We studied the use of rheumatoid arthritis (RA) as an input to FRAX® and trabecular bone score (TBS) to modify FRAX-based probability in individuals with diabetes. Our results help to inform the use of FRAX and FRAXplus in patients with diabetes. Diabetes confers increased risk for fracture independently from FRAX-estimated fracture probability. To compare the relative performance of the rheumatoid arthritis (RA) input and trabecular bone score (TBS) adjustment, alone or in combination, to capture FRAX-independent risk associated with diabetes. We analyzed data on 54,609 individuals from the Manitoba Bone Density Program aged ≥ 40 years with FRAX-based probability and TBS measurements (mean age 63.8 years, 89.9

Background:Artificial Intelligence (AI)-based opportunistic risk stratification solutions can help to counter rising fragility fracture rates. Existing tools estimate bone mineral density (BMD) alone, while the present study incorporates trabecular bone score (TBS), a surrogate of bone microarchitecture, more closely mirroring fracture pathophysiology. We aimed to evaluate the performance of an AI tool that estimates bone fragility directly from standard radiographs to identify individuals at highest risk of fracture. Methods:This retrospective, multinational cohort study included 18,858 paired radiographs and lumbar spine dual-energy X-ray absorptiometry (DXA) scans from adult patients (aged at least 20 years) from three clinical sites in Europe and two sites in the United States. Routine clinical radiographs of the spine, abdomen, chest, or pelvis acquired in the anteroposterior or posteroanterior view and including visualisation of the lumbar spine were included. Eligible radiographs had an in-plane spatial resolution of ≤0.2 mm per pixel, independent of vendor, and had a corresponding DXA examination within 6 months, and included at least two lumbar vertebrae (L1-L4). The AI model training used a composite Bone Fragility Index, combining TBS and BMD. Training, internal validation and testing was performed on two European sites (n = 10,692; Italy and Austria); and external validation involved three sites with ethnically diverse populations (n = 7079): Slovakia, US site 1 (Wisconsin) and US site 2 (New York). Model performance for identifying very high bone fragility (characterised by degraded TBS and osteoporosis) prioritised specificity (as per the intended clinical use to prioritise low false-positive rates) and was evaluated with accuracy, sensitivity, specificity, AUC and precision. Findings:Between Jan 1, 2010 and Dec 31, 2023, 18,858 paired radiographs and lumbar spine dual-energy X-ray absorptiometry (DXA) scans from 11,138 participants across five international sites were retrospectively aggregated. Internal testing on two European sites demonstrated an accuracy of 0.86 (95% CI: 0.78, 0.92), specificity of 0.93 (0.85, 0.99), and sensitivity of 0.53 (0.41, 0.67). External testing on three sites demonstrated consistently high specificity of 0.88 (0.77, 0.99) in the European cohort, 0.94 (0.91, 0.97) in the American White dataset, and 0.96 (0.81, 0.99) in the American Non-White. External sensitivity ranged from 0.53 (0.41, 0.67) to 0.64 (0.50, 0.88). Interpretation:The proposed approach provides rapid identification of individuals with very high bone fragility from routine radiographs. Its specificity across diverse populations supports clinical use for opportunistic osteoporosis screening in real-world settings. Future work should assess the model's performance in more sex-balanced cohorts without prior DXA assessment, and evaluate its ability to predict incident fractures. Funding:The Swiss National Science Foundation, the Foundation of the Orthopaedic Hospital of the Vaudois University Hospital (Lausanne, Switzerland), and Medimaps Group SA, Switzerland.

