Review ArticleThe Association between Helicobacter pylori Infection andGlycated Hemoglobin A in Diabetes: A Meta-Analysis

Jinhu Chen ,1 Yuling Xing ,2 Liying Zhao,2 and Huijuan Ma 1,3,4

1Department of Endocrinology, Hebei General Hospital, Shijiazhuang 050017, China2Graduate School of Hebei Medical University, Shijiazhuang 050017, China3Hebei Key Laboratory of Metabolic Diseases, Hebei General Hospital Shijiazhuang, Hebei 050051, China4Department of Internal Medicine, Hebei Medical University, Shijiazhuang, Hebei 050017, China

Correspondence should be addressed to Huijuan Ma; huijuanma19@163.com

Received 15 May 2019; Accepted 10 August 2019; Published 9 September 2019

Academic Editor: Almudena Gómez-Hernández

Copyright © 2019 Jinhu Chen et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background. The association between Helicobacter pylori infection and glycated hemoglobin A has been confirmed in manystudies, but these conclusions are still contradictory and controversial. Therefore, we conducted a meta-analysis to resolve theproblem of inconsistent results in diabetes. Methods. A comprehensive search was conducted on related researches published inPubMed, Embase, and China Academic Journal Full-text Database (CNKI) from the inception of each database to April 2019.Fixed or random effects model was used to pool the weighted mean difference with 95% confidence interval from individualstudies. Subgroup and sensitivity analyses were also performed. Publication bias was estimated by funnel plot, Egger’s test, andfail-safe numbers. Results. 35 studies with 4,401 participants with diabetes were included in the meta-analysis. Glycatedhemoglobin A levels were elevated in patients with Helicobacter pylori infection compared with patients without Helicobacterpylori infection (WMD= 0 50, 95% CI: 0.28-0.72, p < 0 001). In subgroup analysis by the subtype of diabetes, there was acorrelation between Helicobacter pylori infection and elevated glycated hemoglobin A in type 1 diabetes (I2 = 74%, p < 0 001,WMD= 0 46, 95% CI: 0.12-0.80), and in type 2 diabetes (I2 = 90%, p < 0 001, WMD= 0 59, 95% CI: 0.28-0.90, p < 0 001). Insubgroup analysis by the study design, there was a correlation in cross-sectional study (I2 = 89%, p < 0 001, WMD= 0 42, 95%CI: 0.16-0.69, p ≤ 0 003) and in case-control study (I2 = 83%, p < 0 001, WMD= 0 39, 95% CI: 0.14-0.64, p ≤ 0 003). By differentmethods for detecting Helicobacter pylori, there was a correlation in the biopsy group (I2 = 83%, p < 0 001, WMD= 0 6, 95%CI: 0.11-1.09, p ≤ 0 03) and in other groups of test methods (I2 = 87%, p < 0 001, WMD= 0 37, 95% CI: 0.17-0.56, p < 0 001).Sensitivity analysis showed that our results were reliable, and no evidence of substantial publication bias was detected.Conclusion. The meta-analysis might indicate a correlation between Helicobacter pylori infection and glycated hemoglobin Alevels in diabetes.

1. Introduction

In 2007, the International Federation of Clinical Chemistry(IFCC) clearly defined glycated hemoglobin A (HbA1c)as a stable adduct formed by glucose and the free aminogroup of the hemoglobin β chain-N-terminal proline [1].In 2011, World Health Organization officially recom-mended HbA1c ≥ 6 5% as a diagnostic cutoff point for dia-betes [2]. The American Diabetes Association (ADA)recommends that HbA1c should be measured in patientswith newly developed diabetes, and it plays an important

role in the monitoring of diabetes as an evaluation indexto judge the effect of blood glucose control. Helicobacterpylori infection is now considered the most importantcause of gastritis and peptic ulcer in humans. And studieshave reported on the potential links between H. pyloriinfection and a variety of extra-gastroduodenal manifesta-tions ischemic as heart disease, liver diseases, skin diseases,blood disorders, neurologic disorders, and others [3].Christie et al. found serological evidence of H. pyloriinfection which was associated with an increased rate ofincident diabetes in a Latino elderly cohort [4]. However,

HindawiJournal of Diabetes ResearchVolume 2019, Article ID 3705264, 10 pageshttps://doi.org/10.1155/2019/3705264

studies on the relationship between H. pylori infection andHbA1C in diabetic patients are inconsistent and some-times contradictory. The finding of Bazmamoun et al.showed that there was no correlation between Helicobacterpylori infection and HbA1c levels [5]. Studies by Akınet al. found that HbA1c levels in Helicobacter pylori-positive patients were significantly higher than those inHelicobacter pylori-negative patients [6]. Due to these dis-crepancies, we performed a meta-analysis investigating therelationship between H. pylori infection and glycatedhemoglobin A in patients with diabetes.

2. Methods

2.1. Literature Search Strategy. “Helicobacter pylori” and“glycated hemoglobin A” were jointly searched in PubMeddatabase, the Embase database, and China National Knowl-edge Infrastructure (CNKI) as keywords for all relevantliterature published before April 2019. Moreover, we alsoreviewed the reference not captured by our database search.

