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手指汗液分析可实现人体短时间间隔代谢生物监测。

Finger sweat analysis enables short interval metabolic biomonitoring in humans.

机构信息

Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria.

Department of Inorganic Chemistry, Faculty of Chemistry, University of Vienna, Vienna, Austria.

出版信息

Nat Commun. 2021 Oct 13;12(1):5993. doi: 10.1038/s41467-021-26245-4.

Abstract

Metabolic biomonitoring in humans is typically based on the sampling of blood, plasma or urine. Although established in the clinical routine, these sampling procedures are often associated with a variety of compliance issues, which are impeding time-course studies. Here, we show that the metabolic profiling of the minute amounts of sweat sampled from fingertips addresses this challenge. Sweat sampling from fingertips is non-invasive, robust and can be accomplished repeatedly by untrained personnel. The sweat matrix represents a rich source for metabolic phenotyping. We confirm the feasibility of short interval sampling of sweat from the fingertips in time-course studies involving the consumption of coffee or the ingestion of a caffeine capsule after a fasting interval, in which we successfully monitor all known caffeine metabolites as well as endogenous metabolic responses. Fluctuations in the rate of sweat production are accounted for by mathematical modelling to reveal individual rates of caffeine uptake, metabolism and clearance. To conclude, metabotyping using sweat from fingertips combined with mathematical network modelling shows promise for broad applications in precision medicine by enabling the assessment of dynamic metabolic patterns, which may overcome the limitations of purely compositional biomarkers.

摘要

人体代谢生物监测通常基于血液、血浆或尿液的采样。尽管这些采样程序已经在临床常规中确立,但它们通常与各种合规性问题相关,这些问题阻碍了时间进程研究。在这里,我们证明了从指尖采样的微量汗液的代谢分析能够解决这一挑战。从指尖采集汗液是非侵入性的、稳健的,并且未经训练的人员可以重复进行。汗液基质代表了代谢表型分析的丰富来源。我们通过在涉及饮用咖啡或在禁食间隔后摄入咖啡因胶囊的时间进程研究中证实了从指尖短时间间隔采集汗液的可行性,在这些研究中,我们成功地监测了所有已知的咖啡因代谢物以及内源性代谢反应。通过数学建模来解释汗液生成率的波动,以揭示咖啡因摄取、代谢和清除的个体速率。总之,使用指尖汗液进行代谢组学分析结合数学网络建模显示出在精准医学中的广泛应用前景,能够评估动态代谢模式,这可能克服了纯成分生物标志物的局限性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/28e5/8514494/f91574a62624/41467_2021_26245_Fig1_HTML.jpg

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