Trends, clinical characteristics, and risk factors of young-onset diabetes at a tertiary care diabetes centre in India

Trends, clinical characteristics, and risk factors of young-onset diabetes at a tertiary care diabetes centre in India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Trends, clinical characteristics, and risk factors of young-onset diabetes at a tertiary care diabetes centre in India Shyama Reji, Ganesan Umasankari, Venkatesan Ulagamathesan, Sadhasivam Ganesan, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8314170/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 25 You are reading this latest preprint version Abstract Background & Aim There has been an increase in the occurrence of young-onset type 1 diabetes (T1D) and type 2 diabetes (T2D) in India. In this report, we outline the trends of young-onset T1D and T2D observed at a tertiary care diabetes centre in India. Methods We conducted a retrospective study involving 10,449 individuals diagnosed with diabetes at age ≤25 years who were registered from 2010 to 2023 at a network of diabetes centre in India. T1D was identified through a history of ketoacidosis, low C-peptide levels, or the necessity of insulin from the time of diagnosis. T2D was diagnosed based on the absence of ketosis, sufficient C-peptide levels, a positive response to oral hypoglycemic agents for over two years, and the presence of acanthosis nigricans. Results Of 10,449 participants, 45.5% had T1D, 51.6% had T2D, and 2.8% had maturity-onset diabetes (MODY), Gestational diabetes (GDM), and Fibrocalculous Pancreatic Diabetes (FCPD). The average age at diagnosis of diabetes was higher in T2D (21.1 ± 3.8 years) than in T1D (13.5 ± 6.4 years). The prevalence of young-onset T1D and T2D (as a proportion of all registered patients) increased from 1.1% in 2010 to 1.3% and 1.6% respectively, in 2023. Among those with diabetes with a duration of ≥15 years, retinopathy was found in 61.0% of T1D and 53.9% of T2D, nephropathy in 14.0% and 19.0%, and neuropathy in 29.7% and 50.1%, respectively. Logistic regression, adjusting for relevant factors, indicated that age at diagnosis, BMI, physical inactivity, and parental history were associated with increased risk of young-onset T2D. The Shapley Additive exPlanations (SHAP) analysis identified underweight, age at diagnosis, and parental history as significant determinants for T1D. Conclusion Young-onset diabetes is increasing in prevalence and represents a significant public health challenge because of prolonged disease exposure and increased risk of complications. Early screening and targeted interventions are essential to prevent these complications. Prevalence young-onset diabetes type 1 diabetes type 2 diabetes India Figures Figure 1 Figure 2 Figure 3 Figure 4 Highlights of the study The prevalence of young-onset T1D and T2D is steadily increasing in the clinic setting The frequency of young-onset T2D id higher than that of T1D at this private centre Unhealthy lifestyle and urbanisation are likely to have contributed to the increase of the prevalence of young-onset T2D Introduction Globally, the International Diabetes Federation (IDF) and most epidemiological studies define young-onset diabetes as a diagnosis before 20 years of age. Generally, type 1 diabetes (T1D), which arises from the autoimmune destruction of β-cells, dominates young-onset diabetes, affecting 9.5 million children and adolescents worldwide (1). However, type 2 diabetes (T2D), once rare in youth, now accounts for a significant and growing share of new diabetes cases worldwide (2,3), reflecting a concerning shift in disease patterns. Asian Indians have been shown to develop T2D, 2 to 3 decades earlier than Western populations (4–6). Many Indian studies now suggest that T2D slightly exceeds T1D in urban referral populations (7–9). T2D in youth is characterized by insulin resistance, early β-cell decline, and a more aggressive disease course with earlier complications (2). As the global burden of diabetes continues to rise, particularly among younger populations, there is an urgent need for localized data to support early screening, targeted interventions, and health system planning. Benchmarking prevalence rates across specialized clinical settings provides valuable insights into real-world trends and informs both clinical practice and public health policy. A 2011 study from our group reported that the prevalence of young-onset diabetes had increased from 0.55% of the total patient population in 1992 to 2.5% in 2009, with the majority of cases being T2D (7). Building on this earlier observation, we therefore analysed data from participants diagnosed with diabetes at ≤25 years of age and registered at the same centre between 2010 and 2023 to examine trends in the prevalence of both T1D and T2D. Furthermore, the present study assesses the prevalence of complications associated with diabetes and the factors related to the onset of T1D and T2D in this age group. Additionally, we have documented the seasonal occurrence of early-onset T1D and T2D at our facility . Methodology This retrospective study included 10,449 individuals with an age at diagnosis of ≤25 years, retrieved from the Diabetes Electronic Medical Records (DEMR) of the centre, registered between 2010 and 2023. Among these, 4757 had T1D (45.5%), 5392 had T2D (51.6%), and 300 (2.8%) had Maturity onset diabetes (MODY), Gestational diabetes (GDM) and Fibrocalculous Pancreatic Diabetes (FCPD). Figure 1 illustrates the study flowchart. Information pertaining signs and symptoms, physical activity, family background, and lifestyle elements, such as dietary habits, smoking, and alcohol use, was gathered to determine possible predictors of young-onset T1D and T2D. During their first visit, each participant received a unique identification number, allowing for thorough tracking over time. After the registration process, all participants provided a detailed medical history, which includes diabetes symptoms history of infections, current medications, and a family history of diabetes to a dietician or diabetes educator. Additionally, lifestyle history (including dietary habits assessed via 24-hour recall, physical activity, smoking habits, and alcohol consumption) was also elicited. Anthropometric and biochemical measurements, and evaluations for diabetes-related complications, were performed according to standard protocols, documented in the DEMR (10) and updated at every appointment. Height was recorded in centimeters, and weight was measured to the nearest 0.1 kg using a traditional spring scale. A qualified professional measured waist circumference with a rigid tape at the midpoint between the lumber region and crista iliaca, ensuring that the participant was standing and at the end of expiration, with a precision of 0.1 cm. Body Mass Index (BMI) was determined by dividing weight in kilograms by the square of height in meters (kg/m²). Blood pressure was measured to the nearest two mmHg in the right arm using a mercury sphygmomanometer while the participant was seated and at rest. The analysis used the average of two measurements taken five minutes apart. A fasting blood sample was taken from all participants after they fasted overnight for a minimum of 8 hours. Individuals with a history of diabetes were served a standard breakfast, followed by a venous blood draw 90 minutes later to measure their postprandial glucose level. Biochemical analyses were performed at both the main and branch laboratories, which undergo regular standardization and strict quality control procedures in coordination with the Central Laboratory (11). Plasma glucose was measured using the glucose oxidase technique. Serum cholesterol was measured with the cholesterol oxidase-peroxidase (CHOD-PAP) technique, while triglycerides were assessed using the glycerol phosphate oxidase-peroxidase-amidopyrine (GPO-PAP) method. HDL cholesterol was estimated through direct immunoinhibition, and LDL cholesterol was calculated using the Friedewald formula (12). All biochemical tests were performed with a Beckman Coulter AU2700 analyzer (Fullerton, CA). HbA1c levels were measured by high-performance liquid chromatography (HPLC) on the Variant II Turbo system (Bio-Rad, Hercules, CA). Fasting and post-breakfast (stimulated) C-peptide concentrations were measured by chemiluminescence on the Siemens ADVIA Centaur XPT immunoassay analyzer (11,13). Glutamic acid decarboxylase (GAD) antibodies were measured using an enzyme-linked immunosorbent assay (ELISA) kit (EUROIMMUN, Lübeck, Germany) on a Bio-Rad plate reader (Model 680). Serum creatinine levels were assessed using the Jaffe kinetic method. The study was approved by the Institutional Ethics Committee (MDRF/NCT/09/01/2024), and the informed consent process was waived due to the inclusion of de-identified retrospective cases. Definition Diabetes was diagnosed if the individual presented with a fasting plasma glucose (FPG) level of 126 mg/dL (7.0 mmol/L) or greater, or a 2-hour post-load glucose level of 200 mg/dL (11.1 mmol/L) or greater. The condition was also considered present if the patient reported a history of diabetes managed by a physician, or if they were actively using diabetes medications like insulin or oral hypoglycemic agents (14). T1D was diagnosed if there was a history of diabetic ketoacidosis or fasting and stimulated C-peptide values were <0.6pmol/mL, or if insulin treatment was required since diagnosis (15). T2D was defined by the lack of ketosis, a sufficient β-cell reserve indicated by a stimulated C-peptide assay value of ≥0.6 pmol/mL and a positive response to oral hypoglycemic medications (15). Body Mass Index : Individuals aged 1 to 18 were classified according to BMI z-scores following the standards established by the Indian Academy of Paediatrics (IAP) (16): underweight: ≤2.1; normal: -2.0 to 0.9; overweight: 1.0 to 1.9; and obese: >2.0. For individuals aged 18 and above, classification was based on the Asia Pacific BMI guidelines (17), which define categories as underweight (<18.5 kg/m2), normal (18.5 to 22.9), overweight (23.0 to 24.9), or obese (≥25.0). Retinopathy was evaluated by fundus examination (direct and indirect ophthalmoscopy) by a retinal specialist. Grading of retinal lesions followed a standardized international system (18,19). Nephropathy was diagnosed when 24-h urine protein excretion was >500 mg (20) or urine albumin-to-creatinine ratio >300 µg/mg (21). Neuropathy was assessed using a Biothesiometer and diagnosed if mean vibratory perception threshold (VPT) at the great toes was ≥20 V (22,23). Statistical methods Statistical analyses, conducted using SPSS version 24.0, involved summarizing participant baseline characteristics with descriptive statistics. Categorical data is presented as frequencies and percentages, while continuous data is shown as means and standard deviations. To identify factors associated with the outcome, a binary logistic regression analysis was performed. Variables with p-values <0.05 in the univariate analysis were included in the multivariable model. Adjusted odds ratios (AORs) with corresponding 95% confidence intervals (CIs) were calculated to assess the strength and direction of associations. In addition to regression modelling, a Random Forest classifier was applied to evaluate the relative importance of predictors in classifying the outcome. Feature importance was derived using the mean decrease in Gini impurity (24), indicating each variable’s contribution to improving model performance. Furthermore, SHapley Additive exPlanations (SHAP) analysis was conducted to quantify the individual impact of each predictor on T1D classification and enhance interpretability of the machine learning model. Results Out of 10,449 individuals, 4757 had T1D (45.5%), 5392 had T2D (51.6%), and 300 (2.8%) had other types of diabetes. The present study focuses solely on individuals with T1D and T2D. Table 1 presents the clinical and biochemical features of the study participants. The mean age at diagnosis was significantly higher in T2D (21.1 ± 3.8 years) compared to T1D (13.5 ± 6.4 years). Males constituted a larger proportion in both groups: 52.8% in T1D and 52.0% in T2D. The BMI z-score for participants aged 1 to 18 was -0.3 ± 0.8 for T1D and 1.2 ± 1.0 for T2D. For those over 18, the average BMI was 19.3 ± 4.7 kg/m² in T1D and 26.8 ± 5.3 kg/m² in T2D. The mean HbA1c was higher in T1D (10.7 ± 2.8%) than T2D (9.6 ± 2.5%). Only 9.4% of individuals with T1D achieved good glycaemic control (HbA1c <7%), compared to 17.9% with T2D. Most participants had poor control, as defined by HbA1c ≥8%: 82.0% in T1D and 71.6% in T2D. All T1D patients were prescribed insulin only (90.0%) or insulin with OHA (9.9%), whereas only 16.9% of T2D patients were on insulin, and 1.9% were managed with diet and exercise. The Figure 2 shows a gradual rise in young-onset diabetes registrations from 2.3% in 2010 to 2.9% in 2023 . The percentages fluctuated slightly over the years, ranging from 2.2% to 2.7% between 2011 and 2019 followed by a dip to 2.0% in 2020 . After this decline, the trend steadily increased, reaching 2.9% in 2023 . Additionally, the trends of T1D (Figure 3A) and T2D (Figure 3B) registrations at the centre also showed an upward pattern, rising from 1.1% to 1.3% for T1D and reaching 1.6% for T2D. Osmotic symptoms such as polyuria and polydipsia were observed in 57.0% of T1D cases, compared to 27.2% in T2D. Weight loss was also more prevalent among T1D participants (29.7% vs. 14.0%). Conversely, T2D was more often identified incidentally during routine check-ups (37.1%). A majority of T1D participants (65.4%) had no parental history of diabetes, whereas among T2D, 47.5% had a parent with diabetes, and 31.7% had both parents affected. Regarding diet, T2D participants consumed more total calories (1410 ± 449 kcal vs. 1308 ± 441 kcal). For both T1D and T2D, over 60% of energy intake came from carbohydrates, with only 13% derived from protein (Supplementary File 1). Figure 2 illustrates the annual trend in young-onset diabetes cases registered at a tertiary diabetes care centre, illustrated as a percentage of the total number of registered participants in each period. The prevalence of young-onset T1D increased from 1.1% in 2010 to 1.3% in 2023 (Figure 3A), while T2D prevalence rose from 1.1% to 1.6% (Figure 3B). Figure 4 shows the percentage distribution of age at onset for T1D and T2D. The graph clearly indicates that nearly half of T1D cases (49.4%) were diagnosed between ages 6 and 15, while more than 60% of T2D cases were diagnosed between ages 21 and 25. The microvascular complications increase with longer diabetes duration in both T1D and T2D (Supplementary Figure 3). For those with ≥15 years of diabetes duration, the prevalence of retinopathy, nephropathy, and neuropathy was 61.0%, 14.0%, 29.7% and 53.9%, 19.0%, 50.1% among T1D and T2D respectively. Table 2 displays the results of a logistic regression analysis of various factors associated with young-onset T2D, with young-onset T1D as the reference group. In the unadjusted model, factors such as age at onset (OR: 1.25, 95% CI: 1.23-1.28, P <0.001), BMI (OR: 1.42, 95% CI: 1.38-1.46, P <0.001), and asymptomatic presentation (OR: 3.96, 95% CI: 2.89-5.43, P <0.001) were significantly linked to young-onset T2D. Parental history showed strong associations, with one parent increasing risk more than threefold (OR: 3.35, 95% CI: 2.43-4.61, P <0.001) and both parents raising the odds over twelvefold (OR: 12.11, 95% CI: 7.61-19.27, P <0.001). Physical