Abstract
Hepatic steatosis (HS) is frequently observed in HIV-infected patients. It is not known whether HIV infection is an independent risk factor for HS development. We aimed to analyze whether HIV coinfection was associated with a higher frequency of HS in patients with chronic hepatitis C. This was a retrospective cross-sectional study. 574 subjects with chronic hepatitis C virus (HCV) infection were included, 246 (43%) of them coinfected with HIV. All of them underwent transient elastography with controlled attenuation parameter (CAP) measurement. HS was defined as CAP ≥ 248 dB/m. 147 individuals (45%) showed HS in the HCV-monoinfected group and 100 (40.7%) in the HIV/HCV-coinfected group (p = 0.318). HS was associated with body mass index (BMI) [<25 Kg/m2 vs. ≥25 Kg/m2, 67 (23.5%) vs. 171 (62.9%); p = 0.001], with plasma HDL-cholesterol [<50 mg/dL vs. ≥50 mg/dL, 122 (48.6%) vs. 95 (37.5%), p = 0.012], with plasma triglycerides [<150 mg/dL vs. ≥150 mg/dL, 168 (40.2%) vs. 65 (52.4%); p = 0.016] and with plasma total cholesterol [<200 mg/dL vs. ≥200 mg/dL, 181 (41%) vs. 53 (52.5%); p = 0.035]. In the multivariate analysis, HS was associated with BMI [adjusted OR (AOR) = 1.264 (1.194–1.339); p = 0.001], age [AOR = 1.029 (1.001–1.058); p = 0.047] and HCV genotype 3 infection [AOR = 1.901 (1.081–2.594); p = 0.026]. HIV coinfection was not associated with HS [AOR = 1.166 (0.719–1.892); p = 0.534]. In conclusion, HIV coinfection is not related with an increased frequency of HS in HCV-infected patients.
Subject terms: Viral hepatitis, HIV infections
Introduction
The main causes of hepatic steatosis (HS) are metabolic factors1, alcohol use2 and HCV infection, particularly HCV genotype 33,4. Non-alcoholic fatty liver disease (NAFLD), HS in the absence of other causes than metabolic factors, is recognized as one of the most common liver diseases worldwide with an estimated prevalence of 25% among the general population in Western countries5,6. NAFLD has a spectrum of liver disease ranging from simple fatty liver to nonalcoholic steatohepatitis (NASH), fibrosis and cirrhosis7–10.
In HIV-uninfected patients, the prevalence and risk factors of NAFLD and its complications have been evaluated, specifically in industrialized countries6,8,11–13. In HIV infection, studies carried out in unselected patients with or without HCV coinfection have reported a prevalence of HS, determined by the controlled attenuation parameter (CAP), of approximately 40%14–17. This rate nearly doubles that found of the general population in similar areas5 and it is closer to the NAFLD prevalence observed in obesity and type 2 diabetes mellitus8. Despite this difference between HIV-infected patients and the general population, there are very few direct comparisons of HS frequency between individuals with and without HIV infection. Indeed, a study comparing men who have sex with men (MSM) with and without HIV infection found a lower prevalence of HS in HIV-infected MSM than in MSM without HIV infection18. On the contrary, two case-control studies conducted in small samples found higher frequencies of HS in HIV-infected individuals than in controls19,20. Because of this, the actual impact of HIV infection on the risk of HS still remains unknown.
Among HIV-infected patients with chronic hepatitis C, the frequency of HS estimated by liver biopsy showed a wide range from 23% to 72%21. In a metanalysis of HCV-infected patients who underwent liver biopsy, the rates of HS were similar in HIV/HCV-coinfected and HCV-monoinfected subjects21. However, patients undergoing a liver biopsy are not representative of the overall population with HCV infection. Thus, information provided by this study on the risk of HS inherent to HIV infection is very limited. Because of this, comparative studies of the HS prevalence conducted in unselected populations of HCV-infected patients with and without HIV coinfection are required. Non-invasive techniques may allow these studies, as the overall HCV-infected population, irrespective the estimated liver stage or the therapy perspectives, may be included9,10. This kind of studies may provide very valuable data on the risk of HS due to HIV infection minimizing the possible confounding effect of the HCV coinfection.
Because of these, we aimed to compare the prevalence of HS, evaluated using CAP, in patients with chronic HCV infection, with and without HIV coinfection, in order to appraise the effect of HIV infection on the HS presence in this setting.
