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Abstract

Objective

The aim of the study is to examine the association between long-term occupational exposure to Mn and Fe and their health effects in workers.

Methods

108 Mn workers were selected for the Mn exposure groups; 92 non-Mn workers were in the control group. Inductively coupled plasma-mass spectrometry was used to determine the Mn and Fe concentration in the working environment. Graphite furnace-atomic absorption spectroscopy was used to determine the blood Mn concentration of workers. Serum inflammatory factors were measured by enzyme-linked immunosorbent assay.

Results

The blood Mn concentration, positive rate of clinical symptoms and serum inflammatory response in the Mn exposure group was higher than in the control group.

Conclusions

Low levels of Mn exposure may increase blood Mn concentrations, the rate of complaints of neurological symptoms and promote increased serum inflammatory response in workers.

Keywords

Manganese and iron occupational exposure workers blood Mn serum inflammatory factors

Introduction

Neurodegenerative diseases, such as Parkinson’s disease (PD) and Alzheimer’s disease (AD) are associated with neuroinflammation. The occurrence of neuroinflammation is related to the activation of microglia and astrocytes. Activated microglia and astrocytes are common in brains of PD patients, and neuroinflammaory factors are greatly increased, including nitric oxide (NO), reactive oxygen species (ROS), TNF-α, IL-1β, IL-6 and so on.^1–3 AD is characterized by extracellular β amyloid (Aβ) deposition and microglial neuroinflammatory release, inhibiting cerebrolinase and insulin-degrading enzyme. Therefore, adverse stimuli may cause microglia and astrocytes to activate, leading to their dysfunction, releasing inflammatory factors, stimulating and possibly intensifying the inflammatory response, impairing neuronal function, leading to the occurrence of neurodegenerative diseases.^4–6

Manganese (Mn) is an essential trace element. It regulates normal growth and homeostasis within the cells and is essential for the activity of multiple enzymes and proteins.^7,8 However, excessive Mn exposure in welders, smelters, foundries, finishers and other workers may cause neurological dysfunction. Mn accumulates in the hypothalamus, olfactory bulbs, globus pallidus, hippocampus, and cortex, to name a few regions of the brain.⁹ Mn poisoning is characterized by extrapyramidal damage, causing motor dysfunction, “gear-like” increase in muscle tone, micro-tremors in the fingers, abnormal tendon reflexes, agitation, euphoria, and emotional instability, analogous to several of the symptoms inherent to PD. The mechanism of Mn poisoning is associated with inflammation, apoptosis, autophagy, oxidative stress, amino acid metabolism disorders, and abnormal metal metabolism.^10–12

Iron (Fe) is one of the most abundant trace elements in human body. Fe plays a key role in brain development and physiology, neurotransmitter synthesis, cytoplasmic protein function, and mitochondrial responses. Recent studies suggest that excess Fe may be detrimental to neurodevelopmental, behavioral, and cognitive functions, especially at extreme ages.^13,14 Excessive Fe deposition can cause neurological damage such as neurodegeneration and cognitive impairment, leading to neurological diseases.^15,16

Mn poisoning activates immune effector cells (such as microglia) in a resting state of the central nervous system, stimulates their phagocytic function, prompting them to release inflammatory factors.^17,18 High levels of Mn tend to accumulate in astrocytes and activate astrocytes, leading to cell enlargement. Activation occurs by recognizing Toll-like receptors and NOD-like receptors that secrete inflammatory cytokines in the central nervous system. The activated astrocytes are mainly marked by increased GFAP activity and can secrete proinflammatory chemokines CCL2 and CXCL1 to generate ROS in NF-κB signaling pathway.¹⁹ Activated astrocytes can also stimulate activated microglia to release neuroinflammatory factors through oxidative stress, and induce neurodegeneration. Thus, microglia and astrocytes play an important role in the development of Mn poisoning. However, the neuroinflammatory mechanisms of Mn toxicity have yet to be fully understood.

Simultaneously, excessive Fe exposure can lead to the accumulation of Fe in the brain, leading to the development of AD or PD.²⁰ Mn and Fe readily accumulate in the central nervous system and cause neurotoxicity. The toxic mechanism may be related to oxidative stress, mitochondrial dysfunction, apoptosis, autophagy, protein homeostasis imbalance and inflammation.

