Reproductive Hormones and Subclinical Cardiovascular Disease in Midlife Women

Reproductive Hormones and Subclinical Cardiovascular Disease in Midlife Women Abstract Context Reproductive hormones are important to the pathophysiology of cardiovascular disease (CVD) in women. However, standard estradiol (E2) and testosterone (T) assays lack sensitivity at the levels of postmenopausal women. Objective Investigate relations of mass spectrometry–assessed estrone (E1), E2, and T and SHBG and subclinical CVD in women. Design, Setting, and Participants Three hundred and four perimenopausal and postmenopausal women aged 40 to 60 years underwent subclinical CVD measurements. E1, E2, and T were assayed using liquid chromatography–tandem mass spectrometry; free T (FT) was estimated using ensemble allostery models. Regression models were adjusted for CVD risk factors. Main Outcome Measures Carotid artery intima media thickness, interadventitial diameter (IAD), and plaque; brachial flow mediated dilation (FMD). Results Higher E1 was related to higher FMD [β(SE) = 0.77 (0.37), P = 0.04], indicating better endothelial function. Higher E2 was related to lower IAD [β(SE) = −0.07 (0.02), P = 0.004], indicating less carotid remodeling. Higher SHBG was related to higher FMD [β(SE) = 1.31 (0.40), P = 0.001], yet higher IAD [β(SE) = 0.15 (0.06), P = 0.02] and plaque [OR (95% CI) = 1.84 (1.16 to 2.91), P = 0.009]; FT showed a similar yet inverse pattern of relations as SHBG. Thus, higher SHBG and lower FT were associated with better endothelial function, yet greater carotid remodeling and plaque. Conclusions Endogenous E1 levels were related to endothelial function and E2 to vascular remodeling, suggesting distinct roles of these estrogens. SHBG and FT have complex roles depending on the vessel under study. Cardiovascular disease (CVD) is the leading cause of death in women (1). Reproductive hormones have long been postulated to be involved in the pathophysiology of CVD in women. Women develop clinical CVD largely during their postmenopausal years, on average several years later than do men (2). Reproductive hormones such as estradiol (E2) change markedly with menopause, with implications for cardiovascular health (3). Although a large literature has considered the impact of exogenous hormone administration on midlife women’s cardiovascular health (3), a more limited body of research considers endogenous hormones. As the cardiovascular impact of exogenous vs endogenous hormones may not be comparable (4), further research on the role of endogenous reproductive hormones in women’s cardiovascular health is warranted. Traditional assays for the measurement of sex hormones have problems of suboptimal sensitivity, precision, and accuracy in the low range present in postmenopausal women. Most postmenopausal women have endogenous E2 and testosterone (T) levels that fall below the sensitivity of standard immunoassays, calling into question the validity of findings using these assays in aging women (5, 6). For example, several Endocrine Society position statements have underscored that many standard direct immunoassays for E2 and T have limited accuracy <30 pg/mL and <300 ng/dL for E2 (7) and T (8), respectively, rendering these assays of limited utility for postmenopausal women. Liquid chromatography–tandem mass spectrometry (LC-MS/MS) has emerged as the reference method for the measurement of sex steroids that can quantify with high precision and accuracy the low levels of these hormones observed in postmenopausal women (5, 8, 9). Few studies have used LC-MS/MS when considering sex steroids and women’s cardiovascular health. E2, the hormone that is typically the focus for women’s cardiovascular health, is an ovarian estrogen that binds to estrogen receptors with high affinity and shows dramatic changes during the menopause (10). However, other reproductive hormones are likely critical to women’s vascular health. SHBG is a protein that binds to E2 and T and impacts their bioavailability. In addition to its role as a binding protein for E2 and T, SHBG is being recognized as a risk factor for diabetes and atherosclerosis development, particularly in women (11, 12). T may play an important role in women’s vascular health, yet investigation has been limited by the poor sensitivity and accuracy of standard T immunoassays in women (13–16). Estrone (E1), produced in the peripheral tissues by aromatization of δ-4-androstenedione, does not show the degree of decline of E2 during menopause, and may have particular relevance among postmenopausal women who typically have low E2 levels (17). E1 has received limited attention in relation to cardiovascular health. We tested LC-MS/MS-assessed E1, E2, and T as well as free T (FT) (18) and two-site directed immunometric-assessed SHBG in relation to subclinical CVD measures in a community sample of nonsmoking midlife women free of clinical CVD. Use of subclinical CVD indices is useful in understanding the development of CVD risk in midlife women, as midlife is typically before the onset of clinical CVD in women (2). We considered several subclinical CVD indices, including carotid artery intima media thickness (IMT) and carotid plaque, a measure of subclinical atherosclerosis; carotid interadventitial diameter (IAD), a measure of vascular remodeling; and brachial artery flow mediated dilation (FMD), an index of endothelial function. All are well-validated subclinical CVD indices that predict later CVD outcomes (19, 20). We consider a range of potential explanatory factors in these relations, including CVD risk factors. Patients and Methods The MsHeart Study recruited 304 late perimenopausal and postmenopausal (21) nonsmoking women aged 40 to 60 from the community. Reflecting the parent study aims (22), half of the women reported hot flashes, and half reported no hot flashes. Participants underwent physical measurements; psychosocial and medical history assessments; ambulatory monitoring; and after an overnight fast, phlebotomy, and a brachial artery and carotid artery ultrasounds. Procedures were approved by the University of Pittsburgh Institutional Review Board. Participants provided written informed consent. Exclusion criteria included hysterectomy and/or bilateral oophorectomy; history of heart disease, stroke, arrhythmia, gynecological cancer, pheochromocytoma, pancreatic tumor, kidney failure, seizures, Parkinson disease, Raynaud phenomenon; current pregnancy; or having used the following medications (past 3 months): oral/transdermal estrogen or progesterone, selective estrogen receptor modulators, selective serotonin reuptake inhibitors, serotonin norepinephrine reuptake inhibitors, gabapentin, insulin, β blockers, calcium channel blockers, α-2 adrenergic agonists, or other antiarrhythmic agents. Women were not excluded on the basis of body mass index (BMI) or history of polycystic ovary syndrome. Of the 304 women, exclusions due to missing or poor quality data were as follows [IMT: n = 4; IAD/plaque: n = 2; FMD: n = 33; E1: n = 2; T: n = 2; and homeostatic model assessment (HOMA)/low-density lipoprotein: n = 5]. The final sample sizes ranged from n = 265 to 271 for FMD models, n = 293 to 300 for IMT models, and n = 295 to 302 for plaque/IAD models. Hormones E1, E2, and T were measured using LC-MS/MS. E1 and E2 were assessed at the University of Pittsburgh’s Small Biomarker Core and T in the Brigham and Women’s Hospital Research Assay Core Laboratory, certified by the Center for Disease Control’s Hormone Assay Standardization Program for Testosterone, under the supervision of Dr. Bhasin (23). LC-MS/MS assay employs liquid-liquid extraction, derivatization, and detection with a triple quad mass spectrometer (24). For estrogens (E1 and E2), the lower limit of detection was 1.0 pg/mL and of quantitation was 2.5 pg/mL. Intraday statistics showed errors below 8.1% and relative SDs (RSDs) below 10.4%; interday statistics showed errors below 5.0% with RSDs below 7.4%. For E2 values below the sensitivity of the assay (n = 40), a random number