From ‘Endophenotypes’ to ‘Biomarkers’
The concept of endophenotypes has been described as early as in 1966 and originated from a review on geographical distribution in insects where a clear case was made for not only investigating the exophenotype (“…the obvious and the external…”) but also the endophenotype (“…the microscopic and internal”) (John & Lewis, 1966). This term was further adopted by Gottesman and Shields (1967; 1972) in their studies on schizophrenia as ‘biochemical test or microscopic examination’ (Gottesman & Gould, 2003). The idea behind an endophenotype is that it is the intermediary step between genotype and behavior and thus is more closely related to genotype than behavior is. Therefore, endophenotypes can be investigated to yield more information on the underlying genotype. Given the interest in the last couple of years for genetic linkage studies, this term has become more topical again. In parallel there have also been many studies using the term biological marker, trait, biomarker etc. Here it is important that in line with Gottesman and Gould (2003), an ‘endophenotype’ refers to a marker when also certain heritability indicators are fulfilled, whereas a ‘Biomarker’ simply refers to differences between patient groups, which do not necessarily have a hereditary basis.
In the context of Psychiatry Gordon (2007) proposed the term ‘neuro-marker’, and Johnstone et al. (Johnstone, Gunkelman & Lunt, 2005) proposed the term ‘EEG Phenotype’ as examples of biomarkers or intermediate phenotypes. In another context EEG-vigilance regulation has also been proposed as a state-dependent trait (Hegerl, Himmerich, Engmann & Hensch, 2010; Hegerl, Sander, Olbrich & Schoenknecht, 2009). The underlying idea behind these concepts is that neuroimaging data such as from EEG, fMRI, PET scans etc. can be considered stable endophenotypes or biomarkers incorporating both the effects of nature and nurture. This potentially makes such markers ideal candidate biomarkers, which have the potential to predict treatment outcome for treatments such as antidepressants or stimulants, but also for alternative treatments such as rTMS and neurofeedback (explained below). These developments, currently subsumed under the umbrella term ‘personalized medicine’, are not completely new.
The quest for biomarkers to predict treatment outcome has a long history. For example Satterfield et al. (Satterfield, Cantwell, Saul, Lesser & Podosin, 1973; Satterfield, Lesser & Podosin, 1971) were the first to investigate the potential use of EEG in predicting treatment outcome to stimulant medication (main results outlined further on). In 1957 Roth et al (Roth, Kay, Shaw & Green, 1957) investigated barbiturate induced EEG changes (delta increase) and found this predicted to some degree the long-term outcome (3-6 months) to ECT in depression. This latter finding was replicated measuring delta activity during the inter-seizure period, and as Fink summarized this finding eloquently: ‘Slowing of EEG rhythms was necessary for clinical improvement in ECT’ (Fink, 2010). In this development of personalized medicine the focus is hence more on ‘prognostics’ rather than ‘diagnostics’.
Our main specialty is personalized medicine in ADHD and depression with a main focus on neurophysiological techniques such as the EEG and Event Related Potentials (ERPs). In the Personalized Medicine section the promises and limitations for this personalized medicine approach will be reviewed first.