BJD
British Journal of Dermatology
E P I DE M I O L O G Y A N D HE A L T H S E R V IC E S RE SE AR CH
Hidradenitis suppurativa and metabolic syndrome: a comparative cross-sectional study of 3207 patients* G. Shalom,1,2 T. Freud,3 I. Harman-Boehm,2,4 I. Polishchuk2,4 and A.D. Cohen3,5 1
Department of Dermatology and Venereology and 4Department of Internal Medicine C, Soroka Medical Center, P.O.B. 151, Beer-Sheva 84101, Israel Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel 3 Siaal Research Center for Family Medicine and Primary Care, Division of Community Health, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel 5 Department of Quality Measurements and Research, Chief Physician’s Office, Clalit Health Services, Tel Aviv, Israel 2
Summary Correspondence Guy Shalom. E-mail: [email protected]
Accepted for publication 7 March 2015
Funding sources None.
Conflicts of interest A.D.C. serves as a consultant to AbbVie, Agis, BMZ, Dexcel Pharma, Dexxon, Etwal, Glaxo, Janssen, LEO Pharma, Lev Bar, Medison, Neopharm, Novartis, Perrigo, Pfizer, Rafa, Roche, Schering-Plough, Serono, Taro, Tetrapharm, Teva and Trima, and has received research grants from Novartis. G.S. and T.F. contributed equally to this study. *Plain language summary available online. DOI 10.1111/bjd.13777
Background Hidradenitis suppurativa (HS) is a chronic relapsing inflammatory skin disease. Objectives To evaluate the association between HS and metabolic syndrome and its component morbidities in a large, community-based cohort of patients with HS, using the database of Clalit Health Services, the largest public healthcare provider in Israel. Methods A cross-sectional study was performed. Metabolic syndrome was defined as the presence of at least three of the following conditions: diabetes, hyperlipidaemia, hypertension and obesity. The association between HS and metabolic syndrome was assessed by a multivariate logistic regression model, adjusting for age, sex, diabetes, hypertension, hyperlipidaemia, obesity and smoking status. Results The study included 3207 patients with HS (general frequency of 007%) diagnosed by a dermatologist in primary-care centres, and 6412 age- and sexmatched control patients without HS. HS was significantly associated with metabolic syndrome [odds ratio (OR) 161, 95% confidence interval (CI) 136–189], diabetes (OR 141, 95% CI 119–166), obesity (OR 171, 95% CI 153–191), hyperlipidaemia (OR 114, 95% CI 102–128) and hypertension (OR 119, 95% CI 103–138). Conclusions We found an association between HS and diabetes, hyperlipidaemia, obesity, hypertension and metabolic syndrome among a large community-based cohort of patients with HS. Clinicians should take into account that patients with HS may have one or more undiagnosed components of metabolic syndrome despite their young age. Thus, appropriate targeted screening is advised.
What’s already known about this topic?
•
Hidradenitis suppurativa (HS) is known to be associated with several comorbidities.
What does this study add?
•
We found an association between HS and diabetes, hyperlipidaemia, obesity, hypertension and metabolic syndrome in a large community-based cohort of patients with HS.
Hidradenitis suppurativa (HS) is a chronic relapsing suppurative skin disease, localized in the apocrine-gland-bearing areas of the body, predominantly in the axillae and groin.1 Clinical 464
British Journal of Dermatology (2015) 173, pp464–470
manifestations include the presence of nodules that may develop into subcutaneous abscesses with the formation of sinus tracts and discharging lesions. The disease course is © 2015 British Association of Dermatologists
Hidradenitis suppurativa and metabolic syndrome, G. Shalom et al. 465
characterized by flares that may involve suppuration of foulsmelling discharge, as well as pain.2–4 These painful, recurring episodes can cause significant quality-of-life impairment.3,4 Most knowledge about the epidemiology of HS is derived from screening studies of limited selected populations, with a relative paucity of population-based studies. HS occurs more frequently in women than in men, with an estimated ratio of 3 : 1, particularly in the genitofemoral area.1,5–7 Frequency estimates vary considerably, maybe in part because of the hidden nature of HS (mostly involving areas covered by clothing, thus the disease might often be ignored or misdiagnosed).6,8,9 A 1% frequency of HS was estimated recently in two population-based studies from Europe;5,10 however, other estimates based on the same population were as high as 4%.10 One of the recently published U.S.A.-specific estimates comes from the PharMetrics Integrated Database and was based on healthcare claims for HS. The study suggests a frequency of 0053% clinically detected HS and is one of the first studies to overcome the issue of sample size encountered by previous epidemiological studies of HS.11 Several associations between HS and other morbidities have been reported in the last decade. These include inflammatory bowel disease, depression, hormonal disorders, psoriasis and arthritis.12–21 In several studies it has been suggested that HS may be associated with metabolic syndrome or some of its components.12,22–30 Recently, a study from Massachusetts General Hospital reported significantly higher rates of comorbidities among patients with HS than in controls, including dyslipidaemia, polycystic ovarian syndrome, obesity, hypertension, diabetes, thyroid disease, alcohol dependence and lymphoma, in an unadjusted analysis.25 Epidemiological data regarding HS have many limitations. The data are often based on anecdotal reports or are based on studies that are limited by their small sample size, by poorly controlled investigations or by demographic characteristics that are not easily generalizable. The current literature suffers substantial selection bias as it is based on self-reports or highly selected specialist-treated samples, with few population-based studies. Moreover, recent reports regarding HS and comorbidities that have tried to overcome these limitations rely mainly on secondary- and tertiary-care centres, and therefore may not represent the vast majority of patients presenting in the community setting. Early detection of comorbidities may prevent long-term complications; therefore, further large populationbased studies are clearly warranted. The purpose of our study was to investigate the association between HS and metabolic syndrome and its components – diabetes, hyperlipidaemia, obesity and hypertension – in a large population-based study, utilizing the database of Clalit Health Services (CHS), which is based mainly on patients from the community setting.
Materials and methods Clalit Health Services is the largest health maintenance organization in Israel, serving a population of approximately 42 © 2015 British Association of Dermatologists
million, covering 52% of the population. The CHS-insured population is representative of the general Israeli population. The CHS database is a comprehensive computerized database with continuous real-time input from pharmaceutical, medical and administrative operating systems. The database was started in 1998 in order to improve the provided healthcare services, and it holds accurate information on each medication prescribed and claimed by the patient, including its date, dose and mode of administration. The National Program for Quality Indicators in Community Healthcare in Israel (QICH) has brought about relatively high screening ratios for chronic morbidities and chronic conditions, with high measurement ratios and negligible missing data.31 Diagnoses of chronic diseases such as diabetes, hypertension and hyperlipidaemia are validated by the CHS register using a systematic methodology. The validation process is based on repeated appraisal of diagnoses made by CHS physicians, registered prescriptions, pharmacy claims, laboratory tests (e.g. glucose blood level) and auxiliary tests (e.g. blood pressure values on routine screening and follow-up tests), for each patient. Comparison between diagnoses, drug administration and laboratory and auxiliary tests from various sources is performed regularly. If a diagnosis is not consistent through all sources, it is removed from the register. This validation process ensures an accuracy of 85–90% in the CHS Chronic Disease Register.31,32 All of the exposure variables/confounders defined in the current study are listed as targets in the QICH. Our study was designed as a cross-sectional study of patients with HS and age- and sex-matched control patients. The HS group consisted of patients with at least one documented diagnosis of HS in their medical records between 1998 and 2013, registered by a CHS dermatologist in a primary-care centre. The reference group consisted of age- and sex-matched patients without HS, drawn from the general population of CHS enrollees. We extracted information for all patients with HS diagnosed from January 1998 until the end of November 2013. The observation period for the entire study population extended until the end of December 2013. The extracted information includes the age, sex and smoking status of the subject, and presence of concomitant diabetes, hypertension, hyperlipidaemia or obesity. Diagnosis of metabolic syndrome is not available as an independent code in the CHS database. Hence, in this study metabolic syndrome was a dependent variable, defined as the presence of at least three of the following: diabetes, hyperlipidaemia, hypertension and obesity (body mass index > 30 kg m 2).33 In CHS, diabetes is diagnosed according to one of the following criteria: (i) two random tests of blood glucose > 200 mg dL 1; (ii) one random test of blood glucose > 200 mg dL 1 with evident target organ damage; and (iii) two fasting glucose tests > 125 mg dL 1. Diagnosis of hypertension in CHS is established in patients with several measurements of systolic blood pressure > 140 mmHg or diastolic > 90 mmHg during at least a 1-month period of follow-up. Socioeconomic status was defined according to the average socioeconomic status of the clinics where each British Journal of Dermatology (2015) 173, pp464–470
466 Hidradenitis suppurativa and metabolic syndrome, G. Shalom et al.
enrollee was registered. Clinics were classified as 1 (low), 2 (intermediate) or 3 (high). Follow-up data covering all diagnoses for each patient were extracted from the database for further analysis. Frequency rates were calculated for each group. Additionally, a manual search with focus on metabolic syndrome-related conditions was performed in patients’ files that are available at the Soroka University Medical Center, in which the first author’s (G.S.) dermatology department is located, in order to evaluate the temporal relationships between HS and the metabolic syndrome components. Statistical analysis Demographic data, general data and patient characteristics at baseline were collected. Continuous variables were compared using ANOVA or t-tests, and dichotomous variables were compared by Pearson’s v2-test or Fisher’s exact test, as appropriate. The proportion of each predefined morbidity was calculated in both study groups. A univariate comparative analysis was performed for the entire study population and for the young patient population (age ≤ 50 years). In order to test the association between metabolic syndrome and HS without the effect of obesity, we performed a stratified analysis in obese and nonobese patients with HS. For the stratified analysis, we adopted the World Health Organization (WHO) 1999 criteria, which include the presence of diabetes mellitus, impaired glucose tolerance, impaired fasting glucose or insulin resistance, together with two of the following: elevated blood pressure, dyslipidaemia, obesity and microalbuminuria (urinary albumin excretion ≥ 20 lg min 1 or albumin-tocreatinine ratio ≥ 30 mg g 1).34 The association between HS and each of the investigated morbidities was further tested by a multivariate logistic regression model, adjusted for age, sex, diabetes, hypertension, hyperlipidaemia, obesity and smoking status. Statistical analysis was performed using the SPSS package, version 20 (IBM,
Armonk, NY, U.S.A.). A P-value < 005 was considered statistically significant. The study was approved by the institutional review board of the Meir Medical Center, Kfar Saba, Israel (approval number 0003-12-com), and was exempted from the need to sign an informed consent form. The Declaration of Helsinki protocols were followed.
Results The study included 3207 patients with HS and 6412 control patients. The general frequency of HS in the CHS database was 007%. The baseline characteristics of the study population are presented in Table 1. Female sex was predominant in our study population, with 1975 female patients (616%) with HS and 3948 (616%) without HS. The age distribution of the study population and the proportion of morbidities in each specific age group are presented in Table 2. Most of our study subjects were aged < 50 years (743%). Interestingly, greater differences in the proportion of patients with morbidities were observed at younger ages between patients with HS and controls (Table 2). The univariate comparative analysis for the association between HS and the investigated morbidities is presented in Table 3, for the entire study population and for the youngerage patient population. In this univariate analysis significant associations between diabetes, hyperlipidaemia, obesity and metabolic syndrome and HS were observed in the entire study population. A trend for an association between HS and hypertension was detected, but it did not reach statistical significance [odds ratio (OR) 113, 95% confidence interval (CI) 099–128; P = 005]. In the younger patient population (age ≤ 50 years), the univariate analysis revealed a trend towards a greater association between HS and diabetes, hyperlipidaemia, obesity, hypertension and metabolic syndrome, with higher ORs and stronger statistical significance compared
Table 1 Baseline characteristics of the study population
Total number Female Male Socioeconomic statusa Low Intermediate High Ethnicity Arabic Orthodox Jew General Age (years), mean SD Primary physician visits, mean SDb Smoking
Patients with HS
Reference group
P-value
3207 1975 (616) 1232 (384)
6412 3948 (616) 2464 (384)
NS
1156 (376) 1311 (426) 609 (198)
2692 (428) 2290 (364) 1312 (208)
< 001 < 001 < 001
478 (149) 78 (24) 2651 (827) 396 155 77 69 1520 (474)
1412 (220) 209 (33) 4791 (747) 396 155 54 49 1923 (299)
< 001 < 001 < 001 NS < 001 < 001
Values are n (%) unless stated otherwise. NS, not significant. aSubtotals do not add up to the total value due to missing data. bPer year, per patient.
British Journal of Dermatology (2015) 173, pp464–470
© 2015 British Association of Dermatologists
© 2015 British Association of Dermatologists 452 (7)
226 (7)
3207
6412
10 (02)
964 (15)
482 (15)
5 (02)
1016 (16)
508 (16)
74 (12)
1532 (24)
766 (24)
37 (12)
1728 (27)
864 (27)
142 (22)
484 (75)
242 (75)
71 (22)
10 (02)
No HS
6 (02)
HS
Total
0 – 2 (08) NS 12 (13) NS 29 (38) 0011 63 (12) 0013 120 (25) < 001 86 (38) < 001 30 (42) NS 15 (41) NS 1 (20) NS 358 (11) < 001
HS
Diabetes
Values are n (%). HS, hidradenitis suppurativa; NS, not significant.
