Abstract
Introduction
Hidradenitis suppurativa (HS) is a chronic, inflammatory skin condition characterized by painful, recurrent abscesses and tunnels under the skin. It is influenced by a complex interplay of genetic, environmental, and metabolic factors. Smoking, diabetes mellitus, obesity, and other metabolic disorders have been identified as risk factors for HS, potentially exacerbating the severity and progression of the condition. Given the higher prevalence of HS in individuals with type 2 diabetes mellitus (T2DM), understanding modifiable risk factors such as smoking is crucial for improving patient outcomes.
Objectives
To investigate the association between changes in smoking intensity and the risk of HS in patients with T2DM, with the goal of elucidating how smoking contributes to the development or worsening of HS in this high-risk population.
Methods
This retrospective cohort study analyzed data from the Korean National Health Insurance Service, comprising 1,705,427 participants. The study examined smoking status, changes in smoking intensity, and the incidence of HS in individuals with T2DM, adjusting for potential confounders such as age, gender, and comorbid conditions.
Results
The study found a 23.6% increased risk of HS in individuals who continued smoking compared to nonsmokers (adjusted hazard ratio (aHR), 1.236; 95% confidence interval (CI), 1.075–1.421). Additionally, participants with increased cigarette consumption had a 28.5% higher risk of HS compared to nonsmokers (aHR, 1.285; 95% CI, 1.048–1.577). However, no significant differences were observed in the association between changes in smoking intensity and the risk of HS when stratified by BMI.
Conclusions
This study highlights the association between smoking and the increased risk of HS in individuals with T2DM, underscoring the importance of smoking cessation as a potential strategy for mitigating HS risk in at-risk DM populations. Additional research is needed to further explore the mechanisms by which smoking exacerbates HS in T2DM and to identify effective interventions for this group.
Introduction
Hidradenitis suppurativa (HS), also known as acne inversa, is a persistent inflammatory skin condition characterized by recurrent painful lesions [1,2]. These lesions are deep-seated and inflamed, primarily occurring in regions of the body with apocrine glands, such as the axillae, inguinal, and anogenital areas. The annual incidence of HS falls between 4 to 10 cases per 100,000 people during the period from 1968 and 2008 [3], with a prevalence ranging from 0.2% to 4% [1,4]. The typical age of onset is approximately 23 years old [5]. The condition affects females more frequently, with a male-to-female ratio ranging from 1:2.7 to 1:3.3 in European and North American populations [5,6]. Conversely, Asian populations exhibit a reverse pattern, with a female-to-male ratio of approximately 1:2 to 1:5.6 [7–11].
While the exact cause of HS remains unknown, it is recognized as a multifactorial condition influenced by genetic, metabolic, and environmental factors. HS is more commonly observed in females and often improves during pregnancy, with premenstrual flares and early onset before menopause suggesting a potential hormonal or metabolic origin [12]. Additionally, HS has been associated with smoking, increased body mass index (BMI), metabolic syndrome, inflammatory bowel disease, and polycystic ovary syndrome [13].
Type 2 diabetes mellitus (T2DM) is a chronic disease that develops when the body cannot effectively produce and use insulin [14]. Several studies have reported a notable association between HS and DM [15–18]. However, the precise occurrence of DM in HS patients remains uncertain, with reported rates ranging from approximately 4% to 33% [12,19,20]. Two meta-analyses found a 1.69–3.00 fold increase in the odds of DM in HS populations [12,21,22]. Despite these findings, a causal relationship between DM and HS has yet to be established [12,23]. It has been hypothesized that HS might contribute to DM through chronic systemic inflammation leading to elevated levels of tumor necrosis factor-alpha (TNF-α). Conversely, insulin resistance in DM may predispose individuals to HS [23].
Smoking, in particular, has been strongly linked to HS, with a large US-based retrospective cohort study reporting a twofold increase in HS incidence among smokers [24], and a meta-analysis indicating that HS patients are four times more likely to be smokers than the general population [25]. Smoking rates of 70–90% have been observed in HS populations [26,27], and surveys of disease severity have shown a positive correlation between smoking and the severity of HS [28]. However, it remains unclear whether smoking is merely a risk factor or directly contributes to the pathogenesis of HS [29]. Smoking cessation appears to reduce the risk of adverse outcomes in HS patients [30], but limited research has explored how changes in smoking intensity influence the risk of HS, particularly in individuals with comorbidities such as DM. DM and smoking are known risk factors for HS, but their combined impact on HS development or progression remains poorly understood. This study aims to explore how changes in smoking behavior influence HS risk in DM patients, using a nationally representative Korean claims database, to identify modifiable risk factors in this high-risk population.
Materials and methods
Study design and population
This retrospective cohort study utilized population-level data from the Korean National Health Insurance Service (KNHIS), a compulsory universal insurance system providing coverage for all individuals in Korea. The standardized protocol for data acquisition by the KNHIS has been previously described [31].
The KNHIS database includes demographic information linked to healthcare claims, data from the national health checkup program, and self-reported, health-related questionnaires. It also contains measurements of height, weight, and blood pressure, as well as results from laboratory tests, including blood and urine analyses.
This study included participants with complete smoking status data from their initial health checkup conducted between 2009 and 2012, along with a second health checkup within a two-year interval. Specifically, the presence of diabetes mellitus was determined for all participants. The cohort consisted of patients who had been diagnosed previously with T2DM and those who were newly diagnosed during health checkups. Initially, 2,746,078 patients with T2DM were included. Subjects were excluded from the study if their records indicated any of the following: 1) the second health checkup not been conducted (n = 915,219), 2) under the age of 20 (n = 4), 3) a diagnosis of HS before the first health checkup (n = 2,280), 4) the development of incident HS or death during the one-year lag period (n = 11,246), or 5) having missing data for at least one variable (n = 111,902). In total, 1,705,427 participants were included in the main analysis (Fig 1). The baseline for the study was established at the date of the second health checkup, and eligible participants were followed until the date of death or the date of HS diagnosis or December 31, 2018.
Fig 1. A flow diagram of study population selection.
This study received approval from the Institutional Review Board of Catholic Medical Center (IRB No. KC23ZASI0891) and the requirement for informed consent was waived since the study relied on anonymized retrospective data. We accessed the data for research purposes on December 15, 2023.
Definition of disease
DM was identified based on previously established criteria [32–35]: (1) at least one annual claim for the prescription of anti-diabetic drugs categorized under ICD-10 codes E11–14; or alternatively, (2) fasting plasma glucose level equal to or greater than 126 mg/dL during health examinations. Anti-diabetic medications included insulin, metformin, sulfonylureas, meglitinides, dipeptidyl peptidase 4 (DPP-4) inhibitors, thiazolidinediones, and α-glucosidase inhibitors. HS was identified using the diagnostic ICD-10 code of L73.2. The incidence rate of HS was calculated per 100,000 person-years.
Definition of a change in smoking intensity
Smoking status was determined through self-reported surveys conducted during the initial health screening assessments and again after 2 years (subsequent health assessment). Data on smoking status and changes in smoking intensity were collected. Participants who answered that they had smoked at least 100 cigarettes in their lifetime were asked about their current smoking status according to the World Health Organization’s definition [36]. Among the current smokers, additional information was gathered regarding the duration of smoking and the average number of cigarettes smoked per day. Based on the cigarette smoking status reported during the initial health examination in 2009–2012, participants were grouped into never smokers, former smokers, and current smokers. In this study, changes in cigarette smoking intensity were assessed as the relative change in daily cigarette consumption. We classified participants into seven categories based on the relative change in smoking amount between the initial (2009–2012) and follow-up (after 2 years) health examinations. Participants were grouped into seven categories, defined based on prior research [37–39]. Quitters were individuals who completely stopped smoking, meaning they were smokers at the initial checkup but had quit by the follow up checkup. 50% Reducers were participants who reduced their daily cigarette consumption by 50% or more compared with their initial level. 20% Reducers were individuals who decreased their daily cigarette consumption by 20% to 50% compared with their previous level. Sustainers were those who maintained their daily cigarette consumption or either increased or decreased consumption by less than 20%. Increasers were participants who increased their daily cigarette consumption by 20% or more compared with their previous amount. Never smokers were participants who responded as never smoking at both biennial health examinations. Starters were those who reported being nonsmokers at the first examination but identified as current smokers at the second examination (Fig 2).
Fig 2. The classification based on changes in smoking intensity at initial and follow-up assessments.
Demographic factors
We collected demographic and health-related information from all participants, including age, smoking habits, alcohol consumption, regular physical activity, height, weight, blood pressure, total serum cholesterol, glucose levels, and medical history related to diabetes. Blood samples for serum glucose and lipid level measurements were obtained after an overnight fast, following quality control procedures in accordance with the Korean Association of External Quality Assessment guidelines [40]. Weight measurements were conducted while participants wore light clothing. Height was measured without shoes, with participants maintaining proper posture (heels, buttocks, shoulders, and head aligned), and recorded in meters to two decimal places. BMI was calculated using the formula: weight (kg)/ height2 (m2) and classified based on the World Health Organization’s Asia-Pacific criteria, with a BMI of 25 kg/m2 or greater categorized as obesity [41,42]. Systolic and diastolic blood pressure were measured in a seated position after a minimum of 5 minutes of rest. Regular physical activity was defined as engaging in ≥20 minutes of vigorous-intensity physical activity on ≥3 days per week or ≥30 minutes of moderate-intensity physical activity on ≥5 days per week [43,44]. Alcohol consumption data, including the frequency of drinking per week and the amount consumed per occasion, were converted into daily alcohol intake using previously established methods [45]. Levels of alcohol consumption were categorized as nondrinking, mild drinking (<30 g per day), and heavy drinking (≥30 g per day).
Statistical analysis
All statistical analyses were performed using SAS software (version 9.4, SAS Institute, Cary, NC). The cumulative incidence of HS between groups was compared using the Kaplan-Meier method. Adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for HS risk were estimated using multivariable Cox proportional-hazards models.
Covariates included age, sex, BMI, income, alcohol consumption (categorized as non-drinking, mild drinking, or heavy drinking), regular exercise (yes or no), medical history of hypertension, dyslipidemia, chronic kidney disease (yes or no), fasting glucose levels, duration of diabetes, and the use of oral hypoglycemic agents or insulin.
Additionally, HRs for incident HS associated with changes in smoking behavior were further adjusted for BMI status, categorized as either <25 kg/m2 or ≥25 kg/m2. All statistical tests were two-tailed, and a p-value <0.05 was considered statistically significant.
Results
Baseline characteristics of the study population
The cohort was composed of 1,705,427 participants (581,618 males [34.1%], 1,123,809 females [65.9%]; mean [SD] age, 59.03 [11.76] years). Table 1 shows the baseline characteristics during the 2009–2012 health examination according to relative changes in smoking intensity (never smokers, starters, quitters, 50% reducers, 20% reducers, sustainers, and increasers). At the initial health examination, 56.2% of participants were nonsmokers, 19.3% were former smokers, and 24.6% were current smokers. Among smokers, 9.8% were mild smokers, 34.1% were moderate smokers, and 56.1% were heavy smokers. At the second health examination, 4.7% of participants had quit smoking, and 5.2% had reduced their smoking (2.0% in the 50% reducer group and 3.2% in the 20% reducer group). Meanwhile, 3.9% of participants increased the number of cigarettes they smoked during the 2-year interval. Proportions of starter and sustainer groups were estimated as 3.3 (%) and 10.8 (%), respectively.
Table 1. Baseline characteristics of the study population with DM according to changes in smoking intensity in 2009–2012.
| Never | Starter | Quitter | 50% Reducer | 20% Reducer | Sustainer | Increaser | P value | |
|---|---|---|---|---|---|---|---|---|
| (n = 1229850) | (n = 56633) | (n = 79829) | (n = 33327) | (n = 54102) | (n = 184565) | (n = 67121) | ||
| Age, years | 61.17 ± 11.2 | 54.99 ± 11.22 | 55.97 ± 11.44 | 54.93 ± 11.81 | 52.16 ± 11.25 | 52.5 ± 10.97 | 52.36 ± 11.5 | <.0001 |
| Sex, male | 581618 (47.29) | 51392 (90.75) | 72594 (90.94) | 31157 (93.49) | 52093 (96.29) | 178308 (96.61) | 63456 (94.54) | <.0001 |
| Income, Lowest Quartile 1 | 245419 (19.96) | 11009 (19.44) | 15231 (19.08) | 7223 (21.67) | 10057 (18.59) | 34384 (18.63) | 13209 (19.68) | <.0001 |
| Alcohol consumption | <.0001 | |||||||
| None | 841638 (68.43) | 18069 (31.91) | 36417 (45.62) | 11099 (33.3) | 14750 (27.26) | 49286 (26.7) | 18706 (27.87) | |
| Mild | 323478 (26.3) | 29721 (52.48) | 34229 (42.88) | 17984 (53.96) | 30008 (55.47) | 98986 (53.63) | 34146 (50.87) | |
| Heavy | 64734 (5.26) | 8843 (15.61) | 9183 (11.5) | 4244 (12.73) | 9344 (17.27) | 36293 (19.66) | 14269 (21.26) | |
| Regular exercise | 292717 (23.8) | 12782 (22.57) | 18990 (23.79) | 7374 (22.13) | 11191 (20.69) | 35977 (19.49) | 12779 (19.04) | <.0001 |
| Comorbidities | ||||||||
| Hypertension | 753815 (61.29) | 29290 (51.72) | 42745 (53.55) | 17114 (51.35) | 25604 (47.33) | 87726 (47.53) | 31769 (47.33) | <.0001 |
| Dyslipidemia | 587899 (47.8) | 23018 (40.64) | 34843 (43.65) | 13400 (40.21) | 20921 (38.67) | 70869 (38.4) | 25729 (38.33) | <.0001 |
| CKD | 147074 (11.96) | 3901 (6.89) | 6568 (8.23) | 2377 (7.13) | 2884 (5.33) | 9244 (5.01) | 3514 (5.24) | <.0001 |
| DM Duration, ≥ 5 years | 509078 (41.39) | 18452 (32.58) | 26601 (33.32) | 10375 (31.13) | 14951 (27.63) | 51484 (27.89) | 18907 (28.17) | <.0001 |
| OHA, ≥ 3 | 218704 (17.78) | 9424 (16.64) | 14507 (18.17) | 5672 (17.02) | 8150 (15.06) | 27809 (15.07) | 10423 (15.53) | <.0001 |
| Insulin | 108611 (8.83) | 4021 (7.1) | 7339 (9.19) | 2559 (7.68) | 3353 (6.2) | 10885 (5.9) | 4286 (6.39) | <.0001 |
| BMI, kg/m2, mean±SD | 24.96 ± 3.29 | 24.75 ± 3.23 | 24.89 ± 3.21 | 24.6 ± 3.36 | 24.75 ± 3.36 | 24.69 ± 3.3 | 24.75 ± 3.4 | <.0001 |
| Waist circumference, cm | 84.63 ± 8.68 | 85.93 ± 8.16 | 86.4 ± 8.13 | 85.89 ± 8.34 | 85.98 ± 8.3 | 85.92 ± 8.21 | 85.97 ± 8.45 | <.0001 |
| SBP, mmHg | 128.34 ± 15.3 | 126.42 ± 14.7 | 127.13 ± 14.72 | 126.87 ± 15.03 | 126.67 ± 14.55 | 126.66 ± 14.56 | 126.64 ± 14.8 | <.0001 |
| DBP, mmHg | 77.82 ± 9.85 | 78.21 ± 10 | 78.4 ± 9.92 | 78.42 ± 10.02 | 78.84 ± 9.94 | 78.78 ± 9.92 | 78.76 ± 10.03 | <.0001 |
| Laboratory findings | ||||||||
| Fasting glucose, mg/dL | 131.2 ± 42.45 | 135.4 ± 49.37 | 136.47 ± 50.02 | 136.46 ± 51.52 | 135.8 ± 50.4 | 136.54 ± 51.03 | 138.07 ± 53.4 | <.0001 |
| Total Cholesterol, mg/dL | 187.84 ± 40.49 | 188.76 ± 40.6 | 189.1 ± 41.75 | 189.47 ± 42.22 | 191.52 ± 41.17 | 192.08 ± 41.24 | 191.84 ± 41.33 | <.0001 |
| HDL -C, mg/dL | 51.53 ± 14.9 | 49.68 ± 16.03 | 49.86 ± 16.2 | 49.29 ± 15.16 | 49.28 ± 14.52 | 49.48 ± 15.79 | 49.66 ± 16 | <.0001 |
| LDL -C, mg/dL | 107.1 ± 36.58 | 105.21 ± 37.96 | 105.64 ± 37.35 | 104.87 ± 38.61 | 106.13 ± 38.23 | 106.55 ± 38.69 | 105.96 ± 37.85 | <.0001 |
| eGFR, mL/min/1.73 m2 | 85.22 ± 38.14 | 89.68 ± 41.92 | 88.84 ± 47.29 | 90.63 ± 43.87 | 92.1 ± 46.97 | 92.29 ± 48.01 | 92.65 ± 48.64 | <.0001 |
| Smoking amount (1st Exam) | <.0001 | |||||||
| mild: < 10 cigarettes/d | – | 28456(50.25) | 13951(17.48) | 1335(4.01) | 2969(5.49) | 6774(3.67) | 14670(21.86) | |
| moderate: 10–19 cigarettes/d | – | 10695(18.88) | 30366(38.04) | 6642(19.93) | 17623(32.57) | 57690(31.26) | 36979(55.09) | |
| heavy: ≥ 20 cigarettes/d | – | 17482(30.87) | 35512(44.49) | 25350(76.06) | 33510(61.94) | 120101(65.07) | 15472(23.05) | |
| Smoking duration (1st Exam) | <.0001 | |||||||
| <5 | – | 26530(46.85) | 3610(4.52) | 687(2.06) | 837(1.55) | 2713(1.47) | 2233(3.33) | |
| 5—9 | – | 1633(2.88) | 3744(4.69) | 1182(3.55) | 1647(3.04) | 5114(2.77) | 3047(4.54) | |
| 10—19 | – | 7326(12.94) | 15426(19.32) | 6237(18.71) | 11440(21.15) | 37236(20.18) | 16150(24.06) | |
| 20 | – | 21144(37.34) | 57049(71.46) | 25221(75.68) | 40178(74.26) | 139502(75.58) | 45691(68.07) | |
| Pack-years (1st Exam) | <.0001 | |||||||
| <10 | – | 32841(57.99) | 22050(27.62) | 4092(12.28) | 7039(13.01) | 25003(13.55) | 23117(34.44) | |
| 10 to <20 | – | 7721(13.63) | 20415(25.57) | 6796(20.39) | 13296(24.58) | 47532(25.75) | 23017(34.29) | |
| 20 to <30 | – | 6467(11.42) | 15474(19.38) | 7380(22.14) | 11839(21.88) | 49064(26.58) | 11201(16.69) | |
| ≥30 | – | 9604(16.96) | 21890(27.42) | 15059(45.19) | 21928(40.53) | 62966(34.12) | 9786(14.58) | |
| Smoking amount (2nd Exam) | <.0001 | |||||||
| mild: < 10 cigarettes/d | – | 11247(19.86) | 31853(39.9) | 11552(34.66) | 7562(13.98) | 6797(3.68) | 3956(5.89) | |
| moderate: 10–19 cigarettes/d | – | 22035(38.91) | 18298(22.92) | 18042(54.14) | 30126(55.68) | 57748(31.29) | 19498(29.05) | |
| heavy: ≥ 20 cigarettes/d | – | 23351(41.23) | 29678(37.18) | 3733(11.2) | 16414(30.34) | 120020(65.03) | 43667(65.06) | |
| Smoking duration (2nd Exam) | <.0001 | |||||||
| <5 | – | 4371(7.72) | 28146(35.25) | 1137(3.41) | 759(1.4) | 1919(1.04) | 1203(1.79) | |
| 5-9 | – | 2682(4.74) | 2106(2.64) | 1442(4.33) | 1375(2.54) | 3860(2.09) | 1997(2.98) | |
| 10-19 | – | 10272(18.14) | 10412(13.04) | 6670(20.01) | 10715(19.81) | 31612(17.13) | 12879(19.19) | |
| ≥20 | – | 39308(69.41) | 39165(49.06) | 24078(72.25) | 41253(76.25) | 147174(79.74) | 51042(76.04) | |
| Pack-years (2nd Exam) | <.0001 | |||||||
| <10 | – | 17962(31.72) | 37743(47.28) | 14324(42.98) | 11515(21.28) | 21642(11.73) | 8902(13.26) | |
| 10 to <20 | – | 14487(25.58) | 12661(15.86) | 11430(34.3) | 18717(34.6) | 44659(24.2) | 15566(23.19) | |
| 20 to <30 | – | 10470(18.49) | 10732(13.44) | 4957(14.87) | 11549(21.35) | 48980(26.54) | 14521(21.63) | |
| ≥30 | – | 13714(24.22) | 18693(23.42) | 2616(7.85) | 12321(22.77) | 69284(37.54) | 28132(41.91) | |
CKD: Chronic kidney disease, OHA: Oral hypoglycemic agents, BMI: Body mass index, SBP: Systolic blood pressure, DBP: Diastolic blood pressure.
Association between smoking status, change in smoking intensity, and risk of HS
During the median IQR follow-up period of 5.2 (4.1–6.1) years, there were 2,102 HS events. Table 2 shows the hazard ratio (HR) of HS based on smoking status. Before adjustment, in the initial health checkup, the HR for HS among current smokers was 1.42 (95% CI, 1.29–1.57, unadjusted model). After adjusting for variables, the HR for current smokers was 1.23 (95% CI, 1.08–1.39, model 4), indicating an increased risk compared to nonsmokers and former smokers. Additionally, current smoking was associated with a 21% increased risk of HS during the 2-year follow-up examination (aHR, 1.21; 95% CI, 1.07–1.38, model 4). The results of the Cox regression analyses are presented in Table 3. A higher risk of HS was found in the sustainer (aHR, 1.236; 95% CI, 1.075–1.421) and increaser (aHR, 1.285; 95% CI, 1.048–1.577) groups compared with nonsmoking group. HRs of other groups including starter, quitter, and 50% reducer and 20% reducer were not significant.
