diabetes control compared with their Chinese counterparts ( 31 ). Although the mechanism for ethnic disparities in outcomes is unclear, postulated reasons include a complex interplay between environmental and socioeconomic factors as well as increased genetic predisposition to insulin resistance among Indians and Malays, which may affect their diabetes control. The design of interventions for this class of patients should address these interethnic differences during diabetes care, e.g., education programs tailored for culture-specific dietary habits.

Currently, there exists a myriad of population segmentation frameworks such as Johns Hopkins Adjusted Clinical Groups System and Bridges to Health for population health ( 32 ). However, the optimal segmentation framework for T2DM patients has not been established. As such, it is inevitable that there will be interstudy variations pertaining to derived patient clusters, which arise due to the differences in population segmentation methodology used, selection of segmentation variables, and subjectivity in the naming of patient clusters. Nonetheless, our study generally concurred with findings from studies that have used similar or overlapping latent variables within their segmentation approaches. For example, a study by Jiang et al. ( 9 ) identified four unique clusters of T2DM patients, for which patients in the “high comorbidity/moderate treatment” class had significantly higher risk for diabetes-related nephropathy and its progression compared with patients in the “low comorbidity/low treatment” class.

The main strength of the study is that it is one of the largest Asian studies that have evaluated differential health care use, diabetes-related complications, and mortality patterns among subclasses of T2DM patients. Another strength was that the diagnoses of patients made in other public institutions and regional health systems in Singapore were captured within the database, which increases the robustness and generalizability of our findings.

Nonetheless, our study results should be interpreted with the following limitations. First, due to the inherent limitations of data and lack of data granularity available in the administrative database, variables related to patients’ socioeconomic status (e.g., household income), control of diabetes (e.g., HbA 1c ), diabetes-related retinopathy, and types of antidiabetes medications, which may affect patients’ health care use and mortality, could not be evaluated. Furthermore, modifiable risk factors such as control of concomitant hypertension and obesity, which play a role in predicting diabetes control and disease trajectories, could not be examined. Given the complexity of diabetes care, the use of data-driven care models may complement risk stratification approaches derived from population segmentation techniques in predicting clinical outcomes of T2DM patients ( 33 , 34 ). An example of a data-driven integrated diabetes care program is the Risk Assessment and Management Programme–Diabetes Mellitus (RAMP-DM), which was shown to reduce cardiovascular disease, nephropathy, and mortality by 30–60% ( 35 ). It identifies high-risk T2DM patients using a validated scoring algorithm derived from large data registries and refers these patients for comanagement with nursing personnel and family practitioners for optimization of diabetes control ( 35 ). For health care use, we could only evaluate all-cause health care use instead of T2DM-specific health care use, and health care costs could not be assessed. With the rising use of electronic health records, which can capture more comprehensive medical data, future studies should consider exploring the use of socioeconomic and clinical variables as potential latent class indicators for the segregation of patients as well as to assess T2DM-specific health care use, costs, and mortality. Another limitation of the study was that the patients who used private health care exclusively were not captured in the database. Nevertheless, as the majority (>80%) of the health care demand in Singapore is catered for by the public health care institutions, we expect the number of patients who fall within this group to be small ( 7 ). Lastly, we were unable to assess the long-term clinical trajectories and interclass migration of patients, as the longitudinal data for patients were limited within the administrative database. Future studies may wish to explore these using statistical modeling techniques such as latent class growth analysis and follow-up patients for a longer period of time.

Our study identified five distinct subgroups of T2DM patients with differential health care use, diabetes-related complications, and mortality patterns, using routine sociodemographic and clinical data available in clinical practice. There is a need to screen for concomitant psychiatric diseases such as depression among two identified subgroups of T2DM patients. Our findings serve as an important foundation for guiding researchers and policy makers in designing clinical trials and health care policies to optimize the outcomes of T2DM patients, respectively.

Funding. This study is supported by the Singapore Ministry of Health’s National Medical Research Council under the Fellowship Programme by SingHealth Regional Health System, Population-based, Unified, Learning System for Enhanced and Sustainable (PULSES) Health Centre Grant (NMRC/CG/C027/2017_SHS).

Singapore Ministry of Health’s National Medical Research Council and SingHealth Regional Health System, Population-based, Unified, Learning System for Enhanced and Sustainable (PULSES) Health Centre did not play any role in the study design, data collection, data analysis, data interpretation, manuscript writing, or decision to submit the manuscript for publication.

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. L.L.L. was the study’s principal investigator and was responsible for the conception and design of the study. J.J.B.S., Y.H.K., V.S.Y.L., C.S.T., S.B.Z., and J.T. were the co-investigators. Access to data was provided by L.L.L. and V.S.Y.L. J.J.B.S., C.S.T., and V.S.Y.L. were responsible for analyzing the data. All authors contributed to the interpretation of data and literature review. J.J.B.S. prepared the initial draft of the manuscript. All authors revised the draft critically for important intellectual content and agreed to the final submission. L.L.L. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

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Multiple Biomarkers Improved Prediction for the Risk of Type 2 Diabetes Mellitus in Singapore Chinese Men and Women

Affiliations.

• 1 Health Services and Systems Research, Duke-NUS Medical School, Singapore. [email protected].
• 2 Health Services and Systems Research, Duke-NUS Medical School, Singapore.
• 3 Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore.
• 4 UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA.
• 5 Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
• 6 Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
• PMID: 31769241
• PMCID: PMC7188981
• DOI: 10.4093/dmj.2019.0020

Background: Multiple biomarkers have performed well in predicting type 2 diabetes mellitus (T2DM) risk in Western populations. However, evidence is scarce among Asian populations.

Methods: Plasma triglyceride-to-high density lipoprotein (TG-to-HDL) ratio, alanine transaminase (ALT), high-sensitivity C-reactive protein (hs-CRP), ferritin, adiponectin, fetuin-A, and retinol-binding protein 4 were measured in 485 T2DM cases and 485 age-and-sex matched controls nested within the prospective Singapore Chinese Health Study cohort. Participants were free of T2DM at blood collection (1999 to 2004), and T2DM cases were identified at the subsequent follow-up interviews (2006 to 2010). A weighted biomarker score was created based on the strengths of associations between these biomarkers and T2DM risks. The predictive utility of the biomarker score was assessed by the area under receiver operating characteristics curve (AUC).

Results: The biomarker score that comprised of four biomarkers (TG-to-HDL ratio, ALT, ferritin, and adiponectin) was positively associated with T2DM risk ( P trend <0.001). Compared to the lowest quartile of the score, the odds ratio was 12.0 (95% confidence interval [CI], 5.43 to 26.6) for those in the highest quartile. Adding the biomarker score to a base model that included smoking, history of hypertension, body mass index, and levels of random glucose and insulin improved AUC significantly from 0.81 (95% CI, 0.78 to 0.83) to 0.83 (95% CI, 0.81 to 0.86; P =0.002). When substituting the random glucose levels with glycosylated hemoglobin in the base model, adding the biomarker score improved AUC from 0.85 (95% CI, 0.83 to 0.88) to 0.86 (95% CI, 0.84 to 0.89; P =0.032).

Conclusion: A composite score of blood biomarkers improved T2DM risk prediction among Chinese.

Keywords: Biomarkers; Case-control studies; Diabetes mellitus, type 2; Epidemiology; Prognosis.

Copyright © 2020 Korean Diabetes Association.

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Conflict of interest statement

No potential conflict of interest relevant to this article was reported.

Fig. 1. Odds ratio for type 2…

Fig. 1. Odds ratio for type 2 diabetes mellitus by the biomarker score and percentages…

• Biomarker Score in Risk Prediction: Beyond Scientific Evidence and Statistical Performance. Bang H. Bang H. Diabetes Metab J. 2020 Apr;44(2):245-247. doi: 10.4093/dmj.2020.0073. Diabetes Metab J. 2020. PMID: 32347026 Free PMC article. No abstract available.

