Feature selection approaches identify potential plasma metabolites in postmenopausal osteoporosis patients

IL-16 Mediates the Effect of Circulating Metabolites on Postmenopausal Osteoporosis: A Two-Step, Multivariable Mendelian Randomization Study

Objectives: This study aimed to explore the relationship between circulating metabolites and postmenopausal osteoporosis (PMOP) and to assess the mediating role of inflammatory factors. Methods: Utilizing summary-level data from genome-wide association studies (GWAS) and employing a Mendelian Randomization approach, a two-sample MR analysis was conducted to assess the relationship between circulating metabolites and PMOP. Additionally, a two-step MR was used to quantify the mediating impact of inflammatory factors on the effect of circulating metabolites on PMOP. Results: The results revealed a significant association between certain metabolites and the risk of PMOP, notably the ratio of free cholesterol to total lipids in very large VLDL particles (OR: 1.399, 95% CI: 1.002-1.954, p = 0.048) and IL-16 (OR: 0.773, 95% CI: 0.608-0.983, p = 0.036). IL-16 was found to partially mediate the impact of circulating metabolites on PMOP, with a mediation effect of 10.4%. Conclusion: This study underscores the crucial role of circulating metabolites and inflammatory factors in PMOP pathogenesis. A causal relationship between circulating metabolites and PMOP was established, with IL-16 mediating some effects. These findings hold promise for clinical applications in early detection, personalized medicine, and the identification of therapeutic targets for PMOP.

Grape seed extract prevents oestrogen deficiency-induced bone loss by modulating the gut microbiota and metabolites

Proanthocyanidin-rich grape seed extract (GSE) has been shown to have the potential to protect bones, although the underlying mechanism remains unknown. The current study aims to explore GSE's preventive and therapeutic impact on bone loss induced by oestrogen deficiency and the underlying mechanism through the gut microbiota (GM) and metabolomic responses. In oestrogen-deficient ovariectomized (OVX) mice, GSE ameliorated bone loss by inhibiting the expansion of bone marrow adipose tissue (BMAT), restoring BMAT lipolysis and promoting bone formation. GSE regulated OVX-induced GM dysbiosis by reducing the abundance of opportunistic pathogenic bacteria, such as Alistipes, Turicibacter and Romboutsia, while elevating the abundance of beneficial bacteria, such as Bifidobacterium. The modified GM primarily impacted lipid and amino acid metabolism. Furthermore, the serum metabolites of GSE exhibited a significant enrichment in lipid metabolism. In summary, GSE shows potential as a functional food for preventing oestrogen deficiency-induced bone loss by modulating GM and metabolite-mediated lipid metabolism.

Cistanche deserticola improves ovariectomized-induced osteoporosis mainly by regulating lipid metabolism: Insights from serum metabolomics using UPLC/Q-TOF-MS

ETHNOPHARMACOLOGICAL RELEVANCE

Cistanche deserticola (C. deserticola) is an edible and traditional medicine widely used in China, which has been confirmed to be effective in the treatment of postmenopausal osteoporosis (PMOP). Despite its proven efficacy, the exact role of C. deserticola in bone metabolism and its underlying mechanism has remained unclear.

AIM OF THE STUDY

In this research, we employed an in vivo model utilizing ovariectomized (OVX) rats to characterize the anti-osteoporotic activity and metabolic mechanism of the ethanol extract of C. deserticola (CHE).

MATERIALS AND METHODS

Fifty female Sprague-Dawley (SD) rats were randomly divided into five groups including sham operation group, model group, 0.1 g/kg estradiol valerate (EV) group as the positive control, low (0.6 g/kg) and high (1.2 g/kg) dosage CHE groups. Biochemical parameter analyses and histopathological experiments were conducted to assess the pharmacodynamic effects. Metabolomic analysis was conducted on serum samples to examine the metabolic profiles, identify potential biomarkers, and elucidate the metabolic pathways associated with CHE in OVX rats.

