Abstract
Objective:
To examine whether type 2 diabetes mellitus (T2DM) is more common among women with HIV (WWH) than men with HIV (MWH).
Design:
A cross-sectional analysis of a demographically heterogenous population-based sample of more than 64 million patients in the United States
Methods:
Using the Explorys (IBM) database, compare the prevalence of T2DM among men and women without HIV and influence of HIV on T2DM by sex controlling for confounding factors
Results:
From 19,182,775 persons included in the study, 39,485 were with HIV. Rates of obesity was higher among WWH than MWH (58% vs 35%). Prevalence of T2DM among WWH was 23% compared with 16% among MWH (p<0.001). In sex-stratified adjusted analysis, WWH had 1.31 (95% confidence interval [CI], 1.24-1.38) times the odds of having T2DM than women without HIV. Women with HIV was associated with T2DM across all demographic subgroups. In contrast, no association between HIV and T2DM was observed among men (OR 1.01; 95% CI 0.98-1.05).
Conclusion:
These data suggest that HIV confers a sex-specific increase in odds of T2DM among women, but not men.
Keywords: women living with HIV, HIV and aging, HIV and diabetes, sex differences, human immunodeficiency virus
Introduction
Modern combination antiretroviral (ARV) treatment has greatly improved the lives and well-being for persons with HIV (PWH). However, PWH have a greater burden of inflammatory-related diseases, including heart disease, diabetes, and cancer despite being fully suppressed with modern ARV [1, 2]. Of note, these observations are often from predominantly male cohorts, and it remains unclear whether women with HIV (WWH) bear similar burdens of inflammatory related diseases [3,4,5].
Sex differences among PWH can impact HIV acquisition, disease progression, and treatment response [3,4]. Ongoing translational investigations suggest sex differences likely extends into comorbidity development [5, 6]. In aging-HIV studies, WWH have higher levels of immune activation than men despite lower mean viral load [4,7]. Moreover, ARV related weight-gain has been demonstrated to be several folds higher among WWH than men with HIV (MWH) [8,9]. Increased inflammation and excessive adiposity likely put WWH at discordant risk for metabolic disorders, such as T2DM.
Few studies have investigated sex-specific associations between HIV and T2DM and among the limited studies, data are conflicting [10–15]. Moreover, minimal studies had adequate female representation to robustly control for confounding variables. To assess the hypothesis that WWH are disproportionately at risk for T2DM relative to MWH, we sought to evaluate the sex-specific prevalence and association of T2DM among WWH and MWH in a population-based cohort.
Methods
Database Description
We performed a retrospective cross-sectional studying using IBM Watson Health Explorys, a data analytics platform that contains deidentified electronic health record (HER) data from 26 healthcare networks encompassing over 360 hospitals across all 4 US census regions, representing approximately 15% of the US population (64 million unique lives). Within this database, clinical information from electronic medical records, laboratories, practice management systems, and claims systems were matched using the single set of Unified Medical Language System ontologies to create longitudinal records for unique patients. Data are standardized and curated according to common controlled variables and classification systems including ICD-9 or ICD-10, SNOMED-CT, LOINC, and RxNorm [17–21]. Hundreds of publications have used Explorys since its inception in 1999, demonstrating its efficiency and utility in analyzing large-scale data.
