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Article

Tissue-Specific Accumulation of Heavy Metals and Oxidative Stress in Atlantic Bonito (Sarda sarda, Bloch 1793) Marketed in Kütahya

by
Özge Nur Ekiz
and
Gözde Karabulut
*
Department of Biology, Faculty of Arts and Sciences, Kütahya Dumlupınar University, 43000 Kütahya, Türkiye
*
Author to whom correspondence should be addressed.
Biology 2026, 15(4), 341; https://doi.org/10.3390/biology15040341
Submission received: 14 January 2026 / Revised: 6 February 2026 / Accepted: 12 February 2026 / Published: 15 February 2026
(This article belongs to the Special Issue Metals in Biology (2nd Edition))
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Simple Summary

Heavy metal pollution in oceans and seas is a growing concern because these toxic substances can accumulate in fish, which are a major part of the human diet. In this study, we investigated the Atlantic bonito (Sarda sarda, Bloch 1793) sold in local markets in Kütahya, Türkiye, to see how much heavy metals and arsenic they contain and how these metals affect the fish’s health. We specifically looked at two parts of the fish: the muscle (the part we eat) and the gills (which the fish uses to breathe). Our results showed that the gills had much higher levels of metals like cadmium and copper compared to the muscle. These metals caused “oxidative stress” in the fish, which is a type of internal cell damage that weakens their natural defense systems. However, when we calculated the potential risk for humans, we found that the metal levels in the edible parts were within safe limits.

Abstract

Heavy metal pollution in aquatic environments presents a considerable risk to fish populations, primarily through the induction of oxidative damage. This study aimed to investigate the relationship between heavy metals (Cu, Cd, Hg, and Pb) and As accumulation and oxidative stress biomarkers, including malondialdehyde (MDA), total oxidant status (TOS), and oxidative stress index (OSI), together with antioxidant defenses such as reduced glutathione (GSH) and total antioxidant status (TAS), in the muscle and gill tissues of Atlantic bonito (Sarda sarda, Bloch 1793). Furthermore, human health risks were evaluated using the target hazard quotient (THQ), hazard index (HI), and carcinogenic risk (CR) metrics. Our findings indicate that heavy metals such as cadmium (Cd) and copper (Cu) accumulate significantly more in the gills than in the muscle tissue (p < 0.05). This accumulation seems to cause an unusual biological response, evidenced by a notable increase in oxidative stress markers—namely MDA, TOS, and OSI—within the gill tissues (p < 0.01). Specifically, gill MDA concentrations (5.43 ± 1.86 nmol/mg) were significantly higher than those observed in the muscle tissue (4.07 ± 1.63 nmol/mg). Concerning human safety, both the HI (0.8393) and CR values remained within established safety thresholds. These observations suggest that the gills are the primary site of metal-induced oxidative damage, and the robust correlation between metal accumulation and OSI/MDA levels implies that these parameters are reliable indicators for evaluating aquatic metal pollution.

1. Introduction

Heavy metals have been used and processed worldwide for a long time, but they pose serious risks to both human health and the environment. These metals can enter living organisms through various ways, such as soil, waste from homes and industries, drinking water, cooking tools, packaging materials and the food chain. Therefore, heavy metal pollution, especially in water, is a significant environmental and health problem [1,2]. When heavy metals enter aquatic ecosystems, they can remain in their original form, attach to different particles, or settle as sediment. This process results in the accumulation of heavy metals within aquatic organisms, including fish, potentially endangering human health via the consumption of contaminated fish [3]. Cadmium (Cd), mercury (Hg), copper (Cu), and lead (Pb) are among the most frequently observed heavy metals in fish, together with arsenic (As) as a trace element. Consumption of these metals in excess of acceptable levels can result in various health problems such as neurological impairments, liver and kidney damage, endocrine system disruptions, cardiovascular complications, hematological irregularities and cancer [4,5]. Heavy metals are substances that should be controlled by monitoring their levels in foods because they can bind to sulfur, nitrogen and other functional groups that disrupt enzyme functions. The toxic effects of metals reveal the fact that especially toxic ones (such as Pb, Cd, Hg) can lead to health problems in humans and animals. Even in small amounts, these metals can lead to diseases such as neurological problems, headaches, non-carcinogenic hazards such as liver and kidney diseases, stomach ailments, anorexia, heart diseases, hypertension, and cancer [4,6,7,8]. Heavy metals can be classified into essential and non-essential (toxic) metals. Essential metals, such as copper, manganese, iron, zinc, and cobalt, are necessary in trace amounts for living beings to maintain their normal functions. However, while excessive intake of these metals can lead to functional disorders in the organism, exposure to toxic metals causes potentially more serious health problems [6]. Other heavy metals, such as cadmium, lead, thallium, and mercury, serve no biological role. However, they will inevitably enter the human body due to their presence in the environment. Similar to essential metals, they induce toxicity once specific concentrations are reached [9]. For this reason, monitoring heavy metals in seafood is a necessity for human health. Heavy metals can be found in a dissolved form in water, as particles suspended in water or in bottom sediments, and can cause serious damage to ecosystems. These metals can accumulate in aquatic organisms and pass to humans through the food chain. Seafood, especially, is one of the main sources of human exposure to heavy metals and other toxic chemicals. Therefore, determining the chemical quality of marine organisms and periodically monitoring heavy metal levels are crucial for evaluating potential risks to human health [8].
The Atlantic bonito (Sarda sarda, Bloch 1793) is an epipelagic species that is a member of the Scombridae family and is found in schools in the Mediterranean, Black Sea, and Atlantic coasts [10]. This species, which has great economic importance for Türkiye, migrates between the Aegean Sea and the Black Sea through the Dardanelles and the Bosphorus for feeding and reproduction purposes. Atlantic bonito, one of the most common species of the Black Sea and the Sea of Marmara, migrates to the Black Sea, especially in the spring months; here it feeds and matures its gonads for reproduction. Atlantic bonito has a voracious and predatory structure and feeds on fish living in schools, such as anchovies, chub mackerel, Atlantic mackerel, horse mackerel, and sardines [11].
Increased free oxygen radicals and lipid peroxidation play an important role in the development of many diseases. It is known that conditions such as myocardial infarction, neurological diseases, asthma, diabetes mellitus, rheumatological diseases (such as rheumatic diseases), cancer and aging are associated with oxidative stress [12]. Free radicals are extremely unstable because they have unpaired electrons. Due to this instability, they quickly interact with the organic molecules around them and try to become stable. In biological systems, free radicals are generally found in various forms, such as hydroxyl (•OH), superoxide (O2•−), nitric oxide (NO•) and lipid peroxide radicals (LOO•). The most important free radicals are radicals derived from oxygen and they are generally called reactive oxygen species (ROS). To prevent or reduce oxidative damage caused by these ROS, the body has developed “antioxidant defense systems” [13,14]. However, when these defense systems are insufficient, free radical accumulation increases and cellular degradations called “oxidative stress” occur. This process usually results in tissue damage [15]. Glutathione (GSH) is a tripeptide consisting of glutamic acid, glycine, and cysteine compounds and undertakes the task of cleaning reactive oxygen species. GSH, which constitutes the first step of the antioxidant defense system, combines with free radicals produced by phase I enzymes during xenobiotic metabolism. Thanks to this combination, free radicals cannot bind to DNA, RNA and proteins; hence, cell damage is prevented [16]. During lipid peroxidation, carbon bonds in organic compounds break and degradation products such as 4-hydroxynonenal and malondialdehyde (MDA) emerge. MDA and 4-hydroxynonenal can quickly diffuse within the cell and damage cellular components such as mitochondria, nuclear membranes and DNA. MDA reacts with proteins to form cross-links between lipids and proteins, which leads to tissue damage. As a result of this damage, cellular mechanisms, especially physiological processes such as ion transport and enzyme activity, are disrupted.
Recently, fish have caused researchers to focus on this issue due to the heavy metals they contain. Studies have been conducted in different countries of the world for the analysis of heavy metal content in fish [17,18,19,20,21]. Regarding the research conducted on fish, it has been stated that tissues and organs such as muscle, liver, kidney, gill and skin are used as biomarkers [22,23]. Since fish are very sensitive to environmental changes, such as increased pollution, it has been suggested that fish health is a reliable indicator in determining the general state of the entire aquatic ecosystem [24]. Heavy metal accumulation in fish tissues carries serious risks for ecosystem health and human consumption. However, the relationship between heavy metals and As accumulation and oxidative stress responses in the physiological systems of fish has not been sufficiently addressed in the existing literature. Most previous studies have focused either on the accumulation of heavy metals and As or on oxidative stress biomarkers separately, leaving a gap in the integrated evaluation of these two interrelated processes. In the present study, muscle tissue was selected due to its relevance for human consumption, whereas gill tissue was chosen because of its direct interaction with the aquatic environment and its key role in metal uptake and exchange. Therefore, this study presents original scientific research by simultaneously examining the tissue-specific accumulation of heavy metals and As and their associations with oxidative stress and antioxidant defense indicators in Atlantic bonito.

