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Article

Blood Concentration of Macro- and Microelements in Women Who Are Overweight/Obesity and Their Associations with Serum Biochemistry

by
Zuzana Knazicka
1,
Maros Bihari
1,
Ivona Janco
2,
Lubos Harangozo
3,
Julius Arvay
3,
Anton Kovacik
4,
Peter Massanyi
4,
Branislav Galik
1,
Jorge M. A. Saraiva
5 and
Marta Habanova
1,*
1
Institute of Nutrition and Genomics, Faculty of Agrobiology and Food Resources, Slovak University of Agriculture, Trieda Andreja Hlinku 2, 94976 Nitra, Slovakia
2
AgroBioTech Research Center, Slovak University of Agriculture, Trieda Andreja Hlinku 2, 94976 Nitra, Slovakia
3
Institute of Food Sciences, Faculty of Biotechnology and Food Sciences, Slovak University of Agriculture, Trieda Andreja Hlinku 2, 94976 Nitra, Slovakia
4
Institute of Applied Biology, Faculty of Biotechnology and Food Sciences, Slovak University of Agriculture, Trieda Andreja Hlinku 2, 94976 Nitra, Slovakia
5
LAQV-REQUIMTE, Department of Chemistry, Campus Universitário de Santiago, University of Aveiro, 3810-193 Aveiro, Portugal
*
Author to whom correspondence should be addressed.
Life 2024, 14(4), 465; https://doi.org/10.3390/life14040465
Submission received: 15 February 2024 / Revised: 27 March 2024 / Accepted: 28 March 2024 / Published: 2 April 2024
(This article belongs to the Special Issue Obesity: Precision Diagnosis, Personalised Treatment, Patient Engagement and Recognition as a Disease)
Versions Notes

Abstract

:
Risk elements in blood matrices can affect human health status through associations with biomarkers at multiple levels. The aim of this study was to analyze 15 macro- and microelements in the blood serum of women with overweight (BMI of ≥25 kg/m2) and obesity (BMI of ≥30 kg/m2) and to examine possible associations with biochemical, liver enzymatic parameters, and markers of oxidative stress. Based on the power calculation, the study involved women (in the postmenopausal stage) with overweight (n = 26) and obesity (n = 22), aged between 50–65 years. Multifrequency bioelectrical impedance analysis was used to measure body composition parameters. Concentrations of elements were determined by inductively coupled plasma optical emission spectrometry, and Hg was measured using cold-vapor atomic absorption spectroscopy. Individuals with obesity, as indicated by a higher BMI, percentage of body fat, and visceral fat area, had elevated serum levels of Ca, Mg, Fe, Al, Sr, Pb, and Hg. Concentrations of Al, Cu, K, Sb, Zn, and Pb significantly affected biochemical and liver function markers in women with overweight or obesity. Elements such as Cu and Al were associated with increased total cholesterol. The correlation analysis between total antioxidant status and Cu, Al, and Ni confirmed associations in both groups. Our findings underscore the importance of addressing excess body weight and obesity in relation to risk elements. The results of the research could be beneficial in identifying potential targets for the treatment or prevention of comorbidities in people with obesity.

1. Introduction

Investigation of heavy or trace elements has remained one of the primary focuses of toxicological studies in recent years [1,2,3,4]. Elements can interact with various biomarkers at multiple levels [5,6]. The human body utilizes inorganic compounds, including mineral elements, to support a variety of biological and physiological processes [7,8]. Elements can be classified into essential and non-essential (or toxic) based on their health effects [9]. Essential elements crucial for human nutrition include potassium (K), sodium (Na), chloride (Cl), calcium (Ca), selenium (Se), manganese (Mn), iodine (J), chromium (Cr), cobalt (Co), molybdenum (Mo), fluorine (F), nickel (Ni), silicon (Si), iron (Fe), copper (Cu), zinc (Zn), and boron (B). These elements serve as crucial cofactors in the structure of specific enzymes and are indispensable in various biochemical processes [4,8]. They are divided into two categories: macroelements [K, Na, Ca, magnesium (Mg), and phosphorus (P)], which are present in concentrations exceeding 0.01% of total body mass and mediate structural and regulatory functions such as fluid balance, bone and tooth formation, nerve transmission, and oxygen transport [8,10]. The second group, microelements (or trace elements) in adults, are present in much lower concentrations yet play crucial roles in numerous metabolic processes and in maintaining healthy immune functions [10]. Non-essential elements such as lead (Pb), arsenic (As), cadmium (Cd), and mercury (Hg) exhibit toxic effects even at very low concentrations, posing a significant risk to human health [9,11,12]. Although each toxic element exhibits unique toxicological properties, common symptoms include oxidative damage, disruption in cellular and enzymatic mechanisms, and the formation of adducts with deoxyribonucleic acid or proteins [13]. Deviations from optimal levels of elements can correlate with a variety of diseases. Thus, understanding the associations of elements in biological matrices may enhance the diagnosis and treatment of diseases. Developing reliable analytical methods to determine macro- and microelement levels in samples is crucial for creating biological models that deepen our understanding of the relationship between elements and diseases [14].
Obesity and associated metabolic disorders are global public health concerns [15,16,17]. Currently, the prevalence of obesity is increasing across all age groups and in both sexes, irrespective of geographical location, ethnicity, or socioeconomic status [18]. According to current trends, it is predicted to affect more than one billion adults, or one-sixth of the world’s population, by 2030 [19]. The World Obesity Federation’s Global Obesity Observatory [20] has provided a comprehensive overview of rates of overweight and obesity in the Slovak population from 2000 to 2019. The prevalence of overweight among Slovak adults (aged 18 years and older) reached approximately 39% in 2019, with obesity affecting more than 19% of this population. Among these, the rates of obesity were 20.80% for men and 18.70% for women.
The World Health Organization (WHO) defines obesity and overweight as abnormal or excessive body fat accumulation that may have negative health effects [21]. Discussing obesity, it is essential to introduce the concept of Body Mass Index (BMI), with a BMI of ≥25 kg/m2 generally considered overweight and a BMI of ≥30 kg/m2 classified as obesity [22,23]. This index is the most commonly used criterion for identifying individuals with overweight or obesity [24], and it is applicable to both sexes and all adult age groups. However, it should be considered basic guidance, as it does not capture the proportion of fat mass and fat-free mass or the changes in these compartments among individuals [25,26,27]. Similarly, Wu et al. [28] and Bihari et al. [24] reported that evaluating adults with overweight or obesity requires not only BMI but also other diagnostic elements. The percentage of body fat (PBF; %) may offer a more reliable and accurate indicator compared to BMI when assessing overweight or obesity [29,30,31]. Furthermore, attention should also be focused on the visceral fat area (VFA; cm2), which appears to be a valuable parameter for diagnosing multifactorial diseases and assessing associated health risks [24,27,32]. For most of the population, obesity results from a complex interplay of an individual’s genetic predisposition and various biological, behavioral, psychosocial, socioeconomic, and environmental factors (including chronic stress) [33]. High-energy-dense diets, sedentary lifestyles, low physical activity, and eating disorders are key risk factors [34]. Furthermore, macro- and micronutrients have been identified as crucial in regulating metabolic processes, contributing to the etiology of this disease [35]. Obesity is associated with a wide range of comorbidities, such as cardiovascular disease, hypertension, stroke, various chronic diseases, including type 2 diabetes mellitus, gallbladder disorders, dyslipidemia, osteoarthritis, gout, and various pulmonary diseases, notably sleep apnea, among many other health complications. Additionally, a link has been identified between obesity and several types of cancer [23,36,37,38,39].
Blood analysis serves as a valuable alternative for evaluating health status [5]. Parameters, such as serum biochemistry and enzymatic markers, may offer insights into the physiological responses of the organism in relation to various elements [6]. To our knowledge, there are currently a limited number of human blood studies involving participants with overweight or obesity and that have utilized inductively coupled plasma optical emission spectrometry (ICP-OES) for the analysis of risk elements. Our primary goal was to determine the concentrations of 15 macro- and microelements in the blood serum of women with overweight/obesity (in the postmenopausal phase) and to investigate potential associations between selected markers and risk elements. Specifically, we focused on the quantitative composition of these essential, potentially toxic, and toxic elements (mg/mL): aluminum (Al), barium (Ba), Ca, Cu, Fe, K, Mg, Na, Ni, Pb, antimony (Sb), Se, strontium (Sr), and Zn, using ICP-OES. Furthermore, we determined the total Hg concentration (ng/µL) using cold-vapor atomic absorption spectroscopy (CV-AAS). Our subsequent objective was to assess anthropometric parameters (body weight, body height, BMI, PBF, VFA, and systolic and diastolic blood pressures), biochemical and enzymatic parameters [serum total cholesterol (TC), glucose, aspartate aminotransferase (AST), alanine aminotransferase (ALT), γ-glutamyltransferase (GGT), total bilirubin (tbil), direct/conjugated bilirubin (dbil)], and total antioxidant status (TAS).

