Multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants

Effects of Ganjianglingzhu Decoction on Lean Non-Alcoholic Fatty Liver Disease in Mice Based on Untargeted Metabolomics

Non-alcoholic fatty liver disease (NAFLD) is usually associated with obesity. However, it is crucial to recognize that NAFLD can also occur in lean individuals, which is frequently overlooked. Without an approved pharmacological therapy for lean NAFLD, we aimed to investigate whether the Ganjianglingzhu (GJLZ) decoction, a representative traditional Chinese medicine (TCM), protects against lean NAFLD and explore the potential mechanism underlying these protective effects. The mouse model of lean NAFLD was established with a methionine-choline-deficient (MCD) diet in male C57BL/6 mice to be compared with the control group fed the methionine-choline-sufficient (MCS) diet. After four weeks, physiological saline, a low dose of GJLZ decoction (GL), or a high dose of GJLZ decoction (GH) was administered daily by gavage to the MCD group; the MCS group was given physiological saline by gavage. Untargeted metabolomics techniques were used to explore further the potential mechanism of the effects of GJLZ on lean NAFLD. Different doses of GJLZ decoction were able to ameliorate steatosis, inflammation, fibrosis, and oxidative stress in the liver; GL performed a better effect on lean NAFLD. In addition, 78 candidate differential metabolites were screened and identified. Combined with metabolite pathway enrichment analysis, GL was capable of regulating the glucose and lipid metabolite pathway in lean NAFLD and regulating the glycerophospholipid metabolism by altering the levels of sn-3-O-(geranylgeranyl)glycerol 1-phosphate and lysoPC(P-18:0/0:0). GJLZ may protect against the development of lean NAFLD by regulating glucose and lipid metabolism, inhibiting the levels of sn-3-O-(geranylgeranyl)glycerol 1-phosphate and lysoPC(P-18:0/0:0) in glycerophospholipid metabolism.

Chinmedomics: a potent tool for the evaluation of traditional Chinese medicine efficacy and identification of its active components

As an important part of medical science, Traditional Chinese Medicine (TCM) attracts much public attention due to its multi-target and multi-pathway characteristics in treating diseases. However, the limitations of traditional research methods pose a dilemma for the evaluation of clinical efficacy, the discovery of active ingredients and the elucidation of the mechanism of action. Therefore, innovative approaches that are in line with the characteristics of TCM theory and clinical practice are urgently needed. Chinmendomics, a newly emerging strategy for evaluating the efficacy of TCM, is proposed. This strategy combines systems biology, serum pharmacochemistry of TCM and bioinformatics to evaluate the efficacy of TCM with a holistic view by accurately identifying syndrome biomarkers and monitoring their complex metabolic processes intervened by TCM, and finding the agents associated with the metabolic course of pharmacodynamic biomarkers by constructing a bioinformatics-based correlation network model to further reveal the interaction between agents and pharmacodynamic targets. In this article, we review the recent progress of Chinmedomics to promote its application in the modernisation and internationalisation of TCM.

The Relationship of Circulating Choline and Choline-Related Metabolite Levels with Health Outcomes: A Scoping Review of Genome-Wide Association Studies and Mendelian Randomization Studies

Choline is essential for proper liver, muscle, brain, lipid metabolism, cellular membrane composition, and repair. Understanding genetic determinants of circulating choline metabolites can help identify new determinants of choline metabolism, requirements, and their link to disease endpoints. We conducted a scoping review to identify studies assessing the association of genetic polymorphisms on circulating choline and choline-related metabolite concentrations and subsequent associations with health outcomes. This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement scoping review extension. Literature was searched to September 28, 2022, in 4 databases: Embase, MEDLINE, Web of Science, and the Biological Science Index. Studies of any duration in humans were considered. Any genome-wide association study (GWAS) investigating genetic variant associations with circulating choline and/or choline-related metabolites and any Mendelian randomization (MR) study investigating the association of genetically predicted circulating choline and/or choline-related metabolites with any health outcome were considered. Qualitative evidence is presented in summary tables. From 1248 total reviewed articles, 53 were included (GWAS = 27; MR = 26). Forty-two circulating choline-related metabolites were tested in association with genetic variants in GWAS studies, primarily trimethylamine N-oxide, betaine, sphingomyelins, lysophosphatidylcholines, and phosphatidylcholines. MR studies investigated associations between 52 total unique choline metabolites and 66 unique health outcomes. Of these, 47 significant associations were reported between 16 metabolites (primarily choline, lysophosphatidylcholines, phosphatidylcholines, betaine, and sphingomyelins) and 27 health outcomes including cancer, cardiovascular, metabolic, bone, and brain-related outcomes. Some articles reported significant associations between multiple choline types and the same health outcome. Genetically predicted circulating choline and choline-related metabolite concentrations are associated with a wide variety of health outcomes. Further research is needed to assess how genetic variability influences choline metabolism and whether individuals with lower genetically predicted circulating choline and choline-related metabolite concentrations would benefit from a dietary intervention or supplementation.

