This is the first study to project the cumulative incidence of knee OA in the US population aged 60–64 and estimate the effects of obesity on incidence and progression of knee OA over 10 years. Results of our projections revealed that among the 14.3 million US adults aged 60–64, as defined by the 2008 Census population estimates, 10 years from the baseline 11.9 million will still be alive and 2.4 million of these survivors will have symptomatic, advanced or end-stage knee OA.
Our study illustrates and quantifies the relative impact of the higher risk on knee OA development among obese individuals over a 10-year time horizon from the population perspective. We limited our projections to a 10-year time horizon as longer time spans may have more limited value due to the likely development of new therapies with structure-modifying properties. These estimates and other well-established risks of obesity support ongoing public health efforts to educate non-obese and obese adults about the importance of weight management. Helping adults achieve and maintain a healthy BMI will significantly reduce the incidence of new OA cases.
Several prospective population-based studies note a relationship between obesity and rates of OA incidence and/or progression4, 5, 23–29
. Following the Framingham Knee Osteoarthritis (FOS) Cohort, investigators demonstrated that obesity not only precedes onset of knee OA, but also increases risk for incident OA by an odds ratio of 3.8 per 2 kilogram increase in baseline BMI over 8 years23
. In a separate analysis, investigators showed that a subset of women (in the FOS cohort) who lost approximately 5 kilograms over 10 years were half as likely to develop symptomatic knee OA29
. Further, investigators found that obesity and morbid obesity in the Multicenter Osteoarthritis Study (MOST) Cohort increased patient risk for incident knee OA by factors of 2.4 and 3.2 respectively25
In another projection study, Murphy et al. predicted that the lifetime risk of developing OA in the Johnston County Osteoarthritis Project Cohort was 44.7%30
. Though we based our projections on progression estimates from the Johnston County Osteoarthritis Project in the OAPol Model, our baseline OA prevalence distributions came from the US population, not the Johnston County Cohort. In addition, our outcome measure, symptomatic advanced and end-stage knee OA (K-L 3 or K-L 4 with symptoms), was less inclusive than the measure used by Murphy et al. (K-L 2+ with symptoms). These differences make our 10-year projection of symptomatic advanced or end-stage knee OA difficult to directly compare with Murphy et al.’s projection of 44.7%. Both these studies, however, indicate that obesity augments lifetime risk for knee OA by at least a factor of at least 2.
By using a computer simulation model, we project cumulative rates of symptomatic advanced and end stage knee OA in a national sample of US citizens 60–64 years of age, fully accounting for all sources of mortality and eliminating uncertainty due to loss to follow up, pertinent to many population-based cohorts. The modest difference in the estimated proportion of the baseline population with OA at baseline and after ten years is due primarily to the fact that persons with OA are more likely to be obese and therefore have lower life expectancy.
Our projections should be considered within the scope of several limitations. Our analysis was restricted to a population cohort aged 60–64 years at the baseline, which in 10 years reached 70–75 years of age. Were a similar simulation for other age groups in the population to be added, increases in the expected number of OA cases would be higher. Specifically, if we extended the analyses to subjects 70–74 years old at baseline, the mortality would be much higher, and while the proportion of survivors developing osteoarthritis over a 10-year timeframe would also be considerably higher, the population-based implications are highest for the 60–64 year age group.
We made the assumption that the obesity status of adults aged 60–64 does not change over time. While we used published data to account for small fluctuations in BMI, obesity status, defined by BMI>30 kg/m2
remained stable. A large volume of data suggests that fluctuations in BMI occur mostly between 25 and 55 years of age, and by age 60 BMI remains relatively stable31
. Therefore, we did not model changes in BMI in this population. Following subjects in the NHANES, investigators found that US adults aged 55–64 and 65–74 at the baseline (1971–1975) had on average lost 0.3–0.5 and 1.1–1.7 BMI units (kg/m2
) respectively by the 10-year follow-up (1981–1984)32
. We acknowledge that some obese individuals simulated by the OAPol Model may have been categorized as non-obese at the 10-year follow-up points had they lost up to 1.7 BMI units. Finally, recent studies have suggested that the effect of BMI on rates of knee OA progression is modified by knee alignment25, 33, 34
. Because the OAPol Model progression rates were derived from population-based studies, our obese and non-obese progression rates are weighted averages for those with varying degrees of malalignment. The specifics of the interaction between malalignment and obesity, however, are beyond the scope of this analysis. We recognize as well that our binary categorization of obesity status (BMI < 30 vs. ≥ 30) may obscure associations between obesity, incidence and progression. In fact, the risk for progression to TKR increased with increasing BMI, even within the non-obese range24
. However, available data on BMI-stratified OA structural progression limited us to a somewhat crude (binary) level of granularity.
We used K-L 1 as a proxy for ‘pre-radiographic OA’ acknowledging that the detection of a questionable osteophyte (the definition of K-L 1) may have little bearing on MRI diagnosis of early osteoarthritic changes (such as cartilage defects that are not visible on radiographs). Our rationale is that patients with K-L 1 are known to be at a greater risk of development of incident early OA (K-L 2), suggesting that K-L 1 is indeed an early OA state5, 35
. Our findings document the large increase in incidence and prevalence of OA that would result from using MRI to identify early OA. We suggest that in the absence of therapeutic interventions that would delay progression (such as a structure-modifying agent), a large increase in the number of patients diagnosed with OA would likely lead to more utilization of imaging, physician visits and other resources, without obvious structural benefit for the patient. Such increases in health care utilization will lead to further increases in health care expenditures, without clear evidence of justification of such expenses.
Our sensitivity analyses showed that altering progression rates has a modest impact on the cumulative incidence of symptomatic advanced and end-stage knee OA. The calibration and validation of 8-year OAPol Model incidence and progression elements demonstrated that the progression rates used for our main analyses were comparable to rates found in real US cohorts22, 23
. In addition, our calibration of the OAPol model to data from the FOS provided more conservative incidence estimates, while our calibration of the model to original Johnston County Project data provided more liberal estimates. Differences in the rates of knee OA incidence and progression used in this analysis may be attributed in part to differences in study sample populations. Subjects in the Johnston County Project were more racially diverse (81.5% Caucasian and 18.5% African American), had a higher average BMI, were more likely to live in rural areas, and were younger, on average than subjects followed in the Framingham Knee OA Cohort2,23
In summary, 20% of the surviving cohort or 17% of the baseline population cohort, totaling 2.4 million adults aged 60–64 at baseline will have symptomatic advanced or end-stage OA in 10 years. Obesity greatly augments patient risk for incident and progressive knee OA. More sensitive imaging tools may lead to higher health care utilization. Retarding the onset of OA by preventative obesity control is likely to yield the greatest economic and patient health gains. Effective and sustainable weight management plans may delay OA incidence and reduce the risk for a host of associated and costly chronic conditions including diabetes and heart disease.