Rising to the challenge of cardio-renal-metabolic disease in the 21st century: Translating evidence into best clinical practice to prevent and manage atherosclerosis.

Rising rates of obesity-associated cardiometabolic disorders allied to ageing populations are driving increases in cardiovascular morbidity and mortality. These adverse trends present challenges for healthcare systems that are struggling to prevent and manage the burgeoning cardiometabolic nexus of multiple long-term conditions. While potent new medications and non-pharmacological interventions have ushered in a promising new therapeutic era, translating clinical trial data to real-world clinical practice is often suboptimal.

Machine Learning Applied to Cholesterol-Lowering Pharmacotherapy: Proof-of-Concept in High-Risk Patients Treated in Primary Care.

Machine learning has potential to improve the management of lipid disorders. We explored the utility of machine learning in high-risk patients in primary care receiving cholesterol-lowering medications. Machine learning algorithms were created based on lipid management guidelines for England [National Institute for Health and Care Excellence (NICE) CG181] to reproduce the guidance with >95% accuracy.

Functional, textural, and rheological properties of mixed casein micelle and pea protein isolate co-dispersions.

In the midst of rising consumer health and environmental concerns, pea protein has increased in popularity as an alternative to animal-origin proteins. However, the use of pea protein in food systems is largely hindered by its poor functionality, including low solubility. The objective of this study was to measure the textural, functional, and rheological properties of a mixed plant- and animal-based protein system.