Abstract
-
Background
The global use of pesticides steadily increased until the early 2010s. Pesticides play a significant role in agriculture in Korea. Metabolic syndrome is more prevalent in rural areas than in urban areas. This study explored the potential association between organophosphate and pyrethroid pesticide exposure and metabolic syndrome.
-
Methods
This study enrolled 1,317 individuals who participated in the Pesticide Exposure and Intoxication Study conducted by the Dankook University Hospital Center for Farmers’ Safety and Health from 2014 to 2019. Urinary levels of dimethylphosphate, dimethylthiophosphat, diethylphosphate, and diethylthiophosphate were measured to assess organophosphate pesticide exposure and urinary levels cis-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropane carboxylic acid, trans-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropane carboxylic acid, cis-3-(2,2-dibromovinyl)-2,2-dimethylcyclopropane carboxylic acid, and 3-phenoxybenzoic acid were measured to assess pyrethroid pesticide exposure.
-
Results
The odds ratio for the 4th quartile group of organophosphate metabolites concentration was 1.48 (95% confidence interval: 1.06–2.09) compared to the 1st quartile group after adjustment for general factors. In addition, a positive trend was observed across the quartile groups of organophosphate metabolites concentration. A positive trend was noted across the quartile groups of organophosphate metabolites in males, while no significant association was observed in females. Furthermore, no significant associations were observed between metabolic syndrome and pyrethroid metabolites concentration.
-
Conclusions
A positive correlation was observed between the prevalence of metabolic syndrome and the concentrations of urinary organophosphate metabolites, consistent with previous research finding. This association may be attributed to the action of organophosphates as acetylcholinesterase inhibitors, stimulating beta cells in the islets of Langerhans. This can lead to alterations in lipid metabolism and insulin resistance, ultimately leading to metabolic syndrome development. Metabolic syndrome is a major contributor to cardiovascular disease; therefore, it is necessary to identify the risk factors unique to rural areas, such as pesticide exposure.
-
Keywords: Pesticides; Pyrethrins; Organophosphates; Metabolic syndrome; Farmers; Agriculture
BACKGROUND
Global pesticide use steadily increased until the early 2010s. From 2000 to 2019, global pesticide use increased by 36%, reaching 4.2 million tons in 2019.
1 In the same year, pesticide use per cropland in South Korea was 10.59 kg per hectare, higher than the worldwide average of 2.69 kg per hectare.
1 Despite various eco-friendly farming methods, pesticides continue to play a crucial role in South Korea's agriculture.
Pesticides are typically categorized into four primary groups based on their chemical composition and the nature of their active ingredients: organochlorines, organophosphorus compounds, carbamates, and pyrethrin/pyrethroids.
2 Organophosphates are the most widely used insecticides in agriculture, homes, gardens, and veterinary practices. They exhibit toxicity by inhibiting acetylcholinesterase in the central and peripheral nervous system.
3 Pyrethroids, commonly employed in household and commercial applications such as fly control, are less persistent in the environment and less toxic to mammals than other pesticides.
2
Several studies have been conducted to identify the health effects of pesticides based on their chemical compositions. The health effects of pesticides involving the nervous system, such as Parkinson’s disease, have been well-documented.
4
The relationship between pesticide exposure and metabolic syndrome has been extensively studied recently. Several studies have assessed the relationship between organochlorine pesticide exposure and metabolic syndrome.
5,6 A systematic analysis demonstrated that overall exposure to pesticides and their contaminants increases metabolic syndrome risk by an odds ratio of 1.30 (95% confidence interval [CI]: 1.22–1.37), and organochlorine pesticide exposure increases the risk by an odds ratio of 1.23 (95% CI: 1.14–1.32).
7 However, the relationship between organophosphate or pyrethroid pesticide exposure and metabolic syndrome has not been clearly identified.
6
Metabolic syndrome, characterized by a combination of factors, such as obesity, high blood pressure, insulin resistance, and abnormal lipid profiles, has complex underlying causes, including genetic, lifestyle, and environmental factors.
8 Metabolic syndrome is a significant health concern because it is strongly associated with an increased risk of atherosclerotic cardiovascular disease development.
8 In a cohort study conducted in South Korea including 10,044 individuals, metabolic syndrome prevalence in rural communities was higher than that in urban communities.
9 However, the underlying factors for the higher prevalence of metabolic syndrome in rural areas remain unclear. Given that pesticide use is more abundant in rural areas, we hypothesized that pesticide exposure may be a contributing factor.
Therefore, this study aimed to investigate the relationship between pesticide exposure and metabolic syndrome using data from the Pesticide Exposure and Intoxication Study conducted by the Dankook University Hospital Center for Farmers’ Safety and Health. In addition, this study aimed to assess the relationship between organophosphate and pyrethroid pesticide exposure and metabolic syndrome by measuring urinary pesticide metabolites concentrations.
METHODS
Study population
This study used data from the Pesticide Exposure and Intoxication Study conducted by Dankook University Hospital Center for Farmers’ Safety and Health between 2014 and 2019. The study enrolled farmers who were members of local agricultural cooperatives in eight minor counties in Chungcheongnam-do Province (Gongju, Cheongyang, Yesan, Cheonan, Hongseong, Nonsan, Geumsan, and Taean). The purpose of the study was explained through face-to-face surveys, and consent was obtained from all volunteers prior to the participation. Total of 1,685 farmers from Chungcheongnam-do Province, Republic of Korea, initially participated in the survey.
Among the 1,685 participants, those who did not fully respond to relevant survey questions, those who did not affirmatively respond to the question, ‘During the past 12 months, have you been farming as an occupation?’, those who refused to provide blood or urine samples, and those with urine creatinine < 30 mg/dL were excluded. Finally, 1,317 participants were included in the study.
