Abstract 17

Biomarker discovery in osteoarthritis

Sudeepa Bhattacharyya¹, Eric Siegel², David M. Findlay³, Nicola L. Fazzalari⁴, Mellick Chehade³, Susan Neale³, Gary Wittert⁵ and the Florey Adelaide Male Aging Study and Larry J. Suva¹.

¹Departments of Orthopaedic Surgery and ² Biometry, UAMS, Little Rock, AR, Departments of ³Orthopaedics, ⁴Pathology, ⁵Medicine, The University of Adelaide, South Australia, Australia

Purpose: Osteoarthritis (OA) is the most common joint disorder, producing pain and disability that leads eventually to the destruction of articular cartilage. Although many of the risk factors for OA have been defined (genetic factors, BMI, female sex), the etiology of this disease is poorly understood. Recent data, from examination of bone in OA, from MRI and from circulating bone markers, suggest a role for subchondral bone in initiation and progression of OA. Sensitive disease markers, which will permit diagnosis of OA during the early pre-radiologic stages, and which facilitate monitoring of disease progression, are urgently required. In this study, surface enhanced laser desorption/ionization time-of-flight-mass spectroscopy (SELDI TOF MS) was used to screen for specific serum biomarkers in end-stage OA patients presenting for hip replacement surgery.

Methods: Serum samples from 31 OA patients (median age of 67 years) and 30 agematched unaffected patients (median age of 64.5 years) were fractionated based on charge (6 fractions) and SELDI spectra were collected from fraction 1 of each patient, in triplicate, and in three molecular weight ranges [1.5-10 kDa(low), 6.5-30 kDa(mid) & 25-150 kDa(high)].

Results: All spectral data were normalized, standardized and subjected to multivariate statistical analysis and data mining procedures, ultimately leading to the detection of a number of peaks that optimally separated the two groups. Multiple peaks were identified from each mass range and combined across all three mass ranges in each patient sample. Twenty-two specific peaks were identified that could discriminate between the two sample sets; 10 peaks (m/z 2-9 kDa), 7 peaks (m/z 7-16 kDa) and 5 peaks (m/z 31-71 kDa) were significantly different (p <0.05) between the two groups. This profile represents a diagnostic fingerprint of the multiple biomarkers that discriminates OA from normal controls. Ongoing analyses involve the correlation of the SELDI profile with patient characteristics.

Conclusions: This study has potentially identified a panel of biomarkers that represents a diagnostic fingerprint of risk factors and/or predictors of OA progression, which we will now seek to validate in patients earlier in disease progression. The identification of validated biomarkers will greatly accelerate therapeutic development for this major public health concern.

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