Proof-of-Principle Study on ECT Illustrates Challenges and Possible Merits of Using Polygenic Risk Scores to Predict Treatment Response in Psychiatry

The finding of dissociative effects of PRS-MDD and PRS-BP (and PRS-SCZ) suggests that genetic variation underlying bipolar disorder (and schizophrenia) is more strongly connected to the potency to activate or reactivate certain biological processes in response to ECT (and thereby the clinical response) than the genetic variation underlying major depressive disorder. While indeed the genetic correlation of bipolar disorder with schizophrenia is strong (r_g=0.68), and moderate with major depressive disorder (r_g=0.44), recent work has modeled the genetic correlations of a range of psychiatric diagnoses (including major depressive disorder, bipolar disorder, and schizophrenia), identifying four transdiagnostic factors: “neurodevelopmental, compulsive, psychotic, and internalizing factors” (14). It would be interesting in future analyses to investigate whether the genetic variants underlying ECT response (as observed in the Sigström et al. study) may indeed be enriched in the genetic variants underlying the “psychotic factor” (in line with the clinical observation that people with major depressive disorder with psychotic features respond better to ECT) and the “internalizing factor” (in line with the clinical observation that people with chronic and treatment-resistant depression respond less well to ECT) (5, 6).

In fact, the field of psychiatric genetics has been advancing from cross-sectional analyses on categorical diagnoses toward longitudinal analyses of trans-syndromal and specific phenotypes as well as on the differential impact of treatments and other environmental exposures (early-life risk factors, etc.) on such trajectories (e.g., 15). Given 1) the strong developments in the fundamental insights of genetic background linked to broad and specific phenotypes, 2) the relatively low cost of a genome-wide analysis, and 3) the current culture and accepted value of collaboration in large-scale networks within the international scientific and clinical communities (such as in the existing infrastructure of the Swedish registry and the Psychiatric Genomics Consortium), we foresee that the next phase of research will allow for putting to the test the predictive value of PRSs of treatment response in clinical psychiatry through appropriately designed and adequately powered studies.

It should be noted that the predictive power of the PRSs to explain clinical outcome after ECT was very small in this study; they explained less than 1% of the variance in the ECT-related clinical improvement, while in another recent study (12), PRS-SCZ explained 5%–7% of clinical response to ECT. Technical and conceptual optimization of the computation of PRSs (larger samples sizes, advanced statistical models, more phenotypes and traits) is currently ongoing and may lead to incremental improvements in explained variance in the near future. Nevertheless, the predictive value of measurable genetic background is expected to remain minor to modest, and other factors are expected to have higher predictive value than genetic background (by itself), particularly when combined into multifactorial prediction models.

Replication of the present analysis in ECT studies as well as in genetic studies on other treatment modalities (e.g., pharmacotherapy, repetitive transcranial magnetic stimulation, psychotherapies) through large-scale scientific and clinical collaborative efforts is now needed to establish the possible value of genetic prediction of treatment response in psychiatry.

Setups of standardized longitudinal clinical monitoring (exemplified in the Sigström et al. study) also facilitate other scientific endeavors with potential impact in the near future. For example, longitudinal follow-up studies enable the study of other biological factors, such as the immune system and epigenetic regulation of gene expression of neurotrophins (e.g., 16, 17) in relation to response to ECT. Such studies can complement studies at the genetic level, thereby forming a strong basis for identification of actionable biological targets and the establishment of markers and biomarkers predicting and monitoring response to ECT.

Although the potential of prediction models for treatment response in depression is promising, this field is still in its infancy, and the models are not yet ready to inform treatment selection in clinical practice (18, 19). As depression is highly heterogeneous, multiple features from different domains (e.g., sociodemographic, clinical, genetic, and neuroimaging data) have to be considered to accurately predict the complex processes that underlie response to depression treatment. In addition, the modest results from prediction models that include variables from only one domain could support this notion (18). One excellent study that combined both clinical and genetic data (20) developed two models that accurately predicted outcomes of 12-week treatment with escitalopram and nortriptyline in an independent data set (a hold-out data set—i.e., one that was not used for model development). Interestingly, the prediction models were drug specific, meaning that remission was accurately predicted in the hold-out sample treated with the same type of antidepressant medication. These treatment-specific predictions hold promise for treatment selection. Another example of a prediction model based on multifactorial data has been reported by Taliaz et al. (21), who found that combined demographic, clinical, and genetic data accurately predicted response to various types of antidepressant medication in an independent data set. Although both studies (20, 21) tested the prediction accuracy of their models within a hold-out data set, this process of external validation has been done infrequently in other prediction studies, making it difficult to estimate their generalizability to clinical practice. This is especially worrying, as prediction models are often developed from relatively small data sets (an analysis using simulations has suggested that at least 300 patients per treatment arm are needed [22]), leading to overfitting, meaning that the model perfectly fits the data set in which it is developed, but performs poorly in new data. One way to solve this sample size issue is by using naturalistic data for model development (23), although for treatment selection, confounders that guide real-world treatment allocation should be considered carefully. With these considerations, collaborations such as the Swedish national quality registers are key to the future of accurate and clinically relevant prediction (13).

Research on ECT has important advantages over research on other treatment modalities in psychiatry. The strong effect sizes (in a distinct time window), the individual variation in effects of ECT, its relatively good controllability, the relatively homogeneous clinical settings in which ECT is performed, and the good setup for longitudinal outcome monitoring provide important advantages for adequate designs for biomarker studies. The current wave of studies on prediction of response to ECT can therefore be considered proof-of-principle studies for testing the potential value for personalized genetic prediction of treatment response in psychiatry and the elucidation of biological mechanisms underlying treatment-associated changes in mental health status.

One hypothesis on the mechanism of action of ECT is the neuroplasticity hypothesis, which was originally based on preclinical animal studies that reported a dose-dependent neurogenesis following ECT (24) and is partly supported by neuroimaging studies (25). Although this mechanism may apply to other depression treatments as well, the onset and effect of ECT are more pronounced, presumably resulting in a “plasticity window.” As relapse rates following ECT discontinuation are high, cognitive and behavioral interventions to consolidate the plasticity changes may have some (synergistic) promise for sustainable mental health care (26). Recent studies provide evidence that cognitive-behavioral therapy may indeed be an effective intervention for sustaining the effects of a successful course of ECT (e.g., [27]).

In summary, Sigström et al. provide a proof-of-principle study on the predictive value of PRSs for response to ECT. With their large-scale longitudinal design embedded in clinical practice, they found that genetic variation underlying bipolar disorder was associated with successful ECT. Although the predictive value was small, this could be considered a step toward the optimization of (transdiagnostic) PRSs and the development of multifactorial prediction models. ECT research provides a very useful context for biomarker research, and future studies hold promise in providing a better understanding of underlying biological processes, and better prediction of individual response to ECT.