Urinary pesticide level
Spot urine samples were collected and stored at –20°C. Frozen urine specimens were thawed approximately 30 minutes prior to analysis, and the supernatant obtained after centrifugation was used. Quantitative analysis was performed by gas chromatography/mass spectrometry (GC/MS). The concentrations of metabolites in the samples were determined by establishing calibration curves based on the peak areas of each compound relative to the standard substances. The analysis instrument employed was the AG 7693 autosampler connected to the Agilent Technologies 7000C GC/MS Triple Quad. Neonatal urine was used as a blank to verify the calibration curves, recovery rates, and detection limits. Additionally, for validation purposes, the absence of each analyte was confirmed before conducting the experiments.
To assess organophosphate pesticide exposure, urinary dialkyl phosphate levels, particularly dimethylphosphate (DMP), dimethylthiophosphate (DMTP), diethylphosphate (DEP), and diethylthiophosphate (DETP) levels, were measured. To assess pyrethroid pesticide exposure, urinary levels of cis-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropane carboxylic acid (cis-DCCA), trans-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropane carboxylic acid (trans-DCCA), cis-3-(2,2-dibromovinyl)-2,2-dimethylcyclopropane carboxylic acid (DBCA), and 3-phenoxybenzoic acid (3-PBA) were measured as biomarkers. The limit of detection (LOD) varies annually owing to adjustments in the analysis equipment settings. Levels measured below the LOD were regarded as LOD/√2.
10 Urinary metabolites levels were measured after creatinine adjustment. For statistical analysis, the total concentrations of urinary DMP, DMTP, DEP, and DETP were used as indicators of organophosphate pesticide exposure.
11 Similarly, the total concentrations of urinary cis-DCCA, trans-DCCA, DBCA, and 3-PBA were used as indicators of pyrethroid pesticide exposure.
12
Metabolic syndrome was defined according to the clinical practice guidelines of the Korean Society of Cardiometabolic Syndrome, based on the modified National Cholesterol Education Program Adult Treatment Panel-III criteria.
13 The criteria are as follows: waist circumference ≥ 90 cm (males) or ≥ 85 cm (females), triglycerides ≥ 150 mg/dL, high-density cholesterol (HDL) < 40 mg/dL (males) or < 50 mg/dL (females), blood pressure ≥ 130/85 mmHg or under antihypertensive medication, fasting glucose ≥ 100 mg/dL or under oral hypoglycemic agents. Metabolic syndrome was defined as meeting three or more of these criteria.
The demographic and social characteristics included alcohol consumption, smoking history, regular exercise, marital status, body mass index (BMI), and working hours. Subjects who reported never drinking alcohol in their lifetime were classified as non-drinkers, whereas those who reported having previously drunk or were currently drinking alcohol were classified as former or current drinkers. Subjects who reported smoking fewer than five packs of cigarettes in their lifetime were classified as non-smokers, while those who reported smoking more than five packs in their lifetime were classified as former or current smokers. Subjects who exercised more than once per week were classified as participating in regular exercise. Marital status was classified into two groups: those who were married or cohabiting, and others. Individuals with A BMI of 25 kg/m² or greater were classified as obese, whereas those with a BMI of less than 25 kg/m² were classified as normal weight.
Statistical analysis
Pearson’s chi-squared test and t-test were performed to compare urinary organophosphate and pyrethroid metabolites levels among general characteristics. Pearson’s chi-squared test, Cochran-Armitage test, and analysis of variance were conducted to compare the general characteristics of the different urinary organophosphate and pyrethroid concentration groups.
A multivariate logistic regression analysis was performed to adjust for multiple confounding factors. All statistical analyses were performed using R version 4.1.1 (R Foundation for Statistical Computing, Vienna, Austria), with the significance level set at p < 0.05.
Ethics statement
The data used in present study and approved by the Institutional Review Board of Dankook University Hospital (IRB No. 2020-04-009). Informed consent was submitted by all subjects when they were enrolled.
RESULTS
Table 1 summarizes the general characteristics of the study population. The proportion of former or current alcohol drinkers among males (75.3%) was greater than that observed among females (31.8%). Similarly, the proportion of former or current smokers among males (69.1%) was markedly higher than that among women (0.8%).
A comparison of the general characteristics between the normal and metabolic syndrome groups is presented in
Table 2. Of the 1,317 study participants, 537 (40.8%) were classified as having metabolic syndrome. The average age in the metabolic syndrome group was significantly higher than that in the normal group. The BMI of the metabolic syndrome group was significantly greater compared to the normal group. In terms of the major farming methods, open-field farming was most prevalent in the metabolic syndrome group, whereas irrigated farming was most prevalent in the normal group. No significant differences were found in age, alcohol consumption, smoking history, regular exercise, marital status, duration of agricultural work, or average working hours between the normal and metabolic syndrome groups.
Table 3 presents a comparison of the general characteristics among the quartile groups based on urinary organophosphate metabolites concentrations. There were significant differences in the average age and duration of agricultural work between the quartile groups. The highest average age was found in the 2nd quartile group (mean age, 64.5 ± 8.7 years). The duration of agricultural work was the longest in the 1st quartile group (mean duration, 34.1 ± 14.0 years), while it was the shortest in the 4th quartile group (mean duration, 30.6 ± 15.6 years). Notably, the prevalence of metabolic syndrome exhibited a positive trend across the quartile groups, with the highest prevalence observed in the 4th quartile (48.6%) and the lowest prevalence in the 1st quartile (35.8%).
A comparison of the general characteristics among the quartile groups based on urinary pyrethroid metabolites concentrations is presented in
Table 4. Significant differences in the prevalence of metabolic syndrome were observed among the quartile groups. The highest prevalence was observed in the 3rd quartile group, whereas the lowest was in the 4th quartile group. Although there is a decrease in the 4th quartile group, the prevalence of metabolic syndrome showed a positive trend across quartile groups.
The odds ratio for metabolic syndrome according to general factors and organophosphate and pyrethroid metabolites concentration are shown in
Table 5. The odds ratio for females was 1.59 (95% CI: 1.10–2.32) compared to males. The odds ratio for age was 1.04 (95% CI: 1.02–1.06) for each additional year of age. Former or current smokers had significantly higher odds of metabolic syndrome than non-smokers, with an odds ratio of 1.52 (95% CI: 1.08–2.15). Obesity was also associated with notably higher odds of metabolic syndrome, with an odds ratio of 5.95 (95% CI: 4.59–7.71). The urinary organophosphate metabolites concentration showed the odds ratio of 1.04 (95% CI: 1.00–1.08) for metabolic syndrome, although this slight difference was not statistically significant (
p = 0.053). Urinary pyrethroid metabolites concentration did not show a significant odds ratio. Furthermore, alcohol consumption, regular exercise, marital status, and duration of agricultural work were not significant predictors of metabolic syndrome.
Table 6 shows the odds ratio for metabolic syndrome among the quartile groups based on urinary organophosphate and pyrethroid metabolites concentrations. In terms of organophosphate metabolites, the odds ratio for the 4th quartile group was 1.48 (95% CI: 1.06–2.09) compared to the 1st quartile group after adjustment for general factors. Notably, a positive trend was observed across the quartile groups. In terms of pyrethroid metabolites, the odds ratio for the 3rd quartile group was 1.35 (95% CI: 0.97–1.90) compared to the 1st quartile group after adjustment.
Table 7 shows the odds ratios for metabolic syndrome after stratification according to sex. In males, the odds ratio for metabolic syndrome in the 4th organophosphate metabolites concentration quartile group was 1.77 (95% CI: 1.15–2.75) compared to the 1st quartile group. Notably, a positive trend was observed across the quartile groups. The odds ratio for metabolic syndrome in the 3rd quartile group of pyrethroid metabolites concentration was 1.61 (95% CI: 1.03–2.52), although no trend was evident. In the female group, no significant odds of metabolic syndrome were observed for either organophosphate or pyrethroid metabolites concentrations.
DISCUSSION
Significant differences in the prevalence of metabolic syndrome were observed among different urinary organophosphate metabolites concentrations. Additionally, a positive correlation was observed between the prevalence of metabolic syndrome and the concentrations of urinary organophosphate metabolites. In a previous study by Lee et al., the total concentration of urinary organophosphate metabolites was higher in the metabolic syndrome group than in the normal group; however, the difference was not statistically significant, with a
p-value of 0.074.
6 The study enrolled 104 male individuals, and the authors acknowledge the study’s limitations regarding a relatively small sample size. The present study used a large sample size and demonstrated a positive correlation between metabolic syndrome and organophosphate metabolites concentrations. A study conducted by Glover et al. utilized a cohort of 916 participants and reported that among the quartiles of organophosphate exposure, the odds of metabolic syndrome for individuals in the 3rd quartile of exposure increased to 3.61 (95% CI: 1.16–30.50). The results of the present study are consistent with those of the aforementioned study, indicating an increase in the prevalence of metabolic syndrome with higher organophosphate exposure.
14
After stratification by sex, males showed a significant difference in the prevalence of metabolic syndrome in the 4th organophosphate metabolites concentration quartile, and a significant trend was noted across quartile groups. However, no significant differences were observed among females.
Cardiovascular disease has been the second most common cause of death in South Korea for over a decade. In 2022, the mortality rate due to cardiovascular disease in South Korea was 65.8 per 100,000, an increase from 52.5 per 100,000 in 2012.
15 Metabolic syndrome is associated with a 2.35-fold (95% CI: 2.02–2.73) higher risk of cardiovascular disease, a 2.40-fold (95% CI: 1.87–3.08) higher risk of cardiovascular disease mortality, and a 1.99-fold (95% CI: 1.61–2.46) higher risk of myocardial infarction.
16 In particular, this study revealed that the prevalence of metabolic syndrome was higher in rural communities than in urban communities in South Korea.
9 However, a similar study conducted in India indicated a significantly higher prevalence of metabolic syndrome in urban areas than in rural areas (54.8% vs. 46.2%,
p = 0.002).
17 This disparity may be attributed to variations in demographic or social factors across countries, as well as the distinct occupational structures between rural and urban areas across these countries.
18 The pesticide usage per cropland in South Korea in 2019 was 10.59 kg per hectare, substantially higher than the 0.36 kg per hectare in India.
1 This study indicates that pesticide exposure, especially organophosphate exposure, maybe a potential risk factor for metabolic syndrome.
However, the mechanism by which organophosphates influence the development of metabolic syndrome remains unclear. Considering that organophosphate is an acetylcholinesterase inhibitor, one plausible explanation is that it may stimulate muscarinic receptors in beta cells in the islets of Langerhans, resulting in an increase in insulin. Hyperinsulinemia may lead to metabolic dysfunction such as incident diabetes or nonalcoholic fatty liver disease.
19 A previous systematic review showed that multiple animal studies indicated that organophosphate exposure can lead to alterations in lipid metabolism, glucose homeostasis disturbance, and insulin resistance.
20,21 This can lead to alterations in lipid metabolism and insulin resistance, ultimately leading to metabolic syndrome.
21 In addition, the sex disparity in organophosphate exposure among individuals with metabolic syndrome may be attributed to the protective endocrine effects of estrogens. Specifically, 17β-estradiol (E2) promotes energy homeostasis, enhances body fat distribution, ameliorates insulin resistance, improves β-cell function, and reduces inflammation.
