Presenter: Susan Redline, MD, MPH, Peter C. Farrell Professor of Sleep Medicine, Professor of Epidemiology, Harvard T.H. Chan School of Public Health Abstract Experimental, clinical and epidemiological studies have identified multiple inter-relationships of sleep with glucose regulation and metabolic disease. In one meta-analysis, after overweight and family history of diabetes, the next 7 top risk factors for incident diabetes were measures of sleep health. These included poor sleep quality, insomnia, short or extremely long sleep duration, and sleep apnea; each sleep problem was associated with incident diabetes with relative risks ranging from 1.38 to 1.74. A mechanism linking sleep apnea with diabetes is through the effects of intermittent hypoxemia on insulin sensitivity. However, studies using neurophysiological markers of sleep in healthy adults showed that selective reduction of slow wave sleep reduced glucose tolerance by 23%, thus additionally suggesting the importance neurophysiological mechanisms during sleep in glucose regulation. In support of this, longitudinal epidemiological studies demonstrated that higher proportions of slow wave sleep (N3) were protective for the development of type 2 diabetes. Recent animal and human studies also point to the effects of sleep micro-architecture—specifically the coupling of slow waves and spindles- on short-term and long-term glucose regulation, possibly through the effects on signaling between the hippocampus and hypothalamus, and changes in autonomic nervous system output. Experimental data also demonstrate a prominent role of the circadian system in regulating glucose and lipid levels. In support of those studies, epidemiological associations have identified significant associations between actigraphy-based measures of sleep irregularity (a marker of circadian disruption) with incident metabolic dysfunction and hypertension. This rich data implicating sleep disturbances as drivers of metabolic disease, coupled with data indicating a high prevalence of sleep and circadian disorders in the population, suggest novel opportunities to target sleep and circadian pathways for preventing or treating metabolic dysfunction, as well as key knowledge gaps. The National Sleep Research Resource (NSRR; sleepdata.org) provides a large and growing repository of well-annotated polysomnograms (PSGs), actigraphy studies, and questionnaires, some associated with clinical and biochemical data relevant to understanding the links between sleep and circadian disorders with metabolic disease. Notably, the NSRR includes over 50,000 PSGs, which concurrently include multiple physiological signals with high temporal resolution, allowing generation of thousands of variables summarizing dynamic physiological changes and “cross-talk” between physiological systems...(Please see https://dknet.org/about/blog/2674 for full abstract) Upcoming webinars schedule: https://dknet.org/about/webinar

1. The National Sleep Resource Research:
Opportunities for large-scale sleep and circadian data to
promote understanding of metabolic diseases
Susan Redline, MD,MPH
Brigham and Women’s Hospital
Harvard Medical School
sredline@bwh.harvard.edu

2. Outline
• Overview of sleep and circadian measurements and their
relationship to metabolic health
– Multi-dimensional sleep
– Associations with glucose impairment and diabetes
– Potential mechanisms and pathways
• Overview of NSRR
– Aims, structure, data sharing
– Relevant datasets
– Interface with TOPMed and BDC
• Opportunities to advance sleep-metabolic knowledge

3. Health and Well-Being
Physical
Activity
Nutrition
Stress
Manage
Sleep
>30% of individuals get
insufficient sleep
Higher proportions in low
income and racial/ethnic
minorities

4. Multiple Sleep-Circadian Domains
• Average duration; short and long; variable
Sleep Duration
• When you sleep and its variation
• social jet lag; night to night variability
• Strength of rhythm and alignment with other activities
Sleep Timing and Rhythm
• Sleep/wake; Arousals, Architecture (Stages, transitions)
• Quality (perceived)
Sleep Fragmentation,
Efficiency and Depth
• Spectral power, Spindles, Slow wave oscillations
EEG micro-architecture
• Sleep Apnea (Hypoxemia; Arousals)
• Periodic Limb Movement Disorders (PLMs)
• Insomnia: Problems initiating or maintaining sleep
Presence of a Sleep Disorder
• Sleepiness
• Functional Impairment
Daytime Sequelae

5. Polysomnography Actigraphy/wearables Questionnaires
Insomnia,
Chronotype,
Sleep Duration,
Perceived Quality,
Diagnoses
Assessing Sleep

6. Polysomnography
●Considered the “gold standard”
●Precise neurophysiological monitoring of multiple channels of data
●Sleep macro and macro-architecture
●Extensive cardiorespiratory measures may be captured
●Leg movements

