Sabine Bahn

Background: Mental health disorders cause substantial patient suffering, which could be alleviated through early diagnostic biomarkers. While biomarker discovery is costly, genetic methods utilizing data from large-scale studies, such as Mendelian randomization, may provide a cost-effective approach. Methods: A two-sample Mendelian randomization analysis was conducted to identify potential urinary biomarkers of seven psychiatric disorders using summary statistics from GWAS data. Results: The analysis revealed 67 analyte-disorder associations, of which 21 were exclusive to a single disorder. Notable associations were observed between tyrosine and schizophrenia (β = -0.041, SE = 0.013, Q = 0.027), creatine and bipolar disorder (β = -0.077, SE = 0.019, Q = 0.002), pyridoxal (β = 0.10, SE = 0.03, Q = 0.042) and ferulic acid 4-sulfate (β = 0.077, SE = 0.025, Q = 0.037) to anorexia nervosa, and N, N-dimethylglycine to ADHD (β = -0.39, SE = 0.11, Q = 0.008). Conclusion: The results identify candidate urinary biomarkers and demonstrate the utility of genetic instruments for biomarker discovery, warranting experimental validation in independent cohorts. Clinical trial number: Not applicable.

BACKGROUND Digital tools are increasingly used in mental health care since the COVID-19 pandemic. Because of this, an updated review is needed on the diagnostic accuracy of digital mental health assessment tools. OBJECTIVE This systematic review expands on a previous review (Martin-Key et al., 2022). This review aimed to explore and provide a summary of the quality and validity of current literature on digital self-report question- and answer-based screening tools for mental health across age groups. METHODS The search terms in this systematic review were defined using the Population, Intervention, Comparator, and Outcome framework (PICO) and adhered to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. To expand on the previous review, the searches included literature focusing on children, young people, and older adults. The review was pre-registered with the International Prospective Register of Systematic Reviews (PROSPERO; CRD420250654734) as well as published in a protocol (Babbitt et al., 2026). Various databases were searched (ie, MEDLINE, Embase, Cochrane Library, ASSIA, Web of Science Core Collection, CINAHL, and PsycINFO) by a medical librarian. Grey literature searches were also conducted. This review focused on literature published after the searches of the previous review in October 2021. Characteristics of the studies and information on diagnostic accuracy were extracted from the full texts. The final included studies were assessed for risk of bias using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. RESULTS 34 studies were included in the final review. Tools showed poor to excellent diagnostic accuracy (area under the receiver operating characteristic curve=0.49-0.98, sensitivity=0.00-1.00, specificity=0.10-1.00). Many studies had high risk of bias and applicability concerns. No mental health tools were identified for older adults over 65 years old. More neurodiversity screening tools were identified, with most of the tools for children and young people being for these disorders. Multiple machine learning algorithms were also identified. CONCLUSIONS The evidence for digital mental health assessment tools still varies greatly in the years after the COVID-19 pandemic. While there have been some changes in the focus on conditions, there is opportunity for stronger generation of evidence and development of new tools for psychiatric diagnoses. INTERNATIONAL REGISTERED REPORT RR2-10.2196/73492

Introduction: Premenstrual symptoms are common physical, psychological, and behavioural symptoms that occur before the onset of menstruation. Despite their cyclical nature, these symptoms can be severe and burdensome, potentially interfering with daily life. Although interventions are available, help-seeking remains infrequent, raising questions about the barriers to engaging in formal care. This study employed a mixed-methods approach to examine both barriers and facilitators to help-seeking and assess their relative impact. Methods: An online survey was developed, incorporating questions about premenstrual symptoms and quantitative and qualitative questions on barriers and facilitators to formal help-seeking. Participants included both previous help-seekers and non-help-seekers to allow for group comparisons. Data were collected from 592 UK-based participants. Quantitative data were analysed descriptively, with group differences assessed using Mann–Whitney U and Chi-square tests, as appropriate. Qualitative responses were analysed using thematic analysis. Results: 42.74% ( n = 253) had not previously sought formal help for premenstrual symptoms, while 57.26% ( n = 339) had. Overall, the most common and strongest barrier to help-seeking regarded concerns that healthcare professionals (HCPs) would not take symptoms seriously or would act dismissively. Significant group differences were observed in the barriers endorsed, with non-help-seekers being more likely to think professional care would be ineffective and wanting to solve problems on their own. Previous help-seekers were more likely to endorse previous poor care experiences as a barrier. Qualitative analysis revealed that anticipated HCP knowledge and attitudes, along with lack of awareness and education, were the most frequently reported barriers. Among non-help-seekers, improving education and awareness was commonly cited as a potential facilitator of formal help-seeking. Discussion: Concerns of being dismissed or not taken seriously by HCPs was the most influential factor in deciding whether to seek help or not. Additionally, lack of awareness and education was identified as a key barrier, including uncertainty about whether symptoms were “severe” enough to justify seeking formal care and doubt regarding the range and efficacy of treatment options. To facilitate help-seeking, efforts should focus on improving education for individuals experiencing premenstrual symptoms and enhancing the quality of care interactions to address concerns about poor care experiences.

