Objectives While vertebral fracture assessment (VFA) on dual-energy X-ray absorptiometry (DXA) images is widely used to detect osteoporotic vertebral fractures, existing deep learning approaches typically perform either fracture detection or vertebral localization, not both. We developed VERTE-X L, a deep learning model that simultaneously localizes vertebrae (T7–L5) and detects fractures on DXA VFA images, and evaluated its performance in a community-dwelling older adult cohort. Methods We used DXA VFA images from 1000 randomly sampled participants (age ≥65 years) in the Korean Urban Rural Elderly (KURE) cohort, split into training (N = 800) and test (N = 200) sets. VERTE-X L employs a DINOv2-L encoder, a localization decoder for per-vertebra coordinate prediction, and a consistent rank logits (CORAL) ordinal regression module for fracture detection. Performance was evaluated at image and vertebra levels and compared with previously reported detection-only models (VERTE-X and VERTE-X model fine-tuned on VFA data) and localization-only models (VerteNet and Multi-Context Hybrid CNN Transformer). Results On the held-out test set, VERTE-X L achieved an image-level area under the receiver operating characteristic curve (AUROC) of 0.952 (sensitivity 89.7%, specificity 94.2%, net present value [NPV] 98.2%) and vertebra-level AUROC of 0.957 (NPV 99.7%). Localization success (within 15 pixels) was 94.7% for fracture vertebrae. VERTE-X L outperformed VERTE-X in detection (AUROC 0.952 vs 0.913, P = 0.034) and outperformed VerteNet in localization (mean radial error 10.0 vs. 20.3 px, P < 0.001). Conclusions The proposed method enables simultaneous fracture detection and vertebral localization on DXA VFA images.
Objectives To re-evaluate fall incidence and its determinants in a contemporary cohort of ambulatory women with rheumatoid arthritis (RA) in a follow-up study based on our previously published cohort data. Methods We prospectively evaluated 124 ambulatory female patients aged ≥50 years with RA at a single institution in 2022–2023, monitoring falls for 1 year. Outcomes included patient characteristics, sarcopenia, disease activity, and physical function. Multivariable logistic regression and Firth-penalized logistic regression were used in sensitivity analysis to assess the interaction between sarcopenia and glucocorticoid (GC) use. Results The annual fall rate in the 2022–2023 cohort was lower than in our previous cohort (27.4% vs. 50.0%). History of falls (odds ratio [OR] 2.76, 95% confidence interval [CI] 1.02–7.45, P = 0.045) and hypnotics/anxiolytics use (OR 3.92, 95% CI 1.01–15.20, P = 0.048) were independent risk factors for falls. A significant interaction between sarcopenia and GC use was observed in the initial analysis but was no longer statistically significant in sensitivity analyses. Conclusions Compared with our previously reported cohort, the contemporary RA cohort showed a lower fall rate and a different pattern of associated risk factors. Further research is needed to explore the potential interaction between sarcopenia and glucocorticoid use. The observed interaction should be considered exploratory.
