As cognitive impairment increasingly burdens low- and middle-income countries, scalable tools for early detection are pivotal. This pilot study evaluated the feasibility and performance of a tablet-based digital Clock Drawing Test (DCTclock) in 303 adults aged ≥ 50 years from the population-based CARRS cohort in urban India. Participants completed both the tablet-based DCTclock and the paper-based Mini-Cog, which was used to classify cognitive status (impaired: ≤2 vs. unimpaired: ≥3). The DCTclock required under four minutes to administer, and 99.3% of tests yielded analyzable data. Compared with cognitively unimpaired participants (n = 252), those classified as impaired (n = 51) scored significantly lower on the DCTclock total score and subdomains, particularly spatial reasoning and information processing. Performance was lower with older age and lower educational attainment (both p < 0.001) but did not differ by sex. The DCTclock demonstrated moderate discriminative accuracy for Mini-Cog-defined impairment (AUC = 0.669), and each interquartile range higher total score was associated with 52% lower odds of impairment (OR = 0.48; 95% CI, 0.32–0.70). Similar patterns were observed using FDA-recommended thresholds, while in this cohort, a ROC-derived cut point of 38 yielded slightly stronger discrimination (3.95-fold greater odds). These findings support the DCTclock’s feasibility and potential as a scalable digital tool for community-based cognitive screening.

Aim: To assess the screening, diagnostic, and management practices for early gestational diabetes mellitus (EGDM) at obstetric clinics across India. Materials and Methods: A pan-India online survey was conducted among 135 obstetric clinics (both government and private) across 27 Indian states/union territories, using a structured questionnaire to capture information on institutional or hospital settings, screening strategies, diagnostic criteria, and treatment modalities for EGDM. Results: More respondents were from private sector institutions (71.9%), with the majority (63.7%) reporting high antenatal case loads (>500 pregnancies annually). Universal screening for EGDM was the predominant approach (92.6%), with early screening at <8 weeks reported by 34.4% and at 8-12 weeks by 36.8%. Various methods like oral glucose tolerance test, fasting blood glucose (FBG), glycated hemoglobin, or even random blood glucose were used. There was considerable heterogeneity in diagnostic criteria and glucose cutoffs, with FBG >= 92 mg/dL (38.5%) being the most common criterion used to diagnose EGDM. About 49.6% of EGDM women were managed on nutritional therapy alone, whereas the rest received pharmacotherapy. Conclusion: Screening for EGDM in the first trimester of pregnancy is common in India. However, the lack of standardization for screening or diagnosis of EGDM highlights the urgent need for national guidelines for screening, diagnosis, and management of EGDM in India and indeed in other countries as well.

Depressive symptoms during pregnancy are linked to adverse offspring outcomes, but the underlying biological mechanisms remain unclear. Evidence suggests that maternal stress may alter placental biology and function. We, therefore, examined whether placental telomere length (TL) and mitochondrial DNA alterations, including copy number and heteroplasmic burden, could be associated with antenatal depressive symptoms (ADS). Placental samples (n = 107) from the STRiDE pregnancy cohort were collected at birth. Participants were classified as ADS (n = 54) and controls (n = 53) based on PHQ-9 scores at 24-28 gestational weeks. Placental TL and mtDNA copy number (mtDNA-CN) were measured by qRT-PCR, and mitochondrial genome sequencing was performed using next generation sequencing. Placental TL (Median: 0.430 vs 0.440; p = 0.006) and mtDNA-CN (Median: 0.715 vs 0.990; p = 0.005) were significantly reduced in the ADS group compared to controls. Linear regression analyses showed that higher PHQ-9 scores were significantly associated with shorter placental TL (β = -0.012, p < 0.001) and lower placental mtDNA-CN (β = -0.072, p < 0.001) after adjusting for potential confounders. Mitochondrial genome analysis revealed a higher heteroplasmic burden, including four novel heteroplasmic variants and one deleterious mutation (m.7458G > A in MT-TS1) in ADS. In conclusion, lower placental TL and mtDNA-CN were associated with ADS, suggesting impaired mitochondrial and telomeric integrity. A higher heteroplasmic burden and the presence of a potentially deleterious variant in ADS suggest that mitochondrial genome instability could be mediated by ADS.

