Causal relationships between obesity and the leading causes of death in women and men
Autoři:
Jenny C. Censin aff001; Sanne A. E. Peters aff003; Jonas Bovijn aff001; Teresa Ferreira aff001; Sara L. Pulit aff001; Reedik Mägi aff007; Anubha Mahajan aff002; Michael V. Holmes aff009; Cecilia M. Lindgren aff001
Působiště autorů:
Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
aff001; Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
aff002; The George Institute for Global Health, University of Oxford, Oxford, United Kingdom
aff003; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
aff004; Department of Genetics, Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
aff005; Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, United States of America
aff006; Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
aff007; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
aff008; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
aff009; Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
aff010; Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, Big Data Institute Building, Roosevelt Drive, University of Oxford, Oxford, United Kingdom
aff011
Vyšlo v časopise:
Causal relationships between obesity and the leading causes of death in women and men. PLoS Genet 15(10): e32767. doi:10.1371/journal.pgen.1008405
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pgen.1008405
Souhrn
Obesity traits are causally implicated with risk of cardiometabolic diseases. It remains unclear whether there are similar causal effects of obesity traits on other non-communicable diseases. Also, it is largely unexplored whether there are any sex-specific differences in the causal effects of obesity traits on cardiometabolic diseases and other leading causes of death. We constructed sex-specific genetic risk scores (GRS) for three obesity traits; body mass index (BMI), waist-hip ratio (WHR), and WHR adjusted for BMI, including 565, 324, and 337 genetic variants, respectively. These GRSs were then used as instrumental variables to assess associations between the obesity traits and leading causes of mortality in the UK Biobank using Mendelian randomization. We also investigated associations with potential mediators, including smoking, glycemic and blood pressure traits. Sex-differences were subsequently assessed by Cochran’s Q-test (Phet). A Mendelian randomization analysis of 228,466 women and 195,041 men showed that obesity causes coronary artery disease, stroke (particularly ischemic), chronic obstructive pulmonary disease, lung cancer, type 2 and 1 diabetes mellitus, non-alcoholic fatty liver disease, chronic liver disease, and acute and chronic renal failure. Higher BMI led to higher risk of type 2 diabetes in women than in men (Phet = 1.4×10−5). Waist-hip-ratio led to a higher risk of chronic obstructive pulmonary disease (Phet = 3.7×10−6) and higher risk of chronic renal failure (Phet = 1.0×10−4) in men than women. Obesity traits have an etiological role in the majority of the leading global causes of death. Sex differences exist in the effects of obesity traits on risk of type 2 diabetes, chronic obstructive pulmonary disease, and renal failure, which may have downstream implications for public health.
Klíčová slova:
Blood pressure – Body Mass Index – Cancer risk factors – Genome-wide association studies – Chronic obstructive pulmonary disease – Ischemic stroke – Obesity
Zdroje
1. GBD 2015 Obesity Collaborators, Afshin A, Forouzanfar MH, Reitsma MB, Sur P, Estep K, et al. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. N Engl J Med. 2017;377: 13–27. doi: 10.1056/NEJMoa1614362 28604169
2. Seidell JC, Oosterlee A, Thijssen MA, Burema J, Deurenberg P, Hautvast JG, et al. Assessment of intra-abdominal and subcutaneous abdominal fat: relation between anthropometry and computed tomography. Am J Clin Nutr. 1987;45: 7–13. doi: 10.1093/ajcn/45.1.7 3799506
3. Prospective Studies Collaboration. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet. 2009;373: 1083–1096. doi: 10.1016/S0140-6736(09)60318-4 19299006
4. Taylor AE, Ebrahim S, Ben-Shlomo Y, Martin RM, Whincup PH, Yarnell JW, et al. Comparison of the associations of body mass index and measures of central adiposity and fat mass with coronary heart disease, diabetes, and all-cause mortality: a study using data from 4 UK cohorts. Am J Clin Nutr. 2010;91: 547–556. doi: 10.3945/ajcn.2009.28757 20089729
5. WHO. Obesity: Preventing and Managing the Global Epidemic. Report of a WHO consultation. Geneva: World Health Organization; 2000.
