Uddanam nephropathy in India: a challenge for epidemiologists

Gadde, Praveen; Sanikommu, Suresh; Manumanthu, Ramesh; Anitha, A.

doi:10.2471/blt.17.196758

Cited by 23 publications

(15 citation statements)

References 5 publications

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“…Another CKDu hotspot has been identified in the southeastern region of Andhra Pradesh. 33, 45 Though CKDu in this region has been less intensively studied than other sites with high CKDu prevalence, the disease in India also chiefly affects farmers, with agrichemicals, heat stress, and metals as the prime suspects. The possibility of toxin-related gene-environment interactions has prompted candidate gene studies.…”

Section: Ckdu In Indiamentioning

confidence: 99%

Genes and environment in chronic kidney disease hotspots

Friedman

2019

Current Opinion in Nephrology and Hypertension

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Purpose of Review: Chronic Kidney Disease (CKD) can cluster in geographic locations or in people of particular genetic ancestries. We explore APOL1 nephropathy and Balkan Nephropathy as examples of CKD clustering that illustrate genetics and environment conspiring to cause high rates of kidney disease. Unexplained hotspots of kidney disease in Asia and Central America are then considered from the perspective of potential gene x environment interactions. Recent Findings: We report on evidence supporting both genes and environment in these CKD hotspots. Differing genetic susceptibility between populations and within populations may explain why causal environmental risk factors have been so hard to identify conclusively. Similarly, one cannot explain why these epidemics of kidney disease are happening now without invoking environmental changes. Summary: Approaches to these CKD hotspots are of necessity becoming more holistic. Genetic studies may help us identify the environmental triggers by teaching us about disease biology and may empower environmental risk factor studies by allowing for stratification of study participants by genetic susceptibility.

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Section: Ckdu In Indiamentioning

confidence: 99%

Genes and environment in chronic kidney disease hotspots

Friedman

2019

Current Opinion in Nephrology and Hypertension

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“…Several concerns were raised regarding the high prevalence of CKD due to environmental causes in the Uddanam region, resulting in a high death rate. Local surveys conducted pointed toward some possible causes that could contribute to the disease, but no systematic epidemiological studies were conducted and no published data are available 9 . In these circumstances, we have undertaken a systematic population study with the following objectives: to establish prevalence and the characteristics of CKD in the population of Uddanam and examine whether CKD in Uddanam can be explained by traditional risk factors like diabetes and long-standing hypertension, or if this is CKDu.…”

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confidence: 99%

High Prevalence of CKD of Unknown Etiology in Uddanam, India

Tatapudi

Rentala

Gullipalli³

et al. 2019

Kidney International Reports

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Introduction There were concerns raised regarding a high prevalence of chronic kidney disease (CKD) in Uddanam, a fertile subtropical low-altitude territory in the southern Indian state of Andhra Pradesh. The present study was undertaken to ascertain the prevalence of CKD, disease characteristics, and risk factor profile in this area. Methods We selected 2210 subjects (age >18 years) using multistage sampling. After obtaining demographic and anthropometric data, urinary protein-creatinine ratio, serum creatinine, and blood glucose were measured in all the subjects. Glomerular filtration rate was estimated (eGFR) using the Modification of Diet in Renal Disease equation. Results Mean age of the subjects was 43.2 ± 14.2 years (range: 18–98), 44.3% were men and 55.7% were women. Mean eGFR of subjects was 94.3 ± 33.4. Low eGFR (<60 ml/min per 1.73 m 2 ) was seen in 307 (13.98%) patients with a mean eGFR of 34.8 ± 16.6. The prevalence of subjects having low eGFR and with proteinuria (CKD) was 18.23%. Major risk factors, such as diabetes, long-standing hypertension, and significant proteinuria, were absent in 73% of patients with CKD, implying that a significant proportion of the population is afflicted with the entity “CKD of unknown etiology (CKDu).” Conclusion The prevalence of CKD and CKDu in Uddanam is much higher than other earlier studies in either rural or urban communities in India. We suggest that there is a dire need to review health policies and allocate resources for prevention and treatment of CKD in the Uddanam region.

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“…Kidney failure malady is being seen as a genuine test to the therapeutic field with its effect on a gigantic populace of the world. Without side effects, kidney illnesses are regularly recognized past the point of no return when dialysis is required direly [12] [16]. Boukenze et al, (2016) examined that propelled Data mining advancements can help give choices to deal with this circumstance by finding concealed examples and connections in medicinal information.…”

Section: Machine Learning Models and Neural Networkmentioning

confidence: 99%

Machine Learning Models and Neural Network Techniques for Predicting Uddanam CKD

Anusha¹,

Vital²,

Sangeeta³

2019

IJRTE

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Chronic kidney disease (CKD) is one of the most widely spread diseases across the world. Mysteriously some of the areas in the world like Srilanka, Nicrgua and Uddanam (India), this disease affect more and it is cause of thousands of deaths particular areas. Now days, the prevention with utilizing statistical analysis and early detection of CKD with utilizing Machine Learning (ML) and Neural Networks (NNs) are the most important topics. In this research work, we collected the data form Uddanam (costal area of srikakulam district, A.P, India) about patient's clinical data, living styles (Habits and culture) and environmental conditions (water, land and etc.) data from 2016 to 2019. In this paper, we conduct the statistical analysis, Machine Learning (ML) and Neural Network application on clinical data set of Uddanam CKD for prevention and early detection of CKD. As per statistical analysis we can prevent the CKD in the Uddanam area. As per ML analysis Naive Bayes model is the best where the process model is constructed within 0.06 seconds and prediction accuracy is 99.9%. In the analysis of NNs, the 9 neurons hidden layer (HL) Artificial Neural Network (ANN) is very accurate than other all models where it performs 100% of accuracy for predicting CKD and it takes the 0.02 seconds process time.

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Uddanam nephropathy in India: a challenge for epidemiologists

Cited by 23 publications

References 5 publications

Genes and environment in chronic kidney disease hotspots

Genes and environment in chronic kidney disease hotspots

High Prevalence of CKD of Unknown Etiology in Uddanam, India

Machine Learning Models and Neural Network Techniques for Predicting Uddanam CKD

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