Category Archives: Diabetes

Updated Jan 20, 2022 | 12:31IST

Experts say that making a few changes in your routine and being consistent with those habits can help you reverse prediabetes and achieve normal blood sugar levels, which is anything less than 140 mg/dL (7.8 mmol/L).

Several studies have shed light on how an early breakfast and timely dinner are linked to lower risk of insulin resistance a major contributor to diabetes risk.   |  Photo Credit: iStock Images

New Delhi: Pre-diabetes is a state when the blood sugar levels are high enough to be a cause of concern, yet not high enough to be diagnosed as diabetes. Although an unpleasant diagnosis, the best part about pre-diabetes is that it is a reversible state all you need to do is practice a few dietary changes and get ample workouts. When an individuals blood sugar levels cross the 8 A1C mark, he or she is considered diabetic. However, when blood sugar levels range from 140 to 199 mg/dL (7.8 to 11.0 mmol/L), the state is known as pre-diabetes.

Fret not, experts say that making a few changes in your routine and being consistent with those habits can help you reverse prediabetes and achieve normal blood sugar levels, which is anything less than 140 mg/dL (7.8 mmol/L). Take a look at ourtips to reverse pre-diabetes without medication.

Disclaimer: Tips and suggestions mentioned in the article are for general information purposes only and should not be construed as professional medical advice. Always consult your doctor or a dietician before starting any fitness programme or making any changes to your diet.

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Reverse pre-diabetes with diet, pro tips to ease you into the process - Times Now

We have a longstanding tradition here at DiabetesMine to query prominent diabetes advocacy organizations at the start of each new year about their past-year accomplishments and goals for the year ahead.

Much like the year before, 2021 was a difficult one, dominated by the COVID-19 pandemic impacting our lives and the diabetes community in so many ways. Heres what five of the most well-known diabetes nonprofit orgs are currently telling us about milestones and what they hope to accomplish soon.

This is of course not an exhaustive list of groups involved in diabetes advocacy, but these five stand out as some of the largest and most influential representing type 1 diabetes (T1D) here in the United States.

Former CEO Tracey D. Brown announced her resignation in mid-2021 and left the org in October 2021.

During the search for a new chief exec, three internal ADA leaders Scientific and Medical Officer Dr. Robert Gabbay, Chief of Development Officer Charles Henderson, and Chief Financial Officer Charlotte Carter formed an interim Office of the CEO to ensure a smooth transition period.

There is no timeline on when that search may be complete, but its highly likely the ADA will name a new CEO prior to their big annual Scientific Sessions conference scheduled for June 3 to 7, 2022. The org has already announced that it plans to hold a hybrid virtual and in-person event, as it did in both 2021 and 2020 because of the COVID-19 pandemic. This summers in-person event will take place in New Orleans, Louisiana.

As to 2021 highlights and 2022 plans, an organization spokeswoman told DiabetesMine it will prioritize 6 advocacy areas in the year ahead:

As the longest-running and largest organization dedicated to type 1 diabetes (T1D) research, advocacy and support, the JDRF has a number of efforts planned for 2022 that take into consideration its work during the past year.

In response to DiabetesMines inquiry about JDRF highlights in 2021, an organization spokesperson calls out the orgs work advocating on a number of different fronts from Congressional meetings, FDA regulatory advocacy on new technology and medications, big research funding efforts on treatments and tech, and federal legislative efforts on the Build Back Better plan proposing a $35 insulin copay cap for all federal employer-covered plans, Medicare and Marketplace Exchange plans. The JDRF updated its Health Insurance Guide with timely and relevant information in English and Spanish, to help people find information on affording insulin and diabetes supplies and other insurance topics tailored for the T1D community.

Additionally, the JDRF advocated for COVID-19 vaccine prioritization policies in 2021 and were one of the many groups pushing the CDC to include T1D in the same high-risk category as T2D in over 25 states. This advocacy work helped lead all remaining states to eventually follow suit.

These are the JDRFs priorities for 2022, per the organization:

This California-based organization was marked by tragedy at the end of 2021, as CEO Thom Scher suddenly and unexpectedly passed away in early December. Scher did not live with diabetes himself, but was a passionate advocate who had a bold vision to challenge the status quo in terms of what a nonprofit organization could do. He had been at the helm of the organization since the beginning of 2019 (see DiabetesMines interview with him here.)

As a new leader is being selected by the groups board, theyve named Arizona D-Mom Tracey McCarter as interim CEO. Shes been Involved with BT1 since its inception and on the governing board for several years. Her 4-year-old daughter Charlize was diagnosed with T1D in 2009.

All of us at Beyond Type 1 are touched by the outpouring of support weve received since Thoms passing, McCarter told DiabetesMine. We know that, together, well continue his legacy of collaboration for the greater good of the entire diabetes community. In 2022, we look forward to growing our programs, partnerships, and platforms, further uniting the global diabetes community, and providing resources and solutions that improve the lives of those impacted by diabetes. In everything we do, the memory of Thom will serve as our guiding light.

As to its 2021 achievements, the organization shared this blog post that summarized its efforts during the year and pointed to efforts to make connections worldwide, expanding its international reach, addressing language barriers, and much more.

One highlight involved launching a new Advocacy Portal, which focuses on both federal and state legislative priorities, including insulin pricing and copay caps.

For 2022, BT1 tells DiabetesMine they have many plans for the year but in particular theyre looking forward to the following:

In 2021, ADCES focused on maximizing its advocacy efforts in the virtual environment and reaching out to the new Biden Administration and members of the 117th Congress.

A spokesperson explains:

We worked with our congressional champions and activated our grassroots network to reintroduce and promote the Expanding Access to DSMT Act in the U.S. Senate (S. 2203) in June and in the U.S. House of Representatives (H.R. 5804) in November. This legislation would make necessary improvements to the Medicare benefit for diabetes self-management, education and support, referred to by Medicare as DSMT.

On the regulatory and payment front, ADCES worked with the Diabetes Technology Access Coalition (DTAC) and other partners to make changes to the Medicare local coverage determination (LCD) for CGMs. Thanks to those efforts, the LCD was updated effective July 18, 2021, to remove the requirement that Medicare beneficiaries check their blood glucose 4 times per day to be eligible for a CGM and changed language around injecting insulin to administering insulin to account for inhaled insulin products.

In addition to our involvement with DTAC, ADCES also serves as a co-chair of the Diabetes Advocacy Alliance (DAA). This year, the DAA conducted extensive outreach to the Biden Administration and met with top officials including CMS Administrator Chiquita Brooks-LaSure and Elizabeth Fowler, PhD, deputy administrator and director of the Center for Medicare and Medicaid Innovation, to discuss the DSMT benefit and Medicare Diabetes Prevention Program.

As the year comes to a close, we await the public release of the National Clinical Care Commissions final report to Congress. This report is expected to contain recommendations regarding improvements to federal diabetes policy advocated for by ADCES and other advocacy partners in the diabetes community.

This global advocacy group based in the United Kingdom is focused on the #insulin4all movement to improve access and affordability for those who use insulin, particularly in the United States where outrageously high prices are at crisis levels. DiabetesMine reached out to founder and fellow type 1 Elizabeth Pfiester about her organizations work in 2021 and plans for 2022.

She pointed to the groups top 2021 accomplishments that include the following:

For the coming year, Pfiester says, Our efforts will be focused on continuing to train and support our advocates to reach their local goals, while coordinating on a federal U.S. and global level to lower the cost of insulin and supplies.

T1International notes that it hopes for tangible outcomes will come from the Compact moving forward into 2022, saying that we continue to encourage the WHO to do this, and are part of various consultation groups to hold them accountable and work with them to improve the lives of people with diabetes.

No doubt, theres a lot to look forward to in 2022 on the insulin affordability and access front and beyond. Heres hoping for a brighter, and ideally productive, new year.

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Diabetes Advocacy Orgs: 2022 Goals After Another Tough Year - Healthline

In order to keep your gut health intact, nutritionists advise including food items like fruits, vegetables, beans, millets, more probiotics in the form of buttermilk or curds in the diet.

Good gut health is a must for people, as it helps to keep diabetes, which is one of the fastest-growing diseases, at bay. It is rapidly rising due to several factors including the modern lifestyle, family history of stress and more. It is suggested that people must avoid a diet high in carbs and fats in order to reduce the risk of metabolic disorders. PCOS and Gut Health Nutritionist Avantii Deshpaande told Hindustan Times that Type 1 diabetes is an autoimmune disorder, while type 2 diabetes has been linked to lifestyle, genetics, and environmental conditions. The expert stated that even though several types of research on the matter are underway, it is evident that there is a connection between gut health and diabetes.

According to Ms Deshpaande, eating a diet high in fibre and probiotics can assist in keeping the gut health in top shape, while consuming more carbohydrates and fats does the opposite. The latter leads to the formation of bacteria causing toxin accumulation in the body and increasing bad cholesterol, which can cause obesity and increase the chances to get diabetes.

