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[This corrects the article DOI: 10.1002/jbm4.10346.].

Type 2 diabetes mellitus (T2DM) increases fracture risk despite normal or increased BMD. Abaloparatide reduces fracture risk in patients with postmenopausal osteoporosis (PMO); however, its efficacy in women with T2DM is unknown. This post hoc analysis evaluated the efficacy and safety of abaloparatide in patients with T2DM. The analysis included patients with T2DM from the Abaloparatide Comparator Trial In Vertebral Endpoints (ACTIVE), a phase 3, double-blind, randomized, placebo- and active-controlled trial. In ACTIVE, participants were randomized 1:1:1 to daily s.c. injections of placebo, abaloparatide (80 μg), or open-label teriparatide (20 μg) for 18 months. A total of 198 women with PMO and T2DM from 21 centers in 10 countries were identified from ACTIVE through review of their medical records. The main outcomes measured included effect of abaloparatide versus placebo on BMD and trabecular bone score (TBS), with secondary outcomes of fracture risk and safety, in patients from ACTIVE with T2DM. Significant (p

An abundance of medical data and enhanced computational power have led to a surge in artificial intelligence (AI) applications. Published studies involving AI in bone and osteoporosis research have increased exponentially, raising the need for transparent model development and reporting strategies. This review offers a comprehensive overview and systematic quality assessment of AI articles in osteoporosis while highlighting recent advancements. A systematic search in the PubMed database, from December 17, 2020 to February 1, 2023 was conducted to identify AI articles that relate to osteoporosis. The quality assessment of the studies relied on the systematic evaluation of 12 quality items derived from the minimum information about clinical artificial intelligence modeling checklist. The systematic search yielded 97 articles that fell into 5 areas; bone properties assessment (11 articles), osteoporosis classification (26 articles), fracture detection/classification (25 articles), risk prediction (24 articles), and bone segmentation (11 articles). The average quality score for each study area was 8.9 (range: 7–11) for bone properties assessment, 7.8 (range: 5–11) for osteoporosis classification, 8.4 (range: 7–11) for fracture detection, 7.6 (range: 4–11) for risk prediction, and 9.0 (range: 6–11) for bone segmentation. A sixth area, AI-driven clinical decision support, identified the studies from the 5 preceding areas that aimed to improve clinician efficiency, diagnostic accuracy, and patient outcomes through AI-driven models and opportunistic screening by automating or assisting with specific clinical tasks in complex scenarios. The current work highlights disparities in study quality and a lack of standardized reporting practices. Despite these limitations, a wide range of models and examination strategies have shown promising outcomes to aid in the earlier diagnosis and improve clinical decision-making. Through careful consideration of sources of bias in model performance assessment, the field can build confidence in AI-based approaches, ultimately leading to improved clinical workflows and patient outcomes. [ABSTRACT FROM AUTHOR]

Objective: Anorexia Nervosa (AN) is characterized by a distortion of body image, very low body weight, malnutrition and hormonal dysregulations, resulting in reduced bone mineral density (BMD) and impaired bone microarchitecture. The updated Trabecular Bone Score (TBS) algorithm accounts for soft tissue thickness (TBSTT) instead of BMI (TBSBMI). The aim of the study was to assess both TBS algorithms in adult AN patients compared to normal-weight controls(CTRL). Method: This retrospective cross-sectional study investigated 34 adult female anorexia nervosa (AN) patients and 26 healthy normal-weighted age- and sex-matched controls (CTRL). Bone texture analysis was assessed by TBSTT and TBSBMI (TBS iNsight® V4.0 and V3.1), bone mineral density (BMD; lumbar spine LS, femoral neck, total hip) and body composition by DXA (GE Lunar iDXATM). Laboratory analyses included bone turnover markers (CTX; P1NP; sclerostin). Data analysis was performed using parametric (t-test) or non-parametric test (Mann-Whitney-U-Test) depending on normality, one-way ANCOVA and correlation analysis (Perason's or Spearman's). Results: AN patients (BMI 14.7(1.6)) and CTRL (BMI 22.4(4.0)) were of comparable age (22.8(7.1) vs.25.0(4.0)years, p = 0.145). TBSTT(1.319±0.09 vs.1.502±0.07, p<0.001) and TBSBMI(1.317±0.10 vs.1.548±0.09, p<0.001) were significantly lower in AN patients compared to CTRL. Soft tissue thickness was lower in AN (p<0.001). Within the CTRL group, but not in AN, TBSTT and TBSBMI were significantly different (p<0.001). BMD was lower at all sites in AN patients (p<0.001 for all), being lowest at LS. Bone Mineral Content, Lean Body mass and Fat Mass were lower in AN (p<0.001). AN patients had lower P1NP (p = 0.05), but higher CTX (p = 0.001) and sclerostin (p = 0.003) levels. Conclusion: Adult AN patients have lower TBSTT and TBSBMI, reduced BMD and an uncoupling of bone turnover. In AN both TBS algorithms show similar reduced trabecular bone microarchitecture. The observed difference of TBSTT and TBSBMI in CTRL with normal body composition highlight the importance of the new algorithm. [ABSTRACT FROM AUTHOR]

