暂无摘要。

UNASSIGNED: Multiple studies have shown that skeletal muscle index (SMI) measured on abdominal computed tomography (CT) is strongly associated with bone mineral density (BMD) and fracture risk as estimated by the fracture risk assessment tool (FRAX). Although some studies have reported that SMI at the level of the 12th thoracic vertebra (T12) measured on chest CT images can be used to diagnose sarcopenia, it is regrettable that no studies have investigated the relationship between SMI at T12 level and BMD or fracture risk. Therefore, we further investigated the relationship between SMI at T12 level and FRAX-estimated BMD and fracture risk in this study.
UNASSIGNED: A total of 349 subjects were included in this study. After 1∶1 propensity score matching (PSM) on height, weight, hypertension, diabetes, hyperlipidemia, hyperuricemia, body mass index (BMI), age, and gender, 162 subjects were finally included. The SMI, BMD, and FRAX score of the 162 participants were obtained. The correlation between SMI and BMD, as well as SMI and FRAX, was assessed using Spearman rank correlation. Additionally, the effectiveness of each index in predicting osteoporosis was evaluated through the receiver operating characteristic (ROC) curve analysis.
UNASSIGNED: The BMD of the lumbar spine (L1-4) demonstrated a strong correlation with SMI (r = 0.416, p < 0.001), while the BMD of the femoral neck (FN) also exhibited a correlation with SMI (r = 0.307, p < 0.001). SMI was significantly correlated with FRAX, both without and with BMD at the FN, for major osteoporotic fractures (r = -0.416, p < 0.001, and r = -0.431, p < 0.001, respectively) and hip fractures (r = -0.357, p < 0.001, and r = -0.311, p < 0.001, respectively). Moreover, the SMI of the non-osteoporosis group was significantly higher than that of the osteoporosis group (p < 0.001). SMI effectively predicts osteoporosis, with an area under the curve of 0.834 (95% confidence interval 0.771-0.897, p < 0.001).
UNASSIGNED: SMI based on CT images of the 12th thoracic vertebrae can effectively diagnose osteoporosis and predict fracture risk. Therefore, SMI can make secondary use of chest CT to screen people who are prone to osteoporosis and fracture, and carry out timely medical intervention.

We present comprehensive guidelines for osteoporosis management in Qatar. Formulated by the Qatar Osteoporosis Association, the guidelines recommend the age-dependent Qatar fracture risk assessment tool for screening, emphasizing risk-based treatment strategies and discouraging routine dual-energy X-ray scans. They offer a vital resource for physicians managing osteoporosis and fragility fractures nationwide.
OBJECTIVE: Osteoporosis and related fragility fractures are a growing public health issue with an impact on individuals and the healthcare system. We aimed to present guidelines providing unified guidance to all healthcare professionals in Qatar regarding the management of osteoporosis.
METHODS: The Qatar Osteoporosis Association formulated guidelines for the diagnosis and management of osteoporosis in postmenopausal women and men above the age of 50. A panel of six local rheumatologists who are experts in the field of osteoporosis met together and conducted an extensive review of published articles and local and international guidelines to formulate guidance for the screening and management of postmenopausal women and men older than 50 years in Qatar.
RESULTS: The guidelines emphasize the use of the age-dependent hybrid model of the Qatar fracture risk assessment tool for screening osteoporosis and risk categorization. The guidelines include screening, risk stratification, investigations, treatment, and monitoring of patients with osteoporosis. The use of a dual-energy X-ray absorptiometry scan without any risk factors is discouraged. Treatment options are recommended based on risk stratification.
CONCLUSIONS: Guidance is provided to all physicians across the country who are involved in the care of patients with osteoporosis and fragility fractures.

