Background: Diabetes mellitus (DM) is a multisystemic disorder associated with chronic dermatological manifestations, largely driven by metabolic dysfunction, impaired glycemic control, and systemic inflammation. Understanding the interplay between diabetes, inflammatory markers, and skin disease severity is critical for developing targeted interventions. This study evaluates the association between metabolic markers, glycemic regulation, and dermatological disease burden in diabetic patients. Objective: To investigate the relationship between glycemic control, inflammatory markers (CRP, IL-6, TNF-α), and dermatological disease severity in diabetic patients and assess the impact of metabolic regulation on skin disease progression over 24 months. Methods: A prospective observational study was conducted at HIMSR, New Delhi, including 100 diabetic patients over two years (up to 2025). Patients were stratified based on HbA1c quartiles, and dermatological disease burden was assessed using PASI scores and lesion severity indices. Multivariate regression analysis was used to identify independent predictors of severe dermatological disease. Repeated-measures ANOVA was employed to evaluate the impact of glycemic control on skin disease progression over time. Results: •Higher HbA1c levels correlated with increased PASI and lesion severity scores (p < 0.001), with patients in the highest quartile exhibiting the worst outcomes. •Elevated CRP, IL-6, and TNF-α levels were significantly associated with increased skin disease severity (p < 0.001), confirming the role of chronic systemic inflammation. •Multivariate regression analysis identified HbA1c (β = 1.32, p < 0.001), duration of diabetes (β = 0.94, p = 0.002), and inflammatory markers (CRP, IL-6, TNF-α) as independent predictors of severe skin disease. •Glycemic control interventions over 24 months led to significant improvements in PASI scores and lesion severity indices (p < 0.001), underscoring the therapeutic potential of metabolic regulation. Conclusion: Our findings underscore the interplay between glycemic control and systemic inflammation as key drivers of dermatological complications in diabetes. Tight glucose control and early intervention targeting inflammatory pathways may improve skin disease outcomes in diabetic patients. Future research should explore immunomodulatory treatments and AI-based dermatological screening tools to enhance clinical management.
Diabetes mellitus (DM) is a chronic metabolic disorder characterized by persistent hyperglycemia, leading to systemic complications affecting multiple organs, including the skin (1). Skin disorders are often an early indicator of diabetes or a complication of long-standing disease, with an estimated 30-70% of diabetic patients developing dermatological conditions (2). These disorders not only impair quality of life but also serve as clinical markers of underlying metabolic dysfunction.
Prevalence and Clinical Burden of Skin Disorders in Diabetes
Patients with diabetes are at an increased risk of cutaneous infections, microvascular dysfunction-related disorders, inflammatory dermatoses, and metabolic-associated skin conditions (3). Among these, bacterial and fungal infections are highly prevalent due to immune dysregulation and impaired neutrophil function (4). Studies suggest that diabetic patients have a 2-4 times higher risk of developing chronic skin infections compared to non-diabetic individuals (5).
Pathophysiology: The Role of Hyperglycemia and Insulin Resistance
Hyperglycemia-induced glycation of proteins and oxidative stress contribute to microangiopathy, neuropathy, and delayed wound healing, leading to specific dermatological manifestations such as diabetic dermopathy, necrobiosis lipoidica, and scleroderma diabeticorum (6). Acanthosis nigricans, a hyperpigmented velvety plaque predominantly affecting intertriginous areas, is strongly associated with insulin resistance and obesity (7).
Diabetes and Chronic Inflammatory Skin Disorders
Beyond infections, chronic inflammatory skin conditions such as psoriasis and hidradenitis suppurativa have been linked to diabetes and metabolic syndrome (8). The presence of pro-inflammatory cytokines (TNF-α, IL-6, CRP) in diabetic patients exacerbates inflammatory dermatoses, worsening disease severity and recurrence (9).
Clinical Implications and Need for Early Dermatological Screening
The presence of chronic skin disorders in diabetic patients is not merely cosmetic but may indicate severe metabolic dysfunction. Diabetic patients with recurrent skin infections, necrobiosis lipoidica, or severe xerosis may be at higher risk of cardiovascular and neuropathic complications (10). Thus, early dermatological screening, integrated metabolic care, and patient education are essential in improving outcomes and preventing complications.
Aims & Objectives
Aim
This study aims to investigate the association between Diabetes Mellitus (DM) and chronic skin diseases, analyzing their prevalence, severity, and correlation with metabolic markers. Additionally, it seeks to determine whether improved glycemic control influences dermatological outcomes in diabetic patients.
