JAIDS Journal of Acquired Immune Deficiency Syndromes:
Implementation and Operational Research: Clinical Science
High Risk of Obesity and Weight Gain for HIV-Infected Uninsured Minorities
Taylor, Barbara S. MD, MS*,†,‡; Liang, Yuanyuan PhD†,‡,§; Garduño, L. Sergio MA*; Walter, Elizabeth A. MD*,‖; Gerardi, Margit B. PhD, WHNP*,¶; Anstead, Gregory M. MD, PhD*,‖; Bullock, Delia MD*; Turner, Barbara J. MD, MSEd†,‡
*Division of Infectious Diseases, Department of Medicine, University of Texas Health Science Center San Antonio, San Antonio, TX;
†Research to Advance Community Health (ReACH), University of Texas Health Science Center San Antonio, San Antonio, TX;
‡University of Texas School of Public Health, San Antonio Regional Campus, San Antonio, TX;
§Department of Epidemiology and Biostatistics, University of Texas Health Science Center San Antonio, San Antonio, TX;
‖Division of Infectious Diseases, Department of Medicine, South Texas Veterans Health Care System, San Antonio, TX; and
¶Department of Family & Community Health Systems, School of Nursing, University of Texas Health Science Center San Antonio, San Antonio, TX.
Correspondence to: Barbara S. Taylor, MD, MS, Division of Infectious Diseases, Department of Medicine, UTHSCSA, 7703 Floyd Curl Drive, MSC 7881, San Antonio, TX 78229-3900 (e-mail: email@example.com).
B.J.T. received salary support from University Health System as Director of Health Outcomes Improvement. The development of the South Texas HIV Cohort data repository was funded by a pilot grant to B.S.T. from the UTHSCSA Institute for Integration of Medicine and Science's Clinical and Translational Science Award (8UL1TR000149). B.S.T. receives support from the National Institute for Allergy and Infectious Diseases (K23AI081538). The remaining authors have no funding or conflicts of interest to disclose.
Two previous analyses of this cohort have been presented at the Society for General Internal Medicine 35th Annual Meeting (B.S.T., L.S.G., G.M.A., E.A.W., D.B., and B.J.T. on “Evaluating Failures in Weight and Diabetes Management in an HIV Cohort”), May 10, 2012, Orlando, FL and Texas Infectious Diseases Society Annual Meeting on “Overlapping Epidemics: HIV, Diabetes, and Obesity in the San Antonio HIV Cohort,” June 10, 2012, San Antonio, TX.
Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (www.jaids.com).
Received May 24, 2013
Accepted September 10, 2013
Background: Obesity and HIV disproportionately affect minorities and have significant health risks, but few studies have examined disparities in weight change in HIV-seropositive (HIV+) cohorts.
Objective: To determine racial and health insurance disparities in significant weight gain in a predominately Hispanic HIV+ cohort.
Methods: Our observational cohort study of 1214 nonunderweight HIV+ adults from 2007 to 2010 had significant weight gain [≥3% annual body mass index (BMI) increase] as the primary outcome. The secondary outcome was continuous BMI over time. A 4-level race–ethnicity/insurance predictor reflected the interaction between race–ethnicity and insurance: insured white (non-Hispanic), uninsured white, insured minority (Hispanic or black), or uninsured minority. Logistic and mixed-effects models adjusted for baseline BMI, age, gender, household income, HIV transmission category, antiretroviral therapy type, CD4+ count, plasma HIV-1 RNA, observation months, and visit frequency.
Results: The cohort was 63% Hispanic and 14% black; 13.3% were insured white, 10.0% uninsured white, 40.9% insured minority, and 35.7% uninsured minority. At baseline, 37.5% were overweight, 22.1% obese. Median observation was 3.25 years. Twenty-four percent of the cohort had significant weight gain, which was more likely for uninsured minority patients than insured whites [adjusted odds ratio = 2.85, 95% confidence intervals (CIs): 1.66 to 4.90]. The rate of BMI increase in mixed-effects models was greatest for uninsured minorities. Of 455 overweight at baseline, 29% were projected to become obese in 4 years.
Conclusions and Relevance: In this majority Hispanic HIV+ cohort, 60% were overweight or obese at baseline, and uninsured minority patients gained weight more rapidly. These data should prompt greater attention by HIV providers for prevention of obesity.
Obesity has become a leading health threat in the United States. In the National Health and Nutrition Examination Survey from 2009 to 2010, 35.7% of US adults were obese.1 Obesity is more prevalent in Hispanic and non-Hispanic black populations and in persons of lower socioeconomic status (SES).2–6 Minority race and low SES are also associated with increased risk for HIV infection.7,8 Thus, communities most severely affected by the HIV epidemic are also more likely to have a high prevalence of obesity.7,9 However, few studies have examined the health disparities in the prevalence of obesity and weight gain in HIV-infected (HIV+) populations.
