On December 17, 2018, the North American branch of the International Life Sciences Institute (ILSI North America) convened a workshop "Can We Begin to Define a Healthy Gut Microbiome Through Quantifiable Characteristics?" with >40 invited academic, government, and industry experts in Washington, DC. The workshop objectives were to 1) develop a collective expert assessment of the state of the evidence on the human gut microbiome and associated human health benefits, 2) see if there was sufficient evidence to establish measurable gut microbiome characteristics that could serve as indicators of "health," 3) identify short- and long-term research needs to fully characterize healthy gut microbiome-host relationships, and 4) publish the findings. Conclusions were as follows: 1) mechanistic links of specific changes in gut microbiome structure with function or markers of human health are not yet established; 2) it is not established if dysbiosis is a cause, consequence, or both of changes in human gut epithelial function and disease; 3) microbiome communities are highly individualized, show a high degree of interindividual variation to perturbation, and tend to be stable over years; 4) the complexity of microbiome-host interactions requires a comprehensive, multidisciplinary research agenda to elucidate relationships between gut microbiome and host health; 5) biomarkers and/or surrogate indicators of host function and pathogenic processes based on the microbiome need to be determined and validated, along with normal ranges, using approaches similar to those used to establish biomarkers and/or surrogate indicators based on host metabolic phenotypes; 6) future studies measuring responses to an exposure or intervention need to combine validated microbiome-related biomarkers and/or surrogate indicators with multiomics characterization of the microbiome; and 7) because static genetic sampling misses important short- and long-term microbiome-related dynamic changes to host health, future studies must be powered to account for inter- and intraindividual variation and should use repeated measures within individuals.

Background: Fiber regulates the rate and site of lipid and carbohydrate digestion and absorption and thus can modify the alimentary responses to a meal. When fiber sources containing viscous polysaccharides are included in a meal, a slower rate of carbohydrate and lipid absorption will modify the alimentary hormone and lipid responses. Objective: We investigated in 11 healthy men the response of insulin, glucose, cholecystokinin, and lipid to 2 test meals containing β-glucan. Design: One of the meals was high in fiber (15.7 g) and the other meal was low in fiber (5.0 g). The low-fiber meal contained pasta made with wheat flour. The high-fiber meals contained pasta prepared by replacing 40% of the wheat with 2 types of barley flour: barley naturally high in β-glucan and the other a flour enriched in β-glucan during processing. Results: Plasma glucose and insulin concentrations increased significantly after all meals but the insulin response was more blunted after the barley-containing meals. The test meals were low in fat (25% of energy) but elicited an increase in plasma triacylglycerol and cholecystokinin. Cholecystokinin remained elevated for a longer time after the barley-containing meals. After the low-fiber meal, plasma cholesterol concentrations did not change significantly; however, 4 h after the barley-containing meals, the cholesterol concentration dropped below the fasting concentration and was significantly lower than that after the low-fiber meal. Conclusions: Carbohydrate was more slowly absorbed from the 2 high-fiber meals. Consumption of the barley-containing meals appeared to stimulate reverse cholesterol transport, which may contribute to the cholesterol-lowering ability of barley.

While the health benefit of a functional food may be a metabolic response that lowers risk for disease, the actual target for the food or food component may be on the functioning of the gastrointestinal tract (GIT). For example, slowing absorption from the intestine, as measured by examining the appearance of the nutrient or food component in the blood, the hormone response associated with absorption of the compound or excretion of the compound, may provide a health benefit. However, the food component may slow absorption by delaying gastric emptying, altering the mixing within the intestinal contents or decreasing the availability of digestive enzymes in the intestine. These measures of GIT function provide validation of the mechanisms by which the functional food or food components affect metabolism. Bioavailability of physiologically active compounds from foods will be determined by the digestibility of foods that contain these compounds, their subsequent absorption and utilization by tissues. The physical structure of foods contributes to the functional effects of foods as well as to the availability of compounds from foods. For example, recent studies have demonstrated that changing the viscosity of the gut contents alters absorption and GIT response. Additionally, food structures such as the plant cell wall change the availability of absorbable compounds along the gastrointestinal contents. The areas of probiotics and prebiotics have highlighted the potential importance of gut microflora in health. While evidence suggests biological activity relevant to disease risk reduction, the long-term implications of the microbial activity have yet to be established.

