Abstract Since there are several predicting factors associated with the comet assay parameters, we have decided to assess the impact of seasonal variations on the comet assay results. A total of 162 volunteers were retrospectively studied, based on the date when blood donations were made. The groups (winter, spring, summer and autumn) were matched in terms of age, gender, smoking status, body mass index and medical diagnostic exposure in order to minimise the impact of other possible predictors. Means and medians of the comet assay parameters were higher when blood was sampled in the warmer period of the year, the values of parameters being the highest during summer. Correlation of meteorological data (air temperature, sun radiation and sun insolation) was observed when data were presented as the median per person. Using multivariate analysis, sampling season and exposure to medical radiation were proved to be the most influential predictors for the comet assay parameters. Taken together, seasonal variation is another variable that needs to be accounted for when conducting a cohort study. Further studies are needed in order to improve the statistical power of the results related to the impact of sun radiation, air temperature and sun insolation on the comet assay parameters. Introduction Humans are continuously exposed to a complex environment in which chemical and physical agents interfere with biomolecules important to sustain normal cell and organism functions (1). DNA is one such molecule and several biomarkers for the detection of its damage were developed during the past 30 years. Using the cytokinesis-block micronucleus (CBMN) assay and chromosome aberrations (CA) test, it is possible to determine structural changes in the chromosomes, chromatid breakage and errors in mitotic apparatus (2,3). Furthermore, cancer predictive potential was set for the parameters of both CBMN assay and CA test (4,5). The alkaline comet assay was designed to recognise DNA damage derived from single-strand breaks (SSB) and double-strand breaks, SSB associated with incomplete excision repair sites, DNA cross-links and alkali-labile sites (ALS) (6) and it is presently applied worldwide with growing number of publications (7). One of the objectives of the recently launched hCOMET project (www.hcomet.org) is to build a database and to establish a link between the comet assay parameters and human diseases. Another goal is to determine those factors (age, gender, smoking status, lifestyle, etc.) that might affect the comet assay results, thus challenging its reliability and reproducibility, which was also highlighted by the ComNet working group (8). Seasonal variations (air temperature, sun radiation and sun insolation) as predictors of the comet assay results were assessed by several studies, but most of these dealt with a relatively small number of volunteers (n ≤ 100) and the results were rather inconsistent (9–13). The factors contributing to a greater DNA damage in summer are longer sun exposure, use of sunbeds and higher sun radiation (14). However, during winter, more environmental pollution and lower antioxidant consumption can be expected (15,16). In our previous study on baseline levels of the comet assay parameters, higher tail length (TL) and long-tailed nuclei was noticed during winter. However, such result is the consequence of high ratio of volunteers diagnostically exposed to X-rays and the smoking habits of volunteers who donated blood in winter rather than seasonal variations that were not explored in more detail (17). Therefore, the aim of the present study was to assess the impact of seasonal variations (air temperature, sun irradiation and sun insolation) on different parameters of the comet assay. To get reliable results, we conducted a retrospective study in which 162 volunteers from general population were involved and special attention was paid to reduce the influence of predictors, such as age, gender, smoking