Relying on repeated biospecimens to reduce the effects of classical-type exposure measurement error in studies linking the exposome to health.

The exposome calls for assessing numerous exposures, typically using biomarkers with varying amounts of measurement error, which can be assumed to be of classical type. We evaluated the impact of classical-type measurement error on the performance of exposome-health studies, and the efficiency of two measurement error correction methods relying on the collection of repeated biospecimens: within-subject biospecimens pooling and regression calibration. In a simulation study, we generated 237 exposures from a realistic correlation matrix, with various amounts of classical-type measurement error, and a continuous health outcome linearly influenced by exposures. Measurement error decreased the sensitivity to identify exposures influencing health from a value of 75% down to 46%, increased false discovery proportion from 26% to 49% and increased attenuation bias in the slope of true predictors from 45% to 66%. Assuming that repeated biospecimens were available, within-subject pooling and regression calibration improved sensitivity (which increased to 63%), false discovery proportion (down to 37%) and bias (down to 49%) compared to an error-prone study with a single biospecimen per subject. Performances were poorer for the exposures with the largest amount of measurement error, and increased with the number of available biospecimens. Relying on repeated biospecimens only for the exposures with the largest amount of measurement error provided similar performance improvement. Exposome studies relying on spot exposure biospecimens suffer from decreased performances if some biomarkers suffer from measurement error due to their temporal variability; performances can be improved by collecting repeated biospecimens per subject, in particular for non persistent chemicals.