Predictor factors associated with liver fibrosis and steatosis by transient elastography in HIV mono-infected patients under long-term combined antiretroviral therapy Hugo Perazzo, Sandra W Cardoso, Carolyn Yanavich, João Carlos Soares, Juliana Fittipaldi, Michelle Morata, Claudia Cardoso, Paula Simplicio, Cristiane de Almeida, Valdilea G Veloso, Beatriz Grinsztejn National Institute of Infectious Diseases Evandro Chagas (INI) Oswaldo Cruz Foundation (FIOCRUZ) Rio de Janeiro - Brazil Abstract MOAB0305 HIV and Liver: Co-Infection and Complications July 24th, 2017

Disclosures The authors have nothing to disclosure

Background General population HIV – scare data Çç l HIV – scare data Chronic inflammation and immune activation ? Hepatotoxicity associated with c-ART ?

Aims To evaluate factors associated with liver fibrosis and steatosis in HIV mono-infected patients under long-term combined-antiretroviral therapy (c-ART)

Methods Cohort of HIV patients at INI-FIOCRUZ Study design ~4,000 patients have been followed from 1990 (c-ART regimens, CD4 count, viral load, co-infections, AIDS-related events) Study design Cross-sectional study (PROSPEC-HIV; NCT02542020) Inclusion criteria HIV infection Exclusion criteria Viral hepatitis co-infection c-ART naïve

Methods Performed at the same day (fasting status) Clinical evaluation: anthropometric measures, alcohol intake (AUDIT score), co-morbidities and co-medication use, history of HIV infection and c-ART treatment (current and previous) Blood tests Transient elastography by FibroScan ® Parameter Assessment Cut-offs LSM Fibrosis ≥ 8.0 kPa † CAP Steatosis ≥ 250 dB/m § Reliability criteria 10 valid measures IQR/LSM < 30% / IQR/CAP < 30% Success rate ≥ 60% † Koehler et al Hepatology 2016 § De Ledinghen et al J Hepatol 2014

Methods Metabolic features / Metabolic syndrome: according to the International Diabetes Federation Backbone ART: AZT = use of AZT, ddI, ddC, D4T or ddI-EC TDF = use of TDF, ABC, Emtricitabine or TAF Core Drugs ART: NNRTI = use of EFV, NVP, ETV, CAP or TMC PI = use of any protease inhibithor II = use of any integrase inhibithor Cummulative ART: years of use and most used class of drug for Backbone and Core Drug

Methods Outcomes Multivariate Logistic regression Liver fibrosis (LSM ≥ 8.0 kPa) in reliable LSM Liver steatosis (CAP ≥ 250 dB/m ) in reliable CAP measures Multivariate Logistic regression Univariate analysis: p ≤ 0.05 - into multivariate model Multivariate analysis adjusted for age, gender and confounding factors Significance level: p < 0.05 two-tailed tests STATA software (2017; StataCorp LP, TX, USA)

Flow-chart of the study

Characteristics of patients - I   All (n=395) Female gender 236 (60%) Age, years 45 [35-52] Black/Mixed skin color 210 (53%) Education level > 8 years of study 202 (51%) AUDIT score ≥ 8 90 (23%) Former or current smoking 184 (47%) Metabolic features BMI, Kg/m² 25.7 [23.2-29.4] Central obesity (WC > 94 cm in men and > 88 cm in women) 266 (68%) Type-2 diabetes 37 (10%) Dyslipidemia 234 (61%) Hypertension 118 (30%) Metabolic syndrome 117 (32%) Biochemistry ALT, IU/L 30 [23-42] AST, IU/L 26 [20-34] GGT, IU/L 46 [34-76] Alkaline Phosphatase, IU/L 88 [69-107] Total bilirubin, mg/dL 0.43 [0.30-0.77] Albumin, mg/dL 3.9 [3.7-4.1] Fasting glucose, mg/dL 93 [87-100] Triglycerides, mg/dL 127 [87-178] Total cholesterol, mg/dL 185 [155-219] LDL-cholesterol, mg/dL 112 [88-138] HDL-cholesterol, mg/dL 42 [35-54] Data expressed as n (%) or median [IQR]

Characteristics of patients - II   All (n=395) HIV infection history Duration of HIV infection, years 10 [6-16] CD4 count (cells/mm3) 667 [427-906] HIV viral load > 40 copies/mm3 80 (20%) Nadir CD4 < 100 cells/mm3) 104 (26%) c-ART history Duration of c-ART, years 7 [4-14] Current treatment Backbone drug classes TDF 309 (78%) AZT 86 (22%) Core Drugs classes NNRTI 175 (44%) PI or II 220 (56%) Most used drugs during HIV infection 225 (57%) 170 (43%) 197 (50%) 198 (50%) Cummulative time of use of Backbone drugs, years 3 [1-6] 2 [0-9] Cummulative time of use of Core Drugs, years 2 [1-6] PI 2 [0-7] II 0 [0-1] Data expressed as n (%) or median [IQR]

