research paper on diabetes in india

Advertisement

A Strategic Research Framework for Defeating Diabetes in India: A 21st-Century Agenda

• Review Article
• Published: 21 March 2023
• Volume 103 , pages 33–54, ( 2023 )

Cite this article

• K. M. Venkat Narayan   ORCID: orcid.org/0000-0001-8621-5405 1 , 2 ,
• Jithin Sam Varghese 1 , 2 ,
• Yara S. Beyh 3 ,
• Soura Bhattacharyya 4 ,
• Shweta Khandelwal 5 ,
• Gokul S. Krishnan 6 ,
• Karen R. Siegel 1 , 2 ,
• Tinku Thomas 7 &
• Anura V. Kurpad 8  

3268 Accesses

3 Citations

28 Altmetric

Explore all metrics

Indian people are at high risk for type 2 diabetes (T2DM) even at younger ages and lower body weights. Already 74 million people in India have the disease, and the proportion of those with T2DM is increasing across all strata of society. Unique aspects, related to lower insulin secretion or function, and higher hepatic fat deposition, accompanied by the rise in overweight (related to lifestyle changes) may all be responsible for this unrelenting epidemic of T2DM. Yet, research to understand the causes, pathophysiology, phenotypes, prevention, treatment, and healthcare delivery of T2DM in India seriously lags behind. There are major opportunities for scientific discovery and technological innovation, which if tapped can generate solutions for T2DM relevant to the country’s context and make leading contributions to global science. We analyze the situation of T2DM in India, and present a four-pillar (etiology, precision medicine, implementation research, and health policy) strategic research framework to tackle the challenge. We offer key research questions for each pillar, and identify infrastructure needs. India offers a fertile environment for shifting the paradigm from imprecise late-stage diabetes treatment toward early-stage precision prevention and care. Investing in and leveraging academic and technological infrastructures, across the disciplines of science, engineering, and medicine, can accelerate progress toward a diabetes-free nation.

Similar content being viewed by others

Collaborative research and actions on both sides of the US-Mexico border to counteract type 2 diabetes in people of Mexican origin

Simón Barquera, Dean Schillinger, … Mexico-California Diabetes collaborative group

Feasibility of prevention of type 2 diabetes in low- and middle-income countries

Andre P. Kengne & Ambady Ramachandran

Modifiable and emerging risk factors for type 2 diabetes in Africa: a systematic review and meta-analysis protocol

Ayuba Issaka, Yin Paradies & Christopher Stevenson

Avoid common mistakes on your manuscript.

1 Why is Diabetes Research Important for India?

Within India, 74 million people (of the total population of 1.4 billion) currently have diabetes. This represents 14% of the global burden of diabetes (95% of which is type 2 diabetes [T2DM]), and making India an epicenter for the disease, along with the USA and China. Furthermore, the diabetes epidemic in India is accelerating across all geographic and socioeconomic groups, and it is projected that there will be at least 124 million people with diabetes by 2045. 1 , 2 Yet, India currently contributes to 1% or less of the world’s peer-reviewed diabetes research output. 3 This represents a major potential opportunity for scientific discovery to impact public health, led by researchers across India and their international collaborators.

Opportunities for innovative research into different facets of diabetes in India are immense (Box 1 ). For example, Indians are highly susceptible to T2DM at younger ages and lower body weights relative to other world populations. Several intriguing and understudied factors may drive this unique, heightened risk: first, Indians have a high propensity for innate deficiency in insulin secretion or function and for hepatic fat deposition. 4 Second, the increase in overall prevalence of adult-onset diabetes (from 2% in 1972 to 9% in 2018) across all strata of Indian society (age, gender, socioeconomic, urban/rural) has coincided with rapid economic development and rise in overweight. 5 India’s population is also experiencing dramatic changes in lifestyle and environmental factors—notably: food, nutrition, and activity, air pollution, and stress—but their relative contributions are understudied. 6 , 7 , 8

Research opportunities

Although T2DM is largely a polygenic disease, current genetic knowledge explains only 10–20% of etiological variability in incidence globally. Furthermore, available data are predominantly from European populations, and a recent study indicated that only 22% of diabetes gene associations identified in European populations were replicable in Indians. 9 Unidentified genes and gene–environment interactions and their accompanying epigenetic changes are also likely to be important drivers for the rapid rise in diabetes prevalence across all strata of Indian society.

The emerging question of diabetes phenotypes, and resulting implications for precision medicine and public health, is of great topical interest worldwide. 10 Well-conducted research in Indian populations—in which some phenotypes are more widely prevalent and novel phenotypes may manifest—can provide ground-breaking and widely generalizable findings with global impact. 11 Yet, gradients in risk of complications across phenotypes as the diabetes epidemic evolves, coupled with the vast population diversity, offer contextual opportunities to explore important questions regarding gene–environment interactions in diabetes causation and in the differences manifest in disease phenotypes and pathophysiology.

Given India’s large and growing population, there are opportunities for scalable and affordable high-quality innovations in diabetes technologies, medications, and delivery of care of models tailored to local contexts. Many advances in diabetes prevention and care currently come from high-income countries and are thus cost-prohibitive for large swaths of the Indian population, which may seriously limit the ability of such advances to benefit those living in the Indian subcontinent.

Beyond research on etiology, precision approaches, and technology-enabled innovations, there ought to be emphasis on developing and evaluating cost-effectiveness of policies that enable prevention and better care management. For example, policies that encourage healthy diets (such as supply- or demand-side subsidies) and more physical activity may have implications for both diabetes prevention and control. 12 , 13 Research reports that half of those with diabetes in India are unaware of their condition, more than half of those diagnosed are not currently taking medication, and nearly two-thirds of those diagnosed do not have their diabetes under control, with substantial between-state and within-state disparities. 5 , 14 Innovative national and regional policies, tailored to local settings, along with decentralized monitoring (i.e., precision public health), to improve management of diabetes must become a priority area for research in India. 15

India is thus a fertile environment for shifting the paradigm of diabetes management, away from imprecise late-stage treatment toward precision early-stage prevention and care. In this regard, the environment in India offers notable and unique advantages to accelerate scientific progress. The country has strong foundations and skilled personnel in information technology, bioinformatics, biotechnology, engineering, and analytics, and reputable industries specializing in affordable technologies and scaling challenges. 16 Previously, India has leveraged these resources in areas such as vaccine research and development, and the potential for similar interdisciplinary work in diabetes and related diseases is enormous. 17 , 18

2 A Strategic Research Framework for Diabetes in India

In the section below, we describe four thematic pillars that can form a strategic research framework for defeating diabetes in India: etiology, precision medicine, implementation research, and health policy (See Fig.  1 ). We also provide suggested key research questions for each pillar (Table 1 ), as well as infrastructure needs and improvements for each.

Four thematic pillars of the strategic framework.

2.1 Pillar 1: Etiology

As described above, the reasons for rise in diabetes prevalence across India is likely multifactorial (Fig.  2 )—a combination of genetics and environment, and coincides with dramatic shifts in lifestyle due accompanying economic development, namely, wide availability of processed foods high in refined carbohydrates, fat, and sugar; mechanized transport labor; and increase in sedentary behaviors, driven by television and digital technologies. 1 , 19 In combination, these changes in food and in activity lead to an imbalance in energy metabolism, thus increasing the risk of non-communicable diseases, such as diabetes. 6 These are modifiable factors that could be tackled with the appropriate actions in individual, societal, and policy arenas; yet, more research is needed on how to facilitate effective modification across multiple levels. Further, India is known for its substantial diversity in terms of culture, languages, faiths, and socioeconomic groups; hence, a diversity of factors—varying from social norms to gut microbiota, which can influence the function of the host’s metabolic phenotype—offer opportunities for investigation. Several large and complex questions remain: What is the role of the environment in dictating the mechanism of action in diabetes? How do gene–environment interactions increase susceptibility of Indians to diabetes? Do microbiota, pollution, or other environmental toxins alter biochemical or biological pathways in the body increase the risk of diabetes?

Research framework for diabetes etiology. The figure above (modified from Narayan and Kanaya, 2020) proposes a pathophysiological pathway connecting both common causes leading to diabetes in Asian Indians. The natural evolution leads to the poor beta cell function and hence an impaired insulin secretion; while the intrahepatic and intramuscular fat accumulation caused by lifestyle and inadequate body composition leads to impaired insulin action. Further, the diagram summarizes the proposed areas for research to better understand the etiology of diabetes in Asian Indians to curate a more targeted treatment and prevention.

Despite the established overweight/obesity link with diabetes, substantial data show increasing occurrence of T2DM in Indians at low body mass indices and young ages. Some have postulated the thin-fat Indian phenotype, characterized by small body size and thin lower limbs but high visceral fat, to cause metabolic obesity. 4 However, recent data from several independent sources have offered additional potential but unexplored explanations for the risk of T2DM in Indians, especially, in non-obese and young people. Studies comparing diabetes pathophysiology in Indians with that of the Pima Indians of Arizona—two high-risk populations differing substantially in body weight—have suggested that innate defects in insulin secretion may be the primary driver in Indians, while insulin resistance may be the dominant influence in Pima Indians, suggesting different diabetes phenotypes. 21 , 22 In fact, Indian men 20–44 years old with a body mass index (BMI) < 25 kg/m 2 have a five-fold incidence of diabetes compared to Pima Indians, while those with a BMI > 30 kg/m 2 have approximately the same incidence as Pima Indians. 21 One study showed Pima Indians have four-fold greater insulin resistance compared to Indians, while Indians secreted half to a third of insulin than Pima Indians. 22 Further, studies comparing T2DM among Europeans (the UK and the Netherlands) to Indians have shown that Indians had lower insulin secretion, lower beta cell reserves, and poorer beta cell adaptation. 4

The notion of multiple phenotypes has gained popularity with time. Intriguingly, the frequency of insulin-deficient phenotypes is far more common in Indians than in other ethnic populations. 11 , 23 , 24 In an investigation of young onset T2DM (< 45 years of age) in Pune, India, 53% were of the insulin-deficient phenotype. 25 Furthermore, recent meta-analysis on diabetes phenotypes reported that Asians in general (including Indians) may have poorer insulin secretion across all reported phenotypes. 26 , 27

Understanding the distribution of fat in the abdominal area may be critical to understanding the etiology of diabetes in Indians. The main culprit may be hepatic fat—fat accumulated inside the liver—which has been shown to be higher in Indians compared to Europeans and Pima Indians. 21 One possible explanation to this increased hepatic fat is the high intake of carbohydrates and high concentration of saturated fat in traditional Indian foods. 28 Indian carbohydrate intake that traditionally comprised healthier cereals (millets) is now predominantly from rice and wheat, with an increasing intake of sugar (free sugar as well as from beverages and desserts), often reinforced by government subsidies and child feeding programs. 29 , 30 , 31 Moreover, Indian diets are low in high-quality protein. 32 Very few studies have examined the association between hepatic fat and the risk of diabetes, 33 , 34 although the Chennai Urban Rural Epidemiology (CURES) study found that hepatic fat was associated with progression from pre-diabetes to diabetes. 35 As recent data suggest that Indians are at high risk of hepatic fat deposition, and overall visceral adiposity is higher in Indians than in Europeans, 36 , 37 suitable methods are needed to clearly discriminate between ectopic, visceral, and hepatic fat deposition—opening avenues for imaging and artificial intelligence research.

Data also point to low muscle mass in Indians. 36 , 38 Some have postulated that the increased risk of diabetes is related to long-term intergenerational influences that inflicted negative effects on women of childbearing age and their offspring during early childhood, causing a diminished metabolic capacity. 39 The latter is reflected as short stature, lower lean muscle mass, and increased risk to diabetes due to poor pancreatic development. 38 , 40 Lean muscle mass plays an important role in glucose metabolism, as skeletal muscle is responsible for the clearance, uptake, and metabolism for the majority of glucose in the circulation. Furthermore, Indians have, on average, a diminished beta cell capacity and lower insulin secretion, compared to European and most other populations. 41 Hence, peripheral insulin resistance could cause beta cell exhaustion over time, which has greater implications when insulin secretion is not optimal at baseline. 4 It may be that the nexus of innate defects in insulin secretion, ectopic hepatic and pancreatic and muscle fat deposition, along with low lean muscle mass conspire in an evolutionarily susceptible population to drive high diabetes risk.

This warrants a closer look at pancreatic islet cells, requiring advances in imaging technologies and creation of pancreatic biobanks. Imaging of the pancreatic islet cells can assess the existence of apoptosis or differentiation to aid in diabetes prevention or treatment or enable a precision medicine approach. In fact, both magnetic resonance imaging (MRI) and computed tomography (CT) have been widely used to diagnose pancreatic tissue heterogeneity and fat distribution and accumulation in various pancreatic disorders. 42 Several studies have favored MRI over CT, as MRI has been shown to be very useful in the identification of internal lesions and inflammation in the pancreas specifically, such as in pancreatic cancer, which gives insight to cell functionality. 42 Further, MRI can help assess tissue infiltration, namely ectopic fat, which helps assess progression toward diabetes or reversal after treatment. 43 CT has been shown to be useful in identifying cell heterogeneity and fat distribution, although to a lesser extent than MRI. 43 Hence, it is worth further investigating both methods to adopt the best practices for assessing diabetes development and prevention in Indians. Additionally, the creation of a pancreatic and organ biobank could be a major resource for diabetes research, as it would offer ways to identify the fundamental causes of diabetes, as well as cancers and genetic disorders. For instance, a recent study utilized the UK biobank to predict risk of pancreatic cancer among those with T2DM. 44

Genome-wide association studies (GWAS) have garnered attention from a large number of researchers interested in understanding the genetic etiology of diabetes. The most recent and largest GWAS studies included 228,499 T2DM cases and 1,178,783 controls from five ancestral groups and reported 568 associations and 318 novel risk loci for T2DM, 45 and over the last decades, GWAS studies have identified over 700 genetic variants associated with T2DM. 45 One study identified six variants ( GRB14 , ST6GAL1 , VPS26A , HMG20A , P3S2 , and HNF4A) unique to the South Asian population, also noting their absence in European populations 46 . However, the large number of currently known variants explains less than 20% of the cases of diabetes, leaving unanswered questions as to how to explain the remaining 80%, especially with recent studies suggesting data-driven subtypes of newly diagnosed T2DM that require different treatments and having different risks of complications. 47 Furthermore, where do genes stop and epigenetics takes over? What are the differences in gene expressions in Indians compared to Pima Indians, Europeans, and other races/ethnicities? 48 How do we understand the mechanistic and cellular actions of the variants discovered by GWAS?