RESULTS

CHE treatment demonstrated significant anti-osteoporosis activity by regulating serum biochemical markers of bone turnover, improving cancellous bone structure, and reversing the decrease in bone mineral density. Furthermore, the clinical equivalent dose group (CHL) achieved superior overall outcomes. The main interventions of CHE on OVX rats involved the modulation of several key pathways, including steroid hormone biosynthesis, arachidonic acid metabolism, tyrosine and tryptophan metabolism, biotin metabolism, regulation of TRP channels by inflammatory mediators, primary bile acid biosynthesis, regulation of lipolysis in adipocytes, and bile secretion. 23 potential efficacy-related biomarkers within the metabolic network were identified. Among them, long-chain unsaturated fatty acids (eg. DHA and docosapentaenoic acid), steroid hormones, amino acids and carbohydrates were strongly correlated with bone resorption and formation markers. Additionally, it was observed four pathways (nucleotide, carbon, amino acid, and lipid metabolism) were implicated in the effects of CHE.

CONCLUSION

This study demonstrates that CHE improves bone loss in PMOP mainly through regulating lipid metabolism pathways, which provides an evidence base for CHE treatment of PMOP.

The Weight-Based Feature Selection (WBFS) Algorithm Classifies Lung Cancer Subtypes Using Proteomic Data

Feature selection plays an important role in improving the performance of classification or reducing the dimensionality of high-dimensional datasets, such as high-throughput genomics/proteomics data in bioinformatics. As a popular approach with computational efficiency and scalability, information theory has been widely incorporated into feature selection. In this study, we propose a unique weight-based feature selection (WBFS) algorithm that assesses selected features and candidate features to identify the key protein biomarkers for classifying lung cancer subtypes from The Cancer Proteome Atlas (TCPA) database and we further explored the survival analysis between selected biomarkers and subtypes of lung cancer. Results show good performance of the combination of our WBFS method and Bayesian network for mining potential biomarkers. These candidate signatures have valuable biological significance in tumor classification and patient survival analysis. Taken together, this study proposes the WBFS method that helps to explore candidate biomarkers from biomedical datasets and provides useful information for tumor diagnosis or therapy strategies.

Untargeted metabolomics unravel serum metabolic alterations in smokers with hypertension

Background: Cigarette smoking is an important environmental risk factor for cardiovascular events of hypertension (HTN). Existing studies have provided evidence supporting altered gut microbiota by cigarette smoking, especially in hypertensive patients. Metabolic biomarkers play a central role in the functional potentials of the gut microbiome but are poorly characterized in hypertensive smokers. To explore whether serum metabolomics signatures and compositions of HTN patients were varied in smokers, and investigate their connecting relationship to gut microbiota, the serum metabolites were examined in untreated hypertensive patients using untargeted liquid chromatography-mass spectrometry (LC/MS) analysis. Results: A dramatic difference and clear separation in community features of circulating metabolomics members were seen in smoking HTN patients compared with the non-smoking controls, according to partial least squares discrimination analysis (PLS-DA) and orthogonal partial least squares discrimination analysis (OPLS-DA). Serum metabolic profiles and compositions of smoking patients with HTN were significantly distinct from the controls, and were characterized by enrichment of 12-HETE, 7-Ketodeoxycholic acid, Serotonin, N-Stearoyl tyrosine and Deoxycholic acid glycine conjugate, and the depletion of Tetradecanedioic acid, Hippuric acid, Glyceric acid, 20-Hydroxyeicosatetraenoic acid, Phenylpyruvic acid and Capric acid. Additionally, the metabolome displayed prominent functional signatures, with a majority proportion of the metabolites identified to be discriminating between groups distributed in Starch and sucrose metabolism, Caffeine metabolism, Pyruvate metabolism, Glycine, serine and threonine metabolism, and Phenylalanine metabolic pathways. Furthermore, the observation of alterations in metabolites associated with intestinal microbial taxonomy indicated that these metabolic members might mediate the effects of gut microbiome on the smoking host. Indeed, the metabolites specific to smoking HTNs were strongly organized into co-abundance networks, interacting with an array of clinical parameters, including uric acid (UA), low-denstiy lipoprotein cholesterol (LDLC) and smoking index. Conclusions: In conclusion, we demonstrated disparate circulating blood metabolome composition and functional potentials in hypertensive smokers, showing a linkage between specific metabolites in blood and the gut microbiome.