Obesity and Multiple Sclerosis: A Mendelian Randomization Study

Using a Mendelian randomization approach, Brent Richards and colleagues examine the possibility that genetically raised body mass index could affect risk of multiple sclerosis.

Vyšlo v časopise: Obesity and Multiple Sclerosis: A Mendelian Randomization Study. PLoS Med 13(6): e32767. doi:10.1371/journal.pmed.1002053
Kategorie: Research Article
prolekare.web.journal.doi_sk: 10.1371/journal.pmed.1002053


Using a Mendelian randomization approach, Brent Richards and colleagues examine the possibility that genetically raised body mass index could affect risk of multiple sclerosis.


1. Compston A, Coles A. Multiple sclerosis. Lancet. 2008;372: 1502–1517. doi: 10.1016/S0140-6736(08)61620-7 18970977

2. Trisolini M, Honeycutt A, Wiener J, Lesesne S. MS International Federation. In: Global Economic Impact of Multiple Sclerosis. 2010 [cited 21 Apr 2016]. http://www.msif.org/wp-content/uploads/2014/09/Global_economic_impact_of_MS.pdf

3. Hartung DM, Bourdette DN, Ahmed SM, Whitham RH. The cost of multiple sclerosis drugs in the US and the pharmaceutical industry: Too big to fail? Neurology. 2015;84: 2185–2192. doi: 10.1212/WNL.0000000000001608 25911108

4. Munger KL, Chitnis T, Ascherio A. Body size and risk of MS in two cohorts of US women. Neurology. 2009;73: 1543–1550. doi: 10.1212/WNL.0b013e3181c0d6e0 19901245

5. Munger KL, Bentzen J, Laursen B, Stenager E, Koch-Henriksen N, Sørensen TIA, et al. Childhood body mass index and multiple sclerosis risk: a long-term cohort study. Mult Scler. 2013;19: 1323–1329. doi: 10.1177/1352458513483889 23549432

6. Hedstrom a. K, Olsson T, Alfredsson L. High body mass index before age 20 is associated with increased risk for multiple sclerosis in both men and women. Mult Scler J. 2012;18: 1334–1336. doi: 10.1177/1352458512436596

7. Esposito K, Pontillo A, Di Palo C, Giugliano G, Masella M, Marfella R, et al. Effect of weight loss and lifestyle changes on vascular inflammatory markers in obese women: a randomized trial. JAMA. 2003;289: 1799–804. doi: 10.1001/jama.289.14.1799 12684358

8. Visser M, Bouter LM, McQuillan GM, Wener MH, Harris TB. Low-Grade Systemic Inflammation in Overweight Children. Pediatrics. 2001;107: e13. 11134477

9. Timpson NJ, Nordestgaard BG, Harbord RM, Zacho J, Frayling TM, Tybjærg-Hansen A, et al. C-reactive protein levels and body mass index: elucidating direction of causation through reciprocal Mendelian randomization. Int J Obes. 2011;35: 300–308. doi: 10.1038/ijo.2010.137

10. Würtz P, Wang Q, Kangas AJ, Richmond RC, Skarp J, Tiainen M, et al. Metabolic signatures of adiposity in young adults: Mendelian randomization analysis and effects of weight change. PLoS Med. 2014;11: e1001765. doi: 10.1371/journal.pmed.1001765 25490400

11. van Dielen FM, van’t Veer C, Schols AM, Soeters PB, Buurman WA, Greve JW. Increased leptin concentrations correlate with increased concentrations of inflammatory markers in morbidly obese individuals. Int J Obes Relat Metab Disord. 2001;25: 1759–1766. doi: 10.1038/sj.ijo.0801825 11781755

12. Engeli S, Feldpausch M, Gorzelniak K, Hartwig F, Heintze U, Janke J, et al. Association Between Adiponectin and Mediators of Inflammation in Obese Women. Diabetes. 2003;52: 942–947. doi: 10.2337/diabetes.52.4.942 12663465

13. Davey Smith G, Ebrahim S. “Mendelian randomization”: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32: 1–22. 12689998

14. Juonala M, Magnussen CG, Berenson GS, Venn A, Burns TL, Sabin MA, et al. Childhood adiposity, adult adiposity, and cardiovascular risk factors. N Engl J Med. 2011;365: 1876–1885. doi: 10.1056/NEJMoa1010112 22087679

15. Reilly JJ, Kelly J. Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: systematic review. Int J Obes (Lond). 2011;35: 891–898. doi: 10.1038/ijo.2010.222

16. Barlow SE, Dietz WH. Management of child and adolescent obesity: summary and recommendations based on reports from pediatricians, pediatric nurse practitioners, and registered dietitians. Pediatrics. 2002;110: 236–238. 12094001

