Next Article in Journal
Poly(3-hydroxybutyrate) 3D-Scaffold–Conduit for Guided Tissue Sprouting
Next Article in Special Issue
Modulation of Disease-Associated Pathways in Hidradenitis Suppurativa by the Janus Kinase 1 Inhibitor Povorcitinib: Transcriptomic and Proteomic Analyses of Two Phase 2 Studies
Previous Article in Journal
Downregulation of Sirtuin 1 Does Not Account for the Impaired Long-Term Potentiation in the Prefrontal Cortex of Female APPswe/PS1dE9 Mice Modelling Alzheimer’s Disease
Previous Article in Special Issue
Screening for Diabetes Mellitus in Patients with Hidradenitis Suppurativa—A Monocentric Study in Germany
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 24
Issue 8
10.3390/ijms24086969
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Metformin Treatment of Hidradenitis Suppurativa: Effect on Metabolic Parameters, Inflammation, Cardiovascular Risk Biomarkers, and Immune Mediators

by
Roisin Hambly
1,2,*,
Niamh Kearney
1,2,
Rosalind Hughes
1,
Jean M. Fletcher
3,4 and
Brian Kirby
1,2
1
The Charles Centre, Department of Dermatology, St Vincent’s University Hospital, D04 T6F4 Dublin, Ireland
2
Charles Institute of Dermatology, School of Medicine, University College Dublin, D04 V1W8 Dublin, Ireland
3
School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, D02 R590 Dublin, Ireland
4
School of Medicine, Trinity Biomedical Sciences Institute, Trinity College Dublin, D02 R590 Dublin, Ireland
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(8), 6969; https://doi.org/10.3390/ijms24086969
Submission received: 27 February 2023 / Revised: 5 April 2023 / Accepted: 6 April 2023 / Published: 9 April 2023
(This article belongs to the Special Issue Hidradenitis Suppurativa and Related Disorders)
Browse Figures
Review Reports Versions Notes

Abstract

:
Hidradenitis suppurativa (HS) is a common cutaneous and systemic inflammatory disease with a significant impact on mental health and quality of life. It is associated with obesity, insulin resistance, metabolic syndrome, cardiovascular (CV) disease, and increased all-cause mortality. Metformin is used frequently in HS treatment and is effective for some patients. The mechanism of action of metformin in HS is unknown. A case-control study of 40 patients with HS (20 on metformin and 20 controls) was conducted to assess differences in metabolic markers, inflammation (C-reactive protein [CRP], serum adipokines, and CV risk biomarkers), and serum immune mediators. Body mass index (BMI), insulin resistance (77%), and metabolic syndrome (44%) were high overall, but not significantly different between the groups. This highlights the need for co-morbidity screening and management. A significant reduction in fasting insulin and a trend towards a reduction in insulin resistance were identified in the metformin group compared with pre-treatment levels. CV risk biomarkers were significantly favourable in the metformin group (lymphocytes, monocyte–lymphocyte ratio, neutrophil–lymphocyte ratio, and platelet–lymphocyte ratio). CRP was lower in the metformin group but was not statistically significant. Adipokines were dysregulated overall but were not different between the two groups. Serum IFN-γ, IL-8, TNF-α, and CXCL1 trended lower in the metformin group but did not reach significance. These results suggest that metformin improves CV risk biomarkers and insulin resistance in patients with HS. When the results of this study are considered alongside other studies in HS and related conditions, it is likely that metformin also has beneficial effects on metabolic markers and systemic inflammation in HS (CRP, serum adipokines, and immune mediators), warranting further research.

1. Introduction

Hidradenitis suppurativa (HS) is a cutaneous and systemic inflammatory disease characterised by recurrent painful, inflammatory lesions in the axillae and groin [1]. These can progress to scarring and sinus tract formation and can have a profound impact on quality of life [1]. Pathogenesis remains poorly understood and current evidence points to a multifactorial process including genetics, physiological/environmental factors, the microbiome, follicular occlusion, immune dysregulation, and inflammation [2]. There is a known association with obesity [3,4,5,6,7,8], insulin resistance [9,10,11], and metabolic syndrome [12,13,14,15], and these possibly play a role in pathogenesis by contributing to a systemic proinflammatory state [16,17,18]. These conditions should be screened for and addressed in the management of HS [19].
The available treatments for HS are suboptimal and include surgical options (incision and drainage, excision with primary closure, and closure by secondary intention, skin flap, or graft) [20] and a variety of medical treatments, most of which are used off-label. [21,22,23,24,25,26,27,28,29]. Adalimumab, a monoclonal antibody to TNF-α, the only licensed treatment, is approved for moderate to severe HS [30], and is effective in approximately 50% of recipients [31,32]. Metformin is used frequently in the treatment of HS. To date, there are two published studies evaluating the efficacy of metformin in HS: one prospective and one retrospective [10,26]. Efficacy has not been evaluated in a clinical trial, or by using the recommended outcome measures HS clinical response (HiSCR) [33] or the International HS Severity Score System (IHS4) [34]. The use of metformin is included in clinical guidelines under certain circumstances [20,35]. It has been suggested that metformin may be of particular benefit in mild HS, where treatment options are limited and largely consist of topical or oral antibiotics [36]. Recurrent courses of antibiotics over several years comes at a risk of antibiotic resistance [36,37,38]; therefore, it is of great clinical importance to find a viable alternative.
Metformin is an oral biguanide licensed for the treatment of type 2 diabetes mellitus (T2DM) [39] and functions to regulate glucose metabolism [40]. It is used off-label for several diseases [41,42,43,44], including other inflammatory skin diseases [45]. It has been reported to reduce cardiovascular (CV) mortality, have a protective role against cancer and dementia, and improve longevity [40,46,47]. Metformin is an indirect agonist of adenosine monophosphate-activated protein kinase (AMPK) [48] and a mammalian target of rapamycin complex 1 (mTORC1) inhibitor [46,49]. It has been shown to alter immune cells in laboratory settings and other diseases [50,51,52,53,54,55] and may have antiandrogenic properties [56].
The precise mechanism of action of metformin in HS is uncertain. It is possible that it works through a combination of actions including reducing insulin resistance, influencing immune cells, and reducing chronic inflammation. Improved knowledge of metformin’s mechanism of action in HS may lead to an improved understanding of HS pathogenesis, and in clinical practice, it may help to identify which HS patients would benefit from metformin.
In this paper, we report results of a case-control study comparing HS patients on metformin with those not on metformin. The primary aim was to assess differences in metabolic markers including body mass index (BMI), waist circumference, blood pressure (BP), insulin resistance, and metabolic syndrome. The secondary aims were to assess differences in inflammation as measured by C-reactive protein (CRP); CV risk biomarkers; serum cytokines and chemokines; and serum adipokines.
We have identified some potential benefits of metformin in HS including on CV risk biomarkers and insulin resistance, and we review the potential mechanisms of action of metformin in HS based on its action in related conditions.

2. Results

2.1. Demographics

There was no statistically significant difference in gender, age, disease duration, smoking status, or Hurley stage between the HS metformin and control groups. The mean age overall was 38 years, 85% were female (n = 34), and the mean disease duration was 11.73 years. A total of 45% described themselves as current smokers, 22.5% as ex-smokers, and 32.5% as non-smokers. The majority (85%) had Hurley stage 2 disease. All were Caucasian (Table 1).

2.2. Metformin Dosing, Duration, and Response (n = 20)

A total of 3 patients were taking 500 mg daily; 4 were taking 1 g daily (500 mg twice daily); 2 were taking 1.5 g daily (500 mg three times daily or 850 mg twice daily); and 11 were taking 2 g daily (1 g twice daily). The mean duration of metformin treatment was 35.2 ± 52 months (range 6–240). Response to treatment with metformin was documented by the examining clinician as ‘partial’ in 14 (70%) and as ‘in remission’ in 6 (30%).

2.3. Other Medications for the Management of HS

In the metformin group (n = 20), 4 were taking concomitant spironolactone, 3 were taking a tetracycline and 1 was using topical treatment for the management of their HS. Five out of six who were ‘in remission’ were taking a concomitant medication (two tetracyclines, two spironolactone, and one topical treatment). Within the control group, HS medications were tetracyclines (n = 3), dapsone (n = 2), spironolactone (n = 1), clindamycin and rifampicin (n = 1), and topical treatment (n = 1). Four controls had previously taken metformin (more than six months prior).

2.4. Metabolic Parameters

There is an association between HS and obesity, metabolic syndrome, and insulin resistance [11,57], and it has been proposed that metformin may treat metabolic abnormalities in HS [10]. Comparisons of metabolic parameters were completed to identify if these were more favourable in metformin-treated patients relative to HS controls.

2.4.1. BMI and Waist Circumference

The mean BMI was high overall at 34.4 kg/m2. A total of 25 were classified as obese (BMI ≥ 30), 10 as overweight (BMI 25–29.9), 4 as normal weight (BMI 18.5–24.9), and 1 as underweight (BMI < 18.5). A total of 10 had BMIs greater than 40: 5 in the metformin group and 5 in the control group (WHO obese class III).
The mean waist circumference was high overall at a mean of 103 cm. A waist circumference below 94 cm is ‘low risk’, 94–102 cm is ‘high risk’, and more than 102 cm is ‘very high’ for Caucasian men [58,59]. For Caucasian women, below 80 cm is low risk, 80–88 cm is high risk, and more than 88 cm is very high risk [58,59]. Of 34 female participants, the mean waist circumference was 104.5 ± 19.3 cm, and this was significantly higher than the cut-off for ‘very high risk’ (88 cm) using a one-sample t-test (p < 0.0001). A total of 4 females had a waist circumference in the ‘low risk’ category, 5 were in the ‘high risk’ category, and 25 were in the ‘very high-risk’ category. Of 6 male participants, the mean waist circumference was lower at 94.8 ± 18.5 cm and this was not significantly different to the cut-off for ‘very high risk’ (102 cm) (p = 0.382). Three men were in the ‘low-risk’ category, one in the ‘high-risk’ category, and two in the ‘very high-risk’ category.
There was no statistically significant difference in BMI or waist circumference between the metformin and HS control groups (Table 1).

2.4.2. Blood Pressure

Normal BP is <120/80 [60]. Systolic in the range 120–129 is classified as ‘elevated’ and BP ≥ 130/80 is classified as ‘hypertension’ [60]. Elevated BP forms one of the components for diagnosis of metabolic syndrome and is associated with increased risk of CV disease [60]. The mean BP overall in this study was elevated at 130.7/80.2, but there was no significant difference in BP between the metformin and HS control groups (Table 1).

