Genomics and metabolomics of muscular mass in a community-based sample of UK females

Abstract

The contribution of specific molecular-genetic factors to muscle mass variation and sarcopenia remains largely unknown. To identify endogenous molecules and specific genetic factors associated with appendicular lean mass (APLM) in the general population, cross-sectional data from the TwinsUK Adult Twin Registry were used. Non-targeted mass spec-based metabolomic profiling was performed on plasma of 3953 females (mostly dizygotic and monozygotic twins). APLM was measured using dualenergy X-ray absorptiometry (DXA) and genotyping was genome-wide (GWAS). Specific metabolites were used as intermediate phenotypes in the identification of single-nucleotide polymorphisms associated with APLM using GWAS. In all, 162 metabolites were found significantly correlated with APLM, and explained 17.4% of its variation. However, the top three of them (unidentified substance X12063, urate, and mannose) explained 11.1% (P ≤ 9.25 × 10 − 26 ) so each was subjected to GWAS. Each metabolite showed highly significant (P ≤ 9.28 × 10 − 46 ) associations with genetic variants in the corresponding genomic regions. Mendelian randomization using these SNPs found no evidence for a direct causal effect of these metabolites on APLM. However, using a new software platform for bivariate analysis we showed that shared genetic factors contribute significantly (P ≤ 4.31 × 10 − 43 ) to variance in both the metabolites and APLM -independent of the effect of the associated SNPs. There are several metabolites, having a clear pattern of genetic inheritance, which are highly significantly associated with APLM and may provide a cheap and readily accessible biomarker of muscle mass. However, the mechanism by which the genetic factor influences muscle mass remains to be discovered.

Sex-specific differences in risk factors for sarcopenia amongst community-dwelling older adults

“…The implications of sarcopenia in the older adult have been reported extensively, including impairment in physical performance, mobility limitations, frailty and its consequences of falls, fractures and hospitalizations Clark and Manini (2010). Although progressive loss of muscle mass appears inevitable, with annual rate of decline of 1-2 % from as early as age 50, and muscle strength decline of 1.5 % per year between ages 50 and 60 increasing to 3 % annually thereafter (von Haehling et al 2010), there exists considerable variation in the rate of skeletal muscle mass loss (Korostishevsky et al 2015) which, importantly, remains potentially reversible as even the most frail of older adults had exhibited improvements with exercise interventions (Fiatarone et al 1994).…”

Metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects

“…Nevertheless, associations between various single metabolites and LBM calculated by linear regression models have been shown before [20,22,23,45]. However, these studies did not aim to predict LBM based on metabolite profiles and therefore such data are still missing.…”

“…Only limited data from metabolomics studies are available so far [20][21][22][23]. In the present crosssectional study, we therefore investigated whether urine as well as plasma metabolite profiles are associated with REE or LBM in healthy subjects under resting conditions.…”

“…It is known from other studies that age and sex differences exist in the human plasma and urine metabolome [12][13][14][15][16][17][18]. Nevertheless, if and to which extent the discrimination of sex is responsible for the discrimination of body composition, has not been clarified definitely [20][21][22][23]. Furthermore, to which extent sex is important in the prediction of REE still remains unknown.…”

“…Metabolomics (blood, urine) can accurately distinguish age and sex differences in humans [12][13][14][15][16][17][18][19], but can only partially explain the components of body composition [20][21][22][23] such as LBM, muscle and fat mass. It remains unclear whether the discrimination of LBM is nothing more than the discrimination of sex, because most of the metabolites for sex seem to be identical to the metabolites discriminating LBM [24].…”