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

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.

Keywords: Machine learning; Osteoporosis; Postmenopause; Red clover; Trifolium pretense; XGBoost.

Fig. 1

A typical optimized classification decision tree to map the relationship between serum biomarkers and osteoporosis

Fig. 2

A typical illustration of a neural network to map the relationship between serum biomarkers and osteoporosis

Fig. 3

The effects of TPEE (T125, T250 and T500) or positive controls (Sham, PomE and E) on uterus index in OVX rats. Data were presented as mean ± SD, n = 6. ***p < 0.001 compared with NC group determined by two-way ANOVA test. Representative images of H&E staining of the rats’ uterus. The arrows indicated the ciliated cells on the linings of the epithelium cells. All pictures are stained with H&E and examined under × 400 magnification. Scale bar, 50 µm

Fig. 4

The effects of TPEE on cortical and trabecular bones. A The cortical bone thickness and the representative images of H&E staining of rats’ femur. All pictures are stained with H&E and examined under × 2.2 magnification. Scale bar, 500 µm. Data were presented as mean ± SD, n = 6. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001 determined by two-way ANOVA test. B Trabecular bone tissue was stained with H&E and examined under × 400 magnification. Scale bar, 200 µm. C The representative images of the 3D architecture of trabecular bone analyzed by microCT analysis

Fig. 5

Real-time PCR analysis for mRNA expression of RANKL, OPG, OCN, ALP, ColA, and ERβ in the tibia (A). The levels of osteoblastic and osteoclastic biomarkers in OVX rats’ tibia (B) and serum (C). Data are expressed as mean ± SD (n = 6). Data were presented as mean ± SD, n = 6. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001 determined by two-way ANOVA test, followed by Tukey’s post hoc test

Fig. 6

Recursive Feature Elimination with a Cross-Validation (RFECV) ranking for each serum biomarkers based on F1-score (A). Boxplot of each model performance B F1-score and C accuracy over 50 iterations

Fig. 7

A schematic of the XGBoost model evaluation workflow. The shaded area indicates the data pre-processing (including normalization and feature selection) and data partitioning. The boxes within the dashed lines represents training and testing procedures where F_i(X) denoting the tree function, where i denoting the ith tree