Abstract
Background
Transgender and gender diverse (trans) populations are at elevated risk for atherosclerotic cardiovascular disease (ASCVD).
Objective
Measure the association of gender identity and gender-affirming hormone therapy (GAHT) with ASCVD outcomes.
Design
Cohort study.
Participants
Over 1 million veterans receiving care in the Veterans Health Administration.
Main Measures
Gender identity was identified via a validated natural language processing (NLP) algorithm. Incident ASCVD (acute myocardial infarction, ischemic stroke, or revascularization after the baseline date) was identified via International Classification of Diseases diagnosis codes among veterans without prevalent ASCVD. We calculated sample statistics stratified by gender identity and used Cox proportional hazard regression to assess associations of gender identity and GAHT with incident ASCVD.
Key Results
Among 1,105,082 veterans, 42,149 were classified as trans (8013 transfeminine, 7127 transmasculine, and 27,009 uncategorized trans) while 918,843 were cisgender men and 144,090 were cisgender women. During a median follow-up of 9.39 years, 92,910 veterans had incident ASCVD (2806 among trans veterans). Adjusting for age, race, Hispanic ethnicity, and sexual orientation, trans veterans had 1.52 [1.45, 1.59] and 0.92 [0.89, 0.96] times the hazard of ASCVD compared to cisgender women and cisgender men, respectively. Compared to trans veterans not receiving GAHT, GAHT among trans veterans assigned female at birth was significantly associated a reduced hazard of ASCVD (0.89 [0.80, 0.98]); GAHT was not associated with ASCVD among trans veterans assigned male at birth (0.99 [0.89, 1.09]).
Limitations
With NLP, there is potential for selection bias as clinicians may preferentially document the gender identity for trans more than cisgender veterans.
Conclusions
This is one of the first studies to examine the association of both gender identity and GAHT with incident ASCVD in veterans. Future research must comprehensively evaluate ASCVD outcomes and the effects of gender-affirming care (including hormone therapy) in trans populations.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11606-025-09701-5.
INTRODUCTION
Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of morbidity and mortality, particularly for marginalized populations.1–3 Research exploring the effects of ASCVD risk factors has expanded to focus on gender minority (e.g., transgender and gender diverse [trans]) populations.4 For trans populations, 0.4–0.6% of US adults,5 cardiovascular health is of particular concern given disparate rates of ASCVD.6 As highlighted by the American Heart Association (AHA),7 disparities in cardiovascular health for trans populations emphasize the need to understand factors that influence outcomes, such as incident ASCVD.8
As most longitudinal cardiovascular studies do not collect gender identity,7,9–11 electronic health record (EHR) data are of value in researching trans populations.12,13 As the largest integrated healthcare system in the USA, the Veteran Healthcare Administration (VHA) provides an excellent data source to understand the health of marginalized populations.14 Notably, trans people are approximately twice as likely to have served in the military compared to cisgender (cis) people,15 making the VHA a unique data source. Further, while research shows veterans experience higher rates of ASCVD,16–19 and are more likely to develop ASCVD over 20-year follow-up compared with non-veteran populations,20 few studies characterize the health of marginalized veteran populations (e.g., racial and ethnic marginalized groups),21–23 especially trans veterans.24,25
As trans veterans have unique factors affecting their well-being (e.g., minority stress, gender-affirming treatments),7 adequately addressing their health requires identifying disparities in traditional8 as well as unique cardiovascular risk factors.7 Doing so will inform research and interventions for all veterans as well as potentially the general adult population.7,9,26 Additionally, while there is an appropriate focus on the AHA Life’s Essential 8,8 research continues to explore the impact of gender-affirming hormone therapy (GAHT) on cardiovascular risk factors.7,24 With over half of trans veterans receiving GAHT,27 exploring the effect of GAHT on their health is warranted.
This study aimed to assess whether gender identity was associated with risk of ASCVD by adjusting for other measured stressors as well as traditional ASCVD risk factors. We also sought to assess the association of GAHT with incident ASCVD among trans veterans.
METHODS
Study Population
We used data from a VHA EHR-based cohort study (IIR 18–035), a prospective nationwide observational study of veterans with ≥ 1 primary care visits in the VHA between October 1, 2009, and September 30, 2019. Veterans were identified using a validated algorithm that leverages the VHA EHR.28 The cohort contains data on 1,146,626 veterans who were classified as LGBT or non-LGBT by a natural language processing (NLP) algorithm. Among this sample, 39,132 participants were excluded for having prevalent ASCVD (defined as ≥ 1 inpatient or outpatient codes for acute myocardial infarction, ischemic stroke, or revascularization; eTable 3). Another 2412 veterans were excluded because they were classified as both a “cisgender male” and “lesbian” which was indicative of a potential classification error. Upon chart review, many in this group were categorized by the NLP tool based on EHR notes in which non-sexual minority cisgender men discussed their lesbian family members. Our final analytic sample included data on 1,105,082 veterans.
