̽��ֱ�� of Cambridge - Nick Wareham

Red and processed meat consumption associated with higher type 2 diabetes risk

Anonymous — Tue, 20 Aug 2024 22:30:56 +0000

̽��ֱ��findings are published today in ̽��ֱ��Lancet Diabetes and Endocrinology.

Global meat production has increased rapidly in recent decades and meat consumption exceeds dietary guidelines in many countries. Earlier research indicated that higher intakes of processed meat and unprocessed red meat are associated with an elevated risk of type 2 diabetes, but the results have been variable and not conclusive.

Poultry such as chicken, turkey, or duck is often considered to be an alternative to processed meat or unprocessed red meat, but fewer studies have examined the association between poultry consumption and type 2 diabetes.

To determine the association between consumption of processed meat, unprocessed red meat and poultry and type 2 diabetes, a team led by researchers at the ̽��ֱ�� of Cambridge used the global InterConnect project to analyse data from 31 study cohorts in 20 countries. Their extensive analysis took into account factors such as age, gender, health-related behaviours, energy intake and body mass index.

̽��ֱ��researchers found that the habitual consumption of 50 grams of processed meat a day - equivalent to 2 slices of ham - is associated with a 15% higher risk of developing type 2 diabetes in the next 10 years. ̽��ֱ��consumption of 100 grams of unprocessed red meat a day - equivalent to a small steak - was associated with a 10% higher risk of type 2 diabetes.

Habitual consumption of 100 grams of poultry a day was associated with an 8% higher risk, but when further analyses were conducted to test the findings under different scenarios the association for poultry consumption became weaker, whereas the associations with type 2 diabetes for each of processed meat and unprocessed meat persisted.

Professor Nita Forouhi of the Medical Research Council (MRC) Epidemiology Unit at the ̽��ֱ�� of Cambridge, and a senior author on the paper, said: “Our research provides the most comprehensive evidence to date of an association between eating processed meat and unprocessed red meat and a higher future risk of type 2 diabetes. It supports recommendations to limit the consumption of processed meat and unprocessed red meat to reduce type 2 diabetes cases in the population.

“While our findings provide more comprehensive evidence on the association between poultry consumption and type 2 diabetes than was previously available, the link remains uncertain and needs to be investigated further.”

InterConnect uses an approach that allows researchers to analyse individual participant data from diverse studies, rather than being limited to published results. This enabled the authors to include as many as 31 studies in this analysis, 18 of which had not previously published findings on the link between meat consumption and type 2 diabetes. By including this previously unpublished study data the authors considerably expanded the evidence base and reduced the potential for bias from the exclusion of existing research.

Lead author Dr Chunxiao Li, also of the MRC Epidemiology Unit, said: “Previous meta-analysis involved pooling together of already published results from studies on the link between meat consumption and type 2 diabetes, but our analysis examined data from individual participants in each study. This meant that we could harmonise the key data collected across studies, such as the meat intake information and the development of type 2 diabetes.

“Using harmonised data also meant we could more easily account for different factors, such as lifestyle or health behaviours, that may affect the association between meat consumption and diabetes.”

Professor Nick Wareham, Director of the MRC Epidemiology Unit, and a senior author on the paper said: “InterConnect enables us to study the risk factors for obesity and type 2 diabetes across populations in many different countries and continents around the world, helping to include populations that are under-represented in traditional meta-analyses.

“Most research studies on meat and type 2 diabetes have been conducted in USA and Europe, with some in East Asia. This research included additional studies from the Middle East, Latin America and South Asia, and highlighted the need for investment in research in these regions and in Africa.”

InterConnect was initially funded by the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 602068.

Reference
Li, C et al. Meat consumption and incident type 2 diabetes: a federated meta-analysis of 1·97 million adults with 100,000 incident cases from 31 cohorts in 20 countries. Lancet Diabetes Endocrinol.; 20 August 2024

Adapted form a press release from the MRC Epidemiology Unit

Meat consumption, particularly consumption of processed meat and unprocessed red meat, is associated with a higher type 2 diabetes risk, an analysis of data from almost two million participants has found.

Our research supports recommendations to limit the consumption of processed meat and unprocessed red meat to reduce type 2 diabetes cases in the population

Nita Forouhi

Lauri Patterson

Preparing a Monte Cristo Sandwich, with Black Forest Ham

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Rare genetic variants confer largest increase in type 2 diabetes risk seen to date

cjb250 — Wed, 07 Jul 2021 11:10:25 +0000

Type 2 diabetes is thought to be driven in part by inherited genetic factors, but many of these genes are yet unknown. Previous large-scale studies have depended on efficient ‘array genotyping’ methods to measure genetic variations across the whole genome. This approach typically does a good job at capturing the common genetic differences between people, though individually these each confer only small increases in diabetes risk.

