The use of atypical antipsychotics (AAPs) is associated with increasing the risk of the metabolic syndrome (MetS), which is an important risk factor for cardiovascular disease and diabetes. Two insulin-induced gene (INSIG) isoforms, designated INSIG-1 and INSIG-2 encode two proteins that mediate feedback control of lipid metabolism. In this genetic case–control study, we investigated whether the common variants in INSIG1 and INSIG2 genes were associated with MetS in schizophrenic patients treated with atypical antipsychctics. The study included 456 schizophrenia patients treated with clozapine (n=171), olanzapine (n=91) and risperidone (n=194), for an average of 45.5±27.6 months. The prevalence of MetS among all subjects was 22.8% (104/456). Two single-nucleotide polymorphisms (SNPs) of the INSIG1 gene and seven SNPs of the INSIG2 gene were chosen as haplotype-tagging SNPs. In single-marker-based analysis, the INSIG2 rs11123469-C homozygous genotype was found to be more frequent in the patients with MetS than those without MetS (P=0.001). In addition, haplotype analysis showed that the C-C-C haplotype of rs11123469-rs10185316- rs1559509 of the INSIG2 gene significantly increased the risk of MetS (P=0.0023). No significant associations were found between polymorphisms of INSIG1 gene and MetS, however, INSIG1 and INSIG2 interactions were found in the significant 3-locus and 4-locus gene–gene interaction models (P=0.003 and 0.012, respectively). The results suggest that the INSIG2 gene may be associated with MetS in patients treated with AAPs independently or in an interactive manner with INSIG1.
Metabolic syndrome (MetS) is a combination of risk factors, including weight gain, abdominal or visceral adiposity, dyslipidemia and elevated plasma glucose that increase the risk of developing cardiovascular disease and diabetes. Individuals with schizophrenia are a high-risk group for MetS due to genetic predisposition, medication effects and lifestyle.1, 2 Regarding the medication effects, the increased risk to develop MetS under atypical antipsychotic (AAP) agents, such as clozapine or olanzapine, have been a major concern for the treatment of schizophrenia.3 In a study of MetS in first-episode patients with schizophrenia treated with typical or AAPs, it was found that, at first episode, there was no difference in the prevalence of MetS between the historical and the current cohort.4 However, rates of MetS increased over time in both groups, but patients treated with AAPs had a three times higher incidence rate of MetS (odds ratio 3.6). In another study of 3-month follow-up of female patients with schizophrenia treated with AAPs, it was found, at baseline, 15% fulfilled criteria for MetS, and, after 3 months of treatment, 27% fulfilled criteria for MetS.5 Recently, Correll et al. investigated patients with bipolar disorder or schizophrenia patients treated with AAPs.6 They found both bipolar disorder and schizophrenia patients had comparable and high rates of MetS (43.2 and 45.9%, respectively). The pathogenesis underlying AAP-related MetS is still unknown but some risk factors such as patients older than 50 years, higher body mass index (BMI) at initiation of antipsychotic treatment, BMI increase after initiation of antipsychotic treatment, duration of antipsychotic drug exposure, and those taking clozapine or multiple antipsychotics have been reported.7, 8, 9
During the past decade, dramatic advances in molecular biology have led to the modern era of pharmacogenetics, with variability in drug response, which includes both therapeutic and adverse effect, attributed to genetic factors discovered in different populations. The identification of the genetic variants that influence drug-related adverse effect may help pre-treatment selection and development of drugs that are safe for individual patients on the basis of their idiosyncratic pharmacogenetic profile.10 Currently, there are three reports of pharmacogenetic studies of antipsychotic-related MetS. In 2007, Mulder et al. first demonstrated that genetic variants in 5-hydroxytryptamine (serotonin) receptor 2C is associated with MetS in patients with schizophrenia.11 They replicated the finding in a recent report and further demonstrated that, the increased risk for MetS, is particularly strong in carriers of the 5-hydroxytryptamine (serotonin) receptor 2C rs1414334 C allele using clozapine or risperidone.12 Another study investigated association between methylenetetrahydrofolate reductase genetic variants and MetS in schizophrenic patients receiving AAPs and demonstrated that the methylenetetrahydrofolate reductase 677T allele is associated with a 3.6-fold greater risk for developing AAP-associated MetS and the 677TT genotype may place individuals at greater risk for insulin resistance with greater central adiposity.13
