TY - JOUR
AU - Landini, Eleonora
AB - Abstract A biomonitoring study was carried out to investigate whether exposure to complex pesticide mixtures in ornamental crop production represents a potential genotoxic risk. Exposed and control subjects were selected in western Liguria (Italy). The area was chosen for its intensive use of pesticides. The main crops produced were roses, mimosas, carnations and chrysanthemums, as ornamental non-edible plants, and tomato, lettuce and basil, as edible ones. The levels of micronuclei (MN) were analysed in peripheral blood lymphocytes of 107 floriculturists (92 men and 15 women) and 61 control subjects (42 men and 19 women). A statistically significant increase in binucleated cells with micronuclei (BNMN) was detected in floriculturists with respect to the control population (4.41 ± 2.14 MN/1000 cells versus 3.04 ± 2.14, P < 0.001). The mean number of BNMN varied as a function of sex and age. Smoking habit had no effect on MN frequency. A positive correlation between years of farming and MN frequency in peripheral blood lymphocytes was observed (r = 0.30, P = 0.02). The conditions of exposure were also associated with an increase in cytogenetic damage, with a 28% higher MN frequency in greenhouse workers compared with subjects working only outdoors in fields. Workers not using protective measures during high exposure activities showed an increase in MN frequency. Our findings suggest a potential genotoxic risk due to pesticide exposure. Introduction Effective control of insect and disease pests is a major concern in the large-scale production and exportation of ornamental plants and cut flowers grown mainly in greenhouses. A large consumption of a wide variety of compounds belonging to different chemical classes is common in producing ornamental crops that are highly susceptible to pests, considering also the low health hazard for consumers of flowers marketed exclusively for aesthetic appeal. The environmental conditions in the greenhouses, i.e. enclosed spaces, high temperature and high humidity, favour pesticide exposure. Potential health hazards for acute and chronic effects in floriculturists and commercial florists constantly exposed to contaminated cut flowers have been reported (Morse et al., 1979; Brouwer et al., 1992; Illing, 1997). The main toxic effects include asthma, allergic dermatitis, respiratory diseases (Zuskin et al., 1993) and sensory abnormalities (Morse et al., 1979). Pesticides have also been extensively studied for their long-term risk, including the increased incidence of some cancers. Epidemiological studies on cancer in farmers have yielded conflicting results. Meta-analyses showed that farmers were at risk of specific tumors, including leukaemia (Daniels et al., 1997; Zahm et al., 1997; Zahm and Ward, 1998) and multiple myeloma (Khuder and Mutgi, 1997). A number of reproductive effects, such as birth defects (Restrepo et al., 1990; Petrelli et al., 2000) and decreased male fertility (de Cock et al., 1994; Tielmans et al., 1999; Rojas et al., 2000), have also been associated with pesticide exposure. Genotoxic potential is a primary risk factor for long-term effects such as carcinogenic and reproductive disorders. The approach of monitoring workers using specific biomarkers could allow the evaluation of the possible genotoxic effects of a defined exposure. Cytogenetic damage, evaluated as chromosomal aberrations, has been successfully applied as a reliable biomarker for chronic health risk (Hagmar et al., 1998; Bonassi et al., 2000). The frequency of micronuclei (MN) in peripheral blood lymphocytes is a useful biomarker to evaluate cytogenetic damage. MN can be formed from acentric chromosomal fragments or whole chromosomes left behind during mitotic cellular division, allowing the detection of clastogenic and aneugenic compounds. Several biomonitoring studies in human populations exposed to pesticides showed an increase in MN frequency (Bolognesi et al., 1993a,b; Joksik et al., 1997; Meng and Zhang, 1997; Calvert et al., 1998; Falck et al., 1999; Garaj-Vrhovac and Zeljezic, 1999; Gomez-Arroyo et al., 2000) while no increase in this parameter was reported in a number of other studies (Scarpato et al., 1996a,b; Titenko-Holland et al., 1997; Davies et