Evaluation of DNA damage in long-term paint exposed workers by non-invasive buccal micronucleus assay

Abstract

Workers involved in paint production or application are extensively exposed to various hazardous substances like organic solvents, lead-based pigments, adhesives, and residual plastic monomers. Therefore, workers in the paint industry are at high risk of suffering adverse health effects. Studies of the lymphocytes of paint workers have demonstrated that industrial paint induces DNA damage and cellular changes. The aim of the present study was to assess DNA damage in 54 paint workers from paint production and application areas and 54 age-gender matched control subjects using a non-invasive buccal micronucleus (MN) assay. Buccal MN frequencies were significantly increased in workers compared to controls. MN frequencies significantly increased among workers from paint production areas compared with workers from paint application areas. MN frequencies in long-term workers (>10 years) were found to be significantly higher than those of short-term workers (≤10 years), which indicates that the duration of exposure to paints causes cytogenetic damage. MN frequencies increased with increasing age, while smoking status and the use of protective masks had no additional effect on MN frequencies within groups. In conclusion, it appears that long-term exposure to complex chemical mixtures during paint production may increase DNA damage in the workers. Understanding the possible causes of occupational exposure-induced genotoxicity in paint industry workers is of great importance for the protection of public health. Monitoring variables related to genotoxic damage in the paint workers using non-invasive methods will facilitate and improve risk assessment in the paint production sector.

Keywords

Paint workers buccal micronucleus DNA damage occupational exposure biomonitoring of DNA damage

Introduction

The chemicals used for diverse applications within the paint industry can pose serious adverse human health effects. Paint production and paint application workers are exposed to an extensive mixture of hazardous chemicals, including organic solvents (such as petroleum derivatives, alcohols, esters, and ketones), heavy metals (such as lead-containing pigments), as well as lacquers, resins, oils, adhesives, degreasers, and residual plastic monomers. Procedures in spray painting and powder coating, such as the use, handling, and storage of these hazardous substances, are particularly dangerous. Paint application occurs through the use of brushes, paint rollers, or by aerosol sprays, while the removal is primarily done by sandblasting. Most exposures occur during the manufacture of the paint, which includes processes such as weighing components (pigments, extenders, resins, additives), adding them to a mixer, pouring solvents into a milling machine, and cleaning equipment. Additional exposures may arise during thinning, tinting, and shading of the solvents, as well as during filling and varnish-filtering activities (Bae et al., 2010; Bergamaschi et al., 2022; Cavallo et al., 2021; De Oliveira et al., 2011; O'Connor et al., 2018; Saironi et al., 2023; Singh et al., 2016).

Many paint components have the potential to cause diverse negative health effects after exposure, even if both the technological development and company safety requirements have lowered exposure levels. Solvents used in paints are volatile at room temperature, so workers occupationally exposed to organic solvents are exposed to significant amount of volatile organic compounds (VOCs) via inhalation of vapors and gases (David and Niculescu, 2021; Ghobakhloo et al., 2023). The VOCs consist of aromatic and aliphatic hydrocarbons, ketones, esters, alcohols, and glycolethers. Toluene is the most commonly used VOCs in the paint production sector. Chlorinated solvents such as trichloroethylene, dichloromethane (methylene chloride), and perchlorethylene are also used to clean paint residues (Scélo et al., 2009). These VOCs contribute to air pollution by releasing VOCs into the atmosphere during painting operations. The focus of environmental concerns for VOCs is the formation of ozone through photochemical reactions with nitrogen oxides (NOx) (David and Niculescu, 2021). Recently, these efforts have been made to change the nature of paints to reduce VOC emissions which include the development of paints with low VOC content or without VOC content (new environmentally friendly paints). In some cases, water-based alternatives have been preferred instead of solvent-based products to reduce toxicity. When this is not feasible, different technologies are applied to reduce VOC emissions, of which thermal oxidation is the default choice for many manufacturers. Most VOCs (some hydrophilic VOCs are captured and retained in water) are captured by an adsorption system and thermally oxidized. This involves burning the emissions to convert VOCs into carbon dioxide or water, which can then be released into the atmosphere harmlessly (David and Niculescu, 2021; Ghobakhloo et al., 2023; Kim, 2011).

