Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

A meta-analysis of associations of LEPR Q223R and K109R polymorphisms with Type 2 diabetes risk

  • Yunzhong Yang,

    Roles Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States of America

    ⨯
  • Tianhua Niu

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    tniu@tulane.edu

    Affiliation Department of Biochemistry and Molecular Biology, Tulane University School Medicine, New Orleans, LA, United States of America

    ⨯

Abstract

Background

Leptin receptor (LEPR) plays a pivotal role in the control of body weight, energy metabolism, and insulin sensitivity. Various genetic association studies were performed to evaluate associations of LEPR genetic variants with type 2 diabetes (T2D) susceptibility.

Methods

A comprehensive search was conducted to identify all eligible case-control studies for examining the associations of LEPR single nucleotide polymorphisms (SNPs) Q223R (rs1137101) and K109R (rs1137100) with T2D risk. Odds ratios (OR) and corresponding 95% confidence intervals (CIs) were used to measure the magnitudes of association.

Results

For Q223R, 13 studies (11 articles) consisting of a total of 4030 cases and 2844 controls, and for K109R 7 studies (7 articles) consisting of 3319 cases and 2465 controls were available. Under an allele model, Q223R was not significantly associated with T2D risk (OR = 1.09, 95% CI: 0.80–1.48, P-value = 0.5989), which was consistent with results obtained under four genotypic models (ranges: ORs 1.08–1.20, 95% CIs: 0.58–2.02 to 0.64–2.26; P-values, 0.3650–0.8177, which all exceeded multiplicity-adjusted α = 0.05/5 = 0.01). In addition, no significant association was found between K109R and T2D risk based on either an allele model (OR = 0.93, 95% CI: 0.85–1.03, P-value = 0.1868) or four genotypic models (ranges: ORs 0.81–0.99, 95% CIs: 0.67–0.86 to 0.97–1.26, P-values, 0.0207–0.8804 which all exceeded multiplicity-adjusted α of 0.01). The magnitudes of association for these two SNPs were not dramatically changed in subgroup analyses by ethnicity or sensitivity analyses. Funnel plot inspections as well as Begg and Mazumdar adjusted rank correlation test and Egger linear regression test did not reveal significant publication biases in main and subgroup analyses. Bioinformatics analysis predicted that both missense SNPs were functionally neutral and benign.

Conclusions

The present meta-analysis did not detect significant genetic associations between LEPR Q223R and K109R polymorphisms and T2D risk.

Citation: Yang Y, Niu T (2018) A meta-analysis of associations of LEPR Q223R and K109R polymorphisms with Type 2 diabetes risk. PLoS ONE 13(1): e0189366. https://doi.org/10.1371/journal.pone.0189366

Editor: Roberto Amendola, ENEA Centro Ricerche Casaccia, ITALY

Received: July 14, 2016; Accepted: November 26, 2017; Published: January 2, 2018

Copyright: © 2018 Yang, Niu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: T.N. is supported by a start-up fund of the Center for Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University. There was no additional external funding received for this study.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Type 2 diabetes (T2D), a metabolic disorder that is characterized by hyperglycemia (i.e., high blood glucose) in the context of insulin resistance and a relative lack of insulin, is the most common form of diabetes, accounting for at least 90% of diabetic individuals globally [1]. Recent studies suggest that T2D is increasing rapidly worldwide [2]. The development of T2D is multifactorial, which involves both environmental factors and genetic variants [3].

Leptin (LEP, also called OB for obese) is an adipocyte-derived hormone produced mainly by white adipose tissue, which regulates appetite, energy metabolism, body weight, and insulin sensitivity [4–6]. The word “leptin”, which is from the Greek word ‘leptos’, means ‘thin’, referring to its regulating functions on appetite, food intake and energy homeostasis. LEP exerts its important physiological effect on the regulation of fat metabolism by binding to LEP receptor (LEPR, also called CD295 and OBR) [6–8], which is a single transmembrane protein that belongs to class I cytokine receptor family distributed in a variety of tissue types [9]. Both LEP and LEPR genes have been cloned in humans [10, 11], and have been mapped to chromosome regions 7q32.1 [12] and 1p31.3 [13, 14], respectively.

The LEPR protein has six isoforms designated OBRa, OBRb, OBRc, OBRd, OBRe, and OBRf, which are obtained by alternative splicing [15]. Although all six isoforms share an identical extracellular domain [16], only OBRb (i.e., the long full-length isoform) contains intracellular motifs required for the transduction of intracellular signaling [17, 18]. Of them, OBRb is considered to be the major isoform involved in appetite control [19], which is primarily expressed in hypothalamic regions [16]. Nevertheless, OBRb is found to be expressed in pancreatic islets, mediating the inhibitory effects of LEP on insulin secretion [20]. Upon LEP binding to OBRb, an OBRb/Janus kinase 2 (JAK2) complex is formed, resulting in cross-phosphorylation. The tyrosine residue, Tyr1138 on OBRb, is important for signal transducer and activator of transcription 3 (STAT3) activation, which activates suppressor of cytokine signaling 3 (SOCS3) expression. This leads to a negative inhibition of LEP signaling through Tyr985 and additional sites on JAK2. Mitogen-activated protein kinase (MAPK) and insulin receptor substrate/phosphatidyl-inositol 3’ kinase (PI3K) pathways can also be activated following JAK2 phosphorylation [21]. Through binding to OBRb, LEP can activate multiple signal transduction pathways and particularly the JAK2/STAT3 pathway for controlling food intake and energy balance.

To evaluate the potential roles of LEPR’s molecular variants in T2D risk, several individual genetic association studies have been conducted by different research groups on polymorphisms located in this gene in different ethnic populations. However, results of these studies are controversial and inconclusive (e.g., for Q223R, studies of [22] and [23] showed effects in opposite directions). Seven LEPR genetic polymorphisms, i.e., K109R (rs1137100), Q223R (rs1137101), S343S (rs1805134, formerly rs3790419), N567N (rs2228301), K656N (rs1805094, formerly rs8179183), P1019P (rs1805096), and 3’ untranslated region (UTR) Ins/Del polymorphisms have been previously studied for their associations with T2D risk [24] (Fig 1), however, only two missense single nucleotide polymorphisms (SNPs)—Q223R (rs1137101) and K109R (rs1137100) located in exons 6 and 4 respectively, were most widely examined with regard to their roles in T2D risk, for which sufficient numbers of single studies (i.e., > 5) were obtained for each SNP. We therefore conducted a comprehensive meta-analysis focusing exclusively on these two missense SNPs aiming at elucidating their associations with T2D susceptibility.

thumbnail
Download:
Fig 1. A schematic diagram of LEPR exon-intron gene structure spanning 168-kilobase (kb) displaying genomic locations of LEPR K109R (rs1137100) (exon 4), Q223R (rs1137101) (exon 6), S343S (rs1805134, formerly rs3790419) (exon 9), N567N (rs2228301) (exon 12), K656N (rs1805094, formerly rs8179183) (exon 14), P1019P (rs1805096) (exon 20), and 3’ untranslated region (UTR) Ins/Del polymorphisms (exon 20) based on gene structures shown in Thompson et al. (1997) [81] and Hansel et al. (2009) [82], with applications of SeqVISTA [83, 84] to map the locations of these genetic variants.

Only Q223R, K109R, K656N, P1019P and 3’ UTR Ins/Del (i.e., underlined) polymorphisms were meta-analyzed by Yang et al. (2016) [24]. Only Q223R was meta-analyzed by Liu et al. (2015) [69], and only Q223R, K109R, K656N, and P1019P were meta-analyzed by Su et al. (2016) [70]. Filled boxes indicate protein-coding regions, and open boxes indicate non-protein-coding regions, i.e., UTRs. Abbreviations: Del deletion; Ins, insertion; UTR, untranslated region. Unfilled boxes are non-coding regions. Not drawn to scale.

https://doi.org/10.1371/journal.pone.0189366.g001

Materials and methods

Search strategy

We searched relevant studies from the following electronic databases: PubMed, Excerpta Medica Database (EMBASE), Cochrane Library, and Google Scholar up to February 1, 2016. The following search terms were used in the electronic searches: “leptin receptor”, “gene”, “lepr”, “T2D”, “T2D and Type 2 Diabetes” with language restrictions to either English or Chinese. This study was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement checklist (S1 PRISMA Checklist) and the Meta-analysis of Genetic Association Studies checklist (S2 Checklist).

