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Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

Objective

To explore the association of measures of body composition with disability in patients with rheumatoid arthritis (RA).

Methods

Patients with RA underwent total body dual-energy x-ray absorptiometry for measurement of total and regional body fat and lean mass. The associations of measures of fat and lean mass with disability, measured with the Health Assessment Questionnaire (HAQ), were explored for the total cohort and by sex, controlling for pertinent demographic, lifestyle, and RA disease and treatment covariates.

Results

We studied 197 subjects (118 women, 79 men). Median (interquartile range) HAQ score was 0.625 (0.125–1.25) and was significantly higher, indicating worse physical function, in women than in men. HAQ score was strongly correlated with depression, pain, RA duration, duration of morning stiffness, Disease Activity Score in 28 joints, radiographic damage scores, levels of physical and sedentary activities, and body composition, with increasing fat and decreasing lean mass associated with higher HAQ scores. Appendicular fat and lean mass demonstrated the strongest association per kilogram with HAQ. Mean HAQ score was 0.52 units higher for subjects in the highest versus the lowest quartile of appendicular fat mass (P < 0.001), and 0.81 units higher for subjects in the lowest versus the highest quartile of appendicular lean mass (P < 0.001). Adjusting for demographic and RA characteristics partially attenuated these associations. The joint associations of appendicular fat and lean mass on HAQ were additive without significant interaction.

Conclusion

Body composition, particularly the amount of fat and lean mass located in the arms and legs, is strongly associated with disability in RA patients.


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

Rheumatoid arthritis (RA) is a highly inflammatory disease of joints and extraarticular tissues that often leads to significant articular damage and destruction. Impairment in physical function (i.e., disability) is a prominent feature of RA that can occur very early in the disease course (1), often resulting in loss of work productivity and employment (2), difficulty with activities of daily living (3), impaired health-related quality of life (4), and increased mortality (5). The direct and indirect costs of disability associated with RA are large, amounting to billions of US dollars per year (6).

In RA, reversible joint pain and swelling combined with irreversible joint damage and deformity are assumed to represent the largest contribution to functional limitation (7). However, other factors such as sex, generalized pain, and depression have been shown to significantly contribute to functional limitation in patients with RA (8–11). Body composition may also affect physical functioning in a variety of ways. For example, decreases in lean body mass (i.e., loss of skeletal muscle) may lead to decreased muscle strength and endurance, resulting in difficulties with ambulation and manual tasks. Similarly, increases in fat mass have been linked to greater disability (12).

Reduced lean and increased fat mass have both been linked to the RA disease state (13, 14). However, little is known about how these unfavorable changes in body composition affect disability in patients with RA. Understanding this is important because body composition is potentially modifiable, and interventions promoting healthy body composition in patients with RA may offer promise in attenuating RA-related disability and improving quality of life.

In the present study of patients with RA, we explored whether body composition was associated with disability, after controlling for sociodemographic, lifestyle, and disease-related risk factors. We hypothesized that individuals with high levels of adiposity and low lean body mass would report significantly higher levels of disability.

SUBJECTS AND METHODS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

Study subjects.

Subjects were men and women participating in the Evaluation of Subclinical Cardiovascular Disease and Predictors of Events in Rheumatoid Arthritis (ESCAPE RA) cohort study, which investigated the prevalence, progression, and risk factors for subclinical cardiovascular disease in RA. In total, 197 patients with RA were enrolled, all of whom met the American College of Rheumatology (formerly the American Rheumatism Association) 1987 classification criteria for RA (15), were 45–84 years of age, and did not report any prior prespecified cardiovascular events or procedures, including myocardial infarction, congestive heart failure, coronary artery bypass grafting, angioplasty (with or without arterial stent placement), peripheral vascular disease or peripheral arterial vascular procedures, implanted pacemaker or defibrillator devices, and current atrial fibrillation. Subjects weighing >300 pounds were also excluded due to the weight limitations of the cardiovascular imaging equipment used in the study.

ESCAPE RA participants were recruited from patients followed at the Johns Hopkins Arthritis Center and by referral from local rheumatologists. The study was approved by the Institutional Review Board of the Johns Hopkins Hospital, with all subjects providing written informed consent prior to enrollment. Enrollment began in October 2004 and concluded in May 2006.

Assessments.

Outcomes.

