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. Author manuscript; available in PMC: 2022 Nov 1.
Published in final edited form as: AIDS Care. 2020 Aug 18;33(11):1464–1474. doi: 10.1080/09540121.2020.1808160

Diabetes mellitus control in a large cohort of people with HIV in care-Washington, D.C.

David E Wallace 1, Michael A Horberg 2, Debra A Benator 3,4, Alan E Greenberg 1, Amanda D Castel 1, Anne K Monroe 1,#, Lindsey Powers Happ 1,#, on behalf of the DC Cohort Executive Committee
PMCID: PMC8091517  NIHMSID: NIHMS1620949  PMID: 32811173

Abstract

With more effective antiretroviral therapy (ART), people with HIV (PWH) are living longer and have more chronic diseases, including diabetes mellitus (DM). The prevalence of DM has been estimated in PWH previously, however there is less research regarding DM control. Our objectives were to determine the prevalence of DM and DM control and determine factors associated with DM control in a large urban cohort of PWH in care. We examined DC Cohort participants aged ≥18 years old to determine DM prevalence and to assess DM control (HbA1c measurement <7.0%). Demographic, clinical, and HIV-related factors associated with DM control were identified using multivariate logistic regression. The cohort of 5876 participants was predominantly male (71.3%), Non-Hispanic Black (78.1%) and had a median age of 52.0 years. DM prevalence was 17.4% (1023/5876). Among participants with recent HbA1c data available (39.9%) the proportion with DM control was 60.0% (245/408). In multivariate analysis, higher BMI (aOR: 0.47; 95% CI 0.28, 0.79) and use of non-insulin DM medication (aOR 0.43, 95% CI 0.25, 0.73) or insulin (aOR 0.010, 95% CI 0.04,0.24) compared to no medication use. Our findings suggest that individuals on medication for their DM likely need enhanced support to reach their treatment goals.

Keywords: diabetes, Hemoglobin A1c, metabolic, comorbidities

Introduction

Advances in combination antiretroviral therapy (ART) over the past two decades have led to significantly improved HIV-related outcomes. Today’s ART is highly efficacious and has reduced toxicity and improved tolerability compared to prior regimens (Boyd, 2009). Due to these advances in therapy, people with HIV (PWH) have longer life expectancies than in the past (Marcus et al., 2016; Samji et al., 2013). With a greater life expectancy, PWH are more likely to develop noncommunicable diseases and co-morbidities (Deeks, Lewin, & Havlir, 2013). Studies show that diabetes mellitus (DM) is more prevalent in adult populations with HIV than in the United States (US) general population where diabetes has long been a common and costly disease (Hernandez-Romieu, Garg, Rosenberg, Thompson-Paul, & Skarbinski, 2017).

Studies estimating the prevalence of DM in the US among adult PWH show considerable variety with estimates ranging from as low as 7% (Hasse et al., 2015; Myerson et al., 2014) to as high as 20% (Armah et al., 2012; Sobieszczyk et al., 2008). The substantial variability among the estimates likely stems from differences in study design including age, sex, and location of participants. Most of the recent published estimates of larger cohorts of PWH reveal a DM prevalence between 11% and 16% (Duncan, Goff, & Peters, 2018; Guaraldi et al., 2011; Hernandez-Romieu et al., 2017; Levy et al., 2017; Monroe et al., 2015; Puhr et al., 2019; Sico et al., 2015).

Among PWH, multiple HIV related, sociodemographic, and clinical factors are associated with DM development. Greater time since HIV diagnosis (Levy et al., 2017), a longer duration on ART (Wong et al., 2017), and a number of older ART medications are associated with DM (Brambilla et al., 2003; De Wit et al., 2008; Justman et al., 2003; Lumpkin, 1997). Additionally, older age, female sex, and Black race were associated with increased rates of first occurrence of DM among the North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) (Wong et al., 2017). A higher BMI (Duncan et al., 2018; Levy et al., 2017), particularly one above 30 kg/m2, and Hepatitis C virus coinfection (HCV) (Jain et al., 2007; Visnegarwala, Chen, Raghavan, & Tedaldi, 2005) have also been shown to be significantly associated with the development of DM in PWH. Risk behaviors such as injection drug use (IDU), alcohol abuse, and cigarette smoking are also associated with DM in PWH (Levy et al., 2017; Wong et al., 2017).

With both a high DM prevalence and increased risk of DM in PWH, it is useful to examine the management of diabetes within this population. There is evidence that PWH with DM are undertreated for DM compared to the general population (N. Reinsch et al., 2012). DM control has been defined as a HbA1c measurement of less than 7.0 %, though there is ongoing debate following the American College of Physician (ACP) guidance advising a less restrictive target of 7–8% in some populations (Nico Reinsch et al., 2012). There have been limited studies that have assessed DM control among PWH in the US which have found varying estimates of DM control ranging from 40% to 73%(Adeyemi, 2007; Adeyemi, Vibhakar, & Max, 2009; Colasanti et al., 2018; Davies, Johnson, Brown, Bryan, & Townsend, 2015; Han et al., 2012; Malek W; Satlin, Hoover, & Glesby, 2011; Zuniga, Nguyen, & Holstad, 2016). Characteristics identified as associated with poor DM control include older age (Zuniga et al., 2016), Black race (Zuniga et al., 2016), more recent HIV diagnosis (Satlin et al., 2011). There are considerable differences in study population, sample size, and their definition of DM control between studies.

The DC Cohort, which is a longitudinal cohort study of PWH across 15 sites in Washington, DC (Greenberg et al., 2016), presents an excellent opportunity to study DM control among PWH. Washington DC is a large urban area with a 1.9% prevalence of HIV (“District of Columbia HIV/AIDS, Hepatitis, STD, TB, Hepatitis, STD, and TB Administration epidemiology and surveillance report: Surveillance data through December 2015,”). Though the prevalence of DM and associated factors have been studied among the DC Cohort (Levy et al., 2017), the extent of DM control within the cohort has not been studied previously. Understanding the sociodemographic factors and risk behaviors associated with DM control among PWH is valuable research which could allow HIV clinicians to improve DM control among the HIV patient population.

Materials and Methods

Study design:

We used a cross-sectional approach to determine the prevalence of DM and DM control among adult PWH in care who have DM in Washington, DC. The DC Cohort is an observational longitudinal cohort study across 15 major community, government, and academic HIV clinical sites that have been enrolling participants on an ongoing basis beginning in January 2011. The details of the study have been previously described (Greenberg et al., 2016). In brief, following informed consent, PWH are enrolled and contribute data longitudinally from their routine HIV care visits. DC Cohort data included in this analysis included sociodemographic, clinical, and laboratory measurements gathered through manual and electronic abstraction from the electronic medical record (EMR) at each site. The DC Cohort has Institutional Review Board (IRB) approval from George Washington University and participating sites with their own IRBs. This secondary analysis assessing the prevalence of DM in the DC Cohort included participants who were ≥ 18 years of age and were actively enrolled in the study. Participants were eligible to be included in the assessment of DM control if they had a diagnosis of DM, had at least one HbA1c measurement in the year prior to the end of data available (April 1, 2017 to March 31, 2018) with at least one clinical encounter at a DC Cohort clinical site in the year prior to the HbA1c measurement, and received primary care at a DC Cohort clinical site as of March 31, 2018.