Population
Clinical data were collected between January 14, 2016 and January 14, 2021. All persons aged 18 years or older and not missing data on age, sex, race or BMI were included (supplement figure 1). Persons with HIV were identified using at least 1 ICD-9 (042, 042, 043, 044, V08, 079.53, 795.71, 795.78) or ICD 10 (B20, Z21) diagnosis code with prescription for ARV therapy. These criteria have previously demonstrated a sensitivity of 77% and specificity of 100% for the diagnosis of HIV [22]. T2DM was defined using SNOMED-CT Terms “Diabetes mellitus type 2,” “Type II diabetes mellitus uncontrolled,” “Disorder due to type 2 diabetes mellitus,” “Neurologic disorder associated with type II diabetes mellitus,” or “Peripheral circulatory disorder associated with type II diabetes mellitus,” which are associated with ICD-9 codes 250.x0 and 250.x2 and ICD-10 codes E11.x. This definition in the Veterans Affairs Diabetes Epidemiology Cohort demonstrated 96% specificity and 78% sensitivity [23]. Moreover, this definition yielded a prevalence of 15.4% for T2DM in our database, which is similar to the prevalence estimates of 12% to 14% for T2DM in the United States [24]. Lastly, to further validate the T2DM cohort, we performed a separate age-, sex-, race-, hypertension-, hyperlipidemia-, and smoking-adjusted analysis assessing the relationship between obesity (body mass index [BMI] ≥ 30 kg/m2) and a diagnosis of T2DM within our database. Within the database, persons with T2DM were 2.38 times as likely to be obese than patients without T2DM, suggesting a strong association.
Statistical Analysis
Population demographics were collated and categorized. Age in years was recorded in 3 groups: 18-44, 45-64, and 65 years and older. Race was dichotomized as Black and non-Black. BMI was derived using height and weight measurements with a BMI over 30 being defined as obese.
Tobacco use was defined using the SNOMED-CT terms “History of tobacco use”, “nicotine dependence”, and “tobacco user” which corresponded to ICD 9 codes V15.82, 305.1 or ICD 10 codes Z87.891, Z72.0, or F17.2. Individuals with no record of these terms were considered nonsmokers. Hypertension was defined by the SNOMED-CT terms “essential hypertension” corresponding to ICD-9 code 401.xx; and hyperlipidemia was defined by the SNOMED-CT term “hyperlipidemia” corresponding to ICD-9 code 272.0 to 272.4. The of these ICD-9 codes were validated in prior studies (supplementary table 1).
Population level counts of persons with and without a diagnosis of T2DM for each combination of covariates (HIV status, sex, age, race, obesity, smoking status, hypertension, and hyperlipidemia) were obtained. We calculated the overall prevalence of T2DM in persons with and without HIV, as well as within subgroups of these cohorts. We assessed crude associations between HIV and T2DM with each explanatory variable using separate univariable logistic regression models. Multivariable logistic regression was performed to compare the prevalence between persons with and without HIV while controlling for sex, age, race, obesity, smoking status, hypertension, and hyperlipidemia. We assessed potential subgroup differences between the relationship between HIV and T2DM by testing the significance of the interaction between HIV and the subgroup variable of interest individually. All analyses were performed using Stata 16.0 and significance was set at p < 0.01.
Ethics
Human subjects committee review was waived because there was no personal health information associated with the analyzed data.
Results
Baseline characteristics
In total, 39,485 PWH and 19,143,240 persons without HIV were included. Among PWH, 10,125 (24%) were women and 29,360 (76%) were men. Demographics of the population stratified by sex and HIV serostatus are summarized in Table 1. Notably, PWH were younger (87% <65 years of age vs 70% <65 years of age), more likely to be Black (43% vs 12%), and more likely to smoke (55% vs 32%).
Table 1:
Clinical Characteristics of population included in the study stratified by HIV serostatus and sex.
