The association between HIV infection and diabetes mellitus is poorly understood and complicated by the differential prevalence of risk factors for diabetes mellitus in HIV-infected persons compared with HIV-uninfected persons [1–3]. There is general agreement that the traditional risk factors for diabetes mellitus (increasing age, minority race, obesity) are still responsible for most of the increased risk in the HIV-infected population . However, the role of more novel risk factors [e.g. hepatitis C virus (HCV) coinfection, combination antiretroviral therapy (CART)] is less clear. Few studies have directly compared HIV-infected individuals with HIV uninfected, and the results are conflicting. For example, in the Multicenter AIDS Cohort Study, 5% of the HIV uninfected and 7% of the HIV infected individuals not taking CART had prevalent diabetes at baseline, compared with 14% of individuals who were on CART . The prevalence of HCV was very low in this cohort. In contrast, HIV infection was not associated with an increased incidence of diabetes mellitus in the Women's Interagency HIV Study or the Community Programs for Clinical Research of AIDS study [6,7].
The role of HCV coinfection in HIV-infected persons is also unclear. In the FIRST study, HCV coinfection was associated with a higher risk of diabetes mellitus in the antiretroviral-naive HIV-infected population who were less than 50 years old , whereas no increased risk was found in the Swiss HIV Cohort Study  or an urban cohort of HIV-infected persons in New York city . Although it is generally accepted that protease inhibitor use, or care in the CART era is associated with an increased risk of diabetes mellitus , at least two studies do not support this assertion [10,11].
We determined the association of HIV infection itself with diabetes mellitus, and studied the factors that predicted diabetes mellitus in HIV infected and uninfected groups in the Veterans Aging Cohort Study (VACS), which is one of the largest prospective studies of HIV-infected persons and uninfected controls.
VACS has been described in detail in previous publications [1,12–15]. Briefly, VACS is a live prospective cohort study being conducted at eight Veterans Affairs facilities in the United States (Atlanta, Georgia; Baltimore, Maryland; Bronx, New York, Houston, Texas; Los Angeles, California; New York City, New York; Pittsburgh, Pennsylvania and Washington, District of Columbia, USA). Enrollment in VACS began in June 2002 and reached its initial target of 3000 HIV infected individuals and 3000 HIV-uninfected controls in August 2004, and continues ongoing enrollment. HIV-infected individuals are recruited from the Infectious Diseases clinics at the participating sites. All patients presenting to each participating site are eligible and a majority of the patients in care at the sites are enrolled in the study. HIV-uninfected controls are recruited from the General Internal Medicine clinics at the same site, and are targeted to match the demographics of the Infectious Diseases clinics on 5-year age blocks, race and sex. At enrollment, the individuals complete a comprehensive survey which includes demographic information and information on tobacco and drug use, comorbidities, height and weight. It also includes the three-item AUDIT C [16,17], and the Alcohol Dependence Scale. Complete electronic medical records data (including data prior to enrollment in VACS) are also routinely collected from each local site and includes laboratory information with dates, values, and reference range for all laboratory tests. Outpatient pharmacy information is collected nationally through the Veterans Affairs Pharmacy Benefits Management (PBM) program (Hines, Illinois, USA) and includes medication name, dose, number dispensed and number of refills ordered. An advantage of national pharmaceutical data is the ability to capture outpatient prescriptions filled by any Veterans Affairs facility.