Unraveling phenotypic variance in metabolic syndrome through multi-omics

Complex multi-omics effects drive the clustering of cardiometabolic risk factors, underscoring the imperative to comprehend how individual and combined omics shape phenotypic variation. Our study partitions phenotypic variance in metabolic syndrome (MetS), blood glucose (GLU), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and blood pressure through genome, transcriptome, metabolome, and exposome (i.e., lifestyle exposome) analyses. Our analysis included a cohort of 62,822 unrelated individuals with white British ancestry, sourced from the UK biobank. We employed linear mixed models to partition phenotypic variance using the restricted maximum likelihood (REML) method, implemented in MTG2 (v2.22). We initiated the analysis by individually modeling omics, followed by subsequent integration of pairwise omics in a joint model that also accounted for the covariance and interaction between omics layers. Finally, we estimated the correlations of various omics effects between the phenotypes using bivariate REML. Significant proportions of the MetS variance were attributed to distinct data sources: genome (9.47%), transcriptome (4.24%), metabolome (14.34%), and exposome (3.77%). The phenotypic variances explained by the genome, transcriptome, metabolome, and exposome ranged from 3.28% for GLU to 25.35% for HDL-C, 0% for GLU to 19.34% for HDL-C, 4.29% for systolic blood pressure (SBP) to 35.75% for TG, and 0.89% for GLU to 10.17% for HDL-C, respectively. Significant correlations were found between genomic and transcriptomic effects for TG and HDL-C. Furthermore, significant interaction effects between omics data were detected for both MetS and its components. Interestingly, significant correlation of omics effect between the phenotypes was found. This study underscores omics' roles, interaction effects, and random-effects covariance in unveiling phenotypic variation in multi-omics domains.

Elucidation of genetic determinants of dyslipidaemia using a global screening array for the early detection of coronary artery disease

Dyslipidemia is a major risk factor for the development of coronary artery disease (CAD). Understanding the genetic determinants of dyslipidemia can provide valuable information on the pathogenesis of CAD and aid in the development of early detection strategies. In this study, we used a Global Screening Array (GSA) to elucidate the genetic factors associated with dyslipidemia and their potential role in the prediction of CAD. We conducted a GSA-based association study in 265 subjects to identify the genetic loci associated with dyslipidemia traits using Multiple Linear Regression (MLR) and Logistic Regression (LR), Classification and Regression Tree (CART), and Manhattan plots. We identified an association between dyslipidemia and variants identified in genes such as JCAD, GLIS3, CD38, FN1, CELSR2, MTNR1B, GIPR, DYM, APOB, APOE, ADCY5. The MLR models explained 62%, 71%, and 81% of the variability in HDL, LDL, and triglycerides, respectively. The Area Under the Curve (AUC) values in the LR models of HDL, LDL, and triglycerides were 1.00, 0.94, and 0.95, respectively. CART models identified novel gene-gene interactions influencing the risk for dyslipidemia. To conclude, we have identified the association of 12 SNVs with dyslipidemia and demonstrated their clinical utility in four different models such as MLR, LR, CART, and Manhattan plots. The identified genetic variants and associated pathways shed light on the underlying biology of dyslipidemia and offer potential avenues for precision medicine strategies in the management of CAD.

Genetic and Lifestyle-Related Factors Influencing Serum Hyper-Propionylcarnitine Concentrations and Their Association with Metabolic Syndrome and Cardiovascular Disease Risk

The genetic and environmental determinants of serum propionylcarnitine concentrations (PC) remain largely unexplored. This study investigated the impact of genetic and environmental factors on serum propionylcarnitine levels in middle-aged and elderly participants from the Ansan/Ansung cohort of the Korean Genome and Epidemiology Study. Our goal was to understand the role of PC on the risk of metabolic syndrome (MetS) leading to cardiovascular disease, particularly concerning branched-chain amino acid (BCAA) metabolism. We analyzed participants' demographic, lifestyle, and biochemical data with and without MetS. Serum metabolite concentrations, including carnitine, acylcarnitine, and amino acid concentrations, were measured, and the components of MetS were evaluated. Genetic variants associated with low and high PC were selected using genome-wide association studies after adjusting for MetS-related parameters. Further, genetic variants and lifestyle factors that interacted with the polygenic risk score (PRS) were analyzed. Participants with MetS were older and less educated, and their alcohol intake was higher than non-MetS participants. PC was significantly associated with the MetS risk and increased the serum levels of BCAAs and other amino acids. Higher PC positively correlated with MetS components, insulin resistance, and cardiovascular risk factors. Intake of calcium, sodium, and vitamin D were inversely associated with PC, but coffee consumption was positively linked to PC. Multiple C2 And Transmembrane Domain Containing-1 (MCTP1)_rs4290997, Kinesin Family Member-7 (KIF7)_rs2350480, Coagulation Factor-II (F2)_rs2070850, Peroxisomal Biogenesis Factor-3 (PEX3)_rs223231, TBC1 Domain Family Member-22A (TBC1D22A)_rs910543, and Phospholipase A2 Group-IV-C (PLA2G4C)_rs7252136 interact with each other to have a threefold influence on PC. The PRS for the six-genetic variant model also interacted with age; the diet rich in beans, potato, and kimchi; and smoking status, influencing PC. In conclusion, elevated PC was associated with MetS and cardiovascular disease risk, suggesting their potential as disease biomarkers.

