research paper on diabetes mellitus in india

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.

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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.

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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

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• Published: 15 April 2024

Risk of diabetes and expected years in life without diabetes among adults from an urban community in India: findings from a retrospective cohort

• Palak Sharma   ORCID: orcid.org/0000-0001-8870-9097 1 ,
• T.R. Dilip 1 ,
• Anjali Kulkarni 2 ,
• Udaya Shankar Mishra 3 &
• Yogesh Shejul 2  

BMC Public Health volume  24 , Article number:  1048 ( 2024 ) Cite this article

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Diabetes prevalence has increased over the past few decades, and the shift of the burden of diabetes from the older population to the younger population has increased the exposure of longer durations in a morbid state. The study aimed at ascertaining the likelihood of progression to diabetes and to estimate the onset of diabetes within the urban community of Mumbai.

This study utilized an observational retrospective non-diabetic cohort comprising 1629 individuals enrolled in a health security scheme. Ten years of data were extracted from electronic medical records, and the life table approach was employed to assess the probability of advancing to diabetes and estimate the expected number of years lived without a diabetes diagnosis.

The study revealed a 42% overall probability of diabetes progression, with age and gender variations. Males (44%) show higher probabilities than females (40%) of developing diabetes. Diabetes likelihood rises with age, peaking in males aged 55–59 and females aged 65–69. Males aged 30–34 exhibit a faster progression (10.6 years to diagnosis) compared to females (12.3 years).

The study’s outcomes have significant implications for the importance of early diabetes detection. Progression patterns suggest that younger cohorts exhibit a comparatively slower rate of progression compared to older cohorts.

High Diabetes Progression Rates : A striking finding is that approximately 2 out of every 5 individuals aged 30 years and older advanced to diabetes within a mere decade.

Another intriguing observation is the gender disparity in diabetes progression. Males, in particular, exhibited a notably higher likelihood of transitioning to diabetes during this timeframe.

When we delve into age-specific data, it was found that among women aged 30–34 years, there’s an average of 12.3 years expected to pass before a diabetes diagnosis is made. However, for their male counterparts, this waiting time shrinks significantly to just 10.6 years. This discrepancy indicates a faster progression toward diabetes among males.

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The non-communicable diseases (NCD) are the leading cause of premature morbidity and mortality. Globally, there are around 537 million adults aged 20–79 years currently living with diabetes and India alone accommodates around 77 million diabetic people [ 1 ]. The prevalence of diabetes has increased many fold in recent decades worldwide, yet among major NCDs, it has usually been underestimated in terms of the cause of death. Diabetes is considered to have little impact on mortality directly when compared to morbidity as it mainly expedites the risk of microvascular complications [ 2 ] and various other severe chronic conditions [ 3 ]. Only a minority of patients uniquely die due to diabetes [ 3 , 4 ].

The evolving demographics, epidemiology, and longer lifespans in recent decades have raised concerns about the quality of life for individuals with diabetes. Hence, understanding disease progression and a life-course perspective is vital for designing studies that evaluate intervention impacts in terms of preventing and delaying diabetes [ 5 , 6 ].

Observing diabetes progression across different age groups and genders reveals the significant impact of demographic variables on both the development and duration of morbidity. It is known that blood glucose concentrations tend to rise with age [ 7 ], and previous national surveys reported that 54% people of those who develop diabetes were in the most productive years of their lives (< 50 years) with a higher risk of developing chronic complications of diabetes [ 8 , 9 ]. Most studies, both in India and internationally, indicate a higher prevalence and risk of diabetes among men [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ], while a few studies reported otherwise [ 4 , 20 ]. The shift in diabetes onset among the young and increased life expectancy for those with diabetes has prolonged the duration of time spent with diabetes and related sufferings [ 21 , 22 ].

Determining the onset of diseases, particularly for asymptomatic conditions like diabetes with delayed symptom manifestation, poses a significant challenge. Hence, some studies diverge by reporting the age at diabetes detection rather than onset, introducing variations [ 22 ]. Also, numerous studies have employed the life table approach to explore the transition from non-diabetic states to diabetes, assessing the proportion of remaining life with and without diabetes [ 21 , 23 , 24 ]. However, these studies couldn’t estimate diabetes onset and progression patterns in real-time cohorts with continuous follow-up and clinical diagnosis using laboratory tests, as many longitudinal studies in India lack consistent follow-up and were primarily conducted cross-sectionally with an interval of eight to ten years [ 12 , 25 , 26 , 27 ]. With current technological advancements, early detection and screening for diabetes is crucial for preventing or delaying its onset [ 28 ]. Hence, this study’s main objective is to assess the probability of diabetes progression in a diabetes-free cohort and estimate the remaining diabetes-free life years in the population.

This retrospective cohort study examines diabetes progression among beneficiaries of a Contributory Health Service Scheme (CHSS), using Electronic Medical Records (EMRs) from a Government hospital in urban Mumbai. The beneficiary population features uniform and universal access to healthcare under CHSS, with moderate to high educational attainment and economic stability.

In 2010-12, about 30,463 CHSS beneficiaries were registered with the hospital, and clinical tests revealed thatof these, 835 beneficiaries aged 30 years and above were newly diagnosed with diabetes. Hence, a sample double the size of these cases i.e. 1669 beneficiaries, matched for age and sex, without diabetes diagnosis during this period, was followed up for next ten years (January 2012 to December 2021) to investigate diabetes progression and its patterns. Detailed population characteristics are available elsewhere [ 29 ]. Further, 40 beneficiaries were lost to follow-up in the first year (2012) and were excluded from the analysis due to the unavailability of data on the last visited date. Hence, the analysis in this study is restricted to a total of 1629 beneficiaries.

The main clinical endpoint was the transition from non-diabetic status to clinically diagnosed diabetes. The study adopted the American Diabetes Association 2021 definition to categorize individuals with diabetes [ 30 ]. An individual is considered to have diabetes if the fasting plasma glucose (FPG) ≥ 126 mg/dl (7.0 mmol/l), or postprandial glucose ≥ 200 mg/dl (11.1 mmol/l), or HbA1c ≥ 6.5% or that individual is on anti-diabetes medication. Table  1 presents the parameters and the cut off values used for the diagnosis of diabetes in a tabular format (Table  1 ).

A single decrement life table was constructed to analyze the progression of diabetes within the cohort. In this table, individuals are considered as having exited the study when they are diagnosed with diabetes or when they are lost to follow-up. Loss to follow-up, although an event that leads to exit from the cohort, is not considered a separate decrement in this particular life table.

The probability of an individual being diagnosed with diabetes is given by the formula:

Where ndx is the number of beneficiaries in the age group x to x + n who turned diabetic in ten years and lx is the number of person years contributed over the period by all individuals at risk in age group x to x + n.

Further, using the age and sex-specific probabilities of progressing to diabetes determined over the ten-year follow-up period, the study estimated the average number of years an individual is expected to live before being diagnosed with diabetes based on the life table columns and formulas. The mathematical formulas used to estimate each column of the life table have been published elsewhere [ 31 ]. This analysis provides insights into the expected age of onset of diabetes in the synthetic cohort of beneficiaries with normoglycemia across various age groups for both males and females.

Sample characteristics

Table  2 presents the age-sex distribution of the selected synthetic cohort of beneficiaries who were followed up for a period of ten years. Of the total sample of 1629, 56% were female, and the mean age of the beneficiaries included in the study was 55.2 years. Additionally, more than one-third of the sample (36%) was aged 60 years and above, implying the population under consideration is slightly an aged population. Notably, the proportion of males aged 60 + years was higher (45%) than that of their female counterparts (29.2%).

Ten-year aggregate probability of getting diabetes

Table  3 shows the total number of individuals at the beginning of the year 2012 for each of the age groups, the number and proportion of individuals who turned diabetic at the end of ten years of follow-up with the probability of getting diagnosed with diabetes. As expected with an increase in age, the probability of getting diabetes increases over the period of ten years. There is around an 18% chance that an individual aged 30–34 years will progress to diabetes by the time they turn 40–44 years. The chances of an individual aged 55–59 years progressing to diabetes is as high as 59% (Table  3 ). The probability of getting diabetes is found to be higher among younger males aged 30–44 years as compared to females of the same age. However, the chi-square value from the log rank test indicated that there is no statistical difference in the probability of getting diabetes among males and females across age groups at any given point in time (Table  3 ).