Background: The type of cooking oil used is an under recognized dietary factor influencing cardiometabolic disease in India. Little is known about how commonly used oils relate to cardiometabolic risk in Asian Indians. Methods: We analyzed data from 18,090 adults (mean age=42y, 51% female) in the nationally representative ICMR-INDIAB study. Diet was assessed by a validated food frequency questionnaire where participants reported their top 3 cooking oils. Dyslipidemia was defined as high LDL-C (≥130 mg/dl), high triglycerides (≥150 mg/dl), and low HDL-C (men <40mg/dl, women <50 mg/dl) per NCEP ATP III guidelines. Logistic regression using sampling weights evaluated associations between main cooking oil, dyslipidemia, general (BMI≥25 kg/m 2 ) and abdominal obesity (waist >90cm men or >80cm women), adjusting for sociodemographic, lifestyle, and dietary factors. Results: Cooking oil preferences showed distinct regional patterns: mustard oil was predominant in North, East, Northeast, and Central regions; peanut (groundnut) oil in the West; palm, sunflower, and peanut oils in South, coconut oil in the state of Kerala (South); and soybean oil in states of Maharashtra (West), Madhya Pradesh (Central), and Mizoram (Northeast). Across India, users of coconut oil [OR=2.89 (2.03-4.11)], butter/ghee [OR=3.45 (1.48-8.03)] sunflower [OR=1.49 (1.12-1.97)], soybean [OR=1.81 (1.32-2.48)], or palm oil [OR=1.77 (1.27-2.46)] had a significantly higher odds of high LDL-C than those using peanut oil. Conversely, compared to peanut oil users, users of mustard oil [OR=0.68 (0.55-0.86)], sunflower oil [OR=0.80 (0.64-0.99)], and soybean oil [OR=0.72 (0.57-0.91)] had lower odds of low HDL-C. The likelihood of general obesity was higher among those using coconut oil [OR=1.45 (1.08-1.96)], sunflower oil [OR=1.45 (1.17-1.80)], or butter/ghee [OR=2.34 (1.12-4.89)] when compared to peanut oil users. No associations were observed with abdominal obesity. When examining fatty acid subtypes from cooking oils, each 2%E from saturated fat was associated with 6% higher odds of high LDL-C and abdominal obesity, while each 2%E from PUFA was linked to 3% lower odds of abdominal obesity. Conclusions: In this nationally representative sample of Indian adults, cooking oil choice was significantly associated with cardiometabolic risk. Promoting healthier oils, such as peanut and mustard oil, via dietary guidelines and the Public Distribution System, may help reduce dyslipidemia and obesity in India.

OBJECTIVE:We examined associations between age at type 2 diabetes (T2D) diagnosis and long-term mortalityand lifetime health loss. RESEARCH DESIGN AND METHODS:We analyzed data from the population-based CARRS cohort. T2D was defined by self-report, glucose-lowering medication use, or glycemic thresholds and categorized by age at diagnosis (20-29, 30-39, 40-59, or ≥60 years). Hazard ratios (HRs) were estimated using time-dependent Cox models with participants without T2D as reference group. Model-based projections estimated years of life lost (YLL), years lived with disability (YLD), disability-adjusted life years (DALYs), and excess life-years lost (LYL). RESULTS:Among 21,574 participants (mean age 43.3 years), 6,251 had diabetes. Over a median follow-up of 8.7 years, 2,163 deaths occurred. Younger age at T2D diagnosis was associated with higher risks of mortality and cardiovascular events. Adjusted HRs for all-cause mortality were 2.98 (95% CI 1.60-5.54) for T2D diagnosis at 20-29 years, 2.28 (1.74-2.98) at 30-39 years, 1.73 (1.47-2.04) at 40-59 years, and 1.61 (1.30-1.99) at ≥60 years. Projected lifetime DALYs were greatest with younger diagnosis, ranging from 24.5 years for diagnosis at 20-29 years to 5.5 years at > 60 years, with similar gradients for excess LYL.This pattern was also observed for CVD eventsacross age groups. CONCLUSIONS:T2D diagnosed at a younger age was associated with higher mortalityand greater LYL in South Asians.

