Skip to main content

Advertisement

SpringerLink
Log in

Metabolite signatures of exercise training in human skeletal muscle relate to mitochondrial remodelling and cardiometabolic fitness

  • Article
  • Published:
Diabetologia Aims and scope Submit manuscript

Abstract

Aims/hypothesis

Targeted metabolomic and transcriptomic approaches were used to evaluate the relationship between skeletal muscle metabolite signatures, gene expression profiles and clinical outcomes in response to various exercise training interventions. We hypothesised that changes in mitochondrial metabolic intermediates would predict improvements in clinical risk factors, thereby offering novel insights into potential mechanisms.

Methods

Subjects at risk of metabolic disease were randomised to 6 months of inactivity or one of five aerobic and/or resistance training programmes (n = 112). Pre/post-intervention assessments included cardiorespiratory fitness (\( \overset{\cdot }{V}{\mathrm{O}}_{2\mathrm{peak}} \)), serum triacylglycerols (TGs) and insulin sensitivity (SI). In this secondary analysis, muscle biopsy specimens were used for targeted mass spectrometry-based analysis of metabolic intermediates and measurement of mRNA expression of genes involved in metabolism.

Results

Exercise regimens with the largest energy expenditure produced robust increases in muscle concentrations of even-chain acylcarnitines (median 37–488%), which correlated positively with increased expression of genes involved in muscle uptake and oxidation of fatty acids. Along with free carnitine, the aforementioned acylcarnitine metabolites were related to improvements in \( \overset{\cdot }{V}{\mathrm{O}}_{2\mathrm{peak}} \), TGs and SI (R = 0.20–0.31, p < 0.05). Muscle concentrations of the tricarboxylic acid cycle intermediates succinate and succinylcarnitine (R = 0.39 and 0.24, p < 0.05) emerged as the strongest correlates of SI.

Conclusions/interpretation

The metabolic signatures of exercise-trained skeletal muscle reflected reprogramming of mitochondrial function and intermediary metabolism and correlated with changes in cardiometabolic fitness. Succinate metabolism and the succinate dehydrogenase complex emerged as a potential regulatory node that intersects with whole-body insulin sensitivity. This study identifies new avenues for mechanistic research aimed at understanding the health benefits of physical activity.

Trial registration ClinicalTrials.gov NCT00200993 and NCT00275145

Funding This work was supported by the National Heart, Lung, and Blood Institute (National Institutes of Health), National Institute on Aging (National Institutes of Health) and National Institute of Arthritis and Musculoskeletal and Skin Diseases (National Institutes of Health).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

BCAA:

Branched-chain amino acid

CrAT:

Carnitine acetyltransferase

SI :

Insulin sensitivity index

SDH:

Succinate dehydrogenase

STRRIDE:

Studies of Targeted Risk Reduction Interventions through Defined Exercise

TCA:

Tricarboxylic acid

TG:

Triacylglycerol

\( \overset{\cdot }{V}{\mathrm{O}}_{2\mathrm{peak}} \) :

Cardiopulmonary fitness as determined by a maximal treadmill test

References

  1. Physical Activity Guidelines Advisory Committee (2008) Physical activity guidelines advisory committee report, 2008. Washington, DC: US Department of Health and Human Services

  2. Duscha BD, Slentz CA, Johnson JL et al (2005) Effects of exercise training amount and intensity on peak oxygen consumption in middle-age men and women at risk for cardiovascular disease. Chest 128:2788–2793

    Article  PubMed  Google Scholar 

  3. Houmard JA, Tanner CJ, Slentz CA, Duscha BD, McCartney JS, Kraus WE (2004) Effect of the volume and intensity of exercise training on insulin sensitivity. J Appl Physiol 96:101–106

    Article  PubMed  CAS  Google Scholar 

  4. Kraus WE, Houmard JA, Duscha BD et al (2002) Effects of the amount and intensity of exercise on plasma lipoproteins. N Engl J Med 347:1483–1492

