Zinc–Copper Imbalance and Lipid Peroxidation Mark Glycaemic Control in Type 2 Diabetes Mellitus
Promise
Chineye
Nwaejigh
1✉
Emailnwaejighp@babcock.edu.ng
Tamunoibim
Jessica
Chima-Nwogwugwu
1
Michael
Yusuf
Odegbo
1
Stephen
Sunday
Udofia
1
1
Department of Medical Laboratory Science, School of Allied Health
Babcock University
Ilishan
Ogun State
Nigeria
Promise Chineye Nwaejigh1*, Tamunoibim Jessica Chima-Nwogwugwu1, Michael Yusuf Odegbo1, Stephen Sunday Udofia1
1Department of Medical Laboratory Science, School of Allied Health, Babcock University, Ilishan, Ogun State, Nigeria.
*Corresponding author
Corresponding author e-mail: nwaejighp@babcock.edu.ng (PCN)
A
ABSTRACT
Background:
Oxidative stress and trace element imbalance are implicated in the pathogenesis of type 2 diabetes mellitus (T2DM). Zinc serves as an antioxidant and insulin cofactor, whereas dysregulated copper may amplify oxidative damage. This study investigated serum zinc, copper, their ratio (Zn/Cu), and lipid peroxidation markers across glycaemic control states.
Methods:
In this cross-sectional study (January–May 2025), 110 adults were classified as normoglycaemic (n = 40), well-controlled T2DM (HbA1c ≤ 7.0%, n = 35), or poorly controlled T2DM (HbA1c > 7.0%, n = 35). Serum zinc and copper were measured by flame atomic absorption spectrophotometry; malondialdehyde (MDA) by TBARS assay; and 8-iso-prostaglandin F₂α (8-iso-PGF₂α) by ELISA. MANCOVA adjusted for age, sex, BMI, blood pressure, diabetes duration, and waist circumference. Discriminant analysis was performed to identify biomarkers distinguishing glycaemic groups.
Results:
Poor glycaemic control was linked to significantly higher 8-iso-PGF₂α, MDA, and copper levels, and lower zinc and Zn/Cu ratios (p < 0.001). The Zn/Cu ratio and copper exhibited the largest effect sizes. Strong correlations were observed between oxidative stress indices and trace element levels (p < 0.001). Discriminant analysis correctly classified 88.7% of participants, identifying Zn/Cu ratio and 8-iso-PGF₂α as key discriminators.
Conclusion:
Glycaemic dysregulation in T2DM is strongly associated with oxidative stress and trace element imbalance. The Zn/Cu ratio demonstrates potential as a biomarker for metabolic risk stratification and individualised disease monitoring.
A
Clinical trial number: Not applicable.’Keywords:
Type 2 Diabetes Mellitus
Oxidative Stress
Zinc
Copper
Zn/Cu Ratio
Lipid Peroxidation
8-iso-Prostaglandin F₂α
Malondialdehyde
A
A
A
A
INTRODUCTION
Type 2 diabetes mellitus (T2DM) is a widespread metabolic disorder characterized by persistent high blood sugar levels, along with increased oxidative stress, inflammation, and blood vessel dysfunction [1,2]. While controlling blood glucose remains the main treatment goal, research shows that imbalances in trace elements like zinc (Zn) and copper (Cu) can also influence oxidative stress and disease progression [3,4].
Zinc plays a key role in protecting cells by supporting antioxidant enzymes such as superoxide dismutase (SOD) and metallothioneins, and by helping regulate insulin production and activity [5,6]. When zinc levels are low, glucose metabolism suffers and cells become more vulnerable to oxidative damage [7]. Copper is also essential, contributing to energy production and antioxidant defenses. However, if copper is not properly regulated, it can promote the production of harmful reactive oxygen species, leading to damage of cell membranes and blood vessels, and worsening metabolic imbalance [8,9]. The balance between zinc and copper measured as the Zn/Cu ratio has been suggested as a useful marker of oxidative stress and related complications in diabetes [10].
Markers of lipid peroxidation, such as malondialdehyde (MDA) and 8-iso-prostaglandin F2α (8-iso-PGF2α), provide insight into oxidative damage and are linked to higher cardiovascular risk in people with T2DM [11]. However, many studies simply compare individuals with and without diabetes, without considering differences in blood sugar control. Since poorly controlled diabetes tends to cause more severe oxidative and metabolic disturbances, examining patients by their glycemic control status can offer a clearer understanding of disease impact [12,13].