Annual progression over the period of ten years

The probability of progressing to diabetes at the end of each year of follow-up for all age groups is presented in Table  4 . This analysis reveals a progression pattern to diabetes over the years where 42% of the non-diabetic subjects at the onset of the study move into the diabetic state in a span of ten years. This progression pattern indicates that the shift into the diabetic state increases with age. For instance, the younger group aged 30–34 have a relatively slower progression to diabetes (16%) than those who were aged 55 at the start of the study (52%). Subjects aged between 30 and 34 years experienced their first encounter with diabetes after four years (2016) with a very low probability of 0.02, whereas older age groups developed diabetes within one year of the follow-up. Those aged 55–59 years had the highest probability of progression, i.e., a 10% chance of progression to diabetes within a year. The most revealing of this is perhaps the probability of entering the diabetic state at the end of the study period over time ages remains cumulatively greater till the age of 60 following which the systematic pattern gets violated as a major share of the older cohorts become diabetic in the middle of the study period. It was observed that if an individual has not been diagnosed with diabetes at ages 50–59 years, the probability of having diabetes at age 60–64 is almost equal to that for subjects aged 45–49 years.

The annual progression pattern among males and females is presented in Table  5 . Overall, 44% of males and 40% of the females progressed to diabetes in ten years. The results show that the probability of a male aged 35–39 years of developing diabetes at the end of five years (in 2016) is 16% whereas among females of the same age it is only 8%. The fastest progression over ten years was observed among males aged 55–59 years (54%) and females 65–69 years (60%). The same analysis was repeated for a few selected ages and it was found that individuals aged 30 years have nearly zero probability of developing diabetes in the next ten years, while the overall probability ranged from 15% at age 35 years to 61% at age 65 years (Fig.  1 ).

Probability of progression to diabetes over the ten years of follow-up among non-diabetic individuals at a few selected terminal ages ( N  = 270)

A life table for a synthetic cohort of the non-diabetic population

Using the ten-year probability of progressing to diabetes (from Table  3 ), an abridged life table was constructed to estimate the expected number of years remaining without being diagnosed with diabetes for individuals free from diabetes before that age. It was found that an individual aged 30–34 years has 11.9 years without being diagnosed with diabetes, implying that the probability of having diabetes is almost negligible till he/she turns 40–44 years. The estimated age at onset is therefore around 42 years in this urban community. An individual aged 50–54 years is expected to have around 6.3 years before progressing to diabetes, whereas an individual aged 65–69 years is only expected to live for 3.5 years more without being diagnosed with diabetes (Table  6 ).

Females aged 30–34 years have around 12.3 years more without diabetes diagnosis whereas males of the same age are expected to live around 10.6 more years before progressing to diabetes. These results reveal that males have a shorter waiting time and faster progression to diabetes in comparison to females. The number of years before progressing to diabetes is higher among females up to the age of 45 years only, and later, the pattern changes though the difference in the number of years before being diagnosed with diabetes among males and females isn’t statistically significant (Table  7 ).

Diabetes is a chronic disease that significantly increases the risk of developing other potentially life-threatening conditions. Due to shifts in urbanization and lifestyle factors, there’s been a noted change in the onset of diabetes, with the condition now affecting a younger demographic [ 22 , 32 ]. In light of the limited available evidence regarding the progression and risk of diabetes onset, this study aimed to estimate the likelihood of transitioning to diabetes within a ten-year period among a non-diabetic synthetic cohort. Additionally, the research sought to examine variations in this progression among different age groups and between males and females, employing a life table approach for analysis. The study revealed that, over a ten-year period, 42% of the beneficiaries in the study population were diagnosed with diabetes. Females had a lower likelihood of developing diabetes compared to males. In a recent publication authored by Sharma and colleagues, an investigation within the same study population revealed a lifetime diabetes risk estimated at 40% [ 19 ]. This observation underscores the considerable susceptibility of the community to diabetes, indicating a high rate of diabetes incidence within this demographic. Additionally, a parallel study on the lifetime risk of diabetes conducted by Luhar et al. demonstrated a decline in the remaining lifetime risk (95% confidence interval) with age, reaching 37.7% at the age of 60 for women and 27.5% for men [ 33 ]. In a previous investigation involving Asian Indians, it was discovered that over a ten-year period, the incidence of individuals progressing from impaired glucose tolerance (IGT) to diabetes varied between 13% and 52% [ 9 ]. Another study conducted in India, employing the life table methodology, revealed that the likelihood of maintaining a diabetes-free life diminishes with age. Specifically, only 30% of the overall participants remained free of diabetes beyond the age of 77 [ 23 ].

The current study observed that males had a slightly higher likelihood of developing diabetes compared to females. However, it’s important to note that these differences in diabetes risk between males and females were not found statistically significant across various age groups at any point during the follow-up period of ten years. Consistent with our findings, several previous studies have also noted an elevated diabetes risk among males [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. An Indian study specifically highlighted that the likelihood of remaining diabetes-free is notably lower for men compared to women, particularly for urban residents and those belonging to higher socioeconomic classes [ 23 ]. This investigation utilized data from the India Human Development Survey (IHDS) and discovered that only 26% of urban males and 33% of urban females remained diabetes-free until the age of 73 in India [ 23 ]. It has been argued that the observed discrepancy in survival probabilities can possibly be attributed to the high male mortality in the non-diabetic population [ 34 ]. It has also been found that the western parts of India (including Maharashtra and Goa), in comparison to other regions of India, had an overall higher probability of diabetes progression, which was around 71% for males and 79% for females [ 23 ].

As age increased, the likelihood of developing diabetes also rose, peaking at around 59 years of age. This age-related pattern aligns with findings from prior research studies. Notably, the highest probability of diabetes progression was observed among older men (55–59 years) and women (65–69 years), consistent with earlier investigations [ 10 , 13 ]. While some studies have reported variations in the precise age peak for diabetes incidence, most have identified the highest incidence of type II diabetes in men aged 55–64 years and women aged 65–69 years, with some indicating an overall peak occurring at 55–59 years [ 10 , 13 , 15 ]. For instance, a study led by Singh and colleagues identified the highest likelihood of developing diabetes between the ages of 38 and 58, with an 8% probability [ 23 ]. Another ten-year study found that the risk of diabetes increased until the age of 75, after which it declined. Importantly, this study underscored that the rate of progression from pre-diabetes to diabetes among older adults was one-third of that seen in middle-aged individuals. This suggests that, at later stages of life, the probability of transitioning to diabetes is lower when compared to the higher risk observed in middle-aged individuals [ 35 ].

Diabetes is typically a condition that doesn’t show noticeable symptoms, and tends to develop slowly, sometimes taking several years to manifest, making it easy to overlook. Consequently, this study utilized a ten-year age and sex stratified approach to estimate the probability of progressing to diabetes. Also, it then constructed a synthetic cohort to estimate the additional years a person can expect to live before a diabetes diagnosis. The study found that the population aged 30–34 years is expected to live around 11.9 years before progressing to diabetes, yielding the age at onset to be around 42 years. The onset of diabetes in our study was observed to occur at an age that is relatively early compared to some other Indian studies. For instance, a ten-year prospective study conducted in Kerala, India, found that the average age at which individuals developed diabetes was 47.3 years [ 12 ]. The Chennai Urban-Rural Epidemiological study (CURES study) reported the mean age at diagnosis of diabetes among the incident diabetes cases to be 50.9 ± 12.8 years [ 26 ]. Studies have also reported that the onset of diabetes in urban India is about a decade earlier than among their western counterparts [ 36 ]. The study found a faster progression to diabetes among males, which is in line with previous studies. It has been reported that males are at a higher risk of developing diabetes at an earlier age than females, i.e. age at onset of newly diagnosed diabetes is earlier among males [ 13 , 15 ].

The observed variations in the estimates can be predominantly attributed to disparities in the age and sex demographics of the current study population compared to those in previous studies as a majority of the mentioned studies have focused on populations aged 20 years and above. Also, research has demonstrated that the principal factors associated with the progression to diabetes in India are urbanization and elevated economic status [ 23 ], a trend that is also apparent in the distribution pattern of diabetes-free survival within our study. Furthermore, these elevated estimates in our study may also be attributed to distinctive factors, including an urban-dwelling population, moderate to high levels of educational attainment, economic stability, and the presence of universal and uniform access to healthcare. Moreover, the study’s population benefits from a preventative and universal access medical scheme with rigorous screening and diagnostic evaluations. This unique characteristic partly explains the faster diabetes progression observed in this urban community.

