SPISE index and ensemble machine learning refine cardiovascular risk stratification in stage 0-3 CKM syndrome
Clinical Snapshot
PICO Framework
| P — Population | 6,480 participants with CKM syndrome stages 0-3 from the China Health and Retirement Longitudinal Study |
| I — Intervention | SPISE index assessment and ensemble machine learning model (LR+GMM) |
| C — Comparator | SPISE quartiles (Q1-Q4) for risk stratification |
| O — Outcomes | Cardiovascular disease development over follow-up period |
Bottom Line
This large Chinese cohort study suggests the SPISE index, combined with routine clinical parameters, may improve cardiovascular risk prediction in early CKM syndrome. While the ensemble machine learning model showed impressive performance metrics (AUC 0.932), the exceptionally high accuracy (98.6%) raises concerns about overfitting. The study's strength lies in its large sample size and focus on early disease stages where preventive interventions are most valuable. However, critical limitations include lack of external validation, unclear follow-up methodology, and absence of comparison with established risk prediction tools. The SPISE index offers promise as an accessible insulin resistance marker, but clinicians should await external validation studies before incorporating this model into routine practice. The findings support further research into insulin resistance markers for cardiovascular risk stratification, particularly in patients with metabolic dysfunction.
Key Findings
P Value: Not provided in abstract
Effect Size: Inverse association between higher SPISE quartiles and CVD risk
Primary Outcome: Cardiovascular disease development in 967 of 6,480 participants (14.9%)
Nnt Or Sensitivity: Model AUC: 0.932, Accuracy: 0.986
Confidence Interval: Not provided in abstract
Clinical Application
High - uses readily available clinical parameters (SPISE, HDL-c, DBP, BMI, HbA1c) that are routinely measured Relevant to Australian practice as CKM syndrome aligns with RACGP cardiovascular risk assessment guidelines. SPISE calculation requires only routine pathology available through PBS. Could complement existing Framingham and Australian CVD risk calculators Adults with early-stage CKM syndrome (stages 0-3) in primary care and specialist settings
Abstract
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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