Volume 12, Issue 12 pp. 929-941

ORIGINAL ARTICLE

Alternative kidney filtration markers and the risk of major macrovascular and microvascular events, and all-cause mortality in individuals with type 2 diabetes in the ADVANCE trial

ADVANCE试验中2型糖尿病患者的替代肾脏滤过标志物与主要大血管和微血管事件的风险以及全因死亡率的关系

Hyunju Kim,

Hyunju Kim

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA

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Dan Wang,

Dan Wang

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA

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John Chalmers,

John Chalmers

The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia

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Min Jun,

Min Jun

The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia

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Sophia Zoungas,

Sophia Zoungas

The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia

School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia

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Michel Marre,

Michel Marre

Department of Diabetology, Endocrinology, and Nutrition, Assistance Publique-Hôpitaux de Paris, Bichat Hospital, Paris, France

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Pavel Hamet,

Pavel Hamet

Department of Medicine, Centre Hospitalier de I'Universite de Montreal (CHUM) | CHUM, Montreal, Québec, Canada

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Stephen Harrap,

Stephen Harrap

The University of Melbourne and Royal Melbourne Hospital, Parkville, Victoria, Australia

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Giuseppe Mancia,

Giuseppe Mancia

Department of Medicine and Surgery, University of Milan-Bicocca, Milan, Italy

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Neil R. Poulter,

Neil R. Poulter

The International Centre for Circulatory Health, Imperial College, London, UK

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Mark E. Cooper,

Mark E. Cooper

Diabetes Department, Central Clinical School, Monash University, Melbourne, Victoria, Australia

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Mark Woodward,

Mark Woodward

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia

The George Institute for Global Health, University of Oxford, Oxford, UK

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Elizabeth Selvin,

Elizabeth Selvin

orcid.org/0000-0001-6923-7151

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA

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Casey M. Rebholz,

Corresponding Author

Casey M. Rebholz

[email protected]

orcid.org/0000-0002-5442-8745

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA

Correspondence

Casey M. Rebholz, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E Monument St, Suite 2-500, Baltimore, MD 21205, USA.

Email: [email protected]

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Hyunju Kim,

Hyunju Kim

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA

Search for more papers by this author

Dan Wang,

Dan Wang

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA

Search for more papers by this author

John Chalmers,

John Chalmers

The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia

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Min Jun,

Min Jun

The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia

Search for more papers by this author

Sophia Zoungas,

Sophia Zoungas

The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia

School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia

Search for more papers by this author

Michel Marre,

Michel Marre

Department of Diabetology, Endocrinology, and Nutrition, Assistance Publique-Hôpitaux de Paris, Bichat Hospital, Paris, France

Search for more papers by this author

Pavel Hamet,

Pavel Hamet

Department of Medicine, Centre Hospitalier de I'Universite de Montreal (CHUM) | CHUM, Montreal, Québec, Canada

Search for more papers by this author

Stephen Harrap,

Stephen Harrap

The University of Melbourne and Royal Melbourne Hospital, Parkville, Victoria, Australia

Search for more papers by this author

Giuseppe Mancia,

Giuseppe Mancia

Department of Medicine and Surgery, University of Milan-Bicocca, Milan, Italy

Search for more papers by this author

Neil R. Poulter,

Neil R. Poulter

The International Centre for Circulatory Health, Imperial College, London, UK

Search for more papers by this author

Mark E. Cooper,

Mark E. Cooper

Diabetes Department, Central Clinical School, Monash University, Melbourne, Victoria, Australia

Search for more papers by this author

Mark Woodward,

Mark Woodward

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

The George Institute for Global Health, University of New South Wales, Sydney, New South Wales, Australia

The George Institute for Global Health, University of Oxford, Oxford, UK

Search for more papers by this author

Elizabeth Selvin,

Elizabeth Selvin

orcid.org/0000-0001-6923-7151

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA

Search for more papers by this author

Casey M. Rebholz,

Corresponding Author

Casey M. Rebholz

[email protected]

orcid.org/0000-0002-5442-8745

Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA

Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA

Correspondence

Casey M. Rebholz, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 2024 E Monument St, Suite 2-500, Baltimore, MD 21205, USA.