Answering these complex questions requires the integration of multi-omics modalities, including metabolomics, proteomics, transcriptomics, and metagenomics, driven by recent advances in technology. For instance, transcriptomics and proteomics are essential to map gene expression, which helps understand transcriptional and post-transcriptional regulation. 49 Further, it is important to complement such studies with other methods such as single-cell RNA sequencing, a novel approach that provides insights on cellular mechanisms at the epigenetic level. Single-cell RNA sequencing can reveal complex and rare cell populations, uncover regulatory relationships between genes, and track trajectories of distinct cell lineages in development. 50

In summary, it is important to channel resources to answer crucial scientific questions with interdisciplinary research teams across the spectrum of basic, population, and clinical research, as well as policy sciences and engineering. Examples of research questions may include: Why are Indians innately more insulin-deficient on average than other ethnic groups, what upstream factors are involved (e.g., intergenerational influences, genetics, epigenetics, nutrition, and environmental toxins), and how can approaches to prevention, diagnosis, and treatment be tailored for different diabetes subtypes that may be present in the population? Why are Indians more prone to ectopic fat deposition and low lean muscle mass than Europeans? How do genes, lifestyle, and environmental factors interact in a rapidly transitioning society to manifest differences in diabetes phenotypes and pathophysiology? This integration of different scientific disciplines will require funding and infrastructure to: (1) develop new imaging techniques to understand baseline function of the pancreas and fat distribution; (2) integrate multi-omics modalities to understand the etiology of diabetes; (3) create organ biobanks (e.g., pancreas, liver, muscle); and (4) build and retain diverse deep phenotype cohorts to facilitate clinical and translational studies.

2.2 Pillar 2: Precision Medicine

Precision medicine and prevention involves identifying subgroups for whom some particular sets or sequence of interventions, either preventative or therapeutic, may be more effective. 51 Personalized medicine is similar, but we disambiguate it as the clinical translation of precision medicine after considering each patient’s clinical and socio-demographic profile. Current approaches to precision medicine for diabetes mainly involve diagnosis and treatment. Recently, researchers characterized subgroups of diabetes beyond existing classifications such as type 1 diabetes (T1DM), type 2 diabetes (T2DM), latent auto-immune diabetes in adults (LADA), and maturity onset diabetes of the young (MODY). T1DM and T2DM are complex polygenic diseases, while MODY refers to types of monogenic diabetes (with an unknown prevalence among Indians). 52 An important advancement in precision treatment was the identification of differential treatment responses of monogenic forms of diabetes. For example, MODY 1, 3, and 12 are responsive to sulfonylureas (hypoglycemic agents), while MODY 2 may not require treatment. 51 , 52 However, research on prevention on an individual- or subgroup-level, and understanding the cost-effectiveness of current precision medicine strategies to improve diabetes care, are lacking in India.

Precision medicine for diabetes may involve population segmentation for prevention, diagnosis of disease, treatment choice, and prognosis of complications, with tailored monitoring (using wearables and smartphones) incorporating anticipated cost–benefits and risk of complications (Fig.  3 ). 10 Data modalities typically used for such segmentation in diabetes include both routinely collected clinical data (laboratory and anthropometry) and deep phenotyping data (biological samples for multi-omics, sensor data to monitor health-related behaviors, and sociodemographic factors). Recently, data-driven subtypes of newly diagnosed T2DM were identified in Indian populations using routine clinical data. 11 , 53 Among these novel subtypes, those characterized by insulin deficiency constituted a higher proportion of newly diagnosed T2DM in Indians than among their European counterparts. 27 Beyond clinical and biological characteristics, environmental exposures (such as air pollution and enteric pathogens) may have moderating influences. These factors must be integrated in precision medicine research, 54 or studies may end up with poor generalizability from unmeasured effect modification. 55 There is also growing interest in studying psychosocial phenotypes, using cognitive and behavioral attributes of the patient, to tailor interventions for long-term behavioral change and adherence to interventions. 56 , 57

Research framework for precision medicine.

Collaborations between clinicians, epidemiologists, computer scientists, and statisticians to pursue approaches, such as multi-modal data integration and population segmentation, may be useful to characterize phenotypes for prevention, diagnosis, treatment, and prognosis. 58 Data fusion techniques to combine heterogeneous data from various sources, coupled with data analytics and machine learning algorithms, have significant potential to capture patterns and nuances in the phenotypes that may not be visible in the initial raw data extraction phase. 59 , 60 This would enable stratification of patients and could extend the paradigm of precision medicine and prevention for diabetes. For instance, unsupervised data mining or machine learning techniques, including deep learning, such as clustering and auto-encoders, can be used to learn features as a basis for building effective clinical decision support systems. 61 However, this requires assessing model performance in external datasets and validating explainable aspects before implementing the model in clinical settings. 62 , 63 Going forward, interpretable (and explainable) artificial intelligence, and causal inference approaches have important roles to play. 64 , 65 , 66

By its very nature, precision medicine initiatives must be localized and their benefits be made available to the population on which the studies were based. 67 This implies either calibrating existing models for local settings using secondary data or developing models for prevention, diagnosis, treatment and prognostics using primary data. 68 , 69 , 70 , 71 A research agenda for India should consist of both these approaches.

Results from studies using secondary data for precision medicine face challenges of causal inference from disparate data sources via data fusion methods, which then need to be validated via randomized trials or additional high-quality observational studies. First, data fusion is expected to be hard in practice in India, where data integration concerns and challenges are prevalent at organizational, departmental, and even household levels. For example, studies of mobility and user interaction with mobile applications may present issues when multiple users in a family share a smartphone. Second, analysis of electronic health records (EHRs) from different care providers present challenges, such as harmonization of variables, varying laboratory standards, missing data, and fragmentation of care. 72 , 73 Third, integration of health records and other sensitive information across providers present not only the aforementioned implementation challenges but also concerns of data security and privacy. 74 , 75 The National Health Stack (NHS), proposed in September 2021 as a part of the National Digital Health Mission (NDHM), is a significant initial step toward a solution to address some of these issues in India. 16 The NHS aims to provide unique person-level health identifiers and an application programming interface (API) for submission and retrieval of sensitive health data to authenticated providers. This initiative presents opportunities for large-scale clinical data research, such as developing prediction algorithms for diagnosis, treatment response, and prognosis. However, concerns with storing sensitive health data remain, such as standards for anonymization, data migration from legacy systems, cybersecurity and fraud control risks, and must be abated.

The primary data approach is an expensive endeavor that should be supported by a mix of government and industry funding. The U.S. government is launching an ambitious high-throughput research program for healthcare as part of the Advanced Research Projects Agency for Health (ARPA-H). 76 This initiative is designed along the lines of the Defense Advanced Research Projects Agency (DARPA) that gave rise to technologies, such as the internet, global positioning system (GPS), and micro-electro-mechanical system (MEMS) sensors. 77 Recent initiatives of industry–academia collaborations, such as the Innovative Medicines Initiative in Europe ($5B), the Accelerating Medicines Partnership in the U.S. ($215M), and the Precision Medicine Initiative in China ($9.2B), are parts of larger strategies with expected long-term benefits. 78 , 79 , 80 These initiatives invest in basic science and biomedical research capabilities in universities. India could leverage its strength in pharmaceuticals and information technology by undertaking a similar initiative. For example, India’s National Health Mission, in partnership with a health-focused non-profit (Piramal Swasthya) and a technology services company (Cisco), is implementing an integrated health technology platform for early diagnosis of chronic disease and high-risk pregnancy. 81 Moreover, partnering with technology companies may add another dimension—user behavior—to tailor precision medicine interventions, that is presently underexplored except by companies like Google (FitBit + Android), Amazon, and Apple.

India experiences high levels of undernutrition and poverty, and previous research has shown how early life undernutrition may confer higher risk of cardio-metabolic disease in adulthood. 39 , 40 As a result, there is a constant struggle to balance the priorities of the most impoverished billion with initiatives to tailor care for the more affluent 400 million who are at higher risk of diabetes. 82 It is important to replicate successful research models from other countries and tailor these for Indian settings. One potential approach may be to provide “freemium” services (free with fewer features than paid services) to government health facilities to facilitate digital inclusion with respect to both data and solutions. As internet connectivity has grown rapidly across the country, there is a tremendous opportunity to facilitate further digital onboarding by investing in Health Information Management Systems (HIMS) nationally and identify trained personnel to support this initiative. 83 That India’s young population is digitally adept provides an added benefit. Initiatives such as the Deen Dayal Upadhyaya-Grameen Kaushalya Yojana (part of the National Rural Livelihood Mission) as well as National Skills Development Mission, which upskill young Indians, could facilitate this process.

Representative sampling of the national population, and oversampling of those at higher risk, for biobanks may provide long-term benefits in prevention and management of diabetes. Such initiatives may present opportunities to design tailored prevention strategies (precision prevention). At a global level, there is increasing recognition of ethnic variability in predisposition to diabetes. A recent study reported that only 22% of the loci in Europeans were replicated and only 33% (9 out of 27) of causal genetic loci were shared by those of South Asian (Pakistani and Bangladeshi) descent. 9 India has experienced high geographic genetic isolation for the last 1900 years, with inter-caste marriages continuing to occur infrequently. 84 , 85 Moreover, there is substantial variability in environmental exposures, providing opportunities to study gene–environment interactions. 86 , 87 Therefore, a precision medicine approach that would benefit the nation as a whole must incorporate the geographic, ethnic, and socio-demographic diversity that characterizes India.

2.3 Pillar 3: Implementation Research

In 2020, 700,000 Indians died of diabetes, hyperglycemia, kidney disease, or other complications of diabetes. At-home, community-based, and primary care facilities for early diagnosis, appropriate care, and ethical treatment have not grown to address diabetes early and adequately, whereas tertiary care services dominated by procedure-driven care have grown exponentially. The result is detection at a late stage and treatment at a very high cost, often unaffordable to much of the Indian population, both in terms of money and mortality. To access diagnosis and detection services, patients have to go to established hospitals, lose wages, and incur out-of-pocket expenses, which disproportionately affects the most vulnerable in the society and consequently, results in neglect at initial stages of the disease.

As early identification of those at risk reduces lifetime treatment costs, 88 there is a pressing need for affordable solutions that both individuals and communities can access regularly for timely screening and diagnosis. 89 , 90 , 91 , 92 , 93 , 94 The paradigm of diabetes care delivery in India has traditionally been provider-centric; however, given the magnitude of the diabetes epidemic and long-term need for management, a shift to a patient-centric paradigm is needed. This requires both process and technology innovations, targeting patients and providers.

Frontline health workers (FLHWs) are often the target of new digital initiatives, but are chronically underserved among providers 95 , 96 , 97 Consider the wave of mobile applications targeting FLHWs, deployed for data collection or decision support, with the hope of systemic benefits. 98 Systemic benefits, however, have to be accompanied by value for the user. 99 From the user’s perspective, immediate benefits gained from using the application often do not outweigh the effort required to do so. Care providers accustomed to paper records find it hard to transition to an English language keyboard or handheld devices. 100 Although the latter offers benefits of data storage and retrieval for a trained provider, the former does not run out of battery or memory, and crash from unforeseen technical bugs in the middle of a patient interaction. Hence, digital transformations not only need to minimize costs of transition, but also provide benefits that paper cannot provide. 101 By exploring the dimensions on which digital interventions offer markedly greater value than paper, we can deliver on the promise of technology. 102 We propose five areas for exploration to improve the value generated by digital initiatives targeting FLHWs (Fig.  4 ): voice, direct benefit transfers, geolocation, task streamlining, and digital systems for aggregation of data from both patients and providers.

Research framework for implementation research.

2.3.1 Voice

Voice may be useful for both data capture and communication. Can we use voice as the fundamental data entry method for a community health app? WhatsApp’s voice messaging system sends billions of messages a day, breaking barriers of both language and literacy. Visualize a future where a community health app “gets out of the way” and quietly listens to the interactions between an FLHW and a patient. The FLHW looks at the patient, not at their phone, and the app works in the background—where it generates a transcript, fills forms, provides cues to the FLHW, helps refer the patient to primary care facilities, and triggers reports for administrators to consume.

2.3.2 Direct Benefit Transfers

Direct benefit transfers can provide immediate and secure access to funds for FLHWs, who often have to wait for months’ before receiving compensation for their fieldwork. Reports (on paper) have to wind their way through the administrative system before they are approved and processed. Can we design a transparent payment system that leverages the UPI infrastructure, and interfaces between traditional government systems and FLHWs?

2.3.3 Geolocation

Geolocation or the ability to track ‘where’ and ‘when’ is now possible with a smartphone. Can this geolocation data be used to develop an understanding of how location (and consequently food habits, occupations, movement and migration patterns) correlate with health indices? Geolocation has to be managed judiciously, with strong privacy controls that only allow information to be reported in aggregated form.

2.3.4 Task Streamlining

Task streamlining is the idea that beyond digitization of the provider experience, there needs to be a greater emphasis on improving patient agency. Greater agency for patients can be viewed from Amartya Sen’s capabilities approach by deconstructed into two steps: (1) knowing what to do (information) and (2) having the means to do it (resources). The third step—taking action (or functioning)—follows attainment of agency. “Knowing what to do” requires access to the right information at the right time. COVID-19 demonstrated that traditional and digital media could be harnessed to emphasize simple, but actionable messages. However, there ought to be further research into how tailored communication for diabetes management, a more long duration intervention, should be operationalized. Unless we frame diabetes as the pandemic that it is, communication may not match the urgency that the situation demands. “Having the means” has to be driven by cost reduction, since the majority of cost of care continues to be out-of-pocket (> 60%). 103 (p. 202), 104 India has attained substantial progress in reducing glucometer strip costs from Rs. 22.7 (in 2016) to Rs. 13.6 (in 2022), a 63% reduction in real terms. 105 (p. 1), 106 Similar focus on cost reduction and ease-of-use is required for other tests like HbA1c, and essential medication like insulin. 107

2.3.5 Digital Systems for Data Aggregation

Finally, digital systems enable us to combine information that has traditionally been siloed—such data self-reported by patients, observed and recorded by FLHWs, and captured in a healthcare facility during treatment. While data from healthcare facilities has high fidelity, it has low coverage. Conversely, self-reported data may be low fidelity, but has wide coverage. Integrating hitherto disparate data sources gives us the ability to combine scale with precision. A powerful example of such an initiative is the use of smartphone accelerometers to detect earthquakes and alert users. 108 Though far less precise than seismographs, a large network of devices has the potential to eliminate localized noise, and provide alerts at little to no incremental expense. Can similar models be developed for healthcare, where patients and providers contribute to a common pool, and gain more than they invest?

