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Dec 2, 2023
Abstract Little is known about exposure determinants of acrylamide (AA), a genotoxic food-processing contaminant, in Europe. We assessed determinants of AA exposure, measured by urinary mercapturic acids of AA (AAMA) and glycidamide (GAMA), its main metabolite, in 3157 children/adolescents and 1297 adults in the European Human Biomonitoring Initiative. Harmonized individual-level questionnaires data and quality assured measurements of AAMA and GAMA (urine collection: 2014–2021), the short-term validated biomarkers of AA exposure, were obtained from four studies (Italy, France, Germany, and Norway) in children/adolescents (age range: 3–18 years) and six studies (Portugal, Spain, France, Germany, Luxembourg, and Iceland) in adults (age range: 20–45 years). Multivariable-adjusted pooled quantile regressions were employed to assess median differences (β coefficients) with 95% confidence intervals (95% CI) in AAMA and GAMA (µg/g creatinine) in relation to exposure determinants. Southern European studies had higher AAMA than Northern studies. In children/adolescents, we observed significant lower AA associated with high socioeconomic status (AAMA:β = − 9.1 µg/g creatinine, 95% CI − 15.8, − 2.4; GAMA: β = − 3.4 µg/g creatinine, 95% CI − 4.7, − 2.2), living in rural areas (AAMA:β = − 4.7 µg/g creatinine, 95% CI − 8.6, − 0.8; GAMA:β = − 1.1 µg/g creatinine, 95% CI − 1.9, − 0.4) and increasing age (AAMA:β = − 1.9 µg/g creatinine, 95% CI − 2.4, − 1.4; GAMA:β = − 0.7 µg/g creatinine, 95% CI − 0.8, − 0.6). In adults, higher AAMA was also associated with high consumption of fried potatoes whereas lower AAMA was associated with higher body-mass-index. Based on this large-scale study, several potential determinants of AA exposure were identified in children/adolescents and adults in European countries. Introduction Acrylamide (AA) is a genotoxic food-processing contaminant classified as probably carcinogenic to humans (group 2A) by the International Agency for Research on Cancer (IARC) 1 . It is mainly formed in commonly consumed food, existing in high content in starch e.g., coffee, crisps, fried potatoes, biscuits and cereals, when processed at temperatures above 120 °C under low moisture conditions 2 . AA can be also formed from acrolein, exists in smoking tobacco and, in the occupational setting, it is used as a chemical for the production of polyacrylamides 3 . However, the latter source of AA exposure is considered of less concern 4 . In vivo and in vitro studies have shown that AA and its main metabolite glycidamide (GA) are carcinogenic, neurotoxic, reprotoxic and toxic for the developmental system 3 . Emerging evidence also links AA exposure to several other diseases 5 , 6 , 7 , 8 . In humans, the association between AA and cancer risk, investigated in epidemiological studies, remains unclear 9 . Recently, the European Food Safety Agency (EFSA) reported additional evidence of its genotoxic and non-genotoxic effects and concluded that a risk associated to dietary intake of AA cannot be discarded 10 . Despite the adoption of mitigation, monitoring and regulatory measures at European level to reduce AA content in food 11 , 12 , dietary exposure to AA still remains widespread representing a global concern in the general population 3 . Identification of determinants of exposure might be of great importance as a first step to implement effective measures to reduce AA exposure, especially in vulnerable population groups 13 . Also, it might help to explain the high variability in AA exposure across different populations and countries 14 . Current knowledge on determinants of AA exposure in the general European population is limited, especially in children 15 , 16 , 17 . Human biomonitoring studies could be useful tools to identify potential factors of exposure to chemical pollutants in children and adults by measuring them and/or their metabolites (biomarkers) in biological samples, such as urine, blood, hair, together with the use of questionnaires 18 , 19 . For AA, the validated biomarkers are AA and its metabolite GA measured as hemoglobin adducts in blood, including cord blood 20 , and as mercapturic acids in urine samples. These biomarkers are used as long- and short-term exposure biomarkers to this compound, respectively 14 . In addition, evidence suggests that acrylamide levels could also be measured in other human samples, such as breast milk and placenta 21 . Hence, we aim to assess potential determinants of exposure to AA measured via its urinary biomarkers, AAMA (N-acetyl-S-(2-carbamoylethyl)-L-cysteine) and GAMA (N-acetyl-S-(2-carbamoyl-2-hydroxyethyl)-L-cysteine), in children/adolescents and adults using harmonized data from the European Human Biomonitoring Initiative HBM4EU participating studies, covering different European regions. Materials