Abstract
Objective
To quantify muscle outcomes, independent of fat mass, in rheumatoid arthritis (RA) patients compared to healthy controls.
Methods
Quantitative computed tomography scans measured calf muscle and fat cross-sectional area (CSA) and muscle density (an index of intramuscular adipose tissue), and isometric dynamometry was used to measure ankle muscle strength in 50 participants with RA ages 18–70 years and 500 healthy controls. Multivariable linear regression models assessed muscle deficits in RA after adjusting for group differences in adiposity and assessing for an altered muscle–fat association. Associations between RA disease characteristics and fat-adjusted muscle outcomes were also assessed.
Results
Compared to controls, RA subjects had significantly greater body mass index (BMI) and fat area, and lower muscle area, muscle density, and muscle strength (P < 0.001 for all). Strength deficits were eliminated with adjustment for the smaller muscle area. The magnitude of muscle deficits, relative to controls, was significantly greater (P < 0.03 for interaction) in participants with lower fat area and BMI. Among those in the lower tertiles of adiposity, RA subjects demonstrated more significant deficits compared to controls with similar adiposity. In contrast, among those in the highest tertile for adiposity, RA was not associated with muscle deficits. Among RA, greater Sharp/van der Heijde scores were associated with lower muscle CSA and muscle density. Greater disease activity and disability were associated with low muscle density.
Conclusion
Deficits in muscle area and muscle density are present in RA patients compared to controls and are most pronounced in subjects with low fat mass. Greater joint destruction is associated with greater muscle deficits.
INTRODUCTION
Rheumatoid arthritis (RA) is associated with an increased risk of disability, fractures, and early death. Rheumatoid cachexia has been defined as low lean mass, frequently associated with normal or greater total fat mass (1–4); this pattern has also been referred to as cachectic obesity. Muscle deficits and excess adiposity have implications for comorbidities in RA (5–8); therefore, it is important to quantify alterations in body composition and identify risk factors in RA patients.
Among healthy subjects, lean mass is positively correlated with fat mass (8–10) such that obese subjects have greater lean mass compared to nonobese subjects. Therefore, the assessment of muscle outcomes in RA should consider the greater fat mass frequently observed in these patients (11). Furthermore, RA patients may have reduced muscle strength due to greater intramuscular fat infiltration, which is indicated by decreased muscle density on peripheral quantitative computed tomography (QCT) scans. Studies in a large community-based cohort demonstrated that greater fat indices were associated with greater intramuscular fat infiltration (12,13). One should also recognize that the association between muscle outcomes and adiposity might be altered in a disease state characterized by inflammatory cachexia such as RA. In this context, making a simple adjustment for adiposity without inclusion of an interaction term would be inappropriate because the extent of muscle deficits in RA patients compared to controls may vary according to the extent of adiposity (14).
To our knowledge, prior studies evaluating muscle outcomes in RA have not included the robust sample of healthy controls necessary to adjust for demographic characteristics and adiposity. We hypothesized that RA would be associated with deficits in muscle cross-sectional area (CSA), muscle density, and muscle strength after adjusting for differences in adiposity. Furthermore, we hypothesized that the association between muscle and fat outcomes may be altered in an inflammatory disease state such as RA. The objectives of this study were to 1) quantify the differences in muscle CSA, muscle density, and muscle strength between RA patients and healthy controls after adjusting for group differences in adiposity; 2) determine if there is an altered muscle–fat association in RA subjects compared to controls; and 3) evaluate associations between disease characteristics and muscle outcomes in RA adjusted for adiposity.
SUBJECTS AND METHODS
Study setting and participants
RA subjects ages 18–70 years who met the 2010 American College of Rheumatology criteria (15) were recruited from the University of Pennsylvania (UPenn) rheumatology practices. Subjects with juvenile idiopathic arthritis (or another inflammatory arthritis), active cancer, a history of chronic diseases known to affect bone health (e.g., chronic kidney disease, liver disease, malabsorption syndromes), or pregnancy were excluded. Adults ages 21–78 years (239 men and 261 women) were enrolled as healthy reference participants for multiple bone studies at UPenn, as previously described (8). These participants were recruited from UPenn internal medicine clinics and the surrounding community using flyers and newspaper advertisements. Exclusion criteria included a history of chronic diseases or medications known to affect nutrition or bone health, such as a reported history of diabetes mellitus, malabsorption syndromes, chronic kidney disease, liver disease, thyroid disease, or malignancy. The bone and body composition results in the controls have been described previously (8). All study subjects (RA and controls) underwent scans in the same laboratory using identical equipment and methods. For each subject, all studies were performed at the same study visit. The protocols were approved by the Institutional Review Board at UPenn and informed consent was obtained from all participants.