Measures:

Predictor variables included sociodemographic characteristics (age, sex at birth, race, state of residence, clinic type attended (clinic in a hospital vs. a community-based clinic), employment, housing, and insurance status), behavioral risk factors (smoking, alcohol abuse, and drug use), HIV-related variables (ART, HIVRNA, CD4 cell count, duration of HIV infection and ART, ART regimen as of 3/31/2018, whether DM was present at time of DC Cohort enrollment, any record of use of older ART medications correlated with DM (stavudine, zidovudine, didanosine, indinavir, saquinavir, lopinavir/ritonavir, or nelfinavir)), use of any non-insulin DM medications, use of insulin, or both (medication use as of 3/31/18), and any record of an International Classification of Diseases, 9th or 10th Revision (ICD-9 or ICD-10) code for chronic hepatitis C (HCV), however, HCV status classification as chronic, uncured or chronic, cured is not available. Behavioral risk factors were abstracted via chart review at enrollment only and were classified as current, previous or never.

A participant was determined to have DM if they met at least one of the following three criteria: 1) a record of an ICD-9 or ICD-10 code for Type II, unspecified, or a related diabetes mellitus diagnosis, 2) a record of any current drug prescription in the EMR suggesting treatment of DM, including metformin only, or 3) glycated hemoglobin (HbA1c) ≥ 6.5% or serum glucose ≥ 200 mg/dL or on at least two occasions. The outcome measure for DM control was defined during the period of April 1, 2017 to March 31, 2018. A participant was considered to have achieved DM control if their most recent HbA1c laboratory result was < 7.0%. This cutoff value of 7.0% was used as this value has been recommended for the management of care in most diabetics in the general population (Association, 2017) as well as for diabetic PWH (Aberg et al., 2014).

Statistical Analysis:

Descriptive statistics were calculated for demographic and clinical characteristics by DM status and by DM control status using a Chi Square test or Fisher’s exact test for categorical variables and a Wilcoxon-rank-sum test for continuous variables. Additionally, multivariate logistic regression to examine factors associated with DM control was performed. All sociodemographic, behavioral, and clinical covariates were assessed using univariate analysis. Multivariate modeling proceeded with any factors with a p-value <0.10 in the univariate analysis. Age, sex at birth, race/ethnicity, BMI, HCV comorbidity, insurance status, DM medication use and PI use were all chosen a priori to also be included in the model. Variables with multiple categories, for example, race/ethnicity, BMI, and insurance status were reclassified into dichotomous variables for the multivariable analyses. The final race variable was Race other than Non-Hispanic Black (compared with Non-Hispanic Black), the final BMI variable was obese (vs non obese) and the final insurance status variable was any insurance category other than public compared with public.

Unadjusted and adjusted odds ratios for the included variables are presented with corresponding p-values and 95% confidence intervals. Statistical significance for all the above results was determined if the associated p-value was <0.05. All statistical analyses were completed using SAS version 9.4 (SAS Institute, Cary, NC, USA).

Results

There were a total of 5,876 participants from 14 of the 15 DC Cohort sites meeting the eligibility criteria. One site was excluded due to no participant data being available at the time of analysis. Supplemental Figure 1 shows the distribution in how the participants were identified as meeting DM criteria. Of the 1,023 participants, 961 (93.9%) had an ICD-9 or ICD-10, Type 2, unspecified type, or associated DM diagnosis, 604 (59.0%) had a current DM medication, and 552 (54.0%) had at least two elevated serum glucoses or HbA1c laboratory measurement values. 408 (39.9%) met all three criteria for a DM diagnosis. The total population was predominantly male (71.3%), Non-Hispanic (NH) Black (78.1%) and had a median age of 52.0 years (see Table 1). About one-fifth of participants in this sample, N=1,023 (17.4%) met criteria for DM. Participants with diabetes were more likely to be female (34.3% vs. 27.5%; p<.0001), older (median age of 57.0 years vs. 50.0 years; p<.0001) and NH Black (84.5% vs. 76.8%; p<.0001). Additionally, they were more likely to have a recent BMI ≥ 30 kg/m2 (41.1% vs. 25.5%; p<.0001), HCV diagnosis (14.2% vs 9.2%; p<.0001), and to have taken an antiretroviral medication historically associated with DM (32.4% vs. 25.4%; p<.0001). With regards to their HIV status, diabetic patients, compared with non-diabetic patients, were more likely to be virally suppressed (HIV RNA < 200 copies/mL) (90.1% vs. 86.7%; p=0.007), and have a longer duration of both HIV (median of 17.9 years vs. 13.8 years; p<.0001), and ART use (median of 8.4 years vs. 7.5 years; p<.0001). Finally, they were more likely to have public insurance (79.4% vs 70.0%, p<.0001) and to have ever smoked (56.6% vs. 53.1%; p=0.047).

Table 1.

Characteristics of DC Cohort participants by DM status (N=5876)