The 21-item Stanford Health Assessment Questionnaire (HAQ) (16) was used to assess disability related to common activities in 8 categories: dressing, rising, eating, walking, hygiene, reach, grip, and errands/chores. Each subcategory is scored by the patient on a graded scale of 0 to 3, where 0 indicates no difficulty and 3 indicates inability to perform the task. Subcategory scores are modified if assistance from others or assistive devices are commonly used to accomplish the task. The total HAQ score represents the mean of the sum of the highest scores for each subcategory, and has a possible range of 0 to 3. Increasing HAQ scores are indicative of increasing disability.

Independent variables.

For body composition assessment, all subjects underwent total body dual x-ray absorptiometry (DXA) scanning on a Lunar Prodigy DXA scanner (GE/Lunar Radiation, Madison, WI). Prodigy software, version 05.60.003 (GE/Lunar Radiation), was utilized to analyze and measure fat, lean, and bone mass for the total body (minus the head) and per body region (arms, legs, and trunk). Subjects were all scanned on the same DXA scanner, located at the Johns Hopkins Bayview General Clinical Research Center. Quality control and calibration procedures were performed daily using standard procedures provided by the manufacturer.

Measures of height were obtained using a wall-mounted stadiometer. Weight was measured, with subjects wearing light indoor clothing and no shoes, by a Detecto platform (Webb City, MO). Body mass index was calculated as body weight (kg) divided by height (m2).

The covariates (demographic, lifestyle, and RA characteristics) were assessed on the same day as body composition assessment. A structured interview with formalized questionnaires was administered by trained evaluators.

The demographic and lifestyle characteristics (age, sex, race, and highest level of education attained) were assessed by patient self-report. Medical comorbidity was assessed using the Charlson Index of Comorbidity (17), depressive symptoms by the Center for Epidemiologic Studies Depression Scale (CES-D) (18), and physical activity by the 7-Day Physical Activity Recall questionnaire (19). Current smokers were those reporting smoking with >100 lifetime cigarettes smoked. Subjects were classified as engaging in regular exercise if the daily average of the weekly total of physical activity for intentional exercise activities (moderate or brisk walking for exercise and moderate or vigorous individual or team sports and conditioning activities) amounted to ≥30 minutes per day on ≥5 days per week, consistent with current recommendations for physical activity (20). In addition, subjects reported the duration of daily television watching, a measure of sedentary activities.

To assess RA disease characteristics, 44 joints were examined by a single trained assessor for swelling, tenderness, deformity, and surgical replacement or fusion. RA disease duration was assessed by patient self-report of the date of diagnosis. RA disease activity was calculated with the Disease Activity Score for 28 joints (DAS28) using C-reactive protein (CRP) level. Severity of pain and patient global assessment of health were assessed by the patient using a 100-mm visual analog scale. Current and past use of glucocorticoids and biologic and nonbiologic disease-modifying antirheumatic drugs (DMARDs) was queried by detailed, examiner-administered questionnaires.

Single-view anteroposterior radiographs of the hands and feet were obtained and scored using the modified Sharp/van der Heijde method (21) by a single trained radiologist blinded to patient characteristics. Five subjects had incomplete radiographic assessments (3 had hand but not feet radiographs, 2 had feet but not hand radiographs). For these subjects, the missing score (hand or foot) was imputed from the available hands and feet based on a regression equation relating the expected hand or foot erosion and joint space narrowing scores derived from the remaining subjects in the cohort.

For the laboratory assessments, fasting serum and plasma samples were collected on the day of body composition analysis and stored at −70°C. Serum CRP level was measured by nephelometry (Dade Behring, Deerfield, IL). Rheumatoid factor was assessed by enzyme-linked immunosorbent assay (ELISA), with seropositivity defined at a level of ≥40 units. Anti–cyclic citrullinated peptide antibody was assessed by ELISA, with seropositivity defined at a level of ≥60 units.

Statistical analysis.

The distributions of all variables were examined. Means and SDs were calculated for all normally distributed continuous variables, and medians and interquartile ranges were calculated for continuous variables that were not normally distributed. For categorical variables, counts and percentages were calculated. Simple linear regression models were constructed to explore the unadjusted associations between patient characteristics and HAQ scores. Multivariable linear regression models were constructed for the cohort and by sex. Regression coefficients (β's) and 95% confidence intervals were used to estimate the magnitude and extent of the associations of body composition variables on HAQ score, adjusting for potentially confounding demographic, lifestyle, and disease characteristics.