Two insulin-induced gene (INSIG) isoforms, designated INSIG-1 and INSIG-2, are closely related proteins of the endoplasmic reticulum, encoded by individual gene (INSIG1 and INSIG2, respectively).14, 15 In the mouse, an integrating quantitative trait loci and high-density single-nucleotide polymorphism (SNP) analyses identified INSIG2 as a strong susceptibility gene for plasma cholesterol levels.16 INSIG proteins block the processing of sterol regulatory element binding proteins (SREBPs) by sterol-dependent binding to SREBP cleavage-activating protein (SCAP), and thus prevent SCAP from escorting SREBPs to the Golgi. Thus, the interaction of INSIG proteins, SREBPs and SCAP have a crucial role in feedback regulation of lipid metabolism.17 In addition of lipid metabolism, Herbert et al., in a genome-wide association study, demonstrated a genetic variant (rs7566605) situated 10-kb upstream of the transcription start site of INSIG2 was associated with obesity that was replicated by several replication studies, though not by all.18
From the above findings, INSIG proteins may be implicated in the pathogenesis of MetS in patients treated with AAPs. Skelly et al. conducted the first study on the association of INSIG2 genetic variation and BMI in 756 schizophrenia patients before and after 18-month Clinical Antipsychotics Trials of Intervention Effectiveness (CATIE) phase 1A trail consisting of four AAPs: olanzapine, risperidone, quetiapine and ziprasidone.19 Most of the participants in CATIE study were European, African, Spanish, Hispanic and Latino origins. However, they could not observe an association between INSIG2-rs7566605 and the baseline BMI or BMI change across all CATIE phase 1A treatment. A later study consisted of 160 German patients with clozapine treatment for 12.0±1.2 weeks.20 Although they also did not observe the association of rs7566605, they found an association between three SNPs (rs17587100, rs10490624 and rs17047764) localized with or near INSIG2 gene and clozapine-related BMI changes.20 The two studies yielded divergent results. Furthermore there was no report regarding the association of INSIG1 or INSIG2 genetic variations with body weight changes or MetS in patients treated with AAP in Oriental population. In the present study, we hypothesized that the INSIG1 or INSIG2 genes may be associated with MetS in schizophrenic patients treated with AAPs. Thus we conducted a case–control study in Chinese population to investigate the relationship between the genetic variants in INSIG1 or INSIG2 and risk of MetS. We also determined whether significant gene–gene interactions exist between these two genes in affecting MetS, using a novel algorithm called generalized multifactor dimensionality reduction (GMDR) method, an extension of the MDR method.21
Materials and methods
Subjects and measurements
This study was approved by the Ethnic Review Committee of Yuli Veterans Hospital and was carried out in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained from all subjects before commencement. This study was conducted in the largest psychiatric hospital in Taiwan, which has approximately 2500 inpatients. As our previous report,8 patients selected for the study had used clozapine, olanzapine or risperidone for at least 3 months for treatment of schizophrenia, as diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders (fourth edition) (DSM-IV).22 Consensus diagnoses for each patient was made according to DSM-IV criteria for schizophrenia by two certificated psychiatrists on the basis of an interview, clinical observation, medical record and past history. Patients with the following exclusion criteria were not recruited: history of mood disorder, psychotic disorder or mood disorder due to general medical condition, dementia, mental retardation, substance use or neurological illness. All study subjects were continuously hospitalized, used the same antipsychotic drug and underwent routine monthly body weight assessment throughout the study period. Alcohol consumption was prohibited on all wards. These conditions ensured optimal control of drug treatment compliance and fasting status for assessment of metabolic parameters. With the inclusion and exclusion, there were initially 629 patients with schizophrenia treated with the three AAPs for at least 3 months. Among them, 173 subjects did not have at least one of the information necessary for making a diagnosis of MetS. Therefore, there were 456 included for further study.