The paint vapors may directly contact to workers’ respiratory system and can deposit onto their body. Occupational exposure via inhalation can be significantly reduced by using protective equipment, but exposure still occurs via dermal absorption (Chang et al., 2011). Thus, occupational exposure to VOCs in paint workers comes from two main routes, inhalation and dermal exposure, although some minor cases come from oral exposure (Chang et al., 2011; Edokpolo et al., 2014; Ghosh et al., 2023; Saironi et al., 2023). Inhalation of the emitted VOCs from paint mixtures increases risk for developing adverse health effects among painters due to toxicological impacts on multiple organ systems, including the central nervous system, hepatic and renal systems, immune system, and cardiovascular system, and the reproductive system. Moreover, prolonged exposure is associated with an increased risk of cancer (Brown, 2010; Chang et al., 2011; Chen et al., 2001; De Oliveira et al., 2011; Dick, 2006; Kaukiainen et al., 2004; Park et al., 2006; Saironi et al., 2023). Organic solvents in paints and their metabolites have also been suggested to cause hematotoxicity and may result in decreased production of red blood cells, white blood cells, and platelets. Their adverse effects range widely from anemia to leukemia (Kamal and Malik, 2012). There is also evidence that VOCs exposure has been shown to damage to nucleic acids, leading to oxidative stress, genotoxicity, and inflammation. Studies published in recent years on paint workers have demonstrated that industrial paint induces DNA damage and cellular changes, DNA strand breaks, and incomplete excision repair of DNA/RNA in single cells and lymphocytes (Brum et al., 2021; Saironi et al., 2023). Increased proportions of aneuploid lymphocytes and chromosome deletions and higher values of chromosomal aberrations (CAs), sister chromatid exchange (SCE), and DNA damage have been reported in leukocytes of paint exposed workers (Cavallo et al., 2021; Duan et al., 2009, Kianmehr et al., 2017; Kim et al., 2011; de Oliveira et al., 2011). Chemicals in paints are involved in hydroxylation of the deoxyguanosine residue in DNA, via generation of oxygen radicals, which forms 8-hydroxydeoxyguanosine (8-OHdG) (Anetor, 2010; Chang et al., 2011; Fuchs et al., 1996; Maksoud et al., 2018; Rossner et al., 2005). Studies have shown that titanium dioxide pigment in paint induces DNA single- and double-strand breaks and chromosomal damage, and increases 8-OHdG levels, MN frequencies, and DNA deletions (Laurence, 2001). The increase in oxidative DNA was determined by the formamidopyrimidine-glycosylase (Fpg) comet assay on the lymphocytes of car painters (Londoño-Velasco et al., 2019). Cohort studies of cancer in painters have revealed a higher carcinogenic risk than the average population (Guha et al., 2010, 2011; Kim et al., 2013). Genotoxicity studies and persistent exposure of paint workers to multiple mutagens and carcinogens continue to raise concerns about cancer risks (De Oliveira et al., 2011; Kasperczyk et al., 2025; Saironi et al., 2023). The IARC classified occupational exposure to paints as a high-risk group for lung and bladder cancer development (IARC, 2012).

The micronucleus (MN) test allows the detection of chromosome loss and chromosome breaks, that is, a stabilized DNA damage (Bonassi et al., 2009; Fenech, 2008). The cytokinesis-block micronucleus cytome (CBMN) assay is the most frequently used method to biomonitor in vivo and in vitro exposure to genotoxicants in peripheral blood lymphocytes, whereas the buccal MN cytome assay using buccal exfoliated cells provides a complementary method for measuring DNA damage and cytotoxic effects in an easily accessible tissue not requiring ex vivo/in vitro culture. It has become increasingly important since it allows the simultaneous measure of the frequency of micronuclei and cell proliferation, cytokinesis abnormalities, different stages of apoptosis, and cell death in buccal cells (Bolognesi et al., 2013; Bonassi et al., 2009). The MN assay has been widely used in the biomonitoring of human exposed to different airborne genotoxic compounds. Although the buccal cells can be easily collected by non-invasive sampling, the application of buccal MN assay in occupational settings remains limited (Bolognesi et al., 2013; Bonetta et al., 2024; Villarini et al., 2021).

An increased frequency of MN in lymphocytes and buccal epithelial cells was observed among paint industry workers, which was attributed to exposure to the organic solvents present in the working areas (Cassini et al., 2011; Celik et al., 2010; Dos Reis Filho et al., 2019; Pinto et al., 2000; Testa et al., 2005). In contrast, some studies have not identified an increase in the MN frequency paint workers (Cárdenas-Bustamante et al., 2007; De Oliveira et al., 2011).

In this study, we aimed to assess DNA damage in buccal epithelial cells of workers involved in paint production and application areas using a non-invasive buccal MN assay. Little is known about current practices in the production and application of paints in developing countries, particularly regarding worker exposures. Although some studies have shown a positive relationship between paint exposure and DNA damage, to our knowledge, our study is a noteworthy contribution to monitoring the health effects of on-going paint exposure in occupational settings under current conditions (Brum et al., 2021; Cavallo et al., 2021; Saironi et al., 2023). This study contributes to the assessment of occupational exposure of painters by using an auxiliary marker test, which reveals not only DNA damage status but also early biomarkers for disease and cancer risk associated with changes in genome instability.

Methods

Study design

The study group consisted of 54 male workers employed in the paint industry for at least 1 year. Of these, 32 and 22 have been employed in paint production and paint application areas, respectively. To demonstrate the significance of exposure duration, a 10-year period was selected, considering the similar sample sizes. The workers employed between 1 and 10 years and over 10 years were in the short-term exposure groups and long-term exposure groups, respectively. The control group consisted of 54 male office personnel who were not occupationally exposed to paints or other chemicals and were similar to the workers in terms of age, gender, smoking, alcohol consumption, and nutritional habits. In order to mitigate the influences of genetic and lifestyle factors, all male participants were selected from the same geographic region of Türkiye. The participants in our study had a healthy balanced diet, with three to four meals per day. The study subjects reported that their diets consisted of vegetables, fruits, whole grains, fat-free or low-fat dairy products, lean meats, poultry, fish, eggs, legumes, and nuts, with limited saturated and trans fats, sodium, and added sugars. Owing to the similar dietary habits of the participants, the dietary effect was excluded in MN formation. All volunteers were employed in Ankara, Türkiye. Subjects who reported active infection, chronic diseases, respiratory symptoms, radiotherapy, or chemotherapy were excluded. Participants of the study were selected using the non-probability purposive/judgment sampling method.