Type 2 diabetes is often due to intake of high calories foods in the diet, higher carbohydrates and fats in the diet can lead to it. The nutritionist suggested that it also alters the microbiome, meaning it decreases the number of beneficial bacteria and increases the number of bacteria which in turn increases the toxins accumulated in the body. Ms Deshpaande shared that these accumulated toxins increase the levels of triglycerides, LDL which are bad cholesterol. Further, they also reduce the good HDL cholesterol. Hence, this ends up becoming the root cause of obesity which in turn can lead to insulin resistance.

In order to keep your gut health intact, the nutritionist advised to focus on a diet - high in fibre, and to do so one must consume food items like fruits, vegetables, beans, millets and also include more probiotics in the form of buttermilk or curds. 1 tsp of ghee in the diet every day will also improve the gut health. She also advised that people should consume protein and good fats in the diet and reduce the consumption of carbs.

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Here's Why Poor Gut Health Can Lead To Diabetes - News18

How one man diagnosed with diabetes achieved his life ambitions Calendar An icon of a desk calendar. Cancel An icon of a circle with a diagonal line across. Caret An icon of a block arrow pointing to the right. Email An icon of a paper envelope. Facebook An icon of the Facebook "f" mark. Google An icon of the Google "G" mark. Linked In An icon of the Linked In "in" mark. Logout An icon representing logout. Profile An icon that resembles human head and shoulders. Telephone An icon of a traditional telephone receiver. Tick An icon of a tick mark. Is Public An icon of a human eye and eyelashes. Is Not Public An icon of a human eye and eyelashes with a diagonal line through it. Folder An icon of a paper folder. Folder An icon of a paper folder. Breaking An icon of an exclamation mark on a circular background. Camera An icon of a digital camera. Caret An icon of a caret arrow. Clock An icon of a clock face. Close An icon of the an X shape. Close Icon An icon used to represent where to interact to collapse or dismiss a component Ellipsis An icon of 3 horizontal dots. Envelope An icon of a paper envelope. Facebook An icon of a facebook f logo. Camera An icon of a digital camera. Home An icon of a house. Instagram An icon of the Instagram logo. Linked In An icon of the Linked In logo. Magnifying Glass An icon of a magnifying glass. Search Icon A magnifying glass icon that is used to represent the function of searching. Next An icon of an arrow pointing to the right. Notice An explanation mark centred inside a circle. Previous An icon of an arrow pointing to the left. Rating An icon of a star. Tag An icon of a tag. Video Camera An icon of a video camera shape. Speech Bubble Icon A icon displaying a speech bubble WhatsApp An icon of the WhatsApp logo.

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'I cried when I was diagnosed with diabetes': How one man overcame adversity to achieve his life ambitions - Press and Journal

Scientists believe so and, far from sketchy herbal supplements or vitamin pills, these are real medicines that could defer or prevent the decline we all experience as we get older. And they could be with us sooner than you think. Though most of us think of ageing as inevitable, researchers have been working for years to uncover different ways to slow it down, and even reverse it.

We now have dozens of methods of altering the ageing process in the lab, from diets, to drugs, to gene therapies, making everything from flies to fish to mice biologically younger. But can we do this in humans?

The good news is scientists increasingly believe there may be ways to slow human ageing. One of the most promising results of this research is that we may not even need to develop new medicines to make this possible: existing drugs might be able to slow down ageing meaning it could be just a few years before we can all benefit.

One leading contender is diabetes treatment metformin one of the world's most widely prescribed drugs. Millions of people in the UK already take it. It's usually prescribed to treat the high blood sugar levels that diabetes causes, but a 2014 study showed its effects could be much broader.

Medical records from 180,000 NHS patients were analysed to see how metformin stacked up against other diabetes drugs, and a "control" group of people without diabetes, and therefore not taking medicine for it.

The breakthrough finding was that diabetics taking metformin actually outlived the non-diabetic controls who weren't on the drug all the more surprising because patients without diabetes tended to be less overweight and have fewer other conditions such as heart disease.

Other studies have shown metformin may decrease the risk of cancer, heart disease and dementia, too. So should we all be taking it?

Though the evidence is compelling, it's probably worth waiting for the results of a proper trial about to start in the US of metformin as a drug to fight ageing.

The TAME trial short for Targeting Ageing with Metformin will give 1,500 60-80-year-old volunteers the drug while another 1,500 receive placebo tablets. Then the patients will be watched for several years.

If those taking the real pills get less cancer, heart disease, dementia and so on than those on the fakes, we'll know for certain that metformin slows down ageing as we suspect it might.

One of the challenges of getting an anti-ageing drug approved by regulators is that drugs usually need to treat a particular disease and ageing isn't currently recognised as one.

However, the other exciting aspect of the TAME trial is that the scientists involved have worked closely with American medicines regulator, the FDA, when designing their trial.

That means, even if metformin turns out to be a dud, they've created a pathway to get these kinds of drugs approved in future.

And there are plenty more waiting to follow in metformin's footsteps.

Another contender started its journey in the unusual location of tropical Easter Island, deep in the Pacific Ocean, and home of the famous stone Moai statues with their gigantic heads.

A soil sample collected from the island known in Polynesian as Rapa Nui was found to contain bacteria with antifungal properties. The drug isolated from these bacteria was christened rapamycin after its place of discovery.

Further work uncovered that it also suppresses our immune systems, which would be somewhat counter-productive in an antifungal medicine because it would weaken our bodies' own defences against infection.

Rapamycin eventually found its place as a drug used to help stop the immune systems of transplant patients from rejecting their new organs but research since has shown it could have an even more significant application as an anti-ageing medicine.

Rapamycin, it turns out, can mimic a process called "dietary restriction", where we feed animals less and find that, as a result, they live longer.

This isn't just the kind of diet you might go on to lose a bit of weight, but a lifelong cutting back on calories, carefully balanced to ensure the animals get all the nutrients they need.

On such a diet, mice can live more than 50 percent longer than siblings allowed to eat what they like.

When scientists noticed that rapamycin caused similar biological effects to dietary restriction, they were keen to test it out and they found it extended lifespan in mice by 10 percent.

Even better, this result holds when it's given to old mice, the biological equivalent of human 60-somethings meaning, if it does work in people, we could find out in time to benefit even those of us who are no longer spring chickens.

Counterintuitively, though large doses of rapamycin suppress the immune system, smaller doses seem to rejuvenate it, probably through the drug's anti-ageing effects.

Trials using a related drug showed that it could improve older people's response to a flu jab, and reduce their subsequent risk of infection.

There were also proposals to trial it as a preventative pill to reduce the impact of Covid-19 in British care homes.

Hopefully some of these anti-ageing drugs will be available in time for the next pandemic: we've all seen how much extra risk older people were at if they caught the virus, so if we could all have biologically younger immune systems, so much the better.

One finding, however, of anti-ageing science may come as a shock: vitamin supplements are mostly ineffective when it comes to extending our lives.

The rationale for many vitamin pills is that they work as "antioxidants", clearing up so-called "free radicals" which can damage the DNA and proteins that make up the inside of our cells, and were thought for decades to be a key villain in the ageing process.

However, recent research has overturned this theory, and the biggest and most reliable studies of vitamin pills have found that they don't have much effect on how long we live.

When scientists combined studies, including more than 300,000 people taking the supplements, they found vitamins A and C, along with selenium, had no effect on lifespan, and vitamin E and beta-carotene actually slightly increased risk of death.

So, unless your doctor has told you to take them because you're lacking a particular vitamin, it might be worth rethinking any supplements you're taking, because they might be doing more to reduce your bank balance than extend your life.

Thankfully, however, there are many other drugs with potential anti-ageing properties under investigation.

A compound called spermidine, found in lots of foods, including mushrooms and cheddar cheese, is another potential mimic of dietary restriction. And scientists are also investigating drug combinations.

A chemo drug called dasatinib plus a supplement called quercetin has been found to remove aged cells from our bodies, and make mice live longer and healthier.

And a cocktail of metformin plus two hormones has improved immune function in older men, and reduced their biological age.

This range of different approaches shows anti-ageing medicine isn't some sci-fi pipe dream or a strange fluke seen only in mice in the lab, but that they could be with us pretty soon. With so many drugs in the pipeline to potentially slow ageing, it's a truly exciting time to be alive.

It's also a very important time to understand the science of ageing, because in only a few years we might all be able to get pills to keep us alive and healthy a little longer.

Ageless: The New Science Of Getting Older Without Getting Old by Andrew Steele (Bloomsbury, 9.99) is out now.

