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

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.

SUBMITTER: Colston JM

PROVIDER: S-EPMC6881068 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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

Colston Josh M JM Peñataro Yori Pablo P Moulton Lawrence H LH Paredes Olortegui Maribel M Kosek Peter S PS Rengifo Trigoso Dixner D Siguas Salas Mery M Schiaffino Francesca F François Ruthly R Fardus-Reid Fahmina F Swann Jonathan R JR Kosek Margaret N MN

PLoS neglected tropical diseases 20191115 11

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 ...[more]

PMID: 31730639

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Project description:BackgroundEnvironmental 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.MethodsMany 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.ResultsThrough 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.ConclusionsPenalized 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.Trial registrationThis study was retrospectively registered with ClinicalTrials.gov, number NCT01375647, on June 3, 2011.

Project description:Background: Environmental enteric dysfunction (EED) is a major impediment to the Sustainable Development Goals of improved childhood survival and healthy growth worldwide. Few studies have longitudinally and endoscopically examined EED in children, limiting the development of effective interventions. Methods: The Study of Environmental Enteropathy and Malnutrition (SEEM, Pakistan) followed 416 at-risk children prospectively from birth to 24 months of age in a rural district of Pakistan with a high prevalence of undernutrition. The duodenal genome-wide methylome and transcriptome was determined in 52 undernourished SEEM participants refractory to nutritional interventions and 42 North American pediatric controls and coeliac disease patients. Biomarkers were measured at 9 months and tested for association with growth at 24 months. Findings: The EED core transcriptome exhibited suppression of genes regulating antioxidant and detoxification functions and lipid metabolism, and induction of genes encoding anti-microbial responses, interferon signaling, and lymphocyte activation. Relative to coeliac disease, suppression of genes encoding antioxidant and detoxification functions and induction of anti-microbial responses were EED-specific. At the epigenetic level, EED was notable for hyper-methylation of genes involved in epithelial metabolism and barrier function, and hypo-methylation of genes involved in immune responses and cell proliferation. Duodenal co-expression modules revealed associations between genes regulating lymphocyte proliferation and epithelial metabolism with histologic severity, faecal energy loss, and wasting (weight-for-height Z<-2.0), with data supporting epigenetic regulation. Immune response genes were attenuated by Giardia colonization. After accounting for growth at study entry, IGF-1 and ferritin predicted linear growth, whereas leptin correlated with expression of epithelial carbohydrate metabolism and stem cell renewal genes, and weight gain. Interpretation: Data suggest immune and metabolic pathways which may inform more effective interventions to improve outcomes in EED. A validated model may be used to identify infants at greatest risk for future poor growth.

Project description:BackgroundChild stunting remains a poorly understood, prevalent public health problem. Environmental enteric dysfunction (EED) is hypothesized to be an important underlying cause.ObjectivesWithin a subgroup of 1169 children enrolled in the SHINE (Sanitation Hygiene Infant Nutrition Efficacy) trial in rural Zimbabwe, followed longitudinally from birth to 18 mo of age, we evaluated associations between the concentration of 11 EED biomarkers and linear growth velocity.MethodsAt infant ages 1, 3, 6, 12, and 18 mo, nurses measured child length and collected stool and blood; the lactulose-mannitol urine test was also conducted at all visits except at 1 mo. Stool neopterin, α-1 antitrypsin, myeloperoxidase, and regenerating gene 1β protein; urinary lactulose and mannitol; and plasma kynurenine, tryptophan, C-reactive protein, insulin-like growth factor-1 (IGF-1), soluble CD14, intestinal fatty acid binding protein, and citrulline were measured. We analyzed the change in relative [∆ length-for-age z score (LAZ)/mo] and absolute (∆ length/mo) growth velocity during 4 age intervals (1-3 mo; 3-6 mo; 6-12 mo; and 12-18 mo) per SD increase in biomarker concentration at the start of each age interval.ResultsIn fully adjusted models, we observed only 3 small, statistically significant associations: kynurenine:tryptophan ratio at 12 mo was associated with decreased mean LAZ velocity during the 12-18 mo interval (-0.015 LAZ/mo; 95% CI: -0.029, -0.001 LAZ/mo); mannitol excretion at 6 mo was associated with increased LAZ velocity during the 6-12 mo interval (0.013 LAZ/mo; 95% CI: 0.001, 0.025 LAZ/mo), and plasma IGF-1 at 1 mo was associated with increased LAZ velocity during the 1-3 mo interval (0.118 LAZ/mo; 95% CI: 0.024, 0.211 LAZ/mo). Results for absolute growth velocity were similar, except IGF-1 was also associated with growth during the 12-18 mo interval. We found no other associations between any EED biomarker and linear growth velocity.ConclusionsNone of 11 biomarkers of EED were consistently associated with linear growth among Zimbabwean children.This trial was registered at clinicaltrials.gov as NCT01824940.

Project description:This paper concerns the folded concave penalized sparse linear regression (FCPSLR), a class of popular sparse recovery methods. Although FCPSLR yields desirable recovery performance when solved globally, computing a global solution is NP-complete. Despite some existing statistical performance analyses on local minimizers or on specific FCPSLR-based learning algorithms, it still remains open questions whether local solutions that are known to admit fully polynomial-time approximation schemes (FPTAS) may already be sufficient to ensure the statistical performance, and whether that statistical performance can be non-contingent on the specific designs of computing procedures. To address the questions, this paper presents the following threefold results: (i) Any local solution (stationary point) is a sparse estimator, under some conditions on the parameters of the folded concave penalties. (ii) Perhaps more importantly, any local solution satisfying a significant subspace second-order necessary condition (S3ONC), which is weaker than the second-order KKT condition, yields a bounded error in approximating the true parameter with high probability. In addition, if the minimal signal strength is sufficient, the S3ONC solution likely recovers the oracle solution. This result also explicates that the goal of improving the statistical performance is consistent with the optimization criteria of minimizing the suboptimality gap in solving the non-convex programming formulation of FCPSLR. (iii) We apply (ii) to the special case of FCPSLR with minimax concave penalty (MCP) and show that under the restricted eigenvalue condition, any S3ONC solution with a better objective value than the Lasso solution entails the strong oracle property. In addition, such a solution generates a model error (ME) comparable to the optimal but exponential-time sparse estimator given a sufficient sample size, while the worst-case ME is comparable to the Lasso in general. Furthermore, to guarantee the S3ONC admits FPTAS.

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

Publications

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

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