One of the most topical areas of human nutrition is the role of the gut in health and disease. Specifically, this involves interactions between the resident microbiota and dietary ingredients that support their activities. Currently, it is accepted that the gut microflora contains pathogenic, benign and beneficial components. Some microbially induced disease states such as acute gastroenteritis and pseudomembranous colitis have a defined aetiological agent(s). Speculation on the role of microbiota components in disorders such as irritable bowel syndrome, bowel cancer, neonatal necrotising enterocolitis and ulcerative colitis are less well defined, but many studies are convincing. It is evident that the gut microflora composition can be altered through diet. Because of their perceived health-promoting status, bifidobacteria and lactobacilli are the commonest targets. Probiotics involve the use of live micro-organisms in food; prebiotics are carbohydrates selectively metabolized by desirable moieties of the indigenous flora; synbiotics combine the two approaches. Dietary intervention of the human gut microbiota is feasible and has been proven as efficacious in volunteer trials. The health bonuses of such approaches offer the potential to manage many gut disorders prophylactically. However, it is imperative that the best methodologies available are applied to this area of nutritional sciences. This will undoubtedly involve a genomic application to the research and is already under way through molecular tracking of microbiota changes to diet in controlled human trials.
BackgroundDietary assessment is complex, and strategies to select the most appropriate dietary assessment tool (DAT) in epidemiological research are needed. The DIETary Assessment Tool NETwork (DIET@NET) aimed to establish expert consensus on Best Practice Guidelines (BPGs) for dietary assessment using self-report.MethodsThe BPGs were developed using the Delphi technique. Two Delphi rounds were conducted. A total of 131 experts were invited, and of these 65 accepted, with 48 completing Delphi round I and 51 completing Delphi round II. In all, a total of 57 experts from North America, Europe, Asia and Australia commented on the 47 suggested guidelines.ResultsForty-three guidelines were generated, grouped into the following four stages: Stage I. Define what is to be measured in terms of dietary intake (what? who? and when?); Stage II. Investigate different types of DATs; Stage III. Evaluate existing tools to select the most appropriate DAT by evaluating published validation studies; Stage IV. Think through the implementation of the chosen DAT and consider sources of potential biases.ConclusionsThe Delphi technique consolidated expert views on best practice in assessing dietary intake. The BPGs provide a valuable guide for health researchers to choose the most appropriate dietary assessment method for their studies. These guidelines will be accessible through the Nutritools website, www.nutritools.org.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-017-0962-x) contains supplementary material, which is available to authorized users.
Context Overestimation or underestimation of portion size leads to measurement error during dietary assessment. Objective To identify portion size estimation elements (PSEEs) and evaluate their relative efficacy in relation to dietary assessment, and assess the quality of studies validating PSEEs. Data Selection and Extraction Electronic databases, internet sites, and cross-references of published records were searched, generating 16 801 initial records, from which 334 records were reviewed and 542 PSEEs were identified, comprising 5% 1-dimensional tools (eg, food guides), 46% 2-dimensional tools (eg, photographic atlases), and 49% 3-dimensional tools (eg, household utensils). Out of 334 studies, 21 validated a PSEE (compared PSEE to actual food amounts) and 13 compared PSEEs with other PSEEs. Conclusion Quality assessment showed that only a few validation studies were of high quality. According to the findings of validation and comparison studies, food image–based PSEEs were more accurate than food models and household utensils. Key factors to consider when selecting a PSEE include efficiency of the PSEE and its applicability to targeted settings and populations.
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