Oxidative stress markers and inflammation in type 1 and 2 diabetes are affected by BMI, treatment type, and complications

Abstract

Diabetes mellitus (DM) is a common global metabolic disease. Oxidative stress from reactive oxygen species (ROS) contributes to its development and leads to complications like heart disease, kidney failure, and stroke. Chronic inflammation in diabetes is associated with insulin resistance and elevated glucose levels, as indicated by increased markers of interleukin-6 (IL-6), C-reactive protein (CRP), and tumor necrosis factor-alpha (TNF-α). This study investigates the activity and concentration of antioxidant enzymes (SOD, GPX1, CAT) and inflammatory markers (IL-6, CRP, TNF-α) in patients with type 1 and type 2 diabetes compared to healthy controls. The study included 73 patients—33 with type 1 diabetes (18 men, 15 women) and 40 with type 2 diabetes (20 men, 20 women)—and 41 healthy controls (23 men, 18 women). Antioxidant enzymes and inflammatory markers were measured using enzyme-linked immunosorbent assay (ELISA), and HbA1c levels were assessed. Program R and Statistica 13 were used to analyze the results. Group membership had a significant impact on SOD and CAT activity (p < 0.0001) and GPX1 (p < 0.001). BMI correlated with CAT concentration (p < 0.0001). SOD activity was affected by comorbidities, such as arthritis and urinary tract issues (p = 0.03). Diabetes markedly altered inflammatory markers, particularly CRP and TNF-α (p < 0.0001), and higher IL-6 levels were found in patients using medications other than metformin (p = 0.01). Type 1 and 2 diabetes significantly affect antioxidant enzyme activity and concentration. High SOD and GPX activity suggests chronic oxidative stress, while increased BMI is linked to lower enzyme levels. Additionally, TNF-α levels rise with diabetes duration, which may serve as a biomarker for disease progression and complications, potentially helping to predict diabetic complications and insulin resistance.

Introduction

Metabolic diseases encompass a broad range of disorders resulting from abnormal biochemical changes within the body. They constitute a significant epidemiological problem, with the number of patients increasing yearly. This rise is primarily attributed to inappropriate lifestyle choices, including poor nutrition, sedentary behavior, and the consequences of modern civilization’s development. Among these diseases, diabetes mellitus (DM) is the most prevalent, characterized by chronic hyperglycemia due to impaired insulin secretion or action. This condition is associated with damage, dysfunction, and eventual failure of various organs, particularly the liver, kidneys, nervous system, eyes, and cardiovascular system. Several types of diabetes are distinguished based on their etiopathogenesis, including type 1, type 2, gestational diabetes, and other specific forms, as recommended by the World Health Organization¹. Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease characterized by persistent hyperglycemia due to insulin resistance in peripheral tissues and an inadequate compensatory insulin secretory response from pancreatic β-cells³. It accounts for over 90% of diabetes cases worldwide² and has reached pandemic proportions. As of 2024, an estimated 590 million adults globally are living with diabetes, a figure projected to rise to ~ 800–850 million by 2050². This dramatic increase has been driven by population aging, urbanization, sedentary lifestyles, and the obesity epidemic². The burden of type 2 diabetes mellitus (T2DM) is pervasive across all regions, with low- and middle-income countries accounting for ~ 80% of cases¹. Alarmingly, the onset of disease occurs at younger ages; the incidence in adults under 40 has increased twofold to threefold in recent decades³. T2DM confers substantial morbidity and mortality, mainly through its complications—it is a leading cause of blindness, renal failure, cardiovascular disease, and limb amputations¹. In 2021, about 2 million deaths were directly attributable to diabetes (and its kidney complications), and high blood glucose contributed to ~ 11% of global cardiovascular deaths¹. These stark figures underscore the urgent public health challenge of type 2 diabetes mellitus (T2DM)¹ and motivate intensive research into its epidemiology and pathophysiology. The global burden of type 2 diabetes mellitus (T2DM) has increased substantially, with projections estimating that over 700 million individuals will be affected by 2045². Etiological classification of diabetes according to WHO (2023), and pathomechanism of type 1 and type 2 diabetes are presented in Fig. 1a and b. Aging populations, urbanization, and the increasing prevalence of obesity exacerbate this trend³. Recent studies have identified several genetic mutations and epigenetic changes associated with type 2 diabetes mellitus (T2DM), which are believed to play crucial roles in its development and progression⁴. Notably, oxidative stress has emerged as a crucial factor in the pathogenesis of type 2 diabetes mellitus (T2DM). This condition involves the excessive generation of reactive oxygen species (ROS), which damage cellular components, including proteins, lipids, and DNA. Oxidative stress disrupts insulin signaling pathways by modifying molecules, such as insulin receptor substrate-1 (IRS-1), through serine phosphorylation, thereby further impairing glucose uptake in tissues and exacerbating pancreatic β-cell dysfunction. Additionally, oxidative stress mediates the induction of inflammation, causing an increase in proinflammatory cytokines and adipokines^5,6. Furthermore, oxidative stress has been implicated in sustaining "metabolic memory," where early glycemic imbalances contribute to long-term complications even after glucose levels are normalized⁶.

Fig. 1.

(a) Etiological classification of diabetes according to WHO (2023); (b) Pathomechanism of type 1 and type 2 diabetes; Created in BioRender. Cecerska-heryć, E. (2025) https://BioRender.com/undefined.

The pathogenesis of insulin resistance and type 2 diabetes mellitus (T2DM) is closely linked to chronic, low-grade inflammation and persistent immune system activation. Studies indicate that patients with T2DM exhibit elevated levels of inflammatory markers such as interleukin-6 (IL-6), C-reactive protein (CRP), tumor necrosis factor-alpha (TNF-α), plasminogen activator inhibitor-1 (PAI-1), vascular cell adhesion molecule-1 (VCAM-1), and intercellular adhesion molecule-1 (ICAM-1)⁷. Chronic systemic inflammation promotes insulin resistance and impairs endothelial function, possibly accelerating cardiovascular complications⁸. Obesity, particularly visceral adiposity, is a significant risk factor for the development of type 2 diabetes mellitus (T2DM). Adipose tissue functions as an endocrine organ, influencing systemic metabolism by producing various cytokines, including TNF-α and IL-6, as well as other biologically active molecules like leptin, resistin, and monocyte chemoattractant protein-1 (MCP-1/CCL2)⁹. In obese individuals, adipose tissue is infiltrated by several pro-inflammatory immune cells, including CD8 + T lymphocytes, Th1 IFN-γ + cells, B cells, mast cells, neutrophils, and M1 macrophages¹⁰. These cells are attracted by chemokines secreted by stressed adipocytes in response to the accumulation of excess lipids. The expression of these cytokines can be influenced by single-nucleotide polymorphisms (SNPs) located in the regulatory regions of genes. Research has demonstrated associations between gene polymorphisms, including those of TNF-α and IL-6, and diseases associated with metabolic disorders¹¹. TNF-α, an adipocytokine involved in systemic inflammation, stimulates the acute phase reaction and is primarily secreted by macrophages, with adipocytes contributing to this process to a lesser extent. TNF-α inhibits the insulin transduction pathway and affects glucose metabolism by reducing the expression of insulin-regulated glucose transporter type 4 (GLUT4), present in adipose cells, skeletal muscle cells, and cardiac muscle cells. This cytokine also induces serine phosphorylation of IRS-1, acting as an insulin inhibitor in peripheral tissues, consequently leading to insulin resistance⁹.

