Penalized regression models to select biomarkers of environmental enteric dysfunction associated with linear growth acquisition in a Peruvian birth cohort
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.
Biomarkers/blood, Biostatistics, Child, Preschool, Cohort Studies, Developmental Disabilities/diagnosis, Environmental Illness/diagnosis, Female, Humans, Infant, Infant, Newborn, Male, Malnutrition/diagnosis, Peru
1-20
Colston, Josh M.
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Peñataro Yori, Pablo
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Moulton, Lawrence H.
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Paredes Olortegui, Maribel
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Kosek, Peter S.
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Rengifo Trigoso, Dixner
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Siguas Salas, Mery
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Schiaffino, Francesca
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François, Ruthly
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Fardus-Reid, Fahmina
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Swann, Jonathan R.
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Kosek, Margaret N.
bb8eddcd-e28a-47f5-8686-c47ae6bd70e5
15 November 2019
Colston, Josh M.
3dc60171-1270-4d7e-9351-5107942e5a62
Peñataro Yori, Pablo
02985d35-51ca-49bc-b16a-71fda6cc52e3
Moulton, Lawrence H.
19ff592d-9f22-45e4-874e-db6e80f552ef
Paredes Olortegui, Maribel
91dd9b95-3429-447d-b611-fd381a90dbcd
Kosek, Peter S.
7be56fcc-2720-440f-9fb6-be724b3bf33d
Rengifo Trigoso, Dixner
ce197c99-1bcf-47db-91d5-bdc5a41c4522
Siguas Salas, Mery
7be650ae-4003-42a4-992c-377785aac983
Schiaffino, Francesca
cd2691bb-86e9-4d85-b39f-115036e476b1
François, Ruthly
2a381087-aeea-4f29-ba4b-eda99d49b9f4
Fardus-Reid, Fahmina
69e50f8c-6fff-469c-b69a-792a08c8682a
Swann, Jonathan R.
7c11a66b-f4b8-4dbf-aa17-ad8b0561b85c
Kosek, Margaret N.
bb8eddcd-e28a-47f5-8686-c47ae6bd70e5
Colston, Josh M., Peñataro Yori, Pablo, Moulton, Lawrence H., Paredes Olortegui, Maribel, Kosek, Peter S., Rengifo Trigoso, Dixner, Siguas Salas, Mery, Schiaffino, Francesca, François, Ruthly, Fardus-Reid, Fahmina, Swann, Jonathan R. and Kosek, Margaret N.
(2019)
Penalized regression models to select biomarkers of environmental enteric dysfunction associated with linear growth acquisition in a Peruvian birth cohort.
PLoS Neglected Tropical Diseases, 13 (11), , [e0007851].
(doi:10.1371/journal.pntd.0007851).
Abstract
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.
Text
journal.pntd.0007851
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Accepted/In Press date: 16 October 2019
Published date: 15 November 2019
Keywords:
Biomarkers/blood, Biostatistics, Child, Preschool, Cohort Studies, Developmental Disabilities/diagnosis, Environmental Illness/diagnosis, Female, Humans, Infant, Infant, Newborn, Male, Malnutrition/diagnosis, Peru
Identifiers
Local EPrints ID: 440707
URI: http://eprints.soton.ac.uk/id/eprint/440707
ISSN: 1935-2727
PURE UUID: 1c382a8d-d405-41de-8c07-28394b6ac930
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Date deposited: 13 May 2020 17:07
Last modified: 17 Mar 2024 04:00
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Contributors
Author:
Josh M. Colston
Author:
Pablo Peñataro Yori
Author:
Lawrence H. Moulton
Author:
Maribel Paredes Olortegui
Author:
Peter S. Kosek
Author:
Dixner Rengifo Trigoso
Author:
Mery Siguas Salas
Author:
Francesca Schiaffino
Author:
Ruthly François
Author:
Fahmina Fardus-Reid
Author:
Margaret N. Kosek
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