Traditionally, the focus of HIV providers has been on preventing HIV-related wasting, weight loss, and lipodystrophy.10–12 With the advent of highly active antiretroviral therapy (ART), HIV-specific morbidity and mortality have diminished, whereas non-HIV–specific conditions such as cardiovascular disease have grown as health threats for HIV+ persons.13–17 In this environment, providers may need to pay greater attention for preventing obesity and related conditions such as diabetes and cardiovascular disease.18–24
The prevalence of obesity in HIV+ cohorts ranges from 17% to 32% in cross-sectional studies.24–31 However, point prevalence studies do not elucidate weight change patterns that are indicative of the future severity of this problem. Previous studies of weight change in HIV+ cohorts have focused on the first 12–24 months on ART, when weight gain may be considered beneficial,12,29,32,33 or on military cohorts with low baseline rates of obesity.34,35 To the best of our knowledge, longitudinal analyses of weight changes have not been conducted in HIV+ cohorts on long-term ART.
We examined change in body mass index (BMI) over a 4-year time frame in a Hispanic-majority HIV+ cohort receiving care from the largest HIV clinic in south-central Texas. This region is greatly affected by obesity. In 2010, 32.4% of adult residents in south-central Texas were obese, and 66.3% were either overweight or obese.36 Because the vast majority of the cohort is receiving chronic ART, we hypothesized that the prevalence of obesity would approximate that observed in the local population. We hypothesized that there would be significant disparities in weight gain such that uninsured minorities would be the most severely affected by significant weight gain, as seen in general populations.2,4,6,37 Furthermore, we hypothesized that health insurance status, as a correlate of SES,38,39 would modify the association of race–ethnicity with weight gain, such that uninsured minorities would be the most severely affected by significant weight gain, as in general populations.2,4–6,40–42
Description of the South Texas HIV Cohort
The South Texas HIV Cohort includes patients receiving care from January 1, 2007 to December 31, 2010 in the Family Focused AIDS Clinical Treatment and Services clinic. This clinic is the largest HIV treatment center in south-central Texas and located in a publicly funded county hospital affiliated with an academic medical center in San Antonio, Texas. Study data were obtained from an electronic medical record (EMR) system and included demographics, health insurance information, physiologic measures, clinical diagnoses, laboratory data, visit data, and prescribed medications. Cohort patients were ≥18 years old at their first clinic encounter and not known to be incarcerated per Institutional Review Board specifications. If patients under 18 years of age were seen in the clinic, EMR data were censored before their 18th birthday. HIV diagnosis was validated by an ICD-9-CM code for HIV infection (v08 or 042.xx) in the EMR or a visit to the HIV clinic and confirmatory laboratory results (positive HIV ELISA and Western blot, or plasma HIV-1 RNA level >1000 copies/mL). The 19 questionable cases were resolved by chart review. The resultant cohort totaled 1890 individuals who received longitudinal care in the HIV clinic.
For our analysis of weight change, we selected patients with: (1) at least 2 HIV clinic visits during the observation period from January 1, 2007 to December 31, 2010, (2) at least 6 months between consecutive BMIs, (3) no pregnancy in this time frame, and (4) white, black, or Hispanic race–ethnicity because other racial-ethnic groups comprised only 1.5% of the cohort. We excluded patients with missing plasma HIV-1 RNA or initial CD4+ count data. We also excluded patients who were underweight (BMI, <18.5 kg/m2 at baseline measurement) because weight gain for underweight individuals is often a treatment goal. Our final cohort totaled 1214 individuals (Fig. 1). Of the 959 excluded patients, 20% were women; median age was 39 years (intraquartile range, 30–47); and race–ethnicity was 34% white, 42% Hispanic, 17% black, and 7% other.
The primary study outcome was significant weight gain specified dichotomously as ≥3% annual increase in BMI, calculated as the difference between the first (baseline) and last BMI in the observation period divided by the observation time in years. We selected this outcome because a 3% annual weight gain leads to obesity in only 3 years among persons at the midpoint of the overweight range (eg, 27.5 kg/m2), and it is 5–10 times greater than the average increase in BMI for the general US population, which is 3.4% in men and 5.2% in women over a 10-year period.43,44 To calculate change in BMI, we excluded heights that differed by >10 cm for an individual (5.4% of all measurements) and used each patient's average height to calculate BMI using the formula: weight/(average height during observation period).2 All BMIs <15 kg/m2 or >50 kg/m2 and any BMI that changed more than ±5% from the previous recorded BMI were manually checked, and apparent data entry errors were excluded (406 of 16,451 or 2.5%).
