[Study on the risk factors of development for mild cognitive impairment to Alzheimer's disease based on the competitive risk joint model].

Abstract

Objective: To identify the risk factors for the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) using a competing risks joint model. Methods: This study was based on the Alzheimer's Disease Neuroimaging Initiative database. Three gradient boosting tree algorithm, namely CatBoost, XGBoost, and LightGBM, were employed to reduce the dimensionality of the high-dimensional lipoprotein and metabolite data (including 250 amino acids, lipids, and energy metabolism-related components, etc.). A random survival forest model (RSF) was used to screen out key demographic, cognitive function scores and metabolic variables. A competing risk joint model was constructed to identify the risk factors for the progression from MCI to AD. Results: A total of 8 lipoprotein and metabolite variables were identified by the three algorithms [creatinine, lactate, glycine, large high-density lipoprotein phospholipids percent (L_HDL_PL_PCT), acetic acid, tyrosine, β-hydroxybutyric acid and valine]. The RSF model identified 14 main variables, including cognitive function indicators [Functional Activities Questionnaire (FAQ), Alzheimer's Disease Assessment Scale-13 items (ADAS13), Alzheimer's Disease Assessment Scale word recognition item 4 (ADASQ4), and Alzheimer's Disease Assessment Scale-11 items (ADAS11)], lipoprotein and metabolites (acetic acid, L_HDL_PL_PCT, β-hydroxybutyrate, glycine, and creatinine), and baseline characteristics [age, years of education, marital status, retirement status, and apolipoprotein E ε4 allele (APOE-ε4)]. The univariate competing risk joint model showed that the longitudinal changes of ADAS11, ADAS13, ADASQ4, FAQ, and glycine, as well as baseline age, marital status, and APOE-ε4, were positively associated with the progression of MCI to AD (P<0.05). The results of the multivariate competing risk joint model further indicated that the longitudinal changes of ADAS13, ADASQ4, FAQ, and glycine, as well as baseline age, marital status, and APOE-ε4 were positively associated with AD incidence (P<0.05). Conclusions: Longitudinal increases in ADAS13, ADASQ4, FAQ, and glycine, as well as older age, unfavorable marital status, and APOE-ε4 carriage at baseline, were all risk factors for progression from MCI to AD.