Timely Questions Emerging in Chronobiology: The Circadian Clock Keeps on Ticking

Chronobiology investigations have revealed much about cellular and physiological clockworks but we are far from having a complete mechanistic understanding of the physiological and ecological implications. Here we present some unresolved questions in circadian biology research as posed by the editorial staff and guest contributors to the Journal of Circadian Rhythms. This collection of ideas is not meant to be comprehensive but does reveal the breadth of our observations on emerging trends in chronobiology and circadian biology. It is amazing what could be achieved with various expected innovations in technologies, techniques, and mathematical tools that are being developed. We fully expect strengthening mechanistic work will be linked to health care and environmental understandings of circadian function. Now that most clock genes are known, linking these to physiological, metabolic, and developmental traits requires investigations from the single molecule to the terrestrial ecological scales. Real answers are expected for these questions over the next decade. Where are the circadian clocks at a cellular level? How are clocks coupled cellularly to generate organism level outcomes? How do communities of circadian organisms rhythmically interact with each other? In what way does the natural genetic variation in populations sculpt community behaviors? How will methods development for circadian research be used in disparate academic and commercial endeavors? These and other questions make it a very exciting time to be working as a chronobiologist.

Reflections on Several Landmark Advances in Circadian Biology

Circadian Biology intersects with diverse scientific domains, intricately woven into the fabric of organismal physiology and behavior. The rhythmic orchestration of life by the circadian clock serves as a focal point for researchers across disciplines. This retrospective examination delves into several of the scientific milestones that have fundamentally shaped our contemporary understanding of circadian rhythms. From deciphering the complexities of clock genes at a cellular level to exploring the nuances of coupled oscillators in whole organism responses to stimuli. The field has undergone significant evolution lately guided by genetics approaches. Our exploration here considers key moments in the circadian-research landscape, elucidating the trajectory of this discipline with a keen eye on scientific advancements and paradigm shifts.

Sleep deprivation attenuates neural responses to outcomes from risky decision-making

Sleep loss impacts a broad range of brain and cognitive functions. However, how sleep deprivation affects risky decision-making remains inconclusive. This study used functional MRI to examine the impact of one night of total sleep deprivation (TSD) on risky decision-making behavior and the underlying brain responses in healthy adults. In this study, we analyzed data from N = 56 participants in a strictly controlled 5-day and 4-night in-laboratory study using a modified Balloon Analogue Risk Task. Participants completed two scan sessions in counter-balanced order, including one scan during rested wakefulness (RW) and another scan after one night of TSD. Results showed no differences in participants' risk-taking propensity and risk-induced activation between RW and TSD. However, participants showed significantly reduced neural activity in the anterior cingulate cortex and bilateral insula for loss outcomes, and in bilateral putamen for win outcomes during TSD compared with RW. Moreover, risk-induced activation in the insula negatively correlated with participants' risk-taking propensity during RW, while no such correlations were observed after TSD. These findings suggest that sleep loss may impact risky decision-making by attenuating neural responses to decision outcomes and impairing brain-behavior associations.

The Negative Effects of Travel on Student Athletes Through Sleep and Circadian Disruption

Collegiate athletes must satisfy the academic obligations common to all undergraduates, but they have the additional structural and social stressors of extensive practice time, competition schedules, and frequent travel away from their home campus. Clearly such stressors can have negative impacts on both their academic and athletic performances as well as on their health. These concerns are made more acute by recent proposals and decisions to reorganize major collegiate athletic conferences. These rearrangements will require more multi-day travel that interferes with the academic work and personal schedules of athletes. Of particular concern is additional east-west travel that results in circadian rhythm disruptions commonly called jet lag that contribute to the loss of amount as well as quality of sleep. Circadian misalignment and sleep deprivation and/or sleep disturbances have profound effects on physical and mental health and performance. We, as concerned scientists and physicians with relevant expertise, developed this white paper to raise awareness of these challenges to the wellbeing of our student-athletes and their co-travelers. We also offer practical steps to mitigate the negative consequences of collegiate travel schedules. We discuss the importance of bedtime protocols, the availability of early afternoon naps, and adherence to scheduled lighting exposure protocols before, during, and after travel, with support from wearables and apps. We call upon departments of athletics to engage with sleep and circadian experts to advise and help design tailored implementation of these mitigating practices that could contribute to the current and long-term health and wellbeing of their students and their staff members.

"Count on Sleep": an OSA awareness project update

Obstructive sleep apnea (OSA) is a common, chronic sleep-related breathing disorder that affects approximately 12% of the US adult population. Greater public awareness of OSA is necessary to decrease the number of people with undiagnosed or untreated OSA and reduce the negative health consequences of unrecognized OSA. In 2021, the American Academy of Sleep Medicine initiated the "Count on Sleep" project in partnership with key stakeholders with the objective of raising the awareness of OSA among the public, health care providers, and public health officials. Four workgroups implemented strategies and completed tasks focused on increasing OSA awareness in their targeted areas to address the objectives of the project including (1) Public Awareness and Communications, (2) Provider Education, (3) Tool Development and Surveillance, and (4) a Strategic Planning workgroup that coordinated efforts across the project. Over the first 2 years, workgroups made substantial progress toward project goals including holding "listening sessions" with representatives of communities disproportionately affected by OSA and its consequences, developing resources for primary care providers that can be easily accessed and used in practice, and developing a brief survey for use in estimating and tracking OSA risk across the population. Over the first 2 project years, workgroups made significant progress in advancing efforts to increase awareness of OSA in US communities. The third year of the project will focus on dissemination of campaign materials and resources for all targeted groups, including the public, health care professionals, and public health professionals.

CITATION

Martin JL, Rowley J, Goel N, et al. "Count on Sleep": an OSA awareness project update. J Clin Sleep Med. 2024;20(2):303-307.

Bright Light Therapy for Major Depressive Disorder in Adolescent Outpatients: A Preliminary Study

BACKGROUND

Bright light therapy (BLT) has not been well-studied in adolescents with major depressive disorder, particularly in outpatient settings.

METHODS

We conducted an 8-week clinical trial of BLT in adolescents recruited from a primary care practice with moderate to severe major depression. Acceptability and feasibility were defined by daily use of the light box and integration into daily routines. To assess treatment effects, we utilized the Short Mood and Feelings Questionnaire (SMFQ) and actigraphic sleep variables.

RESULTS

Of the nine enrolled adolescents, the rate of daily use of the light therapy box was 100% at week 2, 78% at week 4 (n = 7), and 67% at weeks 6 and 8 (n = 6). Participants were better able to integrate midday BLT compared to morning BLT into their day-to-day routines. Mean depression scores improved during the 2-week placebo lead-in (dim red light-DRL) and continued to show significant improvement through 6 weeks of BLT. Sleep efficiency increased significantly (p = 0.046), and sleep onset latency showed a trend toward a significant decrease (p = 0.075) in the BLT phase compared to the DRL phase.

CONCLUSION

Bright light treatment that was self-administered at home was feasible, acceptable, and effective for adolescent outpatients with depression. Findings support the development of larger, well-powered, controlled clinical trials of BLT in coordination with primary care.

Short-term and long-term phenotypic stability of actigraphic sleep metrics involving repeated sleep loss and recovery

For the first time, we determined whether actigraphic-assessed sleep measures show inter-individual differences and intra-individual stability during baseline (BL) and recovery (REC) phases surrounding repeated total sleep deprivation (TSD). We conducted a 5-day experiment at Months 2 and 4 in two separate studies (N = 11). During each experiment, sleep measures were collected via wrist actigraphy during two BL 8 h time-in-bed (TIB) nights (B1, B2) and during two REC 8-10 h TIB nights (R1, R2). Intraclass correlation coefficients (ICCs) assessed actigraphic measure long-term stability between 2 and 4 months for (1) the pre-experimental phase before BL; and (2) the BL (B1 + B2), REC (R1 + R2), and BL and REC average (BL + REC) phases; and short-term stability at Month 2 and at Month 4; and (3) between B1 versus B2 and R1 versus R2 in each 5-day experiment. Nearly all ICCs during the pre-experimental, BL, REC, and BL + REC phases were moderate to almost perfect (0.446-0.970) between Months 2 and 4. B1 versus B2 ICCs were more stable (0.440-0.899) than almost all R1 versus R2 ICCs (-0.696 to 0.588) at Month 2 and 4. Actigraphic sleep measures show phenotypic long-term stability during BL and REC surrounding repeated TSD between 2 and 4 months. Furthermore, within each 5-day experiment at Month 2 and 4, the two BL nights before TSD were more stable than the two REC nights following TSD, likely due to increased R1 homeostatic pressure. Given the consistency of actigraphic measures across the short-term and long-term, they can serve as biomarkers to predict physiological and neurobehavioral responses to sleep loss.

Reward sensitivity and social rhythms during goal-striving: An ecological momentary assessment investigation of bipolar spectrum disorders

BACKGROUND

The reward/circadian rhythm model of bipolar spectrum disorders (BSDs) posits that when individuals with hypersensitive reward systems encounter reward-relevant events, they experience social and circadian rhythm disruption, leading to mood symptoms. The aim of the current study is to test an element of this theoretical model by investigating changes in social rhythms during and after an ecologically-valid reward-relevant event and evaluating whether the strength of these associations differ by trait reward sensitivity and BSD diagnostic group.

METHODS

Young adults from three groups (low BSD risk with moderate reward sensitivity [MRew], high BSD risk with high reward sensitivity [HRew], and high reward sensitivity with BSD [HRew+BSD]) completed a reward responsiveness task and 20-day ecological momentary assessment study structured around a participant-specific goal occurring on day 15. Social rhythm disruption (SRD) and social rhythm regularity (SRR) were assessed daily. Multilevel models examined whether reward sensitivity and group moderated associations between study phase (baseline [days 1-5], goal-striving [days 16-20], or outcome [days 16-20]) and social rhythms.

RESULTS

Participants experienced greater SRD after the goal-striving event during the outcome phase, compared to the baseline phase. The HRew+BSD group had significant decreases in SRR during the outcome phase, and this pattern differed significantly from the low-risk and high-risk groups. Greater task reward responsiveness also was associated with significant decreases in SRR during the outcome phase.

LIMITATIONS

This study did not test whether social rhythm irregularity was associated with subsequent mood change.

CONCLUSIONS

Participants exhibited social rhythm changes over the course of this ecologically valid goal-striving period, providing evidence for the interplay between reward-activating events and social rhythms. The HRew+BSD group showed a distinct pattern in which their social rhythms were more irregular after completing reward-relevant goal-striving that was not observed for the low-BSD risk or high-BSD risk groups. These findings provide additional support for Interpersonal and Social Rhythms Therapy.

Social jetlag and trajectories of mood symptoms and reward responsiveness in individuals at low-risk, high-risk, and with bipolar spectrum disorders: An ecological momentary assessment study

A specific type of sleep disruption, social jetlag, involves an incongruence of sleep time between weekends and weekdays. This study investigated relationships between social jetlag and mood symptom lability and trajectories of daily reward responsiveness and mood symptoms. Participants (N = 130) from three groups (moderate reward sensitivity, high reward sensitivity, and high reward sensitivity with a diagnosed bipolar spectrum disorder [BSD]) were recruited from an ongoing longitudinal study based on their self-reported reward sensitivity and a diagnostic interview. For this study, they completed 20 days of ecological momentary assessment (EMA) of reward responsiveness and mood symptoms and a daily sleep diary. Social jetlag was significantly associated with differences in trajectories of depressive symptoms between groups. Specifically, greater social jetlag was associated with a greater increase in depressive symptoms over the 20 days for participants in the high reward sensitivity and BSD groups compared to the moderate reward sensitivity group. Social jetlag also was significantly associated with depressive symptom lability during the EMA period, but this finding was reduced to a trend toward significance when controlling for self-reported sleep duration. The study adds to the literature with methodological strengths including the EMA design and assessment of symptom and reward responsiveness trajectories.

