pii: sp-00553-15
http://dx.doi.org/10.5665/sleep.5132
EDITORIAL
In Pursuit of Sleep-Circadian Biomarkers Commentary on Chua et al. Changes in plasma lipids during exposure to total sleep deprivation. SLEEP 2015;38:1683–1691. Janet Mullington, PhD1,2; Allan I. Pack, MBChB, PhD, FRCP3; Geoffrey S. Ginsburg, MD, PhD4 Neurology, Harvard Medical School, Boston MA; 2Human Sleep and Inflammatory Systems Laboratory, Beth Israel Deaconess Medical Center, Boston, MA; 3Department of Medicine and Center for Sleep and Circadian Neurobiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; 4Duke Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC 1
The paper by Eric Chern-Pin Chua and colleagues1 at the DukeNational University of Singapore Graduate Medical School, “Changes in plasma lipids during exposure to total sleep deprivation” in this issue of SLEEP, applies extensive behavioral and computational unmasking methodologies to separate the circadian and dose-dependent effects of sleep loss. The field standard “constant routine” was used, involving bedrest in dim light, equicaloric hourly snacks, and continuous wakefulness imposed for 40 hours. This approach to human physiological phenotyping has important advantages for enabling the separation of sleep and circadian components in physiological parameters, while minimizing the effects of variable bright light levels, gross body movement, and postural effects, those of variable diet in composition, and timing. With these careful methodological considerations, Chua and colleagues have used targeted lipidomics-based methods to characterize the effects of acute exposure to sleep loss on plasma lipids. Close to 18% of the individually analyzed lipid profiles demonstrated a significant circadian rhythm, 9.3% individually analyzed lipid profiles showed a significant decrease, and 17.8% a significant increase, across the deprivation phase of the protocol. In particular, they found that choline plasmalogen levels decreased during sleep loss, and that some phosphatidylcholines (PCs) and triacylglycerides (TAGs) that carry polyunsaturated fatty acids increased. The results strengthen the building evidence for the importance of lipids in the search for both circadian and sleep sufficiency markers. This paper is timely and exciting because it adds to the building evidence for potential mechanisms connecting metabolic syndrome and cardiovascular risk to sleep loss. It is also exciting because it illustrates the power of rapidly developing metabolomics tools, paired with rich physiological phenotyping approaches commonly used, in our field. We have a timely opportunity, seized by Chua and colleagues in this paper and other pioneers in this sleep-circadian-biomarker frontier (cited therein), to apply new -omics methods. These methods lend themselves well for use in blood, saliva, and urinary samples that have been collected under carefully controlled in-lab protocols in labs around the world, and can now
Submitted for publication September, 2015 Accepted for publication September, 2015 Address correspondence to: Janet Mullington, PhD, Associate Professor of Neurology, Harvard Medical School, Director, Human Sleep and Inflammatory Systems Laboratory, Beth Israel Deaconess Medical Center, Boston, MA 02115; Tel: (617) 667-0434; Fax: (617) 667-5210; Email: [email protected] SLEEP, Vol. 38, No. 11, 2015
be used to multiply efforts in our field, to develop markers of sleep healthiness and sleep sufficiency. To this end, sleep and circadian thought leaders have come together in meetings to discuss best available approaches and strategies for developing such biomarkers.2,3 Given the growing opportunities to develop precision medicine tools, it is timely to launch a coordinated effort to create a bio-bank of specimens for use in sleep-circadian–omics research. Of course, even these carefully controlled studies have limitations. One is the implementation of those controls themselves. In order to examine the small intra-individual changes in a physiological marker, such as lipid levels, investigators use subjects as their own controls. Due to challenges, including cost, of doing repeated visits for multi-day research protocols, investigators using the constant routine do not typically run an additional normal sleep control, and thus the baseline is the only control used. Authors acknowledge that the increase in TAG and PC levels during deprivation may have been caused by a buildup in postprandial lipids due to the hourly snack protocol and suggest future work might include additional controls, such as a fasting condition, to address this issue. While research environments influence outcomes, and in this case include diet, posture, exercise, light levels, and social exposure, the metabolomics approach applied to specimens gathered under multiple conditions will bring forward the most important biomarkers of sleep loss, in replication. The recent announcement of the Precision Medicine Initiative and (PMI) and the plan to launch a Million Person Cohort4 presents our field with both the urgency and tremendous opportunity to vastly advance the field of biomarker development to measure a marker and tracker of sleep health and circadian amplitude and phase alignment status. At the same time, there is an explosion of wearable device development, marketed aggressively, and with very little input from experts in sleep and circadian physiology. At a workshop organized jointly by NHLBI, NIA, and SRS earlier this year on developing biomarker panels for the prediction of sleep and circadian-coupled risks to health, the consensus was that these efforts needed to occur in parallel, and coordinated when possible.3 The meeting underscored the importance of developing devices, developing biomarkers, and developing international collaboration. The current study exemplifies the potential advances possible in applying -omics methodologies, ripe for leveraging secondary analyses of well characterized data sets for metabolomics and -omics methodologies. In order to achieve the most from these opportunities for advancement of the understanding of the role of sleep and
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Editorial—Mullington et al.
