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Human plasma proteomic profiles indicative of cardiorespiratory fitness

Nature Metabolism volume 3, pages 786–797 (2021)Cite this article

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An Author Correction to this article was published on 27 August 2021

Abstract

Maximal oxygen uptake (VO2max) is a direct measure of human cardiorespiratory fitness and is associated with health. However, the molecular determinants of interindividual differences in baseline (intrinsic) VO2max, and of increases of VO2max in response to exercise training (ΔVO2max), are largely unknown. Here, we measure ~5,000 plasma proteins using an affinity-based platform in over 650 sedentary adults before and after a 20-week endurance-exercise intervention and identify 147 proteins and 102 proteins whose plasma levels are associated with baseline VO2max and ΔVO2max, respectively. Addition of a protein biomarker score derived from these proteins to a score based on clinical traits improves the prediction of an individual’s ΔVO2max. We validate findings in a separate exercise cohort, further link 21 proteins to incident all-cause mortality in a community-based cohort and reproduce the specificity of ~75% of our key findings using antibody-based assays. Taken together, our data shed light on biological pathways relevant to cardiorespiratory fitness and highlight the potential additive value of protein biomarkers in identifying exercise responsiveness in humans.

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Fig. 1: Proteins associated with baseline VO2max among offspring and parents.
Fig. 2: Plasma proteins associated with baseline and ΔVO2max.
Fig. 3: Muscle proteins positively associated with baseline VO2max.
Fig. 4: GSEA for proteins associated with ΔVO2max or baseline VO2max.
Fig. 5: ROC curves for relative VO2max changes with exercise training > 15%.
Fig. 6: Spearman’s correlations between aptamer-based and antibody-based assays among top findings.

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Data availability

Deidentified, individual-level proteomics and phenotypic data that support the HERITAGE findings within this paper are available at https://motrpac-data.org/related-studies/heritage-proteomics. Overlapping aptamer-based and antibody-based proteomics data on the HERITAGE sample are included Supplementary Data Table 1. GWAS summary statistics for FHS and JHS are available through restricted access via the database of Genotypes and Phenotypes (dbGaP), a publicly available resource developed to archive data from human studies of genotype–phenotype relationships and can be accessed here (https://www.ncbi.nlm.nih.gov/gap/; FHS accession number: phs000363.v19.p13; JHS accession number: phs000964). FHS proteomics data have also been deposited in dbGaP and are available through the same accession number. JHS proteomics data have been deposited in the JHS Data Coordinating Center and are being deposited in dbGaP (accession number: phs002256.v1.p1); pending its receipt in dbGaP, all JHS data are available from the JHS Data Coordinating Center on request (JHSccdc@umc.edu). In addition, proteogenetics findings (precise SNP IDs) included in Supplementary Table 15 from FHS/MDCS meta-analysis and JHS have been provided in Tables 2 and 3 in the Supplementary Data, respectively. Additional data supporting the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Hawley, J. A., Hargreaves, M., Joyner, M. J. & Zierath, J. R. Integrative biology of exercise. Cell 159, 738–749 (2014).

    Article  CAS  PubMed  Google Scholar 

  2. Hawkins, M. N., Raven, P. B., Snell, P. G., Stray-Gundersen, J. & Levine, B. D. Maximal oxygen uptake as a parametric measure of cardiorespiratory capacity. Med. Sci. Sports Exerc. 39, 103–107 (2007).

    PubMed  Google Scholar 

  3. Ross, R. et al. Importance of assessing cardiorespiratory fitness in clinical practice: a case for fitness as a clinical vital sign: a scientific statement from the american heart association. Circulation 134, e653–e699 (2016).

    Article  PubMed  Google Scholar 

  4. Myers, J. et al. Exercise capacity and mortality among men referred for exercise testing. N. Engl. J. Med. 346, 793–801 (2002).

    Article  PubMed  Google Scholar 

  5. Mora, S. et al. Ability of exercise testing to predict cardiovascular and all-cause death in asymptomatic women: a 20-year follow-up of the lipid research clinics prevalence study. JAMA 290, 1600–1607 (2003).

