Abstract
Objective
To explore the relationship of body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) with cognition in women with (HIV+) and without HIV (HIV-) infection.
Design/Methods
1690 participants (1196 HIV+, 494 HIV-) in the Women's Interagency HIV Study (WIHS) with data available on anthropometric measures comprise the analytical sample. Cross-sectional analyses using linear regression models estimated the relationship between anthropometric variables and Trails A, Trails B, Stroop interference time, Stroop word recall, Stroop color naming and reading, and Symbol Digit Modalities Test (SDMT) with consideration for age, HIV infection status, Wide Range Achievement Test score, CD4 count, insulin resistance, drug use, and race/ethnicity.
Results
Among HIV+ women, BMI < 18.5 kg/m2 was associated with poorer cognitive performance evidenced by longer Trails A and Trails B and shorter SDMT completion times. An obese BMI (30 kg/m2 or higher) was related to better performance on Trails B and worse performance on the Stroop Interference test. Among HIV- women, an obese BMI was related to worse performance on the Stroop – Color naming test. Few and inconsistent associations were observed between WC, WHR and cognition.
Conclusion
Among women at mid-life with chronic (at least 10 years) HIV infection, common anthropometric measures, primarily BMI, were differentially related to cognitive test performance by cognitive domain. Higher levels of BMI were associated with better cognitive function. In this era of antiretroviral therapies, restoration of health evidenced as higher BMI due to effective antiretroviral therapies, may improve cognitive function in middle-aged HIV infected women.
Keywords: Cognition, HIV, Women, Overweight, Obesity, Middle-Aged
Introduction
Human Immunodeficiency Virus (HIV) infection is one of the most prevalent infectious diseases worldwide. Since survival of those with HIV infection is extended because of adherence to antiretroviral therapies, HIV infection will become a chronic infection of aging.
According to the CDC, in 2010 there were an estimated 47,500 people newly infected with HIV in the United States. Of these, 15% (N=7125) were aged 45-55 years and 5% (n=2375) were aged 55 years and older.(Prevention, 2012a) Add to this, prevalent infections in 2009, when there were 381,000 prevalent HIV infections among those aged 45-54 years, and almost 200,000 prevalent HIV infections among those aged 55 years and older.(Prevention, 2012b) This totals almost 600,000 HIV infected adults age 45 years or older in the U.S., which is an underestimate. Thus, these data, illustrate the potential burden of HIV associated cognitive disorders due to the high proportion of HIV/AIDS survivors over age 45 years.
In many countries, the advent of antiretroviral therapy (ART) has been accompanied by an increase in BMI due to normalization of health that also mirrors trends in the general population. However, WIHS women are overweight and obese on average, and overweight and obesity are leading causes of disability and death in the United States and around the world and have been linked to cognitive impairments. Obesity is associated with hypertension, dyslipidemias, Type 2 diabetes mellitus, and cardiovascular disease, and these metabolic complications may be more prevalent in HIV.(2006; Sobieszczyk et al, 2008) These factors also increase risk for age-related, late onset cognitive impairments (CI) and eventually clinical Alzheimer's disease (AD) and vascular dementias.(Gustafson and Skoog, 2009) Given the aging HIV-infected population, it is important to understand how HIV-infection influences aging, as well as accompanying morbidities such as obesity and outcomes such as cognitive impairments and dementia.
Obesity is associated with age-related cognitive impairments and dementia in populations without HIV. Mid-life obesity, estimated using BMI, waist circumference (WC), or waist-to-hip ratio (WHR), measured decades before dementia onset has been linked to higher risk of AD.(Fitzpatrick et al, 2009; Gustafson et al, 2009a; Whitmer et al, 2007) However, associations of obesity with cognitive impairments in mid-life are unclear.(Gustafson et al, 2009a) It has been suggested that the aging HIV population may experience more than the anticipated burden of cognitive impairments since many HIV infected individuals have experienced metabolic and vascular consequences of early ART regimens and other medications, in addition to earlier, mid-life cognitive impairments due to the HIV infection. While use of ART has been suggested to decrease the incidence and prevalence of AIDS- or HIV-associated dementias,(Berger and Brew, 2005; Bhaskaran et al, 2008; González-Scarano and Martín'García, 2005; Moroni and Antinoria, 2003; Saktor, 2002) the frequency of cognitive impairments has not changed(Heaton et al, 2011). The impact of HIV, multi-morbidities and ART on late life sporadic dementias and AD (incident after age 65 years) remains to be understood. Some studies suggest that the prevalence of cognitive impairments, ranging from 15 to 50%, will worsen as the HIV population ages, thus putting them at risk for AD.(Cohen and Gongvatana, 2010; Heaton et al, 2010; McArthur and Brew, 2010)
In the present study, we determined whether anthropometric indices are differentially associated with cognitive performance in women who are HIV+ or HIV- from the Women's Interagency HIV Study (WIHS).(Bacon et al, 2005; Barkan et al, 1998) Demographic, cardiovascular, and HIV-related factors were considered as confounders.
Methods
The WIHS(Bacon et al, 2005) is an ongoing prospective study of HIV in women. The WIHS began in 1994 and enrolled 3766 women across six sites in San Francisco, Los Angeles, Chicago, Washington, DC, Brooklyn and the Bronx (New York). WIHS initially recruited 2054 HIV infected (HIV+) and 569 HIV uninfected (who in WIHS, are their ‘at risk’ counterparts, HIV-) women in 1994-95, and an additional 737 HIV-infected and 406-HIV uninfected women in 2001-2002. Participants are evaluated every six 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.
Demographic measures
All demographic measures were self-reported. Race(Bacon et al, 2005; Barkan et al, 1998) was self-reported as white, Hispanic, African-American, or ‘other’ (self-reported as Native American/Alaskan, Asian/Pacific Islander or other) for all participants. Participants are also asked to report their current smoking status, and use of marijuana, ‘crack’, cocaine, and heroin.