Impact of bimonthly repeated total sleep deprivation and recovery sleep on cardiovascular indices

Since short sleep duration adversely affects cardiovascular (CV) health, we investigated the effects of exposures to total sleep deprivation (TSD), and baseline (BL) and recovery (REC) sleep on CV measures. We conducted a 5-day experiment at months 2 and 4 in two separate studies (N = 11 healthy adults; 5 females). During these repeated experiments, CV measures [stroke volume (SV), cardiac index (CI), systemic vascular resistance index (SVRI), left ventricular ejection time, heart rate (HR), systolic and diastolic blood pressure (SBP and DBP) and mean arterial pressure (MAP)] were collected at three assessment time points after: (1) two BL 8 h time-in-bed (TIB) sleep opportunity nights; (2) a TSD night; and (3) two REC 8-10 h TIB nights. CV measures were also collected pre-study. TSD significantly increased SV and CI, and decreased SVRI, with large effect sizes, which importantly were reversed with recovery, indicating these measures are possible novel biomarkers for assessing the adverse consequences of TSD. Pre-study SV, CI, SVRI, HR, SBP, and MAP measures also significantly associated with TSD CV responses at months 2 and 4 [Pearson's r: 0.615-0.862; r2 : 0.378-0.743], indicating they are robust correlates of future TSD CV responses. Our novel findings highlight the critical impact of sleep on CV health across time.

Real-world implementation of non-endoscopic triage testing for Barrett's oesophagus during COVID-19

BACKGROUND

The Coronavirus pandemic (COVID-19) curtailed endoscopy services, adding to diagnostic backlogs. Building on trial evidence for a non-endoscopic oesophageal cell collection device coupled with biomarkers (Cytosponge), an implementation pilot was launched for patients on waiting lists for reflux and Barrett's oesophagus surveillance.

AIMS

(i) To review reflux referral patterns and Barrett's surveillance practices. (ii) To evaluate the range of Cytosponge findings and impact on endoscopy services.

DESIGN AND METHODS

Cytosponge data from centralized laboratory processing (trefoil factor 3 (TFF3) for intestinal metaplasia (IM), haematoxylin & eosin for cellular atypia and p53 for dysplasia) over a 2-year period were included.

RESULTS

A total of 10 577 procedures were performed in 61 hospitals in England and Scotland, of which 92.5% (N = 9784/10 577) were sufficient for analysis. In the reflux cohort (N = 4074 with gastro-oesophageal junction sampling), 14.7% had one or more positive biomarkers (TFF3: 13.6% (N = 550/4056), p53: 0.5% (21/3974), atypia: 1.5% (N = 63/4071)), requiring endoscopy. Among samples from individuals undergoing Barrett's surveillance (N = 5710 with sufficient gland groups), TFF3-positivity increased with segment length (odds ratio = 1.37 per cm (95% confidence interval: 1.33-1.41, P < 0.001)). Some surveillance referrals (21.5%, N = 1175/5471) had ≤1 cm segment length, of which 65.9% (707/1073) were TFF3 negative. Of all surveillance procedures, 8.3% had dysplastic biomarkers (4.0% (N = 225/5630) for p53 and 7.6% (N = 430/5694) for atypia), increasing to 11.8% (N = 420/3552) in TFF3+ cases with confirmed IM and 19.7% (N = 58/294) in ultra-long segments.

CONCLUSIONS

Cytosponge-biomarker tests enabled targeting of endoscopy services to higher-risk individuals, whereas those with TFF3 negative ultra-short segments could be reconsidered regarding their Barrett's oesophagus status and surveillance requirements. Long-term follow-up will be important in these cohorts.

Light Therapy for Adolescent Depression: A Scoping Review

PURPOSE

Depressive disorders in adolescents are a major health concern associated with developmental, social, and educational impairment. Bright Light Therapy (BLT) is a feasible and effective treatment for depressive disorders in adults, but few controlled trials have been conducted with children or adolescents. This scoping review focuses on the current state of knowledge for BLT in the treatment of adolescent depression. We reviewed the literature for novel data and methodologic approaches using BLT and pediatric and young adult populations.

RECENT FINDINGS

BLT is a tolerable treatment with few side effects. However, there is a marked lack of well-powered studies to support BLT as a treatment for depressive disorders in adolescent populations. Given evidence of tolerability and positive treatment effect on depression in the adult literature, research is needed to establish the efficacy, feasibility, and acceptability of BLT in adolescents.

Cardiovascular measures display robust phenotypic stability across long-duration intervals involving repeated sleep deprivation and recovery

Introduction

We determined whether cardiovascular (CV) measures show trait-like responses after repeated total sleep deprivation (TSD), baseline (BL) and recovery (REC) exposures in two long-duration studies (total N = 11 adults).

Methods

A 5-day experiment was conducted twice at months 2 and 4 in a 4-month study (N = 6 healthy adults; 3 females; mean age ± SD, 34.3 ± 5.7 years; mean BMI ± SD, 22.5 ± 3.2 kg/m2), and three times at months 2, 4, and 8 in an 8-month study (N = 5 healthy adults; 2 females; mean age ± SD, 33.6 ± 5.17 years; mean BMI ± SD, 27.1 ± 4.9 kg/m2). Participants were not shift workers or exposed to TSD in their professions. During each experiment, various seated and standing CV measures were collected via echocardiography [stroke volume (SV), heart rate (HR), cardiac index (CI), left ventricular ejection time (LVET), and systemic vascular resistance index (SVRI)] or blood pressure monitor [systolic blood pressure (SBP)] after (1) two BL 8h time in bed (TIB) nights; (2) an acute TSD night; and (3) two REC 8-10 h TIB nights. Intraclass correlation coefficients (ICCs) assessed CV measure stability during BL, TSD, and REC and for the BL and REC average (BL + REC) across months 2, 4, and 8; Spearman's rho assessed the relative rank of individuals' CV responses across measures.

Results

Seated BL (0.693-0.944), TSD (0.643-0.962) and REC (0.735-0.960) CV ICCs showed substantial to almost perfect stability and seated BL + REC CV ICCs (0.552-0.965) showed moderate to almost perfect stability across months 2, 4, and 8. Individuals also exhibited significant, consistent responses within seated CV measures during BL, TSD, and REC. Standing CV measures showed similar ICCs for BL, TSD, and REC and similar response consistency.

Discussion

This is the first demonstration of remarkably robust phenotypic stability of a number of CV measures in healthy adults during repeated TSD, BL and REC exposures across 2, 4, and 8 months, with significant consistency of responses within CV measures. The cardiovascular measures examined in our studies, including SV, HR, CI, LVET, SVRI, and SBP, are useful biomarkers that effectively track physiology consistently across long durations and repeated sleep deprivation and recovery.

Prolonged Use of an Automated Insulin Delivery System Improves Sleep in Long-Standing Type 1 Diabetes Complicated by Impaired Awareness of Hypoglycemia

BACKGROUND

This study assessed changes in actigraphy-estimated sleep and glycemic outcomes after initiating automated insulin delivery (AID).

METHODS

Ten adults with long-standing type 1 diabetes and impaired awareness of hypoglycemia (IAH) participated in an 18-month clinical trial assessing an AID intervention on hypoglycemia and counter-regulatory mechanisms. Data from eight participants (median age = 58 years) with concurrent wrist actigraph and continuous glucose monitoring (CGM) data were used in the present analyses. Actigraphs and CGM measured sleep and glycemic control at baseline (one week) and months 3, 6, 9, 12, 15, and 18 (three weeks) following AID initiation. HypoCount software integrated actigraphy with CGM data to separate wake and sleep-associated glycemic measures. Paired sample t-tests and Cohen's d effect sizes modeled changes and their magnitude in sleep, glycemic control, IAH (Clarke score), hypoglycemia severity (HYPO score), hypoglycemia exposure (CGM), and glycemic variability (lability index [LI]; CGM coefficient-of-variation [CV]) from baseline to 18 months.

RESULTS

Sleep improved from baseline to 18 months (shorter sleep latency [P < .05, d = 1.74], later sleep offset [P < .05, d = 0.90], less wake after sleep onset [P < .01, d = 1.43]). Later sleep onset (d = 0.74) and sleep midpoint (d = 0.77) showed medium effect sizes. Sleep improvements were evident from 12 to 15 months after AID initiation and were preceded by improved hypoglycemia awareness (Clarke score [d = 1.18]), reduced hypoglycemia severity (HYPO score [d = 2.13]), reduced sleep-associated hypoglycemia (percent time glucose was < 54 mg/dL, < 60 mg/dL,< 70 mg/dL; d = 0.66-0.81), and reduced glucose variability (LI, d = 0.86; CV, d = 0.62).

CONCLUSION

AID improved sleep initiation and maintenance. Improved awareness of hypoglycemia, reduced hypoglycemia severity, hypoglycemia exposure, and glucose variability preceded sleep improvements.This trial is registered with ClinicalTrials.gov NCT03215914 https://clinicaltrials.gov/ct2/show/NCT03215914.

Enhanced amygdala-cingulate connectivity associates with better mood in both healthy and depressive individuals after sleep deprivation

Sleep loss robustly disrupts mood and emotion regulation in healthy individuals but can have a transient antidepressant effect in a subset of patients with depression. The neural mechanisms underlying this paradoxical effect remain unclear. Previous studies suggest that the amygdala and dorsal nexus (DN) play key roles in depressive mood regulation. Here, we used functional MRI to examine associations between amygdala- and DN-related resting-state connectivity alterations and mood changes after one night of total sleep deprivation (TSD) in both healthy adults and patients with major depressive disorder using strictly controlled in-laboratory studies. Behavioral data showed that TSD increased negative mood in healthy participants but reduced depressive symptoms in 43% of patients. Imaging data showed that TSD enhanced both amygdala- and DN-related connectivity in healthy participants. Moreover, enhanced amygdala connectivity to the anterior cingulate cortex (ACC) after TSD associated with better mood in healthy participants and antidepressant effects in depressed patients. These findings support the key role of the amygdala-cingulate circuit in mood regulation in both healthy and depressed populations and suggest that rapid antidepressant treatment may target the enhancement of amygdala-ACC connectivity.

Automated Insulin Delivery for Hypoglycemia Avoidance and Glucose Counterregulation in Long-Standing Type 1 Diabetes with Hypoglycemia Unawareness

Objective: Automated insulin delivery (AID) may benefit individuals with long-standing type 1 diabetes where frequent exposure to hypoglycemia impairs counterregulatory responses. This study assessed the effect of 18 months AID on hypoglycemia avoidance and glucose counterregulatory responses to insulin-induced hypoglycemia in long-standing type 1 diabetes complicated by impaired awareness of hypoglycemia. Methods: Ten participants mean ± standard deviation age 49 ± 16 and diabetes duration 34 ± 16 years were initiated on AID. Continuous glucose monitoring was paired with actigraphy to assess awake- and sleep-associated hypoglycemia exposure every 3 months. Hyperinsulinemic hypoglycemic clamp experiments were performed at baseline, 6, and 18 months postintervention. Hypoglycemia exposure was reduced by 3 months, especially during sleep, with effects sustained through 18 months (P ≤ 0.001) together with reduced glucose variability (P < 0.01). Results: Hypoglycemia awareness and severity scores improved (P < 0.01) with severe hypoglycemia events reduced from median (interquartile range) 3 (3-10) at baseline to 0 (0-1) events/person·year postintervention (P = 0.005). During the hypoglycemic clamp experiments, no change was seen in the endogenous glucose production (EGP) response, however, peripheral glucose utilization during hypoglycemia was reduced following intervention [pre: 4.6 ± 0.4, 6 months: 3.8 ± 0.5, 18 months: 3.4 ± 0.3 mg/(kg·min), P < 0.05]. There were increases over time in pancreatic polypeptide (Pre:62 ± 29, 6 months:127 ± 44, 18 months:176 ± 58 pmol/L, P < 0.01), epinephrine (Pre: 199 ± 53, 6 months: 332 ± 91, 18 months: 386 ± 95 pg/mL, P = 0.001), and autonomic symptom (Pre: 6 ± 2, 6 months: 6 ± 2, 18 months: 10 ± 2, P < 0.05) responses. Conclusions: AID led to a sustained reduction of hypoglycemia exposure. EGP in response to insulin-induced hypoglycemia remained defective, however, partial recovery of glucose counterregulation was evidenced by a reduction in peripheral glucose utilization likely mediated by increased epinephrine secretion and, together with improved autonomic symptoms, may contribute to the observed clinical reduction in hypoglycemia.

An examination of bidirectional associations between physical activity and mood symptoms among individuals diagnosed and at risk for bipolar spectrum disorders

OBJECTIVES

Activation, a construct including energy and activity, is a central feature of Bipolar Spectrum Disorders (BSDs). Prior research found motor activity is associated with affect, and this relationship may be stronger for individuals with BSDs. The aims of this study were to investigate bidirectional relationships between physical activity and mood and evaluate whether bipolar risk status moderated potential associations.

