1
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Sparks R, Rachmaninoff N, Lau WW, Hirsch DC, Bansal N, Martins AJ, Chen J, Liu CC, Cheung F, Failla LE, Biancotto A, Fantoni G, Sellers BA, Chawla DG, Howe KN, Mostaghimi D, Farmer R, Kotliarov Y, Calvo KR, Palmer C, Daub J, Foruraghi L, Kreuzburg S, Treat JD, Urban AK, Jones A, Romeo T, Deuitch NT, Moura NS, Weinstein B, Moir S, Ferrucci L, Barron KS, Aksentijevich I, Kleinstein SH, Townsley DM, Young NS, Frischmeyer-Guerrerio PA, Uzel G, Pinto-Patarroyo GP, Cudrici CD, Hoffmann P, Stone DL, Ombrello AK, Freeman AF, Zerbe CS, Kastner DL, Holland SM, Tsang JS. A unified metric of human immune health. Nat Med 2024:10.1038/s41591-024-03092-6. [PMID: 38961223 DOI: 10.1038/s41591-024-03092-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 05/28/2024] [Indexed: 07/05/2024]
Abstract
Immunological health has been challenging to characterize but could be defined as the absence of immune pathology. While shared features of some immune diseases and the concept of immunologic resilience based on age-independent adaptation to antigenic stimulation have been developed, general metrics of immune health and its utility for assessing clinically healthy individuals remain ill defined. Here we integrated transcriptomics, serum protein, peripheral immune cell frequency and clinical data from 228 patients with 22 monogenic conditions impacting key immunological pathways together with 42 age- and sex-matched healthy controls. Despite the high penetrance of monogenic lesions, differences between individuals in diverse immune parameters tended to dominate over those attributable to disease conditions or medication use. Unsupervised or supervised machine learning independently identified a score that distinguished healthy participants from patients with monogenic diseases, thus suggesting a quantitative immune health metric (IHM). In ten independent datasets, the IHM discriminated healthy from polygenic autoimmune and inflammatory disease states, marked aging in clinically healthy individuals, tracked disease activities and treatment responses in both immunological and nonimmunological diseases, and predicted age-dependent antibody responses to immunizations with different vaccines. This discriminatory power goes beyond that of the classical inflammatory biomarkers C-reactive protein and interleukin-6. Thus, deviations from health in diverse conditions, including aging, have shared systemic immune consequences, and we provide a web platform for calculating the IHM for other datasets, which could empower precision medicine.
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Grants
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1ZIAAI001152 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- Z01 AI000825-24 LIR Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 1 ZIA AI001202-07 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- Z-01-A-00646 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- Z-01-A-00647 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- Z-01-A-00646 Division of Intramural Research, National Institute of Allergy and Infectious Diseases (Division of Intramural Research of the NIAID)
- 5R01AI170116 U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (NIAID)
- U19AI089992 U.S. Department of Health & Human Services | NIH | Office of Extramural Research, National Institutes of Health (OER)
- U19AI089992 U.S. Department of Health & Human Services | NIH | Office of Extramural Research, National Institutes of Health (OER)
- ZIA-HL006089-12 U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI)
- 1ZIAHG200371 U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
- 1ZIAHG200373 U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
- 1ZIAHG200374 U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
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Affiliation(s)
- Rachel Sparks
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Nicholas Rachmaninoff
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
- Graduate Program in Biological Sciences, University of Maryland, College Park, MD, USA
| | - William W Lau
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Dylan C Hirsch
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Neha Bansal
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Andrew J Martins
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Jinguo Chen
- NIH Center for Human Immunology, Inflammation, and Autoimmunity, NIAID, NIH, Bethesda, MD, USA
| | - Candace C Liu
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Foo Cheung
- NIH Center for Human Immunology, Inflammation, and Autoimmunity, NIAID, NIH, Bethesda, MD, USA
| | - Laura E Failla
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Angelique Biancotto
- NIH Center for Human Immunology, Inflammation, and Autoimmunity, NIAID, NIH, Bethesda, MD, USA
| | - Giovanna Fantoni
- NIH Center for Human Immunology, Inflammation, and Autoimmunity, NIAID, NIH, Bethesda, MD, USA
| | - Brian A Sellers
- NIH Center for Human Immunology, Inflammation, and Autoimmunity, NIAID, NIH, Bethesda, MD, USA
| | - Daniel G Chawla
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Katherine N Howe
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - Darius Mostaghimi
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA
| | - Rohit Farmer
- NIH Center for Human Immunology, Inflammation, and Autoimmunity, NIAID, NIH, Bethesda, MD, USA
| | - Yuri Kotliarov
- NIH Center for Human Immunology, Inflammation, and Autoimmunity, NIAID, NIH, Bethesda, MD, USA
| | - Katherine R Calvo
- Hematology Section, Department of Laboratory Medicine, Clinical Center, NIH, Bethesda, MD, USA
| | - Cindy Palmer
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - Janine Daub
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - Ladan Foruraghi
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - Samantha Kreuzburg
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - Jennifer D Treat
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - Amanda K Urban
- Clinical Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Anne Jones
- Inflammatory Disease Section, NHGRI, NIH, Bethesda, MD, USA
| | - Tina Romeo
- Inflammatory Disease Section, NHGRI, NIH, Bethesda, MD, USA
| | | | | | | | - Susan Moir
- Laboratory of Immunoregulation, NIAID, NIH, Bethesda, MD, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, NIA, Baltimore, MD, USA
| | - Karyl S Barron
- Division of Intramural Research, NIAID, NIH, Bethesda, MD, USA
| | | | - Steven H Kleinstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | | | - Neal S Young
- Hematology Branch, NHLBI, NIH, Bethesda, MD, USA
| | | | - Gulbu Uzel
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | | | | | | | | | | | - Alexandra F Freeman
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - Christa S Zerbe
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | | | - Steven M Holland
- Laboratory of Clinical Immunology and Microbiology, NIAID, NIH, Bethesda, MD, USA
| | - John S Tsang
- Multiscale Systems Biology Section, Laboratory of Immune System Biology, NIAID, NIH, Bethesda, MD, USA.
- NIH Center for Human Immunology, Inflammation, and Autoimmunity, NIAID, NIH, Bethesda, MD, USA.
- Center for Systems and Engineering Immunology, Departments of Immunobiology and Biomedical Engineering, Yale University School of Medicine, New Haven, CT, USA.
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2
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Miller PE, Gajjar P, Mitchell GF, Khan SS, Vasan RS, Larson MG, Lewis GD, Shah RV, Nayor M. Clusters of multidimensional exercise response patterns and estimated heart failure risk in the Framingham Heart Study. ESC Heart Fail 2024. [PMID: 38943268 DOI: 10.1002/ehf2.14797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 02/28/2024] [Accepted: 03/21/2024] [Indexed: 07/01/2024] Open
Abstract
AIMS New tools are needed to identify heart failure (HF) risk earlier in its course. We evaluated the association of multidimensional cardiopulmonary exercise testing (CPET) phenotypes with subclinical risk markers and predicted long-term HF risk in a large community-based cohort. METHODS AND RESULTS We studied 2532 Framingham Heart Study participants [age 53 ± 9 years, 52% women, body mass index (BMI) 28.0 ± 5.3 kg/m2, peak oxygen uptake (VO2) 21.1 ± 5.9 kg/m2 in women, 26.4 ± 6.7 kg/m2 in men] who underwent maximum effort CPET and were not taking atrioventricular nodal blocking agents. Higher peak VO2 was associated with a lower estimated HF risk score (Spearman correlation r: -0.60 in men and -0.55 in women, P < 0.0001), with an observed overlap of estimated risk across peak VO2 categories. Hierarchical clustering of 26 separate CPET phenotypes (values residualized on age, sex, and BMI to provide uniformity across these variables) identified three clusters with distinct exercise physiologies: Cluster 1-impaired oxygen kinetics; Cluster 2-impaired vascular; and Cluster 3-favourable exercise response. These clusters were similar in age, sex distribution, and BMI but displayed distinct associations with relevant subclinical phenotypes [Cluster 1-higher subcutaneous and visceral fat and lower pulmonary function; Cluster 2-higher carotid-femoral pulse wave velocity (CFPWV); and Cluster 3-lower CFPWV, C-reactive protein, fat volumes, and higher lung function; all false discovery rate < 5%]. Cluster membership provided incremental variance explained (adjusted R2 increment of 0.10 in women and men, P < 0.0001 for both) when compared with peak VO2 alone in association with predicted HF risk. CONCLUSIONS Integrated CPET response patterns identify physiologically relevant profiles with distinct associations to subclinical phenotypes that are largely independent of standard risk factor-based assessment, which may suggest alternate pathways for prevention.
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Affiliation(s)
- Patricia E Miller
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Priya Gajjar
- Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | | | - Sadiya S Khan
- Division of Cardiology, Department of Medicine and Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Ramachandran S Vasan
- Boston University's and NHLBI's Framingham Heart Study, Framingham, MA, USA
- University of Texas School of Public Health San Antonio, San Antonio, TX, USA
- Department of Medicine, University of Texas Health Science Center, San Antonio, TX, USA
- Department of Population Health Sciences, University of Texas Health Science Center, San Antonio, TX, USA
| | - Martin G Larson
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Boston University's and NHLBI's Framingham Heart Study, Framingham, MA, USA
| | - Gregory D Lewis
- Division of Cardiology, Cardiovascular Research Center, and Pulmonary Critical Care Unit, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ravi V Shah
- Division of Cardiology, Vanderbilt Translational and Clinical Research Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Matthew Nayor
- Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Boston University's and NHLBI's Framingham Heart Study, Framingham, MA, USA
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, 72 E Concord Street, Suite L-516, Boston, MA, 02118, USA
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3
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Perry AS, Farber-Eger E, Gonzales T, Tanaka T, Robbins JM, Murthy VL, Stolze LK, Zhao S, Huang S, Colangelo LA, Deng S, Hou L, Lloyd-Jones DM, Walker KA, Ferrucci L, Watts EL, Barber JL, Rao P, Mi MY, Gabriel KP, Hornikel B, Sidney S, Houstis N, Lewis GD, Liu GY, Thyagarajan B, Khan SS, Choi B, Washko G, Kalhan R, Wareham N, Bouchard C, Sarzynski MA, Gerszten RE, Brage S, Wells QS, Nayor M, Shah RV. Proteomic analysis of cardiorespiratory fitness for prediction of mortality and multisystem disease risks. Nat Med 2024; 30:1711-1721. [PMID: 38834850 PMCID: PMC11186767 DOI: 10.1038/s41591-024-03039-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 04/30/2024] [Indexed: 06/06/2024]
Abstract
Despite the wide effects of cardiorespiratory fitness (CRF) on metabolic, cardiovascular, pulmonary and neurological health, challenges in the feasibility and reproducibility of CRF measurements have impeded its use for clinical decision-making. Here we link proteomic profiles to CRF in 14,145 individuals across four international cohorts with diverse CRF ascertainment methods to establish, validate and characterize a proteomic CRF score. In a cohort of around 22,000 individuals in the UK Biobank, a proteomic CRF score was associated with a reduced risk of all-cause mortality (unadjusted hazard ratio 0.50 (95% confidence interval 0.48-0.52) per 1 s.d. increase). The proteomic CRF score was also associated with multisystem disease risk and provided risk reclassification and discrimination beyond clinical risk factors, as well as modulating high polygenic risk of certain diseases. Finally, we observed dynamicity of the proteomic CRF score in individuals who undertook a 20-week exercise training program and an association of the score with the degree of the effect of training on CRF, suggesting potential use of the score for personalization of exercise recommendations. These results indicate that population-based proteomics provides biologically relevant molecular readouts of CRF that are additive to genetic risk, potentially modifiable and clinically translatable.
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Affiliation(s)
- Andrew S Perry
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Eric Farber-Eger
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Tomas Gonzales
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Toshiko Tanaka
- Longtidudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Jeremy M Robbins
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | | | - Lindsey K Stolze
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shilin Zhao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shi Huang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Laura A Colangelo
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Shuliang Deng
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Donald M Lloyd-Jones
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Keenan A Walker
- Multimodal Imaging of Neurodegenerative Disease (MIND) Unit, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Luigi Ferrucci
- Longtidudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, MD, USA
| | - Eleanor L Watts
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Jacob L Barber
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Prashant Rao
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Michael Y Mi
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Kelley Pettee Gabriel
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Bjoern Hornikel
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Nicholas Houstis
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
| | - Gregory D Lewis
- Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
| | - Gabrielle Y Liu
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, University of California Davis, Sacramento, CA, USA
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minnesota, MN, USA
| | - Sadiya S Khan
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Bina Choi
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - George Washko
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Ravi Kalhan
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Nick Wareham
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Claude Bouchard
- Human Genomic Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Mark A Sarzynski
- Department of Exercise Science, University of South Carolina Columbia, Columbia, SC, USA
| | - Robert E Gerszten
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Soren Brage
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Quinn S Wells
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Matthew Nayor
- Sections of Cardiovascular Medicine and Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Ravi V Shah
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University School of Medicine, Nashville, TN, USA.
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4
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Walzik D, Wences Chirino TY, Zimmer P, Joisten N. Molecular insights of exercise therapy in disease prevention and treatment. Signal Transduct Target Ther 2024; 9:138. [PMID: 38806473 PMCID: PMC11133400 DOI: 10.1038/s41392-024-01841-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 04/17/2024] [Accepted: 04/23/2024] [Indexed: 05/30/2024] Open
Abstract
Despite substantial evidence emphasizing the pleiotropic benefits of exercise for the prevention and treatment of various diseases, the underlying biological mechanisms have not been fully elucidated. Several exercise benefits have been attributed to signaling molecules that are released in response to exercise by different tissues such as skeletal muscle, cardiac muscle, adipose, and liver tissue. These signaling molecules, which are collectively termed exerkines, form a heterogenous group of bioactive substances, mediating inter-organ crosstalk as well as structural and functional tissue adaption. Numerous scientific endeavors have focused on identifying and characterizing new biological mediators with such properties. Additionally, some investigations have focused on the molecular targets of exerkines and the cellular signaling cascades that trigger adaption processes. A detailed understanding of the tissue-specific downstream effects of exerkines is crucial to harness the health-related benefits mediated by exercise and improve targeted exercise programs in health and disease. Herein, we review the current in vivo evidence on exerkine-induced signal transduction across multiple target tissues and highlight the preventive and therapeutic value of exerkine signaling in various diseases. By emphasizing different aspects of exerkine research, we provide a comprehensive overview of (i) the molecular underpinnings of exerkine secretion, (ii) the receptor-dependent and receptor-independent signaling cascades mediating tissue adaption, and (iii) the clinical implications of these mechanisms in disease prevention and treatment.
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Affiliation(s)
- David Walzik
- Division of Performance and Health (Sports Medicine), Institute for Sport and Sport Science, TU Dortmund University, 44227, Dortmund, North Rhine-Westphalia, Germany
| | - Tiffany Y Wences Chirino
- Division of Performance and Health (Sports Medicine), Institute for Sport and Sport Science, TU Dortmund University, 44227, Dortmund, North Rhine-Westphalia, Germany
| | - Philipp Zimmer
- Division of Performance and Health (Sports Medicine), Institute for Sport and Sport Science, TU Dortmund University, 44227, Dortmund, North Rhine-Westphalia, Germany.
| | - Niklas Joisten
- Division of Performance and Health (Sports Medicine), Institute for Sport and Sport Science, TU Dortmund University, 44227, Dortmund, North Rhine-Westphalia, Germany.
- Division of Exercise and Movement Science, Institute for Sport Science, University of Göttingen, 37075, Göttingen, Lower Saxony, Germany.
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5
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Wang X, Tazearslan C, Kim S, Guo Q, Contreras D, Yang J, Hudgins AD, Suh Y. In vitro heterochronic parabiosis identifies pigment epithelium-derived factor as a systemic mediator of rejuvenation by young blood. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.02.592258. [PMID: 38746475 PMCID: PMC11092633 DOI: 10.1101/2024.05.02.592258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Several decades of heterochronic parabiosis (HCPB) studies have demonstrated the restorative impact of young blood, and deleterious influence of aged blood, on physiological function and homeostasis across tissues, although few of the factors responsible for these observations have been identified. Here we develop an in vitro HCPB system to identify these circulating factors, using replicative lifespan (RLS) of primary human fibroblasts as an endpoint of cellular health. We find that RLS is inversely correlated with serum donor age and sensitive to the presence or absence of specific serum components. Through in vitro HCPB, we identify the secreted protein pigment epithelium-derived factor (PEDF) as a circulating factor that extends RLS of primary human fibroblasts and declines with age in mammals. Systemic administration of PEDF to aged mice reverses age-related functional decline and pathology across several tissues, improving cognitive function and reducing hepatic fibrosis and renal lipid accumulation. Together, our data supports PEDF as a systemic mediator of the effect of young blood on organismal health and homeostasis and establishes our in vitro HCPB system as a valuable screening platform for the identification of candidate circulating factors involved in aging and rejuvenation.
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Affiliation(s)
- Xizhe Wang
- Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY
- These authors contributed equally
| | - Cagdas Tazearslan
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY
- These authors contributed equally
| | - Seungsoo Kim
- Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY
| | - Qinghua Guo
- Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY
| | - Daniela Contreras
- Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY
| | - Jiping Yang
- Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY
| | - Adam D. Hudgins
- Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY
| | - Yousin Suh
- Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY
- Department of Genetics and Development, Columbia University Medical Center, New York, NY
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6
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Wei W, Raun SH, Long JZ. Molecular Insights From Multiomics Studies of Physical Activity. Diabetes 2024; 73:162-168. [PMID: 38241506 PMCID: PMC10796296 DOI: 10.2337/dbi23-0004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 06/04/2023] [Indexed: 01/21/2024]
Abstract
Physical activity confers systemic health benefits and provides powerful protection against disease. There has been tremendous interest in understanding the molecular effectors of exercise that mediate these physiologic effects. The modern growth of multiomics technologies-including metabolomics, proteomics, phosphoproteomics, lipidomics, single-cell RNA sequencing, and epigenomics-has provided unparalleled opportunities to systematically investigate the molecular changes associated with physical activity on an organism-wide scale. Here, we discuss how multiomics technologies provide new insights into the systemic effects of physical activity, including the integrative responses across organs as well as the molecules and mechanisms mediating tissue communication during exercise. We also highlight critical unanswered questions that can now be addressed using these high-dimensional tools and provide perspectives on fertile future research directions.
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Affiliation(s)
- Wei Wei
- Department of Pathology, Stanford University School of Medicine, Stanford, CA
- Sarafan ChEM-H, Stanford University, Stanford, CA
| | - Steffen H. Raun
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jonathan Z. Long
- Department of Pathology, Stanford University School of Medicine, Stanford, CA
- Sarafan ChEM-H, Stanford University, Stanford, CA
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA
- Wu Tsai Human Performance Alliance, Stanford University, Stanford, CA
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7
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Hota M, Barber JL, Ruiz-Ramie JJ, Schwartz CS, Lam DTUH, Rao P, Mi MY, Katz DH, Robbins JM, Clish CB, Gerszten RE, Sarzynski MA, Ghosh S, Bouchard C. Omics-driven investigation of the biology underlying intrinsic submaximal working capacity and its trainability. Physiol Genomics 2023; 55:517-543. [PMID: 37661925 PMCID: PMC11178266 DOI: 10.1152/physiolgenomics.00163.2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 07/21/2023] [Accepted: 08/31/2023] [Indexed: 09/05/2023] Open
Abstract
Submaximal exercise capacity is an indicator of cardiorespiratory fitness with clinical and public health implications. Submaximal exercise capacity and its response to exercise programs are characterized by heritability levels of about 40%. Using physical working capacity (power output) at a heart rate of 150 beats/min (PWC150) as an indicator of submaximal exercise capacity in subjects of the HERITAGE Family Study, we have undertaken multi-omics and in silico explorations of the underlying biology of PWC150 and its response to 20 wk of endurance training. Our goal was to illuminate the biological processes and identify panels of genes associated with human variability in intrinsic PWC150 (iPWC150) and its trainability (dPWC150). Our bioinformatics approach was based on a combination of genome-wide association, skeletal muscle gene expression, and plasma proteomics and metabolomics experiments. Genes, proteins, and metabolites showing significant associations with iPWC150 or dPWC150 were further queried for the enrichment of biological pathways. We compared genotype-phenotype associations of emerging candidate genes with reported functional consequences of gene knockouts in mouse models. We investigated the associations between DNA variants and multiple muscle and cardiovascular phenotypes measured in HERITAGE subjects. Two panels of prioritized genes of biological relevance to iPWC150 (13 genes) and dPWC150 (6 genes) were identified, supporting the hypothesis that genes and pathways associated with iPWC150 are different from those underlying dPWC150. Finally, the functions of these genes and pathways suggested that human variation in submaximal exercise capacity is mainly driven by skeletal muscle morphology and metabolism and red blood cell oxygen-carrying capacity.NEW & NOTEWORTHY Multi-omics and in silico explorations of the genes and underlying biology of submaximal exercise capacity and its response to 20 wk of endurance training were undertaken. Prioritized genes were identified: 13 genes for variation in submaximal exercise capacity in the sedentary state and 5 genes for the response level to endurance training, with no overlap between them. Genes and pathways associated with submaximal exercise capacity in the sedentary state are different from those underlying trainability.
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Affiliation(s)
- Monalisa Hota
- Centre for Computational Biology, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Jacob L Barber
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States
| | - Jonathan J Ruiz-Ramie
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States
- Department of Kinesiology, Augusta University, Augusta, Georgia, United States
| | - Charles S Schwartz
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States
| | - Do Thuy Uyen Ha Lam
- Centre for Computational Biology, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Prashant Rao
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States
| | - Michael Y Mi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States
| | - Daniel H Katz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States
| | - Jeremy M Robbins
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States
| | - Clary B Clish
- Metabolomics Platform, Broad Institute, Boston, Massachusetts, United States
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States
| | - Mark A Sarzynski
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, United States
| | - Sujoy Ghosh
- Centre for Computational Biology, Duke-National University of Singapore Medical School, Singapore, Singapore
- Bioinformatics Section, Human Genomics Core, Pennington Biomedical Research Center, Baton Rouge, Louisiana, United States
- Program in Cardiovascular and Metabolic Disorders, Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, United States
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8
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Nieman DC, Sakaguchi CA, Pelleigrini M, Thompson MJ, Sumner S, Zhang Q. Healthy lifestyle linked to innate immunity and lipoprotein metabolism: a cross-sectional comparison using untargeted proteomics. Sci Rep 2023; 13:16728. [PMID: 37794065 PMCID: PMC10550951 DOI: 10.1038/s41598-023-44068-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 10/03/2023] [Indexed: 10/06/2023] Open
Abstract
This study used untargeted proteomics to compare blood proteomic profiles in two groups of adults that differed widely in lifestyle habits. A total of 52 subjects in the lifestyle group (LIFE) (28 males, 24 females) and 52 in the control group (CON) (27 males, 25 females) participated in this cross-sectional study. Age, education level, marital status, and height did not differ significantly between LIFE and CON groups. The LIFE and CON groups differed markedly in body composition, physical activity patterns, dietary intake patterns, disease risk factor prevalence, blood measures of inflammation, triglycerides, HDL-cholesterol, glucose, and insulin, weight-adjusted leg/back and handgrip strength, and mood states. The proteomics analysis showed strong group differences for 39 of 725 proteins identified in dried blood spot samples. Of these, 18 were downregulated in the LIFE group and collectively indicated a lower innate immune activation signature. A total of 21 proteins were upregulated in the LIFE group and supported greater lipoprotein metabolism and HDL remodeling. Lifestyle-related habits and biomarkers were probed and the variance (> 50%) in proteomic profiles was best explained by group contrasts in indicators of adiposity. This cross-sectional study established that a relatively small number of proteins are associated with good lifestyle habits.
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Affiliation(s)
- David C Nieman
- Human Performance Laboratory, Biology Department, Appalachian State University, North Carolina Research Campus, Kannapolis, NC, USA.
| | - Camila A Sakaguchi
- Human Performance Laboratory, Biology Department, Appalachian State University, North Carolina Research Campus, Kannapolis, NC, USA
| | - Matteo Pelleigrini
- Department of Molecular, Cell, and Developmental Biology, University of California Los Angeles, Los Angeles, CA, USA
| | - Michael J Thompson
- Department of Molecular, Cell, and Developmental Biology, University of California Los Angeles, Los Angeles, CA, USA
| | - Susan Sumner
- Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, 28081, USA
| | - Qibin Zhang
- UNCG Center for Translational Biomedical Research, University of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, NC, USA
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9
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Cai X, Xue Z, Zeng FF, Tang J, Yue L, Wang B, Ge W, Xie Y, Miao Z, Gou W, Fu Y, Li S, Gao J, Shuai M, Zhang K, Xu F, Tian Y, Xiang N, Zhou Y, Shan PF, Zhu Y, Chen YM, Zheng JS, Guo T. Population serum proteomics uncovers a prognostic protein classifier for metabolic syndrome. Cell Rep Med 2023; 4:101172. [PMID: 37652016 PMCID: PMC10518601 DOI: 10.1016/j.xcrm.2023.101172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 09/02/2023]
Abstract
Metabolic syndrome (MetS) is a complex metabolic disorder with a global prevalence of 20%-25%. Early identification and intervention would help minimize the global burden on healthcare systems. Here, we measured over 400 proteins from ∼20,000 proteomes using data-independent acquisition mass spectrometry for 7,890 serum samples from a longitudinal cohort of 3,840 participants with two follow-up time points over 10 years. We then built a machine-learning model for predicting the risk of developing MetS within 10 years. Our model, composed of 11 proteins and the age of the individuals, achieved an area under the curve of 0.774 in the validation cohort (n = 242). Using linear mixed models, we found that apolipoproteins, immune-related proteins, and coagulation-related proteins best correlated with MetS development. This population-scale proteomics study broadens our understanding of MetS and may guide the development of prevention and targeted therapies for MetS.
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Affiliation(s)
- Xue Cai
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Zhangzhi Xue
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Fang-Fang Zeng
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510080, China
| | - Jun Tang
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Liang Yue
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Bo Wang
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1 Yunmeng Road, Cloud Town, Xihu District, Hangzhou, Zhejiang 310024, China
| | - Weigang Ge
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1 Yunmeng Road, Cloud Town, Xihu District, Hangzhou, Zhejiang 310024, China
| | - Yuting Xie
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Zelei Miao
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Wanglong Gou
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Yuanqing Fu
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Sainan Li
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Jinlong Gao
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Menglei Shuai
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Ke Zhang
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Fengzhe Xu
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Yunyi Tian
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Nan Xiang
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1 Yunmeng Road, Cloud Town, Xihu District, Hangzhou, Zhejiang 310024, China
| | - Yan Zhou
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China
| | - Peng-Fei Shan
- Department of Endocrinology, the Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, Zhejiang 310009, China
| | - Yi Zhu
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China.
| | - Yu-Ming Chen
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Ju-Sheng Zheng
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China.
| | - Tiannan Guo
- Center for Intelligent Proteomics, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China; School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China.
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10
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Benson MD, Eisman AS, Tahir UA, Katz DH, Deng S, Ngo D, Robbins JM, Hofmann A, Shi X, Zheng S, Keyes M, Yu Z, Gao Y, Farrell L, Shen D, Chen ZZ, Cruz DE, Sims M, Correa A, Tracy RP, Durda P, Taylor KD, Liu Y, Johnson WC, Guo X, Yao J, Chen YDI, Manichaikul AW, Jain D, Yang Q, Bouchard C, Sarzynski MA, Rich SS, Rotter JI, Wang TJ, Wilson JG, Clish CB, Sarkar IN, Natarajan P, Gerszten RE. Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma. Cell Metab 2023; 35:1646-1660.e3. [PMID: 37582364 PMCID: PMC11118091 DOI: 10.1016/j.cmet.2023.07.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 04/12/2023] [Accepted: 07/24/2023] [Indexed: 08/17/2023]
Abstract
Although many novel gene-metabolite and gene-protein associations have been identified using high-throughput biochemical profiling, systematic studies that leverage human genetics to illuminate causal relationships between circulating proteins and metabolites are lacking. Here, we performed protein-metabolite association studies in 3,626 plasma samples from three human cohorts. We detected 171,800 significant protein-metabolite pairwise correlations between 1,265 proteins and 365 metabolites, including established relationships in metabolic and signaling pathways such as the protein thyroxine-binding globulin and the metabolite thyroxine, as well as thousands of new findings. In Mendelian randomization (MR) analyses, we identified putative causal protein-to-metabolite associations. We experimentally validated top MR associations in proof-of-concept plasma metabolomics studies in three murine knockout strains of key protein regulators. These analyses identified previously unrecognized associations between bioactive proteins and metabolites in human plasma. We provide publicly available data to be leveraged for studies in human metabolism and disease.
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Affiliation(s)
- Mark D Benson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Aaron S Eisman
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Center for Biomedical Informatics, Brown University, Providence, RI, USA
| | - Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Daniel H Katz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Debby Ngo
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Jeremy M Robbins
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Alissa Hofmann
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Xu Shi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Shuning Zheng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michelle Keyes
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zhi Yu
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yan Gao
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Laurie Farrell
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Dongxiao Shen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zsu-Zsu Chen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Daniel E Cruz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Mario Sims
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Adolfo Correa
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Russell P Tracy
- Department of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Peter Durda
- Department of Pathology Laboratory Medicine, Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yongmei Liu
- Department of Medicine, Division of Cardiology, Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - W Craig Johnson
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Jie Yao
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii-Der Ida Chen
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ani W Manichaikul
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA; Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | | | - Qiong Yang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Claude Bouchard
- Human Genomic Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Mark A Sarzynski
- Department of Exercise Science, University of South Carolina, Columbia, Columbia, SC, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Thomas J Wang
- Department of Medicine, UT Southwestern Medical Center, Dallas, TX, USA
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Clary B Clish
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Indra Neil Sarkar
- Center for Biomedical Informatics, Brown University, Providence, RI, USA
| | - Pradeep Natarajan
- Broad Institute of Harvard and MIT, Cambridge, MA, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA; Department of Medicine Harvard Medical School, Boston, MA, USA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA; Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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11
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Montgomery MK, De Nardo W, Watt MJ. Exercise training induces depot-specific remodeling of protein secretion in skeletal muscle and adipose tissue of obese male mice. Am J Physiol Endocrinol Metab 2023; 325:E227-E238. [PMID: 37493472 DOI: 10.1152/ajpendo.00178.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/21/2023] [Accepted: 07/21/2023] [Indexed: 07/27/2023]
Abstract
Acute exercise induces changes in circulating proteins, which are known to alter metabolism and systemic energy balance. Skeletal muscle is a primary contributor to changes in the plasma proteome with acute exercise. An important consideration when assessing the endocrine function of muscle is the presence of different fiber types, which show distinct functional and metabolic properties and likely secrete different proteins. Similarly, adipokines are important regulators of systemic metabolism and have been shown to differ between depots. Given the health-promoting effects of exercise, we proposed that understanding depot-specific remodeling of protein secretion in muscle and adipose tissue would provide new insights into intertissue communication and uncover novel regulators of energy homeostasis. Here, we examined the effect of endurance exercise training on protein secretion from fast-twitch extensor digitorum longus (EDL) and slow-twitch soleus muscle and visceral and subcutaneous adipose tissue. High-fat diet-fed mice were exercise trained for 6 wk, whereas a Control group remained sedentary. Secreted proteins from excised EDL and soleus muscle, inguinal, and epididymal adipose tissues were detected using mass spectrometry. We detected 575 and 784 secreted proteins from EDL and soleus muscle and 738 and 920 proteins from inguinal and epididymal adipose tissue, respectively. Of these, 331 proteins were secreted from all tissues, whereas secretion of many other proteins was tissue and depot specific. Exercise training led to substantial remodeling of protein secretion from EDL, whereas soleus showed only minor changes. Myokines released exclusively from EDL or soleus were associated with glycogen metabolism and cellular stress response, respectively. Adipokine secretion was completely refractory to exercise regulation in both adipose depots. This study provides an in-depth resource of protein secretion from muscle and adipose tissue, and its regulation following exercise training, and identifies distinct depot-specific secretion patterns that are related to the metabolic properties of the tissue of origin.NEW & NOTEWORTHY The present study examines the effects of exercise training on protein secretion from fast-twitch and slow-twitch muscle as well as visceral and subcutaneous adipose tissue of obese mice. Although exercise training leads to substantial remodeling of protein secretion from fast-twitch muscle, adipose tissue is completely refractory to exercise regulation.
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Affiliation(s)
- Magdalene K Montgomery
- Faculty of Medicine, Dentistry & Health Sciences, Department of Anatomy and Physiology, School of Biomedical Sciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - William De Nardo
- Faculty of Medicine, Dentistry & Health Sciences, Department of Anatomy and Physiology, School of Biomedical Sciences, The University of Melbourne, Melbourne, Victoria, Australia
| | - Matthew J Watt
- Faculty of Medicine, Dentistry & Health Sciences, Department of Anatomy and Physiology, School of Biomedical Sciences, The University of Melbourne, Melbourne, Victoria, Australia
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12
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Mi MY, Barber JL, Rao P, Farrell LA, Sarzynski MA, Bouchard C, Robbins JM, Gerszten RE. Plasma Proteomic Kinetics in Response to Acute Exercise. Mol Cell Proteomics 2023; 22:100601. [PMID: 37343698 PMCID: PMC10460691 DOI: 10.1016/j.mcpro.2023.100601] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 05/09/2023] [Accepted: 06/11/2023] [Indexed: 06/23/2023] Open
Abstract
Regular exercise has many favorable effects on human health, which may be mediated in part by the release of circulating bioactive factors during each bout of exercise. Limited data exist regarding the kinetic responses of plasma proteins during and after acute exercise. Proteomic profiling of 4163 proteins was performed using a large-scale, affinity-based platform in 75 middle-aged adults who were referred for treadmill exercise stress testing. Plasma proteins were quantified at baseline, peak exercise, and 1-h postexercise, and those with significant changes at both exercise timepoints were further examined for their associations with cardiometabolic traits and change with aerobic exercise training in the Health, Risk Factors, Exercise Training and Genetics Family Study, a 20-week exercise intervention study. A total of 765 proteins changed (false discovery rate < 0.05) at peak exercise compared to baseline, and 128 proteins changed (false discovery rate < 0.05) at 1-h postexercise. The 56 proteins that changed at both timepoints included midkine, brain-derived neurotrophic factor, metalloproteinase inhibitor 4, and coiled-coil domain-containing protein 126 and were enriched for secreted proteins. The majority had concordant direction of change at both timepoints. Across all proteins assayed, gene set enrichment analysis showed increased abundance of coagulation-related proteins at 1-h postexercise. Forty-five proteins were associated with at least one measure of adiposity, lipids, glucose homeostasis, or cardiorespiratory fitness in Health, Risk Factors, Exercise Training and Genetics Family Study, and 20 proteins changed with aerobic exercise training. We identified hundreds of novel proteins that change during acute exercise, most of which resolved by 1 h into recovery. Proteins with sustained changes during exercise and recovery may be of particular interest as circulating biomarkers and pathways for further investigation in cardiometabolic diseases. These data will contribute to a biochemical roadmap of acute exercise that will be publicly available for the entire scientific community.
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Affiliation(s)
- Michael Y Mi
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA; CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.
| | - Jacob L Barber
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Prashant Rao
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA; CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Laurie A Farrell
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Mark A Sarzynski
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
| | - Jeremy M Robbins
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA; CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA; CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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13
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Wei W, Riley NM, Lyu X, Shen X, Guo J, Raun SH, Zhao M, Moya-Garzon MD, Basu H, Sheng-Hwa Tung A, Li VL, Huang W, Wiggenhorn AL, Svensson KJ, Snyder MP, Bertozzi CR, Long JZ. Organism-wide, cell-type-specific secretome mapping of exercise training in mice. Cell Metab 2023; 35:1261-1279.e11. [PMID: 37141889 PMCID: PMC10524249 DOI: 10.1016/j.cmet.2023.04.011] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 02/21/2023] [Accepted: 04/05/2023] [Indexed: 05/06/2023]
Abstract
There is a significant interest in identifying blood-borne factors that mediate tissue crosstalk and function as molecular effectors of physical activity. Although past studies have focused on an individual molecule or cell type, the organism-wide secretome response to physical activity has not been evaluated. Here, we use a cell-type-specific proteomic approach to generate a 21-cell-type, 10-tissue map of exercise training-regulated secretomes in mice. Our dataset identifies >200 exercise training-regulated cell-type-secreted protein pairs, the majority of which have not been previously reported. Pdgfra-cre-labeled secretomes were the most responsive to exercise training. Finally, we show anti-obesity, anti-diabetic, and exercise performance-enhancing activities for proteoforms of intracellular carboxylesterases whose secretion from the liver is induced by exercise training.
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Affiliation(s)
- Wei Wei
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Biology, Stanford University, Stanford, CA 94305, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA
| | - Nicholas M Riley
- Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA; Department of Chemistry, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Xuchao Lyu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA; Wu Tsai Human Performance Alliance, Stanford University, Stanford, CA 94305, USA
| | - Xiaotao Shen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94035, USA
| | - Jing Guo
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Steffen H Raun
- Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Meng Zhao
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Maria Dolores Moya-Garzon
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA
| | - Himanish Basu
- Department of Immunology, Harvard Medical School, Boston, MA 02115, USA
| | - Alan Sheng-Hwa Tung
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA
| | - Veronica L Li
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA; Department of Chemistry, Stanford University, Stanford, CA 94305, USA
| | - Wentao Huang
- Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Amanda L Wiggenhorn
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA; Department of Chemistry, Stanford University, Stanford, CA 94305, USA
| | - Katrin J Svensson
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94035, USA; Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Carolyn R Bertozzi
- Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA; Department of Chemistry, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Jonathan Z Long
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA; Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA 94305, USA; Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA; Wu Tsai Human Performance Alliance, Stanford University, Stanford, CA 94305, USA.
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14
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Ren Z, Fu L, Feng Z, Song Z, Liu Y, Zhang T, Wu N. Skeletal muscle mass is a strong predictor of cardiorespiratory fitness in the Chinese population with obesity. Nutr Metab Cardiovasc Dis 2023; 33:1407-1414. [PMID: 37149447 DOI: 10.1016/j.numecd.2023.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 04/03/2023] [Accepted: 04/04/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND AND AIMS Although skeletal muscle is well-known as physiologically related to VO2max, the independent predictive value of skeletal muscle mass (SMM) VO2max in people with obesity has not been studied. This study aims to determine the relationships between maximal oxygen uptake (VO2max) and SMM in the Chinese population with obesity. METHODS AND RESULTS Overall, 409 participants with obesity were included in this cross-sectional study. A maximal and graded exercise testing measured VO2max, and body compositions were measured by bioelectrical impedance analysis. Subsequently, correlation coefficients and stepwise multiple linear regression analyses were used to determine the relationships between VO2max and body compositions. SMM was found to have a significant correlation with VO2max (r = 0.290, P < 0.001) after adjusting for sex, age, body mass index (BMI), waist-to-hip ratio, and percent body fat (PBF). In previous studies, BMI was widely recognized as a strong predictor of VO2max. This study revealed surprising results: after SMM was controlled, the correlation between BMI and VO2max was reduced (from r = 0.381, P < 0.001 to r = 0.191, P < 0.001). SMM was found the most important independent predictor. In the regression model, the variance of VO2max was explained by the SMM which accounted for 27.4%. CONCLUSIONS In summary, SMM is a stronger independent predictor of cardiorespiratory fitness in the Chinese population with obesity than sex, age, BMI, waist-to-hip ratio, and PBF.
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Affiliation(s)
- Zhengyun Ren
- College of Medicine, Southwest Jiaotong University, Chengdu, China; Center of Gastrointestinal and Minimally Invasive Surgery, Department of General Surgery, The Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University & The Second Affiliated Hospital of Chengdu, Chongqing Medical University, Chengdu, Sichuan, China; Medical Research Center, The Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University & The Second Affiliated Hospital of Chengdu, Chongqing Medical University, Chengdu, Sichuan, China
| | - Luo Fu
- Center of Gastrointestinal and Minimally Invasive Surgery, Department of General Surgery, The Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University & The Second Affiliated Hospital of Chengdu, Chongqing Medical University, Chengdu, Sichuan, China
| | - Zhonghui Feng
- Center of Gastrointestinal and Minimally Invasive Surgery, Department of General Surgery, The Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University & The Second Affiliated Hospital of Chengdu, Chongqing Medical University, Chengdu, Sichuan, China; Medical Research Center, The Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University & The Second Affiliated Hospital of Chengdu, Chongqing Medical University, Chengdu, Sichuan, China
| | - Zhiheng Song
- School of Chemistry and Life Sciences, Suzhou University of Science and Technology, Suzhou, Jiangsu, China
| | - Yanjun Liu
- Center of Gastrointestinal and Minimally Invasive Surgery, Department of General Surgery, The Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University & The Second Affiliated Hospital of Chengdu, Chongqing Medical University, Chengdu, Sichuan, China.
| | - Tongtong Zhang
- Center of Gastrointestinal and Minimally Invasive Surgery, Department of General Surgery, The Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University & The Second Affiliated Hospital of Chengdu, Chongqing Medical University, Chengdu, Sichuan, China; Medical Research Center, The Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University & The Second Affiliated Hospital of Chengdu, Chongqing Medical University, Chengdu, Sichuan, China.
| | - Nianwei Wu
- Center of Gastrointestinal and Minimally Invasive Surgery, Department of General Surgery, The Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University & The Second Affiliated Hospital of Chengdu, Chongqing Medical University, Chengdu, Sichuan, China; Medical Research Center, The Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University & The Second Affiliated Hospital of Chengdu, Chongqing Medical University, Chengdu, Sichuan, China.
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15
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Abstract
Cardiometabolic diseases, including cardiovascular disease and diabetes, are major causes of morbidity and mortality worldwide. Despite progress in prevention and treatment, recent trends show a stalling in the reduction of cardiovascular disease morbidity and mortality, paralleled by increasing rates of cardiometabolic disease risk factors in young adults, underscoring the importance of risk assessments in this population. This review highlights the evidence for molecular biomarkers for early risk assessment in young individuals. We examine the utility of traditional biomarkers in young individuals and discuss novel, nontraditional biomarkers specific to pathways contributing to early cardiometabolic disease risk. Additionally, we explore emerging omic technologies and analytical approaches that could enhance risk assessment for cardiometabolic disease.
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Affiliation(s)
- Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School
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16
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Robbins JM, Rao P, Deng S, Keyes MJ, Tahir UA, Katz DH, Beltran PMJ, Marchildon F, Barber JL, Peterson B, Gao Y, Correa A, Wilson JG, Smith JG, Cohen P, Ross R, Bouchard C, Sarzynski MA, Gerszten RE. Plasma proteomic changes in response to exercise training are associated with cardiorespiratory fitness adaptations. JCI Insight 2023; 8:e165867. [PMID: 37036009 PMCID: PMC10132160 DOI: 10.1172/jci.insight.165867] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 02/21/2023] [Indexed: 04/11/2023] Open
Abstract
Regular exercise leads to widespread salutary effects, and there is increasing recognition that exercise-stimulated circulating proteins can impart health benefits. Despite this, limited data exist regarding the plasma proteomic changes that occur in response to regular exercise. Here, we perform large-scale plasma proteomic profiling in 654 healthy human study participants before and after a supervised, 20-week endurance exercise training intervention. We identify hundreds of circulating proteins that are modulated, many of which are known to be secreted. We highlight proteins involved in angiogenesis, iron homeostasis, and the extracellular matrix, many of which are novel, including training-induced increases in fibroblast activation protein (FAP), a membrane-bound and circulating protein relevant in body-composition homeostasis. We relate protein changes to training-induced maximal oxygen uptake adaptations and validate our top findings in an external exercise cohort. Furthermore, we show that FAP is positively associated with survival in 3 separate, population-based cohorts.
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Affiliation(s)
- Jeremy M. Robbins
- Division of Cardiovascular Medicine
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Prashant Rao
- Division of Cardiovascular Medicine
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Shuliang Deng
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Michelle J. Keyes
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, USA
| | - Usman A. Tahir
- Division of Cardiovascular Medicine
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Daniel H. Katz
- Division of Cardiovascular Medicine
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | | | - François Marchildon
- Laboratory of Molecular Metabolism, The Rockefeller University, New York, New York, USA
| | - Jacob L. Barber
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA
| | - Bennet Peterson
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Yan Gao
- Jackson Heart Study, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - Adolfo Correa
- Jackson Heart Study, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - James G. Wilson
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Jackson Heart Study, University of Mississippi Medical Center, Jackson, Mississippi, USA
| | - J. Gustav Smith
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- The Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, Gothenburg University and the Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
- Wallenberg Center for Molecular Medicine and
- Lund University Diabetes Center, Lund, Sweden
- Department of Heart Failure and Valvular Disease, Skåne University Hospital, Lund, Sweden
| | - Paul Cohen
- Laboratory of Molecular Metabolism, The Rockefeller University, New York, New York, USA
| | - Robert Ross
- School of Kinesiology and Health Studies, Queen’s University, Kingston, Ontario, Canada
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
| | - Mark A. Sarzynski
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine
- CardioVascular Institute, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
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17
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Muli S, Brachem C, Alexy U, Schmid M, Oluwagbemigun K, Nöthlings U. Exploring the association of physical activity with the plasma and urine metabolome in adolescents and young adults. Nutr Metab (Lond) 2023; 20:23. [PMID: 37020289 PMCID: PMC10074825 DOI: 10.1186/s12986-023-00742-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 03/29/2023] [Indexed: 04/07/2023] Open
Abstract
BACKGROUND Regular physical activity elicits many health benefits. However, the underlying molecular mechanisms through which physical activity influences overall health are less understood. Untargeted metabolomics enables system-wide mapping of molecular perturbations which may lend insights into physiological responses to regular physical activity. In this study, we investigated the associations of habitual physical activity with plasma and urine metabolome in adolescents and young adults. METHODS This cross-sectional study included participants from the DONALD (DOrtmund Nutritional and Anthropometric Longitudinally Designed) study with plasma samples n = 365 (median age: 18.4 (18.1, 25.0) years, 58% females) and 24 h urine samples n = 215 (median age: 18.1 (17.1, 18.2) years, 51% females). Habitual physical activity was assessed using a validated Adolescent Physical Activity Recall Questionnaire. Plasma and urine metabolite concentrations were determined using ultra-high-performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) methods. In a sex-stratified analysis, we conducted principal component analysis (PCA) to reduce the dimensionality of metabolite data and to create metabolite patterns. Multivariable linear regression models were then applied to assess the associations between self-reported physical activity (metabolic equivalent of task (MET)-hours per week) with single metabolites and metabolite patterns, adjusted for potential confounders and controlling the false discovery rate (FDR) at 5% for each set of regressions. RESULTS Habitual physical activity was positively associated with the "lipid, amino acids and xenometabolite" pattern in the plasma samples of male participants only (β = 1.02; 95% CI: 1.01, 1.04, p = 0.001, adjusted p = 0.042). In both sexes, no association of physical activity with single metabolites in plasma and urine and metabolite patterns in urine was found (all adjusted p > 0.05). CONCLUSIONS Our explorative study suggests that habitual physical activity is associated with alterations of a group of metabolites reflected in the plasma metabolite pattern in males. These perturbations may lend insights into some of underlying mechanisms that modulate effects of physical activity.
