RESUMEN
Suicide accounts for more than 700,000 deaths annually and is the fourth leading cause of death among individuals aged 15 to 29. Despite years of research to understand the aetiology and pathophysiology of suicidal behaviour, many questions remain unresolved; for example, whether suicidal behaviour has a unique genetic or neurobiological basis and how these differ from related psychiatric conditions, such as depression, bipolar disorder, schizophrenia, etc. Identifying these biological correlates is paramount to advancing our understanding of the underlying mechanisms of suicidal behaviour. This literature review delves into the complex nature of suicidal thoughts and behaviours, integrating insights from recent large-scale genetic and neuroimaging studies published between 2018 and 2023. Recent genome-wide association studies have uncovered specific genomic loci associated with specific suicidal behaviours. However, there is a need for larger and more diverse samples in these studies to overcome challenges in replicability and generalisability. Neuroimaging studies have also revealed structural brain differences associated with suicidal behaviour, thanks to international consortium-level efforts that have enabled data sharing, collaboration, and coordinated analyses that improve the robustness and reliability of findings. Despite promising progress in identifying the genetic and neurobiological underpinnings of suicidal behaviour, the translation of these advances and findings into effective suicide prevention strategies and clinical tools remains a crucial challenge; consequently, future studies must focus on integrating biological elements into an improved mechanistic understanding of the aetiology of suicidal behaviour, which in turn can translate into new strategies for early detection, intervention and treatment.
RESUMEN
Background: Standardized definitions of suicidality phenotypes, including suicidal ideation (SI), attempt (SA), and death (SD) are a critical step towards improving understanding and comparison of results in suicide research. The complexity of suicidality contributes to heterogeneity in phenotype definitions, impeding evaluation of clinical and genetic risk factors across studies and efforts to combine samples within consortia. Here, we present expert and data-supported recommendations for defining suicidality and control phenotypes to facilitate merging current/legacy samples with definition variability and aid future sample creation. Methods: A subgroup of clinician researchers and experts from the Suicide Workgroup of the Psychiatric Genomics Consortium (PGC) reviewed existing PGC definitions for SI, SA, SD, and control groups and generated preliminary consensus guidelines for instrument-derived and international classification of disease (ICD) data. ICD lists were validated in two independent datasets (N = 9,151 and 12,394). Results: Recommendations are provided for evaluated instruments for SA and SI, emphasizing selection of lifetime measures phenotype-specific wording. Recommendations are also provided for defining SI and SD from ICD data. As the SA ICD definition is complex, SA code list recommendations were validated against instrument results with sensitivity (range = 15.4% to 80.6%), specificity (range = 67.6% to 97.4%), and positive predictive values (range = 0.59-0.93) reported. Conclusions: Best-practice guidelines are presented for the use of existing information to define SI/SA/SD in consortia research. These proposed definitions are expected to facilitate more homogeneous data aggregation for genetic and multisite studies. Future research should involve refinement, improved generalizability, and validation in diverse populations.
RESUMEN
Antidepressant medications are frequently used as the first line of treatment for depression. However, their effectiveness is highly variable and influenced by genetic factors. Recently, pharmacogenetic studies, including candidate-gene, genome-wide association studies or polygenic risk scores, have attempted to uncover the genetic architecture of antidepressant response. Genetic variants in at least 27 genes are linked to antidepressant treatment response in both coding and non-coding genomic regions, but evidence is largely inconclusive due to the high polygenicity of the trait and limited cohort sizes in published studies. Future studies should increase the number and diversity of participants to yield sufficient statistical power to characterize the genetic underpinnings and biological mechanisms of treatment response, improve results generalizability and reduce racial health-related inequities.