METHODS

Young adults at low-risk, high-risk, and diagnosed with BSD participated in a 20-day EMA study in which they wore an actiwatch to measure physical activity and sleep/wake cycles. They also reported depressive and hypo/manic symptoms three times daily. Multilevel linear models were estimated to examine how bipolar risk group moderated bidirectional relationships between physical activity and mood symptoms at within-day and between-day timescales.

RESULTS

Physical activity was significantly associated with subsequent mood symptoms at the within-day level. The relationship between physical activity and depressive symptoms was moderated by BSD risk group. An increase in physical activity resulted in a greater reduction of depressive symptoms for the BSD group compared to the low-risk and high-risk groups.

CONCLUSIONS

Interventions targeting activity like behavioral activation may improve residual inter-episode mood symptoms.

The relationship between physical activity states and depressive symptoms: Using ambulatory assessment to characterize day-to-day associations among individuals with and without bipolar spectrum disorder

INTRODUCTION

The role of activation in the pathogenesis of bipolar spectrum disorders (BSD) is of growing interest. Physical activity is known to improve mood, but it is unclear whether low activity levels contribute to inter-episode depressive symptoms observed in BSD. This study examined whether sedentary and vigorous activity, as well as the timing of the activity, were differentially associated with next-day depressive symptoms for individuals at low risk for BSD, high-risk for BSD, and diagnosed with BSD.

METHODS

Young adults (n = 111, ages 18-27) from three groups (low BSD risk, high BSD risk, and BSD diagnosis), participated in a 20-day ecological momentary assessment study. Physical activity was measured via wrist actigraphy counts. The percentage of time awake spent in sedentary, light, moderate, and vigorous activity states was calculated, as was the percentage of morning hours and evening hours in each activity state. Multilevel models examined whether the BSD risk group moderated associations between sedentary and vigorous activity and depressive symptoms, which were assessed three times daily.

RESULTS

There were no between-group differences in time spent in each activity state, nor were there main effects of sedentary or vigorous activity on depression. Increased time spent engaging in vigorous activity was associated with a greater reduction in subsequent depressive symptoms for the BSD group. An increase in the evening, but not morning, vigorous activity was significantly associated with a reduction in subsequent depressive symptoms for the BSD group after controlling for chronotype.

CONCLUSIONS

Interventions targeting physical activity may effectively help regulate inter-episode mood disturbances in BSD.

Keeping an eye on circadian time in clinical research and medicine

BACKGROUND

Daily rhythms are observed in humans and almost all other organisms. Most of these observed rhythms reflect both underlying endogenous circadian rhythms and evoked responses from behaviours such as sleep/wake, eating/fasting, rest/activity, posture changes and exercise. For many research and clinical purposes, it is important to understand the contribution of the endogenous circadian component to these observed rhythms.

CONTENT

The goal of this manuscript is to provide guidance on best practices in measuring metrics of endogenous circadian rhythms in humans and promote the inclusion of circadian rhythms assessments in studies of health and disease. Circadian rhythms affect all aspects of physiology. By specifying minimal experimental conditions for studies, we aim to improve the quality, reliability and interpretability of research into circadian and daily (i.e., time-of-day) rhythms and facilitate the interpretation of clinical and translational findings within the context of human circadian rhythms. We describe protocols, variables and analyses commonly used for studying human daily rhythms, including how to assess the relative contributions of the endogenous circadian system and other daily patterns in behaviours or the environment. We conclude with recommendations for protocols, variables, analyses, definitions and examples of circadian terminology.

CONCLUSION

Although circadian rhythms and daily effects on health outcomes can be challenging to distinguish in practice, this distinction may be important in many clinical settings. Identifying and targeting the appropriate underlying (patho)physiology is a medical goal. This review provides methods for identifying circadian effects to aid in the interpretation of published work and the inclusion of circadian factors in clinical research and practice.

POS1342 DEPLETION OF KLRG1+ T CELLS IN A FIRST-IN-HUMAN CLINICAL TRIAL OF ABC008 IN INCLUSION BODY MYOSITIS (IBM)

Background

Inclusion body myositis (IBM), a relentlessly progressive autoimmune skeletal muscle disease, has no effective available pharmacological therapy. A prominent feature of IBM on microscopy is highly differentiated effector CD8+ cytotoxic T (Tc) cells invading non-necrotic myofibers [1]. These Tc cells, known to be relatively resistant to apoptosis, express markers including killer cell lectin-like receptor G1 (KLRG1) [2]. ABC008, a first-in-class humanized, afucosylated monoclonal antibody (mAb) specific for KLRG1, selectively depletes these highly differentiated Tc cells while sparing other blood cell populations, e.g., naïve, central memory, and regulatory T cells and B cells. ABC008 has been designed to treat diseases mediated by these Tc cells, including IBM and T-cell large granular lymphocytic leukemia (T-LGLL). IBM and rheumatoid arthritis overlap clinically with T-LGLL and share similar expansions of large granular lymphocytes (LGLs), which also express KLRG1. We report here our preliminary data from our ongoing trial of ABC008 in IBM (NCT04659031).

Objectives

Evaluate the safety, pharmacodynamics (PD), and pharmacokinetics (PK) of ABC008 administered subcutaneously (SC) in adults with IBM.

Methods

In this first-in-human, open-label, single ascending dose trial with 3 + 3 design evaluating ABC008 SC, eligible participants must have clinicopathologically defined, clinically defined, or probable IBM according to the European Neuromuscular Centre 2011 research diagnostic criteria [3] and an IBM Functional Rating Scale (IBMFRS) score ≤38. Four dose cohorts are planned: ABC008 0.1, 0.5, 2.0, and 5.0 mg/kg. PD, PK, safety, and disease severity assessments are performed pre-dose (Day 0) and during the 6-month follow-up period.

Results

Five of the 6 (83.3%) participants were male with baseline mean age = 65.7 years, mean IBM disease duration = 6.8 years, and mean IBMFRS score = 27.5 (Table 1). Each received a single dose of ABC008 SC: Cohorts 1 (C1) and 2 (C2) received 0.1 and 0.5 mg/kg and have completed 168 and 56 days of follow-up, respectively.

Table 1.

Baseline Demographics

Baseline CharacteristicsABC008 SC Treatment GroupsCohort 10.1 mg/kg SC (n=3)Cohort 20.5 mg/kg SC (n=3)ABC008 Overall (N=6)Age (years), mean ± SD64.0 ± 11.3667.3 ± 6.6665.7 ± 8.52Male sex, n (%)3 (100)2 (66.7)5 (83.3)Caucasian3 (100)3 (100)6 (100)Body Mass Index (kg/m2)28.5 ± 3.5828.3 ± 4.2528.4 ± 3.52Disease Duration (years), mean ± SD9.7 ± 5.973.9 ± 4.486.8 ± 5.70IBMFRS score, mean ± SD30.0 ± 4.0825.0 ± 6.1627.5 ± 5.80

Abbreviations: IBMFRS, Inclusion Body Myositis Functional Rating Scale; n or N, number; SC, subcutaneous; SD, standard deviation.

Maximum depletion of CD8+KLRG1+ cells for C1 and C2 ranged from 46-96% and 98-99%, respectively (Figure 1A), with depletion evident by the next assessment on Day 1. Recovery in C1 began at Day 84 with Day 168 depletion at 29-71%. Other hematologic parameters generally were stable (e.g., T regulatory and B cells). CD8+CD57+ LGLs, mostly KLRG1+, were also depleted (Figure 1B). Preliminary PK showed that ABC008 SC displays a long absorption phase and slow clearance properties typical of mAb therapies. No severe adverse events (AEs) or discontinuations due to AEs have been reported. One unrelated serious AE of fall with muscle tear in a C1 participant with a prior history of falls occurred.

Figure 1.

Key pharmacodynamic parameters in Study ABC008-IBM-101. Baseline and change (A) of KLRG1+CD8+ T cells and (B) CD8+CD57+ large granular lymphocytes (LGLs). C1 and C2 are Cohorts 1 and 2; P1, P2, and P3 are Participants 1, 2, and 3.

Conclusion

In study participants with IBM, single SC doses of 0.1 and 0.5 mg/kg of ABC008 resulted in the depletion of CD8+KLRG1+ cells and CD8+CD57+ LGLs with clear evidence of a dose response for KLRG1+ T cell depletion and no apparent safety signals. Based on these results, a study evaluating ABC008 for the treatment of T-LGLL is planned.

References

[1]Engel AG, et al. Ann Neurol. 1984;16:209-15.

[2]Greenberg SA, et al. Brain. 2016;139:1348-60.

[3]Rose MR, et al. Neuromuscul Disord. 2013;23:1044-55.

Disclosure of Interests

Niti Goel Shareholder of: UCB, Abcuro, Inc, Employee of: Abcuro, Inc, Merrilee Needham Consultant of: Abcuro, Inc, Grant/research support from: Abcuro, Inc, Dulce Soler-Ferran Shareholder of: Abcuro, Inc, Employee of: Abcuro, Inc, Monette M. Cotreau Consultant of: Abcuro, Inc, Jorge Escobar Shareholder of: Abcuro, Employee of: Abcuro, Steven Greenberg Shareholder of: Abcuro, Inc, Consultant of: Abcuro, Inc

AB0953 Lack of Racial Diversity in Clinical Trials of Psoriatic Arthritis

Background

Participant diversity in clinical trials of therapeutics in rheumatology is important to understand how persons of different races and ethnicities might respond differently to therapeutics. Specifically, for psoriatic arthritis (PsA), although people of color (POC) have lower disease prevalence, prevalence still ranges from 0.04-0.19% in Blacks, 0.13-0.19% in Asians, and 0.09-0.30% in Hispanics versus 0.19-0.34% in Whites in the insured population of the US1. Literature on the diversity of PsA clinical trials remains limited, though data suggest that minorities are underrepresented in clinical trials2. This analysis aims to evaluate the diversity of participants in randomized clinical trials (RCTs) of US Food and Drug Administration approved targeted therapies for PsA.

Objectives

To evaluate the reporting of race and ethnicity in published RCTs of targeted therapeutics approved for the treatment of PsA in the US.

Methods

Targeted therapies approved for use in the treatment of PsA in the US were identified. Package inserts and ClinicalTrials.gov (CT.gov) were used to identify the pivotal double blind, RCTs in PsA which supported the approval of the identified therapeutics in the US. The articles reporting the primary endpoint data were obtained. Race and ethnicity data were extracted from the published data. Countries in which the studies were conducted were identified from the publications or CT.gov. Descriptive analyses were performed.

Results

Twenty-nine pivotal RCTs in PsA evaluating targeted therapeutics, published from 2002 – 2022, were identified; 24 reported race; non-White race was reported in only 13 (45%) (Table 1 and Figure 1). In the latter, people of Black race comprised <1% of the overall population in 12 RCTs and 2.7% in the remaining RCT. People of Asian race comprised 6.1% of the overall population reflecting <10% of the population in 11 studies and 11.3% and 19.0% in the remaining 2 studies. Overall, 19 (65.6%) trials recruited participants from Asia Pacific countries. Hispanic/Latinx ethnicity was not reported in any study. Studies published from 2017-2022 reported non-White race (n=7 of 15 [47%]) no more frequently than studies published from 2004-2016 (n=6 of 14 [43%]). Although the 13 RCTs reporting non-White race may not reflect unique individuals, the total number of people included across these RCTs was 7261, of which 48 (0.7%), 441 (6.1%), and 6598 (90.9%) were Black, Asian, and White, respectively.

Table 1.

Race Reporting in Psoriatic Arthritis Pivotal Clinical Trials of Targeted Therapeutics

Race Reporting StatusTotal Trials (N)Trials which recruited in AP countries(n [% of N])Total Participants (N)White (n [% of N])Black (n [% of N])Asian (n [% of N])Other* (n [% of N])Not reported51 (20.0)1572----Non-White race reported1313 (44.8)72616598 (90.9)48 (0.7)441 (6.1)172 (2.4)Non-White race not reported115 (45.5)65885803 (88.1)---Total (N)2919 (65.5)15421----

Abbreviations: AP, Asia Pacific; N and n, number.

*Other also includes American Indian/Alaskan Native, mixed race, unknown race

Note: Numbers across rows may not add up to 0, and the total number of individuals reported in any one group may not be unique. Dashes reflect data not provided or able to be calculated.