The human body’s defense against various pathogens relies on a well-functioning immune system. When this natural defense is compromised, the body attempts to mitigate damage through the acute-phase reaction, during which specific proteins, such as C-reactive protein (CRP), are synthesized. CRP is a significant, albeit non-specific, marker of ongoing inflammatory responses. Its determination is commonly used in clinical practice due to the relatively short measurement time and the stability of its levels in plasma and serum. Elevated CRP concentrations are observed in numerous clinical conditions, including infections, autoimmune diseases, injuries, neoplasms, tissue ischemia characterized by necrotic changes, and post-surgical states. Therefore, CRP levels should not be considered in isolation but rather as part of a comprehensive laboratory assessment. It is recommended to concurrently determine the concentrations of other acute-phase proteins and inflammatory mediators, as well as serial measurements of CRP itself, to monitor the dynamics of its concentration changes^7,12. CRP is produced by hepatocytes, with its synthesis induced by IL-6. Leukocytes and fibroblasts produce IL-6, with approximately 30% of the production originating from adipose tissue. IL-6 exerts pleiotropic effects on inflammation, immune responses, and hematopoiesis. In addition to inducing CRP production, IL-6 stimulates the synthesis of complement component C3, serum amyloid A (SAA), fibrinogen, thrombopoietin, hepcidin, haptoglobin, and α-1-antichymotrypsin, making it a pivotal mediator of the acute-phase reaction^7,12. Prolonged elevated serum SAA concentrations may lead to the development of amyloidosis. Furthermore, IL-6-induced hepcidin inhibits the action of the iron transporter ferroportin-1 in the intestine, thereby decreasing serum iron concentrations and leading to hypoferritinemia and anemia associated with chronic inflammation^12,13.

A comprehensive understanding of the interactions between oxidative stress markers and inflammation markers is essential for elucidating the complex pathophysiology of diabetes mellitus, particularly type 2 diabetes. These interactions represent a key link between metabolic imbalance, immune system activation, and progressive tissue damage. Oxidative stress can initiate and amplify inflammatory cascades by overproduction of reactive oxygen species (ROS). At the same time, inflammation exacerbates oxidative processes by activating immune cells and promoting further ROS generation. This bidirectional relationship contributes to insulin resistance, β-cell dysfunction, and the development of chronic complications associated with diabetes.

By studying these molecular and cellular mechanisms in greater depth, researchers can gain critical insights into the dynamic feedback loops that drive disease progression. Such insights are crucial for identifying novel therapeutic targets that can interrupt this vicious cycle. Furthermore, elucidating these mechanisms may facilitate the discovery of reliable and disease-specific biomarkers, enabling earlier detection, better patient stratification, and more precise monitoring of disease activity and treatment efficacy. Ultimately, this knowledge can support the development of personalized medicine strategies to improve the prevention, diagnosis, and management of diabetes.

Materials and methods

Characteristics of the study group

The study included 73 participants diagnosed with type 1 and type 2 diabetes, consisting of 38 men and 35 women. These individuals were divided into two groups according to their type of diabetes. The first group comprised 33 patients with type 1 diabetes, including 18 men and 15 women, whose ages ranged from 18 to 68 years (mean age, 40.1 ± 16.1 years). The second group had 40 patients with type 2 diabetes, evenly split with 20 men and 20 women, aged between 25 and 90 years (mean age, 71.8 ± 12.5 years). A control group consisted of 41 healthy adults, comprising 23 men and 18 women, aged 21 to 56 years (mean age, 26.6 ± 8.23 years).

The health status of all participants was confirmed through an assessment of key biochemical markers in blood plasma, including cholesterol, HDL, LDL, triglycerides, glucose, albumin, iron, uric acid, creatinine, and total protein. These analyses utilized reagent kits from BioMaxima S.A., Lublin, and an EnVision microplate reader from PerkinElmer. We gathered comprehensive data about participants’ age, weight, height, waist circumference, duration of their condition, medications (including insulin), dietary habits, physical activity, combined therapies, complications from diabetes, other chronic conditions, recent hospitalizations, smoking habits, use of hormonal contraception, and any additional medications.

The inclusion criteria for the study required participants in the study group to have a confirmed history of either type 1 or type 2 diabetes. In contrast, the control group consisted of healthy volunteers over the age of 18 without any chronic illnesses such as diabetes, kidney disease, heart issues, or liver dysfunction. Those excluded from the study had any of the following: (1) declared chronic diseases (such as diabetes or kidney disease); (2) active infections; (3) any surgical procedures within the last six months; (4) pregnancy or the use of contraceptives; (5) failure to provide written consent to participate in the study.

Detailed participant characteristics and the medications taken are summarized in Table 1a and b, while the biochemical test results and information on chronic diseases are provided in the Supplementary Materials (Supplementary Tables 1, 2, and 3). Additionally, Table 2 provides detailed information regarding diabetic complications, chronic illnesses, physical activity, and other factors that may have influenced the test outcomes. All participants provided written informed consent and were briefed about the study’s purpose and their right to withdraw at any time. The study received approval from the Pomeranian Medical University Bioethics Committee in Szczecin (Resolution No. KB 006/59/2022).

Table 1.

a and b General characteristics of people qualified for the study.

Parameter	Control	Diabetes type 1	Diabetes type 2	p*	p**
Sex	F-18 M-23	F-14 M-18	F-21 M-20	NS	NS
Age [years]	26.6 ± 8.23	40.1 ± 16.1	71.8 ± 12.5	< 0.0001	< 0.0001
BMI	23.7 ± 3.82	25.3 ± 4.85	31.9 ± 7.18	< 0.0001	< 0.0001
Duration of the disease [years]	–	19.5 ± 14.8	13.4 ± 10.7	NS	–

Medications taken	Diabetes type 1			Diabetes type 2
	Number	Number of components	%	Number	Number of components	%
Insulin	31	31	96.88%	12	12	29.27
Metformin	4	35	12.50%	16	28	39.02
Other tablets	0	35	0.00%	12	40	29.27

p—p-value.