22
Among the pyrethroid metabolites level quartile groups, the prevalence of metabolic syndrome was the highest in the 3rd quartile group (47.4%), and the lowest in the 4th quartile group (36.8%). Although a positive trend was observed among groups, no significant association was observed after adjustment. The reduced prevalence of metabolic syndrome observed in the 4th quartile group may be attributable to its lower obesity rates. Although the results were not statistically significant, the average working hours were the longest in the 4th quartile group, and the work intensity might have been higher compared to the other groups, leading to greater exposure to pyrethroids. This increased work intensity and longer working hours may have contributed to the reduced obesity rates and the lower prevalence of metabolic syndrome in this group. A previous study reported sex-based differences in the relationship between 3-PBA exposure and obesity risk, with a notable association in females. However, in the same study, no significant associations were observed between obesity and cis-DCCA or trans-DCCA.
23 One possible explanation for this is that our study measured the total concentration of pyrethroid metabolites (cis-DCCA, trans-DCCA, DBCA, and 3-PBA), whereas the previous study analyzed these metabolites individually.
Pyrethroid exposure has been reported to alter lipid metabolism in adipocytes and impair glucose homeostasis in myotubes.
24 Furthermore, other studies have shown that urinary 3-PBA concentration is associated with an increased risk of diabetes.
25 These findings suggest that pyrethroid exposure may induce endocrine disruption and potentially contribute to the development of metabolic syndrome. However, it should be noted that the results of the present study show discrepancies with those of previous studies.
The present study has several limitations. The response rate for some survey questionnaires was low, leading to constraints in considering socioeconomic factors such as income, education status, or food consumption. Additionally, this study assumed pesticide exposure using the sum of the biomarkers. The sum of dialkyl phosphates was used to assess organophosphate exposure, and the sum of cis-DCCA, trans-DCCA, DBCA, and PBA was used to assess pyrethroid exposure. The respective biomarkers were not analyzed. Therefore, in future studies, such biomarkers should be analyzed individually. Furthermore, the study was conducted on an exposure group. However, in this study, 333 individuals exhibited non-detectable levels of organophosphate metabolites, and 111 had non-detectable levels of pyrethroid metabolites. This indicates that the 1st quartile group of urinary organophosphate metabolites was similar to that of the control group. Lastly, the LOD of biomarkers varied over the years due to differences in laboratory settings.
The strength of the present study lies in the use of a relatively large population of 1,317 participants. Additionally, this study measured biomarkers to assess pesticide exposure, which enhanced the ability to evaluate the effects of different classes of pesticides.
Further studies are needed to address these limitations. This may involve establishing a control group for comparison, conducting a thorough survey of socioeconomic status, and analyzing each biomarker. Given the significance of farming methods and cultivation types in pesticide exposure, it is essential to implement appropriate measures to protect against such exposure.
26
CONCLUSIONS
This study explored the relationship between organophosphate and pyrethroid pesticide exposure and metabolic syndrome in Korean farmers. A positive correlation was found between the prevalence of metabolic syndrome and the concentrations of urinary organophosphate metabolites. No significant association between metabolic syndrome and urinary pyrethroid metabolites concentration was observed. This study contributes to the existing literature by utilizing a substantial sample size of 1,317 participants and delving into the dose-response relationship between pesticide exposure and metabolic syndrome. Rural healthcare typically receives less attention than urban healthcare and has distinct demographic and occupational characteristics. Therefore, it is essential to identify rural-specific risk factors for chronic diseases such as metabolic syndrome, and regular check-ups are essential. However, further studies are needed to validate these findings and address the study's limitations.
Abbreviations
cis-3-(2,2-dichlorovinyl)-2
cis-3-(2,2-dibromovinyl)-2
gas chromatography/mass spectrometry
trans-3-(2,2-dichlorovinyl)-2
NOTES
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Competing interests
The authors declare that they have no competing interests.
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Author contributions
Conceptualization: Kim S, Kim MG. Data curation: Kim S, Yoon J, Moon SI. Formal analysis: Kim S, Yoon J. Investigation: Kim S, Yoon J, Moon SI. Methodology: Kim S, Roh S, Kim MG. Validation: Kim S, Roh S, Kim MG, Rhie J. Writing - original draft: Kim S. Writing - review & editing: Kim S, Roh S, Kim MG, Rhie J, Yoon J.
-
Acknowledgments
The authors thank Dankook University Hospital Center for Farmers’ Safety and Health for providing the data.