Primary Independent Variable
Presumed gender modality (cis vs. trans) of veterans was identified via (1) a NLP algorithm and (2) a validated algorithm using International Classification of Diseases (ICD) codes and receipt of GAHT in the year preceding baseline.(28–31) The NLP tool was validated in the Women’s Veterans Cohort Study (a cohort of 1141 veterans in which women were oversampled to ensure similar percentages of men [49%] and women [51%]) and possessed 88.2% sensitivity, 91.5% specificity, and 85.9% positive predictive (PPV) value in a binary classification task (LGBT veteran vs. non-LGBT veteran) on three random document test sets.28 The ICD code–based algorithm was validated in a non-VHA EHR cohort of 52,746 adults and possessed 97.6% sensitivity, 92.7% specificity, and 93.2% PPV.29 The number of trans veterans identified by each method are displayed in Table 1. Keywords and key phrases used in the NLP algorithm which classified veterans as trans are reported in eTable 1; GAHT used to classify veterans’ gender identity is presented in eTable 2.29,30 Additional details on the NLP algorithm development and performance are provided by Workman et al. and in the Supplemental Methods.28 Because veterans’ gender modality and identity were classified based on an algorithm rather than self-report, all references to gender modality and identity herein are presumed.
Table 1.
Method of Identifying Trans Veterans
| Method of identification | Transfeminine (N = 8013) | Transmasculine (N = 7127) | Uncategorized trans (N = 27,009) | Total trans (N = 42,149) |
|---|---|---|---|---|
| NLP tool | 2644 (33.0) | 5448 (76.4) | 23,478 (86.9) | 31,570 (74.9) |
| ICD codes and GAHT | 202 (2.5) | 142 (2.0) | 1230 (4.6) | 1574 (3.7) |
| Both | 5167 (64.5) | 1537 (21.6) | 2301 (8.5) | 9005 (21.4) |
Dependent Variable
Incident ASCVD (i.e., acute myocardial infarction, ischemic stroke, revascularization, or cardiac cause of death) between an individual’s baseline date and date of death or end of study period (September 30, 2019) was our outcome of interest. Here, the baseline date was defined as 1 year after the index date such that each veteran’s index date was the date of their first primary care encounter from October 1, 2009, to September 30, 2019. Incident ASCVD was identified as the presence of ≥ 1 inpatient and/or ≥ 2 outpatient ICD-9/10 or CPT codes (eTable 3) during the follow-up period, or a cardiac cause of death in the underlying position (eTable 3) which is consistent with prior research using VHA cohorts.
Additional Variables
Models included age, race, Hispanic ethnicity, sexual minority status (i.e., lesbian, gay, or bisexual), lipid levels, statin prescriptions, diabetes diagnosis, race-free estimated glomerular filtration rate (eGFR),32–34 body mass index (BMI), hypertension, smoking status, alcohol use disorder, HIV status, anxiety, depression, homelessness, area deprivation index (ADI),35,36 VHA enrollment priority group (based on income, medical conditions, and other factors),37 and a tally of state-level gender identity policies within veterans’ state of residence38 as covariates. Additional descriptors and the ascertainment method of each variable are detailed in the Supplemental Methods with relevant ICD codes (eTable 4).
Missing Data
We accounted for missing covariate data using multiple imputation via chained equations techniques to produce ten complete datasets. We used regression-based predictive mean matching to produce biologically plausible values.39 We combined results over imputations according to Rubin’s rules.40
Statistical Analysis
Sample Characteristics
We calculated sample statistics stratified by gender identity (cisgender men, cisgender women, transfeminine, transmasculine, uncategorized trans). Continuous variables were reported as mean (standard deviation) and median (interquartile range), while categorical variables were reported as frequency and percent. Kruskal–Wallis tests were used to compare continuous characteristics across groups; chi-square tests were used to compare categorical characteristics across groups.
Assessment of Algorithm to Classify Gender Modality and Identity
First, we examined the distribution of baseline year across gender identity groups to assess whether trans veterans were identified later in the follow-up period as a result of the military’s “Don’t Ask, Don’t Tell” policy, which was repealed in September 2011. Then, in a subset of 145,288 veterans who self-reported their gender identity, we calculated the sensitivity, specificity, and PPV of our algorithm’s ability to classify veterans’ gender modality. Results of the assessment of this algorithm are in eTables 5 and 6.
Primary and Secondary Analyses
Crude incidence rates for each gender identity were calculated per 1000 person-years along with corresponding Poisson 95% confidence intervals (95% CIs). We confirmed that the proportional hazards assumption was reasonably satisfied through visual assessment of log–log plots stratified by gender identity. Using multiply imputed data, we performed Cox proportional hazards regression analyses to assess the association between gender identity and incident ASCVD. First, we performed Cox regression analyses adjusting for only age to assess the association between gender identity and incident ASCVD. Then, guided by the minority stress framework,41–43 and the conceptual model of cardiovascular health among trans individuals,4,7 we created a directed acyclic graph (Fig. 1) to determine which variables to include in our regression models in order to estimate the total and direct effects of gender identity on incident ASCVD. To estimate the total effect of gender identity on incident ASCVD, we adjusted for presumed confounders of the association between gender identity and ASCVD: age, race, Hispanic ethnicity, and sexual orientation (Supplemental Methods).
