1. Top of page
  2. Abstract
  8. Acknowledgements
  10. Supporting Information


To summarize the overall relative risk of knee osteoarthritis (OA) associated with body mass index, and to estimate the potential risk reduction due to the control of this risk factor.


Six electronic databases were searched up to July 2010. Relative risk was estimated using odds ratio (OR). A random-effects model was used to pool the results. Risk reduction was estimated using population-attributable risk percentage (PAR%), i.e., the proportion of knee OA that would have been avoided if obesity had not been present in the population. The percentage of obesity in different populations was obtained from the International Obesity Task Force.


Forty-seven studies (446,219 subjects) were included in the meta-analysis, of which there were 14 cohort, 19 cross-sectional, and 14 case–control studies. The overall pooled ORs for overweight and obese individuals were 2.02 (95% confidence interval [95% CI] 1.84–2.22) and 3.91 (95% CI 3.32–4.56), respectively. Risk reduction in terms of PAR% for knee OA varied from 8% in China to 50% in the US, depending on the prevalence of overweight and obesity. The reduction was greater in severe symptomatic OA than in asymptomatic radiographic OA.


Obesity is a risk factor for many conditions, including knee OA. The benefit of modifying this risk factor may cause significant risk reduction of knee OA in the general population, especially in Western countries where obesity is prevalent.


  1. Top of page
  2. Abstract
  8. Acknowledgements
  10. Supporting Information

Osteoarthritis (OA) is the most common type of arthropathy and a leading cause of pain, restricted mobility, and functional decline, particularly in older people. It is estimated that approximately 25% of people ages >55 years have persistent knee pain, 10% of whom report having painful disabling knee OA (1). OA contributes substantially to direct and indirect health costs. Currently, there is no cure and the benefits of pharmacologic treatments commonly used for its management are often outweighed by their side effects (2).

Development of knee OA is associated with heredity, age, sex, obesity, previous knee injury, occupational factors (e.g., kneeling and squatting), physical activity, and knee malalignment (3). Obesity is one of the greatest health risks facing the world today, and the prevalence has increased globally in recent decades. Moreover, populations in developed countries are living longer. As obesity is growing progressively in older age (4), the prevalence of obesity-related symptomatic knee OA is anticipated to increase and is threatening to become a major public health problem worldwide. OA is also a major cause of total joint replacement. More than 450,000 primary total knee arthroplasties were performed in the US in 2004, and the number is projected to increase to 3.48 million by 2030 (5). Consequently, the economic impact of OA on society is anticipated to increase in parallel.

Obesity is one of the most important modifiable risk factors for the development (6–12) and progression (6, 12–15) of knee OA. It is suggested that excess weight increases mechanical loading of joints, leading to increased cartilage degradation and subsequent failure of the entire joint (16). Another proposed mechanism is mediation through metabolic and hormonal factors that disadvantage the health of joint tissues (16). In the Framingham Knee OA study, weight loss of approximately 5.1 kg over a 10-year period was found to reduce the risk of incident knee OA by 54% (odds ratio [OR] 0.46, 95% confidence interval [95% CI] 0.24–0.86) in women (17). Recently, a meta-analysis of randomized controlled trials (RCTs) has shown weight loss to reduce physical disability in obese patients diagnosed with knee OA (18). However, despite convincing evidence from observational studies and RCTs of the causal effect of obesity in knee OA, the benefit of preventing obesity at the population level has yet to be assessed (19). This is of considerable importance in support of public health initiatives aimed at reducing the burden of disease in the population.

We therefore carried out this study to 1) estimate the overall relative risk (RR) of knee OA associated with obesity using a meta-analysis of observational studies, and 2) calculate the risk reduction of knee OA in different populations according to the overall RR estimate and the prevalence of obesity.

Significance & Innovations

  • Body mass index (BMI) is one of the modifiable risk factors for knee osteoarthritis (OA). However, despite convincing evidence on the causal effect of the elevated BMI in knee OA, the potential risk reduction of knee OA due to the control of overweight/obesity at the population level has not been quantified. This is of considerable importance in support of public health initiatives aimed at reducing the burden of the disease in the general population.