| Patients, No. (%) |
||||
|---|---|---|---|---|
| Characteristic | Women with HIV (n=10,125) |
Men with HIV (n=29,360) |
Women without HIV (n=10,881,130) |
Men without HIV (n=8,262,110) |
| Age, y | ||||
| 18-44 | 3,330 (33) | 9,205 (31) | 4,259,410 (39) | 3,003,660 (36) |
| 45-64 | 5,585 (55) | 15,970 (54) | 3,381,770 (31) | 2,699,520 (33) |
| 65+ | 1,210 (12) | 4,185 (14) | 3,239,950 (30) | 2,558,930 (31) |
| Race | ||||
| Black | 6,150 (61) | 10,950 (37) | 1,423,120 (13) | 963,300 (12) |
| Nonblack | 3,975 (39) | 18,410 (63) | 9,458,010 (87) | 7,298,810 (88) |
| Obese (BMI ≥ 30.0 kg/m2) | 5,895 (58) | 10,145 (35) | 5,305,570 (49) | 3,836,110 (46) |
| Tobacco Smoker | 5,515 (53) | 16,160 (55) | 3,046,280 (28) | 3,138,620 (38) |
| Hypertension | 5,095 (50) | 12,985 (44) | 3,963,300 (36) | 3,505,500 (43) |
| Hyperlipidemia | 3,740 (37) | 12,060 (41) | 3,583,640 (33) | 3,242,970 (39) |
| T2DM | 2,315 (23) | 4,810 (16) | 1,541,960 (14) | 1,403,830 (17) |
Abbreviations: BMI – body mass index; T2DM – type 2 diabetes mellitus
Sex Differences of Clinical Characteristics Among PWH
Among 10,125 WWH and 29,360 MWH, most were younger than 65 years of age (88% vs 85%), while black race was more common among WWH (61% vs 37%). Metabolic and cardiovascular risk factors disproportionately affected WWH compared to MWH including the proportion with obesity (58% vs. 35%) and hypertension (50% vs. 44%). Tobacco use was similar between WWH and MWH (53% vs. 55%).
Prevalence of Type 2 Diabetes Mellitus
The prevalence of T2DM among WWH (23%) was higher compared to MWH (16%) and persons without HIV (15%). WWH were more likely to have T2DM than women without HIV across all age subgroups (Figure 1). In contrast, MWH had a similar prevalence of T2DM as men without HIV (Figure 1). In unadjusted analysis, HIV was associated with a diagnosis of T2DM among women (unadjusted OR 1.80, 95% CI, 1.71-1.88), but not among men (unadjusted OR 0.96, 95% CI, 0.93-0.99).
Figure 1. Sex Differences in Type 2 Diabetes Mellitus Prevalence.
Proportion of persons with T2DM stratified by HIV serostatus, age and sex indicating increased prevalence among WWH compared to men without HIV.
Abbreviations: T2DM – type 2 diabetes mellitus; WWH – women with HIV, NS - non-significant; *** denotes p<0.001 Statistical significance was set at P<.01
In multivariable logistic regression analysis, there was a significant interaction between HIV and sex. WWH had 1.31 (95% CI, 1.24-1.38) times the odds of having T2DM compared to women without HIV (Figure 2). Moreover, T2DM was associated with WWH across all demographic and clinical subgroups (Figure 3). The strength of relationship between WWH and T2DM was often stronger in the absence of a risk factor (i.e. nonobese WWH had stronger relationship with T2DM than obese WWH). In contrast, no association between HIV and T2DM was observed among men (aOR 1.01, 95% CI 0.98-1.05) (Figure 1) including when stratified by subgroup variables of interest (Figure 4).
Figure 2. Sex Differences in the Association of Type 2 Diabetes Mellitus Among Persons with HIV.
Representation of sex-stratified odds of T2DM among persons with HIV. The odds ratio compares the odds of T2DM among WWH compared to women without HIV vs MWH compared to men without HIV. Odds were calculated based on a multivariable logistic regression analysis after controlling for age, race, obesity, smoking, hypertension, and hyperlipidemia. Interaction p-value refers to the null hypothesis that the strength of relationship between HIV and T2DM does not differ among sex.
Abbreviations: aOR - adjusted odds ratio; T2DM – type 2 diabetes mellitus; WWH -women with HIV; MWH-men with HIV
Figure 3. Type 2 Diabetes Mellitus Among Women with HIV.
