Epidemiological studies show that despite the episodic nature, the long-term trajectory of depression can be variable. This study evaluated the heterogeneity of 10-year trajectory of major depressive disorder (MDD) related service utilization and associated clinical characteristics among US Veterans with a first diagnosis after 9/11.

Using a cohort design, electronic health record data for 293,265 Operation Enduring Freedom and Iraqi Freedom (OEF/OIF) Veterans were extracted to identify those with MDD between 2001 and 2021 with a full preceding year of clinical data and 10 years following the diagnosis. Latent class growth analysis compared clinical characteristics associated with four depression trajectories. Across all Veterans Affairs (VA)hospitals, 25,307 Veterans met our inclusion criteria. Demographic and clinical information from medical records was extracted and used as predictors of depression 10-year trajectories.

Among the study cohort (N = 25,307), 27.7% were characterized by brief contact, 41.7% were later re-entry, 17.6% were persistent contact and 12.9% were prolonged initial contact for depression related services. Compared to Veterans with trajectories showing brief contact, those with protracted treatment (persistent or prolonged initial contact) were more likely to be diagnosed with comorbid posttraumatic stress disorder (PTSD) and with MDD that was moderate to severe or recurrent.

Depression is associated with a range of treatment trajectories. The persistent and prolonged initial contact trajectories may have distinct characteristics and uniquely high resource utilization and disability income. We can anticipate that patients with comorbid PTSD may need longer-term care which has implications for brief models of care.

To characterise the association between risk of poor glycaemic control and self-reported and area-level food insecurity among adult patients with type 2 diabetes.

We performed a retrospective, observational analysis of cross-sectional data routinely collected within a health system. Logistic regressions estimated the association between glycaemic control and the dual effect of self-reported and area-level measures of food insecurity.

The health system included a network of ambulatory primary and speciality care sites and hospitals in Bronx County, NY.

Patients diagnosed with type 2 diabetes who completed a health-related social need (HRSN) assessment between April 2018 and December 2019.

5500 patients with type 2 diabetes were assessed for HRSN with 7·1 % reporting an unmet food need. Patients with self-reported food needs demonstrated higher odds of having poor glycaemic control compared with those without food needs (adjusted OR (aOR): 1·59, 95 % CI: 1·26, 2·00). However, there was no conclusive evidence that area-level food insecurity alone was a significant predictor of glycaemic control (aOR: 1·15, 95 % CI: 0·96, 1·39). Patients with self-reported food needs residing in food-secure (aOR: 1·83, 95 % CI: 1·22, 2·74) and food-insecure (aOR: 1·72, 95 % CI: 1·25, 2·37) areas showed higher odds of poor glycaemic control than those without self-reported food needs residing in food-secure areas.

These findings highlight the importance of utilising patient- and area-level social needs data to identify individuals for targeted interventions with increased risk of adverse health outcomes.

The aim of this project is to study to which extent salience alterations influence the severity of psychotic symptoms. However, rather than studying them individually, we decided to focus on their interplay with two additional variables, that is: observing their effect in a vulnerability phase (adolescence) and with another added, well-recognized risk factor (cannabis use).

The reason for this study design lies in the fact that, in our opinion, it is fundamental to observe the trajectory of psychotic symptoms over a continuum; however, rather than adopting a longitudinal approach, we decided to structure it as a cross-sectional study confronting patients from two age brackets - adolescence and adulthood.

The primary purpose of this study was to assess a difference between THC-abusing and non-abusing patients in adolescent and adult cohorts, using the Italian version of the psychometric scale “Aberrant Salience Inventory” (ASI), and the possible correlation with more severe psychotic symptoms. The employment of several different psychometric scales and the inclusion of a variegated cohort allowed to pursue multiple secondary objectives.

We recruited 192 patients, subsequently divided into six subgroups based on age and department of recruitment (whether adolescent or adult psychiatric or neurologic units - the latter serving as controls). Each individual was administered a set of questionnaires and a socio-demographic survey; the set included: Aberrant Salience Inventory (ASI), Community Assessment of Psychic Experiences (CAPE), Positive and Negative Syndrome Scale (PANSS), Montgomery-Asberg Depression Rating Scale (MADRS), Mania Rating Scale (MRS), Hamilton Anxiety Scale (HAM-A), Association for Methodology and Documentation in Psychiatry (AMDP) and Cannabis Experience Questionnaire (CEQ).

The data analysis showed statistically significant (p<0.05) differences between adolescents and adults with psychotic symptoms in all of the three scales of PANSS and in MADRS. These two groups were homogenous for both cannabis use and ASI score. The intra-group comparison (either adolescent or adult) showed a hierarchical pattern in the scores of psychometric scales according to the diagnostic subgroup of allocation: patients with psychotic symptoms showed an higher level of psychopathology in all measures when compared to patients from the psychiatric unit without psychotic symptoms, which in turn scored higher than the patients from the neurologic unit.

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The results of the present study may suggest that when salience alterations occur in adolescents with cannabis exposure, we might observe worsened positive and negative psychotic symptoms; their influence might be relevant also in other domains, especially regarding the depressive and anxiety spectrums.