  • We found risk reduction in knee OA to vary from 8% in China to 60% in the US, depending on the prevalence of obesity. The reduction was greatest in severe symptomatic OA awaiting total joint replacement, followed by symptomatic knee OA and asymptomatic radiographic OA.


  1. Top of page
  2. Abstract
  8. Acknowledgements
  10. Supporting Information

Literature search: data sources and search strategy.

A systematic search of literature published up to July 2010 in 6 English-indexed electronic databases was conducted. Ovid Medline (1950), EMBase (1980), and AMED (1985) databases were searched using a comprehensive search strategy (see Supplementary Appendix A, available in the online version of this article at, and a keyword search using the terms “knee osteoarthritis or knee pain” and “obesity or body mass index or risk factor” and “cohort or case-control or cross-sectional” was applied to PubMed, ISI Web of Science, and CINAHL. Additional studies were obtained from reference lists of relevant articles. No language restrictions were applied.

Study selection.

All relevant abstracts obtained from each database were exported to Endnote, version X4.0.2 (Thomson Reuters), and duplicate articles were excluded. Only observational studies in humans were included. Diseases other than OA were excluded.

Eligible articles were retrieved and reviewed. For inclusion, studies had to fulfill the following criteria: 1) cohort, cross-sectional, or case–control study evaluating the association between overweight or obesity and incidence/development, radiographic progression of knee OA, or knee pain; 2) use of body mass index (BMI) in kg/m2 as a measure of overweight or obesity; and 3) knee OA as the primary outcome of interest, which was defined using radiographs and clinical or physician-diagnosed OA. Where knee pain was the primary outcome of interest, the mean age of the study participants had to be ≥45 years to minimize the chance of knee pain due to other musculoskeletal conditions.

Data extraction.

Study design–specific data extraction forms were used to record extracted information on study characteristics (source of population, sample selection, total number of study participants, mean age or age range of participants, sex ratio, BMI mean/range, and country of study), whether BMI was a primary exposure variable, definition of OA, reported or calculated RRs (OR, RR, and hazard ratio [HR]) and their corresponding 95% CIs, confounding factors, and statistical methods performed. For studies with more than one article from the same study population, we selected the one providing the best evidence based on study design, whether BMI was the primary exposure or sample size, and/or date of publication, so that only one article per study was included. For example, we included results from a cohort article rather than case–control or cross-sectional analysis. If the study design was the same, articles reporting risk estimates from BMI as a primary exposure were included.

Quality assessment.

All of the articles were fully reviewed by one investigator (SGM) and validated by a second investigator (MH) for key study characteristics such as study design, setting, and outcome measures. In addition, a 10% random sample was extracted independently by MH for other characteristics to determine the disagreement. Double extractions were applied when the percent disagreement was more than 2% for each article. Consensus was achieved by discussion and the third investigator (WZ) was involved, if necessary. We followed criteria recommended by the Meta-Analysis of Observational Studies in Epidemiology (20) to assess the methodologic quality of the publications.

Statistical analysis.

BMI was classified using the World Health Organization (WHO) criteria (normal: <25 kg/m2, overweight: 25–29.9 kg/m2, and obese: ≥30 kg/m2). RR (OR, RR, or HR) of overweight and/or obese compared with normal weight was calculated and OR was used in this meta-analysis as a general term for all RR measures. For studies where WHO categories were not applied, we selected the category most similar to the WHO, or converted the RR obtained from a continuous scale to reflect overweight or obese BMI categories. For example, if the RR was measured as an OR per unit increase of the BMI, we multiplied the logarithm of the OR by 5 or 10 for overweight or obese, respectively. If the RR was measured as an OR per 5 kg/m2 unit increase of the BMI, we multiplied the logarithm of this OR by 1 or 2 to represent the overweight or obese categories, respectively.

Potential publication bias was examined by funnel plots and Egger's test (21). Forest plots were used to examine the distribution of the effect sizes. We assessed heterogeneity using Cochran's Q test (22) and the I2 statistic (23). Subgroup analyses were undertaken according to different study-level characteristics, such as study design, setting, definition of knee OA, sex, study region, etc. Dose- response data were extracted and analyzed separately, if available.

