Dataset Information

Predictive validity of biochemical biomarkers in knee osteoarthritis: data from the FNIH OA Biomarkers Consortium.

ABSTRACT: To investigate a targeted set of biochemical biomarkers as predictors of clinically relevant osteoarthritis (OA) progression.Eighteen biomarkers were measured at baseline, 12?months (M) and 24?M in serum (s) and/or urine (u) of cases (n=194) from the OA initiative cohort with knee OA and radiographic and persistent pain worsening from 24 to 48?M and controls (n=406) not meeting both end point criteria. Primary analyses used multivariable regression models to evaluate the association between biomarkers (baseline and time-integrated concentrations (TICs) over 12 and 24?M, transposed to z values) and case status, adjusted for age, sex, body mass index, race, baseline radiographic joint space width, Kellgren-Lawrence grade, pain and pain medication use. For biomarkers with adjusted p<0.1, the c-statistic (area under the curve (AUC)), net reclassification index and the integrated discrimination improvement index were used to further select for hierarchical multivariable discriminative analysis and to determine the most predictive and parsimonious model.The 24?M TIC of eight biomarkers significantly predicted case status (ORs per 1 SD change in biomarker): sCTXI 1.28, sHA 1.22, sNTXI 1.25, uC2C-HUSA 1.27, uCTXII, 1.37, uNTXI 1.29, uCTXI? 1.32, uCTXI? 1.27. 24 M TIC of uCTXII (1.47-1.72) and uC2C-Human Urine Sandwich Assay (HUSA) (1.36-1.50) both predicted individual group status (pain worsening, joint space loss and their combination). The most predictive and parsimonious combinatorial model for case status consisted of 24 M TIC uCTXII, sHA and sNTXI (AUC 0.667 adjusted). Baseline uCTXII and uCTXI? both significantly predicted case status (OR 1.29 and 1.20, respectively).Several systemic candidate biomarkers hold promise as predictors of pain and structural worsening of OA.

SUBMITTER: Kraus VB

PROVIDER: S-EPMC5851287 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Predictive validity of biochemical biomarkers in knee osteoarthritis: data from the FNIH OA Biomarkers Consortium.

Kraus Virginia Byers VB Collins Jamie E JE Hargrove David D Losina Elena E Nevitt Michael M Katz Jeffrey N JN Wang Susanne X SX Sandell Linda J LJ Hoffmann Steven C SC Hunter David J DJ

Annals of the rheumatic diseases 20160613 1

<h4>Objective</h4>To investigate a targeted set of biochemical biomarkers as predictors of clinically relevant osteoarthritis (OA) progression.<h4>Methods</h4>Eighteen biomarkers were measured at baseline, 12 months (M) and 24 M in serum (s) and/or urine (u) of cases (n=194) from the OA initiative cohort with knee OA and radiographic and persistent pain worsening from 24 to 48 M and controls (n=406) not meeting both end point criteria. Primary analyses used multivariable regression models to eva ...[more]

PMID: 27296323

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Project description:ObjectiveKnee osteoarthritis (KOA) is a heterogeneous condition representing a variety of potentially distinct phenotypes. The purpose of this study was to apply innovative machine learning approaches to KOA phenotyping in order to define progression phenotypes that are potentially more responsive to interventions.DesignWe used publicly available data from the Foundation for the National Institutes of Health (FNIH) osteoarthritis (OA) Biomarkers Consortium, where radiographic (medial joint space narrowing of ≥0.7 mm), and pain progression (increase of ≥9 Western Ontario and McMaster Universities Osteoarthritis Index [WOMAC] points) were defined at 48 months, as four mutually exclusive outcome groups (none, both, pain only, radiographic only), along with an extensive set of covariates. We applied distance weighted discrimination (DWD), direction-projection-permutation (DiProPerm) testing, and clustering methods to focus on the contrast (z-scores) between those progressing by both criteria ("progressors") and those progressing by neither ("non-progressors").ResultsUsing all observations (597 individuals, 59% women, mean age 62 years and BMI 31 kg/m2) and all 73 baseline variables available in the dataset, there was a clear separation among progressors and non-progressors (z = 10.1). Higher z-scores were seen for the magnetic resonance imaging (MRI)-based variables than for demographic/clinical variables or biochemical markers. Baseline variables with the greatest contribution to non-progression at 48 months included WOMAC pain, lateral meniscal extrusion, and serum N-terminal pro-peptide of collagen IIA (PIIANP), while those contributing to progression included bone marrow lesions, osteophytes, medial meniscal extrusion, and urine C-terminal crosslinked telopeptide type II collagen (CTX-II).ConclusionsUsing methods that provide a way to assess numerous variables of different types and scalings simultaneously in relation to an outcome of interest enabled a data-driven approach that identified key variables associated with a progression phenotype.