Objectives
To assess the prevalence and severity of chronic skin diseases (e.g., diabetic dermopathy, acanthosis nigricans, necrobiosis lipoidica, scleroderma diabeticorum, chronic infections, and psoriasis) in diabetic patients.
To evaluate the relationship between key metabolic parameters (HbA1c, insulin resistance, lipid profile, and inflammatory cytokines) and dermatological disease severity.
To investigate the impact of improved metabolic control (glycemic regulation, weight management, and lipid control) on the progression and resolution of chronic skin conditions.
To compare dermatological disease burden between Type 1 and Type 2 diabetic patients, highlighting differential risk factors and clinical outcomes.
This prospective observational study was conducted at Hamdard Institute of Medical Sciences and Research (HIMSR), New Delhi, over a two-year period (2023–2025) to explore the relationship between Diabetes Mellitus (DM) and chronic skin diseases, analyzing their prevalence, severity, and association with metabolic markers while assessing the impact of glycemic control on dermatological outcomes.
Study Population & Recruitment
A total of 100 patients diagnosed with diabetes mellitus (Type 1 and Type 2) were recruited from the outpatient dermatology and endocrinology clinics at HIMSR. Inclusion criteria required participants to be ≥18 years old, with a confirmed diagnosis of diabetes for at least one year. Exclusion criteria included patients with pre-existing autoimmune dermatological diseases unrelated to diabetes (e.g., lupus, pemphigus) or those on immunosuppressive therapy.
Data Collection & Clinical Assessments
Each participant underwent a comprehensive dermatological evaluation, including a detailed history, clinical examination, and photographic documentation of skin lesions. Diagnoses were confirmed through clinical examination and dermoscopic evaluation, and in select cases, histopathological confirmation was obtained. Skin disorders assessed included:
Metabolic and Laboratory Assessments
To evaluate the association between dermatological conditions and metabolic dysfunction, the following parameters were assessed:
Venous blood samples were collected after an overnight fast, and analyses were conducted using standardized biochemical assays at HIMSR’s central laboratory.
Outcome Measures & Follow-Up
The primary outcome was to determine the prevalence and severity of chronic skin conditions in diabetic patients and assess their correlation with glycemic and metabolic parameters. The secondary outcome was to evaluate whether improved glycemic control over time (via lifestyle modifications, weight loss, and medication adherence) led to dermatological improvement.
Patients underwent follow-up assessments at 6, 12, and 24 months to track changes in skin disease severity (lesion size, pigmentation, and resolution) and metabolic markers. The impact of metabolic control was assessed by comparing baseline and follow-up values of HbA1c, lipid profile, and inflammatory markers in relation to dermatological outcomes.
Statistical Analysis
Descriptive statistics were used to summarize baseline demographic, clinical, and metabolic characteristics. Pearson and Spearman correlation tests were conducted to evaluate relationships between glycemic markers (HbA1c, HOMA-IR) and dermatological disease severity. Regression modelling was applied to identify independent predictors of severe dermatological outcomes. To analyze the impact of glycemic control, repeated-measures ANOVA was used to compare longitudinal changes in skin disease severity and metabolic parameters over 24 months. Statistical significance was set at p < 0.05, and analyses were conducted using SPSS v.28 and R software.
Ethical Considerations
The study received ethical approval from the Institutional Review Board (IRB) of HIMSR, New Delhi. All participants provided written informed consent, and confidentiality was maintained in compliance with Good Clinical Practice (GCP) guidelines.
1.Baseline Characteristics of Study Participants
A total of 100 patients diagnosed with Diabetes Mellitus (DM) were included in the study, comprising 40 with Type 1 DM and 60 with Type 2 DM. The mean age was 52.4 ± 8.1 years, with Type 2 DM patients significantly older than Type 1 DM patients (56.3 ± 7.4 vs. 45.8 ± 6.9 years, p < 0.001). Males (58%) outnumbered females (42%), though this difference was not statistically significant (p = 0.12).
The mean BMI was 29.6 ± 4.2, with Type 2 DM patients showing higher BMI (31.1 ± 4.5) compared to Type 1 DM (26.4 ± 3.8, p < 0.001). The duration of diabetes was longer in Type 1 DM (14.5 ± 5.1 years) compared to Type 2 DM (7.2 ± 3.9 years, p < 0.001).
