Association of Serum and Hair Antioxidant Minerals with an Oxidative Stress Marker in Relation with Characteristics of Healthy Adults: A Cross-Sectional Study

doi:10.21203/rs.3.rs-3309935/v1

Excess oxidative stress generated in the body causes various types of cellular damage, including DNA damage. Certain trace minerals act as antioxidants by functioning as cofactors for antioxidant enzymes. This study was conducted to evaluate the serum and hair concentrations of major antioxidant trace minerals (zinc, manganese, selenium, and chromium) and to determine the association between the oxidative stress marker urinary 8-hydroxy-2′-deoxyguanosine (8-OHdG) and serum or hair antioxidant trace mineral concentrations, according to the general characteristics of healthy adults. Study participants were selected after screening, and 108 participants aged 19–69 years were finally included. Serum and hair trace mineral concentrations were analyzed using inductively coupled plasma mass spectrometry, and urine 8-OHdG levels were quantified using an ELISA kit. Results showed that urinary 8-OHdG levels were significantly higher in exercisers than in those who did not exercise. Correlation analysis revealed that urinary 8-OHdG was negatively correlated with hair zinc in participants over 60 years of age and with poor health status, and positively correlated with hair chromium in participants with irregular dietary habits. In conclusion, these results suggest that urinary 8-OHdG is particularly correlated with hair zinc and chromium levels. Additional large-scale epidemiological studies are needed to generally confirm these findings.

oxidative stress

antioxidants

trace elements

8-hydroxy-2&prime

-deoxyguanosine (8-OHdG)

zinc

chromium

Oxidative stress is an imbalance between the production of reactive oxygen species (ROS) and the biological ability to detoxify reactive intermediates or repair damage caused by ROS (1). ROS are generated during redox reactions in the body and are produced by both exogenous factors, such as smoking and alcohol consumption, and endogenous factors, such as oxidative phosphorylation and redox enzyme activity in the mitochondria (2). The main tissues affected by oxidative stress in the body are fat, proteins, and DNA. Excessive ROS levels have been implicated in the development of cancer, diabetes, inflammatory diseases, ischemia and reperfusion injury, neurodegenerative disorders, and aging (3). In particular, DNA damage may lead to potentially carcinogenic mutations (4). 8-Hydroxy-2-deoxyguanosine (8-OHdG) is widely used as a primary marker to measure the extent of DNA oxidative damage because it is a relatively accurate indicator of oxidative damage and repair activity in DNA, which is largely unaffected by diet (5).

When the body is exposed to oxidative stress, it triggers mechanisms to counter it through enzymatic and non-enzymatic defenses (6). Enzymatic defense mechanisms directly affect ROS production and involve enzymes such as superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPX). Non-enzymatic defense factors might be endogenous (those that eukaryotic cells may synthesize) or exogenous (those that must be obtained through diet). Endogenous factors include glutathione (GSH), α-lipoic acid, melatonin, coenzyme Q10, and ubiquinone, which are responsible for scavenging several ROS and supporting the activities of other antioxidants (vitamins C and E). Exogenous factors include vitamins C and E, carotenoids, polyphenols, flavonoids, organosulfur, and epigallocatechin-3-gallate. They reduce oxidative stress by inhibiting the activity of free radicals. In particular, vitamin C is an effective scavenger of ROS that directly and indirectly activates oxidized vitamin E, GSH, and carotenoids (7).

For the proper functioning of defense mechanisms against oxidative stress, the presence of antioxidant trace minerals that directly and indirectly affect enzymatic and non-enzymatic defense mechanisms is essential. Zinc acts in the form of Cu/Zn-SOD, where it is bound to SOD with copper and has an indirect effect on the mechanism of action of CAT, serving as an activator of the mechanism (8). Manganese acts in the form of MnSOD, which is the most important enzyme for removing ROS from mitochondria (9). Selenium is an essential dietary element that plays a central role in maintaining good health by producing approximately 25 different selenoproteins, which are part of the active site of the antioxidant enzyme GPX (10). GPX is a selenium-dependent redox enzyme that uses H₂O₂ or an organic hydroperoxide as an oxidant and the tripeptide GSH as an electron donor to remove ROS from the body (11). Chromium can be broadly categorized into hexavalent and trivalent forms. Trivalent chromium plays a role in glucose regulation in type 2 diabetes and is involved in lipid and protein metabolism (12). In particular, chromium appears to function as an antioxidant enzyme cofactor in patients with diabetes, preventing the amount and activity of the antioxidant enzymes Cu/ZnSOD and GPX from being reduced as a result of the disease; however, the exact mechanism for the enzymatic cofactor remains unclear (13, 14).