6. Emdin CA, Khera AV, Natarajan P, Klarin D, Zekavat SM, Hsiao AJ, et al. Genetic Association of Waist-to-Hip Ratio With Cardiometabolic Traits, Type 2 Diabetes, and Coronary Heart Disease. JAMA. 2017;317: 626–634. 28196256
7. Millard LAC, Davies NM, Tilling K, Gaunt TR, Davey Smith G. Searching for the causal effects of body mass index in over 300 000 participants in UK Biobank, using Mendelian randomization. Ripatti S, editor. PLOS Genet. 2019;15: e1007951. doi: 10.1371/journal.pgen.1007951 30707692
8. Lyall DM, Celis-Morales C, Ward J, Iliodromiti S, Anderson JJ, Gill JMR, et al. Association of Body Mass Index With Cardiometabolic Disease in the UK Biobank. JAMA Cardiol. 2017;2: 882. 28678979
9. Huang T, Qi Q, Zheng Y, Ley SH, Manson JE, Hu FB, et al. Genetic Predisposition to Central Obesity and Risk of Type 2 Diabetes: Two Independent Cohort Studies. Diabetes Care. 2015;38: 1306–11. doi: 10.2337/dc14-3084 25852209
10. Dale CE, Fatemifar G, Palmer TM, White J, Prieto-Merino D, Zabaneh D, et al. Causal Associations of Adiposity and Body Fat Distribution With Coronary Heart Disease, Stroke Subtypes, and Type 2 Diabetes Mellitus: A Mendelian Randomization Analysis. Circulation. 2017;135: 2373–2388. doi: 10.1161/CIRCULATIONAHA.116.026560 28500271
11. Lotta LA, Wittemans LBL, Zuber V, Stewart ID, Sharp SJ, Luan J, et al. Association of Genetic Variants Related to Gluteofemoral vs Abdominal Fat Distribution With Type 2 Diabetes, Coronary Disease, and Cardiovascular Risk Factors. JAMA. 2018;320: 2553. 30575882
12. Finucane MM, Stevens GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ, et al. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9·1 million participants. Lancet. 2011;377: 557–567. doi: 10.1016/S0140-6736(10)62037-5 21295846
13. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet (London, England). 2014;384: 766–81. doi: 10.1016/S0140-6736(14)60460-8 24880830
14. Pulit SL, Stoneman C, Morris AP, Wood AR, Glastonbury CA, Tyrrell J, et al. Meta-analysis of genome-wide association studies for body fat distribution in 694,649 individuals of European ancestry. Hum Mol Genet. 2018; doi: 10.1093/hmg/ddy327 30239722
15. Rost S, Freuer D, Peters A, Thorand B, Holle R, Linseisen J, et al. New indexes of body fat distribution and sex-specific risk of total and cause-specific mortality: a prospective cohort study. BMC Public Health. 2018;18: 427. doi: 10.1186/s12889-018-5350-8 29609587
16. Lind L, Ärnlöv J, Lampa E. The Interplay Between Fat Mass and Fat Distribution as Determinants of the Metabolic Syndrome Is Sex-Dependent. Metab Syndr Relat Disord. 2017;15: 337–343. doi: 10.1089/met.2017.0006 28586263
17. Dagenais GR, Yi Q, Mann JFE, Bosch J, Pogue J, Yusuf S. Prognostic impact of body weight and abdominal obesity in women and men with cardiovascular disease. Am Heart J. 2005;149: 54–60. doi: 10.1016/j.ahj.2004.07.009 15660034
18. Meisinger C, Döring A, Thorand B, Heier M, Löwel H. Body fat distribution and risk of type 2 diabetes in the general population: are there differences between men and women? The MONICA/KORA Augsburg Cohort Study. Am J Clin Nutr. 2006;84: 483–489. doi: 10.1093/ajcn/84.3.483 16960160
19. Wannamethee SG, Papacosta O, Whincup PH, Carson C, Thomas MC, Lawlor DA, et al. Assessing prediction of diabetes in older adults using different adiposity measures: a 7 year prospective study in 6,923 older men and women. Diabetologia. 2010;53: 890–898. doi: 10.1007/s00125-010-1670-7 20146052
20. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518: 197–206. doi: 10.1038/nature14177 25673413
21. Shungin D, Winkler TW, Croteau-Chonka DC, Ferreira T, Locke AE, Mägi R, et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature. 2015;518: 187–196. doi: 10.1038/nature14132 25673412
22. Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age. PLOS Med. 2015;12: e1001779. doi: 10.1371/journal.pmed.1001779 25826379
23. Bycroft C, Freeman C, Petkova D, Band G, Elliott LT, Sharp K, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562: 203–209. doi: 10.1038/s41586-018-0579-z 30305743
24. Mccarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, et al. A reference panel of 64,976 haplotypes for genotype imputation. 2016;48. doi: 10.1038/ng.3643 27548312
25. The UK10K Consortium. The UK10K project identifies rare variants in health and disease. Nature. 2015;526: 82–90. doi: 10.1038/nature14962 26367797
26. Durbin RM, Altshuler DL, Durbin RM, Abecasis GR, Bentley DR, Chakravarti A, et al. A map of human genome variation from population-scale sequencing. Nature. 2010;467: 1061–1073. doi: 10.1038/nature09534 20981092
27. Yengo L, Sidorenko J, Kemper KE, Zheng Z, Wood AR, Weedon MN, et al. Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry. Hum Mol Genet. 2018;27: 3641–3649. doi: 10.1093/hmg/ddy271 30124842
28. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, et al. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. Am J Hum Genet. 2007;81: 559–575. doi: 10.1086/519795 17701901
29. World Health Organization. The top 10 causes of death [Internet]. 2018 [cited 7 Jul 2018]. http://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
30. Campbell JM, Lane M, Owens JA, Bakos HW. Paternal obesity negatively affects male fertility and assisted reproduction outcomes: a systematic review and meta-analysis. Reprod Biomed Online. 2015;31: 593–604. doi: 10.1016/j.rbmo.2015.07.012 26380863
31. van der Steeg JW, Steures P, Eijkemans MJC, Habbema JDF, Hompes PGA, Burggraaff JM, et al. Obesity affects spontaneous pregnancy chances in subfertile, ovulatory women. Hum Reprod. 2007;23: 324–328. doi: 10.1093/humrep/dem371 18077317
32. Williams CD, Stengel J, Asike MI, Torres DM, Shaw J, Contreras M, et al. Prevalence of Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis Among a Largely Middle-Aged Population Utilizing Ultrasound and Liver Biopsy: A Prospective Study. Gastroenterology. 2011;140: 124–131. doi: 10.1053/j.gastro.2010.09.038 20858492
33. Chalasani N, Younossi Z, Lavine JE, Diehl AM, Brunt EM, Cusi K, et al. The diagnosis and management of non-alcoholic fatty liver disease: Practice Guideline by the American Association for the Study of Liver Diseases, American College of Gastroenterology, and the American Gastroenterological Association. Hepatology. 2012;55: 2005–2023. doi: 10.1002/hep.25762 22488764
34. Vernon G, Baranova A, Younossi ZM. Systematic review: the epidemiology and natural history of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in adults. Aliment Pharmacol Ther. 2011;34: 274–285. doi: 10.1111/j.1365-2036.2011.04724.x 21623852
35. Estes C, Razavi H, Loomba R, Younossi Z, Sanyal AJ. Modeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease. Hepatology. 2018;67: 123–133. doi: 10.1002/hep.29466 28802062
36. Macaluso M, Wright-Schnapp TJ, Chandra A, Johnson R, Satterwhite CL, Pulver A, et al. A public health focus on infertility prevention, detection, and management. Fertil Steril. 2010;93: 16.e1–16.e10. doi: 10.1016/J.FERTNSTERT.2008.09.046 18992879
37. Eastwood SV, Mathur R, Atkinson M, Brophy S, Sudlow C, Flaig R, et al. Algorithms for the Capture and Adjudication of Prevalent and Incident Diabetes in UK Biobank. Herder C, editor. PLoS One. 2016;11: e0162388. doi: 10.1371/journal.pone.0162388 27631769
38. Nelson CP, Goel A, Butterworth AS, Kanoni S, Webb TR, Marouli E, et al. Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nat Genet. 2017;49: 1385–1391. doi: 10.1038/ng.3913 28714975
39. Tobin MD, Sheehan NA, Scurrah KJ, Burton PR. Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure. Stat Med. 2005;24: 2911–2935. doi: 10.1002/sim.2165 16152135
40. International Consortium for Blood Pressure Genome-Wide Association Studies, Ehret GB, Munroe PB, Rice KM, Bochud M, Johnson AD, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature. 2011;478: 103–9. doi: 10.1038/nature10405 21909115
41. Lagou V, Mägi R, Hottenga J-JJ. Fasting glucose and insulin variability: sex-dimorphic genetic effects and novel loci. Prep. 2018;
42. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29: 308–11. http://www.ncbi.nlm.nih.gov/pubmed/11125122 doi: 10.1093/nar/29.1.308