Clinical measures
Anthropometric measures were conducted according to NHANES III protocol and included body weight (pounds), body height (inches), waist and hip circumferences (cm), and BMI (kg/m2).(Justman et al, 2008) Anthropometric measurements were conducted with participants wearing only undergarments. Those who conduct the measurements are recertified every two years. Body weight was recorded to the nearest 1.0 pound, and body height was measured to the nearest 1.0 inch. After conversion of body weight and height to metric units, body mass index (BMI) was calculated as kilograms per meter squared (kg/m2). Categories of BMI used to denote total body adiposity were ≥ 25 kg/m2 for overweight and obesity, and ≥ 30 kg/m2 for obesity.(1989) Waist and hip circumferences were measured to the nearest 0.5 cm. WHR was calculated as the ratio of waist to hip circumference. Central obesity was defined as WHR > 0.80 or WC ≥ 88 cm.(Croft et al, 1995)
Eight hour fasted blood samples were collected and total cholesterol levels were determined as previously described.(Crystal et al, 2011) Systolic (SBP) and diastolic blood pressures (DBP) were recorded using a standardized protocol.(Mansoor et al, 2009) Hypertension was defined as either average measured systolic BP >140 mm Hg, or diastolic BP >90 mm Hg, or a self-reported hypertension with use of antihypertensive medications. Previous myocardial infarction (MI) and diabetes mellitus (DM) were self-reported.(Bacon et al, 2005; Barkan et al, 1998)
HIV-related variables
Methods for determining HIV status, AIDS diagnosis, CD4 count, viral load, and duration of ART use were described previously.(Bacon et al, 2005; Barkan et al, 1998; Kaplan et al, 2008)
Cognitive tests
The cognitive tests were administered to all English-speaking WIHS participants during visits 21 to 24 (October 2004 to September 2006) as part of the WIHS core assessment; the Comalli-Kaplan Stroop was administered to a subgroup during visits 25-28, October 2006 to September 2008 (Table 1). These tests have been previously described.(Crystal et al, 2011) These tests were selected for a number of reasons. Most importantly, these tests overlap with those that had already been used for several years in the Men's AIDS cooperative study (MACS),(Miller et al, 1990) which is a “brother” study to the WIHS. In addition, these tests are sensitive to the domains most affected in HIV in the pre-ART era, are relatively short, can be administered reliably by trained personnel who are not neuropsychologists, are less sensitive to practice effects than many other cognitive tests, and cover a number of important cognitive domains shown to be affected early in late onset Alzheimer's disease, in addition to being sensitive to HIV-associated neurocognitive disorder (HAND). Among participants who completed testing on multiple visits, and therefore have more than one score, only the first score was used. Times greater than 240 seconds were coded as 240 seconds. Errors were recorded, but were not used to adjust interference times. For all cognitive tests, we used raw scores rather than normalized data.
Table 1.
| Cognitive Domain | Test |
|---|---|
| Executive Function | Trails A, Trails B Stroop Interference27,28 |
| Speed of Information Processing | Symbol Digit Modalities Test (SDMT)*
29,30 Stroop Color Naming and Reading |
| Learning and Memory | Stroop Word Recall |
The SDMT score is the number of correct items in 90 seconds, all other test scores are times with lesser time indicating better performance.
Inclusion criteria
We include all data collected by visit 28, concluding in September 2008 on 1690 participants (494 uninfected and 1196 HIV-infected) with data available on both anthropometric and cognitive measures.
Statistical analysis
Anthropometric factors were considered as continuous and categorical variables according to commonly defined cutpoints (stated above). BMI, WHR and WC, were categorized into tertiles to assist in understanding nonlinear relationships between the anthropometric measures and cognitive test scores, and to practically apply the results to commonly used overweight and obese categories. Linear regression analyses were used to examine the association between the continuous or categorical anthropometric factors and cognitive test scores (time to completion) of Trails A, Trails B, Stroop Interference, Stroop Color Naming, and Stroop Word Recall; or SDMT score. A longer time to completion (higher score) indicates worse cognitive performance for the first 6 tests; in contrast, a lower SDMT score denotes worse performance. Regression models were run separately for HIV+ and HIV-.
Several covariates were considered, including: age, race, highest educational level attained, Wide Range Achievement Test (WRAT) score, HIV status, ART, CD4 count, CD4 nadir, prevalent DM, SBP, DBP, use of anti-hypertensive medications, use of exogenous insulin, blood cholesterol level, current smoking status, and use of marijuana, ‘crack’, cocaine, and/or heroin. Potential covariates were included if significant in age-adjusted models at a level of p < 0.05. Given this significance level, final models included the following covariates: age, WRAT, race, exogenous insulin, and any recreational drug use. Other covariates evaluated, such as CD4 count or CD4 nadir, were not significant. In analyses of women who were HIV+, we also adjusted for HIV viral load. In analyses of both HIV+ and HIV- women we included and excluded drug users. STATA 12 was used for all statistical analyses. Results were considered statistically significant at p < 0.05.
Results
Both anthropometric factors and cognition were available for 1664 WIHS participants (1196 HIV+ and 494 HIV- women). Demographic, anthropometric, and health characteristics are presented in Table 2. Notably, based on average age, these women were not at risk for late-onset, aging-related cognitive impairments or dementias. HIV+ women were approximately 4 years older than HIV- women, however educational attainment, a key influencer of cognitive performance, did not differ between HIV+ and HIV-women. In addition, most (69%) women were overweight or obese (≥ 25.0 kg/m2) and the prevalence of central obesity was high. There was a very low percentage of women with BMI < 18.5, in concordance with the low prevalence of AIDS among those who were HIV+. HIV+ women had lower average BMI and WC, and higher WHR, compared to HIV- women. Among published vascular risk factors for dementia, hypertension was also more prevalent in HIV+ women, however average total blood cholesterol levels and the prevalence of diabetes mellitus did not differ.
Table 2.
Demographic and health characteristics of Women's Interagency HIV Study (WIHS) participants with anthropometric measurements.
| ALL | HIV+ | HIV- | p-value | ||||
|---|---|---|---|---|---|---|---|
| Characteristic | N | mean (SD)/n(%) | N | mean (SD)/n(%) | N | mean (SD)/n(%) | |
| Age (years) | 1690 | 41.3 (9.4) | 1196 | 42.5 (8.9) | 494 | 38.4 (10.1) | <0.0001 |
| Race | 1690 | 1196 | 494 | 0.187 | |||
