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Estimation Of Haematological Parameters Among Sudanese Worker Exposed to Pesticides

Copyright: © 2021 Ariff S. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Background: exposure to Pesticides may lead to high risk of toxicity, in which these chemical compounds can affect blood component causing chronic diseases and inflammations.

Design: This descriptive case control study was conducted in Khartoum state from November to December 2021.

Objectives: The goal is to measure the effect of pesticides on blood parameters among exposures compared to healthy individuals using the analysis of complete blood count. And to correlate between duration of work according to age and gender.

Materials and Methods: 3 ml of intravenous blood samples were collected from 500 participants, 400 samples from men and 100 samples from women aged between 20-65 years, majority were dealers and the rest of them were working at farms and the structural questionnaire was used.

Results: The results of these study show marked increase in red blood cells and absolute lymphocytes count accompanied by decreased in platelets count, platelets distribution width, mean platelets volume, mean corpuscular haemoglobin, mean corpuscular haemoglobin concentration and red cells distribution width upon exposures to pesticides for long time.

Conclusion: this study concluded that there is decrease in PLTs count, PDW, MPV, MCH, MCHC, RDW, and an increase in RBCs count and absolute lymphocytes count among pesticides exposures.

Keywords:Hematology Parameters, Sudanese, Pesticides

Introduction

Pesticides are a broad group of heterogeneous chemicals that have a significant public health benefit by increasing food production productivity and decreasing food-borne and vector-borne diseases. However, depending on the agent and the exposure, they may pose health risks. Because of their behavior, acute accidental toxic exposures occur more commonly in children. Pesticides are substances that are used to prevent, repel, or destroy pests organisms that compete for food supply, adversely affect comfort, or endanger human health [1]. Several classes of compounds are used for this purpose. More than 20 000 pesticide products with nearly 900 active ingredients are registered for use as insecticides, miticides, herbicides, rodenticides, nematocides, fungicides, fumigants, wood preservatives, and plant growth regulators. Pesticides are ubiquitous in the environment. They are found in food, water, homes, schools, workplaces, and gardens. Pesticides have significant economic, environmental, and public health impacts. Pesticide usage helps improve human nutrition through greater availability, which affect millions of children and adults and kill thousands annually in the United States alone [2,3]. Pesticides also pose human health concerns because they are toxic substances and widely released into the environment. Although the toxic actions of pesticides are targeted at specific pest species. The most commonly used classes of pesticides are composed of different types of chemicals with different mechanisms of action, most insecticides work by interfering with nervous system function. Organophosphates. which account for approximately one half of the insecticides used in the United States, and carbamates, which are widely used in homes and gardens, inhibit the activity of acetylcholinesterase at nerve endings, resulting in an excess of the neurotransmitter acetylcholine and a depolarizing blockade of neural transmission. The effects of carbamates are more readily reversible and of shorter duration. Organochlorines, such as dichlorodiphenyltrichloroethane (DDT) and lindane, interfere with nerve cell membrane cation transport, resulting in neural irritability and excitation of the central nervous system, they are used in varying concentrations to reduce the risk of insect stings and vector-borne diseases such as Lyme disease, West Nile disease, and Rocky Mountain spotted fever. Toxic encephalopathy and seizures can result from ingestion or, less common, from dermal absorption [4,5]. Herbicides, including the chlorophenoxy compounds are primarily irritative to the skin and respiratory tract during acute exposures and work by different mechanisms. Some substances, such as paraquat [6] are highly corrosive and can cause multisystem injury and progressive pulmonary failure [4, 7].

Materials and Methods

This is a case control study was conduct in Khartoum state during the period of two months Jun 2021- December 2021, blood sample was collected from 500 Sudanese workers exposed to pesticides and 500 normal controls. 3 ml of venous blood samples were collected by syringes in EDTA container from individuals that exposed to pesticides and healthy people. Each blood sample was analysed by haematology analyser and results was recorded.

Methodology:
Principle of haematology analyser:

Coulter principle is used in almost every hematology analyzer. The coulter principle is based on the detection and measurement of changes in electrical resistance produced by a particle or cell suspended in a conductive liquid (diluent) traversing through a small aperture. When particles or cells are suspended in a conductive liquid, they function as discrete insulators. When a dilute suspension of particles is drawn through a small cylindrical aperture, the passage of each individual cell momentarily modulates the impedance of the electrical path between two submerged electrodes located on each side of the aperture, creating an electrical pulse. Illustrates the passage of a cell through an aperture. The number of electrical pulses indicates cell count, while the amplitude of the electrical pulse produced depends on the cell’s volume [7].

