The etiology of multiple myeloma (MM) is poorly understood. Summary data from genome-wide association studies (GWASs) of multiple phenotypes can be exploited in a Mendelian randomization (MR) phenome-wide association study (PheWAS) to search for factors influencing MM risk. We performed an MR-PheWAS analyzing 249 phenotypes, proxied by 10 225 genetic variants, and summary genetic data from a GWAS of 7717 MM cases and 29 304 controls. Odds ratios (ORs) per 1 standard deviation increase in each phenotype were estimated under an inverse variance weighted random effects model. A Bonferroni-corrected threshold of P = 2 × 10-4 was considered significant, whereas P < .05 was considered suggestive of an association. Although no significant associations with MM risk were observed among the 249 phenotypes, 28 phenotypes showed evidence suggestive of association, including increased levels of serum vitamin B6 and blood carnitine (P = 1.1 × 10-3) with greater MM risk and ω-3 fatty acids (P = 5.4 × 10-4) with reduced MM risk. A suggestive association between increased telomere length and reduced MM risk was also noted; however, this association was primarily driven by the previously identified risk variant rs10936599 at 3q26 (TERC). Although not statistically significant, increased body mass index was associated with increased risk (OR, 1.10; 95% confidence interval, 0.99-1.22), supporting findings from a previous meta-analysis of prospective observational studies. Our study did not provide evidence supporting any modifiable factors examined as having a major influence on MM risk; however, it provides insight into factors for which the evidence has previously been mixed.

Biomedical Center Faculty of Medicine in Pilsen Charles University Prague Pilsen Czech Republic

Broad Institute Cambridge MA; and

Department of Hematology Erasmus MC Cancer Institute Rotterdam The Netherlands

Department of Internal Medicine 5 University of Heidelberg Heidelberg Germany

Division of Cancer Epidemiology DKFZ DKTK Heidelberg Germany

Division of Genetics and Epidemiology The Institute of Cancer Research London United Kingdom

Division of Molecular Genetic Epidemiology German Cancer Research Center Heidelberg Germany

Division of Molecular Pathology The Institute of Cancer Research London United Kingdom

Division of Pediatric Neurooncology DKFZ DKTK Heidelberg Germany

Hematology and Transfusion Medicine Department of Laboratory Medicine Biomedical Centre Lund University Lund Sweden

Hematology Clinic Skåne University Hospital Lund Sweden

Hopp Children's Cancer Center Heidelberg Germany

National Centre of Tumor Diseases Heidelberg Germany

NYU Langone Health Care New York NY

Zobrazit více v PubMed

Zweegman S, Palumbo A, Bringhen S, Sonneveld P. Age and aging in blood disorders: multiple myeloma. Haematologica. 2014;99(7):1133-1137. PubMed PMC

Wallington-Beddoe CT, Pitson SM. Novel therapies for multiple myeloma. Aging (Albany NY). 2017;9(8):1857-1858. PubMed PMC

Cowan AJ, Allen C, Barac A, et al. . Global burden of multiple myeloma: a systematic analysis for the Global Burden of Disease Study 2016. JAMA Oncol. 2018;4(9):1221-1227. PubMed PMC

Carson KR, Bates ML, Tomasson MH. The skinny on obesity and plasma cell myeloma: a review of the literature. Bone Marrow Transplant. 2014;49(8):1009-1015. PubMed

De Pergola G, Silvestris F. Obesity as a major risk factor for cancer. J Obes. 2013;2013:291546. PubMed PMC

Teras LR, Kitahara CM, Birmann BM, et al. . Body size and multiple myeloma mortality: a pooled analysis of 20 prospective studies. Br J Haematol. 2014;166(5):667-676. PubMed PMC

Birmann BM, Giovannucci E, Rosner B, Anderson KC, Colditz GA. Body mass index, physical activity, and risk of multiple myeloma. Cancer Epidemiol Biomarkers Prev. 2007;16(7):1474-1478. PubMed PMC

Wallin A, Larsson SC. Body mass index and risk of multiple myeloma: a meta-analysis of prospective studies. Eur J Cancer. 2011;47(11):1606-1615. PubMed

Thordardottir M, Lindqvist EK, Lund SH, et al. . Dietary intake is associated with risk of multiple myeloma and its precursor disease. PLoS One. 2018;13(11):e0206047. PubMed PMC

