Skip to main content
PMC home page
BMJ Open Diabetes Research & Care logoLink to BMJ Open Diabetes Research & Care
. 2017 Jan 5;5(1):e000304. doi: 10.1136/bmjdrc-2016-000304

Is diabetes prevalence higher among HIV-infected individuals compared with the general population? Evidence from MMP and NHANES 2009–2010

Alfonso C Hernandez-Romieu 1, Shikha Garg 2,3, Eli S Rosenberg 1, Angela M Thompson-Paul 4, Jacek Skarbinski 2,3
PMCID: PMC5293823  PMID: 28191320

Abstract

Background

Nationally representative estimates of diabetes mellitus (DM) prevalence among HIV-infected adults in the USA are lacking, and whether HIV-infected adults are at increased risk of DM compared with the general adult population remains controversial.

Methods

We used nationally representative survey (2009–2010) data from the Medical Monitoring Project (n=8610 HIV-infected adults) and the National Health and Nutrition Examination Survey (n=5604 general population adults) and fit logistic regression models to determine and compare weighted prevalences of DM between the two populations, and examine factors associated with DM among HIV-infected adults.

Results

DM prevalence among HIV-infected adults was 10.3% (95% CI 9.2% to 11.5%). DM prevalence was 3.8% (CI 1.8% to 5.8%) higher in HIV-infected adults compared with general population adults. HIV-infected subgroups, including women (prevalence difference 5.0%, CI 2.3% to 7.7%), individuals aged 20–44 (4.1%, CI 2.7% to 5.5%), and non-obese individuals (3.5%, CI 1.4% to 5.6%), had increased DM prevalence compared with general population adults. Factors associated with DM among HIV-infected adults included age, duration of HIV infection, geometric mean CD4 cell count, and obesity.

Conclusions

1 in 10 HIV-infected adults receiving medical care had DM. Although obesity contributes to DM risk among HIV-infected adults, comparisons to the general adult population suggest that DM among HIV-infected persons may develop at earlier ages and in the absence of obesity.

Keywords: HIV/Aids, Health Disparities, National Health Surveys, Type 2 Diabetes

Key messages.

  • Among a nationally representative US sample of HIV-infected adults receiving medical care, the prevalence of diagnosed diabetes mellitus (DM) was 10.3%.

  • HIV-infected adults may be likely to have DM at younger ages and in the absence of obesity compared with the general US adult population.

  • The prevalence of DM among HIV-infected adults is high and HIV-care providers should follow existing screening guidelines, which recommend FBG and HbA1c be obtained prior to and after starting antiretroviral therapy.

Introduction

Diabetes mellitus (DM) is an important cause of morbidity and mortality in the USA. In 2014, there were an estimated 29.1 million persons with DM, of whom 27.8% were undiagnosed.1 Uncontrolled DM can result in significant disability due to complications such as blindness and end-stage renal disease, and is associated with premature mortality due to cancer and vascular disease.2 3 Furthermore, the medical and societal costs of DM are substantial. In 2012, in the USA alone, DM accounted for $176 billion US in direct medical costs and $69 billion US in reduced productivity.4

In the USA, advances in treatment of HIV infection have led to decreased mortality and increased life expectancy among HIV-infected persons.5 6 Consequently, chronic metabolic and cardiovascular diseases such as DM are gaining importance as causes of morbidity and mortality among HIV-infected persons.7 While the burden of DM among the general US adult population has been well described, nationally representative estimates of DM prevalence among HIV-infected adults are lacking. In addition, whether HIV-infected adults are at increased risk of developing DM compared with the general adult population remains controversial.8–11

We analyzed nationally representative data from the Medical Monitoring Project (MMP) with the following objectives: (1) estimate DM prevalence among a nationally representative sample of HIV-infected adults; (2) compare the prevalence of DM in HIV-infected adults versus the general US adult population; and (3) identify factors associated with prevalent DM among HIV-infected adults.

Methods

Data sources and study design

We used 2009–2010 data from MMP and the National Health and Nutrition Examination Survey (NHANES) to estimate DM prevalence among HIV-infected adults and the general US adult population, respectively. Our analyses were restricted to adults aged ≥20 years, and excluded pregnant women.

MMP is a surveillance system that produces nationally representative estimates of behavioral and clinical characteristics of HIV-infected adults who receive HIV medical care in the USA. MMP is a cross-sectional survey with a multistage probability design. Detailed descriptions of the sampling methodology and data collection procedures have been published elsewhere.12 Briefly, sampling was conducted in three consecutive stages: (1) USA and dependent areas, (2) outpatient HIV care facilities, and (3) HIV-infected adults aged ≥18 years who made at least one medical care visit to a sampled facility between January and April of 2009 and 2010. Data were collected during June 2009 through May 2011. Facility response rates were 76% (461/603) in 2009 and 81% (474/582) in 2010. Approximately 50% of persons sampled from these facilities completed an interview and had their medical records abstracted. After excluding 81 individuals who were either <20 years of age or pregnant, our MMP sample included 8610 participants, representing an estimated average of 427 928 HIV-infected adults. The Centers for Disease Control and Prevention (CDC) National Center for HIV, Viral Hepatitis, STD, and TB Prevention has determined MMP to be a non-research public health surveillance activity, and thus, it was not reviewed by a federal institutional review board (IRB). Participating states or territories and facilities obtained local IRB approval to conduct MMP if required locally. Informed consent was obtained from all interviewed participants.

NHANES is a cross-sectional health examination survey with a stratified multistage probability design representative of the general non-institutionalized US population. Descriptions of the sampling plan, and examination and interview protocol are published elsewhere.13 In the 2009–2010 cycle of NHANES, the unweighted response rate for the interviewed and examined persons was 77.3%, resulting in a final sample of 10 253 persons. After excluding individuals <20 years and pregnant women, 5604 (54.6%) adults remained in the sample, representing an estimated 2.1 million non-institutionalized adults living in the USA in 2009–2010. NHANES was approved by CDC's National Center for Health Statistics Institutional Research Ethics Review Board.

Survey sample weights in NHANES and MMP account for the differential probabilities of selection, non-response to survey instruments, and differences between the final sample and the total population.

Measures

The primary outcome variable was DM. In MMP, DM was defined using the following criteria documented in the medical record: (1) physician-diagnosed DM listed on a problem list or in the assessment/plan portion of a progress note; or (2) prescription of insulin or oral hypoglycemic medications (excluding metformin monotherapy). In NHANES, DM was defined using the following criteria: (1) answered ‘Yes’ to the question: ‘Other than during pregnancy, have you ever been told by a doctor or health professional that you have diabetes or sugar diabetes?’; or (2) answered ‘Yes’ to any of the following questions: (a) ‘Are you now taking insulin?’; or (b) ‘Are you now taking diabetic pills to lower your blood sugar? These are sometimes called oral agents and oral hypoglycemic agents’. Prescription medication data available in NHANES were used to exclude individuals treated with metformin monotherapy who had responded ‘Yes’ to question 2b. Exclusion of patients on metformin monotherapy who were not classified as having DM in MMP or answered ‘No’ to question 1 in NHANES were excluded due to the use of this medication for pre-diabetes and polycystic ovarian syndrome. Laboratory criteria to establish the diagnosis of DM were available for MMP and NHANES; however, they were not used because the fasting nature of blood glucose measurements from laboratory data abstracted from medical charts was unknown, and HbA1c measurements have not been validated for the diagnosis of DM among HIV-infected individuals.10 14 15 Our analyses of DM prevalence were therefore restricted to comparisons of diagnosed DM, as described above.