inactivity (OR: 1.84, 95% CI: 1.42-2.39, P <0.001), alcohol consumption (OR: 2.31, 95% CI: 1.64-3.25, P <0.001), and smoking (OR: 4.47, 95% CI: 2.5-7.98, P <0.001) were also significant predictors, while daily carbohydrate intake showed only a marginal effect (OR: 1.00, 95% CI: 1.00-1.00, P = 0.046). In the adjusted model, after controlling for gender, only a few variables such as age at diagnosis (OR: 1.16, 95% CI: 1.08-1.25, P < 0.001), BMI (OR: 1.47, 95% CI: 1.36-1.59, P < 0.001), having both parents with a history of diabetes (OR: 3.73, 95% CI: 1.61-8.64, P = 0.002), and physical inactivity (OR: 3.09, 95% CI: 1.68-5.67, P < 0.001) emerged as predictors of T2D risk. After the SHAP analysis (Supplementary Figure 4), it was interpreted that, the features are arranged based on the risk factors contributing to T1D. Among them, BMI had the most decisive influence. People with lower BMI were more likely to have T1D. The age at onset was the next important factor, with those developing diabetes at a younger age more often falling into the T1D. Parental history also played a role, but its effect was negligible compared to BMI and age at onset. Being symptomatic at the time of diagnosis added to the likelihood of T1D, while asymptomatic people were less likely. Alcohol consumption had the least influence among the factors studied. Additionally, we compared the seasonal distribution of T1D and T2D. Prevalence of both T1D and T2D ( Supplementary Figure 5A & 5B) was highest during the Monsoon season (33.6% and 36.6%), followed by Winter, Summer, and Autumn. Discussion Our results show that the prevalence of young-onset diabetes increased from 2.3% in 2010 to 2.9% in 2023, with 45.5% of participants having T1D and 51.6% T2D. The proportions of T1D and T2D were remarkably similar across the two time periods, indicating that both forms contribute almost equally to the overall burden of young-onset diabetes at this centre. Logistic regression analysis identified higher BMI, older age at diagnosis, strong parental history of diabetes, and physical inactivity as key predictors of T2D, while SHAP analysis identified lower BMI, younger onset, no parent history, and symptomatic presentation as strongly associated with T1D. A study conducted in China between 2012 and 2019 reported a yearly increase in the number of young-onset diabetes participants, with a male predominance. However, the numbers declined sharply in 2020 and 2021, which is consistent with our findings (25). This decline was likely due to disruptions to routine hospital visits during the COVID-19 pandemic. Although studies show varying prevalence rates, evidence suggests that 95% of T1D cases are diagnosed based on symptoms such as weight loss, infections, and osmotic symptoms, with no significant change over time (26). Conversely, a growing number of T2D cases are identified during routine health checks, increasing from 26.4% in 2002 to 45.2% in 2018. This shift is accompanied by a decrease in symptom-based diagnoses (68.9% to 51.8%), reflecting trends consistent with those observed in this study (26). Most of the young diabetes cases in this study were reported during the monsoon and winter periods. Data from other regions suggest that T1D may show seasonal variation, with higher incidence in autumn and winter (27), possibly linked to viral infections (28). However, large-scale Indian studies and systematic reviews do not report consistent or significant seasonal trends in the onset of diabetes (29). Still, studies report that seasonal variation in the onset of T1D is well established, with most reporting a peak in winter. Additionally, geographic factors play a role, with a higher incidence observed in colder regions and areas with lower sunlight exposure, possibly related to vitamin D status (30). T1D is common among younger individuals, with some overlap and diagnostic difficulties (4,31). Traditionally seen as a childhood and adolescent disease, T1D can occur at any age, although young adults often exhibit a different phenotype than children (32–34). In this study, a large proportion (63.5%) of those with T1D were diagnosed before the age of 15. Consequently, young adult T1D groups identified by clinical diagnosis or islet autoantibodies alone may include some T2D cases, leading to a mixed phenotype that is more similar to T2D than to childhood T1D, where T1D is more common (35,36). A family history of diabetes, especially in first-degree relatives or siblings (37–39), genetic susceptibility (13,40,41), and environmental or early-life factors, such as viral infections, may trigger islet autoimmunity (30,42,43). These findings highlight the significant influence of obesity, age at diagnosis, family history, and clinical presentation in determining the risk profile of young-onset diabetes (44,45). Other factors, such as perinatal factors including higher birthweight, caesarean delivery, maternal obesity, and preterm birth, are modestly associated with young-onset T1D (46,47). Additionally, nutritional influences, such as shorter breastfeeding duration, early cow’s milk exposure, and vitamin D deficiency, as well as maternal and early-life overweight, may also increase risk. However, evidence is mixed (30,43). Rapid lifestyle changes, urbanisation, and increased stress are linked to a higher incidence of T2D in young adults (5,48). Young Indians often have lower measured and genetically determined beta cell function, making them more susceptible to diabetes even at lower BMI (46). Moreover, a lack of physical activity (49) and sedentary habits are major modifiable risk factors, and high-calorie, high-fat diets and poor nutrition (50) contribute to early-onset T2D (5,47,48,51). In the present study, carbohydrates contributed more than 60% of total energy intake, while protein intake was relatively low (12-14%), particularly among participants with T2D. Over half of young-onset diabetes participants were obese; higher BMI is strongly linked to early onset and poor control (4,31,52,53). In India, traditional diets are often high in carbohydrates and low in protein, which can contribute to poor glycemic control, muscle loss, and insulin resistance (50,54,55). The incidence of diabetes-related complications and comorbidities is generally low during the first five years following diagnosis but rises sharply thereafter (56,57). Among the microvascular complications, diabetic retinopathy is the most prevalent, contributing to more than 10,000 new cases of blindness each year (58). It is strongly associated with prolonged hyperglycemia and typically progresses gradually (59), a pattern consistent with the findings of the present study. Notably, evidence indicates that retinopathy can begin developing up to seven years before the clinical diagnosis of T2D (59–61). Similarly, the reported prevalence of neuropathy varies widely, ranging from 13.1% to 45.0% across different populations. This variability may reflect differences in population characteristics, including the distribution of diabetes types (62,63). Previous studies have identified longer diabetes duration (62,64) as key risk factors for neuropathy. Our study also demonstrated a significant association with both of these factors. Finally, evidence from previous studies indicates that the prevalence of diabetic nephropathy in Asian Indians is comparatively lower (21). However, findings from the SEARCH study report that approximately 25% of young individuals with diabetes have diabetic kidney disease (65). Key risk factors for nephropathy include poor glycemic control, longer duration of diabetes, and elevated systolic blood pressure (21,66). Therefore, achieving and maintaining optimal glycemic control remains essential for reducing the risk of diabetes-related complications (65,67). A recent study in India indicates that increasing both the quantity and quality of protein intake can significantly improve health outcomes and is crucial for preventing and managing T2D (68). To better control the rising rates of young-onset diabetes in India, early screening and diagnosis should be prioritized at the national level. The surge in diabetes among adolescents and young adults, caused by sedentary habits, poor nutrition, and genetics, requires prompt action through school health initiatives, community outreach, and awareness campaigns. Conclusion The present research focuses on the trends in the occurrence and potential predictors of young-onset T1D and T2D within a clinic setting in southern India. Over the years, the incidence of diabetes among children, adolescents, and young adults has been on the rise, particularly for T2D due to lifestyle and genetic factors. Also, hospital and cohort studies in India show a high and rising incidence of diabetes, especially in urban populations, emphasising the need for regular testing and preventive approaches in clinical settings. This trend poses a significant public health challenge, as early-onset diabetes leads to longer disease duration and higher risk of complications. Early screening, awareness, prevention, and targeted interventions are urgently needed. Abbreviations Abbreviation Expansion CHOD-PAP Cholesterol Oxidase-Peroxidase Technique AORs Adjusted Odds Ratios BMI Body Mass Index CI Confidence Interval DEMR Diabetes Electronic Medical Records DKA Diabetic Ketoacidosis ELISA Enzyme-Linked Immunosorbent Assay FCPD Fibrocalculous pancreatic diabetes, FPG Fasting Plasma Glucose GAD Glutamic acid decarboxylase GPO-PAP Glycerol Phosphate Oxidase-Peroxidase-Amidopyrine HbA1c Glycated Haemoglobin HDL High-Density Lipoprotein HPLC High-Performance Liquid Chromatography ICMR Indian Council of Medical Research IQR Interquartile Range LDL Low-Density Lipoprotein mg/dL Milligrams Per Decilitre mmol/L Millimoles Per Litre MODY Maturity-Onset Diabetes of The Young OHA Oral Hypoglycemic Agents Pmol/ml Picomoles per millilitre SHAP SHapley Additive exPlanations T1D Type 1 diabetes T2D Type 2 diabetes YDR Young Diabetes Registry Declarations Ethics approval and consent to participate: The study was approved by the Institutional Ethics Committee (MDRF/NCT/09/01/2024), and the informed consent process was waived due to the inclusion of de-identified retrospective cases. We also confirm that the study was performed in accordance with the Helsinki Declaration of 1964, and its later amendments and as per the Indian Council of Medical Research (ICMR) Ethical guidelines. Consent for publication: Not Applicable Availability of data and materials: The datasets used and/or analysed for the study will be available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests Funding: No funding was received for this article. Authors' contributions: VM, AA, and SR conceived the study. SG and SJ retrieved the data. SR and AA checked the integrity of the data and the accuracy of the results. UG and VU helped in the statistical analysis. SR wrote the first draft of the article. SR and AA carried out the corrections in consecutive drafts.VM, RMA, RU and RP provided critical and intellectual feedback on several versions of the article. Acknowledgements: We thank the Madras Diabetes Research Foundation (MDRF) for providing the research infrastructure, clinical data resources, and institutional support that made this work possible. Conflict of interest : The authors hereby declare there is no potential conflict of interest. Authors' information Shyama Reji, M.Sc., M. Phil Madras Diabetes Research Foundation (ICMR CCoE), Chennai, India and University of Madras, Chepauk, Chennai, India G. Umasankari, M.Sc. Madras Diabetes Research Foundation (ICMR CCoE), Chennai, India U. Venkatesan U, BSMS, M.Sc. Madras Diabetes Research Foundation (ICMR CCoE), Chennai, India S. Ganesan, BE Madras Diabetes Research Foundation (ICMR CCoE), Chennai, India S. Jebarani, MBA Madras Diabetes Research Foundation (ICMR CCoE), Chennai, India R. Pradeepa, M.Sc., Ph.D. Madras Diabetes Research Foundation (ICMR CCoE), Chennai, India R. M. Anjana, MD, Ph.D. Dr. Mohan’s Diabetes Specialities centre and Madras Diabetes Research Foundation (ICMR CCoE), Chennai, India Ranjit Unnikrishnan, MD Dr. Mohan’s Diabetes Specialities centre and Madras Diabetes Research Foundation (ICMR CCoE), Chennai, India V. Mohan, MD, Ph.D., D.Sc. Dr. Mohan’s Diabetes Specialities centre and Madras Diabetes Research Foundation (ICMR CCoE), Chennai, India A. Amutha, M.Sc., Ph.D. Madras Diabetes Research Foundation (ICMR CCoE), Chennai, India References IDF Diabetes Atlas 2025 | Global Diabetes Data & Insights [Internet]. [cited 2025 Sep 4]. Available from: https://diabetesatlas.org/resources/idf-diabetes-atlas-2025/ Anjana RM, Unnikrishnan R, Deepa M, Pradeepa R, Tandon N, Das AK, et al. Metabolic non-communicable disease health report of India: the ICMR-INDIAB national cross-sectional study (ICMR-INDIAB-17). Lancet Diabetes Endocrinol. 2023 Jul 1;11(7):474–89. Reji S, Sankaraeswaran M, Ulagamathesan V, Wesley H, Ramesh G, Srinivasan S, et al. Cohort prevalence of young-onset type 2 diabetes in South Asia: A systematic review. Diabetes Res Clin Pract [Internet]. 2025 Mar 1 [cited 2025 Feb 18];221. Available from: https://www.diabetesresearchclinicalpractice.com/article/S0168-8227(25)00027-0/abstract Dutta D, Ghosh S. Young-onset diabetes: An Indian perspective. Indian J Med Res. 2019 Apr;149(4):441–2. Mohan V, Jaydip R, Deepa R. Type 2 diabetes in Asian Indian youth. Pediatr Diabetes. 2007 Dec;8 Suppl 9:28–34. Mohan V, Ramachandran A, Snehalatha C, Mohan R, Bharani G, Viswanathan M. High prevalence of maturity-onset diabetes of the young (MODY) among Indians. Diabetes Care. 1985;8(4):371–4. Amutha A, Datta M, Unnikrishnan IR, Anjana RM, Rema M, Narayan KMV, et al. Clinical profile of diabetes in the young seen between 1992 and 2009 at a specialist diabetes centre in south India. Prim Care Diabetes. 2011 Dec 1;5(4):223–9. Fu JF, Liang L, Gong CX, Xiong F, Luo FH, Liu GL, et al. Status and trends of diabetes in Chinese children: analysis of data from 14 medical centers. World J Pediatr WJP. 2013 May;9(2):127–34. Praveen PA, Madhu SV, Viswanathan M, Das S, Kakati S, Shah N, et al. Demographic and clinical profile of youth onset diabetes patients in India-Results from the baseline data of a clinic based registry of people with diabetes in India with young age at onset-[YDR-02]. Pediatr Diabetes. 2021 Feb;22(1):15–21. Pradeepa R, Prabu AV, Jebarani S, Subhashini S, Mohan V. Use of a large diabetes electronic medical record system in India: clinical and research applications. J Diabetes Sci Technol. 2011 May 1;5(3):543–52. Anjana RM, Pradeepa R, Deepa M, Jebarani S, Venkatesan U, Parvathi SJ, et al. Acceptability and Utilization of Newer Technologies and Effects on Glycemic Control in Type 2 Diabetes: Lessons Learned from Lockdown. Diabetes Technol Ther. 2020 Jul;22(7):527–34. Fukuyama N, Homma K, Wakana N, Kudo K, Suyama A, Ohazama H, et al. Validation of the Friedewald Equation for Evaluation of Plasma LDL-Cholesterol. J Clin Biochem Nutr. 2008 Jul;43(1):1–5. Mohan V, Shanthi Rani CS, Saboo B, Mukhopadhyay S, Chatterjee S, Dharmarajan P, et al. Clinical Profile of Long-Term Survivors and Nonsurvivors with Type 1 Diabetes in India. Diabetes Technol Ther. 2022 Feb;24(2):120–9. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med J Br Diabet Assoc. 1998 Jul;15(7):539–53. Amutha A, Datta M, Unnikrishnan IR, Anjana RM, Rema M, Narayan KMV, et al. Clinical profile of diabetes in the young seen between 1992 and 2009 at a specialist diabetes centre in south India. Prim Care Diabetes. 2011 Dec;5(4):223–9. Khadikar V, Khadilkar AV, Lohiya NN, Karguppikar MB. Extended growth charts for Indian children. J Pediatr Endocrinol Metab JPEM. 2021 Mar 26;34(3):357–62. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet Lond Engl. 2004 Jan 10;363(9403):157–63. Rema M, Deepa R, Mohan V. Prevalence of retinopathy at diagnosis among type 2 diabetic patients attending a diabetic centre in south India. Br J Ophthalmol. 2000 Sep;84(9):1058–60. Grading diabetic retinopathy from stereoscopic color fundus photographs--an extension of the modified Airlie House classification. ETDRS report number 10. Early Treatment Diabetic Retinopathy Study Research Group. Ophthalmology. 1991 May;98(5 Suppl):786–806. Mohan V, Meera R, Premalatha G, Deepa R, Miranda P, Rema M. Frequency of proteinuria in type 2 diabetes mellitus seen at a diabetes centre in southern India. Postgrad Med J. 2000 Sep;76(899):569–73. Unnikrishnan RI, Rema M, Pradeepa R, Deepa M, Shanthirani CS, Deepa R, et al. Prevalence and risk factors of diabetic nephropathy in an urban South Indian population: the Chennai Urban Rural Epidemiology Study (CURES 45). Diabetes Care. 2007 Aug;30(8):2019–24. Ashok S, Ramu M, Deepa R, Mohan V. Prevalence of neuropathy in type 2 diabetic patients attending a diabetes centre in South India. J Assoc Physicians India. 2002 Apr;50:546–50. Deepa M, Pradeepa R, Rema M, Mohan A, Deepa R, Shanthirani S, et al. The Chennai Urban Rural Epidemiology Study (CURES)--study design and methodology (urban component) (CURES-I). J Assoc Physicians India. 2003 Sep;51:863–70. Venkatesan U, Amutha A, Anjana RM, Unnikrishnan R, Mappillairaju B, Mohan V. Predictive Modeling for Diabetes Subtype Classification in India: A Machine Learning Approach. J Diabetol. 2025 Jun;16(2):165. Dong W, Zhang S, Yan S, Zhao Z, Zhang Z, Gu W. Clinical characteristics of patients with early-onset diabetes mellitus: a single-center retrospective study. BMC Endocr Disord. 2023 Oct 10;23(1):216. Wagenknecht LE, Lawrence JM, Isom S, Jensen ET, Dabelea D, Liese AD, et al. Trends in Incidence of Youth-Onset Type 1 and Type 2 Diabetes, 2002–2018: Results from the US Population-Based SEARCH for Diabetes in Youth Study. Lancet Diabetes Endocrinol. 2023 Apr;11(4):242–50. Patterson CC, Harjutsalo V, Rosenbauer J, Neu A, Cinek O, Skrivarhaug T, et al. Trends and cyclical variation in the incidence of childhood type 1 diabetes in 26 European centres in the 25 year period 1989–2013: a multicentre prospective registration study. Diabetologia. 2019 Mar 1;62(3):408–17. Harvey JN, Hibbs R, Maguire MJ, O’Connell H, Gregory JW, Brecon Group (The Wales Paediatric Diabetes Interest Group). The changing incidence of childhood-onset type 1 diabetes in Wales: Effect of gender and season at diagnosis and birth. Diabetes Res Clin Pract. 2021 May;175:108739. Chauhan S, Khatib MN, Ballal S, Bansal P, Bhopte K, Gaidhane AM, et al. The rising burden of diabetes and state-wise variations in India: insights from the Global Burden of Disease Study 1990–2021 and projections to 2031. Front Endocrinol [Internet]. 2025 May 12 [cited 2025 Jul 21];16. Available from: https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1505143/full Zorena K, Michalska M, Kurpas M, Jaskulak M, Murawska A, Rostami S. Environmental Factors and the Risk of Developing Type 1 Diabetes-Old Disease and New Data. Biology. 2022 Apr 16;11(4):608. Reddy PK, Jevalikar G, SIinghal AA, Kaur P, Guptha A, MISHRA SK, et al. 1457-P: Clinical, Biochemical, and Genetic Profile of Patients with Young-Onset Diabetes in North India. Diabetes N Y N. 2020;69(Supplement_1). Thomas NJ, Jones SE, Weedon MN, Shields BM, Oram RA, Hattersley AT. Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetically stratified survival analysis from UK Biobank. Lancet Diabetes Endocrinol. 2018 Feb;6(2):122–9. Diaz-Valencia PA, Bougnères P, Valleron AJ. Global epidemiology of type 1 diabetes in young adults and adults: a systematic review. BMC Public Health. 2015 Mar 17;15:255. Harding JL, Wander PL, Zhang X, Li X, Karuranga S, Chen H, et al. The Incidence of Adult-Onset Type 1 Diabetes: A Systematic Review From 32 Countries and Regions. Diabetes Care. 2022 Apr 1;45(4):994–1006. Jones AG, Shields BM, Dennis JM, Hattersley AT, McDonald TJ, Thomas NJ. The challenge of diagnosing type 1 diabetes in older adults. Diabet Med J Br Diabet Assoc. 2020 Oct;37(10):1781–2. Thomas NJ, Jones AG. The challenges of identifying and studying type 1 diabetes in adults. Diabetologia. 2023;66(12):2200–12. Barone B, Rodacki M, Zajdenverg L, Almeida MH, Cabizuca CA, Barreto D, et al. Family history of type 2 diabetes is increased in patients with type 1 diabetes. Diabetes Res Clin Pract. 2008 Oct;82(1):e1-4. Wędrychowicz A, Grzelak T, Pietraszek A, Skrzyszowska M, Minasjan M, Starzyk JB. Affected brother as the highest risk factor of type 1 diabetes development in children and adolescents: One center data before implementing type 1 diabetes national screening. Adv Clin Exp Med Off Organ Wroclaw Med Univ. 2024 Aug;33(8):781–90. Ramachandran A, Snehalatha C, Premila L, Mohan V, Viswanathan M. Familial aggregation in type 1 (insulin-dependent) diabetes mellitus: a study from south India. Diabet Med J Br Diabet Assoc. 1990 Dec;7(10):876–9. Pociot F, Lernmark Å. Genetic risk factors for type 1 diabetes. Lancet Lond Engl. 2016 Jun 4;387(10035):2331–9. Goyal S, Rani J, Bhat MA, Vanita V. Genetics of diabetes. World J Diabetes. 2023 Jun 15;14(6):656–79. Rewers M, Ludvigsson J. Environmental risk factors for type 1 diabetes. Lancet Lond Engl. 2016 Jun 4;387(10035):2340–8. Craig ME, Kim KW, Isaacs SR, Penno MA, Hamilton-Williams EE, Couper JJ, et al. Early-life factors contributing to type 1 diabetes. Diabetologia. 2019 Oct;62(10):1823–34. Radha V, Mohan V. Genetic predisposition to type 2 diabetes among Asian Indians. Indian J Med Res. 2007 Mar;125(3):259–74. Indulekha K, Anjana RM, Surendar J, Mohan V. Association of visceral and subcutaneous fat with glucose intolerance, insulin resistance, adipocytokines and inflammatory markers in Asian Indians (CURES-113). Clin Biochem. 2011 Mar;44(4):281–7. Siddiqui MK, Anjana RM, Dawed AY, Martoeau C, Srinivasan S, Saravanan J, et al. Correction to: Young-onset diabetes in Asian Indians is associated with lower measured and genetically determined beta cell function. Diabetologia. 2022 Jul;65(7):1237. Mingwal BS, Gogoi JB, And KG, Rawat P. Susceptibility and Risk Factors of developing type 2 Diabetes and Pre-diabetes among Young Indian Population in Uttarakhand, India. CPD Bull Clin Biochem. 2023 Mar 28;8:22–9. Nagarathna R, Bali P, Anand A, Srivastava V, Patil S, Sharma G, et al. Prevalence of Diabetes and Its Determinants in the Young Adults Indian Population-Call for Yoga Intervention. Front Endocrinol. 2020;11:507064. Mohan V, Gokulakrishnan K, Deepa R, Shanthirani CS, Datta M. Association of physical inactivity with components of metabolic syndrome and coronary artery disease—the Chennai Urban Population Study (CUPS no. 15). Diabet Med. 2005;22(9):1206–11. Anjana RM, Sudha V, Abirami K, Gayathri R, Manasa VS, Deepa M, et al. Dietary profiles and associated metabolic risk factors in India from the ICMR-INDIAB survey-21. Nat Med. 2025 Sep 30; Mohan V, Spiegelman D, Sudha V, Gayathri R, Hong B, Praseena K, et al. Effect of brown rice, white rice, and brown rice with legumes on blood glucose and insulin responses in overweight Asian Indians: a randomized controlled trial. Diabetes Technol Ther. 2014 May;16(5):317–25. Narayanan N, Dwarakanath C, Venkataraman S, Manikandan R, Narendra B, Sambit D, et al. 2174-PUB: Profiling of Young Diabetes in India: A Cross-Sectional Analysis Report. Diabetes N Y N. 2020;69(Supplement_1). Sosale B, Sosale AR, Mohan AR, Kumar PM, Saboo B, Kandula S. Cardiovascular risk factors, micro and macrovascular complications at diagnosis in patients with young onset type 2 diabetes in India: CINDI 2. Indian J Endocrinol Metab. 2016;20(1):114–8. Mohan V, Sudha V, Shobana S, Gayathri R, Krishnaswamy K. Are Unhealthy Diets Contributing to the Rapid Rise of Type 2 Diabetes in India? J Nutr. 2023 Apr;153(4):940–8. Mohan V, Vijayachandrika V, Gokulakrishnan K, Anjana RM, Ganesan A, Weber MB, et al. A1C Cut Points to Define Various Glucose Intolerance Groups in Asian Indians. Diabetes Care. 2010 Mar;33(3):515–9. Pradeepa R, Anjana RM, Unnikrishnan R, Ganesan A, Mohan V, Rema M. Risk factors for microvascular complications of diabetes among South Indian subjects with type 2 diabetes--the Chennai Urban Rural Epidemiology Study (CURES) Eye Study-5. Diabetes Technol Ther. 2010 Oct;12(10):755–61. Bhansali A, Dhandania VK, Deepa M, Anjana RM, Joshi SR, Joshi PP, et al. Prevalence of and risk factors for hypertension in urban and rural India: the ICMR-INDIAB study. J Hum Hypertens. 2015 Mar;29(3):204–9. Farmaki P, Damaskos C, Garmpis N, Garmpi A, Savvanis S, Diamantis E. Complications of the Type 2 Diabetes Mellitus. Curr Cardiol Rev. 2020 Nov;16(4):249–51. Harris R, Leininger L. Preventive care in rural primary care practice. Cancer. 1993 Aug 1;72(3 Suppl):1113–8. Pradeepa R, Anitha B, Mohan V, Ganesan A, Rema M. Risk factors for diabetic retinopathy in a South Indian Type 2 diabetic population--the Chennai Urban Rural Epidemiology Study (CURES) Eye Study 4. Diabet Med J Br Diabet Assoc. 2008 May;25(5):536–42. Rema M, Saravanan G, Deepa R, Mohan V. Familial clustering of diabetic retinopathy in South Indian Type 2 diabetic patients. Diabet Med J Br Diabet Assoc. 2002 Nov;19(11):910–6. Pradeepa R, Rema M, Vignesh J, Deepa M, Deepa R, Mohan V. Prevalence and risk factors for diabetic neuropathy in an urban south Indian population: the Chennai Urban Rural Epidemiology Study (CURES-55). Diabet Med J Br Diabet Assoc. 2008 Apr;25(4):407–12. Shaw JE, Hodge AM, de Courten M, Dowse GK, Gareeboo H, Tuomilehto J, et al. Diabetic neuropathy in Mauritius: prevalence and risk factors. Diabetes Res Clin Pract. 1998 Nov;42(2):131–9. Franklin GM, Shetterly SM, Cohen JA, Baxter J, Hamman RF. Risk factors for distal symmetric neuropathy in NIDDM. The San Luis Valley Diabetes Study. Diabetes Care. 1994 Oct;17(10):1172–7. TODAY Study Group, Bjornstad P, Drews KL, Caprio S, Gubitosi-Klug R, Nathan DM, et al. Long-Term Complications in Youth-Onset Type 2 Diabetes. N Engl J Med. 2021 Jul 29;385(5):416–26. Premalatha G, Vidhya K, Deepa R, Ravikumar R, Rema M, Mohan V. Prevalence of non-diabetic renal disease in type 2 diabetic patients in a diabetes centre in Southern India. J Assoc Physicians India. 2002 Sep;50:1135–9. Hamman RF, Bell RA, Dabelea D, D’Agostino RB, Dolan L, Imperatore G, et al. The SEARCH for Diabetes in Youth study: rationale, findings, and future directions. Diabetes Care. 2014 Dec;37(12):3336–44. Mohan V, Misra A, Bhansali A, Singh AK, Makkar B, Krishnan D, et al. Role and Significance of Dietary Protein in the Management of Type 2 Diabetes and Its Complications in India: An Expert Opinion. J Assoc Physicians India. 2023 Dec;71(12):36–46. Tables Table 1: Clinical and Biochemical details of individuals with young-onset diabetes * in a tertiary diabetes care centre Variables T1D (n = 4757) T2D (n = 5392) p-Value Age at first Visit (in years) 18.3 ± 10.1 29.8 ± 11.1 <0.001 Age at diagnosis of diabetes* (in years) 13.5 ± 6.4 21.1 ± 3.8 <0.001 Duration (in years) 4.8 ± 7.3 8.7 ± 10.2 <0.001 Gender (males), n (%) 2510 (52.8) 2804 (52.0) <0.001 Height (cm) 150 ± 21 163 ± 10 <0.001 Weight (kg) 45.4 ± 18.1 71.1 ± 16.8 <0.001 BMI Z-score 1 (1-18 years) -0.3 ± 0.8 1.2 ± 1.0 18.0 Years) 19.3 ± 4.7 26.8 ± 5.3 <0.001 Obesity Classification, n (%) Underweight 2195 (48.9) 233 (4.8) <0.001 Normal 1358 (30.2) 801 (16.5) <0.001 Overweight 377 (8.4) 737 (15.2) <0.001 Obesity 562 (12.5) 3070 (63.4) <0.001 Waist Circumference (cm) 71.4 ± 14.2 92.4 ± 13.1 <0.001 Systolic Blood Pressure (mmHg) 108 ± 15 121 ± 16 <0.001 Diastolic Blood Pressure (mmHg) 72 ± 9 79 ± 9 <0.001 Fasting plasma glucose (mg/dl) 218 ± 109 197 ± 90 <0.001 Glycated haemoglobin (%) 10.7 ± 2.8 9.6 ± 2.5 <0.001 Good Control (< 7%), n (%) 406 (9.4) 891 (17.9) <0.001 Fair Control (7.0 – 7.9%) n (%) 369 (8.6) 522 (10.5) <0.005 Poor Control (≥8.0%) n (%) 3537 (82.0) 3554 (71.6) <0.001 Total Cholesterol (mg/dl) 170 ± 41 179 ± 44 <0.001 Serum Triglycerides (mg/dl) Median (IQR) 82 (54) 139 (108) <0.001 HDL Cholesterol (mg/dl) 48 ± 12 41 ± 11 <0.001 LDL Cholesterol (mg/dl) 104 ± 35 108 ± 39 <0.001 C Peptide Fasting (pmol/ml) Median (IQR) 0.3 (0.0) 0.8 (0.6) <0.001 C Peptide Stimulated (pmol/ml) Median (IQR) 0.3 (0.2) 1.7 (1.4) <0.001 GAD positive (U/ml), n (%) 1613/2497 (64.6) 38/1216 (3.1) <0.001 Creatinine (mg/dl) 0.7 ± 0.4 0.8 ± 0.4 <0.001 Diabetes Treatment, n (%) OHA alone - 2195 (40.7) <0.001 Insulin 4311 (90.6) 867 (16.9) <0.001 Insulin + OHA 446 (9.9) 2231 (43.4) <0.001 Diet and Exercise - 99 (1.9) <0.001 1 Indian Academy of Paediatrics (IAP); 2 Asia Pacific BMI guidelines Table 2: Logistic regression analysis of predictors associated with young-onset T2D Variables Unadjusted Adjusted OR (95% CI) p- value OR (95% CI) p- value Gender (Female) 1.10 (0.92 – 1.30) 0.266 1.61 (0.88 – 2.95) 0.120 Age at Onset 1 (Years) 1.25 (1.23 - 1.28) <0.001 1.16 (1.08 - 1.25) <0.001 Body Mass Index 2 (Kg/m2) 1.42 (1.38 - 1.46) <0.001 1.47 (1.36 - 1.59) <0.001 Symptomatic v/s Asymptomatic 3 3.96 (2.89 - 5.43) <0.001 1.75 (0.91 - 3.38) 0.095 Parental History 4 Single parent 3.35 (2.43 - 4.61) <0.001 1.22 (0.64 - 2.32) 0.547 Both parents 12.11 (7.61 - 19.27) <0.001 3.73 (1.61 - 8.64) 0.002 Physical Inactivity 5 1.84 (1.42 - 2.39) <0.001 3.09 (1.68 - 5.67) <0.001 Alcohol Intake 6 (Yes) 2.31 (1.64 - 3.25) <0.001 0.44 (0.16 - 1.16) 0.098 Smoking 7 (Yes) 4.47 (2.5 - 7.98) <0.001 1.04 (0.24 - 4.60) 0.955 Daily carbohydrate Intake 8 (g) 1.00 (1.00 - 1.00) 0.046 1.00 (0.99 - 1.00) 0.316 1 Age at diagnosis (years) was treated as a continuous variable; 2 Body mass index (BMI) was continuous variable; 3 Symptomatic status was defined based on the presence of symptoms such as polyuria, polyphagia, polydipsia, weight loss, or infections, which served as the reference group, while asymptomatic participants (detected during general check-up, pre-pregnancy check-up, or annual check-up) were coded as 1; 4 Parental history of diabetes was coded with no parental history as reference, single parent = 1, and both parents = 2; ; 5 Physical Activity: Yes = 0, No = 1; 6&7 Alcohol consumption and smoking: Yes = 1, No = 0; 8 Daily Carbohydrate intake was a continuous variable Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Mar, 2026 Reviews received at journal 20 Jan, 2026 Reviews received at journal 20 Jan, 2026 Reviews received at journal 16 Jan, 2026 Reviews received at journal 16 Jan, 2026 Reviews received at journal 12 Jan, 2026 Reviewers agreed at journal 12 Jan, 2026 Reviews received at journal 11 Jan, 2026 Reviews received at journal 11 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviews received at journal 09 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviews received at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers invited by journal 07 Jan, 2026 Editor invited by journal 15 Dec, 2025 Editor assigned by journal 12 Dec, 2025 Submission checks completed at journal 12 Dec, 2025 First submitted to journal 09 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8314170","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571641731,"identity":"cd0f3962-aed5-45c2-a7fd-ff7b407cd567","order_by":0,"name":"Shyama Reji","email":"","orcid":"","institution":"Madras Diabetes Research Foundation (ICMR