In our earlier animal experiments, we found that Mn exposure increased the content of Mn in blood and brain, activated glial cells in thalamus, cortex and hippocampus, and apoptotic neurons in rats exposed to Mn. Levels of inflammatory factors (TNF-α, IL-1β, IL-6, PGE2, etc.) in these tissues were higher than those in the control group, and the serum inflammatory factors (TNF-α, IL-1β, IL-6, PGE2, etc.) in the rats exposed to Mn were higher than those in the control group, suggesting that Mn exposure may cause elevation in brain and serum inflammatory factors.^21–23 However, at present, there is scant information on neuroinflammatory factors affected by occupational Mn exposure in workers. According to these studies, Mn can cause increased blood Mn, inflammatory indicators, and globulin globus indices in workers.^24,25 In this study, inductively coupled plasma-mass spectrometry (ICP-MS) was used to determine the concentration of Mn in the working environment. Graphite furnace-atomic absorption spectrometry (GFAAS) was used to study the effect of Mn exposure on blood Mn in workers, and enzyme-linked immunosorbent assay (ELISA) was used to investigate the effects of Mn exposure on serum inflammatory factors.

Methods and materials

Study design and participants

The cross-sectional survey was carried out four factories in two cities in Guangxi, three are located in Baise City, Guangxi Province, which is rich in Mn ore, with workers in Mn and Fe smelting and related work (Mn exposure group), the other factory is in Guigang City, Guangxi Province, which mainly engages in sugar production, with workers in sugar making operations (control groups). A total of 200 workers were included (108 workers in Mn and Fe smelting and related work were selected as Mn exposure groups, which were stratified by job type, gender, age and working age; 92 non-Mn workers were used as control group.

Inclusion criteria: Age ≥18 years, worker with no physical illness, consent to participate in the study and sign informed consent. Subjects with current or past central nervous system disorders, such as cardiovascular and cerebrovascular diseases such as transient ischemic attack or brain tumors or other inflammatory diseases, alcohol or drug abusers, were not eligible for inclusion. Those who refused to participate in the survey were not considered for participation.

This study was approved by the ethics committee of Guangxi Medical University. All survey subjects participated voluntarily, and informed written consent was obtained from each participant.

Questionnaires

Basic information, occupational history, occupational disease history, family health history, menstrual history, marriage and childbirth history, symptoms, physical examination, laboratory examination, etc. Data were obtained using semi-structured questionnaires by interview to the participants.

Main reagents

Mn ion standard solution (National Reference Standards Research Center), Palladium chloride (Sinopharm Chemical Reagent Co., Ltd), TritonX-100 (Beijing Tianlai Biomedical Technology Co., Ltd), Nitric acid (superior grade pure), Human tumor necrosis factor α (TNF-α), prostaglandin E2 (PGE2), interleukin 1β (IL-1β), interleukin 2 (IL-2), interleukin 6 (IL-6), Interleukin 8 (IL-8) ELISA Kit (Shanghai Enzyme Exemption Company, China).

Exposure biomarkers and measurements

Blood samples were collected into a suitable vacuum tube by venipuncture of the elbow. 5 mL of biochemical coagulation blood and 5 mL of heparin sodium anticoagulation blood was collected from each worker. The biochemical coagulation blood was centrifuged to obtain the supernatant and stored at ultra-low temperature −80°C. Heparin sodium anticoagulated whole blood was stored in the refrigerator at −4°C, until further experimentation.

Graphite furnace-atomic absorption spectrometry (GFAAS) blood measurements: The collected whole blood of workers was divided into groups for pretreatment, the setting of graphite furnace parameters and working conditions were set (Table 1), and the experimental consumables needed for Mn element determination were configured according to the measurement instructions.

Table 1.

Graphite furnace working.

