Identification of a Preliminary Plasma Metabolome-based Biomarker for Circadian Phase in Humans.
Identification of a Preliminary Plasma Metabolome-based Biomarker for Circadian Phase in Humans.
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DOI：
10.1177/07487304211025402
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发表时间：
2021-08
影响因子：
3.5
通讯作者：
Depner, C. M.
中科院分区：
文献类型：
--
作者：
Cogswell, D.;Bisesi, P.;Markwald, R. R.;Cruickshank-Quinn, C.;Quinn, K.;McHill, A.;Melanson, E. L.;Reisdorph, N.;Wright, K. P., Jr.;Depner, C. M.
关键词：
测量个体的昼夜节律时相对于诊断和治疗昼夜节律睡眠-觉醒障碍和昼夜节律失调、为时辰疗法提供信息以及促进昼夜节律科学的发展具有重要意义。使用血液转录物来预测昼夜节律阶段标记物暗淡褪黑素发作(DLMO)的初步发现显示出希望。或者，使用代谢组学来预测DLMO的尝试有限，还没有已知的基于组学的生物标记物预测微弱的褪黑素抵消(DLMOff)。我们使用一份血液样本分析了睡眠充足和睡眠不足时的人体血浆代谢组，以预测DLMO和DLMOff。16名健康受试者(男8人，女8人)，年龄22.4±4.8岁(平均值±SD)，完成了一项实验室研究，实验持续3天(9h睡眠机会/晚)，然后随机交换方案，9h睡眠充足和5h睡眠不足，每种情况持续5天。在每种情况的最后24小时内每小时采集一次血，以独立测定DLMO和DLMOff。每4h采集一次的血液样本进行非靶向代谢组学分析，并随机分为训练组(68%)和测试组(32%)进行生物标志物分析。在训练集中使用偏最小二乘回归建立DLMO和DLMOff生物标记物模型，然后使用测试集进行性能评估。在基线时，DLMOff模型表现出最高的性能(0.91R2和1.1±1.1h中位数绝对误差±四分位数范围[MdAE±IQR])，预测误差显著(p<；0.01)低于DLMO模型。当所有条件(基线、9h和5h)都包括在性能分析中时，DLMO(0.60R2；2.2±2.8hMdAE；44%的样本误差在2 h以下)和DLMOff(0.62 R2；1.8±2.6hMdAE；51%的样本误差在2 h以下)模型没有统计学差异。这些发现显示了基于代谢组学的昼夜节律相生物标记物的前景，并强调了测试生物标记物在不同生理条件下预测多个昼夜节律相标记物的必要性。
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.
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影响因子：
5.6
作者：
Duffy JF;Abbott SM;Burgess HJ;Crowley SJ;Emens JS;Epstein LJ;Gamble KL;Hasler BP;Kristo DA;Malkani RG;Rahman SA;Thomas SJ;Wyatt JK;Zee PC;Klerman EB
通讯作者：
Klerman EB
影响因子：
3.4
作者：
Buijs, RM;Wortel, J;Kalsbeek, A
通讯作者：
Kalsbeek, A
影响因子：
9.5
作者：
Boulesteix, Anne-Laure;Strimmer, Korbinian
通讯作者：
Strimmer, Korbinian
DOI：
10.1073/pnas.1010666108
发表时间：
2011-09-13
影响因子：
11.1
作者：
Duffy, Jeanne F.;Cain, Sean W.;Czeisler, Charles A.
通讯作者：
Czeisler, Charles A.
影响因子：
6.4
作者：
Innominato, Pasquale F.;Giacchetti, Sylvie;Levi, Francis A.
通讯作者：
Levi, Francis A.