Results Transient elastography by FibroScan (reliable exams)   Liver stiffness measurement (n=367) Fibrosis LSM, kPa 5.3 [4.5-6.4] IQR, kPa 0.7 [0.5-1.0] IQR/LSM, % 13.3 [9.1-17.7] Sucess rate, % 100 [91-100] LSM > 8.0 kPa 33 (9%) Controlled Attenuation Parameter (n=344) Steatosis CAP, dB/m 230 [202-262] IQR, dB/m 30 [23-39] IQR/CAP, % 13.5 [9.4-18.0] CAP > 250 dB/m 121 (35%) Data expressed as median [IQR]

Factors associated with fibrosis Univariate Analysis Multivariate Analysis OR [95%CI] p value Social and demographics characteristics   Female gender 1.10 [0.53-2.28] 0.805 1.04 [0.47-2.29] 0.927 Age (per 10 years) 1.78 [1.31-2.42] < 0.001 1.80 [1.27-2.55] 0.001 White skin color 1.44 [0.70-2.95] 0.323 Education < 8 years of study 1.73 [0.83-3.58] 0.143 AUDIT score ≥ 8 0.57 [0.21-1.52] 0.258 Former or current smoking 1.21 [0.59-2.48] 0.598 HIV infection history Duration of HIV infection (per 10 years) 1.30 [0.79-2.16] 0.306 CD4 count < 200 cells/mm3 3.69 [1.12-12.2] 0.032 7.80 [2.09-29.09] 0.002 HIV viral load > 40 copies/mm3 0.98 [0.41-2.33] 0.957 Nadir CD4 < 100 cells/mm3 1.42 [0.69-2.93] 0.338 c-ART history Duration of c-ART (per 10 years) 1.55 [0.89-2.69] 0.120 Current treatment AZT Backbone drug class (vs TDF) 1.19 [0.51-2.74] 0.691 PI or II Core Drugs class (vs NNRTI) 1.73 [0.81-3.68] 0.154 Most used drugs during HIV infection 1.49 [0.73-3.05] 0.275 1.44 [0.70-2.97] 0.322 Metabolic features Central obesity 1.21 [0.56-2.63] 0.632 Type-2 diabetes 3.78 [1.48-9.68] 0.006 2.67 [0.96-7.46] 0.061 Dyslipidemia 1.03 [0.48-2.20] 0.937 Hypertension 2.19 [1.04-4.59] 0.038 1.30 [0.56-3.02] 0.535 Biochemistry ALT ≥ 1.5 ULN 3.40 [0.34-33.6] 0.296 GGT ≥ 1.5 ULN 1.90 [0.77-4.65] 0.161 Alkaline Phosphatases ≥ 1.5 ULN 1.29 [0.15-10.7] 0.813

Factors associated with steatosis Univariate Analysis Multivariate Analysis   Model with metabolic features Metabolic syndrome OR [95%CI] p value Social and demographics characteristics Male gender 1.88 [1.20-2.94] 0.006 6.06 [2.85-12.88] < 0.001 1.94 [1.14-3.30] 0.015 Age (per 10 years) 1.48 [1.21-1.80] <0.001 0.99 [0.75-1.32] 0.983 1.13 [0.89-1.44] 0.318 White skin color 1.85 [1.82-2.89] 0.007 1.45 [0.82-2.56] 0.204 1.64 [0.97-2.78] 0.067 Education < 8 years of study 0.77 [0.49-1.19] 0.239 AUDIT score ≥ 8 0.86 [0.51-1.45] 0.563 Former or current smoking 1.05 [0.68-1.64] 0.818 HIV infection history Duration of HIV infection (per 10 years) 1.76 [1.27-2.43] 0.001 1.47 [0.59-3.62] 0.408 1.34 [0.57-3.12] 0.503 CD4 count < 200 cells/mm3 0.54 [0.15-2.00] 0.355 HIV viral load > 40 copies/mm3 0.53 [0.30-0.95] 0.034 0.58 [0.28-1.21] 0.145 0.59 [0.30-1.17] 0.133 Nadir CD4 < 100 cells/mm3 1.11 [0.71-1.73] 0.647 c-ART history Duration of c-ART (per 10 years) 1.72 [1.21-2.45] 0.003 0.98 [0.35-2.75] 0.966 1.17 [0.44-3.14] 0.758 Current treatment AZT Backbone drug class (vs TDF) 1.55 [0.92-2.62] 0.102 PI or II Core Drugs class (vs NNRTI) 0.93 [0.59-1.45] 0.737 Most used drugs during HIV infection 2.03 [1.30-3.19] 0.002 1.62 [0.86-3.06] 0.135 1.29 [0.71-2.33] 0.410 0.91 [0.59-1.42] 0.685 Metabolic features Central obesity 4.27 [2.39-7.62] 10.74 [4.40-26.20]  - - Type-2 diabetes 9.56 [3.52-25.97] 9.74 [3.15-30.10] Dyslipidemia 5.34 [0.02-9.43] 2.61 [1.35-5.05] 0.004 Hypertension 1.96 [1.20-3.19] 0.67 [0.34-1.33] 0.253 Presence of metabolic syndrome 4.82 [2.87-8.07] 4.28 [2.45-7.46] Biochemistry ALT ≥ 1.5 ULN 1.84 [0.26-13.22] 0.545 GGT ≥ 1.5 ULN 1.43 [0.76-2.68] 0.264 Alkaline Phosphatases ≥ 1.5 ULN 0.91 [0.22-3.72] 0.900

Correlation and collinearity Variance Inflation Factor (VIF) quantify the severity of multicollinearity VIF < 1 = not correlated. VIF = 1 - 5 = moderately correlated VIF > 5 = highly correlated Multivariate Model for steatosis Variable VIF Duration of c-ART 7.05 Duration of HIV infection 5.27 Duration of AZT Backbone 2.67 Age 1.58 Central obesity 1.55 Gender 1.46 Dyslipidemia 1.30 Hypertension 1.22 Type-2 diabetes 1.10 White skin color 1.08 HIV viral load 1.03

Sensitivity analysis Factors associated with steatosis Multivariate Analysis Model A Model B Model C Duration of HIV infection Duration on c-ART Cumulative use of AZT BB OR [95%CI] p value Social and demographics characteristics   Male gender 6.18 [2.93-13.06] < 0.001 6.36 [3.00-13.44] 5.82 [2.77-12.21] Age (per 10 years) 1.01 [0.77-1.34] 0.929 1.02 [0.77-1.35] 0.920 1.07 [0.82-1.40] 0.610 White skin color 1.45 [0.82-2.55] 0.200 0.201 1.47 [0.83-2.59] 0.186 HIV infection history Duration of HIV infection (per 10 years) 1.64 [1.05-2.54] 0.029 HIV viral load > 40 copies/mm3 0.58 [0.28-1.20] 0.141 0.60 [0.29-1.24] 0.165 c-ART history Duration of c-ART (per 10 years) 1.68 [1.03-2.72] 0.036 AZT BB as the most used ART (vs TDF) 1.90 [1.07-3.38] 0.028 Metabolic features Central obesity 10.35 [4.29-25.00] 10.72 [4.43-25.97] 10.75 [4.44-25.99] Type-2 diabetes 9.44 [3.08-28.96] 9.30 [3.05-28.39] 9.42 [3.07-28.86] Dyslipidemia 2.70 [1.40-5.20] 0.003 2.74 [1.42-5.30] 2.60 [1.35-5.03] 0.004 Hypertension 0.66 [0.34-1.30] 0.229 0.68 [0.35-1.34] 0.266 0.69 [0.35-1.35] 0.280 Variance Inflation Factor (VIF) Absence of severe multicollinearity in Model A, B and C Variables Model A VIF Age 1.55 Central obesity Gender 1.45 Dyslipidemia 1.29 Duration of HIV infection 1.26 Hypertension 1.21 Type-2 diabetes 1.09 White skin color 1.08 HIV viral load 1.03 Variables Model B VIF Age 1.57 Central obesity 1.54 Gender 1.45 Dyslipidemia 1.29 Duration on c-ART 1.24 Hypertension 1.21 Type-2 diabetes 1.09 White skin color 1.08 HIV viral load 1.03 Variables Model C VIF Central obesity 1.54 Gender 1.44 Age 1.41 Dyslipidemia 1.30 Hypertension 1.21 Type-2 diabetes 1.09 AZT BB most used drug class 1.08 White skin color HIV viral load 1.03

Conclusions In mono-infected HIV patients : Low CD4 count was independently associated with presence of liver fibrosis by transient elastography Metabolic features (central obesity, type-2 diabetes and dyslipidemia) and metabolic syndrome were independently associated with presence of liver steatosis by CAP Higher duration of c-ART, especially by AZT as Backbone, was associated with steatosis independently of metabolic factors

Thank you for your attention Acknowledgement Participants of the PROSPEC-HIV study Colleagues from LAPCLIN-AIDS at INI-FIOCRUZ Agencies that have been supporting the PROSPEC study Thank you for your attention