Abstract Body (Do not enter title and authors here): Background: Cardiometabolic syndrome (CMS) poses a significant public health concern. The study aimed to investigate the predictive value of age and CMS for incident Alzheimer’s disease (AD) in women aged≥50.
Methods: A cohort of women aged 50-79 (n = 63,117) who participated in the Women’s Health Initiative Observational Study (WHIOS) in 1993-1998, without baseline AD and followed through to March 1, 2019, were analyzed. CMS was defined as having ≥3 of five CMS components: large waist circumference, HBP, elevated triglycerides, elevated glucose, and low HDL-cholesterol. AD was classified by physician-diagnoses of incident AD. Hazards ratios (HR) of AD risk associated with CMS by age were analyzed using Cox’s proportional hazards regression analysis. Machine learning (ML)-XGBoost and Lasso Cox models clustered individuals with low, mild, moderate, and severe risk of incident AD.
Results: During a median follow-up of 20 years (range: 3.36 to 23.36 years), 8340 developed incident AD. The incident rate (95%CI) of AD was 8.6 (8.1-9.1) per 1000 person-years (PY) in women with CMS, and 7.0 (6.9-7.2) per 1000 PY in those without CMS (p<0.001). Multivariate Cox’s regression analysis indicated that women with CMS versus non-CMS had significantly higher risks of AD in those aged 50-59 [HR (95%CI): 1.37(1.12-1.68)], followed by in those aged 60-69 [1.26 (1.14-1.40)], but nonsignificant in those aged≥70 [1.00 (0.87-1.14)]. Elevated glucose concentration ranked as the top predictor for AD, followed by triglyceride and HBP. Four levels (low to severe) of AD risk were significantly clustered using ML-XGBoost and Lasso Cox models.
Conclusions: Women aged 50-69 with CMS vs those without CMS had significantly higher relative risks of AD, but the risk was not significant in those aged≥70. The application of ML prediction models holds potential promise in the early identification of AD risk among U.S. adults.