Metabolomic Profiling Demonstrates Postprandial Changes in Fatty Acids and Glycerophospholipids Are Associated with Fasting Inflammation in Guatemalan Adults

Polygonatum kingianum Polysaccharides Enhance the Preventive Efficacy of Heat-Inactivated Limosilactobacillus reuteri WX-94 against High-Fat-High-Sucrose-Induced Liver Injury and Gut Dysbacteriosis

Limosilactobacillus reuteri (L. reuteri) is an efficacious probiotic that could reduce inflammation and prevent metabolic disorders. Here, we innovatively found that Polygonatum kingianum polysaccharides (PKP) promoted proliferation and increased stability of L. reuteri WX-94 (a probiotic strain showing anti-inflammation potentials) in simulated digestive fluids in vitro. PKP was composed of galactose, glucose, mannose, and arabinose. The cell-free supernatant extracted from L. reuteri cultured with PKP increased ABTS•+, DPPH•, and FRAP scavenging capacities compared with the supernatant of the medium without PKP and increased metabolites with health-promoting activities, e.g., 3-phenyllactic acid, indole-3-lactic acid, indole-3-carbinol, and propionic acid. Moreover, PKP enhanced alleviating effects of heat-inactivated L. reuteri on high-fat-high-sucrose-induced liver injury in rats via reducing inflammation and regulating expressions of protein and genes involved in fatty acid metabolism (such as HIF1-α, FAβO, CPT1, and AMPK) and fatty acid profiles in liver. Such benefits correlated with its prominent effects on enriching Lactobacillus and short-chain fatty acids while reducing Dubosiella, Fusicatenilacter, Helicobacter, and Oscillospira. Our work provides novel insights into the probiotic property of PKP and emphasizes the great potential of the inactivated L. reuteri cultured with PKP in contracting unhealthy diet-induced liver dysfunctions and gut dysbacteriosis.

Multi-omics data reveals aberrant gut microbiota-host glycerophospholipid metabolism in association with neuroinflammation in APP/PS1 mice

Numerous studies have described the notable impact of gut microbiota on the brain in Alzheimer's disease (AD) via the gut - brain axis. However, the molecular mechanisms underlying the involvement of gut microbiota in the development of AD are limited. This study aimed to explore the potential mechanisms of gut microbiota in AD by integrating multi-omics data. In this study, APP/PS1 and WT mice at nine months of age were used as study mouse model. Cognitive function was assessed using the Morris water maze test. The levels of Aβ plaque and neuroinflammation in the brain were detected using immunofluorescence and PET/CT. In addition, we not only used 16S rRNA gene sequencing and metabolomics to explore the variation characteristics of gut microbiota and serum metabolism abundance, but also combined spatial metabolomics and transcriptomics to explore the change in the brain and identify their potential correlation. APP/PS1 mice showed significant cognitive impairment and amyloid-β deposits in the brain. The abundance of gut microbiota was significantly changed in APP/PS1 mice, including decreased Desulfoviobrio, Enterococcus, Turicibacter, and Ruminococcus and increased Pseudomonas. The integration of serum untargeted metabolomics and brain spatial metabolomics showed that glycerophospholipid metabolism was a common alteration pathway in APP/PS1 mice. Significant proliferation and activation of astrocyte and microglia were observed in APP/PS1 mice, accompanied by alterations in immune pathways. Integration analysis and fecal microbiota transplantation (FMT) intervention revealed potential association of gut microbiota, host glycerophospholipid metabolism, and neuroinflammation levels in APP/PS1 mice.