between 0 and the lower limit was generated. For T, the lower limit of quantitation was 1.0 ng/dL, with intra-assay variation of <4% RSDs and interassay variation of <5% RSDs (23). Standards, blanks, calibrators, and control pools were run simultaneously with all samples. SHBG was measured at the University of Pittsburgh Chemistry and Nutrition Laboratory via ELISA (ALPCO, Salem, NH), with a sensitivity of 0.1 nmol/L and intra-assay and interassay coefficients of variation of 1.3% and 4.9%, respectively, at 20 nM. FT was calculated from total T and SHBG, using the ensemble allostery model, a method that has been validated against the equilibrium dialysis method, and that provides accurate estimates of FT that closely match FT determined using the equilibrium dialysis method (18, 25). Carotid ultrasound Trained and certified sonographers at the University of Pittsburgh’s Ultrasound Research Laboratory obtained bilateral carotid images via B-mode ultrasound using a Sonoline Antares (Siemens, Malvern, PA) high-resolution duplex scanner equipped with a VF10-5 transducer. Digitized images were obtained from eight locations (four locations each from the left and right carotid arteries): near and far walls of the distal common carotid artery [(CCA); 1 cm proximal to the carotid bulb], the far walls of the carotid bulb (the point in which the near and far walls of the common carotid are no longer parallel, extending to the flow divider), and internal carotid artery (from the flow divider to 1 cm distal to this point). IMT measures were obtained by electronically tracing the lumen-intima interface and the media-adventitia interface across a 1-cm segment for each of the eight segments described above; one measurement was generated for each pixel over the area, for a total of approximately 140 measures for each segment. The average values for these measures were recorded for each of the eight locations, and the mean of the average readings across these eight locations was used for analyses. CCA IADs were measured directly as the distance from the adventitial-medial interface on the near wall to the medial-adventitial interface on the far wall at end diastole across the same CCA segments used for IMT measurement. Images were read using semiautomated reading software (26). Reproducibility of IMT measures was excellent (intraclass correlation coefficient between sonographers = 0.87 to 0.94, between readers = 0.94 to 0.99). Higher IMT and IAD indicate greater subclinical atherosclerosis and more adverse vascular remodeling, respectively. Sonographers evaluated the presence and extent of plaque in each of five segments of the left and right carotid artery (distal and proximal CCA, carotid bulb, and proximal internal and external carotid arteries). Consistent with the Mannheim Consensus Statement (27), plaque was defined as a focal area protruding into the vessel lumen that was at least 50% thicker than the adjacent IMT and summarized as the presence or absence of plaque. For each segment, the degree of plaque was graded: grade 0 = no observable plaque; grade 1 = one small plaque (<30% of vessel diameter); grade 2 = one medium plaque (30% to 50% of vessel diameter) or multiple small plaques; and grade 3 = one large plaque (≥50% of vessel diameter) or multiple plaques with at least one medium plaque. Grades from all segments were summed to create the plaque index (28), categorized as any/none for analysis. Between-sonographer agreement for plaque assessment was high (κ = 0.78). Brachial ultrasound FMD was measured when women were fasting and had refrained from exercise and caffeine for 6 hours. FMD was measured after 10 minutes of supine rest by high resolution B-mode ultrasound imaging of the right brachial artery, 2 to 10 cm proximal to the antecubital crease by trained sonographers using a standardized protocol. Images were obtained at rest (baseline) and after 5 minutes of forearm blood flow occlusion (postdeflation) with a pneumatic tourniquet set to 50 mm Hg above the participant’s systolic blood pressure. For baseline diameters, digitized images were recorded for 20 seconds. Immediately after deflation, images were recorded continuously for 3 minutes. The arterial diameter was measured as the distance between the anterior and posterior arterial wall media-adventitia interfaces on images captured on the R wave using edge-detection software. Images were read by a single trained reader using the Brachial Analysis System software allowing continuous tracking of the brachial artery diameter across images so that the peak diameter change can be accurately determined. FMD was calculated as the maximum percentage of change in arterial diameter relative to baseline. This methodology is reproducible at this laboratory (intraclass correlation coefficients = 0.70 to 0.72) (29). Lower FMD corresponds to poorer endothelial function. Covariates Height and weight were measured, and BMI calculated (kg/m2). Seated blood pressure was measured via a Dinamap device (GE Medical Systems Information Technologies Inc., Milwaukee, WI) after a 10-minute rest, with blood pressure the mean of second and third measurements. Medical, reproductive, and psychosocial history was assessed by standard instruments. Medication use (e.g., antidepressants, antihypertensives, lipid-lowering medications, medications for glucose control, β agonists, and anticonvulsants) was reported. Menopause status was obtained from reported menstrual bleeding patterns (21). Anxiety and physical activity were assessed with validated instruments (30). Hot flashes were quantified via physiologic monitoring (22). Glucose, high-density lipoprotein cholesterol (HDL-C), and triglycerides were measured enzymatically (Vital Diagnostics, Lincoln, RI). Total cholesterol was determined enzymatically and low-density lipoprotein cholesterol (LDL-C) calculated using the Friedewald formula. Insulin was measured via radioimmunoassay. HOMA, an indicator of insulin resistance, was calculated (31). Analyses Variables were examined for outliers and deviations from normality. E1, E2, SHBG, T, HOMA, and triglycerides were log-transformed and physical activity square root transformed for analysis. Associations between each hormone and each outcome were tested using linear (IMT, IAD, and FMD) and logistic (plaque) regression. Primary models considered each hormone separately; additional models considered several hormones simultaneously (SHBG and E2 or SHBG and T). Covariates were selected based upon their associations with the outcome at P < 0.10. Expanded models for FMD were also estimated with inclusion of multiple CVD risk factors. Hot flashes and menopause stage were considered as additional covariates. Given debates about the optimal method to statistically analyze FMD, allometric scaling methods to analyze FMD were considered in sensitivity analyses (32). T models were repeated excluding several high values (>52 ng/dL), and E2 models repeated excluding very low values (<2.5 pg/mL). Interactions between hormones and menopause stage were tested. Residual analysis and diagnostic plots were conducted to verify model assumptions. Analyses were performed with SAS version 9.4 (SAS Institute, Cary, NC). Models were two-sided at α = 0.05. Results Participants were on average 54 years old, overweight, and postmenopausal (Table 1). Seventy-two percent of the sample was non-Hispanic white, with the remaining 28% of minority race/ethnicity. Hormone levels were consistent with a sample of primarily postmenopausal women. Almost half of the women had evidence of carotid plaque. Table 1. Sample Characteristics N 304 Age, y, M (SD) 54.1 (4.0) Race/ethnicity, n (%)  Non-Hispanic white 220 (72.4)  Black/othera 84 (27.6) Education, n (%)  High school/some college/vocational 129 (42.4)  College or higher 175 (57.6) Menopause stage, n (%)  Perimenopausal 49 (16.12)  Postmenopausal 255 (83.88) Parity, number of live births, median (IQR) 2.0 (1.0–3.0) BMI, M (SD) 28.99 (6.76) SBP, mm Hg, M (SD) 119.85 (14.48) DBP, mm Hg, M (SD) 70.18 (9.11) LDL-C, mg/dL, M (SD) 130.42 (33.47) HDL-C, mg/dL, M (SD) 62.86 (14.84) Triglycerides, mg/dL, median (IQR) 96.00 (71.0–129.0) HOMA, median (IQR) 2.20 (1.68–3.18) Medications, n (%)  Blood pressure–lowering 48 (15.8)  Antidiabetic 10 (3.3)  Lipid-lowering 39 (12.8)  β-Agonists 14 (4.61) Anxiety, median (IQR) 32.10 (9.89) Physical activity, leisure time, median (IQR) 396 (0–1383) Hot flashes, physiologically monitored number/24 h, median (IQR) 6 (0–15) E1, pg/mL, median (IQR) 25.00 (16.50–37.00) E2, pg/mL, median (IQR) 4.95 (2.00–11.30) T, ng/dL, median (IQR) 25.55 (20.40–31.50) SHBG, nmol/L, median (IQR) 79.60 (50.00–122.10) FT, median (IQR) 0.33 (0.23–0.43) FMD, %, M (SD) 7.33 (3.91) IMT, mm, M (SD) 0.68 (0.11) IAD, mm, M (SD) 6.99 (0.60) Plaque, n (%)  None 162 (53.6)  Any 140 (46.4) N 304 Age, y, M (SD) 54.1 (4.0) Race/ethnicity, n (%)  Non-Hispanic white 220 (72.4)  Black/othera 84 (27.6) Education, n (%)  High school/some college/vocational 129 (42.4)  College or higher 175 (57.6) Menopause stage, n (%)  Perimenopausal 49 (16.12)  Postmenopausal 255 (83.88) Parity, number of live births, median (IQR) 2.0 (1.0–3.0) BMI, M (SD) 28.99 (6.76) SBP, mm Hg, M (SD) 119.85 (14.48) DBP, mm Hg, M (SD) 70.18 (9.11) LDL-C, mg/dL, M (SD) 130.42 (33.47) HDL-C, mg/dL, M (SD) 62.86 (14.84) Triglycerides, mg/dL, median (IQR) 96.00 (71.0–129.0) HOMA, median (IQR) 2.20 (1.68–3.18) Medications, n (%)  Blood pressure–lowering 48 (15.8)  Antidiabetic 10 (3.3)  Lipid-lowering 39 (12.8)  β-Agonists 14 (4.61) Anxiety, median (IQR) 32.10 (9.89) Physical activity, leisure time, median (IQR) 396 (0–1383) Hot flashes, physiologically monitored number/24 h, median (IQR) 6 (0–15) E1, pg/mL, median (IQR) 25.00 (16.50–37.00) E2, pg/mL, median (IQR) 4.95 (2.00–11.30) T, ng/dL, median (IQR) 25.55 (20.40–31.50) SHBG, nmol/L, median (IQR) 79.60 (50.00–122.10) FT, median (IQR) 0.33 (0.23–0.43) FMD, %, M (SD) 7.33 (3.91) IMT, mm, M (SD) 0.68 (0.11) IAD, mm, M (SD) 6.99 (0.60) Plaque, n (%)  None 162 (53.6)  Any 140 (46.4) Sample sizes for all variables: N = 304, except HOMA: n = 301; LDL-C: n = 302; E1: n = 302; T: n = 302; FT: n = 302; FMD: n = 271; IMT: n = 300; IAD: n = 302; and plaque: n = 302. Abbreviations: DBP, diastolic blood pressure; IQR, interquartile range; M, mean; SBP, systolic blood pressure. a Black and other race/ethnicity n (%): black: 67 (22.04); Asian: 11 (3.62); Hispanic: 2 (0.66); and biracial: 4 (1.32). View Large Table 1. Sample Characteristics N 304 Age, y, M (SD) 54.1 (4.0) Race/ethnicity, n (%)  Non-Hispanic white 220 (72.4)  Black/othera 84 (27.6) Education, n (%)  High school/some college/vocational 129 (42.4)  College or higher 175 (57.6) Menopause stage, n (%)  Perimenopausal 49 (16.12)  Postmenopausal 255 (83.88) Parity, number of live births, median (IQR) 2.0 (1.0–3.0) BMI, M (SD) 28.99 (6.76) SBP, mm Hg, M (SD) 119.85 (14.48) DBP, mm Hg, M (SD) 70.18 (9.11) LDL-C, mg/dL, M (SD) 130.42 (33.47) HDL-C, mg/dL, M (SD) 62.86 (14.84) Triglycerides, mg/dL, median (IQR) 96.00 (71.0–129.0) HOMA, median (IQR) 2.20 (1.68–3.18) Medications, n (%)  Blood pressure–lowering 48 (15.8)  Antidiabetic 10 (3.3)  Lipid-lowering 39 (12.8)  β-Agonists 14 (4.61) Anxiety, median (IQR) 32.10 (9.89) Physical activity, leisure time, median (IQR) 396 (0–1383) Hot flashes, physiologically monitored number/24 h, median (IQR) 6 (0–15) E1, pg/mL, median (IQR) 25.00 (16.50–37.00) E2, pg/mL, median (IQR) 4.95 (2.00–11.30) T, ng/dL, median (IQR) 25.55 (20.40–31.50) SHBG, nmol/L, median (IQR) 79.60 (50.00–122.10) FT, median (IQR) 0.33 (0.23–0.43) FMD, %, M (SD) 7.33 (3.91) IMT, mm, M (SD) 0.68 (0.11) IAD, mm, M (SD) 6.99 (0.60) Plaque, n (%)  None 162 (53.6)  Any 140 (46.4) N 304 Age, y, M (SD) 54.1 (4.0) Race/ethnicity, n (%)  Non-Hispanic white 220 (72.4)  Black/othera 84 (27.6) Education, n (%)  High school/some college/vocational 129 (42.4)  College or higher 175 (57.6) Menopause stage, n (%)  Perimenopausal 49 (16.12)  Postmenopausal 255 (83.88) Parity, number of live births, median (IQR) 2.0 (1.0–3.0) BMI, M (SD) 28.99 (6.76) SBP, mm Hg, M (SD) 119.85 (14.48) DBP, mm Hg, M (SD) 70.18 (9.11) LDL-C, mg/dL, M (SD) 130.42 (33.47) HDL-C, mg/dL, M (SD) 62.86 (14.84) Triglycerides, mg/dL, median (IQR) 96.00 (71.0–129.0) HOMA, median (IQR) 2.20 (1.68–3.18) Medications, n (%)  Blood pressure–lowering 48 (15.8)  Antidiabetic 10 (3.3)  Lipid-lowering 39 (12.8)  β-Agonists 14 (4.61) Anxiety, median (IQR) 32.10 (9.89) Physical activity, leisure time, median (IQR) 396 (0–1383) Hot flashes, physiologically monitored number/24 h, median (IQR) 6 (0–15) E1, pg/mL, median (IQR) 25.00 (16.50–37.00) E2, pg/mL, median (IQR) 4.95 (2.00–11.30) T, ng/dL, median (IQR) 25.55 (20.40–31.50) SHBG, nmol/L, median (IQR) 79.60 (50.00–122.10) FT, median (IQR) 0.33 (0.23–0.43) FMD, %, M (SD) 7.33 (3.91) IMT, mm, M (SD) 0.68 (0.11) IAD, mm, M (SD) 6.99 (0.60) Plaque, n (%)  None 162 (53.6)  Any 140 (46.4) Sample sizes for all variables: N = 304, except HOMA: n = 301; LDL-C: n = 302; E1: n = 302; T: n = 302; FT: n = 302; FMD: n = 271; IMT: n = 300; IAD: n = 302; and plaque: n = 302. Abbreviations: DBP, diastolic blood pressure; IQR, interquartile range; M, mean; SBP, systolic blood pressure. a Black and other race/ethnicity n (%): black: 67 (22.04); Asian: 11 (3.62); Hispanic: 2 (0.66); and biracial: 4 (1.32). View Large When considering FMD, higher E1 was related to greater FMD (Table 2), indicating better endothelial function. Higher SHBG and lower FT were each also related to greater FMD. Neither E2 nor T were related to FMD. Associations persisted adjusting for CVD risk factors and covariates. Table 2. Relations Between Sex Hormones, SHBG, and FMD FMD β(SE) Model 1 Model 2 Model 3 E1 0.60 (0.36) 0.69 (0.35)a 0.77 (0.37)a E2 0.06 (0.15) 0.11 (0.15) 0.14 (0.16) T 0.15 (0.69) –0.08 (0.65) –0.05 (0.67) SHBG 1.27 (0.36)b 1.12 (0.36)c 1.31 (0.40)c FTd −1.41 (0.47)c −1.42 (0.47)c −1.58 (0.52)c FMD β(SE) Model 1 Model 2 Model 3 E1 0.60 (0.36) 0.69 (0.35)a 0.77 (0.37)a E2 0.06 (0.15) 0.11 (0.15) 0.14 (0.16) T 0.15 (0.69) –0.08 (0.65) –0.05 (0.67) SHBG 1.27 (0.36)b 1.12 (0.36)c 1.31 (0.40)c FTd −1.41 (0.47)c −1.42 (0.47)c −1.58 (0.52)c Hormones log-transformed. Model 1: unadjusted. Model 2: baseline lumen diameter, age, race, BMI, leisure time physical activity, β-agonist meds, parity, and anxiety. Model 3: Model 2 plus education, SBP, HOMA, LDL-C, triglycerides, lipid-lowering medications, blood pressure-lowering medications, and diabetes medications. Abbreviation: SBP, systolic blood pressure. a P < 0.05. b P < 0.001. c P < 0.01. d FT estimated using ensemble allostery models. View Large Table 2. Relations Between Sex Hormones, SHBG, and FMD FMD β(SE) Model 1 Model 2 Model 3 E1 0.60 (0.36) 0.69 (0.35)a 0.77 (0.37)a E2 0.06 (0.15) 0.11 (0.15) 0.14 (0.16) T 0.15 (0.69) –0.08 (0.65) –0.05 (0.67) SHBG 1.27 (0.36)b 1.12 (0.36)c 1.31 (0.40)c FTd −1.41 (0.47)c −1.42 (0.47)c −1.58 (0.52)c FMD β(SE) Model 1 Model 2 Model 3 E1 0.60 (0.36) 0.69 (0.35)a 0.77 (0.37)a E2 0.06 (0.15) 0.11 (0.15) 0.14 (0.16) T 0.15 (0.69) –0.08 (0.65) –0.05 (0.67) SHBG 1.27 (0.36)b 1.12 (0.36)c 1.31 (0.40)c FTd −1.41 (0.47)c −1.42 (0.47)c −1.58 (0.52)c Hormones log-transformed. Model 1: unadjusted. Model 2: baseline lumen diameter, age, race, BMI, leisure time physical activity, β-agonist meds, parity, and anxiety. Model 3: Model 2 plus education, SBP, HOMA, LDL-C, triglycerides, lipid-lowering medications, blood pressure-lowering medications, and diabetes medications. Abbreviation: SBP, systolic blood pressure. a P < 0.05. b P < 0.001. c P < 0.01. d FT estimated using ensemble allostery models. View Large We next considered carotid indices. Higher E2 was associated with lower IAD, indicating less adverse vascular remodeling (Table 3). Higher SHBG and lower FT were each related to higher IAD and carotid plaque presence in multivariable models, indicating greater remodeling and plaque. Neither E1 nor T were related to carotid indices. Associations persisted with multivariable adjustment. Table 3. Relations Between Hormones and Carotid IMT, Adventitial Diameter, and