For free UK P&P on orders over 20, call Express Bookshop on 020 3176 3832 or visit expressbookshop.com

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Could an ordinary diabetes pill slow the ageing process? - Express

NEW YORK, Jan. 18, 2022 /PRNewswire/ --YumVs Zero Diabetic Multivitamin is the first-ever daily dietary gummy supplement specially formulated for adults with diabetes, containing 14 essential vitamins and minerals vital for health and wellness, including those commonly deficient in diabetics. More than just a sugar-free vitamin, YumVs Zero Diabetic Gummy Multivitamins contain Vitamins C, A, D3, E, B12, plus Chromium, Thiamine, Folate and Magnesium with ZERO of the bad stuff: 0 sugars or sugar alcohols, 0 artificial flavors, 0 gluten, 0 GMOs.

"We are thrilled to bring this product to the hundreds of millions of American adults who have diabetes or are prediabetic," states Asher Tyberg, CEO of Teelah Corporation, the maker of YumVs Zero supplements. "Sugar-free gummy vitamins are a tremendously popular platform of supplements due to their great taste, but their sugar content placed these off limits for people with diabetes and those watching sugar content. Not only is this a sugar-free option, it's the first time we've formulated a product to address the specific deficiencies that can be seen in people with diabetes. It's exciting and our retail partners have really gotten behind this innovation, pushing to get the product on shelves as fast as possible."

YumVs Zero Diabetic Gummy Multivitamins contain 14 essential vitamins and minerals, as well as the vitamins and minerals important for people with diabetes, including:

For those adults who are prediabetic, or just monitoring their sugar intake, YumV's Zero Gummy Multivitamins are delicious, sugar-free and keto-friendly. The natural raspberry-flavored chewable gummies have 0g net carbs. Additionally, YumVs Zero Diabetic Gummy Multivitamins are made with only the highest-quality, natural ingredients. Packed with nutritional value, YumVs Zero gummy vitamins are sugar free, non-GMO, gluten free, gelatin free and contain only naturally sourced flavors and colors. YumVs Zero Diabetic Gummy Multivitamins are available at Walgreens, Amazon.com and https://yumvs.com/product/diabetic-multivitamin-gummies/. Serving size: 2 Gummies. 60 count per bottle.

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Introducing YumVs Zero, the First-Ever Gummy Multivitamin Specially Formulated for People with Diabetes - PRNewswire

Scientists do not understand why exercise produces different benefits depending on the time of day.

New research published in Cell Metabolism may further our understanding of how these changes occur.

The study examined the effects of exercise done in the early morning and late evening in mice.

The effects of time on the benefits of exercise were found to be specific to different tissues and organs.

Samples were taken from seven tissues around the body, as well as the blood.

The timing of exercises produced different effects on the heart, muscles, brain, liver and more.

This makes the question of a 'best' time to exercise more complicated, and there isn't currently a catch-all answer.

The main gain from this research is an improved understanding of how these organs communicate with each other and how the timekeeping mechanisms (circadian clocks) can be rewritten by exercise.

READ MORE:Diabetes: Doctor shares worst breakfast choices for blood sugar control

The study does present several limitations that future research will need to overcome.

The research group examined mice, who are nocturnal.

This means the biochemistry of their circadian rhythms might differ to ours more than other aspects of their biology.

The researchers also examined only one type of exercise, treadmill running, which may not activate all of the same responses as other types of exercise.

Co-first author Assistant Professor Shogo Sato from Texas A&M University said: "Despite the limitations, it's an important study that helps to direct further research that can help us better understand how exercise, if timed correctly, can help to improve health."

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Diabetes: Exercising at a certain time of day could reduce your risk of the condition - Daily Express

DIABETES can be an overwhelming condition to manage and you constantly have to worry about what you're eating.

When you're diabetic your body can struggle to produce enough insulin, or the insulin isn't effective.

1

Doctors have said that you need to be careful and monitor your blood sugar levels in order to avoid a spike.

With type 1diabetes, a persons pancreas produces no insulin, but in type 2, cells in the body become resistant to insulin - so a greater amount of insulin is needed to keep glucose levels within a normal range.

Even if you have a balanced diet, it can be hard to keep your levels in check.

While a little bit of what you fancy is good in moderation, there are some foods that you can avoid to help keep your blood sugar levels in check.

Speaking to The Sun GP Dr Sally Roxburgh fromThe Fleet Street Clinicsaid it is important for those with type 2 diabetes to control their sugar intake as well as control their weight, portion sizes, and calorie intake.

She added: "Getting enough exercise and lifestyle choices are equally important.

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"There are certain foods that should be avoided if you have type 2 diabetes, while other foods should be consumed in moderation."

Dr Roxburgh said that fruit juices and smoothies that are often consumed over breakfast are one the main culprits for raising blood sugar levels.

She explained: "Fruit juice and smoothies should be avoided as they can put blood sugar up very quickly.

"Fruit in general can be consumed and I would never recommend excluding any fruit totally because of the nutritional value that fruit contains, however, there are certain fruits that I would recommend over others for someone with type 2 diabetes.

"For example, berries or an apple rather than pineapple, oranges or bananas. Tinned fruit, however, I would avoid as it is kept in a high-sugar syrup."

Healthy smoothies can sometimes havejust as much sugar as fizzy drinksin them - even though the sugars are natural these can contribute to weight gain.

If you want something sweet at breakfast then you could try having a handful of blueberries.

Experts previously found that the sweet treat can help you control your blood sugar levels.

Experts at the Department of Human Ecology at the University of Marylandin the US found that blueberries are arich source of polyphenols, which includes anthocyanin bioactive compounds.

Anthocyanins possessantidiabetic, anticancer, anti-inflammatory, antimicrobial, and anti-obesity effects, as well as prevention of cardiovascular diseases, experts say.

In aresearch paperthey stated: "Epidemiological evidence indicates that incorporating blueberries into the diet may lower the risk of developing type 2 diabetes."

If you enjoy a bacon and sausage sandwich for breakfast then bad luck, as experts say processed meat should be avoided for people who are trying to keep their blood sugar levels low.

Dr Roxburgh said: "These can cause weight gain which makes diabetes harder to control. Instead, opt for leaner meats such as chicken and turkey.

"Ready meals and processed foods should be avoided as they often contain hidden sugars and fats that make blood sugars high, cause weight gain and predispose to heart disease.

"A combination of a healthy, balanced diet, weight management, regular exercise and healthy lifestyle choices will be the best way to manage type 2 diabetes."

What should my blood sugar be?

Diabetics are urged to monitor their sugar levels and if you're diabetic it's likely you will have been given a device so you can do this at home.

You will be told what your average blood sugar level is and this is referred to as yourHbA1c level.

While they differ for everyone, the NHS says that if you monitor your levels at home then a normal target is 4 to 7mmol/l before eating and under 8.5 to 9mmol/l 2 hours after a meal.

If it's tested every few months then a normal HbA1c target is below 48mmol/mol (or 6.5% on the older measurement scale).

Diabetics need to constantly monitor their carbohydrate levels.

Speaking to The Sun, one expert highlighted the dangers of what can happen if a diabetics carbohydrate consumption is off-kilter.

Dr Will Cave GP from The Fleet Street Clinic in London explains that carbohydrates are foods that can be easily turned into glucose.

The ease with which foods are turned into glucose is referred to as the Glycaemic Index or GI. Foods such as white bread and white rice are turned rapidly into glucose causing a sudden spike in the glucose levels in the blood, while carbohydrates with a low GI, such as nuts, whole grain cereals and most vegetables, will cause a slow rise in blood glucose.

Type 1 diabetics generally avoid foods with a high GI because they know it makes controlling their glucose levels more difficult.

Low glucose levels in the blood might cause them to feel faint or even pass out [a so-called hypo], while high glucose levels are harmful to the blood vessels and over time this can cause damage to the heart, kidneys, eyes - in fact most organs and systems within the body.

In order to avoid any damage, you should cut down on foods with any added sugar like croissants and white bread products.

If you already enjoy a bowl of cereal with milk for breakfast, youll be glad to know youre on the right track.

Scientists found in 2018that starting the day with a high-protein milk meal could help keep type 2 diabetes at bay, and even help you to lose weight.

If you fancy some toast in the morning, just make sure to stick to the wholemeal stuff.

Diabetes UK says: Switch from white toast to wholegrain versions like seeded batch bread, multi-seed, granary, soya and linseed.

These are better for your diabetes and digestive health. They're more filling, too.

Another great way to start the day is with yoghurt - but only of a certain kind.

Many yogurts are high in free sugar, Diabetes UK warns.

While sugar, especially the free kind that is added to foods, isnt necessarily the cause of type 2 diabetes, it does contribute to excess weight - which is linked to the condition.

Being overweight can make it difficult to manage your diabetes and increase your risk of getting serious health problems such as heart disease and stroke in the future, Diabetes UK says.

Too much sugar is bad for your teeth too.

Oats are a great choice for some people with type 2 diabetes thanks to having a lower glycemic index.