    Article  CAS  PubMed  Google Scholar 

  6. Blair, S. N. et al. Changes in physical fitness and all-cause mortality. A prospective study of healthy and unhealthy men. JAMA 273, 1093–1098 (1995).

    Article  CAS  PubMed  Google Scholar 

  7. Clausen, J. S. R., Marott, J. L., Holtermann, A., Gyntelberg, F. & Jensen, M. T. Midlife cardiorespiratory fitness and the long-term risk of mortality: 46 years of follow-p. J. Am. Coll. Cardiol. 72, 987–995 (2018).

    Article  PubMed  Google Scholar 

  8. di Prampero, P. E. & Ferretti, G. Factors limiting maximal oxygen consumption in humans. Respir. Physiol. 80, 113–127 (1990).

    Article  PubMed  Google Scholar 

  9. González-Alonso, J. & Calbet, J. A. Reductions in systemic and skeletal muscle blood flow and oxygen delivery limit maximal aerobic capacity in humans. Circulation 107, 824–830 (2003).

    Article  PubMed  Google Scholar 

  10. Wagner, P. D. CrossTalk proposal: diffusion limitation of O2 from microvessels into muscle does contribute to the limitation of V̇O2max. J. Physiol. 593, 3757–3758 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Joyner, M. J. & Coyle, E. F. Endurance exercise performance: the physiology of champions. J. Physiol. 586, 35–44 (2008).

    Article  CAS  PubMed  Google Scholar 

  12. Bouchard, C. et al. Familial resemblance for VO2max in the sedentary state: the HERITAGE family study. Med. Sci. Sports Exerc. 30, 252–258 (1998).

    Article  CAS  PubMed  Google Scholar 

  13. Skinner, J. S. et al. Age, sex, race, initial fitness, and response to training: the HERITAGE Family Study. J. Appl. Physiol. 90, 1770–1776 (2001).

    Article  CAS  PubMed  Google Scholar 

  14. Williams, C. J. et al. Genes to predict VO. BMC Genomics 18, 831 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Sarzynski, M. A., Ghosh, S. & Bouchard, C. Genomic and transcriptomic predictors of response levels to endurance exercise training. J. Physiol. 595, 2931–2939 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Lewis, G. D. et al. Metabolic signatures of exercise in human plasma. Sci. Transl. Med. 2, 33ra37 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Overmyer, K. A. et al. Maximal oxidative capacity during exercise is associated with skeletal muscle fuel selection and dynamic changes in mitochondrial protein acetylation. Cell Metab. 21, 468–478 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wewer Albrechtsen, N. J. et al. Plasma proteome profiling reveals dynamics of inflammatory and lipid homeostasis markers after Roux-En-Y gastric bypass surgery. Cell Syst. 12, 601–612(2018).

    Article  CAS  Google Scholar 

  19. Jacob, J. et al. Application of large scale aptamer-based proteomic profiling to ‘planned’ myocardial infarctions. Circulation 137, 1270–1277 (2017).

  20. Gold, L. et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS ONE 5, e15004 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Kim, C. H. et al. Stability and reproducibility of proteomic profiles measured with an aptamer-based platform. Sci. Rep. 8, 8382 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Williams, S. A. et al. Plasma protein patterns as comprehensive indicators of health. Nat. Med. 25, 1851–1857 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ross, R., Hudson, R., Stotz, P. J. & Lam, M. Effects of exercise amount and intensity on abdominal obesity and glucose tolerance in obese adults: a randomized trial. Ann. Intern. Med. 162, 325–334 (2015).

    Article  PubMed  Google Scholar 

  24. St Hilaire, C. et al. NT5E mutations and arterial calcifications. N. Engl. J. Med. 364, 432–442 (2011).

    Article  Google Scholar 

  25. Hasnain, S. Z. et al. Glycemic control in diabetes is restored by therapeutic manipulation of cytokines that regulate beta cell stress. Nat. Med. 20, 1417–1426 (2014).

