Startseite Correlation between PPARGC1A gene rs8192678 G>A polymorphism and susceptibility to type-2 diabetes
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Correlation between PPARGC1A gene rs8192678 G>A polymorphism and susceptibility to type-2 diabetes

  • Fei Du , Kang-Juan Yang EMAIL logo und Lian-Shan Piao
Veröffentlicht/Copyright: 21. Mai 2019
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Open Life Sciences
Aus der Zeitschrift Open Life Sciences Band 14 Heft 1

Abstract

Objective

To systematically investigate the correlation between the G>A polymorphism of the peroxisome proliferator-activated receptor γ coactivator 1α (PPARGC1A or PGC-1alpha) gene rs8192678 locus and the susceptibility to type-2 diabetes mellitus (T2DM).

Methods

The inclusion and exclusion criteria and retrieval strategies of original literatures were formulated. Then, subjects and free words “PPARGC1A”,”gene polymorphism”, and “T2DM” were retrieved from the PubMed, EMBASE, and Cochrane Library databases. Case-control studies on the G>A polymorphism of the PPARGC1A gene rs8192678 locus and susceptibility to T2DM were included for the meta-analysis.

Results

The number of cases in the T2DM group and control group was 5,607 and 7,596, respectively. The meta-analysis revealed that the PPARGC1A gene rs8192678 locus G>A polymorphism is associated with susceptibility to T2DM. There are differences in each group of genetic models, of which three groups of genetic models are highly significant. In the allele model, OR=1.249, 95% CI: 1.099-1.419, and P=0.001. In the dominant inheritance model, OR=1.364, 95% CI: 1.152-1.614, and P=0.000. In the additive inheritance model, OR=0.828, 95% CI: 0.726-0.945, and P=0.005. And one group is significant, in the recessive inheritance model, OR=1.187, 95% CI: 1.021-1.381, and P=0.026.

Conclusion

In Western Asian, South Asian, European and African populations, the A allele of the PPARGC1A gene rs8192678 locus may be one of the risk factors for T2DM.

Keywords: Type-2 diabetes mellitus; peroxisome proliferator-activated receptor γ coactivator 1α gene; single nucleotide polymorphisms; meta-analysis

Type-2 diabetes mellitus (T2DM) is a disorder of the metabolism of sugar, fat and protein caused by insulin resistance (IR) or the relative insufficiency of insulin secretion in islet beta cells, in which its occurrence and development are impacted by the dual factors of environment and heredity [1]. T2DM is a complex polygenic genetic disease, and the molecular and genetic mechanism of gene mutation, as well as the occurrence and development of T2DM or gene interaction, remain unclear. The expression products of the peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PPARGC1A or PGC-1alpha) gene can regulate lipid secretion, fatty acid metabolism and insulin sensitivity [2]. Therefore, the PPARGC1A gene is a candidate gene for studying the metabolic syndrome and T2DM. The genetic variation at the 1444th locus (rs8192678) in exon 8 of the PPARGC1A gene leads to G>A base substitution, causing the substitution of glycine by serine (Gly482Ser) in the amino acid sequence and structural change [3]. Initially, EK et al [4] used single strand conformational polymorphism analysis followed by nucleotide sequence to scan the PPARGC1A gene in 53 Danish T2DM patients. The study showed that the Gly482Ser polymorphism was associated with T2DM. Subsequently, Lacquemant et al [5] found that this locus was also significantly associated with T2DM in the British Caucasian population, but this association was not found in studies of the French Caucasian and Pima Indians. Moreover, there was no correlation between rs8192678 polymorphism and T2DM in East Asian Chinese population studies [6]. It was found that the association analysis of this locus with the T2DM case-control showed inconsistency in the population of many countries and regions. The inconsistency of this locus with T2DM suggests that statistical power may be reduced due to the small size of a single study, and random sample background or ethnic differences. It is difficult to determine the reasons for T2DM rs8192678 locus inconsistency using a single study. Meta-analysis by comprehensive analysis of all similar studies not only enhances statistical power and increases the credibility of gene polymorphisms in case-control association analysis, but also reduces random errors, false positives and false negatives. A meta-analysis of the PPARGC1A gene rs8192678 polymorphism and T2DM in recent years showed a significant association for this locus and T2DM in the Indian population (OR=1.19, 95% CI: 1.05-1.34, P=0.006). However, no significant associations were found among Caucasians or East Asians [7]. There are also meta-analysis results showing that the A allele of rs8192678 in the Chinese Han population of East Asia increases the risk of developing T2DM (OR=1.54, 95% CI: 1.34-1.81, P<0.001) [8]. Because the results of previous meta-analyses still have some differences, the heterogeneity between the studies is relatively large, especially in East Asia. Therefore, we believe that it is necessary to conduct a subgroup analysis by age and sample size of the method of applying meta-analysis to explore the cause of heterogeneity. The reason is to clarify the correlation between the PPARGC1A gene rs8192678 polymorphism and T2DM in different ethnicities, which will provide a basis of future biological functional research and clinical research of the PPARGC1A gene.