circadian rhythms in health, we need to create opportunities to bring sleep-circadian researchers and biomarker developers together. The Chua et al. paper is a nice example of the advances made possible by such partnerships. CITATION Mullington J, Pack AI, Ginsburg GS. In pursuit of sleep-circadian biomarkers. SLEEP 2015;38(11):1665 –1666.
REFERENCES
1. Chua EC, Shui G, Cazenave-Gassiot A, Wenk MR, Gooley JJ. Changes in plasma lipids during exposure to total sleep deprivation. Sleep 2015;38:1683–91. 2. Quan SF. Finding a research path for the identification of biomarkers of sleepiness. J Clin Sleep Med 2011;7(5 Suppl):S4–5. 3. http://www.nhlbi.nih.gov/research/reports. 4. https://www.whitehouse.gov/the-press-office/2015/01/30/fact-sheetpresident-obama-s-precision-medicine-initiative.
DISCLOSURE STATEMENT The authors have indicated no financial conflicts of interest.
SLEEP, Vol. 38, No. 11, 2015
1666
Editorial—Mullington et al.
http://dx.doi.org/10.5665/sleep.5132
EDITORIAL
In Pursuit of Sleep-Circadian Biomarkers Commentary on Chua et al. Changes in plasma lipids during exposure to total sleep deprivation. SLEEP 2015;38:1683–1691. Janet Mullington, PhD1,2; Allan I. Pack, MBChB, PhD, FRCP3; Geoffrey S. Ginsburg, MD, PhD4 Neurology, Harvard Medical School, Boston MA; 2Human Sleep and Inflammatory Systems Laboratory, Beth Israel Deaconess Medical Center, Boston, MA; 3Department of Medicine and Center for Sleep and Circadian Neurobiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; 4Duke Center for Applied Genomics and Precision Medicine, Duke University, Durham, NC 1
The paper by Eric Chern-Pin Chua and colleagues1 at the DukeNational University of Singapore Graduate Medical School, “Changes in plasma lipids during exposure to total sleep deprivation” in this issue of SLEEP, applies extensive behavioral and computational unmasking methodologies to separate the circadian and dose-dependent effects of sleep loss. The field standard “constant routine” was used, involving bedrest in dim light, equicaloric hourly snacks, and continuous wakefulness imposed for 40 hours. This approach to human physiological phenotyping has important advantages for enabling the separation of sleep and circadian components in physiological parameters, while minimizing the effects of variable bright light levels, gross body movement, and postural effects, those of variable diet in composition, and timing. With these careful methodological considerations, Chua and colleagues have used targeted lipidomics-based methods to characterize the effects of acute exposure to sleep loss on plasma lipids. Close to 18% of the individually analyzed lipid profiles demonstrated a significant circadian rhythm, 9.3% individually analyzed lipid profiles showed a significant decrease, and 17.8% a significant increase, across the deprivation phase of the protocol. In particular, they found that choline plasmalogen levels decreased during sleep loss, and that some phosphatidylcholines (PCs) and triacylglycerides (TAGs) that carry polyunsaturated fatty acids increased. The results strengthen the building evidence for the importance of lipids in the search for both circadian and sleep sufficiency markers. This paper is timely and exciting because it adds to the building evidence for potential mechanisms connecting metabolic syndrome and cardiovascular risk to sleep loss. It is also exciting because it illustrates the power of rapidly developing metabolomics tools, paired with rich physiological phenotyping approaches commonly used, in our field. We have a timely opportunity, seized by Chua and colleagues in this paper and other pioneers in this sleep-circadian-biomarker frontier (cited therein), to apply new -omics methods. These methods lend themselves well for use in blood, saliva, and urinary samples that have been collected under carefully controlled in-lab protocols in labs around the world, and can now