al., 1998; Venegas et al., 1998; Lucero et al., 2000; Pastor et al., 2001a,b). Our previous investigations of farming populations involved in floriculture and horticulture revealed a significant increase in MN frequency (Bolognesi et al., 1993a,b). The present study was carried out in a large number of exposed subjects mainly involved in ornamental crop production, to investigate whether the current exposure levels to pesticide mixtures could represent a potential genotoxic risk. Materials and methods Population The study population reported here consisted of 107 floriculturists and 61 unexposed subjects, selected from healthy blood donors living in the same area. All the participants were informed of the study aims and asked to sign an informed consent form and complete a standardized questionnaire. In addition to demographic information the questionnaire contained personal data, smoking habits and history of recent illness and medical treatment. For the exposed group further questions related to farming were included, such as kind of crops, pesticide use, duration of exposure, type of working activity and protective measures. The characteristics of the referent and exposed subjects (age, smoking habits, years of exposure and conditions of cultivation) are shown in Table I. The groups differed with respect to the percentage of males (85% in the exposed group versus 68% in the control group). The number of smokers and former smokers was higher among the floriculturists than among the controls. The floriculturists considered in our study worked in open fields (45.7%), in greenhouses (17.9%) or in both environments (36.4%). The main crops produced were roses, mimosas, carnations and chrysanthemums as ornamental non-edible plants and tomato, lettuce and basil as edible ones. With respect to their work activity, 75.7% of the subjects harvested only ornamental plants while 24.3% harvested both types of crops, ornamentals and vegetables. The majority of the population (82.2%) reported preparing pesticides. Moreover, with respect to the use of protective measures, most of the floriculturists (84.1%) used protective devices, such as mask, gloves and boots. During the year of the blood sampling the workers included in this study were exposed to complex mixtures of >50 pesticides, including insecticides, fungicides and herbicides, belonging to different chemical classes. The large majority of floriculturists used about eight agrochemical ingredients. No group of subjects could be identified using only one compound. Table II shows the pesticides most commonly used by the floriculturists and their frequency of use estimated on a personal basis. The chemical class of organophosphates and carbamates were more represented than the others (67% of the total pesticide consumption). A small group of six chemicals (monocrotophos, glyphosate, metamidophos, zineb, benomyl and deltamethrin) belonging to different chemical classes were used by 50 or more workers. In addition, the application of pesticides involves exposure not only to active ingredients, but also to by-products present in technical formulations, such as impurities, solvents and other compounds produced during the storage procedure. The large majority of pesticides used by the exposed group have mutagenic properties inducing different genetic end-points: gene mutation, chromosomal alteration or DNA damage. The genotoxic potential of a large number of agrochemical ingredients is low: they give weak positive results in few genotoxicity tests. The lower effective dose in a single test is generally very high. Specimen collection Blood samples were obtained from each subject by venipuncture. All blood specimens were collected in sterile sodium heparin tubes (Becton & Dickinson). As a routine, specimens were received in the laboratory within a few hours of collection and were processed immediately. Cell cultures Whole blood (0.4 ml) was added to 4.5 ml of RPMI 1640 complete medium and 10% foetal calf serum (Gibco BRL Life Technologies, Milano, Italy) with 1% phytohaemoagglutinin (Murex, Biotech, Dartford, UK). Cells were cultured for 72 h, with cytochalasin B (Sigma Chemical Co., St Louis, MO) being added after 44 h (final concentration 6 μg/ml). At the end of the incubation, at 37°C for 72 h, whole blood cultures were centrifuged (1000 r.p.m. for 10 min), resuspended in buffer (0.9 mM NH4HCO3 and 132 mM NH4Cl) for 20 min at room temperature and centrifuged for 15 min at 3000 r.p.m. This procedure was repeated twice. Cells were then fixed twice in cold fixative (methanol:acetic acid 3:1) for 20 min at room temperature. Samples for microscopic observation were obtained by carefully dropping the cell suspension onto clean wet slides. They were air dried, stained with 3% Giemsa (Merck-Bracco, Milano) in distilled water and mounted in Eukitt. Slides were coded and analysed blind by a single scorer. To determine the frequency of binucleated cells with micronuclei (BNMN) a total of 2000 binucleated lymphocytes with preserved cytoplasm were scored for each subject on coded slides. Statistical analysis Parametric and non-parametric statistical tests were used. Student’s t-test for independent samples was applied to detect differences in the mean of BNMN in the exposed subjects and controls. Differences among the group means were evaluated by non-parametric Mann–Whitney U-test. The relationship between BNMN and use of protection measures was evaluated using regression analysis. All the data were analysed using the SPSS statistical software package (SPSS 9.0 for Windows). The level of significance was taken as P < 0.05. Results Table III shows the means ± SD of BNMN per 1000 cells in both groups. An overall comparison shows a statistically significant difference (P < 0.001) between floriculturists and referent subjects. A statistically significant increase in frequency of BNMN in females was evident in the floriculturists as well as in the referents, confirming the data from the literature (Bonassi et al., 1995). Age was associated with an increase in frequency of BNMN mainly in the floriculturists. Due to the low number of females and to the high proportion of elderly subjects in this group, the age effect was not appreciated in the controls for the oldest class. No correlation between smoking habit and frequency of BNMN was observed in either group. To analyse the effect of exposure to pesticides on BNMN frequency independently of smoking habits, we stratified the exposed subjects and referents into groups of non-smokers, smokers and ex-smokers. A decrease in MN incidence (not statistically significant) is evident in smokers with respect to non-smokers only in the floriculturist group. Table IV summarizes the MN frequencies in the floriculturists according to the conditions of exposure. A number of parameters were considered to classify the exposure: the lifetime pesticide exposure, the condition of exposure, the year’s pesticide consumption and involvement in pesticide mixture preparation. A dose–response relationship was observed between duration of exposure in years and MN frequency in floriculturists as the subjects exposed for ≥20 years showed a significant increase in BNMN. A positive correlation between years of farming and BNMN in peripheral blood lymphocytes was observed (Figure 1) (r = 0.30, P = 0.02). The large majority of workers directly involved in mixing the pesticides (82%) were potentially exposed to highly concentrated compounds and showed a higher, although not statistically significant, frequency of BNMN in comparison with subjects involved only in other activities. An increase in MN incidence related to the years of pesticide exposure (Figure 2) was detected for subjects not using protective devices such as gloves, breathing masks and boots, confirming the importance of personal protection during high exposure activities. The condition of exposure was also found to be associated with an increase in MN frequency. A 28% higher occurrence of MN was observed in greenhouse workers when compared with subjects working only in open fields. The difference is not significant due to the small number of subjects working exclusively in greenhouses. The pesticide consumption, recorded as kg/year, did not influence the MN frequency, although these data could suffer from inaccuracies in the questionnaire compilation. Other parameters recorded in the questionnaire, such as the type of crops produced and the area of the greenhouses and open fields, did not influence the cytogenetic