Table 2. Risk of HS in DM according to smoking status.
| Smoking status | N | (%) | HS event | Duration, Person-years | IR, 1000 Person-years | aHR (95% C.I) | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model 1 | p-value | Model 2 | p-value | Model 3 | p-value | Model 4 | p-value | ||||||
| Initial examination | <.0001 | 0.0051 | 0.001 | 0.0011 | |||||||||
| Non | 957893 | 56.2 | 1051 | 4890535.31 | 0.2149 | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | ||||
| Former | 328590 | 19.3 | 413 | 1645213.19 | 0.25103 | 1.16 (1.04, 1.31) | 1.01 (0.88, 1.15) | 1.01 (0.88, 1.16) | 1.01 (0.88, 1.15) | ||||
| Current | 418944 | 24.6 | 638 | 2075405.92 | 0.30741 | 1.42 (1.29, 1.57) | 1.19 (1.06, 1.35) | 1.23 (1.08, 1.39) | 1.23 (1.08, 1.39) | ||||
| Second examination | <.0001 | 0.0277 | 0.0056 | 0.0057 | |||||||||
| Non | 958192 | 56.2 | 1051 | 4882195.08 | 0.21527 | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | 1 (Ref.) | ||||
| Former | 351487 | 20.6 | 455 | 1767536.25 | 0.25742 | 1.19 (1.07, 1.33) | 1.03 (0.90, 1.17) | 1.04 (0.91, 1.18) | 1.03 (0.90, 1.18) | ||||
| Current | 395748 | 23.2 | 596 | 1961423.09 | 0.30386 | 1.41 (1.27, 1.55) | 1.17 (1.03, 1.33) | 1.21 (1.07, 1.38) | 1.21 (1.07, 1.38) | ||||
Model 1: Not adjusted;
Model 2: Age and sex-adjusted;
Model 3: Model 2 + Adjusted for income, drinking, regular exercise, hypertension, and dyslipidemia;
Model 4: Model 3 + CKD, fasting glucose, DM duration, OHA use, insulin use
CKD: chronic kidney disease, OHA: oral hypoglycemic agents
Table 3. Association between changes in smoking intensity and risk of HS.
| N | HS event | Duration, person-years | IR, 1000 Person-years | aHR (95% CI) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Model 1 | p-value | Model 2 | p-value | Model 3 | p-value | Model 4 | p-value | |||||
| Smoking change | <.0001 | 0.0454 | 0.0116 | 0.0115 | ||||||||
| Never smoker | 1229850 | 1398 | 6250268.57 | 0.22367 | 1 (Ref.) | NA | 1 (Ref.) | NA | 1 (Ref.) | NA | 1 (Ref.) | NA |
| Starter | 56633 | 66 | 285479.94 | 0.23119 | 1.032 (0.806, 1.321) | NA | 0.907 (0.706, 1.166) | NA | 0.932 (0.724, 1.198) | NA | 0.930 (0.724, 1.197) | NA |
| Quitter | 79829 | 108 | 399462.77 | 0.27036 | 1.207 (0.993, 1.468) | NA | 1.067 (0.873, 1.304) | NA | 1.077 (0.881, 1.316) | NA | 1.073 (0.878, 1.312) | NA |
| 50% reducer | 33327 | 53 | 163633.51 | 0.32389 | 1.443 (1.097, 1.898) | NA | 1.260 (0.954, 1.664) | NA | 1.295 (0.980, 1.711) | NA | 1.293 (0.979, 1.709) | NA |
| 20% reducer | 54102 | 84 | 268484.87 | 0.31287 | 1.394 (1.118, 1.737) | NA | 1.190 (0.949, 1.493) | NA | 1.227 (0.977, 1.540) | NA | 1.226 (0.976, 1.539) | NA |
| Sustainer | 184565 | 286 | 913593.98 | 0.31305 | 1.394 (1.227, 1.583) | NA | 1.193 (1.039, 1.369) | NA | 1.236 (1.075, 1.420) | NA | 1.236 (1.075, 1.421) | NA |
| Increaser | 67121 | 107 | 330230.8 | 0.32402 | 1.442 (1.185, 1.755) | NA | 1.237 (1.009, 1.516) | NA | 1.285 (1.047, 1.576) | NA | 1.285 (1.048, 1.577) | NA |
Model 1: Non-Adjusted.
Model 2: Age and sex-adjusted.
Model 3: Model 2 + Income, drinking, regular exercise, hypertension, and dyslipidemia-adjusted.
Model 4: Model 3 + CKD, fasting glucose, DM duration, OHA use, insulin use.
Kaplan-Meier estimates showed that the incidence of HS was higher in increasers than in quitters. (Fig 3)
Fig 3. Kaplan-Meier estimates for the probability of incident Hidradenitis suppurativa (HS) according to smoking cessation.
Analysis stratified by BMI
There was no significant difference in the association between smoking intensity change and risk of HS categorized by BMI, even though no significant results were found in BMI ≥ 25 kg/m2 due to the small number analyzed in the study (Table 4).
Table 4. Association between change in smoking intensity and risk of HS stratified by BMI.
| BMI < 25 kg/m2 | BMI ≥ 25 kg/m2 | P value | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| N | HS event | Duration, person-years | IR, 1000 PY | aHR (95% CI) | N | HS event | Duration, person-years | IR, 1000 PY | aHR (95% CI) | ||
| Smoking change | 0.0972 | ||||||||||
| Never smoker | 650327 | 748 | 3298567.87 | 0.22677 | 1 (Ref.) | 579523 | 650 | 2951700.7 | 0.22021 | 1 (Ref.) | NA |
| Starter | 31276 | 30 | 157188.25 | 0.19085 | 0.762 (0.528, 1.102) | 25357 | 36 | 128291.68 | 0.28061 | 1.138 (0.811, 1.598) | NA |
| Quitter | 42392 | 55 | 210993.62 | 0.26067 | 1.025 (0.777, 1.353) | 37437 | 53 | 188469.15 | 0.28121 | 1.129 (0.850, 1.499) | NA |
| 50% reducer | 19046 | 25 | 92859.43 | 0.26922 | 1.070 (0.716, 1.598) | 14281 | 28 | 70774.08 | 0.39563 | 1.584 (1.081, 2.320) | NA |
| 20% reducer | 29919 | 36 | 148341.87 | 0.24268 | 0.948 (0.675, 1.331) | 24183 | 48 | 120143 | 0.39952 | 1.570 (1.165, 2.118) | NA |
| Sustainer | 103441 | 155 | 512125.65 | 0.30266 | 1.188 (0.991, 1.426) | 81124 | 131 | 401468.32 | 0.3263 | 1.293 (1.062, 1.574) | NA |
| Increaser | 37125 | 50 | 182116.65 | 0.27455 | 1.085 (0.810, 1.453) | 29996 | 57 | 148114.15 | 0.38484 | 1.528 (1.158, 2.016) | NA |
Discussion
A direct relationship between the severity of HS and smoking was previously presented [28]. For example, a study reported that nonsmokers showed an 11% higher rate of remission than current smokers [30]. Moreover, smoking cessation was associated with a lower rate of new lesion formation after radical excisional surgery in HS patients [46]. While such studies have explored smoking cessation as a modifiable risk factor for HS, to our knowledge, the link between changes in smoking amounts and the predisposition to HS has not been examined in prior research. Therefore, this study investigated the association between changes in smoking levels and the risk of HS in patients with T2DM.
Among those who responded to this survey, 43% were active or former smokers. Notably, DM patients tend to have a lower smoking rate compared to the general population [47]. In the current study, smoking continuation was associated with a 23.6% increased risk of HS compared to never smokers over the two health examinations conducted at a 2-year interval. Furthermore, participants who increased their cigarette use showed a 28.5% increase in risk of HS. Two extensive retrospective studies in North America [24] and Israel [17], along with a single-center case-control study [13], also identified a significant association between smoking and HS using electronic records. Notably, one study reported a 90% increased risk (adjusted odds ratio, 1.9; 95% confidence interval, 1.8–2.0) of new HS diagnosis in smokers compared with nonsmokers, suggesting smoking as a potential risk factor for HS [24].
The relationship between smoking and HS remains an area of significant debate. While smoking is often suggested as a potential trigger for HS, its exact mechanism has yet to be clearly defined. One proposed pathway involves alterations in the chemotaxis of polymorphonuclear neutrophils, a mechanism similar to that in palmoplantar pustulosis [26,48]. Additionally, Kurzen et al.[49] proposed potential mechanisms for how cigarette smoking might contribute to the development of HS. Alkaloids in cigarette smoke may selectively inhibit most microorganisms while promoting the growth and proliferation of Staphylococcus aureus. This could potentially create a feedback loop altering the skin microbiome. Moreover, nicotine suppresses the production of antimicrobial peptides such as human beta-defensin 2, making follicles more vulnerable to bacterial invasion [50]. However, the role of S. aureus is complicated by its low prevalence in active HS lesions, suggesting that its involvement might occur in preclinical stages before symptoms manifest.
Another hypothesis involves the prolonged release of nicotine through sweat. Specifically, nicotine is emphasized as a modulator of the inflammatory response, contributing to various pathologies characterized by a strong immunological basis. These may include conditions like atherosclerosis, rheumatoid arthritis, inflammatory bowel disease, and skin disorders [2,51,52]. Nicotine interacts with nicotinic acetylcholine receptors (nAChRs) present on various cell types involved in hidradenitis suppurativa (HS) lesions, including keratinocytes, neutrophils, lymphocytes, macrophages, mast cells, and sebocytes [49,53]. In the epidermis of HS patients, nAChRs are strongly expressed around the pilosebaceous unit, contributing to excessive growth of the infundibular epithelium and the blockage of hair follicles [54]. Nicotine is also known to stimulate the release of pro-inflammatory cytokines like TNF-α [55] and inhibit the activity of immune cells such as macrophages and lymphocytes. This impairment may hinder the resolution of inflammation, contributing to the chronic nature of HS [49].
Additionally, cigarette smoke also contains polycyclic aromatic hydrocarbons (PAHs) and dioxin-like chemicals that activate the aryl hydrocarbon receptor (AhR) on keratinocytes, sebocytes, and immune cells [56–58]. In the context of HS, AhR activation may promote the expansion of Th17 cells [59], which are involved in chronic inflammatory conditions like Crohn’s disease [60]. Also, PAHs can disrupt the normal differentiation of follicular keratinocytes, leading to the formation of comedones [61,62]. Overall, the combination of nicotine, PAHs and other chemicals from smoking can create a highly inflammatory environment, which may promote the pathogenesis of HS through both direct cellular effects and modulation of immune responses. The increased risk of HS in the sustainer and increaser group in this study supports these suggested mechanisms.
Meanwhile, there is a potential connection between hyperglycemia, insulin resistance, and diabetes, which may contribute to the development or worsening of HS [21]. Hyperglycemia is known to overactivate pathways associated with the mammalian target of rapamycin complex 1, a process implicated in keratinocyte proliferation, sebaceous gland proliferation, and lipogenesis [63,64]. This activation may predispose individuals to manifest clinical features of HS, such as follicular occlusion, sinus tract formation, and eventual scarring [65,66]. Although previous studies have refrained from drawing causal conclusions, they strongly support a notable and independent association between HS and diabetes [21].
Interestingly, no difference was observed in the association between changes in smoking intensity and the risk of HS according to BMI. Across seven studies, patients with HS were found to have a higher prevalence of obesity compared to control individuals, with prevalence rates ranging from 5.9% to 73.1% [13,17,67–71]. A meta-analysis involving eight studies reported that the adjusted pooled odds of obesity among HS patients were 3.5 times higher (95% CI, 2.2–5.4) than those of control individuals [72]. Elevated BMI is recognized as a potential risk factor for HS [71,73,74], and screening for obesity is recommended in HS patients [75]. Studies have also suggested a correlation between the severity of HS and BMI, with higher BMI levels being associated with more severe cases [28,76]. For instance, a cohort study in Italy found that obesity independently contributed to the severity of HS, as shown in both univariate and multivariate analyses [19]. Additionally, a survey suggested that individuals who quit smoking were more likely to achieve remission, particularly among those with normal weight, although a similar trend was observed in overweight patients as well [30]. In this study, no significant association was observed between changes in smoking intensity and the risk of HS across different BMI categories. This finding may be partly attributable to the limited number of HS events, which could have reduced the statistical power to detect meaningful differences and increased the likelihood of a Type II error. Furthermore, a relatively homogeneous or skewed BMI distribution among participants may have constrained the ability to observe differential effects across BMI strata. Beyond these methodological considerations, the interplay between smoking, BMI, and HS risk is inherently complex and may involve multifactorial biological mechanisms that are not yet fully understood. For instance, inflammation, hormonal regulation, and metabolic dysregulation—factors that are independently influenced by both smoking and adiposity—may confound or mediate the observed associations. These underlying processes could obscure direct relationships in stratified analyses. Consequently, future studies with larger sample sizes and more diverse populations are warranted to elucidate these interactions and clarify the potential modifying role of BMI in the association between smoking behavior and HS risk.
The present study indicates that maintaining cigarette smoking increases the risk of HS, while increasing the amount of smoking exacerbates this risk. The impact of smoking reduction on health outcomes may vary depending on the underlying causes. For example, smoking reduction has been linked to a decreased risk of certain cancers, such as lung cancer, but not necessarily to a lower risk of cardiovascular diseases [38,77].
Several limitations of the study should be noted. First, smoking data were self-reported, introducing potential subjectivity as individuals might underestimate their cigarette consumption. Second, since the recognition of HS was derived from nationwide claims data, access to clinical details and imaging results was limited. This raises the possibility of misdiagnosing abscesses as HS. However, given the recurrent nature of HS, the impact of misdiagnosis is likely minimal. Third, the observational nature of the study precludes establishing causal relationships between the variables. Additionally, the possibility of unmeasured confounders cannot be excluded. Lastly, it is essential to note that this study exclusively involved Korean participants, emphasizing the necessity for validation in diverse ethnic populations.
The findings suggest that reducing smoking does not directly lower the risk of HS. This lack of association may stem from the multifactorial pathogenesis of HS, suggesting it does not exhibit a dose-response relationship with cigarette consumption. It could also be due to the relatively short follow-up period. Nevertheless, the absence of an increased risk of HS in reducers and quitters underscores the importance of smoking reduction as a crucial initial step toward complete cessation.
Data Availability
The data in this study was obtained from KNHIS where licence may apply. Further enquiries can be directed to the corresponding author. We have identified and included a non-author institutional point of contact to ensure long-term data availability. The details are as follows: Institutional Point of Contact: Research Office; Contact Information: dermaseoul@gmail.com
Funding Statement
The author(s) received no specific funding for this work.
