Next Article in Journal
A Next-Generation Sequencing-Based Approach to Identify Genetic Determinants of Antibiotic Resistance in Cambodian Helicobacter pylori Clinical Isolates
Next Article in Special Issue
Molecular Profiling of Cutaneous Lupus Lesions Identifies Subgroups Distinct from Clinical Phenotypes
Previous Article in Journal
Safety Evaluation of Tadalafil Treatment for Fetuses with Early-Onset Growth Restriction (TADAFER): Results from the Phase II Trial
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 8
Issue 6
10.3390/jcm8060857
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Impact of Cytokines on the Health-Related Quality of Life in Patients with Systemic Lupus Erythematosus

by
Warren David Raymond
1,*,
Gro Østli Eilertsen
2,
Sharanyaa Shanmugakumar
1 and
Johannes Cornelis Nossent
1,2,3
1
Rheumatology Group, School of Medicine, The University of Western Australia, Perth 6009, Australia
2
Molecular Inflammation Research Group, Department of Clinical Medicine, Arctic University, 9037 Tromsø, Norway
3
Department of Rheumatology, Sir Charles Gairdner Hospital, Perth 6009, Australia
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2019, 8(6), 857; https://doi.org/10.3390/jcm8060857
Submission received: 21 May 2019 / Revised: 7 June 2019 / Accepted: 12 June 2019 / Published: 15 June 2019
(This article belongs to the Special Issue New Insights into Systemic Lupus Erythematosus: From Immunopathogenesis to Treatment Strategies)
Review Reports Versions Notes

Abstract

:
Introduction: Systemic lupus erythematosus (SLE) reduces the health-related quality of life (HRQoL), even during periods of disease quiescence. We investigated whether subclinical inflammation as reflected by cytokine levels is linked with reduced HRQoL. Methods: A cross-sectional study of SLE patients (n = 52, mean age 47.3, 86.5% female) who completed a Short Form Health Survey-36 (SF-36) questionnaire. The clinical and demographic data, scores for the disease activity (SLEDAI-2K), organ damage (SDI), and laboratory data were collected simultaneously. The autoantibody and cytokine levels (IFN-γ, IL-1β, IL-4, IL-6, IL-10, IL-12, IL-17, BAFF, TNF-α, TGF-β1, MIP-1α, MIP-1β and MCP-1 (levels in pg/mL) were quantified by sandwich ELISA. The comparisons and associations were assessed non-parametrically, and a multiple regression determined the effect sizes (ES) of the variables on the SF-36 domain and summary scores. Results: The SF-36 summary and domain scores for SLE patients were significantly (20–40%) lower than in a comparable control group, with the exception of the Mental Health scores (p = 0.06). SLE patients had a normal body mass index (BMI) (median, 24.2 kg/m2), a high rate of smoking (69.2%), and usage of social security benefits (90.4%). TGF-β1 (ES 0.06), IL-12 (ES −0.11), IFN-γ (ES 0.07) and MCP-1 (ES 0.06) influenced the SF-36 domain scores; and MCP-1 (ES 0.04) influenced the Mental Health Summary Score (MCS). Obvious manifestations, including patient visual analogue scale (VAS) (ES −2.84 to −6.29), alopecia (ES −14.89), malar rash (ES −14.26), and analgesic requirement (ES −19.38), independently influenced the SF-36 items; however, the SF-36 scores were not reflected by the physician VAS or disease activity (SLEDAI-2K). Conclusions: Cytokines had a minimal impact on HRQoL in SLE patients, especially compared to visible skin manifestations, central nervous system (CNS) damage, and pain. Better tools are needed to capture HRQoL in measures of disease activity.

1. Introduction

Systemic lupus erythematosus (SLE) is a systemic autoimmune condition with an unclear aetiology, with wide ranging manifestations. SLE leads to a poorer health-related quality of life (HRQoL) by negatively affecting the patient’s physical and mental health [1]. The reduction of HRQoL experienced by SLE patients is equivalent to patients with other chronic diseases, such as acquired immunodeficiency and congestive heart failure [2].
Patients with SLE experience a reduction of HRQoL early in their disease, which continues for at least 5 years following diagnosis [3]. The initial reduction in HRQoL has been associated with a range of disease-specific complications and psychosocial factors [2,3,4,5,6,7,8,9,10,11]. Importantly, the HRQoL reduction in SLE patients persists in the absence of an active disease, the need of glucocorticoids, or accrued damage [12], which indicates that clinically quiescent or well-controlled SLE can still reduce HRQoL [13]. The reduction of HRQoL in the absence of obvious physical manifestations may result from the persistent immune dysfunction in SLE; which contributes to the upregulation of proinflammatory cytokines, such as interleukin 6 (IL-6) and tumour necrosis factor α (TNF-α), which have been associated with fatigue, irritability, depressive moods and social withdrawal [14]. Given that the current disease activity tools are not well suited to capture the underlying immunological activity [15], this study investigated the influence of cytokines on HRQoL using the SF-36 domain and summary scores in patients with SLE.

2. Methods

This cross-sectional study of patients in the Tromso Lupus Cohort (n = 52, mean age 47.3, 86.5% female) who completed a Short Form Health Survey-36 (SF-36) questionnaire during an outpatient visit. SF-36 has been validated for use in Norwegian studies [16] and is comprised of 8 dimension scales: physical function (PF), role limitations due to physical problems (role physical, RP), bodily pain (BP), general health (GH), vitality (VT), social function (SF), role limitations due to emotional problems (role emotional, RE), and mental health (MH) [17]. Each scale ranges from 0 (lowest) to 100 (highest). These 8 dimensions can be summarised into two global scores, the physical component summary (PCS) and the mental component summary (MCS). The scores for patients in our study were compared to Norwegian reference values for women (n ≥ 1097) in 2015 [18].
All study patients fulfilled the American College of Rheumatology’s (ACR) classification criteria for SLE [19,20]. During the same visit as the SF-36 reporting, data was also collected on clinical characteristics, the disease activity with the Systemic Lupus Erythematosus Disease Activity Index—2000 (SLEDAI-2K) [15], and accrued organ damage with the Systemic Lupus International Collaborating Clinics—Damage Index (SDI) [21]. The disease activity was also scored on a 0–10 cm visual analogue scale (VAS) scale by the patient (patVAS) and attending physicians (pVAS).
The disease activity was grouped as remission (SLEDAI-2K = 0), low disease activity (SLEDAI-2K 1 ≤ x ≤ 4), and high disease activity (SLEDAI-2K > 4). Organ damage was grouped as no damage (SDI = 0), moderate damage (SDI 1 ≤ x ≤ 3), and severe organ damage (SDI > 3). Medication use was grouped as: glucocorticosteroids (GC), anti-malarials (AM), and immunosuppressives (IS), which included: azathioprine, mycophenolate, methotrexate, rituximab or calcineurin inhibitors. We defined, “government benefits” as receiving a disability pension or a stipend for vocational training; “cardiovascular events” were defined as the occurrence of a heart attack or blood clots; “gastrointestinal problems” included the requirement of proton pump inhibitors, irritable bowel or diarrhoea; “mental health problems” as the presence or need for the treatment for depression, insomnia, anxiety or other mood disorders; and “omega-3 supplementation” as any use of seal, fish or cod liver oil.
Serum samples were collected at a routine blood monitoring and stored in 1 ml aliquots at −70 degrees, and they underwent 1–2 freeze-thaw cycles only. Anti-double-stranded DNA antibodies (anti-dsDNA Ab) and other autoantibody assays were performed at the clinical immunology laboratory with a validated ELISA (EliATM and VarelisA®; Phadia GmbH, Freiburg, Germany). The measurements of interleukin 17A (IL-17A), B-cell-activating factor (BAFF), interleukin 1 beta (IL-1β), interleukin 4 (IL-4), IL-6, interleukin 10 (IL-10), interleukin 12 (IL-12), IFN-γ, macrophage inflammatory protein 1-alpha (MIP-1α), macrophage inflammatory protein 1-beta (MIP-1β), monocyte chemoattractant protein 1 (MCP-1), TNF-α, and transforming growth factor beta 1 (TGF-β1) were done with a quantitative sandwich immunoassay (Single Analyte ELISArray™ kit; SuperArray Bioscience Corp., Frederick, MD, USA). The manufacturer’s recommendations were followed throughout; the same lot was used for each cytokine, and the results were the averaged duplicate assay runs. For statistical purposes, the values below the limit of detection (LOD) were replaced by the LOD value.

Statistical Analysis

Continuous data are described as either a mean with a standard deviation or a median with an inter-quartile range (IQR); and, categorical data are described as a frequency and proportion. The differences between the groups were assessed with the non-parametric Mann–Whitney U or the Chi-square tests, where appropriate. Spearman’s rank correlation coefficient (Rs) describes the association between the clinical parameters and the SF-36 domain and summary scores. Univariate and multiple regression models determined the effect size (ES) of the clinical variables on the SF-36 domain and summary scores, i.e., the amount change in the SF-36 when the clinical variable increases by 1 unit [22]. Multiple regression models of the SF-36 domains and component summary scores were adjusted for age and disease duration [3,7,23]. A statistical analysis was conducted with IBM SPSS for Windows Version 24.0 (IBM Corporation: Armonk, NY, USA); and, p-values (p) < 0.05 were considered statistically significant.

3. Results

This SLE cohort had a female preponderance (86.5%), a median age of 46.8 years (IQR 32.4, 60.2), and a median disease duration of 10 years ((IQR 5.3, 18.3) range: 0.0, 34.7) (Table 1). Thirty-six patients (69%) smoked a median of 8 cigarettes per day (IQR 5, 10). The patients were mostly high school graduates and averaged 12 years of schooling, yet the majority (90.4%) were reliant on government benefits. The prevalent comorbidities were hypertension (38.5%), gastrointestinal problems (25%), cardiovascular events (34.5%), and psychiatric issues (19%). Patients required prednisone (54%) at a median dose of 6.3 mg/day (IQR 5, 10), antimalarials (69%) and immunosuppressants (60%); and they required anticoagulants (52%) and antihypertensives (46%) to treat the most common comorbidities. The routine blood and urine biomarkers were generally unremarkable (Table 1), with thrombocytopenia present in 2%, leukocytopenia present in 8%, and proteinuria in 15%, respectively. Anti-dsDNA Ab were seen in 39%, and 23% had hypocomplementemia.
The median SLEDAI-2K score was 7 points (IQR 4, 10), while the median patient VAS was 3 points (IQR 2, 5), and the median physician VAS was 2 points (IQR 1, 4). The most frequent clinical symptoms of SLE were malar rash (31%), alopecia (31%), arthritis (25%), and oropharyngeal ulcers (21%). Organ damage (SDI > 0) had occurred in 63.5% (n = 33), mostly in the musculoskeletal (27%), cardiovascular (21%), and central nervous (neuropsychiatric) (19%) systems.
SLE patients scored significantly lower on all of the SF-36 domain and summary scores than a Norwegian female reference sample in 2015 [18], but this failed to reach a statistical significance for Mental Health (MH) (Table 2). SLE patients with SLEDAI ≤ 4 (n = 18) or SLEDAI > 4 (n = 34), as well as SLEDAI ≤ 6 or SLEDAI > 6, were equivalent across all of the SF-36 summary and domain scores (all, p > 0.05); however, the SF levels (50 vs. 75, p = 0.031) were lower in those with SLEDAI > 10 (n = 18) compared to SLEDAI ≤ 10 (n = 34). The associations between the SF-36 scores and clinical and biochemical findings are shown in Supplementary Table S1. There was a clear negative effect of age and SDI > 0 on PF while the use of osteoporosis medication and analgesics had a large impact on the overall physical health. Age and the use and dosage of prednisone were negatively correlated with mental health. The patVAS was stronger and more broadly correlated with the physical and mental domain scores than pVAS, and SLEDAI-2K was inversely associated with SF only.

3.1. Cytokines

The levels of certain cytokines correlated with some SF-36 sub-scales and summary scores but lacked a discernible pattern (Table 3). The PCS summary score was associated with IFN-γ, IL-1β, IL-12, MCP-1, and TNF-α, while the physical domain scores was associated with IFN-γ, IL-1β, IL-12. IL-4, MCP-1, MIP-1β, and TNF-α. The MCS summary score was associated with IL-12, MCP-1 and MIP-1β, while the mental domains were associated with IL-1β, IL-12, MCP-1 and MIP-1β. In the univariate linear regression models, PF increased with increasing levels of IFN-γ and IL-12. RP increased with increasing levels of TGF-β1. BP, GH or the PCS were unaffected by changing cytokine levels. VT increased with increasing levels of MCP-1. RE increased with increasing levels of BAFF and MIP-1β. MH increased with increasing levels of MCP-1. The MCS increased with increasing levels of MCP-1 and MIP-1β. However, all statistically significant effect sizes were quite small in absolute terms.

3.2. Multiple Regression Model

After adjusting for age and disease duration, the PCS worsened with an increasing patient VAS (ES –4.09), analgesic use (ES –19.38), alopecia (ES –14.89) and platelet count (ES –0.09) (Table 4). The physical domains were reduced by a worsening patVAS, the presence of alopecia, increased steroid requirement, IS use, analgesic use, and falling levels of IFN-γ and TGF-β1. Similarly, MCS worsened with an increasing patient VAS (ES –2.84) or malar rash (ES –14.86), but improved slightly with MCP-1 levels (ES 0.04). The mental domains were reduced by a worsening patVAS, IS use, malar rash, damage accrual, falling MCP-1 level, falling C3 level, greater daily cigarette consumption, and increasing HbA1c (%).

4. Discussion

Patients in the Tromso Lupus Cohort demonstrated significantly lower SF-36 scores than a normative Norwegian female population. The reduction in HRQoL was best captured by the patient’s own assessment of the global disease activity (patVAS), to a lesser extent by the physician VAS score, and, marginally by the total SLEDAI-2K score, which was only associated with the SF domain. While reduced TGF-β1, IFN-γ and MCP-1 levels and increased IL-12 levels were significantly associated with lower HRQoL scores, there were much larger absolute independent effect sizes on age adjusted HRQoL for the presence of malar rash or alopecia, analgesic use, daily cigarette consumption, and poor glycaemic control (HbA1c (%)), compared to the unitary changes in the cytokine levels.
Relative to the normative female Norwegian population, and in line with the literature, SLE patients had a 20–40% reduction in all of the SF-36 domains, with the exception of MH [7,12,23,24,25,26]. The PCS and MCS scores were similar to other SLE cohorts, with the exception of a higher MCS compared to the PCS scores [23,24,27,28]. This could be due to the low RP scores, which to date are the lowest reported in SLE, and correlated with the PCS, MCS and each domain (all, Rs > 0.35), with the exception of RE and MH [12,28]. The dissociation of the mental HRQoL items herein might reflect the socioeconomic and healthcare factors in Norway, coupled with the malleability of mental health over time. This is especially relevant given that 90.4% of participants utilised social security services, which in spite of our insignificant findings, were shown to be protective of HRQoL [1,23]. However, given our higher average RE compared to RP scores, we suspect that greater societal acceptance and reduced stigmatism toward those who could benefit from social security services, especially those with chronic diseases like SLE, may have masked the true effects of these programmes on HRQoL [6,27,29]. Second, the Norwegian healthcare system offered free psychiatric outpatient care to help patients accept the potential for lifestyle changes caused by a chronic disease. Finally, mental HRQoL may have been protected through the regular clinical appointments over an average follow-up time of 13 years, which would have provided patients with a disease-specific stress reduction and coping strategies [12,13,26,30,31].
Our patients had a comparable age, gender, and disease duration to other studies reporting on HRQoL in SLE [23,24,28]. We confirmed the negative association of age and disease duration within the physical HRQoL items [3,6,7,23,24,28]. With the high rates of smoking compared to other studies, we confirmed that an increasing daily cigarette consumption leads to worsened MH [7,13,32]. However, whether smoking contributes to or is a reflection of worse MH remains an open question. Omega-3 supplementation (40.4%) did not influence HRQoL, which was confirmatory given the data showing a lack of efficacy of omega-3 supplementation in patients with SLE [33].
The cytokine levels were equivalent across patients with different levels of disease activity and organ damage. This contradicted other studies, but this may have been due to the small sample size, as well as to different definitions of the outcome measures [4,13]. The weak but positive correlations between cytokines and HRQoL were mostly attenuated in the regression model, with the exception of TGF-β1, IFN-γ, IL-12, and MCP-1. Increasing TGF-β1 levels were associated with higher RP in SLE. While this is a novel finding in the context of HRQoL, it aligns with the evidence demonstrating that SLE patients produce less TGF-β1 than healthy individuals [34], and that a deficiency in TGF-β1 can contribute to the onset and exacerbation of SLE [34,35]; thus, pathways to optimise TGF-β1 may help improve HRQoL in SLE. IFN-γ levels have been associated with disease flares and lupus nephritis, while we found that the IFN-γ level was mildly associated with physical functioning [35,36,37]. Anti-IFN-γ therapy was inefficacious in SLE and resulted in more adverse events and fatigue; however, the HRQoL data was not formally captured [38]; in spite of this, oncology patients receiving anti-IFN-γ therapy had a similar increase of adverse events and demonstrated worsened HRQoL [38,39,40]. Increasing IL-12 levels were seen with a greater SLE disease activity [41] and were associated with worsened HRQoL in rheumatoid and psoriatic arthritis [42,43]; thus, its association with decreased PF in SLE, is a novel, yet unsurprising finding. Increasing MCP-1 increased VT and MCS, which contradicts the data on the disease activity in SLE, where the serum, cerebrospinal and urinary MCP-1 levels were increased with disease exacerbation; and MCP-1 was also shown to be associated with fatigue and depression in SLE; while simultaneously beingnegatively associated with all SF-36 items, (all, p < 0.01) [4]. Our results were similar to other studies which showed that the MCP-1 levels remain elevated after a long-term immunosuppression, even when the disease activity had subsided [44,45,46]. More generally, MCP-1 levels have been shown to be equivalent between women with and without Exhaustion Disorders, anxiety or depression [47]; and MCP-1 is important for combating bacterial infections [48]. Thus, the beneficial effect of MCP-1 on HRQoL seen here may represent a protection against infection in the context of chronic inflammation and IS requirement. However, compared to the clinical parameters, the absolute effect size of cytokine levels on HRQoL was very low, suggesting a large overlap of cytokine level between various HRQoL states, and indicates that a substantial change in cytokine level would be required to cause meaningful differences to a patient’s HRQoL.
Given the limited association for cytokines, we performed ad-hoc analyses of the clinical, serological, immunological data to determine their influence on HRQoL in patients with SLE.
While the rates of comorbidities, such as Raynaud’s phenomenon, cardiovascular events, hypertension, gastrointestinal problems, thyroid dysfunction, and mental health problems were comparable to other studies, in contrast to other studies they did not influence HRQoL [2,13,26,49,50,51,52,53,54,55].
Medication usage was overall comparable to other studies [7,56,57]; and while confirming that antimalarial usage had no effect on HRQoL, prednisone requirement and dosage was associated with lower PF, as shown by Chaigne et al. [7], and IS requirement negatively impacted GH, which confirmed earlier findings [57]. Analgesic requirement was negatively associated with BP and PCS, which confirmed that pain or pain syndromes, such as fibromyalgia, can reduce HRQoL in SLE [7,26,58]. We found no impact of anticoagulation [55], antihypertensives (no comparable data), mood stabilisers [55], and NSAIDs [59] on HRQoL. For the routine laboratory findings, we found that HbA1c (%) negatively impacted MH, aligning with the glycaemic control literature [60,61], but that the levels of proteinuria and anti-dsDNA antibodies were not related to HRQoL [7,26,62,63]. An increasing C3 level led to higher SF; however, the levels or hypocomplementemia of C3 or C4 did not influence any other HRQoL item. Those with low C3 (n = 11) had higher SLEDAI-2K (8 vs. 6, p = 0.021), clinical SLEDAI-2K (6 vs. 4, p = 0.046; which excludes hypocomplementemia and the presence of anti-dsDNA Abs), and more oropharyngeal ulcers (63.6% vs. 9.8%, p < 0.01). Given that oropharyngeal ulcers are not associated with low C3 [61]; and, we demonstrated that SF was reduced by an increasing disease activity (patVAS), especially new malar rash, and was negatively associated with the SLEDAI-2K score (Rs −0.31, p = 0.03); this would suggest that significant complement activation of C3, perhaps driven by a fulminant disease activity or potentially by other non-immune mediated pathways, may contribute to reduced SF.
The reported SLEDAI-2K scores and physician VAS scores were comparable to other studies, but were dissociated from HRQoL, as reported elsewhere [24,56,57,62,64]. In contrast, the patVAS scores, which were similar to the global scores in comparable studies [6,24], had the greatest impact on HRQoL. This points towards an important discrepancy between the “objective” and “subjective” scoring of disease activity, whereby the SLEDAI-2K and physician VAS are primarily weighted to the prognostically important, immunological, renal and CNS manifestations, whereas the patVAS better captures the current impact of disease on the health status and functioning. This is nicely illustrated by the negative impact of alopecia and new onset malar rash on HRQoL in this study, which may be less appreciated by physicians, yet the presence of these manifestations would hinder participation in either schooling or the workplace.
Finally, organ damage accrual was similar to other studies [23,24,57] and showed an independent reduction of PF and SF, especially for those who sustained neuropsychiatric damage. The negative impact on PF was also found by Hanly et al. as part of a broader reduction of the mental HRQoL items [24,56,57].
The generalisability of our findings are limited by the cross-sectional study design, which does not allow for the study of longitudinal changes in disease-related and external factors. Furthermore, the relatively small sample size may have a limited statistical power, and we lacked uniform data on fibromyalgia, which has been shown to impact HRQoL in SLE [6,58]. The strength of this study lies in the comprehensive and synchronized data collection in a homogeneous Caucasian cohort a with well-controlled disease.