Conclusion

Our data show under-reporting of race and ethnicity in publications of pivotal PsA RCTs, and no evidence of improved reporting over time. Whites were overrepresented in pivotal trials of PsA, especially when considering 72 and 62% of the US population was White in 2010 and 2020 (US Census data), respectively, and the reported prevalence of PsA by race in the insured population of the US1. Improved reporting of race/ethnicity and increased representation of racial/ethnic minorities in PsA RCTs are needed.

References

[1]Ogdie A, et al. Rheumatol Ther. 2021;8(4):1725-39.

[2]https://trialfacts.com/diversity-inclusion/ accessed 22 Aug 2021

Disclosure of Interests

Niti Goel Shareholder of: UCB, Abcuro, Employee of: Abcuro

0592 Hybrid Closed Loop Insulin Delivery Systems Reduce Perceived Hypoglycemia During Sleep in Adults With Long-Standing Type 1 Diabetes and Hypoglycemia Unawareness

Introduction

Sleep-associated hypoglycemia is a major concern for individuals with type 1 diabetes (T1D). Hybrid closed loop insulin delivery systems with continuous glucose monitoring (HCL-CGM) may reduce the perceived frequency, severity, and impact of sleep-associated hypoglycemia. This analysis assessed changes in perceived sleep-associated hypoglycemia in individuals with T1D at high risk for hypoglycemia after initiating HCL-CGM.

Methods

Seven adults (median age=53y) with long-standing T1D (median duration=41y) and hypoglycemia unawareness participated in an ongoing 18-month clinical trial assessing effectiveness of HCL-CGM. At baseline and every 6 months thereafter, participants completed the validated Hypoglycemia Awareness Questionnaire (HypoA-Q), a 33-item tool consisting of three subscales (impaired awareness, symptom level, and symptom frequency), and 16 conceptually distinct items, including six items that relate to the frequency, severity, and impact of sleep-associated hypoglycemia, each which is scored and assessed individually. Friedman Tests assessed changes in items over the 18-month interval and Kendall’s W determined effect sizes.

Results

HCL-CGM significantly reduced the reported frequency of the following questions: (a) “How often you have had a hypo during your sleep?” (χ2(3)=8.4, p&lt;0.05; moderate effect size, W=0.40) and (b) “…and were unable treat yourself when you woke up?” (χ2(3)=12.1, p&lt;0.05; large effect size, W=0.57). HCL-CGM also reduced the reported frequency to: (c) “…and someone else gave you sugar by mouth?” (χ2(3)=7.2, p&lt;0.07; moderate effect size, W=0.34). By contrast, HCL-CGM did not affect reported frequency to the questions: (d) “…which led to a major problem?” (p&gt;0.05; moderate effect size, W=0.26)”; (e) “…and someone else gave you a glucagon injection?” (p&gt;0.05; small effect size, W=0.14); and (f) “…where you stayed asleep and only later realized that you had been hypo?” (p&gt;0.05; small effect size, W=0.19).

Conclusion

HCL-CGM improved various critical aspects of perceived sleep-associated hypoglycemic events in individuals most at-risk for hypoglycemia. Our results have important implications for self-care and patient treatment in this population.

Support (If Any)

NIH R01DK117488 (NG), R01DK091331 (MRR), K99NR017416 (SKM), and UL1TR001878 (University of Pennsylvania Center for Human Phenomic Science); NASA NNX14AN49G and 80NSSC20K0243 (NG); Pennsylvania Department of Health SAP 4100079750 (IL).

0575 Characterization of the Prevalence of Sleep Disturbances in Cardiovascular and Neurological Patients from the Rush Heart Center for Women

Introduction

Anecdotally, patients with cardiovascular and neurological medical conditions present with sleep disturbances in duration, efficiency, and timing, or with sleep disorders such as obstructive sleep apnea. The frequency of such disturbances remains unknown as does whether they result from cardiovascular and neurological conditions or contribute to the development of these conditions. Thus, characterizing the prevalence of sleep disturbances in this unique clinical population is a first step to establishing the important role of sufficient, healthy sleep for patient care and treatment.

Methods

We conducted a retrospective electronic chart review of patients from the Rush Heart Center for Women (RHCW) based on the following: age, sex, race, body mass index (BMI), blood pressure (BP), sleep studies, cardiac testing, and neurologic testing. Patients were also characterized based on prevalent disease (cardiac, neurologic, or both) including the following: cardiac—coronary artery disease, ischemic vs. non-ischemic heart disease, atrial fibrillation, heart failure, etc; neurologic—vascular etiology, mild cognitive impairment, dementia, Alzheimer’s Disease, etc. We then performed a database search to identify patients who also met the criteria for sleep apnea or for whom a sleep study was ordered due to sleep disturbances.

Results

103 patients (mean age±SD, 68.71±12.55 years; 78 females; mean BMI±SD, 28.65±5.28; 79 White, 14 African American, 4 Asian, 1 Native Hawaiian, 5 Other; mean BP±SD, 128/72±20/9; mean±SD Mini-Mental State Examination (MMSE), 27.47±3.24) had cardiovascular and/or neurological conditions. Of these, 53 patients (mean age±SD, 68.31±9.35 years; 40 females; mean BMI±SD, 29.91±5.73; 39 White, 7 African American, 2 Asian, 1 Native Hawaiian, 4 Other; mean BP±SD, 129/73±21/9; mean MMSE±SD, 27.67±2.99) also presented with sleep apnea symptoms or sleep disturbances.

Conclusion

A strikingly high percentage (51.5%) of patients at the RHWC who had cardiovascular and/or neurological conditions also presented with sleep apnea or sleep disturbance symptoms. The prevalence of this trifecta of disease (cardiovascular, neurological, and sleep apnea/disturbance) demonstrates the criticality of considering the interplay between these various domains when administering clinical care to patients. Future research capitalizing on the physiological and neurobehavioral benefits of adequate, healthy sleep in this population is warranted.

Support (If Any)

RMC Dean’s Award Summer Research Fellowship; NASA NNX14AN49G and 80NSSC20K0243; NIH R01DK117488

0585 Use of a Hybrid Closed Loop Insulin Delivery System Improves Sleep and Glycemic Control in Adults with Long-Standing Type 1 Diabetes and Hypoglycemia Unawareness

Introduction

Insulin delivery and continuous glucose monitoring systems (CGMs) have been reported to disrupt sleep in individuals with type 1 diabetes (T1D), potentially thwarting the adoption and continued use of diabetes therapeutic technologies. This study assessed changes in actigraphic sleep and glycemic outcomes in individuals at high risk for life threatening nocturnal hypoglycemia after initiating a hybrid closed loop (HCL) insulin delivery system with integrated CGM.

Methods

10 adults (median age=51y) with long-standing T1D (median duration=34y) and hypoglycemia unawareness participated in an 18-month ongoing clinical trial assessing the effectiveness of a HCL system. Wrist actigraphs and CGMs measured sleep and glycemic control, respectively, at baseline (1 week) and at months 3, 6, 9, 12, 15, and 18 (3 weeks) following HCL initiation. Body mass index and hemoglobin A1c (HbA1c) were also collected at these timepoints. Hypoglycemia awareness was assessed using the Clarke hypoglycemia questionnaire, HYPO score, and glycemic lability index. Paired sample t-tests and Cohen’s d effect sizes modeled changes in sleep, glycemic control, and hypoglycemic awareness and the magnitude of these changes from baseline to 18 months.

Results

Sleep improved from baseline to 18 months [shorter sleep latency (p&lt;0.01), later sleep offset (p&lt;0.05), and less wake after sleep onset (WASO) (&lt;0.01)]. Medium effect sizes were found for later sleep onset (d=0.74) and later sleep midpoints (d=0.77). HCL also improved hypoglycemia awareness from baseline to 18 months [Clarke score (p&lt;0.01), HYPO score (p&lt;0.01), lability index (p&lt;0.05)]. Medium to large effect sizes were found for reduced nocturnal hypoglycemia (percent time glucose was &lt;54mg/dL,&lt;60mg/dL,&lt;70mg/dL; d=0.66 – 0.81), daytime and nocturnal hypoglycemia (percent time glucose was&lt;54mg/dL,&lt;60mg/dL,&lt;70mg/dL; d=0.61 – 0.69), and glucose variability (coefficient of variability; d=0.62).

Conclusion

HCL insulin delivery with CGM improved sleep over time as indicated by shorter sleep onset latency, later sleep offset, and less WASO. HCL insulin delivery also improved hypoglycemia awareness and led to clinically significant reductions in hypoglycemia and glucose variability.

Support (If Any)

NIH (R01DK117488 (NG), R01DK091331 (MRR), K99NR017416 (SKM), and UL1TR001878 (University of Pennsylvania Center for Human Phenomic Science)). National Aeronautics and Space Administration (NASA) (NNX14AN49G and 80NSSC20K0243 (NG)) and from the Pennsylvania Department of Health (SAP 4100079750 (IL)).

0283 Neurobehavioral Resilience and Vulnerability to Sleep Loss Differs Between Objective and Self-Rated Metrics Regardless of Categorization Method Utilized

Introduction

Trait-like individual differences in neurobehavioral responses to sleep restriction (SR) and total sleep deprivation (TSD) are robust and phenotypic. We investigated whether the concordance between multiple approaches for defining differential vulnerability depends on the methods and metrics utilized for categorization, including comparisons between objective and self-rated metrics. Trait-like individual differences in neurobehavioral responses to sleep restriction (SR) and total sleep deprivation (TSD) are robust and phenotypic. We investigated whether the concordance between multiple approaches for defining differential vulnerability depends on the methods and metrics utilized for categorization, including comparisons between objective and self-rated metrics.

Methods

Forty-one adults (33.9±8.9y; 18 females) participated in a 13-day experiment (two baseline nights [10h-12h time-in-bed, TIB], 5 SR nights [4h TIB], 4 recovery nights [12h TIB], and 36h TSD). The 10-minute Psychomotor Vigilance Test (PVT), Digit Symbol Substitution Test (DSST), Digit Span Task (DS), Karolinska Sleepiness Scale (KSS), Profile of Mood States Fatigue (POMS-F) and Vigor (POMS-V) were administered every 2h during wakefulness. Three approaches (Raw Score [average SR score], Change from Baseline [average SR minus average baseline score], and Variance [intraindividual SR score variance]), and six thresholds (±1 standard deviation, and the best and worst performing 12.5%, 20%, 25%, 33%, and 50%) categorized Resilient and Vulnerable groups. Kendall’s tau-b correlations assessed the group categorization’s concordance between pairings of PVT lapses (reaction time [RT]&gt;500ms), PVT mean response speed (1/RT), DSST number correct, DS total number correct, KSS score, POMS-F score, and POMS-V score (tau-b=0.0: zero; 0.1: weak; 0.4: moderate; 0.70: strong; 1.0: perfect).

Results

Generally, tau-b correlations comparing Resilient and Vulnerable categorizations between two objective metrics (i.e., PVT, DSST, DS) revealed weak to moderate significant relationships (tau-b=0.29-0.53, p&lt;0.001-0.049) between at least two of the approaches at most thresholds. However, comparisons between one objective (i.e., PVT, DSST, DS) and one self-rated metric (i.e., KSS, POMS) revealed a general lack of significant relationships (tau-b=-0.25-0.28, p=0.052-1.00), regardless of approach or threshold.

Conclusion

Comparisons between two objective metrics revealed significantly concordant Resilient and Vulnerable categorizations, whereas categorizations were generally not significantly correlated between one objective and one subjective metric, regardless of the method utilized. Our findings support and extend previous assertions that SR differentially impacts objective and subjective neurobehavioral domains and have important implications when assessing resilience and vulnerability to sleep loss in both laboratory and applied settings.

