Risk of primary osteoporosis score (RPOPs): an algorithm model for primary osteoporosis risk assessment in grass-roots hospital

BMC Musculoskeletal Disorders

Research

Yanfei Zhao¹  Xinhua Jiang²  Yaqin Zheng²  Jintao Yang³  Na Yan³ 

[1] Department of Laboratory Medicine, Quzhou Maternal and Child Health Care Hospital, Quzhou, Zhejiang Province, China;Department of Laboratory Medicine, Zhejiang Quhua Hospital, Quzhou, Zhejiang Province, China;Key Laboratory of Digital Technology in Medical Diagnostics of Zhejiang Province, Dian Diagnostics Group Co.,Ltd, Hangzhou, Zhejiang Province, China;

关键词: Primary osteoporosis;    Disease screening;    miRNA;    β-CTX;    t-P1NP;    LASSO;    Hazard Assessment;   

DOI  :  10.1186/s12891-022-06014-0

 received in 2022-09-15, accepted in 2022-11-22,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundThis study aimed to develop and validate a lasso regression algorithm model which was established by correlation factors of bone mineral density (BMD) and could be accurately predicted a high-risk population of primary osteoporosis (POP). It provides a rapid, economical and acceptable early screening method for osteoporosis in grass-roots hospitals.MethodsWe collected 120 subjects from primary osteoporosis screening population in Zhejiang Quhua Hospital between May 2021 and November 2021 who were divided into three groups (normal, osteopenia and osteoporosis) according to the BMD T-score. The levels of three micro-RNAs in the plasma of these people were detected and assessed by qRT-PCR. At the same time, the levels of β-CTX and t-P1NP in serum of the three groups were determined. Based on the cluster random sampling method, 84 subjects (84/120, 70%) were selected as the training set and the rest were the test set. Lasso regression was used to screen characteristic variables and establish an algorithm model to evaluate the population at high risk of POP which was evaluated and tested in an independent test cohort. The feature variable screening process was used 10-fold cross validation to find the optimal lambda.ResultsThe osteoporosis risk score was established in the training set: Risk of primary osteoporosis score (RPOPs) = -0.1497785 + 2.52Age − 0.19miR21 + 0.35miR182 + 0.17β-CTx. The sensitivity, precision and accuracy of RPOPs in an independent test cohort were 79.17%, 82.61% and 75%, respectively. The AUC in the test set was 0.80. Some risk factors have a significant impact on the abnormal bone mass of the subjects. These risk factors were female (p = 0.00013), older than 55 (p < 2.2e-16) and BMI < 24 (p = 0.0091) who should pay more attention to their bone health.ConclusionIn this study, we successfully constructed and validated an early screening model of osteoporosis that is able to recognize people at high risk for developing osteoporosis and remind them to take preventive measures. But it is necessary to conduct further external and prospective validation research in large sample size for RPOPs prediction models.

【 授权许可】

CC BY   
© The Author(s) 2022

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