Recent technical advances have allowed more comprehensive genetic measurement by reading the complete DNA sequences of over 20,000 genes that code for proteins in humans. Proteins are essential molecules that enable our bodies to function. In particular, this new approach has allowed for the first time a large-scale approach to study the impact of rare genetic variants on several diseases, including type 2 diabetes.

By looking at data from more than 200,000 adults in the UK Biobank study, researchers from the Medical Research Council (MRC) Epidemiology Unit at the ̽��ֱ�� of Cambridge used this approach to identify genetic variants associated with the loss of the Y chromosome. This is a known biomarker of biological ageing that occurs in a small proportion of circulating white blood cells in men and indicates a weakening in the body’s cellular repair systems. This biomarker has been previously linked to age-related diseases such as type 2 diabetes and cancer.

In results published today in Nature Communications, the researchers identified rare variants in the gene GIGYF1 that substantially increase susceptibility to loss of the Y chromosome, and also increase an individual’s risk of developing type 2 diabetes six-fold. In contrast, common variants associated with type 2 diabetes confer much more modest increases in risk, typically much lower than two-fold.

Around 1 in 3,000 individuals carries such a GIGYF1 genetic variant. Their risk of developing type 2 diabetes is around 30%, compared to around 5% in the wider population. In addition, people who carried these variants had other signs of more widespread ageing, including weaker muscle strength and more body fat.

GIGYF1 is thought to control insulin and cell growth factor signalling. ̽��ֱ��researchers say their findings identify this as a potential target for future studies to understand the common links between metabolic and cellular ageing, and to inform future treatments.

Dr John Perry, from the MRC Epidemiology Unit and a senior author on the paper, said: “Reading an individual’s DNA is a powerful way of identifying genetic variants that increase our risk of developing certain diseases. For complex diseases such as type 2 diabetes, many variants play a role, but often only increasing our risk by a tiny amount. This particular variant, while rare, has a big impact on an individual’s risk.”

Professor Nick Wareham, Director of the MRC Epidemiology Unit, added: “Our findings highlight the exciting scientific potential of sequencing the genomes of very large numbers of people. We are confident that this approach will bring a rich new era of informative genetic discoveries that will help us better understand common diseases such as type 2 diabetes. By doing this, we can potentially offer better ways to treat – or even to prevent – the condition.”

Ongoing research will aim to understand how the loss of function variants in GIGYF1 lead to such a substantial increase in the risk of developing type 2 diabetes. Their future research will also examine other links between biomarkers of biological ageing in adults and metabolic disorders.

̽��ֱ��research was funded by the Medical Research Council. UK Biobank is supported by Wellcome, the Medical Research Council, British Heart Foundation, Cancer Research UK, the UK Department of Health, Northwest Regional Development Agency and the Scottish Government.

Reference
Zhao, Y et al. GIGYF1 loss of function is associated with clonal mosaicism and adverse metabolic health. Nature Communications 2021; 07 Jul 2021; DOI: 10.1038/s41467-021-24504-y

Scientists at the ̽��ֱ�� of Cambridge have identified rare genetic variants – carried by one in 3,000 people – that have a larger impact on the risk of developing type 2 diabetes than any previously identified genetic effect.

For complex diseases such as type 2 diabetes, many variants play a role [in disease risk], but often only increasing our risk by a tiny amount. This particular variant, while rare, has a big impact on an individual’s risk

John Perry

qimono

DNA jigsaw

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Study highlights potential for ‘liquid health check’ to predict disease risk

cjb250 — Mon, 02 Dec 2019 16:00:36 +0000

Preventative medicine programmes such as the UK National Health Service’s Health Check and Healthier You programmes are aimed at improving our health and reducing our risk of developing diseases. While such strategies are inexpensive, cost effective and scalable, they could be made more effective using personalised information about an individual’s health and disease risk.

̽��ֱ��rise and application of ‘big data’ in healthcare, assessing and analysing detailed, large-scale datasets makes it increasingly feasible to make predictions about health and disease outcomes and enable stratified approaches to prevention and clinical management.