Generally, lower GI foods can be useful for managing blood glucose levels, Diabetes UK says.

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Diabetes warning: Im a doctor and these are the breakfast foods to avoid to stop a blood sugar spike... - The Sun

The Saudi Health Council, represented by the National Diabetes Centre, signed a memorandum of understanding (MoU) with Sanofi, to collaborate on various initiatives in research and development in the field of diabetes in the kingdom.

In the presence of Dr Nahar bin Mazki Al-Azmi, Secretary-General of the Saudi Health Council, and Dr Moatazbellah bin Muhammad Alruwaithi, Assistant Secretary-General, Dr Suleiman bin Nasser Alshehri, Director General of Saudi Diabetes Centre, Tarja Stenvall, Senior Vice President, General Medicines, Key Markets, Sanofi and Niven Al- Khoury, General Manager General Medicines KSA & Gulf MCO at Sanofi, the event has been held at the headquarters of the General Secretariat of the Council.

This collaboration will serve as a great impetus in offering efficient healthcare initiatives to develop the ecosystem of diabetes management in the country. Sanofi and SHC will collaborate in building the nucleus of scientific research and generating disease local data supported by establishing a comprehensive diabetes research network. In addition, the agreement includes launching a digital tool that aim to improve the efficiency of diabetes management among people living with diabetes in the country. This MoU will include collaboration in the development and implementation of tailored training courses and workshops for physicians and diabetes educators with specific criteria and performance indicators. Dr. Al-Shehri said.

Dr. Al-Shehri acknowledges Sanofis effort in supporting scientific research, enhancing disease management capabilities, and patients awareness programs. He also conveyed his desire for continued effective and successful cooperation in the future between the two entities, in order to further improve the countrys healthcare sector.

Meanwhile, Tarja Stenvall, Senior Vice President, General Medicines Key Markets, Sanofi, welcomed the collaboration and highlighted its substantial role in facilitating comprehensive, high-quality healthcare to people living with diabetes.

Drawing on our unrivalled experience in the fight against diabetes, we are implementing a series of strategic projects in the healthcare sector of Saudi Arabia. Were grateful to the Saudi Health Council for its sustained efforts in coordination with various authorities to prevent and control diabetes as part of its commitment to bolster the countrys public health. Tarja Stenvall said.

Excerpt from:
Saudi Health Council, Sanofi partner to boost diabetes studies and research in the kingdom - ArabianBusiness.com

Methods

We followed the instructions outlined in the Cochrane Handbook for Systematic Reviews to conduct our systematic review.13 We also followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for reporting this systematic review.14

We systematically searched PubMed (Medline), Scopus, and Web of Science from inception up to 1 May 2021. We developed and performed the literature search (AJ), and two reviewers (AJ and AE) screened the titles and abstracts. The same two reviewers independently assessed the full text of the articles for eligibility. Differences were resolved by discussion. We also screened the reference lists of all published meta-analyses of observational studies on the association between adiposity and the risk of type 2 diabetes. We limited the search to articles in the English language. Table S1 provides the complete search strategy used to find articles of original research for inclusion in our systematic review.

Two of the authors (AJ and AE) screened the title and abstract of all studies found in the systematic search to identify studies that met our criteria for inclusion in the meta-analysis. We selected studies that had a cohort design (prospective and retrospective cohort studies); that were conducted in general adult populations (>18 years); that measured body mass index, hip circumference, waist circumference, thigh circumference, waist-to-hip ratio, waist-to-thigh ratio, waist-to-height ratio, body adiposity index, body shape index, percentage body fat, fat mass, and visceral adipose tissue as the exposure and across two or more quantitative categories; that considered the incidence of type 2 diabetes as the outcome; and that reported the number of participants or person years and the number of individuals with diabetes, and adjusted effect estimates (relative risk, hazard ratio, or odds ratio) with 95% confidence intervals for type 2 diabetes across categories of measures of adiposity. Studies that reported effect estimates for a specific unit increase in measures of adiposity (eg, an increase in body mass index of 1 unit) were also eligible. For duplicate publications from the same cohort, those with more complete information for dose-response meta-analyses (eg, those that expressed exposures as categories and reported sufficient information across categories) were selected. Otherwise, publications that included the largest number of participants were selected. Excluded were cross sectional and case-control studies, studies conducted in patients with diseases, inpatient populations, or elderly patients in institutions, and studies that used self-reported anthropometric measures as exposures. To be included in our review, studies had to have an explicit statement about measuring anthropometric indicators. For cohort studies with incomplete information about the method of measuring anthropometric indices, we read other publications from that cohort or cohort protocol to obtain accurate information about the measurement method. Also, studies that used specific body fat content as the exposure, such as abdominal or leg fat mass, were excluded.

After the study selection process, two reviewers (AJ and SS) independently extracted data from the original cohort studies. The characteristics extracted from each cohort were: the last name of the first author, year of publication, study name, location of the study (country), sex, sample size and number of individuals with type 2 diabetes, age range, length of follow-up, method for identifying outcome, and variables that were entered into the multivariable analyses in primary cohorts.

We also extracted data for performing the analyses: range of exposures, number of individuals with type 2 diabetes and participants or person years, and reported effect estimates with 95% confidence intervals in each category for measures of anthropometric indicators. For studies that did not report sufficient data for the analyses, we contacted the authors by email. Disagreements were resolved by consensus between the two reviewers. We evaluated the quality of the cohort studies with the ROBINS-I tool (Risk Of Bias In Non-randomised Studies-of Interventions).15 This tool was developed by Cochrane to assess the quality of non-randomised studies of interventions and is recommended to evaluate the potential biases associated with observational studies.16 Quality assessments were conducted in duplicate by two independent reviewers (AJ and SS-B). Disagreements were resolved by consensus.

In our registered protocol in PROSPERO (CRD42021255338), we planned to evaluate the quality of the primary studies with the Newcastle-Ottawa scale.17 Because of the limitations of this tool,18 however, we decided to use the ROBINS-I tool. The ROBINS-I tool is increasingly used for assessing the risk of bias of observational studies. The tool includes seven domains and considers potential biases resulting from confounding, selection of participants, assessing the exposure, misclassification during follow-up, missing data, measuring the outcome, and selective reporting of the outcome.15

We selected the relative risk and 95% confidence interval as the effect size for reporting the results of our meta-analysis. We considered hazard ratios equal to relative risk.19 For studies that reported effect estimates as odds ratios, we converted them to relative risk according to the method of Zhang et al.20 We used the random effects model (DerSimonian and Laired method) to generate summary relative risks and 95% confidence intervals.21

For the linear dose-response meta-analyses, we estimated summary relative risks and 95% 95% confidence intervals for an increase in body mass index of 5 units, 10 cm larger waist and hip circumferences, 5 cm larger thigh circumference, increase in waist-to-hip ratio and waist-to-height ratio of 0.1 units, 10% higher percentage body fat, 10% higher body adiposity index, increase in body shape index of 0.005 units, and increase in visceral adiposity index of 1 unit in each primary prospective cohort study according to the methods of Crippa et al.22 We then used random effects models to pool the individual study results.

For the analyses, the median of each category, number of individuals with type 2 diabetes and participants or person years, and adjusted effect estimates across at least two categories of exposures were extracted from each primary cohort study. For studies reporting exposures as a range in each category, the midpoint of the lower and upper bounds was used as a proxy for the median. The widths of the open ended categories were considered equal to the adjacent categories. We pooled relative risks for men and women within each study with a fixed effects model if studies only reported effect sizes specific for sex. For studies that reported the effect estimates graphically, a web plot digitiser (www://plotdigitizer.sourceforge.net/) was used to estimate effect estimates from the graphs. We checked the accuracy of these tools in our previous meta-analysis of central fatness and mortality, which indicated precise estimations by these tools.7 For studies that reported a category other than the lowest one as a reference, we recalculated the relative risks assuming the lowest category as the reference, according to the method of Hamling et al.23

After a comment from referees, we performed a sensitivity analysis after exclusion of studies that reported odds ratios as the effect size to ensure the robustness of the findings. Prespecified subgroup analyses were done according to location of the population, age, sex, number of individuals with type 2 diabetes, length of follow-up, and adjustment for confounders, including intake of alcohol, smoking status, physical activity, and family history of diabetes, and for the intermediate variables blood pressure and blood glucose. We also performed post hoc subgroup analyses by the method of case ascertainment (ie, method of identifying individuals with type 2 diabetes). P values for differences between subgroups were calculated by meta-regression analyses. We also performed sensitivity analyses in healthy participants. For the analyses of healthy individuals, any definition of healthy individuals used in primary cohorts, including those without cardiovascular disease, non-communicable chronic diseases, or cardiometabolic abnormalities, were acceptable (because primary cohorts used a wide range of definitions for healthy individuals). Publication bias was assessed when at least 10 studies were available.13 The potential for publication bias was tested with Eggers test,24 Beggs test,25 and by inspection of funnel plots. To differentiate asymmetry caused by publication bias from that caused by other factors,26 we applied contour enhanced funnel plots.27