Support (If Any)

ONR Award No. N00014-11-1-0361; NIH UL1TR000003; NASA NNX14AN49G and 80NSSC20K0243; NIH R01DK117488

The 3-Minute Psychomotor Vigilance Test Demonstrates Inadequate Convergent Validity Relative to the 10-Minute Psychomotor Vigilance Test Across Sleep Loss and Recovery

The Psychomotor Vigilance Test (PVT) is a widely used behavioral attention measure, with the 10-min (PVT-10) and 3-min (PVT-3) as two commonly used versions. The PVT-3 may be comparable to the PVT-10, though its convergent validity relative to the PVT-10 has not been explicitly assessed. For the first time, we utilized repeated measures correlation (rmcorr) to evaluate intra-individual associations between PVT-10 and PVT-3 versions across total sleep deprivation (TSD), chronic sleep restriction (SR) and multiple consecutive days of recovery. Eighty-three healthy adults (mean ± SD, 34.7 ± 8.9 years; 36 females) received two baseline nights (B1-B2), five SR nights (SR1-SR5), 36 h TSD, and four recovery nights (R1-R4) between sleep loss conditions. The PVT-10 and PVT-3 were completed every 2 h during wakefulness. Rmcorr compared responses on two frequently used, sensitive PVT metrics: reaction time (RT) via response speed (1/RT) and lapses (RT > 500 ms on the PVT-10 and > 355 ms on the PVT-3) by day (e.g., B2), by study phase (e.g., SR1-SR5), and by time point (1000-2000 h). PVT 1/RT correlations were generally stronger than those for lapses. The majority of correlations (48/50 [96%] for PVT lapses and 38/50 [76%] for PVT 1/RT) were values below 0.70, indicating validity issues. Overall, the PVT-3 demonstrated inadequate convergent validity with the "gold standard" PVT-10 across two different types of sleep loss and across extended recovery. Thus, the PVT-3 is not interchangeable with the PVT-10 for assessing behavioral attention performance during sleep loss based on the design of our study and the metrics we evaluated. Our results have substantial implications for design and measure selection in laboratory and applied settings, including those involving sleep deprivation.

Left Ventricular Ejection Time Measured by Echocardiography Differentiates Neurobehavioral Resilience and Vulnerability to Sleep Loss and Stress

There are substantial individual differences (resilience and vulnerability) in performance resulting from sleep loss and psychosocial stress, but predictive potential biomarkers remain elusive. Similarly, marked changes in the cardiovascular system from sleep loss and stress include an increased risk for cardiovascular disease. It remains unknown whether key hemodynamic markers, including left ventricular ejection time (LVET), stroke volume (SV), heart rate (HR), cardiac index (CI), blood pressure (BP), and systemic vascular resistance index (SVRI), differ in resilient vs. vulnerable individuals and predict differential performance resilience with sleep loss and stress. We investigated for the first time whether the combination of total sleep deprivation (TSD) and psychological stress affected a comprehensive set of hemodynamic measures in healthy adults, and whether these measures differentiated neurobehavioral performance in resilient and vulnerable individuals. Thirty-two healthy adults (ages 27-53; 14 females) participated in a 5-day experiment in the Human Exploration Research Analog (HERA), a high-fidelity National Aeronautics and Space Administration (NASA) space analog isolation facility, consisting of two baseline nights, 39 h TSD, and two recovery nights. A modified Trier Social Stress Test induced psychological stress during TSD. Cardiovascular measure collection [SV, HR, CI, LVET, BP, and SVRI] and neurobehavioral performance testing (including a behavioral attention task and a rating of subjective sleepiness) occurred at six and 11 timepoints, respectively. Individuals with longer pre-study LVET (determined by a median split on pre-study LVET) tended to have poorer performance during TSD and stress. Resilient and vulnerable groups (determined by a median split on average TSD performance) showed significantly different profiles of SV, HR, CI, and LVET. Importantly, LVET at pre-study, but not other hemodynamic measures, reliably differentiated neurobehavioral performance during TSD and stress, and therefore may be a biomarker. Future studies should investigate whether the non-invasive marker, LVET, determines risk for adverse health outcomes.

Cortisol and C-Reactive Protein Vary During Sleep Loss and Recovery but Are Not Markers of Neurobehavioral Resilience

Cortisol and C-reactive protein (CRP) typically change during total sleep deprivation (TSD) and psychological stress; however, it remains unknown whether these biological markers can differentiate robust individual differences in neurobehavioral performance and self-rated sleepiness resulting from these stressors. Additionally, little is known about cortisol and CRP recovery after TSD. In our study, 32 healthy adults (ages 27-53; mean ± SD, 35.1 ± 7.1 years; 14 females) participated in a highly controlled 5-day experiment in the Human Exploration Research Analog (HERA), a high-fidelity National Aeronautics and Space Administration (NASA) space analog isolation facility, consisting of two baseline nights, 39 h TSD, and two recovery nights. Psychological stress was induced by a modified Trier Social Stress Test (TSST) on the afternoon of TSD. Salivary cortisol and plasma CRP were obtained at six time points, before (pre-study), during [baseline, the morning of TSD (TSD AM), the afternoon of TSD (TSD PM), and recovery], and after (post-study) the experiment. A neurobehavioral test battery, including measures of behavioral attention and cognitive throughput, and a self-report measure of sleepiness, was administered 11 times. Resilient and vulnerable groups were defined by a median split on the average TSD performance or sleepiness score. Low and high pre-study cortisol and CRP were defined by a median split on respective values at pre-study. Cortisol and CRP both changed significantly across the study, with cortisol, but not CRP, increasing during TSD. During recovery, cortisol levels did not return to pre-TSD levels, whereas CRP levels did not differ from baseline. When sex was added as a between-subject factor, the time × sex interaction was significant for cortisol. Resilient and vulnerable groups did not differ in cortisol and CRP, and low and high pre-study cortisol/CRP groups did not differ on performance tasks or self-reported sleepiness. Thus, both cortisol and CRP reliably changed in a normal, healthy population as a result of sleep loss; however, cortisol and CRP were not markers of neurobehavioral resilience to TSD and stress in this study.

Impulsivity and sleep and circadian rhythm disturbance predict next-day mood symptoms in a sample at high risk for or with recent-onset bipolar spectrum disorder: An ecological momentary assessment study

BACKGROUND

Impulsivity and sleep and circadian rhythm disturbance are core features of bipolar spectrum disorders (BSDs) that are antecedents to onset and persist even between mood episodes; their pervasive presence in BSD suggests that they may be particularly relevant to understanding BSD onset and course. Considerable research demonstrates bidirectional associations between impulsivity and sleep disturbance in healthy individuals; thus, it is important to examine how these features interact to impact BSD symptomatology.

METHODS

Young adults (N = 107, 55% female, M age = 21.82 years) at high risk for developing BSD (based on high self-reported reward sensitivity) or with recent-onset BSD participated in ecological momentary assessment (EMA) to examine relationships between impulsivity, sleep and circadian rhythm alterations, and mood symptoms in everyday life. Impulsivity was measured via self-report/behavioral task, sleep was measured via actigraphy, circadian rhythms were measured via dim light melatonin onset (DLMO) time, and mood symptoms were measured three times daily via self-report.

RESULTS

Multi-level modeling revealed that less total sleep time predicted increased next-day mood symptoms. Moreover, DLMO, total sleep time, and sleep onset latency moderated the relationship between impulsivity and EMA-assessed mood symptoms. Fewer minutes of sleep and later DLMO strengthened the positive relationship between impulsivity and mood symptoms.

LIMITATIONS

Mood symptoms in our sample were mild; future studies should replicate findings in populations with more severe mood symptoms.

CONCLUSIONS

This multi-method assessment of dynamic relationships revealed novel associations between impulsivity, sleep and circadian rhythm disturbance, and symptoms within individuals at high-risk for or with recent-onset BSD.

Concordance of multiple methods to define resiliency and vulnerability to sleep loss depends on Psychomotor Vigilance Test metric

STUDY OBJECTIVES

Sleep restriction (SR) and total sleep deprivation (TSD) reveal well-established individual differences in Psychomotor Vigilance Test (PVT) performance. While prior studies have used different methods to categorize such resiliency/vulnerability, none have systematically investigated whether these methods categorize individuals similarly.

METHODS

41 adults participated in a 13-day laboratory study consisting of 2 baseline, 5 SR, 4 recovery, and one 36h TSD night. The PVT was administered every 2h during wakefulness. Three approaches (Raw Score [average SR performance], Change from Baseline [average SR minus average baseline performance], and Variance [intraindividual variance of SR performance]), and within each approach, six thresholds (±1 standard deviation and the best/worst performing 12.5%, 20%, 25%, 33%, and 50%) classified Resilient/Vulnerable groups. Kendall's tau-b correlations examined the concordance of group categorizations of approaches within and between PVT lapses and 1/reaction time (RT). Bias-corrected and accelerated bootstrapped t-tests compared group performance.

RESULTS

Correlations comparing the approaches ranged from moderate to perfect for lapses and zero to moderate for 1/RT. Defined by all approaches, the Resilient groups had significantly fewer lapses on nearly all study days. Defined by the Raw Score approach only, the Resilient groups had significantly faster 1/RT on all study days. Between-measures comparisons revealed significant correlations between the Raw Score approach for 1/RT and all approaches for lapses.

CONCLUSION

The three approaches defining vigilant attention resiliency/vulnerability to sleep loss resulted in groups comprised of similar individuals for PVT lapses but not for 1/RT. Thus, both method and metric selection for defining vigilant attention resiliency/vulnerability to sleep loss is critical.

Raw scores on subjective sleepiness, fatigue, and vigor metrics consistently define resilience and vulnerability to sleep loss

STUDY OBJECTIVES

Although trait-like individual differences in subjective responses to sleep restriction (SR) and total sleep deprivation (TSD) exist, reliable characterizations remain elusive. We comprehensively compared multiple methods for defining resilience and vulnerability by subjective metrics.

METHODS

41 adults participated in a 13-day experiment:2 baseline, 5 SR, 4 recovery, and one 36h TSD night. The Karolinska Sleepiness Scale (KSS) and the Profile of Mood States Fatigue (POMS-F) and Vigor (POMS-V) were administered every 2h. Three approaches (Raw Score [average SR score], Change from Baseline [average SR minus average baseline score], and Variance [intraindividual SR score variance]), and six thresholds (±1 standard deviation, and the highest/lowest scoring 12.5%, 20%, 25%, 33%, 50%) categorized Resilient/Vulnerable groups. Kendall's tau-b correlations compared the group categorization's concordance within and between KSS, POMS-F, and POMS-V scores. Bias-corrected and accelerated bootstrapped t-tests compared group scores.

RESULTS

There were significant correlations between all approaches at all thresholds for POMS-F, between Raw Score and Change from Baseline approaches for KSS, and between Raw Score and Variance approaches for POMS-V. All Resilient groups defined by the Raw Score approach had significantly better scores throughout the study, notably including during baseline and recovery, whereas the two other approaches differed by measure, threshold, or day. Between-measure correlations varied in strength by measure, approach, or threshold.

CONCLUSION

Only the Raw Score approach consistently distinguished Resilient/Vulnerable groups at baseline, during sleep loss, and during recovery‒‒we recommend this approach as an effective method for subjective resilience/vulnerability categorization. All approaches created comparable categorizations for fatigue, some were comparable for sleepiness, and none were comparable for vigor. Fatigue and vigor captured resilience/vulnerability similarly to sleepiness but not each other.

Genetic Markers of Differential Vulnerability to Sleep Loss in Adults

In this review, we discuss reports of genotype-dependent interindividual differences in phenotypic neurobehavioral responses to total sleep deprivation or sleep restriction. We highlight the importance of using the candidate gene approach to further elucidate differential resilience and vulnerability to sleep deprivation in humans, although we acknowledge that other omics techniques and genome-wide association studies can also offer insights into biomarkers of such vulnerability. Specifically, we discuss polymorphisms in adenosinergic genes (ADA and ADORA2A), core circadian clock genes (BHLHE41/DEC2 and PER3), genes related to cognitive development and functioning (BDNF and COMT), dopaminergic genes (DRD2 and DAT), and immune and clearance genes (AQP4, DQB1*0602, and TNFα) as potential genetic indicators of differential vulnerability to deficits induced by sleep loss. Additionally, we review the efficacy of several countermeasures for the neurobehavioral impairments induced by sleep loss, including banking sleep, recovery sleep, caffeine, and naps. The discovery of reliable, novel genetic markers of differential vulnerability to sleep loss has critical implications for future research involving predictors, countermeasures, and treatments in the field of sleep and circadian science.

Cognitive throughput and working memory raw scores consistently differentiate resilient and vulnerable groups to sleep loss

STUDY OBJECTIVES

Substantial individual differences exist in cognitive deficits due to sleep restriction (SR) and total sleep deprivation (TSD), with various methods used to define such neurobehavioral differences. We comprehensively compared numerous methods for defining cognitive throughput and working memory resiliency and vulnerability.

