The metabolomic profile associated with clustering of cardiovascular risk factors—A multi-sample evaluation

Comparing the metabolic signatures of obesity defined by waist circumference, waist-hip ratio, or BMI

OBJECTIVE

Measuring obesity is crucial for assessing health risks and developing effective prevention and treatment strategies. The most common methods used to measure obesity include BMI, waist circumference, and waist-hip ratio. This study aimed to determine the metabolic signatures associated with each measure of obesity in the Qatari population.

METHODS

Metabolomics profiling was conducted to identify, quantify, and characterize metabolites in serum samples from the study participants. Inverse rank normalization, principal component analysis, and orthogonal partial least square-discriminant analysis were used to analyze the metabolomics data.

RESULTS

This study revealed significant differences in metabolites associated with obesity based on different measurements. In men, phosphatidylcholine and phosphatidylethanolamine metabolites were significantly enriched in individuals classified as having obesity based on the waist-hip ratio. In women, significant changes were observed in leucine, isoleucine, and valine metabolism metabolites. Unique metabolites were found in the different categorization groups that could serve as biomarkers for assessing many obesity-related disorders.

CONCLUSIONS

This study identified unique metabolic signatures associated with obesity based on different measurements in the Qatari population. These findings contribute to a better understanding of the molecular pathways involved in obesity and may have implications for developing personalized prevention and treatment strategies.

Metabolic profiling of galectin-1 and galectin-3: a cross-sectional, multi-omics, association study

OBJECTIVES

Experimental studies indicate a role for galectin-1 and galectin-3 in metabolic disease, but clinical evidence from larger populations is limited.

METHODS

We measured circulating levels of galectin-1 and galectin-3 in the Prospective investigation of Obesity, Energy and Metabolism (POEM) study, participants (n = 502, all aged 50 years) and characterized the individual association profiles with metabolic markers, including clinical measures, metabolomics, adipose tissue distribution (Imiomics) and proteomics.

RESULTS

Galectin-1 and galectin-3 were associated with fatty acids, lipoproteins and triglycerides including lipid measurements in the metabolomics analysis adjusted for body mass index (BMI). Galectin-1 was associated with several measurements of adiposity, insulin secretion and insulin sensitivity, while galectin-3 was associated with triglyceride-glucose index (TyG) and fasting insulin levels. Both galectins were associated with inflammatory pathways and fatty acid binding protein (FABP)4 and -5-regulated triglyceride metabolic pathways. Galectin-1 was also associated with several proteins related to adipose tissue differentiation.

CONCLUSIONS

The association profiles for galectin-1 and galectin-3 indicate overlapping metabolic effects in humans, while the distinctly different associations seen with fat mass, fat distribution, and adipose tissue differentiation markers may suggest a functional role of galectin-1 in obesity.

A Proactive Intervention Study in Metabolic Syndrome High-Risk Populations Using Phenome-Based Actionable P4 Medicine Strategy

The integration of predictive, preventive, personalized, and participatory (P4) healthcare advocates proactive intervention, including dietary supplements and lifestyle interventions for chronic disease. Personal profiles include deep phenotypic data and genetic information, which are associated with chronic diseases, can guide proactive intervention. However, little is known about how to design an appropriate intervention mode to precisely intervene with personalized phenome-based data. Here, we report the results of a 3-month study on 350 individuals with metabolic syndrome high-risk that we named the Pioneer 350 Wellness project (P350). We examined: (1) longitudinal (two times) phenotypes covering blood lipids, blood glucose, homocysteine (HCY), and vitamin D3 (VD3), and (2) polymorphism of genes related to folic acid metabolism. Based on personalized data and questionnaires including demographics, diet and exercise habits information, coaches identified 'actionable possibilities', which combined exercise, diet, and dietary supplements. After a 3-month proactive intervention, two-thirds of the phenotypic markers were significantly improved in the P350 cohort. Specifically, we found that dietary supplements and lifestyle interventions have different effects on phenotypic improvement. For example, dietary supplements can result in a rapid recovery of abnormal HCY and VD3 levels, while lifestyle interventions are more suitable for those with high body mass index (BMI), but almost do not help the recovery of HCY. Furthermore, although people who implemented only one of the exercise or diet interventions also benefited, the effect was not as good as the combined exercise and diet interventions. In a subgroup of 226 people, we examined the association between the polymorphism of genes related to folic acid metabolism and the benefits of folate supplementation to restore a normal HCY level. We found people with folic acid metabolism deficiency genes are more likely to benefit from folate supplementation to restore a normal HCY level. Overall, these results suggest: (1) phenome-based data can guide the formulation of more precise and comprehensive interventions, and (2) genetic polymorphism impacts clinical responses to interventions. Notably, we provide a proactive intervention example that is operable in daily life, allowing people with different phenome-based data to design the appropriate intervention protocol including dietary supplements and lifestyle interventions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00115-z.

Investigating the Influence of Heavy Metals and Environmental Factors on Metabolic Syndrome Risk Based on Nutrient Intake: Machine Learning Analysis of Data from the Eighth Korea National Health and Nutrition Examination Survey (KNHANES)

This study delves into the complex interrelations among nutrient intake, environmental exposures (particularly to heavy metals), and metabolic syndrome. Utilizing data from the Korea National Health and Nutrition Examination Survey (KNHANES), machine learning techniques were applied to analyze associations in a cohort of 5719 participants, categorized into four distinct nutrient intake phenotypes. Our findings reveal that different nutrient intake patterns are associated with varying levels of heavy metal exposure and metabolic health outcomes. Key findings include significant variations in metal levels (Pb, Hg, Cd, Ni) across the clusters, with certain clusters showing heightened levels of specific metals. These variations were associated with distinct metabolic health profiles, including differences in obesity, diabetes prevalence, hypertension, and cholesterol levels. Notably, Cluster 3, characterized by high-energy and nutrient-rich diets, showed the highest levels of Pb and Hg exposure and had the most concerning metabolic health indicators. Moreover, the study highlights the significant impact of lifestyle habits, such as smoking and eating out, on nutrient intake phenotypes and associated health risks. Physical activity emerged as a critical factor, with its absence linked to imbalanced nutrient intake in certain clusters. In conclusion, our research underscores the intricate connections among diet, environmental factors, and metabolic health. The findings emphasize the need for tailored health interventions and policies that consider these complex interplays, potentially informing future strategies to combat metabolic syndrome and related health issues.

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.