Total Sample
N (%)		 Diabetes mellitus
N (%)		 No diabetes mellitus+
N (%)		 p-value
Variable Category*
Total sample 5876 1023 (17.4) 4853 (82.6)
Age at Baseline
Median (IQR) 52.0 (42.0, 59.0) 57.0 (51.0, 63.0) 50.0 (40.0, 58.0) <.0001
Sex at birth
Male 4190 (71.3) 672 (65.7) 3518 (72.5) <.0001
Race
Non-Hispanic Black 4589 (78.1) 864 (84.5) 3725 (76.8)
Non-Hispanic White 691 (11.8) 86 (8.4) 605 (12.5)
Hispanic 346 (5.9) 44 (4.3) 302 (6.2)
Other/Unknowna 250 (4.3) 29 (2.8) 221 (4.6) <.0001
BMIb
Normal weight (<25 kg/m2) 1825 (31.1) 211 (20.6) 1614 (33.3)
Overweight (≥25 and <30 kg/m2) 1786 (30.4) 281 (27.5) 1505 (31.0)
Obese (≥30 kg/m2) 1656 (28.2) 420 (41.1) 1236 (25.5)
Unknown 609 (10.4) 111 (10.9) 498 (10.3) <.0001
Clinic type
Hospital clinic 3329 (56.6) 606 (59.2) 2723 (56.1)
Community-based 2547 (43.4) 417 (40.8) 2130 (43.9) 0.07
ART Regimen
INSTI-based 2580 (43.9) 421 (41.2) 2159 (44.5)
NNRTI-based 1042 (17.7) 180 (17.6) 862 (17.8)
PI-based 691 (11.8) 103 (10.1) 588 (12.1)
Dual Class 1029 (17.5) 200 (19.6) 829 (17.1)
Other/Unknownc 534 (9.1) 119 (11.6) 415 (8.6) 0.002
On INSTI
Yes 3779 (64.3) 661 (64.6) 3118 (64.3) 0.82
On PI
Yes 1654 (28.2) 277 (27.1) 1377 (28.4) 0.40
HCV Comorbidityd
Yes 592 (10.1) 145 (14.2) 447 (9.2) <.0001
History of ART Associated with DMe
Yes 1563 (26.6) 331 (32.4) 1232 (25.4) <.0001
Most Recent CD4 Count Category
≥ 500 cells/mm3 3847 (65.5) 686 (67.1) 3161 (65.1)
201–499 cells/mm3 1583 (26.9) 271 (26.5) 1312 (27.0)
≤ 200 cells/mm3 403 (6.9) 60 (5.9) 343 (7.1)
Unknown 43 (0.7) 6 (0.6) 37 (0.8) 0.44
Viral load suppression (< 200 copies/mL)
No 720 (12.3) 96 (9.4) 624 (12.9)
Yes 5130 (87.3) 922 (90.1) 4208 (86.7)
Unknown 26 (0.4) 5 (0.5) 21 (0.4) 0.009
Years known HIV Positive
Median (IQR) 14.3 (8.8, 21.8) 17.9 (10.8, 24.6) 13.8 (8.4, 21.3) <.0001
Years on ART
Median (IQR) 7.7 (5.2, 10.4) 8.4 (6.0, 11.8) 7.5 (5.0, 10.1) <.0001
Insurance status (baseline)
Public 4207 (71.6) 812 (79.4) 3395 (70.0)
Private 1469 (25.0) 188 (18.4) 1281 (26.4)
Unknown 200 (3.4) 23 (2.3) 177 (3.7) <.0001
Employment (baseline)
Employed 1811 (30.8) 241 (23.6) 1570 (32.4)
Unemployed 1884 (32.1) 357 (34.9) 1527 (31.5)
Otherf 421 (7.2) 131 (12.8) 290 (6.0)
Unknown 1760 (30.0) 294 (28.7) 1466 (30.2) **
Housing Status (baseline)
Permanent/Stable 4833 (82.3) 831 (81.2) 4002 (82.5)
Temporary/Unstable/Other 512 (8.7) 86 (8.4) 426 (8.8)
Unknown 531 (9.0) 106 (10.4) 425 (8.8) 0.26
State of Residence (baseline)
DC 4544 (77.3) 797 (77.9) 3747 (77.2)
MD 994 (16.9) 172 (16.8) 822 (16.9)
VA 280 (4.8) 45 (4.4) 235 (4.8)
Other/Unknown 58 (1.0) 9 (0.9) 49 (1.0) 0.91
Smoking History (baseline)
Never 2059 (35.0) 324 (31.7) 1735 (35.8)
Ever 3154 (53.7) 579 (56.6) 2575 (53.1) `
Unknown/Missing 663 (11.3) 120 (11.7) 543 (11.2) 0.045
Alcohol Abuse History (baseline)
Never 2718 (46.3) 461 (45.1) 2257 (46.5)
Ever 1646 (28.0) 312 (30.5) 1334 (27.5)
Unknown/Missing 1512 (25.7) 250 (24.4) 1262 (26.0) **
Recreational Drug Use History (baseline)
Never 2009 (34.2) 359 (35.1) 1650 (34.0)
Ever 2071 (35.3) 351 (34.3) 1720 (35.4)
Unknown/Missing 1796 (30.6) 313 (30.6) 1483 (30.6) **
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Abbreviations: DM: Diabetes mellitus; ART: antiretroviral treatment INSTI: integrase strand transfer inhibitor; NNRTI: nonnucleoside reverse transcriptase inhibitor; PI: protease inhibitor; HCV: Hepatitis C virus; IQR: interquartile range.

*

Unless otherwise specified, all variables were assessed as of March 31st, 2018

+

No recorded diagnosis of DM, no DM medications, no elevated labs

a

Other/ unknown race includes mixed race, Asians, Alaska Natives, American Indians, Pacific Islanders

b

Most recent BMI within two years of March 31st 2018; otherwise “unknown”

c

Other/ unknown ART regimen includes entry inhibitors and nucleoside reverse transcriptase inhibitors

d

Hepatitis C comorbidity defined to include any reported diagnosis of HCV no matter if it is resolved, acute, or chronic

e

Stavudine, zidovudine, didanosine, idinavir, saquinavir, lopinavir/ritonavir, or nelfinavir

f

Other employment includes retired, student, disabled, and termination of student

**

Unknown/missing data was greater than 15% so no p-value was calculated

Of the 1,023 PWH with DM, 408 (39.9%) from seven sites met inclusion criteria for the diabetes control analysis. Supplemental Figure 2 shows how the original sample was limited to participants meeting the eligibility criteria for the DM control analysis. We only included people receiving their primary care at the DC Cohort site to include participants who would be expected to have their HbA1c tests performed at their HIV primary care site.

For the evaluation of diabetes control status, the sample was mostly male (73.8%), NH Black (84.1%), and had a median age of 57.5 years (see Table 2). There was a statistically significant difference by DM control status for hospital vs community based clinic (49.8% vs 60.7%; p=0.03) Examining medication use, a higher proportion of individuals in the DM uncontrolled group were on insulin, either alone (16.0 vs 3.7%) or in combination with another non-insulin medication (26.4% vs 5.3%) (p<0.0001 for the comparison across all medication groups). Additionally, individuals in the DM uncontrolled group were more likely to have had diabetes at the time of DC Cohort enrollment rather than having a new diagnosis during the observation period (79.1% vs 48.6%, p<0.0001)

Table 2.

