Genetically determined body mass index is associated with diffuse large B‐cell lymphoma in polygenic and Mendelian randomization analyses

Amy Moore; Eleanor Kane; Lauren R Teras; Mitchell J Machiela; Joshua Arias; Orestis A Panagiotou; Alain Monnereau; Nicole Wong Doo; Zhaoming Wang; Susan L Slager; Roel C H Vermeulen; Claire M Vajdic; Karin E Smedby; John J Spinelli; Joseph Vijai; Graham G Giles; Brian K Link; Alan A Arslan; Alexandra Nieters; Paige M Bracci; Nicola J Camp; Gilles Salles; Wendy Cozen; Henrik Hjalgrim; Immaculata De Vivo; Hans‐Olov Adami; Demetrius Albanes; Nikolaus Becker; Yolanda Benavente; Simonetta Bisanzi; Paolo Boffetta; Paul Brennan; Angela R Brooks‐Wilson; Federico Canzian; Jacqueline Clavel; Lucia Conde; David G Cox; Karen Curtin; Lenka Foretova; Hervé Ghesquières; Bengt Glimelius; Thomas M Habermann; Jonathan N Hofmann; Qing Lan; Mark Liebow; Anne Lincoln; Marc Maynadie; James McKay; Mads Melbye; Lucia Miligi; Roger L Milne; Thierry J Molina; Lindsay M Morton; Kari E North; Kenneth Offit; Marina Padoan; Sara Piro; Alpa V Patel; Mark P Purdue; Vignesh Ravichandran; Elio Riboli; Richard K Severson; Melissa C Southey; Anthony Staines; Lesley F Tinker; Ruth C Travis; Sophia S Wang; Elisabete Weiderpass; Stephanie Weinstein; Tongzhang Zheng; Stephen J Chanock; Nathaniel Rothman; Brenda M Birmann; James R Cerhan; Sonja I Berndt

doi:10.1002/ijc.70039

. 2025 Sep 5;158(1):45–59. doi: 10.1002/ijc.70039

Genetically determined body mass index is associated with diffuse large B‐cell lymphoma in polygenic and Mendelian randomization analyses

Amy Moore ¹, Eleanor Kane ², Lauren R Teras ³, Mitchell J Machiela ¹, Joshua Arias ¹, Orestis A Panagiotou ^4,⁵, Alain Monnereau ^6,⁷, Nicole Wong Doo ^8,⁹, Zhaoming Wang ^10,¹¹, Susan L Slager ¹², Roel C H Vermeulen ^13,¹⁴, Claire M Vajdic ¹⁵, Karin E Smedby ^16,¹⁷, John J Spinelli ^18,¹⁹, Joseph Vijai ²⁰, Graham G Giles ^9,^21,²², Brian K Link ²³, Alan A Arslan ^24,^25,²⁶, Alexandra Nieters ²⁷, Paige M Bracci ²⁸, Nicola J Camp ²⁹, Gilles Salles ¹⁹, Wendy Cozen ³⁰, Henrik Hjalgrim ^31,^32,^33,³⁴, Immaculata De Vivo ^35,³⁶, Hans‐Olov Adami ^36,^37,³⁸, Demetrius Albanes ¹, Nikolaus Becker ³⁹, Yolanda Benavente ^40,⁴¹, Simonetta Bisanzi ⁴², Paolo Boffetta ^43,⁴⁴, Paul Brennan ⁴⁵, Angela R Brooks‐Wilson ^46,⁴⁷, Federico Canzian ⁴⁸, Jacqueline Clavel ^6,⁴⁹, Lucia Conde ⁵⁰, David G Cox ⁵¹, Karen Curtin ²⁹, Lenka Foretova ⁵², Hervé Ghesquières ^53,⁵⁴, Bengt Glimelius ⁵⁵, Thomas M Habermann ⁵⁶, Jonathan N Hofmann ¹, Qing Lan ¹, Mark Liebow ⁵⁶, Anne Lincoln ¹⁹, Marc Maynadie ⁵⁷, James McKay ⁴⁵, Mads Melbye ^32,^58,^59,⁶⁰, Lucia Miligi ⁶¹, Roger L Milne ^9,^21,²², Thierry J Molina ⁶², Lindsay M Morton ¹, Kari E North ^63,⁶⁴, Kenneth Offit ¹⁹, Marina Padoan ⁶⁵, Sara Piro ⁶¹, Alpa V Patel ¹⁹, Mark P Purdue ¹, Vignesh Ravichandran ¹⁹, Elio Riboli ⁶⁶, Richard K Severson ⁶⁷, Melissa C Southey ^9,^22,⁶⁸, Anthony Staines ⁶⁹, Lesley F Tinker ⁷⁰, Ruth C Travis ⁷¹, Sophia S Wang ⁷², Elisabete Weiderpass ⁴⁵, Stephanie Weinstein ¹, Tongzhang Zheng ⁷³, Stephen J Chanock ¹, Nathaniel Rothman ¹, Brenda M Birmann ³⁵, James R Cerhan ¹¹, Sonja I Berndt ^1,^✉

^¹Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA

^²Department of Health Sciences, University of York, York, UK

^³Department of Population Science, American Cancer Society, Atlanta, Georgia, USA

^⁴Department of Health Services, Policy, and Practice, Brown University School of Public Health, Providence, Rhode Island, USA

^⁵Center for Gerontology and Healthcare Research, Brown University School of Public Health, Providence, Rhode Island, USA

^⁶Unit of Mixed Research (UMR1153), Centre for Research in Epidemiology and Statistics (CRESS), INSERM, Université Paris‐Cité, Paris, France

^⁷Registre des hémopathies malignes de la Gironde, Institut Bergonié, Bordeaux Cedex, France

^⁸Concord Clinical School, University of Sydney, Concord, New South Wales, Australia

^⁹Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia

^¹⁰Department of Computational Biology, St Jude Children's Research Hospital, Memphis, Tennessee, USA

^¹¹Laboratory of Translational Genomics, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA

^¹²Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA

^¹³Department of Population Health Sciences, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands

^¹⁴Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands

^¹⁵Surveillance and Evaluation Research Program, The Kirby Institute, University of New South Wales, Sydney, New South Wales, Australia

^¹⁶Department of Medicine, Solna, Karolinska Institutet, Stockholm, Sweden

^¹⁷Hematology Center, Karolinska University Hospital, Stockholm, Sweden

^¹⁸Cancer Control Research, BC Cancer, Vancouver, British Columbia, Canada

^¹⁹School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada

^²⁰Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA

^²¹Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia

^²²Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia

^²³Department of Internal Medicine, Carver College of Medicine, The University of Iowa, Iowa City, Iowa, USA

^²⁴Department of Obstetrics and Gynecology, New York University School of Medicine, New York, New York, USA

^²⁵Department of Environmental Medicine, New York University School of Medicine, New York, New York, USA

^²⁶Perlmutter Cancer Center, NYU Langone Medical Center, New York, New York, USA

^²⁷Center for Chronic Immunodeficiency, Institute for Immunodeficiency, University Medical Center Freiburg, Freiburg, Baden‐Württemberg, Germany

^²⁸Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA

^²⁹Department of Internal Medicine and Huntsman Cancer Institute, University of Utah School of Medicine, Salt Lake City, Utah, USA