For the purpose of our study, all individuals enrolled in VACS were eligible to be included in the analysis. HIV infection was defined based on International Classification of Diseases (ICD)-9 codes and verified by an antibody test with a western blot confirmation. The primary outcome was diabetes at baseline, which was defined as having any of the following: glucose at least 200 mg/dl on two separate occasions; ICD-9 codes (two outpatient OR one inpatient) PLUS treatment with an oral hypoglycemic or insulin for at least 30 days; ICD-9 codes (two outpatient OR one inpatient) PLUS glucose at least 126 mg/dl on two separate occasions; glucose at least 200 mg/dl on one occasion PLUS treatment with an oral hypoglycemic or insulin for at least 30 days. We created this multifacted definition to avoid biases that might be introduced by an exclusive focus on ICD-9 diagnostic codes. Diabetes medication prescription and ICD-9 codes have previosuly been used as diagnostic criteria for diabetes in the veterans . We compared our definition with presence of at least one inpatient or at least two outpatient codes for diabetes. Our definition had a sensitivity of 86.6%, specificity of 97.5%, agreement of 95.5% with a kappa value of 0.85 [95% confidence interval (CI) = 0.83–0.86], suggesting excellent agreement beyond chance. Laboratory data were obtained closest to the baseline date. Over 94% of the individuals had a biochemical profile performed within 360 days prior to the baseline visit. HCV coinfection was determined based on at least one inpatient or two outpatient ICD-9 codes. Quantity and frequency of alcohol use was determined using the AUDIT-C instrument. Illicit drug use was determined by individual self-report of presence of at least one inpatient or two outpatient ICD-9 codes for drug abuse or dependence. Height and weight were measured as a part of routine clinical care and extracted from individual medical records at the enrolment date. Hypoglycemic medication and antiretroviral use and duration at baseline was determined and calculated from the individuals' electronic medical records retrieved locally and through the PBM database.
We conducted analyses on all individuals stratified by HIV infection. We also conducted analyses including alanine and aspartate aminotransferase levels with and without HCV in the model, as well as separate comparative analyses on individuals with and without alcohol and drug use to explain some of the observed differences in the risk of diabetes mellitus. Individuals were considered to have received CART if they received at least three drugs from at least two classes of antiretrovirals. Individual class use was calculated as the cumulative number of days each class of antiretrovirals [nucleaside reverse transcriptase inhibitors (NRTI), nonnucleoside reverse transcriptase inhibitor (NNRTI), protease inhibitor was used in any given individual. We analyzed antiretroviral use as ‘any CART’, ‘use of each class per year’, as well as combinations of classes for at least 1 year to understand fully the role of antiretroviral therapy upon the risk of diabetes mellitus within the HIV-infected group.
We compared baseline demographic, clinical and laboratory characteristics of HIV infected and uninfected individuals, and individuals with and without diabetes mellitus at baseline using chi-square and Student's t-test as appropriate. Univariable and multivariable logistic regression analysis was used to determine factors associated with the risk of diabetes mellitus. We used Stata 8.2 (Stata Corp., College Station, Texas, USA) for all statistical analyses.
There were 3227 HIV-infected individuals and 3240 HIV-uninfected controls. HIV-infected individuals were younger, more likely to be black race, male, and have HCV coinfection and had a significantly lower BMI compared with HIV-uninfected controls. In addition, HIV-infected patients had less use of alcohol but more drug use (Table 1). The baseline prevalence of diabetes was 14.9% in the HIV infected and 21.4% in the HIV-uninfected group (P < 0.0001). Most of this difference was driven by the difference in the category with the lowest BMI.
In a univariable logistic regression model, HIV was associated with a lower risk of diabetes mellitus at baseline [odds ratio (OR) = 0.64, 95% CI = 0.56–0.73] (Table 2). In the overall group, increasing age, male sex, minority race, and BMI were associated with an increased risk of diabetes mellitus. Increasing amount of alcohol use and a history of drug abuse or dependence were associated with a lower risk of diabetes mellitus. The effect of increasing age, minority race and BMI were more pronounced in the HIV-infected group compared with HIV-uninfected controls. HCV coinfection was associated with a higher risk of diabetes mellitus in the HIV-infected, but not in the HIV-uninfected group (Table 2).
In multivariable analysis, HIV was still associated with a lower risk of diabetes mellitus (OR = 0.84, 95% CI = 0.72–0.97) (Table 3). Other factors associated with diabetes mellitus were similar to the univariable analysis. Increasing age and BMI had a more pronounced effect on the risk of diabetes in the HIV-infected compared with HIV-uninfected persons. HCV remained a significant predictor only in the HIV-infected group.
In the HIV-infected group, use of CART was associated with a higher risk of diabetes mellitus (OR = 1.11, 95% CI = 1.05–1.17, result not otherwise shown). Although each class of CART was associated with a higher risk of diabetes mellitus in the univariable model, only NRTI and NNRTI use was associated with a higher risk in the multivariable model (Tables 2 and 3). When we analyzed various combinations of CART classes for varying durations (≤1 year vs. >1 year), cumulative use of NRTI or protease inhibitor or both was associated with a significantly higher risk of diabetes mellitus compared with the group of individuals with cumulative exposure to both classes for at least 1 year (data not shown). Increasing CD4+ lymphocyte count was associated with an increased risk in univariable analysis, but not in the multivariable analysis. There was no association of diabetes mellitus with HIV RNA levels (data not shown).