    Article  CAS  PubMed  Google Scholar 

  26. Lee, E. J. et al. Fibromodulin: a master regulator of myostatin controlling progression of satellite cells through a myogenic program. FASEB J. 30, 2708–2719 (2016).

    Article  CAS  PubMed  Google Scholar 

  27. Hynes, R. O. & Naba, A. Overview of the matrisome — an inventory of extracellular matrix constituents and functions. Cold Spring Harb. Perspect. Biol. 4, a004903 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Ngo, D. et al. Aptamer-based proteomic profiling reveals novel candidate biomarkers and pathways in cardiovascular disease. Circulation 134, 270–285 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ko, D. et al. Proteomics profiling and risk of new-onset atrial fibrillation: Framingham Heart Study. J. Am. Heart Assoc. 8, e010976 (2019).

    PubMed  PubMed Central  Google Scholar 

  30. Emilsson, V. et al. Co-regulatory networks of human serum proteins link genetics to disease. Science 361, 769–773 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Whitham, M. et al. Extracellular Vesicles Provide a Means for Tissue Crosstalk during Exercise. Cell Metab. 27, 237–251.e4 (2018).

    Article  CAS  PubMed  Google Scholar 

  32. Whittle, A. J. et al. BMP8B increases brown adipose tissue thermogenesis through both central and peripheral actions. Cell 149, 871–885 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Pellegrinelli, V. et al. Adipocyte-secreted BMP8b mediates adrenergic-induced remodeling of the neuro-vascular network in adipose tissue. Nat. Commun. 9, 4974 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Seldin, M. M. et al. A strategy for discovery of endocrine interactions with application to whole-body metabolism. Cell Metab. 27, 1138–1155 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Karsenty, G. & Olson, E. N. Bone and muscle endocrine functions: unexpected paradigms of inter-organ communication. Cell 164, 1248–1256 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Santos-Parker, J. R., Santos-Parker, K. S., McQueen, M. B., Martens, C. R. & Seals, D. R. Habitual aerobic exercise and circulating proteomic patterns in healthy adults: relation to indicators of healthspan. J. Appl. Physiol. 125, 1646–1659 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Wang, C. Y. et al. Cardiorespiratory fitness levels among US adults 20-49 years of age: findings from the 1999–2004 national health and nutrition examination survey. Am. J. Epidemiol. 171, 426–435 (2010).

    Article  PubMed  Google Scholar 

  38. Swift, D. L. et al. Low cardiorespiratory fitness in African Americans: a health disparity risk factor? Sports Med. 43, 1301–1313 (2013).

    Article  PubMed  Google Scholar 

  39. Fleg, J. L. et al. Accelerated longitudinal decline of aerobic capacity in healthy older adults. Circulation 112, 674–682 (2005).

    Article  PubMed  Google Scholar 

  40. Abe, T., Loenneke, J. P. & Thiebaud, R. S. Fat-free adipose tissue mass: impact on peak oxygen uptake (VO2peak) in adolescents with obesity. Sports Med. 49, 9–15 (2019).

  41. Bray, M. S. et al. The human gene map for performance and health-related fitness phenotypes: the 2006–2007 update. Med. Sci. Sports Exerc. 41, 35–73 (2009).

    Article  PubMed  CAS  Google Scholar 

  42. Timmons, J. A. et al. Using molecular classification to predict gains in maximal aerobic capacity following endurance exercise training in humans. J. Appl. Physiol. 108, 1487–1496 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Bouchard, C. et al. Genomic predictors of the maximal O2 uptake response to standardized exercise training programs. J. Appl. Physiol. 110, 1160–1170 (2011).

    Article  CAS  PubMed  Google Scholar 

  44. Ghosh, S. et al. Exploring the underlying biology of intrinsic cardiorespiratory fitness through integrative analysis of genomic variants and muscle gene expression profiling. J. Appl. Physiol. 126, 1292–1314 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Ghosh, S. et al. Integrative pathway analysis of a genome-wide association study of (V)O2max response to exercise training. J. Appl. Physiol. 115, 1343–1359 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Carvalho-Silva, D. et al. Open Targets Platform: new developments and updates two years on. Nucleic Acids Res. 47, D1056–D1065 (2018).

