JAIDS Journal of Acquired Immune Deficiency Syndromes:
Oral Glucose Tolerance and Insulin Sensitivity Are Unaffected by HIV Infection or Antiretroviral Therapy in Overweight Women
Danoff, Ann MD*; Shi, Qiuhu PhD†; Justman, Jessica MD‡; Mulligan, Kathleen PhD§; Hessol, Nancy PhD, MSPH§; Robison, Esther PhD¶; Lu, Dalian PhD¶; Williams, Tania¶; Wichienkuer, Paula BS; Anastos, Kathryn MD¶
From *Harbor Health Care (Manhattan), VAH & New York University School of Medicine, New York, NY; †New York Medical College, Valhalla, NY; ‡Bronx Lebanon Hospital Center; Bronx, NY; §University of California at San Francisco, CA; and ¶Montefiore Medical Center, Bronx, NY.
Received for publication April 5, 2004; July 21, 2004.
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); Washington, DC, Metropolitan Consortium (Mary Young); The Connie Wofsy Study Consortium (Ruth Greenblatt, Phyllis Tien); Los Angeles County/Southern California Consortium (Alexandra Levine); Chicago Consortium (Mardge Cohen); Data Coordinating Center (Alvaro Mun˜oz). The WIHS is funded by the National Institute of Allergy and Infectious Diseases and the National Institute of Child Health and Human Development, with supplemental funding from the National Cancer Institute, the National Institute on Drug Abuse, and the National Institute of Dental Research. U01-AI-35004, U01-AI-31834, U01-AI-34994, U01-AI-34989, U01-HD-32632, U01-AI-34993, U01-AI-42590, N01-AI-35161, MO1-RR-00079, MO1-RR-00083, DK54615 (K.M.).
Reprints: Ann Danoff, Harbor Health Care (Manhattan), Veteran's Hospital, 423 East 23rd Street, New York, NY 10010 (e-mail: email@example.com).
Objective: To assess the frequency of diabetes, prediabetes, and insulin resistance among a subset of participants in the Women's Interagency HIV Study (WIHS).
Design: Cross-sectional substudy nested within a prospective multicenter cohort study. Women underwent 75 g oral glucose tolerance testing. Diagnoses of diabetes and prediabetes were made according to the American Diabetes Association criteria, and insulin resistance was determined by area under the curve insulin and homeostasis model assessment values.
Setting: Six urban clinical sites in the United States (Brooklyn, NY; Bronx, NY; Washington, DC; Chicago, IL; San Francisco, CA; Los Angeles, CA) participate in the entire WIHS. The Bronx, NY, and San Franscisco, CA, WIHS sites participated in this substudy.
Participants: A total of 258 women, 88 HIV negative, 74 HIV positive not on highly active antiretroviral therapy (HAART), and 96 HIV positive taking HAART were enrolled in the study.
Main Outcomes: Prevalence of diabetes, prediabetes, and insulin resistance was compared among the HIV-uninfected and HIV-infected women.
Results: The frequency of diabetes, prediabetes, or insulin resistance was unrelated to HIV status or antiretroviral treatment. Increasing body mass index was the only characteristic associated with the combined endpoints of diabetes and prediabetes (odds ratio = 1.104, P = 0.0002).
Conclusions: Routine oral glucose tolerance testing of HIV-infected women is not supported by these findings. Elucidation of putative perturbations from HIV or antiretroviral medications requires direct studies of insulin resistance and β-cell function.
Soon after the introduction of highly active antiretroviral therapy (HAART) in 1996, a number of abnormalities of glucose metabolism were noted in HIV-infected individuals, including insulin resistance, impaired glucose tolerance, and type 2 diabetes mellitus. This was in contrast to the relative increase in insulin sensitivity (ie, antidiabetogenic effect) in HIV-infected men that had been described in the pre-HAART era.1,2 Several case reports3-5 followed soon after an alert from the Food and Drug Administration6 describing a possible association between protease inhibitor (PI) use and hyperglycemia. Data from larger cohorts subsequently emerged reporting insulin resistance and impaired glucose tolerance in as many as 60% of HIV-infected patients on HAART.6-12
A number of hypotheses have been proposed and investigated to elucidate whether the metabolic derangements observed in HIV-infected individuals are a consequence of HIV infection per se,13 side effects of antiretroviral medications,13-18 a function of recovery,19 or result from a combination of factors.19 It has been proposed that the metabolic derangements, which include dyslipidemia, and body habitus changes in addition to hyperglycemia and insulin resistance may be interrelated and, as such, resemble the metabolic syndrome (syndrome X).20 However, elegant studies have revealed that in HIV-uninfected persons both dyslipidemia21 and insulin resistance22 may occur independently of each other and independent of changes in body fat22 and as a direct result of exposure to antiretroviral medications.23 Insulin resistance can occur within 30 minutes of ingestion of a single dose of indinavir24; some of the molecular mechanisms responsible for the insulin resistance have been characterized.25,26
Previous work from our group27 demonstrated that PI use was associated with a nearly 3-fold increase in the risk of self-reported incident diabetes in a cohort of 1785 nonpregnant women. Based on our earlier work, as well as the existing literature in men, we hypothesized that diabetes would be more common in HIV-infected women as compared with HIV-uninfected, body mass index (BMI)-matched controls. Further, we predicted that insulin resistance, as determined by serum insulin area under the curve (AUC) during an oral glucose tolerance test (OGTT) and by homeostasis model assessment (HOMA) scores would be significantly greater in HIV-infected women compared with HIV-uninfected controls. To test these hypotheses, additional studies that focused on glycemic control and insulin sensitivity were performed in a subset of women and are the subject of this report.
The Women's Interagency HIV Study (WIHS) is a multicenter prospective study of the natural history of HIV-1 infection in women, conducted in 5 locations within the United States: New York City (2 sites), Washington DC, Chicago, Southern California, and the San Francisco Bay area. The WIHS methods and baseline cohort characteristics have been described previously.28 Briefly, from October 1994 through November 1995, 2628 women (2059 HIV-1 seropositive and 569 seronegative) were enrolled. A second enrollment occurred from October 2001 through September 2002, with the addition of 735 HIV-positive and 407 HIV-negative women to the cohort. For this 2nd wave of enrollees, medical record abstraction was performed for all those reporting HAART use at enrollment, to ascertain their pre-HAART CD4 and HIV RNA and to verify date of HAART initiation and regimen. Consent materials were reviewed and approved by the committee on human experimentation at each of the collaborating institutions and informed consent was obtained from the participants. Every 6 months, WIHS participants were interviewed using a structured questionnaire and received a physical examination. Multiple gynecologic and blood specimens were collected at each visit. At each study visit, self-reported antiretroviral use in the period since the previous study visit was assessed by interviewers, stating the name of each drug, both by brand and generic drug name, and showing participants' photo medication cards
Women who met the eligibility criteria at the Bronx/Manhattan and San Francisco Bay area WIHS consortia were invited to participate in this cross-sectional substudy, with enrollment from April 2001 through March 2003. Women with known type 1 diabetes, pregnant women, and women taking glucocorticoids, estrogenic, or progestational agents (including megestrol acetate) were excluded from this report. Because of technical requirements of other aspects of this study to be reported elsewhere (ie, size limitations of dual-energy x-ray absorptiometry scanner), women weighing >264 pounds (122 kg) were also excluded.
Definition of race was based on self-categorization, and for this analysis was stratified as: African American (Hispanic and non-Hispanic), white (Hispanic and non-Hispanic), and Latina (women identifying as Hispanic but neither black nor white). Women who reported being African American and Hispanic were thus classified as African American, and white women who also reported being Hispanic were classified as white.
A standard 75-g OGTT was performed following an 8- to 12-hour overnight fast; blood glucose and insulin levels were obtained at 0, 30, 60, 90, and 120 minutes. Timing of ingestion of antiretroviral medications was not standardized; some women may have omitted medication on the morning of the OGTT. BMI was calculated from the weight obtained at the study visit during which the OGTT was performed. Participants wore street clothes and removed coats and other outer garments, shoes, socks, keys, and heavy necklaces before being weighed. Height was measured with a height measure, attached to the weighing scale (Bronx), or wall-mounted taximeter (San Francisco), at the baseline visit.