BACKGROUND:Dyslipidemia, characterized by abnormal lipid levels including low high-density lipoprotein cholesterol (HDL-C), high triglycerides, and elevated low-density lipoprotein cholesterol (LDL-C) or total cholesterol (TC), poses a significant cardiovascular threat in India amid rapid urbanization and lifestyle shifts. OBJECTIVE:To assess the prevalence of various components of dyslipidemia in India using data from the large, nationally representative, Indian Council of Medical Research-India Diabetes (ICMR-INDIAB) study. METHODS:The ICMR-INDIAB study employed a stratified multistage cluster sampling design across representative urban and rural clusters in all Indian states and union territories, targeting adults (n = 121,078) aged ≥20 years. In every 5th participant (23,665-urban: 7026 and rural: 16,636), fasting venous blood samples were analyzed for lipids using standardized enzymatic methods. Dyslipidemia was defined using the Lipid Association of India guidelines [high TC ≥200 mg/dL, high triglycerides ≥150 mg/dL, high LDL-C ≥100 mg/dL, and low HDL-C (<40 mg/dL men, <50 mg/dL women)]. Generalized obesity was defined as BMI ≥25 kg/m² (World Health Organization Asia Pacific guidelines). RESULTS:The overall weighted prevalence of dyslipidemia was 87.3%, significantly higher in urban areas and among females. The burden of dyslipidemia in India was predominantly driven by low HDL-C (66.8%). Dyslipidemia prevalence increased with worsening glycemic status, rising body mass index (BMI), and the presence of hypertension; it was also present in 40% of individuals without diabetes, hypertension, or obesity. Younger age, female sex, sedentary lifestyle, obesity, hypertension, and diabetes were associated with dyslipidemia. CONCLUSION:Dyslipidemia is highly prevalent among Indian adults, especially at younger ages and among those with adverse glycemic status, obesity, and hypertension. However, there is a substantial burden of dyslipidemia among metabolically normal individuals, highlighting the need for broader lipid screening strategies and early preventive interventions to curb the rising trend of cardiovascular disease risk in India.

Oral glucose tolerance test (OGTT) is universally recommended for screening and diagnosis of gestational diabetes (GDM). This pure glucocentric approach is inadequate as other risk factors contribute to adverse perinatal outcomes. Our study tested the performance of a population-specific composite risk score-based risk stratification on perinatal outcomes. This was carried out in three prospective early pregnancy cohorts in the UK, India and Kenya and was externally validated in two real-world datasets. Our analysis illustrated clear differences in risk factors and rates of perinatal outcomes across the populations, highlighting the need for a population-specific approach. Our data also showed that rates of perinatal outcomes increased with increasing risk categories. Incorporation of key maternal risk factors that contribute to adverse perinatal outcomes with HbA1c offers a move away from an OGTT-based approach to GDM screening and a pragmatic solution for settings with limited health infrastructure where OGTT is difficult to perform.

