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Body mass index and subsequent fracture risk: a meta-analysis to update FRAX

Meta-analysis first published in Journal of Bone and Mineral Research (2025), reprinted in full under its CC BY 4.0 licence.

Nicholas C Harvey1*ORCID iD 0000-0002-8194-2512, Helena Johansson2, Eugene V McCloskey3ORCID iD 0000-0003-0177-8140, Enwu Liu4ORCID iD 0000-0003-2580-3523, Kristina E Åkesson5ORCID iD 0000-0003-3024-2804, Fred A Anderson6, Rafael Azagra-Ledesma7, Cecilie L Bager8, Charlotte Beaudart9, Heike A Bischoff-Ferrari10, Emmanuel Biver11ORCID iD 0000-0001-6174-1951, Olivier Bruyère12, Jane A Cauley13, Jacqueline R Center14ORCID iD 0000-0002-5278-4527, Roland Chapurlat15, Claus Christiansen8, Cyrus Cooper16ORCID iD 0000-0003-3510-0709, Carolyn J Crandall17ORCID iD 0000-0001-9441-0198, Steven R Cummings18, José A P da Silva19, Bess Dawson-Hughes20ORCID iD 0000-0003-3136-0386, Adolfo Diez-Perez21, Alyssa B Dufour22ORCID iD 0000-0001-8061-9599, John A Eisman23ORCID iD 0000-0002-7162-7471, Petra J M Elders24, Serge Ferrari11ORCID iD 0000-0002-1372-4417, Yuki Fujita25, Saeko Fujiwara26, Claus-Christian Glüer27, Inbal Goldshtein28, David Goltzman29, Vilmundur Gudnason30, Jill Hall31, Didier Hans32ORCID iD 0000-0003-1826-5958, Mari Hoff33, Rosemary J Hollick34, Martijn Huisman35, Masayuki Iki36ORCID iD 0000-0003-2128-5255, Sophia Ish-Shalom37, Graeme Jones38, Magnus K Karlsson39ORCID iD 0000-0002-0616-5928, Sundeep Khosla40ORCID iD 0000-0002-2936-4372, Douglas P Kiel41ORCID iD 0000-0001-8474-0310, Woon-Puay Koh42, Fjorda Koromani43ORCID iD 0000-0002-6942-5889, Mark A Kotowicz44, Heikki Kröger45, Timothy Kwok46ORCID iD 0000-0001-9253-3549, Olivier Lamy47ORCID iD 0000-0001-6512-6008, Arnulf Langhammer48, Bagher Larijani49ORCID iD 0000-0002-0023-1568, Kurt Lippuner50, Fiona E A McGuigan51ORCID iD 0000-0002-8033-9981, Dan Mellström52, Thomas Merlijn24ORCID iD 0000-0003-3003-6488, Tuan V Nguyen53, Anna Nordström54, Peter Nordström55, Terence W O’Neill56, Barbara Obermayer-Pietsch57, Claes Ohlsson58ORCID iD 0000-0002-9633-2805, Eric S Orwoll59ORCID iD 0000-0002-8520-7355, Julie A Pasco60, Fernando Rivadeneira61, Berit Schei62, Anne-Marie Schott63, Eric J Shiroma64, Kristin Siggeirsdottir65, Eleanor M Simonsick66ORCID iD 0000-0002-5460-3494, Elisabeth Sornay-Rendu67, Reijo Sund68ORCID iD 0000-0002-6268-8117, Karin M A Swart69, Pawel Szulc67ORCID iD 0000-0003-4168-6697, Junko Tamaki70ORCID iD 0000-0003-3930-9729, David J Torgerson71, Natasja M van Schoor72, Tjeerd P van Staa73, Joan Vila74, Nicholas J Wareham75, Nicole C Wright76ORCID iD 0000-0001-8414-4842, Noriko Yoshimura77, M Carola Zillikens61ORCID iD 0000-0001-9186-3423, Marta Zwart78, Liesbeth Vandenput79, Mattias Lorentzon80ORCID iD 0000-0003-0749-1431, William D Leslie81ORCID iD 0000-0002-1056-1691, John A Kanis2

1MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, SO16 6YD, United Kingdom; NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, United Kingdom2Centre for Metabolic Bone Diseases, University of Sheffield, Sheffield, United Kingdom3Centre for Metabolic Bone Diseases, University of Sheffield, Sheffield, United Kingdom; MRC Versus Arthritis Centre for Integrated Research in Musculoskeletal Ageing, Mellanby Centre for Musculoskeletal Research, University of Sheffield, Sheffield, United Kingdom4South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia5Clinical and Molecular Osteoporosis Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden; Department of Orthopedics, Skåne University Hospital, Malmö, Sweden6GLOW Coordinating Center, Center for Outcomes Research, University of Massachusetts Medical School, Worcester, MA, United States7Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain; Docencia Atenció Primària, Metropolitana Nord, Institut Català de la Salut, Barcelona, Spain; GROIMAP/GROICAP (research groups), Unitat de Suport a la Recerca de Girona, Institut Universitari d’Investigació en Atenció Primària Jordi Gol, Girona, Spain; PRECIOSA-Fundación para la investigación, Barberà del Vallés, Barcelona, Spain8Nordic Bioscience A/S, Herlev, Denmark9Public Health Aging Research and Epidemiology (PHARE) Group, Research Unit in Clinical Pharmacology and Toxicology (URPC), NAmur Research Institute for LIfe Sciences (NARILIS), Faculty of Medicine, University of Namur, 5000 Namur, Belgium10Department of Aging Medicine, Felix Platter, University of Basel, 4055 Basel, Switzerland; Centre on Aging and Mobility, University of Zurich and City Hospital, Zurich, Switzerland11Division of Bone Diseases, Department of Medicine, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland12Research Unit in Public Health, Epidemiology and Health Economics, University of Liège, Liège, Belgium13Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States14Garvan Institute of Medical Research, Sydney, NSW, Australia; St Vincent's Clinical School, School of Medicine and Health, University of New South Wales Sydney, Sydney, NSW, Australia15INSERM UMR 1033, Université Claude Bernard-Lyon1, Hôpital Edouard Herriot, Lyon, France16MRC Lifecourse Epidemiology Centre, University of Southampton, Southampton, SO16 6YD, United Kingdom; NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, United Kingdom; NIHR Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom17Division of General Internal Medicine and Health Services Research, David Geffen School of Medicine, University of California, Los Angeles, CA, United States18San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, CA, United States19Centre for Innovative Biomedicine and Biotechnology, Faculty of Medicine, University of Coimbra, Coimbra, Portugal; Rheumatology Department, University Hospital and University of Coimbra, Coimbra, Portugal20Bone Metabolism Laboratory, Jean Mayer US Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, Boston, MA, United States21Department of Internal Medicine, Hospital del Mar and CIBERFES, Autonomous University of Barcelona, Barcelona, Spain22Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, United States; Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States23St Vincent's Clinical School, School of Medicine and Health, University of New South Wales Sydney, Sydney, NSW, Australia; Skeletal Diseases Program, Garvan Institute of Medical Research, Sydney, NSW, Australia; School of Medicine Sydney, University of Notre Dame Australia, Sydney, NSW, Australia24Department of General Practice, Amsterdam UMC, location AMC, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands25Department of Hygiene and Public Health, Faculty of Medicine, Kansai Medical University, Osaka, Japan26Department of Pharmacy, Yasuda Women’s University, Hiroshima, Japan27Section Biomedical Imaging, Molecular Imaging North Competence Center, Department of Radiology and Neuroradiology, University Medical Center Schleswig-Holstein Kiel, Kiel University, Kiel, Germany28Maccabitech Institute of Research and Innovation, Maccabi Healthcare Services, Tel Aviv, Israel; Department of Epidemiology and Preventive Medicine, Sackler Faculty of Medicine, School of Public Health, Tel Aviv University, Tel Aviv, Israel29Department of Medicine, McGill University and McGill University Health Centre, Montreal, QC, Canada30Icelandic Heart Association, Kopavogur, Iceland; University of Iceland, Reykjavik, Iceland31MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, United Kingdom32Interdisciplinary Centre of Bone Diseases, Bone and Joint Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland33Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway; Department of Rheumatology, St. Olavs Hospital, Trondheim, Norway34Aberdeen Centre for Arthritis and Musculoskeletal Health, Epidemiology Group, University of Aberdeen, Aberdeen, United Kingdom35Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, VU University Medical Center, Amsterdam, The Netherlands; Department of Sociology, VU University, Amsterdam, The Netherlands36Department of Public Health, Kindai University Faculty of Medicine, Osaka, Japan37Endocrine Clinic, Elisha Hospital, Haifa, Israel38Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia39Clinical and Molecular Osteoporosis Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden; Department of Orthopaedics, Skåne University Hospital, Malmö, Sweden40Robert and Arlene Kogod Center on Aging and Division of Endocrinology, Mayo Clinic College of Medicine, Mayo Clinic, Rochester, MN, United States41Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States; Marcus Institute for Aging Research, Hebrew Senior Life, Boston, MA, United States42Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore43Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands; Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands44Institute for Mental and Physical Health and Clinical Translation (IMPACT), Deakin University, Geelong, VIC, Australia; Barwon Health, Geelong, VIC, Australia; Department of Medicine-Western Health, The University of Melbourne, St Albans, VIC, Australia45Department of Orthopedics and Traumatology, Kuopio University Hospital, Kuopio, Finland; Kuopio Musculoskeletal Research Unit, University of Eastern Finland, Kuopio, Finland46Department of Medicine and Therapeutics, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong; Jockey Club Centre for Osteoporosis Care and Control, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong47Centre of Bone Diseases, Lausanne University Hospital, Lausanne, Switzerland; Service of Internal Medicine, Lausanne University Hospital, Lausanne, Switzerland48HUNT Research Centre, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway; Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway49Endocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran50ARTORG Center for Biomedical Engineering Research, Faculty of Medicine, University of Bern, Bern, Switzerland51Clinical and Molecular Osteoporosis Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden52Geriatric Medicine, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Geriatric Medicine, Sahlgrenska University Hospital Mölndal, Mölndal, Sweden53School of Biomedical Engineering, University of Technology Sydney, Sydney, NSW, Australia; School of Population Health, UNSW Medicine, UNSW Sydney, Kensington, NSW, Australia; Tam Anh Research Institute, Tam Anh Hospital, Ho Chi Minh City, Vietnam54School of Sport Sciences, UiT The Arctic University of Norway, Tromsø, Norway; Department of Health Sciences, Swedish Winter Sports Research Centre, Mid Sweden University, Östersund, Sweden; Department of Medical Sciences, Uppsala University, Uppsala, Sweden55Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden56National Institute for Health Research Manchester Biomedical Research Centre, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom; Centre for Epidemiology Versus Arthritis, University of Manchester, Manchester, United Kingdom57Division of Endocrinology and Diabetology, Department of Internal Medicine, Medical University Graz, Graz, Austria58Sahlgrenska Osteoporosis Centre, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Drug Treatment, Sahlgrenska University Hospital, Gothenburg, Sweden59Department of Medicine, Oregon Health and Science University, Portland, OR, United States60Institute for Physical and Mental Health and Clinical Translation (IMPACT), Deakin University, Geelong, VIC, Australia; Department of Medicine-Western Health, The University of Melbourne, St Albans, VIC, Australia; Barwon Health, Geelong, VIC, Australia; Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia61Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands62Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway; Department of Obstetrics and Gynaecology, St Olavs Hospital, Trondheim, Norway63Université Claude Bernard Lyon 1, RESHAPE U INSERM U-1290, Lyon, France64Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Baltimore, MD, United States65Icelandic Heart Association, Kopavogur, Iceland; Janus Rehabilitation, Reykjavik, Iceland66Translational Gerontology Branch, National Institute on Aging Intramural Research Program, Baltimore, MD, United States67INSERM UMR 1033, University of Lyon, Hôpital Edouard Herriot, Lyon, France68Kuopio Musculoskeletal Research Unit, University of Eastern Finland, Kuopio, Finland69Department of General Practice, Amsterdam UMC, location VUmc, Amsterdam Public Health Research Institute, Amsterdam, The Netherlands; PHARMO Institute for Drug Outcomes Research, Utrecht, The Netherlands70Department of Hygiene and Public Health, Faculty of Medicine, Educational Foundation of Osaka Medical and Pharmaceutical University, Osaka, Japan71York Trials Unit, Department of Health Sciences, University of York, York, United Kingdom72Department of Epidemiology and Data Science, Amsterdam UMC location Vrije Universiteit, Amsterdam, The Netherlands; Amsterdam Public Health Research Institute, Aging and Later Life Program, Amsterdam, The Netherlands73Centre for Health Informatics, Faculty of Biology, Medicine and Health, School of Health Sciences, University of Manchester, Manchester, United Kingdom74Statistics Support Unit, Hospital del Mar Medical Research Institute, CIBER Epidemiology and Public Health (CIBERESP), Barcelona, Spain75MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom76Center for Health Outcomes, Implementation, and Community-Engaged Sciences, Tulane University, New Orleans, LA, United States77Department of Preventive Medicine for Locomotive Organ Disorders, The University of Tokyo Hospital, Tokyo, Japan78PRECIOSA-Fundación para la investigación, Barberà del Vallés, Barcelona, Spain; Health Center Can Gibert del Pla, Catalan Institute of Health, Girona, Spain; Department of Medical Sciences, University of Girona, Girona, Spain; GROICAP (Research Group), Institut Universitari d’Investigació en Atenció Primària Jordi Gol, Girona, Spain79Sahlgrenska Osteoporosis Centre, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden80Sahlgrenska Osteoporosis Centre, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden; Region Västra Götaland, Geriatric Medicine, Sahlgrenska University Hospital, Mölndal, Sweden81Department of Medicine, University of Manitoba, Winnipeg, MB, Canada*Corresponding author

Reprinted 2026-10-01 18 min read Living reprint · journal article Version of record: Journal of Bone and Mineral Research 2025Licence: CC BY 4.0

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In plain languageMeta-analysis first published in Journal of Bone and Mineral Research (2025), reprinted in full under its CC BY 4.0 licence.

Meta-analysis first published in Journal of Bone and Mineral Research (2025), reprinted in full under its CC BY 4.0 licence.

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Abstract

The aim of this international meta-analysis was to quantify the predictive value of BMI for incident fracture and relationship of this risk with age, sex, follow-up time, and BMD. A total of 1 667 922 men and women from 32 countries (63 cohorts), followed for a total of 16.0 million person-years were studied. 293 325 had FN BMD measured (2.2 million person-years follow-up). An extended Poisson model in each cohort was used to investigate relationships between WHO-defined BMI categories (Underweight: <18.5 kg/m2; Normal: 18.5-24.9 kg/m2; Overweight: 25.0-29.9 kg/m2; Obese I: 30.0-34.9 kg/m2; Obese II: ≥35.0 kg/m2) and risk of incident osteoporotic, major osteoporotic and hip fracture (HF). Inverse-variance weighted β-coefficients were used to merge the cohort-specific results. For the subset with BMD available, in models adjusted for age and follow-up time, the hazard ratio (95% CI) for HF comparing underweight with normal weight was 2.35 (2.10-2.60) in women and for men was 2.45 (1.90-3.17). Hip fracture risk was lower in overweight and obese categories compared to normal weight [obese II vs normal: women 0.66 (0.55-0.80); men 0.91 (0.66-1.26)]. Further adjustment for FN BMD T-score attenuated the increased risk associated with underweight [underweight vs normal: women 1.69 (1.47-1.96); men 1.46 (1.00-2.13)]. In these models, the protective effects of overweight and obesity were attenuated, and in both sexes, the direction of association reversed to higher fracture risk in Obese II category [Obese II vs Normal: women 1.24 (0.97-1.58); men 1.70 (1.06-2.75)]. Results were similar for other fracture outcomes. Underweight is a risk factor for fracture in both men and women regardless of adjustment for BMD. However, while overweight/obesity appeared protective in base models, they became risk factors after additional adjustment for FN BMD, particularly in the Obese II category. This effect in the highest BMI categories was of greater magnitude in men than women. These results will inform the second iteration of FRAX®.