2. Materials and Methods

2.1. Characteristics of Participants

Before recruiting subjects, we conducted a power analysis to determine the necessary number of volunteers, ensuring that the obtained results would be meaningful. We aimed for a total sample size with sufficient power (80%). The present study included 51 volunteers, adult women from the staff of the Slovak University of Agriculture (SUA) in Nitra (Slovakia), who underwent eligibility screening for participation in the study. Forty-eight (n = 48) non-medicated women with overweight (n = 26) and obesity (n = 22), with a mean age of 56.92 years, were enrolled in the study. The volunteers had to meet the following inclusion criteria: an age range of 50–65 years (in the postmenopausal stage), a BMI of ≥25 kg/m2 (women with overweight), and a BMI of ≥30 kg/m2 (women with obesity) as defined by the WHO [21,22]. Additionally, participants were required to maintain a constant body weight (±3 kg) over the last 3 months and limit alcohol consumption to ≤30 g/day. Individuals were excluded if they had a history of diabetes mellitus, cardiovascular and cerebrovascular diseases, uncontrolled hypertension treated with medication, were using medication for weight loss, cholesterol-lowering medications or supplements, had gastrointestinal tract disorders, undergone gastrointestinal surgery, had chronic hepatitis, renal diseases, cancer, thyroid abnormalities, were regular smokers, or had alcohol or drug addiction. Of the total registered volunteers (n = 51), three participants did not meet these criteria and were subsequently excluded from the study.
This study was carried out according to the guidelines of the Declaration of Helsinki and approved by the Ethical Committee of the Specialized Hospital of St. Svorad Zobor in Nitra, Slovakia (protocol No. 4/071220/2020), as well as by the SUA in Nitra, Institute of Nutrition and Genomics, Slovakia. All participants provided written informed consent prior to their participation in the study.

2.2. Anthropometric Measurements

Trained personnel carried out anthropometric measurements. Body weight (kg) and height (cm) were measured using outpatient electronic medical scales (Tanita WB-3000, Tanita Co., Tokyo, Japan). BMI was calculated by dividing the body weight in kilograms (kg) by the square of the height in meters (m2). PBF (%) and VFA (cm2) were determined using multifrequency bioelectrical impedance analysis (MF-BIA) with the InBody 720 analyzer (Biospace Co., Ltd., Seoul, Republic of Korea). Participants were provided with information regarding the procedure and informed about the risks of MF-BIA measurement in the case of an electrical device implanted in the body on the heart. The measurements were conducted in a controlled laboratory setting, adhering to the manufacturer’s guidelines. Systolic and diastolic blood pressures (mm Hg) were measured in duplicate using the digital upper arm electronic monitor (Omron M7 Intelli IT, HEM-7361T-EBK, Omron Healthcare Co., Ltd., Tokyo, Japan). Participants were instructed to maintain a seated and calm posture, ensuring they had rested for a minimum of 15 min prior to each measurement [29,40]. All anthropometric characteristics were recorded as the average values.

2.3. Biological Material

The blood (n = 48) for biochemical analysis was collected in the morning after 8 h of fasting from the participants. Venous blood from the peripheral vein was collected in a standard manner by a qualified person using 2.50 mL tubes containing ethylenediaminetetraacetic acid (EDTA) and 7.50 mL serum gel tubes. Once the blood fractions were separated and centrifuged in serum gel tubes at 1800 rpm for 15 min at 10 °C (Hettich® MIKRO 220R, Andreas Hettich GmbH & Co., Tuttlingen, Germany), the blood serum samples were stored at −80 °C until analysis [40].