Characteristics of diabetic DC Cohort participants with a Hemoglobin HbA1c measurement from April 1, 2017 to March 31, 2018 by DM control status (N=408)

Total Sample
N (%)		 Diabetes mellitus controlled (HbA1c<7.0%), N (%) Diabetes mellitus
uncontrolled, N (%)		 p-value
Variable Category*
Total sample 408 245 (60.0) 163 (40.0)
Age (as of HbA1c measurement)
Median (IQR) 57.5 (51.9, 64.0) 57.3 (51.2, 63.3) 58.5 (53.0, 65.7) 0.12
Sex at birth
Male 301 (73.8) 179 (73.1) 122 (74.9) 0.69
Race
Non-Hispanic Black 343 (84.1) 201 (82.0) 142 (87.1)
Non-Hispanic White 30 (7.4) 20 (8.2) 10 (6.1)
Hispanic 27 (6.6) 17 (6.9) 10 (6.1)
Other/Unknowna 8 (2.0) 7 (2.9) 1 (0.6) 0.33
BMIb
Normal weight (<25 kg/m2) 96 (23.5) 60 (24.5) 36 (22.1)
Overweight (≥25 and <30 kg/m2) 119 (29.2) 79 (32.2) 40 (24.5)
Obese (≥30 kg/m2) 190 (46.6) 104 (42.5) 86 (52.8)
Unknown 3 (0.7) 2 (0.8) 1 (0.6) 0.21
Clinic type
Hospital clinic 221 (54.2) 122 (49.8) 99 (60.7)
Community-based 187 (45.8) 123 (50.2) 64 (39.3) 0.03
Diabetes diagnosis present at DC cohort consent 248 (60.8) 119 (48.6) 129 (79.1) <0.0001
Diabetes medication use
No medication 162 (39.7) 128 (52.2) 34 (20.9)
Non-insulin medication only 155 (38.0) 95 (38.8) 60 (36.8)
Insulin only 35 (8.6) 9 (3.7) 26 (16.0)
Both insulin and non-insulin medication 56 (13.7) 13 (5.3) 43 (26.4) <0.0001
Current ART Regimen (as of HbA1c measurement)
INSTI-based 150 (36.8) 86 (35.1) 64 (39.3)
NNRTI-based 71 (17.4) 44 (18.0) 27 (16.6)
PI-based 45 (11.0) 29 (11.8) 16 (9.8)
Dual Class 91 (22.3) 54 (22.0) 37 (22.7)
Other/Unknownc 51 (12.5) 32 (13.1) 19 (11.7) 0.89
On INSTI (as of HbA1c measurement)
Yes 258 (63.2) 151 (61.6) 107 (65.6) 0.41
On PI (as of HbA1c measurement)
Yes 119 (29.2) 79 (32.4) 40 (24.5) 0.09
HCV Comorbidityd
Yes 64 (15.7) 43 (17.6) 21 (12.9) 0.20
History of ART Associated with DMe
Yes 148 (36.3) 85 (34.7) 63 (38.7) 0.42
Most Recent
CD4 Count Category (as of HbA1c measurement)
≥ 500 cells/mm3 274 (67.2) 162 (66.1) 112 (68.7)
201–499 cells/mm3 110 (27.0) 70 (28.6) 40 (24.5)
≤ 200 cells/mm3 23 (5.6) 12 (4.9) 11 (6.8)
Unknown 1 (0.3) 1 (0.4) 0 (0.0) 0.58
Viral load suppression (as of HbA1c measurement)
No 44 (10.8) 30 (12.2) 14 (8.6)
Yes 361 (88.5) 213 (87.0) 148 (90.8)
Unknown 3 (0.7) 2 (0.8) 1 (0.6) 0.49
Years HIV Positive (as of HbA1c measurement)
Median (IQR) 19.3 (12.0, 24.6) 19.2 (11.7, 24.1) 19.4 (13.6, 25.5) 0.39
Years on ART (as of HbA1c measurement)
Median (IQR) 8.3 (6.1, 10.9) 8.5 (6.4,10.4) 8.0 (5.8, 12.6) 0.49
Insurance status (baseline)
Public 350 (85.8) 206 (84.1) 144 (88.3)
Private 45 (11.0) 31 (12.7) 14 (8.6)
No known/unknown 13 (3.2) 8 (3.3) 5 (3.1) 0.43
Employment
Employed 83 (20.3) 46 (18.8) 37 (22.7)
Unemployed 101 (24.8) 66 (26.9) 35 (21.5)
Otherf 39 (9.6) 15 (6.1) 24 (14.7)
Unknown 185 (45.3) 118 (48.2) 67 (41.1) **
Housing Status
Permanent/Stable 333 (81.6) 200 (81.6) 133 (81.6)
Temporary/Unstable 49 (12.0) 28 (11.4) 21 (12.9)
Other/Unknown 26 (6.4) 17 (6.9) 9 (5.5) 0.79
State of Residence
DC 310 (76.0) 195 (79.6) 115 (70.6)
MD 78 (19.1) 39 (15.9) 39 (23.9)
VA 20 (4.9) 11 (4.5) 9 (5.5) 0.10
Smoking History (baseline)
Never 92 (22.6) 49 (20.0) 43 (26.4)
Ever 242 (59.3) 148 (60.4) 94 (57.7)
Unknown/Missing 74 (18.1) 48 (19.6) 26 (16.0) **
Alcohol Abuse History (baseline)
Never 125 (30.6) 70 (28.6) 55 (33.7)
Ever 149 (36.5) 90 (36.7) 59 (36.2)
Unknown/Missing 134 (32.8) 85 (34.7) 49 (30.1) **
Recreational Drug Use History (baseline)
Never 90 (22.1) 48 (19.6) 42 (25.8)
Ever 160 (39.2) 96 (38.2) 64 (39.3)
Unknown/Missing 158 (38.7) 101 (41.2) 57 (35.0) **
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Abbreviations: DM: Diabetes mellitus; ART: antiretroviral treatment: INSTI: integrase strand transfer inhibitor; NNRTI: nonnucleoside reverse transcriptase inhibitor; PI: protease inhibitor; HCV: Hepatitis C virus; IQR: interquartile range.

*

Unless otherwise specified, all variables were assessed as of March 31st, 2018

a

Other/ unknown race includes mixed race, Asians, Alaska Natives, American Indians, Hawaiians, and Pacific Islanders

b

Most recent BMI within two years of A1c measurement; otherwise “unknown”

c

Other/ unknown ART regimen includes entry inhibitors and nucleoside reverse transcriptase inhibitors

d

Hepatitis C comorbidity defined to include any reported diagnosis of HCV no matter if it is resolved, acute, or chronic

e

Stavudine, zidovudine, didanosine, idinavir, saquinavir, lopinavir/ritonavir, or nelfinavir

f

Other employment includes retired, student, disabled, and termination of student

**

Unknown/missing data was greater than 15% so no p-value was calculated

We determined the unadjusted and adjusted odds ratios (aOR) for the association between the independent variables and the DM control outcome (see Table 3). In the multivariate analysis, a BMI ≥30 (aOR: 0.477; 95% CI 0.28, 0.79), DM medication use was inversely associated with DM control, comparing non-insulin vs. no medication (aOR 0.43), insulin vs no medication (0.10) and non-insulin and insulin together (0.08) vs. no medication (p<0.05 for all), Although not statistically significant, the aOR for the association between HCV and DM control was 1.93 (; 95% CI 0.97, 3.86).

