The aging male : the official journal of the International Society for the Study of the Aging Male · 2026
SPISE index and ensemble machine learning refine cardiovascular risk stratification in stage 0-3 CKM syndrome.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wang Lin, Wang Hao, Liu Ting, Zhang Nianzhu, Zhao Lifen
Аннотация
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.
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.