Metabolome-wide association study on physical activity.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
09 02 2023
09 02 2023
Historique:
received:
13
06
2022
accepted:
14
12
2022
entrez:
9
2
2023
pubmed:
10
2
2023
medline:
14
2
2023
Statut:
epublish
Résumé
The underlying mechanisms linking physical activity to better health are not fully understood. Here we examined the associations between physical activity and small circulatory molecules, the metabolome, to highlight relevant biological pathways. We examined plasma metabolites associated with self-reported physical activity among 2217 participants from the Airwave Health Monitoring Study. Metabolic profiling was conducted using the mass spectrometry-based Metabolon platform (LC/GC-MS), measuring 828 known metabolites. We replicated our findings in an independent subset of the study (n = 2971) using untargeted LC-MS. Mendelian randomisation was carried out to investigate potential causal associations between physical activity, body mass index, and metabolites. Higher vigorous physical activity was associated (P < 0.05/828 = 6.03 × 10
Identifiants
pubmed: 36759570
doi: 10.1038/s41598-022-26377-7
pii: 10.1038/s41598-022-26377-7
pmc: PMC9911764
doi:
Substances chimiques
Fatty Acids
0
Lactates
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2374Subventions
Organisme : Medical Research Council
ID : MR/R023484/1
Pays : United Kingdom
Informations de copyright
© 2023. The Author(s).
Références
Erik, D. X. et al. Physical activity and performance impact long-term quality of life in older adults at risk for major mobility disability. Am. J. Prev. Med. (Elsevier Inc.) 56(1), 141–146 (2019).
doi: 10.1016/j.amepre.2018.09.006
Patti, G. J., Yanes, O. & Siuzdak, G. Innovation: Metabolomics: The apogee of the omics trilogy. Nat. Publ. Gr. (Nature Publishing Group) 13(4), 263–269 (2012).
Jacob, M., Lopata, A. L., Dasouki, M. & Abdel Rahman, A. M. Metabolomics toward personalized medicine. Mass Spectrom. Rev. 38(3), 221–238 (2019).
doi: 10.1002/mas.21548
pubmed: 29073341
Johnson, C. H., Ivanisevic, J. & Siuzdak, G. Metabolomics: Beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 17, 451–459 (2016).
doi: 10.1038/nrm.2016.25
pubmed: 26979502
pmcid: 5729912
Brennan, A. M. et al. Plasma metabolite profiles in response to chronic exercise. Med. Sci. Sports Exerc. 50(7), 1480–1486 (2018).
doi: 10.1249/MSS.0000000000001594
pubmed: 29509640
pmcid: 6027642
Fukai, K. et al. Metabolic profiling of total physical activity and sedentary behavior in community-dwelling men. PLoS ONE 11(10), 1–14 (2016).
doi: 10.1371/journal.pone.0164877
Xiao, Q. et al. Objectively measured physical activity and plasma metabolomics in the Shanghai Physical Activity Study. Int. J. Epidemiol. 45(5), 1433–1444 (2016).
doi: 10.1093/ije/dyw033
pubmed: 27073263
pmcid: 5100606
Ding, M. et al. Metabolome-wide association study of the relationship between habitual physical activity and plasma metabolite levels. Am. J. Epidemiol. 188(11), 1932–1943 (2019).
doi: 10.1093/aje/kwz171
pubmed: 31364705
pmcid: 6825824
Smith, G. D. & Hemani, G. Mendelian randomization : Genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 23(1), 89–98 (2014).
doi: 10.1093/hmg/ddu328
Elliott, P. et al. The Airwave Health Monitoring Study of police of fi cers and staff in Great Britain : Rationale, design and methods. Environ Res. (Elsevier) 134, 280–285 (2014).
doi: 10.1016/j.envres.2014.07.025
Evans, A. M., Dehaven, C. D., Barrett, T., Mitchell, M. & Milgram, E. Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal. Chem. 81(16), 6656–6667 (2009).
doi: 10.1021/ac901536h
pubmed: 19624122
Lewis, M. R. et al. Development and application of ultra-performance liquid chromatography-TOF MS for precision large scale urinary metabolic phenotyping. Anal. Chem. 88(18), 9004–9013 (2016).
doi: 10.1021/acs.analchem.6b01481
pubmed: 27479709
Sarafian, M. H. et al. Objective set of criteria for optimization of sample preparation procedures for ultra-high throughput untargeted blood plasma lipid profiling by ultra performance liquid chromatography–mass spectrometry. Anal. Chem. 86(12), 5766–5774 (2014).
doi: 10.1021/ac500317c
pubmed: 24820162
Bauman, A. E. et al. International Physical Activity Questionnaire : 12-country reliability and validity. Med. Sci. Sport Exerc. 35(8), 1381–1395 (2000).