Figure 1.
Directed acyclic graph (DAG).
To estimate the direct effect of gender identity on incident ASCVD, we additionally adjusted for (1) presumed mediators of this association which we directly measured in our data (hypertension, lipids, diabetes mellitus status, BMI, eGFR, substance use, anxiety, depression, and HIV); (2) proxies for unmeasured mediators (homelessness, VA enrollment priority group, and ADI as proxies for individual-level and neighborhood-level socioeconomic status); and (3) an instrumental variable (tally of trans laws and policies in state of residence) to account for any lingering unmeasured confounding (Supplemental Methods). First, we compared the hazard of incident ASCVD of trans persons as a group to that of cisgender women and cisgender men separately.44 Analyses were repeated with trans persons stratified by gender identity groups (transfeminine, transmasculine, uncategorized trans). In secondary analyses, we limited the sample to trans veterans to assess the association of GAHT with incident ASCVD.
Sensitivity Analyses
We performed three sensitivity analyses to assess the robustness of our results. First, because baseline age differed significantly across groups, each trans veteran was matched to two cisgender women and two cisgender men veterans on age ± 2 years. Primary Cox proportional hazards regression models were repeated in the matched sample, stratifying on the matched sets. Second, we excluded trans veterans who were identified by the NLP only, focusing on those identified via the validated algorithm from Jasuja et al. and re-ran the primary analyses.29–31 Third, we limited our sample to 145,288 veterans (2378 trans) who self-reported their gender identity and estimated ASCVD incidence in trans veterans (as a group) compared to cisgender men and women veterans. Sensitivity analysis results are in eTables 7–9.
Analyses were performed in SAS 9.4 (Cary, NC) and R (version 4.2.0).45 A two-sided p-value < 0.05 was considered statistically significant.
Results
Sample Characteristics
Among 1,105,082 veterans, 42,149 were classified as trans (8013 transfeminine, 7127 transmasculine, and 27,009 uncategorized trans) while 918,843 were presumed cisgender men and 144,090 were presumed cisgender women (Table 2).
Table 2.
Summary Statistics by Presumed Gender Identity
| Cis women (N = 144,090)* | Cis men (N = 918,843)† | Transfeminine (N = 8013)‡ | Transmasculine (N = 7127)¶ | Uncategorized trans (N = 27,009)§ | p-value | Overall (N = 1,105,082) | |
|---|---|---|---|---|---|---|---|
| Age, years | 40.4 (13.8) | 52.8 (17.0) | 43.9 (14.3) | 47.2 (14.8) | 46.6 (14.6) | < 0.001 | 51.0 (17.0) |
| Race | < 0.001 | ||||||
| Unknown | 11,496 (8.0) | 69,540 (7.6) | 393 (4.9) | 382 (5.4) | 1393 (5.2) | 83,204 (7.5) | |
| American Indian | 1499 (1.0) | 6352 (0.7) | 116 (1.4) | 73 (1.0) | 352 (1.3) | 8392 (0.8) | |
| Asian | 2013 (1.4) | 8945 (1.0) | 81 (1.0) | 90 (1.3) | 320 (1.2) | 11,449 (1.0) | |
| Black | 41,685 (28.9) | 175,020 (19.0) | 721 (9.0) | 992 (13.9) | 6439 (23.8) | 224,857 (20.3) | |
| Mixed race | 2526 (1.8) | 9013 (1.0) | 135 (1.7) | 110 (1.5) | 379 (1.4) | 12,163 (1.1) | |
| Pacific Islander | 1681 (1.2) | 8450 (0.9) | 53 (0.7) | 75 (1.1) | 245 (0.9) | 10,504 (1.0) | |
| White | 83,190 (57.7) | 641,523 (69.8) | 6514 (81.3) | 5405 (75.8) | 17,881 (66.2) | 754,513 (68.3) | |
| Ethnicity | < 0.001 | ||||||
| Non-Hispanic | 127,883 (88.8) | 83,1674 (90.5) | 7546 (94.2) | 6563 (92.1) | 24,842 (92.0) | 998,508 (90.4) | |
| Hispanic | 12,342 (8.6) | 62,429 (6.8) | 442 (5.5) | 536 (7.5) | 1903 (7.0) | 77,652 (7.0) | |
| Unknown | 3865 (2.7) | 24,740 (2.7) | 25 (0.3) | 28 (0.4) | 264 (1.0) | 28,922 (2.6) | |
| Marital status | < 0.001 | ||||||
| Married | 46,490 (33.4) | 433,703 (48.5) | 2329 (29.3) | 3438 (48.8) | 8880 (33.3) | 494,840 (46.0) | |
| Divorced/separated | 48,525 (34.9) | 253,791 (28.4) | 3250 (40.9) | 1970 (28.0) | 9788 (36.7) | 317,324 (29.5) | |
| Never married/single | 39,400 (28.3) | 164,166 (18.3) | 2147 (27.0) | 1514 (21.5) | 7068 (26.5) | 214,295 (19.9) | |
| Widowed | 4629 (3.3) | 42,978 (4.8) | 225 (2.8) | 125 (1.8) | 903 (3.4) | 48,860 (4.5) | |
| Sexual minority identity | 41,545 (28.8) | 123,104 (13.4) | 2324 (29.0) | 1158 (16.2) | 5798 (21.5) | 173,929 (15.7) | |
| Gender-affirming hormone therapy | – | – | 8013 (100.0) | 7127 (100.0) | 534 (2.0) | < 0.001 | 15,674 (1.4) |
| Priority score | 3.4 (2.4) | 3.9 (2.4) | 3.8 (2.2) | 3.7 (2.4) | 3.9 (2.2) | < 0.001 | 3.9 (2.4) |
| BMI, kg/m2 | 29.3 (6.4) | 29.7 (5.8) | 29.3 (6.3) | 31.4 (6.1) | 29.2 (5.9) | < 0.001 | 29.7 (5.9) |
| Hypertension, JNC8 | < 0.001 | ||||||
| None | 82,973 (66.7) | 364,026 (44.2) | 3669 (48.9) | 3324 (50.0) | 13,535 (54.9) | 467,527 (47.3) | |
| Controlled | 26,063 (21.0) | 301,106 (36.5) | 2683 (35.8) | 2129 (32.0) | 6637 (26.9) | 338,618 (34.3) | |
| Uncontrolled | 15,273 (12.3) | 159,278 (19.3) | 1152 (15.4) | 1198 (18.0) | 4485 (18.2) | 181,386 (18.4) | |
| Smoking status | < 0.001 | ||||||
| Never | 69,305 (50.4) | 287,103 (32.4) | 3024 (37.9) | 2507 (35.3) | 7723 (29.0) | 369,662 (34.7) | |
| Former | 23,654 (17.2) | 240,067 (27.1) | 1977 (24.8) | 2236 (31.5) | 5785 (21.7) | 273,719 (25.7) | |
| Current | 44,604 (32.4) | 357,962 (40.4) | 2978 (37.3) | 2351 (33.1) | 13,100 (49.2) | 420,995 (39.6) | |
| Total cholesterol, mg/dL | < 0.001 | ||||||
| < 200 | 57,212 (62.7) | 461,837 (69.9) | 3814 (64.7) | 3749 (67.8) | 12,996 (67.0) | 539,608 (69.0) | |
| 200–239 | 23,420 (25.7) | 140,327 (21.3) | 1480 (25.1) | 1237 (22.4) | 4508 (23.3) | 170,972 (21.9) | |
| ≥ 240 high | 10,614 (11.6) | 58,088 (8.8) | 597 (10.1) | 540 (9.8) | 1880 (9.7) | 71,719 (9.2) | |
| HDL cholesterol, mg/dL | < 0.001 | ||||||
| < 35 | 6553 (7.2) | 165,043 (25.0) | 968 (16.4) | 1435 (26.0) | 3870 (20.0) | 177,869 (22.7) | |
| 35–54 | 44,422 (48.7) | 377,882 (57.2) | 3229 (54.8) | 3201 (57.9) | 10,891 (56.2) | 439,625 (56.2) | |
| ≥ 55 | 40,271 (44.1) | 117,327 (17.8) | 1694 (28.8) | 890 (16.1) | 4623 (23.8) | 164,805 (21.1) | |
| LDL cholesterol, mg/dL | < 0.001 | ||||||
| < 130 | 64,893 (71.1) | 492,360 (74.6) | 4209 (71.4) | 4033 (73.0) | 14,085 (72.7) | 579,580 (74.1) | |
| 130–159 | 17,406 (19.1) | 112,433 (17.0) | 1162 (19.7) | 989 (17.9) | 3540 (18.3) | 135,530 (17.3) | |
| ≥ 160 | 8947 (9.8) | 55,459 (8.4) | 520 (8.8) | 504 (9.1) | 1759 (9.1) | 67,189 (8.6) | |
| Statin use | 23,928 (16.6) | 333,135 (36.3) | 1799 (22.5) | 2483 (34.8) | 6732 (24.9) | < 0.001 | 368,077 (33.3) |
| Diabetes | < 0.001 | ||||||
| Controlled | 4393 (3.1) | 57,035 (6.3) | 323 (4.1) | 470 (6.6) | 1163 (4.3) | 63,384 (5.8) | |
| Uncontrolled | 6273 (4.4) | 10,6842 (11.7) | 508 (6.4) | 737 (10.4) | 2194 (8.2) | 116,554 (10.6) | |
| None | 132,747 (92.6) | 747,477 (82.0) | 7139 (89.6) | 5880 (83.0) | 23,503 (87.5) | 916,746 (83.6) | |
| eGFR, mL/min/1.73 m2 | 86.9 (20.7) | 81.2 (20.3) | 84.4 (20.4) | 84.3 (19.1) | 85.0 (19.7) | < 0.001 | 82.1 (20.4) |
| Chronic kidney disease | < 0.001 | ||||||
| Stage 1: eGFR > 90 | 47,797 (44.7) | 264,586 (36.0) | 2695 (40.1) | 2382 (39.5) | 9220 (42.1) | 326,680 (37.2) | |
| Stage 2: 60 < eGFR < 9 | 53,321 (48.9) | 381,052 (51.8) | 3439 (51.1) | 3205 (53.1) | 11,090 (50.6) | 451,107 (51.4) | |
| Stage 3a: 45 < eGFR < 59 | 5411 (5.1) | 62,269 (8.5) | 483 (7.2) | 366 (6.1) | 1228 (5.6) | 69,757 (8.0) | |
| Stage 3b: 30 < eGFR < 44 | 1118 (1.0) | 19,927 (2.7) | 84 (1.2) | 54 (0.9) | 273 (1.2) | 21,456 (2.4) | |
| Stage 4: 15 < eGFR < 29 | 276 (0.3) | 5274 (0.7) | 13 (0.2) | 17 (0.3) | 75 (0.3) | 5655 (0.6) | |
