Search Results (431)

Background/Objectives: Comprehensive knowledge of body composition and bone status across the lifespan is critical for clinical evaluation and public health initiatives. This study aimed to develop age- and sex-specific reference curves for body composition and bone status in a physically active Greek population aged 18–80 using dual-energy X-ray absorptiometry (DXA). A secondary objective was to examine age- and sex-related trends in fat distribution, lean mass (LM), and bone status. Methods: A cross-sectional analysis was conducted on 637 participants (275 men and 362 women). Physical activity was assessed through structured interviews evaluating type, frequency, and intensity, categorized using established guidelines from organizations such as the American Heart Association and World Health Organization. Anthropometric data and DXA scans were utilized to measure parameters including fat mass (FM), LM, and BMD. Participants were stratified into age categories, and percentile curves were generated using generalized additive models for location, scale, and shape (GAMLSS). Results: Among women, body mass increased by 20.9% and body fat percentage rose by 38.3% from the youngest to the oldest age group, accompanied by a 5.7% reduction in bone mineral density (BMD) and an 11.5% decline in bone mineral content (BMC). Men exhibited a 49.1% increase in body fat percentage, with LM remaining stable across age groups. In men, BMD decreased by 1.7%, while BMC showed minimal variation. Notable sex differences were observed in fat redistribution, with android fat (AF) increasing significantly in older individuals, particularly among women, highlighting distinct age-related patterns. Conclusions: This study provides essential reference data on body composition and bone status, emphasizing the need for tailored interventions to address sex- and age-related changes, particularly in fat distribution and bone density, to support improved health outcomes in aging populations. Full article

Background and Objectives: The vitamin K epoxide reductase complex subunit 1 (VKORC1) plays a central role in the vitamin K cycle, which is essential for γ-carboxylation of multiple bone-related proteins. Genetic variants in VKORC1 may influence bone mineral density (BMD) and osteoporosis risk. Materials and Methods: A systematic review and meta-analysis were conducted to evaluate the association between VKORC1 polymorphisms and osteopenia and osteoporosis. Relevant studies were identified through PubMed, Scopus, and Web of Science databases. Data on study characteristics, genotypes, BMD measurement, ethnicity, sex, and menopausal status were extracted. Results: Six studies comprising 7335 participants were included. All studies assessed BMD using dual-energy X-ray absorptiometry (DXA). The mean participant age ranged from 41.9 to 63.7 years. The VKORC1 variants most frequently studied, which were included in the meta-analysis, were rs9923231 and rs9934438. The overall effect of VKORC1 risk alleles on osteopenia/osteoporosis was significant with a p = 0.041 (fixed effects OR = 1.16, 95% CI = 1.01–1.35). Heterogeneity among studies was insignificant (I² = 0%, p = 0.893). Conclusions: A modest association was observed for the VKORC1 variants. The current body of evidence requires further studies to elucidate whether VKORC1 polymorphisms have a clinically meaningful role in bone health. Full article

Background and Objectives: People living with HIV (PLWH) have excess osteoporosis and fractures not fully captured by dual-energy X-ray absorptiometry (DXA). We evaluated whether trabecular bone score (TBS), calcaneal quantitative ultrasound (QUS) and bone turnover markers improve vertebral fracture risk assessment beyond areal bone mineral density (BMD) in PLWH. Methods: In this cross-sectional study, 87 antiretroviral-treated adults undergoing DXA had lumbar spine TBS and calcaneal QUS. Morphometric vertebral fractures were identified, correlates of degraded TBS were analyzed using multivariable regression, and sequential logistic models quantified the incremental contribution of TBS and CTX to discriminate for prevalent morphometric vertebral fractures. Results: Low BMD (osteopenia/osteoporosis) was present in 62% of participants, degraded TBS in 37% and morphometric vertebral fractures in 17%. Degraded versus normal TBS was associated with older age (49.1 vs. 39.7 years), longer HIV duration and lower nadir CD4+ count, as well as more frequent tenofovir disoproxil fumarate exposure (66% vs. 52%; all p ≤ 0.04). In multivariable analysis, age (per 10-year increase; adjusted odds ratio [aOR] 1.78; 95% CI 1.13–2.83) and nadir CD4+ < 200 cells/mm³ (aOR 2.29; 95% CI 1.06–4.97) independently predicted degraded TBS. In sequential cross-sectional models for prevalent morphometric vertebral fractures, the area under the curve increased from 0.71 (clinical variables) to 0.79 after adding lumbar spine T-score and to 0.85 after adding TBS; adding CTX yielded 0.87 without a statistically significant incremental gain. Conclusions: In PLWH, TBS captures bone quality deficits and improves vertebral fracture risk discrimination beyond BMD, supporting its integration alongside DXA in routine HIV care. Full article

