http://orcid.org/0000-0002-6601-1015
Figures
Abstract
Background
Obesity is a leading comorbidity in psoriatic disease, including both psoriasis (PsO) and psoriatic arthritis (PsA), and is associated with adverse metabolic and cardiovascular (CV) outcomes. Anthropometric parameters, such as weight, body mass index (BMI) and waist-to-hip ratio, have been extensively reported in psoriatic disease. However, the associations of body composition and fat distribution with psoriasis have not yet been fully defined.
Objectives
To identify whether patients with psoriatic disease, including psoriatic arthritis, have altered body composition compared with the general population, and to review existing modalities for the assessment of body composition.
Methods
Electronic searches of the literature were conducted in PubMed, Medline (Ovid®), Embase (Ovid®), Cochrane Central Register of Controlled Trials (CENTRAL) and Google Scholar. Titles and abstracts were reviewed by two authors independently against a set of prespecified inclusion/exclusion criteria. The research question was answered with a systematic literature review and results were summarized narratively.
Results
Twenty-five full text articles met the inclusion criteria and were included in the final narrative analysis. The studies were of heterogeneous design and used a range of objective measures to assess body composition, including simple anthropometric measures, bioimpedance analysis (BIA), dual energy X-ray absorptiometry (DXA) and computed tomography (CT). Few studies met all the quality assessment criteria. Clinical heterogeneity prevented meta-analysis.
Conclusions
Patients with psoriatic disease reveal defined body composition changes that are independent of obesity and the customary metabolic syndrome, including higher overall body fat, visceral fat and sarcopenia. These findings emphasize that patients with psoriatic disease should be screened for abnormal adipose effects beyond their weight and body mass index (BMI). Our findings show that the last decade has seen an exciting expansion of research interest in the development and validation of new modalities for the assessment of body composition. There is no consensus on the optimal assessment method of body composition for this diverse group; hence there is a need for validation of existing modalities and standardization of assessment tools.
Citation: Blake T, Gullick NJ, Hutchinson CE, Barber TM (2020) Psoriatic disease and body composition: A systematic review and narrative synthesis. PLoS ONE 15(8): e0237598. https://doi.org/10.1371/journal.pone.0237598
Editor: Michael Nurmohamed, VU University Medical Center, NETHERLANDS
Received: May 18, 2020; Accepted: July 29, 2020; Published: August 13, 2020
Copyright: © 2020 Blake et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Psoriasis (PsO) is an immune-mediated chronic inflammatory disease affecting the skin, entheses and joints, with an estimated prevalence in adults ranging from 0.5 to 11.4% and in children from 0 to 1.4% [1]. It is characterized at the skin level by infiltration of immune cells in the dermis and epidermis, vascular proliferation and atypical keratinocyte differentiation. Pathogenesis is complex and thought to result from the interaction between genetic, environmental and immunologic factors; key players in this process are T cells, antigen presenting cells, keratinocytes, Langerhans' cells, macrophages, natural killer cells, as well as multiple cytokines and growth factors including vascular endothelial growth factor and keratinocyte growth factor [2]. In recent years, the mindset has shifted from one of a Th1-driven immune response with IFN-γ and IL-12 as the signature cytokines to one in which the IL-23/Th17 axis with IL-17, IL-21 and IL-22 plays a more central role [3]. Fig 1 illustrates the immunopathogenesis of psoriasis.
Cell subsets and cytokine signaling pathways implicated in the pathogenesis of psoriasis. Damage to the epidermis triggers release of antimicrobial peptides including LL-37, which complexes with self-DNA released from cellular membrane rupture. DNA-LL-37 complexes are autoantigens of psoriasis, which are taken up by dendritic cells, resulting in IL-12 and IL-23 production. The IL-23/Th17 axis actuates a feedforward loop that favors keratinocyte proliferation, ultimately forming a psoriatic plaque. bFGF: basic fibroblast growth factor; DDC: dermal dendritic cell; GFs: growth factors; pDC: plasmacytoid dendritic cell; VEGF: vascular endothelial growth factor.
There is increasing recognition that psoriasis is more than skin deep and has important consequences beyond the skin. Proinflammatory molecules released during chronic inflammation are implicated in certain co-morbidities, such as obesity, hypertension, diabetes mellitus, cardiovascular disease and depression. Paradoxically, epidemiologic evidence infers that obesity, via pro-inflammatory pathways, predisposes to both development and progression of psoriasis [4]. This association is shared with metabolic syndrome (MetS), not least the increased prevalence of cardiovascular risk factors and the ensuing cardiovascular morbidity [5–9]. Recent studies have suggested that adipokines, such as leptin, adiponectin and resistin, produced by adipocytes and dysregulated in obesity and MetS, are the linchpins of this metabolic association and the so called ‘psoriatic march’: a concept of how severe psoriasis can drive cardiovascular comorbidity [10]. They have been shown to contribute independently to the adverse cardiovascular outcomes in patients with PsO and can be viewed as biomarkers of obesity-related inflammation and cardiovascular risk [4]. With this is mind, one should consider adipose tissue as an endocrine organ that has the capabilities, through local and systemic factors, to induce a low-level inflammatory state. Moreover, specific IL-17-secreting Th17 cells and IL-22-secreting Th22 cells have been seen to infiltrate the adipose tissue and represent local mediators of inflammation and insulin resistance, something that is being studied in more detail. Fig 2 represents this relationship between psoriasis, obesity and metabolic dysfunction.
