1.
INTRODUCTION
Obesity is a chronic disease characterized
by excessive fat accumulation that could impair health(WHO,
2024). The
World
Health Organization (WHO) recognizes the obesity as a
global epidemic, it affects individuals of all ages and socioeconomic
backgrounds. As of 2022, over 890 million adults worldwide were obese, with
numbers projected to rise in the coming years.
Obesity is linked to serious health conditions, such as type 2 diabetes,
cardiovascular disease, hypertension, non-alcoholic fatty liver disease, and specific
cancers(Kirichenko
et al., 2022; WHO, 2024).
One of the critical pathophysiological mechanisms
contributing to the development of obesity-related complications is oxidative
stress (Čolak
& Pap, 2021). The imbalance between the reactive
oxygen species (ROS) production and the ability of the body to neutralize them
through antioxidant defenses is known as oxidative stress (Ji
& Yeo, 2021; Yousif & Hami, 2025). In normal physiological
state, hydrogen peroxide (H₂O₂), superoxide anion
(O₂⁻), and hydroxyl radicals (OH•) are
essential for normal cellular signaling and immune defense. The
overproduction of these ROS leads to the damage of lipids, proteins and DNA,
resulting in cellular dysfunction, inflammation, and programmed
cell death (Sachdev
et al., 2021; Sadiq, 2023). In obesity, excessive adipose tissue
becomes a site of increased oxidative metabolism and chronic inflammation, both
enhance ROS production and reduce antioxidant capacity (Yu
et al., 2023).
The
human antioxidant defense systems comprises both enzymatic and non-enzymatic
systems, such as catalase, glutathione peroxidases (GPx), superoxide dismutase
(SODs), glutathione S-transferases (GST), glutathione reductase (GR), vitamins
C, E, carotenoids, flavonoids, uric acid, bilirubin, and glutathione which collectively
act to neutralize reactive oxygen species (ROS) and repair molecules damaged by
oxidative stress (Janciauskiene,
2020; Irato & Santovito, 2021; Jomova et al., 2024; Vilchis-Landeros
et al., 2024)
Trace elements play a vital role in
maintaining oxidative balance. These essential micronutrients serve as
cofactors for many enzyme-catalyzed reactions and redox reactions (Wróblewski
et al., 2024). Zinc (Zn) and copper (Cu) are essential
for the integrity and catalytic activity of Cu/Zn-SOD (Székely
et al., 2024). Manganese (Mn) is an essential cofactor
for Mn-SOD, enabling its antioxidant activity in mitochondria. Magnesium (Mg)
is involved in ATP production and stabilizes antioxidant enzymes, while
chromium (Cr) and molybdenum (Mo) contribute to maintaining redox homeostasis (Jomova
et al., 2022). Trace element imbalances, whether from
deficiency or excess, can disrupt antioxidant enzyme function and facilitating
oxidative stress, particularly in obesity (Amin
et al., 2020).
Although
the role of oxidative stress and trace element imbalance in obesity has been
well studied in many parts of the world, data from specific populations remain
limited (Jakubiak
et al., 2021; Amerikanou et al., 2023).
Local variations in dietary habits, genetic predispositions, and environmental
exposure may influence these biochemical interactions(Barakat
& Almeida, 2021).
This study aims to evaluate the
oxidative–antioxidant balance and trace element status in obese versus
non-obese adult males in Zakho City, and to assess the association of these
antioxidant markers with various anthropometric and biochemical parameters.
2.
MATERIALS AND METHODS
Design of the Study:
This
comparative cross-sectional study was carried out over four months, from
October 2024 to January 2025, at Zakho General Hospital in Zakho City,
Kurdistan Region, Iraq. Ninety adult males aged between 18 and 44 were
recruited for the investigation. Prior enrollment, adult males were informed about
the objective of this study and written consent was singed from each
participant. Basic demographic and health information, such as age, location,
lifestyle behaviors, and medical history, was obtained through a questionnaire
developed for this research.
