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. Author manuscript; available in PMC: 2021 Feb 1.
Published in final edited form as: J Neurovirol. 2019 Aug 29;26(1):52–59. doi: 10.1007/s13365-019-00794-3

Monocyte Activation, HIV and Cognitive Performance in East Africa

L Arnoldo Muñoz-Nevárez 1,*, Brandon M Imp 3,*, Michael A Eller 4,5, Francis Kiweewa 6, Jonah Maswai 4,7,8, Christina Polyak 4,5, Omalla Allan Olwenyi 6, I Elaine Allen 1,9, Eric Rono 4,7,8, Benedetta Milanini 2, Hannah Kibuuka 6, Julie A Ake 4, Leigh Anne Eller 4,5, Victor G Valcour 1,2
PMCID: PMC7044045  NIHMSID: NIHMS1066587  PMID: 31468471

Abstract

Chronic inflammation associated with monocyte activation has been linked to HIV-related cognitive outcomes in resource-rich settings. Few studies have investigated this relationship in the African context where endemic non-HIV infections may modulate effects. We characterized immune activation biomarkers in Kenyan and Ugandan participants in relation to neuropsychological testing performance (NTP) from the African Cohort Study (AFRICOS). We focused on activation markers associated with monocytes (sCD14, sCD163, neopterin), T-cells (HLA-DR+CD38+ on CD4+ and CD8+ T lymphocytes), and microbial translocation (intestinal fatty acid-binding protein, I-FABP). The HIV-infected (n=290) vs. HIV-uninfected (n=104) groups were similar in age with mean (SD) of 41 (9.5) vs. 39 (9.9) years, respectively (p=0.072). Among HIV-infected participants, the mean (SD) current CD4+ count was 402 (232); 217 (75%) were on combination antiretroviral therapy (cART) and 199 (69%) had suppressed plasma HIV RNA. sCD14 was inversely correlated to NTP (r=−0.14, p=0.037) in models that included both HIV-infected and uninfected individuals, adjusted for HIV status and research site, whereas sCD163 was not (r=0.041, p= 0.938). Neither of the T-cell activation markers correlated with NTP. In the HIV-infected group, I-FABP was inversely associated with NTP (r=−0.147, p=0.049), even among those with suppressed plasma virus (r=−0.0004, p=0.025). Among the full group, HIV status did not appear to modulate the effects observed. In this cohort from East Africa, sCD14, but not sCD163, is associated with cognitive performance regardless of HIV status. Findings among both HIV-infected and HIV-uninfected groups is supportive that HIV and non-HIV-related inflammatory sources contribute to cognitive performance in this setting.

Keywords: sCD14, Intestinal Fatty Acid-Binding Protein, Eastern Africa, HIV, Cognition Disorders, sCD163

INTRODUCTION

Cognitive impairment impacts up to 50% of people living with HIV (PLWH) in resource-rich settings (Heaton et al, 2011). The estimated prevalence may be lower in Africa as reported in Zambia (33%) (Robertson et al, 2010), Uganda (31%) (Wong et al, 2007), and Botswana (37%) (Lawler et al, 2011), although methodological challenges make it difficult to provide firm estimates. Nevertheless, these studies confirm the presence of HIV-associated cognitive impairment in Africa, a low-resource setting, where little is known about potential determinants of neuropathogenesis.

Non-HIV sources of inflammation are of interest because population-based reports link geographical burden of infectious disease to intelligence quotient (IQ), a measure of cognitive capacity (Eppig et al, 2010; Nakku et al, 2013). This raises the possibility that, in a setting such as the African continent, both HIV and non-HIV-associated immune activation pathways contribute to cognitive impairment in PLWH. Published reports focused on HIV-specific pathways describe links to the virus itself and viral particles as well as chronic immune activation playing active and overlapping roles (Rao et al, 2014).

Two potential immune activation pathways among PLWH include tissue and intra-cellular levels of HIV DNA (e.g. monocytes, lymph nodes, the brain) and gut microbial translocation (Jiang et al, 2009; Brenchley et al, 2006). It is theorized that HIV-activated monocytes cross the blood brain barrier (BBB) and release cytokines and chemokines in the brain parenchyma, which may be directly or indirectly neurotoxic (Persidsky et al, 1999). Viral proteins gp120, Tat, Vpr, and Nef have also been demonstrated in vitro to directly damage neurons (Vesce et al, 1997; Pu et al, 2003; van Marle et al, 2009). Additionally, infected astrocytes can damage the BBB, allowing more pro- inflammatory cells and molecules into the brain (Eugenin et al, 2011). Chronic immune activation from gut microbial translocation may further perpetuate systemic and neuroinflammation, affecting cognitive reserve and contributing to cognitive dysfunction (Anuta et al, 2008).

