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Journal of Cellular and Molecular Medicine logoLink to Journal of Cellular and Molecular Medicine
. 2018 Jan 4;22(3):1720–1732. doi: 10.1111/jcmm.13453

European versus Asian differences for the associations between paraoxonase‐1 genetic polymorphisms and susceptibility to type 2 diabetes mellitus

Jian‐Quan Luo 1,2,†,✉, Huan Ren 3,4,†, Mou‐Ze Liu 1,2, Ping‐Fei Fang 1,2, Da‐Xiong Xiang 1,2
PMCID: PMC5824408  PMID: 29314660

Abstract

Many studies have examined the associations between paraoxonase‐1 (PON1) genetic polymorphisms (Q192R, rs662 and L55M, rs854560) and the susceptibility to type 2 diabetes mellitus (T2DM) across different ethnic populations. However, the evidence for the associations remains inconclusive. In this study, we performed a meta‐analysis to clarify the association of the two PON1 variants with T2DM risk. We carried out a systematic search of PubMed, Embase, CNKI and Wanfang databases for studies published before June 2017. The pooled odds ratios (ORs) for the association and their corresponding 95% confidence intervals (CIs) were calculated by a random‐ or fixed‐effect model. A total of 50 eligible studies, including 34 and 16 studies were identified for the PON1 Q192R (rs662) and L55M (rs854560) polymorphism, respectively. As for the PON1 Q192R polymorphism, the 192R allele was a susceptible factor of T2DM in the South or East Asian population (OR > 1, P < 0.05) but represented a protective factor of T2DM in European population (OR = 0.66, 95% CI = 0.45–0.98) under a heterozygous genetic model. With regard to the PON1 L55M polymorphism, significant protective effects of the 55M allele on T2DM under the heterozygous (OR = 0.77, 95% CI = 0.61–0.97) and dominant (OR = 0.80, 95% CI = 0.65–0.99) genetic models were found in the European population, while no significant associations in the Asian populations under all genetic models (P > 0.05). In summary, by a comprehensive meta‐analysis, our results firmly indicated that distinct effects of PON1 genetic polymorphisms existed in the risk of T2DM across different ethnic backgrounds.

Keywords: type 2 diabetes mellitus, susceptibility, paraoxonase‐1, polymorphism, ethnic difference

Introduction

The rise of diabetes prevalence poses one of the important challenges to global health. It is estimated that approximately 422 million adults were diagnosed with the disease in 2014 worldwide 1. Diabetes is one of the main causes of cardiovascular disease, blindness and kidney failure and is the sixth leading driver of disability 2. Therefore, the prevention and control of diabetes are growing up to be an ever‐increasing global health priority 3. Type 2 diabetes mellitus (T2DM) comprises the majority of cases of diabetes around the world. T2DM is a metabolic disorder of multifactorial aetiology involving many environmental factors and genetic variants 4, 5.

Human paraoxonase‐1 (PON1) is a calcium‐dependent 45‐kD glycoprotein composed of 355 amino acids. The esterase is synthesized mainly by the liver and secreted into the circulation where it associates with high‐density lipoprotein (HDL) and assists in the antioxidant effect of preventing oxidation of low‐density lipoprotein (LDL). PON1 in human beings is encoded by the PON1 gene which maps to the long arm of chromosome 7 (q21‐22). It has been observed that serum PON1 activity has an important role in susceptibility and progression of T2DM 6, 7.

Single nucleotide polymorphisms (SNPs) in the PON1 gene can significantly account for the catalytic ability of the enzyme. A missense SNP at position 192 (glycine (Q) to arginine (R) substitution) (rs662) is an important determinant of the PON1 activity 8. Although the R‐alloenzyme is more active towards some substrates, for example paraoxon, other substrates such as diazoxon and sarin are hydrolysed more rapidly by the Q‐alloenzyme 9. In addition, the PON1 Q192R polymorphism was the major determinant of individual variation in the ability of HDL in protecting LDL against lipid peroxidation. For example, the Q‐alloenzyme confers least ability 10. Another SNP in the coding region causes a leucine (L) to methionine (M) substitution at position 55 (rs854560), which may also affect the PON1 activity and levels 11.

As Ikeda et al. first found that serum PON activity was significantly decreased in the patients with T2DM 12, a large number of studies have been conducted over the last two decades to investigate the association of Q192R (rs662) and L55M (rs854560) polymorphism in PON1 gene with susceptibility to T2DM. However, the previously published results remain controversial. Hence, to firmly elucidate the association between PON1 genetic polymorphisms (Q192R, rs662 and L55M, rs854560) and the risk of T2DM, we conducted a systematic review and meta‐analysis of data from 50 studies and also established the association according to the ethnicity.