Steps	Heating process	Temperature (°C)	Time (s)	Gas flow rate (ml/min)
1	Dry	150	30	0.1
2	Dry	200	20	0.1
3	Graying	800	10	1.0
4	Graying	800	10	1.0
5	Graying	800	2	0
6	Atomization	2000	2	0
7	Cleanse	2300	2	1.0

Determination of the detection limit: determination of the blank tube solution was determined for 20 times, followed by calculation of the standard deviation of the blank tube solution. Next, 3 times of the standard deviation of the blank tube solution was divided by the slope of the standard curve to calculate the minimum detection limit of Mn. The standard curve of Mn metal in whole blood was obtained and drawn, and the detection limit, recovery rate and precision of Mn metal in whole blood were determined.

Determination of standard recovery rate: 3 samples were randomly selected, each sample was divided into 4 small parts, one part was directly determined, and the other three small parts were added to low, medium and high concentration of Mn standard application solution for standard recovery rate determination. The background value of Mn and the content of Mn in the samples were measured, respectively, and the recovery rate was calculated. If the recovery rate is between 95% and 105%, it indicates that the measurement method is accurate. The specific method is as follows: recovery rate = (measured value of added standard sample − measured value of original sample)/added scalar × 100%.

Determination of relative standard deviation: 5 samples were randomly selected, each sample was measured 3 times, and the relative standard deviation (RSD) was calculated, and the relative standard deviation could not exceed 7%, indicating high measurement precision.

Enzyme-Linked Immunosorbent Assay (ELISA): The levels of TNF-α, PGE2, IL-1β, IL-2, IL-6, and IL-8 in serum samples were measured with commercially available ELISA kits. Serum samples collected above were processed and analyzed for TNF-α, PGE2, IL- 1β, IL-2, IL-6, and IL-8 levels, respectively, according to the manufacturer’s protocol.

Atmospheric dust exposure assessment

Adhering to the requirements of the “Workplace Air Hazardous Substances Monitoring and Sampling Specification GBZ159-2004,” atmospheric sampling in each factory was carried out. Fixed-point short-term fixed sampling of each workshop of each factory (including welding, furnace front operation, furnace surface operation, finishing, distribution room, etc.) was carried out, sampling at the breathing height of the worker’s operation at a flow rate of 10 L/min 15 min. Next, the microporous membrane was collected with atmospheric specimens and stored in a membrane cassette. Removing dust microporous membrane filter in the working environment pressure pot, add 3 ∼ 5 mL of nitric acid, 200°C heat, dust has been heated to nitric acid solution clear transparent, digestive juices again transferred to the test tube, the capacity, the use of deionized water editing instrument measurement equation and jamming equation, adjust the internal standard, according to the regression equation of dust Mn and Fe concentration.

ICP-MS atmospheric dust measurements: The Mn and Fe content in the air of the working environment was collected by microporous filter membrane, and the ICP-MS was used for measurement after heating digestion with 3–5 mL of concentrated nitric acid at 200°C. Before measurement, the ICP-MS parameter settings and working conditions were set, and the solution concentration gradient required for determination of Mn and in the air were configured. The concentration of Mn and Fe in dust was calculated according to the obtained regression equation.

Statistical analyses

Excel and SPSS24.0 statistical software are used to process and analyze the data. The data are expressed in $\bar{X}$ ± S, the comparison of rates is a chi-square test (χ² test), and the multiple comparisons of rates are performed using a chi-square split test (Bonferroni calibration level adjustment method); The t test was used for the comparison of two independent samples of experimental data, and the approximate t test (t' test) was used for the uneven variance. Analysis of Variance (ANOVA) was used for multiple sets of independent samples, LSD was used for two-to-two comparisons, and Tamhane’s T2 test was used for uneven variances. Pearson correlation analysis was used for correlation analysis of each indicator. p < 0.05 was considered statistically significant.

Results

Distribution of exposure

For the work location, position and job type characteristics, 19 air samples of Mn and Fe in the working environment were collected, and the geometric average concentrations of Mn in the working air environment, finishing operation point, smelting operation point, distribution room operation point, welding operation point, were 6 ug/m³, 3 ug/m³, 4 ug/m³, 5 ug/m³, 7 ug/m³, and 1 ug/m³, respectively. The current allowable concentration of Mn time-weighted in the air in China’s workplace (Mn-TWA, GBZ2.1-2007, 150 ug/m³). In addition, we measured the concentration of Fe, where the geometric average concentration in the working air environment, finishing operation point, smelting operation point, distribution room operation point, and welding operation point were 23.4 ug/m³, 19 ug/m³, 21 ug/m³, 13 ug/m³, and 15 ug/m³, respectively.

