Abstract
Objective
Depression is common in the rheumatoid arthritis (RA) population, yet little is known of its effect on the course of disease activity. The aim of our study was to determine if prevalent and incident depressive symptoms influenced longitudinal changes in RA disease activity.
Methods
RA patients with and without depressive symptoms were identified using single-item questions from an existing registry sample. Mixed-effects models were used to examine changes in disease activity over 2 years in those with and without prevalent and incident depressive symptoms. Outcome variables included composite disease activity, joint counts, global assessments, pain, function, and acute-phase reactants. Model-based outcome estimations at the index dates and corresponding 1- and 2-year changes were calculated.
Results
Rates of disease activity change were significantly different in patients with a lifetime prevalence of symptomology, but not incident depressive symptoms, when compared to controls. Prior symptoms were associated with slower rates of disease activity decline, evidenced by the estimated 1-year Clinical Disease Activity Index changes: −3.0 (−3.3, −2.6) and −4.0 (−4.3, −3.6) in patients with and without lifetime prevalence, respectively. Analogous results were obtained for most of the other disease activity outcomes; although, there was no temporal effect of prevalent symptoms of depression on swollen joints and acute-phase reactants.
Conclusion
Depressive symptoms temporally influence the evolution of RA disease activity, and the magnitude is dependent on the time of symptomatic onset. However, the effect is limited to patient-reported pain, global assessment, and function, as well as physician-reported global assessment and tender joints.
INTRODUCTION
Rheumatoid arthritis (RA), the most prevalent autoimmune arthritic disorder, is a systemic disease. In addition to symmetric polyarthritis, patients have a high burden of comorbid conditions (1,2). Despite treatment innovations that have increased the ability to control joint inflammation via biologic disease-modifying antirheumatic drugs (DMARDs), there is still an immense gap in our understanding and clinical care of comorbidity in the RA population (2,3). With an estimated point prevalence of 16.8%, depression is one of the most common co-occurring conditions among RA patients (4). National Institute for Health and Care Excellence guidelines recommend routine depression screening in patients with chronic physical conditions like RA; however, this psychiatric comorbidity is typically underrecognized by rheumatologists (5,6).
The relationship between these 2 conditions is bidirectional, where each disorder simultaneously influences the manifestation of the other, but the underlying mechanisms are poorly understood (7–10). Studies of psoriatic (PsA) and early undifferentiated inflammatory arthritis (EIA) patients have demonstrated significant temporal bidirectional effects between depression and disease activity, and possible causal mechanisms include biological, psychological, and behavioral factors, as well as the interaction of these different domains (11–13). Cross-sectional research has consistently demonstrated strong positive associations between depression and composite disease activity, pain, function, global assessments, and acute-phase reactants (14–17). Yet, the intrinsic lack of temporality prohibits our ability to make causal interpretations.
The temporal impact of depression on RA symptoms has not been well studied. Research has focused on patient-reported pain and depression as a moderator of psychological factors related to this outcome (18–21). Data suggest both present and past depressive symptoms are predictive of elevations in future pain, but that concurrent symptomology is a stronger temporal predictor (18,20,21). Evidence also implies that depression moderates changes in RA disease activity due to stress; although, this relationship was not temporally related to acute-phase reactants and other immunologic markers (19). Existing research has methodological issues with statistical adjustment, small samples, and limited followup, and no studies have evaluated the direct temporal effect of depression on functionality, joint counts, acute-phase reactants, and composite disease activity (11).
Despite the high prevalence of depression in RA patients and the difficulties depression creates regarding medical management, a prohibitive gap exists in the understanding of how depressive symptoms influence disease activity (3,4,11). Therefore, we proposed to temporally assess longitudinal changes in RA disease activity among patients with and without depressive symptoms in a national registry sample, while differentiating between the effect of prevalent and incident symptomology. It was hypothesized that symptoms of depression would be associated with worse prospective RA disease activity outcomes.
MATERIALS AND METHODS
Data source
The Consortium of Rheumatology Researchers of North America (CORRONA) is an existing observational registry of American RA patients (22). Individuals are recruited by participating rheumatologists at academic and community rheumatology practices, and data are gathered from both patients and their treating providers. Collected information includes patient demographics, disease severity, disease activity measures, comorbidity, medication utilization, radiographic changes, laboratory results, and adverse events. Approvals of data collection and analyses for private and academic clinical sites were acquired from central and local institutional review boards, respectively, and the specific operating and funding mechanisms for the CORRONA database have been previously published (22).
Study populations
Analytic samples were drawn from RA patients (n = 33,743) enrolled into the CORRONA registry between October 2001 and August 2012. Based on this cohort, 2 separate longitudinal studies were conducted. The first analysis assessed the impact of a self-reported baseline lifetime prevalence of depressive symptoms on subsequent changes in RA disease activity from study entry over followup. Therefore, we excluded patients without self-reported depressive symptom data at study entry (n = 1,681), no observed clinical followup visits (n = 7,530), and missing baseline data on disease activity and covariate measures (n = 7,525) (see Supplementary Figure 1, available in the online version of this article at http://onlinelibrary.wiley.com/doi/10.1002/acr.22515/abstract). Participants with and without a history of depressive symptoms were compared concerning their longitudinal rates of RA disease activity change. This work was replicated substituting the physician-reported lifetime prevalence depressive symptom indicator for the patient self-report, restricted to data where the measure was available.
The second analysis evaluated the influence of incident self-reported depressive symptoms. Correspondingly, participants with a self-reported history of depressive symptoms (n = 8,141), missing lifetime prevalence data (n = 1,681), and no followup visits (n = 4,920) were excluded (see Supplementary Figure 2, available in the online version of this article at http://onlinelibrary.wiley.com/doi/10.1002/acr.22515/abstract). Patients with incident self-reported depressive symptoms were compared regarding their changes in RA disease activity to controls, participants with no self-reported symptoms of depression during study entry and followup. The visits where incident self-reports occurred were assigned as the index date among participants with depressive symptoms, which were then matched to a randomly selected followup visit from among the controls. Eligible index visits were required to have relevant disease activity and covariate data and observed followup time. A total of 2,372 and 10,073 possible patients with symptoms of depression and randomly selected controls, respectively, were available for inclusion. This process was replicated using an alternative case definition, sustained depressive symptoms, defined as the first 2 consecutive self-reports of symptoms of depression.
Depression
The depressive symptom measures were single-item evaluations collected from enrollment and followup surveys. A patient-reported depressive symptom measure was used for the primary study, while the clinician-reported measure was used for sensitivity analyses. CORRONA surveys capture data on a history of depressive symptoms at enrollment, associated time of onset, and the existence of symptomology during followup. Patient enrollment surveys ask participants to do the following: “Please fill a ‘NO’ or ‘YES’ circle for each of the following conditions you have EVER had,” including “depression (feeling blue),” and the categorized time of comorbidity onset (e.g., “YES less than 1 year ago”). Physician surveys collect comorbidity data from enrollment surveys that ask the physicians to indicate, “if the patient has or has had any of the following, fill in the box,” and an item for “depression” is included. Followup forms ask patients to acknowledge any “medical condition or symptom you have had SINCE YOU LAST FILLED OUT THIS FORM,” which contains an item for “depression (feeling blue).”
While these measures do not meet diagnostic criteria for depression, patient-reported depressive symptoms are frequently employed to evaluate the presence of the condition in everyday clinical care and have been used in prior RA registry research (23–26). Single-item depression symptom screeners have been developed and validated in patients with chronic physical diseases, and evidence would suggest a high sensitivity but low specificity with regard to case ascertainment (27–29). Ultra-brief depressive symptom measures are valuable tools that provide important clinical information and, moreover, when validated against chart abstractions and laboratory measures, patients have consistently proven a strong capacity to recall prior comorbid conditions (24,30–32).
Disease activity
Outcome variables for the analysis comprised composite disease activity, joint counts, global assessments, pain, function, and acute-phase reactants. Composite disease activity measures included the Clinical Disease Activity Index (CDAI) and the 28-joint Disease Activity Score (DAS28) (33,34). The CDAI is a score calculated by summing the tender joint count (TJC), swollen joint count (SJC), patient global assessment (PGA; visual analog scale [VAS] 0–10 cm), and physician global assessment (EGA; VAS 0–10 cm) (34). The DAS28 is a continuous value computed from the TJC, SJC, PGA, and erythrocyte sedimentation rate (ESR) with a mathematical formula (33). Additional outcome measures were the previously described core component disease activity metrics: TJC, SJC, PGA, EGA, and ESR and the following: patient-reported pain (VAS 0–10 cm), Health Assessment Questionnaire (HAQ) score converted from modified HAQ values (35), and C-reactive protein (CRP) level.
Covariates
For the prevalence analysis, all pertinent measures were obtained from study baseline and therefore time-invariant factors. The incidence study used followup visits, and covariate data were obtained from both time-invariant and varying measures.
Self-reported demographic and socioeconomic measures included the following: sex, age, race, ethnicity, education, marital status, employment, and health insurance. Race and ethnicity were combined into the categories of white, African American, Asian, Hispanic, and other, and age was categorized into groupings of 18–24, 25–34, 35–44, 45–54, 55–64, 65–74, 75–84, and ≥85 years. Patients’ education was captured as primary, high school, college/university, and don’t remember. Marital status included the following characterizations: single, married/partnered, widowed, and divorced/separated, and employment was measured using 6 groups: full time, part time, unemployed, student, disabled, and retired. Finally, health insurance was captured as 1 of the following: none, Medicaid, Medicare, and private. Age group, education, marital status, employment, and insurance type were all obtained as a time-varying measure at patients’ index dates in the incidence study.
Behavioral and clinical characteristics included smoking, alcohol use, exercise, disease duration, body mass index (BMI; kilograms/meters2), and medical comorbidity. Smoking and drinking were both collected as binary variables (ever versus never). Exercise was captured as 1 of 5 categories: not at all, 1–2 times per week, 3–4 times per week, 5–6 times per week, and daily. Comorbidity was measured using a validated composite score calculated from reports of past or present conditions from either patients or rheumatologists: myocardial infarction, stroke, hypertension, other cardiovascular disorders, diabetes mellitus, pulmonary disease, cancer, peptic ulcer, other gastrointestinal disorders, and fractures (26). Disease duration, BMI, comorbidity, exercise, smoking, and alcohol use were all also time-varying covariates at patients’ index time points for the incidence analysis.
Concomitant RA treatments that were time-varying measures and only considered as possible confounders in the incidence analysis included biologic and nonbiologic DMARDs, prior biologic agent use, combination therapy, no DMARD utilization, and prednisone. Biologic DMARD use included the initiation of any of one of the following commercially available agents: etanercept, adalimumab, infliximab, certolizumab pegol, golimumab, tocilizumab, abatacept, anakinra, and rituximab. Biologic history (experienced versus naive) was defined as the reported prior utilization of any of the noted biologic DMARDs. Combination therapy was characterized as the use of a biologic and nonbiologic DMARD, which included the following medications: leflunomide, sulfasalazine, azathioprine, methotrexate, hydroxychloroquine, minocycline, auranofin, D-penicillamine, and cyclosporine. No therapy was defined as not having any concurrently prescribed DMARDs.
Confounding
Propensity score (PS) matching was used to account for possible confounders and patients with depressive symptoms were matched to controls (36). Patients with a self-reported history of depressive symptoms were matched to patients with no prior symptoms at the time of study enrollment. At followup visits, patients with incident self-reports of depressive symptoms were matched in stratified blocks of yearly calendar time to controls. This matching approach was used to account for the potential influence of changes in RA treatment patterns on the evolution of disease activity during followup. Data were stratified into 1-year incremental blocks of time; within each calendar year of time, visits for patients with incident reports of depressive symptoms were matched to visits for randomly selected controls with a PS using random number generation.
The likelihood of reporting symptoms of depression was estimated with logistic regression models conditioned on confounders associated with their presence. For lifetime prevalence, PS model variables were the following: CDAI, patient pain, HAQ, sex, age group, race, ethnicity, health insurance, marital status, employment, BMI, comorbidity, smoking, drinking, and exercise. Incorporating the CDAI, patient pain, and HAQ was used to balance differences in disease severity for all the outcome measures simultaneously. For incident onset, covariates included the same variables used in the prevalence model except alcohol use, in addition to education and concomitant treatment factors as follows: prior biologic DMARD use, no current DMARD prescriptions, and prednisone use. Patients with depressive symptoms were matched to controls at ratios of 1:1 and 1:2 for the prevalence and incidence analyses, respectively, using nearest neighbor matching and a caliper of 0.05 without replacement (36).
Statistical analysis
To examine baseline characteristics of the samples, chi-square tests were used for categorical covariates, t-tests for continuous variables, and Wilcoxon’s rank sum tests for continuous measures with skewed distributions. Evaluations were conducted separately in the full and PS-matched groups, and participants with depressive symptoms were compared to controls in terms of the a priori selected covariates.
The method of analysis was linear mixed-effects modeling, appropriate for repeated continuous measures and other clustered data, and outcome variables constituted the previously described RA disease activity measures (37). The laboratory measures (CRP level and ESR) were log transformed due to highly skewed multivariate distributions. Followup was restricted to 2 years because this provided enough time to observe disease activity changes, and the median time to recovery from a depressive episode is between 6 and 12 months (38). Locally weighted scatterplot smoothing (lowess) curves were used to explore functional forms. Due to nonlinear trends over followup, time was modeled as a quadratic function in all analyses. Random effects were incorporated regarding intercepts and slopes for followup time. These observational data are hierarchical, where patients are nested within physicians and clustered by practice sites. The resulting mixed-effects models only accounted for clustering of patients within practice sites, as adjusting for clustering by physicians among each practice had no impact on the results.
Of interest was the depressive symptom by followup time interaction, testing whether the evolution of disease activity differed among patients with and without symptoms of depression, which represents the difference in the rates of disease activity change between these 2 groups. To account for any remaining baseline differences in RA disease activity, models were further adjusted for both the main effect of the outcome (categorized quintiles) and its interaction with followup time. Model-based estimations of disease activity at baseline and changes at 1- and 2-year followup were calculated. Sensitivity analyses replicated these processes using the physician-reported lifetime prevalence measure and sustained symptoms case definition. Secondary analyses were also performed in the matched prevalence sample using patients’ reported time of onset; lifetime prevalent depressive symptom status was subdivided as an “onset within the last year” or “onset equal to or greater than 1 year.”
RESULTS
Propensity score matching
The full lifetime prevalence sample comprised 17,006 patients with and without a history of depressive symptoms (4,303 versus 12,307, respectively). Patients with depressive symptomology (n = 4,131) were matched to controls (1:1). The PS model C statistic was 0.71, and 96% of those with prevalent symptoms of depression were retained. Among the full prevalence analytic cohort, a history of depressive symptoms correlated to being of lower socioeconomic status, female, younger, widowed and divorced/separated, smoking, and physically inactive, as well as greater BMI, comorbidity, and disease activity (Table 1). PS matching resulted in a rebalancing of characteristics that were associated with prior depressive symptoms (Table 1), including most disease activity measures (see Supplementary Table 1, available in the online version of this article at http://onlinelibrary.wiley.com/doi/10.1002/acr.22515/abstract). Similar results were obtained using the physician-reported lifetime prevalence measure (data not shown).
Table 1.
