Global metabolic profiling to model biological processes of aging in twins

Abstract

Aging is intimately linked to system‐wide metabolic changes that can be captured in blood. Understanding biological processes of aging in humans could help maintain a healthy aging trajectory and promote longevity. We performed untargeted plasma metabolomics quantifying 770 metabolites on a cross‐sectional cohort of 268 healthy individuals including 125 twin pairs covering human lifespan (from 6 months to 82 years). Unsupervised clustering of metabolic profiles revealed 6 main aging trajectories throughout life that were associated with key metabolic pathways such as progestin steroids, xanthine metabolism, and long‐chain fatty acids. A random forest (RF) model was successful to predict age in adult subjects (≥16 years) using 52 metabolites (R2 = .97). Another RF model selected 54 metabolites to classify pediatric and adult participants (out‐of‐bag error = 8.58%). These RF models in combination with correlation network analysis were used to explore biological processes of healthy aging. The models highlighted established metabolites, like steroids, amino acids, and free fatty acids as well as novel metabolites and pathways. Finally, we show that metabolic profiles of twins become more dissimilar with age which provides insights into nongenetic age‐related variability in metabolic profiles in response to environmental exposure.

Effects of myocardial ischemia/reperfusion injury on plasma metabolomic profile during aging

“…Plasma metabolomic alterations have been reported during aging and heart disease in both humans and animal models (Bunning et al, 2020; Houtkooper et al, 2011; Johnson et al, 2018; McGarrah et al, 2018). Accordingly, we investigated whether age and myocardial ischemic injury may induce a combined effect on circulating metabolites.…”

“…Wide metabolic changes in different organs occur during both aging and heart failure and, some of them can be captured with the assessment of circulating metabolites (Houtkooper et al, (2011); Bunning et al, 2020; McGarrah et al, 2018; Johnson et al, 2018; Lee et al, 2014; Zordoky et al, 2015; Chaleckis et al, 2016; Gonzalez‐Freire et al, 2019). However, in the clinical setting, it may be complicated to study whether some of those metabolic alterations were influenced by additional diseases as several older adults especially with heart dysfunction show multiple comorbidities (Dharmarajan & Dunlay, 2016).…”

“…However, in the clinical setting, it may be complicated to study whether some of those metabolic alterations were influenced by additional diseases as several older adults especially with heart dysfunction show multiple comorbidities (Dharmarajan & Dunlay, 2016). Plasma metabolomics allows the unbiased profiling of a large panel of circulating metabolites and has become a useful analytical tool to evaluate the pathophysiological mechanisms involved in aging as well as to predict outcomes in patients with heart disease (Bunning et al, 2020; Houtkooper et al, 2011; McGarrah et al, 2018).…”

“…Of note, a common feature of both HF and aging itself is the involvement of metabolic alterations in different organs including liver, muscle, and adipose tissue (Bunning et al, 2020; Houtkooper et al, 2011). Plasma metabolomics is a useful approach to study biological processes occurring in various tissues, which provides large‐scale analysis of the global metabolic state during disease.…”

Measuring biological age using metabolomics

“…All the hallmarks of aging may be expected to have detectable effects on the metabolome and overlap significantly with the effects of metabolic disorders [ 6 ]. The metabolomic study of age is an active area with many important recent contributions [ 7 ], including the development of a searchable database of age-associated metabolites [ 8 ]. However large challenges in the field remain including comparability and reproducibility across studies, metabolome coverage and metabolite annotation.…”