Investigation into potential mechanisms of metabolic syndrome by integrative analysis of metabolomics and proteomics

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.

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.

Urinary 1H NMR Metabolomic Analysis of Prenatal Maternal Stress Due to a Natural Disaster Reveals Metabolic Risk Factors for Non-Communicable Diseases: The QF2011 Queensland Flood Study

Prenatal stress alters fetal programming, potentially predisposing the ensuing offspring to long-term adverse health outcomes. To gain insight into environmental influences on fetal development, this QF2011 study evaluated the urinary metabolomes of 4-year-old children (n = 89) who were exposed to the 2011 Queensland flood in utero. Proton nuclear magnetic resonance spectroscopy was used to analyze urinary metabolic fingerprints based on maternal levels of objective hardship and subjective distress resulting from the natural disaster. In both males and females, differences were observed between high and low levels of maternal objective hardship and maternal subjective distress groups. Greater prenatal stress exposure was associated with alterations in metabolites associated with protein synthesis, energy metabolism, and carbohydrate metabolism. These alterations suggest profound changes in oxidative and antioxidative pathways that may indicate a higher risk for chronic non-communicable diseases such obesity, insulin resistance, and diabetes, as well as mental illnesses, including depression and schizophrenia. Thus, prenatal stress-associated metabolic biomarkers may provide early predictors of lifetime health trajectories, and potentially serve as prognostic markers for therapeutic strategies in mitigating adverse health outcomes.

Value of the triglyceride-glucose index and non-traditional blood lipid parameters in predicting metabolic syndrome in women with polycystic ovary syndrome

PURPOSE

Insulin resistance (IR) is common in patients with polycystic ovary syndrome (PCOS). Metabolic syndrome (MS) includes, inter alia, IR, hypertension, dyslipidemia, and disturbances in glucose metabolism. The triglyceride-glucose (TyG) index and non-traditional lipid parameters are strong predictors of IR and cardiovascular disease and can be considered as screening indicators for MS. This study aimed to evaluate the predictive potential of non-traditional lipid parameters and the TyG index to identify MS in PCOS.

METHODS

This cross-sectional study included 134 women diagnosed with PCOS (50 patients with comorbid MS and 84 patients without MS). Biochemical indices were collected, and triglycerides (TG)/high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC)/HDL-C, low-density lipoprotein cholesterol (LDL-C)/HDL-C, non-HDL-C, TyG, and TyG-BMI indices were calculated. Logistic regression analysis was used to compare and determine the association of the six parameters with MS, and the receiver operating characteristic (ROC) curve was used to evaluate the performance of each parameter in identifying MS in the PCOS population.

RESULTS

After adjusting for age and body mass index (BMI), TG/HDL-C, TC/HDL-C, LDL-C/HDL-C, non-HDL-C, TyG, and TyG-BMI were associated with MS (all P<0.05). The odds ratios were 4.075 (0.891, 1.107), 3.121 (1.844, 5.282), 3.106 (1.734, 5.561), 2.238 (1.302, 3.848), 13.422 (4.364, 41.282), and 1.102 (1.056, 1.150), respectively. TG/HDL-C, TC/HDL-C, LDL-C/HDL-C, non-HDL-C, TyG, and TyG-BMI are effective predictors of MS in PCOS, and their cut-off values can be used for the early detection of MS. TyG-BMI had the strongest performance in predicting MS (area under the curve 0.905, 95% CI 0.855-0.956), and its optimal critical value for predicting MS was 202.542.

CONCLUSIONS

TG/HDL-C, TC/HDL-C, LDL-C/HDL-C, non-HDL-C, TyG, and TyG-BMI are novel, clinically convenient and practical markers for the early identification of MS risk in PCOS patients.

Exploration of the Mechanism of Linoleic Acid Metabolism Dysregulation in Metabolic Syndrome

We aimed to explore the mechanism of the linoleic acid metabolism in metabolic syndrome (MetS). RNA-seq data for 16 samples with or without MetS from the GSE145412 dataset were collected. Gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), and gene differential expression analysis were performed. Expression data of differentially expressed genes (DEGs) involved in the linoleic acid metabolism pathway were mapped to the pathway by using Pathview for visualization. There were 19 and 10 differentially expressed biological processes in the disease group and healthy group, respectively. 9 KEGG pathways were differentially expressed in the disease group. Linoleic acid metabolism was the only differentially expressed pathway in the healthy group. The GSVA enrichment score of the linoleic acid metabolism pathway in the disease group was markedly lower than that in the healthy group. The GSEA result showed that the linoleic acid metabolism pathway was significantly downregulated in the disease group. JMJD7-PLA2G4B, PLA2G1B, PLA2G2D, CYP2C8, and CYP2J2 involved in the pathway were significantly downregulated in the disease group. This study may provide novel insight into MetS from the point of linoleic acid metabolism dysregulation.