Introduction and Objective: To examine associations between cardiovascular-kidney-metabolic (CKM) stages and cardiovascular disease (CVD) and all-cause mortality in South Asians, and to evaluate age-specific differences in these risks. Methods: We analyzed data from 17,430 (51% women; median age 42 y) adults in the cArdiometabolic Risk Reduction in South Asia (CARRS) cohort. We included participants from CARRS-1 (2010-12) and CARRS-2 (2014-16), with a median follow up of 9 y through 2024. CKM stages, based on the 2023 AHA CKM framework, were grouped as 0-1 (optimal/at-risk), 2 (metabolic risk or moderate-to-high risk CKD), and 3-4 (subclinical/clinical CVD or CKD). Cox models with age as the timescale estimated hazard ratios (HRs) for mortality, adjusting for sex, education, income, smoking, and included CKM × age interactions. Results: At baseline, 59% were in CKM stage 2 and 9% in stages 3-4. Compared with stage 0-1, adjusted HRs (95% CI) for CVD mortality for stage 2 were 2.9 (1.8, 4.9), 2.0 (1.4, 2.9), and 2.4 (1.2, 5.0), and for stages 3-4 were 13.0 (6.4, 26.1), 7.6 (5.3, 10.9), and 4.3 (2.1, 8.7) among those aged <45, 45-64, and ≥65 y, respectively. Similar trends were observed for all-cause mortality (Table). Conclusion: Advancing CKM stage was associated with progressively higher CVD and all-cause mortality, with the largest relative hazards at younger ages. These findings highlight the deleterious effects of early emergence of CKM in South Asians. Disclosure R. Jagannathan: None. W. Quarpong: None. D. Kondal: None. K. Singh: None. S.A. Patel: None. M. Deepa: None. R. Anjana: None. U. Gujral: None. S. Mohan: None. H.H. Chang: None. M.K. Ali: Consultant; Ended; Eli Lilly and Company, Siemens, Novo Nordisk. V. Mohan: None. A.A. Quyyumi: None. D. Prabhakaran: None. K. Narayan: None. S. Anand: Other - Site PI for VOICE study at US Renal Care; Current; US Renal Care. N. Tandon: None. Funding National Institutes of Health (HHSN2682009900026C; P01HL154996), National Institute on Aging (R01AG89759), National Institute of Diabetes and Digestive and Kidney Diseases (R01DK139632; R21DK105891)

Introduction and Objective: Concordance in prevalent diabetes among spouses is well known. It is not clear if having a spouse with diabetes independently predicts incident disease and is relevant for identifying prevention opportunities. Methods: Longitudinal data from 2775 spousal dyads residing in two large metropolises in India, in the CARRS cohort were used to model the relative risk (RR) of incident diabetes by partner’s diabetes status over 10 years, with and without adjustment for health behaviors. Heterogeneity of associations by education, BMI, and chronic conditions was evaluated. Results: At baseline, 30.7% of dyads were discordant in diabetes status. In women without diabetes at baseline, 10-y diabetes risk was 21.5% when their husbands were diabetes-free vs. 24.1% when their husband had diabetes. In men without diabetes at baseline, 10-y diabetes risk was 23.4% when their wives were diabetes-free vs. 31.9% when their wives had diabetes. There was no association between spouse’s diabetes at baseline and incident diabetes in women (Table). In men, the age-adjusted (RR= 1.29; 1.07 - 1.55) and demographic adjusted models (RR=1.26; 1.05 - 1.51) showed higher risk in men when wives had diabetes; this association was mitigated after adjusting for health behaviors and body mass index (RR=1.17; 0.97-1.40). Conclusion: The RR of incident diabetes was higher if the spouse had diabetes for men but not for women. The raised RR in men may be due to shared health behaviors and may be explored in future studies. Disclosure M. Siddiqui: None. M. Deepa: None. G. Rautela: None. R.K. Lata: None. M. Duraivel: None. A.J. Shah: None. S. Argeseanu Cunningham: None. R. Anjana: None. S.A. Patel: None.