Curvilinear dose-response associations were modelled with a one stage weighted mixed effects meta-analysis.22 We modelled the exposures by using restricted cubic splines with three knots, according to Harrells recommended centiles (10%, 50%, and 90%) of the distribution.28 The correlation within each category of published relative risks was taken into account and the estimates specific to each study were combined in a one stage weighted mixed effects meta-analysis.2229 This method estimates the slope lines specific to each study and combines them to obtain an overall average slope in one stage.3031 For the analyses of body mass index, we considered the nadir of the curve of the association between body mass index and mortality (body mass index of 23) as the reference.32 For other anthropometric measures, we selected the median of the baseline values of the exposures of the included studies as a reference, and so we avoided self-selection bias in choosing the baseline for the curve and used a data driven approach. For all anthropometric measures, we first performed a main analysis including all eligible cohorts. Then, separate analyses were conducted according to sex, age, geographical region, ethnicity, and in healthy individuals. We applied the best fitting second order fractional polynomial to model curvilinear associations when restricted cubic splines could not be calculated because of the limited number of studies (n2) included in the analyses.22 We used the Wald test to evaluate deviation of the data from linearity.

To compare the associations across different measures, we performed another analysis to estimate the summary relative risk for an increase of one standard deviation in each measure. For this analysis, we estimated the relative risk for each increase in standard deviation in each measure in each study. Then, the results for each study were pooled with a random effects model. For studies that reported the effect size for an increase of one standard deviation in exposure, the effect size was included in the meta-analysis as reported. For studies that reported the effect size for a specific amount of increase in measures, we exponentiated the log (relative risk) times the standard deviation of the anthropometric measure for each study to obtain the relative risk for an increase of one standard deviation in the level of the measures. Similar to a linear dose-response meta-analysis, for studies that reported exposures across quantiles, we estimated the relative risks for an increase of one standard deviation with Crippa et als method.22 Statistical analyses were conducted with STATA software, version 16.0. P<0.05 was considered significant.

We applied the updated Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach to rate the overall certainty of the evidence for each association.3334 The authors (AJ and SS-B) independently performed GRADE assessments. GRADE rates the certainty of evidence as high, moderate, low, or very low. Text S1 provides detailed descriptions about the domains of the GRADE tool and how to judge each domain.

No patients were involved in setting the research question or the outcome measures, nor were they involved in developing plans for design or implementation of the study because of lack of resources to allow their participation.

The initial search of the databases and reference lists identified 119246 records (fig S1). After excluding 24128 duplicates and 94109 irrelevant articles based on screening of the title and abstract, 1102 full text articles were reviewed in detail for eligibility. Overall, 212 articles provided sufficient information and were considered eligible to be included in this dose-response meta-analysis. Four cohort studies reported data from two separate cohort studies, and thus 216 cohort studies with 25999148 participants and 2310697 individuals with type 2 diabetes were included in the analyses. All included studies were original studies published between 1991 and 2021. All studies were population based cohort studies conducted in the general population of adults, and patients with a history of diabetes at baseline were excluded.

In brief, 56 studies (54 publications) were conducted in Europe, including the UK, Scandinavia (Finland, Norway, Denmark, and Sweden), west and central Europe (Germany, Netherlands, Switzerland, and France), and south Europe (Italy and Spain), 32 studies were conducted in North America (US and Canada), five in South America, eight in Australia, one in Africa, 93 (91 publications) in the Far East (China, Japan, South Korea, and Taiwan), four in South Asia (India and Bangladesh), four in South East Asia (Thailand and Singapore), and 14 in the Middle East. Twenty one studies were conducted in men, eight in women, and the rest in both sexes.

Of the 216 studies included in the meta-analysis, 190 (88%) were prospective cohort studies and 26 (12%) were retrospective cohort studies. All studies used measured anthropometry as the exposure. Fifty six studies (26%) performed repeated measurements during the follow-up period and the other 160 studies (74%) performed baseline measurements only. For method of case ascertainment, 56 studies (26%) performed direct blood glucose measurements, 15 studies (7%) used self-reported methods, 15 (7%) used medical registries, and the other 130 studies (60%) used mixed methods, including a combination of two or more of these methods.

All studies reported multivariable effect estimates. Forty nine studies (23%) excluded participants with a history of non-communicable chronic diseases (mainly cardiovascular disease) at baseline, 207 cohorts (96%) controlled for age in their multivariable analyses, 197 studies (91%) for sex, 149 (69%) for smoking status, 114 (53%) for alcohol drinking, 100 (46%) for physical activity, 87 (40%) for a family history of type 2 diabetes, and 39 (18%) controlled for all of these confounders. For intermediate variables, 95 studies (44%) controlled for blood pressure or hypertension, 84 (39%) for blood glucose, and 24 (11%) for both intermediate variables. Twenty four studies (11%) controlled for all of the confounders and intermediate variables. Based on the ROBINS-I tool, 55 studies (25%) were rated as having a serious risk of bias and 161 studies (75%) a moderate risk of bias. Table S2 provides a list of studies excluded after assessment of the full text, with reasons for exclusions. Table S3 shows the general characteristics of the studies included in this review. Table S4 presents the quality of the studies, assessed by the ROBINS-I tool.

We identified 182 cohort studies (178 publications) with 228695 individuals with type 2 diabetes among 5585850 participants for the analysis of body mass index and type 2 diabetes. Each increase in body mass index by 5 units was linked to a 72% higher risk of type 2 diabetes (relative risk 1.72, 95% confidence interval 1.65 to 1.81, I2=99%, table 1).

Subgroup analysis of body mass index (for an increase of 5 units) and risk of type 2 diabetes

A sensitivity analysis after excluding studies that reported odds ratio as the effect size indicated the same results as the main findings (relative risk 1.69, 95% confidence interval 1.60 to 1.78, I2=99%, n=137). The relative risk was 1.75 (1.64 to 1.86; n=43) in men and 1.69 (1.61 to 1.79; n=64) in women (P for subgroup difference=0.71, table 1). The association was significant (P<0.001) in all subgroups defined by participants and study characteristics, and across all ethnicities and regions. The association was stronger in studies with a longer follow-up and in those that performed baseline rather than repeated anthropometric measurements, and significantly weaker in studies that performed blood glucose measurements for case ascertainment compared with self-reported methods and medical registries (P for subgroup difference <0.001, table 1). In the subgroup analysis by region, we saw a stronger association in studies conducted in Europe (table 1). The association was weaker in adults older than 60 years (relative risk 1.26, 95% confidence interval 1.06 to 1.49; I2=93%, n=9), and was significant after controlling for all confounders and intermediate variables, including age, sex, physical activity, smoking status, alcohol drinking, family history of type 2 diabetes, blood pressure, and plasma glucose (1.67, 1.42 to 1.96, P<0.001, I2=97%, n=20). We found evidence of publication bias with Eggers test (P=0.01) but not with Beggs test (P=0.21), however, the contour enhanced funnel plot suggested evidence of asymmetry in the funnel plot owning to publication bias (figs S2-3).

Of the cohort studies, 121 provided sufficient information for the non-linear dose-response meta-analysis. We found a strong linear association between body mass index and risk of type 2 diabetes in the main analysis (Pnon-linearity=0.06, Pdose-response<0.001; R2=0.74, fig 1), with no indication of change from linearity at a specific cut-off value. These same results were seen in men (Pnon-linearity=0.23, n=46), women (Pnon-linearity=0.54, n=43), and in healthy individuals (Pnon-linearity=0.57, n=21). We found some indications of non-linearity in the analysis of younger adults (Pnon-linearity=0.03, n=6) and adults older than 60 years (Pnon-linearity=0.009, n=8), with a steep upward curve at a body mass index of >25, especially in younger adults (fig 1).

Dose-response association between body mass index and the risk of type 2 diabetes in all studies (Pnon-linearity=0.06, n=121), healthy adults (Pnon-linearity=0.57, n=21), men (Pnon-linearity=0.23, n=46), women (Pnon-linearity=0.54, n=43), young adults (Pnon-linearity=0.03, n=6), and older adults (Pnon-linearity=0.009, n=8). The solid line represents the non-linear dose response and the dotted lines the 95% confidence interval. The circles represent the relative risk point estimates for adiposity categories from each study with the size of the circle proportional to the inverse of the standard error

Figures S4-S7 show the associations between body mass index and type 2 diabetes specific to ethnicity and region. Figure 2 provides a summary of the associations between body mass index and type 2 diabetes for all regions and ethnicities. Dose-response meta-analyses indicated similar positive linear or monotonic associations in different regions and ethnicities. Non-linear dose-response meta-analyses indicated a steep upward curve for the US, Scandinavian countries, and in countries located in west and central Europe.