Two monocyte activation markers associated with cognitive impairment in PLWH in resource-rich settings are soluble CD163 (sCD163) and soluble CD14 (sCD14) (Imp et al, 2017). sCD163 is a monocyte-associated hemoglobin-haptoglobin complex scavenger receptor cleaved from activated monocytes (Moller, 2012). In resource-rich settings, plasma sCD163 is higher in PLWH compared to HIV-uninfected people, even with effective cART, and levels are inversely linked to cognitive performance (Burdo et al, 2011; Alcaide et al, 2013; Burdo et al, 2013). In Africa, plasma sCD163 is a risk factor for poor outcomes in HIV-infected individuals starting cART (Scriven et al, 2015; Bestawros et al, 2015) and is inversely linked to cognitive performance (Lyons et al, 2011). sCD14 is a marker of monocyte activation linked to gut microbial translocation as it is derived from a monocyte lipopolysaccharide receptor that is cleaved and released as sCD14. (Triantafilou and Triantafilou, 2002) Higher levels of plasma sCD14 have been associated with cognitive impairment in PLWH (Anuta et al, 2008). The relationship of sCD14 and cognition studied in the African context is limited, but one study of HIV-infected Nigerian women found correlation between higher sCD14 levels and cognitive impairment (Royal et al, 2016). Neopterin is a third marker of monocyte activation; it is a biochemical product of the guanosine triphosphate pathway (Fuchs et al, 1988). Studies have demonstrated a higher expression in HIV-infected compared to HIV-uninfected individuals, also linked to cognitive dysfunction (Fuchs et al, 1989; Griffin et al, 1991). Finally, intestinal fatty acid-binding protein (I-FABP) is expressed in the epithelial cells of the mucosal layer of intestinal tissue. I-FABP is released into the circulation following intestinal mucosal injury, therefore, its plasma concentration has been associated with enterocyte damage and gut microbial translocation. (Lau E et al, 2016).

In this study, we investigated the association of cognitive performance and immune activation in HIV-infected and HIV-uninfected adults in a setting expected to have added endemic infectious vulnerabilities compared to resource-rich areas. We hypothesized that monocyte-associated inflammatory markers would be associated with worse cognitive performance.

MATERIALS AND METHODS

Study design

AFRICOS is a prospective cohort study enrolling HIV-infected (n=3000) and HIV-uninfected (n=600) adults at US Military HIV Research Program (MHRP) President’s Emergency Plan for AIDS Relief (PEPFAR)-associated clinical sites in rural Kenya, Tanzania, Uganda, and Nigeria. Inclusion criteria among PLWH were known HIV infection, age 18 years or older, signed informed consent, intent of long-term residency in the area, HIV care recipient, and ability to provide contact information. Exclusion criteria included any significant condition that, in the opinion of the investigators, would interfere with study conduct. The same inclusion and exclusion criteria were applied to HIV-uninfected participants but were required to consent to HIV testing and pre- and post-test counseling. Participants undergo neuropsychological testing at enrollment and every twelve months thereafter. For this analysis, we evaluated the first enrolled participants from the Kenya South Rift Valley (n=304) and Uganda (n=100) sites, chosen considering their geographical proximity. All participants from these sites who completed neuropsychological testing at baseline and had concurrent inflammatory markers measured were analyzed. All participants consented by institutional review boards approved consent forms.

Clinical Characterization

AFRICOS visits involve comprehensive medical and socio-demographic evaluations with self-reported level of education. The neuropsychological testing battery includes the WHO Auditory Learning Visual Test (WHO AVLT) to test attention (AVLT trial 1), learning efficiency (AVLT sum of trials 1–5), and memory (AVLT recall), Trail Making Test Part A (psychomotor speed), Action Fluency (fluency), and the Grooved Pegboard (manual dexterity) (Maj et al, 1991; Reitan and Wolfson, 1985; Matthews and Klove, 1964). Testers were trained on all neuropsychological tests, certified, and re-certified every 6 months to assure consistent testing across all sites. Individual z-scores were calculated based on normative data collected at the same sites (Milanini et al, 2018). A composite global score was then derived by averaging the individual z-scores (NPZ-6). Assessments of CD4+ T-lymphocyte count and plasma HIV RNA were performed either at CAP-certified MHRP research laboratories or at PEPFAR site clinic labs. Co-infections were defined by concurrent laboratory measures, including a positive hepatitis B surface antigen ELISA, hepatitis C antibody, positive malaria smear, sputum Xpert MTB/RIF, QuantiFERON interferon gamma release essay (performed if ART-naïve), serum cryptococcus antigen positivity (performed if CD4+ count <200 cells/mm3), and elevated titer for serum Rapid Plasma Reagin (RPR) or Venereal Disease Research Laboratory (VDRL) for syphilis.

Quantification of Immune Activation

Peripheral blood from participants was processed and reserved for plasma, serum, and peripheral blood mononuclear cells analysis. Soluble factors were measured independently at each site using commercial enzyme-linked immunosorbent assay (EIA) kits or multiplexed assays as per manufacturer’s instructions. Levels of sCD14 and sCD163 were measured by Quantikine ELISA (R&D Systems, Minneapolis, MN) and the R&D systems human I-FABP Duo Set EIA kit was used to measure levels of I-FABP. T-cell activation (percentage of CD4 or CD8 T-cells co-expressing HLA-DR and CD38) was determined using a whole blood lyse no-wash flow cytometry procedure as previously described. Commercial antibodies included CD3 PerCP (SK7), CD4 FITC (SK3), CD8 FITC (SK1), CD38 PE (HB7), and HLA-DR APC (L243) (BD Biosciences, San Jose, CA). Samples were examined on a BD FACSCalibur or FACS Canto II and data analyzed using FlowJo version 9.8 (Treestar, Ashland, OR). Cut offs for CD38 and HLA-DR co-expression were set based on CD Chex Normal control gates. For Ugandan samples, EIAs were used to additionally quantify serum neopterin (Immuno-Biological Laboratories America, Minneapolis, MN).