status, body mass index and residence (similar air pollution and sun exposure) between compared groups by matching the volunteers for the above-mentioned factors. Subjects and methods Volunteers and blood sampling Volunteers involved in this retrospective study were recruited during the 2008–2016 period from the general population of the city of Zagreb, Croatia (45°48′55″N, 15°57′59″E, elevation 145 m). At the time of blood sampling volunteers reported no acute medical condition and no exposure to medical diagnostics [X-ray, computed tomography (CT), magnetic resonance imaging (MRI) or ultrasound] for at least 1 month before the sampling. A total of 162 (114 female and 48 male) volunteers were involved in this study with their mean age 39.42 ± 12.96 years. Their BMI was 24.12 ± 3.86 kg/m2, 26.54% of them were active smokers and 35.80% were exposed to medical diagnostics (CT, MRI or ultrasound) in the period of 1 year to 1 month before blood sampling. The characteristics of the study group when divided into seasons are summarised in Table 1. Before taking part in particular study, participants signed a written consent form that was approved by the Ethics Committee. Blood samples (10 ml) were drawn by antecubital venipuncture into heparinised tubes (Becton Dickinson, USA) coded and immediately placed at 4°C until the sample preparation. The period between blood sampling and the sample preparation did not exceed 4 h for all blood samples. Table 1. Characteristics of the study population: number of volunteers, gender, age, body mass index, smoking status, and medical radiation exposure Autumn (September–November) Winter (December–February) Spring (March–May) Summer (June–August) Number (n) 27 50 50 35 Gender ratio (F:M) 19:12 35:15 35:15 25:10 Age (years) 40.1 ± 11.75 40.7 ± 15.0 39.1 ± 12.55 37.3 ± 11.43 Body mass index (m/kg2) 23.2 ± 3.14 24.3 ± 3.95 24.0 ± 4.07 24.6 ± 3.73 Active smokers (%) 25.9 30.0 20.0 28.6 Radiation exposure (%) 25.9 40.0 38.0 34.3 Autumn (September–November) Winter (December–February) Spring (March–May) Summer (June–August) Number (n) 27 50 50 35 Gender ratio (F:M) 19:12 35:15 35:15 25:10 Age (years) 40.1 ± 11.75 40.7 ± 15.0 39.1 ± 12.55 37.3 ± 11.43 Body mass index (m/kg2) 23.2 ± 3.14 24.3 ± 3.95 24.0 ± 4.07 24.6 ± 3.73 Active smokers (%) 25.9 30.0 20.0 28.6 Radiation exposure (%) 25.9 40.0 38.0 34.3 All participants reported their residence in the Zagreb agglomeration, Croatia. The results are expressed as numbers, ratio, mean ± SD or percentage. View Large Comet assay The alkaline comet assay was performed according to the protocol by Singh et al. (18) with minor modifications. Normal melting point (1% NMP) agarose (Sigma, USA) was placed on fully frosted slides. The first agarose layer was removed from the slide using a coverslip, after 10 min of solidification. The second agarose layer (0.6% NMP) was added on every slide and left for 10 min to solidify. Then we placed a layer of 0.5% low melting point (LMP) agarose (Sigma) containing 5 μl of whole blood and left it for another 10 min on ice. The slides were then covered with the final layer of 0.5% LMP agarose. After solidification, coverslips were removed from slides, which were then immersed in the lysis solution at 4°C [10 mM Tris–HCl (Sigma), 2.5 M NaCl (Kemika, Zagreb, Croatia), 100 mM disodium EDTA (Sigma) and 1% sodium sarcosinate (Sigma), pH 10] with 10% dimethyl sulfoxide (Kemika) and 1% Triton X-100 (Sigma) and left overnight. The next step was to move slides to freshly prepared cold electrophoresis buffer [300 mM NaOH (Kemika), 1 mM disodium EDTA (Sigma), pH 13.0] for 20 min that enabled DNA unwinding and ALS expression as SSB. The slides were then moved into a horizontal gel electrophoresis tank with new electrophoresis buffer. Denaturation and electrophoresis were performed at 4°C under dim light. Electrophoresis was carried out at 25 V (300 mA) for 20 min, followed by the neutralisation using buffer (0.4 