1–10 P-value 11–20 P-value 21–30 P-value 31–40 P-value 41–50 P-value 51–60 P-value 61–70 P-value 71–80 P-value 81–90 P-value 91–100 P-value Total P-value
Age group (years)
471 (74)
2 (20)
25 (34)
45 (32)
110 (24)
158 (16)
85 (84)
30 (19)
14 (08)
2 (04)
0
No HS
Table 2 The proportions of morbidities in each age group among the study population
0 – 4 (17) NS 68 (79) 0033 147 (19) < 001 185 (36) 0037 291 (60) NS 170 (75) NS 56 (79) NS 29 (78) NS 2 (40) NS 952 (30) < 001
HS
1703 (27)
6 (60)
60 (81)
112 (86)
329 (73)
553 (57)
315 (31)
214 (14)
98 (57)
6 (12)
0
No HS
Hyperlipidaemia
Obesity
1 (17) NS 48 (20) 0005 124 (14) < 001 123 (17) < 001 145 (29) < 001 171 (35) < 001 74 (33) 0086 19 (27) NS 6 (16) NS 0 NS 711 (22) < 001
HS
903 (14)
3 (30)
16 (22)
35 (25)
118 (26)
252 (26)
188 (19)
136 (89)
98 (57)
57 (12)
0
No HS
Hypertension
0 – 1 (04) NS 14 (16) 0035 23 (3) NS 75 (15) 0047 154 (32) NS 114 (50) NS 44 (62) NS 27 (73) NS 3 (60) NS 455 (14) 0060
HS
820 (13)
8 (80)
57 (77)
89 (63)
220 (49)
277 (29)
113 (11)
44 (28)
12 (07)
0
0
No HS
0 – 0 – 9 (1) < 001 17 (22) 0022 54 (11) < 001 116 (24) 0012 90 (40) < 001 30 (42) NS 18 (49) NS 0 NS 334 (10) < 001
453 (7)
2 (20)
24 (32)
51 (36)
117 (26)
177 (18)
65 (64)
15 (09)
2 (01)
0
0
No HS
Metabolic syndrome HS
Hidradenitis suppurativa and metabolic syndrome, G. Shalom et al. 467
British Journal of Dermatology (2015) 173, pp464–470
468 Hidradenitis suppurativa and metabolic syndrome, G. Shalom et al. Table 3 The association between hidradenitis suppurativa (HS) and metabolic syndrome and its components by univariate analysis Component
Patients with HSa
Reference groupa
All patients Diabetes Obesity Hyperlipidaemia Hypertension Metabolic syndrome Young patients (age ≤ 50 years) Diabetes Obesity Hyperlipidaemia Hypertension Metabolic syndrome
n = 3207 358 (112) 711 (222) 952 (297) 455 (142) 334 (104) n = 2386 106 (44) 441 (185) 404 (169) 113 (47) 80 (34)
n = 6412 471 (74) 903 (141) 1703 (26. 6) 820 (128) 453 (71) n = 4770 131 (28) 479 (10) 633 (133) 169 (35) 82 (17)
OR
95% CI
P-value
159 174 117 113 153
137–183 156–194 106–128 099–128 132–177
< 0001 < 0001 0001 005 < 0001
165 203 133 135 198
127–214 177–234 116–153 106–173 145–271
< 0001 < 0001 < 0001 001 < 0001
OR, odds ratio; CI, confidence interval. aValues are n (%).
with the entire study population (Table 3). Using the WHO definition, HS and metabolic syndrome were also significantly associated, in the entire study population (OR 172, 95% CI 145–21; P < 001), among the nonobese patients (OR 145, 95% CI 11–19; P < 001) and among the obese patients (OR 16, 95% CI 12–21; P < 001). Consistently, the multivariate analysis revealed a significant association between HS and diabetes, hyperlipidaemia, obesity, hypertension and metabolic syndrome. Logistic regression results between each categorical variable and HS, adjusted for age, sex, diabetes, hypertension, hyperlipidaemia, obesity and smoking status are presented in Table 4. For the 3207 patients with HS in our study, 423 complete patient files were available at the Soroka University Medical Center. These patients presented to the emergency room or were admitted during 1998–2013, and had an average age of 41 years at the time of HS diagnosis. Thirty of these presented with HS exacerbation during that time period. Among these 423 patients with HS, 135 had at least one component of metabolic syndrome and 12 patients fulfilled the full criteria for metabolic syndrome. In this subgroup of patients, the majority of the metabolic syndrome components, with the exception of obesity, occurred after HS, with a latency period of 38–95 years (Table 5). Table 4 Logistic regression results for the association between hidradenitis suppurativa and metabolic syndrome and its components Component
OR
95% CI
P-value
Diabetesa Obesityb Hyperlipidaemiac Hypertensionc Metabolic syndrome
141 171 114 119 161
119–166 153–191 102–128 103–138 136–189
< 0001 < 0001 002 001 < 0001
OR, odds ratio; CI, confidence interval. aOR adjusted for obesity, age, sex and smoking status. bOR adjusted for age, sex and smoking status. cOR adjusted for obesity, diabetes, age, sex and smoking status.
British Journal of Dermatology (2015) 173, pp464–470
Discussion Our observation is based on a large, community-based cohort of 3207 patients with HS. This shows a prevalence of 007% among the CHS-insured population, in agreement with recent reports.11,25 Multivariate analysis demonstrated an increased frequency of metabolic syndrome among patients with HS compared with age- and sex-matched control patients. Accordingly, the frequencies of diabetes, hyperlipidaemia, obesity and hypertension were significantly elevated in patients with HS compared with control patients. The control group in the present study reflects the general population, and its morbidity rates are in line with Israeli national estimates.32 The univariate and multivariate analysis, together with the stratified analysis (based on the WHO criteria), suggest that the association between HS and metabolic syndrome that we observed is true and does not stem from the presence of other confounders such as obesity. Our observations are consistent with recent studies based on secondary- and tertiary-centres regarding HS and metabolic syndrome.25,26 However, this study adds essential data as is relies mainly on the sector of patients stemming from the community and primary-care centres, which accounted for the vast majority of patients. Accordingly, our observations augment these recent studies and overcome a potential selection bias of tertiary-centre-based investigations. Metabolic syndrome, diabetes, obesity, hypertension and hyperlipidaemia increase the risk for ischaemic heart disease, myocardial infarction and stroke, leading to reduced life quality and expectancy. Despite an overall reduction in the death rate from cardiovascular disease in Western countries over the last few decades, mortality has declined mostly in men, but not in women.35 Recognizing risk factors for long-term cardiovascular morbidities among young women may facilitate early prevention and, therefore, is clearly mandatory. Based on our findings, patients with HS are more likely to be young and female, and have a significantly higher frequency of metabolic syndrome or one or more of its components, all risk factors for cardiovascular morbidity. We should expect to see an effect of female sex and age on comorbidities, yet the associations © 2015 British Association of Dermatologists
Hidradenitis suppurativa and metabolic syndrome, G. Shalom et al. 469 Table 5 Temporal relationship between hidradenitis suppurativa (HS) and the metabolic syndrome components among 135 patients from the Soroka University Medical Center Metabolic syndrome component
Patients with HS before diagnosis, n (%)
Diabetes (n = 64) Hypertension (n = 10) Dyslipidaemia (n = 30) Obesity (n = 61)
51 10 26 15
(80) (100) (87) (25)