The paint manufacturing facility selected in our study covered a total area of 7,000 square meters, with 4,000 square meters designated as enclosed operational space and the remaining 3,000 square meters designated as open areas. The facility encompassed various functional zones, including application, production, filling, packaging, quality control, research and development, and shipment departments. Paint applications and productions were performed indoors. Throughout the areas, ventilation motors (fans) were installed to ensure adequate air circulation and maintain occupational health standards. Employee work schedules were also recorded, with operations running from Monday to Friday, 08:00 to 18:00. During workdays, employees were provided with scheduled breaks: a 15-minute morning break, a 1-hour lunch break, and a 20-minute afternoon break. This operational framework supported efficient manufacturing processes while also adhering to labor regulations and employee well-being.

All volunteers were informed about the aim of the study and their written consent was obtained. Each participant completed a detailed questionnaire that included questions regarding working conditions, possible confounding factors such as smoking, alcohol consumption, and nutritional habits. This study was approved by the local ethics commission of Hacettepe University and conducted in accordance with the ethical standards in the 1964 Declaration of Helsinki (Date: February 15, 2022 and number: GO 21/884). The samples from paint workers were taken at the Ankara Occupational and Environmental Diseases Hospital.

Sample preparation and analysis of micronucleus frequency

The buccal MN assay for the determination of chromosomal genetic damage was performed according to the procedure described by Thomas et al. (2009) and Bonassi et al. (2009) and as further modified by Becit et al. (2021).

Samples were collected every weekday for 2 months before lunch and immediately after work. Volunteers were asked to rinse their mouths with distilled water and keratinized tissue was removed from buccal mucosa with an abeslang and discarded. Exfoliated buccal epithelial cells were collected by softly scraping from the mucosa of both cheeks with a swab. Buccal samples were taken three times from each subject. The cells were shaken in a centrifuge tube containing 10 mL of phosphate-buffered saline (PBS). The cell suspensions were centrifuged at 1200 r/min (300 x g) for 5 min to remove cell debris and the supernatant was discarded. The cells were then collected onto the swab and spread on a clean and coded slide moistened with distilled water then air-dried at room temperature. The cells were then fixed in 80% methanol for 20 min in the dark. This process was repeated twice. For staining, the slides were covered with 60 µl 0.1% acridine orange (Sigma-Aldrich, St. Louis, Missouri, USA) and incubated for 15 min in the dark (Becit et al., 2021; Bonetta et al., 2024; Cao et al., 2002; Thomas et al., 2009).

Three slides were prepared for each subject and a total of 1000 cells per individual were classified and scored using a fluorescent microscope (Leica Microsystems DM2500, Wetzlar, Germany) equipment with a 440–490 excitation and 520 nm emission filters at 400x. Micronuclei were evaluated by a well-trained MN specialist according to the scoring criteria described by Tolbert et al. (1992). The results were expressed as MN frequency. The following criteria for MN analyses in oral epithelial cells were followed: a single MN must be less than one-third the diameter of the main nucleus; be on the same plane of focus; have the same color, texture, and refraction as the main nucleus; have a smooth, oval, or round shape; be clearly separated from the main nucleus.

There are still technological gaps to be resolved regarding the buccal MN test, as image analysis algorithms capable of distinguishing MN from other nuclear anomalies are not yet considered adequate (Fenech et al., 2024). Therefore, the following cells were not analyzed during the evaluation of micronucleated cells: basal cells, binucleated cells, nuclear buds, pyknotic cells, cells containing condensed chromatin, and karyoretic and karyolytic cells.

Statistical analysis

The IBM SPSS software (IBM® SPSS Statistics 23.0) was used for the statistical analysis for both quantitative and qualitative variables. The experiment was repeated three times. The results were expressed as the mean ± standard deviation for continuous variables and the number of cases percent (%) for categorical variables. To determine the normality of the distribution and the homogeneity of variance, the Kolmogorov-Smirnov test and the Levene test were applied to our data, respectively. Statistical differences between groups with normal distribution were determined by the one-way analysis of variance (ANOVA) test. Post hoc analysis of group differences was used with the least significant difference (LSD) test. Statistical differences between groups without normal distribution were analyzed using the Kruskal-Wallis test. Comparison among proportions was performed using Chi-squared proportion test. Pearson correlation analysis was used to measure the magnitude of linear relationship between two variables between MN frequencies and age, exposure duration, work areas, daily cigarette smoking, and mask use. In addition, a multivariate logistic regression analysis was carried out to examine the possible association between the characteristic variables (independent variable) including age, exposure duration, and behavioral factors (smoking, alcohol consumption), using mask and the buccal MN assay positivity (dependent variable). The p-value of less than 0.05 was considered statistically significant.