As our understanding of the functions of antioxidant trace minerals and analytical techniques improve, the number of studies on the roles of antioxidant trace minerals in disease treatment and prevention is expected to increase. Therefore, to facilitate relevant studies, it is important to determine the normal ranges of trace mineral concentrations of antioxidants in each population. Antioxidant trace mineral concentrations in the body are quantified by analyzing serum, hair, and urine. Comparative studies of trace mineral concentrations using both serum and hair in healthy Koreans have been scarce compared to overseas studies, and most of them have focused on identifying associations with specific diseases (15, 16).

This study aimed to determine the serum and hair antioxidant trace mineral concentrations in healthy individuals and to investigate the differences in antioxidant trace mineral concentrations according to general characteristics. In addition, an association between serum or hair antioxidant trace minerals and the oxidative stress marker 8-OHdG was observed, which serves as a basis for future studies on the application of antioxidant trace minerals in predicting and preventing certain diseases associated with oxidative damage.

Subjects

This study was approved by the Institutional Review Board of Bundang Jesaeng Hospital, Korea (project number: IMG14-03). Before the study, the subjects were informed of the purpose of the study, research procedures, expected benefits, possible risks, and precautions, and signed written informed consent was obtained.

Study participants were recruited through bulletin boards at Bundang Jesaeng Hospital and through local newspaper advertisements. Subjects were selected after screening, and 108 participants aged 19–69 years were finally included in the study. The exclusion criteria were as follows: (i) patients with results outside the reference range for general chemistry and blood tests; (ii) patients who were being treated or had been treated within the last three months for any of the following diseases: malignancy, diabetes, hypertension, thyroid disease, kidney disease, liver disease, renal disease, neurological disease, hematologic disease, and human immunodeficiency virus, hepatitis B virus, and hepatitis C virus infection; (iii) patients who had been treated for an infectious or inflammatory disease within the last month; and (iv) alcoholics or drug abusers. The overall flow of subject selection is shown in Fig. 1.

General characteristics survey

The survey was administered to subjects by study staff. Subjects were randomly serialized, and they received no special dietary controls prior to sample collection. The characteristics included sex, age, body mass index (BMI), smoking, drinking, nutritional supplementation, exercise, dietary habits, and health status.

Serum and hair collection and analysis of trace minerals

Venous blood (at least 4 mL) was collected, injected into a BD Vacuum Injector trace element serum container, and placed at room temperature for 30 min. After 30 min, the blood was centrifuged at a maximum speed of 1300 ×g for 10 min. The supernatant was separated, transferred to a cryo tube (screw type, 2 mL), and stored below − 80 ℃ until analysis. Serum selenium, zinc, manganese, and chromium concentrations were measured using inductively coupled plasma mass spectrometry (ICP-MS; Sciex Elan 6100, Perkin-Elmer Corporation, Foster, CA, USA) in a specialized contract laboratory (Center for Bioenvironmental Sciences, Seegene Medical Foundation, Seoul, Korea).

Scalp hair samples were cut from three locations from the occipital region of the head (3–5 cm; at least 150 mg). For subjects who had permed, dyed, or bleached their hair before sample collection, hair samples were collected at least two weeks after those treatments. Hair samples were washed serially with acetone, deionized water, and 0.5% Triton X-100 solution. They were digested in a series of nitric acid, hydrogen peroxide, and deionized water, and then diluted to 10 mL (17). Trace element concentrations in hair were measured by ICP-MS (Sciex Elan 6100, Perkin-Elmer Corporation) at Trace Elements, Inc. (Seoul, Korea).

Urine collection and 8-OHdG assay

At least 20 mL of urine was collected from the subjects who provided informed consent. The collected urine was centrifuged at 2000–5000 ×g for 10–15 min. Next, the supernatant was separated and stored below − 80 ℃ until analysis. Urinary 8-OHdG levels were measured using an Epoch microplate spectrophotometer (BioTek, Santa Clara, CA, USA) and a DNA/RNA oxidative damage enzyme-linked immunosorbent assay kit (501130; Cayman Chemical, Ann Arbor, MI, USA) according to the manufacturer’s instructions. Each sample was assayed in duplicate, and the results were expressed as the ratio of 8-OHdG to creatinine. Creatinine was measured using an Epoch microplate spectrophotometer (BioTek) and a urinary creatinine colorimetric assay kit (500701, Cayman Chemical), according to the manufacturer’s instructions.

Statistical analysis

Statistical analyses of all the collected data were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Continuous variables are expressed as mean ± standard deviation, and categorical variables are expressed as n and percentage (%) to compare the characteristics of the study subjects. One-way analysis of variance was used to compare the mean values of three or more groups; a two-tailed t-test was used to compare the mean values of two specific groups; and differences between categorical variables were assessed for significance using the chi-square test. To determine the association between serum or hair antioxidant trace minerals and 8-OHdG levels, Pearson’s correlation analysis was performed after adjusting for sex, age, BMI, smoking, drinking, exercise, health status, eating status, and supplement use as potential confounding factors. Statistical significance was set at p < 0.05.

