Aging clinical and experimental research · 2026
Comparison of eight insulin resistance surrogate indices in predicting incident stroke among middle-aged and older individuals without diabetes: a nationwide prospective cohort study.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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.
Ran Jun, Wang Jinxi, Zhao Xuemei, Zhou Qiong, Huang Yan, Zhai Mei, Quan Xin, Zhang Yuhui
Аннотация
BACKGROUND: Although insulin resistance (IR) surrogate indices have demonstrated associations with and predictive value for incident stroke, previous studies focused mainly on diabetic or general populations, with limited comprehensive comparisons in non-diabetic populations.
AIMS: To systematically compare eight IR surrogate indices for predicting incident stroke in middle-aged and older individuals without diabetes.
METHODS: Using data from the China Health and Retirement Longitudinal Study (CHARLS), we analyzed 6,377 non-diabetic participants aged ≥ 45 years followed from 2011 to 2020. Multivariate Cox proportional hazards regression models and restricted cubic spline (RCS) analyses were used to assess associations between IR surrogate indices and incident stroke. Harrell's C-index, time-dependent C-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate and compare the predictive performance of different IR surrogate indices for incident stroke. Subgroup and sensitivity analyses were conducted to verify robustness.
RESULTS: Over 9 years of follow-up, 550 incident stroke events (8.6%) were recorded. All eight IR surrogate indices were significantly associated with incident stroke. Regarding predictive performance, the estimated glucose disposal rate (eGDR) model significantly outperformed the base model and other IR surrogate index models across multiple evaluation dimensions, including overall C-index (0.660, 95% CI: 0.638-0.681), time-dependent C-index, NRI, and IDI. Subgroup analyses and sensitivity analyses confirmed the robustness of the eGDR model's predictive advantages.
CONCLUSIONS: eGDR outperformed other IR surrogate indices in predicting incident stroke among middle-aged and older individuals without diabetes, suggesting potential utility for early stroke risk assessment.
AIMS: To systematically compare eight IR surrogate indices for predicting incident stroke in middle-aged and older individuals without diabetes.
METHODS: Using data from the China Health and Retirement Longitudinal Study (CHARLS), we analyzed 6,377 non-diabetic participants aged ≥ 45 years followed from 2011 to 2020. Multivariate Cox proportional hazards regression models and restricted cubic spline (RCS) analyses were used to assess associations between IR surrogate indices and incident stroke. Harrell's C-index, time-dependent C-index, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate and compare the predictive performance of different IR surrogate indices for incident stroke. Subgroup and sensitivity analyses were conducted to verify robustness.
RESULTS: Over 9 years of follow-up, 550 incident stroke events (8.6%) were recorded. All eight IR surrogate indices were significantly associated with incident stroke. Regarding predictive performance, the estimated glucose disposal rate (eGDR) model significantly outperformed the base model and other IR surrogate index models across multiple evaluation dimensions, including overall C-index (0.660, 95% CI: 0.638-0.681), time-dependent C-index, NRI, and IDI. Subgroup analyses and sensitivity analyses confirmed the robustness of the eGDR model's predictive advantages.
CONCLUSIONS: eGDR outperformed other IR surrogate indices in predicting incident stroke among middle-aged and older individuals without diabetes, suggesting potential utility for early stroke risk assessment.