Background: Irisin, a muscle-derived hormone, enhances the energy metabolism by activating the brown adipose tissue and acts as a bone-forming agent across the entire life span. No consistent clinical data in humans have been published so far to highlight if blood irisin as glucose/bone biomarker should be refined based on the vitamin D status (deficient or sufficient). Therefore, we aimed to objectively assess the level of irisin in female adults with abnormal and normal vitamin D status, as reflected by the level of 25-hydroxyvitamin (25OHD) in relationship with glucose and bone metabolic parameters. Methods: This pilot, prospective, exploratory study included eighty-nine menopausal women aged over 50. We excluded subjects with malignancies, bone and metabolic disorders, insulin treatment, and active endocrine disorders. Fasting profile included glycaemia, insulin, and glycated haemoglobin A1c (HbA1c). Then, 75 g oral glucose tolerance test (OGTT) included glycaemia and insulin assay after 60 and 120 min. Bone status involved bone turnover markers and central dual-energy X-ray absorptiometry providing bone mineral density (BMD) and trabecular bone score. Results: Eighty-nine subjects were included in the following two groups depending on 25OHD: vitamin D-deficient (VDD) group (N = 48; 25OHD < 30 ng/mL) and vitamin D-sufficient (VDS) group (N = 41; 25OHD ≥ 30 ng/mL). The two groups had similar age and menopausal period (62.29 ± 10.19 vs. 63.56 ± 8.16 years, respectively; 15.82 ± 9.55 vs. 16.11 ± 9.00 years, p > 0.5 for each). A statistically significant higher body mass index (BMI) was found in VDD vs. VDS group (32.25 ± 5.9 vs. 28.93 ± 4.97 kg/m², p = 0.006). Circulating irisin was similar between the groups as follows: median (IQR) of 91.85 (44.76–121.76) vs. 71.17 (38.76–97.43) ng/mL, p = 0.506. Fasting profile and OGTT assays showed no between-group difference. Median HOMA-IR in VDD group pointed out insulin resistance of 2.67 (1.31–3.29). Lowest mean/median T-scores at DXA for both groups were consistent with osteopenia category, but they were confirmed at different central sites as follows: femoral neck in both groups [VDD versus VDS group: −1.1 (−1.20–−0.90) vs. −1.1 (−1.49–−0.91), p = 0.526, respectively], only at lumbar spine for VDS group (T-score of −1.18 ± 1.13). The correlations between irisin and the mentioned parameters displayed a different profile when the analysis was performed in the groups with different 25OHD levels. In VDD group, irisin levels statistically significantly correlated with serum phosphorus (r = −0.32, p = 0.022), osteocalcin (r = −0.293, p = 0.038), P1NP (r = −0.297, p = 0.04), HbA1c (r = 0.342, p = 0.014), and BMI (r = 0.408, p = 0.003). Conclusions: This pilot study brings awareness in the analysis of irisin in relationship with glucose and bone-related biomarkers correlates, showing a distinct type of association depending on 25OHD level, which might represent an important crossroad in the multitude of irisin-activated signal transduction pathways. Full article

Background: This study investigated the relationships between hematological and bone metabolism variables in 35 elite female trail runners, focusing on identifying key hematological correlates of bone health. Methods: Forty-four hematological variables, including biochemical, hormonal, metabolic, liver enzyme, and iron profiles, as well as complete blood count and platelet indices, were analyzed. Bone mineral density (BMD) and bone mineral content (BMC) were assessed at multiple skeletal regions via dual-energy X-ray absorptiometry (DXA). A cross-sectional design was employed, utilizing descriptive statistics, correlation analyses, and multiple linear regression to analyze the associations between hematological markers and BMC and BMD. Results: Significant but moderate associations were identified: magnesium consistently emerged as a negatively associated factor, particularly associated with BMC and BMD in the lumbar spine (L1–L4) and whole-body, potentially reflecting hypothesized mineral mobilization during chronic physical stress. Follicle-stimulating hormone showed positive associations with BMD, suggesting a potential protective association in bone turnover regulation. Additionally, calcium and thyroid hormones were linked to regional bone properties, highlighting site-specific skeletal vulnerabilities. Conclusions: These findings suggest a complex interplay between mineral homeostasis and hormonal balance that may be related to skeletal integrity in elite female trail runners. This work provides a foundation for developing evidence-based guidelines to support the health and performance of female endurance athletes. Further research is warranted to confirm these results through longitudinal evaluations. Full article

