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. Author manuscript; available in PMC: 2012 Jan 30.
Published in final edited form as: Psychiatry Res. 2010 May 23;185(1-2):232–237. doi: 10.1016/j.psychres.2009.08.020

Interactive Effects of HIV/AIDS, Body Mass, and Substance Abuse on the Frontal Brain: A P300 Study

Lance O Bauer 1,*
PMCID: PMC2947604  NIHMSID: NIHMS144061  PMID: 20580839

Abstract

In view of the rising prevalence of an overweight body mass among patients living with HIV/AIDS, clinicians must now be mindful of possible adverse outcomes resulting from the co-occurrence. The present study was designed to examine the additive and interactive effects of HIV/AIDS and an excess body mass, as well as the additional contributions of substance abuse or dependence. The dependent variable was brain function estimated by the measurement of P300 electroencephalographic potentials. P300 potentials were recorded during a task designed to elicit subcomponents with frontal (P300a) and both frontal and nonfrontal (P300b) generators. Analyses revealed greater frontal P300a latencies among the 102 HIV-1 seropositive versus the 68 seronegative participants. In addition, frontal P300a latency was further increased by a synergistic interaction of HIV-1 serostatus with a body mass index (BMI) ≥ 25 kg/m2. A history of substance abuse/dependence did not alter these changes. However, it did combine with HIV/AIDS to produce a smaller P300a amplitude than was seen in participants with neither disorder. The findings suggest that white matter changes accompanying an excess BMI may exacerbate those that attend HIV/AIDS and thereby slow frontal brain function. Substance abuse likewise interacts with HIV/AIDS but may impair frontal brain function via a different mechanism.

Keywords: HIV-1, Obesity, BMI, Evoked Potentials, EEG, White Matter, Substance Dependence

1. Introduction

Loss of weight and lean muscle mass is a common feature of HIV/AIDS (Tang et al., 2005). Many causes have been described, including nausea and fatigue precipitated by the disease and its treatment as well as HIV-related abnormalities in gastrointestinal and hepatic function. If severe, weight loss can be a sign of a poor prognosis, including a more rapid progression to death (Shor-Posner et al., 2000; Tang et al., 2002).

Although weight loss remains problematic for many patients, its incidence and prevalence have declined dramatically in recent years. In the U.S. Multicenter AIDS Cohort Study, for example, incidence declined from a peak of 22.1 per 1000 person-years in 1994-1995 to 13.4 in 1996-1999 (Smit et al., 2002). Similarly, Hodgson and colleagues (Hodgson et al., 2001) reported a 77% decline in the prevalence of wasting as an AIDS-defining event from a survey of 162 HIV/AIDS cases studied between 1992 and 2001 in the United Kingdom. Other surveys have yielded comparable results (Mocroft et al., 1999; Dworkin and Williamson, 2003). The declining occurrence of wasting and weight loss coincides with the increased availability and prescription of Highly Active Antiretroviral Therapy (HAART) and is often attributed to that cause. Across multiple studies, protease-inhibitor-based HAART has been associated with many factors that either promote or accompany weight gain, including insulin resistance (Walli et al., 1998), hyperglycemia (Dever et al., 2000; Tsiodras et al., 2000), new onset Type 2 diabetes mellitus (Justman et al., 2003; Ledergerber et al., 2007), hyperlipidemia (Mulligan et al., 2005; Rimland et al., 2006), lipodystrophy (McDermott et al., 2001), and the metabolic syndrome.

Because of HAART, excess body weight is emerging as a new and unexpected threat to the HIV-1 seropositive community. The results of a recent analysis (Bauer, 2008c) support this assertion. In the analysis, obesity was a prevalent condition, affecting 30.1% of the 159 HIV-1 seropositive patients. This rate is greater than the 9-28% prevalence rates reported in previous studies (Shor-Posner et al., 2000; Hodgson et al., 2001; Amorosa et al., 2005; Jacobson et al., 2006) of HIV/AIDS patients and exceeds the 25.6% rate found in U.S. adults generally (CDC, 2008).