7. Actigraphy
●Multi-day sleep and circadian patterns
●Sleep duration, efficiency
● Fragmentation
●Circadian- timing, amplitude
●Regularity of duration and timing

8. ● General
● PROMIS Sleep Related Impairment/Sleep Disturbance
● Pittsburgh Sleep Quality Index
● Chronotype
● Horne-Osberg
● Munich
● Sleep Apnea
● Berlin, STOPBANG
● Sleep Apnea Quality of Life Questionnaire
● Insomnia
● Insomnia Severity Index
● Women’s Health Insomnia Severity Rating Scale
● Restless Legs
● International RLS Questionnaire
● Sleep patterns and related exposures: diaries/logs
Questionnaires

9. Common Primary Sleep Disorders
• Problems initiating or maintaining
sleep/unrefreshed sleep
Insomnia (15-30%)
• Recurrent pauses of breathing
Sleep Apnea (2 to 25%)
• Recurrent leg jerks during sleep
Periodic Limb
Movements (10 to 15%)
• Internal “clock” (jet lag, phase shifts, shift work)
Circadian rhythm
disorders (2%)

10. Sleep Disorders: Obstructive Sleep Apnea
Repetitive episodes of partial or complete upper airway
obstruction during sleep, associated with hypoxemia, snoring,
and daytime sleepiness
Autonomic nervous system changes, inflammation, oxidative
stress, endothelial dysfunction, and insulin resistance
Normal Breathing Apneas

11. Evidence Relating Sleep Disturbances and
Metabolic Disease
• Experimental studies
– Sleep deprivation
– Sleep misalignment
– Intermittent Hypoxemia
• Epidemiological data
– Sleep duration
– Sleep variability
– SWS/REM reduction
– Sleep apnea
Inflammatory proteins, gene expression,
endothelial dysfunction, dyslipidemia,
glucose metabolism, etc
HTN, CHD, stroke, diabetes/MetSyn,
mortality, cognitive deficits, cancer
Accidents, reduced quality of life, mood,
behavior

12. Potential mechanisms: sleep apnea and insulin resistance/diabetes
Karine Spiegel et al. J Appl Physiol 2005;99:2008-2019
©2005 by American Physiological Society

13. Potential mechanisms: short sleep duration and
cardio-metabolic disease
Tobaldini et al. 2019. Nat Rev Cardiol

14. Circadian Clocks
• Influence sleep-wake patterns
– Disturbed by sleep wake problems
• Central and peripheral “clocks”
Coordinate a wide variety of metabolic
processes with the external environment
• Circadian Misalignment (shift workers)
associated with obesity, diabetes, dyslipidemia
www.pharma.uzh.ch

16. Lancet Diabetes Endocrinol; 2015 3(1):52-62

17. Anothaisintawee Sleep Med Rev 2016
Traditional vs Sleep-Related Risk Factors for
Incident Diabetes

18. Sleep duration and incident diabetes
Shan et al. 2015. Diabetes Care
Short Sleep
Long Sleep

20. Summary: Incident Diabetes
• Sleep and circadian disruption are associated with obesity,
insulin resistance and diabetes
– Insomnia symptoms, short or long sleep, irregular sleep, shift work,
and low SWS
• Sleep apnea and its associated hypoxemia decrease insulin
sensitivity and impair pancreatic insulin secretion
• OSA also reported to increase risk for diabetic microvascular complications
• Well-controlled experiments show improvement in insulin sensitivity with sleep
apnea treatment (2 weeks)
• Potential bi-directional associations of sleep apnea and diabetes

21. Knowledge Gaps
• Population variability?
– Gender/sex differences
– Genetic background, lifestyle, discrimination stress, etc
• Causal/bi-directional pathways?
• Mechanisms?
– Role of multiple sleep/circadian-related stressors
– Interactions with other lifestyle and risk factors
– Cross-talk between physiological systems
– Genetic and molecular pathways

22. Opportunity:
Untapped
Physiological
Information
• Hours of physiological signals
• Days of physiological/behavioral
signals
• Quantitative metrics
• Cross-talk between physiological
systems
• Temporal and dynamic features

23. Beyond Simple Summary Metrics: Advanced
Quantitative Metrics and Feature Extraction

24. A community resource to deposit and access sleep data including physiological signals
Sleep Data: polysomnography and/or actigraphy and self-report measures
Other Data: demographics, anthropometry, medical history, symptoms,
cardiometabolic health indices, lung function, blood pressure, blood biomarkers,
cognitive tests, physical activity, health behaviors and medications
New Data: animal experiments, circadian data
Open Source Tools
Community Engagement

