To determine the association of obesity, defined as a body mass index (BMI) ≥30 or ≥28 kg/m2 or by waist circumference (WC), with disease activity and severity, as well as its relationship to comorbidities in rheumatoid arthritis (RA).
To determine the association of obesity, defined as a body mass index (BMI) ≥30 or ≥28 kg/m2 or by waist circumference (WC), with disease activity and severity, as well as its relationship to comorbidities in rheumatoid arthritis (RA).
The study population comprised 1,596 patients with early RA (mean ± SD age 55.6 ± 14.6 years, 67.8% women) who had been included in the Better Anti-Rheumatic Farmacotherapy observational study from 1992–2006. In 2010, data on lifestyle factors and comorbidities were collected through a postal questionnaire, answered by 1,391 patients. Clinical outcomes were the Disease Activity Score in 28 joints, sustained remission, physical function (Health Assessment Questionnaire [HAQ]), and pain and global health assessed on a visual analog scale, as well as predefined comorbidities.
After a mean ± SD of 9.5 ± 3.7 years, the mean ± SD BMI had increased from 25.4 ± 4.2 to 26.0 ± 4.5 kg/m2 (P = 0.000). The prevalence of BMI ≥30 kg/m2 was 12.9% at baseline and 15.8% at followup. In multivariable regression, BMI and obesity, defined as a BMI ≥30 or ≥28 kg/m2, at both inclusion and the time of the survey were independently associated with higher disease activity, fewer patients in sustained remission, higher HAQ score, more pain, and worse general health. Also, BMI and obesity independently conferred to higher odds for being diagnosed with hypertension, diabetes mellitus, and chronic pulmonary disease. Further, BMI and WC were independently associated with angina pectoris/acute myocardial infarction/coronary revascularization. In contrast, none of the examined obesity variables was associated with the prevalence of stroke or transient ischemic attack. Lifestyle changes during the observational period, such as quitting smoking or diet change, had no impact on the outcomes.
Obesity was associated with worse RA disease outcomes and a higher prevalence of comorbidities. Body measurements are recommended to improve prediction of the disease course.
The disease course of rheumatoid arthritis (RA) is heterogeneous, ranging from a mild self-limiting form to a very aggressive form. Beyond disease-specific autoantibodies, rheumatoid factor (RF), and antibodies to cyclic citrullinated peptide (anti-CCP), several factors have been identified that predict development of a more severe disease, such as older age at disease onset, female sex, smoking, and genetics, as well as the extent of physical disability and early erosive disease (1, 2). However, there is still a need for individualized predictors of the RA disease course.
During the last decade, research for the role of body composition, adipose tissue, and obesity in the pathophysiology of RA and its severity has increased. It has been recognized that RA, already early in the disease, associates with altered body composition, characterized by reduced lean mass and often preserved or increased fat mass, which is associated with maintained or increased body mass index (BMI) (3), a condition believed to accelerate morbidity and mortality in RA (4). It is uncertain whether low lean mass or an increased amount of fat contributes to the development of the plethora of comorbidities, primarily cardiovascular disease (CVD). Therefore, survival advantage observed in obese patients with RA compared with patients with the lowest BMI was lost when adjusting for comorbidity and RA severity (5). Further, patients with a low BMI had an approximately 3-fold higher risk of cardiovascular death compared with non-RA subjects with normal BMI (6). On the other hand, increasing BMI has been shown to be independently associated with cardiovascular risk (7).
Studies of the impact of BMI on joint destruction indicate that increased BMI protects the joints. Therefore, patients with early RA and a BMI ≥27 kg/m2 had less progressive joint destruction over 2 years than those with a BMI <27 kg/m2 (8). In other cohorts, RF- and anti-CCP–positive (but not negative) obese patients (BMI ≥30 kg/m2) had less joint damage already at baseline and/or less radiographic progression during 3 years than patients with normal weight (9, 10).
In the general population, a BMI of 25–30 kg/m2 indicates overweight and a BMI of >30 kg/m2 indicates obesity, according to the World Health Organization (WHO) definition (11). BMI is, however, only a proxy measure of body fat, and in RA it does not detect all individuals with increased body fat (3). Thus, for a given body fat content, patients with RA had a significantly lower BMI reduced by approximately 2 kg/m2 compared to healthy controls (12). Therefore, for an RA-specific measurement of adiposity, BMI cutoff points of 23 kg/m2 for overweight and 28 kg/m2 for obesity have been suggested (12). Waist circumference (WC) and obesity are highly correlated, but little if anything is known about the role of central adiposity in rheumatic disease.
This background prompted us to examine whether obesity determined by different definitions of BMI and also by WC was associated with disease outcomes and comorbidity over a long observational period in patients with early RA followed since disease onset.
