Categorizing Concentration Confidence: A Framework for Reporting Concentration Measures from Mass Spectrometry-Based Assays.

Abstract

Background: Innovation in mass spectrometry-based methods to both quantify and perform discovery has blurred the lines between targeted and untargeted assays of biospecimens. Continuous data-concentrations or intensity values generated from both methods-can be used in statistical analysis to determine associations with health outcomes, but concentration values are needed to compare measurements from one study to another to inform policy making decisions and to develop clinically relevant thresholds. As a single solution for discovery and quantitation, new hybrid-type assays derive concentration values for chemicals or metabolites but with varying degrees of uncertainty that may be greater than traditional quantitative assays. There is no current single standard to guide reporting bioassay concentrations or their uncertainty in concentration values from hybrid assays. Even when measures are robust, obtained with high scientific rigor, and provide valuable data toward risk assessment, unknown uncertainty can lead to bias in interpretation of reported data or omission of reported data that does not meet the strict criteria for absolute quantitation.

Objective: The objective of this commentary is to articulate a scheme that enables investigators across bioanalytical fields to easily report analyte measurement assurance on the same scale from quantitative, untargeted, or hybrid assays that include a range of concentration confidences.

Discussion: We propose a simple scheme to report concentrations for targeted and untargeted analytes. Level 1 is a confirmed concentration following established tolerances in a fully quantitative assay while level 5 is a tentative intensity from a typical untargeted assay. This framework enables easy communication of uncertainty in concentration measurements to aid cross-validation, meta-analysis, and extrapolation across studies. It will facilitate interpretation while supporting analytical advancement and allow clear and concise measurement reporting across a broad range of confidences. https://doi.org/10.1289/EHP15465.