Journal of Bone and Mineral Metabolism最新文献

Qi et al. presented a prognostic model for hip refracture survival, identifying age, comorbidities, lymphocyte count, and surgery as key predictors. Although clinically relevant, the model lacks external validation and uses suboptimal predictor handling. The selection bias and underpowered subgroup analysis limit the generalizability of the results. Further robust multicenter studies are needed to refine risk stratification in this high-risk population.

Introduction: Hip fracture (HF) is one of the leading causes of mortality and disability in older populations. Hip refracture (HRF) is more serious than HF. We examined the survival rates of patients with HRFs at the Affiliated Dongyang Hospital of Wenzhou Medical University and developed a prediction model for their survival outcomes.

Materials and methods: This study involved identifying and analyzing patients with HRFs from January 2013 to August 2023. Utilizing our hospital's database, we systematically extracted these patients' information. We monitored the survival time and constructed Kaplan-Meier (K-M) survival curve. Through Cox proportional hazards regression analyses, independent risk factors were identified. Based on these factors, a survival prediction model was developed and its reliability was evaluated by the receiver operating characteristic (ROC) curve. In addition, patients who refused surgery were excluded, the eligible patients were divided into a long waiting group (> 48 h) and a short waiting group (≤ 48 h). According to a similar process, independent risk factors were identified. Another nomogram chart and ROC curve were conducted.

Results: Data from 174 patients were used, presenting a median survival time of 40 months. Both univariate and multivariate analyses identified age (HR = 1.71, 95% CI: 1.26-2.32), surgical intervention (HR = 0.34, 95% CI: 0.21-0.55), number of comorbidities (HR = 1.44, 95% CI: 1.09-1.92) and lymphocyte levels (HR = 0.75, 95% CI: 0.61-0.92) as independent influence factors. Based on these factors, a nomogram chart was constructed. The areas under the ROC curve for predicting 1-year, 2-year, and 3-year survival rates were 0.748, 0.794 and 0.806 respectively. Additionally, through regression analysis of surgical patients, some factors (age, number of comorbidities and lymphocyte levels) were supported as independent influence factors.

Conclusion: Advanced age, non-surgical management, multiple comorbidities and lower lymphocyte levels showed a higher risk of all-cause mortality among the patients with HRFs, and these factors were associated with a poor prognosis in this patient population. This study may be useful for improving these patients' prognosis.

Introduction: Few studies assessed comprehensive effects of composite unhealthy lifestyles on aging and musculoskeletal health. This study aimed to address such issues with the UK Biobank datasets.

Materials and methods: An unhealthy lifestyle score (UHLS) was constructed based on 9 lifestyle behaviors. Aging indicators were calculated from 18 clinical traits. General linear and Cox proportional hazards regression models were used to analyze associations between UHLS, aging, and musculoskeletal morbidity. Mendelian randomization (MR) analysis was performed for causal relationship exploration.

Results: Among 396,037 participants, 54.5% and 3.3% of them were designated to the low (scored 0-2) and high UHLS (scored 6-9) groups, respectively. Increasing UHLS was associated with elevated aging acceleration (AA) based on biological age (0.343 per unit; 95% CI: 0.331, 0.355) and phenotypic age (AA_PA) (0.408 per unit; 95% CI: 0.394, 0.422), and higher morbidity of low grip strength (HR = 1.025; 95% CI: 1.001, 1.050), slow walking pace (HR = 1.134; 95% CI: 1.074, 1.198), osteoporosis (HR = 1.077; 95% CI: 1.063, 1.091), fracture (HR = 1.059; 95% CI: 1.048, 1.069) and osteoarthritis (HR = 1.036; 95% CI: 1.030, 1.042). Unhealthy lifestyles in conjunction with AA jointly increased musculoskeletal morbidity. Besides, AA mediated UHLS effects on slow walking pace, osteoporosis and fracture, with mediating proportion of 4.85%-12.79%. MR analyses revealed causal relationships between UHLS and low grip strength, osteoarthritis, and reduced femoral neck bone mineral density. In addition, AA_PA suggestively mediated the UHLS-osteoarthritis association.

Conclusions: Composite unhealthy lifestyles accelerate aging and impair musculoskeletal health. Both mediating and joint effects of AA showed unhealthy lifestyle-associated musculoskeletal morbidity.