M Tris–HCl, pH 7.5) for 3 × 5 min. Each sample was dyed with ethidium bromide (Sigma) (10 μg/mL) and stored in sealed boxes protected from the light at 4°C until analysis. Positive control samples treated with 1 mM H2O2 for 10 min on ice were included in every electrophoresis run confirming the reliability of the protocol (data not shown). Image analysis and data presentation The analysis was carried out using the Comet assay II image software connected to an epifluorescence microscope (Zeiss, Göttingen, Germany). A total of 100 randomly captured nuclei were scored per replication and two replicates per volunteer were done. Damaged parts of the gel and edges as well as debris, superimposed comets, and comets without distinct head (‘clouds’, ‘ghost cells’ or ‘hedgehogs’) were not analysed. All three parameters of the comet assay were presented: TL, tail intensity (TI) and tail moment (TM). Data were presented as mean ± SD. The cut-off values for highly damaged nuclei were set as 90th, 95th and 99th percentile of all nuclei scored. On the basis of the TL, TI and TM results, the cut-offs were as follows: for LTN (LTN90, 95 and 99) 17.31, 19.23 and 26.28 μm, respectively; for atypically sized tails (AST90, 95 and 99) 4.21, 6.21 and 14.45%, respectively and for TM extremes (TME90, 95 and 99) 0.54, 0.88 and 1.93, respectively. Meteorological parameters Data on the mean daily temperature (°C), mean daily sun global radiation (J/cm2) and daily insolation (h) were provided by the Croatian Meteorological and Hydrological Service. Seasons were assigned as: winter (December–February), spring (March–May), summer (June–August) and autumn (September–November) based on the mean monthly values of meteorological parameters as presented in Table 2 and according to Møller et al. Meteorological data represent average values (daily temperature, sun global radiation and daily insolation) for 1, 3 and 7 days before the blood sampling. Table 2. Average meteorological parameters (air temperature, sun insolation and sun global irradiation) for Zagreb, Croatia in the frame of the sampling period Winter (December– February) Spring (March–May) Summer (June–August) Autumn (September– November) Air temperature (°C) 2.37 ± 0.42 12.50 ± 4.66 21.93 ± 1.12 12.20 ± 4.81 Sun insolation (h) 2.07 ± 0.64 6.17 ± 1.50 9.10 ± 0.35 4.00 ± 1.61 Sun global irradiation (J/cm2) 40.23 ± 12.74 106.83 ± 19.01 137.05 ± 3.06 68.16 ± 26.26 Winter (December– February) Spring (March–May) Summer (June–August) Autumn (September– November) Air temperature (°C) 2.37 ± 0.42 12.50 ± 4.66 21.93 ± 1.12 12.20 ± 4.81 Sun insolation (h) 2.07 ± 0.64 6.17 ± 1.50 9.10 ± 0.35 4.00 ± 1.61 Sun global irradiation (J/cm2) 40.23 ± 12.74 106.83 ± 19.01 137.05 ± 3.06 68.16 ± 26.26 The results are expressed as mean ± SD View Large Statistical analysis Basic statistical parameters were obtained using descriptive statistics. Statistical evaluation was done using the STATISTICA 11.0 software package (StatSoft, USA). Basic statistical parameters were calculated by applying the basic statistic method and frequency tables. The difference in the mean values of the comet assay parameters between the two groups was assessed by the Student t-test and among multiple groups by the Duncan test. The influence of the predictor variables on the values of the comet assay parameters was tested by multiple regression analysis, general regression model. The correlation of the comet assay parameters with different meteorological parameters was done by Pearson correlation coefficients. Significance level in all tests was set to P < 0.05. Results Basic statistical parameters of the comet assay The average values of TL, TI and TM for the entire studied population (n = 162) were 14.46 ± 1.47 μm, 1.59 ± 0.80% and 0.21 ± 0.12, respectively, whereas the median±interquartile ranges were 13.97 ± 1.92 μm, 0.66 ± 0.75% and 0.09 ± 0.09, respectively. When comparing the parameters of the comet assay based on four