Table 3.

Factors associated with DM control in diabetic DC Cohort participants with an A1C measurement from April 1, 2017 to March 31, 2018 (N=408)

OR (95% CI), p-value aORa (95% CI), p-value
Variable Category
Age (per 5 years) 0.91 (0.82, 1.02), 0.11 0.94 (0.81, 1.09), 0.43
Sex at birth
Female 1.10 (0.70, 1.73), 0.69 1.01 (0.58, 1.78), 0.96
Race
Race other than Non-Hispanic Black (compared with Non-Hispanic Black)b 1.48 (0.84, 2.60), 0.17 0.98 (0.51, 1.90), 0.95
BMI
≥ 30 kg/m2 0.66 (0.44, 0.99), 0.04 0.47 (0.28, 0.79), 0.005
Clinic type
Community-based 1.56 (1.04, 2.33), 0.03 1.30 (0.78, 2.16), 0.32
Diabetes diagnosis present at DC cohort consent 0.25 (0.16, 0.39), <0.0001 0.31(0.19, 0.52), <0.0001
Diabetes medication use
No medication Ref Ref
Non-insulin medication only 0.42 (0.26, 0.69), 0.0006 0.43(0.25, 0.73), 0.002
Insulin only 0.09 (0.04, 0.22), <0.0001 0.10(0.04, 0.24), <0.0001
Both insulin and non-insulin medication 0.08 (0.04, 0.17), <0.0001 0.08(0.03,0.17), <0.0001
On PI*
Yes 1.46 (0.94, 2.29), 0.09 1.62 (0.94, 2.80), 0.08
HCV Comorbidity
Yes 1.44 (0.82, 2.53), 0.21 1.93 (0.97, 3.86), 0.06
Insurance status (baseline)
Private/Other/Unknownc 1.44 (0.80, 2.58), 0.23 1.00 (0.50, 2.02), 1.00
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Abbreviations: DM: Diabetes mellitus; aOR: adjusted Odds Ratio; HCV: Hepatitis C virus. PI: protease inhibitor.

*

At time of HbA1c measurement

a

Adjusted for age, sex at birth, race, BMI, clinic type, diabetes medication, insulin, current PI use, HCV comorbidity, and insurance status

b

Category includes NH White, Hispanic, mixed race, Asians, Alaska Natives, American Indians, Hawaiians, Pacific Islanders, and unknown race.

c

Referent category is public insurance.

As a higher HbA1c value is recommended for certain forms of diabetes and for older adults with extensive comorbidities,40 we ran a separate analysis using additional cutoff HbA1c values of 7.5% and 8.0% to define DM control achieved by 67.9% and 73.5%, respectively.

Discussion

In a cross-sectional analysis of a large cohort of PWH in Washington D.C, there was a DM prevalence of 17.4%. Among these diabetics with a HbA1c measurement in the final year of observation, the prevalence of DM control was 60.0%. Factors inversely associated with DM control included higher BMI and the use of DM medication, either non-insulin medication alone, insulin alone, or non-insulin and insulin together. Our finding related to DM medication use was somewhat surprising, because we expected that DM medication use would be associated with DM control. However, the finding suggests that individuals taking medication have more severe diabetes and need additional support to meet their A1c goals. A prior study in a large cohort of U.S. veterans with HIV showed similar findings (Davies et al., 2015). This study adds to the growing literature regarding HIV and DM by providing estimates from a large, diverse city-wide cohort of PWH.

The DM prevalence estimate from this study is higher than most estimates identified in the published literature. The estimate of 17.4% is higher than the range of 11–16% previously described (Duncan et al., 2018; Guaraldi et al., 2011; Monroe et al., 2015; Puhr et al., 2019; Sico et al., 2015). Except for one study (Puhr et al., 2019), the median age of our sample was older. The estimate we present, is lower than the 19% estimate in the Women’s Interagency HIV Study (WIHS)(Sobieszczyk et al., 2008) (younger mean age than our median age) and the 20% estimate in the Veteran Aging Cohort Study (VACS) (same median age as our median age)(Myerson et al., 2014). The prevalence of DM has been estimated in the DC Cohort previously using longitudinal data from 2011 to 2015(Levy et al., 2017). The overall period prevalence was calculated to be 13% in that study which is substantially different than the estimate found in this data analysis. Our study only included active participants while the previous study included active and inactive participants. Our study also included data after 2015. These differences in inclusion criteria potentially explain the difference between the two estimates.

Our estimate of DM control in PWH is similar to prior published estimates. Our estimate is lower than the 72% estimate for a 2012 study investigating newly diagnosed Type II diabetics in the Centers for AIDS Research (CFAR) Network of Integrated Clinical Systems (CNICS) cohort (Han et al., 2012). That study included only newly diagnosed diabetics, which may represent more individuals with less-severe diabetes, explaining the higher proportion controlled. However, we had a similar result to the recent estimates of 58% from the WIHS (Colasanti et al., 2018) and 59.5% in a study by Malek and colleagues (Malek W). Additionally, our estimate was similar to the 67% estimate of a 2008 study of PWH in New York City (quarterly HbA1c value less than 7.5% in Type 2 diabetics only)(Satlin et al., 2011) when also using our DM control estimate for HbA1c less than 7.5%. Meanwhile, our estimate was higher than other previous studies which calculated 40–54%(Adeyemi et al., 2009; Bury, Stroup, Stephens, & Baker, 2007; Davies et al., 2015; Zuniga et al., 2016) using the same DM control definition we did (HbA1c value of less than 7.0%). Studies with substantially lower estimates had much smaller sample sizes (less than 60 participants)(Bury et al., 2007; Davies et al., 2015) or studied very different populations (Davies et al., 2015; Zuniga et al., 2016) which likely explains the variation seen compared to our results.

In our multivariate analysis, DM medication use (any type) and higher BMI were inversely associated. Although it seems counterintuitive that taking DM medication would be inversely associated with DM control, this inverse association has been reported in prior studies of PWH with DM (Davies et al., 2015; Satlin et al., 2011; Zuniga et al., 2016). One potential explanation is that individuals on DM medication and insulin have more severe and/or difficult to control forms of diabetes prior to this study’s inclusion. The finding that 28.2% of this DC Cohort sample of PWH met criteria for obesity (BMI > 30) was notable and particularly striking that 41.1% of those with DM were obese. The inverse association between higher BMI and DM control is a significant, clinically relevant finding. A lower weight has been associated with DM control in PWH (Davies et al., 2015) and BMI has long been inversely associated with DM control in the general population (Nichols, Hillier, Javor, & Brown, 2000). In the general population, even a 5% to 10% reduction in body weight may have benefits for diabetes control and reduced CVD risk factors which is an important message that HIV clinicians can reinforce with their patients (Wing et al., 2011).

