Long day's journey into sleep.

My long day's journey into sleep began as an adolescent trying to manage my evening chronotype. The relief, I felt when my undergraduate finals were scheduled at night and as a medical student being able to select psychiatry over surgery deepened my interest in sleep and chronobiology. That interest was allowed to flourish at the National Institute of Mental Health and then at Yale Medical School in setting up a sleep laboratory. The decision to move to the University of Pittsburgh in 1973 led to a 42-year adventure in which we were able to initiate research efforts on the psychobiology of depression. Our interest in social zeitgebers (daily routines) led directly to the development and testing of a treatment intervention for mood disorders, interpersonal, and social rhythm therapy. Our continued emphasis on sleep and circadian rhythms convinced us that sleep and circadian factors were central to all of health, based on the importance of connectivity between sleep and major metabolic and cell functions. This ongoing research motivated our strong desire to study the developmental aspects of sleep. Our success was influenced immensely by the presence of young scientists and a strong subsequent interest in career mentoring. Finally, as we left Pittsburgh in 2015, we became involved in the field of continuous objective monitoring using the commercial smartphone's behavioral sensing capabilities. Our journey is not over. We hope to explore the potential of these remarkable devices to improve our understanding of sleep/wake and circadian factors across all of health.

Corrigendum: Personalized digital intervention for depression based on social rhythm principles adds significantly to outpatient treatment.

[This corrects the article DOI: 10.3389/fdgth.2022.870522.].

Personalized digital intervention for depression based on social rhythm principles adds significantly to outpatient treatment.

We conducted a 16-week randomized controlled trial in psychiatric outpatients with a lifetime diagnosis of a mood and/or anxiety disorder to measure the impact of a first-of-its-kind precision digital intervention software solution based on social rhythm regulation principles. The full intent-to-treat (ITT) sample consisted of 133 individuals, aged 18-65. An exploratory sub-sample of interest was those individuals who presented with moderately severe to severe depression at study entry (baseline PHQ-8 score ≥15; N = 28). Cue is a novel digital intervention platform that capitalizes on the smartphone's ability to continuously monitor depression-relevant behavior patterns and use each patient's behavioral data to provide timely, personalized "micro-interventions," making this the first example of a precision digital intervention of which we are aware. Participants were randomly allocated to receive Cue plus care-as-usual or digital monitoring only plus care as usual. Within the full study and depressed-at-entry samples, we fit a mixed effects model to test for group differences in the slope of depressive symptoms over 16 weeks. To account for the non-linear trajectory with more flexibility, we also fit a mixed effects model considering week as a categorical variable and used the resulting estimates to test the group difference in PHQ change from baseline to 16 weeks. In the full sample, the group difference in the slope of PHQ-8 was negligible (Cohen's d = -0.10); however, the Cue group demonstrated significantly greater improvement from baseline to 16 weeks (p = 0.040). In the depressed-at-entry sample, we found evidence for benefit of Cue. The group difference in the slope of PHQ-8 (Cohen's d = -0.72) indicated a meaningfully more rapid rate of improvement in the intervention group than in the control group. The Cue group also demonstrated significantly greater improvement in PHQ-8 from baseline to 16 weeks (p = 0.009). We are encouraged by the size of the intervention effect in those who were acutely ill at baseline, and by the finding that across all participants, 80% of whom were receiving pharmacotherapy, we observed significant benefit of Cue at 16 weeks of treatment. These findings suggest that a social rhythm-focused digital intervention platform may represent a useful and accessible adjunct to antidepressant treatment (https://clinicaltrials.gov/ct2/show/NCT03152864?term=ellen+frank&draw=2&rank=3).

A game changer for bipolar disorder diagnosis using RNA editing-based biomarkers.