2.4.3. Insulin Resistance

Insulin resistance was calculated using HOMA-IR for 30 patients overall, with a mean of 4.46 ± 3.3. A total of 77% had a diagnosis of insulin resistance (HOMA-IR ≥ 2.6). The HOMA-IR correlated positively with both BMI (r = 0.68, p < 0.0001) and waist circumference (r = 0.72, p < 0.0001) using Spearman’s correlation. There was no statistically significant difference in mean HOMA-IR or insulin resistance between the two groups (Table 1).
Given that no difference was identified in levels of insulin resistance between HS controls and those on metformin, levels of fasting glucose, fasting insulin, and HbA1c were sought retrospectively for patients in the metformin group, prior to commencing metformin. Matched results were available on nine patients. Median fasting insulin was significantly lower on metformin than pre-metformin (22.0 mIU vs. 3.3 mIU, p = 0.008). The median HOMA-IR pre-metformin was 4.79 and on metformin was 3.27. This reduction was not statistically significant (p = 0.43). Fasting glucose and HbA1c did not differ significantly pre-metformin and on metformin (Figure 1, Table 2).

2.4.4. Metabolic Syndrome

Overall, 44% of the patients were diagnosed with metabolic syndrome using NCEP/ATP III guidelines (n = 39). This was not significantly different between the two groups (Table 1).

2.5. C-Reactive Protein (CRP)

CRP is a non-specific acute-phase protein that is elevated in plasma in response to infection, inflammation, tissue damage, and malignancy [61]. It is used frequently in clinical practice as a marker of HS disease activity and severity at a given time. The median value in healthy adults is 0.8 mg/L [61]. Metformin significantly reduces CRP in women with polycystic ovarian syndrome (PCOS) [62], a disease that is associated with HS [63]. It is not yet known whether metformin has this effect on CRP in HS.
CRP was elevated overall at 6.72 mg/L (n = 39) and trended lower in those treated with metformin relative to controls (8.19 vs. 5.17 mg/L). This was not statistically significant (Table 1).

2.6. Cardiovascular Disease Biomarkers

There is a known association between HS and CV disease [57,64], and it would be useful to be able to identify which HS patients are at increased CV risk in order to manage this appropriately. Components of the full blood count (FBC) and their ratios are reported as predictors of CV risk. Higher levels of neutrophils, platelets, and monocytes, and lower levels of lymphocytes are thought to predict higher CV risk [65,66,67,68]. Ratios calculated from the FBC are also predictors of higher CV risk: neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), monocyte–lymphocyte ratio (MLR), and monocyte–HDL ratio (MHL) [69,70,71,72,73]. We analysed these CV biomarkers in 36 HS patients (17 controls and 19 on metformin) in order to identify if metformin alters CV risk.
Absolute lymphocyte count was significantly higher in the metformin group than the HS control group (p = 0.037; Table 1, Figure 2). There was no statistically significant difference in neutrophil, platelet, or monocyte counts between groups (Table 1).
NLR was higher in HS controls than metformin-treated patients but did not reach statistical significance (Table 1, Figure 2). However, when compared with the general population mean [74] using a one-sample t-test it was significantly higher in HS controls (p = 0.027) but not in the metformin group (p = 0.094, Table 3). Similarly, PLR was higher in HS controls than in metformin-treated patients but did not reach significance (Table 1, Figure 2). It was higher in HS controls compared with the general population (p = 0.01) but not in metformin-treated patients compared with the general population (p = 0.35, Table 3) [75].
MLR was significantly higher in the HS control group than the metformin group (p = 0.032; Table 1, Figure 2). MLR was not significantly different to the mean for patients with acute coronary syndrome (ACS) in the HS control group (p = 0.33), but the metformin group was significantly lower than patients with ACS (p = 0.001, Table 3) [76]. MLR has not been studied in a general population which is why ACS was used. The monocyte–HDL ratio (MHL) was not significantly different between the two groups (Table 1, Figure 2).
None of these CV disease biomarkers correlated with CRP or HOMA-IR (Table 4).

2.7. Serum Cytokines and Chemokines

Metformin has been shown to inhibit expression of IL-6 and TNF-α in HaCaT keratinocytes [46,50], and to reduce IL-6 and IL-8 in rheumatoid arthritis synovial explants [51]. Metformin lowers serum TNF-α and IFN-γ in patients with T2DM [77]. Its effect on serum cytokines and chemokines in HS is not yet known.
Serum samples were collected from 35 HS patients (18 controls and 17 metformin). Thirteen cytokines and chemokines were analysed by multiplex assay to examine whether there were differences in serum immune mediators between the two groups.
There was no statistically significant difference in levels between the two groups for IFN-γ, IL-6, IL-8, IL-17a, IL-17af, IL-18, IL-23, TNF-α, CXCL1, or CCL20, but there was a trend towards a reduction in IFN-γ, IL-8, TNF-α, and CXCL1 (Table 5, Figure 3). IL-1α, IL-1β, and IL-17c were not detected in most serum samples.

2.8. Adipokines

Serum adipokines are known to be dysregulated in HS [78,79]. Adiponectin is anti-inflammatory and serum levels have been shown to be decreased in HS, obesity, and psoriasis [78,79]. Resistin and leptin are both proinflammatory and their serum levels have been shown to be elevated in HS [78,79]. To examine this in our study cohort, levels of adiponectin, resistin, and leptin were analysed using ELISA in serum samples from 35 HS patients. Healthy control samples were not available in this study; however, our research group previously published a study comparing levels of these adipokines between HS and healthy controls, measured using the same technique. One-sample t-test was used to compare the mean level of adipokines in HS patients in our current study (n = 35) with the mean level of adipokines in healthy controls in our previous study (n = 20) [78]. Levels of adiponectin were significantly lower in HS compared with healthy controls (p = 0.015). Levels of leptin and resistin were significantly higher in HS compared with healthy controls (both p < 0.0001).
Metformin has a positive effect on adipokines in PCOS and T2DM [80,81]. The effect of metformin on adipokines in HS is not known. Levels were analysed using ELISA in serum samples from 35 HS patients (18 controls and 17 on metformin). There was no statistically significant difference between groups (Figure 4).

