Dyslipidemia frequently occurs alongside type 2 diabetes mellitus (T2DM) and increases the risk of atherosclerosis. VPS26A rs1802295 (C > T) is a susceptibility locus for T2DM identified in a genome-wide association study among South Asians, but its connection with lipid traits in North Indians remains unclear. This study aims to examine the association between rs1802295 genotypes and lipid profile abnormalities. This observational case-control study was carried out at King George’s Medical University in India from November 2024 to November 2025, and enrolled 50 T2DM cases and 30 BMI- and ethnicity-matched normoglycemic controls according to the American Diabetes Association criteria: glycated hemoglobin (HbA1c) ≥ 6.5
The observation and identification of ocular disorders depend heavily on fundus images. However, it is difficult and complex to use deep learning approaches to detect numerous ocular diseases from fundus images. The intricacy of fundus disease structures is one difficulty that results in poor detection accuracy. The class imbalance issue that arises frequently in multi-label image classification presents another barrier, making algorithm evaluation and training more challenging. This research uses deep learning to provide a categorization system for ocular diseases in order to overcome these problems. The goal of this proposed research is to identify and classify lesions related to various eye diseases, including diabetic macular edema (DME), diabetic retinopathy (DR), cataract, glaucoma, and normal. The Improved InceptionV3 model is proposed to classify the different types of diabetic eye diseases. The Enhanced DeepLabV3+ model is used to accurately localize small objects, which makes it suitable for detecting lesions in retinal images. In the proposed research, the DenseNet-121 model is used to capture the spatial and texture features of the images to improve classification and segmentation performance. We have developed a large-scale combined dataset for performance evaluation based on fundus images. The proposed segmentation (Enhanced DeepLabV3+) model demonstrated remarkable performance, achieving a DSC of 98.06
Diabetic kidney disease (DKD) is the primary cause of end-stage renal failure, yet current screening methods show limited early detection sensitivity. This study assessed the cross-sectional association of TyG-ABSI—combining triglyceride-glucose index and a body shape index to reflect insulin resistance and visceral fat distribution—with prevalent DKD in US adults. Data were analyzed from 22,106 adults participating in the National Health and Nutrition Examination Survey (NHANES) spanning 1999 to 2018. Indices computed encompassed the triglyceride-glucose index (TyG), TyG-body mass index (TyG-BMI), TyG-waist-to-height ratio (TyG-WHtR), and TyG-ABSI. The links between these indicators and DKD underwent evaluation through multivariable logistic regression, receiver operating characteristic (ROC) curve analysis, restricted cubic spline (RCS) modelling, and threshold effect analysis. After adjusting for confounders, all indicators showed positive associations with DKD. TyG-ABSI (AUC = 0.79) outperformed TyG-WHtR (0.77), TyG (0.75), and TyG-BMI (0.70). RCS analysis revealed a linear dose-response association between TyG-ABSI and the prevalence of DKD without threshold effects. TyG-ABSI demonstrates superior discriminative ability (AUC = 0.79) over TyG, TyG-BMI, and TyG-WHtR for identifying prevalent DKD. Its linear dose-response association with DKD prevalence suggests no “safe threshold.” As an economical, noninvasive marker using routine measurements, TyG-ABSI shows promise as a screening aid for identifying individuals with prevalent DKD in both developed and developing countries, particularly benefiting resource-constrained healthcare settings; its prognostic value, however, requires confirmation in prospective cohort studies.