43. Cochran W. The combination of estimates from different experiments. Biometrics. 1954;10: 101–29.
44. Burgess S, Thompson SG. Mendelian Randomization—Methods for Using Genetic Variants in Causal Estimation. 1st ed. Boca Raton, FL, USA: CRC Press, Taylor & Francis Group, Chapman and Hall; 2005.
45. Wald A. The Fitting of Straight Lines if Both Variables are Subject to Error. Ann Math Stat. 1940;11: 284–300. doi: 10.1214/aoms/1177731868
46. Bautista LE, Smeeth L, Hingorani AD, Casas JP. Estimation of Bias in Nongenetic Observational Studies Using “Mendelian Triangulation”. Ann Epidemiol. 2006;16: 675–680. doi: 10.1016/j.annepidem.2006.02.001 16621596
47. Thomas DC, Lawlor DA, Thompson JR. Re: Estimation of Bias in Nongenetic Observational Studies Using “Mendelian Triangulation” by Bautista et al. Ann Epidemiol. 2007;17: 511–513. doi: 10.1016/j.annepidem.2006.12.005 17466535
48. Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46: 1734–1739. doi: 10.1093/ije/dyx034 28398548
49. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37: 658–65. doi: 10.1002/gepi.21758 24114802
50. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44: 512–525. doi: 10.1093/ije/dyv080 26050253
51. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40: 304–14. doi: 10.1002/gepi.21965 27061298
52. Python Software Fondation. Python, version 3.5.2 [Internet]. https://www.python.org/
53. McKinney W. Data Structures for Statistical Computing in Python. Proc 9th Python Sci Conf. 2010; 51–56.
54. Oliphant TE. A guide to NumPy. USA: Trelgol Publishing; 2006.
55. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria [Internet]. 2017. https://www.r-project.org/
56. Wickham H, Francois R, Henry L, Müller K. dplyr: A Grammar of Data Manipulation. R package version 0.7.4. 2017; https://cran.r-project.org/package=dplyr%0A
57. Free Software Foundation. bash 4.1.2(2) [Internet]. 2007. https://www.gnu.org/software/bash/
58. Free Software Foundation. GNU AWK 3.1.7 [Internet]. 1989. https://www.gnu.org/software/gawk/manual/gawk.html
59. Wickham H. ggplot2: Elegant Graphics for Data Analysis. New York: Springer-Verlag; 2009.
60. Doebler P. mada: Meta-Analysis of Diagnostic Accuracy. R package version 0.5.8. In: 2017 [Internet]. https://cran.r-project.org/package=mada%0A
61. Auguie B. gridExtra: Miscellaneous Functions for “Grid” Graphics. In: R package version 2.3 [Internet]. 2017. https://cran.r-project.org/package=gridExtra
62. Sarkar D. Lattice: Multivariate Data Visualization with R. New York: Springer; 2008.
63. Kassambara A. ggpubr: “ggplot2” Based Publication Ready Plots. In: R package version 0.1.7. 2018.
64. World Health Organization. Global Health Estimates 2016: Deaths by Cause, Age, Sex, by Country and by Region, 2000–2016. Geneva; 2018.