between HS and metabolic syndrome and its components were still significant after adjusting for these factors. Chronic inflammatory diseases such as psoriasis are known to be risk factors for metabolic syndrome.36,37 The underlying mechanism in chronic inflammatory conditions leading to the induction of metabolic disturbances might be either a primary or secondary pathological process, thus the major driver of the metabolic alterations is chronic inflammation inducing insulin resistance. Tumour necrosis factor-a inhibitors are considered an effective treatment for psoriasis, and have recently been introduced as a potential treatment for HS.38 Thus, the thought of possible common denominators in the pathogenesis of both psoriasis and HS has been raised, leading to the question of a possible association between HS and metabolic syndrome, as previously reported for psoriasis by our group.36,37 There seem to be similarities and differences between psoriasis and HS in this regard. The frequencies of diabetes, hypertension, hyperlipidaemia, obesity and metabolic syndrome in patients with HS appear to be quite similar to those reported by our group in patients with psoriasis, from the CHS database.36,37 On the other hand, in patients with psoriasis but not HS, there is an association between disease duration and the appearance of metabolic syndrome. Furthermore, metabolic syndrome preferentially mostly affects patients with psoriasis at a higher age,39 while in patients with HS, associated metabolic syndrome morbidities were more prevalent in the younger age groups. Based on clinical trials data, higher levels of obesity are more likely in patients with HS than in patients with psoriasis. The implication in terms of cardiovascular morbidity for patients with HS is possibly even greater than for patients with psoriasis, in whom increased mortality from cardiovascular diseases has been documented.40 These observations indicate that the metabolic alterations observed in patients with HS might be the primary process rather than a secondary inflammatory process. Our investigation is based on a computerized database with a large sample size from the community setting. Nevertheless, the study suffers some limitations. The diagnosis of HS was based on digitally transmitted data. Therefore, undiagnosed cases of HS may exist and lead to underestimation of the true frequency of HS. A second limitation is the inability to determine the temporal relationship between the diagnoses. The manual search among the 423 patient files available at the © 2015 British Association of Dermatologists
Mean latency period from HS to diagnosis (years) 55 95 38 5
Mean age at HS diagnosis (years) 446 565 536 37
Soroka University Medical Center provided some restricted data, which may suggest that HS appears prior to metabolic syndrome (except for obesity, which was present before HS), and the authors suggest that HS might be considered a risk factor for metabolic syndrome. Nevertheless, as this subgroup analysis is based on a limited number of patients, all presenting to tertiary hospital emergency rooms for any reason, a firm conclusion cannot be drawn as it may be exposed to certain biases, such as selection bias. Alternatively, both HS and the other components of metabolic syndrome may arise as a consequence of enhanced low-grade inflammation associated with obesity. We cannot definitely ascertain whether HS appeared first and led to the diagnosis of metabolic syndrome or possibly contributed to it via a chronic inflammatory pathway, or whether HS appeared in patients with poorly controlled diabetes and/or metabolic syndrome in the entire study population. Kim et al.41 reported a positive predictive value (PPV) of 819% for only two diagnostic codes in a 5-year period, and therefore some would consider our case definition (one code) as a potential limitation. Notably, the codes in the above-mentioned study were diagnosed by any physician, and the option of defining a case by one diagnostic code by a dermatologist was not investigated. Moreover, in that study it was noticed that if the HS code was entered by a dermatologist, the PPV was superior to the PPV of a code entered by a nondermatologist. In our investigation, the use of codes entered solely by dermatologists, together with the validation process embedded in the CHS register (which promises a relatively high degree of accuracy), ensures data validity, in spite of the fact that only one code was used to define cases of HS. This argument can be further supported by the consistency between our findings (general frequency of 007%) and other recently published observational studies from the U.S.A. (general frequency of 005–008%).11,25 As the patients with HS were seen more frequently by their treating physician, detection bias is another potential bias in our study. However, the authors feel that this point is controversial, as higher morbidity rates may prompt more frequent visits, and therefore this observation is the outcome and not the cause of our observation. Nevertheless, in spite of these limitations, the present study reveals important data regarding the association between HS and metabolic morbidities. In conclusion, in our study we observed an association between HS and both metabolic syndrome and its components (diabetes, hyperlipidaemia, obesity and hypertension). Early British Journal of Dermatology (2015) 173, pp464–470
470 Hidradenitis suppurativa and metabolic syndrome, G. Shalom et al.
detection and treatment of these morbidities in patients with HS may prevent late complications, particularly cardiovascular disease. Clinicians should take into account that patients with HS may have undiagnosed metabolic syndrome or its associated morbidities, and suggest appropriate screening and treatment.
References 1 Alikhan A, Lynch PJ, Eisen DB. Hidradenitis suppurativa: a comprehensive review. J Am Acad Dermatol 2009; 60:539–61. 2 Karampetsou MP, Liossis SN, Sfikakis PP. TNF-a antagonists beyond approved indications: stories of success and prospects for the future. QJM 2010; 103:917–28. 3 Harrison BJ, Mudge M, Hughes LE. Recurrence after surgical treatment of hidradenitis suppurativa. BMJ 1987; 294:487–9. 4 Matusiak L, Bieniek A, Szepietowski JC. Psychophysical aspects of hidradenitis suppurativa. Acta Derm Venereol 2010; 90:264–8. 5 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. 6 Grant A, Gonzalez T, Montgomery MO et al. Infliximab therapy for patients with moderate to severe hidradenitis suppurativa: a randomized, double-blind, placebo controlled crossover trial. J Am Acad Dermatol 2010; 62:205–17. 7 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. 8 Mebazaa A, Ben Hadid R, Cheikh Rouhou R et al. Hidradenitis suppurativa: a disease with male predominance in Tunisia. Acta Dermatovenerol Alp Pannonica Adriat 2009; 18:165–72. 9 Smith HS, Chao JD, Teitelbaum J. Painful hidradenitis suppurativa. Clin J Pain 2010; 26:435–44. 10 Jemec GB, Heidenheim M, Nielsen NH. The prevalence of hidradenitis suppurativa and its potential precursor lesions. J Am Acad Dermatol 1996; 35:191–4. 11 Cosmatos I, Matcho A, Weinstein R et al. Analysis of patient claims data to determine the prevalence of hidradenitis suppurativa in the United States. J Am Acad Dermatol 2013; 68:412–19. 12 Sabat R, Chanwangpong A, Schneider-Burrus S et al. Increased prevalence of metabolic syndrome in patients with acne inversa. PLoS One 2012; 7:e31810. 13 Arun B, Loffeld A. Long-standing hidradenitis suppurativa treated effectively with metformin. Clin Exp Dermatol 2009; 34:920–1. 14 van der Zee HH, van der Woude CJ, Florencia EF, Prens EP. Hidradenitis suppurativa and inflammatory bowel disease: are they associated? Br J Dermatol 2010; 162:195–7. 15 Roussomoustakaki M, Dimoulios P, Chatzicostas C et al. Hidradenitis suppurativa associated with Crohn’s disease and spondyloarthropathy. J Gastroenterol 2003; 38:1000–4. 16 Onderdijk AJ, van der Zee HH, Esmann S et al. Depression in patients with hidradenitis suppurativa. J Eur Acad Dermatol Venereol 2013; 27:473–8. 17 Kraft JN, Searles GE. Hidradenitis suppurativa in female patients. J Cutan Med Surg 2007; 11:125–31. 18 Tanaka A, Goto Y, Iwade M et al. Rapid progression of hidradenitis suppurativa in the lower leg in a patient with psoriasis vulgaris. Acta Derm Venereol 2012; 92:105–6. 19 Scheinfeld N. Treatment of coincidental seronegative arthritis and hidradenitis suppurativa with adalimumab. J Am Acad Dermatol 2006; 55:163–4. 20 Thein M, Hogarth B, Acland K. Seronegative arthritis associated with the follicular occlusion triad. Clin Exp Dermatol 2004; 29:550–2.