^³⁰Division of Hematology‐Oncology, Department of Medicine, University of California Irvine, Orange, California, USA

^³¹Department of Epidemiology Research, Division of Health Surveillance and Research, Statens Serum Institut, Copenhagen, Denmark

^³²Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark

^³³Department of Haematology, Rigshospitalet, Copenhagen, Denmark

^³⁴Danish Cancer Society Research Center, Danish Cancer Society, Copenhagen, Denmark

^³⁵Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA

^³⁶Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, Massachusetts, USA

^³⁷Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden

^³⁸Institute of Health and Society, Clinical Effectiveness Research Group, University of Oslo, Oslo, Norway

^³⁹Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Baden‐Württemberg, Germany

^⁴⁰Cancer Epidemiology Research Programme, Catalan Institute of Oncology‐IDIBELL, Barcelona, Spain

^⁴¹Unidad de Infecciones y Cáncer, CIBER de Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain

^⁴²Regional Laboratory for Cancer Prevention, Institute for Cancer Research, Prevention and Oncological Network (ISPRO), Florence, Italy

^⁴³Stony Brook Cancer Center, Stony Brook University, Stony Brook, New York, USA

^⁴⁴Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy

^⁴⁵Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC), Lyon, France

^⁴⁶Genome Sciences Centre, BC Cancer, Vancouver, British Columbia, Canada

^⁴⁷Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada

^⁴⁸Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany

^⁴⁹National Registry of Childhood Cancers, APHP, CHU Paul Brousse, Villejuif and CHU de Nancy, France

^⁵⁰Bill Lyons Informatics Centre, UCL Cancer Institute, University College London, London, UK

^⁵¹Cancer Research Center of Lyon, INSERM U1052, Centre Léon Bérard, Lyon, France

^⁵²Department of Cancer Epidemiology and Genetics, Masaryk Memorial Cancer Institute, Brno, Czech Republic

^⁵³Department of Hematology, Hospices Civils de Lyon, Lyon Sud Hospital, Pierre Benite, France

^⁵⁴CIRI, Centre International de Recherche en Infectiologie, Team Lymphoma Immuno‐Biology, Univ Lyon, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, Lyon, France

^⁵⁵Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden

^⁵⁶Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA

^⁵⁷INSERM U1231, EA 4184, Registre des Hémopathies Malignes de Côte d'Or, University of Burgundy and Dijon University Hospital, Dijon, France

^⁵⁸Medicine and Health Sciences Department, Jebsen Center for Genetic Epidemiology, NTNU, Trondheim, Norway

^⁵⁹Danish Cancer Institute, Danish Cancer Society, Copenhagen, Denmark

^⁶⁰Department of Genetics, Stanford University Medical School, Stanford, California, USA

^⁶¹Environmental and Occupational Epidemiology Unit, Cancer Prevention and Research Institute (ISPO), Florence, Italy

^⁶²Department of Pathology, APHP, Necker and Robert Debré, Université Paris Cité, Institut Imagine, INSERM U1163, Paris, France

^⁶³Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

^⁶⁴Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

^⁶⁵Unit of Medical Statistics and Epidemiology, Department Translational Medicine, University of Eastern Piedmont “Amedeo Avogadro”, Novara, Italy

^⁶⁶Cancer Epidemiology and Prevention Research Unit, School of Public Health, Imperial College London, London, UK

^⁶⁷Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, Michigan, USA

^⁶⁸Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia

^⁶⁹School of Nursing, Psychotherapy and Community Health, Dublin City University, Dublin, Ireland

^⁷⁰Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, DC, USA

^⁷¹Cancer Epidemiology Unit, University of Oxford, Oxford, UK

^⁷²Division of Health Analytics, City of Hope Beckman Research Institute, Duarte, California, USA

^⁷³Department of Epidemiology, Brown University, Providence, Rhode Island, USA

Correspondence , Sonja I. Berndt, Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology & Genetics, National Cancer Institute, NIH, DHHS, 9609 Medical Center Drive, Rockville, MD 20850, USA. Email: berndts@mail.nih.gov

^✉

Corresponding author.

PMCID: PMC12588556 PMID: 40910475

Abstract

Obesity has been associated with non‐Hodgkin lymphoma (NHL), but the evidence is inconclusive. We examined the association between genetically determined adiposity and four common NHL subtypes: diffuse large B‐cell lymphoma (DLBCL), follicular lymphoma, chronic lymphocytic leukemia, and marginal zone lymphoma, using eight genome‐wide association studies of European ancestry (N = 10,629 cases, 9505 controls) and constructing polygenic scores for body mass index (BMI), waist‐to‐hip ratio (WHR), and waist‐to‐hip ratio adjusted for BMI (WHRadjBMI). Higher genetically determined BMI was associated with an increased risk of DLBCL [odds ratio (OR) per standard deviation (SD) = 1.18, 95% confidence interval (95% CI): 1.05–1.33, p = .005]. This finding was consistent with Mendelian randomization analyses, which demonstrated a similar increased risk of DLBCL with higher genetically determined BMI (OR_{per SD} = 1.12, 95% CI: 1.02–1.23, p = .03). No significant associations were observed with other NHL subtypes. Our study demonstrates a positive link between a genetically determined BMI and an increased risk of DLBCL, providing additional support for increased adiposity as a risk factor for DLBCL.

What's new?

Obesity has been linked to the risk of non‐Hodgkin lymphoma (NHL), but the evidence remains inconclusive. Here, the authors investigate the possibility of shared genetic risk factors between obesity and four common NHL types. Drawing on data from 8 genome‐wide association studies, they found that a higher genetically determined BMI was associated with a higher risk of just one NHL type, diffuse large B cell lymphoma (DLBCL). This positive association was confirmed using Mendelian randomization.

graphic file with name IJC-158-45-g006.jpg

Abbreviations

BMI: body mass index
CLL: chronic lymphocytic leukemia
DLBCL: diffuse large B‐cell lymphoma
FL: follicular lymphoma
IARC: International Agency for Research on Cancer
MZL: marginal zone lymphoma
NHL: non‐Hodgkin lymphoma
OR: odds ratio
PGS: polygenic score
SNP: single nucleotide polymorphism
WHR: waist‐to‐hip ratio
WHRadjBMI: waist‐to‐hip ratio adjusted for BMI
95% CI: 95% confidence interval

1. INTRODUCTION

Excess adiposity, possibly through mechanisms involving chronic inflammation, is a suggested risk factor for B‐cell lymphomas. Although the findings are not entirely consistent, ¹ , ² , ³ several observational studies of non‐Hodgkin lymphoma have shown an association between obesity and lymphoma risk. ⁴ , ⁵ , ⁶ , ⁷ , ⁸ Of more common lymphoma subtypes, the evidence is most convincing for diffuse large B‐cell lymphoma (DLBCL), where excess weight‐for‐height in later adulthood has been found to increase the risk of disease. ² , ⁴ , ⁵ , ⁶ , ⁷ , ⁹ , ¹⁰ There is also some evidence that being overweight or obese in young adulthood may contribute to DLBCL risk. ¹⁰ , ¹¹ , ¹² , ¹³ , ¹⁴ The evidence is less clear for other B‐cell lymphomas, such as follicular lymphoma (FL), chronic lymphocytic leukemia (CLL), and marginal zone lymphoma (MZL). ⁵ , ⁶ , ¹⁵ These lymphomas are more indolent in nature, and weight loss in the years leading up to diagnosis could have attenuated any observed association of later adult obesity with disease etiology. Although no associations were observed with body mass index (BMI) and risk of CLL or MZL, ¹⁵ , ¹⁶ the InterLymph Consortium reported an association between higher BMI in young adulthood and increased risk of FL in a large pooled analysis. ¹⁷

