Identification of glucose and insulin patterns during a 5-H glucose tolerance test and association with cardiometabolic risk factors

• Paulina B. Crespo-Morfin ^[1] ; Carlos Alberto Aguilar Salinas ^[1] ; Paloma Almeda Valdés ^[2] ; Raúl Alfaro-Pastrana ^[3] ; Omar Yaxmehen Bello Chavolla ^[4] ; Jhoana Cano-Castillo ^[3] ; Francisco J. Gómez Pérez ^[1] ; Ivette Cruz Bautista ^[5]
  1. [1] Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán

     Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán

     México

     
  2. [2] Deparments of 1Endocrinology and Metabolism. Metabolic Research Diseases Unit, INCMNSZ, Mexico City
  3. [3] Metabolic Research Diseases Unit, INCMNSZ, Mexico City
  4. [4] Research Division, Instituto Nacional de Geriatría, Mexico City
  5. [5] Deparments of Endocrinology Metabolic Research Diseases Unit, INCMNSZ, Mexico City

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• Localización: Revista de investigación clínica, ISSN 0034-8376, ISSN-e 2564-8896, Vol. 74, Nº. 4, 2022, págs. 193-201
• Idioma: inglés
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  • Background: Insulin resistance is key in the pathogenesis of the metabolic syndrome and cardiovascular disease. Objective: We aimed to identify glucose and insulin patterns after a 5-h oral glucose tolerance test (OGTT) in individuals without diabetes and to explore cardiometabolic risk factors, beta-cell function, and insulin sensitivity in each pattern. Methods: We analyzed the 5-h OGTT in a tertiary healthcare center. We identified classes using latent class trajectory analysis and evaluated their association with cardiometabolic risk factors, beta-cell function, and insulin sensitivity surrogates by multinomial logistic regression analysis. Results: We included 1088 5-h OGTT performed between 2013 and 2020 and identified four classes. Class one was associated with normal insulin sensitivity and secretion. Class two showed hyperglycemia, dysinsulinism, and a high-risk cardiometabolic profile (obesity, hypertriglyceridemia, and low high-density lipoprotein [HDL] cholesterol). Class three included older individuals, a higher proportion of males, and a greater prevalence of hypertension, hyperglycemia, hyperinsulinemia, and postprandial hypoglycemia. Finally, class four showed hyperglycemia, dysinsulinism, and hyperinsulinemia; this class had the worst cardiometabolic profile (a high proportion of males, greater age, hypertension, obesity, hypertriglyceridemia, and low HDL cholesterol, p < 0.001 vs. other classes). Conclusions: The latent class analysis approach allows the identification of groups with an adverse cardiometabolic risk factor, and who might benefit from frequent follow-ups and timely multidisciplinary interventions.