Abstract

Purpose: Knee osteoarthritis is one of the most disabling diseases in the elderly population, occurring with a higher prevalence in women and possibly due to other phenotypic attribution. In addition, there is a steep increase in incidence of knee osteoarthritis in women after the age of 50. There are several studies that report on numerous risk factors for the development of knee osteoarthritis, but there is a lack of studies that investigate the sex-specific differences in the association of these risk factors to the disease. The aim of this study was to identify sex-specific risk factors for the incidence of radiographic knee osteoarthritis (RKOA). Methods: We used the Rotterdam Study cohort, a population based prospective study, with in total 11,781 participants (of whom 11,012 did not have knee OA at baseline) who underwent longitudinal radiographic measurements of the knee and baseline measurements of several lifestyle and joint related factors. A total number of 1064 incidence knee cases were present in the cohort, with a median follow-up time of 9.62 years (IQR 4.95 years). We estimated the association of each of these available risk factors with incidence of RKOA using sex stratified multivariate regression models with generalized estimating equations. We report OR per 1 SD with 95% CI for continuous variables and per presence of risk factor in case of dichotomous variables. Results: In addition to well known risk factors such as age, BMI and weight, we found significant associations between femoral neck - bone mineral density (FN-BMD; in men OR 1.32, 95%CI 1.16 - 1.5; in women OR 1.21, 95%CI 1.1 - 1.33), bilateral finger OA (in men OR 1.38, 95%CI 1.03 - 1.84; in women OR 1.54, 95%CI 1.26 - 1.87), KL sum score across all hand joints (in men OR 1.03 95%CI 1.01 - 1.04; in women OR 1.03, 95%CI 1.02 - 1.04), KL sum score thumb joints (in men OR 1.08, 95%CI 1.04 - 1.03; in women OR 1.12, 95%CI 1.08 - 1.05), KL sum score finger joints (in men OR 1.03, 95%CI 1.01 - 1.04; in women OR 1.03, 95%CI 1.02 - 1.04), KL score of 1 at baseline (in men OR 7.14, 95%CI 5.71 - 8.93; in women OR 5.70, 95%CI 4.85 - 6.70) and incidence of RKOA as illustrated in Figure 1 and Figure 2. Using the same model adjusted for age, BMI and time of follow-up, in men we found physical activity (OR 1.24, 95%CI 1.12 - 1.36), whereas in women we found alcohol intake (OR 1.29, 95%CI 1.01 - 1.65) to be significantly associated with a higher risk of RKOA at follow-up. Smoking showed a significant and protective effect only in women (OR 0.69, 95%CI 0.54 - 0.88). We used sex-stratified multivariable models, to test which ones among the lifestyle factors are independently associated with our outcome. Our results showed that age, BMI, weight, height, FN-BMD, physical activity in men, and age, BMI, weight, height, FN-BMD and current smoking in women, were significantly associated with our outcome independently of the other risk factors. The OR from the multivariable models resulted in similar OR with the adjusted OR (adj. OR, see Figure 1 and Figure 2) from the model with only age, BMI and time between radiograph assessments as covariates. We additionally tested whether there is interaction between sex and each risk factor and we found near and significant interactions with age (p=0.087), height (p=0.024), weight (p=0.007), physical activity (p= 0.073) and the lower compared to the highest education level (p=0.077). Furthermore, we have also tested the interaction between sex and joint related factors. Hip OA at baseline (p=0.075), bilateral thumb OA (p=0.064) and disability index (p=0.070) showed borderline significance for the interaction with sex only in multivariable models where we adjusted for lifestyle risk factors significantly associated with out outcome as resulted from a previous step. Conclusions: Our results show significant sex-specific differences in the association between the incidence of radiographic knee osteoarthritis and height, weight and borderline significance with age, physical activity, education level, hip OA, bilateral thumb OA and disability index. With further in-depth analyses we will disentangle these differences.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call