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. Author manuscript; available in PMC: 2022 Dec 1.
Published in final edited form as: Am J Prev Med. 2021 Aug 5;61(6):804–811. doi: 10.1016/j.amepre.2021.05.019

Cardiovascular disease in a population-based sample of transgender and cisgender adults

Tonia Poteat 1, Shahrzad Divsalar 2, Carl Streed Jr 3,4, Jamie Feldman 5, Walter Bockting 6, Ilan H Meyer 7
PMCID: PMC8608688  NIHMSID: NIHMS1714502  PMID: 34364725

Abstract

Introduction:

Existing data on cardiovascular disease (CVD) among transgender people are inconsistent and derived from non-representative samples or population-based data sets that do not include transgender-specific risk factors such as gender affirming hormone use and gender minority stressors. A nationally representative sample of cisgender and transgender adults age 40 years and older was used to assesses prevalence and correlates of smoking, select CVD conditions, and venous thromboembolism (VTE).

Methods:

Participants were recruited from 2016-2018, with analysis conducted in December 2020 with 114 transgender and 964 cisgender individuals. Sample weights and multiple imputation were used for all estimates except descriptive statistics. Logistic regression models estimated odds ratios and 95% confidence intervals expressing the relationship between each outcome variable and a set of independent variables. Each model controlled for race and age.

Results:

No meaningful differences between cisgender and transgender participants were found in smoking or CVD conditions. However, there was an increased odds of VTE among transgender women compared to cisgender women. Transgender people had a greater odds of discrimination, psychological distress, and adverse childhood experiences. These stressors were associated with increased odds of a CVD condition; and everyday discrimination and adverse childhood experiences were associated with increased odds of smoking. Discrimination and psychological distress were associated with VTE.

Conclusion:

Transgender people face disparities in CVD risk. This study provides support for the gender minority stress model as a framework for understanding CVD disparities. Future research with larger samples and adjudicated outcomes are needed to advance the field.

Keywords: cardiovascular disease, transgender adults, hormone therapy

Introduction

Evidence suggests transgender populations experience disparities in cardiovascular health.1,2 However, only one federal population based survey, the Behavioral Risk Factor Surveillance Survey (BRFSS), reports on adult cardiovascular health by transgender identity. These data are limited since they are derived from an optional module used by less than two-thirds of U.S. states.3 Nonetheless, BRFSS data indicate that transgender adults experience disparities in traditional cardiovascular disease (CVD) risk factors and outcomes compared with cisgender adults.4–7

Gender affirming hormones (i.e., exogenous estrogen and testosterone) are commonly taken by transgender people to align their bodies with their identities.8 Observational studies have consistently identified an increased risk for venous thromboembolism (VTE) among transgender women taking estrogen.9–12 However, data on other cardiovascular outcomes and their relationship to gender affirming hormones have been more limited.5 A systematic review found exogenous testosterone to be associated with elevated prevalence of cardiovascular risk factors (e.g. hypertension, insulin resistance, and dyslipidemia) in transgender men; however, there were no associations with cardiovascular disease or death.13 In contrast, population-based data found that transgender men had a significantly higher odds of a myocardial infarction (MI) than cisgender men (OR 2.53) and cisgender women (OR 4.90).7 Transgender women had higher odds of a MI compared with cisgender women (OR 2.56) but not cisgender men.7 Another study using the same data found that gender non-conforming individuals reported the highest prevalence of coronary heart disease or MI (17.8%) compared with transgender men (6.6%), transgender women (8.0%), cisgender men (9.0%), and cisgender women (4.8%).6

The gender minority stress model posits that transgender stigma and discrimination increase stress and drive health disparities.14 Psychosocial stressors, such as discrimination and adverse childhood experiences (ACEs), play an important role in CVD risk.15,16 An investigation of CVD determinants in 52 countries identified psychosocial stress as a powerful predictor of MI, comparable in impact to smoking.17,18 Discrimination is a common stressor for transgender people and associated with a range of negative health behaviors and outcomes, including smoking and CVD. 19–21 Transgender people are more likely to experience ACEs compared with cisgender people;22 and ACEs have also been associated with smoking and CVD. 16,23 However, population-based data assessing relationships between these psychosocial stressors and CVD among transgender people are absent.