Now, an international team of researchers from the UK and USA, working with biotech company SomaLogic, has shown that large-scale measurement of proteins in a single blood test can provide important information about our health and can help to predict a range of different diseases and risk factors.

Our bodies contain around 30,000 different proteins, which are coded for by our DNA and regulate biological processes. Some of these proteins enter the blood stream by purposeful secretion to orchestrate biological processes in health or in disease, for example hormones, cytokines and growth factors. Others enter the blood through leakage from cell damage and cell death. Both secreted and leaked proteins can inform health status and disease risk.

In a proof-of-concept study based on five observational cohorts in almost 17,000 participants, researchers scanned 5,000 proteins in a plasma sample taken from each participant. Plasma is the single largest component of blood and is the clear liquid that remains after the removal of red and white blood cells and platelets. ̽��ֱ��study resulted in around 85 million protein targets being measured.

̽��ֱ��technique involves using fragments of DNA known as aptamers that bind to the target protein. In general, only specific fragments will bind to particular proteins – in the same way that only a specific key will fit in a particular lock. Using existing genetic sequencing technology, the researchers can then search for the aptamers and determine which proteins are present and in what concentrations.

̽��ֱ��researchers analysed the results using statistical methods and machine learning techniques to develop predictive models – for example, that an individual whose blood contains a certain pattern of proteins is at increased risk of developing diabetes. ̽��ֱ��models covered a number of health states, including levels of liver fat, kidney function and visceral fat, alcohol consumption, physical activity and smoking behaviour, and for risk of developing type 2 diabetes and cardiovascular disease.

̽��ֱ��accuracy of the models varied, with some showing high predictive powers, such as for percentage body fat, while others had only modest prognostic power, such as for cardiovascular risk. ̽��ֱ��researchers report that their protein-based models were all either better predictors than models based on traditional risk factors or would constitute more convenient and less expensive alternatives to traditional testing.

Many of the proteins are linked to a number of health states or conditions; for example, leptin, which modulates appetite and metabolism, was informative for predictive models of percentage body fat, visceral fat, physical activity and fitness.

One difference between genome sequencing and so-called ‘proteomics’ – studying an individual’s proteins in depth – is that whereas the genome is fixed, the proteome changes over time. It might change as an individual becomes more obese, less physically active or smokes, for example, so proteins will be able to track changes in an individual's health status over a lifetime.

“Proteins circulating in our blood are a manifestation of our genetic make-up as well as many other factors, such as behaviours or the presence of disease, even if not yet diagnosed,” said Dr Claudia Langenberg, from the MRC Epidemiology Unit at the ̽��ֱ�� of Cambridge. “This is one of the reasons why proteins are such good indicators of our current and future health state and have the potential to improve clinical prediction across different and diverse diseases.”

“It’s remarkable that plasma protein patterns alone can faithfully represent such a wide variety of common and important health issues, and we think that this is just the tip of the iceberg,” said Dr Stephen Williams, Chief Medical Officer of SomaLogic, who led the study. “We have more than a hundred tests in our SomaSignal pipeline and believe that large-scale protein scanning has the potential to become a sole information source for individualised health assessments.”

While this study shows a proof-of-principle, the researchers say that as technology improves and becomes more affordable, it is feasible that a comprehensive health evaluation using a battery of protein models derived from a single blood sample could be offered as routine by health services.

“This proof of concept study demonstrates a new paradigm that measurement of blood proteins can accurately deliver health information that spans across numerous medical specialties and that should be actionable for patients and their healthcare providers,” said Peter Ganz, MD, co-leader of this study and the Maurice Eliaser Distinguished Professor of Medicine at the UCSF and Director of the Center of Excellence in Vascular Research at Zuckerberg San Francisco General Hospital and Trauma Center. “I expect that in the future we will look back at this Nature Medicine proteomic study as a critical milestone in personalising and thus improving the care of our patients.”

Reference
Williams, SA et al. Plasma protein patterns as comprehensive indicators of health; Nat Med; 2 Dec 2019; DOI: 10.1038/s41591-019-0665-2

Competing interests
̽��ֱ��research was a collaboration with SomaLogic Inc, which has a commercial interest in the results. Several co-authors were or are employees of SomaLogic. ̽��ֱ��company has provided funding to the ̽��ֱ�� of Cambridge. Dr Peter Ganz is a member of the SomaLogic Medical Advisory board, for which he receives no remuneration of any kind.

