Physical activity, sedentary behavior and risk of coronary artery disease, myocardial infarction and ischemic stroke: a two-sample Mendelian randomization study.
Accelerometry
Adult
Aged
Brain Ischemia
/ epidemiology
Coronary Artery Disease
/ epidemiology
Exercise
/ genetics
Female
Genome-Wide Association Study
Humans
Ischemic Stroke
/ epidemiology
Male
Mendelian Randomization Analysis
Middle Aged
Myocardial Infarction
/ epidemiology
Polymorphism, Single Nucleotide
Risk Factors
Sedentary Behavior
2 sample MR
Coronary Artery Disease
Myocardial infarction
Physical activity
Journal
Clinical research in cardiology : official journal of the German Cardiac Society
ISSN: 1861-0692
Titre abrégé: Clin Res Cardiol
Pays: Germany
ID NLM: 101264123
Informations de publication
Date de publication:
Oct 2021
Oct 2021
Historique:
received:
11
08
2020
accepted:
15
03
2021
pubmed:
29
3
2021
medline:
28
1
2022
entrez:
28
3
2021
Statut:
ppublish
Résumé
Observational evidence suggests that physical activity (PA) is inversely and sedentarism positively related with cardiovascular disease risk. We performed a two-sample Mendelian randomization (MR) analysis to examine whether genetically predicted PA and sedentary behavior are related to coronary artery disease, myocardial infarction, and ischemic stroke. We used single nucleotide polymorphisms (SNPs) associated with self-reported moderate to vigorous PA (n = 17), accelerometer based PA (n = 7) and accelerometer fraction of accelerations > 425 milli-gravities (n = 7) as well as sedentary behavior (n = 6) in the UK Biobank as instrumental variables in a two sample MR approach to assess whether these exposures are related to coronary artery disease and myocardial infarction in the CARDIoGRAMplusC4D genome-wide association study (GWAS) or ischemic stroke in the MEGASTROKE GWAS. The study population included 42,096 cases of coronary artery disease (99,121 controls), 27,509 cases of myocardial infarction (99,121 controls), and 34,217 cases of ischemic stroke (404,630 controls). We found no associations between genetically predicted self-reported moderate to vigorous PA, accelerometer-based PA or accelerometer fraction of accelerations > 425 milli-gravities as well as sedentary behavior with coronary artery disease, myocardial infarction, and ischemic stroke. These results do not support a causal relationship between PA and sedentary behavior with risk of coronary artery disease, myocardial infarction, and ischemic stroke. Hence, previous observational studies may have been biased.
Identifiants
pubmed: 33774696
doi: 10.1007/s00392-021-01846-7
pii: 10.1007/s00392-021-01846-7
pmc: PMC8484185
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1564-1573Informations de copyright
© 2021. The Author(s).
Références
Benjamin EJ et al (2018) Heart disease and stroke statistics-2018 update: a report from the American Heart Association. Circulation 137(12):e67–e492
doi: 10.1161/CIR.0000000000000558
pubmed: 29386200
Atlas Writing G et al (2018) European society of cardiology: cardiovascular disease statistics 2017. Eur Heart J 39(7):508–579
doi: 10.1093/eurheartj/ehx628
Wang H et al (2016) Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet 388(10053):1459–1544
doi: 10.1016/S0140-6736(16)31012-1
Physical Activity Guidelines Advisory Committee, Physical activity guidelines advisory committee scientific report, in Washington, DC: US Department of Health and Human Services (2018)
Kraus WE et al (2019) Physical activity, all-cause and cardiovascular mortality, and cardiovascular disease. Med Sci Sports Exerc 51(6):1270–1281
pubmed: 31095084
pmcid: 6527136
doi: 10.1249/MSS.0000000000001939