Fritschi L, Ambrosini GL, Kliewer EV, Johnson KC; Canadian Cancer Registries Epidemiologic Research Group . Dietary fish intake and risk of leukaemia, multiple myeloma, and non-Hodgkin lymphoma. Cancer Epidemiol Biomarkers Prev. 2004;13(4):532-537. PubMed

Brown LM, Gridley G, Pottern LM, et al. . Diet and nutrition as risk factors for multiple myeloma among blacks and whites in the United States. Cancer Causes Control. 2001;12(2):117-125. PubMed

Gascoyne DM, Lyne L, Spearman H, Buffa FM, Soilleux EJ, Banham AH. Vitamin D receptor expression in plasmablastic lymphoma and myeloma cells confers susceptibility to vitamin D. Endocrinology. 2017;158(3):503-515. PubMed PMC

Burwick N. Vitamin D and plasma cell dyscrasias: reviewing the significance. Ann Hematol. 2017;96(8):1271-1277. PubMed

Lindqvist EK, Goldin LR, Landgren O, et al. . Personal and family history of immune-related conditions increase the risk of plasma cell disorders: a population-based study. Blood. 2011;118(24):6284-6291. PubMed PMC

Hsu W-L, Preston DL, Soda M, et al. . The incidence of leukemia, lymphoma and multiple myeloma among atomic bomb survivors: 1950-2001. Radiat Res. 2013;179(3):361-382. PubMed PMC

Preston DL, Kusumi S, Tomonaga M, et al. . Cancer incidence in atomic bomb survivors. Part III: Leukemia, lymphoma and multiple myeloma, 1950-1987. Radiat Res. 1994;137(suppl 2):S68-S97. PubMed

Yarmolinsky J, Wade KH, Richmond RC, et al. . Causal inference in cancer epidemiology: what is the role of Mendelian randomization? Cancer Epidemiol Biomarkers Prev. 2018;27(9):995-1010. PubMed PMC

Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23(R1):R89-R98. PubMed PMC

Went M, Sud A, Law PJ, et al. . Assessing the effect of obesity-related traits on multiple myeloma using a Mendelian randomisation approach. Blood Cancer J. 2017;7(6):e573. PubMed PMC

Chattopadhyay S, Thomsen H, Weinhold N, et al. . Eight novel loci implicate shared genetic etiology in multiple myeloma, AL amyloidosis, and monoclonal gammopathy of unknown significance. Leukemia. 2020;34(4):1187-1191. PubMed

Millard LAC, Davies NM, Timpson NJ, Tilling K, Flach PA, Davey Smith G. MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization. Sci Rep. 2015;5:16645. PubMed PMC

Went M, Sud A. Identification of multiple risk loci and regulatory mechanisms influencing susceptibility to multiple myeloma [published correction appears in Nat Commun. 2019;10(1):213]. Nat Commun. 2018;9(1):3707. PubMed PMC

Mitchell JS, Li N, Weinhold N, et al. . Genome-wide association study identifies multiple susceptibility loci for multiple myeloma. Nat Commun. 2016;7:12050. PubMed PMC

Broderick P, Chubb D, Johnson DC, et al. . Common variation at 3p22.1 and 7p15.3 influences multiple myeloma risk. Nat Genet. 2011;44(1):58-61. PubMed PMC

Chubb D, Weinhold N, Broderick P, et al. . Common variation at 3q26.2, 6p21.33, 17p11.2 and 22q13.1 influences multiple myeloma risk. Nat Genet. 2013;45(10):1221-1225. PubMed PMC

Swaminathan B, Thorleifsson G, Jöud M, et al. . Variants in ELL2 influencing immunoglobulin levels associate with multiple myeloma. Nat Commun. 2015;6:7213. PubMed PMC

Hemani G, Zheng J, Elsworth B, et al. . The MR-Base platform supports systematic causal inference across the human phenome. Elife. 2018;7:e34408. PubMed PMC

Disney-Hogg L, Cornish AJ, Sud A, et al. . Impact of atopy on risk of glioma: a Mendelian randomisation study. BMC Med. 2018;16(1):42. PubMed PMC

Brion MJ, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol. 2013;42(5):1497-1501. PubMed PMC

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(8):1133-1163. PubMed

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(4):304-314. PubMed PMC

Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46(6):1985-1998. PubMed PMC

Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27(R2):R195-R208. PubMed PMC

Fan Q, Maranville JC, Fritsche L, et al. . HDL-cholesterol levels and risk of age-related macular degeneration: a multiethnic genetic study using Mendelian randomization. Int J Epidemiol. 2017;46(6):1891-1902. PubMed PMC

