Protection against death and renal failure by renin-angiotensin system blockers in patients with diabetes and kidney disease

Abstract

Introduction:

Angiotensin-converting enzyme inhibitors (ACEis) and angiotensin receptor blockers (ARBs) are widely used to block the renin-angiotensin system (RAS). Yet it remains uncertain whether these drugs are equally effective and safe.

Methods:

Systematic reviews and meta-analyses of ACEis/ARBs in diabetes and kidney disease published in PubMed, Chinese National Knowledge Infrastructure (CNKI) and Wanfang databases were searched for clinical outcomes including all-cause mortality, end-stage renal disease (ESRD), hyperkalemia and cough.

Results:

Eight meta-analyses included 2177–61,264 patients with follow-up of 6–108 months. RAS blockers reduced mortality (relative risk ratio (RR), 0.90, 95% confidence interval (CI), 0.86–0.95) without heterogeneity. The death protection was significant specifically with ACEis (RR, 0.85, 95% CI, 0.79–0.91), but not with ARBs. Protection against ESRD was homogenously evident by ARBs (RR, 0.79, 95% CI, 0.73–0.87), ACEis (RR, 0.79, 95% , 0.64–0.94), and both (RR, 0.79, 95% CI, 0.73–0.87). Significant side effects were hyperkalemia by ARBs (RR, 2.44, 95% CI, 1.13–5.26), and cough by ACEis (RR, 2.38, 95% CI, 1.75–3.22)

Conclusions:

In patients with diabetes and kidney disease, ACEis and ARBs are consistently protective for the development of ESRD. Use of ACEis alone additionally reduces deaths and increases the risk for cough. Use of ARBs alone increases the risk for hyperkalemia without additional benefit of death protection.

Keywords

Angiotensin-converting enzyme inhibitor angiotensin II receptor blocker renin-angiotensin system meta-analysis mortality end-stage renal disease

Introduction

Diabetes mellitus (DM) and kidney disease are strong independent risk factor for death and renal failure.^1,2 Moreover, DM and kidney disease hasten vascular complications.^3–6 The combination of diabetes and kidney disease relates to a two- to four-fold increase in the risk of cardiovascular disease and death.^7–11 The renin-angiotensin system (RAS) plays a major role in cardiovascular and renal dysfunction.^12–15 Thus, RAS blockade by angiotensin-converting enzyme inhibitors (ACEis) and angiotensin-receptor blockers (ARBs) has been shown to reduce all-cause mortality in DM and kidney disease.^16–18

Recently, a meta-analysis has shown that ACEis and ARBs may exert different effects on all-cause mortality in DM patients.¹⁹ Yet, it remains uncertain whether ACEis and ARBs have differential effects on all-cause mortality, end-stage renal disease (ESRD), and adverse reactions. The objective of this updated study is to perform a meta-analysis of meta-analyses to assess the comparative effects of ACEis and ARBs on all-cause mortality, ESRD, and adverse reactions in patients with DM and kidney disease.

Methods

Eligibility criteria

All studies that met the following criteria were included: (a) systematic reviews and meta-analyses; (b) patients with DM and kidney disease; (c) randomized controlled clinical trials of ACEis and ARBs (any dose or type); and (d) clinical outcomes including all-cause mortality, ESRD, and adverse events such as hyperkalemia (defined as plasma potassium in excess of 5.5 mmol /l), cough, and headache; (e) relative risk ratios (RRs) were calculated with corresponding 95% confidence intervals (CIs).

Exclusion criteria

Exclusion criteria included: (a) studies of patients with a diagnosis of acute kidney injury, as defined by an abrupt (within 48 h) reduction in renal function, manifested by an increase in serum creatinine level (≥0.3 mg/dl or ≥26.5 µmol/l) with or without reduced urine output (<400 ml/day ); (b) duplicated articles; and (c) meta-analysis not meeting the inclusion criteria. Eligibility assessment was performed independently by two investigators (JS, YMH), using pre-designed eligibility forms, with all questions resolved by consensus with other authors.

