BJD

British Journal of Dermatology

S Y S TE M A T IC R E V IE W

Cardiovascular disease risk factors in patients with hidradenitis suppurativa: a systematic review and metaanalysis of observational studies T. Tzellos,1 C.C. Zouboulis,2 W. Gulliver,3 A.D. Cohen,4,5 P. Wolkenstein6 and G.B.E. Jemec7 1

Department of Dermatology, Faculty of Health Sciences, University Hospital of North Norway, St Olavsgate 70, 9406 Harstad, Norway Departments of Dermatology, Venereology, Allergology and Immunology, Dessau Medical Center, Dessau, Germany 3 Faculty of Medicine, Memorial University of Newfoundland, St John’s, NL, Canada 4 Department of Quality Measurements and Research, Chief Physician’s Office, Clalit Health Services, Tel-Aviv, Israel 5 Division of Community Health, Faculty of Health Sciences, Siaal Research Center for Family Medicine and Primary Care, Ben-Gurion University of the Negev, Beer-Sheva, Israel 6 Department of Dermatology, Henri-Mondor Hospital, APHP, University Paris Est Creteil, Creteil, France 7 Department of Dermatology, Health Sciences Faculty, Roskilde Hospital, University of Copenhagen, Copenhagen, Denmark 2

Linked Comment: Simpson, Br J Dermatol 2015; 173: 1118–19.

Summary Correspondence Thrasivoulos Tzellos. E-mail: [email protected]

Accepted for publication 2 July 2015

Funding sources No external funding.

Conflicts of interest See Appendix 1 DOI 10.1111/bjd.14024

Hidradenitis suppurativa (HS) is a chronic, inflammatory, debilitating skin disease. The aim of the study was to systematically review the literature and critically answer the question: In patients with HS, do cardiovascular risk factors appear at a significantly higher rate compared with controls? The main search was conducted in Medline, Embase and the Cochrane Central Register. Studies eligible for inclusion were of case–control, cross-sectional and cohort design, and included comparison of any cardiovascular risk factor(s) in patients with HS with those of control groups. An I2 value > 50% was considered to show substantial heterogeneity. In this case, DerSimonian and Laird random-effect models were considered to compute pooled odds ratios (OR). Otherwise, a fixed-effects model was suitable. Nine studies, with 6174 patients with HS and 24 993 controls, were included. Significant association of HS with obesity [OR 3
45, 95% confidence interval (CI) 2
20–5
38, P < 0
001], central obesity (OR 2
97, 95% CI 1
41–6
25, P = 0
004), active smoking (OR 4
34, 95% CI 2
48–7
60, P < 0
001), history of smoking (OR 6
34, 95% CI 2
41– 16
68, P < 0
001), hypertriglyceridemia (OR 1
67, 95% CI 1
14–2
47, P = 0
009), low high-density lipoprotein (HDL) (OR 2
48, 95% CI 1
49– 4
16, P < 0
001), diabetes (OR 2
85, 95% CI 1
34–6
08, P = 0
007) and metabolic syndrome (OR 2
22, 95% CI 1
62–3
06, P < 0
001) was detected. Associations were significant both in population and hospital patients with HS, with hospital HS groups having uniformly higher ORs than the population HS groups. Causality could not be assessed. Heterogeneity was substantial in all analyses. This systematic review indicated that cardiovascular risk factors appear at a significantly higher rate in patients with HS compared with controls. The need for screening of patients with HS for modifiable cardiovascular risks is emphasized.

What’s already known about this topic?

• • 1142

Patients with hidradenitis suppurativa (HS) present several cardiovascular risk factors more often and to a higher degree than healthy controls. Results of epidemiological studies vary depending on the populations studied.

British Journal of Dermatology (2015) 173, pp1142–1155

© 2015 British Association of Dermatologists

Cardiovascular disease risk factors in HS, T. Tzellos et al. 1143

What does this study add?

• •

A significant association of HS with obesity, active smoking, hypertriglyceridemia, low high-density lipoprotein, diabetes and metabolic syndrome was detected. Hospital HS groups had uniformly higher association with comorbidity than the population HS groups

Hidradenitis suppurativa/acne inversa (HS) is a chronic, inflammatory, recurrent, debilitating skin disease.1,2 It presents after puberty with painful, inflamed lesions in apocrine glandbearing areas of the body and inflicts significant burden on patients, leading to significantly impaired quality of life, depression and inactivity.1,3,4 Epidemiological studies suggest that patients with HS present several cardiovascular risk factors to a higher degree than healthy controls. This is expected to raise the risk of cardiovascular comorbidities. It has been suggested that HS may also be associated with metabolic syndrome (MetS). MetS is a multifaceted disorder associated with inflammation, which includes parameters such as obesity, dyslipidemia and hypertension and is correlated with increased risk for cardiovascular disease.5–7 However, the results of epidemiological studies vary depending on the populations studied. Other chronic inflammatory diseases, such as psoriasis and rheumatoid arthritis, have been associated with MetS, suggesting possible underlying common mechanisms between MetS and inflammatory disorders in general.8,9 In order to systematically review cardiovascular risk factors in patients with HS we systematically evaluated the relationship between HS and cardiovascular risk factors from published epidemiological studies.

Materials and methods This systematic review was conducted in accordance with MOOSE (Meta-analysis Of Observational Studies in Epidemiology) guidelines for meta-analyses and systematic reviews of observational studies.10 Search strategy To identify eligible studies for inclusion, the main search was conducted in electronic databases Medline, Embase and the Cochrane Central Register from inception until March 2015, using Medical Subject Headings (MeSH) terms. The search strategy used was [“acne inversa” (MeSH) OR “hidradenitis suppurativa” (MeSH)] AND [“hypertension” (MeSH) OR “smoking” (MeSH) OR “diabetes” (MeSH) OR “obesity” (MeSH) OR “metabolic syndrome” (MeSH) OR “dyslipidemia” (MeSH) OR “hyperlipidemia” (MeSH)]. We searched references of retrieved articles manually for additional studies. The main search as well as screening of titles and abstracts was completed independently by two reviewers (T.T. and W.G.) with expertise in conducting systematic reviews. © 2015 British Association of Dermatologists

