Past or Present; Which Exposures Predict Metabolomic Aging Better? The Doetinchem Cohort Study

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.

Keywords: Exposome; Human aging; Metabolomics; Sex differences.

Figure 1.

Variable importance ranking of Random Forest (RF) with MetaboHealth as outcome and cross-sectionally measured exposures of Round 4 in men and women. The x-axis shows the percentage increase in mean square error (MSE) when a particular predictor is removed from the RF model.

Figure 2.

Root mean square error (RMSE) plot of prediction performance of X number of selected exposures in Random Forest model with MetaboHealth Round 4 (R4) as outcome and cross-sectionally measured exposures of R4 in the training data set in men and women. The dotted gray line reflects the optimal number of selected exposures (X = 10 men, X = 15 women).