Individual reference intervals in practice: A guide to personalise clinical and omics level data with IRIS

Reference intervals (RI) are the best-established methodology used for the interpretation of numerical clinical level data in healthcare and clinical practice. As the test results are interpreted by comparing with the (population-derived) reference intervals, the quality of the calculation and implementation of reference intervals play a major role in decision-making process at the subject level. Here we describe the IRIS workflow to compute Individual Reference Intervals (IRI) based on multiple "healthy" data points from the same subjects and also utilising peers test results. We have improved the IRI models so they allow for covariate adjustments, such as sex and age. The IRI is expected to play pivotal roles in i) early detection of disease transition in chronic diseases by facilitating the detection of small deviations in clinical measurements, ii) monitoring personal disease progression, either using the standard clinical biochemistry test results or the omics level data. We demonstrate the utility of IRI in clinical and omics level data (proteomics and metabolomics) from two different longitudinal studies, including prior data processing and data quality check procedures. We have created an integrated application IRIS incorporating all described steps in an easy-to-use tool in research and/or clinical practice. We compute the IRI estimates in a healthy population to demonstrate its diagnostic utility in chronic diseases and from a diseased cohort to demonstrate its potential in disease monitoring.