3.1. Air monitoring inside the foundry plant
Twelve air samples were collected from the manufacturing department. The levels of Al, Cd, Cu, Zn, Cr and Ni in the workplace environment had a mean value ± SD (10.31 ± 3.73, 0.01 ± 0.006, 0.67 ± 0.3, 1.14 ± 0.22, 0.05 ± 0.02 and 0.07 ± 0.04 mg/m3, respectively), with the total of suspended particles (TSP) 1.5 ± 0.19 (mg/m3), as shown in Table (1) and Table (2).
Table (1): Statistical analysis of metals concentration (mg/m 3 ) in airborne particles in the production unit of the foundry plant
Metals (mg/m3)
|
Mean ± SD
|
Percent (%)
|
OSHA mg/m3
PEL-TWA
|
ACGIH mg/m3
TLV-TWA
|
NIOSH mg/m3
REL-TWA
|
MAC mg/m3
|
Al
|
10.31 ± 3.73
|
84.0%
|
15
|
10
|
10
|
15
|
Cd
|
0.01 ± 0.006
|
0.09%
|
0.2
|
0.05
|
0.05
|
0.01
|
Cu
|
0.67 ± 0.3
|
5.0%
|
1
|
1
|
1
|
1
|
Zn
|
1.14 ± 0.22
|
9.0%
|
5
|
2
|
5
|
5
|
Cr
|
0.05 ± 0.02
|
0.44%
|
1
|
0.5
|
0.5
|
0.5
|
Ni
|
0.07 ± 0.04
|
0.62%
|
1
|
1.5
|
0.015
|
1
|
Table (2): Particle mass (mg/m 3 ) in the different areas of the manufacturing department in the foundry plant (n = 12)
Manufacturing department
|
Mean
|
SD
|
Min
|
Max
|
TSP mg/m3
|
1.5
|
0.19
|
0.75
|
2.1
|
From previous data, the Al level was above the recommended exposure limit (REL) and threshold limit value (TLV) set by NIOSH (10 mg/m3) and ACGIH (10 mg/m3), and the Ni level was above the REL value set by NIOSH (0.015 mg/m3). However, the Cu, Cd, Cr, and Zn levels were below the REL value and TLV set by NIOSH, ACGIH, and OSHA.
3.2. Biological monitoring
High levels of Al and Ni in workplace air were due to the generation of high amounts of metal dust and fumes combined with poor exhaust ventilation systems at the source of fumes. According to our previous measurements of the mentioned metals in workplace air, employees were exposed to them by inhalation during their work. Hence, these metals' levels should be measured in their blood, as shown in Table (3). Blood levels of the metals Al, Cu, Zn and Cd were observed in highly significantly increased concentration levels ((P < 0.0001) in exposed employees compared to the control group. Ni and Cr blood levels were observed in highly non-significant increases (P > 0.05) compared with the control group.
Likewise, copper levels in the blood were significantly higher in exposed workers than in the control group[27].
Table (3): Statistical analysis of blood Al, Ni, Cu, Zn, Cd and Cr (µg/l) in control and exposed groups.
Sample
|
Control (30)
|
Exposed (60)
|
P-value
|
B-Al (µg/l)
|
7.21 ± 3.18
|
58.3 ± 18.43
|
0.0001
|
B-Ni (µg/l)
|
59.87 ± 11.78
|
62.45 ± 17.44
|
0.693
|
B-Cu (µg/l)
|
709.38 ± 174.57
|
1308.4 ± 126.4
|
0.0001
|
B-Zn (µg/l)
|
4475.8 ± 1201
|
6743.4 ± 1449
|
0.0001
|
B-Cd (µg/l)
|
0.92 ± 0.31
|
1.37 ± 0.72
|
0.0001
|
B-Cr (µg/l)
|
3.57 ± 0.67
|
3.62 ± 0.93
|
0.807
|
Many agents can cause oxidative stress. The human body's cellular defence mechanisms can be divided into enzyme and non-enzyme. A balanced balance of oxidants and antioxidants leads to a healthy body. The word oxidative stress indicates that equilibrium and balance are disrupted[28].
In our study, serum MDA concentration and the activity of blood GSH were determined to establish the effect of employees' exposure to metal dust and whether it may initiate lipid peroxidation. In this study, the mean values of MDA and 8-OHdG were found to be higher with a high statistical significance (16.91 ± 3.87 nmol/ml), 37.48 ± 3.37 ng/ml, respectively with (P < 0.0001). In contrast, the mean levels of GSH were lower among exposed workers compared to the control, with a high statistical significance of 18.87 ± 5.08 (mg/dl) (P < 0.0001,) as reported in Table (4).
Table (4): MDA, GSH and 8-OHdG levels in the control and exposed groups.
Sample
|
Control (30)
|
Exposed (60)
|
P-value
|
MDA (nmol/ml)
|
9.73 ± 1.62
|
16.91 ± 3.87
|
0.0001
|
GSH (mg/dl)
|
29.15 ± 4.18
|
18.77 ± 5.08
|
0.0001
|
8-OHdG (ng/ml)
|
20.85 ± 3.28
|
37.48 ± 3.37
|
0.0001
|
There was a non-significant positive correlation between the composite exposure index and MDA with (p > 0.05), while there was a significant positive correlation with 8 OHdG with (P < 0.05). Simultaneously, there was a significant negative correlation observed between the composite exposure index and GSH with (p < 0.05), as reported in Table (5).