To date, the majority of epidemiologic studies of lymphoma have used BMI to estimate adiposity, largely due to the ease of obtaining height and weight information, but other measures, such as waist‐to‐hip ratio (WHR), may provide better indicators of body fat distribution. A larger WHR is indicative of greater abdominal, rather than gluteal, fat deposition, and has been associated with an increased risk of type 2 diabetes, cardiovascular disease, and some cancers. ¹⁸ , ¹⁹ , ²⁰ The relationship between WHR and NHL has been studied less often. Although most studies have not observed an association with WHR, ² , ¹³ , ²¹ , ²² , ²³ , ²⁴ these studies have generally been small and underpowered to detect associations with specific NHL subtypes. A meta‐analysis suggested that higher WHR may be associated with DLBCL, but not FL. ¹⁴ Some of the observed discrepancies could be due to the known difficulties in studying anthropometry. These include the reliability of height and weight when self‐reported, as is common in studies of lymphoma; the timeframe of anthropometric information relative to lymphoma diagnosis, especially when collected retrospectively; and the different ages, time periods, and places of study where the population prevalence of obesity may vary. Changes in body composition over an individual's lifetime complicate the study of the relationships between obesity and disease risk.

Although adult adiposity is a marker of multiple biological influences reflecting the interplay of genetics and environment, positive associations between adiposity and NHL risk raise the possibility of shared genetic risk factors. Genetic alleles are randomly allocated at conception, not affected by disease status or environmental exposures, measured reliably, and unchanged over the life course of an individual. Hundreds of genetic variants have been identified to be associated with measures of adiposity, BMI, WHR, and WHR adjusted for BMI (WHRadjBMI); the variants for BMI and WHRadjBMI explain 6.0% and 3.9% of the variance, respectively. ²⁵ , ²⁶ Many of the loci identified for WHR without adjustment for BMI overlap those loci identified for BMI, making interpretation of the results difficult as associations observed for WHR may be due to correlation with BMI. In contrast, the loci identified for WHRadjBMI are distinct from and independent of those identified for BMI and reflect different biological pathways. By combining SNP‐specific associations with adiposity into a single measure, a polygenic score (PGS), ²⁷ one can examine the association between genetically determined adiposity and NHL risk while circumventing some of the aforementioned methodological complexities for characterizing body size. Mendelian randomization, an analytic approach using genetic variation, can be used to investigate potentially causal relationships between risk factors, such as obesity, and disease risk. ²⁸ Indeed, previous Mendelian randomization studies have used adiposity‐related polygenic scores as instruments to further elucidate and characterize the etiology of obesity‐related diseases. ²⁹ , ³⁰

Here, we examined the association between genetically determined adiposity and NHL risk using data from eight GWAS, including 10,629 NHL cases and 9505 controls. As evidence suggests that subtypes of NHL have distinctly different etiologies, ³¹ , ³² we focused our analysis on the risk of four major NHL subtypes, DLBCL, FL, CLL, and MZL, and investigated associations with BMI, WHR, and WHRadjBMI.

2. MATERIALS AND METHODS

We used previously genotyped samples of European ancestry and imputed data from eight GWAS of NHL (Supplementary Table 1). Full details of participating studies, including detailed information on quality control and data cleaning, have been previously reported for DLBCL, FL, CLL, and MZL. ³³ , ³⁴ , ³⁵ , ³⁶ Briefly, the largest GWAS, the InterLymph NHL GWAS, was a pooled study consisting of NHL cases and controls from 22 studies of NHL: nine prospective cohort studies, eight population‐based case–control studies, and five hospital or clinic‐based case–control or case‐series studies. The other seven GWAS were the University of California at San Francisco Molecular Epidemiology of Non‐Hodgkin Lymphoma study (UCSF2), ³⁷ University of California at San Francisco Molecular Epidemiology of Non‐Hodgkin Lymphoma study (UCSF1)/Nurses' Health Study (NHS), ³⁸ Scandinavian Lymphoma Etiology Study (SCALE), ³⁹ Genetic Epidemiology of CLL Consortium (GEC), ⁴⁰ Groupe d'Etude des Lymphomes de l'Adulte (GELA)/European Prospective Investigation into Cancer, Chronic Diseases, Nutrition, and Lifestyles (EPIC), ⁴¹ , ⁴² Mayo Clinic Case–Control Study of Diffuse Large B‐cell Lymphoma (Mayo‐DLBCL), ⁴³ and the Utah Chronic Lymphocytic Leukemia Study (Utah). Genotyping was performed on commercially available Illumina and Affymetrix platforms (Supplementary Table 1), and standard quality control and filtering metrics (i.e., low SNP and sample call rates, sex discordance, deviation from Hardy–Weinberg proportions) were applied to each GWAS. Imputation was performed separately for each of the eight GWAS using IMPUTE2 ⁴⁴ and the 1000 Genomes Project reference panel. ⁴⁵ Across the eight GWAS, genotype data were available for a total of 10,629 NHL cases, including 3100 CLL, 3857 DLBCL, 2847 FL, and 825 MZL cases, and up to 9505 controls.

To evaluate the genetically determined adiposity and the risk of NHL, we constructed separate PGS for BMI, WHR, and WHRadjBMI using published genetic loci from the Genetic Investigation of ANthropometric Traits (GIANT) Consortium, which included 941 independent SNPs associated with BMI, 382 independent SNPs associated with WHR, and 463 independent SNPs associated with WHRadjBMI. ²⁵ , ²⁶ Two SNPs for BMI, 4 SNPs for WHR, and nine SNPs for WHRadjBMI were not available in our GWAS and were excluded. After Steiger filtering, ⁴⁶ 917 SNPs remained for BMI, 358 SNPs for WHR, and 430 SNPs for WHRadjBMI. The polygenic scores were computed for each individual (i) as the sum of the allelic dosage for each SNP multiplied by the reported weight of the association with BMI, WHR, or WHRadjBMI, as shown below:

PG S_{i} = \sum_{j = 1}^{k} w_{j} x_{ij}

where w _j is the weight or beta coefficient for the jth SNP derived from the literature and x _ij is the allelic dosage of the jth SNP. In our study, we used the previously published per‐risk‐allele β‐estimates for the European sex‐combined meta‐analysis for BMI, WHR, or WHRadjBMI. ²⁵ , ²⁶