2. Materials and Methods

2.1. Sample Collection

Twenty-four Atlantic bonito (Sarda sarda) samples were collected in September 2024 from a local fish market in Kütahya, Türkiye. All specimens were already dead at the time of purchase. The fish were caught on the same day and kept under commercial cold-chain conditions (on ice) during transport from the capture site to the market, which was further supported by organoleptic freshness criteria (e.g., bright convex eyes, red gills, and firm elastic texture). Upon purchase, the samples were immediately transferred to styrofoam boxes under strict cold-chain protocols and transported to the laboratory within 15 min. Upon arrival, total length and weight were recorded for each specimen. Subsequently, dorsal muscle and gill tissues were excised from each of the 24 individuals using sterile instruments, rinsed with distilled water to remove potential surface contaminants, and stored at −80 °C until biochemical analyses and heavy metals and As determinations were conducted.

2.2. Heavy Metals and As Analysis by ICP-MS

Muscle and gill tissue samples, each weighing roughly 0.5 g, were placed in Teflon vessels for mineralization. Acid digestion was carried out using a microwave digestion system (Milestone Ethos UP, Sorisole, Italy), applying a combination of 6 mL concentrated nitric acid (65%, Suprapur®, Merck, Darmstadt, Germany) and 2 mL hydrogen peroxide (30%, Suprapur®, Merck, Darmstadt, Germany). Following digestion, the samples were permitted to reach room temperature and subsequently diluted to a final volume of 25 mL with ultrapure water (Millipore, Bedford, MA, USA). Prior to instrumental analysis, all samples underwent filtration through 0.45 µm filters. Concentrations of Cu, As, Cd, Hg, and Pb were measured by using an Inductively Coupled Plasma Mass Spectrometer (Thermo Scientific iCAP RQ ICP-MS, Bremen, Germany). The instrument was operated in Kinetic Energy Discrimination (KED) mode, with helium (He) as the collision gas to prevent spectral interferences, especially those resulting from polyatomic species. In addition, internal standards were used to correct for instrumental drift and matrix effects. Analytical accuracy was validated through the analysis of the certified reference material TORT-2 lobster hepatopancreas (Institute for Environmental Chemistry, National Research Council Canada (NRC), Ottawa, ON, Canada). Recovery rates for individual elements were within narrow and acceptable ranges: Cu (98.1–103.8%), As (96.4–101.9%), Cd (97.2–102.6%), Hg (96.8–100.5%), and Pb (99.0–103.2%). The limits of detection (LODs) and limits of quantification (LOQs) (mg kg−1) were determined based on procedural blanks and were as follows: Cu (LOD: 0.005, LOQ: 0.015), As (LOD: 0.003, LOQ: 0.010), Cd (LOD: 0.001, LOQ: 0.003), Hg (LOD: 0.002, LOQ: 0.006), and Pb (LOD: 0.001, LOQ: 0.004). The amounts of heavy metals and As were subsequently reported in milligrams per wet weight (mg/kg ww) for Atlantic bonito.

2.3. Biochemical Assays

2.3.1. Tissue Homogenization and Protein Determination

Muscle and gill tissues were first rinsed with cold distilled water. Then, they were homogenized in ice-cold phosphate-buffered saline (PBS, pH 7.4, 1:10 w/v) using a T18 digital ULTRA-TURRAX homogenizer (IKA, Staufen, Germany). The homogenates were centrifuged at 10,000 rpm for 20 min at 4 °C. The clear supernatants that formed were then collected for biochemical analysis.

2.3.2. Determination of Malondialdehyde (MDA)

MDA concentrations were quantified using a commercial ELISA kit (Reed Biotech, Wuhan, China; Cat. No. RE10165), according to the Competitive-ELISA protocol provided by the manufacturer. This kit includes a micro-ELISA plate pre-coated with MDA. During the assay, MDA present in the supernatant samples competed with a fixed amount of MDA bound to the solid-phase support for binding sites on the biotinylated detection antibody, which showed specificity for MDA. After the removal of unbound conjugates through washing, Avidin–Horseradish Peroxidase (HRP) conjugate and TMB substrate solution were introduced into each well. The enzyme–substrate reaction was subsequently halted by the addition of a stop solution. The optical density (OD) was then assessed at 450 nm utilizing a microplate reader (REL-Microplate reader). The concentration of MDA was determined by comparing the optical density (OD) of the samples to the established standard curve, and the results were normalized to protein content, expressed as nmol MDA/mg protein.

