PURPOSE OF REVIEW:Arterial spin labeling (ASL) MRI noninvasively quantifies cerebral blood flow (CBF), but its role as a mechanistic biomarker and surrogate endpoint in trials of cognitive impairment remains undefined. This review evaluates evidence linking regional CBF to cognition across Alzheimer's disease, mild cognitive impairment (MCI), and cerebral small vessel disease (SVD), with particular emphasis on CADASIL as a mechanistically informative model. RECENT FINDINGS:Multipostlabeling-delay ASL, spatial coefficient of variation metrics, and harmonized processing pipelines have improved reproducibility. Cross-sectional and longitudinal studies consistently link reduced CBF to cognitive impairment, with baseline CBF predicting cognitive decline, MCI-to-Alzheimer's disease conversion, white matter hyperintensity progression, and vascular events. In CADASIL, CBF is independently associated with cognitive performance and predicts subcortical hyperintensity progression over 2 years. Interventional data from SPRINT MIND, PRESERVE, and pilot lecanemab studies indicate that ASL-CBF is responsive to therapy. SUMMARY:Within the FDA-NIH BEST framework, ASL-CBF appears to meet several criteria consistent with a reasonably likely surrogate endpoint, capturing upstream, potentially reversible hemodynamic dysfunction beyond established structural markers. CADASIL provides an optimal context for qualifications. Larger clinical trials linking treatment-induced CBF changes to clinical benefit, further protocol harmonization, and regulatory engagement are needed to translate ASL-CBF into a validated trial endpoint.
PURPOSE OF REVIEW:This review synthesizes research on orthorexia nervosa published in 2025 and early 2026. RECENT FINDINGS:Orthorexia nervosa is characterized by an excessive focus on healthy eating. Widely used measures, particularly the ORTO-15, have been criticized for poor reliability, prompting the development of new assessment instruments. Recent prevalence estimates of orthorexia nervosa vary widely. Meta-analyses based on ORTO-15 assessment in highly heterogeneous settings suggest an overall prevalence of 27.5%, with men and women equally at risk. However, more recent studies indicate a substantially lower prevalence, 0.5-1.5% for orthorexia nervosa and 7.5-15% for elevated orthorexic tendencies. High-risk groups include dietitians, nutrition and sport science students, athletes, and individuals required to monitor their diet. Dietary patterns involving plant-based eating are associated with orthorexia. Other factors associated with increased risk include societal stressors, such as pandemics and climate concerns, social media use, particularly image-based platforms, and psychological traits, such as perfectionism, emotion regulation difficulties, obsessive-compulsive features, alexithymia, and autistic traits. Evidence on long-term outcomes and treatment remains limited, although psychoeducation appears promising. SUMMARY:Orthorexia nervosa is an eating disorder that also reflects broader nutritional, sociocultural, and moral trends. Longitudinal research is needed on its course and management.
PURPOSE OF REVIEW:This review examines how prenatal inflammation may contribute to psychiatric and neurodevelopmental disorders, with a focus on maternal immune activation, microglial programming, and epigenetic mechanisms. RECENT FINDINGS:Recent studies have found that inflammation during pregnancy is associated with changes in brain development and neurodevelopmental outcomes in children. Animal research indicates that maternal immune activation can cause long-lasting changes in gene expression, neural circuitry, and microglial activity. Two-hit models suggest that prenatal inflammation may prime microglia, making the brain more vulnerable to later stress, infections, or adversity. Interventions that target microglia, reduce inflammation, or provide enriched environments may reduce downstream behavioral and neurodevelopmental effects. SUMMARY:Inflammation in early life may increase the risk of psychiatric disorders by altering placental signals, fetal brain development, and neuroimmune regulation. Preventing inflammation during pregnancy and reducing stress and adversity after birth may help protect against later mental health problems.