Keywords: BMI; meta-analysis; hip fracture; osteoporosis; epidemiology; major osteoporotic fracture; FRAX

Introduction

Body mass index (BMI), calculated as weight divided by height squared, is an accepted surrogate for adiposity in population studies and as a clinical risk factor in assessment for outcomes, such as myocardial infarction and fracture.1,2 Multiple studies have documented the complex relationships between fat mass, lean mass, and bone mineral density (BMD) in the determination of fracture risk.3,4 Obesity is associated with an increased risk of falling.5 Both low and high BMI have been associated with greater risk of fracture, but importantly, with different fracture sites at either end of the BMI spectrum: Findings from the Global Longitudinal study of Osteoporosis in Women (GLOW) suggested high risk of ankle and upper leg fractures with obesity,6 with similar results in the Spanish SIDIAP dataset.7 More recently, a Mendelian Randomization study demonstrated causal associations between low BMI and risk of forearm fracture.8 Consistent with these observations, in our previous meta-analysis of 398 610 women, mean age 63 years, low BMI was a risk factor for hip and all osteoporotic fractures, but appeared to be protective for lower leg/ankle fracture.2 In contrast, high BMI was associated with increased risk for humerus and elbow fractures. This relationship was dependent partly on BMD. Thus, after adjustment for FN BMD, high BMI remained a risk factor for upper arm fracture but also predicted increased risk of all osteoporotic fractures. BMI therefore conveys risk information for incident fracture, but with important considerations regarding the relationships with BMD and fracture site. However, the evidence base thus far principally focuses on women, with relationships in men, and indeed across ethnicities in either sex, still requiring detailed elucidation.

FRAX, currently available in 86 territories, is the most widely used fracture risk assessment tool and is incorporated into a large number of assessment guidelines,9 recommended by the Committee for Medicinal Products for Human Use (CHMP),10 and approved by the National Institute for Health and Care Excellence (NICE).11 The incorporation of BMI as an input variable for risk prediction was based on an earlier meta-analysis.12 Since then, many more prospectively studied cohorts have become available that have the potential to improve the accuracy of FRAX and understand better the potential influences of BMD, sex, and ethnicity in these relationships.13

The aim of the present study was to quantify magnitude of the risk relationship between BMI and incident fracture in an international setting, and to explore the dependence of this risk on age, sex, ethnicity, time since baseline assessment, and BMD.

Materials and methods

Details of our initial systematic review undertaken to identify appropriate cohorts, and of the cohorts included, have been documented previously13 and are summarized in Table S1. This was registered in the PROSPERO international prospective register of systematic reviews (CRD42021227266), and followed the Preferred Reporting Items for Systematic Reviews (PRISMA) guidelines. In brief, we studied 1 667 922 men and women from 63 prospectively studied cohorts. A total of 57 cohorts included women (n = 1 127 206) and 40 cohorts included men (n = 540 716). Of these 293 325 men and women had femoral neck (FN) BMD measured, from 53 cohorts in 20 countries, with a total follow-up time of 2.2 million person-years.

Baseline and outcome variables

BMI (kg/m2) was calculated from height and weight assessed at baseline. We considered incident fractures in the following categories: all, osteoporotic, major osteoporotic (MOF: clinical spine, distal forearm, hip, or humerus), and hip fracture (HF). For the purpose of this analysis, and related meta-analyses informing the next iteration of FRAX,14–16 we defined “osteoporotic fractures” on the basis of previously identified sites at which fractures were more common with increasing age and decreasing BMD, thus excluding fractures of the skull, face, hands, feet, ankle, and patella, as well as tibia and fibula fractures in men.17,18 This should not be taken to mean that anyone experiencing such a fracture has osteoporosis, merely that they appear more frequent in those with low BMD and/or higher age. No distinction was made according to trauma since both high- and low-trauma fractures show similar relationships with low BMD and future fracture risk.19,20 Details of fracture ascertainment and validation have been published previously.13

Statistical methods

The risk of incident fracture, expressed as hazard ratio (HR) per 1 kg/m2 greater baseline BMI, was estimated using an extended Poisson model applied separately to each cohort (and also separately by sex for those cohorts with both men and women).21 Because of an embargo on data transfer, Cox regression was used on the Manitoba cohort. Covariates included current time since start of follow-up, current age, prior history of fracture, and BMD T-score at the FN. BMD was adjusted for manufacturer and T-scores were calculated from the NHANES III White female reference values.13 We categorized BMI according to WHO recommendations (Underweight: <18.5; Normal: 18.5-24.9 (referent); Overweight: 25.0-29.9; Obese I: 30.0-34.9; Obese II: ≥35.0 kg/m2)22 for analyses by category. We illustrated non-linear associations using spline models with nodes at 21, 25, and 33 kg/m2. Results of each cohort and the 2 sexes were weighted according to the variance and merged to determine the weighted means and standard deviations. The HR was equal to eweighted mean of β. A priori, a random effects model was used in the meta-analysis. Assessment of the effects of ethnicity was confined to those cohorts recording more than one ethnic group [Asian, Black, Hispanic, White and the Black, Asian and Minority Ethnic category (BAME)], comprising Health ABC, CAMOS, MrOS USA, LASA, WHI, SOF, Manitoba, and UK Biobank. Ascertainment of these data was via self-report. On the basis of these 8 cohorts there was sufficient data to undertake pairwise comparisons. Finally, we investigated associations between BMI and risk of death during follow-up.

Results

Of 1 667 922 men and women studied in 32 countries, the average BMI was 27.2 kg/m2. At follow up, 151 473 men and women were identified as having a subsequent fracture of any kind; 125 135 were characterized as osteoporotic in men and women, 95 748 men and women sustained a MOF; 31 383 were HFs. The total follow-up time was 16.0 million-person years in men and women. BMD measurements were available in 17.6% (293325) of individuals. Table S1 summarizes key characteristics of the cohorts.

Association between BMI and risk of incident fracture

In continuous models, summarized in Table 1, after adjustment for age and time since baseline, greater BMI was associated with lower risk of incident fracture in both men and women, for example, HR (95% CI) for major osteoporotic fracture per unit greater BMI in women was 0.98 (0.97, 0.98); in men 0.98 (0.97, 0.98). Figure 1 documents these associations for HF and major osteoporotic fracture in men and women combined as Forest plots. Associations appeared similar by sex and were not materially different in the subset of cohorts in which BMD was measured. In this subset, after further adjustment for BMD, there was a reversal in the relationships such that greater BMI was weakly associated with higher fracture risk. For example, among women HR for major osteoporotic fracture per unit greater BMI was 1.01 (1.00, 1.02) and for men 1.02 (1.01, 1.03) (Table 1). Overall, associations were similar between men and women (Table S2).

Table 1. Hazard ratio (HR) and 95% CI for fracture at the sites indicated per unit greater BMI (linear models). HRs are adjusted for age and time since baseline.
Adjusted for age and time since baselineAdjusted for age and time since baseline—for those with BMDAdjusted for age, time since baseline and BMD
Outcome fractureNumber of cohorts I 2 HR (95% CI)Number of cohorts I 2 HR (95% CI)Number of cohorts I 2 HR (95% CI)
Female
Any55730.99 (0.98, 0.99)46650.98 (0.98, 0.99)46421.01 (1.01, 1.01)
Hip57570.95 (0.94, 0.95)46410.95 (0.94, 0.96)46330.99 (0.98, 1.00)0.060
MOF52830.98 (0.97, 0.98)45770.98 (0.97, 0.98)45571.01 (1.00, 1.02)0.0018
Ost51830.98 (0.97, 0.98)44730.98 (0.97, 0.98)44551.01 (1.00, 1.01)0.0051
Male
Any38250.99 (0.98, 0.99)3200.99 (0.98, 0.99)3201.02 (1.01, 1.02)
Hip36480.95 (0.93, 0.96)29320.95 (0.93, 0.97)2801.03 (1.01, 1.04)
MOF35600.98 (0.97, 0.98)3000.98 (0.97, 0.98)3011.02 (1.01, 1.03)
Ost34560.98 (0.97, 0.99)30180.98 (0.97, 0.99)3001.02 (1.01, 1.03)

Except where indicated, p < .001.