Retrospective reviews of the study subjects’ medical records were performed to obtain demographic data, including age at initiation of antipsychotic treatment, BMI at initiation of antipsychotic treatment and change in BMI after initiation of antipsychotic treatment. A cross-sectional assessment of anthropometric and metabolic parameters was performed to determine the prevalence of MetS according to the 2005 International Diabetes Federation Asia criteria: waist circumference >90 cm in males or >80 cm in females, as the essential criterion for central obesity, plus two of the following four criteria: (a) fasting serum triglyceride levels ⩾150 mg/dl; (b) fasting high-density lipoprotein cholesterol (HDL) cholesterol level <40 mg per 100 ml in men or <50 mg per 100 ml in women; (c) blood pressure ⩾130/85 mm Hg; and (d) fasting glucose level ⩾100 mg per 100 ml. Overnight fasting blood samples were drawn between 0700 and 0800 h from all patients. Serum glucose, triglyceride and cholesterol levels were measured using a glucose oxidase autoanalyzer, a triglyceride enzyme autoanalyzer and a cholesterol oxidase autoanalyzer, respectively (Dimension RxL, DADE Behring Company, Newark, DE, USA).
The final study sample consisted of 456 patients treated with clozapine (n=171), olanzapine (n=91) and risperidone (n=194) with average antipsychotic treatment duration of 45.5±27.6 months in the total group. Table 1 shows the demographic data and treatment characteristics, metabolic parameters and prevalence of MetS among the three groups.
SNPs selection and genotyping
We selected candidate markers from the CHB (Han Chinese in Beijing, China) database in the International HapMap Project (HapMap data Rel24/phase II, dbSNP 126, www.hapmap.org), except of rs7566605 in INSIG2, which has been found to be associated with obesity and BMI.18 We used the program Tagger implemented in Haploview v4.0 and the option ‘two-and-three aggressive tagging’ (r2 threshold) to selected tagSNPs (tSNPs) that could represent the overall genetic variations of INSIG1 and INSIG2 genes. With the approach, two tSNPs of the INSIG1 gene and six tSNPs of the INSIG2 gene were chosen (Table 2). The eight tSNPs and rs7566605 were included for the study.
Peripheral venous blood was taken from the study subjects for genotyping of the INSIG1 and INSIG2 SNPs. Genomic DNA was isolated using the PUREGENE DNA purification system (Gentra Systems, Minneapolis, MN, USA). For DNA quality examination, all the samples were genotyped for eight unrelated SNPs. The samples were diluted onto 96-well plates, and only the plate with an average successful genotyping rate greater than 95% for the eight SNPs were used for further study. Briefly, primers and probes were designed with SpectroDESIGNER software (Sequenom, San Diego, CA, USA). The sequences of the primers are summarized in the Supplementary Table 1. A multiplex polymerase chain reaction was performed, and unincorporated double stranded nucleotide triphosphate bases were dephosphorylated with shrimp alkaline phosphatase (Hoffman-LaRoche, Basel, Switzerland) followed by primer extension. The purified primer extension reaction was spotted on to a 384-element silicon chip (SpectroCHIP, Sequenom) and analyzed in the Bruker Biflex III matrix-assisted laser desorption or ionization-time of flight SpectroREADER mass spectrometer (Sequenom). The resulting spectra were processed with SpectroTYPER (Sequenom). The genotyping successful rate ranges from 95.6 to 98.2% with the exception of rs2161829 that has a successful rate of 77.8%. All the experiments were done by investigators who were blind to the phenotypes.
