Joshua Freeman

Ph.D., M.P.H.

National Cancer Institute, Division of Cancer Epidemiology and Genetics

Postdoctoral Fellow

IDATA (Learn more about this study)

IDATA-63

Feb 24, 2023

Sleep Duration, Sleep Midpoint, Social Jetlag and Associations with Blood and Urine Metabolites in IDATA

The International Agency for Research on Cancer recognizes shift work causing circadian disruption as a probable carcinogen based on strong evidence in animals and some epidemiologic evidence for prostate, breast, and colorectal cancers. However, less is known about the role of circadian disruption among non-shift workers despite research that suggests sleep disruption (short sleep duration and inconsistent sleep timing) may lead to mild circadian disruption and consequently cancer. To date, epidemiologic evidence from studies of self-reported sleep inconsistently suggest a role for sleep timing and circadian disruption (sleep midpoint and social jetlag) with breast and prostate cancer. How disrupted sleep timing is associated with these cancers is hypothesized to involve peripheral tissue regulation of lipid, glucose, and amino acid metabolism. Perturbations to these processes have been linked to poor cardiometabolic health, obesity, and cancer.

However, evidence supporting links between sleep and metabolism have primarily come from studies of sleep disorders and sleep deprivation protocols, and metabolomics. Studies evaluating typical sleep characteristics, including sleep duration and sleep midpoint (the median interval of sleep, which characterizes circadian rhythms), among a general population without sleep disorders remains rare. To our knowledge, five studies have evaluated typical sleep duration and metabolomics assayed from blood samples with inconsistent findings—either no association between sleep duration and blood metabolites, or associations between short or long sleep duration and blood metabolic signatures related to fatty acids and amino acids. Three studies have evaluated chronotype or sleep midpoint, which characterizes circadian rhythms. Unfortunately, this literature is similarly inconsistent with one study noting no association between chronotype and blood metabolites or that sleep midpoints were associated with multiple metabolites. This literature was predominantly based on self-reported sleep characteristics, which may be reported with error, and thus would underestimate associations with metabolites. Sleep collected by accelerometers, such as an ActivPAL, can help overcome some of the limitations in self-reported sleep given accelerometers are not based on perceived timing of sleep and total sleep duration.

We propose using both self-reported and ActivPAL-derived sleep measures within IDATA to evaluate associations between sleep duration, sleep midpoint, and social jetlag (i.e., the difference in weekend and weekday sleep midpoints, which characterizes mild circadian disruption) with blood, first morning void (FMV) urine, and 24-hour urine metabolites. This study will be significant for evaluating sleep characteristics and metabolites by using an untargeted approach to identify novel associations with metabolites, and a targeted approach to explore key metabolites related to sleep homeostasis, such as melatonin, which is a key sleep-wake hormone that has anticarcinogenic properties. We will innovate over previous work by being one of the first to utilize accelerometry-based measurements of sleep to compare longitudinal changes in metabolites sampled from blood, FMV urine, and 24-hour urine which will expand upon previous sleep and metabolomics studies that have been limited to self-reported sleep and metabolites sampled from blood only.

Aim 1: To examine sleep duration, sleep midpoint, social jetlag, and averaged metabolites among blood, FMV urine, and 24-hour urine specimens.

Aim 2: To examine associations between sleep duration, sleep midpoint, social jetlag, and melatonin among blood, FMV urine, and 24-hour urine specimens.

Aim 3: To examine sleep duration, sleep midpoint, social jetlag, and metabolites longitudinally among blood, FMV urine, and 24-hour urine specimens.

Hypotheses:

Hypothesis 1:

A: Short (<7 hours) and long (≥9 hours) sleep duration will be associated with metabolites compared to 7-9 hours sleep duration.

B: Early (<2:00AM) and late (≥5:00AM) sleep midpoints will be associated with metabolites compared to intermediate sleep midpoints (2:00-4:59AM).

C: Social jetlag ≥2 hours will be associated with metabolites compared to social jetlag <2 hours.

Hypothesis 2:

A: Sleep duration <7 hours will be associated with lower melatonin vs. 7-9 hours. Sleep duration ≥9 hours will be associated with higher melatonin vs. 7-9 hours.

B: Early vs. intermediate sleep midpoints will be associated with lower melatonin. Later vs. intermediate sleep midpoints will be associated with higher melatonin.

C: Social jetlag ≥2 hours will be associated with lower melatonin compared to social jetlag <2 hours.

Hypothesis 3:

We hypothesize that sleep duration, sleep midpoint, and social jetlag will be longitudinally associated with variation in metabolites that will differ by specimen type.

Methods:

Among (n=718) IDATA participants, sleep was measured via self-report and a wearable accelerometer (ActivPAL) at multiple timepoints. Sleep duration was self-reported as average total sleep duration and napping over 12 months on the NIH-AARP Physical Activity Questionnaire (PAQ). Participants also self-reported their time in and out of bed on physical activity logs. From the log data, we will calculate sleep duration, sleep midpoint, and social jetlag. We also propose exploring sleep duration, time in bed, time out of bed, sleep midpoint, and social jetlag using ActivPAL data. ActivPAL data was collected for ≤7 days at two timepoints and we will average total wear time at each timepoint to generate measures of typical sleep. We will compare sleep estimates as self-reported on the NIH-AARP PAQ and on the physical activity logs to ActivPAL derived estimates for quality control.

We propose using blood, FMV urine, and 24-hour urine metabolite data, collected at two timepoints in the iDATA study, as measured by our collaborator, Dr. Erikka Loftfield from the Metabolic Epidemiology Branch in the Division of Cancer Epidemiology and Genetics.

Data Analysis:

We will use linear models to evaluate multivariate associations between averaged sleep duration, sleep midpoint, social jetlag and metabolites adjusting for age (measured at baseline), sex, and BMI (measured at baseline and averaged over repeated measures). We will also use linear mixed models to also evaluate change in metabolites over time and account for change in sleep characteristics between timepoints. We will account for multiple comparisons using false discovery rate correction with q-values for significance defined as q<0.05. We will also explore longitudinal associations between sleep characteristics and metabolites stratified by metabolite specimen type.

Charles E. Matthews (PI): Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, National Institutes of Health

Pedro F. Saint-Maurice (PI): Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, National Institutes of Health

Joshua Freeman (Lead Investigator): Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, National Institutes of Health

Erikka Loftfield: Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, National Institutes of Health

Qian Xiao: Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health

Eleanor Watts: Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, National Institutes of Health

Hyokyoung G. Hong: Division of Cancer Epidemiology and Genetics, Biostatistics Branch, National Cancer Institute, National Institutes of Health

Steve Moore: Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, National Cancer Institute, National Institutes of Health