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Disponible online el 7 de Septiembre de 2021
The impact of angiotensin converting enzyme insertion/deletion gene polymorphism on diabetic kidney disease: A debatable issue
El impacto del polimorfismo del gen de inserción/deleción de la enzima convertidora de la angiotensina en la enfermedad renal diabética: una cuestión discutible
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Wen-li Zenga, Shi-kun Yangb, Na Songb, Fen-fen Chua,
Autor para correspondencia
chufenfen0556@163.com

Corresponding author.
a Department of Nephrology, The First Affiliated Hospital of the University of South China, Hengyang 421001, Hunan Province, China
b Department of Nephrology, The Third Xiangya Hospital of Central South University, Changsha 410013, Hunan Province, China
Recibido 06 marzo 2021. Aceptado 21 julio 2021
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Tablas (3)
Table 1. Characteristics of studies included in the meta-analysis.
Table 2. Characteristics of the studies evaluating the effects of ACE I/D gene polymorphisms on DKD risk.
Table 3. Meta analysis of the association of ACE I/D gene polymorphisms on DKD risk.
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Abstract
Objective

The objective of this study was to evaluate the influence of ACE I/D gene polymorphisms on diabetic kidney disease (DKD) risk.

Methods

All eligible investigations were identified, the number of various genotype in the case and control group were reviewed. The pooled analysis was performed using Stata software.

Results

In overall subjects, 24,321 participants with 12,961 cases and 11,360 controls were included. the pooled analysis showed a significant link between D allele, DD or II genotype and DKD risk (D versus I: OR=1.316, 95% CI: 1.213–1.427, P=0.000; DD versus ID+II: OR=1.414, 95% CI: 1.253–1.595, P=0.000; II versus DD+ID: OR=0.750, 95% CI: 0.647–0.869, P=0.000). The subgroup pooled analysis showed that ACE I/D gene polymorphism was correlated with DKD both in Asian and in Chinese population. In addition, ACE I/D gene polymorphism was correlated with type 2 DKD (D versus I: OR=1.361, 95% CI: 1.243–1.490, P=0.000; DD versus ID+II: OR=1.503, 95% CI: 1.310–1.726, P=0.000; II versus DD+ID: OR=0.738, 95% CI: 0.626 –0.870, P=0.000). However, there was no obvious correlation in Caucasian subjects and type 1 diabetic patients.

Conclusion

ACE I/D polymorphisms were correlated with DKD in Asian and type 2 diabetic populations. ACE D allele/DD genotype might be a risk factor, while ACE II genotype might be a protective factor for DKD.

Keywords:
ACE
Gene polymorphism
Diabetic kidney disease
Pooled-analysis
Abbreviations:
SNPs
HWE
OR
CIs
Resumen
Objetivo

El objetivo de este estudio fue evaluar la influencia de los polimorfismos del gen I/D de la ECA en el riesgo de enfermedad renal diabética (ERD).

Métodos

Se identificaron todas las investigaciones elegibles, se revisó el número de varios genotipos en el grupo de casos y controles. El análisis combinado se realizó con el software Stata.

Resultados

En el conjunto de los sujetos, se incluyeron 24.321 participantes con 12.961 casos y 11.360 controles. El análisis combinado mostró una relación significativa entre el alelo D, el genotipo DD o II y el riesgo de DKD (D frente a I: OR=1,316, IC del 95%: 1,213–1,427, P=0,000; DD frente a ID+II: OR=1,414, IC del 95%: 1,253-1,595, P=0,000; II frente a DD+ID: OR=0,750, 95% CI: 0,647-0,869, P=0,000). El análisis de subgrupos mostró que el polimorfismo del gen I/D de la ECA se correlacionaba con la DMD tanto en la población asiática como en la china. Además, el polimorfismo del gen I/D de la ECA se correlacionó con la DKD de tipo 2 (D frente a I: OR=1,361, IC del 95%: 1,243-1,490, P=0,000; DD frente a ID+II: OR=1,503, IC del 95%: 1,310-1,726, P=0,000; II frente a DD+ID: OR=0,738, 95% CI: 0,626 -0,870, P=0,000). Sin embargo, no hubo una correlación evidente en los sujetos caucásicos y en los pacientes diabéticos de tipo 1.

Conclusión

Los polimorfismos I/D de la ECA se correlacionaron con la DKD en poblaciones asiáticas y diabéticas de tipo 2. El alelo D de la ECA/genotipo DD podría ser un factor de riesgo, mientras que el genotipo II de la ECA podría ser un factor de protección para la DKD.

Palabras clave:
AS
Polimorfismo del gen
Enfermedad diabética del riñón
Análisis combinado
Texto completo
Introduction

Diabetic kidney disease (DKD) is a severe and common complications in diabetic patients, it brings serious economic burden on society both in Western and Eastern countries.1 Recent studies indicated that chronic kidney disease (CKD) induced by diabetes was more common than primary glomerulonephritis in China.2 It has been demonstrated that albuminuria, elevated blood pressure, metabolic abnormalities, excessive oxidative stress and mitochondrial dysfunction were vital pathogenic factors in DKD.3,4 Unfortunately, the detailed pathogenesis of DKD is still not fully understood, and the mainstay of current treatment for DKD including controlling blood glucose and blood pressure are not fully effective. Hence a better understanding of the DKD pathogenesis is urgently needed.