In this study, the average age of all subjects was 42.51 years and BMI was 23.12 kg/m². There were significant differences in BMI (p < 0.001), smoking (p < 0.001), drinking (p < 0.001), and supplement use (p < 0.05) between sexes; however, there were no significant differences in age, exercise, health status, or eating habits (Table 1).

Serum zinc (p < 0.01) and selenium (p < 0.05) levels decreased with age. However, serum manganese and chromium levels were not significantly different among age groups. Exercisers had higher urinary 8-OHdG levels (p < 0.05) and lower serum zinc levels (p < 0.01) than non-exercisers (Table 2). As shown in Table 3, hair antioxidant trace mineral concentrations were not significantly different by age, but hair manganese was higher with better health status (p < 0.01, Table 3). In addition, hair chromium levels were higher in men than in women (p < 0.05), in smokers than in non-smokers (p < 0.05), and in subjects with regular dietary habits (p < 0.05). In the correlation analysis between urinary 8-OHdG and minerals in serum and hair according to general characteristics, urinary 8-OHdG levels were negatively correlated with hair zinc in subjects aged > 60 years (p < 0.05) and those with poor health status (p < 0.05), and positively correlated with hair chromium in subjects with irregular dietary habits (p < 0.05, Table 4).

Considering the importance of antioxidant capacity in oxidative stress defense in the body, this study investigated blood and hair levels of potential antioxidant trace minerals and their associations with the oxidative stress marker 8-OHdG according to various health parameters. Different levels of serum or hair trace minerals were observed according to subjects’ characteristics such as age or exercise. We found significant associations of hair zinc and chromium with urinary 8-OHdG among older participants, those with poor health status, and those with an irregular diet.

The mean serum zinc concentration in this study was 1003.71 µg/L, which is within the range reported in published Korean studies (600–1900 µg/L) (18, 19). Hair zinc concentrations also ranged within the reference range of Medinex Korea (20), which performed the hair test (mean of 181.67 µg/g), and corresponded with values ranging from 150 to 210 µg/g in published Korean studies (20–22). An international study also reported a range of 120–250 µg/g (23). Zinc concentration may vary depending on factors such as age, BMI (obesity status), and residence, with younger people (24, 25), those who are not obese (20), those who exercise (26), and those who live in cities (27) showing higher zinc concentrations. In this study, serum zinc concentration differed significantly with age. Serum zinc concentration also tended to decrease with increasing BMI; however, the difference was not significant. Despite not showing a significant difference, the concentration change with BMI followed the same trend as that reported in a previous study (28). A sustained deficiency of zinc may lead to increased fat mass and decreased lean body mass. In obesity, zinc metabolism is prone to instability, with reduced absorption in the small intestine and increased excretion from the body (29, 30). Therefore, zinc concentration is of particular interest in the context of obesity. Additionally, serum zinc concentration was significantly different depending on the exercise status. Serum zinc concentrations in the exercise group were lower than those in the control group, which may have been due to higher 8-OHdG concentrations in the exercise group than in the control group. These findings suggest that serum zinc acts as an antioxidant against exercise-induced stress. Compared to previous studies on exercise, in one case, the control group had higher levels than the exercise group (31), but in other cases, the exercise group had higher levels than the control group (32). These differences may be due to interactions with other factors, such as age, sex, and diet, which may influence serum zinc concentration. Therefore, we suggest that further studies should be conducted to investigate changes in concentration with exercise after controlling for these factors. As shown in the results above, this study showed that serum zinc concentration was significantly different from the general characteristic factors, but hair zinc concentration was not.

In the present study, serum manganese concentration was 1.45 µg/L, in line with the range reported in previous studies (0.7–3.4 µg/L) (33, 34). Hair manganese concentration averaged 0.19 µg/g, which was slightly below the reference range of Medinex Korea (20). Compared to the concentrations reported in domestic and international studies (average 0.15–0.4 µg/g), the result was within the range (20, 21, 35, 36). In this study, serum manganese concentration tended to decrease with age, although the difference was not significant. Hair manganese levels were significantly higher in those in good health than in those in poor health. Manganese is a particularly diet-sensitive nutrient, characterized by a decrease in the body owing to low food intake or when its bioavailability is reduced in conditions such as disease (37). Manganese concentration may also be influenced by occupation and environment, with welding workers and those living near steel industrial parks having higher manganese concentrations than control subjects (33, 38). Excessive accumulation of manganese in the body has been reported to cause anemia and to negatively affect the nervous system (39). Constant exposure to metals in the environment or due to occupation necessitates close attention to this factor.