2.2. Inclusion Criteria. The inclusion criteria were as follows:(1) observational studies; (2) studies are related to the rela-tionship between H. pylori and diabetes, including casegroups and control groups, and providing the exact samplesize, the number of patients with H. pylori infection, andthe mean and standard deviation of the level of HbA1c; (3)the diagnosis of diabetes was in agreement with internationalguidelines [7]; (4) H. pylori infection is judged by at least onediagnostic method; and (5) the studies are not directly relatedbut with the abovementioned requirements.

2.3. Exclusion Criteria. The exclusion criteria were as follows:(1) case report and observational studies without controlgroups; (2) studies in which the data of the level of HbA1cwere not available for either diabetes group or control group;(3) subset of a published article by the same authors or

repeated published literature; (4) studies limited to animal;and (5) the data of literature are incomplete with little infor-mation, and the extraction of original data is not enough tocalculate the statistics of this study.

2.4. Study Selection. Two researchers independently screenedthe literature, extracted the data, and cross-checked. If theresults were inconsistent, those would be discussed togetheror judged by a third senior researcher. This study used pre-established data extraction forms to extract data from the lit-erature that will eventually be included in the meta-analysis.The excerpts included the first author, the year of publica-tion, the study area, the diagnostic criteria for H. pylori infec-tion, the sample size of the case and control groups, and themean and standard deviation of HbA1c.

2.5. Statistical Analysis. The data and the database were orga-nized and checked carefully according to the requirements ofthe meta-analysis. RevMan 5.3 was used for statistical analy-sis, and weighted mean difference (WMD) with 95% CI wasused for quantitative analysis of measurement data. I2 wasused to quantitatively test the heterogeneity among differentstudies. If I2 ≤ 50%, the heterogeneity had no statistical sig-nificance, and fixed effects model was used to analyze it. Onthe contrary, if I2 > 50% the heterogeneity had statistical sig-nificance, and random effects model was used to analyze.Moreover, subgroup analysis was carried out to explore thesources of heterogeneity according to the factors that mightproduce heterogeneity. To ensure the stability of the resultsof the meta-analysis, the sensitivity analysis (after theincluded studies removed one by one, the combined analysiswas performed again, and the significant difference betweenthe effect values before and after the combination was com-pared) was performed. The funnel plot, Egger’s test, andfail-safe number were used to quantitatively evaluate the

Obtained relevantliterature, n = 459

PubMed = 44Embase = 291CNKI = 114

Excluded literature, n = 423Inconsistent with the

subjectAnimal researchReview literature

No dataInclusion literature,

n = 38

Type 1diabetes,n = 969

Type 2diabetes,n = 2758

Untyped,n = 674

Exclude thesame data,

n = 3

Figure 1: Flow chart of study selection.

2 Journal of Diabetes Research

publication bias. p < 0 05 was considered statistically signifi-cant, suggesting that publication bias is not excluded.

3. Results

3.1. Study Selection and Characteristics. A total of 459 articleswere initially searched by terms, and 35 studies eventuallymet the predetermined inclusion and exclusion criteria

(Figure 1). The relevant literature was published from 2000to 2018 (Table 1). A total of 4,401 diabetic patients wereincluded in the meta-analysis, including 1176 patients withtype 1 diabetes, 2877 patients with type 2 diabetes, and 348patients who were not typed. The included literatureincluded 20 case-control studies involving 1970 patients withdiabetes and 15 cross-sectional studies involving 2,431people with diabetes.

