The aim of this study was to investigate the factors influencing urban–rural differences in depressive symptoms among old people in China and to measure the contribution of relevant influencing factors.

A cross-sectional research. The 2018 data from The Chinese Longitudinal Health Longevity Survey (CLHLS).

Twenty-three provinces in China.

From the 8th CLHLS, 11,245 elderly participants were selected who met the requirements of the study.

We established binary logistic regression models to explore the main influencing factors of their depressive symptoms and used Fairlie models to analyze the influencing factors of the differences in depressive symptoms between the urban and rural elderly and their contribution.

The percentage of depressive symptoms among Chinese older adults was 11.72%, and the results showed that rural older adults (12.41%) had higher rates of depressive symptoms than urban (10.13%). The Fairlie decomposition analysis revealed that 73.96% of the difference in depressive symptoms could be explained, which was primarily associated with differences in annual income (31.51%), education level (28.05%), sleep time ( − 25.67%), self-reported health (24.18%), instrumental activities of daily living dysfunction (20.73%), exercise (17.72%), living status ( − 8.31%), age ( − 3.84%), activities of daily living dysfunction ( − 3.29%), and social activity (2.44%).

The prevalence of depressive symptoms was higher in rural than in urban older adults, which was primarily associated with differences in socioeconomic status, personal lifestyle, and health status factors between the urban and rural residents. If these factors were addressed, we could make targeted and precise intervention strategies to improve the mental health of high-risk elderly.

Genome-wide association studies (GWAS) have consistently revealed that a variant of microRNA 137 (MIR137) shows a quite significant association with schizophrenia. Identifying the network of genes regulated by MIR137 could provide insights into the biological processes underlying schizophrenia. In addition, DLPFC functional connectivity, a robust correlate of MIR137, may provide plausible endophenotypes. However, the regulatory role of the MIR137 gene network in the disrupted functional connectivity remains unclear. Here, we tested the effects of the MIR137 regulated genes on the risk for schizophrenia and DLPFC functional connectivity.

To evaluate the additive effects of the MIR137 regulated genes (N = 1274), we calculated a MIR137 polygenic risk score (PRS) for schizophrenia and tested its association with the risk for schizophrenia in the genomic data of a Han Chinese population that included schizophrenia patients (N = 589) and normal controls (N = 575). We then investigated the association between MIR137 PRS and DLPFC functional connectivity in two independent young healthy cohorts (N = 356 and N = 314).

We found that the MIR137 PRS successfully captured the differences in genetic structure between the patients and controls, but the single gene MIR137 did not. We then consistently found that a higher MIR137 PRS was correlated with lower functional connectivities between the DLPFC and both the superior parietal cortex and the inferior temporal cortex in two independent cohorts.

The findings suggested that these two functional connectivities of the DLPFC could be important endophenotypes linking the MIR137-regulated genetic structure to schizophrenia.

Schizophrenia is a complex mental disorder with high heritability and polygenic inheritance. Multimodal neuroimaging studies have also indicated that abnormalities of brain structure and function are a plausible neurobiological characterisation of schizophrenia. However, the polygenic effects of schizophrenia on these imaging endophenotypes have not yet been fully elucidated.

To investigate the effects of polygenic risk for schizophrenia on the brain grey matter volume and functional connectivity, which are disrupted in schizophrenia.

Genomic and neuroimaging data from a large sample of Han Chinese patients with schizophrenia (N = 509) and healthy controls (N = 502) were included in this study. We examined grey matter volume and functional connectivity via structural and functional magnetic resonance imaging, respectively. Using the data from a recent meta-analysis of a genome-wide association study that comprised a large number of Chinese people, we calculated a polygenic risk score (PGRS) for each participant.

The imaging genetic analysis revealed that the individual PGRS showed a significantly negative correlation with the hippocampal grey matter volume and hippocampus–medial prefrontal cortex functional connectivity, both of which were lower in the people with schizophrenia than in the controls. We also found that the observed neuroimaging measures showed weak but similar changes in unaffected first-degree relatives of patients with schizophrenia.

These findings suggested that genetically influenced brain grey matter volume and functional connectivity may provide important clues for understanding the pathological mechanisms of schizophrenia and for the early diagnosis of schizophrenia.