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基于对选定患者和对照进行的核磁共振代谢组学研究的纤维肌痛综合征诊断生物标志物谱。

A diagnostic biomarker profile for fibromyalgia syndrome based on an NMR metabolomics study of selected patients and controls.

作者信息

Malatji Bontle G, Meyer Helgard, Mason Shayne, Engelke Udo F H, Wevers Ron A, van Reenen Mari, Reinecke Carolus J

机构信息

Centre for Human Metabolomics, Faculty of Natural Sciences, North-West University (Potchefstroom Campus), Private Bag X6001, Potchefstroom, South Africa.

Department of Family Medicine, Kalafong Hospital, University of Pretoria, Private Bag X396, Pretoria, South Africa.

出版信息

BMC Neurol. 2017 May 11;17(1):88. doi: 10.1186/s12883-017-0863-9.

Abstract

BACKGROUND

Fibromyalgia syndrome (FMS) is a chronic pain syndrome. A plausible pathogenesis of the disease is uncertain and the pursuit of measurable biomarkers for objective identification of affected individuals is a continuing endeavour in FMS research. Our objective was to perform an explorative metabolomics study (1) to elucidate the global urinary metabolite profile of patients suffering from FMS, and (2) to explore the potential of this metabolite information to augment existing medical practice in diagnosing the disease.

METHODS

We selected patients with a medical history of persistent FMS (n = 18), who described their recent state of the disease through the Fibromyalgia Impact Questionnaire (FIQR) and an in-house clinical questionnaire (IHCQ). Three control groups were used: first-generation family members of the patients (n = 11), age-related individuals without any indications of FMS or related conditions (n = 10), and healthy young (18-22 years) individuals (n = 20). All subjects were female and the biofluid under investigation was urine. Correlation analysis of the FIQR showed the FMS patients represented a well-defined disease group for this metabolomics study. Spectral analyses of urine were conducted using a 500 MHz H nuclear magnetic resonance (NMR) spectrometer; data processing and analyses were performed using Matlab, R, SPSS and SAS software.

RESULTS AND DISCUSSION

Unsupervised and supervised multivariate analyses distinguished all three control groups and the FMS patients, and significant increases in metabolites related to the gut microbiome (hippuric, succinic and lactic acids) were observed. We have developed an algorithm for the diagnosis of FMS consisting of three metabolites - succinic acid, taurine and creatine - that have a good level of diagnostic accuracy (Receiver Operating Characteristic (ROC) analysis - area under the curve 90%) and on the pain and fatigue symptoms for the selected FMS patient group.

CONCLUSION

Our data and comparative analyses indicated an altered metabolic profile of patients with FMS, analytically detectable within their urine. Validation studies may substantiate urinary metabolites to supplement information from medical assessment, tender-point measurements and FIQR questionnaires for an improved objective diagnosis of FMS.

摘要

背景

纤维肌痛综合征(FMS)是一种慢性疼痛综合征。该疾病合理的发病机制尚不确定,寻找可测量的生物标志物以客观识别受影响个体是FMS研究中持续进行的一项工作。我们的目的是进行一项探索性代谢组学研究:(1)阐明FMS患者的整体尿液代谢物谱;(2)探索这种代谢物信息在增强现有医学实践以诊断该疾病方面的潜力。

方法

我们选择了有持续性FMS病史的患者(n = 18),他们通过纤维肌痛影响问卷(FIQR)和一份内部临床问卷(IHCQ)描述了自己近期的疾病状态。使用了三个对照组:患者的第一代家庭成员(n = 11)、无FMS或相关疾病迹象的年龄匹配个体(n = 10)以及健康的年轻个体(18 - 22岁,n = 20)。所有受试者均为女性,所研究的生物流体为尿液。FIQR的相关性分析表明,FMS患者代表了该代谢组学研究中一个明确的疾病组。使用500 MHz氢核磁共振(NMR)光谱仪对尿液进行光谱分析;数据处理和分析使用Matlab、R、SPSS和SAS软件。

结果与讨论

无监督和有监督的多变量分析区分了所有三个对照组和FMS患者,并且观察到与肠道微生物群相关的代谢物(马尿酸、琥珀酸和乳酸)显著增加。我们开发了一种用于诊断FMS的算法,该算法由三种代谢物——琥珀酸、牛磺酸和肌酸组成,对选定的FMS患者组的疼痛和疲劳症状具有良好的诊断准确性(受试者工作特征(ROC)分析——曲线下面积为90%)。

结论

我们的数据和比较分析表明FMS患者的代谢谱发生了改变,在其尿液中可通过分析检测到。验证研究可能会证实尿液代谢物可补充医学评估、压痛点测量和FIQR问卷中的信息,以改善FMS的客观诊断。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a474/5426044/4dbd1dcbb866/12883_2017_863_Fig1_HTML.jpg

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