Study selection

The inclusion criteria were: (1) an original human-based case-control study using either a hospital-based or a population-based design; (2) a clear definition of T2D; (3) the relationship between either Q223R (rs1137101) or K109R (rs1137100) and T2D risk was evaluated; and (4) providing sufficient data for calculating genotype and allele odds ratios (ORs) with their respective corresponding 95% confidence intervals (CIs). The exclusion criteria were: (1) reviews, conference abstracts, editorials and letters, (2) animal and in vitro studies, and (3) data about genotype frequencies could not be obtained. In case of overlapping or repeated studies, the one with most completed information was chosen. In addition, if more than one study shared the same subjects, the one with smaller sample size is excluded. All assessments were performed independently by two reviewers (YY and TN).

Data extraction

Data extraction was performed independently by two investigators (YY and TN) based on a pre-defined standard protocols. Any disagreements were solved by discussion. From each qualified study, the following information was collected: year of publication, first author’s name, study location, ethnicity, source of controls (population-based or hospital-based), diagnosis criteria of T2D (i.e., how T2D is defined), sample sizes and respective genotypic frequencies in case and control groups, mean±standard deviation (SD) of age, distribution of gender, genotyping methods, and Hardy-Weinberg equilibrium (HWE) in controls (To present study characteristics more succinctly, T2D diagnosis criteria, genotyping methods, and HWE in controls were omitted from Tables 1 and 2). For each variable, corresponding measurements were shown using the same unit.

thumbnail
Download:
Table 1. General characteristics of 13 included studies for LEPR Q223R*.

https://doi.org/10.1371/journal.pone.0189366.t001

thumbnail
Download:
Table 2. General characteristics of 7 included studies for LEPR K109R*.

https://doi.org/10.1371/journal.pone.0189366.t002

Quality assessment

Two authors (YY and TN) evaluated each individual study’s quality independently according to the Newcastle-Ottawa scale (NOS) [25], which assesses the quality of each individual study in three sections: (1) selection of study subjects: 0–4; (2) comparability of study subjects: 0–2; and (3) clinical outcome: 0–3. The NOS score has a range of 0–9; and a score ≥ 7 is indicative of a good quality, e.g., [26, 27]. Studies with a NOS score ≥ 6 are considered to be of sufficient quality for inclusion in a meta-analysis (e.g., [24, 28]).

Statistical analysis

The ORs with 95% CIs were computed to evaluate respective associations of LEPR Q223R and K109R SNPs with T2D risk. For each polymorphism, 5 genetic models were employed, i.e., (1) an allele model (G vs. A), (2) a homozygote model (GG vs. AA), (3) a heterozygote model (AG vs. AA), (4) a dominant model (GG+AG vs. AA), and (5) a recessive model (GG vs. AG+AA).

Heterogeneity among studies was assessed by Cochrane’s Q-test [29], which follows a chi-square distribution. I2 statistic, which is on a scale of 0–100% (0–25%, no heterogeneity; 25–50%, moderate heterogeneity; 50–75%, large heterogeneity; 75–100%, extreme heterogeneity) [30], is also computed. A Cochrane’s Q test P-value < 0.10 [30] or an I2 > 50% [31] was considered indicative of a statistically significant heterogeneity. A random effects model (the DerSimonian and Laird method) [32] was employed when a significant heterogeneity was detected among studies. Otherwise, a fixed effects model (the Mantel-Haenszel method) [33] was applied. Subgroup analyses stratified by ethnicity (Chinese populations vs. non-Chinese populations) were performed. The stability of the results was assessed using sensitivity analysis by removing each single study involved in the meta-analysis one at a time to reflect the influence of the individual study to the pooled ORs. The potential presence of publication bias was assessed by means of funnel plot inspection, and both Begg and Mazumdar adjusted rank correlation test [34] and Egger’s linear regression test [35] were applied to test for funnel plot asymmetry. All statistical analyses were conducted using R version 3.2.3 software meta package (https://cran.r-project.org/web/packages/meta/index.html) and metafor package (https://cran.r-project.org/web/packages/metafor/index.html).

Bioinformatics analysis

A total of 7 in silico tools were applied for functional prediction of LEPR Q223R and K109R: (1) Mutation Assessor [36] (http://mutationassessor.org), (2) BLOSUM62 [37] (https://www.ncbi.nlm.nih.gov/Class/FieldGuide/BLOSUM62.txt), (3) PROVEAN [38] (http://provean.jcvi.org/index.php), (4) PolyPhen-2 [39] (http://genetics.bwh.harvard.edu/pph2/), (5) PANTHER [40], (6) SNPs&GO [41] (http://snps-and-go.biocomp.unibo.it/snps-and-go/), and (7) SNPs3D [42] (http://www.snps3d.org/). Mutation Assessor [36] calculates a functional impact (FI) score for a protein mutation. A functional impact (FI) score ≤ 0.8, 0.8–1.9, 1.9–3.5 and > 3.5 is indicative of “neutral”, “low impact”, “medium impact”, and “high impact”, respectively [43]. BLOSUM62 is a scoring matrix for amino acid substitutions, such that a negative score is indicative of an evolutionarily less acceptable substitution, and a positive score is indicative of an evolutionarily more acceptable substitution [37]. PROVEAN (Protein Variation Effect Analyzer) computes a PROVEAN score by using a delta alignment score approach [38]. A score ≤ -2.5 and > -2.5 is indicative of “deleterious”, and “neutral”, respectively [44]. PolyPhen-2 [39] computes a Position-Specific Independent Count (PSIC) score ranging from 0 to 1. A criterion used by [44] is that a PSIC score ≤ 0.5 and > 0.5 is indicative of “probably damaging”, and “benign”, respectively. PANTHER [40] computes a substitution position-specific evolutionary conservation (subPSEC). A subPSEC score ≤ -3 (corresponding to a Pdeleterious ≥ 0.5) and > -3 (corresponding to a Pdeleterious < 0.5) is indicative of “deleterious” and “neutral”, respectively [45, 46]. A greater Pdeleterious indicates a tendency to exert more severe impairments on protein function [47]. A SNPs&GO Disease Probability score > 0.5 and ≤ 0.5 is indicative of “deleterious”, and “neutral”, respectively [41]. SNPs3D [42] computes a support vector machine (SVM) score. An SVM score < 0 and ≥ 0 is indicative of “deleterious” and “neutral”, respectively [48].

Results

Characteristics of included studies

A flow diagram depicting the study selection process is shown in Fig 2. An initial literature search identified 578 potentially relevant articles (S3 Electronic Search Strategy and Results). After removing duplicates, there were 363 potentially relevant articles. Based on reviews of titles and abstracts of them, 335 articles were excluded (including 165 animal studies, 106 review articles, 4 articles that are not case-control studies, and 59 studies that were not relevant). Full texts were reviewed for the remaining 29 articles, and 16 of them were further excluded. Finally, 13 articles (10 English articles and 3 Chinese articles) were included in this meta-analysis. For LEPR Q223R, 13 studies (7 in Chinese populations and 6 studies in non-Chinese populations) from 11 articles [22, 23, 49–57] were included, comprising 4030 cases and 2844 controls. For LEPR K109R, 7 studies (3 in Chinese populations and 4 studies in non-Chinese populations) from 7 articles [23, 50, 52, 53, 55, 58, 59] were included, comprising 3319 cases and 2465 controls. The characteristics of the included studies are presented in Tables 1 and 2 for Q223R and K109R, respectively. The mean±SD for NOS score was 7.82±0.75 (range, 7–9) for Q223R and 7.83±0.89 (range, 7–9) for K109R, respectively. Specifically, for Q223R (variant allele: R223), higher variant allele frequencies (VAFs) were observed in Chinese T2D cases (Mean±SD: 0.82±0.10; range, 0.63–0.89) and controls (Mean±SD: 0.79±0.20; range, 0.34–0.89) than in non-Chinese T2D cases (Mean±SD: 0.64±0.12; range, 0.49–0.80) and controls (Mean±SD: 0.63±0.19; range, 0.33–0.84), respectively (S1 Fig). Further, for K109R (variant allele: R109), higher VAFs were observed in Chinese T2D cases (Mean±SD: 0.83±0.013; range, 0.82–0.84) and controls (Mean±SD: 0.82±0.021; range, 0.81–0.85) than in non-Chinese T2D cases (Mean±SD: 0.40±0.28; range, 0.20–0.80) and controls (Mean±SD: 0.42±0.27; range, 0.23–0.82), respectively (S2 Fig).

thumbnail
Download:
Fig 2. A PRISMA flow diagram depicting the literature search and study selection process.