Recent studies showed that genetic factors damage was involved in the onset of DKD.5 Additionally, the susceptibility of DKD was associated with some single genes polymorphism (e.g. methylenetetrahydrofolate reductase and angiotensin converting enzyme).6 Angiotensin converting enzyme (ACE) gene contained 21kb base, it was located on 17q23 including 26 exons and 25 introns. Single nucleotide polymorphisms (SNPs) frequently occurs in the ACE gene, it has been identified 6 polymorphism markers of ACE, and Alu insertion/deletion (I/D) fragment in the 16th intron is the most investigated, ACE gene polymorphism could be divided into DD, ID, II genotype based on this I/D polymorphic marker locus.7 Some previous studies has found that ACE I/D polymorphism could influence the occurrence of diabetes-related renal damage.8 In addition, some pooled analysis as regards the impacting of ACE I/D gene polymorphism on DKD susceptibility has been completed.9,10 However, the pooled results were controversial and inconsistent. In this study, we further assess the potential impact of ACE I/D gene polymorphism on DKD through analyzing much more trials.

MethodsSearch strategy

The eligible trials were carefully searched form various databases (e.g. PubMed, Cochrane databases, Embase and China National Knowledge Infrastructure Database). Various search terms were used as follows: angiotensin-converting enzyme, ACE, ACE insertion/deletion, ACE I/D, diabetic nephropathy, diabetic kidney disease, DN, DKD, diabetes mellitus, kidney, renal, gene, gene polymorphism.

Study inclusion criteria

The inclusion criteria were used as follows: (1) the study including two comparison group (DKD patients vs control patients), (2) the association between ACE I/D gene polymorphism and DKD has been reported, (3) the detailed number of ACE genotypes has been provided, (4) the ACE I/D genotype distributions of control group was conformed to Hardy–Weinberg equilibrium (HWE) testing.

Data extraction and analysis

Each study characteristics was extracted, the pooled analysis was performed using the Stata software (version 12.0). An odds ratio (OR) with a 95% confidence interval (CI) was calculated. It was considered statistically significant for the pooled OR when a P-value<0.05. The impact of ACE I/D gene polymorphism on DKD risk was analyzed using different four models: Method 1, D allele versus I allele; Method 2, DD genotype versus ID genotype+II genotype; Method 3, II genotype versus DD genotype+ID genotype; Method 4, ID genotype versus DD genotype+II genotype. The heterogeneity was assessed using Q and I2 statistics. In addition, Begg's adjusted rank correction test was performed to evaluate the publication bias, there was potential publication bias when a P value<0.05.11

ResultsStudy characteristics

After carefully searching and checking in various databases, we finally included 77 studies in this research.12–88 The principal characteristics of included trials are described in Table 1. 24,321 participants with 12,961 cases and 11,360 controls were included, 31 studies were published in Chinese and 46 in English, from a total 22 countries. In this studies, both type 1 and type 2 diabetic patients were analyzed. The average age of participants ranged from 4 to 74 years. According to the Newcastle-Ottawa Scale (NOS), the quality of included studies was generally at the medium level. As shown in Table 2, 10 studies were not included in this pooled analysis due to they failing to meet the HWE testing.24,39,56,57,64,65,69,74,82,85 In addition, we have extracted the number of various genotype in the case and control group (Table 2).

Table 1.

Characteristics of studies included in the meta-analysis.