These
individuals were divided into two groups depending on their body mass index
(BMI): Group 1 comprised 50 individuals classified as obese; BMI ≥ 30
kg/m˛, while Group 2, considered as control group, included 40 participants;
BMI between 18.5 and 24.99 kg/m˛. Individuals were excluded from participation
if they had any diagnosed chronic illnesses such as diabetes mellitus, thyroid
disorders, or cardiovascular disease, or if they were taking medications,
drinking alcohol, or using tobacco in any form.
Anthropometric
measurements were recorded for all participants. These measurements included: body
weight and height for calculating BMI, and waist circumference (WC). In
addition, systolic (SBP) and diastolic blood pressure (DBP) readings were measured
for all adult males.
Six
mL of venous blood was drawn from each participant between 8:30 a.m. to 12:00
p.m., following a minimum of 8 hours of overnight fasting and then transferred
into gel-based serum separation tubes. After that, the samples are allowed to
clot at room temperature, then centrifuged at 3000 rpm for 10 minutes using a
Universal 320 centrifuge (Hettich, Germany). The resulting serum was transferred
into pre-labeled 1.0 mL Eppendorf tubes and stored at –75 °C in deep
freezers at the Zakho Blood Bank laboratory until further biochemical analyses
were performed.
Biochemical
Analysis:
The
following biochemical parameters; fasting blood glucose (FBG), urea, creatinine,
uric acid, lipid profile, liver function tests (alanine aminotransferase (ALT),
aspartate aminotransferase (AST), alkaline phosphatase (ALP); and gamma-glutamyl
transferase (GGT), and high-sensitivity C-reactive protein (hs-CRP) were measured
using an automated Cobas 6000 c501 chemistry analyzer (Roche Diagnostics,
HITACHI). Fasting insulin (FI) levels are analyzed using the Cobas 6000 c601
immunoassay system (Roche Diagnostics). Some parameters, which include very low-density
lipoprotein cholesterol (VLDL-C), low-density lipoprotein cholesterol (LDL-C),
and Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), were
estimated by using these equations (VLDL-C = TG/5), (LDL-C = TC − HDL-C
− TG/5)(Friedewald
et al., 1972), and (HOMA-IR) = FBS (mg/dL) * FI
(mU/ml)/405(Pourhabibi‐Zarandi
et al., 2022).
The
enzymatic activities of glutathione S-transferase (GST), superoxide dismutase
(SOD), and catalase (CAT) in human serum were determined using
spectrophotometric methods. All absorbance readings were measured using a
UV/VIS spectrophotometer (Perkin Elmer Lambda 35, USA).
GST
and SOD activities were determined using the methods described by (Ibrahiem
et al., 2024). GST assay is
based on the conjugation of reduced glutathione (GSH) with
1-chloro-2,4-dinitrobenzene (CDNB), catalyzed by GST to form a GS-DNB complex
that absorbs light at 340 nm. The reaction mixture contained 100 µL of serum,
100 µL of CDNB (2.5 mM), 100 µL of GSH (5 mM), and 700 µL of potassium
phosphate buffer (0.1 M, pH 5.5). A blank was prepared using buffer instead of
serum. After incubation at 37 °C for 6 minutes, the absorbance was
measured at 340 nm. The enzymatic activity was calculated based on the
Beer–Lambert Law with the extinction coefficient ε = 9.6 mM⁻ą
cm⁻ą.
Superoxide dismutase (SOD) activity was
determined using the photochemical nitroblue tetrazolium (NBT) method. The
assay principle is based on the ability of SOD to inhibit the photoreduction of
NBT by superoxide radicals generated in a riboflavin–methionine–light system.
The assay mixture contained 3 mL of reaction solution prepared as follows: 117
mL of phosphate buffer (50 mM, pH 7.8), 1.25 mL of L-methionine (0.2 M), 1 mL
of NBT (1.75 mM), and 0.75 mL of Triton X-100 (1%). To this, 39 µL of sodium
cyanide (2 mM) was added as a peroxidase inhibitor, followed by 150 µL of
serum, 532 µL of working buffer (prepared by dissolving 0.0375 g EDTA and 0.25
mL Triton X-100 in 100 mL of 50 mM phosphate buffer, pH 7.8), and 37.8 µL of
riboflavin solution (117 µM). A blank was prepared using working buffer in
place of serum. After thorough mixing, the samples were placed in a closed
wooden box lined with aluminum foil and exposed to intense light from two
20-watt fluorescent lamps for 15 minutes. The absorbance was measured at 560 nm
before and after light exposure. The difference in absorbance was used to
estimate the SOD activity.