As previous studies have demonstrated substantial etiologic heterogeneity among NHL subtypes, ³¹ , ³² we analyzed each of the four NHL subtypes in this study separately. Logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (95% CI) for the association with genetically determined BMI, WHR, and WHRadjBMI, separately by GWAS and by subtype. Each PGS was modeled as a continuous variable and categorized into quartiles with quartile cutoffs defined by the distribution among controls. Models were adjusted for sex, age at NHL diagnosis or control selection, and GWAS‐specific statistically significant (p <.05) principal components for population stratification. The regression model for the UCSF1/NHS study was not adjusted for sex, as all controls were female. As previous studies have demonstrated sex differences for waist‐related traits, ²⁶ as secondary analyses, we also performed sex‐stratified analyses using: (1) the same weights for the PGS as the sex‐combined analysis, which allowed us to test for differences in the effect size estimates using a Wald test, and (2) sex‐specific PGS, which were constructed using the SNPs and weights previously identified for each sex. ²⁵ , ²⁶ All analyses were adjusted for age and study‐specific statistically significant principal components. The UCSF1/NHS study was excluded from sex‐specific analyses, as all controls were women. Fixed‐effects meta‐analysis was used to combine association results from the included GWAS for each NHL subtype. Between‐study heterogeneity was evaluated by Cochran's Q test and was quantified using the I ² metric. The meta‐analysis was conducted using the package metan in Stata v16 (StataCorp, College Station, TX). The p‐values for between‐sex heterogeneity were calculated using a Wald test. A p‐value <.05 was considered nominally statistically significant, and p‐values of 0.05–0.10 were considered suggestive. To account for multiple testing and control for the false discovery rate, q‐values were calculated using Benjamini‐Hochberg, which is less conservative than Bonferroni. ⁴⁷

In addition, we conducted a Mendelian Randomization analysis to assess the potential causal relationship using the inverse‐variance weighted (IVW), MR‐Egger, weighted median, and weighted mode methods as implemented in the TwoSampleMR R package. ⁴⁸ We focused on the IVW approach as our primary method for analysis. Fixed effects models were used for the IVW analysis. The same SNPs were used for the Mendelian analysis as for the PGS analysis. Using only summary‐level data and under certain strict assumptions, ²⁸ the exponentiated coefficient calculated in this manner can be interpreted as the causal OR of a given NHL subtype associated with a one‐unit increase in the adiposity trait. The Mendelian randomization analysis requires several assumptions: (1) PGS is related to the exposure, (2) the PGS is not related to other exposures that could be confounders, and (3) the PGS affects the outcome only through the exposure. There should be no directional or horizontal pleiotropy. To assess the potential presence of directional pleiotropic effects, we performed Egger regression and estimated the heterogeneity of SNP association using Cochrane's Q‐statistic. Scatter plots were used to visualize the associations, and leave‐one‐out analyses were used to evaluate the impact of outlier SNPs.

3. RESULTS

Overall, higher genetically determined BMI was associated with an elevated risk of DLBCL (OR_{per SD} = 1.18, 95% CI = 1.05–1.33, p = .005) (Figure 1), which remained significant after adjustment for multiple testing (q = 0.02). Compared to the lowest quartile, those in the highest quartile of genetically determined BMI had an 18% increased risk of DLBCL (OR = 1.18, 95% CI:1.04–1.32). After stratifying by sex (Supplementary Table 2), we observed a positive association with increasing BMI PGS and risk of DLBCL for men (OR_{per SD} = 1.23, 95% CI 1.05–1.44, p = .01) with weaker evidence of an association in women (OR_{per SD} = 1.10, 95% CI 0.92–1.31, p = .29); however, the difference was not statistically significant (p_{heterogeneity} = 0.35). Further analyses using sex‐specific PGS for BMI yielded similar positive associations for both men and women (Supplementary Table 3). Overall, there was no association between genetically predicted BMI and the risk of FL, CLL, or MZL (Figure 1). After stratifying by sex (Supplementary Table 2), higher genetically determined BMI was suggestively associated with an increased risk of CLL among men (OR_{per SD} = 1.17, 95% CI: 1.00–1.37, p = .05) with men in the highest quartile displaying a 15% increased risk of CLL compared to the lowest quartile of BMI PGS (OR = 1.15, 95% CI: 0.98–1.34). No association was observed for women (OR_{per SD} = 0.97, 95% CI: 0.79–1.18, p = .73), and there was no significant heterogeneity by sex for CLL risk (p_{heterogeneity} = 0.14). Analyses using sex‐specific PGS showed a significant positive association for genetically predicted BMI among men but not women (Supplementary Table 3). Little evidence of heterogeneity among GWAS was observed for the BMI PGS (P_{heterogeneity} >0.05, Supplementary Table 4).

Risk of four non‐Hodgkin lymphoma subtypes associated with genetically determined body mass index (BMI). Odds ratios (ORs) and 95% confidence intervals (CIs) are shown for quartiles (Q1‐Q4) of the BMI polygenic score (PGS) and BMI PGS modeled as a linear term.

For men and women combined, no significant associations were observed for any NHL subtype with genetically determined body fat distribution as measured by WHR or WHRadjBMI PGS (Figures 2 and 3), although there was a suggestive association with higher WHRadjBMI and increased FL risk (OR = 1.19 [1.00–1.43], p = 0.06). In sex‐specific analyses (Supplementary Tables 5), although not significant after adjustment for multiple testing (q = 0.18), higher WHRadjBMI PGS was nominally associated with lower risk of MZL for men (OR_{per SD} = 0.60, 95% CI = 0.38–0.94, p = .03) but not women (OR_{per SD} = 1.25, 95% CI = 0.84–1.88, p = .28), with evidence of heterogeneity between the sexes (p_{heterogeneity} = 0.02). Men in the highest quartile of WHRadjBMI PGS had a 33% reduction in the risk of MZL compared to the lowest quartile (OR = 0.67, 95% CI: 0.49–0.93). Additional adjustment for genetically determined BMI in the WHRadjBMI analyses did not substantially alter the ORs observed (data not shown). Consistent with the results for WHRadjBMI, a suggestive reduction in MZL risk was also observed for men with higher genetically predicted WHR (OR_{per SD} = 0.58 [0.34–1.02], p = .06), but not women (OR_{per SD} = 1.13 [0.68–1.86], p = .64) (P_{heterogeneity} = 0.08, Supplementary Table 6). Analyses using sex‐specific PGS showed a suggestive reduction in MZL risk for WHRadjBMI among men (Supplementary Tables 7 and 8). No significant linear trends were observed with other subtypes in sex‐specific analyses. With the exception of a few individual quartiles, there was little evidence for heterogeneity among the individual GWAS (Supplementary Tables 9 and 10).

Risk of four non‐Hodgkin lymphoma subtypes associated with genetically determined waist‐to‐hip ratio adjusted for BMI (WHRadjBMI). Odds ratios (ORs) and 95% confidence intervals (CIs) are shown for quartiles (Q1‐Q4) of the WHRadjBMI polygenic score (PGS) and WHRadjBMI PGS modeled as a linear term.

Risk of four non‐Hodgkin lymphoma subtypes associated with genetically determined waist‐to‐hip ratio (WHR). Odds ratios (ORs) and 95% confidence intervals (CIs) are shown for quartiles (Q1‐Q4) of the WHR polygenic score (PGS) and WHR PGS modeled as a linear term.