Diabetic chronic kidney disease (DCKD) represents a significant subset of chronic kidney disease (CKD) cases and is associated with distinct metabolic disturbances, including alterations in iron metabolism. Hepcidin, a key regulator of iron homeostasis, may be influenced by diabetes and CKD, yet its relationship with iron profile parameters in DCKD compared to non-diabetic CKD (NDCKD) remains unclear. This study aimed to investigate and compare the correlation between iron profiles and serum hepcidin levels in diabetic and non-diabetic CKD patients. This observational study included 80 DCKD patients and 80 age- and gender-matched NDCKD patients. Serum hepcidin levels, iron profile parameters, glycemic control markers, and CKD stage were assessed. Correlation analyses were conducted to investigate the relationships between serum hepcidin, iron profile parameters, and CKD stage in both groups. The mean age (± SD) for DCKD was 52.46 ± 7.95 years, and for NDCKD, it was 51.36 ± 9.64 years. In DCKD, 51.25
In rural India, diabetes management is often challenged by limited healthcare access, making family and community support essential. This study evaluates the availability and impact of social support on diabetes management among adults aged 18–59 years in villages under Sarjapur PHC, Anekal Taluk, Bangalore Urban District. A cross-sectional study was conducted from August 2022 to August 2024, involving 328 type 2 diabetes mellitus (DM) patients diagnosed for at least three months. Participants from 25 villages were selected via proportional random sampling. Data was collected using a semi-structured questionnaire covering sociodemographic and diabetes-related details, along with social support domains, including transport assistance, financial support, medication reminders, dietary support, physical activity encouragement, and emotional support (IEC Ref No: 114/2022). Among 328 participants (mean age 48.5 ± 1.85 years), 59.1
Diabetic retinopathy (DR) is a leading cause of blindness worldwide. Although corticosteroids possess potent anti-inflammatory and antiangiogenic properties, systemic corticosteroid therapy in patients with diabetes may adversely affect retinal outcomes. This study evaluated the association between systemic corticosteroid use and DR progression, visual outcomes, retinal structural changes, and ocular complications. A prospective comparative observational study was conducted at a tertiary care ophthalmology centre between August 2024 and May 2025. Forty adults with DR were enrolled and allocated into a corticosteroid-exposed group (n = 20) and a matched non-corticosteroid control group (n = 20). Participants were followed for 9 months, and assessments were performed for best-corrected visual acuity (BCVA), intraocular pressure (IOP), Early Treatment Diabetic Retinopathy Study (ETDRS) scores, central macular thickness (CMT) measured by optical coherence tomography, DR progression, ocular adverse events, and vision-related quality of life using the National Eye Institute Visual Function Questionnaire-25 (NEI VFQ-25). Baseline demographic and clinical characteristics were largely comparable between the groups. The corticosteroid group demonstrated greater deterioration in BCVA (0.43 ± 0.15 to 0.52 ± 0.17 logMAR vs. 0.68 ± 0.18 to 0.72 ± 0.19 logMAR; p < 0.001) and a larger decline in ETDRS scores (− 6.5 vs. − 5.2 letters). DR progression was observed more frequently among corticosteroid users than among controls (70
Hyperglycaemia in pregnancy (HIP), encompassing gestational diabetes mellitus (GDM) and diabetes in pregnancy (DIP), is increasingly recognized as an early marker in the life-course trajectory of noncommunicable diseases (NCDs). In developing countries such as India, a substantial proportion of women enter pregnancy with preexisting metabolic risk factors, limiting the preventive impact of antenatal-only screening strategies. To propose an India-specific framework for prevention of HIP through preconceptional metabolic optimization, aligned with the life-course approach endorsed by the International Federation of Gynaecology and Obstetrics (FIGO) and supported by FOGSI. This position statement synthesizes global evidence, epidemiological data and DIPSI experience to develop a pragmatic framework for preconceptional screening, risk stratification and intervention using both globally accepted diagnostic standards and context-specific approaches. DIPSI recommends opportunistic preconceptional screening using standard diagnostic criteria for nonpregnant adults, including fasting 75 g OGTT and HbA1c, while permitting nonfasting glucose testing for initial risk identification in resource-constrained settings. Women are stratified into normoglycaemia, prediabetes and pregestational diabetes. Core interventions include lifestyle modification, micronutrient optimization, pharmacological management where indicated and family- and couple-centric counselling. Selective use of self-monitoring and continuous glucose monitoring is recommended. Integration into existing health systems with differentiated urban and rural strategies is emphasized. Preconceptional care represents the earliest, most effective and most equitable opportunity to prevent GDM and future NCDs. A simplified, scalable, woman- and family-centred approach can interrupt the intergenerational cycle of metabolic disease.