Glucose analysis was performed on the Roche Modular automated system (Roche Diagnostics Corp., Indianapolis, IN), utilizing the hexokinase method. Glucose determination was validated using standard antibodies purchased from the National Institute of Standards and Technology (SRM) 909a and SRM 909b. Imprecision is determined by internal quality control (BioRad Laboratories) performed using 2 levels, 3 times per day, over the past 30 days. Level 1 is 62 mg/dL, SD 0.9, CV = 1.45%. Level 2 is 355 mg/dL, SD 5.4, CV = 1.52%. Insulin analysis was performed on the Immulite 2000 (Diagnostic Products Corp., Los Angeles, CA), with a solid-phase 2-site sequential chemiluminescent immunometric assay utilizing a bead coated with monoclonal murine anti-insulin antibody and polyclonal chicken anti-insulin antibody conjugated to alkaline phosphatase. Accuracy is validated by comparison to external proficiency materials provided by College of American Pathologists (CAP) and New York State Department of Health. Imprecision is determined by internal quality control (DPC) performed using 3 levels, 3 times per day, over the past 30 days. Level 1 is 5.3 μIU/mL, SD 0.82, CV = 15.47%. Level 2 is 19.5 μIU/mL, SD 1.34, CV = 6.87%. Level 3 is 56.9 μIU/mL, SD 5.07, CV = 8.91%. There is an 8% cross-reactivity with proinsulin in this assay, at proinsulin levels of 10 ng/mL, respectively. Hemoglobin A1c was performed on the CLC385 instrument (Primus Corp., Kansas City, MO), utilizing an automated high-performance boronate affinity liquid chromatography system for the quantitative measurement of the percentage of glycated hemoglobins in whole blood. Imprecision is determined by internal quality control (BioRad Laboratories) performed using 2 levels, 3 times per day, over the past 30 days. Level 1 is 5.1%, SD 0.19, CV = 3.72%. Level 2 is 11.2%, SD 0.61, CV = 5.45.
Quantification of HIV-1 RNA in plasma was performed using the isothermal nucleic acid sequence-based amplification (NASBA/Nuclisens) method (Organon Teknika Corp., Durham, NC) in laboratories participating in the NIH/NIAID Virology Quality Assurance Laboratory proficiency testing program. The lower limit of quantification was 80 copies/mL using 1.0 mL sample input.
Lymphocyte subsets were quantified using standard flow cytometric methods in laboratories participating in the NIH/NIAID Flow Cytometry Quality Assessment Program.29
Prediabetes and diabetes were defined using the American Diabetes Association criteria.30,31 A fasting plasma glucose from ≥100-125 mg/dL or a 2-hour glucose value following a 75-g glucose challenge between ≥140 mg/dL-199 mg/dL was considered prediabetes. A fasting plasma glucose of ≥126 mg/dL or a 2-hour glucose of ≥200 mg/dL following a 75-g glucose challenge was considered diabetes. Participants who self-reported diabetes and were taking exogenous hypoglycemic or antihyperglycemic agents (2 HIV-uninfected women, 2 HIV-infected women not on HAART, and 3 HIV-infected women on HAART) were excluded from the OGTT but were included as diabetic in analyses. An OGTT was performed and data included in all analyses for women self-reporting diabetes who were not taking hypoglycemic or antihyperglycemic agents (2 HIV-uninfected women, 3 HIV-infected women not on HAART, and 1 HIV-infected woman on HAART), and they were classified as normal, prediabetic, or diabetic based on their OGTT results. The HOMA32 and the insulin sensitivity index (ISI)33 were used to evaluate insulin sensitivity. Additional outcome variables included hemoglobin A1C and AUC for both glucose and insulin. BMI categories are based on National Heart, Lung, and Blood Institute (NHLBI) guidelines.34
The primary exposure variables were HIV infection and use of antiretroviral therapy. HAART was defined to include the use of: ≥2 nucleoside analogue reverse transcriptase inhibitors (NRTIs) in combination with at least 1 PI or nonnucleoside reverse transcriptase inhibitor (NNRTI); 1 NRTI in combination with at least 1 PI and at least 1 NNRTI; an abacavir-containing regimen of ≥3 NRTIs in the absence of PIs and NNRTIs. Combinations of zidovudine and stavudine with a PI or NNRTI were not considered HAART. For purposes of this analysis, we also included in the HAART group women reporting use of a 3-NRTI regimen that contained tenofovir and women reporting use of 1 NRTI with 2 PIs. Approximately 48% of the women classified as current HAART users were on a PI-containing regimen. Fewer than 5% of women included in the no-HAART group had past exposure to PI therapy. Antiretroviral category was assigned based on medication use the day on which the OGTT was performed at the study visit that preceded. Assignment of antiretroviral category was also made based on medication used (HAART vs. no-HAART, and PI vs. no-PI) within 3 days preceeding the day on which the OGTT was performed. Menpause was defined as the absence of a menstrual period for ≥1 year, and exercise was categorized as > or <6 hours/wk as reported on the day of the dual-energy x-ray absorptiometry scan.
Demographic and clinical characteristics were organized by HIV status and HAART use, specifically categorizing participants as HIV negative, HIV positive not on HAART, and HIV positive on HAART. Categorical variables, such as race and menopausal status, were presented by number and percentage, and continuous variables such as age and BMI were summarized by mean, median, and SD in each of these 3 categories. The χ2 test was used to compare percentage across these 3 groups, and analysis of variance (ANOVA) was employed to test means across these 3 groups. In summarized ANOVA with adjustment of factors and covariates, least-squares mean and its standard error were reported. Since age, BMI, and CD4 count were statistically significant across HIV status and HAART use groups, they were categorized for multivariate analyses. Age was stratified as age <45 vs. >45 years; BMI was categorized as <25, 25-30, and >30; and median of nadir CD4 was used to categorize high CD4 count and low CD4 count. Primary outcome variables included diabetic status, AUC of insulin, AUC of glucose, and HOMA and were analyzed by each of these 3 categorical variables (age, BMI, and nadir CD4). Race and menopausal status were believed to have an impact on diabetic status; therefore, primary outcomes were also analyzed by race and menopausal status.
The primary outcome of diabetic status by HIV status and HAART use was analyzed using the Cochran Mantel-Haenszel statistic controlling for age, BMI, race, menopausal status, and CD4 count. If the overall test was statistically significant, pairwise comparisons (HIV negative vs. HIV positive not on HAART, HIV negative vs. HIV positive on HAART, and HIV positive not on HAART vs. HIV positive on HAART) were performed. Pairwise P value was adjusted by Bonferroni criteria.
Multiple logistic regression was used to analyze the association between diabetic status and HIV status and HAART use. Diabetic status was regrouped as diabetes vs. prediabetes plus normal, and diabetes plus prediabetes vs. normal. The model included HIV status and HAART use, age, BMI, race, menopausal status, and CD4 cell count. Unadjusted and adjusted odds ratio were reported with their P value. A P value <0.05 was considered as statistically significant.
A total of 272 potential participants were screened for eligibility; 14 HIV-positive women were excluded because they were taking megestrol acetate. The demographic and clinical characteristics of the 258 included women are shown in Table 1. The HIV-uninfected women were younger (P < 0.0001), had a significantly higher mean BMI (P = 0.001), and had a greater likelihood of smoking that approached statistical significance (P = 0.001). The mean and median BMIs were in the overweight range (25-30 kg/m2) for all groups. Among the HIV-positive women, the BMI was higher in those not on HAART compared with those on HAART. The HIV-negative and HIV-positive women did not differ significantly with respect to race or exercise level, and these characteristics did not differ among the HIV-positive women on HAART compared with those not on HAART. The prevalence of hepatitis C did not differ among the 3 groups. Among the HIV-positive women, the nadir CD4 cell count was significantly lower (P < 0.0010) but the CD4 cell count at the most recent core visit was not significantly different (P = 0.4345) in the women on HAART when compared with those not on HAART. Neither the peak HIV-1 RNA nor the HIV-1 RNA at the most recent core visit was significantly different in the HIV-infected women in different treatment groups.
Analysis of the unadjusted metabolic data did not reveal any association of race, smoking status, menopausal status, amount of exercise, and alcohol intake with diabetes, prediabetes, glucose or insulin AUCs, or differences in HOMA or hemoglobin A1C values (data not shown). Higher BMI was the only unadjusted factor that was associated with prediabetes and diabetes and higher AUC insulin levels, with group P values of 0.0038 and 0.0077, respectively.
When the OGTT data were adjusted for BMI, age, race, and menopausal status, no significant differences were noted among the HIV-positive and HIV-negative women, or between the HIV-positive women in different treatment categories (Table 2). No significant difference (P = 0.79) was observed in the prevalence of prediabetes, which was detected in 17% of the HIV-uninfected women, 17.6% of the HIV-infected women not on HAART, and 11.5% of the HAART users, when using either fasting or 2-hour postglucose challenge criteria. Using either the fasting or the 2-hour postglucose challenge criteria to define diabetes, again, no significant differences were noted among the groups, with 10.2% of the HIV-uninfected, 8.1% of the HIV-infected women not on HAART, and 4.2% of the women on HAART being classified as diabetic. Whether the classification of diabetes and prediabetes was made based on fasting or 2-hour postglucose challenge values, the frequency of these diagnoses occurred at similar rates, independent of HIV or HAART category (data not shown). No significant differences were observed in the HOMA score or ISI between the HIV-positive and HIV-negative women, or between the HIV-positive women in different treatment categories. Mean HgA1C levels were in the normal range in all groups and were not different from each other based on HIV status or treatment group. Interestingly, as demonstrated in Figure 1, at every time point during the OGTT, glucose levels were lower and insulin levels higher in the HIV-infected women, compared with the uninfected women.