In clinical practice, differentiating Bipolar Disorder (BD) from unipolar depression is a challenge due to the depressive symptoms, which are the core presentations of both disorders. This misdiagnosis during depressive episodes results in a delay in proper treatment and a poor management of their condition. In a first step, using A-to-I RNA editome analysis, we discovered 646 variants (366 genes) differentially edited between depressed patients and healthy volunteers in a discovery cohort of 57 participants. After using stringent criteria and biological pathway analysis, candidate biomarkers from 8 genes were singled out and tested in a validation cohort of 410 participants. Combining the selected biomarkers with a machine learning approach achieved to discriminate depressed patients (n = 267) versus controls (n = 143) with an AUC of 0.930 (CI 95% [0.879-0.982]), a sensitivity of 84.0% and a specificity of 87.1%. In a second step by selecting among the depressed patients those with unipolar depression (n = 160) or BD (n = 95), we identified a combination of 6 biomarkers which allowed a differential diagnosis of bipolar disorder with an AUC of 0.935 and high specificity (Sp = 84.6%) and sensitivity (Se = 90.9%). The association of RNA editing variants modifications with depression subtypes and the use of artificial intelligence allowed developing a new tool to identify, among depressed patients, those suffering from BD. This test will help to reduce the misdiagnosis delay of bipolar patients, leading to an earlier implementation of a proper treatment.

Can exercise alleviate sleep disturbances during acute nicotine withdrawal in cigarette smokers?

Sleep disturbances, including insomnia (difficulty falling or staying asleep), are common nicotine withdrawal symptoms particularly during the initial stage of nicotine abstinence, and increase the likelihood of relapse within the first 4 weeks of quitting. Although clinically recognized as a key symptom of nicotine withdrawal, sleep disturbances are not addressed in the clinical guidelines for nicotine dependence treatment. Unfortunately, Nicotine Replacement Therapy (NRT) and other pharmacologic interventions do not attenuate withdrawal-provoked sleep disturbances, with several even exacerbating sleep disruption. The present study tested the impact of 30-min of daily moderate exercise, morning versus evening, on key polysomnographic indicators of sleep disturbances during initial 3 days (72 hr) of nicotine withdrawal. Forty-nine daily smokers (53% male) completed 3 separate abstinence periods, during which they completed either morning exercise, evening exercise, or a nonexercising magazine reading control condition. Order of condition was counterbalanced across subjects with a 1-week wash out in between each 3-day abstinence period. Exercise engagement mitigated several changes in sleep architecture associated with acute nicotine deprivation and other time-related effects on sleep, specifically frequency of arousals (B = -2.8, SE = .95; t(1271) = -3.0, p = .003) and reductions in sleep maintenance (B = .58, SE = .21; t(1270) = 2.8, p = .005). Additionally, smokers who reported greater perceived withdrawal severity had the longest latency to fall asleep but experienced the greatest attenuation of this effect following PM exercise. Overall, results suggest a role for exercise as an adjunct smoking cessation treatment to specifically target sleep disturbances during early acute nicotine withdrawal. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Development and Validation of Computerized Adaptive Assessment Tools for the Measurement of Posttraumatic Stress Disorder Among US Military Veterans.

Importance: Veterans from recent and past conflicts have high rates of posttraumatic stress disorder (PTSD). Adaptive testing strategies can increase accuracy of diagnostic screening and symptom severity measurement while decreasing patient and clinician burden. Objective: To develop and validate a computerized adaptive diagnostic (CAD) screener and computerized adaptive test (CAT) for PTSD symptom severity. Design, Setting, and Participants: A diagnostic study of measure development and validation was conducted at a Veterans Health Administration facility. A total of 713 US military veterans were included. The study was conducted from April 25, 2017, to November 10, 2019. Main Outcomes and Measures: The participants completed a PTSD-symptom questionnaire from the item bank and provided responses on the PTSD Checklist for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) (PCL-5). A subsample of 304 participants were interviewed using the Clinician-Administered Scale for PTSD for DSM-5. Results: Of the 713 participants, 585 were men; mean (SD) age was 52.8 (15.0) years. The CAD-PTSD reproduced the Clinician-Administered Scale for PTSD for DSM-5 PTSD diagnosis with high sensitivity and specificity as evidenced by an area under the curve of 0.91 (95% CI, 0.87-0.95). The CAT-PTSD demonstrated convergent validity with the PCL-5 (r = 0.88) and also tracked PTSD diagnosis (area under the curve = 0.85; 95% CI, 0.79-0.89). The CAT-PTSD reproduced the final 203-item bank score with a correlation of r = 0.95 with a mean of only 10 adaptively administered items, a 95% reduction in patient burden. Conclusions and Relevance: Using a maximum of only 6 items, the CAD-PTSD developed in this study was shown to have excellent diagnostic screening accuracy. Similarly, using a mean of 10 items, the CAT-PTSD provided valid severity ratings with excellent convergent validity with an extant scale containing twice the number of items. The 10-item CAT-PTSD also outperformed the 20-item PCL-5 in terms of diagnostic accuracy. The results suggest that scalable, valid, and rapid PTSD diagnostic screening and severity measurement are possible.