In addition, trace element levels and oxidative stress markers can vary depending on diet, environment, and genetics, which differ across populations [14–16]. Despite a growing burden of poorly controlled T2DM in sub-Saharan Africa, few studies have explored these factors in this region.
This study investigates serum zinc and copper levels, the Zn/Cu ratio, and lipid peroxidation markers across three groups: healthy individuals, well-controlled T2DM, and poorly controlled T2DM. By linking trace element balance with oxidative stress across these groups, we hope to identify markers that can improve risk assessment and help guide treatment strategies.
METHODS
Study Design
A
This cross-sectional, observational study was conducted at Babcock University Teaching Hospital, Ilishan-Remo, Ogun State, Nigeria, between January and May 2025. A total of 110 adults aged 30–60 years were enrolled and classified into three groups based on glycaemic status:Healthy Controls: Normoglycaemic individuals with no history of diabetes, fasting plasma glucose (FPG) < 100 mg/dL, and HbA1c < 5.7%.
Well-Controlled T2DM: Diagnosed cases of type 2 diabetes mellitus (≥ 5 years), receiving treatment, with HbA1c ≤ 7.0%.
Poorly-Controlled T2DM: Diagnosed cases of type 2 diabetes mellitus (≥ 5 years) with HbA1c > 7.0%.
Including only those with a diabetes duration of five years or more ensured adequate disease chronicity for evaluating trace element alterations and oxidative stress, reducing variability associated with early disease stages.
Participants were recruited from the endocrinology clinic using medical records and a structured questionnaire (Supplementary Figure S1), with support from the attending consultant endocrinologist. Healthy controls were selected from hospital staff and relatives of patients. A simple random sampling approach was applied across all groups to minimise selection bias.
Exclusion criteria included type 1 diabetes, gestational diabetes, chronic kidney or liver disease, recent cardiovascular events (within 6 months), active infections, inflammatory or malignant conditions, recent micronutrient supplementation (within 3 months), smoking, or alcohol dependence.
Anthropometric Assessment
Participants were assessed in a standing position, barefoot, and in light clothing. Height was measured to the nearest centimetre using a non-elastic measuring tape. Weight was recorded with a digital scale (OMRON HBF-514C, Kyoto, Japan), and BMI was calculated as weight (kg) divided by height squared (m²). Waist circumference was measured in centimetres at the midpoint between the lower rib margin and iliac crest using a flexible tape during normal expiration. Blood pressure was measured using an automated sphygmomanometer (OMRON HEM-907XL, Kyoto, Japan) after five minutes of rest.
Sample Collection and Biochemical Analysis
Following an overnight fast (8–12 hours), 10 mL of venous blood was drawn from each participant into potassium-EDTA, sodium fluoride, and plain vacutainer tubes. Samples were immediately stored in a cold chain and processed under controlled conditions. Serum was separated by centrifugation at 3,000 rpm for 10 minutes and stored at − 80°C until analysis.
Glycemic Parameters: FPG and HbA1c were measured using the glucose oxidase method [17] and the diazyme enzymatic HbA1c assay [18], respectively.
Lipid Peroxidation Markers: Serum MDA levels were quantified spectrophotometrically using the thiobarbituric acid reactive substances (TBARS) assay as described by Nadigar et al. [19]. Serum 8-iso-PGF2α was measured via sandwich ELISA [20] using commercial kits (Elabscience Biotechnology Co., Wuhan, China).
Trace Elements: Serum zinc and copper concentrations were determined by flame atomic absorption spectrophotometry [21] (AAnalyst 400, PerkinElmer Inc., Waltham, MA, USA) at 213.9 nm (Zn) and 324.8 nm (Cu). Certified reference standards were used for calibration, and all glassware and instruments were acid-washed to minimise contamination. The Zn/Cu ratio was computed as the quotient of serum zinc and copper (µmol/L).
Quality Control
All biochemical assays were conducted in duplicate. Internal quality control materials were included in each analytical run. Intra- and inter-assay coefficients of variation (CV) were maintained below 10%.