2.4. Analysis of Biochemical Parameters

The selected biochemical markers, such as serum TC (mmol/L), glucose (mmol/L), and enzymatic parameters including AST (µkat/L), ALT (µkat/L), GGT (µkat/L), tbil (µmol/L), and dbil (µmol/L), were determined in the biochemical laboratory of the Specialized Hospital of St. Svorad Zobor in Nitra (Slovakia). These parameters were analyzed using standard commercial DiaSys kits (Diagnostic Systems GmbH, Holzheim, Germany) on the ultra-compact automated clinical-chemistry analyzer BioMajesty® JCA-BM6010/C (JEOL Ltd., Tokyo, Japan). TAS (mmol/L) was determined from thawed samples and analyzed in the biochemical laboratory of the Institute of Nutrition and Genomics (SUA in Nitra, Slovakia), using a Biolis 24i Premium (Tokyo Boeki Machinery Ltd., Tokyo, Japan) with commercially available diagnostic Randox reagents (Randox Laboratories Ltd., Crumlin, UK).

2.5. Analysis of Elements in Blood Serum

The concentrations of macro- and microelements (Al, Ba, Ca, Cu, Fe, K, Mg, Na, Ni, Pb, Sb, Se, Sr, and Zn) in the blood serum were determined by the ICP-OES.
Initially, a pre-analytical procedure was conducted. All chemicals utilized in sample preparation were of high purity and intended for trace-select analysis. The samples (1.0 mL) were mineralized in a high-performance microwave digestion system ETHOS-One (Milestone Srl., Sorisole, BG, Italy), in a solution of 5.0 mL nitric acid (HNO3; ≥69%; for trace analysis; from Lambda Life s r.o. Bratislava, Slovakia; producer: Sigma-Aldrich Chemie GmbH, Steiheim, Germany) and 1.0 mL hydrogen peroxide (H2O2; ≥30%; for trace analysis; from Lambda Life s r.o. Bratislava, Slovakia; producer: Sigma-Aldrich Chemie GmbH, Steiheim, Germany), which were added directly to the PTFE vessels. The obtained samples, including the blank sample, underwent digestion following the manufacturer’s recommended tissue method to ensure the attainment of the most dependable results. The method included heating and cooling phases (the heating phase: heating to 200 °C over 15 min and maintaining at 200 °C for 15 min; the cooling phase: active cooling to achieve a temperature of 50 °C in 15 min. Through a quantitative Whatman® filter paper Grade No. 595 (basis weight 68 g/m2, thickness 150 μm, pore size 4–7 μm; VWR International, Leuven, France), the digested samples were filtered into 50 mL volumetric flasks and filled with deionized water (ddH2O; 18.2 MΩ cm−1; 25 °C, Synergy UV, Merck Millipore, Guyancourt, France) to the final volume. Sample solutions were stored in polyethylene tubes until ICP-OES analysis [4,41].
Micro- and macroelements in the blood serum were quantified using an inductively coupled plasma-optical emission spectrometer Agilent ICP-OES 720 (Agilent Technologies Inc., Santa Clara, CA, USA), with an axial plasma configuration equipped, and an auto-sampler, the SPS-3 (Agilent Technologies, GmbH, Basel, Switzerland). The detailed experimental conditions are described in Table 1. Limits of detection (LoD; μg/L) for measured elements in serum (mg/mL) were as follows: Ag 0.30; Al 0.20; As 1.50; Ba 0.03; Ca 0.01; Cd 0.05; Co 0.20; Cr 0.15; Cu 0.30; Fe 0.10; K 0.30; Li 0.06; Mg 0.01; Mn 0.03; Na 0.15; Ni 0.30; Pb 0.80; Sb 2.00; Se 2.00; Sr 0.01; and Zn 0.20. The limit of quantification (LoQ; μg/L) for measured elements in serum (mg/mL) were as follows: Ag 0.99; Al 0.66; As 4.95; Ba 0.10; Ca 0.03; Cd 0.17; Co 0.66; Cr 0.50; Cu 0.99; Fe 0.33; K 0.99; Li 0.20; Mg 0.03; Mn 0.10; Na 0.50; Ni 0.99; Pb 2.64; Sb 6.60; Se 6.60; Sr 0.03; and Zn 0.66. All samples were analyzed in triplicate for the concentration of 21 elements. In the study, Multielement Standard Solution V for ICP (Sigma-Aldrich Production, GmbH, Buchs, Switzerland) was used. Argon and carbon were used as internal standard elements. The validity of the entire method was confirmed through the use of certified reference material (CRM) ERM®-CE278k (muscle tissue; IRMM, Geel, Belgium). The results of the analysis were expressed in mg/mL.
The total Hg concentration in blood serum samples was determined using CV-AAS on the AMA 254 Hg analyzer (Altec spol. s r.o., Prague, Czech Republic), with a detection limit of 1.50 ng/kg DM (dry matter). The analysis was performed directly on the sample (100 µL) without pretreatment. Quantitative Hg determination occurred at λ = 253.65 nm. CRM materials ASTASOL® (Czech Metrology Institute, Brno, Czech Republic; CZ 9024 (1N) Hg) were assessed to verify the quality of the measurements. The CRM measurement was performed six times [42]. The obtained results were expressed as ng/µL.

2.6. Statistical Analysis

Power calculations were used to determine the appropriate number of participants for the study using G*Power 3.1.9.7 software [43]. The minimum power (1-β error probability, with α = 0.05) required for our study was set at 80%. Before conducting statistical analysis, the collected data underwent normality testing using the Kolmogorov-Smirnov test. Results are presented as the arithmetic mean ± standard deviation (SD). Other descriptive characteristics are also presented (minimum, maximum, and coefficient of variation). Thereafter, significant differences between selected groups were evaluated using an unpaired t-test for parametric data. The Spearman R correlations were used to determine mutual associations between element levels and all parameters tested in the blood serum of participants. We also evaluated the relationships between macro- and microelements as well as biomarkers in groups of women with overweight or obesity. Statistical software GraphPad Prism 6.01 (GraphPad Software Incorporated, San Diego, CA, USA) and STATGRAPHICS Centurion (© StatPoint Technologies, Inc., Warrenton, VA, USA) were used for statistical evaluation. Statistical significance was set at three levels: *** (p < 0.001); ** (p < 0.01); * (p < 0.05).