25. To add value and stimulate the use of NSRR data:
Improved documentation/search capabilities
Harmonize/standardize core terms and signals
Provide meta-data: FAIR/TRUST
Developing and sharing open analysis tools
Exemplar applications of NSRR data
Linking to, and integration with, other resources
Building community: outreach and education
Not just a collection of valuable, well-curated datasets… … but also driving discovery, supporting the research community

26. Available Data
32 datasets and growing….
> 4, 643 Approved DUAs
Ø 2.0 TB downloaded
per week
Ø 2 PB shared
49,932 PSG
studies
30,980 PSGs
with EEG signals
6,699 actigraph
files
14,314 terms annotated
to structured definitions
4,681 with provenance
attributes

27. ~ 50,000 individuals
> 30,000 full PSGs
2 TB of data shared weekly
4,643 Data Access Use Agreements

28. Explore the Cohort Matrix:
Find data relevant for
metabolic researchers

29. Data are only as valuable as their metadata
Standardized data dictionaries
Standardized folder structures
Collate/share key device metadata (e.g.
make/model, software versions, filters, etc.)

30. > 5,000 defined variables
Ongoing harmonization of variables
across studies, mapping to CDEs
Extensive documentation on study
design

32. Raw signals (EDF) & annotations
on 10,000s of individuals
In total, ~30 years’ worth of
multi-modal sleep signal data

33. http://zzz.bwh.harvard.edu/luna/
Sharing tools/code as well as data
Luna: Open-source tool for sleep signal
analysis
- documented w/ tutorials & vignettes
- underlying codebase accessible via the
command line (lunaC), as an R library
(lunaR), or in the browser (Moonlight)
Issue/activity
Fix in
EDFs
Fix in
analysis
Flag
(not “fix”)
Channel labels ✅ ✅
Annotation labels ✅ ✅
Reformat annotations (to .annot) ✅ ✅
Drop undocumented channels ✅ ✅
Standardize physical units ✅ ✅
EDF record/annotation alignment ✅ ✅
Standardize EDF record size (1 sec) ✅ ✅
Re-reference EEGs as needed ✅ ✅
Reduce to a subset of core channels ❌ ✅
Resample signals to uniform rates ❌ ✅
Bandpass filter (e.g. 0.3-45 Hz EEG) ❌ ✅
(Likely) incorrect EEG polarity ❌ ✅ ✅
Gross artifacts (flat/clipped signals) ❌ ✅ ✅
Inconsistent/truncated staging ❌ ❌ ✅
Strong line noise (spikes in the PSD) ❌ ❌ ✅
(Likely) incorrect physical units ❌ ❌ ✅
Cardiac contamination in the EEG ❌ ❌ ✅
NAP: NSRR Automated
Pipeline
Harmonize/flag issues w/
labels, referencing, units, sample
rates, filtering, polarities,
corrupt signals, artifact, non-
standard channels, (automated)
staging alignment, etc
Uniform annotation format
Senthil Palanivelu
Shaun Purcell

34. Normative
distributions
“Self-contained”
analyses of
new data
Towards “reference-based”
analyses:
- using prior data/models
to inform/augment
newly collected data
- a tighter coupling of data &
code
Predictive
models
Shaun Purcell

35. Bring
BringYour Own Data
Moonbeam: directly pull NSRR
data into the browser, for
interactive viewing (Moonlight)
and analysis (Luna)
https://remnrem.net
Shaun Purcell

36. Active outreach to the sleep research community:
- users, potential data depositors & professional bodies
Webinar series Social media
Twitter/X @SleepDataNSRR

37. Blogs at sleepdata.org
Showcasing:
-new papers
- new datasets
- new tools
Guest bloggers
General announcements
Forum for technical questions

38. E-mail Newsletter
Researcher Spotlights
w/ video interviews

39. Integration
with NHLBI’s
BioData Catalyst
(BDC)

40. Examples from data within NSRR

41. Variation of sleep apnea and abnormal fasting glucose by race,
MESA
Adjusted for age, race/ethnicity ,BMI, sex, study center Bakker J AJRCCM 2015

