Metabolomics-Based Frailty Biomarkers in Older Chinese Adults

Abstract

Background/ObjectivesOwing to accelerated population aging, health in older adults is becoming increasingly important. Frailty can reflect the health status and disease risks of older adults; however, appropriate biomarkers for early screening of frailty have not been identified. Here, we applied metabolomics to identify frailty biomarkers and potential pathogenic mechanisms of frailty.MethodsSerum metabolic profiles from 25 frail and 49 non-frail (control) older adults were systematically investigated by liquid chromatography-mass spectrometry-based metabolomics.ResultsWe identified 349 metabolites of 46 classes, with four increased and seven decreased metabolites in frail older adults. Pearson correlation analysis identified 11 and 21 metabolites that were positively and negatively correlated with grip strength, and 7 and 76 metabolites that were positively and negatively correlated with gait speed, respectively. Pathway analysis identified 10 metabolite sets and 13 pathways significantly associated with one or more frailty phenotype criteria.ConclusionThese results revealed the metabolite characteristics of serum in frail older adults. Intermediates of carbohydrate metabolism (e.g., isocitrate, malate, fumarate, cis-aconitate, glucuronate, and pyruvate), saturated fatty acids (e.g., palmitic acid), unsaturated fatty acids (e.g., arachidonate and linoleic acid), and certain essential amino acids (e.g., tryptophan) may be candidate biomarkers for the early diagnosis of frailty. Mitochondrial function disorders, saturated fatty acid-mediated lipotoxicity, aberrant unsaturated fatty acid metabolism, and increased tryptophan degradation could be potential mechanisms of frailty.

Identification of novel biomarkers for frailty diagnosis via serum amino acids metabolomic analysis using UPLC‐MS/MS

“…Muscle biopsies from frail older adults also showed lower Trp levels[22]. The age-related decrease in serum Trp levels in rats was comparable to that observed in humans[31]. These studies reported that Trp levels decrease with age or frailty, although the mechanism of this phenomenon is unclear.Analysis of mouse and human metabolomes has shown that the Trp catabolic pathway modulates Trp levels and activates Trp metabolites such as kynurenine, which are associated with Trp catabolism in sensitive, upregulated individuals[32].…”

“…Muscle biopsies from frail older adults also showed lower Trp levels [22]. The age‐related decrease in serum Trp levels in rats was comparable to that observed in humans [31]. These studies reported that Trp levels decrease with age or frailty, although the mechanism of this phenomenon is unclear.…”

Dietary patterns, metabolomics and frailty in a large cohort of 120 000 participants

“…54 Metabolomics is also associated with frailty, and several metabolite biomarkers, such as intermediates of carbohydrate metabolism, palmitic acid, arachidonate, tryptophan and creatinine, have been found. 55,56 However, the mediating role of metabolites in the association between dietary patterns and frailty is still limited. Our study included a large sample size and incorporated four dietary patterns to provide new insights into this topic.…”

Proteomic architecture of frailty across the spectrum of cardiovascular disease

“…Given the prognostic relevance and reversibility of frailty (Chang et al, 2004; Guralnik et al, 1994, 1995; Pandey et al, 2023; Perera et al, 2006; Puthoff, 2008; Volpato et al, 2008), clinical studies and care in advanced heart disease have recently prioritized frailty to optimize outcome after cardiac intervention (Afilalo et al, 2017; Denfeld et al, 2017; Flint et al, 2012; Murali‐Krishnan et al, 2015; Patel et al, 2018). Nevertheless, varied definitions across studies, difficulties in standardizing measures (e.g., grip strength Cooper et al, 2021), and lack of specificity of common metrics (e.g., grip strength, walk speed, and albumin) for specific biology challenges clinical application and mechanistic discovery outside of controlled, non‐clinical cohort studies (Kameda et al, 2020; Landino et al, 2021; Lehallier et al, 2019; Liu et al, 2022; Pan et al, 2021; Rizza et al, 2014; Santos‐Lozano et al, 2020; Sathyan et al, 2020; Walston et al, 2002). With an aging population at high‐risk for advanced heart disease eligible for high‐risk intervention (e.g., destination left ventricular assist device Flint et al, 2012), objective measures that personalize variations in clinical status and biology across individuals are critical.…”