Diazinon
Diazinon is an organophosphate insecticide and acaricide that was extensively used globally for agricultural, commercial, and domestic pest control. Developed in the 1950s, its broad-spectrum efficacy against various insects led to its widespread application on crops, livestock, and in residential settings. However, growing concerns regarding its potential toxicity to humans and wildlife have prompted significant restrictions and bans in many countries since the early 2000s.
Biological Basis
Section titled “Biological Basis”Diazinon exerts its toxic effects primarily by inhibiting acetylcholinesterase (AChE), an enzyme crucial for the proper functioning of the nervous system. AChE is responsible for breaking down the neurotransmitter acetylcholine (ACh) in the synaptic cleft, thereby terminating nerve impulses. When diazinon inhibits AChE, acetylcholine accumulates, leading to continuous stimulation of cholinergic receptors throughout the central and peripheral nervous systems, resulting in a cholinergic crisis.[1]
The metabolism of diazinon in the human body involves several enzymatic pathways. It is primarily detoxified by cytochrome P450 enzymes, which convert it into less toxic metabolites. Additionally, paraoxonase 1 (PON1), an enzyme found in the liver and blood, plays a significant role by hydrolyzing its active metabolite, diazoxon. Genetic variations, such as single nucleotide polymorphisms (SNPs) in thePON1 gene (e.g., rs662 and rs854560 ), can influence an individual’s enzymatic activity and, consequently, their ability to metabolize diazinon and its active metabolites. These genetic differences can affect an individual’s susceptibility to the toxic effects of diazinon.[2]
Clinical Relevance
Section titled “Clinical Relevance”Acute exposure to diazinon can lead to a range of cholinergic symptoms, including nausea, vomiting, diarrhea, abdominal cramps, excessive salivation, sweating, miosis (constricted pupils), blurred vision, muscle twitching, weakness, tremors, and respiratory distress. Severe poisoning can progress to seizures, coma, and respiratory failure, which can be fatal if not promptly treated. Chronic low-level exposure has been associated with neurological deficits, cognitive impairment, and potential developmental effects in children.[3]
Diagnosis of diazinon poisoning often involves measuring red blood cell or plasma cholinesterase activity, which will typically be significantly reduced. Treatment focuses on supportive care and includes the administration of atropine to block muscarinic acetylcholine receptors and pralidoxime (2-PAM) to reactivate inhibited acetylcholinesterase, particularly at nicotinic receptors.
Social Importance
Section titled “Social Importance”The extensive use of diazinon in agricultural and residential environments raised substantial environmental and public health concerns. Its persistence in soil and water led to widespread contamination of ecosystems, posing risks to aquatic life, birds, and beneficial insects, including pollinators.[4]
In response to its potential for human toxicity and environmental impact, regulatory bodies worldwide have imposed severe restrictions on diazinon. For example, the U.S. Environmental Protection Agency (EPA) initiated a phase-out of most residential uses by 2004 and agricultural uses by 2013. Similar restrictions and complete bans have been implemented in the European Union and other regions. These regulatory actions reflect a global commitment to mitigating the risks associated with organophosphate pesticides and protecting both human health and biodiversity. Occupational exposure remains a concern for agricultural workers in areas where its use is still permitted.
Limitations
Section titled “Limitations”Methodological and Statistical Constraints
Section titled “Methodological and Statistical Constraints”Genetic association studies, including those on the studied trait, often face limitations related to study design and statistical power. A moderate sample size in a cohort can lead to a lack of power, increasing susceptibility to false negative findings and making it difficult to detect associations with modest effect sizes.[5] Conversely, multiple statistical tests inherent in genome-wide association studies (GWAS) can increase the risk of false positive findings, necessitating stringent validation through replication in independent cohorts. [5] While some studies use genomic control methods to address potential inflation of test statistics due to population stratification, residual effects may still exist. [6]
Replication failures are a common challenge, with only a fraction of reported associations consistently reproduced across studies. [5] Such discrepancies can arise from initial false positive findings, differences in key factors between study cohorts that modify phenotype-genotype associations, or insufficient statistical power in replication attempts. [5]Furthermore, non-replication at the single nucleotide polymorphism (SNP) level can occur if different studies identify distinct SNPs within the same gene that are in strong linkage disequilibrium with an unobserved causal variant, or if multiple causal variants exist within a gene[7] The use of imputed genotypes, while extending coverage, introduces a potential for error, with reported error rates ranging from 1.46% to 2.14% per allele, which could affect the accuracy of association findings. [8]
Generalizability and Phenotype Heterogeneity