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Research Article

Direct Medical Cost of Type 2 Diabetes in Singapore

Affiliation Department of Pharmacy, Faculty of Science, National University of Singapore, Singapore, Singapore

* E-mail: [email protected] (JYL); [email protected] (MPT)

Affiliations Information Management, Central Regional Health Office, National Healthcare Group, Singapore, Singapore, Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore

Affiliation School of Pharmacy, College of Pharmacy, Taipei Medical University, Taipei, Taiwan

• Charmaine Shuyu Ng, 
• Matthias Paul Han Sim Toh, 
• Yu Ko, 
• Joyce Yu-Chia Lee

• Published: March 27, 2015
• https://doi.org/10.1371/journal.pone.0122795
• Reader Comments

Due to the chronic nature of diabetes along with their complications, they have been recognised as a major health issue, which results in significant economic burden. This study aims to estimate the direct medical cost associated with type 2 diabetes mellitus (T2DM) in Singapore in 2010 and to examine both the relationship between demographic and clinical state variables with the total estimated expenditure. The National Healthcare Group (NHG) Chronic Disease Management System (CDMS) database was used to identify patients with T2DM in the year 2010. DM-attributable costs estimated included hospitalisations, accident and emergency (A&E) room visits, outpatient physician visits, medications, laboratory tests and allied health services. All charges and unit costs were provided by the NHG. A total of 500 patients with DM were identified for the analyses. The mean annual direct medical cost was found to be $2,034, of which 61% was accounted for by inpatient services, 35% by outpatient services, and 4% by A&E services. Independent determinants of total costs were DM treatments such as the use of insulin only (p<0.001) and the combination of both oral medications and insulin (p=0.047) as well as having complications such as cerebrovascular disease (p<0.001), cardiovascular disease (p=0.002), peripheral vascular disease (p=0.001), and nephropathy (p=0.041). In this study, the cost of DM treatments and DM-related complications were found to be strong determinants of costs. This finding suggests an imperative need to address the economic burden associated with diabetes with urgency and to reorganise resources required to improve healthcare costs.

Citation: Shuyu Ng C, Toh MPHS, Ko Y, Yu-Chia Lee J (2015) Direct Medical Cost of Type 2 Diabetes in Singapore. PLoS ONE 10(3): e0122795. https://doi.org/10.1371/journal.pone.0122795

Academic Editor: Ulla Kou Griffiths, London School of Hygiene and Tropical Medicine, UNITED KINGDOM

Received: October 23, 2014; Accepted: February 23, 2015; Published: March 27, 2015

Copyright: © 2015 Shuyu Ng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All relevant data are within the paper.

Funding: This work was supported by a MOH Health Services Research Competitive Research Grant (HSRG/0027/2012). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Globally, the total number of people with diabetes mellitus (DM) is projected to rise from 171 million in 2000 to 366 million in 2030 [ 1 ]. There is a growing epidemic of diabetes mellitus, type 2 in particular, in the Asia-Pacific region [ 2 , 3 ]. According to current estimates, the DM population in this region is the largest in the world, with approximately 47.3 million, which is 46% of the global burden of this disease [ 4 ]. In Singapore, as in many developed countries, DM is a growing public health problem. The prevalence of DM has risen to 12.3% in 2013, from 8.2% in 2004 and 9% in 1998 [ 5 – 7 ], surpassing other Asian countries such as Hong Kong (9.5%), Japan (7.2%) and Taiwan (5.7%) [ 8 ]. Moreover, DM is the tenth leading cause of death in Singapore, accounting for 1.7% of total deaths in 2011 [ 9 ].

Diabetes is a chronic medical condition associated with numerous complications that makes it a substantial economic burden incurred by individuals, healthcare systems and society as a whole [ 10 ]. In 2007, the global health expenditure to treat and prevent DM and its complications was estimated to be at least US$232 billion [ 8 ]. Depending on available treatments and local prevalence, the direct costs of DM consume from 2.5% to 15.0% of annual healthcare budgets [ 11 ].

Despite the large number of people with DM, the financial burden in Singapore attributed to DM has not been investigated. Because type 2 diabetes mellitus (T2DM) accounts for approximately 90% of DM cases and its prevalence increases with ageing, understanding the patterns of resource use and cost associated with T2DM is becoming increasingly important for policymakers and budget planners. Therefore, this study aims to identify the total direct medical cost of T2DM in Singapore and to examine the relationship between direct medical costs and individual demographic characteristics, DM treatments (exercise or diet, taking oral medications only, taking insulin only and taking both insulin and oral medications), disease control, complications and comorbidities.

Study design

This study adopted a prevalence-based ‘epidemiological’ approach, employing a bottom-up methodology to estimate different cost components. The prevalence approach can yield more precise estimates because it ascertains the current economic burden of a disease rather than projected ones [ 12 , 13 ]. The perspective for this study was that of the healthcare system (i.e., National Healthcare Group (NHG) institutions). This study was approved by the National Healthcare Group Domain Specific Review Board (NHG-DSRB).

Data source

This was a cross-sectional study of T2DM patients who had received care in any of the NHG institutions in 2010. The NHG is public funded and provides inpatient and ambulatory care (primary care, specialist outpatient and 24-hour emergency) services through a network of 3 acute hospitals, 1 national center, 9 primary care clinics and 3 specialty institutes serving the population in the central and western parts of Singapore. The 9 primary care clinics, also known as polyclinics, had a service load of 3.7 million attendances in 2010, which accounted for 60% of all public sector primary care attendances [ 14 ]. Data was drawn from the NHG Chronic Disease Management System (CDMS), which serves as an operational disease registry within the NHG. The CDMS was commissioned in 2007 to enhance the delivery of care for patients with DM and to facilitate greater efficiency in outcome measurement. It links key clinical data of patients with DM across the NHG healthcare cluster, including records of visits to physicians, nurses, and allied health professionals, as well as medication and laboratory test records [ 15 ]. In addition, it also includes registration and financial cost data related to the care of chronic diseases.

Patient selection

Patients with T2DM were identified using the International Classification of Diseases Ninth Revision (ICD-9-CM) with diagnostic code of 250 as primary or secondary diagnosis, or using pharmacy medication records or laboratory data in the CDMS. Diabetes complications and comorbidities were also identified using ICD-9-CM codes, while only DM-related medications and laboratory data were based on inpatient and outpatient encounters at the hospital or outpatient clinics that were registered with the CDMS. Systematic sampling was conducted for 98,592 identified DM patients (i.e., every 197 th patient was selected). Informed consent was not obtained from the patients as the data was de-identified prior to analysis.

This study included patients who satisfied at least one of the following three criteria: (1) assigned ICD-9-CM code of 250; (2) attended treatment for DM for 1 year in any NHG institution; or (3) prescribed any anti-diabetic medication. Patients with type 1 DM and women with gestational diabetes were excluded.

Laboratory-derived measures related to DM

Measures for DM-related physical examinations were included and categorised as follow: (1) body mass index (BMI) (kg/m 2 ): <18.50 = underweight; 18.50–24.99 = normal; >25.00 = overweight and obese [ 16 ], (2) glycated haemoglobin (HbA1c) (%): ≤7.0 = good disease control; 7.1–8.0 = sub-optimal disease control; >8.0 = poor disease control, (3) low-density lipoprotein cholesterol (LDL-c) (mmol/L): <2.6 = optimal; 2.6–4.0 = near optimal; >4.0 = high, (4) urine albumin-to-creatinine ratio (UACR) (albumin/24h): <30mg = normal; 30-299mg = microalbuminuria; >300mg = macroalbuminuria [ 17 , 18 ].