Because no overall statistically significant differences in primary outcome of diabetes status by HIV status were noted when analyzed controlling for age, BMI, race, menopausal status, or CD4 count, meaningful pairwise comparisons could not be performed between HIV-negative vs. HIV-positive women, or between the HIV-positive women taking vs. those not taking HAART. Exploratory analysis of pairwise comparisons did not reveal any differences between the HIV-infected women taking HAART compared with those not on HAART, or those on PI compared with those not on PI-containing regimens.
The unadjusted and adjusted odds ratios for diabetes, compared with normal and pre-patients with diabetes by HIV infection status, HAART use, BMI, nadir CD4 count, and menopausal status using multiple logistic regression can be seen in Table 3. Table 4 shows the same analysis, but here comparing all glucose-intolerant women (diabetes plus prediabetes) to normal. The only parameter that emerged as significant in this model was BMI (P = 0.0081 and 0.0502 in the unadjusted and adjusted models, respectively, for diabetes (3), and P < 0.0001 and P = 0.0002 in the unadjusted and adjusted model, respectively, for any glucose intolerance (4). HIV infection or HAART use had no effect on diabetes status in this model.
In view of the emergence of BMI as the single most important predictor of diabetes and insulin resistance, and because the BMIs of the 3 groups of women were imperfectly matched, an additional analysis of the data was undertaken. The cohort was subdivided into BMI categories based on NHLBI criteria of normal, overweight, and obese (ie, BMI <25, BMI ≥25-<30, and >30, respectively). Within the BMI-matched categories, no difference was observed in AUCglucose, AUCinsulin, HOMA, prevalence of prediabetes or diabetes, based on HIV serostatus or HIV treatment class (data not shown).
There are numerous reports describing insulin resistance, glucose intolerance, and diabetes in HIV-infected individuals, including those on regimens containing PIs22-27,35,36,37 or NRTIs.35,38,39 In contrast, in our study of 258 well-characterized HIV-infected women and their demographically matched, HIV-uninfected counterparts, no statistically significant association was observed between HIV serostatus or antiretroviral regimen and fasting blood sugar, glucose AUC, or insulin AUC in response to a standard OGTT, insulin resistance (assessed by HOMA and ISI, measures generally agreed to be good surrogates for insulin resistance), HgA1C, or frequency of diabetes or prediabetes. The only feature that emerged as significantly associated with diabetes and prediabetes was BMI.
Our study differs from previous reports in several important ways. The demographic characteristics of our participants are distinct from those of most other large cohort studies. Our participants are exclusively female, 81% African American or Latina, largely (72%) overweight or obese, in general less sick (mean CD4 cell count >300 cells/μL), and have been on HAART for a longer duration (median time on HAART 40.45 ± 22.31 months) than participants in previously published reports. In contrast, many other large cohort studies reporting hyperglycemia, insulin resistance, or diabetes were composed predominantly or exclusively of men of European descent9,10,12 and do not include demographically and clinically well-matched HIV-negative or HIV-positive untreated control groups. In addition, the mean BMI in other reported cohorts is significantly lower than ours. In one moderate-sized series,11 composed entirely of women, none of the women were obese, and 59/75 (79%) of the women had AIDS-related wasting. The differences in demographic and clinical profiles between our study and other large cohorts may partially explain the discrepancy between our findings and previously published results and may explain the rates of diabetes, prediabetes, and fasting hyperinsulinemia that we observed. Although HIV-uninfected men and women are at equal risk for diabetes, African Americans and Latinos are at higher risk for diagnosed and undiagnosed diabetes and prediabetes.40,41 Increasing BMI is also a powerful predictor of type 2 diabetes, prediabetes, and insulin resistance.42 Therefore, by virtue of race and BMI, a higher prevalence of diabetes and prediabetes would be anticipated in our cohort and might also influence the host response of our participants to HIV or antiretroviral therapies.