None Declared

Understanding the factors contributing to optimal cognitive function throughout the aging process is essential to better understand successful cognitive aging. Processing speed is an age sensitive cognitive domain that usually declines early in the aging process; however, this cognitive skill is essential for other cognitive tasks and everyday functioning. Evaluating brain network interactions in cognitively healthy older adults can help us understand how brain characteristics variations affect cognitive functioning. Functional connections among groups of brain areas give insight into the brain’s organization, and the cognitive effects of aging may relate to this large-scale organization. To follow-up on our prior work, we sought to replicate our findings regarding network segregation’s relationship with processing speed. In order to address possible influences of node location or network membership we replicated the analysis across 4 different node sets.

Data were acquired as part of a multi-center study of 85+ cognitively normal individuals, the McKnight Brain Aging Registry (MBAR). For this analysis, we included 146 community-dwelling, cognitively unimpaired older adults, ages 85-99, who had undergone structural and BOLD resting state MRI scans and a battery of neuropsychological tests. Exploratory factor analysis identified the processing speed factor of interest. We preprocessed BOLD scans using fmriprep, Ciftify, and XCPEngine algorithms. We used 4 different sets of connectivity-based parcellation: 1)MBAR data used to define nodes and Power (2011) atlas used to determine node network membership, 2) Younger adults data used to define nodes (Chan 2014) and Power (2011) atlas used to determine node network membership, 3) Older adults data from a different study (Han 2018) used to define nodes and Power (2011) atlas used to determine node network membership, and 4) MBAR data used to define nodes and MBAR data based community detection used to determine node network membership.

Segregation (balance of within-network and between-network connections) was measured within the association system and three wellcharacterized networks: Default Mode Network (DMN), Cingulo-Opercular Network (CON), and Fronto-Parietal Network (FPN). Correlation between processing speed and association system and networks was performed for all 4 node sets.

We replicated prior work and found the segregation of both the cortical association system, the segregation of FPN and DMN had a consistent relationship with processing speed across all node sets (association system range of correlations: r=.294 to .342, FPN: r=.254 to .272, DMN: r=.263 to .273). Additionally, compared to parcellations created with older adults, the parcellation created based on younger individuals showed attenuated and less robust findings as those with older adults (association system r=.263, FPN r=.255, DMN r=.263).

This study shows that network segregation of the oldest-old brain is closely linked with processing speed and this relationship is replicable across different node sets created with varied datasets. This work adds to the growing body of knowledge about age-related dedifferentiation by demonstrating replicability and consistency of the finding that as essential cognitive skill, processing speed, is associated with differentiated functional networks even in very old individuals experiencing successful cognitive aging.

The association between sleep quality and cognition is widely established, but the role of aging in this relationship is largely unknown.

To examine how age impacts the sleep–cognition relationship and determine whether there are sensitive ranges when the relationship between sleep and cognition is modified. This investigation could help identify individuals at risk for sleep-related cognitive impairment.

Sample included 711 individuals (ages 36.00–89.83, 59.66 ± 14.91, 55.7 % female) from the Human Connectome Project-Aging (HCP-A).

The association between sleep quality (Pittsburgh Sleep Quality Index, PSQI) and cognition (Crystallized Cognition Composite and Fluid Cognition Composite from the NIH Toolbox, the Trail Making Test, TMT, and the Rey Auditory Verbal Learning Test, RAVLT) was measured using linear regression models, with sex, race, use of sleep medication, hypertension, and years of education as covariates. The interaction between sleep and age on cognition was tested using the moderation analysis, with age as both continuous linear and nonlinear (quadratic) terms.

There was a significant interaction term between the PSQI and nonlinear age term (age2) on TMT-B (p = 0.02) and NIH Toolbox crystallized cognition (p = 0.02), indicating that poor sleep quality was associated with worse performance on these measures (sensitive age ranges 50–75 years for TMT-B and 66–70 years for crystallized cognition).

The sleep–cognition relationship may be modified by age. Individuals in the middle age to early older adulthood age band may be most vulnerable to sleep-related cognitive impairment.

Little is known about the effect of ethnicity on the response to antipsychotic medication in patients with schizophrenia.

To determine whether ethnicity moderates the response to antipsychotic medication in patients with schizophrenia, and whether this moderation is independent of confounders.

We analysed 18 short-term, placebo-controlled registration trials of atypical antipsychotic medications in patients with schizophrenia (N = 3880). A two-step, random-effects, individual patient data meta-analysis was applied to establish the moderating effect of ethnicity (White versus Black) on symptom improvement according to the Brief Psychiatric Rating Scale (BPRS) and on response, defined as >30% BPRS reduction. These analyses were corrected for baseline severity, baseline negative symptoms, age and gender. A conventional meta-analysis was performed to determine the effect size of antipsychotic treatment for each ethnic group separately.

