Hepatitis C virus (HCV) is the most common cause of chronic liver damage in the United States.1 Many patients are infected with both HCV and HIV; rates of coinfection range from 16%–40% with higher rates in intravenous drug abusers.2,3 In the last decade, several studies have suggested that patients with HCV have a high prevalence of cognitive impairments and that the effects on cognition are unrelated to hepatic encephalopathy (see reviews within3,4). Autopsy studies have shown replicative forms of HCV virus in the brain of some HCV patients,5–7 and brain magnetic resonance spectroscopy studies demonstrated patterns consistent with inflammation in the basal ganglia and white matter.8,9 Moreover, several studies have suggested that coinfection with HCV and HIV may have worse effects on cognition than monoinfection.10,11
Nonetheless, most previous series of the effects of HCV and HIV on cognition have been small, often too small to control for the numerous confounding factors that influence cognition. At least 2 larger studies that attempted to control for these confounding factors did not find a significant effect for HCV infection.2,12 Women were underrepresented in most previous studies of the effects of HCV and HIV on cognition, and some have suggested that HIV-related cognitive impairment is worse in women.13 Because HCV is prevalent in the large women's interagency HIV study (WIHS), we assessed the effects of HCV and HIV on cognition in analyses.
The WIHS14,15 is an ongoing prospective study of HIV in women. The WIHS began in 1994 and has enrolled 3766 women across 6 sites in San Francisco, Los Angeles, Chicago, Washington, DC, Brooklyn, and the Bronx (New York). WIHS initially recruited 2054 HIV-infected and 569 HIV-uninfected women in 1994–1995 and an additional 737 HIV-infected and 406 HIV-uninfected women in 2001–2002. Participants are evaluated every 6 months with an extensive interview that includes history of interval illnesses and interval substance abuse, current medications and medication adherence, physical exam, and blood and gynecological specimen collection. Details of standardized data collection protocols and training of interviewers have been previously described.14,15 Toxicological testing for illicit substances was not performed. Cerebrospinal fluid collection has not been a part of WIHS protocols. Data regarding significant past head trauma were not available.
The symbol-digit modalities test (SDMT)16,17 was administered to all English-speaking WIHS participants during visits 21–24 (October 2004 to September 2006) as part of the core assessment. The SDMT is a measure of speed of information processing and perceptual motor ability. This substitution test requires participants to use a code table at the top of the page to quickly write the correct digit in an empty box below a symbol with a time limit of 90 seconds. Score was the total number of boxes that were correctly filled within the time limit.
Some participants completed the testing on all 4 visits. Only the first score is used for each subject. Trails A and B tests18,19 were also administered to English-speaking participants during visits 21–24 and again we used results from the first time tested. The trail-making test (parts A and B) measures processing speed and cognitive flexibility. Part A is a page with 25 numbered circles randomly arranged. Part B is a page with circles containing the letters A through L and 13 numbered circles intermixed and randomly arranged. Participants instructed to draw lines connecting the circles although alternating between numbers and letters in sequential order. Time to completion were the measures used in this report.
We administered the Comalli–Kaplan Stroop20,21 during visits 25–28, October 2006 to September 2008 Women who spoke Spanish, but not English, did not take the Stroop test. We report condition 3, the interference task. In this test, the words “red”, “blue”, and “green” are printed in red, green, and blue ink, but the meaning of the word and the color of the ink are not always congruent. Subjects have to report the color of the ink. One hundred such words are arranged in rows. Errors were recorded, but we only include time to completion in these analyses. Times greater than 240 seconds were not censored (9 of 1426 participants had scores greater than 240 seconds).
Cofactors and Covariates
Ethnicity was self-reported as described previously.15 About 96.1% of participants described themselves as white, Hispanic, or African American. We combined the remaining 5 groups into one “other” group (n = 50).
