Longitudinal pre-diagnostic samples allow early osteoporosis diagnosis

Abstract

Biomarker discovery for degenerative diseases is challenging due to low statistical power, selection bias, and biological variability. To address these problems, we introduced pre-diagnostic longitudinal sampling using samples from the Danish Blood Donor Study. We obtained up to six longitudinal metabolomics profiles using one-year intervals with the latest profile within one year before osteoporosis diagnosis, including 99 cases and 99 controls. We matched the patients with controls based on sex, age, sampling site, disease history, body mass index, analytical batch, and sample storage time. Our longitudinal model of molecular changes improved the signal from non-significant in single-sample modeling between patient cases and controls to an area under the curve (AUC) of 0.75. This pilot study demonstrates the advantages of longitudinal data in biomarker research, including robustness to day-to-day biological variance, inter-individual variance, and post-diagnostic biases.