Causes of Variation in Food Preference in the Netherlands

Contains fulltext : 221836.pdf (publisher's version ) (Open Access)Our current society is characterized by an increased availability of industrially processed foods with high salt, fat and sugar content. How is it that some people prefer these unhealthy foods while others prefer more healthy foods? It is suggested that both genetic and environmental factors play a role. The aim of this study was to (1) identify food preference clusters in the largest twin-family study into food preference to date and (2) determine the relative contribution of genetic and environmental factors to individual differences in food preference in the Netherlands. Principal component analysis was performed to identify the preference clusters by using data on food liking/disliking from 16,541 adult multiples and their family members. To estimate the heritability of food preference, the data of 7833 twins were used in structural equation models. We identified seven food preference clusters (Meat, Fish, Fruits, Vegetables, Savory snacks, Sweet snacks and Spices) and one cluster with Drinks. Broad-sense heritability (additive [A] + dominant [D] genetic factors) for these clusters varied between .36 and .60. Dominant genetic effects were found for the clusters Fruit, Fish (males only) and Spices. Quantitative sex differences were found for Meat, Fish and Savory snacks and Drinks. To conclude, our study convincingly showed that genetic factors play a significant role in food preference. A next important step is to identify these genes because genetic vulnerability for food preference is expected to be linked to actual food consumption and different diet-related disorders.9 p

Prevalence and clustering of health behaviours and the association with socio-demographics and mental well-being in Dutch university students

The college years represent a vulnerable period for developing health-risk behaviours (e.g., physical inactivity/unhealthy eating habits/substance use/problematic internet use/insufficient sleep). This study examined current health behaviour levels (RQ1), health behaviour classes (RQ2) and between-class differences in socio-demographics (RQ3) and mental well-being (RQ4) among Dutch university students (n = 3771). Participants (Mage = 22.7 (SD = 4.3); 71.2% female/27.3% male/1.5% other) completed an online survey (Oct-Nov 2021). Descriptive statistics (RQ1), Latent Class Analysis (RQ2), and Kruskal-Wallis/Chi-square tests (RQ3-4) were used. RQ1: Prevalence rates suggest that a subsequent proportion of the student sample engages in health-risk behaviours. RQ2: Four classes were identified: class 1 (n = 862) “Licit substance use health-risk group”, class 2 (n = 435) “Illicit and licit substance use health-risk group”, class 3 (n = 1876) “Health-protective group” and class 4 (n = 598) “Non-substance use health-risk group”. RQ3: Class 1 represents relatively more international students and students in a steady relationship. Class 2 represents relatively more older/male/(pre-)master students and students living with roommates/in a steady relationship/with more financial difficulty. Class 3 represents relatively more younger/female students and students living with family/with lower Body Mass Index (BMI)/less financial difficulty. Class 4 represents relatively more younger/non-Western/international/bachelor students and students living with children/single/part of LGBTIQ+ community/with higher BMI. RQ4: Class 3 has significantly higher mental well-being while class 4 has significantly lower mental well-being, relative to the other classes. Above findings provide new insights which can help educational institutes and governments better understand the clustering of students’ health behaviours and between-class differences in socio-demographics and mental well-being

Is the Association Between Alcohol Consumption and Mental Well-Being in University Students Linear, Curvilinear or Absent?

Background: Both alcohol consumption and mental well-being problems have been found to be prevalent in higher educated students and often have severe consequences. However, previous findings of the association between these constructs are mixed, possibly because often linear models are fitted, while some theories suggest a curvilinear association between the two concepts. Objectives: To clarify previously mixed findings, the current study compared curvilinear and linear models for the relationship between alcohol consumption and mental well-being in university students. Because of potential gender differences in this relationship, these models were explored for females and males separately. Data from the first cross-sectional online survey wave of the Healthy Student Life project including 2,631 female and 998 male students was used. The Alcohol Use Disorders Identification Test-consumption was used to measure alcohol consumption. Mental well-being was assessed by six sub-concepts (i.e., depressive symptoms, anxiety, stress, life satisfaction, happiness, and self-rated mental health). Results: For females both linear (for anxiety, life satisfaction, and self-rated mental health) and curvilinear (for depression, stress, and happiness) associations were found, while for males no support for either curvilinear or linear models was found. Conclusions: Results should be interpreted with caution due to the small effect sizes in the relationships for females but may suggest that testing the curvilinear association between alcohol consumption and mental well-being is an important future endeavor.</p

Genetic variants in RBFOX3 are associated with sleep latency

Time to fall asleep (sleep latency) is a major determinant of sleep quality. Chronic, long sleep latency is a major characteristic of sleep-onset insomnia and/or delayed sleep phase syndrome. In this study we aimed to discover common polymorphisms that contribute to the genetics of sleep latency. We performed a meta-analysis of genome-wide association studies (GWAS) including 2 572 737 single nucleotide polymorphisms (SNPs) established in seven European cohorts including 4242 individuals. We found a cluster of three highly correlated variants (rs9900428, rs9907432 and rs7211029) in the RNA-binding protein fox-1 homolog 3 gene (RBFOX3) associated with sleep latency (P-values=5.77 × 10-08, 6.59 × 10- 08 and 9.17 × 10- 08). These SNPs were replicated in up to 12 independent populations including 30 377 individuals (P-values=1.5 × 10- 02, 7.0 × 10- 03 and 2.5 × 10- 03; combined meta-analysis P-values=5.5 × 10-07, 5.4 × 10-07 and 1.0 × 10-07). A functional prediction of RBFOX3 base

Genetic studies of body mass index yield new insights for obesity biology

Note: A full list of authors and affiliations appears at the end of the article. Obesity is heritable and predisposes to many diseases. To understand the genetic basis of obesity better, here we conduct a genome-wide association study and Metabochip meta-analysis of body mass index (BMI), a measure commonly used to define obesity and assess adiposity, in up to 339,224 individuals. This analysis identifies 97 BMI-associated loci (P 20% of BMI variation. Pathway analyses provide strong support for a role of the central nervous system in obesity susceptibility and implicate new genes and pathways, including those related to synaptic function, glutamate signalling, insulin secretion/action, energy metabolism, lipid biology and adipogenesis.</p