Muscle Density in Rheumatoid Arthritis: Associations with Disease Features and Functional Outcomes

Abstract

OBJECTIVE

To explore the associations of thigh computed tomography (CT)-derived measures of body composition with functional outcomes in patients with rheumatoid arthritis (RA).

METHODS

Patients with RA underwent bilateral mid-femoral quantitative CT for measurement of thigh fat area (TFA), muscle area (TMA), and muscle density (TMD). The associations of thigh composition measures with disability and physical performance, measured with the Health Assessment Questionnaire (HAQ), Valued Life Activities (VLA) scale, and Short Physical Performance Battery (SPPB), were explored for the total cohort and by gender, controlling for pertinent demographic, lifestyle, and RA disease and treatment covariates.

RESULTS

A total of 152 RA patients were studied. Among potential determinants of TMD, higher age, higher duration of sedentary activity, longer RA duration, higher tender joint count, higher serum IL-6 levels, use of glucocorticoids, and non-use of hydroxychloroquine were all significantly associated with lower TMD in multivariable modeling. RA characteristics accounted for 77% of the explainable variability in TMD. When co-modeled, higher TFA and lower TMD, but not lower TMA, were significantly and independently associated with higher HAQ scores, lower SF-36 total physical scores, lower composite SPPB scores, and a greater proportion of affected obligatory VLAs.

CONCLUSIONS

Thigh CT-derived measures of body composition, particularly fat area and muscle density, were strongly associated with disability and physical performance in RA patients, with RA disease features as potential determinants. Efforts to reduce fat and improve muscle quality may reduce disability in this population with impaired physical functioning.

INTRODUCTION

Rheumatoid arthritis (RA) is a systemic inflammatory disorder affecting 1–2% of adults that frequently leads to progressive joint deformity and disability. This disability is costly, with the direct medical cost of RA disability amounting to between 9,000 and 19,000 USD per year (1), and indirect costs, such as loss of work, compound the expense (2). In addition, greater disability in RA portends poorer health outcomes, including higher all-cause and cardiovascular mortality (3).

Contributors to disability in RA go beyond articular swelling, tenderness, and deformity. Several of the major extra-articular determinants include mood (4), sleep (5), and more recently studied, body composition. RA patients, on average, have lower body cell mass (6), lower lean mass (7) and higher fat mass (7, 8)compared to otherwise similar non-RA controls. Lower lean and higher fat mass were independently associated with higher levels of disability in RA patients (9). However, distinct from the amount of muscle, the density of muscle may also affect physical functioning. Low muscle density, reflecting reduced muscle contractile units accompanied by fatty replacement (10, 11), was associated with aging, deconditioning, and disuse in the general population (12, 13). As these factors are features of the RA disease state, it is conceivable that low muscle density, in addition to—or even independent of—low muscle mass, may contribute to physical dysfunction in RA patients.

Computed tomography (CT) scanning at the level of the mid-thigh with quantification of the areas and densities of fat, muscle, and bone is a validated, reproducible technique for directly assessing whole and regional body composition (14–16). Studies of CT-assessed muscle density in the general population have shown it to be a suitable determinant of muscle quality, in terms of prediction of strength (17, 18), physical performance (18–20), and incidence of mobility limitations (21, 22). However, to date, there have been no studies using CT to assess muscle area or density in the RA population, with most studies employing indirect measures, such as bioelectrical impedance, total body DXA, or potassium counting.

Therefore, for the present study, we explored the associations of RA disease characteristics with thigh muscle density (TMD), muscle area (TMA), and fat area (TFA), assessed with quantitative CT. We hypothesized that low thigh muscle density would be associated with disability and low physical performance scores, independent of muscle and fat area.

METHODS

Study Participants and Timing of Visits

Study subjects were participants in ESCAPE RA (Evaluation of Subclinical Cardiovascular disease And Predictors of Events in Rheumatoid Arthritis), a cohort study investigating the prevalence, progression, and risk factors for subclinical cardiovascular disease in RA (23). A total of 197 RA patients completed the baseline study visit, all of whom met 1987 classification criteria for RA (24), were 45–84 years of age, and did not report any prior pre-specified cardiovascular events or procedures. The study was approved by the Institutional Review Board of the Johns Hopkins Hospital. Thigh computed tomography (CT) was performed at the third study visit, for which a total of 158 (80%) returned, occurring an average of 39 ± 4 months post-baseline.

Assessments

Body Composition Assessments

Study participants underwent bilateral mid-thigh CT on the same Aquilon 64-slice CT scanner (Toshiba America Medical Systems, Tustin, California). A single transverse section was obtained midway between the greater trochanter and femoral condyles, localized from a coronal scout film. The scans were analyzed with BonAlyse (BonAlyse Oy, Jyvaskyla, Finland) software, which quantifies areas and densities of muscle, fat, and bone in operator selected regions. To exclude artifacts and objects outside the patient, the outline of the thigh was traced manually by a technician blinded to patient characteristics (HRK). TFA was defined as the quantity of tissue between −190 to −30 Hounsfield units (HU) and included both subcutaneous fat tissue and fat tissue intercalated between muscle tissue. Marrow-associated fat tissue was excluded from TFA.

Participants also underwent total body DXA on a Lunar Prodigy DXA scanner (GE/Lunar Radiation Corp., Madison WI) to measure total and regional fat and lean mass. Anthropometric measures [height, weight, body mass index (BMI), and waist and hip circumferences] were assessed as previously described (25). Body mass index (BMI) was calculated as body weight in kilograms divided by the square of the height in meters (kg/m²).

Functional Outcomes

Functional outcomes were assessed concurrently with body composition assessments. The Stanford Health Assessment Questionnaire (HAQ) (26) and the physical performance subscale of the SF-36 questionnaire (SF-36PF) (27) were used to assess disability related to common activities. The HAQ has a range of 0–3, with higher HAQ scores indicative of greater disability. SF-36PF scores range from 0–100, with higher scores indicative of less disability.

Physical performance was assessed using the extended Short Physical Performance Battery (SPPB)(28). The SPPB includes assessments of balance (side-by-side, semi-tandem, and one-leg stands), strength (single and repeated chair stands), and gait and endurance (usual and fast paced 6-meter walk, 400-meter walk test). Scoring range is 0–4 based on completion/time for each of the 5 testing domains, for a total possible score of 20.

Disability in a broader range of activities, including activities outside of the fundamental physical actions assessed with HAQ, was assessed with the Valued Life Activities (VLA) questionnaire (29). This 29-item instrument assesses RA-related limitations in participation in obligatory activities (i.e. self-care, ambulation, transit; 4 items), committed activities (i.e. housework, preparing meals, shopping, care of family members; 10 items), and discretionary activities (i.e. travelling, leisure activities, recreation, socializing; 15 items) as a consequence of the participant’s RA. Activities not engaged in by, or unimportant to, the participant do not count towards the score.

Other Assessments

Age, gender, race/ethnicity, and current and past smoking were assessed by self-report. Forty-four joints were examined for swelling and tenderness by a single trained assessor and RA disease activity was calculated using the Disease Activity Score for 28 joints with CRP (DAS28-CRP) (30). Radiographs of the hands and feet were obtained and scored using the van der Heijde modification of the Sharp method (SHS) (31) by a single experienced reader blinded to patient characteristics. Physical activity was assessed with the 7-Day Physical Activity Recall Questionnaire (32) with 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) calculated for each participant. Duration of television watching, a measure of sedentariness, was also assessed by self-report. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale (CES-D) (33). Fatigue was assessed using the FACIT-fatigue questionnaire (34). Thyroid disease was classified based on the use of thyroid replacement therapy. Current and past use of glucocorticoids, and biologic and non-biologic disease modifying anti-rheumatic drugs (DMARDs) were queried by detailed examiner-administered questionnaires.

Laboratory Assessments

Fasting blood samples were collected and stored at −80C. C-reactive protein (CRP) was measured by nephelometry and IL-6 by chemiluminescent enzyme immunoassay (CLEIA) at the Laboratory for Clinical Biochemistry Research (University of Vermont, Burlington, VT). Rheumatoid factor (RF) was assessed by ELISA, with seropositivity defined as a level at or above 40units. Anti-CCP antibody was assessed by ELISA, with seropositivity defined as a level at or above 60 units. Exon 2 of HLA-DR1 was sequenced for shared epitope alleles as previously described (35).

Statistical Analysis

The distributions of all variables were examined. Univariate linear regression models were constructed to explore the associations of thigh composition outcomes with participant characteristics included as covariates, with beta coefficients, 95% confidence intervals (CIs) and their associated p-values calculated. Where required, variables were transformed to normality. To identify indicators of thigh fat and muscle measures, multivariable models were constructed with covariates carried over from univariate models with p≤0.20 (to allow for residual confounding). More parsimonious multivariable models were constructed by excluding covariates with the weakest associations with the outcome, with the impact of excluding each covariate tested using Akaike’s Information Criterion (AIC) for nested models. The adjusted coefficient of variability (R²) was used to estimate the total proportion of the variability in the outcome predicted by the modeled covariates. Variance inflation factors (VIF) were calculated to ensure that variables with excessive collinearity were not modeled simultaneously.

To investigate the association of thigh fat and muscle measures with functional outcomes, multivariable models were constructed with functional measures modeled as outcomes and thigh composition measures modeled as exposures of interest. We considered confounding covariates as those associated with both the thigh composition measures of interest and the functional outcomes. More parsimonious multivariable models were constructed as described above. Spearman correlation coefficients were calculated for correlation of anthropometric and DXA-derived body composition measures with functional outcomes compared to those of thigh composition measures. All statistical calculations were performed using Intercooled Stata 10 (StataCorp, College Station, TX). A two-tailed α of 0.05 was used throughout.

RESULTS

Characteristics of the 152 RA patients are summarized in Table 1. Participants were middle-aged or older (range 47 – 84 years; mean age 63 years), predominantly female (65%) and Caucasian (88%). On average, the cohort was comprised of patients with longer-standing disease (median disease duration=12 years) with current disease activity in the low to moderate range, although high disease activity (DAS28>5.1) was observed in 17 patients (11%). As a whole, the cohort reported mild to moderate disability (median HAQ=0.69), achieved sub-maximal scores on physical performance testing (mean SPPB score=12 out of a possible 20), and reported a median of 31% of their valued life activities affected by their RA.

Table 1.

Characteristics of 152 RA Patients Undergoing Thigh Fat and Muscle Composition Assessment

Characteristic	Total (n = 152)	Women (n = 98)	Men (n = 54)	p
Age, years (Range 47–84)	63 ± 8	63 ± 8	62 ± 9	0.74
Caucasian, n (%)	133 (88)	84 (86)	49 (91)	0.37
Any college, n (%)	118 (78)	76 (78)	42 (78)	0.97
Exercise, minutes/day	29 (9 – 75)	26 (6 – 62)	51 (9 –112)	0.028
TV watching, hours/day	2 (1 – 3)	2 (1 – 3)	2 (1 – 3)	0.66
CES-Depression score	5 (3 – 10)	6 (3 – 11)	5 (2 – 9)	0.063
Hormone replacement*, n (%)	9 (9)	9 (9)	n/a	--
Ever smoking, n (%)	81 (53)	43 (44)	38 (70)	0.002
Current smoking, n (%)	12 (8)	5 (5)	7 (13)	0.085
Thyroid disease, n (%)	26 (17)	20 (20)	6 (11)	0.14
Diabetes, n (%)	11 (7)	7 (7)	4 (7)	0.97
RA duration, years	12 (8 – 20)	13 (7 – 20)	10 (9 – 18)	0.60
RF or anti-CCP seropositivity, n (%)	113 (75)	74 (76)	39 (72)	0.58
Any shared epitope alleles, n (%)	105 (70)	66 (68)	39 (74)	0.48
DAS28-CRP	3.1 (2.3 – 4.1)	3.3 (2.7 – 4.3)	2.5 (1.8 – 3.4)	<0.001
CRP, mg/L	2.4 (0.9 – 6.2)	2.6 (1.1 – 7.4)	2.3 (0.8 – 4.9)	0.11
IL-6, pg/mL	4.3 (2.5 – 9.6)	4.9(2.5 – 10.4)	3.8 (2.6 – 7.3)	0.37
Total SHS	13 (3 – 53)	20 (4 – 59)	10 (2 – 29)	0.16
Pain (100mm VAS)	20 (10 – 50)	30 (10 – 50)	10 (10 – 40)	0.005
HAQ (0 – 3)	0.69 (0.13 – 1.38)	1.0 (0.38 – 1.38)	0.19 (0 – 1.12)	<0.001
SF-36 Physical Performance Score	64 ± 26	61 ± 25	71 ± 26	0.015
Physical Performance Battery Score (0–20)	12 ± 5	11± 5	14 ± 4	0.002
Proportion of VLAs Affected	0.31(0.05–0.71)	0.52 (0.17–0.75)	0.09 (0 – 0.50)	<0.001
Proportion of Obligatory VLAs	0.20 (0 – 0.60)	0.20 (0 – 0.75)	0 (0 – 0.40)	0.006
Proportion of Committed VLAs	0.50(0 – 0.86)	0.60 (0.29–1.0)	0.07 (0–0.67)	<0.001
Proportion of Discretionary VLAs	0.29 (0 – 0.69)	0.50 (0.13–0.75)	0.09 (0–0.43)	<0.001
Current prednisone, n (%)	38 (25)	25 (26)	13 (24)	0.82
Current non-biologic DMARDs, n (%)	130 (86)	88 (91)	42 (78)	0.028
Methotrexate, n (%)	106 (70)	74 (76)	32 (59)	0.028
Hydroxychloroquine, n (%)	29 (19)	20 (21)	9 (17)	0.56
Current biologic DMARDs, n (%)	70 (46)	54 (56)	16 (30)	0.002
TNF inhibitors, n (%)	53 (35)	40 (41)	13 (24)	0.034
Thigh CT-Derived Measures
Total thigh area, cm2	201 ± 63	204 ± 70	196 ± 48	0.38
Thigh muscle area, cm2	78 ± 34	65 ± 24	100 ± 36	<0.001
Thigh muscle density, mg/cm3	39 ± 5	39 ± 6	40 ± 4	0.23
Thigh fat area, cm2	124 ± 51	139 ± 54	96 ± 26	<0.001
Anthropometrics
Weight, kg	79 ± 18	73 ± 16	90 ± 16	<0.001
Height, meters	1.66 ± 0.10	1.61 ± 0.06	1.74 ± 0.09	<0.001
Body mass index, kg/m2	28.5 ± 5.3	28.9 ± 5.7	29.4 ± 4.5	0.084
Waist circumference, cm	96 ± 16	91 ± 16	104 ± 13	<0.001
Waist-to-hip ratio	0.91 ± 0.10	0.86 ± 0.07	1.01 ± 0.05	<0.001
DXA-Derived Measures
Total body fat, kg	31 ± 11	32 ± 12	29 ± 9	0.12
Fat mass index (FMI), kg/m2	11.2 ± 4.0	12.1 ± 4.2	9.5 ± 3.0	<0.001
Body fat percentage	39 ± 9	43 ± 8	32 ± 7	<0.001
Total body lean, kg	44 ± 12	38 ± 6	55 ± 10	<0.001
Fat free mass index (FFMI), kg/m2	15.9 ± 3.1	14.6 ± 2.0	18.3 ± 3.1	<0.001
Trunk fat, kg	16 ± 6	16 ± 6	17 ± 6	0.18
Appendicular fat, kg	13 ± 6	15 ± 6	11 ± 4	<0.001
Appendicular lean, kg	19 ± 6	16 ± 3	25 ± 4	<0.001

values are mean ± SD or median (IQR) unless otherwise noted

CES=Centers for Epidemiologic Studies; RA=rheumatoid arthritis; RF=rheumatoid factor; CCP=cyclic citrullinated peptide; DAS=disease activity score; CRP=C-reactive protein; IL=interleukin; SHS=Sharp van der Heijde Score; HAQ=Health Assessment Questionnaire; SF=Short Form; VLA=Valued Life Activities; DMARD=disease modifying anti-rheumatic drug; TNF=tumor necrosis factor; CT=computed tomography; DXA=dual-energy X-ray absorptiometry; SD=standard deviation; IQR=interquartile range

