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.

测量个体昼夜节律相位对于诊断和治疗昼夜节律睡眠觉醒障碍和昼夜节律失调、为时间疗法提供信息以及推进昼夜节律科学非常重要。使用血液转录组学来预测昼夜节律阶段标记物弱光褪黑激素起始（DLMO）的初步结果显示出希望。另外，使用代谢组学来预测 DLMO 的尝试也很有限，并且没有已知的基于组学的生物标志物可以预测弱光褪黑激素抵消 (DLMOff)。我们分析了充足和睡眠不足期间的人类血浆代谢组，以使用一份血液样本预测 DLMO 和 DLMOff。 16 名（8 名男性/8 名女性）年龄为 22.4 ± 4.8 岁（平均值 ± 标准差）的健康参与者完成了一项为期 3 天的实验室研究（每晚 9 小时睡眠机会），随后采用 9- h充足睡眠和5h睡眠不足的情况，各持续5天。在每种情况的最后 24 小时内每小时采集一次血液，以独立测定 DLMO 和 DLMOff。每 4 小时收集一次血液样本，通过非靶向代谢组学进行分析，并随机分为训练组 (68%) 和测试组 (32%) 进行生物标志物分析。 DLMO 和 DLMOff 生物标志物模型是在训练集中使用偏最小二乘回归开发的，然后使用测试集进行性能评估。在基线时，DLMOff 模型显示出最高的性能（0.91 R ²和 1.1 ± 1.1 h 中值绝对误差 ± 四分位数范围 [MdAE ± IQR]），与 DLMO 模型相比，预测误差显着降低 ( p < 0.01)。当所有条件（基线、9 小时和 5 小时）均包含在性能分析中时，DLMO (0.60 R ² ；2.2 ± 2.8小时MdAE； 44% 的样本误差低于 2 h) 和 DLMOff (0.62 R ² ; 1.8 ± 2.6 h MdAE; 51% 的样本误差低于 2 h) 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阶段生物标志物的前景，并强调需要测试在不同生理条件下预测多个昼夜节律阶段标志物的生物标志物。