n-3 fatty acid consumption during pregnancy is recommended for optimal pregnancy outcomes and offspring health. We examined characteristics associated with self-reported fish or n-3 supplement intake.

Pooled pregnancy cohort studies.

Cohorts participating in the Environmental influences on Child Health Outcomes (ECHO) consortium with births from 1999 to 2020.

A total of 10 800 pregnant women in twenty-three cohorts with food frequency data on fish consumption; 12 646 from thirty-five cohorts with information on supplement use.

Overall, 24·6 % reported consuming fish never or less than once per month, 40·1 % less than once a week, 22·1 % 1–2 times per week and 13·2 % more than twice per week. The relative risk (RR) of ever (v. never) consuming fish was higher in participants who were older (1·14, 95 % CI 1·10, 1·18 for 35–40 v. <29 years), were other than non-Hispanic White (1·13, 95 % CI 1·08, 1·18 for non-Hispanic Black; 1·05, 95 % CI 1·01, 1·10 for non-Hispanic Asian; 1·06, 95 % CI 1·02, 1·10 for Hispanic) or used tobacco (1·04, 95 % CI 1·01, 1·08). The RR was lower in those with overweight v. healthy weight (0·97, 95 % CI 0·95, 1·0). Only 16·2 % reported n-3 supplement use, which was more common among individuals with a higher age and education, a lower BMI, and fish consumption (RR 1·5, 95 % CI 1·23, 1·82 for twice-weekly v. never).

One-quarter of participants in this large nationwide dataset rarely or never consumed fish during pregnancy, and n-3 supplement use was uncommon, even among those who did not consume fish.

Identifying neuroimaging biomarkers of antidepressant response may help guide treatment decisions and advance precision medicine.

To examine the relationship between anhedonia and functional neurocircuitry in key reward processing brain regions in people with major depressive disorder receiving aripiprazole adjunct therapy with escitalopram.

Data were collected as part of the CAN-BIND-1 study. Participants experiencing a current major depressive episode received escitalopram for 8 weeks; escitalopram non-responders received adjunct aripiprazole for an additional 8 weeks. Functional magnetic resonance imaging (on weeks 0 and 8) and clinical assessment of anhedonia (on weeks 0, 8 and 16) were completed. Seed-based correlational analysis was employed to examine the relationship between baseline resting-state functional connectivity (rsFC), using the nucleus accumbens (NAc) and anterior cingulate cortex (ACC) as key regions of interest, and change in anhedonia severity after adjunct aripiprazole.

Anhedonia severity significantly improved after treatment with adjunct aripiprazole.

There was a positive correlation between anhedonia improvement and rsFC between the ACC and posterior cingulate cortex, ACC and posterior praecuneus, and NAc and posterior praecuneus. There was a negative correlation between anhedonia improvement and rsFC between the ACC and anterior praecuneus and NAc and anterior praecuneus.

Eight weeks of aripiprazole, adjunct to escitalopram, was associated with improved anhedonia symptoms. Changes in functional connectivity between key reward regions were associated with anhedonia improvement, suggesting aripiprazole may be an effective treatment for individuals experiencing reward-related deficits. Future studies are required to replicate our findings and explore their generalisability, using other agents with partial dopamine (D2) agonism and/or serotonin (5-HT2A) antagonism.

Cardiovascular disease (CVD) is excessively prevalent and premature in bipolar disorder (BD), even after controlling for traditional cardiovascular risk factors. The increased risk of CVD in BD may be subserved by microvascular dysfunction. We examined coronary microvascular function in relation to youth BD.

Participants were 86 youth, ages 13–20 years (n = 39 BD, n = 47 controls). Coronary microvascular reactivity (CMVR) was assessed using quantitative T2 magnetic resonance imaging during a validated breathing-paradigm. Quantitative T2 maps were acquired at baseline, following 60-s of hyperventilation, and every 10-s thereafter during a 40-s breath-hold. Left ventricular structure and function were evaluated based on 12–15 short- and long-axis cardiac-gated cine images. A linear mixed-effects model that controlled for age, sex, and body mass index assessed for between-group differences in CMVR (time-by-group interaction).

The breathing-paradigm induced a significant time-related increase in T2 relaxation time for all participants (i.e. CMVR; β = 0.36, p < 0.001). CMVR was significantly lower in BD v. controls (β = −0.11, p = 0.002). Post-hoc analyses found lower T2 relaxation time in BD youth after 20-, 30-, and 40 s of breath-holding (d = 0.48, d = 0.72, d = 0.91, respectively; all pFDR < 0.01). Gross left ventricular structure and function (e.g. mass, ejection fraction) were within normal ranges and did not differ between groups.

Youth with BD showed evidence of subclinically impaired coronary microvascular function, despite normal gross cardiac structure and function. These results converge with prior findings in adults with major depressive disorder and post-traumatic stress disorder. Future studies integrating larger samples, prospective follow-up, and blood-based biomarkers are warranted.

Despite replicated cross-sectional evidence of aberrant levels of peripheral inflammatory markers in individuals with major depressive disorder (MDD), there is limited literature on associations between inflammatory tone and response to sequential pharmacotherapies.

To assess associations between plasma levels of pro-inflammatory markers and treatment response to escitalopram and adjunctive aripiprazole in adults with MDD.