Objectives To investigate whether an artificial intelligence (AI) model could demonstrate high diagnostic accuracy in identifying dual-energy X-ray absorptiometry (DXA) confirmed osteoporosis, outperform traditional human-read metrics, and deliver substantial clinical net benefit. Methods This retrospective multicenter cohort study included 382 patients with hip fractures who had both preoperative pelvic radiographs and postoperative DXA scans. An AI model analyzed the intact contralateral proximal femur to predict both bone mineral density (BMD) values and T-scores. Diagnostic performance was evaluated against DXA-confirmed osteoporosis (T-score ≤−2.5) and compared with manual cortical thickness index (CTI) and femoral neck width (FNW) measurements using Spearman rank-order correlation analysis, receiver operating characteristic (ROC) curves, the DeLong test, and decision curve analysis (DCA). Results The AI model showed a strong correlation with DXA-derived BMD values for both the femoral neck (ρ = 0.76, R2 = 0.55, mean absolute error (MAE) = 0.06 g/cm2) and the total hip (ρ = 0.69, R2 = 0.51, MAE = 0.08 g/cm2). Correspondingly, the AI-predicted T-scores correlated well with DXA-derived T-scores for the femoral neck (ρ = 0.76, R2 = 0.55, MAE = 0.47) and the total hip (ρ = 0.69, R2 = 0.51, MAE = 0.65). The AI model demonstrated excellent discriminative performance, achieving an area under the ROC curve (AUC) of 0.88 (95% confidence interval [CI] 0.83–0.93) for the femoral neck and 0.87 (95% CI 0.82–0.92) for the total hip, both significantly higher than CTI (0.72 [95% CI 0.65–0.79]; P < 0.001) and FNW (0.53 [95% CI 0.44–0.62]; P < 0.001). DCA illustrated that the AI models provided greater clinical net benefit than manual measurements, remaining clinically advantageous up to a threshold probability exceeding 0.90. Conclusions Deep learning AI offers a superior, automated alternative to traditional manual morphometric measurements for identifying osteoporosis on routine pelvic radiographs in patients with hip fractures, potentially facilitating timely secondary fracture prevention.
Objectives To compare serum 25-hydroxyvitamin D (25[OH]D) concentrations and the prevalence of vitamin D deficiency between the Korea National Health and Nutrition Examination Survey (KNHANES) VI and IX, and to evaluate demographic, seasonal, and lifestyle factors associated with vitamin D status in South Korea. Methods Data from KNHANES IX (2022–2023), in which serum 25(OH)D concentrations were measured by liquid chromatography–tandem mass spectrometry, were compared with data from KNHANES VI (2013–2014), in which radioimmunoassay was used. Information on dietary supplement intake (≥2 weeks per year) was obtained through a standardized questionnaire. Vitamin D status was defined according to serum 25(OH)D concentrations as deficiency (<20 ng/mL), adequacy (≥20 and <50 ng/mL), and excess (≥50 ng/mL). Results Serum 25(OH)D levels significantly increased across both sexes and all age groups. The odds of vitamin D deficiency were significantly lower in KNHANES IX compared to VI, with adjusted odds ratios of 0.358 for men and 0.214 for women. The proportion of subjects taking dietary supplements increased significantly from 43.2% to 66.3%. Body mass index was significantly associated with higher odds of vitamin D deficiency exclusively among women in KNHANES IX. Winter remained persistently associated with higher odds of deficiency. Conclusions Over the past decade, South Korea has experienced an upward shift in population-level vitamin D concentrations. These changes may be associated with increased intake of dietary supplements, but because direct standardization of analytical methods has not been achieved, the relative contributions of behavioral changes and analytical method-related differences cannot be quantified.
Age-related sarcopenia, defined as the progressive decline in muscle function, strength, and mass with age, is a growing health concern in South Asian countries (Bangladesh, India, and Sri Lanka) due to their rapidly aging populations. It tends to appear earlier, is frequently overlooked, and worsens after age 60. Despite its prevalence, standardized tools for screening and assessing sarcopenia and dietary status are lacking. Since diet quality is a modifiable risk factor, nutritional strategies play a key role in prevention and management. However, international dietary guidelines are often unsuitable for South Asians due to regional dietary differences and variations within countries. As sarcopenia onset occurs earlier in this population, there is an urgent need to implement standardized screening, diagnosis, and nutrition assessments during dietetic consultations. To address this gap, a panel of experienced dietitians from Bangladesh, India, and Sri Lanka developed this South Asian consensus to provide evidence-based recommendations for identifying and managing age-related sarcopenia. The panel emphasized the use of locally available, affordable, and nutritionally adequate foods to support practical, region-specific interventions.