Table 1

Authors Year Country TypeHP+ HP−

Mean ± SD (%) n Mean ± SD (%) n

Chobot et al. [8] 2014 Poland T1DM 7 8 ± 1 42 17 7 60 ± 1 66 132

Fernandini-Paredes et al. [9] 2008 Peru T2DM 7 6 ± 0 23 49 7 25 ± 0 49 26

Hamed et al. [10] 2008 Egypt T1DM/T2DM 8 1 ± 0 8 68 8 3 ± 0 1 6

Candelli et al. [11] 2004 Rome T1DM 8 2 ± 1 06 29 8 4 ± 1 7 29

Yingjian [12] 2018 China T2DM 8 7 ± 1 45 45 6 77 ± 1 52 49

Qing et al. [13] 2018 China T2DM 8 2 ± 0 77 51 7 10 ± 0 55 59

Ziyue et al. [14] 2017 China T2DM 8 6 ± 1 84 85 7 70 ± 0 75 51

Chunying et al. [15] 2016 China T2DM 8 4 ± 1 68 62 8 53 ± 1 96 69

Licheng et al. [16] 2016 China T2DM 7 7 ± 1 34 106 6 24 ± 0 58 64

Li [17] 2016 China T2DM 8 4 ± 2 06 112 8 01 ± 2 01 68

Zhili et al. [18] 2014 China T2DM 9 0 ± 0 89 48 8 02 ± 0 23 25

Yi et al. [19] 2011 China T2DM 9 21 ± 2 4 43 9 34 ± 2 1 22

Candelli et al. [20] 2003 Rome T1DM 8 3 ± 1 1 34 8 2 ± 1 5 87

Fayed et al. [21] 2014 Egypt T1DM 7 4 ± 1 6 40 7 9 ± 1 3 13

Vafaeimanesh et al. [22] 2016 Iran T1DM/T2DM 8 1 ± 1 67 139 8 08 ± 1 32 72

Nasif et al. [23] 2016 Saudi Arabia T2DM 6 9 ± 0 69 33 7 30 ± 0 55 17

Anandani et al. [24] 2014 Indonesia T2DM 9 52 + 1 12 5 9 08 + 1 22 25

Toporowska-Kowalska et al. [25] 2007 Poland T1DM 7 87 ± 1 51 48 7 17 ± 1 46 150

Bazmamoun et al. [6] 2016 Iran T1DM 8 ± 0 65 48 7 90 ± 0 40 32

Huang et al. [26] 2015 China T2DM 8 34 ± 2 02 195 8 47 ± 2 04 220

Demir et al. [27] 2008 Turkey T2DM 7 9 ± 1 8 87 8 7 ± 7 1 54

Arslan et al. [28] 2000 Turkey T1DM 11 08 ± 3 17 49 10 32 ± 2 63 39

Colombo et al. [29] 2002 Italy T1DM 8 16 ± 1 7 41 7 8 ± 1 6 97

Gulcelik et al. [30] 2005 Turkey T2DM 8 2 ± 1 4 59 7 9 ± 2 2 19

Zekry et al. [31] 2013 Egypt T1DM 7 75 ± 1 67 24 5 72 ± 1 2 36

Ko et al. [32] 2001 China T2DM 8 09 ± 2 11 32 8 42 ± 2 37 31

Jones et al. [33] 2002 Australia DM 8 8 ± 0 4 15 8 6 ± 0 2 48

Candelli et al. [34] 2012 Italy T1DM 8 8 ± 0 8 17 8 4 ± 0 7 52

Zhou et al. [35] 2012 China T2DM 8 45 ± 2 46 84 8 18 ± 2 13 96

El-Eshmawy et al. [36] 2011 Egypt T1DM 8 3 ± 1 58 128 6 8 ± 2 3 34

Senmaru et al. [37] 2013 Japan T2DM 7 3 ± 1 0 146 7 4 ± 1 2 187

Tanrivedi [38] 2011 Turkey T2DM 6 9 ± 1 2 53 6 1 ± 1 4 40

Peng et al. [39] 2013 China T2DM 9 53 ± 1 96 43 7 08 ± 1 53 42

Akın. et al. [6] 2014 Turkey T2DM 8 0 ± 2 1 159 6 9 ± 1 2 156

El Hadidy et al. [40] 2009 Saudi Arabia T2DM 9 2 ± 2 1 23 8 92 ± 2 1 37

3Journal of Diabetes Research

3.2. Results of Meta-Analysis. RevMan 5.3 was used to test theheterogeneity, I2 = 89%, p < 0 001, so the random effectsmodel was used to conduct a combined analysis. 35 studies(WMD= 0 50, 95% CI: 0.28-0.72, p < 0 001) showed thatthere was a significant difference in the level of HbA1cbetween patients infected with H. pylori and that of nonin-fected patients. H. pylori infection was correlated with theincreased level of HbA1c (Figure 2).

3.3. Subgroup Analysis. In order to further increase thereliability of the study, the subtypes of diabetes mellitus,the design of the studies and different detection methodsof H. pylori were analyzed, which were divided into threesubgroups: type 1 diabetes mellitus and type 2 diabetesmellitus; cross-sectional studies and case-control studies;and biopsy and other detection methods. The results ofthe analysis are shown in Figures 3–5. (1) In subgroupanalysis by the type of diabetes, it is indicated that thereis a significant correlation between H. pylori infection andincreased HbA1c in type 1 diabetic patients (I2 = 74%,p < 0 001, WMD= 0 46, 95% CI: 0.12-0.80) and in type

2 diabetes (I2 = 90%, p < 0 001, WMD= 0 59, 95% CI:0.28-0.90, p < 0 001). (2) In subgroup analysis by designof studies, 15 of them were cross-sectional studies, ofwhich 2217 were infected with H. pylori and 2184 wereuninfected. 20 studies were case-control studies, ofwhich 524 patients with H. pylori infection and 1046patients without. A total of 35 studies were analyzedby meta-analysis. It is showed that H. pylori infection wasassociated with HbA1c in cross-sectional study (I2 = 89%,p < 0 001, WMD= 0 42, 95% CI: 0.16-0.69, p ≤ 0 003) andin case-control study (I2 = 83%, p < 0 001, WMD= 0 39,95% CI: -0.14-0.64, p ≤ 0 003). (3) Gastric biopsy groupand other detection methods group: biopsy was used in 6studies and other detection methods were used in 29 stud-ies, including serological detection and breath test. Therewas statistical significance in the biopsy group (I2 = 83%,p < 0 001, WMD= 0 6, 95% CI: 0.11-1.09, p ≤ 0 03) andin other groups of test methods (I2 = 87%, p < 0 001,WMD= 0 37, 95% CI: 0.17-0.56, p < 0 001). It shows thatH. pylori infection is correlated with the level of HbA1cwhether in biopsy or other methods.