Osteoporosis is a major and growing health concern in the Asia-Pacific region, y et it remains widely underdiagnosed and undertreated due to limited access to dual-energy X-ray absorptiometry (DXA) in many areas. Artificial intelligence (AI) offers new opportunities to improve osteoporosis screening and management, but unvalidated tools pose risks of inconsistent care. This consensus was developed to provide regionally harmonized guidance on the safe, effective, and equitable use of AI in osteoporosis care. PURPOSE:The aim of this work was to establish expert consensus recommendations on the role of AI in osteoporosis screening and management in the Asia-Pacific region. Key objectives were to define appropriate applications of AI (e.g., imaging-based bone assessment and fracture risk prediction) and specify minimum standards for validation and reporting, addressing region-specific implementation challenges and ensuring that AI use aligns with clinical guidelines and ethical principles. METHODS:This consensus was developed through multidisciplinary collaboration among experts across the Asia-Pacific region. Each participant reviewed draft statements, contributed feedback during virtual meetings, and provided insights based on clinical experience and current evidence. Consensus was reached iteratively until full agreement was achieved for all statements. The process integrated global best practices and regional adaptations, drawing from peer-reviewed studies, international AI guidelines, and local fracture registry data. The final recommendations emphasize the validation, transparency, and ethical implementation of AI within regional healthcare systems, ensuring compatibility with local regulations. Ultimately, twelve consensus statements were established to guide the responsible use of AI for osteoporosis screening and management in the Asia-Pacific region. RESULTS:The panel produced 12 consensus statements covering the role of AI as an adjunct for opportunistic osteoporosis screening rather than a diagnostic tool, requirements for imaging quality and AI model transparency, standards for validation and performance reporting, integration of AI with clinical risk stratification, demonstration of clinical utility in real-world settings, adherence to data protection laws and ethical AI principles, training of clinicians in AI use, strategies for implementation and monitoring (including post-market surveillance and feedback loops), and recognition of technical, clinical, and equity limitations of AI. All 12 statements give extensive recommendations for using AI to improve osteoporosis management while ensuring patient safety, accuracy, and equity. CONCLUSION:This first Asia-Pacific consensus on AI in osteoporosis concludes that AI, when appropriately validated and implemented, can help bridge the osteoporosis care gap by identifying high-risk patients who would otherwise remain undiagnosed, thus facilitating earlier intervention. It emphasizes that AI should complement-not replace-standard diagnostic methods and clinical judgment. The guidance emphasizes validation, transparency, and ethical oversight to facilitate early intervention while minimizing risks associated with unvalidated or premature AI adoption.

Abdominal aortic calcification (AAC), a marker of subclinical cardiovascular disease, has previously shown to be associated with low BMD and fracture. However, it remains unclear whether AAC is associated with trabecular bone score (TBS), a gray-level textural measure, or whether it predicts fracture risk independent of this measure. Here, we examined the cross-sectional association of AAC scored using a validated machine learning algorithm (ML-AAC24) with TBS, and their simultaneous associations with incident fractures in 7691 individuals (93.4% women) through the Manitoba BMD Registry (mean age 75.3 yr). The association between ML-AAC24 and TBS was tested using generalized linear regression. Cox proportional hazards models tested the simultaneous relationships of ML-AAC24 and TBS with incident fractures. At baseline, 41.3% of the study cohort had low (<2), 32.4% had moderate (2 to <6), and 26.3% had high (≥6) ML-AAC24. Compared to low ML-AAC24, high ML-AAC24 was associated with a 0.81% lower TBS in the multivariable-adjusted model. Independent of each other and multiple established fracture risk factors, ML-AAC24 and TBS were each associated with an increased risk of incident fractures. Specifically, high ML-AAC24 (HR 1.41, 95% CI: 1.15-1.73, compared to low ML-AAC24) and lower TBS (HR 1.13, 95% CI: 1.05-1.22, per SD decrease) were associated with increased relative hazards for any incident fracture. High ML-AAC24 and lower TBS were also associated with incident major osteoporotic fracture (HR 1.48, 95% CI: 1.18-1.87 and HR 1.15, 95% CI: 1.06-1.25, respectively) and hip fracture (HR 1.56, 95% CI: 1.05-2.31 and HR 1.25, 95% CI: 1.08-1.44, respectively). In conclusion, high ML-AAC24 is associated with lower TBS in older adults attending routine osteoporosis screening. Both measures were associated with incident fractures. The findings of this study highlight high ML-AAC24, seen in more than 1 in 4 of the study cohort, and lower TBS provide complementary prognostic information for fracture risk.