Rheumatoid arthritis (RA) patients with obesity at disease onset are often obese after several years as well. Obesity in RA is associated with decreased physical capacity and global health as well as increased pain, markers of inflammation, and comorbidities.
To detect patients with RA at risk for overall ill health, the World Health Organization definition for obesity is not optimal; instead, a lower body mass index cutoff and measurement of waist circumference should be used for assessing adiposity in RA patients.
The increase of inflammatory markers detected in obese patients should be carefully judged as to disease activity.
The current study provides a rationale for further studies on the role of adipose tissue in RA and the mechanisms behind weight changes during the course of RA disease, as well as the potential effect of weight control on the efficacy of medication, disease severity, and complications.
The patients were recruited from the Better Anti-Rheumatic Farmacotherapy (BARFOT) multicenter (6 centers) prospective observational study, which covers both urban and rural patient referral areas (13). From 1992–2006, 2,608 patients with early RA had been consecutively included with the intention of regular followup during the first 15 years of disease. At inclusion, these patients had an RA diagnosis according to the American College of Rheumatology criteria (14) and a disease duration of ≤12 months. The patients were started with disease-modifying antirheumatic drugs (DMARDs) in accordance with the recommended treatment strategy in Sweden. In the 1990s, that strategy implied initial monotherapy and early use of low-dose oral glucocorticoids (GC), and “step-up” combination therapy was reserved for more severe disease. In the 2000s, that strategy was changed toward biologic agents when the first DMARD or combination of DMARDs failed.
In the BARFOT program, the patients were followed at predefined intervals of 1, 2, 5, 8, and 15 years. In addition, most of the patients were also registered annually in the Swedish Rheumatology Quality Register.
Disease activity was measured by the Disease Activity Score calculated on 28 joints (DAS28) (15). For patients assessed early in the BARFOT study, their original DAS values at inclusion were transformed to the DAS28 using the formula described by van Gestel et al (16). Remission was defined as a DAS28 <2.6 (17) and sustained remission was defined as a DAS28 <2.6 at all followup visits since the 1-year assessment.
Disability status was self-reported by the validated Swedish version of the Stanford Health Assessment Questionnaire (HAQ; range 0–3) (18). Patient-experienced pain and estimated global health were assessed on a visual analog scale (VAS; range 0–100 mm).
Information on body weight and height at inclusion was extracted from medical records and from the questionnaire at the time of the survey, and BMI was calculated as weight/height squared (kg/m2). The patients were grouped based on BMI: BMI ≤20 kg/m2, BMI >20 to <25 kg/m2, BMI ≥25 to <30 kg/m2 (overweight), and BMI ≥30 kg/m2 (obesity) (11). Furthermore, the proposed cutoff point for obesity in RA patients (BMI ≥28 kg/m2) (12) was used when indicated.
At inclusion into the BARFOT study, diet and smoking habits were recorded. Diet types were defined as traditional mixed, Mediterranean, vegetarian, gluten free, or other. Smoking was defined as daily ever smoking, current or past smoking, and never smoking.
In March 2010, a questionnaire was sent to all 1,899 members of the study population who were alive and had not migrated or denied further followup (n = 709). Answers were received from 1,391 (73.3%) of the patients (Figure 1). The questionnaire covered several lifestyle factors, including diet and smoking habits (defined as above), weight and height to date and at disease onset, and WC.
To determine WC, the patients were instructed to stand up, locate a measuring tape midway between the iliac crest and the lower rib margin, and bring it all the way around the abdomen, ensuring that the tape is horizontal. Central obesity was defined as a WC ≥94 cm in men and ≥80 cm in women (19).
The questionnaire also asked whether the patients had been diagnosed with hypertension; diabetes mellitus; pulmonary disease as chronic obstructive pulmonary disease (COPD), emphysema, or asthma; heart disease as angina pectoris, acute myocardial infarction (MI), or coronary revascularization; and stroke or transient ischemic attack (TIA).
Clinical outcomes included were the DAS28, HAQ, VAS pain and general health, DAS28 remission at the time of the survey, and DAS28 sustained remission, as well as the aforementioned comorbidities.
The study was approved by the ethics committee at Lund University and was performed in accordance with the Declaration of Helsinki.
Continuous data are shown as the mean ± SD or median (interquartile range [IQR]), where appropriate, and categorical data are shown as the frequency (percentage). Differences between groups were analyzed with the t-test, chi-square test, or Kruskal-Wallis or Mann-Whitney test, where appropriate. Because of important differences ascribed to being underweight, those with a BMI ≤20 kg/m2 were a priori excluded from the further analyses, including the 2 definitions of obesity. The analyses of BMI, WC, central obesity, changes of smoking habits, and diet were performed on all available data.
