Identification of a Preliminary Plasma Metabolome-based Biomarker for Circadian Phase in Humans

2021 ◽  
pp. 074873042110254
Author(s):  
D. Cogswell ◽  
P. Bisesi ◽  
R. R. Markwald ◽  
C. Cruickshank-Quinn ◽  
K. Quinn ◽  
...  
Keyword(s):  
Absolute Error ◽  
Performance Assessments ◽  
Least Squares Regression ◽  
Sleep Wake Disorders ◽  
Insufficient Sleep ◽  
Circadian Phase ◽  
Plasma Metabolome ◽  
Dim Light ◽  
Healthy Participants ◽  
Blood Transcriptomics

Measuring individual circadian phase is important to diagnose and treat circadian rhythm sleep-wake disorders and circadian misalignment, inform chronotherapy, and advance circadian science. Initial findings using blood transcriptomics to predict the circadian phase marker dim-light melatonin onset (DLMO) show promise. Alternatively, there are limited attempts using metabolomics to predict DLMO and no known omics-based biomarkers predict dim-light melatonin offset (DLMOff). We analyzed the human plasma metabolome during adequate and insufficient sleep to predict DLMO and DLMOff using one blood sample. Sixteen (8 male/8 female) healthy participants aged 22.4 ± 4.8 years (mean ± SD) completed an in-laboratory study with 3 baseline days (9 h sleep opportunity/night), followed by a randomized cross-over protocol with 9-h adequate sleep and 5-h insufficient sleep conditions, each lasting 5 days. Blood was collected hourly during the final 24 h of each condition to independently determine DLMO and DLMOff. Blood samples collected every 4 h were analyzed by untargeted metabolomics and were randomly split into training (68%) and test (32%) sets for biomarker analyses. DLMO and DLMOff biomarker models were developed using partial least squares regression in the training set followed by performance assessments using the test set. At baseline, the DLMOff model showed the highest performance (0.91 R2 and 1.1 ± 1.1 h median absolute error ± interquartile range [MdAE ± IQR]), with significantly ( p < 0.01) lower prediction error versus the DLMO model. When all conditions (baseline, 9 h, and 5 h) were included in performance analyses, the DLMO (0.60 R2; 2.2 ± 2.8 h MdAE; 44% of the samples with an error under 2 h) and DLMOff (0.62 R2; 1.8 ± 2.6 h MdAE; 51% of the samples with an error under 2 h) models were not statistically different. These findings show promise for metabolomics-based biomarkers of circadian phase and highlight the need to test biomarkers that predict multiple circadian phase markers under different physiological conditions.

scholarly journals 0050 Identification of a Plasma Metabolome-Based Biomarker for Dim-Light Melatonin Offset and Onset in Humans

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A20-A21
Author(s):  
D T Cogswell ◽  
P J Bisesi ◽  
R R Markwald ◽  
C Cruickshank-Quinn ◽  
K Quinn ◽  
...  
Keyword(s):  
Absolute Error ◽  
Research Society ◽  
Training Dataset ◽  
Validation Dataset ◽  
Least Squares Regression ◽  
Test Dataset ◽  
Circadian Phase ◽  
Plasma Metabolome ◽  
Research Findings ◽  
Dim Light

Abstract Introduction Easily measuring individual circadian timing is increasingly important to inform personalized chronotherapy, screen for circadian disorders and circadian misalignment, and advance circadian research. Findings from multiple studies show that transcriptomics is a viable method to estimate dim-light melatonin onset (DLMO), but no published omics-based findings have predicted dim-light melatonin offset (DLMOff), and only one known study has used metabolomics to predict DLMO. Here, we developed and tested a plasma metabolomics-based biomarker of circadian phase using DLMO and DLMOff as phase markers. Methods Sixteen (8M/8F) healthy participants aged 22.4 ± 4.8y (mean ± SD) completed an in-laboratory study with 3 baseline days (9h sleep opportunity/night), followed by a randomized cross-over protocol with 9h sleep and 5h sleep conditions, each lasting 5 days. Blood was collected every 4h on the final 24h of each condition for untargeted metabolomics analyses. DLMO and DLMOff were determined during the final 24h of each condition. Samples from all conditions were randomly split into training (68%) and test (32%) datasets. DLMO and DLMOff models were developed using partial least squares regression in the training dataset and validated in the test dataset. Results When validating with the test dataset, R2 for the DLMO model was 0.60, median absolute error (MdAE) was 2.2 ± 2.8h (± interquartile range), and 44% of samples had MdAE under 2h. R2 for the DLMOff model was 0.62, MdAE was 1.8 ± 2.6, and 51% of samples had MdAE under 2h. The DLMOff model predicted baseline samples, under conditions of 9h sleep and controlled food intake, with an R2 of 0.91 and MdAE 1.1 ± 1.1h. Conclusion These findings show promise for metabolomics-based biomarkers of circadian phase and highlight the need for biomarker efforts to predict multiple circadian phase markers. Additional analyses with an independent validation dataset will help advance these initial findings. Support NIH-R01HL085705, NIH-R01HL109706, NIH-R01HL132150, NIH-K01HL145099, NIH-F32DK111161, and NIH-UL1TR000154; and Sleep Research Society Foundation 011-JP-16;

scholarly journals Separate and interacting effects of the endogenous circadian system and behaviors on plasma aldosterone in humans

2019 ◽  
Vol 316 (2) ◽  
pp. R157-R164 ◽  
Author(s):  
Saurabh S. Thosar ◽  
Jose F. Rueda ◽  
Alec M. Berman ◽  
Michael R. Lasarev ◽  
Maya X. Herzig ◽  
...  
Keyword(s):  
Circadian System ◽  
Plasma Aldosterone ◽  
Jet Lag ◽  
Circadian Cycle ◽  
Circadian Period ◽  
Nocturnal Sleep ◽  
Circadian Phase ◽  
Dim Light ◽  
And Behaviors ◽  
Healthy Participants

Measurements of aldosterone for diagnosis of primary aldosteronism are usually made from blood sampled in the morning when aldosterone typically peaks. We tested the relative contributions and interacting influences of the circadian system, ongoing behaviors, and prior sleep to this morning peak in aldosterone. To determine circadian rhythmicity and separate effects of behaviors on aldosterone, 16 healthy participants completed a 5-day protocol in dim light while all behaviors ranging from sleep to exercise were standardized and scheduled evenly across the 24-h circadian period. In another experiment, to test the separate effects of prior nocturnal sleep or the inactivity that accompanies sleep on aldosterone, 10 healthy participants were studied across 2 nights: 1 with sleep and 1 with maintained wakefulness (randomized order). Plasma aldosterone was measured repeatedly in each experiment. Aldosterone had a significant endogenous rhythm ( P < 0.001), rising across the circadian night and peaking in the morning (~8 AM). Activity, including exercise, increased aldosterone, and different behaviors modulated aldosterone differently across the circadian cycle (circadian phase × behavior interaction; P < 0.001). In the second experiment, prior nocturnal sleep and prior rested wakefulness both increased plasma aldosterone ( P < 0.001) in the morning, to the same extent as the change in circadian phases between evening and morning. The morning increase in aldosterone is due to effects of the circadian system plus increased morning activities and not prior sleep or the inactivity accompanying sleep. These findings have implications for the time of and behaviors preceding measurement of aldosterone, especially under conditions of shift work and jet lag.

Ambulatory monitoring in humans: a new method to objectively assess circadian phase as compared with dim light melatonin onset (DLMO)

2013 ◽  
Vol 14 ◽  
pp. e79
Author(s):  
M. Bonmati-Carrion ◽  
B. Middleton ◽  
V. Revell ◽  
D. Skene ◽  
A. Rol ◽  
...  
Keyword(s):  
Ambulatory Monitoring ◽  
New Method ◽  
Circadian Phase ◽  
Dim Light Melatonin Onset ◽  
Dim Light

scholarly journals Circadian tau differences and rhythm associations in delayed sleep–wake phase disorder and sighted non-24-hour sleep–wake rhythm disorder

SLEEP ◽  
2020 ◽  
Author(s):  
Gorica Micic ◽  
Nicole Lovato ◽  
Sally A Ferguson ◽  
Helen J Burgess ◽  
Leon Lack
Keyword(s):  
Circadian Rhythm ◽  
Core Body Temperature ◽  
Sleep Onset ◽  
Phase Relationship ◽  
Biological Rhythms ◽  
Circadian Phase ◽  
Behavioral Rhythm ◽  
Rhythm Disorder ◽  
Dim Light ◽  
Delayed Sleep