Trials  Design  CountryEthnicity  Year  Sex (M/F)  Case  Control  Source of control  Diabetes type  Genotyping method  Control type  NOS scores 
Ahluwalia 2009  Case–control  IndiaAsian  C:58.4±5.8D:54.9±7.6  C:159/81D:94/106  240  200  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
An 2015 in Chinese  Case–control  ChinaAsian  C:56.6±15.1D:62.1±13.1  C:70/75D:37/49  86  145  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Araz 2001  Case–control  TurkeyAsian/Europe  C:57±7D:51±C:49/67D:39/84  116  123  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Arzu 2004  Case–control  TurkeyAsian/Europe  C:59.6±13.5D:57.1±14.5  C:20/5D:33/17  25  50  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Azar 2001  Case–control  LebanonAsian  C:22.8±5.2D:26±C:24/28D:5/5  52  10  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Bai 2012 in Chinese  Case–control  ChinaAsian  C:64.3±9.7D:62.2±11.2  NR  69  75  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Barnas 1997  Case–control  AustriaEurope  C:47±11D:47±12  C:35/15D:22/18  50  40  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Bu 2008 in Chinese  Case–control  ChinaAsian  C:57.9±10.0D:56.8+8.2  C:33/32D:46/46  65  92  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Chen 2010 in Chinese  Case–control  ChinaAsian  C:60.1±12.2D:60.0±11.7  C:49/71D:30/44  120  74  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Cheng 2005 in Chinese  Case–control  ChinaAsian  C:53.1±17.7D:52.0±15.2  C:17/20D:22/50  37  72  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Chowdhury 1996  Case–control  BritainEurope  C:39.3±7.6D:37.9±6.3  C:132/110D:79/87  242  166  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
De Cosmo 1999  Case–control  ItalyEurope  C:43±11D:43±13  C:107/68D:70/66  175  136  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Ding 2012 in Chinese  Case–control  ChinaAsian  C:50.1±16.2D:48.0±14.1  C:21/29D:20/36  50  56  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Doi 1996  Case–control  JapanAsian  C:62±12D:61±13  C:28/36D:50/74  64  124  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Dudley 1995  Case–control  BritainEurope  NR  NR  163  267  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Eroglu 2008  Case–control  TurkeyAsian/Europe  C:58.3±10.5D:52.3±9.5  C:19/27D:22/34  46  56  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Freire 1998  Case–control  IsraelAsian  C:10±6D:11±C:48/29D:39/50  77  89  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Fu 2002 in Chinese  Case–control  ChinaAsian  NR  NR  44  47  PB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Gallego 2008  Case–control  AustraliaAustralia  C:4.0–10.6D:5.9–11.9  C:16/25D:199/213  41  412  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Gao 2014 in Chinese  Case–control  ChinaAsian  C:57.6±11.3D:54.6±16.8  C:19/9D:21/9  28  30  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Grzeszczak 1998  Case–control  PolandEurope  C:61.8±9.4D:62.7±8.3  NR  462  254  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Gu 2010 in Chinese  Case–control  ChinaAsian  NR  NR  75  100  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Guo 2007 in Chinese  Case–control  ChinaAsian  27–83  NR  27  33  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Gutiérrez 1997  Case–control  SpainEurope  C:60.1±10.6D:64.2±9.2  C:28/32D:47/53  60  100  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Hadjadj 2003  Case–control  FranceEurope  C:65.7±8.3D:65.0±7.3  C:2285/854D:292/313  3139  605  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Hadjadj 2007  Case–control  Denmark,Finland,France.Europe  C:42.0±10.2D:44.8±11.0  C:757/544D:671/744  1301  1415  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Hibberd 1997  Case–control  BritainEurope  C:43.0±11.6D:50.9±13.6  C:34/38D:45/41  72  86  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Hsieh 2000  Case–control  TaiwanAsian  C:59.6±9.5D:59.5±10.4  C:87/92D:68/89  179  157  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Huang 1998  Case–control  FinlandEurope  56.2±7.2  NR  13  46  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Huang 2004 in Chinese  Case–control  ChinaAsian  C:59.8±7.5D:57.3±6.4  C:44/49D:46/48  93  94  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Ilić 2014  Case–control  SerbiaEurope  C:25.8±6.8D:28.1±5.8  NR  46  33  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Jayapalan 2010  Case–control  MalaysiaAsian  C:59.8±10.2D:57.0±10.2  C:79/96D:31/50  175  81  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Jeffers 1997  Case–control  USAAmerica  NR  NR  50  459  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Lee 2002  Case–control  TaiwanAsian  NR  NR  294  417  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Li 2003 in Chinese  Case–control  ChinaAsian  C:64.2±1.2D:63.5±1.0  NR  97  105  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Li 2004 in Chinese  Case–control  ChinaAsian  C:63.6±12.6D:64.2±10.3  C:116/102D:35/45  218  80  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Li 2005 in Chinese  Case–control  ChinaAsian  NR  NR  38  21  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Liao 2002 in Chinese  Case–control  ChinaAsian  NR  C:20/14D:31/21  34  52  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Liu 2015 in Chinese  Case–control  ChinaAsian  NR  NR  100  100  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Liu 2018 in Chinese  Case–control  ChinaAsian  C:45.9±9.0D:46.0±9.4  