Interest is rapidly growing around the role of the human gut microbiota in facilitating beneficial health effects associated with consumption of dietary fiber. An evidence map of current research activity in this area was created using a newly developed database of dietary fiber intervention studies in humans to identify studies with the following broad outcomes: (1) modulation of colonic microflora; and/or (2) colonic fermentation/short-chain fatty acid concentration. Study design characteristics, fiber exposures, and outcome categories were summarized. A sub-analysis described oligosaccharides and bacterial composition in greater detail. One hundred eighty-eight relevant studies were identified. The fiber categories represented by the most studies were oligosaccharides (20%), resistant starch (16%), and chemically synthesized fibers (15%). Short-chain fatty acid concentration (47%) and bacterial composition (88%) were the most frequently studied outcomes. Whole-diet interventions, measures of bacterial activity, and studies in metabolically at-risk subjects were identified as potential gaps in the evidence. This evidence map efficiently captured the variability in characteristics of expanding research on dietary fiber, gut microbiota, and physiological health benefits, and identified areas that may benefit from further research. We hope that this evidence map will provide a resource for researchers to direct new intervention studies and meta-analyses.

Results from some observational studies suggest that higher whole grain (WG) intake is associated with lower risk of weight gain. Ovid Medline was used to conduct a literature search for observational studies and randomized controlled trials (RCTs) assessing WG food intake and weight status in adults. A meta-regression analysis of cross-sectional data from 12 observational studies (136,834 subjects) and a meta-analysis of nine RCTs (973 subjects) was conducted; six prospective cohort publications were qualitatively reviewed. Cross-sectional data meta-regression results indicate a significant, inverse correlation between WG intake and body mass index (BMI): weighted slope, −0.0141 kg/m² per g/day of WG intake (95% confidence interval (CI): −0.0207, −0.0077; r = −0.526, p = 0.0001). Prospective cohort results generally showed inverse associations between WG intake and weight change with typical follow-up periods of five to 20 years. RCT meta-analysis results show a nonsignificant pooled standardized effect size of −0.049 kg (95% CI −0.297, 0.199, p = 0.698) for mean difference in weight change (WG versus control interventions). Higher WG intake is significantly inversely associated with BMI in observational studies but not RCTs up to 16 weeks in length; RCTs with longer intervention periods are warranted.

Greater whole grain (WG) consumption is associated with reduced risk of cardiovascular disease (CVD); however, few prospective studies have examined WG or refined grain (RG) intake and intermediate cardiometabolic risk factors. We examined the longitudinal association between WG and RG intake on changes in waist circumference (WC); fasting HDL cholesterol, triglyceride, and glucose concentrations; and blood pressure. Subjects were participants in the Framingham Offspring cohort study [n = 3121; mean ± SD baseline age: 54.9 ± 0.2 y; BMI (kg/m²) 27.2 ± 0.1]. FFQ, health, and lifestyle data were collected approximately every 4 y over a median 18-y follow-up. Repeated measure mixed models were used to estimate adjusted mean changes per 4-y interval in risk factors across increasing categories of WG or RG intake. Greater WG intake was associated with smaller increases in WC (1.4 ± 0.2 compared with 3.0 ± 0.1 cm in the highest compared with the lowest category, respectively; P-trend < 0.001), fasting glucose concentration (0.7 ± 0.4 compared with 2.6 ± 0.2 mg/dL; P-trend < 0.001), and systolic blood pressure (SBP; 0.2 ± 0.5 compared with 1.4 ± 0.3 mm Hg; P-trend < 0.001) per 4-y interval. When stratified by sex, a stronger association with WC was observed among females than males. Higher intake of WG was associated with greater increases in HDL cholesterol and declines in triglyceride concentrations; however, these differences did not remain significant after adjustment for change in WC. Conversely, greater RG intake was associated with greater increases in WC (2.7 ± 0.2 compared with 1.8 ± 0.1 cm, P-trend < 0.001) and less decline in triglyceride concentration (−0.3 ± 1.3 compared with −7.0 ± 0.7 mg/dL, P-trend < 0.001). Among middle- to older-age adults, replacing RG with WG may be an effective dietary modification to attenuate abdominal adiposity, dyslipidemia, and hyperglycemia over time, thereby reducing the risk of cardiometabolic diseases.