5. Conclusions

HRQoL in Norwegian SLE patients is significantly lower than in the general population; but does not affect mental health to the same degree as seen in other cohorts, possibly as a result of the chronic disease support, including social security in the Norwegian health system The reduced HRQoL was better captured by patient VAS scores than by global physician and SLEDAI-2K scores. Clinical and lifestyle factors have a larger effect on HRQoL than cytokine levels.

Supplementary Materials

The following are available online at https://www.mdpi.com/2077-0383/8/6/857/s1, Table S1: Spearman rank correlation coefficients (Rs) for relation between SF36 domain and summary scores with clinical findings and disease activity and presence of organ damage.

Author Contributions

Conceptualization (W.D.R., G.Ø.E., S.S. and J.C.N.); Methodology (W.D.R. and J.C.N.); Formal Analysis (W.D.R.); Data Curation (G.Ø.E. and J.C.N.); Writing (W.D.R., S.S., G.Ø.E. and J.C.N.); Supervision (J.C.N.); Project Administration (G.Ø.E. and J.C.N.); Funding Acquisition (W.D.R. and J.C.N.).

Funding

W.D.R. received a PhD Scholarship in Memory of John Donald Stewart issued by the Arthritis Foundation of Western Australia. J.C.N. received an unrestricted grant from the Arthritis Foundation of Western Australia.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Olesińska, M.; Saletra, A. Quality of life in systemic lupus erythematosus and its measurement. Reumatologia 2018, 56, 45–54. [Google Scholar] [CrossRef]
  2. Jolly, M. How does quality of life of patients with systemic lupus erythematosus compare with that of other common chronic illnesses? J. Rheumatol. 2005, 32, 1706–1708. [Google Scholar]
  3. Urowitz, M.; Gladman, D.D.; Ibanez, D.; Sanchez-Guerrero, J.; Bae, S.C.; Gordon, C.; Fortin, P.R.; Clarke, A.; Bernatsky, S.; Hanly, J.G.; et al. Changes in quality of life in the first 5 years of disease in a multicenter cohort of patients with systemic lupus erythematosus. Arthritis Care Res. 2014, 66, 1374–1379. [Google Scholar] [CrossRef]
  4. Pettersson, S.; Idborg, H.; Eketjäll, S.; Gunnarsson, I.; Svenungsson, E. FRI0265 Cytokines and correlations with patient reported outcomes in systemic lupus erythematosus and population controls. Ann. Rheum. Dis. 2017, 76, 586. [Google Scholar]
  5. Figueiredo-Braga, M.; Mota-Garcia, F.; O’Connor, J.E.; Garcia, J.R.; Mota-Cardoso, R.; Cardoso, C.S.; de Sousa, M. Cytokines and anxiety in systemic lupus erythematosus (SLE) patients not receiving antidepressant medication: A little-explored frontier and some of its brief history. Ann. N. Y. Acad. Sci. 2009, 1173, 286–291. [Google Scholar] [CrossRef]
  6. Wolfe, F.; Michaud, K.; Li, T.; Katz, R.S. EQ-5D and SF-36 quality of life measures in systemic lupus erythematosus: Comparisons with rheumatoid arthritis, noninflammatory rheumatic disorders, and fibromyalgia. J. Rheumatol. 2010, 37, 296–304. [Google Scholar] [CrossRef]
  7. Chaigne, B.; Chizzolini, C.; Perneger, T.; Trendelenburg, M.; Huynh-Do, U.; Dayer, E.; Stoll, T.; von Kempis, J.; Ribi, C.; Swiss Systemic Lupus Erythematosus Cohort Study Group. Impact of disease activity on health-related quality of life in systemic lupus erythematosus—A cross-sectional analysis of the Swiss Systemic Lupus Erythematosus Cohort Study (SSCS). BMC Immunol. 2017, 18, 17. [Google Scholar] [CrossRef]
  8. Apostolopoulos, D.; Kandane-Rathnayake, R.; Raghunath, S.; Hoi, A.; Nikpour, M.; Morand, E.F. Independent association of glucocorticoids with damage accrual in SLE. Lupus Sci. Med. 2016, 3, e000157. [Google Scholar] [CrossRef] [Green Version]
  9. Henderson, L.K.; Masson, P.; Craig, J.C.; Roberts, M.A.; Flanc, R.S.; Strippoli, G.F.; Webster, A.C. Induction and maintenance treatment of proliferative lupus nephritis: A meta-analysis of randomized controlled trials. Am. J. Kidney Dis. 2013, 61, 74–87. [Google Scholar] [CrossRef]
  10. Farinha, F.; Freitas, F.; Águeda, A.; Cunha, I.; Barcelos, A. Concerns of patients with systemic lupus erythematosus and adherence to therapy—A qualitative study. Patient Prefer. Adherence 2017, 11, 1213–1219. [Google Scholar] [CrossRef]
  11. Hanly, J.G.; Su, L.; Urowitz, M.B.; Romero-Diaz, J.; Gordon, C.; Bae, S.-C.; Bernatsky, S.; Clarke, A.E.; Wallace, D.J.; Merrill, J.T.; et al. Mood Disorders in Systemic Lupus Erythematosus: Results From an International Inception Cohort Study. Arthritis Rheumatol. 2015, 67, 1837–1847. [Google Scholar] [CrossRef]
  12. Gilboe, I.M.; Kvien, T.K.; Husby, G. Health status in systemic lupus erythematosus compared to rheumatoid arthritis and healthy controls. J. Rheumatol. 1999, 26, 1694–1700. [Google Scholar]
  13. Mak, A.; Tang, C.S.; Ho, R.C. Serum tumour necrosis factor-alpha is associated with poor health-related quality of life and depressive symptoms in patients with systemic lupus erythematosus. Lupus 2013, 22, 254–261. [Google Scholar] [CrossRef]
  14. Himmerich, H.; Fulda, S.; Linseisen, J.; Seiler, H.; Wolfram, G.; Himmerich, S.; Gedrich, K.; Kloiber, S.; Lucae, S.; Ising, M.; et al. Depression, comorbidities and the TNF-alpha system. Eur. Psychiatry 2008, 23, 421–429. [Google Scholar] [CrossRef]
  15. Gladman, D.D.; Ibanez, D.; Urowitz, M.B. Systemic lupus erythematosus disease activity index 2000. J. Rheumatol. 2002, 29, 288–291. [Google Scholar]
