Mendelian Randomization Analysis Identifies Inverse Causal Relationship between External Eating and Metabolic Phenotypes.
Yanina R TimashevaZhanna BalkhiyarovaDiana AvzaletdinovaTatyana MorugovaGulnaz F KorytinaArie NouwenInga ProkopenkoOlga V KochetovaPublished in: Nutrients (2024)
Disordered eating contributes to weight gain, obesity, and type 2 diabetes (T2D), but the precise mechanisms underlying the development of different eating patterns and connecting them to specific metabolic phenotypes remain unclear. We aimed to identify genetic variants linked to eating behaviour and investigate its causal relationships with metabolic traits using Mendelian randomization (MR). We tested associations between 30 genetic variants and eating patterns in individuals with T2D from the Volga-Ural region and investigated causal relationships between variants associated with eating patterns and various metabolic and anthropometric traits using data from the Volga-Ural population and large international consortia. We detected associations between HTR1D and CDKAL1 and external eating; between HTR2A and emotional eating; between HTR2A , NPY2R , HTR1F , HTR3A , HTR2C , CXCR2 , and T2D. Further analyses in a separate group revealed significant associations between metabolic syndrome (MetS) and the loci in CRP , ADCY3 , GHRL , CDKAL1 , BDNF , CHRM4 , CHRM1 , HTR3A , and AKT1 genes. MR results demonstrated an inverse causal relationship between external eating and glycated haemoglobin levels in the Volga-Ural sample. External eating influenced anthropometric traits such as body mass index, height, hip circumference, waist circumference, and weight in GWAS cohorts. Our findings suggest that eating patterns impact both anthropometric and metabolic traits.
Keyphrases
- weight loss
- body mass index
- physical activity
- weight gain
- type diabetes
- genome wide
- metabolic syndrome
- insulin resistance
- cardiovascular disease
- cell proliferation
- magnetic resonance imaging
- magnetic resonance
- birth weight
- high resolution
- deep learning
- artificial intelligence
- data analysis
- total hip arthroplasty
- gestational age