Dataset Information

First qualification study of serum biomarkers as indicators of total body burden of osteoarthritis.

ABSTRACT:

Background

Osteoarthritis (OA) is a debilitating chronic multijoint disease of global proportions. OA presence and severity is usually documented by x-ray imaging but whole body imaging is impractical due to radiation exposure, time and cost. Systemic (serum or urine) biomarkers offer a potential alternative method of quantifying total body burden of disease but no OA-related biomarker has ever been stringently qualified to determine the feasibility of this approach. The goal of this study was to evaluate the ability of three OA-related biomarkers to predict various forms or subspecies of OA and total body burden of disease.

Methodology/principal findings

Female participants (461) with clinical hand OA underwent radiography of hands, hips, knees and lumbar spine; x-rays were comprehensively scored for OA features of osteophyte and joint space narrowing. Three OA-related biomarkers, serum hyaluronan (sHA), cartilage oligomeric matrix protein (sCOMP), and urinary C-telopeptide of type II collagen (uCTX2), were measured by ELISA. sHA, sCOMP and uCTX2 correlated positively with total osteophyte burden in models accounting for demographics (age, weight, height): R(2) = 0.60, R(2) = 0.47, R(2) = 0.51 (all p<10(-6)); sCOMP correlated negatively with total joint space narrowing burden: R(2) = 0.69 (p<10(-6)). Biomarkers and demographics predicted 35-38% of variance in total burden of OA (total joint space narrowing or osteophyte). Joint size did not determine the contribution to the systemic biomarker concentration. Biomarker correlation with disease in the lumbar spine resembled that in the rest of the skeleton.

Conclusions/significance

We have suspected that the correlation of systemic biomarkers with disease has been hampered by the inability to fully phenotype the burden of OA in a patient. These results confirm the hypothesis, revealed upon adequate patient phenotyping, that systemic joint tissue concentrations of several biomarkers can be quantitative indicators of specific subspecies of OA and of total body burden of disease.

SUBMITTER: Kraus VB

PROVIDER: S-EPMC2840035 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature

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Publications

First qualification study of serum biomarkers as indicators of total body burden of osteoarthritis.

Kraus Virginia B VB Kepler Thomas B TB Stabler Thomas T Renner Jordan J Jordan Joanne J

PloS one 20100317 3

<h4>Background</h4>Osteoarthritis (OA) is a debilitating chronic multijoint disease of global proportions. OA presence and severity is usually documented by x-ray imaging but whole body imaging is impractical due to radiation exposure, time and cost. Systemic (serum or urine) biomarkers offer a potential alternative method of quantifying total body burden of disease but no OA-related biomarker has ever been stringently qualified to determine the feasibility of this approach. The goal of this stu ...[more]

PMID: 20305824

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Project description:BackgroundOsteoarthritis (OA) is a slowly developing and debilitating disease, and there are no validated specific biomarkers for its early detection. To improve therapeutic approaches, identification of specific molecules/biomarkers enabling early determination of this disease is needed. This study aimed at identifying, with the use of proteomics/mass spectrometry, novel OA-specific serum biomarkers. As obesity is a major risk factor for OA, we discriminated obesity-regulated proteins to target only OA-specific proteins as biomarkers.MethodsSerum from the Osteoarthritis Initiative cohort was used and divided into 3 groups: controls (n=8), OA-obese (n=10) and OA-non-obese (n=10). Proteins were identified and quantified from the liquid chromatography-tandem mass spectrometry analyses using MaxQuant software. Statistical analysis used the Limma test followed by the Benjamini-Hochberg method. To compare the proteomic profiles, the multivariate unsupervised principal component analysis (PCA) followed by the pairwise comparison was used. To select the most predictive/discriminative features, the supervised linear classification model sparse partial least squares regression discriminant analysis (sPLS-DA) was employed. Validation of three differential proteins was performed with protein-specific assays using plasma from a cohort derived from the Newfoundland Osteoarthritis.ResultsIn total, 509 proteins were identified, and 279 proteins were quantified. PCA-pairwise differential comparisons between the 3 groups revealed that 8 proteins were differentially regulated between the OA-obese and/or OA-non-obese with controls. Further experiments using the sPLS-DA revealed two components discriminating OA from controls (component 1, 9 proteins), and OA-obese from OA-non-obese (component 2, 23 proteins). Proteins from component 2 were considered related to obesity. In component 1, compared to controls, 7 proteins were significantly upregulated by both OA groups and 2 by the OA-obese. Among upregulated proteins from both OA groups, some of them alone would not be a suitable choice as specific OA biomarkers due to their rather non-specific role or their strong link to other pathological conditions. Altogether, data revealed that the protein CRTAC1 appears to be a strong OA biomarker candidate. Other potential new biomarker candidates are the proteins FBN1, VDBP, and possibly SERPINF1. Validation experiments revealed statistical differences between controls and OA for FBN1 (p=0.044) and VDPB (p=0.022), and a trend for SERPINF1 (p=0.064).ConclusionOur study suggests that 4 proteins, CRTAC1, FBN1, VDBP, and possibly SERPINF1, warrant further investigation as potential new biomarker candidates for the whole OA population.

Dataset Information

First qualification study of serum biomarkers as indicators of total body burden of osteoarthritis.

Background

Methodology/principal findings

Conclusions/significance

Publications

First qualification study of serum biomarkers as indicators of total body burden of osteoarthritis.

Similar Datasets

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