The effect of heat stress on hematological parameters and oxidative stress among bakery workers

Abstract

Heat stress causes physiological changes, and changes in hematological parameters and hormonal levels in the human body, known as thermal strain. This study was conducted to determine the effect of exposure to heat stress on hematological parameters and oxidative stress in the bakers of Shahroud City, Iran. A total of 163 bakery workers (exposed group) and 135 office workers (unexposed group) with a minimum of 1-year working experience were selected. Exposure to heat stress was measured using ISO-7243 criteria on the hottest days of the year (late July and August). Wet-bulb globe temperature (WBGT) was calculated based on indoor environments. Oxidative stress indices including malondialdehyde (MDA), nitric oxide (NO), total antioxidant capacity (TAC) in the bakers' and office workers’ serum and hematological parameters were measured. Statistical analysis was done through independent t-test, and multivariate linear regression using SPSS v24. Analysis of hematological parameters showed that about 70% and 68% of the bakers had abnormal mean cell volume (MCV) and white blood cell (WBC) count, respectively, while only around 12% of them had abnormal mean cell hemoglobin concentration (MCHC). The counts of red blood cells (RBC), WBC, lymphocytes (LYM), and MCV showed significant differences in different occupational groups (p < 0.05). The levels of MDA and NO were significantly higher in bakers with WBGT more than the threshold value (p < 0.05). The WBGT index for assessing heat stress can be used as a predictor variable for MDA and NO levels. In addition, heat stress exposure could be a risk factor for abnormal WBC, RBC, LYM, and MCV.

Keywords

Bakers heat stress lymphocytes malondialdehyde nitric oxide red blood cell total antioxidant capacity wet-bulb globe temperature white blood cell

Introduction

Bakers are exposed to heat stress in their work environment, which can result in a variety of outcomes ranging from relatively mild heat cramps to heatstroke (Argaud et al., 2007). Heat stress is one of the most important factors capable of enhancing reactive oxygen species (ROS) production (Li et al., 2017). Mitochondria are the most important site for ROS formation, which is an oxygen metabolism by-product (Starkov, 2008). In addition, ROS is also generated from cellular responses to xenobiotics, cytokines, and bacterial invasion (Ray et al., 2012). Uncontrolled high concentrations of ROS and reactive nitrogen species cause the formation of free radical-mediated chain reactions resulting in direct and indirect damages to proteins, nucleic acids, and lipids, followed by activation of the apoptotic pathway and the development of cancer, arteriosclerosis, aging, neurodegenerative diseases, and diabetes (Haigis and Yankner, 2010; Paravicini and Touyz, 2006; Shukla et al., 2011; Trachootham et al., 2009). Malondialdehyde (MDA) is the most mutagenic compound produced during lipid peroxidation and increases during exposure to heat stress in the mitochondria and plasma (Wang et al., 2018). In addition, abnormal increases in body temperature increase the level of nitric oxide (NO) synthase and stimulate the release of NO (Chen et al., 2012). The formation of NO free radicals in mitochondria may have direct consequences, since this compound binds to the heme group of cytochromes, prevents cellular oxygen intake, and stimulates $O_{2}^{- \cdot}$ production (Poderoso et al., 1996; Zhu and Silverman, 2008). The antioxidant systems, which include enzymatic and nonenzymatic antioxidants, counteract free radical and neutralize oxidants (Birben et al., 2012). In addition, some studies have shown that heat stress causes physiological changes and changes in hematological parameters and hormonal levels in the human body, known as thermal strain (Hoorfarasat et al., 2015; Melesse et al., 2011; Wilson et al., 2006). Choi and Pai showed that although short-term exposure to heat stress does not have an effect on erythrocyte count, hemoglobin (Hb) concentration, and mean corpuscular Hb, under the same conditions, it does increase mean cell volume (MCV) and hematocrit (HCT) (Choi and Pai, 2002).