Abstract Study Objectives We investigated biological and behavioral rhythm period lengths (i.e. taus) of delayed sleep–wake phase disorder (DSWPD) and non-24-hour sleep–wake rhythm disorder (N24SWD). Based on circadian phase timing (temperature and dim light melatonin onset), DSWPD participants were dichotomized into a circadian-delayed and a circadian non-delayed group to investigate etiological differences. Methods Participants with DSWPD (n = 26, 17 m, age: 21.85 ± 4.97 years), full-sighted N24SWD (n = 4, 3 m, age: 25.75 ± 4.99 years) and 18 controls (10 m, age: 23.72 ± 5.10 years) participated in an 80-h modified constant routine. An ultradian protocol of 1-h “days” in dim light, controlled conditions alternated 20-min sleep/dark periods with 40-min enforced wakefulness/light. Subjective sleepiness ratings were recorded prior to every sleep/dark opportunity and median reaction time (vigilance) was measured hourly. Obtained sleep (sleep propensity) was derived from 20-min sleep/dark opportunities to quantify hourly objective sleepiness. Hourly core body temperature was recorded, and salivary melatonin assayed to measure endogenous circadian rhythms. Rhythm data were curved using the two-component cosine model. Results Patients with DSWPD and N24SWD had significantly longer melatonin and temperature taus compared to controls. Circadian non-delayed DSWPD had normally timed temperature and melatonin rhythms but were typically sleeping at relatively late circadian phases compared to those with circadian-delayed DSWPD. Conclusions People with DSWPD and N24SWD exhibit significantly longer biological circadian rhythm period lengths compared to controls. Approximately half of those diagnosed with DSWPD do not have abnormally delayed circadian rhythm timings suggesting abnormal phase relationship between biological rhythms and behavioral sleep period or potentially conditioned sleep-onset insomnia.

scholarly journals 0433 Targeting Light Sensitivity Parameters to Optimize Circadian Phase Predictions

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A166-A166
Author(s):  
J E Stone ◽  
E M McGlashan ◽  
S W Cain ◽  
A J Phillips
Keyword(s):  
Sensitivity Analysis ◽  
Circadian Clock ◽  
Mean Absolute Error ◽  
Light Response ◽  
Absolute Error ◽  
Light Sensitivity ◽  
Model Parameters ◽  
Circadian Period ◽  
Individual Level ◽  
Circadian Phase

Abstract Introduction Existing models of the human circadian clock accurately predict phase at group-level but not at individual-level. Interindividual variability in light sensitivity is not currently accounted for in these models and may be a practical approach to improving individual-level predictions. Using the gold-standard predictive model, we (i) identified whether varying light sensitivity parameters produces meaningful changes in predicted phase in field conditions; and (ii) tested whether optimizing parameters can significantly improve accuracy of circadian phase prediction. Methods Healthy participants (n=12, 7 women, aged 18-26) underwent continuous light and activity monitoring for 3 weeks (Actiwatch Spectrum). Salivary dim light melatonin onset (DLMO) was measured each week. A model of the human circadian clock and its response to light was used to predict the three weekly DLMO times using the individual’s light data. A sensitivity analysis was performed varying three model parameters within physiological ranges: (i) amplitude of the light response [p]; (ii) advance vs. delay bias of the light response [K]; and (iii) intrinsic circadian period [tau]. These parameters were then fitted using least squares estimation to obtain optimal predictions of DLMO for each individual. Accuracy was compared between optimized parameters and default parameters. Results The default model predicted DLMO with mean absolute error of 1.02h. Sensitivity analysis showed the average range of variation in predicted DLMOs across participants was 0.65h for p, 4.28h for K and 3.26h for tau. Fitting parameters independently, we found mean absolute error of 0.85h for p, 0.71h for K and 0.75h for tau. Fitting p and K together reduced mean absolute error to 0.57h. Conclusion Light sensitivity parameters capture similar or greater variability in phase as intrinsic circadian period, indicating they are a viable option for individualising circadian phase predictions. Future prospective work is needed using measures of light sensitivity to validate this approach. Support N/A

scholarly journals Home Circadian Phase Assessments with Measures of Compliance Yield Accurate Dim Light Melatonin Onsets

SLEEP ◽  
2015 ◽  
Author(s):  
Helen J. Burgess ◽  
James K. Wyatt ◽  
Margaret Park ◽  
Louis F. Fogg
Keyword(s):  
Circadian Phase ◽  
Dim Light

scholarly journals 0010 A Putative Biomarker Fingerprint of Insufficient Sleep Derived from the Human Plasma Metabolome

SLEEP ◽  
2018 ◽  
Vol 41 (suppl_1) ◽  
pp. A5-A5
Author(s):  
C M Depner ◽  
R R Markwald ◽  
C Cruickshank-Quinn ◽  
K Quinn ◽  
E L Melanson ◽  
...  
Keyword(s):  
Human Plasma ◽  
Insufficient Sleep ◽  
Plasma Metabolome

scholarly journals The Effect of Cognitive Behaviour Therapy on Sleep and Circadian Rhythm in Young College Students