Plaque Mean IMT IAD Plaque Presence β(SE) β(SE) OR (95% CI) Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 E1 0.008 (0.009) 0.01 (0.009) 0.02 (0.05) –0.003 (0.05) 1.14 (0.82–1.59) 1.31 (0.88–1.95) E2 –0.0003 (0.004) 0.001 (0.004) –0.04 (0.02)a –0.06 (0.02)b 1.01 (0.88–1.16) 1.10 (0.93–1.32) T 0.016 (0.018) 0.007 (0.016) –0.006 (0.10) –0.06 (0.10) 0.84 (0.43–1.64) 0.91 (0.44–1.89) SHBG –0.01 (0.009) 0.01 (0.01) 0.02 (0.05) 0.15 (0.06)b 1.27 (0.89–1.79) 1.84 (1.16–2.91)c FTd 0.02 (0.01) –0.013 (0.01) –0.03 (0.07) –0.19 (0.08)b 0.69 (0.43–1.09) 0.49 (0.28–0.88)b Mean IMT IAD Plaque Presence β(SE) β(SE) OR (95% CI) Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 E1 0.008 (0.009) 0.01 (0.009) 0.02 (0.05) –0.003 (0.05) 1.14 (0.82–1.59) 1.31 (0.88–1.95) E2 –0.0003 (0.004) 0.001 (0.004) –0.04 (0.02)a –0.06 (0.02)b 1.01 (0.88–1.16) 1.10 (0.93–1.32) T 0.016 (0.018) 0.007 (0.016) –0.006 (0.10) –0.06 (0.10) 0.84 (0.43–1.64) 0.91 (0.44–1.89) SHBG –0.01 (0.009) 0.01 (0.01) 0.02 (0.05) 0.15 (0.06)b 1.27 (0.89–1.79) 1.84 (1.16–2.91)c FTd 0.02 (0.01) –0.013 (0.01) –0.03 (0.07) –0.19 (0.08)b 0.69 (0.43–1.09) 0.49 (0.28–0.88)b Hormones log-transformed. Model 1: unadjusted. Model 2: age, race (white/nonwhite), BMI, education, SBP, DBP, HOMA, LDL-C, HDL-C, triglycerides, lipid-lowering medications, blood pressure-lowering medications, and diabetes medications. Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure. a P < 0.10. b P < 0.05. c P < 0.01. d FT estimated using ensemble allostery models. View Large Table 3. Relations Between Hormones and Carotid IMT, Adventitial Diameter, and Plaque Mean IMT IAD Plaque Presence β(SE) β(SE) OR (95% CI) Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 E1 0.008 (0.009) 0.01 (0.009) 0.02 (0.05) –0.003 (0.05) 1.14 (0.82–1.59) 1.31 (0.88–1.95) E2 –0.0003 (0.004) 0.001 (0.004) –0.04 (0.02)a –0.06 (0.02)b 1.01 (0.88–1.16) 1.10 (0.93–1.32) T 0.016 (0.018) 0.007 (0.016) –0.006 (0.10) –0.06 (0.10) 0.84 (0.43–1.64) 0.91 (0.44–1.89) SHBG –0.01 (0.009) 0.01 (0.01) 0.02 (0.05) 0.15 (0.06)b 1.27 (0.89–1.79) 1.84 (1.16–2.91)c FTd 0.02 (0.01) –0.013 (0.01) –0.03 (0.07) –0.19 (0.08)b 0.69 (0.43–1.09) 0.49 (0.28–0.88)b Mean IMT IAD Plaque Presence β(SE) β(SE) OR (95% CI) Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 E1 0.008 (0.009) 0.01 (0.009) 0.02 (0.05) –0.003 (0.05) 1.14 (0.82–1.59) 1.31 (0.88–1.95) E2 –0.0003 (0.004) 0.001 (0.004) –0.04 (0.02)a –0.06 (0.02)b 1.01 (0.88–1.16) 1.10 (0.93–1.32) T 0.016 (0.018) 0.007 (0.016) –0.006 (0.10) –0.06 (0.10) 0.84 (0.43–1.64) 0.91 (0.44–1.89) SHBG –0.01 (0.009) 0.01 (0.01) 0.02 (0.05) 0.15 (0.06)b 1.27 (0.89–1.79) 1.84 (1.16–2.91)c FTd 0.02 (0.01) –0.013 (0.01) –0.03 (0.07) –0.19 (0.08)b 0.69 (0.43–1.09) 0.49 (0.28–0.88)b Hormones log-transformed. Model 1: unadjusted. Model 2: age, race (white/nonwhite), BMI, education, SBP, DBP, HOMA, LDL-C, HDL-C, triglycerides, lipid-lowering medications, blood pressure-lowering medications, and diabetes medications. Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure. a P < 0.10. b P < 0.05. c P < 0.01. d FT estimated using ensemble allostery models. View Large We conducted several additional sensitivity analyses. We considered SHBG and E2 or SHBG and T together in multivariable models in relation to outcomes (FMD, IAD, IMT, and plaque). Findings were comparable to that of each hormone considered separately (data not shown). We considered additional covariates of menopause stage and hot flashes; findings were unchanged (data not shown). Analyses were repeated excluding women with very high T (>52 ng/dL, n = 7) or low E2 values (<2.0 pg/mL, n = 40); findings were unchanged. We considered alternate methods to analyze FMD (32), and conclusions were unchanged (data not shown). Moreover, we examined interactions by menopause stage, given evidence that (exogenous) estrogens may exert menopause state-specific effects (3). Menopause stage was a modifier of select associations, with higher E1 and T related to a more favorable vascular profile (lower IAD, higher FMD) among perimenopausal women, whereas lower SHBG was related to poorer endothelial function (lower FMD) among postmenopausal women (Supplemental Table 1). Discussion Using highly accurate and sensitive LC-MS/MS assays for the measurement of E1, E2, and T, we show here that among nonsmoking midlife women free of clinical CVD, higher E1 levels are related to indicators of better endothelial function; in contrast, higher E2 levels were associated with less carotid remodeling. Higher SHBG and lower FT were associated with better endothelial function, yet greater carotid remodeling and plaque. Taken together, these findings suggest a unique potential physiologic role of E1 in endothelial function and of E2 in vascular remodeling and a complex role of SHBG and free androgen that depends on the specific aspect of the vasculature under study. The vascular endothelium is well known to be sensitive to estrogens (3), which upregulate endothelial nitric oxide synthase expression and activity (33). However, existing work has largely focused on exogenous E2. The vascular impact of other endogenous estrogens, especially E1, has received little attention. E1 is produced mostly by aromatization of adrenal androgen precursors in peripheral tissues, particularly body fat. The present findings point to a unique, potential beneficial relation of endogenous E1 to endothelial function. Although E1 is a weaker estrogen than E2 in some bioassays, circulating E1 levels in perimenopausal and postmenopausal women are higher than those of E2. The association of E1, but not E2, with endothelial function may be related to the differential activity of these two ligands in estrogen receptor (ER) subtypes. Whether E1 exerts additional nongenomic effects on endothelial function is not known. This work indicated that higher E2 was related to less adverse vascular remodeling, or lower IAD. Our finding is consistent with the limited prior work on E2 and carotid remodeling. The Study of Women’s Health Across the Nation (SWAN) showed higher endogenous E2 associated with lower IAD with adjustment for multiple risk factors (34). Many automated immunoassays have limited accuracy <30 pg/mL (7). Notably, 90% of the women in our study had values that fell below 30 pg/mL. Thus, here we consider these relations with state-of-the-art measurements of E2, with a lower limit of quantitation of 2.5 pg/mL, sensitive to the low E2 levels of postmenopausal women. The ER subtype specificity of various ER ligands may contribute to their differential physiologic effects. This work highlights the potential importance of SHBG. SHBG is a protein produced in the liver. Its classical role is as a carrier protein that binds the steroid hormones and regulates their bioavailability (25). However, SHBG is now being recognized to have additional physiologic significance as a risk factor for diabetes and insulin resistance (11, 12). Multiple studies have linked lower SHBG to poorer vascular health, even after adjustment for CVD risk factors in some work (14, 16), but not in other work (15, 35, 36). BMI is particularly important to consider in these analyses given its close relation to SHBG and its role as a strong risk factor for CVD. Notably, little work has considered SHBG in relation to endothelial function. We show higher SHBG, measured via two-site directed immunometric assay, associated with higher FMD (better endothelial function), yet when considering carotid indices, higher SHBG was associated with carotid remodeling and plaque. Thus, SHBG was associated with better endothelial function, yet poorer carotid health. As relations between SHBG and vascular outcomes remained significant even after adjusting for E2 or T, they are unlikely driven by the steroid hormone itself. The reasons for this divergent pattern of associations of SHBG with different vascular indices are unclear, yet other studies have shown similar findings. MESA showed higher SHBG associated with lower carotid IMT, yet higher coronary artery calcification (CAC) (15). Together, findings