Few studies have been conducted on the impact of heat stress on oxidative stress indices and hematological parameters, in particular, among bakery workers. Baking requires a lot of physical effort, and bakers are exposed to excessive heat, especially in traditional bakeries, with uncovered ovens (Rabeiy, 2019). Thus, this study was designed to determine the effect of heat stress on hematological parameters and oxidative stress levels of plasma among bakers who were exposed to high-temperature bakeries.

In this study, wet-bulb globe temperature (WBGT) index was used to assess heat stress; the MDA, NO, and total antioxidant capacity (TAC) serum biomarkers were used to measure oxidative stress, and blood cell parameters were considered as the hematological parameters.

Materials and methods

Study population

The study population in the present cross-sectional study included workers employed in different parts of bakeries of Shahroud city in Semnan province, Iran. The inclusion criteria were at least 1-year work experience and full-time employment at a bakery. The exclusion criteria included having a history of hypertension, heart disease, or fever and subjects with immune system diseases. In total, 163 bakery workers exposed to heat stress routinely and acclimated were included as the exposed group. Participants were divided into four groups including dough making, dough forming, baking of goods, and counterhand based on their tasks. The main task of dough-making workers was mixing ingredients with water and flour to make straight and sponge dough. Dough-forming workers were responsible for kneading, rolling, cutting, and shaping dough. The baking of goods workers placed and baked items in hot ovens. The counterhand workers stood behind a counter and took orders. The unexposed group (n = 135) included the office workers of a hospital who were not exposed to heat stress and had the same level of physical activities. They mainly worked in indoor places, and assessments showed that the temperature in their working places was not more than 23.9 ± 2.14°C. The control and exposed groups mainly worked with their hands in standing or sitting positions. All participants were selected based on the inclusion and exclusion criteria, and the participation rate was 99.3%. Written consent was inquired from all participants.

Data collection

Personal and organizational data were collected using a demographic questionnaire including age, height, weight, marital status, educational level, smoking (current smokers/nonsmokers), work experience and type of clothing (for determining the Clo coefficient) by face to face interviews.

Environmental parameters in the workplace measurement

Heat stress in the environment was evaluated using the WBGT index, which has been approved by the World Health Organization (WHO), the International Organization for Standardization (ISO), and the National Institute for Occupational Safety and Health (NIOSH). In addition, previous studies have shown an acceptable correlation between this index and physiological parameters of the body such as deep body temperature (OSHA, 2009; Ravandi et al., 2016). The measurements were performed in accordance with ISO-7243 on the hottest days of the year (late July and August) from 10:00 a.m. to 3:00 p.m. The WBGT is calculated in indoor environments using the following equation (Parsons, 2006):

{WBGT}_{indoor} = 0.7 \times t_{w} + 0.3 \times t_{g}

In the above formula, t _w is natural wet-bulb temperature and t _g is black globe temperature. This index was measured by a digital WBGT meter (MK427JY model, Casella, Australia) according to the standards. Because of environmental heterogeneity, the WBGT was measured at three heights in worker’s standing position including head (1.7 m), trunk (1.1 m), and ankle (0.1 m). The heat stress index was calculated using the following equation (Parsons, 2006):

{WBGT}_{i} = \frac{{WBGT}_{head} + (2 \times {WBGT}_{abd}) + {WBGT}_{leg}}{4}

Because temperature values were measured at four different times during work shifts (2-h intervals), the WBGT time-weighted average (TWA) was calculated according to the below formula:

{WBGT}_{average} = \frac{\sum_{i = 1}^{n} . (t_{i} \times W B G T_{i})}{\sum_{i = 1}^{n} t_{i}}

The thermal resistance of workers’ clothing was assumed to be 0.6. This was based on the type of clothing, all of which was summer clothing (ACGIH, 2015; Occupational Exposure Limit, 2016). To determine the permissible values of the WBGT index in the environment, the working metabolic rate of individuals was extracted from the ISO-8996 standard table, based on the 2B method (observation level) with 20% accuracy and the posture, work speed, body segment involved in the work, and the workload for each body segment (ISO-9886:2003, 2004). The permissible level of heat stress was determined based on the metabolism level, clothing material, and the WBGT index. The mean WBGT for the dough making, dough forming, baking of goods, and counterhand workers were 28.04 (SD = 1.59), 28.38 (SD = 2.68), 27.41 (SD = 2.39), and 26.96 (SD = 1.74), respectively.

