TY - JOUR
T1 - Penalized regression models to select biomarkers of environmental enteric dysfunction associated with linear growth acquisition in a Peruvian birth cohort
AU - Colston, Josh M.
AU - Yori, Pablo Peñataro
AU - Moulton, Lawrence H.
AU - Olortegui, Maribel Paredes
AU - Kosek, Peter S.
AU - Trigoso, Dixner Rengifo
AU - Salas, Mery Siguas
AU - Schiaffino, Francesca
AU - François, Ruthly
AU - Fardus-Reid, Fahmina
AU - Swann, Jonathan R.
AU - Kosek, Margaret N.
N1 - Publisher Copyright:
© 2019 Colston et al.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pntd.0007851
DO - 10.1371/journal.pntd.0007851
M3 - Article
C2 - 31730639
AN - SCOPUS:85075813349
VL - 13
JO - PLoS Neglected Tropical Diseases
JF - PLoS Neglected Tropical Diseases
SN - 1935-2727
IS - 11
M1 - e0007851
ER -