Search strategy and databases

This updated systematic review and meta-analysis is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. We selected relevant studies published between 1 January 1980–15 June 2015, by searching PubMed, Medline, Chinese National Knowledge Infrastructure (CNKI), Chinese Science and Technology Periodical Database, and Wanfang databases. All potentially relevant articles including reference lists of retrieved papers were investigated as full text in English. For ambiguous or missing information, we contacted the authors where possible. We used the following combined text and MeSH terms: “Angiotensin converting enzyme inhibitors,” “Angiotensin II receptor blockers,” “Renin-angiotensin system,” “Meta analysis,” “Diabetes mellitus,” and “Kidney disease”. We also did a manual search, using the reference lists of published articles. In the next step, the title and then the abstract of papers were examined. Duplicated articles were excluded, and then potentially eligible studies were retrieved for perusal in full text. The extracted data included first author name or study title, year of publication date, country of origin, number characteristics of participants, details of intervention, outcome measures, intervention durations, adverse events, and relative risk ratio (RR) with the corresponding 95% confidence interval (CI). All articles were read by two independent reviewers (XNS, XZH), who archived data from the articles according to a standardized data extraction form. Disagreements were resolved in all cases by discussion among our team members.

Quality assessment

The methodological quality of systematic reviews and meta-analyses was assessed by the Assessment of Multiple Systematic Reviews (AMSTAR) guidelines.^20,21 This form of assessment has good inter-rater reliability, validity, and responsibility, and has been widely applied for measuring the methodological quality of systematic reviews.²² Based on the recommendations of the Canadian Agency for Drugs and Technologies in Health (CADTH), those with score <4 were considered as low quality, 5–8 were considered as moderate, and 9–11 as high quality.²³ Two independent reviewers (JS, YMH) appraised the quality of the included reviews using AMSTAR scores. Each article was given an AMSTAR total score, based on the number of AMSTAR criteria that were fulfilled.^24,25 A third reviewer (HLZ) served to resolve disputes.

Outcome measures

Outcomes were risks of all-cause mortality, ESRD, hyperkalemia (defined as plasma potassium in excess of 5.5 mmol/l), cough and headache.

Synthesis of data

Dichotomous outcome data from individual trials were analyzed by using RR measure and corresponding 95% CI. Data were pooled using the random-effects model when the heterogeneity was significant and the fixed effect model when the included studies showed homogeneity. We assessed the p value of the Chi-square test to determine heterogeneity and I² to measure for inconsistency. Heterogeneity was assessed using the Chi-square test, with values greater than 50% regarded as being indicative of moderate-to-high heterogeneity and were calculated by using a random-effects meta-analysis model;²⁶ otherwise, we used the fixed-effects meta-analysis model.²⁷ The possibility of publication bias was quantified using the Begg’s and Egger’s test.^28,29 A two-tailed p>0.05 was considered to show no bias. This was followed by confirmation with performing a visual inspection of Begg funnel plots in which RRs were plotted against their standard errors (SEs).

Results

Description of the included studies

The study selection process that resulted from our meta-analysis is shown in Supplementary Material, Figure 1. A total of 295 articles were initially identified. Eventually, eight meta-analyses met our inclusion criteria, enrolling 2177–61,264 (median, 21,871) patients.^30–37 All the eight meta-analyses were published between 2005–2015 (Table 1). Two studies were conducted in China, three in Australia, one in England, one in New Zealand, and one in Thailand. Diabetes including diabetic kidney disease was found in seven studies and five studies concerned kidney disease including diabetic kidney disease. Four studies compared ARBs with placebo, and seven studies compared ACEis with control using other anti-hypertensive agents. The age of the participants ranged from 18–80 years. The duration of studies ranged from six months to nine years. Table 1 shows the characteristics of the meta-analyses included in the analysis.

Table 1.

Characteristics of the eight included meta-analyses.