Estimation of costs

Direct DM-related costs were classified by the type of service, including inpatient hospitalisation, accident and emergency (A&E) and ambulatory outpatient care (physician visits, allied health visits, laboratory tests and medications). Allied health visits include foot screening, eye screening, dietary services and health education. The total medical costs were estimated by the total before-subsidy charges, which is the total medical bill before any deduction for government subsidies or insurance claims. All costs reported were in Singapore currency (S$) for year 2010 prices.

The cost of inpatient care and A&E services were estimated by the total charge based on the length of stay and resources used. Any A&E visits that resulted in hospitalisation were included as inpatient care costs. Unit costs used in the estimation of physician visits, which included visits to primary care clinics (polyclinics) and specialist outpatient clinics (hospitals), were equal to the standardised rate for physician visits at all NHG primary care clinics and hospitals. Therefore, costs were estimated by multiplying the number of physician visits by the unit cost of a visit. Unit costs for allied health visits, laboratory tests and medications were estimated via the same method as physician visits. The cost for drugs other than anti-diabetic medications was not included. Unit costs for all services rendered were provided by the NHG and are in Singapore dollars. Direct non-medical costs, such as transportation expenses and indirect costs were not included.

Statistical methods

Healthcare cost data are often positively skewed because a relatively small proportion of patients incur extremely high costs [ 19 , 20 ]. Such problems were dealt with by logarithmic transformation of the cost data [ 21 ]. Descriptive statistics (frequency, percentage, mean, median, standard deviation and 90 th percentile) were used for demographic information and expenditures. To identify the factors affecting total costs, a multiple linear regression model was developed to evaluate the relationship of both demographic and clinical state variables (HbA1c, DM treatments, complications and comorbidities) to the total calculated expenditure. All statistical analyses were performed using SPSS version 21.0 (SPSS Inc., Chicago, IL, USA).

Patient characteristics

A total of 98,592 patients in the NHG CDMS (2010) were identified as patients with DM. After applying the selection criteria and a systematic sampling, 500 patients were included in the analyses. The socio-demographic profile of the patients is shown in Table 1 . The patients were equally distributed between the two genders (55.4% female). The mean (±SD) age was 69.0 ± 9.4 years, and most study patients were Chinese (77.6%) and non-smokers (89.8%). Although a greater proportion of patients was overweight (42.6%), most had good disease control (44.6%), optimal LDL-c (43.2%) and normal UACR (41.2%). Of the 69.2% of DM patients who were on anti-diabetic medications, the majority used oral medications (57.2%), while only 3% were treated with insulin and the remaining 9% used both insulin and oral medications. Nephropathy (57.2%) and cardiovascular conditions (34.2%) were common DM complications among the cohort. The distributions of subgroups were similar between patients with at least one inpatient visit and those without any inpatient visit.

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https://doi.org/10.1371/journal.pone.0122795.t001

Annual costs of diabetes

The mean annual direct cost was S$2,034.6 (US$1.0 = S$1.3 as of 31 December 2010) [ 22 ], of which S$1,237.2 accounted for by inpatient services, S$84.2 by A&E services and S$713.2 by outpatient services ( Table 2 ). Of the total healthcare expenditure, the main cost driver was inpatient costs (60.8%), while A&E services (4.1%) were only a small portion of the total costs. The major source of costs for outpatient services was physician visits, which accounted for 22.6% of the total healthcare expenditure and 64.0% of total outpatient expenditure ( Fig. 1 ).

https://doi.org/10.1371/journal.pone.0122795.t002

https://doi.org/10.1371/journal.pone.0122795.g001

Patients with at least one inpatient admission had higher mean total costs (S$8,787.8) than those who had no inpatient admission (S$690.5), with the bulk of costs resulting from inpatient services (S$7,453.3). Conversely, patients with no inpatient visits had a substantially higher proportion of overall outpatient costs.

Factors affecting the total costs

Using multiple linear regression with log transformation, the total cost of DM was significantly associated with DM treatments (taking insulin only or both oral medications and insulin) and DM-related complications (cerebrovascular, cardiovascular, and peripheral vascular diseases and nephropathy). This model explained 23.0% of the variance in costs ( Table 3 ). Age, gender, race, smoking status, disease control, taking only oral medication, having retinopathy and comorbidities were not independently associated with cost. The combination of oral medications and insulin resulted in an average increment in annual total cost (17.5%, p = 0.047), while the use of only insulin led to a higher increment (53.2%, p<0.001) when compared with patients who were only on dietary control and healthy lifestyle advice alone. Taking the absence of complications as reference, the cost of DM was higher when complications were present except in the case of retinopathy.

https://doi.org/10.1371/journal.pone.0122795.t003

This prevalence-based cost-of-illness study involved a large captive population with T2DM in Singapore. The analysis was based on cost and administrative data retrieved from the NHG disease registry for 2010. This is the first study to provide estimates of costs associated with diabetes care in Singapore.

The cost per patient estimate in this present study was S$2,034.6 (US$1,575.6), and this appears to be higher than the costs reported in other Asian countries. A study in India reported an estimate of US$525.5 per patient [ 23 ], while a study in China reported costs of US$1,501.7 per patient [ 11 ] for the management of DM. However, the costs reported in these studies were presented without accounting for inflation or difference between currency. Notably, hospital costs reported in the American and European continents were much higher than those obtained in this study [ 24 – 26 ]. Despite the cost differences, inpatient costs still remained the main cost driver of the total estimated expenditure, which was also noted in the earlier DM COI studies [ 25 , 27 – 29 ]. Although the length of stay (LOS) was not reported in this study, the high cost of inpatient services were often strongly correlated to LOS [ 30 , 31 ], with higher LOS resulting in higher costs. This suggested that attempts to expedite services or reduce unnecessary utilisation of diagnostic tests to reduce LOS may be worthwhile in reducing overall costs.

In terms of outpatients costs, physician services contributed to the bulk of the total expenditure in our study, and this was understandable since the growth in the number of physicians and specialists have increased over the years to meet with higher patient demands [ 32 ]. In addition, the introduction of new medical technologies and prescription drugs have also shown significant association with physician cost growth because consumers generally require physician visits to obtain diagnostic tests and prescriptions [ 32 ]. Because physicians are central to the healthcare system, efforts to contain physician spending reverberate through all healthcare services, especially with DM being a chronic condition requiring continuous follow-ups.

Our results from the regression analyses have generally confirmed what might have been expected based on the epidemiologic evidence in the literature [ 11 , 20 , 33 – 35 ], that microvascular and macrovascular complications tend to increase the cost of care. On the contrary, comorbidities such as hypertension and dyslipidaemia did not have an association with overall cost. This result is surprising since cost-effectiveness and medication adherence studies [ 36 – 39 ] have reported that achieving therapeutic clinical parameters would lead to an increase in cost of care albeit increasing the quality-adjusted life years (QALY). A possible explanation could be that hypertension and dyslipidaemia may have been controlled or at a steady state that did not require treatment, resulting in no costs incurred.

In our study, patients with sub-optimal and poor disease control had lower overall costs. This may be due to underutilisation of healthcare services compared to those with good disease control. The importance of managing DM to prevent or delay complications requires effort [ 40 ] and good control of DM results in long-term cost savings due to fewer complications [ 41 ]. Furthermore, The use of insulin only or both insulin and oral antidiabetic medications were found to be associated with higher costs. Consistent with other studies, the most expensive component of total outpatient costs after physician costs were medications [ 24 , 25 , 29 , 42 ]. This rise in cost indicated a growth in the consumption of prescription medications, which may be due to increase adherence to medications. Evidence has shown that better adherence results in better healthcare outcomes and reduces the need for physician visits [ 43 , 44 ], and lead to a net decrease in overall healthcare cost.