Representation of subgroup odds for T2DM among WWH. The odds ratio compares the odds of T2DM between women with and without HIV within subgroups (i.e women 18-44 years of age with HIV were 31% more likely to have T2DM than women 18-44 years of age without HIV after controlling for all confounding variables). They were calculated based on a multivariable logistic regression analysis after controlling for age, race, obesity, smoking, hypertension, and hyperlipidemia.
Abbreviations: aOR - adjusted odds ratio; T2DM – type 2 diabetes mellitus; WWH -women with HIV; y-years. Statistical significance was set at P<.01
Figure 4. Type 2 Diabetes Mellitus Among Men with HIV.
Representation of subgroup odds for T2DM among MWH. The odds ratio compares the odds of T2DM between men with and without HIV within subgroups (i.e men 18-44 years of age with HIV were 11% less likely to have T2DM than men 18-44 years of age without HIV after controlling for all confounding variables). They were calculated based on a multivariable logistic regression analysis after controlling for age, race, obesity, smoking, hypertension, and hyperlipidemia.
Abbreviations: aOR - adjusted odds ratio; T2DM – type 2 diabetes mellitus; MWH -men with HIV; y-years. Statistical significance was set at P<.01
Discussion
Our results suggest that there is a sex-specific association between HIV and T2DM. T2DM prevalence was increased among all WWH-subgroups and remained increased after controlling for demographic and clinical risk factors. Our findings are consistent with the hypothesis that the greater inflammation observed among WWH combined with discordant prevalence of obesity may disproportionately increases the risk for T2DM among WWH
Our data also provides further insight on the effect of HIV on the strength of the relationship between T2DM and subgroups of WWH. For example, non-obese WWH were 1.41 times more likely to have T2DM than non-obese WWH (Figure 3). In contrast, obese WWH in comparison to obese women without HIV were 1.24 times more likely to have T2DM. These results suggest that HIV may more strongly increase the odds of T2DM in those whose risk of diabetes is not compounded by the presence of excess adiposity. However, among WWH with obesity, the effect is attenuated as obesity contributes to the strength of the relationship.
Few studies have estimated a sex-specific prevalence of T2DM among PWH with a large population-based control sample. Our analysis is consistent with previous work including an analysis of nationally representative survey data from the Medical Monitoring Project and the National Health and Nutrition Exam Survey where the prevalence of T2DM among WWH was 12.4% compared to 7.4% in the general population [10]. Similarly, a retrospective analysis of 3,851 PWH derived from two tertiary hospitals in Boston, MA demonstrated 12.4% of WWH had diabetes compared to 5.5% of HIV seronegative controls [11]. In both studies, the difference in T2DM prevalence between women with HIV and women without HIV was twice that of men with and without HIV.
Our finding that T2DM is associated with HIV among women, but not among men, suggests that finding an association between HIV and diabetes may be dependent on the degree of women representation. Past studies that utilized predominantly male participants found no association between T2DM and HIV. For example, the Veterans Aging Cohort Study in which 98% of 3,227 veterans with HIV were male, the prevalence of T2DM among veterans with and without HIV was 15% and 21%, respectively [12]. After adjusting for risk factors, PWH had a lower risk for T2DM. In contrast, studies which had adequate female representation (~13-20%), have often demonstrated an association with HIV and T2DM [10, 13].
Our study is limited by being a retrospective epidemiological analysis and thus unable to ascertain causality. Important resources for understanding HIV disease progression and temporal relations have been both the Women’s Interagency HIV Study (WIHS) and the Multicenter AIDS Cohort Study (MACS). Both are large, prospective multi-cohort studies in the United States, and consist of women (WIHS) and men (MACS) entirely, respectively [14, 15]. In the WIHS cohort, a similar prevalence of diabetes was found among WWH as shown in our study (22% vs 23%), but their study did not find a difference in T2DM prevalence by HIV serostatus (22% vs 24%) [14]. Between HIV+ and HIV-seronegative women recruited into WIHS, the HIV-seronegative comparison group have higher rates of obesity, tobacco use, alcohol use, and substance use. Therefore, it is likely this mitigates differences and a regression analysis may have revealed T2DM to be more common among WWH [25, 26]. These results importantly suggest though that disparities in lifestyle risk factors and comorbidity development are likely implicated in sex differences in T2DM prevalence.