The Fracture Risk Assessment Tool (FRAX®) is a widely utilized country-specific calculator for identifying individuals with high fracture risk; its score is calculated from 12 variables, but its formulation is not publicly disclosed. We aimed to decompose and simplify the FRAX® by utilizing a nationwide community survey database as a reference module for creating a local assessment tool for osteoporotic fracture community screening in any country. Participants (n = 16384; predominantly women (75%); mean age = 64.8 years) were enrolled from the Taiwan OsteoPorosis Survey, a nationwide cross-sectional community survey collected from 2008 to 2011. We identified 11 clinical risk factors from the health questionnaires. BMD was assessed via dual-energy X-ray absorptiometry in a mobile DXA vehicle, and 10-year fracture risk scores, including major osteoporotic fracture (MOF) and hip fracture (HF) risk scores, were calculated using the FRAX®. The mean femoral neck BMD was 0.7 ± 0.1 g/cm2, the T-score was -1.9 ± 1.2, the MOF was 8.9 ± 7.1%, and the HF was 3.2 ± 4.7%. Following FRAX® decomposition with multiple linear regression, the adjusted R2 values were 0.9206 for MOF and 0.9376 for HF when BMD was included and 0.9538 for MOF and 0.9554 for HF when BMD was excluded. The FRAX® demonstrated better prediction for women and younger individuals than for men and elderly individuals after sex and age stratification analysis. Excluding femoral neck BMD, age, sex, and previous fractures emerged as 3 primary clinical risk factors for simplified FRAX® according to the decision tree analysis in this study population. The adjusted R2 values for the simplified country-specific FRAX® incorporating 3 premier clinical risk factors were 0.8210 for MOF and 0.8528 for HF. After decomposition, the newly simplified module provides a straightforward formulation for estimating 10-year fracture risk, even without femoral neck BMD, making it suitable for community or clinical osteoporotic fracture risk screening.

Using race and ethnicity in clinical algorithms potentially contributes to health inequities. The American Society for Bone and Mineral Research (ASBMR) Professional Practice Committee convened the ASBMR Task Force on Clinical Algorithms for Fracture Risk to determine the impact of race and ethnicity adjustment in the US Fracture Risk Assessment Tool (US-FRAX). The Task Force engaged the University of Minnesota Evidence-based Practice Core to conduct a systematic review investigating the performance of US-FRAX for predicting incident fractures over 10 years in Asian, Black, Hispanic, and White individuals. Six studies from the Women\'s Health Initiative (WHI) and Study of Osteoporotic Fractures (SOF) were eligible; cohorts only included women and were predominantly White (WHI > 80% and SOF > 99%), data were not consistently stratified by race and ethnicity, and when stratified there were far fewer fractures in Black and Hispanic women vs White women rendering area under the curve (AUC) estimates less stable. In the younger WHI cohort (n = 64 739), US-FRAX without bone mineral density (BMD) had limited discrimination for major osteoporotic fracture (MOF) (AUC 0.53 (Black), 0.57 (Hispanic), and 0.57 (White)); somewhat better discrimination for hip fracture in White women only (AUC 0.54 (Black), 0.53 (Hispanic), and 0.66 (White)). In a subset of the older WHI cohort (n = 23 918), US-FRAX without BMD overestimated MOF. The Task Force concluded that there is little justification for estimating fracture risk while incorporating race and ethnicity adjustments and recommends that fracture prediction models not include race or ethnicity adjustment but instead be population-based and reflective of US demographics, and inclusive of key clinical, behavioral, and social determinants (where applicable). Research cohorts should be representative vis-à-vis race, ethnicity, gender, and age. There should be standardized collection of race and ethnicity; collection of social determinants of health to investigate impact on fracture risk; and measurement of fracture rates and BMD in cohorts inclusive of those historically underrepresented in osteoporosis research.
Using race or ethnicity when calculating disease risk may contribute to health disparities. The ASBMR Task Force on Clinical Algorithms for Fracture Risk was created to understand the impact of the US Fracture Risk Assessment Tool (US-FRAX) race and ethnicity adjustments. The Task Force reviewed the historical development of FRAX, including the assumptions underlying selection of race and ethnicity adjustment factors. Furthermore, a systematic review of literature was conducted, which revealed an overall paucity of data evaluating the performance of US-FRAX in racially and ethnically diverse groups. While acknowledging the existence of racial and ethnic differences in fracture epidemiology, the Task Force determined that currently there is limited evidence to support the use of race and ethnicity–specific adjustments in US-FRAX. The Task Force also concluded that research is needed to create generalizable fracture risk calculators broadly applicable to current US demographics, which do not include race and ethnicity adjustments. Until such population–based fracture calculators are available, clinicians should consider providing fracture risk ranges for Asian, Black, and/or Hispanic patients and should engage in shared decision-making with patients about fracture risk interpretation. Future studies are required to evaluate fracture risk tools in populations inclusive of those historically underrepresented in research.