3. Results

The study included 48 women, ranging in age from 50 to 65 years, with a mean age of 56.92 years. The descriptive characteristics of all participants are presented in Table 2. Regarding body composition parameters, the average values of PBF and VFA calculated for all participants did not fall below the reference range. In the assessment of PBF, it was observed that women with obesity had higher values compared to those classified as overweight (44.86 ± 4.31% and 38.74 ± 2.83%, respectively, p < 0.001). The average values of VFA were 120.03 ± 13.08 cm2 for women who were overweight and 148.90 ± 18.97 cm2 for women who with obesity. Blood pressure measurements exceeded normal levels (≤120/80 mmHg) in both monitored groups; however, they remained below the hypertension cutoff of 140/90 mmHg.
The average concentrations of biochemical and liver enzymatic parameters, as well as TAS of blood serum in all monitored groups, are reported in Table 3. Regarding metabolic characteristics, all participants exhibited high or higher levels of serum TC (6.17 ± 1.04 mmol/L; p > 0.05 for women with overweight and 6.58 ± 0.91 mmol/L; p > 0.05 for women with obesity) compared to the reference value (<5.20 mmol/L) [45]. Concentrations of glucose, tbil, and dbil in both groups exceeded normal levels. The liver function biomarkers for all participants were within the reference range; however, levels of ALT and GGT were significantly higher in women with obesity. Identical values of TAS were detected in both monitored groups, falling within the optimal reference range (1.30–1.77 mmol/L) [46].
In the present study, blood serum was used as a matrix to quantify macro- and microelements. The minimum and maximum concentrations of risk elements are presented in Table 4. For women with overweight, the general trend of decreasing element levels in the serum was as follows: Na > K > Ca > Mg > Fe > Al > Cu > Zn > Sr > Ba > Se > Sb > Ni > Pb > Hg. Sodium was the most frequently detected macroelement in both groups. The lowest concentration was determined for Hg, measured by the CV-AAS method. In the blood serum samples of women with obesity, the scheme was as follows: Na > Ca > K > Mg > Fe > Al > Cu > Zn > Sr > Ba > Se > Sb > Pb > Ni > Hg. Differences were noted for Ca, Mg, Fe, Al, Sr, Pb, and Hg, with higher levels of these elements observed in women with obesity. Concentrations of Ag, As, Cd, Co, Cr, Li, and Mn were below the detection limits of the method.
Statistically significant positive associations were observed between glucose and liver enzymes, AST (r = 0.4529; p < 0.05) and ALT (r = 0.5334; p < 0.01), as presented in Table 5. Moderate positive correlations were found between TC and tbil (r = 0.4575; p < 0.05), as well as between AST and ALT (r = 0.5693; p < 0.01) in women with overweight. A strong, statistically significant correlation was detected between tbil and dbil (r = 0.9139; p < 0.001). For women with obesity, positive correlations were confirmed between tbil and dbil (r = 0.8916; p < 0.001) and between AST and ALT (r = 0.7652; p < 0.001) (Table 6).
Associations between macro- and microelements in the blood serum of women who are overweight are presented in Table 7. The analysis revealed strong positive correlations between Ca and Mg (r = 0.9285; p < 0.001), Ca and Sr (r = 0.9515; p < 0.001), Ca and Al (r = 0.7623; p < 0.01), Mg and Sr (r = 0.8777; p < 0.001), and Al and Sr (r = 0.6789; p < 0.01). Moderate, statistically significant (p < 0.01) positive correlations were confirmed between Ca and Ba (r = 0.6254), Mg and Al (r = 0.6103), Mg and Ba (r = 0.5785), and Ba and Sr (r = 0.6623). Sodium is positively correlated with Mg (r = 0.4523; p < 0.05) and Hg (r = 0.5036; p < 0.05). The analysis also revealed significant negative associations between Se and Al (r = −0.6152; p < 0.01) and Ba and Ni (r = −0.5833; p < 0.05). For women with obesity, statistically significant (p < 0.001) strong positive correlations were found between Na and Ca (r = 0.7764), Mg and Ca (r = 0.8938), Sr and Ca (r = 0.9187), Na and Mg (r = 0.7154), Na and Sr (r = 0.7290), as well as Mg and Sr (r = 0.9176). Significant, positive correlations were also observed between Ba and Ca, Zn and Ca, Na and K, Fe and K, Mg and Ba, Mg and Zn, Mg and Sb, Ba and Sr, as well as Cu and Ni. Selenium showed strong or moderate, statistically significant negative correlations with various risk elements (Table 8).
Statistically significant correlations between risk elements and all investigated biomarkers in monitored groups are presented in Table 9. The correlation analysis in the blood serum of women who were overweight showed significant positive associations between Al and TC (r = 0.5441), Pb and GGT (r = 0.6263), and Cu and TAS (r = 0.4055). The levels of Zn were negatively correlated with tbil (r = −0.5400) and dbil (r = −0.5243). For women with obesity, a statistically significant positive correlation was found between Cu and TC (r = 0.5530). Furthermore, the correlation analysis confirmed significant positive associations between TAS and Al (r = 0.5939), as well as Ni (r = 0.6485). Liver enzymes, such as AST, positively correlated with K (r = 0.4529) and ALT with Sb (r = 0.5241).