In the MACS, the prevalence of T2DM among 568 MWH was 14% compared to 5% of 711 HIV-seronegative men [15]. After adjusting for age and body mass index, the incidence of diabetes was four times higher among the MWH cohort. Being a seminal study conducted early in aging HIV research, significant comorbidities now recognized among persons with HIV were not taken into consideration, in particular tobacco use. Further, the study was conducted in an era where most participants were exposed to metabolically toxic antiretroviral therapy [27]. More recent studies have demonstrated that the use of less toxic drugs has mitigated diabetes risk [28]. Taken together, modern ARV therapy has likely changed the burden of T2DM from off-target toxicity of ARV to maladaptive inflammation and obesity, explaining the unequal risk among sexes.
Our finding that obesity disproportionately affects women with HIV, may be an emerging complication of newer ARV therapy [8]. Also to consider, women with HIV in the United States represents a demographic of racial minorities that suffer disproportionately from poverty and food insecurity which can contribute to the higher rates of obesity found in our study [29, 30]. Of concern, the data in our study suggests the sex-biased rates of obesity coincides with sex-biased metabolic consequences. The prevalence of T2DM in our obese cohort was two times higher than the prevalence in the non-obese cohort (data not shown). Alongside T2DM, obesity is closely linked to cardiovascular disease, neurocognitive impairment, and cancer; therefore, higher rates of obesity may put WWH at higher risk than MWH for a multitude of HIV-related comorbidities and necessitates further investigation and targeted-care.
While prevalence of obesity is markedly different between women and men with HIV, even after controlling for obesity, WWH remain more likely to have diabetes. This suggests other mechanisms are at play, which may include excessive chronic inflammation. Biologic factors should be considered as women have a stronger antiviral response than males attributed to sex-specific expression of toll-like receptor (TLR) genes and enhanced innate immune responses [6, 31]. Although beneficial in acute infection, the heightened, persistent immune response against residual HIV likely contributes to maladaptive inflammation and higher risk for T2DM [5, 32]
Our study has limitations. Persons with HIV may be more likely to seek care, and we could not capture PWH who did not seek care in health systems included in the database, which may result in detection bias. Moreover, given our data is from persons seeking care in the United States, our results may not be generalizable to non-US persons with HIV. Our analysis also did not establish a temporal relationship or causal link between HIV and T2DM. Further, we could not assess the influence of HIV-specific factors (i.e. CD4, viral load, specific antiretrovirals) on the strength of the association. Also, additional confounding factors such as family history of T2DM, lipodystrophy, waist circumference, BMI 25-30 kg/m2, ethnicity, and hepatitis C could not be assessed due to limitations of the database. Lastly, this study was also not able to evaluate persons who may have been transgender, which may be important since transwomen bear a disproportionate burden of HIV and use of sex hormones may impact our observed associations. Further work in this area will need to include and evaluate these persons. Despite these limitations, the present study describes important associations between HIV and T2DM, and the prevalence and strength of association reported in this study are based on the largest and most ethnically diversified cohort of PWH worldwide. Further, because the population sample is drawn from various health care settings across all US census regions, this study overcomes selection biases associated with tertiary single or multi-center investigations; thus, reinforcing the confidence that WWH carry a disproportionate burden of T2DM, suggesting that aggressive diagnosis, prevention and management measures are needed.
Supplementary Material
Funding:
This work was supported by grants from the National Institutes of Health (San Diego Center for AIDS Research, CFAR, AI036214, AI00665) and T32 training grant (5T32AI007384-28 to TCSM). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Footnotes
Conflict of Interest: None to disclose.
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