Alcohol consumption was associated with a lower risk of diabetes mellitus in both HIV infected and uninfected persons. The lower risk was more pronounced in HIV-infected group, and this risk decreased with increasing amount of alcohol consumption, except in those who consumed more than 60 drinks per month. Compared with nondrinkers, the odds of diabetes mellitus in HIV-infected person who consumed 31–60 drinks per months were 0.40 (95% CI = 0.22–0.72). To further understand the lower risk of diabetes mellitus associated with alcohol use, we analyzed this variable by age and BMI. There was no difference in BMI among persons with varying quantity/frequency of alcohol use. However, a higher proportion of nondrinkers were more than 60 years old and, conversely, a smaller proportion among the older age groups were moderate to heavy drinkers (data not shown). We also categorized alcohol use by AUDIT score and into three categories: never used, used more than 12 months ago, and used in the past 12 months. Compared with never drinkers, those who drank more than 12 months ago did not have a significantly lower risk but those who reported drinking in the past 12 months had markedly lower odds of diabetes mellitus (OR = 0.46, 95% CI = 0.32–0.67) in the multivariable model (data not shown). Since alcohol and drug abuse or dependence frequently coexist, or both, we determined prevalence of diabetes mellitus in individuals who used neither, both or either alcohol or drugs. The prevalence of diabetes mellitus was highest in those who used neither (22.5%) compared with those who used both (12.9%), alcohol alone (20%) or drugs alone (15.7%) (overall P < 0.001).
We determined the relationship between HIV infection and other risk factors for prevalent diabetes in one of the largest prospective cohorts of HIV-infected persons and HIV-uninfected controls and found that HIV infection per se was not associated with a higher risk of diabetes mellitus. In fact, the risk of diabetes mellitus at baseline was lower in the HIV infected (OR = 0.84, 95% CI = 0.72–0.97) compared with HIV-uninfected persons. Most of this difference was driven by the difference in the group with lowest BMI suggesting a role of improving health status leading to a higher risk. This observation is further strengthened by the association of higher CD4+ lymphocyte counts with an increased risk of diabetes. There were many differences in the prevalence of risk factors for diabetes mellitus in the HIV infected and uninfected persons. HIV-infected persons were younger and had a lower BMI, which decreases the risk for diabetes mellitus, but were more likely to be racial minorities and had a higher prevalence of HCV, which increases risk. Even after adjusting for these risk factors, HIV was associated with a lower risk of diabetes mellitus. We believe that the net risk of diabetes mellitus is determined by a complex interplay of individual factors, with the traditional risk factors dominating the profile leading to an overall lower risk in HIV-infected persons. Lower prevalence and risk in the HIV-infected group may also reflect a referral/diagnostic bias, with more people in the general medicine clinics seeking care for evaluation and treatment of diabetes mellitus. Another possible mechanism is the differential level of immune activation and inflammatory response in HIV infected and uninfected persons. Although HIV-infected persons may have higher levels of high sensitivity C-reactive protein levels, those with HCV/HIV coinfection have lower levels than uninfected persons [19,20]. How these interact in a given patient to determine the net risk of diabetes requires further study.
We found that HCV infection is associated with a higher risk of diabetes mellitus in the HIV-infected group, and demonstrated a similar trend in the uninfected group in multivariable analysis (although this trend did not reach statistical significance, the effect size was similar). This confirms the suggestion from some previous studies that HCV affects the risk of diabetes mellitus in a complex manner, and such risk is influenced significantly by other more traditional risk factors [10,21]. In our analysis, the risk conferred by HCV is not altered by the presence of liver damage as measured by elevated alanine and aspartate aminotransferase levels (data not shown). Whether HCV and HIV act synergistically at a cellular level or through other factors to increase the risk of diabetes mellitus is not known. Insulin resistance and higher levels of inflammatory cytokines are also seen in patients with chronic HCV (but not with HCV/HIV coinfection as referenced in the previous paragraph), and may be one common pathway leading to a higher risk of diabetes mellitus [22–25].
