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.

Public interest in popular diets is increasing, in particular whole-food plant-based (WFPB) and vegan diets. Whether these diets, as theoretically implemented, meet current food-based and nutrient-based recommendations has not been evaluated in detail. Self-identified WFPB and vegan diet followers in the Adhering to Dietary Approaches for Personal Taste (ADAPT) Feasibility Survey reported their most frequently used sources of information on nutrition and cooking. Thirty representative days of meal plans were created for each diet. Weighted mean food group and nutrient levels were calculated using the Nutrition Data System for Research (NDSR) and data were compared to DRIs and/or USDA Dietary Guidelines/MyPlate meal plan recommendations. The calculated HEI-2015 scores were 88 out of 100 for both WFPB and vegan meal plans. Because of similar nutrient composition, only WFPB results are presented. In comparison to MyPlate, WFPB meal plans provide more total vegetables (180%), green leafy vegetables (238%), legumes (460%), whole fruit (100%), whole grains (132%), and less refined grains (−74%). Fiber level exceeds the adequate intakes (AI) across all age groups. WFPB meal plans failed to meet the Recommended Dietary Allowances (RDA)s for vitamin B12 and D without supplementation, as well as the RDA for calcium for women aged 51–70. Individuals who adhere to WFBP meal plans would have higher overall dietary quality as defined by the HEI-2015 score as compared to typical US intakes with the exceptions of calcium for older women and vitamins B12 and D without supplementation. Future research should compare actual self-reported dietary intakes to theoretical targets.

Consumption of whole grains have been associated with reduced risk of chronic diseases in many observational studies; yet, results of intervention studies are mixed. We aimed to use evidence mapping to capture the methodological and reporting variability in whole grain intervention studies that may contribute to this inconsistency. We conducted a reproducible search in OVID Medline for whole grain human intervention studies (published 1946 to February 2018). After screening based on a priori criteria, we identified 202 publications describing a total of 213 unique trials. Over half (55%) were acute trials, lasting ≤1 day, 30% were moderate duration studies (up to 6 weeks) and 15% were of longer duration (more than 6 weeks). The majority of acute trials (75%) examined measures of glycaemia and/or insulinemia, while most of the longer trials included measures of cardiometabolic health (71%), appetite/satiety (57%) and weight/adiposity (56%). Among the moderate and long duration trials, there was a wide range of how whole grains were described but only 10 publications referenced an established definition. Only 55% of trials reported the actual amount of whole grains (in grams or servings), while 36% reported the amount of food/product and 9% did not report a dose at all. Of the interventions that provided a mixture of whole grains, less than half (46%) reported the distribution of the different grain types. Reporting of subject compliance also varied and only 22% used independent biomarkers of whole grain intake. This evidence map highlights the need to standardize both study protocols and reporting practices to support effective synthesis of study results and provide a stronger foundation to better inform nutrition scientists and public health policy.

Background: ChREBP (carbohydrate responsive element binding protein) is a transcription factor that responds to sugar consumption. Sugar-sweetened beverage (SSB) consumption and genetic variants in the CHREBP locus have separately been linked to HDL-C (high-density lipoprotein cholesterol) and triglyceride concentrations. We hypothesized that SSB consumption would modify the association between genetic variants in the CHREBP locus and dyslipidemia. Methods: Data from 11 cohorts from the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium (N=63 599) and the UK Biobank (N=59 220) were used to quantify associations of SSB consumption, genetic variants, and their interaction on HDL-C and triglyceride concentrations using linear regression models. A total of 1606 single nucleotide polymorphisms within or near CHREBP were considered. SSB consumption was estimated from validated questionnaires, and participants were grouped by their estimated intake. Results: In a meta-analysis, rs71556729 was significantly associated with higher HDL-C concentrations only among the highest SSB consumers (β, 2.12 [95% CI, 1.16–3.07] mg/dL per allele; P <0.0001), but not significantly among the lowest SSB consumers ( P =0.81; P _Diff <0.0001). Similar results were observed for 2 additional variants (rs35709627 and rs71556736). For triglyceride, rs55673514 was positively associated with triglyceride concentrations only among the highest SSB consumers (β, 0.06 [95% CI, 0.02–0.09] ln-mg/dL per allele, P =0.001) but not the lowest SSB consumers ( P =0.84; P _Diff =0.0005). Conclusions: Our results identified genetic variants in the CHREBP locus that may protect against SSB-associated reductions in HDL-C and other variants that may exacerbate SSB-associated increases in triglyceride concentrations. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT00005133, NCT00005121, NCT00005487, and NCT00000479.