Cross-sectional associations between central and general adiposity with albuminuria: observations from 400,000 people in UK Biobank.
Journal
International journal of obesity (2005)
ISSN: 1476-5497
Titre abrégé: Int J Obes (Lond)
Pays: England
ID NLM: 101256108
Informations de publication
Date de publication:
11 2020
11 2020
Historique:
received:
19
12
2019
accepted:
06
07
2020
revised:
28
06
2020
pubmed:
18
7
2020
medline:
4
11
2021
entrez:
18
7
2020
Statut:
ppublish
Résumé
Whether measures of central adiposity are more or less strongly associated with risk of albuminuria than body mass index (BMI), and by how much diabetes/levels of glycosylated haemoglobin (HbA1c) explain or modify these associations, is uncertain. Ordinal logistic regression was used to estimate associations between values of central adiposity (waist-to-hip ratio) and, separately, general adiposity (BMI) with categories of urinary albumin-to-creatinine ratio (uACR) in 408,527 UK Biobank participants. Separate central and general adiposity-based models were initially adjusted for potential confounders and measurement error, then sequentially, models were mutually adjusted (e.g. waist-to-hip ratio adjusted for BMI, and vice versa), and finally they were adjusted for potential mediators. Levels of albuminuria were generally low: 20,425 (5%) had a uACR ≥3 mg/mmol. After adjustment for confounders and measurement error, each 0.06 higher waist-to-hip ratio was associated with a 55% (95%CI 53-57%) increase in the odds of being in a higher uACR category. After adjustment for baseline BMI, this association was reduced to 32% (30-34%). Each 5 kg/m Conventional epidemiological approaches suggest that higher waist-to-hip ratio and BMI are independently positively associated with albuminuria. Adiposity-albuminuria associations appear strong among people with normal HbA1c, as well as people with pre-diabetes or diabetes.
Sections du résumé
BACKGROUND
Whether measures of central adiposity are more or less strongly associated with risk of albuminuria than body mass index (BMI), and by how much diabetes/levels of glycosylated haemoglobin (HbA1c) explain or modify these associations, is uncertain.
METHODS
Ordinal logistic regression was used to estimate associations between values of central adiposity (waist-to-hip ratio) and, separately, general adiposity (BMI) with categories of urinary albumin-to-creatinine ratio (uACR) in 408,527 UK Biobank participants. Separate central and general adiposity-based models were initially adjusted for potential confounders and measurement error, then sequentially, models were mutually adjusted (e.g. waist-to-hip ratio adjusted for BMI, and vice versa), and finally they were adjusted for potential mediators.
RESULTS
Levels of albuminuria were generally low: 20,425 (5%) had a uACR ≥3 mg/mmol. After adjustment for confounders and measurement error, each 0.06 higher waist-to-hip ratio was associated with a 55% (95%CI 53-57%) increase in the odds of being in a higher uACR category. After adjustment for baseline BMI, this association was reduced to 32% (30-34%). Each 5 kg/m
CONCLUSIONS
Conventional epidemiological approaches suggest that higher waist-to-hip ratio and BMI are independently positively associated with albuminuria. Adiposity-albuminuria associations appear strong among people with normal HbA1c, as well as people with pre-diabetes or diabetes.