Summary of the characteristics of the included meta-analyses
First author, year	Country	Disease	Age range (year)	Trials/ participants	ACEis/ARBs vs placebo	Outcome	Follow up (month)
Palmer et al., 2015³⁰	New Zealand	Type 2 diabetes mellitus, chronic kidney disease	40–64	157/43,256	ACEis, ARBs, placebo	Mortality, end-stage kidney disease, hyperkalemia, cough	>24
Cheng et al., 2014³²	China	Diabetes mellitus	30-76	35/56,444	ACEis, placebo	Mortality	12–108
Hao et al., 2014³¹	China	Type 2 diabetes mellitus	25–70	10/21,871	ACEis, ARBs, placebo	Mortality	30–108
Lv et al., 2012³⁴	Australia	Diabetic kidney disease	>18	26/61,264	ACEis, ARBs, placebo	Mortality, end-stage kidney disease, hyperkalemia, cough, headache	6–72
Vejakama et al., 2012³³	Thailand	Type 2 diabetes mellitus	44–65	28/12,728	ACEis, ARBs, placebo	End-stage kidney disease	6–101
Sharma et al., 2011³⁵	England	Chronic kidney disease	18–70	4/2177	ACEis, placebo	Mortality	36–79
Strippoli et al., 2006³⁶	Australia	Diabetic kidney disease	18–80	49/12,067	ACEis, ARBs, placebo	Mortality, end-stage kidney disease, hyperkalemia, cough, headache	12-65
Strippoli et al., 2005³⁷	Australia	Diabetic kidney disease	20–70	16/7603	ACEis, placebo	Mortality, hyperkalemia, cough, headache	6–72

ACEis: Angiotensin-converting enzyme inhibitors; ARBs: angiotensin receptor blockers.

Risk of bias within studies

As shown in Supplementary Material, Table 1, the range in the total AMSTAR score for the eight meta-analyses was 7–10 (theoretical range 0–11) and the mean (standard deviation (SD)) was 8.5 (0.77). Study quality in general was good: five of eight of studies had an AMSTAR score of ≥9, and the other three had an AMSTAR score of 7–8. Based on the recommendations of the CADTH, five studies were considered high quality, and three considered moderate quality.

Publication bias was quantified using the Begg’s and Egger’s test, p>0.05 was considered to be no bias. The p-values were 0.13 and 0.12 for all-cause mortality, 1 and 0.65 for ESRD, 1 and 0.79 for hyperkalemia, 0.73 and 0.45 for cough, and 0.30 and 0.28 for headache, indicating no evidence for publication bias.

Primary outcomes

All-cause mortality

Seven studies reported all-cause mortality (Supplementary Material, Table 2). Treatment with ACEis/ARBs significantly reduced all-cause mortality (RR: 0.90, 95% CI: 0.86–0.95), with homogeneity and consistency of the study results (I²=0.0%, p=0.618). Therefore, the fixed-effects model was used for statistical analysis (Supplementary Material, Figure 2(a)), the symmetric funnel plot suggested no significant publication bias (Supplementary Material, Figure 2(b)). Specifically, the level of death protection was significant by ACEis (RR: 0.85, 95% CI: 0.79–0.91, p<0.0001), but not ARBs (RR: 0.98, 95% CI: 0.89–1.07, p=0.579) (Figure 1(a)). No significant publication bias was found in the symmetric funnel plot (Figure 1(b)).

Figure 1.

The differential effect of angiotensin-converting enzyme inhibitors (ACEis) and angiotensin receptor blockers (ARBs) on the risk all-cause mortality in patients with diabetes and kidney disease. (a) Forest plot; (b) Funnel plot. CI: confidence interval; RR: relative risk ratio; SE: standard error.

End-stage renal disease

ESRD was the outcome in four studies (Supplementary Material, Table 3). Use of ACEis/ARBs reduced the risk of ESRD (RR: 0.79, 95% CI: 0.73–0.87, p<0.0001) (Supplementary Material, Figure 3(a)) in fixed-effects model analysis (I²=0.0%, p=0.814). The symmetry of the funnel plot revealed little publication bias (Supplementary Material, Figure 3(b)). As shown in Figure 2(a), the protection against ESRD was similar for ACEis (RR: 0.78, 95% CI, 0.64–0.94) and ARBs (RR, 0.79, 95% CI, 0.73–0.87). The funnel plot demonstrated publication bias unlikely (Figure 2(b)).

