Exploring Covariation between Traditional Markers of Metabolic Health and the Plasma Metabolomic Profile: A Classic Twin Design

Perspective: Council for Responsible Nutrition Science in Session. Optimizing Health with Nutrition-Opportunities, Gaps, and the Future

Achieving optimal health is an aspirational goal for the population, yet the definition of health remains unclear. The role of nutrition in health has evolved beyond correcting malnutrition and specific deficiencies and has begun to focus more on achieving and maintaining 'optimal' health through nutrition. As such, the Council for Responsible Nutrition held its October 2022 Science in Session conference to advance this concept. Here, we summarize and discuss the findings of their Optimizing Health through Nutrition - Opportunities and Challenges workshop, including several gaps that need to be addressed to advance progress in the field. Defining and evaluating various indices of optimal health will require overcoming these key gaps. For example, there is a strong need to develop better biomarkers of nutrient status, including more accurate markers of food intake, as well as biomarkers of optimal health that account for maintaining resilience-the ability to recover from or respond to stressors without loss to physical and cognitive performance. In addition, there is a need to identify factors that drive individualized responses to nutrition, including genotype, metabotypes, and the gut microbiome, and to realize the opportunity of precision nutrition for optimal health. This review outlines hallmarks of resilience, provides current examples of nutritional factors to optimize cognitive and performance resilience, and gives an overview of various genetic, metabolic, and microbiome determinants of individualized responses.

Genetic and environmental influences on serum oxylipins, endocannabinoids, bile acids and steroids

Lipid bioactivity is a result of direct action and the action of lipid mediators including oxylipins, endocannabinoids, bile acids and steroids. Understanding the factors contributing to biological variation in lipid mediators may inform future approaches to understand and treat complex metabolic diseases. This research aims to determine the contribution of genetic and environmental influences on lipid mediators involved in the regulation of inflammation and energy metabolism. This study recruited 138 monozygotic (MZ) and dizygotic (DZ) twins aged 18-65 years and measured serum oxylipins, endocannabinoids, bile acids and steroids using liquid chromatography mass-spectrometry (LC-MS). In this classic twin design, the similarities and differences between MZ and DZ twins are modelled to estimate the contribution of genetic and environmental influences to variation in lipid mediators. Heritable lipid mediators included the 12-lipoxygenase products 12-hydroxyeicosatetraenoic acid [0.70 (95% CI: 0.12,0.82)], 12-hydroxyeicosatetraenoic acid [0.73 (95% CI: 0.30,0.83)] and 14‑hydroxy-docosahexaenoic acid [0.51 (95% CI: 0.07,0.71)], along with the endocannabinoid docosahexaenoy-lethanolamide [0.52 (95% CI: 0.15,0.72)]. For others such as 13-hydroxyoctadecatrienoic acid and lithocholic acid the contribution of environment to variation was stronger. With increased understanding of lipid mediator functions in health, it is important to understand the factors contributing to their variance. This study provides a comprehensive analysis of lipid mediators and extends pre-existing knowledge of the genetic and environmental influences on the human lipidome.

Genetic and Environmental Contributions to Variation in the Stable Urinary NMR Metabolome over Time: A Classic Twin Study

Genes, sex, age, diet, lifestyle, gut microbiome, and multiple other factors affect human metabolomic profiles. Understanding metabolomic variation is critical in human nutrition research as metabolites that are sensitive to change versus those that are more stable might be more informative for a particular study design. This study aims to identify stable metabolomic regions and determine the genetic and environmental contributions to stability. Using a classic twin design, ¹H nuclear magnetic resonance (NMR) urinary metabolomic profiles were measured in 128 twins at baseline, 1 month, and 2 months. Multivariate mixed models identified stable urinary metabolites with intraclass correlation coefficients ≥0.51. Longitudinal twin modeling measured the contribution of genetic and environmental influences to variation in the stable urinary NMR metabolome, comprising stable metabolites. The conservation of an individual’s stable urinary NMR metabolome over time was assessed by calculating conservation indices. In this study, 20% of the urinary NMR metabolome is stable over 2 months (intraclass correlation (ICC) 0.51–0.65). Common genetic and shared environmental factors contributed to variance in the stable urinary NMR metabolome over time. Using the stable metabolome, 91% of individuals had good metabolomic conservation indices ≥0.70. To conclude, this research identifies 20% of the urinary NMR metabolome as stable, improves our knowledge of the sources of metabolomic variation over time, and demonstrates the conservation of an individual’s urinary NMR metabolome.

Genetic and environmental influences on covariation in reproducible diet-metabolite associations

BACKGROUND

Early applications of metabolomics in nutrition and health research identified associations between dietary patterns and metabolomic profiles. Twin studies show that diet-related phenotypes and diet-associated metabolites are influenced by genes. However, studies have not examined whether diet-metabolite associations are explained by genetic or environmental factors and whether these associations are reproducible over multiple time points.

OBJECTIVE

This research aims to examine the genetic and environmental factors influencing covariation in diet-metabolite associations that are reproducible over time in healthy twins.

METHODS

The UCD Twin Study is a semi-longitudinal classic twin study that collected repeated dietary, anthropometric, and urinary data over 2 months. Correlation analysis identified associations between diet quality measured using the Healthy Eating Index (HEI) and urinary metabolomic profiles at 3 time points. Diet-associated metabolites were examined using linear regression to identify those significantly influenced by familial factors between twins and those significantly influenced by unique factors. Cholesky decomposition modeling quantified the genetic and environmental path coefficients through associated dietary components onto the metabolites.

RESULTS

The HEI was associated with 14 urinary metabolites across 3 metabolomic profiles (r: ±0.15-0.49). For 8 diet-metabolite associations, genetic or shared environmental factors influencing HEI component scores significantly influenced variation in metabolites (β: 0.40-0.52). A significant relation was observed between dietary intakes of whole grain and acetoacetate (β: -0.50, P < 0.001) and β-hydroxybutyrate (β: -0.46, P < 0.001), as well as intakes of saturated fat and acetoacetate (β: 0.47, P < 0.001) and β-hydroxybutyrate (β: 0.52, P < 0.001). For these diet-metabolite associations a common shared environmental factor explained 66-69% of variance in the metabolites.

CONCLUSIONS

This study shows that diet-metabolite associations are reproducible in 3 urinary metabolomic profiles. Components of the HEI covary with metabolites, and covariation is largely due to the shared environment.