Charles Reynolds Iii

Although evidence-based treatments for Prolonged Grief Disorder (PGD) exist, pretreatment characteristics associated with differential improvement trajectories have not been identified. To identify clinical factors relevant to optimizing PGD treatment outcomes, we used unsupervised and supervised machine learning to study treatment effects from a double-blinded, placebo-controlled, randomized clinical trial. Participants were randomized into four treatment groups for 20 weeks: citalopram with grief-informed clinical management, citalopram with prolonged grief disorder therapy (PGDT), pill placebo with PGDT, or pill placebo with clinical management. The trial included 333 PGD patients aged 18–95 years ( M _age = 53.9; SD ± 14.4), predominantly female (77.4%) and white (84.4%). Symptom trajectories were assessed using latent growth mixture modeling based on Inventory for Complicated Grief scores collected at six time points every 4 weeks. The relationship between patient-level characteristics and assigned trajectories was examined using logistic regression with elastic net regularization based on the administration of citalopram, PGDT, and risk factors for developing PGD. Three distinct response trajectories were identified: lesser severity responders (60%, n = 200), greater severity responders (18.02%, n = 60), and non-responders (21.92%, n = 73). Differences between greater severity responders and non-responders emerged as statistically significant by Week 8. The elastic net model demonstrated acceptable discrimination between responders and non-responders (AUC = .702; accuracy = .684). Higher baseline depression severity, grief-related functional impairment, and absence of PGDT were associated with reduced treatment response likelihood. These findings underscore the importance of early identification of clinical factors to optimize individualized PGD treatment strategies. Trial Registration: clinicaltrials.gov Identifier: NCT01179568 .

In this study, we aimed to improve upon a published population pharmacokinetic (PK) model for venlafaxine (VEN) in the treatment of depression in older adults, then investigate whether CYP2D6 metabolizer status affected model‐estimated PK parameters of VEN and its active metabolite O‐desmethylvenlafaxine. The model included 325 participants from a clinical trial in which older adults with depression were treated with open‐label VEN (maximum 300 mg/day) for 12 weeks and plasma levels of VEN and O‐desmethylvenlafaxine were assessed at weeks 4 and 12. We fitted a nonlinear mixed‐effect PK model using NONMEM to estimate PK parameters for VEN and O‐desmethylvenlafaxine adjusted for CYP2D6 metabolizer status and age. At both lower doses (up to 150 mg/day) and higher doses (up to 300 mg/day), CYP2D6 metabolizers impacted PK model‐estimated VEN clearance, VEN exposure, and active moiety (VEN + O‐desmethylvenlafaxine) exposure. Specifically, compared with CYP2D6 normal metabolizers, (i) CYP2D6 ultra‐rapid metabolizers had higher VEN clearance; (ii) CYP2D6 intermediate metabolizers had lower VEN clearance; (iii) CYP2D6 poor metabolizers had lower VEN clearance, higher VEN exposure, and higher active moiety exposure. Overall, our study showed that including a pharmacogenetic factor in a population PK model could increase model fit, and this improved model demonstrated how CYP2D6 metabolizer status affected VEN‐related PK parameters, highlighting the importance of genetic factors in personalized medicine.