The mean serum selenium concentration in this study was 156.43 µg/L, corresponding with the range reported in Korean studies (84–284 µg/L) (40, 41). Comparisons with other countries (average 67–136 µg/L), including China, Malaysia, Syria, Iran, and Sweden, indicated higher concentrations in Korea (42). Differences in concentrations between countries may be attributed to diet and environment. Hair selenium in this study averaged 0.60 µg/g, within the reference range of Medinex Korea (20). Serum selenium concentrations decreased with increasing age and were significantly different between the groups. The results of published studies on selenium concentrations with age vary. We found some studies that showed a positive correlation (43), others that showed a negative correlation (44), and others that showed no association (45). Different age ranges and characteristic dietary profiles, as well as different health conditions of the sample population, may have triggered these discrepancies.

The serum chromium concentration in this study was 0.34 µg/L, slightly lower than the range (0.37–3.96 µg/L) reported in domestic and international studies (46–48). Hair chromium concentration averaged 0.40 µg/g, falling within the reference range of Medinex Korea (20) and that of domestic and international studies (0.15–2.2 µg/g). Serum chromium concentration was not significantly different from the general characteristic factors; however, hair chromium concentration differed significantly according to sex (men > women), alcohol consumption (drinker > non-drinker), and eating habits (regular > irregular). Hexavalent chromium is toxic, whereas trivalent chromium has beneficial effects in humans. Drinking alcohol sensitizes the organs of the body, such as the liver, to hexavalent chromium toxicity (49). In this study, 89% of the men and 52% of the women were drinkers. Sex differences in alcohol consumption may have influenced hair chromium concentration. However, there have been only a few clinical and mechanistic studies on the relationship between alcohol consumption and hair chromium concentration. Therefore, further studies are needed to understand the mechanisms of action and association between alcohol and chromium.

The mean urinary 8-OHdG level was 130.83 ng/mg creatinine. When comparing men and women, men had a mean concentration of 122.91 ng/mg creatinine, and women had a mean concentration of 136.71 ng/mg creatinine, with women having a slightly higher mean concentration than men, but there was no significant difference between the sexes. A review of published studies suggests that establishing a reference range for 8-OHdG concentrations may be difficult because it is sensitive to sex, age, body composition, smoking status, diet, ionizing radiation (50, 51), and occupational and environmental factors, resulting in a wide range of measured concentrations and difficulty in assessing the impact of only one factor (52). In one clinical study, urinary 8-OHdG concentration increased with age (53), whereas other studies showed no difference (54). A comparison of urinary 8-OHdG concentration in smokers and non-smokers showed that smokers had 2.84 times higher concentration of urinary 8-OHdG than non-smokers among subjects with a BMI of ≤ 25 (50), contrary to the results that 8-OHdG was a good prognostic factor in non-small cell lung cancer and was positively correlated with non-smokers (55). Another study examining changes in 8-OHdG concentration by alcohol consumption and exercise did not show a significant correlation, but an association was observed between 8-OHdG concentration and nutritional supplement intake (56). Additional studies are required to determine the sensitivity of this marker to environmental factors and nutritional status.

In the present study, 8-OHdG concentrations were significantly higher in exercisers than in non-exercisers. Although there were differences in the type and duration of exercise, previous studies have reported similar results (57, 58). We found no significant differences between 8-OHdG concentrations and any of the other general characteristic factors except exercise status. In the correlation analysis, 8-OHdG and hair zinc concentrations were negatively correlated in subjects aged > 60 years with poor health status, whereas 8-OHdG and hair chromium concentrations were positively correlated in subjects with irregular eating habits. However, no correlation was found between 8-OHdG concentration and serum antioxidant trace minerals. One possible explanation for this result is that the subjects in this study were relatively healthy adults who were influenced by defense systems other than antioxidant trace minerals (59). However, it is possible that the study participants would have had different results if their concentrations of trace antioxidant minerals were below or above this range. Accordingly, our finding of a significant association between hair zinc and chromium levels and urinary 8-OHdG levels in more vulnerable subjects with older age, poorer health status, and irregular diet is significant. However, this was a cross-sectional study with limitations in the causal interpretation of these associations, and there is a need for future longitudinal studies to elucidate the relationship between these minerals and various oxidative markers.