Statistical Analysis
Data analysis was performed using IBM SPSS Statistics version 23 (IBM Corp., Armonk, NY, USA). Continuous variables were expressed as mean ± standard deviation (SD), and categorical variables as frequencies and percentages.
A
Group comparisons were made using one-way analysis of variance (ANOVA) followed by Tukey’s HSD post hoc test. Chi-square tests assessed differences in categorical variables. Statistical significance was set at p < 0.05, with adjustments applied for multiple comparisons as appropriate.Multivariate analysis of covariance (MANCOVA) was conducted to examine group differences in trace element levels and oxidative stress biomarkers, adjusting for covariates including age, sex, BMI, blood pressure, diabetes duration, and waist circumference. Results were presented as adjusted means ± standard error (SE), and effect sizes were reported using partial eta squared (η²).
Pearson’s correlation analysis was used to explore linear associations among variables. Multicollinearity was assessed using Principal Component Analysis (PCA), retaining components with eigenvalues > 1 and based on scree plot inspection.
To evaluate the discriminative power of biomarkers across glycaemic phenotypes, Discriminant Function Analysis (DFA) was employed. Standardised canonical coefficients and structure matrix loadings were examined to determine variable contributions. Model performance was assessed using Wilks’ Lambda, associated chi-square values, and p-values. Leave-one-out cross-validation was used to validate classification accuracy.
| Variables Poorly-Controlled Well-Controlled Healthy-controls | |||||
| DM DM | |||||
| (HbA1c > 7.0%) (HbA1c < 7.0%) (HbA1c < 5.7%) | |||||
| (N = 35) (N = 35) (N = 40) | |||||
| Mean ± SE Mean ± SE Mean ± SE F (df1, df2) p-value Partial η² | |||||
| (Adjusted ) (Adjusted) (Adjusted) |
Variables Poorly-Controlled DM Well-Controlled DM Healthy-controls p-value
(HbA1c > 7.0%) (HbA1c < 7.0%) (HbA1c < 5.7%)
(N = 35) (N = 35) (N = 40)
Mean ± SD Mean ± SD Mean ± SD
Age (years) 42.11ab ± 5.20 46.09b ± 7.66 40.78a ± 10.69 0.020*
Sex (M) % 14 (40) 18 (51) 18 (45) 0.629
(F) % 21 (60) 17 (49) 22 (55)
Duration of DM − 6.69 ± 1.32 6.29 ± 1.38 0.262
(Years)
SBP (mm/Hg) 134.40a ± 14.94 127.86b ± 9.51 118.13c ± 7.36 < 0.001*
DBP (mm/Hg) 86.83a ± 12.99 80.20b ± 9.86 76.65b ± 7.43 < 0.001*
BMI (kg/m²) 26.78 ± 2.52 27.29 ± 2.35 26.98 ± 2.52 0.708
WC (cm) 98.37a ± 5.68 95.57a ± 4.35 91.15b ± 4.26 < 0.001*
HbA1c (%) 9.5a ± 1.14 6.5b ± 0.34 5.1c ± 0.30 < 0.001*
Values are presented as mean ± standard deviation (SD) for continuous variables and as number (percentage) for categorical variables. Group comparisons were performed using one-way ANOVA with Tukey’s post hoc test for continuous variables and the Chi-square test for categorical variables. Superscript letters (a, b, c) indicate statistically significant differences between groups (p < 0.05); identical letters denote no significant difference.
Abbreviations
BMI, body mass index; DBP, diastolic blood pressure; DM, diabetes mellitus; FPG, fasting plasma glucose; HbA1c, glycated hemoglobin; SBP, systolic blood pressure; WC, waist circumference.
*Significant differences (p < 0.05) were observed among glycaemic phenotype groups for indicated variables.
Clinical and metabolic profiles across glycaemic phenotypes
Key clinical parameters varied significantly among the three glycaemic groups (Table 1). Participants with well-controlled type 2 diabetes were significantly older than both the poorly controlled and non-diabetic control groups (p = 0.020), while sex distribution remained similar across groups (p = 0.629).
Poor glycaemic control was associated with significantly elevated systolic and diastolic blood pressures, as well as increased waist circumference (all p < 0.001). However, BMI did not differ significantly across groups (p = 0.708). As anticipated, both HbA1c and fasting plasma glucose were highest in the poorly controlled group (p < 0.001), confirming the validity of the glycaemic classification. The duration of diabetes did not differ between the diabetic subgroups (p = 0.262), suggesting that the observed metabolic disparities were not attributable to disease duration alone.