2.3.3. Determination of Glutathione (GSH)

GSH concentrations were quantified by applying a commercially available ELISA kit (Reed Biotech, Wuhan, China; Cat. No. RE10155), which operates on the competitive ELISA principle. The micro-ELISA plate supplied within the kit was pre-coated with GSH. Throughout the assay, GSH present in the samples competed with a constant quantity of GSH immobilized on the solid phase for binding sites on the biotinylated detection antibody, which is specific to GSH. Following the removal of unbound conjugates through washing, Avidin–Horseradish Peroxidase (HRP) conjugate and TMB substrate solution were introduced to each well. The reaction was subsequently halted by the addition of a stop solution. The optical density (OD) was then assessed at 450 nm utilizing a microplate reader (REL-Microplate reader). The results were normalized to protein content and presented as nmol GSH/mg protein.

2.3.4. Determination of Total Antioxidant Status (TAS)

TAS was evaluated using a commercial ABTS assay kit (Reed Biotech, Wuhan, China; Cat. No. RBC0031), adhering to the manufacturer’s instructions. This assay capitalizes on the ability of antioxidants to neutralize the stable ABTS radical cation (ABTS.+). The ABTS.+ working solution was mixed with the sample supernatant and then incubated. The presence of antioxidants in the sample promotes the reduction of the radical cation, which in turn results in a decrease in absorbance. Optical density (OD) was subsequently measured at 405 nm using a REL microplate reader (Mega Tıp Sanayi ve Ticaret Ltd. Şti., Şehitkamil, Gaziantep, Turkey). The obtained data were calculated using the standard curve and presented as mmol Trolox equivalent/L.

2.3.5. Determination of Total Oxidant Status (TOS)

TOS was assessed utilizing a commercially available colorimetric kit (Reed Biotech, Wuhan, China; Cat. No. RBC0032). This particular assay operates on the principle that oxidants within the sample oxidize the ferrous ion (Fe2+) to its ferric form (Fe3+) within an acidic environment. Subsequently, the ferric ions interact with the chromogen, resulting in the formation of a colored complex. The color intensity, which correlates with the overall concentration of oxidant molecules, was quantified spectrophotometrically at 590 nm using a REL microplate reader (Mega Tıp Sanayi ve Ticaret Ltd. Şti., Şehitkamil, Gaziantep, Turkey). The findings were reported as micromolar hydrogen peroxide equivalent (µmol H2O2 equiv/L).

2.3.6. Calculation of Oxidative Stress Index (OSI)

OSI, an indicator of the degree of oxidative stress, was calculated as the ratio of TOS to TAS. To perform the calculation, the unit of TAS was converted to µmol/L.
OSI (Arbitrary Unit) = [TOS, μmol H2O2 equiv/L]/([TAS, mmol Trolox equiv/L] × 100).

2.4. Human Health Risk Assessment

2.4.1. Estimated Daily Intake (EDI)

EDI of heavy metals and As through the consumption of Atlantic bonito was calculated to assess potential human exposure. EDI values were estimated using metal concentrations measured in muscle tissue, as muscle represents the edible portion for humans. The calculation was performed according to the following equation:
EDI = (MC × MS)/BW
MC denotes the metal concentration in muscle tissue, quantified in milligrams per kilogram of wet weight. MS denotes the mean daily fish consumption rate of the Turkish population, measured at 0.0211 kg/day, equating to an annual consumption of 7.7 kg [25]. Moreover, BW denotes the mean adult body weight, calculated at 70 kg [26]. Since only inorganic arsenic is acknowledged as harmful, it was presumed that inorganic arsenic concentrations constituted 3% of the overall arsenic levels, a figure that aligns with prior studies [27].

2.4.2. Non-Carcinogenic Risk Assessment (THQ and HI)

Non-carcinogenic health risks associated with heavy metals and As intake were evaluated using the target hazard quotient (THQ), calculated by dividing the EDI value of each metal by its corresponding oral reference dose (RfD) established by the United States Environmental Protection Agency (USEPA) [28] and European Union (EU) [29]. The cumulative non-carcinogenic risk was assessed using the hazard index (HI), defined as the sum of individual THQ values [28,30]. A THQ or HI value lower than 1 indicates the absence of a significant non-carcinogenic health risk [31,32].
THQ = EDI/RfD
HI = THQ (Cu) + THQ (As) + THQ (Cd) + THQ (Hg) + THQ (Pb)

2.4.3. Carcinogenic Risk Assessment (CR)

Carcinogenic risk (CR) was assessed only for inorganic arsenic, as it is classified as a human carcinogen. CR values were calculated by multiplying the EDI of inorganic arsenic by the oral slope factor (SF = 1.5 mg/kg/day−1) recommended by USEPA. CR was only calculated for As, Cd, and Pb, as these metals have established Cancer Slope Factors (CSFs) provided by the USEPA [28,33]. For metals such as Cu and Hg, although they pose significant non-carcinogenic toxicity, there is currently insufficient evidence or consensus on their oral carcinogenic potency to calculate a definitive CR value, and according to international guidelines, a CR value between 10−6 and 10−4 is considered acceptable [34].
CR = EDI × SF

2.5. Statistical Analysis

Statistical analyses were conducted utilizing SPSS 26.0 and Python v3.10.12 (Python Software Foundation, Wilmington, DE, USA) analytical tools, including the SciPy v1.11.2 and Seaborn v0.12.2 libraries. Descriptive statistical techniques were utilized to summarize all biometric attributes, including length, weight, and condition factor. The data are presented as mean ± standard deviation (SD), including the minimum and maximum observed values. The Shapiro–Wilk test was performed to evaluate the normality of the data distribution. Following the violation of normality assumptions, non-parametric approaches were later utilized.
The Mann–Whitney U test was employed to assess differences in heavy metals and As concentrations and oxidative stress indicators (MDA, GSH, TAS, TOS, and OSI) between muscle and gill tissues. To ensure the reproducibility of the results and minimize experimental error, all biochemical assays were performed in triplicate as technical replicates for each tissue sample. The mean value of these three technical replicates was used for subsequent statistical analyses. Spearman’s rank correlation analysis was used to examine the associations between heavy metals (Cu, Cd, Hg, and Pb) and As accumulation and the corresponding physiological responses. The strength of these relationships was demonstrated through heatmaps and scatter plots with linear regression lines. Data are presented as mean ± standard deviation (SD) and median values. Statistical significance was established at p < 0.05, with more stringent criteria set at p < 0.01 and p < 0.001.

