An artificial pancreas developed by researchers at the University of Cambridge has been granted approval by the USA’s Food and Drug Administration (FDA) for use by individuals with type 1 diabetes aged two and older, including during pregnancy.
Cambridge researchers have identified genetic variants in two genes that have some of the largest impacts on obesity risk discovered to date.
New research finds around a quarter of Labrador retriever dogs face a double-whammy of feeling hungry all the time and burning fewer calories due to a genetic mutation.
A Cambridge-led study has shown why many women experience nausea and vomiting during pregnancy – and why some women, including the Duchess of Cambridge, become so sick they need to be admitted to hospital.
The National Institute for Health and Care Excellence (NICE) has today (7 November 2023) recommended hybrid closed loop systems including the CamAPS FX app for use in managing type 1 diabetes, meaning that even more people living with the disease will be able to use this life-changing app.
A study in mice has found that fetuses use a copy of a gene inherited from their dad to force their mum to release as much nutrition as possible during pregnancy.
A study of the DNA of more than 55,000 people worldwide has shed light on how we maintain healthy blood sugar levels after we have eaten, with implications for our understanding of how the process goes wrong in type 2 diabetes.
The protection offered by COVID-19 vaccination declines more rapidly in people with severe obesity than in those with normal weight, scientists at the Universities of Cambridge and Edinburgh have found. The study suggests that people with obesity are likely to need more frequent booster doses to maintain their immunity.
Cambridge has been awarded two of Wellcome’s eight new Discovery Research Platforms, the global charitable foundation announced today.
Cambridge scientists have successfully trialled an artificial pancreas for use by patients living with type 2 diabetes. The device – powered by an algorithm developed at the University of Cambridge – doubled the amount of time patients were in the target range for glucose compared to standard treatment and halved the time spent experiencing high glucose levels.
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