Email: [email protected]

Search for more papers by this author

First published: 01 July 2020

https://doi.org/10.1111/1753-0407.13083

Citations: 5

ClinicalTrials.gov identifier: NCT00145925.

Funding information: National Institute of Diabetes and Digestive and Kidney Diseases, Grant/Award Number: R01DK108784

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Abstract

Background

Creatinine-based estimated glomerular filtration rate (eGFR) is biased in the setting of obesity and other conditions. Alternative kidney filtration markers may be useful in adults with diabetes, but few studies examined the associations with risk of clinical outcomes.

Methods

In the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) trial, we evaluated whether baseline levels and change in eGFR based on creatinine (Cr), cystatin c (Cys), β₂-microglobulin (B2M), eGFR_Cr-Cys, and the average of three estimates (eGFR_Cr-Cys-B2M) assessed in 7217 participants at baseline and a random sample of 640 participants at the 1-year visit are associated with clinical outcomes. We examined associations with major macrovascular and microvascular events together and separately and all-cause mortality using Cox regression models, adjusting for established risk factors.

Results

Over a median follow-up of 5 years, 1313 major macrovascular (n = 748) and microvascular events (n = 637), and 743 deaths occurred. Lower levels of eGFR based on all filtration markers individually and combined were associated with 1.4 to 3.0 times higher risk of major macrovascular and microvascular events (combined and separately) and all-cause mortality. Per 30% decline in eGFR_Cys, eGFR _Cr-Cys, and eGFR_Cr-Cys-B2M were associated with a >2-fold higher risk of all clinical outcomes.

Conclusions

In adults with type 2 diabetes, baseline levels of eGFR based on alternative filtration markers and per 30% decline in eGFR_Cys, eGFR _Cr-Cys, and eGFR_Cr-Cys-B2M were associated with clinical outcomes. Measurement of alternative filtration markers, particularly B2M in adults with type 2 diabetes may be warranted.

摘要

背景

基于肌酐的估计肾小球滤过率(eGFR)在肥胖和其他情况中是有偏差的。替代肾滤过标记物可能对成人糖尿病患者有用, 但很少有研究分析其与临床结果风险的关系。

方法

在糖尿病和血管疾病行动(Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation, ADVANCE)试验中, 我们评估了eGFR基线水平和基于肌酐(Cr), 胱抑素c(Cys), 表皮生长因子受体2-微球蛋白(B2M), eGFR_Cr-Cys以及以上三者平均值(eGFR_Cr-Cys-B2M)的变化水平是否与临床结局有关, 检测了7217名参与者的基线水平和随机抽样的640名参与者的1年随访水平。我们使用Cox回归模型检验了与主要大血管和微血管事件共同发生, 各自发生, 以及全因死亡率之间的关系, 并校正了既定的危险因素。

结果

平均随访5年, 共发生1313例大血管事件(748例)和微血管事件(637例), 743例死亡。根据所有滤过标志物单独或联合计算的低水平eGFR, 发生主要大血管和微血管事件(合并和单独发生)的风险增加1.4到3.0倍, 并且和全因死亡率相关。eGFR_Cys, eGFR_Cr-Cys和eGFR_Cr-Cys-B2M每下降30%, 所有临床结果的风险增加2倍以上。

结论

在成人2型糖尿病患者中, 基于替代滤过标志物的eGFR基线水平,以及eGFR_Cys, eGFR_Cr-Cys和eGFR_Cr-Cys-B2M下降30%与临床结局相关。可能有必要在成人2型糖尿病患者中测量替代的肾小球滤过标志物, 特别是B2M。

Supporting Information

Filename

Description

jdb13083-sup-0001-Supinfo.docxWord 2007 document , 465.4 KB

Supplemental Table 1 Pearson's correlations between estimated glomerular filtration rate (eGFR) estimates

Supplemental Table 2. Adjusted hazard ratios and 95% confidence intervals for the association between reduced eGFR (<60 mL/min/1.73m²) at baseline and the risk of clinical outcomes^†‡