To further explore the relationship between adiposity and NHL risk, we conducted Mendelian randomization analyses; we discovered generally high concordance between the associations calculated using individual‐level PGS with those estimated using the inverse variance‐weighted (IVW), weighted median, and weighted mode methods and summary‐level statistics for BMI, WHR, and WHRadjBMI for all NHL subtypes (Figures 4, 5, 6, Supplementary Table 11). Similar to what we observed with the individual‐level PGS, higher genetically determined BMI was associated with an increased risk of DLBCL using the IVW method (OR_{per SD} = 1.12, 95% CI: 1.02–1.23, p = .03) with similar results for the weighted median and weighted mode. No significant associations were observed for the other NHL subtypes. Egger regression did not detect evidence of overall bias in the IVW estimate due to directional pleiotropy in any of the analyses (p >.05 for all, Supplementary Table 11). Scatterplots showing SNP‐specific associations for BMI, WHR, and WHRadjBMI with each NHL subtype are shown in Supplementary Figures 1–3; no individual SNPs were associated with any NHL subtype at a Bonferroni significance level of 5.5 × 10⁻⁵ for BMI, 1.4 × 10⁻⁴ for WHR, and 1.2 × 10⁻⁴ for WHRadjBMI. There was little evidence of heterogeneity in SNP effects (p>.01 for all, Supplementary Table 11) Leave‐one‐SNP‐out analyses yielded similar results and did not provide evidence of any SNP unduly influencing the results (data not shown).

Mendelian randomization analyses of body mass index (BMI) and the risk of four non‐Hodgkin lymphoma subtypes. Odds ratios (ORs) and 95% confidence intervals (CIs) are shown for each subtype using the inverse variance weighted (IVW), weighted median, weighted mode, and MR Egger methods.

Mendelian randomization analyses of waist‐to‐hip ratio adjusted for body mass index (WHRadjBMI) and the risk of four non‐Hodgkin lymphoma subtypes. Odds ratios (ORs) and 95% confidence intervals (CIs) are shown for each subtype using the inverse variance weighted (IVW), weighted median, weighted mode, and MR Egger methods.

Mendelian randomization analyses of waist‐to‐hip ratio (WHR) and the risk of four non‐Hodgkin lymphoma subtypes. Odds ratios (ORs) and 95% confidence intervals (CIs) are shown for each subtype using the inverse variance weighted (IVW), weighted median, weighted mode, and MR Egger methods.

4. DISCUSSION

In this large study of genetically determined adiposity and NHL risk, we observed a positive association between genetically determined BMI and risk of DLBCL. The International Agency for Research on Cancer (IARC) previously concluded that there was limited, but insufficient, evidence for an association between excess body fatness and DLBCL. ⁴⁹ Our study provides additional evidence for a role of adiposity in DLBCL risk. Our results are consistent with epidemiologic studies of measured or self‐reported BMI, including a pooled analysis of prospective cohorts that reported a statistically significant positive association between BMI and risk of DLBCL ¹⁰ and a pooled analysis based on case–control data from the InterLymph Consortium that discovered elevated risks for DLBCL with increasing young adult BMI. ⁵⁰ Prospective data from the Nurses' Health Study and Health Professionals Follow‐up Study and other studies further support a role for BMI throughout the life course. ¹¹ , ¹² , ¹³ , ¹⁴

A positive association between BMI and DLBCL risk was further supported by our Mendelian randomization analysis, suggesting a potential causal relationship between BMI and DLBCL risk. Obesity has been linked to many altered metabolic processes, including alterations in the insulin‐like growth factor pathway, sex hormones, and adipokines as well as chronic low‐grade inflammation, ⁵¹ which may promote lymphomagenesis. However, some caution should be exercised in inferring causality from this analysis. Although the intercept term calculated using Egger regression for DLBCL was not significantly different from zero, this statistical test does not rule out the existence of directional pleiotropy. BMI is a polygenic trait, and the etiology of DLBCL has yet to be fully elucidated. The existence of biological pleiotropy at one or more loci has the potential to violate the Instrument Strength Independent of Direct Effect (InSIDE) assumption of Mendelian randomization if those variants are related to an unmeasured confounder or act by the same pleiotropic mechanism (e.g., innate immunity).

Given the epidemiologic associations between measured or reported adult BMI and risk of DLBCL in both men and women, it is intriguing that our study found a positive association between the genetically determined BMI and DLBCL risk in men, but less evidence for women. There is ample evidence for sex‐specific aspects of adiposity. For instance, women are relatively protected from certain obesity‐related diseases, such as cardiovascular disease, until menopause when the resulting hormonal changes result in a shift in fat distribution. ⁵² Twin studies suggest that the genetic influence on BMI may differ between men and women, ⁵³ and, across multiple populations, that sex‐specific genetic influences on BMI are strongest after puberty. ⁵⁴ It is possible that overall adiposity plays a stronger role in the risk of DLBCL for men. However, it is important to note that the difference in risk between men and women was not statistically significant in our study, and the elevated risk observed for men could be a chance finding.

For genetically inferred WHRadjBMI, we discovered a nominally lower risk of MZL with higher WHRadjBMI PGS among men but not women. Although collider bias is possible, additional adjustment for genetically determined BMI in the WHRadjBMI analyses did not substantially alter the associations observed, and results were similar for WHR analyses. Although the difference between sexes was significant, some caution should be exercised in drawing inferences as this was a subgroup finding. The result was not statistically significant after adjustment for multiple testing (q = 0.18), and heritability of WHRadjBMI and variance explained by the reported loci is less for men compared to women. ²⁶ Although not statistically significant, we also observed a borderline increased risk of FL in the PGS analysis with similar, albeit weaker, evidence in the Mendelian randomization analyses. Although we used over 400 genetic variants to construct the PGS and Mendelian randomization instrument for WHRadjBMI, these variants combined explain only 1.0%–3.9% of the variation. ²⁶ As such, there is likely misclassification in the ranking of individuals for these traits based on their PGS. Assuming that the misclassification is non‐differential, this could have biased our observed results toward the null. Other studies have reported positive associations between increasing genetically inferred BMI and WHRadjBMI with risk of colorectal cancer using a similar approach, ²⁹ , ⁵⁵ indicating that it is possible to uncover associations with cancer risk with only a small percentage of the variation captured by the score. However, these studies had a larger sample size, and we may have been underpowered to detect a modest association with risk in our study. Burgess et al. reported that a sample size of ~10,000 cases is required to detect a modest association in Mendelian randomization analyses using an instrumental variable explaining 5% of the variance. ⁵⁶

Although the literature on WHR and cancer risk is limited compared with that on BMI and cancer, previous work that examined WHR as a risk factor, most of which has been conducted in women, has not found associations with NHL risk. ² , ²¹ , ²² , ²³ , ²⁴ , ⁵⁷ WHR represents a different measure of adiposity that incorporates the observed protective effects of an increased femoral‐gluteal muscle, ⁵⁸ and studies of cardiovascular disease have shown associations with WHR independent of BMI. ¹⁹ WHRadjBMI loci have been shown to be enriched for genes expressed in adipose tissue itself, and pathway analyses implicated genes involved in angiogenesis, transcriptional regulation, and insulin resistance, ⁵⁹ the latter of which are also established pathways for carcinogenesis. However, we only found limited evidence for a role of WHR and WHRadjBMI in NHL risk in subgroup analyses in our study.

