Decreased HDL-C in Hidradenitis Suppurativa: Association with Systemic Inflammatory Phenotype and Implications for Risk Stratification

Decreased HDL-C in Hidradenitis Suppurativa: Association with Systemic Inflammatory Phenotype and Implications for Risk Stratification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Decreased HDL-C in Hidradenitis Suppurativa: Association with Systemic Inflammatory Phenotype and Implications for Risk Stratification Chao Wu, Yong-jun Wang, Tie-jun Liu, Hong-Zhong Jin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8838333/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Apr, 2026 Read the published version in Lipids in Health and Disease → Version 1 posted 9 You are reading this latest preprint version Abstract Background Hidradenitis suppurativa (HS) is a chronic inflammatory skin disorder increasingly recognized as a systemic disease with significant metabolic comorbidity. Disturbances in lipid metabolism, particularly reduced high-density lipoprotein cholesterol (HDL-C), have been consistently reported in patients with HS. However, the extent to which HDL-C levels are associated with clinical characteristics, systemic inflammatory burden, and disease severity in HS remains insufficiently defined. Methods In this retrospective single-center study, clinical and laboratory data were analyzed for 109 patients with HS and 109 age- and sex-matched healthy controls. Serum lipid parameters were compared between groups. Patients with HS were further stratified according to HDL-C status, and differences in demographic characteristics, clinical features, and laboratory indices were examined to assess the clinical relevance of reduced HDL-C in HS. Results Compared with healthy controls, patients with HS had a significantly higher body mass index (BMI), triglyceride (TG) concentrations and significantly lower HDL-C levels ( p < 0.001). Among patients with HS, those with abnormal HDL-C levels exhibited significantly higher BMI, white blood cell count, platelet count, plateletcrit, gamma-glutamyl transferase, and high-sensitivity C-reactive protein levels than those with normal HDL-C levels ( p < 0.05). In addition, axillary involvement was significantly more frequent in the abnormal HDL-C group ( p = 0.017). A higher proportion of Hurley stage III disease and higher IHS4 scores were observed in the HDL-C abnormal group. Conclusions Patients with HS demonstrate a characteristic reduction in HDL-C that is associated with elevated systemic inflammatory markers and increased axillary involvement. These findings support HDL-C as a readily accessible indicator of inflammatory burden and disease severity in HS and highlight its potential value in clinical risk stratification. hidradenitis suppurativa high-density lipoprotein cholesterol triglyceride inflammation Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Hidradenitis suppurativa (HS) is a chronic, relapsing inflammatory skin disease characterized by painful, deep-seated nodules, abscesses, sinus tracts, and fibrotic scarring that predominantly affect intertriginous regions, including the axillary, inguinal, perianal, perineal, and inframammary areas[ 1 ]. Although HS has traditionally been regarded as a cutaneous disorder, growing evidence supports its classification as a systemic inflammatory disease with substantial extracutaneous involvement. Patients with HS exhibit a high prevalence of metabolic comorbidities, including obesity, insulin resistance, dyslipidemia, and an increased risk of cardiovascular risk[ 2 – 6 ]. Obesity represents one of the most well-established and clinically significant risk factors for HS and is closely associated with both disease severity and chronicity[ 5 ]. Individuals with obesity frequently exhibit concomitant metabolic disturbances, particularly dyslipidemia. Similar patterns of inflammation-associated dyslipidemia have been observed in other chronic inflammatory diseases, reinforcing the relevance of lipid abnormalities as systemic markers rather than isolated metabolic findings. Accumulating evidence suggests that HS is associated with a characteristic dyslipidemic profile, most notably reduced high-density lipoprotein cholesterol (HDL-C) levels and elevated triglyceride (TG) concentrations. A systematic review and meta-analysis demonstrated significantly lower HDL-C and higher TG levels in patients with HS compared with healthy controls, while total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) did not differ significantly between groups[ 7 ]. Consistent with these findings, a large cohort study reported a 10%–14% reduction in HDL-C and an 11%–21% increase in TG levels among individuals with HS[ 5 ]. More recently, a cross-sectional study involving more than 3,000 patients confirmed a significantly increased risk of dyslipidemia in HS, identifying low HDL-C as the most prominent lipid abnormality, particularly in patients with obesity[ 8 ]. Collectively, these data indicate a strong association between HS and lipid metabolism abnormalities, with HDL-C emerging as a central component of this metabolic phenotype. Despite these advances, existing studies have largely described lipid alterations at the population level, and direct comparisons of clinical characteristics between patients with HS who have abnormal versus normal lipid profiles remain limited. More importantly, it remains unclear whether lipid abnormalities, particularly reduced HDL-C, are associated with distinct inflammatory phenotypes or differential disease expression in HS. Clarifying these associations is clinically relevant, as HDL-C plays a central role in lipid metabolism and possesses anti-inflammatory and immunomodulatory properties that may influence disease activity. Accordingly, the present study aimed to characterize lipid profiles in patients with HS and to compare clinical features and laboratory parameters between HS patients with abnormal and normal HDL-C levels. Through this stratified approach, the study sought to clarify the clinical relevance of HDL-C–related metabolic alterations in HS and to explore their potential implications for disease development and clinical management. Methods Study design and patients This single-center retrospective study included patients with HS who had available lipid profile data and were treated at Peking Union Medical College Hospital between January 1, 2020, and November 1, 2025. Patients were clinically diagnosed with HS, follicular occlusion triad, or acne inversa. Diagnosis was established based on characteristic clinical features, including recurrent inflammatory lesions such as nodules, cysts, scars, and sinus tracts, in combination with typical anatomical distribution involving intertriginous regions, including the axillae, groin, buttocks, and genital area. A total of 109 patients with HS were included in the analysis. For each patient with HS, one age- and sex-matched healthy individual was selected as a control from the Health Examination Center of Peking Union Medical College Hospital between January 1, 2020, and November 1, 2025. Fasting lipid parameters, including TG, TC, HDL-C, and LDL-C, were collected and analyzed in both the HS and control groups. Patients with HS were further stratified according to HDL-C levels into an HDL-C abnormal group (n = 64) and an HDL-C normal group (n = 45), and differences in clinical characteristics and laboratory parameters between these groups were subsequently examined. The study was conducted in accordance with the Declaration of Helsinki and was approved by the institutional ethics committee (approval number I-25PJ2964). Data collection and Outcome Definitions Data collection and Outcome Definitions Clinical and laboratory data were obtained from medical records, with all clinical assessments performed by dermatologists. Data collected from patients with HS included sex, age at disease onset, disease duration, family history, body mass index (BMI), disease severity assessed using the Hurley staging system and the International Hidradenitis Suppurativa Severity Score System (IHS4), lesion distribution, types of cutaneous lesions, comorbidities, and laboratory test results. Lesion locations were recorded for the head, neck, axillae, groin, buttocks, and genital region. Cutaneous lesion types included cysts, scars, nodules, and sinus tracts. Abnormal lipid profiles were defined as either TG ≥ 2.3 mmol/L, TC ≥ 6.2 mmol/L, HDL-C < 1.0 mmol/L, or LDL-C ≥ 4.1 mmol/L. For healthy controls, available data included sex, age, and serum lipid parameters (TG, TC, HDL-C, and LDL-C). Statistical analysis Normality of continuous variables was assessed using the Shapiro–Wilk test in combination with quantile–quantile (Q–Q) plots for variables with a sample size less than 50, and the Kolmogorov–Smirnov test combined with Q–Q plots for variables with sample sizes of 50 or greater. For continuous variables, normally distributed data are presented as mean ± standard deviation and were compared between groups using the Welch t-test. Non-normally distributed data are presented as median with interquartile range (25th–75th percentiles) and were compared using the Mann–Whitney U test. Categorical variables are presented as counts and proportions. Group comparisons were performed using Fisher’s exact test for variables with two categories and the chi-square test for variables with more than two categories. All statistical analyses were conducted using Python with the statsmodels library. A two-sided p value < 0.05 was considered statistically significant. Results Differences in lipid profiles between patients with HS and healthy controls A total of 109 patients with HS were included in the analysis, comprising 27 inpatients and 82 outpatients. The cohort consisted of 95 males and 14 females, with a mean age of 30.03 ± 11.46 years. An equal number of age- and sex-matched healthy individuals without HS were selected as the control group. No significant differences were observed between the HS and control groups with respect to sex distribution ( p =1.000) or age ( p =0.139). In contrast, BMI was significantly higher in patients with HS compared with healthy controls (28.61 ± 5.87 vs. 22.35 ± 2.06, p <0.001) (Table 1). Table 1. Clinical characteristics of HS patients and healthy controls Variable HS patients (n=109) Healthy controls (n=109) P value Demographic data Sex (male) 95 (87.2%) 95 (87.2%) 1.000 Age (years) 29.0 (20.0, 36.0) 29.0 (24.0, 36.0) 0.139 BMI 28.61±5.87 22.35±2.06 <0.001*** Laboratory test results TG (mmol/L) 1.13 (0.84, 1.64) 0.79 (0.59, 1.08) <0.001*** TC (mmol/L) 4.26±0.95 4.29±0.71 0.783 HDL-C (mmol/L) 0.96±0.23 1.36±0.28 <0.001*** LDL-C (mmol/L) 2.61 (2.29, 3.25) 2.69 (2.28, 3.17) 0.966 Note: In this table, the Shapiro-Wilk test is for assessed normality for the continues variables. Variables that followed a normal distribution are presented as mean±standard deviation. Variables that did not conform to a normal distribution are expressed as median with interquartile range. Significant associations are marked: p-values *** < 0.001 Abbreviations: HS, hidradenitis suppurativa; BMI, body mass index; TG, triglycerides; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol. Comparison of serum lipid parameters demonstrated no significant differences between the HS and control groups in TC ( p =0.783) or LDL-C ( p =0.966). However, patients with HS exhibited significantly higher TG concentrations, with a median value of 1.13 (0.84, 1.64) mmol/L compared with 0.79 (0.59, 1.08) mmol/L in healthy controls ( p <0.001), as well as significantly lower HDL-C levels (0.96±0.23 vs. 1.36±0.28, p <0.001) (Figure 1). Demographic differences between HS patients with abnormal and normal HDL-C levels The 109 patients with HS were stratified according to HDL-C status into an HDL-C abnormal group (HDL-C <1.0 mmol/L) (Figure 2) and an HDL-C normal group (Figure 3). The HDL-C abnormal group consisted of 59 men and 5 women, whereas the HDL-C normal group included 36 men and 9 women. Baseline demographic and clinical characteristics are summarized in Table 2. A significant difference in BMI was observed between the two groups. Patients in the HDL-C abnormal group had a higher mean BMI than those in the HDL-C normal group (30.07 ± 5.90 vs. 25.80 ± 4.83, p =0.019), indicating a greater degree of adiposity among patients with reduced HDL-C levels. Table 2. Clinical characteristics of HS patients with abnormal and normal HDL-C Abnormal HDL-C (n=64) Normal HDL-C (n=45) P value Demographic data Sex (male) 59 (92.2%) 36 (80%) 0.114 BMI 30.07±5.90 25.80±4.83 0.019* HS Clinical Features Age at onset (years) 20.92 (16.00, 28.00) 19.38 (15.25, 26.75) 0.466 Disease duration (years) 5.00 (2.50, 10.00) 6.50 (3.00, 10.23) 0.323 Family history Yes 13 9 — No 30 21 Hurley stage Stage I 13 (20.3%) 10 (22.2%) 0.067 Stage II 23 (35.9%) 25 (55.6%) Stage III 25 (39.1%) 9 (20.0%) IHS4 18.00 (10.00, 23.00) 8.00 (5.50, 20.50) 0.451 Lesion locations Head 46 (70.1%) 29 (66.7%) 0.565 Neck 22 (34.3%) 7 (14.3%) 0.051 Axillae 57 (86.6%) 31 (71.4%) 0.017* Groin 31 (49.3%) 15 (31.0%) 0.169 Buttock 27 (40.3%) 15 (35.7%) 0.462 Genital region 11 (17.9%) 7 (14.3%) 1.000 Lesion types Nodules 47 (76.1%) 35 (73.8%) 1.000 Abscesses 47 (70.1%) 34 (81.0%) 0.865 Scars 41 (65.7%) 25 (52.4%) 0.219 Sinus tracts 27 (40.3%) 13 (31.0%) 0.127 Laboratory test results WBC ( /L) 10.41 (8.93,12.22) 8.41 (6.63, 10.64) 0.002** NUET% (%) 69.90 (63.50, 75.10) 64.80 (58.08, 72.70) 0.082 Hb (g/L) 143.00 (127.75, 151.50) 141.00 (130.00, 160.25) 0.396 PLT ( /L) 315.00 (284.00, 385.00) 282.00 (236.25, 348.25) 0.021* PCT (%) 0.31 (0.27, 0.37) 0.29 (0.24, 0.32) 0.011* PDW (fL) 10.90 (9.60, 