Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies

Perrier, Flavie; Giorgis-Allemand, Lise; Slama, Rémy; Philippat, Claire

doi:10.1097/ede.0000000000000460

Cited by 205 publications

(203 citation statements)

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“…Overall, the induced sensitivity loss was consistent across all methods and did not help in further discriminating the statistical methods under investigation. Importantly, we did not consider measurement error or misclassification in exposure covariates, although they have a potentially large impact on statistical power and bias, particularly in the case of classical type error (de Klerk et al 1989; Perrier et al 2016; Rappaport et al 1995). As a result, method performance may be hampered in real-life situations, but there is no a priori reason to think that the statistical methods investigated in this study would be differentially affected by these issues.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Comparison of Linear Regression–Based Statistical Methods to Assess Exposome-Health Associations

Agier

Portengen

Chadeau‐Hyam

et al. 2016

Environ Health Perspect

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178

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Background:The exposome constitutes a promising framework to improve understanding of the effects of environmental exposures on health by explicitly considering multiple testing and avoiding selective reporting. However, exposome studies are challenged by the simultaneous consideration of many correlated exposures.Objectives:We compared the performances of linear regression–based statistical methods in assessing exposome-health associations.Methods:In a simulation study, we generated 237 exposure covariates with a realistic correlation structure and with a health outcome linearly related to 0 to 25 of these covariates. Statistical methods were compared primarily in terms of false discovery proportion (FDP) and sensitivity.Results:On average over all simulation settings, the elastic net and sparse partial least-squares regression showed a sensitivity of 76% and an FDP of 44%; Graphical Unit Evolutionary Stochastic Search (GUESS) and the deletion/substitution/addition (DSA) algorithm revealed a sensitivity of 81% and an FDP of 34%. The environment-wide association study (EWAS) underperformed these methods in terms of FDP (average FDP, 86%) despite a higher sensitivity. Performances decreased considerably when assuming an exposome exposure matrix with high levels of correlation between covariates.Conclusions:Correlation between exposures is a challenge for exposome research, and the statistical methods investigated in this study were limited in their ability to efficiently differentiate true predictors from correlated covariates in a realistic exposome context. Although GUESS and DSA provided a marginally better balance between sensitivity and FDP, they did not outperform the other multivariate methods across all scenarios and properties examined, and computational complexity and flexibility should also be considered when choosing between these methods.Citation:Agier L, Portengen L, Chadeau-Hyam M, Basagaña X, Giorgis-Allemand L, Siroux V, Robinson O, Vlaanderen J, González JR, Nieuwenhuijsen MJ, Vineis P, Vrijheid M, Slama R, Vermeulen R. 2016. A systematic comparison of linear regression–based statistical methods to assess exposome-health associations. Environ Health Perspect 124:1848–1856; http://dx.doi.org/10.1289/EHP172

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Section: Discussionmentioning

confidence: 99%

A Systematic Comparison of Linear Regression–Based Statistical Methods to Assess Exposome-Health Associations

Agier

Portengen

Chadeau‐Hyam

et al. 2016

Environ Health Perspect

Self Cite

178

149

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“…To help reduce exposure variability, we averaged 16- and 26-week maternal urinary BPA concentrations to estimate prenatal exposure, but we only have one measurement at each postnatal visit to reflect approximately year-long exposures. Future studies might consider pooling multiple urine samples from each individual and analyzing the pooled sample to reduce the potential for exposure misclassification bias (Perrier et al 2016). Our sample size was modest, which reduced our ability to precisely estimate associations.…”

Section: Discussionmentioning

confidence: 99%

Early life bisphenol A exposure and neurobehavior at 8 years of age: Identifying windows of heightened vulnerability