4. Discussion

Our previous studies [24,29,51] confirmed that body composition analysis should be the basis for assessing obesity risk. In the present study, we found that women with obesity had a higher body composition parameter PBF compared to women with overweight (44.86 ± 4.31% and 38.74 ± 2.83%, respectively, p < 0.001). The average values of VFA were 120.03 ± 13.08 cm2 for women with overweight and 148.90 ± 18.97 cm2 for women with obesity, exceeding the reference level (<100 cm2). Jeon et al. [52] found that VFA positively correlated with BMI, blood pressure, and biochemical parameters, as well as other components of metabolic syndrome. Furthermore, they suggest that combining BMI assessment with VFA determination by the BIA method could serve as a method for predicting the risk of metabolic syndrome. Zając-Gawlak et al. [53] demonstrated that postmenopausal women with VFA > 100 cm2 have a 12 times higher risk of developing metabolic syndrome compared to those women with VFA < 100 cm2.
Clinical research suggests that excess body weight and obesity are linked to metabolism malfunctions and are associated with alterations in the levels of mineral elements in the body [54,55]. The present study revealed associations between serum concentrations of macro- and microelements and biochemistry markers in women with overweight/obesity. The general trend of decreasing serum concentrations of elements in the group of women with obesity was as follows: Na > Ca > K > Mg > Fe > Al > Cu > Zn > Sr > Ba > Se > Sb > Pb > Ni > Hg. Differences were noted for Ca, Mg, Fe, Al, Sr, Pb, and Hg, with higher levels of these elements in women with obesity. Sodium emerged as the predominant macroelement observed in both groups. It has been linked to an increased risk of obesity [56], which may affect the metabolism of insulin and glucose, accelerate leptin production or secretion, and enhance leptin resistance. This can lead to an energy imbalance and the accumulation of adipose tissue mass [57]. Our results demonstrate statistically significant (p < 0.001) strong positive correlations between Na and Mg (r = 0.7154), as well as between Na and Sr (r = 0.7290), in women with obesity. An important metabolic disturbance in individuals with obesity is adipose tissue inflammation, and Mg deficiency appears to activate proinflammatory pathways [58]. The association between individuals with excess body weight and Sr levels has not been thoroughly investigated; therefore, it requires further research. Furthermore, our analysis revealed a significant (p < 0.001) association between serum Ca levels and Na (r = 0.7764), Mg (r = 0.8938), and Sr (r = 0.9187) in women with obesity. Strong, statistically significant (p < 0.001), positive correlations were confirmed between Ca and Mg (r = 0.9285), Ca and Al (r = 0.7623), and Ca and Sr (r = 0.9515) in women with overweight. Recently, clinical observations have confirmed a significant association between metabolic syndrome and increased serum Ca levels in adults with overweight and obesity [59]. Previous studies have reported inconsistent results regarding the association between obesity and serum Ca levels, with some indicating a positive correlation, while others report an inverse correlation [60,61].
In our study, the concentrations of Ag, As, Cd, Co, Cr, Li, and Mn in the human blood matrix were determined to be below the detection limits for the ICP-OES method. Harrington et al. [14] confirmed that some elements can be problematic for human analysis with this method. As a result, elements such as Cr, Co, Mo, and Mn were identified at relatively low levels.
It is important to take into account that the results obtained using the ICP-OES method provide concentrations of total elements found in the samples without accounting for the species of the element present. This distinction is particularly significant for Zn, Fe, and Cu. The human body has evolved various biochemical mechanisms to sequester elements and minimize the potential toxicological impact posed by free ions. These mechanisms often involve binding excess elements with proteins or small molecules to prevent chemical reactions. Some examples of this principle include transferrin and metallothioneins [62,63]. According to Lecube et al. [64], postmenopausal women with obesity had higher levels of the soluble transferrin receptor than non-obese postmenopausal women. Menzie et al. [65] observed significantly lower levels of serum Fe and transferrin saturation in adults with obesity compared to individuals without obesity. Additionally, fat mass was identified as a significant negative predictor of serum Fe concentration. Our correlation analysis showed a positive association between Fe and Ni (r = 0.5049; p < 0.05) for women with overweight and between Fe and K (r = 0.4907; p < 0.05) for women with obesity. Some studies indicate a link between obesity and Fe deficiency anemia, potentially resulting from elevated hepcidin levels caused by chronic inflammation [66].
Copper is important for antioxidation processes, serves as a coenzyme in mitochondrial homeostasis, and is involved in inflammatory responses as well as Fe metabolism [67]. Obesity is associated with an imbalance in Cu levels [55]. Disturbances in Cu metabolism may trigger hypercholesterolemia by increasing the production of reactive oxygen species (ROS), causing oxidative stress, and leading to the oxidation of low-density lipoproteins [68]. This effect of increased serum Cu intensifies the unfavorable impact of excess body weight on health and appears to be one of the many mechanisms linking obesity with oxidative stress and atherosclerosis [55]. In our study, we found a moderate, statistically significant positive correlation between Cu and increased serum TC levels (r = 0.5530) in women with obesity, and an association with TAS (r = 0.4055) in women with overweight. Our results proved a moderate positive correlation between Al and TC (r = 0.5441) in the blood serum of women with overweight, which could disrupt metabolic processes. Aluminum is known to displace Fe from important enzymatically active proteins, resulting in dysfunctional mitochondria geared towards lipogenesis rather than energy production. Additionally, Al toxicity leads to an increase in very low-density lipoprotein secretion and a decrease in β-oxidation of fatty acids [69]. This fact is directly linked to the accumulation of fatty tissue seen in obesity [70].
Generally, elements generate and promote the overproduction of ROS [71]. We observed many significant correlations between TAS and elements, such as Al (r = 0.5939), and Ni (r = 0.6485), in women with obesity. Shi et al. [72] confirmed that Ni toxicity is associated with ROS generation, subsequent lipid peroxidation, and alkyl and alkoxyl radical production. Additionally, oxidative stress can disrupt the balance of glutathione reductase and the mitochondrial antioxidant defense system through the formation of Ni-mercaptan complexes [73,74].
The total bilirubin value is a sensitive indicator of liver damage. Kipp et al. [75] found that bilirubin negatively correlated with BMI and adiposity in individuals with obesity compared to those without obesity. Our results proved a moderate negative correlation between Zn and tbil (r = −0.5400), and dbil (r = −0.5243) in women with overweight. Zinc plays an important role in regulating zinc-α2-glycoprotein (ZAG) homeostasis, which is essential for lipid metabolism and homeostasis of glucose. The primary biological function of ZAG involves the mobilization of lipids, particularly in white adipose tissues [76]. Additionally, the authors demonstrated that excess body weight is negatively associated with Zn levels in both erythrocytes and plasma. The clinical study of Payahoo et al. [77] indicated that Zn supplementation could lead to improvements in BMI, body weight, and triglyceride, without considerable effects on the lipid profile and glucose levels.
The blood levels of elements have been significantly associated with liver function parameters [78,79]. Our present study observed an association between certain elements and liver function biomarkers. We found a moderate, statistically significant positive correlation between K and AST (r = 0.4529), and between Sb and ALT (r = 0.5241) in women with obesity. Furthermore, Pb showed a positive association with the liver enzyme GGT (r = 0.6263) in women with overweight. Our findings highlight the necessity of addressing obesity to reduce the risk of liver injury.
Li et al. [80] concluded that Pb, Cd, and Hg had inverse associations with the risk of peripheral or abdominal obesity. Similarly, a study by Rothenberg et al. [81] reported that blood Hg levels were also inversely related to BMI in adults. In our present study, serum Hg concentrations were 0.06 ± 0.02 ng/µL in women with overweight and 0.07 ± 0.03 ng/µL in women with obesity, using the CV-AAS method. Our findings did not reveal associations between Hg and biochemical parameters, or oxidative status markers linked with excess body weight. However, we noted a moderate positive correlation between Hg and Na (r = 0.5036; p < 0.05) in women with overweight. In the group of women with obesity, we detected a negative correlation analysis between Hg and K (r = −0.4605; p < 0.05). Women are more vulnerable to toxic elements compared to men due to differences in redox homeostasis processes, hormonal influences, and immunological responses between the sexes [82,83]. Furthermore, older individuals may have higher concentrations of these toxic elements compared to younger populations, a consequence of their accumulative effect on tissues and organs [84].