65. Vazquez G, Duval S, Jacobs DR, Silventoinen K. Comparison of Body Mass Index, Waist Circumference, and Waist/Hip Ratio in Predicting Incident Diabetes: A Meta-Analysis. Epidemiol Rev. 2007;29: 115–128. doi: 10.1093/epirev/mxm008 17494056
66. Logue J, Walker JJ, Colhoun HM, Leese GP, Lindsay RS, McKnight JA, et al. Do men develop type 2 diabetes at lower body mass indices than women? Diabetologia. 2011;54: 3003–3006. doi: 10.1007/s00125-011-2313-3 21959958
67. Bray GA. Medical Consequences of Obesity. J Clin Endocrinol Metab. 2004;89: 2583–2589. doi: 10.1210/jc.2004-0535 15181027
68. Kautzky-Willer A, Harreiter J, Pacini G. Sex and Gender Differences in Risk, Pathophysiology and Complications of Type 2 Diabetes Mellitus. Endocr Rev. 2016;37: 278–316. doi: 10.1210/er.2015-1137 27159875
69. Colditz GA, Willett WC, Rotnitzky A, Manson JE. Weight gain as a risk factor for clinical diabetes mellitus in women. Ann Intern Med. 1995;122: 481–6. http://www.ncbi.nlm.nih.gov/pubmed/7872581 doi: 10.7326/0003-4819-122-7-199504010-00001
70. Chan JM, Rimm EB, Colditz GA, Stampfer MJ, Willett WC. Obesity, fat distribution, and weight gain as risk factors for clinical diabetes in men. Diabetes Care. 1994;17: 961–9. http://www.ncbi.nlm.nih.gov/pubmed/7988316 doi: 10.2337/diacare.17.9.961
71. Sierra-Johnson J, Johnson BD, Bailey KR, Turner ST. Relationships between insulin sensitivity and measures of body fat in asymptomatic men and women. Obes Res. 2004;12: 2070–7. doi: 10.1038/oby.2004.258 15687409
72. Masharani U, Goldfine ID, Youngren JF. Influence of gender on the relationship between insulin sensitivity, adiposity, and plasma lipids in lean nondiabetic subjects. Metabolism. 2009;58: 1602–8. doi: 10.1016/j.metabol.2009.05.012 19604524
73. Quon MJ. Limitations of the Fasting Glucose to Insulin Ratio as an Index of Insulin Sensitivity. J Clin Endocrinol Metab. 2001;86: 4615–4617. doi: 10.1210/jcem.86.10.7952 11600512
74. Verbeeten KC, Elks CE, Daneman D, Ong KK. Association between childhood obesity and subsequent Type 1 diabetes: a systematic review and meta-analysis. Diabet Med. 2011;28: 10–18. doi: 10.1111/j.1464-5491.2010.03160.x 21166841
75. Censin JC, Nowak C, Cooper N, Bergsten P, Todd JA, Fall T. Childhood adiposity and risk of type 1 diabetes: A Mendelian randomization study. Langenberg C, editor. PLoS Med. 2017;14: e1002362. doi: 10.1371/journal.pmed.1002362 28763444
76. Felix JF, Bradfield JP, Monnereau C, van der Valk RJP, Stergiakouli E, Chesi A, et al. Genome-wide association analysis identifies three new susceptibility loci for childhood body mass index. Hum Mol Genet. 2016;25: 389–403. doi: 10.1093/hmg/ddv472 26604143
77. Li C, Engström G, Hedblad B, Calling S, Berglund G, Janzon L. Sex differences in the relationships between BMI, WHR and incidence of cardiovascular disease: a population-based cohort study. Int J Obes. 2006;30: 1775–1781. doi: 10.1038/sj.ijo.0803339 16607382
78. Dhana K, Kavousi M, Ikram MA, Tiemeier HW, Hofman A, Franco OH. Body shape index in comparison with other anthropometric measures in prediction of total and cause-specific mortality. J Epidemiol Community Health. 2016;70: 90–6. doi: 10.1136/jech-2014-205257 26160362
79. Hu G, Tuomilehto J, Silventoinen K, Sarti C, Männistö S, Jousilahti P. Body Mass Index, Waist Circumference, and Waist-Hip Ratio on the Risk of Total and Type-Specific Stroke. Arch Intern Med. 2007;167: 1420. 17620537
80. Abete I, Arriola L, Etxezarreta N, Mozo I, Moreno-Iribas C, Amiano P, et al. Association between different obesity measures and the risk of stroke in the EPIC Spanish cohort. Eur J Nutr. 2015;54: 365–375. doi: 10.1007/s00394-014-0716-x 24903807