Materials and methods

Search strategy and inclusion criteria

A systematic search was conducted in the electronic databases PubMed, Embase, China National Knowledge Infrastructure (CNKI) and Wanfang Data, and all relevant articles were published in English or Chinese from their starting dates to June 2017. The search strategy used the following keywords relating to the paraoxonase‐1 gene (‘paraoxonase‐1′, ‘PON1′) or variations (e.g. ‘mutation’, ‘polymorphism’, ‘single nucleotide polymorphism’, ‘SNP’, ‘variant’, ‘variation’) in combination with TD2M (e.g. ‘Diabetes Mellitus, Type 2′, ‘Noninsulin‐Dependent Diabetes Mellitus’, ‘Type 2 Diabetes’, ‘Diabetes Mellitus, Noninsulin‐Dependent’). We supplemented this search by reviewing the cited references for all possible studies.

All identified abstracts were carefully reviewed by two investigators (J. Q. Luo, H. Ren) independently for eligibility. The inclusion criteria were as follows: (i) case‐control design, regardless of sample size; (ii) study assessing the associations between Q192R (rs662) and L55M (rs854560) of PON1 gene and type 2 diabetes; (iii) numbers for the PON1 genotypes could be available or calculated in case and control groups; and (iv) genotype distribution in the controls was in Hardy‐Weinberg equilibrium (HWE). If the two investigators (J. Q. Luo, H. Ren) disagreed about the eligibility of an article, it was resolved by consensus with a third reviewer (M. Z. Liu).

Data extraction

For the eligible articles included in this study, data were also extracted by two reviewers (J. Q. Luo, H. Ren), who reached a consensus on all of the data extraction items. The following information was extracted from each study: name of the first author, publication year, country of the study, ethnicity of the population, genotype and allele distributions in case and control groups, and also sample size, mean age and gender distribution in case and control groups.

Statistical analysis

The goodness‐of‐fit chi‐square analysis was used to test the HWE of the genotype distribution of controls. The distribution was considered deviated significantly from HWE with P < 0.05. The pooled odds ratio (OR) with 95% confidence interval (CI) was used to evaluate the strength of association in the allelic, homozygous, heterozygous, recessive and dominant models, respectively. The statistical significance of the pooled estimates of the OR was determined by the Z test. The Cochran's Q test and I 2 metric were performed to examine the possibility of between‐study heterogeneity. Heterogeneity was considered to be statistically significant at P < 0.05 for the Q statistic and I 2 > 50% for the I 2 metric 13. If substantial heterogeneity existed, random effect model (the DerSimonian and Laird method) was selected as the pooling method. Otherwise, the fixed‐effect model (the Mantel‐Haenszel method) was adopted. Subgroup analysis based on ethnicity (categorized as Europeans, East Asians, South Asians and Canadian Aboriginal) and meta‐regression with restricted maximum likelihood estimation were conducted to assess the sources of heterogeneity across the studies. Potential publication bias was assessed by Begg's test and Egger's test 14, 15, with P < 0.05 considered representative of significant publication bias. All statistical analyses were performed with STATA version 12.0 (Stata, College Station, TX, USA).

Results

Description of eligible studies

The initial screening yielded 332 articles, and 1 article was found to be eligible by reviewing the cited references. A total of 111 articles were excluded because of duplicate publication. Then, 57 articles were excluded from screening based on the titles and/or abstracts. Finally, 37 articles 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52 involving 50 eligible studies were included in the current meta‐analysis according to the study inclusion criteria (Fig. 1). All the included articles were case‐control designs with sample sizes varied from 61 to 593. A total of 34 and 16 eligible studies were identified for the PON1 Q192R (rs662) and L55M (rs854560) polymorphism, respectively. The general characteristics of the studies included in the meta‐analysis are presented in Table 1.

Figure 1.

Figure 1

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Flow diagram of the search strategy and study selection. The terms ‘n’ in the boxes represent the number of corresponding studies.

Table 1.

Characteristics of the included studies of the association of the PON1 Q192R and L55M genetic polymorphism with type 2 diabetes