In short, existing research on CVD among transgender people is limited. Existing data are inconsistent and derived from non-representative samples or population-based data sets that do not include transgender-specific risk factors such as hormone use and psychosocial stressors. Research is needed that assesses both biological (e.g., hormone use) and psychosocial (e.g., discrimination) CVD risk factors. This study sought to address this research gap.

The first aim was to describe the distribution of smoking, select CVD conditions, and VTE among transgender adults by gender identity and compared with cisgender adults using data from the first national probability sample of the U.S. transgender population. Based on prior literature summarized above, this study hypothesized that transgender participants, and transgender women in particular, would have a higher odds of smoking, select CVD conditions, and VTE than cisgender participants.

The second aim was to assess the effect of psychosocial factors and hormone therapy on smoking, CVD conditions, and VTE among transgender participants. The study hypothesized that higher scores for everyday discrimination, psychological distress, and ACEs would predict a higher odds of smoking, CVD conditions, and VTE. The study further hypothesized that transgender participants who had taken hormone therapy would have no higher odds of CVD conditions than transgender participants who had never taken it.

Methods

Study design

This analysis used data from the TransPop study, the first national probability sample of transgender adults in the United States.24 The survey collected demographic, health outcomes and behaviors, experiences of discrimination, and gender affirming interventions (e.g. hormone use).

Data Sources

The TransPop data set uses two sources. One source was a survey administered to a nationally representative sample of transgender adults in two waves, April-August 2016 and June 2017-December 2018. The second source included a comparable survey administered to a nationally representative sample of cisgender respondents administered February 19-23, 2018 and November 12-December 10, 2018.

Recruitment and Data Collection

Participants were recruited by Gallup, Inc., a survey research consulting company25 using two methodologies that corresponded to changes over the study period. The first method recruited a probability sample of U.S. adults using random digit dialing to reach cellphone and landline users. Following industry trends, the second method recruited a probability sample of the entire U.S. adult population using address-based sampling that mailed the survey followed by a reminder mailing. All respondents were sent English language questionnaires to be self-administered online or on paper. The current analysis was restricted to participants aged 40 years and older to correspond to the age when healthcare providers begin calculating cardiovascular disease risk scores.26 The analytic sample included 114 transgender and 964 cisgender individuals. Sample weights account for selection probability and are corrected for unit nonresponse.

Screening and Eligibility

Transgender respondents were identified using the question, “Do you, personally, identify as lesbian, gay, bisexual, or transgender?” Respondents who answered “yes,” were then screened using a 2-step question including self-reported gender identity (“do you currently describe yourself as a man, a woman, or transgender?”) and sex assigned at birth (“on your original birth certificate, was your sex assigned as female or male?”). Respondents were categorized as transgender if they identified as man or woman and that differed from their sex assigned at birth or if they identified as transgender. Participants who selected “transgender” as their gender identity were asked if they identified as a trans woman, trans man, or non-binary/genderqueer. Additional eligibility included being age 18 years or older, having 6 years of education or more, and competency in English language. Detailed information about the methodology is provided elsewhere.27

Ethics Statement

The study protocol was reviewed by the Gallup institutional review board (IRB) and the IRB at the University of California Los Angeles and collaborating investigators’ universities.

Measures

Sociodemographic Measures

Race was dichotomized as Black or non-Black to be consistent with the American College of Cardiology calculator for CVD risk.26

Education was dichotomized as high school or less and more than high school.

Employment was dichotomized as full-time or less than full-time employment.

Poverty was calculated using weighted Census estimates for 2018 poverty thresholds; respondents were categorized as living in poverty (below 100% FPL) or not, based upon their reported household income and number of people living on that income.

Ever Hormone use was measured by participant report of ever taking hormones for gender identity or transition.

Psychosocial Measures (Table 1)

Table 1.