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21 Bhalla R, Sequeira W. Arthritis associated with hidradenitis suppurativa. Ann Rheum Dis 1994; 53:64–6. 22 Vazquez BG, Alikhan A, Weaver AL et al. Incidence of hidradenitis suppurativa and associated factors: a population-based study of Olmsted County, Minnesota. J Invest Dermatol 2013; 133:97–103. 23 Centers for Disease Control (CDC). Trends in ischemic heart disease mortality – United States, 1980–1988. MMWR Morb Mortal Wkly Rep 1992; 41:548–9, 556. 24 Wiltz O, Schoetz DJ Jr, Murray JJ et al. Perianal hidradenitis suppurativa. The Lahey Clinic experience. Dis Colon Rectum 1990; 33:731–4. 25 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. 26 Miller IM, Ellervik C, Vinding GR et al. Association of metabolic syndrome and hidradenitis suppurativa. JAMA Dermatol 2014; 150:1273–80. 27 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. 28 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. 29 Canoui-Poitrine F, Revuz JE, Wolkenstein P et al. Clinical characteristics of a series of 302 French patients with hidradenitis suppurativa, with an analysis of factors associated with disease severity. J Am Acad Dermatol 2009; 61:51–7. 30 von der Werth JM, Jemec GB. Morbidity in patients with hidradenitis suppurativa. Br J Dermatol 2001; 144:809–13. 31 Jaffe DH, Shmueli A, Ben-Yehuda A et al. Community healthcare in Israel: quality indicators 2007–2009. Isr J Health Policy Res 2012; 1:3. 32 Rennert G, Peterburg Y. Prevalence of selected chronic diseases in Israel. Isr Med Assoc J 2001; 3:404–8. 33 Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001; 285:2486–97. 34 Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 1998; 15:539–53. 35 Sempos C, Cooper R, Kovar MG, McMillen M. Divergence of the recent trends in coronary mortality for the four major race-sex groups in the United States. Am J Public Health 1988; 78:1422–77. 36 Cohen AD, Sherf M, Vidavsky L et al. Association between psoriasis and the metabolic syndrome. A cross-sectional study. Dermatology 2008; 216:152–5. 37 Shapiro J, Cohen AD, David M et al. The association between psoriasis, diabetes mellitus, and atherosclerosis in Israel: a case–control study. J Am Acad Dermatol 2007; 56:629–34. 38 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. 39 Gisondi P, Tessari G, Conti A et al. Prevalence of metabolic syndrome in patients with psoriasis: a hospital-based case–control study. Br J Dermatol 2007; 157:68–73. 40 Gelfand JM, Neimann AL, Shin DB et al. Risk of myocardial infarction in patients with psoriasis. JAMA 2006; 296:1735–41. 41 Kim GE, Shlyankevich J, Kimball AB. The validity of the diagnostic code for hidradenitis suppurativa in an electronic database. Br J Dermatol 2014; 171:338–42.
© 2015 British Association of Dermatologists
British Journal of Dermatology
E P I DE M I O L O G Y A N D HE A L T H S E R V IC E S RE SE AR CH
Hidradenitis suppurativa and metabolic syndrome: a comparative cross-sectional study of 3207 patients* G. Shalom,1,2 T. Freud,3 I. Harman-Boehm,2,4 I. Polishchuk2,4 and A.D. Cohen3,5 1
Department of Dermatology and Venereology and 4Department of Internal Medicine C, Soroka Medical Center, P.O.B. 151, Beer-Sheva 84101, Israel Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel 3 Siaal Research Center for Family Medicine and Primary Care, Division of Community Health, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel 5 Department of Quality Measurements and Research, Chief Physician’s Office, Clalit Health Services, Tel Aviv, Israel 2
Summary Correspondence Guy Shalom. E-mail: [email protected]
Accepted for publication 7 March 2015
Funding sources None.
Conflicts of interest A.D.C. serves as a consultant to AbbVie, Agis, BMZ, Dexcel Pharma, Dexxon, Etwal, Glaxo, Janssen, LEO Pharma, Lev Bar, Medison, Neopharm, Novartis, Perrigo, Pfizer, Rafa, Roche, Schering-Plough, Serono, Taro, Tetrapharm, Teva and Trima, and has received research grants from Novartis. G.S. and T.F. contributed equally to this study. *Plain language summary available online. DOI 10.1111/bjd.13777
Background Hidradenitis suppurativa (HS) is a chronic relapsing inflammatory skin disease. Objectives To evaluate the association between HS and metabolic syndrome and its component morbidities in a large, community-based cohort of patients with HS, using the database of Clalit Health Services, the largest public healthcare provider in Israel. Methods A cross-sectional study was performed. Metabolic syndrome was defined as the presence of at least three of the following conditions: diabetes, hyperlipidaemia, hypertension and obesity. The association between HS and metabolic syndrome was assessed by a multivariate logistic regression model, adjusting for age, sex, diabetes, hypertension, hyperlipidaemia, obesity and smoking status. Results The study included 3207 patients with HS (general frequency of 007%) diagnosed by a dermatologist in primary-care centres, and 6412 age- and sexmatched control patients without HS. HS was significantly associated with metabolic syndrome [odds ratio (OR) 161, 95% confidence interval (CI) 136–189], diabetes (OR 141, 95% CI 119–166), obesity (OR 171, 95% CI 153–191), hyperlipidaemia (OR 114, 95% CI 102–128) and hypertension (OR 119, 95% CI 103–138). Conclusions We found an association between HS and diabetes, hyperlipidaemia, obesity, hypertension and metabolic syndrome among a large community-based cohort of patients with HS. Clinicians should take into account that patients with HS may have one or more undiagnosed components of metabolic syndrome despite their young age. Thus, appropriate targeted screening is advised.
What’s already known about this topic?
•
Hidradenitis suppurativa (HS) is known to be associated with several comorbidities.
What does this study add?
•
We found an association between HS and diabetes, hyperlipidaemia, obesity, hypertension and metabolic syndrome in a large community-based cohort of patients with HS.
