Obesity is a global health crisis affecting developing nations, including India. The management of obesity continues to evolve with newer drugs, metabolic and bariatric surgery and endoscopic interventions, requiring family physicians and specialists to adapt their clinical practice accordingly. There is an urgent need for a standardized algorithm to diagnose, stage, and treat obesity. The Endocrine Society of India (ESI) and the Obesity Surgeons Society of India (OSSI) appointed a steering committee to develop an evidence-based algorithm for managing patients with obesity in India. This was put to vote by 80 specialists (38 from OSSI and 42 from ESI) in a physical meeting. A proposed stage-wise algorithm based on Edmonton Obesity Staging System, Asian definition of obesity, and resources in India, received 100
Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous and increasingly prevalent syndrome that remains challenging to diagnose and risk-stratify using conventional clinical and echocardiographic parameters. Advances in high-throughput proteomic and metabolomic technologies, combined with machine learning methods, have enabled the development of predictive models that capture complex molecular signatures associated with heart failure. This systematic review synthesizes current evidence on machine learning models derived from proteomic, metabolomic, and multi-omics datasets for the diagnosis, early detection, and prognostic assessment of heart failure, with particular focus on HFpEF. A structured search of PubMed, Scopus, and Web of Science identified eight eligible studies published between 2010 and 2026. Included studies applied machine learning techniques to high-dimensional molecular data to predict incident HF, classify HFpEF, identify molecular subtypes, or estimate mortality risk. Several models demonstrated strong discriminatory performance, with reported area under the curve (AUC) or C-index values generally ranging from approximately 0.78 to 0.98, and in some studies, demonstrated improved performance compared with established clinical tools such as natriuretic peptides (e.g., NT-proBNP) and conventional risk scores, including the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score. Multi-omics integration showed particular promise in identifying individuals at risk of developing HFpEF years before symptom onset. However, substantial heterogeneity across molecular platforms, limited external validation in some studies, and vulnerability to overfitting in smaller datasets restrict generalizability. Methodological quality assessment using the Prediction model Risk Of Bias ASsessment tool (PROBAST) tool indicated variable risk of bias, with higher concerns observed in smaller, non-externally validated studies. No randomized trials have yet evaluated the clinical impact of ML-omics-guided risk stratification. Overall, machine learning-based molecular profiling represents a promising direction for refining HFpEF phenotyping and risk prediction, but standardization, cross-platform validation, and outcome-based testing are necessary before routine clinical implementation.
Obesity in India is rising rapidly, with higher body fat at lower BMI and younger age compared to Western populations, leading to earlier onset of type 2 diabetes and cardiovascular disease in a resource-constrained health system. Protocols for obesity care therefore need to address region-specific challenges and ensure culturally acceptable, feasible treatment options. The Obesity and Metabolic Surgery Society of India (OSSI) and the Endocrine Society of India (ESI) jointly developed India-specific obesity management protocols using a modified Delphi consensus. A protocol development team generated 73 statements based on literature review and expert experience. Seventy-eight experts (38 OSSI, 40 ESI) participated; 100
ABSTRACT Background: Post-Intensive Care Syndrome (PICS) includes cognitive, psychological, and physical impairments following critical illness. The long-term impact of COVID-19-related ARDS on PICS domains remains under-explored, particularly in resource-limited settings. Objective: To assess the prevalence and trajectory of cognitive, mental, and physical impairments among COVID-19 ARDS survivors at ICU discharge and at 6 months, and to explore associated risk factors. Methods: This was an observational cohort study of 30 mechanically ventilated COVID-19 patients admitted to a tertiary ICU in South India during the second wave (Delta variant). Patients were assessed at ICU discharge (or first follow-up) and at 6 months using the Montreal Cognitive Assessment (MoCA), SF-36 health survey, modified MRC dyspnea scale, 6-minute walk test (6MWT), and physical examination. Risk factors were analyzed using multivariable linear regression. Results: At discharge, mild cognitive impairment was prevalent (MoCA: 25.17,3.63), with significant improvement at 6 months (27.07,2.72, p<0.001). SF-36 domains showed persistent deficits in emotional well-being , fatigue , and pain (all p<0.01). Functional capacity improved on 6MWT, with >350m walked increasing from 23% to 53%. Risk factors included steroid duration, SOFA score, antifungal exposure, and fasting hypoglycemia. Other parameters like Muscle wasting, dyspnea, and gastrointestinal symptoms also showed partial recovery. Conclusion: COVID-19 ARDS survivors experience significant but partially reversible PICS across multiple domains. Structured post-ICU rehabilitation and early identification of modifiable risk factors may improve recovery trajectories. Findings highlight the need for integrated post-ICU care pathways in similar settings. Keywords: Post-ICU Syndrome, COVID-19, ARDS, Cognition, Mental Health, Functional Recovery, India ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Kovai Medical Center and Hospital (KMCH), KMCH Ethics committee, approval granted. Ref: EC/AP/885/03/2022 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors
