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士兵非靶向尿液代谢组学预测超重和肥胖的诊断模型。

A diagnostic model for overweight and obesity from untargeted urine metabolomics of soldiers.

机构信息

Escuela Militar de Graduados de Sanidad, Universidad del Ejército y Fuerza Aérea Mexicanos, Secretaría de la Defensa Nacional, Mexico City, Mexico.

Centro de Investigación en Ciencias de la Salud (CICSA), FCS, Universidad Anahuac Mexico, Campus Norte, Mexico City, Mexico.

出版信息

PeerJ. 2022 Jul 22;10:e13754. doi: 10.7717/peerj.13754. eCollection 2022.

Abstract

Soldiers in active military service need optimal physical fitness for successfully carrying out their operations. Therefore, their health status is regularly checked by army doctors. These inspections include physical parameters such as the body-mass index (BMI), functional tests, and biochemical studies. If a medical exam reveals an individual's excess weight, further examinations are made, and corrective actions for weight lowering are initiated. The collection of urine is non-invasive and therefore attractive for frequent metabolic screening. We compared the chemical profiles of urinary samples of 146 normal weight, excess weight, and obese soldiers of the Mexican Army, using untargeted metabolomics with liquid chromatography coupled to high-resolution mass spectrometry (LC-MS). In combination with data mining, statistical and metabolic pathway analyses suggest increased S-adenosyl-L-methionine (SAM) levels and changes of amino acid metabolites as important variables for overfeeding. We will use these potential biomarkers for the ongoing metabolic monitoring of soldiers in active service. In addition, after validation of our results, we will develop biochemical screening tests that are also suitable for civil applications.

摘要

现役士兵需要最佳的身体素质才能成功执行任务。因此,他们的健康状况由军队医生定期检查。这些检查包括身体参数,如体重指数 (BMI)、功能测试和生化研究。如果体检显示个体超重,会进行进一步检查,并开始采取减肥措施。尿液采集是非侵入性的,因此非常适合频繁进行代谢筛查。我们使用非靶向代谢组学结合液相色谱-高分辨质谱联用 (LC-MS) 分析了 146 名墨西哥军队中正常体重、超重和肥胖士兵的尿液样本的化学特征。结合数据挖掘、统计和代谢途径分析,提示超重与 S-腺苷甲硫氨酸 (SAM) 水平升高以及氨基酸代谢物变化有关,这些可能是过度喂养的重要变量。我们将使用这些潜在的生物标志物对现役士兵进行代谢监测。此外,在验证我们的结果后,我们将开发也适用于民用的生化筛选测试。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f94b/9310780/1c23342f9f23/peerj-10-13754-g001.jpg

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