As a prevalence-based cost-of-illness study, the strength of this study was that all DM cases were included from a specified year, regardless of whether or not they were diagnosed before or during that year. This breadth allows for analysis of patients at various stages of the illness, since different severities of DM may be associated with different costs. However, there were several limitations in this study. First, data was drawn from a healthcare database, hence relied on the accuracy and completeness of the records. The NHG CDMS has, however, been used in several studies and is recognised for providing well-validated and comprehensive data [ 14 , 45 ]. Second, patients with undiagnosed diabetes as well as indirect/intangible costs and out-of-pocket expenses were not included, which may contribute to an underestimation of the true cost of diabetes. Lastly, the study population was relatively small and limited to the public healthcare sector in Singapore. Future studies may consider these shortcomings to further assess different aspects of diabetes costs.

This study provided a comprehensive cost analysis of expenditures incurred in the treatment of DM in Singapore. The results indicated that both medications and DM complications were strong determinants of costs. With projected increase in diabetes prevalence coupled with obesity and growing need for medical treatment in Singapore, diabetes will continue to be a heavy burden on health budgets. Therefore, evidence on the economic burden related to diabetes-related complication and its drives are indispensable for a health-system reform that seeks to minimise the long-term economic burden of this growing epidemic.

Author Contributions

Conceived and designed the experiments: CSN YK MPT. Performed the experiments: CSN. Analyzed the data: CSN YK JYL MPT. Contributed reagents/materials/analysis tools: CSN. Designed the study: CSN YK MPT. Performed the analysis and prepared the manuscript: CSN. Provided data analysis advice and revision of the final manuscript: YK JYL MPT. Read and approved the manuscript: CSN YK JYL MPT.

• 1. World Health Organisation, International Diabetes Federation. Diabetes action now: An initiative of the World Health Organisation and the International Diabetes Federation. WHO, 2004.
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• 5. Health Promotion Board. Information Paper On Diabetes In Singapore. 2011 [cited 2014 April 23]; Available from: http://www.nrdo.gov.sg/uploadedFiles/NRDO/Publications/%28INP-11-7%29 20111103 Diabetes Information Paper 2011.pdf.
• 6. International Diabetes Federation. Diabetes Atlas. Brussels, Belgium: International Diabetes Federation, 2013.
• 7. Ministry of Health Singapore. Disease burden. 2014 [cited 2014 December 09]; Available from: http://www.moh.gov.sg/content/moh_web/home/statistics/Health_Facts_Singapore/Disease_Burden.html .
• 8. International Diabetes Federation. Diabetes Atlas Brussels, Belgium: International Diabetes Federation, 2006.
• 12. World Health Organisation. WHO Guide to Identifying the Economic Consequences of Disease and Injury. Geneva, Switzerland: WHO, 2009.
• 16. World Health Organisation. BMI classification—global database on body mass index. 2006 [cited 2014 April 4]; Available from: http://apps.who.int/bmi/index.jsp?introPage=intro_3.html .
• 17. Ministry of Health Singapore. Clinical practice guidelines—diabetes mellitus. Singapore. 2006 [cited 2014 June 05]; Available from: http://www.moh.gov.sg/content/dam/moh_web/HPP/Doctors/cpg_medical/withdrawn/cpg_Diabetes Mellitus-Jun 2006.pdf.
• 18. National Healthcare Group Polyclinic. Diabetes mellitus. In: Leong H, Lee C, editors. Singapore. 2010.
• 21. Johnson RA, Wichern DW. Applied multivariate statistical analysis. 3rd ed. Englewood Cliffs, NJ: Prentice-Hall; 1992.
• 22. OANDA Corporation. Currency converter. New York, NY: OANDA Corporation [cited 2014 March 31]; Available from: http://www.oanda.com/currency/converter/ .
• 40. International Diabetes Federation. IDF—Global diabetes plan 2011–2021. Brussels, Belgium: International Diabetes Federation, 2010.

Development of a diabetes-related nutrition knowledge questionnaire for individuals with type 2 diabetes mellitus in Singapore: Diabetes-related nutrition knowledge questionnaire

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Beating Type 2 diabetes: How one woman achieved remission

SINGAPORE – Life was going smoothly for Mrs Suja Padmanabhan until she suffered two strokes in 2016, leaving the left side of her body paralysed.

She was 44 years old and living in Gurgaon near New Delhi with her husband and daughter, then 19. 

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How a consistent sleep schedule affects type 2 diabetes risk – new research

• New study finds that people with the greatest irregular sleep patterns have a 34 per cent higher risk of developing type 2 diabetes

Here is another good reason you need a good night’s sleep every night: consistent sleep could be the key to preventing type 2 diabetes.

The study, led by researchers at Boston’s Brigham and Women’s Hospital, looked at sleep patterns over the course of seven nights, and then followed participants for more than seven years.

A regular sleep pattern is one in which your bedtime and wake-up times stay consistent from day to day.

“Our study identified a modifiable lifestyle factor that can help lower the risk of developing type 2 diabetes,” said Sina Kianersi, a research fellow in the Channing Division of Network Medicine at the Brigham.

“Our findings underscore the importance of consistent sleep patterns as a strategy to reduce type 2 diabetes.”

Type 2 diabetes affects close to half a billion people worldwide, and it is one of the top 10 leading causes of death and disability. The number of people with type 2 diabetes is expected to more than double to 1.3 billion by 2050.

The new study, published in Diabetes Care, analysed accelerometry data from more than 84,000 participants in the UK Biobank study to investigate any possible association between sleep and type 2 diabetes. Participants were an average age of 62 years, and were initially free of diabetes.

They wore accelerometers – devices like watches that monitor movement – for seven nights. The participants were followed for about 7.5 years, tracking diabetes development mostly through medical records.

Our findings have the potential to improve diabetes prevention on multiple levels

The researchers found that more irregular sleep duration was associated with higher diabetes risk after adjusting for a wide range of risk factors. Irregular sleep was defined as day-to-day sleep duration varying by more than 60 minutes on average.

“Our findings have the potential to improve diabetes prevention on multiple levels,” Kianersi said.

“Clinically, they might inform better patient care and treatment plans. Public health guidelines could promote regular sleep patterns. However, more research is needed to fully understand the mechanism and confirm the results in other populations.”

New research finds nearly 19% increase in cases of Type 2 diabetes over a decade

New research found a nearly 19% increase in cases of Type 2 diabetes between 2012 and 2022.

More than one in five individuals aged 65 or older had the condition, and the same age group was more than 10 times as likely to be diagnosed with diabetes than people in the 18 to 24 age bracket, according to a new study from the University of Georgia published in the Diabetes, Obesity and Metabolism journal.

There were disparities in the prevalence of the disease between sociodemographic groups, showing higher rates among racial and ethnic minorities in the results of the study.

Where people lived in the U.S. also showed a difference in the number of cases – with the Midwest and South experiencing more pronounced increases, the researchers said. Specifically, 10 states saw increases of 25% or more over the decade-long study period: Arkansas, Kentucky, Nebraska, Texas, Alabama, Minnesota, Illinois, West Virginia, Delaware and Massachusetts.

RELATED STORY | Study: 1.3 billion people could be living with diabetes by 2050

The study also noted a correlation between cases of diabetes and levels of income. People with higher incomes were 41% less likely to be diagnosed with diabetes.

Lastly, the research confirmed what is already known: Obesity is linked to a higher risk of diabetes and addressing the obesity epidemic is a crucial step in combating the disease.