PURPOSE OF REVIEW:Peer support interventions have emerged as a promising adjunct to the treatment of personality disorders, particularly borderline personality disorder (BPD). This review summarizes the rapidly expanding literature on peer support in personality disorders and proposes a dimensional reinterpretation of its potential mechanisms of action through dimensional models of personality functioning. RECENT FINDINGS:Recent literature supports the feasibility, acceptability, and preliminary efficacy of peer support interventions in BPD. Benefits have been reported across recovery-related outcomes, including validation, hope, empowerment, social connectedness, emotion regulation, and, more recently, BPD symptoms and personality functioning. Despite these advances, evidence remains almost exclusively restricted to BPD. Reinterpreting literature through a dimensional framework suggests that the therapeutic processes most consistently reported may influence core domains of personality functioning, including identity, self-direction, empathy, and intimacy. SUMMARY:Peer support is a feasible and promising complement to evidence-based treatments for personality disorders, provided that adequate training, supervision, role clarity, and organizational support are ensured. A dimensional perspective offers a coherent framework for understanding its potential transdiagnostic relevance by targeting impairments in personality functioning rather than diagnosis-specific symptoms. Future research should extend investigations beyond BPD and clarify the mechanisms through which peer support contributes to recovery.
PURPOSE OF REVIEW:The aim of this study was to promote the universal recommendation that lithium carbonate remains the first-choice mood stabilizer for maintenance treatment of older adults with bipolar disorder (OABD) as concluded in a Delphi survey of the International Society for Bipolar Disorders (ISBD) as well as all other official guidelines. RECENT FINDINGS:Prescriptions of lithium have continued to decline in OABD worldwide despite these recommendations and in the face of evidence for its effectiveness as a mood stabilizer and lithium's potential for suicide prevention and neuroprotection from dementia. This study describes the results of a recent literature review of the role of lithium in OABD, finding further support from the recommended therapeutic range of 0.4-0.8 mmol/l for those aged 60-79 years and 0.4-0.7 mmol/l for those 80 and over, and summarizing prescribing considerations. We also provide evidence of the practicality of measuring and reporting age-specific ranges from a laboratory medicine perspective. Finally, we report on an international initiative of the ISBD OABD task force that aims to implement the lower recommended therapeutic ranges for maintenance lithium treatment as reported by laboratories around the world. SUMMARY:Initial efforts in Canada have resulted in the implementation of widespread changes in laboratory reporting. International efforts to implement separate and lower therapeutic ranges for lithium in OABD are continuing; so far, 18 dissemination attempts have yielded three successful implementations. This initiative aims to provide well tolerated and effective lithium treatment for OABD while reducing the risk of toxicity in this vulnerable group of patients.
PURPOSE OF REVIEW:To synthesize longitudinal evidence of mental health outcomes, including prepandemic symptom measurements, among individuals with preexisting mental disorders during the COVID-19 pandemic. RECENT FINDINGS:Symptom exacerbation commonly occurred in bulimia nervosa, binge-eating disorder, substance use disorders and in obsessive-compulsive psychopathology related to contamination, while symptom improvement was consistently observed in bipolar disorders. In autism spectrum, schizophrenia spectrum and posttraumatic stress disorders, contrasting symptom patterns were reported, possibly reflecting differences in preexisting demographic and clinical profiles and contextual influences. Symptoms in depressive and anxiety disorders, anorexia nervosa and obsessive-compulsive psychopathology unrelated to contamination remained largely stable. Across most disorders, individuals with chronic mental disorders or higher prepandemic symptom levels more frequently showed stable or improving trajectories than those with milder presentations or recent-onset disorders. SUMMARY:Given the small number of methodologically diverse studies, definitive statements regarding differential impact of the pandemic across disorders or underlying mechanisms remain premature. Nevertheless, current evidence does not indicate a generalised deterioration in individuals with preexisting mental disorders, including those with the highest prepandemic symptom burden. While some disorder-specific differences emerged, likely reflecting interactions between contextual changes and preexisting disorder processes, the overall picture is more nuanced than frequently portrayed in pandemic-related literature.
PURPOSE OF REVIEW:Harmful behaviours and suicidality are often linked to mental disorders, making it crucial to gain a deeper understanding of these issues. One significant area of focus is bipolar disorder in older adults, a condition that requires further exploration to better address its impact on this age group. RECENT FINDINGS:Longer hospital stays increased harmful behaviour among older adults with bipolar disorder. Older adults with bipolar disorder have lower suicide rates than younger individuals, possibly due to heightened risks shortly after diagnosis, so, it makes it important to pay attention to cases of late-onset bipolar disorder. Early-onset bipolar disorder is often genetically influenced, while late-onset bipolar disorder was more affected by environmental factors, with cognitive impairments leading to poorer functioning. The early years following a diagnosis of dementia associated with bipolar disorder show a higher risk of suicide. SUMMARY:Individuals experiencing manic episodes of bipolar disorder often display a heightened propensity for harmful behaviours. In contrast, during depressive episodes, these individuals are at an increased risk of contemplating or attempting suicide. Research highlights that the threat of suicide is particularly pronounced in the initial years after a bipolar disorder diagnosis, underscoring the critical need for vigilant monitoring during this vulnerable period. This concern is especially significant for cases of late-onset bipolar disorder in older adults, where the unique complexities of age may compound the challenges and risks associated with the condition.
PURPOSE OF REVIEW:The current review discusses the most recent developments in pharmacological maintenance treatment of older age bipolar disorder. RECENT FINDINGS:Older age bipolar disorder (OABD) applies to patients with bipolar disorder aged 50 years and over, a threshold reflecting the association between bipolar disorder and premature aging. Although medication effective in younger age bipolar disorder (YABD) are generally assumed to remain effective in older adults, the disease course becomes more complex in time and age-related changes in pharmacokinetics and pharmacodynamics require careful consideration. Despite the growing aging population, research targeting OABD remains limited. Therefore, the Global Aging and Geriatric Experiments in Bipolar Disorder (GAGE-BD) was initiated by the ISBD (International Society for Bipolar Disorder) OABD taskforce in 2016. Several recent studies on pharmacological maintenance therapy originated from this initiative and are discussed in this narrative review alongside other recent findings. However, no intervention studies have been published in the last 18 months, and the current evidence base largely consists of prospective cohort studies and cross-sectional data. SUMMARY:When looking at maintenance therapy in OABD, lithium remains first line of treatment but with an adjusted lower therapeutic range. Lithium therapy should not be withheld due to concerns of future kidney function decline.
PURPOSE OF REVIEW:This narrative review synthesizes recent empirical studies on the mental health impacts of wildfires on children and adolescents, with specific attention to how urbanization modifies risk, access, and recovery. It addresses a critical gap in the literature by examining spatial and infrastructural inequities in post-disaster mental health outcomes while identifying key gaps in this emerging research domain. RECENT FINDINGS:Youth exposed to wildfires exhibit high rates of PTSD, depression, and anxiety, often persisting well beyond the acute event. Urbanization intensifies these risks by expanding the wildland-urban interface and stratifying access to care. Rural and peri-urban youth face the most significant disruption, from displacement to delayed service access, yet are the least likely to receive sustained, culturally responsive interventions. This review identified a limited evidence base: among studies published since June 2024 that explicitly examined youth mental health and urbanization in wildfire contexts, only three peer-reviewed empirical studies met inclusion criteria. Among the broader wildfire-youth literature, only one employed longitudinal tracking, none used systematic geospatial mapping, and few included Indigenous or marginalized populations. Intervention models such as telehealth, allied health services, and trauma-informed school support show promise but remain unevenly implemented. SUMMARY:The psychological burden of wildfires on youth is enhanced by trauma exposure and spatial inequity. The scarcity of research explicitly addressing urbanization's role in youth wildfire mental health represents a critical gap. Urban planning and mental health policy must be integrated to ensure that services reach underserved regions. Future research should incorporate urban-rural typologies, geospatial analysis, cultural dimensions, and developmental trajectories to inform equitable, place-based interventions for wildfire-exposed youth.