Table 2.

Characteristics of the study group in terms of the occurrence of diabetic complications.

Diabetic complications	Diabetes type 1			Diabetes type 2
	Number	Number of components	%	Number	Number of components	%
No complications	19	19	59.38	7	7	17.07
Diabetic foot	4	23	12.50	1	8	2.44
Retinopathy	10	33	31.25	5	13	12.20
Nephropathy	12	45	37.50	3	16	7.32
Neuropathy	10	55	31.25	4	20	9.76
Other complications	20	75	62.50	39	59	95.12

All procedures in this study adhered to the ethical guidelines established by the institutional and/or national research committee, following the principles outlined in the 1964 Helsinki Declaration and its subsequent amendments, or comparable ethical standards.

Material

The study consisted of blood collected once from the elbow bend for K₂EDTA and a clot. After collection, the blood was centrifuged for 10 min at 20 °C (2600 rpm). Then, the plasma and serum were transferred to new Eppendorf tubes and stored at − 80 °C in a freezer until further analysis. The following biochemical blood tests were performed in the patient’s serum: cholesterol, direct HDL, total protein, urea, uric acid, creatinine, glucose, albumin, and triglycerides. The plasma was tested for the activity and concentration of antioxidant enzymes, such as CAT, SOD, and GPX1, as well as inflammatory markers including IL-6, CRP, and TNF-α. Additionally, in patients with both types of diabetes, the concentration of glycated hemoglobin (HbA1C) was measured.

CAT concentration determination

Catalase concentration was determined using an ELISA (enzyme-linked immunosorbent assay) kit, specifically the Human CAT (Catalase) ELISA Kit from ELK (Wuhan) Biotechnology Co., Ltd.

SOD activity determination

Superoxide dismutase (SOD) activity was determined by the ELISA method using a ready-made Human SOD (Superoxide Dismutases) Elisa Kit from ELK (Wuhan) Biotechnology CO., Ltd.

GPx concentration determination

The concentration of glutathione peroxidase-1 (GPX1) was determined using an ELISA kit, specifically a ready-made Human GPx1 (Glutathione Peroxidase 1) ELISA Kit from ELK (Wuhan) Biotechnology Co., Ltd.

Hb1_AC concentration determination

The concentration of glycated hemoglobin (HbA1c) was determined by ELISA using a ready-made Human HbA1c ELISA Kit from BT Laboratory (BT Lab) (China).

IL-6 concentration determination

The concentration of interleukin-6 (IL-6) was determined by ELISA, using a ready-made set of Human IL-6 (Interleukin 6) ELISA Kit from ELK (Wuhan) Biotechnology CO., Ltd.

CRP concentration determination

The concentration of C-reactive protein (CRP) was determined using an ELISA kit for Human CRP (C-reactive protein) from ELK (Wuhan) Biotechnology Co., Ltd.

TNF-α concentration determination

The concentration of tumor necrosis factor-alpha (TNF-α) was determined using an ELISA kit for Human TNF-α (Tumor Necrosis Factor Alpha) from ELK (Wuhan) Biotechnology Co., Ltd.

Statistical analysis

The results of all measurements were subjected to statistical analysis utilizing the R programming language within RStudio (version 2023.12.1) and Statistica 13. The Shapiro–Wilk test was employed to evaluate the normality of the parameter distributions, which is a widely recognized and rigorous method for this purpose. The findings indicated that all variables exhibited a distribution that deviates from normality. Each parameter was described using statistical measures, including sample size, arithmetic mean, standard deviation, median, interquartile range (IQR), and minimum and maximum values. Given that the distribution of all data was determined to be nonparametric, the Kruskal–Wallis test was applied to assess unrelated variables. The Mann–Whitney U post hoc test with Bonferroni correction was conducted to ascertain the significance of the results. This approach facilitates the comparison of all possible pairs of groups, where a statistically significant result denotes a meaningful difference between the groups.

The strength of the correlation among individual variables was evaluated using the Spearman rank correlation coefficient, which is suitable for data that does not conform to a normal distribution. A sensitivity analysis for the Kruskal–Wallis ANOVA was performed using G*Power software. The analysis indicated that a Kruskal–Wallis ANOVA involving 114 participants distributed across three groups would have sufficient sensitivity to detect effects of η² = 0.113 with a power of 95% (p = 0.05). Consequently, this suggests that the study may not be adequately equipped to reliably identify effects smaller than η² = 0.113. An alpha level of 0.05 was established for all analyses, whereby a p-value of less than 0.05 was considered statistically significant.

Results

Superoxide dismutase activity in plasma of type 1 and type 2 diabetics and control subjects

The study investigated the relationship between the activity of superoxide dismutase (SOD) in the control group and the two study groups. The analysis revealed a significant correlation between SOD activity [U/ml] in the control group and the study groups (p < 0.0001). The highest SOD activity was found in participants with type 1 diabetes. At the same time, the control group exhibited the lowest activity (see Fig. 2). Post-hoc analysis also indicated a significant difference in SOD activity between the control group and individuals with type 1 diabetes (p < 0.001), as well as between the control group and those with type 2 diabetes (p < 0.0001). Detailed results of the SOD activity test are presented in Table 3.

Fig. 2.

Kruskal–Wallis ANOVA analysis of the SOD activity [U/ml] in the control group and the study group; p < 0.0001.

Table 3.

SOD activity in the control group and the study groups (with type 1 or 2 diabetes).

Group	Mean	SD	Median	IQR	Min	Max
Control	4.48	10.5	0.291	1.31	0.073	41.2
Diabetes type 1	3.14	2.07	2.63	1.87	1.13	11.9
Diabetes type 2	2.15	0.77	2.06	0.905	0.802	3.74

Min–minimum value, Max–maximum value.

The following statistical analysis investigated the relationship between superoxide dismutase (SOD) activity [U/ml] and body mass index (BMI). BMI classifications define normal weight as an index of 18.5–24.99, overweight as 25.0–29.99, and obesity as a BMI of 30.0 or higher. The analysis revealed no significant correlation between BMI and SOD activity in individuals with diabetes (p = 0.66). However, a trend was observed indicating that SOD activity decreased as BMI increased. The highest levels of SOD activity were noted in individuals with normal weight, while the lowest levels were found in those with obesity (see Supplementary Fig. 1).