5. Conclusions

The determination of risk elements in blood matrices is an essential tool that provides information related to a variety of health outcomes. Our findings underscore the importance of addressing excess body weight and obesity in relation to essential, potentially toxic, and toxic elements. The present study revealed associations between serum concentrations of macro- and microelements and biochemistry markers in postmenopausal women with overweight/obesity. Notably, individuals who have obesity, as indicated by a higher BMI, percentage of body fat, and visceral fat area, had elevated serum levels of Ca, Mg, Fe, Al, Sr, Pb, and Hg. Sodium was the most frequently detected macroelement in both groups. Concentrations of Al, Cu, K, Sb, Zn, and Pb affected biochemical and liver function markers at multiple levels. Our results showed that Cu and Al were associated with increased serum total cholesterol. Furthermore, the correlation analysis between TAS and Cu, Al, and Ni confirmed associations in women with overweight/obesity. Our findings did not reveal associations between Hg and biochemical parameters or oxidative status markers. Additional prospective research could be valuable in understanding the consequences of element accumulation and biomarker changes, as well as in identifying potential targets for the treatment or prevention of comorbidities in people with obesity.

Author Contributions

Conceptualization, Z.K., P.M. and M.H.; methodology, Z.K., I.J., L.H., J.A., A.K. and M.H.; software, Z.K. and A.K.; validation, Z.K., M.H. and P.M.; formal analysis, M.B. and Z.K.; investigation, Z.K., M.B., I.J., L.H., J.A., A.K., P.M., B.G., J.M.A.S. and M.H.; resources, Z.K., P.M., J.M.A.S. and M.H.; data curation, Z.K., M.B., A.K. and M.H.; writing—original draft preparation, Z.K. and M.H.; writing—review and editing, Z.K., M.B., P.M. and M.H.; visualization, Z.K., M.B., A.K., P.M. and M.H.; supervision, M.H., P.M., Z.K. and J.M.A.S.; project administration, P.M., M.H. and Z.K.; funding acquisition, P.M., M.H., J.M.A.S., Z.K. and B.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Scientific Agency of the Slovakia VEGA No. 1/0698/22 (P.M.), APVV-21-0168 (P.M.), the project of Grant Agency of the Faculty of Agrobiology and Food Resources SUA in Nitra (Slovakia) No. 4/2024 (Z.K.), and by the Operational Programme Integrated Infrastructure for the project: Long-term strategic research of prevention, intervention, and mechanisms of obesity and its comorbidities (IMTS 313011V344), co-financed by the European Regional Development Fund (M.H.).

Institutional Review Board Statement

This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethical Committee of the Specialized Hospital of St. Svorad Zobor in Nitra, Slovakia (protocol No. 4/071220/2020), and by the Slovak University of Agriculture (SUA) in Nitra, Institute of Nutrition and Genomics, Slovakia.

Informed Consent Statement

All participants involved in the study provided written informed consent.

Data Availability Statement

All datasets related to the results and consequences of the study are available from the corresponding author (M.H.) upon reasonable request.

Acknowledgments

The authors are thankful to all the colleagues and technical staff from the Institute of Nutrition and Genomics (Faculty of Agrobiology and Food Resources, SUA, Nitra, Slovakia), Institute of Applied Biology and Institute of Food Sciences (Faculty of Biotechnology and Food Sciences, SUA, Nitra, Slovakia), the AgroBioTech Research Centre (SUA, Nitra, Slovakia) and the LAQV-REQUIMTE (Department of Chemistry, Campus Universitário de Santiago, University of Aveiro, Portugal).

Conflicts of Interest

The authors declare no conflict of interest.