Statistical analysis was performed using SPSS 10.0. Differences in continuous variables were evaluated using Student's t-test. The categorical data were analyzed using the χ2-test or the Fisher exact test if necessary. The criterion for significance was set at P<0.05 for tests for demographic and clinical data. Logistic regression analyses were conducted in single-marker analysis, and gender, age, baseline BMI, duration of current AAP exposure and types of AAPs were entered as covariates. For the analyses of difference in the mean of quantitative factors between the genotypes, we applied analysis of covariance with controlling for appropriate covariates, followed by post hoc Bonferroni procedure. The quantitative factors included those for defining MetS, such as (triglycerides, HDL, systolic blood pressure, diastolic blood pressure, fasting blood sugar and waist circumstance, as well as baseline BMI and BMI changes after AAP treatment. For multi-marker analysis, we used Haploview v4.0 to the status of pair-wise linkage disequilibrium between the studied polymorphisms, and used Unphased 3.0.1323 to evaluate the haplotypic association with MetS in a sliding-window fusion. The overall significance in each window size was determined after 1000 permutation procedures to correct for the multiple haplotypes tested. To further correct the inflation of significance because of the multiple comparisons in genetic analysis, we considered all single-marker and haplotype-based tests and assumed a false discovery rate less than 0.05 as significant after correction.24
To investigate gene–gene interactions, we employed the GMDR method, which is described in detail elsewhere.21 Briefly, the n-dimensional space formed by a given set of SNPs is reduced to a single dimension to analyze n-way interactions. And each class is classified as either high risk or low risk according to the relative proportion of cases (MetS) to controls (non-MetS) in that class. This is performed for all possible combinations of SNPs, and the combination with the lowest misclassification error is selected. To remove the possibility of spurious associations due to missing genotypes, we only included cases and controls for which genotypes were called for all SNPs. Moreover, we tested all possible two- to five-loci interactions using 10-fold cross-validation in an exhaustive search, which considers all possible SNP combinations. GMDR is a non-parametric data mining approach that can apply to both continuous and dichotomous phenotypes.21 In addition, GMDR permits adjustment for discrete and quantitative covariates in various population-based studies with unbalanced case–control subjects.21 The GMDR software provides a number of output parameters, including cross-validation consistency, the testing balanced accuracy and the sign test, to assess each selected interaction.21 The cross-validation consistency score is a measure of the degree of consistency with which the selected interaction is identified as the best model among all possibilities considered. Furthermore, the testing balanced accuracy is a measure of the degree to which the interaction accurately predicts case–control status with scores between 0.50 (indicating that the model predicts no better than chance) and 1.00 (indicating perfect prediction). Finally, we provided gender, age, baseline BMI, duration and type of current AAP exposure used as covariates in our gene–gene interaction analyses. Permutation testing obtains empirical P-values of prediction accuracy as a benchmark based on 1000 shuffles. In addition, we performed logistic regression models to confirm the results from GMDR analyses, with gender, age, baseline BMI, type and duration of current AAP exposure as the covariates.
The powers for the studied variants contributing to various levels of odds ratio (OR) are summarized in Supplementary Table 2. Briefly, for a risk allele of MetS with OR=1.2, the study has powers ranging from 0.13 ∼0.20, 0.45 ∼0.70 at OR=1.5 and 0.89 ∼0.99 at OR=2.0.
This study sample consisted of 456 patients treated with clozapine, olanzapine or risperidone. The prevalence of MetS was 22.8% (104/456) in the total group, and the rates for the subgroups were 27.5%, 24.2% and 18.0% for patients taking clozapine, olanzapine and risperidone, respectively (P=0.094) (Table 1). The mean age, sex distribution and duration of AAPs used were similar between patients with and without MetS, however, patients with MetS had a larger baseline BMI than patients without MetS (P<0.001).