Chance-adjusted inter-rater agreement was calculated using Cohen’s kappa statistic and was found to be satisfactory. Any discrepancy was solved by consultation with a third reviewer (C.C.Z. or G.B.E.J.). Study selection Our search was limited to human-subject studies written in English, German, Greek, French, Italian, Spanish, Swedish, Norwegian or Danish, which were of case–control, crosssectional, cohort or nested case–control design and included examination and comparison of any cardiovascular risk factor(s) in patients with HS with those of control groups. Exclusion criteria were abstracts only, unpublished studies and lack of a suitable control group. All inclusion/exclusion criteria were specified prior to the literature search. Data extraction and quality assessment Information from each study was extracted independently by two reviewers (T.T. and W.G.), using a standardized data extraction form and double-checked. Briefly, data collected for each study included (i) study characteristics, e.g. year, duration, country, data collection processes (prospective or retrospective), setting, design; (ii) study population characteristics, e.g. sex, mean age, numbers of case and control subjects, case definition, ascertainment of exposure, origin of control subjects (general population or dermatology patients); and (iii) outcome characteristics, e.g. HS severity, outcome definition, whether results were a primary analysis of the study, and whether results were adjusted for comorbidities/confounding variables, odds ratio (OR) and 95% confidence interval (CI). When not provided in the paper, OR and/or corresponding 95% CIs were calculated from available data of selected articles. Study methodological quality was evaluated with the Newcastle–Ottawa scale for assessing the quality of nonrandomized studies in meta-analyses, as suggested by the Cochrane Handbook.11,12 We consider high-quality studies to be those that achieve seven or more stars, medium-quality studies four to six stars and poor-quality study fewer than four stars. Statistics Statistical heterogeneity among selected studies was tested on the basis of the Q-test (chi-square), using a significant level of 0
05, and reported with the I2 statistic in which high values indicate high heterogeneity. An I2 value > 50% is considered British Journal of Dermatology (2015) 173, pp1142–1155

1144 Cardiovascular disease risk factors in HS, T. Tzellos et al.

to show substantial heterogeneity. In this case, DerSimonian and Laird random-effect models13 were considered in order to compute pooled OR. Otherwise, with I2 < 50%, the betweenstudy heterogeneity was not substantial and a fixed-effects model was suitable. ORs and 95% CIs were shown on forest plots for each outcome. All computations were performed using the Revman 5.1.6 software package (the Cochrane Collaboration). All P-values were two-sided, and values lower than 0
05 were considered significant. The presence of publication bias was investigated graphically by constructing a funnel plot.

tologists. The community-based study by Revuz et al.6 included patients with self-reported HS, which is not an adequate case definition and may have led to misclassification. The community-based study by Shalom et al.19 included patients with documented medical diagnosis of HS and reference to the primary record source or medical records (adequate case definition). Miller et al.7 identified patients with HS with a questionnaire that was previously validated (adequate case definition).

Obesity

Results Search results Our search identified 87 articles. After evaluation of the abstract and when necessary, the full text, nine studies were found to be eligible for inclusion in this systematic review (Fig. 1).5–7,14–19 General characteristics of the eligible studies included are presented in Table 1. Two of these studies included one hospital-based and community-based study each.6,7 Miller et al.7 used the same control group for both studies. Three of the included studies were cross-sectional and six were case–control studies. Studies included 6174 patients with HS and 24 993 controls. Three studies identified patients with HS in the general population6,7,19 while the other studies included HS cases from hospital-based clinics. In all hospitalbased studies, the HS diagnosis was made by qualified derma-

Eight studies with 6006 patients with HS and 24 783 controls were included in obesity [body mass index (BMI) > 30 kg m 2] analysis. Significant association with obesity was detected with a pooled OR of 3
45 (95% CI 2
20–5
38, P < 0
001) (Fig. 2, Table 2). No evidence of publication bias was detected (Supplementary Fig. S1). Heterogeneity was significant (I2 = 93%). Only one study with patients with HS from the general population (Revuz 2008a)6 did not detect significant association, but this study identified obesity with self-reported questionnaires, which may have resulted in detection bias. Sensitivity analysis for origin of patients with HS (general population or hospital-based) did not change the results and the associations continued to be significant for both populations. For patients with HS from the general population the pooled OR was 1
82 (95% CI 1
56–2
12), while the association for patients with HS from dermatology clinics was 5
37 (95% CI 2
89–9
97).

Fig 1. Flow diagram of the search strategy. British Journal of Dermatology (2015) 173, pp1142–1155

© 2015 British Association of Dermatologists

© 2015 British Association of Dermatologists Cross-sectional

Cross-sectional

Cross-sectional

Case–control

Case–control

Case–control

Case–control

Case–control

Case–control

Case–control

Cross-sectional

Design 109 (78 F, 31 M) patients with HS, dermatology inpatients, mean age = 31
2 years 63 (27 M, 36 F) patients with HS, dermatology inpatients, mean age = 25
7 years 67 (18 M, 49 F) patients with HS, general population, mean age = 43
2 years 302 (70 M, 232 F) patients with HS, hospital-based, mean age = 32 years 15 patients with HS, dermatology outpatient clinic, mean age = 32
3 years 80 patients with HS, dermatology inpatients and outpatient clinic, mean age = 40 years 243 (194 F, 49 M) patients with HS, dermatology outpatient clinic, mean age = 41
9 years 1730 (460 M, 1270 F) patients with HS, dermatology inpatient and outpatient clinic, mean age = 43
8 years 326 (218 F, 108 M) patients with HS, general population, mean age = 47 years 32 (25 F, 7 M) patients with HS, dermatology outpatients, mean age = 42 years 3207 (1975 F, 1232 M) patients with HS, general population, mean age = 39
6 years

Patients

6412 (3948 F, 2464 M) controls, general population

14 851 (8090 F, 6761 M) controls, general populationb 14 851 (8090 F, 6761 M) controls, general populationb

1730 (460 M, 1270 F) controls, hospital-based

222 (172 F, 50 M) controls, dermatology outpatient clinic

200 (53 M, 147 F) controls, general population 906 (210 M, 696 F) controls, general population 45 controls, dermatology outpatient clinic 100 controls, dermatology outpatients

63 controls, dermatology inpatients

464 controls (247 F, 217 M) general population

Controls

HS, hidradenitis suppurativa. aEvaluated with the Newcastle–Ottawa scale. bMiller (a) and (b) share the same control group.

