Blood epigenome-wide association studies of suicide attempt in adults with bipolar disorder.

Suicide attempt (SA) risk is elevated in individuals with bipolar disorder (BD), and DNA methylation patterns may serve as possible biomarkers of SA. We conducted epigenome-wide association studies (EWAS) of blood DNA methylation associated with BD and SA. DNA methylation was measured at >700,000 positions in a discovery cohort of n = 84 adults with BD with a history of SA (BD/SA), n = 79 adults with BD without history of SA (BD/non-SA), and n = 76 non-psychiatric controls (CON). EWAS revealed six differentially methylated positions (DMPs) and seven differentially methylated regions (DMRs) between BD/SA and BD/non-SA, with multiple immune-related genes implicated. There were no epigenome-wide significant differences when BD/SA and BD/non-SA were each compared to CON, and patterns suggested that epigenetics differentiating BD/SA from BD/non-SA do not differentiate BD/non-SA from CON. Weighted gene co-methylation network analysis and trait enrichment analysis of the BD/SA vs. BD/non-SA contrast further corroborated immune system involvement, while gene ontology analysis implicated calcium signalling. In an independent replication cohort of n = 48 BD/SA and n = 47 BD/non-SA, fold changes at the discovery cohort's significant sites showed moderate correlation across cohorts and agreement on direction. In both cohorts, classification accuracy for SA history among individuals with BD was highest when methylation at the significant CpG sites as well as information from clinical interviews were combined, with an AUC of 88.8% (CI = 83.8-93.8%) and 82.1% (CI = 73.6-90.5%) for the combined epigenetic-clinical classifier in the discovery and replication cohorts, respectively. Our results provide novel insight to the role of immune system functioning in SA and BD and also suggest that integrating information from multiple levels of analysis holds promise to improve risk assessment for SA in adults with BD.

Blood epigenome-wide association studies of suicide attempt in adults with bipolar disorder.

Suicide attempt (SA) risk is elevated in individuals with bipolar disorder (BD), and DNA methylation patterns may serve as possible biomarkers of SA. We conducted epigenome-wide association studies (EWAS) of blood DNA methylation associated with BD and SA. DNA methylation was measured at > 700,000 positions in a discovery cohort of n = 84 adults with BD with a history of SA (BD/SA), n = 79 adults with BD without history of SA (BD/non-SA), and n = 76 non-psychiatric controls (CON). EWAS revealed six differentially methylated positions (DMPs) and seven differentially methylated regions (DMRs) between BD/SA and BD/non-SA, with multiple immune-related genes implicated. There were no epigenome-wide significant differences when BD/SA and BD/non-SA were each compared to CON, and patterns suggested that epigenetics differentiating BD/SA from BD/non-SA do not differentiate BD/non-SA from CON. Weighted gene co-methylation network analysis and trait enrichment analysis of the BD/SA vs. BD/non-SA contrast further corroborated immune system involvement, while gene ontology analysis implicated calcium signalling. In an independent replication cohort of n = 48 BD/SA and n = 47 BD/non-SA, fold-changes at the discovery cohort's significant sites showed moderate correlation across cohorts and agreement on direction. In both cohorts, classification accuracy for SA history among individuals with BD was highest when methylation at the significant CpG sites as well as information from clinical interviews were combined, with an AUC of 88.8% (CI = 83.8-93.8%) and 82.1% (CI = 73.6-90.5%) for the combined epigenetic-clinical predictor in the discovery and replication cohorts, respectively. Our results provide novel insight to the role of immune system functioning in SA and BD and also suggest that integrating information from multiple levels of analysis holds promise to improve risk assessment for SA in adults with BD.

Temporal multi-step predictive modeling of remission in major depressive disorder using early stage treatment data; STAR*D based machine learning approach.

BACKGROUND: Artificial intelligence is currently being used to facilitate early disease detection, better understand disease progression, optimize medication/treatment dosages, and uncover promising novel treatments and potential outcomes. METHODS: Utilizing the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, we built a machine learning model to predict depression remission rates using same clinical data as features for each of the first three antidepressant treatment steps in STAR*D. We only used early treatment data (baseline and first follow up) in each STAR*D step to temporally analyze predictive features of remission at the end of the step. RESULTS: Our model showed significant prediction performance across the three treatment steps, At step 1, Model accuracy was 66 %; sensitivity-65 %, specificity-67 %, positive predictive value (PPV)-65.5 %, and negative predictive value (NPV)-66.6 %. At step 2, model accuracy was 71.3 %, sensitivity-74.3 %, specificity-69 %, PPV-64.5 %, and NPV-77.9 %. At step 3, accuracy reached 84.6 %; sensitivity-69 %, specificity-88.8 %, PPV-67 %, and NPV-91.1 %. Across all three steps, the early Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR) scores were key elements in predicting the final treatment outcome. The model also identified key sociodemographic factors that predicted treatment remission at different steps. LIMITATIONS: The retrospective design, lack of replication in an independent dataset, and the use of "a complete case analysis" model in our analysis. CONCLUSIONS: This proof-of-concept study showed that using early treatment data, multi-step temporal prediction of depressive symptom remission results in clinically useful accuracy rates. Whether these predictive models are generalizable deserves further study.