Background: Cholecystokinin is associated with satiety. Fat stimulates cholecystokinin release, and fiber appears to prolong cholecystokinin elevation during the alimentary period. Objective: We tested whether adding fiber or fat to a low-fat, low-fiber meal increases cholecystokinin release and enhances subjective measures of satiety and whether the cholecystokinin response correlates with subjective measures of satiety. Design: Three isoenergetic breakfast meals were tested in a randomized crossover design: low fiber, low fat; high fiber, low fat; and low fiber, high fat. Blood samples were drawn from fasted subjects (7 men and 8 women) before and at different time points after test meal consumption for 6 h. Plasma was analyzed for cholecystokinin, insulin, glucose, and triacylglycerols. Visual analogue scales were used to assess subjects' hunger, desire to eat, fullness, and prospective consumption. Results: In the women, the meals higher in fiber or in fat resulted in greater feelings of satiety and in significantly higher cholecystokinin responses than did the low-fat, low-fiber meal. In the men, the increase in cholecystokinin concentration did not differ between meals, but the 2 low-fat meals elicited a greater feeling of satiety than did the high-fat meal. The insulin response was significantly higher for the low-fiber, low-fat meal than for the other 2 meals, and the triacylglycerol response was greatest for the high-fat, low-fiber meal. Conclusion: In women, the feeling of satiety caused by cholecystokinin release is enhanced by increasing either the fiber or fat content of a low-fat, low-fiber meal.

Dry beans lower plasma cholesterol, an effect that has been associated with both the fiber and the protein content of beans. The objective of this study was to determine the acute hormone and lipid responses to a test meal that contained dry beans as a source of dietary fiber. A crossover design was employed in which men consumed the test meal and a control meal in random order. Both meals contained egg, bread, jelly, orange juice, milk and margarine. The high fiber meal contained white beans, whereas the low fiber (control) meal contained rice and dry milk. The men maintained their normal dietary pattern and fasted overnight before the study days. After a fasting blood sample was drawn, the men consumed the test meal and blood samples were collected over the next 6 h. Blood samples were analyzed for cholecystokinin (CCK), insulin and glucose. Plasma was separated into lipoprotein fractions and the triglyceride, cholesterol, apolipoprotein (apo) B100 and B48 content of triglyceride-rich lipoproteins determined. Insulin and glucose responses did not differ significantly between test meals; however, the CCK response was twice as high after the bean-containing meal than after the low fiber meal (P = 0.03). The increase in apo B48 concentration was significantly higher after the bean meal than after the low fiber meal (P < 0.05). Adding beans to a meal to increase fiber content prolongs the postprandial presence of intestinally derived lipoproteins and augments the CCK response to the meal.