1 Materials and Methods

1.1 Literature retrieval strategy

Literature about the correlation between PPARGC1A gene polymorphism and T2DM was retrieved from the PubMed, EMBASE and Cochrane Library databases. Literature containing the subjects and free words “PPARGC1A”, “gene polymorphism”, and “T2DM” was retrieved from these databases. In addition, the cited references in relevant treatises and reviews were manually retrieved to collected case-control studies on the correlation between the G>A polymorphism of the PPARGC1A gene rs8192678 locus and susceptibility to T2DM. The retrieval time range was from the establishment of the databases to June 2018. By reading the titles and abstracts, and reading the full text when necessary, two investigators conducted an independent evaluation of the literature on the basis of the inclusion and exclusion criteria. When these two investigators disagreed with each other, all researchers in our team participated in the assessment of whether the literature should be included.

1.1.1 All included literature must conform to the following criteria

(1) The study must be a case-control study, cohort study, or cross-sectional study on the association between PPARGC1A gene polymorphism and T2DM, and the case group should comprise of diabetic patients, who have combined diseases with no special restrictions. The control group should comprise of subjects with normal blood glucose levels and no family history of diabetes mellitus. (2) The literature must provide the genotype distribution frequency in the case group and control group, or the genotype distribution frequency could be calculated from the data provided by the literature. (3) If an article contains studies in two or more ethnic or regional populations, each study in one population was considered as one independent study. (4) The language in which the article was published was confined to English, and the sample size was unrestricted. For studies with incomplete data, the investigators did not intend to contact the authors.

1.1.2 Exclusion criteria

Literature with incomplete data, literature in which the observed disease was not T2DM, literature in which the polymorphic loci did not meet the requirements, and literature with animal study subjects, reviews and meta-analysis studies were excluded.

1.2 Data extraction

After reading the full text of the literature, two investigators filled in a standard form with the following data in advance: author information, year of publication, ethnicity and country of the study subjects, the sample sizes of the case group and control group, the number of genotypes, and whether the genotype distribution in the control group was in accordance with the Hardy-Weinberg equilibrium (HWE). When these two investigators disagreed with each other, a third researcher would participate in determining the accuracy of the extracted data.

1.3 Quality evaluation of literature

The Newcastle Ottawa scale (NOS) was used to assess the quality of THE selected case-control studies, in which literature with a NOS score ≥5 was considered as high quality literature [9]. The Agency for Healthcare Research and Quality (AHRQ) score was used to evaluate the quality of cross-sectional studies, in which literature with an AHRQ score ≥8 was considered as high quality literature [10].

1.4 Data processing and statistical analysis

(1) Chi-square goodness of fit test was used to determine whether the distribution of genotypes in the control group in each study was in accordance with HWE. The odds ratio (OR) and 95% confidence interval (CI) were used to evaluate the correlation between PPARGC1A gene polymorphism and risk of T2DM in four different genetic inheritance models: allele model (A vs. G), dominant inheritance model (AA+GA vs. GG), recessive inheritance model (AA vs. GG+GA), and additive inheritance model (AA+GG vs. GA). (3) In order to determine whether there was significant heterogeneity among studies, the q statistic based on Chi-square test was used for qualitative analysis, while I2 statistic was used for quantitative analysis. If I2<50% and P>0.1, it was considered that there was no statistical heterogeneity among studies. Then, a fixed-effects model (M-H) was used for data consolidation, If I2≥50% and P<0.1, it was considered that there was statistical heterogeneity among studies. Then, a random-effects model (D-L) was used for data consolidation. (4) In order to explore for potential heterogeneity sources and robustness of the test results, a subgroup analysis was carried out based on ethnicity, sample size (number of case groups ≥300 or <300) and age (≥60 and <60), respectively. In order to explore the impact of a single study on the overall result, the step-by-step elimination method was used for the sensitivity analysis. That is, merely one study was eliminated at a time, and the effect size of the remaining studies was reconsolidated to observe the stability of results. If the OR value of the remaining studies was outside the range of the total effect size of 95% CI after one study was excluded, it was considered that the study results impacted the overall results. Finally, funnel plot and Begg’s method were used to detect for publication bias in the included literature. If the funnel plot was asymmetrical or P<0.05, publication bias was inferred.