Figure 2.

The differential effect of angiotensin-converting enzyme inhibitors (ACEis) and angiotensin receptor blockers (ARBs) on the risk of end-stage renal disease in patients with diabetes and kidney disease: (a) forest plot; (b) funnel plot. CI: confidence interval; RR: relative risk ratio; SE: standard error.

Hyperkalemia

The pooled data of four meta-analyses (Supplementary Material, Table 4) showed an increased risk of hyperkalemia with the use of ACEis/ARBs (RR, 1.76, 95% CI, 1.04–2.97) (Supplementary Material, Figure 4(a)). The symmetric funnel plot suggested no publication bias (Supplementary Material, Figure 4(b)). In fixed-effects model analysis shown in Figure 3(a), the increased risk of hyperkalemia was attributed to ARBs (RR, 2.44, 95% CI, 1.13–5.26), but not ACEis (RR, 1.33, 95% CI, 0.65–2.71). Consistently, little publication bias was evident by the symmetric funnel plot (Figure 3(b)).

Figure 3.

The differential effect of angiotensin-converting enzyme inhibitors (ACEis) and angiotensin receptor blockers (ARBs) on the risk of hyperkalemia in patients with diabetes and kidney disease: (a) forest plot; (b) funnel plot. CI: confidence interval; RR: relative risk ratio; SE: standard error.

Cough

The synthesized data of four studies (Supplementary Material, Table 5) suggested an increased risk of cough by the treatment with ACEis/ARBs (RR, 2.27, 95% CI, 1.64-3.15) (Supplementary Material, Figure 5(a)). Concerning the significant heterogeneity between the four studies (I²=68.5%, p=0.023), we used the random-effects model for statistical analysis. Publication bias was not significant in the funnel plot (Supplementary Material, Figure 5(b)). In the random-effects model analysis shown in Figure 4(a), the increased risk of cough was attributed to the use of ACEis (RR, 2.38, 95% CI, 1.75–3.22) rather than ARBs (RR, 1.47, 95% CI, 0.72–3.03). The symmetric funnel plot suggested no significant publication bias (Figure 4(b)).

Figure 4.

The differential effect of angiotensin-converting enzyme inhibitors (ACEis) and angiotensin receptor blockers (ARBs) on the risk of cough in patients with diabetes and kidney disease: (a) forest plot; (b) funnel plot. CI: confidence interval; RR: relative risk ratio; SE: standard error.

Headache

The pooled data of three studies (Supplementary Material, Table 6) revealed that the risk of headache was not increased by the treatment with ACEis/ARBs (RR, 0.81, 95% CI, 0.61–1.08) (Figure 5(a)) in the fixed-effects model analysis (I²=0.0%). Consistently, the symmetric funnel plot suggested no significant publication bias (Figure 5(b)).

Figure 5.

The mixed effect of angiotensin-converting enzyme inhibitors (ACEis) and angiotensin receptor blockers (ARBs) on the risk of headache in patients with diabetes and kidney disease: (a) forest plot; (b) funnel plot. CI: confidence interval; RR: relative risk ratio; SE: standard error.

Discussion

In this updated meta-analysis of meta-analyses with moderate to high methodological quality, ACEis and ARBs are consistently protective for the development of ESRD in patients with diabetes and kidney disease, Use of ACEis alone additionally reduces the risk of death and increases the risk for cough. In contrast, use of ARBs alone increases the risk for hyperkalemia without additional benefit of death protection.

