Gender-affirming hormone therapy: effects on cardiovascular risk and vascular function

in Endocrine Connections
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Kirsty A McGinley School of Cardiovascular and Metabolic Health Sciences, University of Glasgow, Glasgow, UK

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Angela K Lucas-Herald School of Cardiovascular and Metabolic Health Sciences, University of Glasgow, Glasgow, UK

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Paul Connelly School of Cardiovascular and Metabolic Health Sciences, University of Glasgow, Glasgow, UK

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Christian Delles School of Cardiovascular and Metabolic Health Sciences, University of Glasgow, Glasgow, UK

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https://orcid.org/0000-0003-2238-2612

Correspondence should be addressed to A L-H: Angela.Lucas-Herald@glasgow.ac.uk

This systematic review was presented as a poster at the Scottish Cardiovascular Forum 2024 and the abstract has been published in the BMJ Heart Online Supplement.

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Graphical abstract

Gender-affirming hormone therapy and cardiovascular disease risk in transgender individuals. CIMT, carotid intima–media thickness. CVD, cardiovascular disease. FMD, flow-mediated dilatation. PWV, pulse wave velocity. TGM, transgender men. Created using BioRender.com.

Abstract

Background

Gender-affirming hormone therapy (GAHT) is used in individuals with gender identity dysphoria to align their secondary sexual characteristics with their affirmed gender. We conducted a systematic review of the literature to explore the mechanisms regarding the effects of GAHT on the vasculature.

Methods

A literature search using PubMed, Embase, Scopus and LILACS was performed using search terms for GAHT, cardiovascular disease (CVD) risk and transgender. Studies were screened by two independent reviewers. Comparison to a cohort of transgender individuals naive or prior to GAHT or a cisgender population was required. Quality assessment was done using the relevant Critical Appraisal Skills Programme checklists.

Results

Out of 2,564 potentially eligible studies, 69 studies met the inclusion criteria. Studies provided evidence of beneficial changes in CVD risk profile, including reduced haemoglobin and pro-inflammatory markers, and atheroprotective changes in lipids in transgender women. In transgender men, there was evidence of negative changes in CVD risk profile, including atherogenic changes in lipids and increased haemoglobin, arterial stiffness and pro-inflammatory markers.

Conclusions

There is a paucity of research across non-traditional measures of CVD risk, which in combination with heterogeneous study design, loss of follow-up, low sample sizes and lack of diversity in age and ethnicity requires the results to be interpreted with caution. More evidence is required to elucidate the mechanisms behind the increased risk of CVD in the transgender population and determine whether GAHT is a contributing factor.

Abstract

Graphical abstract

Gender-affirming hormone therapy and cardiovascular disease risk in transgender individuals. CIMT, carotid intima–media thickness. CVD, cardiovascular disease. FMD, flow-mediated dilatation. PWV, pulse wave velocity. TGM, transgender men. Created using BioRender.com.

Abstract

Background

Gender-affirming hormone therapy (GAHT) is used in individuals with gender identity dysphoria to align their secondary sexual characteristics with their affirmed gender. We conducted a systematic review of the literature to explore the mechanisms regarding the effects of GAHT on the vasculature.

Methods

A literature search using PubMed, Embase, Scopus and LILACS was performed using search terms for GAHT, cardiovascular disease (CVD) risk and transgender. Studies were screened by two independent reviewers. Comparison to a cohort of transgender individuals naive or prior to GAHT or a cisgender population was required. Quality assessment was done using the relevant Critical Appraisal Skills Programme checklists.

Results

Out of 2,564 potentially eligible studies, 69 studies met the inclusion criteria. Studies provided evidence of beneficial changes in CVD risk profile, including reduced haemoglobin and pro-inflammatory markers, and atheroprotective changes in lipids in transgender women. In transgender men, there was evidence of negative changes in CVD risk profile, including atherogenic changes in lipids and increased haemoglobin, arterial stiffness and pro-inflammatory markers.

Conclusions

There is a paucity of research across non-traditional measures of CVD risk, which in combination with heterogeneous study design, loss of follow-up, low sample sizes and lack of diversity in age and ethnicity requires the results to be interpreted with caution. More evidence is required to elucidate the mechanisms behind the increased risk of CVD in the transgender population and determine whether GAHT is a contributing factor.

Introduction

Gender-affirming hormone therapy (GAHT) is used to align the secondary sexual characteristics of an individual with their affirmed gender, where the aim is for virilisation in transgender men (TGM) and feminisation in transgender women (TGW). At least 80% of transgender individuals have received or intend to receive GAHT in their lifetime (1).

GAHT consists of oestrogen, in combination with an anti-androgen, in TGW and testosterone in TGM. Oestrogen has been shown to have a protective effect on the vascular system through the reduced incidence of cardiovascular (CV) disease (CVD) in premenopausal cisgender women (CGW). However, long-term oestrogen exposure in the case of hormone replacement therapy in postmenopausal CGW and oral contraceptives have shown increased risk of CVD (2). The effects of androgens in those who were identified male sex at birth can be positive or negative depending on the physiological environment of the vasculature (3). There is a paucity of research investigating the impact of oestrogen on male sex vasculature and testosterone on female sex vasculature.

Recent studies have reported a higher risk of CVD in the transgender population. In TGW, the higher risk of stroke, venous thromboembolism (VTE) and CVD events is well documented throughout the literature (4, 5). In TGM, the evidence is less conclusive with many studies reporting no increased incidence or risk of CVD in terms of stroke, VTE or CVD events compared to CGW and cisgender men (CGM) (4, 5) and others reporting increased risk (1). A recent systematic review and meta-analysis (6) demonstrated that, even with high heterogeneity and conflicting studies, transgender individuals have a 40% higher risk of CVD compared with cisgender individuals. The incidence of stroke, myocardial infarction and VTE was higher in TGW than in TGM in two recent reviews (6, 7), but both groups had a higher incidence and relative risk compared with cisgender individuals of the same sex at birth (6).

The mechanisms responsible for the increased CVD risk are poorly understood, which poses a challenge for clinicians when assessing and managing CVD in this population. Clearly, there is a need for an improved understanding of these mechanisms in order to institute appropriate preventative strategies to prevent the burden of CVD. Already a complex issue clinically due to patients carrying multiple CVD risk factors, the multifactorial approach required in cisgender individuals (8) becomes more complex when taking into account the impact of gender, health inequalities, differences in behavioural risk factors or utilisation of GAHT (9).

Mechanistic studies investigating vascular function and GAHT are scarce, but a previous review (10) investigated the effect of GAHT on the risk of subclinical atherosclerosis. Twelve studies were identified within the review, which reported on measures of vascular function and blood biomarkers of endothelial function, inflammation or coagulation. Overall, there was limited evidence in both TGM and TGW, often indicating no changes in parameters. Due to the limited studies included within this review (10), which reported on studies dated up to 2020, the present review aims to provide additional evidence on CV risk in the transgender population whilst expanding the range of CV phenotypes investigated. The clinical phenotypes selected for this review were traditional CV and metabolic risk factors, including body composition, blood pressure, lipid profile and insulin sensitivity, in addition to the phenotypes investigated in the previous review by Moreira and coworkers (10). These have been closely linked to major CVD (8), are known to be influenced by sex hormones (3) and are useful intermediate phenotypes that can be easily used to assess CV risk clinically. The primary aim of this systematic review is to establish the proposed mechanisms delineated in the literature to date regarding the effects of GAHT on the vasculature in the transgender population.

Methods

Search strategy

This systematic review builds on the review by Moreira Allgayer and coworkers (10). Potential studies investigating the impact of GAHT on the vascular system were searched for on PubMed, Embase, Scopus and LILACS until 02 July 2024. The search strategy consisted of the following terms for GAHT, CV risk and transgender: ‘hormone therapy’ OR ‘gender hormone therapy’ OR ‘transgender’ OR ‘oestrogen’ OR ‘testosterone’ AND ‘vascular’ OR ‘CV’ OR ‘inflammation’ OR ‘metabolic syndrome’ OR ‘metabolic’ OR ‘cytokine’ AND ‘transgender’ in PubMed and equivalent in the other databases. The reference lists of the studies identified by the search were hand-searched by one author (KAM).

Inclusion/exclusion criteria

Studies had to be in humans, published in the English language and with full text available. Studies could be cohort (retrospective or prospective), cross-sectional, case–control or randomised clinical trials.

Studies had to meet the following criteria for inclusion: i) report on a healthy transgender group (either TGW or TGM); ii) contain a transgender group receiving GAHT; iii) investigate the impact of GAHT on the vascular system; iv) consider at least one assessment of clinical or laboratory measurement of CV risk or vascular phenotype; and v) compare a transgender cohort either before and after GAHT or to a cisgender population or transgender population not treated with GAHT. Studies were excluded if they only provided data on the incidence of CVD events/deaths or just reported CV risk ratios.

Study selection

Potentially eligible studies, identified by the search strategy, were inputted into Covidence (https://www.covidence.org/) for screening. Duplicates were automatically removed by Covidence, and any missed studies were manually removed. The studies were screened by both title and abstract based on the inclusion/exclusion criteria independently by two reviewers (KAM and ALH). Any disagreements between the reviewers were resolved by discussion to reach a consensus. Full texts were screened to exclude any further studies that did not meet the criteria and to assess the eligibility for data extraction.

Data extraction and analysis

The general characteristics of each study were extracted, including vascular phenotype (anthropometry, blood pressure, insulin sensitivity, etc.), study design, sample size, age of participants (range and mean), ethnicity, country and time on and administration route of GAHT. Details of study design included type of study, comparator groups, inclusion/exclusion criteria and measurements taken. Statistically significant results were extracted. When P values were not available to identify statistical significance, confidence intervals were used. When papers carried out multiple models accounting for confounding variables, only the most controlled model was reported.

Quality assessment

To determine the quality of the studies, the appropriate Critical Appraisal Skills Programme (CASP) cohort study checklist was utilised (https://casp-uk.net/casp-tools-checklists/cohort-study-checklist/). Methodological quality was assessed by one author (KAM) with input from another (ALH). Each study was defined as low, medium or high risk of bias based on the answers using the CASP tool.

Results

Search results

The initial search yielded 2,564 results from PubMed (n = 952), Embase (n = 944), Scopus (n = 658) and LILACS (n = 10) (Fig. 1). Twenty-two additional studies were added, which were identified from reference lists (11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32). One-hundred eleven studies were left after duplicate removal (n = 1,623, 37.5%), title and abstract screening (n = 1,494, 58.3%) and lack of access (n = 18, 0.7%). An attempt to email the authors of unretrievable studies was unsuccessful; therefore, these studies were excluded without full text screening. Forty-two (1.6%) further studies were excluded by full-text screening due to no GAHT treatment, outcomes not being relevant, not being available in the English language and abstract available only. This resulted in a total of 69 studies for inclusion in the review.

Figure 1
Figure 1

PRISMA flow chart of the search and inclusion/exclusion of studies investigating the impact of gender-affirming hormone therapy on cardiovascular risk or vascular function.

Citation: Endocrine Connections 14, 2; 10.1530/EC-24-0222

Study characteristics

Table 1 shows the characteristics of the 69 studies included in this review. Thirty-two (46%) studies were identified as low risk of bias and 37 (54%) as medium risk of bias (Table 1).

Table 1

Study characteristics.