8-iso-PGF2α (pg/mL) 205.49a ± 7.23 143.02b ± 7.25 96.83c ± 10.81 57.45 (2,100) < 0.001* 0.535
MDA (µmol/L) 2.30a ± 0.12 1.53b ± 0.12 0.91b ± 0.182 32.49 (2,100) < 0.001* 0.394
Copper (µmol/L) 21.6a ± 0.54 16.64b ± 0.54 13.63b ± 0.80 62.99 (2,100) < 0.001* 0.557
Zinc (µmol/L) 9.17a ± 0.40 12.21b ± 0.40 14.18b ± 0.60 43.55 (2,100) < 0.001* 0.466
Values are presented as adjusted mean ± standard error (SE), derived from multivariate analysis of covariance (MANCOVA), controlling for age, sex, BMI, blood pressure, waist circumference, and duration of diabetes. Group comparisons were conducted using Bonferroni post hoc tests. Superscript letters (a, b, c) indicate statistically significant pairwise differences between groups (p < 0.05); groups sharing the same letter are not significantly different. F-statistics and partial eta squared (η²) values reflect effect sizes and variance explained by group differences.
Abbreviations
8-iso-PGF₂α, 8-iso-prostaglandin F₂α; BMI, body mass index; DM, diabetes mellitus; MDA, malondialdehyde; SE, standard error; WC, waist circumference.
*Statistically significant differences among glycaemic phenotype groups (p < 0.05).
Trace element and oxidative stress profiles across glycaemic phenotypes
Significant differences were observed across all oxidative stress and trace element parameters, even after adjusting for age, sex, BMI, blood pressure, waist circumference, and diabetes duration (p < 0.001 for all comparisons; Table 2).
Participants with poorly controlled diabetes exhibited markedly higher serum levels of 8-iso-PGF₂α and MDA, indicative of exacerbated oxidative stress. These markers demonstrated strong effect sizes (partial η² = 0.535 and 0.394, respectively).
Serum copper levels were significantly elevated in the poorly controlled group, whereas serum zinc concentrations and the Zn/Cu ratio were notably reduced. The Zn/Cu ratio and serum copper exhibited the largest effect sizes (partial η² = 0.616 and 0.557, respectively), highlighting their potential as discriminative biomarkers of metabolic dysfunction in type 2 diabetes.
A
A
Fig. 1
8-Iso-prostaglandin factor 2α levels across glycaemic phenotype.
Highest levels of 8-Iso-prostaglandin factor 2α were observed in poorly-controlled diabetes mellitus individuals.
A
A
Fig. 2
Zinc, copper, Zinc/Copper ratio, and malondialdehyde levels across glycaemic phenotype.
Serum copper was significantly elevated in the poorly controlled group, whereas zinc levels and the zinc/copper ratio were significantly reduced.
A
Table 3Pearson correlation matrix of trace elements and oxidative stress biomarkers
.
Variables r p-value
8-iso-PGF2α (pg/mL) - MDA (µmol/L) 0.815 < 0.001**
8-iso-PGF2α (pg/mL) - Copper (µmol/L) 0.861 < 0.001**
8-iso-PGF2α (pg/mL) – Zinc (µmol/L) − 0.802 < 0.001**
8-iso-PGF2α (pg/mL) – Zinc/Copper ratio − 0.877 < 0.001**
MDA (µmol/L) – Copper (µmol/L) 0.776 < 0.001**
MDA (µmol/L) - Zinc (µmol/L) − 0.719 < 0.001**
MDA (µmol/L) - Zinc/Copper ratio − 0.780 < 0.001**
Copper (µmol/L) - Zinc/Copper ratio − 0.915 < 0.001**
Copper (µmol/L) - Zinc (µmol/L) − 0.767 < 0.001**
Zinc (µmol/L) - Zinc/Copper ratio − 0.941 < 0.001**
Values represent Pearson’s correlation coefficients (r) and corresponding p-values (2-tailed) for associations between trace element levels and oxidative stress biomarkers. All correlations were statistically significant (p < 0.001), indicating strong interrelationships and suggesting substantial multicollinearity among the studied variables.
