insulin resistance

Background: The countries of the Gulf Cooperation Council (GCC) face a significant diabetes burden. Preventing, or at least delaying onset of Type 2 diabetes mellitus (T2DM) requires accurate and timely identification of at-risk individuals with prediabetes. Traditional screening focuses on glucose and HbA1c alone and does not include other readily available data that can better identify risk and guide intervention. Methods: We developed a comprehensive web-based cardiometabolic risk calculator combining the classic homeostasis model assessment (HOMA1), triglyceride-glucose (TyG) index, metabolic syndrome evaluation (ATP III and IDF criteria), diabetes risk prediction adapted from validated models including QDiabetes-2018 and UK Biobank cohort studies, and cardiovascular risk trajectory with Lipoprotein(a) integration. The calculator uses ethnicity-specific thresholds for Middle Eastern, South Asian, and other populations, and provides AI-generated clinical summaries with multi-language patient education materials. Case: An 18-year-old Emirati male with obesity, prediabetes, severe insulin resistance, and compensatory beta-cell hypersecretion had a calculated 10-year diabetes risk exceeding 55%. This quantitative risk estimate motivated intensive lifestyle modification combined with tirzepatide and metformin. After six months, he achieved 17% weight loss, normalization of insulin resistance and beta-cell function, prediabetes resolution and diabetes risk reduction to under 10%. Conclusion: Comprehensive metabolic phenotyping with quantitative risk communication can identify high-risk individuals, characterize their metabolic dysfunction, and motivate behavioral change. This freely available tool addresses the need to identify individuals at high 10-year risk of progression to T2DM and elevated lifetime cardiovascular risk, thereby allowing more timely intervention.

Introduction: Type 2 diabetes is a complex endocrine-metabolic disorder in which insulin resistance, beta-cell dysfunction, chronic low-grade inflammation and environmental exposures interact. The gut microbiota has emerged as a biologically plausible determinant of this process because it regulates nutrient fermentation, microbial metabolite production, gut barrier integrity, immune tone and endocrine-metabolic signalling. Methods: A structured review of the literature was conducted using PubMed/MEDLINE, ScienceDirect and Google Scholar. English and French publications were screened using terms related to gut microbiota, type 2 diabetes, dysbiosis, insulin resistance, inflammation, probiotics, prebiotics, microbiota-targeted therapy and fecal microbiota transplantation. Forty-two records were identified, and 32 references were retained for qualitative synthesis after screening and full-text eligibility assessment. Results: The evidence consistently indicates that type 2 diabetes is associated with dysbiosis, reduced microbial diversity, depletion of butyrate-producing or metabolically favorable taxa and enrichment of opportunistic or pro-inflammatory communities. Recurrent mechanisms include intestinal barrier dysfunction, lipopolysaccharide-mediated metabolic endotoxemia, impaired short-chain fatty acid signalling, bile acid dysregulation, excessive imidazole propionate, branched-chain amino acid metabolism, altered incretin responses and immune-metabolic activation. Microbiota-targeted interventions, including fermented foods, probiotics, prebiotics, synbiotics, resistant starch and fecal microbiota transplantation, show promise but remain clinically heterogeneous. Conclusion: The gut microbiota is best understood as a dynamic metabolic interface rather than a passive marker of type 2 diabetes. Microbiome-informed strategies may enrich future precision endocrinology, but standardised human trials integrating taxonomic, functional and clinical endpoints are required before routine implementation.

Introduction: Dietary interventions are central to the management of obesity, insulin resistance, type 2 diabetes and related cardiometabolic disorders, yet clinical responses vary substantially between individuals exposed to apparently similar nutritional prescriptions. The gut microbiota may explain part of this variability because it transforms dietary substrates into bioactive metabolites and influences intestinal, immune and endocrine pathways. Methods: A narrative review informed by a structured literature search was conducted using PubMed, ScienceDirect and Google Scholar. Search terms combined concepts related to gut microbiota, intestinal microbiota, dietary intervention, nutrition, fiber, prebiotics, probiotics, metabolic response, obesity, insulin sensitivity and glycemic response. Forty records were initially identified, and 21 references were retained for qualitative synthesis after duplicate removal, screening and full-text eligibility assessment. Results: The reviewed literature indicates that baseline microbial diversity, community structure and functional capacity are associated with differential metabolic responses to calorie restriction, fiber-rich dietary patterns, probiotics, prebiotics and glycemia-oriented personalized nutrition. Key taxa and communities discussed in the evidence include Bacteroides, Prevotella, Akkermansia muciniphila, Bifidobacterium, Lactobacillus and Christensenella-associated communities. Mechanistic pathways include short-chain fatty acid generation, bile acid transformation, tryptophan-derived signaling, incretin-related pathways, epithelial barrier integrity and low-grade inflammation. Conclusion: The gut microbiota should be regarded as both a biomarker and a potential mediator of response to dietary interventions. Microbiota-informed nutrition is promising for precision diabetes and endocrinology care, but standardized prospective studies integrating microbiome profiling with robust metabolic outcomes remain necessary before routine clinical implementation.