12.83) 10.45 (9.78, 11.50) 0.264 MPV (fL) 9.55±0.96 9.63±0.87 0.664 P-LCR (%) 21.55 (16.20, 27.40) 21.50 (18.10, 26.00) 0.918 ALT (U/L) 17.00 (11.00, 39.00) 18.00 (12.00, 34.00) 1.000 AST (U/L) 18.50 (13.00, 24.00) 17.00 (14.00, 25.25) 0.681 GGT (U/L) 37.00 (26.00, 70.00) 25.50 (18.25, 42.25) 0.006** Cr (mmol/L) 66.00 (58.00, 74.50) 67.00 (57.25, 75.00) 0.879 Glu (mmol/L) 5.00 (4.38, 5.53) 4.90 (4.70, 5.45) 1.000 UA (mmol/L) 392.00 (345.00, 439.00) 379.00 (319.00, 455.00) 0.513 ESR (mm/h) 46.17±30.45 26.88±25.51 0.100 hsCRP (mg/L) 25.48 (11.68, 49.96) 3.37 (1.70, 16.78) <0.001*** Note: In this table, the Shapiro-Wilk test is for assessed normality for the continues variables. Variables that followed a normal distribution are presented as mean±standard deviation. Variables that did not conform to a normal distribution are expressed as median with interquartile range. Significant associations are marked: p-values * < 0.05, ** < 0.01, *** < 0.001 Abbreviations: BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; HS, hidradenitis suppurativa; IHS4, International Hidradenitis Suppurativa Severity Score System; WBC, white blood cell count; NEUT%, neutrophil percentage; Hb, hemoglobin; PLT, platelet count; PCT, plateletcrit; PDW, platelet distribution width; MPV, mean platelet volume; P-LCR, platelet-large cell ratio; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma-glutamyl transferase; Cr, creatinine; Glu, glucose; UA, uric acid; ESR, erythrocyte sedimentation rate; hsCRP, high-sensitivity C-reactive protein. Clinical feature differences between HS patients with abnormal and normal HDL-C levels Analysis of lesion distribution revealed that axillary involvement was significantly more frequent in the HDL-C abnormal group than in the HDL-C normal group (86.6% vs. 71.4%, p =0.017). No significant differences were observed between the two groups in the prevalence of lesions involving the head (70.1% vs. 66.7%, p =0.565), neck (34.3% vs. 14.3%, p =0.050), groin (49.3% vs. 31.0%, p =0.169), buttocks (40.3% vs. 35.7%, p =0.462), or genital region (17.9% vs. 14.3%, p =1.000). Differences in disease severity indices were also evaluated. Although differences in Hurley stage and International Hidradenitis Suppurativa Severity Score System (IHS4) scores between the HDL-C abnormal and normal groups did not reach statistical significance ( p =0.067 for Hurley stage and p =0.451 for IHS4), notable trends were observed. In the HDL-C abnormal group, which included three patients with missing Hurley stage data, 20.3% of patients were classified as Hurley stage I, 35.9% as stage II, and 39.1% as stage III, with a median IHS4 score of 18.00 (10.00, 23.00). In the HDL-C normal group, which included one patient with missing Hurley stage data, the corresponding proportions were 22.2%, 55.6%, and 20.0%, respectively, with a median IHS4 score of 8.00 (5.50, 20.50). While the proportion of patients with Hurley stage I disease was similar between groups, a higher proportion of Hurley stage III disease and higher IHS4 scores were observed in the HDL-C abnormal group. Regarding comorbidities, 38 patients in the HDL-C abnormal group reported at least one comorbid condition. These included metabolic disorders in 28 patients (e.g., obesity, diabetes mellitus, hyperlipidemia), cardiovascular and cerebrovascular diseases in 7 patients (e.g., hypertension, cardiovascular disease, cerebral infarction), immune- and inflammation-related diseases in 8 patients (e.g., inflammatory bowel disease, arthritis, allergies), hepatobiliary diseases in 2 patients (e.g., liver dysfunction, hepatitis B, cholecystitis), infectious diseases in 3 patients (e.g., pulmonary tuberculosis, syphilis), kidney diseases in 3 patients (chronic renal insufficiency), anemia in 3 patients, drug allergies in 10 patients, and other conditions in 9 patients (e.g., accessory spleen nodules, history of prior surgery, psychiatric disorders such as depression). In the HDL-C normal group, 21 patients reported comorbidities, including metabolic disorders in 10 patients, cardiovascular and cerebrovascular diseases in 5 patients, immune- and inflammation-related diseases in 8 patients, hepatobiliary diseases in 5 patients, infectious diseases in 1 patient, kidney diseases in 1 patient, drug allergies in 3 patients, and other conditions in 4 patients. Differences in laboratory test parameters between HS patients with abnormal and normal HDL-C levels In addition to lipid-related indices, laboratory parameters reflecting hematologic, biochemical, and inflammatory status were compared between the HDL-C abnormal and normal groups (Table 2). These analyses included complete blood count indices, biochemical markers, and inflammatory parameters such as white blood cell (WBC) count, alanine aminotransferase (ALT), and erythrocyte sedimentation rate (ESR). Significant differences were observed between the two groups in several laboratory markers. Patients in the HDL-C abnormal group had significantly higher WBC counts (10.46×10 9 /L vs. 8.41×10 9 /L; p =0.002), platelet counts (PLT; 318.00×10 9 /L vs. 282.00×10 9 /L; p =0.021), plateletcrit (PCT; 0.31% vs. 0.29%; p =0.011), gamma-glutamyl transferase (GGT; 36.50 U/L vs. 25.50 U/L; p =0.006), and high-sensitivity C-reactive protein (hsCRP; 25.48 mg/L vs. 3.37 mg/L; p <0.001) compared with patients in the HDL-C normal group (Figure 4). Discussion In this study, patients with HS exhibited significantly higher BMI and TG levels, along with significantly lower HDL-C levels compared with healthy controls. Further stratified analyses demonstrated that patients with HS with abnormal HDL-C levels had significantly higher BMI, WBC, PLT, PCT, GGT, and hsCRP levels than those with normal HDL-C levels. In addition, axillary involvement was more frequent in the abnormal HDL-C group. Collectively, these findings indicate that reduced HDL-C in HS is associated with a heightened systemic inflammatory burden and specific clinical features, supporting HDL-C as a readily accessible marker of inflammatory status and disease severity in this population. Importantly, HDL-C captures aspects of systemic inflammation that may not be fully reflected by conventional inflammatory markers alone. Metabolic alterations and dyslipidemia in HS The present study identified a significantly higher BMI and TG levels, together with significantly lower HDL-C levels in patients with HS compared with healthy controls. These findings are consistent with previous epidemiologic studies establishing obesity as a major risk factor for HS[9] and demonstrating increased TG concentrations and reduced HDL-C concentrations in HS populations, thereby reinforcing the close association between HS and an atherogenic lipid profile[5,7,8,10,11]. Given that HS is increasingly recognized as a chronic inflammatory disease with systemic metabolic involvement, these findings are biologically plausible within the broader framework of inflammation-driven lipid dysregulation. HDL-C plays a central role in reverse cholesterol transport and exerts a range of protective effects, including anti-inflammatory, antioxidant, antithrombotic, and endothelial-protective functions[12–15]. Chronic inflammatory states are known to disrupt lipid metabolism, resulting in a proatherogenic lipid profile characterized by reduced HDL-C and elevated TG levels. This pattern has been extensively described in other chronic inflammatory conditions, such as rheumatoid arthritis, systemic lupus erythematosus, and psoriasis, where inflammation-associated lipid abnormalities contribute to increased cardiovascular risk[10,16]. Within this context, the observed reduction in HDL-C among patients with HS aligns with established mechanisms linking chronic inflammation to adverse lipid profiles. Importantly, emerging evidence suggests that HDL-related abnormalities in HS may not be limited to quantitative reductions in circulating HDL-C. Persistent inflammatory and oxidative stress can induce qualitative changes in HDL particles, leading to impaired anti-inflammatory and antioxidant functionality[15]. A prospective study reported significantly reduced paraoxonase-1 activity, a key indicator of HDL antioxidant capacity, in patients with HS even in the absence of marked HDL-C reduction[17]. This dissociation between HDL-C concentration and functional capacity suggests that inflammation-driven HDL-C dysfunction may contribute to the metabolic and cardiovascular risk observed in HS. Moreover, clinical studies have demonstrated that effective biologic therapy accompanied by attenuation of systemic inflammation is associated with favorable changes in lipid profiles, further supporting the bidirectional relationship between inflammation and lipid metabolism[10]. Taken together, the findings of the present study are consistent with the concept of a coherent inflammatory–metabolic phenotype in HS rather than a nonspecific lipid disturbance. Clinical characteristics associated with normal and abnormal HDL-C in HS When comparing clinical characteristics between HS patients with normal and abnormal HDL-C levels, a higher proportion of patients with abnormal HDL-C were classified as Hurley stage III, although the difference in Hurley stage distribution did not reach statistical significance. This trend suggests a potential association between reduced HDL-C levels and more severe disease. The categorical nature of the Hurley staging system and the limited sample size following stratification may have reduced statistical power to detect significant differences; nevertheless, the observed pattern may still be clinically meaningful. The association between abnormal HDL-C and disease severity may be partially explained by the higher BMI observed in the abnormal HDL-C group, as increased adiposity is a well-established determinant of more severe HS[9,18]. Beyond adiposity, reduced HDL-C may reflect broader disturbances in lipid metabolism that are closely intertwined with chronic systemic inflammation. Given the immunomodulatory and anti-inflammatory properties of HDL, reduced HDL-C levels may indicate a metabolic environment that facilitates sustained inflammatory activity, thereby contributing to disease persistence or progression. From this perspective, HDL-C abnormalities may function not only as markers of metabolic comorbidity but also as indicators of inflammation-driven disease aggravation in HS. In addition, patients with HS and abnormal HDL-C levels were more likely to exhibit axillary involvement. The axilla is one of the most frequently affected anatomical sites in HS[1,18], and axillary lesions have been identified as a risk factor associated with greater disease severity[18,19]. The axillary region is particularly vulnerable to mechanical stress, friction, and local inflammation[20]. Systemic inflammatory and metabolic dysregulation reflected by reduced HDL-C levels may further lower the threshold for lesion development in anatomically predisposed regions such as the axilla, where mechanical stress and local immune activation are already prominent. These findings suggest that disease pathogenesis in patients with predominant axillary involvement may be influenced by both inflammatory and metabolic factors, whereas disease expression at other anatomical sites may be driven more predominantly by inflammatory mechanisms alone. Association of HDL-C abnormalities with clinical laboratory characteristics in HS In this study, patients with HS and abnormal HDL-C levels demonstrated more pronounced abnormalities in inflammation-related laboratory parameters. Specifically, WBC, PLT, PCT, and hsCRP levels were significantly higher in patients with low HDL-C compared with those with normal HDL-C levels. These findings suggest that reduced HDL-C is associated with a heightened systemic inflammatory state, underscoring the close interplay between inflammatory activity and lipid metabolic dysregulation in HS. Elevated WBC levels primarily reflect increased systemic immune activation and inflammatory burden. Previous studies have shown that WBC levels in patients with HS increase in parallel with disease activity and severity, particularly among those with Hurley stage II–III disease [21]. In the present analysis, patients in the low HDL-C group exhibited significantly higher WBC levels, suggesting a more pronounced state of systemic immune activation. Beyond its established role in reverse cholesterol transport, HDL exerts important anti-inflammatory effects by inhibiting monocyte–macrophage activation and neutrophil chemotaxis[12,13]. Reduced HDL-C levels may attenuate these immunomodulatory functions, thereby amplifying HS-associated inflammatory cascades and sustaining leukocyte activation. This mechanism may partially explain why patients with low HDL-C exhibit higher WBC levels under otherwise comparable disease conditions. Alterations in platelet-related parameters further support an inflammation-driven disease phenotype in patients with low HDL-C. Both PLT and PCT were significantly elevated in this subgroup, consistent with prior evidence demonstrating positive correlations between platelet indices and HS disease severity[21,22]. As a chronic inflammatory disorder, HS is characterized by persistent immune activation that can stimulate bone marrow hematopoiesis via pro-inflammatory cytokines such as TNF-α and IL-6, leading to reactive thrombocytosis[21]. In parallel, reduced HDL-C levels may diminish HDL-mediated inhibition of platelet proliferation, aggregation, and activation[23–25], thereby contributing to increased PLT and PCT values. Notably, the sustained elevation of PCT, an indicator of total platelet mass, suggests that