    Article  PubMed Central  CAS  Google Scholar 

  47. Lee, P. S. et al. Plasma gelsolin depletion and circulating actin in sepsis: a pilot study. PLoS ONE 3, e3712 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Lee, P. S. et al. Plasma gelsolin and circulating actin correlate with hemodialysis mortality. J. Am. Soc. Nephrol. 20, 1140–1148 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Egerstedt, A. et al. Profiling of the plasma proteome across different stages of human heart failure. Nat. Commun. 10, 5830 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Witke, W. et al. Hemostatic, inflammatory, and fibroblast responses are blunted in mice lacking gelsolin. Cell 81, 41–51 (1995).

    Article  CAS  PubMed  Google Scholar 

  51. Lee, W. M. & Galbraith, R. M. The extracellular actin-scavenger system and actin toxicity. N. Engl. J. Med. 326, 1335–1341 (1992).

    Article  CAS  PubMed  Google Scholar 

  52. Goetzl, E. J. et al. Gelsolin binding and cellular presentation of lysophosphatidic acid. J. Biol. Chem. 275, 14573–14578 (2000).

    Article  CAS  PubMed  Google Scholar 

  53. Li, G. H., Arora, P. D., Chen, Y., McCulloch, C. A. & Liu, P. Multifunctional roles of gelsolin in health and diseases. Med Res. Rev. 32, 999–1025 (2012).

    Article  CAS  PubMed  Google Scholar 

  54. Baird, G. S. & Hoofnagle, A. N. A novel discovery platform: aptamers for the quantification of human proteins. Clin. Chem. 63, 1061–1062 (2017).

    Article  PubMed  CAS  Google Scholar 

  55. Bouchard, C. et al. The HERITAGE family study. Aims, design, and measurement protocol. Med. Sci. Sports Exerc. 27, 721–729 (1995).

    Article  CAS  PubMed  Google Scholar 

  56. Skinner, J. S. et al. Heart rate versus %VO2max: age, sex, race, initial fitness, and training response — HERITAGE. Med. Sci. Sports Exerc. 35, 1908–1913 (2003).

    Article  PubMed  Google Scholar 

  57. Skinner, J. S. et al. Reproducibility of maximal exercise test data in the HERITAGE family study. Med. Sci. Sports Exerc. 31, 1623–1628 (1999).

    Article  CAS  PubMed  Google Scholar 

  58. Candia, J. et al. Assessment of variability in the SOMAscan assay. Sci. Rep. 7, 14248 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Benson, M. D. et al. The genetic architecture of the cardiovascular risk proteome. Circulation 137, 1158–1172 (2017).

  60. Suhre, K. et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat. Commun. 8, 14357 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Batista, G. E. A. P. A. & Monard, M. C. An analysis of four missing data treatment methods for supervised learning. Appl. Artif. Intell. 17, 519–533 (2003).

    Article  Google Scholar 

  63. Tsamardinos, I., Brown, L. E. & Aliferis, C. F. The max-min hill-climbing Bayesian network structure learning algorithm. Mach. Learn. 65, 31–78 (2006).

    Article  Google Scholar 

  64. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Merico, D., Isserlin, R., Stueker, O., Emili, A. & Bader, G. D. Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS ONE 5, e13984 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

This study is supported by the National Institute of Health grants K23 HL150327-01A1 (J.M.R.); R01 HL132320; HL133870 (R.E.G.); U24 DK112340 (R.E.G., S.A.C.), R01 HL45670, HL47317, HL47321, HL47323 and HL47327 (all in support of the HERITAGE Family Study); NR019628 (M.A.S., R.E.G.); and HL146462 (M.A.S.). C.B. is partially funded by the John W. Barton Sr. Chair in Genetics and Nutrition. S.G. and C.B. are partially supported by the NIH-funded COBRE grant (NIH 8 P30GM118430-01). S.G. is supported in part by 2 U54 GM104940 from the National Institute of General Medical Sciences (NIGMS) of the National Institutes of Health, which funds the Louisiana Clinical and Translational Science Center. This research was also supported by the National Medical Research Council, Ministry of Health, Singapore (WBS R913200076263) to S.G. D.S. is supported with a doctoral scholarship from the German Academic Scholarship Foundation (Studienstiftung des deutschen Volkes).

