ArticleArthritis & rheumatology (Hoboken, N.J.)2023
Peripheral Blood DNA Methylation-Based Machine Learning Models for Prediction of Knee Osteoarthritis Progression: Biologic Specimens and Data From the Osteoarthritis Initiative and Johnston County Osteoarthritis Project.
Article in Arthritis & rheumatology (Hoboken, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Predictive Value of Machine Learning in Knee Osteoarthritis Progression: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Advances in heterogeneity and classification of osteoarthritis.Bone research · 2026Review
- Multimodal Peripheral Blood Immunophenotyping and Single-Cell Epigenetic Analysis of Knee Osteoarthritis Reveals Expansion and Epigenetic Dysregulation of Treg Cell and Nonclassical Monocyte Populations.Arthritis & rheumatology (Hoboken, N.J.) · 2026Article
- Review
- Borderline acceleration in blood epigenetic age of unclear biological importance in knee osteoarthritis progression: Specimens and data from the Osteoarthritis Initiative and Johnston County Osteoarthritis Project.Osteoarthritis and cartilage · 2026Article
- Integrated analysis of bulk RNA and single-cell RNA sequencing data reveals potential biomarkers and immune infiltrates associated with N7-methylguanosine in osteoarthritis.International journal of surgery (London, England) · 2026Article
- Peripheral blood DNA methylation changes are predictive of incident radiographic osteoarthritis development: Data and biospecimens from the osteoarthritis initiative.Osteoarthritis and cartilage open · 2025Article
- DNA methylation and machine learning: challenges and perspective toward enhanced clinical diagnostics.Clinical epigenetics · 2025Review
- From mechanism to medicine: The progress and potential of epigenetics in osteoarthritis.Osteoarthritis and cartilage open · 2025Review
- Advancing osteoarthritis research: the role of AI in clinical, imaging and omics fields.Bone research · 2025Review
- Progress in multi-omics studies of osteoarthritis.Biomarker research · 2025Review
- Research on the diagnosis model of osteoarthritis based on methylation-related genes using machine learning algorithms.Frontiers in genetics · 2025Article
- Machine Learning Approaches to the Prediction of Osteoarthritis Phenotypes and Outcomes.Current rheumatology reports · 2023Review
- The Role of Genetics and Epigenetic Regulation in the Pathogenesis of Osteoarthritis.International journal of molecular sciences · 2023Review
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveThe lack of accurate biomarkers to predict knee osteoarthritis (OA) progression is a key unmet need in OA clinical research. The objective of this study was to develop baseline peripheral blood epigenetic biomarker models to predict knee OA progression.
methodsGenome-wide buffy coat DNA methylation patterns from 554 individuals from the Osteoarthritis Biomarkers Consortium (OABC) were determined using Illumina Infinium MethylationEPIC 850K arrays. Data were divided into model development and validation sets, and machine learning models were trained to classify future OA progression by knee pain, radiographic imaging, knee pain plus radiographic imaging, and any progression (pain, radiographic, or both). Parsimonious models using the top 13 CpG sites most frequently selected during development were tested on independent samples from participants in the Johnston County Osteoarthritis (JoCo OA) Project (n = 128) and a previously published Osteoarthritis Initiative (OAI) data set (n = 55).
resultsFull models accurately classified future radiographic-only progression (mean ± SEM accuracy 87 ± 0.8%, area under the curve [AUC] 0.94 ± 0.004), pain-only progression (accuracy 89 ± 0.9%, AUC 0.97 ± 0.004), pain plus radiographic progression (accuracy 72 ± 0.7%, AUC 0.79 ± 0.006), and any progression (accuracy 78 ± 0.4%, AUC 0.86 ± 0.004). Pain-only and radiographic-only progressors were not distinguishable (mean ± SEM accuracy 58 ± 1%, AUC 0.62 ± 0.001). Parsimonious models showed similar performance and accurately classified future radiographic progressors in the OABC cohort and in both validation cohorts (mean ± SEM accuracy 80 ± 0.3%, AUC 0.88 ± 0.003 [using JoCo OA Project data], accuracy 80 ± 0.8%, AUC 0.89 ± 0.002 [using previous OAI data]).
conclusionOur data suggest that pain and structural progression share similar early systemic immune epigenotypes. Further studies should focus on evaluating the pathophysiologic consequences of differential DNA methylation and peripheral blood cell epigenotypes in individuals with knee OA.
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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.