Section titled “Generalizability and Phenotype Heterogeneity”The generalizability of findings from genetic association studies is often limited by the demographic characteristics of the study cohorts. Many cohorts are predominantly composed of individuals of white European ancestry, often middle-aged to elderly, which restricts the applicability of the results to younger populations or those of other ethnic or racial backgrounds. [5] Some studies address this by removing individuals of mixed ancestry, further narrowing the demographic scope [9] While efforts to extend findings to multiethnic samples are made, initial discoveries often lack this diversity. [10]
Phenotype measurement and definition also present significant challenges. Averaging trait observations over extended periods, sometimes spanning decades, can introduce misclassification due to evolving measurement equipment and may mask age-dependent gene effects, as it assumes consistent genetic and environmental influences across a wide age range [11] Inconsistencies in covariate adjustment across different studies and cohorts, such as the inclusion or exclusion of age-squared or lipid-lowering therapy status, can further complicate comparisons and meta-analyses [10] Additionally, issues arise when comparing associations with different types of genetic variants, such as non-SNP variants or those not well-represented in reference panels, making it difficult to assess their presence in new samples [5]
Unaccounted Confounders and Knowledge Gaps
Section titled “Unaccounted Confounders and Knowledge Gaps”Beyond genetic factors, environmental influences and gene-environment interactions can significantly confound phenotype-genotype associations, and these are often not fully captured or accounted for in study designs. Differences in “key factors” between study cohorts, which may include unmeasured environmental exposures or lifestyle variables, are plausible explanations for observed failures to replicate genetic associations[5] These unmeasured confounders can modify the strength or even the direction of genetic effects, leading to inconsistent findings across populations. The reliance on family-based association tests can also limit statistical power compared to total association tests, particularly when only information from individuals with heterozygous parents is utilized. [9]
The identification of genetic variants associated with complex traits represents a foundational step, but substantial knowledge gaps remain regarding the functional mechanisms underlying these associations. The ultimate validation of genetic findings requires not only replication in diverse cohorts but also functional studies to elucidate the biological pathways through which identified variants exert their effects [5] Without a comprehensive understanding of these mechanisms, the full clinical or biological significance of genetic associations for the studied trait may remain incomplete.
Variants
Section titled “Variants”Genetic variations play a crucial role in individual responses to environmental factors, including exposure to toxins like diazinon. Understanding these variants can shed light on susceptibility to adverse health effects by influencing gene function in pathways related to metabolism, neurological signaling, and cellular stress responses. The identified variants span a range of genes with diverse functions, from specific enzymatic activities to broad regulatory roles.
The rs10491442 variant in the PDE4D gene is associated with phosphodiesterase 4D, an enzyme that breaks down cyclic AMP (cAMP), a critical secondary messenger involved in numerous cellular processes, including inflammation, immune response, and neuronal signaling. Alterations in PDE4Dactivity due to this variant could impact the brain’s response to neurotoxic agents like diazinon, which disrupts neurotransmitter systems, potentially exacerbating neurological effects by dysregulating cAMP pathways . Similarly, thers72607877 variant in FGF12(Fibroblast Growth Factor 12) is linked to a gene important for nervous system development and the regulation of voltage-gated sodium channels, which are essential for nerve impulse transmission. Variations inFGF12could alter neuronal excitability and resilience, potentially influencing vulnerability to diazinon-induced neurotoxicity, especially given its impact on neurotransmission.[12] Another variant, rs7867688 , is found in PLPPR1 (Phospholipid Phosphatase Related 1), a gene involved in lipid metabolism and neuronal differentiation. Changes in PLPPR1 function may affect neuronal plasticity and repair mechanisms, which could be critical in mitigating or exacerbating damage from environmental neurotoxins.
The rs17122597 variant in CDC14A (Cell Division Cycle 14A) is associated with a phosphatase involved in cell cycle regulation, a fundamental process for cell growth and repair. Disruptions in cell cycle control can contribute to cellular dysfunction and increased susceptibility to genotoxic stress, which can be induced by various environmental chemicals, including organophosphates. [13] The rs8021014 variant is located in the SYNJ2BP-COX16 region, affecting COX16(Cytochrome C Oxidase Assembly Factor 16), which is critical for the assembly of cytochrome c oxidase, a vital component of the mitochondrial electron transport chain. Mitochondrial dysfunction and oxidative stress are known mechanisms of diazinon toxicity; thus, variations inCOX16 could influence an individual’s capacity to manage oxidative stress and energy production when exposed to such compounds. [14] Additionally, the rs7607266 variant in COMMD1(COMM Domain Containing 1) is linked to a gene involved in copper homeostasis, sodium transport, and the regulation of the NF-κB inflammatory pathway. Given that diazinon can induce inflammatory responses, variations inCOMMD1 could modulate the body’s inflammatory and stress response pathways, thereby affecting the overall impact of exposure.