Supplemental Table 3. Association between baseline levels of eGFR and change in eGFR with risk of major macrovascular and microvascular events (combined and separately) after excluding blood pressure-lowering treatment and glucose treatment as covariates^†

Supplemental Table 4. Hazard ratios (HR) and 95% confidence interval (CI) for the association between change in eGFR in 5 categories and the risk of combined major macrovascular and microvascular events in ADVANCE trial^†

Supplemental Table 5. Adjusted hazard ratios and 95% confidence intervals for the association between baseline levels of estimated glomerular filtration rate (eGFR) and the risk of clinical outcomes, with additional adjustment of body mass index (BMI), lipids, and smoking status^†

Supplemental Table 6. Adjusted hazard ratios and 95% confidence intervals for the association between change in estimated glomerular filtration rate (eGFR) and the risk of clinical outcomes, with additional adjustment of body mass index (BMI), lipids, and smoking status^†

Supplemental Figure 1. Flow diagram of participant selection

Supplemental Figure 2. Kernel density plots of the distribution of change in estimated glomerular filtration rate (eGFR) based on creatinine (eGFR_Cr), eGFR based on cystatin C (eGFR_Cys), eGFR based on beta-2-microglobulin (eGFR_B2M), eGFR based on creatinine and cystatin C (eGFR_Cr-Cys), and the average across the three GFR estimates

Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

REFERENCES

1 American Diabetes Association. Microvascular complications and foot care: standards of medical care in diabetes—2019. Diabetes Care. 2019; 42(Supplement 1): S124-S138. https://doi.org/10.2337/dc19-S011.
10.2337/dc19-S011
PubMed Web of Science® Google Scholar
2Afkarian M, Zelnick LR, Hall YN, et al. Clinical manifestations of kidney disease among US adults with diabetes, 1988-2014. JAMA. 2016; 316(6): 602-610. https://doi.org/10.1001/jama.2016.10924.
10.1001/jama.2016.10924
PubMed Web of Science® Google Scholar
3Alicic RZ, Rooney MT, Tuttle KR. Diabetic kidney disease: challenges, progress, and possibilities. Clin J Am Soc Nephrol. 2017; 12(12): 2032-2045. https://doi.org/10.2215/CJN.11491116.
10.2215/CJN.11491116
CAS PubMed Web of Science® Google Scholar
4 National Kidney Foundation. KDOQI clinical practice guideline for diabetes and CKD: 2012 update. Am J Kidney Dis. 2012; 60(5): 850-886. https://doi.org/10.1053/j.ajkd.2012.07.005.
10.1053/j.ajkd.2012.07.005
PubMed Web of Science® Google Scholar
5Fox CS, Matsushita K, Woodward M, et al. Associations of kidney disease measures with mortality and end-stage renal disease in individuals with and without diabetes: a meta-analysis. Lancet. 2012; 380(9854): 1662-1673. https://doi.org/10.1016/S0140-6736(12)61350-6.
10.1016/S0140-6736(12)61350-6
PubMed Web of Science® Google Scholar
6Ninomiya T, Perkovic V, de Galan BE, et al. Albuminuria and kidney function independently predict cardiovascular and renal outcomes in diabetes. J Am Soc Nephrol. 2009; 20(8): 1813-1821. https://doi.org/10.1681/ASN.2008121270.
10.1681/ASN.2008121270
PubMed Web of Science® Google Scholar
7Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009; 150(9): 604-612.
10.7326/0003-4819-150-9-200905050-00006
PubMed Web of Science® Google Scholar
8Levey AS, Gansevoort RT, Coresh J, et al. Change in albuminuria and GFR as end points for clinical trials in early stages of CKD: a scientific workshop sponsored by the National Kidney Foundation in collaboration with the US Food and Drug Administration and European medicines agency. Am J Kidney Dis. 2019; 75: 84-104. https://doi.org/10.1053/j.ajkd.2019.06.009.
10.1053/j.ajkd.2019.06.009
PubMed Web of Science® Google Scholar
9Ohkuma T, Jun M, Chalmers J, et al. Combination of changes in estimated GFR and albuminuria and the risk of major clinical outcomes. Clin J Am Soc Nephrol. 2019; 14(6): 862-872. https://doi.org/10.2215/CJN.13391118.
10.2215/CJN.13391118
CAS PubMed Web of Science® Google Scholar
10Stevens LA, Coresh J, Greene T, Levey AS. Assessing kidney function—Measured and estimated glomerular filtration rate. N Engl J Med. 2006; 354(23): 2473-2483. https://doi.org/10.1056/NEJMra054415.
10.1056/NEJMra054415
CAS PubMed Web of Science® Google Scholar
11Silveiro SP, Araújo GN, Ferreira MN, Souza FDS, Yamaguchi HM, Camargo EG. Chronic kidney disease epidemiology collaboration (CKD-EPI) equation pronouncedly underestimates glomerular filtration rate in type 2 diabetes. Diabetes Care. 2011; 34(11): 2353-2355. https://doi.org/10.2337/dc11-1282.
10.2337/dc11-1282
CAS PubMed Web of Science® Google Scholar
12Camargo EG, Soares AA, Detanico AB, et al. The chronic kidney disease epidemiology collaboration (CKD-EPI) equation is less accurate in patients with type 2 diabetes when compared with healthy individuals. Diabet Med. 2011; 28(1): 90-95. https://doi.org/10.1111/j.1464-5491.2010.03161.x.
10.1111/j.1464-5491.2010.03161.x
CAS PubMed Web of Science® Google Scholar
13MacIsaac RJ, Ekinci EI, Premaratne E, et al. The chronic kidney disease-epidemiology collaboration (CKD-EPI) equation does not improve the underestimation of glomerular filtration rate (GFR) in people with diabetes and preserved renal function. BMC Nephrol. 2015; 16(1):198. https://doi.org/10.1186/s12882-015-0196-0.
10.1186/s12882-015-0196-0
PubMed Web of Science® Google Scholar
14Inker LA, Schmid CH, Tighiouart H, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012; 367(1): 20-29. https://doi.org/10.1056/NEJMoa1114248.
10.1056/NEJMoa1114248
CAS PubMed Web of Science® Google Scholar