Self-reported race–ethnicity was categorized as Hispanic, non-Hispanic black (black), or non-Hispanic white (white). Health insurance status, based on the most common type for all visits, was classified as: (1) insured (ie, Medicare, Medicaid, and all private insurance programs) or (2) uninsured (ie, medical care provided through a county or federal financial assistance plan, available on a sliding scale for those with incomes <300% of the Federal Poverty Level). Insurance status serves as an indicator of SES as in other studies.38,39 We specified the hypothesized interaction between insurance and race/ethnicity in a 4 category variable: (1) insured white, (2) uninsured white, (3) insured Hispanic or black (minority), or (4) uninsured minority.
These characteristics included age at first clinic visit in the observation period, gender, and mean household income per year for the patient's residential zip code as an alternative measure of SES.45 Self-reported HIV transmission categories were heterosexual sex, men who have sex with men, injection drug use (IDU), and other, including unknown or missing. The IDU category includes those men who have sex with men reporting IDU due to small sample size of that dual risk category (n = 13).
BMI is measured in the clinic before each provider visit using one of 2 regularly calibrated standing scales to calculate weight. Height is calculated using a stadiometer, and BMI is calculated as weight in kg/(height in meters)2.” The first or baseline BMI in the observation period was categorized as normal (18.5 to <25 kg/m2), overweight (25 to <30 kg/m2), or obese (≥30 kg/m2).46 Immunologic status, based on the first CD4+ cell count in the observation period (hereafter initial CD4+ cell count), was categorized as <200, 200–349, or ≥350 cells per microliter. Because of changes in plasma HIV-1 RNA assay threshold during the observation period, virologic failure was defined as any 2 plasma HIV-1 RNA levels >1000 copies per milliliter at least 24 weeks after starting ART. Diagnosis of diabetes mellitus was based on ICD-9-CM diagnosis codes 250.XX at ≥2 visits or hemoglobin A1c ≥6.5%; and hypertension from ICD-9-CM codes (401.xx at ≥2 visits) or 2 blood pressure measurements ≥140 mm Hg systolic or ≥90 mm Hg diastolic. Hepatitis C Virus (HCV) infection was determined by positive HCV antibody test or 2 ICD-9-CM codes for chronic active HCV. ART type was based on EMR prescription records during the observation period: (1) receipt of ART without a protease inhibitor (PI) prescription, (2) receipt of ART with a PI prescription, and (3) no prescriptions for ART, because of the known association of PIs with metabolic syndrome, obesity, and weight change in previous studies.29,32,47
Health Care Utilization Variables
The per-patient observation period was the time between the first and last available BMI measurement within the 2007 to 2010 observation period. The number of HIV clinic visits with documented weights during the observation period was used as a metric of care intensity, with those patients in the lowest quartile (<7 visits) characterized as receiving infrequent HIV clinic follow-up.
We first examined the interaction between race–ethnicity and health insurance status with the primary outcome, significant annual weight gain. We then estimated multivariate logistic regression models to examine the association of race–ethnicity/insurance status with significant weight gain after adjustment for demographic, clinical, and health care utilization variables. We excluded HIV transmission risk from the final model because it was not significant in ours or other studies.29 Because of collinearity with insurance type, we also excluded mean household income in residential zip code from the final model. We excluded indicators for diabetes or hypertension because these conditions may result from weight gain instead of being in the causal pathway. HCV status was excluded because it did not contribute specificity to any of the models. Interactions between individual predictors were examined using second-order interaction terms in the logistic regression model.
In sensitivity analyses, we excluded the few patients who were not treated with ART (4%) and conducted a comparison between only Hispanic and non-Hispanic white patients. We also examined an alternative specification of the outcome as absolute change in BMI per year over the observation period. These additional analyses did not change our conclusions so they not reported.
We examined the longitudinal trajectory of BMI change using all BMI measurements for each patient over the observation period in a mixed-effects model, including both a random intercept and a random slope and using continuous BMI values as the response variable. The fully adjusted model included: the 4-level race/ethnicity-insurance variable, years since baseline BMI, age at the first visit, length of observation, gender, virologic failure, initial CD4+ cell count, initial BMI, ART type, and infrequent HIV clinic follow-up. Second-order interactions between explanatory variables were also examined.