17. Goodin DS. The epidemiology of multiple sclerosis: insights to disease pathogenesis. Handb Clin Neurol. 2014;122: 231–266. doi: 10.1016/B978-0-444-52001-2.00010-8 24507521

18. 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

19. Beecham AH, Patsopoulos NA, Xifara DK, Davis MF, Kemppinen A, Cotsapas C, et al. Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat Genet. 2013;45: 1353–1360. doi: 10.1038/ng.2770 24076602

20. Sawcer S, Hellenthal G, Pirinen M, Spencer CCA, Patsopoulos NA, Moutsianas L, et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature. 2011;476: 214–219. doi: 10.1038/nature10251 21833088

21. Walter K, Min JL, Huang J, Crooks L, Memari Y, McCarthy S, et al. The UK10K project identifies rare variants in health and disease. Nature. 2015;526: 82–90. doi: 10.1038/nature14962 26367797

22. Johnson AD, Handsaker RE, Pulit SL, Nizzari MM, O’Donnell CJ, De Bakker PIW. SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics. 2008;24: 2938–2939. doi: 10.1093/bioinformatics/btn564 18974171

23. SNAP Proxy Search. In: Broad Institute. 2016 [cited 21 Apr 2016]. https://www.broadinstitute.org/mpg/snap/ldsearch.php

24. Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey Smith G. Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27: 1133–1163. 17886233

25. 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

26. 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

27. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315: 629–634. doi: 10.1136/bmj.316.7129.469 9310563

28. 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: 304–314. doi: 10.1002/gepi.21965 27061298

29. Huckins LM, Boraska V, Franklin CS, Floyd JAB, Southam L, Sullivan PF, et al. Using ancestry-informative markers to identify fine structure across 15 populations of European origin. Eur J Hum Genet. 2014;22: 1190–1200. doi: 10.1038/ejhg.2014.1 24549058

30. Mokry LE, Ross S, Ahmad OS, Forgetta V, Davey Smith G, Leong A, et al. Vitamin D and Risk of Multiple Sclerosis: A Mendelian Randomization Study. PLoS Med. 2015;12: e1001866. doi: 10.1371/journal.pmed.1001866 26305103

31. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37: 658–665. doi: 10.1002/gepi.21758 24114802

32. Dastani Z, Hivert M-FF, Timpson NJ, Perry JRB, Yuan X, Scott RA, et al. Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals. PLoS Genet. 2012;8: e1002607. doi: 10.1371/journal.pgen.1002607 22479202

33. Patsopoulos NA, Evangelou E, Ioannidis JPA. Sensitivity of between-study heterogeneity in meta-analysis: proposed metrics and empirical evaluation. Int J Epidemiol. 2008;37: 1148–1157. doi: 10.1093/ije/dyn065 18424475

34. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7: 177–188. 3802833

35. Patsopoulos NA, De Bakker PIW. Genome-wide meta-analysis identifies novel multiple sclerosis susceptibility loci. Ann Neurol. 2011;70: 897–912. doi: 10.1002/ana.22609 22190364

36. Wood AR, Esko T, Yang J, Vedantam S, Pers TH, Gustafsson S, et al. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet. 2014;46: 1173–1186. doi: 10.1038/ng.3097 25282103

37. Berghofer A, Pischon T, Reinhold T, Apovian CM, Sharma AM, Willich SN. Obesity prevalence from a European perspective: a systematic review. BMC Public Health. 2008;8: 200. doi: 10.1186/1471-2458-8-200 18533989

38. Ahrens W, Pigeot I, Pohlabeln H, De Henauw S, Lissner L, Molnar D, et al. Prevalence of overweight and obesity in European children below the age of 10. Int J Obes. 2014;38: S99–S107.

39. Pugliatti M, Rosati G, Carton H, Riise T, Drulovic J, Vécsei L, et al. The epidemiology of multiple sclerosis in Europe. Eur J Neurol. 2006;13: 700–722. 16834700

40. WHO. Body mass index—BMI. [cited 3 Dec 2015]. http://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi

41. 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. 2014;384: 766–781. doi: 10.1016/S0140-6736(14)60460-8 24880830

42. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA. 2014;311: 806–814. doi: 10.1001/jama.2014.732 24570244

43. Flegal KM, Carroll MD, Kit BK, Ogden CL. Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAMA. 2012;307: 491–497. doi: 10.1001/jama.2012.39 22253363