Disease progression models often rely on long-term patient observations, but in India, such longitudinal studies are limited due to extended follow-up periods and high associated costs [ 37 , 38 , 39 ]. This study stands out in that it utilizes data from an Electronic Medical Record system within a hospital, leveraging clinically confirmed incident diabetes cases to estimate diabetes progression over a decade of follow-up. The study’s findings have significant implications for early diabetes detection and the optimal age to commence diabetes screening in the population. While the American Diabetes Association (ADA) currently recommends regular screening for individuals aged 45 and above, evidence from this urban community suggests a need to reassess the starting age for diabetes screening in India. The study corroborates the need to rigorously implement the national guideline to screen all adults aged 30 years and above for diabetes. The screening capacity of health care systems needs to be strengthened exponentially to reach out to these age-groups in the populations [ 40 ]. This approach can aid in identifying high-risk or pre-diabetic individuals and may contribute to delaying diabetes onset through prescribed interventions. These data and its outcomes hold substantial importance for implementing preventive strategies against diabetes among a large patient population, with far-reaching economic, social, and psychological implications. Furthermore, such real-time data-driven decisions can serve as compelling case studies in the Indian context, illustrating potential strategies for early detection and diabetes prevention based on estimated onset age and observed progression patterns.

Limitations of the study

The study has a few limitations that warrant acknowledgment. Firstly, it’s important to recognize that the study population consisted of individuals who were beneficiaries of the Contributory Health Service Scheme and received healthcare services under a uniform system. Hence, it’s essential to exercise caution when attempting to generalize the findings of this study to other regions of the country, as the incidence and progression of diabetes can differ across different settings. Secondly, when comparing our results with those of previous studies, caution is advised due to variations in the age and sex distributions of the populations under study, as well as differences in the definitions used for diabetes. Lastly, the study was unable to account for other factors that can influence the development and progression of diabetes, such as diet, obesity, physical inactivity, and the presence of various comorbid conditions. Consequently, establishing a causal relationship between these variables and diabetes was not feasible within the scope of this study.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to data privacy norms of the hospital under consideration. Data are however available from the corresponding author upon reasonable request to [email protected] and with permission of Medical Division, Bhabha Atomic Research Center, Mumbai, after due approvals.

Abbreviations

American Diabetes Association

Body mass index

Contributory Health Service Scheme

Chennai urban-rural epidemiological study

Electronic medical records

Fasting plasma glucose

Glycated hemoglobin

Impaired fasting glucose

Impaired glucose tolerance

India Human Development survey

Non-communicable diseases

Postprandial glucose

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Acknowledgements

This research is part of the CHIPS cohort study jointly undertaken by the Bhabha Atomic Research Centre (BARC), Mumbai, and International Institute for Population Sciences (IIPS), Mumbai. The authors are thankful to the Head of the Medical Division, Bhabha Atomic Research Centre, Mumbai, and the BARC research ethics committee, for all their support for this research study.

This study received no funding from any of the government, non-governmental organizations or any other institution.

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Palak Sharma & T.R. Dilip

Medical Division, Bhabha Atomic Research Center, Mumbai, 400088, India

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Department of Bio-statistics and Epidemiology, International Institute for Population Sciences, Mumbai, 400088, India

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Contributions

All authors have contributed significantly. PS, TRD and USM conceptualized the idea. PS did all the literature review and analysis, and wrote the first draft of the manuscript. PS, TRD, USM, AK and YS reviewed and revised the manuscript to its present form. The manuscript represents honest work. It is original and has not been submitted to any other journal. The manuscript has been read and approved by all the authors.

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Correspondence to Palak Sharma .

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The need for consent to participate was waived by an Institutional Review Board (IRB) and was deemed unnecessary according to national regulations by the “ Bhaba Atomic Research Center’s Hospital Medical Ethics Committee ” on 7th July, 2021 in the 47th Medical Ethics Committee Meeting. All methods carried out in the study were in accordance with relevant guidelines and regulations. The study used the masked ID of the patients and the authors had no direct contact with the patients.

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Sharma, P., Dilip, T., Kulkarni, A. et al. Risk of diabetes and expected years in life without diabetes among adults from an urban community in India: findings from a retrospective cohort. BMC Public Health 24 , 1048 (2024). https://doi.org/10.1186/s12889-024-18465-2

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DOI : https://doi.org/10.1186/s12889-024-18465-2

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• Cohort study
• Type 2 diabetes
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Whither diabetes research in India today?

Affiliations.

• 1 Vice Chairman & Consultant Diabetologist, Dr.Mohan's Diabetes Specialities Centre & Madras Diabetes Research Foundation, Chennai, India.
• 2 Chairman & Chief Diabetologist, Dr.Mohan's Diabetes Specialities Centre & Madras Diabetes Research Foundation, Chennai, India. Electronic address: [email protected].
• PMID: 32145680
• DOI: 10.1016/j.dsx.2020.02.007

Background and aims: India has the second largest number of patients with diabetes, and research to contain it and limit its complications is needed.

Methods: A literature search was done using Pubmed and Google Scholar search engines to prepare a narrative review on this topic.

Results: India's contribution to research on diabetes remains inadequate, both quantitatively and qualitatively. Most of the work thus far has been done by a limited number of organisations and individuals, and has been confined to certain limited areas of interest. Nearly 40% of the publications on diabetes in India between 2000 and 2009 originated from just 20 institutions. Many important aspects of diabetes in India remain uninvestigated. In this review we make an attempt to evaluate the current status of diabetes research in India and to understand the hurdles dissuading a large proportion of healthcare professionals in India from embarking on a career in research. We also suggest solutions for overcoming these hurdles.

Conclusions: Considering the major health and economic problems posed by the unrestrained diabetes epidemic in India, research in this area remains highly inadequate.

Keywords: Diabetes; India; Research; Type 1 diabetes; Type 2 diabetes.

Copyright © 2020 Diabetes India. Published by Elsevier Ltd. All rights reserved.

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• Diabetes Complications / therapy
• Diabetes Mellitus / therapy*
• Research / trends*

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• Published: 18 April 2024

Assessing the predictive value of insulin resistance indices for metabolic syndrome risk in type 2 diabetes mellitus patients

• Hadi Bazyar 1 , 2 ,
• Ahmad Zare Javid 3 , 4 ,
• Mahmood Reza Masoudi 5 ,
• Fatemeh Haidari 6 ,
• Zeinab Heidari 7 ,
• Sohrab Hajializadeh 1 ,
• Vahideh Aghamohammadi 8 &
• Mehdi Vajdi 9  

Scientific Reports volume  14 , Article number:  8917 ( 2024 ) Cite this article

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• Endocrinology
• Medical research

Limited research has explored the effectiveness of insulin resistance (IR) in forecasting metabolic syndrome (MetS) risk, especially within the Iranian population afflicted with type 2 diabetes mellitus (T2DM). The present investigation aimed to assess the efficacy of IR indices in predicting the risk of MetS among T2DM patients. Convenient sampling was utilized to select four hundred subjects with T2DM. Metabolic factors and IR indices, including the Waist Circumference-Triglyceride Index (WTI), Triglyceride and Glucose Index (TyG index), the product of TyG index and abdominal obesity indices, and the Metabolic Score for Insulin Resistance (METS-IR), were evaluated. Logistic regression, coupled with modeling, was employed to explore the risk of MetS. The predictive performance of the indices for MetS stratified by sex was evaluated via receiver operating characteristic (ROC) curve analysis and estimation of the area under the curve (AUC) values. The TyG-Waist Circumference (TyG-WC) index exhibited the largest AUCs in both males (0.91) and females (0.93), while the TyG-Body Mass Index (TyG-BMI) demonstrated the smallest AUCs (0.77 in males and 0.74 in females). All indices significantly predicted the risk of MetS in all subjects before and after adjustment (p < 0.001 for all). The TyG-WC index demonstrated the highest odds ratios for MetS (8.06, 95% CI 5.41–12.00). In conclusion, all IR indices assessed in this study effectively predicted the risk of MetS among Iranian patients with T2DM, with the TyG-WC index emerging as the most robust predictor across both genders.

