Application of Metabolomics and the Discovery of Potential Serum Biomarkers for Diuretic Resistance in Heart Failure

Abstract

Background: Diuretic resistance (DR) is characterized by insufficient fluid and sodium excretion enhancement despite maximum loop diuretic doses, indicating a phenotype of refractory heart failure (HF). Recently, metabolomics has emerged as a crucial tool for diagnosing and understanding the pathogenesis of various diseases. This study aimed to differentiate diuretic-resistant patients from non-resistant HF to identify biomarkers linked to the emergence of DR.

Methods: Serum samples from HF patients, both with and without DR, were subjected to non-targeted metabolomic analysis using liquid chromatography-tandem mass spectrometry. Metabolite variations between groups were identified using principal component analysis and orthogonal partial least-square discriminant analysis. Metabolic pathways were assessed through the Kyoto Encyclopedia of Genes and Genomes database enrichment analysis, and potential biomarkers were determined using receiver operating characteristic curves (ROCs).

Results: In total, 192 metabolites exhibited significant differences across the two sample groups. Among these, up-regulation was observed in 164 metabolites, while 28 metabolites were down-regulated. A total of 28 pathways involving neuroactive ligand-receptor interaction and amino acid biosynthesis were affected. The top five metabolites identified by ROC analysis as potential DR biomarkers were hydroxykynurenine, perillic acid, adrenic acid, 5-acetamidovalerate, and adipic acid.

Conclusions: Significant differences in metabolite profiles were observed between the diuretic-resistant and non-diuretic-resistant groups among patients with HF. The top five differentially expressed endogenous metabolites were hydroxykynurenine, perillic acid, adrenic acid, 5-acetamidovalerate, and adipic acid. The metabolic primary pathways implicated in DR were noted as amino acid, energy, and nucleotide metabolism.

Clinical trial registration: This study was registered with the China Clinical Trials Registry (https://www.chictr.org.cn/hvshowproject.html?id=197183&v=1.7, ChiCTR2100053587).

Keywords: biomarker; diuretic resistance; heart failure; metabolomic.

Fig. 1.

Case inclusion flowchart. NYHA, New York Heart Association.

Fig. 2.

PCA score chart for QC samples. (A) Positive ion mode: graph of PCA scores for QC samples. (B) Negative ion mode: graph of PCA scores for QC samples. CG, non-diuretic-resistant group; EG, diuretic-resistant group; PCA, principal component analysis; QC, quality control; PC1, the first principal component; PC2, the second principal component.

Fig. 3.

Orthogonal partial least-squares discriminant analysis OPLS-DA scores and replacement test results for serum samples from both groups. (A) Positive ion mode: OPLS-DA scores. (B) Negative ion mode, OPLS-DA scores. (C) Positive ion mode: OPLS-DA replacement inspection chart. (D) Negative ion mode: OPLS-DA replacement inspection. OPLS-DA, orthogonal partial least-square discriminant analysis; R2 and Q2 respectively refer to the values of the intersection points of the two regression lines R and Q with the y-axis.

Fig. 4.

Hierarchical clustering heat map of differential metabolites. The color difference in the graph indicates the relative content. A redder color represents a higher expression, while a bluer color indicates a lower expression. The columns stand for the samples, and the rows represent the names of metabolites. The differential metabolite clustering tree is located on the left side of the graph. When the number of metabolites exceeds 150, their names will not be displayed.

Fig. 5.

Volcanic plot. In the figure, each point stands for a metabolite. The x-axis represents the log2 value of the quantitative difference of a metabolite between two samples, while the y-axis represents the log10 value of the p value. A larger absolute value of the x-axis indicates a greater difference in the expression multiplicity of a metabolite between the two samples. A larger y-axis value indicates more significant differential expression, and the differentially expressed metabolites obtained through screening are more reliable. By default, the names of the top 5 metabolites with the smallest p values are displayed. ns refers to substances that have no significant difference.

Fig. 6.

Receiver operating characteristic analysis of the top five differential metabolites (hydroxykynurenine, perillic acid, adrenic acid, 5-acetamidovalerate, and adipic acid). AUC, area under curve.

Fig. 7.

Metabolic pathways influencing the factor histograms. The vertical axis represents metabolic pathways, and the horizontal axis represents the Impact values enriched in different metabolic pathways. The higher the value, the greater the contribution of the metabolites detected under that pathway. The color is related to the p-value; the redder the color, the smaller the p-value, and the bluer the color, the larger the p-value. A smaller p-value indicates that the detected differential metabolites have a more significant impact on the pathway. ABC, adenosine triphosphate-binding cassette.

Fig. 8.

Network diagram for the KEGG pathway enrichment analysis. Circles in blue denote the pathways, whereas the other circles symbolize the metabolites. The magnitude of the pathway circles corresponds to the quantity of associated metabolites; the greater the number of metabolites, the larger the circle appears. The metabolite circles are shaded with a gradient to reflect the extent of the log2(FC) values, with no log2(FC) data presented for multiple comparisons. KEGG, kyoto encyclopaedia of genes and genomes.