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. Author manuscript; available in PMC: 2022 Mar 1.
Published in final edited form as: Drug Alcohol Depend. 2021 Jan 19;220:108531. doi: 10.1016/j.drugalcdep.2021.108531

Heroin Use is Associated with Liver Fibrosis in the Miami Adult Studies on HIV (MASH) Cohort

Marianna K Baum 1,*, Javier A Tamargo 1, Richard L Ehman 2, Kenneth E Sherman 3, Jun Chen 2, Qingyun Liu 1, Raul N Mandler 4, Colby Teeman 1, Sabrina S Martinez 1, Adriana Campa 1
PMCID: PMC7889727  NIHMSID: NIHMS1664606  PMID: 33508691

Abstract

Background:

People who use opioids and people living with HIV (PLWH) are at increased risk for liver-related morbidity and mortality. Although animal models suggest that chronic opioid use may cause liver damage, research in humans is limited. We aimed to determine whether opioid use, particularly heroin, was associated with liver fibrosis.

Methods:

Cross-sectional analysis of 679 participants (295 HIV/HCV uninfected, 218 HIV mono-infected, 87 HCV mono-infected, 79 HIV/HCV coinfected) from the Miami Adult Studies on HIV (MASH) cohort. Liver fibrosis was assessed via magnetic resonance elastography (MRE) on a 3T Siemens MAGNETOM Prisma scanner.

Results:

A total of 120 (17.7%) participants used opioids. Liver fibrosis was present in 99 (14.6%) participants and advanced liver fibrosis in 31 (4.6%). Heroin use (N=46, 6.8%) was associated with HCV-seropositivity, smoking, misuse of prescription opioids, and polysubstance use. The use of heroin, but not misuse of prescription opioids, was significantly associated with liver fibrosis (OR=2.77, 95% CI: 1.18–6.50) compared to heroin non-users, after adjustment for confounders including excessive alcohol consumption, polysubstance use and HIV and HCV infections. Both HIV and HCV infections were associated with liver fibrosis, whether virally suppressed/undetectable or viremic.

Conclusions:

Heroin use was independently associated with increased risk for liver fibrosis, irrespective of the use of other substances and HIV or HCV infections. Both HIV and HCV were associated with higher risk for liver fibrosis, even among those with suppressed or undetectable viral loads. The exact mechanisms for opioid-induced liver fibrosis remain to be fully elucidated.

Keywords: Opioids, Liver Diseases, HIV, Hepatitis C, Substance Abuse

1. Introduction

The opioid epidemic is a public health emergency of global proportions, with the United States (U.S.) having the highest prevalence of opioid use disorders (GBD 2016 Disease and Injury Incidence and Prevalence Collaborators, 2017; UNODC, 2020). Opioids were used by an estimated 57.8 million people worldwide in 2018 and accounted for two-thirds of all drug-related deaths (UNODC, 2020). In the U.S., some 9.9 million people misused prescription narcotics, close to 1 million used heroin, and 2 million had an opioid use disorder in 2018 (SAMHSA, 2019). People who use opioids (PWO) are at increased risk for liver-related morbidity and mortality (Hser et al., 2017). In turn, chronic liver disease in PWO is associated with high-risk opioid use patterns and worse social, economic, and physical health outcomes (Dennis et al., 2020). The majority of liver diseases in PWO may be attributed to injection drug use (IDU) and subsequent viral infections, in particular hepatitis C virus (HCV) (Degenhardt et al., 2016; Lanini et al., 2016; Verna et al., 2019).

Approximately 1 in 15 HIV infections in the U.S. can be attributed to IDU, driven largely by the opioid crisis (CDC, 2018). In turn, people living with HIV (PLWH) are prescribed opioids at higher rates and higher doses than HIV-uninfected persons (Becker et al., 2016), increasing their risk for misuse of prescription opioids (Canan et al., 2018) and progression to heroin use (Cicero et al., 2014). Liver disease is accelerated in PLWH (Sherman et al., 2017) and HIV may be a stronger predictor of liver-related mortality among people who inject drugs than HCV infection (Hayashi et al., 2014). Thus, the hepatotoxic properties of opioids may be particularly harmful for PLWH.

Chronic exposure to morphine (the active metabolite of heroin) in animal models increased liver enzymes, lipid peroxidation, histopathological changes, oxidative stress, and hepatocyte apoptosis (Atici et al., 2005; Bekheet, 2010; Payabvash et al., 2006; Samarghandian et al., 2014; Zhang et al., 2004). Similar findings have been reported with methadone (Amraei et al., 2018)–an opioid agonist typically used to treat opioid use disorder, but also abused for its euphoric effects at high doses. Nonetheless, research in humans is limited. Autopsies have shown chronic active hepatitis and cirrhosis in liver tissues of people who used heroin (Ilic et al., 2010). On the other hand, Brunet et al. (2016a) were unable to show a significant association between opioid use and liver fibrosis among people living with HIV/HCV coinfection using the aspartate aminotransferase-to-platelet ratio index (APRI). Using transient elastography (FibroScan), Van Santen et al. (2018) showed that the duration of methadone abuse, but not heroin, was associated with liver stiffness. However, both of these studies may have been affected by the use of noninvasive assessments of liver fibrosis that have been shown to be insufficiently sensitive or specific (Patel and Sebastiani, 2020).

Currently, magnetic resonance elastography (MRE) is considered the most accurate noninvasive measure of liver fibrosis, with sensitivity and specificity nearly equal to histological assessment (Dulai et al., 2016). However, no studies have examined the associations between substance abuse and liver fibrosis using MRE technology. Consequently, the aim of this study was to determine whether opioid use, particularly heroin, is associated with liver fibrosis, as assessed via MRE, in a cohort of people living with and without HIV and/or HCV. We hypothesized that heroin use is significantly associated with increased risk for liver fibrosis in this population.

2. Materials and methods

This was a cross-sectional analysis of data from the Miami Adult Studies on HIV (MASH) cohort, which follows people living with and without HIV for patterns of substance abuse, liver disease, and related comorbidities (Abdel-Hameed et al., 2020; Campa et al., 2016; Martinez et al., 2017; Tamargo et al., 2021; Zarini et al., 2020). The MASH cohort is largely comprised of Black and Hispanic middle-aged individuals from the Metropolitan sector of Miami, Florida, USA. Eligibility for MASH cohort participation consists of being 40 years of age or older and a medical chart examination for: 1) documented HIV and HCV status, 2) hepatitis B seronegative, 3) free of inherited liver disease, cirrhosis, or 4) any condition that may impair participation in the study (e.g. active cancer, dementia). Participants who could not complete magnetic resonance assessment of the liver (i.e. claustrophobia, metal device or implant) were excluded from this analysis. All participants provided written consent for participation in the study and for release of medical records. The study protocol was approved by the Florida International University Institutional Review Board.

2.1. Assessments

In this cohort, participants’ demographic data was self-reported. Anthropometrics were measured by trained staff and used to calculate body mass index (BMI, kg/m2). Obesity was defined as having a BMI ≥30 kg/m2. Fasting blood samples were obtained from all participants and used to measure routine laboratory values and the liver enzymes aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP). Hyperglycemia was defined as having a fasting blood glucose ≥126 mg/dL as per the American Diabetes Association (2020). Use of antiretroviral therapy (ART) was self-reported. HIV and HCV viral loads and CD4 cell counts were abstracted from medical charts. HIV viral suppression was defined as plasma HIV RNA <200 copies/mL. HCV infection was categorized as undetectable (<15 IU/mL) or viremic.

2.2. Substance use

Substance use (dichotomized as positive or negative) was determined by self-reported use in the last 30-days or a positive result on urine toxicology (American Bio Medica, Kinderhook, NY) performed on the same day as the questionnaire. Participants were asked to name “any drugs that you may have used on your own—that is, either without a doctor’s prescription; in greater amounts, more often, or longer than prescribed; or for a reason other than a doctor said you should use them” (Grant et al., 2015). Hazardous alcohol consumption was assessed with the Alcohol Use Disorders Identification Test (AUDIT) (Babor et al., 2001). An AUDIT score of 8 or more is an indicator of hazardous drinking, while scores of 13 or greater for women and 15 or greater for men may be indicative of alcohol dependence. A history of problems with alcohol was determined from self-report and evaluation of medical records. Polysubstance was determined as use of two or more of: hazardous drinking, tobacco, cannabis, cocaine, prescription opioids, and heroin.

2.3. Liver steatosis and fibrosis

Magnetic resonance assessments of the liver were conducted on a 3T Siemens MAGNETOM Prisma scanner located at the Center for Imaging Science (CIS), Florida International University. Liver steatosis (fatty liver) was assessed via magnetic resonance imaging-derived measurement of proton density fat-fraction (MRI-PDFF). Liver fat content is reported as percentages (%) and steatosis was considered as present if greater than 5% (Chalasani et al., 2018). Liver fibrosis was assessed via liver stiffness (LS) measurement by magnetic resonance elastography (MRE) (Hoodeshenas et al., 2018). MRI-PDFF and MRE are currently regarded as the most accurate noninvasive techniques for assessment of liver steatosis and fibrosis (Dulai et al., 2016; Gu et al., 2019; Hoodeshenas et al., 2018; Yokoo et al., 2018). Liver fibrosis (stage 1 fibrosis or higher) was defined as a LS ≥2.9 kilopascal (kPa) and advanced fibrosis (stage 3 fibrosis or higher) was defined as a LS of ≥3.8 kPa (Singh et al., 2016).

2.4. Statistical analysis

All data analysis was generated using SAS software, version 9.4. Descriptive statistics are reported as No. (%), mean ± standard deviation (SD), or median (interquartile range, IQR). Group differences were tested with Chi-square for categorical outcomes and T-test or Wilcoxon rank-sum test for continuous outcomes. We performed linear regressions and binary logistic regressions to determine the relationship between heroin use and liver fibrosis. Other factors explored included HIV and HCV infections and other illicit drugs. Multiple regression models were built by including variables with p-values under 0.25 in univariate regressions, and were adjusted for age, sex, race/ethnicity, income, alcohol (current and lifetime), polysubstance use, BMI, hyperglycemia, liver fat content, HIV and HCV regardless of their p-values. Results were considered statistically significant at an alpha level of 0.05, and two-sided p-values are reported unless otherwise specified.

3. Results

3.1. Population characteristics

The population characteristics are shown in Table 1, consisting of 679 participants who were 54.5 ± 7.5 years old, mostly Black non-Hispanic (n=421, 62.0%) and Hispanic (n=198, 29.2%). There were 295 HIV/HCV uninfected, 218 HIV mono-infected, 87 HCV mono-infected, 79 HIV/HCV coinfected. Also, 273 (40.2%) were obese and 54 (8.0%) had hyperglycemia.

Table 1.