Knee Osteoarthritis (KOA) is a prevalent musculoskeletal disorder that severely impacts mobility and quality of life, particularly among older adults. Its diagnosis often relies on subjective assessments using the Kellgren-Lawrence (KL) grading system, leading to variability in clinical evaluations. To address these challenges, we propose a confidence-driven deep learning framework for early KOA detection, focusing on distinguishing KL-0 and KL-2 stages. The Siamese-based framework integrates a novel multi-level feature extraction architecture with a hybrid loss strategy. Specifically, multi-level Global Average Pooling (GAP) layers are employed to extract features from varying network depths, ensuring comprehensive feature representation, while the hybrid loss strategy partitions training samples into high-, medium-, and low-confidence subsets. Tailored loss functions are applied to improve model robustness and effectively handle uncertainty in annotations. Experimental results on the Osteoarthritis Initiative (OAI) dataset demonstrate that the proposed framework achieves competitive accuracy, sensitivity, and specificity, comparable to those of expert radiologists. Cohen's kappa values (k > 0.85)) confirm substantial agreement, while McNemar's test (p > 0.05) indicates no statistically significant differences between the model and radiologists. Additionally, Confidence distribution analysis reveals that the model emulates radiologists' decision-making patterns. These findings highlight the potential of the proposed approach to serve as an auxiliary diagnostic tool, enhancing early KOA detection and reducing clinical workload.

In this meta-analysis of international cohorts, current smoking is confirmed as a significant BMD-independent predictor of future fracture with a stronger relationship in men than in women. A causative and reversible effect of smoking on fracture risk is suggested by past smoking having a significantly lower risk than current smoking. In this meta-analysis of international cohorts, the aim was to examine the relationship of current and past smoking with fracture risk to provide an update for future iterations of the FRAX tool. The risk of fracture associated with current and past smoking was estimated using an extended Poisson model applied separately to each of 58 prospective international cohort studies. Covariates included current time since start of follow up, current age, and in an additional model, BMD at the femoral neck. The results of the different studies were merged by using inverse-variance weighted β-coefficients. This analysis included a total of 1,691,024 participants (61.2

The relationship between bone mineral density (BMD) at the femoral neck and fracture risk was determined in a meta-analysis of primary data of 307205 men and women from 53 cohort studies. Low BMD was an important predictor of fracture risk, particularly for hip fracture. This study aimed to quantify the relationship between DXA-measured femoral neck BMD and fracture risk and examine the effect of age, sex, time since measurement, and initial BMD value on fracture risk, with a view to updating FRAX®. We studied 307,205 men and women from within 53 predominately population-based cohorts followed up for an average of 8.7 years and a total of 2,683,185 person-years. The association of BMD and fracture risk was examined using a Poisson model in each cohort separately by sex. Results were expressed as a gradient of risk (GR, hazard ratio/standard deviation decrease in BMD). The different studies were then merged using weighted coefficients. Most hip fractures arose in men and women with low bone mass or osteoporosis at baseline (73 = 0.12 for women and p = 0.89 for men). A significant decrease in GR for hip fracture was observed with increasing duration of follow-up, but the magnitude of the effect was modest compared with the effect of age. For other fracture outcomes, including non-hip major osteoporotic fracture, the gradient of risk was lower than for hip fracture. Femoral neck BMD is a risk factor for fracture of substantial importance, particularly for future hip fracture. The lower magnitude of association at older age is consistent with other non-skeletal factors contributing to hip fracture risk with advancing age. Its validation on an international basis supports its use in case finding strategies. Its use should, however, take account of the variations in predictive value of BMD with age, sex, length of follow-up, and BMD.