Study per SNP Year Country (Population)a Male/Female Age(years) Sample sizeb Case genotypes or allelesc Control genotypes or allelesc MAFd HWE Pe
Case Control Case Control 11 12 22 1 2 11 12 22 1 2
PON1 Q 192R
Mackness 1998 United Kingdom(EUR) 162/90 147/135 59.1 ± 11.3 42.2 ± 12.2 252/282 117 99 34 333 167 156 99 24 411 147 0.263 0.153
Sakai 1998 Japan(EAS) 65/74 179/61 61.7 ± 13.6 48.0 ± 8.7 139/240 14 63 62 91 187 24 102 114 150 330 0.688 0.866
Fanella 2000 Canada NA NA 44.5 ± 15.4 25.4 ± 12.6 115/478 74 36 5 184 46 276 175 27 727 229 0.240 0.915
Koch(a) 2001 Germany(EUR) NA NA NA NA 39/202 25 12 2 62 16 102 87 13 291 113 0.280 0.328
Koch(b) 2001 Germany(EUR) NA NA NA NA 36/113 17 19 0 53 19 62 45 6 169 57 0.252 0.554
Sampson 2001 United Kingdom(EUR) 18/22 14/16 56.6 ± 7.3 52.0 ± 7.4 40/30 17 16 2 50 20 12 11 3 35 17 0.327 0.844
Letellier 2002 France(EUR) 57/39 52/53 56.8 ± 10.7 46.7 ± 10.9 167/105 92 67 8 251 83 40 50 14 130 78 0.375 0.794
Hu YM 2003 China(EAS) 95/57 83/45 58.5 ± 12.1 54 ± 11.5 152/128 30 77 45 137 167 25 74 29 124 132 0.516 0.075
Zhang 2003 Japan(EAS) 39/17 61/28 64.5 ± 7.5 62.7 ± 8.3 56/89 10 23 23 43 69 7 33 49 47 131 0.736 0.665
Ma RX 2003 China(EAS) 40/40 55/49 63.0 ± 8.0 64.0 ± 7.0 176/104 16 84 76 116 236 22 48 34 92 116 0.558 0.511
Wang Y 2003 China(EAS) 24/12 29/9 64.8 ± 11.9 70.8 ± 10.8 75/38 18 41 16 77 73 15 18 5 48 28 0.368 0.912
Pu X 2003 China(EAS) 14/16 55/45 67.0 ± 5.0 64.0 ± 4.0 100/100 6 64 30 76 124 18 52 30 88 112 0.56 0.581
Hao YL 2003 China(EAS) 50/55 44/36 54.5 ± 10.2 51.6 ± 6.3 187/80 26 86 75 138 236 6 32 42 44 116 0.725 0.978
Ren T 2003 China(EAS) 65/55 57/26 NA NA 112/83 22 95 78 139 251 9 42 29 60 100 0.625 0.283
Li SY 2004 China(EAS) 22/14 21/12 56.0 ± 8.0 57.0 ± 11.0 63/33 16 24 23 56 70 13 13 7 39 27 0.409 0.287
Zhang Z 2004 China(EAS) 30/26 49/31 63.6 ± 11.4 63.7 ± 11.5 116/80 16 41 59 73 159 12 41 27 65 95 0.594 0.577
Deng YG 2004 China(EAS) 37/43 45/45 60.1 ± 2.7 54.8 ± 3.7 169/90 20 57 90 97 237 13 44 33 70 110 0.611 0.786
Sun YD 2005 China(EAS) 92/85 50/47 64.5 ± 10.3 62.4 ± 10.9 162/97 53 161 95 267 351 14 56 27 84 110 0.567 0.083
Mastorikou 2006 United Kingdom(EUR) 21/15 10/9 57.7 ± 5.2 57.7 ± 4.8 36/19 NA NA NA 48 24 NA NA NA 28 10 0.263 NA
Qi L 2007 China(EAS) 44/49 35/54 56.6 ± 7.0 57.9 ± 6.8 183/89 32 97 54 161 205 18 42 29 78 100 0.562 0.695
Shi GH 2007 China(EAS) 49/43 43/38 60.9 ± 7.3 62.4 ± 6.3 179/81 33 69 77 135 223 12 38 31 62 100 0.617 0.949
Irace 2008 Italy(EUR) NA NA 55.2 ± 9.2 55.9 ± 6.6 118/65 64 42 12 170 66 26 31 8 83 47 0.362 0.790
Unür 2008 Turkey(EUR) 20/31 27/26 52.5 ± 5.6 55.5 ± 8.0 51/53 31 14 6 76 26 25 22 6 72 34 0.321 0.730
Flekac 2008 Czech(EUR) 114/132 55/45 58 ± 18 41 ± 9 246/110 177 64 5 418 74 32 54 24 118 102 0.464 0.892
Gorshunska 2009 Ukraine(EUR) 28/68 86/47 56.2 ± 1.4 55.3 ± 2.5 96/123 55 25 16 135 57 48 64 11 160 86 0.350 0.110