Logistic regression was performed to describe the relationship of 2 definitions of obesity with baseline demographic and disease characteristics; significant associations were later adjusted for identified potential confounders.
Linear regression (for continuous outcomes: DAS28, HAQ, and VAS pain and global health) or logistic regression (for binary outcomes: DAS28 remission at the time of the survey, DAS28 sustained remission, and separate comorbidities) was used to examine the association of BMI, obesity, weight gain, change of smoking habits, and diet with the study outcomes. To correct for possible confounding effects, multivariable linear regression models were then developed with adjustments for variables considered significant in univariable regressions. Potential confounders were chosen a priori, i.e., age; duration of followup; sex; smoking; presence of RF; ever use of DMARDs, methotrexate, GC, or biologic agents for the RA disease outcomes; and in addition, DAS28 for the comorbidity outcomes. A potentially mediating effect of hypertension and diabetes mellitus in the association between obesity and angina/acute MI/coronary revascularization was explored by the introduction of these variables as additional explanatory factors in separate models. The level of statistical significance was an alpha level less than 0.05.
Baseline characteristics for the 1,596 patients with information on BMI at inclusion grouped into the 4 BMI categories are shown in Table 1. The BMI values at inclusion were obtained both through medical records and the questionnaires. In those 640 individuals with information from both sources, there were no statistical differences between BMI (mean ± SD 25.4 ± 4.0 versus 25.3 ± 4.2 kg/m2; P = 0.12). Since these values did not differ, the memorized reported measures (36% of BMI values at inclusion) were used if BMI information was lacking in medical records.
|All (n = 1,596)||BMI ≤20 kg/m2 (n = 89 [5.6%])||BMI >20 to <25 kg/m2 (n = 775 [48.5%])||BMI ≥25 to <30 kg/m2 (n = 526 [33%])||BMI ≥30 kg/m2 (n = 206 [12.9%])||P|
|Age, mean ± SD years||55.6 ± 14.6||48.6 ± 17.1||53.9 ± 15.4||58.5 ± 13.0||57.9 ± 12.2||0.000|
|Smoking ever, %||58.4||68.2||55.8||60.4||59||0.09|
|RF positive, %||62.4||65.5||62.2||62.4||61.9||0.9|
|Radiographic changes according to ACR criteria (14), %||24.0||28.0||23.6||23.4||25.3||0.8|
|CRP level, median (IQR) mg/liter||19 (9–42)||26 (9–56)||17 (9–42)||20 (9–44)||17 (9–40)||0.08|
|ESR, mean ± SD mm/hour||35.2 ± 24.7||35.5 ± 25.0||35.0 ± 25.9||34.7 ± 22.9||37.1 ± 24.6||0.8|
|DAS28-ESR, mean ± SD||5.2 ± 1.2||5.3 ± 1.2||5.2 ± 1.2||5.2 ± 1.2||5.4 ± 1.1||0.07|
|HAQ score, mean ± SD||1.0 ± 0.6||1.0 ± 0.6||1.0 ± 0.69||1.0 ± 0.6||1.1 ± 0.7||0.006|
|VAS pain, mean ± SD||46.4 ± 23.8||44.2 ± 23.59||45.7 ± 23.6||46.1 ± 23.9||50.4 ± 24.6||0.012|
|VAS global health, mean ± SD||45.5 ± 26.4||45.2 ± 24.2||45.7 ± 25.2||43.7 ± 25.9||49.3 ± 24.7||0.026|
|DMARD use, %||77.8||76.4||77.9||74.9||84.9||0.033|
|GC use, %||36.8||44.8||36.7||37.8||30.9||0.13|
|GC dosage, median (IQR) mg/day||7.5 (7.5–10)||8.75 (7.5–10)||7.5 (7.5–10)||7.5 (7.5–10)||7.5 (7.5–10)||0.4|
Patients with obesity were older, were more often men, had higher HAQ and VAS pain and global health scores, and were generally more often started on DMARDs; however, inflammatory markers and the DAS28 did not differ.
At disease onset, 92.6% of the patients reported intake of a traditional mixed diet and 4.1% reported Mediterranean, vegetarian, or gluten-free diets, with equal distribution across the BMI categories (data not shown). At followup, there had been changes of diet in all groups, but most distinctly in the obese group. Therefore, the frequency of Mediterranean, vegetarian, and gluten-free diets had increased from 4.1% to 10.9% (P = 0.000) in the entire population and from 2.8% to 10.8% (P = 0.004) in the obese group, according to BMI at inclusion. At inclusion, 332 of the patients were daily current smokers, and of them, 111 patients (33.4%) stopped smoking during the observation time.