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Table 1. Parameters for the determination of elements using the ICP-OES technique.
Table 1. Parameters for the determination of elements using the ICP-OES technique.
Method Parameters/Units
RF power (kW)0.90
Plasma gas flow (L/min)15.0
Auxiliary gas flow (L/min)1.50
Nebulizer gas flow (L/min)1.0
Replicated read time (s)3.0
Instrument stabilization (s)20.0
Sample uptake delay (s)25.0
Pump rate (rpm)15.0
Rinse time (s)20.0
CCD detector temperature (°C)−35
Element (λ/nm)Ag 328.068; Al 167.019; As 188.980; Ba 455.403; Ca 315.887; Cd 226.502; Co 228.615; Cr 267.716; Cu 324.754; Fe 234.350; K 766.491; Li 670.783; Mg 383.829; Mn 257.610; Na 589.592; Ni 231.604; Pb 220.353; Sb 206.834; Se 196.026; Sr 407.771; Zn 206.200
Table 2. Baseline descriptive characteristics of the study group (n = 48).
Table 2. Baseline descriptive characteristics of the study group (n = 48).
Parameters/
Units		Standard/Optimal Reference Range for AdultWomen (n = 48)
Overweight (n = 26)Obesity (n = 22)p-Value
x ± SDminmaxCV (%)x ± SDminmaxCV (%)
Age (years)-56.92 ± 4.2050.0064.007.3956.91 ± 3.8950.0063.006.84ns
Anthropometric parameters
Body height (cm)-166.30 ± 5.82156.00180.003.50163.20 ± 6.04151.00174.003.70ns
Body weight (kg)-75.86 ± 5.5563.5086.807.3190.12 ± 10.8670.80113.7512.05<0.001
BMI (≥25–29.90 kg/m2) 18.50–24.90 127.43 ± 1.3725.1129.724.98 <0.001
BMI (≥30 kg/m2) 33.76 ± 2.8130.2739.478.31<0.001
PBF (%)18.0–28.0 238.74 ± 2.8333.8843.847.3144.86 ± 4.3135.7050.699.60<0.001
VFA (cm2)<100 2120.03 ± 13.0897.91144.0310.88148.90 ± 18.97116.31188.7812.75<0.001
Blood pressure
Systolic blood pressure
(mm Hg)		<120130.00 ± 13.47105.50157.0010.36130.14 ± 12.38106.00150.509.52ns
Diastolic blood pressure
(mm Hg)		<8085.85 ± 6.7570.50102.507.8687.30 ± 6.6375.00101.007.59ns
BMI—body mass index (kg/m2); PBF—percentage of body fat (%); VFA—visceral fat area (cm2); x—arithmetic mean; ±SD—standard deviation; min—minimum; max—maximum; CV (%)—coefficient of variation; Bold values are statistically significant; ns—not significant; 1 BMI classified according to the standards of the WHO [21,22]; 2 InBody 720—the precision body composition analyzer (user’s manual) (Biospace Co., Ltd., Seoul, Republic of Korea) [44], the optimal reference range is presented on the result sheet by the analyzer.
Table 3. The selected biochemical, enzymatic, and oxidative status parameters of blood serum in women with overweight/obesity.
Table 3. The selected biochemical, enzymatic, and oxidative status parameters of blood serum in women with overweight/obesity.
Parameters/
Units		Standard/Optimal Reference Range for AdultWomen (n = 48)
Overweight (n = 26)Obesity (n = 22)p-Value
x ± SDminmaxCV (%)x ± SDminmaxCV (%)
Biochemical parameters
TC (mmol/L)<5.20 16.17 ± 1.044.378.4916.856.58 ± 0.915.348.5913.90ns
Glucose (mmol/L)3.90–6.10 25.17 ± 0.564.096.3510.815.35 ± 0.604.627.0611.17ns
tbil (µmol/L)1.70–21.0 29.16 ± 3.063.7915.4233.428.63 ± 1.874.6512.3821.67ns
dbil (µmol/L)<3.40 23.07 ± 0.871.705.2528.422.86 ± 0.551.734.0419.16ns
Liver enzymatic parameters
AST (µkat/L)<0.52 30.33 ± 0.080.230.5525.940.34 ± 0.100.250.7430.29ns
ALT (µkat/L)<0.57 40.30 ± 0.090.180.5730.170.39 ± 0.150.200.7637.980.010
GGT (µkat/L)<0.63 50.40 ± 0.200.210.9349.980.53 ± 0.220.240.9741.270.036
Oxidative parameters
TAS (mmol/L)1.30–1.77 61.72 ± 0.131.421.987.701.71 ± 0.131.411.957.84ns
TC—total cholesterol (mmol/L); tbil—total bilirubin (µmol/L); dbil—direct/conjugated bilirubin (µmol/L); AST—aspartate aminotransferase (µkat/L); ALT—alanine aminotransferase (µkat/L); GGT—γ-glutamyltransferase (µkat/L); TAS—total antioxidant status (mml/L); x—arithmetic mean; ±SD—standard deviation; min—minimum; max—maximum; CV (%)—coefficient of variation; Bold values are statistically significant; ns—not significant; 1 [45]; 2 [47]; 3,4 [48,49]; 5 [50]; 6 [46].
Table 4. The concentration of the macro- and microelements in the blood serum of women who were overweight/obesity.
Table 4. The concentration of the macro- and microelements in the blood serum of women who were overweight/obesity.
Elements/λ (nm)Women (n = 48)
Overweight (n = 26)Obesity (n = 22)p-Value
x ± SDx ± SD
Al mg/mL/167.0191.19 ± 0.921.23 ± 0.87ns
Ba mg/mL/455.4030.62 ± 0.150.61 ± 0.14ns
Ca mg/mL/315.887157.39 ± 18.89162.51 ± 19.98ns
Cu mg/mL/324.7541.12 ± 0.111.09 ± 0.17ns
Fe mg/mL/234.3501.31 ± 0.361.36 ± 0.61ns
K mg/mL/766.491159.78 ± 10.81159.33 ± 15.82ns
Mg mg/mL/383.8293.57 ± 0.453.73 ± 0.48ns
Na mg/mL/589.5922558.87 ± 102.372555.03 ± 175.78ns
Ni mg/mL/231.6040.14 ± 0.120.08 ± 0.06ns
Pb mg/mL/220.3530.14 ± 0.040.15 ± 0.06ns
Sb mg/mL/206.8340.22 ± 0.070.21 ± 0.07ns
Se mg/mL/196.0260.44 ± 0.150.40 ± 0.15ns
Sr mg/mL/407.7710.67 ± 0.180.72 ± 0.14ns
Zn mg/mL/206.2000.99 ± 0.330.93 ± 0.26ns
Hg ng/µL 1/253.650.06 ± 0.02 10.07 ± 0.03 1ns
Ag (328.068); As (188.980); Cd (226.502); Co (228.615); Cr (267.716); Li (670.783); Mn (257.610)nd
x—arithmetic mean; ±SD—standard deviation; nd—not detected; ns—not significant. 1 Concentration of Hg in blood serum was determined by CV-AAS on the AMA 254 Hg analyzer. Values were reported in units of ng/µL.
Table 5. Spearman correlation of biochemical and liver enzymatic parameters in women who are overweight.
Table 5. Spearman correlation of biochemical and liver enzymatic parameters in women who are overweight.