Abbreviations: EMBASE, Excerpta Medica Database; T2D, type 2 diabetes.

https://doi.org/10.1371/journal.pone.0189366.g002

Meta-analysis results

For assessing the relationship between LEPR Q223R polymorphism and T2D risk, a total of 13 studies (11 articles) were included (Table 3) and a random effects model was employed because of the presence of significant heterogeneity. Under an allelic model, a comparison of G vs. A produced an OR of 1.09 (95% CI: 0.80–1.48), which was not statistically significant (P-value = 0.5989) (Table 3 and Fig 3). Under genotypic models, comparisons of GG vs. AA, AG vs. AA, GG/AG vs. AA, and GG vs. AG/AA gave rise to ORs of 1.20, 1.08, 1.13, and 1.13 with P-values of 0.5741, 0.8177, 0.6871, and 0.3650, respectively, which also did not attain statistical significance. For assessing the relationship between LEPR K109R polymorphism and T2D risk, a total of 7 studies (7 articles) were included (Table 4) and a fixed effects model was employed because of a lack of significant heterogeneity. Under an allelic model, a comparison of G vs. A produced an OR of 0.93 (95% CI: 0.85–1.03), which did not reach statistical significance (P-value = 0.1868) (Table 4 and Fig 4). Under genotypic models, comparisons of GG vs. AA, AG vs. AA, GG/AG vs. AA, and GG vs. AG/AA produced ORs of 0.97 (95% CI: 0.74–1.26), 0.81 (95% CI: 0.67–0.97), 0.83 (95% CI: 0.70–0.99), and 0.99 (95% CI: 0.86–1.17) respectively, with P-values of 0.8087, 0.0207, 0.0384, and 0.8804 respectively, which all exceeded multiplicity-adjusted α = 0.05/5 = 0.01 with control for 5 genetic models.

thumbnail
Download:
Fig 3. Forest plot for association of LEPR Q223R polymorphism with T2D risk under an allele model in total sample (n = 13 studies, random effects model).

https://doi.org/10.1371/journal.pone.0189366.g003

thumbnail
Download:
Fig 4. Forest plot for association of LEPR K109R polymorphism with T2D risk under an allele model in total sample (n = 7 studies, fixed effects model).

https://doi.org/10.1371/journal.pone.0189366.g004

thumbnail
Download:
Table 3. Meta-analysis results of the association between LEPR Q223R and T2D for 5 genetic models*.

https://doi.org/10.1371/journal.pone.0189366.t003

thumbnail
Download:
Table 4. Meta-analysis results of the association between LEPR K109R and T2D for 5 genetic models*.

https://doi.org/10.1371/journal.pone.0189366.t004

Test of heterogeneity

In the pooled analysis, for LEPR Q223R, a significant heterogeneity was detected for comparisons under 5 different genetic models, i.e., G vs. A, GG vs. AA, AG vs. AA, GG/AG vs. AA, and GG vs. AG/AA, such that I2 was 90.20%, 86.10%, 82.90%, 88.00%, and 75.40%, respectively (P-value for heterogeneity < multiplicity-corrected α = 0.05/5 = 0.01 for considering 5 genetic models), as shown in Table 3. Therefore, a random effects model was chosen to estimate this SNP’s pooled OR. For LEPR K109R, no statistically significant heterogeneity was detected for comparisons under 5 different genetic models, i.e., G vs. A, GG vs. AA, AG vs. AA, GG/AG vs. AA, and GG vs. AG/AA, such that I2s ranged from 0.00% to 13.60%, and P-values for heterogeneity ranged from 0.3274 to 0.8044, which exceeded multiplicity-corrected α = 0.05/5 = 0.01, as shown in Table 4. Because I2 was under 50% and P-values for heterogeneity were not significant for all these genetic models, a fixed effects model was applied in estimating this SNP’s pooled effect.

Subgroup analysis

To explore sources of heterogeneity across studies, subgroup analyses by ethnicity (i.e., Chinese populations vs. non-Chinese populations) were conducted. For LEPR Q223R, 7 studies were performed in Chinese populations. Under genotypic models, a significant heterogeneity was detected for comparisons under 5 different genetic models, i.e., G vs. A, GG vs. AA, AG vs. AA, GG/AG vs. AA, and GG vs. AG/AA, such that I2 was 91.70%, 86.00%, 82.10%, 87.70%, and 78.80%, respectively (P-value for heterogeneity < multiplicity-adjusted α = 0.01 for each comparison), as shown in Table 5. For this SNP (i.e., Q223R), 6 studies were performed in non-Chinese populations. Under 5 different genetic models, i.e., G vs. A, GG vs. AA, AG vs. AA, GG/AG vs. AA, and GG vs. AG/AA, respectively (I2 was 88.50%, 85.10%, 78.50%, 84.90%, and 73.50%, and P-value for heterogeneity < multiplicity-adjusted α = 0.01 for each comparison), as shown in Table 6. Therefore, a random effects model was employed under each of these 5 genetic models in Chinese and non-Chinese populations, respectively. Pooled ORs (95% CIs) in Chinese populations had a range from 1.09 (95% CI: 0.31–3.88) to 1.17 (95% CI: 0.35–3.89) with P-values ranged 0.5476–0.8944 (Table 5; and the pooled effect under an allele model were displayed in a forest plot shown in S3 Fig) and in non-Chinese populations had a range from 0.98 (95% CI: 0.51–1.86) to 1.20 (95% CI: 0.58–2.47) with P-values ranged 0.5816–0.9436 (Table 6; and the pooled effect under an allele model were displayed in a forest plot shown in S4 Fig). For LEPR K109R, three studies were performed in Chinese populations (Table 7). Under each of 5 genetic models, no significant heterogeneity was detected (I2 ranged from 0% to 55.10%, and P-value for heterogeneity ranged from 0.1078 to 0.4121). For this SNP, 4 studies were performed in non-Chinese populations (Table 8). Under each of 5 genetic models, no significant heterogeneity was detected (I2 was consistently 0.00% for each comparison, and P-value for heterogeneity ranged from 0.5877 to 0.7808). Therefore, a fixed effects model was employed under each of these 5 genetic models in Chinese and non-Chinese populations, respectively. Pooled ORs (95% CIs) in Chinese populations had a range from 0.96 (95% CI: 0.45–2.03) to 1.03 (95% CI: 0.81–1.31) with P-values ranged 0.8044–0.959 (Table 7; and the pooled effect under an allele model were displayed in a forest plot shown in S5 Fig) and in non-Chinese populations had a range from 0.80 (95% CI: 0.66–0.96) to 0.97 (95% CI: 0.73–1.29) with P-values ranged 0.0167–0.8284 (Table 8; and the pooled effect under an allele model were displayed in a forest plot shown in S6 Fig).

thumbnail
Download:
Table 5. Meta-analysis results of the association between LEPR Q223R and T2D for 5 genetic models in Chinese population*.

https://doi.org/10.1371/journal.pone.0189366.t005

thumbnail
Download:
Table 6. Meta-analysis results of the association between LEPR Q223R and T2D for 5 genetic models in Non-Chinese population*.

https://doi.org/10.1371/journal.pone.0189366.t006

thumbnail
Download:
Table 7. Meta-analysis results of the association between LEPR K109R and T2D for 5 genetic models in Chinese population*.

https://doi.org/10.1371/journal.pone.0189366.t007

thumbnail
Download:
Table 8. Meta-analysis results of the association between LEPR K109R and T2D for 5 genetic models in Non-Chinese population*.