raise the potential of complex relations of SHBG to different vessels and aspects of vascular health that warrant further attention. The LC-MS/MS measurement of T is particularly important in women, as most automated T immunoassays have poor sensitivity and precision below 100 ng/dL (5) or even 300 ng/dL (8), levels far above that of most women. Notably, 99% and 100% of the women in this study had values that fell below 100 ng/dL and 300 ng/dL, respectively. Existing work with older androgen assays yields mixed findings, with several studies finding no associations between T and vascular indices (IMT, CAC) (13, 14), others showing higher T associated with better vascular health (lower IMT) (16, 37), and still others showing higher androgens associated with poorer cardiovascular health [FMD (38), IMT (39), and coronary heart disease (40)]. MESA shows mixed results, with higher T and bioavailable T related to higher IMT, yet less CAC (15). We found no significant relation between total T and vascular outcomes. In contrast, higher FT was related to poorer endothelial function, yet less carotid remodeling and plaque. Thus, our results suggest distinctly different relations of T, E1, and E2 with markers of endothelial function, vascular remodeling, and plaque formation. It should be noted that the similar yet inverse effects of SHBG and FT in relation to outcomes would be anticipated given the binding physiology of T to SHBG and determination of FT, which incorporates SHBG. To further understand these findings, future work should consider the independent effects of each of T, estrogens, and SHBG on the underlying physiological mechanisms driving FMD (e.g., nitric oxide–mediated vasodilatation, etc.) vs vascular remodeling and plaque formation (e.g., collagen/elastin changes, lipoprotein-driven inflammation, smooth muscle cell proliferation, etc.). These findings underscore the importance of ongoing investigation of T in relation to women’s vascular health. Findings were robust to sensitivity analyses, such as inclusion of additional covariates, exclusion of extreme T and E2 values, and alternate methods of analyzing FMD. Several interactions emerged for menopause stage; evidence suggested that higher E1 and T were associated with a more favorable vascular profile (lower IAD or higher FMD) among perimenopausal women, whereas lower SHBG was related to poorer endothelial function principally among postmenopausal women. These post hoc interactions with a limited number of perimenopausal women should be interpreted with caution. However, stage-specific actions warrant consideration in future work. There were several study limitations. T was measured, yet FT was calculated using the ensemble allostery model, which has been shown to provide FT values that match closely those measured using the equilibrium dialysis. Future work should consider more direct measures of free or bioavailable T. The sample size was smaller than epidemiologic studies, yet the rigor and expense of LC-MS/MS steroid hormone assays are often prohibitive for larger samples. E2 assays were state of the art, yet there were women who had values below the limit of quantitation of our assay, underscoring the need for continued development of increasingly sensitive methods for postmenopausal women. Women were not characterized with respect to polycystic ovary syndrome, and its role in study findings is unclear. However, analyses excluding women with elevated T (>52 ng/dL) yielded unchanged conclusions. Our study sample, reflecting the demographics of the geographic area, included primarily non-Hispanic white and African American women. Future studies should include additional ethnic groups. We used a range of subclinical CVD indices, but did not include CAC. As this sample of women were nonsmoking midlife women free of clinical CVD, the choice of outcome measures was informed by the typically low prevalence of CAC among nonsmoking midlife women (41). This study had several strengths. We used gold-standard, state-of-the-art measures of steroid hormones. We considered a wide range of hormonal exposures, including E1, an understudied hormone in relation to vascular outcomes. FT was estimated using the unique ensemble allostery model that takes into account the complex, nonlinear allosteric binding of T to SHBG and provides values that closely match those derived from equilibrium dialysis (18). We included several well-validated subclinical CVD measures, including indices of endothelial function, carotid atherosclerosis, and vascular remodeling. We considered a wide range of confounders and potential explanatory factors, including a full array of CVD risk factors. Associations persisted with adjustment for these CVD risk factors. The implications of this work are multiple. First, E1, a previously underexamined hormone, is likely important to women’s endothelial health and requires further investigation. Second, E2 appears important to vascular remodeling. Third, an androgenic milieu may not have exclusively adverse implications for women’s health. Finally, further research into the physiologic actions of SHBG and its impact on vascular health is critical to understand the role of reproductive hormones and this protein carrier in women’s cardiovascular health. These data underscore the importance of endogenous reproductive hormones to women’s vascular health as they age. Abbreviations: Abbreviations: BMI body mass index CAC coronary artery calcification CCA common carotid artery CVD cardiovascular disease E1 estrone E2 estradiol ER estrogen receptor FMD flow-mediated dilation FT free testosterone HDL-C high-density lipoprotein cholesterol HOMA homeostatic model assessment IAD interadventitial diameter IMT intima media thickness LC-MS/MS liquid chromatography–tandem mass spectrometry LDL-C low-density lipoprotein cholesterol RSD relative SD T testosterone Acknowledgments We thank Samar El Khoudary for providing critical feedback on this manuscript. Financial Support: This work was supported by the National Institutes of Health (NIH), National Heart Lung and Blood Institute (R01HL105647, K24123565 to R.C.T.) and the University of Pittsburgh Clinical and Translational Science Institute (NIH Grant UL1TR000005). Allosteric framework for free testosterone determination is supported by the National Institutes of Health, National Institute on Aging (R43 AG045011 and R44 AG045011 to R.J.). Assay development and validation research was supported by the National Institutes of Health, National Institute on Aging (R01AG31206 to S.B.). Additional support to S.B. was provided by the Boston Claude D. Pepper Older Americans Independence Center grant P30AG031679 from the National Institute on Aging and by a grant from the CDC Foundation. This project used the University of Pittsburgh Small Molecule Biomarker Core (NIH Grant S10RR023461). Disclosure Summary: R.C.T. consults for MAS Innovation and Guidepoint. S.B. has received research grants from the National Institute on Aging, the National Institute of Nursing Research, the Patient Centered Outcomes Research Institute, the Foundation for the National Institutes of Health, AbbVie, Alivegen, MIB, Althea Biosciences, and Transition Therapeutics; these research grants are managed by his institution. S.B. has received consulting fees from AbbVie, Novartis, and Opko. S.B. holds an equity interest in FPT, LLC. N.S. consults for Astellas, serves on the scientific advisory board for Menogenix, and owns stock options in Menogenix. The remaining authors have nothing to disclose. References 1. 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Google Scholar CrossRef Search ADS PubMed Copyright © 2018 Endocrine Society http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Clinical Endocrinology and Metabolism Oxford University Press

Reproductive Hormones and Subclinical Cardiovascular Disease in Midlife Women

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Endocrine Society
Copyright
Copyright © 2018 Endocrine Society
ISSN
0021-972X
eISSN
1945-7197
D.O.I.