METHODS

41 adults participated in a 13-day experiment: 2 baseline, 5 SR, 4 recovery, and one 36h TSD night. The Digit Symbol Substitution Test (DSST) and Digit Span Test (DS) were administered every 2h. Three approaches (Raw Score [average SR performance], Change from Baseline [average SR minus average baseline performance], and Variance [intraindividual variance of SR performance]), and six thresholds (±1 standard deviation, and the best/worst performing 12.5%, 20%, 25%, 33%, 50%) classified Resilient/Vulnerable groups. Kendall's tau-b correlations compared the group categorizations' concordance within and between DSST number correct and DS total number correct. Bias-corrected and accelerated bootstrapped t-tests compared group performance. .

RESULTS

The approaches generally did not categorize the same participants into Resilient/Vulnerable groups within or between measures. The Resilient groups categorized by the Raw Score approach had significantly better DSST and DS performance across all thresholds on all study days, while the Resilient groups categorized by the Change from Baseline approach had significantly better DSST and DS performance for several thresholds on most study days. By contrast, the Variance approach showed no significant DSST and DS performance group differences.

CONCLUSION

Various approaches to define cognitive throughput and working memory resilience/vulnerability to sleep loss are not synonymous. The Raw Score approach can be reliably used to differentiate resilient and vulnerable groups using DSST and DS performance during sleep loss.

OP0229 THE GROUP FOR RESEARCH AND ASSESSMENT OF PSORIASIS AND PSORIATIC ARTHRITIS (GRAPPA) TREATMENT RECOMMENDATIONS 2021

Background:

Since the 2015 GRAPPA treatment recommendations were published, therapeutic options and management strategies for psoriatic arthritis (PsA) have advanced considerably.

Objectives:

The goal of the GRAPPA recommendations update is to develop high quality, evidence-based recommendations for the treatment of PsA, including related conditions and comorbidities.

Methods:

GRAPPA rheumatologists, dermatologists and patient research partners (PRPs) updated overarching principles for the management of adults with PsA by consensus. Principles considering use of biosimilars and tapering/discontinuing of therapy were added to this update. Systematic literature searches based on data publicly available from three databases (MEDLINE, EMBASE, and Cochrane CENTRAL) were conducted from the end of the previous recommendations’ searches through August 2020. Additional abstract searches were performed for conference presentations in 2017-2020. Searches covered PsA treatments (peripheral arthritis, axial arthritis, enthesitis, dactylitis, skin, and nail disease). Additional searches were performed for related conditions (uveitis and IBD) and comorbidities evaluating their impact on safety and treatment outcomes. Individual groups assessed the risk of bias and applied the GRADE system to generate strong or conditional recommendations for therapies within the domain groups and for the management of comorbidities and related conditions. These recommendations were then incorporated into an overall treatment schema.

Results:

Updated, evidence-based treatment recommendations are shown (Table 1). Since 2015, many new medications have been incorporated. Additional results for older medications, such as methotrexate, have been published across PsA domains. Based on the evidence, the treatment recommendations developed by individual groups were incorporated into the overall schema including principles for management of arthritis, spondylitis, enthesitis, dactylitis, skin, and nail disease in PsA, and associated conditions (Figure 1). Choice of therapy for an individual should ideally address all of the domains that impact on that patient, supporting shared decision making with the patient involved. Additional consideration in the recommendations was given to key associated conditions and comorbidities as these often impact on therapy choice.

Conclusion:

These GRAPPA treatment recommendations provide up to date, evidence-based guidance to providers who manage and treat adult patients with PsA. These recommendations are based on domain-based strategy for PsA and supplemented by overarching principles developed by consensus of GRAPPA members.

IndicationStrongForConditional ForConditionalAgainstStrongAgainstInsufficient evidencePeripheral Arthritis DMARD NaïvecsDMARDs, TNFi, PDE4i, IL-12/23i, IL-17i, IL-23i, JAKiNSAIDs, oral CS, IA CS,IL-6i,Peripheral Arthritis DMARD IRTNFi, IL-12/23i, IL-17i, IL-23i, JAKiPDE4i, other csDMARD, NSAIDs, oral CS, IA CS,IL-6i,Peripheral ArthritisbDMARD IRTNFi, IL-17i, IL-23i, JAKi,NSAIDs, oral CS, IA CS, IL-12/23i, PDE4i, CTLA-4-IgIL-6i,Axial arthritis, Biologic NaïveNSAIDs, Physiotherapy, simple analgesia, TNFi, IL-17i, JAKiCS SIJ injections, bisphosphonatescsDMARDs, IL-6i,IL-12/23i, IL-23iAxial PsA, Biologic IRNSAIDs, Physiotherapy, simple analgesia, TNFi, IL-17i, JAKi csDMARDs, IL-6i,IL-12/23i, IL-23iEnthesitisTNFi, IL-12/23i, IL-17i, PDE4i, IL-23i, JAKiNSAIDs, physiotherapy, CS injections, MTXIL-6i,Other csDMARDsDactylitisTNFi IL-12/23i, IL-17i, IL-23i, JAKi, PDE4iNSAIDs, CS injections, MTXOther csDMARDsPsoriasisTopicals, phototherapy, csDMARDs, TNFi, IL-12/23i, IL-17i, IL-23i, PDE4i, JAKi AcitretinNail psoriasisTNFi, IL12/23i, IL17i, IL23i, PDE4iTopical CS, tacrolimus and calcipotriol combination or individual therapies, Pulsed dye laser, csDMARDs, acitretin, JAKiTopical Cyclosporine / Tazarotene, Fumarate, Fumaric Acid Esters, UVA and UVB Phototherapy, AlitretinoinIBDTNFi (not ETN), IL-12/23i, JAKiIL-17iUveitisTNFi (not ETN)

Disclosure of Interests:

Laura C Coates Speakers bureau: AbbVie, Amgen, Biogen, Celgene, Gilead, Eli Lilly, Janssen, Medac, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol-Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Amgen, Celgene, Eli Lilly, Pfizer, and Novartis, Enrique Soriano Speakers bureau: AbbVie, Amgen, Bristol-Myers Squibb,GSK, Genzyme, Janssen, Lilly, Novartis, Pfizer, Roche, Sandoz, Sanofi, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb,GSK, Genzyme, Janssen, Lilly, Novartis, Pfizer, Roche, Sandoz, Sanofi, UCB, Grant/research support from: AbbVie, Janssen, Novartis Pharma, Pfizer, Roche, and UCB, Nadia Corp: None declared, Heidi Bertheussen Consultant of: Pfizer, Kristina Callis-Duffin Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Lilly, Janssen, Novartis, Pfizer, Sienna Biopharmaceuticals, Stiefel Laboratories, UCB, Ortho Dermatologics, Inc, Regeneron Pharmaceuticals, Inc., Anaptys Bio, Boehringer Ingelheim., Cristiano Barbosa Campanholo Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Bristol-Myers Squibb, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Jeffrey Chau: None declared, Lihi Eder Consultant of: Abbvie, UCB, Janssen, Eli Lily, Pfizer, Novartis, Grant/research support from: Abbvie, UCB, Janssen, Eli Lily, Pfizer, Novartis, Daniel Fernandez Consultant of: Abbvie, UCB, Roche, Janssen, Pfizer, Amgen and Brystol, Grant/research support from: Abbvie, UCB, Roche, Janssen, Pfizer, Amgen and Brystol, Oliver FitzGerald Speakers bureau: AbbVie, Janssen and Pfizer Inc, Consultant of: BMS, Celgene, Eli Lilly, Janssen and Pfizer Inc, Grant/research support from: AbbVie, BMS, Eli Lilly, Novartis and Pfizer Inc, Amit Garg Consultant of: Abbvie, Amgen, Asana Biosciences, Bristol Myers Squibb, Boehringer Ingelheim, Incyte, InflaRx, Janssen, Pfizer, UCB, Viela Biosciences, Grant/research support from: Abbvie, Dafna D Gladman Consultant of: Abbvie, Amgen, BMS, Eli Lilly, Galapagos, Gilead, Jansen, Novartis, Pfizer and UCB, Grant/research support from: Abbvie, Amgen, Eli Lilly, Jansen, Novartis, Pfizer and UCB, Niti Goel: None declared, Suzanne Grieb: None declared, Philip Helliwell Speakers bureau: Janssen, Novartis, Pfizer, Consultant of: Eli Lilly, M Elaine Husni Consultant of: Abbvie, Amgen, Janssen, Novartis, Lilly, UCB, Regeneron, and Pfizer, Deepak Jadon Speakers bureau: AbbVie, Amgen, Celgene, Eli Lilly, Gilead, Healthcare Celltrion, Janssen, MSD, Novartis, Pfizer, Roche, Sandoz, UCB, Consultant of: AbbVie, Amgen, Celgene, Eli Lilly, Gilead, Healthcare Celltrion, Janssen, MSD, Novartis, Pfizer, Roche, Sandoz, UCB, Grant/research support from: AbbVie, Amgen, Celgene, Eli Lilly, Gilead, Healthcare Celltrion, Janssen, MSD, Novartis, Pfizer, Roche, Sandoz, UCB, Arnon Katz: None declared, Dhruvkumar Laheru: None declared, John Latella: None declared, Ying Ying Leung Speakers bureau: Novartis, AbbVie, Eli Lilly, Janssen, Consultant of: Pfizer and Boehringer Ingelheim, Grant/research support from: Pfizer and conference support from AbbVie, Christine Lindsay Shareholder of: Amgen, Employee of: Aurinia pharmaceuticals, Ennio Lubrano Speakers bureau: Alfa-Sigma, Abbvie, Galapagos, Janssen Cilag, Lilly., Consultant of: Alfa-Sigma, Abbvie, Galapagos, Janssen Cilag, Lilly., Luis Mazzuoccolo Speakers bureau: Abbvie, Amgen, Novartis, Elli Lilly, Jansen, Consultant of: Abbvie, Amgen, Novartis, Elli Lilly, Jansen, Roland McDonald: None declared, Philip J Mease Speakers bureau: AbbVie, Amgen, Eli Lilly, Janssen, Novartis, Pfizer and UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lilly, Galapagos, Gilead Sciences, GlaxoSmithKline, Janssen, Novartis, Pfizer, SUN and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Eli Lilly, Galapagos, Gilead Sciences, Janssen, Novartis, Pfizer, SUN and UCB, Denis O’Sullivan: None declared, Alexis Ogdie Consultant of: AbbVie, Amgen, BMS, Celgene, Corrona, Gilead, Janssen, Lilly, Novartis, and Pfizer, Grant/research support from: Novartis and Pfizer and Amgen, Wendy Olsder: None declared, Lori Schick: None declared, Ingrid Steinkoenig: None declared, Maarten de Wit Consultant of: AbbVie, BMS, Celgene, Janssen, Lilly, Novartis, Pfizer, Roche, Danielle van der Windt: None declared, Arthur Kavanaugh Speakers bureau: AbbVie, Amgen, BMS, Eli Lilly, Gilead Janssen, Novartis, Pfizer, UCB, Consultant of: AbbVie, Amgen, BMS, Eli Lilly, Gilead Janssen, Novartis, Pfizer, UCB

128 Raw Scores Best Differentiate Resilience and Vulnerability to Sleep Loss for Cognitive Throughput and Working Memory

Introduction

Substantial individual differences exist in cognitive deficits due to sleep restriction (SR) and total sleep deprivation (TSD), but the best approach to define such resilience and vulnerability remains a critical question. We compared multiple approaches and cutoff thresholds to define resilience and vulnerability using the Digit Symbol Substitution Task (DSST) and the Digit Span Task (DST).

Methods

Forty-one healthy adults (mean±SD ages,33.9±8.9y) participated in a 13-night experiment [two baseline nights (10h-12h time-in-bed, TIB), 5 SR nights (4h TIB), 4 recovery nights (12h TIB), and 36h TSD]. The DSST [measuring cognitive throughput] and DST [measuring working memory] were administered every 2h during wakefulness. Resilient/vulnerable groups were defined by average performance (DSST: number correct; DST: total correct from forward and backward versions) during SR1-5, average performance change from baseline during SR1-5, and variance in performance during SR1-5. Within each approach, groups were defined by +/-1 standard deviation (SD) and the top and bottom 12.5%, 20%, 25%, 33%, 50%. Bias-corrected and accelerated bootstrapped t-tests compared performance between resilient and vulnerable groups during baseline and SR1-5. Kendall’s tau correlations compared the ranking of individuals in each group.

