Machine learning drives genetic discovery for binge eating disorder

Identifying genetic risk factors for binge-eating disorder (BED) is vital to understand its etiology and develop effective prevention and intervention strategies. To overcome under-reporting of clinical BED diagnosis, a new study uses machine learning to identify genetic variants associated with quantitative BED risk scores and finds evidence for a pathological role of heme metabolism.

Automated summary

Identifying genetic variants associated with binge-eating disorder (BED) risk scores using machine learning provides important insights into its etiology and potential intervention targets. This study highlights the pathological role of heme metabolism in BED, overcoming the limitations of under-reporting clinical diagnoses.