Furthermore, how can such a paradigm help up prioritize disease surveillance, and help identify which communities are at higher risk? A localized approach that examines the cascades of care (screening, diagnosis, treatment, control) may provide some insights 5 , 14 , 109 . Infectious diseases have a location-based risk paradigm (tuberculosis or Cholera). Are there similar paradigms for diabetes, such as risk clustering within families or communities, which can help optimize surveillance efforts? 110

Use of self-reported data requires intermediaries to guarantee a strong layer of data security and privacy. Contributors must have the ability to restrict access and remove their data, similar to the framework proposed by the National Digital Health Mission. 16 Such a system should either be enabled by trusted intermediaries (for instance, banks in the case of financial transactions), or by removing intermediaries altogether. The latter approach has been attempted using public block-chains in Syrian refugee camps set up in Jordan, which has enabled identity information to be isolated from (and made inaccessible to) authentication systems. Another key issue in any large-scale public health intervention is interoperability. The adoption of FHIR as a standard for data exchange can greatly increase information motility. These and many other technological developments may pave the way for better delivery of diabetes care.

2.4 Pillar 4: Health Policy

While health services research and quality of care improvement frameworks can help to address important aspects of patient self-management of diabetes, equally important is creating an environment in India that promotes health and well-being to facilitate T2DM prevention. 111 Policies and practices relevant for T2DM prevention can include those promoting good nutrition through (a) improved availability and affordability of healthy foods, such as agricultural policies to encourage production of healthier crops; (b) improved distribution of, and access to, foods that encourage proper nutrition for all, in terms of caloric and macro- and micronutrient needs; (c) ensuring sustainable production practices that consider both human and planetary health; and (d) nutrition-labeling regulation to ensure the population is empowered with the tools to choose healthier foods and promotion of increased physical activity through urban planning policies to ensure walkable, inclusive cities and other initiatives. In this section, we focus on these policies that lie outside of the healthcare sector (Fig.  5 ).

Research framework for health policy.

Currently, India’s National Health Policy (adopted in 2017) aims to attain the highest possible level of good health and well-being for the entire population through preventive and promotive health care orientation in all developmental policies, with universal access to good quality, affordable health care services. 112 A specific goal is to reduce premature mortality from cardio-metabolic diseases by 25% by 2025. To accomplish this, the Indian government launched the National Program for Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases, and Stroke (NPCDCS). 113 The objectives are to: (1) prevent and control common NCDs through behavior and lifestyle changes; (2) provide early diagnosis and management of common NCDs; (3) build capacity at various levels of health care for prevention, diagnosis, and treatment of common NCDs; (4) train human resources within the public health system; and (5) develop capacity for palliative and rehabilitative care. Although the choice to eat healthy foods or to increase physical activity is made at the individual level, there is also responsibility at a country level to encourage these behaviors. The NPCDCS can be more effective in this regard with an additional objective to create and redesign environments to prevent T2DM, and by conducting a needs assessment to determine what gaps currently exist in this space.

As a first step, an inventory of relevant policies and programs that currently exist in India and that could be leveraged for creating health-promoting environments must be created. Such an inventory could also help to identify potential gaps and/or duplication of efforts across departments and sectors. In a previous study that could provide a roadmap for such inventories, researchers analyzed the policy environment related to fruit and vegetable supply in India. The analysis included 29 policies across the supply chain (including production, distribution, processing, retailing, and demand in consumer level) and 55 interviews with stakeholders at the national and state levels. 114 Results suggested growing government support for agriculture over the past decade, with an increasingly diversified fruit and vegetable supply chain. The study also identified the need to better integrate nutrition and improve coordination across stakeholders, especially at central government and state levels, to avoid duplication of efforts. 115 Specific strategies for improving consumers’ external food environment for fruit and vegetables in India included development of strategic public–private partnerships to increase access to diverse expertise across the supply chain; linking health, economic, and agricultural policy agendas; strengthening surveillance of policy impacts on consumer access to fruit and vegetables; and leveraging public health actors to advocate for ‘consumer oriented’ fruit and vegetable supply policies. This study demonstrated the usefulness of ‘policy learning’-oriented qualitative policy analysis in identifying ‘points of entry’ for food policy change and research, and extended the understanding of political dynamics in engendering agricultural policy change for nutrition. 116 Similar studies could be performed for other sectors of the food supply chain (grains, legumes, meat, and dairy), and implementation research can help to identify ways to address gaps or insufficiencies.

In addition to an inventory of existing policies, research into how to build upon and improve pre-existing policies is also needed. For example, the government’s Integrated Child Development Services (ICDS) program provides nutritional meals, preschool education, and primary healthcare (including immunizations, health check-ups, and referral services) to children under six years of age and their mothers. 117 The program also provides rations for all pregnant women; however, these rations are given without any consideration of the mother’s risk for gestational diabetes or other high-risk conditions. 118 Moreover, the rations are typically cereal-based and include more calories than are needed. No screening is currently in place to suggest discretionary use of dry rations for a woman with diabetes or prediabetes compared to a healthy woman without diabetes; this is an important area for future research and one that could help to address inter-generational causes of T2DM.

One current major obstacle to healthy food availability and safety is the large amount of food loss and waste at all levels of the food chain. Globally, 40–50% of root crops, fruits, and vegetables are wasted each year, perishing during harvest, storage, transport, packaging, and distribution. 119 In India’s warm climate, where fruits and vegetables are prone to spoiling before reaching their market destinations, the lack of adequate distribution systems may lead to supply-side wastage and disincentives for their production—in fact, United Nations Environment Programme estimates 50 kg of food wasted per capita per year in India. 120 A sustainable solution to the lack of cold chains that can link producers to markets in India is urgently needed and could potentially prevent up to one-quarter of food waste. 121 This is an area in which clean technology, powered by renewable energy, could help tremendously—again highlighting the need for cross-sector partnerships. Local distribution systems can also play a role here, as well as initiatives to bolster local food systems through community gardens. Some of the authors were working with the Karnataka State Horticultural Department and Panchayat Raj Department to create such an initiative, and estimate that it could have the potential to provide up to 50 g of vegetables to each child per day (or 1 kg/day to an Anganwadi that serves 20 children). Current challenges to implementation include working across multiple sectors and departments, as well as securing land, seeds, fertilizers, organic pesticides, and labor.

Greater investments in research aimed at reducing production costs for fruits, vegetables, and other healthy crops could greatly benefit population health by helping to lower prices and make these foods more accessible to the populations that need them. While this is a long-term endeavor, shorter- or intermediate-term research can examine ways to improve availability, such as direct cash transfers. Ongoing research in Bihar, India is examining whether direct bank transfers mitigate the impacts of food insecurity (specifically in the context of the COVID pandemic) on household food quality (consumption of foods like pulses, green leafy vegetables, and milk) and modeling the potential impact of such cash transfers on stunting in India. 90 , 91 This is an area of research with large potential for expansion. For example, what would be the impact of these cash transfers in combination with nutrition education?

The cost-effectiveness of nutrition interventions also remains a relatively understudied area, both in India and globally. In the India-specific context, a better understanding is needed to determine whether provision of fruits and vegetables in meal schemes for children is cost-effective. For example, is an approach to provide more vegetables and fruits through the Integrated Child Development Scheme (ICDS) and the Mid-Day Meal program cost-effective in terms of the resulting health benefits, including adequate growth? Ongoing efforts with the Karnataka government ICDS to provide an additional 50 g ration of fruit and vegetables plus 100 ml of milk per day per child (including encouraging the Panchayati Raj Department to provide funding for additional high-quality foods) has a very high cost of four rupees per day, 50% more than is currently spent. Partnerships between agriculture, education, health, and finance sectors must address such questions jointly to provide cost-effective nutrition health solutions.

Policies to promote physical activity among the population are equally important as those promoting better nutrition. Physical activity policies may include providing support for comprehensive physical activity programs in schools or even requiring physical activity education in schools as part of a comprehensive health education curriculum to support an active lifestyle. 124 Educating the next generation of children can help to create a cultural shift that understands and views these issues as urgent. Additionally, and of particular importance, are research and efforts to design cities and towns that are conducive to physical activity. Without suitable policies and infrastructure to manage India’s recent urban population growth, urban sprawl and traffic congestion has occurred, made worse by inadequate quality and safety of public transport and non-motorized transport infrastructure. Pedestrians and cyclists are most vulnerable to poor urban planning; pedestrians account for more than 40% of road traffic fatalities in New Delhi. In most Indian cities, barely 30% of streets have pedestrian pathways, and even where they do exist, they are often taken over for parking or driving. There are encouraging signs of improvement, however: Chennai has drafted non-motorized transport policies to create footpaths along 80% of its streets, along with infrastructure such as cycle lanes, with the promise that they will begin prioritizing people over vehicles. 125 Research evaluating the impact of such initiatives, as well as how to implement similar initiatives in other Indian cities, is needed. This could include experimental research to examine how the initiatives impact behavior (walking and cycling over driving), community engagement, and ultimately health. Community-engaged research could also uncover remaining obstacles/barriers to successful implementation, enabling such initiatives to fully realize their potential.

While many of the policies mentioned in this section are universal and are being implemented in countries around the world, there are unique features to implementation in the Indian context. For example, 176 million (13.4%) of India’s population lives in poverty. 126 Designing cities and societies in which health-promoting attributes — whether these are screening initiatives, access to medicines, access to healthier food options, or safer walking paths—are either low-cost or free is essential. Additionally, the adverse effects of malnutrition in utero (due to a high burden of maternal malnutrition) means that metabolic conditions appear in younger ages in those exposed to malnutrition in utero. 20 This also has implications for screening and prevention services, which need to start at younger ages in India, and underscores the importance of health promotion across policies and sectors. 127

3 Infrastructure Needs for Diabetes Research

Infrastructure needs for diabetes research in India should support deep dive physiological and molecular mechanistic research with animal and human models, tissue (like cadaveric islet, liver, and adipose tissue) biobanks, and the development of well-phenotyped cohorts that allow for developing risk models and disease progression. The needs can be summarized as four inter-related “C’s”- cohorts, collaboration, capacity building, and community engagement and empowerment.

3.1 Establishing Cohorts for Research and Learning

India has always had an excellent clinical infrastructure (and patient load) to conduct high-quality clinical trials, validating approaches toward clinical care of established patients. However, these trials should move toward prevention at an early stage, perhaps even in childhood, and evolve toward a paradigm of precision in care and prevention across the lifespan. This will require the careful building of cohorts. Early age cohorts may be a good place to start, as the finding of an extraordinary burden of chronic disease biomarkers in a cross-sectional survey of Indian children between 5 and 19 years, including those who were underweight or stunted, suggests that the rapid increase in diabetes in the Indian population has its origins in early childhood. 20 This includes establishing representative and large cohorts of normal young individuals to understand the natural history of beta cell function, risk factors for development of diabetes, and development of diabetes-related complications. One can go further with nested trials to evaluate risk reduction efforts to prevent the development of insulin resistance and the deterioration of beta cell function. Cohorts of individuals (young and older) with and without existing diabetes and prediabetes are also required to understand the clinical course of the disease in Indian populations, which will also shape treatment outcomes. The last will also require nested trials of different risk reduction efforts and treatments for established disease, with long-term follow-up of these trial participants. As a starting point, existing Indian cohorts should be identified and collated, such that cross-cohort analyses are possible, simultaneously resulting in the creation of a network of investigators.

3.2 Collaborative Skills and Approaches in Sciences, Humanities, Engineering, and Precision Medicine

Beyond etiology, both public health and implementation science would benefit from interdisciplinary research, such as learning systems deployed in healthcare settings, and novel technologies for integrated care of comorbidities. Precision is fast becoming a buzzword, related to both clinical care and public health. Collaboration with engineering sub-disciplines, such as biomedical engineering and computer science, to produce wearables, develop artificial intelligence systems and validate predictive algorithms requires infrastructure to support computing needs and engineering R&D. A more ambitious infrastructure need relates to the development of biobanks, which would exist with the population cohorts as well as with human cadaveric islet banks, to enable molecular investigations to understand mechanisms of development of functional deterioration of beta cell function. Other banked tissues could include liver, skeletal muscle, and adipose tissue samples. This ambitious task includes multidisciplinary efforts to access human islet and liver in cadavers as well as muscle and adipose tissue in cohorts, with large implications for specimen management and tracking to facilitation efficient utilization of these assets in research while adhering to data privacy and cybersecurity policies. India has developed biobanks for diseases, such as cancer, brain disorders and liver diseases, but a biobank for diabetes research is lacking, and there is limited spare capacity beyond the needs of studies that receive funding to set them up. 128 Integration of such biobanks with electronic health records may provide an avenue for novel insights into disease etiology among Indians. This requires an extensive effort for transdisciplinary research and collaboration across medical and basic biological institutions. Barriers can include the siloed way in which research is often conducted, with separate siloes for basic and clinical science. Facilitating horizontal interactions between siloes and developing a new cohort of multidisciplinary researchers would be transformative in this area. Additionally, current resources are either not shared or are ineffectively shared. Sharing of resources developed under the proposed research framework, such as cohort data, HIMS data, banked specimens, and clinical trial information will not only allow for testing of new hypotheses, but the cross-pollination will also generate new hypotheses.

3.3 Human Capital Investments

Human capital, i.e., skills, knowledge and training, is important and must be specifically deployed for transdisciplinary research; no investment in infrastructure is complete without this component. 129 Research that spans transdisciplinary cell-to-society approaches must include intentional building of individuals as well as transdisciplinary teams that span institutions. Essentially, borders between institutions must become much more porous than their current state, and access and availability of resources (cohort or HIMS data, biobanks, etc.) should be widely advertised, such that multidisciplinary grants utilize these resources. This requires opportunities for training across different disciplines, including basic biology and whole-body physiology to measure insulin sensitivity and beta cell function, as well as innovations in monitoring individuals through precise measurements of functionality. Fields, such as computational biology, statistical genetics, engineering, nanotechnology, imaging, medical informatics, bioinformatics, mathematics, and social sciences, are also key toward building sufficient capacity to address India’s diabetes research challenges. Although it will take time to build transdisciplinary human capital, this needs to begin by inserting these principles into curricular teaching and mission statements of colleges and institutions and used to develop opportunities for post-medical or post-doctoral training—to develop human capital for innovation interdisciplinary and inter-sectoral collaborations.

3.4 Community Engagement and Empowerment

The general public—both patients and non-patients—is critically important to advance diabetes research in India. They are not simply valuable resources as volunteer participants for studies, but also as drivers of research, as user-inspired research is becoming increasingly important to funding agencies. Public engagement is thus a critical part of infrastructure development, and the public, healthy or otherwise, should be educated and engaged as enthusiastic participants in cohorts, trials, and basic physiological research, and empowered as voices that can call for more research in specific areas.

4 Concluding Remarks

Diabetes represents a major public health and economic challenge for India, and its solution requires an ambitious and innovative interdisciplinary research agenda. Implementing such an agenda can propel science in India toward approaches to solve diabetes, and can also be an exemplar for other areas and problems.

Data availability

Not applicable for this perspective article.

