MOLECULAR PATHWAYS ANALYSIS THROUGH MULTI-OMICS APPROACHES TO STUDY THE BIOLOGICAL BASIS OF ANOREXIA NERVOSA
Anorexia nervosa is a complex and multifactorial disorder with a high genetic component and severe metabolic consequences. Despite its high heritability, the molecular mechanisms underlying AN remain unclear, and current treatment strategies are often ineffective. Recent advancements in genomics and metabolomics offer promising tools for understanding the genetic and biochemical pathways involved in AN. This study aimed to investigate the genetic and metabolomic basis of AN using next-generation sequencing, polygenic risk scoring, and hair metabolomic profiling, with the ultimate goal of improving diagnosis and treatment strategies. This study analyzed three distinct case studies involving AN patients. The first case study involved sequencing the DNA of 68 AN patients using a panel of 162 genes to identify rare variants associated with the disorder. The second case study expanded the cohort to 135 AN patients and included the calculation of PRS using a newly designed panel of 163 genes and 730 single nucleotide polymorphisms. The third case study focused on metabolomic analysis of hair samples from 25 AN patients and 25 healthy controls, with an emphasis on amino acid and carnitine profiles. NGS, statistical analysis, and machine learning algorithms were employed to analyze genetic variants and metabolomic profiles. In the first case study, rare variants in key genes were identified, suggesting a potential monogenic form of AN. The second case study revealed a significant genetic contribution to AN risk, with the PRS explaining 21.3% of the variance in the phenotype. Variants in genes related to dopamine signaling, skeletal muscle function, and appetite regulation were identified, highlighting diverse molecular mechanisms contributing to AN. The third case study demonstrated significant metabolic alterations in AN patients, particularly deficiencies in branched-chain amino acids and essential amino acids. These findings suggest a biochemical basis for appetite dysregulation observed in AN patients. Given the emerging evidence from genetic studies, genetic testing in AN is becoming a clinical reality, with identified candidate genes such as NNAT, EPHX2, LEP, and MC4R offering the potential to tailor treatments, predict disease progression, and assess recurrence risk within families. Moreover, genetic testing is crucial for distinguishing between true AN and syndromic forms that mimic AN but have distinct etiologies, enabling more accurate diagnoses and avoiding mismanagement. This study provides insights into the genetic and metabolic foundations of AN. The integration of genomic and metabolomic data highlights the potential for personalized medicine approaches in AN diagnosis and treatment. Genetic testing, particularly for rare variants, could play a key role in early diagnosis and risk assessment, while targeted nutritional interventions may address the metabolic imbalances associated with the disorder. This study investigates a large Italian cohort of 228 anorexia nervosa (AN) patients to validate genetic associations with AN. We developed a genetic test targeting syndromic forms that mimic AN phenotypically but have distinct underlying etiologies. Our analysis identified key variants in genes such as NNAT, EPHX2, and ESR2, highlighting their role in AN pathogenesis. Syndromic forms related to genes like ARG1 and ALPL were also explored. This genetic test represents a crucial tool for differential diagnosis, improving clinical outcomes for patients with AN and its syndromic forms. Future research should focus on expanding these findings to larger cohorts and further exploring the interaction between genetic predisposition and environmental factors in AN.
Anorexia nervosa: Genomic, Metabolomic, Next-generation sequencing, Polygenic Risk Score, Diagnostic genetics, Settore BIO/12 - Biochimica Clinica e Biologia Molecolare Clinica, Settore BIOS-09/A - Biochimica clinica e biologia molecolare clinica
Networked Digital Library of Theses and Dissertations