ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2024
Past or Present; Which Exposures Predict Metabolomic Aging Better? The Doetinchem Cohort Study.
Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Article
- Article
- Advanced Glycation End-Products and Metabolomics Are Independently Associated With Frailty: The Longitudinal Doetinchem Cohort Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
People age differently. Differences in aging might be reflected by metabolites, also known as metabolomic aging. Predicting metabolomic aging is of interest in public health research. However, the added value of longitudinal over cross-sectional predictors of metabolomic aging is unknown. We studied exposome-related exposures as potential predictors of metabolomic aging, both cross-sectionally and longitudinally in men and women. We used data from 4 459 participants, aged 36-75 of Round 4 (2003-2008) of the long-running Doetinchem Cohort Study (DCS). Metabolomic age was calculated with the MetaboHealth algorithm. Cross-sectional exposures were demographic, biological, lifestyle, and environmental at Round 4. Longitudinal exposures were based on the average exposure over 15 years (Round 1 [1987-1991] to 4), and trend in these exposure over time. Random Forest was performed to identify model performance and important predictors. Prediction performances were similar for cross-sectional and longitudinal exposures in both men (R2 6.8 and 5.8, respectively) and women (R2 14.8 and 14.4, respectively). Biological and diet exposures were most predictive for metabolomic aging in both men and women. Other important predictors were smoking behavior for men and contraceptive use and menopausal status for women. Taking into account history of exposure levels (longitudinal) had no added value over cross-sectionally measured exposures in predicting metabolomic aging in the current study. However, the prediction performances of both models were rather low. The most important predictors for metabolomic aging were from the biological and lifestyle domain and differed slightly between men and women.
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