Development and reporting of artificial intelligence in osteoporosis management.

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 17th, 2020, to February 1st, 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 MI-CLAIM checklist. The systematic search yielded 97 articles that fell into five 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 6th area, AI-driven clinical decision support, identified the studies from the five preceding areas which 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.

This review covers the recent advancements in artificial intelligence (AI) for managing osteoporosis, an increasingly prevalent condition that weakens bone tissues and increases fracture risk. Analyzing 97 studies from December 2020 to February 2023, the present work highlights how AI enhances bone properties assessment, osteoporosis classification, fracture detection and classification, risk prediction, and bone segmentation. A systematic qualitative assessment of the studies revealed improvements in study quality compared with the earlier review period, supported by innovative and more explainable AI approaches. AI shows promise in clinical decision support by offering novel screening tools that can help in the earlier identification of the disease, improve clinical workflows and patient prognosis. New pre-processing strategies and advanced model architectures have played a critical role in these improvements. Researchers have enhanced the accuracy and predictive performance of traditional methods by integrating clinical data with imaging data through advanced multi-factorial AI techniques. These innovations, paired with standardized development and validation processes, promise to personalize medicine and enhance patient care in osteoporosis management.

Standardization of body composition parameters between GE Lunar iDXA and Hologic Horizon A and their clinical impact.

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.

Muscle parameters in fragility fracture risk prediction in older adults: A scoping review.

Half of osteoporotic fractures occur in patients with normal/osteopenic bone density or at intermediate or low estimated risk. Muscle measures have been shown to contribute to fracture risk independently of bone mineral density. The objectives were to review the measurements of muscle health (muscle mass/quantity/quality, strength and function) and their association with incident fragility fractures and to summarize their use in clinical practice. This scoping review follows the PRISMA-ScR guidelines for reporting. Our search strategy covered the three overreaching concepts of 'fragility fractures', 'muscle health assessment' and 'risk'. We retrieved 14 745 references from Medline Ovid SP, EMBASE, Web of Science Core Collection and Google Scholar. We included original and prospective studies on community-dwelling adults aged over 50 years that analysed an association between at least one muscle parameter and incident fragility fractures. We systematically extracted 17 items from each study, including methodology, general characteristics and results. Data were summarized in tables and graphically presented in adjusted forest plots. Sixty-seven articles fulfilled the inclusion criteria. In total, we studied 60 muscle parameters or indexes and 322 fracture risk ratios over 2.8 million person-years (MPY). The median (interquartile range) sample size was 1642 (921-5756), age 69.2 (63.5-73.6) years, follow-up 10.0 (4.4-12.0) years and number of incident fragility fractures 166 (88-277). A lower muscle mass was positively/not/negatively associated with incident fragility fracture in 28 (2.0), 64 (2.5) and 10 (0.2 MPY) analyses. A lower muscle strength was positively/not/negatively associated with fractures in 53 (1.3), 57 (1.7 MPY) and 0 analyses. A lower muscle function was positively/not/negatively associated in 63 (1.9), 45 (1.0 MPY) and 0 analyses. An in-depth analysis shows how each single muscle parameter was associated with each fragility fractures subtype. This review summarizes markers of muscle health and their association with fragility fractures. Measures of muscle strength and function appeared to perform better for fracture risk prediction. Of these, hand grip strength and gait speed are likely to be the most practical measures for inclusion in clinical practice, as in the evaluation of sarcopenia or in further fracture risk assessment scores. Measures of muscle mass did not appear to predict fragility fractures and might benefit from further research, on D3-creatine dilution test, lean mass indexes and artificial intelligence methods.

Update on the clinical use of trabecular bone score (TBS) in the management of osteoporosis: results of an expert group meeting organized by the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO), and the International Osteoporosis Foundation (IOF) under the auspices of WHO Collaborating Center for Epidemiology of Musculoskeletal Health and Aging.

