Although behavioral mechanisms in the association among depression, anxiety, and cancer are plausible, few studies have empirically studied mediation by health behaviors. We aimed to examine the mediating role of several health behaviors in the associations among depression, anxiety, and the incidence of various cancer types (overall, breast, prostate, lung, colorectal, smoking-related, and alcohol-related cancers).

Two-stage individual participant data meta-analyses were performed based on 18 cohorts within the Psychosocial Factors and Cancer Incidence consortium that had a measure of depression or anxiety (N = 319 613, cancer incidence = 25 803). Health behaviors included smoking, physical inactivity, alcohol use, body mass index (BMI), sedentary behavior, and sleep duration and quality. In stage one, path-specific regression estimates were obtained in each cohort. In stage two, cohort-specific estimates were pooled using random-effects multivariate meta-analysis, and natural indirect effects (i.e. mediating effects) were calculated as hazard ratios (HRs).

Smoking (HRs range 1.04–1.10) and physical inactivity (HRs range 1.01–1.02) significantly mediated the associations among depression, anxiety, and lung cancer. Smoking was also a mediator for smoking-related cancers (HRs range 1.03–1.06). There was mediation by health behaviors, especially smoking, physical inactivity, alcohol use, and a higher BMI, in the associations among depression, anxiety, and overall cancer or other types of cancer, but effects were small (HRs generally below 1.01).

Smoking constitutes a mediating pathway linking depression and anxiety to lung cancer and smoking-related cancers. Our findings underline the importance of smoking cessation interventions for persons with depression or anxiety.

This article takes stock of the 2030 Agenda and focuses on five governance areas. In a nutshell, we see a quite patchy and often primarily symbolic uptake of the global goals. Although some studies highlight individual success stories of actors and institutions to implement the goals, it remains unclear how such cases can be upscaled and develop a broader political impact to accelerate the global endeavor to achieve sustainable development. We hence raise concerns about the overall effectiveness of governance by goal-setting and raise the question of how we can make this mode of governance more effective.

A recent meta-analysis on the political impact of the Sustainable Development Goals (SDGs) has shown that these global goals are moving political processes forward only incrementally, with much variation across countries, sectors, and governance levels. Consequently, the realization of the 2030 Agenda for Sustainable Development remains uncertain. Against this backdrop, this article explores where and how incremental political changes are taking place due to the SDGs, and under what conditions these developments can bolster sustainability transformations up to 2030 and beyond. Our scoping review builds upon an online expert survey directed at the scholarly community of the ‘Earth System Governance Project’ and structured dialogues within the ‘Taskforce on the SDGs’ under this project. We identified five governance areas where some effects of the SDGs have been observable: (1) global governance, (2) national policy integration, (3) subnational initiatives, (4) private governance, and (5) education and learning for sustainable development. This article delves deeper into these governance areas and draws lessons to guide empirical research on the promises and pitfalls of accelerating SDG implementation.

As SDG implementation lags behind, this article explores 5 governance areas asking how to strengthen the global goals.

Blood-based biomarkers represent a scalable and accessible approach for the detection and monitoring of Alzheimer’s disease (AD). Plasma phosphorylated tau (p-tau) and neurofilament light (NfL) are validated biomarkers for the detection of tau and neurodegenerative brain changes in AD, respectively. There is now emphasis to expand beyond these markers to detect and provide insight into the pathophysiological processes of AD. To this end, a reactive astrocytic marker, namely plasma glial fibrillary acidic protein (GFAP), has been of interest. Yet, little is known about the relationship between plasma GFAP and AD. Here, we examined the association between plasma GFAP, diagnostic status, and neuropsychological test performance. Diagnostic accuracy of plasma GFAP was compared with plasma measures of p-tau181 and NfL.

This sample included 567 participants from the Boston University (BU) Alzheimer’s Disease Research Center (ADRC) Longitudinal Clinical Core Registry, including individuals with normal cognition (n=234), mild cognitive impairment (MCI) (n=180), and AD dementia (n=153). The sample included all participants who had a blood draw. Participants completed a comprehensive neuropsychological battery (sample sizes across tests varied due to missingness). Diagnoses were adjudicated during multidisciplinary diagnostic consensus conferences. Plasma samples were analyzed using the Simoa platform. Binary logistic regression analyses tested the association between GFAP levels and diagnostic status (i.e., cognitively impaired due to AD versus unimpaired), controlling for age, sex, race, education, and APOE e4 status. Area under the curve (AUC) statistics from receiver operating characteristics (ROC) using predicted probabilities from binary logistic regression examined the ability of plasma GFAP to discriminate diagnostic groups compared with plasma p-tau181 and NfL. Linear regression models tested the association between plasma GFAP and neuropsychological test performance, accounting for the above covariates.

