Primary care diabetes · 2025
Non-insulin-based markers of insulin resistance at diabetes diagnosis: A pooled analysis of 14 national health surveys.
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Carrillo-Larco Rodrigo M, Guzman-Vilca Wilmer Cristobal, Xu Xiaolin, Bernabe-Ortiz Antonio
Аннотация
BACKGROUND: The extent to which people with newly diagnosed diabetes exhibit similar insulin resistance profiles across countries is unknown. Understanding the heterogeneity in glycemic and cardiometabolic profiles at the time of diabetes diagnosis could provide insights for precision medicine.
METHODS: We analyzed 14 nationally representative surveys (STEPS). We used anthropometric measures, fasting glucose, triglycerides and HDL cholesterol to calculate eight non-insulin-based markers of insulin resistance: triglyceride and glucose ratio (TyG), TyG with BMI (TyGBMI), TyG with waist circumference (TyGwaist), TyG with waist-to-height ratio (TyGWHtR), triglyceride-to-HDL ratio (TGHDL_r), metabolic score for insulin resistance (METS-IR), lipid accumulation product (LAP), and visceral adiposity index (VAI). We only included subjects with newly diagnosed diabetes. Age- and sex-adjusted multilevel linear regression models were used to estimate country-specific margins of the z-scores for each insulin resistance surrogate (_z suffix refers to predicted z-score margins).
RESULTS: A total of 2531 individuals were analyzed (mean age 47.4 years; 56.6 % were women). The widest disparities across countries in predicted z-scores were observed for TyG-based markers. For instance, the predicted margins for TyGBMI_z ranged from -0.985 (Ethiopia) to 0.682 (Kuwait). In contrast, narrower disparities were noted for other markers, with the smallest gap observed for VAI_z ranging from -0.688 (Bhutan) to 0.491 (Guyana).
CONCLUSIONS: The study highlights heterogeneity in metabolic profiles at diabetes diagnosis across countries. These findings emphasize the importance of incorporating population-specific factors into strategies for understanding and addressing the global diabetes burden, particularly in recognizing the diverse cardiometabolic profiles at the time of diagnosis.
METHODS: We analyzed 14 nationally representative surveys (STEPS). We used anthropometric measures, fasting glucose, triglycerides and HDL cholesterol to calculate eight non-insulin-based markers of insulin resistance: triglyceride and glucose ratio (TyG), TyG with BMI (TyGBMI), TyG with waist circumference (TyGwaist), TyG with waist-to-height ratio (TyGWHtR), triglyceride-to-HDL ratio (TGHDL_r), metabolic score for insulin resistance (METS-IR), lipid accumulation product (LAP), and visceral adiposity index (VAI). We only included subjects with newly diagnosed diabetes. Age- and sex-adjusted multilevel linear regression models were used to estimate country-specific margins of the z-scores for each insulin resistance surrogate (_z suffix refers to predicted z-score margins).
RESULTS: A total of 2531 individuals were analyzed (mean age 47.4 years; 56.6 % were women). The widest disparities across countries in predicted z-scores were observed for TyG-based markers. For instance, the predicted margins for TyGBMI_z ranged from -0.985 (Ethiopia) to 0.682 (Kuwait). In contrast, narrower disparities were noted for other markers, with the smallest gap observed for VAI_z ranging from -0.688 (Bhutan) to 0.491 (Guyana).
CONCLUSIONS: The study highlights heterogeneity in metabolic profiles at diabetes diagnosis across countries. These findings emphasize the importance of incorporating population-specific factors into strategies for understanding and addressing the global diabetes burden, particularly in recognizing the diverse cardiometabolic profiles at the time of diagnosis.