3. Discussion

Metformin is used commonly in the management of HS, either alone or in combination with other medications. It is a cheap and generally safe medication, and in clinical practice it appears to be beneficial for some patients. There is, however, very little evidence regarding its efficacy in HS and its mechanism of action in HS has not been studied to date. It has been suggested that it may work through a combination of actions including reducing insulin resistance, influencing immune cells, and reducing chronic inflammation.
This case-control study first set out to identify if there were differences in metabolic markers between HS controls and those taking metformin. Patients were representative of the known demographics of the disease and did not differ significantly between the two groups.
BMI, waist circumference, BP, metabolic syndrome, and insulin resistance were high overall, but not significantly different between the groups. In the 40 HS patients in this study, the mean BMI was 34.4 kg/m2, and only 12.5% had a BMI in the normal or underweight category. Similarly, waist circumference was elevated at 103 cm, with only 4 of 34 female patients having a waist circumference in the ‘low-risk’ category. In contrast, 3 out of 6 male participants had a waist circumference in the ‘low-risk’ category. This highlights phenotypic differences between males and females with HS, as has been previously described [82,83]. Waist circumference is used as a marker of central obesity, a body fat distribution which is associated with increased cardiometabolic risk [84]. Elevated waist circumference is also one of five criteria for diagnosing metabolic syndrome as per the NCEP/APT III guidelines, of which three must be fulfilled [85]. Blood pressure was elevated overall and this represents increased CV risk [60]. HS has a known association with hypertension [63].
The prevalence of insulin resistance (HOMA-IR) was high at 77%. This is in keeping with 2 prior studies by our research group, which found a prevalence of 64% in 50 patients [9] and 75% in 36 patients [10]. These figures are higher than what has been reported in a case-control study, where insulin resistance was reported to occur in 43% of 76 HS patients compared with 16% of 61 controls [11]. PCOS is associated with insulin resistance and also with HS [86], so it is a relevant comparator in this context. The prevalence of insulin resistance in PCOS varies widely between studies, with one reporting 10.3% of lean and 31% of obese [87], and another reporting it in 70% of studied [88]. As there was no difference in insulin resistance between HS controls and those taking metformin in our current study, pre-treatment values were evaluated for those treated with metformin (available for 9 of 20). Median fasting insulin was significantly lower in HS patients taking metformin compared with their pre-treatment levels, and HOMA-IR trended lower.
There was a 44% prevalence of metabolic syndrome, in keeping with international figures, where the prevalence is said to be 40% [89]. This is similar to the numbers reported in PCOS, with one study showing a prevalence of 43% [90].
There is a well-established significant association between HS and obesity [3,4,5,6,7,8], insulin resistance [9,10,11], metabolic syndrome [12,13,14], CV disease [57,64], T2DM [63], and all-cause mortality [64]. Insulin resistance, hormonal dysregulation, and obesity contribute to a proinflammatory state [16,17] and there is a strong correlation between the presence of insulin resistance and CV risk [91,92], as well as the risk of subsequent development of T2DM [93]. Metabolic syndrome is also an important predictor of risk of T2DM, CV disease, and all-cause mortality [94]. Taking this into account, the high prevalence of insulin resistance and metabolic syndrome in HS patients is likely to contribute to the increased all-cause mortality seen in HS. The results of our study reinforce the need to screen for and manage these important co-morbidities, and indeed this has recently been highlighted in guidelines recommending co-morbidity screening in HS [19]. Studies have shown that weight loss and bariatric surgery are associated with improvements in HS [4,95] and current guidelines incorporate weight loss advice as part of the standard management of HS [20,22,35]. These interventions should also be effective in reducing insulin resistance and metabolic syndrome [96,97], particularly as HOMA-IR correlated positively with both BMI and waist circumference.
Metformin improves insulin resistance in people with T2DM [98]. It is recommended second line in the management for PCOS in several PCOS guidelines [99], and is sometimes used off-label to manage metabolic syndrome in obesity [100,101]. Given the association of metabolic syndrome and insulin resistance with HS, it has been considered whether metformin’s mechanism of action in HS is through improvement of insulin resistance [10]. This forms part of the rationale of treating HS patients with metformin and also raises the question of whether a patient should remain on metformin for the management of insulin resistance/metabolic syndrome/long-term CV risk, even in the absence of an improvement of their HS. In our case-control study, there was no difference in insulin resistance in 16 control HS patients compared with 14 HS patients on metformin, or in metabolic syndrome in 19 control patients compared with 20 on metformin. There was, however, a significant reduction in fasting insulin in the metformin group compared with their pre-treatment level, and a trend towards a reduction in HOMA-IR. Based on this result and what is known from other conditions, it is reasonable to conclude that metformin does have beneficial effects on insulin resistance in at least some HS patients. This should be further examined, ideally in a prospective study of HS patients commencing metformin.
Having examined the effect of metformin on metabolic parameters in HS, we next assessed the effect of metformin on inflammation, as measured by CRP. CRP was high overall indicating a high systemic inflammatory burden of the disease. CRP was higher in HS controls than those on metformin but did not reach statistical significance. This is similar to our previous finding that CRP was reduced in 41 patients taking metformin (7.9 mg/L pre-treatment to 5.2 mg/L on treatment) which trended towards significance [10]. To the best of our knowledge, the effect of metformin on CRP in HS patients has not been otherwise assessed. Metformin significantly reduces CRP in people with rheumatoid arthritis [44], PCOS [62], and T2DM [102]. Although not definitively confirmed in our current study, it is likely that metformin reduces inflammation in HS as measured by CRP and this warrants further investigation.
HS is associated with CV disease and it would be useful to ascertain whether treatment with metformin can ameliorate this risk. In this study we compared CV disease biomarkers between HS controls and those on metformin. Higher levels of neutrophils, platelets, and monocytes, and lower levels of lymphocytes are thought to predict higher risk [65,66,67,68]. The ratios NLR, PLR, MLR, and MHR are predictors of higher CV risk [69,70,71,72,73]. We found a significant difference in some CV biomarkers in the HS metformin group compared with the HS controls, suggesting that metformin may have a cardioprotective effect in HS. Metformin has been shown to reduce NLR in patients with T2DM [103], and to reduce lymphocytes [104] and neutrophils [105] in PCOS. We previously found a reduction in these CV risk biomarkers in HS patients treated with biologic medications [106]. It would be useful to examine this effect of metformin in a larger cohort of patients.
It is well established that cytokines, chemokines, and inflammatory proteins are abnormal in HS skin [107] and serum [108]. Systematic review identified the following immune mediators as elevated in HS serum: IL-1β, IL-6, IL-8, IL-10, IL-12p70, IL-17, TNF-α, sTNFR1, CRP, ESR, LC2, and MMP2, with conflicting results for IL-10, IL-17, and IFN-γ [109]. Metformin reduces serum inflammation and immune mediators in T2DM and may do so in other conditions associated with insulin resistance and metabolic syndrome [77,110]. The effect of metformin on serum cytokines and chemokines in HS has not previously been studied. With this in mind, 13 inflammatory cytokines and chemokines were measured from the serum of 35 HS patients using a multiplex panel for comparison between those on metformin and HS controls. There was no statistically significant difference in levels between the two groups for IFN-γ, IL-6, IL-8, IL-17a, IL-17af, IL-18, IL-23, TNF-α, CXCL1, or CCL20, but levels of IFN-γ, IL-8, TNF-α, and CXCL1 demonstrated a non-significant reduction in the metformin group. In T2DM, metformin has been shown to reduce levels of IL-6 [111], TNF-α, and IFN-γ, but not IL-17a [77], and it is plausible that the same might be seen in HS with a larger sample size. A metanalysis of the effect of metformin on IL-6 in PCOS did not find it to change significantly following treatment [62], although two studies have found reductions in serum IL-6 following metformin treatment [112].
Next, we examined adipokines in the serum of 35 HS patients and compared levels with the mean level of 20 healthy control patients in our previous study [78]. This showed that adiponectin was significantly lower in HS than healthy controls, and both leptin and resistin were significantly higher in HS than healthy controls, reinforcing that serum adipokines are abnormal in HS. Adipokines are produced by adipocytes and adipose-tissue-associated macrophages and have an important role in regulating glucose metabolism, insulin sensitivity, immunity, and inflammation [79]. Serum adipokines are known to be dysregulated in HS [78,79]. Adiponectin is anti-inflammatory and serum levels have been shown to be decreased in HS, obesity, and psoriasis [78,79]. It promotes anti-inflammatory IL-10 and suppresses TNF [113]. Resistin and leptin are both proinflammatory and their serum levels have been shown to be elevated in HS [78,79]. Leptin stimulates production of IFN-γ, IL-1β, IL-2, IL-6, and TNF as well as CCL3–5 which activates JAK2-STAT3 [113,114]. Resistin promotes expression of IL-6 and TNF-α [113]. Proinflammatory cytokines stimulate production of proinflammatory adipokines and suppress anti-inflammatory adipokines [114]. Adipokines are also abnormal in obesity, CV disease, metabolic syndrome, and T2DM [81,115], as well as chronic inflammatory conditions such as psoriasis, psoriatic arthritis, and rheumatoid arthritis [79].
Improving the adipokine profile in HS would likely be of benefit both in reducing cardiometabolic risk and through reducing systemic inflammation, which may in turn have a positive effect on disease burden. The effect of metformin on adipokines has not previously been investigated in HS but metformin has been shown to have a positive effect on adipokines in PCOS, metabolic syndrome, and T2DM [80,81]. Levels of adiponectin, leptin, and resistin were not different between HS controls and metformin-treated patients in our study. However, given what is known about the effect of metformin on adipokines in other conditions, it would be worth assessing adipokine levels either in a larger case-control study, or preferably prospectively when starting metformin.
Efficacy of metformin was not evaluated in this study. However, 20 patients were taking metformin for a mean of 35 months and were noted to have either a partial or complete response to treatment, albeit some were taking concomitant medications for HS management. This indicates that for some HS patients, metformin is at least partially effective. Two published studies have examined this to date. The first demonstrated that metformin improved clinical severity of HS over 24 weeks [26]. Outcome measures were the Sartorius score which improved in 18 patients (72%) with a reduction from an average of 33.8 to 18.1 and dermatology life quality index (DLQI), which dropped significantly (more than 50%) in 64%, with a reduction from an average of 14 to 4.1. A study of 52 HS patients in our department also found metformin to be effective [10]. As this was a retrospective study, no formal severity assessment score could be used. Subjective clinical response (as assessed by the reviewing Consultant Dermatologist) was seen in 68% (36/52) with 19% (7/36) of these having quiescent disease with metformin monotherapy. A total of 75% had insulin resistance and its presence did not predict clinical response to metformin. High-quality prospective studies of metformin in HS using a recommended outcome measure (e.g., HiSCR/IHS4) would be beneficial for guiding treatment decisions. Mild HS accounts for approximately 70% of all HS cases and can have an adverse effect on quality of life, similar to more severe disease [36]. There are limited treatment options available for this cohort and recently metformin has been highlighted as potentially useful [36]. However, HiSCR and IHS4 are not practical in assessing treatment response in mild disease, so alternative outcome measures need to be considered, e.g., number of flares [36]. There is one randomised controlled trial of metformin in HS underway (ClinicalTrials.gov NCT04649502) and the results are eagerly awaited. In the era of antimicrobial resistance, finding a viable alternative to recurrent antibiotic courses for patients with mild or moderate HS is of increasing importance, and the role of metformin either as a solo or adjuvant treatment needs to be adequately assessed.
This study was limited by its case-control design, and also by missing data for some patients. A total of 20 patients were recruited to each group but some blood results were not available on all patients due to hospital laboratory processing/phlebotomy factors.

4. Materials and Methods

Forty HS patients were recruited from a specialty HS clinic for this case-control study: 20 who were taking metformin for at least 6 months and 20 who were not taking metformin for at least 6 months (HS controls). Patients were ≥18 years old. Patients were not taking liraglutide, other oral hypoglycaemic agents, or biologic medication (potential confounders)
The following demographic data were collected for each patient on history taking and chart review: gender; age; disease duration; smoking status; co-morbidities; metformin dose; metformin duration; concomitant medications; previous treatments for HS; and Hurley stage.
Patients were examined and the following data recorded: weight; height; waist circumference; BP; and clinical response to treatment with metformin (in remission; partial response; and no response). Body mass index was calculated.
The following blood tests were completed as per standard of care and analysed in the hospital laboratory of St Vincent’s University Hospital: full blood count (FBC); fasting lipids; fasting glucose; fasting insulin; HbA1c; and CRP. Insulin resistance was calculated using the homeostasis model of insulin resistance (HOMA-IR) with the following formula: (fasting insulin [mIU/L] × fasting glucose [mmol/L])/22.5 [116]. A value of ≥2.6 was classified as having insulin resistance, as this is the most widely used cut-off [117]. It should be noted that the cut-off for diagnosing insulin resistance may differ in different populations, e.g., by ethnicity, age, and gender [118]. Metabolic syndrome was defined according to the NCEP ATP III criteria [119,120].
A total of 13 serum cytokines and chemokines were analysed by multiplex using a custom Meso Scale Discovery (MSD, Rockville, MD, USA) U-plex™ panel (IFN-γ, IL-1α, IL-1β, IL-6, IL-8, IL-17a, IL-17af, IL-17c, IL-18, IL-23, TNF-α, CXCL1, and CCL20) according to the manufacturer’s instructions. These were chosen for their known relevance to HS pathogenesis. Levels of adiponectin, leptin, and resistin in serum were analysed using ELISA (Quantikine ELISA, R&D Systems™, Minneapolis, MN, USA) according to the manufacturer’s instructions.
Statistical analysis was completed using SPSS (IBM Corp. Released 2020. IBM SPSS Statistics for Macintosh, Version 27.0, Armonk, NY, USA: IBM Corp) and GraphPad Prism (version 9 for Mac OS, GraphPad Software, San Diego, CA, USA). Demographic and baseline clinical data were summarised using descriptive statistics by group. Data were tested for normality and the appropriate parametric or non-parametric statistical test applied. Pearson’s Chi square was used for categorical data. For numerical data, analysis of two groups was completed using an independent t-test or a non-parametric equivalent (Mann–Whitney test for unpaired and Wilcoxon test for paired samples). Correlations were calculated using Pearson’s or Spearman’s correlation for parametric or non-parametric data. P values of 0.05 were considered significant.
Ethics approval was granted by the St Vincent’s Healthcare Group Research Ethics Committee (reference number RS18-063) and informed written consent was obtained from all participants.

5. Conclusions

Our findings indicate a high prevalence of metabolic co-morbidities in HS patients as well as elevated CRP and serum adipokines, highlighting the systemic burden of the disease and the need to diagnose and treat these co-morbidities. Our results suggest that metformin improves CV risk biomarkers and fasting insulin in HS. The effect on insulin resistance, metabolic syndrome, serum immune mediators, and serum adipokines are less clear based on this study alone; however, when our results are considered alongside results from other studies in HS and related conditions, it is likely that metformin has beneficial effects on metabolic markers and systemic inflammation in HS patients. Given that patients with HS have increased all-cause mortality, a medication that can improve cardiometabolic risk and systemic inflammation in this condition would be very welcome. This warrants further clinical and translational research; ideally, a prospective study of patients commencing metformin should be conducted with analysis of metabolic markers, inflammation (CRP), CV risk biomarkers, serum immune mediators, and adipokines, as well as skin immune mediators at baseline and after 6 months of treatment. A prospective study would also allow an assessment of efficacy using a recommended outcome measure such as HiSCR and/or IHS4. This would help to guide treatment decisions in the management of HS and allow a more evidence-based approach to treatment. Mechanistic studies examining the effect of metformin on HS skin either in vitro or ex vivo would be of complementary benefit and represent an important next step to further understanding the role of metformin in treating this debilitating condition.