Multinomial machine learning identifies independent biomarkers by integrated metabolic analysis of acute coronary syndrome

A multi-class classification model for acute coronary syndrome (ACS) remains to be constructed based on multi-fluid metabolomics. Major confounders may exert spurious effects on the relationship between metabolism and ACS. The study aims to identify an independent biomarker panel for the multiclassification of HC, UA, and AMI by integrating serum and urinary metabolomics. We performed a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics study on 300 serum and urine samples from 44 patients with unstable angina (UA), 77 with acute myocardial infarction (AMI), and 29 healthy controls (HC). Multinomial machine learning approaches, including multinomial adaptive least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), and assessment of the confounders were applied to integrate a multi-class classification biomarker panel for HC, UA and AMI. Different metabolic landscapes were portrayed during the transition from HC to UA and then to AMI. Glycerophospholipid metabolism and arginine biosynthesis were predominant during the progression from HC to UA and then to AMI. The multiclass metabolic diagnostic model (MDM) dependent on ACS, including 2-ketobutyric acid, LysoPC(18:2(9Z,12Z)), argininosuccinic acid, and cyclic GMP, demarcated HC, UA, and AMI, providing a C-index of 0.84 (HC vs. UA), 0.98 (HC vs. AMI), and 0.89 (UA vs. AMI). The diagnostic value of MDM largely derives from the contribution of 2-ketobutyric acid, and LysoPC(18:2(9Z,12Z)) in serum. Higher 2-ketobutyric acid and cyclic GMP levels were positively correlated with ACS risk and atherosclerosis plaque burden, while LysoPC(18:2(9Z,12Z)) and argininosuccinic acid showed the reverse relationship. An independent multiclass biomarker panel for HC, UA, and AMI was constructed using the multinomial machine learning methods based on serum and urinary metabolite signatures.

Small High-Density Lipoprotein and Omega-3 Fatty Acid Intake Differentiates Japanese and Japanese-Americans: The INTERLIPID Study

AIM

To identify the most differentiated serum lipids, especially concerning particle size and fractions, between Japanese living in Japan and Japanese-Americans in Hawaii, in the absence of possible genetic confounders, and cross-sectionally examine the associated modifiable lifestyle factors.

METHODS

Overall, 1,241 (aged 40-59 years) Japanese living in Japan and Japanese-Americans in Hawaii were included. We quantified 130 serum lipid profiles (VLDL 1-5, IDL, LDL 1-6, high-density lipoprotein [HDL] 1-4, and their subfractions) using Bruker's 1H-nuclear magnetic resonance spectrometer for the primary outcome. Modifiable lifestyle factors included body mass index (BMI), physical activity, alcohol and smoking habits, and 70 nutrient parameters. We evaluated the different lipids between the groups using partial least squares-discriminant analysis and association between extracted lipids and lifestyle factors using multivariable linear regression analysis.

RESULTS

Concentrations of HDL4, HDL with the smallest particle size, were lower in Japanese than in Japanese-Americans of both sexes. Higher fish-derived omega-3 fatty acid intake and lower alcohol intake were associated with lower HDL4 concentrations. A 1% higher kcal intake of total omega-3 fatty acids was associated with a 9.8-mg/dL lower HDL4. Fish-derived docosapentaenoic acid, eicosapentaenoic acid, and docosahexaenoic acid intake were inversely associated with HDL4 concentration. There was no relationship between country, sex, age, or BMI.

CONCLUSIONS

Japanese and Japanese-Americans can be differentiated based on HDL4 concentration. High fish intake among the Japanese may contribute to their lower HDL4 concentration. Thus, HDL particle size may be an important clinical marker for coronary artery diseases or a fish consumption biomarker.

The Effects of 12 Weeks Colostrum Milk Supplementation on the Expression Levels of Pro-Inflammatory Mediators and Metabolic Changes among Older Adults: Findings from the Biomarkers and Untargeted Metabolomic Analysis

Senescence is a normal biological process that is accompanied with a series of deteriorations in physiological function. This study aimed to investigate the effects of bovine colostrum milk supplementation on metabolic changes and the expression of various biomarkers on inflammation, antioxidant and oxidative damage, nutrient metabolism, and genomic stability among older adults. Older adults (50-69 years old) who participated in the 12-week randomized, double-blinded, placebo-controlled trial were instructed to consume the IgCo bovine colostrum-enriched skim milk or regular skim milk (placebo) twice daily. Following 12 weeks of intervention, participants in the intervention group had lower expression levels in pro-inflammatory mediators (CRP, IL-6, and TNF-α), with significant (p < 0.05) interaction effects of the group and time observed. However, no significant interaction effect was observed in the vitamin D, telomerase, 8-OHdG, MDA, and SOD activities. UPLC-MS-based untargeted metabolomics analysis revealed that 22 metabolites were upregulated and 11 were downregulated in the intervention group compared to the placebo group. Glycerophospholipid metabolism, along with cysteine and methionine metabolism were identified as the potential metabolic pathways that are associated with bovine colostrum milk consumption. In conclusion, consuming bovine colostrum milk may induce metabolic changes and reduce the expression of various pro-inflammatory mediators, thus improving the immune function in older adults.

