Dataset Information

Discovery of an autoantibody signature for the early diagnosis of knee osteoarthritis: data from the Osteoarthritis Initiative.

ABSTRACT: OBJECTIVE:To find autoantibodies (AAbs) in serum that could be useful to predict incidence of radiographic knee osteoarthritis (KOA). DESIGN:A Nucleic-acid Programmable Protein Arrays (NAPPA) platform was used to screen AAbs against 2125 human proteins in sera at baseline from participants free of radiographic KOA belonging to the incidence and non-exposed subcohorts of the Osteoarthritis Initiative (OAI) who developed or not, radiographic KOA during a follow-up period of 96 months. NAPPA-ELISA were performed to analyse reactivity against methionine adenosyltransferase two beta (MAT2?) and verify the results in 327 participants from the same subcohorts. The association of MAT2?-AAb levels with KOA incidence was assessed by combining several robust biostatistics analysis (logistic regression, Receiver Operating Characteristic and Kaplan-Meier curves). The proposed prognostic model was replicated in samples from the progression subcohort of the OAI. RESULTS:In the screening phase, six AAbs were found significantly different at baseline in samples from incident compared with non-incident participants. In the verification phase, high levels of MAT2?-AAb were significantly associated with the future incidence of KOA and with an earlier development of the disease. The incorporation of this AAb in a clinical model for the prognosis of incident radiographic KOA significantly improved the identification/classification of patients who will develop the disorder. The usefulness of the model to predict radiographic KOA was confirmed on a different OAI subcohort. CONCLUSIONS:The measurement of AAbs against MAT2? in serum might be highly useful to improve the prediction of OA development, and also to estimate the time to incidence.

SUBMITTER: Camacho-Encina M

PROVIDER: S-EPMC6900252 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Discovery of an autoantibody signature for the early diagnosis of knee osteoarthritis: data from the Osteoarthritis Initiative.

Camacho-Encina María M Balboa-Barreiro Vanesa V Rego-Perez Ignacio I Picchi Florencia F VanDuin Jennifer J Qiu Ji J Fuentes Manuel M Oreiro Natividad N LaBaer Joshua J Ruiz-Romero Cristina C Blanco Francisco J FJ

Annals of the rheumatic diseases 20190830 12

<h4>Objective</h4>To find autoantibodies (AAbs) in serum that could be useful to predict incidence of radiographic knee osteoarthritis (KOA).<h4>Design</h4>A Nucleic-acid Programmable Protein Arrays (NAPPA) platform was used to screen AAbs against 2125 human proteins in sera at baseline from participants free of radiographic KOA belonging to the incidence and non-exposed subcohorts of the Osteoarthritis Initiative (OAI) who developed or not, radiographic KOA during a follow-up period of 96 month ...[more]

PMID: 31471297

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Project description:Background The methods for assessing knee osteoarthritis (OA) do not provide enough comprehensive information to make robust and accurate outcome predictions. Purpose To develop a deep learning (DL) prediction model for risk of OA progression by using knee radiographs in patients who underwent total knee replacement (TKR) and matched control patients who did not undergo TKR. Materials and Methods In this retrospective analysis that used data from the OA Initiative, a DL model on knee radiographs was developed to predict both the likelihood of a patient undergoing TKR within 9 years and Kellgren-Lawrence (KL) grade. Study participants included a case-control matched subcohort between 45 and 79 years. Patients were matched to control patients according to age, sex, ethnicity, and body mass index. The proposed model used a transfer learning approach based on the ResNet34 architecture with sevenfold nested cross-validation. Receiver operating characteristic curve analysis and conditional logistic regression assessed model performance for predicting probability and risk of TKR compared with clinical observations and two binary outcome prediction models on the basis of radiographic readings: KL grade and OA Research Society International (OARSI) grade. Results Evaluated were 728 participants including 324 patients (mean age, 64 years ± 8 [standard deviation]; 222 women) and 324 control patients (mean age, 64 years ± 8; 222 women). The prediction model based on DL achieved an area under the receiver operating characteristic curve (AUC) of 0.87 (95% confidence interval [CI]: 0.85, 0.90), outperforming a baseline prediction model by using KL grade with an AUC of 0.74 (95% CI: 0.71, 0.77; P < .001). The risk for TKR increased with probability that a person will undergo TKR from the DL model (odds ratio [OR], 7.7; 95% CI: 2.3, 25; P < .001), KL grade (OR, 1.92; 95% CI: 1.17, 3.13; P = .009), and OARSI grade (OR, 1.20; 95% CI: 0.41, 3.50; P = .73). Conclusion The proposed deep learning model better predicted risk of total knee replacement in osteoarthritis than did binary outcome models by using standard grading systems. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Richardson in this issue.