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Authors and Affiliations

  1. Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA

    Jeremy M. Robbins, Usman A. Tahir, Daniel H. Katz & Robert E. Gerszten

  2. CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, MA, USA

    Jeremy M. Robbins, Bennet Peterson, Daniela Schranner, Usman A. Tahir, Shuliang Deng, Michelle J. Keyes, Daniel H. Katz, Changyu Shen & Robert E. Gerszten

  3. Exercise Biology Group, Faculty of Sports and Health Sciences, Technical University of Munich, Munich, Germany

    Daniela Schranner

  4. Institute of Health Care Engineering with Testing Center of Medical Devices, Graz University of Technology, Graz, Austria

    Theresa Rienmüller & Christian Baumgartner

  5. National Heart, Lung and Blood Institute’s Framingham Heart Study, Framingham, MA, USA

    Michelle J. Keyes

  6. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Pierre M. Jean Beltran, Steven A. Carr & Robert E. Gerszten

  7. Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA

    Jacob L. Barber & Mark A. Sarzynski

  8. Cardiovascular & Metabolic Disorders Program and Center for Computational Biology, Duke-NUS Graduate Medical School, Singapore, Singapore

    Sujoy Ghosh

  9. Novartis Institutes for Biomedical Research, Cambridge, MA, USA

    Lori L. Jennings

  10. School of Kinesiology and Health Studies, Queen’s University, Kingston, Ontario, Canada

    Robert Ross

  11. Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA

    Claude Bouchard

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  1. Jeremy M. Robbins
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Contributions

J.M.R., M.A.S., C.B. and R.E.G. conceptualized the study. J.M.R., B.P., D.S., T.R., S.D., M.J.K., C.S., P.M.J.B, R.R. and R.E.G designed research, performed biochemical experiments and analysed the proteomics data. J.M.R., U.A.T. and D.H.K. performed genetics analyses. J.L.B., C.B., S.A.C., S.G. and L.L.J. provided technical assistance and/or conceptual advice. J.M.R. and R.E.G. wrote the manuscript with assistance from the coauthors.

Corresponding author

Correspondence to Robert E. Gerszten.

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Competing interests

S.A.C. is a member of the scientific advisory boards of Kymera, PTM BioLabs and Seer and is a scientific advisor to Pfizer and Biogen. The other authors declare no competing interests.

Additional information

Peer review information Nature Metabolism thanks Manuel Mayr and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Christoph Schmitt; Pooja Jha.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Secreted proteins positively related to bone homeostasis and baseline VO2max.

Functional representation of proteins‘ role in bone metabolism and homeostasis. Left and middle: SMOC1 regulates osteoblast differentiation. BMPs are related to bone formation via the TGF-ß pathway and are mediated by extracellular signalling molecules such as NOG. Right: simplified schematic of proteins related to cartilage formation and their location within cartilage tissue.

Extended Data Fig. 2 Receiver-operating characteristic curve for relative VO2max changes with exercise training > 15% using overlapping targets between aptamer- and antibody-based proteomic platforms.

7/10 overlapping proteins on both platforms demonstrated moderate-strong correlation (SELE, TCL1A, COMP, CREG1, STC1, IL1RL2, LILRA2; ρ = 0.41–0.91) and were used in modeling.

Supplementary information

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Supplementary Tables

Supplementary Tables 1–15

Supplementary Data

Aptamer-based (SOMAscan) and antibody-based (Olink) assays for the HERITAGE subset

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Robbins, J.M., Peterson, B., Schranner, D. et al. Human plasma proteomic profiles indicative of cardiorespiratory fitness. Nat Metab 3, 786–797 (2021). https://doi.org/10.1038/s42255-021-00400-z

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