Lu Chengcan, Liu Chunyan, Mei Di, Yu Mengjie, Bai Jian, Bao Xue, Wang Min, Fu Kejia, Yi Xin, Ge Weihong, Shen Jizhong, Peng Yuzhu, Xu Wei
Nanjing Drum Tower Hospital, China Pharmaceutical University, Nanjing, China.
Department of Pharmacy, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
Front Cardiovasc Med. 2022 Aug 8;9:911845. doi: 10.3389/fcvm.2022.911845. eCollection 2022.
Using human humoral metabolomic profiling, we can discover the diagnostic biomarkers and pathogenesis of disease. The specific characterization of atrial fibrillation (AF) subtypes with metabolomics may facilitate effective and targeted treatment, especially in early stages.
By investigating disturbed metabolic pathways, we could evaluate the diagnostic value of biomarkers based on metabolomics for different types of AF.
A cohort of 363 patients was enrolled and divided into a discovery and validation set. Patients underwent an electrocardiogram (ECG) for suspected AF. Groups were divided as follows: healthy individuals (Control), suspected AF (Sus-AF), first diagnosed AF (Fir-AF), paroxysmal AF (Par-AF), persistent AF (Per-AF), and AF causing a cardiogenic ischemic stroke (Car-AF). Serum metabolomic profiles were determined by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Metabolomic variables were analyzed with clinical information to identify relevant diagnostic biomarkers.
The metabolic disorders were characterized by 16 cross-comparisons. We focused on comparing all of the types of AF (All-AFs) plus Car-AF vs. Control, All-AFs vs. Car-AF, Par-AF vs. Control, and Par-AF vs. Per-AF. Then, 117 and 94 metabolites were identified by GC/MS and LC-QTOF-MS, respectively. The essential altered metabolic pathways during AF progression included D-glutamine and D-glutamate metabolism, glycerophospholipid metabolism, etc. For differential diagnosis, the area under the curve (AUC) of specific metabolomic biomarkers ranged from 0.8237 to 0.9890 during the discovery phase, and the predictive values in the validation cohort were 78.8-90.2%.
Serum metabolomics is a powerful way to identify metabolic disturbances. Differences in small-molecule metabolites may serve as biomarkers for AF onset, progression, and differential diagnosis.
通过人体体液代谢组学分析，我们可以发现疾病的诊断生物标志物和发病机制。利用代谢组学对心房颤动（AF）亚型进行特异性表征，可能有助于实现有效且有针对性的治疗，尤其是在疾病早期。
通过研究紊乱的代谢途径，我们可以评估基于代谢组学的生物标志物对不同类型AF的诊断价值。
纳入363例患者，分为发现集和验证集。患者因疑似AF接受心电图（ECG）检查。分组如下：健康个体（对照组）、疑似AF（Sus-AF）、初诊AF（Fir-AF）、阵发性AF（Par-AF）、持续性AF（Per-AF）以及导致心源性缺血性卒中的AF（Car-AF）。采用气相色谱-质谱联用（GC-MS）和液相色谱-四极杆飞行时间质谱联用（LC-QTOF-MS）测定血清代谢组学谱。将代谢组学变量与临床信息进行分析，以识别相关的诊断生物标志物。
通过16次交叉比较对代谢紊乱进行了表征。我们重点比较了所有类型的AF（All-AFs）加Car-AF与对照组、All-AFs与Car-AF、Par-AF与对照组以及Par-AF与Per-AF。然后，分别通过GC/MS和LC-QTOF-MS鉴定出117种和94种代谢物。AF进展过程中基本改变的代谢途径包括D-谷氨酰胺和D-谷氨酸代谢、甘油磷脂代谢等。在鉴别诊断方面，特定代谢组学生物标志物在发现阶段的曲线下面积（AUC）范围为0.8237至0.9890，在验证队列中的预测值为78.8-90.2%。
血清代谢组学是识别代谢紊乱的有力方法。小分子代谢物的差异可能作为AF发病、进展和鉴别诊断的生物标志物。
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