Results

Demographic properties of study groups

The demographics of the study groups are summarized in Table 1. The mean ages of the workers and controls were 41.05 years (range 18–62 years) and 42.12 years (range 18–61 years), respectively. There was no statistically significant difference in age between the workers and the controls. Volunteers aged between 40 and 62 years were designated as the middle-aged group and those aged between 18 and 39 years were in the young-aged group (Noam, 2001). The proportion of young-aged groups was 46.30% among workers and 44.44% among controls, whereas the proportion of middle-aged groups was 53.70% and 55.56%, respectively. The mean ages of young-aged groups were 30.64 years among workers and 31.50 years among controls, whereas the mean ages of middle-aged groups were 50.03 years and 50.63 years, respectively, and these differences between the groups were not statistically significant.

Table 1.

Characteristics of the study groups.

	Workers (n = 54)	Controls (n = 54)
Age (years)	41.05 ± 11.52 (18–62)	42.12 ± 11.48 (18–61)
Young-aged group [n (%)]	30.64 ± 6.33 [25 (46.30%)]	31.50 ± 6.41 [23 (44.44%)
Middle-aged group [n (%)]	50.03 ± 6.05 [29 (53.70%)]	50.63 ± 6.33 [30 (55.56%)]
Duration of exposure (years)	15.29 ± 12.69 (1–45)	-
≤10 years (short exposure) [n (%)]	3.77 ± 2.83 [24 (44.4%)]
Smokers/non-smokers^a (n/n)	13/11
Cigarette/day	20.00 ± 7.07 (10–40)
Mask use in smokers/non-smokers (n/n)	6/7
>10 years (long exposure) [n (%)]	24.50 ± 9.50 [30 (55.6%)]
Smokers/non-smokers^a (n/n)	15/15
Cigarette/day	15.00 ± 8.73 (5–30)
Mask use in smokers/non-smokers (n/n)	5/7
Working area
Paint production [n (%)]	32 (59.26%)	-
Smokers/non-smokers^a (n/n)	16/16
Cigarette/day	16.88 ± 9.52 (5–30)
Mask use in smokers/non-smokers (n/n)	6/7
Paint application [n (%)]	22 (40.74%)
Smokers/non-smokers^a (n/n)	12/10
Cigarette/day	17.92 ± 6.55 (5–30)
Mask use in smokers/non-smokers (n/n)	5/7
Smoking status
Smokers^a [n (%)]	28 (51.85%)	28 (51.85%)
Non-smokers [n (%)]	26 (48.15%)	26 (48.15%)
Cigarette/day	17.32 ± 8.26 (5–40)	15.21 ± 8.14 (3–30)
Alcohol intake
No [n (%)]	50 (92.60%)	53 (98.15%)
Yes [n (%)]	4 (7.40%)	1 (1.85%)
Using protective mask
No [n (%)]	29 (53.70%)	-
Yes [n (%)]	25 (46.30%)	-

The values are given as the mean ± standard deviation (range). The study group consisted of male workers who had been working in the paint industry for at least 1 year. The control group consisted of male office workers who were not occupationally exposed to paint or other chemicals and were similar to the workers in terms of age, gender, smoking, and alcohol consumption habits.

^aIn both groups, individuals who smoked more than one cigarette/day for at least 1 year were considered smokers. Volunteers aged between 40 and 62 years were in the middle-aged group and those aged between 18 and 39 years were in the young-aged group. n: number of subjects.

The average working years of the paint workers was 15.29 years (range 1–45 years). The subjects employed between 1 and 10 years (short-term) (range 1–10 years) and over 10 years (long-term) (range 13–45 years) were 44.4% and 55.6%, respectively. The average number of working years for short- and long-term employees was 3.77 and 24.50, respectively. In the workers, the percentages of subjects from the production and application areas were 59.26% and 40.74%, respectively (Table 1).

The mean daily smoking was 17.32 cigarettes/day (range 5–40) and 15.21 cigarettes/day (range 3–30) in the workers (51.85%, n = 28) and controls (51.85%, n = 28), respectively. The mean daily smoking was 20.00 cigarettes/day (range 10–40) and 15.00 cigarettes/day (range 5–30) in short-term workers (44.40%, n = 24) and long-term workers (55.60%, n = 30), respectively. The mean daily smoking was 16.88 (range 5–40) cigarettes/day and 17.92 (range 5–30) cigarettes/day in paint production workers (59.26%, n = 32) and paint application workers (40.74%, n = 22), respectively (Table 1). There were no statistically significant differences in smoking status among the groups. The number of subjects consuming alcohol was very low in all study groups and not sufficient for statistical evaluation. Therefore, alcohol consumption was not taken into consideration as a factor.

Only 25 (46.30%) workers claimed to use protective masks. Of those wearing masks, 13 and 12 were short-term and long-term workers, respectively, and 13 and 12 were working in the paint production and paint application areas, respectively (Table 1). There were no statistically significant differences in the use of protective masks between short-term and long-term workers.