Author Contributions

Conceptualization, R.H. (Roisin Hambly), R.H. (Rosalind Hughes), J.M.F. and B.K.; methodology, R.H. (Roisin Hambly), J.M.F. and B.K.; validation, B.K.; formal analysis, R.H. (Roisin Hambly); investigation, R.H. (Roisin Hambly); resources, R.H. (Roisin Hambly), R.H. (Rosalind Hughes) and B.K.; data curation, R.H. (Roisin Hambly); writing—original draft, R.H. (Roisin Hambly); writing—review and editing, R.H. (Roisin Hambly), N.K., R.H. (Rosalind Hughes), J.M.F. and B.K.; supervision, J.M.F. and B.K.; project administration, R.H. (Roisin Hambly); funding acquisition, R.H. (Roisin Hambly) and B.K. All authors have read and agreed to the published version of the manuscript.

Funding

RH (Roisin Hambly) was supported by an academic training grant under the Irish Clinical Academic Training Programme, supported by the Wellcome Trust and the Health Research Board Ireland (Grant Number 203930/B/16/Z).

Institutional Review Board Statement

Ethics approval was granted by the St Vincent’s Healthcare Group Research Ethics Committee (reference number RS18-063).

Informed Consent Statement

Informed written consent was obtained from all participants.

Data Availability Statement

The data that support the findings of this study are available from https://figshare.com/articles/dataset/Data_supporting_findings_of_study/22575025.

Conflicts of Interest

R.H. (Roisin Hambly) and N.K. received honoraria from AbbVie, Janssen, and UCB and have acted as sub-investigators in clinical trials for AbbVie and UCB. B.K. received research support/principal investigator (clinical trials) from: AbbVie, Almirall, Janssen, Merck Sharpe Dolme, Moonlake, Novartis, Pfizer, and UCB; was a consultant for AbbVie, Almirall, Celgene, Janssen, Merck Sharpe Dolme, Moonlake, Novartis, Pfizer, and UCB; received honoraria from AbbVie, Almirall, Celgene, Janssen, Lilly, Moonlake, Novartis, Pfizer, and UCB; and was a member of scientific advisory boards for AbbVie, Almirall, Celgene, Janssen, Lilly, Moonlake, Novartis, Pfizer, and UCB.

