The Possible Effect of Dietary Fiber Intake on the Metabolic Patterns of Dyslipidemia Subjects: Cross-Sectional Research Using Nontargeted Metabolomics

• Published in: The Journal of nutrition Vol. 153; no. 9; pp. 2552 - 2560
• Main Authors: Han, Youngmin, Jang, Kyunghye, Kim, Unchong, Huang, Ximei, Kim, Minjoo
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2023; American Institute of Nutrition
• Subjects: Cholesterol; Cluster analysis; Clustering; Complications; Density; Diet; Dietary fiber; Dietary intake; Dyslipidemia; fiber intake; Food intake; High density lipoprotein; High performance liquid chromatography; K-means clustering; LC/MS; Lipids; Liquid chromatography; Low density lipoprotein; Mass spectrometry; Mass spectroscopy; Metabolic disorders; Metabolism; Metabolites; Metabolomics; Nutrition; Regression analysis; Statistical analysis; Triglycerides; Vector quantization; SBP; RCT; IL; LDL-c; PGF2α; ROC; MDA; DASH; TNF; K-means clustering; TC; LC/MS; fiber intake; CVD; HDL-c; QC; DBP; TG; metabolomics; XGBoost; ISTD; dyslipidemia; BMI; Metabolomics; Fiber intake; Dyslipidemia
• ISSN: 0022-3166; 1541-6100; 1541-6100
• DOI: 10.1016/j.tjnut.2023.07.014

Dyslipidemia is important because of its association with various metabolic complications. Numerous studies have sought to obtain scientific evidence for managing dyslipidemia patients. This study aims to identify differences in the nutritional traits of dyslipidemia subjects based on metabolite patterns. Dyslipidemia (n = 73) and control (n = 80) subjects were included. Dyslipidemia was defined as triglycerides ≥200 mg/dL, total cholesterol ≥240 mg/dL, low density lipoprotein cholesterol ≥160 mg/dL, high-density lipoprotein cholesterol <40 mg/dL (men) or 50 mg/dL (women), or lipid-lowering medicine use. Nontargeted metabolomics based on ultra-high performance liquid chromatography–mass spectrometry identified plasma metabolites, and K-means clustering was used to reconstitute groups based on the similarity of metabolomic patterns across all subjects. Then, with eXtreme Gradient Boosting, metabolites significantly contributing to the new grouping were selected. Statistical analysis was conducted to analyze traits demonstrating appreciable differences between the groups. Dyslipidemia subjects were divided into 2 groups based on whether they were (n = 24) or were not (n = 56) in a similar metabolic state as the controls by K-means clustering. The considerable contribution of 4 metabolites (3-hydroxybutyrylcarnitine, 2-octenal, 1,3,5-heptatriene, and 5β-cholanic acid) to this new subset of dyslipidemia was confirmed by eXtreme Gradient Boosting. Furthermore, fiber intake was significantly higher in dyslipidemia subjects whose metabolic state was similar to that of the control than in the dissimilar group (P = 0.002). Moreover, significant correlations were observed between the 4 metabolites and fiber intake. Regression analysis determined that the ideal cutoff for fiber intake was 17.28 g/d. Dyslipidemia patients who consume 17.28 g/d or more of dietary fiber may maintain similar metabolic patterns to healthy individuals, with substantial effects on the changes in the concentrations of 4 metabolites. Our findings could be applied to developing dietary guidelines for dyslipidemia patients.