Corsi DJ, Subramanian SV (2019) Socioeconomic gradients and distribution of diabetes, hypertension, and obesity in India. JAMA Netw Open 2(4):e190411. https://doi.org/10.1001/jamanetworkopen.2019.0411

Article   Google Scholar  

International Diabetes Federation (2022) IDF diabetes atlas. International Diabetes Federation. https://diabetesatlas.org/ . Accessed 6 October 2022

Soniwala A. Global equity in diabetes precision medicine research (personal communication).

Narayan KMV, Kanaya AM (2020) Why are South Asians prone to type 2 diabetes? A hypothesis based on underexplored pathways. Diabetologia 63(6):1103–1109. https://doi.org/10.1007/s00125-020-05132-5

Mathur P, Leburu S, Kulothungan V (2022) Prevalence, awareness, treatment and control of diabetes in India from the countrywide national NCD monitoring survey. Front Public Health 10:748157. https://doi.org/10.3389/fpubh.2022.748157

Baby J, Varghese JS, Cyriac S et al (2021) Contribution of economic and nutritional context to overweight/obesity dynamics in Indian women from 1998 to 2016: a multilevel analysis of national survey data. BMJ Open 11(12):e050598. https://doi.org/10.1136/bmjopen-2021-050598

Chaudhary E, Dey S, Ghosh S et al (2022) Reducing the burden of anaemia in Indian women of reproductive age with clean-air targets. Nat Sustain. https://doi.org/10.1038/s41893-022-00944-2 ( published online August 25, 2022 )

Walia GK, Mandal S, Jaganathan S et al (2020) Leveraging existing cohorts to study health effects of air pollution on cardiometabolic disorders: India global environmental and occupational health hub. Environ Health Insights 14:117863022091568. https://doi.org/10.1177/1178630220915688

Hodgson S, Huang QQ, Sallah N et al (2022) Integrating polygenic risk scores in the prediction of type 2 diabetes risk and subtypes in British Pakistanis and Bangladeshis: a population-based cohort study. PLoS Med 19(5):e1003981. https://doi.org/10.1371/journal.pmed.1003981

Article   CAS   Google Scholar  

Nolan JJ, Kahkoska AR, Semnani-Azad Z et al (2022) ADA/EASD precision medicine in diabetes initiative: an international perspective and future vision for precision medicine in diabetes. Diabetes Care 45(2):261–266. https://doi.org/10.2337/dc21-2216

Anjana RM, Baskar V, Nair ATN et al (2020) Novel subgroups of type 2 diabetes and their association with microvascular outcomes in an Asian Indian population: a data-driven cluster analysis: the INSPIRED study. BMJ Open Diab Res Care. 8(1):e001506. https://doi.org/10.1136/bmjdrc-2020-001506

Black AP, Brimblecombe J, Eyles H, Morris P, Vally H, O’Dea K (2012) Food subsidy programs and the health and nutritional status of disadvantaged families in high income countries: a systematic review. BMC Public Health 12(1):1099. https://doi.org/10.1186/1471-2458-12-1099

An R (2013) Effectiveness of subsidies in promoting healthy food purchases and consumption: a review of field experiments. Public Health Nutr 16(7):1215–1228. https://doi.org/10.1017/S1368980012004715

Prenissl J, Jaacks LM, Mohan V et al (2019) Variation in health system performance for managing diabetes among states in India: a cross-sectional study of individuals aged 15 to 49 years. BMC Med 17(1):92. https://doi.org/10.1186/s12916-019-1325-6

Arnold C (2022) Is precision public health the future—or a contradiction? Nature 601(7891):18–20. https://doi.org/10.1038/d41586-021-03819-2

National Health Authority (2020) National Digital Health Mission: strategy overview—making India a digital health nation enabling digital healthcare for all. Government of India

Google Scholar  

Diseases TLI (2021) The rocky road to universal COVID-19 vaccination. Lancet Infect Dis 21(6):743. https://doi.org/10.1016/S1473-3099(21)00275-9

Knowledge at Wharton Staff (2020) How technology is changing health care in India. Knowledge at Wharton. https://knowledge.wharton.upenn.edu/article/technology-changing-health-care-india/ . Published January 28, 2020. Accessed 6 October 2022

Geldsetzer P, Manne-Goehler J, Theilmann M et al (2018) Diabetes and hypertension in India: a nationally representative study of 1.3 million adults. JAMA Intern Med 178(3):363. https://doi.org/10.1001/jamainternmed.2017.8094

Sachdev HS, Porwal A, Sarna A et al (2021) Intraindividual double-burden of anthropometric undernutrition and “metabolic obesity” in Indian children: a paradox that needs action. Eur J Clin Nutr 75(8):1205–1217. https://doi.org/10.1038/s41430-021-00916-3

Narayan KMV (2016) Type 2 diabetes: why we are winning the battle but losing the war? 2015 Kelly West Award lecture. Diabetes Care 39(5):653–663. https://doi.org/10.2337/dc16-0205

Staimez LR, Deepa M, Ali MK, Mohan V, Hanson RL, Narayan KMV (2019) Tale of two Indians: heterogeneity in type 2 diabetes pathophysiology. Diabetes Metab Res Rev. https://doi.org/10.1002/dmrr.3192

Ahlqvist E, Storm P, Käräjämäki A et al (2018) Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol 6(5):361–369. https://doi.org/10.1016/s2213-8587(18)30051-2

Herder C, Roden M (2022) A novel diabetes typology: towards precision diabetology from pathogenesis to treatment. Diabetologia. https://doi.org/10.1007/s00125-021-05625-x ( published online January 4, 2022 )

Prasad RB, Asplund O, Shukla SR et al (2022) Subgroups of patients with young-onset type 2 diabetes in India reveal insulin deficiency as a major driver. Diabetologia 65(1):65–78. https://doi.org/10.1007/s00125-021-05543-y

Varghese JS, Narayan KMV (2022) Ethnic differences between Asians and non-Asians in clustering-based phenotype classification of adult-onset diabetes mellitus: a systematic narrative review. Prim Care Diabetes. https://doi.org/10.1016/j.pcd.2022.09.007 ( published online 2022 )

Ke C, Narayan KMV, Chan JCN, Jha P, Shah BR (2022) Pathophysiology, phenotypes and management of type 2 diabetes mellitus in Indian and Chinese populations. Nat Rev Endocrinol. https://doi.org/10.1038/s41574-022-00669-4 ( published online May 4, 2022 )

Misra A, Khurana L, Isharwal S, Bhardwaj S (2008) South Asian diets and insulin resistance. Br J Nutr 101(4):465–473

Rahman A (2016) Universal food security program and nutritional intake: evidence from the hunger prone KBK districts in Odisha. Food Policy 63:73–86. https://doi.org/10.1016/j.foodpol.2016.07.003

Chakrabarti S, Kishore A, Roy D (2018) Effectiveness of food subsidies in raising healthy food consumption: public distribution of pulses in India. Am J Agr Econ 100(5):1427–1449. https://doi.org/10.1093/ajae/aay022

Makkar S, Minocha S, Swaminathan S, Kurpad AV (2019) Millets in the Indian plate: a policy perspective. Econ Polit Weekly 54(36):41–48

Shivakumar N, Minocha S, Kurpad A (2018) Protein quality & amino acid requirements in relation to needs in India. Indian J Med Res 148(5):557. https://doi.org/10.4103/ijmr.IJMR_1688_18

Goldberg RB, Tripputi MT, Boyko EJ et al (2021) Hepatic Fat in Participants with and without incident diabetes in the diabetes prevention program outcome study. J Clin Endocrinol Metab 106(11):e4746–e4765. https://doi.org/10.1210/clinem/dgab160

Wagner R, Heni M, Tabák AG et al (2021) Pathophysiology-based subphenotyping of individuals at elevated risk for type 2 diabetes. Nat Med 27(1):49–57. https://doi.org/10.1038/s41591-020-1116-9

Anjana RM, Shanthi Rani CS, Deepa M et al (2015) Incidence of diabetes and prediabetes and predictors of progression among Asian Indians: 10-year follow-up of the Chennai urban rural epidemiology study (CURES). Diabetes Care 38(8):1441–1448. https://doi.org/10.2337/dc14-2814

Shah AD, Kandula NR, Lin F et al (2016) Less favorable body composition and adipokines in South Asians compared with other US ethnic groups: results from the MASALA and MESA studies. Int J Obes 40(4):639–645. https://doi.org/10.1038/ijo.2015.219

Eastwood SV, Tillin T, Dehbi HM et al (2015) Ethnic differences in associations between fat deposition and incident diabetes and underlying mechanisms: the SABRE study: adiposity measures and incident diabetes. Obesity 23(3):699–706. https://doi.org/10.1002/oby.20997

Pomeroy E, Mushrif-Tripathy V, Cole TJ, Wells JCK, Stock JT (2019) Ancient origins of low lean mass among South Asians and implications for modern type 2 diabetes susceptibility. Sci Rep 9(1):10515. https://doi.org/10.1038/s41598-019-46960-9

Yajnik CS, Bandopadhyay S, Bhalerao A et al (2021) Poor in utero growth, and reduced β-cell compensation and high fasting glucose from childhood, are harbingers of glucose intolerance in young Indians. Diabetes Care 44(12):2747–2757. https://doi.org/10.2337/dc20-3026

Thomas N, Grunnet LG, Poulsen P, Christopher S (2012) Born with low birth weight in rural Southern India: what are the metabolic consequences 20 years later? Eur J Endocrinol 166(4):647–655

Staimez LR, Weber MB, Ranjani H, Ali MK (2013) Evidence of reduced β-cell function in Asian Indians with mild dysglycemia. Diabetes Care 36(9):2772–2778

Schima W (2006) MRI of the pancreas: tumours and tumour-simulating processes. Cancer Imaging 6(1):199–203. https://doi.org/10.1102/1470-7330.2006.0035

Costache MI, Costache CA (2017) Which is the best imaging method in pancreatic adenocarcinoma diagnosis and staging. Curr Health Sci J 2:132–136. https://doi.org/10.12865/CHSJ.43.02.05

Sharma S, Tapper WJ, Collins A, Hamady ZZR (2022) Predicting pancreatic cancer in the UK Biobank cohort using polygenic risk scores and diabetes mellitus. Gastroenterology 162(6):1665–1674. https://doi.org/10.1053/j.gastro.2022.01.016

Vujkovic M, Keaton JM, Lynch JA et al (2020) Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat Genet 52(7):680–691. https://doi.org/10.1038/s41588-020-0637-y

Saxena R, Saleheen D, Been LF, Garavito ML (2013) Genome-wide association study identifies a novel locus contributing to type 2 diabetes susceptibility in Sikhs of Punjabi origin from India. Diabetes 62(5):1746–1755. https://doi.org/10.2337/db12-1077

Laakso M, Silva LF (2022) Genetics of type 2 diabetes: past, present, and future. Nutrients 4(14):3201. https://doi.org/10.3390/nu14153201

Chambers JC, Loh M, Lehne B, Drong A, Kriebel J (2015) Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study. Lancet Diabetes Endocrinol 3(7):526–534

Wang N, Zhu F, Chen L, Chen K (2018) Proteomics, metabolomics and metagenomics for type 2 diabetes and its complications. Life Sci 212:194–202. https://doi.org/10.1016/j.lfs.2018.09.035

Hwang B, Lee JH, Bang D (2018) Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med 50(8):1–14. https://doi.org/10.1038/s12276-018-0071-8

Chung WK, Erion K, Florez JC et al (2020) Precision medicine in diabetes: a consensus report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care 43(7):1617–1635. https://doi.org/10.2337/dci20-0022

Bonner C, Saponaro C (2022) Where to for precision treatment of HNF1A-MODY? Diabetologia. https://doi.org/10.1007/s00125-022-05696-4 ( published online April 12, 2022 )

Anjana RM, Siddiqui MK, Jebarani S et al (2022) Prescribing patterns and response to antihyperglycemic agents among novel clusters of type 2 diabetes in Asian Indians. Diabetes Technol Ther 24(3):190–200. https://doi.org/10.1089/dia.2021.0277

Beulens JWJ, Pinho MGM, Abreu TC et al (2022) Environmental risk factors of type 2 diabetes-an exposome approach. Diabetologia 65(2):263–274. https://doi.org/10.1007/s00125-021-05618-w

Rothman KJ, Greenland S (2014) Validity and generalizability in epidemiologic studies. In: Balakrishnan N, Colton T, Everitt B, Piegorsch W, Ruggeri F, Teugels JL (eds) Wiley StatsRef: statistics reference online, 1st edn. Wiley, New Jersey. https://doi.org/10.1002/9781118445112.stat05223

Chapter   Google Scholar  

Kim MT, Radhakrishnan K, Heitkemper EM, Choi E, Burgermaster M (2021) Psychosocial phenotyping as a personalization strategy for chronic disease self-management interventions. Am J Transl Res 13(3):1617–1635

Burgermaster M, Rodriguez VA (2022) Psychosocial-behavioral phenotyping: a novel precision health approach to modeling behavioral, psychological, and social determinants of health using machine learning. Ann Behav Med. https://doi.org/10.1093/abm/kaac012 ( Published online April 21, 2022 )

Boehm KM, Khosravi P, Vanguri R, Gao J, Shah SP (2022) Harnessing multimodal data integration to advance precision oncology. Nat Rev Cancer 22(2):114–126. https://doi.org/10.1038/s41568-021-00408-3

Meng T, Jing X, Yan Z, Pedrycz W (2020) A survey on machine learning for data fusion. Inf Fusion 2020(57):115–129. https://doi.org/10.1016/j.inffus.2019.12.001

Nowak R, Biedrzycki R, Misiurewicz J (2012) Machine learning methods in data fusion systems. In: IEEE. https://doi.org/10.1109/IRS.2012.6233354

Pratella D, Ait-El-Mkadem Saadi S, Bannwarth S, Paquis-Fluckinger V, Bottini S (2021) A survey of autoencoder algorithms to pave the diagnosis of rare diseases. Int J Mol Sci 22(19):10891. https://doi.org/10.3390/ijms221910891

Ravaut M, Harish V, Sadeghi H et al (2021) Development and validation of a machine learning model using administrative health data to predict onset of type 2 diabetes. JAMA Netw Open 4(5):e2111315. https://doi.org/10.1001/jamanetworkopen.2021.11315

Steyerberg EW, Vickers AJ, Cook NR et al (2010) Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21(1):128–138. https://doi.org/10.1097/EDE.0b013e3181c30fb2

Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 1:206–215

Prosperi M, Guo Y, Sperrin M et al (2020) Causal inference and counterfactual prediction in machine learning for actionable healthcare. Nat Mach Intell 2(7):369–375. https://doi.org/10.1038/s42256-020-0197-y