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Introduction

Insulin resistance (IR) arises from inadequate physiological responses due to reduced sensitivity of peripheral tissues to insulin, leading to elevated insulin levels through compensatory mechanisms involving pancreatic β-cell insulin production 1 . Predominantly affecting muscle, liver, and adipose tissue, IR onset in muscle tissue stems from immune-induced inflammatory changes and excess free fatty acids. With impaired glucose uptake by muscles, surplus glucose is redirected to the liver, triggering increased lipogenesis and release of free fatty acids, thereby promoting fat accumulation outside adipose tissue and exacerbating IR 2 , 3 . In individuals with compromised insulin signaling, such as those with type 2 diabetes mellitus (T2DM), insulin fails to suppress hepatic gluconeogenesis, even in the fed state, critically influencing blood glucose regulation 4 . Recognized as a major risk factor for metabolic syndrome (MetS), T2DM, and cardiovascular diseases (CVD), early detection of IR is vital for preventing these conditions 5 , 6 , 7 . While the glucose clamp technique serves as the gold standard for quantifying IR 8 , its complexity, cost, and invasiveness limit its routine use in laboratories 9 , 10 . Therefore, simpler methods like the homeostasis model assessment of insulin resistance (HOMA-IR) have been widely adopted since its proposal in 1985 11 . However, challenges with insulin measurement availability and standardization have prompted the exploration of alternative IR prediction approaches, including lipid ratios and visceral fat index (VAI) 9 , 12 , 13 . The triglyceride-glucose index (TyG index), derived from circulating triglyceride and glucose concentrations, has emerged as a promising tool for IR assessment, outperforming HOMA-IR in predictive accuracy 13 , 14 , 15 . Its strong correlation with IR, high diagnostic sensitivity and specificity, and ease of clinical application make it particularly valuable 9 , 10 , 12 , 13 , 14 . Additionally, obesity, prevalent among individuals with T2DM, is closely linked to IR. Anthropometric measures such as body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHtR) are commonly used due to their practicality. Combined TyG-related parameters, such as TyG-BMI and TyG-WC, exhibit superior performance compared to the standalone TyG index in IR evaluation 16 , 17 , 18 . Simultaneous consideration of WC and TG values, known as the waist circumference-triglyceride index (WTI), offers enhanced effectiveness in investigating MetS, T2DM, and CVD prevalence compared to individual parameters 19 , 20 , 21 . Moreover, the majority of individuals with T2DM are overweight or obese, and it is anticipated that a significant proportion of them will develop MetS 22 , 23 .

Given the scarcity of research on IR index effectiveness in predicting MetS risk among Iranian T2DM patients, this study seeks to evaluate the predictive capacity of IR indices, including WTI, TyG index, the product of TyG index and abdominal obesity indices, and METS-IR, in this population.

The prevalence of Metabolic Syndrome (MetS), as per the International Diabetes Federation (IDF) criteria, was found to be 63.3% in the study sample. The demographic and clinical characteristics of subjects across quartiles of the Triglyceride-Glucose (TyG) index scores are presented in Tables 1 and 2 . Compared to individuals in quartile 4, those in quartile 1 exhibited significantly lower values for weight, Body Mass Index (BMI), Waist Circumference (WC), Hip Circumference (HC), Fasting Blood Glucose (FBG), Hemoglobin A1C (HbA1C), Triglycerides (TG), Total Cholesterol (TC), Low-Density Lipoprotein Cholesterol (LDL-C), LDL/HDL Cholesterol ratio (LDL.HDL-c), Atherogenic Index of Plasma (AIP), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Mean Arterial Pressure (MAP), prevalence of MetS, TyG index score, TyG-BMI, TyG-WC, TyG-Waist-to-Hip Ratio (TyG-WHR), TyG-Waist-to-Height Ratio (TyG-WHtR), Waist Circumference-Triglyceride Index (WTI), and Metabolic Score for Insulin Resistance (METS-IR) (p < 0.001). Post-hoc pairwise comparisons revealed a significant reduction in FBG, HbA1C, TG, TC, LDL-C, AIP, TyG index score, TyG-BMI, TyG-WC, TyG-WHR, TyG-WHtR, and WTI in the third quartile compared to the fourth quartile (p < 0.001). Additionally, a significant reduction in mean weight, BMI, WC, HC, FBG, TG, LDL-C, LDL.HDL-c, AIP, TyG index score, TyG-BMI, TyG-WC, TyG-WHR, TyG-WHtR, WTI, and METS-IR were observed in the second quartile of TyG index score compared to the third and fourth quartiles (p < 0.001). Conversely, a significant increase in HDL-C was observed in the first quartile compared to the fourth quartile (p < 0.001).

Optimal cut-off values for IR indices in predicting MetS risk in patients with T2DM are presented in Table 3 . The predictive performance of anthropometric indices (TyG index, TyG-BMI, TyG-WC, TyG-WHR, TyG-WHtR, WTI, and METS-IR) for MetS, stratified by sex, was evaluated using receiver operating characteristic (ROC) curve analysis, and the corresponding area under the curve (AUC) values are depicted in Figs.  1 , 2 , 3 , 4 , 5 .

Roc Curve for TyG index ( a male, b female, c total).

Roc Curve for TyG-BMI index ( a male, b female, c total).

Roc Curve for TyG.WC index ( a male, b female, c total).

Roc Curve for total TyG-WHR index ( a male, b female, c total).

Roc Curve for total TyG-WHtR index ( a male, b female, c total).

The TyG-WC index exhibited the largest AUCs in both males and females (0.91 and 0.93, respectively) (Fig.  3 , Table 3 ), while the TyG-BMI demonstrated the smallest AUCs (0.77 in males and 0.74 in females) (Fig.  2 , Table 3 ).

Odds ratios (95% CI) for MetS, with IR indices as independent variables among participants, are presented in Table 4 . All indices significantly predicted the risk of MetS in all subjects before and after adjustment (p < 0.001). The TyG-WC index presented the highest odds ratios for MetS (8.06, 95% CI 5.41–12.00).

The coexistence of Metabolic Syndrome (MetS) in diabetic individuals is associated with the development of both microvascular and macrovascular complications, as evidenced by previous research 24 , 25 . Within our study sample, MetS was found to be prevalent in 63.3% of participants. Notably, all insulin resistance (IR) indices investigated demonstrated predictive potential for MetS risk. Among these indices, the TyG-WC index exhibited the most pronounced area under the curve (AUC) values and highest odds ratios for MetS among patients diagnosed with Type 2 Diabetes Mellitus (T2DM).

In line with our findings, prior investigations have also demonstrated the predictive capability of the TyG index for Metabolic Syndrome (MetS). The predictive capacity of the TyG index can be elucidated by mechanisms involving glucotoxicity and lipotoxicity, alongside the intimate associations of its constituent components (triglycerides and fasting plasma glucose) with insulin resistance, a pivotal factor in MetS pathogenesis 18 , 26 , 27 , 28 . However, combining the TyG index with measures of adiposity such as body mass index (BMI) and waist circumference (WC) may enhance predictive accuracy 29 . Indeed, in our study, the composite of the TyG index with abdominal obesity indices such as WC and waist-to-height ratio (WHtR) demonstrated higher odds ratios for MetS compared to the TyG index alone. Khan et al. revealed that the TyG index, with its robust area under the curve (AUC) of 0.764, outperforms other traditional markers such as fasting blood glucose, triglycerides, small dense LDL-c, non-HDL-c, and HOMA-IR in predicting MetS 26 . Similarly, Gui et al. demonstrated the predictive potential of various obesity- and lipid-related indices for MetS in middle-aged and older adults, with TyG-BMI and the Chinese visceral adiposity index (CVAI) emerging as the most effective markers for predicting MetS in men and women, respectively 30 . In a cross-sectional study, Raimi et al. assessed the utility of the TyG index in identifying MetS among Nigerians, concluding that it was effective in predicting MetS. Furthermore, combining anthropometric and TyG index indicators enhanced predictive accuracy, consistent with our findings 18 . Similarly, in our study, TyG-WC and TyG-WHtR exhibited the largest AUCs in both genders, with overall AUC values higher than those reported by Raimi et al. This suggests that TyG-WC and TyG-WHtR may possess greater predictive utility in our population. Both waist circumference (WC) and waist-to-height ratio (WHtR) serve as markers of visceral adiposity, which correlates more strongly with cardiovascular disease (CVD) risk than BMI, a measure of overall obesity 31 . Notably, WHtR, corrected for height, may offer superior predictive capability compared to WC alone. Indeed, previous studies have demonstrated that WHtR identifies individuals at early health risks more effectively than a composite index combining BMI and WC 18 , 32 , 33 . Moreover, in a study by Laurindo et al. conducted among the Brazilian population, the TyG-WC index exhibited the largest AUC (0.849) for detecting MetS using IR indices 34 . Differences in AUC values between studies may be attributed to differences in mean fasting plasma glucose and triglyceride levels, variation in study populations (diabetic versus non-diabetic individuals), and ethnic diversity.

