Retrospective analysis of the relationship between bone mineral density and body composition in a health check-up Chinese population

The impact of bone mineral density on the risk of falling: evidence from genetic correlation and Mendelian randomization analysis

BACKGROUND

Falls are the most common consequence of low bone mineral density (BMD). However, due to limitations inherent in observational studies, the causal relationship between the two remains unestablished.

METHODS

This study utilized Mendelian Randomization (MR) analysis to explore the causal relationship between BMD and the risk of falling, incorporating linkage disequilibrium score (LDSC) regression for genetic correlation assessment. The primary method was inverse-variance weighted (IVW), supplemented with sensitivity analyses and the causal analysis using summary effect estimates (CAUSE) to address heterogeneity and pleiotropy biases.

RESULTS

LDSC analysis indicated significant genetic correlations between BMD at various sites and falling risk (rg range: -0.82 to 0.76, all P < 0.05). IVW analysis, with False Discovery Rate (FDR) correction, showed a protective causal effect of total body BMD (OR = 0.85, 95% CI 0.82-0.88, P = 7.63 × 10-17, PFDR = 1.91 × 10-16), femoral neck BMD (OR = 0.81, 95% CI 0.75-0.88, P = 3.33 × 10-7, PFDR = 5.55 × 10-7), lumbar spine BMD (OR = 0.85, 95% CI 0.79-0.91, P = 9.56 × 10-7, PFDR = 1.20 × 10-6), and heel BMD (OR = 0.82, 95% CI 0.79-0.81, P = 1.69 × 10-39, PFDR = 8.45 × 10-39) on falling risk. No causal relationship was found for forearm BMD (OR = 1.02, 95% CI 0.94-1.11, P = 0.64, PFDR = 0.64). Replication datasets and CAUSE analysis provided causal evidence consistent with the main findings.

CONCLUSION

The study established a causal relationship between BMD at four different sites and the risk of falling, highlighting potential areas for targeted prevention strategies.

Prevalence and Risk Factors of Osteoporosis in a Chinese Population: A Cross-Sectional Study in Xi'an, Shaanxi Province, China

BACKGROUND Osteoporosis is a systemic chronic disease characterized by bone mineral density (BMD) reduction. This study aimed to assess the prevalence of osteoporosis and fracture risks in northwestern China and investigate the related anthropometric risk factors. MATERIAL AND METHODS Between July 2022 and August 2022, 1429 participants (1295 females, 134 males) with measured BMD were recruited to participate in this cross-sectional study. Data on height, weight, and T score were collected. Spearman's correlation and multiple linear regression analysis were used to investigate the relationships between various demographic factors and BMD and the 10-year risk of major osteoporotic fracture (MO) and hip fracture (HP). RESULTS The overall prevalence of osteoporosis in northwest China was 42.34%, with 44.56% in females and 20.90% in males. Age negatively affects females' T scores (r=-0.304, P<0.05), and height positively influences both sexes' T scores (r=0.059 P<0.05). Age (r=0.148, P<0.05) and height were positive predictors of MO (r=0.027, P<0.05), while weight was a negative predictor (r=-0.035, P<0.05). The conclusion for HP was consistent with that of MO, except for the T score, which was a positive predictor of HP (r=0.014, P<0.05). CONCLUSIONS The prevalence of osteoporosis in northeast China is high. The association between anthropometric parameters and osteoporosis in adults in northwest China is different between sexes.

Prediction of the Risk of Bone Mineral Density Decrease in Type 2 Diabetes Mellitus Patients Based on Traditional Multivariate Logistic Regression and Machine Learning: A Preliminary Study

Purpose

There remains a lack of a machine learning (ML) model incorporating body composition to assess the risk of bone mineral density (BMD) decreases in type 2 diabetes mellitus (T2DM) patients. We aimed to use ML algorithms and the traditional multivariate logistic regression to establish prediction models for BMD decreases in T2DM patients over 50 years of age, and compare the performance of the two methods.

Patients and Methods

This cross-sectional study was conducted among 450 patients with T2DM from 1 August 2016 to 31 December 2022. The participants were divided into a normal BMD group and a decreased BMD group. Traditional multivariate logistic regression and six ML algorithms were selected to construct male and female models. Two nomograms were constructed to evaluate the risk of BMD decreases in the male and female T2DM patients, respectively. The ML models with the highest area under the curve (AUC) were compared with the traditional multivariate logistic regression models in terms of discriminant ability and clinical applicability.

Results

The optimal ML model was the extreme gradient boost (XGBoost) model. The AUCs of the traditional multivariate logistic regression and the XGBoost models were 0.722 and 0.800 in the male testing dataset, respectively, and 0.876 and 0.880 in the female testing dataset, respectively. The decision curve analysis results suggested that using the XGBoost models to predict the risk of BMD decreases obtained more net benefits compared with the traditional models in both sexes.

Conclusion

We preliminarily proved that the XGBoost models outperformed most other ML models in both sexes and achieved higher accuracy than traditional analyses. Due to the limited sample size in the study, it is necessary to validate our findings in larger prospective cohort studies.

The Role of BIA Analysis in Osteoporosis Risk Development: Hierarchical Clustering Approach

Osteoporosis is a common musculoskeletal disorder among the elderly and a chronic condition which, like many other chronic conditions, requires long-term clinical management. It is caused by many factors, including lifestyle and obesity. Bioelectrical impedance analysis (BIA) is a method to estimate body composition based on a weak electric current flow through the body. The measured voltage is used to calculate body bioelectrical impedance, divided into resistance and reactance, which can be used to estimate body parameters such as total body water (TBW), fat-free mass (FFM), fat mass (FM), and muscle mass (MM). This study aims to find the tendency of osteoporosis in obese subjects, presenting a method based on hierarchical clustering, which, using BIA parameters, can group patients who show homogeneous characteristics. Grouping similar patients into clusters can be helpful in the field of medicine to identify disorders, pathologies, or more generally, characteristics of significant importance. Another added value of the clustering process is the possibility to define cluster prototypes, i.e., imaginary patients who represent models of "states", which can be used together with clustering results to identify subjects with similar characteristics in a classification context. The results show that hierarchical clustering is a method that can be used to provide the detection of states and, consequently, supply a more personalized medicine approach. In addition, this method allowed us to elect BIA as a potential prognostic and diagnostic instrument in osteoporosis risk development.