https://doi.org/10.1371/journal.pone.0189366.t008

Sensitivity analysis

In order to assess the influence of each individual study on the pooled OR, we performed a sensitivity analysis by excluding each single study involved in the meta-analysis one at a time. For LEPR Q223R, the pooled ORs (95% CIs) ranged from 0.99 (95% CI: 0.78–1.27) to 1.17 (95% CI: 0.86–1.59) under an allelic model (Table 9), which was not dramatically changed from a pooled OR of 1.09 (95% CI: 0.80–1.48) under the same genetic model in the total sample (Table 3). For LEPR K109R, the pooled ORs (95% CIs) ranged from 0.91 (95% CI: 0.81–1.03) to 0.95 (95% CI: 0.85–1.06) under an allelic model (Table 10), which was not substantially altered from a pooled OR of 0.93 (95% CI: 0.85–1.03) under the same genetic model in the total sample (Table 4). These findings show that our results were statistically robust for both of these two polymorphisms.

thumbnail
Download:
Table 9. Sensitivity analysis results of the association between LEPR Q223R and T2D for allelic model*.

https://doi.org/10.1371/journal.pone.0189366.t009

thumbnail
Download:
Table 10. Sensitivity analysis results of the association between LEPR K109R and T2D for allelic model*.

https://doi.org/10.1371/journal.pone.0189366.t010

Publication bias evaluation

Visual inspections of respective funnel plots revealed no obvious asymmetry for associations of LEPR Q223R and T2D and LEPR K109R and T2D in total sample (Figs 5 and 6), Chinese populations (S7 and S8 Figs), and non-Chinese populations (S9 and S10 Figs), respectively. Begg and Mazumdar adjusted rank correlation test and Egger’s linear regression test were used to assess the publication bias for each SNP. No significant publication bias was observed in this meta-analysis [For LEPR Q223R: (1) an allele model (G vs. A): Begg and Mazumdar’s P-value = 0.7650, Egger’s P-value = 0.1932; (2) a homozygote model (GG vs. AA): Begg and Mazumdar’s P-value = 0.3674, Egger’s P-value = 0.5606; (3) a heterozygote model (AG vs. AA): Begg and Mazumdar’s P-value = 1.0000, Egger’s P-value = 0.2857; (4) a dominant model (GG+AG vs. AA): Begg and Mazumdar’s P-value = 0.5098, Egger’s P-value = 0.5570; and (5) a recessive model (GG vs. AG+AA): Begg and Mazumdar’s P-value = 0.9524, Egger’s P-value = 0.4236. For LEPR K109R: (1) an allele model (G vs. A): Begg and Mazumdar’s P-value = 0.2389, Egger’s P-value = 0.0463; (2) a homozygote model (GG vs. AA): Begg and Mazumdar’s P-value = 0.5619, Egger’s P-value = 0.8058; (3) a heterozygote model (AG vs. AA): Begg and Mazumdar’s P-value = 0.7726, Egger’s P-value = 0.8902; (4) a dominant model (GG+AG vs. AA): Begg and Mazumdar’s P-value = 0.7726, Egger’s P-value = 0.6220; and (5) a recessive model (GG vs. AG+AA): Begg and Mazumdar’s P-value = 0.7726, Egger’s P-value = 0.9867]. All the above P-values exceeded multiplicity-adjusted α = 0.05/5 = 0.01.

thumbnail
Download:
Fig 5. Funnel plot for association of LEPR Q223R polymorphism with T2D risk under an allele model in total sample (n = 13 studies).

https://doi.org/10.1371/journal.pone.0189366.g005

thumbnail
Download:
Fig 6. Funnel plot for association of LEPR K109R polymorphism with T2D risk under an allele model in total sample (n = 7 studies).

https://doi.org/10.1371/journal.pone.0189366.g006

Bioinformatics analysis

Based on 7 different in silico tools, both LEPR Q223Rand K109R are predicted to exert a low impact on protein function (by Mutation Assessor), to be evolutionarily more acceptable (by BLOSUM62) neutral (by PROVEAN, PANTHER, SNPs&GO, and SNPs3D) and benign (by PolyPhen-2) (Table 11).

thumbnail
Download:
Table 11. In silico predicted functional effects of LEPR Q223R and K109R*.

https://doi.org/10.1371/journal.pone.0189366.t011

Discussion

LEP, a pleiotropic hormone produced primarily by adipose tissue, plays an essential role in signaling energy status to the central nervous system (CNS), which has helped to redefine adipose tissue as an endocrine organ [60]. By binding to LEPRs expressed by neurons in CNS [61], leptin exerts its physiological effects on food intake, body weight, glucose and lipid metabolism, and regulation of immune function [15]. Although several independent studies identified significant associations between genetic variants of LEPR and obesity (e.g., [62, 63]), others did not (e.g., [58, 64]). Three meta-analysis studies (i.e., [65–67]) did not find significant relationships of LEPR polymorphisms with either obesity or obesity-related outcomes. In current study, 13 studies (11 articles; 4030 cases and 2844 controls) for Q223R, and 7 studies (7 articles; 3319 cases and 2465 controls) for K109R were included, which far exceed the sample size of any individual study. By employing 5 different genetic models to meta-analyze potential effects of these two missense SNPs on T2D risk, we did not detect statistically significant associations of either Q223R or K109R with T2D risk in either main analyses or subgroup analyses. Further, based on 7 software tools, both missense SNPs were predicted to be functionally neutral and benign.

The VAFs for Chinese and non-Chinese populations for LEPR Q223R and K109R are not uniform across different ethnic groups. For Q223R, higher VAFs were observed in Chinese T2D cases (0.82) and controls (0.79) than in non-Chinese T2D cases (0.64) and controls (0.63), respectively (S1 Fig). Further, for K109R, higher VAFs were observed in Chinese T2D cases (0.83) and controls (0.82) than in non-Chinese T2D cases (0.40) and controls (0.42), respectively (S2 Fig). VAFs for both missense SNPs in Chinese populations of current study were similar to those reported in other studies, e,g., [61] and [68], which appear to be higher than in non-Chinese populations. As shown in Fan and Say (2014) [61], even among Asians, the respective allele frequencies of variant alleles R223 and R109 were notably higher in Chinese than Indians and Malays.

A comparison between the current meta-analysis and three other meta-analysis studies, i.e., Yang et al. (2016) [24], Liu et al. (2015) [69], Su et al. (2016) [70], is shown in Table 12. For Yang et al. (2016) [24], 7 LEPR gene’s molecular variants, i.e., Q223R (rs1137101), K109R (rs1137100), S343S (rs1805134, formerly rs3790419), N567N (rs2228301), K656N (rs1805094, formerly rs8179183), P1019P (rs1805096), and the 3’ UTR Ins/Del in T2D risk were assessed (11, 7, 1, 1, 5, 3, and 2 studies were included for them, respectively). However, only 5 LEPR polymorphisms, i.e., Q223R, K109R, K656N, P1019P and 3’ UTR Ins/Del, were meta-analyzed because only 1 article was found for each of S343S and N567N, respectively. For Liu et al. (2015) [69], only Q223R was studied, whereas for Su et al. (2016) [70], 4 LEPR polymorphisms, i.e., Q223R, K109R, K656N, and P1019P, were meta-analyzed. With respect to Q223R, our results were concordant with those of Liu et al. (2015) [69] and Su et al. (2016) [70] such that no statistically significant associations were found. However, significant association was found by Yang et al. (2016) [24]. With respect to K109R, our results were concordant with those of Yang et al. (2016) [24] and Su et al. (2016) [70], such that no significant relationship was found between this missense SNP and T2D risk. With respect to another LEPR missense SNP, i.e., K656N, which was meta-analyzed by Yang et al. (2016) [24] and Su et al. (2016) [70], 5 and 4 studies were included in each of these two meta-analysis studies, respectively, which limited their abilities to draw robust conclusions on them. Therefore, to ensure that there are sufficiently large numbers of individual studies (i.e., > 5) amenable to subgroup analyses, only Q223R and K109R were assessed in the current study. We found that neither of these two missense SNPs is significantly associated with T2D risk. Taken together, based on our careful assessments, for Yang et al. (2016) [24], Liu et al. (2015) [69], Su et al. (2016) [70], there are errors (i.e., the genotype count data were incorrectly assigned to at least one included study) in data extraction from individual studies (affecting all these three studies) (affecting all of [24], [69], and [69), and errors (i.e., included studies contain overlapping data) in the number of individual studies included for meta-analysis a SNP (affecting [24] and [69]) (Table 12).