| White | 347 (20.5%) | 257 (21.5%) | 90 (18.2%) | ||||
| African American (AA) | 1078 (63.8%) | 746 (62.4%) | 332 (67.2%) | ||||
| Non-white, non-AA Hispanic | 214 (12.7%) | 159 (13.3%) | 55 (11.1%) | ||||
| Other | 51 (3.0%) | 34 (2.8%) | 17 (3.4%) | ||||
| Highest education | 1685 | 1194 | 491 | 0.571 | |||
| Grades 7-11 | 586 (34.8%) | 428 (35.9%) | 158 (32.2%) | ||||
| Completed High School | 539 (32.0%) | 377 (31.6%) | 162 (33.0%) | ||||
| Some college | 436 (25.9%) | 305 (25.5%) | 131 (26.7%) | ||||
| 4-year degree | 94 (5.6%) | 62 (5.2%) | 32 (6.5%) | ||||
| attend/completed graduate school | 30 (1.8%) | 1176 | 22 (1.8%) 463.7 (275.4) | 8 (1.6%) | |||
| CD4 count | 1176 | 463.7 (275.4) | |||||
| Viral load | 1187 | 20626.1 (107073.0) | |||||
| Cognitive test scores | |||||||
| Trails A | 1674 | 38.2 (16.5) | 1183 | 39.2 (16.5) | 491 | 36.0 (14.2) | <0.001 |
| Trails B | 1631 | 91.1 (49.6) | 1147 | 93.2 (50.8) | 484 | 86.0 (46.3) | 0.007 |
| Symbol Digit | 1650 | 42.4 (12.5) | 1163 | 41.3 (12.5) | 487 | 45.0 (12.2) | <0.0001 |
| Stroop - Color Name Time | 972 | 71.6 (19.8) | 658 | 73.1 (19.8) | 314 | 68.4 (15.7) | <0.001 |
| Stroop -- Word Read Time | 974 | 55.1 (15.2) | 661 | 56.1 (15.9) | 313 | 52. (13.5) | 0.002 |
| Stroop -- Interference Time | 957 | 129.4 (31.2) | 647 | 131.8 (31.9) | 310 | 124.5 (29.1) | <0.001 |
| Anthropometric Indices | |||||||
| BMI (kg/m2) | 1680 | 29.3 (7.8) | 1188 | 28.6 (7.5) | 492 | 30.9 (8.3) | <0.0001 |
| <18.5 | 27 (3.1%) | 5 (1.0%) | |||||
| 18.5 – 24.9 | 380 (32.%) | 120 (24.4%) | |||||
| 25 - < 29.9 | 355 (29.9% | 129 (26.2%) | |||||
| ≥ 30 | 426 (35.9%) | 238 (48.4%) | <0.001 | ||||
| Waist measurement (cm) | 1501 | 92.0 (16.3) | 1054 | 91.3 (15.5) | 447 | 93.7 (18.1) | 0.01 |
| < 80 | 261 (24.8%) | 108 (24.2%) | |||||
| 80-87.9 | 220 (20.9%) | 83 (18.6%) | |||||
| ≥ 88 | 573 (54.4%) | 256 (57.3%) | 0.509 | ||||
| WHR | 1498 | 0.89 (0.08) | 1053 | 0.90 (0.08) | 445 | 0.86 (0.08) | <0.0001 |
| < 0.85 | 309 (29.3%) | 202 (45.4%) | |||||
| 0.85 - <0.90 | 241 (22.9%) | 92 (20.7%) | |||||
| 0.90 - <0.95 | 243 (23.1%) | 76 (17.1%) | |||||
| ≥ 0.95 | 260 (24.7%) | 75 (16.9%) | <0.001 | ||||
| Marijuana Use since last visit | 1684 | 335 (19.9%) | 1190 | 205 (17.2%) | 494 | 130 (26.3%) | <0.001 |
| Any indicator of hypertension* | 1690 | 727 (43.0%) | 1196 | 543 (45.4%) | 494 | 184 (37.3%) | 0.002 |
| Diabetes mellitus | 1463 | 102 (7.0%) | 1040 | 74 (7.1%) | 423 | 28 (6.6%) | 0.736 |
| Total Cholesterol (mg/dl) | 1689 | 175.6 (40.9) | 1195 | 175.5 (42.3) | 494 | 175.8 (37.4) | 0.872 |
Either SBP>=140, DBP>=90, self-reported hypertension, or taking anti-hypertensive medication.
Multivariate linear regression analyses relating anthropometric measures to cognitive test scores showed divergent results by test and anthropometric measure. It is important to note that the covariates included in the final multivariate models are those that were significant in age-adjusted models. Variables associated with HIV disease and typically included in models of HIV, such as CD4 count, are not included in final models since they were not significantly associated in age-adjusted models.
Body mass index, BMI (Table 3)
Table 3.
Body Mass Index (BMI) category in relation to cognitive test scores: the Women's Interagency HIV Study (WIHS).
| BMI category (kg/m2) | |||||
|---|---|---|---|---|---|
| <18.5 | 18.5-24.9 | 25-29.9 | ≥ 30 | ||
|
| |||||
| Betaa (95% CI) p-value | referent | Beta (95% CI) p-value | Beta (95% CI) p-value | ||
| Executive Function | |||||
| Trails A | |||||
| HIV+ | |||||
| Crudeb | 8.13 (2.63, 13.63) | 0.0 | -0.35 (-2.71, 2.01) | 0.47 (-1.80, 2.75) | |
| 0.004 | 0.771 | 0.797 | |||
| Adjustedc | 8.62 (3.40, 13.83) | 0.0 | -0.32 (-2.66, 2.02) | -0.53 (-2.82, 1.76) | |
| 0.001 | 0.79 | 0.649 | |||
| HIV- | |||||
| Crude | -8.04 (-20.42, 4.33) | 0.0 | -0.45 (-3.90, 3.00) | 1.02 (-2.04, 4.08) | |
| 0.202 | 0.797 | 0.513 | |||
| Adjusted | -7.81 (-20.05, 4.44) | 0.0 | -1.54 (-5.08, 2.01) | -0.36 (-3.61, 2.88) | |
| 0.211 | 0.394 | 0.826 | |||
| Trails B | |||||
| HIV+ | Crude | 18.89 (1.74, 36.04) | 0.0 | -5.33 (-12.71, 2.04) | -6.38 (-13.46, 0.70) |
| 0.031 | 0.156 | 0.078 | |||
| Adjusted | 17.66 (1.77, 33.54) | 0.0 | -5.59 (-12.73, 1.55) | -7.84 (-14.78, -0.91) | |
| 0.029 | 0.125 | 0.027 | |||
| HIV- | Crude | -16.87 (-56.55, 22.80) | 0.0 | 11.12 (-0.02, 22.26) | 2.45 (-7.50, 12.40) |
| 0.404 | 0.050 | 0.629 | |||
| Adjusted | -14.83 (-52.94, 23.28) | 0.0 | 7.11 (-4.01, 18.22) | -2.86 (-13.08, 7.37) | |
| 0.445 | 0.210 | 0.583 | |||
| Stroop Interference | |||||
| HIV+ | Crude | 1.61 (-14.81, 18.04) | 0.0 | 2.29 (-3.89, 8.47) | 5.83 (0.02, 11.65) |
| 0.847 | 0.467 | 0.049 | |||
| Adjusted | 2.11 (-12.87, 17.09) | 0.0 | 2.41 (-3.36, 8.18) | 5.17 (-0.40, 10.73) | |
| 0.782 | 0.413 | 0.069 | |||
| HIV- | Crude | -7.51 (-40.07, 25.04) | 0.0 | 6.33 (-2.60, 15.26) | 5.69 (-2.20, 13.59) |
| 0.650 | 0.164 | 0.157 | |||
| Adjusted | -10.47 (-41.28, 20.34) | 0.0 | 4.12 (-4.59, 12.83) | 3.06 (-4.77, 10.88) | |
| 0.504 | 0.353 | 0.442 | |||
| Speed of Information Processing | |||||
| Symbol Digit Modalities Test (SDMT) | |||||
| HIV+ | Crude | -5.44 (-9.60, -1.28) | 0.0 | 0.28 (-1.49, 2.06) | 0.50 (-1.21, 2.21) |
| 0.010 | 0.755 | 0.565 | |||
| Adjusted | -5.54 (-9.32, -1.75) | 0.0 | 0.29 (-1.39, 1.98) | 1.53 (-0.12, 3.18) | |
| 0.004 | 0.732 | 0.070 | |||
| HIV- | Crude | 5.62 (-4.69, 15.94) | 0.0 | 0.80 (-2.10, 3.69) | 1.06 (-1.50, 3.62) |
| 0.285 | 0.588 | 0.417 | |||
| Adjusted | 4.95 (-4.29, 14.18) | 0.0 | 1.60 (-1.10, 4.29) | 2.45 (-0.02, 4.91) | |
| 0.293 | 0.245 | 0.052 | |||
| Stroop Color Naming | |||||
| HIV+ | Crude | -0.78 (-11.10, 9.53) | 0.0 | -0.18 (-4.05, 2.68) | 0.67 (-2.95, 4.29) |
| 0.882 | 0.925 | 0.715 | |||
| Adjusted | 0.07 (-9.45, 9.59) | 0.0 | 0.40 (-3.24, 4.05) | 0.35 (-3.14, 3.84) | |
| 0.988 | 0.828 | 0.844 | |||
| HIV- | Crude | 3.63 (-13.58, 20.83) | 0.0 | 3.06 (-1.62, 7.74) | 7.21 (3.06, 11.37) |
| 0.679 | 0.199 | 0.001 | |||
| Adjusted | 2.53 (-13.27, 18.63) | 0.0 | 1.51 (-3.01, 6.03) | 5.20 (1.12, 9.27) | |
| 0.757 | 0.512 | 0.013 | |||
| Learning and Memory | |||||
| Stroop Word Recall | |||||
| HIV+ | Crude | 1.27 (-7.02, 9.56) | 0.0 | -0.02 (-3.11, 3.08) | 1.49 (-1.41, 4.39) |
| 0.764 | 0.990 | 0.314 | |||
| Adjusted | 1.70 (-5.55, 8.96) | 0.0 | 0.54 (-2.23, 3.31) | 0.95 (-1.71, 3.61) | |
| 0.645 | 0.704 | 0.483 | |||
| HIV- | Crude | 3.15 (-12.04, 18.35) | 0.0 | 1.79 (-2.35, 5.94) | 4.67 (1.00, 8.36) |
| 0.683 | 0.395 | 0.013 | |||
| Adjusted | 2.37 (-11.31, 16.06) 0.733 | 0.0 | 0.22 (-3.62, 4.07) 0.909 | 2.89 (-0.58, 6.36) 0.102 | |
A positive beta coefficient indicates a positive relationship between anthropometric measure and cognitive test score. For all tests except SDMT, a positive relationship indicates worse cognitive performance with higher level of anthropometric measure. Thus, in fully adjusted models, a BMI < 18.5 kg/m2 is related to longer Trails A completion among HIV+ compared to those with a BMI within the normal range of 18.5-24.9; a BMI ≥ 30 kg/m2 is related to a shorter Trails A completion time.
Crude models adjust for age.
Multivariate models adjust for the covariates: age, WRAT, race, insulin, and any drug use.
Among women with HIV, compared to those with normal BMI (18.5-25.0 kg/m2), having a very low BMI (< 18.5 kg/m2) was associated with a longer time to completion (poorer performance) on Trails A and Trails B. Compared to a BMI in the healthy range (20-24.9 kg/m2), an obese BMI (30 kg/m2 or higher), and an increasing continuous BMI were related to shorter Trails B completion time (better performance), and longer time to Stroop Interference test completion (worse performance), both executive function measures. A BMI < 18.5 kg/m2 was associated with lower SDMT score, a measure of psychomotor speed, and evidence of worse performance. Among HIV- women, an obese BMI was related to worse performance on the Stroop–Color naming test.
Waist circumference (Table 4)
Table 4. Waist circumference category related to cognitive test scores: the Women's Interagency HIV Study (WIHS).
| Waist circumference category (cm) | ||||
|---|---|---|---|---|
| <80 | 80-87.9 | ≥ 88 | ||
|
| ||||
| referent | Beta (95% CI) p-value | Beta (95% CI) p-value | ||
| Executive Function | ||||
| Trails A | ||||
| HIV+ | ||||
| Crude | 0.0 | -1.21 (-4.13, 1.72) | -0.24 (-4.26, 3.79) | |
| 0.417 | 0.907 | |||
| Adjusted | 0.0 | -0.32 (-2.66, 2.02) | 0.26 (-2.96, 3.48) | |
| 0.194 | 0.875 | |||
| HIV- | ||||
| Crude | 0.0 | -1.79 (-4.72, 1.14) | -0.66 (-4.73, 3.41) | |
| 0.231 | 0.748 | |||
| Adjusted | 0.0 | -3.04 (-5.48, -0.60) | -0.89 (-4.27, 2.49) | |
| 0.015 | 0.606 | |||
| Trails B | ||||
| HIV+ | Crude | 0.0 | -7.19 (-16.37, 1.98) | -8.19 (-15.74, -0.65) |
| 0.124 | 0.033 | |||
| Adjusted | 0.0 | -10.98 (-19.92, -2.04) | -10.95 (-18.43, -3.46) | |
| 0.016 | 0.004 | |||
| HIV- | Crude | 0.0 | 3.66 (-8.76, 16.08) | 1.95 (-8.02, 11.91) |
| 0.562 | 0.701 | |||
| Adjusted | 0.0 | 0.91 (-11.29, 13.12) | -3.16 (-13.32, 6.99) | |
| 0.883 | 0.540 | |||
| Stroop Interference | ||||
| HIV+ | Crude | 0.0 | 0.59 (-6.94, 8.12) | 6.36 (0.14, 12.58) |
| 0.878 | 0.045 | |||
| Adjusted | 0.0 | 0.54 (-6.55, 7.63) | 5.50 (-0.49, 11.50) | |
| 0.881 | 0.072 | |||
| HIV- | Crude | 0.0 | -0.07 (-10.57, 10.44) | 6.03 (-2.77, 14.83) |
| 0.990 | 0.179 | |||
| Adjusted | 0.0 | -3.32 (-13.49, 6.86) | 3.03 (-5.74, 11.80) | |
| 0.522 | 0.497 | |||
| Speed of Information Processing | ||||
| Symbol Digit Modalities Test | ||||
| HIV+ | Crude | 0.0 | 1.76 (-0.44, 3.96) | 1.30 (-0.50, 3.11) |
| 0.116 | 0.156 | |||
| Adjusted | 0.0 | 2.56 (0.47, 4.64) | 2.61 (0.86, 4.36) | |
| 0.016 | 0.070 | |||
| HIV- | Crude | 0.0 | 0.80 (-2.10, 3.69) | 1.06 (-1.50, 3.62) |
| 0.157 | 0.064 | |||
| Adjusted | 0.0 | 1.60 (-1.10, 4.29) | 2.45 (-0.02, 4.91) | |
| 0.245 | 0.052 | |||
| Stroop Color Naming | ||||
| HIV+ | Crude | 0.0 | -2.30 (-6.91, 2.31) | 0.24 (-3.56, 4.05) |
| 0.328 | 0.901 | |||
| Adjusted | 0.0 | -1.58 (-5.91, 2.74) | -0.27 (-3.92, 3.38) | |
| 0.473 | 0.885 | |||
| HIV- | Crude | 0.0 | 2.30 (-3.04, 7.63) | 5.65 (1.16, 10.14) |
| 0.398 | 0.014 | |||
| Adjusted | 0.0 | 0.72 (-4.32, 5.76) | 3.50 (-0.86, 7.87) | |
| 0.779 | 0.115 | |||
| Learning and Memory | ||||
| Stroop Word Recall | ||||
| HIV+ | Crude | 0.0 | 0.62 (-3.12, 4.37) | 0.62 (-2.47, 3.70) |
| 0.744 | 0.696 | |||
| Adjusted | 0.0 | 1.57 (-1.73, 4.87) | -0.40 (-3.18, 2.38) | |
| 0.349 | 0.776 | |||
| HIV- | Crude | 0.0 | 0.77 (-3.97, 5.50) | 3.29 (-0.69, 7.28) |
| 0.750 | 0.104 | |||
| Adjusted | 0.0 | -0.98 (-5.25, 3.28) | 1.16 (-2.54, 4.85) | |
| 0.650 | 0.538 | |||
A positive beta coefficient indicates a positive relationship between anthropometric measure and cognitive test score. For all tests except SDMT, a positive relationship indicates worse cognitive performance with higher level of anthropometric measure. For example, in fully adjusted models, a waist circumference ≥88 cm is related to longer Trails A completion among HIV+ compared to a waist circumference of < 80 cm.