2 Results

2.1 Literature retrieval results

According to the retrieval strategy, a total of 70 pieces of literature were primarily obtained. After reading the titles and abstracts, 29 irrelevant pieces of literature were excluded. Among the remaining 41 pieces of literature, after reading the full text, 25 literature pieces were excluded, which included two reviews, four meta analyses, 11 literature items with incomplete data, four literature items that did not study the gene polymorphic locus, one literature piece with suspected data duplication, and three literature items that studied diseases that were not T2DM. Finally, a total of 16 pieces of literature were included in the present meta-analysis [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25]. The entire retrieval process is presented in Figure 1. These 16 pieces of literature included 12 case-control studies and five cross sectional studies, which comprised of 5,607 patients and 7,596 controls. The distribution of genotype in the control group for all the included studies was in accordance with the HWE. Four cross-sectional studies were evaluated as low and medium quality literature by AHRQ, and were excluded. The remaining literature was high quality (Table 1).

Figure 1 Retrieval flow chart of literatures for inclusion in the meta–analysis
Figure 1

Retrieval flow chart of literatures for inclusion in the meta–analysis

Table 1

Basic information on the association between PPARGC1A gene rs8192678 locus G>A polymorphism and type 2 diabetes mellitus

AuthorYearEthnicityAgeCasesControlsGenotypes (cases / controls)HWEQuality Score
casescontrols(n)(n)GGGAAA
Pei et al2017East Asian56.71±11.9651.57±13.1383445353414>0.058
16821463
Zhu et al.(1)2017East Asian65.20±9.5164.67±9.80497782138251108>0.055
250382150
Shokouhi et al.2015Wast54.97±11.0150.29±9.64173173127433>0.057
Asian
159131
Jemaa et al.2015African43.60±12.2049.10±9.5048740216623190>0.055
17617056
Weng et al.2010East Asian60.30±8.4054.50±10.8027610499112956>0.057
340502207
Zhu et al.(2)2009East Asian58.90±12.4061.90±8.80595495181303111>0.056
143240112
Bhat et al. (group1)2007South50.80±9.5049.80±9.301992136810328>0.055
Asian
1128021
Bhat et al. (group2)2007South54.40±9.6052.10±8.50152258697013>0.055
Asian
1439619
Lu et al.2007East Asian63.64±5.5361.25±4.962632829712145>0.058
14411127
Sun et al.2006East Asian45.69±8.7845.15±7.3239052512219078>0.057
18125688
Wang et al.2005East AsianNSNS152111378431>0.056
415515
Chen et al.2004East Asian57.20±9.3859.49±8.7349455515525584>0.057
185264106
EK et al.2001European64.90±9.057.90±9.065549126229796>0.057
24319652

2.2 Meta-analysis results

A total of 12 case-control studies and one cross-sectional study, which involved 4,416 patients and 5,781 controls, were included in the meta-analysis. The results of meta-analysis revealed that the gene polymorphism rs8192678 locus was significantly correlated to T2DM in the general population. In the allele model, OR=1.249, 95% CI: 1.099-1.419, P=0.001, and Pheterogeneity=0.000. In the dominant inheritance model, OR=1.364, 95% CI: 1.152-1.614, P=0.000, and Pheterogeneity=0.000. In the recessive inheritance model, OR=1.187, 95% CI: 1.021-1.381, P=0.026, and Pheterogeneity=0.077. In the additive inheritance model, OR=0.828, 95% CI: 0.726-0.945, P=0.005, and Pheterogeneity=0.006. In addition, the aggregated data exhibited relatively robust heterogeneity, and a subgroup analysis was further applied to explore the sources of heterogeneity. Taking into account the differences in genetic backgrounds and environmental factors among different ethnicities, the same gene locus may have different effects on the same disease. Therefore, the investigators further explored the correlation in different ethnicities. The results revealed that in the allele model, gene polymorphism rs8192678 was associated with T2DM in the Western Asian population (OR=3.641, 95% CI: 2.000-6.628, P=0.000), South Asian population (OR=1.448, 95% CI: 1.187-1.865, P=0.001, Pheterogeneity=0.288), European population (OR=1.354, 95% CI: 1.136-1.615, P=0.001), and African population (OR=1.351, 95% CI: 1.114-1.639, P=0.002), and the associations were all statistically significant. However, no significant correlation was detected in the East Asian population (OR=0.964, 95% CI: 0.685-1.358, P=0.106, Pheterogeneity=0.004). In the dominant inheritance model, the correlation between gene polymorphism rs8192678 and risk of T2DM in Western Asian, South Asian, European and African populations was further confirmed. However, in the recessive inheritance model, no significant correlation between gene polymorphism rs8192678 and risk of T2DM was found in East Asian, West Asian, South Asian and African populations. In the additive inheritance model, no significant correlation between gene polymorphism rs8192678 and risk of T2DM was found in East Asian, European and African populations. The results of the meta-analysis on the correlation between rs8192678 and T2DM are presented in Table 2. A forest map of allele models for the ethnicity subgroups analysis is presented in Figure 2. The heterogeneity test revealed that there was greater heterogeneity among studies in the East Asian population, and the heterogeneity among studies in the South Asian population was smaller. However, a heterogeneity test could not be conducted in the remaining populations due to the small number of studies.