STUDY AIMS: Sarcopenia is a progressive, age-related loss of muscle mass, strength and function. Given the ageing population and the adverse outcomes associated with sarcopenia, monitoring its epidemiology is particularly important. This study aimed to describe sarcopenia prevalence, 5-year incidence and agreement between definitions using the latest operational criteria in Swiss postmenopausal women. METHODS: Postmenopausal women from the last 5 years of the CoLaus/OsteoLaus prospective population-based cohort were included based on complete case analysis (April 2015 to October 2022; Lausanne, Switzerland). We assessed appendicular lean mass via Dual X-ray Absorptiometry (GE Lunar iDXA), handgrip strength using a Jamar Dynamometer and 6-metre gait speed at multiple visits. Sarcopenia was defined based on handgrip strength and/or appendicular lean mass and/or gait speed using 11 definitions, including that from the European Working Group on Sarcopenia in Older People (EWGSOPII, 2019). Prevalence was measured as the number and rate of sarcopenic cases at the last visit, while incidence was measured as the number and rate of new sarcopenic cases over 2.5 or 5 years. RESULTS: A total of 930 women were included, with a mean (standard deviation) age of 72.9 (6.9) years, BMI of 25.7 (4.8) kg/m2, appendicular lean mass 16.8 (2.5) kg, handgrip strength 21.2 (5.5) kg, gait speed 1.1 (0.2) m/s. Sarcopenia prevalence based on EWGSOPII definitions ranged from 2.2% to 5.7%, while other definitions varied from 0.5% to 13.4%. The 5-year incidence rates based on EWGSOPII were 1.9% to 4.7%. Prevalence and incidence increased significantly between the lowest and highest age tertiles (Fisher’s exact test, p <0.05) for most definitions. Agreement between definitions was predominantly “none” or “minimal” according to the Cohen Kappa score. CONCLUSION: This population-based cohort of postmenopausal women highlights an increase in sarcopenia prevalence and incidence beginning in the seventh decade of life, underscoring the accelerated decline in muscle health with age. The minimal agreement between the definitions highlights the need for a consensus, which would improve future research and clinical implementations.

In the largest meta-analysis of international cohorts to date, a family history of fracture is confirmed as a significant BMD-independent predictor of future fracture risk. Parental and sibling histories of fracture carry the same significance for future fracture, including the impact of family hip fracture on future hip fracture risk. PURPOSE:We have undertaken a meta-analysis of international prospective cohorts to quantify the relationship between a family history of fracture and future fracture incidence. METHODS:The analysis dataset comprised 350,542 men and women from 42 cohorts in 29 countries followed for 2.8 million person-years. We investigated the relationship between family history of hip fracture or any fracture and the risk of any clinical fracture, any osteoporotic fracture, major osteoporotic fracture (MOF), and hip fracture alone using an extended Poisson model in each cohort. Models were adjusted for current age, sex, BMD, and follow-up time. RESULTS:As no difference in influence of family history of fracture was seen between genders, results are presented for men and women combined. A parental history of hip fracture was associated with a higher risk of incident fracture across all fracture outcome categories, with a stronger relationship with future hip fracture (hazard ratios (HR, 95% CI) for hip and MOF 1.37, 1.23-1.52 and 1.19, 1.12-1.27, respectively). Associations were slightly reduced but remained significant when additionally adjusted for BMD and did not vary by baseline offspring age, follow-up time, or parent affected. In a more limited analysis, parental history of any fracture or a sibling history of hip or any fracture showed similar associations to those observed with parental history of hip fracture. CONCLUSIONS:A family history of fracture is confirmed as a significant BMD-independent predictor of future fracture risk. While parental hip fracture appears the strongest factor for future hip fracture, a family history of other fractures might be appropriate for inclusion in future iterations of the FRAX tool.

Proton pump inhibitors (PPI) are widely prescribed medications. Proton pump inhibitors exposure may be associated with lower trabecular bone score (TBS), but has not shown a consistent effect on BMD. We hypothesized that abdominal obesity, which is associated with both gastroesophageal disease and PPI use, could confound the relationship between PPI use and TBS. We assessed the effect of PPI use on TBS (primary measurement) and BMD (secondary measurements) before and after adjustment for sagittal abdominal diameter (SAD), a DXA-derived measure of abdominal soft-tissue thickness. The study population comprised 60 930 individuals (90.3% women, mean age 65.7 yr) that included 11 340 (18.6%) with PPI use in the preceding 12 mo. PPI exposure was categorized from medication persistence ratio (MPR) as non-use (referent), minimal (MPR 0.01-0.25), mild (MPR 0.26-0.5), moderate (MPR 0.51-0.75), and high use (MPR 0.76-1). When logistic regression models were minimally adjusted for age, sex, and scanner, increasing PPI use versus non-use was associated with progressively increasing odds ratios (ORs) for TBS in the lowest tertile (minimal 1.11 [95% CI 1.02-1.22], mild 1.18 [1.04-1.34], moderate 1.34 [1.17-1.53], high 1.41 [1.31-1.52]) but inversely with osteoporotic BMD (minimal 0.97 [0.89-1.06], mild 0.85 [0.75-0.97], moderate 0.82 [0.72-0.94]), and high 0.76 [0.70-0.82]). Sagittal abdominal diameter was greater in PPI users than non-users. After further adjustment for SAD, PPI use was not associated with lower TBS or BMD. Similar patterns were seen in men and women, and for longer durations of PPI use. Among 4742 with a second DXA (mean interval 3.4 yr), PPI use was not associated with more rapid TBS or BMD loss compared to non-users. In conclusion, PPI use is associated with greater SAD, an indicator of abdominal obesity. SAD and other clinical variables have a confounding effect on TBS and BMD measurements. When fully adjusted, PPI exposure did not significantly decrease TBS or BMD.