We simultaneously determined antioxidant trace minerals in the serum and hair and compared the antioxidant trace mineral concentrations between groups within the general characteristics. Some results showed significant differences in concentrations based on general characteristics. In addition, we correlated serum and hair antioxidant trace mineral concentrations with oxidative stress markers and found a partially significant correlation. However, the limitations of this study include the fact that participants were recruited from a limited space in and around a university hospital; therefore, the sample size was small and not representative of the entire population. Second, only one oxidative stress marker, 8-OHdG, was used, and a variety of other oxidative stress markers were not included. Third, the study did not include an assessment of the subjects’ intake of macrominerals and antioxidant trace minerals. Fourth, because this study was conducted in normal subjects, a study of normal subjects with a wide range of measured concentrations and a comparative study of antioxidant trace mineral concentrations between normal and abnormal subjects is required. Despite these limitations, this study is significant for several reasons. First, this study compared antioxidant trace mineral concentrations between groups within general characteristic factors in healthy subjects, which may be used as a basis for future observational studies on antioxidant trace minerals according to general characteristics. Second, we observed correlations between serum or hair antioxidant trace minerals and 8-OHdG levels, with some results showing significant correlations. These results may be used as a basis for evaluating the applicability of antioxidant trace minerals to specific disease groups associated with oxidative stress.

Serum zinc and selenium concentrations decreased with age; in particular, serum zinc concentrations were lower in exercisers than in non-exercisers. Hair manganese concentrations were higher in patients with good health status than in those with poor health status, and hair chromium concentration differed significantly according to sex (male > female), alcohol consumption (drinker > non-drinker), and eating habits (regular > irregular). Urinary 8-OHdG concentrations were higher in exercisers than in non-exercisers. In addition, correlations between urinary 8-OHdG and serum or hair antioxidant trace minerals showed a negative association with hair zinc concentration for the age of ≥60 years and poor health status, and a positive association with hair chromium in subjects with irregular diets. Future epidemiological studies with larger numbers of participants, particularly analyses including multiple markers of oxidative stress, are needed to confirm the applicability of these findings.

Conflict of Interest

The authors declare that this study was conducted in the absence of commercial or financial relationships that could be construed as potential conflicts of interest.

Author Contributions

All authors have conceived of the idea. Kang and Sung wrote the articles in the literature. Kang and Sung drafted the manuscript. Choi and Baik finalized the manuscript. All authors have revised the manuscript, contributed intellectual content, and approved the final version of the manuscript.

Funding

This work was supported by the National Research Foundation of Korea (NRF) (2022R1A2C1004626 to M.K.S.) and BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF).

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Table 1 General characteristics of subjects

Characteristics	Total (n = 108)	Men (n = 46)	Women (n = 62)	p-value
Age (years)	42.51 ±13.22	40.61 ± 11.48	43.92 ± 14.30
19−39	29.74 ± 5.28	30.45 ± 5.28	29.12 ± 5.30	0.200
40−59	48.94 ± 5.49	48.73 ± 5.44	49.12 ± 5.64
≥60	63.79 ± 2.67	63.00 ± 2.83	63.92 ± 2.75
BMI (kg/m²)†	23.12 ± 3.05	24.66 ± 2.87	21.98 ± 2.67
<23	20.70 ± 1.58	21.31 ± 1.69	20.59 ± 1.55	0.0001^***
≥23	25.29 ± 2.33	25.37 ± 2.56	25.13 ± 1.85	0.0001^***
Smoking
Current smoker	12 (11.11)	12 (100)	0 (0.00)	0.0001^***
Non-smoker	96 (88.89)	34 (35.42)	62 (64.58)	0.0001^***
Drinking
Drinker	73 (67.59)	41 (56.16)	32 (43.84)	0.0001^***
Non-drinker	35 (32.41)	5 (14.29)	30 (85.71)	0.0001^***
Exercise
Yes	48 (44.44)	23 (47.92)	25 (52.08)	0.317
No	60 (55.56)	23 (38.33)	37 (61.67)	0.317
Health status
Good	98 (90.74)	41 (41.84)	57 (58.16)	0.780
Poor	10 (9.26)	5 (50)	5 (50)	0.780
Eating habits
Regular	84 (77.78)	36 (42.86)	48 (57.14)	0.327
Irregular	24 (22.23)	10 (41.67)	14 (58.33)	0.327
Supplement use
Yes	21 (19.44)	13 (61.90)	8 (38.10)	0.046^*
No	87 (80.56)	33 (37.93)	54 (62.07)	0.046^*

^*p < 0.05, ^*** p < 0.001 statistically significant

p-values were evaluated using Student’s t-test and the chi-square test.

Data are presented as mean ± standard deviation or n (%).