One way health experts are looking to address obesity is through certain medications, like Zepbound. Maker of the drug, Eli Lilly, just released the results of a 3-year study that shows its tirzepatide medication reduced the risk of developing Type 2 diabetes by 94% in adults with pre-diabetes.

RELATED STORY | Hydrogel could be the future of popular diabetes and weight-loss drugs

However, researchers at the University of Georgia said, “Promoting healthy eating habits, increasing physical activity and implementing community-based interventions to support weight management can play a significant role in reducing diabetes prevalence.”

The study on the prevalence of Type 2 diabetes in society was observational, using data from the Behavioral Risk Factor Surveillance System, which is an ongoing health survey involving more than 400,000 adult interviews each year.

RELATED STORY | Amid rise in childhood diabetes, man describes how to 'thrive' with disease

The goal was to examine national trends and disparities in self-reported diabetes cases to increase the comprehension of the risk factors – insight that researchers said is “crucial” for developing focused prevention strategies.

“Improving access to quality care, implementing diabetes prevention programs focusing on high-risk groups, and addressing social determinants through multilevel interventions may help curb the diabetes epidemic in the United States,” researchers said in the study.

According to the University of Virginia Health , Type 2 diabetes is more common and often associated with insulin resistance and obesity whereas Type 1 diabetes is an autoimmune disease.

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Eating Meat Is Linked With Diabetes Risk, New Studies Suggest

The research builds on previous findings connecting red and processed meats with Type 2 diabetes.

By Alice Callahan

For sausage, salami and steak lovers, the news has not been good. Scientists have been consistently finding links between red and processed meat consumption and heart disease , some types of cancer and earlier death .

And now, two recent studies have added to the growing body of evidence that a meat-heavy diet may increase the risk of Type 2 diabetes.

In one of the studies, published today in The Lancet Diabetes and Endocrinology , researchers analyzed data from nearly two million adults participating in 31 studies across 20 countries, including the United States and parts of Europe and Asia.

The researchers reviewed survey data on participants’ diets and then looked at their health an average of 10 years later. After adjusting for other risk factors like smoking, a higher body mass index, physical inactivity and a family history of diabetes, they found that for every 1.8 ounces of processed meat the participants ate each day, their risk for Type 2 diabetes increased by 15 percent. (This is equivalent to a medium-sized sausage or two to three slices of bacon.) For every 3.5 ounces of unprocessed red meat they consumed daily, their risk increased by 10 percent. (This is about the size of a small steak.)

The data also suggested that one serving of poultry per day was associated with an 8 percent increase in Type 2 diabetes risk, but this finding was less consistent and only significant in the European studies, so more research is needed, said Dr. Nita Forouhi, a professor of population health and nutrition at the University of Cambridge who led the study.

The takeaway, she said, is that the less red and processed meat you eat, the better.

Why Eating Meat Might Increase Your Risk

These findings jibe with previous research, including a large U.S. study published in October .

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Cohort profile

Cohort profile: the singapore diabetic cohort study.

1 Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore

Linda Wei Lin Tan

Xueling sim, milly khiam hoon ng, rob van dam, e shyong tai.

2 Division of Endocrinology, National University Hospital, Singapore

Kee Seng Chia

Wern ee tang.

3 National Healthcare Group Polyclinics, Singapore

Darren EJ Seah

Kavita venkataraman, associated data.

bmjopen-2019-036443supp001.pdf

The diabetic cohort (DC) was set up to study the determinants of complications in individuals with type 2 diabetes and examine the role of genetic, physiological and lifestyle factors in the development of complications in these individuals.

Participants

A total of 14 033 adult participants with type 2 diabetes were recruited from multiple public sector polyclinics and hospital outpatient clinics in Singapore between November 2004 and November 2010. The first round of follow-up was conducted for 4131 participants between 2012 and 2016; the second round of follow-up started in 2016 and is expected to end in 2021. A questionnaire survey, physical assessments, blood and urine sample collection were conducted at recruitment and each follow-up visit. The data set also includes genetic data and linkage to medical and administrative records for recruited participants.

Findings to date

Data from the cohort have been used to identify determinants of diabetes and related complications. The longitudinal data of medical records have been used to analyse diabetes control over time and its related outcomes. The cohort has also contributed to the identification of genetic loci associated with type 2 diabetes and diabetic kidney disease in collaboration with other large cohort studies. About 25 scientific papers based on the DC data have been published up to May 2019.

Future plans

The rich data in DC can be used for various types of research to study disease-related complications in patients with type 2 diabetes. We plan to further investigate disease progression and new biomarkers for common diabetic complications, including diabetic kidney disease and diabetic neuropathy.

Strengths and limitations of this study

• The cohort focuses on diabetes, one of the biggest public health challenges in this century, with a relatively large pool of multiethnic participants comprising Chinese, Malay and Indians, and follow-up duration of more than a decade.
• This cohort has collected a variety of data on sociodemographics, lifestyle, health-related quality of life, anthropometric and other physical measurements, biochemical characteristics and genetic profiles through questionnaires, physical assessment, collection of biological samples and linkage to medical and administrative records.
• Diabetic cohort is a prevalence cohort, which recruited participants with varying durations of disease, and potentially at different stages of the natural history of disease.
• This cohort may be limited by the low rate of active follow-up of participants, which has been substantially overcome through the linkage with medical records and disease registries.

Introduction

Diabetes mellitus is a major public health problem that has reached epidemic proportions globally. The International Diabetes Federation has estimated that there were more than 400 million individuals with diabetes worldwide in 2017, which is projected to increase over 600 million by 2045, 1 2 with around 90% having type 2 diabetes. Diabetes also causes vascular damage in multiple organ systems, leading to increased risk of cardiovascular diseases, chronic kidney disease, retinopathy and lower limb amputations. The global burden of such complications is huge, with diabetes now a leading cause for end-stage renal disease, blindness and disability. 3–7 Therefore, diabetes and diabetes-related complications contribute substantially to the global burden of disease, in terms of morbidity, 8 mortality, 9 reduced quality of life 10 11 and economic cost. 12

Rates of type 2 diabetes and related complications vary significantly across countries and regions. In particular, Asians are not only at higher risk for type 2 diabetes at lower levels of obesity and younger ages but also at increased risk of adverse outcomes. 13 14 In Singapore, the prevalence of diabetes has been rising, with prevalences of 8.3% and 8.6% being reported, using fasting plasma glucose measurements only, in the consecutive National Health Surveys in 2010 and 2017, respectively. 15 Incidence rates of serious diabetes-related complications like end-stage renal disease and lower extremity amputations are much higher in Singapore as compared with other high-income countries. 6 16

The diabetic cohort (DC), therefore, was set up to study the determinants of complications in individuals with type 2 diabetes and examine the role of genetic, physiological and lifestyle factors in the development of complications in these individuals. As a disease cohort in multiethnic Singapore, the DC provides the opportunity to examine intra-Asian variations in risk of complications and downstream outcomes and the role of the factors mentioned above in any differential risk.

Through the DC, we hope to provide a more complete understanding of the etiopathogenesis of type 2 diabetes and its related complications in Asia, with the long-term objective of improving care and outcomes in individuals with type 2 diabetes.

Cohort description

The DC is a part of the Singapore Population Health Studies in the Saw Swee Hock School of Public Health, National University of Singapore. It is a closed cohort with a total of 14 033 adults with diabetes enrolled during two recruitment phases in 2004–2006 and 2006–2010 from multiple public sector polyclinics and hospital outpatient clinics. In phase 1, 5324 participants were recruited from Clementi and Toa Payoh Polyclinics, Tan Tock Seng Hospital and National University Hospital in Singapore. An additional set of 8709 participants were recruited from Choa Chu Kang, Jurong, Yishun and Pasir Ris Polyclinics, as well as Changi General Hospital, and Alexandra Hospital in phase 2. Inclusion criteria for the DC were adult Singaporeans and permanent residents (aged 21 years and above) with physician-diagnosed type 2 diabetes. Subjects with mental illness, clinically obvious non-diabetic kidney disease (such as polycystic kidney disease), type 1 diabetes or diabetes mellitus resulting from endocrinopathies were excluded.

