Abstract 4147625: Cardiometabolic Syndrome and Incident Alzheimer’s Disease: The Predicative Value of Age and CMS Using Cox and Machine Learning Models

Circulation, Volume 150, Issue Suppl_1, Page A4147625-A4147625, November 12, 2024. 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

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Novembre 2024