Environmental enteric dysfunction (EED) is associated with chronic undernutrition. Efforts
to identify minimally invasive biomarkers of EED reveal an expanding number of candidate
analytes. An analytic strategy is reported to select among candidate biomarkers and systematically express the strength of each marker’s association with linear growth in infancy
and early childhood. 180 analytes were quantified in fecal, urine and plasma samples taken
at 7, 15 and 24 months of age from 258 subjects in a birth cohort in Peru. Treating the subjects’ length-for-age Z-score (LAZ-score) over a 2-month lag as the outcome, penalized linear regression models with different shrinkage methods were fitted to determine the bestfitting subset. These were then included with covariates in linear regression models to obtain
estimates of each biomarker’s adjusted effect on growth. Transferrin had the largest and
most statistically significant adjusted effect on short-term linear growth as measured by
LAZ-score–a coefficient value of 0.50 (0.24, 0.75) for each log2 increase in plasma transferrin concentration. Other biomarkers with large effect size estimates included adiponectin,
arginine, growth hormone, proline and serum amyloid P-component. The selected subset
explained up to 23.0% of the variability in LAZ-score. Penalized regression modeling
approaches can be used to select subsets from large panels of candidate biomarkers of
EED. There is a need to systematically express the strength of association of biomarkers
with linear growth or other outcomes to compare results across studies.