A random-effects model was used to pool results if studies were heterogeneous or if the reason of heterogeneity could not be reasonably identified; otherwise, a fixed-effects model was used (24). Analyses were undertaken using StatsDirect, version 2.7.8.

A meta–regression model was used to adjust for covariates, where study design as case–control (0,1), adjustment for potential confounders (0,1), overweight or obese (1, 2), hospital setting (0,1), and type of knee OA (radiographic OA, symptomatic OA, and OA requiring total knee replacement [TKR]) (1, 2, 3) were examined. SPSS, version 16, was used to carry out meta–regression analysis.

Population-attributable risk percentage (PAR%) was used to determine risk reduction. It is defined as the proportion of the disease that could be eliminated if the exposure was prevented in a population (25). The PAR% is calculated by (It − Iu)/It × 100%, where It = the incidence of a disease in the total population and Iu = the incidence of a disease in the unexposed population. Alternatively, it may be derived from the following formula: Pe (OR − 1)/[Pe (OR − 1) + 1] × 100% (25), where Pe = the current prevalence of obesity, if the RR (the OR in this case) and percentage of exposure (e.g., overweight/obesity) in the population are known. We used pooled ORs and the current prevalence of obesity to compute the PAR% for 3 knee OA phenotypes: radiographic, symptomatic, and OA requiring TKR. Population-specific obesity prevalence estimates were obtained from the International Obesity Task Force (26).


  1. Top of page
  2. Abstract
  8. Acknowledgements
  10. Supporting Information

Study characteristics.

The systematic literature search yielded 3,056 citations. Of these, 409 were potentially relevant. After subsequent abstract and full-article review, 81 articles met our inclusion criteria. A further 27 articles were excluded because they were either duplicate publications or lacked required data. One study (27) only reported OR without 95% CI and the attempt to contact the corresponding author was unsuccessful. Ultimately, 47 studies that examined the association between the development of knee OA and BMI were included in the meta-analysis (Figure 1). We also performed a separate meta-analysis on radiographic knee OA progression (n = 6) and knee pain (n = 7).

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Figure 1. Results of the systematic literature search (up to July 1, 2010). * Knee osteoarthritis (OA) development = 41, knee OA incidence + radiographic progression = 4, radiographic knee OA progression = 2, knee OA development + knee pain = 2, knee pain only = 5. BMI = body mass index.

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BMI and development of knee OA.

Of 47 studies (446,219 subjects) for the development of knee OA, there were 14 (6–12, 15, 28–33) cohort (404,070 subjects), 19 (34–52) cross-sectional (31,041 subjects), and 14 (53–66) case–control (11,108 subjects) studies (Table 1). Cohort studies had younger age, fewer women, and most (93%) examined BMI as a primary exposure. Details for each study can be found in Supplementary Appendix B (available in the online version of this article at http://online

Table 1. Characteristics of observational studies evaluating BMI and the development of knee OA*
 CohortCross-sectionalCase–controlAll studies
  • *

    BMI = body mass index; OA = osteoarthritis; TKR = total knee replacement.

  • Estimate excludes Sahlstrom et al (62), whose proportion of women was not stated.

  • Includes symptomatic radiographic OA, clinical OA (American College of Rheumatology), and self-reported OA.

  • §

    Includes incidence TKR due to OA and cases recruited from TKR waiting lists.