The association between DM control and selected ARVs was not unexpected, as older PIs have been shown to impair glucose transport and cause peripheral insulin resistance (Murata, Hruz, & Mueckler, 2002; Walli, 1998). Furthermore, previous studies identified an association between PI use and DM control in PWH (Davies et al., 2015; Zuniga et al., 2016). However, these studies used older clinical data and thus are not indicative of recent PI medications, which have better safety profiles and less of an effect on insulin sensitivity (Aberg et al., 2012; Overton et al., 2016). Although we found no difference in DM control status by current PI use, the use of PIs may not be ideal due to drug interactions with several medications for DM.

Prior studies have also shown that HCV is associated with a higher likelihood of developing DM (Visnegarwala et al., 2005) and increased insulin resistance (Duong et al., 2001) in PWH. HCV was associated with DM in our sample, however, we did not find at statistically significant association between HCV diagnosis and DM control (aOR 1.93, 95% CI 0.97, 3.86). HCV cure in PWH has been associated with lower HbA1c levels (Ciancio et al., 2018; Rife et al., 2019). However, we are currently limited by not having data on whether HCV was cured. This would certainly be an area for future study within the DC Cohort.

Although lower viral load (Colasanti et al., 2018; Monroe, Chander, & Moore, 2011; Satlin et al., 2011; Zuniga et al., 2016), older age (Zuniga et al., 2016), and Black race (Zuniga et al., 2016) have been previously associated with DM control, we did not observe similar results in our analysis. Viral load has been previously significantly associated with decreased HbA1c when both variables were continuous (Monroe et al., 2011). Both of these variables were dichotomous in our analysis which may explain the discrepancy in results although the univariate analysis showed very minimal difference across groups.

There are several limitations to our study. As the study is cross-sectional in design, temporality and causality for our independent variables cannot be established. Our estimate of diabetes may have been falsely elevated as individuals using metformin only without any elevated labs or recorded diagnosis were included as diabetics. This likely had a minimal effect as these participants were less than three percent of our total calculated diabetic sample. There may also be some misclassification of outcome as it has been suggested that HbA1c underestimates glycemia among PWH (Kim et al., 2009). However, as previously mentioned, the HbA1c cutoff value of 7.0% is recommended clinically (Aberg et al., 2014; Association, 2017). The participants studied are those that present to care to DC Cohort clinical sites and meet the inclusion criteria. To further investigate this, diabetic participants who were used for DM control analysis were compared to excluded diabetic participants. There were several statistically significant differences based on age, sex at birth, race, BMI, and other variables representing differential characteristics that favor seeking care (see Supplemental Table 1). While this somewhat lessens the generalizability of our results, our findings still provide valuable information and answer questions not previously asked.

There are strengths to our study. This is a large sample, and using the DC Cohort data allowed us to collect data from a variety of clinical settings in a large urban area with a high prevalence of HIV thereby strengthening the generalizability and relevance of our findings.

In conclusion, this study was able to estimate the prevalence of DM and DM control in a large urban, longitudinal cohort using a cross-sectional approach. Worse DM control was associated with higher BMI and DM medication use. Our finding regarding BMI suggests that interventions promoting weight loss are potentially important for helping PWH achieve glycemic control. Additionally, our results demonstrate that PWH using medication to control DM my benefit from extra support to achieve their HbA1c targets.

Supplementary Material

Supplemental Figures 1-2
Supplemental Text

Acknowledgements:

Data on this poster were collected by the DC Cohort investigators and research staff located at: Cerner Corporation (Jeffery Binkley, Rob Taylor, Cheryl Akridge, Stacey Purinton, Qingjiang Hou, Jeff Naughton, David Parfitt); Children’s National Medical Center Adolescent (Lawrence D’Angelo) and Pediatric (Natella Rahkmanina) clinics; The Senior Deputy Director of the DC Department of Health HIV/AIDS, Hepatitis, STD and TB Administration (Michael Kharfen); Family and Medical Counseling Service (Michael Serlin); Georgetown University (Princy Kumar); The George Washington University Medical Faculty Associates (David Parenti); The George Washington University Department of Epidemiology and Biostatistics (Alan Greenberg, Maria Jaurretche, Brittany Wilbourn, James Peterson, Morgan Byrne, Yan Ma); Howard University Adult Infectious Disease Clinic (Ronald Wilcox), and Pediatric Clinic (Sohail Rana); La Clinica Del Pueblo, (Ricardo Fernandez); MetroHealth (Annick Hebou); National Institutes of Health (Carl Dieffenbach, Henry Masur; Unity Health Care (Gebeyehu Teferi); Washington Health Institute (Jose Bordon); Washington Hospital Center (Maria Elena Ruiz); and Whitman-Walker Health (Stephen Abbott). We would also like to acknowledge the Research Assistants at all of the participating sites, the DC Cohort Community Advisory Board and the DC Cohort participants.

Collaborations, sources of research funds, and other acknowledgements

The DC Cohort is funded and supported by the National Institute of Allergy and Infectious Diseases, UM1 AI069503

Footnotes

Conflicts of Interest:

For all authors, no conflicts of interest were declared.