Table (5): Correlations between Composite exposure index with MDA (nmol/ml) & GSH (mg/dl) level and 8-OHdG (ng/ml) levels among exposed workers.
Oxidative stress marker
|
Composite exposure index
|
r
|
P-value
|
MDA (nmol/ml)
|
0.251
|
0.085
|
GSH (mg/dl)
|
-0.404
|
0.004
|
8-OHdG (ng/ml)
|
0.747
|
0.0001
|
Oxidative stress arises from a disparity between the generation of reactive oxygen species (ROS) and the protective mechanisms of the antioxidant defence system [14]. ROS can lead to an assault on several cellular components, including antioxidant defences, DNA, and membrane phospholipids; the latter is particularly vulnerable to oxidation, resulting in the generation of peroxyl radicals and, subsequently, malondialdehyde (MDA)[29]. According to our study, when there was an elevation of metals (Al, Cu, Cd, Cr, Ni and Zn) in blood workers, these metals can generate free radicals through different mechanisms, consequently increasing the rate of lipid peroxidation, which is measured as MDA levels, as intracellular levels of free radicals rise, there is a corresponding increase in MDA levels. In response to oxidative stress, the organism mounts a considerable increase in antioxidant activity to protect itself as a protection mechanism[30]. It was reported that there was a significant increase in MDA and a significant decrease in glutathione (GSH) in cement workers compared with control groups[31]. Another study revealed higher oxidative stress in zinc recovery plant workers than in secondary copper smelting plants[32]. Our findings agree with those reported by [33], who found elevated urinary 8-OHdG concentrations in foundry workers occupationally co-exposed to metals and polycyclic aromatic hydrocarbons (PAHs). Heavy metal exposure can cause an increase in oxidative DNA damage, as reported by [34]. Urinary 8-OHdG concentration was significantly correlated with the combined metals exposure index in working children compared with school children [35]. The present study examined the effects of multiple metal exposures by calculating the composite metal exposure index, as the recruited subjects were exposed to several metals. A strong positive connection was observed between 8-OHdG and the composite metal exposure index (r = 0.747, p < 0.0001), as previously reported by [36].
3.3. Mechanism of oxidative stress caused by heavy metals exposure
Multiple studies have demonstrated that oxidative stress in living cells arises from an equilibrium between creating free radicals and forming antioxidants, which are responsible for neutralizing the reactive substances or mending the consequent harm. Figure 3 depicts the impact of heavy metals on a cell and the equilibrium between the generation of reactive oxygen species (ROS) and the subsequent protection provided by antioxidants. Cellular antioxidants, such as glutathione, shield the cell from harmful free radicals such as superoxide anion O2•−, hydroxyl radical (•OH), and hydrogen peroxide (H2O2). When exposed to heavy metals, the amount of reactive oxygen species (ROS) in the body increases, while the level of antioxidants drops, Fig. (3).
Statistical analysis revealed that the mean concentration of Hemoglobin (Hb) was decreased significantly in the exposed group (P < 0.019) compared to the control group. In contrast, no statistically significant differences were found between the two groups as regards WBCs, RBCs, platelets count and hematocrit percentage, table (6).
Table (6): The mean and standard deviation of the haematological parameters among the control and exposed group
|
Control (n = 30)
|
Exposed (n = 60)
|
T-test
|
P-value
|
|
Mean ± SD
|
Mean ± SD
|
|
|
Hb (g/dl)
|
14.7 ± 1.04
|
14.14 ± 1.17
|
2.39
|
0.019
|
WBCs x103 /cmm
|
7.55 ± 1.9
|
7.79 ± 2.18
|
1.59
|
0.624
|
RBCs ×106/cmm
|
5.08 ± 0.3
|
5.17 ± 0.45
|
-0.91
|
0.362
|
HCT %
|
42.72 ± 2.7
|
42.20 ± 3.4
|
0.743
|
0.459
|
Platelets x103 /cmm
|
248.93 ± 50.2
|
240.09 ± 57.3
|
0.720
|
0.473
|
Statistical analysis revealed that the activity of AST, ALT, ALP and γGT significantly elevated in the exposed group at (P < 0.004), (P < 0.006), (P < 0.006), (P < 0.0001) respectively when compared with the control group. At the same time, levels of T.P and Albumin significantly decreased in the exposed group at(P < 0.008) and (P < 0.008), respectively, when compared with the control group. The level of urea and uric acid significantly increased in the exposed group at (P < 0.025), (P < 0.0001), but no significant difference for creatinine in the exposed group when compared with the control group at (P < 0.644). In contrast, the activity of LDH significantly increased in the exposed group at (P < 0.0001) when compared with the control group, table (7).