Oxidative stress indices and hematological parameters measurement

To measure oxidative stress indices and hematological parameters, 8 cm³ blood samples were taken from bakery workers by an experienced nurse. All samples (both exposed and unexposed groups) were taken prior to the last working shift in the week, which should show the chronic effect of exposure to heat. Two cm³ of the blood samples were transferred into complete blood count/ethylenediaminetetraacetic acid tubes and sent to the laboratory for measuring hematological parameters including red blood cells (RBC), white blood cells (WBC), lymphocytes (LYM), coefficient of variation of red blood cell distribution width (RDW), mean platelet volume (MPV), Hb, HCT, MCV, mean cell hemoglobin (MCH), and mean cell hemoglobin concentration (MCHC) using a cell counter device. The rest of the blood sample was transferred into a vial to separate the serum. The serum samples were held at −20°C until laboratory analysis. Oxidative stress biomarkers including MDA, NO, and TAC were measured in serum samples using Hangzhou Eastbiopharm Kits by double-antibody sandwich enzyme-linked immunosorbent assay (DRG, Germany) (Arslan et al., 2016; Boskabady and Mahtaj, 2015). Participants who had hematological parameters out of normal ranges (Rasouli et al., 2017) were classified as the abnormal group.

Statistical analysis

SPSS version 24 software (SPSS, Chicago, Illinois, USA) was used for descriptive and analytical statistical analyses, at a significance level of 0.05. The χ ² test was used to analyze the relation between demographics and occupational characteristics in the exposed and unexposed groups. Based on the American Conference of Governmental Industrial Hygienists ACGIH threshold limit value (TLV), we designated a light working level for counterhands (WBGT threshold value = 31°C), moderate for dough making and dough forming (WBGT threshold value = 29°C), and heavy for baking goods (WBGT threshold value = 27.5°C); the exposed group was classified into two groups of greater and less than the TLV. Differences in the mean of hematological parameters between the bakers (with less and higher WBGT than TLV) and the unexposed group were calculated. Multivariate linear regression was used to determine the predictive variables of oxidative stress biomarkers.

Results

Table 1 presents the demographic data of the participants. Most of them were within the age range of 30–40 years and had normal body mass index based on WHO body mass index (BMI) classification. More than a third of them had less than 10 years’ work experience, and 13% were current smokers.

Table 1.

Demographic characteristics and χ ² test results for the study participants.

Variables	Classification	Frequency, n (%)		p Value
Variables	Classification	Exposed group (n = 163)	Unexposed group (n = 135)	p Value
Age (years)	<30	32 (19.6)	26 (19.3)	0.06
	30–40	74 (45.4)	87 (64.4)
	>40	57 (35)	22 (16.3)
BMI (kg m⁻²)	Underweight	16 (9.8)	4 (2.9)	0.15
	Normal	78 (47.9)	51 (37.8)
	Pre-obesity	47 (28.8)	62 (45.9)
	Obesity	22 (13.5)	18 (13.4)
Experience (years)	<10	59 (36.2)	66 (48.9)	0.14
	10–19	58 (35.6)	51 (37.8)
	>19	46 (28.2)	18 (13.3)
Education level	Under high school diploma	122 (74.9)	80 (59.3)	0.15
	High school diploma	31 (19)	44 (32.6)
	Higher education	10 (6.1)	11 (8.1)
Smoking	Yes	22 (13.5)	25 (18.5)	0.39
Smoking	No	141 (86.5)	110 (81.5)	0.39
Working hours per day	≤8	44 (27)	51 (37.8)	0.18
Working hours per day	>8	119 (73)	84 (62.2)	0.18
Job group	Dough making	38 (23.3)	—	—
	Dough forming	27 (16.6)	—
	Baking	85 (52.1)	—
	Counterhand	13 (8)	—

Comparing hematological parameters of the exposed and non-exposed groups showed that around 70% and 30% of the bakers had abnormal WBC and LYM count, while slightly more than 16% of the nonexposed group had abnormal LYM% and MCH. Other results for hematological parameters are given in Table 2.

Table 2.

The distribution of hematological parameters among the study participants.

Variable	Normal range	Status	Exposed group, n (%)	Unexposed group, n (%)	p Value
RBC (10¹² L⁻¹)	4.5–5.9	Normal	125 (76.7)	117 (86.7)	0.19
RBC (10¹² L⁻¹)	4.5–5.9	Abnormal	38 (23.3)	18 (13.3)	0.19
Hb (g L⁻¹)	140–175	Normal	137 (84)	120 (88.9)	0.43
Hb (g L⁻¹)	140–175	Abnormal	26 (16)	15 (11.1)	0.43
WBC (10⁹ L⁻¹)	4.5–11	Normal	49 (30.1)	128 (94.8)	0.002
WBC (10⁹ L⁻¹)	4.5–11	Abnormal	114 (69.9)	7 (5.2)	0.002
LYM%	28–34	Normal	124 (76.1)	113 (83.7)	0.31
LYM%	28–34	Abnormal	39 (23.9)	22 (16.3)	0.31
LYM (10⁹ L⁻¹)	1–4.8	Normal	114 (69.9)	124 (91.9)	0.006
LYM (10⁹ L⁻¹)	1–4.8	Abnormal	49 (30.1)	11 (8.1)	0.006
HCT	41.5–50.4	Normal	142 (87.1)	118 (87.4)	0.91
HCT	41.5–50.4	Abnormal	21 (12.9)	17 (12.6)	0.91
MCV (fL)	80–96	Normal	136 (83.4)	119 (88.2)	0.48
MCV (fL)	80–96	Abnormal	27 (16.6)	16 (11.8)	0.48
MCH (pg)	27.5–33.2	Normal	133 (81.6)	113 (83.7)	0.75
MCH (pg)	27.5–33.2	Abnormal	30 (18.4)	22 (16.3)	0.75
MCHC (g L⁻¹)	33.4–35.5	Normal	144 (88.3)	131 (97)	0.11
MCHC (g L⁻¹)	33.4–35.5	Abnormal	19 (11.7)	4 (3)	0.11
RDW-CV	11.5–14.5	Normal	153 (93.9)	128 (94.8)	0.86
RDW-CV	11.5–14.5	Abnormal	10 (6.1)	7 (5.2)	0.86
PLT (10⁹ L⁻¹)	150–450	Normal	140 (85.9)	124 (91.9)	0.32
PLT (10⁹ L⁻¹)	150–450	Abnormal	23 (14.1)	11 (8.1)	0.32
MPV (fL)	9.4–12.3	Normal	151 (92.6)	120 (88.9)	0.48
MPV (fL)	9.4–12.3	Abnormal	12 (7.4)	15 (11.1)	0.48

RBC: red blood cell; Hb: hemoglobin; WBC: white blood cell; LYM: lymphocytosis; HCT: hematocrit; MCV: mean cell volume; MCH: mean cell hemoglobin; MPV: mean platelet volume; MCHC: mean cell hemoglobin concentration; RDW-CV: coefficient of variation of red blood cell distribution width; PLT: platelet.

The count of RBC, WBC, LYM, and MCV showed significant differences in different occupational groups (Table 3).

Table 3.

Hematological parameters and differences in the means by independent t-test.

Hematological parameters	NG (mean ± SD)	Exposed group			p Value
		Total (mean ± SD)	WBGT		p Value
		Total (mean ± SD)	≥TLV	<TLV	Total vs. NG	≥TLV vs. <TLV	<TLV vs. NG
RBC (10¹² L⁻¹)	5.34 ± 0.51	4.97 ± 0.6	5.03 ± 0.41	4.87 ± 0.26	<0.001	0.003	<0.001
Hb (g L⁻¹)	16.95 ± 1.37	16.72 ± 1.43	16.72 ± 1.57	16.71 ± 1.18	0.35	0.94	0.35
WBC (10⁹ L⁻¹)	6.79 ± 1.58	4.62 ± 0.84	4.76 ± 0.93	4.37 ± 0.59	<0.001	0.001	<0.001
LYM (10⁹ L⁻¹)	2.78 ± 0.86	2.46 ± 0.66	2.51 ± 0.69	2.39 ± 0.62	0.04	0.29	0.01
HCT	48.58 ± 3.64	48.09 ± 5.91	47.62 ± 5.44	48.88 ± 6.59	0.63	0.18	0.79
MCV (fL)	91.21 ± 7.08	85.15 ± 5.99	86.22 ± 6.42	83.36 ± 4.72	<0.001	0.003	<0.001
MCH (pg)	31.89 ± 3.01	31.19 ± 2.87	31.09 ± 2.86	31.37 ± 2.91	0.19	0.55	0.4
MCHC (g L⁻¹)	34.89 ± 0.82	34.93 ± 1.11	34.93 ± 1.15	34.94 ± 1.04	0.76	0.94	0.78
RDW-CV	11.63 ± 0.65	11.81 ± 0.93	11.85 ± 0.99	11.73 ± 0.82	0.188	0.394	0.548
PLT (10⁹ L⁻¹)	229.27 ± 51.46	255.45 ± 115.26	250.83 ± 109.41	263.18 ± 124.99	0.17	0.52	0.06
MPV (fL)	8.12 ± 0.91	8.2 ± 0.91	8.51 ± 0.89	7.69 ± 0.72	0.63	<0.001	0.01

NG: nonexposed group; WBGT: wet-bulb globe temperature; TLV: threshold limit value; RBC: red blood cell; Hb: hemoglobin; WBC: white blood cell; LYM: lymphocytosis; HCT: hematocrit; MCV: mean cell volume; MCH: mean cell hemoglobin; MPV: mean platelet volume; MCHC: mean cell hemoglobin concentration; RDW-CV: coefficient of variation of red blood cell distribution width; PLT: platelet.

The mean and standard deviation of the WBGT index in the exposed and nonexposed groups were 27.7 ± 2.3 and 22.8 ± 1.3, respectively. The results showed that the mean of MDA and NO levels were significantly higher in the baking and the counterhand employees than dough-making employees. Also, MDA and NO levels were significantly higher in above threshold WBGT levels (Table 4).

Table 4.

Multivariate linear regression of personal and environmental parameters as predictor factors of oxidative stress biomarkers.

Variable	Oxidative stress biomarkers
	MDA (nmol mL⁻¹)			NO (µmol L⁻¹)			TAC (mmol L⁻¹)
	B	SE	p Value	B	SE	p Value	B	SE	p Value
Age (years)
30 vs. <40	−0.98	4.95	0.84	1.44	11.47	0.9	−0.05	0.61	0.92
30–40 vs. >40	1.45	4.03	0.71	1.98	9.35	0.83	0.21	0.51	0.68
Experience (years)
>10 vs. >19	0.29	4.78	0.95	10.6	11.09	0.34	0.61	0.59	0.3
10–19 vs. >19	−2.88	4.44	0.51	3.11	10.3	0.76	0.47	0.55	0.39
Smoking
Yes vs. No	1.57	4.18	0.71	11.29	9.68	0.24	−0.84	0.51	0.11
Worker position^a
DF vs. DM	6.12	4.39	0.16	17.01	10.17	0.09	0.36	0.54	0.51
BG vs. DM	14.8	3.5	<0.001	37.43	8.12	<0.001	0.76	0.43	0.08
CH vs. DM	31.13	6.14	<0.001	45.79	14.24	0.002	0.56	0.76	0.45
Work hours (h day⁻¹)
≤8 vs. >8	−2.61	3.26	0.42	7.08	7.56	0.35	−0.11	0.41	0.78
WBGT (°C)
<TLV vs. ≥TLV	18.78	3.1	<0.001	16.36	7.19	0.02	0.29	0.38	0.44

DF: dough forming; BG: baking goods; CH: counterhand; DM: dough making; MDA: malondialdehyde; NO: nitric oxide; TAC: total antioxidant capacity; WBGT: wet-bulb globe temperature; TLV: threshold limit value.

^a DF (moderate activity), BG (heavy activity), CH (light activity), and DM (moderate activity).

Discussion

In this study, the WBGT index had a significant relationship with some hematological parameters; and RBC, WBC, LYM, and MCV in the case group were significantly lower than in the nonexposed group. Norloei et al. (2017) showed that chronic exposure to heat stress causes an increase in the serum osmolality and a decrease in MCV and the WBC in the exposed group compared with the nonexposed group. Although the RBC count in the exposed group decreased as well, it was not statistically significant (Norloei et al., 2017). According to the results of this study, RBC, WBC, MCV, and MPV were significantly decreased in the group exposed to over the threshold WBGT, compared to those exposed to less than threshold WBGT. Increased serum osmolality and MCHC and decreased WBC, MCV, LYM%, MPV, PDW, and PLC have been reported in casting workers after exposure to heat stress (Norloei et al., 2017). Studies in cell lines and animal models suggest that heat directly results in tissue damage. The severity of the damage depends on the critical thermal maximum, a term that tries to quantify the level and duration of temperature increase that causes tissue damage to begin (Bruneaux et al., 2017). Observations in groups including marathon runners, normal volunteers, and cancer patients treated with whole body hyperthermia suggest that the critical core temperature in humans is 41.6–42°C for 45 min to 8 h (Bynum et al., 1978). Choi and Pai (2002) studied the effects of changes in hematological parameters due to short-term exposure to heat and reported an increase in HCT, MCV, platelet levels, RDW, and eosinophils and a decrease in MCHC.

Researchers have also shown that exposure to heat stress may affect hematological parameters in animals. Ondruska et al. (2011) found that exposed rabbits showed a decrease in RBC, packed vell volume, neutrophils, monocytes, and basophils after exposure to 36°C daily, for 12 h, within 4 weeks, compared to the nonexposed group. However, there was no significant difference in the WBC count and the LYM% between the groups (Ondruska et al., 2011).

A study of the acute effects of passive exposure to heat on arterial stiffness, oxidative stress, and inflammatory parameters showed that exposure of physically active men to a temperature of 42°C and a relative humidity of 18% for 110 min can lead to reduced large artery elasticity index and increase in interleukin 6 (IL-6). However, WBC, Hb, HCT, high-sensitivity C-reactive protein, TAC, and oxidative stress index did not change significantly before and after exposure to heat (Kaldur et al., 2016).

It is difficult to compare the results of studies about chronic and acute exposure to heat, because the differences in the degrees of adaption to heat exposure in the subjects can cause differences in the results.

Based on our results, the WBGT index had a significant relation with MDA and NO. It can be observed that exposure to heat stress above the ACGIH threshold for WBGT can increase MDA and NO significantly. These findings indicate that exposure to thermal stress may cause cell damage and lipid peroxidation, resulting in an increase in MDA levels. This is probably due to oxidation activities and free radical production increase in living systems under thermal stress conditions (Sahin and Kucuk, 2003). In a study on broilers, the effects of heat stress on oxidative stress were studied. The results showed that birds exposed to 38 ± 1°C for 3 h showed increased rectal temperatures, and their MDA concentrations and HCT decreased (Altan et al., 2003).

The production of MDA from the peroxidation process can be considered as an indicator of environmental damage caused by exposure to heat stress. Similar findings have shown that exposure to other factors such as environmental pollutants, noise, heat, infections, and toxins can cause increase in free radicals, ROS, and the production of MDA (Azad et al., 2010; Khaksar et al., 2017; Sahin et al., 2002).