Background: In a recent trial, we observed no effect of omega‐3 fatty acids (omega‐3s) supplementation on cognitive decline. However, brain concentrations and metabolism of lipid molecules in response to omega‐3s may be regulated by apolipoprotein E (APOE)‐ε4 allele status. Further, while APOE‐ε4 is an established risk factor for Alzheimer disease, its association with cognitive decline is uncertain, especially in more diverse samples. The few randomized clinical trials (RCTs) addressing the moderation of effects of omega‐3s on cognitive decline by APOE‐ε4 have yielded inconsistent findings. Thus, we examined moderation by APOE‐ε4 on effects of omega‐3s vs. placebo for global and domain‐specific cognitive change over 2 years in a well‐characterized, diverse sample of older adults.Methods: VITamin D and OmegA‐3 TriaL (VITAL) is a 2×2 factorial RCT of vitamin D3 (2000 IU/day) and/or omega‐3s (1 g/day including 460 mg of eicosapentaenoic acid and 380 mg of docosahexaenoic acid) for cardiovascular disease and cancer prevention. We included 743 VITAL in‐clinic sub‐cohort participants who completed all cognitive tests at baseline and had APOE genotype data. The primary outcome was change in global cognition (averaging z‐scores of 9 tests) over 2 years; episodic memory and executive function/attention were examined separately. We used multiplicative interaction terms in general linear models of response profiles to examine moderation by APOE‐ε4 carrier status on effects of omega‐3s vs. placebo for cognitive change outcomes over 2 years.Results: The mean age (standard deviation) of participants was 67.1 (5.3) years; 50.6% were females; 13.5% were from racial and/or ethnic minority backgrounds; and 24.9% were APOE‐ε4 allele carriers. Over 2 years, compared with APOE‐ε4 non‐carriers, APOE‐ε4 carriers had faster declines in global cognition and episodic memory but not in executive function/attention; e.g., the mean difference in change (95% confidence interval) for episodic memory comparing APOE‐ε4 carriers vs. non‐carriers was ‐0.18 (‐0.33 to ‐0.03). There were no significant interactions between APOE‐ε4 carrier status and randomization status (omega‐3s vs. placebo) on change in global, episodic memory, or executive function/attention scores.Conclusion: APOE‐ε4 significantly predicted cognitive decline but did not modify the effect of omega‐3s on global and domain‐specific cognitive change over 2 years.

Purpose Developmental changes in sleep in youth with autism spectrum disorder (ASD) are understudied. In non-ASD youth, adolescents exhibit a “night owl chronotype” (i.e., later sleep/wake timing) and social jetlag (i.e., shifts in sleep timing across school nights and weekends), with corresponding sleep problems. The purpose of this study is to evaluate age trends in chronotype, social jetlag, and sleep problems in high-risk youth with ASD. Methods Youth with ASD (N = 171), ages 5–21 years old, were enrolled at the time of admission to specialized psychiatric units. Caregivers reported children’s demographic information, habitual sleep timing, and sleep problems. Multivariate analyses evaluated the effect of age on chronotype, social jetlag, and sleep problems and the effects of chronotype and social jetlag on sleep problems. Covariates and moderators included sex, race, verbal ability, autism symptom severity, supplemental melatonin, and pubertal status. Results Older age was associated with later chronotype, more social jetlag, fewer sleep anxiety/co-sleeping problems, fewer night waking and parasomnia problems, and more daytime alertness problems. The effect of age on chronotype was stronger for youth with greater social affective symptom severity. Mediation analyses showed that later chronotype statistically mediated the association between age and daytime alertness problems. Conclusions Youth with ASD may exhibit night owl chronotype behavior and social jetlag as they enter adolescence. Shifts toward a later chronotype may be exacerbated by autism severity and may contribute to alertness problems and sleepiness during the day. Chronotype is modifiable and may be leveraged to improve daytime functioning in youth with ASD.

The article by Lee and colleagues¹ in this issue of JAMA Psychiatry explores 3 domains or components of wisdom: prosocial relations, emotional regulation, and spirituality. The authors’ basic hypothesis is that interventions may enhance these domains of wisdom (although they found no eligible studies that addressed wisdom as an inclusive or unitary construct). Interventions ranged widely, from mindfulness to emotional intelligence training. The literature contains many approaches to measuring wisdom as reviewed by the authors.¹ One helpful distinction is between theoretical wisdom (understanding the deep nature of reality and humans’ place in it) and practical wisdom (also known as phronesis: making good decisions or doing the right thing, at the right time, for the right reasons), as delineated by Jeste and colleagues in a previous article.² Both can be measured, yet practical wisdom is perhaps more easily captured via a questionnaire than theoretical wisdom. In the current study, the authors¹ combine domains characterized as practical (prosocial behavior and emotional regulation) and theoretical (spirituality). They do not include other domains, such as decisiveness and the tolerance of and ability to deal with uncertainty, because of a dearth of intervention studies in these domains.³ Therefore, the reader should focus on the 3 domains examined as components of a much larger and more complex construct of wisdom, a construct that may be beyond the boundaries of empirical exploration or at least pose considerable challenges thereto. Identify all potential conflicts of interest that might be relevant to your comment. Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued. Err on the side of full disclosure. If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response. Not all submitted comments are published. Please see our commenting policy for details.