Evaluation of micronucleus frequency

Micronucleus frequencies in the exfoliated buccal cells are presented in Table 2. Buccal MN frequency of the workers (9.61, range 1–23) were found to be significantly higher (2.91-fold) than the control group (3.30, range 0–9) (p < .05).

Table 2.

MN frequencies in buccal exfoliated cells of paint-workers and controls.

Factors	MN frequencies (1000)		p Value
Factors	Workers (n = 54)	Controls (n = 54)	p Value
	9.61 ± 6.01 (1–23)^a	3.30 ± 2.18 (0–9)	a: <0.001
Age [n = workers/n = controls)
Young-aged group [n = 25/23]	9.36 ± 6.81 (1–23)^a	2.21 ± 1.50 (0–6)^b	a: <0.001
Middle-aged group [n = 29/31]	9.83 ± 5.34 (2–21)^a	4.16 ± 2.26 (1–9)	b: <0.001
Duration of exposure
≤10 years (short exposure) [n = 24]	7.38 ± 5.45 (1–22)^c	-	c: 0.012
Smokers^d [n = 13]	11.40 ± 5.92 (1–22)
Non-smokers [n = 11]	7.09 ± 6.32 (2–21)
>10 years (long exposure) [n = 30]	11.4 ± 5.91 (2–23)	-
Smokers^d [n = 15]	12.27 ± 5.73 (2–23)
Non-smokers [n = 15]	10.53 ± 6.17 (3–20)
Working area
Paint production [n = 32]	11.31 ± 6.41 (2–23)^e	-	d: 0.007
Smokers^d [n = 16]	11.81 ± 6.27 (2–23)
Non-smokers [n = 16]	10.81 ± 6.71 (3–22)
Paint application [n = 22]	7.14 ± 4.43 (1–17)	-
Smokers^d [n = 12]	7.83 ± 4.19 (2–14)
Non-smokers [n = 10]	6.30 ± 4.78 (4–17)
Smoking status [n = workers/n = controls)
Smokers^d [n = 28/28]	10.1 ± 5.74 (2–23)^a	3.96 ± 2.10 (1–9)^f	a: <0.001
Non-smokers [n = 26/26]	9.07 ± 6.34 (1–22)^a	2.57 ± 2.06 (0–8)	e: 0.018
Alcohol intake [n = workers/n = controls)
No [50/53]	9.78 ± 6.03 (1–23)^a	3.26 ± 2.05 (0–8)	a: <0.001
Using protective mask
Yes [n = 29]	8.16 ± 5.50 (1–21)
No [n = 25]	10.87 ± 6.24 (2–23)

The values are given as the mean ± standard deviation (range).

The ANOVA test was used to determine statistical differences between paint-workers and controls and the Kruskal-Wallis test was used to determine the statistical differences between the subgroups separated according to the factors because of nonparametric data.

The study group consisted of male workers who had been working in the paint industry for at least 1 year. The control group consisted of male office workers who were not occupationally exposed to paint or other chemicals and were similar to the workers in terms of age, gender, smoking, and alcohol consumption habits.

MN frequencies were determined using fluorescence microscopy equipment with a 440–490 excitation and 520 nm emission filters at 400x from a total of 1000 buccal epithelial cells per individual. n, number of subjects; MN, micronucleus.

^ap <0.05, workers compared to controls.

^bp < 0.05, young age group compared to middle age group.

^cp < 0.05, long exposure duration compared to short exposure duration.

^dIn both groups, individuals who smoked more than one cigarette/day for at least 1 year were considered smokers. Volunteers aged between 40 and 62 years were in the middle-aged group and those aged between 18 and 39 years were in the young-aged group.

^ep < 0.05, paint production workers compared to paint application workers.

^fp < 0.05, smokers compared to non-smokers.

The buccal MN frequency of young-aged workers was found to be significantly higher (4.23-fold) than the MN frequency of the controls within the same age range (p < .05). The MN frequency of middle-aged workers was found to be significantly higher (2.36-fold) than the MN frequency of middle-aged controls (p < .05). No statistically significant difference was found between young- and middle-aged workers, indicating that age had no additional influence on the frequency of MN in paint workers (p > .05). Alternatively, the MN frequency was significantly higher (1.88-fold) in middle-aged controls compared to young-aged controls (p < .05). Moderate positive correlations were found between age and MN frequencies in workers (r = 0.218), while low positive correlations were found in controls (r = 0.489). When all workers and controls were included, a low positive relationship was found (r = 0.187). Therefore, it can be claimed that age can increase the MN frequency in healthy controls.

MN frequencies were 7.38 (range 1–22) and 11.4 (range 2–23) in the workers with short and long duration of exposure, respectively. MN frequencies in long-term workers (>10 years) were found to be significantly higher (1.54-fold) than short-term workers (≤10 years) (p < .05). The frequency of MN increased with increasing duration of exposure. There was a low positive relationship between the duration of exposure and MN frequencies in workers (r = 0.203). Smoking status did not change MN frequencies in both short-term and long-term workers (p > .05).