References
- 1.Zouboulis CC, Desai N, Emtestam L, Hunger RE, Ioannides D, Juhász I, et al. European S1 guideline for the treatment of hidradenitis suppurativa/acne inversa. J Eur Acad Dermatol Venereol. 2015;29(4):619–44. doi: 10.1111/jdv.12966 [DOI] [PubMed] [Google Scholar]
- 2.Dauden E, Lazaro P, Aguilar MD, Blasco AJ, Suarez C, Marin I, et al. Recommendations for the management of comorbidity in hidradenitis suppurativa. J Eur Acad Dermatol Venereol. 2018;32(1):129–44. doi: 10.1111/jdv.14517 [DOI] [PubMed] [Google Scholar]
- 3.Vazquez BG, Alikhan A, Weaver AL, Wetter DA, Davis MD. Incidence of hidradenitis suppurativa and associated factors: a population-based study of Olmsted County, Minnesota. J Invest Dermatol. 2013;133(1):97–103. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Jemec GB, Heidenheim M, Nielsen NH. The prevalence of hidradenitis suppurativa and its potential precursor lesions. J Am Acad Dermatol. 1996;35(2 Pt 1):191–4. [DOI] [PubMed] [Google Scholar]
- 5.Zouboulis CC, Del Marmol V, Mrowietz U, Prens EP, Tzellos T, Jemec GBE. Hidradenitis Suppurativa/Acne Inversa: criteria for diagnosis, severity assessment, classification and disease evaluation. Dermatology. 2015;231(2):184–90. doi: 10.1159/000431175 [DOI] [PubMed] [Google Scholar]
- 6.Ingram JR. The epidemiology of hidradenitis suppurativa. Br J Dermatol. 2020;183(6):990–8. doi: 10.1111/bjd.19435 [DOI] [PubMed] [Google Scholar]
- 7.Lee JH, Kwon HS, Jung HM, Kim GM, Bae JM. Prevalence and comorbidities associated with hidradenitis suppurativa in Korea: a nationwide population-based study. J Eur Acad Dermatol Venereol. 2018;32(10):1784–90. doi: 10.1111/jdv.15071 [DOI] [PubMed] [Google Scholar]
- 8.Yang JH, Moon J, Kye YC, Kim KJ, Kim MN, Ro YS, et al. Demographic and clinical features of hidradenitis suppurativa in Korea. J Dermatol. 2018;45(12):1389–95. doi: 10.1111/1346-8138.14656 [DOI] [PubMed] [Google Scholar]
- 9.Hayama K, Fujita H, Hashimoto T, Terui T, Japanese HS Research Group. Questionnaire-based epidemiological study of hidradenitis suppurativa in Japan revealing characteristics different from those in Western countries. J Dermatol. 2020;47(7):743–8. doi: 10.1111/1346-8138.15378 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Choi E, Cook AR, Chandran NS. Hidradenitis suppurativa: an Asian perspective from a Singaporean Institute. Skin Appendage Disord. 2018;4(4):281–5. doi: 10.1159/000481836 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Loo CH, Tan WC, Tang JJ, Khor YH, Manikam MT, Low D-E, et al. The clinical, biochemical, and ultrasonographic characteristics of patients with hidradenitis suppurativa in Northern Peninsular Malaysia: a multicenter study. Int J Dermatol. 2018;57(12):1454–63. doi: 10.1111/ijd.14210 [DOI] [PubMed] [Google Scholar]
- 12.Bui T-L, Silva-Hirschberg C, Torres J, Armstrong AW. Hidradenitis suppurativa and diabetes mellitus: A systematic review and meta-analysis. J Am Acad Dermatol. 2018;78(2):395–402. doi: 10.1016/j.jaad.2017.08.042 [DOI] [PubMed] [Google Scholar]
- 13.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(6):1144–50. doi: 10.1016/j.jaad.2014.09.012 [DOI] [PubMed] [Google Scholar]
- 14.Galicia-Garcia U, Benito-Vicente A, Jebari S, Larrea-Sebal A, Siddiqi H, Uribe KB. Pathophysiology of type 2 diabetes mellitus. Int J Mol Sci. 2020;21(17). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Verdolini R, Clayton N, Smith A, Alwash N, Mannello B. Metformin for the treatment of hidradenitis suppurativa: a little help along the way. J Eur Acad Dermatol Venereol. 2013;27(9):1101–8. doi: 10.1111/j.1468-3083.2012.04668.x [DOI] [PubMed] [Google Scholar]
- 16.Arun B, Loffeld A. Long-standing hidradenitis suppurativa treated effectively with metformin. Clin Exp Dermatol. 2009;34(8):920–1. doi: 10.1111/j.1365-2230.2008.03121.x [DOI] [PubMed] [Google Scholar]
- 17.Shalom G, Freud T, Harman-Boehm I, Polishchuk I, Cohen AD. Hidradenitis suppurativa and metabolic syndrome: a comparative cross-sectional study of 3207 patients. Br J Dermatol. 2015;173(2):464–70. doi: 10.1111/bjd.13777 [DOI] [PubMed] [Google Scholar]
- 18.Miller IM, Ellervik C, Vinding GR, Zarchi K, Ibler KS, Knudsen KM, et al. Association of metabolic syndrome and hidradenitis suppurativa. JAMA Dermatol. 2014;150(12):1273–80. [DOI] [PubMed] [Google Scholar]
- 19.Bettoli V, Naldi L, Cazzaniga S, Zauli S, Atzori L, Borghi A. Overweight, diabetes and disease duration influence clinical severity in hidradenitis suppurativa-acne inversa: evidence from the national Italian registry. Br J Dermatol. 2016;174(1):195–7. [DOI] [PubMed] [Google Scholar]
- 20.Andrade TCPC de, Vieira BC, Oliveira AMN, Martins TY, Santiago TM, Martelli ACC. Hidradenitis suppurativa: epidemiological study of cases diagnosed at a dermatological reference center in the city of Bauru, in the Brazilian southeast State of São Paulo, between 2005 and 2015. An Bras Dermatol. 2017;92(2):196–9. doi: 10.1590/abd1806-4841.20175588 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Phan K, Charlton O, Smith SD. Hidradenitis suppurativa and diabetes mellitus: updated systematic review and adjusted meta-analysis. Clin Exp Dermatol. 2019;44(4):e126–32. doi: 10.1111/ced.13922 [DOI] [PubMed] [Google Scholar]
- 22.Kluger N, Nuutinen P, Lybeck E, Ruohoalho T, Salava A. Type 2 diabetes mellitus in a cohort of Finnish patients with hidradenitis suppurativa. J Eur Acad Dermatol Venereol. 2020;34(2):e98–100. doi: 10.1111/jdv.16010 [DOI] [PubMed] [Google Scholar]
- 23.Gold DA, Reeder VJ, Mahan MG, Hamzavi IH. The prevalence of metabolic syndrome in patients with hidradenitis suppurativa. J Am Acad Dermatol. 2014;70(4):699–703. doi: 10.1016/j.jaad.2013.11.014 [DOI] [PubMed] [Google Scholar]
- 24.Garg A, Papagermanos V, Midura M, Strunk A. Incidence of hidradenitis suppurativa among tobacco smokers: a population-based retrospective analysis in the U.S.A. Br J Dermatol. 2018;178(3):709–14. doi: 10.1111/bjd.15939 [DOI] [PubMed] [Google Scholar]
- 25.Acharya P, Mathur M. Hidradenitis suppurativa and smoking: a systematic review and meta-analysis. J Am Acad Dermatol. 2020;82(4):1006–11. [DOI] [PubMed] [Google Scholar]
- 26.König A, Lehmann C, Rompel R, Happle R. Cigarette smoking as a triggering factor of hidradenitis suppurativa. Dermatology. 1999;198(3):261–4. doi: 10.1159/000018126 [DOI] [PubMed] [Google Scholar]