AuthorCountry/regionTypeVascular phenotype themeAge (mean; range (years))/number of participants (n)Ethnicity of majorityTime (months) and administration method of GAHTComparisonQuality assessment
Allen et al. 2021 (9)USARetrospective cohortHaemostasis and lipid profileTGW = 31.1 (n = 126)White≤60 monthsBaseline, then every 3 months for 1 year and then every 6 months up to 5 yearsMedium
TGM = 27.8 (n = 91)TGW: oral or IM
TGM: IM or transdermal
Anike et al. 2024 (33)USARetrospective cohortInsulin sensitivityTGW/TGM /CGW/CGM = 18–55 (n = 2,425/2,127/33,995/38,913)Non-Hispanic White≤120 monthsBaseline and up to 120 monthsLow
No information on administration
Balik et al. 2022 (34)TurkeyCross-sectionalVascular and inflammationCGW = 28.4 ± 4.7 (n = 30)No information>12 monthsCGW to TGMMedium
TGM = 27.4 ± 5.1 (n = 30)No information on administration
Banks et al. 2021 (35)USARetrospective cohortBlood pressureTGW = 29.3 ± 10.1 (n = 247)White<57 monthsBaseline and up to 57 monthsLow
TGM = 26.1 ± 7.1 (n = 223)TGW: oral or IM
TGM: IM or transdermal
Berra et al. 2006 (27)EuropeProspective cohortAnthropometric, lipid profile, inflammation and insulin sensitivityTGM = 30.4 (5.4) (n = 16)No information6 monthsBaseline and after 6 monthsMedium
IM
Boekhout-Berends et al. 2023 (36)EuropeRetrospective cohortHaematologyTGW = 30.8 (23.5–43.5) (n = 1178)White12 monthsBaseline and after 12 monthsLow
TGM = 23.3 (20.1–30.9) (n = 1023)TGW: oral or transdermal
TGM: IM or transdermal
Bretherton et al. 2021 (37)AustraliaCross-sectionalAnthropometric and insulin sensitivityTGM = 28.8; 25.0–33.0 (n = 43)No informationTGW: oral or transdermal (39 months)TGM to CGW and CGMLow
CGW = 28.1; 24.0–38.7 (n = 48)TGM: oral or IM (44 months)TGW to CGW and CGM
TGW = 41.1; 26.4–52.7 (n = 41)
CGM = 32.0; 26.3–40.9 (n = 30)
Chandra et al. 2010 (26)USAProspective cohortAnthropometric, lipid profile, blood, blood pressure and insulin sensitivityTGM = 29 (9) (n = 12)Caucasian12 monthsBaseline and after 12 monthsLow
TGM: IM
Cocchetti et al. 2021 (38)EuropeProspective cohortAnthropometric, blood pressure and lipid profileTGW = 31.8 ± 11.46 (n = 144)No information24 monthsBaseline, 12 and 24 monthsLow
TGM = 26.78 ± 7.48 (n = 165)TGW: oral or transdermal
TGM: IM or transdermal
Colizzi et al. 2015 (25)EuropeRetrospective cohortAnthropometric, blood pressure, lipid profile and insulin sensitivityTGW = 30.24 (9.57) (n = 79)No information24 monthsBaseline, 12 and 24 monthsLow
TGM = 28.77 (5.62) (n = 43)TGW: oral
TGM: IM
Cunha et al. 2023 (39)BrazilCross-sectionalVascularTGM = 44 ± 10; 26–61 (n = 33)No information168 ± 96 monthsTGM to CGW and CGMMedium
CGW = 43 ± 10 (n = 55)TGM: IM
CGM = 42 ± 10 (n = 56)
Defreyne et al. 2018 (24)EuropeProspective cohortBloodTGW = 28.5; 16–69 (n = 239)No information36 monthsBaseline, 3, 6, 9, 12, 18, 24 and 36 monthsLow
TGM = 22.5; 17–62 (n = 192)TGW: oral or transdermal or sublingual
TGM: IM or transdermal
Deischinger et al. 2023 (40)EuropeCross-sectionalAnthropometric, insulin sensitivity, blood pressure, lipid profile and vascularTGM = 24; 21–27 (n = 22)WhiteTGW: oral or transdermal (16.8; 12–26.4)TGW to CGM TGM to CGWMedium
TGW = 35; 29–46 (n = 16)TGM: IM or transdermal (13.2; 12–24 months)
CGW = 23; 22–27 (n = 16)
CGM = 33; 29–48 (n = 17)
Deutsch et al. 2015 (41)USAProspective cohortAnthropometric, blood pressure and lipid profileTGW = 29 (9.4); 19–50 (n = 16)White6 monthsBaseline and after 6 monthsMedium
TGM = 27 (6.9); 18–45 (n = 31)TGW: IM or sublingual or transdermal
TGM: Transdermal or subcutaneous injection
Duro et al. 2023 (42)USACross-sectionalVascularTGW = 40; 36–50 (n = 25 (24))No informationTGW = 38.4; 24–166.8 months; TGM = 66; 22.8–148.8 monthsTGW and TGM to CGMedium
TGM = 38; 32–43 (n = 22 (16))No information on administration
Elbasan et al. 2023 (43)TurkeyProspective cohortAnthropometric and bloodTGM = 24; 21–29 (n = 45)No information12 monthsTGM to CGWLow
CGW = 27; 23–29 (n = 28)TGM: IMBaseline to 6 and 12 months
Elbers et al. 1997 (23)EuropeProspective cohortAnthropometric, lipid profile, insulin sensitivity and blood pressureTGW = 26 (6); 18–36 (n = 20)No information12 monthsBaseline and 12 monthsMedium
TGM = 23 (5); 16–34 (n =17)TGW: oral
TGM: IM
Elbers et al. 2003 (22)EuropeProspective cohortAnthropometric, lipid profile, insulin sensitivity and blood pressureTGW = 26 (6); 18–36 (n = 20)No information12 monthsBaseline and 12 monthsMedium
TGM = 23 (5); 16–34 (n = 17)TGW: oral
TGM: IM
Elbers et al. 1999 (21)EuropeProspective cohortAnthropometric and insulin sensitivityTGW = 26 (6); 18–37 (n = 20)No information12 monthsBaseline and 12 monthsMedium
TGM = 23 (5); 16–34 (n = 17)TGW: oral
TGM: IM
Emi et al. 2008 (29)AsiaCross-sectionalAnthropometric, blood pressure, blood, haemostasis, lipid profile and vascularTGM (GAHT) = 27.9 (5.5) (n = 48)No information45 (38.1) monthsCompared to no GAHT treatmentMedium
TGM (no GAHT) = 26.5 (5.5) (n = 63)TGM: IM
Fernandez et al. 2016 (20)USARetrospective cohortAnthropometric, blood pressure, lipid profile and bloodTGW = 31; 16–56 (n = 33)White (non-Hispanic)18 monthsBaseline to 3–6 and 6–18 monthsMedium
TGM = 27; 19–47 (n = 19)TGM: oral or transdermal or IM
TGM: IM
Giltay et al. 1998 (17)EuropeProspective cohortAnthropometric, blood and lipid profileTGW = 28; 18–40 (n = 17)No information4 monthsBaseline to 4 monthsMedium
TGM = 22; 16–34 (n = 17)TGW: oral
TGM: IM
Goh et al. 1995 (16)AsiaCross-sectionalLipid profileTGM (GAHT) = 31.7 (0.88) (n = 39)No information33 (6–120) monthsCompared to no GAHT treatmentMedium
TGM (no GAHT) = 25.6 (0.78) (n = 29)TGM: IM
Gulanski et al. 2020 (40)USACross-sectionalVascularTGM = 27 ± 4; 20–31 (n = 11)White≥3 monthsTGM and CGWMedium
CGW = 28 ± 5; 18–34 (n = 20)TGM: IM
Iannantuoni et al. 2020 (44)EuropeProspective cohortVascularTGM = 26.2 ± 7.5 (n = 157)No information3 monthsBaseline and 3 monthsMedium
TGM: IM
Jacobeit et al. 2007 (45)EuropeProspective cohortBlood and lipid profileTGM = 33 (6); 26–44 (n = 12)No information12 monthsBaseline and 12 monthsMedium
TGM: IM
Jacobeit et al. 2009 (46)EuropeProspective cohortBlood and lipid profileTGM = 32 (7); 23–47 (n = 17)No information36 monthsBaseline, 6, 12, 18, 24, 30 and 36 monthsMedium
TGM: IM
Klaver et al. 2018 (47)EuropeProspective cohortAnthropometricTGW = 29; 18–66 (n = 179)No information12 monthsBaseline and 12 monthsLow
TGM = 24; 18–58 (n = 162)TGW: oral or transdermal
TGM: IM or transdermal
Klaver et al. 2018 (48)EuropeRetrospective cohortAnthropometricTGW = 16.4 (1.1) (n = 71)CaucasianTGW: oral (33.6; 19.2–40.8 months)Before and after GAHT TGM to CGW/CGWMedium
TGM = 16.9 (0.9) (n = 121)TGM: IM (36; 22.8–40.8)TGW to CGM/CGW
Klaver et al. 2020 (49)EuropeRetrospective cohortAnthropometric, blood pressure, lipid profile and insulin sensitivityTGW/TGM = 22; 20.5–23.5 (n = 71/121)WhiteTGW: oral (26.4 (IQR: 13.2–37.2 months)Before and after GAHT TGM to CGW/CGWLow
TGM: IM (34.8 (IQR: 20.4–40.8) months)TGW to CGM/CGW
Klaver et al. 2022 (50)EuropeProspective cohortAnthropometric, lipid profile and insulin sensitivityTGW = 29 (IQR: 23–42) (n = 179)White12 monthsBaseline and 12 monthsLow
TGM = 24 (IQR: 21–33) (n = 162)TGW: oral or transdermal
TGM: IM or transdermal
Korpaisarn et al. 2021 (51)AsiaRetrospective cohortBlood pressure, haematology and lipid profileTGM = 27.8 ± 6.0 (n = 39)Thai25.52 ± 12.9 monthsBaseline and ∼24 monthsMedium
TGM: IM
Kulprachakarn et al. 2020 (52)AsiaCross-sectionalBlood pressure, insulin sensitivity, lipid profile and vascularTGW (GAHT) = 24.0 ± 5.1 (n = 100)Thai (inferred)79.4 ± 6.2Compared to no GAHT treatmentMedium
TGW (no GAHT) = 24.3 ± 5.7 (n = 100)No information on administration
Lake et al. 2022 (53)USACross-sectionalHaemostasis, insulin sensitivity, inflammation and vascularTGW = 40; 32–48 (GAHT = 31/no GAHT = 16)LatinaNo information on timeCompared to no GAHT treatment TGW to CGMMedium
CGM = 42; 35–52 (n = 40)TGW: oral
Lake et al. 2023 (54)USARetrospective cohortInsulin sensitivity, haemostasis, inflammation and vascularTGW = 27 (n = 138)Black and Hispanic12 monthsBaseline compared to 6 and 12 monthsMedium
No information on administration
Leemaqz et al. 2023 (55)USARetrospective cohortLipid profileTGW = 29.9 ± 9.5 (n = 170)White<57 monthsBaseline up to 57 monthsMedium
TGM = 26.4 ± 7.2 (n = 196)TGW: oral or IM
TGM: IM or transdermal
Lim et al. 2019 (56)AustraliaCross-sectionalHaemostasisTGW = 32.8; 26.7–44.7 (n = 26)No information25.5 monthsTGW to CGMLow
CGM = 28.7; 24.9–57.3 (n = 55)TGW: oral or transdermalTGW to CGW
CGW = 44.9; 25.5–58.3 (n = 98)
Liu et al. 2021 (57)AsiaRetrospective cohortAnthropometry, blood pressure, lipid profile, insulin sensitivity and haematologyTGM = 27.9 ± 0.7 (n = 65)No information≥12 monthsBaseline, 3–6 (V1), 6–12 (V2) and 12–24 (V3) monthsMedium
TGW = 26.0 ± 1.1 (n = 45)TGW: oral
TGM: IM
Martinez-Martin et al. 2023 (58)EuropeRetrospective cohortAnthropometric, insulin sensitivity, blood pressure and lipid profileTGW = 19.1 (5.0) (n = 149)No information60 monthsBaseline and 60 monthsLow
TGM = 20.2 (5.6) (n = 153)TGW: transdermal
TGM: parenteral
McCredie et al. 1998 (30)AustraliaCross-sectionalLipid profile, blood pressure and vascularTGW = 33 (5); 26–46 (n = 12)No information38 (52); 2–177 monthsTGM to CGWMedium
CGW = 31 (7); 26–41 (n = 12)TGM: implant or IM
Meyer et al. 2020 (28)EuropeRetrospective cohortHaematology and lipid profileTGW = 25; 15–61 (n = 155)No information48 monthsBaseline, 3–4, 10–14 months and 3–4 yearsMedium
TGM = 21; 15–52 (n = 233)TGW: oral or transdermal
TGM: IM or transdermal
Millington & Chan 2021 (59)USACross-sectionalLipid profileTGM = 18.4 (IQR: 17.6–19.5) (n = 17)White14.4 ± 9.6 monthsTGM to CGWMedium
CGM = 17.8 (IQR: 17–19.4) (n = 33)TGM: subcutaneous injectionTGM to CGM (models 1 and 2)*
CGW = 17.6 (IQR: 17.1–18.5) (n = 32)
Millington et al. 2024 (60)USAProspective cohortBlood and insulin sensitivityTGW = 17.3; 16.1–18.6 (n = 93)No information24 monthsBaseline, 6, 12 and 24 monthsLow
TGM = 16.2; 15.1–17.6 (n = 200)TGW: oral or transdermal or IM
TGM: subcutaneous or transdermal
Mueller et al. 2007 (12)EuropeProspective cohortAnthropometric, lipid profile, blood, blood pressure, insulin sensitivity and haemostasisTGM = 29.63 (8.95) (n = 35)No information12 monthsBaseline and 12 monthsLow
TGM: IM
Mueller et al. 2010 (11)EuropeProspective cohortAnthropometric, lipid profile, blood pressure and bloodTGW = 30.4 (9.1) (n = 45)Caucasian24 monthsBaseline, 12 and 24 monthsLow
TGM: IM
New et al. 1997 (32)AustraliaCross-sectionalAnthropometric, lipid profile, blood pressure and vascularTGW = 41 (9) (n = 14)No information61 (70) monthsTGW to CGM/CGWMedium
CGM = 41 (7) (n = 14)TGW: oral
CGW = 40 (8) (n = 15)
Nokoff et al. 2020 (61)USACross-sectionalAnthropometric, blood pressure, lipid profile and insulin sensitivityTGM = 17 ± 1.4 (n = 19) (CGW = 15.2 ± 1.9 (n = 42), CGM = 15.3 ± 1.6 (n = 19))White (and non-Hispanic)TGW: oral or sublingual (12.3 ± 9.9 months)TGM with matched CGW and CGMMedium
TGW = 16.3 ± 1.4 (n = 11) (CGW = 15.9 ± 1.4 (n = 23), CGM = 15.7 ± 1.4 (n = 24))TGM: IM or subcutaneous injection (11.2 ± 5.2 months)TGW with matched CGW and CGM
Olson-Kennedy et al. 2018 (62)USAProspective cohortAnthropometric, blood, insulin sensitivity, lipid profile and blood pressureTG = 18; 12–23 (TGW = 23/TGM = 35)Caucasian and Latino(a)24 monthsBaseline and 24 monthsLow
TGW: oral or IM
TGM: subcutaneous
Ott et al. 2011 (19)EuropeRetrospective cohortAnthropometric and lipid profileTGW = 35.7 (11.4) (n = 89)No information60 monthsBaseline and 60 monthsMedium
TGM = 25 (6.3) (n = 80)TGW: transdermal or oral
TGM: IM or oral
Pallotti et al. 2023 (63)EuropeProspective cohortHaematology, insulin sensitivity and lipid profileTGM = 24.8 ± 8; 18–49 (n = 52)Caucasian12 monthsBaseline, 6 and 12 monthsLow
TGM: IM
Pei et al. 2024 (64)AsiaRetrospective cohortAnthropometric, lipid profile and insulin sensitivityTGW (GAHT) = 23 (3) (n = 59)No information12–26 monthsCompared to no GAHT treatmentLow
TGW (no GAHT) = 25 (5) (n = 40)TGW: oral or transdermal
Resmini et al. 2008 (31)EuropeCross-sectionalInsulin sensitivityTGW = 33.21 (2.1) (n = 15)No informationTGW: oral or transdermal (108 (28) months)TGW/TGM and CGW/CGMMedium
TGM = 30.0 (1.81) (n = 11)TGM: IM (120 (18) months)
CGM/CGW = age-matched (n = 15/14)
Roberts et al. 2014 (65)USARetrospective cohortBlood and lipid profileTGW = 46; 27–67 (n = 55)No information48 (>6) monthsTGW to CGM/CGWMedium
CGM = 58; 21–84 (n = 20)No information on administration
CGW = 56; 23–88 (n = 20)
Sanchez-Toscano et al. 2023 (66)EuropeRetrospective cohortAnthropometric, insulin sensitivity, lipid profile and blood pressureTGW = 18; 16–22 (n = 91)No information12 monthsBaseline and 12 monthsLow
TGM = 18; 16–23 (n = 136)TGW: oral
TGM: IM
Scheres et al. 2021 (2)EuropeProspective cohortHaemostasisTGM = 26.9 ± 9.7 (n = 100)No information12 monthsBaseline and 12 monthsLow
TGW = 33.7 ± 12.9 (n = 98)No information on administration
Schutte et al. 2022 (67)EuropeProspective cohortInflammation and haemostasisTGM = 23; 20–26 (n = 47)No information12 monthsBaseline, 3 and 12 monthsLow
TGW = 30; 24–39 (n = 48)TGW/TGM: transdermal
Shadid et al. 2020 (68)EuropeProspective cohortAnthropometric, lipid profile and insulin sensitivityTGM = 26.1 ± 1.3; 18–64 (n = 55)No information≥12 monthsBaseline and after 12 monthsLow
TGW = 34.4 ± 1.5; 18–64 (n = 35)TGW: oral or transdermal
TGM: IM
SoRelle et al. 2019 (13)USARetrospective cohortInsulin sensitivity, blood, lipid profile and blood pressureTGW (no GAHT) = 31 (12) (n = 87)White>6 monthsCompared to no GAHT treatmentMedium
TGW (GAHT) = 33 (12) (n = 133)TGW: oral or transdermal
TGM (no GAHT) = 27 (9) (n = 62)TGM: IM or transdermal
TGM (GAHT) = 30 (9) (n = 89)
Stoffers et al. 2019 (69)EuropeRetrospective cohortAnthropometric, blood pressure, lipid profile and bloodTGM = 17.2; 14.9–18.4 (n = 62)No information12 (5–33) monthsBaseline, 6, 12 and 24 monthsMedium
TGM: IM or transdermal
Suppakitjanusant et al. 2020 (70)USARetrospective cohortAnthropometricTGW (GAHT) = 43.9 ± 15.6 (n = 46)CaucasianTGW: oral or transdermal or IM (79 ± 112 months)Over 84-month periodLow
TGW (no GAHT) = 32.8 ± 11.7 (n = 59)TGM: IM or transdermal (44 ± 41 months)
TGM (GAHT) = 40.4 ± 13.1 (n = 15)
TGM (no GAHT) = 27.4 ± 8.8 (n = 25)
Tebbens et al. 2023 (71)EuropeIntervention study (cohort study)Anthropometric, lipid profile and insulin sensitivityTGW = 26; 20–27 (n = 8)No information12 monthsBaseline and 12 monthsLow
TGM = 22; 19–25 (n = 10)TGW: oral and transdermal
TGM: transdermal or IM
Tsatsanis et al. 2023 (72)EuropeCross-sectionalInflammationTGW = 41.6 (CI: 36.1–47.3) (n = 16)No information≥36 monthsTGW to CGW/CGM TGM to CGW/CGMMedium
TGM = 33.4 (CI: 30.1–36.6) (n = 27)TGW: oral or transdermal or IM
CGW = 35.9 (CI: 32.5–39.2) (n = 26)TGM: transdermal or IM
CGM = 46.0 (CI: 43.6–48.4) (n = 30)
Valentine et al. 2021 (73)USARetrospective cohortAnthropometric and lipid profileTGM = 16.6 ± 1.3 (n = 42)TGM = White CGW = Black10.8 (2.6–25.7) monthsBaseline TGM to CGW baseline and 12 monthsLow
CGW = 15.5 ± 1.8 (n = 82)TGM: IM
Van Caenegem et al. 2015 (18)EuropeProspective cohortAnthropometricTGM = 27 (9) (n = 23)Caucasian12 monthsBaseline and 12 months TGM to CGWLow
CGM = 27 (9) (n = 23)TGM: IM
van Velzen et al. 2021 (74)EuropeProspective cohortLipid profileTGM = 28 ± 13 (n = 15)No information<12 monthsBaseline and after 12 monthsLow
TGW = 33 ± 12 (n = 15)TGW: oral or transdermal
TGM: transdermal or IM
Victor et al. 2014 (75)EuropeProspective cohortAnthropometric, blood pressure, blood, lipid profile, insulin sensitivity and vascularTGM = 29.4 (9.7) (n = 57)No information3 monthsBaseline and 3 monthsMedium
TGM: IM
Vita et al. 2018 (76)EuropeRetrospective cohortAnthropometric, blood pressure, blood, insulin sensitivity, lipid profileTGW = 25.2 (7) (n = 21)No informationTGW: oral (31.3 (34.2) months)Baseline and on GAHTLow
TGM = 25.1 (3.7) (n = 11)TGM: IM (22.2 (11.5) months)
Wierckx et al. 2014 (14)EuropeProspective cohortAnthropometric, blood pressure, blood, insulin sensitivity and lipid profileTGM = 21.7 (5.1)/21.7 (5.1) (n = 53)No information12 monthsBaseline and 12 months oral vs transdermal in TGWLow
TGW = 31.7 (14.8)/19.3 (2.4) (n = 53)TGW: oral or transdermal
TGM: IM
Yun et al. 2021 (15)AsiaProspective cohortAnthropometricTGW = 28.5 (8.1) (n = 11)No information6 monthsBaseline and 6 monthsLow
TGW: oral or IM

