Article Text

OP0130 Prediction model for knee osteoarthritis including clinical, genetic and biochemical risk factors
  1. H.J. Kerkhof1,2,
  2. S. Bierma-Zeinstra3,4,
  3. B. Hofman2,5,
  4. F. Rivadeneira1,2,5,
  5. A. Uitterlinden1,2,5,
  6. C. Janssens5,
  7. J. van Meurs1,2
  1. 1Department of Internal Medicine, Erasmus Medical Center Rotterdam
  2. 2The Netherlands Genomics Initiative-sponsored Netherlands Consortium for Healthy Aging
  3. 3Department of General Practice, Erasmus Medical Center Rotterdam
  4. 4Department of Orthopedics, Erasmus Medical Center
  5. 5Department of Epidemiology, Erasmus Medical Center Rotterdam, Rotterdam, Netherlands


Background Identification of subjects at high risk for knee osteoarthritis (OA) is warranted for biochemical/pharmaceutical research and for prevention/monitoring in the general practice.

Objectives The objective of this study was to create and compare different risk prediction models including age, gender and BMI, clinical, genetic and/or biochemical risk factors, for knee OA in an elderly population and assess the discriminative value of these models in 4 independent studies.

Methods The prediction model was created in the Rotterdam Study-I (474 incident knee OA cases, 2154 controls) including “questionnaire” and easily obtainable data (i.e., age, gender, BMI and general health), x-ray data (knee baseline KL score, hand OA, hip OA) and genetic markers.

The SNPs chosen to create a genetic risk score are 9 SNPs which were found consistently associated to knee OA in large-scale meta-analyses, including GDF5, MCF2L and chr7q22.

Logistic regression models were applied to assess the relationship between the risk factors and incident knee OA. Validation of the model was done in RS-I, RS-II and in the near future in OAI, Chingford and TwinsUK. The area-under-the-curve (AUC), a measure of discrimination between cases and controls with a range from 0.50 (tossing a coin) to 1.00 (perfect discrimination), was assessed.

Results The multivariate analysis showed the strongest association(s) with gender (OR 1.69), BMI (OR per sd increase 1.28), hand OA (OR 1.45), knee pain (OR 1.62) and baseline KL score of 1 (OR 6.97). In RS-I (internal validation) the AUC for age, gender and BMI was 0.66, for questionnaire based variables 0.62, an AUC of 0.61 voor uCTXII levels and 0.76 for X-ray variables. Similar results were observed in RS-II (external validation).

The AUC for the genetic risk score, disregarding age, was 0.62 (95%CI 0.59-0.65). When stratifying according to age 65, the AUC increased in individuals aged <65 years of age up to 0.65 (95%CI 0.62-0.70) and decreased in individuals aged ≥65 to 0.55 (95%CI 0.51-0.59).

In RS-II addition of the questionnaire variables or questionnaire variables + genetic score to age, gender and BMI, did not change the AUC. However, when adding the knee baseline KL score of 0 or 1 to the model the AUC increased from 0.59 to 0.86 (full model). Similar results were observed in the Rotterdam Study-I (internal validation). For uCTXII levels similar results as for the genetic risk score were obtained in a subset of the Rotterdam Study with AUCs of 0.63 (age, gender, BMI + questionnaire), 0.64 (age, gender, BMI, questionnaire, uCTXII) and 0.85 (full model) in RS-II.

Conclusions We showed that predictive value of genetic markers is dependent on age. At a younger age, moderate predictive value for genetic markers in the prediction of knee OA is achieved whilst this is low in older subjects. “Questionnaire” variables, genetic markers, uCTXII levels and OA at other joint sites do not add much predictive value to age, gender and BMI, at least not in an elderly population.

Disclosure of Interest None Declared

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