Dr. Gibbons et al. Reply.

We thank Kaufman et al.1 for their comprehensive review of the many commendable features of the Kiddie-Computerized Adaptive Test (K-CAT). We do wish to clarify what may be a misunderstanding of the intent of the K-CAT and our view of its role in treatment planning.

Computerized Adaptive Tests for Rapid and Accurate Assessment of Psychopathology Dimensions in Youth.

OBJECTIVE: At least half of youths with mental disorders are unrecognized and untreated. Rapid, accurate assessment of child mental disorders could facilitate identification and referral and potentially reduce the occurrence of functional disability that stems from early-onset mental disorders. METHOD: Computerized adaptive tests (CATs) based on multidimensional item response theory were developed for depression, anxiety, mania/hypomania, attention-deficit/hyperactivity disorder, conduct disorder, oppositional defiant disorder, and suicidality, based on parent and child ratings of 1,060 items each. In phase 1, CATs were developed from 801 participants. In phase 2, predictive, discriminant, and convergent validity were tested against semi-structured research interviews for diagnoses and suicidality in 497 patients and 104 healthy controls. Overall strength of association was determined by area under the receiver operating characteristic curve (AUC). RESULTS: The child and parent independently completed the Kiddie-Computerized Adaptive Tests (K-CATs) in a median time of 7.56 and 5.03 minutes, respectively, with an average of 7 items per domain. The K-CATs accurately captured the presence of diagnoses (AUCs from 0.83 for generalized anxiety disorder to 0.92 for major depressive disorder) and suicidal ideation (AUC = 0.996). Strong correlations with extant measures were found (r ≥ 0.60). Test-retest reliability averaged r = 0.80. CONCLUSION: These K-CATs provide a new approach to child psychopathology screening and measurement. Testing can be completed by child and parent in less than 8 minutes and yields results that are highly convergent with much more time-consuming structured clinical interviews and dimensional severity assessment and measurement. Testing of the implementation of the K-CAT is now indicated.

Association analysis of successive events data in the presence of competing risks.

We aim to close a methodological gap in analyzing durations of successive events that are subject to induced dependent censoring as well as competing-risk censoring. In the Bipolar Disorder Center for Pennsylvanians study, some patients who managed to recover from their symptomatic entry later developed a new depressive or manic episode. It is of great clinical interest to quantify the association between time to recovery and time to recurrence in patients with bipolar disorder. The estimation of the bivariate distribution of the gap times with independent censoring has been well studied. However, the existing methods cannot be applied to failure times that are censored by competing causes such as in the Bipolar Disorder Center for Pennsylvanians study. Bivariate cumulative incidence function has been used to describe the joint distribution of parallel event times that involve multiple causes. To the best of our knowledge, however, there is no method available for successive events with competing-risk censoring. Therefore, we extend the bivariate cumulative incidence function to successive events data, and propose non-parametric estimators of the bivariate cumulative incidence function and the related conditional cumulative incidence function. Moreover, an odds ratio measure is proposed to describe the cause-specific dependence, leading to the development of a formal test for independence of successive events. Simulation studies demonstrate that the estimators and tests perform well for realistic sample sizes, and our methods can be readily applied to the Bipolar Disorder Center for Pennsylvanians study.

Exercise attenuates negative effects of abstinence during 72 hours of smoking deprivation.