Ergun 2011 Turkey(EUR) NA NA 59 ± 9.63 47 ± 6.53 171/80 91 50 30 232 110 38 31 11 107 53 0.331 0.262
Bhaskar 2011 India(SAS) NA NA NA NA 310/120 71 184 55 326 294 40 66 14 146 94 0.392 0.091
Gupta 2011 India(SAS) 126/124 151/149 47.4 ± 11.3 43.1 ± 10.7 250/300 81 126 43 288 212 168 108 24 444 156 0.26 0.264
Chen XJ 2011 China(EAS) 50/47 55/50 59.9 ± 10.6 58.7 ± 5.7 210/105 23 109 78 155 265 12 58 35 82 128 0.610 0.100
Elnoamany 2012 Egypt(EUR) 33/12 30/10 51.11 ± 6.7 50.19 ± 5.5 93/40 42 28 23 112 74 25 13 2 63 17 0.213 0.855
Zheng YQ 2012 China(EAS) 51/39 70/66 57.1 ± 12.0 45.5 ± 13.3 184/136 21 66 97 108 260 19 57 60 95 177 0.651 0.363
Gokcen 2013 Turkey(EUR) 20/30 16/14 60.8 ± 9.4 54.2 ± 8.1 50/30 18 25 7 61 39 8 14 8 30 30 0.500 0.715
Shao ZY 2014 China(EAS) 94/83 111/95 63.3 ± 10.9 61.3 ± 11.0 379/206 50 173 156 273 485 35 94 77 164 248 0.602 0.493
Du WL 2015 China(EAS) 28/33 28/36 54.9 ± 12.7 37.7 ± 14.5 125/64 7 43 75 57 193 5 22 37 32 96 0.75 0.505
PON1 L55M
Ikeda 1998 Japan(EAS) 53/55 82/79 58 ± 7 57 ± 8 108/161 95 10 3 200 16 142 19 0 303 19 0.059 0.426
Malin 1999 Finnish(EUR) NA NA NA NA 93/106 33 49 11 115 71 38 54 14 130 82 0.387 0.447
Fanella 2000 Canada NA NA 44.5 ± 15.4 25.4 ± 12.6 115/478 113 2 0 228 2 471 7 0 949 7 0.007 0.872
Letellier 2002 France(EUR) 57/39 52/53 56.8 ± 10.7 46.7 ± 10.9 167/105 65 81 20 211 121 40 52 12 132 76 0.366 0.426
Ren T 2003 China(EAS) 65/55 57/26 NA NA 184/83 177 7 0 361 7 70 2 0 142 2 0.013 0.905
Agachan 2004 Turkish(EUR) 122/91 57/59 59.9 ± 11.6 58.6 ± 16.0 213/116 111 86 8 308 102 51 51 7 153 65 0.298 0.218
Sampson 2005 United Kingdom(EUR) 38/20 18/32 NA NA 58/50 26 32 32 NA NA 21 28 28 NA NA NA NA
Mastorikou 2006 United Kingdom(EUR) 21/15 10/9 57.7 ± 5.2 57.7 ± 4.8 36/19 NA NA NA 49 23 NA NA NA 24 14 0.368 NA
Sun YD 2006 China(EAS) 92/85 50/47 64.5 ± 10.3 62.4 ± 10.9 294/91 121 150 23 392 196 38 45 8 121 61 0.335 0.296
Shao HQ 2006 China(EAS) 29/21 60/60 61.5 ± 3.3 55.0 ± 2.3 92/120 85 7 0 177 7 109 11 0 229 11 0.046 0.599
Flekac 2008 Czech(EUR) 114/132 55/45 58 ± 18 41 ± 9 246/110 84 118 44 286 206 30 55 25 115 105 0.477 0.983
Unür 2008 Turkey(EUR) 20/31 27/26 52.5 ± 5.6 55.5 ± 8.0 51/53 43 5 3 91 11 28 20 5 76 30 0.283 0.609
Altuner 2011 Turkey(EUR) 56/44 21/29 54.4 ± 2.8 44.04 ± 1.1 100/50 43 44 13 130 70 21 25 4 67 33 0.33 0.355
Ergun 2011 Turkish(EUR) NA NA 59 ± 9.6 47 ± 6.5 171/80 35 45 91 115 227 16 30 34 62 98 0.613 0.060
Gupta 2011 India(SAS) 126/124 151/149 47.4 ± 11.3 43.1 ± 10.7 250/300 176 69 5 421 79 193 101 6 487 113 0.188 0.080
Zheng YQ 2012 China(EAS) 51/39 70/66 57.1 ± 12.0 45.5 ± 13.3 184/136 168 15 1 351 17 124 11 1 259 13 0.048 0.194
Shao ZY 2014 China(EAS) 94/83 111/95 63.3 ± 10.9 61.3 ± 11.0 379/206 339 34 6 712 46 184 20 2 388 24 0.058 0.099
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a