Identifiants
pubmed: 32678323
doi: 10.1038/s41366-020-0642-3
pii: 10.1038/s41366-020-0642-3
pmc: PMC7577847
doi:
Substances chimiques
Glycated Hemoglobin A
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2256-2266Subventions
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_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_QA137853
Pays : United Kingdom
Références
Neuen BL, Chadban SJ, Demaio AR, Johnson DW, Perkovic V. Chronic kidney disease and the global NCDs agenda. BMJ Glob Health. 2017;2:e000380 https://doi.org/10.1136/bmjgh-2017-000380
doi: 10.1136/bmjgh-2017-000380
pubmed: 29225940
pmcid: 5717948
Herrington WG, Smith M, Bankhead C, Matsushita K, Stevens S, Holt T, 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. PLoS ONE. 2017;12:e0173515 https://doi.org/10.1371/journal.pone.0173515
doi: 10.1371/journal.pone.0173515
pubmed: 28273171
pmcid: 5342319
Hsu CY, McCulloch CE, Iribarren C, Darbinian J, Go AS. Body Mass Index and Risk for End-Stage Renal Disease. Ann Intern Med. 2006;144:21–28. https://doi.org/10.7326/0003-4819-144-1-200601030-00006
doi: 10.7326/0003-4819-144-1-200601030-00006
pubmed: 16389251
Iseki K, Ikemiya Y, Kinjo K, Inoue T, Iseki C, Takishita S. Body mass index and the risk of development of end-stage renal disease in a screened cohort. Kidney Int. 2004;65:1870–6. https://doi.org/10.1111/j.1523-1755.2004.00582.x
doi: 10.1111/j.1523-1755.2004.00582.x
pubmed: 15086929
Chang AR, Grams ME, Ballew SH, Bilo H, Correa A, Evans M, et al. Adiposity and risk of decline in glomerular filtration rate: meta-analysis of individual participant data in a global consortium. BMJ. 2019;364:k5301. https://doi.org/10.1136/bmj.k5301
doi: 10.1136/bmj.k5301
pubmed: 30630856
pmcid: 6481269
Chen Z, Smith M, Du H, Guo Y, Clarke R, Bian Z, et al. Blood pressure in relation to general and central adiposity among 500 000 adult Chinese men and women. Int J Epidemiol. 2015;44:1305–19. https://doi.org/10.1093/ije/dyv012
doi: 10.1093/ije/dyv012
pubmed: 25747585
pmcid: 4588860
Gnatiuc L, Alegre-Diaz J, Halsey J, Herrington WG, Lopez-Cervantes M, Lewington S, et al. Adiposity and Blood Pressure in 110 000 Mexican Adults. Hypertension. 2017;69:608–14. https://doi.org/10.1161/HYPERTENSIONAHA.116.08791
doi: 10.1161/HYPERTENSIONAHA.116.08791
pubmed: 28223471
pmcid: 5344187
Lee CM, Huxley RR, Wildman RP, Woodward M. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J Clin Epidemiol. 2008;61:646–53. https://doi.org/10.1016/j.jclinepi.2007.08.012
doi: 10.1016/j.jclinepi.2007.08.012
pubmed: 18359190
Yusuf S, Hawken S, Ounpuu S, Bautista L, Franzosi MG, Commerford P, et al. Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study. Lancet. 2005;366:1640–9. https://doi.org/10.1016/S0140-6736(05)67663-5
doi: 10.1016/S0140-6736(05)67663-5
pubmed: 16271645
Peters SAE, Bots SH, Woodward M. Sex differences in the association between measures of general and central adiposity and the risk of myocardial infarction: results from the UK Biobank. J Am Heart Assoc. 2018;7:e008507. https://doi.org/10.1161/JAHA.117.008507
doi: 10.1161/JAHA.117.008507
pubmed: 29490971
pmcid: 5866342
Vivante A, Golan E, Tzur D, Leiba A, Tirosh A, Skorecki K, et al. Body mass index in 1.2 million adolescents and risk for end-stage renal disease. Arch Intern Med. 2012;172:1644–50. https://doi.org/10.1001/2013.jamainternmed.85
doi: 10.1001/2013.jamainternmed.85
pubmed: 23108588
pmcid: 4941233
Kidney Disease Improving Global Outcome (KDIGO) 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. 2012. www.kdigo.org . Accessed 23 May 2020.