Table 1.General characteristics of the target population
|
Characteristic |
Total (n=1,317) |
Male (n=846) |
Female (n=471) |
|
Age (years) |
63.5 ± 9.2 |
64.1 ± 9.2 |
62.5 ± 9.0 |
|
Alcohol |
|
|
|
|
Non-drinker |
530 (40.2) |
209 (24.7) |
321 (68.2) |
|
Former/current drinker |
787 (59.8) |
637 (75.3) |
150 (31.8) |
|
Smoking |
|
|
|
|
Non-smoker |
728 (55.3) |
261 (30.9) |
467 (99.2) |
|
Former/current smoker |
589 (44.7) |
585 (69.1) |
4 (0.8) |
|
Regular exercise |
|
|
|
|
No |
1,063 (80.7) |
687 (81.2) |
376 (79.8) |
|
Yes |
254 (19.3) |
159 (18.8) |
95 (20.2) |
|
Marital status |
|
|
|
|
Married or cohabiting |
1,222 (92.8) |
811 (95.9) |
411 (87.3) |
|
Others |
95 (7.2) |
35 (4.1) |
60 (12.7) |
|
Body mass index |
|
|
|
|
Normal (<25.0 kg/m²) |
776 (58.9) |
510 (60.4) |
264 (56.1) |
|
Obesity (≥25.0 kg/m²) |
541 (41.1) |
334 (39.6) |
207 (43.9) |
|
Duration of agricultural work (years) |
32.9 ± 14.7 |
34.6 ± 14.6 |
29.5 ± 14.5 |
|
Average working hours (hours) |
7.2 ± 2.8 |
7.2 ± 2.7 |
7.2 ± 2.8 |
|
Major farming methods |
|
|
|
|
Irrigated farming |
426 (32.3) |
272 (32.2) |
154 (32.7) |
|
Open-field farming |
443 (33.6) |
306 (36.2) |
137 (29.1) |
|
Greenhouse farming |
292 (22.2) |
174 (20.6) |
118 (25.1) |
|
Orchard farming |
149 (11.3) |
90 (10.6) |
59 (12.5) |
|
Else |
7 (0.5) |
4 (0.5) |
3 (0.6) |
|
Σ Urinary organophosphate metabolites concentration (μg/g creatinine) |
23.22 (24.88) |
20.35 (27.75) |
29.44 (20.03) |
|
Σ Urinary pyrethroid metabolites concentration (μg/g creatinine) |
4.84 (3.74) |
4.89 (3.91) |
4.75 (3.44) |
Table 2.Comparison of general characteristics between metabolic syndrome group and normal group
|
Characteristic |
Normal (n=780) |
Metabolic syndrome (n=537) |
p-value |
|
Sex |
|
|
|
|
Male |
515 (60.9) |
331 (39.1) |
0.103 |
|
Female |
265 (56.3) |
206 (43.7) |
|
Age (years) |
62.9 ± 9.6 |
64.5 ± 8.5 |
0.002**
|
|
Alcohol |
|
|
|
|
Non-drinker |
320 (60.4) |
210 (39.6) |
0.485 |
|
Former/current drinker |
460 (58.4) |
327 (41.6) |
|
Smoking |
|
|
|
|
Non-smoker |
432 (59.3) |
296 (40.7) |
0.925 |
|
Former/current smoker |
348 (59.1) |
241 (40.9) |
|
Regular exercise |
|
|
|
|
No |
633 (59.5) |
430 (40.5) |
0.626 |
|
Yes |
147 (57.9) |
107 (42.1) |
|
Marital status |
|
|
|
|
Married or cohabiting |
728 (59.6) |
494 (40.4) |
0.403 |
|
Others |
52 (54.7) |
43 (45.3) |
|
Body mass index |
|
|
|
|
Normal (<25.0 kg/m²) |
578 (74.3) |
196 (36.5) |
<0.001**
|
|
Obesity (≥25.0 kg/m²) |
200 (25.7) |
341 (63.5) |
|
Duration of agricultural work (years) |
32.6 ± 14.9 |
33.3 ± 14.5 |
0.411 |
|
Average working hours (hours) |
7.3 ± 2.8 |
7.1 ± 2.7 |
0.177 |
|
Major farming methods |
|
|
|
|
Irrigated farming |
278 (35.6) |
148 (27.5) |
0.004*
|
|
Open-field farming |
238 (30.5) |
205 (38.2) |
|
Greenhouse farming |
179 (22.9) |
113 (21.4) |
|
Orchard farming |
82 (10.5) |
67 (12.5) |
|
Else |
3 (0.4) |
4 (0.7) |
|
Σ Urinary organophosphate metabolites concentration (μg/g creatinine) |
19.14 (25.15) |
30.75 (24.07) |
|
|
Σ Urinary pyrethroid metabolites concentration (μg/g creatinine) |
4.85 (4.04) |
4.83 (3.31) |
|
Table 3.Comparison of general characteristics among quartile groups of urinary organophosphate metabolites concentration
|
Characteristic |
Σ Urinary organophosphate metabolitesa (μg/g creatinine)
|
p-valueb
|
p for trendc
|
|
Q1 (0.01–2.97) (n=330) |
Q2 (2.97–51.02) (n=329) |
Q3 (51.14–269.46) (n=329) |
Q4 (271.11–5,983.82) (n=329) |
|
Sex |
|
|
|
|
|
|
|
Male |
222 (67.3) |
211 (64.1) |
207 (62.9) |
206 (62.6) |
0.583 |
0.495 |
|
Female |
108 (32.7) |
118 (35.9) |
122 (37.1) |
123 (37.4) |
|
Age (years) |
63.1 ± 9.1 |
64.5 ± 8.7 |
63.7 ± 9.2 |
62.8 ± 9.5 |
0.004**
|
|
|
Alcohol |
|
|
|
|
|
|
|
Non-drinker |
144 (43.6) |
