Key result
Higher SPISE index quartiles were associated with lower CVD risk, and an LR+GMM ensemble model incorporating SPISE predicted CVD risk with an accuracy of 0.986 and an AUC of 0.932.
Why the study?
The association between the single-point insulin sensitivity estimator and cardiovascular disease in early cardiovascular-kidney-metabolic stages 0 to 3 remains underexplored.
Does the SPISE index and an ensemble machine learning model predict cardiovascular disease risk in individuals with stage 0-3 CKM syndrome?
Cohort (n=6,480)
Yes
Does the SPISE index and an ensemble machine learning model predict cardiovascular disease risk in individuals with stage 0-3 CKM syndrome?
The SPISE index is a significant inverse predictor of CVD risk in stage 0-3 CKM syndrome, and an ensemble machine learning model incorporating SPISE and clinical metrics provides outstanding predictive performance.
BACKGROUND: While the single-point insulin sensitivity estimator (SPISE) shows promise as an insulin resistance biomarker, its association with cardiovascular disease (CVD) in early CKM stages (0-3) remains underexplored. METHODS: We analyzed 6480 participants with CKM stage 0-3 from the China Health and Retirement Longitudinal Study. CVD outcomes were assessed relative to SPISE index levels. An ensemble machine learning model was employed to predict CVD risk. RESULTS: 6480 subjects were enrolled, of whom 967 developed CVD. After stratifying participants into SPISE quartiles (Q1-Q4) and adjusting for covariates, higher quartiles were linked to a lower CVD risk. This study developed an LR+GMM (Logistic Regression + Gaussian Mixture Model) ensemble model to predict CVD risk using five strong predictors: SPISE, high-density lipoprotein cholesterol (HDL-c), diastolic blood pressure (DBP), body mass index (BMI), and glycated hemoglobin (HbA1c). The model performed well, achieving an accuracy (ACC) of 0.986 and an area under the receiver operating characteristic curve (AUC) of 0.932. CONCLUSIONS: The SPISE index is a significant inverse predictor of CVD risk in individuals with stage 0-3 CKM syndrome. The LR+GMM ensemble model, incorporating the SPISE index and four clinical metrics, demonstrated outstanding predictive performance.
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Wang et al. (2026) conducted a cohort in Stage 0-3 CKM syndrome (n=6,480). SPISE index vs. Lower SPISE quartiles was evaluated on Cardiovascular disease (CVD) risk. Higher SPISE index quartiles were associated with lower CVD risk, and an LR+GMM ensemble model incorporating SPISE predicted CVD risk with an accuracy of 0.986 and an AUC of 0.932.
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