Survey Items Used for Psychosocial Measures

Everyday Discrimination Scale Questions
In your day-to-day life, how often do any of the following things happen to you?
1 You are treated with less courtesy than other people
2 You are treated with less respect than other people
3 You receive poorer service than other people at restaurants or stores.
4 People act as if they think you are not smart.
5 People act as if they are afraid of you
6 People act as if they think you are dishonest
7 People act as if they’re better than you are.
8 You are called names or insulted.
9 You are threatened or harassed
Kessler 6 Scale Questions
During the past 30 days, about how often did you feel …
1 …nervous?
2 …hopeless?
3 …restless or fidgety?
4 … so depressed that nothing could cheer you up?
5 … that everything was an effort?
6 … worthless?
Adverse Childhood Experiences Questions
Look back to before you were 18 years of age …
1 Did you live with anyone who was depressed, mentally ill, or suicidal?
2 Did you live with anyone who was a problem drinker or alcoholic?
3 Did you live with anyone who used illegal street drugs or who abused prescription medications?
4 Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
5 Were your parents separated or divorced?
6 How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
7 Before age 18, how often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way? Do not include spanking.
8 How often did a parent or adult in your home ever swear at you, insult you, or put you down?
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The Everyday Discrimination Scale assessed chronic experiences of unfair treatment.28 Scale items asked respondents the frequency of discrimination experiences over the past year. Responses ranged from “often” to “never” on a 4-point Likert scale. Scale scores ranged from 1 to 4 such that higher values represent more discrimination.

Psychological distress was assessed using the Kessler-6.29 Items asked about frequency of various symptoms over the prior 30 days on a 5-point scale. Higher scores indicate greater distress.

Adverse Childhood Experiences (ACEs)30 scale items asked respondents to “look back before you were 18 years of age,” and included 8 items about adverse experiences. For scoring, all items were dichotomized (1= yes, event occurred at least once vs. 0=no, event never occurred). The final score sum32 ranged from 0 to 8, with higher scores indicated more ACEs.

Smoking, CVD conditions, and VTE

Smoking status:

Participants who reported ever smoking at least 100 cigarettes in their lifetime were categorized as ever smoked. Those who reported smoking currently were considered current smokers.

CVD condition:

a binary composite variable that classified as “yes” participants who reported having been told by a doctor or health professional that they had any one of the following: heart condition or heart disease, angina, heart attack, hypertension, or stroke. Participants who reported none of these conditions were coded as “no.”

VTE:

participants who reported having been told by a doctor or health professional they had blood clots in their legs or lungs were coded as “yes,” otherwise they were coded as “no”.

Statistical Analysis

Descriptive statistics were estimated using the unweighted data. Weighted means and confidence intervals were estimated for psychosocial scores and age. Weighted percentages and confidence intervals were calculated for CVD variables and sociodemographic characteristics other than age.

Eight variables used in the analysis models had missing data, ranging from 0.28% to 7.4% missing. Missing values were multiply imputed across 50 data sets, and regression parameter estimates for each data set were pooled using Rubin’s rules. All models used sampling weights to generate population estimates and Taylor series linearization for standard error estimation. All statistical analyses were performed using STATA 16.1.32

Logistic regression models estimated odds ratios and 95% confidence intervals expressing the relationship between each outcome and a set of explanatory variables, controlling for age and race. Whether gender identity predicts differences in odds of each CVD risk factor and outcome was modeled. Then, whether psychosocial factors predict CVD risk factors and outcomes was modeled, stratifying by gender identity and controlling for age and race. Finally, the effect of gender affirming hormone therapy on CVD risk factors and outcomes for the transgender population alone were modeled.

Results

Descriptive Statistics

Transgender respondents were younger than cisgender respondents with a mean age of 53.5 years compared with 59.8 years. A greater proportion of transgender respondents identified as Black than cisgender respondents (18% and 13.1% respectively). A greater proportion of transgender people had an education level of high school or less, received food stamps or WIC, and met criteria for poverty than cisgender people. Mean scores for everyday discrimination, psychological distress, and ACEs were higher for transgender people. There was little difference between cisgender and transgender people in ever smoking; however, a greater proportion of transgender people were current smokers. Approximately 40% of transgender participants had ever used gender affirming hormones. Data disaggregated by gender identity of transgender participants are available in Table 2.