123 (37.4) |
134 (40.7) |
129 (39.2) |
0.411 |
0.417 |
|
Former/current drinker |
186 (56.4) |
206 (62.6) |
195 (59.3) |
200 (60.8) |
|
Smoking |
|
|
|
|
|
|
|
Non-smoker |
178 (53.9) |
183 (55.6) |
188 (57.1) |
179 (54.4) |
0.845 |
0.837 |
|
Former/current smoker |
152 (46.1) |
146 (44.4) |
141 (42.9) |
150 (45.6) |
|
Regular exercise |
|
|
|
|
|
|
|
No |
273 (82.7) |
268 (81.5) |
251 (76.3) |
271 (82.4) |
0.128 |
0.069 |
|
Yes |
57 (17.3) |
61 (18.5) |
78 (23.7) |
58 (17.6) |
|
Marital status |
|
|
|
|
|
|
|
Married or cohabiting |
306 (92.7) |
301 (91.5) |
307 (93.3) |
308 (93.6) |
0.728 |
0.680 |
|
Others |
24 (7.3) |
28 (8.5) |
22 (6.7) |
21 (6.4) |
|
Body mass index |
|
|
|
|
|
|
|
Normal (<25.0 kg/m²) |
210 (63.6) |
190 (57.9) |
203 (61.7) |
172 (52.3) |
0.015*
|
0.011*
|
|
Obesity (≥25.0 kg/m²) |
120 (36.4) |
138 (42.1) |
126 (38.3) |
158 (47.7) |
|
Duration of agricultural work (years) |
34.1 ± 14.0 |
34.1 ± 14.7 |
32.6 ± 14.4 |
30.6 ± 15.6 |
0.008*
|
|
|
Average working hours (hours) |
7.6 ± 2.7 |
7.0 ± 2.8 |
7.1 ± 2.7 |
7.2 ± 2.9 |
0.077 |
|
|
Metabolic syndrome |
118 (35.8) |
127 (38.6) |
132 (40.1) |
160 (48.6) |
0.006**
|
0.003**
|
Table 4.Comparison of general characteristics among quartile groups of urinary pyrethroid metabolites concentration
|
Characteristic |
Σ Urinary pyrethroid metabolitesa (μg/g creatinine)
|
p-valueb
|
p for trendc
|
|
Q1 (0.14–1.97) (n=330) |
Q2 (1.98–4.61) (n=329) |
Q3 (4.61–10.56) (n=329) |
Q4 (10.59–814.96) (n=329) |
|
Sex |
|
|
|
|
|
|
|
Male |
217 (65.8) |
215 (65.3) |
196 (59.6) |
218 (66.3) |
0.240 |
0.142 |
|
Female |
113 (34.2) |
114 (34.7) |
133 (40.4) |
111 (33.7) |
|
|
|
Age (years) |
64.0 ± 8.8 |
63.0 ± 9.3 |
64.1 ± 8.4 |
63.1 ± 10.1 |
0.296 |
|
|
Alcohol |
|
|
|
|
|
|
|
Non-drinker |
128 (38.8) |
130 (39.5) |
143 (43.5) |
129 (39.2) |
0.587 |
0.465 |
|
Former/current drinker |
202 (61.2) |
199 (60.5) |
186 (56.5) |
200 (60.8) |
|
|
|
Smoking |
|
|
|
|
|
|
|
Non-smoker |
180 (54.5) |
173 (52.6) |
192 (58.4) |
183 (55.6) |
0.510 |
0.516 |
|
Former/current smoker |
150 (45.5) |
156 (47.4) |
137 (41.6) |
146 (44.4) |
|
|
|
Regular exercise |
|
|
|
|
|
|
|
No |
273 (82.7) |
267 (81.2) |
261 (79.3) |
262 (79.6) |
0.671 |
0.666 |
|
Yes |
57 (17.3) |
62 (18.8) |
68 (20.7) |
67 (20.4) |
|
|
|
Marital status |
|
|
|
|
|
|
|
Married or cohabiting |
309 (93.6) |
308 (93.6) |
300 (91.2) |
305 (92.7) |
0.581 |
0.517 |
|
Others |
21 (6.4) |
21 (6.4) |
29 (8.8) |
24 (7.3) |
|
|
|
Body mass index |
|
|
|
|
|
|
|
Normal (<25.0 kg/m²) |
207 (63.1) |
195 (59.3) |
175 (53.2) |
197 (59.9) |
0.058 |
0.194 |
|
Obesity (≥25.0 kg/m²) |
121 (36.9) |
134 (40.7) |
154 (46.8) |
132 (40.1) |
|
|
|
Duration of agricultural work (years) |
33.1 ± 14.8 |
32.5 ± 14.4 |
31.8 ± 14.8 |
34.0 ± 15.0 |
0.261 |
|
|
Average working hours (hours) |
6.9 ± 2.9 |
7.4 ± 2.7 |
7.1 ± 2.6 |
7.5 ± 2.8 |
0.296 |
|
|
Metabolic syndrome |
123 (37.3) |
137 (41.6) |
156 (47.4) |
121 (36.8) |
0.019*
|
0.017*
|
Table 5.OR (95% CI) for metabolic syndrome by general factors and pesticide metabolites concentration
|
Characteristic |
B |
SE |
Wald |
p-valuea
|
OR (95% CI) |
|
Female sex |
0.47 |
0.19 |
2.44 |
0.015*
|
1.59 (1.10–2.32) |
|
Age |
0.04 |
0.01 |
4.54 |
<0.001**
|
1.04 (1.02–1.06) |
|
Alcohol |
|
|
|
|
|
|
Non-drinker |
|
|
|
|
- |
|
Former/current drinker |
0.16 |
0.15 |
1.09 |
0.277 |
1.17 (0.88–1.57) |
|
Smoking |
|
|
|
|
|
|
Non-smoker |
|
|
|
|
- |
|
Former/current smoker |
0.41 |
0.18 |
2.40 |
0.016*
|
1.52 (1.08–2.15) |
|
Regular exercise |
|
|
|
|
|
|
No |
|
|
|
|
- |
|
Yes |
0.06 |
0.16 |
0.40 |
0.691 |
1.07 (0.78–1.46) |
|
Marital status |
|
|
|
|
|
|
Married or cohabiting |
|
|
|
|
- |
|
Others |
0.13 |
0.25 |
0.52 |
0.606 |
1.14 (0.69–1.46) |