thumbnail
Download:
Table 12. Comparison of methods and results of current study with three previously published meta-analysis studies*.

https://doi.org/10.1371/journal.pone.0189366.t012

Caution should be taken when interpreting our results on the associations of gene polymorphisms with T2D. A significant heterogeneity was detected for Q223R (P-values for heterogeneity < multiplicity-corrected α = 0.05/5 = 0.01 for considering 5 genetic models (Table 3)], but not for K109R [range of P-values, 0.0205–0.6487, which were > multiplicity-corrected α = 0.05/5 = 0.01 (Table 4)] and subgroup analyses were conducted to explore reasons of heterogeneity. When stratified by ethnicity (i.e., Chinese vs non-Chinese populations), for Q223R, heterogeneity remained significant in each subgroup [P-values for heterogeneity < 0.0001 in Chinese populations (Table 5) and ≤ 0.002 non-Chinese populations (Table 6), respectively, which were all < multiplicity-corrected α = 0.05/5 = 0.01], and therefore, ethnicity did not appear to explain heterogeneity for Q223R. No heterogeneity was detected for K109R in either Chinese populations (Table 7) or non-Chinese populations (Table 8), because P-value for heterogeneity for each model was > multiplicity-corrected α = 0.05/5 = 0.01. In order to evaluate the influence of single studies on the overall estimate, a sensitivity analysis was performed by deleting each single study one at a time for allele model. The omission of any single study did not significantly alter pooled effect estimates for either Q223R (Table 9) or K109R (Table 10), suggesting that our meta-analysis results were both reliable and credible. For assessments of publication bias, funnel plots were generated and their symmetries were tested using Begg and Mazumdar rank correlation and Egger’s linear regression tests. Both tests revealed that no significant biases existed (P-values > 0.05 for all 5 genetic models for each SNP), and inspections of funnel plots also indicated no evidence of publication bias for the entire study sample [Fig 5 (Q223R) and Fig 6 (K109R)], and for either Chinese populations [S7 Fig (Q223R) and S8 Fig (K109R)] or non-Chinese populations [S9 Fig (Q223R) and S10 Fig (K109R)].

Our meta-analysis had several advantages: (1) Compared with the three previously published meta-analysis studies, i.e., Yang et al. (2016) [24], Liu et al. (2015) [69], Su et al. (2016) [70] their mistakes in data extraction were corrected and their weaknesses in considering 4 genetic models were well-addressed. (2) Both subgroup and sensitivity analyses were performed in the current study whereas only one of these two important types of analyses was employed by each of the three previously published meta-analysis studies (Table 12), which demonstrated that our results were statistically stable. (3) The current study applied both Begg and Mazumdar adjusted rank correlation test and Egger’s linear regression test whereas [of the three previously performed meta-analyses, only one study, i.e., Su et al. (2016) [70] employed both, but only for 4 genetic models], we did not detect any publication biases by funnel plot inspections in either main analyses or subgroup analyses, indicating that our results were unbiased. (4) In the current study, all included studies were of sufficiently high quality (i.e., NOS score ≥ 7), which all met our inclusion criteria. (5) To assess functional impacts of these two common missense SNPs, 7 in silico tools were applied, and their results were consistent with each other.

There are several limitations in the current study: (1) Our meta-analysis was based on unadjusted OR estimates due to a lack of individual participants’ data. There is an important potential source of type II error β in the inference that LEPR genetic variants does not contribute to diabetes-susceptibility in our meta-analysis. Some of the individual studies, e.g., Liao et al. (2012) [23] and Roszkowska-Gancarz et al. (2014) [52], which were included for meta-analysis of both Q223R and K109R, did not match body weight and age between cases and controls, or adjust computationally for these important covariates which are critical to penetrance of genes predisposing to T2D. Since T2DM is highly correlated with body weight and age, using thinner and younger control subjects compared to T2D cases (e.g., Etemad et al. (2013) [49]), could confound the estimate of a non-weight dependent T2DM effect of LEPR genetic variants. (2) The study examined two most widely studied missense SNPs of LEPR in T2D, i.e., Q223R (rs1137101) and K109R (rs1137100) which were in a moderate level of linkage disequilibrium (LD) (e.g., r2 = 0.3647 in Caucasians [71]), and haplotype-based association analysis could provide more statistical power than single SNP analysis [72–74]. (3) We applied a Bonferroni procedure to correct for the 5 genetic models tested, as in Wong et al. (2015) [75], and this procedure could be conservative. (4) The number of studies included in our meta-analysis, particularly the subgroup analyses according to ethnicity, was limited. (5) For Q223R, because individual studies had diverse population characteristics, significant between-study heterogeneity was observed, which could affect the precision of results, although the random effects model was applied in the presence of significant heterogeneity to pool ORs for this SNP. (6) T2D is polygenic and multifactorial, and there are a variety of possible genetic (> 80 genetic susceptibility loci have been identified [76], e.g., TCF7L2, PPARG), environmental (e.g., air pollution by nitrogen dioxide, PM2.5, and PM10 [77]), nutritional (e.g., dietary fiber, fat intake [78]), lifestyle (e.g., physical inactivity [79]) and sociodemographic (e.g., age, ethnicity, education [80]) risk factors involved in the etiology of this disease. Because the definition of T2D varies among the individual studies [The World Health Organization (WHO) and American Diabetes Association (ADA) represent the two most widely used criteria (Tables 1 and 2)], over- (i.e., too many) or under-(i.e., too few) inclusion of subjects could be a possibility for each study. (7) Potential gene-gene and gene-environment interactions may influence the associations of LEPR gene Q223R and K109R polymorphisms and T2D risk. (8) This meta-analysis focused only on articles published in the English and Chinese languages, and there may be other eligible studies that were published in other languages.

In conclusion, to the best of our knowledge, the current study is most up-to-date, robust, and unbiased, when compared to previously published meta-analysis studies (i.e., Yang et al. (2016) [24], Liu et al. (2015) [69], Su et al. (2016) [70]) in this field. Neither Q223R nor K109R was significantly associated with T2D risk in the current meta-analysis, and bioinformatics analysis predicted that both SNPs are functionally neutral and benign. Additional well-designed independent studies with sufficiently large sample sizes in various ethnicities could be conducted to confirm our findings.

Supporting information

S1 File. PRISMA checklist.

PRISMA 2009 checklist.

https://doi.org/10.1371/journal.pone.0189366.s001

(DOC)

S2 File. Checklist.

Meta-analysis of genetic association studies checklist.

https://doi.org/10.1371/journal.pone.0189366.s002

(DOC)

S3 File. Electronic search strategy and results.

Electronic search strategy and results for PubMed, EMBASE, Cochrane Library, and Google Scholar.

https://doi.org/10.1371/journal.pone.0189366.s003

(XLS)

S1 Fig. Q223 allele frequencies of LEPR Q223R polymorphism for T2D cases and controls in Chinese (left panel) and non-Chinese (right panel) populations.

https://doi.org/10.1371/journal.pone.0189366.s004

(TIFF)

S2 Fig. R109 allele frequencies of LEPR K109R polymorphism for T2D cases and controls in Chinese (left panel) and non-Chinese (right panel) populations.

https://doi.org/10.1371/journal.pone.0189366.s005

(TIFF)

S3 Fig. Forest plot for association of LEPR Q223R polymorphism with T2D risk under an allele model in Chinese populations (n = 7 studies, random effects model).

https://doi.org/10.1371/journal.pone.0189366.s006

(TIFF)

S4 Fig. Forest plot for association of LEPR Q223R polymorphism with T2D risk under an allele model in non-Chinese populations (n = 6 studies, random effects model).

https://doi.org/10.1371/journal.pone.0189366.s007

(TIFF)

S5 Fig. Forest plot for association of LEPR K109R polymorphism with T2D risk under an allele model in Chinese populations (n = 3 studies, fixed effects model).

https://doi.org/10.1371/journal.pone.0189366.s008

(TIFF)

S6 Fig. Forest plot for association of LEPR K109R polymorphism with T2D risk under an allele model in non-Chinese populations (n = 4 studies, fixed effects model).

https://doi.org/10.1371/journal.pone.0189366.s009

(TIFF)