Vascular phenotype denotes the CV risk phenotypes assessed within the studies. Time on GAHT refers to the time on GAHT in both cross-sectional and cohort studies. In the cohort studies, this also refers to the follow-up time/duration of the study. Quality assessment is defined as low, medium and high risk based on answers from the CASP quality assessment checklist for cohort studies. *Model 1 is the unadjusted P value, and model 2 is based on an adjusted P value based on BMI, race and age (78). For (32), the number of individuals in the brackets is the number of individuals of which the CAC data came from.

Abbreviations: CGM, cisgender men; CGW, cisgender women; GAHT, gender-affirming hormone therapy; IM, intramuscular; IQR, interquartile range; TGM, transgender men; TGW, transgender women.

The 69 studies consisted of 14,363 transgender individuals, where there was an even split of TGW (50%) and TGM (50%). The sample size ranged from as low as 8 TGW and 10 TGM (71) to as high as 2424 TGW and 2127 TGM (33). A high loss of follow-up was identified in some studies, with up to 79% loss in TGW and 82% in TGM in one study (35). Most studies were carried out across Europe (36/69, 52%) or the US (19/69, 28%). Nine (13%) of the studies (2, 14, 18, 24, 38, 48, 67, 68, 74) were part of the European Network for the Investigation of Gender Incongruence (ENIGI), which is a prospective study aiming to provide data-driven information on the safety of GAHT (77). Many studies (43/69, 62%) provided no information on ethnicity, and the rest were mostly of a white (12/69, 17%), Caucasian (9%, 6/69) or white non-Hispanic (3/69, 4%) ethnicity (Table 1).

The mean age range was similar in both TGW and TGM at 16.3–46 years and 16.2–44 years, respectively. The mean time of GAHT was between 3 months and 14 years. In the 62 studies in TGM, most TGM received intramuscular (81%) or transdermal testosterone injections (31%), with two studies providing no information (33, 42). In the 47 studies in TGW, there was more variety in the administration of GAHT, including oral (83%), intramuscular (23%), transdermal (51%) and sublingual (5%), with two studies providing no information (33, 65).

Cohort studies investigating before and after GAHT treatment

Most studies were cohort studies (51/69, 74%), comparing transgender individuals before and after receiving hormone therapy.

Traditional CV and metabolic risk factors

Thirty-eight (38/51, 75%) studies reported on anthropometric measurements of body composition (Table 2). Studies either indicated a significant increase in body mass index or reported no changes in both TGW and TGM. Few studies reported increases in bodyweight in TGW and TGM. Many studies provided more in-depth analysis of body composition (11, 14, 15, 18, 21, 22, 23, 27, 47, 48, 50, 64, 71). In TGW, evidence suggests a reduced lean body mass and visceral adipose tissue with an increase in total body fat and subcutaneous adipose tissue compared to that before GAHT (14, 15, 21, 22, 23, 48, 50, 47, 64, 71). In TGM, the opposite was identified in comparison with the baseline before GAHT (11, 14, 18, 21, 22, 23, 27, 48, 47, 50, 71).