AIMS/HYPOTHESIS:Beyond longer diabetes duration, uncertainty remains regarding the factors that contribute to a higher risk of developing complications in younger vs older people with type 2 diabetes. We investigated whether younger age was associated with a more adverse risk-factor profile compared with older age among people with type 2 diabetes. METHODS:We conducted cross-sectional analyses of demographic and clinical data from individuals with type 2 diabetes participating in national health surveys from four countries: the Australian Diabetes, Obesity and Lifestyle Study (AusDiab), the US National Health and Nutrition Examination Survey (NHANES), the Mauritius Non-Communicable Diseases Survey (Mauritius Survey), and the Indian Council of Medical Research-India Diabetes (ICMR-INDIAB) study. Type 2 diabetes was defined according to each study's criteria, including previously diagnosed and newly diagnosed (screen-detected) diabetes (NDM). Individuals with normal glucose tolerance (NGT) were included for comparison. Regression analyses with natural splines assessed association between age at survey and cardiometabolic risk factors in each cohort. RESULTS:There were 903 participants with type 2 diabetes from AusDiab, 7086 from NHANES, 2682 from the Mauritius Survey and 10,151 from ICMR-INDIAB. In three of the four studies (excluding ICMR-INDIAB), BMI decreased with increasing age. Younger individuals had higher low-density lipoprotein cholesterol (in NHANES and AusDiab) and lower high-density lipoprotein cholesterol (in all studies except ICMR-INDIAB) compared with older individuals. In AusDiab, NHANES and the Mauritius Survey, triglycerides were highest in younger adults, declining with age. In AusDiab, the Mauritius Survey and ICMR-INDIAB, fasting plasma glucose (FPG) increased with age until 45-55 years, after which it declined. In NHANES, younger individuals with diabetes had higher FPG than older individuals. In all four studies, haemoglobin A1c (HbA1c) increased with age, peaking around 50-60 years, before declining. In NDM, 2 h plasma glucose did not vary across age. Systolic blood pressure (SBP) and urinary albumin/creatinine ratio (UACR) were higher in older individuals. Comparing diabetes with NGT, differences in FPG, HbA1c, triglycerides and diastolic blood pressure (DBP) were greater at younger than older ages in most cohorts. The same was observed for BMI and SBP, but only in two studies (AusDiab and NHANES). CONCLUSIONS/INTERPRETATION:Younger individuals with type 2 diabetes have higher BMI and triglycerides (observed in three out of four studies) but lower SBP and UACR than older individuals with type 2 diabetes. FPG and HbA1c peak in middle age. Differences relative to individuals without diabetes were more pronounced at younger than older ages for FPG, HbA1c, triglycerides and DBP, and were possibly greater for BMI and SBP. These age-related patterns likely influence the overall risk of diabetes-related complications.

Background:Current definitions of type 2 diabetes (T2D) and prediabetes do not capture their pathophysiological heterogeneity. We investigated data-driven subtypes of T2D and prediabetes and evaluated their associations with mortality. Methods:We analyzed data from 14,306 South Asian participants from the CArdiometabolic Risk Reduction cohort using unsupervised k-means clustering based on five variables: age, BMI, HbA1c, insulin resistance, and beta-cell dysfunction. For each subtype of T2D or prediabetes, we estimated Cox hazard ratios (HRs) for all-cause and cardiovascular disease (CVD) mortality and excess years of life lost compared to normal glucose tolerance. Results:Among 2,639 participants with T2D, three subtypes emerged: Severe Insulin-Deficient Diabetes (SIDD;23.0%), Mild Insulin-Deficient Diabetes (MIDD;54.5%), and Severe Insulin-Resistant Diabetes (SIRD;22.5%). Among 4,992 participants with prediabetes, two subtypes were identified: Insulin-Deficient Prediabetes (IDPD;66.0%) and Insulin-Resistant Prediabetes (IRPD;34.0%). Over a median follow-up of 10.6 years, 1,076 deaths occurred (405 due to CVD). Compared with normal glucose tolerance, SIDD had the highest all-cause mortality HR (3.34 [95%CI, 2.39 to 4.68]), followed by MIDD (1.39[95%CI, 1.05 to 1.84]) and SIRD (1.67[95%CI, 1.15 to 2.41]). Among prediabetes subtypes, IDPD was associated with increased all-cause (HR: 1.32 [95%CI, 1.03 to 1.68]) and CVD mortality (HR:1.53 [95%CI, 1.00 to 2.34]), whereas IRPD was not. Excess years of life lost were greatest for SIDD (17.7 years), followed by MIDD (12.8 years) and SIRD (12.0 years). Conclusions:Insulin-deficient subtypes made up a high proportion of T2D and prediabetes cases, harboring increased mortality hazards and excess years of life lost relative to normal glucose tolerance.