The Trabecular Bone Score (TBS), a gray-level textural assessment derived from dual-energy X-ray absorptiometry images, serves as a validated index of trabecular bone microarchitecture. Over the past decade, significant evidence has highlighted the usefulness of TBS in primary and secondary osteoporosis, leading to its integration with the Fracture Risk Assessment Tool (FRAX) and bone mineral density (BMD) T-score adjustments. This review explores the role of TBS in fracture prediction, treatment initiation, and monitoring. Studies confirm that TBS enhances fracture risk prediction in both primary and secondary osteoporosis when combined with BMD and clinical risk factors. Evidence also suggests that including TBS alongside BMD and FRAX offers significant potential for treatment stratification, considering the overall skeletal profile, such as bone mass, bone quality, and clinical risk factors. Consequently, TBS has become a standard part of clinical care worldwide. Future enhancements hope to adjust for soft tissue thickness, broadening the applicability of TBS across diverse body types and pediatric populations. [ABSTRACT FROM AUTHOR]

Body composition (BC) measured by DXA differs between devices. We aimed to compare regional and total BC measurements assessed by the Hologic Horizon A and the GE Lunar iDXA devices; to determine device-specific calibration equations for each BC parameter; and to assess the impact of this standardization procedure on the assessment of sarcopenia, lipedema, obesity, and cardiovascular risk with DXA. A total of 926 postmenopausal women (aged 72.9 ± 6.9 yr, height 160.3 ± 6.6 cm, weight 66.1 ± 12.7 kg) underwent BC assessment on each device within 1 h, following the ISCD guidelines. The included sample was split into 80% train and 20% test datasets stratified by age, height, and weight. Inter-device differences in BC parameters were assessed with Bland–Altman analysis, Pearson or Spearman correlation coefficients, and t-tests or Wilcoxon tests. The equations were developed in the train dataset using backward stepwise multiple linear regressions and were evaluated in the test dataset with the R-squared and mean absolute error. We compared the abovementioned BC-derived health conditions before and after standardization in the test set with respect to relative risk, accuracy, Kappa score, and McNemar tests. Total and regional body masses were similar (p>.05) between devices. BMC was greater for all regions in the Lunar device (p<.05), while fat and lean masses differed among regions. Regression equations showed high performance metrics in both datasets. The BC assessment from Hologic classified 2.13 times more sarcopenic cases (McNemar: p<.001), 1.39 times more lipedema (p<.001), 0.40 times less high cardiovascular risk (p<.001), and similarly classified obesity (p>.05), compared to Lunar. After standardization, the differences disappeared (p>.05), and the classification metrics improved. This study discusses how hardware and software differences impact BC assessments. The provided standardization equations address these issues and improve the agreement between devices. Future studies and disease definitions should consider these differences. Lay Summary: DXA is a versatile and accessible imaging technique for the quantification of fat, lean, and bone tissues. We aimed to compare body composition using 2 recent DXA devices in 926 Swiss postmenopausal women who underwent body composition assessment on each device at 1-h intervals. Total body fat, lean, and bone masses differed significantly between devices, with greater fat mass assessed on the Lunar device and greater lean mass on the Hologic device. According to the regional analysis of the arms, legs, and trunk, these trends were inconsistently distributed between the devices. Univariate and multivariate calibration equations were developed in a training dataset with 80% of the participants. These equations accounted for all differences in the DXA measurements, including those in the internal validation dataset based on the remaining 20% of participants. The equations also accounted for up to 2-fold device differences seen in the assessment of sarcopenia, cardiovascular risk, lipedema, and obesity. This study provides insight on how DXA hardware and software affect the final assessment of body composition. We provide calibration equations to standardize measurements and increase agreement between devices. Although external validation is needed, further studies and disease definitions should account for these device differences. Graphical Abstract [ABSTRACT FROM AUTHOR]