For all analyses, predictor variables were considered statistically significant if associated with a P value of <0.05% and 95% confidence intervals (CIs) were used. Pearson χ2, Kruskal–Wallis H test, and logistic regression analyses were conducted using PASW Statistics (Version 17.0, Armonk, New York), and mixed effects models were generated using SAS (Version 9.2, Cary, North Carolina), GLIMMIX procedure.
Of 1890 HIV+ patients receiving care in the HIV clinic from 2007 to 2010, 1214 met study inclusion criteria (Fig. 1). The study cohort was 62.8% Hispanic, 23.4% white, and 13.8% black. Only 7% had private insurance, 33% had Medicare, 14% had Medicaid, and 46% were uninsured. At the start of the observation period (hereafter referred to as baseline), 37.5% of the cohort was overweight and 22.1% obese (Table 1). Of 946 patients who were not obese at baseline, 112 (11.8%) became obese during follow-up. Median observation period was 3.25 years; 45 patients (3.7%) in the cohort had an observation period of 6 months to 1 year, the remainder had ≥1 year. Significant weight gain, defined as a ≥3% annual increase in BMI,43 was observed for 24.0% (n = 291) of the cohort. Baseline BMI categories for those patients who experienced significant weight gain were 148 (50.9%) normal weight, 95 (32.6%) overweight, and 48 (16.5%) obese.
All patient characteristics differed significantly by the 4-level race–ethnicity/insurance status variable except for CD4+ category and virologic failure (Table 1). Uninsured minorities were younger, more likely to be women, and less likely to receive PI-based ART. Regardless of insurance type, minority patients were more likely to be overweight or obese [minority 62.4% vs. whites 50.4%; odds ratio (OR), 1.63; 95% CI: 1.25 to 2.13; P <0.001], and more likely to have diabetes (minority 16.8% vs. white 10.2%; OR, 1.77; CI: 1.16 to 2.70; P = 0.007). Insured patients were more likely to be overweight or obese at baseline (insured 57% vs. uninsured 43%; OR, 1.30; 95% CI: 1.03 to 1.64; P = 0.025). For the distribution of population characteristics by insurance category, see Table S1 (see Supplemental Digital Content, http://links.lww.com/QAI/A468).
In the unadjusted analysis, significant weight gain was more likely for uninsured whites (OR, 2.17; 95% CI: 1.18 to 3.98) and uninsured minorities (OR, 3.33; CI: 2.04 to 5.44) but insured minorities did not differ when compared with insured whites (Table 2). Patients with a normal baseline BMI had increased odds of significant weight gain compared with those who were obese at baseline, but overweight and obese patients did not differ significantly. Patients with severe immunosuppression (initial CD4+ cell count <200 cells/μL) had greater odds of significant weight gain than patients with initial CD4+ cell count ≥350 cells per microliter. Patients with virologic failure were less likely to have significant weight gain.
After adjustment for demographic and clinical variables, the adjusted odds ratio of significant weight gain was 2.85 (CI: 1.66 to 4.90) for uninsured minorities compared with whites with insurance but race–ethnicity/insurance groups did not differ significantly (Table 2). Persons with a normal baseline BMI had nearly 60% higher adjusted odds of significant weight gain compared with those who were obese at baseline. Severe immunosuppression was also associated with increased adjusted odds of significant weight gain than those with initial CD4 ≥350 cells per microliter.
A fully adjusted mixed-effects model including all BMI measurements revealed that uninsured minorities had a significantly more rapid increase in BMI over the observation time than other race–ethnicity/insurance categories (Fig 2; see Table S1, Supplemental Digital Content, http://links.lww.com/QAI/A468). For all 1214 patients in the cohort, the proportion of obese patients was projected to rise from 22.1% at baseline to 31.3% after 4 years. Of the 455 patients who were overweight at baseline, 131 (28.8%) were projected to become obese after 4 years (Table 3). The number of patients projected to be obese also varied by race–ethnicity/insurance category; the adjusted mixed-effects model revealed that 26% of insured whites, 22% of uninsured whites, 30% of insured minorities, and 38% of uninsured minorities were predicted to be obese at 4 years from baseline. Patients with severe immunosuppression and those without virologic failure had a significantly greater annual increases in BMI than those with less severe immunosuppression or with virologic failure (P < 0.001 and P = 0.04, respectively, see Table S2, Supplemental Digital Content, http://links.lww.com/QAI/A468). Compared with obese patients, patients in other BMI categories had significantly greater annual increases in BMI.
