Comprehensive Analysis of the Role of Metabolic Features in Osteoporosis: A Multi-Omics Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40453197", "kind": "science-record", "revision": 4, "content_hash": "sha256:0a235359eccbe9aa59581f68abfdfd3b13f04201d3943cc655417ee4ff050536", "created_at": "2026-11-01T20:30:35Z", "updated_at": "2026-11-01T22:56:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive Analysis of the Role of Metabolic Features in Osteoporosis: A Multi-Omics Analysis.", "doi": "10.2147/IJGM.S515717", "pmid": "40453197", "authors": ["Shengjia Chang", "Weiwei Tao", "Pengwen Shi", "Huashan Wu", "Hongjun Liu", "Junjie Xu", "Jianghua Chen", "Jianfei Zhu"], "journal": "International journal of general medicine", "year": 2025, "abstract": "Purpose: This study aims to comprehensively explore the metabolic features related to the pathogenesis of osteoporosis (OP) through multi-omics analysis strategy.\nPatients and Methods: Gene expression profiles of OP patients (GSE56815) were downloaded from GEO, and metabolism-related genes (MRGs) were extracted. Plasma samples from 45 OP patients and 18 healthy controls (CON) were collected for metabolomics. We predicted miRNA and transcription factors (TFs) regulating the expression of MRGs on public databases ENCORI and JASPAR, and analyzed the expression levels of target miRNAs using miRNA sequencing of femoral tissues from 7 samples (OP:CON=4:3). Three machine learning algorithms were used to evaluate the diagnostic potential of metabolic signatures for OP.\nResults: A total of 402 significantly differentially expressed MRGs (DEMRGs) were identified in the transcriptome, and these DEMRGs were enriched in 11 metabolic pathways (P<0.05). Metabolomics identified 119 differential plasma metabolites, enriched in 5 metabolic pathways (P<0.05). Purine metabolism, Tryptophan metabolism, and Tyrosine metabolism were identified as key metabolic pathways and were significantly enriched in DEMRGs. Femoral miRNA sequencing found 124 differentially expressed miRNAs, with 23 regulating key metabolic pathway gene expression (P<0.05). Additionally, 13 differentially expressed TFs were predicted to regulate the expression levels of these 23 miRNAs. Finally, three MRGs and one plasma metabolite were selected based on the machine learning algorithm, with AUC of 0.782, 0.714, 0.772 and 0.836, respectively. The diagnostic performance of these metabolic features was better than that of traditional bone metabolism biochemical markers.\nConclusion: This multi-omics study comprehensively explores the metabolic landscape in OP progression, highlighting the central role of metabolic features in the disease. The constructed multi-omics regulatory network aids in understanding the molecular mechanisms of metabolic features in OP progression.", "claims": [{"id": "claim-method", "statement": "Differential expression analysis was conducted using the Limma software, defining p < 0.05 and |log2FC|≧1 as significantly differentially expressed miRNAs. Differential expression gene analysis was conducted using the “limma” R package, defining genes with P<0.05 as differentially expressed metabolic-related genes (DEMRGs). In this study, we employed three machine learning algorithms—Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM)—to comprehensively explore metabolic feature biomarkers from MRGs and plasma metabolites.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40453197", "locator": "sections[7] \"Differential Expression Analysis of miRNAs\" paragraph 2", "quote": "Differential expression analysis was conducted using the Limma software, defining p < 0.05 and |log2FC|≧1 as significantly differentially expressed miRNAs."}, {"asset": "source:pmid:40453197", "locator": "sections[8] \"Extraction and Analysis of MRGs\" paragraph 2", "quote": "Differential expression gene analysis was conducted using the “limma” R package, defining genes with P<0.05 as differentially expressed metabolic-related genes (DEMRGs)."}, {"asset": "source:pmid:40453197", "locator": "sections[10] \"Machine Learning-Based Selection of Metabolic Features Biomarkers\" paragraph 2", "quote": "In this study, we employed three machine learning algorithms—Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM)—to comprehensively explore metabolic feature biomarkers from MRGs and plasma metabolites."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40453197", "locator": "sections[20] \"Data Sharing Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Machine-learning analysis selected three metabolic-related genes and one plasma metabolite with respective AUCs of 0.782, 0.714, 0.772, and 0.836 for osteoporosis diagnosis.", "kind": "quantitative", "target": {"value": 0.782, "unit": "auc", "tolerance": null, "source": "Finally, three MRGs and one plasma metabolite were selected based on the machine learning algorithm, with AUC of 0.782, 0.714, 0.772 and 0.836, respectively."}, "evidence": [{"asset": "source:pmid:40453197", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Finally, three MRGs and one plasma metabolite were selected based on the machine learning algorithm, with AUC of 0.782, 0.714, 0.772 and 0.836, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Machine-learning analysis selected three metabolic-related genes and one plasma metabolite with respective AUCs of 0.782, 0.714, 0.772, and 0.836 for osteoporosis diagnosis.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40453197"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE56815"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE56815"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40453197", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/IJGM.S515717", "sha256": "sha256:4046e659767a492f8d32d4f3800b0243070a066ecfa7e5657f19d2eb8c0437f2", "bytes": 35284, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/IJGM.S515717", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12124299", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE56815", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56815", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE56815", "version": null, "note": "The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/Supplementary Material. 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Differential expression gene analysis was conducted using the “limma” R package, defining genes with P<0.05 as differentially expressed metabolic-related genes (DEMRGs). In this study, we employed three machine learning algorithms—Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Support Vector Machine (SVM)—to comprehensively explore metabolic feature biomarkers from MRGs and plasma metabolites."}}, {"id": "method-2", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "Random Forest"}}, {"id": "method-3", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "Support Vector Machine"}}, {"id": "method-4", "family": "correlation_analysis", "specific": "spearman", "label": "Correlation analysis", "dsl_operations": ["spearman_rho"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Spearman correlation"}}, {"id": "method-5", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "GO enrichment"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "cross_validate", "expected_value": 0.782, "unit": "auc", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. 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Am"], "order_basis": "paper_text_order:925", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Differential expression analysis was conducted using the Limma software, defining p < 0.05 and |log2FC|≧1 as significantly differentially expressed miRNAs. Differential expression gene analysis was conducted using the “limma” R package, defining genes with P<0.05 as differentially expressed metabolic-related genes (DEMRGs). 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