Figure 2 Forest map of allele models for the race subgroup analysis
Figure 2

Forest map of allele models for the race subgroup analysis

Table 2

Meta-analysis of rs8192678 gene polymorphisms and T2DM susceptibility

Genetic contrastsOverall and subgroupsStudy groups (n)OR (95% CI)Significance testHeterogeneity
Z-valueP-valueQ-valueP-valueI2(%)
A versus GAll131.249(1.099-1.419)3.400.00149.740.00075.9
East Asian81.111(0.978-1.263)1.610.10620.630.00466.1
West Asian13.641(2.000-6.628)4.230.0000.00——
African11.351(1.114-1.639)3.060.0020.00——
South Asian21.448(1.187-1.865)3.440.0011.130.28811.5
European11.354(1.136-1.615)3.380.0010.00——
(AA+GA) versus GGAll131.364(1.152-1.614)3.600.00042.140.00071.5
East Asian81.155(0.981-1.359)1.740.08315.170.03453.9
West Asian14.114(2.164-7.818)4.320.0000.00——
African11.506(1.147-1.976)2.950.0030.00——
South Asian21.790(1.262-2.539)3.270.0011.520.21734.4
European11.470(1.161-1.861)3.200.0010.00——
(GG+GA) versus AAAll131.187(1.021-1.381)2.230.02619.520.07738.5
East Asian81.114(0.920-1.350)1.100.26914.180.04850.6
West Asian13.035(0.313-29.470)0.960.3380.00——
African11.401(0.974-2.014)1.820.0690.00——
South Asian21.359(0.853-2.166)1.290.1970.250.6190.0
European11.450(1.012-2.078)2.020.0430.00——
(AA+GG) versus GAAll130.828(0.726-0.945)2.800.00527.890.00657.9
East Asian80.931(0.843-1.029)1.400.1625.560.5920.0
West Asian10.246(0.127-0.476)4.160.0000.00——
African10.812(0.622-1.060)1.530.1250.00——
South Asian20.621(0.469-0.824)3.300.0010.550.4590.0
European10.801(0.632-1.015)1.830.0670.00——

2.3 Heterogeneity analysis

Since there was high heterogeneity among studies in the East Asian population, and in order to explore the source of heterogeneity in all genetic inheritance models, a subgroup analysis was carried out based on sample size (number of patients in the T2DM group was ≥300 or <300) and age (≥60 and <60). The results of the subgroup analysis revealed that after grouping, G>A polymorphism rs8192678 remained uncorrelated to susceptibility to T2DM (P>0.05). Except for the recessive inheritance model, when the sample size was ≥300, the heterogeneity among studies was significantly lower than before grouping. When the sample size was <300, the heterogeneity among studies was significantly higher than before grouping (Table 3). In the four models, when the age was ≥60, the heterogeneity among studies was significantly higher than before grouping. When age was <60, the heterogeneity among studies was significantly lower than before grouping (Table 4).

Table 3

rs8192678 gene polymorphisms and T2DM susceptibility in East Asian population based on sample size meta-analysis

Genetic contrastsOverall and subgroupsStudy groups (n)OR (95% CI)Significance testHeterogeneity
Z-valueP-valueQ-valueP-valueI2(%)
A versus GAll81.111(0.978-1.263)1.610.10620.630.00466.1
T2DM≥30041.039(0.923-1.168)0.630.5285.600.13346.6
T2DM<30041.230(0.935-1.618)1.480.13912.030.00775.1
(AA+GA) versus GGAll81.155(0.981-1.359)1.740.08315.170.03453.9
T2DM≥30041.096(0.962-1.249)1.380.1682.420.4900.0
T2DM<30041.263(0.855-1.866)1.170.24011.820.00874.6
(GG+GA) versus AAAll81.114(0.920-1.350)1.100.26914.180.04850.6
T2DM≥30040.995(0.799-1.238)0.050.9616.180.10351.4
T2DM<30041.354(0.983-1.864)1.850.0644.730.19336.6
(AA+GG) versus GAAll80.931(0.843-1.029)1.400.1625.560.5920.0
T2DM≥30040.920(0.815-1.038)1.360.1740.850.8380.0
T2DM<30040.954(0.757-1.202)0.400.6874.590.20534.6
Table 4

rs8192678 gene polymorphisms and T2DM susceptibility in East Asian populations based on age meta-analysis

