BACKGROUND: Children with lethal infections present an early and extreme phenotype, most likely caused by pathogenic gene variants that disrupt the immune system’s response to microbes. Metabolomic profiling and genomic analysis for children who have suffered or succumbed to lethal infections can complement diagnosis by providing biomarkers and identifying known or potential monogenic etiologies of infectious susceptibility.
METHODS: In a cross-sectional study, after securing informed consent, we obtained urine and blood samples from pediatric patients who required intensive care for severe infectious diseases at the INP, to perform WES and clinical metabolome.
Descriptive statistics, including absolute and relative frequencies for demographic, clinical, and family data; central tendency and dispersion for laboratory and other quantitative variables, as well as the pattern of co-occurrence found between pathogenic variants and metabolic alterations.
RESULTS: We included 48 children aged 9 months to 5 years, from 2020 to 2023; 40% were female, and 80% survived. None of the patients had consanguinity or inbreeding. The first clinical manifestation in these patients was: fever, hemophagocytic syndrome, pneumonia, encephalitis, diarrhea, and pediatric multisystem inflammatory syndrome (PIM-S). On physical examination, 33.3% presented cervical adenopathies and 16.6% hepatomegaly and splenomegaly, skin abnormalities occurred in 16% of patients. Regarding laboratory findings, 83% presented hypergammaglobulinemia. 66.6% developed lymphopenia, 10% leukopenia, and 16.6% thrombocytopenia. In 30% of the patients in whom a metabolomic profile was performed, alterations were found. Genetic diagnosis was reached in 30% of cases.
CONCLUSION: WES and Metabolome can help identify inborn errors of immunity in the high-risk group of pediatric patients with lethal infections. We recommend suspecting immune system defects and sequencing patients with no prior medical history and severe infectious disease. Next, we want to expand the sample and perform family segregation analysis and functional validation to prove causality.