Catalase
activity was measured using the method described by (Hadwan
& Abed, 2016), which is based on the decomposition of
hydrogen peroxide (H₂O₂) by catalase in the presence of a phosphate
buffer. The remaining H₂O₂ reacts with ammonium molybdate to form a
yellow complex, which is measured spectrophotometrically at 374 nm. The reaction
mixture for the test sample consisted of 100 µL of serum and 1000 µL of
H₂O₂ (20 mM) in phosphate buffer (50 mM, pH 7.4). After vortexing,
the mixture was incubated at 37 °C for 3 minutes. The reaction was then
stopped by adding 4000 µL of ammonium molybdate (32.4 mM). For the
control-test, H₂O₂ was omitted, while the blank contained only
distilled water (1100 µL) and ammonium molybdate. The standard consisted of
H₂O₂ (1000 µL) and buffer (100 µL), followed by molybdate. All samples
were kept at room temperature after the addition of molybdate, and absorbance
was recorded at 374 nm using a UV/VIS spectrophotometer (Perkin Elmer Lambda
35, USA). Catalase activity was calculated using the first-order reaction rate
constant formula:
t: time. S°: absorbance of standard tube.
S: absorbance of test tube. M: absorbance of control test (correction factor).
Vt: total volume of reagents in test tube. Vs: volume of serum.
The concentrations of trace elements,
including zinc (Zn), copper (Cu), and magnesium (Mg), were determined using
commercially available colorimetric reagent kits supplied by Spectrum
Diagnostics (Germany) and BioSystems (Spain). All measurements were performed
using a JENWAY 6700 visible spectrophotometer (UK).
Serum
zinc concentration was quantified using a colorimetric method based on the
5-Bromo-PAPS assay (Spectrum Diagnostics, REF 232001, Germany). This method is
based on the formation of a red-colored complex between zinc and2-(5-Bromo-2-pyridylazo)-5-(N-propyl-N-sulfopropylamino)-phenol.
The intensity of the color is directly proportional to the concentration of
zinc in the sample. The reagent contained 5-Br-PAPS (0.02 mmol/L), bicarbonate
buffer (200 mmol/L, pH 9.8), sodium citrate (170 mmol/L), dimethylglyoxime (4
mmol/L), and 1% detergent.
In
the assay procedure, 1 ml of the working reagent was mixed with 50 µl of serum
or zin standard solution, while the blank contained only the reagent. The
mixture was incubated at 37 °C for 5 minutes. Absorbance was measured at
546 nm against a reagent blank. Zinc concentration in µg/dl was calculated
using the following formula:
serum copper concentration was determined
using a colorimetric method with Dibromo-PAESA, supplied by Spectrum
Diagnostics (Germany; REF 232001). The method is based on the formation of a
stable colored chelate complex between copper and
4-(3,5-dibromo-2-pyridylazo)-N-ethylsulfopropylaniline under acidic conditions.
The intensity of the complex is directly proportional to the copper
concentration in the sample. The reagent composition included acetate buffer
(0.2 mol/L, pH 5.0) and Dibromo-PAESA (0.02 mmol/L) as a single monoreagent.
In the assay, 1.0 ml of reagent was added
to 50 µl of either the serum sample or copper standard (100 µg/dL), while the
blank contained only the reagent. All tubes were thoroughly mixed and incubated
at 37 °C for 5 minutes. After incubation, the absorbance of the sample
(Aₛ) and standard (Aₛₜ) was measured against the reagent
blank at 580 nm. The copper concentration (µg/dL) was calculated using the
following equation:
where ΔA represents the difference
between sample or standard absorbance and the blank.