Gojaseni P, Phaopha A, Chailimpamontree W, Pajareya T, Chittinandana A. Prevalence and risk factors of microalbuminuria in Thai nondiabetic hypertensive patients. Vasc Health Risk Manag. 2010;6:157–65. https://doi.org/10.2147/vhrm.s9739
doi: 10.2147/vhrm.s9739
pubmed: 20448800
pmcid: 2860447
Seo WJ, Lee GM, Hwang JH, Lee MN, Kang HC. Association between Body Mass Index, Waist Circumference and Prevalence of Microalbuminuria in Korean Adults of Age 30 Years and Older without Diabetes, Hypertension, Renal Failure, or Overt Proteinuria: The 2013 Korean National Health and Nutrition Examination Survey. Korean J Fam Med. 2016;37:57–63. https://doi.org/10.4082/kjfm.2016.37.1.57
doi: 10.4082/kjfm.2016.37.1.57
pubmed: 26885324
pmcid: 4754288
Wang Z, Ding L, Huang X, Chen Y, Sun W, Lin L, et al. Abdominal adiposity contributes to adverse glycemic control and albuminuria in Chinese type 2 diabetic patients: a cross-sectional study. J Diabetes. 2017;9:285–95. https://doi.org/10.1111/1753-0407
doi: 10.1111/1753-0407
pubmed: 27100567
Lin WY, Pi-Sunyer FX, Liu CS, Li CI, Davidson LE, Li TC, et al. Central obesity and albuminuria: both cross-sectional and longitudinal studies in Chinese. PLoS ONE. 2012;7:e47960. https://doi.org/10.1371/journal.pone.0047960
doi: 10.1371/journal.pone.0047960
pubmed: 23251329
pmcid: 3520991
Sato Y, Fujimoto S, Konta T, Iseki K, Moriyama T, Yamagata K, et al. U-shaped association between body mass index and proteinuria in a large Japanese general population sample. Clin Exp Nephrol. 2014;18:75–86. https://doi.org/10.1007/s10157-013-0809-5
doi: 10.1007/s10157-013-0809-5
pubmed: 23652829
Wanner C, Inzucchi SE, Lachin JM, Fitchett D, von Eynatten M, Mattheus M, et al. Empagliflozin and progression of kidney disease in type 2 diabetes. N Engl J Med. 2016;375:1801–2. https://doi.org/10.1056/NEJMc1611290
doi: 10.1056/NEJMc1611290
pubmed: 27806236
Perkovic V, Jardine MJ, Neal B, Bompoint S, Heerspink HJL, Charytan DM, et al. Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. N Engl J Med. 2019;380:2295–30. https://doi.org/10.1056/NEJMoa1811744
doi: 10.1056/NEJMoa1811744
pubmed: 30990260
Gerstein HC, Colhoun HM, Dagenais GR, Diaz R, Lakshmanan M, Pais P, et al. Dulaglutide and renal outcomes in type 2 diabetes: an exploratory analysis of the REWIND randomised, placebo-controlled trial. Lancet. 2019;394:131–8. https://doi.org/10.1016/S0140-6736(19)31150-X
doi: 10.1016/S0140-6736(19)31150-X
pubmed: 31189509
Cherney DZI, Verma S, Parker JD. Dulaglutide and renal protection in type 2 diabetes. Lancet Diabetes Endocrinol. 2018;6:588–90. https://doi.org/10.1016/S2213-8587(18)30125-6
doi: 10.1016/S2213-8587(18)30125-6
pubmed: 29910025
Ogna A, Forni Ogna V, Bochud M, Guessous I, Paccaud F, Burnier M, et al. Association between obesity and glomerular hyperfiltration: the confounding effect of smoking and sodium and protein intakes. Eur J Nutr. 2016;55:1089–97. https://doi.org/10.1007/s00394-015-0923-0
doi: 10.1007/s00394-015-0923-0
pubmed: 25971845
UK Biobank: www.ukbiobank.ac.uk . Accessed 23 May 2020.
UK Biobank: Protocol for a large-scale prospective epidemiological resource. 2007. https://www.ukbiobank.ac.uk/wp-content/uploads/2011/11/UK-Biobank-Protocol.pdf . Accessed 23 May 2020.
Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779. https://doi.org/10.1371/journal.pmed.1001779
doi: 10.1371/journal.pmed.1001779
pubmed: 25826379
pmcid: 4380465
National Obesity Observatory. Measures of central adiposity as an indicator of obesity. 2009.
UK Biobank. Anthropometry. 2011. http://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/Anthropometry.pdf . Accessed 23 May 2020.