Psychiatric diagnosis remains constrained by symptom-based definitions that, while providing a necessary structure for clinical communication, capture symptoms rather than underlying mechanisms and thus limit their value for clinical decision making. Recent research has increasingly integrated biological, behavioural, and digital evidence to refine how mental disorders are defined and understood. This review examines emerging frameworks and technologies that link clinical observation with biological processes, moving beyond categorical checklists toward a data-driven understanding of mental illness as a set of dynamic, interconnected processes. Objective measures offer the potential to identify reproducible biological subtypes, predict illness trajectories, and guide personalised treatment. Successful translation into clinical practice will require validated, interpretable, and accessible tools that can be readily integrated into real-world health systems to complement rather than replace clinical expertise. By combining objective measurement with clinician judgment, psychiatry can advance toward a diagnostic framework that is more empirically grounded, clinically meaningful, and better positioned to improve patient outcomes.

Background: Digital assessment tools in health care are increasingly used to aid clinicians in diagnosing mental health conditions. Particularly since the quarantine and isolation guidelines of the COVID-19 pandemic moved much of health care online, there has been an accelerated adoption of digital assessment tools. The diagnostic accuracy of digital mental health assessment tools for a range of psychiatric conditions has yet to be fully explored, especially for their use in populations of older adults and children. Objective: This systematic review aims to (1) summarize recent studies on digital self-report question-and-answer–based mental health assessment tools for use in all ages across a range of psychiatric conditions (eg, the type and number of questions, if available; reference tests; timing; and blinding procedures), (2) present their validity (ie, diagnostic accuracy), and (3) assess study quality and applicability. Methods: The PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guided the development of this protocol. The protocol has been registered with PROSPERO. The searches were guided by the PICO (population, intervention, comparator, and outcome) framework. A systematic search was conducted of the following databases of literature published since 2021: MEDLINE, Embase, Cochrane Library, ASSIA, Web of Science Core Collection, CINAHL, and PsycINFO. Searches of clinical trial databases and hand searching of reference lists will be completed. Two authors have independently screened titles and abstracts of identified papers and selected studies according to eligibility criteria, resolving inconsistencies through discussion. Full texts were screened following the same process. The authors extracted data using the Covidence data extraction tool (Veritas Health Innovation Ltd; eg, sensitivity and specificity). Two authors will use the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool to assess risk of bias for each full-text inclusion. Results: Scoping for this review began in December 2024. Searches of databases were completed in January 2025. Full-text screening and identification of the relevant gray literature were completed by the end of August 2025, and the final review is expected to be completed by December 2025. Conclusions: The review aims to present the validity and quality of the diagnostic accuracy of digital mental health assessment tools across different ages (including children and older adults), particularly following the COVID-19 pandemic due to the exponential increase in development and use of such tools. This review will provide evidence for the wider deployment of digital mental health assessment tools across a wide age range. There will also be a discussion about future research for digital tools and avenues for policy around digital mental health assessments.

The differentiation of bipolar disorder (BD) from major depressive disorder (MDD) represents a persistent challenge in psychiatry, leading to high rates of misdiagnosis and adverse patient outcomes. Objective biomarkers are critically needed, but their discovery is hampered by limited understanding of the disease etiology. Robust unbiased, hypothesis-free, and untargeted discovery methods combined with machine learning could prove valuable in providing diagnostic utility. In this study, a novel surface-enhanced Raman spectroscopy (SERS)-gold nanoparticle (AuNP)-cucurbit[5]uril (CB[5]) approach for biomarker discovery was applied for the first time in a clinical setting in combination with an explainable AI driven approach to distinguish BD from MDD. Dried blood spot (DBS) samples were collected from 130 patients with BD and MDD and analysed using SERS and AuNP-CB[5] aggregates. An XGBoost machine learning model was trained to differentiate the groups, with the Context Representative Interpretable Model Explanations (CRIME) method used to identify the most predictive spectral features. These features were subsequently correlated with metabolites measured using targeted mass spectrometry to identify underlying biomarkers. The model distinguished BD from MDD with an AUC of 0.66 (P = 0.003). The predictions were driven by two primary spectral peak regions identified through correlation analysis. The most important peak (1450 cm-¹) corresponded to hypoxanthine, which was significantly upregulated in the BD group. The link between hypoxanthine and the SERS signal was supported by experimental confirmation of correlated spectral peaks, validating the method’s specificity. This study validates a comprehensive DBS-SERS-AuNP-CB[5] clinical discovery pipeline, establishing a powerful, unbiased framework that opens new avenues for developing point-of-care diagnostics for complex neuropsychiatric disorders and other conditions.