SOD activity was also examined in individuals with diabetes who had urinary tract diseases (Fig. 3) and joint degeneration (Fig. 4). These patients were compared to those who did not have these additional conditions. The statistical analysis revealed significant differences (p = 0.03) in SOD activity between individuals with and without these diseases. In both instances, higher SOD activity was observed in the group of individuals without the specific disease.

Fig. 3.

U-test analysis of the SOD activity [U/ml] in the study groups in people with and without urinary tract diseases; p = 0.03.

Fig. 4.

U-test analysis of the SOD activity [U/ml] in the study groups of people with joint degeneration compared to people without degeneration; p = 0.03.

Plasma catalase levels in type 1 and type 2 diabetics and control subjects

In our study, the relationship between catalase concentration and the study groups (with type 1 and 2 diabetes) was examined. The analysis revealed a significant relationship between CAT concentration (ng/mL) in the control group and the study groups (p < 0.0001). The highest catalase concentration was noted in the control group, and the lowest in patients with type 1 diabetes (Fig. 5). Significant differences were also observed between the CAT and diabetes groups. Details of the CAT concentration test results are presented in Table 4.

Fig. 5.

Kruskal–Wallis analysis of the CAT concentration (ng/ml) in subjects in the control group and subjects in the study groups, showing a significant difference between the control group and people with type 1 diabetes (p < 0.0001) and type 2 diabetes (p < 0.0001).

Table 4.

CAT concentration in the control group and the study groups.

Group	Mean	SD	Median	IQR	Min	Max
Control	101	64.2	93.5	112	8.05	225
Diabetes type 1	13.2	8.2	10.3	7.87	3.61	35.9
Diabetes type 2	10.80	5.80	9.14	5.14	4.57	31.3

Min–minimum value, Max–maximum value.

Further analysis was conducted to investigate the impact of BMI on catalase concentration in individuals with normal weight, overweight, and obesity. A significant relationship was found between CAT concentration and BMI in these groups (p < 0.001). The highest catalase concentration was found in individuals in the control group and the lowest in those with obesity (BMI > 30) (Fig. 6). Importantly, a clear downward trend in catalase concentration was observed with increasing BMI. Post-hoc analysis also highlighted the significant difference in CAT concentration between individuals with normal BMI and those with obesity (p < 0.001), underscoring the importance of these findings.

Fig. 6.

Kruskal–Wallis analysis of the CAT activity [ng/ml] depending on BMI; p < 0.001.

The following analysis demonstrates the effect of metformin treatment on plasma catalase concentration in patients with type 1 and type 2 diabetes (p = 0.02). A higher CAT concentration was observed in individuals not taking metformin (Fig. 7).

Fig. 7.

U-test analysis of the effect of metformin treatment on CAT concentration [ng/ml] in people with type 1 and type 2 diabetes; p = 0.02.

Plasma glutathione peroxidase levels in type 1 and type 2 diabetics and control subjects

The study examined the relationship between the concentration of glutathione peroxidase-1 (GPX1) across different study groups. The analysis revealed a significant difference (p < 0.001) in GPX1 concentrations (measured in ng/ml) between the control group and the study groups. Notably, the highest concentrations of GPX1 were found in individuals with type 1 diabetes. At the same time, the lowest levels were observed in the control group (see Fig. 8). Additional post-hoc analysis reinforced our main findings, indicating a significant difference in GPX1 concentrations between the control group and those with type 1 diabetes (p < 0.01) as well as those with type 2 diabetes (p < 0.05). Detailed results of the GPX1 concentration assays are presented in Table 5.

Fig. 8.

Kruskal–Wallis ANOVA analysis of the GPX1 concentration (ng/ml) in the control and study groups; p < 0.001.

Table 5.

GPX1 concentrations in the control group and study groups.

Group	Mean	SD	Median	IQR	Min	Max
Control	143	330	11.6	103	0.188	1447
Diabetes type 1	114	66.1	112	77.5	24.8	294
Diabetes type 2	85.30	43.40	74.3	43.8	10.8	190

Another analysis determined the effect of BMI on the concentration of glutathione peroxidase-1 in the studied individuals. There was no significant difference (p = 0.23) between the parameters mentioned. The highest GPX1 concentrations (ng/ml) were observed in the overweight group, while the lowest were in individuals with a BMI within the normal range (Supplementary Materials—Supplementary Fig. 2).

Plasma interleukin-6 levels in control subjects compared to type 1 and type 2 diabetics

This analysis aimed to investigate the relationship between the concentration of interleukin-6 in the control group and the study group, which consisted of individuals with type 1 diabetes and those with type 2 diabetes. The obtained results in the form of a graph indicate a lack of significance (p = 0.11) between the above groups however the highest concentrations of IL-6 [pg/ml] can be observed in the group of patients with type 1 diabetes (Supplementary Materials—Supplementary Fig. 3). Detailed results of the tested concentrations of interleukin-6 are presented in Table 6.

Table 6.

IL-6 concentrations in the control group and study groups.

Group	Mean	SD	Median	IQR	Min	Max
Control	31.5	32.6	19.3	32.2	0.367	127
Diabetes type 1	33.2	18.5	30.5	25.5	0.223	77.6
Diabetes type 2	25.70	21.70	21.4	26.2	0.146	100

Next, studies were conducted to investigate the effect of BMI on the concentration of interleukin-6 in the study participants. A lack of significance (p = 0.71) was observed between the IL-6 concentration (pg/mL) and BMI (Supplementary Materials—Supplementary Fig. 4). The effect of insulin treatment on the concentration of interleukin-6 in type 1 and type 2 diabetes was also examined. Significance (p < 0.05) was demonstrated between the indicated factors. Higher concentrations of IL-6 were observed in the plasma of patients treated with insulin compared to those not (Fig. 9).

Fig. 9.

U-test analysis of the effect of insulin treatment on IL-6 levels in patients with type 1 and type 2 diabetes; p = 0.05.

The following analysis examines the impact of diabetic complications, including nephropathy, neuropathy, retinopathy, and diabetic foot, on the concentration of IL-6 in the plasma of the studied patients. Diabetic complications significantly influence (p = 0.02) the concentration of interleukin-6. It was higher in the group of people with complications than in those without (Fig. 10).

Fig. 10.

U-test analysis of the influence of diabetic complications on IL-6 concentration [pg/ml; p = 0.02.

An analysis of the effect of diabetes treatment with tablets other than metformin on IL-6 levels was also performed. In this case, the significance level was p = 0.01, indicating statistically significant differences in interleukin-6 levels between groups treated with a substance other than metformin. Higher levels were noted in those not using metformin (Fig. 11).

Fig. 11.