Symptoms of depression were recorded with the Center for Epidemiological Studies—Depression scale (CES-D) at every visit.22 Based on previous work, women were divided into those with scores of ≤22 (not depressed or less depressed) and those with scores 23 and higher (more depressed).23 As the dates of cognitive testing varied, for multivariate analyses, we used the CES-D score closest to the time of neuropsychological testing. We used a history of previous psychiatric hospitalizations as a surrogate for history of serious psychiatric illnesses that could influence cognitive scores.
The HCV group was defined as HCV antibody positive and HCV RNA positive. The HCV negative group was defined as HCV antibody negative.
Education was self-reported on an 8-point scale ranging from 1—no education to 8—completed doctoral degree. In this study, the 8 education groups were collapsed into 4 levels as follows: (1) less than completing high school (n = 434), (2) completed high school with no further formal education (n = 349), (3) did not complete undergraduate degree (n = 349), and (4) completed undergraduate degree or higher (n = 100). We assessed reading ability with the Wide-Range Achievement Test-3.24 Our previous work had demonstrated that Wide-Range Achievement Test-3 usually accounted for more variance in cognitive score than educational level.25
FIB-4 was calculated by the following formula: age (yrs) × aspartate aminotransferase (AST) (U/L)/platelets (109/L) × [alanine aminotransferase (109U/L)]1/2 following published methods.26 AST-to-platelet ratio (APRI) was computed with the following formula: AST level (/upper limit of normal) × (platelet count (109/L))−1 × 100 following published methods.27 The APRI and FIB-4 are simple noninvasive measures of liver fibrosis that are consistent with liver biopsy results.
Data presented in this report include all data collected by wave 28, concluding in September 2008 on participants enrolled in the Genetic Predictors of Substance Abuse in HIV substudy. A total of 1426 participants (472 uninfected and 954 HIV infected) completed the Stroop and 1450 (467 uninfected and 983 infected) completed the symbol digit test and trail making tests (TMT). We assembled a dataset consisting of participants who completed all 4 neuropsychological tests and had complete information available concerning depression score, education level, HCV RNA level, and ethnicity—1338 women met these criteria.
Distributions of Trails A and B and Stroop score were skewed slightly positively. Fourteen subjects received a score of 240 seconds on the Stroop; another 9 had scores ranging from 246 to 381 seconds—none were censored. On Trails B, 21 subjects received scores of 300 seconds; these participants were also not censored.
We compared normally distributed variables with analysis of variance and determined post hoc significance with the Games–Howell test (for unequal variances). In univariate analyses, we compared the frequency of neuropsychological impairment for each neuropsychological test and for summary measures across the 6 participant groups. Significance was calculated with the χ2 test. We compared data that were not normally distributed with the Kruskal–Wallis test.
We utilized general linear models to assess the significance of the HCV RNA factor and HIV/AIDs factor with either individual neuropsychological score and included 7 factors as follows: (1) HIV group (HIV seronegative, HIV seropositive without AIDS, and AIDS); (2) Detectable HCV RNA (present/absent); (3) Depression group (CES-D score ≥ 23); (4) ethnicity (white, Hispanic, African American, and other); (5) age quintile; (6) educational group; and (7) reading ability. After these factors were entered in regression models as a block, we individually assessed the possible additional contribution of HIV-related variables [CD4 count, CD4 nadir, and months on highly active antiretroviral therapy (HAART)], liver-related variables (FIB4 and APRI), and substance abuse related (recent or past use of cocaine or heroin or excessive alcohol use).
To assess whether liver disease was associated with cognitive score, we performed separate univariate and multivariate modeling. Level of significance was set at P < 0.05, and we did not correct for multiple comparisons.
Demographic and clinical characteristics of the 6 patient groups defined by 3 HIV groups (not infected, HIV seropositive but not AIDS, and AIDS) and 2 HCV groups (HCV viremic or not HCV) are shown in Table 1. Participants who were HCV positive were 9 years older than HCV RNA–negative subjects (F = 38, P < 0.001). Eighty-five percent of HCV viremic participants had used intravenous drugs before recruitment into WIHS, compared with 12% of those without HCV viremia. Only 2 participants who had not used intravenous drugs before enrolling into WIHS started intravenous drug use during the study.
HCV viral load did not differ among the 3 groups of participants with active HCV infection. As expected, markers of liver disease, FIB-4 and APRI, were significantly higher in the HCV viremic participants. Heroin use in the 6 months before evaluation (already at least 5 years into recruitment into WIHS) was 14% in HCV viremic without HIV and 10% in HCV viremic with AIDS. Heroin use in the previous 6 months in the other 4 groups was only 1%–2%. Cocaine use in the previous 6 months was also more prevalent in the HCV viremic group (17.8%) than in the HCV-negative group (6.9%, χ2 = 27.7, P < 0.001).
Nadir CD4 count was lowest in the 2 AIDS groups. The 2 groups with AIDS had been maintained on HAART longer than those with HIV, but not AIDS. The HIV but not AIDS/HCV-negative group had higher current CD4 counts than the other 3 HIV groups. HIV viral load did not differ among the 4 HIV-infected groups.
In multivariate models, HCV viremia was not associated with any of the cognitive outcomes (Table 2). We assessed the effects of other possible confounders on cognitive score after adjusting for age quintile, ethnicity, education, reading ability, depression group, HIV group, and HCV viremia. Possible confounders such as HAART use, recent or past drug use, or serious psychiatric illness as measured by psychiatric hospitalizations did not contribute significantly to the models.
We also explored the association of FIB-4 with neuropsychological tests. In univariate analyses, FIB-4 score was associated with scores on all 4 neuropsychological tests as follows: Stroop, trails A, trails B, and SDMT with correlation coefficients of 0.15, 0.12, 0.17, and −0.16, and Ns from 1269 to 1301; all significant at P < 0.001. In multivariate modeling using FIB-4 score, no significant associations were found.
Finally, as shown in Table 2, HIV group was associated with score on the Stroop interference test, but not on the SDMT, trails A, or trails B tests.
To our knowledge, this cohort for the study is over twice as large as any previously reported study of the effects of HCV and HIV on cognition. Only about 20% of the participants in the largest 2 previous studies2,28 were women. Thus our sample includes almost 13 times as many women as any previously reported. In addition to the large size of our sample, other strengths include detailed information on liver function and drug history and our multivariate analyses controlling for confounding variables. Standardized data collection protocols with extensive training of interviewers are other strengths. Weaknesses include our limited neuropsychological battery and the relatively small size of HCV-monoinfected persons. Another weakness is that although WIHS is a longitudinal study, virtually all HIV-seropositive or HCV-seropositive participants were seropositive at the time of recruitment rather than acquiring infection during follow-up somewhat limiting the generalizability of our findings.
In considering the effects of HCV and HIV on cognition, 3 fundamental questions need to be asked as follows: (1) Does HCV impair cognitive function via a mechanism independent of hepatic encephalopathy and/or depression or some other comorbid factor? (2) Does dual infection with HIV and HCV have worse effects on cognition than HIV alone? (3) Are there plausible biological mechanisms to explain how HCV might impair cognitive function?
Because of the many factors that influence cognition, in our literature review, we focused on multivariate analyses that attempted to control for severity of liver disease. We were able to identify only 7 previous multivariate studies2,12,28–32 of the effects of HCV with or without HIV on cognition that met these criteria. In one other study,10 exploratory multivariate modeling was performed. Of these, 612,28–32 included an uninfected control group and 22,10 did not. Three multivariate studies with a negative–negative control group12,30,31 found no effect for HCV in fully controlled models, and 3 other studies28,29,31 found a significant effect. However, 2 of 3 positive studies28,32 used basically the same cohort, and almost half of the cohort was methamphetamine dependent. In the other study,29 details concerning multivariate analysis and the negative–negative control group were lacking.
The multivariate studies that compared HIV-monoinfected to doubly infected participants were both led by Clifford.2,10 The first study with only 30 doubly infected patients found that coinfected did worse than monoinfected on the DSMT. In a subsequent study, involving 172 dual infected subjects and 345 HIV-monoinfected participants, no significant differences were found.
Only women were included in our study. Men outnumbered women in virtually all previous studies. Is it possible that there is a sex-specific effect where HCV affects cognition more in one sex than in another? Effects of sex on prevalence have been demonstrated in Alzheimer disease and Parkinson disease.33 Some have suggested that women are more likely to be cognitively impaired in HIV.13 To determine whether there is a sex-specific effect on cognition in HCV will require a large sample of men and women.
We recognize that several reports have suggested plausible mechanisms for HCV infection to cause cognitive impairment. HCV RNA has been demonstrated in brain7,26 including replicative forms within CD68 cells.5 Patients with HCV had higher brain levels of monocyte chemnotactic protein 1, TNF-alpha, and soluble TNF-receptor II than control groups.28 We believe that potential mechanisms by which HCV could directly influence cognitive function have been identified, but a dose-response relationship between these changes and the magnitude of change in cognitive function has not yet been demonstrated.
In conclusion, we were unable to show a significant association between the presence of HCV RNA and performance on our cognitive battery nor that there is an interaction between HIV and HCV in their effect on cognitive function. Our literature review suggests that an effect of HCV on cognition that is independent of liver dysfunction has not been convincingly demonstrated. The question of whether HCV has a direct effect on cognition will require future studies with a complete neuropsychological battery, a large control group, and a large group of HCV-monoinfected subjects, use of both impairment scores and mean scores on neuropsychological tests, complete data on liver function and other cofactors, and a cohort that includes both men and women.
Data in this article were collected by the WIHS Collaborative Study Group with centers (Principal Investigators) at New York City/Bronx Consortium (Kathryn Anastos); Brooklyn, NY (Howard Minkoff); Washington DC, Metropolitan Consortium (Mary Young); The Connie Wofsy Study Consortium of Northern California (Ruth Greenblatt); Los Angeles County/Southern California Consortium (Alexandra Levine); Chicago Consortium (Mardge Cohen); Data Co-ordinating Center (Stephen Gange). We thank the women participating in WIHS for their time, cooperation, and support. The authors thank Dr. Jeremy Weedon for help with statistics and review of the manuscript.
1. Acharya JN, Pacheco VH. Neurologic complications of hepatitis C
. Neurologist. 2008;14:151–156.
2. Clifford DB, Smurzynski M, Park LS, et al.. Effects of active HCV replication on neurologic status in HIV
RNA virally suppressed patients. Neurology. 2009;73:309–314.
3. Clark US, Cohen RA. Brain dysfunction in the era of combination antiretroviral therapy: implications for the treatment of the aging population of HIV
-infected individuals. Curr Opin Investig Drugs. 2010;11:884–900.
4. McArthur JC, Steiner J, Sacktor N, et al.. Human immunodeficiency virus-associated neurocognitive disorders: mind the gap. Ann Neurol. 2010;67:699–714.
5. Wilkinson J, Radkowski M, Laskus T. Hepatitis C
virus neuroinvasion: identification of infected cells. J Virol. 2009;83:1312–1319.
6. Letendre S, Paulino AD, Rockenstein E, et al.. Pathogenesis of hepatitis C
virus coinfection in the brains of patients infected with HIV
. J Infect Dis. 2007;196:361–370.
7. Murray J, Fishman SL, Ryan E, et al.. Clinicopathologic correlates of hepatitis C
virus in brain: a pilot study. J Neurovirol. 2008;14:17–27.
8. Forton DM, Allsop JM, Main J, et al.. Evidence for a cerebral effect of the hepatitis C
virus. Lancet. 2001;358:38–39.
9. Forton DM, Hamilton G, Allsop JM, et al.. Cerebral immune activation in chronic hepatitis C
infection: a magnetic resonance spectroscopy study. J Hepatol. 2008;49:316–322.
10. Clifford DB, Evans SR, Yang Y, et al.. The neuropsychological and neurological impact of hepatitis C
virus co-infection in HIV
-infected subjects. AIDS. 2005;19(suppl 3):S64–S71.
11. Hinkin CH, Castellon SA, Levine AJ, et al.. Neurocognition
in individuals co-infected with HIV
and hepatitis C
. J Addict Dis. 2008;27:11–17.
12. Richardson JL, Nowicki M, Danley K, et al.. Neuropsychological functioning in a cohort of HIV
- and hepatitis C
. AIDS. 2005;19:1659–1667.
13. Maki PM, Martin-Thormeyer E. HIV
, cognition and women
. Neuropsychol Rev. 2009;19:204–214.
14. Bacon MC, von Wyl V, Alden C, et al.. The Women
's Interagency HIV
Study: an observational cohort brings clinical sciences to the bench. Clin Diagn Lab Immunol. 2005;12:1013–1019.
15. Barkan SE, Melnick SL, Preston-Martin S, et al.. The Women
's Interagency HIV
Study. WIHS Collaborative Study Group. Epidemiology. 1998;9:117–125.
16. Smith A. Symbol Digit Modalities Test (SDMT). Manual (revised). Los Angeles, CA: Western Psychological Services; 1982.
17. Lezak M, Howieson DB, Loring DW. Neuropsychological Assessment. 4th ed. New York, NY; Oxford University Press; 2004.
18. Reitan R. Manual for Administration of Neuropsychological Test Batteries for Adults and Children. Tuscon, AZ: Reitan Neuropsychology Laboratories, Inc; 1978.
19. Heaton RK, Grant I, Matthews CG. Comprehensive Norms for an Expanded Halstead-Reitan Battery. Odessa, FL: Psychological Assessment Resources, Inc; 1991.
20. Stroop J. Studies of interference in serial verbal reaction. J Exp Psychol. 1935;18:643–662.
21. Comalli P, Wapner S, Werner H. Interference effects of Stroop color-word test in childhood, adulthood, and aging. J Genet Psychol. 1962;100:47–53.
22. Radloff L. The CES-D Scale: a self-report depression scale for research in the general population. Public Health Rep. 1977;117:233–251.
23. Zich JM, Attkisson CC, Greenfield TK. Screening for depression in primary care clinics: the CES-D and the BDI. Int J Psychiatry Med. 1990;20:259–277.
24. Wilkinson GS. Wide Range Achievement Test-3 Administration Manual. Wilmington, DE: Jastak Associates; 1993.
25. Manly JJ, Smith C, Crystal HA, et al.. Relationship of ethnicity, age, education. and reading level to speed and executive function among HIV
+ and HIV
: The Women
's Interagency HIV
Study (WIHS) Neurocognitive Substudy. J Clin Exp Neuropsychol. 2011;33:853–863.
26. Vallet-Pichard A, Mallet V, Nalpas B, et al.. FIB-4: an inexpensive and accurate marker of fibrosis in HCV infection. comparison with liver biopsy and fibrotest. Hepatology. 2007;46:32–36.
27. Wai CT, Greenson JK, Fontana RJ, et al.. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C
. Hepatology. 2003;38:518–526.
28. Letendre SL, Cherner M, Ellis RJ, et al.. The effects of hepatitis C
, and methamphetamine dependence on neuropsychological performance: biological correlates of disease. AIDS. 2005;19(suppl 3):S72–S78.
29. Forton DM, Thomas HC, Murphy CA, et al.. Hepatitis C
and cognitive impairment in a cohort of patients with mild liver disease. Hepatology. 2002;35:433–439.
30. Soogoor M, Lynn HS, Donfield SM, et al.. Hepatitis C
virus infection and neurocognitive function. Neurology. 2006;67:1482–1485.
31. Karaivazoglou K, Assimakopoulos K, Thomopoulos K, et al.. Neuropsychological function in Greek patients with chronic hepatitis C
. Liver Int. 2007;27:798–805.
32. Cherner M, Letendre S, Heaton RK, et al.. Hepatitis C
augments cognitive deficits associated with HIV
infection and methamphetamine. Neurology. 2005;64:1343–1347.
33. Gao S, Hendrie HC, Hall KS, et al.. The relationships between age, sex, and the incidence of dementia and Alzheimer disease: a meta-analysis. Arch Gen Psychiatry. 1998;55:809–815.