Objectives Use a deep learning model on computerized tomography images to explore how muscle volume and attenuation relate to preinjury ambulatory levels in patients with intertrochanteric or femoral neck fractures. Methods This retrospective study involved 170 female patients with intertrochanteric or femoral neck fractures who had preoperative hip computed tomography (CT) scans. Using a deep learning–based model, muscles on the unfractured side were automatically segmented, and the muscle volume and attenuation were measured for four groups: gluteal muscles, hip adductors, quadriceps, and hamstrings. Preinjury walking ability was categorized into three groups based on the Koval index: Group 1 (Koval index 1–3; walking outdoors), Group 2 (Koval index 4–6; walking indoors), and Group 3 (Koval index 7; chairbound or bedridden). Finally, associations between preinjury ambulatory status and muscle parameters, patients’ age, body mass index (BMI), hip bone mineral density, geriatric nutritional risk index, and Charlson comorbidity index (CCI) were analyzed using ordinal logistic regression models. Results Ambulatory status was associated with muscle attenuation, CCI, and BMI across all four muscle groups. Other factors did not reach statistical significance. Patients with poorer ambulatory function had lower muscle attenuation, higher CCI, and lower BMI. Conclusions Muscle attenuation on CT images correlated with preinjury ambulatory status, whereas muscle volume did not. This indicates that evaluating muscle quality via CT may more accurately reflect functional decline in older adults than assessing muscle quantity alone.
Objectives Knee osteoarthritis (KOA) is a prevalent joint condition affecting multiple structures of the knee. Clinicians need reliable tools for early detection and accurate grading to support timely management, but radiographic grading remains subjective and time-consuming. We aimed to develop a deep learning and large language model (LLM) hybrid system for KOA severity classification and automated clinical reporting. Methods We analysed bilateral knee radiographs from 2435 adults (≥40 years) in the Vietnam Osteoporosis Study (VOS), using the Kellgren–Lawrence (KL) scale. Data were split into training (N = 1944) and independent testing (N = 491) sets. A hybrid deep learning pipeline combined knee-region object detection with KL grading, screening and feature localization. Outputs were integrated into a Gemini LLM module to generate structured clinical reports. Results The hybrid model achieved 79.1% accuracy and AUC 88.8% for five-class KL grading, and 89.5% accuracy (sensitivity: 87.5%, specificity: 90.2%) for KOA screening. The localization module consistently highlighted KOA-relevant features, including osteophytes, joint space narrowing, and subchondral sclerosis. The Gemini module converted model outputs into coherent, structured reports and provided context-aware answers to clinician queries grounded in the imaging results. Conclusions We developed a deployable deep learning–Gemini system for KOA assessment. This integrated framework offers an interpretable, automated approach to KOA assessment that may improve screening and decision support in resource-limited settings. Demo https://www.youtube.com/watch?v=apXipdeXD5k.
Background Vertebral compression fractures (VCFs) are common and often missed on routine imaging. We systematically reviewed the diagnostic performance of artificial intelligence (AI) for VCF detection. Methods A systematic review and meta-analysis were conducted following PRISMA 2020 and PROSPERO registration by searching Ovid Medline, Embase, and Central from January 1, 2020, to November 18, 2025, for studies using AI models for VCF detection and diagnosis. Risk of bias was assessed with QUADAS-2. Studies with complete 2 × 2 data were pooled using random-effects meta-analysis, with subgroup analyses by modality. Results Fifty-one studies were included. Thirty-seven contributed to meta-analysis with around 259,190 participants and 45,346 VCF patients. Original radiology reports missed > 50% of incidental VCFs in many cohorts (up to 81%). The overall sensitivity of AI-assisted VCF detection was 85.0% (95% CI 84.2–85.7) and specificity 94.6% (95% CI 92.8–95.9). The positive and negative likelihood ratios were 15.4 and 0.16. At 22% pre-test probability, post-test probability was 77.7% after a positive AI result and 4.1% after a negative result. CT-based AI models performed best (sensitivity: 88.0%, specificity: 97.5%), while X-ray-based AI models showed lower sensitivity (79.2%) with high specificity (95.1%). Conclusions AI models demonstrate high diagnostic accuracy for VCF detection, particularly in CT imaging. However, improved detection alone does not ensure better outcomes, as substantial treatment gaps persist. The clinical impact of AI depends on rigorous prospective external validation and integration into structured osteoporosis care pathways, linking automated identification to timely evaluation and secondary fracture prevention.
