Iron metabolism in autism spectrum disorder; inference through single nucleotide polymorphisms in key iron metabolism...

Autism spectrum disorder (ASD) is a heterogeneous group of neurodevelopmental problems with various genetic and environmental components. The ASD diagnosis is based on symptom expression without reliance on any biomarkers. The genetic contributions in ASD remain elusive. Various studies have linked ASD with iron. Since iron plays a crucial role in brain development, neurotransmitter synthesis, neuronal myelination and mitochondrial function, we hypothesized that iron dysregulation in the brain could play a role and contribute to the pathogenesis of ASD. In this study, we investigated single nucleotide polymorphisms in ASD in various iron metabolism genes, including the Transferrin Receptor (TFRC) gene (rs11915082), the Solute Carrier Family 11 Member 2 (SLC11A2) gene (rs1048230 and rs224589), the Solute Carrier Family 40 Member 1 (SLC40A1) gene (rs1439816), and hepcidin antimicrobial peptide (HAMP) gene (rs10421768). We recruited 48 patients with ASD and 88 matched non-ASD controls. Our results revealed a significant difference between ASD and controls in the G allele of the TFRC gene rs11915082, and in the C allele of the SLC40A1 gene rs1439816. In silico analysis demonstrated potential positive role of the indicated genetic variations in ASD development and pathogenesis. These results suggest that specific genetic variations in iron metabolism genes may represent part of early genetic markers for early diagnosis of ASD. A significant effect of SNPs, groups (ASD/control) as well as interaction between SNPs and groups was revealed. Follow-up post hoc tests showed a significant difference between the ASD and control groups in rs11915082 (TFRC gene) and rs1439816 (SLC40A1 gene). Backward conditional logistic regression using both the genotype and allele data showed similar ability in detecting ASD using allel model (Nagelkerke R² = 0.350 p = 0.967; Variables: rs1439816, rs11915082) compared to genotype model (Nagelkerke R² = 0.347, p = 0.430; Variables: rs1439816 G, rs1439816 C, rs10421768 A). ROC curve showed 54% sensitivity in detecting ASD compared to 47% for the genotype model. Both models differentiated controls with high accuracy; the allele model had a specificity of 91% compared to 92% for the genotype model. In conclusion, our findings suggest that specific genetic variations in iron metabolism may represent early biomarkers for a diagnosis of ASD. Further research is needed to correlate these markers with specific blood iron indicators and their contribution to brain development and behavior.