Abbreviations: MOF, major osteoporotic fracture; Ost, osteoporotic fracture.

Figure 1
Figure 1. Forest plot showing effect size on hip fracture (HF) risk (left panel, A) and major osteoporotic fracture (right panel, B) per one unit greater BMI in men and women combined adjusted for age and time since baseline.

Categorical and non-linear associations between BMI and risk of incident fracture

On average, those who were underweight tended to be somewhat older than those in other categories and had lower FN BMD T-score (Table S3). Indeed, there was modest but positive gradient of mean FN BMD T-score going from underweight to normal to overweight to obese categories. Tables 2 and 3 and Figure 2 document the associations between BMI category relative to the normal BMI for men and women, in the subset with BMD measurements, unadjusted and adjusted for FN BMD. As with the initial models, associations were not materially different in the subset with BMD, without BMD adjustment, but the increased risk associated with low BMI and the more modest protective effect of higher BMI category was attenuated by adjustment for FN BMD. Indeed, after BMD adjustment, obesity became a risk factor for fracture in both sexes, with evidence of a greater effect size in men than women (Tables 2 and 3, Table S4). Figure S1 presents the continuous relationships between BMI (HR for fracture for a given BMI vs a BMI of 25 kg/m2) and risk of HF in men and women separately, in the subset with BMD, without and then with adjustment for FN BMD.

Table 2. Hazard ratio (HR) for fracture by BMI category (reference normal) in women and men, adjusted for age and time since baseline, in subset with FN BMD measure available (but not adjusted for BMD). (BMI categories: underweight BMI < 18.5, normal BMI 18.5-24.9, overweight BMI 25.0-29.9, obese I BMI 30.0-34.9, obese II BMI ≥ 35.0 kg/m2). HR in bold are <0.001.
1345
Underweight vs NormalOverweight vs NormalObese I vs NormalObese II vs Normal
Outcome fractureN, cohortsHR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)
Women
 Any38, 47, 45, 401.31 [1.20, 1.43]0.94 [0.90, 0.98]0.88 [0.83, 0.93]0.84 [0.77, 0.91]
 Hip27, 41, 33, 222.35 [2.13, 2.60]0.75 [0.72, 0.78]0.64 [0.59, 0.69]0.66 [0.55, 0.80]
 MOF33, 45, 42, 361.43 [1.30, 1.59]0.90 [0.85, 0.95]0.82 [0.76, 0.89]0.78 [0.70, 0.88]
 Ost34, 44, 42, 361.38 [1.27, 1.50]0.90 [0.86, 0.94]0.84 [0.79, 0.90]0.77 [0.70, 0.85]
Men
 Any21, 30, 27, 231.77 [1.54, 2.04]0.87 [0.84-0.90]0.88 [0.82, 0.94]1.04 [1.04, 1.05]
 Hip14, 23, 16, 112.45 [1.90, 3.17]0.69 [0.65-0.74]0.63 [0.53, 0.74]0.91 [0.66, 1.26]
 MOF16, 28, 23, 171.87 [1.60, 2.20]0.85 [0.80-0.90]0.80 [0.72, 0.89]0.96 [0.96, 0.97]
 Ost19, 28, 25, 211.83 [1.58, 2.12]0.85 [0.82-0.89]0.82 [0.76, 0.89]0.96 [0.96, 0.97]
p women vs men
 Any<0.0010.0067>0.30<0.001
 Hip>0.300.024>0.300.088
 MOF0.00400.18>0.30<0.001
 Ost0.00110.053>0.30<0.001

Abbreviations: MOF, major osteoporotic fracture; Ost, osteoporotic fracture.

Table 3. Hazard ratio (HR) for fracture by BMI category (reference normal) in women and men, adjusted for age and time since baseline, in subset with FN BMD measure available, additionally adjusted for BMD. (BMI categories: underweight BMI < 18.5, normal BMI 18.5-24.9, overweight BMI 25.0-29.9, obese I BMI 30.0-34.9, obese II BMI ≥ 35.0 kg/m2). HR in bold are <0.001.
1345
Underweight vs NormalOverweight vs NormalObese I vs NormalObese II vs Normal
Outcome fractureN, cohortsHR (95% CI)HR (95% CI)HR (95% CI)HR (95% CI)
Women
 Any38, 47, 45, 401.09 [1.03, 1.17]1.08 [1.04, 1.12]1.13 [1.07, 1.20]1.19 [1.10, 1.29]
 Hip27, 41, 33, 221.69 [1.47, 1.96]0.99 [0.93, 1.05]0.97 [0.89, 1.06]1.24 [0.97, 1.58]
 MOF33, 45, 42, 361.16 [1.08, 1.25]1.07 [1.01, 1.13]1.12 [1.04, 1.21]1.21 [1.09, 1.35]
 Ost34, 44, 42, 361.13 [1.06, 1.21]1.06 [1.01, 1.11]1.13 [1.05, 1.21]1.17 [1.05, 1.29]
Men
 Any21, 30, 27, 231.23 [1.00, 1.51]1.03 [0.97, 1.09]1.14 [1.05, 1.24]1.43 [1.24, 1.65]
 Hip14, 23, 16, 111.46 [1.00, 2.13]1.06 [0.95, 1.18]1.10 [0.87, 1.38]1.70 [1.06, 2.75]
 MOF16, 28, 23, 171.25 [0.98, 1.60]1.06 [0.98, 1.15]1.14 [1.01, 1.29]1.54 [1.26, 1.88]
 Ost19, 28, 25, 211.23 [1.00, 1.52]1.03 [0.96, 1.09]1.11 [1.02, 1.22]1.53 [1.27, 1.83]
p women vs men
 Any0.270.19>0.300.027
 Hip>0.300.29>0.300.25
 MOF>0.30>0.30>0.300.037
 Ost>0.30>0.30>0.300.045

Abbreviations: MOF, major osteoporotic fracture; Ost, osteoporotic fracture.

Figure 2
Figure 2. Hazard ratio (HR) (95% CI for fracture according to categories of BMI (referent: normal BMI) for men and women. (A) Hip fracture (HF) and (B) major osteoporotic fracture (MOF). Associations are adjusted for age and follow-up time +/− FN BMD T-score, in subset with FN BMD measure available.

Ethnicity

Table S5 documents the fracture outcome models with continuous BMI as the exposure, by ethnicity (HR and 95%CI fracture risk for each one-unit greater BMI for White, Asian, Black, and Hispanic ethnicities, together with p value for a BMI•ethnicity interaction). There was no statistical evidence for a difference in the association between BMI and HF by ethnicity. In contrast to White ethnicity, for Asian and Hispanic ethnicities, higher BMI was associated with a greater risk of any fracture [Asian: HR (95% CI) 1.02 (1.00, 1.04); Hispanic 1.02 (1.00, 1.05)].

Interaction with age and follow-up time

For all, osteoporotic and MOF fracture outcomes, the HR for fracture per one-unit greater BMI tended to decrease with increasing age. Conversely, for the outcome of HF, there was no obvious relationship with age. (Table S6). There was a modest increase in HR with greater follow-up time for the HF and MOF in the whole cohort after adjusting for age (Table S7).

Association between BMI and mortality

Compared to normal weight, both underweight [HR (95%CI): 1.60 (1.44, 1.78) in women and 1.71 (1.50, 1.94) in men] and obese I and II categories [e.g., obese II: 1.31 (1.21, 1.43) in women and 1.27 (1.07, 1.51) in men] were associated with greater risk of death during follow-up, in both sexes. Overweight was not associated with increased mortality risk and indeed was protective in women [0.92 (0.88, 0.95)] and men [0.96 (0.92, 1.00)]. Overall associations were similar after adjustment for FN BMD T-score. Table S8A and B summarizes these associations.