Trabecular bone score (TBS), derived from the spine dual-energy x-ray absorptiometry (DXA) image, and hip axis length (HAL), derived from the hip DXA image, are bone mineral density (BMD)–independent risk factors for fracture. To date, no studies have directly compared the additive benefits of using TBS and HAL in combination. We found that TBS and HAL made independent contributions to fracture risk, with TBS having a larger benefit for major osteoporotic fractures (MOF) whereas HAL had a larger benefit for hip fractures. To compare the effects of TBS and HAL on fracture risk, separately and in combination. A total of 55,068 individuals (mean age 63.5 years, 90.9

The relationship between rheumatoid arthritis (RA) and fracture risk was estimated in an international meta-analysis of individual-level data from 29 prospective cohorts. RA was associated with an increased fracture risk in men and women, and these data will be used to update FRAX®. RA is a well-documented risk factor for subsequent fracture that is incorporated into the FRAX algorithm. The aim of this study was to evaluate, in an international meta-analysis, the association between rheumatoid arthritis and subsequent fracture risk and its relation to sex, age, duration of follow-up, and bone mineral density (BMD) with a view to updating FRAX. The resource comprised 1,909,896 men and women, aged 20–116 years, from 29 prospective cohorts in which the prevalence of RA was 3

Automated grading of Knee Osteoarthritis (KOA) from radiographs is challenged by significant inter-observer variability and the limited robustness of deep learning models, particularly near critical decision boundaries. To address these limitations, this paper proposes a novel framework, Diffusion-based Counterfactual Augmentation (DCA), which enhances model robustness and interpretability by generating targeted counterfactual examples. The method navigates the latent space of a diffusion model using a Stochastic Differential Equation (SDE), governed by balancing a classifier-informed boundary drive with a manifold constraint. The resulting counterfactuals are then used within a self-corrective learning strategy to improve the classifier by focusing on its specific areas of uncertainty. Extensive experiments on the public Osteoarthritis Initiative (OAI) and Multicenter Osteoarthritis Study (MOST) datasets demonstrate that this approach significantly improves classification accuracy across multiple model architectures. Furthermore, the method provides interpretability by visualizing minimal pathological changes and revealing that the learned latent space topology aligns with clinical knowledge of KOA progression. The DCA framework effectively converts model uncertainty into a robust training signal, offering a promising pathway to developing more accurate and trustworthy automated diagnostic systems. Our code is available at https://github.com/ZWang78/DCA.

The osteoanabolic effects of teriparatide are maximal in the first 6 months of treatment. This secondary analysis of a randomized clinical trial investigated whether cyclic teriparatide would improve Trabecular Bone Score (TBS) more than standard dosing over 36 months. Results showed similar improvements in TBS with both teriparatide regimens. The effects of teriparatide (TPTD) are maximal in the first 6 months when bone formation exceeds resorption to the greatest degree. Cyclic use of TPTD was proposed to try to broaden this anabolic window. Prior results from this study showed that 6-month cycles of TPTD and denosumab did not improve BMD compared to standard dosing with TPTD followed by denosumab. The goal of this study was to determine if there was any difference in trabecular bone score improvement (TBS) with cyclic vs standard therapy. 70 postmenopausal women with osteoporosis were randomized to TPTD for 18 months followed by denosumab for 18 months (standard; n = 32) or three cycles of 6 months TPTD, each followed by denosumab (cyclic; n = 32). DXA measurements of the lumbar spine (LS) were performed every 6 months and TBS calculated at baseline, 18 and 36 months on the 50 participants who completed the 36-month final study visit. This paper is a post-hoc analysis of a prior clinical trial looking at BMD change at 36 months for cyclic vs. standard dosing. At baseline, TBS was similar between the 2 groups with mean level 1.24 ± 0.05, considered partially degraded. In the standard group, TBS increased by 1.1