The ‘psoriatic march’: it portrays the causal link between psoriasis as a systemic inflammatory phenomenon and cardiovascular disease. Obesity, a known risk factor for psoriasis, is capable of inducing a low-grade systemic inflammatory state. Continuous effective systemic therapy may halt the ‘psoriatic march’ through interference with insulin resistance and endothelial dysfunction.
Sustained inflammation due to psoriatic disease also leads to loss of muscle mass and muscle weakness termed sarcopenia; however, the reasons for these muscle changes in the context of inflammation are multifactorial and difficult to define. External factors, such as aging, decreased physical activity secondary to stiffness and pain, hormonal changes and disturbances in protein metabolism are likely to exert considerable influence on this process [11].
Despite this knowledge about the associations between psoriatic disease, adipogenesis and metabolic dysfunction, there is less emphasis on body mass alterations and distribution between separate internal compartments: fat-free tissue (lean body mass), extracellular water and adipose tissue. Body composition, a measure of lean and fat mass proportions, provides a useful indicator of metabolic health [12], and its measurement in psoriatic disease could provide a useful insight into cardiometabolic risk, something that could well influence patient management.
Our principal aim was to determine the evidence for the association between psoriasis and abnormal patterns of fat (including preponderance of visceral fat content) and muscle distribution. We also explored and compared different modalities used for assessment of fat distribution. We hope that this analysis may guide future clinical decision making with respect to risk assessment, screening and management of psoriatic patients in day-to-day practice, encouraging more individualized care and leading to better patient outcomes.
Methods
This systematic review was performed following methodology recommended by the Cochrane Collaboration and is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines [13, 14].
Eligibility criteria
The following inclusion and exclusion criteria were applied.
Inclusion criteria:
Types of studies
- Publication date 2004 –present (inclusive)
- Studies from any geographical location
- English language
- Published studies (including conference papers)
- Grey literature (not published in a peer-reviewed journal) including dissertations/theses
- Any quantitative study (RCT, non-RCT, observational, cohort, case control)
- Studies using qualitative methods of analysis (to describe patterns or themes raised by studies) seeking to understand body composition phenotypes of psoriatic disease. This includes original qualitative studies, studies involving secondary analysis of data, and a qualitative study as part of a mixed methods study e.g. the study also has a quantitative component
- The reference lists of the final articles included for full data extraction were hand searched for further relevant articles
Types of participants
- Adults (>18 years)
- Diagnosed with psoriasis, psoriatic disease or psoriatic arthropathy/arthritis
- Being treated in any ‘usual care’ setting: primary; secondary; tertiary care, e.g. in the hospital, hospice, community, home or rehabilitation
- Receiving care typical for that geographical location
Types of outcome measures
- Understanding and learning about body composition and metabolic phenotypes including measures of body fat and lean mass in men and women with psoriatic disease
- To explore the metabolic, anthropometric and biochemical indices of psoriatic disease
Exclusion criteria:
- Non-English language
- Published pre-2004
- Any study where quantitative or qualitative data are not analyzed i.e. uninterpreted data; case reports; any editorial, meta-analysis or review (systematic, narrative, qualitative)
- Treatment guidelines and pathways
- Commentary articles, written to convey opinion or stimulate research/discussion, with no research component
Types of participants
- Children or Young adults (<18 years)
- Diagnosis of Spondyloarthritis or Spondyloarthropathy, Ankylosing Spondylitis, other immune-mediated inflammatory diseases; studies that do not focus on psoriatic disease per se
Types of outcome measures
- Anything that is not concerned with demonstrating either a metabolic, structural or functional relationship to psoriatic disease; studies focusing on individual components of metabolic syndrome (diabetes mellitus, hypertension, hyperlipidemia or fatty liver disease) and any association with psoriatic disease
Databases and searches
The search strategy was developed by two of the authors (TB and NG) and a librarian. Database searches were performed in PubMed, Medline (Ovid®), Embase (Ovid®), Cochrane Central Register of Controlled Trials (CENTRAL) and Google Scholar for reports published between 2004 to November 2019 using a sensitive methodologic filter for studies. A fifteen-year time frame was chosen to capture the most contemporary and innovative studies in this area. Search terms and criteria are shown in Table 1. Search results were combined into a single Endnote file and duplicates removed.
Study selection
Two authors (TB, NG) independently reviewed all titles/abstracts in the web-based software platform Covidence [15] and selected articles for full-text review. Discrepancies were resolved by consensus.
Data extraction and quality assessment
The data extraction tool was designed by TB and included a Quality Assessment incorporating a Critical Appraisal Skills Programme (CASP) checklist [16] specific for the study design. The checklists included criteria for information and selection bias addressing the following domains: participants, controls, measurement of variables, statistical power, confounding factors and applicability of results. All included studies were appraised with the Risk Of Bias In Non-randomized Studies of Interventions assessment tool (ROBINS-I) [17].