Dose-response association between body mass index and the risk of type 2 diabetes for all regions and ethnicities

We identified 78 cohort studies (74 publications) with 21459955 participants and 2006648 individuals with type 2 diabetes for the association between waist circumference and type 2 diabetes. Each 10 cm increase in waist circumference was related to a 61% higher risk of type 2 diabetes (relative risk 1.61, 95% confidence interval 1.52 to 1.70, I2=99%, table S5).

A sensitivity analysis after excluding studies that reported odds ratio as the effect size indicated the same results as the main findings (relative risk 1.62, 95% confidence interval 1.53 to 1.72, I2=98%, n=57). The relative risk was 1.68 (1.54 to 1.82, I2=95%, n=38) in men and 1.68 (1.56 to 1.81, I2=98%, n=38) in women (P for subgroup difference=0.90). The positive association persisted in all subgroups, with stronger associations in Europe (2.00, 1.79 to 2.45, I2=93%, n=17) and North America (1.69, 1.48 to 1.93, I2=99%, n=14) (table S5). We found significant differences between subgroups; studies with longer follow-up periods reported stronger associations (P for subgroup difference <0.001), and studies that used blood glucose measurements reported weaker associations compared with those that used medical registries and self-reported methods for case ascertainment (P for subgroup difference=0.01). The relative risk was 1.68 (1.38 to 2.04, I2=91%, n=11) in studies which controlled for all confounders and intermediate variables. We found evidence of publication bias with Eggers test (P=0.01), but not with Beggs test (P=0.18). Asymmetry in the funnel plot was found (fig S8), and the contour enhanced funnel plot was also asymmetric owning to publication bias (fig S9).

The systematic search identified 43 cohorts for the non-linear dose-response meta-analyses. We found a strong positive linear association in the main analysis (Pnon-linearity=0.10, Pdose-response<0.001; R2=0.80, n=43), in studies that adjusted for body mass index (Pnon-linearity=0.95, n=4), and also in healthy individuals (Pnon-linearity=0.51, n=8), older adults (Pnon-linearity=0.46, n=1), men (Pnon-linearity=0.38, n=22), and women (Pnon-linearity=0.40, n=21) (fig 3).

Dose-response association between waist circumference and the risk of type 2 diabetes in all individuals (Pnon-linearity=0.10, n=43), in studies which controlled for body mass index (Pnon-linearity=0.95, n=4), in healthy individuals (Pnon-linearity=0.51, n=8) older adults (Pnon-linearity=0.46, n=1), men (Pnon-linearity=0.38, n=22), and women (Pnon-linearity=0.40, n=21). The solid line represents the non-linear dose-response and the dotted lines the 95% confidence interval. The circles represent the relative risk point estimates for adiposity categories from each study with the size of the circle proportional to the inverse of the standard error

Figure S10 shows the association between waist circumference and the risk of type 2 diabetes by region and figure S11 shows the association by ethnicity. The strong linear association persisted in the US (Pnon-linearity=0.83, n=4), Europe (Pnon-linearity=0.18, n=10), and Asia (Pnon-linearity=0.10, n=28) (fig S10), and in white (Pnon-linearity=0.92, n=2) and black (Pnon-linearity=0.32, n=2) individuals (fig S11).

We identified 14 cohort studies (13 publications) with 9623 individuals with type 2 diabetes among 231410 participants that reported data for the relation between hip circumference and type 2 diabetes. A 10 cm larger hip circumference was not linked to the risk of type 2 diabetes in the main analysis (relative risk 1.11, 95% confidence 0.98 to 1.27; I2=98%) (fig S12).

A subgroup analysis by adjustment for waist circumference, however, indicated a positive association in studies that did not take waist circumference into account in their multivariable analyses (relative risk 1.35, 95% confidence interval 1.14 to 1.60; I2=97%, n=8). In contrast, we found an inverse association in studies that considered waist circumference as a confounder in their multivariable analyses (0.89, 0.82 to 0.96, I2=91%, n=7) (fig S12). We did not find evidence of publication bias with Eggers test (P=0.24), Beggs test (P=0.68), or with the funnel plot (fig S13).

Three cohorts in the US and Europe were eligible for the non-linear dose-response meta-analysis. We found a non-linear inverse association between hip circumference and type 2 diabetes (Pnon-linearity<0.001, Pdose-response<0.001; R2=0.37, n=3) (fig S14), with the lowest risk at a hip circumference of 107 cm (relative risk107cm 0.81, 95% confidence interval 0.74 to 0.87) and with a slight upward curve at higher values. The association was significant in women (Pnon-linearity<0.001, n=2) but not in men (Pnon-linearity<0.001, n=2).

Thirty four cohort studies with 46763 individuals with type 2 diabetes among 934589 participants were identified for the relation between waist-to-hip ratio and risk of type 2 diabetes. Each increase in waist-to-hip ratio by 0.1 units was linked to a 63% higher risk of type 2 diabetes (relative risk 1.63, 95% confidence interval 1.50 to 1.78, I2=99%) (fig S15). The association was unchanged after excluding studies that reported odds ratio as the effect size (1.61, 1.46 to 1.76, I2=98%, n=24). Similar to the analyses of body mass index and waist circumference, the positive association persisted in all subgroups defined by region, ethnicity, race, length of follow-up, sample size, and in studies that controlled for all confounders and intermediate variables (2.41, 1.96 to 2.96, I2=0%, n=2) (table S6). The association was stronger in Europe and weaker in South America and the Middle East. We found some indications of publication bias with Eggers test (P=0.02), but not with Beggs test (P=0.21). The contour enhanced funnel plot was also asymmetric owing to publication bias towards a stronger effect size (figs S16-17).

The non-linear dose-response meta-analysis indicated a positive linear association in the main analysis (Pnon-linearity=0.17, Pdose-response<0.001; R2=0.82, n=19) (fig 4), which was also seen in healthy individuals (Pnon-linearity=0.55, n=4), older adults (Pnon-linearity=0.16, n=2), men (Pnon-linearity<0.001, n=8), women (Pnon-linearity=0.10, n=10), and in one study which controlled for body mass index (Pnon-linearity=0.40, n=1) (fig 4). We found a similar positive linear or monotonic association for region (US, Europe, and Asia, fig 4). Figure S18 presents the associations specific to race, and the associations in the Far East and the Middle East, indicating similar positive monotonic associations, except for the Middle East where a modest increase in risk was found across the whole range of waist-to-hip ratios.

Dose-response association between waist-to-hip ratio and the risk of type 2 diabetes in all individuals (Pnon-linearity=0.17, n=19), healthy individuals (Pnon-linearity=0.55, n=4), in studies which controlled for body mass index (Pnon-linearity=0.40, n=1), in older adults (Pnon-linearity=0.16, n=2), men (Pnon-linearity<0.001, n=8), and women (Pnon-linearity=0.10, n=10), and in the US (Pnon-linearity=0.52; n=3), Europe (Pnon-linearity<0.001, n=5), and Asian countries (Pnon-linearity=0.19, n=11). The solid line represents the non-linear dose-response and the dotted lines the 95% confidence interval. The circles represent the relative risk point estimates for adiposity categories from each study with the size of the circle proportional to the inverse of the standard error

We identified 25 cohort studies with 210053 participants and 12352 individuals with type 2 diabetes for the link between waist-to-height ratio and the risk of type 2 diabetes. Each increase in waist-to-height ratio by 0.1 units was associated with a 73% higher risk of type 2 diabetes (relative risk 1.73, 95% confidence interval 1.51 to 1.98, I2=97%, n=25) (fig S19). The result was the same after excluding studies that reported odds ratio as the effect size (1.74, 1.47 to 2.06, I2=98%, n=16). Table S7 presents the results across different subgroups. All studies used measured data for the analysis. The effect size was relatively the same in men (1.74, 1.62 to 1.90, I2=82%, n=16) and women (1.72, 1.61 to 1.86, I2=86%, n=15) (P for subgroup difference=0.86). The association was stronger in the US (1.79, 1.39 to 2.31, I2=96%, n=4) and Europe (2.42, 2.21 to 2.64, I2=0%, n=2), and remained significant after adjustment for all confounders and intermediate variables (1.77, 1.61 to 1.94, P<0.001, I2=0%, n=4) (table S7). We found evidence of publication bias with Eggers test (P=0.01) and Beggs test (P=0.08), and the funnel plot indicated some evidence of asymmetry towards a stronger effect size (fig S20).

Fourteen cohort studies were eligible for the non-linear dose-response meta-analysis. We found a positive monotonic association in the main analysis (Pnon-linearity<0.001, Pdose-response<0.001; R2=0.69, n=14), and in the analysis of older adults (Pnon-linearity<0.001, n=2). A positive linear association was seen in healthy individuals (Pnon-linearity=0.29, n=3), in men (Pnon-linearity=0.24, n=8), women (Pnon-linearity=0.56, n=7), and in one study which controlled for body mass index (Pnon-linearity=0.57, n=1) (fig S21). Figure S22 shows the associations by region, indicating similar positive linear or monotonic associations.

Nine cohort studies (eight publications) were identified for the link between visceral adiposity index and risk of type 2 diabetes. Each increase in higher visceral adiposity index by 1 unit was linked with a 42% higher risk of type 2 diabetes (relative risk 1.42, 95% confidence interval 1.27 to 1.58, I2=84%) (fig S23). In a sensitivity analysis, the association did not change substantially after excluding studies that reported odds ratio as the effect size (1.39, 1.29 to 1.49, I2=83%, n=5). Table S8 presents the results across different subgroups. The positive association persisted in all subgroups, especially after adjustment for all confounders (1.43, 1.25 to 1.63, I2=78%, n=3) and intermediate variables (1.53, 1.24 to 1.88, I2=83%, n=4). Analysis of five cohorts indicated a positive monotonic association between visceral adiposity index and the risk of type 2 diabetes (Pnon-linearity<0.001, Pdose-response<0.001; R2=0.61, n=5) (fig 5).

Dose-response association between visceral adiposity index (Pnon-linearity<0.001, n=5) and the risk of type 2 diabetes. The solid line represents the non-linear dose-response and the dotted lines the 95% confidence interval. The circles represent the relative risk point estimates for adiposity categories from each study with the size of the circle proportional to the inverse of the standard error

Six cohort studies with 44593 participants and 2558 individuals with type 2 diabetes evaluated the association between percentage body fat and the risk of type 2 diabetes. The relative risk for a 10% higher percentage body fat was 2.05 (95% confidence interval 1.41 to 2.98, I2=91%) (fig S24). We did not perform subgroup analyses or non-linear dose-response meta-analyses because of insufficient data.

Two cohort studies with 454 individuals with type 2 diabetes among 2971 participants evaluated the association between thigh circumference and risk of type 2 diabetes. We found no association between thigh circumference and type 2 diabetes in the main analysis (relative risk 1.11, 95% confidence interval 0.86 to 1.42; I2=85%) (fig S25).

Five cohort studies with 481870 participants and 26364 individuals with type 2 diabetes were identified for the analysis of body shape index. Each increase in the body shape index by 0.005 units was linked to a 9% higher risk of type 2 diabetes (relative risk 1.09, 95% confidence interval 1.05 to 1.13, I2=71%) (fig S26). We found a positive linear association in the non-linear dose-response meta-analysis (Pnon-linearity=0.05, Pdose-response=0.03; R2=0.51, n=4; fig 6).

Dose-response association between body shape index (Pnon-linearity=0.05, n=4) and the risk of type 2 diabetes. The solid line represents the non-linear dose-response and the dotted lines the 95% confidence interval. The circles represent the relative risk point estimates for adiposity categories from each study with the size of the circle proportional to the inverse of the standard error

Four cohorts (three publication) with 60790 participants and 3576 individuals with type 2 diabetes reported data for body adiposity index. The relative risk for each 10% increase in body adiposity index was 2.55 (95% confidence interval 1.59 to 4.10, I2=98%) (fig S27).

To compare the associations across different measures of fatness, we estimated the summary relative risks for an increase of one standard deviation in each measure. In this analysis, the number of studies might differ from the number of studies included in the main analyses because we could not calculate standard deviation values in some studies. The results indicated that body adiposity index and percentage body fat had the strongest associations with the risk of type 2 diabetes (table 2). Among traditional measures, waist-to-height ratio was superior to waist circumference, waist-to-hip ratio, and body mass index in predicting the risk of type 2 diabetes.

Summary relative risks of type 2 diabetes for an increase of one standard deviation for measures of adiposity

The certainty of evidence was rated by the GRADE approach. The certainty of evidence was rated strong for body mass index, waist circumference, waist-to-hip ratio, waist-to-height ratio, visceral adiposity index, percentage body fat, body shape index, and body adiposity index because of various downgrades for risk of bias, inconsistency, and publication bias, and upgrades for dose-response gradient and large (relative risk >2.00) to very large (relative risk >5.00) effect sizes (table S9). The evidence was upgraded to two levels because of the very large effect size (relative risk >5.00)35 in the non-linear dose-response meta-analyses of body mass index, waist circumference, and waist-to-hip ratio. The certainty of evidence was graded low and very low for hip and thigh circumferences, respectively.

Our comprehensive dose-response meta-analysis evaluated the association between different measures of body weight, waist, and body fat content, and different ratios of these measures, and the risk of type 2 diabetes in the general population. The analyses indicated a strong positive linear association between body mass index and the risk of type 2 diabetes. Similar linear associations, with no evidence of deviation from linearity at a specific cut-off value, were seen in different region, race, and ethnicity subgroups. Indices of central obesity also showed similar linear (waist circumference) or monotonic (waist-to-hip ratio and waist-to-height ratio) associations with type 2 diabetes. These positive linear or monotonic associations were confirmed by the analyses of more objective measures of body fat content, such as percentage body fat and visceral adiposity index. These associations were consistently stronger in European countries. A larger hip circumference was linked to a lower risk of type 2 diabetes.

A limited number of systematic reviews and meta-analyses of cohort studies have been undertaken of the association between measures of adiposity and the risk of type 2 diabetes. A meta-analysis of 18 prospective cohorts indicated that individuals with overweight (body mass index 25-29.9) and obesity (body mass index 30) had a 292% and 728% increased risk of type 2 diabetes, respectively.9 Another meta-analysis of 15 cohort studies found that an increase of one standard deviation for body mass index, waist circumference, waist-to-hip ratio, and waist-to-height ratio was associated with an increased risk of type 2 diabetes of 55%, 63%, 52%, and 62%, respectively.11 A meta-analysis of 31 cohort studies showed a relatively linear association between body mass index and type 2 diabetes,10 but previous meta-analyses did not include a large number of primary cohort studies in their analyses.

In the analysis of body mass index, we found a strong positive linear association with type 2 diabetes, which was confirmed in the analyses for almost all regions and ethnicities. Although there was a non-linear association in some subgroups, no marked deviation from linearity was seen at a specific cut-off value. Our previous meta-analysis also indicated a similar strong positive linear association between body mass index and hypertension.36

A recent large cohort study including >1.4 million adults older than 18 years in the UK suggested that, based on the risk of type 2 diabetes, different ethnicities have specific cut-off values to define obesity.12 The study suggested that cut-off values that were equivalent in risk to a body mass index of 30 in white individuals were 23.9 in South Asian populations, 26.9 in populations in the Far East, and 28.9 in black populations. Our findings indicated positive linear or monotonic associations between body mass index and the risk of type 2 diabetes in all regions, ethnicities, and races. These findings were from different studies (75 cohorts from the Far East v seven cohorts from the Middle East), however, with different sample sizes and lengths of follow-up, and different degrees of statistical control for confounders, and thus should be interpreted with caution.

Analyses of indices of central adiposity also indicated similar positive linear or monotonic associations with the risk of type 2 diabetes, with no marked deviation from linearity at a specific cut-off value. Analyses of different regions and ethnicities indicated similar findings. The results became stronger after adjustment for body mass index, suggesting that deposition of fat in this area of the body, independent of overall fatness, was related to a higher risk.7

Analyses of general and abdominal adiposity were confirmed by more objective measures of fatness. Analyses of the visceral adiposity index, representing deposits of fat in the visceral compartment, showed strong positive associations. Only five cohort studies were available for the non-linear dose-response meta-analysis of visceral fat, however, and thus more research might be needed to reach more confident conclusions.

Analyses of hip circumference confirmed our previous finding about the inverse association of hip circumference and risk of all cause mortality,7 suggesting that deposits of fat in the gluteofemoral compartment could have a protective effect. We also found that the analyses of hip circumference should be controlled for waist circumference to show these protective effects.

In the analyses of body mass index, waist circumference, and waist-to-height ratio, a subgroup analysis by length of follow-up indicated substantial stronger associations in studies with a longer follow-up. A previous meta-analysis of cohort studies of body mass index and all cause mortality indicated similar stronger associations in studies with longer follow-up periods,37 suggesting a weaker potential for bias owing to weight loss from pre-existing disease in studies with a longer follow-up. Also, studies indicated that length of adiposity was positively associated with the risk of type 2 diabetes.3839

In the analyses of body mass index and waist circumference, a stratified analysis based on the method of case ascertainment indicated significant differences between subgroups, where studies that performed blood glucose measurements reported weaker associations than those that used self-reported methods or medical registries (P for subgroup difference=0.01 and <0.001 for waist circumference and body mass index, respectively). Direct blood glucose measurement is probably a more reliable method for case ascertainment, but this method is difficult to perform in large scale population based cohort studies. About 60% of the studies included in the analyses of body mass index and waist circumference used a combination of methods for case ascertainment, and the main findings were close to those reported in these subgroups (1.72 v 1.68 for body mass index and 1.61 v 1.57 for waist circumference).

Studies suggested evidence of a substantial effect modification by age in the association between adiposity and morbidity and mortality.4041 In general, the harmful effects of adiposity are more evident in young adults, and excess mortality associated with obesity decreases along with the increase in age across levels of obesity, in a way that older adults might benefit from being overweight.3242 Our results also indicated that the association between body mass index and type 2 diabetes was significantly stronger in adults younger than 30 years than in those older than 60 years (P for subgroup difference <0.001. Although originally planned in our protocol, we could not perform more efficient subgroup analyses by age.

In the analysis of body mass index, heterogeneity existed when we stratified studies based on how frequently the exposure was assessed, where studies with repeated assessments during follow-up indicated weaker associations compared with studies that performed one baseline measurement. One baseline measurement of exposures did not consider potential changes in the level of exposure over time and is one of the main limitations of epidemiological studies that could result in measurement errors and non-differential misclassification.43 Repeated measurements can probably provide more reliable measurements and thus can give a more precise estimation of the associations, but we did not find significant differences between subgroups in the analyses of other measures.

We performed an additional meta-analysis to compare the associations across different measures. The results indicated that body adiposity index, an alternative calculation representing an indirect estimate of body fat content, had a stronger association with the risk of type 2 diabetes compared with other measures. Body adiposity index integrates height and hip circumference to estimate the amount of body fat.44 We also found a strong association for percentage body fat. Of the six studies included in the analysis of percentage body fat, four used bioelectrical impedance analysis, one used hydrodensitometry (alternative method representing an indirect estimate of body fat content), and one study used dual energy x ray absorptiometry (the gold standard for measuring body composition) to predict percentage body fat.

Among conventional measures, we found a stronger association for waist-to-height ratio than waist circumference, waist-to-hip ratio, and body mass index. This finding was consistent with previous reviews indicating that waist-to-height ratio is better at predicting the risk of all cause mortality7 and levels of cardiometabolic risk factors45 than body mass index, waist circumference, and waist-to-hip ratio. Marked differences existed across measures in terms of the number of studies included in each analysis, geographical location, study population, length of follow-up, and degree of statistical control, however, and thus our conclusions on the superiority of some measures should be interpreted cautiously and allowing for these limitations.

Ours was a comprehensive study of the association between adiposity and the risk of type 2 diabetes across the world. Our findings indicated a strong positive linear association between body mass index and type 2 diabetes. Current recommendations from the World Health Organization suggest a threshold of 27.5 for body mass index to define obesity and start lifestyle interventions in populations in the Far East and Chinese populations,46 which was equivalent in risk to a body mass index of 30 in white individuals. Using a rigorous analytical method and performing a large meta-analysis of cohort studies, we found a strong positive linear association between body mass index and type 2 diabetes across the whole range of body mass index values.

We included several measures representing regional distribution of body fat. The results indicated a strong positive linear association across the whole range of waist circumference values in different regions, ethnicities, and in both sexes. We also included more objective measures of body fat content that confirmed current knowledge about the harmful effects of adiposity on human health. Allowing for limitations, such as different populations and different numbers of studies, our analysis compared the associations across different anthropometric indicators.

We considered the proposed limitations of conventional anthropometric measures, including body mass index (inability to distinguish lean mass from fat mass), waist circumference (strong correlation with body mass index), and waist-to-hip ratio (difficulty in measuring hip circumference) in presenting our comprehensive perspective of the association between important anthropometric indicators and the risk of type 2 diabetes. We also excluded cohort studies that used self-reported anthropometric measures as exposures. Systematic errors in self-reported weight and height have been reported,47 leading to misclassification and thus an upward bias in the magnitude of the effect estimates.4849 By using cohort studies that measured anthropometric data, we presented a more accurate estimation of the associations.

For almost all exposures, we found high heterogeneity in the data. We performed several predefined and post hoc subgroup analyses to present the results across subgroups and find the sources of heterogeneity, but extreme heterogeneity persisted in the subgroups. We present several explanations for the large heterogeneity in the data.

Firstly, we included a large number of studies in the analyses, especially for body mass index and waist circumference. In such cases, high heterogeneity is inevitable, and even a small difference in effect estimates can lead to high heterogeneity in the data.50 Secondly, all studies included in our review were consistent in terms of the PICOS (population, intervention or exposure, comparator, outcome, and study design) framework.51 Therefore, the heterogeneity seen in the data was statistical heterogeneity rather than clinical or methodological heterogeneity.

Thirdly, the large heterogeneity in the data was mainly because of the difference in the magnitude (weak, moderate, or strong) of the effect sizes rather than a difference in the direction (positive or inverse) of the associations. Of 182 studies included in the analysis of body mass index, 178 reported positive associations. Of 78 studies included in the analysis of waist circumference, all reported positive associations. This consistency in the results was also reported for waist-to-hip ratio, waist-to-height ratio, and visceral adiposity index. Consistency was strong across the studies in terms of the direction of the associations. According to the GRADE approach, reviewers should differentiate between situations where the large inconsistency across studies is caused by differences in the magnitude of the associations rather than differences in the direction of the associations.52 Because individual studies were in the same direction, performing meta-analyses is still thought to be appropriate.5354

We performed several prespecified and post hoc subgroup analyses to find the potential sources of the heterogeneity. Because of the large number of studies, however, large variations in participant and study characteristics within each subgroup persisted.

We found evidence of publication bias in the analyses of body mass index, waist circumference, waist-to-hip ratio, and waist-to-height ratio. Contour enhanced funnel plots suggested that the asymmetry in the funnel plots was a result of publication bias.2627 Also, considering that the association between body mass index and type 2 diabetes is thought to be specific to ethnicity,12 the asymmetry in the funnel plots might also be a result of heterogeneity owing to differences in the characteristics of the populations.26 Because of the large asymmetry in the funnel plots, however, our results could have been overestimated for these measures and thus the magnitude of the effect estimates should be interpreted cautiously.

The major limitation of our study was that although we performed several comparisons across different regions, ethnicities, and races, the results were obtained from different cohorts with different sample sizes, follow-up periods, and statistical controls. An individual participant data meta-analysis of cohort studies might provide more accurate information about the shape of the associations. Secondly, we had insufficient data for the analysis of healthy individuals. Primary cohort studies used different definitions to specify healthy individuals and thus more research might be required in healthy individuals. We also had insufficient data for never smokers and for some subgroups, such as South America, Africa, and black individuals. Thirdly, studies published in non-English journals were not included because of resource constraints, which might cause selection bias. Given the large number of studies included in the analyses, especially for body mass index and waist circumference, however, inclusion of studies in non-English journals would probably not have changed the results substantially. Fourthly, of 216 studies included in the analyses, only a quarter performed repeated anthropometric measurements during the follow-up period. Analysis of body mass index indicated a weaker association in studies that performed repeated versus baseline anthropometric measurements. This finding should be considered in future studies evaluating the associations between obesity and morbidity. Fifthly, we did not include fat mass in specific regions, such as liver, muscle, and abdominal fat mass, in our review. Future research should evaluate the association between body fat content in specific regions and the risk of diabetes risk in more detail. Finally, because of the observational design of the studies, residual confounding cannot be excluded and should be considered when interpreting the results. Recent mendelian randomisation studies, however, suggested a casual effect of general and central adiposity on the risk of type 2 diabetes.5556

Our comprehensive dose-response meta-analysis of 216 cohort studies suggested evidence of a strong positive linear association between body mass index and the risk of type 2 diabetes across almost all regions and ethnicities. We found no marked deviation from linearity at a specific cut-off value. For hip circumference, the direction of the association depended on adjustment for waist circumference, where studies that controlled for waist circumference reported an inverse association and studies that did not control for waist circumference reported a positive association. For some measures, such as body shape index, body adiposity index, and visceral fat mass, only a small number of studies were available.

General and central adiposity are associated with the risk of type 2 diabetes

The shapes of the dose-response associations between general and central adiposity and the risk of type 2 diabetes have not been determined

Associations specific to region, race, and ethnicity have not been evaluated

Body mass index had a strong positive linear association with the risk of type 2 diabetes, confirmed in analyses of almost all regions and ethnicities

A larger waist circumference was strongly and linearly associated with a higher risk of type 2 diabetes

For hip circumference, studies that controlled for waist circumference reported an inverse association, and studies that did not control for waist circumference reported a positive association

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Anthropometric and adiposity indicators and risk of type 2 diabetes: systematic review and dose-response meta-analysis of cohort studies - The BMJ