When the workers from paint production and application areas were compared, the MN frequency of the workers from paint production areas was found to be significantly higher (1.58-fold) than that of the workers from paint application areas. Working area significantly changed MN frequencies (p < .05), but there was a low positive relationship between the working areas and MN frequencies (r = 0.203). Smoking status did not change the frequency of MN in workers working in both paint production and application areas (p > .05).

Among both smokers and non-smokers, the MN frequency of workers was significantly higher than the controls (2.55-fold and 3.53-fold, respectively) (p < .05). However, no statistically significant difference was found between smoking and non-smoking workers, suggesting that smoking had no additional effect on the MN frequency (p > .05). When smoker controls were compared with non-smoker controls, there was a statistically significant difference. The MN frequency of smoker controls was significantly higher than the non-smoker controls (1.54-fold) (p < .05). Low negative and strong positive correlations were found between daily cigarette smoking and MN frequencies in workers (r = −0.106) and controls (0.520), respectively, whereas when all workers and controls were included, a low positive relationship was found (r = 0.120). Therefore, it can be claimed that smoking can increase the MN frequency in healthy controls.

MN frequencies were 8.16 (range 1–21) and 10.87 (range 2–23) in the workers using protective masks and not using protective masks, respectively. There was no difference in MN frequencies between the workers using mask and not using mask (p > .05). There was a low positive relationship between using mask and MN frequencies (r = 0.226). Contrary to expectations, it was found that the use of protective masks had no effect on the buccal MN frequency. It is possible the workers did not correctly answer the questionnaire, or the provided masks did not offer adequate protection.

As a result of multiple regression analysis in which all independent variables (age, exposure duration, working areas, daily cigarette smoking, and mask use) were evaluated together, it was determined that 11.6% could explain the effect on MN frequency.

Discussion

Pigments, binders, extenders, solvents (sometimes called thinners), and various chemical compounds used during the application and production of paints can release hazardous gases that threaten human health. Therefore, paint workers are exposed to various harmful chemicals present in paint products, such as solvents, especially VOCs in particular (benzene, toluene, and xylene) (Roma-Torres et al., 2006). All these constituents were reported by many studies to have adverse effects on neurobehavioral, blood, kidney, spleen, liver, cardiac, and respiratory functions (Awodele et al., 2014; Roma-Torres et al., 2006; Smulders et al., 2014). The multiple genetic and cytogenetic effects observed among paint workers and the information on individual chemicals to which painters are exposed provide strong evidence to support genotoxicity as a mode of action underlying increased cancer risk (De Oliveira et al., 2011; Saironi et al., 2023). Genotoxicity assessments have mainly been conducted on biological materials such as blood, peripheral blood lymphocytes, urine, and buccal epithelial cells (De Oliveira et al., 2011; OECD, 2015). The most frequently examined genotoxic effects were DNA damage, chromosomal abnormalities, and micronuclei (Kasperczyk et al., 2025). Nevertheless, because of the complexity and changing composition of paint mixtures, as well as potential interactions of different ingredients within mixtures, other mode of action are also possible through paint exposure (IARC, 2012). Although reducing the amount of solvents and heavy metals (particularly lead) in paints has been prioritized in many developed countries, painters continue to use them with the belief to improve colors or increase paint adhesion to the surface, resulting in significant exposure to hazardous solvents and metals (Hsu et al., 2018).

We were unable to determine the levels of solvents and their metabolites in the workers in this study. Data on air toluene concentrations were available solely from the paint production plant. The concentration of toluene in the plant production area was reported as 41 mg/m³ (∼12 ppm), which was far below the 200 mg/m³ (OSHA, 2024). Similarly, in the limited studies, organic solvents or their metabolites in blood samples of paint workers were generally found to be lower than the detection limits (Jodeh et al., 2023; Pinto et al., 2000). This was due to the lack of intense exposure because the painting activities were mostly carried out in open areas where natural ventilation was effective, such as windows, playgrounds, and most of the paints used were vinyl and acrylic-water-based paints containing a very small percentage of organic solvents. This is likely because the painting activities were primarily carried out in places where natural ventilation was effective, such as outdoors, windows, and playgrounds. Additionally, there was lower exposure because most of the paints used were vinyl and acrylic-water-based paints which contain a very small percentage of organic solvents (Pinto et al., 2000).

Paint toxicity is generally associated by the production of reactive oxygen species that cause cell damage by increasing lipid peroxidation, decreasing antioxidant enzyme activity, and leading to the formation of free radicals, which result in single and double strand DNA breaks, as well as base changes (Scélo et al., 2009).

