日内瓦大学医院

Background The glymphatic system, a perivascular pathway supporting cerebrospinal and interstitial fluid exchange and clearance, has been implicated in neurodegenerative and psychiatric disorders. Diffusion tensor imaging along the perivascular space (DTI-ALPS) provides a non-invasive proxy for glymphatic-related processes, yet its role in psychiatric conditions remains uncertain. Methods Following PRISMA guidelines, we systematically searched PubMed, PsycNET, and Embase for articles published up to September 25th, 2025. The protocol was pre-registered in PROSPERO (CRD420251155430). Studies reporting diffusion-based indices of glymphatic function in psychiatric populations were included. Standardised mean differences (Hedges’ g) were calculated for patient-control comparisons and pooled using random-effects models. Heterogeneity, methodological moderators, and risk of bias were assessed. Results Thirty-two studies met inclusion criteria for the systematic review, covering major psychiatric groups including mood disorders, autism spectrum disorder, ADHD, psychosis, sleep disorders, and substance-related conditions. Twenty-four studies (n = 2855; 1503 patients, 1352 controls) reporting bilateral DTI-ALPS measures were included in the meta-analysis. The pooled random-effects model revealed a significant transdiagnostic reduction in DTI-ALPS index in psychiatric populations compared with healthy controls (Hedges’ g = –0.78, 95% CI –1.01 to –0.55, p < 0.0001). Between-study heterogeneity was substantial (I² = 86.3%), and there was evidence of small-study effects. Conclusions Bilateral DTI-ALPS index showed a robust but heterogeneous reduction across psychiatric disorders. These results suggest that impairments of perivascular diffusion, as indexed by DTI-ALPS, may reflect a shared transdiagnostic pattern across psychiatric conditions. Harmonised imaging pipelines and multimodal validation are needed to clarify the biological and clinical significance of these findings.

Background Implementation and long-term persistence of guideline-directed medical therapy (GDMT) following ST-segment elevation myocardial infarction (STEMI) remain suboptimal. We assessed the implementation and persistence of four-class GDMT after STEMI and its association with outcomes. Methods We performed a patient-level pooled analysis of three randomised trials of patients undergoing percutaneous coronary intervention for STEMI. GDMT was defined by the number of drug classes prescribed (0–4). External validation was conducted using a real-world PCI registry. Findings The study included 2270 patients from the randomised trials and 5643 from a real-world PCI registry. More than two-thirds of patients received all four GDMT classes at discharge (ranging from 66.9% [398/595] to 83.5% [339/406]), but this proportion declined over 5 years (ranging from 40.5% [408/1008] to 53.1% [276/520]). In exploratory analyses, higher GDMT class at discharge was associated with a lower risk of the composite of cardiovascular death, stroke, or myocardial infarction at 5 years (adjusted HR [95% CI] for class 4 versus 0–2: 0.49 [0.33–0.72]; class 3 versus 0–2: 0.49 [0.28–0.85]). In a time-dependent analysis, higher GDMT class during follow-up was associated with a lower risk of the composite outcome (adjusted IRR [95% CI] for class 4 versus 0–2: 0.71 [0.52–0.95]; class 3 versus 0–2: 0.59 [0.41–0.86]). Similar longitudinal patterns were observed in the real-world PCI registry. Interpretation Although the majority of patients received all four GDMT classes at discharge after STEMI, this proportion declined during long-term follow-up. In exploratory analyses, higher GDMT class was associated with a lower risk of cardiovascular events. Funding No specific funding was received for the present study. Funding for the individual trials and registry is reported in the Acknowledgements.

In youth–i.e., adolescents and young adults–mental disorders represent a major burden on well-being and quality of life. This scientometric study aimed to map research trends concerning any mental disorder in youth. We searched the Web of Science Core Collection for studies published between 01/01/1985 and 28/08/2025, using keywords on mental disorders and youth. Co-citation reference networks were extracted. R-bibliometrix/CiteSpace were used. 65,684 studies were included, forming a well-structured co-reference network. A trend in the 1980s concerned the prevalence/outcomes of specific mental disorders such as depressive and anxiety disorders. In the 1990s/2000s, research trends concerned assessment tools, pharmacotherapy (e.g., antidepressants, antipsychotics), bipolar disorders and schizophrenia. In the 2010s, early intervention for psychosis was a dominant focus, influencing prevention in mental health. The 2010s also focused on ADHD, eating disorders, gaming disorder, autism, depression and suicide, fMRI, and transgender health, beyond gender dysphoria. In the 2020s, a recent trend has increasingly focused on global evidence on the age at onset and burden across mental disorders, access to care, network analysis, and COVID-19. Over time, research has broadened the focus from adolescence to youth across mental health problems. Both disorder-specific and transdiagnostic research trends were observed. Novel trends in 2023–2025 included substance use, lifestyle psychiatry, evidence synthesis and non-suicidal self-injury. Most studies were conducted in Western countries. Underrepresented research areas included the involvement of youth with lived/living experience in identifying meaningful outcomes, open science, implementation research, economic evaluations, machine-learning approaches, and social determinants of mental health.

