|Year : 2015 | Volume
| Issue : 2 | Page : 102-109
Neopterin: A candidate biomarker for the early assessment of toxicity of aluminum among bauxite dust exposed mine workers
Shubhangi K Pingle, Lucky R Thakkar, Aruna A Jawade, Rajani G Tumane, Ruchika K Jain, Pravin N Soni
Department of Biochemistry, National Institute of Miners' Health, Nagpur, Maharashtra, India
|Date of Web Publication||14-Sep-2015|
Shubhangi K Pingle
Department of Biochemistry, National Institute of Miners Health, JNARDDC Campus, Wadi, Nagpur - 440 023, Maharashtra
Source of Support: All authors would like to thank the ministry of mines (S and T) for funding the study. Authors would also like to acknowledge the director of National Institute of Miners' Health for their constant support and encouragement, Conflict of Interest: None declared.
Introduction: Bauxite ore is a major source of aluminum (Al) which contains approximately 35–60% Al by weight. Occupational and environmental bauxite dust exposure may cause toxicity by interaction with human biological systems resulting in oxidative stress (OS) and cell death. A neopterin derivative as an antioxidant is able to modulate cytotoxicity by the induction of OS. Materials and Methods: A total of 273 subjects were selected for blood collection from three different major Al producing bauxite mines and were categorized into three groups as experimental (Exp) (n = 150), experimental controls (ExC) (n = 73) and control (Con) (n = 50). Whole blood and serum samples were used for measurement of Al, neopterin, urea and creatinine values. Statistical analysis was performed using R-2.15.1 programming language. Results and Discussion: The result showed that age, body mass index and the behavioral habits, that is, smoking, tobacco and alcohol consumption have possible effects on neopterin level. Serum neopterin levels were found to be significantly higher (P <0.0001) in the experimental group as compared to other groups. Significantly positive correlation (P < 0.0001) was observed between neopterin and creatinine. It was also observed that neopterin level increases as the duration of exposure increases. Conclusion: On the basis of findings it was concluded that exposure to bauxite dust (even at low levels of Al) changes biochemical profile leading to high levels of serum neopterin. Levels of serum neopterin in workers exposed to bauxite dust were probably examined for the 1st time in India. The outcome of this study suggested that serum neopterin may be used as potential biomarker for early detection of health risks associated with bauxite dust exposed population.
Keywords: Aluminum, bauxite, biomarkers, creatinine, enzyme-linked immunosorbent assay, neopterin, oxidative stress
|How to cite this article:|
Pingle SK, Thakkar LR, Jawade AA, Tumane RG, Jain RK, Soni PN. Neopterin: A candidate biomarker for the early assessment of toxicity of aluminum among bauxite dust exposed mine workers. Indian J Occup Environ Med 2015;19:102-9
|How to cite this URL:|
Pingle SK, Thakkar LR, Jawade AA, Tumane RG, Jain RK, Soni PN. Neopterin: A candidate biomarker for the early assessment of toxicity of aluminum among bauxite dust exposed mine workers. Indian J Occup Environ Med [serial online] 2015 [cited 2020 Jul 3];19:102-9. Available from: http://www.ijoem.com/text.asp?2015/19/2/102/165332
| Introduction|| |
Many minerals are mined all over the country among which bauxite is an important mineral which is mined for the extraction of aluminum (Al). India ranks sixth for the production of bauxite and eighth for Al in the world. Bauxite is a naturally occurring, heterogeneous are composed of one or more aluminum hydroxide minerals and mixtures of various minerals such as silica, iron oxide, titania, aluminosilicate and trace impurities. Mine workers are daily exposed to bauxite dusts through smelting, grinding, crushing and various other activities. Long-term occupational exposure to bauxite dusts may lead to adverse health conditions which can be identified by the expression of biomarkers on the commencement of the disease. Bauxite ore mainly contains alumina (Al2O3) approximately 35–60% by weight. Al is the third most abundant mineral present in the earth crust. The toxicity of Al to human beings primarily on the nervous system and the respiratory system has been well documented by Agency for Toxic Substances and Disease Registry. Al also causes hematological, musculoskeletal, genetic disorders and affects bone metabolism. In addition, exposure to Al enhances the oxidative stress (OS) via a variety of ways. The study of effector biomarker may help in the identification of the root cause of the disease caused due to exposure of Al containing bauxite dust.
Neopterin, biomarker of cell-mediated immunity
Neopterin is an early and valuable biomarker of cellular immunity which is a pyrazinopyrimidine compound of molecular weight 253D belongs to the class of pteridine and is soluble in plasma or serum. The neopterin is produced by activated monocytes, macrophages and dendritic cells following stimulation with γ-interferon. It is also known as gate keeper molecule due to its superiority of production before the onset of symptoms in adverse conditions. It has a special ability to produce in the primates. It can be helpful for longer duration of the study because of its stability as compared to other derivatives of pteridine pathway. Neopterin is used as a prognostic indicator for cell-mediated immunity (CMI), chronic infection, immune stimulation and also serve as an indirect indicator for OS. It has been suggested that neopterin plays an important role in the modulation of oxygen radical-mediated processes and activate the reactive oxygen species.
