An Integrated Lifestyle-, Genetic- and Metabolomics-Based Prediction Model for Mild Cognitive Aging in the HCHS/SOL

Authors

• Annah B Wyss
• Yana Hrytsenko
• Linfeng Hu
• Iris J Broce
• Robert C Kaplan
• Wassim Tarraf
• Bing Yu
• Eric Boerwinkle
• Qibin Qi
• Myriam Fornage
• Charles DeCarli
• Hector M González
• Tamar Sofer

Language

English

Publication Date

1-1-2026

Journal

NPJ Dementia

DOI

10.1038/s44400-026-00120-9

PMID

42428793

PMCID

PMC13345926

PubMedCentral® Posted Date

7-8-2026

PubMedCentral® Full Text Version

Post-print

Abstract

Prediction models for cognitive aging measures have largely evaluated demographic variables and APOE carrier status in populations of European ancestry. To comprehensively assess prediction models among Hispanic/Latinos, we considered 12 models (6 predictor sets and 2 methods) for global cognitive score change (GCSC) and mild cognitive impairment (MCI) in the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA) (N = 5856). Based on the average mean squared error (MSE) for GCSC or average area under the curve (AUC) for MCI across 100 randomly split testing and training sets, performance was similar across models, but slightly better for the following models: the chronic conditions model and genetic model (mean MSEs = 0.2464) using gradient-boosted trees for GCSC prediction and the chronic conditions model (mean AUC = 62%) and metabolite model (mean AUC = 60%) using logistic regression for MCI prediction. Using the Shapley Additive Explanations (SHAP) method, age at baseline, time between exams, and sex were the most important predictors for GCSC, followed by diabetes and global ancestral proportions. Diabetes and the metabolite ribitol had the highest influence on prediction of MCI. Although prediction performance was not especially high and did not vary greatly across models, incorporating information on diabetes, ancestry and metabolites may help improve prediction of GCSC and MCI.

Keywords

Biomarkers, Computational biology and bioinformatics, Diseases, Risk factors

Published Open-Access

yes

Recommended Citation

Wyss, Annah B; Hrytsenko, Yana; Hu, Linfeng; et al., "An Integrated Lifestyle-, Genetic- and Metabolomics-Based Prediction Model for Mild Cognitive Aging in the HCHS/SOL" (2026). Faculty, Staff and Student Publications. 1386.
https://digitalcommons.library.tmc.edu/uthsph_docs/1386