Serum biomarker-based osteoporosis risk prediction and the systemic effects of Trifolium pratense ethanolic extract in a postmenopausal model

Chinese Medicine

卷:17

Jireh Chan Yi-Le¹  Yixian Quah²  Yuan Yee Lee²  Man Hee Rhee²  Eon-Bee Lee²  Seung-Chun Park²  Na-Hye Park³  Seung-Jin Lee⁴  Seung-Hee Jang⁵  Min-Jeong Kim⁵ 

[1] Centre of IoT and Big Data, Universiti Tunku Abdul Rahman;

[2] College of Veterinary Medicine and Cardiovascular Research Institute, Kyungpook National University;

[3] Laboratory Animal Center, Daegu-Gyeongbuk Medical Innovation Foundation;

[4] Reproductive and Development Toxicology Research Group, Korea Institute of Toxicology;

[5] Teazen Co. Ltd.;

关键词: Machine learning;    Osteoporosis;    Postmenopause;    Red clover;    Trifolium pretense;    XGBoost;   

DOI  :  10.1186/s13020-022-00622-7

来源: DOAJ

【 摘 要 】

Abstract Background Recent years, a soaring number of marketed Trifolium pratense (red clover) extract products have denoted that a rising number of consumers are turning to natural alternatives to manage postmenopausal symptoms. T. pratense ethanolic extract (TPEE) showed immense potential for their uses in the treatment of menopause complications including osteoporosis and hormone dependent diseases. Early diagnosis of osteoporosis can increase the chance of efficient treatment and reduce fracture risks. Currently, the most common diagnosis of osteoporosis is performed by using dual-energy x-ray absorptiometry (DXA). However, the major limitation of DXA is that it is inaccessible and expensive in rural areas to be used for primary care inspection. Hence, serum biomarkers can serve as a meaningful and accessible data for osteoporosis diagnosis. Methods The present study systematically elucidated the anti-osteoporosis and estrogenic activities of TPEE in ovariectomized (OVX) rats by evaluating the bone microstructure, uterus index, serum and bone biomarkers, and osteoblastic and osteoclastic gene expression. Leverage on a pool of serum biomarkers obtained from this study, recursive feature elimination with a cross-validation method (RFECV) was used to select useful biomarkers for osteoporosis prediction. Then, using the key features extracted, we employed five classification algorithms: extreme gradient boosting (XGBoost), random forest, support vector machine, artificial neural network, and decision tree to predict the bone quality in terms of T-score. Results TPEE treatments down-regulated nuclear factor kappa-B ligand, alkaline phosphatase, and up-regulated estrogen receptor β gene expression. Additionally, reduced serum C-terminal telopeptides of type 1 collagen level and improvement in the estrogen dependent characteristics of the uterus on the lining of the lumen were observed in the TPEE intervention group. Among the tested classifiers, XGBoost stood out as the best performing classification model with the highest F1-score and lowest standard deviation. Conclusions The present study demonstrates that TPEE treatment showed therapeutic benefits in the prevention of osteoporosis at the transcriptional level and maintained the estrogen dependent characteristics of the uterus. Our study revealed that, in the case of limited number of features, RFECV paired with XGBoost model could serve as a powerful tool to readily evaluate and diagnose postmenopausal osteoporosis.

【 授权许可】

Unknown