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In a sample of 300 employees of the Limoges University Hospital, it is proposed to explore the intraindividual and interindividual variability of urinary concentrations of glyphosate and 320 other pesticides. The employees will fill a questionary in which will allow to explore the relationships between the concentrations, lifestyle and dietary habits.
The general population is exposed to pesticides through the ingestion of food and water. The presence of pesticides in human urine has been demonstrated in numerous studies. Conventionally, biological monitoring studies use an undifferentiated urine sample or the first morning urine. The reliability of measuring a single-point urinary concentration to represent the profile of pesticide exposure in individuals over time is, however, questionable. Rare studies reporting intra-individual outcomes of pesticide exposure have been conducted in specific populations, such as children and pregnant women.
It is proposed to carry out a descriptive biomonitoring study of the urinary concentration of approximately 320 pesticides in a sample of the general population. Among these, the study will focus on glyphosate.
The originality of our study is:
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Pesticide measurement | Experimental |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Urine sampling | Other | Among the 300 people recruited:
|
| Measure | Description | Time Frame |
|---|---|---|
| glyphosate overconsumption | Proportion of individuals with an estimated daily consumption of glyphosate greater than the acceptable daily intake of glyphosate | 1 week |
| Measure | Description | Time Frame |
|---|---|---|
| Identification of determinant of exposure to glyphosate | Proportion of individuals with a daily consumption of glyphosate greater than the acceptable daily intake according to socio-demographic determinants(age, sex, profession and socio-professional category) | 1 week |
| Identification of determinant of exposure to glyphosate |
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Inclusion Criteria:
Exclusion Criteria:
- Adult with a professional activity during which he is required to handle or come into contact with glyphosate
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| CHU de Limoges | Limoges | 87042 | France |
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|
Proportion of individuals with a daily consumption of glyphosate greater than the acceptable daily intake according to lifestyles (city center, peri-urban, rural ) |
| 1 week |
| Identification of determinant of exposure to glyphosate | Proportion of individuals with a daily consumption of glyphosate greater than the acceptable daily intake according to proximity to agricultural activities | 1 week |
| Identification of determinant of exposure to glyphosate | Proportion of individuals with a daily consumption of glyphosate greater than the acceptable daily intake according to use of glyphosate at home | 1 week |
| Identification of determinant of exposure to glyphosate | Proportion of individuals with a daily consumption of glyphosate greater than the acceptable daily intake according to dietary habits | 1 week |
| Coefficient of variation of the glyphosate concentrations | Calculation of the coefficient of variation of the glyphosate concentrations measured 3 times over a period of 24 hours | 1 week |
| Intra-individual variability of urinary glyphosate concentrations | Comparison of the average GLP concentrations measured on 3 occasions (at sunrise, in the middle then at the end of the day) over a period of 24 hours. | 1 week |
| Impact of urine sample collection schedule on glyphosate concentration measurement | According to the methodology proposed by Wang et al. : a linear mixed-effects model to determine which measure produces the best estimate of exposure; itself obtained by calculating the mean of the observed values. | 1 week |
| Identification of determinants of exposure to a panel of around 320 pesticides | Proportion of subjects with a detectable concentration among the 320 pesticides measured by specific LC-MS and GC-MS methods and prevalence of individuals with a detectable concentration according to socio-demographic determinants | 1 week |
| Identification of determinants of exposure to a panel of around 320 pesticides | Proportion of subjects with a detectable concentration among the 320 pesticides measured by specific LC-MS and GC-MS methods and prevalence of individuals with a detectable concentration according to lifestyles | 1 week |
| Identification of determinants of exposure to a panel of around 320 pesticides | Proportion of subjects with a detectable concentration among the 320 pesticides measured by specific LC-MS and GC-MS methods and prevalence of individuals with a detectable concentration according to proximity to agricultural activities | 1 week |
| Identification of determinants of exposure to a panel of around 320 pesticides | Proportion of subjects with a detectable concentration among the 320 pesticides measured by specific LC-MS and GC-MS methods and prevalence of individuals with a detectable concentration according to use of glyphosate at home | 1 week |
| Identification of determinants of exposure to a panel of around 320 pesticides | Proportion of subjects with a detectable concentration among the 320 pesticides measured by specific LC-MS and GC-MS methods and prevalence of individuals with a detectable concentration according to dietary habits | 1 week |
| Intra-individual variability of urinary 320 pesticides concentrations | Calculation of the coefficient of variation of the concentrations of each of the pesticides (expressed in µg/L or µg/g of creatinine) measured 3 times (at sunrise, in the middle and then at the end of the day) over a period of 24 hours | 1 week |
| Impact of urine sample collection schedule on 320 pesticides concentration measurement | Comparison of the average concentrations of each of the pesticides measured on 3 occasions (at sunrise, in the middle and then at the end of the day) over a period of 24 hours. According to the methodology proposed by Wang et al.: a linear mixed-effects model to determine which measure produces the best estimate of exposure; itself obtained by calculating the mean of the observed values. | 1 week |