Penalized regression models to select biomarkers of environmental enteric dysfunction associated with linear growth acquisition in a Peruvian birth cohort

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 best-fitting 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.Author summary: Childhood undernutrition is widespread throughout the world and has severe, long-lasting health impacts. Substances measured in blood, urine and stool could be used as biomarkers to identify children undergoing growth failure before these impacts occur. However, it is not yet known which of the many markers that can be identified are accurate and clinically useful predictors of poor growth in infants and children. This study used a large number of candidate biomarkers of immune activation, metabolism and hormones and applied statistical methods to narrow them down from 110 different substances, to the 36 best predictors of growth in 258 Peruvian infants. It also estimated how large the effect of each of these markers was on height two months later. The biomarker with the largest effect was transferrin, a glycoprotein that can be measured in blood samples. 15-month old children with elevated transferrin were around two thirds of a centimeter taller on average at 17 months than those with low levels. Transferrin and other proteins, glycoproteins, hormones and antibodies that this study identified, can be measured easily and affordably in standard laboratories making them feasible to be used broadly as prognostic markers as part of child health and nutrition programs in under-resourced settings.