ATP “potential” of nutrients may regulated plasma corticosteroid concentration: a hypothesis

Fructose, glucose and fat interrelationships with metabolic pathway regulation and effects on the gut microbiota

The purpose of this 30-day feeding study was to elucidate the changes, correlations, and mechanisms caused by the replacement of the starch content of the AIN-93G diet (St) with glucose (G), fructose (F) or lard (L) in body and organ weights, metabolic changes and caecal microbiota composition in rats (Wistar, SPF). The body weight gain of rats on the F diet was 12% less (P = 0.12) than in the St group. Rats on the L diet consumed 18.6% less feed, 31% more energy and gained 58.4% more than the animals on the St diet, indicating that, in addition to higher energy intake, better feed utilisation is a key factor in the obesogenic effect of diets of high nutrient and energy density. The G, F and L diets significantly increased the lipid content of the liver (St: 7.01 ± 1.48; G: 14.53 ± 8.77; F: 16.73 ± 8.77; L: 19.86 ± 4.92% of DM), suggesting that lipid accumulation in the liver is not a fructose-specific process. Relative to the St control, specific glucose effects were the decreasing serum glucagon (-41%) concentrations and glucagon/leptin ratio and the increasing serum leptin concentrations (+26%); specific fructose effects were the increased weights of the kidney, spleen, epididymal fat and the decreased weight of retroperitoneal fat and the lower immune response, as well as the increased insulin (+26%), glucagon (+26%) and decreased leptin (-25%) levels. This suggests a mild insulin resistance and catabolic metabolism in F rats. Specific lard effects were the decreased insulin (-9.14%) and increased glucagon (+40.44%) and leptin (+44.92%) levels. Relative to St, all diets increased the operational taxonomic units of the phylum Bacteroidetes. G and L decreased, while F increased the proportion of Firmicutes. F and L diets decreased the proportions of Actinobacteria, Proteobacteria and Verrucomicrobia. Correlation and centrality analyses were conducted to ascertain the positive and negative correlations and relative weights of the 32 parameters studied in the metabolic network. These correlations and the underlying potential mechanisms are discussed.