Table 2.

Characteristics Among Study Participants Aged 40 Years and Older

%a or meana (95% CI)a
Transgender (n=114) Cisgender (n=964) Trans Woman (n=70) Trans Man (n=25) GNB (n=19) Cis Woman (n=517) Cis Man (n=447)
Sociodemographic Characteristics
Age (mean age in years) 53.5 (51.12, 55.97) 59.8 (58.65, 60.97) 55.6 (52.30, 58.90) 50.6 (46.77, 54.46) 51.9 (46.30, 57.56) 59.1 (57.44, 60.80) 60.6 (59.05, 62.19)
Race, %
 Black 18.0 (9.3, 31.8) 13.1 (9.8, 17.4) 12.2 (4.8, 27.8) 31.5 (11.3, 62.5) 15.5 (2.7, 55.1) 16.6 (11.5, 23.4) 9.1 (5.6, 14.3)
 Not Black 82.0 (68.2, 90.7) 86.9 (82.6, 90.2) 87.8 (72.2, 95.2) 68.5 (37.5, 88.7) 84.5 (44.9, 97.3) 83.4 (76.6, 88.5) 90.9 (85.7, 94.4)
Educational level, %
 High school or less 48.6 (35.7, 61.7) 33.9 (29.3, 38.8) 42.4 (26.9, 59.6) 53.7 (27.9, 77.7) 59.3 (28.8, 84.0) 34.0 (27.8, 40.7) 33.9 (27.3, 41.1)
 More than high school 51.4 (38.3, 64.3) 66.1 (61.2, 70.7) 57.6 (40.4, 73.1) 46.3 (22.3, 72.1) 40.7 (16.0, 71.2) 66.0 (59.3, 72.2) 66.1 (58.9, 72.7)
Full-time employed, % 32.1 (21.6, 44.9) 30.3 (26.3, 34.5) 28.3 (17.3, 42.7) 33.7 (13.2, 62.9) 40.2 (15.8, 70.6) 25.1 (20.2, 30.7) 36.4 (30.4, 42.8)
Assistance receiving food stamps or WIC, % 36.0 (22.4, 52.3) 10.4 (7.6, 14.0) 27.2 (12.3, 50.1) 55.0 (25.8, 81.1) 2 7.9 (9.7, 58.2) 15.6 (11.1, 21.5) 4.2 (2.3, 7.6)
Poverty, % 39.3 (26.6, 53.5) 12.2 (9.1, 16.3) 36.3 (20.9, 55.3) 53.0 (25.2, 79.0) 32.6 (11.4, 64.4) 15.8 (11.2, 21.9) 7.9 (4.5, 13.6)
Psychosocial Factors
Everyday discrimination 1.92 (1.74, 2.10) 1.60 (1.54, 1.65) 1.93 (1.68, 2.18) 1.7 (1.34, 2.12) 2.1 (1.79, 2.41) 1.6 (1.51, 1.66) 1.6 (1.53, 1.69)
Psychological distress 7.2 (5.89, 8.52) 4.3 (3.88, 4.73) 7.5 (5.96 9.02) 5.9 (2.95, 8.82) 1.58 (5.11, 11.32) 0.32 (4.28, 5.52) 0.28 (3.04, 4.16)
Adverse Childhood Experiences 2.56 (1.92, 3.19) 2.14 (1.91, 2.37) 3.15 (2.41, 3.89) 1.67 (0.77, 2.57) 2.28 (0.68, 3.88) 2.37 (2.03, 2.71) 1.89 (1.60, 2.19)
CVD Conditions
Any CVD condition, % 38.5 (27.1, 51.3) 51.2 (46.8, 55.6) 42.3 (27.9, 58.2) 29.3 (12.1, 55.4) 40.6 (15.4, 71.9) 48.4 (42.4, 54.5) 54.5 (48.0, 60.7)
Blood clots in legs or lungs, % 7.8 (3.0, 18.7) 3.1 (1.9, 4.9) 6.8 (2.5, 17.4) 2.1 (0.5, 8.8) 18.0 (3.1, 60.2) 2.0 (1.2, 3.2) 4.4 (2.3, 8.3)
CVD Risk Factors
Ever Smoker, % 47.3 (34.7, 60.3) 50.0 (45.6, 54.4) 53.2 (37.4, 68.4) 50.2 (24.5, 75.9) 27.7 (10.4, 55.9) 48.1 (42.1, 54.2) 52.2 (45.9, 58.5)
Current Smoker, % 44.1 (26.6, 63.2) 33.1 (27.0, 39.7) 46.7 (24.8, 70.0) 44.3 (13.7, 79.9) 30.5 (6.9, 72.2) 35.2 (27.1, 44.2) 30.7 (22.2, 40.8)
Lifetime Hormone Therapy, % 40.2 (28.4, 53.3) n/a 49.8 (34.1, 65.5) 28.8 (11.3, 56.2) 28.8 (8.1, 64.8) NA NA
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a