C:126/110D:98/93  236  191  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Liu 2019 in Chinese  Case–control  ChinaAsian  C:61.4±10.8D:62.3±11.2  C:216/84D:215/85  300  300  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Mansouri 2017  Case–control  MoroccoAfrica  C:63.7±9.2D:60.1±8.9  C:50/80D:15/70  130  85  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Marre 1994  Case–control  FranceEurope  C:39±14D:43±18  C:37/25D:30/32  62  62  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Marre 1997  Case–control  FranceEurope  C:43±13D:46±13  C:193/144D:84/73  337  157  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Miura 1999  Case–control  JapanAsian  C:34.8±7.3D:33.5±8.6  C:33/65D:51/47  98  103  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Möllsten 2008  Case–control  SwedenEurope  C:47.0±10.7D:43.9±11.3  C:30/43D:88/109  73  197  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Movva 2007  Case–control  IndiaAsian  C:57.2±10.5D:55.4±10.8  C:122/52D:133/42  174  175  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Nakajima 1996  Case–control  JapanAsian  C:57.0±7.9D:55.0±6.0  C:65/36D:24/17  101  41  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Oh 1996  Case–control  KoreaAsian  C:34.6±12.6D:35.7±9.8  C:13/18D:16/12  31  28  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Ohno 1996  Case–control  JapanAsian  C:60.5±7.2D:60.3±8.6  C:42/37D:30/23  79  53  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Okuno 2003  Case–control  JapanAsian  C:68.6±8.1D:67.6±10.0  C:6/6D:18/20  12  38  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Oue 1999  Case–control  JapanAsian  C:61±12D:51±10  C:15/12D:20/20  21  30  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Panagiotopoulos 1995  Case–control  AustraliaAustralia  C:61.9±1.8D:64.4±0.9  C:33/17D:49/66  50  115  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Park 2005  Case–control  KoreaAsian  C:60.3±10.1D:60.1±11.0  NR  103  88  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Pong 2001 in chines  Case–control  ChinaAsian  C:74.6±7.7D:73.9±7.5  NR  62  78  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Powrie 1994  Case–control  BritainEurope  NR  NR  19  85  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Prasad 2006  Case–control  IndiaAsian  C:57±12.8D:60.6±11.5  C:65/131D:76/149  196  225  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Ringel 1997#  Case–control  GermanyEurope  C:38.9±13.1D:35.7±11.4  C:76/58D:130/96  134  226  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Ringel 1997#  Case–control  GermanyEurope  C:61.4±10.6D:58.6±9.6  C:84/77D:69/71  161  140  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Schmidt 1995#  Case–control  GermanyEurope  C:45±15.5D:44±15.4  C:71/43D:75/58  114  133  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Schmidt 1995#  Case–control  GermanyEurope  C:65±9.3D:63±9.7  C:119/128D:81/127  247  208  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Schmidt 1997  Case–control  GermanyEurope  C:65±9D:63±C:153/158D:158/189  311  347  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Seruga 2017  Case–control  SloveniaEurope  C:64.7±9.2D:63.7±8.0  C:163/143D:196/179  276  375  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Sun 2006 in Chinese  Case–control  ChinaAsian  C:54.9±7.8D:47.4±6.6  C:26/14D:19/11  40  30  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Tarnow 1995  Case–control  DenmarkEurope  C:40.9±9.6D:42.7±10.2  C:121/77D:118/72  198  190  HB  Type 1  PCR-RFLP  Type 1 diabetic patients 
Tien 2009  Prospective observational  TaiwanAsian  C:61.0±14.4D:59.5±10.9  NR  47  202  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Viswanathan 2001  Case–control  IndiaAsian  C:56.7±8.9D:56.7±9.3  C:57/29D:15/8  86  23  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Wang 1999 in Chinese  Case–control  ChinaAsian  C:63.3±8.5D:59.1±9.1  C:16/33D;26/28  49  54  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Wang 2007 in Chinese  Case–control  ChinaAsian  46–69  74/70  80  64  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Wyawahare 2017  Case–control  IndiaAsian  C:55.4±9.4D:56.2±8.5  NR  129  50  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Xu 2001 in Chinese  Case–control  ChinaAsian  C:59.5±7.4D:57.5±8.2  C:55/56D:68/70  111  138  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Xue 2000 in Chinese  Case–control  ChinaAsian  C:60.1±10D:60.9±11.6  C:76/64D:48/33  140  81  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Yan 2008 in Chinese  Case–control  ChinaAsian  C:57.7±8.9D:60.2±8.1  C:66/59D:56/36  125  92  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Yang 2003 in Chinese  Case–control  ChinaAsian  C:59.1±10.8D:55.2±11.3  C:19/42D:31/40  61  71  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Young 1998  Case–control  ChinaAsian  C:57.4±11.5D:53.5±9.0  C:19/37D:20/34  56  54  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Zhang 2011 in Chinese  Case–control  ChinaAsian  38–71  72/96  42  126  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Zhao 2001 in Chinese  Case–control  ChinaAsian  NR  NR  61  47  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Zhong 2005 in Chinese  Case–control  ChinaAsian  C:52.7±9.6D:51.6±8.9  C:52/41D:53/49  93  102  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 
Zhong 2008 in Chinese  Case–control  ChinaAsian  NR  C:22/31D:30/24  53  54  HB  Type 2  PCR-RFLP  Type 2 diabetic patients 