References

  1. Sabat, R.; Jemec, G.B.E.; Matusiak, L.; Kimball, A.B.; Prens, E.; Wolk, K. Hidradenitis suppurativa. Nat. Rev. Dis. Primers. 2020, 6, 18. [Google Scholar] [CrossRef]
  2. Vossen, A.R.; van der Zee, H.; Prens, E. Hidradenitis Suppurativa: A Systematic Review Integrating Inflammatory Pathways Into a Cohesive Pathogenic Model. Front. Immunol. 2019, 9, 2965. [Google Scholar] [CrossRef] [Green Version]
  3. Sartorius, K.; Emtestam, L.; Jemec, G.B.; Lapins, J. Objective scoring of hidradenitis suppurativa reflecting the role of tobacco smoking and obesity. Br. J. Dermatol. 2009, 161, 831–839. [Google Scholar] [CrossRef]
  4. Kromann, C.B.; Ibler, K.S.; Kristiansen, V.B.; Jemec, G.B. The influence of body weight on the prevalence and severity of hidradenitis suppurativa. Acta Derm. -Venereol. 2014, 94, 553–557. [Google Scholar] [CrossRef] [PubMed]
  5. Boer, J. Resolution of hidradenitis suppurativa after weight loss by dietary measures, especially on frictional locations. J. Eur. Acad. Derm. Venereol. 2016, 30, 895–896. [Google Scholar] [CrossRef] [PubMed]
  6. Revuz, J.E.; Canoui-Poitrine, F.; Wolkenstein, P.; Viallette, C.; Gabison, G.; Pouget, F.; Poli, F.; Faye, O.; Roujeau, J.C.; Bonnelye, G.; et al. Prevalence and factors associated with hidradenitis suppurativa: Results from two case-control studies. J. Am. Acad. Dermatol. 2008, 59, 596–601. [Google Scholar] [CrossRef]
  7. Vazquez, B.G.; Alikhan, A.; Weaver, A.L.; Wetter, D.A.; Davis, M.D. Incidence of hidradenitis suppurativa and associated factors: A population-based study of Olmsted County, Minnesota. J. Investig. Dermatol. 2013, 133, 97–103. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Crowley, J.J.; Mekkes, J.R.; Zouboulis, C.C.; Scheinfeld, N.; Kimball, A.; Sundaram, M.; Gu, Y.; Okun, M.M.; Kerdel, F. Association of hidradenitis suppurativa disease severity with increased risk for systemic comorbidities. Br. J. Dermatol. 2014, 171, 1561–1565. [Google Scholar] [CrossRef] [Green Version]
  9. Hughes, R. Immune Dysregulation and Metabolic Dysfunction in Hidradenitis Suppurativa. Master’s Thesis, Trinity College Dublin, Dublin, Ireland, 2015. [Google Scholar]
  10. Jennings, L.; Hambly, R.; Hughes, R.; Moriarty, B.; Kirby, B. Metformin use in hidradenitis suppurativa. J. Dermatolog. Treat. 2020, 31, 261–263. [Google Scholar] [CrossRef] [PubMed]
  11. Vilanova, I.; Hernandez, J.L.; Mata, C.; Duran, C.; Garcia-Unzueta, M.T.; Portilla, V.; Fuentevilla, P.; Corrales, A.; Gonzalez-Vela, M.C.; Gonzalez-Gay, M.A.; et al. Insulin resistance in hidradenitis suppurativa: A case-control study. J. Eur. Acad. Dermatol. Venereol. 2018, 32, 820–824. [Google Scholar] [CrossRef] [Green Version]
  12. Gold, D.A.; Reeder, V.J.; Mahan, M.G.; Hamzavi, I.H. The prevalence of metabolic syndrome in patients with hidradenitis suppurativa. J. Am. Acad. Dermatol. 2014, 70, 699–703. [Google Scholar] [CrossRef] [PubMed]
  13. Sabat, R.; Chanwangpong, A.; Schneider-Burrus, S.; Metternich, D.; Kokolakis, G.; Kurek, A.; Philipp, S.; Uribe, D.; Wolk, K.; Sterry, W. Increased prevalence of metabolic syndrome in patients with acne inversa. PLoS ONE 2012, 7, e31810. [Google Scholar] [CrossRef]
  14. Miller, I.M.; Ellervik, C.; Vinding, G.R.; Zarchi, K.; Ibler, K.S.; Knudsen, K.M.; Jemec, G.B. Association of metabolic syndrome and hidradenitis suppurativa. JAMA Dermatol. 2014, 150, 1273–1280. [Google Scholar] [CrossRef] [Green Version]
  15. Mintoff, D.; Benhadou, F.; Pace, N.P.; Frew, J.W. Metabolic syndrome and hidradenitis suppurativa: Epidemiological, molecular, and therapeutic aspects. Int. J. Dermatol. 2021, 61, 1175–1186. [Google Scholar] [CrossRef]
  16. Jimenez-Gallo, D.; de la Varga-Martinez, R.; Ossorio-Garcia, L.; Albarran-Planelles, C.; Rodriguez, C.; Linares-Barrios, M. The Clinical Significance of Increased Serum Proinflammatory Cytokines, C-Reactive Protein, and Erythrocyte Sedimentation Rate in Patients with Hidradenitis Suppurativa. Mediat. Inflamm. 2017, 2017, 2450401. [Google Scholar] [CrossRef]
  17. Kanni, T.; Tzanetakou, V.; Savva, A.; Kersten, B.; Pistiki, A.; van de Veerdonk, F.L.; Netea, M.G.; van der Meer, J.W.; Giamarellos-Bourboulis, E.J. Compartmentalized Cytokine Responses in Hidradenitis Suppurativa. PLoS ONE 2015, 10, e0130522. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Zouboulis, C.C.; Nogueira da Costa, A. Drug repurposing through drug-gene interaction profiles for hidradenitis suppurativa/acne inversa treatment. J. Eur. Acad. Dermatol. Venereol. 2020, 35, e251–e254. [Google Scholar] [CrossRef]
  19. Garg, A.; Malviya, N.; Strunk, A.; Wright, S.; Alavi, A.; Alhusayen, R.; Alikhan, A.; Daveluy, S.D.; Delorme, I.; Goldfarb, N.; et al. Comorbidity screening in hidradenitis suppurativa: Evidence-based recommendations from the US and Canadian Hidradenitis Suppurativa Foundations. J. Am. Acad. Dermatol. 2021, 86, 1092–1101. [Google Scholar] [CrossRef] [PubMed]
  20. Ingram, J.R.; Collier, F.; Brown, D.; Burton, T.; Burton, J.; Chin, M.F.; Desai, N.; Goodacre, T.E.E.; Piguet, V.; Pink, A.E.; et al. British Association of Dermatologists guidelines for the management of hidradenitis suppurativa (acne inversa) 2018. Br. J. Dermatol. 2019, 180, 1009–1017. [Google Scholar] [CrossRef] [Green Version]
  21. Scheinfeld, N. Hidradenitis suppurativa: A practical review of possible medical treatments based on over 350 hidradenitis patients. Dermatol. Online J. 2013, 19, 1. [Google Scholar] [CrossRef] [PubMed]
  22. Zouboulis, C.C.; Desai, N.; Emtestam, L.; Hunger, R.E.; Ioannides, D.; Juhasz, I.; Lapins, J.; Matusiak, L.; Prens, E.P.; Revuz, J.; et al. European S1 guideline for the treatment of hidradenitis suppurativa/acne inversa. J. Eur. Acad. Dermatol. Venereol. 2015, 29, 619–644. [Google Scholar] [CrossRef] [PubMed]
  23. Gulliver, W.; Zouboulis, C.C.; Prens, E.; Jemec, G.B.; Tzellos, T. Evidence-based approach to the treatment of hidradenitis suppurativa/acne inversa, based on the European guidelines for hidradenitis suppurativa. Rev. Endocr. Metab. Disord. 2016, 17, 343–351. [Google Scholar] [CrossRef] [Green Version]
  24. van der Zee, H.H.; Gulliver, W.P. Medical Treatments of Hidradenitis Suppurativa: More Options, Less Evidence. Dermatol. Clin. 2016, 34, 91–96. [Google Scholar] [CrossRef] [PubMed]
  25. van Rappard, D.C.; Mekkes, J.R.; Tzellos, T. Randomized Controlled Trials for the Treatment of Hidradenitis Suppurativa. Dermatol. Clin. 2016, 34, 69–80. [Google Scholar] [CrossRef] [PubMed]
  26. Verdolini, R.; Clayton, N.; Smith, A.; Alwash, N.; Mannello, B. Metformin for the treatment of hidradenitis suppurativa: A little help along the way. J. Eur. Acad. Dermatol. Venereol. 2013, 27, 1101–1108. [Google Scholar] [CrossRef] [PubMed]
  27. Blok, J.L. Interventions for hidradenitis suppurativa: An important step towards evidence-based medicine. Br. J. Dermatol. 2016, 174, 953–954. [Google Scholar] [CrossRef] [PubMed]
  28. Balik, E.; Eren, T.; Bulut, T.; Buyukuncu, Y.; Bugra, D.; Yamaner, S. Surgical approach to extensive hidradenitis suppurativa in the perineal/perianal and gluteal regions. World J. Surg. 2009, 33, 481–487. [Google Scholar] [CrossRef]
  29. Jennings, L.; Nestor, L.; Molloy, O.; Hughes, R.; Moriarty, B.; Kirby, B. The treatment of hidradenitis suppurativa with the glucagon-like peptide-1 agonist liraglutide. Br. J. Dermatol. 2017, 177, 858–859. [Google Scholar] [CrossRef]
  30. Medicines.ie. Humira 40mg Solution for Injection in Pre-Filled Pen-Summary of Product Characteristics (SPC) 2021. Available online: https://www.medicines.ie/medicines/humira-40mg-solution-for-injection-in-pre-filled-pen-32384/spc (accessed on 31 October 2021).
  31. Kimball, A.B.; Okun, M.M.; Williams, D.A.; Gottlieb, A.B.; Papp, K.A.; Zouboulis, C.C.; Armstrong, A.W.; Kerdel, F.; Gold, M.H.; Forman, S.B.; et al. Two Phase 3 Trials of Adalimumab for Hidradenitis Suppurativa. N. Engl. J. Med. 2016, 375, 422–434. [Google Scholar] [CrossRef]
  32. Kimball, A.; Jemec, G.; Armstrong, A.; Forman, S.; Shu, L.; Gu, Y.; Williams, D.; O’kun, M. Evaluating Optimal Medium-Term Dosing Strategy for Adalimumab in PAtients with Moderate-to-Severe- Hidradenitis Suppurativa Based on Analysis of Integrated Results from the PIONEER I and II Phase 3, Randomized, Placebo-Controlled Trials. In Proceedings of the 73rd American Academy of Dermatology Congress, San Francisco, CA, USA, 20–24 March 2015. [Google Scholar]
  33. Kimball, A.B.; Sobell, J.M.; Zouboulis, C.C.; Gu, Y.; Williams, D.A.; Sundaram, M.; Teixeira, H.D.; Jemec, G.B. HiSCR (Hidradenitis Suppurativa Clinical Response): A novel clinical endpoint to evaluate therapeutic outcomes in patients with hidradenitis suppurativa from the placebo-controlled portion of a phase 2 adalimumab study. J. Eur. Acad. Dermatol. Venereol. 2016, 30, 989–994. [Google Scholar] [CrossRef] [Green Version]
  34. Zouboulis, C.C.; Tzellos, T.; Kyrgidis, A.; Jemec, G.B.E.; Bechara, F.G.; Giamarellos-Bourboulis, E.J.; Ingram, J.R.; Kanni, T.; Karagiannidis, I.; Martorell, A.; et al. Development and validation of the International Hidradenitis Suppurativa Severity Score System (IHS4), a novel dynamic scoring system to assess HS severity. Br. J. Dermatol. 2017, 177, 1401–1409. [Google Scholar] [CrossRef] [Green Version]
  35. Alikhan, A.; Sayed, C.; Alavi, A.; Alhusayen, R.; Brassard, A.; Burkhart, C.; Crowell, K.; Eisen, D.B.; Gottlieb, A.B.; Hamzavi, I.; et al. North American clinical management guidelines for hidradenitis suppurativa: A publication from the United States and Canadian Hidradenitis Suppurativa Foundations: Part II: Topical, intralesional, and systemic medical management. J. Am. Acad. Dermatol. 2019, 81, 91–101. [Google Scholar] [CrossRef] [Green Version]
  36. van der Zee, H.H.; van de Bunte, M.; van Straalen, K.R. Management of mild hidradenitis suppurativa: Our greatest challenge yet. Br. J. Dermatol. 2022, 186, 355–356. [Google Scholar] [CrossRef]
  37. Hambly, R.; Kirby, B. Prolonged clindamycin and rifampicin for hidradenitis suppurativa: Resist to prevent resistance. Br. J. Dermatol. 2019, 180, 702–703. [Google Scholar] [CrossRef]
  38. Gierek, M.; Ochala-Gierek, G.; Kitala, D.; Labus, W.; Bergler-Czop, B. Hidradenitis suppurativa: Bacteriological study in surgical treatment. Adv. Dermatol. Allergol. /Postępy Dermatol. Alergol. 2022, 39, 1101–1105. [Google Scholar] [CrossRef]
  39. Medicines.ie. Metformin (Glucophage) 500 mg Tablets-Summary of Product Characteristics (SPC) 2018. Available online: https://www.medicines.ie/medicines/glucophage-500mg-film-coated-tablets-32305/ (accessed on 8 April 2019).
