Questioning glucose measurements used in the International Diabetes Federation (IDF) Atlas

Bloomgarden, Zachary T.

doi:10.1111/1753-0407.12453

Cited by 15 publications

(12 citation statements)

References 7 publications

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“…The prevalence of diabetes mellitus is rapidly rising. The International Diabetes Federation (IDF) estimated that 425 million adults (aged 20–79 years) had diabetes mellitus globally in 2017, and further estimated that a US $727 billion (12% of global health expenditure) was spent on treating diabetes mellitus and its related complications [2]. About 1 in 11 adults have diabetes mellitus, with 90% of these adults having type 2 diabetes mellitus (T2D); Asia is the center of this global T2D epidemic [3].…”

Section: Type 2 Diabetesmentioning

confidence: 99%

In Vivo Rodent Models of Type 2 Diabetes and Their Usefulness for Evaluating Flavonoid Bioactivity

et al. 2019

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About 40% of the world’s population is overweight or obese and exist at risk of developing type 2 diabetes mellitus (T2D). Obesity is a leading pathogenic factor for developing insulin resistance (IR). It is well established that IR and a progressive decline in functional β-cell mass are hallmarks of developing T2D. In order to mitigate the global prevalence of T2D, we must carefully select the appropriate animal models to explore the cellular and molecular mechanisms of T2D, and to optimize novel therapeutics for their safe use in humans. Flavonoids, a group of polyphenols, have drawn great interest for their various health benefits, and have been identified in naturally occurring anti-diabetic compounds. Results from many clinical and animal studies demonstrate that dietary intake of flavonoids might prove helpful in preventing T2D. In this review, we discuss the currently available rodent animal models of T2D and analyze the advantages, the limitations of each T2D model, and highlight the potential anti-diabetic effects of flavonoids as well as the mechanisms of their actions.

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Section: Type 2 Diabetesmentioning

confidence: 99%

In Vivo Rodent Models of Type 2 Diabetes and Their Usefulness for Evaluating Flavonoid Bioactivity

et al. 2019

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“… 1 Recent epidemiological data showed that diabetes affects people worldwide, reaching a prevalence of 412 million in 2015, and adding a plethora of people having risk factors that include impaired fasting glucose (IFG), impaired glucose tolerance (IGT), gestational diabetes and euglycemic IR this figure is expected to increase. 2 From this view, it has been estimated that a substantial number of people, 50% of people in the general population 20–79 years of age globally, are unaware of their disease ( http://www.diabetesatlas.org ; International Diabetes Federation, IDF Diabetes Atlas Eighth Edition), exposing them to the risk of increased morbidity and mortality attributable to the onset of microvascular and macrovascular complications. Dysglycemia—also called glucose abnormalities (GAs)—which includes isolated IFG and/or isolated IGT, represents a risk factor for developing diabetes and its related cardiovascular complications, which appear silently and several years before the clinical manifestation of the disease.…”

Section: Introductionmentioning

confidence: 99%

One-hour plasma glucose combined with skin autofluorescence identifies subjects with pre-diabetes: the DIAPASON study

Sala

Tagliabue

Candia

et al. 2020

BMJ Open Diab Res Care

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IntroductionThe major challenge for diabetes prevention is early identification of individuals at risk to allow for implementation of measures to delay the onset of future disease. Measures such as fasting plasma glucose (FPG), 2-hour plasma glucose (2hPG), and glycosylated hemoglobin (HbA1c) are equally appropriate for identifying pre-diabetes and diabetes, but do not all identify the disease in the same individual. We tested the utility of a diagnostic method combining FPG, 2hPG and HbA1c for early evaluation and easy identification of pre-diabetes.Research design and methods531 subjects underwent skin autofluorescence (SAF) and glycemia analyses. We created two classification groups based on the American Diabetes Association diagnosis guidelines: (1) based on 2hPG and (2) based on a new combination of three glycemia parameters (the three-criteria strategy (3-c)). Logistic regression modeling was used to estimate the associations.ResultsSAF showed high associations for both 3-c definition and 2hPG definition alone. These associations appeared stronger in 3-c than those in 2hPG. The non-invasive SAF measurement outperformed 2hPG in the detection of dysglycemia or pre-diabetes. Stepwise selections identified 1-hour postload glucose (1hPG) as variable identifying pre-diabetes using the 2hPG criterion, and the model based on 1hPG plus SAF appeared to be the best association using the 3-c strategy.Conclusions1hPG coupled with SAF showed a strong association in the evaluation of pre-diabetes using the 3-c method.

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“…According to the International Diabetes Federation (IDF) [1] statistics, there were 415 million people suffering from diabetes around the world in 2015. By 2040 this number is expected to rise to over 642 million, as a consequence, diabetes has become the main cause of national disease and death in most countries.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey

Sun

Zhang

2019

Teh. vjesn.

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Diabetes has become one of the major causes of national disease and death in most countries. By 2015, diabetes had affected more than 415 million people worldwide. According to the International Diabetes Federation report, this figure is expected to rise to more than 642 million in 2040, so early screening and diagnosis of diabetes patients have great significance in detecting and treating diabetes on time. Diabetes is a multifactorial metabolic disease, its diagnostic criteria is difficult to cover all the ethology, damage degree, pathogenesis and other factors, so there is a situation for uncertainty and imprecision under various aspects of medical diagnosis process. With the development of Data mining, researchers find that machine learning is playing an increasingly important role in diabetes research. Machine learning techniques can find the risky factors of diabetes and reasonable threshold of physiological parameters to unearth hidden knowledge from a huge amount of diabetes-related data, which has a very important significance for diagnosis and treatment of diabetes. So this paper provides a survey of machine learning techniques that has been applied to diabetes data screening and diagnosis of the disease. In this paper, conventional machine learning techniques are described in early screening and diagnosis of diabetes, moreover deep learning techniques which have a significance of biomedical effect are also described.

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Questioning glucose measurements used in the International Diabetes Federation (IDF) Atlas

Cited by 15 publications

References 7 publications

In Vivo Rodent Models of Type 2 Diabetes and Their Usefulness for Evaluating Flavonoid Bioactivity

In Vivo Rodent Models of Type 2 Diabetes and Their Usefulness for Evaluating Flavonoid Bioactivity

One-hour plasma glucose combined with skin autofluorescence identifies subjects with pre-diabetes: the DIAPASON study

Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey

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