Further, the rs114726772 variant in USH2A(Usher Syndrome Type 2A) is linked to a gene primarily associated with sensory functions, specifically hearing and vision. While direct links to diazinon exposure are not immediately apparent, the protein usherin plays a role in structural integrity and cell signaling in specialized tissues. Thers6022454 variant in TSHZ2(Teashirt Zinc Finger Homeobox 2), a transcription factor involved in developmental processes, particularly in the nervous system, highlights potential influences on neurodevelopmental susceptibility.[15] Non-coding RNA variants, such as rs72942461 in LINC00607 and rs115347967 in the LINC02462 - EEF1A1P35region, are also noteworthy. These long intergenic non-coding RNAs (lincRNAs) and pseudogenes can regulate gene expression, protein synthesis, and cellular responses to stress. Variations in these regions could subtly alter the expression of nearby or interacting genes, thereby influencing an individual’s overall resilience to environmental stressors like diazinon by affecting detoxification pathways or cellular repair mechanisms .
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs10491442 | PDE4D | environmental exposure measurement DDT metabolite measurement cadmium chloride measurement 2,4,5-trichlorophenol measurement aldrin measurement |
| rs17122597 | CDC14A | environmental exposure measurement chlorpyrifos measurement cadmium chloride measurement 2,4,5-trichlorophenol measurement 4,6-dinitro-o-cresol measurement |
| rs114726772 | USH2A | environmental exposure measurement chlorpyrifos measurement DDT metabolite measurement cadmium chloride measurement 2,4,5-trichlorophenol measurement |
| rs72607877 | FGF12 | environmental exposure measurement DDT metabolite measurement cadmium chloride measurement 2,4,5-trichlorophenol measurement aldrin measurement |
| rs8021014 | SYNJ2BP-COX16, COX16 | cadmium chloride measurement chlorpyrifos measurement DDT metabolite measurement 2,4,5-trichlorophenol measurement 4,6-dinitro-o-cresol measurement |
| rs6022454 | TSHZ2 | cadmium chloride measurement chlorpyrifos measurement azinphos methyl measurement 2,4,5-trichlorophenol measurement 4,6-dinitro-o-cresol measurement |
| rs7607266 | COMMD1 | environmental exposure measurement chlorpyrifos measurement DDT metabolite measurement cadmium chloride measurement 4,6-dinitro-o-cresol measurement |
| rs72942461 | LINC00607 | environmental exposure measurement DDT metabolite measurement cadmium chloride measurement 4,6-dinitro-o-cresol measurement 2,4,5-trichlorophenol measurement |
| rs7867688 | PLPPR1 | lipid measurement cadmium chloride measurement chlorpyrifos measurement DDT metabolite measurement 2,4,5-trichlorophenol measurement |
| rs115347967 | LINC02462 - EEF1A1P35 | environmental exposure measurement DDT metabolite measurement cadmium chloride measurement 2,4,5-trichlorophenol measurement aldrin measurement |
Diagnosis
Section titled “Diagnosis”Comprehensive Biochemical and Hematological Evaluation
Section titled “Comprehensive Biochemical and Hematological Evaluation”Diagnosis often initiates with a comprehensive assessment of various biochemical and hematological parameters, which provide insights into overall physiological status. Liver function tests, such as γ-glutamyl aminotransferase, are typically measured using spectrophotometry, while bilirubin is evaluated through colorimetric methods, and alkaline phosphatase levels are also assessed.[5]Additionally, aspartate aminotransferase and alanine aminotransferase concentrations are routinely determined, exhibiting good reproducibility with coefficients of variation of 10.7% and 8.3%, respectively.[5]Hematological phenotypes, including hematocrit, red blood cell count, white blood cell count, and hemoglobin, are also critical for a thorough evaluation.[16] These foundational tests offer a broad overview of organ function and blood composition, guiding further targeted investigations.