Israel

U.S.A.

Gold 201416

Shalom 201519

Germany

Sabat 201218

Denmark

Brazil

Schmitt 201215

Miller 2014 (b)7

France

Revuz 2008 (b)6

Denmark

France

Revuz 2008 (a)6

Miller 2014 (a)7

Germany

Konig 199917

U.S.A.

U.K.

Harrison 198814

Shlyankevich 20145

Setting

Citation

Table 1 General characteristics of the studies included in the systematic review

Retrospective

Prospective

Prospective

Retrospective

Retrospective

Prospective

Prospective

Prospective

Prospective

Retrospective

Prospective

Data collection

7/9

5/9

4/9

6/9

7/9

7/9

3/9

5/9

5/9

4/9

4/9

Quality assessmenta

Yes: age and sex

No

No

Yes: age, race and sex

Yes: age, race and sex

Yes: age and sex

Yes: age and sex

Yes: age and sex

Yes: age and sex

Yes: age and sex

Yes: sex

Matching

Cardiovascular disease risk factors in HS, T. Tzellos et al. 1145

British Journal of Dermatology (2015) 173, pp1142–1155

1146 Cardiovascular disease risk factors in HS, T. Tzellos et al.

(a)

(b)

(c)

(d)

Fig 2. Significant association of hidradenitis suppurativa with obesity (a), central obesity (b), active smoking (c) and history of smoking (active and ex-smokers) (d). Note that Miller 2014 (a) and (b) studies share the same control group.

Three studies with 438 patients with HS and 14 951 controls were included in the central obesity analysis (waist circumference > 102 cm in males, > 88 cm in females). British Journal of Dermatology (2015) 173, pp1142–1155

Significant association with central obesity was detected with a pooled OR of 2
97 (95% CI 1
41–6
25, P = 0
004) (Fig. 2, Table 2). No evidence of publication bias was detected (Sup© 2015 British Association of Dermatologists

© 2015 British Association of Dermatologists

Patients with HS: 52/ 80 (65%) Controls: 24/100 (24%) Patients with HS: 204/233 (88%) Controls: 146/220 (66%) Patients with HS: 201/1730 (12%) Controls: 13/1730 (0
8%)

Sabat 201218

Shlyankevich 20145

Gold 201416

Central obesity: Waist circumference > 102 cm (M), > 88 cm (F) BMI > 30: Obese or notation by physician that patient was obese ICD-9 criteria coding

–

–

Schmitt 201215

Crude: 17
3 (9
8–30
6) Adjusted: 2
0 (1
0–4
2)

Crude: 3
6 (2
2–5
6)

Crude: 5
8 (2
9–11
9)

–

Crude: 3
4 (2
3–4
9) Adjusted: 4
4 (2
8–6
9)

BMI > 30

Patients with HS: 63/ 302 (21%) Controls: 75/906 (8%)

Revuz 2008 (b)6

Crude: 1
4 (0
6–3
3) Adjusted: 1
4 (0
6–3
3)

BMI > 30 Self-reported questionnaire

Patients with HS: 11/ 67 (16%) Controls: 26/200 (13%)

–

–

Konig 199917

Revuz 2008 (a)6

Obesity, OR (95% CI) Crude: 6
1 (3
6–10
5)

BMI > 30

Obesity

Patients with HS: 8/ 31 M, 26/78 F; 34/ 109 (31%) Controls: 15/217 M, 17/247 F; 32/464 (7%) –

Citation

Harrison 198814

Definition and assessment method obesity

Table 2 Results regarding obesity and smoking for the included studies in the systematic review

Smoking

–

–

Active and ex-smokers: Patients with HS: 511/1730 (30%) Controls: 16/1730 (1%)

–

ICD-9 criteria coding

(continued)

Active and ex-smokers: Crude: 44
9 (27
1–74
2) Adjusted: 5
3 (2
0–9
8)

–

Active smokers: Crude: 11
2 (7
8–16
0) Adjusted: 12
5 (8
5–18
3)

Active smokers: Crude: 3
7 (1
8–7
5) Adjusted: 3
8 (1
8–7
7)

–

Standardized questionnaire

Standardized questionnaire

Active and ex-smokers: Crude: 3
32 (1–11
14)

Crude: 9
4 (3
7–23
7)

–

Smoking, OR (95% CI)

Standardized questionnaire

Standardized questionnaire

–

Active smokers: Patients with HS: 56/63 (89%) Controls: 29/63 (46%) Active smokers: Patients with HS: 27/67 (40%) Controls: 38/200 (19%) Ex-smokers: Patients with HS: 19/67 (28%) Controls: 57/200 (29%) Active smokers: Patients with HS: 228/302 (75%) Controls: 222/906 (25%) Ex-smokers: Patients with HS: 28/302 (9%) Controls: 194/906 (21%) Active and ex-smokers: Patients with HS: 9/15 (60%) Controls: 14/45 (31%) –

–

Method of assessment smoking

Cardiovascular disease risk factors in HS, T. Tzellos et al. 1147

British Journal of Dermatology (2015) 173, pp1142–1155

7

British Journal of Dermatology (2015) 173, pp1142–1155

General obesity: Patients with HS: 107/326 (33%) Controls: 2799/ 14 851 (19%) Central obesity: Patients with HS: 168/326 (52%) Controls: 5524/ 14 851 (37%) General obesity: Patients with HS: 16/32 (50%) Controls: 2799/ 14 851 (19%) Central obesity: Patients with HS: 20/32 (63%) Controls: 5524/ 14 851 (37%) Patients with HS: 711/3207 (22%) Controls: 903/6412 (14%)

Obesity

BMI > 30, claims database

General obesity: BMI > 30 Central obesity: Waist circumference > 102 cm (M), > 88 cm (F)

General obesity: BMI > 30 Central obesity: Waist circumference > 102 cm (M), > 88 cm (F)

Definition and assessment method obesity

Crude: 1
7 (1
5–1
9) Adjusted: 1
7 (1
5–1
9)

General obesity: Crude: 4
8 (2
3–10
1) Adjusted: 6
3 (2
9–13) Central obesity: Crude: 2
8 (1
3–5
7) Adjusted: 3
6 (1
7–7
6)

General obesity: Crude: 2
1 (1
6–2
7) Adjusted: 2
5 (2
0–3
2) Central obesity: Crude: 1
8 (1
4–2
2) Adjusted: 2
2 (1
7–2
8)

Obesity, OR (95% CI)

BMI, body mass index; CI, confidence interval; HS, hidradenitis suppurativa; OR, odds ratio.