Stacy

Papandonatos

Calafat

et al. 2017

Environment International

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Background Early life BPA exposure could affect neurobehavior, but few studies have investigated whether there are developmental periods when the fetus or child is more vulnerable to these potential effects. Objectives We explored windows of vulnerability to BPA exposure in a multiethnic cohort of 228 mothers and their children from Cincinnati, Ohio. Methods We measured urinary BPA concentrations at up to two prenatal and six postnatal time points from the 2nd trimester of pregnancy until the child was age 8 years. At age 8 years, we administered the Behavioral Assessment System for Children-2 (BASC-2), Behavior Rating Inventory of Executive Function, and Wechsler Intelligence Scale for Children-IV. We estimated covariate-adjusted differences in composite scores from each instrument using a multiple informant model designed to identify heightened windows of vulnerability. Results Among all children, there was not strong evidence that the associations between BPA and neurobehavior varied by the timing of exposure (Visit × BPA p-values=0.16). However, child sex modified the associations of repeated BPA measures with BASC-2 scores (Visit × Sex × BPA p-values=0.02–0.23). For example, each 10-fold increase in prenatal BPA was associated with more externalizing behaviors in girls (β=6.2, 95% CI: 0.8, 11.6), but not boys (β=−0.8, 95% CI: −5.0, 3.4). In contrast, a 10-fold increase in 8-year BPA was associated with more externalizing behaviors in boys (β=3.9, 95% CI: 0.6, 7.2), but not girls (β=0.3, 95% CI: −3.5, 4.1). Conclusions We found that sex-dependent associations between BPA and child neurobehavior may depend on the timing of BPA exposure.

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“…These findings underscore the importance of collecting more than a single biospecimen for the analytes that had poor to good reliability, to characterize true exposure for those analytes at the lower range of sensitivity for analyses where correct categorization of exposure is important. Although BP-3, BP-1, TCS, 2,4-DCP, BuP, and PrP exhibited excellent reliability; measurement error from comparable levels of reliability can bias effect estimates (Perrier et al, 2016). …”

Section: Discussionmentioning

confidence: 99%

Variability and exposure classification of urinary phenol and paraben metabolite concentrations in reproductive-aged women

Pollack

Perkins

Sjaarda

et al. 2016

Environmental Research

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Background Human exposure to phenols and parabens is widespread. Within-person variability of urinary concentrations in healthy women is not well characterized. Objectives To characterize the variability of urinary phenol and paraben concentrations across two months and evaluate the ability of a single spot urine sample to characterize exposure. Methods 143 women provided 509 spot urine samples collected across two months of study (3–5 samples/woman). We measured urinary concentrations of 8 phenols: bisphenol A (BPA), benzophenone-3 (BP-3), benzophenone-1 (BP-1), 2,4-dichlorophenol (2,4-DCP), 2,5-dichlorophenol (2,5-DCP), 2,4,5-trichlorophenol (2,4,5-TCP), 2,4,6-trichlorophenol (2,4,6-TCP), triclosan (TCS); and 8 parabens and their metabolites (benzyl (BzP), butyl (BuP), ethyl (EtP), heptyl (HeP), methyl (MeP), propyl (PrP), 4-hydro-xybenzoic acid (4-HB), 3,4-dihydroxybenzoic acid (3,4-DHB)). Biomarker variability was characterized using the intraclass correlation coefficient (ICC) and surrogate category analyses were conducted. Results ICCs ranged from very low for BPA (0.04) to moderate for BP-3, BP-1, TCS, BzP, and MeP (0.66, 0.58, 0.55, 0.54, and 0.62, respectively). Surrogate analyses suggested that BP-1, BP-3, TCS, 2,4-DCP, BuP, and PrP may be characterized by a single spot sample (sensitivity range 0.76–0.86) but that additional samples were necessary for BPA, HeP, 4-HB, and 3,4-DHB (sensitivity range 0.47–0.61). Conclusions Urinary phenol and paraben metabolite concentrations were variable across two months in healthy women but the degree of reliability differed by the specific biomarker. A small number of samples may sufficiently characterize typical concentrations for BP-3, BP-1, TCS, BuP, and PrP; but additional biospecimens may be necessary to characterize exposure for other compounds, including BPA.

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Within-subject Pooling of Biological Samples to Reduce Exposure Misclassification in Biomarker-based Studies

Abstract: Supplemental Digital Content is available in the text.

Cited by 205 publications

References 37 publications

A Systematic Comparison of Linear Regression–Based Statistical Methods to Assess Exposome-Health Associations

A Systematic Comparison of Linear Regression–Based Statistical Methods to Assess Exposome-Health Associations

Early life bisphenol A exposure and neurobehavior at 8 years of age: Identifying windows of heightened vulnerability

Variability and exposure classification of urinary phenol and paraben metabolite concentrations in reproductive-aged women

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