Rising concerns about social isolation and loneliness globally have highlighted the need for a greater understanding of their mental and physical health implications. Robust evidence documents social connection factors as independent predictors of mental and physical health, with some of the strongest evidence on mortality. Although most data are observational, evidence points to directionality of effects, plausible pathways, and in some cases a causal link between social connection and later health outcomes. Societal trends across several indicators reveal increasing rates of those who lack social connection, and a significant portion of the population reporting loneliness. The scientific study on social isolation and loneliness has substantially extended over the past two decades, particularly since 2020; however, its relevance to health and mortality remains underappreciated by the public. Despite the breadth of evidence, several challenges remain, including the need for a common language to reconcile the diverse relevant terms across scientific disciplines, consistent multi-factorial measurement to assess risk, and effective solutions to prevent and mitigate risk. The urgency for future health is underscored by the potentially longer-term consequences of the COVID-19 pandemic, and the role of digital technologies in societal shifts, that could contribute to further declines in social, mental and physical health. To reverse these trends and meet these challenges, recommendations are offered to more comprehensively address gaps in our understanding, and to foster social connection and address social isolation and loneliness.

The concept of ultra-high risk for psychosis (UHR) has been at the forefront of psychiatric research for several decades, with the ultimate goal of preventing the onset of psychotic disorder in high-risk individuals. Orygen (Melbourne, Australia) has led a range of observational and intervention studies in this clinical population. These datasets have now been integrated into the UHR 1000+ cohort, consisting of a sample of 1,245 UHR individuals with a follow-up period ranging from 1 to 16.7 years. This paper describes the cohort, presents a clinical prediction model of transition to psychosis in this cohort, and examines how predictive performance is affected by changes in UHR samples over time. We analyzed transition to psychosis using a Cox proportional hazards model. Clinical predictors for transition to psychosis were investigated in the entire cohort using multiple imputation and Rubin's rule. To assess performance drift over time, data from 1995-2016 were used for initial model fitting, and models were subsequently validated on data from 2017-2020. Over the follow-up period, 220 cases (17.7%) developed a psychotic disorder. Pooled hazard ratio (HR) estimates showed that the Comprehensive Assessment of At-Risk Mental States (CAARMS) Disorganized Speech subscale severity score (HR=1.12, 95% CI: 1.02-1.24, p=0.024), the CAARMS Unusual Thought Content subscale severity score (HR=1.13, 95% CI: 1.03-1.24, p=0.009), the Scale for the Assessment of Negative Symptoms (SANS) total score (HR=1.02, 95% CI: 1.00-1.03, p=0.022), the Social and Occupational Functioning Assessment Scale (SOFAS) score (HR=0.98, 95% CI: 0.97-1.00, p=0.036), and time between onset of symptoms and entry to UHR service (log transformed) (HR=1.10, 95% CI: 1.02-1.19, p=0.013) were predictive of transition to psychosis. UHR individuals who met the brief limited intermittent psychotic symptoms (BLIPS) criteria had a higher probability of transitioning to psychosis than those who met the attenuated psychotic symptoms (APS) criteria (HR=0.48, 95% CI: 0.32-0.73, p=0.001) and those who met the Trait risk criteria (a first-degree relative with a psychotic disorder or a schizotypal personality disorder plus a significant decrease in functioning during the previous year) (HR=0.43, 95% CI: 0.22-0.83, p=0.013). Models based on data from 1995-2016 displayed good calibration at initial model fitting, but showed a drift of 20.2-35.4% in calibration when validated on data from 2017-2020. Large-scale longitudinal data such as those from the UHR 1000+ cohort are required to develop accurate psychosis prediction models. It is critical to assess existing and future risk calculators for temporal drift, that may reduce their utility in clinical practice over time.

Psychotherapies are efficacious in the treatment of depression, albeit only with a moderate effect size. It is hoped that personalization of treatment can lead to better outcomes. The network theory of psychopathology offers a novel approach suggesting that symptom interactions as displayed in person-specific symptom networks could guide treatment planning for an individual patient. In a sample of 254 patients with chronic depression treated with either disorder-specific or non-specific psychotherapy for 48 weeks, we investigated if person-specific symptom networks predicted observer-rated depression severity at the end of treatment and one and two years after treatment termination. Person-specific symptom networks were constructed based on a time-varying multilevel vector autoregressive model of patient-rated symptom data. We used statistical parameters that describe the structure of these person-specific networks to predict therapy outcome. First, we used symptom centrality measures as predictors. Second, we used a machine learning approach to select parameters that describe the strength of pairwise symptom associations. We found that information on person-specific symptom networks strongly improved the accuracy of the prediction of observer-rated depression severity at treatment termination compared to common covariates recorded at baseline. This was also shown for predicting observer-rated depression severity at one- and two-year follow-up. Pairwise symptom associations were better predictors than symptom centrality parameters for depression severity at the end of therapy and one year later. Replication and external validation of our findings, methodological developments, and work on possible ways of implementation are needed before person-specific networks can be reliably used in clinical practice. Nevertheless, our results indicate that the structure of person-specific symptom networks can provide valuable information for the personalization of treatment for chronic depression.