OBJECTIVE: The elderly are prone to fragility fractures, especially those suffering from type 2 diabetes mellitus (T2DM) combined with osteoporosis. Although studies have confirmed the association between GNRI and the prevalence of osteoporosis, the relationship between GNRI and fragility fracture risk and the individualized 10-year probability of osteoporotic fragility fractures estimated by FRAX remains unclear. This study aims to delve into the association between the GNRI and a fragility fracture and the 10-year probability of hip fracture (HF) and major osteoporotic fracture (MOF) evaluated by FRAX in elderly with T2DM.
METHODS: A total of 580 patients with T2DM aged ≥60 were recruited in the study from 2014 to 2023. This research is an ambispective longitudinal cohort study. All participants were followed up every 6 months for 9 years with a median of 3.8 years through outpatient services, medical records, and home fixed-line telephone interviews. According to the tertiles of GNRI, all subjects were divided into three groups: low-level (59.72-94.56, n = 194), moderate-level (94.56-100.22, n = 193), and high-level (100.22-116.45, n = 193). The relationship between GNRI and a fragility fracture and the 10-year probability of HF and MOF calculated by FRAX was assessed by receiver operating characteristic (ROC) analysis, Spearman correlation analyses, restricted cubic spline (RCS) analyses, multivariable Cox regression analyses, stratified analyses, and Kaplan-Meier survival analysis.
RESULTS: Of 580 participants, 102 experienced fragile fracture events (17.59%). ROC analysis demonstrated that the optimal GNRI cut-off value was 98.58 with a sensitivity of 75.49% and a specificity of 47.49%, respectively. Spearman partial correlation analyses revealed that GNRI was positively related to 25-hydroxy vitamin D [25-(OH) D] (r = 0.165, P < 0.001) and bone mineral density (BMD) [lumbar spine (LS), r = 0.088, P = 0.034; femoral neck (FN), r = 0.167, P < 0.001; total hip (TH), r = 0.171, P < 0.001]; negatively correlated with MOF (r = -0.105, P = 0.012) and HF (r = -0.154, P < 0.001). RCS analyses showed that GNRI was inversely S-shaped dose-dependent with a fragility fracture event (P < 0.001) and was Z-shaped with the 10-year MOF (P = 0.03) and HF (P = 0.01) risk assessed by FRAX, respectively. Multivariate Cox regression analysis demonstrated that compared with high-level GNRI, moderate-level [hazard ratio (HR) = 1.950; 95% confidence interval (CI) = 1.076-3.535; P = 0.028] and low-level (HR = 2.538; 95% CI = 1.378-4.672; P = 0.003) had an increased risk of fragility fracture. Stratified analysis exhibited that GNRI was negatively correlated with the risk of fragility fracture, which the stratification factors presented in the forest plot were not confounding factors and did not affect the prediction effect of GNRI on the fragility fracture events in this overall cohort population (P for interaction > 0.05), despite elderly females aged ≥70, with body mass index (BMI) ≥24, hypertension, and with or without anemia (all P < 0.05). Kaplan-Meier survival analysis identified that the lower-level GNRI group had a higher cumulative incidence of fragility fractures (log-rank, all P < 0.001).
CONCLUSIONS: This study confirms for the first time that GNRI is negatively related to a fragility fracture and the 10-year probability of osteoporotic fragility fractures assessed by FRAX in an inverse S-shaped and Z-shaped dose-dependent pattern in elderly with T2DM, respectively. GNRI may serve as a valuable predictor for fragility fracture risk in elderly with T2DM. Therefore, in routine clinical practice, paying attention to the nutritional status of the elderly with T2DM and giving appropriate dietary guidance may help prevent a fragility fracture event.