Shalom 201519

Miller 2014 (b)7

Miller 2014 (a)

Citation

Table 2 (continued)

Active smokers: Patients with HS: 1520/3207 (47%) Controls: 1923/6412 (30%)

Active smokers: Patients with HS: 17/32 (53%) Controls: 2683/14 851 (18%) Ex-smokers: Patients with HS: 13/32 (41%) Controls: 5633/14 851 (38%)

Active smokers: Patients with HS: 133/326 (40%) Controls: 2683/14 851 (18%) Ex-smokers: Patients with HS: 114/326 (35%) Controls: 5633/14 851 (38%)

Smoking

Claims database

Self-reporting questionnaire

Self-reporting questionnaire

Method of assessment smoking

Crude: 2
1 (1
9–2
3)

Active smokers: Crude: 5
1 (2
56–10
30)

Active smokers: Crude: 3
12 (2
49–3
91)

Smoking, OR (95% CI)

1148 Cardiovascular disease risk factors in HS, T. Tzellos et al.

© 2015 British Association of Dermatologists

Cardiovascular disease risk factors in HS, T. Tzellos et al. 1149

(a)

(b)

(c)

Fig 3. Nonsignificant association of hidradenitis suppurativa (HS) with hypertension (a). Significant association of HS with hypertriglyceridemia (b) and low high-density lipoprotein (c). Note that Miller 2014 (a) and (b) studies share the same control group.

plementary Fig. S2). Heterogeneity was significant (I2 = 84%). Sensitivity analysis for origin of patients with HS (general population or hospital-based) did not change results and associations continued to be significant for both populations. For patients with HS from the general population the pooled OR was 1
80 (95% CI 1
44–2
24). The association for patients with HS from dermatology clinics was much higher, at 4
13 (95% CI 2
01–8
51). Smoking Six studies with 3997 patients with HS and 22 432 controls were included in the active smoking analysis. A significant association with active smoking was detected with a pooled OR of 4
34 (95% CI 2
48–7
60, P < 0
001) (Fig. 2, Table 2). No evidence of publication bias was detected (Supplementary Fig. S3). Heterogeneity was significant (I2 = 95%). Sensitivity © 2015 British Association of Dermatologists

analysis for origin of patients with HS (general population or hospital-based) did not change the results and associations continued to be significant for both populations. For patients with HS from the general population the pooled OR was 2
58 (95% CI 1
86–3
58). The association for patients with HS from dermatology clinics was 8
30 (95% CI 5
82–11
85). Six studies with 2472 patients with HS and 17 732 controls were included in the history of smoking analysis (active and ex-smokers). Significant association with history of smoking was detected with a pooled OR of 6
34 (95% CI 2
41–16
68, P < 0
001) (Fig. 2, Table 2). No evidence of publication bias was detected (Supplementary Fig. S4). Heterogeneity was significant (I2 = 96%). Sensitivity analysis for origin of patients with HS (general population or hospital-based) did not change the results and associations continued to be significant for both populations. For patients with HS from the general population the pooled OR was 2
45 (95% CI 1
94–3
10). The British Journal of Dermatology (2015) 173, pp1142–1155

1150 Cardiovascular disease risk factors in HS, T. Tzellos et al.

association for patients with HS from dermatology clinics was much higher (10
82, 95% CI 3
05–38
39). Hypertension Seven studies with 5685 patients with HS and 23 515 controls were included in the hypertension analysis. No significant association with hypertension was detected with a pooled OR of 1
50 (95% CI 0
63–3
58, P = 0
36) (Fig. 3, Table 3). No evidence of publication bias was detected (Supplementary Fig. S5). Heterogeneity was significant (I2 = 98%). Sensitivity analysis for origin of patients with HS (general population or hospital-based) did not change the results and associations continued to be nonsignificant for both populations. For patients with HS from the general population the pooled OR was 0
92 (95% CI 0
55–1
53) and for patients with HS from dermatology clinics it was 2
09 (95% CI 0
35–12
33). Shlyankevich et al.5 found significant association with an adjusted OR of 1
84 (95% CI 1
22–2
79). This was a retrospective study and identified the presence of hypertension using the World Health Organization International Classification of Diseases, version 9 (ICD)-9 code for diagnosis. Shalom et al.19 also found also significant association with an adjusted OR of 1
19 (95% CI 1
03–1
38). Dyslipidemia Four studies with 641 patients with HS and 15 148 controls were included in hypertriglyceridemia analysis. Significant association with hypertriglyceridemia was detected with a pooled OR of 1
67 (95% CI 1
14–2
47, P = 0
009) (Fig. 3, Table 3). No evidence of publication bias was detected (Supplementary Fig. S6). Heterogeneity was significant (I2 = 66%). Sensitivity analysis for origin of patients with HS (general population or hospital-based) did not change the results and the associations continued to be significant for both populations. For patients with HS from the general population the result was marginally significant and the pooled OR was 1
25 (95% CI 1
00–1
55, P = 0
05). Interestingly, this result comes from the Miller et al.5 study, which did not use matched controls, and the adjusted OR was significant (1
4, 95% CI 1
1–1
8). The association for patients with HS from dermatology clinics was 2
06 (95% CI 1
49–2
84). Four studies with 639 patients with HS and 15 151 controls were included in the analysis for low high-density lipoprotein (HDL). A significant association with low HDL was detected with a pooled OR of 2
48 (95% CI 1
49–4
16, P < 0
001) (Fig. 3, Table 3). No evidence of publication bias was detected (Supplementary Fig. S7). Heterogeneity was significant (I2 = 81%). A sensitivity analysis for origin of patients with HS (general population or hospital-based) did not change the results and associations continued to be significant for both populations. For patients with HS from the general population the result was significant and pooled OR was 2
24 (95% CI 1
77–2
84). Association for patients with HS from dermatology clinics was 2
72 (95% CI 1
08–6
89). British Journal of Dermatology (2015) 173, pp1142–1155