Because appendicular lean and fat were found to be the lean and fat measures with the largest contribution to measures of physical function, these covariates were retained as the independent variables of interest in subsequent multivariable modeling. Because the relationship between appendicular lean and fat masses and physical function measures were not linear across levels of the independent variables, lean and fat mass were divided into quartiles. Additional adjustment for height was performed in all regression analyses involving lean or fat mass. Model checking was performed visually by plotting residuals versus fitted values and statistically by the Shapiro-Wilk test for normality. Variance inflation factors were calculated to assess for multicollinearity of the independent variables. A nonsignificant level of collinearity was detected among the covariates tested, with the highest variance inflation factors among the nonbody-composition covariates of 1.80. The interaction of appendicular lean and fat mass on disability was explored by examining levels of appendicular fat on HAQ over strata of appendicular lean mass and tested using Wald's test.

Statistical calculations were performed using Intercooled Stata 9 software (StataCorp, College Station, TX). In all tests, a 2-tailed alpha level of 0.05 was defined as the level of statistical significance.

RESULTS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

Subject characteristics and univariate correlations with physical function outcomes.

All 197 ESCAPE RA participants had body composition and disability assessments performed, and none were excluded due to missing data. Baseline characteristics are presented in Table 1. Subjects had a mean ± SD age of 59.4 ± 8.7 years and tended to be female (60%) and white (86.3%). Eighty (40.6%) subjects reported HAQ scores ≥1.00, indicating at least some difficulty with most items on average, and 21 (10.7%) subjects reported HAQ scores ≥2.0, indicating much difficulty with most items on average.

Table 1. Patient characteristics*
CharacteristicTotal subjects (n = 197)
  • *

    Values are the mean ± SD unless otherwise indicated. CES-D = Center for Epidemiologic Studies Depression Scale; RA = rheumatoid arthritis; DAS28 = Disease Activity Score for 28 joints; RF = rheumatoid factor; CCP = cyclic citrullinated peptide; HAQ = Health Assessment Questionnaire; IQR = interquartile range; CRP = C-reactive protein; DMARD = disease-modifying antirheumatic drug.

Age, years59.4 ± 8.7
Female sex, n (%)118 (59.9)
White, n (%)170 (86.3)
CES-D score8.0 ± 7.7
Weight, kg80.0 ± 17.7
Body mass index, kg/m228.4 ± 5.3
Total fat mass, kg29.9 ± 10.7
Appendicular fat mass, kg13.6 ± 5.4
Total lean mass, kg46.4 ± 11.5
Appendicular lean mass, kg19.6 ± 5.6
RA duration, years12.4 ± 10.6
DAS283.66 ± 1.08
RF or anti-CCP seropositivity, n (%)154 (78.2)
HAQ score (range 0–3), median (IQR)0.625 (0.125–1.25)
Total Sharp/van der Heijde score, median (IQR)44 (16–120)
CRP level, median (IQR) mg/liter2.6 (1.1–7.2)
Current prednisone use, n (%)76 (38.6)
Any DMARD use, n (%)12 (6.1)
Nonbiologic DMARD use, n (%)165 (84.2)
Biologic DMARD use, n (%)89 (45.4)
Habitual physical activity, n (%)105 (53.6)
Daily television watching, hours2.3 ± 1.6

Univariate correlations of patient characterisitics are presented in Table 2. Mean HAQ scores were 0.47 units higher in women than in men (P < 0.001). Generally among measures of body composition, increases in fat mass were positively associated with HAQ scores and increases in lean mass were inversely associated with HAQ scores (see Table 2). Among measures of fat and lean mass, appendicular fat and lean masses demonstrated the strongest association, per kilogram, with HAQ score. On average, each 1-kilogram increase in appendicular fat mass was associated with a 0.04 unit increase in HAQ score (P < 0.001), and each 1-kilogram increase in appendicular lean mass was associated with a 0.05 unit decrease in HAQ score (P < 0.001). In addition, appendicular measures of fat and lean mass contributed the largest proportion to the total variability in HAQ compared with total and truncal measures. Finally, models were constructed including truncal fat and appendicular fat simultaneously to determine which fat mass region was more highly associated with HAQ, demonstrating that only appendicular fat was significantly associated with HAQ. A moderate correlation was observed between appendicular fat and lean mass in men and women (Pearson's ρ = 0.359 and 0.540, respectively; P < 0.001 for both).

