Metabolite and protein associations with general health in the population-based CHRIS study

• Published in: Scientific reports Vol. 14; no. 1; pp. 26635 - 13
• Main Authors: Hantikainen, Essi, Weichenberger, Christian X., Dordevic, Nikola, Verri Hernandes, Vinicius, Foco, Luisa, Gögele, Martin, Melotti, Roberto, Pattaro, Cristian, Ralser, Markus, Amari, Fatma, Farztdinov, Vadim, Mülleder, Michael, Pramstaller, Peter P., Rainer, Johannes, Domingues, Francisco S.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 04.11.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/114/2397; 631/114/2415; 692/308; 692/53; 692/699; Age; Aging; CHRIS study; Comorbidity; Health status; Humanities and Social Sciences; Metabolites; Metabolomics; Morbidity; multidisciplinary; Population studies; Proteins; Proteomes; Proteomics; Regression analysis; Science; Science (multidisciplinary); Serotonin; Aging; Metabolomics; Health status; Comorbidity; Proteomics; CHRIS study
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-75627-3

Identifying biomarkers able to discriminate individuals on different health trajectories is crucial to understand the molecular basis of age-related morbidity. We investigated multi-omics signatures of general health and organ-specific morbidity, as well as their interconnectivity. We examined cross-sectional metabolome and proteome data from 3,142 adults of the Cooperative Health Research in South Tyrol (CHRIS) study, an Alpine population study designed to investigate how human biology, environment, and lifestyle factors contribute to people’s health over time. We had 174 metabolites and 148 proteins quantified from fasting serum and plasma samples. We used the Cumulative Illness Rating Scale (CIRS) Comorbidity Index (CMI), which considers morbidity in 14 organ systems, to assess health status (any morbidity vs. healthy). Omics-signatures for health status were identified using random forest (RF) classifiers. Linear regression models were fitted to assess directionality of omics markers and health status associations, as well as to identify omics markers related to organ-specific morbidity. Next to age, we identified 21 metabolites and 10 proteins as relevant predictors of health status and results confirmed associations for serotonin and glutamate to be age-independent. Considering organ-specific morbidity, several metabolites and proteins were jointly related to endocrine, cardiovascular, and renal morbidity. To conclude, circulating serotonin was identified as a potential novel predictor for overall morbidity.