The studies performed with small numbers of painters regarding their genotoxicity are few and have opposing results (Cavallo et al., 2021; Celik et al., 2010; Lee et al., 2003; Maksoud et al., 2018). In many of the studies related to paint genotoxicity, a significant increase in DNA damage was observed using the comet assay in lymphocytes of paint workers (Kianmehr et al., 2017; Maksoud et al., 2018). Similarly, De Oliveira et al. (2011) also reported the induction of DNA damage on buccal cells and lymphocytes in Brazilian paint industry workers via the comet assay. It was also found in car painters via the evaluation of Fpg on lymphocytes (Dos Reis Filho et al., 2019). In a study by Cavallo et al. (2021) with 17 (11 spray and 6 cylinder) shipyard painters, higher oxidative DNA damage was found in all workers using the Fpg-modified comet assay. In a study using urine 8-OHdG as a biomarker of oxidative DNA damage, oxidative DNA damage was found to be higher among spray and industry painters compared to controls (Chang et al., 2011). In a previous study, 110 auto-paint workers showed increased lymphocyte DNA damage, as well as 8-OHdG and malondialdehyde (MDA) levels with the decreased glutathione peroxidase (GPx) and glutathione reductase (GR) levels compared to their controls (Roma-Torres et al., 2006).

In our study with 54 workers from paint production and application areas, buccal MN frequency of the workers was significantly higher than the control group, suggesting that paint exposure increases DNA damage. This result is consistent with the findings of previous studies showing higher values of CAs, SCE, and DNA damage in the leukocytes of paint-exposed workers in automobile coatings and other painting procedures (Cetintepe et al., 2023; de Oliveira et al., 2011; Piña-Calva et al., 1991; Testa et al., 2005). Bonetta et al. (2024) showed increased direct/oxidative DNA damage, MN frequency, and meta-nuclear alterations in buccal cells of workers involved in pigment-grade TiO₂ production (15 exposed and 20 not-exposed). Workers with higher genotoxic/oxidative stress biomarker levels reported early respiratory symptoms, suggesting that molecular alterations can be predictive of early health dysfunctions (Bonetta et al., 2024). Akram et al. (2023) investigated the expressions of genes related to DNA repair (XRCC1, PARP1) and lead toxicity (ALAD) in 200 industrial paint workers and 200 healthy controls. Strong relationship was shown between XRCC1, PARP1, and ALAD gene versus age, total exposure duration, exposure per day, and lead deposition. They revealed altered activity of oxidative stress markers, which would induce oxidative damage to DNA integrity and limit the function of repair enzymes. Failure to repair oxidative DNA lesions is expected to lead to chromosomal damage as measured by the frequency of MN. Occupational exposure increases the chemical burden in exposed workers, which would negatively affect their health and genetic stability as well. KanuPriya et al. (2023) evaluated the glutathione-S-transferase polymorphism with DNA damage in 30 paint workers and 30 healthy control individuals. A significantly higher frequency of CBMN and DNA tail moment was observed in paint workers compared to the control. The polymorphism in the GST gene was significantly different in paint workers. It was suggested that there was a clear association between glutathione-S-transferase polymorphism and genetic damage in paint workers (Bonetta et al., 2024). Brum et al. (2021) investigated DNA damage and inflammatory response in 22 and 12 employees of the gasoline station attendants (GSA) and private painting companies exposed to fuels and paints. GSA and painters did not present changes in the biochemical parameters and the hepatic and renal markers, when compared with the control group. The lack of significant changes in routinely evaluated parameters may mask the actual health status of the workers. Interestingly, although no significant changes were observed in the oxidative stress markers of workers exposed to chemical products, there are other studies that report these alterations. They observed an increased DNA damage and elevated 8-OHdG levels in GSA and painters compared to the control group. The levels of pro-inflammatory cytokines (TNF-alpha, IL-1, IL-6, and IFN-gamma) were increased in both groups of workers when compared to the control group. It seems that pro-inflammatory cytokines increase when an inflammatory process occurs in the organism. The levels of NOx increased in the painters. In cases of chemical exposure, NOx levels increase in cases of sensitivity to more than one chemical. IFN-gamma upregulates a variety of pro-inflammatory cytokines and induces nitric oxide synthase, the enzyme responsible by NOx synthesis. Higher NOx synthesis is implicated in the pathogenesis of inflammation and genotoxicity. Linear correlations showed strong associations between the analyzed inflammatory parameters (IL-1, IL-6, IL-10, TNF-alpha, and IFN-gamma) and DNA alteration biomarkers (DNA damage index and 8-OHdG). These findings suggest that the immunological alterations observed in the workers are associated with DNA damage (Brum et al., 2021). Ghobakhloo et al. (2023) evaluated the occupational exposure to 15 VOCs in Iranian paint production factories and subsequently, the associated health risks. The non-cancer risks of benzene, n-nonane, trichloroethylene, tetrachloroethylene, xylene, and ethylbenzene surpassed the reference value in most of the sectors. It was suggested that there was a risk of carcinogenesis in all studied sections, mainly due to ethylbenzene and benzene.

Although findings related to genotoxic damage in paint workers have been reported, data obtained from the MN assay is limited and contradictory. A limited number of studies have suggested that there is no statistically significant differences in the frequency of MN induction, which investigated the degree of exposure to organic solvents and related genotoxic consequences in the lymphocytes of paint factory workers (Cárdenas-Bustamante et al., 2007; De Oliveira et al., 2011), by determining MN frequency and DNA damage using the MN test and comet assay, respectively. Moro et al. (2012) reported that DNA damage evaluated by the comet assay increased in industrial painters exposed to low levels of toluene, but no increase was in micronuclei. In the study of Cassini et al. (2011), DNA damage significantly increased in 33 paint workers compared to 29 controls. No significant difference was found in the frequency of MN in lymphocytes and buccal cells, except for the increases in the frequency of nuclear buds in lymphocytes and condensed chromatin in the buccal cells of workers (Cassini et al., 2011). Our study with buccal cells of workers from paint production and application areas is consistent with some studies from different countries showing an increase in the frequency of MN in the buccal cells of paint workers (Celik et al., 2010; De Oliveira et al., 2011; Dos Reis Filho et al., 2019; Martino-Roth et al., 2003; Rossner et al., 2005).