In a predominately Hispanic HIV+ cohort, 60% were overweight or obese at baseline, higher than most cross-sectional studies of weight in HIV+ patients,25–28 and our prediction model estimates that overweight/obesity will increase to 66% over 4 years. Over the same observation period, 2007–2010, data from the San Antonio Metropolitan Statistical Area show that overweight/obesity in the surrounding population dropped from 69% to 63%,36 indicating that our cohort may be at more risk than the general population. A significant interaction between race–ethnicity and health insurance status, used as a proxy for SES, demonstrated that uninsured minorities had over 3 times greater odds of significant weight gain than white persons with insurance. Interestingly, insured minorities and uninsured whites did not differ significantly from insured whites after adjustment. The increased risk of weight gain for uninsured minorities was observed for both a dichotomous outcome measure (≥3% BMI increase/yr) and a mixed-effects model examining longitudinal change in BMI as a continuous outcome over a median 3.25 years of follow-up.
Cardiovascular disease has become a major cause of morbidity and mortality in persons with HIV infection.13–17,48–51 Because overweight- and obesity-related complications, such as diabetes mellitus, are associated with an increased risk of cardiovascular disease, prevention of excessive weight gain in HIV-infected persons may represent a currently overlooked opportunity for primary prevention. Although recent research suggests that being overweight may reduce the risk of death in general populations, obesity still carries major health risks.52,53 Nationally, the prevalence of overweight and obesity increased rapidly from the 1960s to 2004, but remained relatively stable over the past decade.54 This is not the case for our HIV-infected cohort, in whom one quarter were gaining weight at a rate over 5 times faster than the general population.43,44 Our analysis of longitudinal changes in BMI predicts that nearly 30% of HIV+ patients who are overweight at baseline will become obese within 4 years.
The association between race–ethnicity and weight gain has not been observed by other investigators, but their minority populations have been dominantly African American.29,32,34 The increased representation of Hispanics in this cohort may be the reason for this discrepancy, as Hispanic men, who comprise the majority of this cohort, have the highest prevalence of overweight/obesity in the general population, compared with non-Hispanic white and non-Hispanic black men.2,31 In the general US population, racial-ethnic minorities and those with lower childhood and adult SES have greater lifetime weight gain, and these trends seem to be consistent with the patterns observed in this study.55 We did not observe significant differences by gender, HIV transmission category, or mean household income in the residential zip code, an alternative measure of SES. Because women comprised only 20% of our cohort, we were likely underpowered to find gender differences. We did not observe the association between ART that includes PIs and significant weight gain that was reported in 2 studies with a primarily black minority population,29,32 but the US Military HIV Natural History Study also did not find this association.34
There are several limitations to this analysis. Our study population received care from only 1 HIV clinic, albeit the largest such clinic in south-central Texas, and although most patients receive care within our county health care system, any outside clinical encounters would not be documented. We did not have a direct measure of SES or time on ART, and some patients only had 2 BMI measurements, though only 3.7% of cohort had <12 months observation time. We also lacked information about the reasons for weight gain, which for some patients and their providers may be a goal of treatment. Before the advent of potent ART, obesity was found to be protective against both mortality and disease progression.28,56–60 Recent studies suggest that overweight may be associated with improved immune response to ART; however, obesity was associated with a less robust immune response.33,35,61 To address this limitation, we excluded underweight patients at baseline and adjusted for the level of immunodeficiency. We also conservatively examined a threshold for significant weight gain that is at least 5 times higher than that observed in the general population.43,44
In this primarily Hispanic cohort in longitudinal HIV care, we observed a high prevalence of overweight and obesity at baseline accompanied by significant weight gain, especially for uninsured minority patients, over more than 3 years of follow-up. If confirmed in other cohorts, these data suggest that HIV providers may need to be as attentive to addressing excessive weight gain as they are at managing weight loss in their patients. To avert severe obesity-related complications for patients with HIV infection, it may be necessary to integrate clinic-based interventions for weight control.62–66 Our data suggest that uninsured minority patients should be a primary focus for such interventions, though the overall prevalence of obesity in the cohort and the high baseline prevalence of obesity in insured patients suggest that a clinic-wide intervention may be necessary. It is also important to explore patients' perceptions about weight, particularly among minority groups who may consider overweight and obesity to be an indication of health.67,68 This study is unique in examining longitudinal changes in weight in a primarily Hispanic HIV+ cohort, but similar analyses need to be conducted in other HIV cohorts to determine whether the alarming trends in weight gain that we observed are a health concern for HIV+ patients nationally.
The authors gratefully recognize University Health System (UHS) for their support of this project. Specific thanks are due to Lisa Wammack, Tracy Jeffers, and Michelle Silva of UHS for their assistance in developing the data repository for the South Texas HIV Cohort, and all of the patients and providers at the UHS Family Focused AIDS Clinical Treatment & Services clinic.