Crude and Adjusted Clinical Indicators of Thigh Fat and Muscle Measures

Univariate analyses of clinical indicators of thigh fat and muscle measures are summarized in the Supplemental Table. Characteristics with at least marginally significant associations were carried into multivariable models (Table 2). When co-modeled, higher age, female gender, longer RA duration, higher tender joint count, and current prednisone use were all significantly and inversely associated with TMA, while height was positively associated. Together, these 6 characteristics accounted for 47% of the variability in TMA (adjusted R²=0.473), with the RA disease-related characteristics accounting for nearly 20% of the explainable variability (Table 2, TMA Model 2). The other characteristics with significant or marginal associations with TMA in univariate models were no longer significantly associated in the multivariable model, and their exclusion did not meaningfully reduce the predictive ability of the model.

Table 2.

Multivariable Associations of Thigh Fat and Muscle Composition Measures with Participant Characteristics

	Model 1		Model 2
	β	p	β	p
Thigh Muscle Area (cm2)
Age, per year	−0.98	0.001	−1.13	<0.001
Male gender	22.9	0.001	21.7	<0.001
Height, per meter	56.3	0.086	66.6	0.022
Exercise, per 30 min	0.62	0.55
CESD, per unit	−0.11	0.77
Ever smoking	1.75	0.72
Current smoking	5.13	0.55
Thyroid disease	1.27	0.83
RA duration, per year	−0.49	0.067	−0.59	0.006
Any SE alleles	−4.48	0.38
log IL-6, per log unit	−2.36	0.30
Square root SJC	−0.21	0.93
Square root TJC	−3.92	0.031	−3.19	0.018
log Total SHS	−0.89	0.62
Pain (VAS), per unit	0.12	0.26
Current prednisone	−15.4	0.007	−15.8	0.001
Total Adjusted R2	0.473		0.474
R2 for RA characteristics	0.076(16% of total)		0.090 (19% of total)
Thigh Muscle Density (mg/cm3)
Age, per year	−0.12	0.003	−0.13	<0.001
Male gender	−0.21	0.82
Height, per meter	7.62	0.098	6.90	0.024
Any college	1.53	0.044	1.32	0.053
Exercise, per 30 min	−0.003	0.99
TV watching, per 30 min	−0.12	0.21
CESD, per unit	0.007	0.90
Thyroid disease	−0.67	0.41
Diabetes	−1.64	0.20
RA duration, per year	−0.04	0.25	−0.06	0.060
RF or anti-CCP	0.72	0.34
Any SE alleles	−0.50	0.50
log IL-6, per log unit	−0.96	0.003	−0.92	0.001
Square root SJC	0.15	0.66
Square root TJC	−0.39	0.11	−0.33	0.040
log Total SHS	0.008	0.97
Pain (VAS), per unit	−0.001	0.94
Current prednisone	−3.72	<0.001	−3.48	<0.001
Current HCQ use	1.69	0.035	1.67	0.017
Total Adjusted R2	0.381		0.414
R2 for RA characteristics	0.210 (55% of total)		0.260(63% of total)
Thigh Fat Area (cm2)
Male gender	−45.6	<0.001	−55.8	<0.001
Height, per meter	74.7	0.17	95.9	0.059
Exercise, per 30 min	0.57	0.75
RA duration, per year	0.72	0.12	0.76	0.046
log IL-6, per log unit	−1.58	0.70
Square root SJC	2.38	0.58
Square root TJC	0.24	0.94
log Total SHS	1.84	0.55
Pain (VAS), per unit	0.09	0.62
Current prednisone	−28.3	0.003	−23.4	0.006
Current MTX use	5.71	0.51
Current biologics	9.07	0.29
Total Adjusted R2	0.201		0.222
R2 for RA characteristics	0.040 (20% of total)		0.051 (23% of total)

CESD=Centers for Epidemiologic Studies Depression Score; RA=rheumatoid arthritis; SE=shared epitope; IL=interleukin; SJC=swollen joint count; TJC=tender joint count; SHS=Sharp-van der Heijde Score; VAS=visual analogue scale; RF=rheumatoid factor; CCP=cyclic citrullinated peptide; HCQ=hydroxychloroquine; MTX=methotrexate

For TMD, all of the clinical indicators of TMA, except gender, were also significantly associated in the final adjusted model (Table 2, TMD Model 2). In addition, higher IL-6 levels were significantly associated with a lower mean adjusted TMD; while participants prescribed hydroxychloroquine (HCQ) had a significantly higher mean adjusted TMD compared to non-users. Together, the 8 characteristics modeled accounted for 41% of the variability in TMD, with 63% of the explainable variability deriving from RA-related characteristics. For TFA, female gender, greater height, and longer RA duration were significantly associated with higher mean adjusted TFA, while the mean adjusted TFA was 23 cm² lower in current prednisone users compared to non-users (p=0.001; Table 2, TFA Model 2). Together, these 4 characteristics accounted for only 20% of the variability in TFA, with RA-related characteristics accounting for 23% of the explainable variability.