Descriptive data by patient-reported lifetime depressive symptom status for the full study sample and propensity score–matched group*
| Full study sample |
Matched sample |
|||||
|---|---|---|---|---|---|---|
| Variable | No depressive symptoms (n = 12,703) |
Depressive symptoms (n = 4,303) |
P | No depressive symptoms (n = 4,141) |
Depressive symptoms (n = 4,141) |
P |
| Female | 9,295 (73.17) | 3,652 (84.87) | < 0.001 | 3,491 (84.30) | 3,480 (84.04) | 0.74 |
| Race/ethnicity | ||||||
| White | 10,749 (84.62) | 3,718 (86.40) | < 0.001 | 3,546 (85.63) | 3,563 (86.04) | 0.97 |
| Hispanic | 589 (4.64) | 204 (4.74) | 205 (4.95) | 201 (4.85) | ||
| African American | 934 (7.35) | 270 (6.27) | 278 (6.71) | 268 (6.47) | ||
| Asian | 267 (2.10) | 42 (0.98) | 41 (0.99) | 43 (1.04) | ||
| Other | 164 (1.29) | 69 (1.60) | 71 (1.71) | 66 (1.59) | ||
| Age, mean ± SD years | 58.2±13.6 | 56.5±12.2 | < 0.001 | 56.8±12.5 | 56.8±12.4 | 0.79 |
| Education | ||||||
| Primary | 529 (4.16) | 175 (4.07) | 0.44 | 198 (4.78) | 159 (3.84) | < 0.001 |
| High school | 5,003 (39.38) | 1,650 (38.35) | 1,765 (42.62) | 1,586 (38.30) | ||
| College/university | 7,042 (55.44) | 2,441 (6.73) | 2,129 (51.41) | 2,362 (57.04) | ||
| Don’t remember | 129 (1.02) | 37 (0.86) | 49 (1.18) | 34 (0.82) | ||
| Insurance | ||||||
| None | 238 (1.87) | 110 (2.56) | < 0.001 | 110 (2.66) | 106 (2.56) | 0.69 |
| Medicaid | 266 (2.09) | 199 (4.62) | 168 (4.06) | 152 (3.67) | ||
| Medicare | 2,439 (19.20) | 834 (19.38) | 796 (19.22) | 775 (18.72) | ||
| Private | 9,760 (76.83) | 3,160 (73.44) | 3,067 (74.06) | 3,108 (75.05) | ||
| Marital status | ||||||
| Single | 1,410 (11.10) | 471 (10.95) | < 0.001 | 453 (10.94) | 455 (10.99) | 0.88 |
| Married/partnered | 8,689 (68.40) | 2,643 (61.42) | 2,579 (62.28) | 2,609 (63.00) | ||
| Widowed | 1,231 (9.69) | 446 (10.36) | 448 (10.82) | 433 (10.46) | ||
| Divorced/separated | 1,373 (10.81) | 743 (17.27) | 661 (15.96) | 644 (15.55) | ||
| Employment | ||||||
| Full time | 5,337 (42.01) | 1,390 (32.30) | < 0.001 | 1,418 (34.24) | 1,422 (34.34) | 0.96 |
| Part time | 1,254 (9.87) | 397 (9.23) | 401 (9.68) | 411 (9.93) | ||
| Unemployed | 1,247 (9.82) | 594 (13.80) | 580 (14.01) | 583 (14.08) | ||
| Student | 113 (0.89) | 27 (0.63) | 27 (0.65) | 26 (0.63) | ||
| Disabled | 969 (7.63) | 911 (21.17) | 736 (17.77) | 704 (17.00) | ||
| Retired | 3,783 (29.78) | 984 (22.87) | 979 (23.64) | 995 (24.03) | ||
| CDAI, mean ± SD | 12.7±12.4 | 16.5±13.7 | < 0.001 | 16.1±13.5 | 15.9±13.5 | 0.56 |
| Disease duration, mean ± SD years | 8.7±9.6 | 8.9±9.8 | 0.13 | 9.3±9.9 | 8.9±9.8 | 0.07 |
| Alcohol use | 6,176 (48.62) | 1,955 (45.43) | < 0.001 | 1,901 (45.91) | 1,899 (45.86) | 0.97 |
| Smoking | 1,769 (13.93) | 791 (18.38) | < 0.001 | 740 (17.87) | 721 (17.41) | 0.58 |
| Exercise | ||||||
| None | 3,927 (30.91) | 1,560 (36.25) | < 0.001 | 1,521 (36.73) | 1,473 (35.57) | 0.71 |
| 1–2 times/week | 3,841 (30.24) | 1,404 (32.63) | 1,326 (32.02) | 1,340 (32.36) | ||
| 3–4 times/week | 2,735 (21.53) | 804 (18.68) | 757 (18.28) | 799 (19.29) | ||
| 5–6 times/week | 844 (6.64) | 205 (4.76) | 208 (5.02) | 200 (4.83) | ||
| Daily | 1,356 (10.67) | 330 (7.67) | 329 (7.94) | 329 (7.94) | ||
| BMI, mean ± SD kg/m2 | 28.8±6.8 | 30.6±7.7 | < 0.001 | 30.2±7.6 | 30.3±7.5 | 0.64 |
| Comorbidity, median (IQR) | 1 (0–2) | 1 (0–2) | < 0.001 | 1 (0–2) | 1 (0–2) | 0.59 |
CDAI = Clinical Disease Activity Index; BMI = body mass index; IQR = interquartile range.
The incidence sample consisted of 12,445 eligible participants: 2,372 incident depressive symptom onset patients and 10,073 without any reports of symptomology. Individuals with incident symptoms (n = 2,108) were matched (1:2) to controls (n = 4,216). In the full incidence analytic cohort, analogous patterns regarding the general characteristics of patients with incident depressive symptoms were observed compared to controls (Table 2), and similarly, those experiencing an incident episode also had a greater baseline disease severity (see Supplementary Table 2, available in the online version of this article at http://onlinelibrary.wiley.com/doi/10.1002/acr.22515/abstract). Concerning RA treatment, incident depressive symptoms were associated with being biologic agent–experienced, no current DMARD prescriptions, and prednisone utilization (data not shown). There was a successful rebalancing of all general baseline variables (Table 2), including most disease activity measures (Supplementary Table 2) and concomitant treatment variables (data not shown). Secondary analyses utilizing the sustained symptom case definition yielded comparable results (data not shown).
Table 2.
