| 引用本文: |
徐雅诗, 易曼婷, 田梦妍, 王聪慧, 李琳, 梁昊.慢性心力衰竭阳虚证患者血清多组学研究[J].湖南中医药大学学报,2026,46(9):1772-1782[点击复制] |
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| 慢性心力衰竭阳虚证患者血清多组学研究 |
| 徐雅诗,易曼婷,田梦妍,王聪慧,李琳,梁昊 |
| (湖南中医药大学, 湖南 长沙 410208;湖南中医药大学中医诊断研究所, 湖南 长沙 410208) |
| 摘要: |
| 目的 联合代谢组学与多肽组学分析慢性心力衰竭（CHF）阳虚证患者的血清生物学特征。方法 选取湖南中医药大学第一附属医院2024年12月至2025年7月收治的CHF阳虚证患者30例（阳虚组）、非阳虚证患者30例（非阳虚组）、健康体检者31例（对照组）为研究对象，采集其血液样本，并将血液离心分离出血清。研究采用非靶向代谢组学技术检测血清代谢物，运用主成分分析验证样本组间代谢轮廓的分离效果；构建偏最小二乘判别分析与正交偏最小二乘判别分析模型，结合丰度倍数变化、P值及VIP值筛选组间差异代谢物，筛选标准为|log2FC|≥0.2、P＜0.05和VIP＞1；对获得的差异代谢物进行GO功能富集和KEGG通路富集分析。采用液相色谱-质谱检测血清多肽，经相同多元统计策略筛选差异多肽，并完成GO功能富集与KEGG通路富集分析。基于上述筛选出的差异代谢物与差异多肽，分别构建受试者工作特征（ROC）曲线，评估其作为潜在生物标志物的诊断效能，并通过Logistic回归构建代谢物-多肽联合诊断模型。结果 与对照组相比，阳虚组最终筛选出2个差异代谢物和2个差异多肽，分别是乙醛酸、尿酸、结合珠蛋白片段（肽段序列：VDSGNDVTDIAD，主蛋白质登录号：P00738）、补体C3片段（肽段序列：PERLGREGVQKEDIPPADLSD，主蛋白质登录号：P01024）。将以上4个差异分子通过Logistic回归构建联合诊断模型，ROC曲线分析显示，该联合模型区分阳虚组与对照组的曲线下面积为0.887，提示该生物标志物组合具有诊断效能。以上4个差异分子共涉及5条富集通路，分别为：氨酰-tRNA生物合成，精氨酸和脯氨酸代谢，亚油酸代谢，缬氨酸、亮氨酸和异亮氨酸生物合成，补体和凝血级联。结论 CHF阳虚证患者血清存在特征性的代谢物及多肽紊乱；乙醛酸、尿酸、结合珠蛋白片段和补体C3片段等可能作为潜在生物标志物，为CHF阳虚证的客观化辨证提供辅助依据。 |
| 关键词: 慢性心力衰竭|阳虚证|代谢组学|多肽组学|生物标志物 |
| DOI：10.3969/j.issn.1674-070X.2026.09.004 |
| 投稿时间：2026-07-01 |
| 基金项目:国家重点研发计划项目(2025YFF0523900);国家自然科学基金面上项目(82274411);芙蓉实验室科技攻关项目(2025PT5018)。 |
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| Serum multi omics profiling of patients with chronic heart failure and yang deficiency pattern |
| XU Yashi, YI Manting, TIAN Mengyan, WANG Conghui, LI Lin, LIANG Hao |
| (Hunan University of Chinese Medicine, Changsha, Hunan 410208, China;Institute of TCM Diagnostic, Hunan University of Chinese Medicine, Changsha, Hunan 410208, China) |
| Abstract: |
| Objective To analyze the serum biological characteristics of patients with chronic heart failure (CHF) and yang deficiency pattern by combining metabolomics and peptidomics. Methods Subjects were recruited at the First Hospital of Hunan University of Chinese Medicine from December 2024 to July 2025, including 30 CHF patients with yang deficiency pattern (yang deficiency group), 30 patients with non-yang-deficiency pattern (non yang deficiency group), and 31 healthy examinees (control group). Blood samples were collected, and serum was separated via centrifugation. Untargeted metabolomics was applied to detect serum metabolites. Principal component analysis (PCA) was used to verify the separation of metabolic profiles across groups. Partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) models were constructed. Differential metabolites among groups were screened based on fold change, P value and variable importance in projection (VIP), with thresholds set at |log2FC|≥0.2, P<0.05 and VIP>1. GO and KEGG metabolic pathway enrichment analysis were performed on the obtained differential metabolites. Liquid chromatography-mass spectrometry (LC-MS) was used to detect serum peptides. Differential peptides were screened using the same multivariate statistical strategy, followed by GO functional enrichment and KEGG pathway enrichment analyses. Based on the screened differential metabolites and differential peptides, receiver operating characteristic (ROC) curves were plotted separately to evaluate their diagnostic performance as potential biomarkers. Logistic regression was applied to establish a combined metabolite-peptide diagnostic model. Results Compared with the control group, two differential metabolites and two differential peptides were finally screened out in the yang deficiency group, namely glyoxylic acid, uric acid, haptoglobin fragment (peptide sequence: VDSGNDVTDIAD, master protein accession: P00738), and complement C3 fragment (peptide sequence: PERLGREGVQKEDIPPADLSD, master protein accession: P01024). A combined diagnostic model was established by Logistic regression using these four differential molecules. ROC curve analysis demonstrated that the area under the curve (AUC) of this combined model for discriminating CHF patients with yang deficiency pattern from healthy examinees was 0.887, indicating that this biomarker panel had certain diagnostic performance. These four differential molecules were collectively involved in five enriched pathways: Aminoacyl-tRNA biosynthesis, Arginine and proline metabolism, Linoleic acid metabolism, Valine, leucine and isoleucine biosynthesis, and Complement and coagulation cascades. Conclusion CHF patients with yang deficiency pattern show characteristic serum metabolic and peptidomic disturbances. Glyoxylic acid, uric acid, haptoglobin fragments and complement C3 fragments may serve as biomarkers for objective auxiliary pattern identification of CHF with yang deficiency pattern. |
| Key words: chronic heart failure|yang deficiency pattern|metabolomics|peptidomics|biomarker |
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