PURPOSE: Trabecular bone score (TBS) is a grey-level textural measurement acquired from dual-energy X-ray absorptiometry lumbar spine images and is a validated index of bone microarchitecture. In 2015, a Working Group of the European Society on Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases (ESCEO) published a review of the TBS literature, concluding that TBS predicts hip and major osteoporotic fracture, at least partly independent of bone mineral density (BMD) and clinical risk factors. It was also concluded that TBS is potentially amenable to change as a result of pharmacological therapy. Further evidence on the utility of TBS has since accumulated in both primary and secondary osteoporosis, and the introduction of FRAX and BMD T-score adjustment for TBS has accelerated adoption. This position paper therefore presents a review of the updated scientific literature and provides expert consensus statements and corresponding operational guidelines for the use of TBS. METHODS: An Expert Working Group was convened by the ESCEO and a systematic review of the evidence undertaken, with defined search strategies for four key topics with respect to the potential use of TBS: (1) fracture prediction in men and women; (2) initiating and monitoring treatment in postmenopausal osteoporosis; (3) fracture prediction in secondary osteoporosis; and (4) treatment monitoring in secondary osteoporosis. Statements to guide the clinical use of TBS were derived from the review and graded by consensus using the Grades of Recommendation, Assessment, Development and Evaluation (GRADE) approach. RESULTS: A total of 96 articles were reviewed and included data on the use of TBS for fracture prediction in men and women, from over 20 countries. The updated evidence shows that TBS enhances fracture risk prediction in both primary and secondary osteoporosis, and can, when taken with BMD and clinical risk factors, inform treatment initiation and the choice of antiosteoporosis treatment. Evidence also indicates that TBS provides useful adjunctive information in monitoring treatment with long-term denosumab and anabolic agents. All expert consensus statements were voted as strongly recommended. CONCLUSION: The addition of TBS assessment to FRAX and/or BMD enhances fracture risk prediction in primary and secondary osteoporosis, adding useful information for treatment decision-making and monitoring. The expert consensus statements provided in this paper can be used to guide the integration of TBS in clinical practice for the assessment and management of osteoporosis. An example of an operational approach is provided in the appendix. This position paper presents an up-to-date review of the evidence base, synthesised through expert consensus statements, which informs the implementation of Trabecular Bone Score in clinical practice.

Heel quantitative ultrasound (QUS) predicts incident fractures independently of trabecular bone score (TBS), bone mineral density (BMD), and FRAX: the OsteoLaus Study.

This study aimed to better define the role of heel-QUS in fracture prediction. Our results showed that heel-QUS predicts fracture independently of FRAX, BMD, and TBS. This corroborates its use as a case finding/pre-screening tool in osteoporosis management. INTRODUCTION: Quantitative ultrasound (QUS) characterizes bone tissue based on the speed of sound (SOS) and broadband ultrasound attenuation (BUA). Heel-QUS predicts osteoporotic fractures independently of clinical risk factors (CRFs) and bone mineral density (BMD). We aimed to investigate whether (1) heel-QUS parameters predict major osteoporotic fractures (MOF) independently of the trabecular bone score (TBS) and (2) the change of heel-QUS parameters over 2.5 years is associated with fracture risk. METHODS: One thousand three hundred forty-five postmenopausal women from the OsteoLaus cohort were followed up for 7 years. Heel-QUS (SOS, BUA, and stiffness index (SI)), DXA (BMD and TBS), and MOF were assessed every 2.5 years. Pearson's correlation and multivariable regression analyses were used to determine associations between QUS and DXA parameters and fracture incidence. RESULTS: During a mean follow-up of 6.7 years, 200 MOF were recorded. Fractured women were older, more treated with anti-osteoporosis medication; had lower QUS, BMD, and TBS; higher FRAX-CRF risk; and more prevalent fractures. TBS was significantly correlated with SOS (0.409) and SI (0.472). A decrease of one SD in SI, BUA or SOS increased the MOF risk by (OR(95%CI)) 1.43 (1.18-1.75), 1.19 (0.99-1.43), and 1.52 (1.26-1.84), respectively, after adjustment for FRAX-CRF, treatment, BMD, and TBS. We found no association between the change of QUS parameters in 2.5 years and incident MOF. CONCLUSION: Heel-QUS predicts fracture independently of FRAX, BMD, and TBS. Thus, QUS represents an important case finding/pre-screening tool in osteoporosis management. The change in QUS over time was not associated with future fractures, making it inappropriate for patient monitoring.

[Does artificial intelligence have a role in osteoporosis management?] / L'intelligence artificielle a-t-elle un rôle dans la prise en charge de l'ostéoporose ?