Exercise is presumed to be a potentially helpful smoking cessation adjunct reputed to attenuate the negative effects of deprivation. The present study examined the effectiveness of moderate within-session exercise to reduce 4 key symptoms of smoking deprivation during 3 72-hr nicotine abstinence blocks in both male and female smokers. Forty-nine (25 male, 24 female) sedentary smokers abstained from smoking for 3 consecutive days on 3 separate occasions. At each session, smokers' abstinence-induced craving, cue-induced craving, negative mood, and withdrawal symptom severity were assessed prior to and after either exercise (a.m. exercise, p.m. exercise) or a sedentary control activity (magazine reading). Abstinence-induced craving and negative mood differed as a function of condition, F(2, 385) = 21, p < .0001; and, F(2, 385) = 3.38, p = .03. Planned contrasts revealed no difference between a.m. and p.m. exercise, but exercise overall led to greater pre-post reduction in abstinence-induced craving, t(385) = 6.23, p < .0001, effect size Cohen's d = 0.64; and negative mood, t(385) = 2.25, p = .03, d = 0.23. Overall exercise also led to a larger pre-post reduction in cue-induced craving in response to smoking cues, F(2, 387) = 8.94, p = .0002; and withdrawal severity, F(2, 385) = 3.8, p = .02. Unlike the other 3 measures, p.m. exercise reduced withdrawal severity over control, t(385) = 2.64, p = .009, d = 0.27, whereas a.m. exercise did not. The results support the clinical potential of exercise to assist smokers in managing common and robust negative symptoms experienced during the first 3 days of abstinence. (PsycINFO Database Record

Assessment of a Person-Level Risk Calculator to Predict New-Onset Bipolar Spectrum Disorder in Youth at Familial Risk.

Importance: Early identification of individuals at high risk for the onset of bipolar spectrum disorder (BPSD) is key from both a clinical and research perspective. While previous work has identified the presence of a bipolar prodrome, the predictive implications for the individual have not been assessed, to date. Objective: To build a risk calculator to predict the 5-year onset of BPSD in youth at familial risk for BPSD. Design, Setting, and Participants: The Pittsburgh Bipolar Offspring Study is an ongoing community-based longitudinal cohort investigation of offspring of parents with bipolar I or II (and community controls), recruited between November 2001 and July 2007, with a median follow-up period of more than 9 years. Recruitment has ended, but follow-up is ongoing. The present analysis included offspring of parents with bipolar I or II (aged 6-17 years) who had not yet developed BPSD at baseline. Main Outcomes and Measures: This study tested the degree to which a time-to-event model, including measures of mood and anxiety, general psychosocial functioning, age at mood disorder onset in the bipolar parent, and age at each visit, predicted new-onset BPSD. To fully use longitudinal data, the study assessed each visit separately, clustering within individuals. Discrimination was measured using the time-dependent area under the curve (AUC), predicting 5-year risk; internal validation was performed using 1000 bootstrapped resamples. Calibration was assessed by comparing observed vs predicted probability of new-onset BPSD. Results: There were 412 at-risk offspring (202 [49.0%] female), with a mean (SD) visit age of 12.0 (3.5) years and a mean (SD) age at new-onset BPSD of 14.2 (4.5) years. Among them, 54 (13.1%) developed BPSD during follow-up (18 with BD I or II); these participants contributed a total of 1058 visits, 67 (6.3%) of which preceded new-onset BPSD within the next 5 years. Using internal validation to account for overfitting, the model provided good discrimination between converting vs nonconverting visits (AUC, 0.76; bootstrapped 95% CI, 0.71-0.82). Important univariate predictors of outcome (AUC range, 0.66-0.70) were dimensional measures of mania, depression, anxiety, and mood lability; psychosocial functioning; and parental age at mood disorder. Conclusions and Relevance: This risk calculator provides a practical tool for assessing the probability that a youth at familial risk for BPSD will develop new-onset BPSD within the next 5 years. Such a tool may be used by clinicians to inform frequency of monitoring and treatment options and for research studies to better identify potential participants at ultra high risk of conversion.

Development of a Computerized Adaptive Test Suicide Scale-The CAT-SS.

