CSF metabolites associate with CSF tau and improve prediction of Alzheimer's disease status

Lipidomic markers for the prediction of progression from mild cognitive impairment to Alzheimer's disease

Dementia is a well-known syndrome and Alzheimer's disease (AD) is the main cause of dementia. Lipids play a key role in the pathogenesis of AD, however, the prediction value of serum lipidomics on AD remains unclear. This study aims to construct a lipid score system to predict the risk of progression from mild cognitive impairment (MCI) to AD. First, we used the least absolute shrinkage and selection operator (LASSO) Cox regression model to select the lipids that can signify the progression from MCI to AD based on 310 older adults with MCI. Then we constructed a lipid score based on 14 single lipids using Cox regression and estimated the association between the lipid score and progression from MCI to AD. The prevalence of AD in the low-, intermediate- and high-score groups was 42.3%, 59.8%, and 79.8%, respectively. The participants in the intermediate- and high-score group had a 1.65-fold (95% CI 1.10 to 2.47) and 3.55-fold (95% CI 2.40 to 5.26) higher risk of AD, respectively, as compared to those with low lipid scores. The lipid score showed moderate prediction efficacy (c-statistics > 0.72). These results suggested that the score system based on serum lipidomics is useful for the prediction of progression from MCI to AD.

Blood metabolites predicting mild cognitive impairment in the study of Latinos-investigation of neurocognitive aging (HCHS/SOL)

Introduction

Blood metabolomics‐based biomarkers may be useful to predict measures of neurocognitive aging.

Methods

We tested the association between 707 blood metabolites measured in 1451 participants from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), with mild cognitive impairment (MCI) and global cognitive change assessed 7 years later. We further used Lasso penalized regression to construct a metabolomics risk score (MRS) that predicts MCI, potentially identifying a different set of metabolites than those discovered in individual‐metabolite analysis.

Results

We identified 20 metabolites predicting prevalent MCI and/or global cognitive change. Six of them were novel and 14 were previously reported as associated with neurocognitive aging outcomes. The MCI MRS comprised 61 metabolites and improved prediction accuracy from 84% (minimally adjusted model) to 89% in the entire dataset and from 75% to 87% among apolipoprotein E ε4 carriers.

Discussion

Blood metabolites may serve as biomarkers identifying individuals at risk for MCI among US Hispanics/Latinos.