Statistical Analysis

Initially, variables were summarized overall, by country, and by HIV status. Continuous variables were summarized using means and standard deviations, and categorical variables were summarized by counts and percentages. Comparisons between groups were completed by Student’s t-test and chi-squared tests. Multivariable linear regressions in the overall sample were used to examine the relationship between the inflammatory markers of interest and cognitive performance. Adjusted models controlled for site (Uganda vs. Kenya) and HIV status. Follow-up analyses investigated the association between inflammatory markers and cognitive performance among HIV-infected participants, HIV-infected participants without co-infections (of tuberculosis, hepatitis B, hepatitis C, syphilis, and cryptococcosis), and HIV-infected participants who were virally suppressed (RNA <500 copies/mL). Covariates for all three models included CD4+ T-lymphocyte count and plasma HIV RNA. Analyses were performed on Stata (version 15.1, StataCorp, College Station, TX).

RESULTS

Demographic and Clinical Characteristics

Among 394 participants included, 290 (74%) were HIV-infected (Tables 1 and 2) with site distribution of 72 from Uganda and 218 from Kenya. As a group, the HIV-infected participants averaged 41 years of age (range 19–68), 58% were female, and 91% were literate. They self-reported educational attainment to be low, with 40% having some primary school or less and 34% completing primary school and some with secondary school but not having completed that level. The mean (SD) proximal and nadir CD4+ T-lymphocyte count was 402 (232) and 247 (258) cells/mm3, respectively. Among HIV-infected, 75% were on cART and 32% were treatment naïve. Differences in sCD14 and I-FABP were noted between the HIV-infected and uninfected groups (Table 3).

Table 1.

Demographic characteristics for all participants at their initial visit (n=394).

Characteristics HIV-Uninfected (n=104), (%) HIV-Infected (n=290), (%) p-value
Sex, female 52 (50) 168 (58) .162
Age, mean (SD; range) 38.9 (9.9; 19–69) 40.9 (9.5; 19–68) .072
Education .713
 Some primary school or less 44 (42) 115 (40) -
 Completed primary school and some secondary school 36 (35) 98 (34) -
 Completed secondary school and some post-secondary school 32 (31) 64 (22) -
Ability to read and write 94 (90) 264 (91) .767
Employed, by self-report 43 (41) 133 (46) .411
Current depressive symptoms .896
 CES-D 15–21 54 (52) 143 (50) -
 CES-D ≥22 21 (20) 59 (20) -
Co-infection
 Hepatitis B (n=100) 6 (6) 16 (6) .856
 Hepatitis C (n=102) 2 (2) 4 (1) .686
 Syphilis (n=103) 7 (7) 29 (10) .332
HIV disease characteristics
 CD4 count, mean (SD) 402.4 (232) -
 Plasma HIV RNA, mean (SD) 53,107.8 (280,454) -
 Virally suppressed 199 (69) -
 cART-naïve 65 (32) -
 On cART 217 (75) -

Includes HIV-infected subjects only. “Current” refers to within the past week. CES-D=Center for Epidemiological Studies Depression scale. Hepatitis B=Hepatitis B surface antigen. Hepatitis C=Hepatitis C antibody. Tuberculosis=QuantiFERON or TBXpert Test. Cryptococcus=Serum Cryptococcus antigen. Syphilis=Serum RPR or VDRL. Virally suppressed<500 copies/mL=combination antiretroviral therapy.

Table 2.

Demographic characteristics by country and HIV serostatus (n=394).

Kenya, n (%) Uganda, n (%)
Characteristics HIV-Uninfected (n=76) HIV-Infected (n=218) p-value HIV-Uninfected (n=28) HIV-Infected (n=72) p-value p-value Kenya vs Uganda HIV-Uninfected p-value Kenya vs Uganda HIV-Infected p-value Kenya vs Uganda Overall
Sex, female 41 (54) 130 (60) 0.386 11 (39) 38 (53) 0.225 0.185 0.307 0.111
Age, mean (SD) 38.4 (9.8) 41 (9.4) 0.036 40.3 (10.4) 40.4 (9.8) 0.969 0.397 0.623 0.989
Education 0.573 0.208 0.345 <0.001 <0.001
 Some primary school or less 29 (38) 69 (32) 0.397 15 (54) 46 (64) 0.369 0.187 <0.001 <0.001
 Completed primary school and some secondary school 22 (29) 71 (33) 0.569 10 (36) 24 (33) 0.818 0.212 0.416 0.319
 Completed secondary school and some post-secondary school 17 (22) 85 (39) 0.003 1 (4) 1 (1) -- -- -- --
Ability to read and write 72 (95) 212 (98) 0.198 22 (79) 52 (72) 0.516 0.022 <.0001 <0.001
Employed 16 (21) 6321 (29) 0.177 27 (96) 70 (97) 0.835 <0.001 <0.001 <0.001
Current depressive symptoms 0.817 0.814 <0.001 <0.001 <0.001
 CES-D ≤14 13 (17) 43 (20) 16 (57) 44 (61) -- -- -- --
 CES-D 15–21 45 (59) 120 (55) 9 (32) 23 (32) -- -- -- --
 CES-D ≥22 18 (24) 54 (25) 3 (11) 5 (7) -- -- -- --
Co-infection
 Hepatitis B 5 (7) 6 (3) 0.106 1 (4) 10 (14) -- -- -- --
 Hepatitis C 0 (0) 1 (<1) -- 2 (7) 3 (4) -- -- -- --
 Syphilis 5 (7) 27 (12) 0.171 2 (7) 2 (3) -- -- -- --
HIV Disease
CD4 count, mean (SD) 397 (249) -- -- 420 (174) -- -- 0.389 --
Plasma HIV RNA, mean (SD) 65,555 (32,205) -- -- 15,938 (45,312) -- -- 0.029 --
Virally suppressed 146 (68) -- 53 (74) -- -- 0.381 --
cART-naïve 53 (24) -- 12 (17) -- -- 0.196 --
On cART 162 (74) -- 55 (76) -- -- 0.757 --

Includes HIV-infected subjects only. “Current” refers to within the past week. CES-D=Center for Epidemiological Studies Depression scale. Hepatitis B=Hepatitis B surface antigen. Hepatitis C=Hepatitis C antibody. Tuberculosis=QuantiFERON or TBXpert Test. Cryptococcus=Serum Cryptococcus antigen. Syphilis=Serum RPR or VDRL. Virally suppressed<500 copies/mL. cART=combination antiretroviral therapy.

Table 3.

Immune activation markers by HIV serostatus (n=394).

Overall HIV-Uninfected HIV-Infected p-value comparing HIV− and HIV+
N Mean SD N Mean SD N Mean SD
sCD14 394 1953 636.5 104 1661 574.1 290 2058 637.0 0.001
sCD163 394 1119 794.4 104 1116 1075.9 290 1120 668.3 0.961
I-FABP 394 2171 2528.3 104 1406 1350.5 290 2447 2784.9 0.003
Neopterin 100 7.4 9.40 28 8.3 13.73 72 7.0 7.15 0.543
CD4+ HLA-DR+CD38+ 333 4.4 5.65 73 3.9 6.60 260 4.5 5.36 0.395
CD8+ HLA-DR+CD38+ 333 8.0 9.22 73 6.7 8.67 260 8.4 9.35 0.164

SD=Standard deviation. sCD14=soluble CD14. sCD163=soluble CD163. I-FABP=Intestinal fatty acid-binding protein. T-lymphocyte CD4+HLA-DR+CD38+. T-lymphocyte CD8+HLA-DR+CD38+.

The HIV-uninfected participants did not differ in age from the HIV-infected group, at 39 years of age (range 19–69, p=0.072). Sex and education did not differ from the HIV-infected group, with 50% female and 90% literate (p=0.162 and 0.713, respectively). They similarly reported a low level of educational attainment with 42% having some primary school or less, 36% having completed primary school and some secondary school.

Monocyte-associated immune activation and cognitive performance

Higher levels of sCD14 were associated with lower NPZ-6 in an analysis of both HIV-infected and HIV-uninfected groups (r=0.0001, p=0.037). No interaction effect of HIV-status was identified. In separate models examining smaller groups stratified by HIV status, no association was observed. Similarly, in sub-analyses among HIV-infected, no associations were noted among virally suppressed participants (p=0.284) or among participants without co-infections (p=0.228). Plasma sCD163 (p=0.938) and neopterin (p=0.659) were not associated with NPZ-6 in the overall sample or in any of the sub-groups (p=0.963, p=0.835).

Intestinal integrity-associated and T-cell activation markers and cognitive performance

Levels of I-FABP were not associated with NPZ-6 scores in the overall sample (p=0.074) and HIV-status interaction effects were not noted. When we examined for associations in only HIV-infected participants, a marginal effect was noted (r=−0.0004, p=0.049) that was stronger among virally suppressed HIV-infected participants examined alone (r=−0.0004, p=0.025). The frequencies of HLA-DR and CD38 expression on both CD4+ and CD8+ T-lymphocytes were not associated with NPZ-6 in the overall sample (p=0.654, p=0.503) or in any of the sub-groups (p=0.763, p=0.927). No association was observed among HIV-infected participants without co-infections.