    Article  PubMed  CAS  Google Scholar 

  5. Jacobs RA, Lundby C (2013) Mitochondria express enhanced quality as well as quantity in association with aerobic fitness across recreationally active individuals up to elite athletes. J Appl Physiol 114:344–350

    Article  PubMed  CAS  Google Scholar 

  6. Bain JR, Stevens RD, Wenner BR, Ilkayeva O, Muoio DM, Newgard CB (2009) Metabolomics applied to diabetes research: moving from information to knowledge. Diabetes 58:2429–2443

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  7. Shah SH, Kraus WE, Newgard CB (2012) Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases: form and function. Circulation 126:1110–1120

    Article  PubMed  Google Scholar 

  8. Huffman KM, Slentz CA, Bateman LA et al (2011) Exercise-induced changes in metabolic intermediates, hormones, and inflammatory markers associated with improvements in insulin sensitivity. Diabetes Care 34:174–176

    Article  PubMed  PubMed Central  Google Scholar 

  9. Willis LH, Slentz CA, Bateman LA et al (2012) Effects of aerobic and/or resistance training on body mass and fat mass in overweight or obese adults. J Appl Physiol 113:1831–1837

    Article  PubMed  PubMed Central  Google Scholar 

  10. Bergman RN (2007) Orchestration of glucose homeostasis: from a small acorn to the California oak. Diabetes 56:1489–1501

    Article  PubMed  CAS  Google Scholar 

  11. Duscha BD, Kraus WE, Keteyian SJ et al (1999) Capillary density of skeletal muscle: a contributing mechanism for exercise intolerance in class II-III chronic heart failure independent of other peripheral alterations. J Am Coll Cardiol 33:1956–1963

    Article  PubMed  CAS  Google Scholar 

  12. Newgard CB, An J, Bain JR et al (2009) A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab 9:311–326

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  13. Haqq AM, Lien LF, Boan J et al (2005) The Study of the Effects of Diet on Metabolism and Nutrition (STEDMAN) weight loss project: rationale and design. Contemp Clin Trials 26:616–625

    Article  PubMed  CAS  Google Scholar 

  14. Tumor Analysis Best Practices Working Group (2004) Expression profiling–best practices for data generation and interpretation in clinical trials. Nat Rev Genet 5:229–237

    Article  Google Scholar 

  15. Seo J, Bakay M, Chen YW, Hilmer S, Shneiderman B, Hoffman EP (2004) Interactively optimizing signal-to-noise ratios in expression profiling: project-specific algorithm selection and detection p-value weighting in Affymetrix microarrays. Bioinformatics 20:2534–2544

    Article  PubMed  CAS  Google Scholar 

  16. Slentz CA, Aiken LB, Houmard JA et al (2005) Inactivity, exercise, and visceral fat. STRRIDE: a randomized, controlled study of exercise intensity and amount. J Appl Physiol 99:1613–1618

    Article  PubMed  Google Scholar 

  17. Huffman KM, Shah SH, Stevens RD et al (2009) Relationships between circulating metabolic intermediates and insulin action in overweight to obese, inactive men and women. Diabetes Care 32:1678–1683

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  18. Huffman KM, Slentz CA, Johnson JL et al (2008) Impact of hormone replacement therapy on exercise training-induced improvements in insulin action in sedentary overweight adults. Metabolism 57:888–895

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  19. Boyle KE, Canham JP, Consitt LA et al (2011) A high-fat diet elicits differential responses in genes coordinating oxidative metabolism in skeletal muscle of lean and obese individuals. J Clin Endocrinol Metab 96:775–781

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  20. Noland RC, Koves TR, Seiler SE et al (2009) Carnitine insufficiency caused by aging and overnutrition compromises mitochondrial performance and metabolic control. J Biol Chem 284:22840–22852

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  21. Koves TR, Li P, An J et al (2005) Peroxisome proliferator-activated receptor-gamma co-activator 1alpha-mediated metabolic remodeling of skeletal myocytes mimics exercise training and reverses lipid-induced mitochondrial inefficiency. J Biol Chem 280:33588–33598

