Key result
METS-IR effectively identifies metabolic syndrome with ~0.88 AUC and tracks with SCORE2-OP cardiovascular risk.
Why the study?
Does the METS-IR score accurately identify metabolic syndrome and correlate with estimated cardiovascular risk compared to iTyG and iTyHDL indices in older adults?
Cross-Sectional (n=4,690)
Does the METS-IR score accurately identify metabolic syndrome and correlate with estimated cardiovascular risk compared to iTyG and iTyHDL indices in older adults?
METS-IR shows strong discriminatory capacity for identifying metabolic syndrome and correlates in a graded manner with estimated cardiovascular risk in older Latin American adults, performing similarly to other triglyceride-based indices.
Background Insulin resistance is the central pathophysiological driver of metabolic syndrome (MetS) and a major contributor to cardiovascular risk in older adults. The Metabolic Score for Insulin Resistance (METS-IR), which integrates fasting glucose, triglycerides, HDL-cholesterol, and body mass index, represents a pragmatic surrogate marker. However, its diagnostic performance in Latin American elderly populations remains insufficiently established. Purpose To evaluate the operational performance of METS-IR for identifying metabolic syndrome and to examine its association with estimated cardiovascular risk, in comparison with triglyceride–glucose (iTyG) and triglyceride–HDL (iTyHDL) indices. Methods We conducted a cross-sectional analytical study in adults aged ≥60 years from a nationally representative survey. From more than 23,000 participants, 4,690 individuals with complete data on glucose, triglycerides, HDL-cholesterol, and anthropometry were included. METS-IR was calculated as ln[(2×fasting glucose)+triglycerides]×(BMI/ln[HDL-C]). Metabolic syndrome was defined according to ATP III/IDF criteria. Cardiovascular risk was estimated using a modified SCORE2-Older Persons (SCORE2-OP) model incorporating age, sex, systolic blood pressure, cholesterol, diabetes, and smoking. A normalized 0–100 index was derived as a relative risk estimator. Discriminatory performance was assessed using receiver operating characteristic (ROC) curves, and areas under the curve (AUC) were compared using DeLong’s method. Results Mean METS-IR was 37.9 ± 11.8, and the prevalence of metabolic syndrome was 58%. METS-IR demonstrated excellent discriminatory capacity for metabolic syndrome (AUC 0.884). The optimal cut-off (42.8) yielded a sensitivity of 83% and specificity of 76%. A progressive increase in estimated cardiovascular risk was observed across METS-IR quartiles, with normalized SCORE2-OP values of 41.2, 49.6, 56.8, and 63.5 for Q1 through Q4, respectively. In comparative ROC analysis, AUC values were 0.884 for METS-IR, 0.840 for iTyG, and 0.815 for iTyHDL. Pairwise comparisons using DeLong’s test showed no statistically significant differences between METS-IR and iTyG (p=0.183), METS-IR and iTyHDL (p=0.071), or iTyG and iTyHDL (p=0.640). Conclusion METS-IR demonstrates excellent operational performance for identifying metabolic syndrome and shows a clear, graded association with estimated cardiovascular risk in older adults. Together with other triglyceride-based indices, it represents a low-cost and scalable tool with potential utility for cardiometabolic risk stratification in elderly populations. Prospective studies are warranted to assess its prognostic value for hard cardiovascular outcomes.
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Medina et al. (2026) conducted a cross-sectional in Metabolic syndrome and cardiovascular risk in older adults (n=4,690). Metabolic Score for Insulin Resistance (METS-IR) vs. Triglyceride-glucose (iTyG) and triglyceride-HDL (iTyHDL) indices was evaluated on Discriminatory performance for metabolic syndrome (ATP III/IDF criteria) and association with estimated cardiovascular risk (modified SCORE2-OP) (AUC 0.884, p=0.183). METS-IR showed excellent discrimination for metabolic syndrome (AUC 0.884; 83% sensitivity, 76% specificity at 42.8) and graded association with SCORE2-OP risk (41.2 in Q1 to 63.5 in Q4).
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