U-test analysis of the treatment’s effect on IL-6 concentration (pg/ml) with tablets other than metformin; p = 0.01.

CRP in control subjects compared to type 1 and type 2 diabetics

Statistical analysis determined the relationship between C-reactive protein concentration in the control and study groups with type 1 and type 2 diabetes. A high significance level (p < 0.0001) was demonstrated in the indicated groups for CRP concentrations. The highest CRP concentrations [pg/ml] were noted in the control group, while in the group of people with type 2 diabetes, these concentrations were the lowest (Fig. 12). Post-hoc analysis also showed a significant difference between the CRP levels in the control group and patients with type 1 diabetes (p < 0.001) and in patients with type 2 diabetes (p < 0.001). Details regarding the results of the CRP concentrations in the study participants are presented in Table 7.

Fig. 12.

Kruskal–Wallis ANOVA analysis of the CRP concentration [pg/ml] depending on group assignment; p < 0.0001.

Table 7.

CRP concentrations in the control group and study groups.

Group	Mean	SD	Median	IQR	Min	Max
Control	2049	1154	1695	1839	381	4061
Diabetes type 1	1119	285	1102	389	640	1845
Diabetes type 2	1023.00	618.00	957	306	443	4508

Then, an analysis was conducted to assess the impact of BMI on the concentration of C-reactive protein in participants who met the study’s eligibility criteria. A significant difference (p = 0.032) was observed between the aforementioned parameters. Higher CRP concentrations (pg/ml) can be observed in the group of people with normal weight, and the lowest in the group of people with obesity (BMI > 30) (Fig. 13).

Fig. 13.

Kruskal–Wallis ANOVA analysis of the influence of BMI on the concentration of C-reactive protein (pg/ml) in the studied individuals; p = 0.032.

Tumor necrosis factor levels in control subjects compared to type 1 and type 2 diabetics

A significant difference was demonstrated between the concentration of tumor necrosis factor in the study groups and the control group. The results are illustrated in the graph below (Fig. 14), which indicates a high level of significance (p < 0.0001) between the concentration of TNF-α (pg/ml) and the occurrence of the disease. The exact results regarding the concentrations of TNF-α are presented in Table 8. The highest concentrations of tumor necrosis factor were found in patients with type 2 diabetes and the lowest in healthy participants (control group) (Fig. 14). Thanks to the post-hoc analysis, a significant difference was also demonstrated between the concentration of TNF-α in the control group and patients with type 1 diabetes (p < 0.0001) and in patients with type 2 diabetes (p < 0.0001).

Fig. 14.

Kruskal–Wallis ANOVA analysis of the relationship between TNF-α concentration [pg/ml] and group affiliation; p < 0.0001.

Table 8.

TNF-α concentration depending on the assignment to a given group.

Group	Mean	SD	Median	IQR	Min	Max
Control	116	559	11.8	26.5	0	3465
Diabetes type 1	177	185	109	157	4.76	775
Diabetes type 2	234.00	199.00	165	229	4.76	794

Then, an analysis was conducted to assess the impact of BMI on TNF-α concentration in participants who met the study criteria. It showed significance (p < 0.01) between the parameters mentioned above. Higher TNF-α concentrations (pg/mL) can be observed in the group of people with obesity (BMI > 30) (Fig. 15). Post-hoc analysis revealed a significant difference (p < 0.01) between the TNF-α concentration in people with obesity and that in the control group.

Fig.15.

Kruskal–Wallis ANOVA analysis of the influence of BMI on the concentration of TNF-α [pg/ml] in people qualified for the study; p < 0.01.

Spearman rank correlations

Our comprehensive study included Spearman rank correlation analysis between the studied inflammatory markers and oxidative stress enzymes and a wide range of factors: age, body weight, waist circumference (control and study groups), duration of diabetes (study groups), and the concentration of biochemical parameters: cholesterol, direct HDL, triglycerides, albumin, glucose, total protein, creatinine, uric acid, urea, and glycated hemoglobin (study groups). The results, illustrated in Fig. 16 below, reflect the depth and breadth of our research. The strength of the correlation assessment revealed significant insights. SOD activity [U/ml] demonstrated a statistically significant negative, weak correlation (r = − 0.2 and r = − 0.28, respectively) with CAT and albumin concentrations, a weak correlation with GPX1 concentration and age (r = 0.23; r = 0.23), and a moderate correlation (r = 0.43) with TNF-α concentration. These findings provide valuable understanding in our study.

Fig. 16.

Spearman correlation strength between the levels of selected antioxidant enzymes, inflammatory markers, biochemical parameters, and other factors in the control group and the study groups.

CAT concentration [ng/ml] correlated weakly with height (r = 0.19), showed a weak negative correlation (r = − 0.25; r = − 0.33) with body weight and HDL concentration, and exhibited a moderate negative correlation (r = − 0.59; r = − 0.60) with TNF-α concentration and age, as well as a moderate positive correlation (r = 0.45) with CRP concentration.

GPX1 concentration [ng/ml] shows a statistically significant, weak negative correlation (r = − 0.19) with HbA1c concentration and a moderate negative correlation (r = − 0.23) with creatinine concentration. IL-6 concentration (pg/ml) correlated weakly (r = 0.21; r = 0.25) with CRP and cholesterol concentrations. In the case of HbA1c [ng/ml], a weak (r = − 0.19) negative correlation with GPX1 concentration and a moderate (r = 0.25) correlation with diabetes duration were observed. However, diabetes duration (in years) correlated moderately (r = 0.34; r = 0.28) with glucose and cholesterol concentrations. The described correlations and all the others are presented in Fig. 16.

Multivariate regression analysis

A multivariate regression analysis was conducted for this study to examine the influence of various factors such as body mass index (BMI), type of diabetes, duration of the disease, and type of therapy (independent variables) on antioxidant enzymes and inflammatory markers (dependent variables), as presented in Table 9.

Table 9.

Multivariate regression analysis of the influence of the studied parameters on the concentration/activity values of individual oxidative stress enzymes and inflammatory markers.