–2 –1 0 1 2

Study or subgroup

Mahmet Demir 2008Sohair B. Fayed 2014 Wesam Ahmed Nasif 2016Ko GT 2001Hamed, S.A. 2008Candelli, M. 2004Yuxin Huang 2015Yi Zhou 2011Takafumi Senmaru 2013Zhu Chunying 2016Jamshid Vafaeimanesh 2016Bazmamoun, H. 2016Marcello Candelli 2003Jones KL 2002Chobot, A 2014Zhou X 2012EI Hadidy M EI Hadidy 2009N. E. Gulcelik 2005Colombo, C 2002Gino G. Fernandini-Paredes 2008Xiaohua Li 2016Candelli, M. 2012Anandani, E. T. 2014Toporowska-Kowalska, E. 2007Duran Arslan 2000Tanrive di 2011Ziyue Song 2017Zhili Lliang 2014Akin, S. 2014Meng Qing 2018Shao Li-Cheng 2016Mervat M EI-Eshmawy, 2011Yingjian Tong 2018Zekry OA 2013Peng WP 2013

Total (95% CI)Heterogeneity: tau2 = 0.34; chi2 = 308.35, df = 34 (P < 0.00001); P = 89%Test for overall effect Z = 4.45 (P < 0.00001)

Mean–0.80 [–2.73. 1.13]–0.80 [–2.73. 1.13]–0.37 [–0.72, –0.02]–0.33 [–1.44, 0.78]–0.20 [–0.41, 0.01]–0.15 [–0.88, 0.58]–0.13 [–0.52, 0.26]–0.13 [–0.26, 1.00]–0.10 [–0.34, 0.14]–0.09 [–0.71, 0.53]0.03 [–0.38, 0.44]0.10 [–0.13, 0.33]0.10 [–0.39, 0.59]0.20 [–0.01, 0.41]0.22 [–0.51, 0.95]0.27 [–0.41, 0.95]0.28 [–0.81, 1.37]0.30 [–0.75, 1.35]0.36 [–0.25, 0.97]0.38 [0.18, 0.58]

0.39 [–0.22, 1.00]0.40 [–0.03, 0.83]0.44 [–0.65, 1.53]0.70 [0.21, 1.19]

0.76 [–0.45, 1.97]0.80 [0.26, 1.34]0.92 [0.48, 1.36]1.01 [0.74, 1.28]1.10 [0.72, 1.48]1.11 [0.86, 1.36]1.50 [1.21, 1.79]1.50 [1.68, 2.32]1.95 [1.35, 2.55]1.03 [1.26, 2.80]1.45 [1.70, 2.20]

0.50 [0.28, 0.72]

HP+ HP–

1.0%2.4%3.4%1.9%3.6%2.6%3.3%1.9%3.6%2.9%3.3%3.6%3.2%3.6%2.6%2.8%1.9%2.0%2.9%3.6%2.9%3.3%1.9%3.2%1.7%3.0%3.2%3.5%3.4%3.6%3.5%2.5%2.9%2.6%2.6%

541317316

2922022

1876972328748

1329637199726685225

15039405125

156596434493642

7.11.3

0.552.370.11.7

2.042.11.2

1.961.320.41.50.2

1.662.162.12.21.6

0.492.010.7

1.221.462.631.4

0.750.231.2

0.550.582.3

1.521.2

1.53

877.97.3

7.428.38.4

8.479.347.4

8.538.087.98.28.67.6

8.188.927.97.8

7.258.018.4

9.087.17

10.326.17.7

8.026.97.1

6.246.8

6.775.727.08

874033326829

19543

14662

139483415178423594149

112175

4849538548

15951

106128452443

2217 2184 100.0%

1.81.6

0.692.110.8

1.062.022.41

1.681.670.651.10.4

1.422.462.11.41.7

0.232.060.8

1.121.513.171.21840.892.1

0.771.341.581.451.671.96

7.97.4

6.938.098.1

8.258.348.347.3

8.448.11

88.38.8

7.828.459.28.2

8.167.638.48.8

9.527.87

11.086.9

8.629.03

88.217.748.3

7.727.759.53

MeanHP+ HP–

SD SDTotal TotalWeight

Mean differenceIV, random, 95% CI

Mean differenceIV, random, 95% CI

Figure 2: Forest plot of Helicobacter pylori infection and glycated hemoglobin level analysis.

4 Journal of Diabetes Research

4. Publication Bias

Egger’s test (p > 0 05) showed no significant publication bias.Funnel plot is basically symmetrical and has no publicationbias (Figure 6). Fail-safe numbers, indicating the publicationbias, are reported in Table 2. The fail-safe numbers were allrelatively large in the meta-analysis, suggesting that theresults were reliable.