44. Orton S-M, Herrera BM, Yee IM, Valdar W, Ramagopalan S V, Sadovnick AD, et al. Sex ratio of multiple sclerosis in Canada: a longitudinal study. Lancet Neurol. 2006;5: 932–936. doi: 10.1016/S1474-4422(06)70581-6 17052660

45. Koch-Henriksen N, Sørensen PS. The changing demographic pattern of multiple sclerosis epidemiology. Lancet Neurol. 2010;9: 520–532. doi: 10.1016/S1474-4422(10)70064-8 20398859

46. Bays HE, Chapman RH, Grandy S. The relationship of body mass index to diabetes mellitus, hypertension and dyslipidaemia: comparison of data from two national surveys. Int J Clin Pract. 2007;61: 737–747. doi: 10.1111/j.1742-1241.2007.01336.x 17493087

47. Lamon-Fava S, Wilson PWF, Schaefer EJ. Impact of Body Mass Index on Coronary Heart Disease Risk Factors in Men and Women: The Framingham Offspring Study. Arterioscler Thromb Vasc Biol. 1996;16: 1509–1515. 8977456

48. CDC. Diabetes Public Health Resource. In: Mean and Median Age at Diagnosis of Diabetes Among Adult Incident Cases Aged 18–79 Years, United States, 1997–2011. 2015 [cited 21 Apr 2016]. http://www.cdc.gov/diabetes/statistics/age/fig2.htm

49. Roger VL, Go AS, Lloyd-Jones DM, Benjamin EJ, Berry JD, Borden WB, et al. Heart disease and stroke statistics—2012 update: a report from the American Heart Association. Circulation. 2012;125: e2–e220. doi: 10.1161/CIR.0b013e31823ac046 22179539

50. Degenhardt A, Ramagopalan S V, Scalfari A, Ebers GC. Clinical prognostic factors in multiple sclerosis: a natural history review. Nat Rev Neurol. 2009;5: 672–682. doi: 10.1038/nrneurol.2009.178 19953117

51. Vimaleswaran KS, Berry DJ, Lu C, Tikkanen E, Pilz S, Hiraki LT, et al. Causal relationship between obesity and vitamin D status: bi-directional Mendelian randomization analysis of multiple cohorts. PLoS Med. 2013;10: e1001383. doi: 10.1371/journal.pmed.1001383 23393431

52. Rotondi M, Batocchi AP, Coperchini F, Caggiula M, Zerbini F, Sideri R, et al. Severe disability in patients with relapsing-remitting multiple sclerosis is associated with profound changes in the regulation of leptin secretion. Neuroimmunomodulation. 2013;20: 341–347. doi: 10.1159/000353567 24008588

53. Emamgholipour S, Eshaghi SM, Hossein-nezhad A, Mirzaei K, Maghbooli Z, Sahraian MA. Adipocytokine profile, cytokine levels and foxp3 expression in multiple sclerosis: a possible link to susceptibility and clinical course of disease. PLoS ONE. 2013;8: e76555. doi: 10.1371/journal.pone.0076555 24098530

54. Musabak U, Demirkaya S, Genç G, Ilikci RS, Odabasi Z. Serum Adiponectin, TNF-α, IL-12p70, and IL-13 Levels in Multiple Sclerosis and the Effects of Different Therapy Regimens. Neuroimmunomodulation. 2011;18: 57–66. doi: 10.1159/000317393 20714168

55. Matarese G, Carrieri PB, La Cava A, Perna F, Sanna V, De Rosa V, et al. Leptin increase in multiple sclerosis associates with reduced number of CD4(+)CD25+ regulatory T cells. Proc Natl Acad Sci U S A. 2005;102: 5150–5155. doi: 10.1073/pnas.0408995102 15788534

56. Wolf AM, Wolf D, Rumpold H, Enrich B, Tilg H. Adiponectin induces the anti-inflammatory cytokines IL-10 and IL-1RA in human leukocytes. Biochem Biophys Res Commun. 2004;323: 630–635. doi: 10.1016/j.bbrc.2004.08.145 15369797

Interné lekárstvo

Článok vyšiel v časopise

PLOS Medicine

2016 Číslo 6
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle

Zvýšte si kvalifikáciu online z pohodlia domova

Eozinofilní granulomatóza s polyangiitidou
nový kurz

Betablokátory a Ca antagonisté z jiného úhlu
Autori: prof. MUDr. Michal Vrablík, Ph.D., MUDr. Petr Janský

Autori: doc. MUDr. Petr Čáp, Ph.D.

Farmakoterapie akutní a chronické bolesti

Získaná hemofilie - Povědomí o nemoci a její diagnostika

Všetky kurzy
Zabudnuté heslo

Nemáte účet?  Registrujte sa

Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.


Nemáte účet?  Registrujte sa