Discussion

Summary of findings

The present study represents the largest meta-analysis to date on the association between BMI and subsequent fracture risk. It confirms our previous observation in women that low BMI is associated with increased fracture risk, persisting after adjustment for FN BMD.2 Furthermore, it confirms in women that overall higher BMI becomes a risk factor for fracture after adjustment for BMD, despite being protective when used unadjusted. In addition to these confirmatory findings, our new analysis documents important novel associations in men: although we observed a similar pattern overall to that in women, the increased risk of fracture associated with obesity after adjustment for BMD was of greater magnitude in men compared with women.

Comparison with previous studies

This analysis provides an updated comparison with our previous meta-analysis, which examined relationships between BMI and fracture risk only in women, based on a much smaller cohort database with substantially less time at risk available.2 There, we observed that low BMI was a risk factor for hip and all osteoporotic fractures; when adjusted for BMD, it remained a risk factor for HF but was weakly protective for osteoporotic fracture. In contrast, high BMI, when adjusted for BMD, was a risk factor for osteoporotic fractures. Our initial 2005 meta-analysis focused on both men and women but with a smaller number of participants and, while the results were broadly comparable, was not able to elucidate any differences by sex.12 Similar adverse effect of high BMI on fracture risk, after adjustment for BMD, were observed in the US MrOS cohort.23 Compston et al. examined the relationship between BMI and fracture outcomes in the GLOW observing, in 52 939 postmenopausal women, a protective association between greater BMI and risk of hip, clinical spine, and wrist fractures.24 In contrast, higher BMI was associated weakly with greater risk of ankle fracture. While this study has sometimes been interpreted by others as demonstrating that obesity is not protective for osteoporotic fracture, there is clearly a question of whether ankle fractures are a consequence of osteoporosis per se, given that the age and sex relationship differs from that with classical osteoporotic fractures.17,18,25–27 Thus rather than being protective for ankle fractures, obesity appears to be a risk factor for these events. The findings are overall consistent with our current meta-analysis in suggesting that associations between BMI and fracture risk are site specific, with potential further mechanistic implications.

While factors, such as chronic inflammation, type 2 diabetes, and propensity to falls, may contribute to fracture risk in obesity,5,28 other considerations may underpin apparent sex differences observed. For example, in the present analysis, the greater risk of HF in men compared with women at high BMI after consideration of FN BMD may reflect sex dependent distribution of adipose tissue, with the female distribution more classically subcutaneous around the hips than abdomen, compared with the visceral abdominal deposition in men.29,30 Biomechanically, the additional adipose tissue overlying the proximal femur would favor a protective effect for HF in women compared with men. Additionally, or alternatively, a further mechanism might relate to sex-dependent differential hormonal patterns in obesity. Excess adipose tissue is associated with increased estrogen activity, with likely positive effects on bone and muscle.31 However, in men, obesity may lead to a concomitant reduction in male sex steroids and thus potentially effective hypogonadism,32,33 with increased fracture risk possibly additionally mediated via impaired muscle health in addition to reduced BMD.34

Strengths and limitations

We undertook the largest meta-analysis to date of mostly population-based cohorts to investigate and quantify associations between BMI and risk of incident fractures. We were able to investigate BMI as a continuous exponential, nonlinear and categorical exposure, and investigate possible interactions by age, sex, ethnicity, and follow-up time. The use of primary data decreases the risk of publication bias and the general consistency of the BMI-fracture association between cohorts provides strong support for the international validity of this risk variable. However, there are limitations which need to be considered in the interpretation of our findings. First, as with nearly all population-based studies, non-response biases may have occurred, which we were unable to document for all cohorts. This is likely to result in a cohort which is healthier than the background population and may thus lead to underestimation of the absolute risk of fracture. In particular, there may have been reduced responses from those at the lowest and highest BMI values. It is therefore likely, if anything, to have had a conservative effect on our risk estimates. Second, it is possible that there were differences in protocols and, for example, execution of height and weight measures between cohorts. This is unavoidable in such a study, and importantly, exposure-outcome analyses were undertaken within each cohort and then the beta coefficients merged through random effects meta-analysis. Third, we were not able to elucidate whether the association between BMI and incident fracture was causal. However, this was not the purpose of the study and for the purpose of risk assessment, a causal relationship is not required. Fourth, BMI is affected by both fat and lean mass and does not account for distribution of fat between subcutaneous and visceral compartments. This is irrelevant in terms risk assessment, but of course limits mechanistic influence. Finally, we were not able to account for levels of physical activity, which might be related to both BMI and fracture risk. Although this might be mechanistically interesting, it would not reflect the way in which BMI is considered in the FRAX algorithm.

Clinical and risk assessment implications

The role of BMI in the current FRAX model was informed by an earlier meta-analysis by De Laet et al.,12 which was broadly consistent with the Johansson et al., 2014 study,2 the conclusion at that time being that the new findings did not necessitate modification of the FRAX engine itself. Here we present novel associations relating to men as well as women, with interactions by sex, follow-up time, ethnicity, and age, which will directly inform the revised FRAX engine, as a continuous exposure, in the second version of this globally established tool. Importantly our current findings also confirm the non-linear association between BMI and fracture. The variably modifying effects of age, ethnicity, and follow-up time, together with differences by sex, support the accommodation of such nuances in the FRAX risk engine, but conversely mitigate against using BMI thresholds in risk assessment, since these would not necessarily convey consistent risk information across these parameters or fracture outcomes. The associations between increased risk of death during follow-up and underweight, and to a lesser extent obesity, are consistent with a large recent analyses,35 and will also be important to consider in the derivation of the revised FRAX tool.

While there are likely to be causal implications, underpinned by our understanding of biology and biomechanics, for the purposes of risk assessment, the principal requirement is quantification of the exposure-outcome association and of interactions between this relationship and with other potential input variables. Notwithstanding, our findings directly inform clinical practice, indicating that greater BMI is not necessarily protective for all fracture types. While this may play into strategies to tackle obesity, there is evidence that weight loss usually leads to concomitant loss of muscle and bone and so approaches addressing obesity in the context of fracture risk must be carefully thought out.36 Adequate calcium and protein nutrition, together with a mix of weight-bearing and resistance exercise training, alongside dietary interventions to reduce calorie intake are likely to be important,37 given the clear demonstration of increasing risk of HF with lower BMI, even after adjustment for BMD.

Conclusion

In the largest meta-analysis to date of prospective cohorts worldwide, we have confirmed inverse associations between fracture risk and BMI, with the apparent protective effect of higher BMI reversed after adjustment for FN BMD. The magnitude of this effect appeared greater in men than women. The increased accuracy of the elucidated effect sizes, and their specificity by age, sex, follow-up time, and ethnicity will inform improved risk assessment in the second iteration of the FRAX tool.

Human and animal rights

This review does not contain any original studies with human participants or animals performed by any of the authors.

Ethics

All individual cohorts with candidate risk factors available have been approved by their local ethics committees and informed consent has been obtained from all study participants. General ethics approval for the use of these cohorts is also given by the University of Sheffield. Participant data are stored in coded, de-identified form. Only summary statistics and aggregate data are published, not allowing for identification of individual study participants.

Supplementary Material

BMI_FRAX_Online_Supplementary_material_zjaf091 — available with the version of record.

