title: Application of penalized linear regression methods to the selection of environmental enteropathy biomarkers.
abstract: BACKGROUND
  Environmental Enteropathy (EE) is a subclinical condition caused by constant fecal-oral contamination and resulting in blunting of intestinal villi and intestinal inflammation. Of primary interest in the clinical research is to evaluate the association between non-invasive EE biomarkers and malnutrition in a cohort of Bangladeshi children. The challenges are that the number of biomarkers/covariates is relatively large, and some of them are highly correlated.
  
  
  METHODS
  Many variable selection methods are available in the literature, but which are most appropriate for EE biomarker selection remains unclear. In this study, different variable selection approaches were applied and the performance of these methods was assessed numerically through simulation studies, assuming the correlations among covariates were similar to those in the Bangladesh cohort. The suggested methods from simulations were applied to the Bangladesh cohort to select the most relevant biomarkers for the growth response, and bootstrapping methods were used to evaluate the consistency of selection results.
  
  
  RESULTS
  Through simulation studies, SCAD (Smoothly Clipped Absolute Deviation), Adaptive LASSO (Least Absolute Shrinkage and Selection Operator) and MCP (Minimax Concave Penalty) are the suggested variable selection methods, compared to traditional stepwise regression method. In the Bangladesh data, predictors such as mother weight, height-for-age z-score (HAZ) at week 18, and inflammation markers (Myeloperoxidase (MPO) at week 12 and soluable CD14 at week 18) are informative biomarkers associated with children's growth.
  
  
  CONCLUSIONS
  Penalized linear regression methods are plausible alternatives to traditional variable selection methods, and the suggested methods are applicable to other biomedical studies. The selected early-stage biomarkers offer a potential explanation for the burden of malnutrition problems in low-income countries, allow early identification of infants at risk, and suggest pathways for intervention.
  
  
  BACKGROUND
  This study was retrospectively registered with ClinicalTrials.gov, number NCT01375647, on June 3, 2011.
authors.affiliation: Department of Statistics, University of Virginia, Charlottesville, USA.
authors.forename: Miao
authors.initials: M
authors.lastname: Lu
authors.affiliation: Department of Statistics, University of Virginia, Charlottesville, USA.
authors.forename: Jianhui
authors.initials: J
authors.lastname: Zhou
authors.affiliation: Division of Infectious Diseases, School of Medicine, University of Virginia, Charlottesville, USA.
authors.forename: Caitlin
authors.initials: C
authors.lastname: Naylor
authors.affiliation: Department of Medicine and Vaccine Testing Center, University of Vermont College of Medicine, Burlington, USA.
authors.forename: Beth D
authors.initials: BD
authors.lastname: Kirkpatrick
authors.affiliation: The International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), Dhaka, Bangladesh.
authors.forename: Rashidul
authors.initials: R
authors.lastname: Haque
authors.affiliation: Division of Infectious Diseases, School of Medicine, University of Virginia, Charlottesville, USA.
authors.forename: William A
authors.initials: WA
authors.lastname: Petri
authors.affiliation: Division of Biostatistics, Department of Public Health Sciences, University of Virginia, Charlottesville, USA.
authors.forename: Jennie Z
authors.initials: JZ
authors.lastname: Ma
keywords: Biomarker selection; Correlated covariates; Environmental enteropathy; Malnutrition; Penalized linear regression