Lipid Hydrolase Enzymes: Pragmatic Prolongevity Targets for Improved Human Healthspan?

Fatty acid amides as potential circulating biomarkers for sarcopenia

BACKGROUND

Sarcopenia is characterized by a progressive decrease in skeletal muscle mass and function with age. Given that sarcopenia is associated with various metabolic disorders, effective metabolic biomarkers for its early detection are required. We aimed to investigate the metabolic biomarkers related to sarcopenia in elderly men and perform experimental studies using metabolomics.

METHODS

Plasma metabolites from 142 elderly men, comprising a sarcopenia group and an age-matched control group, were measured using global metabolome profiling. Muscle and plasma samples from an aging mouse model of sarcopenia, as well as cell media and cell lysates during myoblast differentiation, were analysed based on targeted metabolome profiling. Based on these experimental results, fatty acid amides were quantified from human plasma as well as human muscle tissues. The association of fatty acid amide levels with sarcopenia parameters was evaluated.

RESULTS

Global metabolome profiling showed that fatty acid amide levels were significantly different in the plasma of elderly men with sarcopenia (all Ps < 0.01). Consistent with these results in human plasma, targeted metabolome profiling in an aging mouse model of sarcopenia showed decreased levels of fatty acid amides in plasma but not in muscle tissue. In addition, the levels of fatty acid amides increased in cell lysates during muscle cell differentiation. Targeted metabolome profiling in men showed decreased docosahexaenoic acid ethanolamide (DHA EA) levels in the plasma (P = 0.016) but not in the muscle of men with sarcopenia. DHA EA level was positively correlated with sarcopenia parameters such as skeletal muscle mass index (SMI) and handgrip strength (HGS) (P = 0.001, P = 0.001, respectively). The area under the receiver-operating characteristic curve (AUC) for DHA EA level ≤ 4.60 fmol/μL for sarcopenia was 0.618 (95% confidence interval [CI]: 0.532-0.698). DHA EA level ≤ 4.60 fmol/μL was associated with a significantly greater likelihood of sarcopenia (odds ratio [OR]: 2.11, 95% CI: 1.03-4.30), independent of HGS. The addition of DHA EA level to age and HGS significantly improved the AUC from 0.620 to 0.691 (P = 0.0497).

CONCLUSIONS

Our study demonstrated that fatty acid amides are potential circulating biomarkers in elderly men with sarcopenia. DHA EA, in particular, strongly related to muscle mass and strength, can be a key metabolite to become a reliable metabolic biomarker for sarcopenia. Further research on fatty acid amides will provide insights into the metabolomic changes relevant to sarcopenia from an aging perspective.

Data mining of human plasma proteins generates a multitude of highly predictive aging clocks that reflect different aspects of aging

We previously identified 529 proteins that had been reported by multiple different studies to change their expression level with age in human plasma. In the present study, we measured the q-value and age coefficient of these proteins in a plasma proteomic dataset derived from 4263 individuals. A bioinformatics enrichment analysis of proteins that significantly trend toward increased expression with age strongly implicated diverse inflammatory processes. A literature search revealed that at least 64 of these 529 proteins are capable of regulating life span in an animal model. Nine of these proteins (AKT2, GDF11, GDF15, GHR, NAMPT, PAPPA, PLAU, PTEN, and SHC1) significantly extend life span when manipulated in mice or fish. By performing machine-learning modeling in a plasma proteomic dataset derived from 3301 individuals, we discover an ultra-predictive aging clock comprised of 491 protein entries. The Pearson correlation for this clock was 0.98 in the learning set and 0.96 in the test set while the median absolute error was 1.84 years in the learning set and 2.44 years in the test set. Using this clock, we demonstrate that aerobic-exercised trained individuals have a younger predicted age than physically sedentary subjects. By testing clocks associated with 1565 different Reactome pathways, we also show that proteins associated with signal transduction or the immune system are especially capable of predicting human age. We additionally generate a multitude of age predictors that reflect different aspects of aging. For example, a clock comprised of proteins that regulate life span in animal models accurately predicts age.