The long-term impact of carbohydrate quality on abdominal weight gain is not fully understood. We aimed to examine the prospective relation of a carbohydrate quality index (CQI; defined by four criteria: dietary fiber, glycemic index, whole grain-to-total grain ratio, and solid-to-total carbohydrate ratio), total, cereal grain, vegetable, and fruit fiber, carbohydrate-to-total fiber ratio, and carbohydrate-to-cereal fiber ratio with changes in waist circumference (WC). Subjects were middle-aged to older, mostly white, participants in the Framingham Offspring cohort (n = 3101 subjects), with mean baseline age 54.9 ± 0.2 years (mean ± SE) and body mass index (BMI) 27.2 ± 0.1 kg/m². Food frequency questionnaire (FFQ), health, and lifestyle data were collected approximately every four years over a median total follow-up of 18 years. Repeated measure mixed models were used to estimate adjusted mean change in WC per four-year interval across quartiles of carbohydrate variables. In the most adjusted model, a higher CQI was marginally associated with a smaller increase in WC (2.0 ± 0.1 vs. 2.4 ± 0.1 cm in highest vs. lowest quartile, p-trend = 0.04). Higher ratios of carbohydrate-to-fiber and carbohydrate-to-cereal fiber were associated with greater increases in WC per four-year interval (2.6 ± 0.1 vs. 2.0 ± 0.1 cm, p-trend < 0.001, and 2.5 ± 0.1 vs. 2.1 ± 0.1 cm in highest versus lowest categories, p-trend = 0.007, respectively); whereas higher intake of total fiber (1.8 ± 0.1 vs. 2.7 ± 0.1 cm, p-trend < 0.001), cereal fiber (2.0 ± 0.1 vs. 2.5 ± 0.1 cm, p-trend = 0.001), and fruit fiber (2.0 ± 0.1 vs. 2.7 ± 0.1 cm, p-trend < 0.001) were associated with smaller increases in WC compared to lower intakes. There was a significant interaction between total fiber and total carbohydrate (as % of total energy intake). After stratification, the association between fiber intake and change in WC was not maintained in the context of a high carbohydrate diet. Better carbohydrate quality, primarily higher fiber intake and lower carbohydrate-to-fiber ratios, may help attenuate increases in abdominal adiposity over time.

Background: UVB light from the sun increases serum 25-hydroxyvitamin D [25(OH)D] concentration, but this relation may depend on skin pigmentation amon

The overall aim of precision nutrition is to replace the ‘one size fits all’ approach to dietary advice with recommendations that are more specific to the individual in order to improve the prevention or management of chronic disease. Interest in precision nutrition has grown with advancements in technologies such as genomics, proteomics, metabolomics and measurement of the gut microbiome. Precision nutrition initiatives have three major applications in precision medicine. First, they aim to provide more ‘precision’ dietary assessments through artificial intelligence, wearable devices or by employing omic technologies to characterise diet more precisely. Secondly, precision nutrition allows us to understand the underlying mechanisms of how diet influences disease risk and identify individuals who are more susceptible to disease due to gene–diet or microbiota–diet interactions. Third, precision nutrition can be used for ‘personalised nutrition’ advice where machine-learning algorithms can integrate data from omic profiles with other personal and clinical measures to improve disease risk. Proteomics and metabolomics especially provide the ability to discover new biomarkers of food or nutrient intake, proteomic or metabolomic signatures of diet and disease, and discover potential mechanisms of diet–disease interactions. Although there are several challenges that must be overcome to improve the reproducibility, cost-effectiveness and efficacy of these approaches, precision nutrition methodologies have great potential for nutrition research and clinical application.