Results
Search results
The initial search yielded 5304 titles. After removing duplicates, 1848 titles and abstracts were screened, then 238 retained for full text review. A further 213 studies were excluded due to: incorrect outcomes (197), incorrect study design (9), duplicate material (3), incorrect intervention (3) and non-English language (1). A total of 25 studies from 25 publications met the inclusion criteria (Fig 3) with differing methodology: 1 randomized control trial, 1 case-control study, 19 cross-sectional and 4 prospective cohorts. A search of the grey literature (the first 100 articles sorted by relevance on Google Scholar) retrieved no additional studies. A summary of the included studies is shown in Table 2.
Population sampled
Twenty-five studies represented a population of 2468 psoriatic patients from 10 countries: Belgium, Brazil, Denmark, France, Italy, Poland, Portugal, Spain, Turkey and UK. Demographics are shown in Table 3. Studies did not differentiate outcomes according to subtypes of psoriatic disease; however, five of the studies focused purely on psoriatic arthritis [18–22]. Only four studies [21–24] reported on the racial and ethnic groups of participants. A control group was assessed in 10 studies [18, 20, 21, 23, 25–30], and this information was missing in another study [19]. None of the studies reported on socio-economic status, including employment or educational attainment of subjects.
Clinical anthropometric assessment
All studies included clinical anthropometric data to varying degrees for overall measurement of participant shape and size. In all but one study [19], participants’ weight, height and BMI were measured. Several studies measured waist-to-hip ratio [29, 31–33], and one study supplemented this with skinfold thickness and abdominal circumference [31]. Only two studies commented on the attire of recruited subjects and attempted to reduce confounding through clothing by stipulating a dress code of light clothes and no shoes [24, 34]. These groups were assessed by a standard operator in a consistent manner. Very few studies incorporated participant information on smoking, alcohol, nutrition or physical activity, all of which are known to influence human metabolism [21, 24, 29, 34].
Body composition
In general, 24 studies confirmed discrete biologic and body composition changes in patients with psoriatic disease, which correlated positively with other indicators of metabolic syndrome, including waist circumference, waist-to-hip ratio, weight, BMI, plasma concentrations of low-density lipoprotein (LDL)-cholesterol, leptin and apolipoprotein-B (apo-B).
One study failed to reveal statistically significant differences between psoriatic patients and controls with respect to maximal aerobic capacity, resting metabolic rate, pulmonary function tests, body fatness, body fat distributions and quality of life [31]. Moreover, not all studies found that these changes were correlated to either skin or joint disease activity [11, 19, 35, 36].
Three studies focused on muscle mass and reported levels of sarcopenia [18, 21, 22]. Aguiar et al. evaluated muscle mass by way of a muscle mass index (MMI: muscle/height2) and demonstrated reductions for spondyloarthritis with no significant variation between psoriatic arthritis and ankylosing spondylitis patients. They identified a significant difference in mean MMI between patients and controls (61.7% vs 43.3%: 7.65 ± 0.98 vs 8.25 ± 0.92; p = 0.001, OR = 5.23). MMI showed correlation with disease activity indices (BASDAI and BASMI) in the male AS patients only. However, their findings were not correlated with disease duration, function or radiological indices. A more recent study assessed sarcopenia and presarcopenia on the basis of European Working Group On Sarcopenia in Older People (EWGSOP) criteria and by using defined MMI cut-offs, and identified sarcopenia in 20.0% and presarcopenia in 25.7% of PsA patients. Their assessment did not include a sex- and age-matched control population [21].
Measurement techniques
Ten techniques were used to describe body composition: BIA 10, DXA 6, CT 2, ultrasonography 1, transthoracic echocardiography 1 and other techniques 5 (including novel automated systems for measurement). Body composition outcomes by modality are shown in Table 4. Four studies [23, 24, 27, 34] measured Phase Angle (PhA), a direct measure of BIA, which researchers found was lower in the psoriatic patients. Barrea et al. [24] discovered that PhA was inversely associated with disease severity measured by Psoriasis Area Severity Index (PASI) and Dermatology Life Quality Index (DLQI); this was also found to be independent of BMI (P < 0.001), although this study included a low number of subjects.
The following devices were used to derive body composition data for analysis in the 10 included BIA studies: BIA 101, Akern srl, Pontassieve, Florence, Italy [21]; single-frequency 50 kHz BIA 101 RJL, Akern Bioresearch, Florence [23, 24, 27, 34]; Bodystat 1500, Bodystat Ltd., Douglas, Isle of Man, UK [31]; Tanita SC-330 Body Composition Analyzer, Tanita Corp., Tokyo, Japan [37]; InBody 170, Biospace, South Korea [11]; Tanita TBF300, Tanita Corporation, Tokyo, Japan [35, 36].
All studies bar one [31] demonstrated less favorable body composition data in psoriasis patients compared to controls.