and methods Study design, data sources and data collection The present study was based on the participating studies in the HBM4EU initiative ( https://www.hbm4eu.eu/ ), where newly harmonized, individual-level data, were produced within the so-called HBM4EU Aligned Studies sampled between 2014 and 2021. The HBM4EU survey leveraged existing European capacity by incorporating both new/ongoing and recently conducted studies establishing the first large-scale joint effort to align and harmonize ongoing European HBM initiatives. Key factors contributing to the alignment of these studies include target populations, biomarker analysis quality assurance/control program, data handling, and statistical procedures, all implemented through standardized protocols 22 , 23 , 24 . This unique material enhances inter-study/country comparability. Despite efforts in aligning and harmonizing data, variations persisted in certain variables, such as time sampling (confined to the period 2014–2020), biological matrices, and questionnaires. These differences were acknowledged, and strategies were employed to minimize them, including the use of standardized variables retrieved by the questionnaire and conversion factors for different matrices 22 , 25 . The complete sampling scheme for the inclusion, combination and data harmonization of HBM4EU Aligned Studies is fully described in Gilles et al. 22 . Two additional HBM studies which generated individual-level data on urinary AA biomarkers outside the HBM4EU initiative were included and harmonized following the same procedure. That is, the German Environmental Survey, 2014–2017, (GerES V) 17 on children/adolescents (3–18 years old, only a subset there of being part of the Aligned Studies), and the BETTERMILK study (2015) 26 on adult women (20–45 years old). The criteria for inclusion of studies were: samples in European children/adolescents (3–18 years old) or adults (20–45 years old), in which AA biomarkers were measured in urines collected between 2014 and 2021, and providing questionnaire data to be used for the investigation of exposure determinants. The full description of data access permissions and ethical considerations has been described elsewhere 22 . In brief, all the participating studies followed methods in accordance with national and European guidelines and ethics regulation. Each of the country's Ethics Committees approved the study (The Regional Committees for Medical and Health Research Ethics in Norway; the Ethics Committees of the University of Udine and the Institute for Maternal and Child Health—IRCCS Burlo Garofolo, Trieste, Italy; Ile-de-France Protection, The French Data Protection Agency, French Advisory Committee on Information Processing for Research and The French National Agency for Medicines and Health Products' Safety, France; the Ethics Commission of the Berlin Chamber of Physicians, the Medical Association Westfalen-Lippe, the Medical Faculty of the University of Münster and Medical Association of the Saarland and the Federal Officer for Data Protection and Freedom of Information, Germany; The National Bioethics Committee, Iceland; the National Ethical committee of Luxembourg, Luxembourg; the Ethical Committees of the National Institute of Health Doutor Ricardo Jorge, the Regional Health Administrations of North, Center, Lisbon, Tagus Valey, Alentejo, Algarve, the Health Service of the Autonomous Region of Madeira and of the Hospital of Horta, Portugal; the Clinical Research Ethics Committee of the Public Health Directorate, the Center for Public Health Research of the Valencian Government, and the Biomedical Scientific Ethic Committee of the University and Polytechnic Hospital “La Fe”, Spain). Written informed consent was obtained from all participants. For children, the written consent was obtained by legal tutors. In general, it was the child's parents who signed the consent. However, there may be differences in the exact procedure between the participating studies i.e. in some countries approval of one parent was sufficient whereas in other countries consent from both parents may be needed 22 . Each study also confirmed that informed consent and approval were in place for secondary use of the collected data. In total, 4 studies for children/adolescents (n = 3157) and 6 studies for adults (n = 1297) were included. Determinants of exposure Determinants of exposure were selected based on either prior knowledge (e.g., smoking, BMI, and dietary factors) and/or a non-hypothesis-based approach. The selection was limited due to the availability of data in each participating study since questionnaires differed between some surveys. A full description of the variables considered in this study, together with the harmonization, as well as the treatment as continuous or categorical variables in the association with AA urinary biomarkers, is