15Dharnidharka VR, Kwon C, Stevens G. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am J Kidney Dis. 2002; 40(2): 221-226. https://doi.org/10.1053/ajkd.2002.34487.
10.1053/ajkd.2002.34487
CAS PubMed Web of Science® Google Scholar
16Astor BC, Shafi T, Hoogeveen RC, et al. Novel markers of kidney function as predictors of ESRD, cardiovascular disease, and mortality in the general population. Am J Kidney Dis. 2012; 59(5): 653-662. https://doi.org/10.1053/j.ajkd.2011.11.042.
10.1053/j.ajkd.2011.11.042
CAS PubMed Web of Science® Google Scholar
17Rebholz CM, Inker LA, Chen Y, et al. Risk of ESRD and mortality associated with change in filtration markers. Am J Kidney Dis. 2017; 70(4): 551-560. https://doi.org/10.1053/j.ajkd.2017.04.025.
10.1053/j.ajkd.2017.04.025
CAS PubMed Web of Science® Google Scholar
18Foster MC, Inker LA, Levey AS, et al. Novel filtration markers as predictors of all-cause and cardiovascular mortality in US adults. Am J Kidney Dis Off J Natl Kidney Found. 2013; 62(1): 42-51. https://doi.org/10.1053/j.ajkd.2013.01.016.
10.1053/j.ajkd.2013.01.016
PubMed Web of Science® Google Scholar
19Bhavsar NA, Appel LJ, Kusek JW, et al. Comparison of measured GFR, serum creatinine, cystatin C, and beta-trace protein to predict ESRD in African Americans with hypertensive CKD. Am J Kidney Dis. 2011; 58(6): 886-893. https://doi.org/10.1053/j.ajkd.2011.07.018.
10.1053/j.ajkd.2011.07.018
CAS PubMed Web of Science® Google Scholar
20Tangri N, Inker LA, Tighiouart H, et al. Filtration markers may have prognostic value independent of glomerular filtration rate. J Am Soc Nephrol. 2012; 23(2): 351-359. https://doi.org/10.1681/ASN.2011070663.
10.1681/ASN.2011070663
CAS PubMed Web of Science® Google Scholar
21Foster MC, Coresh J, Hsu C, et al. Serum β-trace protein and β2-microglobulin as predictors of ESRD, mortality, and cardiovascular disease in adults with CKD in the chronic renal insufficiency cohort (CRIC) study. Am J Kidney Dis. 2016; 68(1): 68-76. https://doi.org/10.1053/j.ajkd.2016.01.015.
10.1053/j.ajkd.2016.01.015
CAS PubMed Web of Science® Google Scholar
22Rebholz CM, Grams ME, Matsushita K, et al. Change in multiple filtration markers and subsequent risk of cardiovascular disease and mortality. Clin J Am Soc Nephrol. 2015; 10(6): 941-948. https://doi.org/10.2215/CJN.10101014.
10.2215/CJN.10101014
CAS PubMed Web of Science® Google Scholar
23Rebholz CM, Grams ME, Matsushita K, Selvin E, Coresh J. Change in novel filtration markers and risk of ESRD. Am J Kidney Dis off J Natl Kidney Found. 2015; 66(1): 47-54. https://doi.org/10.1053/j.ajkd.2014.11.009.
10.1053/j.ajkd.2014.11.009
CAS PubMed Web of Science® Google Scholar
24Garlo KG, White WB, Bakris GL, et al. Kidney biomarkers and decline in eGFR in patients with type 2 diabetes. Clin J Am Soc Nephrol. 2018; 13(3): 398-405. https://doi.org/10.2215/CJN.05280517.
10.2215/CJN.05280517
CAS PubMed Web of Science® Google Scholar
25Foster MC, Inker LA, Hsu C, et al. Filtration markers as predictors of ESRD and mortality in southwestern american Indians with type 2 diabetes. Am J Kidney Dis. 2015; 66(1): 75-83. https://doi.org/10.1053/j.ajkd.2015.01.013.
10.1053/j.ajkd.2015.01.013
PubMed Web of Science® Google Scholar
26Schöttker B, Herder C, Müller H, Brenner H, Rothenbacher D. Clinical utility of creatinine- and cystatin C-based definition of renal function for risk prediction of primary cardiovascular events in patients with diabetes. Diabetes Care. 2012; 35(4): 879-886. https://doi.org/10.2337/dc11-1998.
10.2337/dc11-1998
PubMed Web of Science® Google Scholar
27Tsai C-W, Grams ME, Inker LA, Coresh J, Selvin E. Cystatin C- and creatinine-based estimated glomerular filtration rate, vascular disease, and mortality in persons with diabetes in the U.S. Diabetes Care. 2014; 37(4): 1002-1008. https://doi.org/10.2337/dc13-1910.
10.2337/dc13-1910
CAS PubMed Web of Science® Google Scholar
28de BIH, Katz R, Cao JJ, et al. Cystatin C, albuminuria, and mortality among older adults with diabetes. Diabetes Care. 2009; 32(10): 1833-1838. https://doi.org/10.2337/dc09-0191.
10.2337/dc09-0191
PubMed Web of Science® Google Scholar
29 ADVANCE Management Committee. Study rationale and design of ADVANCE: action in diabetes and vascular disease–preterax and diamicron MR controlled evaluation. Diabetologia. 2001; 44(9): 1118-1120.