Funding
̽��ֱ��research was supported by the UK Medical Research Council, US National Institutes on Aging, British Heart Foundation, National Institute for Health Research, the Norwegian Ministry of Health, Norwegian ̽��ֱ�� of Science and Technology and Norwegian Research Council, Central Norway Regional Health Authority, Nord-Trondelag County Council, Norwegian Institute of Public Health, US National Heart, Lung and Blood Institute. SomaScan assays and the Covance study were funded by SomaLogic, Inc.

Proteins in our blood could in future help provide a comprehensive ‘liquid health check’, assessing our health and predicting the likelihood that we will we will develop a range of diseases, according to research published today in Nature Medicine.

Proteins circulating in our blood are a manifestation of our genetic make-up as well as many other factors, such as behaviours or the presence of disease, even if not yet diagnosed

Claudia Langenberg

Geralt

Blood plasma

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Discovery of genetic variants that protect against obesity and type 2 diabetes could lead to new weight loss medicines

cjb250 — Thu, 18 Apr 2019 15:00:18 +0000

Scientists have known for several years that genes can influence a person’s weight. One of the genes that is known to play a key role in regulating weight is MC4R, which codes for the melanocortin 4 receptor. This receptor acts like a switch in the brain to suppress appetite. People who have genetic variants that disrupt this receptor gain weight easily.

Now, in a study published today in the journal Cell, researchers have shown that other genetic variants in the MC4R gene that increase the activity of this brain receptor can protect people from becoming overweight, a finding that could lead to the development of new medicines that ‘copy’ the protective effect of these genetic variants to achieve or maintain weight-loss.

A team led by Professors Sadaf Farooqi and Nick Wareham and Dr Claudia Langenberg at the Wellcome Trust-MRC Institute of Metabolic Science in Cambridge looked at the MC4R gene in half a million volunteers from the UK population who have taken part in the UK Biobank study, finding 61 distinct naturally-occurring genetic variants. While some of these genetic variants predisposed people to become obese, other variants provided protection against obesity and some of its major complications, such as type 2 diabetes and heart disease.

To investigate the reasons for this mystery, Professor Farooqi’s team, who previously showed that MC4R works in the brain as a ‘switch’ to tell us to stop eating after a meal, studied the function of these genetic variants in a number of laboratory experiments. They found that MC4R gene variants linked to higher obesity risk stopped the gene from working, whereas variants that offered protection against obesity kept the gene ‘switched on’.

Around six per cent of study participants carried genetic variants that caused the receptor to remain ‘switched on’. People with these variants would eat less, which could explain their lower weight. People with two copies of these particular variants (1 in over 1,000 people) were on average 2.5 kg lighter than people without the variants and had a 50% lower risk of type 2 diabetes and heart disease.

“This study drives home the fact that genetics plays a major role in why some people are obese – and that some people are fortunate enough to have genes that protect them from obesity,” says Professor Farooqi of the ̽��ֱ�� of Cambridge Metabolic Research Laboratories.

̽��ֱ��discovery adds to recent work by the team which showed that some slim people have a genetic advantage when it comes to maintaining their weight.

“It doesn’t mean that we can’t influence our weight by watching what we eat, but it does mean the odds are stacked against some people and in favour of others,” added Professor Farooqi.

When the researchers looked in detail at the genetic variants in laboratory experiments, they found that MC4R can send signals through a pathway – known as the beta-arrestin pathway – that had not previously been linked to weight regulation. Genetic variants that sent signals preferentially through this pathway were the ones driving the association with protection against obesity and its complications and, importantly, were also associated with lower blood pressure. Designing drugs that mimic the effect of the protective variants in MC4R could provide new, safer weight loss therapies.

“A powerful emerging concept is that genetic variants that protect against disease can be used as models for the development of medicines that are more effective and safer,” said Dr Luca Lotta, Senior Clinical Investigator at the Medical Research Council Epidemiology Unit and joint lead author of the study. “Our findings may pave the way for a new generation of weight loss therapies that activate MC4R preferentially via the beta-arrestin pathway.”

“Our work would not have been possible without the unique blend of expertise in large-scale genetic epidemiology analysis and laboratory experiments at the Institute of Metabolic Science,” says Professor Wareham, Director of the MRC Epidemiology Unit and Co-Director of the Institute.

“Genetic studies of thousands of people and a functional understanding of the mechanisms behind protective genetic variants can really help us inform the development of a new generation of medicines for common diseases like obesity and diabetes that affect millions of people globally.”

̽��ֱ��work was funded by the MRC and Wellcome, with support from the NIHR Cambridge Biomedical Research Centre.