C: case subjects; H: diabetic subjects; HB: hospital-based; PB: population-based; NR: not reported; PCR: polymerase chain reaction; RFLP: restriction fragment length polymorphism. * Months.

Table 2.

Characteristics of the studies evaluating the effects of ACE I/D gene polymorphisms on DKD risk.

Author (year)  Gene sites  CaseControlHWE(p) 
  ACE I/D  DD  ID  II  Total  DD  ID  II  Total   
Ahluwalia 2009    132  64  44  240  89  117  49  255  0.3445 
An 2015 in Chinese    23  37  26  86  15  73  57  145  0.2327 
Araz 2001    34  64  18  116  43  57  23  123  0.5945 
Arzu 2004    11  25  24  21  50  0.8974 
Azar 2001    23  27  52  10  0.1903 
Bai 2012 in Chinese    23  28  18  69  14  34  27  75  0.5720 
Barnas 1997    14  27  50  21  15  40  0.3901 
Bu 2008 in Chinese    26  25  14  65  21  42  29  92  0.4429 
Chen 2010 in Chinese    62  43  15  120  17  34  23  74  0.5188 
Cheng 2005 in Chinese    28  37  40  31  72  0.0635 
Chowdhury 1996    78  124  40  242  55  79  32  166  0.7033 
De Cosmo 1999    73  79  23  175  65  53  18  136  0.1803 
Ding 2012 in Chinese    12  18  20  50  15  20  21  56  0.0379 
Doi 1996    14  30  20  64  12  56  56  124  0.7105 
Dudley 1995    47  85  31  163  70  148  49  267  0.0591 
Eroglu 2008    16  17  13  46  19  24  13  56  0.3200 
Freire 1998    33  32  12  77  34  45  10  89  0.3930 
Fu 2002 in Chinese    17  22  44  16  23  47  0.0972 
Gallego 2008    15  17  41  102  204  103  409  0.9607 
Gao 2014 in Chinese    17  28  13  30  0.4684 
Grzeszczak 1998    129  230  103  462  73  118  63  254  0.2685 
Gu 2010 in Chinese    25  34  16  75  30  48  22  100  0.7352 
Guo 2007 in Chinese    14  27  13  14  33  0.3493 
Hadjadj 2003    1119  1468  552  3139  208  282  115  605  0.2662 
Hibberd 1997    21  42  72  36  43  86  0.2341 
Hsieh 2000    40  59  80  179  21  50  86  157  0.0038 
Huang 1998    13  19  20  46  0.6498 
Huang 2004 in Chinese    32  37  24  93  18  40  36  94  0.2585 
Ilić 2014    10  23  13  46  10  12  11  33  0.1181 
Jayapalan 2010    21  77  77  175  19  31  31  81  0.0504 
Lee 2002    40  137  117  294  39  170  208  417  0.6181 
Li 2003 in Chinese    19  43  35  97  10  42  53  105  0.6910 
Li 2004 in Chinese    50  93  75  218  22  35  23  80  0.2641 
Li 2005 in Chinese    38  47  16  101  21  42  38  101  0.1477 
Liao 2002 in Chinese    16  14  34  13  22  17  52  0.2832 
Liu 2015 in Chinese    17  58  25  100  16  54  30  100  0.3097 
Liu 2019 in Chinese    45  129  126  300  22  124  154  300  0.6633 
Mansouri 2017    76  42  12  130  47  32  85  0.8627 
Marre 1994    23  35  62  19  28  15  62  0.4640 
Marre 1997    119  168  50  337  48  69  40  157  0.1368 
Miura 1999    13  49  36  98  10  58  35  103  0.0459 
Möllsten 2008    16  45  12  73  48  113  36  197  0.0335 
Movva 2007    39  88  47  174  27  74  74  175  0.2415 
Nakajima 1996    14  50  37  101  19  18  41  0.7529 
Oh 1996    10  12  31  10  11  28  0.1518 
Ohno 1996    15  38  26  79  15  33  53  0.1178 
Okuno 2003    12  12  21  38  0.1521 
Oue 1999    21  15  15  30  0.0679 
Panagiotopoulos 1995    15  25  10  50  43  44  28  115  0.0175 
Park 2005    27  49  27  103  51  30  88  0.0220 
Pong 2001 in chines    14  23  25  62  33  38  78  0.9656 
Powrie 1994    19  24  37  24  85  0.2328 
Ringel 1997#    35  68  31  134  57  130  39  226  0.0177 
Ringel 1997#    44  84  33  161  35  69  36  140  0.8662 
Schmidt 1995#    52  38  24  114  55  55  12  122  0.7442 
Schmidt 1995#    101  105  41  247  83  91  34  208  0.2886 
Schmidt 1997    121  129  61  311  131  154  62  347  0.1577 
Seruga 2017    90  143  43  276  115  169  91  375  0.0659 
Sun 2006 in Chinese    15  17  40  10  14  30  0.1221 
Tarnow 1995    63  95  40  198  67  77  46  190  0.0134 
Viswanathan 2001    24  45  17  86  10  23  0.1956 
Wang 1999 in Chinese    15  20  14  49  27  18  54  0.8337 
Wang 2007 in Chinese    19  27  34  80  35  22  64  0.2082 
Wyawahare 2017    21  56  52  129  26  18  50  0.4640 
Xu 2001 in Chinese    42  48  21  111  30  72  36  138  0.5934 
Xue 2000 in Chinese    42  45  53  140  19  35  27  81  0.2520 
Yan 2008 in Chinese    40  64  21  125  12  22  58  92  0.0005 
Yang 2003 in Chinese    19  24  18  61  14  27  30  71  0.0940 
Young 1998    30  24  57  20  26  54  0.2207 
Zhang 2011 in Chinese    12  22  42  24  42  60  126  0.0021 
Zhao 2001 in Chinese    15  23  23  61  17  25  47  0.4239 
Zhong 2005 in Chinese    16  54  23  93  15  56  31  102  0.2041 
Zhong 2008 in Chinese    10  31  12  53  30  16  54  0.3174 
Correlation between ACE I/D gene polymorphism and DKD in overall diabetic patients

The forest plot concerned the impact of ACE I/D gene polymorphism on the risk of DKD in 63 trials. The pooled analysis indicated that ACE I/D gene polymorphism was correlated with the risk of DKD in the overall populations (D allele vs I allele: OR=1.316, 95% CI: 1.213–1.42, P=0.000; DD genotype vs ID+II genotype: OR=1.414, 95% CI: 1.253–1.595, P=0.000; II genotype vs DD+ID genotype: OR=0.750, 95% CI: 0.647–0.869, P=0.000) (shown in Table 3).

Table 3.

Meta analysis of the association of ACE I/D gene polymorphisms on DKD risk.

