Proteomic associations with eating behaviors in young adults: a twin study
Masip, G.; Drouard, G.; Kaprio, J.
Show abstract
IntroductionEating behaviors are consistently associated with weight-related traits, yet the biological factors contributing to individual differences in these behaviors remain poorly characterized. Plasma proteomics offers an opportunity to investigate the biological processes underlying eating behaviors. MethodsParticipants were 730 young adult twins from the FinnTwin12 cohort. Eating behaviors were measured through self-report questionnaires, including the Three-Factor Eating Questionnaire-R18 and four additional items on eating styles. Associations between plasma proteins and eating behaviors were examined using generalized estimating equation models adjusted for age and sex, with additional analyses adjusting for body mass index (BMI). Within-pair analyses were conducted in both monozygotic (MZ) and dizygotic twin pairs to assess whether associations were influenced by genetic or environmental factors. ResultsWe identified 51 significant protein-eating behavior associations involving 35 unique proteins (FDR <0.05). We observed 19 associations for the item "overeating when feeling down" and 12 for the TFEQ factor of emotional eating. The identified proteins were predominantly enriched in immune system pathways, including the complement cascade and adaptive immune signaling. After further adjustment for BMI, 12 associations persisted, most of which were associated with eating-style items, suggesting that BMI had a substantial influence on protein-eating behavior associations. Within-pair analyses of MZ pairs indicated that several associations persist after accounting for genetic effects. ConclusionOur study identifies plasma proteins associated with eating behaviors, largely involving immune-related pathways. While some associations attenuated in twin analyses, several persisted, suggesting environmental influences. These results highlight potential biomarker candidates and indicate that modifiable environmental factors may contribute to the proteomic profiles associated with eating behaviors, with possible implications for weight-related traits.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Relationship between impulsivity, uncontrolled eating and body mass index: a hierarchical model 94%
- Effects of adiposity on the human plasma proteome: Observational and Mendelian randomization estimates 93%
- Genetic and environmental effects on weight gain from young adulthood to old age and its association with body mass index at early young adulthood: an individual-based pooled analysis of 16 twin cohorts 92%
Similar papers in this journal
- Fetal alleles predisposing to metabolically favourable adiposity are associated with higher birth weight 91%
- Blood transcriptomic biomarkers of alcohol consumption and cardiovascular disease risk factors: the Framingham Heart Study 91%
- Heritability and family-based GWAS analyses of the N-acyl ethanolamine and ceramide plasma lipidome 91%
Similar papers in this journal
- Baseline Cardiometabolic Profiles and SARS-CoV-2 Infection in the UK Biobank 92%
- A murine model of the human CREBRFR457Q obesity-risk variant does not influence energy or glucose homeostasis in response to nutritional stress 92%
- Metabolomic profiling identifies complex lipid species associated with response to weight loss interventions 92%
Similar papers in this journal
- Associations between accurate measures of adiposity and fitness, blood proteins, and insulin sensitivity among South Asians and Europeans 94%
- Longitudinal Associations Between MicroRNAs and Weight in the Diabetes Prevention Program 92%
- Metabolic Drivers of Dysglycemia in Pregnancy: Ethnic-Specific GWAS of 146 Metabolites and 1-Sample Mendelian Randomisation Analyses in a UK Multi-Ethnic Birth Cohort 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.