Results

T-tests showed significant differences between resilient/vulnerable groups at all raw score cutoffs for DSST and DST performance during SR and at baseline. Change from baseline t-tests showed significant differences during SR between the DSST groups only at 12.5%, 20%, and SD whereas DST t-tests showed significant differences at all cutoffs. Variance t-tests revealed a significant difference between the DSST groups only at 25% during SR. For the DSST, the variance vs. change from baseline comparison at all cutoffs and between raw score vs. change from baseline for the SD cutoff showed moderate correlations, and for the DST, the raw score vs. change from baseline correlation was moderate for 25% and 33%.

Conclusion

The resilient/vulnerable groups defined by raw score were more consistent than those defined by change from baseline or variance, and raw score did not track these approaches well. As such, raw score is the optimal approach to define cognitive throughput and working memory performance resiliency/vulnerability during sleep loss.

Support (if any)

ONR Award No. N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

119 Behavioral Attention and Sleepiness Display Robust Stable Relationships Across Sleep Loss but not Across Recovery

Introduction

The 10-minute and 3-minute versions of the Psychomotor Vigilance Test (PVT10 and PVT3) and the Karolinska Sleepiness Scale (KSS) are commonly used to assess objective behavioral attention deficits and subjective sleepiness in response to sleep loss, respectively. However, the precise time course of relationships between behavioral attention and subjective sleepiness across sleep loss and recovery remains unknown but is critical for determining whether objective and subjective measures track each other. Repeated measures correlation (rmcorr) examined within-individual association between these measures throughout a highly controlled sleep deprivation study.

Methods

Forty-one healthy adults (ages 21-49;mean±SD, 33.9±8.9y;18 females) participated in a 13-night experiment consisting of two baseline nights (10h-12h time-in-bed, TIB) followed by 5 sleep restriction (SR) nights (4h TIB), 4 recovery nights (12h TIB), and 36h total sleep deprivation (TSD). A neurobehavioral test battery, including the PVT10, PVT3, and KSS, was administered every 2h during wakefulness. Rmcorr compared PVT10 [lapses (reaction time [RT] &gt;500ms) and 1/RT (response speed)], PVT3 (lapses [RT&gt;355ms], 1/RT), and KSS scores by examining correlations by day (e.g., Baseline day 2) and time point (e.g., 1000h-2000h). Rmcorr ranges: r=0.1:small; r=0.3:moderate; r=0.5:large.

Results

Generally, the correlations between the PVT10 and KSS and the PVT3 and KSS showed a similar pattern for lapses and 1/RT. PVT lapses and KSS scores showed small or non-significant correlations during baseline and recovery, whereas SR and TSD showed moderate correlations. PVT 1/RT and KSS scores showed moderate correlations during baseline, moderate to large correlations during SR and TSD, but small correlations during recovery. PVT10 and PVT3 1/RT showed stronger correlations with KSS scores than lapses. Additionally, all relationships showed moderate to large correlations by time point across the study.

Conclusion

Overall, the relationship between behavioral attention and sleepiness was stronger across sleep loss (SR or TSD) relative to fully rested states while it was consistently relatively strong at specific times of day throughout the study. In contrast to published literature, there is a remarkably stable relationship between an individual’s objective behavioral attention performance and perceptions of sleepiness during sleep loss, which is not evident during recovery or at baseline.

Support (if any)

ONR Award No. N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

121 Behavioral Attention Relationships Vary Between Demographic Groups Across Sleep Loss and Recovery

Introduction

The Psychomotor Vigilance Test (PVT) is a behavioral attention measure widely used to describe sleep loss deficits. Although there are reported differences in PVT performance for various demographic groups, no study has examined the relationship between measures on the 10-minute PVT (PVT10) and the 3-minute PVT (PVT3) within sex, age, and body mass index (BMI) groups throughout a highly controlled sleep deprivation study.

Methods

Forty-one healthy adults (mean±SD ages, 33.9±8.9y) participated in a 13-night experiment [2 baseline nights (10h-12h time in bed, TIB) followed by 5 sleep restriction (SR1-5) nights (4h TIB), 4 recovery nights (R1-R4; 12h TIB), and 36h total sleep deprivation (TSD)]. A neurobehavioral test battery, including the PVT10 and PVT3 was completed every 2h during wakefulness. Repeated measures correlation (rmcorr) compared PVT10 and PVT3 lapses (reaction time [RT] &gt;355ms [PVT3] and &gt;500ms [PVT10]) and response speed (1/RT) by examining correlations by day (e.g., baseline day 2) and time point (e.g., 1000h-2000h) within sex groups (18 females), within age groups defined by a median split (median=32, range=21-49y), and within BMI groups defined by a median split (median=25, range=17-31).

Results

PVT10 and PVT3 1/RT was significantly correlated at all study days and time points excluding at baseline for the younger group and at R2 for the higher BMI group. PVT10 and PVT3 lapses showed overall lower correlations across the study relative to 1/RT. Lapses were not significantly correlated at baseline for any group, for males across recovery (R1-R4), for the high BMI group at R2-R4, for the older group at R2-R3, or for the younger group at SR5 or R3.

Conclusion

Differentiating participants based on age, sex, or BMI revealed important variation in the relationship between PVT10 and PVT3 measures across the study. Surprisingly, lapses were not significantly correlated at baseline for any demographic group or across recovery for males or the high BMI or older group. Thus, PVT10 and PVT3 lapses may be less comparable in certain populations when well-rested. These findings add to a growing literature suggesting demographic factors may be important factors to consider when evaluating the effects of sleep loss.

Support (if any)

ONR Award N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243; NIHR01DK117488

116 Behavioral Attention Raw Scores Best Differentiate Cognitive Resilience and Vulnerability to Sleep Loss

Introduction

There are substantial, stable individual differences in cognitive performance resulting from sleep restriction (SR) and total sleep deprivation (TSD). The best method for defining cognitive resilience and vulnerability to sleep loss remains an unanswered, yet important question. To investigate this, we compared multiple approaches and cutoff thresholds to define resilience and vulnerability using the 10-minute Psychomotor Vigilance Test (PVT).

Methods

Forty-one healthy adults (ages 21-49; mean±SD, 33.9±8.9y; 18 females) participated in a 13-night experiment [2 baseline nights (10h-12h time-in-bed, TIB), 5 SR nights (4h TIB), 4 recovery nights (12h TIB), and 36h TSD]. The PVT was administered every 2h during wakefulness. PVT lapses (reaction time [RT]&gt;500 ms) and 1/RT (response speed) were measured. Resilient and vulnerable groups were defined by three approaches: average performance during SR1-5, average performance change from baseline to SR1-5, and variance in performance during SR1-5. Within each approach, resilient/vulnerable groups were defined by +/- 1 standard deviation and by the top and bottom 12.5%, 20%, 25%, 33%, 50%. Bias-corrected and accelerated bootstrapped t-tests compared PVT performance between the resilient and vulnerable groups during baseline and SR1-5. Kendall’s tau correlations compared the ranking of individuals in each group.

Results

T-tests revealed that the resilient and vulnerable PVT lapses groups, defined by all three approaches, had significantly different mean PVT lapses at all cutoffs. Resilient and vulnerable PVT 1/RT groups, defined by raw scores and by change from baseline, had significantly different mean PVT 1/RT at all cutoffs. However, resilient/vulnerable PVT 1/RT groups defined by variance only differed at the 33% and 50% cutoffs. Notably, raw scores at baseline significantly differed between resilient/vulnerable groups for both PVT measures. Variance vs. raw scores and variance vs. change from baseline had the lowest correlation coefficients for both PVT measures.

Conclusion

Defining resilient and vulnerable groups by raw scores during SR1-5 produced the clearest differentiation between resilient and vulnerable groups at every cutoff threshold for PVT lapses and response speed. As such, we propose that using PVT raw score is the optimal approach to define resilient and vulnerable groups for behavioral attention performance during sleep loss.

Support (if any)

ONR Award No.N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

127 Cognitive Throughput, Behavioral Attention, and Sleepiness Show Robust Relationships During Sleep Loss but Not During Recovery

Introduction

The Digit Symbol Substitution Task (DSST) is a frequently used measure to determine cognitive throughput responses to sleep loss. However, the specific time course of relationships between cognitive throughput and behavioral attention [using the 10-minute Psychomotor Vigilance Test (PVT10)] and subjective sleepiness [using the Karolinska Sleepiness Scale (KSS)] across sleep loss and recovery remains unknown yet is critical for assessing whether tasks involving learning and those without learning track each other. Repeated measures correlation (rmcorr) examined within-individual associations between measures of these tests throughout a highly controlled sleep deprivation study.

Methods

Forty-one healthy adults (ages 21-49;mean ± SD, 33.9 ± 8.9y;18 females) participated in a 13-night experiment consisting of two baseline nights (10h-12h time in bed, TIB) followed by 5 sleep restriction (SR) nights (4h TIB), 4 recovery nights (12h TIB), and 36h total sleep deprivation (TSD). A neurobehavioral test battery, including the DSST, the KSS, and the PVT10, was administered every 2h during wakefulness. Rmcorr analyses compared DSST [number correct], KSS score, and PVT10 performance [lapses (reaction time [RT] &gt;500ms) and 1/RT (response speed)] by examining correlations by day (e.g., Baseline day 2) and by time point (e.g., 1000h-2000h). Rmcorr ranges were as follows: r=0.1: small; r=0.3: moderate; r=0.5: large.

Results

During SR and TSD, correlations were significant, ranging from moderate to large, with the strongest correlation occurring during TSD. By contrast, baseline and recovery correlations were not significant or were small for DSST relative to PVT10 lapses, PVT10 response speed, or KSS scores. Additionally, all three pairs showed moderate to large correlations by time point across the entire study.

Conclusion

The various test measure relationships were consistently strong at specific times of day throughout the study. In addition, the associations between cognitive throughput and behavioral attention and sleepiness were strongest during sleep loss, particularly during TSD, suggesting that these measures are most acutely attuned to neurobehavioral changes resulting from sleep loss. The lack of a significant relationship at baseline and at recovery may be due to the learning effect reported for the DSST that is not present for the PVT10 or KSS.

Support (if any)

ONR Award No. N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

118 Relationships Between Perceptions of Subjective States Differ by Sleep Loss and During Recovery in Healthy Adults

Introduction

The Karolinska Sleepiness Scale (KSS) and the Profile of Mood States Fatigue and Vigor subscales (POMS-F and POMS-V) are commonly used to assess subjective sleepiness, fatigue and vigor in response to sleep loss. However, the detailed time course of relationships between these measures across sleep loss and recovery remains unknown yet is critical for assessing varying changes in perception of subjective states. Repeated measures correlation (rmcorr) examined within-individual association between the measures throughout a highly controlled sleep deprivation study.

Methods

Forty-one healthy adults (ages 21-49; mean±SD, 33.9±8.9y; 18 females) participated in a 13-night experiment consisting of two baseline nights (10h-12h time-in-bed, TIB) followed by 5 sleep restriction (SR) nights (4h TIB), 4 recovery nights (12h TIB), and 36h total sleep deprivation (TSD). A neurobehavioral test battery, including the KSS, POMS-F, and POMS-V, was administered every 2h during wakefulness. Rmcorr compared KSS, POMS-F, and POMS-V scores by examining correlations by study day (e.g., Baseline day 2) and by time point (e.g., 1000h-2000h). Rmcorr cutoffs were as follows: r=0.1:small, 0.3:moderate, 0.5:large.

Results

KSS and POMS-F maintained positive correlations throughout the study, whereas POMS-F and POMS-V and KSS and POMS-V were inversely correlated. All correlations were significant except those for POMS-F and POMS-V across recovery day 1 and KSS and POMS-F across recovery day 4. All measure pairs showed moderate to large correlations across baseline and SR1-5, but only small to moderate correlations across recovery. KSS and POMS-F and KSS and POMS-V showed moderate to large correlations across TSD; however, POMS-F and POMS-V only showed a small correlation. All three pairs showed consistent moderate (POMS-F and POMS-V) or large (KSS and POMS-F, KSS and POMS-V [moderate at 2000h]) correlations when analyzed by time point across the study.

Conclusion

Overall, the strength of relationships between KSS, POMS-F, and POMS-V scores varied as a function of type of sleep loss (SR or TSD) and by fully rested states, but not by time of day. This demonstrates the importance of determining perceptions of sleepiness, fatigue, and vigor in relation to each other, especially during recovery for all three constructs.

