The impact of chronic pain and opioid use on cognitive decline and mild cognitive impairment (MCI) is unclear. We investigated these associations in early older adulthood, considering different definitions of chronic pain.

Men in the Vietnam Era Twin Study of Aging (VETSA; n = 1,042) underwent cognitive testing and medical history interviews at average ages 56, 62, and 68. Chronic pain was defined using pain intensity and interference ratings from the SF-36 over 2 or 3 waves (categorized as mild versus moderate-to-severe). Opioid use was determined by self-reported medication use. Amnestic and non-amnestic MCI were assessed using the Jak-Bondi approach. Mixed models and Cox proportional hazards models were used to assess associations of pain and opioid use with cognitive decline and risk for MCI.

Moderate-to-severe, but not mild, chronic pain intensity (β = −.10) and interference (β = −.23) were associated with greater declines in executive function. Moderate-to-severe chronic pain intensity (HR = 1.75) and interference (HR = 3.31) were associated with a higher risk of non-amnestic MCI. Opioid use was associated with a faster decline in verbal fluency (β = −.18) and a higher risk of amnestic MCI (HR = 1.99). There were no significant interactions between chronic pain and opioid use on cognitive decline or MCI risk (all p-values > .05).

Moderate-to-severe chronic pain intensity and interference related to executive function decline and greater risk of non-amnestic MCI; while opioid use related to verbal fluency decline and greater risk of amnestic MCI. Lowering chronic pain severity while reducing opioid exposure may help clinicians mitigate later cognitive decline and dementia risk.

Haemolysis is developing prominence in the setting of supporting increasingly complex children with heart failure with a ventricular assist device. The goal of this study is to better characterise haemolysis and its implications in children supported with pulsatile ventricular assist devices.

This is a single-centre retrospective review of 44 children who were supported by Berlin Heart EXCOR between January 2006 and June 2020. Patients were divided into major haemolysers and non-major haemolysers. Major haemolysers were defined as patients with lactate dehydrogenase > 500U/L (2.5x the upper limits of normal) with either total bilirubin > 2mg/dL (with predominantly indirect hyperbilirubinemia) or anaemia out of proportion to the clinical scenario more than three days following implantation of the ventricular assist device(s). Patient demographics, ventricular assist device factors, and outcomes, including end-organ function and mortality, were compared between major haemolysers and non-major haemolysers.

Forty-four patients supported by the Berlin EXCOR were included in the analytic cohort of the study: 27 major haemolysers and 17 non-major haemolysers. Major haemolysis was more common in those supported with single-ventricle ventricular assist device (i.e., VAD in the context of functionally univentricular anatomy) compared to those with biventricular hearts, p = 0.01. There were no patients with an isolated left ventricular assist device or isolated right ventricular assist device in our analytic cohort of 44 patients. Of the 19 patients with single-ventricle ventricular assist device, 84% (16/19) were major haemolysers. Of the 25 patients with a biventricular assist device, 44% (11/25) were major haemolysers. Major haemolysers and non-major haemolysers had a body surface area of 0.28 and 0.40, respectively (p = 0.01). Overall, survival to discharge from the hospital was 66% (n = 29/44). Survival to discharge from the hospital was 52% (14/27) in major haemolysers versus 88% (15/17) in non-major haemolysers, p = 0.02. Only 3 of the 27 with major haemolysis had severe haemolysis, that is, lactate dehydrogenase > 2000 and bilirubin above 10. Non-major haemolysers had a better improvement in creatinine clearance during ventricular assist device support, p < 0.0001. (During the same era of this study, 22 patients who were supported with Berlin Heart were excluded from the analytic cohort because they did not have any recorded measurement of lactate dehydrogenase. Seventeen of these 22 patients had no clinical evidence of haemolysis. Survival to discharge from the hospital in this excluded cohort was 86% [19/22].)

Major haemolysis in patients with pulsatile ventricular assist device is more likely with single-ventricle ventricular assist device support and smaller body surface area.

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.

Accurate diagnosis of bipolar disorder (BPD) is difficult in clinical practice, with an average delay between symptom onset and diagnosis of about 7 years. A depressive episode often precedes the first manic episode, making it difficult to distinguish BPD from unipolar major depressive disorder (MDD).