Project description:BackgroundOsteoarthritis (OA) is a leading cause of disability worldwide. However, the existing methods for evaluating OA patients do not provide enough comprehensive information to make reliable predictions of OA progression. This retrospective study aimed to develop prediction nomograms based on MRI cartilage that can predict disease progression of OA.MethodsA total of 600 subjects with mild-to-moderate osteoarthritis from the Foundation for National Institute of Health (FNIH) project of osteoarthritis initiative (OAI). The MRI cartilage parameters of the knee at baseline were measured, and the changes in cartilage parameters at 12- and 24-month follow-up were calculated. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to extract the valuable characteristic parameters at different time points including cartilage thickness, cartilage volume, subchondral bone exposure area and uniform cartilage thickness in different sub regions of the knee, and the MRI cartilage parameters score0, scoreΔ12, and scoreΔ24 at baseline, 12 months, and 24 months were constructed. ScoreΔ12, and scoreΔ24 represent changes between 12 M vs. baseline, and 24 M vs. baseline, respectively. Logistic regression analysis was used to construct the nomogram0, nomogramΔ12, and nomogramΔ24, including MRI-based score and risk factors. The area under curve (AUC) was used to evaluate the differentiation of nomograms in disease progression and subgroup analysis. The calibration curve and Hosmer-Lemeshow (H-L) test were used to verify the calibration of the nomograms. Clinical usefulness of each prediction nomogram was verified by decision curve analysis (DCA). The nomograms with predictive efficacy were analyzed by secondary analysis. Internal verification was assessed using bootstrapping validation.ResultsEach nomogram included cartilage score, KL grade, WOMAC pain score, WOMAC disability score, and minimum joint space width. The AUC of nomogram0, nomogramΔ12, and nomogramΔ24 in predicing the progression of radiology and pain were 0.69, 0.64, and 0.71, respectively. All three nomograms had good calibration. Analysis by DCA showed that the clinical effectiveness of nomogramΔ24 was higher than others. Secondary analysis showed that nomogram0 and nomogramΔ24 were more capable of predicting OA radiologic progression than pain progression.ConclusionNomograms based on MRI cartilage change were useful for predicting the progression of mild to moderate OA.

Project description:OBJECTIVE:To investigate the predictive and concurrent validity of magnetic resonance imaging (MRI)-based cartilage thickness change between baseline (BL) and year-two (Y2) follow-up (predictive validity) and between Y2 and Y4 follow-up (concurrent validity) for symptomatic and radiographic knee osteoarthritis (OA) progression during Y2?Y4. METHODS:777 knees from 777 Osteoarthritis Initiative (OAI) participants (age: 61.3 ± 9.0 years, BMI: 30.1 ± 4.8 kg/m2) with Kellgren Lawrence (KL) grade 1-3 at Y2 (visit before progression interval) had cartilage thickness measurements from 3T MRI at BL, Y2 (n = 777), and Y4 (n = 708). Analysis of covariance and logistic regression were used to assess the association of pain progression (?9 WOMAC units [scale 0-100], n = 205/572 with/without progression) and radiographic progression (?0.7 mm minimum joint space width (mJSW) loss, n = 166/611 with/without progression) between Y2 and Y4 with preceding (BL?Y2) and concurrent (Y2?Y4) change in central medial femorotibial (cMFTC) compartment cartilage thickness. RESULTS:Symptomatic progression was associated with concurrent (Y2?Y4: -305 ± 470 ?m vs -155 ± 346 ?m, Odds ratios (OR) = 1.5 [1.2, 1.7]) but not with preceding cartilage thickness loss in cMFTC (-150 ± 276 ?m vs -151 ± 299 ?m, OR = 0.9 95% CI: [0.8, 1.1]). Radiographic progression, in contrast, was significantly associated with both concurrent (-542 ± 550 ?m vs -98 ± 255 ?m, OR = 3.4 [2.6, 4.3]) and preceding cMFTC thickness loss (-229 ± 355 ?m vs -130 ± 270 ?m, OR = 1.3 [1.1, 1.5]). CONCLUSIONS:These results extend previous reports that did not discern predictive vs concurrent associations of cartilage thickness loss with OA progression. The observed predictive and concurrent validity of cartilage thickness loss for radiographic progression and observed concurrent validity for symptomatic progression provide an important step in qualifying cartilage thickness loss as a biomarker of knee OA progression. CLINICALTRIALS. GOV IDENTIFICATION:NCT00080171.

Project description:ObjectiveTo apply biclustering, a methodology originally developed for analysis of gene expression data, to simultaneously cluster observations and clinical features to explore candidate phenotypes of knee osteoarthritis (KOA) for the first time.MethodsData from the baseline Osteoarthritis Initiative (OAI) visit were cleaned, transformed, and standardized as indicated (leaving 6461 knees with 86 features). Biclustering produced submatrices of the overall data matrix, representing similar observations across a subset of variables. Statistical validation was determined using the novel SigClust procedure. After identifying biclusters, relationships with key outcome measures were assessed, including progression of radiographic KOA, total knee arthroplasty, loss of joint space width, and worsening Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, over 96 months of follow-up.ResultsThe final analytic set included 6461 knees from 3330 individuals (mean age 61 years, mean body mass index 28 kg/m2, 57% women and 86% White). We identified 6 mutually exclusive biclusters characterized by different feature profiles at baseline, particularly related to symptoms and function. Biclusters represented overall better (#1), similar (#2, 3, 6), and poorer (#4, 5) prognosis compared to the overall cohort of knees, respectively. In general, knees in biclusters #4 and 5 had more structural progression (based on Kellgren-Lawrence grade, total knee arthroplasty, and loss of joint space width) but tended to have an improvement in WOMAC pain scores over time. In contrast, knees in bicluster #1 had less incident and progressive KOA, fewer total knee arthroplasties, less loss of joint space width, and stable pain scores compared with the overall cohort.SignificanceWe identified six biclusters within the baseline OAI dataset which have varying relationships with key outcomes in KOA. Such biclusters represent potential phenotypes within the larger cohort and may suggest subgroups at greater or lesser risk of progression over time.

Dataset Information

Predictive validity of biochemical biomarkers in knee osteoarthritis: data from the FNIH OA Biomarkers Consortium.

Publications

Predictive validity of biochemical biomarkers in knee osteoarthritis: data from the FNIH OA Biomarkers Consortium.

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