Hidradenitis suppurativa (HS) is a chronic relapsing suppurative skin disease, localized in the apocrine-gland-bearing areas of the body, predominantly in the axillae and groin.1 Clinical 464
British Journal of Dermatology (2015) 173, pp464–470
manifestations include the presence of nodules that may develop into subcutaneous abscesses with the formation of sinus tracts and discharging lesions. The disease course is © 2015 British Association of Dermatologists
Hidradenitis suppurativa and metabolic syndrome, G. Shalom et al. 465
characterized by flares that may involve suppuration of foulsmelling discharge, as well as pain.2–4 These painful, recurring episodes can cause significant quality-of-life impairment.3,4 Most knowledge about the epidemiology of HS is derived from screening studies of limited selected populations, with a relative paucity of population-based studies. HS occurs more frequently in women than in men, with an estimated ratio of 3 : 1, particularly in the genitofemoral area.1,5–7 Frequency estimates vary considerably, maybe in part because of the hidden nature of HS (mostly involving areas covered by clothing, thus the disease might often be ignored or misdiagnosed).6,8,9 A 1% frequency of HS was estimated recently in two population-based studies from Europe;5,10 however, other estimates based on the same population were as high as 4%.10 One of the recently published U.S.A.-specific estimates comes from the PharMetrics Integrated Database and was based on healthcare claims for HS. The study suggests a frequency of 0053% clinically detected HS and is one of the first studies to overcome the issue of sample size encountered by previous epidemiological studies of HS.11 Several associations between HS and other morbidities have been reported in the last decade. These include inflammatory bowel disease, depression, hormonal disorders, psoriasis and arthritis.12–21 In several studies it has been suggested that HS may be associated with metabolic syndrome or some of its components.12,22–30 Recently, a study from Massachusetts General Hospital reported significantly higher rates of comorbidities among patients with HS than in controls, including dyslipidaemia, polycystic ovarian syndrome, obesity, hypertension, diabetes, thyroid disease, alcohol dependence and lymphoma, in an unadjusted analysis.25 Epidemiological data regarding HS have many limitations. The data are often based on anecdotal reports or are based on studies that are limited by their small sample size, by poorly controlled investigations or by demographic characteristics that are not easily generalizable. The current literature suffers substantial selection bias as it is based on self-reports or highly selected specialist-treated samples, with few population-based studies. Moreover, recent reports regarding HS and comorbidities that have tried to overcome these limitations rely mainly on secondary- and tertiary-care centres, and therefore may not represent the vast majority of patients presenting in the community setting. Early detection of comorbidities may prevent long-term complications; therefore, further large populationbased studies are clearly warranted. The purpose of our study was to investigate the association between HS and metabolic syndrome and its components – diabetes, hyperlipidaemia, obesity and hypertension – in a large population-based study, utilizing the database of Clalit Health Services (CHS), which is based mainly on patients from the community setting.
Materials and methods Clalit Health Services is the largest health maintenance organization in Israel, serving a population of approximately 42 © 2015 British Association of Dermatologists
million, covering 52% of the population. The CHS-insured population is representative of the general Israeli population. The CHS database is a comprehensive computerized database with continuous real-time input from pharmaceutical, medical and administrative operating systems. The database was started in 1998 in order to improve the provided healthcare services, and it holds accurate information on each medication prescribed and claimed by the patient, including its date, dose and mode of administration. The National Program for Quality Indicators in Community Healthcare in Israel (QICH) has brought about relatively high screening ratios for chronic morbidities and chronic conditions, with high measurement ratios and negligible missing data.31 Diagnoses of chronic diseases such as diabetes, hypertension and hyperlipidaemia are validated by the CHS register using a systematic methodology. The validation process is based on repeated appraisal of diagnoses made by CHS physicians, registered prescriptions, pharmacy claims, laboratory tests (e.g. glucose blood level) and auxiliary tests (e.g. blood pressure values on routine screening and follow-up tests), for each patient. Comparison between diagnoses, drug administration and laboratory and auxiliary tests from various sources is performed regularly. If a diagnosis is not consistent through all sources, it is removed from the register. This validation process ensures an accuracy of 85–90% in the CHS Chronic Disease Register.31,32 All of the exposure variables/confounders defined in the current study are listed as targets in the QICH. Our study was designed as a cross-sectional study of patients with HS and age- and sex-matched control patients. The HS group consisted of patients with at least one documented diagnosis of HS in their medical records between 1998 and 2013, registered by a CHS dermatologist in a primary-care centre. The reference group consisted of age- and sex-matched patients without HS, drawn from the general population of CHS enrollees. We extracted information for all patients with HS diagnosed from January 1998 until the end of November 2013. The observation period for the entire study population extended until the end of December 2013. The extracted information includes the age, sex and smoking status of the subject, and presence of concomitant diabetes, hypertension, hyperlipidaemia or obesity. Diagnosis of metabolic syndrome is not available as an independent code in the CHS database. Hence, in this study metabolic syndrome was a dependent variable, defined as the presence of at least three of the following: diabetes, hyperlipidaemia, hypertension and obesity (body mass index > 30 kg m 2).33 In CHS, diabetes is diagnosed according to one of the following criteria: (i) two random tests of blood glucose > 200 mg dL 1; (ii) one random test of blood glucose > 200 mg dL 1 with evident target organ damage; and (iii) two fasting glucose tests > 125 mg dL 1. Diagnosis of hypertension in CHS is established in patients with several measurements of systolic blood pressure > 140 mmHg or diastolic > 90 mmHg during at least a 1-month period of follow-up. Socioeconomic status was defined according to the average socioeconomic status of the clinics where each British Journal of Dermatology (2015) 173, pp464–470
466 Hidradenitis suppurativa and metabolic syndrome, G. Shalom et al.
enrollee was registered. Clinics were classified as 1 (low), 2 (intermediate) or 3 (high). Follow-up data covering all diagnoses for each patient were extracted from the database for further analysis. Frequency rates were calculated for each group. Additionally, a manual search with focus on metabolic syndrome-related conditions was performed in patients’ files that are available at the Soroka University Medical Center, in which the first author’s (G.S.) dermatology department is located, in order to evaluate the temporal relationships between HS and the metabolic syndrome components. Statistical analysis Demographic data, general data and patient characteristics at baseline were collected. Continuous variables were compared using ANOVA or t-tests, and dichotomous variables were compared by Pearson’s v2-test or Fisher’s exact test, as appropriate. The proportion of each predefined morbidity was calculated in both study groups. A univariate comparative analysis was performed for the entire study population and for the young patient population (age ≤ 50 years). In order to test the association between metabolic syndrome and HS without the effect of obesity, we performed a stratified analysis in obese and nonobese patients with HS. For the stratified analysis, we adopted the World Health Organization (WHO) 1999 criteria, which include the presence of diabetes mellitus, impaired glucose tolerance, impaired fasting glucose or insulin resistance, together with two of the following: elevated blood pressure, dyslipidaemia, obesity and microalbuminuria (urinary albumin excretion ≥ 20 lg min 1 or albumin-tocreatinine ratio ≥ 30 mg g 1).34 The association between HS and each of the investigated morbidities was further tested by a multivariate logistic regression model, adjusted for age, sex, diabetes, hypertension, hyperlipidaemia, obesity and smoking status. Statistical analysis was performed using the SPSS package, version 20 (IBM,
Armonk, NY, U.S.A.). A P-value < 005 was considered statistically significant. The study was approved by the institutional review board of the Meir Medical Center, Kfar Saba, Israel (approval number 0003-12-com), and was exempted from the need to sign an informed consent form. The Declaration of Helsinki protocols were followed.