Descriptive characteristics by incident depressive symptom status among the full study sample and propensity score–matched group*
| Full study sample |
Matched sample |
|||||
|---|---|---|---|---|---|---|
| Variable | No depressive symptoms (n = 10,073) |
Depressive symptoms (n = 2,372) |
P | No depressive symptoms (n = 4,216) |
Depressive symptoms (n = 2,108) |
P |
| Female | 7,214 (71.62) | 1,881 (79.30) | < 0.001 | 3,299 (78.25) | 1,649 (78.23) | 0.98 |
| Race/ethnicity | < 0.001 | 0.27 | ||||
| White | 8,596 (85.34) | 1,967 (82.93) | 3,523 (83.56) | 1,782 (84.54) | ||
| Hispanic | 485 (4.81) | 168 (7.08) | 245 (5.81) | 135 (6.40) | ||
| African American | 655 (6.50) | 159 (6.70) | 302 (7.16) | 127 (6.02) | ||
| Asian | 198 (1.97) | 34 (1.43) | 69 (1.64) | 26 (1.23) | ||
| Other | 139 (1.38) | 44 (1.85) | 77 (1.83) | 38 (1.80) | ||
| Age, mean ± SD years | 60.7±13.3 | 59.1±13.5 | < 0.001 | 59.8 (13.4) | 59.7 (13.4) | 0.81 |
| Education | 0.99 | |||||
| Primary | 413 (4.10) | 126 (5.31) | 0.004 | 212 (5.03) | 107 (5.08) | |
| High school | 4,057 (40.28) | 1,009 (42.54) | 1,774 (42.08) | 894 (42.41) | ||
| College/university | 5,506 (54.66) | 1,216 (51.26) | 2,187 (51.87) | 1,087 (51.57) | ||
| Don’t remember | 97 (0.96) | 21 (0.89) | 43 (1.02) | 20 (0.95) | ||
| Insurance | < 0.001 | 0.82 | ||||
| None | 130 (1.29) | 59 (2.49) | 77 (1.83) | 33 (1.57) | ||
| Medicaid | 131 (1.30) | 65 (2.74) | 81 (1.92) | 45 (2.13) | ||
| Medicare | 2,179 (21.63) | 491 (20.70) | 874 (20.73) | 442 (20.97) | ||
| Private | 7,633 (75.78) | 1,757 (74.07) | 3,184 (75.52) | 1,588 (75.33) | ||
| Marital status | < 0.001 | 0.80 | ||||
| Single | 1,042 (10.34) | 252 (10.62) | 450 (10.67) | 214 (10.15) | ||
| Married/partnered | 7,032 (69.81) | 1,538 (64.84) | 2,788 (66.13) | 1,398 (66.32) | ||
| Widowed | 1,080 (10.72) | 275 (11.59) | 513 (12.17) | 250 (11.86) | ||
| Divorced/separated | 919 (9.12) | 307 (12.94) | 465 (11.03) | 246 (11.67) | ||
| Employment | < 0.001 | 0.68 | ||||
| Full time | 3,916 (38.88) | 786 (33.14) | 1,482 (35.15) | 741 (35.15) | ||
| Part time | 958 (9.51) | 253 (10.67) | 448 (10.63) | 218 (10.34) | ||
| Unemployed | 993 (9.86) | 305 (12.86) | 549 (13.02) | 250 (11.86) | ||
| Student | 57 (0.57) | 12 (0.51) | 21 (0.50) | 8 (0.38) | ||
| Disabled | 692 (6.87) | 336 (14.17) | 461 (10.93) | 248 (11.76) | ||
| Retired | 3,457 (34.32) | 680 (28.67) | 1,255 (29.77) | 643 (30.50) | ||
| CDAI, mean ± SD | 8.9±10.0 | 14.0±12.7 | < 0.001 | 12.5±11.6 | 12.3±11.4 | 0.65 |
| Disease duration, mean ± SD years | 11.2±9.8 | 11.2±10.3 | 0.85 | 11.8±9.9 | 11.2±10.3 | 0.02 |
| Alcohol use | 4,731 (46.97) | 1,040 (43.84) | 0.006 | 1,840 (43.64) | 930 (44.12) | 0.72 |
| Smoking | 1,173 (11.64) | 400 (16.86) | < 0.001 | 633 (15.01) | 319 (15.13) | 0.90 |
| Exercise | < 0.001 | 0.99 | ||||
| None | 2,823 (28.03) | 823 (34.70) | 1,360 (32.26) | 684 (32.45) | ||
| 1–2 times/week | 3,036 (30.14) | 710 (29.93) | 1,272 (30.17) | 646 (30.65) | ||
| 3–4 times/week | 2,325 (23.08) | 488 (20.57) | 912 (21.63) | 452 (21.44) | ||
| 5–6 times/week | 808 (8.02) | 109 (4.60) | 217 (5.15) | 106 (5.03) | ||
| Daily | 1,081 (10.73) | 242 (10.20) | 455 (10.79) | 220 (10.44) | ||
| BMI, mean ± SD kg/m2 | 28.5±6.5 | 29±6.9 | < 0.001 | 28.9±6.9 | 28.9±6.7 | 0.71 |
| Comorbidity, median (IQR) | 1 (0–1) | 1 (0–2) | < 0.001 | 1 (0–2) | 1 (0–2) | 0.78 |
CDAI = Clinical Disease Activity Index; BMI = body mass index; IQR = interquartile range.
Prevalent depressive symptoms
Unadjusted associations demonstrated no difference in disease activity change by lifetime depressive symptom status (Figure 1). Patients with prior depressive symptomology had a higher predicted baseline disease severity, but the longitudinal decreases were similar between the 2 groups. However, after PS matching, a history of depressive symptoms was associated with significantly slower rates of decline, illustrated by the predicted 1-year CDAI changes: −2.98 (−3.33, −2.64) and −3.97 (−4.31, −3.63) in patients with and without past symptomology, respectively (Table 3). There was statistically significant depressive symptom by time interactions for the CDAI, TJC, PGA, EGA, pain, and HAQ. Effect sizes were larger for the patient-reported disease activity measures, evidenced by the PGA and EGA estimates. The PS-matched physician-reported sample showed larger effect sizes, but only the PGA and pain outcomes reached statistical significance (see Supplementary Table 3, available in the online version of this article at http://onlinelibrary.wiley.com/doi/10.1002/acr.22515/abstract). Secondary analyses showed patients with symptomology manifesting either <1 year and ≥1 year from study entry as having significantly slower disease activity decreases compared to controls. Estimates of 1-year CDAI changes among the controls, recent onset (<1 year), and distal onset (≥1 year) were the following: −3.98 (−4.33, −3.64), −3.26 (−3.86, −2.66), and −2.81 (−3.23, −2.38), respectively (Table 4).
Figure 1.
Unadjusted Clinical Disease Activity Index (CDAI) trajectories.
Table 3.
Model-based estimates of disease activity by patient-reported lifetime depressive symptom status at baseline and the corresponding change at 1-year and 2-year followup among the propensity score–matched sample*
| Baseline† |
Year 1Δ† |
Year 2Δ† |
|||||||
|---|---|---|---|---|---|---|---|---|---|
| Depressive symptoms |
Outcome | No. | Scale | Estimate | 95% CI | Estimate | 95% CI | Estimate | 95% CI |
| No | CDAI | 4,141 | 0–76 | 15.70 | 15.30, 16.10 | −3.97 | −4.31, −3.63 | −3.67 | −4.24, −3.11 |
| Yes | CDAI‡ | 4,141 | 0–76 | 15.59 | 15.91, 15.99 | −2.98 | −3.33, −2.64 | −2.87 | −3.46, −2.28 |
| No | DAS28 | 2,091 | 0–10 | 3.89 | 3.84, 3.94 | −0.36 | −0.41, −0.30 | −0.43 | −0.53, −0.34 |
| Yes | DAS28 | 2,062 | 0–10 | 3.91 | 3.86, 3.96 | −0.27 | −0.33, −0.22 | −0.34 | −0.44, −0.24 |
| No | TJC | 4,141 | 0–28 | 4.90 | 4.70, 5.11 | −1.42 | −1.60, −1.25 | −1.38 | −1.67, −1.09 |
| Yes | TJC§ | 4,141 | 0–28 | 4.92 | 4.72, 5.13 | −1.06 | −1.24, −0.88 | −0.91 | −1.22, −0.61 |
| No | SJC | 4,141 | 0–28 | 4.39 | 4.16, 4.61 | −1.30 | −1.45, −1.16 | −1.25 | −1.48, −1.02 |
| Yes | SJC | 4,141 | 0–28 | 4.29 | 4.07, 4.51 | −1.17 | −1.31, −1.02 | −1.26 | −1.50, −1.02 |
| No | PGA | 4,141 | 0–10 | 3.69 | 3.62, 3.76 | −0.53 | −0.61, −0.45 | −0.36 | −0.49, −0.22 |
| Yes | PGA‡ | 4,141 | 0–10 | 3.69 | 3.62, 3.75 | −0.16 | −0.24, −0.08 | −0.03 | −0.17, −0.11 |
| No | EGA | 4,141 | 0–10 | 2.67 | 2.59, 2.75 | −0.74 | −0.80, −0.69 | −0.68 | −0.78, −0.59 |
| Yes | EGA§ | 4,141 | 0–10 | 2.64 | 2.55, 2.72 | −0.62 | −0.67, −0.56 | −0.53 | −0.62 −0.43 |
| No | Pain | 4,141 | 0–10 | 4.01 | 3.94, 4.09 | −0.56 | −0.65, −0.48 | −0.32 | −0.46, −0.17 |
| Yes | Pain‡ | 4,141 | 0–10 | 4.01 | 3.93, 4.08 | −0.20 | −0.28, −0.12 | −0.05 | −0.20, −0.09 |
| No | HAQ | 4,141 | 0–3 | 1.05 | 1.04, 1.07 | −0.09 | −0.11, −0.08 | −0.07 | −0.09, −0.04 |
| Yes | HAQ‡ | 4,141 | 0–3 | 1.06 | 1.04, 1.07 | −0.02 | −0.04, −0.01 | 0.01 | −0.02, −0.04 |
| No | Log CRP | 1,395 | NA | 0.71 | 0.69, 0.74 | −0.07 | −0.09, −0.04 | −0.09 | −0.14, −0.05 |
| Yes | Log CRP | 1,380 | NA | 0.71 | 0.68, 0.73 | −0.03 | −0.06, −0.01 | −0.08 | −0.12, −0.03 |
| No | Log ESR | 2,099 | NA | 1.24 | 1.23, 1.25 | −0.02 | −0.03, −0.01 | −0.02 | −0.04, −0.01 |
| Yes | Log ESR | 2,070 | NA | 1.24 | 1.23, 1.25 | −0.02 | −0.03, −0.01 | −0.01 | −0.03, −0.01 |
95% CI595% confidence interval; CDAI = Clinical Disease Activity Index; DAS28 = 28 Joint Count Disease Activity Score; TJC = tender joint count; SJC = swollen joint count; PGA = patient global assessment; EGA = physician global assessment; HAQ = Health Assessment Questionnaire; Log CRP = log-transformed C-reactive protein; Log ESR = log-transformed erythrocyte sedimentation rate; NA = the outcome measure was a continuous value with no definitive measurement scale.