Acknowledgements

We are grateful to Dr. Östen Ljunggren for contributing the MrOS Sweden cohort. UK Biobank data are included under approved access agreement 3593. The authors acknowledge the Manitoba Centre for Health Policy for use of Manitoba data contained in the Population Health Research Data Repository (HIPC 2016/2017-29). The results and conclusions are those of the authors and no official endorsement by the Manitoba Centre for Health Policy, Manitoba Health, Seniors and Active Living, or other data providers is intended or should be inferred. N.C.H. acknowledges funding from the UK Medical Research Council [MC_PC_21003; MC_PC_21001] and the NIHR Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, UK. Terence O’Neill is supported by the National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre (BRC) (NIHR203308). 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 75N92021D00001, 75N92021D00002, 75N92021D00003, 75N92021D00004, 75N92021D00005. The Osteoporotic Fractures in Men (MrOS) Study is supported by National Institutes of Health funding. The following institutes provide support: the National Institute on Aging (NIA), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Center for Advancing Translational Sciences (NCATS), and NIH Roadmap for Medical Research under the following grant numbers: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, R01 AG066671, and UL1 TR002369. Funding for the SOF study comes from the National Institute on Aging (NIA), and the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), supported by grants (AG05407, AR35582, AG05394, AR35584, and AR35583). Funding for the Health ABC study was from the Intramural research program at the National Institute on Aging under the following contract numbers: NO1-AG-6-2101, NO1-AG-6-2103 and NO1-AG-6-2106. The Longitudinal Aging Study Amsterdam is supported by a grant from the Netherlands Ministry of Health, Welfare and Sport, Directorate of Long-Term Care. Funding for the Framingham Study comes from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) R01 AR041398 and AR061445 and National Heart, Lung, and Blood Institute Framingham Heart Study (N01-HC-25195, HHSN268201500001I). Funding for the GOS was from the Victorian Health Promotion Foundation: ID 91-0095. For the purpose of Open Access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission.

Declarations

Author contributions

Nicholas C. Harvey and Helena Johansson contributed equally to this work.

Nicholas C. Harvey (Conceptualization, Investigation, Project administration, Supervision, Writing—original draft, Writing—review & editing), Helena Johansson (Conceptualization, Data curation, Formal analysis, Writing—original draft, Writing—review & editing), Eugene V. McCloskey (Conceptualization, Data curation, Funding acquisition, Supervision, Writing—original draft, Writing—review & editing), Enwu Liu (Data curation, Formal analysis, Writing—review & editing), Kristina E. Åkesson (Data curation, Funding acquisition, Writing—review & editing), Fred A. Anderson (Data curation, Funding acquisition, Writing—review & editing), Rafael Azagra-Ledesma (Data curation, Funding acquisition, Writing—review & editing), Cecilie L. Bager (Data curation, Funding acquisition, Writing—review & editing), Charlotte Beaudart (Data curation, Funding acquisition, Writing—review & editing), Heike A. Bischoff-Ferrari (Data curation, Funding acquisition, Writing—review & editing), Emmanuel Biver (Data curation, Funding acquisition, Writing—review & editing), Olivier Bruyère (Data curation, Funding acquisition, Writing—review & editing), Jane A. Cauley (Data curation, Funding acquisition, Writing—review & editing), Jacqueline R. Center (Data curation, Funding acquisition, Writing—review & editing), Roland Chapurlat (Data curation, Funding acquisition, Writing—review & editing), Claus Christiansen (Data curation, Funding acquisition, Writing—review & editing), Cyrus Cooper (Data curation, Funding acquisition, Writing—review & editing), Carolyn J. Crandall (Data curation, Funding acquisition, Writing—review & editing), Steven R. Cummings (Data curation, Funding acquisition, Writing—review & editing), José A.P. da Silva (Data curation, Funding acquisition, Writing—review & editing), Bess Dawson-Hughes (Data curation, Funding acquisition, Writing—review & editing), Adolfo Diez-Perez (Data curation, Funding acquisition, Writing—review & editing), Alyssa B. Dufour (Data curation, Funding acquisition, Writing—review & editing), John A. Eisman (Data curation, Funding acquisition, Writing—review & editing), Petra J.M. Elders (Data curation, Funding acquisition, Writing—review & editing), Serge Ferrari (Data curation, Funding acquisition, Writing—review & editing), Yuki Fujita (Data curation, Funding acquisition, Writing—review & editing), Saeko Fujiwara (Data curation, Funding acquisition, Writing—review & editing), Claus-Christian Glüer (Data curation, Funding acquisition, Writing—review & editing), Inbal Goldshtein (Data curation, Funding acquisition, Writing—review & editing), David Goltzman (Data curation, Funding acquisition, Writing—review & editing), Vilmundur Gudnason (Data curation, Funding acquisition, Writing—review & editing), Jill Hall (Data curation, Funding acquisition, Writing—review & editing), Didier Hans (Data curation, Funding acquisition, Writing—review & editing), Mari Hoff (Data curation, Funding acquisition, Writing—review & editing), Rosemary J. Hollick (Data curation, Funding acquisition, Writing—review & editing), Martijn Huisman (Data curation, Funding acquisition, Writing—review & editing), Masayuki Iki (Data curation, Funding acquisition, Writing—review & editing), Sophia Ish-Shalom (Data curation, Funding acquisition, Writing—review & editing), Graeme Jones (Data curation, Funding acquisition, Writing—review & editing), Magnus K. Karlsson (Data curation, Funding acquisition, Writing—review & editing), Sundeep Khosla (Data curation, Funding acquisition, Writing—review & editing), Douglas P. Kiel (Data curation, Funding acquisition, Writing—review & editing), Woon-Puay Koh (Data curation, Funding acquisition, Writing—review & editing), Fjorda Koromani (Data curation, Funding acquisition, Writing—review & editing), Mark A. Kotowicz (Data curation, Funding acquisition, Writing—review & editing), Heikki Kröger (Data curation, Funding acquisition, Writing—review & editing), Timothy Kwok (Data curation, Funding acquisition, Writing—review & editing), Olivier Lamy (Data curation, Funding acquisition, Writing—review & editing), Arnulf Langhammer (Data curation, Funding acquisition, Writing—review & editing), Bagher Larijani (Data curation, Funding acquisition, Writing—review & editing), Kurt Lippuner (Data curation, Funding acquisition, Writing—review & editing), Fiona E.A. McGuigan (Data curation, Funding acquisition, Writing—review & editing), Dan Mellström (Data curation, Funding acquisition, Writing—review & editing), Thomas Merlijn (Data curation, Funding acquisition, Writing—review & editing), Tuan V. Nguyen (Data curation, Funding acquisition, Writing—review & editing), Anna Nordström (Data curation, Funding acquisition, Writing—review & editing), Peter Nordström (Data curation, Funding acquisition, Writing—review & editing), Terence W. O’Neill (Data curation, Funding acquisition, Writing—review & editing), Barbara Obermayer-Pietsch (Data curation, Funding acquisition, Writing—review & editing), Claes Ohlsson (Data curation, Funding acquisition, Writing—review & editing), Eric S. Orwoll (Data curation, Funding acquisition, Writing—review & editing), Julie A. Pasco (Data curation, Funding acquisition, Writing—review & editing), Fernando Rivadeneira (Data curation, Funding acquisition, Writing—review & editing), Berit Schei (Data curation, Funding acquisition, Writing—review & editing), Anne-Marie Schott (Data curation, Funding acquisition, Writing—review & editing), Eric J. Shiroma (Data curation, Funding acquisition, Writing—review & editing), Kristin Siggeirsdottir (Data curation, Funding acquisition, Writing—review & editing), Eleanor M. Simonsick (Data curation, Funding acquisition, Writing—review & editing), Elisabeth Sornay-Rendu (Data curation, Funding acquisition, Writing—review & editing), Reijo Sund (Data curation, Funding acquisition, Writing—review & editing), Karin M.A. Swart (Data curation, Funding acquisition, Writing—review & editing), Pawel Szulc (Data curation, Funding acquisition, Writing—review & editing), Junko Tamaki (Data curation, Funding acquisition, Writing—review & editing), David J. Torgerson (Data curation, Funding acquisition, Writing—review & editing), Natasja M. van Schoor (Data curation, Funding acquisition, Writing—review & editing), Tjeerd P. van Staa (Data curation, Funding acquisition, Writing—review & editing), Joan Vila (Data curation, Funding acquisition, Writing—review & editing), Nicholas J. Wareham (Data curation, Funding acquisition, Writing—review & editing), Nicole C. Wright (Data curation, Funding acquisition, Writing—review & editing), Noriko Yoshimura (Data curation, Funding acquisition, Writing—review & editing), M Carola Zillikens (Data curation, Funding acquisition, Writing—review & editing), Marta Zwart (Data curation, Funding acquisition, Writing—review & editing), Liesbeth Vandenput (Data curation, Funding acquisition, Writing—review & editing), Mattias Lorentzon (Conceptualization, Investigation, Writing—original draft, Writing—review & editing), William D. Leslie (Conceptualization, Data curation, Supervision, Writing—original draft, Writing—review & editing), and John A. Kanis (Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing—original draft, Writing—review & editing)

Funding

None declared.