Results The study included 3207 patients with HS and 6412 control patients. The general frequency of HS in the CHS database was 007%. The baseline characteristics of the study population are presented in Table 1. Female sex was predominant in our study population, with 1975 female patients (616%) with HS and 3948 (616%) without HS. The age distribution of the study population and the proportion of morbidities in each specific age group are presented in Table 2. Most of our study subjects were aged < 50 years (743%). Interestingly, greater differences in the proportion of patients with morbidities were observed at younger ages between patients with HS and controls (Table 2). The univariate comparative analysis for the association between HS and the investigated morbidities is presented in Table 3, for the entire study population and for the youngerage patient population. In this univariate analysis significant associations between diabetes, hyperlipidaemia, obesity and metabolic syndrome and HS were observed in the entire study population. A trend for an association between HS and hypertension was detected, but it did not reach statistical significance [odds ratio (OR) 113, 95% confidence interval (CI) 099–128; P = 005]. In the younger patient population (age ≤ 50 years), the univariate analysis revealed a trend towards a greater association between HS and diabetes, hyperlipidaemia, obesity, hypertension and metabolic syndrome, with higher ORs and stronger statistical significance compared
Table 1 Baseline characteristics of the study population
Total number Female Male Socioeconomic statusa Low Intermediate High Ethnicity Arabic Orthodox Jew General Age (years), mean SD Primary physician visits, mean SDb Smoking
Patients with HS
Reference group
P-value
3207 1975 (616) 1232 (384)
6412 3948 (616) 2464 (384)
NS
1156 (376) 1311 (426) 609 (198)
2692 (428) 2290 (364) 1312 (208)
< 001 < 001 < 001
478 (149) 78 (24) 2651 (827) 396 155 77 69 1520 (474)
1412 (220) 209 (33) 4791 (747) 396 155 54 49 1923 (299)
< 001 < 001 < 001 NS < 001 < 001
Values are n (%) unless stated otherwise. NS, not significant. aSubtotals do not add up to the total value due to missing data. bPer year, per patient.
British Journal of Dermatology (2015) 173, pp464–470
© 2015 British Association of Dermatologists
© 2015 British Association of Dermatologists 452 (7)
226 (7)
3207
6412
10 (02)
964 (15)
482 (15)
5 (02)
1016 (16)
508 (16)
74 (12)
1532 (24)
766 (24)
37 (12)
1728 (27)
864 (27)
142 (22)
484 (75)
242 (75)
71 (22)
10 (02)
No HS
6 (02)
HS
Total
0 – 2 (08) NS 12 (13) NS 29 (38) 0011 63 (12) 0013 120 (25) < 001 86 (38) < 001 30 (42) NS 15 (41) NS 1 (20) NS 358 (11) < 001
HS
Diabetes
Values are n (%). HS, hidradenitis suppurativa; NS, not significant.
1–10 P-value 11–20 P-value 21–30 P-value 31–40 P-value 41–50 P-value 51–60 P-value 61–70 P-value 71–80 P-value 81–90 P-value 91–100 P-value Total P-value
Age group (years)
471 (74)
2 (20)
25 (34)
45 (32)
110 (24)
158 (16)
85 (84)
30 (19)
14 (08)
2 (04)
0
No HS
Table 2 The proportions of morbidities in each age group among the study population
0 – 4 (17) NS 68 (79) 0033 147 (19) < 001 185 (36) 0037 291 (60) NS 170 (75) NS 56 (79) NS 29 (78) NS 2 (40) NS 952 (30) < 001
HS
1703 (27)
6 (60)
60 (81)
112 (86)
329 (73)
553 (57)
315 (31)
214 (14)
98 (57)
6 (12)
0
No HS
Hyperlipidaemia
Obesity
1 (17) NS 48 (20) 0005 124 (14) < 001 123 (17) < 001 145 (29) < 001 171 (35) < 001 74 (33) 0086 19 (27) NS 6 (16) NS 0 NS 711 (22) < 001
HS
903 (14)
3 (30)
16 (22)
35 (25)
118 (26)
252 (26)
188 (19)
136 (89)
98 (57)
57 (12)
0
No HS
Hypertension
0 – 1 (04) NS 14 (16) 0035 23 (3) NS 75 (15) 0047 154 (32) NS 114 (50) NS 44 (62) NS 27 (73) NS 3 (60) NS 455 (14) 0060
HS
820 (13)
8 (80)
57 (77)
89 (63)
220 (49)
277 (29)
113 (11)
44 (28)
12 (07)
0
0
No HS
0 – 0 – 9 (1) < 001 17 (22) 0022 54 (11) < 001 116 (24) 0012 90 (40) < 001 30 (42) NS 18 (49) NS 0 NS 334 (10) < 001
453 (7)
2 (20)
24 (32)
51 (36)
117 (26)
177 (18)
65 (64)
15 (09)
2 (01)
0
0
No HS
Metabolic syndrome HS
Hidradenitis suppurativa and metabolic syndrome, G. Shalom et al. 467
British Journal of Dermatology (2015) 173, pp464–470
468 Hidradenitis suppurativa and metabolic syndrome, G. Shalom et al. Table 3 The association between hidradenitis suppurativa (HS) and metabolic syndrome and its components by univariate analysis Component
Patients with HSa
Reference groupa
All patients Diabetes Obesity Hyperlipidaemia Hypertension Metabolic syndrome Young patients (age ≤ 50 years) Diabetes Obesity Hyperlipidaemia Hypertension Metabolic syndrome
n = 3207 358 (112) 711 (222) 952 (297) 455 (142) 334 (104) n = 2386 106 (44) 441 (185) 404 (169) 113 (47) 80 (34)
n = 6412 471 (74) 903 (141) 1703 (26. 6) 820 (128) 453 (71) n = 4770 131 (28) 479 (10) 633 (133) 169 (35) 82 (17)
OR
95% CI
P-value
159 174 117 113 153
137–183 156–194 106–128 099–128 132–177
< 0001 < 0001 0001 005 < 0001
165 203 133 135 198
127–214 177–234 116–153 106–173 145–271
< 0001 < 0001 < 0001 001 < 0001
OR, odds ratio; CI, confidence interval. aValues are n (%).
with the entire study population (Table 3). Using the WHO definition, HS and metabolic syndrome were also significantly associated, in the entire study population (OR 172, 95% CI 145–21; P < 001), among the nonobese patients (OR 145, 95% CI 11–19; P < 001) and among the obese patients (OR 16, 95% CI 12–21; P < 001). Consistently, the multivariate analysis revealed a significant association between HS and diabetes, hyperlipidaemia, obesity, hypertension and metabolic syndrome. Logistic regression results between each categorical variable and HS, adjusted for age, sex, diabetes, hypertension, hyperlipidaemia, obesity and smoking status are presented in Table 4. For the 3207 patients with HS in our study, 423 complete patient files were available at the Soroka University Medical Center. These patients presented to the emergency room or were admitted during 1998–2013, and had an average age of 41 years at the time of HS diagnosis. Thirty of these presented with HS exacerbation during that time period. Among these 423 patients with HS, 135 had at least one component of metabolic syndrome and 12 patients fulfilled the full criteria for metabolic syndrome. In this subgroup of patients, the majority of the metabolic syndrome components, with the exception of obesity, occurred after HS, with a latency period of 38–95 years (Table 5). Table 4 Logistic regression results for the association between hidradenitis suppurativa and metabolic syndrome and its components Component
OR
95% CI
P-value
Diabetesa Obesityb Hyperlipidaemiac Hypertensionc Metabolic syndrome
141 171 114 119 161
119–166 153–191 102–128 103–138 136–189
< 0001 < 0001 002 001 < 0001
OR, odds ratio; CI, confidence interval. aOR adjusted for obesity, age, sex and smoking status. bOR adjusted for age, sex and smoking status. cOR adjusted for obesity, diabetes, age, sex and smoking status.