Mao et al. conducted a study aiming to identify the optimal predictors and cut-off points for Metabolic Syndrome (MetS) among Chinese adults with Type 2 Diabetes Mellitus (T2DM). Their findings indicated that TyG-WC was the most effective predictor of MetS among women, while BMI emerged as the best predictor for both genders combined 35 . In contrast, our study revealed that TyG-WC was the superior predictor of MetS for both women and men. Another study utilizing data from the 2013–2016 US National Health and Nutrition Examination Survey found TyG-WC to be more robust in predicting MetS among the non-Hispanic population, though gender-specific analysis was not conducted 36 . Our findings demonstrated that TyG-WC outperformed TyG-BMI in MetS prediction, with TyG-WC exhibiting the largest area under the curve (AUC) and TyG-BMI the smallest. Body Mass Index (BMI) is commonly regarded as a general indicator of obesity, while waist circumference (WC) is considered a measure of central obesity 37 . However, the distribution of adipose tissue, particularly visceral fat, holds greater significance in metabolic dysfunction and insulin resistance. WC is closely associated with cardiometabolic risks 38 , highlighting its importance in predicting MetS. Moreover, in a study by Song et al., in addition to MetS, the product of the TyG index and anthropometric indices was also employed for predicting non-alcoholic fatty liver disease and Type 2 Diabetes Mellitus. TyG-WC exhibited superiority over TyG-BMI in predicting non-alcoholic fatty liver disease, further emphasizing the utility of WC as a predictor of metabolic disorders 39 .

In the present study, both the Waist-Triglyceride Index (WTI) and Metabolic Syndrome-Insulin Resistance (METS-IR) significantly predicted the risk of Metabolic Syndrome (MetS) in all participants, both before and after adjusting for relevant factors. Yang et al. highlighted the Waist-Triglyceride (WT) index, calculated as the product of waist circumference (WC) and triglyceride levels, as strongly associated with coronary heart disease risk 40 . Additionally, the WT index demonstrated effectiveness in screening for MetS in individuals with Type 2 Diabetes Mellitus (T2DM) 41 . Recently, Liu et al. introduced a modified form of the WT index, termed WTI, which exhibited a robust ability to identify MetS 42 . Similarly, Endukuru et al. demonstrated that WTI had the highest predictive ability for detecting low high-density lipoprotein cholesterol (HDL-C), elevated blood pressure, and high triglyceride levels in women compared to other indices 43 . Several studies have also demonstrated the high predictive capacity of WTI for discriminating MetS 44 , 45 . The METS-IR was developed by Chavolla et al. to evaluate insulin sensitivity, validated against the euglycemic–hyperinsulinemic clamp. Moreover, they found that METS-IR was associated with ectopic fat accumulation and could better predict incident T2DM than the triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C) and TyG index in the Mexican population 46 . Han et al. investigated the association of various insulin resistance indicators, including METS-IR, TG/HDL-C, TyG-BMI, and TyG index, with serum uric acid levels in patients with T2DM, revealing significant associations between all indices and serum uric acid levels 47 . Furthermore, Zhang et al. demonstrated that METS-IR could predict the incidence of major adverse cardiovascular events in individuals with ischemic cardiomyopathy and T2DM 48 . It has been reported that METS-IR is strongly associated with hypertension even in individuals with normal weight 49 . Pathophysiological studies have elucidated that insulin resistance can perturb the lipid metabolism of the entire body, increase cardiac lipotoxicity, and induce oxidative stress and endothelial dysfunction, ultimately culminating in dyslipidemia, hypertension, and cardiovascular disease 50 .

Variations in the literature may stem from differences in chosen anthropometric indices, gender, ethnicity, underlying conditions, participant age, confounder variables, and criteria used to define Metabolic Syndrome (MetS), such as those by WHO, IDF, ATP III, and AHA/NHLBI. A limitation of our study is its cross-sectional design, which doesn't establish causality. Additionally, our focus on the Iranian population may limit generalizability. However, our study is the first to explore IR indices in predicting MetS risk among Iranian T2DM patients, and it includes both genders and employs multivariable logistic regression across three models.

All IR indices examined predicted MetS risk in our study, with the TyG-WC index emerging as the most effective predictor for both genders among Iranian T2DM patients.

Study design and participants

In this cross-sectional investigation, 400 Iranian patients diagnosed with Type 2 Diabetes Mellitus (T2DM) were prospectively enrolled from the Endocrine and Metabolism Clinic of Golestan Hospital, located in Ahvaz City, during the period spanning from March to May 2023. Patients were selected utilizing a convenient consecutive sampling method. Inclusion criteria comprised willingness to participate, age between 18 and 60 years, and a minimum of 2 years since the diagnosis of T2DM. Exclusion criteria consisted of insulin usage, pregnancy or lactation, smoking, alcohol consumption, incomplete demographic or anthropometric data, adherence to specialized diets, recent intake of antioxidant supplements within the last 3 months, and presence of comorbidities such as renal, hepatic, thyroidal, neoplastic, HIV, or infectious diseases.

A structured questionnaire was employed to collect demographic and baseline characteristics, encompassing sociodemographic factors such as gender, age, educational level, occupation, ethnicity, duration of diabetes, physical activity, and medication history. The study protocol adhered to the principles outlined in the Declaration of Helsinki and was approved by the Ethics Committee in Research of Sirjan University of Medical Sciences (Ethical code: IR.SIRUMS.REC.1401.017, Approval date: 18-03-2023). Written informed consent was obtained from all participants before their involvement. The sample size was determined based on the study conducted by Zhang et al. 51 and the utilization of the TyG-WHtR index, employing the formula (n = (z1 − a/2) 2 . SD 2 /d 2 ) with a precision (d) of 0.05, a standard deviation (SD) of 0.45, and a confidence level of 95%, resulting in a final sample size of 400 subjects.

Definition of MetS

Metabolic Syndrome (MetS) was defined according to the criteria established by the International Diabetes Federation (IDF), which includes the presence of central obesity, defined as a waist circumference (WC) equal to or greater than 95 cm for both genders based on guidelines provided by the Iranian National Obesity Committee 52 , in addition to meeting two or more of the following criteria: fasting blood glucose (FBG) levels equal to or greater than 100 mg/dL, or receiving medications for hyperglycemia; triglyceride (TG) levels equal to or greater than 150 mg/dL, or receiving medications for hypertriglyceridemia; low levels of high-density lipoprotein cholesterol (HDL-C), defined as less than 40 mg/dL in men and less than 50 mg/dL in women, or receiving drug treatment for low HDL-C; and elevated blood pressure, indicated by systolic blood pressure (SBP) equal to or greater than 130 mmHg or diastolic blood pressure (DBP) equal to or greater than 85 mmHg, or receiving drug treatment for hypertension 53 .

Blood pressure (BP) measurement

Blood pressure (BP) measurements were taken by a trained professional following a 20-min rest period for the patients, between 8:00 and 9:00 AM. This procedure was iterated thrice consecutively, and the average of the three successive readings was utilized for analysis. The mean arterial pressure (MAP) and pulse pressure (PP) were calculated employing the following formulas 54 :

where SBP represents systolic blood pressure and DBP represents diastolic blood pressure, both measured in millimeters of mercury (mmHg).

Biochemical assessment

Serum levels of fasting blood glucose (FBG) with a coefficient of variation (CV) interassay of 1.2% and lipid profile parameters, including triglycerides (TG) with a CV interassay of 1.6%, total cholesterol (TC) with a CV interassay of 2%, high-density lipoprotein cholesterol (HDL-C) with a CV interassay of 1.8%, low-density lipoprotein cholesterol (LDL-C) with a CV interassay of 1.29%, and very low-density lipoprotein (VLDL), were measured following a 12-h fasting period. Blood samples of 5 cc were drawn from each participant. FBG and the lipid profile were determined utilizing the enzymatic method with Pars Azmoon kits (Tehran, Iran) and analyzed on an auto analyzer (Hitachi 902, Japan). The Atherogenic Index of Plasma (AIP) was calculated using the logarithm of the TG to HDL-C ratio 55 . Hemoglobin A1c (HbA1c) levels in whole blood were quantified via automated high-performance liquid chromatography (HPLC) utilizing an exchange ion method with a DS5 set (DREW, United Kingdom).