Genetic contrastsOverall and subgroupsStudy groups (n)OR (95% CI)Significance testHeterogeneity
Z-valueP-valueQ-valueP-valueI2(%)
A versus GAll81.111(0.978-1.263)1.610.10620.630.00466.1
Age≥6031.211(0.950-1.545)1.550.1229.140.01078.1
Age<6040.996(0.894-1.110)0.070.9423.580.31116.1
NS11.489(1.048-2.118)2.220.0270.00——
(AA+GA) versus GGAll81.155(0.981-1.359)1.740.08315.170.03453.9
Age≥6031.269(0.926-1.739)1.480.1397.160.02872.1
Age<6041.028(0.888-1.190)0.370.7112.230.5260.0
NS11.820(1.067-3.107)2.200.0280.00——
(GG+GA) versus AAAll81.114(0.920-1.350)1.100.26914.180.04850.6
Age≥6031.256(0.928-1.700)1.480.1394.270.11853.2
Age<6040.964(0.763-1.219)0.300.7614.990.17239.9
NS11.640(0.837-3.211)1.440.1490.00——
(AA+GG) versus GAAll80.931(0.843-1.029)1.400.1625.560.5920.0
Age≥6030.931(0.796-1.089)0.900.3702.070.3553.5
Age<6040.943(0.821-1.083)0.830.4073.060.3822.0
NS10.795(0.487-1.299)0.920.3600.00——

2.4 Results of the sensitivity analysis and publication bias

As shown in Figure 3, after the 13 studies were excluded one by one, the meta-analysis results for the remaining 12 studies did not significantly change. This indicates that the results of the meta-analysis were stable. As shown in Figure 4, no publication bias was found in the present study (Begg’s test: P=0.059).

Figure 3 Sensitivity analysis
Figure 3

Sensitivity analysis

Figure 4 Begg’s funnel plot
Figure 4

Begg’s funnel plot

3 Discussion

PPARGC1A is a highly conservative transcriptional co-activator, which is abundant in metabolic active tissues, such as the liver, fat, pancreas and muscles. It plays a role in regulating energy metabolism in the whole

body. The single nucleotide variation of the PPARGC1A gene rs8192678 is correlated to susceptibility to T2DM, the relative risk of obesity and insulin resistance, and lower β-cell function index [6]. Research on the correlation between PPARGC1A gene G>A polymorphism rs8192678 and susceptibility to T2DM has gradually become a focus of attention. However, there were inconsistencies in these studies. The results of the present meta-analysis revealed that in non-grouped populations, Pheterogeneity=0.000. In the allele model, OR=1.249, 95% CI: 1.099-1.419, P= 0.001, and Pheterogeneity=0.000. In the dominant inheritance model, OR=1.364, 95% CI: 1.152-1.614, P=0.000, and Pheterogeneity=0.000. In the recessive inheritance model, OR=1.187, 95% CI: 1.021-1.381, P=0.026, and Pheterogeneity=0.077.

In the additive inheritance model, OR=0.828, 95% CI: 0.726-0.945, P=0.005, and Pheterogeneity=0.006. These results reveal that the A allele of the PPARGC1A gene rs8192678 locus is susceptible to T2DM. Since the heterogeneity among studies was high, subgroup analysis was carried out based on the ethnicity of the population. The results suggest that the A allele was associated with the incidence of T2DM in Western Asian, South Asian, European and African populations (P<0.05), but was not associated with T2DM in East Asian population (P>0.05). The meta-analysis of Yang et al showed a significant association for this locus and T2DM in the Indian population (OR=1.19, 95% CI: 1.05-1.34, P=0.006) [7]. However, no significant findings were found in the East Asian population. The association is consistent. But meta-analysis with Chen et al showed that the A allele of rs8192678 in Chinese Han population in East Asia increased the risk of T2DM (OR=1.54, 95% CI: 1.34-1.81, P<0.001) [8]. Since there was heterogeneity among studies in the East Asian population, patients were grouped according to the number of cases and age of the T2DM group, but no positive correlation was found between the A allele and the incidence of T2DM. However, when the number of samples was ≥300 and age was <60, the heterogeneity was significantly reduced. Studies with less than 300 cases probably do not have enough statistical power to obtain OR~1.5 with allelic frequencies between 0.20 and 0.40 in case-control design (see in Quanto software). It is possible that part of the heterogeneity found in this group is because of lack of statistical power. Ling et al [26] concluded that insulin stimulates and aging reduces skeletal muscle expression of PPARGC1A and PPARGC1B, and suggested that they have different regulatory functions on glucose and fat oxidation in muscle cells. The authors suggested that this could provide an explanation by which an environmental trigger (age) modifies genetic susceptibility to T2DM. Heterogeneity at ages greater than 60 years may be related to environmental factors.