The individual and societal burden of osteoporosis is high and will continue to increase due to the demographic situation. Applications based on artificial intelligence models can provide concrete solutions at each step of the management of osteoporosis: screening, diagnostic, therapy management and prognostic assessment. The implementation of such models could assist clinicians in their workflow while improving overall patient care.

L'ostéoporose représente un fléau important, à l'échelle individuelle mais aussi sociétale. Avec le vieillissement de la population, le nombre de patients concernés augmente de manière considérable. Des applications basées sur des modèles d'intelligence artificielle nous apportent des solutions de plus en plus concrètes, à chaque étape de la prise en charge de l'ostéoporose : dépistage, diagnostic, prise en charge médicamenteuse et évaluation pronostique. L'implémentation de tels modèles pourrait aider les professionnels de santé, aussi bien dans l'optimisation du flux du travail que dans la prise en charge clinique du patient.

Long-term effect of denosumab on bone microarchitecture as assessed by tissue thickness-adjusted trabecular bone score in postmenopausal women with osteoporosis: results from FREEDOM and its open-label extension.

In postmenopausal women with osteoporosis, up to 10 years of denosumab treatment significantly and continuously improved bone microarchitecture assessed by tissue thickness-adjusted trabecular bone score, independently of bone mineral density. Long-term denosumab treatment decreased the number of high fracture-risk patients and shifted more patients to lower fracture-risk categories. PURPOSE: To investigate the long-term effect of denosumab on bone microarchitecture assessed by tissue thickness-adjusted trabecular bone score (TBSTT) in post-hoc subgroup analysis of FREEDOM and open-label extension (OLE). METHODS: Postmenopausal women with lumbar spine (LS) or total hip BMD T-score <-2.5 and ≥-4.0 who completed the FREEDOM DXA substudy and continued in OLE were included. Patients received either denosumab 60 mg subcutaneously every 6 months for 3 years and same-dose open-label denosumab for 7 years (long-term denosumab; n=150) or placebo for 3 years and open-label denosumab for 7 years (crossover denosumab; n=129). BMD and TBSTT were assessed on LS DXA scans at FREEDOM baseline, month 1, and years 1-6, 8, and 10. RESULTS: In long-term denosumab group, continued increases from baseline to years 4, 5, 6, 8, and 10 in BMD (11.6%, 13.7%, 15.5%, 18.5%, and 22.4%) and TBSTT (3.2%, 2.9%, 4.1%, 3.6%, and 4.7%) were observed (all P < 0.0001). Long-term denosumab treatment decreased the proportion of patients at high fracture-risk (according to TBSTT and BMD T-score) from baseline up to year 10 (93.7 to 40.4%), resulting in increases in the proportions at medium-risk (6.3 to 53.9%) and low-risk (0 to 5.7%) (P < 0.0001). Similar responses were observed in crossover denosumab group. Changes in BMD and TBSTT were poorly correlated during denosumab treatment. CONCLUSION: In postmenopausal women with osteoporosis, up to 10 years of denosumab significantly and continuously improved bone microarchitecture assessed by TBSTT, independently of BMD, and shifted more patients to lower fracture-risk categories.

Effect of Abaloparatide on Bone Microarchitecture Assessed by Trabecular Bone Score in Women With Osteoporosis: Post Hoc Analysis of ACTIVE and ACTIVExtend.