Sociodemographic variables collected for MMP and NHANES included age, sex at birth, race/ethnicity, education, and poverty level. The number and percentage of participants meeting current poverty guidelines for MMP and NHANES were determined using the US Department of Health and Human Services poverty guidelines. In MMP, body mass index (BMI) measurements were abstracted from medical records for the year prior to the interview. If height was missing (n=1534 (17.7%) in MMP), BMI category was inferred from recorded weight using previously published methods.16 In NHANES, BMI was measured using standardized techniques and equipment. BMI ≥30 kg/m2 was considered indicative of obesity. Clinical MMP variables included time since HIV diagnosis, geometric mean CD4+ T-lymphocyte (CD4) count, documented prescription of antiretroviral therapy (ART), and disease stage per CDC criteria.17

CD4 were described using geometric means, calculated by back transforming the logarithm of CD4; geometric means instead of arithmetic means were used because of the skewed distribution of CD4. MMP participants were classified as being infected with hepatitis C virus (HCV) if any of the following were documented in their medical record: (1) a positive anti-HCV enzyme immunoassay (EIA) or strip immunoblot assay (RIBA); (2) an HCV genotype or (3) HCV-RNA identified through reverse transcriptase–PCR (RT–PCR).18 Indeterminate results of EIA/RIBA, HCV genotype, or HCV-RNA were considered negative. All NHANES participants received a screening HCV antibody test by EIA with confirmation of positive test results using RIBA. Samples with an indeterminate RIBA were tested for HCV-RNA to confirm HCV infection status.

Data analyses

Prevalence of and factors associated with DM among HIV-infected adults

Among HIV-infected adults, we calculated the weighted prevalence and 95% CIs of DM overall and by each of the following characteristics: age (20–44, 45–60, and ≥60 years), sex at birth, race/ethnicity (non-Hispanic White and Black, Hispanic, and Other), education (less than high school, high school or equivalent, and more than high school), poverty level (living at or below the poverty line and living above the poverty line, obesity, time since HIV diagnosis (<5, 5–9, and ≥10 years), geometric mean CD4 count (0–199, 200–349, 350–499, and ≥500 cells/mm3), use of ART during the surveillance period, CDC HIV disease stage (AIDS or nadir CD4 0–199, no AIDS and nadir CD4 200–500, and no AIDS and nadir CD4>500), and HCV coinfection. All characteristics were analyzed as categorical variables.

To identify factors associated with DM in HIV-infected persons, we used multivariable logistic regression models with DM as the dependent variable, and all previously mentioned characteristics as independent variables. We computed model-adjusted prevalences for all levels of each of the selected characteristics with predicted marginal means, and estimated crude and adjusted prevalence ratios (PR) for each characteristic.19 20 We calculated CIs for adjusted-prevalence estimates and PRs.

Comparisons between HIV-infected adults receiving medical care and general US population adults

Weighted percentages and CIs were determined for DM among HIV-infected adults and the general US adult population stratified by age group, sex at birth, race/ethnicity, education, poverty level, obesity, and HCV infection.

We used marginal standardization methods with predicted marginal probabilities to compare the prevalence of DM between MMP and NHANES. In marginal standardization, the predicted probability of the outcome of interest is adjusted to a weighted average reflecting the distribution of covariates in the target population; the marginal effect obtained is the proportion of subjects with the outcome that would have been observed were the study population forced to the exposure level (ie, HIV infection). In other words, given the demographic and clinical characteristics of the populations in MMP and NHANES, what would the predicted probability of DM be were they to be infected with HIV and vice-versa.20

Under the assumption that MMP and NHANES were two independent samples, with independent design variables and weights, we combined the two data sets and constructed a multivariable logistic model using predicted marginal probabilities with DM as the outcome variable and the following independent variables: an indicator variable for survey type (1=MMP; 0=NHANES), all characteristics listed above, and interaction terms between the indicator variable and all characteristics.21 22

Using the predicted marginal prevalence of DM, we computed prevalence differences (PD) comparing the two populations, adjusting for all characteristics in MMP and NHANES included in the model.19 Linear contrasts were used to test for heterogeneity among subgroups between the adjusted estimates of diagnosed DM in the HIV-infected and general US adult populations. To assess whether differences in care-seeking could account for differences in DM prevalence between the two populations, we performed a second analysis restricting our comparison of HIV-infected adults receiving medical care to the general US adult population who had received medical care in the previous year.

All analyses were performed using SAS 9.3 (SAS Institute, Cary, North Carolina, USA) and SAS-callable SUDAAN 10.0.1 (RTI International, Research Triangle Park, North Carolina, USA) and accounted for clustering, unequal selection probabilities, and non-response.

Results

MMP participants had the following characteristics: male (73.6%), non-Hispanic black (41.3%), aged 45 or more years (59.9%), with greater than a high school education (52.2%), and living above the federal poverty level (56.5%) (table 1). A quarter of MMP participants had a BMI ≥30 kg/m2, 20.6% were HCV-positive, 90.0% had been prescribed ART in the previous year, and 73.0% had their most recent HIV viral load reported <200 copies/mL. NHANES participants had the following characteristics: male (49.3%), non-Hispanic black (11.7%), aged 45 or more years (51.4%), with greater than a high school education (58.7%), and living above the federal poverty level (91.5%). More than a third (36.0%) of the general US adult population had a BMI ≥30 kg/m2, and 1.7% had HCV infection.

Table 1.