Background: Physical literacy is a multidimensional construct that may be relevant for promoting active lifestyles and healthy development during adolescence. However, the association between perceived physical literacy (PPL) and body composition assessed by dual-energy X-ray absorptiometry (DXA) remains underexplored. Objective: To examine the association between PPL and DXA-derived body composition parameters in Spanish adolescents. Methods: This cross-sectional study included 56 adolescents (13.2 ± 1.27 years, 28.6% girls). PPL was assessed using the validated Spanish version of the Perceived Physical Literacy Instrument (S-PPLI). Body composition was measured by DXA. Associations between PPL and body composition outcomes were examined using general linear models, adjusting for sex, age, and device-measured moderate-to-vigorous physical activity (MVPA) and sedentary time. Results: Higher PPL was significantly associated with greater lean body mass (β = 0.81; p = 0.02), lean mass index (β = 0.22; p = 0.01), and fat-free mass (β = 0.85; p = 0.01), as well as with higher body mass index (BMI) (β = 0.24; p = 0.03). Conclusions: Higher PPL is associated with more favorable lean-related body composition outcomes in Spanish adolescents, whereas no associations were found with adiposity or bone parameters. These findings highlight PPL as a relevant correlation of lean body composition during adolescence. Given the cross-sectional design, causal inferences cannot be drawn, and future longitudinal and interventional studies are warranted. Full article

Background: Osteoporosis and osteopenia are prevalent bone diseases characterized by reduced bone mineral density (BMD) and an increased risk of fractures, particularly in postmenopausal women. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, it has limitations regarding accessibility, cost, and predictive capacity for fracture risk. Machine learning (ML) approaches offer an opportunity to develop automated and more accurate diagnostic models by incorporating both BMD values and clinical variables. Method: This study retrospectively analyzed BMD data from 142 postmenopausal women, classified into 3 diagnostic groups: normal, osteopenia, and osteoporosis. Various supervised ML algorithms—including Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Decision Trees (DT), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and Artificial Neural Networks (ANN)—were applied. Feature selection techniques such as ANOVA, CHI2, MRMR, and Kruskal–Wallis were used to enhance model performance, reduce dimensionality, and improve interpretability. Model performance was evaluated using 10-fold cross-validation based on accuracy, true positive rate (TPR), false negative rate (FNR), and AUC values. Results: Among all models and feature selection combinations, SVM with ANOVA-selected features achieved the highest classification accuracy (94.30%) and 100% TPR for the normal class. Feature sets based on traditional diagnostic regions (L1–L4, femoral neck, total femur) also showed high accuracy (up to 90.70%) but were generally outperformed by statistically selected features. CHI2 and MRMR methods also yielded robust results, particularly when paired with SVM and k-NN classifiers. The results highlight the effectiveness of combining statistical feature selection with ML to enhance diagnostic precision for osteoporosis and osteopenia. Conclusions: Machine learning algorithms, when integrated with data-driven feature selection strategies, provide a promising framework for automated classification of osteoporosis and osteopenia based on BMD data. ANOVA emerged as the most effective feature selection method, yielding superior accuracy across all classifiers. These findings support the integration of ML-based decision support tools into clinical workflows to facilitate early diagnosis and personalized treatment planning. Future studies should explore more diverse and larger datasets, incorporating genetic, lifestyle, and hormonal factors for further model enhancement. Full article