Table 2

Anthropometric measurements of body composition.

AuthorsStudy designStatistically significant results
MeasurementsParticipantsTime on GAHT (months)ComparisonTGWTGM
Cohort study
Iannantuoni et al. 2020 (44)BMI, BW, WCTGM = 1573Baseline and 3 monthsn.s.
Victor et al. 2014 (75)BW, BMITGM = 573Baseline and 3 monthsn.s.
Giltay et al. 1998 (17)BMI, LBMTGW = 174Baseline to 4 months↑BMI↑BMI, LBM
TGM = 17
Berra et al. 2006 (27)BW, BMI, WC, WHR, TFM%, TFM kg, LBM %, LBM kgTGM = 166Baseline and after 6 months↑BW, BMI, WC, LBM kg, LBM % ↓TFM %
Deutsch et al. 2015 (41)BW, BMITGW = 166Baseline and after 6 monthsn.s.↑BW, BMI
TGM = 31
Yun et al. 2021 (15)BW, BMI, LBM (total body, arm, leg, trunk, head, skeletal muscle mass index), adipose tissue (TBF (kg), arm, leg, trunk, head, android, gynoid, TBF (%), android/gynoid ratio, fat mass ratio, estimated visceral fat mass)386Baseline and 6 months↓Lean body mass (total body, arm, leg, skeletal muscle mass index), adipose tissue (android/gynoid ratio, fat mass ratio)
↑Adipose tissue (TBF kg, leg, gynoid, TBF %)
Valentine et al. 2021 (73)BMITGM = 4210.8 (2.6–25.7)Baseline and 12 months↑BMI (short and long-term)
CGW = 82
Stoffers et al. 2019 (69)BW, BMI, BMI SDSTGM = 6212; 5–33Baseline, 6, 12, 24 months↑BW (6 and 12 months), BMI (6 months)
Chandra et al. 2010 (26)BMITGM = 1212Baseline and after 12 monthsn.s.
Elbasan et al. 2023 (43)BMITGM = 4512TGM to CGWn.s.
CGW = 28Baseline to 6 and 12 months
Elbers et al. 1997 (23)BW, BMI, body fat (skinfolds kg), body fat (bioimpedance kg), SAT area, sum of skinfoldsTGW = 1712Baseline and 12 months↑BW, BMI, body fat (skinfolds kg), body fat (bioimpedance kg), subcutaneous fat area, sum of skinfolds↑BW, BMI, ↓body fat (skinfolds kg), body fat (bioimpedance kg), SAT area, sum of skinfolds
TGM = 15
Elbers et al. 1999 (21)BW, BMI, skinfolds mm (triceps, biceps, subscapular, suprailiac, para-umbilical, sum), % BF-SF, resistance, % BF-BIA, circumferences cm (arm, abdomen, hip, thigh), WHR, SF (abdomen, hip, thigh), visceral fat, V/S, muscle area, total area cm (abdomen, hip, thigh), WHR areaTGW = 2012Baseline and 12 months↑BW, BMI, skinfolds mm (triceps, biceps, subscapular, suprailiac, para-umbilical, sum), % BF-SF, % BF-SF, resistance, % BF-BIA, circumferences cm (abdomen, hip, thigh), SF (abdomen, hip, thigh), visceral fat, total area (abdomen, hip, thigh), ↓V/S, muscle area↑BW, BMI, circumferences cm (arm), WHR, visceral fat, V/S, muscle area, WHR area, ↓skinfolds mm (triceps, biceps, suprailiac, para-umbilical, sum), % BF-SF, resistance, % BF-BIA, circumferences (hip), subcutaneous fat (abdomen, hip, thigh)
TGM = 17
Elbers et al. 2003 (22)BW, BMI, MRI (visceral fat, subcutaneous abdominal fat, subcutaneous gluteofemoral fat)TGW = 2012Compared to no GAHT treatment↑BW, BMI, MRI (visceral fat, subcutaneous abdominal fat, subcutaneous gluteofemoral fat)↑BW, BMI, MRI (visceral fat), ↓MRI (subcutaneous abdominal fat, subcutaneous gluteofemoral fat)
TGM = 17
Klaver et al. 2018 (47)BW, BMI, WC, hip circumference, WHR, body fat % change (TBF, arm, leg, trunk, android, gynoid), LBM % change (total, arm, leg, trunk, android, gynoid)TGW = 17912Baseline and 12 months↑BW, body fat % change (TBF, arm, trunk, android, gynoid, leg), hip (cm), ↓lean body mass % change (total, arm, trunk, gynoid, leg), WHR↑BW, lean body mass % change (total, arm, trunk, android, gynoid, leg), WHR, ↓body fat % change (total, arm, trunk, gynoid, leg), hip (cm)
TGM = 162
Klaver et al. 2022 (50)VAT, VAT/TBF, TBF, LBMTGW = 17912Baseline and 12 months↓VAT/TBF, LBM and ↑TBF↑VAT/TBF, LBM and ↓TBF
TGM = 162
Mueller et al. 2007 (12)BMITGM = 3512Baseline and 12 months↑BMI
Sanchez-Toscano et al. 2023 (66)BW, BMITGW = 9112Baseline and 12 months↑BW, BMI↑BW, BMI
TGM = 136
Tebbens et al. 2023 (71)BMI, liver fat content, VAT, SAT, VAT/SAT ratioTGW = 812Baseline and 12 months↓Liver fat content (18/58 weeks), VAT/SAT (6/58 weeks) and ↑SAT (8–58 weeks)↑Lver fat content (52 weeks), VAT (52 weeks), SAT (at 6/52 weeks), VAT/SAT ratio (12/52 weeks)
TGM = 10
Van Caenegem et al. 2015 (18)BW, BMI, waist (cm), hip circumference (cm), WHR, TFM (kg), TFM (%), trunk fat mass, fat CSA forearm, fat CSA calf, LBM (kg), LBM (%), muscle CSA forearm, muscle CSA calfTGM = 2312Baseline and 12 months↓TFM (kg), TFM (%), trunk fat mass, fat CSA calf, ↑LBM (kg), LBM (%), muscle CSA forearm, muscle CSA calf
CGW = 23
Wierckx et al. 2014 (14)BW, BMI, TFM (kg), TFM (kg), WC, hip circumference, WHRTGW = 5312Baseline and 12 months oral vs transdermal in TGW(Oral): ↑hip circumference, ↓WHR↑BW, BMI, LBM, WHR, ↓TFM, hip circumference
TGM = 53(All): ↑TFM (kg), ↓LBM (kg)
Liu et al. 2021 (57)BW, BMITGM = 65≥12Baseline, 3–6, 6–12 and 12–24 monthsn.s.↑BW and BMI
TGF = 45
Shadid et al. 2020 (68)BW, FFM, WHR, BMITGW = 55≥12Baseline and after 12 months↑BMI, ↓WHR↑BW, BMI, WHR
TGM = 35TGM: ↑BW, BMI, WHR
van Velzen et al. 2021 (74)BMITGM = 15>12Baseline and after 12 months↑BMIn.s.
TGW = 15
Pei et al. 2024 (64)BW, BMI, body fat kg (TBF, arm, leg, gynoid, corrected leg, android/gynoid, visceral), body fat % (TBF, arm, leg, gynoid, corrected leg, android/gynoid)TGW (GAHT) = 5912–36Compared to no GAHT treatment↑Body fat kg (TBF, arm, leg, gynoid, corrected leg), body fat % (TBF, arm, leg, gynoid, corrected leg)
TGW (no GAHT) = 40↓Body fat kg (android/gynoid, visceral), body fat % (android/gynoid)
Fernandez et al. 2016 (20)BMITGW = 3318Baseline to 3–6 and 6–18 monthsn.s.(3–6 months): ↑BMI
TGM = 19
Vita et al. 2018 (76)BMITGW = 21TGW = 31.3 (34.2)Baseline and on GAHTn.s.n.s.
TGM = 11TGM = 22.2 (11.5)
Cocchetti et al. 2021 (38)BMI, BW, WCTGW = 14424Baseline, 12 and 24 months↓WCn.s.
TGM = 165
Colizzi et al. 2015 (25)BMI, WCTGW = 7924Baseline, 12 and 24 months↑BMI (0–12, 12–24, 0–48), WC (0–12,0–24)↑BMI (0–12, 0–24), WC (0–24, 12–24)
TGM = 43
Mueller et al. 2010 (11)BMI, TFM, LBMTGM = 4524Baseline, 12 and 24 months↑LBM (12 and 24 months)
Olson-Kennedy et al. 2018 (62)BMITGW = 2324Baseline and 24 monthsn.s.n.s.
TGM = 35
Korpaisarn et al. 2021 (51)BWTGM = 3925.52 ± 12.9Baseline and ∼24 monthsn.s.
Klaver et al. 2020 (49)BMITGW = 71TGW = 26.4 (IQR: 13.2–37.2)Before and after GAHT↑BMI↑BMI
TGM = 121TGM = 34.8 (IQR: 20.4–40.8)TGM to CGW/CGW
TGW to CGM/CGW
Klaver et al. 2018 (48)BW, BMI, WC, hip circumference, WHR, body fat % (TBF, android, gynoid), LBM %, SDS (for CG comparison)TGW = 71TGW = 33.6; 19.2–40.8Before and after GAHT↑Body fat % (TBF, android, gynoid), WC, hip circumference, ↓LBM %, WHR↓Body fat % (TBF, gynoid), ↑LBM %, WHR, WC, hip circumference
CGW: BMI, WC, hip circumference, WHR, % LBM, ↓% body fat (TBF, android, gynoid) SDS
TGM = 121TGM = 36; 22.8–40.8TGM to CGW/CGWCGM: ↑WC, hip circumference, WHR, % LBM, ↓% TBFCGM: ↑BMI, hip circumference, % body fat (TBF, android, gynoid), ↓WHR, % LBM SDS
TGW to CGM/CGWSDS CGW: ↑hip circumference, % body fat (TBF, android, gynoid) ↓WHR, % LBM SDS
Suppakitjanusant et al. 2020 (70)BMITGW (GAHT) = 46TGW = 79 ± 112Over 84-month period↑BMIn.s.
TGW (no GAHT) = 59TGM = 44 ± 41 (at baseline)
TGM (GAHT) = 15
TGM (no GAHT) = 25
Meyer et al. 2020 (28)BMITGW = 15548Baseline, 3–4, 10–14 months and 3–4 years↑BMI (10–14, 36–48)↑BMI (3–4, 10–14)
TGM = 233
Martinez-Martin et al. 2023 (58)BWTGW = 14960Baseline and 60 monthsn.s.↑BW
TGM = 153
Ott et al. 2011 (19)BMITGW = 8960Baseline and 60 monthsn.s.n.s.
TGM = 80
Allen et al. 2021 (9)BMI (absolute and % change)TGW = 126≤60Baseline, then every 3 months for 1 year, then every 6 months up to 5 yearsn.s.n.s.
TGM = 91
Cross-sectional – cisgender cohort comparison
Valentine et al. 2021 (73)BMITGM = 4210.8 (2.6–25.7)Baseline TGM to CGW↓BMI (short and long term)
CGW = 82
Nokoff et al. 2020 (61)BMI, % LBM, % TFMTGM = 19 (CGW = 42, CGM =19)TGM = 11.2 ± 5.2TGM with matched CGW and CGM. TGW with matched CGW and CGM.CGW: ↑% LBM and ↓% TFMCGW: ↑% LBM and ↓% TFM
TGW = 11 (CGW = 23, CGM = 24)TGW = 12.3 ± 9.9CGM: ↓% LBM and ↑% TFMCGM: ↓% LBM and ↑% TFM
Van Caenegem et al. 2015 (18)BW, BMI, waist (cm), hip circumference (cm), WHR, TFM (kg), TFM (%), trunk fat mass, fat CSA forearm, fat CSA calf, LBM (kg), LBM (%), muscle CSA forearm, muscle CSA calfTGM = 2312TGM to CGW↑WHR
CGW = 23
Balik et al. 2022 (34)BMICGW = 30>12CGW to TGM-n.s.
TGM = 30
Deischinger et al. 2023 (40)BMI, WC, intramyocardial, pancreatic, hepatic fat content, SAT/VATTGW = 16TGM = 13.2; 12–24TGW to CGMCGM: SAT/VATn.s.
TGM = 22TGW = 16.8; 12–26.4TGM to CGW
CGW = 16
CGM = 17
Tsatsanis et al. 2023 (72)BMITGW = 16≥36TGW to CGW/CGMn.s.CGW: ↑BMI
TGM = 27TGM to CGW/CGM
CGW = 26
CGM = 30
Bretherton et al. 2021 (37)LBM, android fat mass, gynoid fat mass, android:gynoid fat ratio, TFMTGW = 41TGM = 44TGM to CGW and CGMCGM: ↑TFM, android fat mass, gynoid fat mass and ↓android: gynoid fat ratio, LBMCGW: ↑android:gynoid fat ratio, LBM
TGM = 43TGW = 39CGW: ↑android fat mass, android:gynoid fat ratio, LBMCGM: ↓LBM
CGW = 48TGW to CGW and CGM
CGM = 30
New et al. 1997 (32)BMI, WHRTGW = 1461 (70)TGW to CGM/CGWCGW: ↑WHR
CGM = 14
CGW = 15
Cunha et al. 2023 (39)BMITGM = 33168 ± 96TGM to CGW and CGMn.s.
CGW = 55
CGM = 56

The arrows indicate either a significant increase/higher (↑) or significant decrease/lower (↓) parameter in transgender individuals in comparison with either the baseline (before GAHT) values or a cisgender control population. Statistical significance was indicated by a reported P value of <0.05 or a non-overlapping confidence interval (CI). Table is sorted in order of time on GAHT and study type.