  16. Loge, J.H.; Kaasa, S.; Hjermstad, M.J.; Kvien, T.K. Translation and performance of the Norwegian SF-36 Health Survey in patients with rheumatoid arthritis. I. Data quality, scaling assumptions, reliability, and construct validity. J. Clin. Epidemiol. 1998, 51, 1069–1076. [Google Scholar] [CrossRef]
  17. Ware, J.E., Jr.; Sherbourne, C.D. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med. Care 1992, 30, 473–483. [Google Scholar] [CrossRef]
  18. Jacobsen, E.L.; Bye, A.; Aass, N.; Fossa, S.D.; Grotmol, K.S.; Kaasa, S.; Loge, J.H.; Moum, T.; Hjermstad, M.J. Norwegian reference values for the Short-Form Health Survey 36: Development over time. Qual Life Res. 2018, 27, 1201–1212. [Google Scholar] [CrossRef]
  19. Hochberg, M.C. Updating the American College of Rheumatology revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum. 1997, 40, 1725. [Google Scholar] [CrossRef]
  20. Tan, E.M.; Cohen, A.S.; Fries, J.F.; Masi, A.T.; McShane, D.J.; Rothfield, N.F.; Schaller, J.G.; Talal, N.; Winchester, R.J. The 1982 revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum. 1982, 25, 1271–1277. [Google Scholar] [CrossRef]
  21. Gladman, D.D.; Goldsmith, C.H.; Urowitz, M.B.; Bacon, P.; Fortin, P.; Ginzler, E.; Gordon, C.; Hanly, J.G.; Isenberg, D.A.; Petri, M.; et al. The Systemic Lupus International Collaborating Clinics/American College of Rheumatology (SLICC/ACR) Damage Index for Systemic Lupus Erythematosus International Comparison. J. Rheumatol. 2000, 27, 373–376. [Google Scholar]
  22. Asuero, A.G.; Sayago, A.; Gonzalez, A.G. The correlation coefficient: An overview. Crit. Rev. Anal. Chem. 2006, 36, 41–59. [Google Scholar] [CrossRef]
  23. Rinaldi, S.; Doria, A.; Salaffi, F.; Ermani, M.; Iaccarino, L.; Ghirardello, A.; Zampieri, S.; Sarzi-Puttini, P.; Gambari, P.F.; Perini, G. Health-related quality of life in Italian patients with systemic lupus erythematosus. I. Relationship between physical and mental dimension and impact of age. Rheumatology 2004, 43, 1574–1579. [Google Scholar] [CrossRef] [Green Version]
  24. Wang, C.; Mayo, N.E.; Fortin, P.R. The relationship between health related quality of life and disease activity and damage in systemic lupus erythematosus. J. Rheumatol. 2001, 28, 525–532. [Google Scholar]
  25. Gladman, D.D.; Urowitz, M.B.; Ong, A.; Gough, J.; MacKinnon, A. Lack of correlation among the 3 outcomes describing SLE: Disease activity, damage and quality of life. Clin. Exp. Rheumatol. 1996, 14, 305–308. [Google Scholar]
  26. Doria, A.; Rinaldi, S.; Ermani, M.; Salaffi, F.; Iaccarino, L.; Ghirardello, A.; Zampieri, S.; Della Libera, S.; Perini, G.; Todesco, S. Health-related quality of life in Italian patients with systemic lupus erythematosus. II. Role of clinical, immunological and psychological determinants. Rheumatology 2004, 43, 1580–1586. [Google Scholar] [CrossRef] [Green Version]
  27. Freire, E.A.; Maia, I.O.; Nepomuceno, J.C.; Ciconelli, R.M. Damage index assessment and quality of life in systemic lupus erythematosus patients (with long-term disease) in Northeastern Brazil. Clin. Rheumatol. 2007, 26, 423–428. [Google Scholar] [CrossRef]
  28. Kulczycka, L.; Sysa-Jedrzejowska, A.; Robak, E. Quality of life and satisfaction with life in SLE patients-the importance of clinical manifestations. Clin. Rheumatol. 2010, 29, 991–997. [Google Scholar] [CrossRef]
  29. Kulczycka, L.; Sysa-Jedrzejowska, A.; Zalewska-Janowska, A.; Miniszewska, J.; Robak, E. Quality of life and socioeconomic factors in Polish patients with systemic lupus erythematosus. J. Eur. Acad. Dermatol. Venereol. 2008, 22, 1218–1226. [Google Scholar] [CrossRef]
  30. Navarrete-Navarrete, N.; Peralta-Ramirez, M.I.; Sabio, J.M.; Martinez-Egea, I.; Santos-Ruiz, A.; Jimenez-Alonso, J. Quality-of-life predictor factors in patients with SLE and their modification after cognitive behavioural therapy. Lupus 2010, 19, 1632–1639. [Google Scholar] [CrossRef]
  31. Dobkin, P.L.; Da Costa, D.; Dritsa, M.; Fortin, P.R.; Senecal, J.L.; Goulet, J.R.; Choquette, D.; Rich, E.; Beaulieu, A.; Cividino, A.; et al. Quality of life in systemic lupus erythematosus patients during more and less active disease states: Differential contributors to mental and physical health. Arthritis Care Res. 1999, 12, 401–410. [Google Scholar] [CrossRef]
  32. Ghaussy, N.O.; Sibbitt, W., Jr.; Bankhurst, A.D.; Qualls, C.R. Cigarette smoking and disease activity in systemic lupus erythematosus. J. Rheumatol. 2003, 30, 1215–1221. [Google Scholar]
  33. Bello, K.J.; Fang, H.; Fazeli, P.; Bolad, W.; Corretti, M.; Magder, L.S.; Petri, M. Omega-3 in SLE: A double-blind, placebo-controlled randomized clinical trial of endothelial dysfunction and disease activity in systemic lupus erythematosus. Rheumatol. Int. 2013, 33, 2789–2796. [Google Scholar] [CrossRef]
  34. Becker-Merok, A.; Eilertsen, G.O.; Nossent, J.C. Levels of transforming growth factor-beta are low in systemic lupus erythematosus patients with active disease. J. Rheumatol. 2010, 37, 2039–2045. [Google Scholar] [CrossRef]
  35. Raymond, W.D.; Eilertsen, G.O.; Nossent, J. Principal component analysis reveals disconnect between regulatory cytokines and disease activity in Systemic Lupus Erythematosus. Cytokine 2019, 114, 67–73. [Google Scholar] [CrossRef]
  36. Crow, M.K. Type I interferon in the pathogenesis of lupus. J. Immunol. 2014, 192, 5459–5468. [Google Scholar] [CrossRef]