Results
Characteristics of the study population
Five hundred and ninety-eight consecutive patients fulfilled the inclusion criteria. A reliable elastography result could not be obtained in 24 (4%) of them. Thus, 574 patients were finally analyzed. Among them, 328 (57.1%) were HCV-monoinfected patients and 246 (42.9%) were HIV/HCV-coinfected individuals. The demographic, anthropometric and laboratory characteristics of these patients are shown in Table 1. BMI and plasma total cholesterol, HDL cholesterol and LDL cholesterol values were lower in HIV/HCV-coinfected than in HCV-monoinfected patients (Table 1). Nearly all of HIV/HCV-coinfected patients were under antiretroviral therapy [241 (99.2%)] with undetectable viral load in 78.9% of the cases (Table 1).
Table 1.
Characteristics of study populations (N = 574).
| Characteristics | HCV monoinfection (N = 328) | HIV/HCV coinfection (N = 246) | p-value |
|---|---|---|---|
| Male sex, n (%) | 271 (82.6) | 214 (87) | 0.152 |
| Age (years)* | 52 (48–57) | 53 (49–56) | 0.695 |
| IDUa, n (%) | 205 (66.3) | 209 (87.1) | 0.001 |
| Alcohol intake ≥50 g/d, n (%) | 168 (51.2) | 60 (24.4) | <0.001 |
| HCV Genotype 3 infection, n (%) | 54 (16.5) | 43 (17.5) | 0.748 |
| BMIb (kg/m2)* | 25.9 (22.6–28.5) | 24 (21.3–27) | 0.001 |
| BMIb (kg/m2), n (%) | |||
| 18–25 | 141 (44.3) | 144 (60.3) | 0.004 |
| 26–30 | 123 (38.7) | 67 (28) | |
| 31–35 | 41 (12.9) | 18 (7.5) | |
| >35 | 11 (3.5) | 9 (3.8) | |
| Fasting plasma glucosec ≥100 mg/dL, n (%) | 72 (24.4) | 75 (30.5) | 0.113 |
| Plasma triglyceridesd ≥150 mg/dL, n (%) | 42 (14.1) | 82 (33.5) | 0.001 |
| Plasma total cholesterole (mg/dL)* | 165 (145–199) | 156 (137–182) | 0.001 |
| Plasma HDL-cholesterolf (mg/dL)* | 52.5 (41.5–65.4) | 47.5 (37.6–59.0) | 0.003 |
| Plasma LDL-cholesterolg (mg/dL)* | 91 (71–115) | 76 (61–99) | 0.001 |
| LS (kPa)* | 7.3 (5.4–13.3) | 9.7 (6.9–16.9) | 0.001 |
| Cirrhosis, n (%) | 78 (23.8) | 80 (32.5) | 0.020 |
| HbsAgh+, n (%) | 1 (0.3) | 8 (3.3) | 0.001 |
| CD4 cell counts/mm3)I * | — | 486 (310.5–726.0) | |
| Plasma HIV-RNA < 50 copies/mL, n (%) | — | 194 (78.9) | |
| ARTj, n (%) | — | 241 (99.2) | |
*Median (Q1-Q3).
aAvailable data for 549 patient; bAvailable data for 557 patients; cAvailable data for 541 patients; dAvailable data for 542 patients; eAvailable data for 543 patients; fAvailable data for 504 patients; gAvailable data for 505 patients; hAvailable data for 562 patients; iAvailable data for 245 patients; jAvailable data for 243 patients.
Abbreviations: HCV: hepatitis C virus; HIV: human immunodeficiency virus; IDU: injecting drug users; BMI: Body mass index; HDL: High-density lipoprotein; LDL: Low-density lipoprotein; LS: Liver Stiffness; ART: antiretroviral therapy.
Prevalence of HS according to HIV infection status
Median CAP was 241 (209–280) dB/m among HCV-infected patients and 237 (202–273) dB/m among HIV/HCV-coinfected patients, (p = 0.296) (Fig. 1). The frequency of HS (CAP ≥ 248 dB/m) in HCV-monoinfected patients was 147 (44.8%) and in HIV/HCV-coinfected patients was 105 (40.7%) (p = 0.318). Eighty-three (25.3%) individuals without HIV infection showed severe steatosis compared to 51 (20.7%) among those with HIV coinfection (p = 0.2). There were no differences in the prevalence of steatosis between the group with and without HIV coinfection by BMI category (Fig. 2). In an analysis excluding patients with alcohol intake ≥50 g/d, there were no differences between the two study groups in the median CAP values nor in the prevalence of HS (Supplementary Fig. S1).
Figure 1.
CAP median and interquartile range in both HCV-infected and HIV/HCV-coinfected group of patients.
Figure 2.
Frequency of steatosis in HCV-infected and HIV/HCV-coinfected populations classified according to the grade of obesity.
Matching HIV/HCV-coinfected patients with HCV-monoinfected individuals by age, sex and BMI (Supplementary Table 1), the prevalence of HS for the HIV/HCV-coinfected group was 100 (40.7%) and 96 (39.0%) for the HCV-monoinfected group (p = 1.000).
Factors associated with the presence of HS
In the univariate analysis, history of injecting drugs, alcohol intake, plasma triglycerides, BMI, plasma total cholesterol and plasma HDL cholesterol were related with HS, whereas HIV coinfection was not (Table 2). In the multivariate logistic analysis, BMI, age and genotype 3 infection were independently associated with HS. HIV coinfection was not related with HS (Table 2).
Table 2.
Univariate and multivariate analysis of factors associated with steatosis.
| Parameter | No. | Frequency of steatosis** N (%) | p univariate | AOR (95% CI) | p multivariate |
|---|---|---|---|---|---|
| Sex | |||||
| Female | 89 | 37 (41.6%) | 0.762 | 1.129 (0.597–2.134) | 0.709 |
| Male | 485 | 210 (43.3%) | |||
| Age* | |||||
| <52 | 238 | 92 (38.7%) | 0.075 | 1.030 (1.001–1.060) | 0.040 |
| ≥52 | 336 | 155 (46.1%) | |||
| Way of HCV infection | |||||
| IDUa | 414 | 167 (40.3%) | 0.058 | 0.923 (0.541–1.572) | 0.767 |
| Other | 135 | 67 (49.6%) | |||
| HIV | |||||
| Positive | 246 | 100 (40.7%) | 0.318 | 1.181 (0.727–1.918) | 0.502 |
| Negative | 328 | 147 (44.8%) | |||
| HCV genotype | |||||
| 3 | 97 | 49 (50.5%) | 0.102 | 1.952 (1.106–3.445) | 0.021 |
| Other | 477 | 198 (41.5%) | |||
| Alcohol intake (g/day) | |||||
| <50 | 346 | 134 (38.7%) | 0.01 | 1.395 (0.882–2.208) | 0.155 |
| ≥50 | 228 | 113 (49.6%) | |||
| FPG (mg/dL)*c | |||||
| <100 | 394 | 163 (41.4%) | 0.148 | 1.003 (0.993–1.013) | 0.537 |
| ≥100 | 147 | 71 (48.3%) | |||
| Plasma triglycerides (mg/dL)*d | |||||
| <150 | 418 | 168 (40.2%) | 0.016 | 1.003 (0.999–1.007) | 0.130 |
| ≥150 | 124 | 65 (52.4%) | |||
| BMI (kg/m2)*b | |||||
| <25 | 285 | 67 (23.5%) | <0.001 | 1.266 (1.195–1.340) | <0.001 |
| ≥25 | 272 | 171 (62.9%) | |||
| Plasma total cholesterol (mg/dL)*e | |||||
| <200 | 442 | 181 (41%) | 0.035 | 1.002 (0.995–1.008) | 0.562 |
| ≥200 | 101 | 53 (52.5%) | |||
| Plasma HDL cholesterol (mg/dL)*f | |||||
| <50 | 251 | 122 (48.6%) | 0.012 | 0.997 (0.986–1.008) | 0.623 |
| ≥50 | 253 | 95 (37.5%) | |||
| Plasma LDL cholesterol (mg/dL)g | |||||
| <160 | 487 | 211 (43.3%) | 0.709 | — | — |
| ≥160 | 18 | 7 (38.9%) | |||
| Cirrhosis* | |||||
| Yes | 416 | 177 (42.5%) | 0.017 | 0.810 (0.495–1.323) | 0.400 |
| No | 158 | 70 (44.3%) | |||
*Introduced as a continuous variable in the multivariate analysis.
**CAP ≥ 248 dB/m.
aAvailable data for 549 patients; bAvailable data for 557 patients; cAvailable data for 541 patients; dAvailable data for 542 patients; eAvailable data for 543 patients; fAvailable data for 544 patients; gAvailable data for 505 patients.
Abbreviations: AOR: Adjusted Odds Ratio; HCV: hepatitis C virus; HIV: human immunodeficiency virus; IDU: injecting drug users; FPG: Fasting plasma glucose; BMI: Body mass index; HDL: High-density lipoprotein; LDL: Low-density lipoprotein.
Discussion
In our study, we found that the prevalence of HS in HIV/HCV-coinfected patients is similar to that observed in of HCV-monoinfected patients. These results suggest that HIV coinfection does not influence the development of HS in patients with chronic hepatitis C.
The prevalence of NAFLD in HIV infection, as estimated with CAP, has ranged from 39% to 41% across different independent reports in Western countries14,15,22. In the general population, NAFLD frequency is expected to affect 25% individuals living in the USA5. Despite this sharp difference between the HIV-infected population and the general population, direct comparisons of large samples of unselected patients with and without HIV infection are lacking. In a recent retrospective study, the frequency of HS, evaluated by CAP, was higher among HCV-monoinfected individuals than HIV/HCV-coinfected patients23. The prevalence of CAP ≥ 238 dB/m was 29.5% for HIV/HCV-coinfected patients and 42.9% for HCV-infected patients23. However, a comparative analysis of risk factors for HS was not reported23. Thus, it is not possible to unequivocally interpret these results as an effect of HIV infection or as the consequence of a different distribution of metabolic risk factors. In addition, the study population reported by Samson et al. was mainly African American23. Given the distinct effect of ethnicity on the likelihood of NAFLD24, it is not possible to extrapolate the results by Sansom et al. to populations with other ethnic background, as our Caucasian study patients.
In the present study, contrary to all expectations, the rates of HS, measured by CAP, among patients with HCV infection followed at a single tertiary care center were similar among those with and without HIV coinfection. However, there were a number of differences between both groups that could have influenced the frequency of HS. Notably, BMI and other metabolic factors were unevenly represented in both groups. The HCV-monoinfected group showed a greater proportion of overweight or obese patients, whereas those with HIV/HCV coinfection presented higher levels of plasma triglycerides and lower levels of plasma HDL-cholesterol. After adjustment for factors associated with HS, including those mentioned before, HIV infection was not a factor related with a higher likelihood of steatosis. Indeed, a stratified analysis by BMI, the strongest predictor of HS in the present study and in previous reports14,15,17,22, did not disclose any differences in the rates of steatosis by BMI category between patients with and without HIV infection. Finally, an analysis excluding patients with alcohol intake ≥50 g/day did not show either significant differences between both groups. Because of all the above reasons, the risk of NAFLD seems to be similar for patients with chronic hepatitis C with and without HIV infection.
The present study may have several limitations. First, this was a retrospective study and that design may involve lack of data unplanned to be gathered in clinical practice, as insulin resistance. However, all patients with HCV infection attended at our unit undergo the same protocol, including assessment of steatosis by CAP, at their initial clinical visit. CAP data was not available only among individuals in whom images could not be acquired. Second, alcohol intake was self-referred by patients during the clinical interview, and this could underestimate the true amount of alcohol consumption. Third, HS in the present study could represent a mixture of causes, from true NAFLD associated with metabolic factors to secondary steatosis related with HCV genotype 3 infection. Despite this, the main factors associated with HS in the present study were metabolic factors, those typically associated with NAFLD. The main strength of this study is the comparison of the prevalence of and factors associated with HS in a homogeneous population within the same unit, according to the same protocol and evaluated using a uniform technique throughout the study. A study of this kind has been claimed before by some experts9,10.
In conclusion, HS is very frequent in patients with chronic hepatitis C, with and without HIV coinfection. Among them, HS shows features of NAFLD, as it is mainly associated with components of metabolic syndrome, and is also related with HCV genotype 3 infection. Our findings indicate that HIV coinfection is not associated with a higher risk of HS in individuals with chronic hepatitis C background. Because of these, the management of HS in HIV/HCV-coinfected patients and HCV-monoinfected patients should be similar and aimed at controlling metabolic risk factors.
Methods
Patients and study design
This was a retrospective cross-sectional study. All Spanish Caucasian patients who were attended at the Unit of Infectious Diseases of the Hospital Universitario Virgen de Valme, Seville (Spain), from November 2010 to March 2019, were selected if they had: (1) Chronic HCV infection, with persistent detection of plasma HCV RNA, with or without HIV coinfection; (2) A valid available hepatic elastography examination with evaluation of HS by CAP. Patients pretreated against HCV infection who did not achieve sustained virological response (SVR) were also included in the study.
Data collection
Data from all patients were recorded following a pre-specified protocol before starting HCV therapy. At that date, electronic clinical records including demographics, self-referred alcohol intake by patients, anthropometry, blood test and hepatic transient elastometry with CAP were gathered. CAP and liver stiffness (LS) were measured by FibroScan (Echosens FibroScan 502, Paris). A cut-off of ≥248 dB/m and of ≥280 dB/m were selected to define the presence of mild HS (steatosis involving ≥10% of hepatocytes) and severe steatosis (≥66% steatotic hepatocytes), respectively25. All CAP and LS measurements were performed in fasting conditions by two trained operators. We had previously proven a high concordance between two trained operators in FibroScan measurements, to determine HS by CAP26 or to evaluate LS27. All the measurements were the result of the evaluation of ten valid shots. For the present study, an hepatic transient elastometry was considered as valid if the interquartile range for liver stiffness was <30% of the median value and the success rate was ≥60%14.
Individuals with a body mass index (BMI) between 18 and 25 kg/m2, between 25 and 30, between 30 and 35, between 35 and 40 and >40 were considered as individuals with normal weight, pre-obesity, obesity class 1, class 2 and class 3 respectively in accordance with the WHO classification (http://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi). We consider overweight a BMI up to 25 kg/m2. High alcohol intake was defined as ≥50 g/day28.
Statistical analysis
For descriptive analysis, continuous variables were expressed as median (Q1–Q3) and categorical variables as frequencies (percentage). The χ2 test or Fisher’s exact test was used to compare the distribution of categorical variables between groups and Student’s t-test or the Mann-Whitney U test was used for continuous variables. Binary logistic regression models were elaborated to assess the factors independently associated with the presence of HS. In those analyses, variables related to this condition with a univariate p value < 0.2, as well as age, sex and HIV infection, were included to obtain odds ratio (OR) values. Differences were considered significant for p values < 0.05.
A case-control study was carried out as secondary analysis. HIV/HCV-coinfected patients were considered cases. HCV-monoinfected patients were matched with cases by age, sex and BMI. Cases and controls were matched by BMI because it was the only independent predictor of HS in a previous study using CAP14. All analysis were carried out using the SPSS software 25.0 (IBM Corporation, Somers, New York, New York, USA).
Ethics
This study was designed and performed according to the Helsinki declaration and was approved by the ethics committee of the Hospital Universitario Virgen de Valme (Seville, Spain). Informed consent was obtained from all individuals.
Supplementary information
Acknowledgements
J.M. is the recipient of a grant from the Servicio Andaluz de Salud de la Junta de Andalucía (grant number B-0037). J.A.P. is recipient of an intensification grant from the Instituto de Salud Carlos III (grant number Programa-I3SNS). This work has been partially funded by the RD12/0017/0012 and RD16/0025/0040 projects as part of the Plan Nacional R + D + I and cofinanced by ISCIII-Subdirección General de Evaluación, the Fondo Europeo de Desarrollo Regional (FEDER), the Fondo de Investigaciones Sanitarias (grant no: PI15/01124) and Consejería de Salud de la Junta de Andalucía (PI-0001/2017).
Author contributions
Study concept and design: M.F.F., J.M., J.A.P. and L.M.R. Acquisition of data: M.F.F., J.M., A.C.G., P.R., N.M., J.G.M., J.A.P. and L.M.R. Analysis and interpretation of data: M.F.F., J.M., J.A.P. and L.M.R. Drafting of the manuscript: M.F.F., J.M., J.A.P. and L.M.R. Critical revision of the manuscript: All authors. Final approval: All authors.
Data availability
All data generated or analyzed during this study are included in this published article (and its Supplementary Information Files).
Competing interests
J.M. has been an investigator in clinical trials supported by Bristol-Myers Squibb, Gilead and Merck Sharp & Dome. He has received lectures fees from Gilead, Bristol-Myers Squibb, and Merck Sharp & Dome, and consulting fees from Bristol Myers-Squibb, Gilead, and Merck Sharp & Dome. J.A.P. reports having received consulting fees from Bristol-Myers Squibb, Abbvie, Gilead, Merck Sharp & Dome, and Janssen Cilag. He has received research support from Bristol-Myers Squibb, Abbvie and Gilead and has received lecture fees from Abbvie, Bristol-Myers Squibb, Janssen Cilag, and Gilead. The remaining authors report no conflict of interest.
Footnotes
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
These authors contributed equally: M. Fernandez-Fuertes, J. Macías, J. A. Pineda and L. M. Real.
Supplementary information
is available for this paper at 10.1038/s41598-020-62671-y.
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Associated Data
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