3. Results

3.1. Analysis of Length, Weight, and Condition Factor (K) of the Sampled Fish

The fish samples had a limited length range of 38 to 43 cm, whereas their body weights displayed a broader spectrum, ranging from 750 to 1020 g. The average overall length and body weight were determined to be 41.50 ± 1.83 cm and 892.83 ± 67.23 g, respectively. The condition factor (K), an indicator of a fish’s physiological and nutritional well-being, was computed using the formula K = 100 × W/L3, where W represents body weight (g) and L represents total length (cm) [35]. The condition factor values in this study varied from 1.06 to 1.59, with a mean of 1.26 ± 0.15. The majority of the examined fish had condition factor values above 1, signifying they were primarily in good health. Despite differences in body weight among fish of similar lengths, the high condition factor values indicate sufficient nutrition and the absence of significant physiological stress at the time of sampling. Table 1 presents the biometric measurements and condition factor values for the sampled fish. The detailed biometric data are provided in Supplementary Table S1.

3.2. Correlation Analysis Between Heavy Metal Concentrations and Morphometric Parameters

Spearman correlation analysis was conducted to evaluate the relationships between fish morphometric indices (total length, weight, and condition factor) and the concentrations of heavy metals and As (Figure 1). Cu and As showed negligible positive correlations with total length (r = 0.08 and r = 0.07, respectively) and body weight (r = 0.09 and r = 0.04, respectively), indicating the absence of a size-dependent accumulation pattern. In contrast, cadmium (Cd), mercury (Hg), and lead (Pb) exhibited weak negative correlations with both total length (r = −0.21, r = −0.25, and r = −0.26, respectively) and body weight (r = −0.21, r = −0.29, and r = −0.25, respectively), suggesting a slight decrease in metal concentrations with increasing fish size, possibly due to growth dilution effects. The condition factor (K) showed weak and inconsistent correlations with all metals, with minor positive associations observed for Cd (r = 0.13) and Hg (r = 0.16), while Cu, As and Pb displayed negligible negative relationships (r = −0.15, r = −0.04, and r = −0.03, respectively). Overall, these findings indicate that the accumulation of heavy metals and As in the sampled fish was generally independent of biometric characteristics and was more strongly influenced by environmental exposure than by individual size or body condition.

3.3. Results of Heavy Metals and as Concentrations in Muscle and Gill Tissues

The content of heavy metals and As in the muscle and gill tissues of fish specimens obtained on site was analyzed using the non-parametric Mann–Whitney U test due to the non-normal distribution of the data (Table 2, Figure 2). The research revealed statistically significant differences across the tissues, particularly for the accumulation of Cd and Cu. The mean Cd concentration was determined as 0.751 ± 0.435 mg/kg in gill tissue, whereas it was 0.207 ± 0.134 mg/kg in muscle tissue; this difference was found to be highly statistically significant (p < 0.001). Similarly, Cu levels were measured to be significantly higher in gill tissue (2.227 ± 0.702 mg/kg) compared to muscle tissue (1.191 ± 2.169 mg/kg) (p = 0.006). It should be noted that large standard deviations were observed in some metal concentrations (e.g., Cu in muscle), reflecting high intraspecific variability and the non-homogeneous nature of metal bioaccumulation in wild fish populations. The fact that gills serve as the primary entry route and active accumulation site for waterborne metal ions explains the higher load of Cu and Cd in this tissue compared to muscle. On the other hand, no statistically significant differences were detected between the two tissues regarding As (p = 0.729), Hg (p = 0.505), and Pb (p = 0.179) concentrations (p > 0.05). The inter-tissue difference for the general population was not evident, with the exception of Cu and Cd.

3.4. Comparison of Oxidative Stress and Antioxidant Status Between Muscle and Gill Tissues

The Mann–Whitney U test was utilized to evaluate oxidative stress markers (MDA, TOS) and antioxidant defense measures (GSH, TAS) in muscle and gill tissues (Table 3, Figure 3). The findings revealed statistically significant differences between the two tissue types for all evaluated biomarkers. MDA, a primary indicator of lipid peroxidation, showed a markedly significant increase in gill tissue (5.43 ± 1.86 nmol/mg) compared to muscle tissue (4.07 ± 1.63 nmol/mg) (p < 0.001). TOS in gills (11.72 ± 3.42 μmol) was substantially higher than in muscle (8.86 ± 4.09 μmol) (p = 0.003). In contrast, levels of GSH (p = 0.019) and TAS (p = 0.008), indicative of enzymatic and non-enzymatic antioxidant defense mechanisms, were considerably reduced in gill tissue compared to muscle. The data indicate that in gill tissue—a locus of considerable heavy metal buildup—oxidative damage, evidenced by increased MDA and TOS levels, is exacerbated, leading to the depletion and ultimate loss of antioxidant defenses, particularly GSH and TAS.
OSI, an indicator of the overall oxidative burden on tissues and the global oxidative equilibrium, was calculated (Table 4). The mean OSI value recorded in gill tissue (0.81 ± 0.27) was much higher than that in muscle tissue (0.47 ± 0.22) (p < 0.001). This significant rise indicates that the elevated oxidant load in the gills surpassed TAS, thus altering the oxidative balance to a pro-oxidant state.

3.5. Correlation Analysis Between Heavy Metals and As Accumulation and Oxidative Stress Biomarkers

Figure 4 illustrates a correlation heatmap showing the relationships between the accumulation of heavy metals and As and oxidative stress indicators. The data analysis reveals that Cu and Cd are the primary contributors to physiological stress, exhibiting strong positive relationships with oxidative damage markers such as MDA, TOS, and OSI. In contrast, these metals exhibit a significant negative connection with antioxidant reserves (TAS and GSH), indicating a systematic decline in the organism’s defensive mechanisms. Regression analysis and scatter plots (Figure 5) were employed to elucidate the dose–response relationship, confirming that cellular damage is directly proportional to the tissue-specific metal burden. Substantial positive linear correlations were observed between MDA and Cd (r = 0.53, p < 0.001) and Cu (r = 0.68, p < 0.01), thereby statistically validating the adverse impacts of these metals on membrane integrity through lipid peroxidation. The spatial distribution of the data points in Figure 4 reveals a clear tissue differentiation; gill samples, marked in red, predominantly aggregate in the upper-right quadrant, indicating both the greatest metal concentrations and the most significant oxidative damage. The negative regression slopes identified in the Cd against TAS and Cu vs. GSH analyses signify a linear decline in antioxidant ability with increasing metal concentrations. The significant inhibitory effect of Cd on TAS, indicated by an r-value of −0.54, suggests that metal poisoning not only enhances radical production but also significantly reduces both enzymatic and non-enzymatic defense systems. The statistical correlations indicate that MDA and GSH concentrations serve as highly sensitive and reliable indicators for evaluating the biological impacts of Cd and Cu contamination in aquatic environments. Arsenic and mercury exhibited modest to moderate associations with oxidative stress indicators, indicating a small, but notable, contribution to oxidative imbalance. Lead (Pb) had negligible relationships with oxidative stress and antioxidant measures, suggesting a limited impact on the oxidative state in this study. The data collectively suggest that Cu and Cd are the primary metals responsible for inducing oxidative stress, while the negative correlations with GSH and TAS underscore the disruption of antioxidant balance. The identified correlation patterns support the idea that the accumulation of heavy metals and As, particularly in tissues immediately exposed to environmental contaminants, leads to oxidative stress through increased oxidant production and reduced antioxidant capacity.