Global population aging and increased chronic stress due to numerous mass disasters including those related to pandemics, climate change, war, displacement, and political unrest all challenge our collective resilience, with a growing burden of late-life neuropsychiatric and neurodegenerative disorders placing unprecedented demands on health and social systems worldwide. With these considerations in mind, understanding and promoting brain health is becoming a priority for the prevention of neuropsychiatric disorders across the lifespan. Brain health represents a dynamic balance of neural, cognitive, and emotional processes that determine resilience to neuropsychiatric illness. In later life, this balance becomes particularly critical as neurobiological and psychosocial stressors converge to shape trajectories of neuropsychiatric and neurodegenerative disorders. This review synthesizes current evidence on the determinants of brain health in aging, emphasizing resilience as a modifiable pathway linking neuropsychiatric illness risk and prevention. We integrate insights from neuroscience, lifestyle medicine, geroscience, and social determinants of health to emphasize the value of a whole-person, life-course approach. Particular attention is given to the interplay between stress physiology, interoceptive regulation, emotional resilience, and cognitive and brain reserve across the aging continuum. Emerging frameworks including brain clocks, precision biomarkers, digital phenotyping, and artificial intelligence, are considered as tools for risk stratification, early detection, and personalized intervention. By linking resilience mechanisms to measurable biological indices, we argue for the integration of neurobiological, psychological, behavioral, and sociocultural domains to inform next-generation strategies in neuropsychopharmacology, prevention science, and the promotion of healthy brain aging.

Osteoporosis is a major and growing health concern in the Asia-Pacific region, y et it remains widely underdiagnosed and undertreated due to limited access to dual-energy X-ray absorptiometry (DXA) in many areas. Artificial intelligence (AI) offers new opportunities to improve osteoporosis screening and management, but unvalidated tools pose risks of inconsistent care. This consensus was developed to provide regionally harmonized guidance on the safe, effective, and equitable use of AI in osteoporosis care. Purpose The aim of this work was to establish expert consensus recommendations on the role of AI in osteoporosis screening and management in the Asia-Pacific region. Key objectives were to define appropriate applications of AI (e.g., imaging-based bone assessment and fracture risk prediction) and specify minimum standards for validation and reporting, addressing region-specific implementation challenges and ensuring that AI use aligns with clinical guidelines and ethical principles. Methods This consensus was developed through multidisciplinary collaboration among experts across the Asia-Pacific region. Each participant reviewed draft statements, contributed feedback during virtual meetings, and provided insights based on clinical experience and current evidence. Consensus was reached iteratively until full agreement was achieved for all statements. The process integrated global best practices and regional adaptations, drawing from peer-reviewed studies, international AI guidelines, and local fracture registry data. The final recommendations emphasize the validation, transparency, and ethical implementation of AI within regional healthcare systems, ensuring compatibility with local regulations. Ultimately, twelve consensus statements were established to guide the responsible use of AI for osteoporosis screening and management in the Asia-Pacific region. Results The panel produced 12 consensus statements covering the role of AI as an adjunct for opportunistic osteoporosis screening rather than a diagnostic tool, requirements for imaging quality and AI model transparency, standards for validation and performance reporting, integration of AI with clinical risk stratification, demonstration of clinical utility in real-world settings, adherence to data protection laws and ethical AI principles, training of clinicians in AI use, strategies for implementation and monitoring (including post-market surveillance and feedback loops), and recognition of technical, clinical, and equity limitations of AI. All 12 statements give extensive recommendations for using AI to improve osteoporosis management while ensuring patient safety, accuracy, and equity. Conclusion This first Asia-Pacific consensus on AI in osteoporosis concludes that AI, when appropriately validated and implemented, can help bridge the osteoporosis care gap by identifying high-risk patients who would otherwise remain undiagnosed, thus facilitating earlier intervention. It emphasizes that AI should complement-not replace-standard diagnostic methods and clinical judgment. The guidance emphasizes validation, transparency, and ethical oversight to facilitate early intervention while minimizing risks associated with unvalidated or premature AI adoption.