Patterns of Dietary Fatty Acids and Fat Spreads in Relation to Blood Pressure, Lipids and Insulin Resistance in Young Adults: A Repeat Cross-Sectional Study.

Determining whether dietary fatty acids and the use of fat spreads are associated with cardiovascular risk factors is difficult due to the multicollinearity of fatty acids and the consumption of multiple spread types. We applied clustering methodologies using data on 31 different fatty acids and 5 different types of fat spreads (high fat: butter, blended butters, and margarines; lower fat: polyunsaturated and monounsaturated) and investigated associations with blood pressure, serum lipid patterns and insulin resistance in the Raine Study Gen2 participants in Western Australia, at 20 and 22 years of age. Amongst n = 785 participants, there were eight distinct clusters formed from the fatty acid data and ten distinct clusters formed from the fat spread data.

Oropharyngeal Staphylococcus aureus is linked to higher mortality in long-term aged care residents.

Background: Biological ageing, healthcare interactions, and pharmaceutical and environmental exposures in later life alter the characteristics of the oropharyngeal (OP) microbiome. These changes, including an increased susceptibility to colonisation by pathobiont species, have been linked with diverse health outcomes.

Objectives: To investigate the relationship between OP microbiome characteristics and all-cause mortality in long-term aged care residents.

Steatotic Liver Disease in Younger Adults is Associated With Altered Gut Microbiology.

Background And Aims: Steatotic liver disease (SLD) is a leading cause of chronic liver disease worldwide. As SLD pathogenesis has been linked to gut microbiome alterations, we aimed to identify SLD-associated gut microbiome features early in SLD development by utilising a highly characterised cohort of community-dwelling younger adults.

Methods And Results: At age 27 years, 588 participants of the Raine Study Generation 2 underwent cross-sectional assessment.

Phenotyping to predict 12-month health outcomes of older general medicine patients.

Background: A variety of unsupervised learning algorithms have been used to phenotype older patients, enabling directed care and personalised treatment plans. However, the ability of the clusters to accurately discriminate for the risk of older patients, may vary depending on the methods employed.

Aims: To compare seven clustering algorithms in their ability to develop patient phenotypes that accurately predict health outcomes.