Baydar et al., pointed out in their study that neopterin level increases with the duration of exposure with Al. There are studies which show that Al causes inhibition of dihydroneopterin reductase (DHPR) activity. DHPR is an important enzyme in pteridine pathway, involved in tetrahydrobiopterin (BH4) synthesis.,, Al induced reduction of DHPR activity account for pathologic association with Al intoxication as reported by certain studies., The main purpose of this study was to investigate serum neopterin levels to find out the effect of Bauxite dust exposure in health related risk to the mine workers.
| Materials and Methods|| |
The three different major bauxite producing open cast mines from three different zones around the country were selected for study. The present study is an exploratory and stratified randomized study. A standard questionnaire was used to record information on baseline characteristics such as age, sex, height, weight, diet, habits like smoking, tobacco chewing, alcohol consumption, duration of exposure to bauxite dust, health history and medication etc. Height and weight were recorded for calculation of body mass index (BMI). Informed consent was obtained from all study subjects. The study was approved by Institutional Ethics Committee and Research Advisory Committee of National Institute of Miners' Health, Nagpur. Workers having exposure period more than 1-year were included in this study and those who were occupationally exposed to any known chemical agents, taking antacids, history of chronic diseases such as neurological disorders, diabetes, cardiovascular diseases, anemia, etc., and female workers were excluded from the study.
Totally, 273 subjects were selected for collection of blood from three different Indian open cast mines (HINDALCO, BALCO and NALCO) and were further divided into three groups. Subjects who were directly exposed to bauxite dust were categorized into experimental group (Exp) (n = 150), for comparison, age and sex matched subjects who were residing at the same geographical region but not directly exposed to bauxite dust were selected as experimental controls (ExC) (n = 73). Healthy individuals were chosen as a control group (Con) (n = 50).
Blood samples were collected in a medical room from subjects before the start of the shift. 5 mL metal free, sterile syringes were used for the collection of blood. 5 mL blood samples were collected. 2 mL of whole blood (WB) was used for Al estimation while remaining blood was allowed to clot and centrifuged at 1000 rpm for 5 min. The resulting serum samples were transferred to acid washed sterile tubes with disposable polyethylene Pasteur pipettes. Aliquots of serum samples were freeze immediately and stored at −40°C in accordance with accepted procedures. Serum samples were used for further analysis.
>Determination of blood aluminum by inductive coupled plasma atomic emission spectroscopy method
Whole blood Al levels were determined by inductive coupled plasma atomic emission spectroscopy (ICP-AES) method. Briefly, 2 mL of WB is digested by the addition of ultrapure grade concentrated hydrochloric acid (HCl) and nitric acid (HNO3). The resulting solution was digested on a hot plate, and final volume was made up to 10 mL with ultrapure distilled water. The solution was used for Al level estimation by ICP-AES method. Each sample was tested twice.
>Determination of serum neopterin by enzyme-linked immunosorbent assay (kit method)
Neopterin was estimated by standard protocol by competitive enzyme-linked immunosorbent assay method using commercially available kit (Kit-DRG International, Inc., USA Catalog No. EIA-2949). Assay was performed as per the instruction manual of the kit. The absorbance was measured at 450 nm. Each sample was tested in duplicate.
>Determination of serum urea
The serum urea level was determined by urea Berthelot method using commercially available kit (Kit-Beacon). The absorbance was measured at 600 nm. Each sample was tested in duplicate.
>Determination of serum creatinine
The serum creatinine level was determined by alkaline picrate method using commercially available kit (Kit-Precision Biotech). The absorbance was measured at 510 nm. Each sample was tested in duplicate.
All statistical analyses were performed using R-2.15.1 programming language with prevalidated programs. One-way analysis of variance (ANOVA), Chi-square test and t-test of independent samples were used to evaluate the results for neopterin, urea, creatinine and Al. When significant differences were found (P < 0.05), conservative Turkey's test was conducted as a post-hoc test to determine differences between individual groups. Pearson's correlation coefficient graphs of respective data were prepared using Prism (version 5) software (Graph Pad Software, Inc., San Diego, CA).
| Results|| |
During the course of the study, total 273 participants were enrolled. The study subjects were divided into three groups; (a) mine workers exposed to bauxite dust as experimental group (n = 150); (b) subjects from same geographical region working in mines but not exposed to dust as experimental control (n = 73); and (c) healthy individuals were categorized under control group (n = 50).