Abbreviations
8-iso-PGF₂α, 8-iso-prostaglandin F₂α; MDA, malondialdehyde; Zn, zinc; r, Pearson correlation coefficient; p, significance level.
Note
All correlations were significant at p < 0.01.
Correlation and multicollinearity assessment
Pearson correlation analysis revealed strong and physiologically consistent relationships among oxidative stress and trace element markers (Table 3). Serum 8-iso-PGF₂α and MDA correlated positively with copper (r = 0.861 and 0.776, respectively; p < 0.001) and inversely with zinc and the Zn/Cu ratio. Copper and zinc were inversely correlated (r = − 0.767), while the Zn/Cu ratio was negatively associated with all other markers, underscoring the presence of multicollinearity.
To explore latent structure, principal component analysis (PCA) was conducted. A single principal component (PC1) explained 86% of the total variance, with uniformly high loadings across all markers (Supplementary Table S1, Figure S2), reflecting shared variance. However, inclusion of PC1 in regression models yielded unstable estimates, limiting interpretability. Consequently, discriminant function analysis (DFA) was pursued to retain the discriminative value of individual biomarkers while accounting for multicollinearity.
A
Table 4. Summary of discriminant function analysis based on oxidative stress and trace element biomarkers.
8-iso-PGF2α − 0.420–0.733
(pg/mL)
MDA (µmol/L) − 0.253–0.522
Copper (µmol/L) 0.328–0.639
Zinc (µmol/L) − 0.306 0.626
Zinc/Copper ratio 1.109 0.866
GROUPS
Healthy-controls 2.848 97.5 96.4
Well-controlled DM − 0.005 80 77. 6
Poorly–Controlled DM − 3.251 88.6 85.7
Standardised canonical coefficients indicate the relative contribution of each variable to group separation, while structure loadings represent their correlation with the discriminant function. Group centroids reflect the position of each glycaemic group along the discriminant axis. Apparent and cross-validated classification accuracies indicate the model’s ability to correctly classify participants based on biomarker profiles.
Function 1 significantly discriminated among glycaemic phenotypes (Wilks’ λ = 0.088, χ² = 255.57, df = 10, p < 0.001). Classification performance was validated using leave-one-out cross-validation.
Abbreviations
8-iso-PGF₂α, 8-iso-prostaglandin F₂α; MDA, malondialdehyde; DM, diabetes mellitus.
Discriminant function analysis of oxidative stress and trace element markers
DFA incorporating the five oxidative stress and trace element markers yielded a highly significant model (Wilks’ λ = 0.088; χ² = 255.57; df = 10; p < 0.001; Table 4). The Zn/Cu ratio had the strongest structure matrix loading (0.866), followed by 8-iso-PGF₂α and serum copper, indicating their prominent roles in distinguishing glycaemic status.
Group centroids demonstrated clear separation: +2.85 (healthy controls), – 0.01 (well-controlled diabetes), and – 3.25 (poorly controlled diabetes), supporting the model's discriminative ability. Overall classification accuracy was 88.7%, with particularly high correct classification for healthy (97.5%) and poorly controlled diabetic (88.6%) participants. Leave-one-out cross-validation confirmed model robustness, yielding a comparable accuracy of 86.6%, reinforcing the utility of individual biomarker profiling in differentiating glycaemic phenotypes.
A
A
Fig. 3
Glycaemic phenotype group centroids based on Discriminant Function Analysis (DFA).
Centroids reflect the mean discriminant scores for each group, illustrating clear separation across glycemic states. Group positions along Function 1 indicate the degree and direction of discriminative influence.
A
A
A
Fig. 4
Standardised discriminant function coefficients and structure matrix loadings for trace elements and oxidative stress biomarkers.
Coefficients indicate the relative contribution of each variable to group discrimination, while structure loadings represent the strength of correlation with the discriminant function. Zinc/Copper ratio showed the strongest discriminatory power, followed by 8-iso-PGF₂α and zinc.
DISCUSSION
This study reinforces the association between trace element dysregulation, oxidative stress, and glycaemic status in individuals with type 2 diabetes mellitus (T2DM). Participants with poor glycaemic control exhibited significantly elevated levels of serum copper and lipid peroxidation markers (8-iso-PGF₂α and MDA) alongside reduced serum zinc concentrations and lower zinc/copper (Zn/Cu) ratios. These findings remained consistent after adjusting for potential confounders including age, sex, BMI, blood pressure, waist circumference, and diabetes duration.