The demographic and disease characteristics of patients with obesity, defined as a BMI ≥30 or ≥28 kg/m2, were substantially similar (data not shown). In logistic regression analyses, the relationships between obesity with a BMI ≥30 or ≥28 kg/m2 and disease characteristics showed comparable significant associations, where the DAS28, HAQ, and VAS pain and patient-assessed global health were positively associated with obesity, independently of age and sex. Therefore, adjusted odds ratios (ORs) for associations between a BMI ≥30 kg/m2 or a BMI ≥28 kg/m2 and the DAS28 were 1.16 (95% confidence interval [95% CI] 1.02, 1.31; P = 0.021) and 1.16 (95% CI 1.05, 1.28; P = 0.005), respectively; for the HAQ were 1.49 (95% CI 1.17, 1.89; P = 0.001) and 1.33 (95% CI 1.09, 1.63; P = 0.004), respectively; for VAS pain (per 10 mm of the VAS) were 1.08 (95% CI 1.02, 1.14; P = 0.013) and 1.05 (95% CI 1.0, 1.11; P = 0.037), respectively; and for the relationship between general health (per 10 mm of the VAS) and a BMI ≥30 kg/m2 at inclusion was 1.07 (95% CI 1.01, 1.13; P = 0.027).
Baseline characteristics did not differ between patients answering the questionnaire and those not answering the questionnaire as to age, sex, smoking, RF positivity, and duration of followup in the BARFOT study. However, BMI was higher in those not responding versus those responding (mean ± SD 26.0 ± 5.4 versus 25.2 ± 4.0 kg/m2; P = 0.033).
At followup, after a mean ± SD of 9.5 ± 3.7 years (range 4–18 years), the mean ± SD BMI had increased from 25.4 ± 4.2 kg/m2 (range 15.1–52.6, n = 1,596) to 26.0 ± 4.5 kg/m2 (range 13.8–64.3, n = 1,333; P = 0.000). The patients that had changed BMI categories had mostly moved to a higher one; therefore, the percentage of patients still in the low weight category at the time of the survey was 4.4% (versus 5.6% at inclusion; P = 0.16), in the normal weight category was 40.4% (versus 48.5%; P = 0.000), in the overweight category was 39.3% (versus 33%; P = 0.004), and in the obese category at followup was 15.8% of the participants (versus 12.9% at baseline; P = 0.000). Overall, 46% of the patients were overweight or obese at the disease onset and 55.1% were overweight at the time of the survey (P = 0.000). Both weight gain and speed of weight gain were mostly pronounced in the obesity group (median weight gain 2.48 kg/m2, IQR 0.35–4.85; P = 0.000 for between-group difference), and speed of weight gain was a median of 0.3 kg/m2 per year (IQR 0.03–0.5; P = 0.000). The weight gain was not dependent on the medication given, as to DMARD, methotrexate, GC, or biologic agents (data not shown).
In further analyses, an association between inflammatory markers at baseline and weight gain during the observational period was found, i.e., for C-reactive protein (CRP) level per 10 mg/liter (OR 1.06 [95% CI 1.02, 1.09], P = 0.001) and erythrocyte sedimentation rate (ESR) per 10 mm/hour (OR 1.08 [95% CI 1.03, 1.13], P = 0.002), independently of age and sex. Nonetheless, on neither of the RA disease outcomes or comorbidities did the weight gain or speed of weight gain have any effect (data not shown).
The characteristics of the patients at the latest record ahead of the date of the survey are shown in Table 2, separated for BMI categories. Therefore, after ∼9 years of disease, patients with obesity compared with other weight categories were more often men and had higher HAQ and VAS pain and global health scores. Moreover, they had more often stopped smoking since disease onset and had a higher CRP level, ESR, and DAS28, whereas the current antirheumatic therapy was similar. Of note, patients with a BMI ≤20 kg/m2 also had higher DAS28 and HAQ scores, and they were more often women. Interestingly, central obesity was frequently found not only in patients with a BMI ≥25 kg/m2, but also in those with normal weight.