OBJECTIVE: Current suicide risk screening and measurement are inefficient, have limited measurement precision, and focus entirely on suicide-related items. For this study, a psychometric harmonization between related suicide, depression, and anxiety symptom domains that provides a more balanced and complete spectrum of suicidal symptomatology was developed. The objective of this article is to describe the results of the early stages of computerized adaptive testing development for a suicide scale and pave the way for the final stage of validation. METHODS: Data from psychiatric outpatients at the University of Pittsburgh and a community health clinic were collected from January 2010 through June 2012. 789 participants were enrolled in the calibration phase; 70% were female, and 30% were male. The rate of major depressive disorder as diagnosed by DSM-5 was 47%. The item bank contained 1,008 items related to depression, anxiety, and mania, including 11 suicide items. Data were analyzed using a bifactor model to identify a core dimension between suicidal ideation, depression, anxiety, and mania items. A computerized adaptive test was developed via simulation from the actual complete item responses in 308 subjects. RESULTS: 111 items were identified that provided an extension of suicidality assessment to include statistically related responses from depression and anxiety domains that are syndromally associated with suicidality. All items had high loadings on the primary suicide dimension (average = 0.67; range, 0.49-0.88). Analyses revealed that a mean of 10 items (5-20) had a correlation of 0.96 with the 111-item scale, with a precision of 5 points on a 100-point scale metric. Preliminary validation data based on 290 clinician interviews revealed a 52-fold increase in the likelihood of current suicidal ideation across the range of the Computerized Adaptive Test Suicide Scale (CAT-SS). CONCLUSIONS: The CAT-SS is able to accurately measure the latent suicide dimension with a mean of 10 items in approximately 2 minutes. Further validation against an independent clinician-administered assessment of suicide risk (ideation and attempts) and prediction of suicidal behavior is underway.

Objective Sleep Duration Is Prospectively Associated With Endothelial Health.

Study Objectives: The mechanisms linking short sleep duration to cardiovascular disease (CVD) are poorly understood. Emerging evidence suggests that endothelial dysregulation may lie along the causal pathway linking sleep duration to cardiovascular risk, although current evidence in humans is based on cross-sectional studies. Our objective was to evaluate the prospective association between objectively assessed sleep duration and clinical indices of endothelial health. Methods: A total of 141 medically healthy adults underwent an overnight laboratory sleep study when they were between the ages of 21 and 60 years. Total sleep time was objectively assessed by polysomnography at study entry. Endothelial health, including brachial artery diameter (BAD) and flow-mediated dilation (FMD), was measured 18.9 ± 4.6 years later. Medical health and psychiatric status were assessed at both time points. Approximately half of the sample had a lifetime history of major depressive disorder. Results: In univariate analyses, shorter sleep duration was associated with increased BAD (ß = -0.24, p = .004) and decreased FMD (ß = 0.17, p = .042). BAD, but not FMD, remained significantly associated with sleep duration after adjusting for sex, age, body mass index (BMI), smoking, diabetes, hypertension, and lifetime history of major depressive disorder (MDD) at T2. The association between sleep duration and BAD was stronger than the association between BAD and an aggregate measure of CVD risk including three or more of the following risk factors: male sex, age ≥ 65 years, smoker, BMI ≥ 30, diabetes, hypertension, and MDD. Conclusions: Objectively assessed short sleep duration was prospectively associated with increased BAD over a 12- to 30-year period.

Longitudinal sleep phenotypes among offspring of bipolar parents and community controls.