10.1210/jc.2018-00579
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Abstract

Abstract Context Reproductive hormones are important to the pathophysiology of cardiovascular disease (CVD) in women. However, standard estradiol (E2) and testosterone (T) assays lack sensitivity at the levels of postmenopausal women. Objective Investigate relations of mass spectrometry–assessed estrone (E1), E2, and T and SHBG and subclinical CVD in women. Design, Setting, and Participants Three hundred and four perimenopausal and postmenopausal women aged 40 to 60 years underwent subclinical CVD measurements. E1, E2, and T were assayed using liquid chromatography–tandem mass spectrometry; free T (FT) was estimated using ensemble allostery models. Regression models were adjusted for CVD risk factors. Main Outcome Measures Carotid artery intima media thickness, interadventitial diameter (IAD), and plaque; brachial flow mediated dilation (FMD). Results Higher E1 was related to higher FMD [β(SE) = 0.77 (0.37), P = 0.04], indicating better endothelial function. Higher E2 was related to lower IAD [β(SE) = −0.07 (0.02), P = 0.004], indicating less carotid remodeling. Higher SHBG was related to higher FMD [β(SE) = 1.31 (0.40), P = 0.001], yet higher IAD [β(SE) = 0.15 (0.06), P = 0.02] and plaque [OR (95% CI) = 1.84 (1.16 to 2.91), P = 0.009]; FT showed a similar yet inverse pattern of relations as SHBG. Thus, higher SHBG and lower FT were associated with better endothelial function, yet greater carotid remodeling and plaque. Conclusions Endogenous E1 levels were related to endothelial function and E2 to vascular remodeling, suggesting distinct roles of these estrogens. SHBG and FT have complex roles depending on the vessel under study. Cardiovascular disease (CVD) is the leading cause of death in women (1). Reproductive hormones have long been postulated to be involved in the pathophysiology of CVD in women. Women develop clinical CVD largely during their postmenopausal years, on average several years later than do men (2). Reproductive hormones such as estradiol (E2) change markedly with menopause, with implications for cardiovascular health (3). Although a large literature has considered the impact of exogenous hormone administration on midlife women’s cardiovascular health (3), a more limited body of research considers endogenous hormones. As the cardiovascular impact of exogenous vs endogenous hormones may not be comparable (4), further research on the role of endogenous reproductive hormones in women’s cardiovascular health is warranted. Traditional assays for the measurement of sex hormones have problems of suboptimal sensitivity, precision, and accuracy in the low range present in postmenopausal women. Most postmenopausal women have endogenous E2 and testosterone (T) levels that fall below the sensitivity of standard immunoassays, calling into question the validity of findings using these assays in aging women (5, 6). For example, several Endocrine Society position statements have underscored that many standard direct immunoassays for E2 and T have limited accuracy <30 pg/mL and <300 ng/dL for E2 (7) and T (8), respectively, rendering these assays of limited utility for postmenopausal women. Liquid chromatography–tandem mass spectrometry (LC-MS/MS) has emerged as the reference method for the measurement of sex steroids that can quantify with high precision and accuracy the low levels of these hormones observed in postmenopausal women (5, 8, 9). Few studies have used LC-MS/MS when considering sex steroids and women’s cardiovascular health. E2, the hormone that is typically the focus for women’s cardiovascular health, is an ovarian estrogen that binds to estrogen receptors with high affinity and shows dramatic changes during the menopause (10). However, other reproductive hormones are likely critical to women’s vascular health. SHBG is a protein that binds to E2 and T and impacts their bioavailability. In addition to its role as a binding protein for E2 and T, SHBG is being recognized as a risk factor for diabetes and atherosclerosis development, particularly in women (11, 12). T may play an important role in women’s vascular health, yet investigation has been limited by the poor sensitivity and accuracy of standard T immunoassays in women (13–16). Estrone (E1), produced in the peripheral tissues by aromatization of δ-4-androstenedione, does not show the degree of decline of E2 during menopause, and may have particular relevance among postmenopausal women who typically have low E2 levels (17). E1 has received limited attention in relation to cardiovascular health. We tested LC-MS/MS-assessed E1, E2, and T as well as free T (FT) (18) and two-site directed immunometric-assessed SHBG in relation to subclinical CVD measures in a community sample of nonsmoking midlife women free of clinical CVD. Use of subclinical CVD indices is useful in understanding the development of CVD risk in midlife women, as midlife is typically before the onset of clinical CVD in women (2). We considered several subclinical CVD indices, including carotid artery intima media thickness (IMT) and carotid plaque, a measure of subclinical atherosclerosis; carotid interadventitial diameter (IAD), a measure of vascular remodeling; and brachial artery flow mediated dilation (FMD), an index of endothelial function. All are well-validated subclinical CVD indices that predict later CVD outcomes (19, 20). We consider a range of potential explanatory factors in these relations, including CVD risk factors. Patients and Methods The MsHeart Study recruited 304 late perimenopausal and postmenopausal (21) nonsmoking women aged 40 to 60 from the community. Reflecting the parent study aims (22), half of the women reported hot flashes, and half reported no hot flashes. Participants underwent physical measurements; psychosocial and medical history assessments; ambulatory monitoring; and after an overnight fast, phlebotomy, and a brachial artery and carotid artery ultrasounds. Procedures were approved by the University of Pittsburgh Institutional Review Board. Participants provided written informed consent. Exclusion criteria included hysterectomy and/or bilateral oophorectomy; history of heart disease, stroke, arrhythmia, gynecological cancer, pheochromocytoma, pancreatic tumor, kidney failure, seizures, Parkinson disease, Raynaud phenomenon; current pregnancy; or having used the following medications (past 3 months): oral/transdermal estrogen or progesterone, selective estrogen receptor modulators, selective serotonin reuptake inhibitors, serotonin norepinephrine reuptake inhibitors, gabapentin, insulin, β blockers, calcium channel blockers, α-2 adrenergic agonists, or other antiarrhythmic agents. Women were not excluded on the basis of body mass index (BMI) or history of polycystic ovary syndrome. Of the 304 women, exclusions due to missing or poor quality data were as follows [IMT: n = 4; IAD/plaque: n = 2; FMD: n = 33; E1: n = 2; T: n = 2; and homeostatic model assessment (HOMA)/low-density lipoprotein: n = 5]. The final sample sizes ranged from n = 265 to 271 for FMD models, n = 293 to 300 for IMT models, and n = 295 to 302 for plaque/IAD models. Hormones E1, E2, and T were measured using LC-MS/MS. E1 and E2 were assessed at the University of Pittsburgh’s Small Biomarker Core and T in the Brigham and Women’s Hospital Research Assay Core Laboratory, certified by the Center for Disease Control’s Hormone Assay Standardization Program for Testosterone, under the supervision of Dr. Bhasin (23). LC-MS/MS assay employs liquid-liquid extraction, derivatization, and detection with a triple quad mass spectrometer (24). For estrogens (E1 and E2), the lower limit of detection was 1.0 pg/mL and of quantitation was 2.5 pg/mL. Intraday statistics showed errors below 8.1% and relative SDs (RSDs) below 10.4%; interday statistics showed errors below 5.0% with RSDs below 7.4%. For E2 values below the sensitivity of the assay (n = 40), a random number between 0 and the lower limit was generated. For T, the lower limit of quantitation was 1.0 ng/dL, with intra-assay variation of <4% RSDs and interassay variation of <5% RSDs (23). Standards, blanks, calibrators, and control pools were run simultaneously with all samples. SHBG was measured at the University of Pittsburgh Chemistry and Nutrition Laboratory via ELISA (ALPCO, Salem, NH), with a sensitivity of 0.1 nmol/L and intra-assay and interassay coefficients of variation of 1.3% and 4.9%, respectively, at 20 nM. FT was calculated from total T and SHBG, using the ensemble allostery model, a method that has been validated against the equilibrium dialysis method, and that provides accurate estimates of FT that closely match FT determined using the equilibrium dialysis method (18, 25). Carotid ultrasound Trained and certified sonographers at the University of Pittsburgh’s Ultrasound Research Laboratory obtained bilateral carotid images via B-mode ultrasound using a Sonoline Antares (Siemens, Malvern, PA) high-resolution duplex scanner equipped with a VF10-5 transducer. Digitized images were obtained from eight locations (four locations each from the left and right carotid arteries): near and far walls of the distal common carotid artery [(CCA); 1 cm proximal to the carotid bulb], the far walls of the carotid bulb (the point in which the near and far walls of the common carotid are no longer parallel, extending to the flow divider), and internal carotid artery (from the flow divider to 1 cm distal to this point). IMT measures were obtained by electronically tracing the lumen-intima interface and the media-adventitia interface across a 1-cm segment for each of the eight segments described above; one measurement was generated for each pixel over the area, for a total of approximately 140 measures for each segment. The average values for these measures were recorded for each of the eight locations, and the mean of the average readings across these eight locations was used for analyses. CCA IADs were measured directly as the distance from the adventitial-medial interface on the near wall to the medial-adventitial interface on the far wall at end diastole across the same CCA segments used for IMT measurement. Images were read using semiautomated reading software (26). Reproducibility of IMT measures was excellent (intraclass correlation coefficient between sonographers = 0.87 to 0.94, between readers = 0.94 to 0.99). Higher IMT and IAD indicate greater subclinical atherosclerosis and more adverse vascular remodeling, respectively. Sonographers evaluated the presence and extent of plaque in each of five segments of the left and right carotid artery (distal and proximal CCA, carotid bulb, and proximal internal and external carotid arteries). Consistent with the Mannheim Consensus Statement (27), plaque was defined as a focal area protruding into the vessel lumen that was at least 50% thicker than the adjacent IMT and summarized as the presence or absence of plaque. For each segment, the degree of plaque was graded: grade 0 = no observable plaque; grade 1 = one small plaque (<30% of vessel diameter); grade 2 = one medium plaque (30% to 50% of vessel diameter) or multiple small plaques; and grade 3 = one large plaque (≥50% of vessel diameter) or multiple plaques with at least one medium plaque. Grades from all segments were summed to create the plaque index (28), categorized as any/none for analysis. Between-sonographer agreement for plaque assessment was high (κ = 0.78). Brachial ultrasound FMD was measured when women were fasting and had refrained from exercise and caffeine for 6 hours. FMD was measured after 10 minutes of supine rest by high resolution B-mode ultrasound imaging of the right brachial artery, 2 to 10 cm proximal to the antecubital crease by trained sonographers using a standardized protocol. Images were obtained at rest (baseline) and after 5 minutes of forearm blood flow occlusion (postdeflation) with a pneumatic tourniquet set to 50 mm Hg above the participant’s systolic blood pressure. For baseline diameters, digitized images were recorded for 20 seconds. Immediately after deflation, images were recorded continuously for 3 minutes. The arterial diameter was measured as the distance between the anterior and posterior arterial wall media-adventitia interfaces on images captured on the R wave using edge-detection software. Images were read by a single trained reader using the Brachial Analysis System software allowing continuous tracking of the brachial artery diameter across images so that the peak diameter change can be accurately determined. FMD was calculated as the maximum percentage of change in arterial diameter relative to baseline. This methodology is reproducible at this laboratory (intraclass correlation coefficients = 0.70 to 0.72) (29). Lower FMD corresponds to poorer endothelial function. Covariates Height and weight were measured, and BMI calculated (kg/m2). Seated blood pressure was measured via a Dinamap device (GE Medical Systems Information Technologies Inc., Milwaukee, WI) after a 10-minute rest, with blood pressure the mean of second and third measurements. Medical, reproductive, and psychosocial history was assessed by standard instruments. Medication use (e.g., antidepressants, antihypertensives, lipid-lowering medications, medications for glucose control, β agonists, and anticonvulsants) was reported. Menopause status was obtained from reported menstrual bleeding patterns (21). Anxiety and physical activity were assessed with validated instruments (30). Hot flashes were quantified via physiologic monitoring (22). Glucose, high-density lipoprotein cholesterol (HDL-C), and triglycerides were measured enzymatically (Vital Diagnostics, Lincoln, RI). Total cholesterol was determined enzymatically and low-density lipoprotein cholesterol (LDL-C) calculated using the Friedewald formula. Insulin was measured via radioimmunoassay. HOMA, an indicator of insulin resistance, was calculated (31). Analyses Variables were examined for outliers and deviations from normality. E1, E2, SHBG, T, HOMA, and triglycerides were log-transformed and physical activity square root transformed for analysis. Associations between each hormone and each outcome were tested using linear (IMT, IAD, and FMD) and logistic (plaque) regression. Primary models considered each hormone separately; additional models considered several hormones simultaneously (SHBG and E2 or SHBG and T). Covariates were selected based upon their associations with the outcome at P < 0.10. Expanded models for FMD were also estimated with inclusion of multiple CVD risk factors. Hot flashes and menopause stage were considered as additional covariates. Given debates about the optimal method to statistically analyze FMD, allometric scaling methods to analyze FMD were