Aberrant One-Carbon Metabolism and Ancestral Genetics Underlie Edematous Severe Acute Malnutrition

Neil Hanchard1, Natasha Lie2, Yixing Han3

  • 1National Human Genome Research Institute.

Research Square
|July 18, 2025
PubMed

Insights

Aberrant one-carbon metabolism (OCM) genetic variants are linked to severe acute malnutrition (SAM) and edematous SAM (ESAM) risk. These findings highlight OCM

Area of Science:

  • Genetics and Molecular Biology
  • Nutritional Science
  • Pediatric Health

Background:

  • Severe acute malnutrition (SAM) is a major cause of mortality in children under five.
  • Edematous SAM (ESAM) is a severe form of SAM, more prevalent in specific regions, with unclear etiological factors.
  • Aberrant one-carbon metabolism (OCM) has been implicated in ESAM pathogenesis.

Purpose of the Study:

  • To investigate the association between genetic variants in OCM loci and the risk of developing ESAM.
  • To explore the role of specific OCM-related genetic variants and their metabolites in ESAM.
  • To examine the population genetics of OCM variants in relation to ESAM risk.

Main Methods:

  • Genotyping of 103 OCM-related loci in 711 samples from Jamaica and Malawi.
  • Analysis of single nucleotide polymorphisms (SNPs) in OCM genes, including those genotyped using cell-free DNA.
  • Leveraging chromosome-level admixture patterns and whole genome sequencing data from African populations.

Main Results:

  • Seven OCM loci showed significant association with ESAM across both populations, with five linked to homocysteine and folate metabolism.
  • Specific SNPs in PLD2, PRICKLE2, and GABBR2 supported causal effects on ESAM risk via homocysteine metabolites.
  • OCM variant associations with ESAM were found on a shared East African ancestral genetic background, with evidence of selection at OCM loci like PLD2.

Conclusions:

  • Aberrant one-carbon metabolism is strongly supported as a key factor in ESAM pathogenesis.
  • Genetic variants influencing OCM contribute to ESAM risk, with population-specific effect magnitudes.
  • Cell-free DNA, admixture analysis, and population genetics offer valuable tools for disease risk mapping in SAM.

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