In the complete data-set, 61% of patients were White, 25.6% of patients were Black and 13.4% of patients were of other ethnicities. Ethnicity did not moderate the efficacy of antipsychotic treatment: pooled β-coefficient for the interaction between treatment and ethnic group was −0.582 (95% CI −2.567 to 1.412) for mean BPRS change, with an odds ratio of 0.875 (95% CI 0.510–1.499) for response. These results were not modified by confounders.

Atypical antipsychotic medication is equally effective in both Black and White patients with schizophrenia. In registration trials, White and Black patients were overrepresented relative to other ethnic groups, limiting the generalisability of our findings.

To evaluate the construct validity of the NIH Toolbox Cognitive Battery (NIH TB-CB) in the healthy oldest-old (85+ years old).

Our sample from the McKnight Brain Aging Registry consists of 179 individuals, 85 to 99 years of age, screened for memory, neurological, and psychiatric disorders. Using previous research methods on a sample of 85 + y/o adults, we conducted confirmatory factor analyses on models of NIH TB-CB and same domain standard neuropsychological measures. We hypothesized the five-factor model (Reading, Vocabulary, Memory, Working Memory, and Executive/Speed) would have the best fit, consistent with younger populations. We assessed confirmatory and discriminant validity. We also evaluated demographic and computer use predictors of NIH TB-CB composite scores.

Findings suggest the six-factor model (Vocabulary, Reading, Memory, Working Memory, Executive, and Speed) had a better fit than alternative models. NIH TB-CB tests had good convergent and discriminant validity, though tests in the executive functioning domain had high inter-correlations with other cognitive domains. Computer use was strongly associated with higher NIH TB-CB overall and fluid cognition composite scores.

The NIH TB-CB is a valid assessment for the oldest-old samples, with relatively weak validity in the domain of executive functioning. Computer use’s impact on composite scores could be due to the executive demands of learning to use a tablet. Strong relationships of executive function with other cognitive domains could be due to cognitive dedifferentiation. Overall, the NIH TB-CB could be useful for testing cognition in the oldest-old and the impact of aging on cognition in older populations.

Cognitive therapy and behavioural activation are both widely applied and effective psychotherapies for depression, but it is unclear which works best for whom. Individual participant data (IPD) meta-analysis allows for examining moderators at the participant level and can provide more precise effect estimates than conventional meta-analysis, which is based on study-level data.

This article describes the protocol for a systematic review and IPD meta-analysis that aims to compare the efficacy of cognitive therapy and behavioural activation for adults with depression, and to explore moderators of treatment effect. (PROSPERO: CRD42022341602)

Systematic literature searches will be conducted in PubMed, PsycINFO, EMBASE and the Cochrane Library, to identify randomised clinical trials comparing cognitive therapy and behavioural activation for adult acute-phase depression. Investigators of these trials will be invited to share their participant-level data. One-stage IPD meta-analyses will be conducted with mixed-effects models to assess treatment effects and to examine various available demographic, clinical and psychological participant characteristics as potential moderators. The primary outcome measure will be depressive symptom level at treatment completion. Secondary outcomes will include post-treatment anxiety, interpersonal functioning and quality of life, as well as follow-up outcomes.

To the best of our knowledge, this will be the first IPD meta-analysis concerning cognitive therapy versus behavioural activation for adult depression. This study has the potential to enhance our knowledge of depression treatment by using state-of-the-art statistical techniques to compare the efficacy of two widely used psychotherapies, and by shedding more light on which of these treatments might work best for whom.

Hypotension is an adverse event that may be related to systemic exposure of milrinone; however, the true exposure–safety relationship is unknown.

Using the Pediatric Trials Network multicentre repository, we identified children ≤17 years treated with milrinone. Hypotension was defined according to age, using the Pediatric Advanced Life Support guidelines. Clinically significant hypotension was defined as hypotension with concomitant lactate >3 mg/dl. A prior population pharmacokinetic model was used to simulate milrinone exposures to evaluate exposure–safety relationships.

We included 399 children with a median (quarter 1, quarter 3) age of 1 year (0,5) who received 428 intravenous doses of milrinone (median infusion rate 0.31 mcg/kg/min [0.29,0.5]). Median maximum plasma milrinone concentration was 110.7 ng/ml (48.4,206.2). Median lowest systolic and diastolic blood pressures were 74 mmHg (60,85) and 35 mmHg (25,42), respectively. At least 1 episode of hypotension occurred in 178 (45%) subjects; clinically significant hypotension occurred in 10 (2%). The maximum simulated milrinone plasma concentrations were higher in subjects with clinically significant hypotension (251 ng/ml [129,329]) versus with hypotension alone (86 ng/ml [44, 173]) versus without hypotension (122 ng/ml [57, 208], p = 0.002); however, this relationship was not retained on multivariable analysis (odds ratio 1.01; 95% confidence interval 0.998, 1.01).

We successfully leveraged a population pharmacokinetic model and electronic health record data to evaluate the relationship between simulated plasma concentration of milrinone and systemic hypotension occurrence, respectively, supporting the broader applicability of our novel, efficient, and cost-effective study design for examining drug exposure–response and –safety relationships.