Objectives To evaluate the performance of an artificial intelligence (AI) system trained via federated learning for opportunistic screening of lumbar-spine DXA-defined osteoporotic-level bone mineral density (T-score ≤ −2.5) using routine chest X-rays (CXRs) in a multi-center independent validation setting. Methods A deep learning model was developed via privacy-preserving federated learning on 5943 paired CXR and dual-energy X-ray absorptiometry (DXA) examinations and validated on an independent dual-center cohort of 3007 adults undergoing same-day CXR and DXA. Ground truth was a lumbar spine DXA T-score ≤ −2.5. The primary metric was AUROC, alongside sensitivity and specificity. Image-only and clinical-only ablation models were evaluated on the same cohort to disentangle imaging from demographic contributions. Results Among 3007 validation cases, 200 (6.7%) had a T-score ≤ −2.5. The AI achieved an AUROC of 0.942 (95% CI, 0.923–0.962), with sensitivity of 91.0% and specificity of 82.7%, and performance was preserved across study sites, sexes, and the clinically relevant older stratum (≥60 years). In ablation analysis, the image-only model achieved AUROC 0.931 (95% CI 0.912–0.950) and the clinical-only baseline 0.780 (0.732–0.827); non-overlapping 95% CIs (image-only higher) support an independent imaging contribution beyond demographics. Conclusions This federated learning-based AI demonstrates high discrimination for identifying lumbar-spine DXA-defined osteoporotic-level bone mineral density (T-score ≤ −2.5) from standard CXRs and shows temporal transportability across two institutions without centralizing raw imaging data. Positioned as a triage adjunct to confirmatory DXA, it offers a scalable, privacy-preserving opportunistic screening solution.
Objectives Osteoporosis is a common comorbidity in patients with rheumatoid arthritis (RA). In this study, we investigated the discordance in T-scores between the lumbar spine and hip in patients with RA. Methods This cross-sectional study included 520 patients with RA with T-scores for the lumbar spine, total hip, and femoral neck. T-score discordance was defined as differences in the WHO T-score categories between the spine and femur. Major discordance indicated osteoporosis at one site and a normal T-score at the other, whereas minor discordance represented a one-category difference. Discordance was also defined as a ≥1.5 difference in T-scores between the two sites. Results A comparison of the major, minor, and no discordance groups regarding T-scores of the lumbar spine and hip identified significant variables, including age (P < 0.001), body mass index (P = 0.008), duration of RA (P = 0.001), Health Assessment Questionnaire Disability Index (P < 0.001), pharmacotherapy for osteoporosis (P < 0.001), scoliosis (P = 0.006), and vertebral fracture (P = 0.030). Analysis of the presence or absence of a discordance ≥1.5 in T-scores between the lumbar spine and hip, showed that age (P = 0.010), sex (P < 0.001), duration of RA (P = 0.033), and type 2 diabetes mellitus (P = 0.034) were associated with discordance. Conclusions This study identified factors associated with discordance in bone mineral density between the lumbar spine and hip in patients with RA, highlighting the need for careful interpretation of site-specific measurements, particularly in high-risk patients.