Serum
magnesium concentration was determined using a colorimetric method based on the
reaction with xylidyl blue, supplied by BioSystems (Spain; COD 11797). The
method relies on the formation of a colored complex between magnesium and
xylidyl blue in an alkaline medium. EGTA is incorporated in the reagent to
eliminate interference from calcium. The intensity of the resulting complex is
directly proportional to the magnesium concentration in the sample. The working
reagent was prepared by combining Reagent B (xylidyl blue 0.5 mmol/L, glycine
25 mmol/L, and chloroacetamide 2.6 g/L) with Reagent A (sodium carbonate 0.1
mol/L, EGTA 0.1 mmol/L, triethanolamine 0.1 mol/L, potassium cyanide 7.7 mmol/L,
and sodium azide 0.95 g/L). In the assay, 1.0 mL of the working reagent was
added to 10 µL of either the serum sample or the magnesium standard (2 mg/dl).
A blank was prepared using only the working reagent. All tubes were mixed
thoroughly and incubated at room temperature for 2 minutes. The absorbance of
the sample (Aₛ) and standard (Aₛₜ) was measured against the
reagent blank at 520 nm. The magnesium concentration in the sample was
calculated using the following formula:
AS: absorbance of the sample, Ast:
absorbance of the standard, Cst: Concentration of standard
Statistical
Analysis:
Statistical
analyses were performed using IBM SPSS Statistics software, version 26.0. Mean
and standard deviation (mean ± SD) were used to express data. The Shapiro–Wilk
test was applied for testing the distribution of continuous variables. The
independent samples t-test for normally distributed variables and the
Mann–Whitney U test for those that were not normally distributed were applied
for comparisons between groups. Correlations between oxidative stress markers
and anthropometric or biochemical parameters were tested using Spearman’s
rank-order correlation. P-value less than 0.05 is considered statistically significant,
less than 0.01 is highly significant, and greater than 0.05 is not significant.
3.
RESULTS
Table
1 summarizes demographic characteristics of the studied groups. No significant
difference between the groups was observed in age, height, and SBP (p >
0.05). However, body weight, BMI, WC, and DBP were significantly higher in
obese groups compared to control group (p < 0.05).
Biochemical
and oxidative stress parameters are presented in Table 2. Obese participants
demonstrated significantly elevated levels of FBG, urea, uric acid, TC, TG,
LDL-C, VLDL-C, liver enzymes (ALT, AST, ALP, and GGT), hs-CRP, FI, and HOMA-IR
values compared to non-obese group (p < 0.05). In contrast, HDL-C concentrations
were significantly lower among obese subjects (p < 0.001).
Regarding
trace elements, Zn and Cu levels were elevated in obese males (p = 0.002 and p
< 0.001, respectively), while Mg concentrations are significantly reduced (p
< 0.001). Antioxidant enzyme activities, including GST, catalase, and SOD,
were markedly lower in obese group (p < 0.05 and p < 0.001, respectively)
4.
DISCUSSION
This
study aimed to evaluate the impact of obesity on oxidative–antioxidant balance
and trace element status in adult males in Zakho City and as well as to
investigate the association of these biomarkers with anthropometric and
biochemical parameters. The findings revealed a substantial oxidative imbalance
and altered trace element levels in obese individuals compared to non-obese
controls.
Obese
participants had significantly higher BMI, WC, DBP, FBG, lipid profile (TC, TG,
LDL-C, and VLDL), liver enzymes (ALT, AST, ALP, and GGT), hs-CRP, FI, and
HOMA-IR. These results were in agreement with previous research indicating that
obesity was strongly associated with insulin resistance, dyslipidemia, hepatic
stress, and systemic inflammation(Liu
et al., 2021; Rohm et al., 2022). The elevated hs-CRP observed
in this study support the presence of chronic inflammation, a well-recognized
characteristic of obesity-associated metabolic dysfunction (Lund
et al., 2020).
A
major focus of this study was the evaluation of oxidative stress and
antioxidant defense. All three measured antioxidant enzymes GST, SOD, and
catalase were significantly lower in obese individuals. This finding suggests a
compromised antioxidant defense in response to increased ROS, likely resulting
from excess adipose tissue and the consequent metabolic overload(Yu
et al., 2023). The reduction in antioxidant activity
reflected a failure of the body to adequately counter oxidative stress, which
may contribute to the progression of obesity-related complications such as
insulin resistance and cardiovascular disease(Idan
& Mohamoud, 2024; Świątkiewicz et al., 2023).