In view of the rising prevalence of overweight/obesity (Amorosa et al., 2005) among seropositive patients, clinicians must now be mindful of possible adverse outcomes resulting from the combination. The most obvious areas for interaction are the heart and vasculature. Both disorders are known to increase risk for atherosclerosis, diabetes, and myocardial infarction.

An additional area for interaction is the white matter of the brain. HIV/AIDS has been shown to demyelinate fronto-striatal white matter tracts in autopsy studies, increase the number of white matter abnormalities or hyperintensities in MRI studies (Pfefferbaum et al., 2007), and slow the neural processing of auditory, visual, or somatosensory information in evoked electroencephalographic potential studies (Harrison et al., 1998; Chao et al., 2004). Interestingly, within the brain, obesity is also, primarily, a white matter disease. An increased waist-to-hip ratio (Jagust et al., 2005) or BMI (Gazdzinski et al., 2008) is associated with an increase in the prevalence of white matter abnormalities in MRI—an increase that can be partially reversed through dieting and weight loss (Haltia et al., 2007). In addition, an overweight body mass increases the latencies of some evoked electroencephalographic potentials (Bauer, (under review)).

The potential therefore exists for HIV/AIDS and overweight/obesity to act synergistically. The goal of the present study was to evaluate the independent and interactive effects of HIV/AIDS and a BMI ≥ 25 kg/m2 on an estimate of white matter integrity. It also evaluated the contribution of a history of substance abuse/dependence -- a common disorder among HIV/AIDS patients (Bing et al., 2001; Galvan et al., 2002) -- to this relationship. To eliminate the potential confounding effect of childhood Conduct Disorder -- a neurodevelopmental disorder which precedes and promotes HIV (Bauer and Shanley, 2006), excess BMI (Anderson et al., 2006), and substance abuse — as well as depression symptoms that also accompany these disorders, the number of DS M-IV childhood Conduct Disorder criteria and the Beck Depression Inventory score were entered as covariates in the analysis.

To assess the effects of HIV/AIDS, excess body mass, and substance abuse/dependence, the study employed simple, noninvasive measures of brain function that could, unlike functional magnetic resonance imaging, be practically used to evaluate participants whose body weights (>300 lbs) or trunk diameters (> 60 cm) exceed the physical limits of most research scanners. The indices were the P300a and P300b components of the event related electroencephalographic potential (ERP). The latencies of these components are inversely correlated with the integrity of white matter pathways connecting their generators (Cardenas et al., 2005) whereas their amplitudes are more closely related to the gray matter volumes of the generators themselves (Egan et al., 1994; McCarley et al., 2002). P300a is generated frontally. In contrast, the P300b has a diffuse distribution of frontal and non-frontal generators (Halgren et al., 1998; Clark et al., 2000).

2. Methods

2.1 Participants

A detailed description of the recruitment, screening, and evaluation procedures can be found in our recent publications (Bauer, 2008b, 2008a). In brief, all HIV-1 seropositive participants were recruited from outpatient infectious disease clinics located in the greater Hartford, CT region. The HIV-1 seronegative control group was also recruited from this region. During recruitment, an attempt was made to equate the groups on demographics, substance abuse/dependence history, and psychiatric history.

Participants who passed the initial recruitment screen were invited to visit the Health Center for a full day of testing. At the beginning of this day, they signed informed consent and medical release documents. Then, they provided blood samples for clinical blood chemistries, serologies, and confirmation of HIV serostatus. Toxicological analyses for cocaine, opiates, amphetamine, and marijuana were performed on urine samples and a breathalyzer was used to detect recent alcohol use. In addition, vision was tested to confirm normal acuity (with correction).

A psychiatric assessment was performed (American, 1994; Robins, 2002) to identify selected DSM-IV Axis I and II disorders. Participants also completed questionnaires or brief interviews assessing medical history, medication use, demographics, psychiatric symptoms, alcohol and drug use, and cognitive status. The assessments included the Addiction Severity Index [ASI (McLellan et al., 1980)], Michigan Alcoholism Screening Test [MAST (Selzer, 1971)], Drug Abuse Screening Test [DAST-10 (Skinner, 1982)], and Beck Depression Inventory Version II [BDI-II (Beck, 1996)]. In addition, the Kaufman Brief Intelligence Test [KBIT (Kaufman, 1990)] was administered to derive an estimate of IQ.

