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

Content Provider	Springer Nature : BioMed Central
Author	Jiang, Xinhua Yan, Na Zheng, Yaqin Yang, Jintao Zhao, Yanfei
Abstract	Background This 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. Methods We 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. Results The 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. Conclusion In 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.
Related Links	https://bmcmusculoskeletdisord.biomedcentral.com/counter/pdf/10.1186/s12891-022-06014-0.pdf
Ending Page	7
Page Count	7
Starting Page	1
File Format	HTM / HTML
ISSN	14712474
DOI	10.1186/s12891-022-06014-0
Journal	BMC Musculoskeletal Disorders
Issue Number	1
Volume Number	23
Language	English
Publisher	BioMed Central
Publisher Date	2022-12-01
Access Restriction	Open
Subject Keyword	Orthopedics Rehabilitation Rheumatology Sports Medicine Internal Medicine Epidemiology Primary osteoporosis Disease screening miRNA β-CTX t-P1NP LASSO Hazard Assessment
Content Type	Text
Resource Type	Article
Subject	Orthopedics and Sports Medicine Rheumatology
Journal Impact Factor	2.2/2023
5-Year Journal Impact Factor	2.6/2023

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