S7 Fig. Funnel plot for association of LEPR Q223R polymorphism with T2D risk under an allele model in Chinese populations (n = 7 studies).

https://doi.org/10.1371/journal.pone.0189366.s010

(TIFF)

S8 Fig. Funnel plot for association of LEPR K109R polymorphism with T2D risk under an allele model in Chinese populations (n = 3 studies).

https://doi.org/10.1371/journal.pone.0189366.s011

(TIFF)

S9 Fig. Funnel plot for association of LEPR Q223R polymorphism with T2D risk under an allele model in non-Chinese populations (n = 6 studies).

https://doi.org/10.1371/journal.pone.0189366.s012

(TIFF)

S10 Fig. Funnel plot for association of LEPR K109R polymorphism with T2D risk under an allele model in non-Chinese populations (n = 4 studies).

https://doi.org/10.1371/journal.pone.0189366.s013

(TIFF)

Acknowledgments

We are thankful for Dr. John Lefante and Dr. Sudesh Srivastav at Department of Global Biostatistics and Data Science, Tulane University School of Public Health and Tropical Medicine for their constructive comments. We are grateful to Dr. Kyong Soo Park, Department of Internal Medicine, College of Medicine, Seoul National University, Korea, and Dr. Bermseok Oh, Department of Biomedical Engineering, School of Medicine, Kyung Hee University, Korea, for their critical help in understanding their published results [i.e., Park et al. J Hum Genet. (2006) PMID: 16333525] on the relationships between LEPR Q223R and K109R polymorphisms and T2D in the Korean study. We thank Dr. Patimah Ismail, Department of Biomedical Science, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia for providing insightful comments on their study [i.e., Etemad et al., Int J Mol Sci. (2013) PMID: 24051404]. We also would like to express our gratitude for Dr. Miguel Cruz, Centro Médico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, México, México, for providing the genotype count data for LEPR K109R for their study [i.e., Cruz et al., Diabetes Metab Res Rev. (2010) PMID: 20503258].