Indicates mixed-effects models were further adjusted for both the main effect of the baseline outcome (categorized quintiles) and its interaction with followup time.
Prior depressive symptoms by followup time interaction: P < 0.001.
Prior depressive symptoms by followup time interaction: P < 0.01.
Table 4.
Model-based estimates of disease activity by patient-reported lifetime depressive symptom status categorized by the self-reported time of onset at baseline and the corresponding changes at 1-year and 2-year followup among the propensity score–matched sample*
| Baseline† |
Year 1Δ† |
Year 2Δ† |
||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Depressive symptoms |
Outcome | Scale | No. | Estimate | 95% CI | Estimate | 95% CI | Estimate | 95% CI | P |
| No | CDAI | 0–76 | 4,099 | 15.73 | 15.32, 16.13 | −3.98 | −4.33, −3.64 | −3.73 | −4.29, −3.16 | < 0.001 |
| <1 year‡ | CDAI | 0–76 | 1,429 | 15.78 | 15.26, 16.30 | −3.26 | −3.86, −2.66 | −2.90 | −4.00, −1.80 | |
| ≥1 year‡ | CDAI | 0–76 | 2,670 | 15.55 | 15.11, 15.99 | −2.81 | −3.23, −2.38 | −2.63 | −3.33, −1.92 | |
| No | DAS28 | 0–10 | 2,076 | 3.89 | 3.84, 3.94 | −0.35 | −0.41, −0.30 | −0.43 | −0.53, −0.33 | 0.08 |
| <1 year | DAS28 | 0–10 | 723 | 3.94 | 3.88, 4.01 | −0.35 | −0.44, −0.25 | −0.37 | −0.56, −0.18 | |
| ≥1 year‡ | DAS28 | 0–10 | 1,320 | 3.89 | 3.83, 3.95 | −0.23 | −0.30, −0.16 | −0.32 | −0.44, −0.19 | |
| No | TJC | 0–28 | 4,099 | 4.91 | 4.70, 5.12 | −1.43 | −1.60, −1.25 | −1.40 | −1.69, −1.10 | 0.04 |
| <1 year | TJC | 0–28 | 1,429 | 5.03 | 4.76, 5.30 | −1.09 | −1.40, −0.78 | −1.06 | −1.63, −0.50 | |
| ≥1 year‡ | TJC | 0–28 | 2,670 | 4.88 | 4.65, 5.10 | −1.05 | −1.27, −0.84 | −0.85 | −1.21, −0.48 | |
| No | SJC | 0–28 | 4,099 | 4.39 | 4.17, 4.61 | −1.31 | −1.45, −1.17 | −1.27 | −1.50, −1.04 | 0.57 |
| <1 year | SJC | 0–28 | 1,429 | 4.26 | 4.00, 4.52 | −1.26 | −1.51, −1.00 | −1.31 | −1.76, −0.86 | |
| ≥1 year | SJC | 0–28 | 2,670 | 4.32 | 4.08, 4.55 | −1.12 | −1.30, −0.95 | −1.27 | −1.55, −0.98 | |
| No | PGA | 0–10 | 4,099 | 3.70 | 3.63, 3.77 | −0.54 | −0.62, −0.42 | −0.38 | −0.51, −0.24 | < 0.001 |
| <1 year‡ | PGA | 0–10 | 1,429 | 3.71 | 3.62, 3.81 | −0.21 | −0.35, −0.07 | −0.08 | −0.34, 0.19 | |
| ≥1 year‡ | PGA | 0–10 | 2,670 | 3.69 | 3.61, 3.77 | −0.15 | −0.24, 20.05 | −0.02 | −0.18, 0.15 | |
| No | EGA | 0–10 | 4,099 | 2.67 | 2.59, 2.75 | −0.75 | −0.80, −0.69 | 20.69 | −0.78, −0.60 | 0.001 |
| <1 year | EGA | 0–10 | 1,429 | 2.65 | 2.56, 2.75 | −0.64 | −0.74, −0.54 | −0.50 | −0.68, −0.32 | |
| ≥1 year‡ | EGA | 0–10 | 2,670 | 2.63 | 2.55, 2.72 | −0.61 | −0.68, −0.54 | −0.54 | −0.66, −0.43 | |
| No | Pain | 0–10 | 4,099 | 4.02 | 3.95, 4.09 | −0.58 | −0.66, −0.50 | −0.33 | −0.47, −0.19 | < 0.001 |
| <1 year‡ | Pain | 0–10 | 1,429 | 4.04 | 3.95, 4.14 | −0.20 | −0.34, −0.06 | −0.20 | −0.47, 0.07 | |
| ≥1 year‡ | Pain | 0–10 | 2,670 | 4.02 | 3.94, 4.10 | −0.22 | −0.32, −0.12 | −0.01 | −0.18, 0.17 | |
| No | HAQ | 0–3 | 4,099 | 1.06 | 1.04, 1.07 | −0.09 | −0.11, −0.08 | −0.07 | −0.10, −0.04 | < 0.001 |
| <1 year‡ | HAQ | 0–3 | 1,429 | 1.06 | 1.04, 1.08 | −0.04 | −0.06, −0.01 | −0.03 | −0.08, −0.03 | |
| ≥1 year‡ | HAQ | 0–3 | 2,670 | 1.06 | 1.04, 1.07 | −0.02 | −0.04, 0.01 | 0.03 | −0.01, 0.06 | |
| No | Log CRP | NA | 1,383 | 0.71 | 0.69, 0.74 | −0.07 | −0.09, −0.04 | −0.10 | −0.14, −0.05 | 0.22 |
| <1 year‡ | Log CRP | NA | 531 | 0.70 | 0.67, 0.73 | −0.01 | −0.05, 0.03 | −0.07 | −0.18, 0.03 | |
| ≥1 year | Log CRP | NA | 828 | 0.71 | 0.68, 0.74 | −0.06 | −0.09, −0.03 | −0.09 | −0.15, −0.33 | |
| No | Log ESR | NA | 2,084 | 1.24 | 1.23, 1.25 | −0.02 | −0.03, −0.01 | −0.02 | −0.04, 0.001 | 0.69 |
| <1 year |
Log ESR | NA | 728 | 1.24 | 1.23, 1.26 | −0.03 | −0.05, −0.01 | −0.02 | −0.06, 0.02 | |
| ≥1 year | Log ESR | NA | 1,323 | 1.24 | 1.23, 1.25 | −0.01 | −0.03, 0.001 | −0.01 | −0.03, 0.02 | |
95% CI = 95% confidence interval; CDAI = Clinical Disease Activity Index; DAS28 = 28 Joint Count Disease Activity Score; TJC = tender joint count; SJC = swollen joint count; PGA = patient global assessment; EGA = physician global assessment; HAQ = Health Assessment Questionnaire; log CRP = log-transformed C-reactive protein; log ESR = log-transformed erythrocyte sedimentation rate; NA = the outcome measure was a continuous value with no definitive measurement scale.
Indicates mixed-effects models were further adjusted for both the main effect of the baseline outcome (categorized quintiles) and its interaction with followup time.
Indicates pairwise comparison versus those with no history of depressive symptoms was statistically significant.
Incident depressive symptoms
Using all participants, there were significant differences in the course of disease activity between the 2 groups (Figure 1). Patients with incident depressive symptoms had a significantly faster rate of disease activity decline compared to the controls. Similar findings were observed for the other outcomes. Depressive symptom by time interactions reached statistical significance for every outcome, except the DAS28 and laboratory-reported measures (data not shown). PS matching resulted in prospective trends that were analogous to the prevalence results, but the effect sizes were smaller in magnitude, demonstrated by the 1-year model-based estimates of CDAI change: −1.52 (−1.92, −1.13) versus −1.90 (−2.18, −1.63) in those with and without symptoms of depression, respectively; and the depressive symptom by time interaction only reached statistical significance for the patient-reported pain outcome (Table 5). The sustained depressive symptoms case definition yielded similar results, but the effect sizes were larger in magnitude (see Supplementary Table 4, available in the online version of this article at http://onlinelibrary.wiley.com/doi/10.1002/acr.22515/abstract ).
Table 5.
Model-based estimates of disease activity by incident depressive symptom status at baseline and the corresponding change at 1-year and 2-year followup among the propensity score–matched sample*
| Baseline† |
Year 1Δ† |
Year 2Δ† |
|||||||
|---|---|---|---|---|---|---|---|---|---|
| Depressive symptoms |
Variable | No. | Scale | Estimate | 95% CI | Estimate | 95% CI | Estimate | 95% CI |
| No | CDAI | 4,216 | 0–76 | 12.24 | 11.89, 12.59 | −1.90 | −2.18, −1.63 | −2.08 | −2.52, −1.63 |
| Yes | CDAI | 2,108 | 0–76 | 12.25 | 11.84, 12.66 | −1.52 | −1.92, −1.13 | −1.62 | −2.25, −0.99 |
| No | DAS28 | 2,912 | 0–10 | 3.46 | 3.42, 3.50 | −0.15 | −0.19, −0.11 | −0.23 | −0.29, −0.16 |
| Yes | DAS28 | 1,491 | 0–10 | 3.48 | 3.43, 3.53 | −0.12 | −0.17, −0.06 | −0.15 | −0.24, −0.06 |
| No | TJC | 4,216 | 0–28 | 3.40 | 3.21, 3.60 | −0.63 | −0.77, −0.49 | −0.78 | −0.99, −0.56 |
| Yes | TJC | 2,108 | 0–28 | 3.45 | 3.23, 2.67 | −0.50 | −0.70, −0.31 | −0.48 | −0.78, −0.18 |
| No | SJC | 4,216 | 0–28 | 3.44 | 3.23, 3.65 | −0.56 | −0.68, −0.43 | −0.73 | −0.93, −0.53 |
| Yes | SJC | 2,108 | 0–28 | 3.43 | 3.20, 3.66 | −0.45 | −0.63, −0.27 | −0.77 | −1.06, −0.49 |
| No | PGA | 4,216 | 0–10 | 3.29 | 3.24, 3.35 | −0.42 | −0.49, −0.35 | −0.18 | −0.30, −0.07 |
| Yes | PGA | 2,108 | 0–10 | 3.29 | 3.22, 3.36 | −0.30 | −0.40, −0.20 | −0.09 | −0.26, 0.07 |
| No | EGA | 4,216 | 0–10 | 1.47 | 1.37, 1.57 | −0.34 | −0.38, −0.30 | −0.33 | −0.40, −0.25 |
| Yes | EGA | 2,108 | 0–10 | 1.46 | 1.36, 1.57 | −0.30 | −0.36, −0.24 | −0.34 | −0.44, −0.23 |
| No | Pain | 4,216 | 0–10 | 3.62 | 3.56, 3.67 | −0.53 | −0.60, −0.46 | −0.33 | −0.45, −0.21 |
| Yes | Pain‡ | 2,108 | 0–10 | 3.64 | 3.57, 3.71 | −0.32 | −0.42, −0.22 | −0.07 | −0.24, 0.10 |
| No | HAQ | 4,216 | 0–3 | 0.97 | 0.96, 0.08 | −0.07 | −0.08, −0.05 | −0.03 | −0.06, −0.01 |
| Yes | HAQ | 2,108 | 0–3 | 0.97 | 0.96, 0.99 | −0.05 | −0.07, −0.03 | −0.01 | −0.05, 0.02 |
| No | Log CRP | 1,765 | NA | 0.56 | 0.53, 0.59 | −0.04 | −0.06, −0.02 | −0.032 | −0.066, 0.003 |
| Yes | Log CRP | 877 | NA | 0.61 | 0.59, 0.64 | −0.04 | −0.07, −0.01 | −0.06 | −0.11, −0.01 |
| No | Log ESR | 2,965 | NA | 1.23 | 1.22, 1.24 | −0.004 | −0.012, 0.05 | 0.004 | −0.01, 0.019 |
| Yes | Log ESR | 1,509 | NA | 1.22 | 1.21, 1.23 | 0.001 | −0.012, 0.013 | 0.004 | −0.017, 0.026 |
95% CI = 95% confidence interval; CDAI = Clinical Disease Activity Index; DAS28 = 28 Joint Count Disease Activity Score; TJC = tender joint count; SJC = swollen joint count; PGA = patient global assessment; EGA = physician global assessment; HAQ = Health Assessment Questionnaire; log CRP = log-transformed C-reactive protein; log ESR = log-transformed erythrocyte sedimentation rate; NA = the outcome measure was a continuous value with no definitive measurement scale.
Indicates mixed-effects models were further adjusted for both the main effect of the baseline outcome (categorized quintiles) and its interaction with followup time.
Incident depressive symptoms by follow-up time interaction: P < 0.01.
DISCUSSION
This study examined the temporal impact of depressive symptoms on changes in RA disease activity, and demonstrated that they were associated with slower rates of disease activity decline. Yet, symptoms of depression had no impact on swollen joints and acute-phase reactants. There were also differences between the effect of prevalent and incident symptomology, and effect sizes were greater for those reporting lifetime prevalence. Moreover, the magnitude of the impact was larger for the patient-reported disease activity outcomes compared to measures reported by physicians. The results indicate that symptoms of depression temporally influence RA disease activity, but the effect is limited to patient- and provider-reported measures and varies by symptomatic onset.
Prevalent depressive symptoms affected every disease activity metric, except the DAS28, joint swelling, and serum biomarkers. Studies have consistently demonstrated temporal associations between depression and pain among RA, PsA, and EIA patients (12,13,18,20). Research also indicates that depression moderates stress-associated changes in pain, EGA, and TJC, but these associations were not linked to immune activation (19). Our results replicated prior data and expanded them to show direct temporal associations between prevalent symptoms of depression and multiple disease activity measures. Stress, negative affect, and the perseverative cognitions which characterize depression may stimulate proinflammatory cytokines and influence the etiology of chronic physical diseases; however, these data and prior studies provide no evidence that depressive symptomology raises levels of acute-phase reactants, measures of serum inflammation (7,39,40). Therefore, it is unclear whether the observed temporal relationships truly reflect a substantive impact on RA disease activity.
The findings also imply that the effect of depressive symptoms varies by the time of onset, evidenced by the differences between the prevalent and incident findings. Despite similar depressive effects, where symptoms of depression were associated with slower disease activity decline, the magnitude was greatest for those reporting lifetime prevalent symptomology. Age at depression onset is predictive of the frequency and strength of depressive episodes, and individuals with depression occurring earlier in life are more likely to have future episodes that are greater in number and persistency; therefore, the observed effect heterogeneity may be a proxy for symptom severity (41,42). The stratified prevalence findings trending by the time of depressive symptom onset and sustained symptomology case definition that yielded stronger temporal effects support this assertion. These data indicate that greater depressive symptom severity creates larger differences in the trajectory of disease activity, which is certainly plausible, given the strong association between this comorbidity and future RA symptoms (18,20).
The effect of symptoms of depression on changes in disease activity was greater for the patient-reported measures. Current research provides little information regarding the simultaneous assessment of all measures of RA disease activity (18–21). Studies using EIA and PsA patients have demonstrated temporal associations between depression and pain but not swollen joints (12,13). Depressive symptomology temporally influences the evolution of RA symptoms, although the effect depends on how disease activity is defined, and the mediating mechanisms are not understood. Two pertinent hypotheses fit into the collective meaning of these results: a measurement response shift created from negative affect and behavioral patterns that cause reductions in physical activity, a loss of natural endorphins, and increases in musculoskeletal pain (11). Nonetheless, depressive symptoms also resulted in more negative physician assessments (i.e., EGA and TJC), which support the premise of a tangible effect on musculoskeletal pain. Conversely, physicians’ interpretations of disease status based on communications with their patients may be impacted by the presence of mood disorders.
Likewise, this research has limitations. The depression measures represented the presence and absence of the core symptom, rather than metrics of the condition as evaluated using diagnostic criteria. This limited method of assessment results in an overestimation of the true prevalence and incidence levels because they are under-determinative of clinical depression and therefore dilute the overall case severity of the samples of patients with depressive symptoms and bias the results in a conservative manner. Also, the propensity score matching approach simultaneously balanced all baseline disease activity measures, which may have resulted in an overadjustment and underestimation of the true longitudinal differences. Lastly, multivariable models did not control for time-varying factors that may have changed after the index time points. Nonetheless, this work is the first to examine the temporal effect of this comorbidity in terms of the multifactorial nature of RA disease activity using a large heterogeneous sample and rigorous statistical adjustment over extended followup durations.
In summary, the temporal impact of depressive symptomology extends beyond measures of pain and includes global assessments, function, and tender joints analogous to prior work identifying these measures as strong predictors of depressive symptom onset in RA patients (43). Further, the effect increases with patients’ depressive symptom severity, as evidenced by the differences in the impact of prevalent symptoms compared to incident episodes; still, these data would indicate no appreciable impact on joint swelling and serum inflammation. Additional work is necessary to determine whether these results are representative of a definitive effect on joint pain, or if the underlying relationship is a consequence of patients’ illness perceptions. Supposing the causal mechanism is the latter, these data call into question the validity of global assessments to evaluate disease activity in routine rheumatology practice. Future research should ascertain how depression is related to patient- and physician-reported disease activity measurement responses, as well as the impact on changes in behavior assessed using objective measures of functional performance and corresponding influence on musculoskeletal pain.
Supplementary Material
Significance & Innovations.
Symptoms of depression temporally influence longitudinal changes in rheumatoid arthritis disease activity and are associated with slower improvement.
Temporal effect of depressive symptoms increases with depression case severity.
There is no appreciable impact of depression symptomology on joint swelling and acute-phase reactants.
Acknowledgments
Supported by the 2013 Rheumatology Research Foundation Student Achievement Award and the Consortium of Rheumatology Researchers of North America (CORRONA). In the last 2 years, AbbVie, Amgen, Genentech, Horizon Pharma, Lilly, Momenta, Novartis, Pfizer, Regeneron, Vertex, and UCB have supported CORRONA through contract subscriptions.
Dr. Harrold is a consulting epidemiologist to CORRONA and has received research grants to her institution from CORRONA. Dr. Reed is Chief Statistical Officer of the CORRONA Registry and holds stock options in CORRONA.
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 submitted for publication. Dr. Rathbun 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. Rathbun, Harrold, Reed.
Analysis and interpretation of data. Rathbun, Harrold, Reed.
REFERENCES
- 1.Helmick CG, Felson DT, Lawrence RC, Gabriel S, Hirsch R, Kwoh CK, et al. for the National Arthritis Data Workgroup. Estimates of the prevalence of arthritis and other rheumatic conditions in the United States: part I. Arthritis Rheum. 2008;58:15–25. doi: 10.1002/art.23177. [DOI] [PubMed] [Google Scholar]
- 2.Cutolo M, Kitas GD, van Riel PL. Burden of disease in treated rheumatoid arthritis patients: going beyond the joint. Semin Arthritis Rheum. 2014;43:479–488. doi: 10.1016/j.semarthrit.2013.08.004. [DOI] [PubMed] [Google Scholar]
- 3.Detweiler-Bedell JB, Friedman MA, Leventhal H, Miller IW, Leventhal EA. Integrating co-morbid depression and chronic physical disease management: identifying and resolving failures in self-regulation. Clin Psychol Rev. 2008;28:1426–1446. doi: 10.1016/j.cpr.2008.09.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Matcham F, Rayner L, Steer S, Hotopf M. The prevalence of depression in rheumatoid arthritis: a systematic review and meta-analysis [letter] Rheumatology (Oxford) 2014;53:578–579. doi: 10.1093/rheumatology/ket439. [DOI] [PubMed] [Google Scholar]
- 5.Rathbun AM, Harrold LR, Reed GW. A description of patient- and rheumatologist-reported depression symptoms in an American rheumatoid arthritis registry population. Clin Exp Rheumatol. 2014;32:523–532. [PubMed] [Google Scholar]
- 6.Pilling S, Anderson I, Goldberg D, Meader N, Taylor C and Two Guideline Development Groups. Depression in adults, including those with a chronic physical health problem: summary of NICE guidance. BMJ. 2009;339:b4108. doi: 10.1136/bmj.b4108. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Maes M. Depression is an inflammatory disease, but cell-mediated immune activation is the key component of depression. Prog Neuropsychopharmacol Biol Psychiatry. 2011;35:664–675. doi: 10.1016/j.pnpbp.2010.06.014. [DOI] [PubMed] [Google Scholar]
- 8.Margaretten M, Julian L, Katz P, Yelin E. Depression in patients with rheumatoid arthritis: description, causes and mechanisms. Int J Clin Rheumatol. 2011;6:617–623. doi: 10.2217/IJR.11.6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Ehring T, Watkins ER. Repetitive negative thinking as a transdiagnostic process. Int J Cogn Ther. 2008;1:192–205. [Google Scholar]
- 10.Sheehy C, Murphy E, Barry M. Depression in rheumatoid arthritis: underscoring the problem. Rheumatology (Oxford) 2006;45:1325–1327. doi: 10.1093/rheumatology/kel231. [DOI] [PubMed] [Google Scholar]
- 11.Rathbun AM, Reed GW, Harrold LR. The temporal relationship between depression and rheumatoid arthritis disease activity, treatment persistence and response: a systematic review. Rheumatology (Oxford) 2013;52:1785–1794. doi: 10.1093/rheumatology/kes356. [DOI] [PubMed] [Google Scholar]
- 12.Schieir O, Thombs BD, Hudson M, Taillefer S, Steele R, Berkson L, et al. Symptoms of depression predict the trajectory of pain among patients with early inflammatory arthritis: a path analysis approach to assessing change. J Rheumatol. 2009;36:231–239. doi: 10.3899/jrheum.080147. [DOI] [PubMed] [Google Scholar]
- 13.Husted JA, Tom BD, Farewell VT, Gladman DD. Longitudinal study of the bidirectional association between pain and depressive symptoms in patients with psoriatic arthritis. Arthritis Care Res (Hoboken) 2012;64:758–765. doi: 10.1002/acr.21602. [DOI] [PubMed] [Google Scholar]
- 14.Cordingley L, Prajapati R, Plant D, Maskell D, Morgan C, Ali FR, et al. and the Biologics in Rheumatoid Arthritis Genetics and Genomics Study Syndicate. Impact of psychological factors on subjective disease activity assessments in patients with severe rheumatoid arthritis. Arthritis Care Res (Hobo-ken) 2014;66:861–868. doi: 10.1002/acr.22249. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Godha D, Shi L, Mavronicolas H. Association between tendency towards depression and severity of rheumatoid arthritis from a national representative sample: the Medical Expenditure Panel Survey. Curr Med Res Opin. 2010;26:1685–1690. doi: 10.1185/03007991003795808. [DOI] [PubMed] [Google Scholar]
- 16.Lok EY, Mok CC, Cheng CW, Cheung EF. Prevalence and determinants of psychiatric disorders in patients with rheumatoid arthritis. Psychosomatics. 2010;51:338. doi: 10.1176/appi.psy.51.4.338. [DOI] [PubMed] [Google Scholar]
- 17.Kojima M, Kojima T, Suzuki S, Oguchi T, Oba M, Tsuchiya H, et al. Depression, inflammation, and pain in patients with rheumatoid arthritis. Arthritis Rheum. 2009;61:1018–1024. doi: 10.1002/art.24647. [DOI] [PubMed] [Google Scholar]
- 18.Zautra AJ, Smith BW. Depression and reactivity to stress in older women with rheumatoid arthritis and osteoarthritis. Psychosom Med. 2001;63:687–696. doi: 10.1097/00006842-200107000-00022. [DOI] [PubMed] [Google Scholar]
- 19.Zautra AJ, Yocum DC, Villanueva I, Smith B, Davis MC, Attrep J, et al. Immune activation and depression in women with rheumatoid arthritis. J Rheumatol. 2004;31:457–463. [PubMed] [Google Scholar]
- 20.Conner TS, Tennen H, Zautra AJ, Affleck G, Armeli S, Fifield J. Coping with rheumatoid arthritis pain in daily life: within-person analyses reveal hidden vulnerability for the formerly depressed. Pain. 2006;126:198–209. doi: 10.1016/j.pain.2006.06.033. [DOI] [PubMed] [Google Scholar]
- 21.Zautra AJ, Parrish BP, van Puymbroeck CM, Tennen H, Davis MC, Reich JW, et al. Depression history, stress, and pain in rheumatoid arthritis patients. J Behav Med. 2007;30:187–197. doi: 10.1007/s10865-007-9097-4. [DOI] [PubMed] [Google Scholar]
- 22.Kremer J. The CORRONA database. Ann Rheum Dis. 2005;64(Suppl 4):iv37–iv41. doi: 10.1136/ard.2005.043497. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Zimmerman M, Ruggero CJ, Chelminski I, Young D, Posternak MA, Friedman M, et al. Developing brief scales for use in clinical practice: the reliability and validity of single-item self-report measures of depression symptom severity, psychosocial impairment due to depression, and quality of life. J Clin Psychiatry. 2006;67:1536–1541. doi: 10.4088/jcp.v67n1007. [DOI] [PubMed] [Google Scholar]
- 24.Kobau R, Safran MA, Zack MM, Moriarty DG, Chapman D. Sad, blue, or depressed days, health behaviors and health-related quality of life, Behavioral Risk Factor Surveillance System, 1995–2000. Health Qual Life Outcomes. 2004;2:40. doi: 10.1186/1477-7525-2-40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Nichols GA, Brown JB. Following depression in primary care: do family practice physicians ask about depression at different rates than internal medicine physicians? Arch Fam Med. 2000;9:478–482. doi: 10.1001/archfami.9.5.478. [DOI] [PubMed] [Google Scholar]
- 26.Wolfe F, Michaud K. Predicting depression in rheumatoid arthritis: the signal importance of pain extent and fatigue, and comorbidity. Arthritis Rheum. 2009;61:667–673. doi: 10.1002/art.24428. [DOI] [PubMed] [Google Scholar]
- 27.Jones D, Vichaya EG, Cleeland CS, Cohen L, Thekdi SM, Wang XS, et al. Screening for depressed mood in patients with cancer using the MD Anderson Symptom Inventory: investigation of a practical approach for the oncologist. J Oncol Pract. 2014;10:e95–e102. doi: 10.1200/JOP.2013.001112. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Mitchell AJ, Coyne JC. Do ultra-short screening instruments accurately detect depression in primary care? A pooled analysis and meta-analysis of 22 studies. Br J Gen Pract. 2007;57:144–151. [PMC free article] [PubMed] [Google Scholar]
- 29.Avasarala JR, Cross AH, Trinkaus K. Comparative assessment of Yale Single Question and Beck Depression Inventory Scale in screening for depression in multiple sclerosis. Mult Scler. 2003;9:307–310. doi: 10.1191/1352458503ms900oa. [DOI] [PubMed] [Google Scholar]
- 30.Mukerji SS, Duffy SA, Fowler KE, Khan M, Ronis DL, Terrell JE. Comorbidities in head and neck cancer: agreement between self-report and chart review. Otolaryngol Head Neck Surg. 2007;136:536–542. doi: 10.1016/j.otohns.2006.10.041. [DOI] [PubMed] [Google Scholar]
- 31.Margolis KL, Lihong Q, Brzyski R, Bonds DE, Howard BV, Kempainen S, et al. for the Women Health Initiative Investigators. Validity of diabetes self-reports in the Women’s Health Initiative: comparison with medication inventories and fasting glucose measurements. Clin Trials. 2008;5:240–247. doi: 10.1177/1740774508091749. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Kroenke K, Spitzer RL, Williams JB. The Patient Health Questionnaire-2: validity of a two-item depression screener. Med Care. 2003;41:1284–1292. doi: 10.1097/01.MLR.0000093487.78664.3C. [DOI] [PubMed] [Google Scholar]
- 33.Prevoo ML, van ‘t Hof MA, Kuper HH, van Leeuwen MA, van Puttede LB, van Riel PL. Modified disease activity scores that include twenty-eight-joint counts: development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. Arthritis Rheum. 1995;38:44–48. doi: 10.1002/art.1780380107. [DOI] [PubMed] [Google Scholar]
- 34.Aletaha D, Nell VP, Stamm T, Uffmann M, Pflugbeil S, Machold K, et al. Acute phase reactants add little to composite disease activity indices for rheumatoid arthritis: validation of a clinical activity score. Arthritis Res Ther. 2005;7:R796–R806. doi: 10.1186/ar1740. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Anderson J, Sayles H, Curtis JR, Wolfe F, Michaud K. Converting modified health assessment questionnaire (HAQ), multidimensional HAQ, and HAQII scores into original HAQ scores using models developed with a large cohort of rheumatoid arthritis patients. Arthritis Care Res (Hoboken) 2010;62:1481–1488. doi: 10.1002/acr.20265. [DOI] [PubMed] [Google Scholar]
- 36.Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res. 2011;46:399–424. doi: 10.1080/00273171.2011.568786. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Rabe-Hesketh S, Skrondal A. Multilevel and longitudinal modeling using Stata. 2nd. College Station (TX): Stata; 2008. [Google Scholar]
- 38.Furukawa TA, Kitamura T, Takahashi K. Time to recovery of an inception cohort with hitherto untreated unipolar major depressive episodes. Br J Psychiatry. 2000;177:331–335. doi: 10.1192/bjp.177.4.331. [DOI] [PubMed] [Google Scholar]
- 39.Brosschot JF, Gerin W, Thayer JF. The perseverative cognition hypothesis: a review of worry, prolonged stress-related physiological activation, and health. J Psychosom Res. 2006;60:113–124. doi: 10.1016/j.jpsychores.2005.06.074. [DOI] [PubMed] [Google Scholar]
- 40.Iwata M, Ota KT, Duman RS. The inflammasome: pathways linking psychological stress, depression, and systemic illnesses. Brain Behav Immun. 2013;31:105–114. doi: 10.1016/j.bbi.2012.12.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Garcia-Toro M, Rubio JM, Gili M, Roca M, Jin CJ, Liu SM, et al. Persistence of chronic major depression: a national prospective study. J Affect Disord. 2013;151:306–312. doi: 10.1016/j.jad.2013.06.013. [DOI] [PubMed] [Google Scholar]
- 42.Monroe SM, Slavich GM, Gotlib IH. Life stress and family history for depression: the moderating role of past depressive episodes. J Psychiatr Res. 2014;49:90–95. doi: 10.1016/j.jpsychires.2013.11.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Rathbun AM, Harrold LR, Reed GW. Temporal associations between the different domains of rheumatoid arthritis disease activity and the onset of patient-reported depressive symptoms. Clin Rheumatol. doi: 10.1007/s10067-014-2759-z. In press. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.