[Table 1] provides the descriptive statistics of basic characteristics of subjects in three study groups. As regards age, the difference in the mean age across study groups was statistically significant with P < 0.0001 using one-way ANOVA. The mean age of subjects in the control group (37.9 ± 8.61 years) was significantly lower than the other two groups. The mean duration of exposure for subjects in experimental control group (20.71 ± 9.36 years) was insignificantly different than that of experimental group (19.63 ± 9.45 years) as indicated by a P value of 0.422 as per t-test of independent samples. Further, the mean BMI of subjects across study groups differed significantly as revealed by a P value of 0.001 (P < 0.05) using one-way ANOVA. The mean BMI in experimental control group (26.11 ± 4.38 kg/m 2) was significantly higher than the control (24.66 ± 4.38 kg/m 2) and experimental groups (24.05 ± 3.58 kg/m 2). The dietary habits of subjects showed insignificant association with the study groups as indicated by a P value of 0.1463 using Chi-square test. The proportion of subjects with smoking habit in experimental group (40.6%) was significantly higher than that of experimental control (24.6%) and control (14%) group as revealed by P value of 0.0007 (P < 0.05) using Chi-square test. As regards tobacco consumption, the proportion of subjects in Control (38%) and experimental control (36.9%) groups was nearly same and differed insignificantly with that of experimental group (50.6%) as indicated by P value of 0.089 (P > 0.05) using Chi-square test. The proportion of subjects consuming alcohol in experimental group (57.3%) was significantly higher than that of experimental control (35.6%) and control (46%) groups as revealed by P value of 0.008 (P < 0.05).
|Table 1: Descriptive statistics for demographic and behavioral parameters according to study groups|
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[Table 2] provides the mean and standard deviation (SD) of different biochemical parameters and neopterin biomarker according to study groups. Considering that age, BMI and the behavioral habits, that is, smoking, tobacco and alcohol consumption could have a possible confounding effect on the levels of these parameters; analysis of covariance was carried out for each parameter independently to adjust for these confounders and to determine the true effect of exposure. As a result, the adjusted parametric levels were obtained for each subject and are summarized in terms of adjusted mean and SD as shown in [Table 3]. The statistical significance of the difference in the overall mean adjusted values of parameters across study groups was evaluated using one-way ANOVA. The parameters violating the assumption of normality were log-transformed and then the significance testing was carried out. One-way ANOVA revealed that all the parameters differed significantly across three groups. For the majority of the parameters, the significance was contributed by the mean levels in the control group as confirmed through Turkey's post-hoc comparison [Table 4]. The Experimental control and experimental groups showed statistically insignificant difference of mean in all the parameters except neopterin (all with P < 0.0001). It was also observed from [Table 4] that there was a slight increase in the level of Al in the experimental group as compared to both the groups, but it was not statistically significant and lies within normal range. This study through light on defined contaminants observed during the process of Al in blood samples.
|Table 2: Unadjusted mean and SD for different biochemical parameters and biomarker according to study groups and mine|
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|Table 3: Adjusted mean and SD for different biochemical parameters and biomarker according to study groups and mines*|
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|Table 4: Comparative study on three mine wise distribution of data, adjusted mean, and SD for different biochemical parameters and biomarkers according to study groups*|
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[Table 5] depicts the percent change in the overall mean levels of parameters in three groups after adjusting for the confounders. Considering a thumb rule of 10% change, marked reduction in the mean neopterin levels was observed in all the groups after adjustment of confounders, indicating their effect on the parameter while for remaining parameters, the percent change was < 10% suggesting that the confounders had a very negligible role on the parametric levels in statistical sense.
|Table 5: Percentage change in the mean levels of each parameter after adjusting with the confounders|
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[Table 5] shows the mean and SD of each parameter according to mines and three groups. The significance of the difference in the mean levels of each parameter across group was evaluated separately for each mine. Majority of the parameters showed statistically significant difference in the mean levels of groups. The significance was mainly contributed by the control group for all the three mines.
[Figure 1] provides the relationship between neopterin and creatinine levels considering subjects from all three groups. It is evident from the scatter that as neopterin level increases, the creatinine level was also found on a higher side, indicating a positive correlation between the two parameters. Pearson's correlation coefficient for the two parameters was 0.59 with a P < 0.0001 indicating significant positive relationship between the two.