3.6. Results of Human Health Risk Assessment

The non-carcinogenic health risks associated with the consumption of Atlantic bonito were evaluated by calculating EDI and THQ for each investigated metal (Table 5). Based on an annual consumption rate of 7.7 kg/year and an average body weight of 70 kg, the daily intake levels were analyzed in detail. For Cu, the EDI was calculated as 0.000370 mg/kg/day, resulting in a notably low THQ of 0.0092, indicating that copper intake is far below the safety threshold. Arsenic (As), following the adjustment for its 3% inorganic fraction, yielded an EDI of 0.000066 mg/kg/day and a THQ of 0.2198. Although arsenic is a significant concern in marine species, this adjusted value demonstrates that it does not pose an individual non-carcinogenic risk.
The estimated daily intake (EDI) for mercury (Hg) was calculated to be 0.000167 mg/kg/day, with a target hazard quotient (THQ) of 0.5557. This result represented the most significant individual contribution to the risk index, although it remained considerably below the critical threshold of 1.0. Furthermore, the EDI values for cadmium (Cd) and lead (Pb) were determined to be 0.000050 mg/kg/day and 0.000015 mg/kg/day, respectively, with corresponding THQ values of 0.0502 and 0.0044.
The cumulative health risk, represented by HI, was determined by aggregating the individual THQ values of all five metals. The resultant HI of 0.8393 is below the unity criterion of 1.0, indicating that the cumulative exposure to these heavy metals and As from the consumption of Atlantic bonito does not present a substantial non-carcinogenic health risk to the adult population.

3.7. Carcinogenic Risk Assessment (CR)

The calculated cancer risk (CR) for inorganic arsenic in this investigation was determined to be 9.90 × 10−5, a value that, while within the acceptable risk range, approaches the upper limit. This finding indicates that although the carcinogenic risk associated with arsenic exposure through Atlantic bonito consumption remains within acceptable parameters, inorganic arsenic constitutes the most significant factor contributing to long-term health risks among the metals examined.
Regarding chronic exposure, the CR values for inorganic arsenic (9.90 × 10−5) and cadmium (1.90 × 10−5) were found to be within the acceptable/tolerable range (10−6 to 10−4) as defined by the USEPA; conversely, the risk associated with lead (1.28 × 10−7) was considered negligible (Table 6). Consequently, this observation indicates that, despite arsenic’s predominant contribution to the overall carcinogenic risk profile, the estimated risk linked to the consumption of Atlantic bonito remains below internationally recognized safety limits.
CR values for Cd were found to be within the acceptable risk parameters defined by the USEPA, thereby indicating a low probability of cancer risk stemming from Cd exposure through fish consumption. This observation is consistent with previous research, which has consistently demonstrated that cadmium-related carcinogenic risks from marine fish consumption are generally limited, except in instances of significantly increased exposure. Similarly, the CR values for lead were comparatively low and remained within the acceptable limits.
The assessment of carcinogenic risk indicates that inorganic arsenic is the most influential element in increasing lifetime cancer risk among the metals studied. However, CR values for As, Cd and Pb, when evaluated collectively, are still within the acceptable risk limits set by international regulatory bodies. As a result, these findings suggest that the consumption of Atlantic bonito available in Kütahya does not pose a significant carcinogenic health risk to adults. Despite this conclusion, continuous monitoring of arsenic species and other toxic metals is recommended to protect long-term consumer health.