Assessment of anthropometrics and race
Weight and height were measured using a digital scale (Scale-Tronix) and stadiometer (Holtain), respectively. Body mass index (BMI) was calculated by dividing weight in kilograms by height in meters squared. Participants self-identified race according to National Institutes of Health categories.
Peripheral QCT
Muscle and fat measures in the left calf were obtained by peripheral QCT (Stratec XCT2000 12-detector unit, Orthometrix) with a voxel size of 0.4 mm, slice thickness of 2.3 mm, and scan speed of 25 mm/second. All scans were analyzed with Stratec software, version 6.00. A scout view was obtained to place the reference line at the proximal border of the distal end plate. Calf muscle and subcutaneous fat CSA (mm2) were assessed 66% proximal to the distal physis using the thresholds 40 mg/cm3 for fat/lean separation and 711 mg/cm3 for lean/bone separation. Quality control was monitored daily using a phantom. In our laboratory, the coefficient of variation (CV) for short-term precision ranged from 0.5–1.6% for peripheral QCT outcomes.
The peripheral QCT measure of muscle density (mg/cm3) was used as a composite index of intra- and extramyocellular fat content, as previously described (5,16). Edge detection and threshold techniques were used to separate tissues (fat, muscle, and bone) based on attenuation characteristics that are directly related to tissue composition and density (17,18). Images were filtered prior to being analyzed using contour mode 3 (−101 mg/cm3) to find skin, and peel mode 2 (40 mg/cm3) to separate adipose and muscle/bone, respectively. Images were filtered subsequently with a combination 3 × 3 and double 5 × 5 kernel image filter that clearly defined the edge of the muscle using contour mode 31 (40 mg/cm3). All bone was identified using a threshold of 150 mg/cm3 and mathematically removed to generate results for muscle density.
Dynamometric measurement of muscle strength
Muscle strength was assessed using Biodex Multi-Joint System 3 Pro Dynamometer. High intrarater (range 0.97–0.99) and interrater (range 0.93–0.96) intraclass correlation coefficients have been reported (19). Peak isokinetic torque (foot-pounds) was measured in triplicate at −10, 0, 10, and 20 degrees and the highest value recorded for both dorsiflexion and plantar flexion. We report strength as peak isometric torque (foot-pounds) in dorsiflexion (with the foot placed in 20° of plantar flexion), since this measurement had the best reproducibility in our laboratory (CV 4.3%) and had the best fit (R2) in prior regression models related to bone outcomes (20).
Physical activity questionnaire
Physical activity was assessed using a detailed questionnaire developed for the Multi-Ethnic Study of Atherosclerosis (21). We used a definition of intentional exercise (the sum of walking for exercise, sports/dancing, and conditioning metabolic equivalent [MET] hours/week) that has been previously defined (8,22). The total number of reported sedentary hours per week was also recorded.
Disease measures and inflammatory markers
Clinical laboratory assays (erythrocyte sedimentation rate [ESR] and C-reactive protein [CRP]) were performed using standard methods. ESR was performed using the Westergren method. CRP levels were measured using a fixed-point immunorate assay. Medication use was determined by subject self-report. Disease activity and disability were measured in a standard manner using the Disease Activity Score in 28 joints (DAS28) using the CRP level and the Health Assessment Questionnaire (HAQ) (23). Standard radiographs of the hands and feet were performed and Sharp/van der Heijde (SHS) scores were determined by a trained radiologist (ET, WK) in a blinded manner.
Statistical analysis
Statistical analyses were performed using Stata 11 software (StataCorp). Differences in means between RA patients and controls were assessed using Student’s t-test or the Wilcoxon rank sum test, as appropriate. Group differences in categorical variables were assessed using the chi-square test.
Determination of muscle and fat Z scores
BMI and the muscle and fat outcomes vary according to sex, race, and age. Therefore, these outcomes were converted to sex- and race-specific Z scores (SD scores) relative to age using the results in the 500 healthy control participants (24). Peripheral QCT muscle and fat CSA were converted to Z scores using the LMS method; this is the same method used to generate body composition Z scores in adult National Health and Nutrition Examination Survey participants (24). These peripheral QCT Z scores were further adjusted for tibia length, as previously described (25,26). The LMS method accounted for the nonlinearity, heteroscedasticity, and skew noted in these outcomes in the reference participants. BMI, muscle density, and muscle strength were converted into sex- and race-specific Z scores relative to age, based on healthy control distributions. Muscle strength Z scores were further adjusted for tibia length.
