Conventional and Genetic Evidence on the Association between Adiposity and CKD.


Journal

Journal of the American Society of Nephrology : JASN
ISSN: 1533-3450
Titre abrégé: J Am Soc Nephrol
Pays: United States
ID NLM: 9013836

Informations de publication

Date de publication:
01 2021
Historique:
received: 18 05 2020
accepted: 10 09 2020
pubmed: 1 11 2020
medline: 8 7 2021
entrez: 31 10 2020
Statut: ppublish

Résumé

The size of any causal contribution of central and general adiposity to CKD risk and the underlying mechanism of mediation are unknown. Data from 281,228 UK Biobank participants were used to estimate the relevance of waist-to-hip ratio and body mass index (BMI) to CKD prevalence. Conventional approaches used logistic regression. Genetic analyses used Mendelian randomization (MR) and data from 394 waist-to-hip ratio and 773 BMI-associated loci. Models assessed the role of known mediators (diabetes mellitus and BP) by adjusting for measured values (conventional analyses) or genetic associations of the selected loci (multivariable MR). Evidence of CKD was found in 18,034 (6.4%) participants. Each 0.06 higher measured waist-to-hip ratio and each 5-kg/m Genetic analyses suggest that conventional associations between central and general adiposity with CKD are largely causal. However, conventional approaches underestimate mediating roles of diabetes, BP, and their correlates. Genetic approaches suggest these mediators explain most of adiposity-CKD-associated risk.

Sections du résumé

BACKGROUND
The size of any causal contribution of central and general adiposity to CKD risk and the underlying mechanism of mediation are unknown.
METHODS
Data from 281,228 UK Biobank participants were used to estimate the relevance of waist-to-hip ratio and body mass index (BMI) to CKD prevalence. Conventional approaches used logistic regression. Genetic analyses used Mendelian randomization (MR) and data from 394 waist-to-hip ratio and 773 BMI-associated loci. Models assessed the role of known mediators (diabetes mellitus and BP) by adjusting for measured values (conventional analyses) or genetic associations of the selected loci (multivariable MR).
RESULTS
Evidence of CKD was found in 18,034 (6.4%) participants. Each 0.06 higher measured waist-to-hip ratio and each 5-kg/m
CONCLUSIONS
Genetic analyses suggest that conventional associations between central and general adiposity with CKD are largely causal. However, conventional approaches underestimate mediating roles of diabetes, BP, and their correlates. Genetic approaches suggest these mediators explain most of adiposity-CKD-associated risk.

Identifiants

pubmed: 33127858
pii: 00001751-202101000-00014
doi: 10.1681/ASN.2020050679
pmc: PMC7894659
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

127-137

Subventions

Organisme : Medical Research Council
ID : MC_PC_17228
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_U137686857
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00017/3
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_U137686861
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_12026/3
Pays : United Kingdom
Organisme : British Heart Foundation
ID : CH/1996001/9454
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/R007764/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00017/2
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_QA137853
Pays : United Kingdom

Informations de copyright

Copyright © 2021 by the American Society of Nephrology.

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Auteurs

Pengfei Zhu (P)

Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom.
Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.

William G Herrington (WG)

Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom.
Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
Oxford Kidney Unit, Churchill Hospital, Oxford, United Kingdom.

Richard Haynes (R)

Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom.
Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
Oxford Kidney Unit, Churchill Hospital, Oxford, United Kingdom.

Jonathan Emberson (J)

Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom.
Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.

Martin J Landray (MJ)

Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom.
Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.
Health Data Research UK, University of Oxford, Oxford, United Kingdom.
National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom.

Cathie L M Sudlow (CLM)

Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, United Kingdom.

Mark Woodward (M)

The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, Australia.
The George Institute for Global Health, Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom.
Welch Center for Prevention, Epidemiology and Clinical Research, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.

Colin Baigent (C)

Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom.
Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.

Sarah Lewington (S)

Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.

Natalie Staplin (N)

Medical Research Council Population Health Research Unit at the University of Oxford, Nuffield Department of Population Health, Oxford, United Kingdom.
Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.

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