  40. Rena, G.; Hardie, D.G.; Pearson, E.R. The mechanisms of action of metformin. Diabetologia 2017, 60, 1577–1585. [Google Scholar] [CrossRef] [Green Version]
  41. Johnson, N.P. Metformin use in women with polycystic ovary syndrome. Ann. Transl. Med. 2014, 2, 56. [Google Scholar]
  42. Pu, R.; Shi, D.; Gan, T.; Ren, X.; Ba, Y.; Huo, Y.; Bai, Y.; Zheng, T.; Cheng, N. Effects of metformin in obesity treatment in different populations: A meta-analysis. Ther. Adv. Endocrinol. Metab. 2020, 11, 2042018820926000. [Google Scholar] [CrossRef]
  43. Rask Larsen, J.; Dima, L.; Correll, C.U.; Manu, P. The pharmacological management of metabolic syndrome. Expert. Rev. Clin. Pharmacol. 2018, 11, 397–410. [Google Scholar] [CrossRef]
  44. Gharib, M.; Elbaz, W.; Darweesh, E.; Sabri, N.A.; Shawki, M.A. Efficacy and Safety of Metformin Use in Rheumatoid Arthritis: A Randomized Controlled Study. Front. Pharmacol. 2021, 12, 726490. [Google Scholar] [CrossRef]
  45. Chang, J.E.; Choi, M.S. A Molecular Perspective on the Potential Benefits of Metformin for the Treatment of Inflammatory Skin Disorders. Int. J. Mol. Sci. 2020, 21, 8960. [Google Scholar] [CrossRef] [PubMed]
  46. Melnik, B.C.; Schmitz, G. Metformin: An Inhibitor of mTORC1 Signaling. J. Endocrinol. Diabetes Obes. 2014, 2, 1029. [Google Scholar]
  47. Bharath, L.P.; Nikolajczyk, B.S. The intersection of metformin and inflammation. Am. J. Physiol. Cell Physiol. 2021, 320, C873–C879. [Google Scholar] [CrossRef] [PubMed]
  48. Zhou, G.; Myers, R.; Li, Y.; Chen, Y.; Shen, X.; Fenyk-Melody, J.; Wu, M.; Ventre, J.; Doebber, T.; Fujii, N.; et al. Role of AMP-activated protein kinase in mechanism of metformin action. J. Clin. Investig. 2001, 108, 1167–1174. [Google Scholar] [CrossRef] [PubMed]
  49. Howell, J.J.; Hellberg, K.; Turner, M.; Talbott, G.; Kolar, M.J.; Ross, D.S.; Hoxhaj, G.; Saghatelian, A.; Shaw, R.J.; Manning, B.D. Metformin Inhibits Hepatic mTORC1 Signaling via Dose-Dependent Mechanisms Involving AMPK and the TSC Complex. Cell Metab. 2017, 25, 463–471. [Google Scholar] [CrossRef] [Green Version]
  50. Liu, Y.; Yang, F.; Ma, W.; Sun, Q. Metformin inhibits proliferation and proinflammatory cytokines of human keratinocytes in vitro via mTOR-signaling pathway. Pharm. Biol. 2016, 54, 1173–1178. [Google Scholar] [CrossRef] [Green Version]
  51. Gallagher, L.; Cregan, S.; Biniecka, M.; Cunningham, C.; Veale, D.J.; Kane, D.J.; Fearon, U.; Mullan, R.H. Insulin-Resistant Pathways Are Associated With Disease Activity in Rheumatoid Arthritis and Are Subject to Disease Modification Through Metabolic Reprogramming: A Potential Novel Therapeutic Approach. Arthritis Rheumatol. 2020, 72, 896–902. [Google Scholar] [CrossRef]
  52. Kang, K.Y.; Kim, Y.K.; Yi, H.; Kim, J.; Jung, H.R.; Kim, I.J.; Cho, J.H.; Park, S.H.; Kim, H.Y.; Ju, J.H. Metformin downregulates Th17 cells differentiation and attenuates murine autoimmune arthritis. Int. Immunopharmacol. 2013, 16, 85–92. [Google Scholar] [CrossRef]
  53. Park, M.J.; Lee, S.Y.; Moon, S.J.; Son, H.J.; Lee, S.H.; Kim, E.K.; Byun, J.K.; Shin, D.Y.; Park, S.H.; Yang, C.W.; et al. Metformin attenuates graft-versus-host disease via restricting mammalian target of rapamycin/signal transducer and activator of transcription 3 and promoting adenosine monophosphate-activated protein kinase-autophagy for the balance between T helper 17 and Tregs. Transl. Res. 2016, 173, 115–130. [Google Scholar]
  54. Lee, S.Y.; Lee, S.H.; Yang, E.J.; Kim, E.K.; Kim, J.K.; Shin, D.Y.; Cho, M.L. Metformin Ameliorates Inflammatory Bowel Disease by Suppression of the STAT3 Signaling Pathway and Regulation of the between Th17/Treg Balance. PLoS ONE 2015, 10, e0135858. [Google Scholar] [CrossRef] [Green Version]
  55. Sun, Y.; Tian, T.; Gao, J.; Liu, X.; Hou, H.; Cao, R.; Li, B.; Quan, M.; Guo, L. Metformin ameliorates the development of experimental autoimmune encephalomyelitis by regulating T helper 17 and regulatory T cells in mice. J. Neuroimmunol. 2016, 292, 58–67. [Google Scholar] [CrossRef] [PubMed]
  56. Kurzthaler, D.; Hadziomerovic-Pekic, D.; Wildt, L.; Seeber, B.E. Metformin induces a prompt decrease in LH-stimulated testosterone response in women with PCOS independent of its insulin-sensitizing effects. Reprod. Biol. Endocrinol. 2014, 12, 98. [Google Scholar] [CrossRef] [Green Version]
  57. Tzellos, T.; Zouboulis, C.C.; Gulliver, W.; Cohen, A.D.; Wolkenstein, P.; Jemec, G.B. Cardiovascular disease risk factors in patients with hidradenitis suppurativa: A systematic review and meta-analysis of observational studies. Br. J. Dermatol. 2015, 173, 1142–1155. [Google Scholar] [CrossRef]
  58. Lean, M.E.; Han, T.S.; Morrison, C.E. Waist circumference as a measure for indicating need for weight management. BMJ 1995, 311, 158–161. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. British Heart Foundation. Why Your Waist Size Matters. Available online: https://www.bhf.org.uk/informationsupport/heart-matters-magazine/medical/measuring-your-waist (accessed on 13 October 2021).
  60. Whelton, P.K.; Carey, R.M.; Aronow, W.S.; Casey, D.E., Jr.; Collins, K.J.; Dennison Himmelfarb, C.; DePalma, S.M.; Gidding, S.; Jamerson, K.A.; Jones, D.W.; et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J. Am. Coll. Cardiol. 2018, 71, e127–e248. [Google Scholar] [PubMed]
  61. Pepys, M.B.; Hirschfield, G.M. C-reactive protein: A critical update. J. Clin. Investig. 2003, 111, 1805–1812. [Google Scholar] [CrossRef] [PubMed]
  62. Wang, J.; Zhu, L.; Hu, K.; Tang, Y.; Zeng, X.; Liu, J.; Xu, J. Effects of metformin treatment on serum levels of C-reactive protein and interleukin-6 in women with polycystic ovary syndrome: A meta-analysis: A PRISMA-compliant article. Medicine 2017, 96, e8183. [Google Scholar] [CrossRef]
  63. Jorgensen, A.R.; Yao, Y.; Ghazanfar, M.N.; Ring, H.C.; Thomsen, S.F. Burden, predictors and temporal relationships of comorbidities in patients with hidradenitis suppurativa: A hospital-based cohort study. J. Eur. Acad. Dermatol. Venereol. 2019, 34, 565–573. [Google Scholar] [CrossRef] [PubMed]
  64. Egeberg, A.; Gislason, G.H.; Hansen, P.R. Risk of Major Adverse Cardiovascular Events and All-Cause Mortality in Patients With Hidradenitis Suppurativa. JAMA Dermatol. 2016, 152, 429–434. [Google Scholar] [CrossRef] [Green Version]
  65. Silvestre-Roig, C.; Braster, Q.; Ortega-Gomez, A.; Soehnlein, O. Neutrophils as regulators of cardiovascular inflammation. Nat. Rev. Cardiol. 2020, 17, 327–340. [Google Scholar] [CrossRef]
  66. Hu, D.; Wang, Z.; Wang, Y.; Liang, C. Targeting Macrophages in Atherosclerosis. Curr. Pharm. Biotechnol. 2021, 22, 2008–2018. [Google Scholar] [CrossRef]
  67. Nording, H.M.; Seizer, P.; Langer, H.F. Platelets in inflammation and atherogenesis. Front. Immunol. 2015, 6, 98. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  68. Nunez, J.; Sastre, C.; D’Ascoli, G.; Ruiz, V.; Bonanad, C.; Minana, G.; Valero, E.; Garcia-Blas, S.; Mollar, A.; Villaescusa, A.; et al. Relation of Low Lymphocyte Count to Frailty and its Usefulness as a Prognostic Biomarker in Patients >65 Years of Age With Acute Coronary Syndrome. Am. J. Cardiol. 2020, 125, 1033–1038. [Google Scholar] [CrossRef]
  69. Delgado, G.E.; Kramer, B.K.; Marz, W.; Hellstern, P.; Kleber, M.E.; Leipe, J. Immune Status and Mortality in Smokers, Ex-smokers, and Never-Smokers: The Ludwigshafen Risk and Cardiovascular Health Study. Nicotine Tob. Res. 2021, 23, 1191–1198. [Google Scholar] [CrossRef]
  70. Sager, H.B.; Koenig, W. Immune cell-based cardiovascular risk assessment: Spotlight on the neutrophil-lymphocyte ratio. Eur. Heart J. 2021, 42, 904–906. [Google Scholar] [CrossRef]
  71. Pujani, M.; Chauhan, V.; Singh, K.; Rastogi, S.; Agarwal, C.; Gera, K. The effect and correlation of smoking with platelet indices, neutrophil lymphocyte ratio and platelet lymphocyte ratio. Hematol. Transfus. Cell Ther. 2021, 43, 424–429. [Google Scholar] [CrossRef]
  72. Ji, H.; Li, Y.; Fan, Z.; Zuo, B.; Jian, X.; Li, L.; Liu, T. Monocyte/lymphocyte ratio predicts the severity of coronary artery disease: A syntax score assessment. BMC Cardiovasc. Disord. 2017, 17, 90. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Zhang, T.Y.; Zhao, Q.; Liu, Z.S.; Zhang, C.Y.; Yang, J.; Meng, K. Relationship between monocyte/lymphocyte ratio and non-culprit plaque vulnerability in patients with acute coronary syndrome: An optical coherence tomography study. Medicine 2020, 99, e21562. [Google Scholar] [CrossRef]
  74. Forget, P.; Khalifa, C.; Defour, J.P.; Latinne, D.; Van Pel, M.C.; De Kock, M. What is the normal value of the neutrophil-to-lymphocyte ratio? BMC Res. Notes 2017, 10, 12. [Google Scholar] [CrossRef] [Green Version]
  75. Cetinarslan, T.; Turel Ermertcan, A.; Ozyurt, B.; Gunduz, K. Evaluation of the laboratory parameters in hidradenitis suppurativa: Can we use new inflammatory biomarkers? Dermatol. Ther. 2021, 34, e14835. [Google Scholar] [CrossRef]
  76. Chen, H.; Li, M.; Liu, L.; Dang, X.; Zhu, D.; Tian, G. Monocyte/lymphocyte ratio is related to the severity of coronary artery disease and clinical outcome in patients with non-ST-elevation myocardial infarction. Medicine 2019, 98, e16267. [Google Scholar] [CrossRef] [PubMed]
  77. Amoani, B.; Sakyi, S.A.; Mantey, R.; Laing, E.F.; Ephraim, R.D.; Sarfo-Katanka, O.; Koffie, S.; Obese, E.; Afranie, B.O. Increased metformin dosage suppresses pro-inflammatory cytokine levels in systemic circulation and might contribute to its beneficial effects. J. Immunoass. Immunochem. 2021, 42, 252–264. [Google Scholar] [CrossRef]
  78. Malara, A.; Hughes, R.; Jennings, L.; Sweeney, C.M.; Lynch, M.; Awdeh, F.; Timoney, I.; Tobin, A.M.; Lynam-Loane, K.; Tobin, L.; et al. Adipokines are dysregulated in patients with hidradenitis suppurativa. Br. J. Dermatol. 2018, 178, 792–793. [Google Scholar] [CrossRef]
  79. Gonzalez-Lopez, M.A.; Vilanova, I.; Ocejo-Vinals, G.; Arlegui, R.; Navarro, I.; Guiral, S.; Mata, C.; Perez-Paredes, M.G.; Portilla, V.; Corrales, A.; et al. Circulating levels of adiponectin, leptin, resistin and visfatin in non-diabetics patients with hidradenitis suppurativa. Arch. Dermatol. Res. 2020, 312, 595–600. [Google Scholar] [CrossRef] [PubMed]
  80. Duan, X.; Zhou, M.; Zhou, G.; Zhu, Q.; Li, W. Effect of metformin on adiponectin in PCOS: A meta-analysis and a systematic review. Eur. J. Obstet. Gynecol. Reprod. Biol. 2021, 267, 61–67. [Google Scholar] [CrossRef]
  81. Dludla, P.V.; Nkambule, B.B.; Mazibuko-Mbeje, S.E.; Nyambuya, T.M.; Mxinwa, V.; Mokgalaboni, K.; Ziqubu, K.; Cirilli, I.; Marcheggiani, F.; Louw, J.; et al. Adipokines as a therapeutic target by metformin to improve metabolic function: A systematic review of randomized controlled trials. Pharmacol. Res. 2021, 163, 105219. [Google Scholar] [CrossRef]