Comprehensive Metabolic Profiling of Inflammation Indicated Key Roles of Glycerophospholipid and Arginine Metabolism in Coronary Artery Disease

Background

Systemic immune inflammation is a key mediator in the progression of coronary artery disease (CAD), concerning various metabolic and lipid changes. In this study, the relationship between the inflammatory index and metabolic profile in patients with CAD was investigated to provide deep insights into metabolic disturbances related to inflammation.

Methods

Widely targeted plasma metabolomic and lipidomic profiling was performed in 1,234 patients with CAD. Laboratory circulating inflammatory markers were mainly used to define general systemic immune and low-grade inflammatory states. Multivariable-adjusted linear regression was adopted to assess the associations between 860 metabolites and 7 inflammatory markers. Least absolute shrinkage and selection operator (LASSO) logistic-based classifiers and multivariable logistic regression were applied to identify biomarkers of inflammatory states and develop models for discriminating an advanced inflammatory state.

Results

Multiple metabolites and lipid species were linearly associated with the seven inflammatory markers [false discovery rate (FDR) <0.05]. LASSO and multivariable-adjusted logistic regression analysis identified significant associations between 45 metabolites and systemic immune-inflammation index, 46 metabolites and neutrophil-lymphocyte ratio states, 32 metabolites and low-grade inflammation score, and 26 metabolites and high-sensitivity C-reactive protein states (P < 0.05). Glycerophospholipid metabolism and arginine and proline metabolism were determined as key altered metabolic pathways for systemic immune and low-grade inflammatory states. Predictive models based solely on metabolite combinations showed feasibility (area under the curve: 0.81 to 0.88) for discriminating the four parameters that represent inflammatory states and were successfully validated using a validation cohort. The inflammation-associated metabolite, namely, β-pseudouridine, was related to carotid and coronary arteriosclerosis indicators (P < 0.05).

Conclusions

This study provides further information on the relationship between plasma metabolite profiles and inflammatory states represented by various inflammatory markers in CAD. These metabolic markers provide potential insights into pathological changes during CAD progression and may aid in the development of therapeutic targets.

Investigating the Postprandial Metabolome after Challenge Tests to Assess Metabolic Flexibility and Dysregulations Associated with Cardiometabolic Diseases

This review focuses on the added value provided by a research strategy applying metabolomics analyses to assess phenotypic flexibility in response to different nutritional challenge tests in the framework of metabolic clinical studies. We discuss findings related to the Oral Glucose Tolerance Test (OGTT) and to mixed meals with varying fat contents and food matrix complexities. Overall, the use of challenge tests combined with metabolomics revealed subtle metabolic dysregulations exacerbated during the postprandial period when comparing healthy and at cardiometabolic risk subjects. In healthy subjects, consistent postprandial metabolic shifts driven by insulin action were reported (e.g., a switch from lipid to glucose oxidation for energy fueling) with similarities between OGTT and mixed meals, especially during the first hours following meal ingestion while differences appeared in a wider timeframe. In populations with expected reduced phenotypic flexibility, often associated with increased cardiometabolic risk, a blunted response on most key postprandial pathways was reported. We also discuss the most suitable statistical tools to analyze the dynamic alterations of the postprandial metabolome while accounting for complexity in study designs and data structure. Overall, the in-depth characterization of the postprandial metabolism and associated phenotypic flexibility appears highly promising for a better understanding of the onset of cardiometabolic diseases.