Decoding the Type 2 Diabetes Epidemic in Rural India

Little, Matthew; Humphries, Sally; Patel, Kirit; Dewey, Cate

doi:10.1080/01459740.2016.1231676

Cited by 33 publications

(25 citation statements)

References 44 publications

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“…4 Moreover, the prevalence of diabetes and prediabetes is increasing even among the rural population due to fast lifestyle changes. 7 Considering the high mortality rate and morbidity due to diabetes, it is necessary to assess the prevalence of this devastating condition among the rural population. The current study was carried out to assess the prevalence of diabetes and prediabetes and the cardio metabolic risk profiles among the rural population in Kuppam.…”

Section: Discussionmentioning

confidence: 99%

“…Diabetes is a complex disease of multi factorial origin. 7 Diabetes is the single most important risk factor for cardiovascular disease. 5 In the present study 69 (23%) participants had postprandial plasma glucose of 200mg/dl and above.…”

Section: Discussionmentioning

confidence: 99%

“…6 This pattern is alarming as 72.2% of the Indian population reside in rural areas with low income, limited resources and poor access to health services. 7 Hence the current study was carried to assess the prevalence of Diabetes Mellitus, Prediabetes and cardio metabolic profile in rural population of Kuppam.…”

Section: Introductionmentioning

confidence: 99%

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A Study of Prevalence of Diabetes Mellitus, Prediabetes and Cardio Metabolic Profile among Rural Population in South India

Sethuram¹,

.²,

Rao³

2019

IJCMR

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Introduction: Diabetes and prediabetes are rapidly growing in India. Diabetes is the single most important risk factor for cardiovascular disease. As 72.2% of the Indian population resides in rural areas, the current study was carried to assess the prevalence of Diabetes Mellitus, prediabetes and cardio metabolic profile in a rural population. Material and methods: A total of 300 people aged more than 18 years were included in this observational, communitybased study. All the relevant parameters were documented in a structured study proforma. The following investigations were done in all subjects: fasting blood glucose, post prandial blood glucose, fasting lipid profile, electrocardiography and echocardiogram. Results: The prevalence of diabetes was 37.33% and pre diabetes was 8.67% in the current study. Among diabetics mean age was 51.17 years and 43.69 years in prediabetics. Among diabetics 69 (69.6%) were males and 43 (38.4%) were females. Among the prediabetics 43 (38.4%) were males and 12 (46.2%) were females. Among diabetes patients mean Triglycerides (TGL) was 175.24 mg/dl. In this study 128 (42.7%) participants were overweight, 114 (38%) were obese, 85 (28.33%) were alcoholics, 44 (14.67%) were smokers. Conclusion: Higher Total cholesterol, Triglycerides levels are associated with higher chance of prediabetes and diabetes. Increasing age is also a risk factor for prediabetes and diabetes. The prevalence of the diabetics and prediabetes is rising in rural population. Hence it is essential to create awareness about diabetes and accessibility to health care services among rural population.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A Study of Prevalence of Diabetes Mellitus, Prediabetes and Cardio Metabolic Profile among Rural Population in South India

Sethuram¹,

.²,

Rao³

2019

IJCMR

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show abstract

“…[20] It becomes the responsibility of the clinicians to do proper screening and early detection of the disease. They should do frequent counselling of the patients and make them aware of therapeutic management of diabetes [21]. Progress in the technology in every field of basic and applied science has revolutionised computational world .…”

Section: Introductionmentioning

confidence: 99%

Statistical and Machine Learning Methods for the Classification of Type -2 Diabetes Mellitus

Razdan¹

2019

IJRASET

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Diabetes mellitus is a chronic disorder of glucose metabolism either due to the relative deficiency of Insulin or due to the failure of body to response to the action of insulin. Diseases leads to critical life threatening complications, if it remains undiagnosed and untreated. There is growing incidence of this disease all over the world. It has assumed epidemic form in Asian countries, China and India. Changing to urban life style, metabolic syndrome and obesity are some factors responsible for this increased prevalence. It is most common (90%) among the different types of diabetes. It is recognised by raised fasting or post prandial blood sugar level. The review of literature shows that one model does not always give good performance in all situations and data frames. The study aimed to address this issue of classification by the use of statistical and machine learning models. Present study is conducted on Pima Indian diabetic dataset which is publicly available and is widely used for diabetes research. Unlike other datasets, medical datasets usually suffer from missing and unwanted data which needs proper handling so that classification accuracy is significantly improved. Therefore, in this study mean, median, and linear regression based imputing techniques are developed and outliers are handled using winsorizing approach. Classification accuracy on imputed data are compared with the accuracy on raw data. Results on mean and median based imputed data significantly outperformed as compared against the raw data.

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“…India is an agricultural nation, with 72.2% of the population residing in its rural sector, hence the trending high prevalence of the rural diabetes epidemic are highly alarming 16 . Several distinct cultural and socioeconomic factors define the epidemic of T2D in rural India 17 . Response and efficacy of lifestyle interventions are governed by cultural practices as well as genetic/ethnic makeup 18 .…”

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confidence: 99%

Yoga-based lifestyle treatment and composite treatment goals in Type 2 Diabetes in a rural South Indian setup- a retrospective study

Arumugam

Nagarathna

Majumdar

et al. 2020

Sci Rep

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This multicentre retrospective study examined the effects of adjunct yoga-treatment in achieving composite cardiovascular goals for type 2 diabetes (T2D), set forth by the American Diabetes Association (ADA) in rural Indian settings. Records were extracted for 146 T2D patients, aged ≥20-70 years, and treated under the "Apollo Total Health Programme" for rural diabetes management, for the period April 2016 to November 2016. The study cohort comprised of two treatment groups (n = 73 each); non-yoga group (standard of care) and yoga group (adjunct yoga-treatment). Propensity score matching was applied between the study groups to define the cohort. Composite cardiovascular scores were based on the combination of individual ADA goals; A1c < 7%, blood pressure (BP) < 140/90 mmHg, stringent BP (<130/80 mmHg) and lipid, LDL-C < 100 mg/dl [risk factor for atherosclerotic cardiovascular disease]. Logistic regression was used to compare between the two treatment groups. Compared to standard of care, adjunct yoga-treatment was found to significantly facilitate the attainment of ADA composite score by 8-fold; A1c, ~2-fold; LDL-C, ~2-fold; BP < 140/90 mmHg and <130/80 mmHg by ~8-and ~6-fold respectively. This study provides the first evidence for significant efficacy of adjunct yoga-treatment for the attainment of favourable treatment goals for T2D in rural Indian settings. Clinical Trial Registration Number: CTRI/2020/02/0232790

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Decoding the Type 2 Diabetes Epidemic in Rural India

Cited by 33 publications

References 44 publications

A Study of Prevalence of Diabetes Mellitus, Prediabetes and Cardio Metabolic Profile among Rural Population in South India

A Study of Prevalence of Diabetes Mellitus, Prediabetes and Cardio Metabolic Profile among Rural Population in South India

Statistical and Machine Learning Methods for the Classification of Type -2 Diabetes Mellitus

Yoga-based lifestyle treatment and composite treatment goals in Type 2 Diabetes in a rural South Indian setup- a retrospective study

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