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

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/m2 increase in BMI were associated with 69% (odds ratio, 1.69; 95% CI, 1.64 to 1.74) and 58% (1.58; 1.55 to 1.62) higher odds of CKD, respectively. In analogous MR analyses, each 0.06-genetically-predicted higher waist-to-hip ratio was associated with a 29% (1.29; 1.20 to 1.38) increased odds of CKD, and each 5-kg/m2 genetically-predicted higher BMI was associated with a 49% (1.49; 1.39 to 1.59) increased odds. After adjusting for diabetes and measured BP, chi-squared values for associations for waist-to-hip ratio and BMI fell by 56%. In contrast, mediator adjustment using multivariable MR found 83% and 69% reductions in chi-squared values for genetically-predicted waist-to-hip ratio and BMI models, respectively. 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.

[1]  B. Zinman,et al.  Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy. , 2019, The New England journal of medicine.

[2]  R. Mägi,et al.  Causal relationships between obesity and the leading causes of death in women and men , 2019, PLoS genetics.

[3]  B. Zinman,et al.  Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy. , 2019, The New England journal of medicine.

[4]  F. Kronenberg,et al.  Adiposity and risk of decline in glomerular filtration rate: meta-analysis of individual participant data in a global consortium , 2019, British Medical Journal.

[5]  P. Donnelly,et al.  The UK Biobank resource with deep phenotyping and genomic data , 2018, Nature.

[6]  G. Davey Smith,et al.  Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians , 2018, British Medical Journal.

[7]  J. Hirschhorn,et al.  GWAS for BMI: a treasure trove of fundamental insights into the genetic basis of obesity , 2018, International Journal of Obesity.

[8]  D. Lawlor,et al.  Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption , 2018, bioRxiv.

[9]  R. Collins,et al.  Effect of diabetes duration and glycaemic control on 14-year cause-specific mortality in Mexican adults: a blood-based prospective cohort study , 2018, The lancet. Diabetes & endocrinology.

[10]  B. Neale,et al.  Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases , 2018, Nature Genetics.

[11]  Samuel E. Jones,et al.  Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry , 2018, bioRxiv.

[12]  P. Visscher,et al.  Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry , 2018, bioRxiv.

[13]  M. Landray,et al.  Prognostic utility of estimated albumin excretion rate in chronic kidney disease: results from the Study of Heart and Renal Protection , 2017, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[14]  D. Bennett,et al.  Mendelian randomisation in cardiovascular research: an introduction for clinicians , 2017, Heart.

[15]  Christopher D. Brown,et al.  Genetic-Variation-Driven Gene-Expression Changes Highlight Genes with Important Functions for Kidney Disease. , 2017, American journal of human genetics.

[16]  Tanya M. Teslovich,et al.  An Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans , 2017, Diabetes.

[17]  Maristella,et al.  Genomic analyses identify hundreds of variants associated with age at menarche and 1 support a role for puberty timing in cancer risk 2 , 2017 .

[18]  M. Woodward,et al.  Body-mass index and risk of advanced chronic kidney disease: Prospective analyses from a primary care cohort of 1.4 million adults in England , 2017, PloS one.

[19]  Stephen Burgess,et al.  Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants , 2016, Epidemiology.

[20]  Dylan S. Small,et al.  A review of instrumental variable estimators for Mendelian randomization , 2015, Statistical methods in medical research.

[21]  T. Jenssen,et al.  Central obesity associates with renal hyperfiltration in the non-diabetic general population: a cross-sectional study , 2016, BMC Nephrology.

[22]  Xiaofeng Zhu,et al.  The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals , 2016, Nature Genetics.

[23]  M. Woodward,et al.  Effects of intensive blood pressure lowering on cardiovascular and renal outcomes: updated systematic review and meta-analysis , 2016, The Lancet.

[24]  K. Kalantar-Zadeh,et al.  Association of age and BMI with kidney function and mortality: a cohort study. , 2015, The lancet. Diabetes & endocrinology.

[25]  P. Elliott,et al.  UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age , 2015, PLoS medicine.

[26]  F. Dudbridge,et al.  Re: "Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects". , 2015, American journal of epidemiology.

[27]  Ross M. Fraser,et al.  Genetic studies of body mass index yield new insights for obesity biology , 2015, Nature.

[28]  S. Thompson,et al.  Multivariable Mendelian Randomization: The Use of Pleiotropic Genetic Variants to Estimate Causal Effects , 2015, American journal of epidemiology.

[29]  Tamara S. Roman,et al.  New genetic loci link adipose and insulin biology to body fat distribution , 2014, Nature.

[30]  F. Greenway,et al.  Effect of a long-term behavioural weight loss intervention on nephropathy in overweight or obese adults with type 2 diabetes: a secondary analysis of the Look AHEAD randomised clinical trial. , 2014, The lancet. Diabetes & endocrinology.

[31]  Alan D. Lopez,et al.  Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013 , 2014, The Lancet.

[32]  Elizabeth Selvin,et al.  Trends in the prevalence of reduced GFR in the United States: a comparison of creatinine- and cystatin C-based estimates. , 2013, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[33]  I. White,et al.  Within-person variability in calculated risk factors: Comparing the aetiological association of adiposity ratios with risk of coronary heart disease , 2013, International journal of epidemiology.

[34]  K. Skorecki,et al.  Body mass index in 1.2 million adolescents and risk for end-stage renal disease. , 2012, Archives of internal medicine.

[35]  Gretchen A. Stevens,et al.  National, regional, and global trends in adult overweight and obesity prevalences , 2012, Population Health Metrics.

[36]  I. Njølstad,et al.  Impaired Fasting Glucose Is Associated With Renal Hyperfiltration in the General Population , 2011, Diabetes Care.

[37]  R Craig,et al.  Adult anthropometric measures, overweight and obesity , 2010 .

[38]  Christian Gieger,et al.  Six new loci associated with body mass index highlight a neuronal influence on body weight regulation , 2009, Nature Genetics.

[39]  J. Griffith,et al.  Waist-to-hip ratio, body mass index, and subsequent kidney disease and death. , 2008, American journal of kidney diseases : the official journal of the National Kidney Foundation.

[40]  R. Collins,et al.  Biomarkers of inflammation predict both vascular and non-vascular mortality in older men. , 2008, European heart journal.

[41]  Sarah Parish,et al.  Fibrinogen and coronary heart disease: test of causality by 'Mendelian randomization'. , 2006, International journal of epidemiology.

[42]  Carlos Iribarren,et al.  Body Mass Index and Risk for End-Stage Renal Disease , 2006, Annals of Internal Medicine.

[43]  H Tunstall-Pedoe,et al.  Plasma fibrinogen level and the risk of major cardiovascular diseases and nonvascular mortality: an individual participant meta-analysis. , 2005, JAMA.

[44]  K. Iseki,et al.  Body mass index and the risk of development of end-stage renal disease in a screened cohort. , 2004, Kidney international.

[45]  B. Brenner,et al.  Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy , 2002 .

[46]  B. Brenner,et al.  Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy. , 2001, The New England journal of medicine.

[47]  R. Collins,et al.  Underestimation of risk associations due to regression dilution in long-term follow-up of prospective studies. , 1999, American journal of epidemiology.