81. The Global Burden of Metabolic Risk Factors for Chronic Diseases Collaboration (BMI Mediated Effects). Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: a pooled analysis of 97 prospective cohorts with 1·8 million participants. Lancet. 2014;383: 970–983. doi: 10.1016/S0140-6736(13)61836-X 24269108
82. Hagg S, Fall T, Ploner A, Magi R, Fischer K, Draisma HH, et al. Adiposity as a cause of cardiovascular disease: a Mendelian randomization study. Int J Epidemiol. 2015;44: 578–586. doi: 10.1093/ije/dyv094 26016847
83. Vozoris NT, O’Donnell DE. Prevalence, risk factors, activity limitation and health care utilization of an obese, population-based sample with chronic obstructive pulmonary disease. Can Respir J. 2012;19: e18–24. doi: 10.1155/2012/732618 22679617
84. Hanson C, Rutten EP, Wouters EFM, Rennard S. Influence of diet and obesity on COPD development and outcomes. Int J Chron Obstruct Pulmon Dis. 2014;9: 723–33. doi: 10.2147/COPD.S50111 25125974
85. Vestbo J, Hurd SS, Agustí AG, Jones PW, Vogelmeier C, Anzueto A, et al. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med. 2013;187: 347–365. doi: 10.1164/rccm.201204-0596PP 22878278
86. Bedell GN, Wilson WR, Seebohm PM. Pulmonary function in obese persons. J Clin Invest. 1958;37: 1049–60. doi: 10.1172/JCI103686 13563634
87. Carreras-Torres R, Johansson M, Haycock PC, Wade KH, Relton CL, Martin RM, et al. Obesity, metabolic factors and risk of different histological types of lung cancer: A Mendelian randomization study. Hu C, editor. PLoS One. 2017;12: e0177875. doi: 10.1371/journal.pone.0177875 28594918
88. Morris RW, Taylor AE, Fluharty ME, Bjørngaard JH, Åsvold BO, Elvestad Gabrielsen M, et al. Heavier smoking may lead to a relative increase in waist circumference: evidence for a causal relationship from a Mendelian randomisation meta-analysis. The CARTA consortium. BMJ Open. 2015;5: e008808. doi: 10.1136/bmjopen-2015-008808 26264275
89. Audrain-McGovern J, Benowitz NL. Cigarette smoking, nicotine, and body weight. Clin Pharmacol Ther. 2011;90: 164–8. doi: 10.1038/clpt.2011.105 21633341
90. Rásky E, Stronegger WJ, Freidl W. The relationship between body weight and patterns of smoking in women and men. Int J Epidemiol. 1996;25: 1208–12. http://www.ncbi.nlm.nih.gov/pubmed/9027526 doi: 10.1093/ije/25.6.1208
91. Burgess S, Scott RA, Timpson NJ, Davey Smith G, Thompson SG, EPIC- InterAct Consortium. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol. 2015;30: 543–52. doi: 10.1007/s10654-015-0011-z 25773750
92. Chiolero A, Faeh D, Paccaud F, Cornuz J. Consequences of smoking for body weight, body fat distribution, and insulin resistance. Am J Clin Nutr. 2008;87: 801–809. doi: 10.1093/ajcn/87.4.801 18400700
93. Fulkerson JA, French SA. Cigarette smoking for weight loss or control among adolescents: gender and racial/ethnic differences. J Adolesc Health. 2003;32: 306–13. http://www.ncbi.nlm.nih.gov/pubmed/12667735 doi: 10.1016/s1054-139x(02)00566-9
94. Geng T, Smith CE, Li C, Huang T. Childhood BMI and Adult Type 2 Diabetes, Coronary Artery Diseases, Chronic Kidney Disease, and Cardiometabolic Traits: A Mendelian Randomization Analysis. Diabetes Care. 2018;41: 1089–1096. doi: 10.2337/dc17-2141 29483184
95. Todd JN, Dahlström EH, Salem RM, Sandholm N, Forsblom C, FinnDiane Study Group the FS, et al. Genetic Evidence for a Causal Role of Obesity in Diabetic Kidney Disease. Diabetes. 2015;64: 4238–46. doi: 10.2337/db15-0254 26307587
96. van Zuydam NR, Ahlqvist E, Sandholm N, Deshmukh H, Rayner NW, Abdalla M, et al. A Genome-Wide Association Study of Diabetic Kidney Disease in Subjects With Type 2 Diabetes. Diabetes. 2018;67: 1414–1427. doi: 10.2337/db17-0914 29703844