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(2):512-525. PubMed PMC

Wootton RE, Lawn RB, Millard LAC, et al. . Evaluation of the causal effects between subjective wellbeing and cardiometabolic health: Mendelian randomisation study. BMJ. 2018;362:k3788. PubMed PMC

Shim H, Chasman DI, Smith JD, et al. . A multivariate genome-wide association analysis of 10 LDL subfractions, and their response to statin treatment, in 1868 Caucasians. PLoS One. 2015;10(4):e0120758. PubMed PMC

Wu JHY, Lemaitre RN, Manichaikul A, et al. . Genome-wide association study identifies novel loci associated with concentrations of four plasma phospholipid fatty acids in the de novo lipogenesis pathway: results from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. Circ Cardiovasc Genet. 2013;6(2):171-183. PubMed PMC

Kim HI, Raffler J, Lu W, et al. . Fine mapping and functional analysis reveal a role of SLC22A1 in acylcarnitine transport. Am J Hum Genet. 2017;101(4):489-502. PubMed PMC

Alexander DD, Mink PJ, Adami HO, et al. . Multiple myeloma: a review of the epidemiologic literature. Int J Cancer. 2007;120(suppl 12):40-61. PubMed

Akram M, Iqbal M, Daniyal M, Khan AU. Awareness and current knowledge of breast cancer. Biol Res. 2017;50(1):33. PubMed PMC

Barta JA, Powell CA, Wisnivesky JP. Global epidemiology of lung cancer. Ann Glob Health. 2019;85(1):8. PubMed PMC

Roncucci L, Mariani F. Prevention of colorectal cancer: how many tools do we have in our basket? Eur J Intern Med. 2015;26(10):752-756. PubMed

World Cancer Research Fund; American Institute for Cancer Research Food, Nutrition, Physical Activity, and the Prevention of Cancer: A Global Perspective. Washington, DC: American Institute for Cancer Research; 2007.

Aran V, Victorino AP, Thuler LC, Ferreira CG. Colorectal cancer: epidemiology, disease mechanisms and interventions to reduce onset and mortality. Clin Colorectal Cancer. 2016;15(3):195-203. PubMed

Masarwi M, DeSchiffart A, Ham J, Reagan MR. Multiple myeloma and fatty acid metabolism. JBMR Plus. 2019;3(3):e10173. PubMed PMC

Caro-Maldonado A, Wang R, Nichols AG, et al. . Metabolic reprogramming is required for antibody production that is suppressed in anergic but exaggerated in chronically BAFF-exposed B cells. J Immunol. 2014;192(8):3626-3636. PubMed PMC

Hanahan D, Weinberg RA. The hallmarks of cancer. Cell. 2000;100(1):57-70. PubMed

Campa D, Martino A, Varkonyi J, et al. . Risk of multiple myeloma is associated with polymorphisms within telomerase genes and telomere length. Int J Cancer. 2015;136(5):E351-E358. PubMed

Aviv A, Anderson JJ, Shay JW. Mutations, cancer and the telomere length paradox. Trends Cancer. 2017;3(4):253-258. PubMed PMC

Andersson U, Degerman S, Dahlin AM, et al. . The association between longer relative leukocyte telomere length and risk of glioma is independent of the potentially confounding factors allergy, BMI, and smoking. Cancer Causes Control. 2019;30(2):177-185. PubMed

Ziakas PD, Karsaliakos P, Prodromou ML, Mylonakis E. Interleukin-6 polymorphisms and hematologic malignancy: a re-appraisal of evidence from genetic association studies. Biomarkers. 2013;18(7):625-631. PubMed

Li Y, Du Z, Wang X, Wang G, Li W. Association of IL-6 promoter and receptor polymorphisms with multiple myeloma risk: a systematic review and meta-analysis. Genet Test Mol Biomarkers. 2016;20(10):587-596. PubMed

Chang SH, Luo S, Thomas TS, et al. . Obesity and the transformation of monoclonal gammopathy of undetermined significance to multiple myeloma: a population-based cohort study. J Natl Cancer Inst. 2016;109(5):djw264. PubMed PMC

Thordardottir M, Lindqvist EK, Lund SH, et al. . Obesity and risk of monoclonal gammopathy of undetermined significance and progression to multiple myeloma: a population-based study. Blood Adv. 2017;1(24):2186-2192. PubMed PMC

Epidemiology, genetics and treatment of multiple myeloma and precursor diseases

Search for AL amyloidosis risk factors using Mendelian randomization