Characteristics of HIV-infected adults and general US population adults, MMP and NHANES 2009–2010

HIV-infected adults*
 General population adults*
Sample n Weighted % (95% CI) Sample n Weighted % (95% CI)
Total 8610 – 5604 –
Sex at birth
 Male 6349 73.6 (70.3 to 76.7) 2745 49.3 (48.1 to 50.6)
 Female 2261 26.4 (23.3 to 29.7) 2859 50.7 (49.4 to 51.9)
Race/ethnicity
 White (non-Hispanic) 2869 34.6 (27.6 to 42.3) 2584 67.3 (59.8 to 74.0)
 Black (non-Hispanic) 3512 41.3 (32.4 to 50.8) 1051 11.7 (10.0 to 13.7)
 Hispanic or Latino 1827 19.3 (14.2 to 25.7) 1656 14.0 (8.8 to 21.3)
 Other 402 4.9 (3.9 to 6.0) 313 7.1 (5.1 to 9.9)
Age in years
 20–44 3412 40.1 (38.7 to 41.5) 2518 48.6 (46.2 to 51.1)
 45–60 4366 50.3 (48.9 to 51.6) 1473 29.8 (28.5 to 31.1)
 ≥60 832 9.6 (8.8 to 10.6) 1613 21.6 (20.1 to 23.2)
Education
 Less than high school 1937 21.5 (19.1 to 24.1) 1570 18.4 (16.5 to 20.5)
 High school or equivalent 2314 26.3 (24.1 to 28.7) 1289 22.9 (20.7 to 25.2)
 More than high school 4356 52.2 (47.7 to 56.6) 2722 58.7 (55.7 to 61.7)
Living at or below poverty line†
 Yes 3790 43.5 (39.6 to 47.5) 713 8.5 (7.0 to 10.3)
 No 4565 56.5 (52.5 to 60.4) 4608 91.5 (89.7 to 93.0)
Obese (BMI ≥30 kg/m2)
 Yes 2071 25.5 (23.9 to 27.2) 2073 36.0 (34.0 to 38.2)
 No 6208 74.5 (72.8 to 76.2) 3291 64.0 (61.9 to 66.0)
Hepatitis C virus‡
 Positive 1556 20.6 (18.7 to 22.7) 106 1.7 (1.2 to 2.4)
 Negative 5590 79.4 (77.3 to 81.3) 5190 98.3 (97.6 to 98.8)
Time since HIV diagnosis
 Less than 5 years 1866 22.4 (20.7 to 24.1) – –
 5–9 years 1932 22.6 (21.6 to 23.6) – –
 10 or more years 4806 55.0 (52.8 to 57.3) – –
AIDS/nadir CD4 count in cells/mm3
 AIDS or nadir CD4 0-199 5952 68.5 (67.2 to 69.7) – –
 No AIDS and nadir CD4 200–500 2098 24.8 (23.4 to 26.2) – –
 No AIDS and nadir CD4 >500 540 6.8 (6.0 to 7.6) – –
Geometric mean CD4 past 12 months
 0–199 cells/mm3 1118 13.0 (12.1 to 13.9) – –
 200–349 cells/mm3 1486 17.9 (16.9 to 19.1) – –
 350–499 cells/mm3 1949 23.6 (22.6 to 24.7) – –
 ≥500 cells/mm3 3739 45.5 (44.0 to 47.0) – –
Prescribed ART
 Yes 7751 90.0 (89.2 to 90.7) – –
 No 828 10.1 (9.3 to 10.8) – –
Most recent viral load undetectable or <200 copies/mL
 Yes 6287 73.0 (71.0 to 75.0) – –
 No 2323 27.0 (25.1 to 29.1) – –
Open in a new tab

*Includes adults ≥20 years of age and excludes pregnant women.

†Calculated using the ratio of annual household income to number of people in the household.

‡A total of n=7146 and n=5295 were screened for hepatitis C (HCV) in MMP and NHANES, respectively. HCV positivity was defined as having a documented positive anti-HCV enzyme immunoassay or strip immunoblot assay RIBA, HCV genotype, or HCV RNA through reverse transcriptase–PCR (RT–PCR).

ART, antiretroviral therapy; BMI, body mass index; MMP, Medical Monitoring Project; NHANES, National Health and Nutrition Examination Survey.

The unadjusted prevalence of DM (CI) among HIV-infected adults was 10.3% (9.1% to 11.5%), and was higher compared with the general US adult population (8.3% (7.2% to 9.4%)) as well as the general US adult population having received care in the previous 12 months (9.7% (8.4% to 11.1%)) (data not shown). Among HIV-infected adults with diagnosed DM, 3.9% (95% CI 2.9% to 5.2%) had DM type 1, 52.3% (CI 46.7% to 57.8%) had DM type 2, and 43.9% (CI 38.1% to 49.8%) had unspecified DM. After adjusting for differences in distributions of sex, age, race/ethnicity, education, poverty, obesity, and HCV infection prevalence, the adjusted PD (aPD) of DM in HIV-infected adults versus the general US adult population was 3.8% (table 2). The largest difference in DM prevalence among HIV-infected adults relative to their counterparts in the general US adult population occurred among those with HCV infection (6.3%), those with a high school or equivalent education (5.1%), women (5.0%), non-Hispanics whites (4.9%), individuals living at or below the poverty line (4.6%), obese individuals (4.4%) and ages 20–44 years (4.1%). After restricting the NHANES population to adults who had received care in the previous 12 months, associations were similar to those described above with a slight decrease in the magnitude of DM PD (table 3).

Table 2.

Predicted marginal prevalence and prevalence comparisons of diagnosed diabetes among HIV-infected adults and general US population adults, MMP and NHANES 2009, 2010

HIV-infected adults General population adults
Adjusted prevalence (95% CI) Adjusted prevalence (95% CI) aPR (95% CI)* aPD (95% CI)† p Value
Total 11.8 (10.1 to 13.7) 8.0 (7.1 to 9.1) 1.47 (1.21 to 1.78) 3.8 (1.8 to 5.8) 0.0002
Sex at birth
 Male 11.2 (9.9 to 12.7) 8.9 (7.2 to 10.7) 1.28 (1.02 to 1.60) 2.4 (0.3 to 4.6) 0.03
 Female 12.4 (10.1 to 15.2) 7.4 (6.4 to 8.5) 1.68 (1.31 to 2.15) 5.0 (2.3 to 7.7) 0.0003
Race/ethnicity
 White (non-Hispanic) 11.4 (9.6 to 13.5) 6.5 (5.3 to 7.9) 1.76 (1.37 to 2.28) 4.9 (2.7 to 7.2) <0.0001
 Black (non-Hispanic) 13.1 (11.1 to 15.4) 11.8 (9.7 to 14.3) 1.11 (0.87 to 1.42) 1.3 (−1.7 to 4.3) 0.4
 Hispanic or Latino 13.1 (10.3 to 16.4) 11.4 (9.4 to 13.8) 1.14 (0.85 to 1.54) 1.6 (−2.1 to 5.4) 0.4
 Other 11.7 (8.5 to 15.9) 14.8 (11.4 to 18.9) 0.79 (0.53 to 1.17) −3.1 (−8.3 to 2.1) 0.2
Age in years
 20–44 6.4 (5.3 to 7.6) 2.3 (1.7 to 3.2) 2.77 (1.91 to 4.02) 4.1 (2.7 to 5.5) <0.0001
 45–60 13.0 (11.0 to 15.2) 9.1 (7.5 to 10.9) 1.43 (1.12 to 1.81) 3.9 (1.3 to 6.5) 0.004
 ≥60 21.9 (17.9 to 26.6) 19.5 (16.8 to 22.6) 1.12 (0.88 to 1.44) 2.4 (−2.8 to 7.6) 0.4
Education
 Less than high school 13.0 (10.3 to 16.2) 10.6 (9.3 to 12.1) 1.22 (0.94 to 1.59) 2.3 (−0.9 to 5.6) 0.2
 High school or equivalent 11.4 (9.9 to 13.6) 6.3 (5.3 to 7.5) 1.81 (1.39 to 2.35) 5.1 (2.6 to 7.5) 0.0001
 More than high school 11.6 (9.9 to 13.6) 7.8 (6.5 to 9.4) 1.48 (1.17 to 1.87) 3.8 (1.5 to 6.1) 0.001
Living at or below poverty line‡
 No 11.8 (10.1 to 13.8) 8.1 (7.2 to 9.1) 1.46 (1.20 to 1.77) 3.7 (1.6 to 5.8) 0.0005
 Yes 11.8 (9.8 to 14.1) 7.2 (5.4 to 9.7) 1.63 (1.17 to 2.27) 4.6 (1.7 to 7.4) 0.002
Obesity (BMI ≥30 kg/m2)
 No 7.8 (6.2 to 9.9) 4.3 (3.5 to 5.4) 1.81 (1.32 to 2.49) 3.5 (1.4 to 5.6) 0.0009
 Yes 18.1 (15.8 to 20.7) 13.7 (12.1 to 15.6) 1.32 (1.10 to 1.58) 4.4 (1.5 to 7.4) 0.003
Hepatitis C virus§
 Positive 13.4 (11.0 to 16.2) 7.1 (3.8 to 13.1) 1.88 (0.98 to 3.61) 6.3 (1.1 to 11.4) 0.02
 Negative 11.8 (10.1 to 13.7) 8.1 (7.1 to 9.1) 1.46 (1.21 to 1.77) 3.7 (1.7 to 5.7) 0.0003
Open in a new tab