4. Discussion

This study offers a comprehensive analysis of heavy metals and As accumulation in various tissues, oxidative stress indicators, and an evaluation of human health concerns in adult Atlantic bonito (Sarda sarda) marketed in Kütahya, Türkiye. This research integrates chemical tests, biochemical indicators, and existing risk assessment models, contributing to the increasing evidence for a comprehensive approach to seafood safety evaluation. The research demonstrated that the gill tissues of Atlantic bonito exhibited a greater propensity for heavy metals and As accumulation, specifically Cu and Cd, in contrast to muscle tissues. This discovery aligns with the fundamental physiological premise that gills, perpetually in direct contact with ambient water, serve as the primary mechanism for metal absorption from the environment.
The findings revealed distinct differences in the distribution of heavy metals and As among tissues. Arsenic (As) had the highest amounts in muscle (7.585 mg/kg) and gill (8.277 mg/kg) tissues. Marked disparities were seen in cadmium (Cd) and copper (Cu) concentrations among tissues (p < 0.001 and p = 0.006, respectively), but arsenic (As), mercury (Hg), and lead (Pb) levels exhibited uniformity among tissues. The data demonstrate that bioaccumulation is contingent upon the metal and the particular organ, aligning with prior research that indicates tissue-specific accumulation affected by environmental and physiological variables [36,37]. Contemporary reviews emphasize that essential metals such as Cu may become toxic when environmental exposure exceeds physiological regulatory capacity [38,39], a mechanism that is consistent with the oxidative stress responses observed in the present study.
The disparities in accumulation among tissues indicate the varied functions and exposure pathways of fish organs. The heightened cadmium (Cd) levels detected in gill tissues (0.751 mg/kg) compared to muscle (0.207 mg/kg) support the established notion that gills serve as the primary site for metal absorption from water, due to their large surface area and active ion exchange mechanisms [40]. Muscle tissue, including the consumable segment of the fish, generally displayed reduced metal concentrations. This trend has been recorded in previous studies on commercially important marine fish species in the Mediterranean and Türkiye; specifically, gills reflect short-term environmental exposure, while muscle tissue offers a more precise indication of long-term accumulation relevant to human consumption [41]. The metal quantities observed in muscle tissue indicate ongoing environmental exposure but do not imply acute contamination. Recent studies focusing specifically on Sarda sarda further indicate that Cd accumulation may vary with biological parameters such as size, age, and seasonal migration [42]. Although the present study was limited to samples collected in September, the elevated gill Cd levels observed may reflect seasonal pollutant inputs or localized contamination events, emphasizing the importance of temporal context in interpreting bioaccumulation data.
The negative correlation observed between fish size (length/weight) and metal concentrations (Hg, Pb, Cd) supports the “growth dilution” hypothesis described by Farkas et al. [43], where rapid somatic growth outpaces the rate of metal accumulation. Regarding biochemical responses, the significant correlation between metal burden and MDA levels indicates the induction of lipid peroxidation. As elucidated by Lushchak [44], transition metals can catalyze the Fenton reaction, generating reactive oxygen species (ROS) that deplete antioxidant defenses (such as GSH). Our results suggest that chronic exposure to these metals, even at sublethal levels, triggers oxidative stress mechanisms in Sarda sarda, similar to the toxicity patterns reported in other teleost species by Sevcikova et al. [45]. Importantly, despite detectable concentrations of toxic metals, the relatively low mean Pb and Cd levels in muscle tissue fall within ranges previously reported for commercially important fish species from the Mediterranean region and are generally considered to pose a limited risk to consumers, although the presence of measurable Hg and As highlights the necessity for continued monitoring [46,47,48].
As exhibited the greatest mean concentrations in both muscle and gill tissues. Elevated total arsenic levels in marine fish are prevalent, frequently ascribed to organic arsenic compounds, such as arsenobetaine, which are deemed non-toxic [49]. Consequently, in accordance with worldwide risk assessment protocols, this study posited that inorganic arsenic constituted 3% of the total arsenic when determining health risk indices [27]. Recent literature highlights the limitations of relying solely on total arsenic measurements and increasingly advocates for arsenic speciation using ICP-MS to reduce uncertainty in carcinogenic risk assessment and to improve long-term exposure characterization [50,51]. Consequently, the estimated daily intake (EDI) values for inorganic arsenic remained consistently below the reference dose established by the United States Environmental Protection Agency (USEPA). In contrast, the cancer risk (CR) values for arsenic were within the tolerable risk range of 10−6 to 10−4, indicating a potential long-term concern rather than an immediate health threat. Similar CR ranges for arsenic have been recorded in fish-related risk assessments conducted in Mediterranean and Turkish coastal regions, highlighting the need for continuous monitoring [41].
The observed mercury concentrations in various tissues correspond to the biomagnification of mercury, particularly methylmercury, within the tissues of predatory fish [36]. Gill tissues displayed significantly higher cadmium concentrations compared to muscle, which is in agreement with the known preference of cadmium for ion-regulating tissues and its association with metallothionein binding in fish [52]. Although muscle cadmium levels were relatively low, cadmium exposure augmented the overall carcinogenic risk, attributable to its cumulative toxicity and extended biological half-life in humans. Lead concentrations were low across both tissue types and showed no significant tissue-specific differences; nonetheless, lead was considered in carcinogenic risk assessments in accordance with USEPA guidelines, given its established carcinogenic properties. Mercury levels in muscle tissue were within acceptable limits, remaining below established human consumption guidelines. Methylmercury, the predominant mercury species in marine fish, often varies in concentration based on the fish’s trophic position, size, and age [53]. The Hg levels observed in this investigation are comparable to those reported for Atlantic bonito and similar pelagic species in the Mediterranean, suggesting minimal health concerns for adult consumers under typical dietary habits. The varied distribution patterns within tissues underscore the necessity of analyzing different tissues when comprehensively evaluating environmental and dietary concerns. Concentrations detected in the gills may signify environmental contamination, but those in muscle tissue are directly associated with the potential danger of human exposure.
The present study investigated oxidative stress biomarkers to determine sublethal biological effects, together with metal accumulation. Increased MDA levels indicated enhanced lipid peroxidation, while alterations in GSH, TAS, and TOS reflected an imbalance between pro-oxidant and antioxidant systems. The data suggest that extended exposure to a mixture of metals may induce oxidative stress, despite tissue metal concentrations remaining within acceptable limits. Cd and Cu are known to induce oxidative stress through multiple mechanisms, including mitochondrial dysfunction, disruption of antioxidant enzyme activity, and depletion of glutathione-dependent defense systems. Recent experimental and field-based studies emphasize that oxidative stress represents one of the earliest cellular responses to metal exposure and may occur even when tissue metal concentrations remain within regulatory limits [54,55]. The significant increase in MDA observed in gill tissues in the present study suggests enhanced lipid peroxidation and potential compromise of membrane integrity, consistent with these mechanistic frameworks. Previous studies have shown that oxidative stress biomarkers can serve as early indicators of environmental pollution, possibly preceding detectable pathological changes in aquatic organisms [56,57]. The simultaneous detection of metal concentrations and oxidative stress responses in Atlantic bonito supports the hypothesis that combined exposure to multiple metals can induce synergistic effects at the biochemical level, thereby emphasizing the necessity of integrating chemical and biological endpoints within ecotoxicological evaluations. These biochemical responses signify environmental stress and adversely affect fish health; persistent oxidative stress can interfere with physiological processes, impede growth, and weaken immune function. Although the present study primarily examined correlations between individual metals (Cu and Cd) and oxidative stress biomarkers, it is important to consider that fish in natural aquatic environments are simultaneously exposed to complex mixtures of metals rather than isolated elements. Numerous experimental and field-based studies have demonstrated that combined exposure to multiple metals may result in additive or synergistic effects on oxidative stress responses, even when individual metal concentrations remain below established toxicity thresholds [52,56]. In particular, co-exposure to redox-active metals such as Cu and non-essential metals such as Cd has been shown to intensify reactive oxygen species production and antioxidant depletion through overlapping mechanisms, including disruption of mitochondrial function, competition for metallothionein binding sites, and depletion of glutathione-dependent defense systems [39]. Moreover, the presence of additional elements such as As and Hg may further modulate oxidative stress responses by interfering with cellular redox signaling and antioxidant enzyme activity, thereby amplifying cumulative oxidative damage [54,55]. Therefore, the elevated oxidative stress observed in gill tissues in the present study likely reflects not only the influence of individual metals but also the combined burden of multiple co-occurring elements. This highlights the importance of interpreting oxidative stress biomarkers within a mixture-based framework when assessing metal contamination under real environmental conditions. Consequently, incorporating oxidative stress biomarkers alongside chemical analyses improves the interpretation of sublethal effects and augments risk assessment methodologies.
The findings were further elucidated through a human health risk assessment, which incorporated EDI, THQ, HI, and CR metrics. Public health implications were considered, and the computed EDI, THQ, and HI values for all examined heavy metals and As were found to be below established threshold levels. This suggests that the consumption of Atlantic bonito, available in Kütahya, does not present a substantial risk to adult consumers. Mercury exhibited the highest individual contribution among the toxic metals (THQ = 0.5557), yet remained below the risk threshold, consistent with reports for pelagic fish species from the Black Sea and Mediterranean regions where Hg frequently represents the dominant contributor to cumulative risk indices [58,59]. Nevertheless, the detection of carcinogenic risk values within acceptable limits for As, Cd, and Pb underscores the need for ongoing monitoring, especially considering the bioaccumulative characteristics of these elements. Recent market-based assessments in Türkiye suggest that arsenic-related carcinogenic risk may become more relevant under high-frequency consumption scenarios or for sensitive subpopulations, such as children [60]. These observations highlight the importance of considering dietary habits, consumption frequency, and demographic factors in comprehensive risk assessment frameworks.
Nevertheless, recent market-based assessments conducted in Türkiye emphasize that arsenic may represent a comparatively more relevant driver of potential carcinogenic risk, particularly under high-frequency consumption scenarios or for sensitive subpopulations such as children, even when non-carcinogenic indices remain within acceptable limits [60]. Similar conclusions have been drawn from broader Mediterranean studies, highlighting that demographic factors, dietary habits, and consumption frequency critically influence health risk interpretation beyond mean concentration values alone [61]. Collectively, these findings underscore the importance of incorporating consumption scenarios and population-specific exposure patterns when translating metal concentrations in fish into public health risk assessments.
The present investigation indicates that, despite the safety of the examined fish samples for current dietary practices, the observed patterns of metal accumulation and the resultant oxidative stress responses suggest continued environmental exposure. These findings highlight the imperative of establishing regular monitoring programs. Such programs should integrate both chemical analyses and biological biomarkers to facilitate a more thorough assessment of seafood safety and the long-term health of the environment.