Metabolite profiles and DNA methylation in metabolic syndrome: a two-sample, bidirectional Mendelian randomization

Introduction: Metabolic syndrome (MetS) increases the risk of cardiovascular disease and death. Previous '-omics' studies have identified dysregulated serum metabolites and aberrant DNA methylation in the setting of MetS. However, the relationship between the metabolome and epigenome have not been elucidated. In this study, we identified serum metabolites associated with MetS and DNA methylation, and we conducted bidirectional Mendelian randomization (MR) to assess causal relationships between metabolites and methylation. Methods: We leveraged metabolomic and genomic data from a national United States cohort of older adults (REGARDS), as well as metabolomic, epigenomic, and genomic data from a family-based study of hypertension (HyperGEN). We conducted metabolite profiling for MetS in REGARDS using weighted logistic regression models and validated them in HyperGEN. Validated metabolites were selected for methylation studies which fit linear mixed models between metabolites and six CpG sites previously linked to MetS. Statistically significant metabolite-CpG pairs were selected for two-sample, bidirectional MR. Results: Forward MR indicated that glucose and serine metabolites were causal on CpG methylation near CPT1A [B(SE): -0.003 (0.002), p = 0.028 and B(SE): 0.029 (0.011), p = 0.030, respectively] and that serine metabolites were causal on ABCG1 [B(SE): -0.008(0.003), p = 0.006] and SREBF1 [B(SE): -0.009(0.004), p = 0.018] methylation, which suggested a protective effect of serine. Reverse MR showed a bidirectional relationship between cg06500161 (ABCG1) and serine [B(SE): -1.534 (0.668), p = 0.023]. Discussion: The metabolome may contribute to the relationship between MetS and epigenetic modifications.

Vaginal homeostasis features of Vulvovaginal Candidiasis through vaginal metabolic profiling

Vulvovaginal candidiasis (VVC) is an inflammatory disease primarily infected by Candida albicans. The condition has good short-term treatment effects, high recurrence, and seriously affects the quality of life of women. Metabolomics has been applied to research a variety of inflammatory diseases. In the present study, the vaginal metabolic profiles of VVC patients and healthy populations (Cnotrol (CTL)) were explored by a non-targeted metabolomics approach. In total, 211 differential metabolites were identified, with the VVC group having 128 over-expressed and 83 under-expressed metabolites compared with healthy individuals. Functional analysis showed that these metabolites were mainly involved in amino acid metabolism and lipid metabolism. In addition, network software analysis indicated that the differential metabolites were associated with mitogen-activated protein kinase (MAPK) signaling and NF-κB signaling. Further molecular docking suggested that linoleic acid can bind to the acyl-CoA synthetase 1 (ACSL1) protein, which has been shown to be associated with multiple inflammatory diseases and is an upstream regulator of the MAPK and NF-κB signaling pathways that mediate inflammation. Therefore, our preliminary analysis results suggest that VVC has a unique metabolic profile. Linoleic acid, a significantly elevated unsaturated fatty acid in the VVC group, may promote VVC development through the ACSL1/MAPK and ACSL1/NF-κB signaling pathways. This study's findings contribute to further exploring the mechanism of VVC infection and providing new perspectives for the treatment of Candida albicans vaginal infection.

Plasma metabolic fingerprints for large-scale screening and personalized risk stratification of metabolic syndrome

Direct diagnosis and accurate assessment of metabolic syndrome (MetS) allow for prompt clinical interventions. However, traditional diagnostic strategies overlook the complex heterogeneity of MetS. Here, we perform metabolomic analysis in 13,554 participants from the natural cohort and identify 26 hub plasma metabolic fingerprints (PMFs) associated with MetS and its early identification (pre-MetS). By leveraging machine-learning algorithms, we develop robust diagnostic models for pre-MetS and MetS with convincing performance through independent validation. We utilize these PMFs to assess the relative contributions of the four major MetS risk factors in the general population, ranked as follows: hyperglycemia, hypertension, dyslipidemia, and obesity. Furthermore, we devise a personalized three-dimensional plasma metabolic risk (PMR) stratification, revealing three distinct risk patterns. In summary, our study offers effective screening tools for identifying pre-MetS and MetS patients in the general community, while defining the heterogeneous risk stratification of metabolic phenotypes in real-world settings.