In our study, it was observed that the frequency of MN significantly increased as the duration of occupational paint exposure increased among workers. In agreement with our results, Cassini et al. (2011) showed a positive correlation between DNA damage in workers exposed to paints and the daily exposure time. The working area also affected MN frequencies, such that MN frequencies in the paint production workers were significantly higher than in the paint application workers. The higher prevalence of MN in the paint production workers suggests that ventilation conditions or the use of protective masks are not sufficient in the work areas. Additionally, it is thought that dermal absorption may be the exposure pathway responsible for the increase in MN frequencies. Solvent gases released into the atmosphere during paint application can come into direct contact with workers’ respiratory systems and can also be absorbed dermally by contact with their clothing or bodies. Londoño-Velasco et al. (2019) demonstrated that DNA damage increased in the spray painters compared to the control using the comet assay. Maksoud et al. (2018) found that oxidative DNA damage in spray painter is higher than that in sandblasting workers. It was reported that using respirators reduced workers’ inhalation of solvent vapors, but cutaneous exposure became the predominant part of total body burden of solvents.

The use of protective masks did not exhibit a protective effect on the frequency of MN in buccal epithelial cells in our study, likely due to the failure to implement suitable and proper protective measures. Also, smoking did not add an additional DNA damage to paint workers since no statistically significant difference was found between smoking and non-smoking workers. Similarly, Silva and Santos-Mello (1996) concluded that smoking habits do not represent a significant factor of chromosome aberrations found in their occupational monitoring of car painters.

The primary limitation of the present study is the small sample size of workers. It was difficult to obtain the necessary permissions from the workplace and to collect samples from volunteers during the sample collection process. In previous similar studies, it has also been observed that the sample size is small. Nevertheless, it appears that the sample size of our study was even higher than that of most previous studies. The other limitations of the present study are the inability to measure air and blood measurements of some solvents and metals found in paints, and lack of additional supporting biochemical analysis, such as biochemical analysis of oxidative stress and gene expression of relevant genes, which would contribute to a better understanding of the picture. In this workplace, lead-free paint was used. Therefore, it is estimated that the genotoxicity cannot be caused by lead since the workers were not exposed to lead from paint. It was observed that workplace air measurements were not sufficient.

It is legally mandatory to measure respirable dust in workplaces in Türkiye, and the employer declared that the dust measurement values at this workplace were below legal limits. However, the VOC levels other than toluene were not measured in this workplace. The toluene level in the workplace was below OSHA limit. Additionally, exposure levels may vary across different paint processing plants. Therefore, estimates obtained from this group do not reflect all paint industries. In the future, more detailed studies with a large number of voluntary participants are necessary to reveal the accuracy relationship of the results.

Conclusion

The possible causes of occupational exposure-induced genotoxicity in paint industry workers are of great importance for the protection of public health. Monitoring variables related to genotoxic damage in paint workers will help in the risk assessment in carcinogenicity. Our results showed that MN frequencies in buccal cells of paint production and application workers exposed to complex paint chemicals increased with increasing age and exposure duration, while smoking status and the use of protective masks had no additional effect on MN frequencies within the workers. It was also seen that the working area significantly changed MN frequencies.

The buccal MN assay is a non-invasive and easy technique that can be used in biomonitoring studies in occupational settings. Our study only evaluated cytological markers, but with other related parameters, they can be used as early biomarkers for future disease risk assessment. Furthermore, the use of personal protective equipment is essential to prevent increased health risk, but approximately half of the individuals in our study declared that they did not use it. Periodic health investigations are suggested for the determination of early toxic effects in long-duration workers. Given the clear association with cancer, relative information should be provided for these individuals, and public policies should be updated, ranging from regular health screenings to limited exposure time and ideal working conditions to guarantee a better quality of life and reduced risk.

Footnotes

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by Hacettepe University Scientific Research Projects Coordination Unit in Turkey (Project number: THD-2021-19840). The sponsoring organization provided financial support for the supplying of experimental materials necessary for the study. The sponsoring organization had no role in study design, collection, analysis and interpretation of data, writing of the report, or decision to submit the article for publication.

Ethical approval

This study was approved by the local ethics commission of Hacettepe University and conducted in accordance with the ethical standards in the 1964 Declaration of Helsinki (Date: February 15, 2022 and grant number: GO 21/884).

Informed consent

All volunteers were instructed with information about the aim of the study and their written consent was obtained.

ORCID iDs

İzem Bilinmiş

Senanur Durna

Sevtap Aydin Dilsiz

Nurşen Başaran

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