Biology subjects

Ordovas, J. M.

Publications and source records attributed to Ordovas, J. M..

5 recordsLinked to original sources

bioRxiv2024

DNA methylation and gene expression trajectories of human postprandial metabolism

Human postprandial metabolism is characterised by a highly individualised response to food that is predictive of cardiometabolic health and underexplored at the molecular level. We profiled blood DNA methylation (DNAm) and gene expression trajectories before and after a test meal in 225 European participants. We identify DNAm changes at fasting, 30 minutes and 4 hours after meal challenge, including in metabolically relevant genes INPP4A, GHRL, ASIP and ABCG1, with changes observed as early as 30mins postprandially. Gene expression trajectories also changed postprandially predominantly at 4 hours, with replication of lipid metabolism (CPT1A) and circadian rhythm (PER1) genes. Genetic variants affect postprandial molecular trajectories at genes linked to obesity (PDE9A) and glucose response (GPT2). Multiple signals associated with postprandial glucose and triglyceride levels, with replication of CPT1A methylation. The postprandial DNAm and expression trajectories target metabolically relevant genes, giving insights towards mechanisms underlying inter-individual response to food and cardiometabolic disease risk.

genomics↗

bioRxiv2022

Characterisation of fasting and postprandial NMR metabolites: insights from the ZOE PREDICT 1 Study

BackgroundPostprandial metabolomic profiles and their inter-individual variability are not well characterised. Here we describe postprandial metabolite changes, their correlations with fasting values and their inter- and intra-individual variability following a standardised meal in the ZOE PREDICT 1 cohort. MethodsIn the ZOE PREDICT 1 study (n = 1,002 (NCT03479866)), 250 metabolites, mainly lipids, were measured by Nightingale NMR panel in fasting and postprandial (4 and 6 h after a 3.7 MJ mixed nutrient meal, with a second 2.2 MJ mixed nutrient meal at 4 h) serum samples. For each metabolite, inter- and intra-individual variability over-time was evaluated using linear mixed modelling and intraclass-correlation coefficients (ICC) calculated. ResultsPostprandially, 85% (of 250 metabolites) significantly changed from fasting at 6h (47% increased, 53% decreased; Kruskal-Wallis), with 37 measures increasing by >25%, and 14 increasing by >50%. The largest changes were observed in very large lipoprotein particles and ketone bodies. Seventy-one percent of circulating metabolites were strongly correlated (Spearmans rho >0.80) between fasting and postprandial timepoints, and 5% were weakly correlated (rho <0.50). The median ICC of the 250 metabolites was 0.91 (range 0.08-0.99). The lowest ICCs (ICC<0.40, 4% of measures) were found for glucose, pyruvate, ketone bodies ({beta}-hydroxybutyrate, acetoacetate, acetate) and lactate. ConclusionsIn this large-scale postprandial metabolomic study, circulating metabolites were highly variable between individuals following a mixed challenge meal. Findings suggest that a meal challenge may yield postprandial responses divergent from fasting measures, specifically for glycolysis, essential amino acid, ketone body and lipoprotein size metabolites.

biochemistry↗

bioRxiv2020

PhyteByte: Identification of foods containing compounds with specific pharmacological properties

BackgroundPhytochemicals and other molecules in foods elicit positive health benefits, often by poorly established or unknown mechanisms. While there is a wealth of data on the biological and biophysical properties of drugs and therapeutic compounds, there is a notable lack of similar data for compounds commonly present in food. Computational methods for high-throughput identification of food compounds with specific biological effects, especially when accompanied by relevant food composition data, could enable more effective and more personalized dietary planning. We have created a machine learning-based tool (PhyteByte) to leverage existing pharmacological data to predict bioactivity across a comprehensive molecular database of foods and food compounds. ResultsPhyteByte uses a cheminformatic approach to structure-based activity prediction and applies it to uncover the putative bioactivity of food compounds. The tool takes an input protein target and develops a random forest classifier to predict the effect of an input molecule based on its molecular fingerprint, using structure and activity data available from the ChEMBL database. It then predicts the relevant bioactivity of a library of food compounds with known molecular structures from the FooDB database. The output is a list of food compounds with high confidence of eliciting relevant biological effects, along with their source foods and associated quantities in those foods, where available. Applying PhyteByte to the PPARG gene, we identified irigenin, sesamin, fargesin, and delta-sanshool as putative agonists of PPARG, along with previously identified agonists of this important metabolic regulator. ConclusionsPhyteByte identifies food-based compounds that are predicted to interact with specific protein targets. The identified relationships can be used to prioritize food compounds for experimental or epidemiological follow-up and can contribute to the rapid development of precision approaches to new nutraceuticals as well as personalized dietary planning.

bioinformatics↗

bioRxiv2019

Type-2 diabetes with low LDL-C: genetic insights into a unique phenotype

Although hyperlipidemia is traditionally considered a risk factor for type-2 diabetes (T2D), evidence has emerged from statin trials and candidate gene investigations suggesting that lower LDL-C increases T2D risk. We thus sought to comprehensively examine the phenotypic and genotypic relationships of LDL-C with T2D. Using data from the UK Biobank, we found that LDL-C was negatively associated with T2D (OR=0.43[0.41, 0.45] per mmol/L unit of LDL-C), despite positive associations of LDL-C with HbA1c and BMI. We then performed the first genome-wide exploration of variants simultaneously associated with lower LDL-C and increased T2D risk, using data on LDL-C from the UK Biobank (n=431,167) and the GLGC consortium (n=188,577), and T2D from the DIAGRAM consortium (n=898,130). We identified 31 loci associated with lower LDL-C and increased T2D, capturing several potential mechanisms. Seven of these loci have previously been identified for this phenotype, and 9 have previously been implicated in non-alcoholic fatty liver disease. Finally, two-sample Mendelian randomization analyses suggest that low LDL-C causes T2D, although causal interpretations are challenging due to pleiotropy. Our findings extend our current understanding of the higher T2D risk among individuals with low LDL-C, and of the underlying mechanisms, including those underlying the diabetogenic effect of LDL-C-lowering medications.

genetics↗

bioRxiv2018

DNA methylation modules associate with incident cardiovascular disease and cumulative risk factor exposure

1 AbstractO_ST_ABSBackgroundC_ST_ABSEpigenome-wide association studies using DNA methylation have the potential to uncover novel biomarkers and mechanisms of cardiovascular disease (CVD) risk. However, the direction of causation for these associations is not always clear, and investigations to-date have generally failed to replicate at the level of individual loci. Here, we undertook module- and region-based DNA methylation analyses of incident CVD in the Womens Health Initiative (WHI) and Framingham Heart Study Offspring Cohort (FHS) in order to find more robust epigenetic biomarkers for cardiovascular risk.\n\nMethods and FindingsWe applied weighted gene correlation network analysis (WGCNA) and the Comb-p algorithm to find methylation modules and regions associated with incident CVD in the WHI dataset. We discovered two modules whose activation correlated with CVD risk and replicated across cohorts. One of these modules was enriched for development-related processes and overlaps strongly with epigenetic aging sites. For the other, we showed preliminary evidence for monocyte-specific effects and statistical links to cumulative exposure to traditional cardiovascular risk factors. Additionally, we found three regions (associated with the genes SLC9A1, SLC1A5, and TNRC6C) whose methylation associates with CVD risk.\n\nConclusionsIn sum, we present several epigenetic associations with incident CVD that reveal disease mechanisms related to development and monocyte biology. Furthermore, we show that epigenetic modules may act as a molecular readout of cumulative cardiovascular risk factor exposure, with implications for the improvement of clinical risk prediction.

genomics↗