|
Body mass index |
|
|
|
|
|
|
Normal (<25.0 kg/m²) |
|
|
|
|
- |
|
Obesity (≥25.0 kg/m²) |
1.78 |
0.13 |
13.50 |
<0.001**
|
5.95 (4.59–7.71) |
|
Duration of agricultural work (years) |
–0.00 |
0.01 |
–0.41 |
0.680 |
1.00 (0.99–1.01) |
|
Σ Urinary organophosphate metabolites concentration (μg/g creatinine)b
|
0.04 |
0.02 |
1.93 |
0.053 |
1.04 (1.00–1.08) |
|
Σ Urinary pyrethroid metabolites concentration (μg/g creatinine)c
|
–0.00 |
0.05 |
–0.10 |
0.923 |
1.00 (0.90–1.10) |
Table 6.OR (95% CI) for metabolic syndrome among quartile groups of urinary organophosphate and pyrethroid metabolites concentration
|
Level |
Unadjusted
|
Adjusteda
|
|
OR (95% CI) |
p-value |
OR (95% CI) |
p-value |
|
Σ Urinary organophosphate metabolitesb (μg/g creatinine) |
|
|
|
|
|
Q1 (0.01–2.97) |
- |
|
- |
|
|
Q2 (2.97–51.02) |
1.13 (0.82–1.55) |
0.450 |
0.99 (0.70–1.40) |
0.969 |
|
Q3 (51.14–269.46) |
1.20 (0.88–1.65) |
0.249 |
1.17 (0.83–1.65) |
0.361 |
|
Q4 (271.11–5,983.82) |
1.70 (1.25–2.33) |
0.001**
|
1.48 (1.06–2.09) |
0.023* |
|
p for linear trend |
0.001**
|
0.014*
|
|
Σ Urinary pyrethroid metabolitesc (μg/g creatinine) |
|
|
|
|
|
Q1 (0.14–1.97) |
- |
|
- |
|
|
Q2 (1.98–4.61) |
1.20 (0.88–1.64) |
0.251 |
1.16 (0.83–1.63) |
0.389 |
|
Q3 (4.61–10.56) |
1.52 (1.11–2.07) |
0.009**
|
1.35 (0.97–1.90) |
0.078 |
|
Q4 (10.59–814.96) |
0.98 (0.71–1.34) |
0.895 |
0.94 (0.66–1.32) |
0.703 |
|
p for linear trend |
0.738 |
0.934 |
Table 7.OR (95% CI) for metabolic syndrome among quartile groups of urinary organophosphate and pyrethroid metabolites concentration by sex
|
Level |
Unadjusted
|
Adjusteda
|
|
OR (95% CI) |
p-value |
OR (95% CI) |
p-value |
|
Male |
|
|
|
|
|
Σ Urinary organophosphate metabolitesb (μg/g creatinine) |
|
|
|
|
|
Q1 (0.01–2.45) |
- |
|
- |
|
|
Q2 (2.51–45.45) |
1.06 (0.71–1.58) |
0.781 |
0.99 (0.64–1.54) |
0.970 |
|
Q3 (47.15–262.70) |
1.37 (0.92–2.03) |
0.120 |
1.41 (0.91–2.19) |
0.129 |
|
Q4 (265.55–5430.48) |
1.89 (1.28–2.79) |
0.001** |
1.77 (1.15–2.75) |
0.010* |
|
p for linear trend |
0.001** |
|
0.003** |
|
|
Σ Urinary pyrethroid metabolitesc (μg/g creatinine) |
|
|
|
|
|
Q1 (0.14–1.91) |
- |
|
- |
|
|
Q2 (1.92–4.45) |
1.53 (1.04–2.27) |
0.032* |
1.55 (1.00–2.40) |
0.050 |
|
Q3 (4.48–11.06) |
1.71 (1.15–2.55) |
0.008** |
1.61 (1.03–2.52) |
0.036* |
|
Q4 (11.06–814.96) |
0.99 (0.67–1.48) |
0.973 |
0.98 (0.63–1.52) |
0.920 |
|
p for linear trend |
0.890 |
|
0.969 |
|
|
Female |
|
|
|
|
|
Σ Urinary organophosphate metabolitesb (μg/g creatinine) |
|
|
|
|
|
Q1 (0.01–3.61) |
- |
|
- |
|
|
Q2 (3.65–56.93) |
1.23 (0.72–2.09) |
0.447 |
1.04 (0.59–1.85) |
0.888 |
|
Q3 (57.87–290.80) |
0.94 (0.56–1.60) |
0.829 |
0.89 (0.50–1.58) |
0.693 |
|
Q4 (291.97–5,983.82) |
1.39 (0.82–2.34) |
0.221 |
1.13 (0.64–2.00) |
0.663 |
|
p for linear trend |
0.395 |
|
0.807 |
|
|
Σ Urinary pyrethroid metabolitesc (μg/g creatinine) |
|
|
|
|
|
Q1 (0.17–1.99) |
- |
|
- |
|
|
Q2 (2.01–4.91) |
0.76 (0.45–1.30) |
0.318 |
0.61 (0.34–1.07) |
0.088 |
|
Q3 (4.93–10.18) |
1.20 (0.73–2.00) |
0.469 |
0.92 (0.53–1.60) |
0.773 |
|
Q4 (10.19–184.67) |
0.96 (0.57–1.63) |
0.880 |
0.83 (0.47–1.46) |
0.514 |
|
p for linear trend |
0.694 |
|
0.871 |
|
REFERENCES
- 1. Food and Agriculture Organization of the United Nations. World food and agriculture: statistical yearbook 2021. https://www.fao.org/3/cb4477en/cb4477en.pdf. Updated 2021. Accessed February 8, 2024 Article
- 2. Weis GC, Alves AO, Assmann CE, Bonadiman BS, Costabeber IH. Pesticides: classifications, exposure and risks to human health. Arch Biosci Health 2019;1(1):29–44.ArticlePDF
- 3. Roberts JR, Reigart JR. Recognition and Management of Pesticide Poisonings. 6th ed. Washington, DC: U.S. Environmental Protection Agency (EPA); 2013, 43.