- 27.Revuz JE, Canoui-Poitrine F, Wolkenstein P, Viallette C, Gabison G, Pouget F, et al. Prevalence and factors associated with hidradenitis suppurativa: results from two case-control studies. J Am Acad Dermatol. 2008;59(4):596–601. doi: 10.1016/j.jaad.2008.06.020 [DOI] [PubMed] [Google Scholar]
- 28.Sartorius K, Emtestam L, Jemec GBE, Lapins J. Objective scoring of hidradenitis suppurativa reflecting the role of tobacco smoking and obesity. Br J Dermatol. 2009;161(4):831–9. doi: 10.1111/j.1365-2133.2009.09198.x [DOI] [PubMed] [Google Scholar]
- 29.Kang S. Fitzpatrick’s dermatology. 9th ed. New York: McGraw-Hill Education. 2019. [Google Scholar]
- 30.Kromann CB, Deckers IE, Esmann S, Boer J, Prens EP, Jemec GBE. Risk factors, clinical course and long-term prognosis in hidradenitis suppurativa: a cross-sectional study. Br J Dermatol. 2014;171(4):819–24. doi: 10.1111/bjd.13090 [DOI] [PubMed] [Google Scholar]
- 31.Cheol Seong S, Kim Y-Y, Khang Y-H, Heon Park J, Kang H-J, Lee H, et al. Data Resource Profile: The National Health Information database of the National Health Insurance Service in South Korea. Int J Epidemiol. 2017;46(3):799–800. doi: 10.1093/ije/dyw253 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Lee Y-B, Han K, Kim B, Lee S-E, Jun JE, Ahn J, et al. Risk of early mortality and cardiovascular disease in type 1 diabetes: a comparison with type 2 diabetes, a nationwide study. Cardiovasc Diabetol. 2019;18(1):157. doi: 10.1186/s12933-019-0953-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Lee Y-B, Han K, Kim B, Jun JE, Lee S-E, Ahn J, et al. Risk of end-stage renal disease from chronic kidney disease defined by decreased glomerular filtration rate in type 1 diabetes: A comparison with type 2 diabetes and the effect of metabolic syndrome. Diabetes Metab Res Rev. 2019;35(8):e3197. doi: 10.1002/dmrr.3197 [DOI] [PubMed] [Google Scholar]
- 34.Kim Y, Han K, Choi J, Boo K, Kim D, Oh S. The impact of body weight and diabetes on new-onset atrial fibrillation: a nationwide population based study. Cardiovasc Diabetol. 2019;18(1):128. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Cheon DY, Han K, Yang YS, Kim Y, Lee SH, Kim C, et al. Associations between migraine and major cardiovascular events in type 2 diabetes mellitus. Cardiovasc Diabetol. 2022;21(1):275. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.World Health Organization. Guidelines for controlling and monitoring the tobacco epidemic. Geneva: World Health Organization; 1998. x, 190 p. p. [Google Scholar]
- 37.Godtfredsen N, Osler M, Vestbo J, Andersen I, Prescott E. Smoking reduction, smoking cessation, and incidence of fatal and non-fatal myocardial infarction in Denmark 1976-1998: a pooled cohort study. J Epidemiol Community Health. 2003;57(6):412–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Yoo JE, Han K, Shin DW, Jung W, Kim D, Lee CM, et al. Effect of smoking reduction, cessation, and resumption on cancer risk: A nationwide cohort study. Cancer. 2022;128(11):2126–37. doi: 10.1002/cncr.34172 [DOI] [PubMed] [Google Scholar]
- 39.Jeong S-M, Park J, Han K, Yoo J, Yoo JE, Lee CM, et al. Association of changes in smoking intensity with risk of dementia in Korea. JAMA Netw Open. 2023;6(1):e2251506. doi: 10.1001/jamanetworkopen.2022.51506 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Kim S, Lee K, Park H, Lee Y, Chun S, Min W. Schemes and performance evaluation criteria of Korean association of external quality assessment (KEQAS) for improving laboratory testing. Ann Lab Med. 2021;41(2):230–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Seo MH, Lee W-Y, Kim SS, Kang J-H, Kang J-H, Kim KK, et al. 2018 Korean Society for the Study of obesity guideline for the Management of Obesity in Korea. J Obes Metab Syndr. 2019;28(1):40–5. doi: 10.7570/jomes.2019.28.1.40 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Koo BK, Park S-H, Han K, Moon MK. Cardiovascular outcomes of obesity according to menopausal status: a Nationwide Population-Based Study. Endocrinol Metab (Seoul). 2021;36(5):1029–41. doi: 10.3803/EnM.2021.1197 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Lee Y-B, Kim DH, Kim SM, Kim NH, Choi KM, Baik SH, et al. Hospitalization for heart failure incidence according to the transition in metabolic health and obesity status: a nationwide population-based study. Cardiovasc Diabetol. 2020;19(1):77. doi: 10.1186/s12933-020-01051-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Lee Y-B, Han K, Park S, Kim SM, Kim NH, Choi KM, et al. Gamma-glutamyl transferase variability and risk of dementia: A nationwide study. Int J Geriatr Psychiatry. 2020;35(10):1105–14. doi: 10.1002/gps.5332 [DOI] [PubMed] [Google Scholar]
- 45.Choi YJ, Lee DH, Han K-D, Kim HS, Yoon H, Shin CM, et al. The relationship between drinking alcohol and esophageal, gastric or colorectal cancer: A nationwide population-based cohort study of South Korea. PLoS One. 2017;12(10):e0185778. doi: 10.1371/journal.pone.0185778 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Kurzen H, Schönfelder-Funcke S, Hartschuh W. Surgical treatment of acne inversa at the University of Heidelberg. Coloproctology. 2000;22:76–80. [Google Scholar]
- 47.Roderick P, Turner V, Readshaw A, Dogar O, Siddiqi K. The global prevalence of tobacco use in type 2 diabetes mellitus patients: A systematic review and meta-analysis. Diabetes Res Clin Pract. 2019;154:52–65. doi: 10.1016/j.diabres.2019.05.035 [DOI] [PubMed] [Google Scholar]
- 48.Smith JB, Fenske NA. Cutaneous manifestations and consequences of smoking. J Am Acad Dermatol. 1996;34(5 Pt 1):717–32. [DOI] [PubMed] [Google Scholar]
- 49.Kurzen H, Kurokawa I, Jemec G, Emtestam L, Sellheyer K, Giamarellos-Bourboulis E. What causes hidradenitis suppurativa? Exp Dermatol. 17(5):455–6. [DOI] [PubMed] [Google Scholar]
- 50.Schlapbach C, Yawalkar N, Hunger RE. Human beta-defensin-2 and psoriasin are overexpressed in lesions of acne inversa. J Am Acad Dermatol. 2009;61(1):58–65. doi: 10.1016/j.jaad.2008.12.033 [DOI] [PubMed] [Google Scholar]
- 51.Orosz Z, Csiszar A, Labinskyy N, Smith K, Kaminski PM, Ferdinandy P, et al. Cigarette smoke-induced proinflammatory alterations in the endothelial phenotype: role of NAD(P)H oxidase activation. Am J Physiol Heart Circ Physiol. 2007;292(1):H130–9. doi: 10.1152/ajpheart.00599.2006 [DOI] [PubMed] [Google Scholar]