An interesting aspect is the A allele frequency in the East Asian populations is 44%, more than 15% in relation to South Asian, 8% of the European population and more than 39% of the African population. This may be due to the fact that this variant does not influence T2DM due to adaptive issues (See www.internationalgenome.org/1000-genomes-browsers/) Another possibility is about the linkage disequilibrium (LD). In LD-based indirect correlation analysis, if a disease-causing locus and genetic markers (polymorphic alleles) have strong LD, then it can be compared to normal individuals by comparing genetic markers. Differences ultimately lead to the relative risk of disease-causing loci in the disease. If LD between the SNP and the causal loci is weaker in East Asian than in South Asian or Europeans, it may lead to a weaker association which may not be detected [27]. If the size effect is very low in East Asian population, it could only be detected by increasing the statistical power with a larger sample size.

In the present study, strict inclusion and exclusion criteria were designed, a stratified analysis was conducted to deal with the confounding bias in the study, and the credibility of results of the analysis was satisfactory. Nevertheless, the present study had certain limitations: (1) the T2DM diagnosis was drawn in most of the included studies, according to the Diagnostic Criteria of Diabetes published by the World Health Organization (WHO), but this was drawn according to the Diagnostic Criteria of Diabetes published by the American Dental Association (ADA) in some studies, which may result in some differences among patients who were included in these studies; (2) merely the PPARGC1A gene polymorphism of one locus was analyzed, and although the results of the analysis revealed a statistically significant correlation, the number of cases in each study was small, and the potential interactions between gene-gene and gene-environment were not included in the present meta-analysis, showing that the results of the analysis could only be used for reference.

In summary, PPARGC1A gene G>A polymorphism rs8192678 may increase the risk of T2DM in Western Asian, South Asian, European, and African populations, while this was not correlated to susceptibility to T2DM in the East Asian population. Taking into account that the occurrence of T2DM is the result of the combined action of genetic and environmental factors, and some limitations in the present study, the exact conclusions need to be further verified through large sample case-controlled or prospective clinical studies.

Acknowledgments

The study was supported by grants from the National Natural Science Foundation of China (no. 81460158,no 31060154 and no. 81560141).

  1. Conflict of interest Authors state no conflict of interest

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Received: 2018-08-02
Accepted: 2019-01-13
Published Online: 2019-05-21