Rapid population aging in the Asia Pacific region will significantly increase its burden of osteoporosis and fragility fractures. The Asia Pacific Consortium on Osteoporosis (APCO)-International Osteoporosis Foundation (IOF) Asia Pacific Regional Audit was developed through a coordinated effort of these two organizations. Spanning data from 22 countries and regions in the Asia Pacific, it comprehensively assembles the most current regionally relevant osteoporosis evidence available. The audit used a structured questionnaire covering 12 domains, supplemented by demographic projections, registry data, government statistics, and expert consensus estimates where formal data were unavailable. The audit revealed that osteoporosis is a national health priority in only six countries, limiting attention, funding, and coordinated service development. It also highlights glaring infrastructural gaps: insufficient fracture surveillance, uneven diagnostic access, misaligned reimbursement, and limited secondary fracture prevention. Collectively, these deficiencies point not to a lack of evidence, but to an implementation deficit in translating evidence into routine care. The audit proposes several key solutions including implementing fracture registries and post-fracture care pathways, developing coherent country-specific guidelines, aligning reimbursement with risk profile, and tracking quality outcomes. These findings provide a timely roadmap for governments and health care systems to reduce fracture burden and improve equitable osteoporosis care across the Asia Pacific region.
A unified consensus statement on medication-related osteonecrosis of the jaw (MRONJ) has not yet been established among the Asian member countries or regions of the Asian Federation of Osteoporosis Societies (AFOS). This study aimed to develop a consensus on MRONJ in patients with osteoporosis across these countries and regions. In this study, the term “Asia-Pacific” refers specifically to the Asian member countries and regions of AFOS. A structured survey consisting of nine MRONJ-related questions was distributed across 10 countries and regions to assess the level of agreement and summarize regional perspectives. In addition, a manual literature review and voting were conducted to evaluate the current evidence on MRONJ. The key aspects of MRONJ, including definition, staging, diagnosis, pathogenesis, risk factors, management, and prevention, were generally consistent among the AFOS countries and regions. The annual incidence and incidence rate of MRONJ associated with low-dose antiresorptive therapy in patients with osteoporosis ranged from 0.025% to 0.136% and 21 to 283 cases per 100,000 person-years, respectively. However, evidence regarding the benefits of drug discontinuation before dental surgery, such as tooth extraction, remains insufficient. Large-scale, multinational studies across AFOS countries and regions are warranted to determine the incidence of MRONJ better and evaluate the impact of antiresorptive drug discontinuation before dental procedures. These findings may contribute to the development of effective evidence-based strategies for preventing MRONJ in patients with osteoporosis.
Objectives:To explore whether bone marrow proton density fat fraction (PDFF) mediates the relationship between liver iron concentration (LIC), assessed via magnetic resonance imaging (MRI)-derived R2∗, and lumbar spine (LS) bone mineral density (BMD) in women. Methods:This prospective study included 103 female volunteers (median age 50 years, range 20-80) recruited between June 2019 and January 2021. All participants underwent 3.0T MRI (liver R2∗ mapping and lumbar VIBE-Dixon sequences) and dual-energy X-ray absorptiometry (DXA) on the same day. Mediation analysis was performed using the PROCESS macro, with age as covariate. Results:Among the 103 participants (median age 50 years, IQR 38-60; 44% with elevated liver R2∗ > 67.7 s-1), liver R2∗ was positively associated with bone marrow PDFF (β = 0.36, P < 0.001) and negatively associated with LS BMD (β = -0.273, P = 0.004). Bone marrow PDFF showed a negative association with LS BMD (β = -0.286, P = 0.006). Mediation analysis revealed that bone marrow PDFF significantly mediated 37.6% of the liver R2∗-BMD relationship (indirect effect = -0.103; 95% CI: 0.210 to -0.028). Subgroup analyses revealed that these associations were statistically significant only in participants aged ≥ 50 years. Conclusions:Bone marrow PDFF partially mediates the inverse relationship between liver iron and BMD in women, particularly in those aged ≥ 50 years, suggesting a potential link between iron metabolism and bone loss that warrants further investigation. Combined MRI assessment of liver R2∗ and bone marrow PDFF may enhance osteoporosis risk stratification.