| Stage 5: eGFR < 15 | 98 (0.1) | 2373 (0.3) | 11 (0.2) | 7 90.1) | 34 (0.20) | 2523 (0.3) | |
| Alcohol use disorder | 8310 (5.8) | 100,324 (10.9) | 692 (8.6) | 516 (7.2) | 4501 (16.7) | < 0.001 | 114,343 (10.3) |
| Opioid use disorder | 1456 (1.0) | 17,859 (1.9) | 94 (1.2) | 158 (2.2) | 854 (3.2) | < 0.001 | 20,421 (1.8) |
| Pain score, 0–10 | 2.9 (3.1) | 2.5 (3.1) | 2.5 (2.9) | 2.8 (3.0) | 2.8 (3.2) | < 0.001 | 2.5 (3.1) |
| Homelessness | 12,636 (8.8) | 89,542 (9.7) | 1126 (14.1) | 617 (8.7) | 5255 (19.5) | < 0.001 | 109,176 (9.9) |
| Suicide attempt | < 0.001 | ||||||
| Not reported | 137,645 (95.5) | 883,939 (96.2) | 7339 (91.6) | 6779 (95.1) | 24,549 (90.9) | 106,0251 (95.9) | |
| > 1 year after enrollment | 3688 (2.6) | 18,363 (2.0) | 431 (5.4) | 213 (3.0) | 1541 (5.7) | 24,236 (2.2) | |
| Present at enrollment | 2757 (1.9) | 16,541 (1.8) | 243 (3.0) | 135 (1.9) | 919 (3.4) | 20,595 (1.9) | |
| Military sexual trauma | < 0.001 | ||||||
| Never screened | 16,188 (11.2) | 83,581 (9.1) | 583 (7.3) | 549 (7.7) | 2127 (7.9) | 103,028 (9.3) | |
| No | 74,210 (51.5) | 793,063 (86.3) | 5387 (67.2) | 5730 (80.4) | 20,383 (75.5) | 898,773 (81.3) | |
| Yes | 53,692 (37.3) | 42,199 (4.6) | 2043 (25.5) | 848 (11.9) | 4499 (16.7) | 103,281 (9.3) | |
| Depression | 24,732 (17.2) | 84,130 (9.2) | 1538 (19.2) | 1035 (14.5) | 4429 (16.4) | < 0.001 | 115,864 (10.5) |
| General anxiety | 4930 (3.4) | 18,189 (2.0) | 312 (3.9) | 225 (3.2) | 914 (3.4) | < 0.001 | 24,570 (2.2) |
| HIV | 301 (0.2) | 16,041 (1.7) | 105 (1.3) | 138 (1.9) | 661 (2.4) | < 0.001 | 17,246 (1.6) |
| Area deprivation index, % | 54.4 (25.3) | 54.9 (26.6) | 53.0 (25.7) | 51.3 (26.0) | 54.6 (26.9) | < 0.001 | 54.8 (26.5) |
| State gender identity policies | 6.0 (11.4) | 6.3 (11.5) | 8.2 (11.7) | 7.6 (11.8) | 7.2 (11.6) | < 0.001 | 6.3 (11.5) |
Summary statistics for continuous variables are presented as mean and standard deviation with p-values from ANOVA; summary statistics for categorical variables are presented as number and column percentage with p-values from chi-square tests
*Number missing: 5046 (marital status), 1123 (priority score), 20,574 (BMI), 19,781 (hypertension), 6527 (smoking status), 52,844 (lipids), 677 (diabetes), 37,069 (eGFR), 19,779 (pain), 28,097 (ADI national rank), 26,362 (state anti-trans policies)
†Number missing: 24,205 (marital status), 3973 (priority score), 100,395 (BMI), 94,433 (hypertension), 33,711 (smoking status), 258,591 (lipids), 7489 (diabetes), 183,362 (eGFR), 92,947 (pain), 118,142 (ADI), 101,524 (state GI policy score)
‡Number missing: 62 (marital status), 11 (priority score), 537 (BMI), 509 (hypertension), 34 (smoking status), 2122 (lipids), 43 (diabetes), 1288 (eGFR), 503 (pain), 771 (ADI), 683 (state anti-trans policies)
¶Number missing: 80 (marital status), 10 (priority score), 504 (BMI), 476 (hypertension), 33 (smoking status), 1601 (lipids), 40 (diabetes), 1096 (eGFR), 476 (pain), 609 (ADI), 542 (state anti-trans policies)
§Number missing: 370 (marital status), 48 (priority score), 2485 (BMI), 2352 (hypertension), 401 (smoking status), 7625 (lipids), 149 (diabetes), 5089 (eGFR), 2203 (pain), 2619 (ADI), 2212 (state anti-trans policies)
Primary Analysis: Association of Gender Identity with Incident ASCVD