Crude models adjust for age.
Multivariate models adjust for the covariates: age, WRAT, race, insulin, and any drug use.
Among women with HIV, a waist circumference of 88 cm or more was related to shorter completion time on Trails B (better performance); and a nonlinear relation with Trails A, such that the shortest time to completion (best performance) was observed among those in the middle category of waist circumference (80-87.9 cm). Among HIV- women, there were no associations between waist circumference and performance on any test after adjusting for potential confounders.
Waist-to-hip ratio, WHR (Table 5)
Table 5.
WHR category by cognitive test scores: the Women's Interagency HIV Study (WIHS).
| WHR category | |||||
|---|---|---|---|---|---|
| <0.85 | 0.85 - < 0.90 | 0.90 - <0.95 | ≥ 0.95+ | ||
|
| |||||
| referent | Beta (95% CI) p-value | Beta (95% CI) p-value | Beta (95% CI) p-value | ||
| Executive Function | |||||
| Trails A | |||||
| HIV+ | |||||
| Crude | 0.0 | 0.09 (-2.68, 2.86) | -0.18 (-2.95, 2.59) | 0.21 (-2.95, 2.98) | |
| 0.951 | 0.897 | 0.885 | |||
| Adjusted | 0.0 | -0.43 (-3.15, 2.28) | -1.57 (-4.34, 1.21) | -1.52 (-4.35, 1.32) | |
| 0.754 | 0.269 | 0.294 | |||
| HIV- | Crude | 0.0 | 0.69 (-2.84, 4.21) | 0.80 (-2.97, 4.56) | 1.51 (-2.34, 5.36) |
| 0.702 | 0.678 | 0.442 | |||
| Adjusted | 0.0 | -0.15 (-3.72, 3.42) | -0.64 (-4.51, 3.24) | 0.20 (-3.80, 4.19) | |
| 0.933 | 0.747 | 0.923 | |||
| Trails B | |||||
| HIV+ | Crude | 0.0 | 0.34 (-1.35, 2.03) | 0.72 (-1.00, 2.44) | 0.08 (-1.66, 1.81) |
| 0.695 | 0.410 | 0.929 | |||
| Adjusted | 0.0 | -6.85 (-15.17, 1.48) | -5.19 (-13.68, 3.30) | -4.88 (-13.62, 3.86) | |
| 0.107 | 0.231 | 0.274 | |||
| HIV- | Crude | 0.0 | 0.75 (-10.09, 11.59) | 8.88 (-2.63, 20.38) | 14.31 (2.48, 26.14) |
| 0.892 | 0.130 | 0.018 | |||
| Adjusted | 0.0 | -2.42 (-13.13, 8.28) | 4.33 (-7.18, 15.84) | 11.03 (-0.93, 22.98) | |
| 0.657 | 0.460 | 0.071 | |||
| Stroop Interference | |||||
| HIV+ | Crude | 0.0 | 6.64 (-0.66, 13.94) | -2.79 (-9.96, 4.38) | -0.06 (-7.17, 7.04) |
| 0.075 | 0.445 | 0.986 | |||
| Adjusted | 0.0 | 4.70 (-2.09, 11.49) | -4.20 (-10.92, 2.51) | -2.92 (-9.86, 4.02) | |
| 0.174 | 0.219 | 0.409 | |||
| HIV- | Crude | 0.0 | 5.12 (-4.01, 14.25) | 10.31 (-0.04, 20.66) | 8.18 (-1.83, 18.19) |
| 0.270 | 0.051 | 0.109 | |||
| Adjusted | 0.0 | 2.44 (-6.43, 11.31) | 8.68 (-1.40, 18.77) | 6.36 (-3.46, 16.18) | |
| 0.589 | 0.091 | 0.204 | |||
| Speed of Information Processing | |||||
| Symbol Digit Modalities Test | |||||
| HIV+ | Crude | 0.0 | 0.49 (-1.58, 2.57) | 1.00 (-1.08, 3.08) | 0.75 (-1.34, 2.84) |
| 0.642 | 0.344 | 0.482 | |||
| Adjusted | 0.0 | 1.41 (-0.52, 3.33) | 2.58 (0.60, 4.56) | 2.58 (0.55, 5.60) | |
| 0.153 | 0.011 | 0.013 | |||
| HIV- | Crude | 0.0 | 0.77 (-2.16, 3.69) | 1.05 (-2.07, 4.16) | -1.06 (-4.25, 2.13) |
| 0.606 | 0.509 | 0.513 | |||
| Adjusted | 0.0 | 1.83 (-0.84, 4.49) | 1.07 (-1.80, 3.95) | 0.13 (-2.85, 3.11) | |
| 0.179 | 0.464 | 0.930 | |||
| Stroop Color Naming | |||||
| HIV+ | Crude | 0.0 | 1.00 (-3.45, 5.44) | -2.92 (-7.29, 1.45) | -1.45 (-5.76, 2.87) |
| 0.659 | 0.190 | 0.510 | |||
| Adjusted | 0.0 | 0.56 (-3.57, 4.69) | -2.44 (-6.51, 1.63) | -1.97 (-6.16, 2.23) | |
| 0.789 | 0.239 | 0.358 | |||
| HIV- | Crude | 0.0 | 3.05 (-1.64. 7.74) | 3.83 (-1.48, 9.14) | 4.92 (-0.23, 10.08) |
| 0.202 | 0.156 | 0.061 | |||
| Adjusted | 0.0 | 1.28 (-3.14, 5.71) | 2.61 (-2.42, 7.64) | 3.13 (-1.78, 8.04) | |
| 0.568 | 0.308 | 0.211 | |||
| Learning and Memory | |||||
| Stroop Word Recall | |||||
| HIV+ | Crude | 0.0 | 1.88 (-1.73, 5.48) | -2.02 (-5.56, 1.52) | 0.06 (-3.44, 3.56) |
| 0.307 | 0.262 | 0.972 | |||
| Adjusted | 0.0 | 1.23 (-1.91, 4.37) | -2.08 (-5.17, 1.02) | -1.16 (-4.36, 2.03) | |
| 0.442 | 0.189 | 0.474 | |||
| HIV- | Crude | 0.0 | 4.25 (0.13, 8.36) | 1.21 (-3.44, 5.86) | 4.17 (-0.35, 8.69) |
| 0.043 | 0.609 | 0.071 | |||
| Adjusted | 0.0 | 2.47 (-1.26, 6.19) | -0.01 (-4.24, 4.22) | 2.52 (-1.61, 6.65) | |
| 0.193 | 0.995 | 0.230 | |||
A positive beta coefficient indicates a positive relationship between anthropometric measure and cognitive test score. For all tests except SDMT, a positive relationship indicates worse cognitive performance with higher level of anthropometric measure. For example, in fully adjusted models, a WHR > 0.95 is related to shorter Trails A completion time among HIV+ compared to those with a ‘healthy’ WHR of <0.85.