Correlations between Thigh Composition Measures

TMD was positively correlated with TMA (Spearman’s rho=0.75; p<0.001) but was not strongly correlated with TFA (Spearman’s rho=0.13; p=0.13). TMA and TFA were also uncorrelated (Spearman’s rho=0.06; p=0.50). Despite moderate to high correlation, TMD and TMA were not sufficiently collinear to refute co-modeling (mean VIF=2.40).

Crude and Adjusted Associations of Thigh Fat and Muscle Measures with Functional Outcomes and Fatigue

Crude and adjusted associations of TFA, TMA, and TMD with HAQ, SF-36PF, SPPB, and FACIT scores are summarized in Table 3. For each of the outcomes, higher TFA and lower TMA were significantly associated with higher reported disability and limitation in performance when only these two covariates were co-modeled (Model 1). Adding TMD to the model (Model 2) supplanted nearly the entirety of the association of TMA with the outcomes, and eliminated the statistical significance of the remaining TMA associations. In models including potential confounders (Model 3), the magnitudes of the associations of TFA and TMD with the outcomes were reduced by 25–75%; however, these associations remained robust in final models including only the confounders with significant associations with the outcomes (Model 4). Accordingly, in the final adjusted models, each 10cm² higher TFA was associated with a 0.042 unit higher HAQ, a 1.35 unit lower SF-36PF score, a 0.35 unit lower SPPB score, and a 0.056 unit higher FACIT score (p<0.01 throughout), while each mg/cm³ increase in TMD was associated with a 0.041 unit lower HAQ, a 2.77 unit higher SF-36PF score, a 0.43 unit higher SPPB score, and a 0.064 unit lower FACIT score (p<0.05 throughout). The adjusted associations of TFA and TMD with functional outcomes did not significantly differ by gender, and there was no statistical evidence for interaction between TFA and TMD on any of the functional outcomes (data not shown).

Table 3.

Crude and Adjusted Associations of Thigh Composition Measures with Functional Outcomes and Fatigue

	Model 1		Model 2		Model 3		Model 4
	β	p	β	p	β	p	β	p
HAQ
TFA, per 10 cm2	0.044	<0.001	0.050	<0.001	0.050	<0.001	0.042	<0.001
TMA, per 10 cm2	−0.072	<0.001	−0.011	0.62	0.018	0.50
TMD, per mg/cm3			−0.062	<0.001	−0.047	0.014	−0.041	<0.001
Age, per year					−0.002	0.74
Male gender					−0.198	0.208
Height, per meter					−1.915	0.003	−1.13	0.018
Any college					−0.037	0.73
RA duration, per year					0.001	0.95
log IL-6, per log unit					0.052	0.24
Square root TJC					0.122	<0.001	0.112	<0.001
Current prednisone					0.217	0.056	0.223	0.044
Current HCQ use					−0.087	0.44
SF-36 Physical Performance
TFA, per 10 cm2	−1.37	<0.001	−1.68	<0.001	−1.52	0.001	−1.35	<0.001
TMA, per 10 cm2	2.16	<0.001	−0.72	0.40	−1.87	0.093
TMD, per mg/cm3			2.93	<0.001	2.32	0.004	2.77	<0.001
Age, per year					−0.06	0.83
Male gender					−6.99	0.28
Height, per meter					68.0	0.010	54.3	0.014
Any college					−1.63	0.71
RA duration, per year					−0.11	0.59
log IL-6, per log unit					−1.68	0.36
Square root TJC					−4.39	<0.001	−4.21	<0.001
Current prednisone					−9.10	0.051	−8.99	0.046
Current HCQ use					0.97	0.83
Short Physical Performance Battery
TFA, per 10 cm2	−0.33	<0.001	−0.38	<0.001	−0.32	<0.001	−0.35	<0.001
TMA, per 10 cm2	0.58	<0.001	0.05	0.72	−0.14	0.59
TMD, per mg/cm3			0.54	<0.001	0.36	0.007	0.43	<0.001
Age, per year					−0.15	<0.001	−0.15	<0.001
Male gender					0.70	0.52
Height, per meter					8.10	0.067
Any college					0.70	0.35
RA duration, per year					−0.02	0.48
log IL-6, per log unit					−0.62	0.047	−0.62	0.045
Square root TJC					−0.37	0.080
Current prednisone					−1.11	0.16
Current HCQ use					0.15	0.85
Square Root FACIT Score
TFA, per 10 cm2	0.068	0.002	0.077	0.001	0.048	0.065	0.056	0.009
TMA, per 10 cm2	−0.096	0.004	−0.017	0.74	0.075	0.25
TMD, per mg/cm3			−0.081	0.044	−0.084	0.070	−0.064	0.011
Age, per year					−0.012	0.40
Male gender					−0.189	0.62
Height, per meter					−1.658	0.28
Any college					0.297	0.25
RA duration, per year					0.004	0.76
log IL-6, per log unit					0.070	0.52
Square root TJC					0.296	<0.001	0.324	<0.001
Current prednisone					0.369	0.18
Current HCQ use					−0.011	0.97

TFA=thigh fat area; TMA=thigh muscle area; TMD=thigh muscle density; TJC=tender joint count; HCQ=hydroxychloroquine

Similar associations were observed for the associations of TFA and TMD with the proportion of obligatory VLAs affected (Table 4). Specifically, on average, each 10cm² higher TFA was associated with a 1.28 percentage point higher adjusted proportion of obligatory VLAs affected (p=0.011), while each mg/cm³ higher TMD was associated with a 1.07 percentage point lower adjusted proportion (p=0.024) (Table 4, obligatory VLA Model 4). TFA, but not TMA or TMD, was significantly associated with the proportion of committed and discretionary VLAs affected, with each 10cm² higher TFA associated with a 1.73 and 1.32 percentage point higher adjusted proportion, respectively, of VLAs affected (p<0.05 for both)(Table 4, Committed and Discretionary VLA Model 4).