Measurement of anthropometric indices and physical activity

All anthropometric assessments were conducted by a trained professional. Weight was measured using a digital scale manufactured in Japan with a precision of 0.1 kg, with participants asked to remove their shoes and wear minimal clothing. Height was determined using a tape measure with a precision of 0.5 cm. Body Mass Index (BMI) was calculated using the formula: weight in kilograms divided by the square of height in meters. Waist circumference (WC) was measured at the narrowest point of the torso with a precision of 0.5 cm, while hip circumference (HC) was assessed at the most prominent part of the hip area using a tape measure 56 , 57 , 58 . Waist-to-hip ratio (WHR) was computed by dividing WC by HC. Additionally, Waist-to-height ratio (WHtR) was obtained by dividing WC by height 59 .

Formulas for calculating novel indices of insulin resistance (IR) were applied as follows 18 , 36 :

Physical activity levels were assessed using the International Physical Activity Questionnaire (IPAQ), and results were reported as metabolic equivalent hours per week (METs hr/wk) 60 .

Statistical analysis

Data analysis was conducted using SPSS version 23 software. The normal distribution of the data was assessed using the Kolmogorov–Smirnov statistical test. Quantitative variables were compared between two groups using the independent t-test, while qualitative variables were compared using the chi-square test. Differences in variables across quartiles of the Triglyceride and glucose index (TyG index) were examined using One-way ANOVA with Post hoc (Least Significant Difference, LSD) analysis. To investigate the risk of Metabolic Syndrome (MetS), logistic regression was utilized, incorporating models with both crude and adjusted effects for potential confounding factors such as age, gender, ethnicity, educational level, occupation, duration of disease, physical activity, and medication usage. The predictive capacity of anthropometric indices (TyG index, TyG-BMI, TyG-WC, TyG-WHR, TyG-WHtR, Waist circumference-triglyceride index (WTI), and Metabolic Syndrome-Insulin Resistance (METS-IR)) for MetS stratified by sex was evaluated through receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC) values calculated. Figures  1 , 2 , 3 , 4 , 5 depict the results, highlighting the best predictors for both genders alongside their optimal threshold values. Quantitative data are presented as mean ± standard deviation (SD), while qualitative data are expressed as frequencies (percentages). A significance level of p < 0.05 was considered statistically significant.

Ethics declarations

The research protocol was in accordance with the guidelines of the Declaration of Helsinki. The Ethics Committee in Research of Sirjan University of Medical Sciences approved the study protocol (Ethical code: IR.SIRUMS.REC.1401.017, Approval date: 18-03-2023). The informed written consent form was acquired from all subjects at the starting of the study. For illiterate participants, informed consent was obtained from their guardian/legally authorized representative.

Data availability

All data and materials are fully presented in the manuscript.

Abbreviations

Area under the curve

Body mass index

Homeostasis model assessment of insulin resistance

Insulin resistance

Mean arterial pressure

• Metabolic syndrome

Metabolic score for insulin resistance

Pulse pressure

Receiver operating characteristic

• Type 2 diabetes mellitus

Triglyceride and glucose index

TyG-waist circumference

TyG-body mass index

Visceral fat index

Waist circumference

Waist to height ratio

Waist circumference-triglyceride index

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Acknowledgements

The authors extend their appreciation to Endocrinology and Metabolism clinic employees of Golestan Hospital and also the laboratory staff of Golestan Hospital in Ahvaz.

This work has been financially supported by the Student Research Committee of Sirjan School of Medical Sciences (No: 401000024).

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Bazyar, H., Zare Javid, A., Masoudi, M.R. et al. Assessing the predictive value of insulin resistance indices for metabolic syndrome risk in type 2 diabetes mellitus patients. Sci Rep 14 , 8917 (2024). https://doi.org/10.1038/s41598-024-59659-3

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Economic menace of diabetes in India: a systematic review

Sumit oberoi.

Mittal School of Business, Lovely Professional University, Phagwara, Punjab India

Pooja Kansra

Associated data.

Diabetes mellitus is recognised as a major chronic pandemic disease that does not consider any ethnic and monetary background. There is a dearth of literature on the cost of diabetes in the Indian context. Therefore, the present study aims to capture the evidence from the literature on the cost of diabetes mellitus in India.

An extensive literature was reviewed from ACADEMIA, NCBI, PubMed, ProQuest, EBSCO, Springer, JSTOR, Scopus and Google Scholar. The eligibility criterion is based on ‘PICOS’ procedure, and only those studies which are available in the English language, published between 1999 and February 2019, indexed in ABDC, EBSCO, ProQuest, Scopus and peer-reviewed journals are included.

A total of thirty-two studies were included in the present study. The result indicates that the median direct cost of diabetes was estimated to be ₹18,890/- p.a. for the north zone, ₹10,585/- p.a. for the south zone, ₹45,792/- p.a. for the north-east zone and ₹8822/- p.a. for the west zone. Similarly, the median indirect cost of diabetes was ₹18,146/- p.a. for the north zone, ₹1198/- p.a. for the south zone, ₹18,707/- p.a. for the north-east and ₹3949/- p.a. for the west zone.

The present study highlighted that diabetes poses a high economic burden on individuals/households. The study directed the need to arrange awareness campaign regarding diabetes and associated risk factors in order to minimise the burden of diabetes.

Electronic supplementary material

The online version of this article (10.1007/s13410-020-00838-z) contains supplementary material, which is available to authorized users.

Introduction

‘Diabetes is a metabolic disease characterised by hyperglycemia resulting from defects in insulin secretion, insulin action or both’ [ 1 ]. With rising pervasiveness globally, diabetes is conceded as a major chronic pandemic disease which does not consider any ethnic background and monetary levels both in developing and developed economies and has also been designated with the status of ‘public health priority’ in the majority of the countries [ 2 , 3 ]. Individuals with diabetes are more susceptible to develop any of the associated complications, viz. macrovascular or microvascular. As a consequence, people experience frequent and exhaustive confrontation with the health care systems [ 4 ]. The treatment cost for diabetes and its associated complications exert an enormous economic burden both at the household and national levels [ 5 – 9 ].

In a developing nation like India, the majority of diabetes patients experience a substantial cost burden from out-of-pocket (OOP). Also, the dearth of insurance schemes and policies escalate the cost of diabetes care [ 2 ]. Instantaneous urbanisation and socio-economic transitions, viz. rural to urban migration, low exercise regimen, lifestyle disorder, etc., have resulted in an escalation of diabetes prevalence in India over the last couple of decades [ 10 – 14 ]. According to the International Diabetes Federation [ 15 ], ‘India is the epicentre of diabetes mellitus and it was found that in 2017 India had the second-largest populace of 73 million diabetic patients, after China. And the figure is expected to be just double 134 million by 2045’. Considering that fact, the epidemiologic transition of diabetes has a colossal economic burden [ 16 ]. The estimated country-level health care expenditure on diabetes mellitus in India after amending purchasing power difference was 31 billion US dollars in 2017, pushing India in fourth place globally after the USA, China and Germany. Looking at the economic burden, in India, diabetes alone exhausts 5 to 25% share of an average Indian household earning [ 17 – 19 ].

Chronic nature and the rising epidemic of diabetes have everlasting consequences on the nation’s economy and health status [ 20 ]. Therefore, managing diabetes and its comorbidities is a massive challenge in India due to several issues and stumbling blocks, viz. dearth of awareness regarding diabetes, its risk factors, prevention strategies, health care systems, poverty-stricken economy, non-adherence to medicines, etc. Altogether, these issues and problems remarkably contribute to the economic menace of diabetes in India [ 20 – 24 ].

After a perspicuous representation of the economic menace of diabetes in India, policymakers and health experts should provide healthier prospects to enhance the quality of life of millions [ 19 ]. Thus, the present study aims at capturing the evidence from the literature on the cost of diabetes mellitus in India, reviewing the materials and methods used to estimate the costs and, lastly, exploring future research area. For the accomplishment of the objective, the paper has been divided into five sections. The ‘ Introduction ’ section of the study discusses diabetes and its economic burden. The ‘ Materials and methods ’ section deals with materials and methods applied for data extraction and quality assessment. The ‘ Results ’ section of the present study reports the results of the study. The ‘ Discussion ’ section concludes the discussion along with policy implications and limitations.

