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Correlation between FIB4, liver stiffness and metabolic parameters in patients with HIV and hepatitis C virus co-infection
Digestive and Liver Disease 43 (2011) 575–578
Contents lists available at ScienceDirect
Digestive and Liver Disease journal homepage: www.elsevier.com/locate/dld
Short Report
Correlation between FIB4, liver stiffness and metabolic parameters in patients with HIV and hepatitis C virus co-infection Raffaele Bruno a,∗,1 , Paolo Sacchi a,1 , Serena Cima a , Laura Maiocchi a , Savino F.A. Patruno a , Catherine Klersy b , Giorgio Barbarini a , Valentina Zuccaro d , Calogero Camma c , Gaetano Filice a a
Division of Infectious and Tropical Diseases, Foundation IRCCS San Matteo Hospital, University of Pavia, Pavia, Italy Biometry and Clinical Epidemiology Service, Foundation IRCCS San Matteo Hospital, Pavia, Italy c Cattedra ed Unità Operativa di Gastroenterologia, DiBiMIS, University of Palermo, Italy d Università del Piemonte Orientale “Amedeo Avogadro” Novara Italy b
a r t i c l e
i n f o
Article history: Received 26 October 2010 Accepted 22 March 2011 Available online 19 May 2011 Keywords: FIB4 HIV–HCV HOMA Liver stiffness
a b s t r a c t Background/Aims: Assessment of liver fibrosis is crucial in HIV/HCV coinfected patients, in whom metabolic disturbances are frequent. Aims of this study were to analyse the association of two noninvasive liver fibrosis evaluation methods, liver stiffness measurement and FIB4, and their correlation with metabolic parameters. Methods: This was a single centre cross-sectional study. All patients underwent biochemical and virological assessment, FIB4 score, HOMA and transient elastography. Results: Seventy-five patients were evaluated. Liver stiffness values positively correlated with FIB4 (R = 0.62; p < 0.0001). By ROC curve analysis the optimal cut-off for liver stiffness to identify high FIB4 was calculated as 10.1 kPa. The area under the ROC curve was 0.78 (95%CI 0.78–0.94, sensitivity 83.3%, specificity 80.7%). Liver stiffness values positively correlated with HOMA score (R = 0.31; p = 0.006). Conclusions: The combination of two non invasive tools provide a useful system for the assessment of fibrosis evolution in patients with HIV–HCV coinfection. © 2011 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.
1. Introduction The rates of liver disease-related death in Human Immunodeficiency Virus (HIV) positive patients are increasing, suggesting that staging and treating liver disease is became a key issue in the management of patients [1]. The gold standard to evaluate liver damage is a liver biopsy, however it is an invasive method, that may lead to complications and that needs the agreement of the patient [2]. Thus many non-invasive tools for the accurate assessment of liver disease severity have been proposed [2]. They consist in the evaluation of several serum markers of liver damage and/or fibrogenesis and so far different scoring systems have been evaluated (such as Fibrostest, aspartate aminotransferase–platelet ratio index (APRI) and FIB4) but they do not perform as well in HCV/HIV co-infected patients as in monoinfected ones, especially to detect lower stages of fibrosis [2–8].
Liver stiffness measurement by transient elastography (TE) is a rapid, low cost, accurate and non-invasive technique designed to predict the stage of fibrosis and portal hypertension [4,5,9]. Several studies have identified that the characteristics associated to insulin resistance (IR) and metabolic syndrome (MS), such as older age, lipodystrophy and high body mass index (BMI) may contribute to hepatic steatosis, which is associated with advanced liver fibrosis [10]. All these factors are associated with the use of antiretroviral therapy in HIV infected subjects. So far, the need to stage liver fibrosis and to understand its relationship with metabolic alterations is priorities in HIV–HCV coinfected patients [11–13]. The aim of this cross-sectional study is to analyse the association of the non-invasive liver fibrosis evaluation methods, Fibroscan and FIB4, and the metabolic parameters in patients with HIV/HCV coinfection. 2. Patients and methods 2.1. Study design and patients
∗ Corresponding author at: Division Infectious and Tropical Diseases, Foundation IRCCS San Matteo Hospital, Hepatology Outpatients Unit, University of Pavia, Via Taramelli, 5, 27100 Pavia, Italy. Tel.: +39 0382 501080; fax: +39 0382 501080. E-mail address: [email protected] (R. Bruno). 1 These two authors contributed equally to the present study.