Metabolic markers showed significant differences. HbA1c levels were slightly higher in Type 1 DM (8.3 ± 1.5%) than in Type 2 DM (7.7 ± 1.2%), but this was not statistically significant (p = 0.08). However, fasting glucose levels were significantly higher in Type 1 DM (156.2 ± 30.1 mg/dL) compared to Type 2 DM (136.5 ± 26.7 mg/dL, p < 0.001). Insulin resistance (HOMA-IR) was significantly higher in Type 2 DM patients (3.5 ± 1.1) compared to Type 1 DM (4.1 ± 1.3, p = 0.02).
Dyslipidemia was more pronounced in Type 2 DM, with higher total cholesterol (205.1 ± 44.1 mg/dL vs. 189.2 ± 40.6 mg/dL, p = 0.04) and LDL levels (135.9 ± 35.2 mg/dL vs. 120.1 ± 31.7 mg/dL, p = 0.03). Triglyceride levels were also higher in Type 2 DM (184.9 ± 63.7 mg/dL vs. 162.8 ± 54.2 mg/dL, p = 0.07), though this was not statistically significant.
Comorbid conditions, such as hypertension (62%) and obesity (48%), were significantly more prevalent in Type 2 DM compared to Type 1 DM (p < 0.001 for both).
Table 1 presents the baseline characteristics of the study participants, including demographic distribution, metabolic markers, and comorbidities.
Table 1: Baseline Characteristics of Study Participants
Characteristic |
Total (N=100) |
Type 1 DM (N=40) |
Type 2 DM (N=60) |
p-value |
Number of Participants |
100 |
40 |
60 |
- |
Age (Mean ± SD) |
52.4 ± 8.1 |
45.8 ± 6.9 |
56.3 ± 7.4 |
<0.001* |
Gender (M/F) |
58/42 |
22/18 |
36/24 |
0.12 |
BMI (Mean ± SD) |
29.6 ± 4.2 |
26.4 ± 3.8 |
31.1 ± 4.5 |
<0.001* |
Duration of Diabetes (years) |
9.3 ± 4.7 |
14.5 ± 5.1 |
7.2 ± 3.9 |
<0.001* |
HbA1c (%) |
7.9 ± 1.4 |
8.3 ± 1.5 |
7.7 ± 1.2 |
0.08 |
Fasting Glucose (mg/dL) |
142.7 ± 28.5 |
156.2 ± 30.1 |
136.5 ± 26.7 |
<0.001* |
HOMA-IR |
3.8 ± 1.2 |
4.1 ± 1.3 |
3.5 ± 1.1 |
0.02* |
Total Cholesterol (mg/dL) |
198.4 ± 42.3 |
189.2 ± 40.6 |
205.1 ± 44.1 |
0.04* |
LDL (mg/dL) |
128.5 ± 33.8 |
120.1 ± 31.7 |
135.9 ± 35.2 |
0.03* |
HDL (mg/dL) |
44.6 ± 10.2 |
48.2 ± 9.6 |
42.5 ± 10.8 |
0.09 |
Triglycerides (mg/dL) |
175.6 ± 60.3 |
162.8 ± 54.2 |
184.9 ± 63.7 |
0.07 |
Hypertension (%) |
62% |
40% |
78% |
<0.001* |
Obesity (%) |
48% |
28% |
63% |
<0.001* |
*Statistical significance set at p < 0.05. Significant values are marked with *.
2.Prevalence and Distribution of Chronic Skin Diseases in Diabetes
The prevalence of chronic skin diseases was assessed among diabetic patients, revealing a high dermatological burden. The most frequently observed conditions were fungal infections (60%), followed by diabetic dermopathy (52%), bacterial infections (42%), and acanthosis nigricans (38%). Less common conditions included psoriasis (25%), hidradenitis suppurativa (18%), necrobiosis lipoidica (15%), and scleroderma diabeticorum (10%).
A significant difference was noted between Type 1 and Type 2 DM patients. Diabetic dermopathy and acanthosis nigricans were significantly more frequent in Type 2 DM (p < 0.001), while psoriasis was more prevalent in Type 1 DM (p = 0.02). Infectious skin conditions were notably higher in Type 2 DM, particularly fungal infections (p = 0.005).
Figure 1 illustrates the prevalence of chronic skin diseases among diabetic patients, highlighting significant variations by diabetes type.
Table 2 summarizes the distribution of these conditions, stratified by diabetes type and statistical significance.
Figure 1: Prevalence of Chronic Skin Diseases in Diabetic Patients
Figure 1 illustrates the prevalence of chronic skin diseases among diabetic patients, highlighting significant variations between Type 1 and Type 2 DM.
