Researchers at Kyushu University, Hiroshima University, Tottori University, and others have found that the previously known discrimination performance of blood biomarkers for depression is dramatically improved in populations with specific characteristics.
In previous studies, the researchers have found blood metabolites (blood biomarkers) that may be associated with the discrimination and severity of depression.Psychological studies, on the other hand, have long suggested that an individual's personality influences the onset and severity of depression.
Therefore, in order to verify the relationship between the blood components related to depression and personality, we first performed a cluster analysis of 5 depressed patients and 100 healthy subjects based on a personality test called BIG-100.As a result, a group with a "depression-related temperament" with a high tendency for neurosis and a low extroversion (majority of depression patients), and a group with the opposite personality tendency (majority of healthy people). It was possible to stratify into 200 groups of a group with less bias (including half of patients and half of healthy people).
Next, when a depression discriminant model was created by machine learning of blood component information and a depression discriminant analysis was performed on the target group, a group with less personality bias (depression) than when all subjects were targeted. It was found that the discriminant performance is dramatically improved when the influence of the related disposition is limited to the intermediate subject group).In this middle group, tryptophan, kynurenine, and serotonin in depressed patients were significantly lower than those in healthy subjects.
This is the first report to show that personality is associated with the discriminating performance of depression blood biomarkers, suggesting that different personalities may result in different "biotypes" of depression.Different biotypes may require different treatment strategies, and future research developments are expected to lead to personalized medicine suitable for each individual depressed patient.