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Affiliation(s)
- Samuel Muli
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany.
| | - Christian Brachem
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany
| | - Ute Alexy
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany
| | - Matthias Schmid
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), University Hospital Bonn, Venusberg Campus 1, 53127, Bonn, Germany
| | - Kolade Oluwagbemigun
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany
| | - Ute Nöthlings
- Nutritional Epidemiology, Department of Nutrition and Food Sciences, University of Bonn, Friedrich-Hirzebruch- Allee 7, 53115, Bonn, Germany
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18
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Hypermethylation of ACADVL is involved in the high-intensity interval training-associated reduction of cardiac fibrosis in heart failure patients. J Transl Med 2023; 21:187. [PMID: 36894992 PMCID: PMC9999524 DOI: 10.1186/s12967-023-04032-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 03/01/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND Emerging evidence suggests that DNA methylation can be affected by physical activities and is associated with cardiac fibrosis. This translational research examined the implications of DNA methylation associated with the high-intensity interval training (HIIT) effects on cardiac fibrosis in patients with heart failure (HF). METHODS Twelve HF patients were included and received cardiovascular magnetic resonance imaging with late gadolinium enhancement for cardiac fibrosis severity and a cardiopulmonary exercise test for peak oxygen consumption ([Formula: see text]O2peak). Afterwards, they underwent 36 sessions of HIIT at alternating 80% and 40% of [Formula: see text]O2peak for 30 min per session in 3-4 months. Human serum from 11 participants, as a means to link cell biology to clinical presentations, was used to investigate the exercise effects on cardiac fibrosis. Primary human cardiac fibroblasts (HCFs) were incubated in patient serum, and analyses of cell behaviour, proteomics (n = 6) and DNA methylation profiling (n = 3) were performed. All measurements were conducted after completing HIIT. RESULTS A significant increase (p = 0.009) in [Formula: see text]O2peak (pre- vs. post-HIIT = 19.0 ± 1.1 O2 ml/kg/min vs. 21.8 ± 1.1 O2 ml/kg/min) was observed after HIIT. The exercise strategy resulted in a significant decrease in left ventricle (LV) volume by 15% to 40% (p < 0.05) and a significant increase in LV ejection fraction by approximately 30% (p = 0.010). LV myocardial fibrosis significantly decreased from 30.9 ± 1.2% to 27.2 ± 0.8% (p = 0.013) and from 33.4 ± 1.6% to 30.1 ± 1.6% (p = 0.021) in the middle and apical LV myocardium after HIIT, respectively. The mean single-cell migration speed was significantly (p = 0.044) greater for HCFs treated with patient serum before (2.15 ± 0.17 μm/min) than after (1.11 ± 0.12 μm/min) HIIT. Forty-three of 1222 identified proteins were significantly involved in HIIT-induced altered HCF activities. There was significant (p = 0.044) hypermethylation of the acyl-CoA dehydrogenase very long chain (ACADVL) gene with a 4.474-fold increase after HIIT, which could activate downstream caspase-mediated actin disassembly and the cell death pathway. CONCLUSIONS Human investigation has shown that HIIT is associated with reduced cardiac fibrosis in HF patients. Hypermethylation of ACADVL after HIIT may contribute to impeding HCF activities. This exercise-associated epigenetic reprogramming may contribute to reduce cardiac fibrosis and promote cardiorespiratory fitness in HF patients. TRIAL REGISTRATION NCT04038723. Registered 31 July 2019, https://clinicaltrials.gov/ct2/show/NCT04038723 .
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19
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Diaz-Canestro C, Chen J, Liu Y, Han H, Wang Y, Honoré E, Lee CH, Lam KSL, Tse MA, Xu A. A machine-learning algorithm integrating baseline serum proteomic signatures predicts exercise responsiveness in overweight males with prediabetes. Cell Rep Med 2023; 4:100944. [PMID: 36787735 PMCID: PMC9975321 DOI: 10.1016/j.xcrm.2023.100944] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 11/11/2022] [Accepted: 01/20/2023] [Indexed: 02/15/2023]
Abstract
The molecular transducers conferring the benefits of chronic exercise in diabetes prevention remain to be comprehensively investigated. Herein, serum proteomic profiling of 688 inflammatory and metabolic biomarkers in 36 medication-naive overweight and obese men with prediabetes reveals hundreds of exercise-responsive proteins modulated by 12-week high-intensity interval exercise training, including regulators of metabolism, cardiovascular system, inflammation, and apoptosis. Strong associations are found between proteins involved in gastro-intestinal mucosal immunity and metabolic outcomes. Exercise-induced changes in trefoil factor 2 (TFF2) are associated with changes in insulin resistance and fasting insulin, whereas baseline levels of the pancreatic secretory granule membrane major glycoprotein GP2 are related to changes in fasting glucose and glucose tolerance. A hybrid set of 23 proteins including TFF2 are differentially altered in exercise responders and non-responders. Furthermore, a machine-learning algorithm integrating baseline proteomic signatures accurately predicts individualized metabolic responsiveness to exercise training.
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Affiliation(s)
- Candela Diaz-Canestro
- State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China; Department of Medicine, The University of Hong Kong, Hong Kong, China
| | - Jiarui Chen
- State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China; Department of Medicine, The University of Hong Kong, Hong Kong, China
| | - Yan Liu
- State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China; Department of Medicine, The University of Hong Kong, Hong Kong, China
| | - Hao Han
- State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China; Department of Medicine, The University of Hong Kong, Hong Kong, China
| | - Yao Wang
- State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China; Department of Medicine, The University of Hong Kong, Hong Kong, China
| | - Eric Honoré
- Université Côte d'Azur, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherche Médicale, Institut de Pharmacologie Moléculaire et Cellulaire, Labex ICST, Valbonne, France
| | - Chi-Ho Lee
- State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China; Department of Medicine, The University of Hong Kong, Hong Kong, China
| | - Karen S L Lam
- State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China; Department of Medicine, The University of Hong Kong, Hong Kong, China
| | - Michael Andrew Tse
- State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China; Centre for Sports and Exercise, The University of Hong Kong, Hong Kong, China.
| | - Aimin Xu
- State Key Laboratory of Pharmaceutical Biotechnology, The University of Hong Kong, Hong Kong, China; Department of Medicine, The University of Hong Kong, Hong Kong, China; Department of Pharmacology and Pharmacy, The University of Hong Kong, Hong Kong, China.
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20
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Belkin TG, Tham YK, McMullen JR. Lipids regulated by exercise and PI3K: potential role as biomarkers and therapeutic targets for cardiovascular disease. CURRENT OPINION IN PHYSIOLOGY 2023. [DOI: 10.1016/j.cophys.2023.100633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
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21
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Coenen L, Lehallier B, de Vries HE, Middeldorp J. Markers of aging: Unsupervised integrated analyses of the human plasma proteome. FRONTIERS IN AGING 2023; 4:1112109. [PMID: 36911498 PMCID: PMC9992741 DOI: 10.3389/fragi.2023.1112109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 02/08/2023] [Indexed: 02/24/2023]
Abstract
Aging associates with an increased susceptibility for disease and decreased quality of life. To date, processes underlying aging are still not well understood, leading to limited interventions with unknown mechanisms to promote healthy aging. Previous research suggests that changes in the blood proteome are reflective of age-associated phenotypes such as frailty. Moreover, experimentally induced changes in the blood proteome composition can accelerate or decelerate underlying aging processes. The aim of this study is to identify a set of proteins in the human plasma associated with aging by integration of the data of four independent, large-scaled datasets using the aptamer-based SomaScan platform on the human aging plasma proteome. Using this approach, we identified a set of 273 plasma proteins significantly associated with aging (aging proteins, APs) across these cohorts consisting of healthy individuals and individuals with comorbidities and highlight their biological functions. We validated the age-associated effects in an independent study using a centenarian population, showing highly concordant effects. Our results suggest that APs are more associated to diseases than other plasma proteins. Plasma levels of APs can predict chronological age, and a reduced selection of 15 APs can still predict individuals' age accurately, highlighting their potential as biomarkers of aging processes. Furthermore, we show that individuals presenting accelerated or decelerated aging based on their plasma proteome, respectively have a more aged or younger systemic environment. These results provide novel insights in the understanding of the aging process and its underlying mechanisms and highlight potential modulators contributing to healthy aging.
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Affiliation(s)
- L Coenen
- Department of Neurobiology and Aging, Biomedical Primate Research Centre, Rijswijk, Netherlands.,Department of Molecular Cell Biology and Immunology, Amsterdam Neuroscience, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | | | - H E de Vries
- Department of Molecular Cell Biology and Immunology, Amsterdam Neuroscience, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - J Middeldorp
- Department of Neurobiology and Aging, Biomedical Primate Research Centre, Rijswijk, Netherlands
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22
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Castro A, Signini ÉF, De Oliveira JM, Di Medeiros Leal MCB, Rehder-Santos P, Millan-Mattos JC, Minatel V, Pantoni CBF, Oliveira RV, Catai AM, Ferreira AG. The Aging Process: A Metabolomics Perspective. Molecules 2022; 27:molecules27248656. [PMID: 36557788 PMCID: PMC9785117 DOI: 10.3390/molecules27248656] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 10/29/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
Aging process is characterized by a progressive decline of several organic, physiological, and metabolic functions whose precise mechanism remains unclear. Metabolomics allows the identification of several metabolites and may contribute to clarifying the aging-regulated metabolic pathways. We aimed to investigate aging-related serum metabolic changes using a metabolomics approach. Fasting blood serum samples from 138 apparently healthy individuals (20−70 years old, 56% men) were analyzed by Proton Nuclear Magnetic Resonance spectroscopy (1H NMR) and Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS), and for clinical markers. Associations of the metabolic profile with age were explored via Correlations (r); Metabolite Set Enrichment Analysis; Multiple Linear Regression; and Aging Metabolism Breakpoint. The age increase was positively correlated (0.212 ≤ r ≤ 0.370, p < 0.05) with the clinical markers (total cholesterol, HDL, LDL, VLDL, triacylglyceride, and glucose levels); negatively correlated (−0.285 ≤ r ≤ −0.214, p < 0.05) with tryptophan, 3-hydroxyisobutyrate, asparagine, isoleucine, leucine, and valine levels, but positively (0.237 ≤ r ≤ 0.269, p < 0.05) with aspartate and ornithine levels. These metabolites resulted in three enriched pathways: valine, leucine, and isoleucine degradation, urea cycle, and ammonia recycling. Additionally, serum metabolic levels of 3-hydroxyisobutyrate, isoleucine, aspartate, and ornithine explained 27.3% of the age variation, with the aging metabolism breakpoint occurring after the third decade of life. These results indicate that the aging process is potentially associated with reduced serum branched-chain amino acid levels (especially after the third decade of life) and progressively increased levels of serum metabolites indicative of the urea cycle.