Table (7): Liver and Kidney function tests in the control and exposed group.
|
Control (n = 30)
|
Exposed (n = 60)
|
T-test
|
P-value
|
|
Mean ± SD
|
Mean ± SD
|
|
|
AST(U/L)
|
27.21 ± 6.61
|
33.55 ± 9.23
|
-3.26
|
0 .002
|
ALT(U/L)
|
24.6 ± 4.67
|
30.76 ± 10.96
|
-2.84
|
0.006
|
ALP(U/L)
|
73.1 ± 14.52
|
81.22 ± 19.08
|
-1.99
|
0.049
|
T.P(g/dl)
|
7.45 ± 0.47
|
7.18 ± 0.4
|
2.71
|
0.008
|
Alb.(g/dl)
|
4.56 ± 0.36
|
4.19 ± 0.68
|
2.69
|
0.008
|
γGT(U/L)
|
23.24 ± 7.14
|
36.44 ± 15.09
|
-4.39
|
0.0001
|
LDH (U/L)
|
249.6 ± 52
|
504.3 ± 117
|
-10.97
|
0.0001
|
Urea(mg/dl)
|
29.1 ± 5.21
|
32.5 ± 6.98
|
-2.3
|
0.025
|
Creatinine(mg/dl)
|
0.79 ± 0.15
|
0.81 ± 0.2
|
-0.46
|
0.644
|
U.A (mg/dl)
|
4.8 ± 1.2
|
6.52 ± 1.3
|
-5.9
|
0.0001
|
3.4. Synergistic effects of the exposure to a cocktail of metals (Al, Cu, Cr, Cd, Ni & Zn) on biochemical parameters level among exposed workers.
Correlations between composite exposure with liver and kidney parameters showed a positive correlation with AST, ALT, ALP, Alb, γGT, LDH, Urea, Creatinine and Uric acid with P< (0.001, 0.044, 0.91, 0.125, 0.314, 0.136, 0.021, 0.425, 0.307), respectively. At the same time, there was a negative correlation with Total protein (P < 0.248), as shown in Table (8).
Table (8): Correlations between Composite exposure index with Liver and kidney parameters
|
Composite exposure index
|
R
|
P-value
|
AST(U/L)
|
0.446
|
0.001
|
ALT(U/L)
|
0.286
|
0.044
|
ALP(U/L)
|
0.016
|
0.91
|
T.P(g/dl)
|
-0.166
|
0.248
|
Alb.(g/dl)
|
0.221
|
0.125
|
γGT(U/L)
|
0.145
|
0.314
|
LDH (U/L)
|
0.214
|
0.136
|
Urea(mg/dl)
|
0.325
|
0.021
|
Creatinine(mg/dl)
|
0.156
|
0.425
|
U.A (mg/dl)
|
0.147
|
0.307
|
3.5. Duration of exposure
The exposed group (G2) was divided into two subgroups based on the Duration of exposure to metal dust to study the relative relation between the Duration of exposure to metal dust and metal level. G2a (including 34 male workers) had less than 15 years of exposure to metal dust, and G2b (including 26 male workers) had more than 15 years of exposure. G1 included 30 males. Statistical analysis revealed that the mean MDA level and median 8-OHdG show a high increase in exposed groups G2b more than G2a with a high significance of P < 0.0001 compared with the control group G1. There was also a significant difference between the exposed groups G2a and G2b (P < 0.05). Statistical analysis revealed that the GSH level decreased in exposed groups G2b less than G2a compared with the control group G1 with a significance of P < 0.01. At the same time, there was a significant difference between exposed groups G2a and G2b at P < 0.05, Fig. (4).
3.6. Diagnostic validity of the single markers
ROC curves were developed to assess the diagnostic efficacy of specific oxidative indicators. The MDA serves as a marker with the highest Area Under the Curve (AUC), enabling effective differentiation between the exposed and control subjects (AUC = 0.968; Sensitivity = 90%, Specificity = 100%; P < 0.0001). At the same time, AUC for GSH was (0.936; Sensitivity = 75%, Specificity = 100%; P < 0.0001), and AUC for 8-OHdG was (0.936; Sensitivity = 60.29%, Specificity = 96.43%; P < 0.0001), respectively as shown in Table (9) and Figure (5), Figure (6). Therefore, MDA may be used as an oxidative stress marker to follow up with exposed workers.
Table (9): Diagnostic sensitivity and Specificity of MDA, 8-OHdG and GSH to distinguish between controls from exposed workers:
Control vs. exposed
|
|
MDA
|
GSH
|
8-OHdG
|
Cutoff
|
> 12.2
|
≤ 22.33
|
> 29.9
|
AUC
|
0.968
|
0.936
|
0.816
|
SE
|
0.0156
|
0.024
|
0.040
|
P-value
|
< 0.0001
|
< 0.0001
|
< 0.0001
|
Sensitivity %
|
90
|
75
|
60.29
|
Specificity %
|
100
|
100
|
96.43
|
PPV %
|
100
|
100
|
97.9
|
NPV %
|
82.4
|
65.1
|
46.6
|
Youden index
|
0.9
|
0.75
|
0.566
|
AUC: Area Under Curve; SE: Standard error; PPV: Positive Predictive Value; NPV: Negative Predictive Value.