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Prevalence of and risk factors for chronic kidney disease of unknown aetiology in India: secondary data analysis of three population-based cross-sectional studies

OBJECTIVES: To assess whether chronic kidney disease of unknown aetiology (CKDu) is present in India and to identify risk factors for it using population-based data and standardised methods. DESIGN: Secondary data analysis of three population-based cross-sectional studies conducted between 2010 and...

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Autores principales: O’Callaghan-Gordo, Cristina, Shivashankar, Roopa, Anand, Shuchi, Ghosh, Shreeparna, Glaser, Jason, Gupta, Ruby, Jakobsson, Kristina, Kondal, Dimple, Krishnan, Anand, Mohan, Sailesh, Mohan, Viswanathan, Nitsch, Dorothea, P A, Praveen, Tandon, Nikhil, Narayan, K M Venkat, Pearce, Neil, Caplin, Ben, Prabhakaran, Dorairaj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6429742/
https://www.ncbi.nlm.nih.gov/pubmed/30850400
http://dx.doi.org/10.1136/bmjopen-2018-023353
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author O’Callaghan-Gordo, Cristina
Shivashankar, Roopa
Anand, Shuchi
Ghosh, Shreeparna
Glaser, Jason
Gupta, Ruby
Jakobsson, Kristina
Kondal, Dimple
Krishnan, Anand
Mohan, Sailesh
Mohan, Viswanathan
Nitsch, Dorothea
P A, Praveen
Tandon, Nikhil
Narayan, K M Venkat
Pearce, Neil
Caplin, Ben
Prabhakaran, Dorairaj
author_facet O’Callaghan-Gordo, Cristina
Shivashankar, Roopa
Anand, Shuchi
Ghosh, Shreeparna
Glaser, Jason
Gupta, Ruby
Jakobsson, Kristina
Kondal, Dimple
Krishnan, Anand
Mohan, Sailesh
Mohan, Viswanathan
Nitsch, Dorothea
P A, Praveen
Tandon, Nikhil
Narayan, K M Venkat
Pearce, Neil
Caplin, Ben
Prabhakaran, Dorairaj
author_sort O’Callaghan-Gordo, Cristina
collection PubMed
description OBJECTIVES: To assess whether chronic kidney disease of unknown aetiology (CKDu) is present in India and to identify risk factors for it using population-based data and standardised methods. DESIGN: Secondary data analysis of three population-based cross-sectional studies conducted between 2010 and 2014. SETTING: Urban and rural areas of Northern India (states of Delhi and Haryana) and Southern India (states of Tamil Nadu and Andhra Pradesh). PARTICIPANTS: 12 500 individuals without diabetes, hypertension or heavy proteinuria. OUTCOME MEASURES: Mean estimated glomerular filtration rate (eGFR) and prevalence of eGFR below 60 mL/min per 1.73 m(2) (eGFR <60) in individuals without diabetes, hypertension or heavy proteinuria (proxy definition of CKDu). RESULTS: The mean eGFR was 105.0±17.8 mL/min per 1.73 m(2). The prevalence of eGFR <60 was 1.6% (95% CI=1.4 to 1.7), but this figure varied markedly between areas, being highest in rural areas of Southern Indian (4.8% (3.8 to 5.8)). In Northern India, older age was the only risk factor associated with lower mean eGFR and eGFR <60 (regression coefficient (95% CI)=−0.94 (0.97 to 0.91); OR (95% CI)=1.10 (1.08 to 1.11)). In Southern India, risk factors for lower mean eGFR and eGFR <60, respectively, were residence in a rural area (−7.78 (-8.69 to –6.86); 4.95 (2.61 to 9.39)), older age (−0.90 (–0.93 to –0.86); 1.06 (1.04 to 1.08)) and less education (−0.94 (-1.32 to –0.56); 0.67 (0.50 to 0.90) for each 5 years at school). CONCLUSIONS: CKDu is present in India and is not confined to Central America and Sri Lanka. Identified risk factors are consistent with risk factors previously reported for CKDu in Central America and Sri Lanka.