During a median follow-up of 9.39 years, 92,910 veterans had an incident ASCVD event (2806 trans). Trans veterans’ overall ASCVD incidence rate (IR) per 1000 person-years (IR [95% CI]: 7.48 [7.21, 7.77]) was lower than that of cisgender men (IR [95% CI]: 11.21 [11.13, 11.28]), higher than that of cisgender women (Table 3; IR [95% CI]: 3.66 [3.55, 3.77]), and was similar across transfeminine, transmasculine, and uncategorized trans subgroups. Upon adjustment for age, trans veterans had 1.47 [1.40, 1.54] and 0.93 [0.90, 0.97] times the hazard of incident ASCVD compared to cisgender women and to cisgender men, respectively (Table 3). The magnitude and significance of these associations were similar when additionally adjusting for presumed confounders (race, Hispanic ethnicity, and sexual orientation) of the association between gender identity and incident ASCVD (Table 3). Upon additional adjustment for ASCVD risk factors, substance use, anxiety, depression, HIV status, homelessness, ADI, VA enrollment priority group, and a tally of state-level gender identity policies in veterans’ state of residence, results attenuated but remained statistically significant.
Table 3.
Association of Gender Identity with Incident ASCVD
| Age-adjusted model | Multivariable model 1† | Multivariable model 2‡ | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Incidence rate [95% CI]* |
Versus cis women | Versus cis men | Versus cis women | Versus cis men | Versus cis women | Versus cis men | ||||||||
| Gender Identity | Incident ASCVDs/N | Hazard ratio [95% CI] | p | Hazard ratio [95% CI] | p | Hazard ratio [95% CI] | p | Hazard ratio [95% CI] | p | Hazard ratio [95% CI] | p | Hazard ratio [95% CI] | p | |
| Cis man | 85,595/918,843 |
11.21 [11.13, 11.28] |
– | – | 1.00 | – | – | – | 1.00 | – | – | – | 1.00 | – |
| Cis woman | 4509/144,090 |
3.66 [3.55, 3.77] |
1.00 | – | – | – | 1.00 | – | – | – | 1.00 | – | – | – |
| Trans | 2806/42,149 |
7.48 [7.21, 7.77] |
1.47 [1.40, 1.54] |
< 0.001 |
0.93 [0.90, 0.97] |
< 0.001 |
1.52 [1.45, 1.59] |
< 0.001 |
0.92 [0.89, 0.96] |
< 0.001 |
1.25 [1.19, 1.32] |
< 0.001 |
0.96 [0.92, 0.99] |
0.021 |
| Trans masculine | 460/7127 |
7.05 [6.42, 7.73] |
1.27 [1.15, 1.40] |
< 0.001 |
0.83 [0.75, 0.90] |
< 0.001 |
1.37 [1.24, 1.51] |
< 0.001 |
0.85 [0.77, 0.93] |
< 0.001 |
1.11 [1.00, 1.22] |
0.045 |
0.86 [0.79, 0.94] |
0.001 |
| Trans feminine | 441/8013 |
6.16 [5.59, 6.76] |
1.43 [1.29, 1.57] |
< 0.001 |
0.86 [0.78, 0.94] |
0.001 |
1.50 [1.36, 1.66] |
< 0.001 |
0.86 [0.79, 0.95] |
0.002 |
1.28 [1.16, 1.42] |
< 0.001 |
0.91 [0.83, 1.00] |
0.052 |
| Unclassified trans | 1905/27,009 |
8.00 [7.65, 8.37] |
1.54 [1.46, 1.63] |
< 0.001 |
0.98 [0.94, 1.03] |
0.422 |
1.56 [1.48, 1.65] |
< 0.001 |
0.96 [0.92, 1.01] |
0.093 |
1.28 [1.21, 1.36] |
< 0.001 |
0.99 [0.95, 1.04] |
0.803 |
*Calculated as number of events over total person-years and reported per 1000 person-years. CI calculated using the Poisson distribution
†Adjusted for race, Hispanic ethnicity, and sexual orientation
‡Additionally adjusted for BMI, diabetes, hypertension, lipids, substance use, anxiety, depression, HIV, homelessness, ADI, VA enrollment priority group, and state-level anti-trans policies in veterans’ state of residence
When disaggregating trans veterans into specific gender identities, we observed that all gender minority identities were associated with greater ASCVD risk compared to cisgender women when adjusting for age alone or in conjunction with race, Hispanic ethnicity, and sexual orientation (Table 3). In fully adjusted models, associations remained statistically significant. Compared to cisgender men, a transmasculine gender identity was associated with lower hazard of incident ASCVD in minimally and fully adjusted models (fully adjusted HR [95% CI]: 0.86 [0.79, 0.94]). Transfeminine veterans’ ASCVD risk was lower than that of cisgender men when adjusting for age, race, Hispanic ethnicity, and sexual orientation (HR [95% CI]: 0.86 [0.79, 0.95]) but was attenuated upon adjustment for presumed mediators of the association between gender identity and incident ASCVD (p = 0.052). Unclassified trans veterans’ ASCVD risk was not significantly different from that of cisgender men.
