Michael Scholz, Andrea Eva Steuer, Akos Dobay, Hans-Peter Landolt, Thomas Kraemer
{"title":"评估睡眠和采样时间对口腔液中代谢物的影响:对代谢组学研究的意义。","authors":"Michael Scholz, Andrea Eva Steuer, Akos Dobay, Hans-Peter Landolt, Thomas Kraemer","doi":"10.1007/s11306-024-02158-3","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>The human salivary metabolome is a rich source of information for metabolomics studies. Among other influences, individual differences in sleep-wake history and time of day may affect the metabolome.</p><p><strong>Objectives: </strong>We aimed to characterize the influence of a single night of sleep deprivation compared to sufficient sleep on the metabolites present in oral fluid and to assess the implications of sampling time points for the design of metabolomics studies.</p><p><strong>Methods: </strong>Oral fluid specimens of 13 healthy young males were obtained in Salivette<sup>®</sup> devices at regular intervals in both a control condition (repeated 8-hour sleep) and a sleep deprivation condition (total sleep deprivation of 8 h, recovery sleep of 8 h) and their metabolic contents compared in a semi-targeted metabolomics approach.</p><p><strong>Results: </strong>Analysis of variance results showed factor 'time' (i.e., sampling time point) representing the major influencer (median 9.24%, range 3.02-42.91%), surpassing the intervention of sleep deprivation (median 1.81%, range 0.19-12.46%). In addition, we found about 10% of all metabolic features to have significantly changed in at least one time point after a night of sleep deprivation when compared to 8 h of sleep.</p><p><strong>Conclusion: </strong>The majority of significant alterations in metabolites' abundances were found when sampled in the morning hours, which can lead to subsequent misinterpretations of experimental effects in metabolomics studies. Beyond applying a within-subject design with identical sample collection times, we highly recommend monitoring participants' sleep-wake schedules prior to and during experiments, even if the study focus is not sleep-related (e.g., via actigraphy).</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11306311/pdf/","citationCount":"0","resultStr":"{\"title\":\"Assessing the influence of sleep and sampling time on metabolites in oral fluid: implications for metabolomics studies.\",\"authors\":\"Michael Scholz, Andrea Eva Steuer, Akos Dobay, Hans-Peter Landolt, Thomas Kraemer\",\"doi\":\"10.1007/s11306-024-02158-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>The human salivary metabolome is a rich source of information for metabolomics studies. Among other influences, individual differences in sleep-wake history and time of day may affect the metabolome.</p><p><strong>Objectives: </strong>We aimed to characterize the influence of a single night of sleep deprivation compared to sufficient sleep on the metabolites present in oral fluid and to assess the implications of sampling time points for the design of metabolomics studies.</p><p><strong>Methods: </strong>Oral fluid specimens of 13 healthy young males were obtained in Salivette<sup>®</sup> devices at regular intervals in both a control condition (repeated 8-hour sleep) and a sleep deprivation condition (total sleep deprivation of 8 h, recovery sleep of 8 h) and their metabolic contents compared in a semi-targeted metabolomics approach.</p><p><strong>Results: </strong>Analysis of variance results showed factor 'time' (i.e., sampling time point) representing the major influencer (median 9.24%, range 3.02-42.91%), surpassing the intervention of sleep deprivation (median 1.81%, range 0.19-12.46%). In addition, we found about 10% of all metabolic features to have significantly changed in at least one time point after a night of sleep deprivation when compared to 8 h of sleep.</p><p><strong>Conclusion: </strong>The majority of significant alterations in metabolites' abundances were found when sampled in the morning hours, which can lead to subsequent misinterpretations of experimental effects in metabolomics studies. Beyond applying a within-subject design with identical sample collection times, we highly recommend monitoring participants' sleep-wake schedules prior to and during experiments, even if the study focus is not sleep-related (e.g., via actigraphy).</p>\",\"PeriodicalId\":18506,\"journal\":{\"name\":\"Metabolomics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-08-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11306311/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Metabolomics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s11306-024-02158-3\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENDOCRINOLOGY & METABOLISM\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Metabolomics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s11306-024-02158-3","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
Assessing the influence of sleep and sampling time on metabolites in oral fluid: implications for metabolomics studies.
Introduction: The human salivary metabolome is a rich source of information for metabolomics studies. Among other influences, individual differences in sleep-wake history and time of day may affect the metabolome.
Objectives: We aimed to characterize the influence of a single night of sleep deprivation compared to sufficient sleep on the metabolites present in oral fluid and to assess the implications of sampling time points for the design of metabolomics studies.
Methods: Oral fluid specimens of 13 healthy young males were obtained in Salivette® devices at regular intervals in both a control condition (repeated 8-hour sleep) and a sleep deprivation condition (total sleep deprivation of 8 h, recovery sleep of 8 h) and their metabolic contents compared in a semi-targeted metabolomics approach.
Results: Analysis of variance results showed factor 'time' (i.e., sampling time point) representing the major influencer (median 9.24%, range 3.02-42.91%), surpassing the intervention of sleep deprivation (median 1.81%, range 0.19-12.46%). In addition, we found about 10% of all metabolic features to have significantly changed in at least one time point after a night of sleep deprivation when compared to 8 h of sleep.
Conclusion: The majority of significant alterations in metabolites' abundances were found when sampled in the morning hours, which can lead to subsequent misinterpretations of experimental effects in metabolomics studies. Beyond applying a within-subject design with identical sample collection times, we highly recommend monitoring participants' sleep-wake schedules prior to and during experiments, even if the study focus is not sleep-related (e.g., via actigraphy).
期刊介绍:
Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to:
metabolomic applications within man, including pre-clinical and clinical
pharmacometabolomics for precision medicine
metabolic profiling and fingerprinting
metabolite target analysis
metabolomic applications within animals, plants and microbes
transcriptomics and proteomics in systems biology
Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.