Mooney SJ, Keil AP, Westreich DJ (2021) Thirteen questions about using machine learning in causal research (you won’t believe the answer to number 10!). Am J Epidemiol 190(8):1476–1482. https://doi.org/10.1093/aje/kwab047

(2021) Precision medicine needs an equity agenda. Nat Med 27(5):737. https://doi.org/10.1038/s41591-021-01373-y

Torrey L, Shavlik J (2009) Transfer learning. In: Handbook of research on machine learning applications, vol 1. IGI Global

Huang Y, Li W, Macheret F, Gabriel RA, Ohno-Machado L (2020) A tutorial on calibration measurements and calibration models for clinical prediction models. J Am Med Inf Assoc 27(4):621–633. https://doi.org/10.1093/jamia/ocz228

Rajaraman S, Ganesan P, Antani S (2022) Deep learning model calibration for improving performance in class-imbalanced medical image classification tasks. PLoS ONE 17(1):e0262838. https://doi.org/10.1371/journal.pone.0262838

Alba AC, Agoritsas T, Walsh M et al (2017) Discrimination and calibration of clinical prediction models: users’ guides to the medical literature. JAMA 318(14):1377–1384. https://doi.org/10.1001/jama.2017.12126

Yellapa V, Devadasan N, Krumeich A et al (2017) How patients navigate the diagnostic ecosystem in a fragmented health system: a qualitative study from India. Glob Health Action 10(1):1350452. https://doi.org/10.1080/16549716.2017.1350452

Fleming KA, Horton S, Wilson ML et al (2021) The Lancet Commission on diagnostics: transforming access to diagnostics. Lancet 398(10315):1997–2050. https://doi.org/10.1016/S0140-6736(21)00673-5

Mithun MK (2021) The risks of storing health records of 1.3 billion Indians on the National Health Stack. The News Minute. https://www.thenewsminute.com/article/risks-storing-health-records-13-billion-indians-national-health-stack-156707 . Accessed September 7, 2022 ( published October 20, 2021 )

Shrivastava S, Srikanth T, Dileep V (2020) e-Governance for healthcare service delivery in India: challenges and opportunities in security and privacy. In: Proceedings of the 13th international conference on theory and practice of electronic governance. ACM DL

Office of Science and Technology Policy, National Institutes of Health (2022) Listening sessions for ARPA-H: summary report. Executive Office of the President of the United States; 2021. https://www.whitehouse.gov/wp-content/uploads/2021/09/093021-ARPA-H-Listening-Session-Summary_Final.pdf . Accessed September 7, 2022

Duncan RE, Gabriel KJ (2013) “Special Forces” innovation: how DARPA attacks problems. Harvard Business Review. https://hbr.org/2013/10/special-forces-innovation-how-darpa-attacks-problems ( published online 2013 )

Meulien P (2021) Innovative medicines initiative plays the long game on research funding. Nat Microbiol 6:137. https://doi.org/10.1038/s41564-020-00862-z

Cyranoski D (2016) China embraces precision medicine on a massive scale. Nature 529:9–10

Hoover P, Der E, Berthier CC et al (2020) Accelerating medicines partnership: organizational structure and preliminary data from the phase 1 studies of lupus nephritis. Arthritis Care Res (Hoboken) 72(2):233–242. https://doi.org/10.1002/acr.24066

Singh B (2022) National Health Mission launches project “Niramay” in Assam. The Economic Times. https://economictimes.indiatimes.com/news/india/national-health-mission-launches-project-niramay-in-assam/articleshow/88780886.cms?from=mdr . Accessed September 7, 2022 ( published January 8, 2022 )

Singh P (2019) India 2024: a healthy India. Brookings India. https://www.brookings.edu/blog/up-front/2019/05/16/a-healthy-india/ . Accessed September 7, 2022 ( published May 16, 2019 )

Chatterjee P, Gupta A, Subramanian SV (2022) Can administrative health data be used to estimate population level birth and child mortality estimates? A comparison of India’s Health Information Management System data with nationally representative survey data. SSM Popul Health 19:101148. https://doi.org/10.1016/j.ssmph.2022.101148

Zerjal T, Pandya A, Thangaraj K et al (2007) Y-chromosomal insights into the genetic impact of the caste system in India. Hum Genet 121(1):137–144. https://doi.org/10.1007/s00439-006-0282-2

Moorjani P, Thangaraj K, Patterson N et al (2013) Genetic evidence for recent population mixture in India. Am J Hum Genet 93(3):422–438. https://doi.org/10.1016/j.ajhg.2013.07.006

Collaborators I-L (2021) Health and economic impact of air pollution in the states of India: the Global Burden of Disease Study 2019. Lancet Planet Health 5(1):e25–e38. https://doi.org/10.1016/S2542-5196(20)30298-9

Tak M, Shankar B, Kadiyala S (2019) Dietary transition in India: temporal and regional trends, 1993 to 2012. Food Nutr Bull 40(2):254–270. https://doi.org/10.1177/0379572119833856

Zhuo X, Zhang P, Barker L, Albright A, Thompson TJ, Gregg E (2014) The lifetime cost of diabetes and its implications for diabetes prevention. Diabetes Care 37(9):2557–2564. https://doi.org/10.2337/dc13-2484

Nagarathna R, Madhava M, Patil SS et al (2020) Cost of management of diabetes mellitus: a pan India study. Ann Neurosci 27(3–4):190–192. https://doi.org/10.1177/0972753121998496

Tripathy JP, Prasad BM (2018) Cost of diabetic care in India: an inequitable picture. Diabetes Metab Syndr 12(3):251–255. https://doi.org/10.1016/j.dsx.2017.11.007

Yesudian CA, Grepstad M, Visintin E, Ferrario A (2014) The economic burden of diabetes in India: a review of the literature. Glob Health 10(1):80. https://doi.org/10.1186/s12992-014-0080-x

Mateti U, Kunduru B, Akari S (2013) Health-care cost of diabetes in South India: a cost of illness study. J Res Pharm Pract 2(3):114. https://doi.org/10.4103/2279-042X.122382

Jacob AM, Devarajan A, Nachimuthu S, Datta M, Viswanathan V (2022) Cost of diabetes treatment in private facilities for low resource urban community in South India. Int J Diabetes Dev Countries. https://doi.org/10.1007/s13410-022-01047-6 ( published online March 22, 2022 )

Oberoi S, Kansra P (2020) Economic menace of diabetes in India: a systematic review. Int J Diabetes Dev Countries 40(4):464–475. https://doi.org/10.1007/s13410-020-00838-z

Kowalski AJ, Poongothai S, Chwastiak L et al (2017) The integrating depression and diabetes treatment (INDEPENDENT) study: design and methods to address mental healthcare gaps in India. Contemp Clin Trials 60:113–124. https://doi.org/10.1016/j.cct.2017.06.013

Balasubramanya B, Isaac R, Philip S et al (2020) Task shifting to frontline community health workers for improved diabetes care in low-resource settings in India: a phase II non-randomized controlled clinical trial. J Glob Health Rep. https://doi.org/10.29392/001c.17609

Agarwal S, Perry HB, Long L, Labrique AB (2015) Evidence on feasibility and effective use of mH ealth strategies by frontline health workers in developing countries: systematic review. Trop Med Int Health 20(8):1003–1014. https://doi.org/10.1111/tmi.12525

Scott K, Ummer O, Chamberlain S et al (2022) At the frontlines of digitisation: a qualitative study on the challenges and opportunities in maintaining accurate, complete and timely digital health records in India’s government health system. BMJ Open 12(2):e051193. https://doi.org/10.1136/bmjopen-2021-051193

Newton-Lewis T, Nanda P (2021) Problematic problem diagnostics: why digital health interventions for community health workers do not always achieve their desired impact. BMJ Glob Health 6(Suppl 5):e005942. https://doi.org/10.1136/bmjgh-2021-005942

Verdezoto N, Bagalkot N, Akbar SZ, Sharma S (2021) The invisible work of maintenance in community health: challenges and opportunities for digital health to support frontline health workers in Karnataka, South India. In: Proceedings of the ACM on human-computer interaction. https://doi.org/10.1145/3449165

Srinidhi V, Karachiwala B, Iyer A et al (2021) ASHA Kirana: when digital technology empowered front-line health workers BMJ Glob Health. 6(Suppl 5):e005039. https://doi.org/10.1136/bmjgh-2021-005039

Chamberlain S, Dutt P, Godfrey A et al (2021) Ten lessons learnt: scaling and transitioning one of the largest mobile health communication programmes in the world to a national government. BMJ Glob Health 6(Suppl 5):e005341. https://doi.org/10.1136/bmjgh-2021-005341

Kaur G, Chauhan AS, Prinja S et al (2022) Cost-effectiveness of population-based screening for diabetes and hypertension in India: an economic modelling study. Lancet Public Health 7(1):e65–e73. https://doi.org/10.1016/S2468-2667(21)00199-7

Ghosh P, Dasgupta A, Paul B, Roy S, Ghose S, Yadav A (2022) Out-of-pocket expenditure for diabetes mellitus and its determinants in recent times in India: a narrative review. J Diabetol 12(4):8

Deshwal S, Barola A, Tiwari P (2016) Costing of ADA-recommended self-monitoring of blood glucose: early results from a northern Indian city couplet. Value Health 19(3):A301–A302. https://doi.org/10.1016/j.jval.2016.03.705

1mg.com. TATA 1mg: glucose test strips. https://www.1mg.com/search/all?name=glucose%20test%20strips . Accessed October 24, 2022

Misra A, Mukherjee R, Luthra A, Singh P (2017) Rising costs of drug/insulin treatment for diabetes: a perspective from India. Diabetes Technol Ther 19(12):693–698. https://doi.org/10.1089/dia.2017.0286

Reilly J, Dashti S, Ervasti M, Bray JD (2013) Mobile phones as seismologic sensors: automating data extraction for the ishake system. In: IEEE transactions on automation science and engineering. IEEE. https://doi.org/10.1109/TASE.2013.2245121

Flood D, Green H, Hu P et al (2022) Prevalence, awareness, treatment, and control of diabetes in India: a nationally representative survey of adults aged 45 years and older. SSRN J. https://doi.org/10.2139/ssrn.4066713

Patel SA, Dhillon PK, Kondal D et al (2017) Chronic disease concordance within Indian households: A cross-sectional study. PLoS Med 14(9):e1002395. https://doi.org/10.1371/journal.pmed.1002395

Siegel K, Narayan KMV, Kinra S (2008) Finding a policy solution to India’s diabetes epidemic. Health Aff 27(4):1077–1090. https://doi.org/10.1377/hlthaff.27.4.1077

Ministry of Health and Family Welfare, Government of India (2017) National Health Policy, 2017. Government of India. https://www.nhp.gov.in/nhpfiles/national_health_policy_2017.pdf . Accessed September 7, 2022

Ministry of Health & Family Welfare, Government of India (2022) National programme for prevention & control of cancer, diabetes, cardiovascular diseases & stroke. National Health Mission. https://www.nhm.gov.in/index1.php?lang=1&level=2&sublinkid=1048&lid=604 . Accessed August 28, 2022

Khandelwal S, Verma G, Shaikh NI et al (2020) Mapping of policies related to fruits and vegetables accessibility in India. J Hunger Environ Nutr 15(3):401–417. https://doi.org/10.1080/19320248.2019.1595254

Khandelwal S, Thow AM, Siegel KR, Shaikh NI, Soni D (2017) Strengthening fruit and vegetable supply-chain policies and programmes in India. Public Health Foundation of India. https://assets.publishing.service.gov.uk/media/5ae8635aed915d42f7c6bcc3/LANSA_FruitVeg_Supply_chain.pdf

Thow AM, Verma G, Soni D, Soni D (2018) How can health, agriculture and economic policy actors work together to enhance the external food environment for fruit and vegetables? A qualitative policy analysis in India. Food Policy 2018(77):143–151. https://doi.org/10.1016/j.foodpol.2018.04.012

Ministry of Women and Child Development, Government of India (2017) Supplementary nutrition under the integrated child development services (ICDS) scheme. http://icds-wcd.nic.in/icdsimg/snrules2017.pdf . Accessed October 6, 2022 ( published online February 20, 2017 )

Kapil U (2011) Integrated child development services (ICDS) scheme : A program for holistic development of children in India. Indian J Pediatr 69:597–601

Food and Agricultural Organization (2021) Your guide to living free of food waste. https://www.fao.org/3/cb6601en/cb6601en.pdf . Accessed September 7, 2022

United Nations Environment Programme (2021) UNEP food waste index report 2021

Institution of Mechanical Engineers (2014) A tank of cold: cleantech leapfrog to a more food secure world. Institution of Mechanical Engineers. https://www.imeche.org/policy-and-press/reports/detail/a-tank-of-cold-cleantech-leapfrog-to-a-more-food-secure-world

Makkar S, Manivannan JR, Swaminathan S et al (2022) Role of cash transfers in mitigating food insecurity in India during the COVID-19 pandemic: a longitudinal study in the Bihar state. BMJ Open 12(6):e060624. https://doi.org/10.1136/bmjopen-2021-060624

Kishore S, Thomas T, Sachdev H, Kurpad AV, Webb P (2022) Modeling the potential impacts of improved monthly income on child stunting in India: a subnational geospatial perspective. BMJ Open 12(4):e055098. https://doi.org/10.1136/bmjopen-2021-055098

Ministry of Youth Affairs and Sports (2020) Fitness protocols and guidelines for 5–18 years, fit India movement. Government of India. https://fitindia.gov.in/wp-content/uploads/doc/Fitness%20Protocols%20for%20Age%2005-18%20Years%20v1%20(English).pdf . Accessed September 7, 2022

Bhaskar H (2021) Chennai’s journey to become a pedestrian friendly space. Citizen consumer and civic action group. https://www.cag.org.in/blogs/chennais-journey-become-pedestrian-friendly-space . Accessed September 7, 2022 ( published June 10, 2021 )

World Bank (2022) World development indicators. https://databank.worldbank.org/source/world-development-indicators . Accessed September 7, 2022

Yajnik CS (2004) Early life origins of insulin resistance and type 2 diabetes in India and other Asian countries. J Nutr 134(1):205–210. https://doi.org/10.1093/jn/134.1.205

Gangadharan C, Wills S, Vangala RK, Sigamani A (2020) Biobanking for translational diabetes research in India. Biores Open Access 9(1):183–189. https://doi.org/10.1089/biores.2019.0052

Tiwari R, Negandhi H, Zodpey S (2022) India’s public health management cadre policy. Lancet Reg Health Southeast Asia 4:100053. https://doi.org/10.1016/j.lansea.2022.100053

Download references

Author information

Authors and affiliations.

Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA

K. M. Venkat Narayan, Jithin Sam Varghese & Karen R. Siegel

Emory Global Diabetes Research Center, Woodruff Health Sciences Center, Emory University, Atlanta, GA, 30322, USA

Laney Graduate School, Nutrition and Health Sciences Doctoral Program, Emory University, Atlanta, USA

Yara S. Beyh

Lattice Innovations, New Delhi, India

Soura Bhattacharyya

Public Health Foundation of India, Gurugram, Haryana, India

Shweta Khandelwal

Robert Bosch Centre for Data Science and Artificial Intelligence, Indian Institute of Technology Madras, Chennai, India

Gokul S. Krishnan

Department of Biostatistics, St. John’s Medical College, Bengaluru, India

Tinku Thomas

Department of Physiology, St. John’s Medical College, Bengaluru, India

Anura V. Kurpad

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to K. M. Venkat Narayan .

Additional information

Publisher's note.

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Narayan, K.M.V., Varghese, J.S., Beyh, Y.S. et al. A Strategic Research Framework for Defeating Diabetes in India: A 21st-Century Agenda. J Indian Inst Sci 103 , 33–54 (2023). https://doi.org/10.1007/s41745-022-00354-5

Download citation

Received : 05 November 2022

Accepted : 14 December 2022

Published : 21 March 2023

Issue Date : January 2023

DOI : https://doi.org/10.1007/s41745-022-00354-5

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

• Find a journal
• Publish with us
• Track your research

ORIGINAL RESEARCH article

Prevalence of diabetes and its determinants in the young adults indian population-call for yoga intervention.

• 1 Vivekananda Yoga Anusandhana Samsthana, Bengaluru, India
• 2 Department of Biophysics, Postgraduate Institute of Medical Education and Research, Chandigarh, India
• 3 Neuroscience Research Lab, Department of Neurology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
• 4 College of Social Work, University of Kentucky, Lexington, KY, United States
• 5 Department of Yoga and Life Science, Swami Vivekananda Yoga Anusandhana Samsthana, Bengaluru, India
• 6 Government Medical College and Hospital Sector 32, Chandigarh, India

Background: The young Indian population, which constitutes 65% of the country, is fast adapting to a new lifestyle, which was not known earlier. They are at a high risk of the increasing burden of diabetes and associated complications. The new evolving lifestyle is not only affecting people’s health but also mounting the monetary burden on a developing country such as India.

Aim: We aimed to collect information regarding the prevalence of risk of diabetes in young adults (<35 years) in the 29 most populous states and union territories (7 zones) of India, using a validated questionnaire.

Methods: A user-friendly questionnaire-based survey using a mobile application was conducted on all adults in the 29 most populous states/union territories of India, after obtaining ethical clearance for the study. Here, we report the estimation of the prevalence of the risk of diabetes and self-reported diabetes on 58,821 young individuals below the age of 35 years. Risk for diabetes was assessed using a standardized instrument, the Indian diabetes risk score (IDRS), that has 4 factors (age, family history of diabetes, waist circumference, and physical activity). Spearman’s correlation coefficient was used to check the correlations.

Results: The prevalence of high (IDRS score > 60), moderate (IDRS score 30–50), and low (IDRS < 30) diabetes risk in young adults (<35 years) was 10.2%, 33.1%, and 56.7%, respectively. Those with high-risk scores were highest (14.4%) in the Jammu zone and lowest (4.1%) in the central zone. The prevalence of self-reported diabetes was 1.8% with a small difference between men (1.7%) and women (1.9%), and the highest (8.4%) in those with a parental history of diabetes. The south zone had the highest (2.5%), and the north west zone had the lowest (4.4%) prevalence.

Conclusions: Indian youth are at high risk for diabetes, which calls for an urgent action plan through intensive efforts to promote lifestyle behavior modifications during the pandemics of both communicable and noncommunicable diseases.

Introduction

India is a fast developing economy with a considerable number of diabetes patients. Its health care cost is rising with a deterioration in health standards among the economic productive young population ( 1 – 4 ). It is the country with the second highest numbers after China with 65.1 million diabetes cases that estimated in 2013. This is expected to increase up to 109.0 million in 2035 ( 5 ). The highest prevalence of diabetes was noted in low-income countries (LIC) and lowest in high-income countries (HIC) ( 6 ). The diabetes primarily affects individuals over 50 years of age in HIC, whereas in middle-income countries (MIC), the prevalence is higher in young individuals, which is the most productive age group. The prevalence in older age again rises as these young individuals age with increased life expectancies ( 5 , 7 ).

Diabetes has become a global pandemic and threat for world health due to demographic variations and cultural differences of societies supplemented by aging phenomena. It is a costly disease that has been identified as the prime causative factor for blindness, lipoprotein abnormalities, or mitochondrial dysfunction causing cardiovascular diseases, renal failure, and amputation in several countries ( 8 – 10 ). The World Health Organization (WHO) has reported 24 million cases of diabetic neuropathy, 5 million cases of retinopathy, and 6 million cases of amputation due to diabetes. The mortality in individuals with diabetes is chiefly due to cardiac complications. Therefore, diabetes can cause undesirable consequences and, hence, needs urgent consideration in the young population in order to timely strategize effective prevention therapies ( 8 , 11 ).

Genetic and environmental factors, such as heredity, change in lifestyle, age, smoking habits, increased alcohol consumption, screen time, parental conflicts, improper sleep, education, and stress, predispose young adults to diabetes, which is exacerbated with diabetic comorbid conditions ( 12 ). Obesity is the main risk factor that accounts for 80%–85% of the risks of developing type-2 diabetes ( 13 ).

The lack of physical activity among the younger population is a matter of concern as 84% of girls and 78% of males in Australia did not meet the criteria for minimum physical activity corresponding to their age. As a consequence, females were found to be more overweight than males ( 14 ). The risk of diabetes in young adults can be managed by routine physical activity and adopting a healthy and balanced diet, which focuses on the increased intake of dietary fiber ( 15 – 17 ). The WHO strongly recommends reducing the intake of free sugars throughout one’s lifetime by avoiding foods or beverages containing added monosaccharides and disaccharides ( 18 ). A study was conducted in an urban slum in a large metropolitan city in northern India, which noted a high prevalence of metabolic disorders, such as obesity, dyslipidemia, and diabetes mellitus in middle age, particularly in females in such an economically deprived population ( 19 ). Hence, such prevalence studies are required even at a national level to examine the important risk factors in this economic productive young population in order to have effective prevention strategies.

Our study was aimed to estimate the prevalence of low, moderate, and high risk of diabetes in young adults. We conducted a nationwide study by collecting information regarding prevalence of risk of diabetes in young adults using a validated questionnaire. Moreover, the contribution of other sociodemographic factors, such as age, physical activities, yoga, family history, vitals, diet, gender, marriage, education, occupation, and socioeconomic status, were further collected to examine diabetic progression.

Sampling and Study Population

The study was conducted after ethical clearance from the ethical committee of the Indian yoga association with reference number RES/IEC-IYA/001. The data used in this analysis has been collected during phase 1 of the NMB 2017 trial, a large translational, multicenter, cluster-sampled research trial aimed to assess the efficacy of yoga-based lifestyle modification as a primary prevention strategy for diabetes in a community setting. The methodological details of the study have been reported previously ( 20 , 21 ). In brief, the data collection aimed at screening 4000 adults per district in 60 randomly selected districts representative of the Indian adult population. There were two research associates (who designed the study and monitored work of senior research fellows), 30 senior research fellows (who worked in each district and monitored the work of yoga volunteers for diabetes movement [YVDMS]). The 1200 YVDMs were involved in data collection and yoga training in the next part of the study. These YVDMs were trained for data collection as per their schedule ( Supplementary Table 3 ).

Sample Size Estimation

Keeping in mind the twin objectives of the study, the sample size estimation was based on the relative risk reduction (30%) in prediabetes individuals reported in the Community Lifestyle Improvement Program study ( 22 ). We used annual incidence rates of diabetes as 18.3% in the control conditions as per IDPP-1 study ( 23 ). This provided a conversion rate at 3-month follow-up of 4.57% and 3.0%, respectively, for control and intervention conditions. Using the sample size calculator ( http://www.sample-size.net ), the required sample size for a two-group design with α = 0.05 and (1− α) = 0.80 was estimated to be 1949 for each group (a total of 3898 individuals). Factoring an attrition of 20%, the final sample size was estimated to be 4678 individuals with prediabetes. To obtain 4678 individuals with prediabetes, it was calculated that there was a need to screen 77,967 adults above the age of 20 years (4678 × 100/6; the least reported prevalence of prediabetes in India has been 6.0% ( 24 ). Thus, the study plan included screening of approximately 155,933 individuals across 60 Indian districts (10% of all districts as per the 2011 Census of India), assuming a nonresponse rate of 50%. Consequently, the study targeted approximately 4000 adults per district with equal involvement of the urban and rural areas.

Assessments

We acquired information on diabetes and risk scores by a door-to-door survey using a mobile application with detailed person-level information about age, gender, income details, educational qualifications, and marital status.

The Indian Diabetes Risk Score (IDRS) developed by Mohan et al. in 2005 was used for risk analysis ( 25 ). IDRS is a validated instrument with optimum sensitivity (72.5%) and specificity (60.1%) used widely in India in several studies ( 26 ). It is a convenient, simple, and economical tool for the detection of a high-risk population that uses age, waist circumference, parental diabetes history, and physical activity ( 27 ) ( Supplementary Table 4 ). The combined scores of the 4 factors contribute to the prediction of risk level of an individual. The individuals with scores > 60, 30–50, and <30 are considered to be high, moderate, and low risk, respectively ( Supplementary Table 1 ). We measured waist circumference in centimeters using a measuring tape. Self-reported diabetes was confirmed by checking the medication that they were taking and/or medical reports during the door-to-door visits. The questionnaire was tested for interrater reliability in a preliminary study between two YVDMs using the Kappa coefficient value, which was found to be 0.83.

Sampling Strategy

Niyantarit Maduhmeha Bharat (NMB) 2017 was a pan-India randomized multicluster translational trial with dual objectives, namely, a survey for prevalence and lifestyle intervention for the population at high risk and known diabetes ( Figure 1 ). Details of the methods have been published (20, 21) earlier. In brief, a four-stage (zone–state–district-urban/rural) strategy was adopted for identifying study locations, using a random cluster sampling method and located households and individuals. Clustering was performed by dividing each state into districts and each district into rural and urban localities. Census enumeration blocks (CEB) were randomly selected from the randomly selected wards, and all eligible individuals (both genders between 20 and 70 years) within the CEB were contacted. The door-to-door survey enlisted eligible individuals and specifically enquired about the status of diabetes and scored them on the IDRS.

Figure 1 Population sampling strategy of nationwide NMB study.

Field personnel [1200 volunteers (20/district), supervised by 35 senior research officers and 5 zonal coordinators] were trained in a 5-day residential program to ask appropriate questions in local languages that included practical tests by visiting nearby villages and urban wards.

Statistical Analysis

Data were analyzed using SPSS (21.0) version. The estimation of prevalence was calculated using the distribution of frequency and percentage using cross tabs descriptive. Chi-square and Fisher exact tests were used for mean differences. Binary logistic regression analysis was done to find the association between independent predictors of diabetes. Self-reported diabetes was considered as a dependent variable. Gender, area, marital status, parental history, IDRS, physical activity, and waist circumference were covariates by keeping the reference factors rural for area, female for gender, vegetarian for diet ( Supplementary Table 2 ) etc. as mentioned in Table 5 .

Prevalence of Self-Reported Diabetes and Its Risks Based on Gender, Marital Status, and Parental History

According to the national survey (NMB-2017), the young diabetes population was screened across the nation on the basis of IDRS and self-reported diabetes, using validated IDRS; 60,194 individuals were selected on the basis of IDRS score, and 58,821 were selected on the basis of self-reported diabetes as young adults (<35 years). Gender-related risk of diabetes was found to be similar in men and women. The prevalence of self-reported diabetes in young females was 1.9% and in men 1.7%. On the basis of IDRS risk, no significant difference was found in the female and male diabetes population ( Table 1 ). The marital status analysis revealed that 1.5% of unmarried, 2.0% of married, and 1.5% of separated individuals were found to have self-reported diabetes. Among these, married (11.6%) and separated (11.0%) individuals were under higher risk of diabetes than unmarried ones ( p < 0.001) ( Table 1 ). Similarly, the frequency distribution of unmarried, married, and separated people based in IDRS was also found to be significantly different among these groups.

Table 1 Frequency distribution of diabetes participants (self-reported) with context to gender, anthropometric parameters, and different geographical locations in India.

Interestingly, it has been found that 1.3%, 5%, and 8.4% of diabetic subjects were self-reported with no parental history of diabetes, one diabetes parent, and both diabetes parents with diabetic history, respectively ( P < 0.001). Frequency distribution based on the parental history of diabetes has also reflected significantly higher numbers in high IDRS scores as compared to low-risk IDRS. Results suggest the inheritance pattern of diabetic condition, which may be triggered with familial lifestyle or genetic susceptibility of parents and trait transmission in siblings.

Prevalence of Self-Reported Diabetes and Its Risk Based on BMI, Physical Activity, and Waist Circumference

Participants were categorized into normal, underweight, and overweight/obese. It was found that the overweight (2.4%) and obese (>30) (3.3%) young population was at significantly higher risk of diabetes than the normal (1.3%) and underweight (1.1%) young population ( Table 1 ). The percentage of self-reported diabetes individuals with normal, high, and moderate health risks based on waist circumference is as follows: 1.3%, 3.2%, 2.0% ( p < 0.001). The IDRS scores (based on waist circumference) were also significantly higher in high-risk participants based on waist circumference of individuals, i.e., almost 33.6% more than moderate (11.7%) and normal (0.4%) individuals ( Table 1 ).

The frequency distribution based on physical activities was also in concordance with the BMI and waist circumference of the participants. It was found that the proportion of individuals who performed no, mild, moderate, or vigorous physical exercise were comparable.

Differential Frequency of Self-Reported Diabetes and Its Risk Factors Based on Different Indian Geographical Location

The zone-wise prevalence of diabetes (self-reported) was significantly different (<0.001) and reported as follows in descending order: south, north, east, northeast, central, west, and Jammu. However, no gender-wise significant differences were found ( Table 2 ). Zone-wise distribution of high and moderate IDRS risk of diabetes was also reported as south, north, west, Jammu, northeast, east, and central ( Table 3 ), and the data showed significant differences among these groups. However, frequency distribution of self-reported diabetes was comparable in urban areas (1.9%) and rural localities (1.8%) that showed statistically insignificant differences between the two ( p = 0.221, Table 1 ). The proportion of individuals taking treatment to control diabetes was estimated. Results demonstrated that only 54.5% of the young diabetes adults were taking treatment to control diabetes, and there were no medications being taken by 45.5% of the diabetes subjects ( Table 4 ).