We found that use of CART was associated with a significantly higher risk of diabetes mellitus in the HIV-infected group. This is essentially a confirmation of multiple previous studies that have demonstrated induction of insulin resistance and a higher risk of diabetes mellitus with the use of CART. The precise role of each class, or each drug in the CART regimens is extremely difficult to determine as such therapy is always used in combination, and often changes in individual participants. We studied the role of CART in several different ways, including cumulative exposure, current exposure and past exposure for each class (as a part of CART) as well as CART itself. The results were generally consistent indicating a higher risk of diabetes mellitus with use of NRTI and NNRTI. However, the association with protease inhibitor use was not significant in the multivariable model. Mitochondrial toxicity associated with nucleoside reverse transcriptase inhibitors (e.g. stavudine, zidovudine and didanosine) [26–29], likely plays a role in the risk associated with NRTI class of drugs. Since protease inhibitors is almost never used alone, it is possible that the risk being attributed to NRTIs is at least partly due to the use of protease inhibitors. It is notable that the mean duration of NRTI use was nearly twice that of protease inhibitor use, and duration of NNRTI use was significantly less than duration of NRTI or protease inhibitor use. In addition, it is also possible that patients with higher risk for diabetes mellitus or with existent diabetes mellitus are more likely to be treated with a NNRTI containing regimen instead of a protease inhibitor-containing regimen. The risk of diabetes mellitus due to each class of drugs may be related to a cumulative dose effect, and individual class exposure cannot currently be separated from concurrent use of a second or third class of the CART regimen.
Our finding of a lower risk of diabetes mellitus associated with increasing alcohol use and drug use is intriguing. Increasing quantity/frequency of alcohol use was associated with increasing protection except in HIV-infected persons who consumed more than 60 drinks per month. Increasing alcohol use is associated with increasing liver damage, and may be expected to increase the risk of diabetes. We conducted separate analyses including liver damage (i.e. defined as alanine or aspartate aminotransferase levels more than five times upper limit of normal) with and without HCV in the models and found no significant association with liver damage. It is also plausible that increased alcohol consumption and drug abuse or dependence may lead to poor nutrition and lower BMI which may indirectly dilute the association with diabetes mellitus. However, we found no significant association between quantity and frequency of alcohol use and BMI. Another possibility is that people with alcohol and drug abuse may not seek medical care and the opportunity to diagnose diabetes mellitus may have been missed. We did find that nondrinkers were older, whereas moderate to heavy drinkers were more likely to be younger. These data suggest that, while some of the decreased association with alcohol is due to the alcohol consuming population being younger, there are other likely mechanisms that modulate this effect.
There are many strengths of our study. This study was conducted in a large, prospective cohort of HIV-infected persons and appropriate controls. VACS has validated measures of alcohol and drug abuse, as well as well defined algorithms for identifying comorbidities. We used multiple sources of data (surveys, electronic medical records, national data) to ensure as complete and accurate data collection as possible. However, certain limitations should also be noted. Most important, we analyzed prevalent diabetes, not incident diabetes. Although we found a negative association between HIV infection and diabetes at baseline, it is entirely possible that incidence rates may be very different. Data on BMI and laboratory data were gathered in the course of routine clinical care. There were few women included in this analysis. Little is known about alcohol consumption patterns among women infected with HIV. Family history of diabetes mellitus is an important risk factor and was not determined in our study. Most of our study individuals were enrolled between 2002 and 2004. With the approval of newer antiretrovirals and updated recommendations about initial regimens, it is possible that the risk associated with CART may have changes. Finally, it has been argued that the veterans in care are a nonrepresentative sample for the US population in general. However, with the exception of this sex difference, HIV-infected veterans are similar to many other HIV-infected persons, being more likely to be people of color, having contracted HIV via injection drug use or heterosexual exposure, and to be of lower socioeconomic status . According to the National Institutes of Health, the estimated prevalence of diagnosed and undiagnosed diabetes mellitus among people age 20 or older is 11.2% in men and 10.2% in women. (http://diabetes.niddk.nih.gov/DM/PUBS/statistics/, accessed 20 February 2009) Prevalence of diabetes mellitus is 9.8% among non-Hispanic Whites and 14.7% among non-Hispanic Blacks. As only 2.5% of the HIV infecteed and 7.9% of the HIV-uninfected individuals in our study were women, caution is warranted when interpreting any sex differences in our study.