Reference
Lotta, LA, Mokrosiński, J et al. Human gain-of-function MC4R variants show signaling bias and protect against obesity. Cell; 18 April 2019; DOI: 10.1016/j.cell.2019.03.044

Around four million people in the UK carry genetic variants that protect them from obesity, type 2 diabetes and heart disease, suggests new research from the ̽��ֱ�� of Cambridge. ̽��ֱ��team say the discovery could lead to the development of new drugs that help people lose weight.

This study drives home the fact that genetics plays a major role in why some people are obese – and that some people are fortunate enough to have genes that protect them from obesity

Sadaf Farooqi

Siora Photography

Tape measure

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Common strength ‘genes’ identified for first time

cjb250 — Wed, 12 Jul 2017 09:28:41 +0000

̽��ֱ��researchers used data on hand grip strength from more than 140,000 participants in the UK Biobank study, combined with 50,000 additional individuals from the UK, Netherlands, Denmark and Australia, to identify sixteen common genetic variants that are associated with muscle strength.

Dan Wright, joint first author on this paper and a PhD student at the Medical Research Council Epidemiology (MRC) Unit at the ̽��ֱ�� of Cambridge, said: “ ̽��ֱ��very large number of individuals participating in UK Biobank provides a powerful resource for identifying genes involved in complex traits such as muscle strength, and helps us understand their underlying biology and its relevance to health.”

Many of these variants were located within or near to genes known to play a role in biological processes highly relevant to muscle function, including the structure and function of muscle fibres, and the communication of the nervous system with muscle cells.

Mutations in some of the genes highlighted are also known to be associated with severe monogenic syndromes – conditions caused by a single genetic mutations – characterised by compromised muscle function. This demonstrates that genetic variation in genes which cause serious muscular conditions may also influence differences in strength in the general population.

Dr Robert Scott, who co-led the study with colleagues from the MRC Epidemiology Unit, said: “While we have long suspected a role for genetics in the variation in muscle strength, these findings give the first insights into some of the specific genetic variants that underpin variation in strength.

“These could be important steps towards identifying new treatments to prevent or treat muscle weakness.”

Hand grip strength has been reported to be associated with many health outcomes, including risk of mortality, cardiovascular disease, and fracture – although it has been unclear whether variation in strength actually causes these outcomes, or simply reflects underlying disease processes.

Using the sixteen genetic variants identified for strength, the researchers were able to investigate the hypothesised causal link between strength and these adverse health outcomes. Their study found no evidence that lower strength causally increases risk of death or cardiovascular disease, but they did find evidence that higher muscular strength reduces risk of fracture, supporting the use of strength training interventions as a strategy to reduce risk of fractures.

Professor Nick Wareham, director of the MRC Epidemiology Unit and a senior author of the study, noted: “This work highlights the importance of muscle strength in the prevention of fractures and the complications which can often follow a fall.”

Reference
Willems, SM et al. Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness. Nature Communications; 12 July 2017; DOI: 10.1038/ncomms16015

Common genetic factors that influence muscle strength in humans have been identified for the first time in a study led by researchers from the ̽��ֱ�� of Cambridge and published today in Nature Communications.

̽��ֱ��very large number of individuals participating in UK Biobank provides a powerful resource for identifying genes involved in complex traits such as muscle strength

Dan Wright

Thomas Morris

Lose Weight and Gain Muscle

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Genetic approach could help identify side-effects at early stages of drug development

cjb250 — Wed, 01 Jun 2016 18:00:33 +0000

̽��ֱ��technique involves identifying genetic variants that mimic the action of a drug on its intended target and then checking in large patient cohorts whether these variants are associated with risk of other conditions, such as cardiovascular disease.

When developing a new drug for market, pharmaceutical companies must not only demonstrate that it is effective at treating a particular condition, but also that the drug does not have any adverse side-effects in patients. For example, the Food and Drug Administration, which approves all new medicines for use in the USA, has defined that any new anti-diabetic medicines need to demonstrate cardiovascular safety. However, in many cases adverse safety profiles do not become apparent until late in the drug development process, by which point millions – possibly even billions – of pounds will have been invested.

In a study published today in the journal Science Translational Medicine, scientists have provided a proof of concept that it is possible to use genetic analyses to demonstrate systematically at a very early stage whether a drug will alter the risk of developing other conditions.

