Association of Insulin Resistance with Dysglycemia in Elder Koreans: Age- and Sex-Specific Cutoff Values.

Background/Objectives: Dysglycemia including pre-diabetes mellitus (Pre-DM) and type 2 diabetes mellitus (T2DM) is associated with insulin resistance. This study aimed to support personalized early diagnosis of dysglycemia by proposing optimal, sex- and age-specific cutoff values for Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) and Homeostatic Model Assessment of Beta-Cell Function (HOMA-β) in Koreans aged ≥65 years. Methods: This study analyzed 3862 older Koreans from the 8th Korea National Health and Nutrition Examination Survey data (2019-2021), excluding those with prior diabetes or medication. The participants were classified into normal and dysglycemia groups, based on fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c). Sex- and age-specific optimal cutoff values were determined using Youden's Index (YI) and area under the curve (AUC). Results: For T2DM, the optimal HOMA-IR cutoff was 2.25 for men and 2.03 for women, with strong discriminative performance (AUCs: 0.828 and 0.823, respectively). Stratifying cutoff values further by sex and age improved the diagnostic accuracy (AUC > 0.83 in most subgroups), underscoring the value of tailored thresholds. For pre-DM, the HOMA-IR cutoff was 1.73 in men and 1.85 in women (AUCs: 0.682 and 0.665, respectively). Age- and sex-specific cutoffs modestly improved AUCs, particularly in men (up to 0.7), although the improvement was less consistent among women. HOMA-β showed no significant association with dysglycemia, and no meaningful cutoff values were identified. Conclusions: HOMA-IR is a promising marker for the early identification of dysglycemia in older adults when interpreted through a personalized lens. Applying sex- and age-specific cutoff values enhances diagnostic precision and supports a more individualized approach to metabolic risk assessment. Further longitudinal studies are warranted to validate these personalized thresholds and to optimize early detection strategies in diverse populations.