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Updated: Aug 12, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Multifactorial stratified analysis of two novel metabolism-related indices for predicting stroke in mild cognitive
Boyuan Qiu1, Hongliang Tang1, Liangyuan Tan2
1Guangxi University of Chinese Medicine, Nanning, Guangxi, China.
Objectives:
This study aimed to evaluate the predictive value of two novel metabo-lism-related indices, the C-reactive protein-triglyceride glucose index (CTI) and the Body Roundness Index (BRI), for new-onset stroke in patients with mild cognitive impairment (MCI). A systematic stratified analysis based on various factors was con-ducted to refine clinical strategies for the primary prevention of stroke.
Methods:
Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS). A total of 907 eligible participants were included in this cohort study. Multivariable logistic regression models were employed to assess the associations of CTI and BRI with stroke risk. Exploratory stratified analyses were performed using piecewise linear regression and receiver operating characteristic (ROC) curves based on key behavioral and demographic characteristics, with optimal cutoff values calculated for each stratum. Subgroup analyses were conducted to verify the stability of the findings.
Results:
During the follow-up period, 81 participants (8.93%) experienced a stroke. After adjusting for potential confounding factors, each one-unit increase in CTI was associated with a 34% higher risk of stroke among MCI patients (Odds Ratio (OR): 1.34, 95% Confidence Interval (CI): 1.04-1.72, p < 0.05), demonstrating a significant increasing trend (P for trend < 0.05). In contrast, BRI showed no significant correlation with stroke occurrence (OR: 1.12, 95% CI: 0.97-1.30, p > 0.05). The multifactorial stratified analysis revealed that among MCI patients who smoked, within the CTI range below 9.91, each unit increase in CTI was significantly associated with an elevated stroke risk (OR: 1.81, 95% CI: 1.09-3.01, p < 0.05). The predictive performance of CTI demonstrated no significant heterogeneity across populations with different characteristics (Areas Under the Curve (AUCs) ranged from 0.55 to 0.61), and the ROC analysis identified optimal CTI cutoff values ranging from 8.20 to 9.28 across subgroups. Subgroup analyses further showed that the association between CTI and stroke was more pro-nounced in females, married individuals, and patients aged below 65 years (p < 0.05).
Conclusion:
Among middle-aged and elderly Chinese patients with MCI, CTI predicts stroke risk better than BRI.