Secondary Analysis: Association of GAHT Use with Incident ASCVD in Trans Veterans
Among trans veterans, masculinizing GAHT was significantly associated with an 11–17% reduced hazard of ASCVD in minimally and fully adjusted Cox proportional hazards regression models (Table 4). There was no significant difference in ASCVD incidence between no known GAHT use and use of feminizing GAHT.
Table 4.
Association of GAHT with Incident ASCVD Among Transgender Veterans
| Age-adjusted model | Multivariable model 1† | Multivariable model 2‡ | ||||||
|---|---|---|---|---|---|---|---|---|
| GAHT | Incident ASCVDs/N | Incidence rate [95% CI]* | Hazard ratio [95% CI] | p-value | Hazard ratio [95% CI] | p-value | Hazard ratio [95% CI] | p-value |
| Absent** | 1884/26,475 | 8.09 [7.73, 8.47] | 1.00 | – | 1.00 | – | 1.00 | – |
| Masculinizing GAHT | 460/7127 | 7.05 [6.42, 7.73] | 0.83 [0.75, 0.92] | < 0.001 | 0.89 [0.80, 0.98] | 0.020 | 0.86 [0.77, 0.95] | 0.004 |
| Feminizing GAHT | 441/8013 | 6.15 [5.59, 6.75] | 0.94 [0.85, 1.04] | 0.23 | 0.99 [0.89, 1.09] | 0.78 | 1.02 [0.91, 1.13] | 0.77 |
*Calculated as number of events over total person-years and reported per 1000 person-years. CI calculated using the Poisson distribution
†Adjusted for race, Hispanic ethnicity, and sexual minority identity
‡Additionally adjusted for BMI, diabetes, hypertension, lipids, substance use, anxiety, depression, HIV, homelessness, ADI, VA enrollment priority group, and a tally of state-level gender identity policies in veterans’ state of residence
**It is possible that some trans veterans in this group receive GAHT outside of the VHA, which we are unable to capture
DISCUSSION
Within a sample of > 1 million veterans, nearly 4% were algorithmically classified as trans, making this one of the larger cohorts of trans adults available to examine ASCVD. Our estimate of the trans veteran population is consistent with prior research.15,46 We found ASCVD risk behaviors consistent with prior literature (e.g., cigarette use, alcohol use).7,47–53 Further, diagnoses of depression and anxiety and experiences of homelessness and suicide attempts were elevated among trans veterans.54–61 These disparities are posited as drivers of coping behaviors such as tobacco and alcohol use, and have been independently linked to elevated 10-year ASCVD risk.20,62 The distribution of additional ASCVD risk factors differed by gender identity (e.g., hypertension, diabetes), consistent with recent research.7,24
Consistent with a recent meta-analysis, we found that trans veterans experience a higher hazard of ASCVD compared to cisgender women.6 However, while they reported no significant difference in risk of MI or stroke among transfeminine individuals versus cisgender men, we observed that transfeminine veterans had lower risk of MI and stroke. Notably, our analyses contained larger samples in both cisgender and trans veterans than the 6 studies in the meta-analysis combined. Our study is unique in that we explore the association of GAHT with ASCVD among trans veterans.63 When adjusting for traditional risk factors for ASCVD, there remained a lower hazard ratio for having ASCVD compared to trans persons who had no indication of receiving GAHT. While prior research suggests that GAHT is associated with worse ASCVD morbidity and mortality,7,64–66 our results could be rooted in several factors. First, receipt of GAHT is linked to improvements in general well-being and mental health;58,67 reductions in gender dysphoria are expected to be associated with a reduction in ASCVD risk. Second, access to gender-affirming care, including GAHT, is linked to improved access to care and optimization of health such as improved uptake of preventive services.68,69 Additionally, veterans who did not receive GAHT were more likely to have comorbidities and documented social stressors than those who were receiving GAHT at the VHA.27 Finally, GAHT is associated with improved body satisfaction and greater engagement in physical activity.7,70 Consequently, the effects of GAHT should be considered in the context of their positive impact on mental health, health behaviors, and downstream physiological effects.71
LIMITATIONS
Despite the strengths of our data source (e.g., sample size, duration, structured coding), limitations exist. Given policies penalizing sexual and gender minority veterans, especially trans veterans (e.g., Don’t Ask Don’t Tell), there is likely underreporting and reduced documentation of gender minority status. This is particularly relevant as policies allowing open participation of trans persons in the military have vacillated based on which administration is in power. However, additional analyses within our study do not reveal varying distributions of year of gender identity ascertainment in trans veterans compared to cisgender veterans (eTable 5). Regardless, the chilling effect of these policies likely discourages veterans from disclosing their trans status.72 However, an underreporting of trans veterans would bias our results to the null. Further, as the current study partially relies on an NLP algorithm, there is potential for