Genetic contrasts  Group and subgroups  Studies number  Q test P value  Model selected  OR (95% CI)  P value  Begg's test 
D versus IOverall  63  0.000  Random  1.316 (1.213–1.427)  0.000  0.006 
Asian  41  0.000  Random  1.513 (1.363–1.679)  0.000  – 
Caucasian  20  0.167  Random  1.058 (0.975–1.149)  0.176  – 
Chinese  27  0.002  Random  1.552 (1.368–1.760)  0.002  – 
Non-Chinese  36  0.000  Random  1.169 (1.066–1.281)  0.000  – 
Type 1 diabetic  13  0.022  Random  1.139 (0.952–1.364)  0.155  – 
Type 2 diabetic  50  0.000  Random  1.361 (1.243–1.490)  0.000  – 
DD versus ID+IIOverall  63  0.000  Random  1.414 (1.253–1.595)  0.000  0.016 
Asian  41  0.016  Random  1.819 (1.559–2.122)  0.016  – 
Caucasian  20  0.755  Random  1.023 (0.92–1.127)  0.755  – 
Chinese  27  0.112  Fixed  1.929 (1.666–2.234)  0.000  – 
Non-Chinese  36  0.008  Random  1.137 (1.045–1.237)  0.003  – 
Type 1 diabetic  13  0.153  Fixed  1.103 (0.884–1.377)  0.153  – 
Type 2 diabetic  50  0.000  Random  1.503 (1.310–1.726)  0.000  – 
II versus DD+IDOverall  63  0.000  Random  0.750 (0.647–0.869)  0.000  0.107 
Asian  41  0.000  Random  0.678 (0.547–0.840)  0.000  – 
Caucasian  20  0.021  Random  0.858 (0.719–1.025)  0.092  – 
Chinese  27  0.040  Random  0.650 (0.548–0.771)  0.000  – 
Non-Chinese  36  0.000  Random  0.845 (0.683–1.046)  0.123  – 
Type 1 diabetic  13  0.011  Random  0.803 (0.568–1.134)  0.212  – 
Type 2 diabetic  50  0.000  Random  0.738 (0.626–0.870)  0.000  – 
ID versus DD+IIOverall  63  0.005  Random  0.999 (0.914–1.091)  0.981  0.822 
Asian  41  0.002  Random  0.949 (0.829–1.085)  0.443  – 
Caucasian  20  0.516  Fixed  1.075 (0.981–1.178)  0.121  – 
Chinese  27  0.276  Fixed  0.915 (0.803–1.043)  0.186  – 
Non-Chinese  36  0.003  Random  1.055 (0.939–1.185)  0.369  – 
Type 1 diabetic  13  0.299  Fixed  1.048 (0.870–1.263)  0.622  – 
Type 2 diabetic  50  0.003  Random  0.990 (0.896–1.095)  0.845  – 
Correlation between ACE I/D gene polymorphism and DKD in Asian diabetic patients

41 included studies analyzed the correlation between ACE I/D gene polymorphism and DKD risk. A significant correlation was observed between ACE D allele/DD genotype and DKD risk in the Asian diabetic patients (D allele vs I allele: OR=1.513, 95% CI: 1.363–1.679, P=0.000; DD genotype vs ID+II genotype: OR=1.819, 95% CI: 1.559–2.122, P=0.016, Table 3). On the contrary, our pooled analysis indicated that the II genotype might be a protective factor against the DKD risk (II genotype vs DD+ID genotype: OR=0.678, 95% CI: 0.547–0.840, P=0.000, Table 3).

Correlation between ACE I/D gene polymorphism and DKD in Caucasian diabetic patients

There were 20 trials evaluating the impact of ACE I/D gene polymorphism on DKD susceptibility in Caucasian diabetic patients. The pooled-analysis indicated no significant correlation between ACE I/D gene polymorphism and DKD (D allele vs I allele: OR=1.058, 95% CI: 0.975–1.149, P=0.176; DD genotype vs ID+II genotype: OR=1.023, 95% CI: 0.920–1.127, P=0.755; II genotype vs DD+ID genotype: OR=0.858, 95% CI: 0.719–1.025, P=0.092; ID genotype vs DD+II genotype: OR=1.075, 95% CI: 0.981–1.178, P=0.121, Shown in Table 3).

Correlation between ACE I/D gene polymorphism and DKD risk in Chinese diabetic patients

27 studies analyzed the correlation between ACE I/D gene polymorphism and DKD risk in Chinese subjects. It showed a significant correlation between the ACE D allele/DD genotype and DKD in the Chinese population (D allele vs I allele: OR=1.552, 95% CI: 1.368–1.760, P=0.002; DD genotype vs ID+II genotype: OR=1.929, 95% CI: 1.666–2.234, P=0.000, Table 3). On the contrary, our pooled analysis showed that the II genotype might have or induce a protective role against DKD in Chinese diabetic patients (II genotype vs DD+ID genotype: OR=0.650, 95% CI: 0.548–0.771, P=0.000, shown in Table 3).

Correlation between ACE I/D gene polymorphism and DKD susceptibility in type 1 diabetic patients

There were 13 studies exploring the impact of ACE I/D gene polymorphism on DKD susceptibility in type 1 diabetic subjects, our pooled analysis showed that there was no association between ACE I/D gene polymorphism and DKD susceptibility in type 1 diabetic patients (D allele vs I allele: OR=1.139, 95% CI: 0.952–1.364, P=0.155; DD genotype vs ID+II genotype: OR=1.103, 95% CI: 0.884–1.377, P=0.153; II genotype vs DD+ID genotype: OR=0.803, 95% CI: 0.568–1.134, P=0.212; ID genotype vs DD+II genotype: OR=1.048, 95% CI: 0.870–1.263, P=0.622, Table 3).