5. Conclusions

This investigation focused on tissue-specific accumulation of heavy metals and As and the corresponding oxidative stress profiles in adult Atlantic bonito (Sarda sarda) sourced from Kütahya, Türkiye. Our analysis reveals a distinct compartmentalization of metals; specifically, the gills functioned as the primary site for Cu and Cd sequestration, whereas the muscle tissue—representing the edible portion—maintained significantly lower concentrations. While As was identified as the most prevalent element, risk assessments based on the inorganic fraction (fixed at 3%) confirmed that estimated daily intake levels remain safely below established reference doses. Furthermore, the calculated carcinogenic risk (CR) values for As, Cd, and Pb fall within internationally recognized safety thresholds, indicating no immediate health threat to the adult population. Crucially, however, the observed perturbations in biochemical markers, such as MDA, GSH, TAS, and TOS, suggest that cumulative metal exposure may induce subclinical oxidative stress even when concentrations remain within regulatory limits. These findings emphasize that chemical analysis alone may be insufficient; a comprehensive seafood safety framework must integrate biological indicators. In conclusion, while current consumption of Atlantic bonito in this region poses no significant health risk, the presence of subclinical physiological shifts necessitates rigorous, long-term monitoring and specialized studies on arsenic speciation to ensure sustained public health.
It is important to note that this study relies on a relatively limited sample size collected from a single location. Therefore, while these findings provide valuable local data regarding heavy metals and As accumulation and potential health risks, they should be interpreted as a preliminary assessment. Future studies with larger sample sizes and broader geographical sampling are recommended to further generalize these findings to the wider Atlantic bonito population.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biology15040341/s1, Table S1: Biometric data of Atlantic bonito samples.

Author Contributions

Conceptualization, G.K. and Ö.N.E.; methodology, G.K. and Ö.N.E.; validation, G.K. and Ö.N.E.; formal analysis, G.K.; investigation, G.K. and Ö.N.E.; data curation, G.K.; writing—original draft preparation, G.K. and Ö.N.E.; writing—review and editing, G.K. and Ö.N.E.; supervision, G.K.; funding acquisition, G.K. and Ö.N.E. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Kütahya Dumlupınar University Scientific Research Projects Coordination Office under grant number #2024–60.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The data presented in this article are derived from the thesis work of Özge Nur Ekiz, conducted at the Institute of Graduate Education of Kütahya Dumlupınar University.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
MDAMalondialdehyde
TOSTotal Oxidant Status
TASTotal Antioxidant Status
GSHGlutathione
OSIOxidative Stress Index
THQTarget Hazard Quotient
HIHazard Index
CRCarcinogenic Risk
ROSReactive Oxygen Species
ICP-MSInductively Coupled Plasma Mass Spectrometer
KEDKinetic Energy Discrimination
HRPAvidin–Horseradish Peroxidase
ODOptical Density
EDIEstimated Daily Intake
USEPAUnited States Environmental Protection Agency
EUEuropean Union
CSFCancer Slope Factors
SDStandard Deviation
KCondition Factor