  82. Canoui-Poitrine, F.; Le Thuaut, A.; Revuz, J.E.; Viallette, C.; Gabison, G.; Poli, F.; Pouget, F.; Wolkenstein, P.; Bastuji-Garin, S. Identification of three hidradenitis suppurativa phenotypes: Latent class analysis of a cross-sectional study. J. Investig. Dermatol. 2013, 133, 1506–1511. [Google Scholar] [CrossRef] [Green Version]
  83. van der Zee, H.H.; Jemec, G.B. New insights into the diagnosis of hidradenitis suppurativa: Clinical presentations and phenotypes. J. Am. Acad. Dermatol. 2015, 73, S23–S26. [Google Scholar] [CrossRef]
  84. Klein, S.; Allison, D.B.; Heymsfield, S.B.; Kelley, D.E.; Leibel, R.L.; Nonas, C.; Kahn, R.; Association for Weight Management and Obesity Prevention; NAASO, The Obesity Society; American Society for Nutrition; et al. Waist circumference and cardiometabolic risk: A consensus statement from shaping America’s health: Association for Weight Management and Obesity Prevention; NAASO, the Obesity Society; the American Society for Nutrition; and the American Diabetes Association. Diabetes Care 2007, 30, 1647–1652. [Google Scholar] [PubMed] [Green Version]
  85. Huang, P.L. A comprehensive definition for metabolic syndrome. Dis. Model. Mech. 2009, 2, 231–237. [Google Scholar] [CrossRef] [Green Version]
  86. Garg, A.; Neuren, E.; Strunk, A. Hidradenitis Suppurativa Is Associated with Polycystic Ovary Syndrome: A Population-Based Analysis in the United States. J. Investig. Dermatol. 2018, 138, 1288–1292. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  87. Legro, R.S.; Kunselman, A.R.; Dodson, W.C.; Dunaif, A. Prevalence and predictors of risk for type 2 diabetes mellitus and impaired glucose tolerance in polycystic ovary syndrome: A prospective, controlled study in 254 affected women. J. Clin. Endocrinol. Metab. 1999, 84, 165–169. [Google Scholar] [CrossRef] [PubMed]
  88. DeUgarte, C.M.; Bartolucci, A.A.; Azziz, R. Prevalence of insulin resistance in the polycystic ovary syndrome using the homeostasis model assessment. Fertil. Steril. 2005, 83, 1454–1460. [Google Scholar] [CrossRef]
  89. Ingram, J.R. The epidemiology of hidradenitis suppurativa. Br. J. Dermatol. 2020, 183, 990–998. [Google Scholar] [CrossRef] [PubMed]
  90. Azziz, R.; Woods, K.S.; Reyna, R.; Key, T.J.; Knochenhauer, E.S.; Yildiz, B.O. The prevalence and features of the polycystic ovary syndrome in an unselected population. J. Clin. Endocrinol. Metab. 2004, 89, 2745–2749. [Google Scholar] [CrossRef] [Green Version]
  91. Gast, K.B.; Tjeerdema, N.; Stijnen, T.; Smit, J.W.; Dekkers, O.M. Insulin resistance and risk of incident cardiovascular events in adults without diabetes: Meta-analysis. PLoS ONE 2012, 7, e52036. [Google Scholar] [CrossRef] [Green Version]
  92. Ormazabal, V.; Nair, S.; Elfeky, O.; Aguayo, C.; Salomon, C.; Zuniga, F.A. Association between insulin resistance and the development of cardiovascular disease. Cardiovasc. Diabetol. 2018, 17, 122. [Google Scholar] [CrossRef] [Green Version]
  93. Taylor, R. Insulin resistance and type 2 diabetes. Diabetes 2012, 61, 778–779. [Google Scholar] [CrossRef] [Green Version]
  94. Shin, J.A.; Lee, J.H.; Lim, S.Y.; Ha, H.S.; Kwon, H.S.; Park, Y.M.; Lee, W.C.; Kang, M.I.; Yim, H.W.; Yoon, K.H.; et al. Metabolic syndrome as a predictor of type 2 diabetes, and its clinical interpretations and usefulness. J. Diabetes Investig. 2013, 4, 334–343. [Google Scholar] [CrossRef]
  95. Sivanand, A.; Gulliver, W.P.; Josan, C.K.; Alhusayen, R.; Fleming, P.J. Weight Loss and Dietary Interventions for Hidradenitis Suppurativa: A Systematic Review. J. Cutan. Med. Surg. 2020, 24, 64–72. [Google Scholar] [CrossRef]
  96. Clamp, L.D.; Hume, D.J.; Lambert, E.V.; Kroff, J. Enhanced insulin sensitivity in successful, long-term weight loss maintainers compared with matched controls with no weight loss history. Nutr. Diabetes 2017, 7, e282. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  97. Phelan, S.; Wadden, T.A.; Berkowitz, R.I.; Sarwer, D.B.; Womble, L.G.; Cato, R.K.; Rothman, R. Impact of weight loss on the metabolic syndrome. Int. J. Obes. 2007, 31, 1442–1448. [Google Scholar] [CrossRef] [Green Version]
  98. Giannarelli, R.; Aragona, M.; Coppelli, A.; Del Prato, S. Reducing insulin resistance with metformin: The evidence today. Diabetes Metab. 2003, 29, S28–S35. [Google Scholar] [CrossRef] [PubMed]
  99. De Sousa, S.M.D.; Norman, R.J.P. Metabolic syndrome, diet and exercise. Best Pract. Res. Clin. Obstet. Gynaecol. 2016, 37, 140–151. [Google Scholar] [CrossRef]
  100. Al Wattar, B.H.; Fisher, M.; Bevington, L.; Talaulikar, V.; Davies, M.; Conway, G.; Yasmin, E. Clinical Practice Guidelines on the Diagnosis and Management of Polycystic Ovary Syndrome: A Systematic Review and Quality Assessment Study. J. Clin. Endocrinol. Metab. 2021, 106, 2436–2446. [Google Scholar] [CrossRef]
  101. Bassols, J.; Martinez-Calcerrada, J.M.; Osiniri, I.; Diaz-Roldan, F.; Xargay-Torrent, S.; Mas-Pares, B.; Dorado-Ceballos, E.; Prats-Puig, A.; Carreras-Badosa, G.; de Zegher, F.; et al. Effects of metformin administration on endocrine-metabolic parameters, visceral adiposity and cardiovascular risk factors in children with obesity and risk markers for metabolic syndrome: A pilot study. PLoS ONE 2019, 14, e0226303. [Google Scholar] [CrossRef] [PubMed]
  102. Shi, L.; Tan, G.S.; Zhang, K. Relationship of the Serum CRP Level With the Efficacy of Metformin in the Treatment of Type 2 Diabetes Mellitus: A Meta-Analysis. J. Clin. Lab. Anal. 2016, 30, 13–22. [Google Scholar] [CrossRef]
  103. Cameron, A.R.; Morrison, V.L.; Levin, D.; Mohan, M.; Forteath, C.; Beall, C.; McNeilly, A.D.; Balfour, D.J.; Savinko, T.; Wong, A.K.; et al. Anti-Inflammatory Effects of Metformin Irrespective of Diabetes Status. Circ. Res. 2016, 119, 652–665. [Google Scholar] [CrossRef] [Green Version]
  104. Orio, F.; Manguso, F.; Di Biase, S.; Falbo, A.; Giallauria, F.; Labella, D.; Tolino, A.; Lombardi, G.; Colao, A.; Palomba, S. Metformin administration improves leukocyte count in women with polycystic ovary syndrome: A 6-month prospective study. Eur. J. Endocrinol. 2007, 157, 69–73. [Google Scholar] [CrossRef] [Green Version]
  105. Ibanez, L.; Jaramillo, A.M.; Ferrer, A.; de Zegher, F. High neutrophil count in girls and women with hyperinsulinaemic hyperandrogenism: Normalization with metformin and flutamide overcomes the aggravation by oral contraception. Hum. Reprod. 2005, 20, 2457–2462. [Google Scholar] [CrossRef]
  106. Kearney, N.; McCourt, C.; Hambly, R.; Hughes, R.; O’Kane, D.; Kirby, B. Association of Biologic Treatment in Hidradenitis Suppurativa With Reduced Neutrophil-Lymphocyte Ratio and Platelet-Lymphocyte Ratio. JAMA Dermatol. 2023, 159, 222–224. [Google Scholar] [CrossRef] [PubMed]
  107. Wolk, K.; Join-Lambert, O.; Sabat, R. Aetiology and pathogenesis of hidradenitis suppurativa. Br. J. Dermatol. 2020, 183, 999–1010. [Google Scholar] [CrossRef]
  108. Navrazhina, K.; Garcet, S.; Frew, J.W.; Zheng, X.; Coats, I.; Guttman-Yassky, E.; Krueger, J.G. The inflammatory proteome of hidradenitis suppurativa skin is more expansive than that of psoriasis vulgaris. J. Am. Acad. Dermatol. 2022, 86, 322–330. [Google Scholar] [CrossRef] [PubMed]
  109. Frew, J.W.; Hawkes, J.E.; Krueger, J.G. A systematic review and critical evaluation of inflammatory cytokine associations in hidradenitis suppurativa. F1000Research 2018, 7, 1930. [Google Scholar] [CrossRef] [PubMed]
  110. Bai, B.; Chen, H. Metformin: A Novel Weapon Against Inflammation. Front. Pharmacol. 2021, 12, 622262. [Google Scholar] [CrossRef] [PubMed]
  111. Chen, W.; Liu, X.; Ye, S. Effects of metformin on blood and urine pro-inflammatory mediators in patients with type 2 diabetes. J. Inflamm. 2016, 13, 34. [Google Scholar] [CrossRef]
  112. Xu, X.; Du, C.; Zheng, Q.; Peng, L.; Sun, Y. Effect of metformin on serum interleukin-6 levels in polycystic ovary syndrome: A systematic review. BMC Women’s Health 2014, 14, 93. [Google Scholar] [CrossRef] [Green Version]
  113. Ouchi, N.; Parker, J.L.; Lugus, J.J.; Walsh, K. Adipokines in inflammation and metabolic disease. Nat. Rev. Immunol. 2011, 11, 85–97. [Google Scholar] [CrossRef]
  114. Wolk, K.; Sabat, R. Adipokines in psoriasis: An important link between skin inflammation and metabolic alterations. Rev. Endocr. Metab. Disord. 2016, 17, 305–317. [Google Scholar] [CrossRef]
  115. Nakamura, K.; Fuster, J.J.; Walsh, K. Adipokines: A link between obesity and cardiovascular disease. J. Cardiol. 2014, 63, 250–259. [Google Scholar] [CrossRef] [Green Version]
  116. Matthews, D.R.; Hosker, J.P.; Rudenski, A.S.; Naylor, B.A.; Treacher, D.F.; Turner, R.C. Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985, 28, 412–419. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  117. Ascaso, J.F.; Pardo, S.; Real, J.T.; Lorente, R.I.; Priego, A.; Carmena, R. Diagnosing insulin resistance by simple quantitative methods in subjects with normal glucose metabolism. Diabetes Care 2003, 26, 3320–3325. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  118. Gayoso-Diz, P.; Otero-Gonzalez, A.; Rodriguez-Alvarez, M.X.; Gude, F.; Garcia, F.; De Francisco, A.; Quintela, A.G. Insulin resistance (HOMA-IR) cut-off values and the metabolic syndrome in a general adult population: Effect of gender and age: EPIRCE cross-sectional study. BMC Endocr. Disord. 2013, 13, 47. [Google Scholar] [CrossRef] [Green Version]
  119. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002, 106, 3143–3421. [Google Scholar] [CrossRef]
  120. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA 2001, 285, 2486–2497. [Google Scholar] [CrossRef]
Ijms 24 06969 g001 550
Figure 1. Fasting insulin levels were lower in patients taking metformin compared with their pre-treatment level. Blood samples from HS patients taking metformin pre-treatment and on treatment (n = 9) were analysed for HOMA-IR and fasting insulin. Graphs represent matched samples. Statistical significance was calculated using the Wilcoxon test. ** p ≤ 0.01. HOMA-IR = homeostatic model assessment for insulin resistance; Met = metformin.
Figure 1. Fasting insulin levels were lower in patients taking metformin compared with their pre-treatment level. Blood samples from HS patients taking metformin pre-treatment and on treatment (n = 9) were analysed for HOMA-IR and fasting insulin. Graphs represent matched samples. Statistical significance was calculated using the Wilcoxon test. ** p ≤ 0.01. HOMA-IR = homeostatic model assessment for insulin resistance; Met = metformin.
Ijms 24 06969 g001