The graded increase in oxidative stress markers across the glycaemic spectrum aligns with the established role of reactive oxygen species (ROS) in the pathogenesis of diabetes. Elevated 8-iso-PGF₂α and MDA, both reliable indicators of lipid peroxidation, have been implicated in endothelial dysfunction and pancreatic β-cell damage, which are pivotal in the development and progression of diabetes and its complications [22, 23].
Our results concerning trace element alterations are consistent with previous research demonstrating increased copper and decreased zinc levels in poorly controlled T2DM [24, 25]. Copper contributes to oxidative stress through its redox cycling capabilities that promote ROS generation [8], while zinc functions as an antioxidant, stabilising cellular membranes and inhibiting NADPH oxidase activity [6]. The observed inverse relationship between zinc and copper and the high discriminative capacity of the Zn/Cu ratio underscore a disturbed redox state that may exacerbate metabolic instability.
A notable strength of this study is its use of multivariate statistical techniques to delineate biochemical profiles. Despite high intercorrelations among biomarkers, discriminant function analysis (DFA) effectively separated glycaemic groups, with the Zn/Cu ratio emerging as the most significant discriminant. While principal component analysis (PCA) corroborated shared variance, it offered less interpretability than DFA. This methodological integration enhances the internal validity of our findings and presents a replicable approach for biomarker identification in clinical settings.
Furthermore, by stratifying participants into three glycaemic categories, we captured a gradient of redox imbalance aligned with glycaemic deterioration. This layered analytical framework allowed for a more nuanced exploration of metabolic alterations and may offer diagnostic and prognostic value in clinical practice.
Crucially, this study contributes novel insights by integrating redox biology with trace element dynamics in a unified analytical model. To our knowledge, few studies have employed both DFA and PCA to differentiate glycaemic phenotypes using this combination of biomarkers.
The clinical relevance of the Zn/Cu ratio merits special attention. Given its simplicity, affordability, and strong association with oxidative stress, this ratio holds potential as a practical screening and monitoring tool in T2DM, especially in resource-limited settings where advanced assays are inaccessible. Emerging evidence also supports its utility in monitoring interventions aimed at redox homeostasis. For instance, zinc supplementation or copper chelation may offer therapeutic benefits [26–28], though such strategies require validation through well-designed longitudinal and interventional studies.
Limitations
This study is limited by its cross-sectional design, which precludes causal inference. Although the sample was representative, the findings may not fully capture ethnic, regional, or genetic variability. Future prospective studies are needed to determine whether correction of trace element imbalances leads to sustained improvements in oxidative stress and glycaemic control. Mechanistic studies examining the molecular effects of zinc and copper modulation on redox signalling pathways would also deepen our understanding of these associations.
Conclusion
This study highlights a strong link between glycaemic control, oxidative stress, and trace element imbalance in T2DM. Elevated serum copper and lipid peroxidation markers, alongside decreased zinc and Zn/Cu ratios in poorly controlled individuals, suggest a redox imbalance that may contribute to disease progression. Among the parameters evaluated, the Zn/Cu ratio demonstrated the highest discriminatory capacity, underscoring its potential as a cost-effective and clinically relevant biomarker. These findings support the incorporation of trace element profiling into metabolic risk assessment and point to possible roles for targeted supplementation as adjuncts in diabetes management.
DECLARATION
Ethical Approval and Consent to Participate
This study received ethical approval from the Babcock University Health Research Ethics Committee (BUHREC), Ilishan-Remo, Ogun State, Nigeria (Reference Number: BUHREC 967/24). Written informed consent was obtained from all participants prior to enrolment. The study protocol adheres to the principles of the 1975 Declaration of Helsinki. To ensure participant anonymity, a barcode-based system was implemented, and all data were managed with strict confidentiality. The data collected were used exclusively for this study.
Consent for publication
Not applicable.
Availability of data and materials
The authors confirm that the data supporting the findings of this study is available within the article and its supplementary information files.
Competing interests
The authors declare no competing interests.