PURPOSE OF REVIEW:To present an up-to-date systematic review and meta-analysis on the risk of dementia in patients with bipolar disorder. RECENT FINDINGS:We included 22 studies investigating the risk of developing dementia among patients with bipolar disorder of which 21 studies provided data for the meta-analysis, comprising a total of 185 306 patients with bipolar disorder and 10 702 744 participants without bipolar disorder. Random effects meta-analysis showed an increased risk of being diagnosed with dementia in patients diagnosed with bipolar disorder [odds ratio (OR): 3.13 (95% CI: 2.40-4.09), I2 = 99.8%, 21 studies]. In a secondary analysis excluding studies with overlapping populations, the pooled effect estimate was OR = 2.60 (95% CI 1.76-3.85, I2 = 99.9%, 11 studies). The evidence was uncertain due to substantial and unexplained between-study heterogeneity, methodological limitations, and risk of bias in the included studies. SUMMARY:Patients with bipolar disorder have an increased risk of developing dementia, but the evidence is uncertain.
PURPOSE OF REVIEW:With this article, we aimed to outline the most recent data, official statistics, and research findings significant to understand current mental health trends in Russia. Furthermore, we, to the best of our knowledge, for the first time since the publication 'wave' of the pandemic-related articles, address mental health in Russia with particular focus on urbanicity. RECENT FINDINGS:Major issues of mental health of the youth and the elderly in Russian cities, suicide and its growing rates in certain populations, maternal mental health, and medical personnel mental health were covered in this article. Beyond prevalence, access to care, and changes attributable to urbanization, we briefly discuss how electronic media became an emerging source of data for Russian researchers who investigated mental health in megacities, and address insights on associations between distance of migration track and risk of mental health conditions. SUMMARY:Reported overview of findings can inform future research, policies, and mental health interventions tailored to the complexities of urban populations in Russia, and globally.
PURPOSE OF REVIEW:Amidst rapid global urbanization, understanding the association between urban planning and mental health is crucial. This review synthesizes fragmented evidence on six core elements - walkability, green spaces, blue spaces, population density, public transport accessibility, and road design-to address gaps in causality and equity, providing timely insights for creating psychologically supportive urban environments. RECENT FINDINGS:Evidence consistently indicates a moderate protective association between urban green space and mental health. Findings for walkability and population density are highly heterogeneous, often moderated by socioeconomic factors and air pollution. Research on blue spaces and road design remains limited, highlighting significant variability and context-dependence across planning elements. SUMMARY:Methodological challenges, such as establishing causality, simplistic exposure metrics, and inadequate consideration of equity-constrain current evidence. Future research should prioritize longitudinal and natural experiment designs, dynamic multiexposure assessments, and explicit equity integration to generate actionable guidance for urban planning that supports mental well being equitably.
PURPOSE OF REVIEW:Current approaches to preventing drug-related harms remain suboptimal. This review describes components of a systems innovation framework for addiction care designed to address escalating drug-related harms and fragmented service delivery through a multidisciplinary, ecosystem-driven approach. RECENT FINDINGS:Innovative approaches to addiction medicine are needed that integrate clinical research with entrepreneurial execution to address three priority areas of need: real-time risk mitigation using digital phenotyping and wearable biosensors for overdose detection; precision surveillance utilizing AI-informed data ecosystems and wastewater analysis; and next-generation therapeutics, including neuromodulation and nanotechnology-driven drug delivery. This creates an innovative "biopsychotechnological" treatment paradigm that moves beyond traditional clinical boundaries. SUMMARY:This framework provides a roadmap for transitioning addiction care from reactive clinical and risk management to innovative and proactive precision medicine. For research, it necessitates a shift toward interdisciplinary validation of "biopsychotechnological" models that bridge bench science and community implementation. For clinical practice, this framework offers a blueprint for integrating real-time biosensing and advanced therapeutics into patient care, promising to reduce mortality and support sustainable recovery through ecosystem-informed and individualized care.
