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

BACKGROUND: Dyslipidemia is important because of its association with various metabolic complications. Numerous studies have sought to obtain scientific evidence for managing dyslipidemia patients. OBJECTIVES: This study aims to identify differences in the nutritional traits of dyslipidemia subjects based on metabolite patterns. METHODS: 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. RESULTS: 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 5ß-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. CONCLUSIONS: 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.

Early Prediction for Prediabetes and Type 2 Diabetes Using the Genetic Risk Score and Oxidative Stress Score.

We aimed to use a genetic risk score (GRS) constructed with prediabetes and type 2 diabetes-related single nucleotide polymorphisms (SNPs) and an oxidative stress score (OSS) to construct an early-prediction model for prediabetes and type 2 diabetes (T2DM) incidence in a Korean population. The study population included 549 prediabetes and T2DM patients and 1036 normal subjects. The GRS was constructed using six prediabetes and T2DM-related SNPs, and the OSS was composed of three recognized oxidative stress biomarkers. Among the nine SNPs, six showed significant associations with the incidence of prediabetes and T2DM. The GRS was profoundly associated with increased prediabetes and T2DM (OR = 1.946) compared with individual SNPs after adjusting for age, sex, and BMI. Each of the three oxidative stress biomarkers was markedly higher in the prediabetes and T2DM group than in the normal group, and the OSS was significantly associated with increased prediabetes and T2DM (OR = 2.270). When BMI was introduced to the model with the OSS and GRS, the area under the ROC curve improved (from 69.3% to 70.5%). We found that the prediction model composed of the OSS, GRS, and BMI showed a significant prediction ability for the incidence of prediabetes and T2DM.

Risk Associated with the LEPR rs8179183 GG Genotype in a Female Korean Population with Obesity.

The difference between metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) phenotypes might be partly attributable to genetic traits modulating body fat distribution and other obesity-related metabolic traits, specifically with regard to LEPR rs8179183 in Korean women with obesity. A total of 177 females with obesity participated in the study and were grouped by genotype (GC or GG) and metabolic health status (MHO and MUO). Between the MHO and MUO groups, significant differences were found in waist circumference, waist-to-hip ratio, lipid profiles, glucose-related markers, biomarkers of liver health, adiponectin, oxidative stress markers, whole fat area (WFA), and subcutaneous fat area (SFA) at the level of the L1 vertebra, and WFA and visceral fat area (VFA) at the level of the L4 vertebra. Lipid profiles, glucose-related markers, adipokines, oxidative stress markers, and WFA and VFA at the L4 level were significantly different between the GC and GG genotypes. Notably, the individuals with the MUO phenotype and the GG genotype had the least favorable values of glucose-related markers, lipid profiles, adipokines, oxidative stress markers, and regional fat distribution. These observations suggest that the development of obesity-related metabolic traits is highly associated not only with the rs8179183 genotype but also with metabolic status in Korean females with obesity.

Inflammatory Markers and Plasma Fatty Acids in Predicting WBC Level Alterations in Association With Glucose-Related Markers: A Cross-Sectional Study.

Aging leads to immune function changes which contribute to occurrence of chronic conditions. White blood cell (WBC) level is a marker widely known to reflect the immune function, thus, prediction of WBC level changes by using certain biomarkers is needed to prevent chronic conditions and to decrease the burdens of aging. In this respect, the present study aimed to explore the relationships between inflammatory markers and plasma fatty acid (FA) composition according to WBC levels for verifying potential predictors of WBC levels. Study subjects were divided into three groups according to their WBC count: moderate-low WBC (MLW), normal WBC, and moderate-high WBC (MHW). Inflammatory markers were measured, and plasma FA profiles were constructed via gas chromatography-mass spectrometry (GC-MS). In the MHW group, insulin, homeostatic model assessment of insulin resistance (HOMA-IR), γ-glutamyltransferase (GGT), high-sensitivity C-reactive protein (hs-CRP), and interferon (IFN)-γ showed significant increases compared to those in the other groups. In addition, the granulocyte-to-lymphocyte ratio (GLR) significantly increased according to the WBC levels, whereas the platelet-to-lymphocyte ratio (PLR) showed the opposite result. Total ω-3 polyunsaturated fatty acids (PUFAs) showed significant differences among the groups. Regarding ω-6 PUFAs, dihomo-γ-linolenic acid and docosatetraenoic acid levels were significantly increased in the MHW group compared to the other groups. Finally, multivariate linear regression analysis revealed that GGT, hs-CRP, IFN-γ, ω-3 PUFAs, and dihomo-γ-linolenic acid were independent factors for altering WBC levels. In conclusion, elevated WBC levels accompanied by an increased GLR and a decreased PLR were associated with the risk of type 2 diabetes based on increased insulin and HOMA-IR levels and decreased adiponectin levels. Additionally, GGT, hs-CRP, IFN-γ, ω-3 PUFAs, and dihomo-γ-linolenic acid levels emerged as independent biomarkers for predicting WBC level alterations. Therefore, this study showed that these inflammatory markers and plasma FAs not only affect WBC level alterations but also may play roles in the risk of type 2 diabetes as one of the chronic conditions by certain mechanisms, which should be further studied. Finally, checking these biomarkers along with WBC levels can be helpful to prevent the chronic conditions.