ObjectivesWe compared skeletal microarchitecture using trabecular bone score (TBS) and evaluated relationships between change in TBS and lumbar spine (LS) bone mineral density (BMD) in women with and without HIV.MethodsDual-energy X-ray absorptiometry was performed on 319 women with HIV and 118 without HIV in the Women's Interagency HIV Study at baseline and 2 and 5 years, to measure regional BMD and lean and fat mass. TBS was extracted from LS dual-energy X-ray absorptiometry images and examined continuously and categorically [normal (≥1.35), intermediate (1.20-1.35), or degraded (≤1.20) microarchitecture]. Pearson correlation and linear regression examined associations of TBS with regional BMD at baseline and over time.ResultsWomen with HIV were older (43 vs. 37 years), more likely to be postmenopausal (27% vs. 4%), have lower baseline total fat mass, trunk fat, and leg fat than uninfected women, degraded microarchitecture (27% vs. 9%, P = 0.001), and lower baseline mean TBS (1.3 ± 0.1 vs. 1.4 ± 0.1, P < 0.001). After adjusting for age, race, menopause status, and body mass index, TBS remained lower in women with HIV (P < 0.0001). Annual change in TBS correlated with LS BMD change among women with HIV (r = 0.36, P < 0.0001) and without HIV (r = 0.26, P = 0.02); however, mean % annual TBS change did not differ by HIV status (-1.0%/yr ± 2.9% for HIV+ vs. -0.8%/yr ± 1.7% for HIV-, P = 0.42).ConclusionsWomen with HIV have worse bone microarchitecture than uninfected women, but annual percent change in LS BMD or TBS was similar. Use of TBS as an adjunct to BMD to improve prediction of fragility fractures in women with HIV merits further study.

Several recent studies suggest that obesity may be a risk factor for fracture. The aim of this study was to investigate the association between body mass index (BMI) and future fracture risk at different skeletal sites. In prospective cohorts from more than 25 countries, baseline data on BMI were available in 398,610 women with an average age of 63 (range, 20-105) years and follow up of 2.2 million person-years during which 30,280 osteoporotic fractures (6457 hip fractures) occurred. Femoral neck BMD was measured in 108,267 of these women. Obesity (BMI ≥ 30 kg/m(2) ) was present in 22%. A majority of osteoporotic fractures (81%) and hip fractures (87%) arose in non-obese women. Compared to a BMI of 25 kg/m(2) , the hazard ratio (HR) for osteoporotic fracture at a BMI of 35 kg/m(2) was 0.87 (95% confidence interval [CI], 0.85-0.90). When adjusted for bone mineral density (BMD), however, the same comparison showed that the HR for osteoporotic fracture was increased (HR, 1.16; 95% CI, 1.09-1.23). Low BMI is a risk factor for hip and all osteoporotic fracture, but is a protective factor for lower leg fracture, whereas high BMI is a risk factor for upper arm (humerus and elbow) fracture. When adjusted for BMD, low BMI remained a risk factor for hip fracture but was protective for osteoporotic fracture, tibia and fibula fracture, distal forearm fracture, and upper arm fracture. When adjusted for BMD, high BMI remained a risk factor for upper arm fracture but was also a risk factor for all osteoporotic fractures. The association between BMI and fracture risk is complex, differs across skeletal sites, and is modified by the interaction between BMI and BMD. At a population level, high BMI remains a protective factor for most sites of fragility fracture. The contribution of increasing population rates of obesity to apparent decreases in fracture rates should be explored.