Correlation between ACE I/D gene polymorphism and DKD susceptibility in type 2 diabetic patients

There were 50 studies exploring the correlation between ACE I/D gene polymorphism and DKD susceptibility in type 2 diabetic subjects, our pooled analysis indicated that the ACE D allele/DD genotype might increase the risk of DKD in type 2 diabetic subjects (D allele vs I allele: OR=1.361, 95% CI: 1.243–1.490, P=0.000; DD genotype vs ID+II genotype: OR=1.503, 95% CI: 1.310–1.726, P=0.000, Table 3). On the contrary, this pooled analysis showed that the ACE II genotype might be a protective factor for DKD in type 2 diabetic patients (II genotype vs DD+ID genotype: OR=0.738, 95% CI: 0.626–0.870, P=0.000, Table 3).

Publication bias

In this study, we used funnel plots and Begg's test to evaluate the publication bias. In the analysis for the association of ACE D allele/DD genotype with DKD susceptibility in overall diabetic patients, there was potential publication bias noted by Begg's test (D vs. I: Begg’ s test P=0.006; DD vs. ID+II: Begg's test P=0.016). In line with this, the funnel plots were asymmetrical (Table 3, Fig. 1).

Fig. 1.

The funnel plot of different model for pooled analysis. (a): D vs I; (b): DD vs ID+II; (c): II vs DD+ID; (d): ID vs DD+II.

(0,33MB).
Discussion

This pooled-analysis showed that the ACE I/D polymorphism was statistically associated with DKD susceptibility, it indicated that ACE D allele/DD genotype might be a risk factor for DKD. On the contrary, ACE II genotype might be a protective factor for DKD.

Genome-wide association studies (GWAS) research was frequently carried out to explore the relationship between various gene single nucleotide polymorphisms (SNPs) and an array of diseases. In such studies, HWE testing for each SNPs was often the first and quality control step. Those SNPs that did not pass the HWE tests were eliminated before moving on to the next step.89 On the other hand, in case-control genetic association studies, departures from HWE in controls have been associated with problems in the design, genotyping error or selection bias.90 In pooled analysis, checking HWE among controls was a good idea for included trials. Trikalinos et al. has demonstrated that exclusion of trials with departures from HWE may sometimes change the estimate pooled analysis result, they advocated that studies with departures from HWE should be excluded in pooled analysis.91 For this reason, in this pooled analysis, we carefully checked all selected trials and excluded these studies failing to meet the HWE test. In addition, the quality of included studies were generally at the medium level according to the Newcastle-Ottawa Scale (NOS), it indicated that the included studies met the criteria accepted for valid SNP-association studies.

Some previous pooled analysis concerning about the impact of ACE I/D gene polymorphism on DKD risk has been completed. In 2012, a meta analysis included 14,108 DKD cases and 12,472 controls from 63 published studies has been performed, it indicated that ACE I/D polymorphism was associated with DKD development in the Asian type 2 diabetes subjects.92 However, the genotype distributions of the control groups do not conform to HWE in some trials. And because of that, in order to gain a more credible pooled analysis result, we re-examined the related studies and included some other more high quality trials. In line with Wang et al., our study also found that the ACE I/D genotype was correlated with DKD risk in type 2 diabetes patients. Due to the fact that we included a plethora of studies in our analysis compared to the aforementioned analyses, we feel that our pooled results are more convincing.

ACE is a pivotal factor of the renin–angiotensin–aldosterone system (RAAS), it contains 26 exons and 25 introns located on 17q23. In 1990, ACE gene polymorphism was firstly described based on the insertion or deletion (I/D) of a 287bp Alu in the 16th intron.93 Whereafter, a series of ACE polymorphic genetic markers have been found (e.g. A240T, T93C, T594lC). Among these polymorphic marker, I/D polymorphism (rs4340) was the most investigated. On account of this I/D polymorphic marker, we could divide ACE gene polymorphism into DD homozygote, II homozygote and ID heterozygote. It has been demonstrated that ACE I/D polymorphism could affect ACE activity level both in plasma and various tissues.94 Additionally, a great number of previous studies have been carried out and verified the impact of ACE I/D gene polymorphism on various diabetes-related diseases.

DKD is a severe complication both in type 1 and type 2 diabetic patients, it damages about 40% of all diabetic subjects and is a crucial cause of chronic renal failure both in the Eastern and Western world. The pathogenesis of DKD is very complicated, it has been verified that various signaling pathways and molecular factors are activated during DKD, such molecular events include activation of systemic and local RAAS, generation of pro-inflammatory cytokines and excessive reactive oxygen species.3 In addition, recent GWAS studies demonstrated that DKD patients always suffer from genetic damage, and genetic factors are involved in the development of DKD, more critically, some specific gene SNPs might be associated with DKD susceptibility, thus it could provide remarkable clinical significance for preventing and early diagnosing of DKD through detailed illuminating the genetic mechanisms involved in DKD.