*Prevalence ratio using NHANES as the referent category.

†Prevalence difference calculated as prevalence(MMP)–prevalence(NHANES).

‡Calculated using the ratio of annual household income to number of people in the household.

§A total of n=7146 and n=5295 were screened for hepatitis C (HCV) in MMP and NHANES, respectively. HCV positivity was defined as having a documented positive anti-HCV enzyme immunoassay or strip immunoblot assay RIBA, HCV genotype, or HCV RNA through reverse transcriptase–PCR (RT–PCR).

aPD, adjusted prevalence difference; aPR, adjusted prevalence ratio; BMI, body mass index; MMP, Medical Monitoring Project; NHANES, National Health and Nutrition Examination Survey.

Table 3.

Predicted marginal prevalence and prevalence comparisons of diagnosed diabetes among HIV-infected adults and general US population adults having received medical care in the previous 12 months, MMP and NHANES 2009–2010

HIV-infected adults General population adults
Adjusted prevalence (95% CI) Adjusted prevalence (95% CI) aPR (95% CI)* aPD (95% CI)† p Value
Total 12.4 (10.6 to 14.4) 9.38 (8.2 to 10.7) 1.32 (1.09 to 1.61) 3.0 (0.8 to 5.2) 0.007
Sex at birth
 Male 11.7 (10.3 to 13.3) 10.6 (8.6 to 13) 1.11 (0.88 to 1.4) 1.1 (−1.4 to 3.7) 0.4
 Female 13.0 (10.5 to 15.9) 8.36 (7.3 to 9.6) 1.55 (1.22 to 1.97) 4.6 (1.7 to 7.5) 0.002
Race/ethnicity
 White (non-Hispanic) 12.0 (10.1 to 14.2) 7.58 (6.2 to 9.2) 1.58 (1.23 to 2.04) 4.4 (1.9 to 6.9) 0.0005
 Black (non-Hispanic) 13.8 (11.7 to 16.2) 13.47 (11.3 to 15.9) 1.02 (0.81 to 1.29) 0.3 (−2.8 to 3.4) 0.8
 Hispanic or Latino 13.8 (10.9 to 17.3) 14 (11.6 to 16.7) 0.98 (0.73 to 1.31) −0.3 (−4.3 to 3.8) 0.9
 Other 12.3 (8.9 to 16.7) 17.22 (13.3 to 22) 0.71 (0.48 to 1.06) −4.9 (−11.0 to 0.8) 0.09
Age in years
 20–44 6.5 (5.4 to 7.8) 2.72 (1.9 to 3.9) 2.37 (1.59 to 3.54) 3.7 (2.2 to 5.3) <0.0001
 45–60 13.1 (11.1 to 15.4) 10.35 (8.6 to 12.4) 1.27 (1.00 to 1.61) 2.8 (−0.0 to 5.6) 0.05
 ≥60 22.2 (18.1 to 26.9) 20.54 (17.8 to 23.6) 1.08 (0.85 to 1.37) 1.6 (−3.6 to 6.9) 0.5
Education
 Less than high school 13.6 (10.8 to 17) 13.12 (11.3 to 15.1) 1.04 (0.79 to 1.36) 0.5 (−3.2 to 4.2) 0.8
 High school or equivalent 11.9 (9.9 to 14.4) 7.32 (6 to 8.9) 1.63 (1.23 to 2.15) 4.6 (1.9 to 7.3) 0.001
 More than high school 12.2 (10.3 to 14.4) 8.94 (7.4 to 10.8) 1.36 (1.07 to 1.74) 3.3 (0.7 to 5.8) 0.01
Living at or below poverty line‡
 No 12.4 (10.6 to 14.5) 9.43 (8.3 to 10.6) 1.31 (1.08 to 1.60) 3.0 (0.7 to 5.2) 0.01
 Yes 12.4 (10.3 to 14.8) 8.72 (6.6 to 11.5) 1.42 (1.03 to 1.95) 3.6 (0.5 to 6.8) 0.02
Obesity (BMI ≥30 kg/m2)
 No 8.1 (6.4 to 10.3) 5.14 (4.1 to 6.5) 1.58 (1.13 to 2.21) 3.0 (0.7 to 5.3) 0.01
 Yes 18.7 (16.2 to 21.4) 15.35 (13.5 to 17.4) 1.22 (1.01 to 1.46) 3.3 (0.2 to 6.5) 0.04
Hepatitis C virus§
 Positive 14.0 (11.5 to 17) 8.22 (4.4 to 14.8) 1.71 (0.90 to 3.23) 5.8 (0.1 to 11.5) 0.04
 Negative 12.4 (10.6 to 14.4) 9.4 (8.3 to 10.7) 1.32 (1.08 to 1.60) 3.0 (0.8 to 5.2) 0.009
Open in a new tab

*Prevalence ratio using NHANES as the referent category.

†Prevalence difference calculated as prevalence(MMP)–prevalence(NHANES).

‡Calculated using the ratio of annual household income to number of people in the household.