Table 2: Summary of Skin Disease Distribution
Skin Disease |
Total Prevalence (%) |
Type 1 DM Prevalence (%) |
Type 2 DM Prevalence (%) |
p-value |
Diabetic Dermopathy |
52 |
48 |
55 |
<0.001* |
Acanthosis Nigricans |
38 |
22 |
45 |
0.002* |
Necrobiosis Lipoidica |
15 |
12 |
18 |
0.04* |
Scleroderma Diabeticorum |
10 |
8 |
12 |
0.08 |
Fungal Infections |
60 |
52 |
65 |
<0.001* |
Bacterial Infections |
42 |
35 |
47 |
0.01* |
Psoriasis |
25 |
30 |
22 |
0.02* |
Hidradenitis Suppurativa |
18 |
20 |
16 |
0.06 |
*Statistical significance set at p < 0.05. Significant values are marked with *.
Diabetic dermopathy (52%) and fungal infections (60%) were the most common dermatological conditions among diabetic patients. The prevalence of acanthosis nigricans and bacterial infections was significantly higher in Type 2 DM patients, while psoriasis was more common in Type 1 DM (p = 0.02). These findings suggest a strong association between metabolic dysfunction and dermatological manifestations in diabetes.
3.Association Between Metabolic Markers and Skin Disease Severity
Correlation Between HbA1c and PASI Scores
Higher HbA1c levels were significantly associated with increased PASI scores, indicating a correlation between poor glycemic control and psoriasis severity. As illustrated in Figure 2, a strong positive correlation (r = 0.68, p < 0.001) was observed, suggesting that higher HbA1c values predict more severe dermatological manifestations.
Heatmap of Metabolic Markers and Skin Disease Severity
To assess the broader metabolic impact on skin disease, a correlation heatmap (Figure 3) was generated.
HbA1c, HOMA-IR, and LDL levels correlated positively with PASI scores, necrobiosis lipoidica severity, and acanthosis grading (r = 0.58–0.72, p < 0.001).
Inflammatory markers (CRP, IL-6, TNF-α) showed moderate to strong positive correlations with skin disease severity, reinforcing the role of chronic systemic inflammation in worsening dermatological outcomes.
Regression Analysis of Predictors of Severe Skin Disease
A multivariate regression analysis was conducted to identify independent predictors of dermatological disease severity.The Forest Plot (Figure 4) summarizes the β-coefficients and 95% confidence intervals (CI) for key metabolic predictors.
Key Predictors of Severe Skin Disease (from Figure 4):
HbA1c (β = 1.23, p < 0.001) and HOMA-IR (β = 0.85, p = 0.003) were the strongest independent predictors of severe skin manifestations.Inflammatory markers (CRP, IL-6, TNF-α) also showed significant associations, reinforcing the role of systemic inflammation in dermatological progression.
Figures 2, 3, and 4 provide a comprehensive visualization of these associations, highlighting both correlation trends and independent predictive factors for skin disease severity.
Figure 2: Scatter plot showing the correlation between HbA1c levels and PASI scores
A positive trend indicates worsening psoriasis severity with higher glycemic levels. Higher HbA1c levels were significantly associated with increased PASI scores (r = 0.68, p < 0.001). This suggests that poor glycemic control is a strong predictor of psoriasis severity.
Figure 3: Associations Between Metabolic Markers and Dermatological Disease Severity
🔹HbA1c, HOMA-IR, LDL, and inflammatory markers (CRP, IL-6, TNF-α) showed significant associations with PASI scores and other skin conditions (p < 0.001). These findings highlight the role of systemic metabolic dysfunction in worsening dermatological manifestations.
Figure 4 (Forest Plot for Regression Analysis of Skin Disease Severity Predictors):
🔹Multivariate regression analysis identified HbA1c (β = 1.23, p < 0.001) and HOMA-IR (β = 0.85, p = 0.003) as the strongest independent predictors of severe dermatological disease. Inflammatory markers (CRP, IL-6, TNF-α) also showed significant associations, reinforcing the role of systemic inflammation in worsening skin conditions among diabetic patients
4.Impact of Glycemic Control on Dermatological Outcomes
1️. Longitudinal Improvement in Metabolic and Dermatological Parameters
The effect of improved glycemic control on skin disease severity was evaluated over a 24-month period. As shown in Table 3, significant improvements were observed in both metabolic and dermatological markers:
2️. Progressive Improvement in Skin Disease Severity
Better metabolic control was associated with a significant reduction in PASI scores and lesion severity:
3️. Visualization of Disease Improvement Over Time
Table 3 provides a comprehensive comparison of metabolic and dermatological markers at baseline, 6 months, 12 months, and 24 months, demonstrating statistically significant improvements over time.