2021 ◽  
pp. 43-47
Author(s):  
Shweta Kanchan ◽  
Sunita Tiwari ◽  
Shweta Singh
Keyword(s):  
Cognitive Behaviour Therapy ◽  
Sleep Time ◽  
Behaviour Therapy ◽  
Circadian Phase ◽  
Dim Light Melatonin Onset ◽  
College Going ◽  
Cognitive Behaviour ◽  
Before And After ◽  
Sleep Parameters ◽  
Dim Light

The present study is to study the effect of cognitive behaviour therapy on various sleep parameters and circadian phase rhythmic in young college going adults. Fifty young college going adults were chosen from the MBBS and BDS students of King George's Medical University Lucknow, their polysomnography was conducted along with it salivary melatonin estimation was conducted to find the time of Dim light melatonin onset (DLMO), the subjects were administered cognitive behaviour therapy (CBT),after completing the sessions of cognitive behaviour therapy another Polysomnographic study and DLMO estimation was conducted, various sleep parameters were compared before and after the CBT. The study showed an improvement in the steep quality, a decrease in daytime sleepiness along with this total sleep time significantly increased, sleep efficiency also improved and there was a decrease in the REM sleep latency. The Dim light melatonin onset advanced for the subjects and the chronotype showed an inclination towards an earlier timings.

scholarly journals 0041 Preliminary Identification and Validation of a Plasma Metabolome-Based Biomarker for Circadian Phase in Humans

SLEEP ◽  
2019 ◽  
Vol 42 (Supplement_1) ◽  
pp. A17-A17
Author(s):  
Dasha Cogswell ◽  
Rachel R Markwald ◽  
Charmion Cruickshank-Quinn ◽  
Kevin Quinn ◽  
Edward L Melanson ◽  
...  
Keyword(s):  
Circadian Phase ◽  
Preliminary Identification ◽  
Plasma Metabolome

156 Evening Light-Induced Circadian Phase Shift in Preschool-Aged Children

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A63-A64
Author(s):  
Lauren Hartstein ◽  
Lameese Akacem ◽  
Cecilia Diniz Behn ◽  
Shelby Stowe ◽  
Kenneth Wright ◽  
...  
Keyword(s):  
Phase Shift ◽  
Light Exposure ◽  
Light Stimulus ◽  
Phase Delay ◽  
Healthy Children ◽  
Dependent Manner ◽  
Circadian Phase ◽  
Light Intensities ◽  
Dim Light ◽  
Dose Dependent

Abstract Introduction In adults, exposure to light at night delays the timing of the circadian clock in a dose-dependent manner with intensity. Although children’s melatonin levels are highly suppressed by evening bright light, the sensitivity of young children’s circadian timing to evening light is unknown. This research aimed to establish an illuminance response curve for phase delay in preschool children as a result of exposure to varying light intensities in the hour before bedtime. Methods Healthy children (n=36, 3.0 – 4.9 years, 39% males), participated in a 10-day protocol. For 7 days, children followed a strict parent-selected sleep schedule. On Days 8-10, an in-home dim-light assessment was performed. On Day 8, dim light melatonin onset (DLMO) was measured through saliva samples collected in 20-30-min intervals throughout the evening until 1-h past habitual bedtime. On Day 9, children were exposed to a white light stimulus (semi-randomly assigned from 5lx to 5000lx) for 1-h before their habitual bedtime, and saliva was collected before, during, and after the exposure. On Day 10, children provided saliva samples in the evening for 2.5-h past bedtime for a final DLMO assessment. Phase angle of entrainment (habitual bedtime – DLMObaseline) and circadian phase delay (DLMOfinal – DLMObaseline) were computed. Results Final DLMO (Day 10) shifted between -8 and 123 minutes (M = 56.1 +/- 33.6 min; negative value = phase advance, positive value = phase delay) compared with DLMO at baseline (Day 8). Raw phase shift did not demonstrate a dose-dependent relationship with light intensity. Rather, we observed a robust phase delay across all intensities. Conclusion These data suggest preschoolers’ circadian clocks are immensely sensitive to a large range of light intensities, which may be mechanistically influenced by less mature ophthalmologic features (e.g. clearer lenses, larger pupils). With young children’s ever-growing use of light-emitting devices and evening exposure to artificial lighting, as well as the prevalence of behavioral sleep problems, these findings may inform recommendations for parents on the effects of evening light exposure on sleep timing in early childhood. Support (if any) This research was supported with funds from the Eunice Kennedy Shriver National Institute of Child Health & Human Development (R01-HD087707).

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