Two studies (Shlyankevich et al.5 and Revuz et al.6) evaluated dyslipidemia. Revuz et al.6 identified patients from the general population with dyslipidemia with self-reported questionnaires for the use of treatment for dyslipidemia, something that may have resulted in detection bias and underestimation of the association. They found no significant association. Shlyankevich et al.5 found a significant association with an adjusted OR of 4
0 (95% CI 2
5–6
4). This study was of retrospective design and identified the presence of dyslipidemia using a diagnosis found by inclusion of the ICD-9 code. Shalom et al.19 evaluated hyperlipidemia and found a marginally significant association with an adjusted OR of 1
14 (95% CI 1
02–1
28). They identified patients from the general population and hyperlipidemia using the diagnosis noted in a claims database. Diabetes Seven studies with 5685 patients with HS and 23 515 controls were included in the diabetes analysis. A significant association with diabetes was detected with a pooled OR of 2
85 (95% CI 1
34–6
08, P = 0
007) (Fig. 4, Table 4). No evidence of publication bias was detected (Supplementary Fig. S8). Heterogeneity was significant (I2 = 95%). Sensitivity analysis for origin of patients with HS (general population or hospitalbased) showed significant association for patients with HS from the general population (1
57, 95% CI 1
37–1
80). The association for patients with HS from dermatology clinics was also significant (4
48, 95% CI 1
26–15
86). Metabolic syndrome Five studies with 3888 patients with HS and 21 585 controls were included in the analysis for MetS. A significant association with MetS was detected with a pooled OR of 2
22 (95% CI 1
62–3
06, P < 0
001) (Fig. 4, Table 4). No evidence of publication bias was detected (Supplementary Fig. S9). Heterogeneity was significant (I2 = 77%). Sensitivity analysis for origin of patients with HS (general population or hospitalbased) did not change the results and the associations continued to be significant for both populations. For patients with HS from the general population the pooled OR was 1
59 (95% CI 1
40–1
80). The association for patients with HS from dermatology clinics was much higher at 3
20 (95% CI 2
07–4
96).

Discussion This systematic review indicates that cardiovascular risk factors appear at a significantly higher rate in patients with HS compared with controls. Only hypertension was not found at a significantly higher rate. Such associations were significant both in population patients with HS and hospital HS groups, with hospital HS groups having uniformly higher ORs than the population HS groups. Whether this is because of detection bias in population patients with HS remains unclear. © 2015 British Association of Dermatologists

© 2015 British Association of Dermatologists

Patients with HS: 38/ 80 Controls: 36/100 (36%)

Hypertension: Patients with HS: 110/243 (45%) Controls: 112/222 (50%)

Patients with HS: 594/1730 (34%) Controls: 52/1730 (3%) Patients with HS: 157/326 (48%) Controls: 9007/ 14 851 (61%)

Patients with HS: 18/ 32 (56%) Controls: 9007/ 14 851 (61%)

Patients with HS: 455/3207 (14%) Controls: 820/6412 (13%)

Revuz 2008 (a)6

Sabat 201218

Gold 201416

Shlyankevich 20145

Miller 2014 (b)7

Shalom 201519

Adjusted: 1 (0
8–1
3)

Adjusted: 2
1 (1–4
5)

Crude: 1
13 (0
9–1
2) Adjusted: 1
19 (1
03–1
38)

Blood pressure > 140/ 90 mmHg or use of medication for hypertension

Blood pressure > 140/ 90 mmHg or use of medication for hypertension

Blood pressure > 140/ 90 mmHg

Crude: 0
8 (0
6–1
2)

Blood pressure > 130/ 85 mmHg or history of hypertension noted by physician in patient chart

Crude: 16
9 (12
6–22) Adjusted: 1
8 (1
2–2)

Crude: 1
6 (0
8–2
9)

Blood pressure > 130/ 85 mmHg or use of medication for hypertension

ICD-9 criteria coding

Crude: 1
4 (0
6–3
1)

Self-reported questionnaire Definition: If patient received treatment for hypertension

Hypertension, OR (95% CI)

Hypertriglyceridemia: Patients with HS: 159/326 (49%) Controls: 6428/14 851 (43%) Low HDL: Patients with HS: 108/326 (33%) Controls: 2687/14 851 (18%) Hypertriglyceridemia: Patients with HS: 16/32 (50%) Controls: 6428/14 851 (43%) Low HDL: Patients with HS: 15/32 (47%) Controls: 2687/14 851 (18%) Hyperlipidemia: Patients with HS: 952/3207 (30%) Controls: 1703/6412 (27%)

Hypertriglyceridemia: Patients with HS: 31/80 (39%) Controls: 22/100 (22%) Low HDL: Patients with HS: 40/80 (50%) Controls: 18/100 (18%) Hypertriglyceridemia: Patients with HS: 98/203 (48%) Controls: 56/197 (28%) Low HDL: Patients with HS: 108/201 (54%) Controls: 97/200 (49%) Dyslipidemia: Patients with HS: 620/1730 Controls: 28/1730

Dyslipidemia: Patients with HS: 7/67 (10%) Controls: 26/200 (13%)

Dislipidemia

Claims database coding

According to NCEPATP III criteria

According to NCEPATP III criteria

ICD-9 criteria coding

According to NCEPATP III criteria

Self-reported questionnaire Definition: If patient received treatment for dyslipidemia According to NCEPATP III criteria

Definition and assessment method dislipidemia

Dyslipidemia, OR (95% CI)

Crude: 1
17 (1
06–1
2) Adjusted: 1
14 (1
0–1
2)

Hypertriglyceridemia: Adjusted: 1
6 (0
8–3
4) Low HDL: Adjusted: 2
9 (1
45–6)

Hypertriglyceridemia: Adjusted: 1
4 (1
1–1
8) Low HDL: Adjusted: 1
9 (1
5–2
4)

Crude: 33
9 (23
0–45) Adjusted: 4
0 (2
5–6
4)

Hypertriglyceridemia: Crude: 2
4 (1
6–3
6) Low HDL: Crude: 1
2 (0
8–1
8)

Hypertriglyceridemia: Crude: 2
2 (1
11–4
54) Low HDL: Crude: 4
56 (2
2–9
4)

Crude: 0
6 (0
2–1
7)

CI, confidence interval; HDL, high-density lipoprotein; ICD-9, World Health Organization International Classification of Diseases, version 9; NCEP-ATP III, National Cholesterol Education Program Adult Treatment Panel III; OR, odds ratio.

