Peripheral blood DNA methylation-based machine learning models for prediction of knee osteoarthritis progression: biospecimens and data from the Osteoarthritis Initiative and Johnston County Osteoarthritis Project

Details

• Alternative Title:
  Arthritis Rheumatol
• Personal Author:
  Dunn, Christopher M. ; Sturdy, Cassandra ; Velasco, Cassandra ; Schlupp, Leoni ; Prinz, Emmaline ; Izda, Vladislav ; Arbeeva, Liubov ; Golightly, Yvonne M. ; Nelson, Amanda E. ; Jeffries, Matlock A.
• Description:
  Objective:
  
  The lack of accurate biomarkers to predict knee osteoarthritis (OA) progression is a key unmet need in the OA field. The objective of this study was to develop baseline peripheral blood epigenetic biomarker models to predict knee OA progression.
  
  Methods:
  
  Genome-wide buffy coat DNA methylation patterns from the Osteoarthritis Biomarkers Consortium (OABC, n=554) were determined using Illumina MethylationEPIC arrays. Data were divided into development and validation sets and machine learning models were trained to classify future knee pain, radiographic, dual (pain + radiographic), and any (pain, radiographic, or dual) progression. Parsimonious models, using the top 13 CpGs most frequently selected during development, were tested on independent samples from participants in the Johnston County Osteoarthritis Project (JoCoOA, n=141) and a previously published Osteoarthritis Initiative dataset (OAI, n=54).
  
  Results:
  
  Full models accurately classified future radiographic (accuracy 87±0.8%, AUC=0.94±0.004, mean±SEM), pain (89±0.9%, 0.97±0.004), dual (72±0.7%, 0.79±0.006), and any progression (78±0.4%, 0.86±0.004). Pain-only and radiographic-only progressors were not distinguishable (accuracy 58±1%, AUC=0.62±0.001). Parsimonious models showed similar performance and accurately classified future radiographic progressors in OABC and in both validation cohorts (JoCoOA: accuracy 80±0.3%, AUC=0.88±0.003, OAI: accuracy 80±0.8%, AUC=0.89±0.002).
  
  Conclusions:
  
  Herein, we developed peripheral blood-based DNA methylation models to predict knee OA progression in the OABC cohort and validated our findings in two independent cohorts. Our data suggest that pain and structural progression share similar early systemic immune epigenotypes. Further work should focus on evaluating the pathophysiological consequences of differential DNA methylation and peripheral blood cell epigenotypes in individuals with knee OA.
  
  
• Subjects:
  Biological Products Biomarkers Disease Progression DNA Methylation Humans Knee Joint Osteoarthritis, Knee Pain
• Source:
  Arthritis Rheumatol. 75(1):28-40
• Pubmed ID:
  36411273
• Pubmed Central ID:
  PMC9797424
• Document Type:
  Journal Article
• Funding:
  U01 DP006266/DP/NCCDPHP CDC HHSUnited States/ ; P60 AR030701/AR/NIAMS NIH HHSUnited States/ ; U01 DP003206/DP/NCCDPHP CDC HHSUnited States/ ; P20GM125528/GM/NIGMS NIH HHSUnited States/ ; K08 AR070891/AR/NIAMS NIH HHSUnited States/ ; P60 AR049465/AR/NIAMS NIH HHSUnited States/ ; R61 AR078075/AR/NIAMS NIH HHSUnited States/ ; R01 AR076440/AR/NIAMS NIH HHSUnited States/ ; P60 AR064166/AR/NIAMS NIH HHSUnited States/ ; P30 AR072580/AR/NIAMS NIH HHSUnited States/ ; P20 GM125528/GM/NIGMS NIH HHSUnited States/
• Volume:
  75
• Issue:
  1
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:684812825019c99f123ff855cdd9176258e16865de8e5a8cde15074e0109dcf6
• Download URL:
  https://stacks.cdc.gov/view/cdc/140160/cdc_140160_DS1.pdf
• File Type:
  [PDF - 1.04 MB ]