Comparison of muscle between RA patients and controls
Linear regression models were used to determine the difference in the muscle CSA Z score outcomes between RA patients and controls, adjusting for fat CSA Z scores. We previously reported that muscle CSA was not associated with muscle density in the reference participants (8). Among controls, the correlation between BMI Z score and muscle density Z score (R = −0.35, P < 0.001) was greater than the correlation between fat CSA Z score and muscle density Z score (R = −0.28, P < 0.001; P = 0.04 for comparison). Therefore, in muscle density Z score models, BMI Z score was used as the primary measure of body fat. Multivariable linear regression models comparing muscle outcomes between RA patients and controls were further adjusted for group differences in physical activity and reported sedentary time.
Assessment of modified association between muscle and fat in RA
In order to determine if the associations between muscle and fat outcomes in RA patients differed compared with controls, we included a multiplicative interaction term (RA × fat) in the linear regression models. The criterion for significance of the interaction term was a P value of less than 0.05. Scatter plots were used to demonstrate the associations between muscle and fat Z scores with attention to RA status and the differences in slopes in RA participants and controls. Differences in the mean muscle CSA and mean muscle density Z scores in RA and controls stratified by tertile of adiposity (fat CSA Z score or BMI Z score) were also assessed.
Assessment of associations between disease characteristics and muscle outcomes
Muscle outcomes (muscle CSA and muscle density Z scores) were adjusted for fat indices (fat CSA or BMI Z scores) by adjusting individual muscle Z scores according to the observed association between muscle and fat Z scores in linear regression analyses within the RA subjects. Disease-specific outcomes were non-normal in distribution. Therefore, correlations between disease characteristics, RA-specific medication use, and muscle outcomes (and fat-adjusted muscle outcomes) were assessed with nonparametric tests of correlation (Spearman’s rank correlation).
RESULTS
Participant characteristics in RA patients and controls are shown in Table 1. BMI and BMI Z scores were significantly greater in the subjects with RA compared with controls. The median RA disease duration was 10 years and the mean ± SD DAS28-CRP score was 3.14 ± 1.17 in the setting of frequent use of methotrexate, prednisone, and therapy with biologic agents.
Table 1.
Characteristics of RA subjects compared to healthy controls*
| RA subjects (n = 50) |
Controls (n = 500) |
P | |
|---|---|---|---|
| Age, years | 51.2 ± 13.3 | 50.0 ± 16.0 | 0.2 |
| Women, no. (%) | 39 (78) | 261 (52) | <0.001 |
| African American, no. (%) | 18 (36) | 220 (44) | 0.3 |
| BMI, kg/m2 | 30.1 ± 8.5 | 26.6 ± 5.6 | <0.001 |
| BMI Z score | 0.63 ± 1.49 | 0.00 ± 0.92 | <0.001 |
| Calf muscle CSA, cm2 | 64.4 ± 12.5 | 71.7 ± 13.0 | <0.001 |
| Calf muscle CSA Z score | −0.42 ± 1.23 | 0.00 ± 1.00 | 0.008 |
| Fat CSA, cm2 | 32.6 ± 14.9 | 25.4 ± 13.0 | <0.001 |
| Fat CSA Z score | 0.35 ± 1.13 | 0.00 ± 1.00 | 0.02 |
| Muscle density, mg/cm3 | 72.9 ± 2.5 | 74.6 ± 2.5 | <0.001 |
| Muscle density Z score | −0.58 ± 0.80 | 0.00 ± 0.81 | <0.001 |
| Ankle torque (strength), foot-pounds | 19.4 ± 7.2 | 23.7 ± 8.5 | <0.001 |
| Intentional exercise, median (IQR) MET hours/week | 17.7 (1.6–47.5) | 26.8 (7.7–69.6) | 0.06 |
| Sedentary time, median (IQR) hours/week | 16.25 (10–28) | 10.5 (5–21) | 0.001 |
| RA disease characteristics | |||
| DAS28-CRP | 3.14 ± 1.17 | – | |
| HAQ score | 0.89 ± 0.70 | – | |
| CCP positive, no. (%) | 38 (76) | – | |
| Disease duration, median (IQR) years | 10 (4–19) | – | |
| Current methotrexate, no. (%) | 32 (64) | – | |
| Current therapy with a biologic agent, no. (%) | 32 (64) | – | |
| Current prednisone, no. (%) | 20 (40) | – | |
| Ever methotrexate, no. (%) | 41 (82) | – | |
| Ever therapy with a biologic agent, no. (%) | 38 (76) | – | |
| Ever prednisone, no. (%) | 40 (80) | – |
Values are the mean ± SD unless indicated otherwise. RA = rheumatoid arthritis; BMI = body mass index; CSA = cross-sectional area; IQR = interquartile range; MET = metabolic equivalent; DAS28-CRP = Disease Activity Score in 28 joints using the C-reactive protein level; HAQ = Health Assessment Questionnaire; CCP = cyclic citrullinated peptide antibody.