Participants

The basic situation of the workers

The Mn exposure group included finishers, smelters, distributors, welders, a total of 108 workers, of which 82 were males and 26 females. The control group included security guards, cleaners, maintenance fitters, etc., a total of 92, of which 69 were male workers and 23 were female workers. There was no statistically significant difference in age and length of service between the workers in the Mn exposure group and the control group (p > 0.05), and there was no statistically significant difference in the distribution of gender, age and length of service between the two groups (p > 0.05) (Table 2).

Table 2.

Population characteristics analysis of workers in two groups of occupational groups.

Constituencies	Manganese exposure group (n = 108).		Control group (n = 92).		χ² values	p-value
Constituencies	Number of examples N	Percentage (%)	Number of examples N	Percentage (%)	χ² values	p-value
Average age (years)	44.22 ± 7.58	42.06 ± 8.99
Length of service (years)	9.66 ± 7.35	8.91 ± 4.91
Gender					0.023	0.879
Man	82	75.9	69	75.0
Woman	26	24.1	23	25.0
Age (years)					0.940	0.332
<45	56	51.8	54	58.7
≥45	52	48.2	38	41.3
Length of service (years)					0.887	0.642
<5	35	32.4	30	32.6
5–10	50	46.3	47	51.8
≥10	23	21.3	15	15.6

Comparison of the main complaint rate of clinical manifestations of two groups of workers

The positive rate of clinical symptoms of autonomic function in the Mn exposure group was higher than that in the control group (χ2 = 69.133, p < 0 .01). And the positive rates of dizziness and headache, memory loss, distraction, and fatigue in the Mn exposure group were higher than those in the control group, and the difference was statistically significant (p < 0.05∼0.01) in Table 3.

Table 3.

Comparison of clinical symptom complaint rates between the two groups.

Clinical symptoms	Manganese exposure group (n = 108)		Control group (n = 92)
Clinical symptoms	Number of examples N	Percentage (%)	Number of examples N	Percentage (%)
Dizziness and headache	19	17.6 ^*	4	4.3
Insomnia	7	6.5	2	2.2
Drowsiness	4	3.7	3	3.3
Memory loss	27	25.0 ^*	5	5.4
Distraction	21	19.4 ^*	3	3.3
Easy fatigue	37	34.3 ^**	7	7.6
Indifferent	9	8.3	5	5.4
Tingling in the limbs	7	6.5	5	5.4
Sweating	22	20.4	10	10.9
Writing exception	3	2.8	1	1.1
Decreased libido	6	5.6	5	5.4

Note: ^*Compared to the control group, ^*p < 0.05, ^**p < 0.01.

Association between exposure to Mn and blood Mn of workers

Graphite furnace - atomic absorption spectroscopy to determine the accuracy and precision of the results

Standard curve and detection limit: the absorbance value ABS of each tube was measured in sequence according to the standard series, and the regression equation of the standard curve of whole blood Mn was y = 0.077993x + 0.11977 (R² = 0.9993). The standard deviation of continuous measurement of blank solution was S_{blank space} = 0.02316, and the ratio of three times to the slope of the standard curve was the detection limit of whole blood Mn 0.89 μg/L;Standard recovery rate and standard deviation results: the average standard recovery rate is 95.7%, and the standard deviation measurement range is 0.8%∼7.0% (Table 4).

Table 4.

Results of quality assurance.

Projects	Steps	Results
The limit of detection (LOD)	The blank tube solution was determined 20 times continuously, and the standard deviation of the blank tube solution was calculated. The minimum detection limit of manganese was calculated by dividing 3 times the standard deviation of the blank tube solution by the slope of the standard curve.	0.89 μg/L
Spiked recovery rate	Three samples were randomly selected, and each sample was divided into four small parts. One part was directly determined, and the other three parts were added with low, medium and high concentrations of manganese standard application solution for standard recovery determination. The background value of manganese and the manganese content of samples after standard addition were measured, and the recovery rate of manganese addition was calculated. If recovery is between 95% and 105%, show that the measurement accuracy is good. Specific means is: standard addition recovery rate = (plus the prototype measurements - the original sample measurements)/scalar x 100%.	95.7%
Standard deviation measurement range	Five samples were randomly selected, and each sample was determined 3 times. The relative standard deviation (RSD) was calculated, and the relative standard deviation could not exceed 7%, indicating that the measurement precision was high.	0.8%∼7.0%

Blood Mn in workers

The blood concentration of Mn in the Mn exposure group was higher than that in the control group (p < 0.01). There was no statistical significance in blood Mn concentration upon stratification by sex, age and working age (p > 0.05. Table 5).