1. Ogden CL, Carroll MD, Kit BK, et al.. Prevalence of obesity in the United States, 2009–2010. In: National Center for Health Statistics Data Brief No. 82, ed. Atlanta, GA: Centers for Disease Control and Prevention; 2012.
2. Wang Y, Beydoun MA. The obesity epidemic in the United States–gender, age, socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis. Epidemiol Rev. 2007;29:6–28.
3. Centers for Disease Control and Prevention (CDC). Vital signs: state-specific obesity prevalence among adults—United States, 2009. MMWR Morb Mortal Wkly Rep. 2010;59:951–955.
4. Robert SA, Reither EN. A multilevel analysis of race, community disadvantage, and body mass index among adults in the US. Soc Sci Med. 2004;59:2421–2434.
5. Zhang Q, Wang Y. Trends in the association between obesity and socioeconomic status in U.S. adults: 1971 to 2000. Obes Res. 2004;12:1622–1632.
6. Zhang Q, Wang Y. Socioeconomic inequality of obesity in the United States: do gender, age, and ethnicity matter? Soc Sci Med. 2004;58:1171–1180.
7. Hall HI, Song R, Rhodes P, et al.. Estimation of HIV incidence in the United States. JAMA. 2008;300:520–529.
8. Centers for Disease Control and Prevention. Characteristics associated with HIV infection among heterosexuals in urban areas with high AIDS prevalence—24 cities, United States, 2006-2007. MMWR Morb Mortal Wkly Rep. 2011;60:1045–1049.
9. Prejean J, Song R, Hernandez A, et al.. Estimated HIV incidence in the United States, 2006-2009. PLoS One. 2011;6:e17502.
10. Grinspoon S, Mulligan K. Weight loss and wasting in patients infected with human immunodeficiency virus. Clin Infect Dis. 2003;36(suppl 2):S69–S78.
11. Gallant JE, Staszewski S, Pozniak AL, et al.. Efficacy and safety of tenofovir DF vs stavudine in combination therapy in antiretroviral-naive patients: a 3-year randomized trial. JAMA. 2004;292:191–201.
12. Shikuma CM, Zackin R, Sattler F, et al.. Changes in weight and lean body mass during highly active antiretroviral therapy. Clin Infect Dis. 2004;39:1223–1230.
13. Achhra AC, Amin J, Law MG, et al.. Immunodeficiency and the risk of serious clinical endpoints in a well studied cohort of treated HIV-infected patients. AIDS. 2010;24:1877–1886.
14. Triant VA. HIV infection and coronary heart disease: an intersection of epidemics. J Infect Dis. 2012;205(suppl 3):S355–S361.
15. Triant VA, Lee H, Hadigan C, et al.. Increased acute myocardial infarction rates and cardiovascular risk factors among patients with human immunodeficiency virus disease. J Clin Endocrinol Metab. 2007;92:2506–2512.
16. Sackoff JE, Hanna DB, Pfeiffer MR, et al.. Causes of death among persons with AIDS in the era of highly active antiretroviral therapy: New York City. Ann Intern Med. 2006;145:397–406.
17. Islam FM, Wu J, Jansson J, et al.. Relative risk of cardiovascular disease among people living with HIV: a systematic review and meta-analysis. HIV Med. 2012;13:453–468.
18. Smit E, Skolasky RL, Dobs AS, et al.. Changes in the incidence and predictors of wasting syndrome related to human immunodeficiency virus infection, 1987-1999. Am J Epidemiol. 2002;156:211–218.
19. Mocroft A, Sabin CA, Youle M, et al.. Changes in AIDS-defining illnesses in a London Clinic, 1987-1998. J Acquir Immune Defic Syndr. 1999;21:401–407.
20. Hogg R, Samji H, Cescon A, et al.. Temporal changes in life expectancy of HIV+ individuals: North America. Paper presented at: 19th Conference on Retroviruses and Opportunistic Infections; March 5–8, 2012; Seattle, WA.
21. Rodger A, Lodwick R, Schechter M, et al.. Mortality in patients with well-controlled HIV and high CD4 counts in the cART arms of the SMART and ESPRIT Randomized Clinical Trials compared to the general population. Paper presented at: 19th Conference on Retroviruses and Opportunistic Infections; March 5–8, 2012; Seattle, WA.
22. Bogers RP, Bemelmans WJ, Hoogenveen RT, et al.. Association of overweight with increased risk of coronary heart disease partly independent of blood pressure and cholesterol levels: a meta-analysis of 21 cohort studies including more than 300 000 persons. Arch Intern Med. 2007;167:1720–1728.