presented in Supplementary Table S1 . Urinary levels of AA biomarkers: chemical analysis Urine sampling strategies across studies were either 24-h (n = 1 study for adults), first-morning spot (n = 2 studies for children/adolescents and 3 studies for adults) or random spot (n = 2 studies for children/adolescents and 2 studies for adults) urine samples. The individual urinary concentrations of AAMA and GAMA obtained from each study were generated using different analytical methods, but their comparability was generally guaranteed by the HBM4EU quality assurance/quality control (QA/QC) programme (further details in Supplementary Information SI-1 ). The AAMA and GAMA urinary levels were standardized for creatinine (µg/g creatinine) for children/adolescents and adults, to account for variation in dilution and to increase comparability of the data. Statistical analysis Descriptive summary statistics of AAMA and GAMA urinary levels (mean, standard deviation, 10th, 25th, 50th, 75th and 90th percentiles) were calculated for children/adolescents and adults, and by European geographical region if available. As quantification frequencies (QFs) of AAMA and GAMA in urine were > 92% in all the studies, those concentration values reported as < LoQ were excluded from the analysis (n = 2 for AAMA, and n = 2 for GAMA in children; n = 14 for AAMA, and n = 17 for GAMA in adults). This decision was supported by experts in the field and statisticians of the HBM4EU, concluding that this exclusion ensured an improvement of homogeneity and accuracy of the data for analysis. Median regression models were employed to investigate the association between continuous urinary levels of AAMA and GAMA and potential exposure determinants in children/adolescents and adults. This statistical approach allows to regress any percentile of the outcome distribution. As the urinary biomarkers of AA tend to have a skewed distribution, the median regression might be considered a better summary measure than the mean and thus preferable to the classical linear regression. Since results from the 10th, 25th, 50th, 75th, and 90th percentile did not show different results across the percentiles of the distribution, we decided to present only those based on the median regression (50th percentile). The results, expressed as beta coefficients and 95% confidence intervals (95% CI), need to be interpreted as median differences in AAMA and/or GAMA in relation to the corresponding predictor, adjusted for all other factors included in the model. To account for potential heterogeneity among studies, we included in the regression model a study-specific fixed effect. The selection of determinants to be included in the multiple regression models was based on data availability, prior findings, knowledge of the field, and collinearity. To retain statistical power, missing values in categorical predictors were treated as a separate category (known as missing indicator method or “dummy variable adjustment”). Because cigarette smoking is known to increase the levels of AA 3 by 3–4 times 27 , main analyses were presented excluding active smokers (n = 39 in children/adolescents, and n = 238 in adults). Additional analyses were performed including both smokers and non-smokers and by participating studies. STATA software (STATA version 12.1, Corp, College Station, TX, USA) was used to perform all statistical analyses. Results The HBM studies providing data on individual AAMA and GAMA levels in urine of European children/adolescents (median age: 10, IQR: 7–13 years old) and adults (median age: 33, IQR: 28–37 years old) are summarized in Table 1 . In total, urinary data on AA metabolites of 3157 children/adolescents and 1297 adults were collected from different countries including European studies from Northern (Norway and Iceland), Southern (Italy, Spain and Portugal) and Western Europe (France, Germany and Luxembourg). No studies on AA urinary levels from Eastern European countries were available and/or agreed to participate. Out of the total number of children/adolescents, 81% were based on studies from Western countries (France and Germany), 9.5% from the North (Norway), and 9.5% from the South (Italy). In adults, the participating studies were more equally distributed within the European regions, with 53% of the individuals being from the West (France, Germany, Luxembourg), 31% from the South (Spain and Portugal), and 16% from the North (Iceland). Sampling years ranged from 2014 to 2017 in studies on children/adolescents, and from 2014 to 2021 in those on adults. Distribution of exposure determinants by geographical area in children and adults, respectively, are shown in Supplementary Table S2 . Table 1 Characteristics of the HBM4EU participating studies with urinary levels of AA in children/adolescents and adults.
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