The list of exclusion criteria included a history of seizures, mental retardation, dementia, schizophrenia, bipolar disorder, neurosurgery, head injury with loss of consciousness for greater than 10 minutes, and recent (past year) pregnancy. Participants were also required to have no acute illness, an IQ score greater than 70, and no major neurological or medical (i.e., chronic obstructive pulmonary disease, Type 1 or Type 2 diabetes, cirrhosis, hepatic encephalopathy, ocular disorders, etc.) disorders unrelated to HIV-1. Positive urine toxicology or breathalyzer tests were additional exclusions. However, current use of methadone was not.

Based upon the information provided by the structured interviews, questionnaires, and clinical laboratory results, participants were assigned to one of 8 mutually exclusive groups. The groups were defined by the factorial combination of three design factors. The first factor was simply the presence versus absence of HIV-1. We could have added a third level to this factor by dividing the seropositive group into subgroups consisting of patients who did, or did not, meet the clinical target of ≥ 95% medication compliance for effective viral suppression. It would then be possible to test the effects of antiretroviral treatment. Unfortunately, the clinical target was rarely achieved and the test could therefore not be performed.

The second factor used for group assignment was a body mass index above versus below the overweight criterion of 25 kg/m2. Admittedly, body mass index is not a valid index of adiposity in all cases, e.g., among participants who are muscular and physically fit. Yet, it is valid in most cases. Alternative indices, such as skinfold thickness and waist-to-hip ratio, were not measured.

The third and final grouping factor was the presence versus absence of a history of treatment for alcohol or illicit drug abuse problems. Defining substance abuse/dependence in this manner is not ideal because there are many variables unrelated to substance abuse/dependence that determine access to, and willingness to seek, treatment. Yet, we believe that it is a reasonable compromise. As an alternative, we might have split the group based upon an arbitrary threshold applied to a quantity by frequency index. However, quantity by frequency indices are highly variable across individuals and show relatively poor reliability within individuals. In addition, it is unclear how one might combine quantity by frequency information across multiple drugs into a single and meaningful summary score.

The second alternative could have been the presence versus absence of a DSM-IV diagnosis of dependence. Unfortunately, diagnoses are not necessarily the best choice in this instance. The number of participants with no lifetime diagnosis was insufficient for a powerful test of the interaction between diagnosis and the other design factors. More importantly, the veracity of self-reports of alcohol/drug use symptoms, and the resulting reliability and validity of substance dependence diagnoses, are questionable in a group of participants (i.e., HIV+ drug users) shown to exhibit a remarkably high rate of antisocial personality disorder features (Brooner et al., 1990; Bauer and Shanley, 2006), including deceitfulness and irresponsibility.

It is for this reason that we chose a history of substance abuse treatment as our operational definition of alcohol/drug exposure. Unlike self-reports of the level of use, or of symptoms and problems resulting from use, a history of substance abuse treatment can be verified through a review of each participant’s medical records. In addition, a criterion based upon the presence versus absence of treatment ensures--unlike a quantity by frequency index--that the level of use was sufficient to affect brain and behavior.

2.2 Procedures

Tin EEG electrodes were applied to 31 scalp sites positioned by an electrode cap (ElectroCap International, Eaton, Ohio, USA). A reference electrode was taped over the bridge of the nose. The ground electrode was applied to the middle of the forehead. Interelectrode impedance was maintained below 5 kOhms.

The task for eliciting P300 was a series of 300 visual symbols flashed sequentially for 200 ms each on a computer monitor. Each stimulus subtended 0.5° of visual angle. The interstimulus interval ranged from 0.8 to 1.2 s. The stimuli were three upper-case letters from the English alphabet. On 80% of the trials, the letter ‘T’ was presented. On the remaining trials, either the letter ‘X’ (P=0.10) or the letter ‘C’ (P=0.10) was presented. The trial types were randomly intermixed with the restriction that no more than three of the rarer stimuli could occur successively. The participant was instructed to press a response key upon seeing the letter ‘X’ (i.e., the rare target) and to ignore the letters ‘C’ (rare nontarget) and ‘T’ (frequent nontarget).

