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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: OBJECTIVE, METHODS, RESULTS. Meshes: .
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Does oral glucose tolerance test-based calculation identify different glucose intolerance phenotypes within the impaired fasting glucose range? Context: The conventional oral glucose tolerance test (OGTT) cannot detect future diabetics among isolated impaired fasting glucose (is-IFG) nor normal glucose tolerant (NGT) groups. By analyzing the relationship between fasting (FPG) and 2-h plasma glucose (2hPG), the present study identifies is-IFG subjects liable to worsening glucose homeostasis. Oral glucose tolerance test was carried out in 619 patients suffering from obesity, hypertension or dyslipidemia, whose FPG was in the 100-125 mg/dL range. We calculated the percentage increment of 2hPG with respect to FPG (PG%) in these patients using the formula: ([2hPG - FPG] / FPG) × 100. Differences in β-cell function within is-IFG patients were assessed by estimated insulin sensitivity index (EISI), first-phase insulin release (1stPH) and 1stPH/1/EISI (1stPHcorrected). Diabetes was diagnosed in 69 patients (11.2%), combined IFG/impaired glucose tolerance (IGT) in 185 patients (29.9%) and is-IFG in 365 patients (58.9%). Is-IFG was subdivided into PG% tertile groups: the percentage of females increased from 25% in the lowest to 45.2% in the highest tertile (χ(2) = 18.7, P < 0.001). Moving from the lowest to the highest PG% tertile group, insulin and 2hPG concentrations rose, whereas FPG, EISI, and 1stPHcorrected decreased progressively and significantly. Furthemore, PG% correlated inversely with EISI (r = -0.44, P < 0.0001) and 1stPHcorrected (r = -0.38, P < 0.0001).
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Oral glucose tolerance test does differentiate the great heterogeneity in metabolic disorders of patients with FPG 100-125 mg/dL. Furthermore, PG% can expand the diagnostic power of OGTT in the is-IFG range by distinguishing metabolic phenotypes very likely to herald different clinical risks. Final Decision: yes