Non-invasive breath-based glucose estimation has emerged as a potential alternative to invasive capillary blood glucose testing. However, preliminary validation against established glucose estimation methods is required before broader clinical applicability can be considered. To evaluate the preliminary feasibility of a prototype breath analyser for non-invasive glucose estimation compared with capillary glucometer measurements under point-of-care conditions. This cross-sectional pilot feasibility and method-comparison study included 76 adults recruited at a tertiary care teaching hospital in India. Paired capillary blood glucose (CBG) and breath-derived glucose measurements were obtained within a 2-min interval. Correlation analysis, Bland–Altman agreement analysis, Clarke Error Grid analysis, mean absolute relative difference (MARD), and exploratory receiver operating characteristic (ROC) analysis were performed. The mean capillary glucose and breath-derived glucose values were 107 ± 24.8 mg/dL and 120 ± 26.2 mg/dL, respectively. Breath-derived glucose estimates showed a moderate positive correlation with capillary glucometer values (Spearman’s r = 0.675, p < 0.001). Bland–Altman analysis demonstrated a mean bias of + 14 mg/dL with 95
Diabetic retinopathy (DR) is an ocular condition affecting the retina’s blood vessels. Patients with diabetes who experience DR risk losing all vision. In this situation, DR must be detected early to save the vision and provide prompt treatment. DR manual diagnosis is time-consuming and prone to mistakes due to its complexities. Numerous machine/deep learning techniques have been suggested to identify DR and its various stages from retinal images automatically. This study presents the deep learning model for automatically classifying DR stages from fundus images. This work proposes a transformer and parallel ensemble-based deep learning model for diabetic retinopathy segmentation and detection called TPEDLM. The proposed work contains three steps: preprocessing, segmentation, and detection. The fundus images are enhanced using contrast-limited adaptive histogram equalization in the preprocessing step. The TransUNet model segments the fundus image that extracts retina blood vessels, optic disc, and DR lesions. The three deep learning models, Inception V3, ResNet50 and VGG16, are used to detect the DR stages. The IDRiD and Messidor-2 datasets are used to evaluate the proposed methodology. The experimental results confirm that the proposed method effectively distinguishes the different stages of DR, achieving over 98
The growing incidence of type 2 diabetes among adult populations of India is particularly troubling considering India’s urbanization, increased sedentary lifestyles, dietary changes, and increasing age of the populations. The increasing prevalence of type 2 diabetes among adults threaten the SDG goal of reducing the number of deaths caused by non-communicable diseases (NCDs) and increasing type 2 diabetes among adults. This study focused on examining how the presence of hypertension, family history of diabetes, and lifestyle(s) of the individual, along with socioeconomic factors, contribute to the incidences of type 2 diabetes among India’s older adult populations. The LASI 2017–2018 was the source of data and information pertaining to individuals 45 years of age and older. The reported data was analyzed using descriptive statistics. A choropleth map was utilized to illustrate variations based on each state. A forest plot was used to illustrate adjusted odds ratios (AOR) for significant variables, which included the following: hypertension, family history, socioeconomic status, and lifestyle behaviors. Urban populated areas demonstrated an increase in the incidences of diabetes among individuals with hypertension and individuals with a parent who was a diabetic. A higher level of education, increased household wealth, and decreased levels of physical activity, were aligned with increased incidences of diabetes. The choropleth map demonstrated significant geographic inequity in the incidences of diabetes. The presence of diabetes is higher among older adult populations with a history of hypertension, and that the incidences are concentrated in urban and affluent populations. Identifying this population and targeting approaches to modify and address lifestyle behaviors to manage the progression of the disease is critical. Addressing the areas of concentrated diabetes cases will help manage the diabetes and assist India in moving towards achieving the SDG-3 goals.
Gestational diabetes mellitus (GDM) is a common pregnancy complication associated with adverse maternal and neonatal outcomes. Increasing attention has focused on hyperglycaemia identified early in pregnancy; however, evidence regarding the impact of timing of diagnosis on pregnancy outcomes remains limited in Indian populations. To evaluate maternal characteristics, treatment patterns, and pregnancy outcomes in women with GDM and compare outcomes between early- and late-diagnosed GDM. This retrospective multicentre observational study included 172 women diagnosed with GDM and managed at three tertiary care centres. Women were classified as early GDM (<20 weeks gestation) or late GDM (≥20 weeks gestation). Maternal characteristics, HbA1c levels, treatment modalities, mode of delivery, gestational age at delivery, and neonatal birth weight were analysed. The cohort comprised a metabolically high-risk population with a mean maternal age of 34.7 ± 4.8 years and mean BMI of 31.1 ± 3.6 kg/m2. Early GDM accounted for 47.1