No. of studies14191447
No. of subjects404,07031,04111,108446,219
Age, range years20–8435–9047–9620–96
Women, no. (%)49,679 (12)17,894 (58)5,789 (56)73,362 (17)
No. of hospital-based studies1135
OA definition, no. (%)    
 Radiographic OA7 (50)15 (79)22 (47)
 Symptomatic OA4 (29)4 (21)7 (50)15 (32)
 OA requiring TKR§3 (21)7 (50)10 (21)
BMI as a primary exposure, no. (%)13 (93)16 (84)8 (57)37 (79)
Study region, no. (%)    
 US4 (29)6 (32)2 (14)12 (26)
 Europe8 (57)4 (21)10 (71)22 (47)
 Others2 (14)9 (47)2 (14)13 (28)

An asymmetric funnel plot showed significant publication bias; smaller studies with larger ORs were more likely to be published (Egger's test bias = 2.96, P = 0.0001) (Figure 2). There was considerable heterogeneity, with I2 of 92.9% across all 47 studies, with a pooled OR of 2.78 (95% CI 2.45–3.15) for overweight/obese (Figure 3).

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Figure 2. A funnel plot showing the association between overweight/obesity and the development of knee osteoarthritis in all 47 studies. I2 (inconsistency) = 92.9%; Egger's test bias = 2.96 (95% confidence interval 1.57–4.35); P = 0.0001.

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thumbnail image

Figure 3. A forest plot showing the association between body mass index and knee osteoarthritis in all 47 studies.

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Pooled ORs varied according to study design. Case–control studies had a greater OR (3.90, 95% CI 3.09–4.91) than cohort (2.51, 95% CI 2.06–3.07) and cross-sectional studies (2.41, 95% CI 2.00–2.91) (Table 2).

Table 2. Subgroup analysis for the association between BMI and knee OA*
 No. of studiesNo. of subjectsI2, % (Cochran's Q P)Random-effects pooled OR (95% CI)Publication bias (Egger's test bias P)
  • *

    BMI = body mass index; OA = osteoarthritis; OR = odds ratio; 95% CI = 95% confidence interval; TKR = total knee replacement.

  • Estimates compare specified BMI category with normal weight BMI category.

  • Assessment of BMI in 2 studies (56, 62) not known.

  • §

    Overweight/obese compared with normal weight BMI category.

By BMI categories     
 Underweight5328,9240.0 (0.637)0.78 (0.62–0.99)0.711
 Overweight35437,14078.3 (< 0.001)2.02 (1.84–2.22)0.020
 Obese37439,69386.3 (< 0.001)3.91 (3.32–4.56)0.037
 Overweight/obese47446,21992.9 (< 0.001)2.78 (2.45–3.15)< 0.001
By study design     
  Overweight12402,53685.1 (< 0.001)1.94 (1.63–2.31)0.358
  Obese11401,92888.6 (< 0.001)3.17 (2.40–4.19)0.700
  Overweight/obese14404,07093.1 (< 0.001)2.51 (2.06–3.07)0.109
  Overweight1526,31031.4 (0.117)1.81 (1.66–1.97)0.390
  Obese1729,28179.3 (< 0.001)3.64 (2.96–4.47)0.106
  Overweight/obese1931,04192.6 (< 0.001)2.41 (2.00–2.91)0.018
  Overweight88,29477.1 (< 0.001)2.66 (2.00–3.55)0.572
  Obese98,48474.2 (< 0.001)6.01 (4.12–8.77)0.506
  Overweight/obese1411,10872.2 (< 0.001)3.90 (3.09–4.91)0.701
By sex     
  Overweight13338,87158.9 (0.004)2.21 (1.82–2.70)0.463
  Obese12338,16767.3 (< 0.001)4.00 (2.98–5.38)0.547
  Overweight/obese16341,18877.3 (< 0.001)2.79 (2.26–3.44)0.420
  Overweight1730,84762.2 (< 0.001)2.09 (1.80–2.42)0.071
  Obese1731,01881.0 (< 0.001)4.26 (3.28–5.55)0.083
  Overweight/obese2133,02986.6 (< 0.001)3.14 (2.53–3.89)0.011
By study setting     
 Community43442,34092.8 (< 0.001)2.70 (2.38–3.08)< 0.001
 Hospital43,87989.4 (< 0.001)3.53 (2.19–5.69)0.566
By assessment of BMI     
 Measured BMI36427,47192.2 (< 0.001)2.69 (2.34–3.08)0.004
 Self-reported BMI916,25995.0 (< 0.001)3.11 (1.99–4.86)0.026
By OA symptoms and phenotypes     
 Radiographic knee OA     
  Overweight1826,40630.0 (0.112)1.85 (1.69–2.03)0.219
  Obese1826,55780.7 (< 0.001)3.36 (2.74–4.13)0.218
  Overweight/obese2229,29992.1 (< 0.001)2.46 (2.06–2.94)0.007
 Symptomatic radiographic OA     
  Overweight1018,57683.1 (< 0.001)1.91 (1.51–2.41)0.052
  Obese1220,95885.4 (< 0.001)3.98 (2.77–5.71)0.010
  Overweight/obese1523,38590.7 (< 0.001)2.66 (2.03–3.48)0.002
 Knee OA requiring TKR     
  Overweight7392,15885.6 (< 0.001)2.53 (2.02–3.19)0.034
  Obese7392,15888.1 (< 0.001)5.49 (3.87–7.80)0.137
  Overweight/obese10393,53593.9 (< 0.001)3.99 (2.83–5.64)0.010
By study location§     
 US1218,59384.1 (< 0.001)2.81 (2.25–3.50)0.050
 Europe22376,43694.4 (< 0.001)3.09 (2.41–3.96)< 0.001
 Others1351,19093.4 (< 0.001)2.37 (1.90–2.97)0.292