Body composition associations
Balci et al. [32] identified increased visceral fat area (VFA) using CT in psoriasis patients vs. controls. Multiple linear regression analysis in all study subjects indicated that VFA was significantly associated with waist:hip ratio, age, body weight and presence of metabolic syndrome, though not PASI score, duration or type of disease, smoking habit and therapies. A second study from this group studied epicardial fat [33], in which epicardial fat area (EFA) and coronary artery calcium scoring (CACS) were measured in patients with psoriasis and controls: EFA was significantly associated with CACS, waist circumference and age in the psoriasis patients only. No clinical features or laboratory findings were coupled to EFA. Barone et al. [21] showed age, CRP and disability were associated with sarcopenia, whereas the type of rheumatic disease (RA, PsA or AS), gender, calorie and protein intake, physical activity level, biologic treatment, duration of disease and ESR were not associated with an increased risk of sarcopenia.
Effect of treatments
Six studies reported on cardiometabolic and body composition improvements on patient profiles after receiving specific interventions: hypocaloric diet with omega-3 supplementation [19], phosphodiesterase-4 (PDE4) inhibition with apremilast [20], narrowband ultraviolet (NB-UVB) therapy [35], anti-TNFα administration with infliximab or etanercept [29], anti-TNFα administration with infliximab or adalimumab [28] and anti-IL-12/23 administration with ustekinumab [23].
Assessment of bias
The risk of bias quality assessment of included studies is presented in Table 5. Six of the studies, whilst identifying as case-control, were reassigned as cross-sectional design on account of their methodology [22, 24, 36, 38–40]. Nine studies did not include a control group [18, 20, 21, 23, 25–29]. Several studies did not fully specify how their participants were recruited [21, 26, 29, 31] and there was uncertainty about this information from an additional four studies [11, 18, 20, 28]. The sample size was small (< 40) for a number of studies [28, 31, 33, 38]. Overall, four studies were regarded to be at high risk of bias [18, 21, 26, 29], two at medium risk of bias [28, 31] and 19 at low risk of bias [11, 19, 20, 22–25, 27, 30, 32–41] using the ROBINS-I tool.
Discussion
To our knowledge, this is the first systematic review examining the relationship between psoriatic disease and whole-body composition as a distinct entity from metabolic syndrome. Strengths of the review include use of a high degree of rigor in its search strategy and screening procedures and using relevant standards for performing systematic reviews. We tried to reflect the complete picture of both published and unpublished literature on this topic including smaller studies and conference proceedings. Evidently, the last decade has seen an exciting expansion of interest in the development and validation of new modalities for the assessment of body composition. Our study provides evidence for a relationship between certain body composition phenotypes and the occurrence of psoriasis, including higher overall body fat, visceral fat and sarcopenia, that is similar, yet distinct, from the metabolic syndrome. Several aspects of body composition, specifically the amount and distribution of body fat and lean mass, are now understood to be important health outcomes in adults and should form an important part of the ongoing clinical assessment of patients with psoriasis. However, the issue of whether body compartment distribution is a result of severe psoriasis or a causative factor in its development remains contentious. It is likely that novel systems will eventually supplement less sophisticated bedside measurements and influence key aspects of risk assessment, prognostication and management.
We found an increased prevalence of body composition derangements in psoriatic patients compared with controls. As expected, parameters associated with obesity, such as weight, body fat percentage, fat mass and degree of obesity, were higher in the psoriasis groups than in the control groups, irrespective of therapy. Such an association between obesity and psoriasis has been well documented, first described in 1986 [42]. Traditional epidemiologic studies have focused on weight or BMI to define obesity rather than altered body composition. We found conflicting data on the association between psoriasis severity, such as PASI, and body composition parameters, indicating that a causal link is not definitive. Previous studies have alluded to a dose-response relationship between psoriasis severity and metabolic syndrome [43], supported by translational studies showing T-helper cell cytokine upregulation in the blood and skin of psoriasis patients, leading to effects on lipid metabolism and insulin resistance [44]. Furthermore, our data exemplify how there is insufficient evidence to infer that PsA carries a higher metabolic burden than PsO; thus, more studies are required in this area to distinguish distinct body composition and metabolic profiles of subtypes of psoriatic disease.
In this review, treatment with anti-IL-12/23 or PDE4 inhibitors was associated with more favorable body composition profiles than anti-TNFα treatments, findings which mirror previous observations of increases in BMI seen with this drug class [45, 46]. IL-17, one of the key proinflammatory cytokines in psoriasis, mechanistically links inflammation with insulin resistance and adipocyte dysfunction [47]. IL-17A producing cells are thought to be pathogenic in driving inflammation in obesity and progression of obesity-related inflammatory diseases, suggesting that causality between psoriasis and adipogenesis is likely to be bidirectional [48]. From this perspective, there are likely to be therapeutic implications of targeting proinflammatory factors like IL-17 or IL-12/23 in metabolic dysfunction associated with psoriatic disease.