Gestational diabetes mellitus (GDM) and preeclampsia (PE) are among the most common medical complications in pregnancy, with prevalence rates of 10–25

Introduction and Objective: There is very little data on glucose profiles using continuous glucose monitoring (CGM) in Gestational Diabetes (GDM). We compared CGM profiles of pregnant Asian Indian women with Early GDM (EGDM), late GDM (LGDM) and no GDM Methods: An observational study was conducted among pregnant women aged 20-45 years recruited early in gestation (<15 weeks). A total of 47 participants were included, comprising 20 women with no GDM, 13 with EGDM and 14 with LGDM diagnosed between 24-28 weeks of gestation (Definitions provided below the Table). CGM (FreeStyle Libre) was performed in all 3 groups. CGM metrics included mean glucose, time in range (TIR), time above range (TAR), time below range (TBR), glucose management indicator (GMI) and coefficient of variation (CV) Results: Baseline characteristics were comparable across 3 groups. CGM profiles showed that mean glucose was higher in EGDM and LGDM compared with No GDM (96±7 and 99±11 vs 89±6 mg/dl; p<0.001). TIR (63-140 mg/dl) was reduced in EGDM and LGDM (91.0±6.0% and 92.8±7.8% vs 97.0±2.0% in No GDM; p<0.001 and p<0.05). TAR was higher in EGDM and LGDM compared to No GDM (5.2±4.0 and 5.9±8.2 vs 1.9±1, p<0.001). GMI values were higher in EGDM and LGDM than No GDM (5.7±0.2% and 5.6±0.3% vs 5.3±0.4%; p<0.05). There were no significant differences between EGDM and LGDM Conclusion: CGM derived glucose metrics demonstrate comparable glycemic abnormalities in EGDM and LGDM women. This is the first CGM study comparing EGDM and LGDM Disclosure V. Mohan: None. J. Lunghar: None. P. Thyparambil Aravindakshan: None. L. Natarajan: None. U. Ram: None. R. Anjana: None. M. Deepa: None. Funding We thank M/s Abbott for supplying Freestyle Libre sensors.

Although recent studies indicate that air pollution exposure affects lower socioeconomic groups more, there is limited evidence regarding the relative influence of household and neighborhood socioeconomic status (SES) on air pollution exposure in lower- and middle-income country settings. We evaluated the associations between neighborhood- and household-level SES metrics and exposure to ambient particulate matter (PM _2.5 ) in two populous Indian cities, ‒Delhi and Chennai. We used data from the Center for cArdiometabolic Risk Reduction in SouthAsia cohort study to obtain household-level SES indicators such as household income, years of education, and wealth index score. Neighborhood-level SES was calculated based on ward-level socio-demographic variables from the 2011 Census of India. Daily average PM _2.5 for the years 2010–2016 at a spatial resolution of 1 km × 1 km, were obtained from a hybrid exposure model for each city. For both cities, we observed a stronger association between neighborhood-level SES and ambient PM _2.5 , than household level SES. The study signifies that neighborhood-level SES factors are important confounders that should be considered while examining the relationship between ambient PM _2.5 exposure and health outcomes in the study area.

Introduction and Objective: South Asians develop type 2 diabetes (T2D) earlier than others. We investigated associations between age at T2D diagnosis and risks of all-cause and CVD mortality, non-fatal CVD events, and years of life lost (YLL). Methods: We analyzed data from 21,861 CARRS cohort participants. T2D was defined as self-reported diagnosis, T2D treatment, fasting plasma glucose ≥126 mg/dL, 2-h plasma glucose >200 mg/dL, or HbA1c ≥6.5%. Participants were grouped by age at diagnosis: 20-29, 30-39, 40-59, and ≥60 years. Cox regression models estimated risks, adjusting for demographics and clinical factors. YLL was calculated using India’s Individual Annuitant Mortality Table. Results: Among 21,861 participants (mean age 43.6 years; 44.5% male), 6,396 had T2D (5,295 prevalent; 181 incident). Over 14 years (132,293 person-years), 2,121 deaths (809 CVD) and 336 non-fatal CVD events occurred. Earlier T2D diagnosis was linked to higher all-cause mortality vs. those without T2D: HR 2.2 (20-29), HR 2.0 (30-39), HR 1.4 (40-59), HR 0.9 (≥60). Similar patterns were seen for CVD mortality and non-fatal CVD events (Table). T2D caused an average 12-year life loss, with YLL increasing for earlier diagnosis: 19.2 (20-29), 16.8 (30-39), 12.2 (40-59), and 6.3 (≥60). Conclusion: T2D was linked to greater years of life lost and higher mortality risks, especially in younger individuals, highlighting the need for early detection and management in South Asians. R. Jagannathan: None. A.S. Oguntade: None. M. Deepa: None. D. Kondal: None. R. Anjana: None. S.A. Patel: None. R.M. Carrillo-Larco: None. S. Mohan: None. M.K. Ali: Advisory Panel; Eli Lilly and Company. A.A. Quyyumi: None. D. Prabhakaran: None. V. Mohan: Speaker's Bureau; Novo Nordisk. Advisory Panel; Abbott. Research Support; Servier Laboratories. Speaker's Bureau; USV Private Limited, Sanofi, Medtronic, Eli Lilly and Company. K. Narayan: None. N. Tandon: None. The Center for cArdiometabolic Risk Reduction in South Asia study were supported by grants from the National Heart, Lung, and Blood Institute (HHSN2682009900026C, P01HL154996), National Institutes of Health (NIH), National Institute on Aging, NIH (R01-AG89759), and National Institute of Diabetes and Digestive and Kidney Diseases (R01DK139632), NIH.