The mean (SD) age of the sample was 74.34 (7.54), 319 (56.3%) were female, 75 (13.2%) were Black, and 223 (39.3%) were APOE e4 carriers. Higher GFAP concentrations were associated with increased odds for having cognitive impairment (GFAP z-score transformed: OR=2.233, 95% CI [1.609, 3.099], p<0.001; non-z-transformed: OR=1.004, 95% CI [1.002, 1.006], p<0.001). ROC analyses, comprising of GFAP and the above covariates, showed plasma GFAP discriminated the cognitively impaired from unimpaired (AUC=0.75) and was similar, but slightly superior, to plasma p-tau181 (AUC=0.74) and plasma NfL (AUC=0.74). A joint panel of the plasma markers had greatest discrimination accuracy (AUC=0.76). Linear regression analyses showed that higher GFAP levels were associated with worse performance on neuropsychological tests assessing global cognition, attention, executive functioning, episodic memory, and language abilities (ps<0.001) as well as higher CDR Sum of Boxes (p<0.001).

Higher plasma GFAP levels differentiated participants with cognitive impairment from those with normal cognition and were associated with worse performance on all neuropsychological tests assessed. GFAP had similar accuracy in detecting those with cognitive impairment compared with p-tau181 and NfL, however, a panel of all three biomarkers was optimal. These results support the utility of plasma GFAP in AD detection and suggest the pathological processes it represents might play an integral role in the pathogenesis of AD.

Blood-based biomarkers offer a more feasible alternative to Alzheimer’s disease (AD) detection, management, and study of disease mechanisms than current in vivo measures. Given their novelty, these plasma biomarkers must be assessed against postmortem neuropathological outcomes for validation. Research has shown utility in plasma markers of the proposed AT(N) framework, however recent studies have stressed the importance of expanding this framework to include other pathways. There is promising data supporting the usefulness of plasma glial fibrillary acidic protein (GFAP) in AD, but GFAP-to-autopsy studies are limited. Here, we tested the association between plasma GFAP and AD-related neuropathological outcomes in participants from the Boston University (BU) Alzheimer’s Disease Research Center (ADRC).

This sample included 45 participants from the BU ADRC who had a plasma sample within 5 years of death and donated their brain for neuropathological examination. Most recent plasma samples were analyzed using the Simoa platform. Neuropathological examinations followed the National Alzheimer’s Coordinating Center procedures and diagnostic criteria. The NIA-Reagan Institute criteria were used for the neuropathological diagnosis of AD. Measures of GFAP were log-transformed. Binary logistic regression analyses tested the association between GFAP and autopsy-confirmed AD status, as well as with semi-quantitative ratings of regional atrophy (none/mild versus moderate/severe) using binary logistic regression. Ordinal logistic regression analyses tested the association between plasma GFAP and Braak stage and CERAD neuritic plaque score. Area under the curve (AUC) statistics from receiver operating characteristics (ROC) using predicted probabilities from binary logistic regression examined the ability of plasma GFAP to discriminate autopsy-confirmed AD status. All analyses controlled for sex, age at death, years between last blood draw and death, and APOE e4 status.

Of the 45 brain donors, 29 (64.4%) had autopsy-confirmed AD. The mean (SD) age of the sample at the time of blood draw was 80.76 (8.58) and there were 2.80 (1.16) years between the last blood draw and death. The sample included 20 (44.4%) females, 41 (91.1%) were White, and 20 (44.4%) were APOE e4 carriers. Higher GFAP concentrations were associated with increased odds for having autopsy-confirmed AD (OR=14.12, 95% CI [2.00, 99.88], p=0.008). ROC analysis showed plasma GFAP accurately discriminated those with and without autopsy-confirmed AD on its own (AUC=0.75) and strengthened as the above covariates were added to the model (AUC=0.81). Increases in GFAP levels corresponded to increases in Braak stage (OR=2.39, 95% CI [0.71-4.07], p=0.005), but not CERAD ratings (OR=1.24, 95% CI [0.004, 2.49], p=0.051). Higher GFAP levels were associated with greater temporal lobe atrophy (OR=10.27, 95% CI [1.53,69.15], p=0.017), but this was not observed with any other regions.

The current results show that antemortem plasma GFAP is associated with non-specific AD neuropathological changes at autopsy. Plasma GFAP could be a useful and practical biomarker for assisting in the detection of AD-related changes, as well as for study of disease mechanisms.

Modern psychometric methods make it possible to eliminate nonperforming items and reduce measurement error. Application of these methods to existing outcome measures can reduce variability in scores, and may increase treatment effect sizes in depression treatment trials.