Our study has limitations that are important to consider. Our participants were sampled from populations of European descent, limiting the generalizability of results to other ethnic groups, but this also has the benefit of reducing bias from population stratification. As many of our subjects came from retrospective case–control studies, there is the possibility of participation bias, as obese subjects may be less likely to be controls; however, genotypes, distributed at birth, are unlikely to be related to study participation. We did not have individual‐level data on BMI or WHR from all studies and could not evaluate the variance explained by the PGS in our study. Although we were able to combine multiple studies of NHL together in one analysis and achieve sample sizes of several thousand cases for three out of the four subtypes, our study had limited power to detect modest associations between these anthropometric traits and specific NHL subtypes. Larger studies are needed to further explore the suggestive findings in this study. Previous studies of measured or self‐reported adiposity and cancer risk have been susceptible to recall bias, reverse causation, residual confounding, and misclassification. By utilizing genetically determined measures of adiposity, our results are less sensitive to these concerns and provide additional insight into the role of adiposity on NHL risk, though at the expense of the non‐genetic contribution.

In conclusion, our study provides evidence supporting a positive association between BMI and DLBCL risk, consistent with the previously reported epidemiologic studies of measured or self‐reported adult BMI and risk of DLBCL. Future exploration of biological pathways that underlie the association with BMI and DLBCL is warranted as well as the evaluation of BMI with DLBCL subtypes, as these may provide more insight into the etiology of NHL.

AUTHOR CONTRIBUTIONS

Amy Moore: Conceptualization; data curation; formal analysis; investigation; methodology; writing – original draft; writing – review and editing. Eleanor Kane: Conceptualization; methodology; resources; writing – review and editing. Lauren R. Teras: Conceptualization; methodology; resources; writing – review and editing. Mitchell J. Machiela: Conceptualization; methodology; writing – review and editing. Joshua Arias: Conceptualization; formal analysis; writing – review and editing. Orestis A. Panagiotou: Conceptualization; methodology; writing – review and editing. Alain Monnereau: Conceptualization; methodology; resources; writing – review and editing. Nicole Wong Doo: Conceptualization; methodology; resources; writing – review and editing. Zhaoming Wang: Conceptualization; formal analysis; methodology; writing – review and editing. Susan L. Slager: Resources; writing – review and editing. Roel C. H. Vermeulen: Resources; writing – review and editing. Claire M. Vajdic: Resources; writing – review and editing. Karin E. Smedby: Resources; writing – review and editing. John J. Spinelli: Resources; writing – review and editing. Joseph Vijai: Resources; writing – review and editing. Graham G. Giles: Resources; writing – review and editing. Brian K. Link: Resources; writing – review and editing. Alan A. Arslan: Resources; writing – review and editing. Alexandra Nieters: Resources; writing – review and editing. Paige M. Bracci: Resources; writing – review and editing. Nicola J. Camp: Resources; writing – review and editing. Gilles Salles: Resources; writing – review and editing. Wendy Cozen: Resources; writing – review and editing. Henrik Hjalgrim: Resources; writing – review and editing. Immaculata De Vivo: Resources; writing – review and editing. Hans‐Olov Adami: Resources; writing – review and editing. Demetrius Albanes: Resources; writing – review and editing. Nikolaus Becker: Resources; writing – review and editing. Yolanda Benavente: Resources; writing – review and editing. Simonetta Bisanzi: Resources; writing – review and editing. Paolo Boffetta: Resources; writing – review and editing. Paul Brennan: Resources; writing – review and editing. Angela R. Brooks‐Wilson: Resources; writing – review and editing. Federico Canzian: Resources; writing – review and editing. Jacqueline Clavel: Resources; writing – review and editing. Lucia Conde: Resources; writing – review and editing. David G. Cox: Resources; writing – review and editing. Karen Curtin: Resources; writing – review and editing. Lenka Foretova: Resources; writing – review and editing. Hervé Ghesquières: Resources; writing – review and editing. Bengt Glimelius: Resources; writing – review and editing. Thomas M. Habermann: Resources; writing – review and editing. Jonathan N. Hofmann: Resources; writing – review and editing. Qing Lan: Resources; writing – review and editing. Mark Liebow: Resources; writing – review and editing. Anne Lincoln: Resources; writing – review and editing. Marc Maynadie: Resources; writing – review and editing. James McKay: Resources; writing – review and editing. Mads Melbye: Resources; writing – review and editing. Lucia Miligi: Resources; writing – review and editing. Roger L. Milne: Resources; writing – review and editing. Thierry J. Molina: Resources; writing – review and editing. Lindsay M. Morton: Resources; writing – review and editing. Kari E. North: Resources; writing – review and editing. Kenneth Offit: Resources; writing – review and editing. Marina Padoan: Resources; writing – review and editing. Sara Piro: Resources; writing – review and editing. Alpa V. Patel: Resources; writing – review and editing. Mark P. Purdue: Resources; writing – review and editing. Vignesh Ravichandran: Resources; writing – review and editing. Elio Riboli: Resources; writing – review and editing. Richard K. Severson: Resources; writing – review and editing. Melissa C. Southey: Resources; writing – review and editing. Anthony Staines: Resources; writing – review and editing. Lesley F. Tinker: Resources; writing – review and editing. Ruth C. Travis: Resources; writing – review and editing. Sophia S. Wang: Resources; writing – review and editing. Elisabete Weiderpass: Resources; writing – review and editing. Stephanie Weinstein: Resources; writing – review and editing. Tongzhang Zheng: Resources; writing – review and editing. Stephen J. Chanock: Resources; writing – review and editing. Nathaniel Rothman: Resources; writing – review and editing. Brenda M. Birmann: Resources; writing – review and editing. James R. Cerhan: Conceptualization; methodology; resources; writing – review and editing. Sonja I. Berndt: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; resources; supervision; writing – original draft; writing – review and editing.

CONFLICT OF INTEREST STATEMENT

G. Salles is on the advisory board for Abbvie, Beigene, BMS, Genentech/Roche, Genmab, Incyte, Ipsen, Janssen, Kite/Gilead, Loxo/Lilly, Merck, Novartis, and Nurix; consults for Abbvie, Atbtherapeutics, Beigene, BMS, Debiopharm, Genentech/Roche, Genmab, Innate Pharma, Incyte, Ipsen, Kite/Gilead, Modex, Molecular Partners, Nordic, Nanovector, Orna Therapeutics, and Treeline; receives research support from Abbvie, Genentech, Genmab, Janssen, Ipsen, and Nurix; and is a shareholder of Owkin. T.M. Habermann is on the data monitoring committee for Seagen and Eli Lilly and receives research program support from Roche/Genentech, BMS, and Genmab. The remaining authors have no conflicts of interest to declare.

ETHICS STATEMENT

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the following Institutional Review Boards: ATBC:(NCI Special Studies Institutional Review Board), BCCA: UBC BC Cancer Research Ethics Board, CPS‐II: American Cancer Society, ELCCS: Northern and Yorkshire Research Ethics Committee, ENGELA: IRB00003888—Comite d’ Evaluation Ethique de l'Inserm IRB # 1, EPIC: Imperial College London, EpiLymph: International Agency for Research on Cancer, HPFS: Harvard School of Public Health (HSPH) Institutional Review Board, Iowa‐Mayo SPORE: University of Iowa Institutional Review Board, Italian GxE: Comitato Etico Azienda Ospedaliero Universitaria di Cagliari, Mayo Clinic Case–Control: Mayo Clinic Institutional Review Board, MCCS: Cancer Council Victoria's Human Research Ethics Committee, MD Anderson: University of Texas MD Anderson Cancer Center Institutional Review Board, MSKCC: Memorial Sloan‐Kettering Cancer Center Institutional Review Board, NCI‐SEER (NCI Special Studies Institutional Review Board), NHS: Partners Human Research Committee, Brigham and Women's Hospital, NSW: NSW Cancer Council Ethics Committee, NYU‐WHS: New York University School of Medicine Institutional Review Board, PLCO: (NCI Special Studies Institutional Review Board), SCALE: Scientific Ethics Committee for the Capital Region of Denmark, SCALE: Regional Ethical Review Board in Stockholm (Section 4) IRB#5, UCSF2: University of California San Francisco Committee on Human Research, WHI: Fred Hutchinson Cancer Research Center, Yale: Human Investigation Committee, Yale University School of Medicine. Informed consent was obtained from all subjects involved in the study.

Supporting information

Data S1: Supporting information

IJC-158-45-s001.pdf^{(1.1MB, pdf)}

ACKNOWLEDGEMENTS

This study was supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH. The contributions of the NIH authors were made as part of their official duties as NIH federal employees, are in compliance with agency policy requirements, and are considered Works of the United States Government. However, the findings and conclusions presented in this paper are those of the authors and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services. Where authors are identified as personnel of the International Agency for Research on Cancer / World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer / World Health Organization. The InterLymph Data Coordinating Centre is funded by NCI 1U01CA257679. The funders had no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of the manuscript; or the decision to submit the manuscript for publication. The authors thank Mr. William Wheeler (Information Management Services, Inc.) for his analytic support. David Cox and Richard Severson were not available to confirm their co‐authorship of this article, but the corresponding author Sonja Berndt affirms that they contributed to the paper and vouches for their co‐authorship status. A complete list of funding sources and acknowledgements for individual studies is listed below: ATBC—The ATBC Study is supported by the Intramural Research Program of the U.S. National Cancer Institute, National Institutes of Health, Department of Health and Human Services. BC Cancer—Canadian Institutes for Health Research (CIHR); Canadian Cancer Society; Michael Smith Foundation for Health Research. CPS‐II—The Cancer Prevention Study‐II (CPS‐II) Nutrition Cohort is supported by the American Cancer Society. Genotyping for all CPS‐II samples was supported by the Intramural Research Program of the National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics. The authors express sincere appreciation to all Cancer Prevention Study‐II participants and to each member of the study and biospecimen management group. The authors would like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention's National Program of Cancer Registries and cancer registries supported by the National Cancer Institute's Surveillance Epidemiology and End Results Program. ELCCS—Blood Cancer UK, United Kingdom. ENGELA—Association pour la Recherche contre le Cancer (ARC), Institut National du Cancer (INCa), Fondation de France, Fondation contre la Leucémie, Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES). EPIC—Coordinated Action (Contract #006438, SP23‐CT‐2005‐006438); HuGeF (Human Genetics Foundation), Torino, Italy; Cancer Research UK. EpiLymph—European Commission (grant references QLK4‐CT‐2000‐00422 and FOOD‐CT‐2006‐023103); We thank CERCA Programme/Generalitat de Catalunya for institutional support. This study has been funded by Instituto de Salud Carlos III through the project PI20/00288 and CIBERESP CB06/02/007 (Co‐funded by European Regional Development Fund. ERDF, a way to build Europe) and the Secretariat for Universities and Research of the Department of Business and Knowledge of the Government of Catalonia grants to support the activities of research groups 2017SGR1085; the NIH (contract NO1‐CO‐12400); the Compagnia di San Paolo—Programma Oncologia; the Federal Office for Radiation Protection grants StSch4261 and StSch4420, the José Carreras Leukemia Foundation grant DJCLS‐R12/23, the German Federal Ministry for Education and Research (BMBF‐01‐EO‐1303); the Health Research Board, Ireland, and Cancer Research Ireland; Czech Republic supported by MH CZ—DRO (MMCI, 00209805); Fondation de France and Association de Recherche Contre le Cancer. GEC/Mayo GWAS—National Institutes of Health (CA118444, CA148690, CA92153). Intramural Research Program of the NIH, National Cancer Institute. Veterans Affairs Research Service. Data collection for Duke University was supported by a Leukemia & Lymphoma Society Career Development Award, the Bernstein Family Fund for Leukemia and Lymphoma Research, and the National Institutes of Health (K08CA134919), National Center for Advancing Translational Science (UL1 TR000135). HPFS (Walter C. Willet)—The HPFS was supported in part by National Institutes of Health grants UO1 CA167552, R01 CA149445, and R01 CA098122. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors would like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention's National Program of Cancer Registries (NPCR) and/or the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program. Central registries may also be supported by state agencies, universities, and cancer centers. Participating central cancer registries include the following: Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Indiana, Iowa, Kentucky, Louisiana, Massachusetts, Maine, Maryland, Michigan, Mississippi, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Puerto Rico, Rhode Island, Seattle SEER Registry, South Carolina, Tennessee, Texas, Utah, Virginia, West Virginia, Wyoming. We would also like to thank the participants and staff of the Health Professionals Follow‐up Study for their valuable contributions. The study protocol was approved by the institutional review boards of the Brigham and Women's Hospital and Harvard T.H. Chan School of Public Health, and those of participating registries as required. Iowa‐Mayo SPORE—NCI Specialized Programs of Research Excellence (SPORE) in Human Cancer (P50 CA97274); National Cancer Institute (P30 CA086862, P30 CA15083); Henry J. Predolin Foundation. Italian GxE—Italian Association for Cancer Research (AIRC, Investigator Grant 11855) (PC); Fondazione Banco di Sardegna 2010‐2012, and Regione Autonoma della Sardegna (LR7 CRP‐59812/2012) (MGE). Mayo Clinic Case–Control—National Institutes of Health (R01 CA92153); National Cancer Institute (P30 CA015083). MCCS—The Melbourne Collaborative Cohort Study recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057, 251553, and 504711 and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database. MSKCC—Geoffrey Beene Cancer Research Grant, Lymphoma Foundation (LF5541); Barbara K. Lipman Lymphoma Research Fund (74419); Robert and Kate Niehaus Clinical Cancer Genetics Research Initiative (57470); U01 HG007033; ENCODE; U01 HG007033. NCI‐SEER—Intramural Research Program of the National Cancer Institute, National Institutes of Health, and Public Health Service (N01‐PC‐65064, N01‐PC‐67008, N01‐PC‐67009, N01‐PC‐67010, N02‐PC‐71105). NHS (Meir J. Stampfer)—The NHS was supported in part by National Institutes of Health grants UM1 CA186107, P01 CA87969, R01 CA49449, R01 CA149445, R01 CA098122, and R01 CA134958. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors would like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention's National Program of Cancer Registries (NPCR) and/or the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program. Central registries may also be supported by state agencies, universities, and cancer centers. Participating central cancer registries include the following: Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Indiana, Iowa, Kentucky, Louisiana, Massachusetts, Maine, Maryland, Michigan, Mississippi, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Puerto Rico, Rhode Island, Seattle SEER Registry, South Carolina, Tennessee, Texas, Utah, Virginia, West Virginia, Wyoming. We also thank the participants and staff of the Nurses' Health Study for their valuable contributions. The study protocol was approved by the institutional review boards of the Brigham and Women's Hospital and Harvard T. H. Chan School of Public Health, and those of participating registries as required. NSW—NSW was supported by grants from the Australian National Health and Medical Research Council (ID990920), the Cancer Council NSW, and the University of Sydney Faculty of Medicine. NYU‐WHS—National Cancer Institute (R01 CA098661, P30 CA016087); National Institute of Environmental Health Sciences (ES000260). PLCO—This research was supported by the Intramural Research Program of the National Cancer Institute and by contracts from the Division of Cancer Prevention, National Cancer Institute, NIH, DHHS. SCALE—Swedish Cancer Society (2009/659). Stockholm County Council (20110209) and the Strategic Research Program in Epidemiology at Karolinska Institutet. Swedish Cancer Society grant (02 6661). National Institutes of Health (5R01 CA69669‐02); Plan Denmark. UCSF2—The UCSF studies were supported by the NCI, National Institutes of Health, CA1046282 and CA154643. The collection of cancer incidence data used in this study was supported by the California Department of Health Services as part of the statewide cancer reporting program mandated by California Health and Safety Code Section 103885; the National Cancer Institute's Surveillance, Epidemiology, and End Results Program under contract HHSN261201000140C awarded to the Cancer Prevention Institute of California, contract HHSN261201000035C awarded to the University of Southern California, and contract HHSN261201000034C awarded to the Public Health Institute; and the Centers for Disease Control and Prevention's National Program of Cancer Registries, under agreement #1 U58 DP000807‐01 awarded to the Public Health Institute. The ideas and opinions expressed herein are those of the authors, and endorsement by the State of California, the California Department of Health Services, the National Cancer Institute, or the Centers for Disease Control and Prevention or their contractors and subcontractors is not intended nor should be inferred. UTAH/Sheffield—National Institutes of Health CA134674. Partial support for data collection at the Utah site was made possible by the Utah Population Database (UPDB) and the Utah Cancer Registry (UCR). Partial support for all datasets within the UPDB is provided by the Huntsman Cancer Institute (HCI) and the HCI Cancer Center Support grant, P30 CA42014. The UCR is funded by the NCI's SEER Program, Contract No. HHSN261201800016I, the US Centers for Disease Control and Prevention's National Program of Cancer Registries, Cooperative Agreement No. NU58DP007131, with additional support from the University of Utah and Huntsman Cancer Foundation. Partial support for data collection in Sheffield, UK was made possible by funds from Yorkshire Cancer Research and the Sheffield Experimental Cancer Medicine Centre. We thank the NCRI Haemato‐oncology Clinical Studies Group, colleagues in the North Trent Cancer Network, and the North Trent Haemato‐oncology Database. WHI—WHI investigators are: Program Office—(National Heart, Lung, and Blood Institute, Bethesda, Maryland) Jacques Rossouw, Shari Ludlam, Dale Burwen, Joan McGowan, Leslie Ford, and Nancy Geller; Clinical Coordinating Center—(Fred Hutchinson Cancer Research Center, Seattle, WA) Garnet Anderson, Ross Prentice, Andrea LaCroix, and Charles Kooperberg; Investigators and Academic Centers—(Brigham and Women's Hospital, Harvard Medical School, Boston, MA) JoAnn E. Manson; (MedStar Health Research Institute/Howard University, Washington, DC) Barbara V. Howard; (Stanford Prevention Research Center, Stanford, CA) Marcia L. Stefanick; (The Ohio State University, Columbus, OH) Rebecca Jackson; (University of Arizona, Tucson/Phoenix, AZ) Cynthia A. Thomson; (University at Buffalo, Buffalo, NY) Jean Wactawski‐Wende; (University of Florida, Gainesville/Jacksonville, FL) Marian Limacher; (University of Iowa, Iowa City/Davenport, IA) Robert Wallace; (University of Pittsburgh, Pittsburgh, PA) Lewis Kuller; (Wake Forest University School of Medicine, Winston‐Salem, NC) Sally Shumaker; Women's Health Initiative Memory Study—(Wake Forest University School of Medicine, Winston‐Salem, NC) Sally Shumaker. The WHI program is funded by the National Heart, Lung, and Blood Institute, National Institutes of Health, U.S. Department of Health and Human Services through contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C. YALE—National Cancer Institute (CA62006); National Cancer Institute (CA165923).