The results were still highly heterogeneous even after stratification by study design. Egger's test revealed a statistically significant publication bias in cross-sectional studies (P = 0.018) but not in cohort or case–control studies. The ORs did not differ significantly between sexes, study setting, assessment of BMI, or study location (Table 2).

A dose-response relationship was seen between BMI and knee OA, regardless of the definition of the disease. The pooled OR for overweight was 2.02 (95% CI 1.84–2.22), and for obese was 3.91 (95% CI 3.32–4.56) as compared with normal weight. We also examined the association between underweight and knee OA in comparison with normal weight. A marginal negative association (OR 0.78, 95% CI 0.62–0.99) was found (Table 2).


Of the 4 covariates included in this analysis, only case–control studies (OR 1.53, 95% CI 1.07–2.19) and obesity (OR 1.84, 95% CI 1.40–2.40) were statistically significant.


Using the pooled OR for radiographic, symptomatic, and knee OA requiring TKR (Table 2) and the country-specific overweight/obesity prevalence, the PAR% was calculated (Table 3). For example, the risk reduction of symptomatic knee OA due to the prevention of overweight varied from 13% in China to 24% in the US, while that for obesity ranged from 8% to 50%, respectively. This variation did not only depend on the prevalence of overweight or obesity, but also on the severity of knee OA. The PAR% was smaller in asymptomatic radiographic OA (data not reported), but greater in severe knee OA requiring TKR (Table 3).

Table 3. Risk reduction (PAR%) of knee OA for overweight and obesity in selected countries*
CountryOverweight (BMI 25–29.9 kg/m2)Obesity (BMI ≥30 kg/m2)
PrevalenceSymptomatic knee OA, PAR% (95% CI)Knee OA requiring TKR, PAR% (95% CI)§PrevalenceSymptomatic knee OA, PAR% (95% CI)Knee OA requiring TKR, PAR% (95% CI)§
  • *

    PAR% = population-attributable risk percentage; OA = osteoarthritis; BMI = body mass index; 95% CI = 95% confidence interval; TKR = total knee replacement.

  • Prevalence estimates were obtained from the International Obesity Task Force (26).

  • Pooled odds ratios (ORs) 1.91 (95% CI 1.51–2.41) for overweight and 3.98 (95% CI 2.77–5.71) for obesity associated with symptomatic knee OA (Table 2) were used.

  • §

    Pooled ORs 2.53 (95% CI 2.02–3.19) for overweight and 5.49 (95% CI 3.87–7.80) for obesity associated with knee OA requiring TKR (Table 2) were used.

  • Average obesity prevalence from the UK.