Single polymorphism analysis
Two INSIG1 SNPs and seven INSIG2 SNPs were genotyped in this study. The genotype distributions of the nine SNPs for the patient with and without MetS are shown in Table 2. Most of the genotype distribution of the nine SNPs in the MetS and non-MetS were in Hardy–Weinberg equilibrium with the exception of rs2161829 in the non-MetS that was deviated significantly from Hardy–Weinberg equilibrium (Supplementary Table 3). The analysis for single locus effects revealed that one INSIG2 SNP (rs11123469) had a statistically significant association with MetS, when analyzed by genotype frequency that the INSIG2 rs11123469-C homozygous genotype was found to be more frequent in the patients with MetS than those without MetS (P=0.00011, false discovery rate=0.043) (Table 2). Comparing with the TT carriers, the OR for rs11123469-C homozygotes to have MetS was 5.10 (95% CI=2.23 ∼11.70). The other six INSIG2 SNPs and the two INSIG1 SNPs did not show any association with MetS.
We also analyzed the association of the nine SNPs on the quantitative serum factors of defining MetS (triglycerides, HDL, systolic blood pressure, diastolic blood pressure, fasting blood sugar and waist circumstance), and also examined the association of baseline BMI and BMI changes after AAP treatment with analysis of covariance, as shown in Supplementary Table 4. The MetS-associated SNP rs11123469 had significant effect on serum HDL levels across AAP treatment: patients with rs11123469-TT had significantly higher HDL levels than those with rs11123469-CC, post hoc Bonferroni P-value=0.005). With regard to other studied SNPs, there were no significant differences in the mean of the eight quantitative factors between different genotypes (Supplementary Table 4).
The association of the genetic sequences with MetS was further investigated by looking into the INSIG1 and INSIG2 haplotypes. Pairwise linkage disequilibrium estimation of the nine SNPs is showed in the Supplementary Figure 1 and 2. The results of the case–control haplotype analysis between groups are presented in Table 3 and Supplementary Table 5. The overall haplotype distributions between groups were significantly different in the two, three and four-marker windows by permutation analysis and by correction for multiple testing (Supplementary Table 5, all permutation P<0.05; false discovery rate for all the three permutation P=0.043). Further analysis for the individual haplotype in each of the three windows identified several haplotypes significantly associated with MetS (Table 3). The most significant haplotype was the C-C-C type of rs11123469-rs10185316- rs1559509 that was more frequent in the patients with MetS (Table 3, P=0.0023, false discovery rate=0.043).
Multi-loci interaction analysis
The GMDR analysis was used to assess the impacts of combinations between the nine SNPs, including gender, age, baseline BMI, duration of current AAP exposure and type of AAPs as covariates because these factors may affect the risk of MetS. Table 4 summarizes the results obtained from GMDR analysis for 2-locus to 5-locus model with covariate adjustment. There was a significant 3-locus model involving INSIG1 rs9767875 and INSIG2 (rs1559509 and rs2161829), indicating a potential gene–gene interaction between INSIG1 and INSIG2 (Table 4). Moreover, there was a significant 4-locus model involving INSIG1 rs9767875 and INSIG2 (rs7566605, rs1559509 and rs2161829). Overall, the 3-locus model had the highest level of testing accuracy (62.29%) and showed good cross-validation consistency (10/10). Therefore, we chose the 3-locus model as the best GMDR model. Furthermore, the significant interactions among the above 3-locus and 4-locus models were confirmed by logistic regression models (P<0.001 and <0.001, respectively).