Miller 2014 (a)7

Hypertension

Patients with HS: 14/ 67 (21%) Controls: 34/200 (17%)

Citation

Definition and assessment method hypertension

Table 3 Results regarding hypertension and dyslipidemia for the included studies in the systematic review

Cardiovascular disease risk factors in HS, T. Tzellos et al. 1151

British Journal of Dermatology (2015) 173, pp1142–1155

1152 Cardiovascular disease risk factors in HS, T. Tzellos et al.

(a)

(b)

Fig 4. Significant association of hidradenitis suppurativa (HS) with diabetes (a) and metabolic syndrome (b). Note that Miller 2014 (a) and (b) studies share the same control group.

However, Miller et al.9 minimized detection bias in population HS groups by employing physical examination and laboratory analysis of blood samples, and reported uniformly higher ORs for the hospital HS groups. These results emphasize the need for screening patients with HS for these modifiable cardiovascular risks. Patients included in the studies had a mean age of 26–47 years. Identification of significant comorbidities in these young age groups indicates findings are clinically important and suggests the need to examine the patients with HS with a view to detecting and treating comorbidities at the earliest possible opportunity. These data further suggest that the increased cardiovascular risk is manifest even in patients identified in population samples rather than only in select hospital-based samples. Assuming that hospital cases may have more severe disease than population-identified cases, these data further suggest a dose– response effect between obesity, smoking, dyslipidemia (low HDL and hypertriglyceridemia), diabetes mellitus, MetS and HS severity, which could be taken to imply causality rather than just co-occurrence. However, such a conclusion requires additional data and cannot be drawn based on the current meta-analysis. Obesity affects a number of other factors that influence the overall morbidity and prognosis of patients with HS. Recent studies imply that weight reduction may have a beneficial effect on HS prevalence and severity.20,21 Long-term prognosis also appears to be influenced by obesity, with spontaneous British Journal of Dermatology (2015) 173, pp1142–1155

resolution of the disease reported more often by patients who lose weight.22 Obesity has been shown to influence the effect of treatments. In HS disease severity appears to co-vary with BMI and recurrence rates after CO2 laser surgery are better in patients with low BMI compared with patients with higher BMI.23 The weight of patients may also prove to be an important factor when treating patients with HS with tumour necrosis factor (TNF)-a inhibitors.24 When using anti-TNF-a agents in chronic inflammatory skin diseases such as psoriasis,25 optimal responses are less frequently observed in patients with psoriasis with increasing weight, especially above 100 kg, when using fixed standard doses. A phase II study of adalimumab for the treatment of HS provided evidence that clinical response showed a larger treatment effect in patients with HS with a BMI greater than or equal to the median compared with the respective corresponding subgroup.26 This may be interpreted to indicate that obesity does not influence response to anti-TNF-a antibodies in HS. Such an observation has to be studied further both in phase III studies and longitudinal studies with appropriately designed registries.27 Smoking appears as another potential risk factor for HS. It is also closely associated with HS for both population-identified cases as well as hospital-based cases. The exact mechanism is not known, but an altered chemotaxis of polymorphic neutrophils possibly plays a role, as previously suggested in diseases such as palmoplantar pustulosis.28,29 The association to © 2015 British Association of Dermatologists

Diabetes: Patients with HS: 3/67 (4%) Controls: 6/200 (3%) Hyperglycemia: Patients with HS: 21/80 (26%) Controls: 8/100 (8%)

Diabetes: Patients with HS: 94/243 (39%) Controls: 54/222 (24%)

Diabetes: Patients with HS: 353/1730 (20%) Controls: 26/1730 (1
5%)

Hyperglycemia: Patients with HS: 23/326 (7%) Controls: 728/14 851 (5%)

Revuz 2008 (a)6

Gold 201416

Shlyankevich 20145

Miller 2014 (a)7

© 2015 British Association of Dermatologists

Diabetes: Patients with HS: 358/3207 (11%) Controls: 471/6412 (7%)

Shalom 201519

HbA1c level of > 48 mmol mol 1 Nonfasting plasma glucose level of > 220 mg dL 1 Diagnosis of diabetes mellitus or use of antidiabetic medication HbA1c level of > 48 mmol mol 1 Nonfasting plasma glucose level of > 220 mg dL 1 Diagnosis of diabetes mellitus or use of antidiabetic medication 2 tests glucose > 200 mg dL 1 or 1 test glucose > 200 mg dL 1 with target organ damage or 2 fasting glucose tests > 125mg dL 1

Hyperglycemia > 110 mg dL 1 fasting or history of glucose intolerance noted by physician in the patient chart ICD-9 criteria coding

Self-reported questionnaire Definition: if patient received treatment for diabetes Hyperglycemia > 110 mg dL 1 fasting or use of medication for hyperglycemia

Crude: 1
59 (1
3–1
8) Adjusted: 1
4 (1
2–1
7)

Adjusted: 5
7 (1
9–17)

Crude: 1
68 (11
2–25) Adjusted: 1
72 (1–2
9) Adjusted: 2
4 (1
5–3
8)

Crude: 2
0 (1
3–2
9)

Crude: 4
0 (1
59–10
8)

Crude: 1
5 (0
36–6
55)

Diabetes (OR with 95% CI)

CI, confidence interval; NCEP-ATP III, National Cholesterol Education Program Adult Treatment Panel III; OR, odds ratio.