	Independent varaibles	β	R2	p	p for model	F
SOD	BMI	− 0.077726	0.24	NS	0.013671	3.21
	Type of diabetes	0.406748		0.008
	Duration of diabetes	0.141466		NS
	Insulin	− 0.052276		NS
	Metformin	− 0.073508		NS

	Independent varaibles	β	R2	p	p for model	F
Il-6	BMI	− 0.059358	0.19	NS	0.06	2.28
	Type of diabetes	0.155293		NS
	Duration of diabetes	− 0.087193		NS
	Insulin	0.268267		NS
	Metformin	0.378347		0.013

	Independent varaibles	β	R2	p	p for model	F
TNF-α	BMI	0.190755	0.16	NS	NS	1.94
	Type of diabetes	0.092845		NS
	Duration of diabetes	− 0.349619		0.017
	Insulin	0.233597		NS
	Metformin	0.166513		NS

	Independent varaibles	β	R2	p	p for model	F
GPx	BMI	− 0.022843	0.27	NS	0.006	2.28
	Type of diabetes	0.532359		< 0.001
	Duration of diabetes	0.009799		NS
	Insulin	− 0.057676		NS
	Metformin	− 0.032112		NS

The analysis revealed that the type of diabetes influenced the activity of superoxide dismutase (SOD) by 24% and glutathione peroxidase (GPx) by 27%. Additionally, it was found that metformin treatment had an impact on interleukin-6 (IL-6) levels in patients, accounting for 19% of the variation, a result that approached statistical significance. Specifically, metformin treatment increased IL-6 levels by 0.37 pg/ml. Moreover, the concentration of tumor necrosis factor-alpha (TNF-α) was found to be affected by the duration of diabetes; as the disease progressed, TNF-α levels decreased by 0.35 pg/ml.

The same multivariate regression analysis was applied to assess the effects of diabetic complications and comorbidities (independent variables) on markers of oxidative stress and inflammation (dependent variables), as shown in Table 10. For SOD, the analysis indicated that all comorbidities and diabetic complications collectively influenced its activity by 59%, with mood changes having the most significant effect. Furthermore, gout and rheumatic diseases were found to primarily increase the levels of C-reactive protein (CRP), with concentrations rising by 0.47 pg/ml and 0.32 pg/ml, respectively.

Table 10.

Multivariate regression analysis of the influence of the diabetic complications on the SOD activity values.

	Independent varaibles	β	R2	p	p	F
					for model
SOD	Diabetic foot	0.065277	0.591222	NS	0.034	2.012
	Retinopathy	− 0.120197		NS
	Nephropathy	0.085037		NS
	Neuropathy	− 0.175142		NS
	Or other chronic diseases? Yes/no	0.222340		NS
	Heart diseases	− 0.198855		NS
	Other diseases of the circulatory system	0.187496		NS
	Arterial hypertension—YES/NO	− 0.289261		NS
	Blood vessel diseases	− 0.193164		NS
	Lung diseases	0.311386		NS
	Digestive system diseases	− 0.350404		NS
	Liver diseases	0.007507		NS
	Urinary system diseases	0.071083		0.022
	Gout	− 0.062458		NS
	Thyroid diseases	0.263306		NS
	Nervous system diseases	− 0.357107		NS
	Muscle and joint diseases	− 0.195964		NS
	Joint degeneration YES/NO	0.001729		NS
	Blood and coagulation system diseases	0.010401		NS
	Eye diseases	0.144916		NS
	Mood changes	1.066001		< 0.001
	Infectious diseases	0.326136		NS
	Rheumatic disease	− 0.173659		NS
	Osteoporosis	− 0.026741		NS

β—standardized coefficient in the regression equation, R2—coefficient of determination, p-value of the significance coefficient; SOD—superoxide dismutase.

Significant values are in bold.

Discussion

Type 1 and type 2 diabetes are chronic diseases that affect millions worldwide each year, creating significant health issues and a heavy burden on healthcare systems. By 2045, it is estimated that the number of adults with diabetes will reach 700 million, predominantly with type 2 diabetes. In Poland, the prevalence of diabetes is also increasing, positioning the country among the highest in Europe for affected individuals¹⁴.

A key factor in diabetes is oxidative stress, which occurs when reactive oxygen species (ROS) exceed the body’s antioxidant defenses, consisting of enzymes like superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase 1 (GPX1). Oxidative stress can lead to serious complications, such as heart, kidney, and eye diseases, by disrupting insulin signaling and worsening pancreatic β-cell function. Consequently, these antioxidant enzymes may serve as important biomarkers for the early diagnosis of diabetes and the detection of potential complications.

In our study, we measured the levels of antioxidant enzymes—superoxide dismutase (SOD), glutathione peroxidase (GPX1), and catalase (CAT)—in patients with type 1 and type 2 diabetes, comparing the results to a control group of healthy individuals. We found a significant difference in SOD activity (p < 0.0001) between the groups. Contrary to earlier studies that reported reduced SOD activity in type 1 diabetes patients, our results showed the highest SOD levels in this group and the lowest in the control. Additionally, there were no statistically significant differences in SOD activity between the type 1 and type 2 diabetes groups, which aligns with recent findings indicating elevated SOD activity in diabetic patients as a compensatory response to increased oxidative stress¹⁵. There was also no significant effect of gender or age on the activity of this enzyme in either patients with diabetes or healthy individuals. The difference in SOD activity between diabetic patients and healthy individuals may be due to the influence of genotypic background combined with various effects associated with diabetes, such as enzyme glycation¹⁶.

Our analysis examined the relationship between SOD activity and BMI. We found no significant correlation (p = 0.66) in diabetic patients, although SOD activity tended to decrease with increasing BMI. Individuals with normal weight exhibited the highest SOD activity, while those with obesity showed the lowest. Obesity is a significant factor in the development of type 2 diabetes, a situation worsened by diets high in sugars and saturated fats and limited physical activity.

According to the study by Dworzanski et al.¹⁷, patients with type 2 diabetes have reduced SOD and GPX activity compared to the group of people without diabetes, which is also confirmed by other researchers¹⁸. The reduced activity of enzymes is linked to the depletion of the body’s antioxidant defenses due to excessive reactive oxygen species (ROS). In diabetes, glucose autoxidation produces hydrogen peroxide, which inactivates superoxide dismutase (SOD), contributing to its decreased activity. This study also examined SOD activity in diabetic patients with additional health issues. Significant reductions in SOD activity were found in those with urinary tract diseases (p = 0.03) and joint degeneration (p = 0.03). Multivariate regression analysis revealed that all comorbidities and diabetic complications collectively accounted for 59% of the variation in dismutase activity, with mood changes having the most substantial impact. Liu et al.¹⁹ suggest that in the case of knee osteoarthritis (KOA), one of its causes is cartilage degeneration, which results from the destruction of dynamic balance caused by chondrocyte activation by various factors. Oxidative stress plays an important role in the pathogenesis of KOA. SOD inside and outside chondrocytes play a key role in the regulation of ROS in cartilage.

Synovial inflammation is a key factor in developing knee osteoarthritis (KOA). Hypoxia in synovial cells damages mitochondria, leading to increased levels of reactive oxygen species (ROS) and exacerbating synovitis. Additionally, oxidative stress accelerates telomere shortening and chondrocyte aging, further promoting KOA by disrupting mitochondrial function and increasing ROS production. This accumulation of ROS may explain the low superoxide dismutase (SOD) activity seen in diabetic individuals with joint degeneration¹⁹.