Ijms 24 06969 g002 550
Figure 2. The absolute number of lymphocytes was higher in HS patients taking metformin than HS controls and the monocyte–lymphocyte ratio (MLR) was lower in those taking metformin than controls. Blood samples from control HS patients (n = 17) and those taking metformin (n = 19) were analysed for the absolute number of lymphocytes, the monocyte to lymphocyte ratio (MLR), neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), and monocyte–HDL ratio (MHL). Graphs represent individual results, mean and standard deviation. Statistical significance was calculated using independent samples t-test. * p < 0.05. ns = not significant. MHR = monocyte–HDL ratio (HDL = high-density lipoprotein); MLR = monocyte–lymphocyte ratio; NLR = neutrophil–lymphocyte ratio; and PLR = platelet–lymphocyte ratio.
Figure 2. The absolute number of lymphocytes was higher in HS patients taking metformin than HS controls and the monocyte–lymphocyte ratio (MLR) was lower in those taking metformin than controls. Blood samples from control HS patients (n = 17) and those taking metformin (n = 19) were analysed for the absolute number of lymphocytes, the monocyte to lymphocyte ratio (MLR), neutrophil–lymphocyte ratio (NLR), platelet–lymphocyte ratio (PLR), and monocyte–HDL ratio (MHL). Graphs represent individual results, mean and standard deviation. Statistical significance was calculated using independent samples t-test. * p < 0.05. ns = not significant. MHR = monocyte–HDL ratio (HDL = high-density lipoprotein); MLR = monocyte–lymphocyte ratio; NLR = neutrophil–lymphocyte ratio; and PLR = platelet–lymphocyte ratio.
Ijms 24 06969 g002
Ijms 24 06969 g003 550
Figure 3. Trend towards a reduction in serum concentration of IFN-γ, IL-8, TNF-α, and CXCL1 in HS patients taking metformin compared with HS controls. Serum levels of cytokines and chemokines from control HS patients (n = 18) and those taking metformin (n = 17) were analysed using a multiplex assay: IFN-γ (A), IL-6 (B), IL-8 (C), IL-17a (D), IL-17af (E), IL-18 (F), IL-23 (G), TNF-α (H), CXCL1 (I), and CCL20 (J). Graphs represent individual samples with median and IQR. Statistical significance was calculated using the Mann–Whitney test. IQR = interquartile range.
Figure 3. Trend towards a reduction in serum concentration of IFN-γ, IL-8, TNF-α, and CXCL1 in HS patients taking metformin compared with HS controls. Serum levels of cytokines and chemokines from control HS patients (n = 18) and those taking metformin (n = 17) were analysed using a multiplex assay: IFN-γ (A), IL-6 (B), IL-8 (C), IL-17a (D), IL-17af (E), IL-18 (F), IL-23 (G), TNF-α (H), CXCL1 (I), and CCL20 (J). Graphs represent individual samples with median and IQR. Statistical significance was calculated using the Mann–Whitney test. IQR = interquartile range.
Ijms 24 06969 g003
Ijms 24 06969 g004 550
Figure 4. No statistically significant difference in serum adipokines between HS controls (HS-Con) and those on metformin (HS-Met). Serum levels of adipokines adiponectin (A), leptin (B), and resistin (C) were analysed using ELISA in 18 HS controls and 17 HS patients taking metformin. Graphs represent individual samples with median and IQR. Statistical significance was calculated using the Mann–Whitney test. IQR = interquartile range.
Figure 4. No statistically significant difference in serum adipokines between HS controls (HS-Con) and those on metformin (HS-Met). Serum levels of adipokines adiponectin (A), leptin (B), and resistin (C) were analysed using ELISA in 18 HS controls and 17 HS patients taking metformin. Graphs represent individual samples with median and IQR. Statistical significance was calculated using the Mann–Whitney test. IQR = interquartile range.
Ijms 24 06969 g004
Table
Table 1. Demographics, metabolic parameters, CRP, and CV risk biomarkers for HS metformin and HS controls groups.
Table 1. Demographics, metabolic parameters, CRP, and CV risk biomarkers for HS metformin and HS controls groups.
HS Metformin
(n = 20)		HS Control
(n = 20)		p-Value
Female (n, %)19 (95%)15 (75%)0.077
Age (mean ± SD)38.1 ± 11.237.95 ± 12.30.968
Disease duration (mean ± SD)12.65 years ± 8.710.8 years ± 8.60.504
Smoking (n, %) 0.374
Current7 (35%)11 (55%)
Ex6 (30%)3 (15%)
Non7 (35%)6 (30%)
Hurley stage (n, %) 0.347
Hurley 12 (10%)2 (10%)
Hurley 216 (80%)18 (90%)
Hurley 32 (10%)0
BMI kg/m2 (mean ± SD)35 ± 9.833.6 ± 90.624
WC cm (mean ± SD)103.7 ± 19102.35 ± 19.80.823
SBP (mean ± SD)131 ± 14.9130.5 ± 18.80.528
DBP (mean ± SD)81.3 ± 979.2 ±12.30.116
HbA1c mmol/mol (mean ± SD)36.2 ± 2.735.8 ± 4.30.74
HOMA-IR (mean ± SD)4.7 ± 4
(n = 14)		4.2 ± 2.8
(n = 16)		0.673
Insulin resistance (HOMA-IR ≥ 2.6) (n, %)10 (71.4%)
(n = 14)		13 (81.3%)
(n = 16)		0.526
Metabolic syndrome (n, %)9 (45%)
(n = 20)		8 (42%)
(n = 19)		0.855
CRP (mg/L) (mean ± SD)5.17 ± 3.8
(n = 19)		8.19 ± 11.9
(n = 20)		0.3
n = 19n = 17
Neutrophils (×109/L) (mean ± SD)4.88 ± 1.55.15 ± 20.658
Lymphocytes (×109/L) (mean ± SD)2.66 ± 0.732.15 ± 0.670.037
Platelets (×109/L) (mean ± SD)314.16 ± 56300.65 ± 57.40.48
Monocytes (×109/L) (mean ± SD)0.59 ± 0.120.62 ± 0.180.596
NLR (mean ± SD)1.92 ± 0.682.69 ± 1.760.108
PLR (mean ± SD)127.65 ± 48.2150.6 ± 47.740.161
MLR (mean ± SD)0.23 ± 0.050.32 ± 0.140.032
MHR (mean ± SD)0.43 ± 0.210.43 ± 0.180.898
Statistical significance was calculated using Pearson’s Chi-square for categorical data and independent samples t-test for numerical data. BMI = body mass index; CRP = C-reactive protein; CV = cardiovascular; DBP = diastolic blood pressure; HOMA-IR = homeostatic model assessment for insulin resistance; HS = hidradenitis suppurativa; MHR = monocyte–HDL ratio (HDL = high-density lipoprotein); MLR = monocyte–lymphocyte ratio; n = number; NLR = neutrophil–lymphocyte ratio; PLR = platelet–lymphocyte ratio; SBP = systolic blood pressure; SD = standard deviation; and WC = waist circumference.
Table
Table 2. Fasting insulin levels were lower in patients taking metformin compared with their pre-treatment level.
Table 2. Fasting insulin levels were lower in patients taking metformin compared with their pre-treatment level.
Pre-Metformin
(n = 9)		On Metformin
(n = 9)		p-Value
Fasting glucose mmol/L
(median, IQR)		4.9
(4.6–5.3)		4.9
(4.8–5.3)		0.69
Fasting insulin mIU/L
(median, IQR)		22.0
(5.7–72.9)		3.3
(2.0–7.5)		0.008
HOMA-IR
(median, IQR)		4.8
(1.2–22.9)		3.3
(2.0–7.5)		0.43
HbA1c mmol/mol
(median, IQR)		35.0
(32.5–37.8)		36.0
(35.0–38.0)		0.11
Statistical significance was calculated using the Wilcoxon test for non-parametric data. IQR = interquartile range.
Table
Table 3. NLR and PLR were higher in the HS control group than the general population mean but not significantly different in the HS metformin group compared with the general population mean. MLR was lower in the metformin group than the ACS mean.
Table 3. NLR and PLR were higher in the HS control group than the general population mean but not significantly different in the HS metformin group compared with the general population mean. MLR was lower in the metformin group than the ACS mean.
CV Risk BiomarkerGroupMeanp-Value
NLRHS Control (n = 17)2.690.027
General population mean a1.65
HS Metformin (n = 19)1.920.094
General population mean1.65
PLRHS Control (n = 17)150.60.01
General population mean a117.05
HS Metformin (n = 19)127.650.35
General population mean a117.05
MLRHS Control (n = 17)0.320.325
ACS mean a0.28
HS Metformin (n = 19)0.230.001
ACS mean a0.28
a The general population mean and ACS mean were identified from the literature (references in text). Statistical significance was calculated using one-sample t-test. ACS = acute coronary syndrome; CV = cardiovascular; NLR = neutrophil–lymphocyte ratio; PLR = platelet–lymphocyte ratio; and MLR = monocyte–lymphocyte ratio.
Table
Table 4. CV biomarkers did not correlate with CRP or HOMA-IR.
Table 4. CV biomarkers did not correlate with CRP or HOMA-IR.
Correlationrp-Value
CRP and lymphocyte count0.1060.546
CRP and neutrophil count0.1790.304
CRP and NLR0.0230.896
CRP and PLR0.0520.766
CRP and MLR−0.0840.631
CRP and MHR0.2180.208
HOMA-IR and lymphocyte count−0.40.841
HOMA-IR and neutrophil count0.2090.296
HOMA-IR and NLR0.0910.653
HOMA-IR and PLR0.0680.763
HOMA-IR and MLR−0.0350.861
HOMA-IR and MHR0.1570.435
Calculated using Pearson’s correlation. n = 35 for CRP calculations; n = 27 for HOMA-IR calculations. CRP = C-reactive protein; CV = cardiovascular; HOMA-IR = homeostatic model assessment for insulin resistance; MHR = monocyte–HDL ratio; MLR = monocyte–lymphocyte ratio; NLR = neutrophil–lymphocyte ratio; and PLR = platelet–lymphocyte ratio.
Table
Table 5. Trend towards a reduction in serum concentration of IFN-γ, IL-8, TNF-α, and CXCL1 in HS patients taking metformin compared with HS controls.
Table 5. Trend towards a reduction in serum concentration of IFN-γ, IL-8, TNF-α, and CXCL1 in HS patients taking metformin compared with HS controls.
AnalyteHS Controls n = 18
Median (IQR)		HS Metformin n = 17
Median (IQR)		p-Value
IFN-γ (pg/mL)6.43
(0–15)		0.19
(0–16.34)		0.52
IL-6 (ng/mL)1.9
(0.84–2.78)		2
(0.99–2.81)		0.8
IL-8 (ng/mL)27.39
(13.34–52.44)		24.03
(20.05–27.75)		0.81
IL-17a (pg/mL)0
(0–0.45)		0
(0–0)		0.74
IL-17af (pg/mL)5.06
(1.65–13.77)		5.83
(0.93–21.24)		0.98
IL-18 (pg/mL)578
(441–908)		613
(506–757)		0.96
IL-23 (pg/mL)1.75
(0.09–6.08)		2.46
(0.63–7.97)		0.68
TNF-α (pg/mL)2.78
(2.15–4.1)		1.7
(1.22–3.38)		0.19
CXCL1 (ng/mL)553
(384–652)		471
(408–635)		0.73
CCL20 (pg/mL)0
(0–24.89)		0
(0–8.11)		0.37
Statistical significance was calculated using the Mann–Whitney test. IQR = interquartile range.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Hambly, R.; Kearney, N.; Hughes, R.; Fletcher, J.M.; Kirby, B. Metformin Treatment of Hidradenitis Suppurativa: Effect on Metabolic Parameters, Inflammation, Cardiovascular Risk Biomarkers, and Immune Mediators. Int. J. Mol. Sci. 2023, 24, 6969. https://doi.org/10.3390/ijms24086969

AMA Style

Hambly R, Kearney N, Hughes R, Fletcher JM, Kirby B. Metformin Treatment of Hidradenitis Suppurativa: Effect on Metabolic Parameters, Inflammation, Cardiovascular Risk Biomarkers, and Immune Mediators. International Journal of Molecular Sciences. 2023; 24(8):6969. https://doi.org/10.3390/ijms24086969

Chicago/Turabian Style

Hambly, Roisin, Niamh Kearney, Rosalind Hughes, Jean M. Fletcher, and Brian Kirby. 2023. "Metformin Treatment of Hidradenitis Suppurativa: Effect on Metabolic Parameters, Inflammation, Cardiovascular Risk Biomarkers, and Immune Mediators" International Journal of Molecular Sciences 24, no. 8: 6969. https://doi.org/10.3390/ijms24086969

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop