Posttraumatic stress disorder (PTSD) has been associated with advanced epigenetic age cross-sectionally, but the association between these variables over time is unclear. This study conducted meta-analyses to test whether new-onset PTSD diagnosis and changes in PTSD symptom severity over time were associated with changes in two metrics of epigenetic aging over two time points.

We conducted meta-analyses of the association between change in PTSD diagnosis and symptom severity and change in epigenetic age acceleration/deceleration (age-adjusted DNA methylation age residuals as per the Horvath and GrimAge metrics) using data from 7 military and civilian cohorts participating in the Psychiatric Genomics Consortium PTSD Epigenetics Workgroup (total N = 1,367).

Meta-analysis revealed that the interaction between Time 1 (T1) Horvath age residuals and new-onset PTSD over time was significantly associated with Horvath age residuals at T2 (meta β = 0.16, meta p = 0.02, p-adj = 0.03). The interaction between T1 Horvath age residuals and changes in PTSD symptom severity over time was significantly related to Horvath age residuals at T2 (meta β = 0.24, meta p = 0.05). No associations were observed for GrimAge residuals.

Results indicated that individuals who developed new-onset PTSD or showed increased PTSD symptom severity over time evidenced greater epigenetic age acceleration at follow-up than would be expected based on baseline age acceleration. This suggests that PTSD may accelerate biological aging over time and highlights the need for intervention studies to determine if PTSD treatment has a beneficial effect on the aging methylome.

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.

Whole genome sequencing (WGS) can help identify transmission of pathogens causing healthcare-associated infections (HAIs). However, the current gold standard of short-read, Illumina-based WGS is labor and time intensive. Given recent improvements in long-read Oxford Nanopore Technologies (ONT) sequencing, we sought to establish a low resource approach providing accurate WGS-pathogen comparison within a time frame allowing for infection prevention and control (IPC) interventions.

WGS was prospectively performed on pathogens at increased risk of potential healthcare transmission using the ONT MinION sequencer with R10.4.1 flow cells and Dorado basecaller. Potential transmission was assessed via Ridom SeqSphere+ for core genome multilocus sequence typing and MINTyper for reference-based core genome single nucleotide polymorphisms using previously published cutoff values. The accuracy of our ONT pipeline was determined relative to Illumina.

Over a six-month period, 242 bacterial isolates from 216 patients were sequenced by a single operator. Compared to the Illumina gold standard, our ONT pipeline achieved a mean identity score of Q60 for assembled genomes, even with a coverage rate as low as 40×. The mean time from initiating DNA extraction to complete analysis was 2 days (IQR 2–3.25 days). We identified five potential transmission clusters comprising 21 isolates (8.7% of sequenced strains). Integrating ONT with epidemiological data, >70% (15/21) of putative transmission cluster isolates originated from patients with potential healthcare transmission links.

Via a stand-alone ONT pipeline, we detected potentially transmitted HAI pathogens rapidly and accurately, aligning closely with epidemiological data. Our low-resource method has the potential to assist in IPC efforts.

In decision-making, especially for sustainability, choosing the right assessment tools is crucial but challenging due to the abundance of options. A new method is introduced to streamline this process, aiding policymakers and managers. This method involves four phases: scoping, cataloging, selection, and validation, combining data analysis with stakeholder engagement. Using the food system as an example, the approach demonstrates how practitioners can select tools effectively based on input variables and desired outcomes to address sustainability risks. This method can be applied across various sectors, offering a systematic way to enhance decision-making and manage sustainability effectively.

Decision making frequently entails the selection and application of assessment tools. For sustainability decisions there are a plethora of tools available for environmental assessment, yet no established and clear approach to determine which tools are appropriate and resource efficient for application. Here we present an extensive inventory of tools and a novel taxonomic method which enables efficient, effective tool selection to improve decision making for policymakers and managers. The tool selection methodology follows four main phases based on the divergence-convergence logic; a scoping phase, cataloging phase, selection phase and validation phase. This approach combines elements of data-driven analysis with participatory techniques for stakeholder engagement to achieve buy-in and to ensure efficient management of progress and agile course correction when needed. It builds on the current limited range and scope of approaches to tool selection, and is flexible and Artificial Intelligence-ready in order to facilitate more rapid integration and uptake. Using the food system as a case study, we demonstrate how practitioners can use available input variables and desired output metrics to select the most appropriate tools to manage sustainability risks, with the approach having wide applicability to other sectors.

New method simplifies tool selection for sustainable decisions, aiding policymakers & managers. #Sustainability #DecisionMaking

Research using latent variable models demonstrates that pre-attentive measures of early auditory processing (EAP) and cognition may initiate a cascading effect on daily functioning in schizophrenia. However, such models fail to account for relationships among individual measures of cognition and EAP, thereby limiting their utility. Hence, EAP and cognition may function as complementary and interacting measures of brain function rather than independent stages of information processing. Here, we apply a data-driven approach to identifying directional relationships among neurophysiologic and cognitive variables.

Using data from the Consortium on the Genetics of Schizophrenia 2, we estimated Gaussian Graphical Models and Bayesian networks to examine undirected and directed connections between measures of EAP, including mismatch negativity and P3a, and cognition in 663 outpatients with schizophrenia and 630 control participants.

Chain structures emerged among EAP and attention/vigilance measures in schizophrenia and control groups. Concerning differences between the groups, object memory was an influential variable in schizophrenia upon which other cognitive domains depended, and working memory was an influential variable in controls.

Measures of EAP and attention/vigilance are conditionally independent of other cognitive domains that were used in this study. Findings also revealed additional causal assumptions among measures of cognition that could help guide statistical control and ultimately help identify early-stage targets or surrogate endpoints in schizophrenia.

Previous research established that white matter hyperintensities (WMH), a biomarker of small vessel cerebrovascular disease, are strong predictors of cognitive function in older adults and associated with clinical presentation of Alzheimer’s disease (AD), particularly when distributed in posterior brain regions. Secondary prevention clinical trials, such as the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s (A4) study, target amyloid accumulation in asymptomatic amyloid positive individuals, but it is unclear the extent to which small vessel cerebrovascular disease accounts for performance on the primary cognitive outcomes in these trials. The purpose of this study was to examine the relationship between regional WMH volume and performance on the Preclinical Alzheimer Cognitive Composite (PACC) among participants screened for participation in the A4 trial. We also determined whether the association between WMH and cognition is moderated by amyloid positivity status.

We assessed demographic, amyloid PET status, cognitive screening, and raw MRI data for participants in the A4 trial and quantitated regional (by cerebral lobe) WMH volumes from T2-weighted FLAIR in amyloid positive and amyloid negative participants at screening. Cognition was assessed using PACC scores, a z-score sum of four cognitive tests: The Mini-Mental State Examination (MMSE), the Free and Cued Selective Reminding Test, Logical Memory Test, and Digit Symbol Substitution Test. We included 1329 amyloid positive and 329 amyloid negative individuals (981 women; mean age=71.79 years; mean education=16.58 years) at the time of the analysis. The sample included Latinx (n=50; 3%), non-Latinx (n=1590; 95.9%), or unspecified ethnicity (n=18; 1.1%) individuals who identified as American Indian/Alaskan Native (n=7; 0.4%), Asian (n=38; 2.3%), Black/African American (n=41; 2.5%), White (n=1551 ; 93.5%), or unspecified (n=21; 1.3%) race. We first examined the associations of total and regional WMH volume and amyloid positivity on PACC scores (the primary cognitive outcome measure for A4) using separate general linear models and then determined whether amyloid positivity status and regional WMH statistically interacted for those WMH regions that showed significant main effects.

Both increased WMH, in the frontal and parietal lobes particularly, and amyloid positivity were independently associated with poorer performance on the PACC, with similar magnitude. In subsequent models, WMH volume did not interact with amyloid positivity status on PACC scores.

Regionally distributed WMH are independently associated with cognitive functioning in typical participants enrolled in a secondary prevention clinical trial for AD. These effects are of similar magnitude to the effects of amyloid positivity on cognition, highlighting the extent to which small vessel cerebrovascular disease potentially drives AD-related cognitive profiles. Measures of small vessel cerebrovascular disease should be considered explicitly when evaluating outcomes in trials, both as potential effect modifiers and as possible targets for intervention or prevention. The findings from this study cannot be generalized widely, as the participants are not representative of the overall population.

Neuropsychiatric symptoms concerning mood are common in Alzheimer's disease (AD), but it is unclear if they are etiologically related to AD pathophysiology or due to factors considered to be non-pathogenic, such as small vessel cerebrovascular disease. New generation clinical trials for AD often enroll participants with evidence of AD pathophysiology, indexed by amyloid PET scanning, but who are cognitively asymptomatic. We used screening data from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) study to examine the extent to which depressive symptoms are associated with amyloid pathophysiology and small vessel cerebrovascular disease, in the form of white matter hyperintensities (WMH).

The A4 study randomizes cognitively healthy older adults with evidence of amyloid pathophysiology on PET scanning. We used screening data, which included amyloid status (positive, negative) by visual read, amyloid PET standard uptake value ratio (SUVR) in cortical regions, and MRI data acquired in a subset (n=1,197, mean age 71.6 +/- 4.8 years, 57% women) to quantitate total WMH volume. Depressive symptoms were evaluated with the 15-item Geriatric Depression Scale, which we used both as a continuous variable and to define 'depressed' and 'non-depressed' groups, based on a cut score of > 5. We examined whether 1) depressive symptoms and proportion of depressed individuals differed between amyloid positive and negative groups, 2) there is a relationship between amyloid SUVR and depressive symptoms that differs as a function of amyloid positivity status, and 3) there is a relationship between WMH volume and depressive symptoms that differs as a function of amyloid positivity status.

Although depressive symptom severity did not differ between groups (t=0.14, p=0.88), a greater proportion of individuals were classified as depressed in the amyloid negative group than the amyloid positive group (3.5% vs. 1.9%, X2=4.60, p=0.032). Increased amyloid SUVR was associated with increased GDS scores among amyloid positive individuals (r=0.117, p=0.002) but not among amyloid negative individuals (r=0.006, p=0.68, Positivity Status x SUVR interaction on GDS: ß=0.817, p=0.029). Increased WMH was associated with higher GDS scores (ß=0.105, p=0.017) but not differentially in amyloid positive and negative participants (Positivity Status x WMH interaction on GDS: ß=-0.010, p=0.243).

These analyses have several implications. First, individuals who are screened to participate in a clinical trial but do not have evidence of amyloidosis may be misattributing concerns about underlying AD pathophysiology to depressive symptoms. Second, the severity of AD pathophysiology, indexed by amyloid PET SUVR, may drive a small increase in depressive symptomatology among individuals over visual diagnostic thresholds. Third, small vessel cerebrovascular changes are additionally associated with depressive symptoms but in a manner that is independent of AD pathophysiology. Overall, depressive symptoms and depression are likely multiply determined among prospective clinical trial participants for preclinical AD.

Several hypotheses may explain the association between substance use, posttraumatic stress disorder (PTSD), and depression. However, few studies have utilized a large multisite dataset to understand this complex relationship. Our study assessed the relationship between alcohol and cannabis use trajectories and PTSD and depression symptoms across 3 months in recently trauma-exposed civilians.

In total, 1618 (1037 female) participants provided self-report data on past 30-day alcohol and cannabis use and PTSD and depression symptoms during their emergency department (baseline) visit. We reassessed participant's substance use and clinical symptoms 2, 8, and 12 weeks posttrauma. Latent class mixture modeling determined alcohol and cannabis use trajectories in the sample. Changes in PTSD and depression symptoms were assessed across alcohol and cannabis use trajectories via a mixed-model repeated-measures analysis of variance.

Three trajectory classes (low, high, increasing use) provided the best model fit for alcohol and cannabis use. The low alcohol use class exhibited lower PTSD symptoms at baseline than the high use class; the low cannabis use class exhibited lower PTSD and depression symptoms at baseline than the high and increasing use classes; these symptoms greatly increased at week 8 and declined at week 12. Participants who already use alcohol and cannabis exhibited greater PTSD and depression symptoms at baseline that increased at week 8 with a decrease in symptoms at week 12.

Our findings suggest that alcohol and cannabis use trajectories are associated with the intensity of posttrauma psychopathology. These findings could potentially inform the timing of therapeutic strategies.

People with neuropsychiatric symptoms often experience delay in accurate diagnosis. Although cerebrospinal fluid neurofilament light (CSF NfL) shows promise in distinguishing neurodegenerative disorders (ND) from psychiatric disorders (PSY), its accuracy in a diagnostically challenging cohort longitudinally is unknown.

We collected longitudinal diagnostic information (mean = 36 months) from patients assessed at a neuropsychiatry service, categorising diagnoses as ND/mild cognitive impairment/other neurological disorders (ND/MCI/other) and PSY. We pre-specified NfL > 582 pg/mL as indicative of ND/MCI/other.

Diagnostic category changed from initial to final diagnosis for 23% (49/212) of patients. NfL predicted the final diagnostic category for 92% (22/24) of these and predicted final diagnostic category overall (ND/MCI/other vs. PSY) in 88% (187/212), compared to 77% (163/212) with clinical assessment alone.

CSF NfL improved diagnostic accuracy, with potential to have led to earlier, accurate diagnosis in a real-world setting using a pre-specified cut-off, adding weight to translation of NfL into clinical practice.

Current psychiatric diagnoses, although heritable, have not been clearly mapped onto distinct underlying pathogenic processes. The same symptoms often occur in multiple disorders, and a substantial proportion of both genetic and environmental risk factors are shared across disorders. However, the relationship between shared symptoms and shared genetic liability is still poorly understood.

Well-characterised, cross-disorder samples are needed to investigate this matter, but few currently exist. Our aim is to develop procedures to purposely curate and aggregate genotypic and phenotypic data in psychiatric research.

As part of the Cardiff MRC Mental Health Data Pathfinder initiative, we have curated and harmonised phenotypic and genetic information from 15 studies to create a new data repository, DRAGON-Data. To date, DRAGON-Data includes over 45 000 individuals: adults and children with neurodevelopmental or psychiatric diagnoses, affected probands within collected families and individuals who carry a known neurodevelopmental risk copy number variant.

We have processed the available phenotype information to derive core variables that can be reliably analysed across groups. In addition, all data-sets with genotype information have undergone rigorous quality control, imputation, copy number variant calling and polygenic score generation.

DRAGON-Data combines genetic and non-genetic information, and is available as a resource for research across traditional psychiatric diagnostic categories. Algorithms and pipelines used for data harmonisation are currently publicly available for the scientific community, and an appropriate data-sharing protocol will be developed as part of ongoing projects (DATAMIND) in partnership with Health Data Research UK.

This is the first report on the association between trauma exposure and depression from the Advancing Understanding of RecOvery afteR traumA(AURORA) multisite longitudinal study of adverse post-traumatic neuropsychiatric sequelae (APNS) among participants seeking emergency department (ED) treatment in the aftermath of a traumatic life experience.

We focus on participants presenting at EDs after a motor vehicle collision (MVC), which characterizes most AURORA participants, and examine associations of participant socio-demographics and MVC characteristics with 8-week depression as mediated through peritraumatic symptoms and 2-week depression.

Eight-week depression prevalence was relatively high (27.8%) and associated with several MVC characteristics (being passenger v. driver; injuries to other people). Peritraumatic distress was associated with 2-week but not 8-week depression. Most of these associations held when controlling for peritraumatic symptoms and, to a lesser degree, depressive symptoms at 2-weeks post-trauma.

These observations, coupled with substantial variation in the relative strength of the mediating pathways across predictors, raises the possibility of diverse and potentially complex underlying biological and psychological processes that remain to be elucidated in more in-depth analyses of the rich and evolving AURORA database to find new targets for intervention and new tools for risk-based stratification following trauma exposure.