† Body mass index

Table 2 Urinary 8-OHdG and serum antioxidant trace mineral concentrations by characteristics of the subjects

Variables	Criteria	Urinary 8-OHdG (ng/mg creatinine)	Serum minerals
Variables	Criteria	Urinary 8-OHdG (ng/mg creatinine)	Zn (μg/L)	Mn (μg/L)	Se (μg/L)	Cr (μg/L)
Total subjects, n = 108		130.83 ± 98.32	1003.71 ± 243.22	1.45 ± 0.75	156.43 ± 24.02	0.34 ± 0.21
Sex	Men, n = 46	122.91 ± 77.75	997.51 ± 281.80	1.49 ± 0.83	158.01 ± 28.49	0.34 ± 0.19
Sex	Women, n =62	136.71 ± 111.43	1008.31± 212.45	1.42 ± 0.69	155.25 ± 20.25	0.35 ± 0.23
p-value		0.450	0.829	0.649	0.577	0.902
Age, years	19–39, n = 47	121.84 ± 71.72	1073.73 ± 258.52	1.56 ± 0.61	163.43 ± 23.31	0.37 ± 0.24
	40–59, n = 47	133.22 ± 124.75	980.23 ± 217.45	1.46 ± 0.87	152.98 ± 24.65	0.34 ± 0.21
	≥60, n = 14	153.04 ± 73.87	847.45 ± 192.60	1.01 ± 0.55	144.48 ± 17.39	0.27 ± 0.08
p-value		0.571	0.006^**	0.050	0.013^*	0.330
BMI, kg/m²	<23, n = 51	134.51 ± 115.28	1037.69 ± 209.79	1.53 ± 0.77	159.83 ± 21.36	0.37 ± 0.26
BMI, kg/m²	≥23, n = 57	129.80 ± 10.93	978.41 ± 259.77	1.44 ± 0.78	154.10 ± 25.27	0.32 ± 0.16
p-value		0.746	0.171	0.276	0.164	0.309
Smoking	Smoker, n = 12	156.25 ± 109.90	987.98 ± 262.56	1.40 ± 0.76	168.41 ± 29.22	0.32 ± 0.15
Smoking	Non-smoker, n = 96	127.66 ± 96.95	1005.68 ± 242.10	1.45 ± 0.75	154.93 ± 23.03	0.35 ± 0.22
p-value		0.345	0.813	0.798	0.067	0.753
Drinking	Drinker, n = 73	129.42 ± 110.48	1007.50 ± 261.18	1.47 ± 0.82	155.69 ± 24.07	0.34 ± 0.23
Drinking	Non-drinker, n = 35	133.78 ± 67.57	995.81 ± 204.01	1.40 ± 0.57	157.97 ± 24.19	0.36 ± 0.18
p-value		0.801	0.817	0.619	0.646	0.632
Exercise	Yes, n = 48	154.22 ± 128.69	928.31 ± 215.97	1.37 ± 0.76	152.59 ± 27.40	0.30 ± 0.15
Exercise	No, n = 60	112.13 ± 59.49	1064.03 ± 248.60	1.51 ± 0.73	159.50 ± 20.66	0.38 ± 0.25
p-value		0.040^*	0.004^**	0.329	0.151	^0.050
Health status	Good, n = 98	135.01 ± 100.23	999.30 ± 244.94	1.41 ± 0.71	155.63 ± 24.15	0.34 ± 0.21
Health status	Poor, n = 10	89.90 ± 67.87	1046.94 ± 233.29	1.80 ± 1.05	164.27 ± 22.30	0.39 ± 0.28
p-value		0.168	0.558	0.122	0.280	0.490
Eating habits	Regular, n = 84	127.42 ± 99.44	982.48 ± 247.03	1.41 ± 0.76	154.05 ± 24.26	0.33 ± 0.18
Eating habits	Irregular, n = 24	142.80 ± 95.38	1078.02 ± 218.15	1.58 ± 0.68	164.73 ± 21.63	0.40 ± 0.31
p-value		0.502	0.090	0.324	0.054	0.259
Supplement use	Yes, n = 21	128.59 ± 98.31	970.57 ± 230.08	1.56 ± 0.70	154.25 ± 27.74	0.32 ± 0.15
Supplement use	No, n = 87	131.38 ± 98.89	1011.71 ± 246.90	1.42 ± 0.76	156.95 ± 23.19	0.35 ± 0.23
p-value		0.908	0.489	0.464	0.646	0.513

^*p < 0.05, ^** p < 0.01 statistically significant

p-values were calculated using one-way analysis of variance or Student’s t-test.