Table 4.

Crude and Adjusted Associations of Thigh Composition Measures with Affected Valued Life Activities

	Model 1		Model 2		Model 3		Model 4
	β	p	β	p	β	p	β	p
Proportion of Obligatory VLAs Affected
TFA, per 10 cm2	1.45	0.007	1.40	0.009	1.66	0.008	1.28	0.011
TMA, per 10 cm2	−2.67	0.001	−1.69	0.062	0.01	0.78
TMD, per mg/cm3			−1.28	0.010	−1.19	0.021	−1.07	0.024
Height, per meter					−5.68	0.17
Age, per year					0.59	0.082	0.55	0.079
Male gender					2.51	0.74
Any college					−4.22	0.49
RA duration, per year					−0.17	0.55
log IL-6, per log unit					1.30	0.60
Square root TJC					7.18	<0.001	7.14	<0.001
Current prednisone					22.39	<0.001	21.90	<0.001
Current HCQ use					−6.49	0.31
Proportion of Committed VLAs Affected
TFA, per 10 cm2	2.34	<0.001	2.32	<0.001	1.81	0.011	1.73	0.002
TMA, per 10 cm2	−3.17	<0.001	−2.70	0.008	−0.31	0.81	−1.40	0.15
TMD, per mg/cm3			−0.61	0.32	−0.25	0.68	−0.38	0.52
Height, per meter					−6.33	0.12
Age, per year					0.37	0.34
Male gender					−6.68	0.44
Any college					3.21	0.64
RA duration, per year					0.10	0.75
log IL-6, per log unit					1.13	0.23
Square root TJC					8.51	<0.001	8.96	<0.001
Current prednisone					4.46	0.46
Current HCQ use					−8.99	0.22
Proportion of Discretionary VLAs Affected
TFA, per 10 cm2	1.80	0.001	1.77	0.001	1.46	0.030	1.32	0.015
TMA, per 10 cm2	−2.69	0.001	−2.31	0.014	−0.37	0.77	−1.28	0.17
TMD, per mg/cm3			−0.49	0.39	−0.25	0.68	−0.35	0.52
Height, per meter					−3.21	0.39
Age, per year					0.29	0.42
Male gender					−4.58	0.57
Any college					1.80	0.78
RA duration, per year					0.14	0.62
log IL-6, per log unit					0.08	0.38
Square root TJC					7.08	<0.001	7.21	<0.001
Current prednisone					4.02	0.48
Current HCQ use					−6.75	0.33

TFA=thigh fat area; TMA=thigh muscle area; TMD=thigh muscle density; TJC=tenderjoint count; HCQ=hydroxychloroquine

Correlations of Other Body Composition Measures with Functional Measures

We compared the strength of correlation of CT-derived thigh composition measures with functional outcomes against those of anthropometric and DXA-derived measures of body composition (Table 5). In general, anthropometric measures were not significantly correlated with functional outcomes. In the few cases of significant correlation of anthropometrics with functional outcomes (e.g. BMI with SF-36PF and proportion of VLAs affected), the strength of correlation was weaker than CT-derived measures. Among DXA-derived measures of fat, the strength of correlation of fat mass index (FMI), body fat percentage (BFP), and appendicular fat with functional outcomes were comparable or slightly higher than TFA. Appendicular lean mass was the strongest DXA-derived measure, with comparable correlation with functional outcomes as TMA or TMD.

Table 5.

Correlations of Body Composition Measures with Functional Outcomes

	HAQ		SF-36 PF		SPPB		Proportion of VLAs Affected
	ρ	p-value	ρ	p-value	ρ	p-value	ρ	p-value
Thigh CT-Derived Measures
Total thigh area, cm2	0.037	0.65	−0.022	0.79	−0.001	0.99	0.084	0.30
Thigh muscle area, cm2	−0.356	<0.001	0.285	<0.001	0.397	<0.001	−0.265	0.001
Thigh muscle density, mg/cm3	−0.353	<0.001	0.373	<0.001	0.460	<0.001	−0.270	<0.001
Thigh fat area, cm2	0.327	<0.001	−0.225	0.005	−0.291	<0.001	0.307	<0.001
Anthropometrics
Weight, kg	−0.080	0.32	0.001	0.99	0.050	0.54	0.005	0.95
Body mass index, kg/m2	0.117	0.14	−0.176	0.027	−0.132	0.10	0.199	0.012
Waist circumference, cm	0.031	0.70	−0.120	0.13	−0.107	0.18	0.117	0.14
Waist-to-hip ratio	−0.166	0.037	0.049	0.54	0.111	0.17	−0.135	0.091
DXA-Derived Measures
Total body fat, kg	0.251	0.001	−0.247	0.002	−0.189	0.018	0.297	<0.001
Fat mass index (FMI), kg/m2	0.355	<0.001	−0.327	<0.001	−0.281	<0.001	0.381	<0.001
Body fat percentage	0.460	<0.001	−0.369	<0.001	−0.362	<0.001	0.443	<0.001
Total body lean, kg	−0.343	<0.001	0.228	0.004	0.271	<0.001	−0.259	0.001
Fat free mass index (FFMI), kg/m2	−0.223	<0.001	0.097	0.228	0.136	0.089	−0.133	0.097
Trunk fat, kg	0.155	0.052	−0.188	0.018	−0.117	0.145	0.213	0.007
Appendicular fat, kg	0.318	<0.001	−0.271	<0.001	−0.259	0.001	0.345	<0.001
Appendicular lean, kg	−0.355	<0.001	0.254	0.001	0.311	<0.001	−0.272	<0.001

ρ = Spearman correlation coefficient

HAQ=Health Assessment Questionnaire; SF=Short Form; SPPB=Short Physical Performance Battery; VLAs=Valued Life Activities

We next explored the linearity of the associations of TMD and TFA with functional outcomes, and the combined associations of TMD and TFA (Figure 1). For HAQ, the change in HAQ per quartile increase in TMD or TFA was roughly linear in adjusted analyses (Figure 1.a.). Mean adjusted HAQ scores were 2-fold higher in the group with the lowest TMD (below the median of 39 mg/cm³) and the highest TFA (above the median of 113 cm²) compared to the group with the highest TMD and lowest TFA (1.18 vs. 0.55 HAQ units, respectively; p<0.001). For SPPB (Figure 1.b.), Mean adjusted scores were 79% higher for participants in the fourth quartile of TMD compared to the first quartile (14.3 vs. 8.0 SPPB units, respectively; p=0.001); however, the difference in mean adjusted SPPB score was the largest between the first and second quartiles of TMD (8.0 vs. 12.9 SPPB units, respectively; p<0.01), and did not differ between the third and fourth quartiles. For TFA, mean adjusted SPPB scores were 25% higher for participants in the lowest TFA quartile compared to the highest quartile (14.1 vs. 9.8 SPPB units, respectively; p<0.001). As with TMD, the trend in SPPB scores across quartiles of TFA was not linear, as scores were not significantly lower until the third quartile of TFA. Mean adjusted SPPB scores were 63% higher for the group with the highest TMD and lowest TFA compared to the group with the lowest TMD and highest TFA (14.5 vs. 8.9 SPPB units, respectively; p<0.001). For the proportion of obligatory VLAs affected (Figure 1.c.), per-quartile differences in the associations of TMD and TFA with the outcome were roughly linear. The proportion of obligatory VLAs affected was 2.4-fold higher, on average, for those with the lowest TMD and highest TFA compared to the group with the highest TMD and lowest TFA (45 vs. 19%, respectively; p=0.002).

Figure 1. Crude and Adjusted Individual and Combined Associations of Thigh Muscle Density and Thigh Fat Area on Functional Outcomes.

Depicted are crude and adjusted means and 95% confidence intervals. Adjustments are for Model 4 covariates from Tables 3 and 4. TMD-Q1 (25.7–36.1 mg/cm³; n=40); TMD-Q2 (36.2–38.9 mg/cm³; n=36); TMD-Q3 (39.1–42.5 mg/cm³; n=38); TMD-Q4 (42.7–49.7 mg/cm³; n=38).

TFA-Q1 (22–88 cm²; n=38); TFA-Q2 (89–112 cm²; n=38); TFA-Q3 (113–148 cm²; n=38); TFA-Q4 (149–336 cm²; n=38).

High TMD + low TFA = participants in TMD-Q3 or Q4 and also in TFA-Q1 or 2 (n=30); High TMD + high TFA = participants in TMD-Q3 or Q4 and also in TFA-Q3 or 4 (n=46); Low TMD + low TFA = participants in TMD-Q1 or Q2 and also in TFA-Q1 or 2 (n=46); Low TMD + high TFA = participants in TMD-Q1 or Q2 and also in TFA-Q3 or 4 (n=30).

TMD=thigh muscle density; TFA=thigh fat area; Q=quartile

DISCUSSION

In this investigation, which is the first to our knowledge to utilize mid-thigh quantitative CT to measure body composition in RA patients, we observed that TMD, more so than TMA and independent of TFA, was a strong indicator of functional outcomes and physical performance, with each mg/cm³ increase in TMD associated with a lower HAQ score, a lower proportion of valued life activities affected, and higher SF36-PF and SPPB scores. Moreover, in multivariable analyses, RA disease features accounted for approximately a quarter of the explainable variability in TMA and TFA and three-quarters of the explainable variability in TMD.

In recent years, CT-derived muscle density has been established as a marker of muscle quality (11). Density is estimated from CT-derived attenuation coefficients, and low muscle density reflects increased myocellular lipid content and fatty infiltration of the muscle compartment (10). Low muscle density at various sites has been associated with adverse outcomes in several studies of non-RA patients, including frailty (36), reduced physical performance (18–20), increased incidence of mobility limitations (21, 22), increased risk of hip fracture (37), and increased risk of hospitalization (18). However, until now, there have been no studies assessing muscle density in the RA population, let alone associations with functional outcomes. The present study confirms the importance of this measure in the RA population. Notably, muscle density was a stronger indicator of functional outcomes and physical performance than muscle area, anthropometrics, or DXA-derived measures of lean body mass or fat mass, a finding concordant with other studies in the general population (18, 37). It is interesting to note that in these studies, in which enrolled patients were generally older than our cohort by an average of about a decade, average thigh muscle area and density were similar to those of our RA cohort (21).

Inflammatory cytokines, such as IL-6 and tumor necrosis factor, have been associated with reduced muscle density in non-RA populations (38, 39). Early studies by Roubenoff and colleagues showing reduced body cell mass in RA patients implicated inflammatory cytokines, suggesting a cytokine driven state of hypermetabolism as the cause (6). Inflammation likely exerts multiple, complex effects on muscle such as reduced insulin/growth-factor sensitivity and accelerated protein degradation. Inflammatory cytokines are clearly associated with muscle wasting in several pathologic states, such as cancer, heart failure, and sepsis (40). Indeed, in our study, IL-6 level was independently and inversely associated with muscle density and area. In addition, use of hydroxychloroquine and use of prednisone were also indicators of thigh muscle area and density. While hydroxychloroquine has not been studied in this context, it is plausible that its effect on muscle density could be mediated by potentiating the activity of lipolytic enzymes by reducing their lysosomal degradation (41, 42), which could, hypothetically, lead to reduced intramyocellular lipid. However, as the association of hydroxychloroquine with muscle density could be confounded by factors related to the indication for prescribing the drug (i.e. milder disease, etc..), then additional study is needed to explore this association. Also notable, both fat and muscle areas were lower in prednisone users in our study. There may be several mechanisms underlying this finding. For one, prednisone-treated patients undergo an apparent redistribution of fat from the periphery to the trunk (43), and for another, glucocorticoid therapy may induce muscle wasting and has been associated with low muscle area (44). These indicators are potential targets for intervention that may improve muscle quality and functional outcomes.

The strong, independent association of lower muscle density and higher fat area with limitation in physical performance and disability suggest that these outcomes might be improved by interventions designed to increase muscle density and/or decrease fat. For example, it has been shown that in a study of non-RA patients, muscle area was the same or greater in obese individuals compared to non-obese controls, but muscle density was lower (45). When obese individuals participated in a 16-week weight loss program, there was an increase in muscle density (46), and other studies have shown that changes in muscle density parallel changes in strength during resistance detraining and retraining (13). From a functional standpoint, resistance exercises have already been shown to be effective in reducing pain and fatigue scores, as well as strength and certain measures of physical performance in the RA population (47–50). However, as no studies in RA patients have used CT to determine body composition, the effects of exercise on muscle density in RA are still unknown. Although duration of intentional exercise and sedentary activities were associated with some thigh composition measures in univariate analyses, we did not observe strong independent associations in multivariable models. This could indicate a true lack of association, or may be a reflection of the imprecision of self-reported physical activity among RA patients. A clinical trial employing a physical activity intervention would be preferred to clarify the role of physical activity in altering body composition in RA patients as a means to improve function.

In addition to CT, we also investigated two other methods of body composition: DXA and anthropometry. In general, anthropometry was poorly correlated with functional outcome measures, and essentially did not add to what was obtained via CT. However, some DXA-derived measures of body composition showed similar associations with functional outcomes as the CT-derived measures, and it would appear that appendicular lean mass and appendicular fat mass are potentially reasonable surrogates (9). In clinical practice, neither CT nor DXA are yet practical for use in measuring body composition. It is possible that other anthropometric measures, such as thigh circumference or caliper skin-fold assessment of thigh fat, could be a feasible surrogate for clinical practice; however, these were not evaluated in our study.

In our study, both lower TMD and higher TFA were significantly associated with a risk of affected obligatory VLAs, but TMD was not associated with committed or discretionary VLAs. Obligatory VLAs are those considered necessary for survival and self sufficiency, including walking to get around, getting around one’s community by car or public transportation, and taking care of one’s basic needs, such as bathing, washing, getting dressed, or taking care of personal hygiene (51). That these types of activities appear to be more strongly influenced by thigh composition than either committed or discretionary VLAs is intriguing. Since our study population was older with generally more long standing RA, it is plausible that our subjects have learned ways to participate in these activities despite their physical performance limitations, or they have already adapted and eliminated these from consideration as important life activities.

The data from our study are cross-sectional, making these findings hypothesis generating. Both a longitudinal, prospective cohort study of body composition and, eventually, a randomized controlled trial for interventions would be needed to confirm these postulations. Aside from the cross sectional nature of our investigation, there are additional limitations. First, our study population was older with relatively long-standing RA, and while there was a broad distribution of RA characteristics represented, most had low-to-moderate disease activity with only mild-to-moderate disability. Second, our CT scans were regional, at only the mid thigh, which may provide a more limited estimate of whole body composition. Analysis of cross sections from multiple body regions would be a useful future study. Third, we did not include measures of muscle strength, which would have permitted a more complete assessment of muscle quality.

In summary, three main conclusions are drawn from this investigation. First, CT of the mid-thigh is a promising technique for direct assessment of regional body composition in RA patients, allowing determination of muscle area, fat area, and muscle density. Second, RA disease features play an important role in thigh composition, accounting for a large proportion of the explainable variability. Third and perhaps most important, muscle density appears to be a stronger indicator of physical functioning than muscle area in RA patients, with associations independent of fat mass. Further studies are needed to determine whether interventions aimed at increasing muscle density will improve the disability and poor physical performance so characteristic of this population.