Because of practical limitations, previous investigations of glucose metabolism in large cohort studies of HIV-infected individuals have relied on self-definition27 or nonrigorous definitions of hyperglycemia based on random blood glucose measures,12 rather than on fasting or 2-hour postchallenge glucose values. Although random glucose values can be informative, and self-definition is considered valid in large epidemiologic studies,43 use of these criteria for categorization of diabetes status may result in misclassification and should be interpreted with caution. Among 13 women in our study who self-reported diabetes, 6 participants were not taking hypoglycemic or antihyperglycemic medications and underwent OGTT. Based on the OGTT results, 3 participants (2 HIV uninfected, 1 HIV infected on HAART) were classified as normal, and 3 participants (all HIV infected, none on HAART) had normal fasting blood sugar values and 2-hour postglucose challenge value that fell into the “impaired glucose challenge” range. We must reconcile a previous report from our own group27 that demonstrated a nearly 3-fold increase in diabetes in HIV-infected women on a PI-containing regimen with our current observations. In that report, the population included the entire WIHS cohort and the definition of diabetes was based on self-report. In addition, approximately 25% of the women in our earlier report were on a regimen containing indinavir (a PI known to cause abnormal glucose transport), compared with only 6.4% in the present cohort. Similar to the women included in this substudy, the HIV-uninfected women in our prior report had a slightly higher BMI than the HIV-infected women (26.4 vs. 25.5 kg/m2). However, the average BMI in both the HIV-infected and uninfected women included in our earlier report was substantially lower than the women included in this study. The higher BMI in these participants (as well as general better health, gender difference, and different methodologies) may also explain why we did not find evidence of decreased glucose in the HIV-infected women not on HAART, in contrast to findings in HIV-infected men.1,2 It is possible that the powerful impact of higher BMI in the women included in this report, with a 10% increased risk for each single-unit increase in BMI, obscures what may be subtle contributions from HIV infection itself or various antiretroviral regimens.
Several additional detailed investigations of glucose homeostasis in HIV-infected individuals seem to contradict our findings. In contrast to our study, in which timing of ingestion of antiretroviral medications was not standardized, when antiretroviral medications were ingested immediately before performance of a euglycemic-hyperinsulinemic clamp, glucose transport and insulin-mediated glucose uptake were reduced.36 Another study that demonstrated peripheral insulin resistance in skeletal muscle and adipose tissue, as well as reduced β-cell function in 13 HIV-infected individuals (7 women and 6 men, notably with mean BMIs that were in the overweight range), is also relevant.37 Although glucose dysregulation was observed at 12 weeks following the introduction of a PI, like the women in our study, baseline glucose and insulin levels were comparable in HIV-uninfected and HIV-infected participants (including those who had been taking combination nucleoside analogues and a non-NRTI for at least 12 months prior to study entry). Although differences between acute vs. chronic effects of antiretroviral therapies may account for these observations, we did not find any differences in the metabolic parameters evaluated, whether we classified women into antiretroviral categories according to the medications taken at the study visit before their OGTT (mean 55 days), or based on antiretroviral regimen (HAART vs. no-HAART or PI vs. no-PI) within the 3 days preceeding the OGTT. A third group44,45 has reported higher mean fasting plasma glucose (5.44 ± 0.11 vs. 5.05 ± 0.11 mM) concentrations in the face of comparable fasting insulin levels, and higher glucose and insulin levels in response to an OGTT in HIV-infected in persons (94% male) on a PI-containing regimen compared with those on a non-PI regimen. They noted 6-fold variability in the steady-state plasma glucose concentration (measured during an octreotide, glucose, insulin infusion), did not find any difference between the PI and non-PI treatment groups, and found only a modest correlation between HOMA values and direct measures of insulin resistance. Their conclusions, that insulin sensitivity varied widely in HIV-infected individuals, irrespective of PI treatment, that the magnitude of the effect of PIs on insulin sensitivity was modest, and that indirect measures of insulin resistance had poor predictive value in HIV-infected individuals, may be relevant to our study.
Although in this large cohort study of overweight women, we did not observe an increased frequency of abnormalities of glucose homeostasis in HIV-infected women compared with well-matched HIV-uninfected controls, we hope these findings are interpreted with appropriate caution. Our study was powered to find a 3-fold difference and it is possible that a smaller but still clinically significant effect may occur. We believe that both in vitro25,26 and in vivo data22,36,37,44,45 provide compelling evidence that antiretroviral therapy may be associated with rapid and clinically relevant insulin resistance and dysregulation of glucose metabolism, and we share the concern regarding growing evidence45 that, as in HIV-uninfected individuals,46 even subtle abnormalities may place individuals at increased cardiovascular risk.47 We encourage further investigation aimed at determining the incidence of clearly defined glucose intolerance, insulin resistance, and frank diabetes in HIV-infected individuals as compared with demographically matched HIV-uninfected persons, as well as investigations aimed at elucidating the relationship between glucose regulation, dyslipidemia, and body habitus changes. We encourage investigators to be cognizant of the potent effect of BMI on glucose regulation and to consider the precise timing that has elapsed between the ingestion of antiretroviral agents and the performance of tests aimed at investigating glucose metabolism. Further, we think that screening HIV-infected individuals with OGTTs in the nonresearch setting is of limited value, unless they have other well-defined risk factors that warrant such testing.48-50 Rather, for the individual patient potentially at increased risk for glucose intolerance, diabetes, and associated cardiovascular complications (such as the overweight African American and Latina women in this study), it would be sensible to urge a prudent lifestyle that emphasizes healthy eating and exercise habits, smoking abstinence, and acquisition or maintenance of a normal body weight. Further longitudinal follow-up of this and other cohorts is critical. In addition, direct in vivo measures of insulin sensitivity and β-cell function, coupled with in vitro investigations studying the effect of HIV or its treatments, will be most illuminating.