Mente A, O’Donnell MJ, Dagenais G, Wielgosz A, Lear SA, McQueen MJ, et al. Validation and comparison of three formulae to estimate sodium and potassium excretion from a single morning fasting urine compared to 24-h measures in 11 countries. J Hypertens. 2014;32:1005–14. https://doi.org/10.1097/hjh.0000000000000122
doi: 10.1097/hjh.0000000000000122
pubmed: 24569420
Clarke R, Shipley M, Lewington S, Youngman L, Collins R, Marmot M, et al. Underestimation of risk associations due to regression dilution in long-term follow-up of prospective studies. Am J Epidemiol. 1999;150:341–53. https://doi.org/10.1093/oxfordjournals.aje.a010013
doi: 10.1093/oxfordjournals.aje.a010013
pubmed: 10453810
Wormser D, White IR, Thompson SG, Wood AM. Within-person variability in calculated risk factors: comparing the aetiological association of adiposity ratios with risk of coronary heart disease. Int J Epidemiol. 2013;42:849–59. https://doi.org/10.1093/ije/dyt077
doi: 10.1093/ije/dyt077
pubmed: 23918853
pmcid: 3733701
Stefansson VT, Schei J, Jenssen TG, Melsom T, Eriksen BO. Central obesity associates with renal hyperfiltration in the non-diabetic general population: a cross-sectional study. BMC Nephrol. 2016;17:172. https://doi.org/10.1186/s12882-016-0386-4
doi: 10.1186/s12882-016-0386-4
pubmed: 27832768
pmcid: 5103601
Melsom T, Mathisen UD, Ingebretsen OC, Jenssen TG, Njolstad I, Solbu MD, et al. Impaired fasting glucose is associated with renal hyperfiltration in the general population. Diabetes Care. 2011;34:1546–51. https://doi.org/10.2337/dc11-0235
doi: 10.2337/dc11-0235
pubmed: 21593291
pmcid: 3120190
Sasson AN, Cherney DZ. Renal hyperfiltration related to diabetes mellitus and obesity in human disease. World J Diabetes. 2012;3:1–6. https://doi.org/10.4239/wjd.v3.i1.1
doi: 10.4239/wjd.v3.i1.1
pubmed: 22253940
pmcid: 3258534
Look AHEAD Research Group. 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. The Lancet Diabetes & Endocrinology. 2014;2:801–9. https://doi.org/10.1016/S2213-8587(14)70156-1
doi: 10.1016/S2213-8587(14)70156-1
Lovshin JA, Cherney DZ. Sodium transport in diabetes: two sides to the coin. Nat Rev Nephrol. 2019;15:125–6. https://doi.org/10.1038/s41581-018-0106-3
doi: 10.1038/s41581-018-0106-3
pubmed: 30580375
D’Agati VD, Chagnac A, de Vries AP, Levi M, Porrini E, Herman-Edelstein M, et al. Obesity-related glomerulopathy: clinical and pathologic characteristics and pathogenesis. Nat Rev Nephrol. 2016;12:453–71. https://doi.org/10.1038/nrneph.2016.75nrneph.2016.75
doi: 10.1038/nrneph.2016.75nrneph.2016.75
pubmed: 27263398
Aburto NJ, Ziolkovska A, Hooper L, Elliott P, Cappuccio FP, Meerpohl JJ. Effect of lower sodium intake on health: systematic review and meta-analyses. BMJ. 2013;346:f1326. https://doi.org/10.1136/bmj.f1326
doi: 10.1136/bmj.f1326
pubmed: 23558163
pmcid: 4816261
Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, 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. Lancet. 2014;384:766–81. https://doi.org/10.1016/S0140-6736(14)60460-8
doi: 10.1016/S0140-6736(14)60460-8
pubmed: 24880830
pmcid: 24880830
Stevens GA, Singh GM, Lu Y, Danaei G, Lin JK, Finucane MM, et al. National, regional, and global trends in adult overweight and obesity prevalences. Popul Health Metr. 2012;10:22. https://doi.org/10.1186/1478-7954-10-22
doi: 10.1186/1478-7954-10-22
pubmed: 23167948
pmcid: 3543235
Statistics on Obesity, Physical Activity and Diet. England; 2018. Available at www.digital.nhs.uk . Accessed 13 July 2020.
Herrington WG, Preiss D, Haynes R, von Eynatten M, Staplin N, Hauske SJ, et al. The potential for improving cardio-renal outcomes by sodium-glucose co-transporter-2 inhibition in people with chronic kidney disease: a rationale for the EMPA-KIDNEY study. Clin Kidney J. 2018;11:749–61. https://doi.org/10.1093/ckj/sfy090
doi: 10.1093/ckj/sfy090
pubmed: 30524708
pmcid: 6275453
Mafham MM, Staplin N, Emberson J, Haynes R, Herrington W, Reith C, et al. Prognostic utility of estimated albumin excretion rate in chronic kidney disease: results from the Study of Heart and Renal Protection. Nephrol Dial Transplant. 2018;33:257–64. https://doi.org/10.1093/ndt/gfw396
doi: 10.1093/ndt/gfw396
pubmed: 28088776
Jedrusik P, Symonides B, Gaciong Z. Estimating 24-hour urinary sodium, potassium, and creatinine excretion in hypertensive patients: can we replace 24-hour urine collection with spot urine measurements? Pol Arch Intern Med. 2019;129:506–15. https://doi.org/10.20452/pamw.14872
doi: 10.20452/pamw.14872
pubmed: 31215902