Dipstick Proteinuria and Reduced Estimated Glomerular Filtration Rate as Independent Risk Factors for Osteoporosis

Prediction of subsequent fragility fractures: application of machine learning

BACKGROUND

Machine learning (ML) has shown exceptional promise in various domains of medical research. However, its application in predicting subsequent fragility fractures is still largely unknown. In this study, we aim to evaluate the predictive power of different ML algorithms in this area and identify key features associated with the risk of subsequent fragility fractures in osteoporotic patients.

METHODS

We retrospectively analyzed data from patients presented with fragility fractures at our Fracture Liaison Service, categorizing them into index fragility fracture (n = 905) and subsequent fragility fracture groups (n = 195). We independently trained ML models using 27 features for both male and female cohorts. The algorithms tested include Random Forest, XGBoost, CatBoost, Logistic Regression, LightGBM, AdaBoost, Multi-Layer Perceptron, and Support Vector Machine. Model performance was evaluated through 10-fold cross-validation.

RESULTS

The CatBoost model outperformed other models, achieving 87% accuracy and an AUC of 0.951 for females, and 93.4% accuracy with an AUC of 0.990 for males. The most significant predictors for females included age, serum C-reactive protein (CRP), 25(OH)D, creatinine, blood urea nitrogen (BUN), parathyroid hormone (PTH), femoral neck Z-score, menopause age, number of pregnancies, phosphorus, calcium, and body mass index (BMI); for males, the predictors were serum CRP, femoral neck T-score, PTH, hip T-score, BMI, BUN, creatinine, alkaline phosphatase, and spinal Z-score.

CONCLUSION

ML models, especially CatBoost, offer a valuable approach for predicting subsequent fragility fractures in osteoporotic patients. These models hold the potential to enhance clinical decision-making by supporting the development of personalized preventative strategies.

High C-reactive protein-to-albumin ratio levels are associated with osteoporosis in patients with primary biliary cholangitis

Objective

Inflammation contributes to the development of metabolic bone diseases. The C-reactive protein-to-albumin ratio (CAR) is an inflammation-based marker with a prognostic value for several metabolic diseases. This study investigated the relationship between the CAR and osteoporosis (OP) in patients with primary biliary cholangitis (PBC).

Methods

Patients with PBC treated at Beijing Ditan Hospital between January 2018 and June 2023 were enrolled. Logistic regression analysis was performed to investigate the factors influencing OP. The predictive value of CAR for OP was evaluated using receiver operating characteristic (ROC) curves. Moreover, a restricted cubic spline (RCS) fitted with a logistic regression model was used to analyze the relationship between CAR and OP.

Results

The prevalence of OP among the patients with PBC was 26.9% (n = 82). CAR levels were higher in the OP group than in the non-OP group (0.33 (0.09, 0.61) vs. 0.08 (0.04, 0.18), P < 0.001). Logistic regression analysis showed that CAR was an independent predictor of OP in patients with PBC (odds ratio = 2.642, 95% confidence interval = 1.537-4.540, P < 0.001). CAR exhibited a good predictive ability for OP, with an areas under the curve (AUC) of 0.741. We found that individuals with CAR values > 0.1 have higher odds of OP. In addition, high CAR levels were associated with an increased prevalence of fragility fractures and high 10-year fracture risk.

Conclusion

High CAR levels were associated with greater odds of developing OP, and the CAR could serve as an independent predictor of OP in patients with PBC.

Proteinuria screening and risk of bone fracture: a retrospective cohort study using a nationwide population-based database

Background and hypothesis

Proteinuria is associated with an increased risk of kidney function deterioration, cardiovascular disease, or cancer. Previous reports suggesting an association between kidney dysfunction and bone fracture may be confounded by concomitant proteinuria and were inconsistent regarding the association between proteinuria and bone fracture. Therefore, we aimed to evaluate the association using a large administrative claims database in Japan.

Methods

Using the DeSC database, we retrospectively identified individuals with laboratory data including urine dipstick test between August 2014 and February 2021. We evaluated the association between proteinuria and vertebral or hip fracture using multivariable Cox regression analyses adjusted for various background factors including kidney function. We also performed subgroup analyses stratified by sex and kidney function and sensitivity analyses with Fine & Gray models considering death as a competing risk.

Results

We identified 603 766 individuals and observed 21 195 fractures. With reference to the negative proteinuria group, the hazard ratio for hip or vertebral fracture was 1.10 [95% confidence interval (CI), 1.05-1.14] and 1.16 (95%CI, 1.11-1.22) in the trace and positive proteinuria group, respectively, in the Cox regression analysis. The subgroup analyses showed similar trends. The Fine & Gray model showed a subdistribution hazard ratio of 1.09 (95%CI, 1.05-1.14) in the trace proteinuria group and 1.15 (95% CI, 1.10-1.20) in the positive proteinuria group.

Conclusions

Proteinuria was associated with an increased risk of developing hip or vertebral fractures after adjustment for kidney function. Our results highlight the clinical importance of checking proteinuria for predicting bone fractures.

From protein uptake to Dent disease: An overview of the CLCN5 gene

Proteinuria is a well-known risk factor, not only for renal disorders, but also for several other problems such as cardiovascular diseases and overall mortality. In the kidney, the chloride channel Cl^-/H⁺ exchanger ClC-5 encoded by the CLCN5 gene is actively involved in preventing protein loss. This action becomes evident in patients suffering from the rare proximal tubulopathy Dent disease because they carry a defective ClC-5 due to CLCN5 mutations. In fact, proteinuria is the distinctive clinical sign of Dent disease, and mainly involves the loss of low-molecular-weight proteins. The identification of CLCN5 disease-causing mutations has greatly improved our understanding of ClC-5 function and of the ClC-5-related physiological processes in the kidney. This review outlines current knowledge regarding the CLCN5 gene and its protein product, providing an update on ClC-5 function in tubular and glomerular cells, and focusing on its relationship with proteinuria and Dent disease.