Our review highlights that there are no data comparing and validating body composition techniques in a psoriatic population; therefore, drawing conclusions about the most precise or reliable technique is not possible. Due to its non-invasiveness, low cost and portability, it is easy to appreciate how BIA was adopted by most researchers. The technique relies on the assumption that the volume of fat-free tissue will be proportional to the electrical conductivity of the body. It employs a small electric current to measure the resistance and reactance at difference frequencies against various tissues in the body e.g. lipid has a high resistance to the flow of current, therefore shows a high impedance reading, whereas muscle, which stores most of our body water, has lower impedance. BIA assessment tools have been considered a promising approach for the quantitative measurement of tissue characteristics over time, as well as demonstrating the direct relativity between fluctuations in body composition and prognosis, clinical condition and quality of life [49]. The technique offers reliable data on body composition provided that suitable (i.e. age-, sex- and population-specific) equations for the calculation of body compartments are applied [50]. However, a major limitation of this technique pertains to measurement discrepancies between devices from different manufacturers and the lack of internationally recognized standard reference values.
PhA is thought to be one of the most clinically relevant parameters of BIA. It is defined as the ratio of resistance (intracellular and extracellular resistance) to reactance (cell membrane-specific resistance), expressed as an angle. It is considered an indicator of cellular health, where higher values reflect cell membrane integrity and better cell function. In healthy populations, increasing age bestows a lower PhA due to a reduction in reactance and a parallel loss of muscle mass and an increase in resistance due to the declining proportion of body water at the expense of fat mass. In disease, PhA is often reduced because of infection, inflammation or disease-specific determinants [51]. Recent studies have reported that PhA in humans follows a linear relationship with cellular health and can be considered a prognostic tool in certain medical disorders, including cancer, cirrhosis and diabetes mellitus [24, 52–56]. It is important to note that not all BIA devices can detect phase-sensitive impedance variation that can be used for assessment of phase angle.
It is clear that there is no single method of body composition measurement that allows for the delineation of all tissues and organs, and there are pros and cons of all techniques. The seemingly unsophisticated measurements of skin thickness, BMI and waist circumference can provide simple longitudinal assessments of fatness and metabolic risk despite their poor accuracy and inability to differentiate fat and lean masses. The value of any approach in supporting clinical practice is enhanced by the availability of reference data. Recent developments include MRI for fat distribution.
Limitations
This systematic review should be interpreted in the context of the reported studies which were heterogeneous in several aspects. Firstly, the observational studies recruited different extents and subtypes of psoriatic disease, some with associated arthritis, and measured different aspects of body composition, making definitive conclusions problematic. Secondly, there was poor matching of patients and controls across all studies and little consideration for the potential confounding effects of key determinants of metabolism, e.g. physical activity, age and smoking. Finally, BIA-estimated percentage of body fat varies greatly with population and age and is directly and closely related to various health outcomes such as cardiovascular diseases. Despite its prognostic potential, BIA has not been validated in population studies or clinical practice due to lack of normal population reference limits for comparison and is also influenced by other factors such as age, sex and race [57]. We aimed to present a wide variety of research endeavors in this upcoming field though we recognize that we may not have captured smaller conference proceedings or non-English publications.
Further considerations
We suggest that body composition indices should be analyzed in more detail using a broader range of techniques and imaging systems across the clinical spectrum of psoriatic patients in order to generate validated methods of assessment, particularly with regards to the prognostic ability of BIA and PhA. Further studies are needed to address the discrepancies in bioimpedance parameters within body compartments and between different devices and the deviation from health to disease. We hope that future studies will reveal insights into drug-specific alterations in body composition profiles in psoriatic disease, enabling clinicians to practice more stratified medicine and treat more effectively the metabolic components of patients’ disease that are so often neglected in clinical practice and associated with worse outcomes.
Conclusions
In conclusion, this study provides evidence for a relationship between certain body composition phenotypes and the occurrence of psoriasis, including higher overall body fat, visceral fat and sarcopenia that are similar, albeit distinct, from the metabolic syndrome. Several aspects of body composition, specifically the amount and distribution of body fat and lean mass, are now understood to be important health outcomes in adults and should form an important part of the ongoing assessment of patients with psoriasis. However, the issue of whether body compartment distribution is a result of severe psoriasis or a causative factor in its development remains contentious. It is hoped that novel systems will eventually supplement less sophisticated bedside measurements and influence key aspects of risk assessment, prognostication and management.
Supporting information
S2 Fig. Full Medline and Embase search strategy.
https://doi.org/10.1371/journal.pone.0237598.s002
(TIF)
Acknowledgments
We thank Petra Meeson, Knowledge Skills Librarian for her valuable input into the design of the search strategy.
References
- 1. Michalek IM, Loring B, John SM. A Systematic Review of Worldwide Epidemiology of Psoriasis. J Eur Acad Dermatol Venereol. 2017 Feb;31(2):205–212. Epub 2016/08/30. pmid:27573025.
- 2. Das RP, Jain AK, Ramesh V. Current concepts in the pathogenesis of psoriasis. Indian J Dermatol. 2009;54(1):7–12. pmid:20049260.