thrombocytosis in patients with low HDL-C represents a chronic process rather than a transient inflammatory response, reinforcing its role in perpetuating systemic inflammation in HS. The marked elevation of hsCRP in patients with low HDL-C provides further evidence of increased systemic inflammatory burden. hsCRP is a key acute-phase reactant reflecting cytokine-driven inflammatory activity and is closely linked to lipid metabolic status. Inflammatory signaling can suppress HDL-C synthesis and impair HDL function[15], while reduced HDL-C may in turn weaken its anti-inflammatory capacity, creating a self-reinforcing cycle of inflammation and lipid dysregulation. The significantly higher hsCRP levels observed in the low HDL-C group support the presence of a pathological process in which inflammatory activity and lipid metabolic abnormalities mutually amplify in HS. Taken together, the concurrent elevations in WBC, PLT, PCT, and hsCRP observed in the low HDL-C group indicate that reduced HDL-C is associated with a greater systemic inflammatory burden in HS. In this context, low HDL-C may represent not only a downstream consequence of chronic inflammation but also a contributing factor that sustains inflammatory signaling through diminished anti-inflammatory, anti-platelet, and immunomodulatory effects. HDL-C may therefore serve as an adjunctive marker that integrates metabolic and inflammatory dimensions of disease activity into routine assessment, providing complementary information to conventional inflammatory markers. In addition, HS patients with low HDL-C levels exhibited significantly higher GGT levels compared with those with normal HDL-C. GGT primarily reflects hepatobiliary injury and liver-related dysfunction. Given that patients in the low HDL-C group tended to have more severe disease, elevated GGT levels in this subgroup may be associated with increased medication exposure in the context of greater disease severity. Furthermore, HDL participates in reverse cholesterol transport[14], and reduced HDL-C levels may reflect hepatic lipid accumulation, which could also contribute to elevated GGT levels. Clinical relevance and novel insights from HDL-C-based stratification in HS Through direct comparison with healthy controls, this study confirmed a significant reduction in HDL-C levels in patients with HS, providing additional support for the emerging concept of HDL-C–centered metabolic dysregulation in this disease. By stratifying patients according to HDL-C levels, a routinely available laboratory parameter with both lipid metabolic and immunomodulatory relevance, this analysis extends current understanding of clinical and biological heterogeneity in HS. Beyond its role in reverse cholesterol transport, HDL-C exerts important anti-inflammatory and immunoregulatory effects, including inhibition of monocyte–macrophage activation, neutrophil chemotaxis, and platelet activation. Accordingly, circulating HDL-C levels may reflect the cumulative impact of chronic systemic inflammation and, when reduced, may further contribute to inflammatory persistence through impaired anti-inflammatory and immunomodulatory capacity. This bidirectional relationship provides a biologically plausible framework for investigating lipid–immune interactions in the pathogenesis and progression of HS and supports a mechanistic link between dysregulated lipid metabolism and systemic inflammation in this condition. In the present study, patients with low HDL-C exhibited concordant elevations across multiple inflammation-related markers, including WBC, PLT, PCT, and hsCRP, defining a composite inflammatory phenotype characterized by enhanced systemic immune activation, increased inflammatory burden, and heightened platelet reactivity. Compared with reliance on individual inflammatory markers, this consistent multi-parameter pattern more comprehensively captures the overall inflammatory status of patients with HS. The pronounced inflammatory profile associated with reduced HDL-C suggests a greater disease burden, a more complex systemic inflammatory milieu, and potentially increased risk of disease-related complications. These findings support the potential utility of HDL-C–based stratification for inflammatory risk assessment, follow-up planning, and optimization of therapeutic decision-making in HS. By identifying biologically meaningful differences through stratified analysis within a relatively small cohort, this study provides an important hypothesis-generating foundation for future investigations employing larger sample sizes and prospective or longitudinal study designs. Study limitations and future perspectives Several limitations of this study should be acknowledged. First, the cross-sectional design permits identification of associations between HDL-C levels, systemic inflammatory markers, and selected clinical features in patients with HS, but does not allow causal inferences to be drawn. Accordingly, the present findings should be interpreted as characterizing an inflammatory phenotype associated with HDL-C status rather than implying directional or causal relationships. Reduced HDL-C may represent a consequence of long-standing chronic inflammation in HS; however, it may also actively contribute to inflammatory persistence through impaired anti-inflammatory, anti-platelet, and immunomodulatory functions. Second, the relatively small sample size may limit statistical power and generalizability. Although the overall cohort comprised 109 patients, stratification according to HDL-C levels resulted in smaller subgroup sizes, which may further reduce statistical power and increase the likelihood of type II error. Estimates derived from limited sample sizes are also more susceptible to the influence of extreme values, thereby constraining the robustness and broader applicability of the findings. Third, although categorization of HDL-C, a continuous variable, facilitates clinical interpretation and subgroup comparisons, this approach may lead to loss of information and obscure potential continuous or non-linear associations between HDL-C levels and inflammatory markers. In addition, missing data for certain laboratory parameters may have introduced selection bias, particularly if data were not missing at random. Finally, residual confounding cannot be excluded. Potentially relevant factors, including medication use, coexisting metabolic disorders, and lifestyle variables, could not be fully accounted for and may have influenced both HDL-C levels and inflammatory status. Therefore, the findings of this study should be interpreted as exploratory and warrant confirmation in larger cohorts using prospective and longitudinal study designs to further clarify the temporal and mechanistic relationships between HDL-C, systemic inflammation, and disease manifestations in HS. Conclusions By integrating lipid, inflammatory, and clinical parameters, this study identifies a metabolically linked inflammatory phenotype in HS. Stratified analyses demonstrated that low HDL-C levels were significantly associated with concordant increases in multiple inflammation-related laboratory markers, indicating a close relationship between reduced HDL-C levels and heightened systemic inflammation. These findings suggest that low HDL-C may identify an inflammatory phenotype in HS characterized by increased inflammatory burden and platelet reactivity, reflecting the interplay between inflammatory processes and lipid metabolic dysregulation. As HDL-C is routinely measured in clinical practice, it may serve as a practical marker for assessing systemic inflammation and stratifying inflammatory risk in HS. Further prospective and longitudinal studies are required to clarify the temporal and mechanistic role of HDL-C in HS pathogenesis and its potential value in clinical management and risk assessment. Abbreviations HS hidradenitis suppurativa IHS4 International Hidradenitis Suppurativa Severity Score System BMI body mass index WBC white blood cell count NEUT% neutrophil percentage Hb hemoglobin PLT platelet PCT plateletcrit PDW platelet distribution width MPV mean platelet volume P-LCR platelet-large cell ratio ALT alanine aminotransferase AST aspartate aminotransferase GGT gamma-glutamyl transferase Cr creatinine Glu glucose UA uric acid TG triglycerides TC total cholesterol HDL-C high-density lipoprotein cholesterol LDL-C low-density lipoprotein cholesterol ESR erythrocyte sedimentation rate hsCRP high-sensitivity C-reactive protein Declarations Ethics approval and consent to participate This study was reviewed and approved by the institutional review board of Peking Union Medical College Hospital (Approval number: I-25PJ2964). The requirement for written informed consent was waived due to the retrospective nature of the study. Consent for publication Not applicable. Data availability The datasets supporting the conclusions of this article are available from Chao Wu on reasonable request. Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding This study was supported by Peking Union Medical College Hospital Talent Cultivation Program Category D [grant number UHB11983]; the National Natural Science Foundation of China [grant number 82573978]; Beijing Key Clinical Specialty Construction Project; National Key Clinical Specialty Project of China; and Beijing Natural Science Foundation [grant number 7242109]. Authors’ contributions YJ Wang and Wu had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors contributed to the study concept and design, and all participated in the acquisition, analysis, or interpretation of data. All authors participated in the drafting of the manuscript and critical revision of the manuscript for important intellectual content. YJ Wang, and TJ Liu performed the statistical analysis. HZ Jin were responsible for study supervision. All authors have read and approved the final version. References Ballard K, Sathe NC, Shuman VL. Hidradenitis Suppurativa. StatPearls [Internet] [Internet]. StatPearls Publishing; 2024 [cited 2025 Dec 26]. https://www.ncbi.nlm.nih.gov/books/NBK534867/. Accessed 26 Dec 2025 Elzawawi KE, Elmakaty I, Habibullah M, Ahmed MB, Lahham SA, Harami SA, et al. Hidradenitis suppurativa and its association with obesity, smoking, and diabetes mellitus: A systematic review and meta‐analysis. [cited 2026 Jan 1]; https://doi.org/10.1111/iwj.70035 Hidradenitis Suppurativa: Beyond Skin Concerns to Focus on Cardiovascular Considerations [Internet]. JCAD - The Journal of Clinical and Aesthetic Dermatology. [cited 2026 Jan 1]. https://jcadonline.com/hidradenitis-suppurativa-beyond-skin-concerns-to-focus-on-cardiovascular-considerations/. Accessed 1 Jan 2026 Jørgensen A-HR, Aarestrup J, Baker JL, Thomsen SF. Association of Birth Weight, Childhood Body Mass Index, and Height With Risk of Hidradenitis Suppurativa. JAMA Dermatol. 2020;156:746. https://doi.org/10.1001/jamadermatol.2020.1047 Miller IM, Ellervik C, Vinding GR, Zarchi K, Ibler KS, Knudsen KM, et al. Association of Metabolic Syndrome and Hidradenitis Suppurativa. JAMA Dermatol. 2014;150:1273–80. https://doi.org/10.1001/jamadermatol.2014.1165 Van Straalen KR, Vanlaerhoven AMJD, Ardon CB, Van Der Zee HH. Body mass index at the onset of hidradenitis suppurativa. J Deutsche Derma Gesell. 2021;19:437–9. https://doi.org/10.1111/ddg.14433 Li Y-H, Chuang S-H, Huang Y-C, Yang H-J. A comprehensive systemic review and meta-analysis of the association between lipid profile and hidradenitis suppurativa. Arch Dermatol Res. 2025;317:225. https://doi.org/10.1007/s00403-024-03762-y Almenara-Blasco M, Gracia-Cazaña T, Poblador-Plou B, Laguna-Berna C, Navarro-Bielsa A, Moreno-Juste A, et al. Multimorbidity of hidradenitis suppurativa: a cross-sectional population-based study of its associated comorbidities. Front Med (Lausanne). 2025;12:1618975. https://doi.org/10.3389/fmed.2025.1618975 Elzawawi KE, Elmakaty I, Habibullah M, Ahmed MB, Al Lahham S, Al Harami S, et al. Hidradenitis suppurativa and its association with obesity, smoking, and diabetes mellitus: A systematic review and meta‐analysis. Int Wound J. 2024;21:e70035. https://doi.org/10.1111/iwj.70035 Cascio Ingurgio R, Alfano A, Matteodo E, Stark M, Valenti M, Narcisi A, et al. Effectiveness of Biologic Therapy in Hidradenitis Suppurativa: Real-World Clinical Outcomes and Lipid Profile Evaluation. Australasian Journal of Dermatology. 2025;66:468–74. https://doi.org/10.1111/ajd.14592 Choi E, Mir SA, Ji S, Ooi XT, Chua EWL, Yi Wei Y, et al. Understanding the systemic burden of disease in hidradenitis suppurativa from plasma lipidomic analysis. Journal of Dermatological Science. 2022;107:133–41. https://doi.org/10.1016/j.jdermsci.2022.08.005 Barker G, Weiner J, Guirgis F, Reddy S. HDL and Persistent Inflammation Immunosuppression and Catabolism Syndrome. Curr Opin Lipidol. 2021;32:315–22. https://doi.org/10.1097/MOL.0000000000000782 Bonacina F, Pirillo A, Catapano AL, Norata GD. HDL in Immune-Inflammatory Responses: Implications beyond Cardiovascular Diseases. Cells. 2021;10:1061. https://doi.org/10.3390/cells10051061 Fisher EA, Feig JE, Hewing B, Hazen SL, Smith JD. HDL Function, Dysfunction, and Reverse Cholesterol Transport. Arterioscler Thromb Vasc Biol. 2012;32:2813–20. https://doi.org/10.1161/ATVBAHA.112.300133 Madaudo C, Bono G, Ortello A, Astuti G, Mingoia G, Galassi AR, et al. Dysfunctional High-Density Lipoprotein Cholesterol and Coronary Artery Disease: A Narrative Review. J Pers Med. 2024;14:996. https://doi.org/10.3390/jpm14090996 Feingold KR, Grunfeld C. Effect of Inflammation and Infection on Lipids and Lipoproteins. Endotext [Internet] [Internet]. MDText.com, Inc.; 2025 [cited 2026 Jan 1]. https://www.ncbi.nlm.nih.gov/books/NBK326741/. Accessed 1 Jan 2026 Molinelli E, Morresi C, Dragonetti ML, Simoni ED, Candelora M, Marasca S, et al. Oxidative Stress, High Density Lipoproteins and Hidradenitis Suppurativa: A Prospective Study. Antioxidants [Internet]. publisher; 2025 [cited 2026 Jan 1];14. https://doi.org/10.3390/antiox14081014 Kaya G, Özgen FP, Kelahmetoğlu O, Su Küçük Ö, Onsun N. Demographic features, clinical characteristics, and comorbid relation in hidradenitis suppurativa: a population-based study. Front Med [Internet]. Frontiers; 2025 [cited 2026 Jan 6];11. https://doi.org/10.3389/fmed.2024.1499509 Chokevittaya P, Jirattikanwong N, Thongngarm T, Phinyo P, Wongsa C. Factors Associated With Dupilumab Response in Atopic Dermatitis: A Systematic Review and Meta-Analysis. The Journal of Allergy and Clinical Immunology: In Practice. 2024;12:3044–56. https://doi.org/10.1016/j.jaip.2024.08.054 Themes UFO. The Role of Mechanical Stress in Hidradenitis Suppurativa [Internet]. Plastic Surgery Key. 2018 [cited 2026 Jan 6]. https://plasticsurgerykey.com/the-role-of-mechanical-stress-in-hidradenitis-suppurativa/. Accessed 6 Jan 2026 Gunter SJ, Holcomb ZE, Lam BD, Porter ML, Kimball AB. Hematologic Abnormalities in Patients With Hidradenitis Suppurativa Receiving Adalimumab. JAMA Dermatol. 2024;160:678–81. https://doi.org/10.1001/jamadermatol.2024.1075 Morss-Walton PC, Greif C, Holcomb ZE, Salian P, Yankama T, Kim D, et al. Treatment of hidradenitis suppurativa resolves associated hematologic abnormalities. International Journal of Women’s Dermatology. 2022;8:e011. https://doi.org/10.1097/JW9.0000000000000011 Aviram M, Brook JG. Platelet interaction with high and low density lipoproteins. Atherosclerosis. 1983;46:259–68. https://doi.org/10.1016/0021-9150(83)90176-4 Murphy AJ, Bijl N, Yvan-Charvet L, Welch CB, Bhagwat N, Reheman A, et al. Cholesterol efflux in megakaryocyte progenitors suppresses platelet production and thrombocytosis. Nature medicine. 2013;19:586. https://doi.org/10.1038/nm.3150 van der Stoep M, Korporaal SJA, Van Eck M. High-density lipoprotein as a modulator of platelet and coagulation responses. Cardiovasc Res. 2014;103:362–71. https://doi.org/10.1093/cvr/cvu137 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Apr, 2026 Read the published version in Lipids in Health and Disease → Version 1 posted Editorial decision: Revision requested 09 Apr, 2026 Reviews received at journal 08 Apr, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviews received at journal 17 Feb, 2026 Reviewers agreed at journal 17 Feb, 2026 Reviewers invited by journal 17 Feb, 2026 Editor assigned by journal 10 Feb, 2026 Submission checks completed at journal 10 Feb, 2026 First submitted to journal 10 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8838333","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593753418,"identity":"85cd2f92-a257-4fc4-bfbd-abd8a2482892","order_by":0,"name":"Chao Wu","email":"","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Wu","suffix":""},{"id":593753419,"identity":"ae9f42cd-0168-4a69-98ab-3d1d0a107879","order_by":1,"name":"Yong-jun Wang","email":"","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yong-jun","middleName":"","lastName":"Wang","suffix":""},{"id":593753421,"identity":"746b7aff-cd6f-45d0-8b84-b165ddc78393","order_by":2,"name":"Tie-jun Liu","email":"","orcid":"","institution":"National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Medical College","correspondingAuthor":false,"prefix":"","firstName":"Tie-jun","middleName":"","lastName":"Liu","suffix":""},{"id":593753424,"identity":"c0668977-3fad-493e-8d90-8219b825f067","order_by":3,"name":"Hong-Zhong Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACxmYGNjDDAER8qLCxI00L44wzackguoGALoQWZt62QyDl+LUwt/Mee/BxR629OXvv4dc2bAeYGaSbjz/A7zC+dMOZZ44zW/acS7PO4bnDxyBzLBGvLYzNPGbSvG3H2Axu5JgZ50g8Y2aQyDEkSgsPWIuFwWHGBon8j8RoqZEAajF+zJAA0pKD3/sgLZIz2w4YGJw5Y8bYcyAtmU0izXAGPi2G/WfMJD621dkbHO8x/vDzn40dv0Tygw94tUDccBhEsEmASXzKQUAeQtWBCGa8ho+CUTAKRsHIBQBzoUhk4dyPdQAAAABJRU5ErkJggg==","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College","correspondingAuthor":true,"prefix":"","firstName":"Hong-Zhong","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2026-02-10 08:24:59","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8838333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8838333/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12944-026-02959-6","type":"published","date":"2026-04-23T15:59:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":103216530,"identity":"90201119-519f-4954-9906-40807d275e31","added_by":"auto","created_at":"2026-02-23 09:36:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1485812,"visible":true,"origin":"","legend":"\u003cp\u003eThe box-and-whisker plots of lipid profile data between HS patients and healthy controls. (A) Triglyceride (TG). (B) Total Cholesterol (TC). (C) High-Density Lipoprotein Cholesterol (HDL-C). (D) Low-Density Lipoprotein Cholesterol (LDL-C).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8838333/v1/694328292c304b2870ea29a4.jpg"},{"id":103216531,"identity":"a0da4374-479d-4d89-a38e-1a764c106878","added_by":"auto","created_at":"2026-02-23 09:36:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2217248,"visible":true,"origin":"","legend":"\u003cp\u003eClinical images of hidradenitis suppurativa lesions in patients with abnormal HDL-C levels. (A) Abscesses, sinus tracts, and scarring in the right axilla (24-year-old female). (B) Deep sinus tracts, erosions, and scarring in the right axilla (31-year-old male). (C) Diffuse erythematous nodules, abscesses, erosions, and scarring on the chest and bilateral upper limbs (24-year-old male). (D) Multiple nodules and abscesses with sinus tracts, erosions, and scarring in the bilateral inguinal regions (18-year-old male). (E) Multiple abscesses with sinus tracts and scarring in the bilateral gluteal regions (31-year-old male). (F) Multiple erythematous nodules, scarring, and hyperpigmentation on the bilateral gluteal regions and thighs (40-year-old male). The patient had a history of long-term oral corticosteroid therapy, resulting in generalized skin thinning and extensive striae.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8838333/v1/fc3e0836b35bb719c4aef1a9.jpg"},{"id":103216533,"identity":"84e35dee-3b36-4915-8d80-432483b5b344","added_by":"auto","created_at":"2026-02-23 09:36:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2290227,"visible":true,"origin":"","legend":"\u003cp\u003eClinical images of hidradenitis suppurativa lesions in patients with normal HDL-C levels. (A) Multiple abscesses with purulent sinus tracts and scarring in the right axilla (25-year-old male). (B) Diffuse nodules and abscesses with scarring on the scalp, nape, and upper back (22-year-old male). (C) Nodules with erosions, sinus tracts, and scarring in the right inguinal region (19-year-old male). (D) Erythematous nodules with erosions, sinus tracts, and scarring in the bilateral inguinal regions (23-year-old male). (E) Multiple erythematous nodules with sinus tracts and scarring in the perianal region (21-year-old male). (F) Multiple abscesses with purulent sinus tracts and scarring in the bilateral gluteal regions (27-year-old male).\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8838333/v1/0792fd6608b1467461f4e351.jpg"},{"id":103216534,"identity":"056fff8b-84a8-458d-9c60-8fe19ff248be","added_by":"auto","created_at":"2026-02-23 09:36:24","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7033345,"visible":true,"origin":"","legend":"\u003cp\u003eThe box-and-whisker plots of Hurley stage and laboratory test results between HS patients with abnormal and normal HDL-C levels. (A) Hurley stages. (B) White Blood Cell (WBC). (C) Neutrophil Percentage (NEUT%). (D) Hemoglobin (Hb). (E) Platelet (PLT). (F) Procalcitonin (PCT). (G) Platelet Distribution Width (PDW). (H) Mean Platelet Volume (MPV). (I) Platelet Large Cell Ratio (P-LCR). (J) Alanine Aminotransferase (ALT). (K) Aspartate Aminotransferase (AST). (L) Gamma-Glutamyl Transferase (GGT). (M) Creatinine (Cr). (N) Glucose (Glu). (O) Uric Acid (UA). (P) Erythrocyte Sedimentation Rate (ESR). (Q) high-sensitivity C-reactive protein (hsCRP).\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8838333/v1/0476fd740cc84b168acce949.jpg"},{"id":107928165,"identity":"aa897fc1-aad9-47b0-8221-9fa6fc33f52e","added_by":"auto","created_at":"2026-04-27 16:08:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13383442,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8838333/v1/66ea425d-9ff2-4d55-a797-4916d8d38a9f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Decreased HDL-C in Hidradenitis Suppurativa: Association with Systemic Inflammatory Phenotype and Implications for Risk Stratification","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHidradenitis suppurativa (HS) is a chronic, relapsing inflammatory skin disease characterized by painful, deep-seated nodules, abscesses, sinus tracts, and fibrotic scarring that predominantly affect intertriginous regions, including the axillary, inguinal, perianal, perineal, and inframammary areas[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although HS has traditionally been regarded as a cutaneous disorder, growing evidence supports its classification as a systemic inflammatory disease with substantial extracutaneous involvement. Patients with HS exhibit a high prevalence of metabolic comorbidities, including obesity, insulin resistance, dyslipidemia, and an increased risk of cardiovascular risk[\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eObesity represents one of the most well-established and clinically significant risk factors for HS and is closely associated with both disease severity and chronicity[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Individuals with obesity frequently exhibit concomitant metabolic disturbances, particularly dyslipidemia. Similar patterns of inflammation-associated dyslipidemia have been observed in other chronic inflammatory diseases, reinforcing the relevance of lipid abnormalities as systemic markers rather than isolated metabolic findings. Accumulating evidence suggests that HS is associated with a characteristic dyslipidemic profile, most notably reduced high-density lipoprotein cholesterol (HDL-C) levels and elevated triglyceride (TG) concentrations. A systematic review and meta-analysis demonstrated significantly lower HDL-C and higher TG levels in patients with HS compared with healthy controls, while total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) did not differ significantly between groups[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Consistent with these findings, a large cohort study reported a 10%\u0026ndash;14% reduction in HDL-C and an 11%\u0026ndash;21% increase in TG levels among individuals with HS[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. More recently, a cross-sectional study involving more than 3,000 patients confirmed a significantly increased risk of dyslipidemia in HS, identifying low HDL-C as the most prominent lipid abnormality, particularly in patients with obesity[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Collectively, these data indicate a strong association between HS and lipid metabolism abnormalities, with HDL-C emerging as a central component of this metabolic phenotype.\u003c/p\u003e \u003cp\u003eDespite these advances, existing studies have largely described lipid alterations at the population level, and direct comparisons of clinical characteristics between patients with HS who have abnormal versus normal lipid profiles remain limited. More importantly, it remains unclear whether lipid abnormalities, particularly reduced HDL-C, are associated with distinct inflammatory phenotypes or differential disease expression in HS. Clarifying these associations is clinically relevant, as HDL-C plays a central role in lipid metabolism and possesses anti-inflammatory and immunomodulatory properties that may influence disease activity. Accordingly, the present study aimed to characterize lipid profiles in patients with HS and to compare clinical features and laboratory parameters between HS patients with abnormal and normal HDL-C levels. Through this stratified approach, the study sought to clarify the clinical relevance of HDL-C\u0026ndash;related metabolic alterations in HS and to explore their potential implications for disease development and clinical management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and patients\u003c/h2\u003e \u003cp\u003eThis single-center retrospective study included patients with HS who had available lipid profile data and were treated at Peking Union Medical College Hospital between January 1, 2020, and November 1, 2025. Patients were clinically diagnosed with HS, follicular occlusion triad, or acne inversa. Diagnosis was established based on characteristic clinical features, including recurrent inflammatory lesions such as nodules, cysts, scars, and sinus tracts, in combination with typical anatomical distribution involving intertriginous regions, including the axillae, groin, buttocks, and genital area.\u003c/p\u003e \u003cp\u003eA total of 109 patients with HS were included in the analysis. For each patient with HS, one age- and sex-matched healthy individual was selected as a control from the Health Examination Center of Peking Union Medical College Hospital between January 1, 2020, and November 1, 2025. Fasting lipid parameters, including TG, TC, HDL-C, and LDL-C, were collected and analyzed in both the HS and control groups. Patients with HS were further stratified according to HDL-C levels into an HDL-C abnormal group (n\u0026thinsp;=\u0026thinsp;64) and an HDL-C normal group (n\u0026thinsp;=\u0026thinsp;45), and differences in clinical characteristics and laboratory parameters between these groups were subsequently examined. The study was conducted in accordance with the Declaration of Helsinki and was approved by the institutional ethics committee (approval number I-25PJ2964).