damage. Discussion The aim of this study was to evaluate whether exposure to a pesticide mixture in ornamental crop production, mainly in greenhouses, produced an increase in MN frequency as an index of chromosomal damage. The analysis concerned a population of floriculturists and a matched unexposed control group. The main result of this study was a significant increase in cytogenetic damage measured as frequency of micronucleated binucleated lymphocytes in floriculturists when they were compared with the referents. The most consistent demographic variables influencing the MN frequency in human lymphocytes are gender and age. In the present study females showed a marked increase in MN incidence independently of the exposure, confirming the data from the large majority of studies from the literature (Bonassi et al., 1995; Barale et al., 1998; Fenech, 1998) that associate the effect with high micronucleation of the X chromosome (Catalan et al., 1998; Bukvic et al., 2001). An increased frequency of MN associated with age was observed mainly in the exposed subjects, while no age-related increase in MN frequency was detected in the control population. This could be due to the small number of females and the high number of elderly subjects, >60 years of age, in the floriculturists as well as in the matched controls. From the scientific literature it is known that age has a strong effect on MN frequency in adult life, mainly in females, followed by a decline above 60 years of age (Bolognesi et al., 1997, 1999). This evidence supports the interpretation that occupational exposure was the main factor in the induction of MN in the exposed subjects considered in our investigation. The incidence of MN was shown to be positively correlated with the duration of exposure: a clear dose–response relationship was evident with years of farming. The chromosomal damage appears to be cumulative for continuous exposure to pesticide mixtures, confirming data reported in other biomonitoring studies (Carbonell et al., 1993a,b, 1995; Davies et al., 1998) where people chronically exposed to genotoxic compounds seem to be more susceptible to chromosomal damage. A number of factors were considered in this study as exposure surrogates (condition of exposure, personal pesticide use and involvement in pesticide mixture preparation) as the individual exposure to complex mixture of pesticides was not evaluated. The involvement of workers in pesticide mixture preparation was associated with an increase in MN frequency, although the result was not statistically significant. The use of gloves, breathing mask and boots seems to protect the workers by reducing the MN frequency. This result confirms previous findings (Lander and Ronne, 1995; Lander et al., 2000; Shaham et al., 2001) emphasizing the importance of personal protection in order to prevent pesticide uptake during pesticide preparation and spraying but also during activities typical of ornamental crop production, such as cutting and trimming the treated plants. Given the complex exposure to a large number of compounds it is not feasible to investigate which agents may be responsible for the observed cytogenetic damage. The most represented chemical classes in our study were organophosphates and carbamates, which have been reported to induce cytogenetic effects in experimental systems (Moriya et al., 1983; Franekic et al., 1994; Wei et al., 1997; Hour et al., 1998). In addition, a recent study (McDuffie et al., 2001) found that the risk of non-Hodgkin lymphoma was statistically significantly increased by exposure to carbamate and organophosphorous insecticides. The significant increase in MN frequency in peripheral lymphocytes of floriculturists in our study indicates a potential genotoxic hazard due to pesticide exposure. A positive association between occupational exposure to complex pesticide mixtures and the presence of cytogenetic damage in human populations has been observed in a large number of studies (Dulout et al., 1985; Rita et al., 1987; Paldy et al., 1987; Nehez et al., 1988; Rupa et al., 1988, 1989a, Rupa et al., b, 1991; De Ferrari et al., 1991; Kourakis et al., 1992, 1996; Carbonell, 1993a, 1995; Mohammad et al., 1995; Joksic et al., 1997; Garaj-Vrhovac and Zeljezic, 1999; Amr, 1999; Au