- 3. Marinoni B, Ceribelli A, Massarotti MS, et al. The Th17 axis in psoriatic disease: pathogenetic and therapeutic implications. Autoimmun Highlights. 2014 Jan 22;5(1):9–19. pmid:26000152
- 4. Rodriguez-Cerdeira C, Cordeiro-Rodriguez M, Carnero-Gregorio M, Lopez-Barcenas A, Martinez-Herrera E, Fabbrocini G, et al. Biomarkers of Inflammation in Obesity-Psoriatic Patients. Mediators Inflamm. 2019.
- 5. Fernandez-Armenteros JM, Gomez-Arbones X, Buti-Soler M, Betriu-Bars A, Sanmartin-Novell V, Ortega-Bravo M, et al. Psoriasis, metabolic syndrome and cardiovascular risk factors. A population-based study. J Eur Acad Dermatol Venereol. 2019 Jan;33(1):128–35. pmid:29953676.
- 6. Gisondi P, Fostini AC, Fossa I, Girolomoni G, Targher G. Psoriasis and the metabolic syndrome. Clin Dermatol. Jan-Feb 2018;36(1):21–282018. Epub 2017/09/08. pmid:29241748.
- 7. Rodriguez-Zuniga MJM, Garcia-Perdomo HA. Systematic review and meta-analysis of the association between psoriasis and metabolic syndrome. J Am Acad Dermatol. 2017 Oct;77(4)657–66.e8. pmid:28917453.
- 8. Curco N, Barriendos N, Barahona MJ, Arteaga C, Garcia M, Yordanov S, et al. Factors influencing cardiometabolic risk profile in patients with psoriasis. Australas J Dermatol. 2018 May;59(2):e93–e98. pmid:28240341.
- 9. Manolis AA, Manolis TA, Melita H, Manolis AS. Psoriasis and cardiovascular disease: the elusive link. Int Rev Immunol. 2019;38(1):33–54. pmid:30457023.
- 10. Boehncke WH, Boehncke S. Tobin AM, Kirby B. The ‘psoriatic march’: a concept of how severe psoriasis may drive cardiovascular comorbidity. Exp Dermatol. 2011 Apr;20(4):303–7. pmid:21410760.
- 11. Krajewska-Wlodarczyk M, Owczarczyk-Saczonek A, Placek W. Changes in body composition and bone mineral density in postmenopausal women with psoriatic arthritis. Reumatologia 2017;55(5):215–221. Epub 2017/10/28. pmid:29332959.
- 12. Andreoli A, Garaci F, Cafarelli FP, Guglielmi G. Body composition in clinical practice. Eur J Radiol. 2016 Aug;85(8):1461–8. Epub 2016/02/15. pmid:26971404.
- 13.
Cochrane Collaboration. In: Cochrane Handbook for Diagnostic Test Accuracy Reviews. http://methods.cochrane.org/sdt/handbookdta-reviews. Accessed 2020/02/26.
- 14. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. Br Med J. 2009;339:b2700. Accessed 2020/02/26.
- 15.
Covidence. In: Covidence–better systematic review management. https://www.covidence.org/home. Accessed 2020/02/26.
- 16.
Critical Appraisal Skills Programme. In: CASP Checklists. https://casp-uk.net/casp-tools-checklists. Accessed 2019/12/12.
- 17. Sterne JA, Hernan MA, Reeves BC, Savovic J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. Br Med J. 2016 Oct;355:i4919. pmid:27733354.
- 18.
Tournadre A, Jaffeux P, Frayssac T, Fan A, Couderc M, Dubost JJ, et al. Prevalence of sarcopenia in patients with chronic inflammatory rheumatic diseases. In: EULAR, editor. Annual European Congress of Rheumatology; Spain: Ann Rheum Dis. 2017. p. 1033.
- 19.
Leite B, De Medeiros Pinheiro M. Dietetic intervention in psoriatic arthritis: The dieta trial. In: Rheumatology Ai, editor. Conference: 35th Brazilian Congress of Rheumatology, SBR 2018; Brazil: Adv Rheumatol. 2018.
- 20.
Ferguson LD, Welsh P, Brown R, Tindell A, Kerrigan S, Sattar N, et al. Effect of Phosphodiesterase 4 Inhibition with Apremilast on Cardiometabolic Outcomes in Psoriatic Arthritis–Initial Results from the Immune Metabolic Associations in Psoriatic Arthritis (IMAPA) Study. 2019 ACR/ARP Annual Meeting; Atlanta: Arthritis Rheumatol. 2019. p. 905–6.
- 21. Barone M, Viggiani MT, Anelli MG, Fanizzi R, Lorusso O, Lopalco G, et al. Sarcopenia in patients with rheumatic diseases: Prevalence and associated risk factors. J Clin Med. 2018 Dec 1;7(12):504. pmid:30513782.
- 22. Aguiar R, Sequeira J, Meirinhos T, Ambrosio C, Barcelos A. SARCOSPA—Sarcopenia in spondyloarthritis patients. Acta Reumatol Port. Oct-Dec 2014;39(4):322–6. pmid:25584619.