DISCUSSION

Among our primary hypotheses, only plasma sCD14 levels were associated with overall cognitive performance in a sample of both HIV-infected and HIV-uninfected participants. Within these models, HIV status did not appear to modulate the effect seen between immune activation and cognition in this cohort from East Africa. In exploratory examinations, plasma I-FABP, a marker of enterocyte damage, was inversely associated with cognitive performance, but only in the HIV-infected group. This effect remained present in those that were virally suppressed. This effect was not seen when examining the group without co-infections. The co-infections we quantified included syphilis, hepatitis B, and hepatitis C. Other gut co-infections are common in Africa, including bacterial pathogens such as E. coli spp (including enterotoxigenic type), Shigella spp, Salmonella spp and Campylobacter spp. (40%), followed by parasites (27%) such as Cryptosporidium spp, Cyclospora spp, Entamoeba spp and Blastocystis hominis and then viruses (22%), mainly rotavirus and adenovirus. A high prevalence of Cryptosporidium parvum, Cystoisospora belli and microsporidia is reported in HIV-infected adults from the same region (Fletcher et al, 2011). In contrast to other reports, we did not find associations between sCD163 and cognitive performance. Other exploratory immune activation markers similarly were not associated with cognitive performance, including neopterin and T-cell activation markers.

Considering that HIV did not modulate the correlations seen suggests that contributions from non-HIV-related inflammation, potentially from gut- associated inflammatory changes, may play an important role. Early depletion of mucosal CD4+ T cells, loss of immune homeostasis in the gut, and alteration of the normal gut microbiome composition (dysbiosis) are all phenomenon that have been linked to loss of intestinal integrity and persisting immune activation, even after cART and virologic control (Klatt et al, 2013).

Our finding of I-FABP correlating to worse cognitive performance has not previously been reported and is in contrast with one other study of HIV- infected women in the U.S. where no association was found (Imp et al, 2017). A prior study from Uganda suggested a contradicting trend-level decrease in I-FABP among ART-naïve PLWH, although the difference did not meet statistical significance (p=0.07) (Olwenyi et al, 2015). Because these findings were noted only in group level sub-analyses and since no serostatus interaction effects were noted in our main models, these findings should be interpreted with caution. Higher than expected levels of I-FABP in HIV-uninfected participants were seen and similar to levels seen among HIV-infected individuals samples taken from the U.S. (Sandler et al, 2011) This leads us to consider unique competing sources of immunological and gut changes in Uganda that could impact I-FABP’s association to HIV outcomes.

These results differ from findings in resource-rich settings that find associations with sCD14 and sCD163, but not I-FABP, which infer a monocyte-driven process (Imp et al, 2017). This discrepancy displays the importance of population context, especially that of co-infections, when assessing the pathogenesis of cognitive impairment. One could postulate that infections endemic to these regions lead to gut microbial translocation and chronic immune activation. For example, intestinal hookworm infection has been suggested to contribute to cognitive impairment, but the evidence is limited and conflicting (Bartsch et al, 2016; Hotez et al, 2011; Welch et al, 2017; Taylor-Robinson et al, 2015).

We evaluated participants without co-infections of tuberculosis, hepatitis B, hepatitis C, syphilis, and cryptococcosis noting that the effects seen were no longer present; however, this may be due to smaller sample size limiting power. AFRICOS’s study design limited our ability to control for other endemic parasitic and enteric diseases, work that might add depth to these hypotheses. However, the known elevated frequency of gut co-infections in this population (Fletcher et al, 2013; Tumwine et al, 2002) that are likely linked to gut microbial translocation, further strengthens our finding that elevated sCD14 and I-FABP levels but not sCD163 are associated with worse cognitive performance. Since gut microbial translocation exists in the context of HIV infection, (Klatt et al, 2013)we can further surmise that this neuropathogenic mechanism is, at least partially, contributing to cognitive impairment in the context of HIV infection in East Africa.

We recognize several potential limitations to our work. As with other epidemiological studies, AFRICOS includes only a limited neuropsychological testing battery, thus, limiting our understanding of global cognition (Robert- son et al, 2010; Wong et al, 2007; Njamnshi et al, 2008; Oshinaike et al, 2010). The AFRICOS methods also limit our ability to assess endemic bio- logical vulnerabilities, especially parasitic and enteric diseases. This reduces our ability to select for HIV-specific contributions to inflammation. Finally, we cannot determine causality because this study was cross-sectional. Prior studies suggest that inflammation has a role in the progression of cognitive impairment (Gorelick, 2010; Simen et al, 2011). Despite these limitations, our measures were developed and validated in the African context and piloted with minor adaptions to local culture for accurate use, strengthening this work (Milanini et al, 2018). The use of co-enrolled HIV-uninfected controls to examine internally standardized performance is a further strength as we did not rely on external normative data that can add external systematic variability.

In conclusion, higher levels of plasma sCD14 but not sCD163, neopterin, or frequency of HLA-DR+CD38+ on both CD4+ or CD8+ T-lymphocytes are inversely associated with worse cognitive performance in men and women from Uganda and Kenya regardless of HIV status. Higher levels of I-FABP were associated with worse cognitive performance, but only in the HIV- infected group, perhaps due to higher propensity for dysbiosis, which has been described in the setting of HIV infection. Our findings add to prior studies by linking inflammation to cognition in the African context.

ACKNOWLEDGEMENTS

We thank AFRICOS study participants and staff at these research sites.

Footnotes

CONFLICTS OF INTEREST AND SOURCE OF FUNDING: This work was supported by the Military Infectious Disease Research Program and also conducted in collaboration with a PEPFAR supported basic program evaluation through the Department of Defense (DoD) and funded via a cooperative agreement (W81XWH-11-2-0174) between the Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., and the U.S. DoD. Dr. Valcour’s contributions were supported by K24MH098759 from the National Institute of Mental Health and by the Global Brain Health Institute (www.GBHI.org).

Dr. Valcour has served as a consultant for ViiV Healthcare, Merck, and IAS-USA on topics related to aging and HIV. All other authors have nothing to declare. The views expressed are those of the authors and should not be construed to represent the positions of the U.S. Army or the Department of Defense.

PRIOR PRESENTATION: This information was presented as a poster at the INTEREST Workshop in Lilongwe, Malawi in May 2017. This manuscript has not been reviewed nor is it under review elsewhere.

REFERENCES

  1. Alcaide ML, Parmigiani A, Pallikkuth S, Roach M, Freguja R, Della Negra M, Bolivar H, Fischi MA, Pahwa S (2013) Immune activation in HIV- infected aging women on antiretrovirals–implications for age-associated comorbidities: a cross-sectional pilot study. PLoS One 8(5):e63804. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Anuta P, Kamat A, Kunstman KJ, Kim EY, Autissier P, Wurcel A, Zaman T, Stone D, Mefford M, Morgello S, Singer EJ, Wolinsky SM, Gabuzda D (2008) Microbial translocation is associated with increased monocyte activation and dementia in AIDS patients. PLoS One 3(6):e2516. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bartsch SM, Hotez PJ, Asti L, Zapf KM, Bottazzi ME, Diemert DJ, Lee BY (2016) The Global Economic and Health Burden of Human Hookworm Infection. PLoS Negl Trop Dis 10(9):e0004922. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bestawros M, Chidumayo T, Blevins M, Canipe A, Bala J, Kelly P, Filteau S, Shepherd BE, Heimburger DC, Koethe JR (2015) Increased systemic inflammation is associated with cardiac and vascular dysfunction over the first 12 weeks of antiretroviral therapy among undernourished, HIV-infected adults in Southern Africa. J AIDS Clin Res 6(3):431. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Brenchley JM, Price DA, Schacker TW, Asher TE, Silvestri G, Rao S, Kazzaz Z, Bornstein E, Lambotte O, Altmann D, Blazar BR, Rodriguez B, Teixeira- Johnson L, Landay A, Martin JN, Hecht FM, Picker LJ, Lederman MM, Deeks SG, Douek DC (2006) Microbial translocation is a cause of systemic immune activation in chronic HIV infection. Nat Med 12(12):1365–1371 [DOI] [PubMed] [Google Scholar]
  6. Burdo TH, Lentz MR, Autissier P, Krishnan A, Halpem E, Letendre S, Rosenberg ES, Ellis RJ, Williams KC, et al. (2011) Soluble CD163 made by monocyte/macrophages is a novel marker of HIV activity in early and chronic infection prior to and after anti-retroviral therapy. J Infect Dis 204(1):154–163 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Burdo TH, Weiffenbach A, Woods SP, Letendre S, Ellis RJ, Williams KC (2013) Elevated sCD163 in plasma but not cerebrospinal fluid is a marker of neurocognitive impairment in HIV infection. AIDS 27(9):1387–1395 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Eppig C, Fincher CL, Thornhill R (2010) Parasite prevalence and the world- wide distribution of cognitive ability. Proc Biol Sci 277(1701):3801–3808 [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
  9. Eugenin EA, Clements JE, Zink MC, Berman JW (2011) Human immunodeficiency virus infection of human astrocytes disrupts blood-brain barrier integrity by a gap junction-dependent mechanism. J Neurosci 31(26):9456–9465 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Fletcher SM, Stark D, Ellis J (2011) Prevalence of gastrointestinal pathogens in Sub-Saharan Africa: systematic review and meta-analysis. J Public Health Africa 5;2(2):e30. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Fletcher SM, McLaws ML, Ellis JT (2013) Prevalence of gastrointestinal pathogens in developed and developing countries: systematic review and meta-analysis. J Public Health Res 2(1):42–53 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Fuchs D, Hausen A, Reibnegger G, Werner ER, Dierich MP, Wachter H (1988) Neopterin as a marker for activated cell-mediated immunity: application in HIV infection. Immunol Today 9(5):150–155 [DOI] [PubMed] [Google Scholar]
  13. Fuchs D, Chiodi F, Albert J, Asjö B, Hagberg L, Hausen A, Norkrans G, Reibnegger G, Werner ER, Wachter H (1989) Neopterin concentrations in cerebrospinal fluid and serum of individuals infected with HIV-1. AIDS 3(5):285–288 [DOI] [PubMed] [Google Scholar]
  14. Gorelick PB (2010) Role of inflammation in cognitive impairment: results of observational epidemiological studies and clinical trials. Ann N Y Acad Sci 1207:155–162 [DOI] [PubMed] [Google Scholar]
  15. Griffin DE, McArthur JC, Cornblath DR (1991) Neopterin and interferon- gamma in serum and cerebrospinal fluid of patients with HIV-associated neurologic disease. Neurology 41(1):69–74 [DOI] [PubMed] [Google Scholar]
  16. Heaton RK, Franklin DR, Ellis RJ, McCutchan JA, Letendre SL, Leblanc S, Corkran SH, Duarte NA, Clifford DB, Woods SP, Collier AC, Marra CM, Morgello S, Mindt MR, Taylor MJ, Marcotte TD, Atkinson JH, Wolfson T, Gelman BB, McArthur JC, Simpson DM, Abramson I, Gamst A, Fennema-Notestine C, Jemigan TL, Wong J, Grant I (2011) HIV-associated neurocognitive disorders before and during the era of combination antiretroviral therapy: differences in rates, nature, and predictors. J Neurovirol 17(1):3–16 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Hotez PJ, Mistry N, Rubinstein J, Sachs JD (2011) Integrating neglected tropical diseases into AIDS, tuberculosis, and malaria control. N Engl J Med 364(22):2086–2089 [DOI] [PubMed] [Google Scholar]
  18. Imp BM, Rubin LH, Tien PC, Plankey MW, Golub ET, French AL, Valcour VG (2017) Monocyte activation is associated with worse cognitive performance in HIV-infected women with virologic suppression. J Infect Dis 215(1):114–121 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Jiang W, Lederman MM, Hunter P, Sieg SF, Haley K, Rodriguez B, Landay A, Martin J, Sinclair E, Asher AI, Deeks SG, Douek DC, Brenchley JM (2009) Plasma levels of bacterial DNA correlate with immune activation and the magnitude of immune restoration in persons with antiretroviral-treated HIV infection. J Infect Dis 199(8):1177–1185 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Klatt NR, Funderburg NT, Brenchley JM (2013) Microbial translocation, immune activation, and HIV disease. Trends Microbiol 21(1):6–13 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Lau E, Marques C, Pestana D, Santoalha M, Carvalho D, Freitas P, Calhau C (2016) The role of I-FABP as a biomarker of intestinal barrier dysfunction driven by gut microbiota changes in obesity. Nutr Metab (Lond) 13:31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Lawler K, Jeremiah K, Mosepele M, Ratcliffe SJ, Cherry C, Seloilwe E, Steenhoff AP (2011) Neurobehavioral effects in HIV-positive individuals receiving highly active antiretroviral therapy (HAART) in Gaborone, Botswana. PLoS One 6(2):e17233. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Lyons JL, Uno H, Ancuta P, Kamat A, Moore DJ, Singer EJ, Morgello S, Gabuzda D (2011) Plasma sCD14 is a biomarker associated with impaired neurocognitive test performance in attention and learning domains in HIV infection. J Acquir Immune Defic Syndr 57(5):371–379 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Maj M, Janssen R, Satz P, Zaudig M, Starace F, Boor D, Sughondhabirom B, Bing EG, Luabeya MK, Ndetei D, et al. (1991) The World Health Organization’s cross-cultural study on neuropsychiatric aspects of infection with the human immunodeficiency virus (HIV-1). Br J Psychiatry 159:351–356 [DOI] [PubMed] [Google Scholar]
  25. van Marle G, Henry S, Todoruk T, Sullivan A, Silva C, Rourke SB, Holden J, McArthur JC, Gill MJ, Power C (2009) Human immunodeficiency virus type 1 Nef protein mediates neural cell death: a neurotoxic role for IP-10. Virology 329(2):302–318 [DOI] [PubMed] [Google Scholar]
  26. Matthews CG, Klove J (1964) Instruction Manual for the Adult Neuropsychology Test Battery. University of Wisconsin Medical School, Madison, WI [Google Scholar]
  27. Milanini B, Paul R, Bahemana E, Adamu Y, Kiweewa F, Langat R, Owuoth J, Allen E, Polyak C, Ake J, Valcour V (2018) Limitations of the International HIV Dementia Scale in the current era. AIDS 32(17):2477–2483 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Moller HJ (2012) Soluble CD163. J Clin Lab Invest 72(1):1–13 [DOI] [PubMed] [Google Scholar]
  29. Nakku J, Kinyanda E, Hoskins S (2013) Prevalence and factors associated with probable HIV dementia in an African population: a cross-sectional study of an HIV/AIDS clinic population. BMC Psychiatry 3(13):126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Njamnshi AK, Djientcheu V, Fonsah JY, Yepnjio FN, Njamnshi DM, Muna WE (2008) The International HIV Dementia Scale is a useful screening tool for HIV-associated dementia/cognitive impairment in HIV-infected adults in Yaoundé-Cameroon. J Acquir Immune Defic Syndr 49(4):393–397 [DOI] [PubMed] [Google Scholar]
  31. Olwenyi OA, Naluyima P, Cham F, Quinn TC, Serwadda D, Sewankambo NK, Gray RH, Sandberg JK, Michael NL, Wabwire-Mangen F, Robb ML, Eller MA (2015) Brief Report: Differential associations of interleukin 6 and intestinal fatty acid-binding protein with progressive untreated HIV-1 infection in Rakai, Uganda. J Acquir Immune Defic Syndr 72(1):15–20 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Oshinaike OO, Okubadejo NU, Ojini FI, Danesi MA (2010) A preliminary study of the frequency of focal neurological deficits in HIV/AIDS patients seropositive for Toxoplasma gondii IgG in Lagos, Nigeria. Nig Q J Hosp Med 20(3):104–107 [PubMed] [Google Scholar]
  33. Persidsky Y, Ghorpade A, Rasmussen J, Limoges J, Liu XJ, Stins M, Fiala M, Way D, Kim KS, Witte MH, Weinand M, Carhart L, Gendelman HE (1999) Microglial and astrocyte chemokines regulate monocyte migration through the blood-brain barrier in human immunodeficiency virus-1 encephalitis. Am J Pathol 155(5):1599–1611 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Pu H, Tian J, Flora G, Lee YW, Nath A, Hennig B, Toborek M (2003) HIV- 1 tat protein upregulates inflammatory mediators and induces monocyte invasion into the brain. Mol Cell Neurosci 24(1):224–237 [DOI] [PubMed] [Google Scholar]
  35. Rao VR, Ruiz AP, Prasad VR (2014) Viral and cellular factors underlying neuropathogenesis in HIV associated neurocognitive disorders (HAND). AIDS Res Ther 19(11):13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Reitan RM, Wolfson D (1985) The Halstead-Reitan Neuropsychological Test Battery: Theory and Clinical Interpretation. Neuropsychology Press, Tucson [Google Scholar]
  37. Robertson K, Liner J, Hakim J, Sankalé JL, Grant I, Letendre S, Clifford D, Diop AG, Jaye A, Kanmogne G, Njamnshi A, Langford TD, Weyessa TG, Wood C, Banda M, Hosseinipour M, Sacktor N, Nakasuja N, Bangirana P, Paul R, Joska J, Wong J, Boivin M, Holding P, Kammerer B, Van Rie A, Ive P, Nath A, Lawler K, Adebamowo C, Royal W 3rd, Joseph J (2010) NeuroAIDS in Africa. J Neurovirol 16(3):189–202 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Royal W 3rd, Cherner M, Burdo TH, Umlauf A, Letendre SL, Jumare J, Abimiku A, Alabi P, Alkali N, Bwala S, Okwuasaba K, Eyzaguirre LM, Akolo C, Guo M, Williams KC, Blattner WA (2016) Associations between Cognition, Gen- der and Monocyte Activation among HIV Infected Individuals in Nigeria. PLoS One 11(2):e0147182. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Sandler NG, Wand H, Roque A, Law M, Nason MC, Nixon DE, Pedersen C, Ruxrungtham K, Lewin SR, Emery S, Neaton JD, Brenchley JM, Deeks SG, Sereti I, Douek DC (2011) Plasma levels of soluble CD14 independently predict mortality in HIV infection. J Infect Dis 203(6):780–790 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Scriven JE, Rhein J, Hullsiek KH, von Hohenberg M, Linder G, Rolfes MA, Williams DA, Taseera K, Meya DB, Meintjes G, Boulware DR (2015) Early ART after cryptococcal meningitis is associated with cerebrospinal fluid pleocytosis and macrophage activation in a multisite randomized trial. J Infect Dis 212(5):769–778 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Simen AA, Bordner KA, Martin MP, Moy LA, Barry LC (2011) Cognitive dysfunction with aging and the role of inflammation. Ther Adv Chronic Dis 2(3):175–195 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Taylor-Robinson DC, Maayan N, Soares-Weiser K, Donegan S, Garner P (2015) Deworming drugs for soil-transmitted intestinal worms in children: effects on nutritional indicators, haemoglobin, and school performance. Cochrane Database Syst Rev 23(7):CD000,371 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Triantafilou M, Triantafilou K (2002) Lipopolysaccharide recognition: CD14, TLRs and the LPS-activation cluster. Trends Immunol 23(6):301–304 [DOI] [PubMed] [Google Scholar]
  44. Tumwine JK, Thompson J, Katua-Katua M, Mujwajuzi M, Johnstone M, Porras I (2002) Diarrhoea and effects of different water sources, sanitation and hygiene behaviour in East Africa. Trop Med Int Health 7(9):750–756 [DOI] [PubMed] [Google Scholar]
  45. Vesce S, Bezzi P, Rossi D, Meldosi J, Volterra A (1997) HIV-1 gp120 glycoprotein affects the astrocyte control of extracellular glutamate by both inhibiting the uptake and stimulating the release of the amino acid. FEBS Lett 411(1):107–109 [DOI] [PubMed] [Google Scholar]
  46. Welch VA, Ghogomu E, Hossain A, Awasthi S, Bhutta ZA, Cumberbatch C, Fletcher R, McGowan J, Krishnaratne S, Kristjansson E, Sohani S, Suresh S, Tugwell P, White H, Wells GA, et al. (2017) Mass deworming to improve developmental health and wellbeing of children in low-income and middle- income countries: a systematic review and network meta-analysis. Lancet Glob Health 5(1):e40–e50 [DOI] [PubMed] [Google Scholar]
  47. Wong MH, Robertson K, Nakasujja N, Skolasky R, Musisi S, Katabira E, McArthur JC, Ronald A, Sacktor N (2007) Frequency of and risk factors for HIV dementia in an HIV clinic in sub-Saharan Africa. Neurology 68(5):350–355 [DOI] [PubMed] [Google Scholar]

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