Patients with diabetes^38,39 or kidney disease^40–42 are at increased risk of death and renal failure. Prevalent in patients with diabetes or kidney disease is comorbidity with hypertension. ACEis and ARBs have been recommended for clinical treatment.^43,44 Previous studies have demonstrated that ACEis and ARBs protect against the progression to renal failure in patients with kidney disease.^44–46 In this updated meta-analysis, both ACEis and ARBs are protective for the development of ESRD in people with diabetes and kidney disease. Furthermore, ACEis are generally superior to ARBs in minimizing the risks of all-cause mortality and hyperkalemia. A major concern for the use of ACEis relate to the occurrence of cough. A possible rationale for the additional benefit of ACEis in contrast to ARBs on death protection may relate to angiotensin-(1-7).^47,48 Moreover, ACEis also increase bradykinin levels to stimulate glucose metabolism⁴⁹ and trigger cough. In cases where the cough is intolerable, ARBs fit patients at risk of renal failure.

There are several potential limitations of this study. First, our results are subject to limitations inherent to any meta-analysis based on pooling data from different meta-analyses. Meta-analyses of ACEis and ARBs were not equivalent. Second, there is the possibility of overlap trials between the included meta-analyses. Third, substantial differences exist in the doses of the used drugs, duration of intervention, period of follow-up, and study population. It is hard to ensure quality control. Fourth, patients included in the reported meta-analyses have uneven baseline data, other concomitant conditions, and background therapies. These potential factors might have impacts on the interpretation of our findings.

Footnotes

Acknowledgements

The authors’ contributions were as follows: JS, YMH, and HLZ designed the study; JS collected and analyzed the data and wrote the manuscript; JS, MW, and XZH contributed to the collection and assembly of the data; WM, XZH, WL, and XXZ conducted the statistical analyses; JS, YNH, XZH, and HLZ interpreted the data; HLZ revised the manuscript and approved the submission. All authors agreed on the final version of the manuscript.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by grants from the Guilin Medical University (KY2011002), Program for Innovative Research Team of Guilin Medical University (PIRTGMU), National Natural Science Foundation of China (81270934, 81471054) and Innovation Project of Guangxi Graduate Education (YCSZ2015213).

References

Lloyd-Jones

Adams

Brown

. Executive summary: Heart disease and stroke statistics–2010 update: A report from the American Heart Association. Circulation 2010; 121: 948–954.

Ritz

Orth

SR.

Nephropathy in patients with type 2 diabetes mellitus. N Engl J Med 1999; 341: 1127–1133.

van de Woestijne

van der Graaf

Westerink

. Effect of statin therapy on incident type 2 diabetes mellitus in patients with clinically manifest vascular disease. Am J Cardiol 2015; 115: 441–446.

Morita

Morimoto

Nakano

. Erratum for renal and vascular protective effects of ezetimibe in chronic kidney disease. Int Med 2015; 54: 1683.

Lewis

. Early kidney disease, an important factor in cognitive decline or merely a harbinger of early vascular brain injury? Am J Nephrol 2015; 41: 303–304.

Fadini

Menegazzo

Bonora

. Effects of age, diabetes, and vascular disease on growth differentiation factor 11: First-in-human study. Diabetes Care 2015; 38: e118-119.

Peralta

Shlipak

Fan

. Risks for end-stage renal disease, cardiovascular events, and death in Hispanic versus non-Hispanic white adults with chronic kidney disease. J Am Soc Nephrol 2006; 17: 2892–2899.

Sarnak

Levey

Schoolwerth

. Kidney disease as a risk factor for development of cardiovascular disease: A statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Circulation 2003; 108: 2154–2169.

Ray

Seshasai

Wijesuriya

. Effect of intensive control of glucose on cardiovascular outcomes and death in patients with diabetes mellitus: A meta-analysis of randomised controlled trials. Lancet 2009; 373: 1765–1772.

10.

Nakamura

Okamura

Hayakawa

. Chronic kidney disease is a risk factor for cardiovascular death in a community-based population in Japan: NIPPON DATA90. Circ J 2006; 70: 954–959.

11.

Foley

Murray

. Chronic kidney disease and the risk for cardiovascular disease, renal replacement, and death in the United States Medicare population, 1998 to 1999. J Am Soc Nephrol 2005; 16: 489–495.