§A total of n=7146 and n=5295 were screened for hepatitis C (HCV) in MMP and NHANES, respectively. HCV positivity was defined as having a documented positive anti-HCV enzyme immunoassay or strip immunoblot assay RIBA, HCV genotype, or HCV RNA through reverse transcriptase–PCR (RT–PCR).

aPD, adjusted prevalence difference; aPR, adjusted prevalence ratio; BMI, body mass index; MMP, Medical Monitoring Project; NHANES, National Health and Nutrition Examination Survey.

Among HIV-infected adults, DM prevalence varied by selected characteristics (table 4). The adjusted DM prevalence was lowest among those aged 20–44 years (6.7%) and highest among those aged ≥60 years (19.6%), and obese (18.9%) (table 4). Factors independently associated with total DM among HIV-infected adults included increasing age, obesity, increasing time since HIV diagnosis, and geometric mean CD4.

Table 4.

Prevalence of and factors associated with diagnosed diabetes mellitus among HIV-infected adults, MMP 2009–2010

Sample n* Weighted prevalence % (95% CI) Adjusted weighted prevalence % (95% CI) cPR (95% CI) aPR (95%CI) p Value†
Total 8610 10.2 (9.1 to 11.4) 10.7 (9.6 to 11.8) – – –
Sex at birth 0.2
 Male 6349 9.5 (8.3 to 10.7) 10.3 (9.2 to 11.5) Ref Ref
 Female 2261 12.4 (10.7 to 14.0) 11.5 (9.7 to 13.6) 1.30 (1.14 to 1.49) 1.12 (0.94 to 1.33)
Race/ethnicity 0.1
 White (non-Hispanic) 2869 8.9 (7.4 to 10.5) 9.4 (8.0 to 10.9) ref ref
 Black (non-Hispanic) 3512 11.3 (9.9 to 12.7) 11.5 (10.0 to 13.2) 1.26 (1.07 to 1.50) 1.23 (1.02 to 1.48)
 Hispanic or Latino 1827 10.4 (8.7 to 12.1) 11.2 (9.3 to 13.6) 1.17 (0.93 to 1.46) 1.20 (0.96 to 1.50)
 Other 402 10.3 (6.7 to 13.9) 10.7 (7.8 to 14.7) 1.16 (0.87 to 1.53) 1.15 (0.87 to 1.52)
Age in years <0.0001
 20–44 3412 5.8 (4.9 to 6.7) 6.7 (5.8 to 7.7) ref ref
 45–60 4366 12.0 (10.7 to 13.4) 11.9 (10.5 to 13.6) 2.07 (1.82 to 2.36) 1.79 (1.53 to 2.08)
 ≥60 832 19.3 (16.1 to 22.6) 19.6 (16.3 to 23.5) 3.33 (2.83 to 3.91) 2.94 (2.36 to 3.67)
Education 0.6
 Less than high school 1937 12.1 (10.1 to 14.1) 11.5 (9.7 to 13.6) 1.24 (1.00 to 1.55) 1.09 (0.89 to 1.34)
 High school or equivalent 2314 9.7 (8.0 to 11.5) 10.3 (8.6 to 12.2) 1.00 (0.84 to 1.18) 0.97 (0.80 to 1.18)
 More than high school 4356 9.7 (8.3 to 11.2) 10.5 (9.3 to 12) ref ref
Living at or below poverty line‡ 0.9
  No 4565 10.4 (8.8 to 11.3) 10.6 (9.2 to 12.2) ref ref
  Yes 3790 10.7 (9.1 to 12.2) 10.8 (9.3 to 12.5) 1.06 (0.92 to 1.23) 1.02 (0.83 to 1.25)
Obesity (BMI ≥30 kg/m2) <0.0001
 No 6208 7.8 (6.4 to 9.1) 7.9 (6.7 to 9.4) ref ref
 Yes 2071 17.7 (16.4 to 19.1) 18.9 (17 to 20.9) 2.29 (1.88 to 2.79) 2.38 (1.91 to 2.95)
Hepatitis C virus§ 0.3
 Positive 1556 12.9 (10.9 to 14.9) 11.6 (9.8 to 13.6) 1.31 (1.10 to 1.58) 1.11 (0.92 to 1.35)
 Negative 5590 9.8 (8.7 to 11.0) 10.4 (9.2 to 11.8) ref ref
Time since HIV diagnosis <0.0001
 Less than 5 years 1866 5.4 (3.9 to 6.9) 7.0 (5.3 to 9.1) ref ref
 5–9 years 1932 8.6 (7.0 to 10.2) 9.8 (8.1 to 11.8) 1.59 (1.22 to 2.08) 1.41 (1.06 to 1.87)
 10 or more years 4806 12.9 (11.6 to 14.2) 12.3 (11.1 to 13.5) 2.40 (1.88 to 3.05) 1.77 (1.38 to 2.27)
AIDS/nadir CD4 count in cells/mm3 0.3
 AIDS or nadir CD4 0–199 5952 10.8 (9.7 to 11.8) 10.5 (9.5 to 11.6) 1.27 (1.08 to 1.52) 1.01 (0.83 to 1.22)
 No AIDS and nadir CD4 200–500 2098 8.5 (6.5 to 10.4) 10.4 (8.6 to 12.6) ref ref
 No AIDS and nadir CD4 >500 540 11.2 (8.0 to 14.3) 13.5 (9.6 to 18.6) 1.32 (0.97 to 1.79) 1.29 (0.94 to 1.78)
Geometric mean CD4 past 12 months 0.0007
 0–199 cells/mm3 1118 9.8 (7.8 to 11.8) 10.1 (7.9 to 12.7) 1.11 (0.87 to 1.40) 1.14 (0.88 to 1.47)
 200–349 cells/mm3 1486 9.8 (7.9 to 11.7) 11.4 (9.5 to 13.7) 1.11 (0.89 to 1.37) 1.29 (1.05 to 1.59)
 350–499 cells/mm3 1949 8.9 (7.8 to 10.0) 8.8 (7.7 to 10.1) ref ref
 ≥500 cells/mm3 3739 11.3 (9.8 to 12.8) 11.5 (10.1 to 13.2) 1.27 (1.13 to 1.90) 1.30 (1.14 to 1.50)
Prescribed ART 0.054
 No 828 7.2 (5.1 to 9.4) 8.3 (6.2 to 10.9) Ref Ref
 Yes 7751 10.6 (9.5 to 11.7) 10.9 (9.8 to 12.1) 1.47 (1.13 to 1.90) 1.32 (0.99 to 1.76)
Open in a new tab

*Includes adults aged ≥ 20 years.

†p Value of association for the multivariable model.

‡Calculated using the ratio of annual household income to number of people in the household.