Table 5.

Effect of manganese exposure on blood manganese concentration in workers ( $\bar{X}$ ± S).

Constituencies	Number of examples	Blood manganese concentration (μg/L)
Control group	92	12.08 ± 4.59
Manganese exposure group	108	27.18 ± 2.14**
Jobs
Finishing	36	23.43 ± 2.18
Smelter	35	25.72 ± 3.91
Distribution electrician	19	28.90 ± 4.30
Welder	18	33.13 ± 4.01
Gender
Man	82	28.90 ± 2.28
Woman	26	24.98 ± 4.49
Age (years)
<45	56	25.35 ± 2.57
≥45	52	27.43 ± 2.97
Length of service (years)
<5	35	24.33 ± 3.73
5–10	50	25.41 ± 3.37
≥10	23	31.07 ± 5.45

Note: Compared with the control group, **p < 0.01.

Binary logistic regression analysis of factors influencing blood Mn levels in workers

Factors that may affect blood Mn levels, such as workers’ type of work, gender, age, and length of service, were included in the binary logistic regression model. The results showed that neither gender, age, nor work duration affected the blood Mn levels, and the blood Mn of workers exposed to Mn was increased (OR = 5.32, 95% CI: 2.7–10.4. Table 6).

Table 6.

Logistic regression analysis of influencing factors of blood manganese levels in workers.

Influencing factors	Beta	p-value	OR	95% CI
Jobs	1.67	0.000	5.32	2.7–10.43
Gender	−0.41	0.507	0.67	0.20–2.22
Age	−0.60	0.331	1.82	0.54–6.08
Seniority	−0.07	0.396	0.93	0.43–2.00

Association between exposure to Mn and serum inflammatory factors in workers

Serum inflammatory factors were measured by ELISA. The levels of serum inflammatory factors TNF-α, PGE2, IL-1β, IL-2, IL-6 and IL-8 in Mn exposed workers were higher than in control workers (p < 0.01), while there is not significant difference when compared in the jobs, gender, age, length of service (Table 7).

Table 7.

Effects of manganese exposure on serum inflammatory factors in workers ( $\bar{X}$ ± S).

Constituencies	n	TNF-α (ng/L)	PGE2 (ng/L)	IL-1β (ng/L)	IL-2 (ng/L)	IL-6 (ng/L)	IL-8 (ng/L)
Control group	92	107.78 ± 8.52	345.80 ± 6.00	18.55 ± 0.37	218.15 ± 8.36	10.81 ± 0.20	294.16 ± 7.50
Manganese exposure group	108	993.18 ± 68.22^**	1065.35 ± 94.88^**	41.88 ± 2.41^**	857.92 ± 80.83^**	26.87 ± 2.36^**	1036.43 ± 84.84^**
Jobs
Finishing	36	952.23 ± 98.69	1012.65 ± 165.80	41.45 ± 4.12	809.57 ± 121.48	25.47 ± 3.51	1003.47 ± 135.23
Smelter	35	990.45 ± 111.66	1105.30 ± 178.87	42.85 ± 4.47	856.29 ± 136.51	26.77 ± 3.89	1057.17 ± 152.84
Distribution electrician	19	986.96 ± 200.21	1041.20 ± 213.64	39.58 ± 5.57	918.57 ± 187.04	29.13 ± 6.02	977.19 ± 209.53
Welder	18	1087.10 ± 201.92	1192.45 ± 267.47	43.47 ± 6.25	1056.71 ± 234.54	30.56 ± 7.05	1128.99 ± 235.91
Gender
Man	82	1016.64 ± 82.64	1122.65 ± 116.38	42.21 ± 2.85	875.36 ± 96.20	27.38 ± 2.85	1059.20 ± 101.44
Woman	26	969.71 ± 132.24	1014.61 ± 195.87	41.55 ± 4.88	840.48 ± 160.24	26.35 ± 4.62	1013.66 ± 166.94
Age (years)
<45	56	1055.58 ± 100.18	1122.27 ± 147.13	43.50 ± 3.46	804.33 ± 105.85	28.73 ± 3.49	954.42 ± 110.81
≥45	52	928.42 ± 97.88	1000.72 ± 131.57	40.20 ± 3.43	921.36 ± 121.64	24.94 ± 3.18	1115.45 ± 128.99
Length of service (years)
<5	35	952.38 ± 105.34	1033.86 ± 168.57	40.55 ± 3.92	877.20 ± 146.27	26.90 ± 4.19	1032.03 ± 148.28
5–10	50	1000.35 ± 102.94	1050.00 ± 143.65	42.19 ± 3.71	894.02 ± 108.66	28.28 ± 3.94	1003.56 ± 118.64
≥10	23	1026.79 ± 163.80	1138.32 ± 222.25	42.89 ± 5.60	884.72 ± 171.75	25.42 ± 4.44	1073.69 ± 197.88

