Veterans Affairs Medical Centers offer multiple weight-loss treatments, including a comprehensive lifestyle intervention program (i.e., MOVE!), anti-obesity medications (AOMs) and bariatric surgery. Yet, most eligible veterans do not receive these treatments.

To describe the design, rationale, and planned evaluation of a comprehensive Weight Management and Metabolic Health program (WMMHP), consisting of (1) weight-focused visits with physicians or pharmacists trained in obesity medicine; (2) patient-centered use of available weight-loss treatments; and (3) coordinated, team-based care.

This is a quality improvement program implemented within the VA Ann Arbor Healthcare System. WMMHP eligibility criteria include body mass index (BMI) ≥ 30 kg/m2 or BMI ≥ 27 kg/m2 and ≥ 1 weight-related condition and participation in the MOVE! program. We plan to conduct an 18-month retrospective program evaluation using a propensity-matched cohort analysis to estimate the added benefit of WMMHP vs. MOVE! alone. The primary outcome will be mean change in weight at 18 months after baseline. Secondary outcomes will include mean weight loss at 6, 12, and 24 months, percentage of patients achieving thresholds of ≥ 5%, ≥ 10%, and ≥ 15% weight loss, initial prescriptions for and refilled prescriptions as a measure of adherence to AOMs, and referrals to, engagement with, and completion of bariatric surgery. We will also examine between-group differences in health system resource utilization.

The WMMHP is an innovative approach to improving treatment and outcomes for veterans with overweight and obesity. If effective, its components may inform obesity care delivery in VA and non-VA settings.

Although workplace mental health screening is often implemented to aid early identification of mental health symptoms and facilitate access to treatment, supporting evidence is limited.

We aimed to evaluate the effect of independently conducted, confidential, online mental health screening, paired with automated tailored feedback recommending referral services, on help-seeking and psychological distress.

We conducted a cluster-randomised controlled trial with firefighters from an Australian fire and rescue service. Randomisation occurred by station (N = 264). Firefighters at stations allocated to the intervention group received tailored information detailing suitable mental health services based on their Kessler-6 psychological distress score (K6). The control group received generic feedback on services irrespective of K6 score. The primary outcome was help-seeking at 3-months post-intervention for those with at least moderate levels of psychological distress at baseline (K6 ≥14). The study was registered with Australian New Zealand Clinical Trials Registry (no. ANZCTR 12621001457831).

Of the 459 firefighters screened, 141 (30.72%) scored ≥14 on K6. Among this subgroup at 3 months, no differences were observed in rates of overall help-seeking between the intervention and control groups (P = 0.31). In contrast, levels of psychological distress remained high in the intervention group but declined in the control group (t[111] = 2.29, 95% CI: 0.24, 3.23, P = 0.024). The difference in psychological distress associated with workplace mental health screening equated to an effect size of −0.42 (95% CI: −0.04, −0.79).

Our findings suggest that independent, confidential online mental health screening, paired with tailored online feedback and information on available treatment, does not significantly increase help-seeking and may sustain psychological distress over time compared with receiving generic information. As such, it should not be implemented to promote help-seeking and reduce levels of psychological distress. These findings are relevant for workplaces, mental health researchers and practitioners alike, highlighting the potential risk and potential harm of mental health screening conducted in this way on individuals.

Electronic Health Record (EHR) data are critical for advancing translational research and AI technologies. The ENACT network offers access to structured EHR data across 57 CTSA hubs. However, substantial information is contained in clinical narratives, requiring natural language processing (NLP) for research. The ENACT NLP Working Group was formed to make NLP-derived clinical information accessible and queryable across the network.

We established the ENACT NLP Working Group with 13 sites selected based on criteria including clinical notes access, IT infrastructure, NLP expertise, and institutional support. We divided sites into five focus groups targeting clinical tasks within disease contexts. Each focus group consisted of two development sites and two validation sites. We extended the ENACT ontology to standardize NLP-derived data and conducted multisite evaluations using the Open Health Natural Language Processing (OHNLP) Toolkit.

The working group achieved 100% site retention and deployed NLP infrastructure across all sites. We developed and validated NLP algorithms for rare disease phenotyping, social determinants of health, opioid use disorder, sleep phenotyping, and delirium phenotyping. Performance varied across sites (F1 scores 0.53–0.96), highlighting data heterogeneity impacts. We extended the ENACT common data model and ontology to incorporate NLP-derived data while maintaining Shared Health Research Informatics NEtwork (SHRINE) compatibility.

This demonstrates feasibility of deploying NLP infrastructure across large, federated networks. The focus group approach proved more practical than general-purpose approaches. Key lessons include the challenge of data heterogeneity and importance of collaborative governance. This work also provides a foundation that other networks can build on to implement NLP capabilities for translational research.

Patients with posttraumatic stress disorder (PTSD) exhibit smaller regional brain volumes in commonly reported regions including the amygdala and hippocampus, regions associated with fear and memory processing. In the current study, we have conducted a voxel-based morphometry (VBM) meta-analysis using whole-brain statistical maps with neuroimaging data from the ENIGMA-PGC PTSD working group.

T1-weighted structural neuroimaging scans from 36 cohorts (PTSD n = 1309; controls n = 2198) were processed using a standardized VBM pipeline (ENIGMA-VBM tool). We meta-analyzed the resulting statistical maps for voxel-wise differences in gray matter (GM) and white matter (WM) volumes between PTSD patients and controls, performed subgroup analyses considering the trauma exposure of the controls, and examined associations between regional brain volumes and clinical variables including PTSD (CAPS-4/5, PCL-5) and depression severity (BDI-II, PHQ-9).

PTSD patients exhibited smaller GM volumes across the frontal and temporal lobes, and cerebellum, with the most significant effect in the left cerebellum (Hedges’ g = 0.22, pcorrected = .001), and smaller cerebellar WM volume (peak Hedges’ g = 0.14, pcorrected = .008). We observed similar regional differences when comparing patients to trauma-exposed controls, suggesting these structural abnormalities may be specific to PTSD. Regression analyses revealed PTSD severity was negatively associated with GM volumes within the cerebellum (pcorrected = .003), while depression severity was negatively associated with GM volumes within the cerebellum and superior frontal gyrus in patients (pcorrected = .001).

PTSD patients exhibited widespread, regional differences in brain volumes where greater regional deficits appeared to reflect more severe symptoms. Our findings add to the growing literature implicating the cerebellum in PTSD psychopathology.

It remains unclear which individuals with subthreshold depression benefit most from psychological intervention, and what long-term effects this has on symptom deterioration, response and remission.

To synthesise psychological intervention benefits in adults with subthreshold depression up to 2 years, and explore participant-level effect-modifiers.

Randomised trials comparing psychological intervention with inactive control were identified via systematic search. Authors were contacted to obtain individual participant data (IPD), analysed using Bayesian one-stage meta-analysis. Treatment–covariate interactions were added to examine moderators. Hierarchical-additive models were used to explore treatment benefits conditional on baseline Patient Health Questionnaire 9 (PHQ-9) values.

IPD of 10 671 individuals (50 studies) could be included. We found significant effects on depressive symptom severity up to 12 months (standardised mean-difference [s.m.d.] = −0.48 to −0.27). Effects could not be ascertained up to 24 months (s.m.d. = −0.18). Similar findings emerged for 50% symptom reduction (relative risk = 1.27–2.79), reliable improvement (relative risk = 1.38–3.17), deterioration (relative risk = 0.67–0.54) and close-to-symptom-free status (relative risk = 1.41–2.80). Among participant-level moderators, only initial depression and anxiety severity were highly credible (P > 0.99). Predicted treatment benefits decreased with lower symptom severity but remained minimally important even for very mild symptoms (s.m.d. = −0.33 for PHQ-9 = 5).

Psychological intervention reduces the symptom burden in individuals with subthreshold depression up to 1 year, and protects against symptom deterioration. Benefits up to 2 years are less certain. We find strong support for intervention in subthreshold depression, particularly with PHQ-9 scores ≥ 10. For very mild symptoms, scalable treatments could be an attractive option.

Negative symptoms are a key feature of several psychiatric disorders. Difficulty identifying common neurobiological mechanisms that cut across diagnostic boundaries might result from equifinality (i.e., multiple mechanistic pathways to the same clinical profile), both within and across disorders. This study used a data-driven approach to identify unique subgroups of participants with distinct reward processing profiles to determine which profiles predicted negative symptoms.

Participants were a transdiagnostic sample of youth from a multisite study of psychosis risk, including 110 individuals at clinical high-risk for psychosis (CHR; meeting psychosis-risk syndrome criteria), 88 help-seeking participants who failed to meet CHR criteria and/or who presented with other psychiatric diagnoses, and a reference group of 66 healthy controls. Participants completed clinical interviews and behavioral tasks assessing four reward processing constructs indexed by the RDoC Positive Valence Systems: hedonic reactivity, reinforcement learning, value representation, and effort–cost computation.

k-means cluster analysis of clinical participants identified three subgroups with distinct reward processing profiles, primarily characterized by: a value representation deficit (54%), a generalized reward processing deficit (17%), and a hedonic reactivity deficit (29%). Clusters did not differ in rates of clinical group membership or psychiatric diagnoses. Elevated negative symptoms were only present in the generalized deficit cluster, which also displayed greater functional impairment and higher psychosis conversion probability scores.

Contrary to the equifinality hypothesis, results suggested one global reward processing deficit pathway to negative symptoms independent of diagnostic classification. Assessment of reward processing profiles may have utility for individualized clinical prediction and treatment.

Research examining (MCI) criteria in diverse and/or health-disparate populations is limited. There is a critical need to investigate the predictive validity for incident dementia of widely used MCI definitions in diverse populations.

Eligible participants were non-Hispanic White or Black Bronx community residents, free of dementia at enrollment, with at least one annual follow-up visit after baseline. Participants completed annual neurological and neuropsychological evaluations to determine cognitive status. Dementia was defined based on DSM-IV criteria using case conferences. Cox proportional hazard models assessed predictive validity for incident dementia of four specific MCI definitions (Petersen, Jak/Bondi, number of impaired tests, Global Clinical Ratings) at baseline, controlling for age, sex, education, and race/ethnicity. Time-dependent sensitivity and specificity at 2–7 years for each definition, and Youden’s index were calculated as accuracy measures.

Participants (N = 1073) ranged in age from 70 to 100 (mean = 78.4 ± 5.3) years at baseline. The sample was 62.5% female, and educational achievement averaged 13.9 ± 3.5 years. Most participants identified as White (70.0%), though Black participants were well-represented (30.0%). In general, MCI definitions differed in sensitivity and specificity for incident dementia. However, there were no significant differences in Youden’s index for any definition, across all years of follow-up.

This work provides an important step toward improving the generalizability of the MCI diagnosis to underrepresented/health-disparate populations. While our findings suggest the studied MCI classifications are comparable, researchers and clinicians may choose to consider one method over another depending on the rationale for evaluation or question of interest.

A clinical tool to estimate the risk of treatment-resistant schizophrenia (TRS) in people with first-episode psychosis (FEP) would inform early detection of TRS and overcome the delay of up to 5 years in starting TRS medication.

To develop and evaluate a model that could predict the risk of TRS in routine clinical practice.

We used data from two UK-based FEP cohorts (GAP and AESOP-10) to develop and internally validate a prognostic model that supports identification of patients at high-risk of TRS soon after FEP diagnosis. Using sociodemographic and clinical predictors, a model for predicting risk of TRS was developed based on penalised logistic regression, with missing data handled using multiple imputation. Internal validation was undertaken via bootstrapping, obtaining optimism-adjusted estimates of the model's performance. Interviews and focus groups with clinicians were conducted to establish clinically relevant risk thresholds and understand the acceptability and perceived utility of the model.

We included seven factors in the prediction model that are predominantly assessed in clinical practice in patients with FEP. The model predicted treatment resistance among the 1081 patients with reasonable accuracy; the model's C-statistic was 0.727 (95% CI 0.723–0.732) prior to shrinkage and 0.687 after adjustment for optimism. Calibration was good (expected/observed ratio: 0.999; calibration-in-the-large: 0.000584) after adjustment for optimism.

We developed and internally validated a prediction model with reasonably good predictive metrics. Clinicians, patients and carers were involved in the development process. External validation of the tool is needed followed by co-design methodology to support implementation in early intervention services.