The burden of diabetes in India

Atre, Sachin

doi:10.1016/s2214-109x(18)30556-4

Cited by 29 publications

(7 citation statements)

References 3 publications

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“…In India, there are around 80 million people with the pre-diabetes condition and an estimated 77 million people (1 in 11 Indians) are formally diagnosed with diabetes [1], which makes India the second most affected country in the world, after China [3]. The number of people with diabetes in India has increased from 26 million in 1990 to 65 million in 2016 [4]. According to the 2019 National Diabetes and Diabetic Retinopathy Survey report released by the Ministry of Health and Family Welfare, the prevalence was found to be 11.8% in people over the age of 50 [5].…”

Section: Introductionmentioning

confidence: 99%

"Now, We Need to Breed the Cereal Food Crop Varieties with Low Glycemic Index for 463 Millions Diabetic Population of the World"

Borkar¹

2023

BJSTR

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Diabetes Mellitus is a serious health issue in the world with around 463 million people suffering from this disease and 10 to 11 percent population of the most affected countries are diabetic. In countries like India, each year 1.5 million people are added to the category of diabetic people and such an increase should be a serious concern for the world health organization and the world community. Cereal food grains with high carbohydrates, high glycemic index, and high glycemic load are mainly responsible for this situation besides other factors. With the crop improvement programs in developed and developing countries like India, during the last century, the high-yielding carbohydrate-rich cereal crop varieties were developed to coup up with the food shortages and food nutrients, and coincidently during the same period, the percentage of diabetes Mellitus population steadily increased with around 80 million people as pre-diabetes and an estimated 77 million people (1 in 11 Indians) with diabetes in India, which makes India the second most affected country in the world, This is attributed to the change in lifestyle and food that we consume. Cereal food grains rich in carbohydrates are supposed to accelerate the glycemic index in the blood and the cereal food grain with high carbohydrates means food with a high glycemic load and thereby high glycemic index (sugar content) in the blood. As long as such high glycemic load food is made available to the diabetic population, their diabetic disease condition will not be cured. Therefore, we need to streamline the cereal crop varietal improvement program with proper carbohydrate and fiber content for millions of diabetic populations. This will also hold good for breeding other fruit crop varieties with a low glycemic load too for the present diabetic population and others on the borderline of diabetics. The paper discusses the present scenario of carbohydrates in cereal food crops and the expected carbohydrate content in these crops for the diabetic population. WHO and FAO of the United Nations with concerned International crop-specific research institutes like CIMMYT(for wheat and Maize), IRRI (for Rice), and ICRISAT (for Millets) should come forward for the supply of low carbohydrate-containing germplasm, landrace or wild derivatives of the concerned cereal crop to tackle this health issue for breeding low glycemic load crop varieties for the diabetic population of the world.

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Section: Introductionmentioning

confidence: 99%

"Now, We Need to Breed the Cereal Food Crop Varieties with Low Glycemic Index for 463 Millions Diabetic Population of the World"

Borkar¹

2023

BJSTR

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“…Most of the diabetes cases are of type-2 diabetes [3]. In 2010, the Government of India started National Program for Preclusion and Control of Cancer, Diabetes, Cardiovascular Diseases, and Stroke (NPCDCS) [4]. The goal of NPCDCS is to organize outside camps for deft testing at all levels in the medicinal services conveyance framework for early identification of diabetes, among different ailments [5].…”

Section: Introductionmentioning

confidence: 99%

“…A lower pervasiveness of 14.3% was perceived in the 50-to 59-year age range [6]. High prevalence of diabetes is observed in economically and epidemiologically boosted states, like, Tamil Nadu and Kerala, in which many research centers are carried out their research on occurrence frequency are additionally present [4].…”

Section: Introductionmentioning

confidence: 99%

Diabetic retinopathy detection and classification using capsule networks

Kalyani

Janakiramaiah

Karuna

et al. 2021

Complex Intell. Syst.

179

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Nowadays, diabetic retinopathy is a prominent reason for blindness among the people who suffer from diabetes. Early and timely detection of this problem is critical for a good prognosis. An automated system for this purpose contains several phases like identification and classification of lesions in fundus images. Machine learning techniques based on manual extraction of features and automatic extraction of features with convolution neural network have been presented for diabetic retinopathy detection. The recent developments like capsule networks in deep learning and their significant success over traditional machine learning methods for a variety of applications inspired the researchers to apply them for diabetic retinopathy diagnosis. In this paper, a reformed capsule network is developed for the detection and classification of diabetic retinopathy. Using the convolution and primary capsule layer, the features are extracted from the fundus images and then using the class capsule layer and softmax layer the probability that the image belongs to a specific class is estimated. The efficiency of the proposed reformed network is validated concerning four performance measures by considering the Messidor dataset. The constructed capsule network attains an accuracy of 97.98%, 97.65%, 97.65%, and 98.64% on the healthy retina, stage 1, stage 2, and stage 3 fundus images.

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“…Especially in developing countries such as India the prevalence of diabetes is more in rural than in urban areas and there is a shift from older to younger people [1]. Moreover, there is a high prevalence of diabetes in economically and epidemiologically advanced states in India such as Tamilnadu and Kerala [2]. Diabetes may cause microvascular changes in the retina leading to diabetic retinopathy (DR), which is the second largest cause of blindness in the working middle‐age population.…”

Section: Introductionmentioning

confidence: 99%

Automated detection of multiple structural changes of diabetic macular oedema in SDOCT retinal images through transfer learning in CNNs

Padmasini

Umamaheswari²

2020

IET image process

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Diabetic Maculopathy, the major cause of vision loss, occurs due to uncontrolled diabetes. This affects the retinal layers of the eye causing bleeding of vessels, which results in Diabetic Macular Edema (DME), macular detachment etc., Basically three structural changes are involved in DME viz., Cystoid Macular Edema (CME), Serous Macular Detachment (SMD) and Intra Retinal Fluid (IRF). These changes may also coexist with each other such as CME with SMD or CME with IRF etc. In this work, the retinal images acquired through Spectral Domain Optical Coherence Tomography (SDOCT) imaging modality, which would provide high level of precision and resolution of the retinal layers are utilized. An automated algorithm to detect and differentiate seven types of DME based on deep learning is implemented using transfer learning on three Convolutional Neural Networks (CNNs), ResNet 50, VGGNet and AlexNet. In detection of DME, ResNet 50 performs excellently well, when compared with Alexnet and VGGNet, because of its depth and skip connection features. The average values of the statistical parameters such as accuracy (0.993), F1 score (0.975), Mathews Correlation Coefficient(MCC)(0.972) of ResNet are high when compared to that of AlexNet and VGGNet

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The burden of diabetes in India

Cited by 29 publications

References 3 publications

"Now, We Need to Breed the Cereal Food Crop Varieties with Low Glycemic Index for 463 Millions Diabetic Population of the World"

"Now, We Need to Breed the Cereal Food Crop Varieties with Low Glycemic Index for 463 Millions Diabetic Population of the World"

Diabetic retinopathy detection and classification using capsule networks

Automated detection of multiple structural changes of diabetic macular oedema in SDOCT retinal images through transfer learning in CNNs

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