Conflicts of interest

N.C.H. has received consultancy/lecture fees/honoraria/grant funding from Alliance for Better Bone Health, Amgen, MSD, Eli Lilly, Radius Health, Servier, Shire, UCB, Consilient Healthcare, Kyowa Kirin, Theramex, and Internis Pharma.

J.A.K. led the team that developed FRAX as director of the WHO Collaborating Centre for Metabolic Bone Diseases. E.V.M., W.D.L., M.L., N.C.H., E.L., L.V., and H.J. are members of the FRAX team. J.A.K., N.C.H., and E.V.M. are members of the advisory body to the National Osteoporosis Guideline Group. J.A.K. reports no additional competing interests.

K.E.Å. has no financial interest related to FRAX; chaired the National SALAR Group for Person-Centered Care Pathway Osteoporosis.

F.A.A. led the team that developed GLOW, while director of the Center for Outcomes Research at the University of Massachusetts Medical School; he has no financial interest in FRAX.

R.A. has received funding for research from Instituto Carlos III of Spanish Ministry of Health, IDIAP Jordi Gol of Catalan Government and from Scientific Societies SEMFYC and SEIOMM.

C.L.B. is employed at Nordic Bioscience and owns stock in Nordic Bioscience. She declares no competing interests in relation to this work.

H.A.B.-F. has no financial interest in FRAX. For the DO-HEALTH trial cohort, Prof. H.A.B.-F. reports independent and investigator-initiated grants from European Commission Framework 7 Research Program, from the University of Zurich, from NESTEC, from Pfizer Consumer Healthcare, from Streuli Pharma, plus non-financial support from DNP. For the study cohort extension, she reports independent and investigator-initiated grants from Pfizer and from Vifor. Further, Prof. H.A.B.-F. reports non-financial support from Roche Diagnostics and personal fees from Wild, Sandoz, Pfizer, Vifor, Mylan, Roche, Meda Pharma, outside the submitted work with regard to speaker fees and travel fees.

J.R.C. has received honoraria for speaking at educational meetings and for advisory boards from Amgen and honoraria for an advisory board from Bayer, all unrelated to this work. R.C. has no financial interest in FRAX. He has received grant funding from Amgen, UCB, Chugai, MSD, Mylan, and Medac. He has received honoraria from Amgen, UCB, Chugai, Galapagos, Biocon, Abbvie, Haoma Medica, Pfizer, Amolyt, MSD, Lilly, BMS, Novartis, Arrow, PKMed, Kyowa-Kirin, and Sanofi.

C.C. owns stock in Nordic Bioscience. He declares no competing interests in relation to this work.

C.C. reports personal fees from Alliance for Better Bone Health, Amgen, Eli Lilly, GSK, Medtronic, Merck, Novartis, Pfizer, Roche, Servier, Takeda, and UCB.

A.D.-P. reports personal fees from Amgen, Lilly, Theramex, and grants from Instituto Carlos III and owns shares of Active Life Scientific, all outside the submitted work.

J.A.E. declares consulting and research support from Actavis, Amgen, Aspen, Lilly, Merck Sharp and Dohme, Novartis, Sanofi-Aventis, Servier, and Theramex.

P.J.M.E. has no financial interest in FRAX. P.J.M.E. reports support for the SOS study by Stichting Achmea Gezondheidszorg, Achmea, and VGZ zorgverzekeraar. Additional support was given by the stichting Artsenlaboratorium en Trombosedienst. Outside the submitted work, she did receive independent investigator driven grants by Zonmw, The Netherlands, de Hartstichting, The Netherlands, The European foundation for the study of Diabetes, Amgen, The Netherlands, TEVA, The Netherlands, and Takeda, The Netherlands.

C.-C.G. reports honoraria and research support from AgNovos, Amgen, osteolabs, and UCB unrelated to this work.

D.P.K. has no financial interest in FRAX but has received support for his work in the Framingham Study over the past 30 yr by the National Institutes of Health, Astra Zeneca, Merck, Amgen, and Radius Health.

M.A.K. has received funding from the National Health and Medical Research Council (NHMRC) Australia, and the Medical Research Future Fund (MRFF) Australia. He has served on advisory boards for Amgen Australia, Novartic, and Eli Lilly—all unrelated to this work and was the Director of the Geelong Bone Densitometry Service until 2022.

A.L. is PI of the HUNT Lung and Osteoporosis project, the project got some funding from AstraZeneca for data collection. He has received lecture or consultant fees from AstraZeneca, GSK, Boehringer Ingelheim, and Diagnostica. No funding related to this study.

M.L. has received lecture fees from Amgen, Lilly, Meda, Gedeon Richter, Medison Pharma, and UCB Pharma and consulting fees from Amgen, Radius Health, UCB Pharma, Medac AB, and Parexel International, all outside the presented work.

E.V.M. has received consultancy/lecture fees/grant funding/honoraria from AgNovos, Amgen, AstraZeneca, Consilient Healthcare, Fresenius Kabi, Gilead, GSK, Hologic, Internis, Lilly, Merck, Novartis, Pfizer, Radius Health, Redx Oncology, Roche, Sanofi Aventis, UCB, ViiV, Warner Chilcott, and I3 Innovus.

C.O. is listed as a coinventor on two patent applications regarding probiotics in osteoporosis treatment.

T.W. O’N. reports honoraria from UCB unrelated to this work.

E.S.O. reports consulting fees from Angios, Biocon, Radius, and Bayer.

J.A.P. has received funding from the National Health and Medical Research Council (NHMRC) Australia, and the Medical Research Future Fund (MRFF) Australia, and Amgen, all unrelated to this work.

K.M.A.S. is an employee of the PHARMO Institute for Drug Outcomes Research. This independent research institute performs financially supported studies for government and related healthcare authorities and several pharmaceutical companies.

N.C.W. sits on the Board of Trustee of the US Bone Health and Osteoporosis Foundation, and has received consulting fees from Radius and ArgenX.

M.C.Z. reports honoraria for lectures and consulting from Amgen and Kyowa Kirin and research support from Kyowa Kirin to her institution, unrelated to this work.

M.Z. has received research funding from national societies (SEMFYC and SEIOMM).

Data availability

Data availability is cohort specific and individual cohort Principal Investigators should be contacted for enquiries about data access.