5. Discussion

The quantitative data of this meta-analysis showed that thelevel of HbA1c in the H. pylori-infected group was signifi-cantly higher than that in the H. pylori-negative group, indi-cating that H. pylori infection and HbA1c were correlated indiabetes. Subgroup analysis revealed that (1) Helicobacterpylori infection was correlated with the level of HbA1c intype 1 diabetes mellitus and type 2 diabetes mellitus. (2)

According to the type of studies, they were divided into twosubgroups: case-control study and cross-sectional study.There was statistical significance in cross-sectional studiesand case-control studies. (3) According to the diagnostic cri-teria of H. pylori infection, the H. pylori infection was corre-lated with the level of HbA1c in the gastric biopsy group.Similarly, the differences in other test groups were also statis-tically significant.

The results of this study are not completely consistentwith the results of a meta-analysis of Dai et al. [41] in 2015.Their 11 studies showed that HbA1c of type 1 diabeticpatients with H. pylori infection was significantly higher thanthat of type 1 diabetic patients without H. pylori infection(WMD= 0 35, 95% CI: 0.05-0.64; p ≤ 0 03). However, therewas no significant difference in the levels of HbA1c betweentype 2 diabetes mellitus with and without H. pylori infection(WMD= 0 51, 95% CI: -0.63-1.65; p ≤ 0 38). According tothe results of increasing sample size, both type 1 diabetes

Study or subgroupMean

HP+ HP–Mean SD SDTotal Total

Weight

7.48.25

88.3

7.828.168.8

7.8711.08

8.37.75

7.96.938.098.349.217.3

8.448.459.28.2

7.638.4

9.526.9

8.629.03

88.217.748.729.53

1.61.060.651.1

1.421.70.8

1.513.171.581.67

1.80.692.112.022.41

1.682.462.11.4

0.232.061.121.2

1.840.892.1

0.771.341.451.96

402948341741174849

12824

475

873332

19543

1466284235949

1125

538548

15951

1064546

1520

7.98.47.98.27.67.88.4

7.1710.32

6.85.72

8.77.3

8.428.479.347.4

8.538.188.927.9

7.258.019.086.17.7

8.026.97.1

6.246.777.08

1.31.70.41.5

1.661.60.7

1.462.632.31.2

7.10.552.372.042.12

1.962.132.12.2

0.492.011.221.4

0.750.231.2

0.550.581.521.53

13293287

1329752

150393436

701

541731

22022

18769963719266825405125

15659644942

1357

2058 100.0%1995

65.8%

2.7%3.0%4.0%3.5%3.0%3.2%3.6%3.5%3.0%2.8%2.9%

34.2%

1.1%3.8%2.2%3.7%2.1%4.0%3.2%3.1%2.2%2.3%4.0%3.2%2.2%3.4%3.6%3.9%3.7%3.9%3.9%3.3%2.9%

0.56 [0.32, 0.79]

Favours [experimental]–2 –1 0 1 2

Favours [control]

0.59 [0.28, 0.90]

–0.50 [–1.36, 0.36]–0.15[–0.88, 058]

0.10 [–0.13, 033]0.10 [–0.39, 0.59]0.22 [–0.51, 0.95]0.36 [–0.25, 0.97]0.40 [–0.03, 0.83]0.70 [0.21, 1.19]

0.76 [–0.45, 1.97]1.50 [0.68, 2.32]2.03 [1.26, 2.80]0.46 [0.12, 0.80]

–0.80 [–2.73, 1.13]

–37 [–0.72, –0.02]

–0.33 [–1.44, 0.78]

–0.13 [–0.52, 0.26]

–0.13 [–1.26, 1.00]

–0.10 [–0.34, 0.14]

–0.09 [–0.71, 0.53]

0.27 [–0.41, 0.95]0.28 [–0.81, 137]0.30 [–0.75, 1.35]0.38 [0.18, 0.58]

0.39 [–0.22, 1.00]0.44 [–0.65, 1.53]0.80 [0.26, 1.34]0.92 [0.48, 1.36]1.01 [0.74, 1.28]1.10 [0.72, 1.48]1.11 [0.86, 1.36]1.50 [1.21, 1.79]1.95 [1.35, 2.55]2.45 [1.70, 3.20]

Total (95% CI)Heterogeneity: tau2 = 0.36; chi2 = 257.35, df = 31 (P < 0.00001); I2 = 88%Test for overall effect: Z = 4.55 (P < 0.00001)Test for subgroup differences; chi2 = 0.30, df = 1 (P = 0.58), I2 = 0%

Sohair B. Fayed 20141.2.1 TIDM

Candelli, M. 2004Bazmamoun, H. 2016Marcello Candelli 2003Chobot, A. 2014Colombo, C 2002Candelli, M. 2012Toporowska-Kowalska, E. 2007Duran Arslan 2000Mervat M EI-Eshmawy, 2011Zekry OA 2013Subtotal (95% CI)Heterogeneity: tau2 = 0.22; chi2 = 39. 10, df = 10 (P < 0.0001); I2 = 74%Test for overall effect: Z = 2. 67 (P = 0.008)