- 52.Aicher A, Heeschen C, Mohaupt M, Cooke JP, Zeiher AM, Dimmeler S. Nicotine strongly activates dendritic cell-mediated adaptive immunity: potential role for progression of atherosclerotic lesions. Circulation. 2003;107(4):604–11. [DOI] [PubMed] [Google Scholar]
- 53.Kurzen H, Wessler I, Kirkpatrick CJ, Kawashima K, Grando SA. The non-neuronal cholinergic system of human skin. Horm Metab Res. 2007;39(2):125–35. doi: 10.1055/s-2007-961816 [DOI] [PubMed] [Google Scholar]
- 54.Hana A, Booken D, Henrich C, Gratchev A, Maas-Szabowski N, Goerdt S, et al. Functional significance of non-neuronal acetylcholine in skin epithelia. Life Sci. 2007;80(24–25):2214–20. doi: 10.1016/j.lfs.2007.02.007 [DOI] [PubMed] [Google Scholar]
- 55.Jeong S, Park J, Kim J, Park Y, Shin S, Lee Y. Up-regulation of TNF-alpha secretion by cigarette smoke is mediated by Egr-1 in HaCaT human keratinocytes. Exp Dermatol. 2010;19(8):e206-12. [DOI] [PubMed] [Google Scholar]
- 56.Moorthy B, Chu C, Carlin DJ. Polycyclic aromatic hydrocarbons: from metabolism to lung cancer. Toxicol Sci. 2015;145(1):5–15. doi: 10.1093/toxsci/kfv040 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Kasai A, Hiramatsu N, Hayakawa K, Yao J, Maeda S, Kitamura M. High levels of dioxin-like potential in cigarette smoke evidenced by in vitro and in vivo biosensing. Cancer Res. 2006;66(14):7143–50. doi: 10.1158/0008-5472.CAN-05-4541 [DOI] [PubMed] [Google Scholar]
- 58.Hu T, Pan Z, Yu Q, Mo X, Song N, Yan M, et al. Benzo(a)pyrene induces interleukin (IL)-6 production and reduces lipid synthesis in human SZ95 sebocytes via the aryl hydrocarbon receptor signaling pathway. Environ Toxicol Pharmacol. 2016;43:54–60. doi: 10.1016/j.etap.2016.02.011 [DOI] [PubMed] [Google Scholar]
- 59.Esser C, Rannug A, Stockinger B. The aryl hydrocarbon receptor in immunity. Trends Immunol. 2009;30(9):447–54. doi: 10.1016/j.it.2009.06.005 [DOI] [PubMed] [Google Scholar]
- 60.Arsenescu R, Arsenescu V, Zhong J, Nasser M, Melinte R, Dingle RWC, et al. Role of the xenobiotic receptor in inflammatory bowel disease. Inflamm Bowel Dis. 2011;17(5):1149–62. doi: 10.1002/ibd.21463 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Patterson AT, Tian FT, Elston DM, Kaffenberger BH. Occluded cigarette smoke exposure causing localized chloracne-like comedones. Dermatology. 2015;231(4):322–5. [DOI] [PubMed] [Google Scholar]
- 62.Frew JW. Hidradenitis suppurativa is an autoinflammatory keratinization disease: A review of the clinical, histologic, and molecular evidence. JAAD Int. 2020;1(1):62–72. doi: 10.1016/j.jdin.2020.05.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Ueno M, Carvalheira JBC, Tambascia RC, Bezerra RMN, Amaral ME, Carneiro EM, et al. Regulation of insulin signalling by hyperinsulinaemia: role of IRS-1/2 serine phosphorylation and the mTOR/p70 S6K pathway. Diabetologia. 2005;48(3):506–18. doi: 10.1007/s00125-004-1662-6 [DOI] [PubMed] [Google Scholar]
- 64.Blagosklonny MV. TOR-centric view on insulin resistance and diabetic complications: perspective for endocrinologists and gerontologists. Cell Death Dis. 2013;4(12):e964. doi: 10.1038/cddis.2013.506 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Egeberg A, Gislason GH, Hansen PR. Risk of major adverse cardiovascular events and all-cause mortality in patients with hidradenitis suppurativa. JAMA Dermatol. 2016;152(4):429–34. doi: 10.1001/jamadermatol.2015.6264 [DOI] [PubMed] [Google Scholar]
- 66.Lim ZV, Oon HH. Management of hidradenitis suppurativa in patients with metabolic comorbidities. Ann Dermatol. 2016;28(2):147–51. doi: 10.5021/ad.2016.28.2.147 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Wertenteil S, Strunk A, Garg A. Overall and subgroup prevalence of acne vulgaris among patients with hidradenitis suppurativa. J Am Acad Dermatol. 2019;80(5):e131–2. doi: 10.1016/j.jaad.2018.11.022 [DOI] [PubMed] [Google Scholar]
- 68.TheutRiis P, Pedersen O, Sigsgaard V, Erikstrup C, Paarup H, Nielsen K. Prevalence of patients with self-reported hidradenitis suppurativa in a cohort of Danish blood donors: a cross-sectional study. Br J Dermatol. 2019;180(4):774–81. [DOI] [PubMed] [Google Scholar]
- 69.Tiri H, Huilaja L, Jokelainen J, Timonen M, Tasanen K. Women with hidradenitis suppurativa have an elevated risk of suicide. J Invest Dermatol. 2018;138(12):2672–4. [DOI] [PubMed] [Google Scholar]
- 70.Schmitt JV, Bombonatto G, Martin M, Miot HA. Risk factors for hidradenitis suppurativa: a pilot study. An Bras Dermatol. 2012;87(6):936–8. doi: 10.1590/s0365-05962012000600024 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 71.Sabat R, Chanwangpong A, Schneider-Burrus S, Metternich D, Kokolakis G, Kurek A, et al. Increased prevalence of metabolic syndrome in patients with acne inversa. PLoS One. 2012;7(2):e31810. doi: 10.1371/journal.pone.0031810 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 72.Tzellos T, Zouboulis CC, Gulliver W, Cohen AD, Wolkenstein P, Jemec GBE. Cardiovascular disease risk factors in patients with hidradenitis suppurativa: a systematic review and meta-analysis of observational studies. Br J Dermatol. 2015;173(5):1142–55. doi: 10.1111/bjd.14024 [DOI] [PubMed] [Google Scholar]
- 73.Dufour DN, Emtestam L, Jemec GB. Hidradenitis suppurativa: a common and burdensome, yet under-recognised, inflammatory skin disease. Postgrad Med J. 2014;90(1062):216–21; quiz 220. doi: 10.1136/postgradmedj-2013-131994 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Yosipovitch G, DeVore A, Dawn A. Obesity and the skin: skin physiology and skin manifestations of obesity. J Am Acad Dermatol. 2007;56(6):901–16; quiz 917–20. doi: 10.1016/j.jaad.2006.12.004 [DOI] [PubMed] [Google Scholar]
- 75.Garg A, Malviya N, Strunk A, Wright S, Alavi A, Alhusayen R, et al. Comorbidity screening in hidradenitis suppurativa: Evidence-based recommendations from the US and Canadian Hidradenitis Suppurativa Foundations. J Am Acad Dermatol. 2022;86(5):1092–101. doi: 10.1016/j.jaad.2021.01.059 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Canoui-Poitrine F, Revuz JE, Wolkenstein P, Viallette C, Gabison G, Pouget F. 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(1):51–7. [DOI] [PubMed] [Google Scholar]
- 77.Jeong S-M, Jeon KH, Shin DW, Han K, Kim D, Park SH, et al. Smoking cessation, but not reduction, reduces cardiovascular disease incidence. Eur Heart J. 2021;42(40):4141–53. doi: 10.1093/eurheartj/ehab578 [DOI] [PubMed] [Google Scholar]