Statistical assessment was carried out with statistical package for social sciences (SPSS) version 17.0 for windows statistical software.

Results

This study shows statistically significant difference in RBCs count, MCH, MCHC and RDW among case and control, p value (0.005), (0.000), (0.024), (0.023) respectively, and statistical insignificant in HB. PCV and MCV (By the grace of Almighty Allah and his help I complete this study, all praise to him). This present study appears statistically significant difference in lymphocyte among case and control p value (0.000) and statistical insignificant in WBCs count, neutrophil, monocyte, eosinophil, basophil. (Table 2). This current study shows statistically significant in PLTs count, PDW, and MPV in study group case and control P value (0.001), (0.002), (0.000) respectively, (Table 3.3).

This study shows statistically significant in pesticides exposures according to gender in RBCs count, HB and PCV p value (0.001), (0.000), (0.000) respectively, and statistical insignificant in MCV, MCH, MCHC and RDW (Table 4). This present study shows statistical insignificant difference in pesticides exposures according to gender in TWBCs, lymphocyte, neutrophil, monocyte, eosinophil and basophil (Table 5)

This present study appears statistically significant difference in pesticides exposure according to gender in PLTs count p value (0.007) and statistical insignificant in PDW and MPV (Table 6). This current study shows statistical insignificant difference in pesticides exposure according to duration and age in RBCs count, HB, PCV, MCV, MCH, MCHC and RDW (Table 7). This present study appears statistical insignificant difference in pesticides exposure according to duration and age in TWBCs, lymphocyte, neutrophil, monocyte eosinophil, and basophile (Table 8). This current study shows statistical insignificant difference in pesticides exposure according to duration and age in PLTs, PDW and MPV (Table 9)

Discussion

Pesticides are substances that are used to prevent, repel, or destroy pest’s organisms that compete for food supply, adversely affect comfort, or endanger human health [1]. Our study present clear changes in hematological parameters in pesticides exposures group compared with control group, some parameters were found to have unusual levels such as RBCs profile (RBCs count, MCH, MCHC), absolute lymphocytes count and PLTs profile (PLTs count, MPV, PDW). In similar study done by Atef M.K. et al which was conduct in (2016) [8] show agreement with our study in which there is significant decrease in PLTs, MPV and PDW, MCH and MCHC among pesticides workers and sprayers. However, Atef M.K et al reported significant difference in HB, TWBC, and differential leukocytes count which was below the normal compared with control group [9]. Another study of Manel Araoud et al (2012) [9] disagree in the same hematological parameters, there were no significant differences in PLT, RBC and total leukocyte count values between exposed workers and the control group, the MCV and HCT were higher in exposures group compared with control group, whereas MCH and MPV were significantly higher [10]. According to this new report, there is considerable evidence that widely used pesticides may suppress immune responses to bacteria, viruses, parasites, and tumors, making people significantly more vulnerable to disease. Pesticides affect the genetic material directly via stimulation of structural and functional DNA damage, or indirectly disrupting the cellular mitochondria, endoplasmic reticulum, and other factors involved in maintaining cell integrity.

Declarations
Ethical Approval and Consent to Participant:

Approval of This study was obtained from hematology department of medical laboratory science (MLS), Elrazi University, and ministry of health issued by the local ethical committee, Khartoum State, Sudan. Written consent was taken from each member of the study.

Consent for Publication

Not applicable.

Availability Of Data and Materials

The datasets generated during and / or analyzed in this study are not publicly available due to Khartoum vaccinate ethical policy in order to protect participant confidentiality.

Competing Interest

The authors declare that they have no competing interests.

Funding

No funding was obtained for this study

Authors Contributions

AH and AA, MA, SK, RT, and KG contributed in literature search and manuscript writing. AH had the main idea of the study and contributed in manuscript writing, AA, MA, SK, RT, and KG contributed to clinic work, AH contributed in statistical analysis. AH supervised the study and critically reviewed the manuscript. All authors read and approved the final draft of the manuscript.

Acknowledgment

By the grace of Almighty Allah and his help I complete this study, all praise to him

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Journal of Medicine & Biological Studies

Tables at a glance
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Table 1
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Table 2
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Table 3
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Table 4
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Table 5
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Table 6
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Table 7
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Table 8
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Table 9
Figures at a glance
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Figure 1
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Figure 2

RBCs profile

Mean±SD Case

Mean±SD Control

P value

RBCs count cell/l

5.36±0.73

5.03±0.34

0.005S

HB g/dl

14.3±2.01

14.5±0.86

0.615NS

PCV %

44.5±5.72

43.9±2.72

0.566NS

MCV fl

84.4±5.32

85.5±2.35

0.213NS

MCH pg

26.86±2.76

28.51±1.17

0.000S

MCHC g/l

32.17±1.75

32.82±1.00

0.024S

RDW-SDfl

15.08±1.83

14.40±0.95

0.023S

Table 1: Mean of red blood cell profile in Sudanese pesticides exposure compared to health control