Abbreviations: BF-SF, body fat-skinfolds; BIA, bioimpedance method; BMI, body mass index; BW, body weight; CGM, cisgender men; CGW, cisgender women; CSA, cross-sectional area; FFM, fat-free mass; GAHT, gender-affirming hormone therapy; LBM, lean body mass; SAT, subcutaneous adipose tissue; SDS, standard deviation score; SF, skinfolds; TBF, total body fat; TFM, total fat mass; TGM, transgender men; TGW, transgender women; VAT, visceral adipose tissue; WC, waist circumference; WHR, waist–hip ratio.

Twenty-two (22/51, 41%) studies investigated systolic blood pressure (SBP) and/or diastolic blood pressure (DBP) (9, 11, 12, 13, 14, 20, 22, 25, 26, 35, 38, 41, 44, 49, 51, 57, 58, 62, 66, 69, 75, 76). Four studies reported an increase in SBP (22, 25, 58, 66) and eight an increase in DBP (12, 25, 35, 49, 58, 62, 66, 69) in TGW. Three studies reported a conflicting decrease in SBP and/or DBP (35, 38, 41) with the rest showing no significant changes. In TGM, nine studies indicated increased SBP (11, 12, 25, 35, 49, 58, 62, 66, 69) and DBP (12, 49, 62, 66). In TGM, one study (76) reported a conflicting decrease in SBP and the rest showed no significant changes. Only one study reported on mean arterial pressure (26) and pulse pressure (13) with no statistical difference found.

Thirty-five (35/51, 69%) studies investigated lipid profile (Table 3). Some studies reported no difference in parameters of lipid profile in TGW, whilst the rest demonstrated a potential shift towards an anti-atherogenic profile through either a decrease in triglycerides (TG), total cholesterol (TC) and low-density lipoprotein (LDL) or an increase in high-density lipoprotein (HDL) (9, 14, 19, 20, 22, 25, 38, 39, 41, 50, 55, 57, 62, 64, 66, 68, 74). In TGM, most studies indicated a pro-atherogenic change, i.e., increased TG, TC and LDL and decreased HDL, under at least one measurement (9, 11, 12, 13, 14, 19, 22, 25, 26, 27, 38, 39, 44, 49, 50, 55, 57, 62, 63, 66, 68, 69, 73, 74, 75) (Table 3).

Table 3

Lipid profile in transgender individuals.

AuthorsStudy designStatistically significant results
MeasurementsParticipantsTime on GAHT (months)ComparisonTGWTGM
Cohort study
Iannantuoni et al. 2020 (44)TC, LDL, HDL, TG, AIPTGM = 1573Baseline and 3 months↓HDL, ↑AIP
Victor et al. 2014 (75)TC, LDL, HDL, TG, AIPTGM = 573Baseline and 3 months↓HDL, ↑AIP
Giltay et al. 1998 (17)TC, LDLTGW = 174Baseline to 4 monthsn.s.n.s.
TGM = 17
Berra et al. 2006 (27)TC, LDL, HDL, TGTGM = 166Baseline and after 6 months↓HDL
Deutsch et al. 2015 (41)TC, LDL, HDL, TGTGW = 166Baseline and after 6 months↑HDL, TG↓HDL
TGM = 31
SoRelle et al. 2019 (13)TC, LDL, HDL, TGTGW (no GAHT) = 87>6TGW no GAHT to GAHTn.s.↑HDL, TG
TGW (GAHT) = 133TGM no GAHT to GAHT
TGM (no GAHT) = 62
TGM (GAHT) = 89
Valentine et al. 2021 (73)TC, LDL, HDL, TGTGM = 4210.8 (2.6–25.7)Baseline TGM to CGW↓HDL
CGW = 82Baseline and 12 months
Stoffers et al. 2019 (69)TC, LDL, HDL, TGTGM = 6212; 5–33Baseline, 6, 12 and 24 months↓TC (6 months), HDL (6, 12 and 24 months)
Chandra et al. 2010 (26)TC, LDL, HDL, TGTGM = 1212Baseline and after 12 months↓HDL
Elbers et al. 2003 (22)TC, HDL, HDL-2, HDL-3, VLDL, FFA, TG, LDL, LDL size, LDL composition (TC, free, cholesterol ester, phospholipids, TG)TGW = 2012Compared to no GAHT treatment↑HDL, HDL-2, HDL-3, TG, LDL composition (TG), ↓LDL, LDL size, LDL composition (TC, free, cholesterol ester)↑TG, ↓HDL, HDL-2, HDL-3, LDL size, LDL composition (free, phospholipids)
TGM = 17
Jacobeit et al. 2007 (46)TC, LDL, HDLTGM = 1212Baseline and 12 months↓TC, LDL
Klaver et al. 2022 (50)TC, LDL, HDL, TGTGW = 17912Baseline and 12 months↓TC, TG, LDL, HDL↓HDL and ↑TG, LDL
TGM = 162
Mueller et al. 2007 (12)TC, LDL, HDL, TGTGM = 3512Baseline and 12 months↑TG, ↓HDL
Pallotti et al. 2023 (63)TC, LDL, HDL, TGTGM = 5212Baseline, 6 and 12 months6 months: ↓HDL
Sanchez-Toscano et al. 2023 (66)TC, LDL, HDL, TGTGW = 9112Baseline and 12 months↓TC↑TG, ↓HDL
TGM = 136
Tebbens et al. 2023 (71)TC, LDL, HDL, TGTGW = 812Baseline and 12 monthsn.s.n.s.
TGM = 10
Wierckx et al. 2014 (14)TC, LDL, HDL, TGTGW = 5312Baseline and 12 months oral vs transdermal in TGW(Oral): ↓TC, LDL, HDL (transdermal): ↓TC, LDL, TG↑TC, LDL, TG, ↓HDL
TGM = 53
Liu et al. 2021 (57)TC, LDL, HDL, TGTGM = 65≥12Baseline, 3–6, 6–12 and 12–24 months↓LDL (3–6 months)↑LDL (12–24 months), ↓HDL
TGF = 45
Shadid et al. 2020 (68)TC, LDL, HDL, TGTGW = 55≥12Baseline and after 12 months↓TC, LDL, HDL, TG↑LDL, ↓HDL
TGM = 35
van Velzen et al. 2021 (74)TC, LDL, HDL, TG, total HDL-CEC, aqueous diffusion HDL-CEC, ABCA1 HDL-CEC, ABCG1 HDL-CEC, HDL-CLCTGM = 15>12Baseline and after 12 months↓TC, LDL, HDL, total HDL-CEC, ABCA1 HDL-CEC, aqueous diffusion HDL-CEC↑TG and ↓HDL, aqueous diffusion HDL-CEC
TGW = 15
Pei et al. 2024 (64)TC, LDL, HDL, TGTGW (GAHT) = 5912–36Compared to no GAHT treatment↓TC
TGW (no GAHT) = 40
Fernandez et al. 2016 (20)TC, LDL, HDL, TGTGW = 3318Baseline to 3–6 and 6–18 months(3–6 months): ↑HDLn.s.
TGM = 19
Vita et al. 2018 (76)TC, LDL, HDL, TGTGW = 21TGW = 31.3 (34.2)Baseline and on GAHTn.s.n.s.
TGM = 11TGM = 22.2 (11.5)
Cocchetti et al. 2021 (38)TC, LDL, HDL, TGTGW = 14424Baseline, 12 and 24 months↓TC, TG, LDL↓HDL and ↑TC, TG, LDL
TGM = 165
Colizzi et al. 2015 (25)TC, LDL, HDL, TG, AIPTGW = 7924Baseline, 12 and 24 months↑TG, TC, LDL, AIP (0–12, 12–24, 0–24), ↓HDL (0–12, 12–24, 0–24)↑TG (0–24, 12–24), ↑TC, LDL (0–12, 0–24, 12–24), AIP (0–12, 0–24) ↓HDL (12–24)
TGM = 43
Mueller et al. 2010 (11)TC, LDL, HDL, TGTGM = 4524Baseline, 12 and 24 months↑TG (12 and 24 months), ↓HDL (12 and 24 months)
Olson-Kennedy et al. 2018 (62)TC, HDL, TGTGW = 2324Baseline and 24 months↑HDL↓HDL, ↑TG
TGM = 35
Korpaisarn et al. 2021 (51)TC, LDL, HDL, TGTGM = 3925.52 ± 12.9Baseline and ∼24 monthsn.s.
Klaver et al. 2020 (49)TC, LDL, HDL, TGTGW = 71TGW = 26.4 (IQR: 13.2–37.2)Before and after GAHT TGM to CGW/CGW↑TG↑TC, LDL, TG and ↓HDL
TGM = 121TGM = 34.8 (IQR: 20.4–40.8)TGW to CGM/CGW
Jacobeit et al. 2009 (46)TC, LDL, HDL, TGTGM = 1736Baseline, 6, 12, 18, 24, 30, 36 months18 months; ↓TC, LDL
Meyer et al. 2020 (28)LDL, HDL, TGTGW = 15548Baseline, 3–4, 10–14 months and 3–4 yearsn.s.n.s.
TGM = 233
Leemaqz et al. 2023 (55)TC, LDL, HDL, TGTGF = 170<57Baseline up to 57 months↑HDL and ↓LDL (22–57)↑TG, TC (22–57), LDL (22–57) and ↓HDL, TC (2–10)
TGM = 196
Allen et al. 2021 (9)LDL, HDL, TC, TG (absolute and % change)TGW = 126≤60Baseline, then every 3 months for 1 year, then every 6 months up to 5 years↑HDL at 3 and 60 months (relative)↑LDL after 36 months, and 48–60 months (% change), LDL at 54 months (absolute) and TC after 60 months, ↓HDL between 3, 9 and 18 months (relative), HDL 6–24 months (absolute)
TGM = 91
Martinez-Martin et al. 2023 (58)LDL, TGTGW = 14960Baseline and 60 months↑LDLn.s.
TGM = 153
Ott et al. 2011 (19)TC, LDL, HDL, TG, TC/HDLTGW = 8960Baseline and 60 months↑TG, TC, HDL↑TG, TC, LDL, TC/HDL and ↓HDL
TGM = 80
Cross-sectional – cisgender cohort comparison
Gulanski et al. 2020 (40)TC, LDL, HDL, TGTGM = 11≥3TGM to CGW-n.s.
CGF = 20
Lim et al. 2020 (56)TCTGF = 26≥6 (25.5)TGW and CGM/CGWCGM/CGW:↓TC-
CGM = 55
CGF = 98
Nokoff et al. 2020 (61)TC, LDL, HDL, TGTGM = 19TGM = 11.2 ± 5.2TGM with matched CGW and CGMCGM: ↑HDLCGW: ↓HDL
(CGW = 42, CGM =19)
TGW = 11TGW = 12.3 ± 9.9TGW with matched CGW and CGM
(CGW = 23, CGM = 24)
Deischinger et al. 2023 (40)TC, LDL, HDL, TGTGW = 16TGM = 13.2; 12–24TGW to CGM↓LDL-C↓HDL-C
TGM = 22TGW = 16.8; 12–26.4TGM to CGW
CGW = 16
CGM = 17
Millington & Chan 2021 (59)TRL particle (total, very large, large, medium, small, very small) particles, LDL-C (mean and particle diameter), LDL particle (total, large, medium, small), ApoB, HDL-C (mean and particle diameter), HDL particle (total, large, medium, small), ApoA1TGM = 1714.4 ± 9.6TGM to CGWCGW: ↑LDL-C mean (model 2), total LDL, small LDL, ApoB (model 1/2) and ↓HDL (model 1/2), HDL particle diameter, large HDL, ApoA1 (model 1/2)
CGM = 33TGM to CGM (model 1 and 2)*CGM: ↑LDL-C mean (model 1), total LDL (model 1/2), ApoB (model 1)
CGF = 32
McCredie et al. 1998 (30)TC, HDLTGM = 1238 (52); 2–177TGM to CGW↓HDL
CGW = 12
Roberts et al. 2014 (65)TC, LDL, HDL, TGTGW = 5548 (>6)TGW to CGM/CGWCGM: ↓LDL, ↑TG
CGM = 20CGW: ↑TG
CGW = 20
New et al. 1997 (32)TC, HDL, LDL, LDL particle size, TGTGW = 1461 (70)TGW to CGM/CGWCGM: ↑HDL, ↓LDL
CGM = 14CGM/CGW: ↑TG, ↓LDL particle size
CGW = 15
Cunha et al. 2023 (39)TC, LDL, HDL, TGTGM = 33168 ± 96TGM to CGW and CGMCGM:↑HDL
CGW = 55CGW:↓HDL
CGM = 56