yearly seasons, winter, spring, summer and autumn, significantly greater baseline DNA damage was detected during summer for TL, TI and TM. These results point to the same trend irrespectively of choosing the average or the median per person for data presentation. Median TI and TM data presentation revealed significantly greater DNA damage in the leucocytes of volunteers sampled during spring compared with those sampled in autumn (Table 3). The number of LTN90, AST90, TME90 and LTN95 was also higher when sampled in summer, compared with other seasons (Table 3). Table 3. Mean values ± SD for the comet assay parameters grouped according to the sampling season and statistical significance (P < 0.05) obtained by the Duncan test among age groups Variable Sampling season Winter (a) Spring (b) Summer (c) Autumn (d) Average TL 14.10 ± 1.46 c 14.39 ± 1.27 c 15.32 ± 1.57 a,b,d 14.16 ± 1.31 c Average TI 1.43 ± 0.80 c 1.58 ± 0.88 c 1.98 ± 0.65 a,b,d 1.38 ± 0.69 c Average TM 0.19 ± 0.12 c 0.21 ± 0.12 c 0.26 ± 0.09 a,b,d 0.19 ± 0.10 c Median TL 13.61 ± 1.03 c 13.89 ± 1.07 c 14.88 ± 1.47 a,b,d 13.60 ± 1.01 c Median TI 0.50 ± 0.52 c 0.69 ± 0.64 c,d 1.08 ± 0.57 a,b,d 0.40 ± 0.42 b,c Median TM 0.06 ± 0.06 c 0.09 ± 0.08c,d 0.14 ± 0.08a,b,d 0.05 ± 0.05 b,c LTN90 6.45 ± 7.18 c 8.35 ± 9.82 c 17.26 ± 15.13 a,b,d 7.46 ± 8.27 c LTN95 3.23 ± 4.36 c 4.44 ± 5.86 c 8.56 ± 9.30a,b,d 3.81 ± 5.57c LTN99 0.96 ± 1.62 0.91 ± 1.55 0.94 ± 1.72 1.06 ± 1.44 AST90 8.71 ± 6.68c 9.98 ± 7.59c 14.06 ± 7.07a,b,d 8.74 ± 6.78c AST95 4.76 ± 4.20 4.77 ± 4.02 6.21 ± 3.62 4.50 ± 4.03 AST99 1.06 ± 1.46 0.91 ± 0.96 0.80 ± 1.15 0.87 ± 1.18 TME90 8.52 ± 6.94c 9.90 ± 7.98c 14.60 ± 8.05a,b,d 8.54 ± 7.14c TME95 4.57 ± 4.50 4.79 ± 4.33 6.34 ± 4.00 4.41 ± 4.33 TME99 1.15 ± 1.63 0.87 ± 1.05 0.80 ± 1.40 0.91 ± 1.26 Variable Sampling season Winter (a) Spring (b) Summer (c) Autumn (d) Average TL 14.10 ± 1.46 c 14.39 ± 1.27 c 15.32 ± 1.57 a,b,d 14.16 ± 1.31 c Average TI 1.43 ± 0.80 c 1.58 ± 0.88 c 1.98 ± 0.65 a,b,d 1.38 ± 0.69 c Average TM 0.19 ± 0.12 c 0.21 ± 0.12 c 0.26 ± 0.09 a,b,d 0.19 ± 0.10 c Median TL 13.61 ± 1.03 c 13.89 ± 1.07 c 14.88 ± 1.47 a,b,d 13.60 ± 1.01 c Median TI 0.50 ± 0.52 c 0.69 ± 0.64 c,d 1.08 ± 0.57 a,b,d 0.40 ± 0.42 b,c Median TM 0.06 ± 0.06 c 0.09 ± 0.08c,d 0.14 ± 0.08a,b,d 0.05 ± 0.05 b,c LTN90 6.45 ± 7.18 c 8.35 ± 9.82 c 17.26 ± 15.13 a,b,d 7.46 ± 8.27 c LTN95 3.23 ± 4.36 c 4.44 ± 5.86 c 8.56 ± 9.30a,b,d 3.81 ± 5.57c LTN99 0.96 ± 1.62 0.91 ± 1.55 0.94 ± 1.72 1.06 ± 1.44 AST90 8.71 ± 6.68c 9.98 ± 7.59c 14.06 ± 7.07a,b,d 8.74 ± 6.78c AST95 4.76 ± 4.20 4.77 ± 4.02 6.21 ± 3.62 4.50 ± 4.03 AST99 1.06 ± 1.46 0.91 ± 0.96 0.80 ± 1.15 0.87 ± 1.18 TME90 8.52 ± 6.94c 9.90 ± 7.98c 14.60 ± 8.05a,b,d 8.54 ± 7.14c TME95 4.57 ± 4.50 4.79 ± 4.33 6.34 ± 4.00 4.41 ± 4.33 TME99 1.15 ± 1.63 0.87 ± 1.05 0.80 ± 1.40 0.91 ± 1.26 a–dStatistical difference to corresponding season. View Large Correlation of the comet assay parameters with meteorological data Meteorological data Global sun radiation (J/cm2), temperature (°C) and daily insolation (h) were assessed in a day, and in period of 3 and 7 days before blood sampling. Besides observing baseline DNA damage levels in a particular period of the year or season, we correlated meteorological data with different parameters of the comet assay. As summarised in Table 4, we observed that the most significant results were obtained when data were presented as the median TI or TM. AST and TME did not correlate significantly with meteorological data. However, meteorological data only occasionally reached statistical significance for the average and median TL, and LTNi (Table 4). Table 4. Correlation coefficients between the parameters of the comet assay and the selected meteorological average parameters for 1, 3 and 7 days before blood sampling Variable Global sun radiation (J/cm2) Temperature (°C) Daily insolation (h) 1 3 7 1 3 7 1 3 7 Average TL 0.38* 0.26 0.26 0.22 0.28* 0.25 0.35* 0.21 0.19 Average TI 0.05 0.01 0.02 0.02 0.03 0.01 0.04 0.05 0.06 Average TM 0.17 0.12 0.11 0.06 0.1 0.1 0.11 0.06 0.03 Median TL 0.38* 0.27* 0.26 0.23 0.29* 0.24 0.34* 0.23 0.21 Median TI 0.48* 0.49* 0.46* 0.50* 0.49* 0.43* 0.46* 0.49* 0.48* Median TM 0.46* 0.47* 0.45* 0.48* 0.47* 0.42* 0.43* 0.46* 0.47* LTN90 0.34* 0.26 0.27* 0.22 0.28* 0.25 0.31* 0.23 0.23 LTN95 0.29* 0.21 0.25 0.22 0.27* 0.26* 0.26* 0.19 0.22 LTN99 0.26 0.19 0.23 0.19 0.24 0.26 0.24 0.16 0.19 AST90 0.06 0.01 0.01 0.02 0.03 0.01 0.01 0.07 0.09 AST95 0.19 0.15 0.15 0.1 0.15 0.13 0.08 0.08 0.07 AST99 0.21 0.2 0.22 0.17 0.23 0.23 0.1 0.12 0.12 TME90 0.1 0.03 0.05 0.03 0.08 0.05 0.03 0.02 0.03 TME95 0.22 0.19 0.19 0.12 0.17 0.15 0.11 0.13 0.12 TME99 0.25 0.23 0.24 0.2 0.25 0.23 0.12 0.15 0.13 Variable Global sun radiation (J/cm2) Temperature (°C) Daily insolation (h) 1 3 7 1 3 7 1 3 7 Average TL 0.38* 0.26 0.26 0.22 0.28* 0.25 0.35* 0.21 0.19 Average TI 0.05 0.01 0.02 0.02 0.03 0.01 0.04 0.05 0.06 Average TM 0.17 0.12 0.11 0.06 0.1 0.1 0.11 0.06 0.03 Median TL 0.38* 0.27* 0.26 0.23 0.29* 0.24 0.34* 0.23 0.21 Median TI 0.48* 0.49* 0.46* 0.50* 0.49* 0.43* 0.46* 0.49* 0.48* Median TM 0.46* 0.47* 0.45* 0.48* 0.47* 0.42* 0.43* 0.46* 0.47* LTN90 0.34* 0.26 0.27* 0.22 0.28* 0.25 0.31* 0.23 0.23 LTN95 0.29* 0.21 0.25 0.22 0.27* 0.26* 0.26* 0.19 0.22 LTN99 0.26 0.19 0.23 0.19 0.24 0.26 0.24 0.16 0.19 AST90 0.06 0.01 0.01 0.02 0.03 0.01 0.01 0.07 0.09 AST95 0.19 0.15 0.15 0.1 0.15 0.13 0.08 0.08 0.07 AST99 0.21 0.2 0.22 0.17 0.23 0.23 0.1 0.12 0.12 TME90 0.1 0.03 0.05 0.03 0.08 0.05 0.03 0.02 0.03 TME95 0.22 0.19 0.19 0.12 0.17 0.15 0.11 0.13 0.12 TME99 0.25 0.23 0.24 0.2 0.25 0.23 0.12 0.15 0.13 *Statistically significant P < 0.05. View Large Multiple regression modelling Multiple regression modelling was performed in order to demonstrate the impact of particular predictors for each parameter of the comet assay. TL presented as the average or the median correlated with the sampling period and season. In addition, the TL average correlated with medical diagnostics (Figure 1A and B). Regarding TI and TM, these parameters correlated with the sampling period and season, and medical diagnostics (Figures 1C–F). Similar was observed for extremely damaged nuclei where LTN90, AST90 and TME90 correlated with the sampling period and season, whereas TME90 also correlated with medical diagnostics (Figures 2A–C). When observing 5% of the highest damaged nuclei, only LTN showed significant correlation with the sampling period and season (Figures 2D–F). Interestingly, active smoking was correlated with 1% of highest damaged nuclei when DNA damage was assessed with AST and TME. Moreover, TME99 was also associated with medical diagnostic radiation (Figures 2G–I). Figure 1. View largeDownload slide The results of the multiple regression model expressed as the Pareto charts of t-values testing for the correlation between the average TL (A), median TL (B), average TI (C), median TI (D), average TM (E), median TM (F) and selected predictor variables. Figure 1. View largeDownload slide The results of the multiple regression model expressed as the Pareto charts of t-values testing for the correlation between the average TL (A), median TL (B), average TI (C), median TI (D), average TM (E), median TM (F) and selected predictor variables. Figure 2. View largeDownload slide The results of the multiple regression model expressed as the Pareto charts of t-values testing for the correlation between the LTN90 (A), AST 90 (B), TME90 (C), LTN95 (D), AST95 (E), TME95 (F), LTN99 (G), AST99 (H), TME99 (I) and selected predictor variables. Note: 90, 95 and 99 represent the cut-off points at 90th, 95th and 99th percentile of all nuclei scored. Figure 2. View largeDownload slide The results of the multiple regression model expressed as the Pareto charts of t-values testing for the correlation between the LTN90 (A), AST 90 (B), TME90 (C), LTN95 (D), AST95 (E), TME95 (F), LTN99 (G), AST99 (H), TME99 (I) and selected predictor