Biomarker Profiles in Psychosis Risk Groups Within Unaffected Relatives Based on Familiality and Age

Investigating biomarkers in unaffected relatives (UR) of individuals with psychotic disorders has already proven productive in research on psychosis neurobiology. However, there is considerable heterogeneity among UR based on features linked to psychosis vulnerability. Here, using the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) dataset, we examined cognitive and neurophysiologic biomarkers in first-degree UR of psychosis probands, stratified by 2 widely used risk factors: familiality status of the respective proband (the presence or absence of a first- or second-degree relative with a history of psychotic disorder) and age (within or older than the common age range for developing psychosis). We investigated biomarkers that best differentiate the above specific risk subgroups. Additionally, we examined the relationship of biomarkers with Polygenic Risk Scores for Schizophrenia (PRSSCZ) in a subsample of Caucasian probands and healthy controls (HC). Our results demonstrate that the Brief Assessment of Cognition in Schizophrenia (BACS) score, antisaccade error (ASE) factor, and stop-signal task (SST) factor best differentiate UR (n = 169) from HC (n = 137) (P = .013). Biomarker profiles of UR of familial (n = 82) and non-familial (n = 83) probands were not significantly different. Furthermore, ASE and SST factors best differentiated younger UR (age <= 30) (n = 59) from older UR (n = 110) and HC from both age groups (age <= 30 years, n=49; age > 30 years, n = 88) (P < .001). In addition, BACS (r = -0.175, P = .006) and ASE factor (r = 0.188, P = .006) showed associations with PRSSCZ. Taken together, our findings indicate that cognitive biomarkers-top-down inhibition impairments in particular-may be of critical importance as indicators of psychosis vulnerability.

Automated summary

Investigating biomarkers in unaffected relatives of individuals with psychotic disorders is productive, with cognitive biomarkers such as Brief Assessment of Cognition in Schizophrenia (BACS) and neurophysiologic biomarkers like antisaccade error (ASE) showing potential in indicating psychosis vulnerability. The heterogeneity among unaffected relatives based on different risk factors highlights the importance of identifying specific risk subgroups for effective biomarker differentiation. The relationship between biomarkers and Polygenic Risk Scores for Schizophrenia (PRSSCZ) suggests a potential genetic association in psychosis vulnerability.