Intestinal microbiology shapes population health impacts of diet and lifestyle risk exposures in Torres Strait Islander communities.

Poor diet and lifestyle exposures are implicated in substantial global increases in non-communicable disease burden in low-income, remote, and Indigenous communities. This observational study investigated the contribution of the fecal microbiome to influence host physiology in two Indigenous communities in the Torres Strait Islands: Mer, a remote island where a traditional diet predominates, and Waiben a more accessible island with greater access to takeaway food and alcohol. Counterintuitively, disease markers were more pronounced in Mer residents. However, island-specific differences in disease risk were explained, in part, by microbiome traits. The absence of Alistipes onderdonkii, for example, significantly (p=0.014) moderated island-specific patterns of systolic blood pressure in multivariate-adjusted models. We also report mediatory relationships between traits of the fecal metagenome, disease markers, and risk exposures. Understanding how intestinal microbiome traits influence response to disease risk exposures is critical for the development of strategies that mitigate the growing burden of cardiometabolic disease in these communities.

DNA extraction approaches substantially influence the assessment of the human breast milk microbiome.

In addition to providing nutritional and bioactive factors necessary for infant development, human breast milk contains bacteria that contribute to the establishment of commensal microbiota in the infant. However, the composition of this bacterial community differs considerably between studies. We hypothesised that bacterial DNA extraction methodology from breast milk samples are a substantial contributor to these inter-study differences. We tested this hypothesis by applying five widely employed methodologies to a mock breast milk sample and four individual human breast milk samples. Significant differences in DNA yield and purity were observed between methods (P < 0.05). Microbiota composition, assessed by 16S rRNA gene amplicon sequencing, also differed significantly with extraction methodology (P < 0.05), including in the contribution of contaminant signal. Concerningly, many of the bacterial taxa identified here as contaminants have been reported as components of the breast milk microbiome in other studies. These findings highlight the importance of using stringent, well-validated, DNA extraction methodologies for analysis of the breast milk microbiome, and exercising caution interpreting microbiota data from low-biomass contexts.

Comparison of flavonoid intake assessment methods.

BACKGROUND: Flavonoids are a diverse group of polyphenolic compounds found in high concentrations in many plant foods and beverages. High flavonoid intake has been associated with reduced risk of chronic disease. To date, population based studies have used the United States Department of Agriculture (USDA) food content database to determine habitual flavonoid intake. More recently, a new flavonoid food content database, Phenol-Explorer (PE), has been developed. However, the level of agreement between the two databases is yet to be explored. AIM: To compare the methods used to create each database, and to explore the level of agreement between the flavonoid intake estimates derived from USDA and PE data. DESIGN: The study population included 1063 randomly selected women aged over 75 years. Two separate intake estimates were determined using food composition data from the USDA and the PE databases. RESULTS: There were many similarities in methods used to create each database; however, there are several methodological differences that manifest themselves in differences in flavonoid intake estimates between the 2 databases. Despite differences in net estimates, there was a strong level of agreement between total-flavonoid, flavanol, flavanone and anthocyanidin intake estimates derived from each database. Intake estimates for flavanol monomers showed greater agreement than flavanol polymers. The level of agreement between the two databases was the weakest for the flavonol and flavone intake estimates. CONCLUSION: In this population, the application of USDA and PE source data yielded highly correlated intake estimates for total-flavonoids, flavanols, flavanones and anthocyanidins. For these sub-classes, the USDA and PE databases may be used interchangeably in epidemiological investigations. There was poorer correlation between intake estimates for flavonols and flavones due to differences in USDA and PE methodologies. Individual flavonoid compound groups that comprise flavonoid sub-classes had varying levels of agreement. As such, when determining the appropriate database to calculate flavonoid intake variables, it is important to consider methodologies underpinning database creation and which foods are important contributors to dietary intake in the population of interest.