- 4. Kamel F, Hoppin JA. Association of pesticide exposure with neurologic dysfunction and disease. Environ Health Perspect 2004;112(9):950–8.ArticlePubMedPMC
- 5. Rosenbaum PF, Weinstock RS, Silverstone AE, Sjodin A, Pavuk M. Metabolic syndrome is associated with exposure to organochlorine pesticides in Anniston, AL, United States. Environ Int 2017;108:11–21.ArticlePubMedPMC
- 6. Lee KM. Pesticide exposure, oxidative stress, and metabolic syndrome among male farmers exposure to pesticide [dissertation]. Seoul: Yonsei University; 2011.
- 7. Lamat H, Sauvant-Rochat MP, Tauveron I, Bagheri R, Ugbolue UC, Maqdasi S, et al. Metabolic syndrome and pesticides: a systematic review and meta-analysis. Environ Pollut 2022;305:119288.ArticlePubMed
- 8. Rochlani Y, Pothineni NV, Kovelamudi S, Mehta JL. Metabolic syndrome: pathophysiology, management, and modulation by natural compounds. Ther Adv Cardiovasc Dis 2017;11(8):215–25.ArticlePubMedPMCPDF
- 9. Lim S, Jang HC, Lee HK, Kimm KC, Park C, Cho NH. A rural-urban comparison of the characteristics of the metabolic syndrome by gender in Korea: the Korean Health and Genome Study (KHGS). J Endocrinol Invest 2006;29(4):313–9.ArticlePubMedPDF
- 10. Croghan C, Egeghy PP. Methods of dealing with values below the limit of detection using SAS. In: Southeastern SAS User Group; 2003 Sep 22-24; St. Petersburg, FL, USA.
- 11. Jusko TA, van den Dries MA, Pronk A, Shaw PA, Guxens M, Spaan S, et al. Organophosphate pesticide metabolite concentrations in urine during pregnancy and offspring nonverbal IQ at age 6 years. Environ Health Perspect 2019;127(1):17007.ArticlePubMed
- 12. Oh J, Roh S. Evaluation of exposure level to pyrethroid pesticides according to protective equipment in male orchard farmers. Korean J Community Living Sci 2017;28(3):391–401.Article
- 13. Huh JH, Kang DR, Kim JY, Koh KK; The Taskforce Team of the Metabolic Syndrome Fact Sheet of the Korean Society of Cardiometabolic Syndrome. Metabolic syndrome fact sheet 2021: executive report. Cardiometab Syndr J 2021;1(2):125–34.ArticlePDF
- 14. Glover F, Eisenberg ML, Belladelli F, Del Giudice F, Chen T, Mulloy E, et al. The association between organophosphate insecticides and blood pressure dysregulation: NHANES 2013-2014. Environ Health 2022;21(1):74.ArticlePubMedPMCPDF
- 15. Causes of death statistics: number of deaths and death rates by cause of death (104 items), sex, and age (5-year age groups). https://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_1B34E01&conn_path=I2. Updated 2023. Accessed March 20, 2024
- 16. Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier P, et al. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol 2010;56(14):1113–32.PubMed
- 17. Sundarakumar JS, Stezin A, Menesgere AL, Ravindranath V; SANSCOG and TLSA Collaborators. Rural-urban and gender differences in metabolic syndrome in the aging population from southern India: two parallel, prospective cohort studies. EClinicalMedicine 2022;47:101395.ArticlePubMedPMC
- 18. Lee H, Cho SY, Kim JS, Yoon SY, Kim BI, An JM, et al. Difference in health status of Korean farmers according to gender. Ann Occup Environ Med 2019;31:7.ArticlePubMedPMCPDF
- 19. Thomas DD, Corkey BE, Istfan NW, Apovian CM. Hyperinsulinemia: an early indicator of metabolic dysfunction. J Endocr Soc 2019;3(9):1727–47.ArticlePubMedPMCPDF
- 20. Karami-Mohajeri S, Abdollahi M. Toxic influence of organophosphate, carbamate, and organochlorine pesticides on cellular metabolism of lipids, proteins, and carbohydrates: a systematic review. Hum Exp Toxicol 2011;30(9):1119–40.ArticlePubMedPDF
- 21. Czajka M, Matysiak-Kucharek M, Jodlowska-Jedrych B, Sawicki K, Fal B, Drop B, et al. Organophosphorus pesticides can influence the development of obesity and type 2 diabetes with concomitant metabolic changes. Environ Res 2019;178:108685.ArticlePubMed
- 22. Mauvais-Jarvis F, Clegg DJ, Hevener AL. The role of estrogens in control of energy balance and glucose homeostasis. Endocr Rev 2013;34(3):309–38.ArticlePubMedPMC
- 23. Zuo L, Chen L, Chen X, Liu M, Chen H, Hao G. Pyrethroids exposure induces obesity and cardiometabolic diseases in a sex-different manner. Chemosphere 2022;291(Pt 2):132935.ArticlePubMed
- 24. Kim J, Park Y, Yoon KS, Clark JM, Park Y. Permethrin alters adipogenesis in 3T3-L1 adipocytes and causes insulin resistance in C2C12 myotubes. J Biochem Mol Toxicol 2014;28(9):418–24.ArticlePubMed
- 25. Park J, Park SK, Choi YH. Environmental pyrethroid exposure and diabetes in U.S. adults. Environ Res 2019;172:399–407.ArticlePubMed
- 26. Kim S, Roh S. Exposure level to organophosphate and pyrethroid pesticides and related agricultural factors in chili and cucumber cultivation among greenhouse and orchard farmers. J Environ Health Sci 2017;43(4):280–97.Article
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