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spelling pubmed-64297422019-04-05 Prevalence of and risk factors for chronic kidney disease of unknown aetiology in India: secondary data analysis of three population-based cross-sectional studies O’Callaghan-Gordo, Cristina Shivashankar, Roopa Anand, Shuchi Ghosh, Shreeparna Glaser, Jason Gupta, Ruby Jakobsson, Kristina Kondal, Dimple Krishnan, Anand Mohan, Sailesh Mohan, Viswanathan Nitsch, Dorothea P A, Praveen Tandon, Nikhil Narayan, K M Venkat Pearce, Neil Caplin, Ben Prabhakaran, Dorairaj BMJ Open Epidemiology OBJECTIVES: To assess whether chronic kidney disease of unknown aetiology (CKDu) is present in India and to identify risk factors for it using population-based data and standardised methods. DESIGN: Secondary data analysis of three population-based cross-sectional studies conducted between 2010 and 2014. SETTING: Urban and rural areas of Northern India (states of Delhi and Haryana) and Southern India (states of Tamil Nadu and Andhra Pradesh). PARTICIPANTS: 12 500 individuals without diabetes, hypertension or heavy proteinuria. OUTCOME MEASURES: Mean estimated glomerular filtration rate (eGFR) and prevalence of eGFR below 60 mL/min per 1.73 m(2) (eGFR <60) in individuals without diabetes, hypertension or heavy proteinuria (proxy definition of CKDu). RESULTS: The mean eGFR was 105.0±17.8 mL/min per 1.73 m(2). The prevalence of eGFR <60 was 1.6% (95% CI=1.4 to 1.7), but this figure varied markedly between areas, being highest in rural areas of Southern Indian (4.8% (3.8 to 5.8)). In Northern India, older age was the only risk factor associated with lower mean eGFR and eGFR <60 (regression coefficient (95% CI)=−0.94 (0.97 to 0.91); OR (95% CI)=1.10 (1.08 to 1.11)). In Southern India, risk factors for lower mean eGFR and eGFR <60, respectively, were residence in a rural area (−7.78 (-8.69 to –6.86); 4.95 (2.61 to 9.39)), older age (−0.90 (–0.93 to –0.86); 1.06 (1.04 to 1.08)) and less education (−0.94 (-1.32 to –0.56); 0.67 (0.50 to 0.90) for each 5 years at school). CONCLUSIONS: CKDu is present in India and is not confined to Central America and Sri Lanka. Identified risk factors are consistent with risk factors previously reported for CKDu in Central America and Sri Lanka. BMJ Publishing Group 2019-03-07 /pmc/articles/PMC6429742/ /pubmed/30850400 http://dx.doi.org/10.1136/bmjopen-2018-023353 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.
spellingShingle Epidemiology
O’Callaghan-Gordo, Cristina
Shivashankar, Roopa
Anand, Shuchi
Ghosh, Shreeparna
Glaser, Jason
Gupta, Ruby
Jakobsson, Kristina
Kondal, Dimple
Krishnan, Anand
Mohan, Sailesh
Mohan, Viswanathan
Nitsch, Dorothea
P A, Praveen
Tandon, Nikhil
Narayan, K M Venkat
Pearce, Neil
Caplin, Ben
Prabhakaran, Dorairaj
Prevalence of and risk factors for chronic kidney disease of unknown aetiology in India: secondary data analysis of three population-based cross-sectional studies
title Prevalence of and risk factors for chronic kidney disease of unknown aetiology in India: secondary data analysis of three population-based cross-sectional studies
title_full Prevalence of and risk factors for chronic kidney disease of unknown aetiology in India: secondary data analysis of three population-based cross-sectional studies
title_fullStr Prevalence of and risk factors for chronic kidney disease of unknown aetiology in India: secondary data analysis of three population-based cross-sectional studies
title_full_unstemmed Prevalence of and risk factors for chronic kidney disease of unknown aetiology in India: secondary data analysis of three population-based cross-sectional studies
title_short Prevalence of and risk factors for chronic kidney disease of unknown aetiology in India: secondary data analysis of three population-based cross-sectional studies
title_sort prevalence of and risk factors for chronic kidney disease of unknown aetiology in india: secondary data analysis of three population-based cross-sectional studies
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6429742/
https://www.ncbi.nlm.nih.gov/pubmed/30850400
http://dx.doi.org/10.1136/bmjopen-2018-023353
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