Although bone mineral density (BMD) is a predictor of fracture, many fractures occur in women with T-scores > -2.5. Bone microarchitecture, assessed by trabecular bone score (TBS), predicts fracture risk independent of BMD. We evaluated whether abaloparatide improves TBS and whether TBS trends were associated with vertebral fracture risk reduction. Women with osteoporosis randomized to abaloparatide or placebo for 18 months (ACTIVE), followed by alendronate for 24 months (ACTIVExtend), with evaluable TBS, were included in this post hoc analysis (N = 911). TBS was calculated from spine BMD scans using an algorithm adjusted for tissue thickness (TBSth ) at baseline, 6, 18, and 43 months. Mean increments in TBSth from baseline within and between treatment groups, proportion of women with TBSth increments above least significant change (LSC) and proportion with degraded TBSth (<1.027) were calculated. Risk estimates for vertebral fracture were compared using binary logistic regressions adjusted for baseline age and spine BMD. At baseline, 42% had degraded TBSth . Mean TBSth increased 4% after 18 months abaloparatide (p < 0.001) and was unchanged with placebo. After 2 subsequent years of alendronate, the total cumulative TBSth increase was 4.4% with abaloparatide/alendronate and 1.7% with placebo/alendronate (group difference, p < 0.001). At 43 months, the proportion of women with degraded TBSth had declined to 21% with abaloparatide/alendronate and 37% with placebo/alendronate (p < 0.05). An increase in TBSth  ≥ LSC was observed in 50% of abaloparatide-treated women at 18 months and was associated with decreased odds (odds ratio [OR]; 95% confidence interval [CI]) of vertebral fracture (0.19; 95% CI, 0.04-0.80, 6 months; 0.30; 95% CI, 0.11-0.79, 43 months). In conclusion, abaloparatide increased TBSth rapidly and progressively over 18 months and increments were maintained over 2 years with alendronate. TBSth increase was associated with vertebral fracture risk reduction. Microarchitectural improvement may be one mechanism by which abaloparatide strengthens vertebral bone. © 2023 Radius Health, Inc and The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Updated trabecular bone score accounting for the soft tissue thickness (TBSTT) demonstrated significantly improved bone microstructure with denosumab in the FREEDOM TBS post hoc analysis.

TBS algorithm has been updated to account for regional soft tissue noise. In postmenopausal women with osteoporosis, denosumab improved tissue thickness-adjusted TBS vs placebo independently of bone mineral density over 3 years, with the magnitude of changes from baseline or placebo numerically greater than body mass index-adjusted TBS. INTRODUCTION: To evaluate the effect of denosumab on bone microarchitecture assessed by trabecular bone score (TBS) in the FREEDOM study using the updated algorithm that accounts for regional soft tissue thickness (TBSTT) in dual-energy X-ray absorptiometry (DXA) images and to compare percent changes from baseline and placebo with classical body mass index (BMI)-adjusted TBS (TBSBMI). METHODS: Postmenopausal women with lumbar spine or total hip bone mineral density (BMD) T score < - 2.5 and ≥ - 4.0 received placebo or denosumab 60 mg subcutaneously every 6 months. TBSBMI and TBSTT were assessed on lumbar spine DXA scans at baseline and months 1, 12, 24, and 36 in a subset of 279 women (129 placebo, 150 denosumab) who completed the 3-year FREEDOM DXA substudy and rolled over to open-label extension study. RESULTS: Baseline characteristics were similar between groups. TBSTT in the denosumab group showed numerically greater changes from both baseline and placebo than TBSBMI at months 12, 24, and 36. Denosumab led to progressive increases in BMD (1.2, 5.6, 8.1, and 10.5%) and TBSTT (0.4, 2.3, 2.6, and 3.3%) from baseline to months 1, 12, 24, and 36, respectively. Both TBS changes were significant vs baseline and placebo from months 12 to 36 (p < 0.0001). As expected, BMD and TBSTT were poorly correlated both at baseline and for changes during treatment. CONCLUSION: In postmenopausal women with osteoporosis, denosumab significantly improved bone microstructure assessed by TBSTT over 3 years. TBSTT seemed more responsive to denosumab treatment than TBSBMI and was independent of BMD.

The fracture predictive ability of lumbar spine BMD and TBS as calculated based on different combinations of the lumbar spine vertebrae.

