Diet Quality, Saturated Fat and Metabolic Syndrome

Harrison, Stephanie L.; Couture, Patrick; Lamarche, Benoı̂t

doi:10.3390/nu12113232

Cited by 58 publications

(34 citation statements)

References 61 publications

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“…However, polyunsaturated fatty acids improve the nutritional properties of food. In this contradiction, in our opinion, two mutually exclusive trends in the modern food market can be traced-the unification of raw material parameters in mass production, and, conversely, attempts to develop individualized approaches to human nutrition [48,49].…”

Section: Nomentioning

confidence: 99%

Artificial Neural Networks for Predicting Food Antiradical Potential

et al. 2022

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Using an artificial neural network (ANN), the values of the antiradical potential of 1315 items of food and agricultural raw materials were calculated. We used an ANN with the structure of a “multilayer perceptron” (MLP) and with the hyberbolic tangent (Tanh) as an activation function. Values reported in the United States Food and Nutrient Database for Dietary Studies (FNDDS) were taken as input to the analysis. When training the ANN, 60 parameters were used, such as the content of plastic substances, food calories, the amount of mineral components, vitamins, the composition of fatty acids and additional substances presented in this database. The analysis revealed correlations, namely, a direct relationship between the value of the antiradical potential (ARP) of food and the concentration of dietary fiber (r = 0.539) and a negative correlation between the value of ARP and the total calorie content of food (r = −0.432) at a significance level of p < 0.001 for both values. The average ARP value for 10 product groups within the 95% CI (confidence interval) was ≈23–28 equivalents (in terms of ascorbic acid) per 1 g of dry matter. The study also evaluated the range of average values of the daily recommended intake of food components (according to Food and Agriculture Organization—FAO, World Health Organization—WHO, Russia and the USA), which within the 95% CI, amounted to 23.41–28.98 equivalents per 1 g of dry weight. Based on the results of the study, it was found that the predicted ARP values depend not only on the type of raw materials and the method of their processing, but also on a number of other environmental and technological factors that make it difficult to obtain accurate values.

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Section: Nomentioning

confidence: 99%

Artificial Neural Networks for Predicting Food Antiradical Potential

et al. 2022

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“…The reader will find a mix of narrative reviews and communications written by well-published investigators, top international experts in the field. We are very grateful to these experts who have agreed to contribute to this issue [ 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ]. Original papers that are relevant to our theme are also included [ 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 ].…”

Section: This Issuementioning

confidence: 99%

Metabolic Syndrome: Past, Present and Future

2020

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“…Metabolic disorder is the prelude of metabolic diseases consisting of obesity, non-alcoholic fatty liver disease (NAFLD), type 2 diabetes (T2DM), hyperlipidemia, and metabolic syndrome (3)(4)(5). It is well known that the prevalence of Western diet is the key factor contributing to the development of metabolic disorders (6)(7)(8)(9).…”

Section: Introductionmentioning

confidence: 99%

Microbial and Transcriptomic Profiling Reveals Diet-Related Alterations of Metabolism in Metabolic Disordered Mice

Zhu

Hong

et al. 2022

Front. Nutr.

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Metabolic disorders are the prelude of metabolic diseases, which are mainly due to the high-energy intake and genetic contribution. High-fat diet (HFD) or high-sucrose diet is widely used for inducing metabolic disorders characterized by increased body weight, insulin resistance, hepatic steatosis, and alteration of gut microbiome. However, the triangle relationship among diets, gut microbiome, and host metabolism is poorly understood. In our study, we investigated the dynamic changes in gut microbiota, and host metabolism in mice that were fed with either chow diet, HFD, or chow diet with 30% sucrose in drinking water (HSD) for continued 12 weeks. The gut microbiota was analyzed with 16S rDNA sequencing on feces. Hepatic gene expression profile was tested with transcriptomics analysis on liver tissue. The host metabolism was evaluated by measuring body weight, insulin sensitivity, serum lipids, and expression of proteins involved in lipid metabolism of liver. The results showed that HFD feeding affected body weight, insulin resistance, and hepatic steatosis more significantly than HSD feeding. 16S rRNA gene sequencing showed that HFD rapidly and steadily suppressed species richness, altered microbiota structure and function, and increased the abundance of bacteria responsible for fatty acid metabolism and inflammatory signaling. In contrast, HSD had minor impact on the overall bacteria structure or function but activated microbial bile acid biosynthesis. Fecal microbiota transplantation suggested that some metabolic changes induced by HFD or HSD feeding were transferrable, especially in the weight of white adipose tissue and hepatic triglyceride level that were consistent with the phenotypes in donor mice. Moreover, transcriptomic results showed that HFD feeding significantly inhibited fatty acid degradation and increase inflammation, while HSD increased hepatic de novo lipogenesis and inhibited primary bile acid synthesis alternative pathway. In general, our study revealed the dynamic and diversified impacts of HFD and HSD on gut microbiota and host metabolism.

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Diet Quality, Saturated Fat and Metabolic Syndrome

Cited by 58 publications

References 61 publications

Artificial Neural Networks for Predicting Food Antiradical Potential

Artificial Neural Networks for Predicting Food Antiradical Potential

Metabolic Syndrome: Past, Present and Future

Microbial and Transcriptomic Profiling Reveals Diet-Related Alterations of Metabolism in Metabolic Disordered Mice

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