We use genome-wide association analyses (GWAS) to identify differential genetic factors and to develop predictors based on polygenic risk scores (PRS) that may aid early differential diagnosis.

Based on individual genotypes from case–control cohorts of BPD and MDD shared through the Psychiatric Genomics Consortium, we compile case–case–control cohorts, applying a careful quality control procedure. In a resulting cohort of 51 149 individuals (15 532 BPD patients, 12 920 MDD patients and 22 697 controls), we perform a variety of GWAS and PRS analyses.

Although our GWAS is not well powered to identify genome-wide significant loci, we find significant chip heritability and demonstrate the ability of the resulting PRS to distinguish BPD from MDD, including BPD cases with depressive onset (BPD-D). We replicate our PRS findings in an independent Danish cohort (iPSYCH 2015, N = 25 966). We observe strong genetic correlation between our case–case GWAS and that of case–control BPD.

We find that MDD and BPD, including BPD-D are genetically distinct. Our findings support that controls, MDD and BPD patients primarily lie on a continuum of genetic risk. Future studies with larger and richer samples will likely yield a better understanding of these findings and enable the development of better genetic predictors distinguishing BPD and, importantly, BPD-D from MDD.

Recent stressful life events (SLE) are a risk factor for psychosis, but limited research has explored how SLEs affect individuals at clinical high risk (CHR) for psychosis. The current study investigated the longitudinal effects of SLEs on functioning and symptom severity in CHR individuals, where we hypothesized CHR would report more SLEs than healthy controls (HC), and SLEs would be associated with poorer outcomes.

The study used longitudinal data from the EU-GEI High Risk study. Data from 331 CHR participants were analyzed to examine the effects of SLEs on changes in functioning, positive and negative symptoms over a 2-year follow-up. We compared the prevalence of SLEs between CHR and HCs, and between CHR who did (CHR-T) and did not (CHR-NT) transition to psychosis.

CHR reported 1.44 more SLEs than HC (p < 0.001), but there was no difference in SLEs between CHR-T and CHR-NT at baseline. Recent SLEs were associated with poorer functioning and more severe positive and negative symptoms in CHR individuals (all p < 0.01) but did not reveal a significant interaction with time.

CHR individuals who had experienced recent SLEs exhibited poorer functioning and more severe symptoms. However, as the interaction between SLEs and time was not significant, this suggests SLEs did not contribute to a worsening of symptoms and functioning over the study period. SLEs could be a key risk factor to becoming CHR for psychosis, however further work is required to inform when early intervention strategies mitigating against the effects of stress are most effective.

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data.

We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors.

The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms).

The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

Adolescence is a critical period for brain development, consolidation of self-understanding, and onset of non-suicidal self-injury (NSSI). This study evaluated the RDoC (Research Domain Criteria) sub-construct of Self-Knowledge in relation to adolescent NSSI using multiple units of analysis.

One hundred and sixty-four adolescents assigned female at birth (AFAB), ages 12–16 years with and without a history of NSSI entered a study involving clinical assessment and magnetic resonance imaging (MRI), including structural, resting-state functional MRI (fMRI), and fMRI during a self-evaluation task. For imaging analyses, we used an a priori defined Self Network (anterior cingulate, orbitofrontal, and posterior cingulate cortices; precuneus). We first examined interrelationships among multi-level Self variables. We then evaluated the individual relationships between NSSI severity and multi-level Self variables (self-report, behavior, multi-modal brain Self Network measures), then conducted model testing and multiple regression to test how Self variables (together) predicted NSSI severity.

Cross-correlations revealed key links between self-reported global self-worth and self-evaluation task behavior. Individually, greater NSSI severity correlated with lower global self-worth, more frequent and faster negative self-evaluations, lower anterior Self Network activation during self-evaluation, and lower anterior and posterior Self Network resting-state connectivity. Multiple regression analysis revealed the model including multi-level Self variables explained NSSI better than a covariate-only model; the strongest predictive variables included self-worth, self-evaluation task behavior, and resting-state connectivity.

Disruptions in Self-Knowledge across multiple levels of analysis relate to NSSI in adolescents. Findings suggest potential neurobiological treatment targets, potentially enhancing neuroplasticity in Self systems to facilitate greater flexibility (more frequently positive) of self-views in AFAB adolescents.