The population codes of EAS, EUR and SAS mean East Asian, European and South Asian, respectively.

b

Sample size means the case‐control groups.

c

For the PON1 Q192R, 11: QQ, 12: QR, 22: RR; for the PON1 L55M, 11: LL, 12:LM, 22: MM.

d

MAF, minor allele frequency; NA, not available.

e

HWE, Hardy‐Weinberg equilibrium.

Quantitative synthesis of the association between PON1 Q912R polymorphism and T2DM

The results of the meta‐analysis of PON1 Q912R polymorphism are summarized in detail in Table 2 and Figure 2. In the overall population, the pooled meta‐analysis revealed that there were no significant associations between the PON1 Q912R genetic polymorphism and T2DM under all genetic models: allelic (OR = 1.02, 95% CI = 0.87–1.20; P = 0.786), homozygous (OR = 1.08, 95% CI = 0.81–1.45; P = 0.596), heterozygous (OR = 0.93, 95% CI = 0.75–1.17; P = 0.544), recessive (OR = 1.12, 95% CI = 0.92–1.35; P = 0.259) and dominant (OR = 0.99, 95% CI = 0.78–1.26; P = 0.921).

Table 2.

Summary of meta‐analysis of the association of the PON1 Q192R and L55M genetic polymorphism with type 2 diabetes

Genetic modela PON1 R192R PON1 L55M
Pooled OR (95%CI) Z P b N c Modeld I 2% P hetero Pooled OR(95%CI) Z P b N c Modeld I 2% P hetero
Allelic 1.02 (0.87–1.20) 0.27 0.786 34 R 81.8% 0.000 0.91 (0.81–1.02) 1.56 0.118 16 F 2.3% 0.427
EUR subgroup 0.80 (0.56–1.16) 1.16 0.246 13 R 87.1% 0.000 0.89 (0.77–1.03) 1.52 0.129 8 F 45.7% 0.075
Canadian Aboriginal 0.79 (0.56–1.13) 1.27 0.203 1 NA NA NA 1.19 (0.25–5.76) 0.22 0.830 1 NA NA NA
SAS subgroup 1.73 (1.17–2.56) 2.72 0.007b 2 R 74.8% 0.046 0.81 (0.59–1.11) 1.32 0.187 1 NA NA NA
EAS subgroup 1.14 (1.01–1.28) 2.1 0.036b 18 R 43.4% 0.026 1.03 (0.81–1.31) 0.22 0.825 6 F 0.0% 0.978
Recessive 1.12 (0.92–1.35) 1.13 0.259 33 R 62.6% 0.000 1.05 (0.81–1.35) 0.35 0.729 12 F 0.0% 0.630
EUR subgroup 0.77 (0.41–1.46) 0.80 0.425 12 R 77.2% 0.000 1.02 (0.76–1.35) 0.11 0.912 7 F 0.0% 0.427
Canadian Aboriginal 0.76 (0.29–2.02) 0.55 0.580 1 NA NA NA NA NA NA NA NA NA NA
SAS subgroup 2.03 (1.35–3.05) 3.39 0.001b 2 F 0.0% 0.365 1.00 (0.30–3.32) 0.00 1.000 1 NA NA NA
EAS subgroup 1.18 (1.04–1.33) 2.6 0.009b 18 F 33.4% 0.084 1.25 (0.63–2.47) 0.63 0.529 4 F 0.0% 0.405
Dominant 0.99 (0.78–1.26) 0.10 0.921 33 R 78.4% 0.000 0.85 (0.73–0.99) 2.15 0.032b 16 F 0.0% 0.628
EUR subgroup 0.69 (0.45–1.06) 1.70 0.089 12 R 83.5% 0.000 0.80 (0.65–0.99) 2.10 0.036b 8 F 31.9% 0.173
Canadian Aboriginal 0.76 (0.50–1.16) 1.29 0.197 1 NA NA NA 1.19 (0.24–5.81) 0.22 0.829 1 NA NA NA
SAS subgroup 2.26 (1.72–2.98) 5.78 0.000b 2 F 57.9% 0.123 0.76 (0.53–1.09) 1.51 0.132 1 NA NA NA
EAS subgroup 1.18 (0.99–1.39) 1.88 0.060 18 F 30.4% 0.108 1.00 (0.75–1.33) 0 0.997 6 F 0.0% 0.997
Homozygous 1.08 (0.81–1.45) 0.53 0.596 33 R 72.1% 0.000 0.92 (0.69–1.23) 0.56 0.577 12 F 0.0% 0.700
EUR subgroup 0.63 (0.30–1.32) 1.22 0.222 12 R 81.6% 0.000 0.85 (0.61–1.19) 0.94 0.348 7 F 0.0% 0.562
Canadian Aboriginal 0.69 (0.26–1.86) 0.73 0.463 1 NA NA NA NA NA NA NA NA NA NA
SAS subgroup 3.01 (1.93–4.67) 4.88 0.000b 2 F 21.2% 0.260 0.91 (0.27–3.05) 0.15 0.883 1 NA NA NA
EAS subgroup 1.28 (1.06–1.54) 2.54 0.011b 18 F 34.5% 0.075 1.28 (0.64–2.59) 0.7 0.487 4 F 0.0% 0.431
Heterozygous 0.93 (0.75–1.17) 0.61 0.544 33 R 71.4% 0.000 0.82 (0.70–0.97) 2.39 0.017b 15 F 0.0% 0.609
EUR subgroup 0.66 (0.45–0.98) 2.09 0.037b 12 R 75.8% 0.000 0.77 (0.61–0.97) 2.24 0.025b 7 F 37.7% 0.141
Canadian Aboriginal 0.77 (0.49–1.19) 1.18 0.239 1 NA NA NA 1.19 (0.24–5.81) 0.22 0.829 1 NA NA NA
SAS subgroup 2.07 (1.54–2.76) 4.89 0.000b 2 F 49.1% 0.161 0.75 (0.52–1.08) 1.54 0.124 1 NA NA NA
EAS subgroup 1.09 (0.91–1.30) 0.95 0.341 18 F 18.9% 0.228 0.96 (0.72–1.29) 0.28 0.778 6 F 0.0% 0.984
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a