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection and Outcome Definitions\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eData collection and Outcome Definitions\u003c/div\u003e \u003cp\u003eClinical and laboratory data were obtained from medical records, with all clinical assessments performed by dermatologists. Data collected from patients with HS included sex, age at disease onset, disease duration, family history, body mass index (BMI), disease severity assessed using the Hurley staging system and the International Hidradenitis Suppurativa Severity Score System (IHS4), lesion distribution, types of cutaneous lesions, comorbidities, and laboratory test results. Lesion locations were recorded for the head, neck, axillae, groin, buttocks, and genital region. Cutaneous lesion types included cysts, scars, nodules, and sinus tracts. Abnormal lipid profiles were defined as either TG\u0026thinsp;\u0026ge;\u0026thinsp;2.3 mmol/L, TC\u0026thinsp;\u0026ge;\u0026thinsp;6.2 mmol/L, HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.0 mmol/L, or LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;4.1 mmol/L. For healthy controls, available data included sex, age, and serum lipid parameters (TG, TC, HDL-C, and LDL-C).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eNormality of continuous variables was assessed using the Shapiro\u0026ndash;Wilk test in combination with quantile\u0026ndash;quantile (Q\u0026ndash;Q) plots for variables with a sample size less than 50, and the Kolmogorov\u0026ndash;Smirnov test combined with Q\u0026ndash;Q plots for variables with sample sizes of 50 or greater. For continuous variables, normally distributed data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and were compared between groups using the Welch t-test. Non-normally distributed data are presented as median with interquartile range (25th\u0026ndash;75th percentiles) and were compared using the Mann\u0026ndash;Whitney U test. Categorical variables are presented as counts and proportions. Group comparisons were performed using Fisher\u0026rsquo;s exact test for variables with two categories and the chi-square test for variables with more than two categories. All statistical analyses were conducted using Python with the \u003cem\u003estatsmodels\u003c/em\u003e library. A two-sided \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDifferences in lipid profiles between patients with HS and healthy controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 109 patients with HS were included in the analysis, comprising 27 inpatients and 82 outpatients. The cohort consisted of 95 males and 14 females, with a mean age of 30.03 \u0026plusmn; 11.46 years. An equal number of age- and sex-matched healthy individuals without HS were selected as the control group. No significant differences were observed between the HS and control groups with respect to sex distribution (\u003cem\u003ep\u003c/em\u003e=1.000) or age (\u003cem\u003ep\u003c/em\u003e=0.139). In contrast, BMI was significantly higher in patients with HS compared with healthy controls (28.61 \u0026plusmn; 5.87 vs. 22.35 \u0026plusmn; 2.06, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) (Table 1).\u003c/p\u003e\n\u003cp\u003eTable 1. Clinical characteristics of HS patients and healthy controls\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6781%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.3056%;\"\u003e\n \u003cp\u003eHS patients (n=109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7414%;\"\u003e\n \u003cp\u003eHealthy controls (n=109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2749%;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6781%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.3056%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7414%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6781%;\"\u003e\n \u003cp\u003eSex (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.3056%;\"\u003e\n \u003cp\u003e95 (87.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7414%;\"\u003e\n \u003cp\u003e95 (87.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6781%;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.3056%;\"\u003e\n \u003cp\u003e29.0 (20.0, 36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7414%;\"\u003e\n \u003cp\u003e29.0 (24.0, 36.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6781%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.3056%;\"\u003e\n \u003cp\u003e28.61\u0026plusmn;5.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7414%;\"\u003e\n \u003cp\u003e22.35\u0026plusmn;2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6781%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory test results\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.3056%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7414%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6781%;\"\u003e\n \u003cp\u003eTG (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.3056%;\"\u003e\n \u003cp\u003e1.13 (0.84, 1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7414%;\"\u003e\n \u003cp\u003e0.79 (0.59, 1.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6781%;\"\u003e\n \u003cp\u003eTC (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.3056%;\"\u003e\n \u003cp\u003e4.26\u0026plusmn;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7414%;\"\u003e\n \u003cp\u003e4.29\u0026plusmn;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e0.783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6781%;\"\u003e\n \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.3056%;\"\u003e\n \u003cp\u003e0.96\u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7414%;\"\u003e\n \u003cp\u003e1.36\u0026plusmn;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 25.6781%;\"\u003e\n \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.3056%;\"\u003e\n \u003cp\u003e2.61 (2.29, 3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 30.7414%;\"\u003e\n \u003cp\u003e2.69 (2.28, 3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.2749%;\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: In this table, the Shapiro-Wilk test is for assessed normality for the continues variables. Variables that followed a normal distribution are presented as mean\u0026plusmn;standard deviation. Variables that did not conform to a normal distribution are expressed as median with interquartile range.\u003c/p\u003e\n\u003cp\u003eSignificant associations are marked: p-values *** \u0026lt; 0.001\u003c/p\u003e\n\u003cp\u003eAbbreviations: HS, hidradenitis suppurativa; BMI, body mass index; TG, triglycerides; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.\u003c/p\u003e\n\u003cp\u003eComparison of serum lipid parameters demonstrated no significant differences between the HS and control groups in TC (\u003cem\u003ep\u003c/em\u003e=0.783) or LDL-C (\u003cem\u003ep\u003c/em\u003e=0.966). However, patients with HS exhibited significantly higher TG concentrations, with a median value of 1.13 (0.84, 1.64) mmol/L compared with 0.79 (0.59, 1.08) mmol/L in healthy controls (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001), as well as significantly lower HDL-C levels (0.96\u0026plusmn;0.23 vs. 1.36\u0026plusmn;0.28,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e\u0026lt;0.001) (Figure 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDemographic differences between HS patients with abnormal and normal HDL-C levels\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 109 patients with HS were stratified according to HDL-C status into an HDL-C abnormal group (HDL-C \u0026lt;1.0 mmol/L) (Figure 2) and an HDL-C normal group (Figure 3). The HDL-C abnormal group consisted of 59 men and 5 women, whereas the HDL-C normal group included 36 men and 9 women. Baseline demographic and clinical characteristics are summarized in Table 2. A significant difference in BMI was observed between the two groups. Patients in the HDL-C abnormal group had a higher mean BMI than those in the HDL-C normal group (30.07 \u0026plusmn; 5.90 vs. 25.80 \u0026plusmn; 4.83, \u003cem\u003ep\u003c/em\u003e=0.019), indicating a greater degree of adiposity among patients with reduced HDL-C levels.\u003c/p\u003e\n\u003cp\u003eTable 2. Clinical characteristics of HS patients with abnormal and normal HDL-C\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003eAbnormal HDL-C (n=64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eNormal HDL-C (n=45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eSex (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e59 (92.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e36 (80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e30.07\u0026plusmn;5.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e25.80\u0026plusmn;4.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.019*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHS Clinical Features\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eAge at onset (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e20.92 (16.00, 28.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e19.38 (15.25, 26.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eDisease duration (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e5.00 (2.50, 10.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e6.50 (3.00, 10.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eFamily history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;Hurley stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eStage I\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e13 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e10 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eStage II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e23 (35.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e25 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eStage III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e25 (39.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e9 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eIHS4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e18.00 (10.00, 23.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e8.00 (5.50, 20.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eLesion locations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eHead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e46 (70.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e29 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.565\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eNeck\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e22 (34.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e7 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;Axillae\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e57 (86.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e31 (71.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.017*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;Groin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e31 (49.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e15 (31.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;Buttock\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e27 (40.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e15 (35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;Genital region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e11 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e7 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eLesion types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eNodules\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e47 (76.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e35 (73.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eAbscesses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e47 (70.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e34 (81.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eScars\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e41 (65.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e25 (52.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eSinus tracts\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e27 (40.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e13 (31.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory test results\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;WBC (\u003cimg width=\"19\" height=\"14\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e10.41 (8.93,12.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e8.41 (6.63, 10.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eNUET% (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e69.90 (63.50, 75.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e64.80 (58.08, 72.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;Hb (g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e143.00 (127.75, 151.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e141.00 (130.00, 160.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;PLT (\u003cimg width=\"19\" height=\"14\" src=\"data:image/png;base64,R0lGODlhHAAVAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAEAAgAaAA8AhQAAAAAAAAAAOgAAZgA6OgA6kABmtjoAADoAOjo6kDpmZjpmkDqQ22YAAGYAOmY6AGY6OmaQ22a222a2/5A6AJBmOpDb/7ZmALbb/7b//9uQOtu2kNv///+2Zv/bkP/btv//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwaWQIBwSCwWPY+AIGJsNkENCcBzmDivws4gI7wwhhuIpRiqBAIJjNCDGH8oBsAnuSWGKAXOuw7QnBUPDB4LGBp8WQJjAB0BVkQgDop9hwAXfFBxQ3NfQ4ZcQnd5QlCidwRSRJ5DoRygeK1Xqq6iAKxYsrWvow2ZsZSWn1Ccvp9Cfo6MjrGJjw15tk1lB2cBBExCc2cLsE1BADs=\" alt=\"image\"\u003e/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e315.00 (284.00, 385.