Support (if any)

ONR Award No. N00014-11-1-0361; NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

122 Different Duration Psychomotor Vigilance Tests Show Robust Stable Relationships Across Sleep Loss That Deteriorate in Recovery

Introduction

The Psychomotor Vigilance Test (PVT), a behavioral attention measure widely used to capture sleep loss deficits, is available in 10-minute (PVT10) and 3-minute (PVT3) versions. The PVT3 is a briefer and presumably comparable assessment to the more commonly used PVT10 yet the relationship between the measures from the two versions across specific time points and in recovery after sleep loss has not been investigated. Repeated measures correlation (rmcorr) evaluated within-individual associations between measures on the PVT10 and PVT3 throughout a highly controlled sleep deprivation study.

Methods

Forty-one healthy adults (ages 21-49; mean±SD, 33.9±8.9y; 18 females) participated in a 13-night experiment consisting of 2 baseline nights (10h-12h time in bed, TIB) followed by 5 sleep restriction (SR1-5) nights (4h TIB), 4 recovery nights (R1-R4; 12h TIB), and 36h total sleep deprivation (TSD). A neurobehavioral test battery, including the PVT10 and PVT3 was completed every 2h during wakefulness. Rmcorr compared PVT10 and PVT3 lapses (reaction time [RT] &gt;355ms [PVT3] or &gt;500ms [PVT10]) and response speed (1/RT) by examining correlations by day (e.g., baseline day 2) and by time point (e.g., 1000h-2000h). Rmcorr ranges were as follows: 0.1-0.3, small; 0.3-0.5, moderate; 0.5-0.7, large; 0.7-0.9, very large.

Results

All time point correlations (1000h-2000h) were significant (moderate to large for lapses; large to very large for 1/RT). Lapses demonstrated large correlations during R1, moderate correlations during SR1-SR5 and TSD, and small correlations during R2 and R4, and showed no significant correlations during baseline or R3. 1/RT correlations were large for SR1-SR4 and TSD, moderate for SR5 and R1-R4, and small for baseline.

Conclusion

The various PVT relationships were consistently strong at specific times of day throughout the study. In addition, higher correlations observed for 1/RT relative to lapses and during SR and TSD relative to baseline and recovery suggest that the PVT10 and PVT3 are most similar and best follow performance when most individuals are experiencing behavioral attention deficits during sleep loss. Both measures track SR and TSD performance well, with 1/RT presenting as more comparable between the PVT10 and PVT3.

Support (if any)

ONR Award N00014-11-1-0361; NIH UL1TR000003; NASA NNX14AN49G and 80NSSC20K0243; NIHR01DK117488

115 Age and Sex Differences in Behavioral Attention Across Baseline, Sleep Loss, and Recovery

Introduction

There are established individual differences in performance resulting from sleep loss. However, differences in behavioral attention performance between demographic subgroups remain unclear, especially during recovery after sleep loss. Thus, we examined demographic subgroup performance differences during baseline, sleep loss (sleep restriction [SR] and total sleep deprivation [TSD]), and recovery (R).

Methods

Forty-one healthy adults participated in a 13-night experiment (2 baseline nights [10h-12h time-in-bed, TIB], 5 SR nights [4h TIB], 4 recovery nights [12h TIB], and 36h TSD). The 10-minute Psychomotor Vigilance Test (PVT), measuring behavioral attention, was administered every 2h during wakefulness. PVT lapses (reaction time [RT]&gt;500ms) and 1/RT (response speed) were measured. PVT performance differences were investigated by sex (18 females) and by median split on age (range: 21-49y; median: 32y). Repeated measures ANOVAs on each study day examined PVT performance with demographic groups as the between-subject factor.

Results

SR1-2 and R1-2 showed significant between-subject effects by age: the older group had faster mean 1/RT than the younger group. SR2 showed a significant time*age group interaction: the older group had faster 1/RT from 0800h-1400h. B2, SR1, and R1 showed significant between-subject effects by sex: males had faster mean 1/RT than females. SR3 showed a significant time*sex interaction: males had faster 1/RT at 0800h and 1200h. PVT lapses (log transformed) analyses by age and by sex revealed significant between-subject effects at SR1 and R1. The direction of effects for lapses paralleled those for 1/RT: the younger group and females had more lapses than the older group and males, respectively. No other study days showed significant between-subjects or interaction effects.

Conclusion

For both age and sex, significant between-subject effects and/or interactions were revealed only in the beginning half of SR or recovery and not during TSD. These findings suggest that group differences exist when the effects of sleep loss are mild (i.e., SR1-3) or when the post-effects of sleep loss have diminished (i.e., R3-4); however, when the effects of sleep loss become more severe (i.e., SR4-5 or after a night of TSD), the well-established individual differences in response to sleep loss may overwhelm group differences.

Support (if any)

ONR Award No.N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

117 Comparison of Various Methods to Differentiate Resilience and Vulnerability to Sleep Loss Using Self-Rated Measures

Introduction

There are robust, trait-like individual differences in subjective perceptions in response to sleep restriction (SR) and total sleep deprivation (TSD). How to best define neurobehavioral resilience and vulnerability to sleep loss remains an open question. We compared multiple approaches and cutoff thresholds for defining resilience and vulnerability using scores on the Karolinska Sleepiness Scale (KSS) and the Profile of Mood States Fatigue and Vigor (POMS-F and POMS-V) subscales.

Methods

Forty-one adults (33.9±8.9y;18 females) participated in a 13-night experiment (two baseline nights [10h-12h time in bed, TIB], 5 SR nights [4h TIB], 4 recovery nights [12h TIB], and 36h TSD). The KSS, POMS-F, and POMS-V were administered every 2h during wakefulness. Resilience and vulnerability were defined by the following: average score during SR1-5, average change from baseline to SR1-5, and variance during SR1-5. Resilient and vulnerable groups were defined by the following cutoffs: the top and bottom 12.5%, 20%, 25%, 33%, 50%, and +/-1 standard deviation. Bias-corrected and accelerated bootstrapped t-tests compared the scores of resilient and vulnerable groups during baseline and across SR1-5. Kendall’s tau correlations compared the ranking of individuals in each group (tau=0.4:moderate,0.7:strong).

Results

Resilient and vulnerable groups for POMS-F, as defined by all three approaches, significantly differed in their scores at all cutoffs during SR. However, only raw score and change from baseline approaches defined significantly different resilient and vulnerable groups during SR for KSS, and only raw score and variance approaches defined significantly different groups during SR for POMS-V. Notably, raw scores at baseline significantly differed between resilient and vulnerable groups for all measures. Correlations revealed moderate to strong associations between all three approaches at all cutoffs for POMS-F, between raw score and change from baseline approaches for KSS, and between raw score and variance approaches for POMS-V.

Conclusion

Defining resilience and vulnerability on self-rated measures by change from baseline was comparable to using raw score for KSS and POMS-F, whereas defining these groups by variance was comparable for POMS-F and POMS-V. Differences across methods may be due to the differential impact of SR on these various distinct subjective states.

Support (if any)

ONR Award No. N00014-11-1-0361;NIH UL1TR000003;NASA NNX14AN49G and 80NSSC20K0243;NIH R01DK117488

Residual, differential neurobehavioral deficits linger after multiple recovery nights following chronic sleep restriction or acute total sleep deprivation

STUDY OBJECTIVES

The amount of recovery sleep needed to fully restore well-established neurobehavioral deficits from sleep loss remains unknown, as does whether the recovery pattern differs across measures after total sleep deprivation (TSD) and chronic sleep restriction (SR).

METHODS

In total, 83 adults received two baseline nights (10-12-hour time in bed [TIB]) followed by five 4-hour TIB SR nights or 36-hour TSD and four recovery nights (R1-R4; 12-hour TIB). Neurobehavioral tests were completed every 2 hours during wakefulness and a Maintenance of Wakefulness Test measured physiological sleepiness. Polysomnography was collected on B2, R1, and R4 nights.

RESULTS

TSD and SR produced significant deficits in cognitive performance, increases in self-reported sleepiness and fatigue, decreases in vigor, and increases in physiological sleepiness. Neurobehavioral recovery from SR occurred after R1 and was maintained for all measures except Psychomotor Vigilance Test (PVT) lapses and response speed, which failed to completely recover. Neurobehavioral recovery from TSD occurred after R1 and was maintained for all cognitive and self-reported measures, except for vigor. After TSD and SR, R1 recovery sleep was longer and of higher efficiency and better quality than R4 recovery sleep.

CONCLUSIONS

PVT impairments from SR failed to reverse completely; by contrast, vigor did not recover after TSD; all other deficits were reversed after sleep loss. These results suggest that TSD and SR induce sustained, differential biological, physiological, and/or neural changes, which remarkably are not reversed with chronic, long-duration recovery sleep. Our findings have critical implications for the population at large and for military and health professionals.

Robust stability of trait-like vulnerability or resilience to common types of sleep deprivation in a large sample of adults

STUDY OBJECTIVES

Sleep loss produces large individual differences in neurobehavioral responses, with marked vulnerability or resilience among individuals. Such differences are stable with repeated exposures to acute total sleep deprivation (TSD) or chronic sleep restriction (SR) within short (weeks) and long (years) intervals. Whether trait-like responses are observed to commonly experienced types of sleep loss and across various demographically defined groups remains unknown.

METHODS

Eighty-three adults completed two baseline nights (10 h-12 h time-in-bed, TIB) followed by five 4 h TIB SR nights or 36 h TSD. Participants then received four 12-h TIB recovery nights followed by five SR nights or 36 h TSD, in counterbalanced order to the first sleep loss sequence. Neurobehavioral tests were completed every 2 h during wakefulness.

RESULTS

Participants who displayed neurobehavioral vulnerability to TSD displayed vulnerability to SR, evidenced by substantial to near perfect intraclass correlation coefficients (ICCs; 78%-91% across measures). Sex, race, age, body mass index (BMI), season, and sleep loss order did not impact ICCs significantly. Individuals exhibited significant consistency of responses within, but not between, performance and self-reported domains.

CONCLUSIONS

Using the largest, most diverse sample to date, we demonstrate for the first time the remarkable stability of phenotypic neurobehavioral responses to commonly experienced sleep loss types, across demographic variables and different performance and self-reported measures. Since sex, race, age, BMI, and season did not affect ICCs, these variables are not useful for determining stability of responses to sleep loss, underscoring the criticality of biological predictors. Our findings inform mathematical models and are relevant for the general population and military and health professions.

Meta-analysis of sleep deprivation in the acute treatment of bipolar depression

BACKGROUND

Sleep deprivation (SD) is an antidepressant intervention with multiple administration formats that has been investigated primarily with uncontrolled clinical trials and qualitative reviews of the literature. The validity and applicability of these findings to the treatment of bipolar depression (BPD) is uncertain.

METHODS

A PRISMA-based systematic review of the literature and meta-analysis were conducted to determine the efficacy of SD in the treatment of BPD and to identify moderator variables that influence response rate.

RESULTS

From a sample of 15 studies covering 384 patients, the overall, mean response rate to SD was 47.6% (CI 36.0%, 59.5%). This response rate compared post-SD to pre-SD depression scores, and not to a placebo control condition. Of several potential moderating variables examined, the use of adjunctive pharmacotherapy achieved statistical significance with response rates of 59.4% [CI 48.5, 69.5] for patients using adjunctive medication vs 27.4% [CI 17.8, 39.8] for patients not using adjunctive medication.

CONCLUSIONS

This meta-analysis of SD in the treatment of BPD found an overall, response rate of almost 50%, reinforcing earlier estimates of efficacy. The use of adjunctive pharmacotherapy had a statistically significant moderating effect on SD response suggesting that clinical practice should routinely pair these interventions. These findings provide a higher level of evidence supporting the use of SD, especially when used with medication, and should inform future management guidelines for the treatment of BPD.

Pulmonary ossifying carcinoid - MEN in a male?

Pulmonary carcinoid tumors are considered as low-grade neoplasms, seen as centrally located endobronchial masses or as peripheral circumscribed nodules. Calcification or ossification is a known phenomenon, but presentation as large bony mass is extremely uncommon. Herein, we report a case of ossifying bronchial carcinoid along with nodular Hashimoto's thyroiditis as incidental autopsy findings in a 32-year-old patient with a prior recent excision of pituitary macroadenoma. This association suggests the possibility of multiple endocrine neoplasia in this young male.