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Affiliation(s)
- Alex Castro
- Department of Chemistry, Universidade Federal de São Carlos, São Carlos 13565-905, Brazil
- Correspondence: (A.C.); (A.G.F.)
| | - Étore F. Signini
- Department of Physiotherapy, Universidade Federal de São Carlos, São Carlos 13565-905, Brazil
| | | | | | - Patrícia Rehder-Santos
- Department of Physiotherapy, Universidade Federal de São Carlos, São Carlos 13565-905, Brazil
| | | | - Vinicius Minatel
- Department of Physiotherapy, Universidade Federal de São Carlos, São Carlos 13565-905, Brazil
| | - Camila B. F. Pantoni
- Department of Physiotherapy, Universidade Federal de São Carlos, São Carlos 13565-905, Brazil
| | - Regina V. Oliveira
- Department of Chemistry, Universidade Federal de São Carlos, São Carlos 13565-905, Brazil
| | - Aparecida M. Catai
- Department of Physiotherapy, Universidade Federal de São Carlos, São Carlos 13565-905, Brazil
| | - Antônio G. Ferreira
- Department of Chemistry, Universidade Federal de São Carlos, São Carlos 13565-905, Brazil
- Correspondence: (A.C.); (A.G.F.)
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23
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Elman RJ. Still Searching for Understanding: The Importance of Diverse Research Designs, Methods, and Perspectives. AMERICAN JOURNAL OF SPEECH-LANGUAGE PATHOLOGY 2022; 31:2444-2453. [PMID: 36001820 DOI: 10.1044/2022_ajslp-21-00348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
BACKGROUND Evidence-based medicine and evidence hierarchies have been widely adopted and have strongly influenced decision making across many fields, including clinical aphasiology. However, questions remain about the creation, usefulness, and validity of current evidence hierarchies. AIMS This article builds on ideas about scientific approaches and evidence originally shared by Elman (1995, 1998, 2006). This article reviews the history of evidence hierarchies and argues that improving the diversity of research designs, methods, and perspectives will improve understanding of the numerous and complex variables associated with aphasia intervention. Researchers and clinicians are encouraged to synthesize diverse types of scientific evidence. It is hoped that this article will stimulate thought and foster discussion in order to encourage high-caliber research of all types. MAIN CONTRIBUTION Concepts from a wide variety of fields including philosophy of science, research design and methodology, and precision medicine are brought together in an attempt to focus research on the scientific understanding of aphasia treatment effects. CONCLUSION It is hoped that by incorporating diverse research designs, methods, and perspectives, clinical aphasiologists will become better able to provide effective, personalized treatments, ensuring that each person with aphasia is able to improve their communication ability and quality of life.
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24
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Katz DH, Robbins JM, Deng S, Tahir UA, Bick AG, Pampana A, Yu Z, Ngo D, Benson MD, Chen ZZ, Cruz DE, Shen D, Gao Y, Bouchard C, Sarzynski MA, Correa A, Natarajan P, Wilson JG, Gerszten RE. Proteomic profiling platforms head to head: Leveraging genetics and clinical traits to compare aptamer- and antibody-based methods. SCIENCE ADVANCES 2022; 8:eabm5164. [PMID: 35984888 PMCID: PMC9390994 DOI: 10.1126/sciadv.abm5164] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 07/07/2022] [Indexed: 05/10/2023]
Abstract
High-throughput proteomic profiling using antibody or aptamer-based affinity reagents is used increasingly in human studies. However, direct analyses to address the relative strengths and weaknesses of these platforms are lacking. We assessed findings from the SomaScan1.3K (N = 1301 reagents), the SomaScan5K platform (N = 4979 reagents), and the Olink Explore (N = 1472 reagents) profiling techniques in 568 adults from the Jackson Heart Study and 219 participants in the HERITAGE Family Study across four performance domains: precision, accuracy, analytic breadth, and phenotypic associations leveraging detailed clinical phenotyping and genetic data. Across these studies, we show evidence supporting more reliable protein target specificity and a higher number of phenotypic associations for the Olink platform, while the Soma platforms benefit from greater measurement precision and analytic breadth across the proteome.
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Affiliation(s)
- Daniel H. Katz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Jeremy M. Robbins
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Shuliang Deng
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Usman A. Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | | | - Akhil Pampana
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Zhi Yu
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Debby Ngo
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Mark D. Benson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Zsu-Zsu Chen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Daniel E. Cruz
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Dongxiao Shen
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Yan Gao
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Claude Bouchard
- Human Genomic Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, USA
| | - Mark A. Sarzynski
- Department of Exercise Science, University of South Carolina, Columbia, SC, USA
| | - Adolfo Correa
- University of Mississippi Medical Center, Jackson, MS, USA
| | - Pradeep Natarajan
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - James G. Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Robert E. Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
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25
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Rao P, Belanger MJ, Robbins JM. Exercise, Physical Activity, and Cardiometabolic Health: Insights into the Prevention and Treatment of Cardiometabolic Diseases. Cardiol Rev 2022; 30:167-178. [PMID: 34560712 PMCID: PMC8920940 DOI: 10.1097/crd.0000000000000416] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Physical activity (PA) and exercise are widely recognized as essential components of primary and secondary cardiovascular disease (CVD) prevention efforts and are emphasized in the health promotion guidelines of numerous professional societies and committees. The protean benefits of PA and exercise extend across the spectrum of CVD, and include the improvement and reduction of risk factors and events for atherosclerotic CVD (ASCVD), cardiometabolic disease, heart failure, and atrial fibrillation (AF), respectively. Here, we highlight recent insights into the salutary effects of PA and exercise on the primary and secondary prevention of ASCVD, including their beneficial effects on both traditional and nontraditional risk mediators; exercise "prescriptions" for ASCVD; the role of PA regular exercise in the prevention and treatment of heart failure; and the relationships between, PA, exercise, and AF. While our understanding of the relationship between exercise and CVD has evolved considerably, several key questions remain including the association between extreme volumes of exercise and subclinical ASCVD and its risk; high-intensity exercise and resistance (strength) training as complementary modalities to continuous aerobic exercise; and dose- and intensity-dependent associations between exercise and AF. Recent advances in molecular profiling technologies (ie, genomics, transcriptomics, proteomics, and metabolomics) have begun to shed light on interindividual variation in cardiometabolic responses to PA and exercise and may provide new opportunities for clinical prediction in addition to mechanistic insights.
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Affiliation(s)
- Prashant Rao
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
- Cardiovascular Research Center, Beth Israel Deaconess Medical Center, Boston, MA
| | | | - Jeremy M. Robbins
- Cardiovascular Research Center, Beth Israel Deaconess Medical Center, Boston, MA
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
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26
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Mitochondrial mutations alter endurance exercise response and determinants in mice. Proc Natl Acad Sci U S A 2022; 119:e2200549119. [PMID: 35482926 PMCID: PMC9170171 DOI: 10.1073/pnas.2200549119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Primary mitochondrial diseases (PMDs) are the most prevalent inborn metabolic disorders, affecting an estimated 1 in 4,200 individuals. Endurance exercise is generally known to improve mitochondrial function, but its indication in the heterogeneous group of PMDs is unclear. We determined the relationship between mitochondrial mutations, endurance exercise response, and the underlying molecular pathways in mice with distinct mitochondrial mutations. This revealed that mitochondria are crucial regulators of exercise capacity and exercise response. Endurance exercise proved to be mostly beneficial across the different mitochondrial mutant mice with the exception of a worsened dilated cardiomyopathy in ANT1-deficient mice. Thus, therapeutic exercises, especially in patients with PMDs, should take into account the physical and mitochondrial genetic status of the patient. Primary mitochondrial diseases (PMDs) are a heterogeneous group of metabolic disorders that can be caused by hundreds of mutations in both mitochondrial DNA (mtDNA) and nuclear DNA (nDNA) genes. Current therapeutic approaches are limited, although one approach has been exercise training. Endurance exercise is known to improve mitochondrial function in heathy subjects and reduce risk for secondary metabolic disorders such as diabetes or neurodegenerative disorders. However, in PMDs the benefit of endurance exercise is unclear, and exercise might be beneficial for some mitochondrial disorders but contraindicated in others. Here we investigate the effect of an endurance exercise regimen in mouse models for PMDs harboring distinct mitochondrial mutations. We show that while an mtDNA ND6 mutation in complex I demonstrated improvement in response to exercise, mice with a CO1 mutation affecting complex IV showed significantly fewer positive effects, and mice with an ND5 complex I mutation did not respond to exercise at all. For mice deficient in the nDNA adenine nucleotide translocase 1 (Ant1), endurance exercise actually worsened the dilated cardiomyopathy. Correlating the gene expression profile of skeletal muscle and heart with the physiologic exercise response identified oxidative phosphorylation, amino acid metabolism, matrisome (extracellular matrix [ECM]) structure, and cell cycle regulation as key pathways in the exercise response. This emphasizes the crucial role of mitochondria in determining the exercise capacity and exercise response. Consequently, the benefit of endurance exercise in PMDs strongly depends on the underlying mutation, although our results suggest a general beneficial effect.