Patient recruitment

Recruitment was conducted in the following ways: (1) patients were identified by their attending physicians and referred to a research nurse or identified by a research nurse based on review of medical records and approached directly for recruitment at the clinic or (2) patients were approached by a trained researcher at the waiting area of the clinic for assessment of eligibility and recruitment. Written informed consent was obtained for (1) participation in the study, (2) extraction of information from case notes, (3) linkage to national disease registries and medical records, (4) storage of biospecimens for future research and (5) recontact. After informed consent, each participant completed an interviewer-administered questionnaire and underwent anthropometric assessments by a trained research nurse. Blood and spot urine specimens were collected at the time of recruitment and stored for future analysis. If the patient had already provided a blood specimen for the quarterly diabetes monitoring tests to the clinic prior to the providing consent, the blood specimen would be collected at the patients’ next quarterly diabetes monitoring visit. Participants’ medical record data of the past 5 years were extracted from the site of recruitment by the trained research nurses after enrolment.

Patient and public involvement

Patients or the public were not involved in the design, conduct, reporting or dissemination plans of our research.

Follow-up information

After the baseline recruitment in 2004 and 2010, 410 participants of the DC were selected for a pilot follow-up in 2010 to study the associations between the glycaemic index of their diet with glycaemic control and risk factors for cardiovascular disease. Following the pilot, the first wave of in-person follow-up with the cohort participants began in 2012 and ended in 2016. Of the 14 033 baseline participants, 4677 (33.3%) were not contactable (ie, contact details changed and no updated information available, frequently travelling, or access to household denied and unable to contact despite six attempts at household visitation) and 1058 (7.5%) were confirmed to have been lost to follow-up (ie, deceased, migrated, declined consent for follow-up at baseline, lost mental competence to give consent to continue the research, institutionalised or physically unfit to participate). Of the 8298 contactable participants, 4131 (29.4% of DC and 49.8% of contactable participants) agreed to participate in the follow-up survey. A second wave of in-person follow-up of the DC has been started in 2016 and is expected to be completed in 2021.

Data collection

Table 1 summarises the information collected from DC participants at baseline and follow-up assessment. At recruitment, participants completed an interviewer-administered questionnaire, which took approximately 15–20 min. Questionnaires were available in English, and interviewers provided additional explanation and translation when necessary in other languages common to both the participant and the interviewer. Participants were interviewed about their demographic characteristics, smoking behaviour and personal and family medical history. Height and weight were based on the last recorded values in the medical record file of the participants which would have been measured on the same day or up to 3 months earlier. Waist circumference was measured at the level of the mid-point between the last rib and the iliac crest, with the participant in light expiration following WHO standards. Hip circumference was measured at the level of the greater trochanter of the femur. 17

Summary of variables collected or derived from the cohort

ADL, activities of daily living; EQ-5D, EuroQol quality of life 5 dimensions; FFQ, food frequency questionnaire; MMSE, mini-mental state examination; PSQI, Pittsburgh sleep quality index; SF-12/SF-36, short form health survey - 12 items/ 36 items.

Follow-up of the DC was conducted in tandem with the follow-up of the Singapore Multiethnic Cohort using the same study protocol. 18 During the first follow-up assessment, participants completed a more comprehensive questionnaire that included additional information on environmental tobacco smoke, alcohol consumption, diet, physical activity, medication use, women’s health, health-related quality of life, stress, distress (Kessler Psychological Distress Scale (K10)) and cognition (mini-mental state examination). This was followed by a physical examination to measure height, weight, waist circumference, hip circumference, brachial and ankle blood pressure, central aortic blood pressure, skinfold thickness, resting ECG, hand grip strength and foot sensory function using monofilament and neurothesiometer. Systolic and diastolic blood pressures were measured using an automated digital monitor (Dinamap Carescape V100, General Electronic). A sphygmomanometer (Accoson, UK) was used for participants with blood pressures beyond the range of the digital monitor. Two readings were recorded for each participant, with a third reading if the difference between the first two readings exceeded 10 mm Hg for systolic or 5 mm Hg for diastolic blood pressure, respectively. Central aortic systolic pressure and arterial pulse waveform were measured on the left arm with the participant seated and the left arm resting on a table at chest level using A-PULSE CASPro Lite (HealthSTATS, Singapore). Skinfold thickness was measured by a Holtain/Tanner skinfold calliper at the left triceps, left biceps, subscapular, supra-iliac and calf regions with the participant in a standing position. A resting electrocardiogram (10 leads) was obtained using the ECG-1350K (Nihon Kohden, Japan). A hand dynamometer (TAKEI A5401, Japan) was used for assessing hand grip strength with three readings recorded for each arm. Foot sensory function was assessed with the participant in supine position with bare feet and closed eyes. A 10 g (5.07) monofilament (Sensory Testing System, USA) was used to test light touch on five least calloused plantar sites per foot—the distal great toe, third toe, fifth toe and the first and fifth metatarsal heads. The number of sites that the participants could feel was recorded for each foot. A neurothesiometer (Horwell, UK) was used to assess foot proprioception. A vibration-emitting probe with gradually increased voltage was applied to the apex of the big toe and medial malleolus of both feet, and the voltage reading was recorded when the participants indicated verbally that they could feel the vibration.

During the second follow-up, assessments of workability and activities of daily living were also included in the questionnaire, and measurements of visual acuity, Timed-Up-and-Go and spirometry have been added to the physical examination.

Medical records extraction and data linkage

After recruitment, relevant data were extracted from patients’ medical case notes available at the site of recruitment. Extraction was restricted to a period of up to 5 years before recruitment and was performed by trained research nurses. Fields extracted included records of diagnosis of diabetes, haemoglobin A1c (HbA1c) levels, treatment regimens for diabetes, hypertension diagnosis, blood pressure levels, antihypertensive treatment, lipid values, usage of lipid-lowering agents, diagnosis of microalbuminuria, proteinuria and diabetic nephropathy; age at dialysis initiation, age at renal transplant, serum creatinine levels, urinalysis results, and diagnosis of diabetic retinopathy, cataract, ischaemic heart disease, stroke and limb amputation.

Additional comprehensive medical data on physical and laboratory investigations and medications for the period 2000–2015 were obtained by linkage with the electronic medical record system of the National Healthcare Group (NHG) Polyclinics for participants who ever visited the NHG polyclinics (n=11 721 participants). Extracted data included blood test results (ie, measurements of fasting or random glucose, HbA1c, lipids, creatinine, haemoglobin, urea and uric acid), physical measurements (ie, blood pressure, height and weight), urine test results (ie, albumin, albumin creatinine ratio, protein, protein creatinine ratio, creatinine, cells and formed elements), medication records, clinic visits records and attendance at diabetic food screening and retinal photography. Cohort data have also been linked with the disease registers maintained by the National Registry of Diseases Office to identify the incidence of acute myocardial infarction, stroke, end-stage renal disease, cancer and death.

Biochemical analyses and biobanking

Blood and urine samples were collected from consenting participants at baseline and follow-up. In the first phase of recruitment, approximately 10 mL of blood (fasting or random) and 50 mL of urine were obtained from each consented participant; in the second phase, 15 mL of blood and 20 mL of urine were taken. During follow-up, participants were asked to fast 8–12 hour before their appointment, and up to 23 mL of blood and 6 mL of urine were stored. The samples were aliquoted and stored at −80°C. DNA, buffy coat, plasma, serum and red blood cells were extracted from blood samples and stored separately. The number of biosamples available is summarised in table 2 .