Background/Objectives: Pediatric osteoporosis is a multifactorial condition characterized by impaired bone mineralization and increased fracture risk, particularly vertebral compression fractures. This study aims to evaluate the diverse etiology, diagnostic challenges, and treatment options for pediatric osteoporosis in a cohort of affected children. Methods: We reviewed eleven pediatric patients (aged 5–16 years) diagnosed with vertebral fractures and osteoporosis, who were hospitalized between 2020 and 2024 at the Department of Endocrinology and Metabolic Diseases at PMMH-RI in Lodz. Clinical evaluation included medical history, physical examination, biochemical markers of bone metabolism, and imaging techniques such as dual-energy X-ray absorptiometry (DXA) to determine underlying causes of bone fragility. Results: The cohort presented a broad etiological spectrum, including seven patients with genetic disorders (e.g., mutations in COL1A1, LRP5, SGMS2, and ALPL genes) and secondary osteoporosis due to chronic diseases requiring prolonged glucocorticoid therapy (two patients with Duchenne muscular dystrophy (DMD), one patient with Crohn’s disease) or endocrinological disorders (one patient with Cushing disease). Vertebral fractures were confirmed in all patients, with back pain as the predominant symptom. Low bone mass (BMD Z-score < −2.0) was observed in eight individuals; in others, clinical signs of skeletal fragility were present despite Z-scores above this threshold. Mild biochemical abnormalities included hypercalciuria (3/11 cases) and vitamin D deficiency (6/11 cases). Height adjustment improved BMD interpretation in short-stature patients. Most children received bisphosphonate therapy, supplemented with calcium and vitamin D. In two patients, bisphosphonates were not used due to lack of parental consent or underlying conditions in which such treatment is not recommended. Conclusions: Pediatric osteoporosis requires a multidisciplinary diagnostic and therapeutic approach, integrating clinical, biochemical, and genetic factors. It is a heterogeneous and often underrecognized condition, with vertebral fractures frequently serving as its earliest sign—even in the absence of overt symptoms or low bone mass. This underscores the need for clinical vigilance, as significant skeletal fragility may occur despite normal BMD values. Importantly, pediatric osteoporosis may also impact the attainment of peak bone mass and ultimately affect final adult height. Early diagnosis through thorough assessment, including height-adjusted DXA, and a multidisciplinary approach are essential to ensure timely management and prevent long-term complications. Full article

Background and Objectives: Osteoporosis is a prevalent skeletal disorder characterized by decreased bone mass and compromised bone microarchitecture, leading to an elevated risk of fractures and significant morbidity, particularly among aging populations. Early diagnosis and personalized management are critical to reducing fracture incidence and associated healthcare burdens. Recent advances in artificial intelligence (AI) and machine learning (ML) have led to potential improvements in enhancing osteoporosis care by enabling accurate diagnostic imaging analysis, robust fracture risk prediction, and personalized therapeutic strategies. Materials and Methods: We performed a narrative review to summarize and critically evaluate the current literature on AI and ML applications in osteoporosis diagnosis and management. We searched relevant literature from inception to January 2025 to provide a comprehensive perspective, focusing on key themes, methodological approaches, and clinical implications. Results: Deep learning models, especially convolutional neural networks, facilitate rapid and accurate bone mineral density assessment from routine radiographs, expanding screening capabilities beyond conventional dual-energy X-ray absorptiometry (DXA). Machine learning algorithms harness clinical and demographic data to generate fracture risk models that often outperform traditional tools, enabling timely identification of high-risk individuals. Furthermore, AI-driven analyses of historical treatment responses coupled with real-time monitoring through wearable technologies and mobile applications allow for personalized therapeutic optimization and enhance patient engagement. Despite these promising advances, challenges remain regarding ethical considerations, data privacy, legal liability, incomplete model validation, lack of standardization, and the need for critical appraisal of real-world clinical efficacy for widespread clinical adoption. Conclusions: This narrative review indicates that AI and ML hold significant promise to revolutionize osteoporosis management by enabling early detection, precise risk stratification, and tailored interventions. However, the current evidence is heterogeneous, often lacking robust external validation and quantitative synthesis. Critical gaps include insufficient evaluation of model robustness across diverse populations, discussion of negative or conflicting results, and a comprehensive assessment of the limitations inherent in current AI evidence. Strategic efforts to validate, regulate, and critically integrate these technologies into routine clinical workflows are essential to realize their full potential and address the growing burden of osteoporosis worldwide. Full article

