Effect of BMI-discordant abdominal tissue thickness on fracture probability: a registry-based study

FRAX, which is used to assess fracture probability, considers body mass index (BMI), but BMI may not reflect individual variation in body composition and distribution. We examined the effect of BMI-discordant abdominal thickness on FRAX-derived fracture probability for major osteoporotic fracture (MOF) and hip fracture. We studied 73,105 individuals, mean age 64.2 years. During mean 8.7 years, 7048 (9.6%) individuals sustained incident MOF, including 2155 (3.0%) hip fractures. We defined abdominal thickness index (ATI) as the difference between abdominal thickness measured by dual-energy X-ray absorptiometry (DXA) and thickness predicted by BMI using sex-stratified regression. ATI was categorized from lower (+2 cm) with referent around zero (−1 to +1 cm). Adjusted for FRAX probability, increasing ATI was associated with incident MOF and hip fracture (p < 0.001). For the highest ATI category, MOF risk was increased (hazard ratio [HR] = 1.23, 95% confidence interval [CI] 1.12–1.35) independent of FRAX probability. Similar findings were noted for hip fracture probability (HR = 1.28, 95% CI 1.09–1.51). There was significant age-interaction with much larger effects before age 65 years (HR = 1.44, 95% CI 1.23–1.69 for MOF; 2.29, 95% CI 1.65–3.18 for hip fracture). In contrast, for the subset of individuals with diabetes, there was also increased risk for those in the lowest ATI category (HR = 1.73, 95% CI 1.12–2.65 for MOF; 2.81, 95% CI 1.59–4.97 for hip fracture). Calibration plots across ATI categories demonstrated deviation from the line of identity in women (calibration slope 2.26 for MOF, 2.83 for hip fracture). An effect of ATI was not found in men, but this was inconclusive as the sex-interaction terms did not show significant effect modification. In conclusion, these data support the need to investigate increased abdominal thickness beyond that predicted by BMI and sex as a FRAX-independent risk factor for fracture. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR)

Association of incident hip fracture with the estimated femoral strength by finite element analysis of DXA scans in the Osteoporotic Fractures in Men (MrOS) study

Finite element model can estimate bone strength better than BMD. This study used such a model to determine its association with hip fracture risk and found that the strength estimate provided limited improvement over the hip BMDs in predicting femoral neck (FN) fracture risk only. INTRODUCTION: Bone fractures occur only when it is loaded beyond its ultimate strength. The goal of this study was to determine the association of femoral strength, as estimated by finite element (FE) analysis of DXA scans, with incident hip fracture as a single condition or with femoral neck (FN) and trochanter (TR) fractures separately in older men. METHODS: This prospective case-cohort study included 91 FN and 64 TR fracture cases and a random sample of 500 men (14 had a hip fracture) from the Osteoporotic Fractures in Men study during a mean ± SD follow-up of 7.7 ± 2.2 years. We analysed the baseline DXA scans of the hip using a validated plane-stress, linear-elastic FE model of the proximal femur and estimated the femoral strength during a sideways fall. RESULTS: The estimated strength was significantly (P < 0.05) associated with hip fracture independent of the TR and total hip (TH) BMDs but not FN BMD, and combining the strength with BMD did not improve the hip fracture prediction. The strength estimate was associated with FN fractures independent of the FN, TR and TH BMDs; the age-BMI-BMD adjusted hazard ratio (95% CI) per SD decrease of the strength was 1.68 (1.07-2.64), 2.38 (1.57, 3.61) and 2.04 (1.34, 3.11), respectively. This association with FN fracture was as strong as FN BMD (Harrell's C index for the strength 0.81 vs. FN BMD 0.81) and stronger than TR and TH BMDs (0.8 vs. 0.78 and 0.81 vs. 0.79). The strength's association with TR fracture was not independent of hip BMD. CONCLUSIONS: Although the strength estimate provided additional information over the hip BMDs, its improvement in predictive ability over the hip BMDs was confined to FN fracture only and limited

Osteoporotic Vertebral Fracture Prevalence Varies Widely Between Qualitative and Quantitative Radiological Assessment Methods: The Rotterdam Study