23. Schafer I, von Leitner EC, Schon G, et al.. Multimorbidity patterns in the elderly: a new approach of disease clustering identifies complex interrelations between chronic conditions. PLoS One. 2010;5:e15941.
24. Kim DJ, Westfall AO, Chamot E, et al.. Multimorbidity patterns in HIV-infected patients: the role of obesity in chronic disease clustering. J Acquir Immune Defic Syndr. 2012;61:600–605.
25. Amorosa V, Synnestvedt M, Gross R, et al.. A tale of 2 epidemics: the intersection between obesity and HIV infection in Philadelphia. J Acquir Immune Defic Syndr. 2005;39:557–561.
26. Crum-Cianflone N, Tejidor R, Medina S, et al.. Obesity among patients with HIV: the latest epidemic. AIDS Patient Care STDS. 2008;22:925–930.
27. Mariz CA, Albuquerque Mde F, Ximenes RA, et al.. Body mass index in individuals with HIV infection and factors associated with thinness and overweight/obesity. Cad Saude Publica. 2011;27:1997–2008.
28. Jones CY, Hogan JW, Snyder B, et al.. Overweight and human immunodeficiency virus (HIV) progression in women: associations HIV disease progression and changes in body mass index in women in the HIV epidemiology research study cohort. Clin Infect Dis. 2003;37(suppl 2):S69–S80.
29. Tate T, Willig AL, Willig JH, et al.. HIV infection and obesity: where did all the wasting go? Antivir Ther. 2012;17:1281–1289.
30. Hodgson LM, Ghattas H, Pritchitt H, et al.. Wasting and obesity in HIV outpatients. AIDS. 2001;15:2341–2342.
31. Buchacz K, Baker RK, Palella F, et al.. Disparities in prevalence of key chronic diseases by gender and race/ethnicity among antiretroviral-treated HIV-infected adults in the US. Antivir Ther. 2013;18:65–75.
32. Lakey W, Yang LY, Yancy W, et al.. From wasting to obesity: initial antiretroviral therapy and weight gain in HIV-infected persons. AIDS Res Hum Retroviruses. 2013;29:435–440.
33. Koethe JR, Jenkins CA, Shepherd BE, et al.. An optimal body mass index range associated with improved immune reconstitution among HIV-infected adults initiating antiretroviral therapy. Clin Infect Dis. 2011;53:952–960.
34. Crum-Cianflone N, Roediger MP, Eberly L, et al.. Increasing rates of obesity among HIV-infected persons during the HIV epidemic. PLoS One. 2010;5:e10106.
35. Crum-Cianflone NF, Roediger M, Eberly LE, et al.. Impact of weight on immune cell counts among HIV-infected persons. Clin Vaccin Immunol. 2011;18:940–946.
36. Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention; 2007–2010.
37. Centers for Disease Control and Prevention (CDC). Differences in prevalence of obesity among black, white, and hispanic adults—United States, 2006–2008. MMWR Morb Mortal Wkly Rep. 2009;58:740–744.
38. DeNavas-Walt C, Proctor BD, Smith JC. Income, poverty, and health insurance coverage in the United States: 2010. In: U.S. Census Bureau, Current Population Reports, P60-239. Washington, DC: U.S. Government Printing Office; 2011.
39. Monheit AC, Vistnes JP. Race/ethnicity and health insurance status: 1987 and 1996. Med Care Res Rev. 2000;57(suppl 1):11–35.
40. Haas JS, Adler NE. The Causes of Vulnerability: Disentangling the Effects of Race, Socioeconomic Status and Insurance Coverage on Health. Washington, DC: The Institute of Medicine; 2001.
41. Adler NE, Rehkopf DH. U.S. disparities in health: descriptions, causes, and mechanisms. Annu Rev Public Health. 2008;29:235–252.
42. Haas JS, Lee LB, Kaplan CP, et al.. The association of race, socioeconomic status, and health insurance status with the prevalence of overweight among children and adolescents. Am J Public Health. 2003;93:2105–2110.
43. Yanovski JA, Yanovski SZ, Sovik KN, et al.. A prospective study of holiday weight gain. N Engl J Med. 2000;342:861–867.
44. Williamson DF, Kahn HS, Remington PL, et al.. The 10-year incidence of overweight and major weight gain in US adults. Arch Intern Med. 1990;150:665–672.
45. U.S. Census Bureau; Census 2000, Summary File 3, Table DP-3; generated by L. Sergio Garduno; using American FactFinder. Available at: http://factfinder2.census.gov
. Accessed April 10, 2012.