12.

Cao

Cooper

ME.

Efficacy of renin-angiotensin system (RAS) blockers on cardiovascular and renal outcomes in patients with type 2 diabetes. Acta Diabetol 2012; 49: 243–254.

13.

Mohamed

Abdel-Aziz

Abd

Motteleb

. Effect of RAS inhibition on TGF-beta, renal function and structure in experimentally induced diabetic hypertensive nephropathy rats. Biomed Pharmacother 2013; 67: 209–214.

14.

Ando

Nitta

Rakugi

. Comparison of the antialbuminuric effects of benidipine and hydrochlorothiazide in Renin-Angiotensin System (RAS) inhibitor-treated hypertensive patients with albuminuria: The COSMO-CKD (COmbination Strategy on Renal Function of Benidipine or Diuretics TreatMent with RAS inhibitOrs in a Chronic Kidney Disease Hypertensive Population) study. Int J Med Sci 2014; 11: 897–904.

15.

Lin

Alexander

Colan

. Clinical, pathological, and molecular analyses of cardiovascular abnormalities in Costello syndrome: A Ras/MAPK pathway syndrome. Am J Med Genet A 2011; 155A: 486–507.

16.

Strippoli

Craig

Deeks

. Effects of angiotensin converting enzyme inhibitors and angiotensin II receptor antagonists on mortality and renal outcomes in diabetic nephropathy: Systematic review. Br Med J 2004; 329: 828.

17.

Ruggenenti

Perna

Remuzzi

on behalf of Gruppo Italiano di Studi Epidemiologici in Nefrologia (GISEN). ACE inhibitors to prevent end-stage renal disease: When to start and why possibly never to stop: A post hoc analysis of the REIN trial results. Ramipril Efficacy in Nephropathy. J Am Soc Nephrol 2001; 12: 2832–2837.

18.

Lewis

Hunsicker

Clarke

. Renoprotective effect of the angiotensin-receptor antagonist irbesartan in patients with nephropathy due to type 2 diabetes. N Engl J Med 2001; 345: 851–860.

19.

Cheng

Zhang

. ACEI/ARB therapy for IgA nephropathy: A meta analysis of randomised controlled trials. Int J Clin Pract 2009; 63: 880–888.

20.

Burda

Norris

Holmer

. Quality varies across clinical practice guidelines for mammography screening in women aged 40–49 years as assessed by AGREE and AMSTAR instruments. J Clin Epidemiol 2011; 64: 968–976.

21.

Shea

Grimshaw

Wells

. Development of AMSTAR: A measurement tool to assess the methodological quality of systematic reviews. BMC Med Res Methodol 2007; 7: 10.

22.

Shea

Hamel

Wells

. AMSTAR is a reliable and valid measurement tool to assess the methodological quality of systematic reviews. J Clin Epidemiol 2009; 62: 1013–1020.

23.

Canadian Agency for Drugs and Technologies in Health. Interventions directed to consumers, https://www.cadth.ca/interventions-directed-consumers (2011, accessed 12 October 2015).

24.

Nuckols

Anderson

Popescu

. Opioid prescribing: A systematic review and critical appraisal of guidelines for chronic pain. Ann Intern Med 2014; 160: 38–47.

25.

Shea

Bouter

Peterson

. External validation of a measurement tool to assess systematic reviews (AMSTAR). PloS One 2007; 2: e1350.

26.

Higgins

Green

Cochrane handbook for systematic reviews of interventions. Oxford: Wiley, 2008.

27.

Whitehead

A general parametric approach to the meta-analysis of randomized clinical trials. Stat Med 1991; 10: 1665–1677.

28.

Egger

Davey Smith

Schneider

. Bias in meta-analysis detected by a simple, graphical test. Br Med J 1997; 315: 629–634.

29.

Begg

Mazumdar

Operating characteristics of a rank correlation test for publication bias. Biometrics 1994; 50: 1088–1101.

30.