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27
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SARZYNSKI MARKA, RICE TREVAK, DESPRÉS JEANPIERRE, PÉRUSSE LOUIS, TREMBLAY ANGELO, STANFORTH PHILIPR, TCHERNOF ANDRÉ, BARBER JACOBL, FALCIANI FRANCESCO, CLISH CLARY, ROBBINS JEREMYM, GHOSH SUJOY, GERSZTEN ROBERTE, LEON ARTHURS, SKINNER JAMESS, RAO DC, BOUCHARD CLAUDE. The HERITAGE Family Study: A Review of the Effects of Exercise Training on Cardiometabolic Health, with Insights into Molecular Transducers. Med Sci Sports Exerc 2022; 54:S1-S43. [PMID: 35611651 PMCID: PMC9012529 DOI: 10.1249/mss.0000000000002859] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The aim of the HERITAGE Family Study was to investigate individual differences in response to a standardized endurance exercise program, the role of familial aggregation, and the genetics of response levels of cardiorespiratory fitness and cardiovascular disease and diabetes risk factors. Here we summarize the findings and their potential implications for cardiometabolic health and cardiorespiratory fitness. It begins with overviews of background and planning, recruitment, testing and exercise program protocol, quality control measures, and other relevant organizational issues. A summary of findings is then provided on cardiorespiratory fitness, exercise hemodynamics, insulin and glucose metabolism, lipid and lipoprotein profiles, adiposity and abdominal visceral fat, blood levels of steroids and other hormones, markers of oxidative stress, skeletal muscle morphology and metabolic indicators, and resting metabolic rate. These summaries document the extent of the individual differences in response to a standardized and fully monitored endurance exercise program and document the importance of familial aggregation and heritability level for exercise response traits. Findings from genomic markers, muscle gene expression studies, and proteomic and metabolomics explorations are reviewed, along with lessons learned from a bioinformatics-driven analysis pipeline. The new opportunities being pursued in integrative -omics and physiology have extended considerably the expected life of HERITAGE and are being discussed in relation to the original conceptual model of the study.
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Affiliation(s)
- MARK A. SARZYNSKI
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC
| | - TREVA K. RICE
- Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO
| | - JEAN-PIERRE DESPRÉS
- Department of Kinesiology, Faculty of Medicine, Laval University, Quebec, QC, CANADA
- Quebec Heart and Lung Institute Research Center, Laval University, Québec, QC, CANADA
| | - LOUIS PÉRUSSE
- Department of Kinesiology, Faculty of Medicine, Laval University, Quebec, QC, CANADA
- Institute of Nutrition and Functional Foods (INAF), Laval University, Quebec, QC, CANADA
| | - ANGELO TREMBLAY
- Department of Kinesiology, Faculty of Medicine, Laval University, Quebec, QC, CANADA
- Institute of Nutrition and Functional Foods (INAF), Laval University, Quebec, QC, CANADA
| | - PHILIP R. STANFORTH
- Department of Kinesiology and Health Education, University of Texas at Austin, Austin, TX
| | - ANDRÉ TCHERNOF
- Quebec Heart and Lung Institute Research Center, Laval University, Québec, QC, CANADA
- School of Nutrition, Laval University, Quebec, QC, CANADA
| | - JACOB L. BARBER
- Department of Exercise Science, Arnold School of Public Health, University of South Carolina, Columbia, SC
| | - FRANCESCO FALCIANI
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UNITED KINGDOM
| | - CLARY CLISH
- Metabolomics Platform, Broad Institute and Harvard Medical School, Boston, MA
| | - JEREMY M. ROBBINS
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
- Cardiovascular Research Center, Beth Israel Deaconess Medical Center, Boston, MA
| | - SUJOY GHOSH
- Cardiovascular and Metabolic Disorders Program and Centre for Computational Biology, Duke-National University of Singapore Medical School, SINGAPORE
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA
| | - ROBERT E. GERSZTEN
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA
- Cardiovascular Research Center, Beth Israel Deaconess Medical Center, Boston, MA
| | - ARTHUR S. LEON
- School of Kinesiology, University of Minnesota, Minneapolis, MN
| | | | - D. C. RAO
- Division of Biostatistics, Washington University in St. Louis School of Medicine, St. Louis, MO
| | - CLAUDE BOUCHARD
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA
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28
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Ho JE, Robbins JM. Exercise Training Across the Spectrum of HFpEF: Time for Tailoring or One Size Fits All? JACC. HEART FAILURE 2022; 10:250-253. [PMID: 35361443 DOI: 10.1016/j.jchf.2022.02.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 02/16/2022] [Indexed: 06/14/2023]
Affiliation(s)
- Jennifer E Ho
- Cardiovascular Institute and Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA.
| | - Jeremy M Robbins
- Cardiovascular Institute and Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
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29
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Long JZ. Molecular transducers and the cardiometabolic benefits of exercise. Nat Rev Endocrinol 2022; 18:77-78. [PMID: 34880406 PMCID: PMC8766953 DOI: 10.1038/s41574-021-00609-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Physical activity stimulates tissue crosstalk and provides powerful protection against cardiometabolic disease. This past year, several studies have expanded our knowledge of the secreted molecules regulated by physical activity, uncovered new circuits of cell and tissue crosstalk and provided fundamental insights into the mechanisms that underlie the cardiometabolic benefits of exercise.
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Affiliation(s)
- Jonathan Z Long
- Department of Pathology, Stanford University School of Medicine and Stanford ChEM-H, Stanford University, Stanford, CA, USA.
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30
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Nieman DC. Multiomics Approach to Precision Sports Nutrition: Limits, Challenges, and Possibilities. Front Nutr 2022; 8:796360. [PMID: 34970584 PMCID: PMC8712338 DOI: 10.3389/fnut.2021.796360] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 11/24/2021] [Indexed: 12/15/2022] Open
Abstract
Most sports nutrition guidelines are based on group average responses and professional opinion. Precision nutrition for athletes aims to improve the individualization of nutrition practices to optimize long-term performance and health. This is a 2-step process that first involves the acquisition of individual-specific, science-based information using a variety of sources including lifestyle and medical histories, dietary assessment, physiological assessments from the performance lab and wearable sensors, and multiomics data from blood, urine, saliva, and stool samples. The second step consists of the delivery of science-based nutrition advice, behavior change support, and the monitoring of health and performance efficacy and benefits relative to cost. Individuals vary widely in the way they respond to exercise and nutritional interventions, and understanding why this metabolic heterogeneity exists is critical for further advances in precision nutrition. Another major challenge is the development of evidence-based individualized nutrition recommendations that are embraced and efficacious for athletes seeking the most effective enhancement of performance, metabolic recovery, and health. At this time precision sports nutrition is an emerging discipline that will require continued technological and scientific advances before this approach becomes accurate and practical for athletes and fitness enthusiasts at the small group or individual level. The costs and scientific challenges appear formidable, but what is already being achieved today in precision nutrition through multiomics and sensor technology seemed impossible just two decades ago.
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Affiliation(s)
- David C Nieman
- North Carolina Research Campus, Human Performance Laboratory, Department of Biology, Appalachian State University, Boone, NC, United States
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31
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Castro A, Duft RG, de Oliveira-Nunes SG, de Andrade ALL, Cavaglieri CR, Chacon-Mikahil MPT. Association Between Changes in Serum and Skeletal Muscle Metabolomics Profile With Maximum Power Output Gains in Response to Different Aerobic Training Programs: The Times Study. Front Physiol 2021; 12:756618. [PMID: 34744794 PMCID: PMC8563999 DOI: 10.3389/fphys.2021.756618] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 09/23/2021] [Indexed: 01/13/2023] Open
Abstract
Purpose: High heterogeneity of the response of cardiorespiratory fitness (CRF) to standardized exercise doses has been reported in different training programs, but the associated mechanisms are not widely known. This study investigated whether changes in the metabolic profile and pathways in blood serum and the skeletal muscle are associated with the inter-individual variability of CRF responses to 8-wk of continuous endurance training (ET) or high-intensity interval training (HIIT). Methods: Eighty men, young and sedentary, were randomized into three groups, of which 70 completed 8 wk of intervention (> 90% of sessions): ET, HIIT, or control. Blood and vastus lateralis muscle tissue samples, as well as the measurement of CRF [maximal power output (MPO)] were obtained before and after the intervention. Blood serum and skeletal muscle samples were analyzed by 600 MHz 1H-NMR spectroscopy (metabolomics). Associations between the pretraining to post-training changes in the metabolic profile and MPO gains were explored via three analytical approaches: (1) correlation between pretraining to post-training changes in metabolites' concentration levels and MPO gains; (2) significant differences between low and high MPO responders; and (3) metabolite contribution to significantly altered pathways related to MPO gains. After, metabolites within these three levels of evidence were analyzed by multiple stepwise linear regression. The significance level was set at 1%. Results: The metabolomics profile panel yielded 43 serum and 70 muscle metabolites. From the metabolites within the three levels of evidence (15 serum and 4 muscle metabolites for ET; 5 serum and 1 muscle metabolites for HIIT), the variance in MPO gains was explained: 77.4% by the intervention effects, 6.9, 2.3, 3.2, and 2.2% by changes in skeletal muscle pyruvate and valine, serum glutamine and creatine phosphate, respectively, in ET; and 80.9% by the intervention effects; 7.2, 2.2, and 1.2% by changes in skeletal muscle glycolate, serum creatine and creatine phosphate, respectively, in HIIT. The most changed and impacted pathways by these metabolites were: arginine and proline metabolism, glycine, serine and threonine metabolism, and glyoxylate and dicarboxylate metabolism for both ET and HIIT programs; and additional alanine, aspartate and glutamate metabolism, arginine biosynthesis, glycolysis/gluconeogenesis, and pyruvate metabolism for ET. Conclusion: These results suggest that regulating the metabolism of amino acids and carbohydrates may be a potential mechanism for understanding the inter-individual variability of CRF in responses to ET and HIIT programs.
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Affiliation(s)
- Alex Castro
- Laboratory of Exercise Physiology, School of Physical Education, University of Campinas (UNICAMP), São Paulo, Brazil.,Nuclear Magnetic Resonance Laboratory, Department of Chemistry, Federal University of São Carlos (UFSCar), São Paulo, Brazil
| | - Renata G Duft
- Laboratory of Exercise Physiology, School of Physical Education, University of Campinas (UNICAMP), São Paulo, Brazil
| | | | | | - Claudia R Cavaglieri
- Laboratory of Exercise Physiology, School of Physical Education, University of Campinas (UNICAMP), São Paulo, Brazil
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