Metabolic score for insulin resistance (METS-IR) provides validated, non-fasting surrogate assessment incorporating waist circumference, triglycerides, high-density lipoprotein, and fasting glucose, outperforming traditional homeostasis model assessment in population studies.
Investigators conducted comprehensive cross-national analysis leveraging China Health and Retirement Longitudinal Study and National Health and Nutrition Examination Survey data augmented by general health examination datasets, with external validation from Shengjing Hospital cohort spanning 2011-2020. The study was published in the Diabetology and Metabolic Syndrome. 
Older adults with CKM syndrome stages 0-3 underwent METS-IR stratification with Cox proportional hazards modeling estimating stroke incidence hazard ratios across continuous and categorical thresholds. Restricted cubic spline analyses characterized dose-response relationships, while K-means clustering and machine learning algorithms including logistic regression, XGBoost, and random forest quantified predictive discrimination.
Dose-Dependent Stroke Risk Across CKM Stages
Cox regression established significant association between quartile-elevated METS-IR and incident stroke among early CKM syndrome patients, demonstrating consistent hazard ratios across discovery cohorts. Clinical validation cohort confirmed generalizability beyond survey-based ascertainment, supporting bedside applicability through routine lipid panel integration.
Nonlinear Relationship Refined By Splines
Restricted cubic spline modeling revealed J-shaped METS-IR-stroke association with inflection at approximately 45 units, identifying therapeutic intervention thresholds guiding precision lifestyle pharmacotherapy. Sensitivity analyses excluding outliers and subgroup stratification by age, sex, and kidney function confirmed effect robustness across demographic spectra.
Clustering Identifies Persistent High-Risk Trajectories
K-means clustering partitioned longitudinal METS-IR patterns into stable low, intermediate, and persistently high-risk clusters, with logistic regression confirming sustained elevation as independent stroke predictor independent of static baseline measurement. This dynamic phenotyping enhances temporal risk assessment beyond cross-sectional snapshots.
Machine Learning Validates Predictive Utility
Gradient boosting and ensemble algorithms demonstrated superior area under receiver operating characteristic curve discrimination for METS-IR versus conventional Framingham stroke components, establishing computational validation of clinical utility. Feature importance rankings prioritized METS-IR above blood pressure and kidney markers in CKM-specific models.
Precision Stroke Prevention Through Metabolic Profiling
Cardiologists, neurologists, and endocrinologists gain accessible biomarker facilitating cardiovascular-kidney-metabolic syndrome risk stratification within routine practice workflows. METS-IR calculation from standard lipid profiles identifies stage 0-3 patients warranting aggressive lifestyle intervention, glucagon-like peptide-1 receptor agonism, or sodium-glucose cotransporter-2 inhibition targeting insulin resistance-mediated microvascular brain injury. Serial monitoring tracks therapeutic trajectory while machine learning-derived cutoffs refine net reclassification improvement over traditional risk calculators. Population health integration supports targeted screening among metabolic syndrome clusters maximizing stroke yield.
Implementation Within CKM Care Pathways
Health systems should embed METS-IR calculators within electronic medical record dashboards prompting stage-specific interventions for scores exceeding 47 units. Multidisciplinary clinics coordinate lipid optimization alongside kidney protection while digital phenotyping platforms forecast clustering trajectories guiding annual stroke risk recalibration. Guideline updates should incorporate METS-IR thresholds paralleling coronary artery calcium scoring utility in primary prevention paradigms.