variables. Note: 90, 95 and 99 represent the cut-off points at 90th, 95th and 99th percentile of all nuclei scored. Discussion As there are open issues regarding the determination of factors that might affect the comet assay results and challenge its reliability and reproducibility, we decided to perform a retrospective study on 162 volunteers in order to explore the impact of seasonal variations on the level of baseline DNA damage. Although the number of volunteers was relatively low, it nevertheless provided interesting data about the impact of meteorological variables on the comet assay parameters. Still, it is important to conduct more similar studies to build a base for performing meta-analysis, as this would provide a more precise insight into this study area. Each study group (per season) was similar in age, female-to-male ratio, smoking habits, body mass index and previous diagnostic exposure. Moreover, all volunteers lived in the same city area, being exposed to similar air pollution and meteorological conditions (Table 1). The geographic latitude of the studied area is located in the northern temperate zone where seasons change four times per year. Tsilimigaki et al. (10) studied the impact of colder and warmer period on the comet assay parameters and the percentage of DNA in tails of the comet assay was higher in the group whose blood was sampled during the warmer period of the year, although they have used isolated and cryopreserved lymphocytes. These results are in line with our results where higher DNA damage was detected during summer season. Opposing results on 11 volunteers were observed in a study by Dušinská et al. (16) where higher DNA damage was observed when blood was sampled during the colder period of the year. This effect was associated with a lower intake of vitamins acting as antioxidants, thus minimising DNA protection from endogenous oxidation. When the groups were broken down to seasons, baseline DNA damage in peripheral blood leucocytes sampled in summer was higher compared with other seasons. This effect was observed for all comet assay parameters and 10% of highly damaged nuclei. In a study by Giovannelli et al. (9), the same effect was observed. The number of DNA breaks was the highest when lymphocytes were sampled in summer and it was correlated with average solar radiation 30 days, average air temperature 3, 7 and 30 days and average ozone level 7 and 30 days before blood sampling. Nevertheless, the most crucial impact was the air temperature and not the number of sunny days that probably influenced on the time that people spent outdoors on the sun. However, the impact of sun radiation was more thoroughly studied in two papers by Møller et al. (13,14). The increase of TM was observed when blood was sampled in the months closer to summer, and at the same time, TM correlated significantly with sun radiation power during 7 days before blood sampling. The highest TM was also reported during summer, and DNA damage correlated with sun radiation. The statistical model revealed that the greatest influence of sun radiation on TM was in the period 3–6 days before sampling and later correlation (up to 50 days) was perhaps a coincidence. A possible explanation for the higher baseline DNA damage observed during summer is that people are more often outdoors and they have less area of skin covered with clothes. The statistical model also showed an association of DNA damage with the time spent outdoors (14). Since blood cells circulate through the body, they also reach outer layers of the skin. At the same time, ultraviolet A (UVA) and UVB radiation has the ability to pass through the skin stratum corrneum (19). UVA light, which represents 95% of solar UV radiation, is considered to induce cyclobutane pyrimidine dimers and oxidative stress, thus, having potential of damaging other biomolecules. The remaining 5% is represented by UVB light that is attributed to the formation of cyclobutane