A major class of anti-diabetic therapies are those known as glucose-lowering glucagon-like peptide-1 receptor (GLP1R)-agonists. These drugs bind to the GLP-1 receptor (which is encoded by the GLP1R gene) to increase insulin production, helping reduce levels of blood sugar. However, the cardiovascular safety, of this class of agents, including the risk of heart disease for example, remains unknown.

By analysing genetic variations in DNA encoding drug targets for type 2 diabetes and obesity in almost 12,000 individuals, the researchers identified a variant in the GLP1R gene that was associated with lower fasting glucose and a lower risk of type 2 diabetes – in other words, the variant appeared to mimic the action of the diabetes drugs. They confirmed this result in a further 40,000 individuals.

̽��ֱ��researchers then used genetic data available through an international data-sharing consortium to study the association of that same variant with coronary heart disease in almost 62,000 individuals with coronary heart disease and over 160,000 controls. In fact, they found that the variant actually reduced the risk of heart disease. Long-term large-scale randomised controlled clinical trials to evaluate the cardiovascular safety of GLP1R-agonists are underway and results from a large trial are scheduled to be released later this month.

“This further suggests that human genetics can support the development of new therapies, and can offer insights into their safety profile early in the development process,” says Dr Robert Scott from the Medical Research Council (MRC) Epidemiology Unit at the ̽��ֱ�� of Cambridge, the study’s first author.

Professor Nick Wareham, Director of the MRC Epidemiology Unit, added: “These findings suggest that beyond their effectiveness in treating diabetes, these drugs may have the added benefit of lowering risk of heart disease.”

“Researching and developing new medicines is a lengthy, expensive and risky journey, and any insights we can gain in to the processes of the body related to disease could help improve our ability to succeed,” says Dr Dawn Waterworth, joint senior author from GSK. “By pooling our resources and expertise in collaborations like this one with Cambridge ̽��ֱ��, we believe there’s an opportunity to expand our knowledge of disease biology, which in turn could help reduce the risk of late-stage failures and accelerate the development of innovative new treatments for patients.”

̽��ֱ��study was primarily funded by GSK and the Medical Research Council.

Reference
Scott, R et al. A genomic approach to therapeutic target validation identifies a glucose-lowering GLP1R variant protective for coronary heart disease. Sci Trans Med; 2 June 2016; DOI: 10.1126/scitranslmed.aad3744

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 ̽��ֱ�� of Cambridge and pharmaceutical company GlaxoSmithKline (GSK).

This further suggests that human genetics can support the development of new therapies, and can offer insights into their safety profile early in the development process

Robert Scott

Fotos GOVBA

Diabetes

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Phone for a doctor

cjb250 — Wed, 28 Oct 2015 12:29:19 +0000

It’s the middle of the afternoon. You hear the trill of an incoming text message on your phone. You pick it up, expecting it to be from a friend.

Skipping breakfast will make you overeat at lunch.

Ah yes, this must be from Professor Ambady Ramachandran. You’ve never met him and he doesn’t know you personally, but he has sent you this helpful reminder because you are one of over 20 million Indians at a high risk of developing type 2 diabetes. Tomorrow, you tell yourself, you will make sure you eat before going to work.

“It seems paradoxical that something as simple as text messaging could help prevent you from developing diabetes,” says Professor Nick Wareham, Director of the Medical Research Council (MRC) Epidemiology Unit at the ̽��ֱ�� of Cambridge. And yet, the evidence suggests it might work.

In 2013, Ramachandran, who founded and runs a diabetes hospital in Chennai, India, and is President of the India Diabetes Research Foundation, reported the results of a study that found almost a third fewer men in the high risk group went on to develop diabetes if they received between two and four texts a week giving advice on diet and exercise.

“This is a big – and surprising – effect,” says Wareham. And India, as with many other countries worldwide, needs something big (and, possibly, surprising) to help it tackle the growing burden of diabetes and obesity. Recent estimates suggest there are 68 million people living with diabetes in India, the majority with type 2 diabetes. A mixture of poor diet and lack of exercise, low birth weight followed by rapid growth, and genetic predisposition – Indians tend to develop diabetes at a lower body-mass index than Caucasians – means that diabetes is twice as common in India as it is in the UK.

While targeted strategies aimed at high risk individuals are likely to be effective, there is no way they could be rolled out to 20 million people, says Wareham. “If you had to individually counsel that many people, it would be unaffordable. Simple, pragmatic, scalable approaches are the only ones that are feasible.”