The electroencephalogram was recorded throughout the task. For the detection of eyeblink and eye movement artifacts, a pair of electrodes were placed diagonally above and below the left eye. The 31 channels of the EEG and 1 channel of eye movement (EOG) activity were appropriately amplified (EEG gain = 20 K, EOG gain = 2 K) and filtered (bandpass = 0.01–12 Hz) using an SA Instrumentation Company amplification system. Along with markers indicating stimulus and response onsets, the EEG and EOG channels were routed to an A/D converter, and sampled at a rate of 200 Hz for 50 ms before and 950 ms after the onset of each stimulus.

During off-line computations, single trial data were sorted by electrode and stimulus type. Before averaging, trials containing an eye movement deviation greater than 50 microvolts were deleted. Trials with A/D converter overflow and incorrect responses were also deleted. Time-point-averaged waveforms were then created from a minimum of 15 artifact-free epochs.

2.3 P300a and P300b Measurement

P300a is a frontally-generated component elicited by the presentation of rare stimuli that do not demand an overt response. It was therefore measured at the Fz electrode site following the presentation of the rare nontarget stimulus. P300b is a diffusely generated component best elicited by rare stimuli that do demand an overt response. It was accordingly measured at the Pz electrode site following the presentation of the rare target stimulus. These P300a and P300b peaks were identified between 250 and 650 ms following stimulus onset. Their amplitudes were expressed as the voltage difference between the peak and the average voltage during the 50 ms prestimulus period. The latencies of the peaks were expressed in milliseconds relative to stimulus onset.

2.4 Data Analysis

The 8 groups formed by the design factors (Table 1) were initially compared on their demographic and psychiatric features. Pearson’s χ2 square test was used to evaluate group equivalence on categorical features. A one-way ANOVA was used to evaluate equivalence on continuously-distributed features. P300a and P300b amplitudes and latencies were evaluated by separate 2 (HIV− / HIV+) by 2 (BMI < 25 / BMI ≥ 25 kg/m2) by 2 [never/ever treated for substance abuse (SA)] ANOVAs with the number of childhood Conduct Disorder symptoms and the BDI-II score specified as covariates. A regression analysis was used to clarify significant interactions.

Table 1.

Background
Characteristicsƒ 		HIV−
BMI<25
No SA Hx		 HIV+
BMI<25
No SA Hx		 HIV−
BMI≥25
No SA Hx		 HIV+
BMI≥25
No SA Hx		 HIV−
BMI<25
SA Hx		 HIV+
BMI<25
SA Hx		 HIV−
BMI≥25
SA Hx		 HIV+
BMI≥25
SA Hx		 Test Result
df=7 or 3
N 14 18 21 11 14 39 19 34
Age (SD) 35(9) 39(6) 39(6) 40(8) 39(5) 41(5) 39(7) 41(6) F=1.1
IQ 99(14) 98(12) 95(12) 94(15) 91(10) 88(11) 93(14) 88(18) F=1.9
% Caucasian 42.9 38.9 33.3 45.5 35.7 25.6 21.1 23.5 χ2= 7
% Female 64.3 44.4 52.4 36.4 35.7 41.0 52.6 44.1 χ2= 4
CD4 (cells/ml) 858(386) a 379(321) b 943(414) a 340(211) b 786(210) a 317(209) b 905(404) a 370(226) b F=19.1*
Log10 Viral Load -- 3.8(0.8) -- 4.1(1.2) -- 4.3(0.9) a -- 3.5(0.8) b F=3.2*
% Receiving Any
Protease Inhibitor		 -- 44.4 -- 36.4 -- 25.6 a -- 55.9 b χ2= 7.1**
% Receiving Any
NRTI		 -- 61.1 -- 72.7 -- 59.0 -- 73.5 χ2= 2.1
# Alcohol Pblms
(MAST)		 0.7(1.0)a 1.0(1.2)a 1.7(2.7)a 0.1(0.3)a 3.8(4.3) 8.1(7.1)b 7.5(6.3)b 5.8(5.7)b F=8.4*
# Drug Pblms (DAST-
10)		 0.7(1.2)a 1.4(1.5)a 0.7(1.4)a 0.2(0.4)a 4.9(2.7)b 4.5(3.4)b 3.7(3.7)b 3.6(3.2)b F=9.1*
% Dependent on
alcohol, cocaine,
opiates (lifetime)		 0a 22.2 a 19.0 a 0 a 92.9 b 100 b 89.5 b 100 b χ2= 129*
% Dependent on
alcohol, cocaine,
opiates (current)		 0 a 11.1 a 4.8 a 0 a 71.4 b 53.8 b 36.8 b 47.1 b χ2= 43.5*
Current # Depression
Symptoms (BDI-II)		 10(9) 13(12) 7(10)a 13(14) 12(9) 16(10) 16(8) 17(11)b F=2.1*
# Childhood Conduct
Disorder Problems		 1.0(1.4)a 1.4(1.8)a 1.1(1.6)a 1.3(2.1)a,c 2.0(1.7)a,b,c 4.5(2.9)b,c 3.0(3.0)a,b,c 3.7(3.3)b,c F=7.3*
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ƒ