Table 3 Hair antioxidant trace mineral concentrations by characteristics of the subjects (n = 108)

Variables	Criteria	Hair minerals
Variables	Criteria	Zn (μg/g)	Mn (μg/g)	Se (μg/g)	Cr (μg/g)
Total subjects, n = 108		181.67 ± 110.56	0.19 ± 0.28	0.60 ± 0.19	0.40 ± 0.18
Sex	Men, n = 46	189.35 ± 146.76	0.18 ± 0.35	0.63 ± 0.20	0.45 ± 0.22
Sex	Women, n = 62	175.97 ± 73.98	0.19 ± 0.21	0.58 ± 0.18	0.36 ± 0.13
p-value		0.573	0.885	0.182	0.012^*
Age, years	19–39, n = 47	191.49 ± 135.93	0.19 ± 0.36	0.58 ± 0.18	0.40 ± 0.22
	40–59, n = 47	180.00 ± 95.46	0.17 ± 0.14	0.60 ± 0.19	0.41 ± 0.13
	≥60, n = 14	154.29 ± 42.56	0.27 ± 0.33	0.67 ± 0.23	0.32 ± 0.16
p-value		0.542	0.456	0.275	0.226
BMI, kg/m²	<23, n = 51	180.39 ± 78.36	0.16 ± 0.16	0.56 ± 0.19	0.38 ± 0.13
BMI, kg/m²	≥23, n = 57	182.81 ± 133.80	0.21 ± 0.35	0.63 ± 0.19	0.42 ± 0.21
p-value		0.908	0.327	0.066	0.316
Smoking	Smoker, n = 12	279.17 ± 266.00	0.33 ± 0.66	0.70 ± 0.22	0.56 ± 0.37
Smoking	Non-smoker, n = 96	169.48 ± 64.99	0.17 ± 0.18	0.59 ± 0.18	0.38 ± 0.13
p-value		0.182	0.414	0.051	0.120
Drinking	Drinker, n = 73	187.12 ± 122.89	0.18 ± 0.29	0.60 ± 0.19	0.42 ± 0.20
Drinking	Non-drinker, n = 35	170.29 ± 79.32	0.21 ± 0.24	0.59 ± 0.19	0.35 ± 0.11
p-value		0.393	0.563	0.829	0.031^*
Exercise	Yes, n = 48	186.67 ± 122.50	0.19 ± 0.20	0.61 ± 0.21	0.38 ± 0.17
Exercise	No, n = 60	177.67 ± 100.88	0.19 ± 0.33	0.59 ± 0.18	0.41 ± 0.19
p-value		0.676	0.953	0.611	0.503
Health status	Good, n = 98	180.61 ± 114.49	0.20 ± 0.29	0.61 ± 0.19	0.39 ± 0.18
Health status	Poor, n = 10	192.00 ± 62.68	0.10 ± 0.04	0.53 ± 0.19	0.41 ± 0.12
p-value		0.758	0.003^**	0.220	0.801
Eating habits	Regular, n = 84	177.14 ± 88.27	0.19 ± 0.30	0.62 ± 0.19	0.41 ± 0.19
Eating habits	Irregular, n = 24	197.50 ± 168.56	0.17 ± 0.17	0.55 ± 0.19	0.35 ± 0.11
p-value		0.574	0.625	0.112	0.039^*
Supplement use	Yes, n = 21	188.57 ± 141.82	0.14 ± 0.12	0.57 ± 0.19	0.37 ± 0.09
Supplement use	No, n = 87	180.00 ± 102.55	0.20 ± 0.30	0.61 ± 0.19	0.40 ± 0.19
p-value		0.796	0.140	0.443	0.206

^*p < 0.05, ^** p < 0.01 statistically significant

p-values were calculated using one-way analysis of variance or Student’s t-test.

Table 4 Pearson’s correlation coefficients between urinary 8-OHdG and antioxidant trace minerals by characteristics of the subjects

