Cross-sectional Association Between Plasma Biomarkers and Multimorbidity Patterns in Older Adults

Abstract

Abstract Multimorbidity is the simultaneous presence of 2 or more chronic conditions. Metabolomics could identify biomarkers potentially related to multimorbidity. We aimed to identify groups of biomarkers and their association with different multimorbidity patterns. Cross-sectional analyses were conducted within the Seniors-ENRICA-2 cohort in Spain, with information from 700 individuals aged ≥65 years. Biological samples were analyzed using high-throughput proton nuclear magnetic resonance metabolomics. Biomarker groups were identified with exploratory factor analysis, and multimorbidity was classified into 3 types: cardiometabolic, neuropsychiatric, and musculoskeletal. Logistic regression was used to estimate the association between biomarker groups and multimorbidity patterns, after adjusting for potential confounders including sociodemographics, lifestyle, and body mass index. Three factors were identified: the “lipid metabolism” mainly reflected biomarkers related to lipid metabolism, such as very-low-density lipoprotein and low-density lipoprotein cholesterol; the “high-density lipoprotein cholesterol” mainly included high-density lipoprotein cholesterol subclasses and other lipids not included in the first factor; and the “amino acid/glycolysis/ketogenesis,” composed of some amino acids, glycolysis-related metabolites, and ketone bodies. Higher scores in the “lipid metabolism” factor were associated with a higher likelihood of cardiometabolic multimorbidity, odds ratio for tertile 3 versus tertile 1 was 1.79 (95% confidence interval: 1.17–2.76). The “high-density lipoprotein cholesterol” factor was associated with lower odds of cardiometabolic multimorbidity [0.51 (0.32–0.82)], and the “amino acid/glycolysis/ketogenesis” factor was associated with more frequent cardiometabolic multimorbidity [1.85 (1.18–2.90)]. Different metabolomic biomarkers are associated with different multimorbidity patterns; therefore, multiple biomarker measurements are needed for a complete picture of the molecular mechanisms of multimorbidity.