Presented by: Eira E Huerta-Avila
Background: The prevalence of the different metabolic syndrome phenotypes (MetSP) such as hypertriglyceridemia, hyperglycemia, hypoalphalipoproteinemia, central obesity and arterial hypertension are increasing and they are risk factors for other comorbidities such as type 2 diabetes and cardiovascular diseases. It has been documented that, even prior to the appearance of MetSP, an altered postprandial metabolism may occur. As if that were not enough, a large number of genetic variants associated with the risk of developing MetSP have been identified. Moreover, it has recently been documented that the gut microbiota (GM) represents an environmental risk factor for the development of these entities. Hence, that it is necessary to implement methodologies that allow genetic and environmental factors to be combined to establish the risk of developing these entities even before the appearance of symptoms. A widely used methodology is the polygenic risk score (PRS) that allows identifying people at risk by combining the environmental and genetic part. Therefore, the aim of this work was to determine if there is a correlation between the composition of the GM, the pre and postprandial metabolic state of the individual and the PRS to develop MetSP.
Methods: A cross-sectional and observational study was carried out with 33 apparently healthy individuals. Blood samples were taken for biochemical analysis (pre and postprandial metabolism), and stool samples. Genotyping and sequencing (metagenomic) were performed. We used previously reported genetic variants for the different MetSP and in combination with the GM metagenomic data, the PRS was performed for the different MetSP. A second analysis consisted of stratifying individuals without preprandial alterations to assess PRS and GM data in their postprandial metabolism.
Results: In the 33 individuals, the mean age of the population was 39.7±13.4 years and more than 70% were female. The prevalence of metabolic syndrome was 42.4% while the prevalence of type 2 diabetes was 3.1%. The average of the studied population presented an overweight nutritional status according to BMI (29.7±5.1 kg/m2) and a high percentage of body fat (39.7±8.9%). Ancestry analysis showed that the population has an average Amerindian ancestry of 79.1%, followed by 18.0% European and 2.9% African. The construction of the different PRS by the FSM showed a power of discrimination between individuals with and without the different components of metabolic syndrome above 0.75. In other words, most PRS have the ability to detect 75% of cases or more of each condition. The metagenomic analysis showed that the dominant phyla were Bacteroidetes, Firmicutes, Proteobacteria and Actinobacteria. Regarding a species level, the most abundant were Prevotella copri, Faecalibacterium prausnitzii and Eubacterium rectale. The relative abundance of Prevotella copri and Parabacteroides merdae was directly proportional to glucose levels, and Eubacterium rectale to blood HbA1c concentration (p<0.05). Other microorganisms had a significant correlation (p<0.05), with postprandial data and even with the different PRS, such as Methanobrevibacter smithii that negatively correlated with the PRS of hypertriglyceridemia. Finally, the alpha diversity (such as the Shannon and Simpson index) in the different MetSP did not show significant correlations and when the analysis was performed only in those who did not have preprandial glucose and triglyceride alterations, we observed higher levels of these indices in people who did not have altered triglyceride curves.
Conclusions: These data suggest that the decrease in gut microbiome diversity could occur in the stages prior to the onset of manifestation MetSP. And that there are species that seem to favor the increase of pre and postprandial metabolites related to cardiovascular risk and polygenic risk for some MetSP. It is necessary to increase the sample size to corroborate these data.