Acta Psychiatrica Scandinavica最新文献

The study conducted by Tapio Paljärvi et al., titled “Cardiovascular mortality in bipolar disorder: population-based cohort study,” sheds crucial light on the alarming rates of cardiovascular disease mortality among individuals with bipolar disorder.¹ The findings underscore a significant excess in mortality due to various cardiovascular causes, including coronary artery disease, cardiomyopathy, and hypertensive heart disease. This issue is part of a larger problem for people with severe mental illnesses (SMI), such as schizophrenia, bipolar disorders, and major depressive disorders. Individuals with SMI die up to 15 years earlier than the general population, and around 70% of this mortality gap is due to physical disorders.² Increased risk of physical disorders in those with mental disorders is due to reduced access to adequate physical health monitoring, care and prevention,^3-5 increased risk of adverse health behaviors⁶ such as sedentary behavior, poor diet, and smoking, comorbid substance abuse, and side effects of psychiatric medications.⁷

Despite the availability of clinical practice guidelines and evidence-based interventions aimed at reducing excess mortality rates due to physical comorbidities in persons with SMI, the problem still persists. There is a crucial need to better understand how to implement clinical practice guidelines and evidence-based interventions successfully in the real world. Individuals with comorbid mental and physical disorders face specific challenges in service organization, requiring complex treatment and intense resource utilization.⁸ The management of multimorbidity is recognized to be complex, with a high treatment burden in terms of understanding and self-managing the conditions, attending multiple appointments, and managing complex drug regimens.⁹ Evidence suggests that a key part of the problem is the lack of integration of care across service settings. There is major fragmentation in how care is coordinated between family doctors and hospitals, between physical and mental health care, and across health and social care. As proposed by Tapio Paljärvi et al.¹ and other authors,¹⁰ person-centered and integrated care models are a critical step to support effective implementation approaches to translate evidence into practice.

However, despite local and innovative experiences in several countries, the large-scale implementation of integrated care models faces numerous barriers and conservatisms. These obstacles hinder progress and maintain the status quo, perpetuating major health inequalities in individuals with SMI. A call for action in Europe, similar to initiatives already established in the United States,¹¹ is necessary. Currently, there are two main types

Background: Affective states influence the sympathetic nervous system, inducing variations in electrodermal activity (EDA), however, EDA association with bipolar disorder (BD) remains uncertain in real-world settings due to confounders like physical activity and temperature. We analysed EDA separately during sleep and wakefulness due to varying confounders and potential differences in mood state discrimination capacities.

Methods: We monitored EDA from 102 participants with BD including 35 manic, 29 depressive, 38 euthymic patients, and 38 healthy controls (HC), for 48 h. Fifteen EDA features were inferred by mixed-effect models for repeated measures considering sleep state, group and covariates.

Results: Thirteen EDA feature models were significantly influenced by sleep state, notably including phasic peaks (p < 0.001). During wakefulness, phasic peaks showed different values for mania (M [SD] = 6.49 [5.74, 7.23]), euthymia (5.89 [4.83, 6.94]), HC (3.04 [1.65, 4.42]), and depression (3.00 [2.07, 3.92]). Four phasic features during wakefulness better discriminated between HC and mania or euthymia, and between depression and euthymia or mania, compared to sleep. Mixed symptoms, average skin temperature, and anticholinergic medication affected the models, while sex and age did not.

Conclusion: EDA measured from awake recordings better distinguished between BD states than sleep recordings, when controlled by confounders.

Background: Knowledge graphs (KGs) remain an underutilized tool in the field of psychiatric research. In the broader biomedical field KGs are already a significant tool mainly used as knowledge database or for novel relation detection between biomedical entities. This review aims to outline how KGs would further research in the field of psychiatry in the age of Artificial Intelligence (AI) and Large Language Models (LLMs).

Methods: We conducted a thorough literature review across a spectrum of scientific fields ranging from computer science and knowledge engineering to bioinformatics. The literature reviewed was taken from PubMed, Semantic Scholar and Google Scholar searches including terms such as "Psychiatric Knowledge Graphs", "Biomedical Knowledge Graphs", "Knowledge Graph Machine Learning Applications", "Knowledge Graph Applications for Biomedical Sciences". The resulting publications were then assessed and accumulated in this review regarding their possible relevance to future psychiatric applications.

Results: A multitude of papers and applications of KGs in associated research fields that are yet to be utilized in psychiatric research was found and outlined in this review. We create a thorough recommendation for other computational researchers regarding use-cases of these KG applications in psychiatry.

Conclusion: This review illustrates use-cases of KG-based research applications in biomedicine and beyond that may aid in elucidating the complex biology of psychiatric illness and open new routes for developing innovative interventions. We conclude that there is a wealth of opportunities for KG utilization in psychiatric research across a variety of application areas including biomarker discovery, patient stratification and personalized medicine approaches.