|All (n = 1,333)||BMI ≤20 kg/m2 (n = 59 [4.4%])||BMI >20 to <25 kg/m2 (n = 539 [40.4%])||BMI ≥25 to <30 kg/m2 (n = 524 [39.3%])||BMI ≥30 kg/m2 (n = 211 [15.8%])||P|
|Age at the time of the survey, mean ± SD years||64.6 ± 13.8||63.2 ± 13.8||63.7 ± 15.3||65.4 ± 12.8||65.5 ± 11.5||0.12|
|Followup, mean ± SD years||9.8 ± 3.7||9.9 ± 3.6||9.5 ± 3.7||9.9 ± 3.6||9.7 ± 3.7||0.5|
|Quit smoking, no. (%)||111 (33.4)||3 (15.0)||27 (21.9)||50 (40.0)||24 (51.1)||0.000|
|CRP level, median (IQR) mg/liter||4 (2–8)||3 (1–5)||4 (1–7)||4 (2–8)||5 (2–9)||0.000|
|ESR, median (IQR) mm/hour||13 (7–21)||12 (7–21)||12 (6–20)||12 (7–21)||15 (10–25)||0.000|
|DAS28, mean ± SD||2.8 ± 1.2||3.0 ± 1.3||2.7 ± 1.3||2.8 ± 1.2||3.0 ± 1.2||0.002|
|HAQ score, mean ± SD||0.6 ± 0.6||0.6 ± 0.5||0.6 ± 0.5||0.6 ± 0.6||0.7 ± 0.6||0.000|
|VAS pain, mean ± SD mm||28.0 ± 23.9||29.2 ± 23.3||25.8 ± 23.8||28.1 ± 23.9||32.9 ± 23.9||0.005|
|VAS global health, mean ± SD mm||27.6 ± 24.1||28.7 ± 23.6||26.0 ± 24.4||27.5 ± 23.8||31.6 ± 23.9||0.033|
|DAS28 sustained remission, %||27.3||28.8||26.6||30.5||20.5||0.048|
|DMARD use at the time of the survey, %||79.0||76.3||78.3||77.5||84.8||0.16|
|DMARD use ever, %||96.0||94.9||95.9||95.4||98.1||0.4|
|GC use ever, %||54.5||57.6||55.1||55.3||50.2||0.6|
|Biologic agent use ever, %||26.8||37.3||26.5||26.0||26.5||0.3|
|WC, median (IQR) cm||92 (84–100)||76 (73–80)||85 (80–90)||96 (90–102)||108 (102–114)||0.000|
|Central obesity, %†||77.7||17||60.4||94.6||100||0.000|
We also further analyzed the disease characteristics at the latest record for patients with a BMI ≥28 kg/m2, and similar data were found to those with a BMI ≥30 kg/m2 (data not shown).
First, by univariable linear regression, the factors statistically significantly associated with the study outcomes were identified. Then, by multivariable regression, the effect of BMI and different definitions of obesity was examined, with correction for the identified potential confounders. As shown in Table 3, BMI and obesity, defined as a BMI ≥30 or ≥28 kg/m2, both at inclusion and at the time of the survey, were independently associated with worse outcomes, as was central obesity at the time of the survey. WC at the time of the survey was associated with a higher HAQ measure. However, quitting smoking after disease onset and change in diet had no impact on any of the RA disease outcomes.
|Variables||DAS28||HAQ||VAS pain||VAS global health|
|β (95% CI)||P||β (95% CI)||P||β (95% CI)||P||β (95% CI)||P|
|BMI at inclusion||0.008 (0.002, 0.014)†||0.006||0.021 (0.009, 0.033)‡||0.001||0.014 (0.003, 0.025)§||0.015||0.014 (0.004, 0.025)§||0.006|
|BMI at the time of the survey||0.008 (0.003, 0.013)†||0.002||0.019 (0.008, 0.030)‡||0.001||0.015 (0.05, 0.025)§||0.004||0.013 (0.003, 0.023)§||0.012|
|BMI ≥30 kg/m2 at inclusion||0.03 (0, 0.06)†||0.11||0.10 (0.03, 0.17)‡||0.006||0.05 (−0.02, 0.12)||0.13||0.04 (−0.03, 0.11)||0.3|
|BMI ≥28 kg/m2 at inclusion||0.04 (0.02, 0.07)†||0.002||0.11 (0.05, 0.17)‡||0.001||0.08 (0.03, 0.14)§||0.002||0.08 (0.03, 0.14)§||0.003|
|BMI ≥30 kg/m2 at the time of the survey||0.05 (0.02, 0.08)†||0.003||0.14 (0.08, 0.20)‡||0.000||0.10 (0.04, 0.15)§||0.001||0.09 (0.03, 0.14)§||0.003|
|BMI ≥28 kg/m2 at the time of the survey||0.03 (0.01, 0.06)†||0.008||0.11 (0.06, 0.16)‡||0.001||0.08 (0.04, 0.13)§||0.001||0.07 (0.02, 0.12)§||0.004|
|WC, per 10 cm||0.01 (−0.01, 0.03)||0.4||0.09 (0.05, 0.11)‡||0.000||0.02 (−0.02, 0.06)||0.3||0.02 (−0.02, 0.06)||0.3|
|Central obesity||0.04 (0.01, 0.08)†||0.019||0.09 (0.01, 0.18)‡||0.027||0.04 (−0.03, 0.1)||0.3||0.05 (−0.02, 0.11)§||0.16|
|Quit smoking||0.03 (−0.01, 0.08)†||0.17||0.06 (−0.04, 0.17)||0.2||0.05 (−0.04, 0.14)||0.3||0.06 (−0.03, 0.16)||0.18|
|Diet change||0.005 (−0.04, 0.05)||0.8||0.05 (−0.04, 0.14)||0.3||0.04 (−0.04, 0.12)||0.3||0.03 (−0.05, 0.11)||0.4|
In multivariable analyses, BMI and obesity both at inclusion and at the time of the survey were associated with a decreased probability of being in disease remission at the time of the survey (Table 4). Furthermore, obesity at inclusion and central obesity were also independently associated with a decreased probability of sustained remission. On neither of these outcomes did the quitting smoking or change of diet have any impact.