CCoE)","correspondingAuthor":false,"prefix":"","firstName":"Shyama","middleName":"","lastName":"Reji","suffix":""},{"id":571641733,"identity":"7abbe00a-3053-483d-bb18-8f83da053793","order_by":1,"name":"Ganesan Umasankari","email":"","orcid":"","institution":"Madras Diabetes Research Foundation (ICMR CCoE)","correspondingAuthor":false,"prefix":"","firstName":"Ganesan","middleName":"","lastName":"Umasankari","suffix":""},{"id":571641735,"identity":"1b564031-1b4b-45d1-b668-7542365b05fc","order_by":2,"name":"Venkatesan Ulagamathesan","email":"","orcid":"","institution":"Madras Diabetes Research Foundation (ICMR CCoE)","correspondingAuthor":false,"prefix":"","firstName":"Venkatesan","middleName":"","lastName":"Ulagamathesan","suffix":""},{"id":571641738,"identity":"5a843436-aa42-45d8-b667-3b5c019f8f97","order_by":3,"name":"Sadhasivam Ganesan","email":"","orcid":"","institution":"Madras Diabetes Research Foundation (ICMR CCoE)","correspondingAuthor":false,"prefix":"","firstName":"Sadhasivam","middleName":"","lastName":"Ganesan","suffix":""},{"id":571641740,"identity":"d32e9a76-1d69-486c-b84c-52ee093980b4","order_by":4,"name":"Saravanan Jebarani","email":"","orcid":"","institution":"Madras Diabetes Research Foundation (ICMR CCoE)","correspondingAuthor":false,"prefix":"","firstName":"Saravanan","middleName":"","lastName":"Jebarani","suffix":""},{"id":571641743,"identity":"427f68e4-a25c-4e84-b062-8f38d00934b4","order_by":5,"name":"Rajendra Pradeepa","email":"","orcid":"","institution":"Madras Diabetes Research Foundation (ICMR CCoE)","correspondingAuthor":false,"prefix":"","firstName":"Rajendra","middleName":"","lastName":"Pradeepa","suffix":""},{"id":571641748,"identity":"f5a10b37-669e-4a4d-9058-e872cfd81c0c","order_by":6,"name":"Ranjit Mohan Anjana","email":"","orcid":"","institution":"Dr. Mohan’s Diabetes Specialities centre and Madras Diabetes Research Foundation (ICMR CCoE)","correspondingAuthor":false,"prefix":"","firstName":"Ranjit","middleName":"Mohan","lastName":"Anjana","suffix":""},{"id":571641755,"identity":"7c1fa399-be4f-44ba-91e9-aac697ecc0dd","order_by":7,"name":"Ranjit Unnikrishnan","email":"","orcid":"","institution":"Dr. Mohan’s Diabetes Specialities centre and Madras Diabetes Research Foundation (ICMR CCoE)","correspondingAuthor":false,"prefix":"","firstName":"Ranjit","middleName":"","lastName":"Unnikrishnan","suffix":""},{"id":571641761,"identity":"95303a5c-14f5-49d3-b610-360373681e09","order_by":8,"name":"Viswanathan Mohan","email":"","orcid":"","institution":"Dr. Mohan’s Diabetes Specialities centre and Madras Diabetes Research Foundation (ICMR CCoE)","correspondingAuthor":false,"prefix":"","firstName":"Viswanathan","middleName":"","lastName":"Mohan","suffix":""},{"id":571641765,"identity":"aa87f1cb-5e31-4a63-a6aa-e9601510bb80","order_by":9,"name":"Anandakumar Amutha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYJACZgiVw/gASPLwEaGDsRmqhdkApIWNFC1sEiCKoBb+aWfMHxfU3JE3Z889Vvk1x06GjYH54aMbeLRI3M4xbJ5x7Jnhzp53abdltyUDHcZmbJyDzxqQFh62w4wbbuSY3ZbcxgzUwsMmjU+LPFjLv8P2IC3FktvqCWsxAGnhbTucCNLC+HHbYcJaDG+nFc7m7TucvOHMG2Npxm3HediYCfhF7nbyhs883w7bbjieY/jx57Zqe3725oeP8XofGTDzgElilYMA4w9SVI+CUTAKRsGIAQDqM0lfXD6NhQAAAABJRU5ErkJggg==","orcid":"","institution":"Madras Diabetes Research Foundation (ICMR CCoE)","correspondingAuthor":true,"prefix":"","firstName":"Anandakumar","middleName":"","lastName":"Amutha","suffix":""}],"badges":[],"createdAt":"2025-12-09 07:08:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8314170/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8314170/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100011681,"identity":"d66e93c3-66d1-451e-8194-14ecf00ce26e","added_by":"auto","created_at":"2026-01-12 06:11:20","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":633964,"visible":true,"origin":"","legend":"","description":"","filename":"Trendsclinicalcharacteristicsandriskfactorsofyoungonsetdiabetes.docx","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/1bb4c61a2922b69f46f8cbf6.docx"},{"id":100011679,"identity":"5b0c7ca5-0566-4b28-ada8-4b306cb5bed5","added_by":"auto","created_at":"2026-01-12 06:11:20","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11155,"visible":true,"origin":"","legend":"","description":"","filename":"75cf1326a2b44c99a26e1c53ad9ae5a1.json","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/804e112ef4ba3efe0c272c88.json"},{"id":100011682,"identity":"e46476e1-0941-4fca-aeca-85816d189453","added_by":"auto","created_at":"2026-01-12 06:11:20","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":184527,"visible":true,"origin":"","legend":"","description":"","filename":"75cf1326a2b44c99a26e1c53ad9ae5a11enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/98cba2e502ffd57f7eb1d6fe.xml"},{"id":100011717,"identity":"5586ea3c-72cb-45a6-8573-eb4dcac281f6","added_by":"auto","created_at":"2026-01-12 06:11:21","extension":"jpeg","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":450002,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/e842c7650b82d08056504d3e.jpeg"},{"id":100011729,"identity":"77ccde44-72bb-4176-9ece-092eff2b093e","added_by":"auto","created_at":"2026-01-12 06:11:21","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":69002,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/4fedcd81294e28b70a963895.png"},{"id":100362318,"identity":"a7fbaa39-2b57-4bac-a273-1787dbf10834","added_by":"auto","created_at":"2026-01-16 07:46:33","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":48041,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/6370485c84ddafbd61433f80.png"},{"id":100011732,"identity":"2bd528a4-78d8-44bd-bb86-2c7e98556acf","added_by":"auto","created_at":"2026-01-12 06:11:21","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":49539,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/110e0676df286edcf87ad2de.png"},{"id":100011715,"identity":"2e99bde6-e8b4-4957-8f3b-2fc121d8a3cd","added_by":"auto","created_at":"2026-01-12 06:11:21","extension":"jpeg","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":341542,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/5b72f8560b89386864eb090e.jpeg"},{"id":100011702,"identity":"7b9af2f7-6d54-4e2f-b689-40151b33642f","added_by":"auto","created_at":"2026-01-12 06:11:21","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":79706,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/9dfcea3c9f4341fdd68dd86b.png"},{"id":100011688,"identity":"d9b06a54-f7be-4b7c-8550-1a78b7329d07","added_by":"auto","created_at":"2026-01-12 06:11:21","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":123543,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/2237b86cda22c7c58f9e638e.png"},{"id":100011683,"identity":"62bd74a9-eb22-4bd7-8a81-ec20c022be1a","added_by":"auto","created_at":"2026-01-12 06:11:21","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":37729,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/d8ed2cd2771f2312947d8e81.png"},{"id":100011730,"identity":"5666665e-d70f-43dc-9419-617711e74772","added_by":"auto","created_at":"2026-01-12 06:11:21","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":39436,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/9f2f9cddc3963232661be8f1.png"},{"id":100011722,"identity":"35064d6d-d808-4763-b99d-cae53bc21ab4","added_by":"auto","created_at":"2026-01-12 06:11:21","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":221576,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/93030cad55256c1626c3e598.png"},{"id":100361129,"identity":"be338db0-3002-4e45-bb1c-97396d612420","added_by":"auto","created_at":"2026-01-16 07:44:28","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":61301,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/6f9cfca01e72f0047140ce2f.png"},{"id":100011725,"identity":"c212eda0-829e-4d60-ae4c-eedcc2048604","added_by":"auto","created_at":"2026-01-12 06:11:21","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":44774,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/6f217c94db954c9ccb8da749.png"},{"id":100361459,"identity":"15590ef5-d5d1-4614-99b9-d2d3958bbfe6","added_by":"auto","created_at":"2026-01-16 07:45:12","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":46871,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/3acedc15e6b16c01ed8ac0df.png"},{"id":100361360,"identity":"6820e3c0-beff-41b5-958e-4b89bf16b952","added_by":"auto","created_at":"2026-01-16 07:45:00","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":56945,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/bc23c4c3fb4f52fe8e412b03.png"},{"id":100011720,"identity":"dfc9bb07-7d67-4c8b-9e14-b218bcc86535","added_by":"auto","created_at":"2026-01-12 06:11:21","extension":"png","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":73844,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/c2fcdad35680414bb953cf13.png"},{"id":100361263,"identity":"68cce299-ce1d-40fc-9757-99e41d7623d7","added_by":"auto","created_at":"2026-01-16 07:44:47","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":25950,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/fc8e5f5b5384cc013af399d9.png"},{"id":100011721,"identity":"823f4f86-dbba-4a40-b6b1-09e82f4acd4b","added_by":"auto","created_at":"2026-01-12 06:11:21","extension":"png","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":36419,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/29eb8287fe47865adfc9722c.png"},{"id":100011727,"identity":"049562df-c606-4771-9a57-401b0bf51f67","added_by":"auto","created_at":"2026-01-12 06:11:21","extension":"png","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":35653,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/775b4152d9b7b2f8286b7675.png"},{"id":100011728,"identity":"73c7caf5-e691-4794-af1d-7ba5ec3e9e2b","added_by":"auto","created_at":"2026-01-12 06:11:21","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":179530,"visible":true,"origin":"","legend":"","description":"","filename":"75cf1326a2b44c99a26e1c53ad9ae5a11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/59e505b29fb5577f47e610b5.xml"},{"id":100011733,"identity":"7febbf68-3381-4e2d-9477-255aac1c759f","added_by":"auto","created_at":"2026-01-12 06:11:21","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":197357,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/35b70a9dd8376b772163f6f6.html"},{"id":100361657,"identity":"ea9d5c3f-6916-4ec6-bbc5-0f4dc3d97de6","added_by":"auto","created_at":"2026-01-16 07:45:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142442,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flow chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/e66580f9cef628eff0a58f00.png"},{"id":100011677,"identity":"7ab84b38-1ae9-4e1c-83ec-a746ef93dfb2","added_by":"auto","created_at":"2026-01-12 06:11:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94006,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eYearly trend on young-onset diabetes registered in a tertiary diabetes care centre\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/a42b9dea5a26f1c1795f7d9b.png"},{"id":100361664,"identity":"70ace0f9-63c6-4f48-b9d6-9be368f67d5a","added_by":"auto","created_at":"2026-01-16 07:45:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":118890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A \u0026amp; B): Trend of young-onset T1D and T2D registered at a tertiary care diabetes centre\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/fd272bf428f9149cce71b859.png"},{"id":100011724,"identity":"54295c66-ab86-4997-a89d-a6b6c2a95b72","added_by":"auto","created_at":"2026-01-12 06:11:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68189,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of age onset of young-onset T1D and T2D at a tertiary diabetes care centre\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/9b7c5885a3dc160016345ce8.png"},{"id":100381347,"identity":"a3d68431-382b-4160-b69a-82cf8664836c","added_by":"auto","created_at":"2026-01-16 10:38:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1481106,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/03cae44b-459d-4c7d-abcb-c043b686650b.pdf"},{"id":100360985,"identity":"62bac42d-f91b-4b36-8d85-684712e4b64e","added_by":"auto","created_at":"2026-01-16 07:44:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":299496,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.docx","url":"https://assets-eu.researchsquare.com/files/rs-8314170/v1/e3a5ef4d6961c9bd7ea7a351.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends, clinical characteristics, and risk factors of young-onset diabetes at a tertiary care diabetes centre in India","fulltext":[{"header":"Highlights of the study","content":"\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe prevalence of young-onset T1D and T2D is steadily increasing in the clinic setting\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe frequency of young-onset T2D id higher than that of T1D at this private centre\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUnhealthy lifestyle and urbanisation are likely to have contributed to the increase of the prevalence of young-onset T2D\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eGlobally, the International Diabetes Federation (IDF) and most epidemiological studies define young-onset diabetes as a diagnosis before 20 years of age. Generally, type 1 diabetes (T1D), which arises from the autoimmune destruction of \u0026beta;-cells, dominates young-onset diabetes, affecting 9.5 million children and adolescents worldwide (1). However, type 2 diabetes (T2D), once rare in youth, now accounts for a significant and growing share of new diabetes cases worldwide (2,3), reflecting a concerning shift in disease patterns. Asian Indians have been shown to develop T2D, 2 to 3 decades earlier than Western populations (4\u0026ndash;6). \u0026nbsp;Many Indian studies now suggest that T2D slightly exceeds T1D in urban referral populations (7\u0026ndash;9). T2D in youth is characterized by insulin resistance, early \u0026beta;-cell decline, and a more aggressive disease course with earlier complications (2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs the global burden of diabetes continues to rise, particularly among younger populations, there is an urgent need for localized data to support early screening, targeted interventions, and health system planning. Benchmarking prevalence rates across specialized clinical settings provides valuable insights into real-world trends and informs both clinical practice and public health policy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA 2011 study from our group reported that the prevalence of young-onset diabetes had increased from 0.55% of the total patient population in 1992 to 2.5% in 2009, with the majority of cases being T2D (7). Building on this earlier observation, we therefore analysed data from participants diagnosed with diabetes at \u0026le;25 years of age and registered at the same centre between 2010 and 2023 to examine trends in the prevalence of both T1D and T2D. Furthermore, the present study assesses the prevalence of complications associated with diabetes and the factors related to the onset of T1D and T2D in this age group. Additionally, we have documented the seasonal occurrence of early-onset T1D and T2D at our facility\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis retrospective study included 10,449\u0026nbsp;individuals with an age at diagnosis of \u0026le;25 years, retrieved from the Diabetes Electronic Medical Records (DEMR) of the centre, registered between 2010 and 2023. Among these, 4757 had T1D (45.5%), 5392 had T2D (51.6%), and 300 (2.8%) had Maturity onset diabetes (MODY), Gestational diabetes (GDM) and Fibrocalculous Pancreatic Diabetes (FCPD). Figure 1 illustrates the study flowchart.