Andrade-Sierra et al.²⁰ investigated early chronic kidney disease (CKD) and its complications associated with diabetes mellitus (DM). The study focused on the roles of oxidative stress (OS) and inflammation in CKD progression among patients with and without type 2 diabetes (T2DM). In their analytical cross-sectional study of patients in the early stages of chronic kidney disease (CKD), stages 1 to 3, they measured proinflammatory cytokines (IL-6, TNF-α), oxidative stress markers (lipoperoxides, nitric oxide), and antioxidant activity (superoxide dismutase, glutathione peroxidase, and total antioxidant capacity).

Results showed that CKD patients with T2DM had significantly lower antioxidant levels (SOD, p < 0.001; GPX, p < 0.01; TAC, p < 0.01) and higher oxidative (NO, p < 0.01) and inflammatory markers (IL-6, p < 0.001; TNF-α) compared to those without T2DM. CKD stages were not associated with oxidative or inflammatory markers in patients with T2DM. Conversely, in patients without T2DM, CKD progression was linked to increased SOD levels (p = 0.04) and decreased NO (p < 0.01). These findings are critical and echo those of our study, where reduced SOD activity was also noted in patients with urinary tract diseases²⁰.

Our study revealed a significant difference (p < 0.001) in GPX1 concentrations between the control and diabetic groups, with the highest levels observed in patients with type 1 diabetes and the lowest in the control group. This finding suggests that GPX1 concentration could serve as a potential clinical marker for diabetes. However, no significant differences were found between the type 1 and type 2 diabetes groups.

Recent research suggests that GPX1 plays a crucial role in maintaining redox homeostasis and protecting pancreatic β-cells from oxidative stress, particularly under hyperglycemic conditions^21,22. Studies have demonstrated that overexpression of GPX1 may prevent β-cell apoptosis and improve insulin secretion, while GPX1 deficiency aggravates glucose intolerance^22,23.

In addition, genetic polymorphisms in the GPX1 gene, such as rs1050450, have been associated with altered enzyme activity and increased susceptibility to type 2 diabetes and obesity-related insulin resistance^21,23. This variant, resulting in a leucine-to-proline substitution, has been associated with higher oxidative stress, dyslipidemia, and increased markers of metabolic syndrome²¹.

Our findings revealed a negative correlation between HbA1c levels and GPX1 concentration (r = − 0.19), although it was not statistically significant. This is consistent with data suggesting decreased GPX1 activity may lead to oxidative stress-induced protein glycation and impaired glucose metabolism²⁴. Moreover, no significant association was found between GPX1 concentration and BMI (p = 0.23), although studies have reported reduced GPX1 levels in obese individuals with insulin resistance^24,25. These results underscore the complex, context-dependent role of GPX1 in glucose homeostasis.

The final enzyme analyzed in this study is catalase. We compared its concentration in the control group with individuals with type 1 and type 2 diabetes. Statistical analysis revealed a significant difference in catalase levels (p < 0.0001) between the control and both diabetes groups, with the highest levels observed in the control group and the lowest in the type 2 diabetes group. Post-hoc analysis confirmed these differences (p < 0.0001 for both diabetes types). According to Nandi et al., catalase is essential for regulating hydrogen peroxide levels, protecting cells from oxidative damage, and acting as a messenger in insulin secretion by inactivating tyrosine phosphatase²⁶. Cells sensitive to oxidative stress include, among others, pancreatic β cells. They are devoid of catalase, but due to their functions, they have many mitochondria, one of the primary sources of hydrogen peroxide and free radicals. They are produced in the respiratory chain, which occurs within mitochondria. Therefore, in individuals with acatalasemia or hypocatalasemia (a lack or deficiency of catalase), prolonged exposure to even small amounts of oxidative stress may damage pancreatic β cells, thereby contributing to the development of diabetes²⁶. This study confirms the low level of catalase in individuals with diabetes, which was also observed in our research. In our further analysis, the effect of BMI on catalase concentration was checked in patients with both types of diabetes. A significant dependence of CAT concentration on BMI was demonstrated. The study also allowed for the observation of a trend in which catalase activity decreases with increasing BMI. This trend suggests that obesity, as indicated by higher BMI, may lead to a reduction in catalase activity, which could potentially exacerbate oxidative stress and contribute to the development and progression of diabetes. González-Domínguez et al.²⁷ describe reduced catalase activity in children with obesity and insulin resistance in their study.

A fascinating study on the relationship between CAT level and BMI in people with diabetes was done by Chatzakis et al.²⁸. They examined the assessment of oxidative capacity and uteroplacental circulation before and after aerobic exercises in patients with gestational diabetes (GDM) and uncomplicated pregnancies. In this cross-sectional study, women with GDM and women with uncomplicated pregnancies (control group) underwent 30 min of moderate-intensity cycling. Total antioxidant capacity (TAC), catalase activity (CAT), reduced glutathione (GSH), uterine artery pulsatility index (UtA PI), and umbilical artery pulsatility index (UmA PI) were assessed before, immediately after, and 1 h after exercise. Twenty-five pregnant women were included in each group. TAC and CAT increased between before and immediately after exercise, whereas GSH decreased (p < 0.001). In GDM, CAT was lower than the control group at all time points (p < 0.05). In the GDM group, delta (Δ) CAT (before and immediately after exercise) was lower than in the control group (p = 0.003). In GDM, BMI and preconception weight gain were negatively predicted by ΔTAC (before and 1 h after exercise). This means that moderate-intensity physical exercise improves oxidative capacity in women with gestational diabetes and uncomplicated pregnancies, although to a lesser extent in those with gestational diabetes. Exercise reduces uterine arterial resistance in women with gestational diabetes mellitus (GDM)²⁹. Our study also demonstrated the effect of metformin treatment on patients with type 1 and 2 diabetes on catalase concentration in plasma (p = 0.02). Higher CAT concentrations were observed in the group of people not taking metformin. This is a particularly interesting result because it does not align with the findings of other researchers. Naghdi et al.²⁶ showed that treatment of diabetic rats with curcumin or metformin restored the activity of CAT, SOD and GPx. Mobasher et al.³⁰ showed, however, that metformin may have potential as a new drug against oxidative stress induced by diabetes or liver cancer as a result of restoring the activity of CAT, SOD, and GSH.