Abbreviations: BMI, body mass index; SA Hx, substance abuse treatment history; MAST, Michigan Alcoholism Screening Test; DAST, Drug Abuse Screening Test; BDI-II, Beck Depression Inventory.

*

p < .05.

**

p < .07.

Nonoverlapping subscripts designate cell means that are significantly different in post hoc tests.

3. Results

3.1 Background Characteristics

The 8 groups of participants were equivalent in their average age and IQ. They were also similar in their gender and racial composition (Table 1). The groups did differ on some background characteristics. However, these differ ences largely reflected the characteristics that defined the groups.

One example of a group difference in a background characteristic is CD4+ T-lymphocyte count, which was significantly lower in HIV-1 seropositive than seronegative groups. CD4 count did not vary as a function of the other grouping factors. Other examples include the number of substance use and depression symptoms, as well as the presence of a DSM-IV substance dependence diagnosis, which were all less common among participants without versus with a history of treated substance abuse/dependence. Neither substance use symptoms nor depression symptoms varied as a function of BMI or HIV.

The only other variable that discriminated the groups was the number of Conduct Disorder problems. In general, participants with HIV/AIDS or a history of substance abuse treatment exhibited more problems of this type during childhood than participants without HIV/AIDS or a substance abuse/dependence history.

3.2 P300 Analyses

Univariate ANOVAs of P300 revealed several significant findings (Table 2). Statistical significance was limited to analyses involving P300a. None of the main or interaction effects involving P300b attained the p<0.05 criterion.

Table 2.

Covariate-adjusted
P300a and P300b
amplitudes and
latencies (SD) by
group		 HIV−
BMI<25
No SA Hx		 HIV+
BMI<25
No SA Hx		 HIV−
BMI≥25
No SA Hx		 HIV+
BMI≥25
No SA Hx		 HIV−
BMI<25
SA Hx		 HIV+
BMI<25
SA Hx		 HIV−
BMI≥25
SA Hx		 HIV+
BMI≥25
SA Hx
N 14 18 21 11 14 39 19 34
P300a Amplitude
(μV) at Fz		 8.2(3.5) 3.5(5.0) 7.6(3.9) 6.3(4.8) 6.7(6.7) 6.8(5.9) 3.5(4.3) 5.1(5.3)
P300a Latency
(msec) at Fz		 418(77) 385(93) 388(100) 467(63) 377(80) 433(82) 370(85) 432(97)
P300b Amplitude
at Pz		 16.4(10.6) 14.3(8.7) 13.4(9.6) 8.6(9.9) 11.9(9.6) 12.3(6.7) 15.0(6.6) 13.3(8.1)
P300b Latency at
Pz		 437(33) 435(51) 442(58) 441(39) 423(60) 442(50) 421(70) 461(48)
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The analysis revealed that the two covariates were significantly related to frontal P300a latency but not to P300a amplitude. The F-ratio for the effect of Conduct Disorder problems was significant [F(1,158)=5.8, p<0.02]. In addition, the main effect of BDI-II score was significant [F(1,158)=7.2, p<0.01]