10.1007/s001250100612
PubMed Web of Science® Google Scholar
30 ADVANCE Collaborative Group, Patel A, MacMahon S, et al. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N Engl J Med. 2008; 358(24): 2560-2572. https://doi.org/10.1056/NEJMoa0802987.
10.1056/NEJMoa0802987
CAS PubMed Web of Science® Google Scholar
31Patel A. Effects of a fixed combination of perindopril and indapamide on macrovascular and microvascular outcomes in patients with type 2 diabetes mellitus (the ADVANCE trial): a randomised controlled trial. Lancet. 2007; 370(9590): 829-840. https://doi.org/10.1016/S0140-6736(07)61303-8.
10.1016/S0140-6736(07)61303-8
CAS PubMed Web of Science® Google Scholar
32Inker LA, Tighiouart H, Coresh J, et al. GFR estimation using β-trace protein and β2-microglobulin in CKD. Am J Kidney Dis. 2016; 67(1): 40-48. https://doi.org/10.1053/j.ajkd.2015.07.025.
10.1053/j.ajkd.2015.07.025
CAS PubMed Web of Science® Google Scholar
33Inker LA, Coresh J, Sang Y, et al. Filtration markers as predictors of ESRD and mortality: individual participant data meta-analysis. Clin J Am Soc Nephrol. 2017; 12(1): 69-78. https://doi.org/10.2215/CJN.03660316.
10.2215/CJN.03660316
CAS PubMed Web of Science® Google Scholar
34Lees JS, Welsh CE, Celis-Morales CA, et al. Glomerular filtration rate by differing measures, albuminuria and prediction of cardiovascular disease, mortality and end-stage kidney disease. Nat Med. 2019; 25(11): 1753-1760. https://doi.org/10.1038/s41591-019-0627-8.
10.1038/s41591-019-0627-8
CAS PubMed Web of Science® Google Scholar
35Wong MG, Perkovic V, Chalmers J, et al. Long-term benefits of intensive glucose control for preventing end-stage kidney disease: ADVANCE-ON. Diabetes Care. 2016; 39(5): 694-700. https://doi.org/10.2337/dc15-2322.
10.2337/dc15-2322
CAS PubMed Web of Science® Google Scholar
36Whelton PK, Carey RM, Aronow WS, et al. ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA guideline for the prevention, detection, evaluation, and Management of High Blood Pressure in adults: a report of the American College of Cardiology/American Heart Association task force on clinical practice guidelines. J Am Coll Cardiol. 2018; 71(19): e127-e248. https://doi.org/10.1016/j.jacc.2017.11.006.
10.1016/j.jacc.2017.11.006
PubMed Web of Science® Google Scholar
37Holtkamp FA, de Zeeuw D, Thomas MC, et al. An acute fall in estimated glomerular filtration rate during treatment with losartan predicts a slower decrease in long-term renal function. Kidney Int. 2011; 80(3): 282-287. https://doi.org/10.1038/ki.2011.79.
10.1038/ki.2011.79
CAS PubMed Web of Science® Google Scholar
38Oshima M, Jun M, Ohkuma T, et al. The relationship between eGFR slope and subsequent risk of vascular outcomes and all-cause mortality in type 2 diabetes: the ADVANCE-ON study. Diabetologia. 2019; 62(11): 1988-1997. https://doi.org/10.1007/s00125-019-4948-4.
10.1007/s00125-019-4948-4
CAS PubMed Web of Science® Google Scholar
39Argyropoulos CP, Chen SS, Ng Y-H, et al. Rediscovering Beta-2 microglobulin as a biomarker across the Spectrum of kidney diseases. Front Med. 2017; 4(73). https://doi.org/10.3389/fmed.2017.00073.
Google Scholar

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Volume12, Issue12

December 2020

Pages 929-941

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Clinical Science

Alternative kidney filtration markers and the risk of major macrovascular and microvascular events, and all-cause mortality in individuals with type 2 diabetes in the ADVANCE trial

ADVANCE试验中2型糖尿病患者的替代肾脏滤过标志物与主要大血管和微血管事件的风险以及全因死亡率的关系

Abstract

Background

Methods

Results

Conclusions

摘要

背景

方法

结果

结论

Supporting Information

REFERENCES

Citing Literature

References

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Alternative kidney filtration markers and the risk of major macrovascular and microvascular events, and all-cause mortality in individuals with type 2 diabetes in the ADVANCE trial

ADVANCE试验中2型糖尿病患者的替代肾脏滤过标志物与主要大血管和微血管事件的风险以及全因死亡率的关系

Abstract

Background

Methods

Results

Conclusions

摘要

背景

方法

结果

结论

Supporting Information

REFERENCES

Citing Literature

References

Related

Information