Data-driven subphenotypic dissection of the clinical heterogeneity of schizophrenia spectrum disorders

Phenotypic heterogeneity within disorders and phenotypic similarities across disorders are some of the main challenges in the current traditional classification of mental disorders, including schizophrenia spectrum disorders. This can be partly attributed to the absence of objective diagnostic criteria, and high comorbidity with other psychiatric and non-psychiatric diseases. This thesis aimed to dissect the phenotypic heterogeneity of schizophrenia spectrum disorders using data-driven approaches within the framework of the Genetic Risk and Outcome of Psychosis (GROUP) project, a naturalistic, longitudinal cohort study in the Dutch population. GROUP enables a comparison between patients with maximum susceptibility for schizophrenia spectrum disorders (N = 1,119), their unaffected siblings who share genetic and environmental risk factors with patients (N = 1,059), and control subjects who have baseline risk (N = 586). This thesis also aimed to investigate the role of sociodemographic and clinical risk factors along with cardiometabolic and genetic risk factors (as measured by polygenic risk score of schizophrenia (PRSSCZ), and type 2 diabetes (PRST2D)). Chapters 2 to 5 found that positive, negative and cognitive symptoms are highly heterogeneous, constituting up to six subtypes with variable trajectories over time. Additionally, several sociodemographic and clinical factors were associated with the course of these symptoms in participants. We found weak evidence that genetic risk influences symptom course. Chapter 6 showed that metabolic dysregulation, as measured by glycated hemoglobin (HbA1c), was associated with late age of illness onset in patients with schizophrenia