© 2019 Fei Du et al.,published by De Gruyter

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Artikel in diesem Heft

  1. Plant Sciences
  2. Extended low temperature and cryostorage longevity of Salix seeds with desiccation control
  3. Genome-wide analysis of the WRKY gene family and its response to abiotic stress in buckwheat (Fagopyrum tataricum)
  4. Differential expression of microRNAs during root formation in Taxus chinensis var. mairei cultivars
  5. Metabolomics Approach for The Analysis of Resistance of Four Tomato Genotypes (Solanum lycopersicum L.) to Root-Knot Nematodes (Meloidogyne incognita)
  6. Beneficial Effects of Salt on Halophyte Growth: Morphology, Cells, and Genes
  7. Phosphate-solubilizing bacteria from safflower rhizosphere and their effect on seedling growth
  8. Anatomy and Histochemistry of the Roots and Shoots in the Aquatic Selenium Hyperaccumulator Cardamine hupingshanensis (Brassicaceae)
  9. Effects of LED light on Acacia melanoxylon bud proliferation in vitro and root growth ex vitro
  10. Ecology and Environmental Sciences
  11. Intensity of stripping and sugar content in the bark and the bast of European beech (Fagus sylvatica)
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  13. Loci identification of a N-acyl homoserine lactone type quorum sensing system and a new LysR-type transcriptional regulator associated with antimicrobial activity and swarming in Burkholderia gladioli UAPS07070
  14. Bacillus methylotrophicus has potential applications against Monilinia fructicola
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  17. Effect of rhamnolipids on microbial biomass content and biochemical parameters in soil contaminated with coal tar creosote
  18. Effects of foliar trichomes on the accumulation of atmospheric particulates in Tillandsia brachycaulos
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https://doi.org/10.1515/biol-2019-0006
Schlagwörter für diesen Artikel
Type-2 diabetes mellitus; peroxisome proliferator-activated receptor γ coactivator 1α gene; single nucleotide polymorphisms; meta-analysis
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Plant Sciences
  2. Extended low temperature and cryostorage longevity of Salix seeds with desiccation control
  3. Genome-wide analysis of the WRKY gene family and its response to abiotic stress in buckwheat (Fagopyrum tataricum)
  4. Differential expression of microRNAs during root formation in Taxus chinensis var. mairei cultivars
  5. Metabolomics Approach for The Analysis of Resistance of Four Tomato Genotypes (Solanum lycopersicum L.) to Root-Knot Nematodes (Meloidogyne incognita)
  6. Beneficial Effects of Salt on Halophyte Growth: Morphology, Cells, and Genes
  7. Phosphate-solubilizing bacteria from safflower rhizosphere and their effect on seedling growth
  8. Anatomy and Histochemistry of the Roots and Shoots in the Aquatic Selenium Hyperaccumulator Cardamine hupingshanensis (Brassicaceae)
  9. Effects of LED light on Acacia melanoxylon bud proliferation in vitro and root growth ex vitro
  10. Ecology and Environmental Sciences
  11. Intensity of stripping and sugar content in the bark and the bast of European beech (Fagus sylvatica)
  12. Influence of monometallic and bimetallic phytonanoparticles on physiological status of mezquite
  13. Loci identification of a N-acyl homoserine lactone type quorum sensing system and a new LysR-type transcriptional regulator associated with antimicrobial activity and swarming in Burkholderia gladioli UAPS07070
  14. Bacillus methylotrophicus has potential applications against Monilinia fructicola
  15. Evaluation of Heavy Metals and Microbiological Contamination of Selected herbals from Palestine
  16. The effect of size of black cherry stumps on the composition of fungal communities colonising stumps
  17. Effect of rhamnolipids on microbial biomass content and biochemical parameters in soil contaminated with coal tar creosote
  18. Effects of foliar trichomes on the accumulation of atmospheric particulates in Tillandsia brachycaulos
  19. Isolation and characterisation of the agarolytic bacterium Pseudoalteromonas ruthenica
  20. Comparison of soil bioconditioners and standard fertilization in terms of the impact on yield and vitality of Lolium perenne and soil biological properties
  21. Biomedical Sciences
  22. The number of regulatory B cells is increased in mice with collagen-induced arthritis
  23. Lactate overload inhibits myogenic activity in C2C12 myotubes
  24. Diagnostic performance of serum CK-MB, TNF-α and hs-CRP in children with viral myocarditis
  25. Correlation between PPARGC1A gene rs8192678 G>A polymorphism and susceptibility to type-2 diabetes
  26. Improving the Detection of Hepatocellular Carcinoma using serum AFP expression in combination with GPC3 and micro-RNA miR-122 expression
  27. The ratio of neutrophil to lymphocyte is a predictor in endometrial cancer
  28. Expression of HER2/c-erbB-2, EGFR protein in gastric carcinoma and its clinical significance
  29. Clinical significance of neuropeptide Y expression in pelvic tissue in patients with pelvic floor dysfunction
  30. Overexpression of RASAL1 indicates poor prognosis and promotes invasion of ovarian cancer
  31. The effect of adrenaline on the mineral and trace element status in rats