Population characteristics

Total
(N=679)		 Heroin User
(N=46)		 Heroin Non-User
(N=633)
N (%)
Mean ± SD		 N (%)
Mean ± SD		 N (%)
Mean ± SD		 P-value
Age, years 54.5 ± 7.5 53.9 ± 8.7 54.4 ± 7.5 0.574
Sex, male 379 (55.8) 29 (63.0) 350 (55.3) 0.307
Race/ethnicity 0.567
Black non-Hispanic 421 (62.0) 27 (60.4) 394 (62.2)
White non-Hispanic 60 (8.8) 6 (12.5) 54 (8.5)
Hispanic 198 (29.2) 13 (27.1) 185 (29.3)
Income 0.706
$15,000 or less 528 (77.8) 37 (80.4) 491 (77.6)
$15,000–$30,000 116 (17.1) 6 (13.0) 110 (17.4)
$30,000 or more 35 (5.2) 3 (0.5) 32 (5.1)
Education 0.598
Less than High-School 266 (39.2) 19 (41.3) 247 (39.0)
High-School or GED 206 (30.3) 11 (23.9) 195 (30.8)
More than High-School 207 (30.5) 16 (34.8) 191 (30.2)
Substances
Alcohol, AUDIT≥8 161 (23.7) 10 (21.7) 151 (23.9) 0.745
Alcohol, AUDIT≥13 for women or 15 for men 72 (10.6) 6 (13.0) 66 (10.4) 0.578
History of problems with alcohol 243 (35.8) 16 (34.8) 227 (35.9) 0.883
Smoker (tobacco) 353 (52.0) 33 (71.7) 320 (50.6) 0.006
Cannabis 192 (28.3) 18 (39.1) 174 (27.5) 0.091
Cocaine 311 (45.8) 27 (58.7) 284 (44.9) 0.069
Prescription opioids 88 (13.0) 14 (30.4) 74 (11.7) 0.0003
Polysubstancea 344 (50.7) 41 (89.1) 303 (47.9) <0.0001
Polysubstance, amountb 2.2 ± 1.1 3.2 ± 1.2 2.1 ± 1.0 <0.0001
Metabolic
BMI, kg/m2 29.2 ± 6.3 28.9 ± 6.5 29.2 ± 6.2 0.769
Obesity 273 (40.2) 18 (39.1) 255 (40.3) 0.878
Hyperglycemia 54 (8.0) 1 (2.2) 53 (8.4) 0.165
Hepatic
AST, U/L 22 (17–29)c 21 (18–31)c 22 (17–29)c 0.601
ALT, U/L 18 (14–27)c 19 (12–25)c 18 (14–27)c 0.598
AST/ALT ratio 1.25 ± 0.4 1.41 ± 0.5 1.24 ± 0.4 0.011
ALP, U/L 79 (66–96)c 82 (75–93)c 79 (66–96)c 0.316
Fat content, % 2.9 (2.0–3.8)c 2.9 (2.0–3.8)c 2.0 (2.0–3.8)c 0.182
Steatosis (>5%) 107 (15.8) 7 (15.2) 100 (15.9) 0.910
Liver stiffness, kPa 2.3 (2.0–2.6)c 2.4 (2.1–2.9)c 2.3 (2.0–2.6)c 0.028d
Fibrosis (≥2.9 kPa) 99 (14.6) 12 (26.1) 87 (13.7) 0.022
HIV 297 (43.7) 19 (41.3) 278 (43.9) 0.730
Taking antiretrovirals 293 (98.7) 18 (94.7) 275 (98.9) 0.233
<200 copies/mL 245 (83.3) 18 (94.7) 227 (82.6) 0.217
CD4 cells/µL 601.3 ± 371.4 629.5 ± 395.7 599.4 ± 370.3 0.733
HCV 166 (24.5) 19 (41.3) 147 (23.2) 0.006
Undetected 86 (51.8) 11 (57.9) 75 (51.0) 0.573
HIV/HCV coinfected 79 (11.6) 7 (15.2) 72 (11.4) 0.433
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Bold values are those that are statistically significant at p < 0.05.

a

Polysubstance was defined as use of 2 or more of hazardous drinking (AUDIT≥8), tobacco, cannabis, cocaine, prescription opioids, and heroin

b

The amount of substances used among participants who used at least 1 substance, N=527 (77.6%)

c

Median (interquartile range)

d

One-sided p-value (heroin user > non-user)

Among participants living with HIV, the vast majority (n=293, 98.7%) reported being on ART and were virally suppressed (n=245, 83.3%), with mean CD4 cell count of 601 ± 371 cells/μL. There was no significant difference in ART use, viral suppression, or CD4 cell counts between the heroin users and non-users. Over half of HCV-seropositive participants had undetectable HCV viremia (n=86, 51.8%), but heroin use was not associated with HCV viremia.