42. *Adjusted for Site, Background, education level, sampling design,
smoking, alcohol, age and sex
Incidence of Hypertension and Diabetes In Association
with OSA or Insomnia in Hispanics/Latinos (HCHS)
Redline, AJRCCM; 189: 2014
Cross-Sectional Association of Sleep Apnea
(AHI>15) with
Hypertension and Diabetes In Latinos
Li X, AJRCCM; 203: 2021
Incidence of Hypertension and Diabetes, for OSA (AHI>5)
or Insomnia

43. Sleep irregularity and metabolic syndrome: cluster analysis
Huang and Redline, 2019, Diabetes Care

44. In prospective analyses of 1251 participants and
129 incident cases over 6346 person-years of
follow-up, a curvilinear relationship was
observed between N3 proportion and incident
diabetes risk. In the fully adjusted model, the
hazard ratio (95% CI) of developing diabetes vs
Q1 was 0.47 (0.26, 0.87) for Q2, 0.34 (0.15, 0.77)
for Q3, and 0.32 (0.10, 0.97) for Q4 (P
nonlinearity = .0213).

45. Stronger Sleep-Metabolic Traits Genetic Correlations in Women,
Hispanic Community Health Study
Elgart, Cell Reports Nat; 2022

46. Adjusted for: age, sex, BMI, study center, Hispanic Background, alcohol use,
smoking status, total physical activity (MET-min/day)
Faquih…….Wang, in prep
Metabolites Associated with Sleepiness
• Metabolites and pathways associated with
excessive sleepiness:
• Biosynthesis of hormonal steroid
• Cortisol and melatonin related pathways
• Tyrosine metabolism
• Sphingomyelin
• A combined effect of metabolism, lifestyle,
and CYP genes

47. Predictive Ability of a SDB-Metabolic Risk Score to Predict
Incident Diabetes
• SDB Trait Summary Scores (PCs)
–PC1 (hypoxia) PC2 (short event)
• Create a Sex-Specific MRS for each SDB
PC
• Test Association with Incident Diabetes
Zhang Y, in revision

49. Vallat et al. 2023

50. Opportunities
• What are the macro- and micro-architecture features of sleep that can predict metabolic dysfunction?
• Understand the dynamic and system-level “cross-talk” between changes in sleep, breathing,
oxygenation, vascular stiffness, heart rate, and glucose/insulin as indicators of autonomic dysfunction
and other pathways linking sleep disorders to metabolic dysfunction
• Future concurrent CGM in MESA with sleep data
• By linking with data within dbGaP or TOPMed, what are the molecular pathways that may explain
associations between sleep and circadian disorders with metabolic dysfunction?
• Sex, race/ethnicity and other differences in these associations
• Role of circadian rhythms and sleep disturbances in relevant transcriptome and metabolomic
pathways
• Use of genetics to interrogate causal pathways and susceptibility

51. Sharing Data with NSRR
• We aim to make data sharing as simple as possibler while ensuring
each data submission provides the highest utility possible to users.
Initiate
• Meet with the
NSRR team to
learn more about
our project and
yours
• Complete Share
Form
• Participate in a
Kick Off meeting
to discuss specific
data to be shared
Regulatory
Approval
• NSRR will ask for
documentation to
submit to our IRB
• A DUA process will
be initiated
• During this time
we can also begin
review of data
dictionaries and
metadata
Share Data
• Specify preferred
sharing
mechanism and
send “test case”
for review
• Submit all data to
be shared to NSRR
team
Data is posted
• Data will be
uploaded to a
staging server to
be approved by
the submitter
• Data goes live!
• Submitter invited
to write a blog
post to publicize
their newly shared
dataset
Continuous
improvement
• Relay feedback
and questions
from users to data
submitters which
may lead to
revision and
improvement to
the meta-data
• Continue to add
harmonized data

52. NSRR
http://sleepdata.org
Luna
http://zzz.bwh.harvard.edu
Moonlight
https://remnrem.net
Whole night delta-band spectral power from 1000 MESA participants
Acknowledgements
Brigham and Women’s Hospital
Shaun Purcell (MPI)
Dennis Dean
Matthew Kim
Sara Mariani
Daniel Mobley
Remo Mueller
Senthil Palanivelu
Michael Prerau
Rebecca Robbins
Michael Rueschman
Paige Sparks
Susan Surovec
Meg Tully
Ying Zhang
NHLBI
University of Kentucky
GQ Zhang, Satya Sahoo, Licong Cui
Beth Israel Deaconess Medical Center
Ary Goldberger, Madalena Costa