|DAS28 <2.6||DAS28 sustained remission|
|OR (95% CI)||P||OR (95% CI)||P|
|BMI at inclusion||0.95 (0.92, 0.98)†||0.001||0.99 (0.96, 1.02)||0.4|
|BMI at the time of the survey||0.95 (0.93, 0.98)†||0.001||0.99 (0.96, 1.02)||0.4|
|BMI ≥30 kg/m2 at inclusion||0.66 (0.45, 0.95)†||0.027||0.65 (0.37, 0.99)‡||0.048|
|BMI ≥28 kg/m2 at inclusion||0.65 (0.48, 0.87)†||0.004||0.67 (0.44, 0.97)‡||0.041|
|BMI ≥30 kg/m2 at the time of the survey||0.59 (0.43, 0.82)†||0.002||0.51 (0.32, 0.84)‡||0.008|
|BMI ≥28 kg/m2 at the time of the survey||0.62 (0.48, 0.81)†||0.001||0.79 (0.58, 1.08)||0.13|
|WC, per 10 cm||0.83 (0.71, 0.94)†||0.003||1.02 (0.91, 1.13)||0.8|
|Central obesity||0.73 (0.53, 0.98)†||0.045||0.85 (0.58, 1.25)‡||0.4|
|Quit smoking||0.83 (0.5, 1.35)||0.4||0.76 (0.41, 1.42)||0.4|
|Diet change||0.68 (0.25, 1.84)||0.4||1.16 (0.37, 3.64)||0.8|
The prevalence of patient-reported comorbidities obtained through the questionnaire was 35.1% for hypertension, 9.9% for diabetes mellitus, 10.5% for COPD/emphysema/asthma, 9.7% for angina pectoris/acute MI/coronary revascularization, and 4.2% for stroke/TIA.
After adjusting for variables that were significantly associated with the comorbidity outcomes in univariable analyses, multivariable regression showed that BMI and obesity both at baseline and at the time of the survey were independently associated with increased odds for hypertension and diabetes mellitus (Table 5). As expected, obesity both at baseline and at the time of the survey was associated with higher odds of being diagnosed with COPD, emphysema, or asthma. However, only BMI and WC were associated with angina pectoris/acute MI/coronary revascularization. Additional adjustment for hypertension and diabetes mellitus did not change the result substantially. In contrast, none of the examined obesity variables was associated with the prevalence of stroke/TIA.
|Hypertension||Diabetes mellitus||COPD/emphysema/asthma||Angina/acute MI/coronary revascularization||Stroke/TIA|
|OR (95% CI)||P||OR (95% CI)||P||OR (95% CI)||P||OR (95% CI)||P||OR (95% CI)||P|
|BMI at inclusion||1.12 (1.08, 1.16)†||0.000||1.13 (1.08, 1.18)‡||0.000||1.03 (0.98, 1.08)§||0.19||1.08 (1.03, 1.14)¶||0.004||1.01 (0.94, 1.08)||0.9|
|BMI at the time of the survey||1.10 (1.07, 1.13)†||0.000||1.12 (1.08, 1.17)‡||0.000||1.03 (0.99, 1.07)§||0.097||1.05 (1.01, 1.10)¶||0.041||1.0 (0.94, 1.06)||0.9|
|BMI ≥30 kg/m2 at inclusion||2.40 (1.61, 3.58)†||0.001||2.09 (1.25, 3.5)‡||0.005||1.81 (1.11, 2.95)§||0.018||1.33 (0.77, 2.31)||0.3||1.17 (0.51, 2.64)||0.7|
|BMI ≥28 kg/m2 at inclusion||2.79 (2.02, 3.86)†||0.000||2.55 (1.66, 3.92)‡||0.001||1.41 (0.92, 2.16)§||0.11||1.22 (0.77, 1.93)||0.4||1.04 (0.53, 2.07)||0.9|
|BMI ≥30 kg/m2 at the time of the survey||2.17 (1.54, 3.06)†||0.001||3.23 (2.09, 5.01)‡||0.000||1.86 (1.2, 2.89)§||0.006||1.24 (0.76, 2.03)||0.4||0.88 (0.41, 1.9)||0.7|
|BMI ≥28 kg/m2 at the time of the survey||2.15 (1.61, 2.87)†||0.000||2.68 (1.79, 4.02)‡||0.001||1.7 (1.15, 2.5)§||0.007||1.08 (0.71, 1.65)||0.7||1.08 (0.6, 1.96)||0.8|
|WC, per 10 cm||1.26 (1.14, 1.39)†||0.001||1.37 (1.19, 1.55)‡||0.001||1.12 (0.96, 1.29)||0.13||1.24 (1.06, 1.43)¶||0.009||1.0 (0.73, 1.28)||0.9|
|Central obesity||1.84 (1.24, 2.71)†||0.002||2.4 (1.2, 4.82)‡||0.013||1.36 (0.77, 2.38)||0.3||2.06 (1.06, 4.01)¶||0.033||1.32 (0.54, 3.23)||0.5|
|Quit smoking||1.14 (0.68, 1.91)||0.6||0.88 (0.32, 2.47)||0.82||1.24 (0.61, 2.54)||0.6||0.86 (0.33, 2.23)||0.7||1.03 (0.39, 2.75)||0.9|
|Diet change||0.6 (0.21, 1.76)||0.3||0.91 (0.11, 7.29)||0.9||1.35 (0.37, 4.9)||0.6||1.57 (0.33, 7.33)||0.6|
In the present study, in which patients with early RA had been followed for a mean of 9.5 years, we analyzed the impact of obesity on disease manifestations. We found that 46% of the patients were overweight or obese at disease onset and 55% were overweight at the time of the survey according to the WHO definition (11). At these time points, obesity according to both definitions used (either a BMI of ≥30 or ≥28 kg/m2) was associated with a higher HAQ score, more pain, and worse global health, and also higher disease activity at the time of the survey. Furthermore, obesity already at disease onset was independently associated with worse disease at the time of the survey, fewer patients in sustained remission, and a higher frequency of comorbidities.
The frequency of obesity (according to the WHO definition) at disease onset of 13% in the patients in the present study is close to that reported in Sweden for women and men between ages 16 and 84 years in 2010 (14%) (20). On the other hand, patients with RA have higher percentages of fat mass compared to healthy controls, and this increase of fat mass can be undetected because it often coincides with a decrease of muscle mass and thus normal BMI (12, 21). BMI is therefore not a good marker of body fat content because it does not distinguish between the tissues that comprise it. We therefore used 2 different definitions of obesity in accordance with the proposal by Stavropoulos-Kalinoglou et al (12). When using a BMI ≥28 kg/m2 as a cutoff for obesity, 27.7% of the patients were obese at disease onset.
Irrespective of which of the 2 definitions of obesity was used (BMI ≥30 or ≥28 kg/m2), analyses of associations with disease severity produced similar results, implying that with the lower cutoff point, more patients with an unfavorable body composition were detected. Of clinical importance is the finding of prevalent central obesity not only among obese patients but also among overweight (95%) and normal weight patients (60%). The effect of central obesity on the outcomes was mostly similar to the effect of obesity in our study. The measurement of WC along with BMI could therefore help to identify patients at high risk for severe RA disease and comorbidities.
Taken in the context of prior research (9), the current study supports the fact that at diagnosis, obese patients already had worse functional capacity, pain, and global health, as well as worse disease activity, also at the time of the survey. Due to the study design, it is impossible to be sure about causality, but since obesity was present already at disease onset, it is unlikely that disease severity caused obesity, but rather that obesity negatively influenced the disease.
Adipose tissue produces a variety of tissue-specific proinflammatory and antiinflammatory cytokines called adipocytokines (22). Adipokines are implicated in the production of several proinflammatory cytokines, such as tumor necrosis factor α (TNFα), interleukin-1 (IL-1), IL-6, and the acute-phase reactant CRP, and have been suggested to be the link that explains the slower joint destructive process in obese patients with RA (22). Interestingly, the association between obesity and less joint damage has been reported only in patients positive for RF or anti-CCP (9, 10). In the present study, the associations between obesity and outcomes were independent of RF positivity.
TNFα, IL-6, and CRP are all increased in obesity (23–25), and CRP levels are independently associated with adiposity in women with RA (26). In obese patients with RA, it might therefore be difficult to determine if increased levels of these cytokines reflect adiposity or disease activity. A close association between adipose tissue and inflammatory state could be supported by our finding of a negative association between ESR and CRP level at baseline and weight gain during the followup, which likely indicates a catabolic state as a result of uncontrolled active inflammation at disease onset.
It seems likely that obesity might have an inflammatory milieu leading to the occurrence of more severe RA and a poorer therapy effect. Here, we showed that despite similar use of DMARDs, GC, and biologic agents in the BMI groups, increasing BMI and obesity were independently associated with a lower chance to achieve remission at the time of the survey or to achieve sustained remission. Our data are in line with previous reports of a higher risk of moderate to severe RA in patients with metabolic syndrome than in those without (27), and also, a negative correlation between BMI at baseline and change in the DAS28 after 16 weeks of followup as well as a reduced rate of remission response to biologic agents in obese patients with RA (28).
In the patients in the present study, obesity had an independent statistically significant negative impact on functional ability, pain, and global health, although it should be noted that the magnitude of the effects was rather small. Physical disability, here assessed by the HAQ, may in RA be associated with the unbalanced body composition per se (29) or be a consequence of both inflammatory activity and joint damage (30). With increasing disease duration, however, there is a link between joint damage and functional disability (31). Therefore, we cannot exclude that in the patients in the present study, obesity, which was associated with a higher HAQ score, was associated with increased joint destruction.
Also in the general population, worsening functional capacity has been linked to increasing fat mass (32). The mechanism behind this association remains unclear, but in both the general population and in patients with RA, pain prevalence and severity have been linked to obesity (33), and pain in RA has a large impact on self-reported health and physical function.
As for global health, obese RA patients with established disease also have a worse quality of life (34), as well as obese individuals in the general population (35). Importantly, weight loss has been associated with improved health-related quality of life (36), but to date, the effect of interventions to control weight in RA is not known.
In RA, patients on average have approximately 1.6 comorbidities (37), and the number increases while aging. In the present study, obesity already at disease onset, irrespective of cutoff points, and WC each had a similar strength of an independent association with hypertension and diabetes mellitus; furthermore, increasing BMI and central obesity had a negative impact on the CVD outcomes. In healthy people, obesity and especially visceral fat accumulation are important in the development of the metabolic syndrome, with diabetes mellitus and hypertension as coexistent disorders, and risk factors of developing CVD (38). Hypertension and CVD are also common in RA (39); however, there have been conflicting results as to whether RA is associated with diabetes mellitus (40–42). When controlling for confounding factors, including BMI, among other things, an association between prevalent RA and diabetes mellitus was not found (43). In contrast to other comorbidities, there is no conclusive evidence about the relationship between RA and chronic pulmonary disease. Nevertheless, in our cohort, this comorbidity was prevalent and should be weighted in assessing disease burden.
When addressing the question of obesity, smoking should be considered because it is associated with a lower BMI and body fat in patients with RA (44). Similarly, we found here that although ever smokers were equally represented in the BMI groups, patients who stopped smoking were more often recognized as being obese at the time of the survey than those who continued smoking. On the other hand, overall, we did not find any associations between ever smoking or quitting smoking per se and the outcomes.
Some limitations of our study merit recognition. First, all patients did not answer the questionnaire, but nonresponders did not differ in baseline characteristics from those answering the questionnaire. Second, it is known that overweight individuals tend to reduce their reported weights, but BMIs were similar for those with both documented and reported values. Next, included comorbidities were obtained through a self-assessment questionnaire and could be misclassified in some participants, with both false-positive and false-negative reports.
The strengths of the study are the large number of participants and the long observational period. To the best of our knowledge, it is the first study including different adiposity measurements along with measures of RA disease activity and outcomes, as well as extended information on comorbidities.
The present results indicate that obesity and central adiposity both at disease onset and during the course of the disease were associated with worse RA disease outcomes and a higher prevalence of comorbidities. BMI with an appropriate cutoff to assess obesity and WC measurements should both be used and encouraged in the RA population and might aid in prediction of the disease course.
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. Hafström 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. Ajeganova, Andersson, Hafström.
Acquisition of data. Ajeganova, Andersson, Hafström.
Analysis and interpretation of data. Ajeganova, Hafström.
Other members of the BARFOT Study Group the authors would like to thank include Valentina Bala, Stefan Bergman, Kristina Forslind, Catharina Keller, Ido Leden, Bengt Lindell, Ingemar Petersson, Christoffer Schaufelberger, Björn Svensson, Annika Teleman, Jan Theander, Anneli Östenson, and especially Maria Söderlin for the initiative to create the lifestyle questionnaire.