Introduction: Despite available safe hormonal and non-hormonal interventions, most women with troublesome menopausal symptoms do not receive effective, evidence-based therapy, with notable international disparities in provision. This study aimed to investigate self-reported menopausal care experiences in a self-selecting sample from five English-speaking countries: Australia, Canada, New Zealand, the United Kingdom, and the United States, through an anonymous online survey. Methods: The 15–20 min survey, delivered via Qualtrics XM®, included questions on sociodemographic characteristics and treatment experiences, such as the number of healthcare professionals (HCP) seen before getting a prescription, ease of obtaining treatment, involvement in treatment discussions, appropriateness of treatment review and optimization, side effect tolerability, and overall satisfaction. Results: Data from 3,062 respondents were analyzed: Australia (16.59%, n = 508), Canada (17.54%, n = 537), New Zealand (16.59%, n = 508), UK (24.00%, n = 735), and US (25.28%, n = 774). Significant international differences were observed in both healthcare access and prescribing patterns. More women in the UK and US consulted an HCP compared with Australia, Canada, and New Zealand [ χ ²(4, N = 3062) = 101.02, p < 0.001, φ c = 0.18]. Prescription rates were higher in New Zealand, the UK, and the US compared with Australia and Canada [ χ ²(4, N = 2,485) = 75.71, p < 0.001, φ c = 0.18]. However, UK respondents, despite longer treatment use, generally reported less involvement in treatment discussions, poorer treatment review, lower side effect tolerability, and reduced satisfaction compared with other countries across treatment types. Discussion: Based on a self-selected cohort, these findings reveal critical gaps in menopause care, including disparities in treatment access and international differences in patient involvement. Greater access to healthcare in the UK and the US did not translate into higher satisfaction, highlighting the need for patient-centered approaches. Improving care requires better clinician education and strategies to enhance communication and shared decision-making.

Background: Menopause is a significant time in a woman’s life, but only recently has there been an open discussion about it in the media, workplaces, and general society. With increasing frequency, women are using the internet to research menopause, making it essential that online sources provide safe, high-quality, and relevant information. Objective: This study aimed to investigate the current state of the online information landscape for menopause from the perspective of information seekers, exploring (1) information-seeking behavior and (2) perceptions of online resources for menopause. Methods: A 10- to 15-minute online survey was conducted asking about the respondents’ use of and opinions about online resources specifically for menopause. We distributed the survey via social media, email, and word of mouth. Quantitative data were explored using means and frequencies. Group differences between menopausal groups were analyzed using chi-square, Fisher exact, or Kruskall-Wallis tests as appropriate. Qualitative data were analyzed using data-driven thematic analysis. Results: Data from 627 participants were analyzed (early perimenopause: n=171, 27.3%, late perimenopause: n=125, 19.9%, postmenopause: n=262, 41.8%, and surgical menopause: n=69, 11%). The majority of respondents had used the internet as a source of information (581/627, 92.7%), with the internet being the first choice of information source (489/581, 84.2%). The most searched-for information online was about menopause symptoms (479/581, 82.4%), menopause treatment options (442/581, 76.1%), and self-help tips or strategies (318/581, 54.7%). The majority of participants trusted online information to some extent (615/627, 98.1%), with many also considering online information accurate to some extent (555/627, 88.5%). Many participants reported finding some but not all of the information they were looking for online (379/581, 65.2%). Thematic analysis revealed 10 themes related to information quality and accessibility and sought-after information (eg, symptom specifics, treatment, and nonformal management strategies). Analysis also indicated that information is lacking for several groups, including those in medically induced or surgical menopause. Conclusions: The study showed that online informational resources are widely accessed and widely perceived as useful and trustworthy. However, it is crucial that the quality of online information is evaluated, especially considering the large number of users who rely on it as their first or only informational source. Online searches were usually performed to find information related to symptoms, treatment, and self-help recommendations, with differences in search behaviors observed across menopausal stages and groups, highlighting the need for tailored informational resources. Thematic analysis revealed gaps in the provision of online information both in terms of content and quality. Participants noted a lack of comprehensive symptom information, inadequate information for groups such as those experiencing medical or surgical menopause, and concerns about outdated content and a lack of source transparency. Future research with more diverse samples is needed to better understand variations in online health information-seeking behaviors across groups.