Project description:OBJECTIVE:Osteoarthritis (OA) is a disease with a substantial public health burden. Quantitative assessments of periarticular bone may be a biomarker capable of monitoring early disease progression. The purpose of this study was to evaluate whether measures of periarticular bone associate with longitudinal structural progression. METHODS:We conducted a 12-18 months longitudinal study using the Osteoarthritis Initiative (OAI). Participants received knee dual-energy x-ray absorptiometry (DXA), trabecular magnetic resonance (MR) imaging, and x-rays. Knee DXAs generated proximal tibial medial:lateral periarticular bone mineral density (paBMD) measures. Proximal tibial trabecular MR images were assessed for trabecular morphometry: apparent bone volume fraction (BVF), trabecular number, thickness, and spacing. Weight-bearing x-rays were assessed for medial tibiofemoral joint space narrowing (JSN). Chi-squared analyses assessed whether periarticular bone measures were predictive of worsening medial tibiofemoral JSN, adjusted for age, sex, and BMI. RESULTS:In all, 444 participants, mean age 64.2 ± 9.2 years, BMI 29.5 ± 4.6kg/m2, and 52% male at baseline. Medial JSN (radiographic progression) occurred in 40 participants (9%). Higher baseline medial:lateral paBMD, apparent BVF, trabecular number and thickness, and lower baseline and decreased trabecular spacing were all associated with more progression of JSN in the medial compartment. From lowest to highest baseline medial:lateral paBMD quartile groups, 2%, 5%, 11%, and 18% had medial JSN progression, respectively, between the 36- and 48-month visits, p-values = 0.001 and 0.002 unadjusted and adjusted. The rate of change in medial:lateral paBMD, apparent BVF, and spacing were associated with more medial JSN. For rate of medial:lateral paBMD change from lowest to highest quartile, the proportion of each group that experienced medial JSN progression were 5%, 5%, 11%, and 18%, with an unadjusted and adjusted p-value of 0.005. CONCLUSION:Baseline and most rates of periarticular bone change associate with knee OA structural progression, highlighting the close relationship between subchondral bone and JSN. Future studies should focus on developing these measures as predictive and pathophysiological biomarkers, and evaluating their deployment in clinical trials testing bone-targeted therapeutics.

Project description:BackgroundEarly diagnosis of knee osteoarthritis (OA) is important in managing this disease, but such an early diagnostic tool is still lacking in clinical practice. The purpose of this study was to develop diagnostic models for early stage knee OA based on the first 2-year clinical course after the patient's initial presentation in primary care and to identify whether these course factors had additive discriminative value over baseline factors.MethodsWe extracted eligible patients' clinical and radiographic data from the CHECK cohort and formed the first 2-year course factors according to the factors' changes over the 2 years. Clinical expert consensus-based diagnosis, which was made via evaluating patients' 5- to 10-year follow-up data, was used as the outcome factor. Four models were developed: model 1, included clinical course factors only; model 2, included clinical and radiographic course factors; model 3, clinical baseline factors + clinical course factors; and model 4, clinical and radiographic baseline factors + clinical and radiographic course factors. All the models were built by a generalized estimating equation with a backward selection method. Area under the receiver operating characteristic curve (AUC) and its 95% confidence interval (CI) were calculated for assessing model discrimination. Delong's method compared AUCs.ResultsSeven hundred sixty-one patients with 1185 symptomatic knees were included in this study. Thirty-seven percent knees were diagnosed as OA at follow-up. Model 1 contained 6 clinical course factors; model 2: 6 clinical and 3 radiographic course factors; model 3: 6 baseline clinical factors combined with 5 clinical course factors; and model 4: 4 clinical and 1 radiographic baseline factors combined with 5 clinical and 3 radiographic course factors. Model discriminations are as follows: model 1, AUC 0.70 (95% CI 0.67-0.74); model 2, 0.74 (95% CI 0.71-0.77); model 3, 0.77 (95% CI 0.74-0.80); and model 4, 0.80 (95% CI 0.77-0.82). AUCs of model 3 and model 4 were slightly but significantly higher than corresponding baseline-factor models (model 3 0.77 vs 0.75, p = 0.031; model 4 0.80 vs 0.76, p = 0.003).ConclusionsFour diagnostic models were developed with "fair" to "good" discriminations. First 2-year course factors had additive discriminative value over baseline factors.

Dataset Information

Discovery of an autoantibody signature for the early diagnosis of knee osteoarthritis: data from the Osteoarthritis Initiative.

Publications

Discovery of an autoantibody signature for the early diagnosis of knee osteoarthritis: data from the Osteoarthritis Initiative.

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