Table 2 Zone-wise frequency distribution of self-reported diabetes participants.

Table 3 Zone-wise risk of diabetes based on IDRS score.

Table 4 Proportion of self-reported diabetes individual prescribed for treatment.

Relative Risk of Diabetes

By using logistic regression, high- and moderate-risk young adults (based on IDRS) were found to have higher odds of developing diabetes as compared to low-risk young adults. Unmarried young adults had 1.290 higher odds ( p < 0.001) of diabetes as compared to married individuals. The comparison was made for relative risk of diabetes ( 28 ) within each parameter using a binary multinomial logistic regression analysis. Both higher and lower odds of diabetes as compared to the reference variable have been reproduced in Table 5 . Logistic regression analysis to see the impact of BMI and food habits on IDRS scoring has revealed the imperative impact of both on diabetes. Obese participants can significantly stimulate the diabetic condition ( Table 6 ).

Table 5 Multinomial logistic regression analysis showing the odds of diabetes within each variable.

Table 6 Logistic regression to see the association of BMI and food habit with IDRS scores.

India is the second-largest populated country in the world. It is estimated that India has more of a young population compared to other countries in the world. According to the 2011 census, out of the total population, about 65% of the population of India are under the age of 35 ( 29 ). India has more than 40 million diabetes cases with a good majority across the nation not aware of the disease and comorbid factors. As diabetes risk varies with increasing age, early detection and intervention may prevent serious health complications and healthcare-related cost. The diabetes population in young adults has a tendency to become readily or more vulnerable to comorbid diabetes illnesses ( 30 ). Complications related to diabetes are becoming a major cause of morbidity and mortality in the young population ( 31 ). Rapidly increasing burden of Diabetes in the young might reder population to early predisposition to age related disorders which have no treatment ( 32 – 35 ). Primarily, the risk of diabetes is associated with age, obesity, parental diabetes history, smoking, type of diet, and physical inactivity ( 36 ).

The studies have shown that diabetes might be linked to genetic and environmental factors ( 37 ). Parental history is generally believed to play a major role in the prediction of diabetes. Therefore, we analyzed the percentage prevalence of self-reported diabetes in both parents with diabetes, no parents with diabetes, and one parent diabetes. This survey revealed that, overall, 1.3% of the diabetes cases had no parental history, which is possibly explained by the change in lifestyle or some epigenetic factors that can contribute to the development of such diabetes cases. Our study demonstrates that young adults with both diabetes and one diabetes parent are at a high risk of developing diabetes as compared to both nondiabetes parents. Comparison of the relative risk of diabetes within each variable showed significant results except gender. We observe that marital status (separated vs. married) was also found to be associated with diabetes risk. The current study suggests that unmarried individuals are also at increased risk of diabetes but less than married and separated people. This could be possibly because of more stress or hormonal changes in unmarried as compared to married people, which may be the contributing factors for developing diabetes risk; however, further studies are required to conclude these possibilities. Although, it is difficult to speculate why unmarried individuals as compared to separate and married were more affected by diabetes, it is possible that the former group ignored health and wellness as compared to the latter.

It was also found that the risk of diabetes varies according to areas and zones. Based on the IDRS score, the study found that the urban young population is under higher risk of diabetes than the rural counterparts. The southern region was found to have more young diabetes population i.e., 2.5%. The study conducted in India shows a similar prevalence of diabetes in the urban population ( 24 ). Nevertheless, the distribution characters in all cities were found to be comparable except socioeconomic status.

Dietary habits played a vital role in enhancing the diabetes risk and awareness, and more attention is required regarding this aspect. Diet, with high glycemic load, results in diabetes complications ( 38 ). Interestingly, the study outcomes reveal that the young vegetarian population was under a higher risk of diabetes than the nonvegetarian self-reported diabetes population. This reflects the predominant consumption of vegetarian diets rich in carbohydrates, such as rice, wheat, oil, and fatty foods. Additionally, it is worth noting that consumption of sweets is also an integral part and parcel of the Indian culture, which could be responsible for the development of diabetes among the young adult population ( 39 ). However, other studies suggest that the typical vegetarian diet helps in reducing the diabetes risk ( 40 ). This controversial fact needs further investigation, including the amount and types of diet with an appropriate control group. India is the habitat of different religions and many cultures having different eating behaviors and unique lifestyles. Hence, these variations, cultural diversity, customs, and heterogeneity across the nation are great challenges to associate it with diabetes even though it has been shown that changes in the dietary pattern may reduce the chance of diabetes ( 41 ).

The individuals who showed high IDRS but did not develop diabetes need to be followed up for any late development of diabetes, especially if it had not manifested in early life (< 35 years). There is a need to develop a cost-effective and preventive management program to reduce or prevent diabetes complications in young adults. As yoga is emerging as a cost-effective lifestyle intervention and alternative, its efficacy in the prevention of diabetes can be examined in Indian population studies where its acceptability is high. The level of physical activity index among young adults with diabetes shows that 26.9% of the young adults with high-risk diabetes did not perform any physical activity, and 9.5% and 3.7% of these individuals were engaged in mild and moderate physical activity, respectively, indicating that a sedentary lifestyle is one of the major risk factors in the development of diabetes among younger adults. Results demonstrated that only 54.5% of the young diabetes adults were taking treatment to control diabetes, and there were no medications being taken by 45.5% of the diabetes subjects ( Table 4 ). The possible reason can be that patients might be asymptomatic as we analyzed in young population.

Studies show that yoga helps in the activation of the hypothalamic pituitary axis and sympatho-adrenal component known to inhibit glucose uptake by inhibiting insulin release, inducing insulin resistance and increasing hepatic glucose production ( 42 ). Vigorous exercises have shown to increase HDL level, and moderate intensity exercises are effective in reducing VLDL ( 43 ). Young adults with higher risk for diabetes may benefit from practicing yoga as well as managing their obesity by engaging in vigorous and moderate intensity exercises to manage their lipid profile ( Figure 2 ).

Figure 2 Yoga benefit in decreasing diabetic risks: The studies show that yoga causes vagal stimulation and, therefore, decreases inflammatory cytokines and heart rate as well as blood pressure. The yoga activates parasympthatic system that possibly leads to decreased perception of stress, activation, or reactivity of the sympathoadrenal system and HPA axis. Further, it may enhance metabolic and psychological responses, insulin sensitivity, glucose tolerance, improved lipid profile, mood, and decreased visceral adiposity.

Interestingly, young diabetes patients are amenable to reversal by intensive lifestyle intervention as seen in this young diabetes study ( 44 ). The diabetes young population has greater chances of reversal because of reduced risk factors as compared to the aged group. Diabetes, if it remains untreated/undetected in the early stage of life, may become more complicated in the later stage of life ( 30 ). Young diabetes often remains undetected as aged people continue to be tested for multiple health problems and identification and corresponding intervention programs are essential for this population. This study suggests that about one fourth of the young adult population in India is at a high risk of developing diabetes and in need of the public provision of lifestyle modification programs.

Limitations

The study used cluster sampling, which might have contributed to the sample selection bias. As a result, some subjects with diabetes might have refused to admit to having diabetes. It is also possible that a few subjects are wrongly believed to have diabetes, and there is no validation of such self-reported diabetes. Furthermore, undiagnosed diabetes could be another confounder. Subjects frequently ignore the subtle signs and symptoms of asymptomatic diabetes. The possibility of underestimation of the prevalence of diabetes in the proposed population may be the main limitation.

Data Availability Statement

All datasets generated for this study are included in the article/ Supplementary Material . Data is available with the principal investigator.

Ethics Statement

Ethical permission obtained from Institutional Ethics Committee (IEC) meeting held at Indian Yoga Association, Morarji Desai National Institute of Yoga with reference no. RES/IEC-IYA/001 dated 16th Dec 2016.

Author Contributions

RN is a grant PI involved in conceptualization, editing of manuscript. PB was involved in original writing and data analysis. VS edited the manuscript. AA was involved in conceptualization of manuscript. VS edited the manuscript. SP was involved in data curation and analysis. GS and AS were involved in the acquisition of data. VP was involved in writing, editing and collection of data on site as physician. HRN was involved in conceptualization of manuscript, obtained resources and mentoring of work. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We acknowledge AYUSH for funding and Department of Biotechnology, India for DBT-RAship program.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2020.507064/full#supplementary-material .

Supplementary Table 1 | Assessments phase.

Supplementary Table 2 | Diet information.

Supplementary Table 3 | Schedule of 5-day training camps of Yoga-Certified Volunteers for Diabetes Movement in different zones.

Supplementary Table 4 | for Physical activity measurement.

1. Owen N, Sparling PB, Healy GN, Dunstan DW, Matthews CE. Sedentary behavior: emerging evidence for a new health risk, Mayo Clinic Proceedings . United States: Elsevier on behalf of the Mayo Clinic (2010). p. 1138–41.

Google Scholar

2. Akari S, Mateti UV, Kunduru BR. Health-care cost of diabetes in South India: A cost of illness study. J Res Pharm Pract (2013) 2:114.

PubMed Abstract | Google Scholar

3. Barik D, Arokiasamy P. Rising health expenditure due to non-communicable diseases in India: an outlook. Front Public Health (2016) 4:268.

4. Mather H, Verma N, Mehta S, Madhu S, Keen H. The prevalence of known diabetes in Indians in New Delhi and London. J Med Assoc Thailand Chotmaihet Thangphaet (1987) 70:54–8.

5. Guariguata L, Whiting DR, Hambleton I, Beagley J, Linnenkamp U, Shaw J, et al. Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res Clin Pract (2014) 103:137–49.

6. Dagenais GR, Gerstein HC, Zhang X, McQueen M, Lear S, Lopez-Jaramillo P, et al. Variations in diabetes prevalence in low-, middle-, and high-income countries: results from the prospective urban and rural epidemiological study. Diabetes Care (2016) 39:780–7.

7. Scully T. Diabetes in Numbers. Nature (2012) 485:S2–3.

8. Tol A, Sharifirad G, Shojaezadeh D, Tavasoli E, Azadbakht L. Socio-economic factors and diabetes consequences among patients with type 2 diabetes. J Educ Health Promotion (2013) 2.

9. Krentz AJ. Lipoprotein abnormalities and their consequences for patients with type 2 diabetes. Diabetes Obes Metab (2003) 5:S19–27.

10. Schrauwen-Hinderling VB, Kooi ME, Schrauwen P. Mitochondrial function and diabetes: consequences for skeletal and cardiac muscle metabolism. Antioxid Redox Signaling (2016) 24:39–51.

11. Connolly V, Roper N, Unwin N, Jones S, Bilous R, Kelly W. Quality of diabetes care, material deprivation and mortality: P80. Diabetic Med Supplement (2002) 19.

12. Murea M, Ma L, Freedman BI. Genetic and environmental factors associated with type 2 diabetes and diabetic vascular complications. Rev Diabetic Stud: RDS (2012) 9:6.

13. Carr MC, Brunzell JD. Abdominal obesity and dyslipidemia in the metabolic syndrome: importance of type 2 diabetes and familial combined hyperlipidemia in coronary artery disease risk. J Clin Endocrinol Metab (2004) 89:2601–7.

14. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJ, Martin BW, et al. Correlates of physical activity: why are some people physically active and others not? Lancet (2012) 380:258–71.

15. Zhao L, Zhang F, Ding X, Wu G, Lam YY, Wang X, et al. Gut bacteria selectively promoted by dietary fibers alleviate type 2 diabetes. Science (2018) 359:1151–6.

16. Galisteo M, Duarte J, Zarzuelo A. Effects of dietary fibers on disturbances clustered in the metabolic syndrome. J Nutr Biochem (2008) 19:71–84.

17. Brennan CS. Dietary fibre, glycaemic response, and diabetes. Mol Nutr Food Res (2005) 49:560–70.

18. Organization WH. Sugar Intake for Adults and Children: Guideline (2015). Available at: http://www.who.int/nutrition/publications/guidelines/sugars_intake/en (Accessed May 10). 2016.

19. Misra A, Pandey R, Devi JR, Sharma R, Vikram N, Khanna N. High prevalence of diabetes, obesity and dyslipidaemia in urban slum population in northern India. Int J Obes (2001) 25:1722.

20. Nagarathna R, Rajesh S, Amit S, Patil S, Anand A, Nagendra H. Methodology of Niyantrita Madhumeha Bharata Abhiyaan-2017, a nationwide multicentric trial on the effect of a validated culturally acceptable lifestyle intervention for primary prevention of diabetes: Part 2. Int J Yoga (2019) 12:193.

21. Nagendra H, Nagarathna R, Rajesh S, Amit S, Telles S, Hankey A. Niyantrita Madhumeha Bharata 2017, methodology for a nationwide diabetes prevalence estimate: Part 1. Int J Yoga (2019) 12:179.

22. Weber MB, Ranjani H, Staimez LR, Anjana RM, Ali MK, Narayan KM, et al. The Stepwise Approach to Diabetes Prevention: Results From the D-CLIP Randomized Controlled Trial. Diabetes Care (2016) 39:1760–7.

23. Ramachandran A, Snehalatha C, Mary S, Mukesh B, Bhaskar AD, Vijay V. The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia (2006) 49:289–97.

24. Ramachandran A, Snehalatha C, Kapur A, Vijay V, Mohan V, Das AK, et al. High prevalence of diabetes and impaired glucose tolerance in India: National Urban Diabetes Survey. Diabetologia (2001) 44:1094–101.

25. Mohan V, Deepa R, Deepa M, Somannavar S, Datta M. A simplified Indian Diabetes Risk Score for screening for undiagnosed diabetic subjects. J Assoc Physicians India (2005) 53:759–63.

26. Adhikari P, Pathak R, Kotian S. Validation of the MDRF-Indian Diabetes Risk Score (IDRS) in another south Indian population through the Boloor Diabetes Study (BDS). J Assoc Physicians India (2010) 58:434–6.

27. Peters T, Brage S, Westgate K, Franks PW, Gradmark A, Tormo Diaz MJ, et al. Validity of a short questionnaire to assess physical activity in 10 European countries. Eur J Epidemiol (2012) 27:15–25.

28. Deshpande AD, Harris-Hayes M, Schootman M. Epidemiology of diabetes and diabetes-related complications. Phys Ther (2008) 88:1254–64.

29. U.P. Division. The impact of population momentum on future population growth . United Nations, Department of Economic and Social Affairs Population Division (2017). Available at: https://esa.un.org/unpd/wpp/publications/Files/PopFacts_2017-4_Population-Momentum.pdf .