Crude models adjust for age.
Multivariate models adjust for the covariates: age, WRAT, race, insulin, and any drug use.
Among women with HIV, a higher WHR was associated with a better SDMT score. However, there were no other associations between WHR and cognitive performance in HIV+ or HIV- women.
Subanalyses
In subsequent analyses, ART use was evaluated as a potential confounder in multivariate models predicting cognitive test scores. Use versus no use of ART use was not found to influence the results. Over 80% of participants were on ART. Similarly, we excluded women who reported recent use of recreational drugs and the results were similar in all essential aspects.
Discussion
Among women at mid-life with HIV for at least 10 years, the association between common anthropometric measures (primarily BMI) and cognitive test performance differed depending on the cognitive domain being assessed. Perhaps these observations illustrate the severity and variable metabolic dysregulation in women with HIV.
Considering any anthropometric measure – BMI, WC, or WHR – in relation to cognition, we observed some patterns in women with HIV infection. There appears to be an association between higher BMI and better performance on the Trails B test, and the SDMT. At a significance level of p < 0.10, an obese phenotype based on BMI or WC, was associated with longer Stroop Interference time, yet higher SDMT score. Considering all observed associations, aside from Trails B and SDMT, being more overweight and obese was related to worse performance.
Similar to other reports in non-HIV samples that relate anthropometric measures to cognition, anthropometric measures of being overweight and obesity are related to worse cognitive performance. Executive function and speed of information processing are the cognitive domains most commonly associated with these measures.
The association between anthropometric measures and cognitive performance was more pronounced among HIV+ women than HIV- women. Among women in whom higher BMI and WC measures were related to better cognitive function, it may be indicative of a normalization of health due to HIV treatments and better medical and nutritional care and status, for which higher BMI and central obesity may be markers. Among HIV+ women with a BMI < 18.5 kg/m2, longer completion times on Trails A and B may be due to the detrimental influence of HIV-related illness, such as AIDS or cancer, on cognition, and/or lack of adherence to ART over time. Attempting to evaluate individuals with AIDS as a group was difficult due to a low number of participants; and adjustment for HIV disease markers was not informative
While high mid-life BMI(Fitzpatrick et al, 2009; Kivipelto et al, 2005; Whitmer et al, 2005) and central obesity (measured as waist circumference or WHR) (Gustafson et al, 2009b; Whitmer et al, 2008) have been shown to increase risk for AD in non-HIV infected populations, cross-sectional relationships with cognition in mid-life are not clear. In addition, these simple anthropometric measures reflect different aspects of body composition, and are crude estimates of body adiposity at best. A high BMI is reasonably correlated with whole body amount of adipose tissue in healthy HIV-negative adults.(Shah and Braverman, 2012) A high waist circumference is a measure of central obesity. However, a high WHR may reflect both higher waist circumference and/or lower hip circumference. In HIV+ individuals who experience lipoatrophy of the hip region, a higher WHR may be more reflective of the lipoatrophy than the central obesity. Thus, these HIV-associated alterations in anthropometric measurements may increase the variability in the measure making it a less accurate measure when compared to an uninfected population. High BMI and WC during mid-life are also related to other vascular risk factors, such as DM,(Capeau et al, 2012) hypertension, and hyperlipidemia, which increase risk for cognitive impairments and dementia in non-HIV populations. Hypertension was more prevalent in HIV+ women in this sample compared to HIV- women. However, adjustment for hypertension and insulin resistance did not alter the association of anthropometric factors with cognition in multivariate models. In HIV+ individuals, mid-life relationships of vascular risk factors and cognition are also unclear. The relationship between mid-life risks and late-life cognitive impairment is not known, but will be investigated when this cohort of women enters later stages of life.
At least one other study has investigated the relationship of anthropometric variables on cognitive function in HIV.(McCutchan et al, 2012) Similar results for BMI in relation to cognitive impairment were reported among 130 participants in the CHARTER study,(McCutchan et al, 2012) in relation to cognitive impairment. Higher BMI was protective for cognitive impairment, after adjustment for WHR among HIV+ participants. Vascular factors associated with being overweight and obese have also been related adversely to cognitive function. For example, prior cardiovascular disease, hypertension, and hypercholesterolemia have been related to measures of motor speed in 292 participants from the SMART study.(Wright et al, 2010) Hypertension has also been reported to adversely influence cognition in WIHS.(Crystal et al, 2011)
The influence of ART on cognition and overall health in HIV is mixed depending on the severity cognitive outcome being assessed and age of the patient. Data suggest no difference in the proportion of individuals with HIV-associated cognitive disorder (HAND) in the pre- versus post-ART eras,(Heaton et al, 2011) however, the prevalence of AIDS dementias, the most severe form of impairment, has fallen precipitously concurrent with optimization of medication regimens and better care overall. As HIV infected populations survive to older ages, they may be at risk for more severe cognitive impairments. This would be a new phenomenon, but is speculative at this time. ART may have cardiovascular side effects, such as atherosclerosis,(Bozzette et al, 2003; Currier, 2007) DM, and hypertension, even in children. These cardiovascular factors have been related to AD in populations without HIV infection; and cardiovascular risk factors are associated with worse cognition in persons with HIV.(Becker et al, 2009; Wright et al, 2010) While, the positive influence of ART on cognition is clear; there are several studies that provide controversial data to suggest a concurrent negative influence. Some studies show that discontinuing ART is associated with improved performance on cognitive tests;(Mary-Krause et al, 2003), (Robertson et al, 2010) and it has been speculated that certain ART regimens are deleterious for cognition.(Andrieu et al, 2011) Alternatively, ART has shown a positive effect on health, eliminating HIV and improving health status. Even so, alterations in adipose tissue, adipose tissue distribution, adipose tissue hormones, and/or lipid metabolism, may create an altered metabolic and hormonal milieu that may be undesirable for the brain.(Gustafson, 2010)
This is a large study of anthropometric measures and cognition in women with and without HIV-infection. Strengths include the large multi-ethnic participant sample, and a variety of anthropometric measures that are commonly used to estimate total and regional body fatness. The primary limitations include a limited battery of cognitive tests and cross-sectional analyses. Due to multiple comparisons, and relatively high p-values one must also consider risk for false discoveries. Only 3 p-values were < 0.005, all among HIV infected women. These included BMI < 18.5 kg/m2 being associated with worse Trails A and worse SDMT performance, and waist ≥ 88 cm associated with better Trails B performance. Our analyses were not adjusted for multiple comparisons. However, this is one of the first reports of a consistently reported risk factor for late-onset AD in relation to cognition in HIV. In summary, these data suggest the need for continued follow up of these women to determine mid-life and late-life effects of adipose tissue and adipose tissue alterations on cognition and dementia in HIV.