Among
the antioxidant enzymes, GST activity was significantly reduced in obesity and
negatively correlated with weight, BMI, WC, FBS, TG, VLDL-C, ALT, GGT, FI, and
HOMA-IR. These results were suggested lower GST activity was associated with
worsening metabolic control and liver dysfunction. Previous studies have shown
that GST played a role in detoxifying lipid peroxidation products and other
oxidative metabolites, and its reduced activity may have enhanced cellular
vulnerability to oxidative injury(Awasthi
et al., 2017).
SOD
activity also decreased significantly in obese individuals and was negatively
associated with multiple parameters, including weight, BMI, lipid profile,
liver enzymes, and inflammatory and insulin-related markers. In particular, SOD
showed a significant negative correlation with Zn and Cu levels, which may be
explained by altered trace element homeostasis under inflammatory conditions.
While Zn and Cu are cofactors for Cu/Zn-SOD, their elevated serum levels in
obesity may reflect redistribution due to stress or altered absorption, rather
than improved enzymatic function(Franco
& Canzoniero, 2024). This paradoxical increase in trace
element levels alongside decreased SOD activity was suggested that oxidative
stress, rather than element deficiency, may have driven enzyme suppression.
Catalase,
the third antioxidant enzyme assessed, showed the strongest inverse
correlations with obesity indicators and biochemical markers. In addition to
its negative correlation with weight, BMI, lipid profile, liver enzymes, and
insulin resistance. On the other way catalase was positively correlated with
magnesium levels. Magnesium is essential for numerous enzymatic processes,
including those involving antioxidant defense, and hypomagnesemia has been
frequently reported in obesity and type 2 diabetes(Piuri
et al., 2021). The positive relationship between
catalase and magnesium suggested that adequate Mg levels may help maintain
antioxidant capacity. Conversely, catalase was negatively associated with
copper, reinforcing the hypothesis that excess copper contributed to oxidative
stress(Sielska
et al., 2024).
Regarding
trace elements, obese individuals had significantly elevated Zn and Cu levels,
but significantly reduced Mg levels. Although Zn and Cu are essential cofactors
in antioxidant enzyme systems, their elevated concentrations in this context
may be part of an acute-phase response to inflammation rather than indicative
of enhanced antioxidant defense(Li
et al., 2023; Olechnowicz et al., 2018).
The consistent finding of lower Mg level in the obese group aligns with
existing literature and may be related to impaired glucose metabolism, reduced
insulin sensitivity, and pro-inflammatory status (Piuri
et al., 2021; Xu et al., 2024).
The
positive correlations among GST, SOD, and catalase observed in the study was
suggested a coordinated antioxidant response to manage oxidative stress. Their
simultaneous decline in the obese group was highlighted a systemic impairment
of antioxidant defense, which likely contributed to the pathophysiology of
obesity and its associated metabolic complications.
CONCLUSION
This
study demonstrated that obesity was associated with reduced antioxidant enzyme
activities and altered trace element levels. Obese individuals showed lower
levels of GST, SOD, and catalase, alongside increased metabolic and
inflammatory markers. Significant correlations between antioxidant enzymes such
as GST, SOD, and catalase and clinical parameters including BMI, lipid profile,
and inflammatory markers suggested that oxidative stress plays a central role
in the development of obesity-related complications. The imbalance in trace
elements, especially decreased Mg and increased Cu and Zn, further highlights
the oxidative burden in obesity. These findings was suggested that monitoring
antioxidant status and trace elements might have valuable in managing obesity
and its metabolic risks.
Acknowledgment:
We
thank the University of Zakho, the College of Science, and the Chemistry
Department for their invaluable support throughout this project.
Research Funding:
This
research was conducted without any financial support from external funding
sources.
Ethical Approval:
Ethical
approval for this research is approved in November 2024 from the Research
Ethics Committee, College of Medicine, University of Zakho (Ref:
NOV2024/UOZE28).
Authors
Contribution :
The
project framework was devised by Assistant Professor Dr. Lina Y. Mohammed, who
also offered guidelines during the research period. Sozdar Ayoub Adil managed
all facets of sample collection, biochemical testing, data analysis, and
literature research. The manuscript of this study was composed by Sozdar Ayoub
Adil. Both the author and the supervisor evaluated and ratified the final
version.
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