Ainsworth, B. E. et al. Compendium of physical activities: An update of activity codes and MET intensities. Med. Sci. Sport Exerc. 32(12), 498–504 (2000).
doi: 10.1097/00005768-200009001-00009
Xia, J., Sinelnikov, I. V., Han, B. & Wishart, D. S. MetaboAnalyst 3.0-making metabolomics more meaningful. Nucleic Acids Res. 43(W1), W251–W257 (2015).
doi: 10.1093/nar/gkv380
pubmed: 25897128
pmcid: 4489235
Minelli, C. et al. The use of two-sample methods for Mendelian randomization analyses on single large datasets. Int. J. Epidemiol. 50(5), 1651–1659 (2021).
doi: 10.1093/ije/dyab084
pubmed: 33899104
pmcid: 8580269
Klimentidis, Y. C. et al. Genome-wide association study of habitual physical activity in over 377,000 UK Biobank participants identifies multiple variants including CADM2 and APOE. Int. J. Obes. 42(6), 1161–1176 (2018).
doi: 10.1038/s41366-018-0120-3
Roshchupkin, G. V., Adams, H. H. H., Vernooij, M. W., Hofman, A. & Van Duijn, C. M. OPEN HASE : Framework for efficient high-dimensional association analyses. Nat. Publ. Gr. (Nature Publishing Group) 6, 1–8 (2016).
Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼ 700 000 individuals of European ancestry. Hum. Mol. Genet. 27(20), 3641–3649 (2018).
doi: 10.1093/hmg/ddy271
pubmed: 30124842
pmcid: 6488973
Shin, S. et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 46(6), 543–550 (2014).
doi: 10.1038/ng.2982
pubmed: 24816252
pmcid: 4064254
Bowden, J., Smith, G. D., Haycock, P. C. & Burgess, S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator genetic epidemiology. Genet. Epidemiol. 40(4), 304–314 (2016).
doi: 10.1002/gepi.21965
pubmed: 27061298
pmcid: 4849733
Bowden, J., Greco, D., Minelli, C., Smith, G. D. & Thompson, J. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat. Med. 36(11), 1783–1802 (2017).
doi: 10.1002/sim.7221
pubmed: 28114746
pmcid: 5434863
Bowden, J., Smith, G. D. & Burgess, S. Mendelian randomization methodology Mendelian randomization with invalid instruments: Effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44(2), 512–525 (2015).
doi: 10.1093/ije/dyv080
pubmed: 26050253
pmcid: 4469799
Devlin, J. T., Brodsky, I., Scrimgeour, A., Fuller, S. & Bier, D. M. Amino acid metabolism after intense exercise. Am. J. Physiol. Endocrinol. Metab. 258(2), 249–255 (1990).
doi: 10.1152/ajpendo.1990.258.2.E249
Greenfield, J. R. et al. Oral glutamine increases circulating glucagon-like peptide 1, glucagon, and insulin concentrations in lean, obese, and type 2 diabetic subjects 1–4. Am. J. Clin. Nutr. 1, 106–113 (2009).
doi: 10.3945/ajcn.2008.26362
Zheng, Y. et al. Metabolites of glutamate metabolism are associated with incident cardiovascular events in the PREDIMED PREvención con DIeta MEDiterránea (PREDIMED) trial. J. Am. Heart Assoc. 5(9), 003755 (2016).
doi: 10.1161/JAHA.116.003755
Morgan, M. Y., Marshall, A. W., Milsom, J. P. & Sherlock, S. Plasma amino-acid patterns in liver disease. Gut 23(5), 362–370 (1982).
doi: 10.1136/gut.23.5.362
pubmed: 7076013
pmcid: 1419690
Eck, H. P., Drings, P. & Dröge, W. Plasma glutamate levels, lymphocyte reactivity and death rate in patients with bronchial carcinoma. J. Cancer Res. Clin. Oncol. 115(6), 571–574 (1989).
doi: 10.1007/BF00391360
pubmed: 2558118
Dröge, W., Eck, H. P., Betzler, M. & Näher, H. Elevated plasma glutamate levels in colorectal carcinoma patients and in patients with acquired immunodeficiency syndrome (AIDS). Immunobiology 174(4–5), 473–479 (1987).
doi: 10.1016/S0171-2985(87)80019-0
pubmed: 3679279
Dröge, W. et al. Plasma glutamate concentration and lymphocyte activity. J. Cancer Res. Clin. Oncol. 114(2), 124–128 (1988).
doi: 10.1007/BF00417824
pubmed: 2895110
Andreadou, E. et al. Plasma glutamate and glycine levels in patients with amyotrophic lateral sclerosis: The effect of riluzole treatment. Clin. Neurol. Neurosurg. 110(3), 222–226 (2008).