§A total of n=7146 were screened for hepatitis C (HCV) in MMP. HCV positivity was defined as having a documented positive anti-HCV enzyme immunoassay or strip immunoblot assay RIBA, HCV genotype, or HCV RNA through reverse transcriptase–PCR (RT–PCR).

aPR, adjusted prevalence ratio; ART, antiretroviral therapy; BMI, body mass index; cPR, crude prevalence ratio; MMP, Medical Monitoring Project.

Discussion

Among a nationally representative US sample of HIV-infected adults receiving medical care in 2009 and 2010, the DM prevalence was 10.3%; increasing age, obesity, longer duration of HIV infection, and geometric mean CD4 were independently associated with a higher DM prevalence. When compared with the general US adult population, HIV-infected individuals had a 3.8% higher prevalence of DM after adjusting for age, sex, race/ethnicity, education, poverty-level, obesity, and HCV infection. This analysis provides the first nationally representative estimate of DM burden among HIV-infected adults and suggests that HIV-infected persons may be more likely to have DM at younger ages and in the absence of obesity compared with the general US adult population.

Our estimates of DM prevalence among HIV-infected adults are lower compared with previous US studies. The Multicenter AIDS Cohort Study reported 14% DM prevalence among 411 men who have sex with men recruited from 1999 to 2003.8 The Veterans Aging Cohort Study Virtual Cohort observed a similar baseline prevalence (14%) in a cohort of 27 350 HIV-infected veterans recruited from 2003 to 2009.23 These differences may reflect the burden of undiagnosed diabetes measured with fasting blood glucose and HbA1c. The HIV Outpatient Study (HOPS) reported a higher DM prevalence among HIV-infected women (19%) compared with HIV-infected men (12%), a finding that although not statistically significant was observed in our sample.24 Conversely, DM prevalence among HIV-infected individuals in non-US cohorts is significantly lower than our estimate, ranging from 2.7% to 3.3%.9 25 26

Similar to previous studies, we observed a strong association between both increasing age and obesity and prevalent DM among HIV-infected individuals,8 10 11 24 26 27 suggesting that these traditional risk factors play a major role in the development of DM among HIV-infected adults. Despite evidence suggesting a link between ART and DM, ART prescription in the past year was not associated with prevalent DM in our study.9 24 26 This may be due to our inability to assess cumulative exposure to ART in MMP. The ARTs indinavir, zidovudine, saquinavir, stavudine, and didanosine have been associated with a higher prevalence of DM;9 24 25 only a small percentage of HIV-infected adults in MMP were currently prescribed these agents (0.4% indinavir, 11.2% zidovudine, 1.0% saquinavir, 1.2% stavudine, and 2.4% didanosine), which may also account for the null association between ART exposure and DM. Time since HIV diagnosis was significantly associated with a higher DM prevalence. Although the exact mechanistic pathways measured by this variable are hard to elucidate, it may serve as a proxy for older age, exposure to chronic inflammation due to HIV infection, as well as cumulative exposure to ART, all of which have been linked to insulin resistance.24 26–30 Another surrogate marker for systemic inflammation, CD4 count nadir, was not associated with increased prevalence of DM after adjusting for other covariates. This is contrary to previous studies which have linked CD4+ nadir <200 cells/μL with increased levels of interleukin 6, high-sensitivity C reactive protein, and soluble tumor necrosis receptors.26 31 32 In addition, geometric mean CD4 count was associated with a higher prevalence of DM in our sample; however, the lack of clear directionality makes it hard to interpret. Taken together, our results highlight the need for more nuanced measures of chronic inflammation present in HIV infection and their interaction with traditional risk factors such as obesity.

The aPD of DM between HIV-infected adults and the general US adult population was heterogeneous by subpopulation. HIV-infected women had a 5% higher prevalence than their counterparts in the general population, an effect that was independent of obesity. There is evidence that the use of ART may increase conversion to DM among women with high-risk genetic polymorphisms;33 however, sex-specific differences in insulin resistance, particularly the role of sex hormones in the setting of HIV infection, remain understudied. Beyond the effect of ART on insulin resistance and development of DM, chronic inflammation during HIV infection may accelerate the development of comorbid conditions such as DM.32 Although this chronic inflammatory state may explain the development of DM among HIV-infected adults at younger ages and among the non-obese, there is a continued need for research assessing other important risk factors for DM among HIV-infected individuals, including diet and exercise, as well as a deeper understanding of insulin and glucose homeostasis in the setting of HIV infection.

Finally, although HCV has been described as a risk factor for DM in the general population, our findings indicate that HIV may compound the deleterious effects of HCV, putting HIV/HCV coinfected individuals at even higher risk of DM.34 Observed differences could be due to a lower engagement in medical care by HCV-infected adults in the general population and suboptimal screening practices.35 Nevertheless, this finding is particularly relevant, given the availability of directly acting antiviral agents as curative HCV therapy and highlights a potential additional benefit of HCV treatment for coinfected patients.36

Based on our findings, as well as current literature regarding DM among HIV-infected individuals, there are several important implications. First, HIV-care providers should follow existing DM screening guidelines, which recommend FBG and HbA1c be obtained prior to and after starting ART.37 Second, existing data from prospective studies should be examined to determine if screening guidelines should be modified, given the increased prevalence of DM among younger and non-obese HIV-infected persons. Third, improved tests for DM diagnosis and monitoring among HIV-infected persons should be explored, given studies that have demonstrated the diagnostic limitations of HbA1c in this population.10 14 15 Finally, additional research will be important to identify optimal DM management strategies among HIV-infected persons as traditional strategies to improve insulin sensitivity such as weight loss and diabetic medical therapy have been shown to be less effective among HIV-infected individuals.38 39

This analysis is subject to several limitations. First, the definition of diagnosed DM was different between MMP (medical record abstraction) and NHANES (self-reported) and may be a source of bias. A recent cohort-based validation of prevalence of DM based on self-report showed a specificity and negative predictive value >95%, with a sensitivity and positive predictive value between 60% and 70%.40 This indicates that were there a bias introduced by self-reporting of DM, it would be towards and increased prevalence of DM with self-report. Furthermore, comparisons between self-reported and medical record-based estimates of DM have shown substantial agreement between both measures, and in some cases, an underestimation of DM prevalence in medical records relative to self-report.41–43 Second, there is a risk of observer bias in our sample, given differences in engagement in care between our samples. We addressed this by performing a sensitivity analysis restricting the NHANES sample to adults having received care in the previous 12 months. Although we observed only slight changes in the magnitude of the associations, there is a possibility of overestimation of the PD between MMP and NHANES. Third, risk factors for DM, such as family history/genetics, diet, and exercise, were not included in this analysis and could explain some of the excess prevalence observed among HIV-infected adults. However, the inclusion of patients with type 1 diabetes is unlikely to have resulted in the excess diabetes prevalence observed in our study as the prevalence of type 1 diabetes in NHANES has been estimated to range between 3.6% and 4.8%.44 Fourth, the measurement of BMI and HCV were standardized for the NHANES population and not for MMP participants resulting in a biased association between these variables and DM when comparing NHANES and MMP participants. Fifth, MMP data are representative of HIV-infected persons receiving medical care and do not necessarily reflect DM prevalence among HIV-infected persons not diagnosed or not receiving care. Sixth, the increased prevalence of DM among HIV-infected women relative to the general US adult population may be due to misclassification bias; although we excluded pregnant women and diagnoses labeled gestational diabetes in the medical record of MMP participants, female patients with gestational diabetes may have been mislabeled and included in our sample. Finally, the NHANES population included HIV-infected adults who may or may not have received medical care. However, the prevalence of HIV-infected individuals in the NHANES population is negligible (0.21%).45 Although diabetes rates were standardized to the combined population of MMP and NHANES, given the very small percentage represented by MMP in the general US adult population, the bias introduced should be minimal.