We aim to determine whether using confirmatory factor analysis techniques can provide better estimates of the true effects of treatments, by conducting secondary analyses of individual patient data from randomised trials of antidepressant therapies.

We will access individual patient data from antidepressant treatment trials through Clinicalstudydatarequest.com and Vivli.org, specifically targeting studies that used the Hamilton Rating Scale for Depression (HRSD) as the outcome measure. Exploratory and confirmatory factor analytic approaches will be used to determine pre-treatment (baseline) and post-treatment models of depression, in terms of the number of factors and weighted scores of each item. Differences in the derived factor scores between baseline and outcome measurements will yield an effect size for factor-informed depression change. The difference between the factor-informed effect size and each original trial effect size, calculated with total HRSD-17 scores, will be determined, and the differences modelled with meta-analytic approaches. Risk differences for proportions of patients who achieved remission will also be evaluated. Furthermore, measurement invariance methods will be used to assess potential gender differences.

Our approach will determine whether adopting advanced psychometric analyses can improve precision and better estimate effect sizes in antidepressant treatment trials. The proposed methods could have implications for future trials and other types of studies that use patient-reported outcome measures.

Women and gender-diverse people with early psychosis are at risk for suboptimal sexual health outcomes, yet little research has explored their sexual health experiences.

This study explored sexual health experiences and related priorities among women and gender-diverse people with early psychosis, to identify opportunities for improvements in sexual health and well-being.

Semi-structured individual qualitative interviews explored how patient participants (n = 19, aged 18–31 years, cisgender and transgender women and non-binary individuals) receiving clinical care from early psychosis programmes in Ontario, Canada, experienced their sexual health, including sexual function and behaviour. Thematic analysis was conducted, with triangulation from interviews/focus groups with clinicians (n = 36) who provide sexual and mental healthcare for this population.

Three key themes were identified based on patient interviews: theme 1 was the impact of psychotic illness and its treatments on sexual function and activity, including variable changes in sex drive, attitudes and behaviours during acute psychosis, vulnerability to trauma and medications; theme 2 related to intimacy and sexual relationships in the context of psychosis, with bidirectional effects between relationships and mental health; and theme 3 comprised autonomy, identity and intersectional considerations, including gender, sexuality, culture and religion, which interplay with psychosis and sexual health. Clinicians raised each of these priority areas, but emphasised risk prevention relative to patients’ more holistic view of their sexual health and well-being.

Women and non-binary people with early psychosis have wide-ranging sexual health priorities, affecting many facets of their lives. Clinical care should incorporate this knowledge to optimise sexual health and well-being in this population.

Experiential learning, such as simulation-based training, is widely used in health education. Dramatic self-expression adds another layer through enacted perspective taking, and embodied self-exploration of interaction with others, to foster situated learning. We describe the evaluation of an innovative drama-based experiential learning project involving collaboration between multidisciplinary mental healthcare staff and people with lived experience of mental illness. The programme was facilitated at East London NHS Foundation Trust by a theatre company experienced in delivering workshops with service users. A weekly group programme took place online over 8 weeks during the COVID-19 pandemic and included activities of improvisation, embodied enactments and debriefing. The programme led to co-production of a drama piece that was filmed and distributed online. It was hypothesised that the experiential learning might result in individual benefits for all participants, such as improved well-being and increased mutual understanding of each other's experience of mental health care. The project aimed to improve relationships between healthcare disciplines, and between staff and service users. Additionally, aims were to empower service users, and support staff to practice core interpersonal skills. Objectives of the evaluation were to study the impact of the experiential learning, understand participants’ experience, and explore challenges and benefits.

A mixed methods approach was taken to evaluate the programme. Following completion of the project, participants were invited to complete a questionnaire utilising a Likert scale rating of overall satisfaction with the project, perceived benefit and impact on specific domains such as working with others. One-to-one semi-structured interviews were conducted according to a topic-guide, and qualitative data were analysed using open & axial coding for thematic analysis.

11 participants, including Psychiatrists, Occupational Therapists and current service users, completed the experiential learning and filming. Questionnaire data suggested participants were highly satisfied with the learning and felt it would be valuable to others. Themes include the positive experience of creativity, dismantling of hierarchy, improved empathy, confidence and connection. Potential challenges were digital inequality and lack of dedicated time for professional development.

A drama-based experiential learning group programme for healthcare staff and service users is a highly beneficial learning experience. Participants describe changes on a personal level as well as improved understanding of others’ perspectives. This form of experiential learning features collaborative working that aligns with principles of co-production and supports the development of interpersonal skills; the findings suggest that drama-based experiential learning is a useful method in health education to complement knowledge acquisition.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.