Study on Risk Factors of Primary Non-traumatic OVCF in Chinese Elderly and a Novel Prediction Model

Abstract

Objective: Prevention of fragility fractures is one of the public health priorities worldwide, whilst the incidence of osteoporotic vertebral compression fractures (OVCF) continues to rise and lacks the corresponding accurate prediction model. This study aimed to screen potential causes and risk factors for primary non-traumatic osteoporotic vertebral compression fractures (NTOVCF) in the elderly by characterizing a patient population with NTOVCF and comparing it with a population of osteoporotic patients.

Methods: Between January 2013 and January 2022, 208 elderly patients with unequivocal evidence of bone fragility manifested as painful NTOVCF were enrolled, and compared with 220 patients with osteoporosis and no fractures. The demographic data, bone turnover markers, blood routine, serum biochemical values, and radiological findings were investigated. Differences between the fracture and non-fracture groups were analyzed, and variables significant in univariate analysis and correlation analysis were included in the logistic analysis to build the risk prediction model for osteoporotic vertebral fractures. Univariate analysis using student's t-tests for continuous variables or a chi-squared test for categorical variables was conducted to identify risk factors.

Results: No significant differences were revealed regarding age, gender, BMI, smoking, alcohol consumption, blood glucose, propeptide of type I procollagen (P1NP), and N-terminal middle segment osteocalcin (N-MID) (P > 0.05). Parathyroid Hormone (PTH), 25(OH)D, serum albumin (ALB), hemoglobin (HB), bone mineral density (BMD), and cross-sectional area (CSA) of the paraspinal muscle in the fracture group were significantly lower than those in the control group; however, b-C-terminal telopeptide of type I collagen (β-CTX), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), non-prostatic acid phosphatase (NACP), and fatty degeneration ratio (FDR) were significantly higher than those in the control group (P < 0.05). Logistic regression analysis showed that ALB, HB, CSA, and BMD were negatively correlated with NTOVCF, while β-CTX, HDL-C, NACP, and FDR were positively correlated with NTOVCF.

Conclusion: Decreased physical activity, anemia, hypoproteinemia, imbalances in bone metabolism, abnormal lipid metabolism, and degenerative and decreased muscle mass, were all risk factors for OVCF in the elderly, spontaneous fractures may be the consequence of cumulative declines in multiple physiological systems over the lifespan. Based on this risk model, timely detection of patients with high OVCF risk and implementation of targeted preventive measures is expected to improve the effect of fracture prevention.

Keywords: Elderly population; Osteoporosis; Osteoporotic vertebral compression fracture; Risk prediction model.

FIG. 1

The cross‐sectional area (CSA) of the paraspinal muscle and vertebral body size (VB) were separately outlined. The fatty degeneration ratio (FDR) of the paraspinal muscle was calculated using the ImageJ, and the gray‐scale ranges for CSA and subcutaneous fat (SCF) were presented as histograms, then the overlapping area (OA) of CSA and SCF grayscale ranges were produce, which indicated the amount of fatty degeneration within the CSA. FDR was formulated by the number of pixels in the overlap area divided by the total number of pixels in the CSA

FIG. 2

(A and B) showed the differences in serum bone turnover markers and related hormones. MRI findings were demonstrated in (C), and blood routine and serum biochemical indicators were shown in (D–F). Statistical differences were expressed as P < 0.05 (*), P < 0.01 (**), and P < 0.001 (***), besides, “ns” meant no significant difference. Parathyroid Hormone (PTH, pg./ml), 25(OH)D (ng/ml), propeptide of type I procollagen (P1NP, ng/ml), b‐C‐terminal telopeptide of type I collagen (β‐CTX, ng/ml), N‐terminal middle segment osteocalcin (N‐MID, ng/ml), alkaline phosphatase (ALP, U/L), acid phosphatase (ACP, U/L), non‐prostatic acid phosphatase (NACP, U/L), prostatic acid phosphatase (PACP, U/L), bone mineral density (BMD, T‐score); Cross‐sectional area (CSA, cm²), vertebral body size (VB, cm²), fatty degeneration ratio (FDR, %), lumbar muscle mass (LMM, cm²), lumbar muscle fat content (LMF, cm²); Red blood cell (RBC, ×10¹²/L), hemoglobin (HB, g/L), white blood cell (WBC, ×10⁹/L). serum albumin (ALB, g/L), total protein (TP, g/L), total cholesterol (TC, mmol/L), triglyceride (TG, mmol/L), high‐density lipoprotein cholesterol (HDL‐C, mmol/L), low‐density lipoprotein cholesterol (LDL‐C, mmol/L), blood glucose (Glu, mmol/L), calcium (Ca, mmol/L), phosphorus (P, mmol/L), magnesium (Mg, mmol/L)

FIG. 3

The performances of the related factors in predicting the occurrence of NTOVCF. Receiver operating characteristic curves (ROC) of hemoglobin (HB), serum albumin (ALB), total cholesterol (TC), high‐density lipoprotein cholesterol (HDL‐C), propeptide of type I procollagen (P1NP), b‐C‐terminal telopeptide of type I collagen (β‐CTX), non‐prostatic acid phosphatase (NACP), bone mineral density (BMD), fatty degeneration ratio (FDR), cross‐sectional area (CSA), and activities of daily living (daily activity) were shown. Area under curve (AUC)

FIG. 4

Comparing the receiver operating characteristic curves (ROC) of the new model to those of BMD T‐score‐based prediction and OSTA tool. Area under curve (AUC), bone mineral density (BMD), osteoporosis self‐assessment tool for Asians (OSTA)