Objective: This study aimed to investigate the relationship between computed tomography (CT)-derived Hounsfield Unit (HU) measurements and dual-energy X-ray absorptiometry (DXA) and to evaluate the feasibility of using contrast-enhanced abdominal CT as a complementary tool in the assessment of bone health in liver transplant recipients. Methods: This retrospective descriptive and analytical study included adult liver transplant recipients who underwent both contrast-enhanced abdominal CT and DXA within a three-month interval. HU measurements were obtained from sagittal and axial reformatted images at the lumbar spine (L1–L4) and femoral neck. All CT examinations were performed using a standardized venous-phase protocol. DXA-derived T-scores from the lumbar spine and femur served as the reference standard. Correlation analyses and receiver operating characteristic (ROC) curves were used to evaluate associations between HU values and BMD, as well as the diagnostic performance of HU in identifying low bone density. Results: A total of 259 recipients (mean age 55.7 ± 14.4 years; 62.9% male) were included. Based on lumbar spine DXA, 17.8% had normal BMD, 36.7% were osteopenic, and 45.5% were osteoporotic. CT-derived HU values at both the lumbar spine and femoral neck were significantly lower in patients with reduced BMD and showed a graded decline across worsening DXA categories. HU values demonstrated positive correlations with corresponding T-scores. Diagnostic performance for detecting osteoporosis was fair, with AUCs of 0.700 (sagittal), 0.698 (axial), and 0.751 (femoral). Conclusion: Contrast-enhanced abdominal CT provides useful ancillary information for opportunistic bone health assessment. CT-derived HU values offer a rapid and cost-effective complementary tool but should not replace DXA in the diagnostic evaluation of osteoporosis Full article

Background: Paralympic athletes represent a highly heterogeneous athletic population, which poses unique challenges for body composition assessment. This study evaluated the accuracy of Bioelectrical Impedance Analysis (BIA), Air Displacement Plethysmography (ADP), and a set of skinfold equations in estimating relative fat mass (%FM) in Paralympians, using Dual-Energy X-Ray Absorptiometry (DXA) as reference method. Methods: Sixty-six male and sixty-seven female Paralympians underwent body composition assessments on the same day. The %FM estimated using BIA, ADP, and six existing skinfold equations was compared with %FM measured by DXA (%FM_DXA). Accuracy and agreement between the methods were evaluated using two-tailed paired-sample t-tests, concordance correlation coefficients, reduced major axis regression, and Bland–Altman analysis. Linear regression analyses with the %FM_DXA as dependent variable and anthropometric measurements as independent variable were also carried out. Results: BIA, ADP, and skinfold equations exhibited poor agreement with DXA and significantly underestimated %FM_DXA, with systematic biases ranging from −1.8% to −10.7% in both men and women. In both groups, skinfold sums showed strong correlations with %FM_DXA (r > 0.7), with the nine-skinfold model providing the best prediction (adjusted R² approximately 0.8). Conclusions: The results of this study indicate a lack of transferability of available methods for assessing body composition (skinfold equations, BIA, and ADP) in estimating %FM_DXA in both male and female Paralympians, as these methods proved inaccurate. Future research is needed to further investigate the accuracy of methods for assessing body composition in this population, taking into account the specific impairment and health condition of the athletes. Full article

This paper provides a comprehensive overview of the application of various radiological modalities, with a critical comparison between human and veterinary medicine. The modalities discussed include conventional radiography, dual-energy X-ray absorptiometry (DXA), computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), quantitative ultrasound (QUS), positron emission tomography-computed tomography (PET-CT) and micro and nano computed tomography (micro-CT, nano-CT) in clinical practice and basic research of skeletal system. Radiological imaging plays a crucial role in the diagnosis, monitoring and research of skeletal system disorders in both human and veterinary medicine. In preclinical research, advanced diagnostic imaging modalities such as micro-CT and nano-CT allow for 3D quantification of trabecular and cortical bone microarchitecture for studies in bone biology, regenerative medicine and pharmacological research. Furthermore, the integration of artificial intelligence is advancing image interpretation, precision diagnostics and disease tracking. Despite their broad utility, imaging modalities must be selected based on clinical indication, species, age and anatomical region with consideration of radiation dose, cost and availability, especially in remote regions. For this reason, clinicians and radiologists remain an irreplaceable part of diagnostic imaging. Full article