Biosample availability for baseline and first follow-up

*Number of participants with at least one aliquot of sample available. Sample availability was updated on 11 March 2020.

Genome-wide array data are available for a subset of the DC cohort from a combination of Illumina genome-wide genotyping arrays (n=2010) 19 and imputed to 1000G Phase 3 reference panels. In addition, whole exome sequence data are also available for some of the participants as part of the T2D-GENES (Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples) Consortium. 20 21

Participant characteristics

The demographic profile of participants at recruitment is presented in table 3 . The mean age of participants was 59.7±10.7 years and 50.8% were men. The ethnic composition was 59.3% Chinese, 22.7% Malay and 17.3% Indian. The median duration of diabetes in the cohort was 7 years with an IQR of 3 to 14. 68.5%, 10.9% and 9.0% of participants reported a prior diagnosis of hypertension, diabetic retinopathy and diabetic kidney disease, respectively. Approximately half of the participants reported a family history of hypertension (48.7%) and 76.6% reported a family history of diabetes. Participants who have been actively followed up were similar to the overall cohort at recruitment, except for slightly younger age and a greater proportion of those working (online supplementary table 1 ).

Participants characteristics at baseline (n=14 033)

Numbers missing: duration of diabetes (n=927), marital status (n=7), educational status (n=6), occupation status (n=23) and smoking status (n=1).

*Educational status: secondary education (‘O’/‘N’ level), vocational training (attended Institute of Technical Education or obtained National Technical Certificate) and postsecondary education (‘A’ level, polytechnic/diploma).

†Participants were asked whether they had been diagnosed with hypertension, diabetic retinopathy or diabetic kidney disease by Western doctors.

‡Family history was defined as having a history of the condition in parents or siblings.

Supplementary data

Disease characteristics of the study participants at recruitment and the latest visit in medical records are presented in table 4 . Participants had a mean HbA1c of 7.7 ± 1.5% at recruitment and similar levels were observed at their latest visit. There was a slight improvement in diastolic blood pressure (from 77 ± 8.9 mm Hg to 70 ± 9.6 mm Hg), and low-density lipoprotein cholesterol (from 2.9 ± 0.9 mmol/L to 2.4 ± 0.8 mmol/L) between recruitment and last visit. The prevalence of obesity, defined as body mass index ≥ 27.5 kg/m 2 using Asian-specific cut-offs, 22 decreased from 38.0% to 31.3% during this time. The median follow-up duration from recruitment to the last visit recorded was 7.5 years.

Disease profile of study participants at recruitment and the latest visit in medical records

Numbers missing at recruitment: HbA1c (n=1124), total cholesterol (n=6656), triglycerides (n=6659), HDL-C (n=6671), LDL-C (n=6753), eGFR (n=5426), blood pressure (n=440), BMI (n=255). Numbers missing at latest visit: total cholesterol (n=5042), triglycerides (n=5040), HDL-C (n=5040), LDL-C (n=5041), eGFR (n=5058), blood pressure (n=194), BMI (n=3191); most missing values are due to lack of records in extracted data.

*Variables measured within 1-year window from date of recruitment were used.

††date of the Latest Visit Was Defined Using the Date of the Last Hba1c Measurement for Each Subject, and Other Variables Measured Closest to This Date Within 1-year Window Were Used as the Latest Measurement.

‡eGFR was calculated based on the CKD-EPI formula.

BMI, body mass index; CKD-EPI, chronic kidney disease epidemiology collaboration; eGFR, estimated glomerular filtration rate; HbA1c, haemoglobin A1c; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.

Data from the cohort have been used to identify determinants of diabetes and related complications. The longitudinal nature of the cohort and the linkage to a variety of records data have allowed the time trend analyses of diabetes control over time. An examination of determinants of poor glycaemic control in primary care over 5 years identified treatment with insulin at baseline, Malay or Indian ethnicity and presence of retinopathy to be associated with poor glycaemic control. 23 The availability of serial HbA1c values has been used to examine patterns of longitudinal HbA1c control. Four distinct patterns were identified, the largest being a low-stable pattern with mean HbA1c of 7.1% over time, followed by a moderate stable pattern with mean HbA1c of 8.5%, a pattern of deteriorating glycaemic control and one of improving glycaemic control. These patterns were associated with differential risks of late-stage complications and death. 24 The role of diabetes treatment in shaping the HbA1c patterns has also been examined, with findings revealing that treatment by and large matched the extent of dysglycemia, and that HbA1c deterioration occurred in spite of treatment intensification and not due to a lack of intensification. 25

Using biological samples collected from the cohort participants, we have also focused on identifying correlates and markers for diabetes and diabetic kidney disease. As part of the Asian Genetic Epidemiology Network Type 2 Diabetes Consortium, the DC has contributed to identification of T2D-associated genetic loci such as KCNQ1 , 26 East Asian-specific PAX4 27 28 and trans-ethnic SSR1-RREB1 and ARL15 which have been implicated in regulation of fasting insulin and fasting glucose. 29 DC has also contributed cases to replication studies of novel T2D susceptibility loci identified first in European populations or other Asian populations 30 31 as well as to transancestral investigations into the genetic architecture of diabetes. 32 33 More recently, whole exome sequencing analyses across multiple ancestries have identified modest rare-variant associations with T2D. 20 21 In addition to diabetes meta-analyses, the DC has also contributed to large-scale meta-analysis of diabetic kidney disease. 34 35 Other analyses have demonstrated the significant associations of plasma tumor necrosis factor α and its receptors, 17 pentosidine 36 (an advanced glycation end product) and urinary excretion of nephrin 37 with reduced kidney function. Metabolomic analysis of urine samples from DC participants through liquid chromatography–mass spectrometry and gas chromatography–mass spectrometry has identified several metabolites that could potentially serve as markers of non-proteinuric diabetic kidney disease. 38

The complete list of publications based on the DC data is available online ( https://blog.nus.edu.sg/sphs/publications/ ).

Strengths and limitations

The main strengths of the DC are the focus on diabetes, which is one of the biggest public health challenges in this century, the relatively large pool of participants and a follow-up duration of more than a decade. The linkage of cohort data with medical records and disease registries is an important advantage for the cohort as this has allowed the in-depth and longitudinal tracking of several key clinical measures and outcomes in these patients. This linkage has also facilitated the capture of clinical data not only prospectively but also retrospectively before recruitment into the cohort. Another strength is the multiethnic composition of the cohort, representing three major ethnic groups in Asia, and thus allowing the evaluation of interethnic variation in diabetes progression, complication risk and outcomes. The stored biological samples are also an asset of the cohort, making it possible to examine novel and emerging biomarkers and genetic determinants in this population.

This study is not without its limitations. DC is a prevalence cohort, which recruited participants with varying durations of disease, and potentially at different stages of the natural history of disease. Another limitation is the low rate of active follow-up of participants, which has been substantially overcome through the linkage with medical records and disease registries. In spite of these limitations, the cohort continues to yield insights into diabetes and its complications in the Asian context.

Collaboration

We welcome potential collaboration with other researchers. Researchers can visit the Saw Swee Hock School of Public Health website ( https://blog.nus.edu.sg/sphs/ ) for information on submitting a request for data and/or samples.

Supplementary Material

Contributors: KV conceived the present manuscript and prepared the final version for the submission. ML drafted the manuscript. ML, LWLT and MKHN conducted the data analysis. RVD, EST, KSC, WET and DEJS established the cohort and provided intellectual inputs to the manuscript. XS critically revised the manuscript. All authors reviewed and approved the final version of the manuscript.