Based on weighted data.

NA = not applicable. CVD = cardiovascular disease. Cis = cisgender. Trans = transgender. GNB = gender non-binary. WIC = special supplemental program for women, infant, and children.

Smoking, CVD Conditions, and VTE by Gender Identity

In models adjusted for age and race (Table 3), the odds of lifetime smoking were not different for transgender and cisgender people. The estimated odds of current smoking were higher for transgender compared to cisgender people with the confidence interval including the null. A greater proportion of cisgender people had a history of a CVD condition; however, transgender people were more likely to report a history of VTE.

Table 3.

Adjusted Odds Ratios of Any CVD Condition, VTE, and Smoking Status by Gender Identity

AORa (95% CI)
Any CVD Condition VTE Ever Smoker Current Smoker
Gender Identity
Trans v. Cis .79 (.43, 1.44) 3.35 (1.07, 10.46) .98 (.55, 1.76) 1.58 (.70, 3.62)
Trans Woman v. Cis Woman .94 (.46, 1.93) 3.94 (1.24, 12.51) 1.26 (.62, 2.57) 1.44 (.49, 4.17)
Trans Woman v. Cis Man .76 (.37, 1.55) 1.90 (.53, 6.81) 1.12 (.54, 2.27) 1.56 (.52, 4.66)
Trans Man v. Cis Woman .61 (.16, 2.26) 1.60 (.32, 7.95) 1.29 (.39, 4.31) .98 (.20, 4.84)
Trans Man v. Cis Man .49 (.13, 1.81) .77 (.14, 4.25) 1.13 (.34, 3.80) 1.07 (.21, 5.36)
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Note: Comparisons for gender non-binary participants not conducted due to small sample size.

a

All models adjusted for age and race

CVD = cardiovascular disease

VTE = venous thromboembolism

Cis = cisgender

Trans = transgender

The adjusted odds of having any of the measured CVD conditions were lower for transgender than cisgender people, but this was not statistically significant as indicated by the confidence intervals that include the null. However, transgender people had more than three times the odds of VTE than cisgender people (aOR 3.35), largely driven by the difference between transgender and cisgender women (aOR 3.94). Transgender women also had higher odds of reporting a history of VTE than cisgender men (aOR 1.90); however, this result was not statistically significant. Confidence intervals included the null for odds ratios comparing transgender men and cisgender women as well as transgender men and cisgender men for each outcome (i.e., any CVD condition, VTE, ever smoker, and current smoker). All analyses in Table 3 were repeated with poverty in the model. No meaningful differences in effect sizes nor inferences were found between models with and without poverty.