1.2.2 T2DMMehmet Demir 2008Wesam Ahmed Nasif 2016Ko GT 2001Yuxin Huang 2015Yi Zhou 2011Yakafumi Senmaru 2013ZhuChunying 2016Zhou X 2012EI Hadidy M EI Hadidy 2009N. E. Gulcelik 2005Gino G. Fernandini-Paredes 2008Xiaohua Li 2016Anandani, E. T. 2014Tanrivedi 2011Ziyue Song 2017Zhili Lliang 2014Akin, S. 2014Meng Qing 2018Shao Li-Cheng 2016Yingjian Tong 2018Peng WP 2013Sub total (95% CI)Heterogeneity: tau2 = 0.41; chi2 = 207.38, df = 20 (P < 0.00001); I2 = 90%Test for overall effect: Z = 3.73 (P = 0.0002)

IV, random, 95% CIMean difference

IV, random, 95% CIMean difference

Figure 3: Forest plot of Helicobacter pylori infection and glycated hemoglobin level: subgroup analysis grouped by disease classification.

5Journal of Diabetes Research

mellitus and type 2 diabetes mellitus patients infected with H.pylori have high levels of HbA1c and poor control of glyce-mic indices.

It is estimated that about 4.4 billion people worldwidewere infected with H. pylori in 2015 [42], and it is estimatedthat diabetes will reach 552 million by 2020 [43]. Kato et al.have shown that H. pylori infection is associated with anincreased risk of diabetes mellitus [44]. Refaeli et al. showedthat the prevalence of metabolic syndrome in H. pylori-infected patients was higher than that in uninfected patients[45]. More and more data indicated that inflammation mayplay a role in the pathogenesis of type 2 diabetes, and thepathogenesis of type 2 diabetes can be regarded as an autoin-flammatory disease [46]. Simultaneously, the inflammatoryresponse caused by H. pylori has also been confirmed byresearches [47, 48]. At the same time, studies have shown

that eradication of H. pylori can improve glucose homeosta-sis in type 2 diabetes mellitus by reducing proinflammatoryfactors [49]. In addition, studies have shown that H. pylorican promote insulin resistance by inducing chronic inflam-mation and affecting insulin regulation of gastrointestinalhormones [50]. Gastritis caused by H. pylori may affect thesecretion of gastric-related hormones, such as leptin andgrowth hormone-releasing hormone, as well as gastrin andsomatostatin, which may affect the susceptibility to diabetes[51]. Studies have also described a positive correlationbetween H. pylori infection and impaired insulin secretion[52]. The results of Zhou et al. showed that H. pylori infec-tion induced hepatic insulin resistance through c-Jun/miR-203/SOCS3 signaling pathway and provided possible impli-cations for insulin resistance [53]. Although the mechanismof the association between H. pylori infection and diabetes

N. E. Gulcelik 2005

Gino G. Fernandini-Paredes 2008

Wesam Ahmed Nasif 20167.2.1 Case-cotrol

Sohair B. Fayed 2014Mehmet Demir 2008Ko GT 2001Candelli, M. 2004Marcello Candelli 2003El Hadidy M El Hadidy 2009Chobot, A. 2014Bazmamoun, H. 2016

Colombo, C 2002Duran Arslan 2000Toporowska-Kowalska, E. 2007Candelli, M. 2012Jones KL 2002Mervat M El-Eshmawy, 2011

Yingjian Tong 2018Shao Li-Cheng 2016Zekry OA 2013Subtotal (95% CI)Heterogeneity: tau2 = 0.26; chi2 = 112.67, df = 19 (P < 0.00001); I2 = 83%Test for overall effect: Z = 3.08 (P = 0.002)

Zhu Chunying 2016

Anandani, E. T. 2014

Peng WP 2013

Hamed, S. A. 20087.2.2 Cross-sectional study

Takafumi Senmaru 2013Yuxin Huang 2015Yi Zhou 2011

Jamshid Vafaeimanesh 2016Zhou X 2012Xiaohua Li 2016

Ziyue Song 2017

Akin, S. 2014Zhili Lliang 2014

Meng Qing 2018

Tanrivedi 2011

Subtotal (95% CI)

Total (95% CI) 2217 2184 0.40 [0.23, 0.58]

Heterogeneity: tau2 = 0.22; chi2 = 12.511, df = 14 (P < 0.00001); I2 = 89%Test for overall effect: Z = 3.13 (P = 0.002)

Heterogeneity: tau2 = 0.23; chi2 = 242.24, df = 34 (P < 0.00001); I2 = 86%Test for overall effect: Z = 4.46 (P < 0.00001)Test for subgroup differences: chi2 = 0.03, df = 1 (P = 0.86); I2 = 0%

2.0%3.4%3.4%2.7%3.1%3.2%3.2%3.2%1.7%3.1%2.9%3.3%2.7%2.8%2.9%

43.7%

2.5%2.4%3.1%2.8%2.7%3.0%2.7%2.7%2.9%2.7%3.1%2.9%3.1%2.6%2.5%3.0%2.7%2.9%3.1%2.6%

56.3%

100.0%

0.39 [0.14, 0.64]