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Biology 15 00341 g001
Figure 1. Spearman correlation heatmap showing the relationships between the concentrations of heavy metals (Cu, Cd, Hg, and Pb) and As and morphometric parameters (total length, weight, and condition factor) in Sarda sarda. The color scale indicates the strength and direction of the correlation: red represents a positive correlation, while blue indicates a negative correlation. The values within the cells represent the correlation coefficients (r).
Figure 1. Spearman correlation heatmap showing the relationships between the concentrations of heavy metals (Cu, Cd, Hg, and Pb) and As and morphometric parameters (total length, weight, and condition factor) in Sarda sarda. The color scale indicates the strength and direction of the correlation: red represents a positive correlation, while blue indicates a negative correlation. The values within the cells represent the correlation coefficients (r).
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Figure 2. Tissue-specific concentrations of heavy metals (Cu, Cd, Hg, and Pb) and As (mg kg−1) in Atlantic bonito (Sarda sarda). Data are presented as mean ± SD. Asterisks indicate statistical significance between muscle and gill tissues (** p < 0.01, *** p < 0.001). Cu: copper, As: arsenic, Cd: cadmium, Hg: mercury, Pb: lead.
Figure 2. Tissue-specific concentrations of heavy metals (Cu, Cd, Hg, and Pb) and As (mg kg−1) in Atlantic bonito (Sarda sarda). Data are presented as mean ± SD. Asterisks indicate statistical significance between muscle and gill tissues (** p < 0.01, *** p < 0.001). Cu: copper, As: arsenic, Cd: cadmium, Hg: mercury, Pb: lead.
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Figure 3. Comparison of biochemical oxidative stress parameters in the muscle and gill tissues of Atlantic bonito (Sarda sarda). (A) MDA: malondialdehyde concentration; (B) TOS: total oxidant status; (C) GSH: reduced glutathione concentration; (D) TAS: total antioxidant status. Data are expressed as mean ± SD. Asterisks indicate statistical significance between tissues based on the Mann–Whitney U test (* p < 0.05, ** p < 0.01, *** p < 0.001).
Figure 3. Comparison of biochemical oxidative stress parameters in the muscle and gill tissues of Atlantic bonito (Sarda sarda). (A) MDA: malondialdehyde concentration; (B) TOS: total oxidant status; (C) GSH: reduced glutathione concentration; (D) TAS: total antioxidant status. Data are expressed as mean ± SD. Asterisks indicate statistical significance between tissues based on the Mann–Whitney U test (* p < 0.05, ** p < 0.01, *** p < 0.001).
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Figure 4. Heatmap illustrating Spearman’s rank correlation coefficients between the concentrations of heavy metals (Cu, Cd, Hg, and Pb) and As and oxidative stress/antioxidant parameters (MDA, TOS, OSI, GSH, and TAS). The color scale indicates the strength and direction of the correlation (red: positive correlation, blue: negative correlation).
Figure 4. Heatmap illustrating Spearman’s rank correlation coefficients between the concentrations of heavy metals (Cu, Cd, Hg, and Pb) and As and oxidative stress/antioxidant parameters (MDA, TOS, OSI, GSH, and TAS). The color scale indicates the strength and direction of the correlation (red: positive correlation, blue: negative correlation).
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Figure 5. Scatter plots and linear regression analysis demonstrating the relationship between significant heavy metal and As concentrations and oxidative stress/antioxidant parameters. The scatter points represent individual data samples from muscle (blue circles) and gill (red circles) tissues. The dashed lines indicate the linear regression trend for the entire dataset. Panels show correlations between (A) Cd and MDA, (B) Cu and MDA, (C) Cd and TAS, and (D) Cu and GSH. Spearman’s correlation coefficient (r) and significance level (p) are provided in each plot. Units: Cd and Cu (mg/kg), MDA (nmol/mg), GSH (nmol/mg), TAS (mmol).
Figure 5. Scatter plots and linear regression analysis demonstrating the relationship between significant heavy metal and As concentrations and oxidative stress/antioxidant parameters. The scatter points represent individual data samples from muscle (blue circles) and gill (red circles) tissues. The dashed lines indicate the linear regression trend for the entire dataset. Panels show correlations between (A) Cd and MDA, (B) Cu and MDA, (C) Cd and TAS, and (D) Cu and GSH. Spearman’s correlation coefficient (r) and significance level (p) are provided in each plot. Units: Cd and Cu (mg/kg), MDA (nmol/mg), GSH (nmol/mg), TAS (mmol).
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Table 1. Descriptive statistics of length, weight, and condition factor (K) of the sampled fish (n = 24).
Table 1. Descriptive statistics of length, weight, and condition factor (K) of the sampled fish (n = 24).
ParameterMeanStandard DeviationMinimumMaximum
Length (cm)41.501.833843
Weight (g)892.8367.237501020
Condition factor (K)1.260.151.061.59
n represents the number of individual fish analyzed (n = 24). All biometric parameters were summarized using descriptive statistical methods.
Table 2. Statistical comparison of heavy metals and As levels between muscle and gill tissues.
Table 2. Statistical comparison of heavy metals and As levels between muscle and gill tissues.
TissueStatisticCuAsCdHgPb
MuscleMean1.1917.5850.2070.5840.051
SD±2.169±1.658±0.134±0.449±0.101
GillMean2.2278.2770.7510.4910.024
SD±0.702±2.433±0.435±0.252±0.014
Mann–Whitneyp-value0.006 **0.729 (ns)0.000 ***0.505 (ns)0.179 (ns)
(ns: not significant, **: p < 0.01, ***: p < 0.001).
Table 3. Comparison of oxidative stress (MDA, TOS) and antioxidant (GSH, TAS) parameters between muscle and gill tissues.
Table 3. Comparison of oxidative stress (MDA, TOS) and antioxidant (GSH, TAS) parameters between muscle and gill tissues.
ParameterTissueMean ± SDMedianp-Value
MDA (nmol/mg)Muscle4.07 ± 1.633.93<0.001 ***
Gill5.43 ± 1.865.00
TOS (μmol)Muscle8.86 ± 4.097.450.003 **
Gill11.72 ± 3.4210.45
GSH (nmol/mg)Muscle10.53 ± 2.6510.700.019 *
Gill8.21 ± 2.108.95
TAS (mmol)Muscle1.89 ± 0.381.830.008 **
Gill1.46 ± 0.311.55
Data are expressed as mean ± standard deviation (SD) and median. Statistical significance was determined using the Mann–Whitney U test (*: p < 0.05, **: p < 0.01, ***: p < 0.001).
Table 4. Comparison of oxidative stress index (OSI) values between muscle and gill tissues.
Table 4. Comparison of oxidative stress index (OSI) values between muscle and gill tissues.
ParameterTissueMean ± SDMedianp-Value
OSI (Arbitrary Unit)Muscle0.47 ± 0.220.41<0.001 ***
Gill0.81 ± 0.270.75
Values are presented as mean ± SD and median. Statistical significance was determined using the Mann–Whitney U test (***: p < 0.001).
Table 5. Non-carcinogenic health risk parameters (EDI, THQ, and HI) for adults via consumption of Atlantic bonito.
Table 5. Non-carcinogenic health risk parameters (EDI, THQ, and HI) for adults via consumption of Atlantic bonito.
MetalMean Conc. (MC) (mg/kg)Adjusted MC (As %3)EDI (mg/kg/day)RfDTHQ
Copper (Cu)1.2751.2750.0003700.04000.0092
Arsenic (As)7.5830.22750.0000660.00030.2198
Mercury (Hg)0.5750.5750.0001670.00030.5557
Cadmium (Cd)0.1730.1730.0000500.00100.0502
Lead (Pb)0.0530.0530.0000150.00350.0044
HI (Total) 0.8393
Table 6. Carcinogenic risk (CR) estimates for adults associated with the ingestion of heavy metals and As in Atlantic bonito.
Table 6. Carcinogenic risk (CR) estimates for adults associated with the ingestion of heavy metals and As in Atlantic bonito.
MetalEDI (mg/kg/day)CSF (mg/kg/day)−1CRRisk Level
Arsenic (As)6.60 × 10−51.59.90 × 10−5Acceptable
Cadmium (Cd)5.00 × 10−50.381.90 × 10−5Acceptable
Lead (Pb)1.50 × 10−50.00851.28 × 10−7Negligible
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Ekiz, Ö.N.; Karabulut, G. Tissue-Specific Accumulation of Heavy Metals and Oxidative Stress in Atlantic Bonito (Sarda sarda, Bloch 1793) Marketed in Kütahya. Biology 2026, 15, 341. https://doi.org/10.3390/biology15040341

AMA Style

Ekiz ÖN, Karabulut G. Tissue-Specific Accumulation of Heavy Metals and Oxidative Stress in Atlantic Bonito (Sarda sarda, Bloch 1793) Marketed in Kütahya. Biology. 2026; 15(4):341. https://doi.org/10.3390/biology15040341

Chicago/Turabian Style

Ekiz, Özge Nur, and Gözde Karabulut. 2026. "Tissue-Specific Accumulation of Heavy Metals and Oxidative Stress in Atlantic Bonito (Sarda sarda, Bloch 1793) Marketed in Kütahya" Biology 15, no. 4: 341. https://doi.org/10.3390/biology15040341

APA Style

Ekiz, Ö. N., & Karabulut, G. (2026). Tissue-Specific Accumulation of Heavy Metals and Oxidative Stress in Atlantic Bonito (Sarda sarda, Bloch 1793) Marketed in Kütahya. Biology, 15(4), 341. https://doi.org/10.3390/biology15040341

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