et al., 1999; Falck et al., 1999; Padmavathi et al., 2000; Lander et al., 2000; Gomez-Arroyo et al., 2000; Shaham et al., 2001; Zeljezic and Garaj-Vrhovac, 2001), although a number of others failed to detect cytogenetic damage (Dulout et al., 1987; Carbonell et al., 1990, 1993b; Gomez-Arroyo et al., 1992; Bolognesi and Merlo, 1995; Pasquini et al., 1996; Scarpato et al., 1996a,b; Kourakis et al., 1996; Hoyos et al., 1996; Davies et al., 1998; Lucero et al., 2000; D’Arce and de Syllos Colus, 2000; Pastor et al., 2001a,b). The variation in the degree of exposure and the use of different chemical mixtures may be the reason for conflicting results. The exposure to pesticides is quite peculiar because each population has different lifestyles, climatic conditions and use different pesticide mixtures and conditions of cultivation. The multiple strategies used to estimate the exposure in biomonitoring studies of pesticide-exposed populations prevent direct comparisons of exposure levels and cytogenetic results. On the whole the evidence of a genetic hazard related to exposures resulting from the intensive use of pesticides stresses the need for educational programmes for farmers in order to reduce the use of chemicals in agriculture and to implement protection measures. Table I. General characteristics of the floriculturists and control group   Floriculturists  Referents  Number of subjects  107  61  Age (years) (mean ± SD)  49.44 ± 13.69  49.50 ± 13.62  Gender          Male  92  42      Female  15  19      Male (%)  85  68  Smoking          Smokers  23 (21.5%)  20 (32.8%)      Former smokers  36 (33.7%)  19 (31.1%)      Non-smokers  48 (44.8%)  22 (36.1%)  Pesticide exposure      Duration of exposure (years) (mean ± SD)  27.8 ± 15.5        Range  2–70    Condition of cultivation          Greenhouse  19(17.9%)        Open field  49(45.7%)        Both  39(36.4%)    Types of crops          Ornamental crops  81 (75.7%)        Vegetables and ornamental crops  26 (24.3%)    Preparation of pesticide mixture          Yes  88 (82.2%)        No  19 (17.8%)    Use of protective devices          Yes  90 (84.1%)        No  17 (15.9%)      Floriculturists  Referents  Number of subjects  107  61  Age (years) (mean ± SD)  49.44 ± 13.69  49.50 ± 13.62  Gender          Male  92  42      Female  15  19      Male (%)  85  68  Smoking          Smokers  23 (21.5%)  20 (32.8%)      Former smokers  36 (33.7%)  19 (31.1%)      Non-smokers  48 (44.8%)  22 (36.1%)  Pesticide exposure      Duration of exposure (years) (mean ± SD)  27.8 ± 15.5        Range  2–70    Condition of cultivation          Greenhouse  19(17.9%)        Open field  49(45.7%)        Both  39(36.4%)    Types of crops          Ornamental crops  81 (75.7%)        Vegetables and ornamental crops  26 (24.3%)    Preparation of pesticide mixture          Yes  88 (82.2%)        No  19 (17.8%)    Use of protective devices          Yes  90 (84.1%)        No  17 (15.9%)    View Large Table II. Pesticides used (kg/yeara) by the floriculturists, frequency of use and genotoxicity datab   Frequency of use (no. of subjects)  CAS no.  Gene (no. of subjects) mutation  Chromosomal mutation  DNA damage  aYear of sampling.  bReferences: Fahrig (1974), Chen et al. (1982), Waters et al. (1982), Haworth et al. (1983), Moriya et al. (1983), Dellarco et al. (1986), Dzwonkowska and Hubner (1986), Garrett et al. (1986), Galloway et al. (1987), IARC (1987, 1991), Zeiger et al. (1987), Ishidate et al. (1988), McGregor et al. (1988), Dearfield et al. (1993), Franekic et al. (1994), Wei et al. (1997) and Hour et al. (1998).  Organophosposphates                Monocrotophos  88  6923-22-4  +  +  +      Glyphosate  57  1071-83-6  +  +  +      Methamidophos  53  10265-92-6  −  +  +      Methidation  48  950-37-8  nd  −  +      Methyl parathion  47  298-00-0  +  +  +      Etoprophos  38  13194-48-4  +/−  nd  nd      Dimethoate  4  60-51-5  +  +  +      Parathion  3  56-38-2  +  +  +      Pirimiphos methyl  3  29232-93-7  −  −  nd      Acephate  1  30560-19-1  +  +  +      Chlorpyrifos  1  2921-88-2  +  +  +      Diazinon  1  333-41-5  +  +  −      Fenthion  1  55-38-9  −  −  +      Phosphamidon  1  13171-21-6  +  +  nd      Profenofos  1  41198-08-7  nd  +  +      Total kg  1871.3 per year (39%)          