Objectives In a mouse model, aldehyde dehydrogenase 2 (ALDH2) knockout resulted in lower bone mineral density; however, higher parathyroid hormone receptor expression than wild-type mice. This study aimed to investigate whether ALDH2 polymorphisms influence efficacy of intermittent parathyroid hormone therapy and bone mineral density changes in humans. Methods Eighty-two patients with primary osteoporosis treated with parathyroid hormone for > 1 year were divided into wild-type ALDH2 (ALDH2∗1) and variant (ALDH2∗2) groups. Bone mineral densities were measured by dual-energy X-ray absorptiometry. Changes in bone mineral density, treatment response, bone turnover markers, and new fracture incidence were evaluated. Furthermore, bone mineral density was analyzed using a mixed-effects model. Results Femoral neck bone mineral density increased by 1.0 ± 7.4% in the ALDH2∗1 group and 4.3 ± 8.1% in the ALDH2∗2 group (P < 0.05), whereas lumbar spine bone mineral density increased by 5.7 ± 8.2% and 9.4 ± 9.1% without significance. Treatment success rates were higher in ALDH2∗2 group (femoral neck 38.7%, lumbar spine 68.8%) compared with ALDH2∗1 (16.3%, 51.0%). Statistical significance was observed only at the femoral neck. Bone turnover markers and fracture incidence were comparable between groups. Mixed-effects analysis adjusting for confounders showed a significant ALDH2 genotype × duration interaction for femoral neck, indicating genotype-related differences in the rate of bone mineral density increase over time. For lumbar spine, the genotype main effect was significant, whereas the interaction was not. Conclusions These findings suggest that ALDH2 polymorphisms may influence the therapeutic response to PTH treatment and highlight the need for larger future studies.
A unified consensus statement on medication-related osteonecrosis of the jaw (MRONJ) has not yet been established among the Asian member countries or regions of the Asian Federation of Osteoporosis Societies (AFOS). This study aimed to develop a consensus on MRONJ in patients with osteoporosis across these countries and regions. In this study, the term “Asia-Pacific” refers specifically to the Asian member countries and regions of AFOS. A structured survey consisting of nine MRONJ-related questions was distributed across 10 countries and regions to assess the level of agreement and summarize regional perspectives. In addition, a manual literature review and voting were conducted to evaluate the current evidence on MRONJ. The key aspects of MRONJ, including definition, staging, diagnosis, pathogenesis, risk factors, management, and prevention, were generally consistent among the AFOS countries and regions. The annual incidence and incidence rate of MRONJ associated with low-dose antiresorptive therapy in patients with osteoporosis ranged from 0.025% to 0.136% and 21 to 283 cases per 100,000 person-years, respectively. However, evidence regarding the benefits of drug discontinuation before dental surgery, such as tooth extraction, remains insufficient. Large-scale, multinational studies across AFOS countries and regions are warranted to determine the incidence of MRONJ better and evaluate the impact of antiresorptive drug discontinuation before dental procedures. These findings may contribute to the development of effective evidence-based strategies for preventing MRONJ in patients with osteoporosis.
Objectives Sarcopenia, an age-related skeletal muscle disorder, is assessed by handgrip strength, gait speed, and muscle mass, yet population-specific reference data and standardized scores for Asian adults remain limited. We aimed to establish sex- and age-specific normative reference distributions, smoothed centile curves, and model-based standardized T scores for these indicators in Asian adults. Methods We retrospectively analyzed adults aged ≥ 40 years attending a tertiary-center Adult Preventive Health Program in 2023–2024. To assess construct validity, we compared sarcopenia indicators across age groups using analysis of variance (ANOVA). Sex- and age-specific reference distributions were estimated using Generalized Additive Models for Location, Scale, and Shape (GAMLSS), from which smooth model-based age-specific centiles (5th–95th) were derived. Individual values were standardized to model-based Z scores and transformed to T scores on an approximate 100-point scale. Asian Working Group for Sarcopenia (AWGS) cut-offs were used in receiver operating characteristic (ROC) analyses to determine corresponding T-score thresholds. Results Among 8095 participants, model-based mean handgrip strength, gait speed, and skeletal muscle mass were 20.03±5 kg, 1.12±0.37 m/s, and 6.93±1.43 kg/m² in women and 30.69±7.46 kg, 1.12±0.37 m/s, and 8.79±1.95 kg/m² in men. GAMLSS-derived centile curves demonstrated age-related declines in strength and gait speed and milder changes in muscle mass. AWGS cut-offs corresponded to T scores in the mid-40s for strength and gait and around 40–41 for muscle mass. Conclusions We established sex- and age-specific normative values and GAMLSS-based reference distributions, and derived model-based Z and T scores that provide standardized sarcopenia metrics in Asian adults, align with AWGS thresholds, and support detection of muscle decline and risk stratification.