In conclusion, we found that HIV itself is not associated with a higher risk of diabetes mellitus. In fact, after adjusting for traditional risk factors, HIV is actually associated with a lower risk. A return to a more healthy state with increasing BMI and CD4+ lymphocyte counts was associated with a higher risk of diabetes. However, the magnitude of association with the traditional risk factors varies between HIV infected and uninfected persons. Further studies are warranted to understand the mechanisms behind our observations.
Funding: Veterans Aging Cohort Study funded by: National Institute on Alcohol Abuse and Alcoholism (U10 AA 13566) and VHA Public Health Strategic Health Core Group. Dr Butt is supported by a Career Development Award from the National Institutes of Health/National Institute on Drug Abuse (DA016175-01A1).
Disclaimer: The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs.
There are no conflicts of interests.
1. Goulet JL, Fultz SL, McGinnis KA, Justice AC. Relative prevalence of comorbidities and treatment contraindications in HIV-mono-infected and HIV/HCV-co-infected veterans. AIDS 2005; 19(Suppl 3):S99–S105.
2. Goulet JL, Fultz SL, Rimland D, Butt A, Gibert C, Rodriguez-Barradas M, et al
. Aging and infectious diseases: do patterns of comorbidity vary by HIV status, age, and HIV severity? Clin Infect Dis 2007; 45:1593–1601.
3. Kilbourne AM, Justice AC, Rabeneck L, Rodriguez-Barradas M, Weissman S. General medical and psychiatric comorbidity among HIV-infected veterans in the post-HAART era. J Clin Epidemiol 2001; 54(Suppl 1):S22–S28.
4. Butt AA, Fultz SL, Kwoh CK, Kelley D, Skanderson M, Justice AC. The risk of diabetes in HIV infected Veterans in the pre and post-HAART era and the role of hepatitis C virus co-infection. Hepatology 2004; 40:115–119.
5. Brown TT, Cole SR, Li X, Kingsley LA, Palella FJ, Riddler SA, et al
. Antiretroviral therapy and the prevalence and incidence of diabetes mellitus in the multicenter AIDS cohort study. Arch Intern Med 2005; 165:1179–1184.
6. Tien PC, Schneider MF, Cole SR, Levine AM, Cohen M, Dehovitz J, et al
. Antiretroviral therapy exposure and incidence of diabetes mellitus in the Women's Interagency HIV Study. AIDS 2007; 21:1739–1745.
7. Brar I, Shuter J, Thomas A, Daniels E, Absalon J. A comparison of factors associated with prevalent diabetes mellitus among HIV-infected antiretroviral-naive individuals versus individuals in the National Health and Nutritional Examination Survey cohort. J Acquir Immune Defic Syndr 2007; 45:66–71.
8. Visnegarwala F, Chen L, Raghavan S, Tedaldi E. Prevalence of diabetes mellitus and dyslipidemia among antiretroviral naive patients co-infected with hepatitis C virus (HCV) and HIV-1 compared to patients without co-infection. J Infect 2005; 50:331–337.
9. Ledergerber B, Furrer H, Rickenbach M, Lehmann R, Elzi L, Hirschel B, et al
. Factors associated with the incidence of type 2 diabetes mellitus in HIV-infected participants in the Swiss HIV Cohort Study. Clin Infect Dis 2007; 45:111–119.
10. Yoon C, Gulick RM, Hoover DR, Vaamonde CM, Glesby MJ. Case-control study of diabetes mellitus in HIV-infected patients. J Acquir Immune Defic Syndr 2004; 37:1464–1469.
11. Jain MK, Aragaki C, Fischbach L, Gibson S, Arora R, May L, et al
. Hepatitis C is associated with type 2 diabetes mellitus in HIV-infected persons without traditional risk factors. HIV Med 2007; 8:491–497.