Submitted for publication September, 2015 Accepted for publication September, 2015 Address correspondence to: Janet Mullington, PhD, Associate Professor of Neurology, Harvard Medical School, Director, Human Sleep and Inflammatory Systems Laboratory, Beth Israel Deaconess Medical Center, Boston, MA 02115; Tel: (617) 667-0434; Fax: (617) 667-5210; Email: [email protected] SLEEP, Vol. 38, No. 11, 2015
be used to multiply efforts in our field, to develop markers of sleep healthiness and sleep sufficiency. To this end, sleep and circadian thought leaders have come together in meetings to discuss best available approaches and strategies for developing such biomarkers.2,3 Given the growing opportunities to develop precision medicine tools, it is timely to launch a coordinated effort to create a bio-bank of specimens for use in sleep-circadian–omics research. Of course, even these carefully controlled studies have limitations. One is the implementation of those controls themselves. In order to examine the small intra-individual changes in a physiological marker, such as lipid levels, investigators use subjects as their own controls. Due to challenges, including cost, of doing repeated visits for multi-day research protocols, investigators using the constant routine do not typically run an additional normal sleep control, and thus the baseline is the only control used. Authors acknowledge that the increase in TAG and PC levels during deprivation may have been caused by a buildup in postprandial lipids due to the hourly snack protocol and suggest future work might include additional controls, such as a fasting condition, to address this issue. While research environments influence outcomes, and in this case include diet, posture, exercise, light levels, and social exposure, the metabolomics approach applied to specimens gathered under multiple conditions will bring forward the most important biomarkers of sleep loss, in replication. The recent announcement of the Precision Medicine Initiative and (PMI) and the plan to launch a Million Person Cohort4 presents our field with both the urgency and tremendous opportunity to vastly advance the field of biomarker development to measure a marker and tracker of sleep health and circadian amplitude and phase alignment status. At the same time, there is an explosion of wearable device development, marketed aggressively, and with very little input from experts in sleep and circadian physiology. At a workshop organized jointly by NHLBI, NIA, and SRS earlier this year on developing biomarker panels for the prediction of sleep and circadian-coupled risks to health, the consensus was that these efforts needed to occur in parallel, and coordinated when possible.3 The meeting underscored the importance of developing devices, developing biomarkers, and developing international collaboration. The current study exemplifies the potential advances possible in applying -omics methodologies, ripe for leveraging secondary analyses of well characterized data sets for metabolomics and -omics methodologies. In order to achieve the most from these opportunities for advancement of the understanding of the role of sleep and
1665
Editorial—Mullington et al.
circadian rhythms in health, we need to create opportunities to bring sleep-circadian researchers and biomarker developers together. The Chua et al. paper is a nice example of the advances made possible by such partnerships. CITATION Mullington J, Pack AI, Ginsburg GS. In pursuit of sleep-circadian biomarkers. SLEEP 2015;38(11):1665 –1666.
REFERENCES
1. Chua EC, Shui G, Cazenave-Gassiot A, Wenk MR, Gooley JJ. Changes in plasma lipids during exposure to total sleep deprivation. Sleep 2015;38:1683–91. 2. Quan SF. Finding a research path for the identification of biomarkers of sleepiness. J Clin Sleep Med 2011;7(5 Suppl):S4–5. 3. http://www.nhlbi.nih.gov/research/reports. 4. https://www.whitehouse.gov/the-press-office/2015/01/30/fact-sheetpresident-obama-s-precision-medicine-initiative.
DISCLOSURE STATEMENT The authors have indicated no financial conflicts of interest.
SLEEP, Vol. 38, No. 11, 2015
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Editorial—Mullington et al.