Note: Compared with the control group, **p < 0.01.

Correlation analysis of working age, age, blood Mn concentration, serum inflammatory factors TNF-α, PGE2, IL-1β, IL-2, IL-6, and IL-8 concentration in Mn exposed workers are shown in Table 8. Concentrations of blood Mn and serum TNF-α, serum PGE2, serum IL-1β, serum IL-2, serum IL-6, and serum IL-8 showed positive correlation (r = 0.889, 0.816, 0.826, 0.855, 0.875, 0.825, p < 0.01). Concentrations of serum TNF-α and serum PGE2, serum IL-1β, serum IL-2, serum IL-6, and serum IL-8 concentrations were positively correlated (r = 0.938, 0.965, 0.982, 0.969, 0.978, p < 0.01). Serum PGE2 concentration was positively correlated with the concentration of serum IL-1β, serum IL-2, serum IL-6 and serum IL-8 concentrations (r = 0.982, 0.974, 0.977, 0.967, p < 0.01). Serum IL-1β concentration and serum IL-2 concentration, serum IL-6 concentration, serum IL-8 concentration were positively correlated (r = 0.982, 0.974, 0.983, p < 0.01). Serum IL-2 concentration was positively correlated with serum IL-6 concentration and serum IL-8 concentration (r = 0.993, 0.990, p < 0.01). Serum IL-6 concentration was positively correlated with serum IL-8 concentration (r = 0.982, p < 0.01).

Table 8.

Analysis of the correlation between duration of work, age, blood manganese concentration and inflammatory factor concentration of manganese exposure workers (n = 108).

Index	Age	Seniority	Blood manganese	TNF-α	PGE2	IL-1β	IL-2	IL-6	IL-8
Age	—	0.045	0.091	0.090	0.072	0.065	0.077	0.078	0.092
Seniority	—	—	−0.013	−0.028	−0.007	−0.027	−0.020	−0.025	−0.013
Blood manganese	—	—	—	0.889**	0.816**	0.826**	0.855**	0.875**	0.825**
TNF-α	—	—	—	—	0.938**	0.965**	0.982**	0.969**	0.978**
PGE2	—	—	—	—	—	0.982**	0.974**	0.977**	0.967**
IL-1β	—	—	—	—	—	—	0.982**	0.974**	0.983**
IL-2	—	—	—	—	—	—	—	0.993**	0.990**
IL-6	—	—	—	—	—	—	—	—	0.982**
IL-8	—	—	—	—	—	—	—	—	—

Note: Pearson correlation analysis was used, **p < 0.01.