Moore A, Kane E, Teras LR, et al. Genetically determined body mass index is associated with diffuse large B‐cell lymphoma in polygenic and Mendelian randomization analyses. Int J Cancer. 2026;158(1):45‐59. doi: 10.1002/ijc.70039

The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/World Health Organization.

DATA AVAILABILITY STATEMENT

Genotype data from the NCI NHL GWAS is available on dbGaP (phs000801.v2.p1) for research purposes in accordance with dbGaP data access policies. Other data that support the findings of this study are available for research purposes through the InterLymph Consortium upon approval in accordance with institutional review boards and general data protection regulations.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1: Supporting information

IJC-158-45-s001.pdf^{(1.1MB, pdf)}

Data Availability Statement

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PERMALINK

Genetically determined body mass index is associated with diffuse large B‐cell lymphoma in polygenic and Mendelian randomization analyses

Amy Moore

Eleanor Kane

Lauren R Teras

Mitchell J Machiela

Joshua Arias

Orestis A Panagiotou

Alain Monnereau

Nicole Wong Doo

Zhaoming Wang

Susan L Slager

Roel C H Vermeulen

Claire M Vajdic

Karin E Smedby

John J Spinelli

Joseph Vijai

Graham G Giles

Brian K Link

Alan A Arslan

Alexandra Nieters

Paige M Bracci

Nicola J Camp

Gilles Salles

Wendy Cozen

Henrik Hjalgrim

Immaculata De Vivo

Hans‐Olov Adami

Demetrius Albanes

Nikolaus Becker

Yolanda Benavente

Simonetta Bisanzi

Paolo Boffetta

Paul Brennan

Angela R Brooks‐Wilson

Federico Canzian

Jacqueline Clavel

Lucia Conde

David G Cox

Karen Curtin

Lenka Foretova

Hervé Ghesquières

Bengt Glimelius

Thomas M Habermann

Jonathan N Hofmann

Qing Lan

Mark Liebow

Anne Lincoln

Marc Maynadie

James McKay

Mads Melbye

Lucia Miligi

Roger L Milne

Thierry J Molina

Lindsay M Morton

Kari E North

Kenneth Offit

Marina Padoan

Sara Piro

Alpa V Patel

Mark P Purdue

Vignesh Ravichandran

Elio Riboli

Richard K Severson

Melissa C Southey

Anthony Staines

Lesley F Tinker

Ruth C Travis

Sophia S Wang

Elisabete Weiderpass

Stephanie Weinstein

Tongzhang Zheng

Stephen J Chanock

Nathaniel Rothman

Brenda M Birmann

James R Cerhan

Sonja I Berndt

Abstract

Abbreviations

1. INTRODUCTION