The arrows indicate either a significant increase/higher (↑) or significant decrease/lower (↓) parameter in transgender individuals in comparison with either the baseline (before GAHT) values or a cisgender control population. Significance was indicated by a reported P-value of <0.05 or a non-overlapping confidence interval (CI). Table is sorted in order of time on GAHT and type of study. *Model 1 is the unadjusted P value, and model 2 is based on an adjusted P value based on BMI, race and age (59).

Abbreviations: ABCA1, ATP-binding cassette A1; ABCG1, ATP-binding cassette G1; AIP, atherogenic index of plasma; ApoA, apolipoprotein A; ApoAII, apolipoprotein II; ApoB, apolipoprotein B; CEC, cholesterol efflux capacity; CGM, cisgender men; CGW, cisgender women; CLC, cholesterol loading capacity; FFA, free fatty acids; GAHT, gender-affirming hormone therapy; HDL, high-density lipoprotein; LDL, low-density lipoprotein; TC, total cholesterol; TG, triglycerides; TGM, transgender men; TGW, transgender women; TRL, triglyceride-rich lipoprotein; VLDL, very low-density lipoprotein.

Twenty-seven (27/51, 53%) studies reported on a number of measures of insulin sensitivity, including fasting plasma glucose (FPG) or glucose (9, 13, 14, 22, 25, 26, 27, 33, 44, 49, 51, 54, 57, 58, 62, 63, 64, 66, 68, 71, 75, 76), insulin (14, 21, 22, 25, 27, 44, 49, 54, 57, 68, 75), homeostatic model assessment of insulin resistance (HOMA-IR) (25, 27, 44, 49, 50, 54, 57, 68, 75) and glycated haemoglobin (HbA1c) (33, 57, 60, 63, 66), with some studies carrying out a more focused analysis, such as a hyperinsulinaemic clamp test (22), gastric inhibitory polypeptide (GIP) (68), glucagon-like peptide-1 (GLP-1) (68), resistin (26), adiponectin (27, 54, 67) and leptin (23, 26, 27, 67). In TGW, six studies found no difference in any measures of insulin resistance (9, 33, 49, 62, 71, 76), four found an increased HOMA-IR (25, 50, 54, 68), six reported increased insulin levels (14, 21, 22, 25, 54, 68), and five demonstrated increased glucose (13, 25, 54, 58, 64). Adiponectin (67) and leptin (23, 67) were reported to increase with GAHT in TGW. Conflicting results with HbA1c were identified in TGW, where one study found lower levels (57), one study found higher levels (60), and other studies found no difference (33, 66). Eleven studies (9, 13, 21, 26, 33, 44, 60, 62, 63, 71, 75) demonstrated no changes across any measurements in TGM. Some studies indicated a reduced HOMA-IR (49, 50, 57), insulin (14, 49, 57) and FPG (51, 76) and an increased HbA1c (66) in TGM, with a conflicting increase in insulin (25) and FPG (58) also reported. TGM showed reduced leptin (23, 27, 67) and adiponectin (27, 67). An additional study indicated a reduced fasting GIP and area under curve (AUC) glucose, GIP and GLP-1 in TGW and increased AUC GIP and reduced GLP-1 in TGM (68). Another study showed reduced glucose utilisation in TGW and M value by hyperinsulinaemic clamp in both TGM and TGW (22).

Blood biomarkers of haematology, haemostasis and inflammation

Twenty-one (21/51, 41%) studies investigated blood parameters, all reporting in TGM and only 11 (2, 9, 14, 17, 24, 28, 36, 57, 60, 62, 76) in TGW; measurements included haematocrit (2, 9, 11, 12, 14, 20, 24, 26, 28, 36, 43, 45, 46, 51, 57, 60, 62, 63, 69, 76), haemoglobin (9, 11, 12, 17, 20, 28, 36, 43, 45, 46, 51, 57, 60, 62, 63, 69, 76), red blood cell count (RBC) (9, 20, 63, 76), mean corpuscle volume (MCV) (9, 43, 51, 76), red cell width distribution (9), packed cell volume (17), total homocysteine (tHcy) (17), platelet count (9, 26, 43, 63, 76), mean platelet volume (MPV) (43, 63) and white blood cell count (WBC) (9, 43, 51, 57, 63, 76). Neutrophils, lymphocytes and the neutrophil/lymphocyte ratio (NLR) were reported in one study (43). In TGW, there was consistent reporting of reduced haematocrit and haemoglobin with GAHT (2, 9, 14, 17, 24, 36, 57, 60, 62, 76), with the exception of one study that showed no difference in clinically relevant levels of haematocrit and haemoglobin (28). All studies reporting on packed cell volume (17), tHcy (17) and RBC (9, 76) in TGW indicated a reduction in these parameters, with one report of increased platelets (9). In TGM, 18 studies (2, 11, 12, 14, 20, 26, 36, 43, 45, 46, 60, 63, 69, 76) reported a significant increase in haematocrit and 14 studies (9, 11, 12, 20, 28, 36, 43, 45, 57, 60, 62, 63, 69, 76) also identified an increase in haemoglobin. Increased RBC (9, 20, 63, 76) and tHcy (17) were also reported in TGM.

Four (4/51, 8%) studies investigated blood markers of haemostasis and coagulation (2, 12, 54, 67); parameters included were fibrinogen (2, 12, 67), plasminogen activator-1 (PAI-1) (67), platelet factor 4 (PR-4) (54, 67), β-thromboglobulin (67), P-selectin (67), activated protein C resistance (APCr) ratio (2), coagulation factor II (FII) (2), coagulation factor IX (FIX) (2), coagulation factor XI (FXI) (2), protein S (2), protein C (2), D-dimer (54) and von Willebrand factor (vWF) (54), prothrombin time (PT) (12), activated partial thromboplastin time (APTT) (12) and thrombin time (12). In TGW, studies identified a significant increase in fibrinogen (67), PF-4 (67), β-thromboglobulin (67), FIX (2) and FXI (2) and a significant decrease in protein C (2) and D-dimer (54). In TGM, there was a reported increase in APCr ratio (2), FIX (2) and protein S (2) and a decrease in PF-4 (67), β-thromboglobulin (67), FII (2) and FXI (2).

Five (5/51, 10%) studies reported on blood markers of inflammation (43, 44, 54, 67, 75). Studies in TGW investigated high sensitivity C-reactive protein (hs-CRP) (43, 67), α-1-antitrypsin (67), tumor necrosis factor alpha (TNF-α) (54, 67), interleukin 6 (IL-6) (54, 67), IL-8 (54, 67), IL-10 (67), IL-22 (67), sCD14 (54), sCD163 (54) and fatty acid binding protein 4 (FABP-4) (54). Across these inflammatory markers, there was a reported reduction in hs-CRP, TNF-α, IL-6 and IL-8 (67). Studies in TGM investigated hs-CRP (43, 44, 67, 75), IL-6 (44, 67), TNF-α (44, 67), α-1-antitrypsin (67), IL-8 (67), IL-10 (67) and IL-22 (67). There was a reported elevation in hs-CRP (43, 67), TNF-α (44) and IL-6 (44) across these parameters in TGM.

Vascular function

Blood markers and studies investigating vascular function are shown in Table 4. In TGM, one study reported an increase in E-selectin and vascular cell adhesion protein 1 (VCAM-1) (44). Another study reported no difference in VCAM-1 or other measurements of endothelial function after GAHT treatment in TGM (67). In TGW, a decrease in VCAM-1 was found in a study (67); however, another study reported no difference (54). No changes in vascular function with GAHT in TGW were demonstrated; the only parameter investigated was heart rate, also showing change in TGM (52). One study investigated leucocyte–endothelium cell interactions in TGM, reporting a reduced rolling velocity and increased rolling flux and adhesion (44). In TGM, there was a reported reduction in oxygen consumption, membrane potential, glutathione and glutathione–glutathione disulphide ratio and an increased reactive oxygen species production (75).

Table 4

Blood markers of vascular function and vascular function studies in transgender individuals.

AuthorsStudy designStatistically significant results
MeasurementsParticipantsTime on GAHT (months)ComparisonTGWTGM
Blood markers of vascular function
Cohort study
Iannantuoni et al. 2020 (44)VCAM-1, ICAM-1, E-selectinTGM = 1573Baseline and 3 months↑E-selectin and VCAM-1
Lake et al. 2023 (54)ET-1, VCAM-1TGW = 13812Baseline, 6 and 12 monthsn.s.
Schutte et al. 2022 (67)VCAM-1 , p-selectinTGF = 4812Baseline, 3 and 12 months(3 months): VCAM-1n.s.
TGM = 47(12 months): ↓VCAM-1
Cross-sectional – cisgender cohort comparison
Lake et al. 2022 (53)ET-1, VCAM-1, ENRAGE, oxLDLTGW (GAHT) = 31TGW = 40; 32–48Compared to no GAHT↑ENRAGE, oxLDL and ↓ET-1
TGW (no-GAHT) = 16; CGM = 40CGM = 42; 35–52Treatment TGW to CGM
Vascular function studies
Cohort studies
Iannantuoni et al. 2020 (44)Leucocyte–endothelium cell interaction assay: PMN rolling velocity, flux and PMN adhesionTGM = 1573Baseline and 3 months↑Leucocyte–endothelium interaction: ↓rolling velocity, ↑rolling flux and adhesion
Victor et al. 2014 (75)O2 consumption, membrane potential, ROS production, GSH, GSH/GSSG ratioTGM = 573Baseline and 3 months↓O2 consumption, membrane potential, GSH, GSH/GSSG ratio, ↑ROS production
Allen et al. 2021 (9)HR (absolute and % change)TGW = 126≤60Baseline, then every 3 months for 1 year, then every 6 months up to 5 yearsn.s.n.s.
TGM = 91
Cross-sectional – cisgender cohort comparison
Gulanski et al. 2020 (40)FMDTGM = 11≥3TGM to CGW↓FMD
CGF = 20
Balik et al. 2022 (34)CIMTCGW = 30>12TGM to CGW↑CIMT
TGM = 30
Deischinger et al. 2023 (40)HR, CRP, pro-BNPTGW = 16TGM = 13.2; 12–24TGW to CGM↑HR, pro-BNP↓pro-BNP
TGM = 22TGW = 16.8; 12–26.4TGM to CGW
CGW = 16
CGM = 17
McCredie et al. 1998 (30)Baseline flow, vessel size, FMD, NTG, hyperaemia (%)TGM = 1238 (52); 2–177TGM to CGW↑Vessel size, ↓NTG (%)
CGW = 12
Duro et al. 2023 (42)CAC % participants score 0, 1–99 and >100TGW = 25 (24)TGW = 38.4; 24–166.8TGW and TGM to CG↓1–99 ,↑>100↑1–99, ↓>100
TGM = 22 (16)
TGM = 66; 22.8–148.8
New et al. 1997 (32)FMD, baseline vessel size, aFMD, baseline flow, hyperaemia (% inc in flow), GTN, aGTNTGW = 1461 (70)TGW to CGM/CGWCGM: ↑FMD, aFMD, GTN, aGTN, ↓baseline vessel size
CGM = 14
CGW = 15
Cunha et al. 2023 (39)cf-PWVTGM = 33168 ± 96TGM to CGW and CGMCGF/CGM: ↑cf-PWV
CGW = 55
CGM = 56

The arrows indicate either a significant increase/higher (↑) or significant decrease/lower (↓) parameter in transgender individuals in comparison with either the baseline (before GAHT) values or a cisgender control population. Significance was indicated by a reported P value of <0.05 or a non-overlapping confidence interval (CI). Table is sorted in order of time on GAHT and study design.