Multiple linear regression analysis of the relationship between blood Mn concentration, serum inflammatory factors TNF-α, PGE2, IL-1β, IL-2, IL-6, and IL-8 concentrations in Mn exposure workers is shown in Table 9. High correlation factors such as blood Mn concentration, serum inflammatory factors TNF-α, PGE2, IL-1β, IL-2, IL-6 and IL-8 were included in the multiple stepwise linear regression model. The results showed that blood Mn concentration, TNF-α, IL-6 and IL-8 contributed to the regression model (partial regression coefficients were −2.054, 0.223, 24.858, 0.144, respectively, p < 0.01). With IL-2 concentration as the dependent variable, the multiple regression equation was: C_IL-2 = −118.331 − 2.054 × C_{blood Mn} + 0.223 × C_TNF-α + 24.858 × C_IL-6 + 0.144 × C_IL-8, R² = 0.995.

Table 9.

Results of multiple linear regression analysis of blood manganese concentration and serum inflammatory factors (n = 108).

Variable	Partial regression coefficients	Adjust the partial regression coefficient	T-value	p-value	R² value
Blood manganese	−2.054	−0.082	−4.801	0.000	0.995
TNF-α	0.223	0.223	7.587	0.000
IL-6	24.858	0.706	17.661	0.000
IL-8	0.144	0.155	3.038	0.003

Discussion

Long-term exposure to low Mn concentrations may cause its brain accumulation, damaging the central nervous system, especially the extrapyramidal system, affecting learning, memory, movement and cognition.^26–29 Mn exposure is common in mining, crushing, transportation, processing and smelting industries, as well as welding.

This study showed that the average geometric concentration of Mn in the working environment air was 6 ug/m³ in the working environment air Mn geometrical concentration, which was significantly lower than the current Mn time-weighted average allowable concentration (PC - TWA, 150 ug/m³). The Mn exposure concentration from high to low was in order of welding, power distribution room, smelting and finishing operations, which were all higher than those in non-Mn working points (control group). We also analyzed the average geometric concentration of Fe in the air of the working environment, which was 23 ug/m³, 3.8 times that of Mn exposure, however, at present, China has not established occupational health standards for Fe. The positive rates of dizziness, headache, memory loss, distraction, fatigue and blood Mn levels in Mn exposed workers were higher than those in the control group. The exposure level of Mn in the ambient air of welders, electricians, smelters and finishing workers increased with blood Mn level. Analysis of occupational populations showed that no differences in the composition of the sex, age and working age of the survey, hence it was speculated that the binary logistic regression model of the influencing factors of blood Mn level contributed little to the influence on blood Mn levels. Combined with the on-site Mn operation conditions, the Mn exposed (welder, distributor, smelter, finisher) and the control workers (cleaner, canteen worker, fitter, etc.) were included in the binary logistic regression model, establishing a 5.32-fold risk for increased blood Mn levels on the Mn-exposed versus non-exposed workers. There was no difference in blood Mn level related to gender, age and length of service, failing to support the notion that the longer the Mn exposure, the higher the blood Mn level.^30,31 We also found a trend suggesting that the higher the working age of the Mn exposed workers, the higher their blood Mn level; however, the lack of statistical significance suggests an insufficient sample size. The positive rate of complaints of discomfort in the Mn-exposed workers was higher than in the control workers, consistent with results of occupational epidemiological investigations.^32,33

Mn neurotoxicity is closely related to inflammation.^34–36 Mn has been shown to activate microglia to produce a variety of inflammatory factors, such as TNF-α, IL-1β, NO, PGE2, causing an inflammatory response, which in turn affects neuronal function.^37–40 Mn also activates astrocytes, releasing neurotoxic cytokines and promoting pro-inflammatory responses, with overexpression of TNF-α and IL-1β as well as release of NO. Upon Mn exposure, the levels of blood Mn, inflammatory factors (TNF-α, IL-1β, IL-6) and pallidal index (PI) in the nonexposed group were lower than in the exposed group,^41,42 and neurological symptoms were less frequent.²⁷ Here, we found that concentrations of serum inflammatory factors TNF-α, PGE2, IL-1β, IL-2, IL-6 and IL-8 in the Mn exposed group were higher than those in the control group. Interestingly, our previous study found that Mn exposure induced increased ROS levels and expression of TNF-α and IL-1β in BV2 cells.⁴³ However, the levels of ROS and inflammatory factors induced by Mn and Fe combined exposure were lower than those of the Mn alone group, but higher than those of the Fe alone group, suggesting that Fe can antagonize the Mn-induced oxidative stress and neuroinflammation.^43,44 Correlation analysis showed that blood Mn concentration was positively correlated with serum TNF-α, PGE2, IL-1β, IL-2, IL-6, and IL-8 concentrations. In addition, the concentrations of various serum inflammatory factors (TNF-α, PGE2, IL-1β, IL-2, IL-6, and IL-8) were positively correlated with each other. Blood Mn concentrations, TNF-α, IL-6, and IL-8 contributed to the regression model. These results indicated that Mn exposure increased Mn concentration in the blood of workers and also led to increased serum TNF-α, PGE2, IL-1β, IL-2, IL-6 and IL-8 levels in workers, whether Mn exposure was related to Fe exposure was not clear. A positive correlation between blood Mn and serum inflammatory factors was noted, suggesting that low levels of Mn exposure promote increased serum inflammatory response in workers. In the present study, we mainly explored the association between Mn exposure and inflammatory factors, and did not address possible interactions between Mn and Fe. Therefore, more studies are needed to clarify the exact mechanisms of the interaction between Mn and Fe.

