ArticleiScience2020
Multi-omics Data Integration for Identifying Osteoporosis Biomarkers and Their Biological Interaction and Causal Mechanisms.
Article in iScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- Metabolomic biomarkers of low BMD: a systematic review.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2021Pooled it
- Robust and Interpretable Metagenomic Modeling Through Structure-Aware Multi-View Learning and Attribution-Guided Biological Insight.Research square · 2026Article
- A Large-Scale Multi-omics Polygenic Risk Score Analysis Identified Candidate Biomarkers Associated with Heel Bone Mineral Density.Calcified tissue international · 2026Article
- Review
- Integrative Harmonization of Phenotypic and Genomic Data Improves Bone Mineral Density Prediction in Multi-Study Osteoporosis Research.Genetic epidemiology · 2026Article
- Repurposing Acebutolol for Osteoporosis Treatment: Insights From Multi-Omics and Multi-Modal Data Analysis.Clinical pharmacology and therapeutics · 2025Article
- Genetically informed causal links between gut microbiota and bone mass: pleiotropy and metabolic mediation.Nature communications · 2025Article
- PF-DAformer: Proximal Femur Segmentation via Domain Adaptive Transformer for Dual-Center QCT.ArXiv · 2025Article
- Emerging applications of feature selection in osteoporosis research: from biomarker discovery to clinical decision support.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2025Review
- N-Lactoyl Amino Acids: Emerging Biomarkers in Metabolism and Disease.Diabetes/metabolism research and reviews · 2025Review
- Mettl7a alleviated bone loss in osteoporosis mice by targeting the O-GlcNAcylation of Bsp via m6A methylation.Stem cells translational medicine · 2025Article
- Beyond Bone Loss: A Biology Perspective on Osteoporosis Pathogenesis, Multi-Omics Approaches, and Interconnected Mechanisms.Biomedicines · 2025Review
- Network-based multi-omics integrative analysis methods in drug discovery: a systematic review.BioData mining · 2025Review
- Molecular Biology: Challenges and Opportunities.Current issues in molecular biology · 2025Article
- Genome-scale models in human metabologenomics.Nature reviews. Genetics · 2025Review
- Comprehensive Analysis of the Role of Metabolic Features in Osteoporosis: A Multi-Omics Analysis.International journal of general medicine · 2025Article
- From Genomics to Metabolomics: Molecular Insights into Osteoporosis for Enhanced Diagnostic and Therapeutic Approaches.Biomedicines · 2024Review
- In-silico analysis predicts disruption of normal angiogenesis as a causative factor in osteoporosis pathogenesis.BMC genomic data · 2024Article
- Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data.Computers in biology and medicine · 2024Article
- Multi Omics Applications in Biological Systems.Current issues in molecular biology · 2024Review
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 2 countries.
Funding
Abstract
Osteoporosis is characterized by low bone mineral density (BMD). The advancement of high-throughput technologies and integrative approaches provided an opportunity for deciphering the mechanisms underlying osteoporosis. Here, we generated genomic, transcriptomic, methylomic, and metabolomic datasets from 119 subjects with high (n = 61) and low (n = 58) BMDs. By adopting sparse multiple discriminative canonical correlation analysis, we identified an optimal multi-omics biomarker panel with 74 differentially expressed genes (DEGs), 75 differentially methylated CpG sites (DMCs), and 23 differential metabolic products (DMPs). By linking genetic data, we identified 199 targeted BMD-associated expression/methylation/metabolite quantitative trait loci (eQTLs/meQTLs/metaQTLs). The reconstructed networks/pathways showed extensive biomarker interactions, and a substantial proportion of these biomarkers were enriched in RANK/RANKL, MAPK/TGF-β, and WNT/β-catenin pathways and G-protein-coupled receptor, GTP-binding/GTPase, telomere/mitochondrial activities that are essential for bone metabolism. Five biomarkers (FADS2, ADRA2A, FMN1, RABL2A, SPRY1) revealed causal effects on BMD variation. Our study provided an innovative framework and insights into the pathogenesis of osteoporosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.