Identification of serum analytes and metabolites associated with aerobic capacity

Lustgarten, Michael S.; Price, Lori Lyn; Logvinenko, Tanya; Padukone, N.; Reo, Nicholas V.; Phillips, Edward M.; Kirn, Dylan; Mills, John A.; Fielding, Roger A.

doi:10.1007/s00421-012-2555-x

Cited by 31 publications

(53 citation statements)

References 39 publications

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“…Recruitment and screening criteria for subjects in the current study has been previously reported [6]. Briefly, 77 subjects, including 28 men and 49 women were admitted into the study.…”

Section: Subjectsmentioning

confidence: 99%

Serum Predictors of Percent Lean Mass in Young Adults

Lustgarten

Price

Phillips

et al. 2016

Journal of Strength and Conditioning Research

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Lustgarten, MS, Price, LL, Phillips, EM, Kirn, DR, Mills, J, and Fielding, RA. Serum predictors of percent lean mass in young adults. J Strength Cond Res 30(8): 2194-2201, 2016-Elevated lean (skeletal muscle) mass is associated with increased muscle strength and anaerobic exercise performance, whereas low levels of lean mass are associated with insulin resistance and sarcopenia. Therefore, studies aimed at obtaining an improved understanding of mechanisms related to the quantity of lean mass are of interest. Percent lean mass (total lean mass/body weight × 100) in 77 young subjects (18-35 years) was measured with dual-energy x-ray absorptiometry. Twenty analytes and 296 metabolites were evaluated with the use of the standard chemistry screen and mass spectrometry-based metabolomic profiling, respectively. Sex-adjusted multivariable linear regression was used to determine serum analytes and metabolites significantly (p ≤ 0.05 and q ≤ 0.30) associated with the percent lean mass. Two enzymes (alkaline phosphatase and serum glutamate oxaloacetate aminotransferase) and 29 metabolites were found to be significantly associated with the percent lean mass, including metabolites related to microbial metabolism, uremia, inflammation, oxidative stress, branched-chain amino acid metabolism, insulin sensitivity, glycerolipid metabolism, and xenobiotics. Use of sex-adjusted stepwise regression to obtain a final covariate predictor model identified the combination of 5 analytes and metabolites as overall predictors of the percent lean mass (model R = 82.5%). Collectively, these data suggest that a complex interplay of various metabolic processes underlies the maintenance of lean mass in young healthy adults.

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“…Recruitment and screening criteria for subjects in the current study has been previously reported [6]. Briefly, 77 subjects, including 28 men and 49 women were admitted into the study.…”

Section: Subjectsmentioning

confidence: 99%

Serum Predictors of Percent Lean Mass in Young Adults

Lustgarten

Price

Phillips

et al. 2016

Journal of Strength and Conditioning Research

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“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Metabolite Profiles Of Lean Body Mass In Plasma and Urinementioning

confidence: 98%

“…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].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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Purpose Differences in resting energy expenditure (REE) between men and women mainly result from sex-related differences in lean body mass (LBM). So far, a little is known about whether REE and LBM are reflected by a distinct human metabolite profile. Therefore, we aimed to identify plasma and urine metabolite patterns that are associated with REE and LBM of healthy subjects. Methods We investigated 301 healthy male and female subjects (18-80 years) under standardized conditions in the crosssectional KarMeN (Karlsruhe Metabolomics and Nutrition) study. REE was determined by indirect calorimetry and LBM by dual X-ray absorptiometry. Fasting blood and 24 h urine samples were analyzed by targeted and non-targeted metabolomics methods using GC × GC-MS, GC-MS, LC-MS, and NMR. Data were evaluated by predictive modeling of combined data using different machine learning algorithms, namely SVM, glmnet, and PLS. Results When evaluating data of men and women combined, we were able to predict REE and LBM with high accuracy (> 90%). This, however, was a clear effect of sex, which is supported by the high degree of overlap in identified important metabolites for LBM, REE, and sex, respectively. The applied machine learning algorithms did not reveal a metabolite pattern predictive of REE or LBM, when analyzing data for men and women, separately. Conclusions We could not identify a sex independent predictive metabolite pattern for REE or LBM. REE and LBM have no impact on plasma and urine metabolite profiles in the KarMeN Study participants. Studies applying metabolomics in healthy humans need to consider sex specific data evaluation.

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“…Plasma metabolomics profiling of healthy versus T2D subjects, along with integrated global transcriptomics, has mapped disease alterations in acute exercise responses (Hansen et al 2015). Moreover, metabolomics studies have uncovered biomarkers of human cardiometabolic fitness in skeletal muscle (Huffman et al 2014) and aerobic capacity in serum (Lustgarten et al 2013). Current and future directions in this growing field include globally profiling the metabolome signature of plasma and other biological fluids (e.g., urine [Wu and Gao 2015], saliva [Wallner-Liebmann et al 2016], and sweat [Hooton et al 2016]) from healthy, athletic, sedentary, and diseased subjects exposed to a range of exercise programs.…”

Section: Metabolome and Lipidomementioning

confidence: 99%

Omics and Exercise: Global Approaches for Mapping Exercise Biological Networks

Hoffman

2017

Cold Spring Harb Perspect Med

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The application of global "-omics" technologies to exercise has introduced new opportunities to map the complexity and interconnectedness of biological networks underlying the tissue-specific responses and systemic health benefits of exercise. This review will introduce major research tracks and recent advancements in this emerging field, as well as critical gaps in understanding the orchestration of molecular exercise dynamics that will benefit from unbiased omics investigations. Furthermore, significant research hurdles that need to be overcome to effectively fill these gaps related to data collection, computation, interpretation, and integration across omics applications will be discussed. Collectively, a cross-disciplinary physiological and omics-based systems approach will lead to discovery of a wealth of novel exercise-regulated targets for future mechanistic validation. This frontier in exercise biology will aid the development of personalized therapeutic strategies to improve athletic performance and human health through precision exercise medicine.T he dynamic human physiological responses to exercise have been widely studied in the context of metabolism and mechanical stress. Health benefits of exercise in prevention, delay, and/or treatment of pathophysiology associated with metabolic disorders and aging are widely appreciated. However, the intricacies of molecular networks and biological mechanisms underlying how humans adapt to exercise and acquire health benefits are not fully understood. In response to perturbations in whole-body homeostasis induced by physical activity and exercise, biological networks are stimulated in various cell types and organs to manage systemic metabolic and mechanical demands (Hawley et al. 2014). Targeted, reductionist-based approaches have laid the foundation for our understanding of distinct biological mechanisms regulating acute versus repeated exercise adaptations using a range of experimental models from cells to animals and humans. However, the complexity and integrated nature of the whole-body exercise response warrants global unbiased systems approaches to unravel the interwoven networks underlying the benefits of exercise in health and disease.In this early stage of the exercise and omics revolution, there is a wealth of molecular information now within reach to help build on our understanding of human metabolism and exercise physiology. There is tremendous potential for omics approaches to fill critical gaps in our

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Identification of serum analytes and metabolites associated with aerobic capacity

Cited by 31 publications

References 39 publications

Serum Predictors of Percent Lean Mass in Young Adults

Serum Predictors of Percent Lean Mass in Young Adults

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

Omics and Exercise: Global Approaches for Mapping Exercise Biological Networks

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