Acknowledgments
Supported in part by grants 1R01MH076537, 1R01MH079880, and IAID U01 318345. Data in this manuscript were collected by the Women's Interagency HIV Study (WIHS) Collaborative Study Group with centers (Principal Investigators) at New York City/Bronx Consortium (Kathryn Anastos); Brooklyn, NY (Howard Minkoff, Deborah Gustafson); 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 Coordinating Center (Stephen Gange). The WIHS is funded by the National Institute of Allergy and Infectious Diseases (UO1-AI-35004, UO1-AI-31834, UO1-AI-34994, UO1-AI-34989, UO1-AI-34993, and UO1-AI-42590) and by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (UO1-HD-32632). The study is co- funded by the National Cancer Institute, the National Institute on Drug Abuse, and the National Institute on Deafness and Other Communication Disorders. Funding is also provided by the National Center for Research Resources (UCSF-CTSI Grant Number UL1 RR024131). Dr. Gustafson received support from NIH/NIAID ARRA Supplement No. 54492, Swedish Research Council Diarienummer: 523-2005-8460, and the SUNY Research Foundation.
The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health.
We thank the women participating in WIHS for their time, cooperation, and support.
Footnotes
Conflict of Interest: All authors, Deborah R. Gustafson, MS, PhD; Michelle M. Mielke, PhD; Phyllis C. Tien, MD; Victor Valcour, MD; Mardge Cohen, MD; Kathryn Anastos, MD; Chenglong Liu, MD, PhD; Leigh Pearce, PhD; Elizabeth T. Golub, PhD; Howard Minkoff, MD; and Howard A. Crystal, MD, have nothing to declare.
References
- Implications for Reducing Chronic Disease Risk. National Academy Press; Washington, D.C: 1989. Diet and Health. [PubMed] [Google Scholar]
- The FRAM Study. Fat distribution in women with HIV infection. J Acquir Immune Defic Syndr. 2006;42:562–71. doi: 10.1097/01.qai.0000229996.75116.da. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Andrieu S, Aboderin I, Baeyens JP, Beard J, Benetos A, Berrut G, Brainin M, Cha HB, Chen LK, Du P, Forette B, Forette F, Franco A, Fratiglioni L, Gilette-Guyonnet S, Gold G, Gomez F, Guimaraes R, Gustafson D. IAGG workshop: health promotion program on prevention of late onset dementia. J Nutr Health Aging. 2011;15:562–575. doi: 10.1007/s12603-011-0142-1. [DOI] [PubMed] [Google Scholar]
- Bacon MC, von Wyl V, Alden C, Sharp G, Robison E, Hessol N, Gange S, Barranday Y, Holman S, Weber K, Young MA. The Women's Interagency HIV Study: an observational cohort brings clinical sciences to the bench. Clin Diagn Lab Immunol. 2005;12:1013–9. doi: 10.1128/CDLI.12.9.1013-1019.2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Barkan SE, Melnick SL, Preston-Martin S, Weber K, Kalish LA, Miotti P, Young M, Greenblatt R, Sacks H, Feldman J. The Women's Interagency HIV Study. WIHS Collaborative Study Group. Epidemiology. 1998;9:117–25. [PubMed] [Google Scholar]
- Becker JT, Kingsley L, Mullen J, Cohen B, Martin E, Miller EN, Ragin A, Sacktor N, Selnes OA, Visscher BR. Vascular risk factors, HIV serostatus, and cognitive dysfunction in gay and bisexual men. Neurology. 2009;73:1292–9. doi: 10.1212/WNL.0b013e3181bd10e7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Berger JR, Brew B. An international screening tool for HIV dementia. AIDS. 2005;19:2165. doi: 10.1097/01.aids.0000194798.66670.6e. [DOI] [PubMed] [Google Scholar]
- Bhaskaran K, Mussini C, Antinori A, et al. Changes in the incidence and predictors of human immunodeficiency virus-associated dementia in the era of highly active antiretroviral therapy. Ann Neurol. 2008;63:213. doi: 10.1002/ana.21225. [DOI] [PubMed] [Google Scholar]
- Bozzette SA, Ake CF, Tam HK, Chang SW, Louis TA. Cardiovascular and cerebrovascular events in patients treated for human immunodeficiency virus infection. N Engl J Med. 2003;348:702–10. doi: 10.1056/NEJMoa022048. [DOI] [PubMed] [Google Scholar]
- Capeau J, Bouteloup V, Katlama C, Bastard JP, Guiyedi V, Salmon-Ceron D, Protopopescu C, Leport C, Raffi F, Chene G. Ten-year diabetes incidence in 1046 HIV-infected patients started on a combination antiretroviral treatment. AIDS. 2012;26:303–14. doi: 10.1097/QAD.0b013e32834e8776. [DOI] [PubMed] [Google Scholar]
- Cohen RA, Gongvatana A. The persistence of HIV-associated neurocognitive dysfunction and the effects of comorbidities. Neurology. 2010;75:2052–3. doi: 10.1212/WNL.0b013e318200d833. [DOI] [PubMed] [Google Scholar]
- Croft JB, Keenan NL, Sheridan DP, Wheeler FC, Speers MA. Waist-to-hip ratio in a biracial population: measurement, implications, and cautions for using guidelines to define high risk for cardiovascular disease. J Am Diet Assoc. 1995;95:60–64. doi: 10.1016/S0002-8223(95)00014-3. [DOI] [PubMed] [Google Scholar]
- Crystal HA, Weedon J, Holman S, Manly J, Valcour V, Cohen M, Anastos K, Liu C, Mack WJ, Golub E, Lazar J, Ho A, Kreek MJ, Kaplan RC. Associations of cardiovascular variables and HAART with cognition in middle-aged HIV-infected and uninfected women. J Neurovirol. 2011;17:469–76. doi: 10.1007/s13365-011-0052-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Currier J. Report from the 14th Retrovirus Conference. Metabolic complications: lipoatrophy, lipohypertrophy, and cardiovascular risk. AIDS Clin Care. 2007;19:40–1. [PubMed] [Google Scholar]
- Fitzpatrick AL, Kuller LH, Lopez OL, Diehr P, O'Meara ES, Longstreth WT, Jr, Luchsinger JA. Midlife and late-life obesity and the risk of dementia: cardiovascular health study. Arch Neurol. 2009;66:336–42. doi: 10.1001/archneurol.2008.582. [DOI] [PMC free article] [PubMed] [Google Scholar]
- González-Scarano F, Martín'García J. The neuropathogenesis of AIDS. Nat Rev Immunol. 2005;5:69. doi: 10.1038/nri1527. [DOI] [PubMed] [Google Scholar]
- Gustafson D, Skoog I. Control of vascular risk factors. In: Wahlund TE LO, Gauthier S, editors. Vascular dementia in clinical practice. Cambridge University Press; London: 2009. [Google Scholar]
- Gustafson DR. Adiposity hormones and dementia. J Neurosci. 2010;299:30–34. doi: 10.1016/j.jns.2010.08.036. [DOI] [PubMed] [Google Scholar]