BACKGROUND: Sleep disturbances are a prominent feature of bipolar disorder (BP). However, it remains unclear how sleep phenotypes may evolve among at-risk youth, and their relevance to BP onset. METHODS: Pittsburgh Bipolar Offspring Study (BIOS) offspring (ages 10-18) and their parents completed assessments approximately every two years pertaining to current psychopathology and offspring sleep habits. A latent transition analysis (LTA) identified latent sleep groups within offspring based on their ratings of six sleep domains using the School Sleep Habits Survey. Demographic and clinical characteristics were compared between sleep groups. Logistic regression tested links between sleep group and BP onset at the subsequent assessment. RESULTS: The LTA model identified latent groups of good, poor, and variable sleepers. We observed an overall trend of good sleep becoming variable, and then poor, as youth age. Offspring in the poor sleep group were more likely to have psychopathology. Adjusting for age and depression, poor sleepers had nearly twice the odds of developing BP relative to good (OR=1.99, CI=0.45-8.91) or variable (OR=2.03, CI=0.72-5.72) sleepers. LIMITATIONS: Limitations include the use of proximal sleep phenotypes to predict BP onset, and a self-report measure of sleep CONCLUSIONS: We found three non-overlapping sleep phenotype groups in a large sample of offspring of bipolar probands and offspring of demographically-matched community control parents. Clinicians should consider that youth will likely experience variable and/or poor sleep as they age, and that at-risk youth with poor sleep may be at increased risk of developing MDD and BP at their next assessment.

The Relationship Between Stressful Life Events and Axis I Diagnoses Among Adolescent Offspring of Probands With Bipolar and Non-Bipolar Psychiatric Disorders and Healthy Controls: The Pittsburgh Bipolar Offspring Study (BIOS).

BACKGROUND: Previous studies have explored the role of stressful life events in the development of mood disorders. We examined the frequency and nature of stressful life events as measured by the Stressful Life Events Schedule (SLES) among 3 groups of adolescent offspring of probands with bipolar (BD), with non-BD psychiatric disorders, and healthy controls. Furthermore, we examined the relationship between stressful life events and the presence of DSM-IV Axis I disorders in these offspring. Stressful life events were characterized as dependent, independent, or uncertain (neither dependent nor independent) and positive, negative, or neutral (neither positive nor negative). METHODS: Offspring of probands with BD aged 13-18 years (n = 269), demographically matched offspring of probands with non-BD Axis I disorders (n = 88), and offspring of healthy controls (n = 81) from the Pittsburgh Bipolar Offspring Study were assessed from 2002 to 2007 with standardized instruments at intake. Probands completed the SLES for their offspring for life events within the prior year. Life events were evaluated with regard to current Axis I diagnoses in offspring after adjusting for confounds. RESULTS: After adjusting for demographic and clinical between-group differences (in probands and offspring), offspring of probands with BD had greater independent (χ² = 11.96, P < .04) and neutral (χ² = 17.99, P < .003) life events compared with offspring of healthy controls and greater number of more severe stressful life events than offspring of healthy controls, but not offspring of probands with non-BD. Offspring of BD probands with comorbid substance use disorder reported more independent stressful life events compared to those without comorbid substance use disorder (P = .024). Greater frequency and severity of stressful life events were associated with current Axis I disorder in offspring of both probands with BD and probands with other Axis I disorders regardless of dependency or valence. Greater frequency and severity of stressful life events were associated with greater current Axis I disorder in all offspring. CONCLUSIONS: Offspring of probands with BD have greater exposure to independent and neutral life events than offspring of healthy controls. Greater frequency and severity of stressful life events were associated with Axis I disorder in offspring of both BD and non-BD affected probands.

Immuno-psychiatry: an agenda for clinical practice and innovative research.

BACKGROUND: The diagnostic scheme for psychiatric disorders is currently based purely on descriptive nomenclature given that biomarkers subtypes and clearly defined causal mechanisms are lacking for the vast majority of disorders. The emerging field of "immuno-psychiatry" has the potential to widen the exploration of a mechanism-based nosology, possibly leading to the discovery of more effective personalised treatment strategies. DISCUSSION: Disturbances in immuno-inflammatory and related systems have been implicated in the aetiology, pathophysiology, phenomenology and comorbidity of several psychiatric disorders, including major mood disorders and schizophrenia. A fundamental challenge in their clinical management is to identify bio-signatures that might indicate risk, state, trait, prognosis or theragnosis. Here, we provide the rationale for a clinical and research agenda to refine future clinical practice and conceptual views, and to delineate pathways toward innovative treatment discovery. CONCLUSION: The development of bio-signatures will allow clinicians to tailor interventions to the abovementioned biomarker subtypes - a major translational goal for research in this field.