The results of our study showed an overall prevalence of MetS of 22.8% among the 456 Chinese schizophrenic patients treated with AAPs. The aims of the present study was to examine not only the main effects but also epistatic effects of the INSIG1 and INSIG2 genes in the risk of MetS in patients treated with AAPs. Among the nine SNPs tested, we found, for the rs11123469 CC carriers, 45.0% met MetS criteria, compared with 18.6% in the TT genotype group, giving an OR=5.10, (95% CI=2.23∼11.70, P=0.00011). Thus, for the CC homozygotes, the risk was five times greater to have MetS in patients treated with AAPs. Our finding might imply that the associated marker, rs11123469, may have effects on INSIG2 functioning, and contribute to the underlying pathophysiology of MetS in patients treated with AAPs directly. For instance, the significant rs11123469 was subjected to in silico prediction for its function with FastSNP (http://fastsnp.ibms.sinica.edu.tw/), an online tool for functional analysis and prioritizing SNP, and found that the C-to-T substitution on the polymorphism creates a binding site for transcription factor octamer-binding factor 1. On the other hand, it is also possible that the association of INSIG2 rs11123469 may not have real biological effects and it is most likely in linkage disequilibrium with the responsible variants that harbor the biological effects on the reported metabolic phenotypes.
The pioneering work of Steen and colleagues has highlighted the possible effect of the SREBP pathway in antipsychotic-induced metabolic adverse effects. Using a microarray approach, they demonstrated that the antipsychotic drugs clozapine and haloperidol both activated the SREBP system.25 In another study, using cell cultures of human liver cells, they found clozapine, which has been strongly associated with metabolic adverse effects, has a very pronounced activation of SREBP.26 Ziprasidone, however, which has less metabolic adverse effects, did hardly stimulate the SREBP system. In a recent study using female rats, they demonstrated that acute clozapine exposure affected SREBP-regulated lipid biosynthesis, as well as other lipid homeostasis pathways, suggesting that such drug-induced effects on lipid metabolism may be related to the metabolic adverse effects associated with clozapine.27 In 2007, Skelly et al. investigated the association between rs7566605 and 10 additional tagging SNPs near INSIG2 with BMI in 756 participants in CATIE study, a double-blinded randomized clinical trial of typical and AAPs.19 They were unable to find the association of rs7566605 or other SNPs investigated with BMI. However, recently, Steen and colleague reported a strong association (P=0.0003–0.00007) between three markers localized within or near the INSIG2 gene (rs17587100, rs10490624 and rs17047764) and antipsychotic-related weight gain from the time before the initiation of antipsychotic treatment (BMI-1) to the time before initiation of clozapine treatment (BMI-2).20 The three SNPs (rs17587100, rs10490624 and rs17047764) were also found to be associated with BMI changes after 12-week clozapine treatment (BMI-3 versus BMI-2).20 In the present study, none of the studied INSIG2 SNPs, including the obesity-associated SNP, rs7566605 were found to have association with BMI changes (Supplementary Table 4). This is consistent with the result of Skelly et al.19 supporting the genetic variation in INSIG2 has no effect on body weight change across AAP treatment, but partially conflicts with the report of Le Hellard et al.20 The discrepancy might be multi-factorial: (1) studied population with different antipsychotic used across studies: olanzapine, risperidone, quetiapine and ziprasidone in Skelly et al.,19 typical antipsychotics alone, AAP alone, combined typical and AAPs and clozapine in Le Hellard et al.20 and risperidone, olanzapine and clozapine in our study (Table 1); (2) various follow-up duration across antipsychotic treatment: average 18 months in Skelly et al.,19 average 2.7 years before and 12 weeks after clozapine treatment in Le Hellard et al.,20 and average 45 months in our studies (Table 1), and (3) ethnic difference in the minor allele frequency of studied SNP: the minor allele frequency of the three weight-change-associated SNPs in Le Hellard et al.20 are all less than 10% in Han population according to the information in the International HapMap Project. Despite of the discrepancy on body weight change after antipsychotic treatment between studies, the observation that patients with rs11123469-TT had significantly higher HDL levels than those with rs11123469-CC (Supplementary Table 4, post hoc Bonferroni P-value=0.005) indirectly supports that Insig2 as an quantitative trait locus for mouse serum cholesterol levels.16 The positive association between INSIG2 genetic variants and MetS in patients treated with AAPs in this study further supports the role of SREBP pathway in AAP-associated metabolic adverse effects.
Another major finding of this study is that, by the GMDR analyses, we further established a two-gene interaction model between INSIG1 and INSIG2 genes in MetS. INSIG1 and INSIG2 interactions were found in the significant 3-locus and 4-locus gene–gene interaction models. Furthermore, the significant interactions among the above 3-locus and 4-locus models were confirmed by logistic regression models (P<0.001 and <0.001, respectively), adjusting for gender, age, baseline BMI, duration and type of current AAP exposure used as covariates. GMDR is a non-parametric data mining approach that can apply to both continuous and dichotomous phenotypes. In addition, GMDR permits adjustment for discrete and quantitative covariates in various population-based studies with unbalanced case–control subjects. The results from the GMDR analyses confirmed our hypothesis that the genes from INSIG1 and INSIG2 may have interactive effects on risk of MetS in patients treated with AAPs. Our findings also suggested that the use of multiple statistical approaches could be a better strategy to elucidate complex gene interaction, such as drug-therapeutic effects. Beside the statistical significance, the potential biological mechanism under this interaction model was our concern. The protein encoded by INSIG2 is highly similar to the protein product encoded by gene INSIG1. Both INSIG proteins are endoplasmic reticulum proteins that block the processing of SREBPs by binding to SCAP, and thus prevent SCAP from escorting SREBPs to the Golgi. Data from knockout study showed that INSIG1 and INSIG2 mouse appear grossly normal, however, the combined disruption of both genes was associated with over-accumulated cholesterol and triglycerides in liver.28 This finding suggested that both proteins may interact or compensate with each other in the regulation of cholesterol and triglyceride synthesis which is in line with our findings.
The notable strength of the present study was that, during the entire study period, these patients were hospitalized, remained on the same antipsychotic with optimal control of drug compliance, and had the same diet and activity. At the same time, there are several limitations of this study. First, the positive associations in this study may have simply been because of chance or a stratification effect in the sample collection. The resolution of this suggestion requires replication in future studies with different samples. Second, we only included nine genetic variations in this study. Therefore, the contributions of other markers in the INSIG1 and INSIG2 genes should be further examined in future work. Finally, INSIG proteins interact with SREBPs and SCAP to control the metabolism of cholesterol and fatty acids. Whether genetic variants of these proteins may interact to influence the MetS in patients treated with AAPs needs further exploration.
In conclusion, our study has tested the association between INSIG1and INSIG2 genetic polymorphisms and MetS in schizophrenic patients treated with AAPs. Our findings suggested that INSIG2 gene might contribute to the risk of MetS independently or in an interactive manner with INSIG1. Independent replications in other population are needed to confirm the role of the INSIG polymorphisms in MetS in patients treated with AAPs.
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This work was supported by grant V98C1–061, V98C1-055 from Taipei Veterans General Hospital, Taiwan, and grants NSC 95-2314-B-075 –011 and NSC 97-2314-B-075-001-MY3 from National Science Council Grant, Taiwan. The authors also thank Jer-Yuan Wu, the National Genomic Center and the National Clinical Core at Academia Sinica, Taiwan, for genotyping support.
The authors declare no conflict of interest.
Supplementary Information accompanies the paper on the The Pharmacogenomics Journal website
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Cite this article
Liou, YJ., Bai, Y., Lin, E. et al. Gene–gene interactions of the INSIG1 and INSIG2 in metabolic syndrome in schizophrenic patients treated with atypical antipsychotics. Pharmacogenomics J 12, 54–61 (2012). https://doi.org/10.1038/tpj.2010.74
- gene–gene interaction
- insulin-induced gene 1
- insulin-induced gene 2
- metabolic syndrome
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