Comparison of muscle between RA patients and controls
In crude comparisons between RA patients and controls, muscle CSA Z scores were significantly lower and fat CSA Z scores were significantly greater in RA patients compared with controls (Table 1). The multivariable model is shown in Table 2. Within controls and RA participants, there was a positive association between fat CSA Z score and muscle CSA Z score (Figure 1A and Table 2). RA was associated with significantly lower muscle density and muscle density Z scores on average (Table 1). Greater BMI Z score was associated with lower muscle density Z scores in controls, as shown in Figure 1B and Table 2.
Table 2.
Multivariable linear regression analysis of associations between RA status, fat indices, physical activity, and muscle outcomes*
| Muscle CSA Z score (R2 0.085), β (95% CI)† |
Muscle density Z score (R2 0.16), β (95% CI) |
Muscle strength Z score (R2 0.087), β (95% CI)† | |
|---|---|---|---|
| RA | −0.60 (−0.93, −0.30)‡ | −0.58 (−0.82, −0.34)‡ | −0.18 (−0.40, −0.037) |
| Muscle CSA Z score | 0.17 (0.11, 0.23)‡ | ||
| Fat CSA Z score | 0.23 (0.24, 0.32)‡ | ||
| RA × fat CSA Z score (interaction) | 0.30 (0.027, 0.57)§ | ||
| BMI Z score | −0.32 (−0.40, −0.24)‡ | ||
| RA × BMI Z score (interaction) | 0.34 (0.14, 0.54)‡ |
RA = rheumatoid arthritis; CSA = cross-sectional area; 95% CI = 95% confidence interval; BMI = body mass index.
Adjusted for tibia length.
P < 0.001.
P < 0.05.
Figure 1.
Scatter plots demonstrating associations between A, peripheral quantitative computed tomography muscle and fat cross-sectional area (CSA) Z scores, and B, muscle density and body mass index Z scores, in rheumatoid arthritis patients and controls.
Muscle strength Z scores were significantly lower in RA (mean ± SD −0.24 ± 0.68; P = 0.02). RA subjects who reported pain during the test demonstrated significantly lower muscle strength than those who did not report pain (mean ± SD 17.9 ± 5.5 versus 21.8 ± 5.4; P = 0.03). The difference in muscle strength between RA patients and controls was not significant after excluding the 12 RA subjects who reported pain during the test (β = −0.087 [95% confidence interval −0.33, 0.15], P = 0.5). In a multivariate model adjusted for muscle CSA Z scores, muscle strength Z scores did not differ between RA patients and controls (Table 2), suggesting that muscle CSA explained a significant proportion of the group differences in muscle strength.
Assessment of modified association between muscle and fat in RA
There was a significant positive coefficient for the RA × fat CSA Z score interaction term that signified that among subjects with a lower fat CSA Z score, there were the greatest differences in CSA Z score between RA participants and controls. This is evident in Figure 1A, where the relative muscle deficits in RA compared with controls were most pronounced among those with low fat CSA Z scores and were substantially attenuated among those with greater fat CSA Z scores. The interaction term indicates that the slope was significantly steeper in the RA participants compared with the controls. Overall, this model suggested that RA-related deficits in muscle CSA compared to controls were more pronounced among subjects with lower fat CSA Z scores (Table 2). Adjustment for group differences in reported sedentary time or MET hours of physical activity did not attenuate these associations. Figure 1A shows the association between muscle and fat CSA Z scores in RA patients and controls.