Table 2. Univariate correlations of patient characteristics with HAQ scores*
CharacteristicβPR2
  • *

    VAS = visual analog scale. For additional definitions, see Table 1.

  • Represents the total variability in HAQ explained by the characteristics.

  • Represents the change in mean HAQ score per unit increase in the characteristic.

  • §

    Represents the difference in mean HAQ score for subjects with the characteristic versus those without.

Age0.0050.4370.003
Female sex0.465§< 0.0010.094
White−0.264§0.0890.015
Charlson comorbidity score0.0630.3360.005
CES-D score0.039< 0.0010.163
Weight−0.0030.2910.006
Body mass index0.0210.0380.022
Total fat mass0.0150.0010.051
Truncal fat mass0.0150.0790.016
Appendicular fat mass0.042< 0.0010.091
Total lean mass−0.022< 0.0010.112
Truncal lean mass−0.032< 0.0010.073
Appendicular lean mass−0.052< 0.0010.150
RA duration0.019< 0.0010.070
DAS280.325< 0.0010.220
RF or anti-CCP seropositivity−0.084§0.5160.002
Pain (1–100-mm VAS)0.019< 0.0010.356
Duration of morning stiffness0.006< 0.0010.167
Total Sharp/van der Heijde score0.004< 0.0010.193
CRP level0.0070.0550.019
Current prednisone use0.174§0.1130.004
Any DMARD use0.352§0.1140.013
Nonbiologic DMARD use−0.171§0.2450.007
Biologic DMARD use0.302§0.0050.041
Habitual physical activity−0.593§< 0.0010.158
Daily television watching0.0920.0060.038

Other than body composition variables, there was a strong correlation of increasing depression score with increasing HAQ score, with each 6 unit increase in CES-D score associated with a 0.24 unit increase in HAQ score (P < 0.001). As expected, RA disease characteristics were strongly correlated with increasing HAQ score, including duration of RA, DAS28 score, pain, duration of morning stiffness, and total Sharp/van der Heijde score. Subjects engaging in regular exercise reported HAQ scores that were 0.59 units lower on average (P < 0.001), and increasing sedentary activity (i.e., television watching) was associated with a 0.09 unit increase in HAQ score per daily hour reported (P = 0.006).

Multivariate associations of appendicular fat mass with physical function.

Multivariable models were constructed with appendicular measures of fat and lean mass as the independent covariates of interest. When we explored the independent association of appendicular fat mass with HAQ score in analyses adjusted only for appendicular lean mass (Table 3), the mean HAQ score was 0.52 units greater for subjects in the highest quartile of appendicular fat mass versus those in the lowest quartile, representing a near doubling in HAQ score (P < 0.001). Adjusting for appendicular lean mass, sex, height, CES-D score, swollen and tender joint count, radiographic damage (Sharp/van der Heijde) score, duration of morning stiffness, pain, and current use of biologic DMARDs reduced the difference in HAQ between highest and lowest quartiles of appendicular fat mass by ∼50% (0.26 units), but the difference remained statistically significant (P = 0.023) (Figure 1A). After adjustment, each quartile increase in appendicular fat mass was associated with an average increase in HAQ score of 0.08 units (P = 0.015 for linear trend).

Table 3. Multivariable models of the association of appendicular fat and lean mass with mean HAQ scores*
CharacteristicModel 1Model 2Model 3§Model 4
β (95% CI)Pβ (95% CI)Pβ (95% CI)Pβ (95% CI)P
  • *

    Characteristics tested but noncontributory to the models included age, race, highest education level attained, total comorbidity (Charlson score), current smoking, RA disease duration, CRP concentration, current prednisone use, current nonbiologic DMARD use, RF seropositivity, and daily hours of television watching. Throughout, analyses of appendicular fat mass are additionally adjusted for appendicular lean mass, and analyses of appendicular lean mass are additionally adjusted for appendicular fat mass. 95% CI = 95% confidence interval. See Table 1 for additional definitions.

  • Crude: adjusted only for appendicular fat or lean mass.

  • Adjusted for model 1 covariates in addition to sex, height, and CES-D score.