Cheng W et al (2018) Associations of leisure-time physical activity with cardiovascular mortality: a systematic review and meta-analysis of 44 prospective cohort studies. Eur J Prev Cardiol 25(17):1864–1872
pubmed: 30157685
doi: 10.1177/2047487318795194
Wahid A et al (2016) Quantifying the association between physical activity and cardiovascular disease and diabetes: a systematic review and meta-analysis. J Am Heart Assoc 5:9
doi: 10.1161/JAHA.115.002495
Howard VJ, McDonnell MN (2015) Physical activity in primary stroke prevention. Stroke 46(6):1735–1739
pubmed: 25882053
doi: 10.1161/STROKEAHA.115.006317
Ding D et al (2019) Towards better evidence-informed global action: lessons learnt from the Lancet series and recent developments in physical activity and public health. Br J Sports Med 2019:bjsports-2019-101001
Powell KE et al (2015) The scientific foundation for the physical activity guidelines for Americans, 2nd edition. J Phys Act Health 16(1):1
doi: 10.1123/jpah.2018-0618
Warren JM et al (2010) Assessment of physical activity—a review of methodologies with reference to epidemiological research: a report of the exercise physiology section of the European Association of Cardiovascular Prevention and Rehabilitation. Eur J Cardiovasc Prev Rehabil 17(2):127–139
pubmed: 20215971
doi: 10.1097/HJR.0b013e32832ed875
Prince SA et al (2008) A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int J Behav Nutr Phys Act 5:56
pubmed: 18990237
pmcid: 2588639
doi: 10.1186/1479-5868-5-56
Diaz KM et al (2017) Patterns of sedentary behavior and mortality in US middle-aged and older adults: a national cohort study. Ann Intern Med 2(017):5
Dohrn I-M et al (2018) Accelerometer-measured sedentary time and physical activity—a 15 year follow-up of mortality in a Swedish population-based cohort. J Sci Med Sport 21(7):702–707
pubmed: 29128418
doi: 10.1016/j.jsams.2017.10.035
LaMonte MJ et al (2018) Accelerometer-measured physical activity and mortality in women aged 63 to 99. J Am Geriatr Soc 66(5):886–894
pubmed: 29143320
doi: 10.1111/jgs.15201
Lee IM et al (2017) Accelerometer-measured physical activity and sedentary behavior in relation to all-cause mortality: the Women’s Health study. Circulation 2017:5
Matthews CE et al (2016) Accelerometer-measured dose-response for physical activity, sedentary time, and mortality in US adults. Am J Clin Nutr 104(5):1424–1432
pubmed: 27707702
pmcid: 5081718
doi: 10.3945/ajcn.116.135129
Evenson KR, Wen F, Herring AH (2016) Associations of accelerometry-assessed and self-reported physical activity and sedentary behavior with all-cause and cardiovascular mortality among US adults. Am J Epidemiol 184(9):621–632
pubmed: 27760774
pmcid: 5100839
doi: 10.1093/aje/kww070
Smith GD (2006) Randomised by (your) god: robust inference from an observational study design. J Epidemiol Community Health 60(5):382–388
pubmed: 16614326
pmcid: 2563965
doi: 10.1136/jech.2004.031880
Hingorani A, Humphries S (2005) Nature’s randomised trials. The Lancet 366(9501):1906–1908
doi: 10.1016/S0140-6736(05)67767-7
Burgess S, Foley CN, Zuber V (2018) Inferring causal relationships between risk factors and outcomes from genome-wide association study data. Annu Rev Genomics Hum Genet 19:303–327
pubmed: 29709202
pmcid: 6481551
doi: 10.1146/annurev-genom-083117-021731
Doherty A et al (2018) GWAS identifies 14 loci for device-measured physical activity and sleep duration. Nat Commun 9(1):5257
pubmed: 30531941
pmcid: 6288145
doi: 10.1038/s41467-018-07743-4
Klimentidis YC et al (2018) 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 (Lond) 42(6):1161–1176
doi: 10.1038/s41366-018-0120-3
Fry A et al (2017) Comparison of sociodemographic and health-related characteristics of UK biobank participants with those of the general population. Am J Epidemiol 186(9):1026–1034