pyrimidine dimers and pyrimidine (6–4) pyrimidone photoproducts (14,20,21). The highest levels of DNA damage in summer were also detected by Verschaeve et al. (12) where the median TI and extremely damaged nuclei correlated with air temperature and ozone level. The same study also provided a mini-review of the literature where the warmer period of the year is associated with higher baseline DNA damage level. On the contrary, there are also some studies indicating that the level of DNA adducts can be higher when blood is sampled during winter (15,22). The reasons why baseline DNA damage level could be higher during the colder period of the year is locally heavier air pollution. Our study is in accordance with the previously published results on the correlation of the comet assay parameters with meteorological variables. The median values of TI, and TM correlated particularly well with sun radiation, air temperature and sun insolation measured for 1, 3 and 7 days before blood sampling, whereas the median TL reached significance only occasionally. At the same time, the average TI and TM showed no significant correlation with meteorological parameters, whereas the average TL, LTN90 and LTN95 reached significance occasionally. Several recent studies investigated the impact of UV radiation and heat shock on the DNA integrity at the cellular level. Pal et al. (23) demonstrated that primary mouse keratinocytes after exposure to UVB exhibited more DNA damage (higher TL, TI, TM and γ-H2AX), higher intracellular reactive oxygen species and more 8-hydroxy-2'-deoxyguanosine. Alternatively, rapid severe heat shocks at temperature 50–52°C inhibited UV-induced DNA repair in human HeLa cells. On the basis of Sapareto–Dewey method, it was shown that 1 min at 50°C has the approximate effects as exposure to 43°C for 128 min (24). The impact of ambient temperature was also explored on lymphocytes in vitro by increasing the incubation temperature from 37 to 38.5°C. The cells incubated for 18 h at higher temperature resulted with a higher number of DNA breaks per 109 Da of DNA (9). Positive correlation between air temperature, collection period and genetic damage was also detected in monitoring study on plant (Tradescantia pallida) and fish (Abramis brama) models (25,26). These results indicate that higher sun radiation and higher temperature might cause DNA damage in different species and in vitro cell models. On the basis of the results of the comet assay and the multiple regression analysis, we observed minimal differences related to data presentation (average vs. median). Similar results regarding data presentation were found in the study by Uno et al. where data gathered by an in vivo comet assay differed minimally depending on data presentation. However, the use of the median was encouraged because it provides higher sensitivity of TI towards the identification of genotoxic effect, while there were no changes in the specificity of the assessment (27). We can hypothesise that the data presented as the median described sun exposure effects more effectively as well, thus improving the correlation with meteorological data. The use of highly damaged nuclei (LTN, AST and TME) is common when presenting the comet assay results, but its implications in human biomonitoring studies need thorough evaluation. The Pareto charts of multivariate analysis revealed that the year season of blood sampling influenced several parameters of the comet assay. In the literature, the effects of age, gender, smoking and other lifestyle factors are often described as predictors (28). Influence from the solar radiation, air pollution (urban areas), diet, allergy and physical exercise are more prone in warmer period and also barbecue food (source of PAHs) may have influence on the seasonal variation among the individuals sampled in the same period. Still, based on our results, and the results from other studies (9,12,14), the impact of the sampling season