Desorption electrospray ionization and matrix-assisted laser desorption/ionization as imaging approaches for biological samples analysis

The imaging of biological tissues can offer valuable information about the sample composition, which improves the understanding of analyte distribution in such complex samples. Different approaches using mass spectrometry imaging (MSI), also known as imaging mass spectrometry (IMS), enabled the visualization of the distribution of numerous metabolites, drugs, lipids, and glycans in biological samples. The high sensitivity and multiple analyte evaluation/visualization in a single sample provided by MSI methods lead to various advantages and overcome drawbacks of classical microscopy techniques. In this context, the application of MSI methods, such as desorption electrospray ionization-MSI (DESI-MSI) and matrix-assisted laser desorption/ionization-MSI (MALDI-MSI), has significantly contributed to this field. This review discusses the evaluation of exogenous and endogenous molecules in biological samples using DESI and MALDI imaging. It offers rare technical insights not commonly found in the literature (scanning speed and geometric parameters), making it a comprehensive guide for applying these techniques step-by-step. Furthermore, we provide an in-depth discussion of recent research findings on using these methods to study biological tissues.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

New Advances in Metabolic Syndrome, from Prevention to Treatment: The Role of Diet and Food

The definition of metabolic syndrome (MetS) has undergone several changes over the years due to the difficulty in establishing universal criteria for it. Underlying the disorders related to MetS is almost invariably a pro-inflammatory state related to altered glucose metabolism, which could lead to elevated cardiovascular risk. Indeed, the complications closely related to MetS are cardiovascular diseases (CVDs) and type 2 diabetes (T2D). It has been observed that the predisposition to metabolic syndrome is modulated by complex interactions between human microbiota, genetic factors, and diet. This review provides a summary of the last decade of literature related to three principal aspects of MetS: (i) the syndrome's definition and classification, pathophysiology, and treatment approaches; (ii) prediction and diagnosis underlying the biomarkers identified by means of advanced methodologies (NMR, LC/GC-MS, and LC, LC-MS); and (iii) the role of foods and food components in prevention and/or treatment of MetS, demonstrating a possible role of specific foods intake in the development of MetS.

PIKE-A Modulates Mitochondrial Metabolism through Increasing SDHA Expression Mediated by STAT3/FTO Axis

Previous studies have shown that phosphoinositide 3-kinase enhancer-activating Akt (PIKE-A) is involved in the regulation of several biological processes in cancer. In our previous study, we demonstrated a crucial function of PIKE-A in cancer energy metabolism by regulating pentose phosphate pathway (PPP) flux. However, whether PIKE-A regulates energy metabolism through affecting mitochondrial changes are poorly understood. In the present study, we show that PIKE-A promotes mitochondrial membrane potential, leading to increasing proliferation of glioblastoma cell. Mechanistically, PIKE-A affects the expression of respiratory chain complex Ⅱ succinate dehydrogenase A (SDHA), mediated by regulating the axis of STAT3/FTO. Taken together, these results revealed that inhibition of PIKE-A reduced STAT3/FTO/SDHA expression, leading to the suppression of mitochondrial function. Thus, our findings suggest the PIKE-A/STAT3/FTO/SDHA axis as promising anti-cancer treatment targets.

The Advent of Nutrigenomics: A Narrative Review with an Emphasis on Psychological Disorders

A new research field is emerging that combines nutrition and genetics at the molecular level, namely nutrigenomics. Several aspects of nutrigenomics are examined in this review, with a particular focus on psychological disorders. The origin of this field in the 20th century and its modern developments have been investigated. Various studies have reported the impact of genetic factors and diet on various chronic disorders, elucidating how the deficiency of several macronutrients results in significant ailments, including diabetes, cancer, cardiovascular disorders, and others. Furthermore, the application of nutrigenomics to diet and its impact on the global disease rate and quality of life have been discussed. The relationship between diet and gene expression can facilitate the classification of diet-gene interactions and the diagnosis of polymorphisms and anomalies. Numerous databases and research tools for the study of nutrigenomics are essential to the medical application of this field. The nutrition-gene interrelationships can be utilized to study brain development, impairment, and diseases, which could be a significant medical breakthrough. It has also been observed that psychological conditions are exacerbated by the interaction between gut microbes and the prevalence of malnutrition. This article focuses on the impact of nutrition on genes involved in various psychological disorders and the potential application of nutrigenomics as a revolutionary treatment method.