Accurate diagnosis of vertebral osteoporotic fractures is crucial for the identification of individuals at high risk of future fractures. Different methods for radiological assessment of vertebral fractures exist, but a gold standard is lacking. The aim of our study was to estimate statistical measures of agreement and prevalence of osteoporotic vertebral fractures in the population-based Rotterdam Study, across two assessment methods. The quantitative morphometry assisted by SpineAnalyzer® (QM SA) method evaluates vertebral height loss that affects vertebral shape whereas the algorithm-based qualitative (ABQ) method judges endplate integrity and includes guidelines for the differentiation of vertebral fracture and nonfracture deformities. Cross-sectional radiographs were assessed for 7582 participants aged 45 to 95 years. With QM SA, the prevalence was 14.2% (95% CI, 13.4% to 15.0%), compared to 4.0% (95% CI, 3.6% to 4.5%) with ABQ. Inter-method agreement according to kappa (κ) was 0.24. The highest agreement between methods was among females (κ = 0.31), participants age >80 years (κ = 0.40), and at the L1 level (κ = 0.40). With ABQ, most fractures were found at the thoracolumbar junction (T12–L1) followed by the T7–T8 level, whereas with QM SA, most deformities were in the mid thoracic (T7–T8) and lower thoracic spine (T11–T12), with similar number of fractures in both peaks. Excluding mild QM SA deformities (grade 1 with QM) from the analysis increased, the agreement between the methods from κ = 0.24 to 0.40, whereas reexamining mild deformities based on endplate depression increased agreement from κ = 0.24 to 0.50 (p <0.001). Vertebral fracture prevalence differs significantly between QM SA and ABQ; reexamining QM mild deformities based on endplate depression would increase the agreement between methods. More widespread and consistent application of an optimal method may improve clinical care

Recent sarcopenia definitions—prevalence, agreement and mortality associations among men: findings from population‐based cohorts

Background The 2019 European Working Group on Sarcopenia in Older People (EWGSOP2) and the Sarcopenia Definitions and Outcomes Consortium (SDOC) have recently proposed sarcopenia definitions. However, comparisons of the performance of these approaches in terms of thresholds employed, concordance in individuals and prediction of important health-related outcomes such as death are limited. We addressed this in a large multinational assembly of cohort studies that included information on lean mass, muscle strength, physical performance and health outcomes. Methods White men from the Health Aging and Body Composition (Health ABC) Study, Osteoporotic Fractures in Men (MrOS) Study cohorts (Sweden, USA), the Hertfordshire Cohort Study (HCS) and the Sarcopenia and Physical impairment with advancing Age (SarcoPhAge) Study were analysed. Appendicular lean mass (ALM) was ascertained using DXA; muscle strength by grip dynamometry; and usual gait speed over courses of 2.4–6 m. Deaths were recorded and verified. Definitions of sarcopenia were as follows: EWGSOP2 (grip strength <27 kg and ALM index <7.0 kg/m2), SDOC (grip strength <35.5 kg and gait speed <0.8 m/s) and Modified SDOC (grip strength <35.5 kg and gait speed <1.0 m/s). Cohen's kappa statistic was used to assess agreement between original definitions (EWGSOP2 and SDOC). Presence versus absence of sarcopenia according to each definition in relation to mortality risk was examined using Cox regression with adjustment for age and weight; estimates were combined across cohorts using random-effects meta-analysis. Results Mean (SD) age of participants (n = 9170) was 74.3 (4.9) years; 5929 participants died during a mean (SD) follow-up of 12.1 (5.5) years. The proportion with sarcopenia according to each definition was EWGSOP2 (1.1%), SDOC (1.7%) and Modified SDOC (5.3%). Agreement was weak between EWGSOP2 and SDOC (κ = 0.17). Pooled hazard ratios (95% CI) for mortality for presence versus absence of each definition were EWGSOP2 [1.76 (1.42, 2.18), I2: 0.0%]; SDOC [2.75 (2.28, 3.31), I2: 0.0%]; and Modified SDOC [1.93 (1.54, 2.41), I2: 58.3%]. Conclusions There was low prevalence and poor agreement among recent sarcopenia definitions in community-dwelling cohorts of older white men. All indices of sarcopenia were associated with mortality. The strong relationship between sarcopenia and mortality, regardless of the definition, illustrates that identification of appropriate management and lifecourse intervention strategies for this condition is of paramount importance

