Association Between Polygenic Risk Scores and Outcome of ECT

Correlation between polygenic risk scores of depression and cortical morphology networks

BACKGROUND

Cortical morphometry is an intermediate phenotype that is closely related to the genetics and onset of major depressive disorder (MDD), and cortical morphometric networks are considered more relevant to disease mechanisms than brain regions. We sought to investigate changes in cortical morphometric networks in MDD and their relationship with genetic risk in healthy controls.

METHODS

We recruited healthy controls and patients with MDD of Han Chinese descent. Participants underwent DNA extraction and magnetic resonance imaging, including T 1-weighted and diffusion tensor imaging. We calculated polygenic risk scores (PRS) based on previous summary statistics from a genome-wide association study of the Chinese Han population. We used a novel method based on Kullback-Leibler divergence to construct the morphometric inverse divergence (MIND) network, and we included the classic morphometric similarity network (MSN) as a complementary approach. Considering the relationship between cortical and white matter networks, we also constructed a streamlined density network. We conducted group comparison and PRS correlation analyses at both the regional and network level.

RESULTS

We included 130 healthy controls and 195 patients with MDD. The results indicated enhanced connectivity in the MIND network among patients with MDD and people with high genetic risk, particularly in the somatomotor (SMN) and default mode networks (DMN). We did not observe significant findings in the MSN. The white matter network showed disruption among people with high genetic risk, also primarily in the SMN and DMN. The MIND network outperformed the MSN network in distinguishing MDD status.

LIMITATIONS

Our study was cross-sectional and could not explore the causal relationships between cortical morphological changes, white matter connectivity, and disease states. Some patients had received antidepressant treatment, which may have influenced brain morphology and white matter network structure.

CONCLUSION

The genetic mechanisms of depression may be related to white matter disintegration, which could also be associated with decoupling of the SMN and DMN. These findings provide new insights into the genetic mechanisms and potential biomarkers of MDD.

Characterizing the genetic architecture of drug response using gene-context interaction methods

Identifying factors that affect treatment response is a central objective of clinical research, yet the role of common genetic variation remains largely unknown. Here, we develop a framework to study the genetic architecture of response to commonly prescribed drugs in large biobanks. We quantify treatment response heritability for statins, metformin, warfarin, and methotrexate in the UK Biobank. We find that genetic variation modifies the primary effect of statins on LDL cholesterol (9% heritable) as well as their side effects on hemoglobin A1c and blood glucose (10% and 11% heritable, respectively). We identify dozens of genes that modify drug response, which we replicate in a retrospective pharmacogenomic study. Finally, we find that polygenic score (PGS) accuracy varies up to 2-fold depending on treatment status, showing that standard PGSs are likely to underperform in clinical contexts.

A prediction model for electroconvulsive therapy effectiveness in patients with major depressive disorder from the Dutch ECT Consortium (DEC)

Reliable predictors for electroconvulsive therapy (ECT) effectiveness would allow a more precise and personalized approach for the treatment of major depressive disorder (MDD). Prediction models were created using a priori selected clinical variables based on previous meta-analyses. Multivariable linear regression analysis was used, applying backwards selection to determine predictor variables while allowing non-linear relations, to develop a prediction model for depression outcome post-ECT (and logistic regression for remission and response as secondary outcome measures). Internal validation and internal-external cross-validation were used to examine overfitting and generalizability of the model's predictive performance. In total, 1892 adult patients with MDD were included from 22 clinical and research cohorts of the twelve sites within the Dutch ECT Consortium. The final primary prediction model showed several factors that significantly predicted a lower depression score post-ECT: higher age, shorter duration of the current depressive episode, severe MDD with psychotic features, lower level of previous antidepressant resistance in the current episode, higher pre-ECT global cognitive functioning, absence of a comorbid personality disorder, and a lower level of failed psychotherapy in the current episode. The optimism-adjusted R² of the final model was 19%. This prediction model based on readily available clinical information can reduce uncertainty of ECT outcomes and hereby inform clinical decision-making, as prompt referral for ECT may be particularly beneficial for individuals with the above-mentioned characteristics. However, despite including a large number of pretreatment factors, a large proportion of the variance in depression outcome post-ECT remained unpredictable.

Polygenic Risk Scores for Bipolar Disorder: Progress and Perspectives

Bipolar disorder (BD) is a common and highly heritable psychiatric disorder, the study of BD genetic characteristics can help with early prevention and individualized treatment. At the same time, BD is a highly heterogeneous polygenic genetic disorder with significant genetic overlap with other psychiatric disorders. In recent years, polygenic risk scores (PRS) derived from genome-wide association studies (GWAS) data have been widely used in genetic studies of various complex diseases and can be used to explore the genetic susceptibility of diseases. This review discusses phenotypic associations and genetic correlations with other conditions of BD based on PRS, and provides ideas for genetic studies and prevention of BD.

Alterations in the Serum Proteome Following Electroconvulsive Therapy for a Major Depressive Episode: A Longitudinal Multicenter Study

Background

Electroconvulsive therapy (ECT) is the most effective treatment for severe depression, but the biological changes induced by ECT remain poorly understood.

Methods

This study investigated alterations in blood serum proteins in 309 patients receiving ECT for a major depressive episode. We analyzed 201 proteins in samples collected at 3 time points (T): just before the first ECT treatment session (T0), within 30 minutes after the first ECT session (T1), and just before the sixth ECT session (T2).

Results

Using statistical models to account for repeated sampling, we identified 152 and 70 significantly (<5% false discovery rate) altered proteins at T1 and T2, respectively. The most pronounced alterations at T1 were transiently increased levels of prolactin, myoglobin, and kallikrein-6. However, most proteins had decreased levels at T1, with the largest effects observed for pro-epidermal growth factor, proto-oncogene tyrosine-protein kinase Src, tumor necrosis factor ligand superfamily member 14, sulfotransferase 1A1, early activation antigen CD69, and CD40 ligand. The change of several acutely altered proteins correlated with electric current and pulse frequency in a dose-response-like manner. Over a 5-session course of ECT, some acutely altered levels were sustained while others increased, e.g., serine protease 8 and chitinase-3-like protein 1. None of the studied protein biomarkers were associated with clinical response to ECT.

Conclusions

We report experimental data on alterations in the circulating proteome triggered by ECT in a clinical setting. The findings implicate hormonal signaling, immune response, apoptotic processes, and more. None of the findings were associated with clinical response to ECT.