Towards sex-specific osteoarthritis risk models: evaluation of risk factors for knee osteoarthritis in males and females
Szilagyi, I. A., Waarsing, J. H., Schiphof, D., Van Meurs, J. B. J., & Bierma-Zeinstra, S. M. A.
Towards sex-specific osteoarthritis risk models: evaluation of risk factors for knee osteoarthritis in males and females. Rheumatology, 61
(2), 648-657. doi:10.1093/rheumatology/keab378.
The aim of this study was to identify sex-specific prevalence and strength of risk factors for the incidence of radiographic knee OA (incRKOA).
Our study population consisted of 10 958 Rotterdam Study participants free of knee OA in one or both knees at baseline. One thousand and sixty-four participants developed RKOA after a median follow-up time of 9.6 years. We estimated the association between each available risk factor and incRKOA using sex stratified multivariate regression models with generalized estimating equations. Subsequently, we statistically tested sex differences between risk estimates and calculated the population attributable fractions (PAFs) for modifiable risk factors.
The prevalence of the investigated risk factors was, in general, higher in women compared with men, except that alcohol intake and smoking were higher in men and high BMI showed equal prevalence. We found significantly different risk estimates between men and women: high level of physical activity [relative risk (RR) 1.76 (95% CI: 1.29–2.40)] or a Kellgren and Lawrence score 1 at baseline [RR 5.48 (95% CI: 4.51–6.65)] was higher in men. Among borderline significantly different risk estimates was BMI ≥27, associated with higher risk for incRKOA in women [RR 2.00 (95% CI: 1.74–2.31)]. The PAF for higher BMI was 25.6% in women and 19.3% in men.
We found sex-specific differences in both presence and relative risk of several risk factors for incRKOA. Especially BMI, a modifiable risk factor, impacts women more strongly than men. These risk factors can be used in the development of personalized prevention strategies and in building sex-specific prediction tools to identify high risk profile patients.