Smoking, CVD Conditions, and VTE by Psychosocial Factors and Gender Identity

Table 4 presents relationships between psychosocial factors and CVD conditions stratified by gender identity. The odds of reporting any CVD condition increased significantly with increases in psychological distress for both transgender (aOR 1.15 [95%CI: 1.02, 1.30]) and cisgender (aOR 1.07 [95%CI: 1.02, 1.12]) participants. The odds of reporting any CVD condition increased with increased number of adverse childhood experiences for both transgender (aOR 1.12 [95%CI: 0.84, 1.51]) and cisgender (aOR 1.12 [95% CI: 1.00, 1.24]) participants; however, results only met statistical significance for cisgender participants. None of the psychosocial factors were significantly associated with VTE.

Table 4.

Adjusted Odds Ratios of CVD, Blood Clots, and Smoking Status by Psychosocial Characteristics and Gender Identity

AORa (95% CI)
Any CVD Condition VTE Ever Smoker Current Smoker
Everyday Discrimination
 Transgender 1.61 (.88, 2.97) 1.33 (.34, 5.26) 2.09 (.99, 4.43) 2.58 (.77, 8.68)
 Cisgender 1.44 (.96, 2.14) 1.30 (.47, 3.60) 1.07 (.75, 1.53) 1.16 (.70, 1.91)
Psychological Distress
 Transgender 1.15 (1.02, 1.30) 1.04 (.91, 1.20) .98 (.89, 1.08) 1.22 (1.03, 1.46)
 Cisgender 1.07 (1.02, 1.12) 1.01 (.92, 1.12) 1.04 (1.00, 1.09) 1.04 (.98, 1.11)
ACES
 Transgender 1.12 (.84, 1.51) .80 (.42, 1.52) 1.28 (.97, 1.70) 1.60 (1.02, 2.51)
 Cisgender 1.12 (1.00, 1.24) .91 (.72, 1.15) 1.22 (1.10, 1.35) 1.01 (.86, 1.18)
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a

All models adjusted for age and race

CVD = cardiovascular disease

VTE = venous thromboembolism

ACES = adverse childhood experiences

Psychological distress was significantly associated with having ever smoked for cisgender participants (aOR 1.04 [95%CI: 1.00, 1.09]) but the relationship did not reach statistical significance for transgender participants (aOR 0.98 [95%CI: 0.89, 1.08]). Likewise, higher scores on ACEs were significantly associated with being a current smoker for cisgender participants (aOR 1.22 [95%CI: 1.10, 1.35)]; but the relationship did not reach statistical significance for transgender participants (aOR 1.28 [95%CI: 0.97, 1.70]). Contrary to results for having ever smoked, increased psychological distress (aOR 1.22 [95%CI: 1.03, 1.46]) and higher ACEs scores (aOR 1.60 [95%CI: 1.02, 2.51]) were significantly associated with being a current smoker for transgender but not cisgender participants. All analyses in Table 3 were repeated with poverty in the model. No meaningful differences in effect sizes nor inferences were found between models with and without poverty.

Smoking, CVD Conditions, and VTE by Gender Affirming Hormone use

Transgender respondents with a history of hormone use had lower odds of any CVD (aOR 0.69 [95%CI: 0.24, 2.00]) compared to those without hormone use. A history of hormone use was associated with higher odds of VTE (aOR 1.49 [95%CI: 0.21, 10.78]). However, these results were not statistically significant.

Discussion

Contrary to hypotheses and some prior literature, this study found no statistically significant differences between cisgender and transgender participants in smoking or CVD conditions, possibly due to the younger age of transgender participants in this study. Consistent with prior research,33 this study found an increased odds of VTE among transgender women compared to cisgender women. However, among transgender participants who had ever received gender affirming hormone therapy, the confidence interval for the adjusted odds of VTE was wide and included the null, likely due to small sample sizes. Sub-analyses by sex assigned at birth were not possible due to limited sample size. Future studies with larger samples are needed. Additional research using adjudicated CVD conditions will be important to advance knowledge on CVD disparities. Routinely assessing assigned sex at birth and gender identity in existing, ongoing CVD cohorts would be an important step in this direction.1 Social determinants such as poverty and/or minority stress may be the main drivers of CVD risk, rather than hormone use or identity, per se; and these factors should be incorporated into future research.