PURPOSE OF REVIEW:Tobacco use remains the leading preventable cause of death worldwide, while the rise of electronic nicotine products has sparked a new wave of initiation. The urgent need for scalable, multilevel tobacco-control interventions converges with the rapid advances in artificial intelligence (AI). This article reviews the most recent literature on integrating machine- and human expertise to enhance tobacco-cessation strategies within a multilevel framework. RECENT FINDINGS:Recent advances in predictive analytics, large-language models (LLM), AI chatbots, and related tools create a framework to strengthen tobacco prevention. Predictive analytics merge electronic health records, behavioral surveys, genetics, and real-time sensor data to model the complex multilevel factors that influence quitting. LLMs instantly uncover informative features, revealing novel predictors that shape targeted interventions. AI-driven conversational agents deliver stage-specific counseling and medication guidance, with preliminary trials showing improved engagement and quit rates. Reinforcement learning personalizes messaging, rewards, and medication schedules to optimize outcomes, while natural-language processing of social media provides fine-grained sentiment data to assess policy impact. SUMMARY:Realizing AI's potential to reduce tobacco's public-health burden requires interdisciplinary collaboration, equity-oriented design, external validation, and strong governance. These safeguards can enable scalable, adaptable, and culturally relevant smoking-cessation interventions and facilitate timely, effective tobacco-control policies.
PURPOSE OF REVIEW:Therapeutic drug monitoring (TDM) and pharmacogenomic testing (PGx) are increasingly important in optimizing psychopharmacological treatments. This review highlights the significance of integrating these tools to enhance pharmacological care in psychiatry. RECENT FINDINGS:TDM has a longstanding tradition in managing psychiatric disorders, showcasing interindividual variability in drug metabolism and response. Recent studies demonstrate that TDM improves symptom control, reduces relapse rates, and decreases hospitalization for patients on psychotropic medications. The introduction of mini-invasive TDM procedures, including point-of-care testing and wearable devices, expands its applicability in real-world settings. Similarly, PGx testing, which assesses genetic variations affecting drug metabolism, has shown a significant increase in remission rates among depressed patients and emphasizes the importance of substrate-specific effects in pharmacogenomics. Recent findings indicate that phenoconversion can alter drug-gene interactions, necessitating careful consideration in treatment planning. Long-read sequencing allows more accurate genotyping results. SUMMARY:The integration of TDM and PGx in clinical practice offers a more personalized approach to psychiatric care, improving treatment outcomes and reducing adverse effects. The development of new guidelines for both TDM and PGx enhances their utility, while emerging technologies may facilitate their implementation. Continuing research in this area is essential for refining these tools, ensuring that clinicians can effectively tailor neuropsychotropic treatments to individual patient needs.
Purpose of reviewLarge language models (LLMs) are increasingly integrated into digital mental health tools, yet their role in substance use disorder (SUD) interventions remains poorly understood. This review synthesizes emerging evidence on the opportunities and risks of applying LLMs across the digital SUD care continuum.Recent findingsStudies report promising applications in early detection, personalized support, continuous monitoring, and relapse prevention. LLMs demonstrate capacity to extract substance-use signals from natural language, generate supportive and motivational responses, and interpret narrative data for patient-reported outcomes. However, risks are substantial. LLMs can produce inaccurate or hallucinated content, may reinforce stigma or demographic bias, and can generate misleading or potentially unsafe advice. Privacy concerns are amplified in SUD contexts, where sensitive data are often managed outside regulated healthcare systems. Existing regulatory frameworks such as the EU AI Act or U.S. device regulations, do not yet provide clear governance for anonymous, AI-supported SUD interventions.SummaryLLMs have potential to expand scalable, low-threshold support for SUDs, but their safe deployment requires validation, bias mitigation, transparent data governance, and robust human oversight. Evidence remains preliminary, and clinical integration should proceed cautiously.