97. Tsuboi N, Utsunomiya Y, Kanzaki G, Koike K, Ikegami M, Kawamura T, et al. Low glomerular density with glomerulomegaly in obesity-related glomerulopathy. Clin J Am Soc Nephrol. 2012;7: 735–41. doi: 10.2215/CJN.07270711 22403274
98. Kovesdy CP, Furth SL, Zoccali C, World Kidney Day Steering Committee on behalf of the WKDS. Obesity and Kidney Disease: Hidden Consequences of the Epidemic. Can J kidney Heal Dis. 2017;4: 2054358117698669. doi: 10.1177/2054358117698669 28540059
99. Hall ME, do Carmo JM, da Silva AA, Juncos LA, Wang Z, Hall JE. Obesity, hypertension, and chronic kidney disease. Int J Nephrol Renovasc Dis. 2014;7: 75–88. doi: 10.2147/IJNRD.S39739 24600241
100. Stender S, Kozlitina J, Nordestgaard BG, Tybjærg-Hansen A, Hobbs HH, Cohen JC. Adiposity amplifies the genetic risk of fatty liver disease conferred by multiple loci. Nat Genet. 2017;49: 842–847. doi: 10.1038/ng.3855 28436986
101. Loomba R, Sanyal AJ. The global NAFLD epidemic. Nat Rev Gastroenterol Hepatol. 2013;10: 686–690. doi: 10.1038/nrgastro.2013.171 24042449
102. Al-Ajmi K, Lophatananon A, Ollier W, Muir KR. Risk of breast cancer in the UK biobank female cohort and its relationship to anthropometric and reproductive factors. Meyre D, editor. PLoS One. 2018;13: e0201097. doi: 10.1371/journal.pone.0201097 30048498
103. Liu K, Zhang W, Dai Z, Wang M, Tian T, Liu X, et al. Association between body mass index and breast cancer risk: evidence based on a dose-response meta-analysis. Cancer Manag Res. 2018;10: 143–151. doi: 10.2147/CMAR.S144619 29403312
104. Jarvis D, Mitchell JS, Law PJ, Palin K, Tuupanen S, Gylfe A, et al. Mendelian randomisation analysis strongly implicates adiposity with risk of developing colorectal cancer. Br J Cancer. 2016;115: 266–272. doi: 10.1038/bjc.2016.188 27336604
105. Thrift AP, Gong J, Peters U, Chang-Claude J, Rudolph A, Slattery ML, et al. Mendelian Randomization Study of Body Mass Index and Colorectal Cancer Risk. Cancer Epidemiol Biomarkers Prev. 2015;24: 1024–1031. doi: 10.1158/1055-9965.EPI-14-1309 25976416
106. Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol. 2000;29: 722–9. http://www.ncbi.nlm.nih.gov/pubmed/10922351 doi: 10.1093/ije/29.4.722
107. Martens EP, Pestman WR, de Boer A, Belitser S V, Klungel OH. Instrumental variables: application and limitations. Epidemiology. 2006;17: 260–7. doi: 10.1097/01.ede.0000215160.88317.cb 16617274
108. Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two-sample Mendelian randomization. Genet Epidemiol. 2016;40: 597–608. doi: 10.1002/gepi.21998 27625185
109. Burgess S, Thompson SG. Use of allele scores as instrumental variables for Mendelian randomization. Int J Epidemiol. 2013;42: 1134–1144. doi: 10.1093/ije/dyt093 24062299
110. Berg JJ, Harpak A, Sinnott-Armstrong N, Joergensen AM, Mostafavi H, Field Y, et al. Reduced signal for polygenic adaptation of height in UK Biobank. bioRxiv. 2018; 354951.
111. Haworth S, Mitchell R, Corbin L, Wade KH, Dudding T, Budu-Aggrey A, et al. Common genetic variants and health outcomes appear geographically structured in the UK Biobank sample: Old concerns returning and their implications. bioRxiv. 2018; 294876.
112. Davey Smith G, Paternoster L, Relton C. When Will Mendelian Randomization Become Relevant for Clinical Practice and Public Health? JAMA. 2017;317: 589. 28196238
Štítky
Genetika Reprodukční medicínaČlánek vyšel v časopise
PLOS Genetics
2019 Číslo 10
Nejčtenější v tomto čísle
- Spatiotemporal cytoskeleton organizations determine morphogenesis of multicellular trichomes in tomato
- Loss of thymidine kinase 1 inhibits lung cancer growth and metastatic attributes by reducing GDF15 expression
- TSEN54 missense variant in Standard Schnauzers with leukodystrophy
- Viral quasispecies