WBCs& differential count

Mean±SD
Case

Mean±SD
Control

P value

TWBCs cell/l

6338±1640

5868±1144

0.100NS

Lymphocyte cell/l

2628±0.830

2068±0.470

0.000S

Neutrophil cell/l

3358±1142

3538±1110

0.426NS

Monocyte cell/l

0.282±0.163

0.262±0.133

0.516NS

Eosinophil cell/l

0.203±0.810

0.082±0.086

0.297NS

Basophil cell/l

00.0±00.0

00.0±00.0

-

Table 2: Mean of WBCs& differential count in Sudanese pesticides exposure compared to health control

PLTS profile

Mean±SD Case

Mean±SD Control

P value

PLTs cell/l

209.2±75.2

252.2±39.5

0.001S

PDW fl

15.5±2.7

14.1±1.7

0.002S

MPV fl

11.6±1.01

9.9±1.25

0.000S

Table 3: Mean of PLTs profile in Sudanese pesticides exposure compared to health control

RBCs profile

Mean±SD Male

Mean±SD Female

P value

RBCs count cell/l

5.32±0.610

4.89±0.420

0.001S

HB g/dl

14.8±1.28

13.3±1.60

0.000S

PCV %

45.2±4.12

41.8±4.37

0.000S

MCV fl

85.2±3.14

84.3±5.89

0.284NS

MCH pg

27.81±1.93

27.37±2.95

0.459NS

MCHC g/l

32.7±1.01

31.9±2.11

0.054NS

RDW-SDfl

14.6±1.01

15.1±2.25

0.144NS

Table 4: Mean of red blood cell profile in Sudanese pesticides exposure according to gender

WBCs& differential count

Mean±SD Male

Mean±SD Female

P value

TWBCs cell/l

6023±1499

6300±1232

0.380NS

Lymphocyte cell/l

2317±0.736

2424±0.715

0.507NS

Neutrophil cell/l

3417±1.128

3524±1131

0.667NS

Monocyte cell/l

0.278±0.138

0.256±0.173

0.502NS

Eosinophil cell/l

0.167±0.862

0.083±0.064

0.508NS

Basophil cell/l

00.0±00.0

00.0±00.0

-

Table 5: Mean of WBCs& differential count in Sudanese pesticides exposure according to gender

PLTS profile

Mean±SD Male

Mean±SD Female

P value

PLTs cell/l

219.9±64.4

257.3±53.5

0.007S

PDW fl

14.8±2.48

14.6±2.04

0.797NS

MPV fl

10.9±1.31

10.5±1.58

0.158NS

Table 6: Table 6: Mean of PLTs profile in Sudanese pesticides exposure according to gender

RBCs profile

Duration

P value

Age

P value

R/Pearson

R/Pearson

RBCs count cell/l

0.152

0.131NS

-0.049

0.627NS

HB g/dl

0.007

0.943NS

-0.036

0.724NS

PCV %

0.099

0.325NS

-0.016

0.873NS

MCV fl

0.073

0.468NS

-0.020

0.846NS

MCH pg

-0.141

0.162NS

-0.082

0.417NS

MCHC g/l

-0.177

0.078NS

-0.167

0.097NS

RDW-SDfl

0.113

0.263NS

-0.031

0.760NS

Table 7: Correlation of red blood cell profile with duration of exposure and age

WBCs&differential
count

Duration

P value

Age

P value

R/Pearson

R/Pearson

TWBCs cell/l

-0.164

0.102NS

0.010

0.919NS

Lymphocyte cell/l

0.082

0.417NS

-0.034

0.737NS

Neutrophil cell/l

-0.259

0.009S

0.071

0.480NS

Monocyte cell/l

-0.088

0.382NS

-0.013

0.898NS

Eosinophil cell/l

-0.021

0.837NS

0.023

0.821NS

Basophil cell/l

-

-

-

-

Table 8: Correlation of WBCs& differential count with duration of exposure and age

PLTs profile

Duration

P value

Age

P value

R/Pearson

R/Pearson

PLTs cell/l

-0.348

0.000S

0.052

0.606NS

PDW fl

0.041

0.686NS

-0.043

0.669NS

MPV fl

0.081

0.424NS

-0.182

0.070NS

Table 9: Correlation of PLTs profile with duration of exposure and age

Figure1: Weak negative correlation

Figure 2: Medium negative correlation