A
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
A
Author Contribution
P.C.N. conceived the study, interpreted the data, and drafted and reviewed the manuscript. T.J.C performed the laboratory procedures, statistical analysis, and reviewed the manuscript. M.Y.O. performed the laboratory procedures, statistical analysis, and reviewed the manuscript. S.S.U. contributed to statistical analysis, data interpretation, and manuscript editing. All authors approve the final version for submission.
Abbreviations
T2DM: Type 2 diabetes mellitus
MDA: malondialdehyde
8-iso-PGF₂α: 8-iso-prostaglandin F₂α
Zn/Cu: zinc/copper
WC: waist circumference
SBP: systolic blood pressure
DBP: diastolic blood pressure
BMI: body mass index
FPG: fasting plasma glucose
TBARS: thiobarbituric acid reactive substances
PCA: Principal Component Analysis
DFA: Discriminant Function Analysis
ROS: reactive oxygen species
SOD: superoxide dismutase
A
BUHREC: Babcock University Health Research Ethics CommitteeMANCOVA: Multivariate analysis of covariance
ANOVA: analysis of variance
Electronic Supplementary Material
Below is the link to the electronic supplementary material
A
Data Availability
The authors confirm that the data supporting the findings of this study is available within the article and its supplementary information files.
REFERENCES
1. Asim M, Awan R, Rashid HM, Hussain F. Oxidative stress in type-2 diabetes mellitus. In: Advances in Experimental Medicine and Biology. 2024. p. 103–121. doi:10.1007/978-3-031-69962-7_5
2. Yousef H, Khandoker AH, Feng SF, Helf C, Jelinek HF. Inflammation, oxidative stress and mitochondrial dysfunction in the progression of type II diabetes mellitus with coexisting hypertension. Front Endocrinol (Lausanne). 2023;14:1173402. doi:10.3389/fendo.2023.1173402
3. Sun Z, Shao Y, Yan K, Yao T, Liu L, Sun F, et al. The link between trace metal elements and glucose metabolism: Evidence from zinc, copper, iron, and manganese-mediated metabolic regulation. Metabolites. 2023;13(10):1048. doi:10.3390/metabo13101048
4. Ruan S, Guo X, Ren Y, Cao G, Xing H, Zhang X. Nanomedicines based on trace elements for intervention of diabetes mellitus. Biomed Pharmacother. 2023;168:115684. doi:10.1016/j.biopha.2023.115684
5. Chen B, Yu P, Chan WN, et al. Cellular zinc metabolism and zinc signaling: from biological functions to diseases and therapeutic targets. Signal Transduct Target Ther. 2024;9:6. doi:10.1038/s41392-023-01679-y
6. Cai L, Tan Y, Watson S, Wintergerst K. Diabetic cardiomyopathy – zinc preventive and therapeutic potentials by its anti-oxidative stress and sensitizing insulin signaling pathways. Toxicol Appl Pharmacol. 2023;477:116694. doi:10.1016/j.taap.2023.116694
7. Asghari K, Shargh Z, Fatehfar S, Chodari L, Sameei P. The impact of zinc on the molecular signaling pathways in the diabetes disease. J Trace Elem Med Biol. 2022;72:126985. doi:10.1016/j.jtemb.2022.126985
8. Vo TTT, Peng TY, Nguyen TH, Bui TNH, Wang CS, Lee WJ, et al. The crosstalk between copper-induced oxidative stress and cuproptosis: a novel potential anticancer paradigm. Cell Commun Signal. 2024;22(1):353. doi:10.1186/s12964-024-01726-3
9. Zheng Y, Sun J, Luo Z, Li Y, Huang Y. Emerging mechanisms of lipid peroxidation in regulated cell death and its physiological implications. Cell Death Dis. 2024;15(11):859. doi:10.1038/s41419-024-07244-x
10. Gouaref I, Otmane A, Makrelouf M, Abderrhmane SA, Haddam AEM, Koceir EA. Crucial interactions between altered plasma trace elements and fatty acids unbalance ratio to management of systemic arterial hypertension in diabetic patients: Focus on endothelial dysfunction. Int J Mol Sci. 2024;25(17):9288. doi:10.3390/ijms25179288
