Nomogram to Predict Cadmium-Induced Osteoporosis and Fracture in a Chinese Female Population.
Miaomiao WangHao ZhouWenjing CuiZhongqiu WangGuoying ZhuXiao ChenTaiyi JinPublished in: Biological trace element research (2021)
Cadmium exposure may increase the risk of osteoporosis. However, there is no quick method to get bone mineral density (BMD) unless dual-energy X-ray absorptiometry (DXA) examinations were performed. In the present study, we aimed to identify associated factors to osteoporosis and fracture in a Chinese female population with cadmium exposure and develop nomograms to predict the risk. A total of 488 women was included in this study. Cadmium in blood (BCd) and urine (UCd) were determined as exposure biomarkers. BMD was determined using single-photon absorptiometry. Urinary N-acetyl-β-d-glucosaminidase (UNAG) and urinary albumin (UALB) were determined as renal function biomarkers. Osteoporosis was defined if T-score < - 2.5. Multiple logistic regression showed that age, BCd, and menopausal status were independent risk factors for osteoporosis. The odds (OR) value was 1.19 (95% confidence interval (CI): 1.14-1.25) for age, 1.05 (95% CI: 1.004-1.10) for BCd, and 4.75 (95% CI: 1.65-13.69) for menopausal status after adjusting with cofounders. Age and UCd were the independent risk factors for bone fracture. Nomograms were developed based on the associated factors. Age was the main determinant for osteoporosis or fracture. Receiver operating curve showed acceptable performance in predicting osteoporosis (area under the curve (AUC) = 0.93, 95CI: 0.90-0.96) and fracture (AUC = 0.67, 95% CI: 0.58-0.75). Linear discriminant analysis (LDA) further showed that 88.9% of osteoporosis and 68.4% of fractures were correctly classified. Our study develops nomograms that may be used to predict cadmium-induced osteoporosis or fracture if BMD data is not available.
Keyphrases
- bone mineral density
- postmenopausal women
- body composition
- dual energy
- heavy metals
- computed tomography
- hip fracture
- high glucose
- type diabetes
- diabetic rats
- image quality
- high resolution
- electronic health record
- skeletal muscle
- drug induced
- machine learning
- contrast enhanced
- insulin resistance
- big data
- risk assessment
- mass spectrometry
- breast cancer risk