Materials and methods

A comprehensive literature review was carried out by following the ‘Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines’ [ 25 ]. The article suggests a minimum set of guidelines and procedures of writing items to enhance the quality of the systematic review. A search was performed between February and March 2019 for the accumulation and review of studies published up to January 2019.

Literature search

An extensive desk search was executed for all published articles and book chapters in relevant databases such as ACADEMIA, NCBI, PubMed, ProQuest, EBSCO, Springer, ResearchGate, Google Scholar, JSTOR and Scopus. For better insight, a literature search was performed on the World Health Organization (WHO) and International Diabetes Federation (IDF) libraries available online. Additional articles were investigated by scrutinising the backward referencing lists or references of the included articles. The search terms and keywords were adjusted by following different databases using words or phrases, viz. ‘India’, ‘Diabetes Mellitus or Diabetes’, ‘Economic Burden’, ‘Economic Menace’, ‘Costs of Diabetes’, ‘Health Care Utilization’, ‘Cost of Illness’, ‘Out-of-Pocket Expenditure’, ‘Diabetes Care’, ‘Health Economics’, ‘Direct/Indirect Costs’, ‘Cost Analysis’, ‘Hospitalization’, ‘Diabetic Complications’, ‘Developing Countries’, ‘Lifestyle Modification’, ‘Non-communicable diseases’, ‘Expenses by patients’, ‘Comorbidity Burden’ and ‘Treatment Costs’ were utilised to attain expected results. A total of 412 studies were acquired including duplicates by exercising the desk search criteria. Further, a comprehensive analysis of the studies was performed as per the recommendations suggested by Moher et al. [ 25 ]. Later, 187 articles were identified to be duplicate and removed immediately.

Inclusion criterion

Of the total 225 articles, limited studies managed to clear the eligibility criterion based upon the significant elements of the ‘Patient Intervention Comparison Outcome Study (PICOS)’ procedure [ 26 ]. Title, abstract and keywords of the remaining 225 studies were assessed to determine their relevance. Those articles which have been included (a) were available in English language; (b) were published between 1999 and February 2019; (c) were indexed under ABDC, EBSCO, ProQuest and Scopus; (d) were under journals that are to be peer-reviewed in nature; (e) highlighted unprecedented research outcomes on costs; and (f) were comprising at least one or more demographic zones. Thus, the screening procedure facilitated the selection of 32 articles. Majority of research publications were excluded on the grounds if they (a) did not provide the detailed analysis of how costs were estimated; (b) were conference articles or posters; (c) only presented the costs of diabetes prevention; and (d) were published in non-peer-reviewed journals.

Data extraction and quality assessment of included studies

The exploration includes those articles which highlight the cost burden of diabetes in India. Whilst performing the analysis, two interdependent excel spreadsheets were developed for data to be summarised. In the very first spreadsheet, a predefined category was used, viz. publication title/year, study type, location, diabetes type, methodology and findings. Relevant information is drawn out and presented in Table ​ Table1, 1 , highlighting the study characteristics of the included articles. The second excel spreadsheet focuses its attention on the list of technical criteria applied to assess the quality of the articles incorporated in the review process. Copious checklist has been put forward for the quality assessment of the included studies and majority of them emphasise on the economic assessment, viz. cost analysis, cost-benefit analysis (CBA), health care utility analysis, etc. [ 27 , 28 ]. Therefore, the quality indicators developed for the present study were grounded on the criterions suggested by prior literature [ 29 – 32 ].

Profile of the studies included for review

*Multiple responses possible

Source: Based on author’s calculation

A symbol of (√) yes, (×) no and (±) moderately available was assigned to individual quality indicator. Each symbol was allocated with a score of 1, which leads to a maximum attainable score of 10 for each study reviewed. Hence, a complete detailed analysis of the parameters utilised is presented in Table ​ Table2 2 .

Quality index of the included studies

Source: Authors’ compilation established on reviewed articles

Study characteristics

The characteristics of the included thirty-two studies are presented in Table ​ Table1. 1 . A majority of 66% (21) of the studies were published between 2010 and 2019 and the remaining 11 studies (34%) were published in 1999–2009. Year of costing was 1999–2003 for 5 studies; between 2009 and 2013, 10 studies (31%) were included; and for 2014–2019, 12 studies (37%) were included. The cost of diabetes was estimated from various locations such as the south zone ( n  = 11), followed by the north zone ( n  = 8), the north-east zone ( n  = 1) and the west zone ( n  = 1). A large proportion of 11 studies (34%) were defined under India as a whole.

Whilst conducting review studies, it is imperative to initially define the type, study interest, sample size, data source and outlook of the study. The included studies majorly focus on type 2 diabetes ( n  = 9), followed by both type 1 and type 2 studies ( n  = 8), 2 studies were identified under type 1 diabetes and only 1 study was acknowledged under gestational/foot ulcer category, whilst the remaining 12 studies did not define any diabetes type (Table ​ (Table1). 1 ). Of the total 32 studies, 94% of studies focus on general costs and the remaining 2 studies emphasise on foot ulcers and others. Whilst discussing the cost interests, the complications associated with diabetes were estimated by merely10 studies and the remaining 22 studies (69%) estimated the diabetes cost without any complications. Defining sample size is the utmost priority of the study, 27 studies (83%) of the total 32 studies have properly identified the sample size to be ≤ 100 respondents, only 2 studies specified the population size to be > 100 respondents and 3 studies (10%) did not define or provide the sample size.

Under the source of the cost data section, 16 studies (50%) retrieved data on cost from the patients themselves; for 11 studies (34%), source of cost data was obtained from medical institutes; and the remaining 5 studies (16%) acquired the data on cost from publications. Studies on the economic burden of illness could be done through several perspectives, viz. household, patient, societal and governmental. In the particular study, the patient’s perspective was most commonly acknowledged by 19 studies (61%), 9 studies considered societal perspective, followed by government perspective for 7 studies and lastly, household perspective was adopted by 6 studies as highlighted in Table ​ Table1 1 .

Quality of the reviewed articles

The quality of the included studies is broadly presented in Table ​ Table2. 2 . For all 32 studies, research questions and findings were discussed and explained in a very well-defined manner. The presentation of the results was completely in synchronisation with the aim and conclusions derived from the reviewed articles. It was found that 60% (19) of the studies have comprehensively defined the epidemiological definition such as type of diabetes (type 1 and type 2). Limitations experienced by the majority of studies that hampered the quality of the reviewed articles were the absence of a broad definition of diabetes and a lack of adequate sample size. A major proportion of 25 studies (78%) did not extensively define diabetes and 18 studies (56%) moderately considered the sample size.

For most of the reviewed articles, the sampling technique for data collection was addressed and only 1 study did not define the sampling technique. However, 56% (18) of studies lucidly defined the tools and technique employed in the reviewed articles and the remaining 14 studies moderately describe the tools and technique. A majority of 27 studies (84%) have properly classified the cost of diabetes and the remaining 5 studies defined moderately. Hence, based on quality index scores, the majority of the studies ( n  = 11) scored ‘6 Yes’ on a 10-point scale. Interestingly, 5 studies attained a marginally higher score of ‘8 Yes’ of the total 32 studies as presented in Table ​ Table2 2 .

Cost of diabetes

The economic burden of diabetes mellitus has led to numerous studies on the cost of illness. The cost exerted by diabetes can be categorised into three groups: direct cost, indirect cost and intangible cost [ 55 , 56 ]. Direct cost includes both direct health care costs (diagnosis, treatment, care and prevention) and direct non-health care costs (transport, housekeeping, social service and legal cost) [ 1 , 57 ]. Indirect cost includes cost for absenteeism, loss of productivity and disability [ 58 , 59 ]. Lastly, intangible costs embrace cost for social isolation and dependence, low socio-economic status, mental health and behavioral disorder and loss of quality of life [ 56 , 60 , 61 ]. All twenty-one reviewed studies put forward data and statistics to evaluate per capita cost of individual/household at zone level and the remaining eleven studies highlighted the cost of diabetes at the national level (Table ​ (Table3). 3 ). To have a clear insight on cost, the reviewed articles have been categorised into four different zones, viz. north zone, west zone, south zone and north-east zone.