Predictive value of sarcopenia components for all-cause mortality: findings from population-based cohorts

Background Low grip strength and gait speed are associated with mortality. However, investigation of the additional mortality risk explained by these measures, over and above other factors, is limited. Aim We examined whether grip strength and gait speed improve discriminative capacity for mortality over and above more readily obtainable clinical risk factors. Methods Participants from the Health, Aging and Body Composition Study, Osteoporotic Fractures in Men Study, and the Hertfordshire Cohort Study were analysed. Appendicular lean mass (ALM) was ascertained using DXA; muscle strength by grip dynamometry; and usual gait speed over 2.4–6 m. Verified deaths were recorded. Associations between sarcopenia components and mortality were examined using Cox regression with cohort as a random effect; discriminative capacity was assessed using Harrell’s Concordance Index (C-index). Results Mean (SD) age of participants (n = 8362) was 73.8(5.1) years; 5231(62.6%) died during a median follow-up time of 13.3 years. Grip strength (hazard ratio (95% CI) per SD decrease: 1.14 (1.10,1.19)) and gait speed (1.21 (1.17,1.26)), but not ALM index (1.01 (0.95,1.06)), were associated with mortality in mutually-adjusted models after accounting for age, sex, BMI, smoking status, alcohol consumption, physical activity, ethnicity, education, history of fractures and falls, femoral neck bone mineral density (BMD), self-rated health, cognitive function and number of comorbidities. However, a model containing only age and sex as exposures gave a C-index (95% CI) of 0.65(0.64,0.66), which only increased to 0.67(0.67,0.68) after inclusion of grip strength and gait speed. Conclusions Grip strength and gait speed may generate only modest adjunctive risk information for mortality compared with other more readily obtainable risk factors

Effect of Abdominal Tissue Thickness on Trabecular Bone Score and Fracture Risk in Adults With Diabetes: The Manitoba BMD Registry.

Individuals with type 2 diabetes have lower trabecular bone score (TBS) and increased fracture risk despite higher bone mineral density (BMD). However, measures of trabecular microarchitecture from high resolution peripheral computed tomography (HRpQCT) are not lower in type 2 diabetes. We hypothesized that confounding effects of abdominal tissue thickness may explain this discrepancy, since central obesity is a risk factor for diabetes and also artifactually lowers TBS. This hypothesis was tested in individuals aged 40 years and older from a large DXA registry, stratified by sex and diabetes status. When DXA-measured abdominal tissue thickness was not included as a covariate, men without diabetes had lower TBS than women without diabetes (mean difference -0.074, p&lt;0.001). TBS was lower in women with versus without diabetes (mean difference -0.037, p&lt;0.001), and men with versus without diabetes (mean difference -0.007, p=0.042). When adjusted for tissue thickness these findings reversed, and TBS became greater in men versus women without diabetes (mean difference +0.053, p&lt;0.001), in women with versus without diabetes (mean difference +0.008, p&lt;0.001) and in men with versus without diabetes (mean difference +0.014, p&lt;0.001). During mean 8.7 years observation, incident major osteoporotic fractures were seen in 7048 (9.6%). Adjusted for multiple covariates except tissue thickness, TBS predicted fracture in all subgroups with no significant diabetes interaction. When further adjusted for tissue thickness, HR per SD lower TBS remained significant and even increased slightly. In conclusion, TBS predicts fractures independent of other clinical risk factors in both women and men, with and without diabetes. Excess abdominal tissue thickness in men and individuals with type 2 diabetes may artifactually lower TBS using the current algorithm, which reverses after accounting for tissue thickness. This supports ongoing efforts to update the TBS algorithm to directly account for the effects of abdominal tissue thickness for improved fracture risk prediction

Development of a manufacturer-independent convolutional neural network for the automated identification of vertebral compression fractures in vertebral fracture assessment images using active learning.