In our study, the bakers with heat exposure greater than TLV had higher levels of MDA and NO rather than the bakers with heat exposure less than TLV. Barbosa et al. (2012) found that the levels of glutathione peroxidase and glutathione transferase as antioxidant enzymes in male workers who were working in sugarcane farms significantly increased during the harvest period when they were exposed to heat stress, compared with the non-harvest period. They found that the WBGT index in these two periods was 18.1 and 24.4°C respectively (Barbosa et al., 2012). Furthermore, Tang et al. (2016) reported that increasing the WBGT index had a significant relation with vitamin C losses in sweat which is an antioxidant and reduces oxidative stress (Tang et al., 2016).

Chang et al. (2009) found that heat stress can induce the production of ROS and increase the expression of the interleukin 8 (IL-8), IL-8 receptor, and NF-E2–related factor 2 (Nrf2) target genes in human dental pulp cells. However, pretreatment with an exogenous antioxidant such as N-acetylcysteine inhibited the heat-induced expression of IL-8 and transcription factor Nrf2-target genes and Nrf2 translocation. They concluded that heat stress stimulates the signaling pathway involved in Nrf2 activation and the subsequent induction of antioxidant enzymes protects the cells from heat-induced proinflammatory cytokine and oxidative damage (Chang et al., 2009). Nrf2 mediates antioxidant response and actives transcription of several antioxidant enzymes such as superoxide dismutase and glutathione peroxidase, and heat-induced Nrf2 activation can be seen as a possible mechanism in which exposure to heat stress may increase the production of ROS. Several previous studies have also reported that heat stress induces ROS in various cells types (Davidson and Schiestl, 2001; Katschinski et al., 2000). For instance, a review by Horváth and Babinszky (2018) stated that heat stress can lead to harmful impacts on birds including increase in the production of ROS, the formation of MDA as an indicator for lipid peroxidation, and decreased vitamin concentrations (Horváth and Babinszky, 2018). It has also been suggested that overproduction of ROS is caused by the reduction of mitochondrial respiratory chain activity due to heat stress (Yang et al., 2010). However, several studies have reported that bakers and pastry cooks are exposed to flour dust and associated aeroallergens during the process of flour manipulation (Mounier-Geyssant et al., 2007; Stobnicka and Górny, 2015); and after exposure to PM2.5, levels of MDA in plasma increase (Sørensen et al., 2003). In contrast, a more recent study by Manawil et al. (2013) did not find significant differences in bakery workers in comparison to the control regarding oxidative stress indicators including MDA and SOD (Manawil et al., 2013). In addition, the levels of MDA in the plasma of sugarcane workers during the harvest period was not significantly different from the non-harvest period, and even after adjusting for age, BMI, and smoking. Nevertheless, the median level of exposure to PM2.5 in these workers in the harvest period was significantly higher than the non-harvest period (Barbosa et al., 2012).

The baking goods and counterhand workers had higher level of MDA and NO than dough maker workers. In our study, the baking goods workers were exposed to heat stress extensively because their workstation was mainly in the front of the oven. It seems like the counterhand workers experienced some risk factors and heat stress as well.

A limitation of this study was that it did not investigate some confounders such as external sources of stress, job demands, and workload. For instance, the counterhands may have had more stress and experienced a higher level of mental workload, because of encountering and interacting with customers. In addition, the level of exposure to flour dust had not been measured in this study, while there is some evidence that PM2.5 may influence the production of ROS (Sørensen et al., 2003).

Conclusion

Our findings indicate that prolonged exposure to heat stress can be a risk factor for abnormal WBC, RBC, LYM, and MCV. The WBGT index for assessing heat stress can be used as a predictor variable for MDA and NO levels.

Footnotes

Acknowledgement

We would like to thank the bakery employers and bakers.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical considerations and informed consent

This study was approved by the Ethics Committee of Shahroud University of Medical Sciences (). The aims and scope of the study were explained to all participants. Informed consent was obtained from all participants. Participation was completely voluntary. Personal data were kept confidential.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was financially supported by Shahroud University of Medical Sciences [Grant No. 9529].

ORCID iD

Majid Bagheri Hosseinabadi

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