Open AccessPenalized regression models to select biomarkers of environmental enteric dysfunction associated with linear growth acquisition in a Peruvian birth cohort
Author(s) -
Josh M. Colston,
Pablo Peñataro Yori,
Lawrence H. Moulton,
Maribel Paredes Olórtegui,
Peter Kosek,
Dixner Rengifo Trigoso,
Mery Siguas Salas,
Francesca Schiaffino,
Ruthly François,
Fahmina FardusReid,
Jonathan R. Swann,
Margaret Kosek
Publication year - 2019
Publication title -
plos neglected tropical diseases
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.99
H-Index - 135
eISSN - 1935-2735
pISSN - 1935-2727
DOI - 10.1371/journal.pntd.0007851
Subject(s) - biomarker , linear regression , cohort , medicine , lasso (programming language) , bayesian multivariate linear regression , covariate , regression , statistics , biology , mathematics , computer science , biochemistry , world wide web
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 log 2 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.