US34.223.7 (14.9–32.5)34.4 (25.9–42.8)33.850.2 (37.4–61.4)60.3 (49.2–69.7)
Australia36.524.9 (15.7–34.0)35.8 (27.1–44.4)24.842.5 (30.5–53.9)52.7 (41.6–62.8)
UK36.925.1 (15.8–34.2)36.1 (27.3–44.7)24.542.2 (30.2–53.6)52.4 (41.3–62.5)
Germany37.525.4 (16.1–34.6)36.5 (27.7–45.1)20.838.3 (26.9–49.5)48.3 (37.4–58.6)
Norway46.629.8 (19.2–39.7)41.6 (32.2–50.5)18.335.2 (24.4–46.2)45.0 (34.4–55.4)
Morocco27.720.1 (12.4–38.1)29.7 (22.0–37.7)15.030.8 (20.9–41.3)40.2 (30.0–50.4)
Spain38.626.0 (16.4–35.2)37.1 (28.2–45.8)14.630.3 (20.5–40.7)39.6 (29.5–49.8)
Finland35.824.5 (15.4–33.5)35.4 (26.7–43.9)14.229.7 (20.1–40.1)38.9 (29.0–49.1)
Sweden35.124.2 (15.2–33.1)34.9 (26.3–43.4)12.927.8 (18.6–37.8)36.7 (27.0–46.7)
The Netherlands36.024.7 (15.5–33.7)35.5 (26.9–44.1)10.323.4 (15.4–32.6)31.5 (22.7–41.1)
Thailand21.516.3 (9.9–23.2)24.7 (18.0–32.0)6.917.1 (10.9–24.5)23.7 (16.5–31.9)
China16.112.7 (7.6–18.5)19.7 (14.1–26.0)2.98.0 (4.9–12.0)11.5 (7.7–16.5)


BMI and radiographic progression of knee OA.

Six studies (5,778 subjects) (6, 11–15) investigated the association between BMI and radiographic progression of knee OA (see Supplementary Appendix C, available in the online version of this article at Of these, 3 studies (13–15) found a positive significant association with overweight and 4 studies (12–15) with obese categories. The pooled ORs were 1.33 (95% CI 1.01–1.75) for overweight and 1.93 (95% CI 1.23–3.04) for obese.

BMI and knee pain.

Seven studies (18,450 subjects) (45, 46, 49, 67–70) examined the association between BMI and persistent knee pain (see Supplementary Appendix D, available in the online version of this article at Of these, 5 studies (46, 49, 67–69) showed a statistically significant association between overweight and knee pain, whereas all of the 7 studies (45, 46, 49, 67–70) found a significant association in the obese category. The pooled ORs were 1.69 (95% CI 1.36–2.11) and 2.58 (95% CI 1.74–3.82) for the overweight and obese BMI categories, respectively.


  1. Top of page
  2. Abstract
  8. Acknowledgements
  10. Supporting Information

BMI is a major risk factor for the development of knee OA (3, 71), and it may also accelerate the progression of the disease (72). This meta-analysis of 47 observational studies with more than 446,000 people found an approximately 3-fold increased risk in the development of knee OA in overweight or obese individuals, and the association was dose dependent. A potential risk reduction may be estimated according to these RRs estimated from meta-analysis. For example, in the US, approximately 50% of symptomatic knee OA may be prevented if the current obesity prevalence in the US population could be controlled. The risk reduction in China, in contrast, is only 8% due to the low prevalence of obesity (Table 3). It is predicted, however, that the prevalence of obesity is increasing dramatically (73, 74). The impact of preventing obesity and the resultant risk reduction are substantial, especially when multiple outcomes of obesity are considered (19).

To our knowledge, this is the first calculation of risk reduction using the RR summarized from a meta-analysis. The advantage of this method is that it first establishes the association between exposure and the condition. Second, it produces a powerful and globally generalizable RR estimate for the estimation of risk reduction. Given a country- or population-specific prevalence of exposure (e.g., obesity), users are able to estimate the risk reduction for their own populations. The utility of the RR estimates provided in Table 2 is significant. We have therefore listed all possible ORs in subgroups to accommodate the application. The country-specific PAR% in Table 3 is based on the current prevalence of overweight and obesity in adults. It is important to note that we have used prevalence data of adult obesity that includes young adults. However, since BMI increases with age while OA is associated with older age, PAR% for obesity may be underestimated; therefore, these estimates are for reference only. Moreover, risk reduction due to the prevention of both overweight and obesity may be additive. Therefore, the incidence of knee OA would be greatly reduced if overweight and obesity were eliminated.