Conclusion

This novel study showed that low-level Mn exposure not only caused increased blood Mn concentrations and positive rates of neurologic symptoms in workers, but were also associated with high serum TNF-α, PGE2, IL-1β, IL-2, IL-6, elevated IL-8 levels and a positive correlation between blood Mn and serum inflammatory factors. Our study suggests that low-level Mn exposure may lead to increased serum inflammatory levels in workers, requiring further investigation.

Limitations

Firstly, in this novel study, our findings indicated that Mn exposure may elevate serum inflammatory levels in workers. However, due to the low concentrations detected in the workplace, we did not explore the relationship between Fe and serum inflammatory cytokine levels. Secondly, the relevant data on the symptoms experienced by workers were obtained through self-reported questionnaires, which may introduce bias when evaluating these diseases. Lastly, our research design is cross-sectional and lacks relevant sample sizes, which limits our ability to verify the effects of long-term or low-level Mn exposure on serum inflammation in workers. In addition, it is still necessary to construct in vitro and in vivo models to verify how Mn exposure leads to elevated levels of inflammatory factors in workers, and to obtain relevant results through long-term follow-up through cohort studies.

Footnotes

Author contributions

Dianyin Liang: conceptualization, investigation, formal analysis, writing—original draft, writing—review and editing. Jianchao Peng and Bingyan Xie: investigation, formal analysis, writing—original draft, supervision, writing—review and editing. Wenxia Qin: investigation, methodology, formal analysis, data curation. Michael Aschner: revised and polished the paper. Shiyan Ou: Investigation, Supervision. Yueming Jiang: supervision, project administration, funding acquisition, writing—review and editing. Dianyin Liang, Jianchao Peng and Bingyan Xie have contributed equally to this article. All authors read and approved the final manuscript.

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.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was supported by grants from the National Natural Science Foundation of China (NSFC 81973094, 81460505).

Ethical statement

Informed consent

Participation was voluntary, and written informed consent was obtained from all parents of the participants before enrollment.

ORCID iDs

Dian-yin Liang

Bing-yan Xie

Data availability statement

All data generated or analyzed during this study are included in this published article.*

Appendix

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Effects of combined exposure of manganese and iron on serum inflammatory factor levels among workers

Abstract

Objective

Methods

Results

Conclusions

Keywords

Introduction

Methods and materials

Study design and participants

Questionnaires

Main reagents

Exposure biomarkers and measurements

Atmospheric dust exposure assessment

Statistical analyses

Results

Distribution of exposure

Participants

The basic situation of the workers

Comparison of the main complaint rate of clinical manifestations of two groups of workers

Association between exposure to Mn and blood Mn of workers

Graphite furnace - atomic absorption spectroscopy to determine the accuracy and precision of the results

Blood Mn in workers

Binary logistic regression analysis of factors influencing blood Mn levels in workers

Association between exposure to Mn and serum inflammatory factors in workers

Discussion

Conclusion

Limitations

Footnotes

Author contributions

Declaration of conflicting interests

Funding

Ethical statement

Informed consent

ORCID iDs

Data availability statement

Appendix

References