References

  1. 1. Zimmet P, Alberti KG, Shaw J. Global and societal implications of the diabetes epidemic. Nature. 2001;414(6865):782–7. pmid:11742409.
  2. 2. Hu FB. Globalization of diabetes: the role of diet, lifestyle, and genes. Diabetes Care. 2011;34(6):1249–57. pmid:21617109; PubMed Central PMCID: PMC3114340.
  3. 3. Wu Y, Ding Y, Tanaka Y, Zhang W. Risk factors contributing to type 2 diabetes and recent advances in the treatment and prevention. Int J Med Sci. 2014;11(11):1185–200. pmid:25249787; PubMed Central PMCID: PMC4166864.
  4. 4. Yadav VK, Oury F, Tanaka KF, Thomas T, Wang Y, Cremers S, et al. Leptin-dependent serotonin control of appetite: temporal specificity, transcriptional regulation, and therapeutic implications. J Exp Med. 2011;208(1):41–52. pmid:21187319; PubMed Central PMCID: PMC3023132.
  5. 5. Himms-Hagen J. Physiological roles of the leptin endocrine system: differences between mice and humans. Crit Rev Clin Lab Sci. 1999;36(6):575–655. pmid:10656540.
  6. 6. Morton GJ, Gelling RW, Niswender KD, Morrison CD, Rhodes CJ, Schwartz MW. Leptin regulates insulin sensitivity via phosphatidylinositol-3-OH kinase signaling in mediobasal hypothalamic neurons. Cell Metab. 2005;2(6):411–20. pmid:16330326.
  7. 7. Considine RV. Human leptin: an adipocyte hormone with weight-regulatory and endocrine functions. Semin Vasc Med. 2005;5(1):15–24. Epub 2005/06/22. pmid:15968576.
  8. 8. de Luis DA, Perez Castrillon JL, Duenas A. Leptin and obesity. Minerva Med. 2009;100(3):229–36. Epub 2009/02/03. pmid:19182739.
  9. 9. Mantzoros CS, Moschos SJ. Leptin: in search of role(s) in human physiology and pathophysiology. Clin Endocrinol (Oxf). 1998;49(5):551–67. pmid:10197068.
  10. 10. Zhang Y, Proenca R, Maffei M, Barone M, Leopold L, Friedman JM. Positional cloning of the mouse obese gene and its human homologue. Nature. 1994;372(6505):425–32. Epub 1994/12/01. pmid:7984236.
  11. 11. Tartaglia LA, Dembski M, Weng X, Deng N, Culpepper J, Devos R, et al. Identification and expression cloning of a leptin receptor, OB-R. Cell. 1995;83(7):1263–71. Epub 1995/12/29. pmid:8548812.
  12. 12. Sober S, Org E, Kepp K, Juhanson P, Eyheramendy S, Gieger C, et al. Targeting 160 candidate genes for blood pressure regulation with a genome-wide genotyping array. PLoS One. 2009;4(6):e6034. pmid:19562039; PubMed Central PMCID: PMCPMC2699027.
  13. 13. Chung WK, Power-Kehoe L, Chua M, Leibel RL. Mapping of the OB receptor to 1p in a region of nonconserved gene order from mouse and rat to human. Genome Res. 1996;6(5):431–8. Epub 1996/05/01. pmid:8743992.
  14. 14. Winick JD, Stoffel M, Friedman JM. Identification of microsatellite markers linked to the human leptin receptor gene on chromosome 1. Genomics. 1996;36(1):221–2. Epub 1996/08/15. pmid:8812446.
  15. 15. Fruhbeck G. Intracellular signalling pathways activated by leptin. Biochem J. 2006;393(Pt 1):7–20. Epub 2005/12/13. pmid:16336196; PubMed Central PMCID: PMCPmc1383660.
  16. 16. Hegyi K, Fulop K, Kovacs K, Toth S, Falus A. Leptin-induced signal transduction pathways. Cell Biol Int. 2004;28(3):159–69. Epub 2004/02/27. pmid:14984741.
  17. 17. Mantzoros CS, Magkos F, Brinkoetter M, Sienkiewicz E, Dardeno TA, Kim SY, et al. Leptin in human physiology and pathophysiology. Am J Physiol Endocrinol Metab. 2011;301(4):E567–84. Epub 2011/07/28. pmid:21791620; PubMed Central PMCID: PMCPmc3191548.
  18. 18. Cottrell EC, Mercer JG. Leptin receptors. Handb Exp Pharmacol. 2012;(209):3–21. Epub 2012/01/18. pmid:22249808.
  19. 19. Suzuki K, Jayasena CN, Bloom SR. Obesity and appetite control. Exp Diabetes Res. 2012;2012:824305. pmid:22899902; PubMed Central PMCID: PMC3415214.
  20. 20. Emilsson V, Liu YL, Cawthorne MA, Morton NM, Davenport M. Expression of the functional leptin receptor mRNA in pancreatic islets and direct inhibitory action of leptin on insulin secretion. Diabetes. 1997;46(2):313–6. pmid:9000710.
  21. 21. Ahima RS, Osei SY. Leptin signaling. Physiol Behav. 2004;81(2):223–41. Epub 2004/05/26. pmid:15159169.
  22. 22. Gan RT, Yang SS. The 223A>G polymorphism of the leptin receptor gene is associated with macroangiopathy in type 2 diabetes mellitus. Mol Biol Rep. 2012;39(4):4759–64. pmid:21938427.
  23. 23. Liao WL, Chen CC, Chang CT, Wu JY, Chen CH, Huang YC, et al. Gene polymorphisms of adiponectin and leptin receptor are associated with early onset of type 2 diabetes mellitus in the Taiwanese population. Int J Obes (Lond). 2012;36(6):790–6. Epub 2011/09/21. pmid:21931325.
  24. 24. Yang MM, Wang J, Fan JJ, Ng TK, Sun DJ, Guo X, et al. Variations in the Obesity Gene "LEPR" Contribute to Risk of Type 2 Diabetes Mellitus: Evidence from a Meta-Analysis. J Diabetes Res. 2016;2016:5412084. pmid:27195302; PubMed Central PMCID: PMC4852360.
  25. 25. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25(9):603–5. pmid:20652370.
  26. 26. Wang HL, Zhou PY, Liu P, Zhang Y. ALDH2 and ADH1 genetic polymorphisms may contribute to the risk of gastric cancer: a meta-analysis. PLoS One. 2014;9(3):e88779. pmid:24633362; PubMed Central PMCID: PMC3954547.
  27. 27. Wang Q, Zhou SB, Wang LJ, Lei MM, Wang Y, Miao C, et al. Seven functional polymorphisms in the CETP gene and myocardial infarction risk: a meta-analysis and meta-regression. PLoS One. 2014;9(2):e88118. pmid:24533069; PubMed Central PMCID: PMC3922770.
  28. 28. Qi D, Li J, Jiang M, Liu C, Hu Y, Li M, et al. The relationship between promoter methylation of p16 gene and bladder cancer risk: a meta-analysis. Int J Clin Exp Med. 2015;8(11):20701–11. pmid:26884993; PubMed Central PMCID: PMC4723838.
  29. 29. Cochran WG. The combination of estimates from different experiments. Biometrics. 1954;10:101–29.
  30. 30. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Bmj. 2003;327(7414):557–60. Epub 2003/09/06. pmid:12958120; PubMed Central PMCID: PMCPmc192859.
  31. 31. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21(11):1539–58. pmid:12111919.
  32. 32. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88. Epub 1986/09/01. pmid:3802833.
  33. 33. Mantel N, Haenszel W. Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst. 1959;22(4):719–48. Epub 1959/04/01. pmid:13655060.
  34. 34. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50(4):1088–101. Epub 1994/12/01. pmid:7786990.
  35. 35. Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. Bmj. 1997;315(7109):629–34. Epub 1997/10/06. pmid:9310563; PubMed Central PMCID: PMCPmc2127453.
  36. 36. Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 2011;39(17):e118. pmid:21727090; PubMed Central PMCID: PMC3177186.
  37. 37. Henikoff S, Henikoff JG. Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci U S A. 1992;89(22):10915–9. pmid:1438297; PubMed Central PMCID: PMC50453.
  38. 38. Choi Y, Sims GE, Murphy S, Miller JR, Chan AP. Predicting the functional effect of amino acid substitutions and indels. PLoS One. 2012;7(10):e46688. pmid:23056405; PubMed Central PMCID: PMC3466303.
  39. 39. Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet. 2013;Chapter 7:Unit7 20. pmid:23315928; PubMed Central PMCID: PMC4480630.
  40. 40. Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, et al. PANTHER: a library of protein families and subfamilies indexed by function. Genome Res. 2003;13(9):2129–41. pmid:12952881; PubMed Central PMCID: PMC403709.
  41. 41. Calabrese R, Capriotti E, Fariselli P, Martelli PL, Casadio R. Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum Mutat. 2009;30(8):1237–44. pmid:19514061.
  42. 42. Yue P, Melamud E, Moult J. SNPs3D: candidate gene and SNP selection for association studies. BMC Bioinformatics. 2006;7:166. pmid:16551372; PubMed Central PMCID: PMC1435944.
  43. 43. Park S, Lee J, Do IG, Jang J, Rho K, Ahn S, et al. Aberrant CDK4 amplification in refractory rhabdomyosarcoma as identified by genomic profiling. Sci Rep. 2014;4:3623. pmid:24406431; PubMed Central PMCID: PMC3887377.
  44. 44. Hepp D, Goncalves GL, de Freitas TR. Prediction of the damage-associated non-synonymous single nucleotide polymorphisms in the human MC1R gene. PLoS One. 2015;10(3):e0121812. pmid:25794181; PubMed Central PMCID: PMC4368538.
  45. 45. Thomas PD, Kejariwal A. Coding single-nucleotide polymorphisms associated with complex vs. Mendelian disease: evolutionary evidence for differences in molecular effects. Proc Natl Acad Sci U S A. 2004;101(43):15398–403. pmid:15492219; PubMed Central PMCID: PMC523449.
  46. 46. Niu T, Liu N, Yu X, Zhao M, Choi HJ, Leo PJ, et al. Identification of IDUA and WNT16 Phosphorylation-Related Non-Synonymous Polymorphisms for Bone Mineral Density in Meta-Analyses of Genome-Wide Association Studies. J Bone Miner Res. 2016;31(2):358–68. pmid:26256109; PubMed Central PMCID: PMC5362379.
  47. 47. Brunham LR, Singaraja RR, Pape TD, Kejariwal A, Thomas PD, Hayden MR. Accurate prediction of the functional significance of single nucleotide polymorphisms and mutations in the ABCA1 gene. PLoS Genet. 2005;1(6):e83. pmid:16429166; PubMed Central PMCID: PMC1342637.
  48. 48. Song J, Yang Y, Mauvais-Jarvis F, Wang YP, Niu T. KCNJ11, ABCC8 and TCF7L2 polymorphisms and the response to sulfonylurea treatment in patients with type 2 diabetes: a bioinformatics assessment. BMC Med Genet. 2017;18(1):64. pmid:28587604; PubMed Central PMCID: PMCPMC5461698.
