TY - JOUR
T1 - Peripheral Blood DNA Methylation–Based Machine Learning Models for Prediction of Knee Osteoarthritis Progression
T2 - Biologic Specimens and Data From the Osteoarthritis Initiative and Johnston County Osteoarthritis Project
AU - Dunn, Christopher M.
AU - Sturdy, Cassandra
AU - Velasco, Cassandra
AU - Schlupp, Leoni
AU - Prinz, Emmaline
AU - Izda, Vladislav
AU - Arbeeva, Liubov
AU - Golightly, Yvonne M.
AU - Nelson, Amanda E.
AU - Jeffries, Matlock A.
N1 - Funding Information:
The Johnston County Osteoarthritis Project is funded in part by the Association of Schools of Public Health (grants S043, S1734, S3486), Centers for Disease Control and Prevention (grants U01 DP003206 and U01 DP006266), and National Institutes of Health/National Institute of Arthritis and Musculoskeletal and Skin Diseases (grants P60AR30701, P60AR049465, P60AR064166, and P30AR072580).
Funding Information:
Supported by the NIH (grants K08‐AR‐070891, P20‐GM‐125528, R61‐AR‐078075, and R01AR076440), along with the Congressionally Directed Medical Research Program (grant PR191652). The OAI is a public‐private partnership comprising 5 contracts (N01‐AR‐2‐2258, N01‐AR‐2‐2259, N01‐AR‐2‐2260, N01‐AR‐2‐2261, and N01‐AR‐2‐2262) funded by the NIH and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories, Novartis Pharmaceuticals Corporation, GlaxoSmithKline, and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health.
Publisher Copyright:
© 2022 American College of Rheumatology.
PY - 2023/1
Y1 - 2023/1
N2 - Objective: The 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. Methods: Genome-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). Results: Full 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]). Conclusion: Our 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. (Figure presented.).
AB - Objective: The 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. Methods: Genome-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). Results: Full 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]). Conclusion: Our 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. (Figure presented.).
UR - http://www.scopus.com/inward/record.url?scp=85143426372&partnerID=8YFLogxK
U2 - 10.1002/art.42316
DO - 10.1002/art.42316
M3 - Article
C2 - 36411273
AN - SCOPUS:85143426372
SN - 2326-5191
VL - 75
SP - 28
EP - 40
JO - Arthritis and Rheumatology
JF - Arthritis and Rheumatology
IS - 1
ER -