46. World Health Organization. Obesity: Preventing and Managing the Global Epidemic. Report of a WHO Consultation. Geneva, Switzerland: World Health Organization; 2000.
47. Silva M, Skolnik PR, Gorbach SL, et al.. The effect of protease inhibitors on weight and body composition in HIV-infected patients. AIDS. 1998;12:1645–1651.
48. Stein JH, Hsue PY. Inflammation, immune activation, and CVD risk in individuals with HIV infection. JAMA. 2012;308:405–406.
49. Obel N, Thomsen HF, Kronborg G, et al.. Ischemic heart disease in HIV-infected and HIV-uninfected individuals: a population-based cohort study. Clin Infect Dis. 2007;44:1625–1631.
50. Guaraldi G, Orlando G, Zona S, et al.. Premature age-related comorbidities among HIV-infected persons compared with the general population. Clin Infect Dis. 2011;53:1120–1126.
51. Currier JS, Taylor A, Boyd F, et al.. Coronary heart disease in HIV-infected individuals. J Acquir Immune Defic Syndr. 2003;33:506–512.
52. Flegal KM, Kit BK, Orpana H, et al.. Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA. 2013;309:71–82.
53. Golubic R, Ekelund U, Wijndaele K, et al.. Rate of weight gain predicts change in physical activity levels: a longitudinal analysis of the EPIC-Norfolk cohort. Int J Obes (Lond). 2013;37:404–409.
54. Flegal KM, Carroll MD, Ogden CL, et al.. Prevalence and trends in obesity among US adults, 1999-2008. JAMA. 2010;303:235–241.
55. Clarke P, O'Malley PM, Johnston LD, et al.. Social disparities in BMI trajectories across adulthood by gender, race/ethnicity and lifetime socio-economic position: 1986-2004. Int J Epidemiol. 2009;38:499–509.
56. Shor-Posner G, Campa A, Zhang G, et al.. When obesity is desirable: a longitudinal study of the Miami HIV-1-infected drug abusers (MIDAS) cohort. J Acquir Immune Defic Syndr. 2000;23:81–88.
57. Guenter P, Muurahainen N, Simons G, et al.. Relationships among nutritional status, disease progression, and survival in HIV infection. J Acquir Immune Defic Syndr. 1993;6:1130–1138.
58. Palenicek JP, Graham NM, He YD, et al.. Weight loss prior to clinical AIDS as a predictor of survival. Multicenter AIDS Cohort Study Investigators. J Acquir Immune Defic Syndr Hum Retrovirol. 1995;10:366–373.
59. Wheeler DA, Gibert CL, Launer CA, et al.. Weight loss as a predictor of survival and disease progression in HIV infection. Terry Beirn Community Programs for Clinical Research on AIDS. J Acquir Immune Defic Syndr Hum Retrovirol. 1998;18:80–85.
60. Womack J, Tien PC, Feldman J, et al.. Obesity and immune cell counts in women. Metabolism. 2007;56:998–1004.
61. Crum-Cianflone NF, Roediger M, Eberly LE, et al.. Obesity among HIV-infected persons: impact of weight on CD4 cell count. AIDS. 2010;24:1069–1072.
62. ter Bogt NC, Bemelmans WJ, Beltman FW, et al.. Preventing weight gain by lifestyle intervention in a general practice setting: three-year results of a randomized controlled trial. Arch Intern Med. 2011;171:306–313.
63. ter Bogt NC, Bemelmans WJ, Beltman FW, et al.. Preventing weight gain: one-year results of a randomized lifestyle intervention. Am J Prev Med. 2009;37:270–277.
64. Tsai AG, Wadden TA, Rogers MA, et al.. A primary care intervention for weight loss: results of a randomized controlled pilot study. Obesity (Silver Spring). 2010;18:1614–1618.
65. Jay M, Gillespie C, Schlair S, et al.. The impact of primary care resident physician training on patient weight loss at 12 months. Obesity (Silver Spring). 2013;21:45–50.
66. Engelson ES, Agin D, Kenya S, et al.. Body composition and metabolic effects of a diet and exercise weight loss regimen on obese, HIV-infected women. Metabolism. 2006;55:1327–1336.
67. Barroso CS, Peters RJ, Johnson RJ, et al.. Beliefs and perceived norms concerning body image among African-American and Latino teenagers. J Health Psychol. 2010;15:858–870.
68. Paeratakul S, White MA, Williamson DA, et al.. Sex, race/ethnicity, socioeconomic status, and BMI in relation to self-perception of overweight. Obes Res. 2002;10:345–350.
HIV; obesity; observational cohort; health disparities; weight gain
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