A chemical found in our breath could provide a flag to warn of dangerously-low blood sugar levels in patients with type 1 diabetes, according to new research from the University of Cambridge. The finding, published in the journal Diabetes Care, could explain why some dogs can be trained to spot the warning signs in patients.
An approach that could reduce the chances of drugs failing during the later stages of clinical trials has been demonstrated by a collaboration between the University of Cambridge and pharmaceutical company GlaxoSmithKline (GSK).
The babies of obese women who develop gestational diabetes are five times as likely to be excessively large by six months of pregnancy, according to new research led by the University of Cambridge. The study, which shows that excessive fetal growth begins weeks before at-risk women are screened for gestational diabetes, suggests that current screening programmes may take place too late during pregnancy to prevent lasting health impacts on the offspring.
Worried you might be at risk from diabetes? Check your phone: it might help stop you getting the disease. And if you already have diabetes? Your phone might even help you monitor your condition at home.
A new genetic study of over 200,000 women reveals the underlying mechanisms of polycystic ovary syndrome, as well as potential interventions.
Graham Ladds, lecturer in pharmacology, discusses the controversy around a group of drugs used to treat Type 2 diabetes.
Scientists have found that some drugs from a group of anti-diabetic treatments may, in certain circumstances, act on glucagon receptors in the body, meaning that they could also potentially enable the release of sugar into the bloodstream.
Technology assisting people with type 1 diabetes edges closer to perfection.
Sugar sweetened drinks may give rise to nearly two million diabetes cases over ten years in the US and 80,000 in the UK, estimates a study published in the BMJ.
Life expectancy for people with a history of both cardiovascular disease and diabetes is substantially lower than for people with just one condition or no disease, a new study harnessing the power of ‘big data’ has concluded.
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