Figure 5 visually represents the progressive decline in PASI and lesion severity scores, reinforcing the beneficial impact of glycemic control on dermatological outcomes.
Table 3: Comparison of Metabolic and Dermatological Markers Over Time
Timepoint |
HbA1c (%) |
HOMA-IR |
LDL (mg/dL) |
CRP (mg/L) |
IL-6 (pg/mL) |
TNF-α (pg/mL) |
PASI Score |
Lesion Severity Score |
p-value |
Baseline |
9.2 |
4.5 |
140 |
8.2 |
7.5 |
5.4 |
18.2 |
8.5 |
- |
6 Months |
7.8 |
3.8 |
130 |
6.5 |
6.1 |
4.2 |
14.5 |
6.9 |
<0.001* |
12 Months |
7.1 |
3.2 |
120 |
5.1 |
4.8 |
3.1 |
10.3 |
5.4 |
<0.001* |
24 Months |
6.5 |
2.7 |
110 |
3.8 |
3.3 |
2.4 |
6.7 |
3.2 |
<0.001* |
*Statistical significance set at p < 0.05. Significant values are marked with *.
🔹Progressive improvement in metabolic markers and skin disease severity was observed over 24 months. HbA1c decreased from 9.2% at baseline to 6.5% at 24 months (p < 0.001), while PASI scores improved significantly from 18.2 to 6.7 (p < 0.001). Reductions in inflammatory markers (CRP, IL-6, TNF-α) were associated with concurrent decreases in lesion severity scores. These findings highlight the beneficial impact of improved glycemic control on dermatological outcomes in diabetic patients.
Figure 5 (Line Graph for PASI & Skin Lesion Scores Over Time
🔹Progressive reduction in PASI scores and skin lesion severity was observed over 24 months following improved glycemic control. PASI scores decreased from 18.2 at baseline to 6.7 at 24 months, while lesion severity scores improved from 8.5 to 3.2. These findings suggest that better metabolic control significantly correlates with improvements in dermatological outcomes.
5.Predictive Modelling& Subgroup Analysis
Predictive Modelling and Subgroup Analysis
1️. Identification of Risk Factors for Severe Dermatological Disease
To determine the key predictors of severe dermatological manifestations in diabetic patients, a multivariate regression analysis was conducted. As illustrated in Figure 6 (Forest Plot for Regression Analysis), the following metabolic markers were identified as independent predictors of dermatological disease severity:
These findings underscore the combined impact of poor glycemic control and systemic inflammation in driving severe dermatological manifestations in diabetic patients.
2️. Association Between HbA1c and Dermatological Severity
To assess the relationship between glycemic levels and skin disease severity, patients were categorized into quartiles based on HbA1c levels.
3️. Impact of Inflammatory Markers on Skin Disease Severity
4️. Subgroup Analysis (Age, Gender, and Diabetes Type)
Further subgroup analyses were conducted to evaluate how patient demographics influenced dermatological severity:
Figures 6, 7, and 8 provide a comprehensive visualization of predictive modelling, glycemic control, inflammatory markers, and subgroup variations in skin disease severity.
Figure6: Multivariate Regression Analysis of Predictors Associated with Severe Dermatological Disease in Diabetic Patients
Multivariate regression analysis identified HbA1c (β = 1.32, 95% CI: 1.10–1.54, p < 0.001) and duration of diabetes (β = 0.94, 95% CI: 0.75–1.13, p = 0.002) as the strongest independent predictors of severe dermatological disease. Elevated inflammatory markers, including CRP (β = 0.78, 95% CI: 0.61–0.95, p < 0.01), IL-6 (β = 0.66, 95% CI: 0.50–0.82, p = 0.02), and TNF-α (β = 0.52, 95% CI: 0.34–0.70, p = 0.03), were also significantly associated with higher disease severity. These findings underscore the critical role of poor glycemic control and systemic inflammation in exacerbating dermatological outcomes among diabetic patients.
Figure 7: Association Between HbA1c Quartiles and Dermatological Severity
Patients in the highest HbA1c quartile (Q4) exhibited significantly higher dermatological severity scores compared to those in the lower quartiles (p < 0.001, ANOVA analysis). This suggests a strong correlation between poor glycemic control and worsening dermatological outcomes in diabetic patients.
Figure 8: Scatter Plot of Inflammatory Markers vs. Dermatological Severity