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e282.00 (236.25, 348.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.021*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;PCT (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e0.31 (0.27, 0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e0.29 (0.24, 0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.011*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;PDW (fL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e10.90 (9.60, 12.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e10.45 (9.78, 11.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;MPV (fL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e9.55\u0026plusmn;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e9.63\u0026plusmn;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;P-LCR (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e21.55 (16.20, 27.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e21.50 (18.10, 26.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;ALT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e17.00 (11.00, 39.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e18.00 (12.00, 34.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;AST (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e18.50 (13.00, 24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e17.00 (14.00, 25.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;GGT (U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e37.00 (26.00, 70.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e25.50 (18.25, 42.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.006**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eCr (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e66.00 (58.00, 74.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e67.00 (57.25, 75.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;Glu (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e5.00 (4.38, 5.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e4.90 (4.70, 5.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;UA (mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e392.00 (345.00, 439.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e379.00 (319.00, 455.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;ESR (mm/h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e46.17\u0026plusmn;30.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e26.88\u0026plusmn;25.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;hsCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 158px;\"\u003e\n \u003cp\u003e25.48 (11.68, 49.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e3.37 (1.70, 16.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: In this table, the Shapiro-Wilk test is for assessed normality for the continues variables. Variables that followed a normal distribution are presented as mean\u0026plusmn;standard deviation. Variables that did not conform to a normal distribution are expressed as median with interquartile range.\u003c/p\u003e\n\u003cp\u003eSignificant associations are marked: p-values * \u0026lt; 0.05, ** \u0026lt; 0.01, *** \u0026lt; 0.001\u003c/p\u003e\n\u003cp\u003eAbbreviations: BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; HS, hidradenitis suppurativa; IHS4, International Hidradenitis Suppurativa Severity Score System; WBC, white blood cell count; NEUT%, neutrophil percentage; Hb, hemoglobin; PLT, platelet count; PCT, plateletcrit; PDW, platelet distribution width; MPV, mean platelet volume; P-LCR, platelet-large cell ratio; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma-glutamyl transferase; Cr, creatinine; Glu, glucose; UA, uric acid; ESR, erythrocyte sedimentation rate; hsCRP, high-sensitivity C-reactive protein.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical feature differences between HS patients with abnormal and normal HDL-C levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of lesion distribution revealed that axillary involvement was significantly more frequent in the HDL-C abnormal group than in the HDL-C normal group (86.6% vs. 71.4%, \u003cem\u003ep\u003c/em\u003e=0.017). No significant differences were observed between the two groups in the prevalence of lesions involving the head (70.1% vs. 66.7%, \u003cem\u003ep\u003c/em\u003e=0.565), neck (34.3% vs. 14.3%, \u003cem\u003ep\u003c/em\u003e=0.050), groin (49.3% vs. 31.0%, \u003cem\u003ep\u003c/em\u003e=0.169), buttocks (40.3% vs. 35.7%, \u003cem\u003ep\u003c/em\u003e=0.462), or genital region (17.9% vs. 14.3%, \u003cem\u003ep\u003c/em\u003e=1.000).\u003c/p\u003e\n\u003cp\u003eDifferences in disease severity indices were also evaluated. Although differences in Hurley stage and International Hidradenitis Suppurativa Severity Score System (IHS4) scores between the HDL-C abnormal and normal groups did not reach statistical significance (\u003cem\u003ep\u003c/em\u003e=0.067 for Hurley stage and \u003cem\u003ep\u003c/em\u003e=0.451 for IHS4), notable trends were observed. In the HDL-C abnormal group, which included three patients with missing Hurley stage data, 20.3% of patients were classified as Hurley stage I, 35.9% as stage II, and 39.1% as stage III, with a median IHS4 score of 18.00 (10.00, 23.00). In the HDL-C normal group, which included one patient with missing Hurley stage data, the corresponding proportions were 22.2%, 55.6%, and 20.0%, respectively, with a median IHS4 score of 8.00 (5.50, 20.50). While the proportion of patients with Hurley stage I disease was similar between groups, a higher proportion of Hurley stage III disease and higher IHS4 scores were observed in the HDL-C abnormal group.\u003c/p\u003e\n\u003cp\u003eRegarding comorbidities, 38 patients in the HDL-C abnormal group reported at least one comorbid condition. These included metabolic disorders in 28 patients (e.g., obesity, diabetes mellitus, hyperlipidemia), cardiovascular and cerebrovascular diseases in 7 patients (e.g., hypertension, cardiovascular disease, cerebral infarction), immune- and inflammation-related diseases in 8 patients (e.g., inflammatory bowel disease, arthritis, allergies), hepatobiliary diseases in 2 patients (e.g., liver dysfunction, hepatitis B, cholecystitis), infectious diseases in 3 patients (e.g., pulmonary tuberculosis, syphilis), kidney diseases in 3 patients (chronic renal insufficiency), anemia in 3 patients, drug allergies in 10 patients, and other conditions in 9 patients (e.g., accessory spleen nodules, history of prior surgery, psychiatric disorders such as depression). In the HDL-C normal group, 21 patients reported comorbidities, including metabolic disorders in 10 patients, cardiovascular and cerebrovascular diseases in 5 patients, immune- and inflammation-related diseases in 8 patients, hepatobiliary diseases in 5 patients, infectious diseases in 1 patient, kidney diseases in 1 patient, drug allergies in 3 patients, and other conditions in 4 patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferences in laboratory test parameters between HS patients with abnormal and normal HDL-C levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to lipid-related indices, laboratory parameters reflecting hematologic, biochemical, and inflammatory status were compared between the HDL-C abnormal and normal groups (Table 2). These analyses included complete blood count indices, biochemical markers, and inflammatory parameters such as white blood cell (WBC) count, alanine aminotransferase (ALT), and erythrocyte sedimentation rate (ESR).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSignificant differences were observed between the two groups in several laboratory markers. Patients in the HDL-C abnormal group had significantly higher WBC counts (10.46\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L vs. 8.41\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L; \u003cem\u003ep\u003c/em\u003e=0.002), platelet counts (PLT; 318.00\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L vs. 282.00\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L; \u003cem\u003ep\u003c/em\u003e=0.021), plateletcrit (PCT; 0.31% vs. 0.29%; \u003cem\u003ep\u003c/em\u003e=0.011), gamma-glutamyl transferase (GGT; 36.50 U/L vs. 25.50 U/L; \u003cem\u003ep\u003c/em\u003e=0.006), and high-sensitivity C-reactive protein (hsCRP; 25.48 mg/L vs. 3.37 mg/L; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) compared with patients in the HDL-C normal group (Figure 4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, patients with HS exhibited significantly higher BMI and TG levels, along with significantly lower HDL-C levels compared with healthy controls. Further stratified analyses demonstrated that patients with HS with abnormal HDL-C levels had significantly higher BMI, WBC, PLT, PCT, GGT, and hsCRP levels than those with normal HDL-C levels. In addition, axillary involvement was more frequent in the abnormal HDL-C group. Collectively, these findings indicate that reduced HDL-C in HS is associated with a heightened systemic inflammatory burden and specific clinical features, supporting HDL-C as a readily accessible marker of inflammatory status and disease severity in this population. Importantly, HDL-C captures aspects of systemic inflammation that may not be fully reflected by conventional inflammatory markers alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolic alterations and dyslipidemia in HS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study identified a significantly higher BMI and TG levels, together with significantly lower HDL-C levels in patients with HS compared with healthy controls. These findings are consistent with previous epidemiologic studies establishing obesity as a major risk factor for HS[9]\u0026nbsp;and demonstrating increased TG concentrations and reduced HDL-C concentrations in HS populations, thereby reinforcing the close association between HS and an atherogenic lipid profile[5,7,8,10,11].\u003c/p\u003e\n\u003cp\u003eGiven that HS is increasingly recognized as a chronic inflammatory disease with systemic metabolic involvement, these findings are biologically plausible within the broader framework of inflammation-driven lipid dysregulation. HDL-C plays a central role in reverse cholesterol transport and exerts a range of protective effects, including anti-inflammatory, antioxidant, antithrombotic, and endothelial-protective functions[12–15]. Chronic inflammatory states are known to disrupt lipid metabolism, resulting in a proatherogenic lipid profile characterized by reduced HDL-C and elevated TG levels. This pattern has been extensively described in other chronic inflammatory conditions, such as rheumatoid arthritis, systemic lupus erythematosus, and psoriasis, where inflammation-associated lipid abnormalities contribute to increased cardiovascular risk[10,16]. Within this context, the observed reduction in HDL-C among patients with HS aligns with established mechanisms linking chronic inflammation to adverse lipid profiles.\u003c/p\u003e\n\u003cp\u003eImportantly, emerging evidence suggests that HDL-related abnormalities in HS may not be limited to quantitative reductions in circulating HDL-C. Persistent inflammatory and oxidative stress can induce qualitative changes in HDL particles, leading to impaired anti-inflammatory and antioxidant functionality[15]. A prospective study reported significantly reduced paraoxonase-1 activity, a key indicator of HDL antioxidant capacity, in patients with HS even in the absence of marked HDL-C reduction[17]. This dissociation between HDL-C concentration and functional capacity suggests that inflammation-driven HDL-C dysfunction may contribute to the metabolic and cardiovascular risk observed in HS. Moreover, clinical studies have demonstrated that effective biologic therapy accompanied by attenuation of systemic inflammation is associated with favorable changes in lipid profiles, further supporting the bidirectional relationship between inflammation and lipid metabolism[10]. Taken together, the findings of the present study are consistent with the concept of a coherent inflammatory–metabolic phenotype in HS rather than a nonspecific lipid disturbance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical characteristics associated with normal and abnormal HDL-C in HS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen comparing clinical characteristics between HS patients with normal and abnormal HDL-C levels, a higher proportion of patients with abnormal HDL-C were classified as Hurley stage III, although the difference in Hurley stage distribution did not reach statistical significance. This trend suggests a potential association between reduced HDL-C levels and more severe disease. The categorical nature of the Hurley staging system and the limited sample size following stratification may have reduced statistical power to detect significant differences; nevertheless, the observed pattern may still be clinically meaningful. The association between abnormal HDL-C and disease severity may be partially explained by the higher BMI observed in the abnormal HDL-C group, as increased adiposity is a well-established determinant of more severe HS[9,18]. Beyond adiposity, reduced HDL-C may reflect broader disturbances in lipid metabolism that are closely intertwined with chronic systemic inflammation. Given the immunomodulatory and anti-inflammatory properties of HDL, reduced HDL-C levels may indicate a metabolic environment that facilitates sustained inflammatory activity, thereby contributing to disease persistence or progression. From this perspective, HDL-C abnormalities may function not only as markers of metabolic comorbidity but also as indicators of inflammation-driven disease aggravation in HS.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, patients with HS and abnormal HDL-C levels were more likely to exhibit axillary involvement. The axilla is one of the most frequently affected anatomical sites in HS[1,18], and axillary lesions have been identified as a risk factor associated with greater disease severity[18,19]. The axillary region is particularly vulnerable to mechanical stress, friction, and local inflammation[20]. Systemic inflammatory and metabolic dysregulation reflected by reduced HDL-C levels may further lower the threshold for lesion development in anatomically predisposed regions such as the axilla, where mechanical stress and local immune activation are already prominent. These findings suggest that disease pathogenesis in patients with predominant axillary involvement may be influenced by both inflammatory and metabolic factors, whereas disease expression at other anatomical sites may be driven more predominantly by inflammatory mechanisms alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of HDL-C abnormalities with clinical laboratory characteristics in HS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, patients with HS and abnormal HDL-C levels demonstrated more pronounced abnormalities in inflammation-related laboratory parameters. Specifically, WBC, PLT, PCT, and hsCRP levels were significantly higher in patients with low HDL-C compared with those with normal HDL-C levels. These findings suggest that reduced HDL-C is associated with a heightened systemic inflammatory state, underscoring the close interplay between inflammatory activity and lipid metabolic dysregulation in HS.\u003c/p\u003e\n\u003cp\u003eElevated WBC levels primarily reflect increased systemic immune activation and inflammatory burden. Previous studies have shown that WBC levels in patients with HS increase in parallel with disease activity and severity, particularly among those with Hurley stage II–III disease\u0026nbsp;[21]. In the present analysis, patients in the low HDL-C group exhibited significantly higher WBC levels, suggesting a more pronounced state of systemic immune activation. Beyond its established role in reverse cholesterol transport, HDL exerts important anti-inflammatory effects by inhibiting monocyte–macrophage activation and neutrophil chemotaxis[12,13]. Reduced HDL-C levels may attenuate these immunomodulatory functions, thereby amplifying HS-associated inflammatory cascades and sustaining leukocyte activation. This mechanism may partially explain why patients with low HDL-C exhibit higher WBC levels under otherwise comparable disease conditions.\u003c/p\u003e\n\u003cp\u003eAlterations in platelet-related parameters further support an inflammation-driven disease phenotype in patients with low HDL-C. Both PLT and PCT were significantly elevated in this subgroup, consistent with prior evidence demonstrating positive correlations between platelet indices and HS disease severity[21,22]. As a chronic inflammatory disorder, HS is characterized by persistent immune activation that can stimulate bone marrow hematopoiesis via pro-inflammatory cytokines such as TNF-α and IL-6, leading to reactive thrombocytosis[21]. In parallel, reduced HDL-C levels may diminish HDL-mediated inhibition of platelet proliferation, aggregation, and activation[23–25], thereby contributing to increased PLT and PCT values. Notably, the sustained elevation of PCT, an indicator of total platelet mass, suggests that thrombocytosis in patients with low HDL-C represents a chronic process rather than a transient inflammatory response, reinforcing its role in perpetuating systemic inflammation in HS.\u003c/p\u003e\n\u003cp\u003eThe marked elevation of hsCRP in patients with low HDL-C provides further evidence of increased systemic inflammatory burden. hsCRP is a key acute-phase reactant reflecting cytokine-driven inflammatory activity and is closely linked to lipid metabolic status. Inflammatory signaling can suppress HDL-C synthesis and impair HDL function[15], while reduced HDL-C may in turn weaken its anti-inflammatory capacity, creating a self-reinforcing cycle of inflammation and lipid dysregulation. The significantly higher hsCRP levels observed in the low HDL-C group support the presence of a pathological process in which inflammatory activity and lipid metabolic abnormalities mutually amplify in HS.\u003c/p\u003e\n\u003cp\u003eTaken together, the concurrent elevations in WBC, PLT, PCT, and hsCRP observed in the low HDL-C group indicate that reduced HDL-C is associated with a greater systemic inflammatory burden in HS. In this context, low HDL-C may represent not only a downstream consequence of chronic inflammation but also a contributing factor that sustains inflammatory signaling through diminished anti-inflammatory, anti-platelet, and immunomodulatory effects. HDL-C may therefore serve as an adjunctive marker that integrates metabolic and inflammatory dimensions of disease activity into routine assessment, providing complementary information to conventional inflammatory markers.\u003c/p\u003e\n\u003cp\u003eIn addition, HS patients with low HDL-C levels exhibited significantly higher GGT levels compared with those with normal HDL-C. GGT primarily reflects hepatobiliary injury and liver-related dysfunction. Given that patients in the low HDL-C group tended to have more severe disease, elevated GGT levels in this subgroup may be associated with increased medication exposure in the context of greater disease severity. Furthermore, HDL participates in reverse cholesterol transport[14], and reduced HDL-C levels may reflect hepatic lipid accumulation, which could also contribute to elevated GGT levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical relevance and novel insights from HDL-C-based stratification in HS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough direct comparison with healthy controls, this study confirmed a significant reduction in HDL-C levels in patients with HS, providing additional support for the emerging concept of HDL-C–centered metabolic dysregulation in this disease. By stratifying patients according to HDL-C levels, a routinely available laboratory parameter with both lipid metabolic and immunomodulatory relevance, this analysis extends current understanding of clinical and biological heterogeneity in HS. Beyond its role in reverse cholesterol transport, HDL-C exerts important anti-inflammatory and immunoregulatory effects, including inhibition of monocyte–macrophage activation, neutrophil chemotaxis, and platelet activation. Accordingly, circulating HDL-C levels may reflect the cumulative impact of chronic systemic inflammation and, when reduced, may further contribute to inflammatory persistence through impaired anti-inflammatory and immunomodulatory capacity. This bidirectional relationship provides a biologically plausible framework for investigating lipid–immune interactions in the pathogenesis and progression of HS and supports a mechanistic link between dysregulated lipid metabolism and systemic inflammation in this condition.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the present study, patients with low HDL-C exhibited concordant elevations across multiple inflammation-related markers, including WBC, PLT, PCT, and hsCRP, defining a composite inflammatory phenotype characterized by enhanced systemic immune activation, increased inflammatory burden, and heightened platelet reactivity. Compared with reliance on individual inflammatory markers, this consistent multi-parameter pattern more comprehensively captures the overall inflammatory status of patients with HS. The pronounced inflammatory profile associated with reduced HDL-C suggests a greater disease burden, a more complex systemic inflammatory milieu, and potentially increased risk of disease-related complications. These findings support the potential utility of HDL-C–based stratification for inflammatory risk assessment, follow-up planning, and optimization of therapeutic decision-making in HS. By identifying biologically meaningful differences through stratified analysis within a relatively small cohort, this study provides an important hypothesis-generating foundation for future investigations employing larger sample sizes and prospective or longitudinal study designs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy limitations and future perspectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral limitations of this study should be acknowledged. First, the cross-sectional design permits identification of associations between HDL-C levels, systemic inflammatory markers, and selected clinical features in patients with HS, but does not allow causal inferences to be drawn. Accordingly, the present findings should be interpreted as characterizing an inflammatory phenotype associated with HDL-C status rather than implying directional or causal relationships. Reduced HDL-C may represent a consequence of long-standing chronic inflammation in HS; however, it may also actively contribute to inflammatory persistence through impaired anti-inflammatory, anti-platelet, and immunomodulatory functions. Second, the relatively small sample size may limit statistical power and generalizability. Although the overall cohort comprised 109 patients, stratification according to HDL-C levels resulted in smaller subgroup sizes, which may further reduce statistical power and increase the likelihood of type II error. Estimates derived from limited sample sizes are also more susceptible to the influence of extreme values, thereby constraining the robustness and broader applicability of the findings. Third, although categorization of HDL-C, a continuous variable, facilitates clinical interpretation and subgroup comparisons, this approach may lead to loss of information and obscure potential continuous or non-linear associations between HDL-C levels and inflammatory markers. In addition, missing data for certain laboratory parameters may have introduced selection bias, particularly if data were not missing at random. Finally, residual confounding cannot be excluded. Potentially relevant factors, including medication use, coexisting metabolic disorders, and lifestyle variables, could not be fully accounted for and may have influenced both HDL-C levels and inflammatory status. Therefore, the findings of this study should be interpreted as exploratory and warrant confirmation in larger cohorts using prospective and longitudinal study designs to further clarify the temporal and mechanistic relationships between HDL-C, systemic inflammation, and disease manifestations in HS.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBy integrating lipid, inflammatory, and clinical parameters, this study identifies a metabolically linked inflammatory phenotype in HS. Stratified analyses demonstrated that low HDL-C levels were significantly associated with concordant increases in multiple inflammation-related laboratory markers, indicating a close relationship between reduced HDL-C levels and heightened systemic inflammation. These findings suggest that low HDL-C may identify an inflammatory phenotype in HS characterized by increased inflammatory burden and platelet reactivity, reflecting the interplay between inflammatory processes and lipid metabolic dysregulation. As HDL-C is routinely measured in clinical practice, it may serve as a practical marker for assessing systemic inflammation and stratifying inflammatory risk in HS. Further prospective and longitudinal studies are required to clarify the temporal and mechanistic role of HDL-C in HS pathogenesis and its potential value in clinical management and risk assessment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;hidradenitis suppurativa\u003c/p\u003e\n\u003cp\u003eIHS4\u0026nbsp; \u0026nbsp; \u0026nbsp;International Hidradenitis Suppurativa Severity Score System\u003c/p\u003e\n\u003cp\u003eBMI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;body mass index\u003c/p\u003e\n\u003cp\u003eWBC\u0026nbsp; \u0026nbsp; \u0026nbsp;white blood cell count\u003c/p\u003e\n\u003cp\u003eNEUT%\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;neutrophil percentage\u003c/p\u003e\n\u003cp\u003eHb\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;hemoglobin\u003c/p\u003e\n\u003cp\u003ePLT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;platelet\u003c/p\u003e\n\u003cp\u003ePCT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;plateletcrit\u003c/p\u003e\n\u003cp\u003ePDW\u0026nbsp; \u0026nbsp; \u0026nbsp;platelet distribution width\u003c/p\u003e\n\u003cp\u003eMPV\u0026nbsp; \u0026nbsp; \u0026nbsp;mean platelet volume\u003c/p\u003e\n\u003cp\u003eP-LCR\u0026nbsp;\u0026nbsp;platelet-large cell ratio\u003c/p\u003e\n\u003cp\u003eALT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;alanine aminotransferase\u003c/p\u003e\n\u003cp\u003eAST\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003eGGT\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;gamma-glutamyl transferase\u003c/p\u003e\n\u003cp\u003eCr\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;creatinine\u003c/p\u003e\n\u003cp\u003eGlu\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;glucose\u003c/p\u003e\n\u003cp\u003eUA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;uric acid\u003c/p\u003e\n\u003cp\u003eTG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;triglycerides\u003c/p\u003e\n\u003cp\u003eTC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;total cholesterol\u003c/p\u003e\n\u003cp\u003eHDL-C\u0026nbsp;\u0026nbsp;high-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eLDL-C\u0026nbsp;\u0026nbsp;low-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eESR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;erythrocyte sedimentation rate\u003c/p\u003e\n\u003cp\u003ehsCRP \u0026nbsp; high-sensitivity C-reactive protein\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the institutional review board of Peking Union Medical College Hospital (Approval number:\u0026nbsp;I-25PJ2964).\u0026nbsp;The requirement for written informed consent was waived due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are available from Chao Wu on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Peking Union Medical College Hospital Talent Cultivation Program Category D [grant number UHB11983]; the National Natural Science Foundation of China [grant number 82573978]; Beijing Key Clinical Specialty Construction Project; National Key Clinical Specialty Project of China; and Beijing Natural Science Foundation [grant number 7242109].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYJ Wang and Wu had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors contributed to the study concept and design, and all participated in the acquisition, analysis, or interpretation of data. All authors participated in the drafting of the manuscript and critical revision of the manuscript for important intellectual content. YJ Wang, and TJ Liu performed the statistical analysis. HZ Jin were responsible for study supervision. All authors have read and approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBallard K, Sathe NC, Shuman VL. Hidradenitis Suppurativa. StatPearls [Internet] [Internet]. StatPearls Publishing; 2024 [cited 2025 Dec 26]. https://www.ncbi.nlm.nih.gov/books/NBK534867/. Accessed 26 Dec 2025\u003c/li\u003e\n\u003cli\u003eElzawawi KE, Elmakaty I, Habibullah M, Ahmed MB, Lahham SA, Harami SA, et al. Hidradenitis suppurativa and its association with obesity, smoking, and diabetes mellitus: A systematic review and meta‐analysis. [cited 2026 Jan 1]; https://doi.org/10.1111/iwj.70035\u003c/li\u003e\n\u003cli\u003eHidradenitis Suppurativa: Beyond Skin Concerns to Focus on Cardiovascular Considerations [Internet]. JCAD - The Journal of Clinical and Aesthetic Dermatology. [cited 2026 Jan 1]. https://jcadonline.com/hidradenitis-suppurativa-beyond-skin-concerns-to-focus-on-cardiovascular-considerations/. Accessed 1 Jan 2026\u003c/li\u003e\n\u003cli\u003eJ\u0026oslash;rgensen A-HR, Aarestrup J, Baker JL, Thomsen SF. Association of Birth Weight, Childhood Body Mass Index, and Height With Risk of Hidradenitis Suppurativa. JAMA Dermatol. 2020;156:746. https://doi.org/10.1001/jamadermatol.2020.1047\u003c/li\u003e\n\u003cli\u003eMiller IM, Ellervik C, Vinding GR, Zarchi K, Ibler KS, Knudsen KM, et al. Association of Metabolic Syndrome and Hidradenitis Suppurativa. JAMA Dermatol. 2014;150:1273\u0026ndash;80. https://doi.org/10.1001/jamadermatol.2014.1165\u003c/li\u003e\n\u003cli\u003eVan Straalen KR, Vanlaerhoven AMJD, Ardon CB, Van Der Zee HH. Body mass index at the onset of hidradenitis suppurativa. J Deutsche Derma Gesell. 2021;19:437\u0026ndash;9. https://doi.org/10.1111/ddg.14433\u003c/li\u003e\n\u003cli\u003eLi Y-H, Chuang S-H, Huang Y-C, Yang H-J. A comprehensive systemic review and meta-analysis of the association between lipid profile and hidradenitis suppurativa. Arch Dermatol Res. 2025;317:225. https://doi.org/10.1007/s00403-024-03762-y\u003c/li\u003e\n\u003cli\u003eAlmenara-Blasco M, Gracia-Caza\u0026ntilde;a T, Poblador-Plou B, Laguna-Berna C, Navarro-Bielsa A, Moreno-Juste A, et al. Multimorbidity of hidradenitis suppurativa: a cross-sectional population-based study of its associated comorbidities. Front Med (Lausanne). 2025;12:1618975. https://doi.org/10.3389/fmed.2025.1618975\u003c/li\u003e\n\u003cli\u003eElzawawi KE, Elmakaty I, Habibullah M, Ahmed MB, Al Lahham S, Al Harami S, et al. Hidradenitis suppurativa and its association with obesity, smoking, and diabetes mellitus: A systematic review and meta‐analysis. Int Wound J. 2024;21:e70035. https://doi.org/10.1111/iwj.70035\u003c/li\u003e\n\u003cli\u003eCascio Ingurgio R, Alfano A, Matteodo E, Stark M, Valenti M, Narcisi A, et al. Effectiveness of Biologic Therapy in Hidradenitis Suppurativa: Real-World Clinical Outcomes and Lipid Profile Evaluation. Australasian Journal of Dermatology. 2025;66:468\u0026ndash;74. https://doi.org/10.1111/ajd.14592\u003c/li\u003e\n\u003cli\u003eChoi E, Mir SA, Ji S, Ooi XT, Chua EWL, Yi Wei Y, et al. Understanding the systemic burden of disease in hidradenitis suppurativa from plasma lipidomic analysis. Journal of Dermatological Science. 2022;107:133\u0026ndash;41. https://doi.org/10.1016/j.jdermsci.2022.08.005\u003c/li\u003e\n\u003cli\u003eBarker G, Weiner J, Guirgis F, Reddy S. HDL and Persistent Inflammation Immunosuppression and Catabolism Syndrome. Curr Opin Lipidol. 2021;32:315\u0026ndash;22. https://doi.org/10.1097/MOL.0000000000000782\u003c/li\u003e\n\u003cli\u003eBonacina F, Pirillo A, Catapano AL, Norata GD. HDL in Immune-Inflammatory Responses: Implications beyond Cardiovascular Diseases. Cells. 2021;10:1061. https://doi.org/10.3390/cells10051061\u003c/li\u003e\n\u003cli\u003eFisher EA, Feig JE, Hewing B, Hazen SL, Smith JD. HDL Function, Dysfunction, and Reverse Cholesterol Transport. Arterioscler Thromb Vasc Biol. 2012;32:2813\u0026ndash;20. https://doi.org/10.1161/ATVBAHA.112.300133\u003c/li\u003e\n\u003cli\u003eMadaudo C, Bono G, Ortello A, Astuti G, Mingoia G, Galassi AR, et al. Dysfunctional High-Density Lipoprotein Cholesterol and Coronary Artery Disease: A Narrative Review. J Pers Med. 2024;14:996. https://doi.org/10.3390/jpm14090996\u003c/li\u003e\n\u003cli\u003eFeingold KR, Grunfeld C. Effect of Inflammation and Infection on Lipids and Lipoproteins. Endotext [Internet] [Internet]. MDText.com, Inc.; 2025 [cited 2026 Jan 1]. https://www.ncbi.nlm.nih.gov/books/NBK326741/. Accessed 1 Jan 2026\u003c/li\u003e\n\u003cli\u003eMolinelli E, Morresi C, Dragonetti ML, Simoni ED, Candelora M, Marasca S, et al. Oxidative Stress, High Density Lipoproteins and Hidradenitis Suppurativa: A Prospective Study. Antioxidants [Internet]. publisher; 2025 [cited 2026 Jan 1];14. https://doi.org/10.3390/antiox14081014\u003c/li\u003e\n\u003cli\u003eKaya G, \u0026Ouml;zgen FP, Kelahmetoğlu O, Su K\u0026uuml;\u0026ccedil;\u0026uuml;k \u0026Ouml;, Onsun N. Demographic features, clinical characteristics, and comorbid relation in hidradenitis suppurativa: a population-based study. Front Med [Internet]. Frontiers; 2025 [cited 2026 Jan 6];11. https://doi.org/10.3389/fmed.2024.1499509\u003c/li\u003e\n\u003cli\u003eChokevittaya P, Jirattikanwong N, Thongngarm T, Phinyo P, Wongsa C. Factors Associated With Dupilumab Response in Atopic Dermatitis: A Systematic Review and Meta-Analysis. The Journal of Allergy and Clinical Immunology: In Practice. 2024;12:3044\u0026ndash;56. https://doi.org/10.1016/j.jaip.2024.08.054\u003c/li\u003e\n\u003cli\u003eThemes UFO. The Role of Mechanical Stress in Hidradenitis Suppurativa [Internet]. Plastic Surgery Key. 2018 [cited 2026 Jan 6]. https://plasticsurgerykey.com/the-role-of-mechanical-stress-in-hidradenitis-suppurativa/. Accessed 6 Jan 2026\u003c/li\u003e\n\u003cli\u003eGunter SJ, Holcomb ZE, Lam BD, Porter ML, Kimball AB. Hematologic Abnormalities in Patients With Hidradenitis Suppurativa Receiving Adalimumab. JAMA Dermatol. 2024;160:678\u0026ndash;81. https://doi.org/10.1001/jamadermatol.2024.1075\u003c/li\u003e\n\u003cli\u003eMorss-Walton PC, Greif C, Holcomb ZE, Salian P, Yankama T, Kim D, et al. Treatment of hidradenitis suppurativa resolves associated hematologic abnormalities. International Journal of Women\u0026rsquo;s Dermatology. 2022;8:e011. https://doi.org/10.1097/JW9.0000000000000011\u003c/li\u003e\n\u003cli\u003eAviram M, Brook JG. Platelet interaction with high and low density lipoproteins. Atherosclerosis. 1983;46:259\u0026ndash;68. https://doi.org/10.1016/0021-9150(83)90176-4\u003c/li\u003e\n\u003cli\u003eMurphy AJ, Bijl N, Yvan-Charvet L, Welch CB, Bhagwat N, Reheman A, et al. Cholesterol efflux in megakaryocyte progenitors suppresses platelet production and thrombocytosis. Nature medicine. 2013;19:586. https://doi.org/10.1038/nm.3150\u003c/li\u003e\n\u003cli\u003evan der Stoep M, Korporaal SJA, Van Eck M. High-density lipoprotein as a modulator of platelet and coagulation responses. Cardiovasc Res. 2014;103:362\u0026ndash;71. https://doi.org/10.1093/cvr/cvu137\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"lipids-in-health-and-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lhad","sideBox":"Learn more about [Lipids in Health and Disease](http://lipidworld.biomedcentral.com/)","snPcode":"12944","submissionUrl":"https://submission.nature.com/new-submission/12944/3","title":"Lipids in Health and Disease","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"hidradenitis suppurativa, high-density lipoprotein cholesterol, triglyceride, inflammation","lastPublishedDoi":"10.21203/rs.3.rs-8838333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8838333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHidradenitis suppurativa (HS) is a chronic inflammatory skin disorder increasingly recognized as a systemic disease with significant metabolic comorbidity. Disturbances in lipid metabolism, particularly reduced high-density lipoprotein cholesterol (HDL-C), have been consistently reported in patients with HS. However, the extent to which HDL-C levels are associated with clinical characteristics, systemic inflammatory burden, and disease severity in HS remains insufficiently defined.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this retrospective single-center study, clinical and laboratory data were analyzed for 109 patients with HS and 109 age- and sex-matched healthy controls. Serum lipid parameters were compared between groups. Patients with HS were further stratified according to HDL-C status, and differences in demographic characteristics, clinical features, and laboratory indices were examined to assess the clinical relevance of reduced HDL-C in HS.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared with healthy controls, patients with HS had a significantly higher body mass index (BMI), triglyceride (TG) concentrations and significantly lower HDL-C levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among patients with HS, those with abnormal HDL-C levels exhibited significantly higher BMI, white blood cell count, platelet count, plateletcrit, gamma-glutamyl transferase, and high-sensitivity C-reactive protein levels than those with normal HDL-C levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, axillary involvement was significantly more frequent in the abnormal HDL-C group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017). A higher proportion of Hurley stage III disease and higher IHS4 scores were observed in the HDL-C abnormal group.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePatients with HS demonstrate a characteristic reduction in HDL-C that is associated with elevated systemic inflammatory markers and increased axillary involvement. These findings support HDL-C as a readily accessible indicator of inflammatory burden and disease severity in HS and highlight its potential value in clinical risk stratification.\u003c/p\u003e","manuscriptTitle":"Decreased HDL-C in Hidradenitis Suppurativa: Association with Systemic Inflammatory Phenotype and Implications for Risk Stratification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 09:36:15","doi":"10.21203/rs.3.rs-8838333/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-09T05:18:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-08T17:15:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333192386506921645373866173214037097084","date":"2026-03-30T20:03:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T15:56:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251593346757920488542937377589869713592","date":"2026-02-17T12:58:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-17T07:29:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-10T22:00:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-10T21:59:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Lipids in Health and Disease","date":"2026-02-10T07:50:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"lipids-in-health-and-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lhad","sideBox":"Learn more about [Lipids in Health and Disease](http://lipidworld.biomedcentral.com/)","snPcode":"12944","submissionUrl":"https://submission.nature.com/new-submission/12944/3","title":"Lipids in Health and Disease","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c34ba293-e4c9-448c-ab77-ad36bcc8976d","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:06:27+00:00","versionOfRecord":{"articleIdentity":"rs-8838333","link":"https://doi.org/10.1186/s12944-026-02959-6","journal":{"identity":"lipids-in-health-and-disease","isVorOnly":false,"title":"Lipids in Health and Disease"},"publishedOn":"2026-04-23 15:59:46","publishedOnDateReadable":"April 23rd, 2026"},"versionCreatedAt":"2026-02-23 09:36:15","video":"","vorDoi":"10.1186/s12944-026-02959-6","vorDoiUrl":"https://doi.org/10.1186/s12944-026-02959-6","workflowStages":[]},"version":"v1","identity":"rs-8838333","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8838333","identity":"rs-8838333","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}