Abbreviations: ABI, ankle-brachial index; aFMD, flow-mediated dilation adjusted for vessel size (percent flow-mediated vasodilation divided by baseline diameter (mm)); baPWV, brachial-ankle pulse wave velocity; CAC, coronary artery calcium score; CAI, carotid augmentation index; CAVI, cardio-ankle vascular index; cf-PWV, carotid-femoral pulse wave velocity; CGM, cisgender men; CGW, cisgender women; CIMT, carotid intima–media thickness; CRP, c-reactive protein; ET-1, endothelin-1; FMD, flow-mediated dilatation; GAHT, gender-affirming hormone therapy; GSH, glutathione; GSSG, glutathione disulphide; HR, heart rate; ICAM-1, intercellular cell adhesion molecule-1; NTG/GTN, nitroglycerine-induced dilatation; PMN, polymorphonuclear leucocytes; PWV, pulse wave velocity; ROS, reactive oxygen species; TGM, transgender men; TGW, transgender women; VCAM-1, vascular adhesion molecule-1.

Cross-sectional studies comparing treated and untreated transgender individuals

Two studies were cross-sectional, comparing a GAHT-treated and untreated group of TGM (16, 29) and TGW (52, 53) across all investigated CVD phenotypes (Table 5). In TGW, there are few changes in parameters with evidence of reduced waist circumference (52), RBC (13), Hb (13), Hct (13), soluble tumor necrosis factor receptor I and II (TNFRI/II) (53), IL-8 (53), endothelin 1 (ET-1) (53) and carotid intima–media thickness (CIMT) (52) and increased platelets (13), PAI-1 (53), extracellular newly-identified receptor for advanced glycation end-products (ENRAGE) (53), ox-LDL (53), HR (52), cardio-ankle vascular index (CAVI) (52) and cardiac troponin (52) (Table 5). No significant differences were reported in markers of insulin sensitivity, lipid profile or blood pressure. In TGM, there was evidence of increased SBP (29), DBP (29), MAP (29), TG (16, 29), TC (16, 29), LDL (16), ApoB (16), LDL/HDL (16), Hct (29), brachial-ankle pulse wave velocity (baPWV) (29) and reduced HDL (16, 29) and ApoA1/ApoB (16) (Table 5). No studies reported on any markers of inflammation or endothelial function, and no significant differences were reported on anthropometric measures or markers of insulin sensitivity and haemostasis.

Table 5

Results of studies investigating CV risk factors in a GAHT treatment-naive population compared to GAHT-treated population.

AuthorsStudy designStatistically significant results
MeasurementsParticipantsTime on GAHT (months)TGWTGM
Anthropometric
Emi et al. 2008 (29)BW, BMITGM (GAHT) = 4845 (38.1)n.s.
TGM (no GAHT) = 63
Kulprachakarn et al. 2020 (52)BMI, WCTGW (GAHT) = 10079.4 ± 6.2↓WC
TGW (no GAHT) = 100
Blood pressure
Emi et al. 2008 (29)SBP, DBP, MAPTGM (GAHT) = 4845 (38.1)↑SBP, DBP, MAP
TGM (no GAHT) = 63
Kulprachakarn et al. 2020 (52)SBP, DBPTGW (GAHT) = 10079.4 ± 6.2n.s.
TGW (no GAHT) = 100
Lipid profile
Goh et al. 1995 (16)TG, TC, LDL, HDL, Apo A1, ApoAII, ApoB, LDL/HDL, ApoA1/ApoBTGM (GAHT) = 3933; 6–120↑TG, TC, LDL, ApoB, LDL/HDL, ↓HDL, ApoA1/ApoB
TGM (no GAHT) = 29
Emi et al. 2008 (29)TC, HDL, TGTGM (GAHT) = 4845 (38.1)↑TC, TG and ↓HDL
TGM (no GAHT) = 63
Kulprachakarn et al. 2020 (52)TC, LDL, HDL, TGTGW (GAHT) = 10079.4 ± 6.2n.s.
TGW (no GAHT) = 100
Insulin sensitivity
Lake et al. 2022 (53)High molecular weight adiponectinTGW (GAHT) = 31TGW = 40; 32–48n.s.
TGW (no-GAHT) = 16CGM = 42; 35–52
CGM = 40
Emi et al. 2008 (29)Glucose, HbA1cTGM (GAHT) = 4845 (38.1)n.s.
TGM (no GAHT) = 63
Kulprachakarn et al. 2020 (52)FPGTGW (GAHT) = 10079.4 ± 6.2n.s.
TGW (no GAHT) = 100
Haematology
SoRelle et al. 2019 (13)WBC, RBC, Hb, Hct, MCV, red cell diameter width, plateletsTGW (no GAHT) = 87>6↓RBC, Hb, Hct, ↑platelets↑RBC, Hb, Hct
TGW (GAHT) = 133
TGM (no GAHT) = 62
TGM (GAHT) = 89
Emi et al. 2008 (29)Hct %TGM (GAHT) = 4845 (38.1)↑Hct %
TGM (no GAHT) = 63
Haemostasis
Lake et al. 2022 (53)PAI-1, vWF, d-dimerTGW (GAHT) = 31TGW = 40; 32–48GAHT: ↑PAI-1
TGW(no-GAHT) = 16CGM = 42; 35–52
CGM = 40
Emi et al. 2008 (29)PT, PT-INR, APTT, fibrinogenTGM (GAHT) = 4845 (38.1)n.s.
TGM (no GAHT) = 63
Inflammation
Lake et al. 2022 (53)sCD14, sCD163, IL-6, IL-8, sTNFR I/II, P-selectin, LpPLA2TGW (GAHT) = 31TGW = 40; 32-48GAHT: ↓sTNFR II, IL-8
TGW(no-GAHT) = 16CGM = 42; 35–52
CGM = 40
Endothelial function
Lake et al. 2022 (53)ET-1, VCAM-1, ENRAGE, oxLDLTGW (GAHT) = 31TGW = 40; 32–48↑ENRAGE, oxLDL and ↓ET-1
TGW (no-GAHT) = 16CGM = 42; 35–52
CGM = 40
Vascular function
Emi et al. 2008 (29)HR, ABI, baPWV, CAITGM (GAHT) = 4845 (38.1)↑baPWV
TGM (no GAHT) = 63
Kulprachakarn et al. 2020 (52)HR, PWV, ABI, CAVI, CIMT, CRP, cardiac troponin I, pro-BNPTGW (GAHT) = 10079.4 ± 6.2↑HR (model 1), CAVI (model 1), cardiac troponin (model) and ↓CIMT (model 1 and 2)
TGW (no GAHT) = 100

The arrows indicate either a significant increase/higher (↑) or significant decrease/lower (↓) parameter in transgender individuals in comparison with an untreated transgender control population. Significance was indicated by a reported P-value of <0.05 or a non-overlapping confidence interval (CI). Table is sorted in order of time on GAHT.

Abbreviations: ABI, ankle-brachial index; ApoA, apolipoprotein A; ApoAII, apolipoprotein II; ApoB, apolipoprotein B; APTT, activated partial thromboplastin time; baPWV, brachial-ankle pulse wave velocity; BMI;,body mass index; BW, body weight; CAI, carotid augmentation index; CAVI, cardio-ankle vascular index; CGM, cisgender men; CGW, cisgender women; CIMT, carotid intima–media thickness; CRP, c-reactive protein; DBP, diastolic blood pressure; ET-1, endothelin-1; FPG, fasting plasma glucose; GAHT, gender-affirming hormone therapy; Hb, haemoglobin. HbA1c, glycated haemoglobin; Hct, haematocrit; HDL, high-density lipoprotein; HR, heart rate; LDL, low-density lipoprotein; LpPLA2, lipoprotein-associated phospholipase A2; MAP, mean arterial pressure; MCV, mean corpuscle volume; PAI-1, plasminogen activator-1; PT, prothrombin time; PT-INR, prothrombin time-international normalised ratio; PWV, pulse wave velocity; RBC, red blood cell; SBP, systolic blood pressure; TC, total cholesterol; TG, triglycerides; VCAM-1, vascular adhesion molecule-1; vWF, von Willebrand factor; WBC, white blood cell; WC, waist circumference.

Cross-sectional studies comparing to a cisgender population

Cross-sectional studies also compared transgender individuals on GAHT to cisgender counterparts (18/69, 26%) (Table 2). The same pattern is observed as in cohort studies for anthropometric measures (Table 2) and lipid profile (Table 3) when TGW are compared to CGM and TGM are compared to CGW. When compared to cisgender individuals of the affirmed gender (CGW in the case of TGW, for example), the opposite pattern was observed in both anthropometric measures (37, 61) and lipids ( 39, 65). Most studies provide no evidence for significant differences in MAP (78), PP (39) or SBP and DBP in both TGW (32, 40) and TGM (30, 40, 78). Nokoff and coworkers (61) indicated a decrease in SBP compared to CGM in both TGW and TGM, whereas Cunha and coworkers (39) indicated an increase in both SBP and DBP compared to CGW and CGM in TGM individuals.

Nine studies (31, 37, 39, 40, 53, 56, 61, 72, 78) compared to a cisgender cohort when considering measures of insulin sensitivity with no differences reported in TGW (37, 53, 72) or TGM (31, 37, 39) in three studies each. Reduced HbA1c (40, 56) and 1/(fasting insulin) (61) and increased insulin (40), HOMA-IR (40, 61), leptin (61), adiponectin (40) and betatrophin (40) were found in TGW in comparison with CGM. When comparing TGW to CGW, HbA1c (56) and leptin (31) were lower and fasting insulin (40) and HOMA-IR (40) were higher. In comparison with CGW, studies identified an increase in HbA1c (78) and HOMA-IR (72) and a reduced leptin (61) in TGM. Compared to CGM, only adiponectin (40) and betatrophin (40) were higher in TGM. Additional measures, such as C-peptide (37), IGF-1 (37) and ghrelin (31), and assessment of insulin resistance through the Matsuda index and Stumvoll I and II indices (40) indicated no difference in either TGW or TGM in comparison with cisgender individuals.

Only two studies (56, 65) compared haematological parameters in TGW to CGM and CGW, reporting on Hb, Hct and platelet count: Hb and Hct were higher, and platelets were lower compared to CGM. Four studies reported differences between TGM and CGW (34, 39, 43, 78), and one compared Hct to CGM showing an increase (39). Parameters compared to CGW were Hct (39, 43, 78), Hb (43, 78), WBC (34, 43), neutrophils (43), lymphocytes (43), NLR (34, 43), MCV (43), platelets (43), MPV (34, 43), platelet–lymphocyte ratio (PLR) (34) and lymphocyte–monocyte ratio (34). Hct (39, 78), WBC (34, 43) and neutrophil (43) counts were higher, and PLR (34) was lower in TGM compared to CGW.