The interaction with fat CSA Z score is also shown in Table 3. There were significant deficits in muscle CSA compared to controls among subjects within the lowest fat CSA Z score tertile. Among subjects in the highest tertile for fat CSA Z score, there was no difference in muscle CSA between RA subjects and controls.
Table 3.
Muscle CSA and muscle density Z scores in RA subjects and healthy controls stratified by measures of adiposity (tertile of fat CSA or BMI)*
| RA patients
|
Controls
|
P | |||
|---|---|---|---|---|---|
| Mean ± SD | N | Mean ± SD | N | ||
| Muscle CSA Z score | |||||
| Fat CSA Z score tertile | |||||
| 1 | −1.39 ± 1.18 | 12 | −0.25 ± 0.98 | 159 | 0.0002 |
| 2 | −0.52 ± 0.95 | 14 | 0.052 ± 0.99 | 157 | 0.04 |
| 3 | 0.24 ± 1.05 | 20 | 0.21 ± 0.99 | 151 | 0.9 |
| Muscle density Z score | |||||
| BMI Z score tertile | |||||
| 1 | −0.63 ± 0.80 | 13 | 0.20 ± 0.74 | 159 | 0.0002 |
| 2 | −0.67 ± 0.96 | 11 | 0.12 ± 0.74 | 160 | 0.001 |
| 3 | −0.50 ± 0.74 | 22 | −0.35 ± 0.86 | 145 | 0.4 |
CSA = cross-sectional area; RA = rheumatoid arthritis; BMI = body mass index.
Differences in muscle CSA Z score and muscle density Z score between RA patients and controls were assessed after adjusting for differences in fat CSA Z score (Table 2). Greater BMI Z score was associated with lower muscle density Z scores in the controls, as shown in Figure 1B and Table 2. There was a significant positive coefficient for the RA × BMI Z score interaction term in Table 2 that signified that the slope of the association between BMI Z score and muscle density was significantly less negative in RA participants compared with controls. Among RA patients, the inverse association between BMI Z score and muscle density seen among controls was not observed. Adjustment for group differences in reported sedentary time or MET hours of physical activity did not attenuate these associations (data not shown). Figure 1B demonstrates that the lower muscle density Z scores in RA patients compared with controls were most pronounced among control and RA participants with lower BMI Z scores.
The interaction with BMI Z score is also shown in Table 3. There were greater differences in muscle density between RA patients and controls among those subjects within the lowest BMI tertile. Among subjects within the highest tertile for BMI Z score, there was no difference in muscle density between RA subjects and controls.
Assessment of associations between disease characteristics and muscle outcomes
Associations between RA disease characteristics and muscle outcomes (before and after fat adjustment) are shown in Table 4. The total SHS score was inversely correlated with the muscle CSA Z score (Spearman’s ρ = −0.33, P = 0.04). None of the other RA disease characteristics correlated with muscle CSA Z scores, with or without adjustment for fat CSA Z score. Current or prior use of methotrexate, therapy with biologic agents, or prednisone was not significantly associated with muscle CSA Z score (data not shown).
Table 4.
Spearman’s rank correlations between disease characteristics and muscle CSA and muscle density outcomes (Z scores) before and after adjusting for fat Z scores (fat CSA or BMI)*
| Muscle CSA | Fat-adjusted muscle CSA | Muscle density | BMI-adjusted muscle density | |
|---|---|---|---|---|
| DAS28-CRP (per 1 unit) | −0.20 | 0.18 | −0.30† | −0.33† |
| HAQ score (per 1 unit) | −0.12 | 0.11 | −0.39† | −0.33† |
| Total SHS score (per 1 unit) | −0.33† | −0.29 | −0.27 | −0.36† |
| Disease duration (per 1 year) | −0.18 | −0.14 | −0.28 | −0.25 |
| Exercise (per MET hour) | 0.052 | 0.012 | 0.011 | −0.050 |
| Sedentary time (per 1 hour) | 0.24 | 0.20 | −0.10 | −0.24 |
CSA = cross-sectional area; BMI = body mass index; DAS28-CRP = Disease Activity Score in 28 joints using the C-reactive protein level; HAQ = Health Assessment Questionnaire; SHS = Sharp/van der Heijde; MET = metabolic equivalent.
P < 0.05.
Analyses within the RA participants identified risk factors for lower muscle density Z scores (Table 4). Lower Z scores were associated with greater DAS28-CRP, greater HAQ scores, and greater SHS scores (P < 0.05 for all). Muscle density Z scores were not significantly associated with disease duration, sedentary hours, or the number of MET hours per week of physical activity in RA. Current or prior use of methotrexate, therapy with biologic agents, or prednisone was not significantly associated with muscle density Z score (data not shown).