Convolutional neural networks (CNNs) can identify vertebral compression fractures in GE vertebral fracture assessment (VFA) images with high balanced accuracy, but performance against Hologic VFAs is unknown. To obtain good classification performance, supervised machine learning requires balanced and labeled training data. Active learning is an iterative data annotation process with the ability to reduce the cost of labeling medical image data and reduce class imbalance. To train CNNs to identify vertebral fractures in Hologic VFAs using an active learning approach, and evaluate the ability of CNNs to generalize to both Hologic and GE VFA images. VFAs were obtained from the OsteoLaus Study (labeled Hologic Discovery A, n = 2726), the Manitoba Bone Mineral Density Program (labeled GE Prodigy and iDXA, n = 12,742), and the Canadian Longitudinal Study on Aging (CLSA, unlabeled Hologic Discovery A, n = 17,190). Unlabeled CLSA VFAs were split into five equal-sized partitions (n = 3438) and reviewed sequentially using active learning. Based on predicted fracture probability, 17.6% (n = 3032) of the unlabeled VFAs were selected for expert review using the modified algorithm-based qualitative (mABQ) method. CNNs were simultaneously trained on Hologic, GE dual-energy and GE single-energy VFAs. Two ensemble CNNs were constructed using the maximum and mean predicted probability from six separately trained CNNs that differed due to stochastic variation. CNNs were evaluated against the OsteoLaus validation set (n = 408) during the active learning process; ensemble performance was measured against the OsteoLaus test set (n = 819). The baseline CNN, prior to active learning, achieved 55.0% sensitivity, 97.9% specificity, 57.9% positive predictive value (PPV), F1-score 56.4%. Through active learning, 2942 CLSA Hologic VFAs (492 fractures) were added to the training data-increasing the proportion of Hologic VFAs with fractures from 4.2% to 12.5%. With active learning, CNN performance improved to 80.0% sensitivity, 99.7% specificity, 94.1% PPV, F1-score 86.5%. The CNN maximum ensemble achieved 91.9% sensitivity (100% for grade 3 and 95.5% for grade 2 fractures), 99.0% specificity, 81.0% PPV, F1-score 86.1%. Simultaneously training on a composite dataset consisting of both Hologic and GE VFAs allowed for the development of a single manufacturer-independent CNN that generalized to both scanner types with good classification performance. Active learning can reduce class imbalance and produce an effective medical image classifier while only labeling a subset of available unlabeled image data-thereby reducing the time and cost required to train a machine learning model

Fracture risk following high-trauma versus low-trauma fracture: a registry-based cohort study

Summary: Prior high-trauma fractures identified through health services data are associated with low bone mineral density (BMD) and future fracture risk to the same extent as fractures without high-trauma. Introduction: Some have questioned the usefulness of distinguishing high-trauma fractures from low-trauma fractures. The aim of this study is to compare BMD measurements and risk of subsequent low-trauma fracture in patients with prior high- or low-trauma fractures. Methods: Using a clinical BMD registry for the province of Manitoba, Canada, we identified women and men age 40 years or older with fracture records from linked population-based healthcare data. Age- and sex-adjusted BMD Z-scores and covariate-adjusted hazard ratios (HR) with 95% confidence intervals (CI) for incident fracture were studied in relation to prior fracture status, categorized as high-trauma if associated with external injury codes and low-trauma otherwise. Results: The study population consisted of 64,428 women and men with no prior fracture (mean age 63.7 years), 858 with prior high-trauma fractures (mean age 65.1 years), and 14,758 with prior low-trauma fractures (mean age 67.2 years). Mean Z-scores for those with any prior high-trauma fracture were significantly lower than in those without prior fracture (P &lt; 0.001) and similar to those with prior low-trauma fracture. Median observation time for incident fractures was 8.8 years (total 729,069 person-years). Any prior high-trauma fracture was significantly associated with increased risk for incident major osteoporotic fracture (MOF) (adjusted HR 1.31, 95% CI 1.08–1.59) as was prior low-trauma fracture (adjusted HR 1.55, 95% CI 1.47–1.63), and there was no significant difference between the two groups (prior trauma versus low-trauma fracture P = 0.093). A similar pattern was seen when incident MOF was studied in relation to prior hip fracture or prior MOF, or when the outcome was incident hip fracture or any incident fracture. Conclusions: High-trauma and low-trauma fractures showed similar relationships with low BMD and future fracture risk. This supports the inclusion of high-trauma fractures in clinical assessment for underlying osteoporosis and in the evaluation for intervention to reduce future fracture risk.</p