The authors thank Dr. Edmund Bini, Harbor Healthcare VAH and New York University School of Medicine, for critical review and suggestions.
1. Hommes MJ, Romijn JA, Endert E, et al. Insulin sensitivity and insulin clearance in human immunodeficiency virus-infected men. Metabolism
2. Heyligenberg R, Romijn JA, Hommes MJ, et al. Non insulin-mediated glucose uptake in human immunodeficiency virus-infected men. Clin Sci
3. Visnegarwala F, Krause KL, Musher DM. Severe diabetes associated with protease inhibitor therapy. Ann Intern Med
4. Dube MP, Johnson DL, Currier JS, et al. Protease inhibitor-associated hyperglycaemia. Lancet
5. Eastone JA, Decker CF. New-onset diabetes mellitus associated with use of protease inhibitor. Ann Intern Med
6. Lumpkin MM. Reports of diabetes and hyperglycemia in patients receiving protease inhibitors for the treatment of human immunodeficiency virus (HIV). FDA Public Health Advisory, June 21, 1997.
7. Walli R, Goebel FD, Demant T. Impaired glucose tolerance and protease inhibitors. Ann Intern Med
8. Walli R. Treatment with protease inhibitors associated with peripheral insulin resistance and impaired oral glucose tolerance in HIV-1 infected patients. AIDS
9. Carr A, Samaras K, Burton S, et al. A syndrome of peripheral lipodystrophy, hyperlipidaemia and insulin resistance in patients receiving HIV protease inhibitors. AIDS
10. Carr A, Samaras K, Thorisdottir A, et al. Diagnosis, prediction, and natural course of HIV-1 protease-inhibitor-associated lipodystrophy, hyperlipidaemia, and diabetes mellitus: a cohort study. Lancet
11. Hadigan C, Miller K, Corcoran C, et al. Fasting hyperinsulinemia and changes in regional body composition in human immunodeficiency virus-infected women. J Clin Endocrinol Metab
12. Tsiodras S, Mantzoros C, Hammer S, et al. Effects of protease inhibitors on hyperglycemia, hyperlipidemia, and lipodystrophy: a 5-year cohort study. Arch Intern Med
13. Saint-Marc T, Partisani M, Paegat-Martin I, et al. Fat distribution evaluated by computed tomography and metabolic abnormalities in patients undergoing anti-retroviral therapy: preliminary results of the LIPOCO study. AIDS
14. Kino T, Gragerov A, Kopp JB, et al. The HIV-1 virion-associated protein vpr is a coactivator of the human glucocorticoid receptor. J Exp Med
15. Carr A, Samaras K, Chisholm DJ, et al. Pathogenesis of HIV-1 protease inhibitor-associated peripheral lipodystrophy, hyperlipidaemia, and insulin resistance. Lancet
16. Goldberg B, Strickler RB. HIV protease and the pathogenesis of AIDS. Res Virol
17. Danoff A, Ling WL. Protease inhibitors do not interfere with prohormone processing. Ann Intern Med
18. Brinkman K, Smeitink JA, Romijn JA, et al. Mitochondrial toxicity induced by nucleoside-analogue reverse-transcriptase inhibitors is a key factor in the pathogenesis of antiretroviral-therapy-related lipodystrophy. Lancet
19. Grunfeld C, Tien P. Difficulties in understanding the metabolic complications of acquired immune deficiency syndrome. Clin Infect Dis
. 2003;37(Suppl 2):S43-S46.