Fracture Risk Assessment Tool is a free, online fracture risk calculator which can be used to predict 10-year fracture risk for women and men over age 50 years. It incorporates seven clinical risk factors and bone density to give a 10-year risk of major osteoporotic fracture and hip fracture. This dynamic tool can be used with patients at the bedside to help guide treatment decisions. There are some limitations to Fracture Risk Assessment Tool, with the most central limitation being the fact that inputs are binary. Much research has been done to try to refine Fracture Risk Assessment Tool to allow for more accurate risk prediction, and this article describes the data for adjusting Fracture Risk Assessment Tool depending on the clinical scenario such as the dose of glucocorticoid use, presence of diabetes and others. Recently, the new FRAXplus tool has been developed to address many of these concerns and will likely replace the old Fracture Risk Assessment Tool in the future. At the current time, it is available in beta form.
Methods for Refining the FRAX® Tool in Patients with Low Bone Density to Help Improve the Accuracy of Osteoporotic Fracture Risk PredictionMany patients who have low bone density develop fragility fractures, even those whose bone density is not yet within the osteoporosis range. Thus, in patients with low bone density, the health care team should estimate the risk of fracture to decide which patients should take medications to prevent fractures. Factors such as age, body mass index, steroid use, family history and other clinical factors can influence the fracture risk, in addition to bone density. There is an online calculator called the Fracture Risk Assessment Tool (FRAX®) which allows patients and doctors to integrate these risk factors with bone density in order to estimate the 10 year risk of osteoporotic fractures. FRAX® asks a series of yes/no questions about the patient’s risks for fracture, and also takes into account the patient’s country of residence, age, gender, race and bone density at the femur neck. However, there are some important limitations of this calculator. For example, we think that steroid medications increase the risk of fractures, and the higher the dose, the higher the risk of fractures. However, FRAX® only allows a “yes” or “no” input to the steroid use question. This paper aims to descibe methods for refining the FRAX® calculation to make the fracture risk prediction more accurate. For example, it describes a mathematical adjustment to FRAX® to account for the dose of steroids used. It also reviews methods for FRAX® adjustment for diabetes type 1 and 2, and severity of rheumatoid arthritis, among other considerations. Importantly, there is a new FRAX® tool that is currently in beta testing which will also further refine the accuracy of fracture risk prediction.

BACKGROUND: The sphingosine 1-phosphate (S1P) concentration is a potential biomarker of osteoporotic fracture and is associated with both the fracture risk assessment tool (FRAX) probability and trabecular bone score (TBS), which are well-known predictors of fracture. We sought to estimate the effect of the S1P concentration on fracture risk using the FRAX probability and TBS as mediators.
METHODS: Plasma S1P concentrations, FRAX variables, and TBSs were measured in 66 postmenopausal women with fractures and 273 postmenopausal women without fractures. Associations between S1P concentration, FRAX probability, TBS, and fracture risk were analyzed using correlation, logistic regression, and mediation analyses.
RESULTS: Subjects in the highest S1P concentration tertile had a higher fracture risk (odds ratio [OR], 5.09; 95% confidence interval [CI], 2.22-11.67) than those in the lowest S1P concentration tertile before adjustment. Subjects in the highest FRAX probability tertile had a higher fracture risk (OR, 14.59; 95% CI, 5.01-42.53) than those in the lowest FRAX probability tertile before adjustment. Subjects in the lowest TBS tertile had a higher fracture risk (OR, 4.76; 95% CI, 2.28-9.93) than those in the highest TBS tertile before adjustment. After adjustment for FRAX probability and TBS, the highest S1P concentration tertile was still associated with a higher fracture risk (OR, 3.13; 95% CI, 1.28-7.66). The FRAX probability and TBS accounted for 32.6% and 21.7%, respectively, of the relationship between the S1P concentration and fracture risk.
CONCLUSIONS: The relationship between the circulating S1P concentration and fracture risk was partly mediated by the FRAX probability, bone microarchitecture, and other factors.

OBJECTIVE: This study aimed to investigate the correlation of sarcopenic obesity with various cardiometabolic risk factors and fracture risk in middle-aged Korean women.
METHODS: In this cross-sectional study, the medical records of 1,775 women who had visited Pusan National University Hospital for routine health screenings from 2010 to 2016 were reviewed. The patients were divided into four groups as follows: group 1, nonsarcopenic, nonobese (NS-NO); group 2, nonsarcopenic, obese (NS-O); group 3, sarcopenic, nonobese (S-NO); and group 4, sarcopenic, obese (S-O). Each patient was assessed based on self-reported questionnaires and individual interviews with a healthcare provider. The Fracture Risk Assessment Tool (FRAX) was used to assess bone fracture risk.
RESULTS: Postmenopausal women accounted for 68.5% of the total patient population. The proportion of each group was as follows: NS-NO, 71.2%; NS-O, 17.9%; S-NO, 10.2%; and S-O, 0.7%. Statistical analysis of various parameters associated with metabolic and cardiovascular risks revealed that the S-O group had more patients with hypertension, diabetes, osteopenia, and metabolic syndrome. The FRAX scores were significantly higher in the S-O group than in other groups.
CONCLUSIONS: Middle-aged women with obesity and reduced muscle mass, known as sarcopenic obesity, are at increased risk of hypertension, diabetes, and metabolic syndrome. Furthermore, sarcopenic obesity, individual cardiometabolic risks, and menopause can increase the bone fracture risk.

UNASSIGNED: To evaluate if Hounsfield units (HU) measured on preoperative computed tomography (CT) scans at the anatomic neck of the proximal humerus correlates with intraoperative findings of the \"thumb test\" in assessment of bone quality in shoulder arthroplasty patients.
UNASSIGNED: Primary anatomic total shoulder and reverse total shoulder arthroplasty patients from 2019-2022 with an available preoperative CT scan of the operative shoulder were prospectively enrolled at a single center with 3 surgeons who perform shoulder arthroplasty. The \"thumb test\" was performed intraoperatively; a positive test signified \"good bone.\" Demographic information, including prior dual x-ray absorptiometry scans, was extracted from the medical record. HU at the cut surface of the proximal humerus were calculated, as was cortical bone thickness on preoperative CT. Fracture risk assessment tool (FRAX) scores were calculated for 10-year risk of osteoporotic fracture.
UNASSIGNED: A total of 149 patients were enrolled. Mean age was 67.6 ± 8.5 years with 69 (46.3%) being males. Patients with a negative thumb test were significantly older (72.3 ± 6.6 vs. 66.5 ± 8.6 years; P < .001) than those with a positive thumb test. Males were more likely to have a positive thumb test than females (P = .014). Patients with a negative thumb test had significantly lower HUs on preoperative CT (16.3 ± 29.7 vs. 51.9 ± 35.2; P < .001). Patients with a negative thumb test had a higher mean FRAX score (14.1 ± 7.9 vs. 8.0 ± 4.8; P < .001). Receiver operator curve analysis was performed to identify a cut-off value for CT HU of 36.67, above which the thumb test is likely to be positive. Furthermore, receiver operator curve analysis also identified optimal cut-off values for 10-year risk of fracture by FRAX score of 7.75 HU, below which the thumb test is likely to be positive. Fifty patients were at high risk based on FRAX and HU; surgeons classified 21 (42%) as having \"poor bone\" quality through a negative thumb test. High-risk patients had a negative thumb test 33.8% (23/68) and 37.1% (26/71) of the time for HU and FRAX, respectively.
UNASSIGNED: Surgeons are poor at identifying suboptimal bone quality at the anatomic neck of the proximal humerus based on intraoperative thumb test when referencing against CT HU and FRAX scores. The objective measures of CT HU and FRAX scoring may be useful metrics to incorporate into surgeons\' preoperative plans for humeral stem fixation using readily available imaging and demographic data.