Funding: The study was funded by grants from the National Medical Research Council (NMRC/0850/2004), Biomedical Research Council of A Star (BMRC/05/1/21/19/425), Ministry of Health, Singapore, National University of Singapore and National University Health System.

Competing interests: None declared.

Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Patient consent for publication: Not required.

Ethics approval: Ethics approval for the DC was provided by the National University of Singapore Institutional Review Board (NUS IRB) and National Health Group Domain Specific Review Board (NHG DSRB).

Provenance and peer review: Not commissioned; externally peer reviewed.

Data availability statement: Data are available upon reasonable request. Researchers can visit the Saw Swee Hock School of Public Health website ( https://blog.nus.edu.sg/sphs/ ) for information on submitting a request for data and/or samples.

Eating Red And Processed Meat—And Even Chicken—Could Increase Risk Of Diabetes, Research Finds

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Consuming meat, particularly red and processed meat, and even poultry like chicken and turkey may increase the risk of developing type 2 diabetes in the future, according to a new study published on Tuesday, adding to growing evidence linking meat and ultra-processed foods to health issues including heart disease, cancer, depression, anxiety and even premature death.

Red meat is associated with a higher risk of type 2 diabetes, researchers found.

Consuming processed meat and unprocessed red meat regularly is associated with a higher risk of developing type 2 diabetes, according to peer reviewed published in The Lancet Diabetes and Endocrinology medical journal.

While previous research has indicated eating more processed meat and unprocessed red meat is linked to a higher risk of type 2 diabetes, the researchers said results have been inconclusive and variable, which has led to confusing and often polarizing debates over whether the foods are safe to eat and, if so, in what quantities.

To assess the link between meat and the risk of type 2 diabetes, the team, led by researchers at the University of Cambridge, analyzed existing data from nearly 2 million people across 31 study groups in 20 countries to see whether their eating habits were associated with a risk of type 2 diabetes when accounting for other factors like age, gender, energy intake, body mass index and health-related behaviors.

Habitually eating 50 grams of processed meat a day—roughly equivalent to two slices of ham—was associated with a 15% higher risk of developing type 2 diabetes in the next 10 years, the researchers found, and consuming 100 grams of unprocessed red meat a day—the equivalent of a small steak—was associated with a 10% higher risk.

Nita Forouhi, a professor of population health and nutrition at the University of Cambridge and a senior author on the paper, said the research “provides the most comprehensive evidence to date” of a link between eating red and processed meat and a higher future risk of type 2 diabetes.

“It supports recommendations to limit the consumption of processed meat and unprocessed red meat to reduce type 2 diabetes cases in the population,” added Forouhi.

Is It Safe To Eat Other Meat Like Chicken And Turkey?

Poultry such as chicken, turkey and duck is often touted as a healthier protein source to red and processed meats. The idea is supported by research, which indicates lower risks for many of the health issues linked to red and processed meat consumption like cancer , heart disease and diabetes , but the issue is a comparative one and it does not mean eating poultry is without risk. Research increasingly indicates regular poultry meat consumption is linked to harmful health effects like gastro-oesophageal reflux disease, gallbladder disease and diabetes. Research on this association is more limited, the researchers noted, taking the opportunity to investigate the potential link as well. They found habitual consumption of 100 grams of poultry a day was associated with an 8% higher risk of developing type 2 diabetes over the next 10 years. However, Forouhi warned the evidence linking poultry consumption and diabetes was much weaker than that for red and processed meat when subjected to further analytical scrutiny. “While our findings provide more comprehensive evidence on the association between poultry consumption and type 2 diabetes than was previously available, the link remains uncertain and needs to be investigated further,” Forouhi said.

Surprising Fact

While often considered a “white meat” alongside poultry like chicken, experts and regulators say pork is a “red meat” like beef, veal and lamb. The U.S. Department of Agriculture says the distinction is determined by the amount of the oxygen-carrying protein myoglobin is in the meat, which determines the color of the meat. Pork is considered red meat because it contains more myoglobin than chicken or fish.

What To Watch For

Growing evidence on the negative health associations of eating different meats has ignited campaigns to limit the consumption of red and processed meat, and sometimes meat in general, as a matter of public health and to reduce the burden of diseases like diabetes. In recent years, this health-driven messaging has been joined by a more climate-focused approach, urging people to limit meat consumption as part of reducing their carbon footprint and tackling the climate crisis. Research has also increasingly identified potential health problems like heart disease and early death linked to ultraprocessed foods, including plant-based ultraprocessed foods .

What We Don’t Know

Most research between food consumption and various health risks are observational in nature. This means causal relationships are very hard to determine. More research—much of which would be difficult or impossible to conduct in humans—is needed to establish causal claims like reducing red meat intake will reduce the risk of developing diabetes.

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The simple sandwich filler linked with a 15% higher risk of type 2 diabetes

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A simple sandwich filler could increase the risk of developing type 2 diabetes by 15 per cent, a study has found.

Data from nearly 2 million people – analysed by a team led by the University of Cambridge – also found that consuming 100 grams of unprocessed red meat a day – equivalent to a small steak – was associated with a 10 per cent higher risk of developing the condition.

The NHS advises those eating more than 90g of red meat such as beef, lamb, mutton, pork, veal, venison and goat, or processed meat such as sausages, bacon, ham, salami and corned beef a day to cut down to 70g or less.

Researchers said that the findings, published in the journal The Lancet Diabetes and Endocrinology , back the recommendations to cut down meat intake.

Senior author Professor Nita Forouhi, of the University of Cambridge’s Medical Research Council (MRC) Epidemiology Unit, said: “Our research provides the most comprehensive evidence to date of an association between eating processed meat and unprocessed red meat and a higher future risk of type 2 diabetes.

“It supports recommendations to limit the consumption of processed meat and unprocessed red meat to reduce type 2 diabetes cases in the population.”

For the study, the researchers analysed data from 31 study cohorts involving 1.97 million people across 20 countries through InterConnect – a project funded by the European Union to understand more about diabetes and obesity across different populations.

They found 50 grams of processed meat a day – equivalent to two slices of ham – was associated with a 15 per cent higher risk of type 2 diabetes in the next 10 years.

But they said the link between eating poultry, such as chicken, turkey, and duck, and type 2 diabetes remains uncertain and needs further investigations.

The researchers said the InterConnect data allowed the team to “more easily account for different factors, such as lifestyle or health behaviours, that may affect the association between meat consumption and diabetes”.

It also included people usually under-represented in scientific research with cohorts from countries in the Middle East, Latin America and South Asia alongside Europe and the US.

Professor Nick Wareham, director of the MRC Epidemiology Unit and a senior author on the paper, said the data “allowed us to provide more concrete evidence of the link between consumption of different types of meat and type 2 diabetes than was previously possible”.

Commenting on the study, experts said that while the research cannot show how or why red and processed meat intake increases the risk of type 2 diabetes, the findings align with the current healthy eating recommendations.

Dr Duane Mellor, dietitian and spokesperson for British Dietetic Association, who was not involved in the study, said: “The overall message to moderate meat intake is in line with national healthy eating guidelines and advice to reduce risk of developing type 2 diabetes, which include eating a diet which is based on vegetables, fruit, nuts, seeds, beans, peas and lentils along with some wholegrain and moderate amounts of meat and dairy with limited amounts of added fat, salt and sugar.

“This should be accompanied by regular physical activity to minimise risk of developing type 2 diabetes.

“If people are considering reducing their meat intake, it is important that the nutrients found in meat are obtained from other foods, these include iron, vitamin B12 and protein.

“It is important when considering reducing or taking a type of food out of the diet, that any replacement foods provide the same nutrients to maintain a healthy diet overall.”

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