Sources

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Version of record: Journal of Bone and Mineral Research 2025
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Journal of Bone and Mineral Research (2025) · doi:10.1093/jbmr/zjaf091
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2025-08-08
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  1. Published in Journal of Bone and Mineral Research2025-08-08
  2. Living reprint on Literature Decoded2026-10-01
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Citation
Harvey NC, Johansson H, McCloskey EV, Liu E, Åkesson KE, Anderson FA, et al. Body mass index and subsequent fracture risk: a meta-analysis to update FRAX. J Bone Miner Res. 2025;40(10):1144–1155. doi:10.1093/jbmr/zjaf091
BibTeX
@article{Harvey2025Body,
  title     = {Body mass index and subsequent fracture risk: a meta-analysis to update FRAX},
  author    = {Nicholas C Harvey and Helena Johansson and Eugene V McCloskey and Enwu Liu and Kristina E Åkesson and Fred A Anderson and Rafael Azagra-Ledesma and Cecilie L Bager and Charlotte Beaudart and Heike A Bischoff-Ferrari and Emmanuel Biver and Olivier Bruyère and Jane A Cauley and Jacqueline R Center and Roland Chapurlat and Claus Christiansen and Cyrus Cooper and Carolyn J Crandall and Steven R Cummings and José A P da Silva and Bess Dawson-Hughes and Adolfo Diez-Perez and Alyssa B Dufour and John A Eisman and Petra J M Elders and Serge Ferrari and Yuki Fujita and Saeko Fujiwara and Claus-Christian Glüer and Inbal Goldshtein and David Goltzman and Vilmundur Gudnason and Jill Hall and Didier Hans and Mari Hoff and Rosemary J Hollick and Martijn Huisman and Masayuki Iki and Sophia Ish-Shalom and Graeme Jones and Magnus K Karlsson and Sundeep Khosla and Douglas P Kiel and Woon-Puay Koh and Fjorda Koromani and Mark A Kotowicz and Heikki Kröger and Timothy Kwok and Olivier Lamy and Arnulf Langhammer and Bagher Larijani and Kurt Lippuner and Fiona E A McGuigan and Dan Mellström and Thomas Merlijn and Tuan V Nguyen and Anna Nordström and Peter Nordström and Terence W O’Neill and Barbara Obermayer-Pietsch and Claes Ohlsson and Eric S Orwoll and Julie A Pasco and Fernando Rivadeneira and Berit Schei and Anne-Marie Schott and Eric J Shiroma and Kristin Siggeirsdottir and Eleanor M Simonsick and Elisabeth Sornay-Rendu and Reijo Sund and Karin M A Swart and Pawel Szulc and Junko Tamaki and David J Torgerson and Natasja M van Schoor and Tjeerd P van Staa and Joan Vila and Nicholas J Wareham and Nicole C Wright and Noriko Yoshimura and M Carola Zillikens and Marta Zwart and Liesbeth Vandenput and Mattias Lorentzon and William D Leslie and John A Kanis},
  journal   = {Journal of Bone and Mineral Research},
  year      = {2025},
  volume    = {40},
  number    = {10},
  pages     = {1144–1155},
  doi       = {10.1093/jbmr/zjaf091},
  pmid      = {40795319},
  publisher = {Oxford University Press}
}
RIS
TY  - JOUR
TI  - Body mass index and subsequent fracture risk: a meta-analysis to update FRAX
AU  - Nicholas C Harvey
AU  - Helena Johansson
AU  - Eugene V McCloskey
AU  - Enwu Liu
AU  - Kristina E Åkesson
AU  - Fred A Anderson
AU  - Rafael Azagra-Ledesma
AU  - Cecilie L Bager
AU  - Charlotte Beaudart
AU  - Heike A Bischoff-Ferrari
AU  - Emmanuel Biver
AU  - Olivier Bruyère
AU  - Jane A Cauley
AU  - Jacqueline R Center
AU  - Roland Chapurlat
AU  - Claus Christiansen
AU  - Cyrus Cooper
AU  - Carolyn J Crandall
AU  - Steven R Cummings
AU  - José A P da Silva
AU  - Bess Dawson-Hughes
AU  - Adolfo Diez-Perez
AU  - Alyssa B Dufour
AU  - John A Eisman
AU  - Petra J M Elders
AU  - Serge Ferrari
AU  - Yuki Fujita
AU  - Saeko Fujiwara
AU  - Claus-Christian Glüer
AU  - Inbal Goldshtein
AU  - David Goltzman
AU  - Vilmundur Gudnason
AU  - Jill Hall
AU  - Didier Hans
AU  - Mari Hoff
AU  - Rosemary J Hollick
AU  - Martijn Huisman
AU  - Masayuki Iki
AU  - Sophia Ish-Shalom
AU  - Graeme Jones
AU  - Magnus K Karlsson
AU  - Sundeep Khosla
AU  - Douglas P Kiel
AU  - Woon-Puay Koh
AU  - Fjorda Koromani
AU  - Mark A Kotowicz
AU  - Heikki Kröger
AU  - Timothy Kwok
AU  - Olivier Lamy
AU  - Arnulf Langhammer
AU  - Bagher Larijani
AU  - Kurt Lippuner
AU  - Fiona E A McGuigan
AU  - Dan Mellström
AU  - Thomas Merlijn
AU  - Tuan V Nguyen
AU  - Anna Nordström
AU  - Peter Nordström
AU  - Terence W O’Neill
AU  - Barbara Obermayer-Pietsch
AU  - Claes Ohlsson
AU  - Eric S Orwoll
AU  - Julie A Pasco
AU  - Fernando Rivadeneira
AU  - Berit Schei
AU  - Anne-Marie Schott
AU  - Eric J Shiroma
AU  - Kristin Siggeirsdottir
AU  - Eleanor M Simonsick
AU  - Elisabeth Sornay-Rendu
AU  - Reijo Sund
AU  - Karin M A Swart
AU  - Pawel Szulc
AU  - Junko Tamaki
AU  - David J Torgerson
AU  - Natasja M van Schoor
AU  - Tjeerd P van Staa
AU  - Joan Vila
AU  - Nicholas J Wareham
AU  - Nicole C Wright
AU  - Noriko Yoshimura
AU  - M Carola Zillikens
AU  - Marta Zwart
AU  - Liesbeth Vandenput
AU  - Mattias Lorentzon
AU  - William D Leslie
AU  - John A Kanis
JO  - Journal of Bone and Mineral Research
PY  - 2025
VL  - 40
IS  - 10
SP  - 1144
EP  - 1155
DO  - 10.1093/jbmr/zjaf091
PB  - Oxford University Press
SN  - 0884-0431
UR  - https://doi.org/10.1093/jbmr/zjaf091
ER  -

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This page reproduces Body mass index and subsequent fracture risk: a meta-analysis to update FRAX by Nicholas C Harvey, Helena Johansson, Eugene V McCloskey, Enwu Liu, Kristina E Åkesson, Fred A Anderson, Rafael Azagra-Ledesma, Cecilie L Bager, Charlotte Beaudart, Heike A Bischoff-Ferrari, Emmanuel Biver, Olivier Bruyère, Jane A Cauley, Jacqueline R Center, Roland Chapurlat, Claus Christiansen, Cyrus Cooper, Carolyn J Crandall, Steven R Cummings, José A P da Silva, Bess Dawson-Hughes, Adolfo Diez-Perez, Alyssa B Dufour, John A Eisman, Petra J M Elders, Serge Ferrari, Yuki Fujita, Saeko Fujiwara, Claus-Christian Glüer, Inbal Goldshtein, David Goltzman, Vilmundur Gudnason, Jill Hall, Didier Hans, Mari Hoff, Rosemary J Hollick, Martijn Huisman, Masayuki Iki, Sophia Ish-Shalom, Graeme Jones, Magnus K Karlsson, Sundeep Khosla, Douglas P Kiel, Woon-Puay Koh, Fjorda Koromani, Mark A Kotowicz, Heikki Kröger, Timothy Kwok, Olivier Lamy, Arnulf Langhammer, Bagher Larijani, Kurt Lippuner, Fiona E A McGuigan, Dan Mellström, Thomas Merlijn, Tuan V Nguyen, Anna Nordström, Peter Nordström, Terence W O’Neill, Barbara Obermayer-Pietsch, Claes Ohlsson, Eric S Orwoll, Julie A Pasco, Fernando Rivadeneira, Berit Schei, Anne-Marie Schott, Eric J Shiroma, Kristin Siggeirsdottir, Eleanor M Simonsick, Elisabeth Sornay-Rendu, Reijo Sund, Karin M A Swart, Pawel Szulc, Junko Tamaki, David J Torgerson, Natasja M van Schoor, Tjeerd P van Staa, Joan Vila, Nicholas J Wareham, Nicole C Wright, Noriko Yoshimura, M Carola Zillikens, Marta Zwart, Liesbeth Vandenput, Mattias Lorentzon, William D Leslie, John A Kanis, first published in Journal of Bone and Mineral Research 2025;40(10):1144–1155, doi:10.1093/jbmr/zjaf091, PMID 40795319, PMC12487781. © The Author(s) 2025. Published by Oxford University Press on behalf of the American Society for Bone and Mineral Research. It is used under the CC BY 4.0 licence.

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