The predicative values of vascular and metabolic disorders for risk of incident mild cognitive impairment and dementia

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Project Summary/Abstract Dementia is a condition affecting thinking, judgment, memory and other cognitive domains to the degree that interferes with performing everyday activities. Alzheimer’s disease (AD) is the most common type of dementia. In 2021, an estimated 6.2 million adults in the US aged ≥65 have dementia, and the number is projected to be nearly 14 million by 2060. Women a have significantly higher risk of AD than men. About two thirds of patients with dementia are women. Mild cognitive impairment (MCI), representing the early stage of the disease, refers to a state in which the patient experiences a decline in short-term memory, or other cognitive domain, but with no significant impairment in everyday functioning. Though dementia mostly affects older adults, it is not a part of normal aging. Given that taking prevention at the early stage of MCI delays the development of the disease. It is of tremendous important to detect MCI during the early pre- symptomatic stage. Several studies observed that vascular, metabolic disorders, and inflammation are associated with risk of MCI, AD and AD related dementia (AD/ADRD). However, research gaps remain: (1) inconsistent findings were observed from the previous studies. (2) Large-scale population-based studies for AD and dementia risk are limited. (3) Although it is known that the risk of MCI and dementia are associated with changes in risk exposures, few studies tested the association between time-varying exposures and risk of outcomes. In the application, we aim at filling these gaps by using a rigorous study design to test the predictive values of vascular and metabolic disorders, inflammation, as well as genetic factors for the risk of incident MCI and dementia, and then develop a novel cumulative (combined) prediction index. We have 2 specific aims. Aim 1: To examine the association of vascular, metabolic and inflammatory biomarkers with risk of incident MCI and dementia in older women. Hypothesis: vascular, metabolic and inflammatory biomarkers, with time-varying measures significantly predict the risk of incident MCI and dementia in women aged 65-79, and these associations are modified by APOE gene (ε4 versus the other alleles). Aim 2: To develop a machine learning (ML)-enabled algorithm to predict individuals who are at high risk of incident MCI and dementia. Hypothesis: A novel and advanced risk prediction model (e.g., a multi-dimensional risk model using ML) that integrates predictive values of multiple risk factors and key covariates, will enhance the degree of the prediction for the risk of incident MCI and dementia. The proposed study addresses a significant public health challenge facing an aging population. The proposed study is innovative, characterized by (1) focusing on sex-specific study in older women; (2) addressing time-varying risk factors that may have significant predictive effects on the study outcomes. (3) We will test whether there are potential modification effects of APOE on the association between the study of exposures and outcomes. The scientifically promising aspects of the study are the fact that it is able to test the natural development of MCI and dementia using data from an unique large-scale population-based study, the Women’s Health Initiative Memory Study. Findings of the study will be disseminated through scientific meetings and research articles published in high impact journals, and add new insights into the prevention and control of MCI and dementia.