DISCUSSION
The term “rheumatoid cachexia” was coined to capture the pattern of low muscle mass and normal to high fat mass observed in adults with inflammatory disorders (3). However, to our knowledge, this is the first study to examine interactions between muscle and fat in RA patients compared with controls, and to identify risk factors for muscle deficits independent of fat mass.
Overall, RA participants demonstrated substantial deficits in muscle CSA, density, and strength compared with controls. The deficits in muscle area and density observed in RA patients compared with controls were significantly greater among those with less adiposity. RA participants in the lowest tertile for adiposity showed profound deficits compared to controls with similar adiposity (based on fat CSA or BMI). Among RA participants in the highest tertile for adiposity, muscle deficits were not present. Therefore, obese RA participants had muscle measures comparable to obese controls. Therefore, while on average RA is associated with adverse muscle outcomes, the excess risk is disproportionately greater among those with low fat stores.
In addition to low muscle CSA, subjects with RA demonstrated lower muscle density. While prior studies have also reported that lower muscle density is associated with greater disease activity in RA (27), this is the first study to include healthy controls. Importantly, the lower muscle density in RA patients compared with controls was not explained by group differences in fat CSA or BMI. Furthermore, we found that the strong inverse association between BMI and muscle density present in controls was not detected in RA patients. Modification of the effect of fat on muscle density suggests mechanisms for low muscle density in this group that are distinct from those in controls. Kramer et al showed that muscle density was lower among subjects with RA who had greater inflammatory disease and worse functional status, suggesting that inflammatory cytokines and decreased physical activity may play a role (27). We similarly found that greater DAS28-CRP and greater HAQ scores were associated with lower muscle density, supporting the concept that the inflammatory disease may be the etiology of these deficits. Differences between RA patients and controls in the distribution of fat (subcutaneous versus visceral) may also be implicated and deserve further study.
We believe that these observations add to previous epidemiologic studies showing greater risk of fracture, joint destruction, and death among RA subjects with low BMI (28–30). The most severe muscle deficits in RA may be the result of a severe disease phenotype that is toxic to muscle and also associated with greater resting energy expenditure over time, resulting in smaller fat stores (31–34). Conversely, greater fat could beneficially modify the disease through the secretion of biologic mediators such as adiponectin and other adipocytokines (35).
This study also specifically explored the association between disease-specific characteristics and muscle deficits in RA. We observed that greater SHS scores were associated with greater deficits in both muscle CSA and muscle density. We also confirmed previous reports that greater disease activity scores and HAQ scores were associated with lower muscle density among RA patients (27). Overall, these observations further suggest that muscle deficits are associated with a severe disease phenotype, perhaps related to greater systemic inflammation.
Limitations of our study include the relatively small number of RA subjects. The level of disease control among these subjects may also limit the generalizability to patients with more active disease. It is noteworthy that we identified significant muscle deficits in a cohort with relatively well-controlled disease. Analyses evaluating group differences in muscle strength in participants with pain may have overestimated RA deficits in strength, while sensitivity analyses limited to those without pain may have underestimated these deficits. Finally, temporal associations between body composition, disease characteristics, and physical activity measures could not be assessed due to the cross-sectional design.
In conclusion, RA is associated with muscle deficits compared to healthy controls, and these deficits are most pronounced among subjects with low fat stores. Further study is needed in this area and could result in novel therapies that may help to improve muscle mass and reduce the risk of fracture, falls, and perhaps other outcomes in diseases associated with cachexia.
Significance & Innovations.
Rheumatoid arthritis (RA) subjects have significant deficits in muscle mass and muscle density compared to controls after considering differences in fat.
RA is associated with an altered association between muscle and fat such that the greater deficits in muscle compared to controls were seen among those with lower adiposity.
Greater joint destruction is associated with low muscle cross-sectional area and low muscle density among RA subjects.
Acknowledgments
Supported by the University of Pennsylvania Clinical and Translational Research Center (UL1-RR024134).
Footnotes
AUTHOR CONTRIBUTIONS
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. Baker 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. Baker, Von Feldt, Leonard.
Acquisition of data. Baker, Leonard.
Analysis and interpretation of data. Baker, Von Feldt, Mostoufi-Moab, Noaiseh, Taratuta, Kim, Leonard.
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