Combination antiretroviral therapy (cART) has dramatically reduced the mortality and AIDS-related illness . However, chronic hepatitis C (CHC) has become a major cause of morbidity and mortality in patients infected with HIV , because of fast progression of fibrosis  and other comorbidities not related to CHC and AIDS . In fact, an increased risk of dyslipidemia, lipodystrophy, insulin resistance, type 2 diabetes mellitus (T2DM), and cardiovascular disease (CVD) has been widely described in HIV-infected individuals on cART . In addition, not only HIV patients on cART have metabolic disturbances, but also HIV-infected patients without cART show lipid disturbances . Moreover, hepatitis C virus (HCV) infection is associated with metabolic disturbance such as insulin resistance and dyslipidemia , which increased risk of CVD in HIV/HCV-coinfected patients [8,9]. An unexplained variability in these clinical traits still remains, suggesting that several host genetic factors may play an important role.
The Solute Carrier Family 30 Member 8 gene (SLC30A8) encodes a zinc transporter-8 (ZnT8), which plays a key role in insulin metabolism at pancreatic β-cells and liver [10,11]. Furthermore, ZnT8 is also expressed in human adipose tissue , involved in the control of adipocyte differentiation  and release of free fatty acids . The SLC30A8 rs13266634 polymorphism, a nonsynonymous single nucleotide polymorphism (SNP), has been related to higher risk for T2DM in a recent meta-analysis . This analysis proposed that major C allele (which encodes Arg) is a T2DM risk factor, whereas minor T allele (which encodes Trp) confers a protective effect. In addition, rs13266634 polymorphism influences to different extents the development of impaired fasting glucose (IFG) and the transition from IFG to T2DM .
The aim of this study was to analyze the relationship of SLC30A8 rs13266634 polymorphism with insulin resistance and dyslipidemia in European white HIV/HCV-coinfected patients.
Patients and methods
We carried out a cross-sectional study on 260 nondiabetic HIV/HCV-coinfected patients between September 2000 and July 2009. The study was approved by the Institutional Review Board and the Research Ethic Committee of the Instituto de Salud Carlos III and conducted in accordance with the Declaration of Helsinki. Patients gave their written consent for the study. All patients were European whites.
All patients were HCV treatment-naive and potential candidates for HCV therapy, and in most cases, underwent a liver biopsy. The inclusion criteria were as follows: detectable HCV-RNA by PCR, negative hepatitis B surface antigen, availability of DNA sample, no clinical evidence of hepatic decompensation, no diabetes mellitus, and stable cART, or no need for cART. Patients with active opportunistic infections, active drug and/or alcohol addiction, and other concomitant diseases were excluded. Of the 495 HIV/HCV-coinfected patients who met the criteria described above, 293 patients had a DNA sample available for DNA genotyping, and only 260 patients were available for statistical analysis: 14 patients were excluded because of DNA genotyping problems and 19 had no data of homeostasis model assessment of insulin resistance (HOMA-IR).
Epidemiological and clinical data
Medical records were used to obtain epidemiological and clinical data when HCV therapy was started and/or liver biopsy was performed. BMI was calculated as the weight in kilograms divided by the square of the height in meters. The duration of HCV infection for patients with a history of intravenous drug use was estimated starting from the first year they shared needles and other injection paraphernalia, which are the most relevant risk practices for HCV transmission. For non-IDU patients, we only included those patients for whom the initiation of their HCV infection could be determined with certainty.
Genotyping of SLC30A8 polymorphism
Genomic DNA was extracted from peripheral blood with Qiagen columns (QIAamp DNA Blood Midi/Maxi; Qiagen, Hilden, Germany). DNA samples were sent to the Spanish National Genotyping Center (CeGen; http://www.cegen.org/) in order to genotype rs13266634 SNP at SLC30A8 gene. Genotyping was performed by using GoldenGate assay with VeraCode Technology (Illumina Inc., San Diego, California, USA).