GlucoseTCtbildbilASTALTGGT
TC0.2628
tbil0.01860.4575 *
dbil−0.26380.27910.9139 ***
AST0.4529 *−0.1155−0.4434 *−0.4781 *
ALT0.5334 **−0.3127−0.2491−0.27150.5693 **
GGT−0.0175−0.2582−0.3652−0.23900.15140.3436
TAS0.1418−0.07770.21180.1274−0.04880.36260.3981
TC—total cholesterol; tbil—total bilirubin; dbil—direct/conjugated bilirubin; AST—aspartate aminotransferase; ALT—alanine aminotransferase; GGT—γ-glutamyltransferase; TAS—total antioxidant status; *** (p < 0.001); ** (p < 0.01); * (p < 0.05).
Table 6. Spearman correlation of biochemical and liver enzymatic parameters in women with obesity.
Table 6. Spearman correlation of biochemical and liver enzymatic parameters in women with obesity.
GlucoseTCtbildbilASTALTGGT
TC0.0403
tbil0.24000.0909
dbil0.2196−0.00970.8916 ***
AST0.0097−0.23900.24300.2749
ALT0.1515−0.13850.07350.07540.7652 ***
GGT0.14680.04890.13060.13250.43480.4194
TAS0.34450.21450.26450.2216−0.1250−0.2691−0.2296
TC—total cholesterol; tbil—total bilirubin; dbil—direct/conjugated bilirubin; AST—aspartate aminotransferase; ALT—alanine aminotransferase; GGT—γ-glutamyltransferase; TAS—total antioxidant status; *** (p < 0.001).
Table 7. Spearman correlation of macro- and microelements in the blood serum of women who are overweight.
Table 7. Spearman correlation of macro- and microelements in the blood serum of women who are overweight.
CaNaKMgAlBaCuFeNiPbSrZnSeSb
Na0.3246
K0.25920.1954
Mg0.9285 ***0.4523 *0.2254
Al0.7623 **−0.0294−0.10540.6103 **
Ba0.6254 **0.01920.18620.5785 **0.3578
Cu0.0989−0.18080.34550.02460.03680.2566
Fe−0.01080.3015−0.17540.01770.0931−0.0885−0.2524
Ni0.03430.23770.0074−0.0221−0.0545−0.5833 *−0.21700.5049 *
Pb0.0797−0.16670.00340.04630.1716−0.1322−0.14920.13790.3964
Sr0.9515 ***0.26150.21850.8777 ***0.6789 **0.6623 **0.0854−0.1446−0.10540.0667
Zn−0.0054−0.1946−0.17620.0077−0.0809−0.0808−0.03080.18000.40930.1904−0.1292
Se−0.1814−0.06180.1388−0.0753−0.6152 **−0.0122−0.3430−0.0035−0.1589−0.0023−0.0874−0.0383
Sb0.4647 *0.24070.10580.4973 *0.32990.31830.2932−0.1404−0.1626−0.23230.4607 *0.1720−0.3792
Hg0.17740.5036 *0.00040.24270.10290.25530.02170.3505−0.06180.07250.1796−0.2909−0.31360.4114
*** (p < 0.001); ** (p < 0.01); * (p < 0.05).
Table 8. Spearman correlation of macro- and microelements in the blood serum of women with obesity.
Table 8. Spearman correlation of macro- and microelements in the blood serum of women with obesity.
CaNaKMgAlBaCuFeNiPbSrZnSeSb
Na0.7764 ***
K0.36650.4828 *
Mg0.8938 ***0.7154 ***0.3371
Al0.27140.2652−0.11660.2487
Ba0.5381 **0.24450.13270.6014 **−0.2921
Cu0.39880.2056−0.00060.42130.2570−0.0260
Fe0.21060.04460.4907 *0.1711−0.45100.0785−0.0768
Ni0.1226−0.02450.22770.10510.4316−0.28020.6410 *0.4413
Pb0.0209−0.2129−0.1112−0.07400.0444−0.00960.24800.09200.4448
Sr0.9187 ***0.7290 ***0.31790.9176 ***0.21980.6657 **0.26490.0480−0.0105−0.0503
Zn0.5508 **0.38360.37800.5085 *0.18800.31300.04630.41190.05610.14070.4192
Se−0.6219 **−0.4637 *−0.3581−0.7365 ***0.0650−0.6224 **−0.1833−0.1909−0.10860.1666−0.7060 ***−0.2577
Sb0.31870.36960.15800.4996 *0.08220.19580.14190.32660.62760.06670.37660.2512−0.4756 *
Hg−0.2696−0.1897−0.4605 *−0.18640.1913−0.15460.0474−0.30200.0729−0.0195−0.1559−0.25760.10200.3096
*** (p < 0.001); ** (p < 0.01); * (p < 0.05).
Table 9. Statistically significant correlations between risk elements and all investigated biomarkers in the blood serum of women with overweight/obesity.
Table 9. Statistically significant correlations between risk elements and all investigated biomarkers in the blood serum of women with overweight/obesity.
Women (n = 48)
Overweight (n = 26)Obesity (n = 22)
Investigated
Parameter		ElementSpearman R (p-Value)Investigated ParameterElementSpearman R (p-Value)
GlucoseSb−0.4451 (0.0465)TCCu0.5530 (0.0134)
TCAl0.5441 (0.0351)ASTK0.4529 (0.0428)
tbilZn−0.5400 (0.0096)ALTSb0.5241 (0.0307)
dbilZn−0.5243 (0.0119)TASAl0.5939 (0.0175)
GGTPb0.6263 (0.0079)TASNi0.6485 (0.0315)
TASCu0.4055 (0.0470)---
TC—total cholesterol; tbil—total bilirubin; dbil—direct/conjugated bilirubin; AST—aspartate aminotransferase; ALT—alanine aminotransferase; GGT—γ-glutamyltransferase; TAS—total antioxidant status.
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Knazicka, Z.; Bihari, M.; Janco, I.; Harangozo, L.; Arvay, J.; Kovacik, A.; Massanyi, P.; Galik, B.; Saraiva, J.M.A.; Habanova, M. Blood Concentration of Macro- and Microelements in Women Who Are Overweight/Obesity and Their Associations with Serum Biochemistry. Life 2024, 14, 465. https://doi.org/10.3390/life14040465

AMA Style

Knazicka Z, Bihari M, Janco I, Harangozo L, Arvay J, Kovacik A, Massanyi P, Galik B, Saraiva JMA, Habanova M. Blood Concentration of Macro- and Microelements in Women Who Are Overweight/Obesity and Their Associations with Serum Biochemistry. Life. 2024; 14(4):465. https://doi.org/10.3390/life14040465

Chicago/Turabian Style

Knazicka, Zuzana, Maros Bihari, Ivona Janco, Lubos Harangozo, Julius Arvay, Anton Kovacik, Peter Massanyi, Branislav Galik, Jorge M. A. Saraiva, and Marta Habanova. 2024. "Blood Concentration of Macro- and Microelements in Women Who Are Overweight/Obesity and Their Associations with Serum Biochemistry" Life 14, no. 4: 465. https://doi.org/10.3390/life14040465

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