Palmer

Mavridis

Navarese

. Comparative efficacy and safety of blood pressure-lowering agents in adults with diabetes and kidney disease: A network meta-analysis. Lancet 2015; 385: 2047–2056.

31.

Hao

Wang

Guo

. Effects of ACEI/ARB in hypertensive patients with type 2 diabetes mellitus: A meta-analysis of randomized controlled studies. BMC Cardiovasc Disord 2014; 14: 148.

32.

Cheng

Zhang

. Effect of angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers on all-cause mortality, cardiovascular deaths, and cardiovascular events in patients with diabetes mellitus: A meta-analysis. JAMA Intern Med 2014; 174: 773–785.

33.

Vejakama

Thakkinstian

Lertrattananon

. Reno-protective effects of renin-angiotensin system blockade in type 2 diabetic patients: A systematic review and network meta-analysis. Diabetologia 2012; 55: 566–578.

34.

Perkovic

Foote

. Antihypertensive agents for preventing diabetic kidney disease. Cochrane Database Syst Rev 2012; 12: CD004136.

35.

Sharma

Blackburn

Parke

. Angiotensin-converting enzyme inhibitors and angiotensin receptor blockers for adults with early (stage 1 to 3) non-diabetic chronic kidney disease. Cochrane Database Syst Rev 2011: CD007751.

36.

Strippoli

Bonifati

Craig

. Angiotensin converting enzyme inhibitors and angiotensin II receptor antagonists for preventing the progression of diabetic kidney disease. Cochrane Database Syst Rev 2006: CD006257.

37.

Strippoli

Craig

JC.

Antihypertensive agents for preventing diabetic kidney disease. Cochrane Database Syst Rev 2005: CD004136.

38.

Hirst

Taylor

Stevens

. The impact of renin-angiotensin-aldosterone system inhibitors on Type 1 and Type 2 diabetic patients with and without early diabetic nephropathy. Kidney Int 2012; 81: 674–683.

39.

Gaede

Lund-Andersen

Parving

. Effect of a multifactorial intervention on mortality in type 2 diabetes. N Engl J Med 2008; 358: 580–591.

40.

Tonelli

Wiebe

Culleton

. Chronic kidney disease and mortality risk: A systematic review. J Am Soc Nephrol 2006; 17: 2034–2047.

41.

Keith

Nichols

Gullion

. Longitudinal follow-up and outcomes among a population with chronic kidney disease in a large managed care organization. Arch Intern Med 2004; 164: 659–663.

42.

Kinchen

Sadler

Fink

. The timing of specialist evaluation in chronic kidney disease and mortality. Ann Intern Med 2002; 137: 479–486.

43.

American Diabetes Association. Standards of medical care in diabetes–2015: Summary of revisions. Diabetes Care 2015; 38: S4-S4.

44.

Brenner

Cooper

de Zeeuw

. Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy. N Engl J Med 2001; 345: 861–869.

45.

Rodby

Rohde

Clarke

. The Irbesartan type II diabetic nephropathy trial: Study design and baseline patient characteristics. For the Collaborative Study Group. Nephrol Dial Transplant 2000; 15: 487–497.

46.

Ruggenenti

Perna

Gherardi

. Renal function and requirement for dialysis in chronic nephropathy patients on long-term ramipril: REIN follow-up trial. Gruppo Italiano di Studi Epidemiologici in Nefrologia (GISEN). Ramipril Efficacy in Nephropathy. Lancet 1998; 352: 1252–1256.

47.

Ruster

Wolf

Renin-angiotensin-aldosterone system and progression of renal disease. J Am Soc Nephrol 2006; 17: 2985–2991.

48.

Kon

Fogo

Ichikawa

Bradykinin causes selective efferent arteriolar dilation during angiotensin I converting enzyme inhibition. Kidney Int 1993; 44: 545–550.

49.

Shiuchi

Cui

. ACE inhibitor improves insulin resistance in diabetic mouse via bradykinin and NO. Hypertension 2002; 40: 329–334.