Background: Androgen deprivation therapy (ADT) is a fundamental component of treatment for metastatic hormone-sensitive prostate cancer (mHSPC), but it accelerates bone mineral density loss and increases fracture risk. International guidelines recommend calcium and vitamin D supplementation, baseline dual-energy X-ray absorptiometry (DXA), and antiresorptive therapy in patients with osteoporosis. Methods: We conducted a retrospective review of 156 mHSPC patients treated with ADT at a tertiary hospital between January 2022 and December 2024. We assessed adherence to guideline-recommended bone health measures. Collected variables included age, ADT duration, calcium/vitamin D supplementation, DXA testing, antiresorptive treatment, and fracture events. Exploratory stratified analyses were performed, and proportions were reported with 95% confidence intervals (CIs). Results: Calcium/vitamin D supplementation was prescribed in 50.6% of patients (95% CI: 42.9–58.4), baseline DXA was performed in 12.8% (95% CI: 8.5–18.9), and denosumab was administered in 5.1% of the cohort (95% CI: 2.6–9.8). The median follow-up was 23 months, with a fracture incidence of 0.67 events per 100 person-years. Stratified analyses showed lower adherence in older patients, those with prolonged ADT exposure, and those with high metastatic burden. Conclusions: Adherence to guideline-recommended bone health measures in patients with mHSPC receiving ADT was markedly suboptimal. These findings underscore the need to implement standardized institutional protocols to ensure systematic supplementation, routine DXA monitoring, and appropriate antiresorptive therapy. Full article

Background/Objectives: Bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) are the gold standard to measure fat mass, but they are unavailable in regular consultations. Relative Fat Mass (RFM) and Pediatric Relative Fat Mass (pRFM) equations are calculated using DXA images in adults and children, but they have not been correlated with BIA. Methods: A longitudinal prospective study was conducted with 531 children from a public school followed over one year; sex, age, weight, height, waist circumference and fat mass percentage were recorded. We calculated body mass index Z-score (Z-BMI), body mass index percentile (Pc BMI), waist-to-height ratio (WHtR), and RFM-pRFM to diagnose Overweight (Ow)/Obesity (Ob). We used descriptive statistics, Pearson’s correlation, sensitivity and specificity, 95% CI, and ROC curves; SPSS version 22 was used. Results: Adiposity was found in 34.5%, 33.2%, 21.5% and 43.5% of children using Z-BMI, Pc BMI, WHtR, and BIA, respectively; excluding children younger than 8 years old, the frequency of adiposity was 51.5% by RFM-pRFM. The highest correlation was between RFM-pRFM and BIA (0.84, p < 0.000). Of the total measurements of each visit considered as normal weight using Z-BMI, 21.5% had adiposity using BIA, and the proportion of girls underdiagnosed was twice that of boys. Conclusions: RFM-pRFM had the highest correlation with BIA but Z-BMI, Pc BMI, and WHtR are also helpful. It is important to consider that 21.5% of children with apparent normal weight present adiposity. Full article

Background/Objectives: A subset of individuals develops persistent symptoms following SARS-CoV-2 infection, including musculoskeletal (MSK) manifestations, a condition known as long COVID (LC). Emerging hypotheses suggest that chronic low-grade inflammation in LC may impair bone metabolism and compromise joint health. However, empirical evidence is limited, and the impact of LC on MSK health, particularly bone and joint integrity, is poorly understood. To determine the influence of LC on MSK function, including bone health, body composition, and joint integrity. Methods: A 12-month longitudinal prospective cohort feasibility study was conducted involving 45 adults with LC and 40 well-recovered (WR) post-COVID-19 controls. Baseline and follow-up assessments included dual-energy X-ray absorptiometry (DXA) for bone mineral density (BMD) and total body composition (TBC), alongside ultrasound of the hand and knee joints to evaluate intra-articular changes. Results: The LC group had more fat in the gynoid, android, and leg regions at each assessment point compared to the controls (p < 0.01). LC showed a significantly lower knee synovial hypertrophy at the baseline, 13.3% compared to WR 45% (p = 0.001), and a marginal improvement in hand synovial hypertrophy, over 12 months, from a median of 2 (IQR 1;5) to 1 (IQR 0;3) (p = 0.012), as observed via MSK ultrasound. No notable differences were found between groups regarding BMD, either in the LC group compared to the control group or overtime. Conclusions: This cohort study of LC adults and controls found no evidence of rapid bone loss; however, adiposity and joint symptoms suggest the need for ongoing monitoring. Future research should focus on MSK markers, muscle function, advanced imaging, and improving MSK health. Full article