The population codes of EAS, EUR and SAS mean East Asian, European and South Asian, respectively.

b

P < 0.05.

c

N means the number of eligible studies for the meta‐analysis.

d

F, fixed‐effects model; NA, not available; R, random‐effects model.

Figure 2.

Figure 2

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Forest plot for PON1 Q192R polymorphism under a recessive genetic model stratified by ethnicity in studies with type 2 diabetes patients.

When we performed subgroup analyses stratified by ethnicity, the distinct effects in different ethnic populations were observed under all genetic models. Significant associations between PON1 Q912R genetic polymorphism and T2DM presented in the South Asian subgroup (under all genetic models) and East Asian subgroup (under four genetic models), while no significant associations were shown in the Canadian Aboriginal subgroup and in the European subgroup under the allelic, homozygous, recessive and dominant genetic models. By contrast, the significant association for the European subgroup under the heterozygous genetic model showed the 192R allele represented a protective factor of T2DM (OR = 0.66, 95% CI = 0.45–0.98; P = 0.037), but a risk factor for T2DM in South Asian subgroup.

Quantitative synthesis of the association between PON1 L55M polymorphism and T2DM

The results of the meta‐analysis of PON1 L55M polymorphism are summarized in detail in Table 2 and Figure 3. In the overall population, the associations between the PON1 L55M genetic polymorphism and T2DM did not reach statistically significant under the allelic genetic model (OR = 0.91, 95% CI = 0.81–1.02; P = 0.118), homozygous genetic model (OR = 0.92, 95% CI = 0.69–1.23; P = 0.577) and recessive genetic model (OR = 1.05, 95% CI = 0.81–1.35; P = 0.729). However, significant associations were found under a heterozygous genetic model (OR = 0.82, 95% CI = 0.70–0.97; P = 0.017) and a dominant genetic model (OR = 0.85, 95% CI = 0.73–0.99; P = 0.032).

Figure 3.

Figure 3

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Forest plot for PON1 L55M polymorphism under a dominant genetic model stratified by ethnicity in studies with type 2 diabetes patients.

In subgroup analyses based on ethnicity, the distinct effects in different ethnic populations were also presented for the PON1 L55M genetic polymorphism. There were significant protective effects of L allele on T2DM in the European subgroup under the heterozygous (OR = 0.77, 95% CI = 0.61–0.97; P = 0.025) and dominant (OR = 0.80, 95% CI = 0.65–0.99; P = 0.036) genetic models, while no significant results were found in the South Asian, East Asian and Canadian Aboriginal subgroup under all genetic models.

Sources of heterogeneity

There was significant heterogeneity in the overall meta‐analysis of PON1 Q912R polymorphism under all of the genetic models (P heterogeneity < 0.05, I 2 > 50%). Subgroup analysis stratified by ethnicity indicated that heterogeneity was significantly reduced in the South Asian and East Asian subgroup, while was increased in the European subgroup. Therefore, ethnicity may be one of the sources of heterogeneity between studies for the PON1 Q912R polymorphism in the overall meta‐analysis.

Because substantial heterogeneity still existed in the European subgroup under all genetic models, meta‐regression was used to explore the source of this heterogeneity. The following three covariates were taken into consideration: publication year, MAF (minor allele frequency) in controls and sample size in the subsequent meta‐regression (Table 3). The results of meta‐regression analysis showed that MAF in the control group could explain the observed between‐study heterogeneity. The proportion of between‐study variance explained by the MAF covariate ranges from 67.81 to 93.06%, depending on the genetic models. However, no significant effects were accounted for by the covariates sample size and publication year under all genetic models.

Table 3.