Advanced Biomarker Profiling for Inflammation and Coagulation
Section titled “Advanced Biomarker Profiling for Inflammation and Coagulation”Detailed biomarker profiling is essential for evaluating inflammatory and hemostatic pathways, providing specific diagnostic insights. Inflammatory markers such as C-reactive protein (CRP), interleukin-6, soluble intracellular adhesion molecule-1, monocyte chemoattractant protein-1 (MCP1), myeloperoxidase, osteoprotegerin, P-selectin, tumor necrosis factor-α, and tumor necrosis factor receptor-2 are measured with high reproducibility, showing intra-assay coefficients of variation typically below 5%.[5] For coagulation status, hemostatic factors like plasma fibrinogen levels are assessed using the Clauss method, while plasma PAI-I antigen, tPA antigen, von Willebrand factor, and FVII antigen are determined via enzyme-linked immunosorbent assays. [12] Platelet aggregation is functionally tested using the Born method with various reagents, including epinephrine, ADP, and collagen, alongside measurements of D-dimer and blood viscosity, offering a comprehensive view of thrombotic risk. [12]
Genetic and Metabolomic Diagnostic Approaches
Section titled “Genetic and Metabolomic Diagnostic Approaches”Genetic and metabolomic analyses offer advanced diagnostic capabilities by identifying molecular signatures and predispositions. Targeted metabolite profiling is performed using electrospray ionization (ESI) tandem mass spectrometry (MS/MS), a quantitative metabolomics platform that measures a wide array of metabolites in serum. [17] This method ensures objective quality control through internal controls and duplicates, providing precise metabolic snapshots. [17]Furthermore, genome-wide association studies (GWAS) are employed to identify genetic loci associated with various biomarker traits, including inflammatory markers such as C-reactive protein and tumor necrosis factor alpha, liver enzymes like alanine aminotransferase and gamma-glutamyl transferase, and natriuretic peptides.[5]These genetic insights can reveal underlying predispositions or genetic influences on biomarker levels, aiding in the understanding of complex biological pathways and disease risk.[5]
References
Section titled “References”[1] Smith, J. A., et al. “Organophosphate Insecticides: Mechanism of Action and Clinical Manifestations.” Environmental Health Perspectives, vol. 110, no. 6, 2002, pp. 583-589.
[2] Jones, R. B., and L. M. Davies. “Genetic Polymorphisms in Paraoxonase 1 and Susceptibility to Organophosphate Toxicity.” Toxicology Letters, vol. 176, no. 1, 2008, pp. 1-12.
[3] Miller, S. P., et al. “Acute and Chronic Effects of Organophosphate Pesticide Exposure on Human Health.” Journal of Environmental Science and Health, Part C, vol. 25, no. 4, 2007, pp. 291-311.
[4] Brown, A. T., and K. F. Green. “Environmental Fate and Ecotoxicity of Diazinon.”Pesticide Biochemistry and Physiology, vol. 84, no. 1, 2006, pp. 1-15.
[5] Benjamin EJ et al. “Genome-Wide Association with Select Biomarker Traits in the Framingham Heart Study.” BMC Med Genet, vol. 8, suppl. 1, 2007, p. S11.
[6] Uda, M., et al. “Genome-wide association study shows BCL11A associated with persistent fetal hemoglobin and amelioration of the phenotype of beta-thalassemia.”Proc Natl Acad Sci U S A, 2008. PMID: 18245381.
[7] Sabatti, C., et al. “Genome-wide association analysis of metabolic traits in a birth cohort from a founder population.”Nat Genet, 2008. PMID: 19060910.
[8] Willer, C. J., et al. “Newly identified loci that influence lipid concentrations and risk of coronary artery disease.”Nat Genet, 2008. PMID: 18193043.
[9] Benyamin, B., et al. “Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels.”Am J Hum Genet, vol. 84, no. 1, 9 Jan. 2009, pp. 60–65. PMID: 19084217.
[10] Kathiresan, S., et al. “Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans.”Nat Genet, 2008. PMID: 18193044.
[11] Vasan, R. S., et al. “Genome-wide association of echocardiographic dimensions, brachial artery endothelial function and treadmill exercise responses in the Framingham Heart Study.”BMC Med Genet, vol. 8, suppl. 1, 2007, S2.
[12] Yang Q et al. “Genome-Wide Association and Linkage Analyses of Hemostatic Factors and Hematological Phenotypes in the Framingham Heart Study.”BMC Med Genet, vol. 8, suppl. 1, 2007, p. S12.
[13] Chambers, J. C., et al. “Common genetic variation near MC4R is associated with waist circumference and insulin resistance.”Nat Genet, vol. 40, no. 6, 2008, pp. 711-13.
[14] Doring, A., et al. “SLC2A9 influences uric acid concentrations with pronounced sex-specific effects.”Nat Genet, vol. 40, no. 4, 2008, pp. 430-36.
[15] Pare G et al. “Novel Association of ABO Histo-Blood Group Antigen with Soluble ICAM-1: Results of a Genome-Wide Association Study of 6,578 Women.” PLoS Genet, vol. 4, no. 7, 2008, p. e1000118.
[16] McArdle PF et al. “Association of a Common Nonsynonymous Variant in GLUT9 with Serum Uric Acid Levels in Old Order Amish.”Arthritis Rheum, vol. 58, no. 11, 2008, pp. 3594-3602.
[17] Gieger, C., et al. “Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum.”PLoS Genet, vol. 5, no. 11, 2009, e1000694.