Ramachandran’s study involved a relatively small sample, but such was its promise that he and Wareham have teamed up to see whether text messaging might be scaled up to a larger population, with support from the MRC and the Indian Council for Medical Research. An additional arm of the study, being carried out by Imperial College London, is looking at whether the same concept would work in the UK.

Wareham and colleagues use a combination of a risk score that they have developed, which looks at factors such as age, sex and weight, and a simple blood test to identify people at risk of developing diabetes: these are the individuals who are targeted by the text messages.

It is the pervasiveness of mobile phones that could make this scheme work: there are almost a billion mobile phones in India – the country ranks second only to China. Smartphones are still much less common, with just around one in six people in the country owning one, but this is expected to increase significantly, potentially making India the second largest market worldwide.

If, as expected, smartphones really do take off, they could hold the answer to helping those people unfortunate enough to develop diabetes to monitor their condition, says Chris Lowe, Emeritus Professor of Biotechnology at the Department of Chemical Engineering and Biotechnology in Cambridge.

“We’re interested in developing diagnostics that are appropriate for taking measurements at home or in the doctor’s surgery, without the need for a specialist,” says Lowe. For over a decade, he has been looking at making ‘smart’ holograms that are sensitive to chemicals or biological compounds.

Unlike conventional holograms, which are two-dimensional, Lowe’s holograms are three-dimensional, created by shining a one-nanosecond laser pulse into a gel, suspended in which are silver nanoparticles. ̽��ֱ��silver nanoparticles arrange themselves into planes, giving the hologram a particular colour. But when glucose – from a blood or urine sample – comes into contact with the hologram, it binds to sensors within the gel, known as receptors, causing the hologram to expand or contract; the planes move closer together or further apart and the light given off by the hologram changes colour, moving towards the blue or red end of the spectrum.

“You can see these changes visually, but to increase the accuracy you need to be able to quantify the change, and this is where smartphones come in,” he says. Using the phone’s camera and a downloadable app, it could be possible to give an accurate measurement of the level of glucose in the body. His colleague Dr Gita Khalili Moghaddam is working on software that would enable the app to operate in a real-world setting, compensating for variability between phones and the environment.

̽��ֱ��technology works in a similar way to QR codes – the black-and-white square patterns which, when scanned on a phone, redirect you to information online. In fact, says Moghaddam, the holograms could themselves be QR codes. “You can save the patient’s information in the holographic QR code, so when you scan it and send off your glucose levels, embedded in this are your own details,” she says.

Lowe and colleagues are exploring ways of delivering the holograms, from strips of holograms through to contact lenses that measure glucose in tear fluid as a surrogate for blood sugar levels, and even to having holograms that can be tattooed onto skin. These could even do away with the need to take regular blood finger-prick tests.

Despite the hi-tech nature of this technology, part of its beauty lies in its cost. ̽��ֱ��holograms could be mass-produced at a very low cost – even if they were embedded into daily-use contact lenses, the cost would be negligible, making them particularly attractive in the developing world. At the moment, patients need special instruments to monitor their glucose levels – in areas such as rural India, these are often given away, but their cost then has to be incorporated into the disposable strips used by the instruments.

“With smart holograms, there is no instrument,” says Lowe. “It’s just your smartphone. And soon, almost everyone will have one of those.”

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.

With smart holograms, there is no instrument. It’s just your smartphone. And soon, almost everyone will have one of those

Chris Lowe

James Theophane

mobile-mobile

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Early detection and treatment of type 2 diabetes may reduce heart disease and mortality

cjb250 — Mon, 18 May 2015 14:00:37 +0000

̽��ֱ��study, led by researchers at the ̽��ֱ�� of Michigan and the MRC Epidemiology Unit, ̽��ֱ�� of Cambridge, used data from the ADDITION-Europe study of diabetes screening and treatment, which it combined with a computer simulation model of diabetes progression. This revealed that screening followed by treatment led to a reduced risk of cardiovascular disease or death within a 5-year follow-up period when compared to patients having no screening.

̽��ֱ��ADDITION-Europe study enrolled people 40 to 69 years of age without known diabetes from 343 general practices in the United Kingdom, Denmark, and the Netherlands. ̽��ֱ��study had two objectives: firstly, to determine if routine screening for type 2 diabetes was feasible; secondly, to determine if early, intensive, treatment of high blood sugar and cardiovascular risk factors such as blood pressure, cholesterol status and smoking in those diagnosed with type 2 diabetes reduced the risk of events such as stroke, heart attack, angioplasty, heart bypass surgery, amputation and death.

