英国剑桥大学Claudia Langenberg研究组取得一项新突破。他们的研究利用血浆代谢产物分析揭示了非传染性多重疾病的发病途径。这一研究成果发表在2021年3月11日出版的国际学术期刊《自然-医学》上。

研究人员使用涵盖超过1,000种代谢物的基线非靶向血浆代谢谱分析作为人类生理学的综合读数，以表征与住院电子病历和癌症登记数据相关的27种非传染性疾病（NCD）相关通路，并利用超过11,000名参与者（219,415人年）的癌症登记数据进行了评估。研究确定了至少2个NCD之间共有420种代谢物，占所有640种重要代谢物-疾病关联的65.5％。研究人员整合了超过50种不同的临床风险因素和特征基线数据，以鉴定与这些代谢物相关的共享代谢途径。

该研究主要发现了肝和肾功能、脂质和葡萄糖代谢、轻度炎症、肠道微生物多样性的替代指标以及与健康相关的特定行为，这些都是常见非传染性多重疾病发病的前兆，具有早期预防潜力。研究人员将这些结果整合到开放式网络服务器（https://omicscience.org/apps/mwasdisease/）中，以方便将来的研究和荟萃分析。 

据悉，多重疾病是指临患多种慢性病，其已成为一个日益严重的全球性健康问题，当务之急是研究其决定因素。

附：英文原文

Title: Plasma metabolites to profile pathways in noncommunicable disease multimorbidity

Author: Maik Pietzner, Isobel D. Stewart, Johannes Raffler, Kay-Tee Khaw, Gregory A. Michelotti, Gabi Kastenmller, Nicholas J. Wareham, Claudia Langenberg

Issue&Volume: 2021-03-11

Abstract: Multimorbidity, the simultaneous presence of multiple chronic conditions, is an increasing global health problem and research into its determinants is of high priority. We used baseline untargeted plasma metabolomics profiling covering >1,000 metabolites as a comprehensive readout of human physiology to characterize pathways associated with and across 27 incident noncommunicable diseases (NCDs) assessed using electronic health record hospitalization and cancer registry data from over 11,000 participants (219,415 person years). We identified 420 metabolites shared between at least 2 NCDs, representing 65.5% of all 640 significant metabolite–disease associations. We integrated baseline data on over 50 diverse clinical risk factors and characteristics to identify actionable shared pathways represented by those metabolites. Our study highlights liver and kidney function, lipid and glucose metabolism, low-grade inflammation, surrogates of gut microbial diversity and specific health-related behaviors as antecedents of common NCD multimorbidity with potential for early prevention. We integrated results into an open-access webserver (https://omicscience.org/apps/mwasdisease/) to facilitate future research and meta-analyses.

DOI: 10.1038/s41591-021-01266-0

Source: https://www.nature.com/articles/s41591-021-01266-0