In a 16-week open-label clinical trial, 211 participants with MDD were treated with escitalopram 10– 20 mg daily for 8 weeks. Responders continued on escitalopram while non-responders received adjunctive aripiprazole 2–10 mg daily for 8 weeks. Plasma levels of pro-inflammatory markers – C-reactive protein, Interleukin (IL)-1β, IL-6, IL-17, Interferon gamma (IFN)-Γ, Tumour Necrosis Factor (TNF)-α, and Chemokine C–C motif ligand-2 (CCL-2) - measured at baseline, and after 2, 8 and 16 weeks were included in logistic regression analyses to assess associations between inflammatory markers and treatment response.

Pre-treatment levels of IFN-Γ and CCL-2 were significantly higher in escitalopram non-responders compared to responders. Pre-treatment IFN-Γ and CCL-2 levels were significantly associated with a lower of odds of response to escitalopram at 8 weeks. Increases in CCL-2 levels from weeks 8 to 16 in escitalopram non-responders were significantly associated with higher odds of non-response to adjunctive aripiprazole at week 16.

Pre-treatment levels of IFN-Γ and CCL-2 were predictive of response to escitalopram. Increasing levels of these pro-inflammatory markers may predict non-response to adjunctive aripiprazole. These findings require validation in independent clinical populations.

None Declared

Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.

We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.

Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.

An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.

Blast related characteristics may contribute to the diversity of findings on whether mild traumatic brain injury sustained during war zone deployment has lasting cognitive effects. This study aims to evaluate whether a history of blast exposure at close proximity, defined as exposure within 30 feet, has long-term or lasting influences on cognitive outcomes among current and former military personnel.

One hundred participants were assigned to one of three groups based on a self-report history of blast exposure during combat deployments: 47 close blast, 14 non-close blast, and 39 comparison participants without blast exposure. Working memory, processing speed, verbal learning/memory, and cognitive flexibility were evaluated using standard neuropsychological tests. In addition, assessment of combat exposure and current post-concussive, posttraumatic stress, and depressive symptoms, and headache was performed via self-report measures. Variables that differed between groups were controlled as covariates.

No group differences survived Bonferroni correction for family-wise error rate; the close blast group did not differ from non-close blast and comparison groups on measures of working memory, processing speed, verbal learning/memory, or cognitive flexibility. Controlling for covariates did not alter these results.

No evidence emerged to suggest that a history of close blast exposure was associated with decreased cognitive performance when comparisons were made with the other groups. Limited characterization of blast contexts experienced, self-report of blast distance, and heterogeneity of injury severity within the groups are the main limitations of this study.

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Studying phenotypic and genetic characteristics of age at onset (AAO) and polarity at onset (PAO) in bipolar disorder can provide new insights into disease pathology and facilitate the development of screening tools.

To examine the genetic architecture of AAO and PAO and their association with bipolar disorder disease characteristics.

Genome-wide association studies (GWASs) and polygenic score (PGS) analyses of AAO (n = 12 977) and PAO (n = 6773) were conducted in patients with bipolar disorder from 34 cohorts and a replication sample (n = 2237). The association of onset with disease characteristics was investigated in two of these cohorts.

Earlier AAO was associated with a higher probability of psychotic symptoms, suicidality, lower educational attainment, not living together and fewer episodes. Depressive onset correlated with suicidality and manic onset correlated with delusions and manic episodes. Systematic differences in AAO between cohorts and continents of origin were observed. This was also reflected in single-nucleotide variant-based heritability estimates, with higher heritabilities for stricter onset definitions. Increased PGS for autism spectrum disorder (β = −0.34 years, s.e. = 0.08), major depression (β = −0.34 years, s.e. = 0.08), schizophrenia (β = −0.39 years, s.e. = 0.08), and educational attainment (β = −0.31 years, s.e. = 0.08) were associated with an earlier AAO. The AAO GWAS identified one significant locus, but this finding did not replicate. Neither GWAS nor PGS analyses yielded significant associations with PAO.

AAO and PAO are associated with indicators of bipolar disorder severity. Individuals with an earlier onset show an increased polygenic liability for a broad spectrum of psychiatric traits. Systematic differences in AAO across cohorts, continents and phenotype definitions introduce significant heterogeneity, affecting analyses.

Research career development awards (CDAs) facilitate development of clinician-scientists. This study compared the academic achievements of individuals in a structured institutional “pre-K” CDA program, the Mayo Clinic Kern Scholars program, with individuals who applied for but were not admitted to the Kern program (“Kern applicants”), and awardees of other unstructured internal CDAs.

This was a longitudinal cohort study of clinicians engaged in research at Mayo Clinic between 2010 and 2019. The primary outcome was time to the 15th new peer-reviewed publication after the program start, adjusted for baseline number of publications. Secondarily, we described successful awarding of federal funding by the NIH or VA.

The median (IQR) number of baseline publications was highest among Kern Scholars compared to Kern Applicants or other CDA awardees [16 (12, 29) vs 5 (1, 11) and 8 (5, 16); P < 0.001]. After adjustment for baseline publications, the time to 15th new publication was significantly shorter for Kern Scholars than for the two comparator groups (P<0.001). Similar findings were observed with total new publications within 5 years (P < 0.001), as well as number of new first-/last-author publications within 5 years (P < 0.001). The overall frequency of K-awards, R-awards (or equivalent), or any funding were similar between groups, with the exception of R03 awards, which were significantly more common among Kern Scholars (P = 0.002).

The Kern Scholars program is a successful training model for clinician-scientists that demonstrated comparatively greater acceleration of scholarly productivity than other internal CDA programs.