Isolating biomarkers for symptomatic states: considering symptom-substrate chronometry.

Abstract

A long-standing goal of psychopathology research is to develop objective markers of symptomatic states, yet progress has been far slower than expected. While prior reviews have attributed this state of affairs to diagnostic heterogeneity, symptom comorbidity, and phenotypic complexity, little attention has been paid to the implications of intra-individual symptom dynamics and inter-relatedness for biomarker study designs. In this critical review, we consider the impact of short-term symptom fluctuations on widely-used study designs that regress the “average level” of a given symptom against biological data collected at a single time-point, and summarize findings from ambulatory assessment studies suggesting that such designs may be sub-optimal to detect symptom-substrate relationships. While such designs play a crucial role in advancing our understanding of biological substrates related to more stable, longer-term changes (e.g., grey matter thinning during a depressive episode), they may be less optimal for the detection of symptoms that exhibit show high frequency fluctuations, are susceptible to common reporting biases, or may be heavily influenced by the presence of other symptoms. We propose that a greater emphasis on intra-individual symptom chronometry may be useful for identifying subgroups of patients with a common, proximal pathological indicators. Taken together, these three recent developments in the areas of symptom conceptualization and measurement raise important considerations for future studies attempting to identify reliable biomarkers in psychiatry.