Background & objectives While obesity usually produces cardio-metabolic dysfunction, some obese individuals are metabolically healthy, and conversely, some nonobese individuals have significant metabolic dysfunction. This study aims to assess the national prevalence of various obesity subtypes and their association with type 2 diabetes (T2D), coronary artery disease (CAD), and chronic kidney disease (CKD) in the Indian Council of Medical Research-India Diabetes (ICMR-INDIAB) study. Methods The ICMR-INDIAB study is a nationally representative cross-sectional survey of 1,13,043 individuals aged ≥20 yr from urban and rural areas across 31 Indian States and Union Territories. In every fifth individual (n=19,370), venous blood glucose and lipids were measured. A body mass index (BMI) ≥25 kg/m 2 was defined as being obese, and metabolic obesity was diagnosed if two risk factors, out of the following: high waist circumference, high blood pressure, elevated blood glucose, raised serum triglycerides, or low HDL cholesterol, were present. Four subgroups were identified: Metabolically Healthy Non-Obese (MHNO), Metabolically Healthy Obese (MHO), Metabolically Obese Non-Obese (MONO), and Metabolically Obese Obese (MOO). Results The prevalence of various obesity subtypes was as follows: MONO: 43.3 per cent [95% confidence interval (CI): 42.6-44%], MOO: 28.3 per cent (27.7-28.9%), MHNO: 26.6 per cent (26-27.2%), and MHO: 1.8 per cent (1.6-2%). MONO was more prevalent in rural areas [Rural vs. Urban: MONO: 46 per cent (45-46.9%) vs. 39.6 per cent (37.8-41.3%), P< 0.001]. MOO showed the highest risk for T2D and CAD, while MONO showed the highest risk of CKD, especially among females. Interpretation & conclusions Individuals with MONO have a distinct phenotype with adverse metabolic consequences, highlighting the need to shift from body weight-focused approaches to broader strategies to identify and tackle non-communicable diseases (NCDs) in India.

Physical inactivity contributes to non-communicable disease (NCD) health burden, making it essential to study and address this issue at a population level. The present research aims to explore the patterns of physical activity (PA) in Haryana through a subgroup analysis of the national Indian Council of Medical Research–India Diabetes (ICMR-INDIAB) study. This study was conducted between December 2018 and July 2019 in Haryana and included 3918 adult participants. Physical activity was assessed using the validated MDRF Physical Activity Questionnaire (MPAQ), which has domain-wise assessments of PA. Weighted prevalence was estimated using state-specific sampling weights, and associations between PA, anthropometric and biochemical profiles were assessed using bivariate analysis done using Student’s unpaired t tests, one-way analysis of variance (ANOVA), or chi-square tests. Factors describing the likelihood of being active were ascertained using a multivariable nominal regression analysis. About 73