The analyses also revealed an underadditive interaction (Figures 1 and 2) between HIV/AIDS and a previous history of substance abuse/dependence [F(1,158)=4.6, p<0.03]. Post-hoc tests showed that, in comparison to participants without HIV infection and without a history of substance abuse treatment (M=7.9 μV, SD=0.9), participants with both HIV and a substance abuse treatment history (M=5.9 μV, SD=0.6) exhibited a P300a amplitude reduction. The amplitude reduction was less than the sum of the reductions independently associated with HIV (HIV+ and No Hx of SA: M=4.9 μV, SD=1.0) and substance abuse/dependence (HIV− and Hx of SA: M=5.1 μV, SD=0.9).

Figure 1.

Figure 1

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P300a amplitude in microvolts (+1 SEM) at the Fz electrode. Note the underadditive interaction of HIV/AIDS and substance abuse/dependence. The effects of body mass on P300a amplitude were not statistically significant and are therefore not shown.

Figure 2.

Figure 2

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Event related potentials at Fz and Pz elicited by the rare nontarget stimulus. The waveforms are averaged within groups and sorted by HIV-1 serostatus and substance abuse treatment history. P300a is the prominent positive peak at approximately 400 ms following stimulus onset.

P300a latency was changed in the predicted direction by HIV/AIDS. Figure 3 illustrates a significant [F(1,158)=7.6, p<0.007] increase in frontal P300a latency among HIV-1 seropositive (M=431 ms, SD=89) versus seronegative (M=384 ms, SD=87) participants. In addition, the figure illustrates a significant synergistic interaction [F(1,158)=4.1, p<0.04] of HIV/AIDS with an overweight BMI, wherein the combination is associated with significantly greater slowing of P300a than can be explained by the sum of their independent effects.

Figure 3.

Figure 3

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P300a latency in milliseconds (+1 SEM) at the Fz electrode. Note the synergistic interaction of HIV/AIDS and BMI. The effects of substance abuse treatment history were not significant.

In an attempt to show that the interactions of HIV/AIDS with BMI and substance abuse/dependence were not artifacts of the arbitrary thresholds used for defining groups, two separate regression analyses were performed. The first analysis treated BMI as continuous variable and regressed it against the residual of frontal P300a latency after the effects of depression, substance abuse, conduct problems, and age were removed. It was performed separately for the HIV− and HIV+ groups.

Figure 4 clearly shows that the relationship between BMI and frontal P300a latency did vary as function of HIV-1 serostatus. Among HIV− participants, the relationship was not statistically significant (rpartial = −0.050, p = 0.69). However, among participants already compromised by HIV/AIDS, an increase in frontal P300a peak latency was found as BMI increased (rpartial = 0.381, p < 0.005).

Figure 4.

Figure 4

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Scatterplots illustrating the relationship between BMI and Fz P300a latency separately for the HIV− and HIV+ groups. For this analysis, Fz P300a latency is the standardized residual after the variance association with depression level (BDI-II score); childhood Conduct Disorder symptoms, and alcohol (MAST) and drug abuse (DAST-10) problems; and age was removed. The figure demonstrates a significant increase in Fz P300a latency as a function of BMI in the HIV+ group. This relationship is absent in the seronegative group.

The second regression analysis tested the relationship between frontal P300a amplitude and the number of previous treatments for alcohol or drug problems. It controlled for the effects of BMI, age, conduct problems, and depression score. The analysis results were consistent with the results of the ANOVA. An increased number of previous treatments was associated with a graded reduction in frontal P300a amplitude (rpartial = −0.282, p = 0.02) among HIV− participants. However, it was associated with no further reduction (rpartial = 0.118, p = 0.25) in their HIV+ counterparts.

