Consensus on Current Landscape and Treatment Trends of Obesity in India for Primary Care Physicians.

Objectives: The objective of this consensus article was to form a list of expert recommendations and an easily adaptable algorithm for obesity management in India by primary care physicians (PCPs). Methods: A Delphi-based model was followed to form a list of the consensus recommendations. Consensus statements were created from the results of a literature review that were graded as per the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) criteria. After being evaluated by an expert panel comprising diabetologists, endocrinologists, cardiologists, bariatric surgeons, and gynecologists, the statements were revised and reevaluated by a larger group of practitioners, including PCPs and diabetologists, to arrive at a consensus. Results: The panel opined that obesity is increasing in prevalence in India and is projected to rise in the coming years. Body mass index and waist circumference were both recommended for better identification of people at risk of obesity-related comorbidities than either of them alone. The Edmonton Obesity Staging System (EOSS) was suggested as being most suitable for the assessment (staging) of obesity. A multidisciplinary team was considered invaluable for assessing and managing patients with obesity. The use of once-a-week semaglutide (2.4 mg) via the subcutaneous route was suggested as the first-choice anti-obesity treatment when pharmacotherapy is deemed necessary. An algorithm considering all these aspects was proposed. Conclusion: Obesity needs to be recognized as a significant contributor to other comorbidities. The diagnosis and management of obesity should be comprehensive and consider patient psychology, the presence or absence of comorbidities, available pharmacologic agents, and long-term outcomes. The proposed algorithm could help clinicians in this aspect and improve the overall outcomes. How to cite this article: Deshpande NR, Kapoor N, Dalal JJ, et al. Consensus on Current Landscape and Treatment Trends of Obesity in India for Primary Care Physicians. J Assoc Physicians India 2023;71(10):69-77.

A tale of two risks: smoking, diabetes and the subgingival microbiome.

Although smoking and diabetes have been established as the only two risk factors for periodontitis, their individual and synergistic impacts on the periodontal microbiome are not well studied. The present investigation analyzed 2.7 million 16S sequences from 175 non-smoking normoglycemic individuals (controls), smokers, diabetics and diabetic smokers with periodontitis as well as periodontally healthy controls, smokers and diabetics to assess subgingival bacterial biodiversity and co-occurrence patterns. The microbial signatures of periodontally healthy smokers, but not diabetics, were highly aligned with the disease-associated microbiomes of their respective cohorts. Diabetics were dominated by species belonging to Fusobacterium, Parvimonas, Peptostreptococcus, Gemella, Streptococcus, Leptotrichia, Filifactor, Veillonella, TM7 and Terrahemophilus. These microbiomes exhibited significant clustering based on HbA1c levels (pre-diabetic (<6.5%), diabetic (6.5-9.9%), diabetics >10%). Smokers with periodontitis evidenced a robust core microbiome (species identified in at least 80% of individuals) dominated by anaerobes, with inter-individual differences attributable largely to the 'rare biosphere'. Diabetics and diabetic smokers, on the other hand, were microbially heterogeneous and enriched for facultative species. In smokers, microbial co-occurrence networks were sparse and predominantly congeneric, while robust inter-generic networks were observed in diabetics and diabetic smokers. Smoking and hyperglycemia impact the subgingival microbiome in distinct ways, and when these perturbations intersect, their synergistic effect is greater than what would be expected from the sum of each effect separately. Thus, this study underscores the importance of early intervention strategies in maintaining health-compatible microbiomes in high-risk individuals, as well as the need to personalize these interventions based on the environmental perturbation.