The meta‐regression results among the European population under all genetic model for the PON1 Q192R genetic polymorphism

Genetic model Covariates Coefficient Standard Error T‐value P‐value 95% confidence interval Adjusted R‐squared
Heterozygous MAF in controls −6.03742 1.641809 −3.68 0.004a −9.6956∼−2.37924 81.00%
Sample size −0.00028 0.001466 −0.19 0.854 −0.00354∼0.002991 −14.64%
Publication year −0.04524 0.03562 −1.27 0.233 −0.12461∼0.034129 13.01%
Allelic MAF in controls −6.41229 1.281261 −5 0.000a −9.23233∼−3.59225 80.80%
Sample size −0.00045 0.001437 −0.32 0.759 −0.00366∼0.002749 −11.10%
Publication year −0.00646 0.037309 −0.17 0.866 −0.08858∼0.075655 −9.84%
Homozygous MAF in controls −12.2995 3.278749 −3.75 0.004a −19.605∼−4.99395 73.94%
Sample size −0.00058 0.003051 −0.19 0.853 −0.00738∼0.006216 −13.55%
Publication year 0.023242 0.081639 0.28 0.782 −0.15866∼0.205145 −13.07%
Dominant MAF in controls −7.93603 1.488279 −5.33 0.000a −11.2521∼−4.61994 93.06%
Sample size −0.00049 0.001638 −0.3 0.771 −0.00414∼0.003161 −12.29%
Publication year −0.02767 0.042206 −0.66 0.527 −0.12171∼0.066367 −4.49%
Recessive MAF in controls −10.0113 3.071858 −3.26 0.009a −16.8558∼−3.16679 67.81%
Sample size −0.00047 0.002671 −0.18 0.864 −0.00642∼0.005483 −14.87%
Publication year 0.035213 0.071726 0.49 0.634 −0.1246∼0.19503 −13.82%
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a

P < 0.05.

MAF, minor allele frequency; Coefficient: regression coefficient. The regression coefficients were the estimated increase in the lnOR per unit increase in the covariates.

In contrast, no significant heterogeneity in the overall meta‐analysis of PON1 L55M polymorphism was showed under all genetic models (P heterogeneity > 0.1, I 2 = 0%). Subgroup analysis stratified by ethnicity also indicated that no substantial between‐study heterogeneity was found in the Asian subgroup (P heterogeneity > 0.1, I 2 = 0%) and in the European subgroup (P heterogeneity > 0.05, I 2 < 50%) under all genetic models.

Publication bias evaluation

Publication bias of the individual articles was evaluated by using the Begg's funnel plot (Fig. 4) and Egger's test. For the PON1 Q192R meta‐analysis (Fig. 4A), no obvious publication bias was visualized in the shape of the funnel plot under all genetic models. Additionally, no evidence of significant publication bias was detected by the Egger's test (P = 0.257 for allelic genetic model; P = 0.452 for heterozygous genetic model; P = 0.527 for dominant genetic model; and P = 0.197 for recessive genetic model). However, there was marginal significant publication bias for the homozygous genetic model (P = 0.047).

Figure 4.

Figure 4

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Begg's funnel plot for studies of the association between type 2 diabetes and PON1 Q192R polymorphism under a dominant genetic model (A) and PON1 L55M polymorphism under a heterozygous genetic model (B).

For the PON1 L55R meta‐analysis (Fig. 4B), there is also no obvious publication bias in the shape of the funnel plot under all genetic models. No evidence of significant publication bias was also detected by the Egger's test (P = 0.961 for allelic genetic model; P = 0.719 for heterozygous genetic model; P = 0.309 for homozygous genetic model; P = 0.871 for dominant genetic model; and P = 0.628 for recessive genetic model) yet.

Discussion

So far, the associations between PON1 genetic polymorphisms and T2DM were conflicting in the previous studies. This is partly because some previous case‐control studies have been too small to be reliable. Thus, our meta‐analysis could overcome the limitations of single study by pooling the individual dataset and provide more reliable results.

In the overall meta‐analysis of the PON1 Q192R polymorphism, no significant association, but strong between‐study heterogeneity, was observed. To address the substantial heterogeneity, we divided the total samples into four subgroups, that is white European, Canadian Aboriginal, South and East Asians. Stratified analyses by ethnicity yielded a significant association of the PON1 Q192R polymorphism with T2DM in South Asian and East Asian populations and, conversely, no association of the PON1 Q192R polymorphism with T2DM in European populations under the allelic, homozygous, recessive and dominant genetic models. In addition, the 192R allele was a susceptible factor of T2DM in the Asian population but represented a protective factor of T2DM in European population under a heterozygous genetic model.