4. Discussion

The HIV/AIDS literature is rife with reports of clinically significant cognitive, neurophysiological, and MRI abnormalities. These reports have become less common in recent years as the effect sizes have diminished. The absence of large differences confirms that antiretroviral therapy is indeed effective in reducing the prevalence of significant central nervous system damage (Bauer and Shanley, 2006; Larussa et al., 2006), despite ongoing debate about the central nervous system permeability and efficacy of specific antiretroviral agents (Nath and Sacktor, 2006). It also suggests that the population of patients afflicted with HIV/AIDS is changing. The population will increasingly consist of individuals possessing a chronic, modest, and non-debilitating neurological disorder. Detecting and tracking the sequelae of HIV-1 infection in the current era of antiretroviral treatment will require new theoretical perspectives and methodologies.

A novel perspective provided by the present report is its examination of a comorbid condition that was not expected to be a problem in this population. The results (Figures 3 and 4) show that an overweight body mass in combination with HIV/AIDS synergistically delays an evoked EEG response, P300a, whose generator resides in the frontal brain. The frontal locus of the synergism is not surprising given other literature documenting the greater vulnerability of frontal regions to the effects of HIV/AIDS (Thompson et al., 2006; McMurtray et al., 2008) and obesity (Gazdzinski et al., 2008). The delay in P300a latency in the absence of a change in P300a amplitude suggests a disruption in the connectivity between neurons that generate this component with a preservation of their overall integrity. Although the disruption is subtle, it is worthy of note for it could presage greater risk for overt neurological signs or symptoms as these middle-aged, overweight or obese HIV/AIDS patients enter senescence.

One qualification in intepreting the synergistic Body Mass x HIV/AIDS interaction is the marginally higher rate of protease inhibitor use (Table 1) by overweight, HIV+ participants. Based upon the information available to us, we cannot presently discern whether the significant delay in P300a found in this group is related to their greater body mass or a neurotoxic effect of protease inhibitor therapy, which permits and promotes their greater mass. Although protease inhibitors are less commonly associated with neuropathies than nucleoside reverse transcriptase inhibitors, evidence of adverse effects is emerging (Pettersen et al., 2006).

The present article is also noteworthy in reporting underadditive effects of HIV/AIDS and a history of substance abuse or dependence on P300a amplitude. It is important to recognize that the underadditive quality of this relationship is tenuous and may only be present under specific circumstances. There are several reasons justifying the concern. The first reason is that an interaction can sometimes be a statistical artifact. Indeed, the results shown in Figures 1 and 2 suggest the presence of measurement floor which prohibits further reductions in P300a amplitude associated with the combined effects of HIV/AIDS and substance abuse/dependence. The second reason for concern originates from the different and contradictory results reported by Fein and colleagues (Fein et al., 1995; Fein et al., 1998). In contrast to our demonstration of an underadditive interaction, they found independent effects of—that is, no interaction between--HIV and their indicant of substance abuse. In addition, Fein and colleagues reported differences in P300a latency whereas our differences were restricted to P300a amplitude. Although their work produced a different pattern of results, the design of their study was also quite different: it employed an auditory task, a smaller number of patients, an HIV-1 seropositive sample with more advanced disease, and a different indicant of substance abuse. Rationalizing the discrepant findings of the two studies is therefore a daunting task that awaits more data.

Despite concerns and caveats about the interpretation of these interactions, as well as the unknown contributions of unstudied factors, including gender, antiretroviral treatment, and recency of substance abuse/dependence, the present results are nonetheless valuable. They compelling suggest that neurophysiological impairments in HIV-1 seropositive patients are not solely determined by their disease. For as long as excess body mass and substance abuse remain prevalent in the HIV-1 seropositive community, and add to, or interact with, the effects of HIV, these patients may face neurophysiological impairments which will impact their quality of life and their risk for frank neurological signs or symptoms.

Acknowledgements

This research was supported by PHS grant R01MH61346 funded jointly by NIMH and NIDA. Additional support was provided by grants P50AA03510, M01RR06192, and R01DA017666. The author has no conflicts of interest to disclose.

Footnotes

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