Carbamates                Zineb  61  12122-67-7  +  +  +      Methomyl  38  16752-77-5  +  +  +      Dazomet  32  533-74-4  nd  +  +      Aldicarb  13  116-06-3  +  +  +      Propineb  3  12071-83-9  −  −  nd      Metham sodium  2  137-42-8  −  −  −      Mancozeb  1  8018-01-7  −  +  +      Total kg  1326 per year (28%)          Benzimidazoles                Benomyl  52  17804-35-2  +  +  +      Carbendazim  33  10605-21-7  +  +  +      Tiofanate methyl  19  23564-05-8  +  +  nd      Total kg  408.5 per year (8.5%)          Pyrethroids                Deltamethrin  53  52918-63-5  −  +  +      Permethrin  4  52645-53-1  +  +  nd      Cypermethrin  2  52315-07-8  +  +  nd      Fenpropathrin  1  64257-84-7  +  +  nd      Total kg  126.5 per year (2.5%)          Tiophthalimides                Captan  19  133-06-2  +  +  +      Folpet  7  133-07-3  +  +  +      Total kg  104 per year (2.1%)          Pyrimidinol compounds                Bupirimate  35  41483-43-6  −  nd  nd      Total kg  422 per year (8.8%)          Organochlorines                Endosulfan  24  115-29-7  +  +  +      Total kg  246 per year (5%)          Bypiridylics                Paraquat  47  1910-42-5  +  +  +      Total kg  192.5 per year (4%)          Amides                Vinclozolin  28  50471-44-8  +  +  +      Total kg  56 per year (1.1%)          Morpholinics                Dodemorph  5  1593-77-7  nd  +  nd      Total kg  50 per year (1%)            Frequency of use (no. of subjects)  CAS no.  Gene (no. of subjects) mutation  Chromosomal mutation  DNA damage  aYear of sampling.  bReferences: Fahrig (1974), Chen et al. (1982), Waters et al. (1982), Haworth et al. (1983), Moriya et al. (1983), Dellarco et al. (1986), Dzwonkowska and Hubner (1986), Garrett et al. (1986), Galloway et al. (1987), IARC (1987, 1991), Zeiger et al. (1987), Ishidate et al. (1988), McGregor et al. (1988), Dearfield et al. (1993), Franekic et al. (1994), Wei et al. (1997) and Hour et al. (1998).  Organophosposphates                Monocrotophos  88  6923-22-4  +  +  +      Glyphosate  57  1071-83-6  +  +  +      Methamidophos  53  10265-92-6  −  +  +      Methidation  48  950-37-8  nd  −  +      Methyl parathion  47  298-00-0  +  +  +      Etoprophos  38  13194-48-4  +/−  nd  nd      Dimethoate  4  60-51-5  +  +  +      Parathion  3  56-38-2  +  +  +      Pirimiphos methyl  3  29232-93-7  −  −  nd      Acephate  1  30560-19-1  +  +  +      Chlorpyrifos  1  2921-88-2  +  +  +      Diazinon  1  333-41-5  +  +  −      Fenthion  1  55-38-9  −  −  +      Phosphamidon  1  13171-21-6  +  +  nd      Profenofos  1  41198-08-7  nd  +  +      Total kg  1871.3 per year (39%)          Carbamates                Zineb  61  12122-67-7  +  +  +      Methomyl  38  16752-77-5  +  +  +      Dazomet  32  533-74-4  nd  +  +      Aldicarb  13  116-06-3  +  +  +      Propineb  3  12071-83-9  −  −  nd      Metham sodium  2  137-42-8  −  −  −      Mancozeb  1  8018-01-7  −  +  +      Total kg  1326 per year (28%)          Benzimidazoles                Benomyl  52  17804-35-2  +  +  +      Carbendazim  33  10605-21-7  +  +  +      Tiofanate methyl  19  23564-05-8  +  +  nd      Total kg  408.5 per year (8.5%)          Pyrethroids                Deltamethrin  53  52918-63-5  −  +  +      Permethrin  4  52645-53-1  +  +  nd      Cypermethrin  2  52315-07-8  +  +  nd      Fenpropathrin  1  64257-84-7  +  +  nd      Total kg  126.5 per year (2.5%)          Tiophthalimides                Captan  19  133-06-2  +  +  +      Folpet  7  133-07-3  +  +  +      Total kg  104 per year (2.1%)          Pyrimidinol compounds                Bupirimate  35  41483-43-6  −  nd  nd      Total kg  422 per year (8.8%)          Organochlorines                Endosulfan  24  115-29-7  +  +  +      Total kg  246 per year (5%)          Bypiridylics                Paraquat  47  1910-42-5  +  +  +      Total kg  192.5 per year (4%)          Amides                Vinclozolin  28  50471-44-8  +  +  +      Total kg  56 per year (1.1%)          Morpholinics                Dodemorph  5  1593-77-7  nd  +  nd      Total kg  50 per year (1%)          View Large Table III. Frequency of BNMN (mean×1000 cells) in floriculturists and controls by gender, age and smoking habit   Floriculturists  Referents    n  Mean ± SD  n  Mean ± SD  aFemales versus males.  