Machine Learning Analyses Reveal Circadian Features Predictive of Risk for Sleep Disturbance
Rebeccah Overton,1 Aziz Zafar,1,2 Ziad Attia,1,3 Ahmet Ay,1,2 Krista K Ingram1 1Department of Biology, Colgate University, Hamilton, NY, USA; 2Department of Mathematics, Colgate University, Hamilton, NY, USA; 3Department of Computer Science, Colgate University, Hamilton, NY, USACorrespondence: Krist...
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| Format: | Article |
| Language: | English |
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Dove Medical Press
2022-10-01
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| Series: | Nature and Science of Sleep |
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| Online Access: | https://www.dovepress.com/machine-learning-analyses-reveal-circadian-features-predictive-of-risk-peer-reviewed-fulltext-article-NSS |
| _version_ | 1811197813213626368 |
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| author | Overton R Zafar A Attia Z Ay A Ingram KK |
| author_facet | Overton R Zafar A Attia Z Ay A Ingram KK |
| author_sort | Overton R |
| collection | DOAJ |
| description | Rebeccah Overton,1 Aziz Zafar,1,2 Ziad Attia,1,3 Ahmet Ay,1,2 Krista K Ingram1 1Department of Biology, Colgate University, Hamilton, NY, USA; 2Department of Mathematics, Colgate University, Hamilton, NY, USA; 3Department of Computer Science, Colgate University, Hamilton, NY, USACorrespondence: Krista K Ingram, Email kingram@colgate.eduIntroduction: Sleep disturbances often co-occur with mood disorders, with poor sleep quality affecting over a quarter of the global population. Recent advances in sleep and circadian biology suggest poor sleep quality is linked to disruptions in circadian rhythms, including significant associations between sleep features and circadian clock gene variants.Methods: Here, we employ machine learning techniques, combined with statistical approaches, in a deeply phenotyped population to explore associations between clock genotypes, circadian phenotypes (diurnal preference and circadian phase), and risk for sleep disturbance symptoms.Results: As found in previous studies, evening chronotypes report high levels of sleep disturbance symptoms. Using molecular chronotyping by measuring circadian phase, we extend these findings and show that individuals with a mismatch between circadian phase and diurnal preference report higher levels of sleep disturbance. We also report novel synergistic interactions in genotype combinations of Period 3, Clock and Cryptochrome variants (PER3B (rs17031614)/ CRY1 (rs228716) and CLOCK3111 (rs1801260)/ CRY2 (rs10838524)) that yield strong associations with sleep disturbance, particularly in males.Conclusion: Our results indicate that both direct and indirect mechanisms may impact sleep quality; sex-specific clock genotype combinations predictive of sleep disturbance may represent direct effects of clock gene function on downstream pathways involved in sleep physiology. In addition, the mediation of clock gene effects on sleep disturbance indicates circadian influences on the quality of sleep. Unraveling the complex molecular mechanisms at the intersection of circadian and sleep physiology is vital for understanding how genetic and behavioral factors influencing circadian phenotypes impact sleep quality. Such studies provide potential targets for further study and inform efforts to improve non-invasive therapeutics for sleep disorders.Keywords: circadian clock, chronotype, sleep disturbance, sleep quality, machine learning, circadian misalignment |
| first_indexed | 2024-04-12T01:20:42Z |
| format | Article |
| id | doaj.art-30b1a9e42d8a4fd3aa62d85ba19888b9 |
| institution | Directory Open Access Journal |
| issn | 1179-1608 |
| language | English |
| last_indexed | 2024-04-12T01:20:42Z |
| publishDate | 2022-10-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | Nature and Science of Sleep |
| spelling | doaj.art-30b1a9e42d8a4fd3aa62d85ba19888b92022-12-22T03:53:48ZengDove Medical PressNature and Science of Sleep1179-16082022-10-01Volume 141887190079147Machine Learning Analyses Reveal Circadian Features Predictive of Risk for Sleep DisturbanceOverton RZafar AAttia ZAy AIngram KKRebeccah Overton,1 Aziz Zafar,1,2 Ziad Attia,1,3 Ahmet Ay,1,2 Krista K Ingram1 1Department of Biology, Colgate University, Hamilton, NY, USA; 2Department of Mathematics, Colgate University, Hamilton, NY, USA; 3Department of Computer Science, Colgate University, Hamilton, NY, USACorrespondence: Krista K Ingram, Email kingram@colgate.eduIntroduction: Sleep disturbances often co-occur with mood disorders, with poor sleep quality affecting over a quarter of the global population. Recent advances in sleep and circadian biology suggest poor sleep quality is linked to disruptions in circadian rhythms, including significant associations between sleep features and circadian clock gene variants.Methods: Here, we employ machine learning techniques, combined with statistical approaches, in a deeply phenotyped population to explore associations between clock genotypes, circadian phenotypes (diurnal preference and circadian phase), and risk for sleep disturbance symptoms.Results: As found in previous studies, evening chronotypes report high levels of sleep disturbance symptoms. Using molecular chronotyping by measuring circadian phase, we extend these findings and show that individuals with a mismatch between circadian phase and diurnal preference report higher levels of sleep disturbance. We also report novel synergistic interactions in genotype combinations of Period 3, Clock and Cryptochrome variants (PER3B (rs17031614)/ CRY1 (rs228716) and CLOCK3111 (rs1801260)/ CRY2 (rs10838524)) that yield strong associations with sleep disturbance, particularly in males.Conclusion: Our results indicate that both direct and indirect mechanisms may impact sleep quality; sex-specific clock genotype combinations predictive of sleep disturbance may represent direct effects of clock gene function on downstream pathways involved in sleep physiology. In addition, the mediation of clock gene effects on sleep disturbance indicates circadian influences on the quality of sleep. Unraveling the complex molecular mechanisms at the intersection of circadian and sleep physiology is vital for understanding how genetic and behavioral factors influencing circadian phenotypes impact sleep quality. Such studies provide potential targets for further study and inform efforts to improve non-invasive therapeutics for sleep disorders.Keywords: circadian clock, chronotype, sleep disturbance, sleep quality, machine learning, circadian misalignmenthttps://www.dovepress.com/machine-learning-analyses-reveal-circadian-features-predictive-of-risk-peer-reviewed-fulltext-article-NSScircadian clockchronotypesleep disturbancesleep qualitymachine learningcircadian misalignment |
| spellingShingle | Overton R Zafar A Attia Z Ay A Ingram KK Machine Learning Analyses Reveal Circadian Features Predictive of Risk for Sleep Disturbance Nature and Science of Sleep circadian clock chronotype sleep disturbance sleep quality machine learning circadian misalignment |
| title | Machine Learning Analyses Reveal Circadian Features Predictive of Risk for Sleep Disturbance |
| title_full | Machine Learning Analyses Reveal Circadian Features Predictive of Risk for Sleep Disturbance |
| title_fullStr | Machine Learning Analyses Reveal Circadian Features Predictive of Risk for Sleep Disturbance |
| title_full_unstemmed | Machine Learning Analyses Reveal Circadian Features Predictive of Risk for Sleep Disturbance |
| title_short | Machine Learning Analyses Reveal Circadian Features Predictive of Risk for Sleep Disturbance |
| title_sort | machine learning analyses reveal circadian features predictive of risk for sleep disturbance |
| topic | circadian clock chronotype sleep disturbance sleep quality machine learning circadian misalignment |
| url | https://www.dovepress.com/machine-learning-analyses-reveal-circadian-features-predictive-of-risk-peer-reviewed-fulltext-article-NSS |
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