  • §

    Adjusted for model 2 covariates in addition to swollen and tender joint counts, radiographic damage, duration of morning stiffness, pain (visual analog scale), and current biologic DMARD use.

  • Adjusted for model 3 covariates in addition to conditioning physical activity.

  • #

    Referent.

  • **

    β coefficients represent the difference in mean HAQ scores between the quartile of interest and Quartile 1 (referent).

  • ††

    β coefficients represent the average change in mean HAQ per 1-quartile increase in appendicular fat or lean mass.

  • ‡‡

    Represents the total variance in HAQ explained by the covariates included in the regression model.

Appendicular fat        
 Quartile 1#- - - - 
 Quartile 2**0.03 (−0.25, 0.32)0.8200.02 (−0.25, 0.28)0.901−0.01 (−0.20, 0.19)0.953−0.01 (−0.20, 0.18)0.921
 Quartile 3**0.27 (−0.02, 0.56)0.0640.22 (−0.04, 0.29)0.0970.08 (−0.12, 0.27)0.4510.06 (−0.13, 0.26)0.532
 Quartile 4**0.52 (0.23, 0.81)< 0.0010.54 (0.23, 0.85)0.0010.29 (0.06, 0.51)0.0130.26 (0.04, 0.49)0.023
 Trend††0.15 (0.07, 0.24)< 0.0010.18 (0.09, 0.28)< 0.0010.09 (0.02, 0.16)0.0090.08 (0.02, 0.16)0.015
Appendicular lean        
 Quartile 1#- - - - 
 Quartile 2**−0.24 (−0.51, 0.04)0.088−0.23 (−0.49, 0.03)0.087−0.11 (−0.31, 0.09)0.276−0.10 (−0.30, 0.10)0.341
 Quartile 3**−0.34 (−0.61, −0.06)0.017−0.46 (−0.82, −0.11)0.011−0.15 (−0.43, 0.12)0.274−0.12 (−0.39, 0.15)0.388
 Quartile 4**−0.81 (−1.09, −0.54)< 0.001−0.85 (−1.31, −0.39)< 0.001−0.37 (−0.74, −0.01)0.043−0.31 (−0.68, 0.05)0.090
 Trend††−0.24 (−0.33, −0.16)< 0.001−0.27 (−0.42, −0.12)< 0.001−0.12 (−0.24, −0.01)0.046−0.10 (−0.21, 0.02)0.110
R2‡‡0.228 0.351 0.669 0.677 
thumbnail image

Figure 1. Crude and adjusted mean Health Assessment Questionnaire (HAQ) scores according to quartiles of A, appendicular fat mass and B, appendicular lean mass. Quartiles of appendicular fat mass were defined for men (Q1, 2.5–8.1 kg; Q2, 8.2–10.2 kg; Q3, 10.3–13.8 kg; Q4, 14.0–24.5 kg) and women (Q1, 3.4–11.6 kg; Q2, 11.7–15.0 kg; Q3, 15.1–18.4 kg; Q4, 18.5–32.6 kg). Quartiles of appendicular lean mass were also defined for men (Q1, 16.9–21.3 kg; Q2, 21.8–24.8 kg; Q3, 25.0–27.3 kg; Q4, 27.4–32.7 kg) and women (Q1, 9.1–13.9 kg; Q2, 14.1–16.0 kg; Q3, 16.1–18.0 kg; Q4, 18.1–27.7 kg). 95% CI = 95% confidence interval.

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Although both men and women demonstrated increasing HAQ score with increasing appendicular fat mass, the magnitude of effect was greatest in women (Table 4); there was a 0.69 unit difference in HAQ between women in the highest versus lowest quartiles of appendicular fat (P = 0.001) in analyses adjusted only for appendicular lean. After full adjustment, the mean HAQ score was 0.42 units higher for women in the highest quartile of appendicular fat compared with those in the lowest quartile (P = 0.004) (Table 4, Figure 1A).

Table 4. Sex-stratified crude and adjusted associations of appendicular fat and lean mass on mean HAQ scores*
CharacteristicMen (n = 79)Women (n = 118)
Crude§AdjustedCrude§Adjusted#
β (95% CI)Pβ (95% CI)Pβ (95% CI)Pβ (95% CI)P
  • *

    Exercise and sedentary activities were considered intermediates and were not tested in the models. Throughout, analyses of appendicular fat are additionally adjusted for appendicular lean and analyses of appendicular lean are additionally adjusted for appendicular fat. 95% CI = 95% confidence interval; VAS = visual analog scale. See Table 1 for additional definitions.