References

  1. Aberg JA, Gallant JE, Ghanem KG, Emmanuel P, Zingman BS, & Horberg MA (2014). 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, 58(1), e1–e34. 10.1093/cid/cit665 [DOI] [PubMed] [Google Scholar]
  2. Aberg JA, Tebas P, Overton ET, Gupta SK, Sax PE, Landay A, … De La Rosa G (2012). Metabolic effects of darunavir/ritonavir versus atazanavir/ritonavir in treatment-naive, HIV type 1-infected subjects over 48 weeks. AIDS Res Hum Retroviruses, 28(10), 1184–1195. 10.1089/aid.2011.0327 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Adeyemi O (2007). Cardiovascular risk and risk management in HIV-infected patients. Top HIV Med, 15(5), 159–162. [PubMed] [Google Scholar]
  4. Adeyemi O, Vibhakar S, & Max B (2009). Are we meeting the American Diabetes Association goals for HIV-infected patients with diabetes mellitus? Clin Infect Dis, 49(5), 799–802. 10.1086/605286 [DOI] [PubMed] [Google Scholar]
  5. Armah KA, McGinnis K, Baker J, Gibert C, Butt AA, Bryant KJ, … Freiberg M (2012). HIV status, burden of comorbid disease, and biomarkers of inflammation, altered coagulation, and monocyte activation. Clin Infect Dis, 55(1), 126–136. 10.1093/cid/cis406 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Association AD (2017). Standards of Medical Care in Diabetes-2017. Diabetes Care, 40. [Google Scholar]
  7. Boyd MA (2009). Improvements in antiretroviral therapy outcomes over calendar time. Curr Opin HIV AIDS, 4(3), 194–199. 10.1097/COH.0b013e328329fc8d [DOI] [PubMed] [Google Scholar]
  8. Brambilla AM, Novati R, Calori G, Meneghini E, Vacchini D, Luzi L, … Lazzarin A (2003). Stavudine or indinavir-containing regimens are associated with an increased risk of diabetes mellitus in HIV-infected individuals. AIDS, 17(13), 1993–1995. 10.1097/00002030-200309050-00022 [DOI] [PubMed] [Google Scholar]
  9. Bury JE, Stroup JS, Stephens JR, & Baker DL (2007). Achieving American Diabetes Association goals in HIV-seropositive patients with diabetes mellitus. Proc (Bayl Univ Med Cent), 20(2), 118–123. 10.1080/08998280.2007.11928265 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Ciancio A, Bosio R, Bo S, Pellegrini M, Sacco M, Vogliotti E, … Saracco GM (2018). Significant improvement of glycemic control in diabetic patients with HCV infection responding to direct-acting antiviral agents. J Med Virol, 90(2), 320–327. 10.1002/jmv.24954 [DOI] [PubMed] [Google Scholar]
  11. Colasanti J, Galaviz KI, Christina Mehta C, Palar K, Schneider MF, Tien P, … Ali MK (2018). Room for Improvement: The HIV-Diabetes Care Continuum Over 15 Years in the Women’s Interagency HIV Study. Open Forum Infect Dis, 5(6), ofy121. 10.1093/ofid/ofy121 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Davies ML, Johnson MD, Brown JN, Bryan WE, & Townsend ML (2015). Predictors of glycaemic control among HIV-positive veterans with diabetes. Int J STD AIDS, 26(4), 262–267. 10.1177/0956462414535207 [DOI] [PubMed] [Google Scholar]
  13. De Wit S, Sabin CA, Weber R, Worm SW, Reiss P, Cazanave C, … Phillips A (2008). 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, 31(6), 1224–1229. 10.2337/dc07-2013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Deeks SG, Lewin SR, & Havlir DV (2013). The end of AIDS: HIV infection as a chronic disease. Lancet, 382(9903), 1525–1533. 10.1016/s0140-6736(13)61809-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. District of Columbia HIV/AIDS, Hepatitis, STD, TB, Hepatitis, STD, and TB Administration epidemiology and surveillance report: Surveillance data through December 2015. Retrieved from https://doh.dc.gov/page/2016-hahsta-annual-reports
  16. Archived at: http://www.webcitation.org/6teNPqRhQ.
  17. Duncan AD, Goff LM, & Peters BS (2018). Type 2 diabetes prevalence and its risk factors in HIV: A cross-sectional study. PLoS One, 13(3), e0194199. 10.1371/journal.pone.0194199 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Duong M, Petit JM, Piroth L, Grappin M, Buisson M, Chavanet P, … Portier H (2001). Association between insulin resistance and hepatitis C virus chronic infection in HIV-hepatitis C virus-coinfected patients undergoing antiretroviral therapy. J Acquir Immune Defic Syndr, 27(3), 245–250. 10.1097/00126334-200107010-00005 [DOI] [PubMed] [Google Scholar]
  19. Greenberg AE, Hays H, Castel AD, Subramanian T, Happ LP, Jaurretche M, … Hart R (2016). Development of a large urban longitudinal HIV clinical cohort using a web-based platform to merge electronically and manually abstracted data from disparate medical record systems: technical challenges and innovative solutions. J Am Med Inform Assoc, 23(3), 635–643. 10.1093/jamia/ocv176 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Guaraldi G, Orlando G, Zona S, Menozzi M, Carli F, Garlassi E, … Palella F (2011). Premature age-related comorbidities among HIV-infected persons compared with the general population. Clin Infect Dis, 53(11), 1120–1126. 10.1093/cid/cir627 [DOI] [PubMed] [Google Scholar]
  21. Han JH, Crane HM, Bellamy SL, Frank I, Cardillo S, & Bisson GP (2012). HIV infection and glycemic response to newly initiated diabetic medical therapy. AIDS, 26(16), 2087–2095. 10.1097/QAD.0b013e328359a8e5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hasse B, Tarr PE, Marques-Vidal P, Waeber G, Preisig M, Mooser V, … Marco Z (2015). Strong Impact of Smoking on Multimorbidity and Cardiovascular Risk Among Human Immunodeficiency Virus-Infected Individuals in Comparison With the General Population. Open Forum Infect Dis, 2(3), ofv108. 10.1093/ofid/ofv108 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Hernandez-Romieu AC, Garg S, Rosenberg ES, Thompson-Paul AM, & Skarbinski J (2017). Is diabetes prevalence higher among HIV-infected individuals compared with the general population? Evidence from MMP and NHANES 2009–2010. BMJ Open Diabetes Res Care, 5(1), e000304. 10.1136/bmjdrc-2016-000304 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Jain MK, Aragaki C, Fischbach L, Gibson S, Arora R, May L, … Lee WM (2007). Hepatitis C is associated with type 2 diabetes mellitus in HIV-infected persons without traditional risk factors. HIV Med, 8(8), 491–497. 10.1111/j.1468-1293.2007.00499.x [DOI] [PubMed] [Google Scholar]
  25. Justman JE, Benning L, Danoff A, Minkoff H, Levine A, Greenblatt RM, … Anastos K (2003). Protease inhibitor use and the incidence of diabetes mellitus in a large cohort of HIV-infected women. J Acquir Immune Defic Syndr, 32(3), 298–302. doi: 10.1097/00126334-200303010-00009 [DOI] [PubMed] [Google Scholar]