Biochemistry panel was measured using a clinical chemistry analyzer (Hitachi 912, Boehringer Mannheim, Germany) in fasting patients. The collected data were total cholesterol (TC), triglycerides, and high-density lipoprotein (HDL-C). The low-density lipoprotein (LDL-C) was calculated by Friedewald estimation: LDL cholesterol = total cholesterol − HDL cholesterol − (triglycerides/5). The atherogenic risk was estimated using the atherogenic index = (total cholesterol − HDL cholesterol)/HDL cholesterol.
The degree of insulin resistance was estimated for each patient using the HOMA-IR method described by Matthews et al.: fasting plasma glucose (mmol/l) times fasting serum insulin (mU/l) divided by 22.5. The HOMA-IR is the most commonly used model for assessing insulin resistance in CHC , but there is great variability in the threshold HOMA-IR levels (from 1.5 to >4) to define insulin resistance in CHC . In this article, we evaluated the thresholds for HOMA-IR at least 2.0, 2.5, 3.0 and 3.8.
We analyzed the serum concentration of TC, triglycerides, HDL-C, LDL-C, LDL-C/HDL-C, and atherogenic index. Moreover, we evaluated the following thresholds: TC at least 200 mg/dl, triglycerides at least 170 mg/dl, LDL-C at least 100 mg/dl, HDL-C 35 mg/dl or less, LDL-C/HDL-C at least 3.0, and atherogenic index at least 3.5.
We analyzed the continuous HOMA-IR values and thresholds (at least 2.0, 2.5, 3.0, and 3.8) for HOMA-IR.
All statistical tests were performed with the Statistical Package for the Social Sciences (SPSS) 19.0 software (IBM Corp., Chicago, Illinois, USA). All P values were two-tailed and statistical significance was defined as P <0.05.
We estimated the statistical power that is expected for two sample populations (CT/TT and CC). For example, accepting an α risk of 0.05 in a two-sided test with 127 rs13266634 CC carriers and 133 rs13266634 TT/CT carriers, the statistical power would be 74% to recognize as statistically significant the difference from two independent proportions (40 versus 25%); and 88% from two independent serum lipid levels (40 versus 35 mg/dl).
Continuous variables were expressed as median (interquartile range) and categorical variables as percentage (absolute frequency). Analysis of normality was performed with the Kolmogorov–Smirnov test. Log transformation was performed for all variables that were not normally distributed (HOMA-IR and serum lipid levels). The association of categorical variables was determined by χ2 analysis or Fisher's exact test. Comparisons of continuous variables were performed with the Mann–Whitney U-test.
For the genetic association study, univariate and multivariate regression analyses were used to compare the outcome variables according to SLC30A8 polymorphisms. The analysis was carried out according to a dominant genetic model (CT/TT versus CC), which was the model that best fit to our data. On the one hand, lineal regression analyses were used to investigate the association between SLC30A8 polymorphism and continuous outcome variables, which were log10-transformed. This test gives the differences between groups and the arithmetic mean ratio (AMR), which gives how many times greater is the value in the presence of CT/TT genotype versus CC genotype. On the other hand, logistic regression analyses were used to investigate the association of SLC30A8 polymorphisms with categorical outcome variables. This test gives the association between groups and the odds ratio (OR), which gives the likelihood of having the outcome in the CT/TT genotype versus CC genotype. We included the SLC30A8 polymorphism and the most significant covariables selected by ‘Stepwise’ algorithm (at each step, factors are considered for removal or entry: a P value for entry and exit of 0.15 and 0.20, respectively). The covariables used were gender, age, BMI, nadir CD4+ T cells, undetectable HIV viral load (<50 copies/ml), time with cART, specific antiretroviral drugs (saquinavir, efavirenz, ritonavir, tenofovir, etc.), HCV genotype 1, HCV viral load at least 500 000 IU/ml, and significant fibrosis (F≥2).
Characteristics of patients
Table 1 shows the characteristics of 260 HIV/HCV-coinfected patients. Note that patient characteristics were similar when they were stratified by SLC30A8 polymorphism (CC versus CT/TT).
The length of HIV/HCV coinfection in our cohort was high (median of 20.9 years) and similar between the two study groups (P = 0.446). The rs13266634 CC carriers had a positive correlation between time of infection for HIV and HCV, and serum levels of TC (r = 0.208; P = 0.025) and LDL-C (r = 0.191; P = 0.041), whereas rs13266634 TT/CT carriers did not have any significant correlation.
Allele frequencies for rs13266634 polymorphism were 0.70 for C allele and 0.30 for T allele. Genotype frequencies were 0.49, 0.42, and 0.09 for CC, CT, and TT genotypes, respectively. These frequencies in our dataset were in accordance with data listed on the NCBI SNP database (http://www.ncbi.nlm.nih.gov/projects/SNP/snp_ref.cgi?rs=13266634).
The rs13266634 polymorphism had fulfilled the minimum allele frequency more than 0.05 and missing values less than 5%. Furthermore, rs13266634 polymorphism was in Hardy–Weinberg equilibrium (P = 0.999).
SLC30A8 polymorphism with metabolic disturbance
Figure 1 shows the lipid profile according to rs13266634 genotypes of HIV/HCV-coinfected patients. rs13266634 CT/TT carriers had significantly higher serum values of HDL-C (P = 0.014) and lower values of LDL-C/HDL-C (P = 0.036) and atherogenic index (P = 0.011) than CC carriers. Additionally, rs13266634 CT/TT carriers had lower percentage of HDL, 35 mg/dl or less (P = 0.050), LDL/HDL at least 3 (P = 0.091), and atherogenic index at least 3.5 (P = 0.003).
When adjusted regression analysis was performed (Supplementary Table 1, http://links.lww.com/QAD/A487), rs13266634 CT/TT genotype was associated with high serum values of HDL-C [AMR = 1.10 (95% confidence interval, CI = 1.03–1.19) P = 0.006], and low values of LDL-C/HDL-C [AMR = 0.88 (95% CI = 0.79–0.99) P = 0.045] and atherogenic index [AMR = 0.89 (95% CI = 0.81–0.98) P = 0.024]. For categorical outcomes, rs13266634 CT/TT carriers had lower likelihood of having atherogenic index at least 3.5 [OR = 0.47 (95% CI = 0.26–0.83) P = 0.009], and very close to statistical significance for LDL-C/HDL-C at least 3 [OR = 0.52 (95% CI = 0.27–1.02) P = 0.056], supporting the protective effect of the rs13266634 T allele (CT and TT genotypes).
Supplementary Figure 1, http://links.lww.com/QAD/A487 shows that no significant relationship was observed between rs13266634 polymorphism and continuous HOMA-IR values or threshold for HOMA-IR at least 3.8. Additionally, we evaluated the cut-offs for HOMA-IR at least 2.0, 2.5, and 3.0, and no statistically significant results were found (data not shown).
In this study, we analyzed the serum lipid profile and HOMA-IR values in HIV/HCV-coinfected patients according to SLC30A8 rs13266634 polymorphism. The major findings were as follows: rs13266634 T allele was related to higher levels of HDL-C and lower values of cardiovascular risk indices (LDL-C/HDL-C and atherogenic index) than rs13266634 CC carriers; and there was a lack of association between rs13266634 polymorphism and HOMA-IR values.
To our knowledge, this is the first study to report about the impact of SLC30A8 rs13266634 polymorphism on dyslipidemia in HIV/HCV-coinfected patients. Dyslipidemia is a complex metabolic disturbance in which genetic and environmental factors may interact. Both the HIV/HCV coinfection and the cART influence by developing steatosis and lipid disturbances . HIV-infected patients without cART show an increase in triglycerides and decreases in TC, LDL-C, and HDL-C levels . Moreover, cART leads to increased levels of TC, LDL-C, and triglycerides and decreased levels of HDL-C [20,21]. cART is also associated with subcutaneous fat wasting (lipoatrophy) and visceral adiposity (lipohypertrophy) . Moreover, HCV infection is related to steatosis and metabolic abnormalities in HCV-monoinfected patients such as insulin resistance and dyslipidemia (reductions in TC, HDL-C, and LDL-C) [8,22]. Although we have no data to attribute a direct effect of SLC30A8 polymorphism on lipid profile, our findings show that patients with rs13266634 CT/TT genotype had an antiatherogenic lipid profile, being able to have a protective role against cardiovascular risk in HIV/HCV-coinfected patients.
In non-HIV-infected patients, the effects of rs13266634 polymorphism on insulin resistance and T2DM vary depending on different studies, but the majority of them indicate the association of CT/TT genotype with reduced rates of insulin resistance and T2DM . However, we did not find any significant association between rs13266634 polymorphism and HOMA-IR values among HIV/HCV-coinfected patients, considering continuous values or several cut-offs, and stratifying by significant fibrosis (F≥2). This lack of significance might be because of the limited number of patients used in the stratified analysis, or also because of the possible distortive effect of direct and indirect factors related to both HIV and HCV infections, and cART . Moreover, we must not discount the fact that our patients had a relatively low BMI (22.5 kg/m2), possibly because of the fact that around 85% of our patients were IDUs. Both HIV infection and chronic drug abuse compromise nutritional status of patients, despite major advances in the HIV treatment , promoting that HIV-positive IDUs had lower BMIs.
There are some issues that have to be considered for a correct interpretation of our data. This is a cross-sectional study with a relatively low sample size. Our study should also include HCV-monoinfected patients in order to evaluate the significance of only CHC, and HIV-monoinfected patients in order to evaluate the significance of HIV infection and cART in the development of metabolic disturbances. It is important to note that cART may increase the risk for unfavorable cardiometabolic profile [5,9], but the cART characteristics were similar between the study groups and they were included in the adjusted regression analyses carried (see Statistical analysis section). Moreover, the patients selected for our study were patients who met a set of criteria for starting HCV treatment and it is possible that this may have introduced a selection bias. Finally, DNA genotyping was performed using customized GoldenGate (VeraCode) for 48 SNPs, which were extracted from a review about SNPs related to T2DM . Therefore, we have not conducted a random search of a meaningful result because these SNPs have previously been associated with metabolic disturbances in patients not infected with HIV and/or HCV , and thus, our results should not be affected by adjusting the ‘P value’ after multiple tests in a clinical-orientated study [25,26].
In conclusion, the presence of rs13266634 CT/TT genotype was associated with a protective cardiometabolic lipid profile, but not with HOMA values. Thus, rs13266634 polymorphism might play a significant role in lipid metabolism and cardiovascular risk in HIV/HCV-coinfected patients. However, we consider that further analysis is needed to determine the potential use of rs13266634 polymorphism as a predictor of dyslipidemia and cardiovascular risk in HIV/HCV-coinfected patients.
The authors thank the Spanish National Genotyping Center (CeGen) for providing the SNP genotyping services (http://www.cegen.org).
D.P.T., J.M.B., and S.R. performed all statistical analyses, interpretation of the data, and wrote the article. J.B. and S.R. participated in the study concept and design. J.B., D.M., C.D., A.C., and T.A.E. participated in patient selection, collection of samples, and acquisition of data. A.F.R., M.A.J.S., P.G.B., M.G.F., and M.G.A. participated in sample preparation, DNA isolation, and genotyping preprocedure, and contributed to critical revision of the article. S.R. supervised the study.
All authors revised the article from a draft by S.R.
This work has been supported by grants given by Fondo de Investigación de Sanidad en España (FIS) [Spanish Health Founds for Research] [grant numbers PI08/0738, PI11/00245; PI08/0928, and PI11/01556], Red Española de Investigación en SIDA (RIS) [AIDS Research Network] [grant numbers RD12/0017/0024 and RD12/0017/0004], and ‘Fundación para la Investigación y la Prevención del Sida en España’ (FIPSE) [grant number 361020/10]. D.P.T., P.G.B., M.A.J.S., A.F.R., and M.G.A. are supported by ‘Instituto de Salud Carlos III’ [grant numbers CM12/00043, FI12/00036, CM10/00105, PI11/00245, CD12/00442, respectively]. J.B. is an investigator from the Programa de Intensificación de la Actividad Investigadora en el Sistema Nacional de Salud (I3SNS).
Conflicts of interest
The authors have nothing to disclose. The authors do not have any commercial or other association that might pose a conflict of interest.
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