  • Other characteristics tested but noncontributory to the male-specific model included age, race, swollen joints, duration of morning stiffness, highest education level attained, total comorbidity (Charlson score), current smoking, RA disease duration, CRP concentration, current prednisone use, current biologic and nonbiologic DMARD use, RF or anti-CCP seropositivity.

  • Other characteristics tested but noncontributory to the female-specific model included age, race, highest education level attained, total comorbidity (Charlson score), current smoking, RA disease duration, C-reactive protein concentration, current prednisone use, current nonbiologic DMARD use, RF or anti-CCP seropositivity.

  • §

    Adjusted only for appendicular fat or lean mass.

  • Adjusted for height, CES-D score, tender joint count, radiographic damage, and pain (VAS).

  • #

    Adjusted for height, CES-D score, swollen and tender joint counts, radiographic damage, pain (VAS), duration of morning stiffness, current biologic DMARD use, and conditioning physical activity.

  • **

    Referent.

  • ††

    β coefficients represent the difference in mean HAQ scores between the quartile of interest and Quartile 1 (referent).

  • ‡‡

    β coefficients represent the average change in mean HAQ per 1-quartile increase in appendicular fat or lean mass.

  • §§

    Represents the total variance in HAQ explained by the covariates included in the regression model.

Appendicular fat        
 Quartile 1**- - - - 
 Quartile 2††0.33 (−0.07, 0.72)0.1010.07 (−0.21, 0.36)0.6070.01 (−0.35, 0.37)0.9500.10 (−0.15, 0.36)0.432
 Quartile 3††0.32 (−0.08, 0.72)0.1140.06 (−0.22, 0.34)0.6760.30 (−0.07, 0.66)0.1110.18 (−0.07, 0.43)0.152
 Quartile 4††0.56 (0.15, 0.97)0.0080.23 (−0.04, 0.51)0.0890.69 (0.27, 1.10)0.0010.42 (0.14, 0.71)0.004
 Trend‡‡0.17 (0.04, 0.30)0.0120.07 (−0.01, 0.16)0.1020.22 (0.09, 0.35)0.0010.13 (0.04, 0.22)0.005
Appendicular lean        
 Quartile 1**- - - - 
 Quartile 2††−0.59 (−0.98, −0.20)0.003−0.30 (−0.61, −0.00)0.050−0.73 (−1.08, −0.39)< 0.001−0.35 (−0.62, −0.08)0.011
 Quartile 3††−0.80 (−1.19, −0.41)< 0.001−0.39 (−0.71, −0.06)0.021−0.71 (−1.06, −0.35)< 0.001−0.41 (−0.69, −0.12)0.006
 Quartile 4††−0.68 (−1.09, −0.27)0.001−0.17 (−0.54, 0.20)0.362−0.91 (−1.32, −0.50)< 0.001−0.44 (−0.79, −0.09)0.012
 Trend‡‡−0.24 (−0.37, −0.10)0.001−0.21 (−0.40, −0.02)0.034−0.28 (−0.41, −0.15)< 0.001−0.14 (−0.25, −0.03)0.016
R2§§0.212 0.655 0.178 0.643 

Multivariate associations of appendicular lean mass with physical function.

The association of increasing lean mass on measures of physical function was opposite that of increasing fat mass (Table 3). The mean HAQ score was 0.81 units lower in subjects in the highest quartile of appendicular lean mass versus those in the lowest quartile (P < 0.001) in analyses adjusted only for appendicular fat mass, resulting in a mean HAQ score >3-fold higher in patients with the lowest appendicular lean mass versus those with the highest (Figure 1B). Adjusting for height, demographics, and depression did not substantially alter this relationship. However, adjusting for confounding RA characteristics reduced the magnitude of the association by >50%, such that the HAQ score in subjects in the highest quartile of appendicular lean mass was 0.37 units lower than in subjects in the lowest quartile (P = 0.043). Only additional covariate adjustment for exercise, a potential intermediate in the appendicular lean/HAQ association, reduced the magnitude of the association such that the difference in HAQ score was no longer statistically significant between subjects with the highest versus the lowest appendicular lean mass (P = 0.090).