Consistent with prior literature, transgender participants reported greater psychosocial stressors. Drawing on the gender minority stress model,14 it was expected that participants who experienced more psychosocial stressors would be more likely to smoke and have a CVD condition than participants with fewer psychosocial stress. The larger effect size of psychological distress on CVD conditions for transgender people is consistent with this model. The lack of statistically significant relationships between CVD conditions and psychological distress or ACEs for transgender participants could be a product of limited statistical power. However, it may also suggest that psychological distress (possibly caused by discrimination and childhood trauma) played a more powerful role in negative health outcomes.

Current smoking was more common among transgender people, as has been found in prior studies,34 and was significantly associated with psychological distress and ACEs for transgender participants. The largest effect size for the relationship between ACEs and current smoking was among transgender participants. Together these data suggest that childhood trauma and current psychological distress (both more common among transgender than cisgender participants) may lead to increased CVD risk behaviors, i.e., continued smoking, which then lead to increased risk of a CVD condition.

These findings support the gender minority stress model. In applying the model to CVD, this study expands beyond its typical application to mental health. More research is needed on the pathways by which psychosocial factors affect cardiovascular health. Smoking is one CVD risk behavior impacted by psychosocial stressors. Transgender-inclusive studies with representative samples should explore the role of psychosocial stressors for other health behaviors (e.g., diet, exercise) and metabolic changes (e.g., blood pressure, cholesterol) that are key to cardiovascular health.1,35 A growing body of research is examining psychosocial stress as a driver of increased allostatic load and chronic inflammation that elevate risk for CVD.36–38 Such studies should include a robust number of transgender participants to ensure findings are applicable to this population. Ideally, such studies would be longitudinal to allow for mediation analyses that could support causal inference.

Limitations

This study has limitations. Data collection periods for transgender and cisgender participants did not overlap, potentially introducing bias. However, general population prevalence of CVD and VTE did not change significantly during the data collection periods, 39 suggesting minimal temporal bias. All measures were self-reported. Transgender people are more likely to delay or avoid seeking medical care due to discrimination.40 Therefore, self-report may underrepresent their CVD burden. Also, information was unavailable on types, dose, or duration of hormone use; nor their temporality with outcomes. This limitation precluded making inferences about relationships between hormone use and CVD conditions or VTE. The number of gender non-binary participants was too small for disaggregated analyses, and data were not available for gender diverse people who did not identify as transgender. Little is known about the health of non-binary people, and much more research is needed.

Transgender participants were significantly younger than cisgender participants. Given this age difference, transgender participants had a shorter period of time in which to develop a CVD condition or VTE. Hence, this analysis may have underestimated the risk of CVD for transgender people. However, the younger age distribution, as well as the higher proportion of transgender people who identified as Black, are consistent with BRFFS data.41,42 Studies specific to aging transgender people are warranted.

Conclusions

This study found no difference in smoking and CVD conditions between cisgender and transgender participants; however transgender participants had three times the odds of VTE compared with cisgender participants –driven by the differences between transgender and cisgender women. This study makes a contribution to the nascent literature on cardiovascular health among transgender people. It is one of very few studies to provide estimates from a nationally representative sample of transgender people. Unlike the BRFSS, this study was designed specifically for transgender people. Therefore, it used a gender ascertainment method that allowed for distinction between assigned sex at birth and current gender identity; and it included data on hormone therapy as well as minority stressors. To advance the knowledge base on CVD and transgender health, research is needed that includes adjudicated CVD measures, follows longitudinal cohorts to assess mediating factors, and includes larger samples of gender non-binary and older transgender people.

Funding Statement

TransPop was funded by a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD grant R01HD090468) and through Supplement to grant R01HD078526 from the National Institutes of Health, Office of Behavioral and Social Sciences Research and the Office of Research on Women’s Health. The TransPop investigators are: Ilan H. Meyer, Ph.D. (PI), Walter O. Bockting, Ph.D., Jody L. Herman, Ph.D., and Sari L. Reisner, ScD (Co-Investigators, listed alphabetically). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Declaration of Interests

Dr. Poteat has received research support from Gilead Sciences and ViiV Healthcare.

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