Using three statistical methods to analyze the associations between a mixture of multi-nutrients and risk of mild cognitive impairment in an elderly population in Northern China

Abstract

Few studies have considered nutrients as a mixture and their impact on Mild Cognitive Impairment (MCI). The generalized linear regression (GLM), weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) models are fitted to estimate the association between intake of a mixture of nutrients and MCI. Comparing the results from these three models, vitamin E and vitamin B6 were identified as the most important factors associated with the risk of MCI. Considering the characteristics of BKMR, it may be more advantageous to use BKMR to estimate the combined the joint effects of nutrients mixture. In the future, studies need to move from a “one nutrient at a time” approach to simultaneous analyses of multiple nutrients intakes in order to understand and quantify the joint effect of nutrients mixture on health.