doi: 10.1016/j.clineuro.2007.10.018
pubmed: 18055102
Iwasaki, Y., Ikeda, K., Shiojima, T. & Kinoshita, M. Increased plasma concentrations of aspartate, glutamate and glycine in Parkinson’s disease. Neurosci. Lett. 145(2), 175–177 (1992).
doi: 10.1016/0304-3940(92)90015-Y
pubmed: 1361223
Paraskevas, G. P. et al. Add-on lamotrigine treatment and plasma glutamate levels in epilepsy: Relation to treatment response. Epilepsy Res. 70(2–3), 184–189 (2006).
doi: 10.1016/j.eplepsyres.2006.05.004
pubmed: 16762531
Aldred, S., Moore, K. M., Fitzgerald, M. & Waring, R. H. Plasma amino acid levels in children with autism and their families. J. Autism Dev. Disord. 33(1), 93–97 (2003).
doi: 10.1023/A:1022238706604
pubmed: 12708584
Alam, Z., Coombes, N. H., Waring, R., Williams, A. C. & Steventon, G. B. Plasma levels of neuroexcitatory amino acids in patients with migraine or tension headache. J. Neurol. Sci. 156(1), 102–106 (1998).
doi: 10.1016/S0022-510X(98)00023-9
pubmed: 9559996
Mitani, H. et al. Correlation between plasma levels of glutamate, alanine and serine with severity of depression. Prog. Neuro-Psychopharmacol. Biol. Psychiatry 30(6), 1155–1158 (2006).
doi: 10.1016/j.pnpbp.2006.03.036
Bergman, B. C., Hunerdosse, D. M., Kerege, A., Playdon, M. C. & Perreault, L. Localisation and composition of skeletal muscle diacylglycerol predicts insulin resistance in humans. Diabetologia 55(4), 1140–1150 (2013).
doi: 10.1007/s00125-011-2419-7
Dubé, J. J. et al. Effects of weight loss and exercise on insulin resistance, and intramyocellular triacylglycerol, diacylglycerol and ceramide. Diabetologia 54(5), 1147–1156 (2013).
doi: 10.1007/s00125-011-2065-0
Pavey, T. G., Peeters, G., Bauman, A. E. & Brown, W. J. Does vigorous physical activity provide additional benefits beyond those of moderate?. Med. Sci. Sports Exerc. 45(10), 1948–1955 (2013).
doi: 10.1249/MSS.0b013e3182940b91
pubmed: 23542895
Swain, D. P. & Franklin, B. A. Comparison of cardioprotective benefits of vigorous versus moderate intensity aerobic exercise. Am. J. Cardiol. 97(1), 141–147 (2006).
doi: 10.1016/j.amjcard.2005.07.130
pubmed: 16377300
Rankin, A. J., Rankin, A. C., Macintyre, P. & Hillis, W. S. Walk or run? Is high-intensity exercise more effective than moderate-intensity exercise at reducing cardiovascular risk?. Scott Med. J. 57(2), 99–102 (2012).
doi: 10.1258/smj.2011.011284
pubmed: 22194404
Erion, D. M. & Shulman, G. I. Diacylglycerol-mediated insulin resistance. Nat. Med. 16(4), 400–402 (2013).
doi: 10.1038/nm0410-400
Henriksen, E. J., Diamond-Stanic, M. K. & Marchionne, E. M. Oxidative stress and the etiology of insulin resistance and type 2 diabetes. Free Radic. Biol. Med. (Internet, Elsevier Inc.) 51(5), 993–999. (2011).
doi: 10.1016/j.freeradbiomed.2010.12.005
Roa Engel, C. A., Straathof, A. J. J., Zijlmans, T. W., Van, G. W. M. & Van Der, W. L. A. M. Fumaric acid production by fermentation. Appl. Microbiol. Biotechnol. 78(3), 379–389 (2008).
doi: 10.1007/s00253-007-1341-x
pubmed: 18214471
pmcid: 2243254
Kainulainen, H., Hulmi, J. J. & Kujala, U. M. Potential role of branched-chain amino acid catabolism in regulating fat oxidation. Exerc. Sport Sci. Rev. 41(4), 194–200 (2013).
doi: 10.1097/JES.0b013e3182a4e6b6
pubmed: 23873132
Kozey, S. L., Lyden, K., Howe, C. A., Staudenmayer, J. W. & Freedson, P. S. Accelerometer output and MET values of common physical activities. Med. Sci. Sports Exerc. 42(9), 1776–1784 (2010).
doi: 10.1249/MSS.0b013e3181d479f2
pubmed: 20142781
pmcid: 2924952