    Article  PubMed  CAS  Google Scholar 

  22. Koves TR, Ussher JR, Noland RC et al (2008) Mitochondrial overload and incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance. Cell Metab 7:45–56

    Article  PubMed  CAS  Google Scholar 

  23. Chao LC, Wroblewski K, Zhang Z et al (2009) Insulin resistance and altered systemic glucose metabolism in mice lacking Nur77. Diabetes 58:2788–2796

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  24. Muoio DM, Noland RC, Kovalik JP et al (2012) Muscle-specific deletion of carnitine acetyltransferase compromises glucose tolerance and metabolic flexibility. Cell Metab 15:764–777

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  25. Wang TJ, Larson MG, Vasan RS et al (2011) Metabolite profiles and the risk of developing diabetes. Nat Med 17:448–453

    Article  PubMed  PubMed Central  Google Scholar 

  26. Anderson EJ, Lustig ME, Boyle KE et al (2009) Mitochondrial H2O2 emission and cellular redox state link excess fat intake to insulin resistance in both rodents and humans. J Clin Invest 119:573–581

Download references

Acknowledgements

We thank the rest of the STRRIDE research team at East Carolina University and Duke University.

Funding

This work was supported by the National Heart, Lung, and Blood Institute (National Institutes of Health) R01HL-57354 (WEK), National Institute on Aging (National Institutes of Health) P30 AGO28716-01 (WEK, KMH), R01AG-028930 and R01DK-089312 (DMM) and National Institute of Arthritis and Musculoskeletal and Skin Diseases (National Institutes of Health) K23AR054904 (KMH).

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Contribution statement

KMH, DMM, and WEK participated in conceptual design, data analysis and data interpretation and drafting and editing of the manuscript. EPH participated in conceptual design, data interpretation and editing of the manuscript. TRK, MJH, HA, NB, LAB, RDS and ORI participated in data collection, data interpretation and editing of the manuscript. All authors approved the final version. KMH is the guarantor of this work.

Author information

Authors and Affiliations

  1. Physical Medicine and Rehabilitation Service, Veterans Affairs Medical Center, Durham, NC, USA

    Kim M. Huffman

  2. Division of Rheumatology, Department of Medicine, Duke University Medical Center, Durham, NC, USA

    Kim M. Huffman

  3. Duke Molecular Physiology Institute, Duke University Medical Center, 300 N. Duke St, Durham, NC, 27701, USA

    Kim M. Huffman, Timothy R. Koves, Robert D. Stevens, Olga R. Ilkayeva, Deborah M. Muoio & William E. Kraus

  4. Division of Geriatrics, Department of Medicine, Duke University Medical Center, Durham, NC, USA

    Timothy R. Koves

  5. Department of Integrative Systems Biology, Children’s National Medical Center, Washington, DC, USA

    Monica J. Hubal & Eric P. Hoffman

  6. Division of Endocrinology, Duke University Medical Center, Durham, NC, USA

    Hiba Abouassi & Deborah M. Muoio

  7. School of Medicine, Duke University, Durham, NC, USA

    Nina Beri

  8. Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC, USA

    Lori A. Bateman & William E. Kraus

  9. Department of Medicine, Duke University Medical Center, Durham, NC, USA

    Deborah M. Muoio

  10. Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC, USA

    Deborah M. Muoio

Authors
  1. Kim M. Huffman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Timothy R. Koves
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Monica J. Hubal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hiba Abouassi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nina Beri
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lori A. Bateman
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Robert D. Stevens
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Olga R. Ilkayeva
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Eric P. Hoffman
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Deborah M. Muoio
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. William E. Kraus
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kim M. Huffman.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM Fig. 1

(PDF 15 kb)

ESM Table 1

(PDF 160 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huffman, K.M., Koves, T.R., Hubal, M.J. et al. Metabolite signatures of exercise training in human skeletal muscle relate to mitochondrial remodelling and cardiometabolic fitness. Diabetologia 57, 2282–2295 (2014). https://doi.org/10.1007/s00125-014-3343-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00125-014-3343-4

Keywords

Search

Navigation