Purpose of reviewWith an estimated 41.1B digital devices, the term "digital biomarkers" has been increasingly bandied about in the research literature. There is, however, a significant disconnect between the presumption of digital biomarkers and the reality of digital biomarkers.Recent findingsThe research literature embraces the concept of digital biomarkers without concomitant evidence for validation of digital measures as biomarkers. Unlike imaging or blood-based biomarkers, there is a woeful lack of research dedicated to validating digital measures as biomarkers. This gap also presents an opportunity. Regulatory agencies worldwide have long-standing protocols used by pharmaceutical and biotech companies to stand up quality management systems (QMS) that track research from inception to regulatory approved submissions. The recent United States (US) Food and Drug Administration (FDA) approval of Alzheimer's disease (AD) plasma biomarkers is another example where successful QMS implementation provided the processes and transparency necessary to obtain approval. Regulatory guidelines for digital technology validation are more circumspect on validation pathways of AD digital biomarkers, but FDA provides a framework for building a QMS that could potentially do so.SummaryBuilding an open source QMS for AD digital biomarker validation will be a critical breakthrough for harnessing the potential of digital technologies for detection, monitoring and treatment of AD and related disorders.
PURPOSE OF REVIEW:Addictive behaviors, including both substance use disorders and behavioral addictions, arise from complex interactions among biological, psychological, social, and environmental factors including digital ones. This review focuses on the assessment of social and psychological risk and protective factors, highlighting how artificial intelligence and machine learning approaches complement conventional qualitative and quantitative methodologies. The aim is to clarify how these tools can enhance understanding, prediction, and prevention of addictive behaviors. RECENT FINDINGS:Recent research identifies impulsivity, emotion dysregulation, peer norms, and family functioning as central psychosocial risk factors for addictive behaviors. Protective factors - such as self-efficacy, social support, and family cohesion - moderate these risks. Conventional analyses provide foundational evidence, while ML methods (predictive machine learning, explainable artificial intelligence, reinforcement learning) now enable integration of multimodal data, detection of nonlinear patterns, and identification of latent psychosocial profiles. Emerging studies demonstrate potential for early-warning prediction and personalized intervention design. SUMMARY:AI/ML offers unprecedented opportunities to advance addiction science by handling high-dimensional psychosocial and behavioral data. Yet, ethical, interpretative, and causal challenges persist. The most promising path forward lies in synergizing theory-driven analytics with data-driven AI approaches to achieve more precise and contextually grounded prevention and intervention strategies for addictive behaviors.
Purpose of reviewPublic discussion has increasingly focused on violent incidents involving individuals diagnosed with schizophrenia, particularly those who are nonadherent with treatment or are in the early stages of illness before treatment needs are recognized.Recent findingsAlthough people with serious mental illness are somewhat more likely to commit violent acts than those in the general population, only a small proportion of individuals with schizophrenia do so, and they are far more often victims than perpetrators of violence. Misconceptions linking schizophrenia with violence contribute to stigma, delay early diagnosis and intervention, and divert attention from contributing factors such as substance use disorders. While structured assessment tools exist, precise methods for identifying those at highest risk for committing a violent act remain limited.SummaryEarly recognition of the prodromal phase of schizophrenia, combined with timely pharmacological and psychosocial interventions, can meaningfully reduce the risk of violence. Ongoing research should emphasize improving predictive tools and promoting effective prevention and treatment strategies.
Purpose of reviewThe U.S. drug landscape is rapidly shifting necessitating early warning surveillance of emerging drug threats. We describe one such surveillance effort from the United States: the National Drug Early Warning System (NDEWS).Recent findingsNDEWS monitors drug indicators with a particular focus on trends in new psychoactive substances (NPS) and emerging adulterants. NDEWS has five major goals: develop a collaboration network, including people with lived experience, initiate methods that deliver the freshest data on drug trends, integrate data from sources to better understand signals, disseminate findings widely, and train the next generation of surveillance scientists. NDEWS collects primary data using venue-based methods (Rapid Street Reporting), Web Monitoring, and 911 (Emergency Medical Service) data, and utilizes secondary data on drug seizures and poisonings. Information is shared bidirectionally with our 16 Sentinel Sites, our Community-Based Health Expert network, and our informal networks which include medical examiners, toxicologists, funeral directors, reporters, and community overdose response workers.SummarySurveillance of emerging drug trends is increasingly important around the world as patterns of drug use continue to shift. With a focus on NPS and nonlagged data strategies, NDEWS warns communities at risk to prevent serious consequences and death.