Hyperglycemia: Patients with HS: 4/32 (13%) Controls: 728/14 851 (5%)

Miller 2014 (b)7

Sabat 201218

Diabetes

Citation

Definition and assessment method diabetes

Table 4 Results regarding diabetes and metabolic syndrome for the included studies in the systematic review

–

Metabolic syndrome: Patients with HS: 334/3207 (15%) Controls: 453/6412 (7%)

Metabolic syndrome: Patients with HS: 17/32 (53%) Controls: 3192/ 14 851 (21%)

Metabolic syndrome: Patients with HS: 105/326 (32%) Controls: 3192/ 14 851 (21%)

–

According to NCEPATP III criteria

According to NCEPATP III criteria

According to NCEPATP III criteria

–

According to NCEPATP III criteria

According to NCEPATP III criteria

–

Metabolic syndrome

Metabolic syndrome: Patients with HS: 32/ 80 (40%) Controls: 13/100 (13%) Metabolic syndrome: Patients with HS: 123/243 (50%) Controls: 67/222 (30%)

Definition and assessment method metabolic syndrome

Crude: 1
5 (1
3–1
7) Adjusted: 1
6 (1
3–1
9)

Adjusted: 3
8 (1
9–8)

Adjusted: 2 (1
6–2
6)

–

Crude: 2
3 (1
6–3
4)

Crude: 4
4 (2
02– 9
9)

–

Metabolic syndrome (OR with 95% CI)

Cardiovascular disease risk factors in HS, T. Tzellos et al. 1153

British Journal of Dermatology (2015) 173, pp1142–1155

1154 Cardiovascular disease risk factors in HS, T. Tzellos et al.

disease severity and the effect of smoking cessation on longterm effect or treatment effect is less pronounced than for obesity. Smoking cessation should nevertheless be encouraged in patients with HS on suspicion of a pathogenic role and the many other diseases associated with the use of tobacco, which may lead to additional comorbidities and increased overall burden of HS. Diabetes mellitus and increased serum insulin and serum insulin growth factor lead to oversensitization of follicular androgen receptors in patients with HS, which has been speculated to influence the disease course.30 This association is further supported by observations that metformin hydrochloride treatment may positively influence HS.31 Τhese observations imply a causal role of elevated blood glucose on HS, and emphasize the need for early identification of this comorbidity. A low glycaemic load diet may be important not only for glucose control but also for ensuring promotion of weight loss in patients with HS. A limitation of this study is that causality cannot be assessed because all studies included were of case–control and cross-sectional design. This systematic review could not appropriately study whether HS severity influences the strength of the associations with various comorbidities. Sabat et al.18 evaluated severity using the Sartorius score and concluded that no correlation was observed between the severity or duration of HS and levels of respective parameters or number of criteria defining MetS. Gold et al.16 assessed HS severity with Hurley staging and found no significant difference in the rate of MetS in different Hurley stages. Miller et al.9 evaluated severity for the population HS group based on self-reported information on number and locations of boils and scarring and in hospital HS groups using the Sartorius score. They also concluded that association between HS and MetS and its components was not influenced by the degree of HS severity with the exception of general obesity. Therefore, whether or not HS severity influences these associations remains to be further examined. Heterogeneity was substantial in all analyses. However, due to the low number of studies included, meta-regression analysis would not have been plausible. Sensitivity analysis with origin of the HS population as a confounding variable provided evidence that part of this heterogeneity can be explained by this variable. The increased heterogeneity may have resulted from differences in participants and outcomes assessments, from residual confounding and from other biases that vary across studies. Thus, this increased heterogeneity may be a factor affecting the validity of the results of the present systematic review. Language bias cannot be fully excluded as we employed language restrictions. However this meta-analysis minimized it by including languages other than English. This meta-analysis used only categorical values and not continuous data. This meta-analysis provides strong support for the notion that HS is associated with significant comorbidities thus increasing the cardiovascular risk for patients. Future studies should try to quantify cardiovascular risk factors by analysing continuous data in order to assess the magnitude of these British Journal of Dermatology (2015) 173, pp1142–1155

associations more precisely and provide evidence for better clinical management. Future studies should aim to further elucidate this association by evaluating possible causality.

References 1 Fimmel S, Zouboulis CC. Comorbidities of hidradenitis suppurativa (acne inversa). Dermatoendocrinol 2010; 2:9–16. 2 Kurzen H, Kurokawa I, Jemec GB et al. What causes hidradenitis suppurativa? Exp Dermatol 2008; 17:455–72. 3 Jemec GBE. Clinical practice: hidradenitis suppurativa. N Engl J Med 2012; 366:158–64. 4 Esmann S, Jemec GBE. Psychosocial impact of hidradenitis suppurativa: a qualitative study. Acta Derm Venereol 2011; 91:328–32. 5 Shlyankevich J, Chen AJ, Kim GE, Kimball AB. Hidradenitis suppurativa is a systemic disease with substantial comorbidity burden: a chart-verified case–control analysis. J Am Acad Dermatol 2014; 71:1144–50. 6 Revuz JE, Canoui-Poitrine F, Wolkenstein P et al. Prevalence and factors associated with hidradenitis suppurativa: results from two case–control studies. J Am Acad Dermatol 2008; 59:596–601. 7 Miller IM, Ellervik C, Vinding GR et al. Association of metabolic syndrome and hidradenitis suppurativa. JAMA Dermatol 2014; 150:1273–80. 8 Dessein PH, Norton GR, Joffe BI et al. Metabolic cardiovascular risk burden and atherosclerosis in African black and Caucasian women with rheumatoid arthritis: a cross-sectional study. Clin Exp Rheumatol 2013; 31:53–61. 9 Miller IM, Ellervik C, Yazdanyar S, Jemec GB. Meta-analysis of psoriasis, cardiovascular disease, and associated risk factors. J Am Acad Dermatol 2013; 69:1014–24. 10 Stroup DF, Berlin JA, Morton SC et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Metaanalysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA 2000; 283:2008–12. 11 Wells GA, Shea B, O’Connell D et al. The Newcastle–Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in metaanalyses, 2014 [WWW document]. Available at: http://www. ohri.ca/programs/clinical_epidemiology/oxford.html (last accessed 14 December 2014). 12 Higgins JPT, Green S (eds). Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 (updated March 2011). The Cochrane Collaboration, 2011. Available at: www.cochrane-handbook.org (last accessed 14 December 2014). 13 DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986; 7:177–88. 14 Harrison BJ, Read GF, Hughes LE. Endocrine basis for the clinical presentation of hidradenitis suppurativa. Br J Surg 1988; 75:972–5. 15 Schmitt JV, Bombonatto G, Martin M, Miot HA. Risk factors for hidradenitis suppurativa: a pilot study. An Bras Dermatol 2012; 87:936–8. 16 Gold DA, Reeder VJ, Mahan MG, Hamzavi IH. The prevalence of metabolic syndrome in patients with hidradenitis suppurativa. J Am Acad Dermatol 2014; 70:699–703. 17 Konig A, Lehmann C, Rompel R, Happle R. Cigarette smoking as a triggering factor of hidradenitis suppurativa. Dermatology 1999; 198:261–4. 18 Sabat R, Chanwangpong A, Schneider-Burrus S et al. Increased prevalence of metabolic syndrome in patients with acne inversa. PLoS One 2012; 7:e31810. 19 Shalom G, Freud T, Harman-Boehm I et al. Hidradenitis suppurativa and the metabolic syndrome: a comparative cross-sectional study of 3207 patients. Br J Dermatol 2015; 173:464–70.