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformation pertaining signs and symptoms, physical activity, family background, and lifestyle elements, such as dietary habits, smoking, and alcohol use, was gathered to determine possible predictors of young-onset T1D and T2D.\u003c/p\u003e\n\u003cp\u003eDuring their first visit, each participant received a unique identification number, allowing for thorough tracking over time. After the registration process, all participants provided a detailed medical history, which includes diabetes symptoms history of infections, current medications, and a family history of diabetes to a dietician or diabetes educator. Additionally, lifestyle history (including dietary habits assessed via 24-hour recall, physical activity, smoking habits, and alcohol consumption) was also elicited.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnthropometric and biochemical measurements, and evaluations for diabetes-related complications, were performed according to standard protocols, documented in the DEMR (10) and updated at every appointment. Height was recorded in centimeters, and weight was measured to the nearest 0.1 kg using a traditional spring scale. A qualified professional measured waist circumference with a rigid tape at the midpoint between the lumber region and crista iliaca, ensuring that the participant was standing and at the end of expiration, with a precision of 0.1 cm. Body Mass Index (BMI) was determined by dividing weight in kilograms by the square of height in meters (kg/m\u0026sup2;). Blood pressure was measured to the nearest two mmHg in the right arm using a mercury sphygmomanometer while the participant was seated and at rest. The analysis used the average of two measurements taken five minutes apart.\u003c/p\u003e\n\u003cp\u003eA fasting blood sample was taken from all participants after they fasted overnight for a minimum of 8 hours. Individuals with a history of diabetes were served a standard breakfast, followed by a venous blood draw 90 minutes later to measure their postprandial glucose level. Biochemical analyses were performed at both the main and branch laboratories, which undergo regular standardization and strict quality control procedures in coordination with the Central Laboratory (11). Plasma glucose was measured using the glucose oxidase technique. Serum cholesterol was measured with the cholesterol oxidase-peroxidase (CHOD-PAP) technique, while triglycerides were assessed using the glycerol phosphate oxidase-peroxidase-amidopyrine (GPO-PAP) method. HDL cholesterol was estimated through direct immunoinhibition, and LDL cholesterol was calculated using the Friedewald formula (12). All biochemical tests were performed with a Beckman Coulter AU2700 analyzer (Fullerton, CA). HbA1c levels were measured by high-performance liquid chromatography (HPLC) on the Variant II Turbo system (Bio-Rad, Hercules, CA). Fasting and post-breakfast (stimulated) C-peptide concentrations were measured by chemiluminescence on the Siemens ADVIA Centaur XPT immunoassay analyzer (11,13). Glutamic acid decarboxylase (GAD) antibodies were measured using an enzyme-linked immunosorbent assay (ELISA) kit (EUROIMMUN, L\u0026uuml;beck, Germany) on a Bio-Rad plate reader (Model 680). Serum creatinine levels were assessed using the Jaffe kinetic method.\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Institutional Ethics Committee (MDRF/NCT/09/01/2024), and the informed consent process was waived due to the inclusion of de-identified retrospective cases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDefinition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e was diagnosed if the individual presented with a fasting plasma glucose (FPG) level of 126 mg/dL (7.0 mmol/L) or greater, or a 2-hour post-load glucose level of 200 mg/dL (11.1 mmol/L) or greater. The condition was also considered present if the patient reported a history of diabetes managed by a physician, or if they were actively using diabetes medications like insulin or oral hypoglycemic agents\u0026nbsp;(14).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT1D\u003c/strong\u003e was diagnosed if there was a history of diabetic ketoacidosis or fasting and stimulated C-peptide values were \u0026lt;0.6pmol/mL, or if insulin treatment was required since diagnosis (15).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eT2D\u003c/strong\u003e was defined by the lack of ketosis, a sufficient \u0026beta;-cell reserve indicated by a stimulated C-peptide assay value of \u0026ge;0.6 pmol/mL and a positive response to oral hypoglycemic medications (15).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBody Mass Index\u003c/strong\u003e: Individuals aged 1 to 18 were classified according to BMI z-scores following the standards established by the Indian Academy of Paediatrics (IAP) (16): underweight: \u0026le;2.1; normal: -2.0 to 0.9; overweight: 1.0 to 1.9; and obese: \u0026gt;2.0. For individuals aged 18 and above, classification was based on the Asia Pacific BMI guidelines (17), which define categories as underweight (\u0026lt;18.5 kg/m2), normal (18.5 to 22.9), overweight (23.0 to 24.9), or obese (\u0026ge;25.0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRetinopathy\u003c/strong\u003e was evaluated by fundus examination (direct and indirect ophthalmoscopy) by a retinal specialist. Grading of retinal lesions followed a standardized international system (18,19).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNephropathy\u003c/strong\u003e was diagnosed when 24-h urine protein excretion was \u0026gt;500 mg (20) or urine albumin-to-creatinine ratio \u0026gt;300 \u0026micro;g/mg (21).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeuropathy\u003c/strong\u003e was assessed using a Biothesiometer and diagnosed if mean vibratory perception threshold (VPT) at the great toes was \u0026ge;20 V (22,23).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses, conducted using SPSS version 24.0, involved summarizing participant baseline characteristics with descriptive statistics. Categorical data is presented as frequencies and percentages, while continuous data is shown as means and standard deviations.\u003c/p\u003e\n\u003cp\u003eTo identify factors associated with the outcome, a binary logistic regression analysis was performed. Variables with p-values \u0026lt;0.05 in the univariate analysis were included in the multivariable model. Adjusted odds ratios (AORs) with corresponding 95% confidence intervals (CIs) were calculated to assess the strength and direction of associations. In addition to regression modelling, a Random Forest classifier was applied to evaluate the relative importance of predictors in classifying the outcome. Feature importance was derived using the mean decrease in Gini impurity (24), indicating each variable\u0026rsquo;s contribution to improving model performance. Furthermore, SHapley Additive exPlanations (SHAP) analysis was conducted to quantify the individual impact of each predictor on T1D classification and enhance interpretability of the machine learning model.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOut of\u0026nbsp;10,449\u0026nbsp;individuals, 4757 had T1D (45.5%), 5392 had T2D (51.6%), and 300 (2.8%) had other types of diabetes. The present study focuses solely on individuals with T1D and T2D. Table 1\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003epresents the clinical and biochemical features of the study participants. The mean age at diagnosis was significantly higher in T2D (21.1 \u0026plusmn; 3.8 years) compared to T1D (13.5 \u0026plusmn; 6.4 years). Males constituted a larger proportion in both groups: 52.8% in T1D and 52.0% in T2D. The BMI z-score for participants aged 1 to 18 was -0.3 \u0026plusmn; 0.8 for T1D and 1.2 \u0026plusmn; 1.0 for T2D. For those over 18, the average BMI was 19.3 \u0026plusmn; 4.7 kg/m\u0026sup2; in T1D and 26.8 \u0026plusmn; 5.3 kg/m\u0026sup2; in T2D. The mean HbA1c was higher in T1D (10.7 \u0026plusmn; 2.8%) than T2D (9.6 \u0026plusmn; 2.5%). Only 9.4% of individuals with T1D achieved good glycaemic control (HbA1c \u0026lt;7%), compared to 17.9% with T2D. Most participants had poor control, as defined by HbA1c \u0026ge;8%: 82.0% in T1D and 71.6% in T2D. All T1D patients were prescribed insulin only (90.0%) or insulin with OHA (9.9%), whereas only 16.9% of T2D patients were on insulin, and 1.9% were managed with diet and exercise.\u003c/p\u003e\n\u003cp\u003eThe Figure 2 shows a gradual rise in young-onset diabetes registrations from \u003cstrong\u003e2.3% in 2010\u003c/strong\u003e to \u003cstrong\u003e2.9% in 2023\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e The percentages fluctuated slightly over the years, ranging from \u003cstrong\u003e2.2% to 2.7%\u003c/strong\u003e between 2011 and 2019 followed by a dip to \u003cstrong\u003e2.0% in 2020\u003c/strong\u003e. After this decline, the trend steadily increased, reaching \u003cstrong\u003e2.9% in 2023\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Additionally, the trends of T1D (Figure 3A) and T2D (Figure 3B) registrations at the centre also showed an upward pattern, rising from 1.1% to 1.3% for T1D and reaching 1.6% for T2D.\u003c/p\u003e\n\u003cp\u003eOsmotic symptoms such as polyuria and polydipsia were observed in 57.0% of T1D cases, compared to 27.2% in T2D. Weight loss was also more prevalent among T1D participants (29.7% vs. 14.0%). Conversely, T2D was more often identified incidentally during routine check-ups (37.1%). A majority of T1D participants (65.4%) had no parental history of diabetes, whereas among T2D, 47.5% had a parent with diabetes, and 31.7% had both parents affected. Regarding diet, T2D participants consumed more total calories (1410 \u0026plusmn; 449 kcal vs. 1308 \u0026plusmn; 441 kcal). For both T1D and T2D, over 60% of energy intake came from carbohydrates, with only 13% derived from protein (Supplementary File 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 2\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eillustrates the annual trend in young-onset diabetes cases registered at a tertiary diabetes care centre, illustrated as a percentage of the total number of registered participants in each period. The prevalence of young-onset T1D increased from 1.1% in 2010 to 1.3% in 2023 (Figure 3A), while T2D prevalence rose from 1.1% to 1.6% (Figure 3B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 4\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eshows the percentage distribution of age at onset for T1D and T2D. The graph clearly indicates that nearly half of T1D cases (49.4%) were diagnosed between ages 6 and 15, while more than 60% of T2D cases were diagnosed between ages 21 and 25.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe microvascular complications increase with longer diabetes duration in both T1D and T2D (Supplementary Figure 3). For those with \u0026ge;15 years of diabetes duration, the prevalence of retinopathy, nephropathy, and neuropathy was 61.0%, 14.0%, 29.7% and 53.9%, 19.0%, 50.1% among T1D and T2D respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 displays the results of a logistic regression analysis of various factors associated with young-onset T2D, with young-onset T1D as the reference group. In the unadjusted model, factors such as age at onset (OR: 1.25, 95% CI: 1.23-1.28, P \u0026lt;0.001), BMI (OR: 1.42, 95% CI: 1.38-1.46, P \u0026lt;0.001), and asymptomatic presentation (OR: 3.96, 95% CI: 2.89-5.43, P \u0026lt;0.001) were significantly linked to young-onset T2D. Parental history showed strong associations, with one parent increasing risk more than threefold (OR: 3.35, 95% CI: 2.43-4.61, P \u0026lt;0.001) and both parents raising the odds over twelvefold (OR: 12.11, 95% CI: 7.61-19.27, P \u0026lt;0.001). Physical inactivity (OR: 1.84, 95% CI: 1.42-2.39, P \u0026lt;0.001), alcohol consumption (OR: 2.31, 95% CI: 1.64-3.25, P \u0026lt;0.001), and smoking (OR: 4.47, 95% CI: 2.5-7.98, P \u0026lt;0.001) were also significant predictors, while daily carbohydrate intake showed only a marginal effect (OR: 1.00, 95% CI: 1.00-1.00, P = 0.046).\u003c/p\u003e\n\u003cp\u003eIn the adjusted model, after controlling for gender, only a few variables such as age at diagnosis (OR: 1.16, 95% CI: 1.08-1.25, P \u0026lt; 0.001), BMI (OR: 1.47, 95% CI: 1.36-1.59, P \u0026lt; 0.001), having both parents with a history of diabetes (OR: 3.73, 95% CI: 1.61-8.64, P = 0.002), and physical inactivity (OR: 3.09, 95% CI: 1.68-5.67, P \u0026lt; 0.001) emerged as predictors of T2D risk.\u003c/p\u003e\n\u003cp\u003eAfter the SHAP analysis (Supplementary Figure 4), it was interpreted that, the features are arranged based on the risk factors contributing to T1D. Among them, BMI had the most decisive influence. People with lower BMI were more likely to have T1D. The age at onset was the next important factor, with those developing diabetes at a younger age more often falling into the T1D. Parental history also played a role, but its effect was negligible compared to BMI and age at onset. Being symptomatic at the time of diagnosis added to the likelihood of T1D, while asymptomatic people were less likely. Alcohol consumption had the least influence among the factors studied. Additionally, we compared the seasonal distribution of \u003cstrong\u003eT1D\u003c/strong\u003e and \u003cstrong\u003eT2D. Prevalence of both T1D and T2D\u0026nbsp;\u003c/strong\u003e(\u003cstrong\u003eSupplementary Figure 5A \u0026amp; 5B)\u003c/strong\u003e was highest during the Monsoon season (33.6% and 36.6%), followed by Winter, Summer, and Autumn.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur results show that the prevalence of young-onset diabetes increased from 2.3% in 2010 to 2.9% in 2023, with 45.5% of participants having T1D and 51.6% T2D. The proportions of T1D and T2D were remarkably similar across the two time periods, indicating that both forms contribute almost equally to the overall burden of young-onset diabetes at this centre. Logistic regression analysis identified higher BMI, older age at diagnosis, strong parental history of diabetes, and physical inactivity as key predictors of T2D, while SHAP analysis identified lower BMI, younger onset, no parent history, and symptomatic presentation as strongly associated with T1D.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA study conducted in China between 2012 and 2019 reported a yearly increase in the number of young-onset diabetes participants, with a male predominance. However, the numbers declined sharply in 2020 and 2021, which is consistent with our findings (25).\u0026nbsp;This decline was likely due to disruptions to routine hospital visits during the COVID-19 pandemic. Although studies show varying prevalence rates, evidence suggests that 95% of T1D cases are diagnosed based on symptoms such as weight loss, infections, and osmotic symptoms, with no significant change over time\u0026nbsp;(26). Conversely, a growing number of T2D cases are identified during routine health checks, increasing from 26.4% in 2002 to 45.2% in 2018. This shift is accompanied by a decrease in symptom-based diagnoses (68.9% to 51.8%), reflecting trends consistent with those observed in this study\u0026nbsp;(26).\u003c/p\u003e\n\u003cp\u003eMost of the young diabetes cases in this study were reported during the monsoon and winter periods. Data from other regions suggest that T1D may show seasonal variation, with higher incidence in autumn and winter (27), possibly linked to viral infections (28). However, large-scale Indian studies and systematic reviews do not report consistent or significant seasonal trends in the onset of diabetes (29). Still, studies report that\u0026nbsp;seasonal variation in the onset of T1D is well established, with most reporting a peak in winter. Additionally, geographic factors play a role, with a\u0026nbsp;higher incidence observed in colder regions and areas with lower sunlight exposure, possibly related to vitamin D status\u0026nbsp;(30).\u003c/p\u003e\n\u003cp\u003eT1D is common among younger individuals, with some overlap and diagnostic difficulties (4,31). Traditionally seen as a childhood and adolescent disease, T1D can occur at any age, although young adults often exhibit a different phenotype than children (32\u0026ndash;34). In this study, a large proportion (63.5%) of those with T1D were diagnosed before the age of 15. Consequently, young adult T1D groups identified by clinical diagnosis or islet autoantibodies alone may include some T2D cases, leading to a mixed phenotype that is more similar to T2D than to childhood T1D, where T1D is more common (35,36). A family history of diabetes, especially in first-degree relatives or siblings (37\u0026ndash;39), genetic susceptibility (13,40,41), and environmental or early-life factors, such as viral infections, may trigger islet autoimmunity (30,42,43). These findings highlight the significant influence of obesity, age at diagnosis, family history, and clinical presentation in determining the risk profile of young-onset diabetes (44,45). Other factors, such as perinatal factors including higher birthweight, caesarean delivery, maternal obesity, and preterm birth, are modestly associated with young-onset T1D (46,47). Additionally, nutritional influences, such as shorter breastfeeding duration, early cow\u0026rsquo;s milk exposure, and vitamin D deficiency, as well as maternal and early-life overweight, may also increase risk. However, evidence is mixed (30,43).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRapid lifestyle changes, urbanisation, and increased stress are linked to a higher incidence of T2D in young adults (5,48). Young Indians often have lower measured and genetically determined beta cell function, making them more susceptible to diabetes even at lower BMI (46). Moreover, a lack of physical activity (49) and sedentary habits are major modifiable risk factors, and high-calorie, high-fat diets and poor nutrition (50) contribute to early-onset T2D (5,47,48,51). In the present study, carbohydrates contributed more than 60% of total energy intake, while protein intake was relatively low (12-14%), particularly among participants with T2D. Over half of young-onset diabetes participants were obese; higher BMI is strongly linked to early onset and poor control (4,31,52,53). In India, traditional diets are often high in carbohydrates and low in protein, which can contribute to poor glycemic control, muscle loss, and insulin resistance (50,54,55).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe incidence of diabetes-related complications and comorbidities is generally low during the first five years following diagnosis but rises sharply thereafter (56,57). Among the microvascular complications, diabetic retinopathy is the most prevalent, contributing to more than 10,000 new cases of blindness each year (58). It is strongly associated with prolonged hyperglycemia and typically progresses gradually\u0026nbsp;(59), a pattern consistent with the findings of the present study. Notably, evidence indicates that retinopathy can begin developing up to seven years before the clinical diagnosis of T2D (59\u0026ndash;61). Similarly, the reported prevalence of neuropathy varies widely, ranging from 13.1% to 45.0% across different populations. This variability may reflect differences in population characteristics, including the distribution of diabetes types (62,63). Previous studies have identified longer diabetes duration (62,64) as key risk factors for neuropathy. Our study also demonstrated a significant association with both of these factors. Finally, evidence from previous studies indicates that the prevalence of diabetic nephropathy in Asian Indians is comparatively lower (21). However, findings from the SEARCH study report that approximately 25% of young individuals with diabetes have diabetic kidney disease (65). Key risk factors for nephropathy include poor glycemic control, longer duration of diabetes, and elevated systolic blood pressure (21,66). Therefore, achieving and maintaining optimal glycemic control remains essential for reducing the risk of diabetes-related complications (65,67).\u003c/p\u003e\n\u003cp\u003eA recent study in India indicates that increasing both the quantity and quality of protein intake can significantly improve health outcomes and is crucial for preventing and managing T2D (68). To better control the rising rates of young-onset diabetes in India, early screening and diagnosis should be prioritized at the national level. The surge in diabetes among adolescents and young adults, caused by sedentary habits, poor nutrition, and genetics, requires prompt action through school health initiatives, community outreach, and awareness campaigns.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present research focuses on the trends in the occurrence and potential predictors of young-onset T1D and T2D within a clinic setting in southern India. Over the years, the incidence of diabetes among children, adolescents, and young adults has been on the rise, particularly for T2D due to lifestyle and genetic factors. Also, hospital and cohort studies in India show a high and rising incidence of diabetes, especially in urban populations, emphasising the need for regular testing and preventive approaches in clinical settings. This trend poses a significant public health challenge, as early-onset diabetes leads to longer disease duration and higher risk of complications. Early screening, awareness, prevention, and targeted interventions are urgently needed.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 29.8077%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 70.1923%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExpansion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eCHOD-PAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eCholesterol Oxidase-Peroxidase Technique\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eAORs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eAdjusted Odds Ratios\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eDEMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eDiabetes Electronic Medical Records\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eDKA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eDiabetic Ketoacidosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eEnzyme-Linked Immunosorbent Assay\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eFCPD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eFibrocalculous pancreatic diabetes,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eFPG\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eFasting Plasma Glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eGAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eGlutamic acid decarboxylase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eGPO-PAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eGlycerol Phosphate Oxidase-Peroxidase-Amidopyrine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eGlycated Haemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eHDL\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eHigh-Density Lipoprotein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eHPLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eHigh-Performance Liquid Chromatography \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eICMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eIndian Council of Medical Research\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eInterquartile Range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eLow-Density Lipoprotein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003emg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eMilligrams Per Decilitre\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003emmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eMillimoles Per Litre\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eMODY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eMaturity-Onset Diabetes of The Young\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eOHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eOral Hypoglycemic Agents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003ePmol/ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003ePicomoles per millilitre\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eSHAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eSHapley Additive exPlanations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eT1D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eType 1 diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eT2D\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eType 2 diabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.8077%;\"\u003e\n \u003cp\u003eYDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70.1923%;\"\u003e\n \u003cp\u003eYoung Diabetes Registry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe study was approved by the Institutional Ethics Committee (MDRF/NCT/09/01/2024), and the informed consent process was waived due to the inclusion of de-identified retrospective cases. We also confirm that the study was performed in accordance with the Helsinki Declaration of 1964, and its later amendments and as per the Indian Council of Medical Research (ICMR) Ethical guidelines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analysed for the study will be available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo funding was received for this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eVM, AA, and SR conceived the study. SG and SJ retrieved the data. SR and AA checked the integrity of the data and the accuracy of the results. UG and VU helped in the statistical analysis. SR wrote the first draft of the article. SR and AA carried out the corrections in consecutive drafts.VM, RMA, RU and RP provided critical and intellectual feedback on several versions of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe thank the Madras Diabetes Research Foundation (MDRF) for providing the research infrastructure, clinical data resources, and institutional support that made this work possible.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e: \u0026nbsp;The authors hereby declare there is no potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShyama Reji, M.Sc., M. Phil\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMadras Diabetes Research Foundation\u0026nbsp;(ICMR CCoE), Chennai, India and University of Madras, Chepauk, Chennai, India\u003c/p\u003e\n\u003cp\u003eG. Umasankari,\u0026nbsp;M.Sc.\u003c/p\u003e\n\u003cp\u003eMadras Diabetes Research Foundation\u0026nbsp;(ICMR CCoE), Chennai, India\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eU. Venkatesan U,\u0026nbsp;BSMS,\u0026nbsp;M.Sc.\u003c/p\u003e\n\u003cp\u003eMadras Diabetes Research Foundation\u0026nbsp;(ICMR CCoE), Chennai, India\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eS.\u0026nbsp;Ganesan,\u0026nbsp;BE\u003c/p\u003e\n\u003cp\u003eMadras Diabetes Research Foundation\u0026nbsp;(ICMR CCoE), Chennai, India\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eS. Jebarani,\u0026nbsp;MBA\u003c/p\u003e\n\u003cp\u003eMadras Diabetes Research Foundation\u0026nbsp;(ICMR CCoE), Chennai, India\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eR. \u0026nbsp; \u0026nbsp; Pradeepa,\u0026nbsp;M.Sc., Ph.D.\u003c/p\u003e\n\u003cp\u003eMadras Diabetes Research Foundation\u0026nbsp;(ICMR CCoE), Chennai, India\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eR. M. Anjana,\u0026nbsp;MD, Ph.D.\u003c/p\u003e\n\u003cp\u003eDr. Mohan\u0026rsquo;s Diabetes Specialities centre and Madras Diabetes Research Foundation\u0026nbsp;(ICMR CCoE), Chennai, India\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRanjit Unnikrishnan,\u0026nbsp;MD\u003c/p\u003e\n\u003cp\u003eDr. Mohan\u0026rsquo;s Diabetes Specialities centre and Madras Diabetes Research Foundation\u0026nbsp;(ICMR CCoE), Chennai, India\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eV. Mohan,\u0026nbsp;MD, Ph.D., D.Sc.\u003c/p\u003e\n\u003cp\u003eDr. Mohan\u0026rsquo;s Diabetes Specialities centre and Madras Diabetes Research Foundation\u0026nbsp;(ICMR CCoE), Chennai, India\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA. Amutha,\u0026nbsp;M.Sc., Ph.D.\u003c/p\u003e\n\u003cp\u003eMadras Diabetes Research Foundation (ICMR CCoE), Chennai, India\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIDF Diabetes Atlas 2025 | Global Diabetes Data \u0026amp; Insights [Internet]. [cited 2025 Sep 4]. Available from: https://diabetesatlas.org/resources/idf-diabetes-atlas-2025/\u003c/li\u003e\n\u003cli\u003eAnjana RM, Unnikrishnan R, Deepa M, Pradeepa R, Tandon N, Das AK, et al. Metabolic non-communicable disease health report of India: the ICMR-INDIAB national cross-sectional study (ICMR-INDIAB-17). Lancet Diabetes Endocrinol. 2023 Jul 1;11(7):474\u0026ndash;89. \u003c/li\u003e\n\u003cli\u003eReji S, Sankaraeswaran M, Ulagamathesan V, Wesley H, Ramesh G, Srinivasan S, et al. Cohort prevalence of young-onset type 2 diabetes in South Asia: A systematic review. Diabetes Res Clin Pract [Internet]. 2025 Mar 1 [cited 2025 Feb 18];221. Available from: https://www.diabetesresearchclinicalpractice.com/article/S0168-8227(25)00027-0/abstract\u003c/li\u003e\n\u003cli\u003eDutta D, Ghosh S. Young-onset diabetes: An Indian perspective. Indian J Med Res. 2019 Apr;149(4):441\u0026ndash;2. \u003c/li\u003e\n\u003cli\u003eMohan V, Jaydip R, Deepa R. Type 2 diabetes in Asian Indian youth. Pediatr Diabetes. 2007 Dec;8 Suppl 9:28\u0026ndash;34. \u003c/li\u003e\n\u003cli\u003eMohan V, Ramachandran A, Snehalatha C, Mohan R, Bharani G, Viswanathan M. High prevalence of maturity-onset diabetes of the young (MODY) among Indians. Diabetes Care. 1985;8(4):371\u0026ndash;4. \u003c/li\u003e\n\u003cli\u003eAmutha A, Datta M, Unnikrishnan IR, Anjana RM, Rema M, Narayan KMV, et al. Clinical profile of diabetes in the young seen between 1992 and 2009 at a specialist diabetes centre in south India. Prim Care Diabetes. 2011 Dec 1;5(4):223\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eFu JF, Liang L, Gong CX, Xiong F, Luo FH, Liu GL, et al. Status and trends of diabetes in Chinese children: analysis of data from 14 medical centers. World J Pediatr WJP. 2013 May;9(2):127\u0026ndash;34. \u003c/li\u003e\n\u003cli\u003ePraveen PA, Madhu SV, Viswanathan M, Das S, Kakati S, Shah N, et al. Demographic and clinical profile of youth onset diabetes patients in India-Results from the baseline data of a clinic based registry of people with diabetes in India with young age at onset-[YDR-02]. Pediatr Diabetes. 2021 Feb;22(1):15\u0026ndash;21. \u003c/li\u003e\n\u003cli\u003ePradeepa R, Prabu AV, Jebarani S, Subhashini S, Mohan V. Use of a large diabetes electronic medical record system in India: clinical and research applications. J Diabetes Sci Technol. 2011 May 1;5(3):543\u0026ndash;52. \u003c/li\u003e\n\u003cli\u003eAnjana RM, Pradeepa R, Deepa M, Jebarani S, Venkatesan U, Parvathi SJ, et al. Acceptability and Utilization of Newer Technologies and Effects on Glycemic Control in Type 2 Diabetes: Lessons Learned from Lockdown. Diabetes Technol Ther. 2020 Jul;22(7):527\u0026ndash;34. \u003c/li\u003e\n\u003cli\u003eFukuyama N, Homma K, Wakana N, Kudo K, Suyama A, Ohazama H, et al. Validation of the Friedewald Equation for Evaluation of Plasma LDL-Cholesterol. J Clin Biochem Nutr. 2008 Jul;43(1):1\u0026ndash;5. \u003c/li\u003e\n\u003cli\u003eMohan V, Shanthi Rani CS, Saboo B, Mukhopadhyay S, Chatterjee S, Dharmarajan P, et al. Clinical Profile of Long-Term Survivors and Nonsurvivors with Type 1 Diabetes in India. Diabetes Technol Ther. 2022 Feb;24(2):120\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eAlberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med J Br Diabet Assoc. 1998 Jul;15(7):539\u0026ndash;53. \u003c/li\u003e\n\u003cli\u003eAmutha A, Datta M, Unnikrishnan IR, Anjana RM, Rema M, Narayan KMV, et al. Clinical profile of diabetes in the young seen between 1992 and 2009 at a specialist diabetes centre in south India. Prim Care Diabetes. 2011 Dec;5(4):223\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eKhadikar V, Khadilkar AV, Lohiya NN, Karguppikar MB. Extended growth charts for Indian children. J Pediatr Endocrinol Metab JPEM. 2021 Mar 26;34(3):357\u0026ndash;62. \u003c/li\u003e\n\u003cli\u003eWHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet Lond Engl. 2004 Jan 10;363(9403):157\u0026ndash;63. \u003c/li\u003e\n\u003cli\u003eRema M, Deepa R, Mohan V. Prevalence of retinopathy at diagnosis among type 2 diabetic patients attending a diabetic centre in south India. Br J Ophthalmol. 2000 Sep;84(9):1058\u0026ndash;60. \u003c/li\u003e\n\u003cli\u003eGrading diabetic retinopathy from stereoscopic color fundus photographs--an extension of the modified Airlie House classification. ETDRS report number 10. Early Treatment Diabetic Retinopathy Study Research Group. Ophthalmology. 1991 May;98(5 Suppl):786\u0026ndash;806. \u003c/li\u003e\n\u003cli\u003eMohan V, Meera R, Premalatha G, Deepa R, Miranda P, Rema M. Frequency of proteinuria in type 2 diabetes mellitus seen at a diabetes centre in southern India. Postgrad Med J. 2000 Sep;76(899):569\u0026ndash;73. \u003c/li\u003e\n\u003cli\u003eUnnikrishnan RI, Rema M, Pradeepa R, Deepa M, Shanthirani CS, Deepa R, et al. Prevalence and risk factors of diabetic nephropathy in an urban South Indian population: the Chennai Urban Rural Epidemiology Study (CURES 45). Diabetes Care. 2007 Aug;30(8):2019\u0026ndash;24. \u003c/li\u003e\n\u003cli\u003eAshok S, Ramu M, Deepa R, Mohan V. Prevalence of neuropathy in type 2 diabetic patients attending a diabetes centre in South India. J Assoc Physicians India. 2002 Apr;50:546\u0026ndash;50. \u003c/li\u003e\n\u003cli\u003eDeepa M, Pradeepa R, Rema M, Mohan A, Deepa R, Shanthirani S, et al. The Chennai Urban Rural Epidemiology Study (CURES)--study design and methodology (urban component) (CURES-I). J Assoc Physicians India. 2003 Sep;51:863\u0026ndash;70. \u003c/li\u003e\n\u003cli\u003eVenkatesan U, Amutha A, Anjana RM, Unnikrishnan R, Mappillairaju B, Mohan V. Predictive Modeling for Diabetes Subtype Classification in India: A Machine Learning Approach. J Diabetol. 2025 Jun;16(2):165. \u003c/li\u003e\n\u003cli\u003eDong W, Zhang S, Yan S, Zhao Z, Zhang Z, Gu W. Clinical characteristics of patients with early-onset diabetes mellitus: a single-center retrospective study. BMC Endocr Disord. 2023 Oct 10;23(1):216. \u003c/li\u003e\n\u003cli\u003eWagenknecht LE, Lawrence JM, Isom S, Jensen ET, Dabelea D, Liese AD, et al. Trends in Incidence of Youth-Onset Type 1 and Type 2 Diabetes, 2002\u0026ndash;2018: Results from the US Population-Based SEARCH for Diabetes in Youth Study. Lancet Diabetes Endocrinol. 2023 Apr;11(4):242\u0026ndash;50. \u003c/li\u003e\n\u003cli\u003ePatterson CC, Harjutsalo V, Rosenbauer J, Neu A, Cinek O, Skrivarhaug T, et al. Trends and cyclical variation in the incidence of childhood type 1 diabetes in 26 European centres in the 25 year period 1989\u0026ndash;2013: a multicentre prospective registration study. Diabetologia. 2019 Mar 1;62(3):408\u0026ndash;17. \u003c/li\u003e\n\u003cli\u003eHarvey JN, Hibbs R, Maguire MJ, O\u0026rsquo;Connell H, Gregory JW, Brecon Group (The Wales Paediatric Diabetes Interest Group). The changing incidence of childhood-onset type 1 diabetes in Wales: Effect of gender and season at diagnosis and birth. Diabetes Res Clin Pract. 2021 May;175:108739. \u003c/li\u003e\n\u003cli\u003eChauhan S, Khatib MN, Ballal S, Bansal P, Bhopte K, Gaidhane AM, et al. The rising burden of diabetes and state-wise variations in India: insights from the Global Burden of Disease Study 1990\u0026ndash;2021 and projections to 2031. Front Endocrinol [Internet]. 2025 May 12 [cited 2025 Jul 21];16. Available from: https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1505143/full\u003c/li\u003e\n\u003cli\u003eZorena K, Michalska M, Kurpas M, Jaskulak M, Murawska A, Rostami S. Environmental Factors and the Risk of Developing Type 1 Diabetes-Old Disease and New Data. Biology. 2022 Apr 16;11(4):608. \u003c/li\u003e\n\u003cli\u003eReddy PK, Jevalikar G, SIinghal AA, Kaur P, Guptha A, MISHRA SK, et al. 1457-P: Clinical, Biochemical, and Genetic Profile of Patients with Young-Onset Diabetes in North India. Diabetes N Y N. 2020;69(Supplement_1). \u003c/li\u003e\n\u003cli\u003eThomas NJ, Jones SE, Weedon MN, Shields BM, Oram RA, Hattersley AT. Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetically stratified survival analysis from UK Biobank. Lancet Diabetes Endocrinol. 2018 Feb;6(2):122\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eDiaz-Valencia PA, Bougn\u0026egrave;res P, Valleron AJ. Global epidemiology of type 1 diabetes in young adults and adults: a systematic review. BMC Public Health. 2015 Mar 17;15:255. \u003c/li\u003e\n\u003cli\u003eHarding JL, Wander PL, Zhang X, Li X, Karuranga S, Chen H, et al. The Incidence of Adult-Onset Type 1 Diabetes: A Systematic Review From 32 Countries and Regions. Diabetes Care. 2022 Apr 1;45(4):994\u0026ndash;1006. \u003c/li\u003e\n\u003cli\u003eJones AG, Shields BM, Dennis JM, Hattersley AT, McDonald TJ, Thomas NJ. The challenge of diagnosing type 1 diabetes in older adults. Diabet Med J Br Diabet Assoc. 2020 Oct;37(10):1781\u0026ndash;2. \u003c/li\u003e\n\u003cli\u003eThomas NJ, Jones AG. The challenges of identifying and studying type 1 diabetes in adults. Diabetologia. 2023;66(12):2200\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eBarone B, Rodacki M, Zajdenverg L, Almeida MH, Cabizuca CA, Barreto D, et al. Family history of type 2 diabetes is increased in patients with type 1 diabetes. Diabetes Res Clin Pract. 2008 Oct;82(1):e1-4. \u003c/li\u003e\n\u003cli\u003eWędrychowicz A, Grzelak T, Pietraszek A, Skrzyszowska M, Minasjan M, Starzyk JB. Affected brother as the highest risk factor of type 1 diabetes development in children and adolescents: One center data before implementing type 1 diabetes national screening. Adv Clin Exp Med Off Organ Wroclaw Med Univ. 2024 Aug;33(8):781\u0026ndash;90. \u003c/li\u003e\n\u003cli\u003eRamachandran A, Snehalatha C, Premila L, Mohan V, Viswanathan M. Familial aggregation in type 1 (insulin-dependent) diabetes mellitus: a study from south India. Diabet Med J Br Diabet Assoc. 1990 Dec;7(10):876\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003ePociot F, Lernmark \u0026Aring;. Genetic risk factors for type 1 diabetes. Lancet Lond Engl. 2016 Jun 4;387(10035):2331\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eGoyal S, Rani J, Bhat MA, Vanita V. Genetics of diabetes. World J Diabetes. 2023 Jun 15;14(6):656\u0026ndash;79. \u003c/li\u003e\n\u003cli\u003eRewers M, Ludvigsson J. Environmental risk factors for type 1 diabetes. Lancet Lond Engl. 2016 Jun 4;387(10035):2340\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eCraig ME, Kim KW, Isaacs SR, Penno MA, Hamilton-Williams EE, Couper JJ, et al. Early-life factors contributing to type 1 diabetes. Diabetologia. 2019 Oct;62(10):1823\u0026ndash;34. \u003c/li\u003e\n\u003cli\u003eRadha V, Mohan V. Genetic predisposition to type 2 diabetes among Asian Indians. Indian J Med Res. 2007 Mar;125(3):259\u0026ndash;74. \u003c/li\u003e\n\u003cli\u003eIndulekha K, Anjana RM, Surendar J, Mohan V. Association of visceral and subcutaneous fat with glucose intolerance, insulin resistance, adipocytokines and inflammatory markers in Asian Indians (CURES-113). Clin Biochem. 2011 Mar;44(4):281\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eSiddiqui MK, Anjana RM, Dawed AY, Martoeau C, Srinivasan S, Saravanan J, et al. Correction to: Young-onset diabetes in Asian Indians is associated with lower measured and genetically determined beta cell function. Diabetologia. 2022 Jul;65(7):1237. \u003c/li\u003e\n\u003cli\u003eMingwal BS, Gogoi JB, And KG, Rawat P. Susceptibility and Risk Factors of developing type 2 Diabetes and Pre-diabetes among Young Indian Population in Uttarakhand, India. CPD Bull Clin Biochem. 2023 Mar 28;8:22\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eNagarathna R, Bali P, Anand A, Srivastava V, Patil S, Sharma G, et al. Prevalence of Diabetes and Its Determinants in the Young Adults Indian Population-Call for Yoga Intervention. Front Endocrinol. 2020;11:507064. \u003c/li\u003e\n\u003cli\u003eMohan V, Gokulakrishnan K, Deepa R, Shanthirani CS, Datta M. Association of physical inactivity with components of metabolic syndrome and coronary artery disease\u0026mdash;the Chennai Urban Population Study (CUPS no. 15). Diabet Med. 2005;22(9):1206\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eAnjana RM, Sudha V, Abirami K, Gayathri R, Manasa VS, Deepa M, et al. Dietary profiles and associated metabolic risk factors in India from the ICMR-INDIAB survey-21. Nat Med. 2025 Sep 30; \u003c/li\u003e\n\u003cli\u003eMohan V, Spiegelman D, Sudha V, Gayathri R, Hong B, Praseena K, et al. Effect of brown rice, white rice, and brown rice with legumes on blood glucose and insulin responses in overweight Asian Indians: a randomized controlled trial. Diabetes Technol Ther. 2014 May;16(5):317\u0026ndash;25. \u003c/li\u003e\n\u003cli\u003eNarayanan N, Dwarakanath C, Venkataraman S, Manikandan R, Narendra B, Sambit D, et al. 2174-PUB: Profiling of Young Diabetes in India: A Cross-Sectional Analysis Report. Diabetes N Y N. 2020;69(Supplement_1). \u003c/li\u003e\n\u003cli\u003eSosale B, Sosale AR, Mohan AR, Kumar PM, Saboo B, Kandula S. Cardiovascular risk factors, micro and macrovascular complications at diagnosis in patients with young onset type 2 diabetes in India: CINDI 2. Indian J Endocrinol Metab. 2016;20(1):114\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eMohan V, Sudha V, Shobana S, Gayathri R, Krishnaswamy K. Are Unhealthy Diets Contributing to the Rapid Rise of Type 2 Diabetes in India? J Nutr. 2023 Apr;153(4):940\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eMohan V, Vijayachandrika V, Gokulakrishnan K, Anjana RM, Ganesan A, Weber MB, et al. A1C Cut Points to Define Various Glucose Intolerance Groups in Asian Indians. Diabetes Care. 2010 Mar;33(3):515\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003ePradeepa R, Anjana RM, Unnikrishnan R, Ganesan A, Mohan V, Rema M. Risk factors for microvascular complications of diabetes among South Indian subjects with type 2 diabetes--the Chennai Urban Rural Epidemiology Study (CURES) Eye Study-5. Diabetes Technol Ther. 2010 Oct;12(10):755\u0026ndash;61. \u003c/li\u003e\n\u003cli\u003eBhansali A, Dhandania VK, Deepa M, Anjana RM, Joshi SR, Joshi PP, et al. Prevalence of and risk factors for hypertension in urban and rural India: the ICMR-INDIAB study. J Hum Hypertens. 2015 Mar;29(3):204\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eFarmaki P, Damaskos C, Garmpis N, Garmpi A, Savvanis S, Diamantis E. Complications of the Type 2 Diabetes Mellitus. Curr Cardiol Rev. 2020 Nov;16(4):249\u0026ndash;51. \u003c/li\u003e\n\u003cli\u003eHarris R, Leininger L. Preventive care in rural primary care practice. Cancer. 1993 Aug 1;72(3 Suppl):1113\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003ePradeepa R, Anitha B, Mohan V, Ganesan A, Rema M. Risk factors for diabetic retinopathy in a South Indian Type 2 diabetic population--the Chennai Urban Rural Epidemiology Study (CURES) Eye Study 4. Diabet Med J Br Diabet Assoc. 2008 May;25(5):536\u0026ndash;42. \u003c/li\u003e\n\u003cli\u003eRema M, Saravanan G, Deepa R, Mohan V. Familial clustering of diabetic retinopathy in South Indian Type 2 diabetic patients. Diabet Med J Br Diabet Assoc. 2002 Nov;19(11):910\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003ePradeepa R, Rema M, Vignesh J, Deepa M, Deepa R, Mohan V. Prevalence and risk factors for diabetic neuropathy in an urban south Indian population: the Chennai Urban Rural Epidemiology Study (CURES-55). Diabet Med J Br Diabet Assoc. 2008 Apr;25(4):407\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eShaw JE, Hodge AM, de Courten M, Dowse GK, Gareeboo H, Tuomilehto J, et al. Diabetic neuropathy in Mauritius: prevalence and risk factors. Diabetes Res Clin Pract. 1998 Nov;42(2):131\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eFranklin GM, Shetterly SM, Cohen JA, Baxter J, Hamman RF. Risk factors for distal symmetric neuropathy in NIDDM. The San Luis Valley Diabetes Study. Diabetes Care. 1994 Oct;17(10):1172\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eTODAY Study Group, Bjornstad P, Drews KL, Caprio S, Gubitosi-Klug R, Nathan DM, et al. Long-Term Complications in Youth-Onset Type 2 Diabetes. N Engl J Med. 2021 Jul 29;385(5):416\u0026ndash;26. \u003c/li\u003e\n\u003cli\u003ePremalatha G, Vidhya K, Deepa R, Ravikumar R, Rema M, Mohan V. Prevalence of non-diabetic renal disease in type 2 diabetic patients in a diabetes centre in Southern India. J Assoc Physicians India. 2002 Sep;50:1135\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eHamman RF, Bell RA, Dabelea D, D\u0026rsquo;Agostino RB, Dolan L, Imperatore G, et al. The SEARCH for Diabetes in Youth study: rationale, findings, and future directions. Diabetes Care. 2014 Dec;37(12):3336\u0026ndash;44. \u003c/li\u003e\n\u003cli\u003eMohan V, Misra A, Bhansali A, Singh AK, Makkar B, Krishnan D, et al. Role and Significance of Dietary Protein in the Management of Type 2 Diabetes and Its Complications in India: An Expert Opinion. J Assoc Physicians India. 2023 Dec;71(12):36\u0026ndash;46. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Clinical and Biochemical details of individuals with young-onset diabetes\u003csup\u003e*\u0026nbsp;\u003c/sup\u003ein a tertiary diabetes care centre\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"718\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1D\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 4757)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2D\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 5392)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-Value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eAge at first Visit (in years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e18.3 \u0026plusmn; 10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e29.8 \u0026plusmn; 11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eAge at diagnosis of diabetes* (in years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e13.5 \u0026plusmn; 6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e21.1 \u0026plusmn; 3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eDuration (in years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e4.8 \u0026plusmn; 7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e8.7 \u0026plusmn; 10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eGender (males), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e2510 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2804 (52.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eHeight (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e150 \u0026plusmn; 21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e163 \u0026plusmn; 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eWeight (kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e45.4 \u0026plusmn; 18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e71.1 \u0026plusmn; 16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eBMI Z-score\u003csup\u003e1\u003c/sup\u003e (1-18 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e-0.3 \u0026plusmn; 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.2 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eBody Mass Index\u003csup\u003e2\u003c/sup\u003e (Kg/m\u003csup\u003e2\u003c/sup\u003e) (\u0026gt;18.0 Years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e19.3 \u0026plusmn; 4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e26.8 \u0026plusmn; 5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99.8607%;\" colspan=\"4\"\u003e\u003cstrong\u003eObesity Classification, n (%)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e2195 (48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e233 (4.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1358 (30.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e801 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e377 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e737 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e562 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e3070 (63.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eWaist Circumference (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e71.4 \u0026plusmn; 14.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e92.4 \u0026plusmn; 13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eSystolic Blood Pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e108 \u0026plusmn; 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e121 \u0026plusmn; 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eDiastolic Blood Pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e72 \u0026plusmn; 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e79 \u0026plusmn; 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eFasting plasma glucose (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e218 \u0026plusmn; 109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e197 \u0026plusmn; 90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlycated haemoglobin (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e10.7 \u0026plusmn; 2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e9.6 \u0026plusmn; 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eGood Control (\u0026lt; 7%), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e406 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e891 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eFair Control (7.0 \u0026ndash; 7.9%) n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e369 (8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e522 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003ePoor Control (\u0026ge;8.0%) n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e3537 (82.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e3554 (71.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eTotal Cholesterol (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e170 \u0026plusmn; 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e179 \u0026plusmn; 44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eSerum Triglycerides (mg/dl) Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e82 (54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e139 (108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eHDL Cholesterol (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e48 \u0026plusmn; 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e41 \u0026plusmn; 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eLDL Cholesterol (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e104 \u0026plusmn; 35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e108 \u0026plusmn; 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eC Peptide Fasting (pmol/ml) Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.3 (0.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e0.8 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eC Peptide Stimulated (pmol/ml) Median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.3 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e1.7 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eGAD positive (U/ml), n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e1613/2497 (64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e38/1216 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eCreatinine (mg/dl)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 161px;\"\u003e\n \u003cp\u003e0.7 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 141px;\"\u003e\n \u003cp\u003e0.8 \u0026plusmn; 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99.8607%;\" colspan=\"4\"\u003e\u003cstrong\u003eDiabetes Treatment, n (%)\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eOHA alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2195 (40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e4311 (90.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e867 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eInsulin + OHA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e446 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e2231 (43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 340px;\"\u003e\n \u003cp\u003eDiet and Exercise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 141px;\"\u003e\n \u003cp\u003e99 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eIndian Academy of Paediatrics (IAP); \u003csup\u003e2\u003c/sup\u003eAsia Pacific BMI guidelines\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Logistic regression analysis of predictors associated with young-onset T2D\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"709\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 249px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 242px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnadjusted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003evalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 249px;\"\u003e\n \u003cp\u003eGender (Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1.10 (0.92 \u0026ndash; 1.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.61 (0.88 \u0026ndash; 2.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 249px;\"\u003e\n \u003cp\u003eAge at Onset\u003csup\u003e1\u003c/sup\u003e (Years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1.25 (1.23 - 1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.16 (1.08 - 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 249px;\"\u003e\n \u003cp\u003eBody Mass Index\u003csup\u003e2\u003c/sup\u003e (Kg/m2)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1.42 (1.38 - 1.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.47 (1.36 - 1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 249px;\"\u003e\n \u003cp\u003eSymptomatic v/s Asymptomatic\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e3.96 (2.89 - 5.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.75 (0.91 - 3.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 249px;\"\u003e\n \u003cp\u003eParental History\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 249px;\"\u003e\n \u003cp\u003eSingle parent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e3.35 (2.43 - 4.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.22 (0.64 - 2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 249px;\"\u003e\n \u003cp\u003eBoth parents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e12.11 (7.61 - 19.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e3.73 (1.61 - 8.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 249px;\"\u003e\n \u003cp\u003ePhysical Inactivity\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1.84 (1.42 - 2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e3.09 (1.68 - 5.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 249px;\"\u003e\n \u003cp\u003eAlcohol Intake\u003csup\u003e6\u003c/sup\u003e (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e2.31 (1.64 - 3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e0.44 (0.16 - 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 249px;\"\u003e\n \u003cp\u003eSmoking\u003csup\u003e7\u003c/sup\u003e (Yes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e4.47 (2.5 - 7.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.04 (0.24 - 4.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.955\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 249px;\"\u003e\n \u003cp\u003eDaily carbohydrate Intake\u003csup\u003e8\u003c/sup\u003e (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003e1.00 (1.00 - 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.046\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e1.00 (0.99 - 1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eAge\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eat diagnosis (years) was treated as a continuous variable; \u003csup\u003e2\u003c/sup\u003eBody mass index (BMI) was continuous variable; \u003csup\u003e3\u003c/sup\u003eSymptomatic status was defined based on the presence of symptoms such as polyuria, polyphagia, polydipsia, weight loss, or infections, which served as the reference group, while asymptomatic participants (detected during general check-up, pre-pregnancy check-up, or annual check-up) were coded as 1; \u003csup\u003e4\u003c/sup\u003eParental history of diabetes was coded with no parental history as reference, single parent = 1, and both parents = 2; ; \u003csup\u003e5\u003c/sup\u003ePhysical Activity: Yes = 0, No = 1; \u003csup\u003e6\u0026amp;7\u003c/sup\u003eAlcohol consumption and smoking: Yes = 1, No = 0; \u003csup\u003e8\u003c/sup\u003eDaily Carbohydrate intake was a continuous variable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Prevalence, young-onset diabetes, type 1 diabetes, type 2 diabetes, India","lastPublishedDoi":"10.21203/rs.3.rs-8314170/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8314170/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u0026amp; Aim\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere has been an increase in the occurrence of young-onset type 1 diabetes (T1D) and type 2 diabetes (T2D) in India. In this report, we outline the trends of young-onset T1D and T2D observed at a tertiary care diabetes centre in India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a retrospective study involving 10,449 individuals diagnosed with diabetes at age ≤25 years who were registered from 2010 to 2023 at a network of diabetes centre in India. T1D was identified through a history of ketoacidosis, low C-peptide levels, or the necessity of insulin from the time of diagnosis. T2D was diagnosed based on the absence of ketosis, sufficient C-peptide levels, a positive response to oral hypoglycemic agents for over two years, and the presence of acanthosis nigricans.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf 10,449 participants, 45.5% had T1D, 51.6% had T2D, and 2.8% had maturity-onset diabetes (MODY), Gestational diabetes (GDM), and Fibrocalculous Pancreatic Diabetes (FCPD). The average age at diagnosis of diabetes was higher in T2D (21.1 ± 3.8 years) than in T1D (13.5 ± 6.4 years). The prevalence of young-onset T1D and T2D (as a proportion of all registered patients) increased from 1.1% in 2010 to 1.3% and 1.6% respectively, in 2023. Among those with diabetes with a duration of ≥15 years, retinopathy was found in 61.0% of T1D and 53.9% of T2D, nephropathy in 14.0% and 19.0%, and neuropathy in 29.7% and 50.1%, respectively. Logistic regression, adjusting for relevant factors, indicated that age at diagnosis, BMI, physical inactivity, and parental history were associated with increased risk of young-onset T2D. The Shapley Additive exPlanations (SHAP) analysis identified underweight, age at diagnosis, and parental history as significant determinants for T1D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYoung-onset diabetes is increasing in prevalence and represents a significant public health challenge because of prolonged disease exposure and increased risk of complications. Early screening and targeted interventions are essential to prevent these complications.\u003c/p\u003e","manuscriptTitle":"Trends, clinical characteristics, and risk factors of young-onset diabetes at a tertiary care diabetes centre in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 06:11:11","doi":"10.21203/rs.3.rs-8314170/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-10T09:28:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-21T02:06:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-20T10:45:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-16T22:54:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-16T05:28:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-12T18:21:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176161402969657256200966072601503896060","date":"2026-01-12T10:50:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-12T04:21:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-11T12:51:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333939440967030277697558102551428263080","date":"2026-01-10T02:37:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212903676170553621321504916489307324864","date":"2026-01-09T17:26:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-09T13:19:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128858142957636489381311841852003733003","date":"2026-01-09T11:47:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-08T17:19:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276036715443186146580688721551216533645","date":"2026-01-08T16:58:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"170578264014385207948266192555363809595","date":"2026-01-08T16:19:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"76636327301352289682337997227671921664","date":"2026-01-08T09:11:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269273983919140804791496788105246002241","date":"2026-01-07T15:08:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157377556053629788624668766245820872695","date":"2026-01-07T13:04:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61039852069410890557822077737366263382","date":"2026-01-07T13:02:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-07T10:22:50+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-15T09:33:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-12T05:40:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-12T05:38:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2025-12-09T06:57:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e81db550-1370-46dd-9cd9-a40428657124","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T10:08:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 06:11:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8314170","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8314170","identity":"rs-8314170","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}