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Mazidi, M.

Publications and source records attributed to Mazidi, M..

2 recordsLinked to original sources

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↗

Food environments and obesity: cannot see the fat for the restaurants?

We recently showed that across the mainland USA there is no association between the density of fast food and full service restaurants and the prevalence of obesity. In a recent editorial it was suggested there are 4 problems with our analysis. The suggested problems were the area of analysis may not reflect adequately the exposure to different outlets, using the absolute numbers of restaurants rather than their ratio, using a global model which assumes the same relationship across all sites and finally the potential for residual confounding. In this short note we address all four of these issues and provide some new analysis of the impact of the ratio of restaurant types on obesity prevalence showing there is only a very weak association (r2 = 0.006). We conclude that none of the supposed weaknesses in our original analysis are valid.

epidemiology↗