pubmed: 28641372
pmcid: 5860371
doi: 10.1093/aje/kwx246
Guo W, Key TJ, Reeves GK (2019) Accelerometer compared with questionnaire measures of physical activity in relation to body size and composition: a large cross-sectional analysis of UK Biobank. BMJ Open 9(1):e024206
pubmed: 30700478
pmcid: 6352868
doi: 10.1136/bmjopen-2018-024206
Doherty A et al (2017) Large scale population assessment of physical activity using wrist worn accelerometers: the UK biobank study. PLoS ONE 12(2):e0169649
pubmed: 28146576
pmcid: 5287488
doi: 10.1371/journal.pone.0169649
Purcell S et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3):559–575
pubmed: 17701901
pmcid: 1950838
doi: 10.1086/519795
Nikpay M et al (2015) A comprehensive 1,000 Genomes-based genome-wide association meta-analysis of coronary artery disease. Nat Genet 47(10):1121–1130
pubmed: 26343387
pmcid: 4589895
doi: 10.1038/ng.3396
Malik R et al (2018) Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet 50(4):524–537
pubmed: 29531354
pmcid: 5968830
doi: 10.1038/s41588-018-0058-3
Deloukas P et al (2013) Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet 45(1):25–33
pubmed: 23202125
doi: 10.1038/ng.2480
Burgess S (2014) Sample size and power calculations in Mendelian randomization with a single instrumental variable and a binary outcome. Int J Epidemiol 43(3):922–929
pubmed: 24608958
pmcid: 4052137
doi: 10.1093/ije/dyu005
Burgess S et al (2019) Guidelines for performing Mendelian randomization investigations. Wellcome Open Res 4(186):186
pubmed: 32760811
doi: 10.12688/wellcomeopenres.15555.1
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol) 57(1):289–300
Hemani G et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. Elife 2018:7
Team RC (2013) R: a language and environment for statistical computing
Smith GD et al (2019) STROBE-MR: guidelines for strengthening the reporting of Mendelian randomization studies. PeerJ Preprints
Bowden J et al (2017) A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med 36(11):1783–1802
pubmed: 28114746
pmcid: 5434863
doi: 10.1002/sim.7221
Hemani G, Bowden J, Davey-Smith G (2018) Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet 27(R2):R195-r208
pubmed: 29771313
pmcid: 6061876
doi: 10.1093/hmg/ddy163
Burgess S, Thompson SG (2011) Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol 40(3):755–764
pubmed: 21414999
doi: 10.1093/ije/dyr036
Kamat MA et al (2019) PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinformatics 2019:5
Buniello A et al (2019) The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res 47(D1):D1005-d1012
pubmed: 30445434
doi: 10.1093/nar/gky1120
Watanabe K et al (2019) A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet 51(9):1339–1348
pubmed: 31427789
doi: 10.1038/s41588-019-0481-0
Dogra S et al (2019) Effects of replacing sitting time with physical activity on lung function: an analysis of the Canadian Longitudinal Study on Aging. Health Rep 30(3):12–23
pubmed: 30892662
Jones PR, Ekelund U (2019) Physical activity in the prevention of weight gain: the impact of measurement and interpretation of associations. Curr Obes Rep 8(2):66–76
pubmed: 30905041
doi: 10.1007/s13679-019-00337-1
Nocon M et al (2008) Association of physical activity with all-cause and cardiovascular mortality: a systematic review and meta-analysis. Eur J Cardiovasc Prev Rehabil 15(3):239–246
pubmed: 18525377
doi: 10.1097/HJR.0b013e3282f55e09
Sanderson E et al (2019) An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. Int J Epidemiol 48(3):713–727
pubmed: 30535378
doi: 10.1093/ije/dyy262
Pulit SL et al (2019) Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum Mol Genet 28(1):166–174