Proton pump inhibitors (PPI) are widely prescribed medications. Proton pump inhibitors exposure may be associated with lower trabecular bone score (TBS), but has not shown a consistent effect on BMD. We hypothesized that abdominal obesity, which is associated with both gastroesophageal disease and PPI use, could confound the relationship between PPI use and TBS. We assessed the effect of PPI use on TBS (primary measurement) and BMD (secondary measurements) before and after adjustment for sagittal abdominal diameter (SAD), a DXA-derived measure of abdominal soft-tissue thickness. The study population comprised 60 930 individuals (90.3% women, mean age 65.7 yr) that included 11 340 (18.6%) with PPI use in the preceding 12 mo. PPI exposure was categorized from medication persistence ratio (MPR) as non-use (referent), minimal (MPR 0.01-0.25), mild (MPR 0.26-0.5), moderate (MPR 0.51-0.75), and high use (MPR 0.76-1). When logistic regression models were minimally adjusted for age, sex, and scanner, increasing PPI use versus non-use was associated with progressively increasing odds ratios (ORs) for TBS in the lowest tertile (minimal 1.11 [95% CI 1.02-1.22], mild 1.18 [1.04-1.34], moderate 1.34 [1.17-1.53], high 1.41 [1.31-1.52]) but inversely with osteoporotic BMD (minimal 0.97 [0.89-1.06], mild 0.85 [0.75-0.97], moderate 0.82 [0.72-0.94]), and high 0.76 [0.70-0.82]). Sagittal abdominal diameter was greater in PPI users than non-users. After further adjustment for SAD, PPI use was not associated with lower TBS or BMD. Similar patterns were seen in men and women, and for longer durations of PPI use. Among 4742 with a second DXA (mean interval 3.4 yr), PPI use was not associated with more rapid TBS or BMD loss compared to non-users. In conclusion, PPI use is associated with greater SAD, an indicator of abdominal obesity. SAD and other clinical variables have a confounding effect on TBS and BMD measurements. When fully adjusted, PPI exposure did not significantly decrease TBS or BMD.

This study proposes age- and sex-specific trabecular bone score (TBS) reference curves for Mexican children and adolescents. Using the latest software version, results highlight significant pubertal changes and provide reference data for assessing pediatric bone health, paving the way for a wider use of this technology in children and adolescents. Trabecular Bone Score (TBS) is a grey scale texture measure that correlates with bone microarchitecture derived from dual-energy X-ray absorptiometry (DXA). While extensively studied in adults, limited data exist for pediatric populations. This study aims to develop age- and sex-specific reference curves for TBS adjusted for abdominal soft tissue thickness in healthy children and adolescents from Mexico City. This cross-sectional study reanalyzed data from 1552 healthy participants (5–18 years) who underwent lumbar spine DXA scans using Lunar iDXA and TBS iNsight 4.0 (Core Module 19.4.0), which accounts for soft tissue thickness. Generalized Additive Models for Location, Scale, and Shape (GAMLSS) were employed to construct smoothed percentile curves. TBS values were stratified by age, sex, and Tanner stage, with descriptive statistics and outlier exclusions. TBS showed distinct age- and sex-related trajectories, with steep increases during puberty. Girls demonstrated a sharper rise in TBS starting at age 9, peaking by age 16, while boys exhibited a more gradual increase starting at age 10–11, peaking by age 18. Differences were also observed between Tanner stages, with the most significant changes occurring from stages 2 to 3. This study proposes the first TBS reference curves for Mexican children and adolescents using the latest software version. This data may prove to be a valuable tool for assessing bone health in pediatric populations. Yet further research to explore TBS’s utility in predicting bone fragility in pediatric population as well as its life-course trends.

Among individuals aged ≥ 40 years old, we found that after controlling for age, sex, FMI, and tissue thickness, an increase of 1kg/m2 of ALMI is associated with an increase in TBS of 0.058, which is approximately half of one population standard deviation, or 4.7

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®.