20. Grundy SM, Brewer HB Jr, Cleeman JI, et al. American Heart Association; National Heart, Lung, and Blood Institute. Definition of metabolic syndrome: Report of the National Heart, Lung, and Blood Institute/American Heart Association conference on scientific issues related to definition. Circulation
21. Purnell JQ, Zambon A, Knopp RH, et al. Effect of ritonavir on lipids and post-heparin lipase activities in normal subjects. AIDS
22. Mulligan K, Grunfeld C, Tai VW, et al. Hyperlipidemia and insulin resistance are induced by protease inhibitors independent of changes in body composition in patients with HIV infection. J Acquir Immune Defic Syndr
23. Noor MA, Lo JC, Mulligan K, et al. Metabolic effects of indinavir in healthy HIV-seronegative men. AIDS
24. Noor M, Seneviratne T, Aweeka F, et al. Indinavir acutely inhibits insulin-stimulated glucose disposal in humans: a randomized placebo-controlled study. AIDS
25. Murata H, Hruz PW, Mueckler M. The mechanism of insulin resistance caused by HIV protease inhibitor therapy. J Biol Chem
26. Hruz PW, Mueckler MM. Structural analysis of the GLUT1 facilitative glucose transporter. Mol Membr Biol
27. Justman JE, Benning L, Danoff A, et al. Protease inhibitor use and the incidence of diabetes mellitus in a large cohort of HIV-infected women. J Acquir Immune Defic Syndr
28. Barkan SE, Melnick SL, Preston-Martin S, et al. The Women's Interagency HIV Study. WIHS Collaborative Study Group. Epidemiology
29. Calvelli T, Denny T, Paxton H, et al. Guidelines for flow cytometric immunophenotyping. Cytometry
30. The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Diabetes Care
31. The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care
32. Mathews D, Hosker J, Rudenski A, et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia
33. Matsuda M, De Fronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care
34. NHLBI Obesity Education Initiative Expert Panel. Clinical Guidelines on the Identification, Evaluation and Treatment of Overweight and Obesity in Adults. Bethesda, MD: National Heart, Lung, and Blood Institute; 1998.
35. Hadigan C, Corcoran C, Stanley T, et al. Fasting hyperinsulinemia in human immunodeficiency virus-infected men: relationship to body composition, gonadal function, and protease inhibitor use. J Clin Endocrinol Metab
36. Behrens G, Dejam A, Schmidt H, et al. Impaired glucose tolerance, beta cell function and lipid metabolism in HIV patients under treatment with protease inhibitors. AIDS
37. Woerle HJ, Mariuz PR, Meyer C, et al. Mechanisms for the deterioration in glucose tolerance associated with HIV protease inhibitor regimens. Diabetes
38. Saint-Marc T, Partisani M, Poizot-Martin I, et al. A syndrome of peripheral fat wasting (lipodystrophy) in patients receiving long-term nucleoside analogue therapy. AIDS
39. Mallal SA, John M, Moore CB, et al. Contribution of nucleoside analogue reverse transcriptase inhibitors to subcutaneous fat wasting in patients with HIV infection. AIDS
40. Harris MI. Epidemiological correlates of NIDDM in Hispanics, whites, and blacks in the US population. Diabetes Care
41. Centers for Disease Control and Prevention (CDC). Prevalence of diabetes and impaired fasting glucose in adults-United States, 1999-2000. MMWR Morb Mortal Wkly Rep
42. Health implications of obesity. NIH Consens Statement
43. Centers for Disease Control. Trends in the prevalence and incidence of self-reported diabetes mellitus: United States, 1980-1994. MMWR Morb Mortal Wkly Rep
44. Chu JW, Abbasi F, Beatty GW, et al. Methods for quantifying insulin resistance in human immunodeficiency virus-positive patients. Metabolism
45. Beaty G, Khalili M, Abbasi F, et al. Quantification of insulin-mediated glucose disposal in HIV-infected individuals: comparison of patients treated and untreated with protease inhibitors. J Acquir Immune Defic Syndr
46. Friis-Moller N, Sabin CA, Weber R, et al. Data Collection on Adverse Events of Anti-HIV Drugs (DAD) Study Group. Combination antiretroviral therapy and the risk of myocardial infarction. N Engl J Med
47. Despres J-P, LaMarchi B, Mauriegi P, et al. Hyperinsulinemia as an independent risk factor for ischemic heart disease. N Engl J Med
48. Schambelan M, Benson CA, Carr A, et al. International AIDS Society-USA. Management of metabolic complications associated with antiretroviral therapy for HIV-1 infection: recommendations of an International AIDS Society-USA panel. J Acquir Immune Defic Syndr
49. Marie C. Gelato insulin and carbohydrate dysregulation. Clin Infect Dis
50. American Diabetes Association. Screening for type 2 diabetes. Diabetes Care
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