pubmed: 30239722
doi: 10.1093/hmg/ddy327
Lee JJ et al (2018) Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet 50(8):1112–1121
pubmed: 30038396
pmcid: 6393768
doi: 10.1038/s41588-018-0147-3
Bowden J, Hemani G, Davey-Smith G (2018) Invited Commentary: Detecting Individual And Global Horizontal Pleiotropy In Mendelian Randomization-A Job For The Humble Heterogeneity Statistic? Am J Epidemiol 187(12):2681–2685
pubmed: 30188969
pmcid: 6269239
van Oort S et al (2020) Modifiable lifestyle factors and heart failure: a Mendelian randomization study. Am Heart J 227:64–73
pubmed: 32682105
doi: 10.1016/j.ahj.2020.06.007
van de Vegte YJ et al (2020) Genome-wide association studies and Mendelian randomization analyses for leisure sedentary behaviours. Nat Commun 11(1):1770
pubmed: 32317632
pmcid: 7174427
doi: 10.1038/s41467-020-15553-w
Zhuang Z et al (2020) Association of physical activity, sedentary behaviours and sleep duration with cardiovascular diseases and lipid profiles: a Mendelian randomization analysis. Lipids Health Dis 19(1):86
pubmed: 32384904
pmcid: 7206776
doi: 10.1186/s12944-020-01257-z
Bahls M et al (2018) Association of domain-specific physical activity and cardiorespiratory fitness with all-cause and cause-specific mortality in two population-based cohort studies. Sci Rep 8(1):16066
pubmed: 30375472
pmcid: 6207740
doi: 10.1038/s41598-018-34468-7
Myers J et al (2015) Physical activity and cardiorespiratory fitness as major markers of cardiovascular risk: their independent and interwoven importance to health status. Prog Cardiovasc Dis 57(4):306–314
pubmed: 25269064
doi: 10.1016/j.pcad.2014.09.011
DeFina LF et al (2015) Physical activity versus cardiorespiratory fitness: two (partly) distinct components of cardiovascular health? Prog Cardiovasc Dis 57(4):324–329
pubmed: 25269066
doi: 10.1016/j.pcad.2014.09.008
Williams PT (2001) Physical fitness and activity as separate heart disease risk factors: a meta-analysis. Med Sci Sports Exerc 33(5):754–761
pubmed: 11323544
pmcid: 2821586
doi: 10.1097/00005768-200105000-00012
Myers J et al (2004) Fitness versus physical activity patterns in predicting mortality in men. Am J Med 117(12):912–918
pubmed: 15629729
doi: 10.1016/j.amjmed.2004.06.047
Bouchard C et al (1999) Familial aggregation of Vo 2 max response to exercise training: results from the HERITAGE Family Study. J Appl Physiol 87:1003–1008
pubmed: 10484570
doi: 10.1152/jappl.1999.87.3.1003
Ren YY et al (2013) Genetic analysis of a rat model of aerobic capacity and metabolic fitness. PLoS ONE 8(10):e77588
pubmed: 24147032
pmcid: 3795692
doi: 10.1371/journal.pone.0077588
Karvinen S et al (2015) Physical activity in adulthood: genes and mortality. Sci Rep 5:18259
pubmed: 26666586
pmcid: 4678877
doi: 10.1038/srep18259
Dowd KP et al (2018) A systematic literature review of reviews on techniques for physical activity measurement in adults: a DEDIPAC study. Int J Behav Nutr Phys Act 15(1):15
pubmed: 29422051
pmcid: 5806271
doi: 10.1186/s12966-017-0636-2
Vink JM et al (2011) Variance components models for physical activity with age as modifier: a comparative twin study in seven countries. Twin Res Hum Genet 14(1):25–34
pubmed: 21314253
doi: 10.1375/twin.14.1.25
Burgess S, Labrecque JA (2018) Mendelian randomization with a binary exposure variable: interpretation and presentation of causal estimates. Eur J Epidemiol 33(10):947–952
pubmed: 30039250
pmcid: 6153517
doi: 10.1007/s10654-018-0424-6
Burgess S, Davies NM, Thompson SG (2014) Instrumental variable analysis with a nonlinear exposure-outcome relationship. Epidemiology 25(6):877–885
pubmed: 25166881
pmcid: 4222800
doi: 10.1097/EDE.0000000000000161