Lumbar spine bone mineral density (BMD) and trabecular bone score (TBS) are both calculated on L1-L4 vertebrae. This study investigated the ability to predict osteoporotic fractures of BMD and TBS as calculated based on all possible adjacent L1-L4 vertebrae combinations. Present findings indicate that L1-L3 is an optimal combination to calculate LS-BMD or TBS. INTRODUCTION: Lumbar spine (LS) BMD and TBS are both assessed in the LS DXA scans in the same region of interest, L1-L4. We aimed to investigate the ability to predict osteoporotic fractures of all the possible adjacent LS vertebrae combinations used to calculate BMD and TBS and to evaluate if any of these combinations performs better at osteoporotic fracture prediction than the traditional L1-L4 combination. METHODS: This study was embedded in OsteoLaus-women cohort in Switzerland. LS-DXA scans were performed using Discovery A System (Hologic). The incident vertebral fractures (VFs) and major osteoporotic fractures (MOFs) were assessed from VF assessments using Genant's method or questionnaires (non-VF MOF). We ran logistic models using TBS and BMD to predict MOF, VF, and non-VF MOF, combining different adjustment factors (age, fracture level, or BMD). RESULTS: One thousand six hundred thirty-two women (mean ± SD) 64.4 ± 7.5 years, BMI 25.9 ± 4.5 kg/m2, were followed for 4.4 years and 133 experienced MOF. The association of one SD decrease L1-L3 BMD with the odds ratios (ORs) of MOF was OR 1.32 (95%CI 1.15-1.53), L2-L4 BMD was 1.25 (95%CI 1.09-1.42), and L1-L4 BMD was 1.30 (95%CI 1.14-1.48). One SD decrease in L1-L3 TBS was more strongly associated with the odds of having a MOF (OR 1.64, 95% CI 1.34-2.00), than one SD decrease in L2-L4 TBS (OR 1.48, 95% CI 1.21-1.81), or in L1-L4 TBS (OR 1.60, CI 95% 1.32-1.95). CONCLUSION: Current findings indicate that L1-L3 is an optimal combination for the TBS or LS-BMD calculation.

Quantitative Ultrasound (QUS) in the Management of Osteoporosis and Assessment of Fracture Risk: An Update.

Quantitative ultrasound (QUS) presents a low cost and readily available alternative to DXA measurements of bone mineral density (BMD) for osteoporotic fracture risk assessment. It is performed in a variety of skeletal sites, among which the most widely investigated and clinically used are first the calcaneus and then the radius. Nevertheless, there is still uncertainty in the incorporation of QUS in the clinical management of osteoporosis as the level of clinical validation differs substantially upon the QUS models available. In fact, results from a given QUS device can unlikely be extrapolated to another one, given the technological differences between QUS devices. The use of QUS in clinical routine to identify individuals at low or high risk of fracture could be considered primarily when central DXA is not easily available. In this later case, it is recommended that QUS bone parameters are used in combination with established clinical risk factors for fracture. Currently, stand-alone QUS is not recommended for treatment initiation decision making or follow-up. As WHO classification of osteoporosis thresholds cannot apply to QUS, thresholds specific for given QUS devices and parameters need to be determined and cross-validated widely to have a well-defined and certain use of QUS in osteoporosis clinical workflow. Despite the acknowledged current clinical limitations for QUS to be used more widely in daily routine, substantial progresses have been made and new results are promising.

Bone Texture Assessment on Lateral VFAs Using the Texture Research Investigational Platform (TRIP) and its Fracture Discrimination Ability.

Texture Research Imaging Platform applies trabecular bone score (TBS) measurement principles to images acquired with multiple modalities to assess bone texture at various skeletal sites. This study aimed to assess the bone texture score in dual-energy X-ray absorptiometry-acquired lateral vertebral fracture assessment (VFA) images (BTSVFA), evaluate its reproducibility, and vertebral fracture discrimination ability. Subjects included 178 VF cases and 178 non-VF controls, 136 women and 42 men in each group, age 55-92 years, from two research centers. Cases and controls were matched for age (±5 years), body mass index (±5 kg/m2) and TBS. All participants underwent dual-energy X-ray absorptiometry TBS assessment from standard posterior-anterior lumbar spine scans and BTSVFA assessment. VF presence was determined using VFA images applying the Genant's method. BTSVFA was lower among fractured women compared to non-fractured (0.626 ± 0.109 vs 0.675 ± 0.099, p < 0.01), but not among men. In a binary logistic regression adjusted for study center and sex, for each SD lower BTSVFA, there was a 40% increase (OR 1.40, 95% CI (1.13-1.74)) in the risk of having a prevalent VF; area under the curve (95% CI) 0.616 (0.557-0.675). Inter-assessor and inter-centers ICCs for BTSVFA measurements were very good; 0.96 (0.64-0.99) and 0.98 (0.95-0.99), respectively. The BTSVFA precision (coefficient of variation) was 2.42%. This feasibility study demonstrates the potential to assess trabecular bone texture in lateral VFA images with good reproducibility. BTSVFA can discriminate between fractured and non-fractured women independent of their age, body mass index and TBS. In conclusion, BTSVFA, a potential trabecular texture assessment that excludes the posterior elements, may have value in fracture prediction or as a novel approach to be further investigated in spine surgery planning.