People with physical diseases are reported to be at elevated risk of subsequent mental disorders. However, previous studies have considered only a few pairs of conditions, or have reported only relative risks. This study aimed to systematically explore the associations between physical diseases and subsequent mental disorders. It examined a population-based cohort of 7,673,978 people living in Denmark between 2000 and 2021, and followed them for a total of 119.3 million person-years. The study assessed nine broad categories of physical diseases (cardiovascular, endocrine, respiratory, gastrointestinal, urogenital, musculoskeletal, hematological and neurological diseases, and cancers), encompassing 31 specific diseases, and the subsequent risk of mental disorder diagnoses, encompassing the ten ICD-10 groupings (organic, including symptomatic, mental disorders; mental disorders due to psychoactive substance use; schizophrenia and related disorders; mood disorders; neurotic, stress-related and somatoform disorders; eating disorders; personality disorders; intellectual disabilities; pervasive developmental disorders; and behavioral and emotional disorders with onset usually occurring in childhood and adolescence). Using Poisson regression, the overall and time-dependent incidence rate ratios (IRRs) for pairs of physical diseases and mental disorders were calculated, adjusting for age, sex and calendar time. Absolute risks were estimated with the Aalen-Johansen estimator. In total, 646,171 people (8.4%) were identified as having any mental disorder during follow-up. All physical diseases except cancers were associated with an elevated risk of any mental disorder. For the nine broad pairs of physical diseases and mental disorders, the median point estimate of IRR was 1.51 (range: 0.99-1.84; interquartile range: 1.29-1.59). The IRRs ranged from 0.99 (95% CI: 0.98-1.01) after cancers to 1.84 (95% CI: 1.83-1.85) after musculoskeletal diseases. Risks varied over time after the diagnosis of physical diseases. The cumulative mental disorder incidence within 15 years after diagnosis of a physical disease varied from 3.73% (95% CI: 3.67-3.80) for cancers to 10.19% (95% CI: 10.13-10.25) for respiratory diseases. These data document that most physical diseases are associated with an elevated risk of subsequent mental disorders. Clinicians treating physical diseases should constantly be alert to the possible development of secondary mental disorders.

Psychiatry shares most ethical issues with other branches of medicine, but also faces special challenges. The Code of Ethics of the World Psychiatric Association offers guidance, but many mental health care professionals are unaware of it and the principles it supports. Furthermore, following codes of ethics is not always sufficient to address ethical dilemmas arising from possible clashes among their principles, and from continuing changes in knowledge, culture, attitudes, and socio-economic context. In this paper, we identify topics that pose difficult ethical challenges in contemporary psychiatry; that may have a significant impact on clinical practice, education and research activities; and that may require revision of the profession's codes of ethics. These include: the relationships between human rights and mental health care, research and training; human rights and mental health legislation; digital psychiatry; early intervention in psychiatry; end-of-life decisions by people with mental health conditions; conflicts of interests in clinical practice, training and research; and the role of people with lived experience and family/informal supporters in shaping the agenda of mental health care, policy, research and training. For each topic, we highlight the ethical concerns, suggest strategies to address them, call attention to the risks that these strategies entail, and highlight the gaps to be narrowed by further research. We conclude that, in order to effectively address current ethical challenges in psychiatry, we need to rethink policies, services, training, attitudes, research methods and codes of ethics, with the concurrent input of a range of stakeholders, open minded discussions, new models of care, and an adequate organizational capacity to roll-out the implementation across routine clinical care contexts, training and research.

Features of autism spectrum disorder, attention-deficit/hyperactivity disorder, learning disorders, intellectual disabilities, and communication and motor disorders usually emerge early in life and are associated with atypical neurodevelopment. These "neurodevelopmental conditions" are grouped together in the DSM-5 and ICD-11 to reflect their shared characteristics. Yet, reliance on categorical diagnoses poses significant challenges in both research and clinical settings (e.g., high co-occurrence, arbitrary diagnostic boundaries, high within-disorder heterogeneity). Taking a transdiagnostic dimensional approach provides a useful alternative for addressing these limitations, accounting for shared underpinnings across neurodevelopmental conditions, and characterizing their common co-occurrence and developmental continuity with other psychiatric conditions. Neurodevelopmental features have not been adequately considered in transdiagnostic psychiatric frameworks, although this would have fundamental implications for research and clinical practices. Growing evidence from studies on the structure of neurodevelopmental and other psychiatric conditions indicates that features of neurodevelopmental conditions cluster together, delineating a "neurodevelopmental spectrum" ranging from normative to impairing profiles. Studies on shared genetic underpinnings, overlapping cognitive and neural profiles, and similar developmental course and efficacy of support/treatment strategies indicate the validity of this neurodevelopmental spectrum. Further, characterizing this spectrum alongside other psychiatric dimensions has clinical utility, as it provides a fuller view of an individual's needs and strengths, and greater prognostic utility than diagnostic categories. Based on this compelling body of evidence, we argue that incorporating a new neurodevelopmental spectrum into transdiagnostic frameworks has considerable potential for transforming our understanding, classification, assessment, and clinical practices around neurodevelopmental and other psychiatric conditions.