  32. Effects of Ischemic Post-Conditioning on the Expressions of LC3-II and Beclin-1 in the Hippocampus of Rats after Cerebral Ischemia and Reperfusion
  33. Long non-coding RNA DUXAP8 regulates the cell proliferation and invasion of non-small-cell lung cancer
  34. Risk factors of regional lymph node metastasis in patients with cervical cancer
  35. Bullous prurigo pigmentosa
  36. Association of HIF-1α and NDRG2 expression with EMT in gastric cancer tissues
  37. Decrease in the level of nervonic acid and increased gamma linolenic acid in the plasma of women with polycystic ovary syndrome after a three-month low-glycaemic index and caloric reduction diet
  38. Depletion of VAX2 restrains the malignant progression of papillary thyroid carcinoma by modulating ERK signaling pathway
  39. Insulin resistance is a risk factor for mild cognitive impairment in elderly adults with T2DM
  40. Nurr1 promotes lung cancer apoptosis via enhancing mitochondrial stress and p53-Drp1 pathway
  41. Predictive significance of serum MMP-9 in papillary thyroid carcinoma
  42. Agmatine prevents oxidative-nitrative stress in blood leukocytes under streptozotocin-induced diabetes mellitus
  43. Effect of platelet-rich plasma on implant bone defects in rabbits through the FAK/PI3K/AKT signaling pathway
  44. The diagnostic efficacy of thrombelastography (TEG) in patients with preeclampsia and its association with blood coagulation
  45. Value of NSE and S100 Protein of Kawasaki Disease with aseptic meningitis in Infant
  46. CB2 receptor agonist JWH133 activates AMPK to inhibit growth of C6 glioma cells
  47. The effects of various mouthwashes on osteoblast precursor cells
  48. Co-downregulation of GRP78 and GRP94 induces apoptosis and inhibits migration in prostate cancer cells
  49. SKA3 up-regulation promotes lung adenocarcinoma growth and is a predictor of poor prognosis
  50. Protective effects and mechanisms of microRNA-182 on oxidative stress in RHiN
  51. A case of syphilis with high bone arsenic concentration from early modern cemetery (Wroclaw, Poland)
  52. Study of LBHD1 Expression with Invasion and Migration of Bladder Cancer
  53. 1-Hydroxy-8-methoxy-anthraquinon reverses cisplatin resistance by inhibiting 6PGD in cancer cells
  54. Andrographolide as a therapeutic agent against breast and ovarian cancers
  55. Accumulation of α-2,6-sialyoglycoproteins in the muscle sarcoplasm due to Trichinella sp. invasion
  56. Astragalus polysaccharides protects thapsigargin-induced endoplasmic reticulum stress in HT29 cells
  57. IGF-1 via PI3K/Akt/S6K signaling pathway protects DRG neurons with high glucose-induced toxicity
  58. Intra-arterial tirofiban in a male nonagenarian with acute ischemic stroke: A case report
  59. Effects of Huaiqihuang Granules adjuvant therapy in children with primary nephrotic syndrome
  60. Immune negative regulator TIPE2 inhibits cervical squamous cancer progression through Erk1/2 signaling
  61. Asymptomatic mediastinal extra-adrenal paraganglioma as a cause of sudden death: a case Report
  62. Primary mucinous adenocarcinoma of appendix invading urinary bladder with a fistula: a case report
  63. Minocycline attenuates experimental subarachnoid hemorrhage in rats
  64. Neural Remodeling of the Left Atrium in rats by Rosuvastatin following Acute Myocardial Infarction
  65. Protective effects of emodin on lung injuries in rat models of liver fibrosis
  66. RHOA and mDia1 promotes apoptosis of breast cancer cells via a high dose of doxorubicin treatment
  67. Bacteria co-colonizing with Clostridioides difficile in two asymptomatic patients
  68. A allele of ICAM-1 rs5498 and VCAM-1 rs3181092 is correlated with increased risk for periodontal disease
  69. Treatment of hepatic cystic echinococcosis patients with clear cell renal carcinoma: a case report
  70. Edaravone exerts brain protective function by reducing the expression of AQP4, APP and Aβ proteins
  71. Correlation between neutrophil count and prognosis in STEMI patients with chronic renal dysfunction: a retrospective cohort study
  72. Bioinformatic analysis reveals GSG2 as a potential target for breast cancer therapy
  73. Nuciferine prevents hepatic steatosis by regulating lipid metabolismin diabetic rat model
  74. Analysis of SEC24D gene in breast cancer based on UALCAN database
  75. Bioengineering and Biotechnology
  76. Co-cultured Bone-marrow Derived and Tendon Stem Cells: Novel Seed Cells for Bone Regeneration
  77. Animal Sciences
  78. Comparative analysis of gut microbiota among the male, female and pregnant giant pandas (Ailuropoda Melanoleuca)
  79. Adaptive immunity and skin wound healing in amphibian adults
  80. Hox genes polymorphism depicts developmental disruption of common sole eggs
  81. The prevalence of virulence genes and multidrug resistance in thermophilic Campylobacter spp. isolated from dogs
  82. Agriculture
  83. Effect of Lactobacillus plantarum supplementation on production performance and fecal microbial composition in laying hens
  84. Identification of Leaf Rust Resistance Genes in Selected Wheat Cultivars and Development of Multiplex PCR
  85. Determining Potential Feed Value and Silage Quality of Guar Bean (Cyamopsis tetragonoloba) Silages
  86. Food Science
  87. Effect of Thermal Processing on Antioxidant Activity and Cytotoxicity of Waste Potato Juice
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