12. Justice AC, Landefeld CS, Asch SM, Gifford AL, Whalen CC, Covinsky KE. Justification for a new cohort study of people aging with and without HIV infection. J Clin Epidemiol 2001; 54(Suppl 1):S3–S8.
13. Justice AC, Erdos J, Brandt C, Conigliaro J, Tierney W, Bryant K. The Veterans Affairs Healthcare System: a unique laboratory for observational and interventional research. Med Care 2006; 44(8 Suppl 2):S7–S12.
14. Fultz SL, Skanderson M, Mole LA, Gandhi N, Bryant K, Crystal S, et al
. Development and verification of a ‘virtual’ cohort using the national VA health information system. Med Care 2006; 44(8 Suppl 2):S25–S30.
15. Justice AC, Dombrowski E, Conigliaro J, Fultz SL, Gibson D, Madenwald T, et al
. Veterans Aging Cohort Study (VACS): overview and description. Med Care 2006; 44(8 Suppl 2):S13–S24.
16. Bush K, Kivlahan DR, McDonell MB, Fihn SD, Bradley KA. The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Ambulatory Care Quality Improvement Project (ACQUIP). Alcohol Use Disorders Identification Test. Arch Intern Med 1998; 158:1789–1795.
17. Gordon AJ, Maisto SA, McNeil M, Kraemer KL, Conigliaro RL, Kelly ME, et al
. Three questions can detect hazardous drinkers. J Fam Pract 2001; 50:313–320.
18. Miller DR, Safford MM, Pogach LM. Who has diabetes? Best estimates of diabetes prevalence in the department of Veterans Affairs based on computerized patient data. Diab Care 2004; 27(suppl_2):B10–B21.
19. Floris-Moore M, Howard AA, Lo Y, Schoenbaum EE, Arnsten JH, Klein RS. Hepatitis C infection is associated with lower lipids and high-sensitivity C-reactive protein in HIV-infected men. AIDS Patient Care STD 2007; 21:479–491.
20. Alter MJ, Moyer LA. The importance of preventing hepatitis C virus infection among injection drug users in the United States. J Acquir Immune Defic Syndr Hum Retrovirol 1998; 18(Suppl 1):S6–S10.
21. Mehta SH, Brancati FL, Strathdee SA, Pankow JS, Netski D, Coresh J, et al
. Hepatitis C virus infection and incident type 2 diabetes. Hepatology 2003; 38:50–56.
22. de Larranaga GF, Wingeyer SD, Puga LM, Alonso BS, Benetucci JA. Relationship between hepatitis C virus (HCV) and insulin resistance, endothelial perturbation, and platelet activation in HIV-HCV-coinfected patients under highly active antiretroviral treatment. Eur J Clin Microbiol Infect Dis 2006; 25:98–103.
23. Maeno T, Okumura A, Ishikawa T, Kato K, Sakakibara F, Sato K, et al
. Mechanisms of increased insulin resistance in noncirrhotic patients with chronic hepatitis C virus infection. J Gastroenterol Hepatol 2003; 18:1358–1363.
24. Lecube A, Hernandez C, Genesca J, Simo R. Proinflammatory cytokines, insulin resistance, and insulin secretion in chronic hepatitis C patients: a case-control study. Diab Care 2006; 29:1096–1101.
25. Yazicioglu G, Isitan F, Altunbas H, Suleymanlar I, Ozdogan M, Balci MK, et al
. Insulin resistance in chronic hepatitis C. Int J Clin Pract 2004; 58:1020–1022.
26. Brown TT, Li X, Cole SR, Kingsley LA, Pallela FJ, Riddler SA, et al
. Cumulative exposure to nucleoside analogue reverse transcriptase inhibitors is associated with insulin resistance markers in the Multicenter AIDS Cohort Study. AIDS 2005; 19:1375–1383.
27. De WS, Sabin CA, Weber R, et al. Incidence and risk factors for new onset diabetes mellitus in HIV infected patients: the D:A:D study
. Diabetes Care
28. Cossarizza A, Moyle G. Antiretroviral nucleoside and nucleotide analogues and mitochondria. AIDS 2004; 18:137–151.
29. Lowell BB, Shulman GI. Mitochondrial dysfunction and type 2 diabetes. Science 2005; 307:384–387.