30. Zheng Y, Ley SH, Hu F.B.J.N.R.E. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol (2018) 14:88.

31. Mohan V. Why are Indians more prone to diabetes? J Assoc Physicians India (2004) 52:468–74.

32. Avijit B, Brown RE, Bamburg J, Khurana DKLD, Friedland RP, Chen W, et al. Translation of Pre-Clinical Studies into Successful Clinical Trials for Alzheimer’s Disease: What are the Roadblocks and How Can They Be Overcome? Journal of Alzheimer's Disease. Scandinavian J Med Sci Sports (2015) 47(4):815–43.

33. Akshay A, Sharma NK, Gupta A, Prabhakar S, Sharma SK, Singh R, et al. Single nucleotide polymorphisms in MCP-1 and its receptor are associated with the risk of age related macular degeneration. PloS One (2012) 7(11):e49905.

34. Kaushal S, Sharma NK, Anand A. Why AMD is a disease of ageing and not of development: mechanisms and insights. Front Aging Neurosci (2014) 6:151.

35. Bali P, Banik A, Banik B, Nehru B, Anand A. Neurotrophic factors mediated activation of astrocytes ameliorate memory loss by amyloid clearance after transplantation of lineage negative stem cells. Mol Neurobiol (2019) 56(12):8420–34.

36. Wong J, Constantino M, Yue D. Morbidity and mortality in young-onset type 2 diabetes in comparison to type 1 diabetes: where are we now? Curr Diab Rep (2015) 15:566.

37. Papazafiropoulou AK, Papanas N, Melidonis A, Maltezos E. Family history of type 2 diabetes: does having a diabetic parent increase the risk? Curr Diabeted Rev (2017) 13:19–25.

38. Sheard NF, Clark NG, Brand-Miller JC, Franz MJ, Pi-Sunyer FX, Mayer-Davis E, et al. Dietary carbohydrate (amount and type) in the prevention and management of diabetes: a statement by the American Diabetes Association. Diabetes Care (2004) 27:2266–71.

39. Tonstad S, Butler T, Yan R, Fraser G. Type of vegetarian diet, body weight and prevalence of type 2 diabetes. Diabetes Care (2009) 32(5):791–6.

40. Maiorino MI, Bellastella G, Giugliano D, Esposito K, i. complications. Can diet prevent diabetes? J Diabetes Complications (2017) 31:288–90.

41. Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med (2002) 346:393–403.

42. Fehm H, Kern W, Peters A. The selfish brain: competition for energy resources. Prog Brain Res (2006) 153:129–40.

43. Iborra R, Ribeiro I, Neves M, Charf A, Lottenberg S, Negrão C, et al. Aerobic exercise training improves the role of high-density lipoprotein antioxidant and reduces plasma lipid peroxidation in type 2 diabetes mellitus. Scandinavian J Med Sci Sports (2008) 18:742–50.

44. Huang TT, Goran M II. Prevention of type 2 diabetes in young people: a theoretical perspective. Pediatr Diabetes (2003) 4:38–56.

Keywords: prevalence, diabetes, young adult Indian population, IDRs, lifestyle - related disease

Citation: Nagarathna R, Bali P, Anand A, Srivastava V, Patil S, Sharma G, Manasa K, Pannu V, Singh A and Nagendra HR (2020) Prevalence of Diabetes and Its Determinants in the Young Adults Indian Population-Call for Yoga Intervention. Front. Endocrinol. 11:507064. doi: 10.3389/fendo.2020.507064

Received: 24 October 2019; Accepted: 07 October 2020; Published: 11 December 2020.

Reviewed by:

Copyright © 2020 Nagarathna, Bali, Anand, Srivastava, Patil, Sharma, Manasa, Pannu, Singh and Nagendra. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Raghuram Nagarathna, [email protected] ; Akshay Anand, [email protected]

† These authors have contributed equally to this work and share first authorship

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Diabetes mellitus and its complications in India

Affiliation.

• 1 Madras Diabetes Research Foundation &Dr Mohan's Diabetes Specialities Centre, WHO Collaborating Centre for Noncommunicable Diseases Prevention and Control, IDF Centre of Education, No. 6 Conran Smith Road, Gopalapuram, Chennai, 600 086, India.
• PMID: 27080137
• DOI: 10.1038/nrendo.2016.53

India is one of the epicentres of the global diabetes mellitus pandemic. Rapid socioeconomic development and demographic changes, along with increased susceptibility for Indian individuals, have led to the explosive increase in the prevalence of diabetes mellitus in India over the past four decades. Type 2 diabetes mellitus in Asian Indian people is characterized by a young age of onset and occurrence at low levels of BMI. Available data also suggest that the susceptibility of Asian Indian people to the complications of diabetes mellitus differs from that of white populations. Management of this disease in India faces multiple challenges, such as low levels of awareness, paucity of trained medical and paramedical staff and unaffordability of medications and services. Novel interventions using readily available resources and technology promise to revolutionise the care of patients with diabetes mellitus in India. As many of these challenges are common to most developing countries of the world, the lessons learnt from India's experience with diabetes mellitus are likely to be of immense global relevance. In this Review, we discuss the epidemiology of diabetes mellitus and its complications in India and outline the advances made in the country to ensure adequate care. We make specific references to novel, cost-effective interventions, which might be of relevance to other low-income and middle-income countries of the world.

Publication types

• Developing Countries*
• Diabetes Complications / epidemiology*
• Diabetes Complications / etiology
• Diabetes Complications / therapy
• Diabetes Mellitus, Type 1 / complications
• Diabetes Mellitus, Type 1 / epidemiology
• Diabetes Mellitus, Type 1 / therapy
• Diabetes Mellitus, Type 2 / complications
• Diabetes Mellitus, Type 2 / epidemiology*
• Diabetes Mellitus, Type 2 / therapy
• Diabetes, Gestational / epidemiology
• Diabetes, Gestational / therapy
• Health Care Costs
• India / epidemiology

We have updated our terms and conditions and privacy policy Click "Continue" to accept and continue with ET HealthWorld

We use cookies to ensure best experience for you

We use cookies and other tracking technologies to improve your browsing experience on our site, show personalize content and targeted ads, analyze site traffic, and understand where our audience is coming from. You can also read our privacy policy , We use cookies to ensure the best experience for you on our website.

By choosing I accept, or by continuing being on the website, you consent to our use of Cookies and Terms & Conditions .

• Leaders Speak
• Study suggests evening physical activity key for health benefits in obesity, diabetes

The researchers did not just track structured exercise, but also moderate to vigorous aerobic activity performed in bouts of three minutes or more, as they said previous research has shown such activity to be strongly associated with glucose control and lowering the risk of heart-related diseases.

• Updated On Apr 12, 2024 at 05:56 PM IST

• Published On Apr 12, 2024 at 05:52 PM IST

All Comments

By commenting, you agree to the Prohibited Content Policy

Find this Comment Offensive?

• Foul Language
• Inciting hatred against a certain community
• Out of Context / Spam

Join the community of 2M+ industry professionals

Subscribe to our newsletter to get latest insights & analysis., download ethealthworld app.

• Get Realtime updates
• Save your favourite articles

• diabetes care journal
• lowest risk of dying prematurely
• vigorous aerobic activity
• Lowering the risk of heart-related diseases
• glucose intolerant
• performing physical activity
• postdoctoral research
• University of Sydney
• greatest health benefits
• Health news

• International
• Today’s Paper
• Premium Stories
• Express Shorts
• Health & Wellness
• Board Exam Results

Can a diabetes drug slow down Parkinson’s disease? Expert explains what it means for patients in India

As it is already in use across the globe, it may soon be used for management of parkinson’s disease, says dr prashanth l k of the parkinson’s research alliance of india.

It may be early days yet but reports of a diabetes drug that can slow down symptoms of Parkinson’s disease as well have given the impetus for a large-scale global trial. The drug, lixisenatide, belongs to a group of medications used in the treatment of diabetes worldwide and was tested on Parkinson’s patients as both diseases result from common risk factors. And it was found to be neuro-protective for those suffering from Parkinson’s, a common neurological disorder causing tremors , slowness of movements, stiffness and walking difficulties in most.

According to a recently published study in the New England Journal of Medicine, which tracked 156 French early-onset patients for a year, the 78 people on lixisenatide reported no further deterioration of their motor skills. The remaining patients on placebo saw a worsening of those symptoms. However, nearly half of the group who took lixisenatide reported nausea while 13 per cent experienced vomiting.

What is lixisenatide?

It is a glucagon-like peptide-1 (GLP-1) receptor agonist that belongs to a group of medications used in the treatment of diabetes. It has proven efficacy in sugar control, weight control and cardiac health. And unlike the more well-known diabetes and weight loss drug semaglutide, lixisenatide can easily cross into the brain. It was used because of common links between diabetes and Parkinson’s.

What are common links between diabetes and Parkinson’s?

Both diabetes and Parkinson’s are a result of insulin resistance, glucose dysregulation, inflammation, oxidative stress and other genetic triggers. Some studies have shown that diabetics may have a higher risk of developing Parkinson’s disease and vice versa.

The logic flows from the fact that insulin not only controls glucose levels in the brain but impacts levels of dopamine, the chemical released by the brain and used to send messages between nerve cells. Research shows that insulin helps with our cognitive functions, thinking, reasoning and memory. So insulin abnormalities in the brain may contribute to Parkinson’s.

How do you know your Parkinson’s is worsening?

Increased numbness in the feet and tips of your fingers, problems in balance while walking, back and knee pain, facial spasms, sleep disturbances, fatigue, depression and anxiety.

Lixisenatide in India?

“Lixisenatide has shown that over a year, it could slow down deterioration of motor skills in people with Parkinson’s disease as against a placebo. The findings fit into one of the highest levels of evidence to get approval for therapy. As this family of medications is already in use across the globe, it may soon be used for management of Parkinson’s Disease,” says Dr Prashanth L K, neurologist and movement disorders specialist with the Parkinson’s Disease and Movement Disorders Clinic, Bengaluru, and lead investigator with Parkinson’s Research Alliance of India.

“Research in India is critical as therapies for Parkinson’s disease are moving from a blanket approach to personalised therapy based upon genetics, geographical and environmental factors,” he says.

According to a report in Frontiers in Neurology, India is home to nearly 0.58 million people living with Parkinson’s Disease (as estimated in 2016) and a major increase in the prevalence is expected.

The Andhra Pradesh Board of Intermediate Education (BIEAP) will announce the results for the first and second year intermediate public exams (IPE) today at 11 am. Over 10 lakh students appeared for the exams, which were held from March 1 to April 20. The results can be checked on the official websites and students must collect their original mark sheet from their schools.

• Manabadi AP Inter Results 2024 Live Updates: Download 1st, 2nd Year marks memo at resultsbie.ap.gov.in 19 mins ago
• AP Inter 1st Year, 2nd Year Results 2024 Live Updates: BIEAP 1st, 2nd year results at 11 am, check at bie.ap.gov.in 20 mins ago
• Delhi News Live Updates: Day after arrest, CBI seeks five-day custody of Kavitha 22 mins ago
• Stock Market Live Updates: Nifty Bank hits record high at 50,500 mark 28 mins ago

Best of Express

Buzzing Now

Apr 12: Latest News

• 01 The many perils of decorative lights wrapped on Mumbai’s trees
• 02 Gujarat: Nomination of candidates to begin today
• 03 MVA is a three-wheeler with mismatching spare parts: Amit Shah
• 04 Canadian brand offers Indian techie $10,000 in apology after mocking his surname
• 05 Won’t relinquish Gadchiroli guardianship: Devendra Fadnavis
• Elections 2024
• Political Pulse
• Entertainment
• Movie Review
• Newsletters
• Gold Rate Today
• Silver Rate Today
• Petrol Rate Today
• Diesel Rate Today
• Web Stories

• 9. India now world's fourth-largest research paper producer

9. India now world's fourth-largest research paper producer

• This year, 69 Indian universities secured positions in the rankings, contributing to a total of 424 entries, marking a substantial 19.4% increase from the previous year, according to the QS Quacquarelli Symonds World University Rankings released on Wednesday.
• Impressively, 72% of Indian entries showed improvement, maintained their positions, or were new additions, reflecting a positive trend, with India's overall performance demonstrating a notable 17% year-on-year improvement.
• In the Asian region, India stands out as the second country with 69 featured universities, following Mainland China.
• Additionally, India holds the fourth spot in the total number of ranked entries, trailing behind China, Japan and South Korea.
• IIM-Ahmedabad is ranked among the top 25 institutions globally for business and management studies with IIM-Bangalore and IIM-Calcutta among the top 50 for business and management studies, showcasing prestigious education quality.
• India's research output surged by 54% from 2017 to 2022, making it one of the fastest-growing research centres globally.
• It now ranks as the fourth-largest producer of academic papers , behind China, the United States, and the United Kingdom as per Ben Sowter, QS’ senior vice president.
• Indian universities excel in various fields, including Computer Science, Chemistry, Biological Sciences, Business Studies and Physics, as showcased by their presence in the rankings across 44 narrow subjects and all five broad subject areas.
• The 12 Institutes of Eminence (IoE) in India played a significant role, contributing 40% of the country's total entries, with 47 IoE positions in the top 100.
• This highlights the success of the IoE program in driving university rankings improvement, with 82% of IoE entries maintaining or climbing up in the rankings.
• The 2024 QS World University Rankings by Subject cover more than 16,400 individual university programmes across 56 academic disciplines and five broad faculty areas across 95 countries.
• These rankings cover 56 academic disciplines and five faculty areas, highlighting India's significant progress in academia. For more

TOP TRENDING

Trending stories.

• Shubman Gill overtakes Virat Kohli for this big record in IPL
• 50+ Happy Eid-ul-Fitr Wishes to Share Joy and Cheer
• Eid Mubarak Cards, Greetings, Pictures and GIFs
• Inspiring Eid Mubarak Quotes: Share the Spirit of Eid-ul-Fitr with Loved Ones
• Eid Mubarak Messages for Sharing on Facebook and WhatsApp
• Eid Mubarak 2024: Messages, wishes and quotes for a joyous Eid
• 50 heartfelt Eid Mubarak wishes
• Best Eid Mubarak messages, quotes & wishes
• 51 Inspiring Eid Mubarak wishes and messages
• Raaj Kumar Anand resigns from Delhi cabinet, quits AAP
• Elon Musk to meet PM Modi on India visit, set to unveil EV plan
• 'Smart' multi-layer perimeter security for 30 more IAF bases
• How AI is bringing dead politicians back to campaign
• Stepbrother held for conning Pandyas of Rs 4cr
• Ramcharitmanas in gold, silver worth Rs 5cr gifted to Ram Lalla
• 'I would have loved to...': Gill after GT edge RR in last-ball finish
• Five reasons why BJP is looking shaky in Haryana
• Infosys tie-up with Intel, to train its employees on AI portfolio
• ‘410’: Sidhu Moose Wala’s 7th song after his death
• 10k women say no to sanitary pads: Why menstrual cups are safest