Despite the potential severity and burden of premenstrual symptoms, few appear to seek formal care. Given that access to many therapeutic interventions requires formal help-seeking, it is important to understand predictors of this health behaviour. This study employed machine learning to identify symptoms, functional impairment, and barriers to accessing care that predict formal help-seeking for premenstrual symptoms. Data was collected from a UK-based sample using online survey software and explored using descriptive analysis. Group differences in ordinal and categorical data between those who have and have not sought formal help specifically for premenstrual symptoms were examined using Mann-Whitney U tests and Chi-square tests, respectively. Predictive models of help-seeking were built using the decision tree-based machine learning method, Extreme Gradient Boosting (XGBoost). A total of 592 participants with complete data who endorsed premenstrual symptoms in consecutive cycles were included for analysis. Of those, 57.26% (n = 339) had previously seen a healthcare professional specifically for premenstrual symptoms. The model predicting formal help-seeking demonstrated fair performance, with an area under the receiver operating characteristic curve (AUROC) of 0.75 (SD = 0.06), a sensitivity of 0.65 (SD = 0.10), and a specificity of 0.71 (SD = 0.11). The strongest predictors of formal help-seeking were impaired social functioning, thinking that symptoms were severe, impairment in work/studies, and a previous poor care experience for gynaecological/reproductive conditions. These insights may be leveraged to encourage help-seeking behaviour, potentially reducing unnecessary distress or impairment. Improved knowledge of premenstrual symptoms and disorders is vital to facilitate identification of severe symptoms and impaired functioning related to the menstrual cycle. Additionally, improved guidance on when to seek help is required to increase the rate of formal help-seeking, particularly for individuals with high-risk psychological symptoms such as suicidality. More work is needed to determine the specific mechanism by which previous poor care experiences drive further help-seeking for premenstrual symptoms.

Background: While there exist safe hormonal and non-hormonal therapeutic interventions for the menopause, their efficacy profiles are not fully characterized. This study sought to use a symptom checklist to examine menopausal symptom relief associated with different treatments. Methods: An online survey study was conducted between December 2023 and February 2024. Convenience sampling was conducted, with participants recruited via social media, email, through relevant foundations and support groups, and by word-of-mouth. Inclusion criteria were: (1) ≥ 18 years, (2) assigned female at birth, (3) strong comprehension of the English language, and (4) must be currently experiencing symptoms of the menopause or menopause transition. 3330 respondents consented to participate in the study and of these, 91.95% (N = 3062) who had completed at least 88% of the survey were included in the analysis. Symptom relief per treatment (transdermal hormone replacement therapy (HRT), oral HRT, vaginal HRT, antidepressants, testosterone, cognitive behavioral therapy (CBT)/other therapy/counseling) was assessed using the symptoms included in the Menopause-Specific Quality of Life (MENQOL) questionnaire, which measures four symptom domains: vasomotor, psychosocial, physical, and sexual. Results: Data from a total of 3062 respondents were included for analysis (mean age = 51.97, SD = 5.24). Treatment response rates differed significantly across the domains (vasomotor: F(5,2340) = 204.93, p < 0.001, η² = 0.31; psychosocial: F(5,2340) = 75.12, p < 0.001, η² = 0.14; physical: F(5,2340) = 65.46, p < 0.001, η² = 0.12; sexual: F(5,2340) = 89.34, p < 0.001, η² = 0.16). Transdermal HRT performed better at reducing vasomotor symptoms relative to all other treatment options. Regarding psychosocial symptoms, CBT/other therapy/counseling outperformed all other treatment options. The use of transdermal HRT and testosterone was associated with greater response rates in physical symptoms relative to other treatments. Finally, vaginal HRT and testosterone were associated with significantly higher response rates in sexual symptoms in comparison to all other treatments. Conclusions: The findings demonstrate differential response rates to menopausal treatments across symptom domains, underscoring the importance of a comprehensive, multidimensional approach to menopausal symptom management. Utilizing a symptom checklist can facilitate the tailoring of treatment plans for specific symptom profiles and patient needs. The outcomes of this study hold considerable implications for improving and shaping treatment guidelines for the menopause.