British Journal of Dermatology (2015) 173, pp464–470
Discussion Our observation is based on a large, community-based cohort of 3207 patients with HS. This shows a prevalence of 007% among the CHS-insured population, in agreement with recent reports.11,25 Multivariate analysis demonstrated an increased frequency of metabolic syndrome among patients with HS compared with age- and sex-matched control patients. Accordingly, the frequencies of diabetes, hyperlipidaemia, obesity and hypertension were significantly elevated in patients with HS compared with control patients. The control group in the present study reflects the general population, and its morbidity rates are in line with Israeli national estimates.32 The univariate and multivariate analysis, together with the stratified analysis (based on the WHO criteria), suggest that the association between HS and metabolic syndrome that we observed is true and does not stem from the presence of other confounders such as obesity. Our observations are consistent with recent studies based on secondary- and tertiary-centres regarding HS and metabolic syndrome.25,26 However, this study adds essential data as is relies mainly on the sector of patients stemming from the community and primary-care centres, which accounted for the vast majority of patients. Accordingly, our observations augment these recent studies and overcome a potential selection bias of tertiary-centre-based investigations. Metabolic syndrome, diabetes, obesity, hypertension and hyperlipidaemia increase the risk for ischaemic heart disease, myocardial infarction and stroke, leading to reduced life quality and expectancy. Despite an overall reduction in the death rate from cardiovascular disease in Western countries over the last few decades, mortality has declined mostly in men, but not in women.35 Recognizing risk factors for long-term cardiovascular morbidities among young women may facilitate early prevention and, therefore, is clearly mandatory. Based on our findings, patients with HS are more likely to be young and female, and have a significantly higher frequency of metabolic syndrome or one or more of its components, all risk factors for cardiovascular morbidity. We should expect to see an effect of female sex and age on comorbidities, yet the associations © 2015 British Association of Dermatologists
Hidradenitis suppurativa and metabolic syndrome, G. Shalom et al. 469 Table 5 Temporal relationship between hidradenitis suppurativa (HS) and the metabolic syndrome components among 135 patients from the Soroka University Medical Center Metabolic syndrome component
Patients with HS before diagnosis, n (%)
Diabetes (n = 64) Hypertension (n = 10) Dyslipidaemia (n = 30) Obesity (n = 61)
51 10 26 15
(80) (100) (87) (25)
between HS and metabolic syndrome and its components were still significant after adjusting for these factors. Chronic inflammatory diseases such as psoriasis are known to be risk factors for metabolic syndrome.36,37 The underlying mechanism in chronic inflammatory conditions leading to the induction of metabolic disturbances might be either a primary or secondary pathological process, thus the major driver of the metabolic alterations is chronic inflammation inducing insulin resistance. Tumour necrosis factor-a inhibitors are considered an effective treatment for psoriasis, and have recently been introduced as a potential treatment for HS.38 Thus, the thought of possible common denominators in the pathogenesis of both psoriasis and HS has been raised, leading to the question of a possible association between HS and metabolic syndrome, as previously reported for psoriasis by our group.36,37 There seem to be similarities and differences between psoriasis and HS in this regard. The frequencies of diabetes, hypertension, hyperlipidaemia, obesity and metabolic syndrome in patients with HS appear to be quite similar to those reported by our group in patients with psoriasis, from the CHS database.36,37 On the other hand, in patients with psoriasis but not HS, there is an association between disease duration and the appearance of metabolic syndrome. Furthermore, metabolic syndrome preferentially mostly affects patients with psoriasis at a higher age,39 while in patients with HS, associated metabolic syndrome morbidities were more prevalent in the younger age groups. Based on clinical trials data, higher levels of obesity are more likely in patients with HS than in patients with psoriasis. The implication in terms of cardiovascular morbidity for patients with HS is possibly even greater than for patients with psoriasis, in whom increased mortality from cardiovascular diseases has been documented.40 These observations indicate that the metabolic alterations observed in patients with HS might be the primary process rather than a secondary inflammatory process. Our investigation is based on a computerized database with a large sample size from the community setting. Nevertheless, the study suffers some limitations. The diagnosis of HS was based on digitally transmitted data. Therefore, undiagnosed cases of HS may exist and lead to underestimation of the true frequency of HS. A second limitation is the inability to determine the temporal relationship between the diagnoses. The manual search among the 423 patient files available at the © 2015 British Association of Dermatologists
Mean latency period from HS to diagnosis (years) 55 95 38 5
Mean age at HS diagnosis (years) 446 565 536 37
Soroka University Medical Center provided some restricted data, which may suggest that HS appears prior to metabolic syndrome (except for obesity, which was present before HS), and the authors suggest that HS might be considered a risk factor for metabolic syndrome. Nevertheless, as this subgroup analysis is based on a limited number of patients, all presenting to tertiary hospital emergency rooms for any reason, a firm conclusion cannot be drawn as it may be exposed to certain biases, such as selection bias. Alternatively, both HS and the other components of metabolic syndrome may arise as a consequence of enhanced low-grade inflammation associated with obesity. We cannot definitely ascertain whether HS appeared first and led to the diagnosis of metabolic syndrome or possibly contributed to it via a chronic inflammatory pathway, or whether HS appeared in patients with poorly controlled diabetes and/or metabolic syndrome in the entire study population. Kim et al.41 reported a positive predictive value (PPV) of 819% for only two diagnostic codes in a 5-year period, and therefore some would consider our case definition (one code) as a potential limitation. Notably, the codes in the above-mentioned study were diagnosed by any physician, and the option of defining a case by one diagnostic code by a dermatologist was not investigated. Moreover, in that study it was noticed that if the HS code was entered by a dermatologist, the PPV was superior to the PPV of a code entered by a nondermatologist. In our investigation, the use of codes entered solely by dermatologists, together with the validation process embedded in the CHS register (which promises a relatively high degree of accuracy), ensures data validity, in spite of the fact that only one code was used to define cases of HS. This argument can be further supported by the consistency between our findings (general frequency of 007%) and other recently published observational studies from the U.S.A. (general frequency of 005–008%).11,25 As the patients with HS were seen more frequently by their treating physician, detection bias is another potential bias in our study. However, the authors feel that this point is controversial, as higher morbidity rates may prompt more frequent visits, and therefore this observation is the outcome and not the cause of our observation. Nevertheless, in spite of these limitations, the present study reveals important data regarding the association between HS and metabolic morbidities. In conclusion, in our study we observed an association between HS and both metabolic syndrome and its components (diabetes, hyperlipidaemia, obesity and hypertension). Early British Journal of Dermatology (2015) 173, pp464–470
470 Hidradenitis suppurativa and metabolic syndrome, G. Shalom et al.
detection and treatment of these morbidities in patients with HS may prevent late complications, particularly cardiovascular disease. Clinicians should take into account that patients with HS may have undiagnosed metabolic syndrome or its associated morbidities, and suggest appropriate screening and treatment.
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