Conclusion

We presented the first nationally representative estimate of DM prevalence among HIV-infected adults receiving medical care in the USA in 2009–2010 where 1 in 10 HIV-infected adults had a diagnosis of DM. Although obesity is a risk factor for prevalent DM among HIV-infected adults, when compared with the general US adult population, HIV-infected adults may have higher DM prevalence at younger ages and in the absence of obesity. Healthcare providers caring for HIV-infected patients should follow existing DM screening guidelines. Given the large burden of DM among HIV-infected adults, additional research would help to determine whether DM screening guidelines should be modified to include HIV infection as a risk factor for DM and to identify optimal management strategies in this population.

Acknowledgments

The authors thank all MMP and NHANES participants and staff members for their time and efforts. The authors also thank Dr. Emma Frazier for MMP data analytic support.

Footnotes

Contributors: ACH-R, SG, and JS had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. ACH-R, SG, ESR, and JS are responsible for study concept and design, analysis and interpretation of data, drafting of the manuscript, and statistical analysis. ACH-R, SG, and JS are responsible for acquisition of data. All authors contributed to critical revision of the manuscript for important intellectual content. JS obtained funding. SG and JS are responsible for administrative, technical, and material support and study supervision.

Funding: This work was supported and funded by CDC through a Cooperative Agreement (PS09-937) with MMP participating areas.

Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the CDC.

Competing interests: None declared.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data sharing statement: NHANES additional data include biometric, social, and demographic characteristics, and data sets are available to the general public through the Centers for Disease Control and Prevention website. MMP additional data include biometric, social, behavioral, and demographic data and are only available to Centers for Disease Control and Prevention employed in the Clinical and Behavioral Branches of domestic HIV surveillance.