IL-6 is a pleiotropic cytokine involved in inflammation, immune response, and metabolic regulation. Our analysis revealed no statistically significant differences in IL-6 levels between diabetic and control groups (p = 0.11), though the highest levels were observed in type 1 diabetes patients. Similarly, no association was found between IL-6 levels and BMI (p = 0.71).

Recent studies have clarified IL-6’s dual role in metabolism. While transient increases in IL-6 may promote insulin secretion and glucose utilization, chronic elevation contributes to inflammation, insulin resistance, and diabetic complications^31–33. Elevated IL-6 has been implicated in the progression of diabetic nephropathy, cardiovascular disease, and retinopathy^34,35. IL-6 has also emerged as a therapeutic target. Inhibition of IL-6 signaling through monoclonal antibodies or small-molecule inhibitors has shown promise in reducing inflammatory burden and improving insulin sensitivity in animal models and early-phase clinical trials^31–33. These findings highlight the biomarker and therapeutic relevance of IL-6 in diabetes.

In our study, we investigated the relationship between interleukin-6 (IL-6) levels and the use of metformin. We found significant differences in IL-6 concentrations between patients on metformin and those on other medications, with higher levels in the latter group. Regression analysis revealed that metformin use accounted for 19% of the variation in IL-6 levels, with a 0.37 pg/ml increase associated with treatment. Xian et al.³⁶ also found that metformin administration inhibited IL-6 secretion in mice, corroborating findings from other research. Its production was already reduced at the level of gene transcription²⁹. However, expanding research in this direction is necessary, especially in human models.

Another marker analyzed in this study is tumor necrosis factor-alpha (TNF-α). This pro-inflammatory cytokine is crucial in developing insulin resistance and type 2 diabetes. Our study demonstrated a statistically significant relationship between TNF-α concentration and the presence of diabetes (p < 0.05). The highest TNF-α levels were observed in patients with type 2 diabetes, while the lowest levels were found in the healthy control group.

These findings are consistent with recent studies indicating that serum TNF-α levels are significantly higher in patients with type 2 diabetes than healthy individuals^37,38. TNF-α interferes with insulin signaling by reducing insulin receptor substrate-1 (IRS-1) phosphorylation and glucose transporter type 4 (GLUT4) translocation, ultimately leading to impaired glucose uptake and insulin resistance^37–40. This mechanism also explains the strong positive correlation between TNF-α and HbA1c levels in diabetic patients (p < 0.05).

The significance level was also assessed for BMI and TNF-α concentrations, revealing the highest levels in individuals with obesity (BMI > 30; p < 0.01). This supports existing evidence that TNF-α is abundantly expressed in adipose tissue and contributes to the chronic low-grade inflammation characteristic of obesity-related insulin resistance⁴¹.

Obese diabetic patients in our study had higher TNF-α levels than non-obese diabetic individuals, and TNF-α levels were also elevated in obese diabetic patients compared to obese individuals without diabetes (p < 0.05). However, in contrast to some studies, our data did not show a consistent correlation between TNF-α and HbA1c across all subgroups.

A regression analysis indicated that the duration of diabetes negatively impacts TNF-α levels, with concentrations decreasing by an average of 0.35 pg/ml as the disease progresses (p < 0.05). This decline may reflect immune system dysregulation over time.

BCG (Bacillus Calmette–Guérin) vaccination has also been associated with modulation of TNF-α. Some studies suggest that BCG can increase TNF-α expression, leading to selective elimination of autoreactive T cells and expansion of regulatory T cells (Tregs), potentially restoring immune balance in autoimmune diabetes⁴². Clinical trials have reported that BCG administration in patients with type 1 diabetes results in temporary restoration of insulin production and long-term glycemic control improvements⁴³.

These findings highlight TNF-α’s central role in the pathogenesis of both type 2 and type 1 diabetes, particularly through inflammation and immune-mediated mechanisms, and suggest potential therapeutic avenues for further research.

Limitations

A notable limitation of our study was the small sample size, which should be expanded in future research to yield more reliable and generalizable results. The reduced number of participants with type 1 diabetes stemmed from the challenges in reaching individuals over the age of 18 who are living with this condition. In aiming for a meaningful comparison between type 1 and type 2 diabetes, we sought to minimize age disparities while ensuring a relatively comparable duration of diabetes among participants, which we successfully achieved. Additionally, many of our participants presented with other significant chronic illnesses, further complicating the findings. Despite these challenges, pursuing further investigation into antioxidant enzymes, specifically superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase 1 (GPX1), as well as inflammatory markers such as interleukin-6 (IL-6), C-reactive protein (CRP), and tumor necrosis factor-alpha (TNF-α), remains crucial. These biomarkers hold substantial diagnostic and prognostic potential in understanding diabetes and its associated complications.

Conclusions

A recent study has demonstrated that diabetes significantly impacts oxidative stress levels, resulting in alterations in the activity of antioxidant enzymes. Metformin did not exhibit any beneficial effects on the antioxidant system or inflammation in this context. While body mass index (BMI) was not correlated with the activities of superoxide dismutase (SOD) and glutathione peroxidase 1 (GPX1), it revealed a significant relationship with catalase (CAT) activity. Furthermore, the levels of tumor necrosis factor-alpha (TNF-α) decreased with an extended duration of diabetes and were elevated in obese patients. Additionally, the presence of coexisting medical conditions markedly influenced the concentration of C-reactive protein (CRP). In conclusion, the findings of this study indicate that all examined biomarkers—interleukin-6 (IL-6), C-reactive protein (CRP), and tumor necrosis factor-alpha (TNF-α)—may serve as valuable prognostic indicators for potential complications associated with diabetes. This emphasizes the necessity of monitoring these biomarkers in clinical practice to enhance the management of diabetic patients’ health.

Author contributions

Conceptualization—E.C-H.; Methodology—J.Mi, W.E., J.Ma., Formal analysis—R.B, and Resources—N.S Writing—review and editing—E.C-H., B.W., B.D Visualization- M.G., A.P. Supervision—B.D Collecting material—R.H., M.W. Funding acquisition,—B.D, E.C-H.

Funding

Pomeranian Medical University in Szczecin financed this research.

Data availability

The data used to support the findings of this study have not been made available because they are the property of the Pomeranian Medical University in Szczecin. Participants in the study did not consent to the disclosure of their data outside of publication. All data generated or analyzed during this study are included in this article. To obtain access to the data contained in the study, please contact the corresponding author—Elżbieta Cecerska—Heryć (elżbieta.cecerska.heryk@pum.edu.pl).

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

All procedures performed in studies involving human participants were conducted according to the ethical standards of the institutional and/or national research committee, the 1964 Helsinki Declaration and its subsequent amendments, or comparable ethical standards.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-05818-z.