  37. Ahn, S.S.; Park, E.S.; Shim, J.S.; Ha, S.-J.; Kim, B.S.; Jung, S.M.; Lee, S.-W.; Park, Y.-B.; Song, J.J. Decreased ex vivo production of interferon-gamma is associated with severity and poor prognosis in patients with lupus. Arthritis Res. Ther. 2017, 19, 193. [Google Scholar] [CrossRef]
  38. Boedigheimer, M.J.; Martin, D.A.; Amoura, Z.; Sánchez-Guerrero, J.; Romero-Diaz, J.; Kivitz, A.; Aranow, C.; Chan, T.M.; Chong, Y.B.; Chiu, K.; et al. Safety, pharmacokinetics and pharmacodynamics of AMG 811, an anti-interferon-γ monoclonal antibody, in SLE subjects without or with lupus nephritis. Lupus Sci. Med. 2017, 4, e000226. [Google Scholar] [CrossRef]
  39. Ziefle, S.; Egberts, F.; Heinze, S.; Volkenandt, M.; Schmid-Wendtner, M.; Tilgen, W.; Linse, R.; Boettjer, J.; Vogt, T.; Spieth, K.; et al. Health-related quality of life before and during adjuvant interferon-alpha treatment for patients with malignant melanoma (DeCOG-trial). J. Immunother. 2011, 34, 403–408. [Google Scholar] [CrossRef]
  40. Reuter, K.; Albrecht, K.; Seelig, H.; Meiss, F.; Mauch, C.; Kreuzberg, N.; Nashan, D. Health-related quality of life, fatigue, and depression under low-dose IFN-alpha therapy in melanoma patients. J. Immunother. 2014, 37, 461–467. [Google Scholar] [CrossRef]
  41. Tokano, Y.; Morimoto, S.; Kaneko, H.; Amano, H.; Nozawa, K.; Takasaki, Y.; Hashimoto, H. Levels of IL-12 in the sera of patients with systemic lupus erythematosus (SLE)—Relation to Th1- and Th2-derived cytokines. Clin. Exp. Immunol. 1999, 116, 169–173. [Google Scholar] [CrossRef]
  42. El-Tantawy, A.M.; El-Sayed, A.E.; Kora, B.A.; Amin, R.T. Psychiatric morbidity associated with some cytokines (IL-1beta, IL-12, IL-18 and TNF-alpha) among rheumatoid arthritis patients. Egypt J. Immunol. 2008, 15, 1–11. [Google Scholar]
  43. Frieder, J.; Kivelevitch, D.; Fiore, C.T.; Saad, S.; Menter, A. The impact of biologic agents on health-related quality of life outcomes in patients with psoriasis. Exp. Rev. Clin. Immunol. 2018, 14, 1–19. [Google Scholar] [CrossRef]
  44. Iikuni, N.; Okamoto, H.; Yoshio, T.; Sato, E.; Kamitsuji, S.; Iwamoto, T.; Momohara, S.; Taniguchi, A.; Yamanaka, H.; Minota, S.; et al. Raised monocyte chemotactic protein-1 (MCP-1)/CCL2 in cerebrospinal fluid of patients with neuropsychiatric lupus. Ann. Rheum. Dis. 2006, 65, 253–256. [Google Scholar] [CrossRef]
  45. Zivkovic, V.; Cvetkovic, T.; Mitic, B.; Stamenkovic, B.; Stojanovic, S.; Radovanovic-Dinic, B.; Jurisic, V. Monocyte chemoattractant protein-1 as a marker of systemic lupus erythematosus: An observational study. Rheumatol. Int. 2018, 38, 1003–1008. [Google Scholar] [CrossRef]
  46. Vega, L.; Barbado, J.; Almansa, R.; Gonzalez-Gallego, R.; Rico, L.; Jimeno, A.; Nocito, M.; Ortiz de Lejarazu, R.; Bermejo-Martin, J.F. Prolonged standard treatment for systemic lupus erythematosus fails to normalize the secretion of innate immunity-related chemokines. Eur. Cytokine Netw. 2010, 21, 71–76. [Google Scholar]
  47. Jonsdottir, I.H.; Hägg, D.A.; Glise, K.; Ekman, R. Monocyte Chemotactic Protein-1 (MCP-1) and Growth Factors Called into Question as Markers of Prolonged Psychosocial Stress. PLoS ONE 2009, 4, e7659. [Google Scholar] [CrossRef]
  48. Balamayooran, G.; Batra, S.; Balamayooran, T.; Cai, S.; Jeyaseelan, S. Monocyte chemoattractant protein 1 regulates pulmonary host defense via neutrophil recruitment during Escherichia coli infection. Infect. Immun. 2011, 79, 2567–2577. [Google Scholar] [CrossRef]
  49. Jipan, X.; Eric, Q.W.; Zhi-Jie, Z.; Patrick, W.S.; Lin, Z.; Darwin, R.L. Patient-Reported Health Status in Coronary Heart Disease in the United States. Circulation 2008, 118, 491–497. [Google Scholar] [Green Version]
  50. Doria, A.; Iaccarino, L.; Ghirardello, A.; Zampieri, S.; Arienti, S.; Sarzi-Puttini, P.; Atzeni, F.; Piccoli, A.; Todesco, S. Long-term prognosis and causes of death in systemic lupus erythematosus. Am. J. Med. 2006, 119, 700–706. [Google Scholar] [CrossRef]
  51. Fawzy, M.; Edrees, A.; Okasha, H.; El Ashmaui, A.; Ragab, G. Gastrointestinal manifestations in systemic lupus erythematosus. Lupus 2016, 25, 1456–1462. [Google Scholar] [CrossRef]
  52. Lee, C.K.; Ahn, M.S.; Lee, E.Y.; Shin, J.H.; Cho, Y.S.; Ha, H.K.; Yoo, B.; Moon, H.B. Acute abdominal pain in systemic lupus erythematosus: Focus on lupus enteritis (gastrointestinal vasculitis). Ann. Rheum. Dis. 2002, 61, 547–550. [Google Scholar] [CrossRef]
  53. Appenzeller, S.; Pallone, A.T.; Natalin, R.A.; Costallat, L.T. Prevalence of thyroid dysfunction in systemic lupus erythematosus. J. Clin. Rheumatol. 2009, 15, 117–119. [Google Scholar] [CrossRef]
  54. De Angelis, R.; Salaffi, F.; Grassi, W. Health-related quality of life in primary Raynaud phenomenon. J. Clin. Rheumatol. 2008, 14, 206–210. [Google Scholar] [CrossRef]
  55. Hanly, J.G.; Urowitz, M.B.; Jackson, D.; Bae, S.C.; Gordon, C.; Wallace, D.J.; Clarke, A.; Bernatsky, S.; Vasudevan, A.; Isenberg, D.; et al. SF-36 summary and subscale scores are reliable outcomes of neuropsychiatric events in systemic lupus erythematosus. Ann. Rheum. Dis. 2011, 70, 961–967. [Google Scholar] [CrossRef]
  56. Kuriya, B.; Gladman, D.D.; Ibanez, D.; Urowitz, M.B. Quality of life over time in patients with systemic lupus erythematosus. Arthritis Rheum. 2008, 59, 181–185. [Google Scholar] [CrossRef]
  57. Elera-Fitzcarrald, C.; Alva, M.; Gamboa-Cardenas, R.; Mora-Trujillo, C.S.; Zevallos, F.; Garcia-Poma, A.; Medina, M.; Rodriguez-Bellido, Z.; Perich-Campos, R.A.; Pastor-Asurza, C.A.; et al. Factors associated with health-related quality of life in Peruvian patients with systemic lupus erythematosus. Lupus 2018, 27, 913–919. [Google Scholar] [CrossRef]
  58. Gladman, D.D.; Urowitz, M.B.; Gough, J.; MacKinnon, A. Fibromyalgia is a major contributor to quality of life in lupus. J. Rheumatol. 1997, 24, 2145–2148. [Google Scholar]
  59. Bertsias, G.; Ioannidis, J.P.; Boletis, J.; Bombardieri, S.; Cervera, R.; Dostal, C.; Font, J.; Gilboe, I.M.; Houssiau, F.; Huizinga, T.; et al. EULAR recommendations for the management of systemic lupus erythematosus. Report of a Task Force of the EULAR Standing Committee for International Clinical Studies Including Therapeutics. Ann. Rheum. Dis. 2008, 67, 195–205. [Google Scholar] [CrossRef]
  60. Elissen, A.M.J.; Hertroijs, D.F.L.; Schaper, N.C.; Bosma, H.; Dagnelie, P.C.; Henry, R.M.; van der Kallen, C.J.; Koster, A.; Schram, M.T.; Stehouwer, C.D.A.; et al. Differences in biopsychosocial profiles of diabetes patients by level of glycaemic control and health-related quality of life: The Maastricht Study. PLoS ONE 2017, 12, e0182053. [Google Scholar] [CrossRef]
  61. Gandino, I.J.; Scolnik, M.; Bertiller, E.; Scaglioni, V.; Catoggio, L.J.; Soriano, E.R. Complement levels and risk of organ involvement in patients with systemic lupus erythematosus. Lupus Sci. Med. 2017, 4, e000209. [Google Scholar] [CrossRef] [PubMed]
  62. Jolly, M.; Pickard, A.S.; Wilke, C.; Mikolaitis, R.A.; Teh, L.S.; McElhone, K.; Fogg, L.; Block, J. Lupus-specific health outcome measure for US patients: The LupusQoL-US version. Ann. Rheum. Dis. 2010, 69, 29–33. [Google Scholar] [CrossRef] [PubMed]
  63. Jolly, M.; Toloza, S.; Goker, B.; Clarke, A.E.; Navarra, S.V.; Wallace, D.; Weisman, M.; Mok, C.C. Disease-specific quality of life in patients with lupus nephritis. Lupus 2018, 27, 257–264. [Google Scholar] [CrossRef] [PubMed]
  64. Postal, M.; Lapa, A.T.; Sinicato, N.A.; de Oliveira Peliçari, K.; Peres, F.A.; Costallat, L.T.L.; Fernandes, P.T.; Marini, R.; Appenzeller, S. Depressive symptoms are associated with tumor necrosis factor alpha in systemic lupus erythematosus. J. Neuroinflammation 2016, 13, 5. [Google Scholar] [CrossRef] [PubMed]
Table
Table 1. Systemic lupus erythematosus (SLE) patient demographic and clinical characteristics at the time of study.
Table 1. Systemic lupus erythematosus (SLE) patient demographic and clinical characteristics at the time of study.
Demographic and Clinical Characteristics at the Time of StudySLE Patients (n = 52)
Median (f)n (%)
Age (years)46.8 (35.4, 60.2)
Time since diagnosis (years)10 (5.3, 18.3)
Female 45 (86.5)
Schooling (years)12 (10.0, 15.0)
Required social security 47 (90.4)
Body mass index (BMI)24.2 (21.8, 27.8)
Smoker 36 (69.2)
Number of cigarettes daily8 (5, 10)
Systolic BP (mm Hg)130 (119, 142)
Diastolic BP (mm Hg)80 (66, 85)
Comorbidities
Cardiovascular events 18 (34.6)
Stroke 6 (11.5)
Hypertension 20 (38.5)
Mental Health Problems 10 (19.2)
Gastrointestinal conditions 13 (25.0)
Thyroid dysfunction 5 (9.6)
Medication
Prednisone daily 28 (53.4)
Prednisone dose (mg/day)6.3 (5.0, 10.0)
Hydroxychloroquine 36 (69.2)
Immunosuppressants 31 (59.6)
Non-steroidal anti-inflammatory drugs (NSAIDs) 11 (21.2)
Anticoagulants 27 (51.9)
Anti-hypertensives 24 (46.2)
Statins 8 (15.4)
Anti-resorptives 9 (17.3)
Mood stabilising drugs 9 (17.3)
Analgesics 7 (13.5)
Fish oil 4 (7.8)
Serology
Anti-dsDNA positive 20 (39.2)
Hypocomplementemia 12 (23.1)
HbA1c (%)5.7 (5.5, 6.0)
Hemoglobin (g/dL)12.8 (12.0, 13.9)
White Blood Cells (109/L)6.2 (4.3, 7.4)
Lymphocytes (mm3)1 (1.0, 2.0)
Erythrocyte Sedimentation Rate (mm/h)18.5 (11.5, 34.0)
High sensitivity C-Reactive Protein (mg/L)2.9 (0.7, 8.5)
Creatinine (mmol/L)71 (45, 92)
Systemic Lupus Erythematosus Disease Activity Index—2000 (SLEDAI-2K) score7 (4, 10)
Patient visual analogue scale (VAS)3 (2, 5)
Physicians VAS2 (1, 4)
Systemic Lupus International Collaborating Clinics—Damage Index (SDI) > 0 33 (63.5)
Median SDI2 (1, 4)
Required social security = disability pension or vocational training stipend; Cardiovascular events = occurrence of heart attack or blood clots; Gastrointestinal problems = use of proton pump inhibitors, irritable bowel or diarrhoea; Mental health problems = treatment for depression, insomnia or anxiety; Immunosuppressants = azathioprine, mycophenolate, methotrexate, rituximab or calcineurin inhibitors; Hypocomplementemia = C3 < 0.84 g/L or C4 < 0.08 g/L. IQR: inter-quartile range. BP: bodily pain.
Table
Table 2. Short Form Health Survey-36 (SF-36) Summary Measures and domain scores for (and within) patients in the Tromso Lupus Cohort against a Norwegian reference value from 2015. Figures indicate mean ± standard deviation.
Table 2. Short Form Health Survey-36 (SF-36) Summary Measures and domain scores for (and within) patients in the Tromso Lupus Cohort against a Norwegian reference value from 2015. Figures indicate mean ± standard deviation.
Norwegian Female, 2015 (n = 1149)
(A)		Overall SLE (n = 52)
(B)		SLEDAI-2K ≤ 4 (n = 18)
(C)		SLEDAI-2K > 4 (n = 34)
(D)		Independent t-Test
A vs. B		Independent t-Test
C vs. D
Physical Functioning84.9 ± 21.066.35 ± 24.5072.78 ± 21.0962.94 ± 25.76<0.0010.171
Role-physical72.6 ± 39.627.88 ± 36.6027.78 ± 40.1227.94 ± 35.23<0.0010.988
Bodily Pain66.9 ± 26.553.74 ± 27.3856.44 ± 27.1952.31 ± 27.79<0.0010.609
General Health72.6 ± 22.544.56 ± 23.5349.72 ± 24.1741.82 ± 23.07<0.0010.253
Vitality57.2 ± 20.638.94 ± 24.80 39.44 ± 25.2638.68 ± 24.93<0.0010.917
Social Functioning85.7 ± 21.662.98 ± 27.1169.44 ± 26.1659.56 ± 27.36<0.0010.214
Role-emotional87.4 ± 28.666.67 ± 41.2274.07 ± 42.0962.75 ± 40.84<0.0010.351
Mental Health79.9 ± 14.876.00 ± 15.6881.33 ± 13.2373.18 ± 16.320.0610.074
Physical Summary 48.13 ± 21.8351.68 ± 22.0046.25 ± 21.83-0.399
Mental Summary 61.15 ± 19.3966.07 ± 19.7458.54 ± 18.98-0.185
Table
Table 3. Spearman rank correlation and linear regression coefficients for the overall strength (Rs) and effect size (ES) of the relation between the inflammatory cytokine levels (pg/ml) and health related quality of life, quantified with the Short Form 36 domain and summary scores.
Table 3. Spearman rank correlation and linear regression coefficients for the overall strength (Rs) and effect size (ES) of the relation between the inflammatory cytokine levels (pg/ml) and health related quality of life, quantified with the Short Form 36 domain and summary scores.
CytokinePhysical DomainsMental Health DomainsPCSMCS
PFRPBPGHVTSFREMH
BAFFRs/ES−0.17/ NS−0.24/NS−0.13/NS−0.27/NS0.07/NS−0.12/NS0.23/0.091 *0.18/NS−0.23/NS0.16/NS
IFN-γRs/ES0.31 */0.04 *0.31 */NS0.24/NS0.17/NS0.09/NS0.16/NS0.09/NS−0.16/NS0.33 */NS0.10/NS
IL-1βRs/ES0.34 */NS0.30 */NS0.19/NS0.13/NS0.16/NS0.24/NS0.28 */NS−0.05/NS0.33 */NS0.27/NS
IL-4Rs/ES0.10/NS0.28 */NS0.16/NS0.10/NS0.22/NS0.12/NS0.17/NS−0.10/NS0.24/NS0.20/NS
IL-6Rs/ES0.14/NS0.13/NS0.180.12/NS0.07/NS0.15/NS0.06/NS−0.05/NS0.17/NS0.11/NS
IL-10Rs/ES0.02/NS0.21/NS−0.01/NS0.12/NS0.07/NS0.11/NS0.23/NS0.07/NS0.13/NS0.23/NS
IL-12Rs/ES0.30 */0.07 *0.31 */NS0.26/NS0.37 **/NS0.22/NS0.30 */NS0.25/NS0.12/NS0.38 */NS0.33 */NS
MCP-1Rs/ES0.15/NS0.30 */NS0.25/NS0.19/NS0.34 */0.064 *0.19/NS0.18/NS0.25/0.034 *0.32 */NS0.34 */0.05 *
MIP-1αRs/ES0.02/NS0.06/NS0.20/NS0.12/NS−0.05/NS0.14/NS−0.07/NS−0.11/NS0.13/NS−0.06/NS
MIP-1βRs/ES0.10/NS0.06/NS0.15/NS0.17/NS0.32 */NS0.26/NS0.25/0.077 *0.29 */NS0.15/NS0.35 */0.040 *
TNF-αRs/ES0.27/NS0.50 **/NS0.01/NS0.32 */NS0.00/NS0.15/NS0.20/NS−0.07/NS0.32 */NS0.16/NS
TGF-β1Rs/ES0.05/NS0.14/0.04 *0.08/NS0.04/NS0.07/NS0.07/NS0.11/NS0.20/NS0.11/NS0.11/NS
*: p < 0.05; **: p < 0.01; NS: not statistically significant, p ≥ 0.05 PF: Physical Function; RP: Role-Physical; BP: Bodily Pain; GH: General Health; VT: Vitality; SF: Social Functioning; RE: Role-Emotional; MH: Mental Health; PCS: Physical Component Summary; MCS: Mental Component Summary.
Table
Table 4. Multiple linear regression model, assessing the effect size (ES) of the significant univariate factors on the health-related quality of life, quantified with the Short Form 36 domain and summary scores.
Table 4. Multiple linear regression model, assessing the effect size (ES) of the significant univariate factors on the health-related quality of life, quantified with the Short Form 36 domain and summary scores.
Physical ScalesMental Health ScalesPCSMCS
PFRPBPGHVTSFREMH
Age−0.39
(−0.73, −0.05)		−0.82
(−1.43, −0.20)		NSNSNSNSNSNSNSNS
Pat. VAS (1–10)−3.36
(−5.20, −1.53)		NS−6.29
(−8.86, −3.72)		−4.99
(−7.28, −2.70)		−3.72
(−6.34, −1.09)		−4.83
(−6.96, −2.70)		NS−3.10
(−5.76, −0.43)		−4.09
(−5.78, −2.40)		−2.84
(−4.79, −0.89)
Number of Cigarettes per dayNSNSNSNSNSNSNS−1.58
(−2.81, −0.35)		NSNS
AlopeciaNS−20.52
(−38.88, −2.15)		NSNSNSNSNSNS−14.89
(−23.35, −6.44)		NS
Malar RashNSNSNSNSNS−18.95
(−30.20, −7.70)		NSNSNS−14.86
(−24.50, −5.22)
SDI > 0−13.60
(−23.19, −4.01)		NSNSNSNS−14.63
(−25.15, −4.12)		NSNSNSNS
NP damage−15.71
(−27.68, −3.73)		NSNSNSNSNSNSNSNSNS
Prednisone. (mg/d)−1.77
(−2.71, −0.825)		NSNSNSNSNSNSNSNSNS
IS drugsNSNSNS−17.15
(−28.13, −6.16)		NSNSNSNSNSNS
AnalgesicsNSNS−22.67
(−40.70, −4.65)		NSNS−22.90
(−38.44, −7.36)		NSNS−19.38
(−31.00, −7.76)		NS
HbA1cNSNSNSNSNSNSNS−6.28
(−11.37, −1.19)		NSNS
C3NSNSNSNSNS37.24
(13.22, 61.27)		NSNSNSNS
ThrombocytesNS−0.22
(−0.34, −0.10)		NSNS−0.10
(−0.18, −0.01)		NSNSNS−0.09
(−0.14, −0.03)		NS
TGF-β1NS0.06
(0.02, 0.09)		NSNSNSNSNSNSNSNS
IFN-γ0.071
(0.024, 0.118)		NSNSNSNSNSNSNSNSNS
IL-12−0.112
(−0.222, −0.002)		NSNSNSNSNSNSNSNSNS
MCP-1NSNSNSNS0.06 (0.01, 0.11)NSNSNSNS0.04 (0.01, 0.08)

Share and Cite

MDPI and ACS Style

Raymond, W.D.; Eilertsen, G.Ø.; Shanmugakumar, S.; Nossent, J.C. The Impact of Cytokines on the Health-Related Quality of Life in Patients with Systemic Lupus Erythematosus. J. Clin. Med. 2019, 8, 857. https://doi.org/10.3390/jcm8060857

AMA Style

Raymond WD, Eilertsen GØ, Shanmugakumar S, Nossent JC. The Impact of Cytokines on the Health-Related Quality of Life in Patients with Systemic Lupus Erythematosus. Journal of Clinical Medicine. 2019; 8(6):857. https://doi.org/10.3390/jcm8060857

Chicago/Turabian Style

Raymond, Warren David, Gro Østli Eilertsen, Sharanyaa Shanmugakumar, and Johannes Cornelis Nossent. 2019. "The Impact of Cytokines on the Health-Related Quality of Life in Patients with Systemic Lupus Erythematosus" Journal of Clinical Medicine 8, no. 6: 857. https://doi.org/10.3390/jcm8060857

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop