Differences in Phenotypic Age Acceleration Between Sexual Minority and Heterosexual Adults in the USA

doi:10.21203/rs.3.rs-3211652/v1

Health disparities have been reported between sexual minority and heterosexual populations, but no study investigated Phenotypic Age Acceleration (PhenoAgeAccel) among sexual minority. To investigate differences in PhenoAgeAccel between sexual minority and heterosexual adults. In this cross-sectional study, 3061 participants from 2007–2010 National Health and Nutrition Examination Survey (NHANES) included adults aged 20 to 59 years who self-identified as being heterosexual, gay, lesbian, bisexual or others. The main outcome was PhenoAgeAccel predicted using nine routine clinical chemical biomarkers and chronological age. The sex-stratified linear regression model was used to examine the differences of PhenoAgeAccel and its nine biomarkers between sexual minorities and heterosexuality. The current study included 3061 participants consisting of 1518 (49.59%) males and 1543 (50.41%) females. Lesbian (β: -1.37, 95% CI: -2.46 to -0.27) and gay (β: -2.71, 95% CI: -4.13 to -1.29) were found to have the slowest PhenoAgeAccel compared with the heterosexual females. Bisexual males (β: -0.45, 95% CI: -0.73 to -0.18), lesbians (β: -0.49, 95% CI: -0.74 to -0.23) and bisexual (β: -0.41, 95% CI: -0.68 to -0.14) females were found to have a lower level of glucose. Gays (β: -0.14 95% CI: -0.25 to -0.03) and bisexual males (β: -0.06, 95% CI: -0.24 to -0.03) showed a lower level of c-reactive protein. The study for the first time suggested that gay and lesbian adults had the slower PhenoAgeAccel as compared to heterosexual adults.

Population aging is a major public health issue since aging is a risk factor for most chronic diseases ^1,2. Therefore, prevention and intervention strategies are crucial to promoting healthy aging³. Individuals exhibit varying rates of aging, and such differences in the aging process are reflected in their varying propensities to succumb to mortality and morbidity^4,5. The objective of decelerating the aging process has been a longstanding pursuit of humanity, and comprehending the determinants linked with aging may aid in our endeavors to combat aging. Recently, a new biological aging index based on nine clinical biomarkers has been developed, Phenotypic Age Acceleration (PhenoAgeAccel)⁶. PhenoAgeAccel, an acronym for the assessment of phenotypic age in relation to chronological age, serves as an indicator of biological aging, with positive values suggesting an accelerated biological aging process beyond what is expected⁷. The utilization of this metric of aging serves as a valuable tool in evaluating the efficacy of interventions aimed at preserving youthfulness, pinpointing potential risk factors, and illuminating the underlying mechanisms of the aging process⁸.

In recent years, there has been increased concern about the health situation and healthcare needs of lesbian, gay, bisexual, and transgender (LGBT) people ⁹. A 2011 report by the Institute of Medicine highlighted health disparities among LGBT people¹⁰. Subsequent to the publication of the report, a multitude of scholars have conducted inquiries into health inequities predicated upon sexual orientation with the aim of eradicating such a gap. Studies have demonstrated variations in mental health status between heterosexual and non-heterosexual individuals, spanning from LGBT adolescents to adulthood¹¹. Most current studies have focused on differences in mental health and behavior among LGBT populations. As an illustration, a cross-sectional investigation demonstrated that lesbian and bisexual females exhibited elevated rates of tobacco consumption in comparison to their heterosexual counterparts¹². Another study of LGBT people in New York City showed that they were more likely to use mental health clothing and take prescription medications for mental/emotional conditions¹³. In addition, a few studies have explored differences in physical health in LGBT populations. For example, in a cross-sectional analysis of data from 12 180 participants, bisexual women had lower cumulative cardiovascular health scores than heterosexual women¹⁴. There are also differences in health and mortality among sexual minorities of different genders^15,16. As of present, there exists a limited number of population-based studies that have explored the correlation between aging and sexual minorities. A study showed that LGBT adults perform better in some aspects of aging because of their experiences as a sexual minority¹⁷. Most early articles described positive psychosocial functioning in older gay men and lesbians, with positive attitudes toward aging, appearance, and sexuality^18,19. In a separate study, 73.4 percent of older transgender people they surveyed reported coping well with aging²⁰. Nevertheless, there is a dearth of research that delves into the intricacies of the aging process and PhenoAgeAccel among sexual minorities.

To fill these knowledge gaps, we firstly assessed the differences in PhenoAgeAccel between sexual minority and heterosexual adults using data from the National Health and Nutrition Examination Survey (NHANES) in 2007–2010. Moreover, our study also explored differences in its nine biomarkers components among sexual minority and heterosexual adults.

2.1Data collection and study population

The NHANES is an ongoing, multistage, stratified survey of the noninstitutionalized population of the United States. The NHANES database contains a wealth of information on a variety of health and nutrition-related topics, including demographics, dietary intake, physical activity, health conditions and environmental exposures. Data are collected through a combination of interviews, physical examinations, and laboratory tests.

This cross-sectional study was conducted across 2 cycles of the NHANES (2007–2010). Since 2007, the NHANES database has been completely harmonized and improved on questionnaires for sexual minorities. The restrictions on available publicly available data vary according to the survey year, but always include complete personal information about those between the ages of 20 and 59. Consequently, respondents in this age group were selected for the current study. Prior to 2010, the NHANES database contained measurements of the complete nine biomarkers that could be used to calculate PhenoAgeAccel. Therefore, we used data from the NHANES database from 2007 to 2010 From 2007 to 2010, a total of 20,686 individuals were included in the NHANES survey. Our study included 7,926 participants aged 20 to 59 years old. Participants who were missing data on the nine biomarkers (albumin, creatinine, glucose, C-reactive protein, lymphocyte percentage, mean cell volume, red cell distribution width, alkaline phosphatase, white blood cell count) that comprise PhenoAgeAccel (N = 4450), or sexual orientation identity (N = 415) were excluded. Ultimately, the study comprised a total of 3061 individuals.

2.2 Variable definitions for sexual orientation identity

The NHANES measures sexual orientation with a question about sexual identity. The participants of the study identified their sexual orientation by selecting from a set of predetermined options, which included heterosexual (meaning sexually attracted solely to the opposite sex), homosexual (meaning sexually attracted solely to the same sex), bisexual (meaning sexually attracted to both men and women), an alternative option, or uncertainty. After excluding those who answered “no,” “don’t know,” or “I don’t know the answer,” we categorized the remaining participants as gay, straight, bisexual, or other based on their responses. Other category included participants who did not identify as gay, heterosexual, or bisexual. The responses homosexual or gay/lesbian, bisexual, and other (LGBT+) were combined to create a sexual minority category.

2.3 Criteria for determining Phenotypic age acceleration⁶

The calculation of phenotypic age involves nine clinical chemical biomarkers (albumin, creatinine, glucose, C-reactive protein, lymphocyte percentage, mean cell volume, red cell distribution width, alkaline phosphatase, white blood cell count) and chronological age. Phenotypic age is a commonly used metric in medical research to evaluate the expected age of a population based on an individual's estimated risk of mortality. This indicator is frequently employed to identify risk factors for morbidity and mortality, evaluate the effectiveness of interventions, and provide insights into the mechanisms of aging. The formula used to calculate phenotypic age is as follows:

Phenotypic Age = 141.50 +\(\frac{{ln}\left\{-0.00553\times {ln}\left(1-mortality risk\right)\right\}}{0.090165}\)

where Mortality risk = 1 − exp (\(\frac{-1.51714 \times e\text{x}p \left(\text{x}b\right)}{0.0076927 }\)) and xb = − 19.907 − 0.0336 × albumin + 0.0095 × creatinine + 0.1953 × glucose + 0.0954 × ln (C − reactive protein) − 0.0120 × lymphocyte percentage + 0.0268 × mean cell volume + 0.3306 ×red blood cell distribution width + 0.00188 × alkaline phosphatase + 0.0554 × white blood cell count + 0.0804 × chronological age

In our study, we calculated PhenoAgeAccel as an indicator of physiological aging, which adjusts for chronological age. This metric quantifies the dissimilarity between an individual’s Phenotypic Age and chronological Age, serving as a tool to ascertain whether said individual exhibits an advanced (positive value) or delayed (negative value) aging phenotype relative to their physiological attributes. The formula used to calculate PhenoAgeAccel is as follows: PhenoAgeAccel = Phenotypic Age - chronological Age. This approach enables us to evaluate the rate of aging and identify factors that influence the aging process in medical research.

2.4 Covariates

The covariates in this study came from the population background section of NHANES (2007–2010) including sex (male, female), age, body mass index (BMI), race/ethnicity (other Hispanic, non-Hispanic black, non-Hispanic white, other), education level (high school and below, college or above), family income to poverty ratio (PIR) (≤ 1.3, 1.3 to 3.5, > 3.5), smoking status (current smoker, former smoker, never smoked), drinking status (yes, no), obesity (yes, no), depression (yes, no) and moderate-to-vigorous physical activity (MVPA). To determine an individual's BMI, their weight in kilograms was divided by the square of their height in meters. Those with a BMI equal to or greater than 30 were considered to have obesity²¹. The analysis focused on identifying clinically significant symptoms of depression, as defined by the standard cut point of The 9-item Patient Health Questionnaire (PHQ-9) scores of 10, in accordance with medical research guidelines.²² The calculation of MVPA was performed by means of the following formula: [the product of moderate leisure-time activity minutes and moderate leisure-time days] added to [the product of vigorous leisure-time activity minutes and vigorous leisure-time days] resulted in the total number of MVPA minutes per week²³.

2.5 Statistical Analyses

Data analysis was performed in accordance with the analysis guidelines and used recommended survey weights for NHANES data. For continuous variables, mean and standard deviation are commonly presented. Analysis of variance (ANOVA) is used for between-group comparison of continuous variables. For categorical variables, frequencies and percentages were presented. The chi-square test was used for analyzing differences among groups of categorical variables. The sex-stratified linear regression model was used to examine the differences of PhenoAgeAccel and nine biomarkers that composed phenotypic age between sexual minorities and heterosexuality. For all regression analyses, model 1 was basic model (unadjusted) and model 2 was adjusted for ethnicity/race, education, PIR, smoking, drinking, obesity, depression and MVPA. All regressions were conducted using heterosexuals as the reference group. In addition, we performed stratified analyses to explore difference in PhenoAgeAccel between sexual minorities and heterosexual adults among different populations based all covariates. The results are presented in the form of β-values, accompanied by their respective 95% confidence intervals (CIs). The data analysis was performed using Empower (R) (www.empowerstats.com, X&Ysolutions, inc. Boston MA) and R (http://www.R-project.org). The plots were all created using R version 4.3.0. Statistical significance was defined as a P value of 0.05.

3.1 Characteristics of participants

The current study included 3061 participants consisting of 1518 (49.59%) males and 1543 (50.41%) females. Sexual identity differences of characteristics among male (Table 1) and female (Table 2) participants were showed. There were significant age differences among males of different sexual identity (P = 0.020), with gay (37.81 [2.88] years) and bisexual (36.68 [2.37] years) having relatively low ages. There were sexual identity differences in race/ethnicity, educational level, PIR and MVPA among male participants (all P < 0.05). Compared with the heterosexual females, lesbian (34.53 [2.89] years) and bisexual (33.65 [1.30] years) females were younger (P < 0.001). We found significant differences in sexual identity by race/ethnicity, educational level, PIR, smoking status, drinking status and depression among female participants (all P < 0.05).

3.2 Sexual Identity Differences in Phenotypic Age and PhenoAgeAccel

Figure 1 and eTable 1 presented the levels of phenotypic age and PhenoAgeAccel in different sexual identity populations. Phenotypic age varied in sexual identity groups among both male (P = 0.003) and female (P < 0.001) participants. We found that gay males had the lowest phenotypic age among males (28.85 [0.64] years), while bisexual females had the lowest phenotypic age compared other sexual identity females (27.17 [1.50] years). Meanwhile, the gay (-8.95 [0.64] years) and lesbian (-7.32 [0.59] years) participants had the lowest PhenoAgeAccel among both males and females. However, the significant difference of PhenoAgeAccel was only found in four sexual identity groups of males (P = 0.001) but not in females.

In linear regression analysis (Table 3), lesbian females were also found to have less PhenoAgeAccel compared with the heterosexual females after adjusting all covariate (β: -1.37, 95% CI: -2.46 to -0.27). Both gay male individuals (β: -2.71, 95% CI: -4.13 to -1.29) and something else individuals (β: -0.70, 95% CI: -2.29 to -0.90) had less PhenoAgeAccel relative to the heterosexual males in model 2. When we expressed sexual identity as binary variable, the GBT+ (β: -1.10, 95% CI: -2.02 to -0.17) and LBT+ (β: -1.38, 95% CI: -2.63 to -0.13) individuals also had lower PhenoAgeAccel than heterosexual individuals (eTable 2).

3.3 Sexual Identity Differences in Nine Biomarkers Components of Phenotypic Age Acceleration

As shown in Fig. 2A, heterosexual males had the highest level of albumin (43.92 [0.13] g/L) than other males and a significant difference was found in sexual identity of male individuals (P = 0.044, eTable 1). In females, lesbian females had the highest albumin (43.92 [0.13 g/L]) but without significant sexual identity differences. In Fig. 2C, gay (5.30 [0.09 mmol/L]) and bisexual (5.77 [0.16 mmol/L]) males had the lower level of glucose relative to heterosexual males and those who identified their sexual identity as something else (both P < 0.001). The same difference was observed in glucose among females with different sexual identity (P < 0.001). Regarding C-reactive protein, both gay males (0.18 [0.02 mg/dL], P < 0.001) and lesbian females (0.25 [0.05 mg/dL], P = 0.006) had the lowest level (Fig. 2D). Less favorable white blood cell count (5.82 [0.27 mg/dL]) was found in gay males (P = 0.010), but more favorable white blood cell count was found in lesbian (7.56 [0.40 mg/dL]) and bisexual (7.46 [0.24 mg/dL]) females (P = 0.003).

Table 4 presented the result in linear regression of sexual identity in nine biomarkers predicting PhenoAgeAccel after adjusting covariates. In males, gays were likely to have a lower level of C-reactive protein (β: -0.14 95% CI: -0.25 to -0.03), and red cell distribution width (β: -0.34, 95% CI: -0.61 to -0.06). Bisexual males were negatively associated with the level of creatinine (β: -4.04, 95% CI: -7.60 to -0.48), glucose (β: -0.45, 95% CI: -0.73 to -0.18), and C-reactive protein (β: -0.06, 95% CI: -0.24 to -0.03). In females, lesbian (β: -0.49, 95% CI: -0.74 to -0.23) and bisexual (β: -0.41, 95% CI: -0.68 to -0.14) females were found to have lower glucose. We also found lesbian (β: -0.09, 95% CI: -0.17 to -0.01) and female participants who identified their sexual identity as something else (β: -0.19, 95% CI: -0.32 to -0.05) was inversely associated with C-reactive protein. When we expressed sexual identity as a binary variable, we found similar patterns of sexual identity differences (eTable 3). In addition, we performed stratified analyses to explore difference in PhenoAgeAccel between sexual minorities and heterosexual adults among different populations (eTable 4).

In this study, we examine the difference in phenotypic age acceleration (PhenoAgeAccel) between sexual minorities and heterosexual adults in the US for the first time. Of major importance was the present finding of the slowest PhenoAgeAccel was observed among gay and lesbian, compared to heterosexual adults. This observation could be attributable to the association between sexual identity and several biomarkers predicting phenotypic age (glucose and c-reactive protein) among both males and females. In comparison to their heterosexual male counterparts, bisexual males exhibited a lower level of creatinine, while gay males demonstrated a lower red cell distribution width.

To our knowledge, no studies have examined the differences in PhenoAgeAccel between sexual minorities and heterosexual adults. PhenoAgeAccel was a novel aging measure commonly used to assess mortality and morbidity^6,7. We found that gay and lesbian were more likely to have the slower PhenoAgeAccel. However, a prospective cohort study in the US found gay had greater all-cause mortality, while no significant difference was found between lesbian female and heterosexual adults¹⁶. The cohort study did not report attributable mortality among sexual minorities. Another study similarly suggested that gay had greater all-cause mortality, with a major contributing factor as HIV²⁴. The previous study also reported no significant sexual orientation difference in all-cause mortality among both males and females, and lesbian evidenced greater risk for suicide mortality than heterosexual females²⁵. Consequently, the cause of higher death risk in sexual minorities may not only because of their adverse physiological factors. Despite a lower PhenoAgeAccel was found in sexual minorities in the current study, many previous studies still reported that sexual minorities were less likely to engage beneficial health-related behaviors^14,26. In addition, an American study found that compared with heterosexual males, gay had the lower risk of cardiovascular disease (CVD), but bisexual males had the higher risk of CVD²⁷. Meanwhile, the study also suggested sexual minority males had younger chronological age of vascular systems²⁷, which supported the findings of our study. Though sexual minorities are increasingly valued, there has been much less research on the physical health of sexual minorities than on their mental health. Our findings may represent an important contribution to existing research on physical health and PhenoAgeAccel disparities among sexual minorities adults.

In addition, the study also investigated the biomarkers associated with PhenoAgeAccel. We found that sexual minorities in general had lower levels of glucose. Two systematic reviews both reported no significant difference in diabetes between sexual minorities and heterosexual adults, but they did not examine the level of glucose^28,29. A cohort study found that sexual minorities had higher risk of diabetes, and the association was stronger among females than among males³⁰. In accordance with our research, a prior investigation discovered that gay had lower BMI, glycemic status, and bisexual male also demonstrated lower lipid level in comparison to their heterosexual counterparts¹⁴. Furthermore, a previous study presented that bisexual female exhibited the lower risk of diabetes, particularly among non-Hispanic white females, whereas non-Hispanic black lesbian females displayed a reduced risk of diabetes³¹. It is possible that sexual minorities exhibit improved glucose outcomes because of their comparatively superior dietary habits. In a prior study, lesbian and bisexual women were found to have superior dietary quality and lower glycemic index diets³². Furthermore, it has been reported that individuals who identify as gay exhibit a decreased likelihood of experiencing overweight and obesity³³, which could also result in comparatively lower levels of glucose. Consistent with previous findings, our study also found that gay, lesbian, and bisexual males had lower levels of C-reactive protein. In a prior study, sexual minority men had higher levels of C-reactive protein, while lesbians had lower levels of C-reactive protein than heterosexual females³⁴. However, a significant body of research indicates that depression, stress, or violence experienced by sexual minorities can result in heightened levels of inflammation^35–37, warranting further investigation.

Our study is the first to explore differences in PhenoAgeAccel among sexual minorities and heterosexual adults. In contrast to prior research endeavors that predominantly relied on self-reported health measures, our study utilized a predictive approach based on the analysis of nine distinct biomarkers, which may represent a more objective evaluation. Future research ought to direct attention towards not solely the psychological well-being of individuals who identify as sexual minorities, but also their physical well-being.

The study also has several limitations. First, it is not possible to infer cause-and-effect associations because NHANES is a cross-sectional study. Second, the restricted sample sizes of LGBT + participants might limit statistical power. However, various investigations have utilized NHANES data to explore the physical and mental well-being of sexual minorities. Third, we are unable to assess the validity of participants responses to sexual identity questions, so external research should be strengthened by asking about coping strategies in terms of sexual identity and sexual orientation. Finally, as is the case with all observational studies, the potential for residual confounding, unmeasured confounding, and measurement error remains.

In conclusion, the study suggested that gay and lesbian adults had the slowest phenotypic age acceleration than heterosexual adults. Moreover, sexual minorities had the lower level of glucose and C-reactive protein compared heterosexual adults among male and female adults. However, only bisexual males presented the lower level of creatinine and only gay had the lower level of red cell distribution width. There is a need for increased attention from professionals towards sexual minorities, as well as a call for further research to provide additional evidence regarding the health status of this population.

Author Contributions: YM, MS and ZM were responsible for study conception and design. YM, MS, ZM, YZ and MH collected the data. MS and ZM performed the data analysis. YM, MS, ZM, YZ, MH, XJ and CW performed the data interpretation. YM, MS and ZM drafted the manuscript. YM, XJ and CW critically reviewed and revised the manuscript for important intellectual content. All authors approved the final version of the manuscript to be published. MS and ZM is responsible for the integrity of the work as a whole. MS and ZM contributed equally as the first co-authors. YM, XJ and CW contributed equally as corresponding co-authors.

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Table 1. Sexual Identity Differences in Characteristics Among Male Participants (n=1518)

Characteristic	Heterosexual	Gay	Bisexual	Something else	P
N	1441 (95.8)	24 (1.70)	30 (1.56)	23 (0.94)
Age, years	39.21 (0.47)	37.81 (2.85)	36.68 (2.37)	47.02 (2.32)	0.020
Race/ethnicity					0.011
Other Hispanic	157 (5.23)	9 (20.59)	4 (8.41)	2 (6.36)
Non-Hispanic Black	650 (67.33)	9 (54.99)	12 (62.60)	8 (50.55)
Non-Hispanic White	262 (10.56)	2 (5.37)	10 (21.73)	2 (7.92)
Other	372 (16.88)	4 (19.05)	4 (7.26)	11 (35.17)
Educational level					0.032
High school and below	772 (42.10)	2 (6.16)	14 (42.44)	18 (70.37)
College or above	667 (57.90)	22 (93.84)	16 (57.56)	5 (29.63)
PIR					0.001
≤1.3	406 (19.84)	2 (11.11)	14 (36.81)	11 (47.37)
>1.3 to 3.5	472 (32.84)	7 (22.54)	8 (31.69)	11 (37.52)
>3.5	436 (47.32)	13 (66.35)	8 (31.50)	1 (15.11)
Smoking status					0.134
Current smoker	418 (26.12)	5 (22.46)	11 (26.54)	9 (32.03)
Former smoker	297 (21.73)	3 (4.81)	3 (6.40)	5 (30.62)
Never smoked	725 (52.14)	16 (72.73)	16 (67.06)	9 (37.34)
Drinking status					0.148
Yes	1243 (87.67)	20 (78.45)	23 (81.64)	17 (69.11)
No	197 (12.33)	4 (21.55)	7 (18.36)	6 (30.89)
Obesity					0.493
Yes	474 (32.21)	5 (23.67)	9 (40.63)	6 (21.65)
No	958 (67.79)	19 (76.33)	21 (59.37)	17 (78.35)
Depression					0.083
Yes	88 (5.05)	0 (0.00)	5 (15.32)	5 (20.29)
No	1351 (94.95)	24 (100.00)	25 (84.68)	18 (79.71)
MVPA, min/week	915.65 (47.42)	824.80 (213.39)	436.41 (147.30)	692.36 (230.53)	0.038
BMI, kg/m²	28.46 (0.20)	28.13 (1.50)	28.66 (1.23)	26.70 (1.39)	0.643

^a Values were means (SD) or n (weighted %).

^b Abbreviation: PIR, Family income to poverty ratio; MVPA, Moderate-to-vigorous physical activity; BMI, Body mass index.

Table 2. Sexual Identity Differences in Participants Characteristics Among Female Participants (n=1543)

Characteristic	Heterosexual	Lesbian	Bisexual	Something else	P
N	1421 (92.62)	18 (1.20)	66 (4.46)	38 (1.37)
Age, year	40.01 (0.39)	34.53 (2.89)	33.65 (1.30)	37.98 (2.73)	<0.001
Race/ethnicity					<0.001
Other Hispanic	189 (5.88)	0 (0.00)	2 (1.53)	4 (4.04)
Non-Hispanic Black	637 (68.20)	10 (70.43)	36 (70.09)	8 (29.93)
Non-Hispanic White	250 (11.69)	6 (24.40)	19 (21.55)	12 (27.50)
Other	345 (14.23)	2 (5.17)	9 (6.84)	14 (38.54)
Educational level					0.045
High school and below	627 (35.83)	8 (36.81)	28 (37.18)	27 (64.52)
College or above	791 (64.17)	10 (63.19)	38 (62.82)	11 (35.48)
PIR					0.008
≤1.3	433 (20.42)	5 (42.38)	29 (35.83)	18 (51.77)
>1.3 to 3.5	475 (34.45)	7 (38.73)	22 (34.96)	8 (22.97)
>3.5	398 (45.13)	3 (11.89)	13 (29.22)	5 (25.26)
Smoking status					0.001
Current smoker	314 (20.53)	7 (27.92)	37 (44.16)	11 (27.61)
Former smoker	230 (19.38)	3 (20.96)	9 (19.33)	2 (3.79)
Never smoked	877 (60.10)	8 (51.120	20 (36.51)	25 (68.60)
Drinking status					0.016
Yes	973 (73.75)	17 (98.32)	55 (80.83)	17 (47.57)
No	448 (26.25)	1 (1.68)	11 (19.17)	21 (52.43)
Obesity					0.970
Yes	541 (34.25)	7 (37.66)	27 (36.39)	18 (39.65)
No	871 (65.75)	11 (65.34)	39 (63.61)	20 (60.35)
Depression					0.004
Yes	176 (9.53)	2 (4.83)	15 (19.41)	9 (25.12)
No	1244 (90.47)	16 (95.17)	51 (80.59)	29 (74.88)
MVPA, min/week	493.74 (28.54)	734.32 (432.37)	660.21 (99.52)	414.08 (92.15)	0.113
BMI, kg/m²	28.47 (0.24)	28.65 (2.58)	28.96 (1.37)	29.73 (1.75)	0.877

^a Values were means (SD) or n (weighted %).

^bAbbreviation: PIR, Family income to poverty ratio; MVPA, Moderate-to-vigorous physical activity; BMI, Body mass index.

Table 3. Linear Regression Models Examining Sexual Identity Differences in Phenotypic Age Acceleration

Sexual Identity	Male individuals		Female individuals
	Model 1	Model 2	Model 1	Model 2
	β (95% CI)	β (95% CI)	β (95% CI)	β (95% CI)
Heterosexual	[Reference]	[Reference]	[Reference]	[Reference]
Gay/lesbian	-3.16 (-4.45, -1.87)	-2.71 (-4.13, -1.29)	-0.85 (-2.18, -0.48)	-1.37 (-2.46, -0.27)
Bisexual	-0.22 (-1.39, 1.83)	-1.49 (-3.03, 0.04)	-0.003 (-1.42, 1.42)	-0.71 (-2.58, 1.17)
Something else	-0.41 (-2.46, -3.27)	-0.70 (-2.29, -0.90)	0.15 (-3.27, 3.57)	-0.46 (-4.11, 3.18)

^aModel 1 Adjusted for none.

^bModel 2 Adjusted for ethnicity/race, education, PIR, smoking, drinking, obesity, depression, MVPA.

Table 4. Linear Regression Models Examining Sexual Identity Differences in Nine Biomarkers (albumin, creatinine, glucose, C-reactive protein, lymphocyte percentage, mean cell volume, red cell distribution width, alkaline phosphatase, white blood cell count) Predicting Phenotypic Age Acceleration

Biomarkers	Male individuals	Female individuals
Biomarkers	β (95% CI)	β (95% CI)
Albumin
Heterosexual	[Reference]	[Reference]
Gay/lesbian	-0.22 (-1.51, 1.07)	0.44 (-0.43, 1.32)
Bisexual	0.40 (-1.23, 0.26)	-0.58 (-1.55, 0.39)
Something else	-1.32 (-2.67, 0.03)	-0.60 (-2.28, 1.07)
Creatinine
Heterosexual	[Reference]	[Reference]
Gay/lesbian	-1.30 (-7.85, 5.25)	3.10 (-3.67, 9.87)
Bisexual	-4.04 (-7.60, -0.48)	-1.43 (-6.71, 3.86)
Something else	2.73 (-6.83, 12.29)	-4.69 (-13.64, 4.26)
Glucose
Heterosexual	[Reference]	[Reference]
Gay/lesbian	-0.23 (-0.55, 0.08)	-0.49 (-0.74, -0.23)
Bisexual	-0.45 (-0.73, -0.18)	-0.41 (-0.68, -0.14)
Something else	0.23 (-0.63, 1.08)	-0.20 (-0.56, 0.16)
C-reactive protein
Heterosexual	[Reference]	[Reference]
Gay/lesbian	-0.14 (-0.25, -0.03)	-0.09 (-0.17, -0.01)
Bisexual	-0.06 (-0.24, -0.03)	-0.07 (-0.23, 0.09)
Something else	0.05 (-0.18, 0.27)	-0.19 (-0.32, -0.05)
Lymphocyte percentage
Heterosexual	[Reference]	[Reference]
Gay/lesbian	2.87 (-1.97, 7.72)	-1.74 (-4.56, 1.07)
Bisexual	0.42 (-2.18, 3.03)	1.42 (-1.31, 4.15)
Something else	0.11 (-2.53, 2.76)	0.15 (-2.25, 2.55)
Mean cell volume
Heterosexual	[Reference]	[Reference]
Gay/lesbian	0.23 (-0.96, 1.42)	-0.61 (-2.78, 1.57)
Bisexual	-0.34 (-1.80, 1.11)	0.49 (-1.57, 2.55)
Something else	0.16 (-3.44, 3.76)	0.15 (-2.88, 4.59)
Red cell distribution width
Heterosexual	[Reference]	[Reference]
Gay/lesbian	-0.34 (-0.61, -0.06)	-0.19 (-0.50, 0.13)
Bisexual	-0.08 (-0.30, 0.15)	-0.18 (-0.64, 0.27)
Something else	0.09 (-0.39, 0.56)	0.06 (-0.57, 0.44)
Alkaline phosphatase
Heterosexual	[Reference]	[Reference]
Gay/lesbian	-4.07 (-12.75, 4.61)	0.46 (-6.71, 7.64)
Bisexual	2.81 (-4.75, 10.37)	-4.49 (-10.80, 1.81)
Something else	4.01 (-4.10, 12.12)	-1.85 (-12.22, 8.51)
White blood cell count
Heterosexual	[Reference]	[Reference]
Gay/lesbian	-0.76 (-1.56, 0.04)	0.40 (-0.48, 1.28)
Bisexual	0.40 (-0.05, 0.86)	0.03 (-0.80, 0.86)
Something else	-0.25 (-0.78, 0.29)	-1.11 (-1.63, 0.59)

^a Adjusted for ethnicity/race, education, PIR, smoking, drinking, obesity, depression, MVPA.

Supplementarymaterials.docx

Differences in Phenotypic Age Acceleration Between Sexual Minority and Heterosexual Adults in the USA

Status:

Version 1

Abstract

Figures

1. Introduction

2. Research Design and Methods

2.1Data collection and study population

2.2 Variable definitions for sexual orientation identity

2.3 Criteria for determining Phenotypic age acceleration⁶

2.4 Covariates

2.5 Statistical Analyses

3. Results

3.1 Characteristics of participants

3.2 Sexual Identity Differences in Phenotypic Age and PhenoAgeAccel

3.3 Sexual Identity Differences in Nine Biomarkers Components of Phenotypic Age Acceleration

4.Discussion

5.Strengths and Limitations

6.Conclusion

Declarations

References

Tables

Supplementary Files

Status:

Version 1

Differences in Phenotypic Age Acceleration Between Sexual Minority and Heterosexual Adults in the USA

Status:

Version 1

Abstract

Figures

1. Introduction

2. Research Design and Methods

2.1Data collection and study population

2.2 Variable definitions for sexual orientation identity

2.3 Criteria for determining Phenotypic age acceleration6

2.4 Covariates

2.5 Statistical Analyses

3. Results

3.1 Characteristics of participants

3.2 Sexual Identity Differences in Phenotypic Age and PhenoAgeAccel

3.3 Sexual Identity Differences in Nine Biomarkers Components of Phenotypic Age Acceleration

4.Discussion

5.Strengths and Limitations

6.Conclusion

Declarations

References

Tables

Supplementary Files

Status:

Version 1

2.3 Criteria for determining Phenotypic age acceleration⁶