Bruce R. Hamaker’s research while affiliated with Rush University Medical Center and other places

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Publications (497)


Digestion profiles of experimental carbohydrate diets. Slowly digestible starch (SDS, represented in yellow) is hydrolyzed in the small intestine whereas resistant starch (RS, represented in blue) is primarily fermented in the colon [49, 50]. Total carbohydrate content of each diet was held constant at 53%. (See Tables S2 and S3 for details on digestibility and composition of the diets.)
We hypothesized that carbohydrates with different digestibilities would affect the utilization of glucose vs. fat as substrates for energy. In particular, we reasoned that dietary carbohydrates with slow digestion rates would decrease the “metabolic gridlock” [9] that is proposed to occur due to the rapid influx of glucose into mitochondria following consumption of a rapidly digestible carbohydrate, and thus enhance metabolic flexibility. To test this hypothesis, we altered carbohydrate digestibility/digestion: (1) in diets with different proportions of slowly digestible starch and resistant starch, (2) using Mgam knockout (null) mice to reduce starch digestibility, and (3) adding amyloglucosidase (AMG) to increase carbohydrate digestion rate (see experimental design in Figure S1). The Mgam null mouse exhibits a 40% reduction in mucosal glucogenesis, but normal energy expenditure [51, 52–53]
The impacts of these alterations were assessed by measuring respiratory exchange ratio (RER), the ratio of carbohydrate oxidation to fat oxidation, and then using RER values to assess metabolic flexibility in mice
Representative respiratory exchange ratio (RER) curves over one 24-h cycle from individual mice when given ad libitum access to the High SDS diet (A), the Sucrose diet (C), the High fat diet (E), and their corresponding percent relative cumulative frequency (PRCF) curves (B, D, and F, respectively). The blue and red shaded bands/ranges are indicative of the proposed ideal ranges for the modes of fat oxidation and carbohydrate oxidation, respectively, and they represent ± 5% of the ideal values for fat oxidation (0.70) and carbohydrate oxidation (1.00). For the High SDS diet, PRCF rose rapidly in the region representing ideal fat metabolism, plateaued in the region of intermediate RER values characterized by mixed metabolism, and then rose rapidly in the region of idea carbohydrate metabolism. This reflected a rapid and complete shift between these metabolic states, leading to a high Metabolic Flexibility Factor (MFF) of 0.85 for the example curve shown, indicating that 85% of the rise in PRCF occurred in the regions close to idea fat or carbohydrate metabolism. For the Sucrose and High-fat diets, mice lingered in periods of mixed metabolism and consequently displayed lower MFFs of 0.22 and 0.36, respectively, indicating that only 22 and 36% of the rise in PRCF for these curves occurred in the regions close to ideal fat or carbohydrate metabolism, with the remainder occurring in the region of mixed metabolism
Mixed Weibull parameter exploration for percent relative cumulative frequency (PRCF) analysis of respiratory exchange ratio (RER). Note that all values shown in this figure are theoretical in order to demonstrate the meaning of the parameters. Shaded areas indicate ideal RER ranges for fat oxidation (red) and carbohydrate oxidation (blue). A1–A3: Different α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} but the same x50F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{F}$$\end{document}, x50C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{C}$$\end{document}, bF\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{F}$$\end{document}, and bC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{C}$$\end{document}. Bar graph of all parameters, with α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} indicated in color (A1). Probability distribution function indicating theoretical distributions of data with three different α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} values (A2). Cumulative distribution function [Mixed Weibull Cumulative Distribution function] for the same α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} values (A3). Related to RER, the first mode represents fat oxidation and the second mode represents carbohydrate oxidation. As shown in the figure, greater α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} causes a greater proportion of the distribution to be allocated to the first mode and consequently a lesser proportion of the distribution constitutes the second mode. In practical terms, this means a greater α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document} shifts RER toward increased fat oxidation. B1–B3 Different x50F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{F}$$\end{document} but the same α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}, x50C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{C}$$\end{document}, bF\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{F}$$\end{document}, and bC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{C}$$\end{document}. Bar graph of all parameters, with x50F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{F}$$\end{document} indicated in color (B1). Probability distribution function indicating theoretical distributions of data with three different x50F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{F}$$\end{document} values (B2). Cumulative distribution function [Mixed Weibull Cumulative Distribution function] for the same x50F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{F}$$\end{document} values (B3). Vertical colored lines indicate x50F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{F}$$\end{document} RER values, which are the median RER values of the carbohydrate mode of the respective distributions. Related to RER, the first mode represents fat oxidation and the second mode represents carbohydrate oxidation. As shown in the figure, greater x50F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{F}$$\end{document} shifts the curve representing the first mode to the right, which signifies a higher median RER value in the fat oxidation mode. The further x50F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{F}$$\end{document} differs from 0.70, the less fat oxidation. C1-C3: Different bF\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{F}$$\end{document} but the same α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}, x50F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{F}$$\end{document}, x50C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{C}$$\end{document}, and bC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{C}$$\end{document}. Bar graph of all parameters, with bF\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{F}$$\end{document} indicated in color (C1). Probability distribution function indicating theoretical distributions of data with three different bF\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{F}$$\end{document} values (C2). Cumulative distribution function [Mixed Weibull Cumulative Distribution function] for the same bF\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{F}$$\end{document} values (C3). Related to RER, the first mode represents fat oxidation and the second mode represents carbohydrate oxidation. As shown in the figure, greater bF\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{F}$$\end{document} steepens the curve representing the first mode, which signifies a smaller spread of RER values in the fat oxidation mode. We interpret this to suggest more efficient switching to fat oxidation and thus enhanced metabolic flexibility. D1-D3 Different x50C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{C}$$\end{document} but the same α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}, x50F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{F}$$\end{document}, bF\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{F}$$\end{document}, and bC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{C}$$\end{document}. Bar graph of all parameters, with x50C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{C}$$\end{document} indicated in color (D1). Probability distribution function indicating theoretical distributions of data with three different x50C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{C}$$\end{document} values (D2). Cumulative distribution function [Mixed Weibull Cumulative Distribution function] for the same x50C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{C}$$\end{document} values (D3). Vertical colored lines indicate x50C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{C}$$\end{document} RER values, which are the median RER values of the carbohydrate mode of the respective distributions. Related to RER, the first mode represents fat oxidation and the second mode represents carbohydrate oxidation. As shown in the figure, greater x50C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{C}$$\end{document} shifts the curve representing the second mode to the right, which signifies a higher median RER value in the carbohydrate oxidation mode. The further x50C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{C}$$\end{document} differs from 1.00, the less carbohydrate oxidation. Decreasing values of x50C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{C}$$\end{document} below 1.0 reflect increasing concurrent fat oxidation and values above 1.0 may indicate de novo lipogenesis. E1–E3 Different bC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{C}$$\end{document} but the same α\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}, x50F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{F}$$\end{document}, x50C\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x50}_{C}$$\end{document}, and bF\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{F}$$\end{document}. Bar graph of all parameters, with bC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{C}$$\end{document} indicated in color (E1). Probability distribution function indicating theoretical distributions of data with three different bC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{C}$$\end{document} values (E2). Cumulative distribution function [Mixed Weibull Cumulative Distribution function] for the same bC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{C}$$\end{document} values (E3). Related to RER, the first mode represents fat oxidation and the second mode represents carbohydrate oxidation. As shown in the figure, greater bC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${b}_{C}$$\end{document} steepens the curve representing the second mode, which signifies a smaller spread of RER values in the carbohydrate oxidation mode. We interpret this to suggest more efficient switching to carbohydrate oxidation and thus enhanced metabolic flexibility. au, arbitrary unit; PRCF percent relative cumulative frequency; RER respiratory exchange ratio
Average percent relative cumulative frequency (PRCF) curves of respiratory exchange ratio (RER) modeled using the Mixed Weibull Cumulative Distribution plotted by taking the means of the modeled parameter values for each group. Split by genotype and amyloglucosidase (AMG) supplementation: wild-type without AMG (A), wild-type with AMG (B), null without AMG (C), null with AMG (D). n = 7–8 per group. AMG, amyloglucosidase; PRCF percent relative cumulative distribution, RCS raw corn starch, RER respiratory exchange ratio
Metabolic Flexibility Factor (MFF) for the diet × genotype × cycle [AMG] groupings. A high MFF is suggested to indicate improved metabolic flexibility. Bars represent mean values, shown with ± standard error of the mean, and symbols represent individual observations (mice) per dietary condition. Different letters indicate statistically significant differences (p < 0.05) per diet. In addition to the main effects of diet shown (p < 0.0001), there was also an effect of cycle [AMG], such that AMG decreased MFF compared to without AMG (p = 0.0002). There was also an interaction effect for diet × cycle for MFF (p = 0.0008). n = 7–8 per group. AMG amyloglucosidase; au, arbitrary units, MFF Metabolic Flexibility Factor
Moderating carbohydrate digestion rate in mice promotes fat oxidation and metabolic flexibility revealed through a new approach to assess metabolic substrate utilization
  • Article
  • Publisher preview available

February 2025

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25 Reads

European Journal of Nutrition

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Bruce R. Hamaker

Purpose Superior metabolic flexibility, or the ability to efficiently switch between oxidation of carbohydrate and fat, is inversely associated with obesity and type 2 diabetes. The influence of dietary factors on metabolic flexibility is incompletely understood. This study examined the impact of dietary carbohydrate digestion rate on metabolic flexibility and metabolic substrate utilization. Methods We employed percent relative cumulative frequency (PRCF) analyses coupled with a new application of modeling using the Mixed Weibull Cumulative Distribution function to examine respiratory exchange ratio (RER) data from adult wild-type mice and mice lacking the mucosal maltase-glucoamylase enzyme (Mgam) under different dietary carbohydrate conditions, with diets matched for total carbohydrate contents and containing different ratios of slowly digestible starch (SDS) and resistant starch (RS), or that were high in sucrose or fat. Fungal amyloglucosidase (AMG) was administered in drinking water to increase carbohydrate digestion rate. We devised a Metabolic Flexibility Factor (MFF) to quantitate metabolic flexibility for each dietary condition and mouse genotype, with higher MFF indicating higher metabolic flexibility. Results Diets high in SDS exhibited lower average RER and higher metabolic flexibility (MFF) than diets high in resistant starch, sucrose, or fat. Diets containing high and intermediate amounts of SDS led to a more complete shift to fat oxidation. While mouse genotype had minimal effects on substrate oxidation and MFF, AMG supplementation shifted substrate utilization to carbohydrate oxidation and generally decreased MFF. Conclusions Consumption of slowly digestible carbohydrates improved measures of metabolic substrate utilization at the whole-body level in adult mice.

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Figure 2. Volatile organic compound output following a bowel movement. This is an example of a typical bowel movement pattern depicting how both the Min/Max fall values and area over the curve data were generated for each individual research participant.
Figure 4. Prebiotic consumption beneficially impacted the microbiota. Analysis of curated genera lists revealed that the relative abundances of (a) total SCFA-producing taxa, (b) total acetate-producing taxa, and (c) total butyrate-producing taxa were all significantly higher following prebiotic intervention. (d) Total propionate-producing taxa relative abundance remained unchanged with prebiotics. In contrast, (e) the relative abundance of Gram-negative proinflammatory taxa and (f) Gram-negative-to-total SCFA ratio were both significantly lower post-intervention. (g) The Firmicutes-to-Bacteroidota ratio trended higher. (h) A significant relationship was observed between the reduction in Gram-negative proinflammatory genera and increased fiber intake following the twoweek prebiotic intervention. Statistical analyses: A line connects each participant at baseline and after prebiotic intervention. Bar height represents the group's mean value and individual samples are indicated. n = 11. (a-g) Wilcoxon signed-rank paired test and (h) linear regression. The microbiota data are provided in Supplementary Data File S1.
Participants' demographics.
Bosch BME 680 VOC Sensor Technical Specification Sheet.
Use of a Novel Passive E-Nose to Monitor Fermentable Prebiotic Fiber Consumption

January 2025

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15 Reads

We developed a home-based electronic nose (E-Nose) to passively monitor volatile organic compounds (VOCs) emitted following bowel movements and assessed its validity by correlating the output with prebiotic fiber intake. Healthy, non-overweight participants followed a three-week protocol which included the following: (1) installing the E-Nose in their bathroom; (2) activating the device following each bowel movement; (3) recording their dietary intake; (4) consuming a fiber bar (RiteCarbs) containing a blend of 10 g of prebiotic fiber daily during weeks two and three; and (5) submit stool specimens at the beginning and end of the study for 16S rRNA gene sequencing and analysis. Participants’ fecal microbiome displayed significantly increased relative abundance of putative total SCFA-producing genera (p = 0.0323) [total acetate-producing genera (p = 0.0214), total butyrate-producing genera (p = 0.0131)] and decreased Gram-negative proinflammatory genera (p = 0.0468). Prebiotic intervention significantly increased the participants’ fiber intake (p = 0.0152), E-Nose Min/Max (p = 0.0339), and area over the curve in VOC–to–fiber output (p = 0.0044). Increased fiber intake was negatively associated (R2 = 0.53, p = 0.026) with decreased relative abundance of putative Gram-negative proinflammatory genera. This proof-of-concept study demonstrates that a prototype E-Nose can noninvasively detect a direct connection between fiber intake and VOC outputs in a home-based environment.


Characterization and In Vitro Digestion Kinetics of Purified Pulse Starches: Implications on Bread Formulation

January 2025

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3 Reads

This study investigated the contribution of pulse starches (PSs) to the slowly digestible starch (SDS) properties observed in pulses. Purified pulse starches from 17 commonly consumed pulses were examined, focusing on their digestion kinetics using a pancreatic alpha-amylase (PAA) and rat intestinal acetone powder (RIAP) mixture. Chickpea starch, exhibiting a slow digestibility profile, was incorporated as an ingredient to confer slow digestibility to refined wheat flour bread. Our findings reveal that some PSs exhibited low digestibility when gelatinized (100 °C, 30 min) and retrograded (7 days, 4 °C). Rapid retrogradation was observed in starch from chickpeas, lentils, field peas, adzuki beans, navy beans, large lima beans, and great northern beans. The incorporation of chickpea starch into fortified bread significantly improved its slow digestibility properties. This study reveals the potential of pulse starch as a promising functional ingredient for baked products, related to the faster retrogradation of many pulse-sourced starches. These findings contribute valuable insights into the slow digestibility attributes of pulse starches for developing food products with enhanced nutritional profiles.


Prebiotics as an adjunct therapy for posttraumatic stress disorder: a pilot randomized controlled trial

January 2025

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29 Reads

Introduction Posttraumatic stress disorder (PTSD) is a debilitating disorder characterized by intrusive memories, avoidance, negative thoughts and moods, and heightened arousal. Many patients also report gastrointestinal symptoms. Cognitive behavioral therapy (CBT) is an evidence-based treatment approach for PTSD that successfully reduces symptoms. However, many patients still meet criteria for PTSD after treatment or continue to have symptoms indicating the need for new treatment strategies for PTSD. Patients with PTSD have a disrupted intestinal microbiome (i.e., dysbiosis) which can promote neuroinflammation; thus, modulation of the microbiome could be an alternative or adjunct treatment approach for PTSD. Methods The current study was a 12-week, double-blind, placebo-controlled trial seeking to understand if CBT combined with a microbiota-modifying, prebiotic fiber intervention would beneficially impact clinical outcomes in veterans with PTSD (n = 70). This proof-of-concept, pilot trial was designed to assess: (1) the relationship between severity of PTSD symptoms and microbiota composition and SCFA levels (i.e., acetate, propionate, butyrate), (2) if CBT treatment with a concomitant prebiotic fiber intervention would beneficially impact clinical outcomes in veterans with PTSD, (3) evaluate the feasibility and acceptability of a prebiotic intervention as an adjunct treatment to CBT, and (4) assess the impact of treatment on the intestinal microbiota and stool SCFA (i.e., mechanism). Results This study found that PTSD severity may be associated with reduced abundance of taxa capable of producing the SCFA propionate, and that a subset of individuals with PTSD may benefit from a microbiota-modifying prebiotic intervention. Conclusion This study suggests that targeting the intestinal microbiome through prebiotic supplementation could represent a promising avenue for enhancing treatment outcomes in some individuals with PTSD. Clinical trial registration https://clinicaltrials.gov/, identifier NCT05424146.




Systematically-designed mixtures outperform single fibers for gut microbiota support

December 2024

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29 Reads

Dietary fiber interventions to modulate the gut microbiota have largely relied on isolated fibers or specific fiber sources. We hypothesized that fibers systematically blended could promote more health-related bacterial groups. Initially, pooled in vitro fecal fermentations were used to design dietary fiber mixtures to support complementary microbial groups related to health. Then, microbial responses were compared for the designed mixtures versus their single fiber components in vitro using fecal samples from a separate cohort of 10 healthy adults. The designed fiber mixtures outperformed individual fibers in supporting bacterial taxa across donors resulting in superior alpha diversity and unexpected higher SCFA production. Moreover, unique shifts in community structure and specific taxa were observed for fiber mixtures that were not observed for single fibers, suggesting a synergistic effect when certain fibers are put together. Fiber mixture responses were remarkably more consistent than individual fibers across donors in promoting several taxa, especially butyrate producers from the Clostridium cluster XIVa. This is the first demonstration of synergistic fiber interactions for superior support of a diverse group of important beneficial microbes consistent across people, and unexpectedly high SCFA production. Overall, harnessing the synergistic potential of designed fiber mixtures represents a promising and more efficacious avenue for future prebiotic development.



Citations (61)


... In COPD patients, sugarcane fiber improved their quality of life despite unaltered symptoms [197]. When talking about fiber, its chemical diversity needs to be considered, as it largely determines the specific health impact on humans [198,199]. Table 1 provides an overview of all common fiber types according to their chemicophysical properties. ...

Reference:

Impact of Dietary Fiber on Inflammation in Humans
Dietary Fiber’s Physicochemical Properties and Gut Bacterial Dysbiosis Determine Fiber Metabolism in the Gut

... Large repositories of genetic diversity with thousands of landraces and improved clones of cassava and wild relatives are maintained in germplasm collections (Hershey, 2008) The primary focus for breeders worldwide has been the development of cassava varieties capable of effectively supporting value chains and addressing food security challenges. Achieving a high, stable, and reliable yield has been of paramount importance, often overshadowing considerations for consumer-preferred traits and postharvest aspects in cassava cultivation for human consumption (Dufour et al., 2024;Eriksson et al., 2018;Thiele et al., 2021). As breeders and food technologists turned their attention to improving the cooking quality of cassava, they discovered a significant lack of understanding regarding the inheritance of quality traits. ...

Tropical roots, tubers and bananas: new breeding tools and methods to meet consumer preferences

... Excess protein that the body cannot utilize undergoes fermentation in the large intestine, which increases the number of proteolytic and pathogenic bacteria. The growth of these harmful bacteria promotes their metabolic processes, leading to the production of several toxic substances, such as urea, indoxyl sulfate (IS), p-cresyl sulfate (PCS), ammonia, hydrogen, and histamine [6]. ...

Protein combined with certain dietary fibers increases butyrate production in gut microbiota fermentation

Food & Function

... Hence, a thermal treatment is required to impede rancidity development in micronutrient-fortified WGMM. Hot-air drying of maize grain to 11.6% moisture substantially impedes rancidity development in fortified WGMM development as reported by Taylor et al. [221]. ...

Reduction in rancidity development in fortified whole‐grain maize meal by hot‐air drying of the grain

Cereal Chemistry

... HMs through gut lining injury and its leak can increase oxidative stress, and inflammation, have cytotoxic, genotoxic, and carcinogenic effects following gut dysbiosis, and lead to some diseases [34]. Hence, enough intake of dietary fiber especially wheat bran and pectin, antioxidant-rich food sources, treatment with probiotics and prebiotics, and following a low to moderate fat diet may be effective strategies for preventing HMs toxicity and gut dysbiosis [55][56][57][58]. Furthermore, we should consider micronutrients deficiency (Iron, zinc, etc.) in particular in high-risk groups such as pregnant women and children [59,60]. ...

Specific dietary fibers prevent heavy metal disruption of the human gut microbiota in vitro
  • Citing Article
  • December 2023

Food Research International

... 7,8 Notably, microbes have different fiber degradation abilities and preferences, [9][10][11] and even small differences in dietary fiber structures can lead to marked shifts in bacterial outcomes. 12,13 Dietary fiber refers mainly to a diverse group of plant-derived carbohydrates that resist digestion in the upper gastrointestinal tract and reach the large intestine intact. 14 Although humans lack the enzymes necessary to break down fiber, it serves as a valuable energy source for gut microbes, which possess the enzymatic machinery to ferment most of these complex carbohydrates. ...

(1 → 3),(1 → 6) and (1 → 3)-β-D-glucan physico-chemical features drive their fermentation profile by the human gut microbiota
  • Citing Article
  • December 2023

Carbohydrate Polymers

... Kurşun (Pb): Lead is toxic and can cause developmental problems in children and various health problems in adults (Román-Ochoa, et al, 2023). Although the level of 0.04 mg/kg Pb in bread is low, caution should be exercised as there is no safe level of lead exposure. ...

Light abrasive decortication of heavy metal contaminated quinoa and rice from southern Perú reduces lead and arsenic contamination, but not cadmium
  • Citing Article
  • October 2023

Journal of Cereal Science

... SCFAs can also promote the differentiation of undifferentiated T cells into Treg cells, promote the production of anti-inflammatory factor IL-10, and alleviate intestinal inflammation. Studies have shown that the integrity of the intestinal barrier is closely related to the development of diseases(Hao et al., 2024;Manfready et al., 2023). Butyrate can enhance the expression of tight junction proteins claudin-1 and zonula occludens-1, regulate the permeability of intestinal intercellular channels, and enhance the protective effect of intestinal barrier, whereas acetate can promote the integrity of intestinal barrier by activating IL-18 receptorXie et al., 2023) In addition, mucin is an important component of the mucous layer. ...

28 INTESTINAL MICROBIAL DYSBIOSIS AND IMPAIRED SCFA PRODUCTION, GLP-1 SECRETION, AND INTESTINAL BARRIER FUNCTION IN PARKINSON'S DISEASE
  • Citing Article
  • May 2023

Gastroenterology

... The issue of UPF consumption is impacting nutrition perspectives and policy. In 2023, the USDA sponsored a 2-day workshop on this topic (O'Connor et al., 2023). Moreover, a question addressed by the 2025-2030 US Dietary Guidelines Advisory Committee is "What is the relationship between consumption of dietary patterns with varying amounts of UPFs and growth, body composition, and risk of obesity?" Nova has been embraced by the World Health Organization (WHO) (Monteiro et al., 2019(Monteiro et al., , 2018 and incorporated into the dietary guidelines of several countries (Koios et al., 2022). ...

Perspective: A Research Roadmap about ultra-processed foods and human health for the US food system: Proceedings from an interdisciplinary, multi-stakeholder workshop

Advances in Nutrition

... Alpha diversity is also reduced when specific components act on intestinal bacteria. Alpha diversity has been reported to either increase or decrease due to increased dietary fiber intake [23]. In this study, the WSCA and DE groups exhibited a prominent decrease in alpha diversity among the NASH groups, which suggests that WSCA and indigestible dextrin affect specific intestinal microbiota. ...

Tuning Expectations to Reality: Don’t Expect Increased Gut Microbiota Diversity with Dietary Fiber
  • Citing Article
  • September 2023

Journal of Nutrition