Objectives:Osteoporosis is a silent disease with low screening rates in many developing countries. This study aimed to evaluate the feasibility of using an artificial intelligence (AI)-based system to screen osteoporosis from pelvic and hip radiographs in Vietnam. Methods:We conducted a cross-sectional study at a tertiary medical center in Central Vietnam in 2023. A total of 2000 consecutive pelvic and hip radiographs from patients aged ≥ 40 years were collected. After excluding poor-quality images, 1987 radiographs were analyzed using an AI-based software designed to estimate bone mineral density (BMD) from plain radiographs and derive T-scores. Osteoporosis was defined as a T-score ≤ -2.5. Patient characteristics, radiographic findings, and risk factors for osteoporosis were analyzed. Results:Among 1987 patients (mean age 66.4 ± 15.1 years; 41.3% men), osteoporosis was identified in 872 patients (43.9%). The prevalence increased with age and was higher in women than in men (58.7% vs 22.8%, P < 0.001). Osteoporosis was associated with femoral neck (OR = 3.8, 95% CI: 2.7-5.2) and intertrochanteric fractures (OR = 7.0, 95% CI: 4.5-11.0). Patients with lower T-scores had a higher risk of hip fractures, especially those with T-scores ≤ -3.0 (OR = 11.5, 95% CI: 5.5-24.5). Conclusions:AI-based analysis of pelvic and hip radiographs is a feasible and effective tool for osteoporosis screening in Vietnam. The prevalence of osteoporosis in this hospital-based setting was high, particularly among elderly women. AI-assisted screening may offer an accessible strategy for early detection of osteoporosis in resource-limited settings.
Objectives Frailty among community-dwelling older adults increases the risk of adverse health outcomes. Sarcopenic obesity (SO), characterized by low muscle mass combined with excess adiposity, may worsen frailty. Locomotive syndrome (LS) shares features with frailty and sarcopenia. This study examined whether LS modifies the association between body composition and frailty. Methods We analyzed cross-sectional data of 481 adults aged ≥ 40 years from the DETECt-L cohort in Japan. Frailty was defined according to the Japanese Cardiovascular Health Study criteria. Body composition was measured using bioelectrical impedance, based on skeletal muscle mass and body fat, to categorize participants into normal, sarcopenia, obesity, or SO phenotypes. Logistic regression-estimated odds ratios for comparing prefrailty/frailty with robustness were calculated. Model 1 was unadjusted; Model 2 (fully adjusted) adjusted for age, sex, pain, fall history, and Timed Up-and-Go and single-leg standing tests; and Model 3 additionally included body mass index (BMI) (sensitivity). Results Overall, 41.6% of participants were classified as prefrail or frail. The distribution of body composition phenotypes was as follows: normal (42.8%), sarcopenic (31.2%), obese (15.8%), and SO (10.2%). In Model 2, both sarcopenia (odds ratio [OR], 2.24; 95% confidence interval [CI], 1.38–3.63) and SO (OR, 2.51; 95% CI, 1.21–5.24) were associated with increased odds of prefrailty/frailty. These associations remained robust after adjusting for BMI. However, LS did not significantly affect these associations. Conclusions Sarcopenia and SO were independently associated with prefrailty/frailty regardless of LS status. Integrating muscle function and adiposity metrics may support early risk detection in community settings.