In the overall meta‐analysis of the PON1 L55M polymorphism, no significance between‐study heterogeneity was observed. The distinct effects across different ethnic backgrounds also presented in the subgroup analysis based on ethnicity. For example, significant protective effects of the 55M allele on T2DM under the heterozygous and dominant genetic models were found in the European population, while no significant results in the Asian populations under all genetic models. Interestingly, the associations of the two PON1 SNPs in our study were generally very similar in South and East Asians, although Asia is known to harbour genetically different origins 53. In Canadian population, only one study investigated the association between the two PON1 SNPs and risk of T2DM, and no significant associations were found in all genetic models. Therefore, it was inferred that the 192R or 55M allele may decrease the risk of developing T2DM in European ancestry population, whereas the 192R increase the risk of T2DM in the South Asian and East Asian populations.

To our knowledge, this is the largest study to underline the importance of ethnicity in the association between PON1 genetic variations and T2DM by a comprehensive meta‐analysis. The question remaining to be addressed is how the PON1 Q192R and L55M variants can exert an impact on T2DM with ethnic difference. One potential explanation is that different populations might have experienced very diverse lifestyle and environmental factors during their long‐period evolution. The PON1 activity may be influenced by several environmental and lifestyle impacts, such as cigarette smoking 54, alcohol intake 55 and physical activity 56. Another possible explanation may be the ethical differences in the distribution of the PON1 Q192R and L55M (rs854560) polymorphisms. Nevertheless, the precise mechanism deserves to be investigated in the future.

According to the included studies among different countries 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52 and the 1000 genomes database (https://www.ncbi.nlm.nih.gov/variation/tools/1000genomes/), there are huge racial and regional differences in the distribution of the PON1 Q192R (rs662) and L55M (rs854560) genetic polymorphisms (Fig. 5). For the PON1 Q192R polymorphism, the R allele predominates in the East Asian populations (>60%), which is significantly higher than in the South Asian populations (about 40%) and the European populations (<35%). For the PON1 L55M polymorphism, the M allele frequency is rare in the East Asian populations (<5%), which is significantly lower than in the South Asian populations (about 20%) and European populations (>30%). Such heterogeneous genetic backgrounds could be, at least in part, responsible for the heterogeneity of effect on the risk of T2DM detected in our overall population meta‐analysis. Furthermore, subgroup analysis stratified by ethnicity also indicated that the heterogeneity in the Asian group was significantly decreased.

Figure 5.

Figure 5

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The frequency of PON1 Q192R and L55M among the different ethnicities. The data were summarized according to the 1000 genomes database. East Asian referred to the Chinese and Japanese; South Asian was from India; European referred to Utah Residents (CEPH) with Northern and Western European Ancestry; African was from Yoruba in Ibadan, Nigeria.

The meta‐analysis results in the current study should be interpreted with particular caution when large between‐study heterogeneity existed. Obvious heterogeneity was present in all the genetic models for the PON1 Q192R polymorphism in the European population subgroup. Meta‐regression was performed to evaluate the potentially important covariates exerting substantial impact on heterogeneity. Our findings have proved that the proportion of heterogeneity explained by the MAF in controls can reach as high as 93.06%. One of the reasons may be the small number of subjects in the control group. For example, the study of Elnoamany et al. 45 included 40 control subjects and the MAF of PON1 Q192R was 0.213, while the study of Gokcen et al. 47. included 30 control subjects and the MAF of PON1 Q192R was 0.5. Accordingly, studies with large sample size are needed to be investigated in the future.

There are some shortcomings in our current meta‐analysis. First, our included studies were limited to English and Chinese language, with some data published in other languages excluded, which may lead to some publication bias and thus affect the pooled results in the meta‐analysis. Second, although there are 34 eligible studies for the PON1 Q192R polymorphism meta‐analysis and 16 eligible studies for the PON1 L55M polymorphism, the populations were restricted to Asians, Europeans and Canadian Aboriginals. Studies from other populations should be conducted to confirm the findings. Last but not the least, the information about exposure to environmental substrates was not available in the included studies. This may explain some between‐study heterogeneity in our meta‐analysis. In addition, the gene×environment interactions are needed to be further evaluated in the future.

In conclusion, we have firmly established that the PON1 genetic polymorphisms (Q192R and L55M) play important roles in the risk of T2DM with distinct effects across European and Asian populations. Further studies from other populations are needed to confirm these results.

Conflict of interest

The authors confirm that there are no conflicts of interest.

Acknowledgement

Conceived and designed the study: JQL and HR. Performed the search: JQL, HR and MZL. Analysed the data: JQL and HR. Contributed reagents/material/analysis tools: JQL, HR, MZL, PFF and DXX. Wrote the manuscript, reference collection, data management, statistical analyses, paper writing and study design: JQL.

Funding source: This work was supported by the National Natural Science Foundation of China (No. 81703623).

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