–0.26 [–1.09, 0.58]–0.09 [–0.31, 0.13]–0.06 [–0.26, 0.13]–0.06 [–0.57, 0.46]–0.05 [–0.39, 0.29]0.02 [–0.27, 0.30]0.12 [–0.18, 0.41]0.19 [–0.11, 0.49]0.35 [–0.61, 1.32]0.60 [0.24, 0.95]0.62 [0.19, 1.04]0.64 [0.41, 0.87]1.36 [0.82, 1.89]1.38 [0.90, 1.85]1.67 [1.23, 2.10]

–0.56 [–1.16, 0.03]–0.32 [–0.95, 0.31]–0.17 [–0.51, 0.17]–0.15 [–0.64, 0.35]–0.10 [–0.62, 0.41]0.07 [–0.33, 0.47]0.13 [–0.39, 0.65]0.13 [–0.37, 0.64]0.18 [–0.27, 0.62]0.18 [–0.34, 0.70]0.22 [–0.15, 0.59]0.26 [–0.17, 0.68]0.47 [0.15, 0.80]

0.55 [–0.01, 1.10]0.76 [0.16, 1.36]0.85 [0.46, 1.24]1.10 [0.59, 1.61]1.30 [0.85, 1.75]1.34 [0.99, 1.68]1.43 [0.85, 2.01]

0.42 [0.16, 0.69]

8.17.3

8.349.218.448.118.458.4

9.528.626.98

9.039.538.21

6.937.47.9

8.098.258.39.2

7.828

8.28.16

11.087.878.88.88.3

7.638.727.747.75

0.81

2.022.4

1.681.672.462.061.121.841.22.1

0.891.960.77

0.691.61.8

2.111.061.12.1

1.420.651.41.7

3.171.510.80.4

1.580.231.451.341.67

924

681461954362

13984

1125

8553

159484351

1293

334087322934231748594149481715

1284945

10624

8.37.4

8.479.348.538.088.188.019.087.76.16.9

8.027.087.1

7.37.98.7

8.428.48.2

8.927.67.97.97.8

10.327.178.48.66.8

7.256.776.245.72

1046

61872202269729668255140

156254259

1138

17135431298737

13232199739

15052483426496436

0.11.2

2.042.1

1.961.322.132.011.220.751.41.2

0.231.530.55

0.551.37.1

2.371.71.52.1

1.660.42.21.6

2.631.460.70.22.3

0.491.520.581.2

–2 –1 0

Case-report Cross-sectional study

1 2

Study or subgroupMean SD Total Mean SD

Cross-sectional studyCase-controlTotal

WeightIV. random, 95% CIStd. mean difference

IV. random, 95% CIStd. mean difference

Figure 4: Forest plot of Helicobacter pylori infection and glycated hemoglobin level: subgroup analysis forest map, grouped by study type.

6 Journal of Diabetes Research

is still unclear, more and more studies have shown that thereis a potential link between them. Because of the large numberof patients with the two diseases, if the causal relationshipbetween them becomes clear, the impact will be great.

Meta-analysis is a secondary literature analysis based onprevious research evidence. Therefore, there are limitationsand biases in the analysis. Case-control studies are inevitablyaffected by selective bias. The results are not as reliable asprospective studies, and there are some limitations. More-over, the results of this study are not grouped by population,region, and race. The limitation of this meta-analysis is that itdoes not consider other characteristics that may affect bloodsugar control besides H. pylori infection, such as treatmentstatus, age, gender, obesity index, or smoking status.

In conclusion, the results of this meta-analysis indicatethat Helicobacter pylori infection is associated with increased

glycosylated hemoglobin A, with a large sample size and acertain degree of confidence. Although some biases affectthe accuracy of the results, it is still possible to provide newreference and guidance for eradication of H. pylori as a sec-ondary prevention or treatment of diabetes. Proper screeningfor H. pylori infection and regular monitoring of blood glu-cose and HbA1c may be effective for early detection of bloodglucose disorders and prevention of type 2 diabetes. Furtherresearch, especially longitudinal studies, is necessary to vali-date current results.

6. Conclusion

This study conducted a meta-analysis of existing literatureand concluded that H. pylori infection may increase the levelof glycosylated hemoglobin A in diabetic patients, and the

Zhu Chunying 2016N. E. Gulcelik 2005Anandani, E. T. 2014Tanrivedi 2011Gino G. Fernandini-Paredes 2008Peng WP 2013Subtotal (95% CI)

5.1.2 Others DM

5.1.1 Biopsy DM

Study or subgroupMean SD Total Mean SD

ControlExperimentalTotal

WeightIV. random, 95% CIStd. mean difference

IV. random, 95% CIStd. mean difference

Wesam Ahmed Nasif 2016Sohair B. Fayed 2014Hamed, S. A. 2008Mehmet Demir 2008Ko GT 2001Candelli, M. 2004Takafumi Senmaru 2013Yuxin Huang 2015Yi Zhou 2011Jamshid Vafaeimanesh 2016Marcello Candelli 2003Zhou X 2012El Hadidy M El Hadidy 2009Chobot, A. 2014Bazmamoun, H. 2016Xiaohua Li 2016Colombo, C 2002Duran Arslan 2000Toporowska-Kowalska, E. 2007Candelli, M. 2012Ziyue Song 2017Akin, S. 2014Jones KL 2002Mervat M El-Eshmawy, 2011Yingjian Tong 2018Shao Li-cheng 2016Zhili Lliang 2014Zekry OA 2013Meng Qing 2018Subtotal (95% CI)

