Department of Genetics, Stanford University, Stanford, CA, 94305, USA.
Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA.
Nat Commun. 2020 Oct 1;11(1):4933. doi: 10.1038/s41467-020-18758-1.
The influence of seasons on biological processes is poorly understood. In order to identify biological seasonal patterns based on diverse molecular data, rather than calendar dates, we performed a deep longitudinal multiomics profiling of 105 individuals over 4 years. Here, we report more than 1000 seasonal variations in omics analytes and clinical measures. The different molecules group into two major seasonal patterns which correlate with peaks in late spring and late fall/early winter in California. The two patterns are enriched for molecules involved in human biological processes such as inflammation, immunity, cardiovascular health, as well as neurological and psychiatric conditions. Lastly, we identify molecules and microbes that demonstrate different seasonal patterns in insulin sensitive and insulin resistant individuals. The results of our study have important implications in healthcare and highlight the value of considering seasonality when assessing population wide health risk and management.
季节对生物过程的影响还不甚清楚。为了根据多样化的分子数据而不是日历日期来确定生物季节性模式，我们对 105 个人进行了长达 4 年的深度纵向多组学分析。在这里，我们报告了超过 1000 种组学分析物和临床测量的季节性变化。这些不同的分子分为两种主要的季节性模式，与加利福尼亚州春末和秋末/初冬的高峰期相关。这两种模式富含参与人类生物学过程的分子，如炎症、免疫、心血管健康以及神经和精神疾病。最后，我们确定了在胰岛素敏感和胰岛素抵抗个体中表现出不同季节性模式的分子和微生物。我们研究的结果在医疗保健方面具有重要意义，并强调在评估人群健康风险和管理时考虑季节性的重要性。
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