could also have strong influence on the results of the comet assay parameters besides the influence of the age, gender, smoking habit or acute illness. This is not an attempt to relativise the possible predicting effects of those variables, but rather to highlight the importance of taking into account the effect of weather conditions as well. The impact of age and gender on the comet assay parameters in this study did not reach statistical significance. The general hypothesis that DNA damage accumulates with age due to exposure to different genotoxicants and due to lower DNA repair capacity is still matter of debate because the results of single studies are still inconsistent (8,29). The same applies for gender differences in human biomonitoring results (29–31). It is expected that higher BMI positively correlates with DNA damage due to higher sensitivity of volunteers towards external temperature variations as they disperse heat less efficiently (9), what was observed in the study by Karaman et al. involving people suffering from metabolic syndrome (32). In our study, volunteers generally had BMI <25 m/kg2; therefore, the impact of high BMI could not be assessed. Multivariate analysis showed that exposure to medical diagnostics that occurred more than 1 month before blood sampling can influence the comet assay parameters. The DNA damaging effect of X-rays, exposure to low doses of ionising radiation and ultrasound have been previously described (33–35). Active smoking and heavy smoking are usually considered as an important comet assay predictor, still as several studies suggest and based on our results this impact is quite low, in fact it can affect only small fraction of extremely damaged cells. It also suggests that the influence of smoking habits on the comet assay parameters should be revised due to bias of small studies, strong influence of studies using arbitrary units and high variability of the method (29,36). In the future, further protocol standardisation, appropriate statistics and data presentation of the comet assay can be expected. If the impact of predictors is taken into account during the study design, the comet assay will become an even more reliable and powerful tool appropriate for human biomonitoring, which will lead to policy making (37,38). Conclusion Taken together, the obtained results of this retrospective study showed an impact of seasonal variations on the comet assay parameters in human biomonitoring. Greater baseline DNA damage was observed in the summer showing the highest levels of DNA damage compared with all the other seasons. Data presented as the median value per person showed a good correlation with meteorological parameters. The multivariate analysis showed that the sampling season affects the parameters of the comet assay. When designing a cohort study, it is important to have as similar groups as possible. Usually, study groups are matched for age, gender, several lifestyle factors and radiation exposure, but the annual changes in weather should also be taken on board. The air temperature, sun radiation and sun insolation are specific to each season. In some climate regions, there could be a distinction between two periods only—a warmer and a colder one. We can, therefore, suggest paying special attention to the period of sampling when developing a cohort study, bearing in mind that the colder period could be the best option to minimise the effect of the interfering factors occurring during the warmer period. Acknowledgements Authors would like to thank all the volunteers who participated in this study. They also would like to thank Ms. Maja Nikolić for her excellent technical assistance and Mr. Damir Mlinek at the Croatian Meteorological and Hydrological Service for providing meteorological data. 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Mutagenesis – Oxford University Press
Published: Jan 1, 2018
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