[Figure 2] shows the bar plots for the mean duration of exposure and the mean neopterin level in experimental control and experimental groups. The mean neopterin level was higher in the experimental group as compared to experimental control group, and the difference was statistically significant with P value of 0.0003 (P < 0.05). The mean neopterin level in Experimental group was 1.12 times higher than the experimental group although the mean duration of exposure was very close in two groups. This again suggests the possibility of some hidden factors influencing the neopterin levels in the experimental group.
|Figure 2: Bar charts with error bars showing mean duration of exposure and neopterin|
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[Table 6] provides the mean and SD for neopterin according to behavioral habits of subjects. As regards, the mean neopterin level in the smoking group (13.98 ± 9.21 nmol/L) was significantly higher than that of nonalcoholic group (11.43 ± 8.49 nmol/L) as indicated by a P value of 0.031 (P < 0.05). Moreover, the mean neopterin level in the alcoholic group (13.41 ± 9.15 nmol/L) was significantly higher than that of nonalcoholic group (11.08 ± 8.29 nmol/L) as indicated by a P value of 0.028 (P < 0.05). Further, the mean level for this parameter was higher in tobacco chewing group (14.60 ± 9.29 nmol/L) as compared to nonchewing group (10.32 ± 7.88 nmol/L) as indicated by a P < 0.0001, although the neopterin level was higher from the normal range in all the groups.
|Table 6: Comparison of selected biomarkers according to behavioral habits|
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| Discussion|| |
The present study focuses on evaluating neopterin biomarker and associated biochemical parameters along with behavioral factors among bauxite dust exposed miners. Literature is limited to estimation of neopterin levels in various other diseases. Furthermore, in bauxite mine workers its level is not yet measured.
Significantly high concentration of serum neopterin was found in the experimental group as compared to control and experimental control groups. In agreement with our results, Prakova et al., have performed similar experiment on asbestos and coal dust exposed mine workers in which they found higher serum neopterin levels among the exposed population as compared to the controls. The increase in the level of neopterin may be due to increased CMI response. Being a predictive biomarker of CMI, neopterin levels are adversely affected by factors such as smoking status, diastolic blood pressure and BMI as reported by Alfrey et al., and Tanigawa et al., in two different studies., Several studies showed an inverse correlation between neopterin level and smoking. Our finding shows enhanced neopterin level among smokers which are a paradoxical to the reported studies. Furthermore, the level of serum neopterin was found to higher among tobacco chewers and alcohol consumers as compared to others. The reason for higher level of neopterin among smokers may be due to aerosols and another component of smoke especially nicotine which may be responsible for the activation of CMI and ultimately leads to rise in neopterin level.,
Urea and creatinine levels were also estimated in serum samples. The high level of urea and creatinine was observed among the experimental group as compared to the control group, although they fall under the normal range indicating negligible effect of bauxite dust exposure among exposed population. These results are in relative resemblance to what obtained by Mohammed and Kahtani, who also reported an elevation in serum urea and creatinine levels in Al treated mice. The results showed that there was significant positive correlation between neopterin and creatinine levels (r = 0.59) with a P < 0.0001 though within normal range. Earlier studies established the relationship between neopterin and creatinine in urine samples, but our study is probably the first to report a positive correlation between neopterin and creatinine levels in serum of bauxite exposed mine workers.
The elevated level of mean WB Al in exposed population as compared to the other group may be explained by occupational exposure of workers to bauxite dusts and their inhalation from work environment. Same trend was obtained by Adams, who made a study on foundry workers exposed to Al dusts and he found that foundry workers had higher blood levels of Al compared to controls., Furthermore, Bogdanovic and Bulat found the same trend on their study.
Earlier reports showed no correlation between Al exposure and enzymes of pteridine pathway., Some studies point out that Al causes inhibition of DHPR activity. It has been documented that synthesis of Neopterin involves important molecules of pteridine pathway like DHPR and BH4.,, Hence, it is clear from the above lines that Al may affect neopterin level. The higher mean value of neopterin (12.04 ± 2.14 nmol/L) in experimental group as compared to experimental control group with statistically significant P value of 0.0003 (P < 0.05) indicates the hidden cause of increase in neopterin level among bauxite miners. Increased neopterin concentration in the blood sample indicates increased activation of cellular immunity among bauxite exposed workers. On the basis of the findings of our study, it is proposed that exposure to bauxite dust changes biochemical profile leading to high level of neopterin. Serum neopterin may be used for early detection of health risks in workers exposed to bauxite dust in mines.
| Conclusion|| |
The present study confirms the involvement cell-mediated immune reactions in the pathogenesis of diseases caused due to exposure to bauxite dusts. Levels of serum neopterin in workers exposed to bauxite dust were examined for the 1st time and were found to be significantly higher than those of the control group. Increased serum neopterin levels could be used as a candidate biomarker for effect of exposure to bauxite dust containing Al even in low concentration. The positive correlation of serum neopterin with creatinine is established in this study which was not reported earlier. Moreover, smoking, tobacco chewing and alcohol consumption have possible effects on serum neopterin. Further investigation with large number of sample is needed to confirm the findings of the present study.
| Acknowledgment|| |
All authors would like to thank the ministry of mines (S and T) for funding the study. Authors would also like to acknowledge the director of National Institute of Miners' Health for their constant support and encouragement.
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[Figure 1], [Figure 2]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]