Harmful Drinking Phenotype in a Large Dutch Community Sample.

AIMS: Harmful drinking patterns are shaped by a broad complex interaction of factors, societal and individual, psychological and behavioral. Although previous studies have focused on a few variables at a time, the current study simultaneously examines a large number of variables in order to create a comprehensive view (i.e. phenotype) of harmful drinking, and to rank the main predictors of harmful and non-harmful drinking by order of importance. METHODS: We surveyed a large sample of Dutch adults about their habitual drinking characteristics and attitudes, perceptions and motives for drinking. We fed 45 variables into a random forest machine learning model to identify predictors for (1) drinking within and in excess of Dutch guideline recommendations and (2) harmful and non-harmful drinking. RESULTS: In both models, respondents' subjective perceptions of 'responsible drinking', both per occasion and per week, showed the strongest predictive potential for different drinking phenotypes. The next strongest factors were respondents' reason for drinking, motives for drinking and age. Other variables, such as drinking location, knowledge about alcohol-related health risks and consumption of different beverage types, were not strong predictors of drinking phenotypes. CONCLUSIONS: Although the direction of the relationship is unclear from the findings, they suggest that interventions and policy measures aimed at individuals and social norms around drinking may offer promise for reducing harmful drinking. Messaging and promotion of drinking guidelines should be tailored with this in mind.

Alcohol Consumption, Drinking Patterns, and Cognitive Performance in Young Adults: A Cross-Sectional and Longitudinal Analysis.

Long-term alcohol abuse is associated with poorer cognitive performance. However, the associations between light and moderate drinking and cognitive performance are less clear. We assessed this association via cross-sectional and longitudinal analyses in a sample of 702 Dutch students. At baseline, alcohol consumption was assessed using questionnaires and ecological momentary assessment (EMA) across four weeks ('Wave 1'). Subsequently, cognitive performance, including memory, planning, and reasoning, was assessed at home using six standard cognition tests presented through an online platform. A year later, 436 students completed the four weeks of EMA and online cognitive testing ('Wave 2'). In both waves, there was no association between alcohol consumption and cognitive performance. Further, alcohol consumption during Wave 1 was not related to cognitive performance at Wave 2. In addition, EMA-data-based drinking patterns, which varied widely between persons but were relatively consistent over time within persons, were also not associated with cognitive performance. Post-hoc analyses of cognitive performance revealed higher within-person variance scores (from Wave 1 to Wave 2) than between-person variance scores (both Wave 1 and Wave 2). In conclusion, no association was observed between alcohol consumption and cognitive performance in a large Dutch student sample. However, the online cognitive tests performed at home may not have been sensitive enough to pick up differences in cognitive performance associated with alcohol consumption.

Allergenicity prediction of novel and modified proteins: Not a mission impossible! Development of a Random Forest allergenicity prediction model.

Alternative and sustainable protein sources (e.g., algae, duckweed, insects) are required to produce (future) foods. However, introduction of new food sources to the market requires a thorough risk assessment of nutritional, microbial and toxicological risks and potential allergic responses. Yet, the risk assessment of allergenic potential of novel proteins is challenging. Currently, guidance for genetically modified proteins relies on a weight-of-evidence approach. Current Codex (2009) and EFSA (2010; 2017) guidance indicates that sequence identity to known allergens is acceptable for predicting the cross-reactive potential of novel proteins and resistance to pepsin digestion and glycosylation status is used for evaluating de novo allergenicity potential. Other physicochemical and biochemical protein properties, however, are not used in the current weight-of-evidence approach. In this study, we have used the Random Forest algorithm for developing an in silico model that yields a prediction of the allergenic potential of a protein based on its physicochemical and biochemical properties. The final model contains twenty-nine variables, which were all calculated using the protein sequence by means of the ProtParam software and the PSIPred Protein Sequence Analysis program. Proteins were assigned as allergenic when present in the COMPARE database. Results show a robust model performance with a sensitivity, specificity and accuracy each greater than ≥85%. As the model only requires the protein sequence for calculations, it can be easily incorporated into the existing risk assessment approach. In conclusion, the model developed in this study improves the predictability of the allergenicity of new or modified food proteins, as demonstrated for insect proteins.