CYP11B1 variants influence skeletal maturation via alternative splicing.

We performed genome-wide association study meta-analysis to identify genetic determinants of skeletal age (SA) deviating in multiple growth disorders. The joint meta-analysis (N = 4557) in two multiethnic cohorts of school-aged children identified one locus, CYP11B1 (expression confined to the adrenal gland), robustly associated with SA (rs6471570-A; ß = 0.14; P = 6.2 × 10-12). rs6410 (a synonymous variant in the first exon of CYP11B1 in high LD with rs6471570), was prioritized for functional follow-up being second most significant and the one closest to the first intron-exon boundary. In 208 adrenal RNA-seq samples from GTEx, C-allele of rs6410 was associated with intron 3 retention (P = 8.11 × 10-40), exon 4 inclusion (P = 4.29 × 10-34), and decreased exon 3 and 5 splicing (P = 7.85 × 10-43), replicated using RT-PCR in 15 adrenal samples. As CYP11B1 encodes 11-ß-hydroxylase, involved in adrenal glucocorticoid and mineralocorticoid biosynthesis, our findings highlight the role of adrenal steroidogenesis in SA in healthy children, suggesting alternative splicing as a likely underlying mechanism.

DXA parameters, Trabecular Bone Score (TBS) and Bone Mineral Density (BMD), in fracture risk prediction in endocrine-mediated secondary osteoporosis.

Osteoporosis, a disease characterized by low bone mass and alterations of bone microarchitecture, leading to an increased risk for fragility fractures and, eventually, to fracture; is associated with an excess of mortality, a decrease in quality of life, and co-morbidities. Bone mineral density (BMD), measured by dual X-ray absorptiometry (DXA), has been the gold standard for the diagnosis of osteoporosis. Trabecular bone score (TBS), a textural analysis of the lumbar spine DXA images, is an index of bone microarchitecture. TBS has been robustly shown to predict fractures independently of BMD. In this review, while reporting also results on BMD, we mainly focus on the TBS role in the assessment of bone health in endocrine disorders known to be reflected in bone.

[Towards an individualized bone health report focused on the patient - Bone parameters and treatment options]. / Vers un rapport de santé osseuse individualisé centré sur le patient - Les paramètres osseux et les possibilités de traite.

This article presents a novel approach of osteoporosis management, starting from the DXA scans performance, image quality, BMD and TBS assessment and interpretation, vertebral fracture assessment, decision making on treatment initiation and being finalized with patient`s further follow-up recommendations. A report based on this approach is soon to be implemented by the CiMO at CHUV. Among the thorough evaluation of the densitometric status of the patient, this report presents the first effort to incorporate into the osteoporosis clinical workflow the current evidence on TBS. It suggests practical ways to use TBS as in conjunction with BMD T-score or FRAX score to come up with the final scores that allow treatment initiation. Implementations in other non-Caucasian or non-Swiss clinical settings are to be accompanied by local validations.

Cet article présente une approche nouvelle de la prise en charge de l'ostéoporose, à partir de la performance des DXA, de la qualité de l'image, de l'interprétation combinée de la densité minérale osseuse et du TBS, de la recherche des fractures vertébrales, de la décision de traiter et des stratégies de suivi du patient. Un nouveau rapport densitométrique basé sur cette approche sera bientôt mis en fonction par le Centre interdisciplinaire des maladies osseuses du CHUV. Il présente le premier effort pour incorporer dans le flux de travail clinique de l'ostéoporose les toutes dernières avancées sur le TBS. Il suggère des moyens pratiques pour utiliser le TBS en conjonction avec le T-score de la DMO ou le FRAX pour obtenir les scores finaux en vue d'initier un traitement spécifique pour un profil de risque individuel donné. L'utilisation dans d'autres contextes cliniques non caucasiens/suisses devra être validée.

Machine Learning Solutions for Osteoporosis-A Review.