Total (95% CI)

–2

Heterogeneity: tau2 = 0.30; chi2 = 30.02, df = 5 (P < 0.0001); I2 = 83%Test for overall effect: Z = 2.40 (P = 0.02)

Heterogeneity: tau2 = 0.23; chi2 = 207.59, df = 28 (P < 0.00001); I2 = 87%Test for overall effect: Z = 3.74 (P = 0.0002)

Heterogeneity: tau2 = 0.23; chi2 = 242.24, df = 34 (P < 0.00001); I2 = 86%Test for overall effect: Z = 4.46 (P < 0.00001)Test for subgroup differences: chi2 = 0.76, df = 1 (P = 0.38); I2 = 0%

8.448.2

9.526.9

7.639.53

1.681.4

1.121.2

0.231.96

7.48.17.9

8.098.257.3

8.349.218.118.3

8.459.2

7.828

8.48.16

11.087.878.8

8.628

8.88.3

8.727.749.037.758.21

6.93

8.537.9

9.086.1

7.257.08

7.37.98.38.7

8.428.47.4

8.479.348.088.2

8.188.927.67.9

8.017.8

0.327.178.47.76.98.66.8

6.776.248.025.727.1

0.691.60.81.8

2.111.06

12.022.4

1.671.1

2.462.1

1.420.652.061.7

3.171.510.8

1.842.10.4

1.581.451.340.891.670.77

62595

534943

271

2217

334068873229

14619543

1393484231748

1124149481785

15915

12845

106482451

1946

691925402642

221

2184

17136

543129

1872202272879637

13232689739

1505251

15648344964253659

1963

0.40 [0.23, 0.58]

–0.05 [–0.39, 0.29]0.18 [–0.34, 0.70]0.35 [–0.61, 1.32]0.62 [0.19, 1.04]1.10 [0.59, 1.61]1.38 [0.90, 1.85]0.60 [0.11, 1.09]

–0.56 [–1.16, 0.03]–0.32 [–0.95, 0.31]–0.26 [–1.09, 0.58]–0.17 [–0.51, 0.17]–0.15 [–0.64, 0.35]–0.10 [–0.62, 0.41]–0.09 [–0.31, 0.13]–0.06 [–0.26, 0.13]–0.06 [–0.57, 0.46]0.02 [–0.27, 0.30]0.07 [–0.33, 0.47]0.12 [–0.18, 0.41]0.13 [–0.39, 0.65]0.13 [–0.37, 0.64]0.18 [–0.27, 0.62]0.19 [–0.11, 0.49]0.22 [–0.15, 0.59]0.26 [–0.17, 0.68]0.47 [0.15, 0.80]

0.55 [–0.01, 1.10]0.60 [0.24, 0.95]0.64 [0.41, 0.87]0.76 [0.16, 1.36]0.85 [0.46, 1.24]1.30 [ 0.85, 1.75]1.34 [0.99, 1.68]1.36 [0.82, 1.89]1.43 [0.85, 2.01]1.67 [1.23, 2.10]0.37 [0.17, 0.56]

3.1%2.7%1.7%2.9%2.7%2.8%

16.1%

100.0%

2.5%2.4%2.0%3.1%2.8%2.7%3.4%3.4%2.7%3.2%3.0%3.2%2.7%2.7%2.9%3.2%3.1%2.9%3.1%2.6%3.1%3.3%2.5%3.0%2.9%3.1%2.7%2.6%2.9%

83.9%

1.962.2

1.221.4

0.491.53

0.551.30.17.1

2.371.71.2

2.042.1

1.321.5

2.132.1

1.660.4

2.011.6

2.631.460.7

0.751.20.22.3

1.520.580.231.2

0.55

–1 0

Favours [experimental] Favours [control]

1 2

Figure 5: Forest plot of Helicobacter pylori infection and glycated hemoglobin level: subgroup analysis, grouped by detection method.

7Journal of Diabetes Research

same conclusions were obtained in both type 1 diabetes andtype 2 diabetes. The sample size included in this paper is largeand has credibility, which can guide clinical work to a certainextent. In clinical practice, individualized prevention andtreatment need to be closely combined with the actual situa-tion of the patient.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Jinhu Chen and Yuling Xing contributed equally to thiswork.

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Funnel plot with pseudo 95% confidence limits0

0.2

0.4

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0.8

1

–2 –1 0 1

WMD

SE (W

MD

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Figure 6: Publication bias test: funnel plot.

Table 2: Fail-safe numbers of all groups for the studies.

Numberof studies

Fail-safe numberα = 0 05 α = 0 01

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Subgroup of type 1 diabetes 11 101.251 44.612

Subgroup of type 2 diabetes 21 936.340 453.288

Subgroup of case-control 20 491.393 233.356

Subgroup of cross-sectional study 15 393.769 187.513

Subgroup of biopsy 6 78.977 36.010

Subgroup of other methods 29 1100.883 530.770

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