Cost profile of the reviewed studies

Under the north zone, 8 studies were included to calculate both direct and indirect costs of diabetes at the individual/household level (Fig. ​ (Fig.1). 1 ). The median direct cost of diabetes is estimated to be ₹18,890/- per annum, ranging from ₹999/- to ₹1,09,344/- [ 19 , 35 , 39 , 44 , 46 , 48 – 50 ]. The most commonly measured costing items under direct cost were expenditure on medicines (7 studies), diagnostic expenses (2 studies), transportation cost (1 study), hospitalisation (2 studies) and consultation fee (3 studies). The median indirect cost of diabetes for the north zone was evaluated to be ₹18,146/- per annum, ranging from ₹4642/- to ₹98,808/- [ 19 , 35 , 46 , 49 ]. For all indirect cost studies, costing items, viz. wage loss and leisure time forgone, were used majorly.

PRISMA Framework for detailed inclusion criterion. Source: Based on Oberoi and Kansra [ 54 ], as suggested by Moher et al. [ 25 ]

South zone includes 11 studies, majorly from Karnataka state (6 studies), followed by Tamil Nadu (4 studies) and Andhra Pradesh (1 study). The median direct cost was assessed to be ₹10,585/-- per annum (Fig. ​ (Fig.1), 1 ), ranging from ₹377/- to ₹21,258/- per annum [ 2 , 6 – 9 , 33 , 37 , 38 , 40 , 42 , 45 ]. Direct costing items, viz. medicine cost (9 studies), consultation fees (4 studies) and hospitalisation (3 studies), were used in the reviewed article. The median indirect cost of diabetes was ₹1198/- per annum, ranging from ₹462/- to ₹3572/- per annum with major cost items such as monitoring cost (1 study), absenteeism (3 studies) and impairment (1 study) [ 7 – 9 , 33 , 37 ].

Under the north-east and west zone, only one-one study was observed, to evaluate the direct and indirect cost of diabetes at the individual/household level [ 47 , 51 ]. The median direct cost of diabetes for north-east was evaluated to be ₹45,792/- per annum and ₹8822/- per annum was observed for the west zone (Fig. ​ (Fig.1). 1 ). Commonly estimated costing items were surgical procedures, expenditure on drugs/medicines, clinical fees, etc. The median indirect cost estimated for the north-east zone was ₹18,707/- per annum and ₹3949/- per annum was analysed for the west zone. Indirect costing items identified for both reviewed studies were loss of wage, spendings on health class, travelling expenditure and spendings on diet control. Lastly, 11 studies were incorporated to estimate the cost of diabetes for India as a whole at the individual/household level [ 5 , 20 , 22 – 24 , 34 , 36 , 43 , 51 – 53 ]. The median direct cost of diabetes for India as a whole was ₹9996/- per annum, ranging from ₹4724/- to ₹25,391/- per annum. Also, the median indirect cost of diabetes at the individual/household level was estimated to be ₹5237/- per annum, ranging from ₹2435/- to ₹12,756/- annually (Figs.  1 and ​ and2 2 ).

Cost estimates of India and zone-wise cost profile. Source: Based on the author’s compilation and reviewed studies

Complications

Diabetes mellitus is associated with a large number of serious and chronic complications, which act as a major cause of hospitalisation, morbidity and premature mortality in diabetic patients [ 2 , 7 , 8 , 42 ]. Diabetes mellitus is commonly associated with chronic complications both macrovascular and microvascular origin [ 2 , 3 ]. Microvascular complications of diabetes mellitus include retinopathy, autonomic neuropathy, peripheral neuropathy and nephropathy [ 3 , 53 ]. The macrovascular complication of diabetes mellitus broadly includes coronary and peripheral arterial disease [ 2 , 7 ]. Of the total reviewed studies, only 10 studies estimated the cost of complications associated with diabetes (Table ​ (Table3). 3 ). A couple of studies on diabetes assessed the cost of illness to be 1.4 times higher for individuals with complications as exhibited in Table ​ Table3 3 [ 8 , 52 ]. A similar study by Sachidananda et al. [ 42 ] concluded that the cost of diabetes is 1.8 times higher for complicated non-hospitalised patients and 2.4 times higher for complicated hospitalised patients. Kapur [ 38 ] inferred that individuals with three or more comorbidities encounter 48% more cost of care, amounting to ₹10,593/- annually. According to Cavanagh et al. [ 5 ], India is the most expensive country for a patient with a complex diabetic foot ulcer, where 68.8 months of income was required to pay for treatment. Three reviewed studies incorporated in the study estimated the cost of individual/household with both macrovascular and microvascular complications [ 2 , 7 , 53 ]. Of these 3 reviewed articles, a couple of them primarily concentrate on the cost of illness prompted by renal (kidney) complication [ 2 , 53 ]. Lastly, Eshwari et al. [ 9 ] estimated the total cost for the treatment of diabetes with comorbidities was ₹9133/- annually. Direct cost with complications was ₹8185/- per annum and indirect cost amounts to be ₹508/- annually.

Rising menace of diabetes has been a major concern for India. With a frightening increase in population with diabetes, India is soon going to be crowned as ‘diabetes capital’ of the world. A swift cultural and social alteration, viz. rising age, diet modification, rapid urbanisation, lack of regular exercise regimen, obesity and a sedentary lifestyle, will result in the continuous incidence of diabetes in India. The primary objective of this article is to detect and capture the evidence from published literature on the per capita cost at the individual/household level for both direct and indirect costs of diabetes in India which are available and published since 1999. Of the total 412 records, 32 studies were identified to meet the inclusion criterion. Therefore, the findings of the present study suggest that per annum median direct and indirect cost of diabetes at the individual/household level is very colossal in India.

A large proportion of health care cost is confronted by the patients themselves, which affects the fulfilment of health care because of financial restraints [ 62 ]. The proportion of public health expenditure by the Indian government is the lowest in the world. As a consequence, out-of-pocket (OOP) spending constitutes to be 70% of the total health expenditure. Hence, financing and delivering health care facilities in India is majorly catered by the private sector for more than 70% of diseases in both rural and urban areas [ 24 ].

Direct cost items (expenditure on medicines, diagnostic expenses, transportation cost, hospitalisation and consultation fee) and indirect cost items (loss of wage, spendings on health class and travelling expenditure) were most commonly reported costing items in the present study [ 8 , 9 , 19 , 37 , 46 , 48 ]. Most of the reviewed studies on the cost of diabetes highlighted expenditure on drugs/medicine as the foremost costing item which accounts for a significant share of all direct costs. The finding of the present study is consistent with Yesudian et al. [ 62 ], ‘cost on drugs constitutes 50% of the total direct costs’. The majority of the reviewed articles included in the study justify that the primary cause for such abnormal costs of medicines is the common practice adopted by physicians to prescribe brand-named medicines, rather than generic medicines.

In context to the quality of tools and techniques incorporated by the included studies, a large number of articles (56%) witnessed to acknowledge the standards of tools and techniques. Similarly, the classification of the cost of diabetes was also determined by the majority of reviewed articles (27 articles). But the absence of a comprehensive definition of diabetes and a small size of individuals/households produce dubiousness about the standards or quality of the study. Hence, the limitations experienced by the majority of reviewed articles hampered the quality of the present study. Thus, it is beneficial to develop and suggest standard procedures and framework to conduct a comprehensive and exhaustive study on the cost of diabetes.

Limitations of the study

The present study holds few limitations. Primarily the exclusion of the relevant articles presented as conference papers and those studies published under non-peer-reviewed journals. With the omission of the above literature, some biasness might have been introduced into the review process. Furthermore, the major limitation of the present study is the non-availability of published articles under the central and east zone of India. Also, the studies published under the north-east zone and west zone were only one. Lastly, the heterogeneity in material and methodology used in cost estimation are not analogous. As a consequence, conducting a meta-analysis is not feasible.

The above discussion highlighted a huge economic burden of diabetes in India and variations were recorded in the different zones. It was observed that the cost of drugs/medicines accounts for a major burden of the cost of diabetes. The study suggested few policy interventions to cope with the high economic burden of diabetes. There is a dire need in the country to arrange awareness programmes on diabetes and associated risk factors. The menace of diabetes can be controlled by devising new health care policies, introducing new generic medicines and taxing alcohol/tobacco. Diabetes is a lifestyle disease so along with the above measures, a change in dietary habits, physical activity, beliefs and behavior can reduce its economic burden.

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Compliance with ethical standards

The authors declare that they have no conflict of interest.

The study is a review-based study, so it does not contain any studies with animals. The present study only reviews those studies which contain individual’s performance.

For the present study, it is not necessary to obtain any consent.

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