  49. 49. Etemad A, Ramachandran V, Pishva SR, Heidari F, Aziz AF, Yusof AK, et al. Analysis of Gln223Agr polymorphism of Leptin Receptor Gene in type II diabetic mellitus subjects among Malaysians. Int J Mol Sci. 2013;14(9):19230–44. pmid:24051404; PubMed Central PMCID: PMC3794830.
  50. 50. Jiang B, Liu Y, Liu Y, Fang F, Wang X, Li B. Association of four insulin resistance genes with type 2 diabetes mellitus and hypertension in the Chinese Han population. Mol Biol Rep. 2014;41(2):925–33. Epub 2014/01/15. pmid:24414038; PubMed Central PMCID: PMCPmc3929032.
  51. 51. Mohammadzadeh G, Nikzamir A, Mohammadi J, Pourdashti S, Shabazian H, Latifi SM. Association of the 223A/G LEPR polymorphism with serum leptin levels in Iranian subjects with type 2 diabetes. Arch Iran Med. 2013;16(11):636–41. Epub 2013/11/12. pmid:24206404.
  52. 52. Roszkowska-Gancarz M, Kurylowicz A, Polosak J, Mossakowska M, Franek E, Puzianowska-Kuznicka M. Functional polymorphisms of the leptin and leptin receptor genes are associated with longevity and with the risk of myocardial infarction and of type 2 diabetes mellitus. Endokrynol Pol. 2014;65(1):11–6. Epub 2014/02/20. pmid:24549597.
  53. 53. Park KS, Shin HD, Park BL, Cheong HS, Cho YM, Lee HK, et al. Polymorphisms in the leptin receptor (LEPR)—putative association with obesity and T2DM. J Hum Genet. 2006;51(2):85–91. Epub 2005/12/08. pmid:16333525.
  54. 54. Zhao L-S, Xiang G-D, Tang Y, Liao Y-H, Yang L, Sun H-L, et al. Association of Gln223Arg variant in leptin receptor gene in type 2 diabetes in Wuhan "Han" population. Military Medical Journal of South China. 2008a;22(2):25–9 [in Chinese].
  55. 55. Murugesan D, Arunachalam T, Ramamurthy V, Subramanian S. Association of polymorphisms in leptin receptor gene with obesity and type 2 diabetes in the local population of Coimbatore. Indian J Hum Genet. 2010;16(2):72–7. Epub 2010/10/30. pmid:21031055; PubMed Central PMCID: PMCPmc2955955.
  56. 56. Zhang Y, Li G, Zhang M, Teng X, Chen C, Tang X. Correlation between polymorphism of leptin receptor gene Gln223Arg and type 2 diabetes mellitus. Journal of Third Military Medical University. 2011;33(18):1932–4 [in Chinese].
  57. 57. Sun H, Miao C-Q, Zhao X-W, Zhang H-J, Li Y-P, Liang L-B, et al. LEPR gene Gln223Arg polymorphism in Chinese families with type 2 diabetes. Progress in Modern Biomedicine. 2011;11(24):4852–6 [in-Chinese].
  58. 58. Qu Y, Yang Z, Jin F, Sun L, Zhang C, Sun H, et al. Analysis of the relationship between three coding polymorphisms in LEPR gene and obesity in northern Chinese. Obes Res Clin Pract. 2007;1(4):223–90. Epub 2007/12/01. pmid:24351585.
  59. 59. Cruz M, Valladares-Salgado A, Garcia-Mena J, Ross K, Edwards M, Angeles-Martinez J, et al. Candidate gene association study conditioning on individual ancestry in patients with type 2 diabetes and metabolic syndrome from Mexico City. Diabetes Metab Res Rev. 2010;26(4):261–70. pmid:20503258.
  60. 60. Wilding JP. Leptin and the control of obesity. Curr Opin Pharmacol. 2001;1(6):656–61. Epub 2002/01/05. pmid:11757823.
  61. 61. Fan SH, Say YH. Leptin and leptin receptor gene polymorphisms and their association with plasma leptin levels and obesity in a multi-ethnic Malaysian suburban population. J Physiol Anthropol. 2014;33:15. Epub 2014/06/21. pmid:24947733; PubMed Central PMCID: PMCPmc4073586.
  62. 62. Yiannakouris N, Yannakoulia M, Melistas L, Chan JL, Klimis-Zacas D, Mantzoros CS. The Q223R polymorphism of the leptin receptor gene is significantly associated with obesity and predicts a small percentage of body weight and body composition variability. J Clin Endocrinol Metab. 2001;86(9):4434–9. Epub 2001/09/11. pmid:11549688.
  63. 63. Furusawa T, Naka I, Yamauchi T, Natsuhara K, Kimura R, Nakazawa M, et al. The Q223R polymorphism in LEPR is associated with obesity in Pacific Islanders. Hum Genet. 2010;127(3):287–94. Epub 2010/02/26. pmid:20183928.
  64. 64. Marti A, Santos JL, Gratacos M, Moreno-Aliaga MJ, Maiz A, Martinez JA, et al. Association between leptin receptor (LEPR) and brain-derived neurotrophic factor (BDNF) gene variants and obesity: a case-control study. Nutr Neurosci. 2009;12(4):183–8. Epub 2009/07/23. pmid:19622243.
  65. 65. Bender N, Allemann N, Marek D, Vollenweider P, Waeber G, Mooser V, et al. Association between variants of the leptin receptor gene (LEPR) and overweight: a systematic review and an analysis of the CoLaus study. PLoS One. 2011;6(10):e26157. Epub 2011/10/27. pmid:22028824; PubMed Central PMCID: PMCPmc3196514.
  66. 66. Heo M, Leibel RL, Fontaine KR, Boyer BB, Chung WK, Koulu M, et al. A meta-analytic investigation of linkage and association of common leptin receptor (LEPR) polymorphisms with body mass index and waist circumference. Int J Obes Relat Metab Disord. 2002;26(5):640–6. Epub 2002/05/29. pmid:12032747.
  67. 67. Paracchini V, Pedotti P, Taioli E. Genetics of leptin and obesity: a HuGE review. Am J Epidemiol. 2005;162(2):101–14. Epub 2005/06/24. pmid:15972940.
  68. 68. An BQ, Lu LL, Yuan C, Xin YN, Xuan SY. Leptin Receptor Gene Polymorphisms and the Risk of Non-Alcoholic Fatty Liver Disease and Coronary Atherosclerosis in the Chinese Han Population. Hepat Mon. 2016;16(4):e35055. pmid:27257426; PubMed Central PMCID: PMC4888499.
  69. 69. Liu Y, Chen SQ, Jing ZH, Hou X, Chen Y, Song XJ, et al. Association of LEPR Gln223Arg polymorphism with T2DM: A meta-analysis. Diabetes Res Clin Pract. 2015;109(3):e21–6. pmid:26094585.
  70. 70. Su S, Zhang C, Zhang F, Li H, Yang X, Tang X. The association between leptin receptor gene polymorphisms and type 2 diabetes mellitus: A systematic review and meta-analysis. Diabetes Res Clin Pract. 2016;121:49–58. pmid:27657457.
  71. 71. Saukko M, Kesaniemi YA, Ukkola O. Leptin receptor Lys109Arg and Gln223Arg polymorphisms are associated with early atherosclerosis. Metab Syndr Relat Disord. 2010;8(5):425–30. pmid:20874424.
  72. 72. Niu T, Qin ZS, Xu X, Liu JS. Bayesian haplotype inference for multiple linked single-nucleotide polymorphisms. Am J Hum Genet. 2002;70(1):157–69. Epub 2001/12/13. pmid:11741196; PubMed Central PMCID: PMCPmc448439.
  73. 73. Zhang H, Liu L, Wang X, Gruen JR. Guideline for data analysis of genomewide association studies. Cancer Genomics Proteomics. 2007;4(1):27–34. Epub 2007/08/30. pmid:17726238.
  74. 74. Niu T. Algorithms for inferring haplotypes. Genet Epidemiol. 2004;27(4):334–47. pmid:15368348.
  75. 75. Wong KH, Rong SS, Chong KK, Young AL, Pang CP, Chen LJ. Genetic Associations of Interleukin-related Genes with Graves' Ophthalmopathy: a Systematic Review and Meta-analysis. Sci Rep. 2015;5:16672. pmid:26578206; PubMed Central PMCID: PMCPMC4649612.
  76. 76. Wang X, Strizich G, Hu Y, Wang T, Kaplan RC, Qi Q. Genetic markers of type 2 diabetes: Progress in genome-wide association studies and clinical application for risk prediction. J Diabetes. 2016;8(1):24–35. pmid:26119161.
  77. 77. Wang B, Xu D, Jing Z, Liu D, Yan S, Wang Y. Effect of long-term exposure to air pollution on type 2 diabetes mellitus risk: a systemic review and meta-analysis of cohort studies. Eur J Endocrinol. 2014;171(5):R173–82. pmid:25298376.
  78. 78. Parillo M, Riccardi G. Diet composition and the risk of type 2 diabetes: epidemiological and clinical evidence. Br J Nutr. 2004;92(1):7–19. pmid:15230984.
  79. 79. Fletcher B, Gulanick M, Lamendola C. Risk factors for type 2 diabetes mellitus. J Cardiovasc Nurs. 2002;16(2):17–23. pmid:11800065.
  80. 80. Thibault V, Belanger M, LeBlanc E, Babin L, Halpine S, Greene B, et al. Factors that could explain the increasing prevalence of type 2 diabetes among adults in a Canadian province: a critical review and analysis. Diabetol Metab Syndr. 2016;8:71. pmid:27833664; PubMed Central PMCID: PMC5103368.
  81. 81. Thompson DB, Ravussin E, Bennett PH, Bogardus C. Structure and sequence variation at the human leptin receptor gene in lean and obese Pima Indians. Hum Mol Genet. 1997;6(5):675–9. pmid:9158141.
  82. 82. Hansel NN, Gao L, Rafaels NM, Mathias RA, Neptune ER, Tankersley C, et al. Leptin receptor polymorphisms and lung function decline in COPD. Eur Respir J. 2009;34(1):103–10. pmid:19196818.
  83. 83. Hu Z, Frith M, Niu T, Weng Z. SeqVISTA: a graphical tool for sequence feature visualization and comparison. BMC Bioinformatics. 2003;4:1. pmid:12513700; PubMed Central PMCID: PMC140037.
  84. 84. Niu T, Hu Z. Dynamic visual data mining: biological sequence analysis and annotation using SeqVISTA. Int J Bioinform Res Appl. 2005;1(1):18–30. pmid:18048119.
  85. 85. Han HR, Ryu HJ, Cha HS, Go MJ, Ahn Y, Koo BK, et al. Genetic variations in the leptin and leptin receptor genes are associated with type 2 diabetes mellitus and metabolic traits in the Korean female population. Clin Genet. 2008;74(2):105–15. pmid:18564365.
  86. 86. Takahashi-Yasuno A, Masuzaki H, Miyawaki T, Matsuoka N, Ogawa Y, Hayashi T, et al. Association of Ob-R gene polymorphism and insulin resistance in Japanese men. Metabolism. 2004;53(5):650–4. pmid:15131772.
  87. 87. Zhao LS, Xiang GD, Tang Y, Hou J, Liao Y-H, Yang L. The polymorphism analysis of leptin receptor gene variants of Gln223Arg in patients with obesity and type 2 diabetes mellitus of Han people in Wuhan district. Chin J Diabetes. 2008b;16(3):159–60 [in Chinese].