11. Shabalala SC, Johnson R, Basson AK, Ziqubu K, Hlengwa N, Mthembu SXH, et al. Detrimental effects of lipid peroxidation in type 2 diabetes: exploring the neutralizing influence of antioxidants. Antioxidants (Basel). 2022;11(10):2071. doi:10.3390/antiox11102071
12. Li Y, Liu Y, Liu S, et al. Diabetic vascular diseases: molecular mechanisms and therapeutic strategies. Signal Transduct Target Ther. 2023;8:152. doi:10.1038/s41392-023-01400-z
13. Guan H, Tian J, Wang Y, et al. Advances in secondary prevention mechanisms of macrovascular complications in type 2 diabetes mellitus patients: a comprehensive review. Eur J Med Res. 2024;29:152. doi:10.1186/s40001-024-01739-1
14. Mizuno Y, Inaba Y, Masuoka H, Kibe M, Kosaka S, Natsuhara K, et al. Determinants of oxidative stress among indigenous populations in Northern Laos: Trace element exposures and dietary patterns. Sci Total Environ. 2023;868:161516. doi:10.1016/j.scitotenv.2023.161516
15. Krishnamurthy HK, Rajavelu I, Pereira M, Jayaraman V, Krishna K, Wang T, et al. Inside the genome: understanding genetic influences on oxidative stress. Front Genet. 2024;15:1397352. doi:10.3389/fgene.2024.1397352
16. Mertaş B, Boşgelmez İİ. The role of genetic, environmental, and dietary factors in Alzheimer’s disease: a narrative review. Int J Mol Sci. 2025;26(3):1222. doi:10.3390/ijms26031222
17. Shaker G, Swift CJ. Peroxidase-coupled glucose method. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 [cited 2025 May 21]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK594277/
18. Diazyme Laboratories. Direct Enzymatic HbA1c Assay [Internet]. 2024 [cited 2025 May 21]. Available from: https://www.diazyme.com/diabetic-markers/direct-enzymatic-hba1c-assay
19. Nadiger HA, Marcus SR, Chandrakala MV, et al. Malondialdehyde levels in different organs of rats subjected to acute alcohol toxicity. Indian J Clin Biochem. 1986; 1:133–136.
20. Smith KA, Shepherd J, Wakil A, Kilpatrick ES. A comparison of methods for the measurement of 8-isoPGF2α: a marker of oxidative stress. Ann Clin Biochem. 2011;48(2):147–154. doi:10.1258/acb.2010.010151
21. Handley SA, Wanandy T, Prentice L. Validation of the Randox colorimetric assays for serum copper and zinc. Ann Clin Biochem. 2024;61(3):182–194. doi:10.1177/00045632231208337
22. Yang DR, Wang MY, Zhang CL, Wang Y. Endothelial dysfunction in vascular complications of diabetes: a comprehensive review of mechanisms and implications. Front Endocrinol (Lausanne). 2024; 15:1359255. doi:10.3389/fendo.2024.1359255
23. Shabalala SC, Johnson R, Basson AK, Ziqubu K, Hlengwa N, Mthembu SXH, et al. Detrimental effects of lipid peroxidation in type 2 diabetes: exploring the neutralizing influence of antioxidants. Antioxidants (Basel). 2022;11(10):2071. doi:10.3390/antiox11102071
24. Wu K, Chen L, Kong Y, Zhuo JF, Sun Q, Chang J. The association between serum copper concentration and prevalence of diabetes among US adults with hypertension (NHANES 2011–2016). J Cell Mol Med. 2024;28(8):e18270. doi:10.1111/jcmm.18270
25. Singh M, Chandey M, Mohan G. Study of serum zinc levels in type 2 diabetes mellitus and its complications. Eur J Cardiovasc Med. 2025;15(4):830–835.
26. Ahmad R, Shaju R, Atfi A, Razzaque MS. Zinc and diabetes: a connection between micronutrient and metabolism. Cells. 2024;13(16):1359. doi:10.3390/cells13161359
27. Daneshvar M, Ghaheri M, Safarzadeh D, Nikfar S, Abdollahi M. Effect of zinc supplementation on glycemic biomarkers: an umbrella of interventional meta-analyses. Diabetol Metab Syndr. 2024; 16:124. doi:10.1186/s13098-024-01366-0
28. Baldari S, Di Rocco G, Toietta G. Current biomedical use of copper chelation therapy. Int J Mol Sci. 2020;21(3):1069. doi:10.3390/ijms21031069