© 2015 British Association of Dermatologists

Cardiovascular disease risk factors in HS, T. Tzellos et al. 1155 20 Kromann CB, Ibler KS, Kristiansen VB, Jemec GB. The influence of body weight on the prevalence and severity of hidradenitis suppurativa. Acta Derm Venereol 2014; 94:553–7. 21 Sartorius K, Emtestam L, Jemec GB, Lapins J. Objective scoring of hidradenitis suppurativa reflecting the role of tobacco smoking and obesity. Br J Dermatol 2009; 161:831–9. 22 Kromann CB, Deckers IE, Esmann S et al. Risk factors, clinical course and long-term prognosis in hidradenitis suppurativa: a cross-sectional study. Br J Dermatol 2014; 171:819–24. 23 Mikkelsen PR, Dufour DN, Zarchi K, Jemec GB. Recurrence rate and patient satisfaction of CO2 laser evaporation of lesions in patients with hidradenitis suppurativa: a retrospective study. Dermatol Surg 2015; 41:255–60. 24 Zouboulis CC, Desai N, Emtestam L et al. European S1 guideline for the treatment of hidradenitis suppurativa/acne inversa. J Eur Acad Dermatol Venereol 2015; 29:619–44. 25 Puig L. Obesity and psoriasis: body weight and body mass index influence the response to biological treatment. J Eur Acad Dermatol Venereol 2011; 25:1007–11. 26 Kimball AB, Kerdel F, Adams D et al. Adalimumab for the treatment of moderate to severe hidradenitis suppurativa: a parallel randomized trial. Ann Intern Med 2012; 157:846–55. 27 Ingvarsson G, Dufour DN, Killasli H et al. Development of a clinical Scandinavian registry for hidradenitis suppurativa; HISREG. Acta Derm Venereol 2013; 93:350–1. 28 O’Doherty CJ, MacIntyre C. Palmoplantar pustulosis and smoking. Br Med J 1985; 291:861–4. 29 Smith JB, Fenske NA. Cutaneous manifestations and consequences of smoking. J Am Acad Dermatol 1996; 34:717–32. 30 Melnik BC, Plewig G. Impaired Notch signalling: the unifying mechanism explaining the pathogenesis of hidradenitis suppurativa (acne inversa). Br J Dermatol 2013; 168:876–8. 31 Verdolini R, Clayton N, Smith A et al. Metformin for the treatment of hidradenitis suppurativa: a little help along the way. J Eur Acad Dermatol Venereol 2013; 27:1101–8.

Appendix Conflicts of interest T.T. has been reimbursed for travel expenses and hotel accommodation to attend dermatology congresses by Janssen-Cilag, MSD and Novartis and participates on the Hidradenitis Suppurativa Advisory Board of AbbVie. C.C.Z. has received honoraria from AbbVie, Almirall, Basilea, Bayer Health Care, Bioderma, Biogen-Idec, Dermira, Galderma, General Topics, Glenmark, Leo, Philips Lifestyle, Pierre Fabre, Stiefel/GSK and Xenon for participation on advisory boards, or as a consultant, investigator or speaker; his department has received grants from

© 2015 British Association of Dermatologists

AbbVie, Astra-Zeneca, Biogen-Idec, Bristol Meyers Squibb, Immundiagnostik AG, Intendis, LVMH, Merz, Novartis, Pierre Fabre and UCB for his participation as an investigator, or on advisory boards. W.G. has received honoraria for participation on advisory boards for AbbVie, Amgen, Bio-K and Janssen and for participation in speaking engagements and consultative meetings for AbbVie, Actelion, Amgen, Janssen, Leo Pharma, Novartis and Roche. A.D.C. serves as a consultant to AbbVie, Agis, BMZ, Dexcel Pharma, Dexxon, Etwal, Glaxo, Janssen, Leo, Lev Bar, Medison, Neopharm, Novartis, Perrigo, Pfizer, Rafa, Roche, Schering-Plough, Serono, Taro, Tetrapharm, Teva and Trima. He has also received research grants from Novartis. P.W. participates on the Hidradenitis Suppurativa Advisory Board of AbbVie. G.B.E.J. has received honoraria from AbbVie, Pfizer and MSD for participation on advisory boards, and grants from AbbVie, Leo Pharma, Actelion, Janssen-Cilag and Novartis for participation as an investigator, and has received speaker honoraria from AbbVie, Galderma, Leo Pharma and MSD; he has furthermore received unrestricted research grants from AbbVie and Leo Pharma.

Supporting Information Additional Supporting Information may be found in the online version of this article at the publisher’s website: Fig S1. Funnel plot for the studies included in the obesity analysis indicated no publication bias. Fig S2. Funnel plot for the studies included in the central obesity analysis indicated no publication bias. Fig S3. Funnel plot for the studies included in the active smoking analysis indicated no publication bias. Fig S4. Funnel plot for the studies included in the history of smoking analysis indicated no publication bias. Fig S5. Funnel plot for the studies included in the hypertension analysis indicated no publication bias. Fig S6. Funnel plot for the studies included in the hypertriglyceridemia analysis indicated no publication bias. Fig S7. Funnel plot for the studies included in the low high-density lipoprotein analysis indicated no publication bias. Fig S8. Funnel plot for the studies included in the diabetes analysis indicated no publication bias. Fig S9. Funnel plot for the studies included in the metabolic syndrome analysis indicated no publication bias.

British Journal of Dermatology (2015) 173, pp1142–1155