RAAS activation play a vital role in the occurrence and development of DKD, the RAAS is a pivotal regulator of renal arterial blood pressure by angiotensin II. However, conversion of low activity angiotensin I to high activity angiotensin II was relying on ACE. It has been showed that the ACE level is strongly correlated with ACE I/D polymorphism. Although the ACE I/D gene polymorphism is taken place in the non-coding gene region, the base insertion or deletion itself might alter the splicing process of the ACE precursor mRNA, then influence the stability of ACE mRNA, and ultimately affect the expression or stabilization of ACE. In situ hybridization for ACE mRNA on renal biopsy studies have found that the expression of ACE mRNA was increased in those subjects with the ACE DD genotype.95 Additionally, the serum ACE levels was also higher in the those individuals with D genotype than those with ID genotype or II genotype.93 And because of that, it was reasonable to consider that ACE I/D genetic variation was associated with the development of DKD. ACE D allele carriers had more higher ACE levels both in serum and kidney tissue, which lead to a more efficient activation of angiotensin II, and consequently resulted in the deterioration of DKD. In line with these, our pooled study further demonstrated that DKD risk was higher in those subjects with D allele than I allele carriers. We observed that the presence of II genotype offered a significant protective effect for DKD, whereas the presence of DD genotype conferred remarkable risk for DKD. The detailed mechanistic aspects that underlie the relationship between ACE I/D gene polymorphism and DKD was not completely clear. As mentioned earlier, the impact of ACE I/D gene polymorphism on DKD could be partially attributed to the effect of the ACE I/D polymorphic variant on the expression of the ACE gene. On the other hand, a recent study performed by Mahwish et al. found that ACE I/D genotypes was associated with dyslipidemia in diabetic patients, the DD genotype subgroup subjects were characterized by a significant higher levels of plasma triglycerides and total cholesteroln.96 In addition, the association of ACE I/D genotypes with atherosclerotic risk factors such as hypertension, dyslipidemia, and obesity in type 2 diabetic patients has been reported.97 Taken together, ACE DD genotype might result in the formation of diabetic renal lesions through elevating angiotensin II levels and a key contributor to dyslipidemia in a hyperglycemic environment further culminating in renal complications.

In this study, we found that the impact of ACE I/D gene polymorphism on DKD susceptibility was inconsonant in different types of diabetes and races. The pooled analysis showed that ACE I/D gene polymorphism was correlated with DKD susceptibility in Asian individuals, but there was no obvious correlation in Caucasian subjects. For another, we found that there was no correlation between them among 13 studies concerning type 1 diabetic patients, while ACE I/D gene polymorphism was correlated with the onset of DKD risk in type 2 diabetic patients. It indicates that ACE I/D genetic factors contribute more in patients with type 2 diabetes mellitus. Likewise, this inconsistency was also found in previous pooled analysis, Ng et al. found that ACE gene polymorphism was associated with DKD among type 2 diabetic Asians, while there was a reduced risk of DKD associated with the ACE I/D gene polymorphism among Caucasians with either type 1 or type 2 diabetes.98 Similarly, a pooled analysis performed by Wang et al. further found that the Asian group with T2DM showed a significant association. However, it failed to find any significant effects for different genetic models in T1DM and Caucasian subjects.92 Conversely, another pooled analysis performed by Xu et al. included 17 case-control studies in 2016 showed that ACE I/D polymorphism was correlated with DKD in the Asian groups with type 1 diabetes.9 While Fujisawa et al. found that the association was significant both in Asian populations and in Caucasian populations.10 Some reasons may account for the different results between Asians and Caucasians. Firstly, different lifestyle, environmental exposure, and different socioeconomic status may modify individual DKD susceptibility in different ethnic groups. Secondly, different genetic backgrounds in different racial subjects may influence genetic phenotypes.9 On the other hand, there are some other explanations for the predisposition to DKD in patients with type 2 DM. As mentioned above, the D allele of the ACE gene has been connected with higher ACE activity and increased level of angiotensin II. It has been found that increased angiotensin II could worsen insulin resistance and lipid metabolism disorders.99 In addition, both muscle capillary density and endogenous hepatic glucose production also could be affected by ACE I/D gene polymorphism.100

This study has several potential limitations. Firstly, most included trials were limited number and size. Second, there were evidences of public bias in this pooled study, in addition, the included trials were from various countries and races, which might decrease the reliability of this pooled analysis. Finally, our research was focused on ACE I/D genetic alteration, but previous studies have indicated that gene polymorphism in many other genes including Interleukin-6 -174G/C and angiotensinogen T174M gene polymorphism were correlated with DKD susceptibility,101,102 thus it can be argue that further pooled analysis concerning these genes SNPs are needed.

Conclusion

ACE I/D gene polymorphism is correlated with DKD risk in Asian, Chinese populations and type 2 diabetic individuals. ACE D allele and DD genotype is a risk factor for DKD. Conversely, ACE II genotype seems to be a protective factor of DKD. However, no correlation between ACE I/D gene polymorphism and the susceptibility of DKD was found in Caucasian or type 1 diabetic patients.

Authors’ contribution

Shi-kun Yang, Wen-li Zeng, Fen-fen Chu analyzed the data for the manuscript and wrote the manuscript. Shi-kun Yang, Na Song, Wen-li Zeng performed the literature search. Fen-fen Chu, Shi-kun Yang edited the manuscript.

Data availability and ethics committee

The data used to support the findings of this study are available from the first author and corresponding author upon request. This is a meta analysis using previous relevant published studies. There is no Human participants and/or Animals informed consent. None of the authors is in any condition that may represent a potential conflict of interest. The experiments were carried out according to the Ethics Review Committee of The Third Xiangya Hospital, Central South University.

Funding

This study was supported by the Hunan Provincial Health Commission Project (202103050178). Hunan Provincial Clinical medical technology innovation guide project (2020SK53601). The science and technology planning project of Hengyang City (2019jh011012).

Conflict of interest

The authors declare that they have no conflict of interest.

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