  26. Kim PS, Woods C, Georgoff P, Crum D, Rosenberg A, Smith M, & Hadigan C (2009). A1C underestimates glycemia in HIV infection. Diabetes Care, 32(9), 1591. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Levy ME, Greenberg AE, Hart R, Powers Happ L, Hadigan C, & Castel A (2017). High burden of metabolic comorbidities in a citywide cohort of HIV outpatients: evolving health care needs of people aging with HIV in Washington, DC. HIV Med, 18(10), 724–735. 10.1111/hiv.12516 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Lumpkin MM (1997). Reports of diabetes and hyperglycemia in patients receiving protease inhibitors for the treatment of human immunodeficiency virus infection. FDA Public Health Advisory. [Google Scholar]
  29. Malek WYJ, Britt N, Morse C, Hecox Z, Hoye-Simek A, Sullivan S, Patel N Clinical Outcomes Associated with Single Tablet Regimens: Relationship between Antiretroviral Therapy Pill Burden and Control of Concomitant Comorbidities among HIV-Infected Patients. J HIV Retrovirus, 3(1), 1–7. [Google Scholar]
  30. Marcus JL, Chao CR, Leyden WA, Xu L, Quesenberry CP Jr., Klein DB, … Silverberg MJ. (2016). Narrowing the Gap in Life Expectancy Between HIV-Infected and HIV-Uninfected Individuals With Access to Care. J Acquir Immune Defic Syndr, 73(1), 39–46. 10.1097/qai.0000000000001014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Monroe AK, Chander G, & Moore RD (2011). Control of medical comorbidities in individuals with HIV. J Acquir Immune Defic Syndr, 58(5), 458–462. 10.1097/QAI.0b013e31823801c4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Monroe AK, Fu W, Zikusoka MN, Jacobson LP, Witt MD, Palella FJ, … Brown TT (2015). Low-Density Lipoprotein Cholesterol Levels and Statin Treatment by HIV Status Among Multicenter AIDS Cohort Study Men. AIDS Res Hum Retroviruses, 31(6), 593–602. 10.1089/aid.2014.0126 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Murata H, Hruz PW, & Mueckler M (2002). Indinavir inhibits the glucose transporter isoform Glut4 at physiologic concentrations. AIDS, 16(6), 859–863. 10.1097/00002030-200204120-00005 [DOI] [PubMed] [Google Scholar]
  34. Myerson M, Poltavskiy E, Armstrong EJ, Kim S, Sharp V, & Bang H (2014). Prevalence, treatment, and control of dyslipidemia and hypertension in 4278 HIV outpatients. J Acquir Immune Defic Syndr, 66(4), 370–377. 10.1097/qai.0000000000000168 [DOI] [PubMed] [Google Scholar]
  35. Nichols GA, Hillier TA, Javor K, & Brown JB (2000). Predictors of glycemic control in insulin-using adults with type 2 diabetes. Diabetes Care, 23(3), 273–277. 10.2337/diacare.23.3.273 [DOI] [PubMed] [Google Scholar]
  36. Overton ET, Tebas P, Coate B, Ryan R, Perniciaro A, Dayaram YK, … Baugh BP (2016). Effects of once-daily darunavir/ritonavir versus atazanavir/ritonavir on insulin sensitivity in HIV-infected persons over 48 weeks: results of an exploratory substudy of METABOLIK, a phase 4, randomized trial. HIV Clin Trials, 17(2), 72–77. 10.1080/15284336.2016.1141468 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Puhr R, Petoumenos K, Huang R, Templeton DJ, Woolley I, Bloch M, … Cooper DA (2019). Cardiovascular disease and diabetes in HIV-positive and HIV-negative gay and bisexual men over the age of 55 years in Australia: insights from the Australian Positive & Peers Longevity Evaluation Study. HIV Med, 20(2), 121–130. 10.1111/hiv.12689 [DOI] [PubMed] [Google Scholar]
  38. Reinsch N, Neuhaus K, Esser S, Potthoff A, Hower M, Mostardt S, … Erbel R (2012). Are HIV patients undertreated? Cardiovascular risk factors in HIV: results of the HIV-HEART study. European journal of preventive cardiology, 19(2), 267–274. [DOI] [PubMed] [Google Scholar]
  39. Reinsch N, Neuhaus K, Esser S, Potthoff A, Hower M, Mostardt S, … Neumann T (2012). Are HIV patients undertreated? Cardiovascular risk factors in HIV: results of the HIV-HEART study. Eur J Prev Cardiol, 19(2), 267–274. 10.1177/1741826711398431 [DOI] [PubMed] [Google Scholar]
  40. Rife K, Lyman A, LeClerc-Kamieniecki S, Falck-Ytter C, Pascuzzi K, Burant CJ, & Falck-Ytter Y (2019). Significant HbA1c Lowering in Patients Achieving a Hepatitis C Virus Cure. Fed Pract, 36(Suppl 2), S26–s32. [PMC free article] [PubMed] [Google Scholar]
  41. Samji H, Cescon A, Hogg RS, Modur SP, Althoff KN, Buchacz K, … Gange SJ (2013). Closing the gap: increases in life expectancy among treated HIV-positive individuals in the United States and Canada. PLoS One, 8(12), e81355. 10.1371/journal.pone.0081355 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Satlin MJ, Hoover DR, & Glesby MJ (2011). Glycemic control in HIV-infected patients with diabetes mellitus and rates of meeting American Diabetes Association management guidelines. AIDS Patient Care STDS, 25(1), 5–12. 10.1089/apc.2010.0237 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Sico JJ, Chang CC, So-Armah K, Justice AC, Hylek E, Skanderson M, … Freiberg MS (2015). HIV status and the risk of ischemic stroke among men. Neurology, 84(19), 1933–1940. 10.1212/wnl.0000000000001560 [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Sobieszczyk ME, Hoover DR, Anastos K, Mulligan K, Tan T, Shi Q, … Justman J (2008). Prevalence and predictors of metabolic syndrome among HIV-infected and HIV-uninfected women in the Women’s Interagency HIV Study. J Acquir Immune Defic Syndr, 48(3), 272–280. 10.1097/QAI.0b013e31817af461 [DOI] [PubMed] [Google Scholar]
  45. Visnegarwala F, Chen L, Raghavan S, & Tedaldi E (2005). 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, 50(4), 331–337. 10.1016/j.jinf.2004.06.001 [DOI] [PubMed] [Google Scholar]
  46. Walli RH,O; Michl GM; Demant T; Jäger H; Dieterle C; Bogner JR; Landgraf R; Goebel FD (1998). Treatment with protease inhibitors associated with peripheral insulin resistance and impaired oral glucose tolerance in HIV-1-infected patients. AIDS, 12(15), F167–F173. [DOI] [PubMed] [Google Scholar]
  47. Wing RR, Lang W, Wadden TA, Safford M, Knowler WC, Bertoni AG, … Wagenknecht L (2011). Benefits of modest weight loss in improving cardiovascular risk factors in overweight and obese individuals with type 2 diabetes. Diabetes Care, 34(7), 1481–1486. 10.2337/dc10-2415 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Wong C, Gange SJ, Buchacz K, Moore RD, Justice AC, Horberg MA, … Althoff KN (2017). First Occurrence of Diabetes, Chronic Kidney Disease, and Hypertension Among North American HIV-Infected Adults, 2000–2013. Clin Infect Dis, 64(4), 459–467. 10.1093/cid/ciw804 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Zuniga J, Nguyen ML, & Holstad M (2016). Predictors of dual control of HIV and diabetes. AIDS Care, 28(9), 1124–1127. 10.1080/09540121.2016.1139667 [DOI] [PubMed] [Google Scholar]

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