- Gustafson DR, Bäckman K, Waern M, Östling S, Guo X, Zandi PP, Mielke MM, Bengtsson C, Skoog I. Adiposity indicators and dementia over 32 years in Sweden. Neurology. 2009a;73:1559–1566. doi: 10.1212/WNL.0b013e3181c0d4b6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Gustafson DR, Bäckman K, Waern M, Östling S, Guo XX, Zandi P, Mielke MM, Bengtsson C, Skoog I. Adiposity indicators and dementia over 32 years in Sweden. Neurology. 2009b;73:1559–1566. doi: 10.1212/WNL.0b013e3181c0d4b6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Heaton RK, Clifford DB, Franklin DR, Jr, Woods SP, Ake C, Vaida F, Ellis RJ, Letendre SL, Marcotte TD, Atkinson JH, Rivera-Mindt M, Vigil OR, Taylor MJ, Collier AC, Marra CM, Gelman BB, McArthur JC, Morgello S, Simpson DM, McCutchan JA, Abramson I, Gamst A, Fennema-Notestine C, Jernigan TL, Wong J, Grant I. HIV-associated neurocognitive disorders persist in the era of potent antiretroviral therapy: CHARTER Study. Neurology. 2010;75:2087–96. doi: 10.1212/WNL.0b013e318200d727. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 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, Jernigan TL, Wong J, Grant I. HIV-associated neurocognitive disorders before and during the era of combination antiretroviral therapy: differences in rates, nature, and predictors. J Neurovirol. 2011;17:3–16. doi: 10.1007/s13365-010-0006-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Justman JE, Hoover DR, Shi Q, Tan T, Anastos K, Tien PC, Cole SR, Hyman C, Karim R, Weber K, Grinspoon S. Longitudinal anthropometric patterns among HIV-infected and HIV-uninfected women. J Acquir Immune Defic Syndr. 2008;47:312–9. doi: 10.1097/QAI.0b013e318162f597. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kaplan RC, Kingsley LA, Gange SJ, Benning L, Jacobson LP, Lazar J, Anastos K, Tien PC, Sharrett AR, Hodis HN. Low CD4+ T-cell count as a major atherosclerosis risk factor in HIV-infected women and men. AIDS. 2008;22:1615–24. doi: 10.1097/QAD.0b013e328300581d. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Kivipelto M, Ngandu T, Fratiglioni L, Viitanen M, Kareholt I, Winblad B, Helkala EL, Tuomilehto J, Soininen H, Nissinen A. Obesity and vascular risk factors at midlife and the risk of dementia and Alzheimer disease. Arch Neurol. 2005;62:1556–60. doi: 10.1001/archneur.62.10.1556. [DOI] [PubMed] [Google Scholar]
- Mansoor A, Althoff K, Gange S, Anastos K, Dehovitz J, Minkoff H, Kaplan R, Holman S, Lazar JM. Elevated NT-pro-BNP levels are associated with comorbidities among HIV-infected women. AIDS Res Hum Retroviruses. 2009;25:997–1004. doi: 10.1089/aid.2009.0038. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Mary-Krause M, Cotte L, Simon A, Partisani M, Costagliola D. Increased risk of myocardial infarction with duration of protease inhibitor therapy in HIV-infected men. AIDS. 2003;17:2479–86. doi: 10.1097/00002030-200311210-00010. [DOI] [PubMed] [Google Scholar]
- McArthur JC, Brew BJ. HIV-associated neurocognitive disorders: is there a hidden epidemic? AIDS. 2010;24:1367–70. doi: 10.1097/QAD.0b013e3283391d56. [DOI] [PubMed] [Google Scholar]
- McCutchan JA, Marquie-Beck JA, Fitzsimons CA, Letendre SL, Ellis RJ, Heaton RK, Wolfson T, Rosario D, Alexander TJ, Marra C, Ances BM, Grant I. Role of obesity, metabolic variables, and diabetes in HIV-associated neurocognitive disorder. Neurology. 2012;78:485–492. doi: 10.1212/WNL.0b013e3182478d64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Miller EN, Selnes OA, McArthur JC, Satz P, Becker JT, Cohen BA, Sheridan K, Machado AM, Van Gorp WG, Visscher B. Neuropsychological performance in HIV-1-infected homosexual men: The Multicenter AIDS Cohort Study (MACS) Neurology. 1990;40:197–203. doi: 10.1212/wnl.40.2.197. [DOI] [PubMed] [Google Scholar]
- Moroni M, Antinoria S. HIV and direct damage of organs: disease spectrum before and during the highly active antiretroviral therapy era. AIDS. 2003;17:S51. [PubMed] [Google Scholar]
- Centers for Disease Control and Prevention. Estimated HIV incidence among adults and adolescents in the United States, 2007–2010 2012a [Google Scholar]
- Centers for Disease Control and Prevention. HIV Surveillance Supplemental Report 2012. Centers for Disease Control and Prevention; Atlanta: 2012b. Monitoring selected national HIV prevention and care objectives by using HIV surveillance data—United States and 6 U.S dependent areas—2010. [Google Scholar]
- Robertson KR, Su Z, Margolis DM, Krambrink A, Havlir DV, Evans S, Skiest DJ. Neurocognitive effects of treatment interruption in stable HIV-positive patients in an observational cohort. Neurology. 2010;74:1260–6. doi: 10.1212/WNL.0b013e3181d9ed09. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Saktor N. The epidemiology of human immunodeficiency virus-associated neurological disease in the era of highly active antiretroviral therapy. J Neurovirol. 2002;8:115. doi: 10.1080/13550280290101094. [DOI] [PubMed] [Google Scholar]
- Shah N, Braverman ER. Measuring adiposity in patients: the utility of body mass index (BMI), percent body fat, and leptin. PLOS One. 2012;7:e33308. doi: 10.1371/journal.pone.0033308. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Sobieszczyk ME, Hoover DR, Anastos K, Mulligan K, Tan T, Shi Q, Gao W, Hyman C, Cohen MH, Cole SR, Plankey MW, Levine AM, Justman J. Prevalence and predictors of metabolic syndrome among HIV-infected and HIV-uninfected women in the Women's Interagency HIV Study. J Acquir Immune Defic Syndr. 2008;48:272–80. doi: 10.1097/QAI.0b013e31817af461. [DOI] [PubMed] [Google Scholar]
- Whitmer RA, Gunderson EP, Barrett-Connor E, Quesenberry CP, Jr, Yaffe K. Obesity in middle age and future risk of dementia: a 27 year longitudinal population based study. BMJ. 2005;330:1360–1364. doi: 10.1136/bmj.38446.466238.E0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Whitmer RA, Gunderson EP, Quesenberry CP, Jr, Zhou J, Yaffe K. Body mass index in midlife and risk of Alzheimer disease and vascular dementia. Curr Alzheimer Res. 2007;4:103–9. doi: 10.2174/156720507780362047. [DOI] [PubMed] [Google Scholar]
- Whitmer RA, Gustafson DR, Barrett-Connor E, Haan MN, Gunderson EP, Yaffe K. Central obesity and increased risk of dementia more than three decades later. Neurology. 2008;71:1057–64. doi: 10.1212/01.wnl.0000306313.89165.ef. [DOI] [PubMed] [Google Scholar]
- Wright EJ, Grund B, Robertson K, Brew BJ, Roediger M, Bain MP, Drummond F, Vjecha MJ, Hoy J, Miller C, Penalva de Oliveira AC, Pumpradit W, Shlay JC, El-Sadr W, Price RW. Cardiovascular risk factors associated with lower baseline cognitive performance in HIV-positive persons. Neurology. 2010;75:864–73. doi: 10.1212/WNL.0b013e3181f11bd8. [DOI] [PMC free article] [PubMed] [Google Scholar]