Osteoporosis and its clinical consequence, bone fracture, is a multifactorial disease that has been the object of extensive research. Recent advances in machine learning (ML) have enabled the field of artificial intelligence (AI) to make impressive breakthroughs in complex data environments where human capacity to identify high-dimensional relationships is limited. The field of osteoporosis is one such domain, notwithstanding technical and clinical concerns regarding the application of ML methods. This qualitative review is intended to outline some of these concerns and to inform stakeholders interested in applying AI for improved management of osteoporosis. A systemic search in PubMed and Web of Science resulted in 89 studies for inclusion in the review. These covered one or more of four main areas in osteoporosis management: bone properties assessment (n = 13), osteoporosis classification (n = 34), fracture detection (n = 32), and risk prediction (n = 14). Reporting and methodological quality was determined by means of a 12-point checklist. In general, the studies were of moderate quality with a wide range (mode score 6, range 2 to 11). Major limitations were identified in a significant number of studies. Incomplete reporting, especially over model selection, inadequate splitting of data, and the low proportion of studies with external validation were among the most frequent problems. However, the use of images for opportunistic osteoporosis diagnosis or fracture detection emerged as a promising approach and one of the main contributions that ML could bring to the osteoporosis field. Efforts to develop ML-based models for identifying novel fracture risk factors and improving fracture prediction are additional promising lines of research. Some studies also offered insights into the potential for model-based decision-making. Finally, to avoid some of the common pitfalls, the use of standardized checklists in developing and sharing the results of ML models should be encouraged. © 2021 American Society for Bone and Mineral Research (ASBMR).

Distinct Subsets of Noncoding RNAs Are Strongly Associated With BMD and Fracture, Studied in Weight-Bearing and Non-Weight-Bearing Human Bone.

We investigated mechanisms resulting in low bone mineral density (BMD) and susceptibility to fracture by comparing noncoding RNAs (ncRNAs) in biopsies of non-weight-bearing (NWB) iliac (n = 84) and weight bearing (WB) femoral (n = 18) postmenopausal bone across BMDs varying from normal (T-score > -1.0) to osteoporotic (T-score ≤ -2.5). Global bone ncRNA concentrations were determined by PCR and microchip analyses. Association with BMD or fracture, adjusted by age and body mass index, were calculated using linear and logistic regression and least absolute shrinkage and selection operator (Lasso) analysis. At 10% false discovery rate (FDR), 75 iliac bone ncRNAs and 94 femoral bone ncRNAs were associated with total hip BMD. Eight of the ncRNAs were common for the two sites, but five of them (miR-484, miR-328-3p, miR-27a-5p, miR-28-3p, and miR-409-3p) correlated positively to BMD in femoral bone, but negatively in iliac bone. Of predicted pathways recognized in bone metabolism, ECM-receptor interaction and proteoglycans in cancer emerged at both sites, whereas fatty acid metabolism and focal adhesion were only identified in iliac bone. Lasso analysis and cross-validations identified sets of nine bone ncRNAs correlating strongly with adjusted total hip BMD in both femoral and iliac bone. Twenty-eight iliac ncRNAs were associated with risk of fracture (FDR < 0.1). The small nucleolar RNAs, RNU44 and RNU48, have a function in stabilization of ribosomal RNAs (rRNAs), and their association with fracture and BMD suggest that aberrant processing of rRNAs may be involved in development of osteoporosis. Cis-eQTL (expressed quantitative trait loci) analysis of the iliac bone biopsies identified two loci associated with microRNAs (miRNAs), one previously identified in a heel-BMD genomewide association study (GWAS). In this comprehensive investigation of the skeletal genetic background in postmenopausal women, we identified functional bone ncRNAs associated to fracture and BMD, representing distinct subsets in WB and NWB skeletal sites. © 2020 The Authors. Journal of Bone and Mineral Research published by American Society for Bone and Mineral Research.

OsteoLaus: raisons d'être et premiers résultats.

OsteoLaus: Right to Exist and First Results Abstract. The OsteoLaus cohort included 1475 women aged 50 to 80 years between 2010 and 2012, and since followed every 2.5 years. The main goal is to better define osteoporosis and the prediction of fracture risk. Using the multiple data available in CoLaus/PsycoLaus, many analyses are being conducted to better understand the relationship between bone health and chronic disease.