3.2. Substance use

A total of 120 (17.7%) participants misused opioids–46 (6.8%) participants used heroin and 88 (13.0%) misused prescription opioids; 14 (2.1%) participants used both. Participants who used heroin were significantly more likely than heroin non-users to be HCV-seropositive, smoke cigarettes, misuse prescription opioids, and to engage in polysubstance use. Also, among participants who used at least one substance, those who used heroin used significantly more substances concomitantly than heroin non-users.

3.3. Liver-related outcomes

The levels of AST, ALT, or ALP did not significantly differ between heroin users and non-users, but heroin users had significantly higher AST/ALT ratios. Liver steatosis was present in 107 (15.8%) participants and median liver fat content was 2.9 (2.0–3.8)%. Although median liver fat percentage was lower among heroin users, there were no significant differences in liver fat content or liver steatosis (liver fat >5%) between heroin users and non-users. Liver fibrosis was present in 99 (14.6%) participants, 31 (4.6%) with advanced liver fibrosis. The median liver stiffness in this population was 2.3 (2.0–2.6) kPa. Heroin users had a higher frequency of liver fibrosis (26.1 vs. 13.7%, respectively; P=0.02) and slightly higher liver stiffness (Z=1.91; one-sided P=0.03) compared to heroin non-users.

Univariate logistic regressions for the presence of liver fibrosis are shown in Table 2. The use of heroin was associated with double the risk for fibrosis compared to heroin non-users (OR: 2.22, 95% CI: 1.10–4.44; P=0.03). Additionally, age, hyperglycemia, HIV viral non-suppression, and HCV infection (undetectable or viremic) were significantly associated with increased risk for liver fibrosis. In multivariable analysis, the use of heroin remained significantly associated with increased risk for liver fibrosis (adjusted odds ratio [AOR]: 2.77, 95% CI: 1.18–6.50 ; P=0.02) after adjustment for covariates. Also, only after adjustment for covariates, HIV infection was significantly associated with increased risk for liver fibrosis, whether virally suppressed or not.

Table 2.

Logistic regressions for liver fibrosis (LS ≥ 2.9 kPa)

Univariate Multivariable
OR (95% CI) P AOR (95% CI) P
Age Years 1.04 (1.01–1.07) 0.021 1.05 (1.02–1.09) 0.005
Sex M vs. F 1.20 (0.78–1.85) 0.413 1.01 (0.61–1.67) 0.977
Race/ethnicity Black non-Hispanic Reference
White non-Hispanic 1.30 (0.64–2.64) 0.397 1.06 (0.48–2.34) 0.878
Hispanic 0.92 (0.56–1.51) 0.448 0.79 (0.44–1.41) 0.415
Income Per $1,000 0.99 (0.97–1.01) 0.243 0.99 (0.97–1.01) 0.434
BMI kg/m2 0.97 (0.94–1.01) 0.145 0.99 (0.95–1.04) 0.769
Hyperglycemia 3.35 (1.81–6.17) 0.0001 3.80 (1.87–7.72) 0.0002
Liver Steatosis % (continuous) 1.02 (0.97–1.07) 0.498
>5% (binary) 1.22 (0.70–2.13) 0.483
HIVa Suppressed 1.51 (0.96–2.38) 0.074 2.21 (1.30–3.76) 0.004
Not suppressed 2.11 (1.01–4.43) 0.047 3.70 (1.51–9.03) 0.004
HCVb Undetectable 2.57 (1.37–4.83) 0.003 3.17 (1.66–6.06) 0.001
Viremicc 7.30 (4.18–12.73) <0.0001 9.05 (4.94–16.6) <.0001
Alcohol AUDIT≥8 1.17 (0.72–1.91) 0.519
AUDIT≥13/15 1.48 (0.79–2.78) 0.219 2.11 (0.90–4.91) 0.085
History of problems 1.14 (0.73–1.77) 0.560 0.84 (0.46–1.55) 0.577
Cigarette Smoking 0.98 (0.64–1.50) 0.919
Cannabis 0.94 (0.86–1.52) 0.810
Cocaine 0.85 (0.55–1.31) 0.466
Opioids Prescription 0.92 (0.48–1.75) 0.788
Heroin 2.22 (1.10–4.44) 0.025 2.77 (1.18–6.50) 0.019
Polysubstance # of substances 0.90 (0.59–1.38) 0.639 0.90 (0.71–1.14) 0.387
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Bold values are those that are statistically significant at p < 0.05.

a

Reference is HIV uninfected

b

Reference is HCV seronegative

c

Viremic HCV-seropositive had 2.84 (95% CI 1.38–5.84) time the odds for liver fibrosis than those with undetectable HCV viral load; P=0.005. In multivariable regression, viremic HCV-seropositive participants had 2.88 (95% CI 1.31–6.32) times the odds for liver fibrosis than those with undetectable HCV viral load; P=0.008

Similar results were obtained with linear regressions for liver stiffness (Table 3). In univariate analyses, heroin was significantly associated with increased mean liver stiffness (B=0.278, SE=0.134; P=0.04) and the association remained significant after adjustment for covariates (B=0.324, SE=0.133; P=0.02).

Table 3.

Linear regressions for liver stiffness (kPa)

Univariate Multivariable
B SE 95% CI t P B SE 95% CI t P
Age Years 0.014 0.004 0.005 0.022 3.08 0.002 0.014 0.004 0.006 0.023 3.26 0.001
Sex M vs. F 0.064 0.068 −0.070 0.197 0.94 0.350 0.009 0.065 −0.120 0.137 0.13 0.895
Race/ethnicity Black non-Hispanic Reference Reference
White non-Hispanic 0.094 0.121 −0.144 0.332 0.77 0.439 −0.039 0.114 −0.262 0.184 −0.34 0.732
Hispanic 0.004 0.078 −0.148 0.157 0.06 0.956 −0.051 0.074 −0.196 0.137 −0.69 0.488
Income Per $1,000 −0.003 0.002 −0.007 0.002 −1.24 0.214 −0.002 0.002 −0.005 0.002 −0.77 0.439
BMI kg/m2 −0.003 0.005 −0.013 0.008 −0.50 0.621 0.002 0.006 −0.008 0.013 0.43 0.667
Hyperglycemia 0.576 0.123 0.334 0.817 4.67 <.0001 0.487 0.115 0.260 0.713 4.23 <.0001
Liver Steatosis % (continuous) 0.008 0.008 0.950 −0.008 0.03 0.340 0.008 0.008 −0.008 0.024 1.02 0.308
>5% (binary) 0.085 0.092 −0.097 0.268 0.92 0.359
HIVa Suppressed 0.090 0.072 −0.051 0.232 1.25 0.211 0.143 0.068 0.009 0.277 2.09 0.034
Not suppressed 0.218 0.134 −0.044 0.479 1.63 0.104 0.329 0.128 0.078 0.580 2.57 0.010
HCVb Undetectable 0.299 0.036 0.110 0.489 3.10 0.002 0.296 0.098 0.104 0.489 3.02 0.003
Viremic 1.052 0.098 0.860 1.244 10.77 <.0001 1.057 0.100 0.859 1.254 10.5 <.0001
Alcohol AUDIT≥8 0.026 0.080 −0.130 0.182 0.33 0.745
AUDIT≥13/15 0.174 0.110 −0.042 0.389 1.58 0.114 0.257 0.116 0.028 0.485 2.21 0.028
History of problems 0.040 0.071 −0.098 0.178 0.57 0.570 −0.017 0.078 −0.171 0.136 −0.22 0.824
Cigarette smoking −0.084 0.068 −0.217 0.049 −1.24 0.214
Cannabis −0.037 0.075 −0.185 0.110 −0.50 0.618
Cocaine −0.033 0.068 −0.167 0.100 −0.49 0.624
Opioids Prescription −0.025 0.101 −0.223 0.173 −0.25 0.802
Heroin 0.278 0.134 0.014 0.541 2.07 0.039 0.324 0.133 0.063 0.585 2.44 0.015
Polysubstance # of substances −0.092 0.068 −0.225 0.040 −1.37 0.172 −0.059 0.030 −0.117 <.0001 −1.97 0.050
Open in a new tab

Bold values are those that are statistically significant at p < 0.05.

a

Reference is HIV uninfected

b

Reference is HCV seronegative

4. Discussion

In this study, we aimed to determine whether misuse of opioids, particularly heroin, was associated with liver fibrosis among MASH cohort participants–a population mostly comprised of socioeconomically disadvantaged Black and Hispanic individuals who use drugs and are living with and without HIV and/or HCV. Our findings showed that heroin use, but not misuse of prescription opioids, was independently associated with increased liver stiffness and increased risk for liver fibrosis as measured by MRE. The prevalence of liver steatosis was 16% and that of liver fibrosis was 15%, with only 5% who had advanced fibrosis. In the general population, the prevalence of liver fibrosis is estimated to range between 0.7–25.7%, and 0.9–2.0% for advanced fibrosis (Harris et al., 2017). This suggests that the MASH cohort was in early stages of liver disease but may be at increased risk for advanced liver disease. Moreover, our findings suggest that heroin promotes liver damage irrespective of HIV or HCV infections. Indeed, the use of heroin was associated with increased liver stiffness and over two and a half times the risk for liver fibrosis compared to heroin non-users. Additionally, the use of heroin was associated with increased AST/ALT ratio, but no effect was seen on liver steatosis or serum levels of AST, ALT, or ALP.

Opioid drugs are primarily metabolized in the liver by cytochrome P450 and uridine diphosphate glucuronosyltransferase enzymes (Smith, 2009). In humans, research on the hepatotoxic effects of opioids is scarce. Animal models suggest that morphine (the active metabolite of heroin) promotes the activity of liver enzymes, lipid peroxidation, and oxidative stress in the liver, subsequently leading to hepatocyte apoptosis and histopathological changes (Amraei et al., 2018; Atici et al., 2005; Bekheet, 2010; Payabvash et al., 2006; Samarghandian et al., 2014; Zhang et al., 2004). It has also been suggested that heroin may cause injury to the liver through direct vascular injury leading to both stellate cell activation and centrolobular fibrosis (de Araújo et al., 1997).

In turn, liver disease can alter opioid pharmacokinetics. Reduced clearance due to hepatic insufficiency leads to prolonged exposure to opioids in circulation. Indeed, this warrants the use of lower doses of opioid analgesics in patients with liver disease, particularly those with advanced fibrosis (Soleimanpour et al., 2016). Therefore, opioid abuse may create a vicious cycle in which opioids promote liver fibrosis, which in turn increases the risk for opioid overdose by decreasing the threshold for toxicity.

In contrast to our findings, Brunet et al. (2016a) were unable to find a significant relationship between opioid use (prescribed or illicit) and liver fibrosis among HIV/HCV coinfected persons, despite having a greater number of opioid users than our study. Van Santen et al. (2018) found that duration since first injection drug use and duration of methadone use, but not heroin, were significantly associated with liver stiffness measurement from transient elastography (FibroScan) among people living with HIV, HCV, and/or HBV. However, these associations were no longer significant when adjusted for age, alcohol consumption, and HIV/HCV co-infection. The difference in results in the studies by Brunet et al. (2016a) and van Santen et al. (2018) to ours is likely related to our use of MRE, currently regarded as the most accurate non-invasive measure of liver stiffness with accuracy nearly equal to histological assessment (Dulai et al., 2016).

Importantly, the misuse/abuse of opioids is associated with HIV viral non-suppression among PLWH (Lemons et al., 2019). It is also a barrier for treatment of HCV, despite evidence of successful treatment among people who inject drugs, regardless of opioid treatment (Verna et al., 2019). Yet, in our population, the use of heroin was not associated with HIV or HCV viral loads. This, and the results seen after adjustment of viral infection with HIV or HCV, suggest that heroin promotes hepatic fibrogenesis independent of these infections. However, the exact mechanisms remain to be established.

Not surprisingly, we found that age, hyperglycemia, HIV, and HCV were associated with increased risk for liver fibrosis. Among PLWH, the risk for fibrosis was particularly elevated in those who were not virally suppressed, but even those who were virally suppressed (83%) showed increased risk for fibrosis compared to HIV-uninfected participants, after adjustment for covariates. Previous publications have shown that uncontrolled HIV viral replication promotes liver fibrosis and that use of modern ART is associated with decreased liver fibrosis (Brunet et al., 2016b; Mohr et al., 2015; Vinikoor et al., 2017). Nonetheless, to the best of our knowledge, this is the first study to show that even people with suppressed HIV viral load have significantly greater liver fibrosis than people who are not infected with HIV.

Similar observations were found in HCV-seropositive people. HCV-seropositive participants, whether HCV-viremic or undetectable, had higher risk for liver fibrosis than those who were HCV-uninfected. In addition, HCV-viremic participants had higher risk for liver fibrosis than those who were HCV-seropositive but had undetectable HCV viral load. This is consistent with studies that have shown improvement in liver stiffness after successful HCV treatment (Beguelin et al., 2018; Hedenstierna et al., 2018). This may reflect improvements in hepatic inflammation that occurs soon after viral eradication, as well as regression of liver fibrosis following successful HCV treatment (Singh et al., 2018). It is also possible that participants who were HCV-seropositive with undetectable viremia are actually a blend of two groups: those with false positive HCV antibodies and those who spontaneously cleared infection.

The MASH cohort participants in this study are representative of socioeconomically disadvantaged minorities and of PLWH who are engaged in care and on suppressive ART. While these findings are highly relevant for the clinical setting, the study was limited by a relatively small number of participants with fibrosis. Additionally, drug use was self-reported for the last 30 days and lifetime use was not available for all participants. The cross-sectional design is another limitation. Longitudinal studies may be able to provide greater insights into the relationship between opioid use–including the misuse of prescription opioids and use of illicit opioids–and the progression of liver disease. Future studies should also consider the duration, method (e.g. oral, injection), and quantity/frequency of use, as well as concurrent use with other substances. Lastly, studies should explore potential mechanisms for opioid-induced liver fibrosis, such as the actions of liver enzymes and oxidative stress.

5. Conclusions

Heroin use was independently associated with increased risk for liver fibrosis determined by MRE, irrespective of HIV or HCV infections. These risks are especially relevant for PLWH, as they are disproportionately affected by the opioid crisis and liver-related morbidity and mortality. The exact mechanisms for opioid-induced liver fibrosis remain to be fully elucidated.

Highlights.

  • HIV and opioids increase risk for liver-related morbidity and mortality

  • We used highly-accurate magnetic resonance elastography to assess liver fibrosis

  • Heroin use was independently associated with increased risk for liver fibrosis

  • Misuse of prescription opioids was not associated with liver fibrosis

  • HIV and HCV were associated with liver fibrosis, even if virally suppressed

Financial Support:

This work was supported by the National Institute on Drug Abuse at the National Institutes of Health under Grant U01-DA040381 and partially supported by R37-EB001981. The views and opinions expressed in this manuscript are those of the authors only and do not necessarily represent the views, official policy or position of the U.S. Department of Health and Human Services or any of its affiliated institutions or agencies.

Footnotes

No conflicts of interest declared by any of the authors. Dr. RN Mandler was substantially involved in the grant U01-DA040381 consistent with his role as Scientific Officer and had no substantial involvement in other cited grants.

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