Variables		Serum minerals				Hair minerals
Variables		Zn	Mn	Se	Cr		Zn	Mn	Se	Cr
Total subjects, n = 108		0.016 (0.876)	0.089 (0.381)	0.021 (0.834)	-0.051 (0.614)		-0.059 (0.564)	-0.016 (0.872)	-0.024 (0.814)	-0.060 (0.553)
Sex	Men, n = 48	0.026 (0.877)	0.289 (0.079)	0.104 (0.536)	0.118 (0.479)		-0.288 (0.079)	-0.098 (0.559)	-0.004 (0.980)	-0.272 (0.098)
Sex	Women, n = 64	0.026 (0.850)	-0.011 (0.937)	-0.042 (0.762)	-0.135 (0.326)		0.120 (0.383)	0.013 (0.926)	-0.029 (0.834)	0.055 (0.692)
Age, years	19–39, n = 47	-0.019 (0.909)	-0.069 (0.682)	-0.112 (0.504)	-0.095 (0.571)		-0.038 (0.820)	-0.044 (0.794)	0.013 (0.939)	0.137 (0.412)
	40–59, n = 47	-0.113 (0.500)	0.049 (0.770)	-0.045 (0.789)	-0.115 (0.493)		-0.134 (0.423)	-0.050 (0.764)	-0.033 (0.842)	-0.153 (0.359)
	≥60, n = 14	0.798 (0.057)	0.768 (0.075)	0.641 (0.171)	-0.629 (0.181)		*-0.901 (0.014^)**	-0.576 (0.232)	-0.591 (0.217)	0.642 (0.169)
BMI, kg/m²	<23, n = 51	-0.090 (0.571)	-0.043 (0.788)	-0.153 (0.333)	-0.127 (0.422)		0.161 (0.310)	0.037 (0.816)	0.026 (0.869)	0.103 (0.516)
BMI, kg/m²	≥23, n = 57	0.178 (0.225)	0.236 (0.106)	0.217 (0.138)	0.089 (0.545)		-0.067 (0.650)	0.019 (0.900)	-0.069 (0.641)	-0.035 (0.813)
Smoking	Smoker, n = 12	-0.476 (0.340)	-0.466 (0.352)	-0.483 (0.331)	0.088 (0.869)		-0.581 (0.227)	0.176 (0.739)	-0.479 (0.336)	-0.122 (0.818)
Smoking	Non-smoker, n = 96	0.042 (0.694)	0.138 (0.199)	0.006 (0.952)	-0.058 (0.593)		0.136 (0.208)	0.006 (0.957)	0.007 (0.952)	-0.034 (0.754)
Drinking	Drinker, n = 73	0.099 (0.433)	0.062 (0.625)	0.044 (0.730)	-0.073 (0.563)		-0.136 (0.279)	-0.026 (0.837)	-0.086 (0.498)	-0.090 (0.476)
Drinking	Non-drinker, n = 35	-0.241 (0.216)	0.149 (0.450)	0.007 (0.970)	-0.088 (0.658)		0.260 (0.182)	0.120 (0.542)	-0.175 (0.374)	0.151 (0.444)
Exercise	Yes, n = 48	0.132 (0.418)	-0.003 (0.985)	0.106 (0.515)	-0.134 (0.411)		-0.114 (0.484)	-0.040 (0.807)	-0.119 (0.466)	-0.092 (0.574)
Exercise	No, n = 60	-0.146 (0.355)	0.204 (0.195)	-0.019 (0.905)	0.006 (0.972)		0.030 (0.849)	0.114 (0.471)	0.092 (0.564)	-0.067 (0.673)
Health status	Good, n = 98	0.021 (0.846)	0.019 (0.862)	0.043 (0.690)	-0.077 (0.469)		-0.065 (0.545)	-0.035 (0.743)	-0.056 (0.601)	-0.071 (0.509)
Health status	Poor, n = 10	-0.452 (0.701)	0.908 (0.275)	0.327 (0.788)	-0.879 (0.317)		*-0.997 (0.047^)**	-0.851 (0.351)	0.781 (0.429)	-0.275 (0.822)
Eating habits	Regular, n = 84	0.065 (0.575)	0.112 (0.337)	0.120 (0.301)	-0.109 (0.348)		0.001 (0.991)	-0.031 (0.793)	-0.060 (0.607)	-0.172 (0.138)
Eating habits	Irregular, n = 24	-0.193 (0.475)	-0.176 (0.513)	-0.378 (0.149)	-0.123 (0.649)		-0.234 (0.382)	-0.261 (0.329)	0.147 (0.588)	*0.554 (0.026^)**
Supplement use	Yes, n = 21	-0.299 (0.320)	0.333 (0.267)	-0.366 (0.218)	0.094 (0.759)		-0.370 (0.213)	-0.447 (0.126)	-0.464 (0.110)	-0.121 (0.694)
Supplement use	No, n = 87	0.048 (0.673)	0.096 (0.400)	0.038 (0.740)	-0.049 (0.671)		0.006 (0.957)	0.055 (0.628)	-0.023 (0.843)	0.020 (0.861)

^* p < 0.05, statistically significant; R and p-values are presented after adjusting for sex, age, body mass index, smoking, drinking, exercise, health status, eating status, and supplement use. R-values greater than 0 indicate a positive correlation, whereas R-values smaller than 0 indicate a negative correlation.

BMI: body mass index

No competing interests reported.

Association of Serum and Hair Antioxidant Minerals with an Oxidative Stress Marker in Relation with Characteristics of Healthy Adults: A Cross-Sectional Study

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Subjects

General characteristics survey

Serum and hair collection and analysis of trace minerals

Urine collection and 8-OHdG assay

Statistical analysis

Results

Discussion

Conclusion

Declarations

References

Tables

Additional Declarations

Status:

Version 1