̽��ֱ��researchers found that screening was feasible, but that intensive treatment did not yield statistically significant benefits over routine care, most likely because of the overall high quality of routine diabetes care delivered in general practice.

An important question is what impact screening followed by treatment has on the later health outcomes of people with undiagnosed type 2 diabetes, as previous research indicates that in Europe onset of the disease typically occurs six years before its clinical diagnosis. Because all participants in ADDITION-Europe were screened for type 2 diabetes at the start of the study, the researchers could not determine what difference would have been observed had screening and treatment for T2DM been compared with no screening, and therefore no treatment, until the time of eventual clinical diabetes diagnosis.

To answer this question the authors used the Michigan Model for Type 2 Diabetes, which simulates the progression of diabetes and its complications, co-morbidities, quality-of-life, and costs, and provides estimates of cardiovascular events and mortality.

In order to assess the impact of a delay in diagnosis and treatment for type 2 diabetes, the researchers simulated scenarios where screening did not take place and there was either a three-year or six-year delay before diagnosis, after which routine care was initiated.

At 10 years after baseline, the simulations predicted that with a delay of 3 years in diagnosis and treatment 22.4 % of those with type 2 diabetes would experience a cardiovascular disease event, such as stroke or heart bypass surgery, which rose to 25.9% with a delay of six years. However, if screening and routine care had been implemented, the simulation predicted only 18.4% would experience a cardiovascular disease event at 10 years after baseline. ̽��ֱ��simulated incidence of all-cause mortality was 16.4% with a delay of three years and 18.2% with a delay of six years, compared to 14.6% for screening and treatment.

This means that over 10 years, the model predicts that for people with undiagnosed type 2 diabetes, screening would be associated with a 29% reduction in relative risk of a cardiovascular disease event compared with a delay of six years in diagnosis and treatment. This amounts to a 7.5% reduction in the absolute risk of adverse cardiovascular outcome in this population. ̽��ֱ��comparable change in all-cause mortality was 20% relative risk and 3.6% absolute risk reduction.

Before they used the Michigan model to assess the impact of a delay in diagnosis and treatment for type 2 diabetes, the authors first evaluated its ability to predict the previous outcomes of ADDITION-Europe by simulating the impact of the intensive treatment and routine care on the composite cardiovascular outcome and all-cause mortality, and comparing these two scenarios. ̽��ֱ��results were similar to those of ADDITION-Europe, with little difference between the intensive treatment and routine care scenarios.

Professor William Herman of the ̽��ֱ�� of Michigan, first author on the paper, said: “Comparing the results of our simulations with the real-world data gave us confidence that our model successfully predicts the impact of delaying diagnosis of type 2 diabetes on future cardiovascular health outcomes.

“Diabetes can be debilitating for patients and costly for healthcare. This research shows that the early identification of diabetes has major health benefits, and supports the introduction of measures such as screening to reduce the time between development of type 2 diabetes and its treatment.”

Professor Nick Wareham, senior author on the paper and Director of the MRC Epidemiology Unit, ̽��ֱ�� of Cambridge, added: “This work shows the value of public health modelling to assess impacts and interventions for diseases such as type 2 diabetes that pose an increasing public health challenge.

“ADDITION-Europe is a large, high quality study, but even so there are limitations in how much direct clinical observation can tell us about the costs and benefits of screening. Computer simulations add an extra dimension which we hope will guide future research as well as the development of public health policy.”

̽��ֱ��authors caution that, even without a diabetes diagnosis, individuals might in the interim receive treatment for conditions such as blood pressure or cease smoking, so that the outcomes might be somewhat less pronounced than their model predicts. However, they note that the model assumes that, as in ADDITION-Europe, all participants were recruited from primary care, and were thus already receiving medical care, so this distortion is not expected to be large.

Reference
Herman, WH et al. Early detection and treatment of type 2 diabetes reduces cardiovascular morbidity and mortality: A simulation of the results of the Anglo-Danish-Dutch Study of Intensive Treatment in People with Screen-Detected Diabetes in Primary Care (ADDITION-Europe). Diabetes Care (2015)

Screening to identify type 2 diabetes followed by early treatment could result in substantial health benefits, according to new research published today in Diabetes Care that combined large scale clinical observations and innovative computer modelling.

This work shows the value of public health modelling to assess impacts and interventions for diseases such as type 2 diabetes that pose an increasing public health challenge

Nick Wareham

Oskar Annermarken

Diabetes testing (crop)

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