Type 2 Diabetes Mellitus (T2DM) and Pulmonary Tuberculosis (PTB) frequently coexist in developing countries, leading to worsened clinical outcomes due to immune suppression and metabolic disturbances. T2DM impairs infection control, while PTB affects glycemic regulation. Therefore, selecting an appropriate antidiabetic agent is crucial in managing both conditions. Metformin has insulin-sensitizing properties and potential host-directed anti-TB effects. In contrast, Glimepiride increases insulin secretion but lacks immunomodulatory action. To compare the effects of metformin and glimepiride on glycemic control, immune response, and tuberculosis (TB) treatment outcomes in people with Type 2 Diabetes Mellitus (T2DM) co-diagnosed with TB. A prospective cohort study was conducted from November 2024 to May 2025 at the NTEP Centre, Government General Hospital, Ananthapuramu. A total of 138 people with T2DM and PTB were enrolled, of whom 76 received Metformin and 62 received Glimepiride. Metformin significantly reduced random blood sugar (RBS) from 248.05 to 181.32 mg/dL, while Glimepiride showed an increase from 253.35 to 273.74 mg/dL (p < 0.0001). As the intensive phase is crucial for early treatment response, sputum conversion at 2 months was assessed, with the Metformin group showing a conversion rate of 34
H syndrome is a rare autosomal recessive disorder caused by SLC29A3 mutations and is characterized by multisystem manifestations, including early-onset diabetes mellitus, hearing loss, and cutaneous abnormalities. This case serires reports three related patients from South India with early-onset insulin-dependent diabetes mellitus, bilateral sensorineural hearing loss, and variable cutaneous features. Genetic testing identified the same homozygous exon 4 deletion in the SLC29A3 gene in all three patients, confi rming H syndrome. This first familial case series from South India highlights the importance of considering H syndrome in patients with early-onset diabetes and hearing loss, and underscores the value of genetic testing in establishing the diagnosis.
India has one of the world’s largest burdens of prediabetes and type 2 diabetes mellitus (T2DM), characterized by early age of onset, high rates of undiagnosed disease, and increased cardiometabolic risk. Early identification and intervention are essential to reduce long-term complications. This position statement integrates national epidemiological data, international evidence, and expert clinical perspectives to provide India-specific recommendations for screening, diagnosis, risk stratification, and management of prediabetes and T2DM. Universal screening from 30 years of age and targeted screening for high-risk individuals are recommended using fasting plasma glucose, OGTT, HbA1c, and validated risk scores such as the Indian Diabetes Risk Score. Prediabetes should be managed with phenotype-based risk stratification and early cardiovascular risk assessment. Lifestyle modification remains the cornerstone of care, including weight reduction, dietary optimization, physical activity, and community-based interventions. Metformin is recommended for high-risk individuals, while SGLT2 inhibitors and GLP-1 receptor agonists may be considered in selected populations. For established T2DM, metformin remains first-line therapy with early combination treatment and individualized pharmacotherapy. Strengthening primary care, improving awareness, expanding access to preventive services, and integrating digital health tools are critical to reducing the growing burden of diabetes and its complications in India.
Gestational diabetes mellitus (GDM) is a common metabolic complication of pregnancy associated with adverse maternal and neonatal outcomes. Emerging evidence suggests that foetal sex may influence maternal glucose metabolism through placental hormonal and inflammatory pathways; however, published findings remain inconsistent. To evaluate the association between foetal sex and maternal glycaemic severity as well as neonatal birth weight among women with GDM. This retrospective observational study included 172 women diagnosed with GDM at tertiary care centres in India. Maternal age, body mass index (BMI), gravida/parity status, HbA1c, neonatal sex, and birth weight were extracted from medical records. GDM was diagnosed using Diabetes in Pregnancy Study Group India (DIPSI) criteria. Comparisons between male and female foetus groups were performed using independent t-tests. Multivariable linear regression analyses adjusted for maternal age and BMI were used to evaluate associations with HbA1c and birth weight. Foetal sex was not significantly associated with maternal HbA1c levels or neonatal birth weight in this cohort of women with GDM. While these findings suggest that foetal sex may not be a major determinant of these outcomes under routine clinical management, small associations cannot be excluded. Further prospective studies with comprehensive metabolic and neonatal outcome assessment are warranted.
Type 2 diabetes mellitus (T2DM) is a multifactorial disease influenced by genetic and environmental factors. This systematic review and meta-analysis aimed to synthesize available evidence on the association between the PON1 gene polymorphism and susceptibility to T2DM. A comprehensive literature search on PubMed, Web of Science, and Google Scholar was conducted for studies published between 2014 and 2025. Eligible case–control, cohort, and cross-sectional studies reporting genotype distributions of the PON1 gene polymorphism in T2DM patients and controls were included, and quality assessment was performed using the Newcastle–Ottawa Scale (NOS) and Q-Genie tool. Pooled odds ratios (ORs) and 95
