Depression arises from diverse environmental and psychosocial risk factors, yet how these factors co-occur within individuals remains unclear. This study identifies profiles of multiple depression risk factors and examines their clinical and neuroimaging correlates.

Among 157,317 UK Biobank participants completing the mental health questionnaire, 24 psychological, environmental, and lifestyle factors were assessed using latent class analysis. Logistic regression evaluated associations between profiles and depression outcomes; linear models examined neuroimaging differences. Imaging transcriptomics and gene-set enrichment analyses contextualized neural findings.

Three latent profiles emerged: low risk profile (81.09%), childhood adversity-related profile (CA; 10.95%), and adulthood adversity-related profile (AA; 7.97%). Both the CA profile and AA profile show significantly higher depression risk than the low risk profile. Compared with the low risk profile, the AA profile shows a 2.7-fold increase in depression risk (OR = 3.701, 95%CI: 3.532~3.881), with appetite change and psychomotor symptoms being more prominent. The CA profile shows a 2.5-fold increase in depression risk (OR = 3.507, 95%CI: 3.353~3.607), with worthlessness, sleep problems, and suicidal ideation being more prominent. Both adversity profiles showed lower white-matter FA in cerebellar–thalamic and associative pathways. The CA profile additionally showed reduced FA in occipital tracts, whereas the AA profile showed greater reductions in prefrontal pathways and lower GMV in insula, amygdala, and cerebellar lobules VIIIb/IX, alongside higher occipital pole GMV. The most pronounced nominally significant difference between CA and AA centered on the right amygdala. Genes overlapping subcortical GMV differences were enriched for psychiatric disorders.

Life-course adversity may be a key feature associated with distinct clinical and neural signatures, helping identify subgroups with co-occurring vulnerabilities. These patterns warrant further investigation in future studies.

Schizophrenia is a severe and complex psychiatric disorder that needs treatment based on extensive experience. Antipsychotic drugs have already become the cornerstone of the treatment for schizophrenia; however, the therapeutic effect is of significant variability among patients, and only around a third of patients with schizophrenia show good efficacy. Meanwhile, drug-induced metabolic syndrome and other side-effects significantly affect treatment adherence and prognosis. Therefore, strategies for drug selection are desperately needed. In this study, we will perform pharmacogenomics research and set up an individualised preferred treatment prediction model.

We aim to create a standard clinical cohort, with multidimensional index assessment of antipsychotic treatment for patients with schizophrenia.

This trial is designed as a randomised clinical trial comparing treatment with different kinds of antipsychotics. A total sample of 2000 patients with schizophrenia will be recruited from in-patient units from five clinical research centres. Using a computer-generated program, the participants will be randomly assigned to four treatment groups: aripiprazole, olanzapine, quetiapine and risperidone. The primary outcomes will be measured as changes in the Positive and Negative Syndrome Scale of schizophrenia, which reflects the efficacy. Secondary outcomes include the measure of side-effects, such as metabolic syndromes. The efficacy evaluation and side-effects assessment will be performed at baseline, 2 weeks, 6 weeks and 3 months.

This trial will assess the efficacy and side effects of antipsychotics and create a standard clinical cohort with a multi-dimensional index assessment of antipsychotic treatment for schizophrenia patients.

This study aims to set up an individualized preferred treatment prediction model through the genetic analysis of patients using different kinds of antipsychotics.