- 23. Galluzzo M, D'Adamio S, Pastorino R, Andreoli A, Servoli S, Bianchi L, et al. Effect of anti-IL-12/23 on body composition: results of bioelectrical impedance analysis in Caucasian psoriatic patients. Expert Opin Biol Ther. 2018 Mar;18(3):229–235. Epub 2017/12/22. pmid:29252034.
- 24. Barrea L, Macchia PE, Di Somma C, Napolitano M, Balato A, Falco A, et al. Bioelectrical phase angle and psoriasis: A novel association with psoriasis severity, quality of life and metabolic syndrome. J Transl Med. 2016 May 10;14(1):130. pmid:27165166.
- 25.
Andreevskaia O, De La Brassinne M, Vanhooteghem O. A novel system for estimating the metabolic syndrome in patients with psoriasis. In: J Eur Acad Dermatol Venereol. Conference: 4th Congress of the Psoriasis International Network, Paris, France, 2013.
- 26.
De La Brassinne M, Vanhooteghem O. An automatic conversational device to estimate the incidence of obesity in psoriasis. In: Venereology JotEAoDa, editor. Conference: 5th Congress of the Psoriasis International Network; France: J Eur Acad Dermatol Venereol. 2016. p. 41.
- 27. Galluzzo M, Talamonti M, Perino F, Servoli S, Giordano D, Chimenti S, et al. Bioelectrical impedance analysis to define an excess of body fat: evaluation in patients with psoriasis. J Dermatol Treat. 2017 Jun;289(4):299–303. pmid:27786575.
- 28. Kofoed K, Clemmensen A, Mikkelsen UR, Simonsen L, Andersen O, Gniadecki R. Effects of anti-tumor necrosis factor therapy on body composition and insulin sensitivity in patients with psoriasis. Arch Dermatol. 2012 Sep;148(9):1089–91. pmid:22986877.
- 29. Renzo LD, Saraceno R, Schipani C, Rizzo M, Bianchi A, Noce A, et al. Prospective assessment of body weight and body composition changes in patients with psoriasis receiving anti-TNF-alpha treatment. Dermatol Ther. Jul-Aug 2011;24(4):446–51. pmid:21910803.
- 30.
Toussirot E, Aubin F, Desmarets M, Wendling D, Augé B, Gillard J, et al. Body Composition and Fat Distribution in Patients with Psoriasis or Psoriatic Arthritis. 2019 ACR/ARP Annual Meeting; Atlanta, Georgia 2019.
- 31. Demirel R, Genc A, Ucok K, Kacar SD, Ozuguz P, Toktas M, et al. Do patients with mild to moderate psoriasis really have a sedentary lifestyle? Int J Dermatol. 2013 Sep;52(9):1129–34. Epub 2013/07/24. pmid:23879519.
- 32. Balci A, Balci DD, Yonden Z, Korkmaz I, Yenin JZ, Celik E, et al. Increased amount of visceral fat in patients with psoriasis contributes to metabolic syndrome. Dermatology 2010;220(1):32–7. Epub 2009/10/29. pmid:19887761.
- 33. Balci A, Celik M, Balci DD, Karazincir S, Yonden Z, Korkmaz I, et al. Patients with psoriasis have an increased amount of epicardial fat tissue. Clin Exp Dermatol. 2014 Mar;39(2):123–8. pmid:24164295.
- 34. Barrea L, Balato N, Di Somma C, Macchia PE, Napolitano M, Savanelli MC, et al. Nutrition and psoriasis: Is there any association between the severity of the disease and adherence to the Mediterranean diet? Journal of Translational Medicine. 2015;13(1). pmid:25622660.
- 35. Romani J, Caixas A, Carrascosa JM, Ribera M, Rigla M, Luelmo J. Effect of narrowband ultraviolet B therapy on inflammatory markers and body fat composition in moderate to severe psoriasis. J Dermatol. 2012 Jun;166(6):1237–44. pmid:22309899.
- 36.
Romani J, Caixas A, Ceperuelo V, Carrascosa JM, Ribera M, Rigla M, et al. Vitamin D, body fat composition and parameters of atherogenesis and inflammation in psoriatic patients treated with narrow-band UVB. 3rd World Psoriasis and Psoriatic Arthritis Conference "Psoriasis—A Global Health Challenge"; Stockholm, Sweden: Dermatol Ther. 2012. p. S25.
- 37. Engin B, Kutlubay Z, Yardimci G, Vehid HE, Ambarcioglu P, Serdaroglu S, et al. Evaluation of body composition parameters in patients with psoriasis. Int. J. Dermatol 2014 Dec;53(12):1468–73. pmid:25267412.
- 38. Akyildiz ZI, Seremet S, Emren V, Ozcelik S, Gediz B, Tastan A, et al. Epicardial fat thickness is independently associated with psoriasis. Dermatology 2014;228(1):55–9. Epub 2013/10/18. pmid:24158189.
- 39. Gonul M, Tatar I, Canpolat F, Kurmus GI, Ergin C, Hekimoglu B. Evaluation of abdominal fat index by ultrasonography and its relationship with psoriasis and metabolic syndrome. Postep Derm Alergol. 2017 Oct;34(5):453–6. pmid:29507560.
- 40. Ganguly S, Ray L, Kuruvila S, Nanda S, Ravichandran K. Lipid accumulation product index as visceral obesity indicator in psoriasis: A Case-control Study. Indian J Dermatol. 2018 Mar-Apr;63(2):136–40. pmid:29692455.
- 41. Diniz Mdos S, Bavoso NC, Kakehasi AM, Lauria MW, Soares MM, Machado-Pinto J. Assessment of adiposity in psoriatic patients by dual energy X-ray absorptiometry compared to conventional methods. An Bras Dermatol. 2016 Apr;91(2):150–5. pmid:27192512.
- 42. Lindegard B. Diseases associated with psoriasis in a general population of 159,200 middle-aged, urban, native Swedes. Dermatologica 1986;172(6):298–304. pmid:3089849.
- 43. Langan SM, Seminara NM, Shin DB, Troxel AB, Kimmel SE, Mehta NN, et al. Prevalence of metabolic syndrome in patients with psoriasis: a population-based study in the United Kingdom. J Invest Dermatol. 2012 Mar;132(3 Pt 1):556–62. Epub 2011/11/24. pmid:22113483.
- 44. Koczan D, Guthke R, Thiesen HJ, Ibrahim SM, Kundt G, Krentz H, et al. Gene expression profiling of peripheral blood mononuclear leukocytes from psoriasis patients identifies new immune regulatory molecules. Eur J Dermatol. Jul-Aug 2005;15(4):251–7. pmid:16048752.
- 45. Saraceno R, Schipani C, Mazzotta A, Esposito M, Di Renzo L, De Lorenzo A, et al. Effect of anti-tumor necrosis factor-alpha therapies on body mass index in patients with psoriasis. Pharmacol Res. 2008 Apr;57(4):290–5. pmid:18400510.
- 46. Florin V, Cottencin AC, Delaporte E, Staumont-Salle D. Body weight increment in patients treated with infliximab for plaque psoriasis. J Eur Acad Dermatol Venereol. 2013 Feb; 27(2): e186–90. pmid:22621415.
- 47. von Stebut E, Boehncke WH, Ghoreschi K, Gori T, Kaya Z, Thaci D, et al. IL-17A in Psoriasis and Beyond: Cardiovascular and Metabolic Implications. Front Immunol. 2020 Jan; 10:3096. pmid:32010143.
- 48. Chehimi M, Vidal H, Eljaafari A. Pathogenic Role of IL-17-Producing Immune Cells in Obesity, and Related Inflammatory Diseases. J Clin Med. 2017 Jul;6(7):68. pmid:28708082.
- 49. Khalil SF, Mohktar MS, Ibrahim F. The theory and fundamentals of bioimpedance analysis in clinical status monitoring and diagnosis of diseases. Sensors (Basel, Switzerland) 2014 Jun;14(6):10895–928. pmid:24949644.
- 50. Dittmar M. Reliability and variability of bioimpedance measures in normal adults: effects of age, gender, and body mass. Am J Phys Anthropol. 2003 Dec;122(4):361–70. pmid:14614757.
- 51. Norman K, Stobaus N, Pirlich M, Bosy-Westphal A. Bioelectrical phase angle and impedance vector analysis—clinical relevance and applicability of impedance parameters. Clin Nutr. 2012 Dec;31(6):854–61. pmid:22698802.
- 52. Di Mauro M, Lazzarini D, Fumelli P, Carle F, Kosmidis A. Bioelectrical impedance analysis and diabetes mellitus: which correlation among fructosamine, glycosylated haemoglobin and exchangeable potassium. Minerva Med. 2007 Dec;98(6):633–8. pmid:18299676.
- 53. de Luis DA, Aller R, Romero E, Duenas A, Perez Castrillon JL. Relation of phase angle tertiles with blood adipocytokines levels, insulin resistance and cardiovascular risk factors in obese women patients. Eur Rev Med Pharmacol Sci. 2010 Jun;14(6):521–6. pmid:20712259.
- 54. Dittmar M, Reber H, Kahaly GJ. Bioimpedance phase angle indicates catabolism in Type 2 diabetes. Diabet Med. 2015 Sep;32(9):1177–85. Epub 2015/02/18. pmid:25661454.
- 55. Norman K, Stobaus N, Zocher D, Bosy-Westphal A, Szramek A, Scheufele R, et al. Cutoff percentiles of bioelectrical phase angle predict functionality, quality of life, and mortality in patients with cancer. Am J Clin Nutr. 2010 Sep;92(3):612–9. pmid:20631202.
- 56.
Bioelectrical Impedance Analysis in Body Composition Measurement. Proceedings of National Institutes of Health Technology Assessment Conference. Bethesda, Maryland, December 12–14, 1994. Am J Clin Nutr. 1996 Sep;64(3 Suppl):387S-532S. pmid:8928699.
- 57. Siddiqui NI, Khan SA, Shoeb M, Bose S. Anthropometric Predictors of Bio-Impedance Analysis (BIA) Phase Angle in Healthy Adults. J Clin Diagn Res. 2016 Jun;10(6):CC01–4. Epub 2016/06/01. pmid:27504280.