Although both men and women demonstrated decreasing HAQ score with increasing appendicular lean mass (Table 4, Figure 1B), there were differences in the direction of effect with increasing appendicular lean mass between men and women. Compared with men with the lowest appendicular lean mass, increasing appendicular lean mass was associated with significantly lower HAQ scores for men in the second and third quartile of increasing lean mass. However, adjusted mean HAQ scores did not significantly differ for men in the highest quartile of appendicular lean mass compared with men in the lowest quartile. For women, those in the highest quartile of appendicular lean mass did demonstrate significantly lower HAQ scores compared with those in the lowest quartile. However, the magnitude of association was not substantially different between women in the third and fourth quartile of increasing appendicular lean mass.

Interaction of appendicular fat and lean mass with physical function.

We next explored the joint association of appendicular fat and lean mass on physical function. Across strata of appendicular lean mass (divided into quartiles), there was no difference in the association of increasing appendicular fat mass with HAQ (P = 0.933 for interaction); each tertile increase in appendicular fat mass was associated, on average, with a 0.25 unit higher HAQ score (Figure 2). Similarly, there was no difference in the association of appendicular lean mass with HAQ score across strata of appendicular fat mass (divided into tertiles); each quartile decrease in appendicular lean mass was associated, on average, with a 0.29 unit lower HAQ score, regardless of stratum of appendicular fat (P = 0.795 for interaction) (Figure 2).

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Figure 2. Mean Health Assessment Questionnaire (HAQ) scores according to tertiles of appendicular fat mass, stratified by quartiles of appendicular lean mass. Mean HAQ scores are presented for the total cohort according to the combined sex-specific quartiles. Ranges indicate 95% confidence intervals for the estimate of the mean HAQ score.

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DISCUSSION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

In this investigation, which is the first to our knowledge to explore relationships between body composition and physical functioning in patients with RA, we observed increasing disability (as measured by the HAQ) with increasing fat and decreasing lean masses, with appendicular fat and lean masses demonstrating the greatest magnitude of effect. Adjusting for demographic, lifestyle, and RA disease and treatment characterisitics attenuated, but did not eliminate, the independent association of increasing appendicular fat mass with worsening HAQ scores. However, accounting for RA disease characteristics and physical activity accounted for much of the association of decreasing appendicular lean mass with worsening HAQ scores. Men and women demonstrated similar findings in sex-stratified analyses, including a ceiling effect on improvement in physical function associated with increasing lean mass. There was no observed joint effect of appendicular fat and lean mass on physical function, which indicates independent effects of fat and lean mass on physical function.

The determinants of functional capacity in RA are complex. Suboptimal physical functioning may be the product of factors such as joint pain and swelling from articular inflammation (22), accumulated joint deformity (7), depressive symptoms (8, 9), and other factors, but also important, as our study identifies, are the amount and distribution of body fat and lean tissue. This is not surprising, because the HAQ assesses aspects of physical functioning that require varying levels of muscular strength and physical fitness. Reduced lean muscle mass in the arms and legs would be expected to impair the ability to perform such actions as rising, climbing, lifting, and carrying. Reduced body cell mass has been identified in patients with RA (23), with cytokine-driven muscle catabolism and reduced physical activity implicated as possible causes (13). Our observation that most of the independent association of decreasing appendicular lean mass on physical functioning was accounted for by RA disease characterisitics and reduced physical activity lends further credence to the construct that these factors may be causally related to both reduced lean mass and physical incapacity.

How increasing amounts of fat, particularly in the arms and legs, can impair physical functioning is less intuitive. One possibility is that increasing fat may interfere with the normal range of motion of the arms and legs, impeding muscular function. Another possibility is that fat may biochemically interfere with adjacent muscular function and tone, because fat is a known source of inflammatory cytokines, such as tumor necrosis factor α and interleukin-6 (24), that are associated with sarcopenia. More likely, however, is that fatty infiltrates in the muscle itself may reflect deleterious changes in muscle quality. The observation of stability in total body weight in patients with RA with reduced body cell mass has suggested that patients with RA may replace lost lean mass with fat (25), which, given the strong independent association of increasing appendicular fat with physical function noted in the present study, would compound the isolated effect of reduced lean mass on physical functioning. Indeed, in contrast to the observed association between decreasing appendicular lean mass and decline in physical function, the association between appendicular fat mass and physical function was not fully explained by demographic, lifestyle, or RA disease-related factors, suggesting that other mechanisms may account for the association. An alternate explanation is that RA-associated reduction in lean mass may be a more dynamic process, influenced more readily by current RA characterisitics, therapies, and physical activity (as assessed by our cross-sectional design). However, RA-associated increase in fat mass may be more resistant to dynamic change because RA disease characteristics improve with effective therapy or as patients become more physically active when pain and stiffness are reduced. Longitudinal followup is underway to address this possibility.

Interestingly, in studies of the general population, increasing fat mass has also been more strongly linked to worsening functional capacity than decreasing lean mass (12, 26, 27), suggesting that efforts to improve physical function require a focus on fat reduction with at least as much emphasis, if not more, than increasing lean mass. This is confirmed in our data with the finding that appendicular fat and lean mass exert independent effects on HAQ score. In this setting, the observation of a leveling off or worsening in HAQ score with increasing appendicular lean mass may be related to the physiologic tendency for lean mass to increase in proportion to increasing fat mass (28), thus counteracting the net effect of the independent individual components on HAQ score. However, caution should be used when interpreting these findings in this way, as our cross-sectional design does not represent response to change within individuals. Interventional studies are required to determine whether increasing muscle and decreasing fat are effective at improving physical function in patients with RA.

Some additional limitations of our study should be acknowledged. Subjects weighing >300 pounds were excluded due to the weight limits of the cardiovascular imaging equipment used in other portions of the study. Thus, patients with the most extreme body fat, and who are thus the most likely to demonstrate the most body fat–associated impairment in physical function, may have been excluded. However, it is likely that inclusion of these subjects would have strengthened, rather than weakened, the findings observed. Lean mass, as quantified by DXA, is not the most precise measure of muscle mass because it includes internal organs and connective tissues that may not contribute to physical functioning. However, these tend to be in relatively stable proportion across individuals of the same stature, within a narrow range of variability (28). Importantly, appendicular lean mass is a close proxy for appendicular muscle mass, as most of the lean mass in the arms and legs is skeletal muscle (29). However, there is a range of variability in the correlation between lean mass and muscle strength (30), with patients of the same lean mass exhibiting different levels of muscle strength. Physical performance measures, which were not assessed in this study, may have helped delineate differences in lean mass and muscle strength in these patients. Finally, some of the subcategories of the HAQ assess endurance as much as muscle strength. In these, cardiopulmonary and other factors may interfere with accomplishing the task despite adequate body composition.

In summary, body composition appears to be an independent determinant of disability in patients with RA, after controlling for known risk factors including active joint inflammation and accumulated deformity. Future studies will delineate whether optimizing body fat and muscle composition (i.e., in the context of a dietary and/or conditioning physical activity intervention) may attenuate disability in patients with RA, independent of articular inflammation and damage. In the absence of interventional trials, these findings suggest that practitioners should encourage muscle strengthening and fat loss in their patients with RA as a method of reducing disability.

AUTHOR CONTRIBUTIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

Dr. Giles 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 design. Giles, Andersen, Bathon.

Acquisition of data. Giles, Bathon.

Analysis and interpretation of data. Giles, Bartlett, Fontaine, Bathon.

Manuscript preparation. Giles, Bartlett, Andersen, Fontaine, Bathon.

Statistical analysis. Giles.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES

We would like to thank the Johns Hopkins Bayview Medical Center General Clinical Research Center and staff for providing support for the DXA scanning used in this study. We are indebted to the dedication and hard work of the ESCAPE RA staff: Marilyn Towns, Michelle Jones, Patricia Jones, Marissa Hildebrandt, and Shawn Franckowiak. Drs. Uzma Haque, Clifton Bingham III, Carol Ziminski, Jill Ratain, Ira Fine, Joyce Kopicky-Burd, David McGinnis, Andrea Marx, Howard Hauptman, Achini Perera, Peter Holt, Alan Matsumoto, Megan Clowse, Gordon Lam, and others generously recommended their patients for this study.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. SUBJECTS AND METHODS
  5. RESULTS
  6. DISCUSSION
  7. AUTHOR CONTRIBUTIONS
  8. Acknowledgements
  9. REFERENCES
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