Phuc D Nguyen, Claire Dunbar, Hannah Scott, Bastien Lechat, Jack Manners, Gorica Micic, Nicole Lovato, Amy C Reynolds, Leon Lack, Robert Adams, Danny Eckert, Andrew Vakulin, Peter G Catcheside
{"title":"一种分离昼夜节律与非昼夜节律掩蔽效应的新方法,以加强从核心体温估计每日昼夜节律的时间和振幅","authors":"Phuc D Nguyen, Claire Dunbar, Hannah Scott, Bastien Lechat, Jack Manners, Gorica Micic, Nicole Lovato, Amy C Reynolds, Leon Lack, Robert Adams, Danny Eckert, Andrew Vakulin, Peter G Catcheside","doi":"arxiv-2408.15295","DOIUrl":null,"url":null,"abstract":"Circadian disruption contributes to adverse effects on sleep, performance,\nand health. One accepted method to track continuous daily changes in circadian\ntiming is to measure core body temperature (CBT), and establish daily,\ncircadian-related CBT minimum time (Tmin). This method typically applies\ncosine-model fits to measured CBT data, which may not adequately account for\nsubstantial wake metabolic activity and sleep effects on CBT that confound and\nmask circadian effects, and thus estimates of the circadian-related Tmin. This\nstudy introduced a novel physiology-grounded analytic approach to separate\ncircadian from non-circadian effects on CBT, which we compared against\ntraditional cosine-based methods. The dataset comprised 33 healthy participants\nattending a 39-hour in-laboratory study with an initial overnight sleep\nfollowed by an extended wake period. CBT data were collected at 30-second\nintervals via ingestible capsules. Our design captured CBT during both the\nbaseline sleep period and during extended wake period (without sleep) and\nallowed us to model the influence of circadian and non-circadian effects of\nsleep, wake, and activity on CBT using physiology-guided generalized additive\nmodels. Model fits and estimated Tmin inferred from extended wake without sleep\nwere compared with traditional cosine-based models fits. Compared to the\ntraditional cosine model, the new model exhibited superior fits to CBT (Pearson\nR 0.90 [95%CI; [0.83 - 0.96] versus 0.81 [0.55-0.93]). The difference between\nestimated vs measured circadian Tmin, derived from the day without sleep, was\nbetter fit with our method (0.2 [-0.5,0.3] hours) versus previous methods (1.4\n[1.1 to 1.7] hours). This new method provides superior demasking of\nnon-circadian influences compared to traditional cosine methods, including the\nremoval of a sleep-related bias towards an earlier estimate of circadian Tmin.","PeriodicalId":501266,"journal":{"name":"arXiv - QuanBio - Quantitative Methods","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel method to separate circadian from non-circadian masking effects in order to enhance daily circadian timing and amplitude estimation from core body temperature\",\"authors\":\"Phuc D Nguyen, Claire Dunbar, Hannah Scott, Bastien Lechat, Jack Manners, Gorica Micic, Nicole Lovato, Amy C Reynolds, Leon Lack, Robert Adams, Danny Eckert, Andrew Vakulin, Peter G Catcheside\",\"doi\":\"arxiv-2408.15295\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Circadian disruption contributes to adverse effects on sleep, performance,\\nand health. One accepted method to track continuous daily changes in circadian\\ntiming is to measure core body temperature (CBT), and establish daily,\\ncircadian-related CBT minimum time (Tmin). This method typically applies\\ncosine-model fits to measured CBT data, which may not adequately account for\\nsubstantial wake metabolic activity and sleep effects on CBT that confound and\\nmask circadian effects, and thus estimates of the circadian-related Tmin. This\\nstudy introduced a novel physiology-grounded analytic approach to separate\\ncircadian from non-circadian effects on CBT, which we compared against\\ntraditional cosine-based methods. The dataset comprised 33 healthy participants\\nattending a 39-hour in-laboratory study with an initial overnight sleep\\nfollowed by an extended wake period. CBT data were collected at 30-second\\nintervals via ingestible capsules. Our design captured CBT during both the\\nbaseline sleep period and during extended wake period (without sleep) and\\nallowed us to model the influence of circadian and non-circadian effects of\\nsleep, wake, and activity on CBT using physiology-guided generalized additive\\nmodels. Model fits and estimated Tmin inferred from extended wake without sleep\\nwere compared with traditional cosine-based models fits. Compared to the\\ntraditional cosine model, the new model exhibited superior fits to CBT (Pearson\\nR 0.90 [95%CI; [0.83 - 0.96] versus 0.81 [0.55-0.93]). The difference between\\nestimated vs measured circadian Tmin, derived from the day without sleep, was\\nbetter fit with our method (0.2 [-0.5,0.3] hours) versus previous methods (1.4\\n[1.1 to 1.7] hours). This new method provides superior demasking of\\nnon-circadian influences compared to traditional cosine methods, including the\\nremoval of a sleep-related bias towards an earlier estimate of circadian Tmin.\",\"PeriodicalId\":501266,\"journal\":{\"name\":\"arXiv - QuanBio - Quantitative Methods\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuanBio - Quantitative Methods\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.15295\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Quantitative Methods","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.15295","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A novel method to separate circadian from non-circadian masking effects in order to enhance daily circadian timing and amplitude estimation from core body temperature
Circadian disruption contributes to adverse effects on sleep, performance,
and health. One accepted method to track continuous daily changes in circadian
timing is to measure core body temperature (CBT), and establish daily,
circadian-related CBT minimum time (Tmin). This method typically applies
cosine-model fits to measured CBT data, which may not adequately account for
substantial wake metabolic activity and sleep effects on CBT that confound and
mask circadian effects, and thus estimates of the circadian-related Tmin. This
study introduced a novel physiology-grounded analytic approach to separate
circadian from non-circadian effects on CBT, which we compared against
traditional cosine-based methods. The dataset comprised 33 healthy participants
attending a 39-hour in-laboratory study with an initial overnight sleep
followed by an extended wake period. CBT data were collected at 30-second
intervals via ingestible capsules. Our design captured CBT during both the
baseline sleep period and during extended wake period (without sleep) and
allowed us to model the influence of circadian and non-circadian effects of
sleep, wake, and activity on CBT using physiology-guided generalized additive
models. Model fits and estimated Tmin inferred from extended wake without sleep
were compared with traditional cosine-based models fits. Compared to the
traditional cosine model, the new model exhibited superior fits to CBT (Pearson
R 0.90 [95%CI; [0.83 - 0.96] versus 0.81 [0.55-0.93]). The difference between
estimated vs measured circadian Tmin, derived from the day without sleep, was
better fit with our method (0.2 [-0.5,0.3] hours) versus previous methods (1.4
[1.1 to 1.7] hours). This new method provides superior demasking of
non-circadian influences compared to traditional cosine methods, including the
removal of a sleep-related bias towards an earlier estimate of circadian Tmin.