Résumé. La cohorte OsteoLaus a inclus 1475 femmes de 50 à 80 ans entre 2010 et 2012, et depuis suivies tous les 2,5 ans. Le but principal est de mieux définir l'ostéoporose et la prédiction du risque de fracture. Grâce aux multiples données à disposition dans CoLaus/PsycoLaus, de nombreuses analyses sont faites pour mieux comprendre le lien entre santé osseuse et maladies chroniques.

Vertebral Fractures in Individuals With Type 2 Diabetes: More Than Skeletal Complications Alone.

OBJECTIVE: We aimed to assess whether individuals with type 2 diabetes (T2D) have increased risk of vertebral fractures (VFs) and to estimate nonvertebral fracture and mortality risk among individuals with both prevalent T2D and VFs. RESEARCH DESIGN AND METHODS: A systematic PubMed search was performed to identify studies that investigated the relationship between T2D and VFs. Cohorts providing individual participant data (IPD) were also included. Estimates from published summary data and IPD cohorts were pooled in a random-effects meta-analysis. Multivariate Cox regression models were used to estimate nonvertebral fracture and mortality risk among individuals with T2D and VFs. RESULTS: Across 15 studies comprising 852,705 men and women, individuals with T2D had lower risk of prevalent (odds ratio [OR] 0.84 [95% CI 0.74-0.95]; I 2 = 0.0%; P het = 0.54) but increased risk of incident VFs (OR 1.35 [95% CI 1.27-1.44]; I 2 = 0.6%; P het = 0.43). In the IPD cohorts (N = 19,820), risk of nonvertebral fractures was higher in those with both T2D and VFs compared with those without T2D or VFs (hazard ratio [HR] 2.42 [95% CI 1.86-3.15]) or with VFs (HR 1.73 [95% CI 1.32-2.27]) or T2D (HR 1.94 [95% CI 1.46-2.59]) alone. Individuals with both T2D and VFs had increased mortality compared with individuals without T2D and VFs (HR 2.11 [95% CI 1.72-2.59]) or with VFs alone (HR 1.84 [95% CI 1.49-2.28]) and borderline increased compared with individuals with T2D alone (HR 1.23 [95% CI 0.99-1.52]). CONCLUSIONS: Based on our findings, individuals with T2D should be systematically assessed for presence of VFs, and, as in individuals without T2D, their presence constitutes an indication to start osteoporosis treatment for the prevention of future fractures.

Skeletal maturation in relation to ethnic background in children of school age: The Generation R Study.

Ethnicity is a well-established determinant of pediatric maturity, but the underlying genetic and environmental contributions to these ethnic differences are poorly comprehended. We aimed to evaluate the influence of ethnicity on skeletal age (SA), an assessment of pediatric maturation widely used in clinical settings. We included children from the Generation R Study, a multiethnic population-based pregnancy cohort, assessed at a mean age of 9.78 (±0.33) years. SA was evaluated by a trained observer on hand DXA scans using the Greulich and Pyle method. Ethnic background was defined as geographic ancestry (questionnaire-based assessment) (N = 5325) and genetic ancestry (based on admixture analysis) (N = 3413). Associations between the ethnic background and SA were investigated separately in boys and girls, using linear regression models adjusted for age, height and BMI. Based on geographic ancestry, 84% of the children were classified as European, 6% as Asian and 10% as African. Children of European background had on average younger SA than those of Asian or African descent. Asian boys had 0.46 (95% CI 0.26-0.66, p-value < 0.0001) and African boys 0.36 years (95% CI 0.20-0.53, p-value < 0.0001) older SA as compared to European boys. Similarly, Asian girls showed 0.64 (95% CI 0.51-0.77, p-value < 0.0001) and African girls 0.38 years (95% CI 0.27-0.48, p-value < 0.0001) older SA as compared to European girls. A similar pattern was observed in the analysis with genetically-defined ancestry. Furthermore, an increase in the proportion of Asian or African component was associated with older SA in both boys (log[Non-European/European]proportion = 0.10, 95% CI 0.06-0.13, p-value < 0.0001) and girls (log[Non-European/European]proportion = 0.06, 95% CI 0.04-0.08, p-value < 0.0001). In summary, children of Asian and African backgrounds have on average older SA as compared to children of European descent, partially explained by a genetic component.