selection bias as clinicians may preferentially document the gender identity for trans veterans more than cisgender veterans. Further, while EHR data have the benefit of objective measures of clinical risk factors and outcomes, data rely on measurement, diagnosis, and documentation by the healthcare system; it is possible that some risk factors or outcomes could be underreported. Additionally, although VHA EHR allows for coding of select social stressors, providers do not routinely or systematically document these stressors, resulting in underreporting. Through the inclusion of proxy variables for socioeconomic status, we were able to explore several factors (e.g., homelessness, VHA enrollment priority score, ADI). However, these data alone are unlikely to capture the full effect of multi-level socioeconomic factors. Further, trans people receiving GAHT may access it outside of the VHA, which affects classification and potentially biases results towards the null. Additionally, trans persons who access GAHT are a select group given they (1) see GAHT as important to their affirmation, (2) can navigate systems to access GAHT, (3) likely do not have contraindications for GAHT,73,74 and (4) likely have a diagnosis of gender identity disorder since this is a requirement per VHA guidelines.74 Therefore, while trans veterans receiving GAHT in our sample displayed decreased ASCVD risk compared to those who were not on GAHT, it may be attributable to a variety of reasons, including increased healthcare utilization and fewer social stressors. Finally, when comparing our algorithm’s classification of veterans’ gender modality to self-identified gender in the subset of veterans with both measures available, our algorithm possessed a low PPV indicating that of those our algorithm classifies as trans, only 30.6% self-identified as trans. However, it has been reported that among trans veterans, only 32.7% selected a gender minority identity (transgender man, transgender woman, non-binary, a gender not listed here); 48.3% self-identified as “woman” or “man” and 19% chose not to disclose. If 48.3% of the individuals we classified as trans self-identified as “man” or “woman,” then 2585 individuals in eTable 6 are misclassified, which would account for our low PPV; reclassification of these individuals would increase our PPV to 64.1%.
CONCLUSION
When examining incident ASCVD, we found that trans adults fared worse than cisgender women but better than cisgender men. These results reflect disparate cardiovascular morbidity of trans veterans and reveal that even when accounting for inequities in risk factors, trans veterans continue to fair worse than cisgender women. Further, among trans veterans, receipt of GAHT was significantly associated with a lower hazard of incident ASCVD. These unique results reveal that clinicians and researchers need to consider additional factors affecting the cardiovascular health of trans persons, and would best view gender-affirming care, including GAHT, as an intervention that should be optimized rather than discontinued to improve cardiovascular health.71,75
Supplementary Information
Below is the link to the electronic supplementary material.
(DOCX 74.3 KB)
Acknowledgements:
The authors wish to acknowledge veterans for their service and sharing their identity with clinicians to improve their care as well as the care of their fellow veterans.
Author Contribution:
Streed, Duncan, Heier, O’Leary, Skanderson, and Goulet take responsibility for the integrity of the data and the accuracy of the data analyses. All authors provided substantial contributions to the conception or design of the work; the acquisition, analysis, or interpretation of data for the work; drafting the work and reviewing it critically for important intellectual content; final approval of the version to be published; and agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
Data Availability
Data are available following appropriate request procedures to access VHA data.
Declarations:
Human Ethics and Consent to Participate:
The VA Connecticut Healthcare System and VA Washington DC Institutional Review Boards approved this study.
Conflict of Interest:
The views expressed in this article are those of the authors and do not necessarily reflect the position or policies of the funders, institutions, the Department of Veterans Affairs or any agencies of the United States Government. Streed reports receiving consulting fees from EverlyWell, L’Oreal, the Texas Health Institute, the Research Institute for Gender Therapeutics, and the US Department of Justice unrelated to this work.
Footnotes
Suggested Tweet
Transgender and gender diverse veterans have a higher hazard of incident cardiovascular disease than cisgender women veterans but not cisgender men veterans. Among trans veterans, gender-affirming hormone therapy was significantly associated with a reduced hazard of CVD.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
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Data Availability Statement
Data are available following appropriate request procedures to access VHA data.