This was a single centre cross-sectional retrospective study. We included all consecutive HIV-infected patients with chronic hepatitis C (CHC) older than 18 years who underwent a transient elastometry (TE) evaluation between December 2007 and
1590-8658/$36.00 © 2011 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.dld.2011.03.009
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R. Bruno et al. / Digestive and Liver Disease 43 (2011) 575–578
September 2008 and biochemical assessment. HCV infection was diagnosed by the presence of serum antibodies against HCV (RIBA test) and detectable serum HCV RNA PCR in all patients. Patients with a previous diagnosis of diabetes, hepatitis B, and with an alcohol intake were excluded.
2.2. Biochemical assessment All the patients underwent a biochemical assessment to detect different serum parameters such as aspartate aminotransferase (AST), alanine aminotransferase (ALT), platelet count, albumin, cholesterol, CD4, HCV-RNA, HIV-RNA, insulin and glucose. The FIB4 score is calculated as follows: (age [years] × AST [U/L])/(platelet count [×109 /L]) × (ALT [U/L]1/2)). FIB4 at a cut-off value >3.25 is consistent with significant (F2–F4) fibrosis at a sensitivity of 70% and a specificity of 86–97% in HIV/HCV co-infected persons [13]. For each patient the IR, was measured using the homeostatic model assessment (HOMA) that is fasting plasma insulin (U/ml) × fasting plasma glucose (mml/L)/22.5 [14].
Table 1 Characteristics of the study population. Parameter
Value median (IQR)
Agea Male HOMAa CD4a (cells/mmc) HIV-RNAa (copies/ml) Time HAART exposurea (months) HCV-RNAa (UI/ml) Genotype 1 Genotype 2 Genotype 3 Genotype 4 Stiffnessa (kPa) FIB-4a Cholesterola Triglyceridesa Insulina
41.5 (41–46) 80% 1.75 (1.09–4.45) 259 (338–609) 50 (50–625) 33.5 (20–45) 1,092,180 (245,013–1,731,660) 50.45% 3.9% 23.38% 12.88% 6.94 (5.7–17.4) 1.71 (0.98–3.04) 148.5 (144–195) 121 (95–206) 8.5 (5.3–17.9)
a
Value median (IQR).
3. Results 3.1. Study population
2.3. Liver stiffness measurement Transient elastography was performed using a FibroScanTM apparatus (Echosens, Paris, France) to measure liver stiffness. The only operator was a staff physician (L.M.) who had previously performed at least 100 determinations in patients with chronic liver disease. We obtained at least ten valid measures of liver stiffness in each patient before ending the examination. To guarantee the validity of TE results, we considered for analysis only the examinations with an interquartile range (IQR) below 30% of the median value and a success rate of acquisitions above 60%.
Seventy-five patients were included in the study. The main characteristics of the study population are summarized in Table 1. 3.2. Factors correlated with liver stiffness Albumin, platelet count and AST level were significantly correlated with liver stiffness, with Spearman R −0.24 (p = 0.038), −0.59 (p < 0.001), and 0.34 (p = 0.003), respectively. Liver stiffness values positively correlated with HOMA (Spearman’s Rho correlation coefficient 0.31; p = 0.006) as shown in Fig. 1.
2.4. Statistical analysis Continuous variables are summarized by their median and IQR. Categorical variables are described by count and relative frequency (%). Spearman’s non-parametric rank-correlation was used to assess the association between continuous variables. Logistic regression was applied to test for association between test concordance and explanatory variables such as bilirubin, aminotransferases, prothrombin time, insulinaemia, albumin, fasting blood glucose, HOMA score, cholesterol. Odds ratios (OR) and their 95% confidence intervals (95%CI) were computed. We also use the test for trend and the Mann Whitney U test to compare biological parameters between classes of liver stiffness and FIB4. ROC analysis was applied to find an optimal cut-off (highest sensitivity and specificity) value for kPa with respect to FIB4 categorized as high ≥ 3.25 and low < 3.25. The limit of significance for all analyses was defined as a twosided p-value <0.05. Sample size calculation: with 75 patients, a 95% confidence interval will extend from 70 to 89% of a Spearman r = 80% between FIB4 and kPa. It will extend from 80 to 95% if Spearman r = 90%. Statistical analyses were carried out with Stata software (StataCorp, 4905 Lakeway Drive, College Station, TX 77845, USA) and MedCalc Software (Broekstraat 52, 9030 Mariakerke, Belgium).
2.5. Ethical aspects The study was designed and conducted following the Helsinki declaration. The Ethics committee of the IRCCS Fondazione Policlinico San Matteo approved the study.
Fig. 1. Liver stiffness values positively correlate with HOMA (R = 0.31; p = 0.006) and inversely correlated with fasting blood cholesterol levels (R = −0.32; p = 0.005).
R. Bruno et al. / Digestive and Liver Disease 43 (2011) 575–578
577
Table 2 Liver stiffness values positively correlate with HOMA and fasting blood insulin levels, where are inversely correlated with fasting blood cholesterol levels. FIB4 values inversely correlated with fasting blood cholesterol levels. The median value of cholesterol decreased with the increase of FIB4 values. A kPa Number of patients HOMAa Insulina Cholesterola
>7 32 1.47 (0.98–2.41) 7.15 (5.17–10.95) 179.5 (148–208.25)
7–13 20 2.56 (1.07–3.25) 13.2 (5.2–16.6) 199 (155.5–196.5)
pc
>13 23 3.4 (1.64–7.13) 13.8 (7.3–40.05) 152 (131.5–170.5)
0.012 0.020 0.006
B pd Fib4 Numbers of patients Insulinb Cholesterola a b c d
<3.25 57 13.71 (2.40–71.40) 176 (148–204.5)
>3.25 18 31.01 (3.10–122.0) 140.5 (127–158)
0.15 0.0007
Median (Q1–Q3). HOMA, homeostatic model assessment. Mean values. p Test for trend. p Mann Whitney U-test.
The median value of HOMA increased according to kPa (see Table 2). The same positive correlation was observed for fasting blood insulin levels (Spearman’s Rho correlation coefficient) (0.28; p = 0.01). Liver stiffness values inversely correlated with fasting blood cholesterol levels (Spearman’s Rho correlation coefficient −0.32; p = 0.005) as shown in Fig. 1. The median value of cholesterol decreased with the increase of kPa (see Table 2). Since cirrhosis may cause hyperinsulinaemia we reanalysed the data excluding cirrhotic patients with liver stiffness values >14 kPa. So, we did not find any correlation between liver stiffness and HOMA (Spearman’s Rho correlation coefficient 0.04; p = 0.77). 3.3. Factors correlated with FIB4 score FIB4 values inversely correlated with fasting blood cholesterol levels (Spearman’s Rho correlation coefficient −0.46; p < 0.001). After dropping two outliers (FIB4 > 20) the correlation was confirmed (Spearman’s Rho correlation coefficient −0.47; p < 0.001). The median value of cholesterol decreased with the increase of FIB4 values (Table 2). 3.4. Correlation between liver stiffness and FIB4 Liver stiffness values positively correlated with FIB4 (Spearman’s R correlation coefficient 0.62; p < 0.0001) as shown in Fig. 2. For the purpose of the analysis, stiffness >13 kPa and a FIB4 > 3.25 were defined as high stiffness/FIB4 according to previously published reports [5–10]. By means of the ROC curve analysis the
Fig. 3. The ROC curve analysis shows that the optimal cut-off for liver stiffness to be able to identify high FIB4 was computed to 10.1 kPa.
optimal cut-off for liver stiffness to be able to identify high FIB4 was computed to 10.1 kPa. The area under the ROC curve was 0.78 (95%CI 0.78–0.94, sensitivity 83.3%, specificity 80.7%) (Fig. 3). 4. Discussion
Fig. 2. Liver stiffness values positively correlate with FIB4 (R = 0.62; p < 0.0001).
This study shows that liver stiffness values positively correlate with FIB4 in HIV/HCV-coinfected patients. By means of the ROC curve analysis the optimal cut-off for liver stiffness to be able to identify high FIB4 was computed as 10.1 kPa. An interesting finding of our work is the association between metabolic parameters, such as insulin resistance (IR) assessed by HOMA, and liver stiffness, confirming the data published by Merchante et al. [15]. Insulin resistance has been systematically associated with fibrosis [10–15] and with portal hypertension in several reports [16,17]. The association between IR and fibrosis has a consistent biological plausibility, considering the ability of insulin to stimulate hepatic stellate cells [15,16], or the induction of portal hypertension via modulation of nitric oxide and endothelin synthesis [18,19]. Therefore, insulin could influence liver stiffness values, even in a pre-cirrhotic state with relatively modest fibrosis; platelet levels also represent an indirect marker of fibrosis severity [10] and
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of portal hypertension [2–20]. Our data shows that also in HIV/HCV coinfected patients platelet count was significantly associated with increased liver stiffness and significantly correlates with liver fibrosis [4–22]. A key issue in the management of coinfected patients is the correct estimate of liver fibrosis, and a tool to assess it should be reliable, non invasive, relatively inexpensive and accurate not only for the staging of fibrosis, but also for the monitoring of disease progression. In HIV/HCV-coinfected patients, most of the indirect scores are based on indices, such as aminotransferases or platelet count that could be modified by HAART, HIV infection, or malnutrition. A poor performance of FIB4 and APRI has been reported by Shire et al. [21] in the co-infected population. In that study, these two biomarkers were found to be disappointing, as overall misclassification was 45%. Conversely, measurement of liver stiffness, which directly assesses liver parenchyma, is not supposed to be influenced by extrahepatic conditions. For this purpose, we combined two easy to perform non-invasive tools to evaluate liver fibrosis in order to limit the need for liver biopsy. Our data show a good concordance of these two non-invasive tests, which are not influenced by biochemical or metabolic parameters, for the evaluation of liver fibrosis. The main limitation of the current study is that it is unable to distinguish the temporal relationship of the associations between IR and liver stiffness, as a surrogate of liver fibrosis, as well as the lack of histological data. Lack of data on other potential confounders, such as nutritional status, fat distribution and longitudinal anthropometric changes or lipodystrophy could also affect the interpretation of our findings. Thus, the combination of two non-invasive tools (a biochemical test combined with transient elastography), could provide us with a useful system for the assessment of fibrosis evolution in patients with HIV–HCV coinfection; given the many factors which contribute to accelerate the course of liver disease in these patients, constant monitoring is required and invasive testing is not easily applied. Conflict of interest statement None declared.
List of abbreviations AIDS, acquired immune deficiency syndrome; ALT, alanine aminotransferase; Apo-1, apolipoprotein; APRI, aspartate aminotransferase–platelet ratio index; ART, antiretroviral therapy; AST, aspartate aminotransferase; BMI, body mass index; CD4, cluster of differentiation 4; CHC, chronic hepatitis C; 95%CI, 95% confidence intervals; FFAs, free fatty acids; FT, fibrotest; GGT, gamma-glutamyl transpeptidase; HAART, highly active antiretroviral therapy; HCV, hepatitis C virus; HIV, Human Immunodeficiency Virus; HDL-C, HDL cholesterol; HOMA, homeostatic model assessment; IQR, interquartile range; IR, insulin resistance; LDL-C, low-density lipoprotein cholesterol; LSM, liver stiffness measurement; MS, metabolic syndrome; OR, Odds ratios; %, relative frequency; TC, total cholesterol; TE, transient elastography; VLDL-C, very low-density lipoprotein cholesterol.
Acknowledgement The Authors are indebted to Mr. Bertie Vitry (Lecturer in English) for the revision of English text. References [1] Bruno R, Sacchi P, Puoti M, et al. Natural history of compensated viral cirrhosis in a cohort of patients with HIV infection. J Acquir Immune Defic Syndr 2007;46:297–303. [2] Vergara S, Macías J, Rivero A, et al. The use of transient elastometry for assessing liver fibrosis in patients with HIV and hepatitis C virus coinfection. Clin Infect Dis 2007;45:969–74. [3] Castera L, Forns X, Alberti A. Non-invasive evaluation of liver fibrosis using transient elastography. J Hepatol 2008;48:835–47. [4] Castera L, Vergniol J, Foucher J, et al. Prospective comparison of transient elastography, Fibrotest, APRI, and liver biopsy for the assessment of fibrosis in chronic hepatitis C. Gastroenterology 2005;128: 343–50. [5] Macias J, Giron-Gonzalez JA, Gonzalez-Serrano M, et al. Prediction of liver fibrosis in human immunodeficiency virus/Hepatitis C virus coinfected patients by simple non-invasive indexes. Gut 2006;55:409–14. [6] Sánchez-Conde M, Montes-Ramírez ML, Miralles P, et al. Comparison of transient elastography and liver biopsy for the assessment of liver fibrosis in HIV/hepatitis C virus-coinfected patients and correlation with noninvasive serum markers. J Viral Hepat 2010;17:280–6 [Epub 2009 Sep 2]. [7] Tural C, Tor J, Sanvisens A, et al. Accuracy of simple biochemical tests in identifying liver fibrosis in patients co-infected with human immunodeficiency virus and hepatitis C virus. Clin Gastroenterol Hepatol 2009;7:339–45 [Epub 2008 Dec 3]. [8] Macías J, González J, Ortega E, et al. Use of simple noninvasive biomarkers to predict liver fibrosis in HIV/HCV coinfection in routine clinical practice. HIV Med 2010;11:439–47 [Epub 2010 Feb 17]. [9] Friedrich-Rust M, Ong MF, Martens S, et al. Performance of transient elastography for the staging of liver fibrosis: a meta-analysis. Gastroenterology 2008;134:960–74. [10] Petta S, Cammà C, Di Marco V, et al. Insulin resistance, and diabetes, increase fibrosis in the liver of patients with HCV genotype 1 infection. Am J Gastroenterol 2008;103:1136–44. [11] Grinspoon S, Carr A. Cardiovascular risk and body-fat abnormalities in HIVinfected adults. N Engl J Med 2005;352:48–62. [12] Kelleher TB, Mehta SH, Bhaskar R, et al. Prediction of hepatic fibrosis in HIV/HCV co-infected patients using serum fibrosis markers: the SHASTA index. J Hepatol 2005;43:78–84. [13] Sterling RK, Lissen E, Clumeck N, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology 2006;43:1317–25. [14] Ikeda Y, Suehiro T, Nakamura T, et al. Clinical significance of the insulin resistance index as assessed by homeostasis model assessment. Endocr J 2001;48:81–6. [15] Merchante N, Rivero A, de Los Santos-Gil I, et al. Insulin resistance is associated with liver stiffness in HIV/HCV co-infected patients. Gut 2009;58: 1654–60. [16] Moucari R, Asselah T, Cazals-Hatem D, et al. Insulin resistance in chronic hepatitis C: association with genotypes 1 and 4, serum HCV RNA level, and liver fibrosis. Gastroenterology 2008;134:416–23. [17] Cammà C, Petta S, Di Marco V, et al. Insulin resistance is a risk factor for esophageal varices in hepatitis C virus cirrhosis. Hepatology 2009;49: 195–203. [18] Vincent MA, Montagnani M, Quon MJ. Molecular and physiologic actions of insulin related to production of nitric oxide in vascular endothelium. Curr Diab Rep 2003;3:279–88. [19] IWakiri Y, Groszmann RJ. Vascular endothelial dysfunction in cirrhosis. J Hepatol 2007;46:927–34. [20] Thabut D, Imbert-Bismut F, Cazals-Hatem D, et al. Relationship between the Fibrotest and portal hypertension in patients with liver disease. Aliment Pharmacol Ther 2007;26:359–68. [21] Shire N, Rao MB, Succo P, et al. Improving non-invasive methods of assessing liver fibrosis in patients with hepatitis C virus/human immunofdeficiency viru coinfection. Clin Gastroentero Hepatol 2009;7: 471–80. [22] Ziol M, Handra-Luca A, Kettaneh A, et al. Noninvasive assessment of liver fibrosis by measurement of stiffness in patients with chronic hepatitis C. Hepatology 2005;41:48–54.
Contents lists available at ScienceDirect
Digestive and Liver Disease journal homepage: www.elsevier.com/locate/dld
Short Report
Correlation between FIB4, liver stiffness and metabolic parameters in patients with HIV and hepatitis C virus co-infection Raffaele Bruno a,∗,1 , Paolo Sacchi a,1 , Serena Cima a , Laura Maiocchi a , Savino F.A. Patruno a , Catherine Klersy b , Giorgio Barbarini a , Valentina Zuccaro d , Calogero Camma c , Gaetano Filice a a
Division of Infectious and Tropical Diseases, Foundation IRCCS San Matteo Hospital, University of Pavia, Pavia, Italy Biometry and Clinical Epidemiology Service, Foundation IRCCS San Matteo Hospital, Pavia, Italy c Cattedra ed Unità Operativa di Gastroenterologia, DiBiMIS, University of Palermo, Italy d Università del Piemonte Orientale “Amedeo Avogadro” Novara Italy b
a r t i c l e
i n f o
Article history: Received 26 October 2010 Accepted 22 March 2011 Available online 19 May 2011 Keywords: FIB4 HIV–HCV HOMA Liver stiffness
a b s t r a c t Background/Aims: Assessment of liver fibrosis is crucial in HIV/HCV coinfected patients, in whom metabolic disturbances are frequent. Aims of this study were to analyse the association of two noninvasive liver fibrosis evaluation methods, liver stiffness measurement and FIB4, and their correlation with metabolic parameters. Methods: This was a single centre cross-sectional study. All patients underwent biochemical and virological assessment, FIB4 score, HOMA and transient elastography. Results: Seventy-five patients were evaluated. Liver stiffness values positively correlated with FIB4 (R = 0.62; p < 0.0001). By ROC curve analysis the optimal cut-off for liver stiffness to identify high FIB4 was calculated as 10.1 kPa. The area under the ROC curve was 0.78 (95%CI 0.78–0.94, sensitivity 83.3%, specificity 80.7%). Liver stiffness values positively correlated with HOMA score (R = 0.31; p = 0.006). Conclusions: The combination of two non invasive tools provide a useful system for the assessment of fibrosis evolution in patients with HIV–HCV coinfection. © 2011 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.
1. Introduction The rates of liver disease-related death in Human Immunodeficiency Virus (HIV) positive patients are increasing, suggesting that staging and treating liver disease is became a key issue in the management of patients [1]. The gold standard to evaluate liver damage is a liver biopsy, however it is an invasive method, that may lead to complications and that needs the agreement of the patient [2]. Thus many non-invasive tools for the accurate assessment of liver disease severity have been proposed [2]. They consist in the evaluation of several serum markers of liver damage and/or fibrogenesis and so far different scoring systems have been evaluated (such as Fibrostest, aspartate aminotransferase–platelet ratio index (APRI) and FIB4) but they do not perform as well in HCV/HIV co-infected patients as in monoinfected ones, especially to detect lower stages of fibrosis [2–8].
Liver stiffness measurement by transient elastography (TE) is a rapid, low cost, accurate and non-invasive technique designed to predict the stage of fibrosis and portal hypertension [4,5,9]. Several studies have identified that the characteristics associated to insulin resistance (IR) and metabolic syndrome (MS), such as older age, lipodystrophy and high body mass index (BMI) may contribute to hepatic steatosis, which is associated with advanced liver fibrosis [10]. All these factors are associated with the use of antiretroviral therapy in HIV infected subjects. So far, the need to stage liver fibrosis and to understand its relationship with metabolic alterations is priorities in HIV–HCV coinfected patients [11–13]. The aim of this cross-sectional study is to analyse the association of the non-invasive liver fibrosis evaluation methods, Fibroscan and FIB4, and the metabolic parameters in patients with HIV/HCV coinfection. 2. Patients and methods 2.1. Study design and patients
∗ Corresponding author at: Division Infectious and Tropical Diseases, Foundation IRCCS San Matteo Hospital, Hepatology Outpatients Unit, University of Pavia, Via Taramelli, 5, 27100 Pavia, Italy. Tel.: +39 0382 501080; fax: +39 0382 501080. E-mail address: [email protected] (R. Bruno). 1 These two authors contributed equally to the present study.
This was a single centre cross-sectional retrospective study. We included all consecutive HIV-infected patients with chronic hepatitis C (CHC) older than 18 years who underwent a transient elastometry (TE) evaluation between December 2007 and
1590-8658/$36.00 © 2011 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.dld.2011.03.009
576
R. Bruno et al. / Digestive and Liver Disease 43 (2011) 575–578
September 2008 and biochemical assessment. HCV infection was diagnosed by the presence of serum antibodies against HCV (RIBA test) and detectable serum HCV RNA PCR in all patients. Patients with a previous diagnosis of diabetes, hepatitis B, and with an alcohol intake were excluded.
2.2. Biochemical assessment All the patients underwent a biochemical assessment to detect different serum parameters such as aspartate aminotransferase (AST), alanine aminotransferase (ALT), platelet count, albumin, cholesterol, CD4, HCV-RNA, HIV-RNA, insulin and glucose. The FIB4 score is calculated as follows: (age [years] × AST [U/L])/(platelet count [×109 /L]) × (ALT [U/L]1/2)). FIB4 at a cut-off value >3.25 is consistent with significant (F2–F4) fibrosis at a sensitivity of 70% and a specificity of 86–97% in HIV/HCV co-infected persons [13]. For each patient the IR, was measured using the homeostatic model assessment (HOMA) that is fasting plasma insulin (U/ml) × fasting plasma glucose (mml/L)/22.5 [14].
Table 1 Characteristics of the study population. Parameter
Value median (IQR)
Agea Male HOMAa CD4a (cells/mmc) HIV-RNAa (copies/ml) Time HAART exposurea (months) HCV-RNAa (UI/ml) Genotype 1 Genotype 2 Genotype 3 Genotype 4 Stiffnessa (kPa) FIB-4a Cholesterola Triglyceridesa Insulina
41.5 (41–46) 80% 1.75 (1.09–4.45) 259 (338–609) 50 (50–625) 33.5 (20–45) 1,092,180 (245,013–1,731,660) 50.45% 3.9% 23.38% 12.88% 6.94 (5.7–17.4) 1.71 (0.98–3.04) 148.5 (144–195) 121 (95–206) 8.5 (5.3–17.9)
a
Value median (IQR).
3. Results 3.1. Study population
2.3. Liver stiffness measurement Transient elastography was performed using a FibroScanTM apparatus (Echosens, Paris, France) to measure liver stiffness. The only operator was a staff physician (L.M.) who had previously performed at least 100 determinations in patients with chronic liver disease. We obtained at least ten valid measures of liver stiffness in each patient before ending the examination. To guarantee the validity of TE results, we considered for analysis only the examinations with an interquartile range (IQR) below 30% of the median value and a success rate of acquisitions above 60%.
Seventy-five patients were included in the study. The main characteristics of the study population are summarized in Table 1. 3.2. Factors correlated with liver stiffness Albumin, platelet count and AST level were significantly correlated with liver stiffness, with Spearman R −0.24 (p = 0.038), −0.59 (p < 0.001), and 0.34 (p = 0.003), respectively. Liver stiffness values positively correlated with HOMA (Spearman’s Rho correlation coefficient 0.31; p = 0.006) as shown in Fig. 1.
2.4. Statistical analysis Continuous variables are summarized by their median and IQR. Categorical variables are described by count and relative frequency (%). Spearman’s non-parametric rank-correlation was used to assess the association between continuous variables. Logistic regression was applied to test for association between test concordance and explanatory variables such as bilirubin, aminotransferases, prothrombin time, insulinaemia, albumin, fasting blood glucose, HOMA score, cholesterol. Odds ratios (OR) and their 95% confidence intervals (95%CI) were computed. We also use the test for trend and the Mann Whitney U test to compare biological parameters between classes of liver stiffness and FIB4. ROC analysis was applied to find an optimal cut-off (highest sensitivity and specificity) value for kPa with respect to FIB4 categorized as high ≥ 3.25 and low < 3.25. The limit of significance for all analyses was defined as a twosided p-value <0.05. Sample size calculation: with 75 patients, a 95% confidence interval will extend from 70 to 89% of a Spearman r = 80% between FIB4 and kPa. It will extend from 80 to 95% if Spearman r = 90%. Statistical analyses were carried out with Stata software (StataCorp, 4905 Lakeway Drive, College Station, TX 77845, USA) and MedCalc Software (Broekstraat 52, 9030 Mariakerke, Belgium).
2.5. Ethical aspects The study was designed and conducted following the Helsinki declaration. The Ethics committee of the IRCCS Fondazione Policlinico San Matteo approved the study.
Fig. 1. Liver stiffness values positively correlate with HOMA (R = 0.31; p = 0.006) and inversely correlated with fasting blood cholesterol levels (R = −0.32; p = 0.005).
R. Bruno et al. / Digestive and Liver Disease 43 (2011) 575–578
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Table 2 Liver stiffness values positively correlate with HOMA and fasting blood insulin levels, where are inversely correlated with fasting blood cholesterol levels. FIB4 values inversely correlated with fasting blood cholesterol levels. The median value of cholesterol decreased with the increase of FIB4 values. A kPa Number of patients HOMAa Insulina Cholesterola
>7 32 1.47 (0.98–2.41) 7.15 (5.17–10.95) 179.5 (148–208.25)
7–13 20 2.56 (1.07–3.25) 13.2 (5.2–16.6) 199 (155.5–196.5)
pc
>13 23 3.4 (1.64–7.13) 13.8 (7.3–40.05) 152 (131.5–170.5)
0.012 0.020 0.006
B pd Fib4 Numbers of patients Insulinb Cholesterola a b c d
<3.25 57 13.71 (2.40–71.40) 176 (148–204.5)
>3.25 18 31.01 (3.10–122.0) 140.5 (127–158)
0.15 0.0007
Median (Q1–Q3). HOMA, homeostatic model assessment. Mean values. p Test for trend. p Mann Whitney U-test.
The median value of HOMA increased according to kPa (see Table 2). The same positive correlation was observed for fasting blood insulin levels (Spearman’s Rho correlation coefficient) (0.28; p = 0.01). Liver stiffness values inversely correlated with fasting blood cholesterol levels (Spearman’s Rho correlation coefficient −0.32; p = 0.005) as shown in Fig. 1. The median value of cholesterol decreased with the increase of kPa (see Table 2). Since cirrhosis may cause hyperinsulinaemia we reanalysed the data excluding cirrhotic patients with liver stiffness values >14 kPa. So, we did not find any correlation between liver stiffness and HOMA (Spearman’s Rho correlation coefficient 0.04; p = 0.77). 3.3. Factors correlated with FIB4 score FIB4 values inversely correlated with fasting blood cholesterol levels (Spearman’s Rho correlation coefficient −0.46; p < 0.001). After dropping two outliers (FIB4 > 20) the correlation was confirmed (Spearman’s Rho correlation coefficient −0.47; p < 0.001). The median value of cholesterol decreased with the increase of FIB4 values (Table 2). 3.4. Correlation between liver stiffness and FIB4 Liver stiffness values positively correlated with FIB4 (Spearman’s R correlation coefficient 0.62; p < 0.0001) as shown in Fig. 2. For the purpose of the analysis, stiffness >13 kPa and a FIB4 > 3.25 were defined as high stiffness/FIB4 according to previously published reports [5–10]. By means of the ROC curve analysis the
Fig. 3. The ROC curve analysis shows that the optimal cut-off for liver stiffness to be able to identify high FIB4 was computed to 10.1 kPa.
optimal cut-off for liver stiffness to be able to identify high FIB4 was computed to 10.1 kPa. The area under the ROC curve was 0.78 (95%CI 0.78–0.94, sensitivity 83.3%, specificity 80.7%) (Fig. 3). 4. Discussion
Fig. 2. Liver stiffness values positively correlate with FIB4 (R = 0.62; p < 0.0001).
This study shows that liver stiffness values positively correlate with FIB4 in HIV/HCV-coinfected patients. By means of the ROC curve analysis the optimal cut-off for liver stiffness to be able to identify high FIB4 was computed as 10.1 kPa. An interesting finding of our work is the association between metabolic parameters, such as insulin resistance (IR) assessed by HOMA, and liver stiffness, confirming the data published by Merchante et al. [15]. Insulin resistance has been systematically associated with fibrosis [10–15] and with portal hypertension in several reports [16,17]. The association between IR and fibrosis has a consistent biological plausibility, considering the ability of insulin to stimulate hepatic stellate cells [15,16], or the induction of portal hypertension via modulation of nitric oxide and endothelin synthesis [18,19]. Therefore, insulin could influence liver stiffness values, even in a pre-cirrhotic state with relatively modest fibrosis; platelet levels also represent an indirect marker of fibrosis severity [10] and
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of portal hypertension [2–20]. Our data shows that also in HIV/HCV coinfected patients platelet count was significantly associated with increased liver stiffness and significantly correlates with liver fibrosis [4–22]. A key issue in the management of coinfected patients is the correct estimate of liver fibrosis, and a tool to assess it should be reliable, non invasive, relatively inexpensive and accurate not only for the staging of fibrosis, but also for the monitoring of disease progression. In HIV/HCV-coinfected patients, most of the indirect scores are based on indices, such as aminotransferases or platelet count that could be modified by HAART, HIV infection, or malnutrition. A poor performance of FIB4 and APRI has been reported by Shire et al. [21] in the co-infected population. In that study, these two biomarkers were found to be disappointing, as overall misclassification was 45%. Conversely, measurement of liver stiffness, which directly assesses liver parenchyma, is not supposed to be influenced by extrahepatic conditions. For this purpose, we combined two easy to perform non-invasive tools to evaluate liver fibrosis in order to limit the need for liver biopsy. Our data show a good concordance of these two non-invasive tests, which are not influenced by biochemical or metabolic parameters, for the evaluation of liver fibrosis. The main limitation of the current study is that it is unable to distinguish the temporal relationship of the associations between IR and liver stiffness, as a surrogate of liver fibrosis, as well as the lack of histological data. Lack of data on other potential confounders, such as nutritional status, fat distribution and longitudinal anthropometric changes or lipodystrophy could also affect the interpretation of our findings. Thus, the combination of two non-invasive tools (a biochemical test combined with transient elastography), could provide us with a useful system for the assessment of fibrosis evolution in patients with HIV–HCV coinfection; given the many factors which contribute to accelerate the course of liver disease in these patients, constant monitoring is required and invasive testing is not easily applied. Conflict of interest statement None declared.
List of abbreviations AIDS, acquired immune deficiency syndrome; ALT, alanine aminotransferase; Apo-1, apolipoprotein; APRI, aspartate aminotransferase–platelet ratio index; ART, antiretroviral therapy; AST, aspartate aminotransferase; BMI, body mass index; CD4, cluster of differentiation 4; CHC, chronic hepatitis C; 95%CI, 95% confidence intervals; FFAs, free fatty acids; FT, fibrotest; GGT, gamma-glutamyl transpeptidase; HAART, highly active antiretroviral therapy; HCV, hepatitis C virus; HIV, Human Immunodeficiency Virus; HDL-C, HDL cholesterol; HOMA, homeostatic model assessment; IQR, interquartile range; IR, insulin resistance; LDL-C, low-density lipoprotein cholesterol; LSM, liver stiffness measurement; MS, metabolic syndrome; OR, Odds ratios; %, relative frequency; TC, total cholesterol; TE, transient elastography; VLDL-C, very low-density lipoprotein cholesterol.
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