Background Chronic conditions cause notable health and economic burdens. While health insurance enables access to healthcare, its effects on chronic care outcomes remain under-explored. Objective To examine the association between health insurance coverage and cardiometabolic risk factors among people with chronic conditions in India. Methods Data from the Centre for Cardiometabolic Risk Reduction in South Asia (CARRS) and Solan studies, including 2,926 adults with chronic conditions were analyzed using propensity score weighting to evaluate the associations between health insurance and cardiometabolic risk factors (HbA1c, low-density lipoprotein cholesterol [LDLc], and blood pressure [BP]) and self-reported health status (measured using European Quality of Life Visual Analogue Scale [EQ-VAS]). Mediation analysis evaluated healthcare visits as a potential mediator. Results Among 2,926 respondents meeting criteria, mean (SD) age was 54.6 years (11.8), and 1630 (55.7%) were women. Health insurance coverage was low (6.5%) and more prevalent among men, higher-income groups, and rural vs urban residents. Insured participants had lower mean diastolic BP (84.8 vs 86.0 mmHg), mean LDLc (113.3 vs 117.2 mg/dl), mean HbA1c (6.9% vs 7.5%), and higher health status (EQ-VAS: 74.6 vs 69.1) than uninsured participants, respectively (p < 0.05). Mediation analysis showed healthcare visits strongly mediated the relationship between insurance and BP and partially mediated effects on LDLc, HbA1c, and self-rated health. Conclusion Health insurance coverage was associated with better cardiometabolic risk profiles and health status, largely mediated by increased healthcare utilization. Expanding insurance coverage to include outpatient chronic care services should be prioritized to improve health outcomes in low- and middle-income countries.

Gestational diabetes mellitus (GDM), a common pregnancy-related metabolic disorder, often goes undiagnosed until the second trimester, limiting early intervention opportunities. Given the higher prevalence of GDM in India, there is a critical need to investigate metabolomic biomarkers among Asian Indians, who exhibit greater insulin resistance and are predisposed to developing type 2 diabetes at an earlier age. This study aimed to identify early pregnancy metabolomic signatures predictive of GDM. Among 2115 pregnant women from the STratification of Risk of Diabetes in Early pregnancy (STRiDE) study, we performed untargeted metabolomic profiling using UPLC-MS/MS at early pregnancy (< 16 weeks) plasma samples from 100 women—comprising 50 with GDM and 50 normal (without GDM) based on oral glucose tolerance test (OGTT) at 24–28 weeks. Statistical and machine learning approaches, including logistic regression and random forest (RF), were applied to identify GDM-associated metabolites and construct predictive models. Pathway enrichment analysis was conducted using KEGG database annotations. A total of 49 metabolites were significantly associated with GDM, primarily involving lipid classes such as phosphatidylcholines, sphingomyelins, and triacylglycerols. RF analysis identified a panel of eight metabolites that achieved best predictive performance (AUC 0.880; 95

Background & objectives The prevalence of gestational diabetes mellitus (GDM) is known to be high among South Asians. However, there is no national study on prevalence of GDM in India and few data comparing prevalence of GDM in early pregnancy (Early GDM) and late pregnancy (Late GDM). Methods This is an analysis of pregnant women who participated in the nationally representative Indian Council of Medical Research-India Diabetes (ICMR-INDIAB) study. Of the 1,206 pregnant women 1,032 who underwent oral glucose tolerance test (OGTT) or had fasting blood glucose measurement and did not have overt diabetes, were included in this study. GDM was diagnosed using the NICE criteria. GDM was classified as Early GDM if diagnosed before 20 wk of gestation and Late GDM if diagnosed ≥ 20 wk of gestation, Results The weighted national prevalence of GDM in India was 22.4 per cent (95% CI: 16.7-28%) with no significant urban rural differences (24.2% vs. 21.6%, NS). The prevalence of Early GDM and Late GDM were 19.2 per cent (9.8-28.7%) and 23.4 per cent (16.7-30.2%), respectively. Central India had the highest prevalence of GDM at 32.9 per cent (17.9-48%), and West India, the lowest at 16 per cent (3.1-29.3%). High systolic blood pressure and family history of diabetes were independently associated with risk of GDM. Interpretation & conclusions Nearly one in four pregnant women in India have GDM with regional variability. The prevalence of Early GDM is also high. Thus, there is a need for screening of all pregnant women for GDM starting in early pregnancy.