References

  • 1.Centers for Disease Control and Prevention. National Diabetes Statistics Report: estimates of diabetes and its burden in the Unites States. Atlanta, GA: US Departments of Health and Human Services, 2014. [Google Scholar]
  • 2.Nathan DM. Long-term complications of diabetes mellitus. N Engl J Med 1993;328:1676–85. 10.1056/NEJM199306103282306 [DOI] [PubMed] [Google Scholar]
  • 3.Seshasai SR, Kaptoge S, Thompson A et al. . Diabetes mellitus, fasting glucose, and risk of cause-specific death. N Engl J Med 2011;364:829–41. 10.1056/NEJMoa1008862 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Economic costs of diabetes in the U.S. in 2012. Diabetes Care 2013;36:1033–46. 10.2337/dc12-2625 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Smith C, Sabin CA, Lundgren JD et al. . Factors associated with specific causes of death amongst HIV-positive individuals in the D:A:D Study. AIDS 2010;24:1537–48. 10.1097/QAD.0b013e32833a0918 [DOI] [PubMed] [Google Scholar]
  • 6.Nakagawa F, Lodwick RK, Smith CJ et al. . Projected life expectancy of people with HIV according to timing of diagnosis. AIDS 2012;26:335–43. 10.1097/QAD.0b013e32834dcec9 [DOI] [PubMed] [Google Scholar]
  • 7.Buchacz K, Baker RK, Palella FJ Jr 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 (Lond) 2013;18:65–75. 10.3851/IMP2450 [DOI] [PubMed] [Google Scholar]
  • 8.Brown TT, Cole SR, Li X et al. . Antiretroviral therapy and the prevalence and incidence of diabetes mellitus in the multicenter AIDS cohort study. Arch Intern Med 2005;165:1179–84. 10.1001/archinte.165.10.1179 [DOI] [PubMed] [Google Scholar]
  • 9.Rasmussen LD, Mathiesen ER, Kronborg G et al. . Risk of diabetes mellitus in persons with and without HIV: a Danish nationwide population-based cohort study. PLoS ONE 2012;7:12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Tien PC, Schneider MF, Cox C et al. . Association of HIV infection with incident diabetes mellitus: impact of using hemoglobin A1C as a criterion for diabetes. J Acquir Immune Defic Syndr 2012;61:334–40. 10.1097/QAI.0b013e31826bfc32 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Tripathi A, Liese AD, Jerrell JM et al. . Incidence of diabetes mellitus in a population-based cohort of HIV-infected and non-HIV-infected persons: the impact of clinical and therapeutic factors over time. Diabet Med 2014;31:1185–93. 10.1111/dme.12455 [DOI] [PubMed] [Google Scholar]
  • 12.Frankel MR, McNaghten A, Shapiro MF et al. . A probability sample for monitoring the HIV-infected population in care in the U.S. and in selected states. Open AIDS J 2012;6:67–76. 10.2174/1874613601206010067 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Centers for Disease Control and Prevention. National Health and Nutrition Examination Survey: Survey Methods and Analytic Guidelines 2014 (cited 8 March 2015). http://www.cdc.gov/nchs/nhanes/survey_methods.htm
  • 14.Eckhardt BJ, Holzman RS, Kwan CK et al. . Glycated Hemoglobin A(1c) as screening for diabetes mellitus in HIV-infected individuals. AIDS Patient Care STDS 2012;26:197–201. 10.1089/apc.2011.0379 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Slama L, Palella FJ Jr, Abraham AG et al. . Inaccuracy of haemoglobin A1c among HIV-infected men: effects of CD4 cell count, antiretroviral therapies and haematological parameters. J Antimicrob Chemother 2014;69:3360–7. 10.1093/jac/dku295 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Thompson-Paul AM, Wei SC, Mattson CL et al. . Obesity among HIV-infected adults receiving medical care in the United States: data from the cross-sectional Medical Monitoring Project and National Health and Nutrition Examination Survey. Medicine (Baltimore) 2015;94:e1081 10.1097/MD.0000000000001081 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Schneider E, Whitmore S, Glynn KM et al. . Revised surveillance case definitions for HIV infection among adults, adolescents, and children aged <18 months and for HIV infection and AIDS among children aged 18 months to <13 years—United States, 2008. MMWR Recomm Rep 2008;57(RR-10):1–12. [PubMed] [Google Scholar]
  • 18.Ghany MG, Strader DB, Thomas DL et al. . Diagnosis, management, and treatment of hepatitis C: an update. Hepatology 2009;49:1335–74. 10.1002/hep.22759 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Bieler GS, Brown GG, Williams RL et al. . Estimating model-adjusted risks, risk differences, and risk ratios from complex survey data. Am J Epidemiol 2010;171:618–23. 10.1093/aje/kwp440 [DOI] [PubMed] [Google Scholar]
  • 20.Muller CJ, MacLehose RF. Estimating predicted probabilities from logistic regression: different methods correspond to different target populations. Int J Epidemiol 2014;43:962–70. 10.1093/ije/dyu029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Korn EL, Graubard BI. Analysis of health surveys. New York, NY: Wiley and Sons, 1999. [Google Scholar]
  • 22.Gregg EW, Cheng YJ, Cadwell BL et al. . Secular trends in cardiovascular disease risk factors according to body mass index in US adults. JAMA 2005;293:1868–74. 10.1001/jama.293.15.1868 [DOI] [PubMed] [Google Scholar]
  • 23.De Wit S, Sabin CA, Weber R et al. . Incidence and risk factors for new-onset diabetes in HIV-infected patients: the Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) study. Diabetes Care 2008;31:1224–9. 10.2337/dc07-2013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Freiberg MS, Chang CC, Kuller LH et al. . HIV infection and the risk of acute myocardial infarction. JAMA Intern Med 2013;173:614–22. 10.1001/jamainternmed.2013.3728 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Glass TR, Ungsedhapand C, Wolbers M et al. . Prevalence of risk factors for cardiovascular disease in HIV-infected patients over time: the Swiss HIV Cohort Study. HIV Med 2006;7:404–10. 10.1111/j.1468-1293.2006.00400.x [DOI] [PubMed] [Google Scholar]
  • 26.Capeau J, Bouteloup V, Katlama C et al. . Ten-year diabetes incidence in 1046 HIV-infected patients started on a combination antiretroviral treatment. AIDS 2012;26:303–14. 10.1097/QAD.0b013e32834e8776 [DOI] [PubMed] [Google Scholar]
  • 27.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;53:1120–6. 10.1093/cid/cir627 [DOI] [PubMed] [Google Scholar]
  • 28.Betene ADC, De Wit S, Neuhaus J et al. . Interleukin-6, high sensitivity C-reactive protein, and the development of type 2 diabetes among HIV-positive patients taking antiretroviral therapy. J Acquir Immune Defic Syndr 2014;67:538–46. 10.1097/QAI.0000000000000354 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Brown TT, Tassiopoulos K, Bosch RJ et al. . Association between systemic inflammation and incident diabetes in HIV-infected patients after initiation of antiretroviral therapy. Diabetes Care 2010;33:2244–9. 10.2337/dc10-0633 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.McComsey GA, Kitch D, Sax PE et al. . Associations of inflammatory markers with AIDS and non-AIDS clinical events after initiation of antiretroviral therapy: AIDS clinical trials group A5224s, a substudy of ACTG A5202. J Acquir Immune Defic Syndr 2014;65:167–74. 10.1097/01.qai.0000437171.00504.41 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Brown TT, Tassioppoulos K, Bosch RJ et al. . Association between systemic inflammation and incident diabetes in HIV-infected patients after initiation of antiretroviral therapy. Diabetes Care 2010;33:2244–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Ghislain M, Bastard JP, Meyer L et al. . Late Antiretroviral Therapy (ART) initiation is associated with long-term persistence of systemic inflammation and metabolic abnormalities. PLoS ONE 2015;10:e0144317 10.1371/journal.pone.0144317 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Frasco MA, Karim R, Van Den Berg D et al. . Antiretroviral therapy modifies the genetic effect of known type 2 diabetes-associated risk variants in HIV-infected women. AIDS 2014;28:1815–23. 10.1097/QAD.0000000000000366 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.White DL, Ratziu V, El-Serag HB. Hepatitis C infection and risk of diabetes: a systematic review and meta-analysis. J Hepatol 2008;49:831–44. 10.1016/j.jhep.2008.08.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Holmberg SD, Spradling PR, Moorman AC et al. . Hepatitis C in the United States. N Engl J Med 2013;368:1859–61. 10.1056/NEJMp1302973 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Kowdley KV, Lawitz E, Poordad F et al. . Phase 2b trial of interferon-free therapy for hepatitis C virus genotype 1. N Engl J Med 2014;370:222–32. 10.1056/NEJMoa1306227 [DOI] [PubMed] [Google Scholar]
  • 37.Aberg JA, Gallant JE, Ghanem KG et al. . Primary care guidelines for the management of persons infected with HIV: 2013 update by the HIV Medicine Association of the Infectious Diseases Society of America. Clin Infect Dis 2014;58:1–10. 10.1093/cid/cit757 [DOI] [PubMed] [Google Scholar]
  • 38.Reeds DC, Patterson WT, Overton BW et al. . Metabolic benefits of weight loss are blunted in obese, HIV-infected women. Obesity 2011;19(Suppl 1):S112. [Google Scholar]
  • 39.Han JH, Crane HM, Bellamy SL et al. . HIV infection and glycemic response to newly initiated diabetic medical therapy. AIDS 2012;26:2087–95. 10.1097/QAD.0b013e328359a8e5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Schneider ALC, Pankow JS, Heiss G et al. . Validity and reliability of self-reported diabetes in the atherosclerosis risk in communities study. Am J Epidemiol 2012;176:738–43. 10.1093/aje/kws156 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Okura Y, Urban LH, Mahoney DW et al. . Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure. J Clin Epidemiol 2004;57:1096–103. 10.1016/j.jclinepi.2004.04.005 [DOI] [PubMed] [Google Scholar]
  • 42.Corser W, Sikorskii A, Olomu A et al. . Concordance between comorbidity data from patient self-report interviews and medical record documentation. BMC Health Serv Res 2008;8:85 10.1186/1472-6963-8-85 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Asao K, McEwen LN, Lee JM et al. . Ascertainment of outpatient visits by patients with diabetes: The National Ambulatory Medical Care Survey (NAMCS) and The National Hospital Ambulatory Medical Care Survey (NHAMCS). J Diabetes Complicat 2015;29:650–8. 10.1016/j.jdiacomp.2015.03.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Menke A, Orchard TJ, Imperatore G et al. . The prevalence of type 1 diabetes in the United States. Epidemiology 2013;24:773–4. 10.1097/EDE.0b013e31829ef01a [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Centers for Disease Control and Prevention. National Health and Nutrition Examination Survey: Data Documentation, Codebook, and Frequencies. 2014 (cited 8 March 2015). http://wwwn.cdc.gov/nchs/nhanes/2009–2010/HIV_F.htm

Articles from BMJ Open Diabetes Research & Care are provided here courtesy of BMJ Publishing Group

RESOURCES