This important public health question was identified by the U.S. Departments of Agriculture (USDA) and Health and Human Services (HHS) to be examined by the 2020 Dietary Guidelines Advisory Committee. The 2020 Dietary Guidelines Advisory Committee, Dietary Patterns Subcommittee conducted a systematic review to answer this question with support from the Nutrition Evidence Systematic Review (NESR) team. The goal of this systematic review was to examine the following question: What is the relationship between dietary patterns consumed and growth, size, body composition, and/or risk of overweight or obesity? Dietary patterns: Children: Limited evidence suggests that dietary patterns consumed by children or adolescents that are lower in fruits, vegetables, whole grains, and low-fat dairy while being higher in added sugars, refined grains, fried potatoes, and processed meats are associated with higher fat-mass index and BMI later in adolescence. (Grade: Limited). Dietary patterns: Adults: The 2020 Dietary Guidelines Advisory Committee reviewed newly published evidence using a systematic evidence scan and determined that the conclusion drawn by the 2015 Dietary Guidelines Advisory Committee generally reflects the current state of science. Moderate evidence indicates dietary patterns emphasizing vegetables, fruits, and whole grains; seafood and legumes; moderate in dairy products (particularly low and non-fat dairy) and alcohol; lower in meats (including red and processed meats), and low in sugar-sweetened foods and beverages, and refined grains are associated with favorable outcomes related to body weight, (including lower BMI, waist circumference, or percent body fat) or risk of obesity. Components of the dietary patterns associated with these favorable outcomes include higher intakes of unsaturated fats and lower intakes of saturated fats, cholesterol, and sodium. (2015 Dietary Guidelines Advisory Committee Grade: Moderate). Diets based on macronutrient distribution: Children: No evidence is available to determine a relationship between diets based on macronutrient distribution consumed during childhood and growth, size, body composition, and risk of overweight/obesity. (Grade: Grade not assignable). Diets Based on macronutrient distribution: Adults: Insufficient evidence is available to determine the relationship between macronutrient distributions with proportions of energy falling outside of the AMDR for at least one macronutrient and growth, size, body composition, and risk of overweight/obesity, due to methodological limitations and inconsistent results. (Grade: Grade not assignable). Two literature searches were conducted using 3 databases (PubMed, Cochrane, and Embase) to identify articles that evaluated the intervention or exposure of dietary patterns consumed and the outcomes of type 2 diabetes. A manual search was conducted to identify articles that may not have been included in the electronic databases searched. Articles were screened by two NESR analysts independently for inclusion based on pre-determined criteria. Data extraction and risk of bias assessment were conducted for each included study, and both were checked for accuracy. The Committee qualitatively synthesized the body of evidence to inform development of a conclusion statement(s), and graded the strength of evidence using pre-established criteria for risk of bias, consistency, directness, precision, and generalizability. Dietary patterns were defined as the quantities, proportions, variety, or combination of different foods, drinks, and nutrients in diets, and the frequency with which they are habitually consumed. Diets based on macronutrient distribution were considered to be those with at least one macronutrient proportion outside of the acceptable macronutrient distribution range (AMDR) for carbohydrate, fat, and/or protein, whether or not the foods/food groups consumed were provided. Studies examining energy-restricted diets that induce weight loss or treat overweight and obesity for the purposes of treating additional or other medical conditions were excluded. Eighty-eight articles were identified that met inclusion criteria and examined the relationship between dietary patterns and/or diets based on macronutrient proportion and growth, size, body composition, and/or risk of overweight or obesity. Twelve articles from prospective cohort studies were identified that examined dietary patterns consumed by children and growth, size, body composition, and/or risk of overweight or obesity, met inclusion criteria, and were published between January 2014 and October 2019. ○. Dietary patterns were assessed using a variety of methods including factor/cluster analysis, indices or scores, latent class analysis, and reduced rank regression. ○. Outcome measures varied across studies and included incidence of overweight/obesity, fat mass, lean mass, body mass index (BMI), central adiposity, and weight/height. ○. Despite variability in methods, dietary patterns in childhood or adolescence that generally reflect poorer diet quality tended to associate with higher fatmass index and BMI later in adolescence. However, the findings should be interpreted with caution due to several limitations. ○. Across the body of evidence, the direction of significant findings were mixed with relatively small and inconsistent magnitude. ○. Most of the studies assessed diet once at baseline with methods that were not necessarily valid, reliable, or applicable in children. Fifty-four articles examined dietary patterns consumed by adults and growth, size, body composition, and/or risk of overweight or obesity. The 2020 Committee determined with a systematic evidence scan that the recently published evidence was generally consistent with the body of evidence from the existing review, and a full systematic review update was not needed at this time. Therefore, the conclusion statement and grade from the existing review were carried forward. No studies identified met inclusion criteria that examined diets based on macronutrient distribution consumed by children or adolescents and growth, size, body composition, and/or risk of overweight or obesity. Thirty-one articles examined diets based on macronutrient distribution consumed by adults and growth, size, body composition, and/or risk of overweight or obesity, met inclusion criteria, and were published between January 2000 and October 2019: ○. Twenty-two articles came from randomized controlled trials (RCTs) and nine articles came from prospective cohort studies. ○. When describing and categorizing studies included in this review, the Committee did not label the diets examined as “low” or “high,” because no standard definition is currently available for, for example, “low-carbohydrate” or “high-fat” diets. Instead, the Committee focused on whether, and the extent to which, the proportions of the macronutrients were below or above the AMDR. ○. Most of the articles examined distributions in which the proportion of energy from carbohydrate was below the AMDR; fat was above the AMDR; and protein was within the AMDR in at least one of the exposure groups compared. ○. Foods or food groups consumed as part of the diet were not reported consistently. ○. Results across studies were inconsistent, with the majority of studies reporting no significant association between diets based on macronutrient distribution and growth, size, body composition, and/or risk of overweight or obesity. ○. Some studies reported diets based on macronutrient distributions with proportions of energy from carbohydrate and/or fat within or close to AMDRs compared to those that were outside AMDRs significantly associated with favorable fat-mass, BMI, waist circumference, and/or change in weight. Notably, these results were closely linked to better quality of the diet where reported, typically consisting of vegetables, fruit, whole-grains, vegetable oils, nuts, pulses, and/or lean meat or fish. ○. Numerous limitations that prevent adequate assessment across this body of evidence were identified: ■. Several studies did not directly test the difference in macronutrient proportions in the context of various dietary patterns during energy balance. ■. Although statistically significant relationships were reported, the gradient between macronutrient distributions was relatively narrow within studies (e.g., 45.3% vs. 43.8% carbohydrate) and between studies. ■. Due to the variety of methods used to estimate macronutrient intake and adjust intake for total energy, the confidence in the reported proportions of energy falling outside the AMDR is low. ■. Several studies reported to be examining one particular macronutrient of interest, such as “high-protein” or “low-carbohydrate” intake, but the proportion for that nutrient was within the AMDR.