This study has several limitations. First, we combined RR estimates from different study designs for the subsequent calculation of PAR%. Yet, the OR from case–control studies is less conservative and can overestimate the RR in common diseases such as knee OA. Moreover, the OR may change with the number of confounding factors being adjusted for. As this was a study-level analysis, we were unable to provide an OR that was adjusted with the same set of confounding factors, and unadjusted ORs were used where adjustment was unavailable. Therefore, the RR may be overestimated. Second, there was a high degree of variation among studies included in the meta-analysis. We carried out a number of subgroup analyses with regard to potential sources of heterogeneity such as study design, setting (hospital versus community), sex, adjustment for confounding factors, disease definition, assessment of BMI (self-reported or measured), and BMI categories. Even so, we were unable to explain some of the heterogeneity found here, but findings from the meta–regression analysis revealed case–control studies and obesity as the main sources. Third, it is conceivable that persons may gain weight after developing knee OA as a consequence of knee pain and physical inactivity. Therefore, cross-sectional BMI assessed in symptomatic knee OA cases may not reflect the body weight prior to the onset of knee OA. This may be a source of bias and may also explain the high pooled risk estimate in case–control studies. Finally, we assessed BMI as a surrogate measure of adiposity. However, BMI neither distinguishes heaviness due to body fat or heaviness due to muscle bulk, nor does it indicate the distribution of body fat. It is speculated that fat distribution may act through metabolic or systematic rather than biomechanical mechanisms to contribute to knee OA. Some epidemiologic studies have demonstrated that certain adiposity measures such as waist circumference and waist to hip ratio or body composition components such as fat mass may be associated with knee OA (31, 34, 43, 75), but not others (36, 76). In spite of these reports, such adiposity measures were beyond the scope of this analysis.

Evidence suggests that excessive body weight may attenuate biomechanical load exerted on weight-bearing joints. It is also plausible that obesity may additionally cause dysfunctional metabolism and joint damage by stimulating adipokines that are known regulators of metabolic homeostasis (77, 78). This may partly explain the different associations between obesity and the incidence of OA in weight-bearing versus non–weight-bearing joints such as hand OA (29, 79). Weight loss has shown significant clinical effects in the treatment of OA in a number of RCTs for weight-bearing joint OA (2). However, to our knowledge there have been no trials to prove whether weight loss can prevent the onset of knee OA. Therefore, we have summarized evidence from the observational studies to examine the potential benefit of preventing knee OA attributable to overweight and obesity. It was not our intention to investigate the effectiveness of weight reduction interventions, but to provide an epidemiologic perspective on the potential risk reduction of knee OA in a general population if overweight/obesity was removed. However, these estimates may carry confounding bias, and further prevention trials on the effect of weight reduction are needed to confirm these results.

In summary, obesity is a common risk factor associated with many conditions, including knee OA. It is one of the few risk factors that may be modified, although effective modification is challenging. The resulting benefits from preventing obesity in the general population are huge, especially in the Western world, where obesity is highly prevalent. Approximately half of symptomatic knee OA, for example, would be prevented in the US if this risk factor was eliminated.


  1. Top of page
  2. Abstract
  8. Acknowledgements
  10. Supporting Information

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. Zhang had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design. Muthuri, Doherty, Zhang.

Acquisition of data. Muthuri, Hui.

Analysis and interpretation of data. Muthuri, Doherty, Zhang.


  1. Top of page
  2. Abstract
  8. Acknowledgements
  10. Supporting Information

We would like to thank Joanna Ramowski for data collection, Helen Richardson for logistics support, and Maggie Wheeler for language translations.


  1. Top of page
  2. Abstract
  8. Acknowledgements
  10. Supporting Information
  • 1
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  • 2
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  • 3
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