bP < 0.05 (Mann−Whitney U-test).  cP < 0.001 (Mann−Whitney U-test).  dOlder classes versus young classes.  eFarmers versus referents.  Gender              Males  92  4.20 ± 2.10  42  2.64 ± 1.28      Females  15  5.65 ± 2.05a,b  19  3.92 ± 1.52a,c  Age              20−35  20  3.12 ± 1.13  11  2.58 ± 1.62      35−50  33  3.91 ± 1.77  23  3.57 ± 1.58      >50  54  5.19 ± 2.33c,d  27  2.77 ± 1.21  Smoking              Non-smokers  48  4.56 ± 2.16  22  3.36 ± 1.33      Smokers  23  3.80 ± 2.00  20  3.34 ± 1.53      Ex-smokers  36  4.60 ± 2.20  19  2.36 ± 1.41  Total  107  4.41 ± 2.14c,e  61  3.04 ± 2.14    Floriculturists  Referents    n  Mean ± SD  n  Mean ± SD  aFemales versus males.  bP < 0.05 (Mann−Whitney U-test).  cP < 0.001 (Mann−Whitney U-test).  dOlder classes versus young classes.  eFarmers versus referents.  Gender              Males  92  4.20 ± 2.10  42  2.64 ± 1.28      Females  15  5.65 ± 2.05a,b  19  3.92 ± 1.52a,c  Age              20−35  20  3.12 ± 1.13  11  2.58 ± 1.62      35−50  33  3.91 ± 1.77  23  3.57 ± 1.58      >50  54  5.19 ± 2.33c,d  27  2.77 ± 1.21  Smoking              Non-smokers  48  4.56 ± 2.16  22  3.36 ± 1.33      Smokers  23  3.80 ± 2.00  20  3.34 ± 1.53      Ex-smokers  36  4.60 ± 2.20  19  2.36 ± 1.41  Total  107  4.41 ± 2.14c,e  61  3.04 ± 2.14  View Large Table IV. Frequency of binucleated cells with micronuclei (mean×1000 cells) in floriculturists according to pesticide exposure   n  Mean ± SD  aP < 0.001 (Mann−Whitney U-test).  Pesticide exposure          No  61  3.04 ± 2.14      Yes  107  4.41 ± 2.14a  Duration of exposure (years of farming)          2−20  36  3.36 ± 1.37      >20  71  4.93 ± 2.28a  Pesticide consumption (kg/year)          0−10  27  4.36 ± 2.47      10−100  65  4.36 ± 1.96      >100  15  4.69 ± 2.39  Condition of exposure          Greenhouse  19  5.27 ± 2.51      Open field  49  4.31 ± 2.07      Open field and greenhouses  39  4.11 ± 1.99  Preparation of pesticide mixture          Yes  88  5.05 ± 2.50      No  19  4.27 ± 2.00  Use of protection devices          Yes  90  4.29 ± 2.13      No  17  5.04 ± 2.06  Types of crops          Ornamental crops  81  4.40 ± 2.02      Vegetable and ornamental crops  26  4.42 ± 2.55    n  Mean ± SD  aP < 0.001 (Mann−Whitney U-test).  Pesticide exposure          No  61  3.04 ± 2.14      Yes  107  4.41 ± 2.14a  Duration of exposure (years of farming)          2−20  36  3.36 ± 1.37      >20  71  4.93 ± 2.28a  Pesticide consumption (kg/year)          0−10  27  4.36 ± 2.47      10−100  65  4.36 ± 1.96      >100  15  4.69 ± 2.39  Condition of exposure          Greenhouse  19  5.27 ± 2.51      Open field  49  4.31 ± 2.07      Open field and greenhouses  39  4.11 ± 1.99  Preparation of pesticide mixture          Yes  88  5.05 ± 2.50      No  19  4.27 ± 2.00  Use of protection devices          Yes  90  4.29 ± 2.13      No  17  5.04 ± 2.06  Types of crops          Ornamental crops  81  4.40 ± 2.02      Vegetable and ornamental crops  26  4.42 ± 2.55  View Large Fig. 1. View largeDownload slide Correlation between BNMN per 1000 cells among floriculturists and years of exposure to pesticides (r = 0.30, P = 0.02). Fig. 1. View largeDownload slide Correlation between BNMN per 1000 cells among floriculturists and years of exposure to pesticides (r = 0.30, P = 0.02). Fig. 2. View largeDownload slide Box plots of BNMN per 1000 cells of floriculturists divided in two groups according to exposure duration (2–20 and >20 years) using or not the protective measures. From the bottom to the top the lines in the figure represent the 10th, 25th, 50th, 75th and 90th percentiles, respectively. Sample size (n) for each box plot is provided on the x-axis. Fig. 2. View largeDownload slide Box plots of BNMN per 1000 cells of floriculturists divided in two groups according to exposure duration (2–20 and >20 years) using or not the protective measures. 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TI - Micronucleus monitoring of a floriculturist population from western Liguria, Italy
JO - Mutagenesis
DO - 10.1093/mutage/17.5.391
DA - 2002-09-01
UR - https://www.deepdyve.com/lp/oxford-university-press/micronucleus-monitoring-of-a-floriculturist-population-from-western-gQqzSJ1j8O
SP - 391
EP - 397
VL - 17
IS - 5
DP - DeepDyve
ER -