Blood-brain barrier dysfunction in bipolar disorder: Molecular mechanisms and clinical implications.

Bipolar disorder (BD) is a severe psychiatric disorder affecting approximately 1-3% of the population and characterized by a chronic and recurrent course of debilitating symptoms. An increasing focus has been directed to discover and explain the function of Blood-Brain Barrier (BBB) integrity and its association with a number of psychiatric disorders; however, there has been limited research in the role of BBB integrity in BD. Multiple pathways may play crucial roles in modulating BBB integrity in BD, such as inflammation, insulin resistance, and alterations of neuronal plasticity. In turn, BBB impairment is hypothesized to have a significant clinical impact in BD patients. Based on the high prevalence of medical and psychiatric comorbidities in BD and a growing body of evidence linking inflammatory and neuroinflammatory mechanisms to the disorder, recent studies have suggested that BBB dysfunction may play a key role in BD's pathophysiology. In this comprehensive narrative review, we aim to discuss studies investigating biological markers of BBB in patients with BD, mechanisms that modulate BBB integrity, their clinical implications on patients, and key targets for future development of novel therapies.

Addressing Firearm Safety Counseling: Integration of a Multidisciplinary Workshop in a Pediatric Residency Program.

BACKGROUND: Firearm-related injuries are the second leading cause of death among US children. Given this, firearm injury prevention should be a key aspect of pediatric anticipatory guidance. OBJECTIVE: We assessed the impact of a firearm safety counseling workshop on pediatric resident knowledge, self-efficacy, and self-reported practice patterns. METHODS: Sixty of 80 residents (75%) participated in a 2-hour multimodal workshop, including video, didactics with experts, and role-play scenarios. Participants were invited to complete pre-workshop, immediate post-workshop, and 3- and 6-month post-workshop self-reported questionnaires evaluating knowledge, comfort, perceived barriers, and reported practice patterns. Data comparing pre- and 6-month post-workshop practice patterns were analyzed via Fischer's exact test. Remaining statistical analysis utilized a one-sided, unpaired Mann-Whitney U test. A binomial exact proportions test was used for open-ended responses. RESULTS: After the workshop, the percentage of participants with perceived concern regarding parental barriers decreased significantly (24% to 7%, P = .001). Participants 6 months post-workshop were 5.14 times more likely to counsel their patients on firearms during more than 75% of their well visits than prior to the intervention (P = .010). Participants reported greater comfort asking patients about firearms, with mean Likert scores increasing from 3.81 pre to 4.33 post (P = .022), which was similar to 3-month (4.39, P = .06) and 6-month evaluations (4.54, P = .003). CONCLUSIONS: Education on firearm safety counseling improved pediatric resident comfort level in discussing the topic. This impact persisted 6 months after the workshop, implying a sustained change in attitudes and behaviors.

Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest.

Brain functional connectivity (FC) changes have been measured across seconds using fMRI. This is true for both rest and task scenarios. Moreover, it is well accepted that task engagement alters FC, and that dynamic estimates of FC during and before task events can help predict their nature and performance. Yet, when it comes to dynamic FC (dFC) during rest, there is no consensus about its origin or significance. Some argue that rest dFC reflects fluctuations in on-going cognition, or is a manifestation of intrinsic brain maintenance mechanisms, which could have predictive clinical value. Conversely, others have concluded that rest dFC is mostly the result of sampling variability, head motion or fluctuating sleep states. Here, we present novel analyses suggesting that rest dFC is influenced by short periods of spontaneous cognitive-task-like processes, and that the cognitive nature of such mental processes can be inferred blindly from the data. As such, several different behaviorally relevant whole-brain FC configurations may occur during a single rest scan even when subjects were continuously awake and displayed minimal motion. In addition, using low dimensional embeddings as visualization aids, we show how FC states-commonly used to summarize and interpret resting dFC-can accurately and robustly reveal periods of externally imposed tasks; however, they may be less effective in capturing periods of distinct cognition during rest.