No studies investigated markers of haemostasis in TGM compared to cisgender cohorts. In TGW, one study (56) investigated haemostatic function through global coagulation assays, which included analysis of efficiency of coagulation through thromboelastography, thrombin generation through a calibrated automated thrombogram and fibrin generation through an overall haemostatic potential assay in addition to PT and APTT (56). Global coagulation assays indicated hypercoagulability and increased fibrinolytic potential compared to CGM through an elevated maximum amplitude, endogenous thrombin potential, peak thrombin, overall fibrinolytic potential and lower K time. Compared to CGW, there were fewer reported differences, but these included reductions in alpha-angle, overall coagulation and haemostatic potential (56). This study and one other also investigated blood parameters of haemostasis, such as PT (56), APTT (56), FVIII (56), vWF (53, 56), D-dimer (53, 56), fibrinogen (56) and PAI-1 (53) in comparison with cisgender individuals. In comparison with CGM, studies reported an elevated D-dimer (56), fibrinogen (56) and PAI-1 (53) in TGW. In contrast to CGW, TGW had a higher D-dimer and reduced FVII and PT (56).

Two studies (53, 72) reported on blood markers of inflammation in comparison with cisgender populations: one study made comparison to both CGM and CGW, finding lower B-cell activating factor (BAFF) and higher lipopolysaccharide-TNF (LPS–TNF) when comparing TGM to CGW and higher BAFF and lower TNF when comparing TGW and TGM to CGM (72), and the other study (53) investigated a wider range of inflammatory parameters in TGW compared to CGM, including sCD14, sCD163, IL-6, IL-8, sTNFR I/II, P-selectin and LpPLA2, reporting an increase in sTNFRII and IL-8.

No cross-sectional studies compared blood markers of vascular function in TGM. TGW reported a higher oxidative LDL and ENRAGE and a lower ET-1 than CGM (53). Three studies investigated vascular function in TGW, showing an increased coronary artery score (32) HR, brain natriuretic peptide (pro-BNP) (40), flow-mediated dilatation (FMD), and nitroglycerine-induced dilatation and a reduced baseline vessel size (32). Six studies investigated vascular function parameters in TGM, showing an increased CIMT (34), PWV (39), vessel size (30) and a reduced FMD (78), pro-BNP (40), nitroglycerine-induced dilatation (30) and coronary artery calcium (CAC) scoring (42).

Discussion

This systematic review identified 69 studies, which reported on traditional CV risk factors and vascular function in transgender individuals. In TGM, evidence suggested potentially negative changes in CV risk profile and, in TGW, potentially positive changes, as shown in Fig. 2. The direction of evidence in the cohort and cisgender comparison cross-sectional studies within each CV phenotype followed the same pattern. More weight can be attributed to studies comparing to a baseline transgender population due to the presence of confounding variables, such as behavioural risk factors, socio-economic differences and health inequalities, which are not directly comparable to cisgender counterparts. Studies comparing to an untreated transgender cohort were scarce but corroborated these findings. In TGW, some of the research opposed this, indicating a procoagulant shift and an increased insulin sensitivity. The studies were inconsistent concerning blood pressure in both TGW and TGM. Across factors such as body composition, insulin sensitivity, haemostasis, endothelial function and vascular function, the evidence was inconclusive for TGM or TGW or both, with studies reporting on a high number of markers with inconsistencies in results. Currently, no standard practice exists in study design, which is required to gain further knowledge in this field. It would be best to compare to a hormone-naive transgender cohort, but where this is not possible, it is always beneficial to compare to both a CGM and CGW control group in both cross-sectional and cohort studies. Of note is the study by Suppakitjanusant and coworkers (70), where transgender individuals were separated by prior hormone use. In addition, great heterogeneity was identified in terms of reported outcomes, suggesting that development of a core outcome set for standardisation of research into the CV effects of gender identity dysphoria would be useful for future study comparison and synchronicity.

Figure 2
Figure 2

Summary of alteration of cardiovascular (CV) risk profile with gender-affirming hormone therapy. In TGW, there were mostly beneficial changes in the CV risk profile, and in TGM, there were mostly negative changes in the CV risk profile. TGM, transgender men. TGW, transgender women. Created using BioRender.com.

Citation: Endocrine Connections 14, 2; 10.1530/EC-24-0222

Another important finding in the research to be addressed is the substantial dropout rate reported by some studies (9, 20, 35, 51, 55, 69). These attrition rates and low sample sizes pose a major problem in the ability to assess CV risk in this population. It is important to build cohorts of transgender individuals to follow for longer term whilst on GAHT. Within these cohorts, real-time feedback from transgender individuals can be given to provide insight into their health priorities.

In addition, in relation to ethnicity, there is a lack of diversity within transgender research. All studies have different inclusion and exclusion criteria, making it difficult to make meaningful comparisons. A number of the included studies investigating differences in CV risk controlled for previous hormone exposure (2, 11, 14, 17, 18, 20, 21, 22, 23, 24, 26, 36, 38, 43, 44, 48, 57, 60, 63, 67, 68, 71, 74). Some studies (11, 12, 17, 22, 30, 37, 39, 42, 43, 44, 52, 56, 57, 59, 61, 72, 75, 78) reported on exclusion based on CV risk to varying extents, whereas the rest did not mention excluding for CV disease or CV risk at baseline.

Overall interpretation of findings

The conclusion of the review by Moreira Allgayer and coworkers (10) was a reported neutral or beneficial effect in TGW and an increased risk of subclinical atherosclerosis in TGM. Considering additional CV risk phenotypes provides more evidence to support these conclusions. Some results have been conflicting, such as the evidence of insulin sensitivity being increased in TGM and reduced in TGW and the inconsistencies in published work concerning BP. There is a paucity of research across non-traditional measurements of CV risk in transgender individuals; however, the research on vascular function is severely lacking. In cross-sectional studies comparing to CGW or CGM, there were often more differences between CGM and less between CGW in TGW, indicating a shift towards the affirmed gender. The converse was often observed in TGM. This was particularly evident concerning lipids, insulin and blood cells, where there are known sex differences. More evidence is required to determine whether GAHT is conferring the risk of the affirmed gender or posing additional CVD risk. No clear patterns were observed regarding time to development of CV risk in individuals who were on GAHT for a longer period. The longest study of transgender individuals before and after GAHT spanned over 5 years (9) indicating stabilisation of haemoglobin and HDL as early as 3–6 months. Some values, such as platelets and LDL, increased later at 18 and 36 months, respectively, providing some evidence of longer-term changes with GAHT. However, based on the lack of a clear trend in both that study and the other studies of different lengths included as part of this review, it is likely that most changes in CV risk are established early into treatment. Much more research is required to confirm this hypothesis.

The changes in parameters of CVD risk reported were mostly either neutral or beneficial in the case of TGW and negative in the case of TGM. However, studies reporting a higher incidence of CVD events in TGW are well documented with less conclusive evidence in TGM (1, 4, 5). The incidence of CV events does not coincide with the mechanistic data. It is unclear whether this is because studies reporting on incidence report on a slightly higher age range with as high as 65+ years (4) and that the early changes are not clinically relevant or become more apparent with age. Studies on CVD incidence report on transgender individuals as a whole and not only on individuals on GAHT, which may explain the inconsistency. This hypothesis was supported in a recent systematic review and meta-analysis (6), which compared two of the studies that frequently reported opposing effects on CVD risk. In the study where approximately half of the participants were on GAHT, a smaller risk of CVD events was reported; however, the study in which all individuals were treated with GAHT reported a larger risk of CVD events. Haemostatic and insulin sensitivity markers in TGW supported an elevated incidence of CVD. It is possible that these play an important role in CVD in transgender individuals. Elevated haematocrit has been associated with CV disease, through increased blood viscosity and association with platelet adhesion, but it is not clear what the strength of this association is and its implications clinically (79). Elevated haemoglobin levels have been associated with increased TC and TG, also conferring an additional CV disease risk (79). Reduced insulin sensitivity, otherwise termed insulin resistance, has been shown to be an independent predictor of high CV risk, including hypertension, obesity and dyslipidaemia (80) which is in direct conflict with the potential CV risk profile in TGW in this review.

Strengths and limitations of this review

As far as we are aware, this is the first review to investigate such a wide range of phenotypes. There have been a variety of reviews on a more limited number of phenotypes (80, 81, 82, 83) published as early as 2017, resulting in gaps within the literature. A recent review by Stobbe and coworkers (7) summarised studies investigating blood pressure, lipid profile and glucose from 2001 to July 2019. Studies reported changes in blood pressure but within normal range, inconsistent change in lipids with pro-atherogenic changes in TGM and an absence of data on glucose, corroborating these results. This review includes data on body composition, blood pressure, lipid profiles, insulin sensitivity and blood cell count, thereby expanding on the number of phenotypes. This review was unable to carry out a meta-analysis due to a wide range of measurements and units of measurement across the studies, but this is similar to other reviews within this field. We did not consider the addition of anti-androgens in TGW, which means oestrogen may not be the only influencing factor on the measure of CV risk or vascular function.

Future work and implications

Future work is required to address the gaps within the data in this field. In particular, there is a need for studies to determine mechanisms behind the increased CV risk and assessment of laboratory values to assess what the reference values should be to assess CV risk. Study design must be refined to allow comparable results. All studies should aim to compare to a cisgender population of both men and women for each transgender individual. Studies should be carried out over a longer term. The production of a more ethnically diverse, wider age range and larger sample size of transgender individuals is of high importance. It is important to distinguish between lifestyle, social factors and biological factors by appropriate inclusion/exclusion criteria. Studies should exclude prior CV disease at the baseline and, if possible, recruit individuals before they begin GAHT. Incentivisation and public outreach will be required to recruit transgender individuals and make individuals aware of their CV health risks due to the high loss of follow-up across studies (84).

Understanding the mechanisms behind the increased risk of CVD in transgender individuals will allow screening of those who are at higher risk, provide therapies or lifestyle interventions and provide evidence-based knowledge to individuals undergoing GAHT. Monitoring and interpreting clinical laboratory values is important as these are used to support clinical decisions, and it is important that transgender individuals receive the same high-quality evidence-based care as CGM and CGM. If physicians are aware of the mechanisms and risk factors for CVD in TGM and TGW, preventative measures can be implemented to reduce the burden of CVD in this population. With further understanding of the laboratory and clinical markers of CVD risk, this information can be utilised in risk stratification of transgender individuals when they seek healthcare treatment. The goal would be to produce a review similar to that of Bays and coworkers (8) to provide an overview of the clinical considerations in preventative cardiology in the transgender population in the future. This will also extend further than the transgender population to those who have received sex hormone-based therapies for breast cancer, prostate cancer, HRT for menopause, oral contraceptives and puberty blockers.

Conclusion

Evidence suggests beneficial or neutral changes in the CVD risk profile in TGW and negative changes in TGM with administration of GAHT. The evidence is, however, often conflicting with studies reporting results contradicting this within each CV phenotype. This suggests that additional, larger and more diverse studies with a standardised study design would allow comparison and meta-analyses to be conducted. This would help elucidate why there is a higher rate of CV events in the transgender population and whether GAHT is a contributing factor. In the meantime, there is a need to counsel TGM regarding lifestyle modifications for CVD.

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the work reported.

Funding

This work has been supported by a collaborative PhD studentship to KAM funded by the Universities of Glasgow and Sydney. KAM and CD are also supported by a Programme of Research on long-term health outcomes of people accessing gender identity healthcare funded by the Scottish Government. PC, ALH and CD are supported by the British Heart Foundation (RE/18/6/34217).

Author contribution statement

KAM helped with methodology design, data collection, investigation, formal analysis and writing the original draft. ALH contributed to data collection, formal analysis, investigation, writing, reviewing, editing and supervision. PC helped with conceptualisation, writing, reviewing and editing. CD helped with conceptualisation, methodology design, writing, reviewing, editing and supervision.

Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.

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  • Figure 1

    PRISMA flow chart of the search and inclusion/exclusion of studies investigating the impact of gender-affirming hormone therapy on cardiovascular risk or vascular function.

  • Figure 2

    Summary of alteration of cardiovascular (CV) risk profile with gender-affirming hormone therapy. In TGW, there were mostly beneficial changes in the CV risk profile, and in TGM, there were mostly negative changes in the CV risk profile. TGM, transgender men. TGW, transgender women. Created using BioRender.com.

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