considered in sensitivity analyses (32). T models were repeated excluding several high values (>52 ng/dL), and E2 models repeated excluding very low values (<2.5 pg/mL). Interactions between hormones and menopause stage were tested. Residual analysis and diagnostic plots were conducted to verify model assumptions. Analyses were performed with SAS version 9.4 (SAS Institute, Cary, NC). Models were two-sided at α = 0.05. Results Participants were on average 54 years old, overweight, and postmenopausal (Table 1). Seventy-two percent of the sample was non-Hispanic white, with the remaining 28% of minority race/ethnicity. Hormone levels were consistent with a sample of primarily postmenopausal women. Almost half of the women had evidence of carotid plaque. Table 1. Sample Characteristics N 304 Age, y, M (SD) 54.1 (4.0) Race/ethnicity, n (%)  Non-Hispanic white 220 (72.4)  Black/othera 84 (27.6) Education, n (%)  High school/some college/vocational 129 (42.4)  College or higher 175 (57.6) Menopause stage, n (%)  Perimenopausal 49 (16.12)  Postmenopausal 255 (83.88) Parity, number of live births, median (IQR) 2.0 (1.0–3.0) BMI, M (SD) 28.99 (6.76) SBP, mm Hg, M (SD) 119.85 (14.48) DBP, mm Hg, M (SD) 70.18 (9.11) LDL-C, mg/dL, M (SD) 130.42 (33.47) HDL-C, mg/dL, M (SD) 62.86 (14.84) Triglycerides, mg/dL, median (IQR) 96.00 (71.0–129.0) HOMA, median (IQR) 2.20 (1.68–3.18) Medications, n (%)  Blood pressure–lowering 48 (15.8)  Antidiabetic 10 (3.3)  Lipid-lowering 39 (12.8)  β-Agonists 14 (4.61) Anxiety, median (IQR) 32.10 (9.89) Physical activity, leisure time, median (IQR) 396 (0–1383) Hot flashes, physiologically monitored number/24 h, median (IQR) 6 (0–15) E1, pg/mL, median (IQR) 25.00 (16.50–37.00) E2, pg/mL, median (IQR) 4.95 (2.00–11.30) T, ng/dL, median (IQR) 25.55 (20.40–31.50) SHBG, nmol/L, median (IQR) 79.60 (50.00–122.10) FT, median (IQR) 0.33 (0.23–0.43) FMD, %, M (SD) 7.33 (3.91) IMT, mm, M (SD) 0.68 (0.11) IAD, mm, M (SD) 6.99 (0.60) Plaque, n (%)  None 162 (53.6)  Any 140 (46.4) N 304 Age, y, M (SD) 54.1 (4.0) Race/ethnicity, n (%)  Non-Hispanic white 220 (72.4)  Black/othera 84 (27.6) Education, n (%)  High school/some college/vocational 129 (42.4)  College or higher 175 (57.6) Menopause stage, n (%)  Perimenopausal 49 (16.12)  Postmenopausal 255 (83.88) Parity, number of live births, median (IQR) 2.0 (1.0–3.0) BMI, M (SD) 28.99 (6.76) SBP, mm Hg, M (SD) 119.85 (14.48) DBP, mm Hg, M (SD) 70.18 (9.11) LDL-C, mg/dL, M (SD) 130.42 (33.47) HDL-C, mg/dL, M (SD) 62.86 (14.84) Triglycerides, mg/dL, median (IQR) 96.00 (71.0–129.0) HOMA, median (IQR) 2.20 (1.68–3.18) Medications, n (%)  Blood pressure–lowering 48 (15.8)  Antidiabetic 10 (3.3)  Lipid-lowering 39 (12.8)  β-Agonists 14 (4.61) Anxiety, median (IQR) 32.10 (9.89) Physical activity, leisure time, median (IQR) 396 (0–1383) Hot flashes, physiologically monitored number/24 h, median (IQR) 6 (0–15) E1, pg/mL, median (IQR) 25.00 (16.50–37.00) E2, pg/mL, median (IQR) 4.95 (2.00–11.30) T, ng/dL, median (IQR) 25.55 (20.40–31.50) SHBG, nmol/L, median (IQR) 79.60 (50.00–122.10) FT, median (IQR) 0.33 (0.23–0.43) FMD, %, M (SD) 7.33 (3.91) IMT, mm, M (SD) 0.68 (0.11) IAD, mm, M (SD) 6.99 (0.60) Plaque, n (%)  None 162 (53.6)  Any 140 (46.4) Sample sizes for all variables: N = 304, except HOMA: n = 301; LDL-C: n = 302; E1: n = 302; T: n = 302; FT: n = 302; FMD: n = 271; IMT: n = 300; IAD: n = 302; and plaque: n = 302. Abbreviations: DBP, diastolic blood pressure; IQR, interquartile range; M, mean; SBP, systolic blood pressure. a Black and other race/ethnicity n (%): black: 67 (22.04); Asian: 11 (3.62); Hispanic: 2 (0.66); and biracial: 4 (1.32). View Large Table 1. Sample Characteristics N 304 Age, y, M (SD) 54.1 (4.0) Race/ethnicity, n (%)  Non-Hispanic white 220 (72.4)  Black/othera 84 (27.6) Education, n (%)  High school/some college/vocational 129 (42.4)  College or higher 175 (57.6) Menopause stage, n (%)  Perimenopausal 49 (16.12)  Postmenopausal 255 (83.88) Parity, number of live births, median (IQR) 2.0 (1.0–3.0) BMI, M (SD) 28.99 (6.76) SBP, mm Hg, M (SD) 119.85 (14.48) DBP, mm Hg, M (SD) 70.18 (9.11) LDL-C, mg/dL, M (SD) 130.42 (33.47) HDL-C, mg/dL, M (SD) 62.86 (14.84) Triglycerides, mg/dL, median (IQR) 96.00 (71.0–129.0) HOMA, median (IQR) 2.20 (1.68–3.18) Medications, n (%)  Blood pressure–lowering 48 (15.8)  Antidiabetic 10 (3.3)  Lipid-lowering 39 (12.8)  β-Agonists 14 (4.61) Anxiety, median (IQR) 32.10 (9.89) Physical activity, leisure time, median (IQR) 396 (0–1383) Hot flashes, physiologically monitored number/24 h, median (IQR) 6 (0–15) E1, pg/mL, median (IQR) 25.00 (16.50–37.00) E2, pg/mL, median (IQR) 4.95 (2.00–11.30) T, ng/dL, median (IQR) 25.55 (20.40–31.50) SHBG, nmol/L, median (IQR) 79.60 (50.00–122.10) FT, median (IQR) 0.33 (0.23–0.43) FMD, %, M (SD) 7.33 (3.91) IMT, mm, M (SD) 0.68 (0.11) IAD, mm, M (SD) 6.99 (0.60) Plaque, n (%)  None 162 (53.6)  Any 140 (46.4) N 304 Age, y, M (SD) 54.1 (4.0) Race/ethnicity, n (%)  Non-Hispanic white 220 (72.4)  Black/othera 84 (27.6) Education, n (%)  High school/some college/vocational 129 (42.4)  College or higher 175 (57.6) Menopause stage, n (%)  Perimenopausal 49 (16.12)  Postmenopausal 255 (83.88) Parity, number of live births, median (IQR) 2.0 (1.0–3.0) BMI, M (SD) 28.99 (6.76) SBP, mm Hg, M (SD) 119.85 (14.48) DBP, mm Hg, M (SD) 70.18 (9.11) LDL-C, mg/dL, M (SD) 130.42 (33.47) HDL-C, mg/dL, M (SD) 62.86 (14.84) Triglycerides, mg/dL, median (IQR) 96.00 (71.0–129.0) HOMA, median (IQR) 2.20 (1.68–3.18) Medications, n (%)  Blood pressure–lowering 48 (15.8)  Antidiabetic 10 (3.3)  Lipid-lowering 39 (12.8)  β-Agonists 14 (4.61) Anxiety, median (IQR) 32.10 (9.89) Physical activity, leisure time, median (IQR) 396 (0–1383) Hot flashes, physiologically monitored number/24 h, median (IQR) 6 (0–15) E1, pg/mL, median (IQR) 25.00 (16.50–37.00) E2, pg/mL, median (IQR) 4.95 (2.00–11.30) T, ng/dL, median (IQR) 25.55 (20.40–31.50) SHBG, nmol/L, median (IQR) 79.60 (50.00–122.10) FT, median (IQR) 0.33 (0.23–0.43) FMD, %, M (SD) 7.33 (3.91) IMT, mm, M (SD) 0.68 (0.11) IAD, mm, M (SD) 6.99 (0.60) Plaque, n (%)  None 162 (53.6)  Any 140 (46.4) Sample sizes for all variables: N = 304, except HOMA: n = 301; LDL-C: n = 302; E1: n = 302; T: n = 302; FT: n = 302; FMD: n = 271; IMT: n = 300; IAD: n = 302; and plaque: n = 302. Abbreviations: DBP, diastolic blood pressure; IQR, interquartile range; M, mean; SBP, systolic blood pressure. a Black and other race/ethnicity n (%): black: 67 (22.04); Asian: 11 (3.62); Hispanic: 2 (0.66); and biracial: 4 (1.32). View Large When considering FMD, higher E1 was related to greater FMD (Table 2), indicating better endothelial function. Higher SHBG and lower FT were each also related to greater FMD. Neither E2 nor T were related to FMD. Associations persisted adjusting for CVD risk factors and covariates. Table 2. Relations Between Sex Hormones, SHBG, and FMD FMD β(SE) Model 1 Model 2 Model 3 E1 0.60 (0.36) 0.69 (0.35)a 0.77 (0.37)a E2 0.06 (0.15) 0.11 (0.15) 0.14 (0.16) T 0.15 (0.69) –0.08 (0.65) –0.05 (0.67) SHBG 1.27 (0.36)b 1.12 (0.36)c 1.31 (0.40)c FTd −1.41 (0.47)c −1.42 (0.47)c −1.58 (0.52)c FMD β(SE) Model 1 Model 2 Model 3 E1 0.60 (0.36) 0.69 (0.35)a 0.77 (0.37)a E2 0.06 (0.15) 0.11 (0.15) 0.14 (0.16) T 0.15 (0.69) –0.08 (0.65) –0.05 (0.67) SHBG 1.27 (0.36)b 1.12 (0.36)c 1.31 (0.40)c FTd −1.41 (0.47)c −1.42 (0.47)c −1.58 (0.52)c Hormones log-transformed. Model 1: unadjusted. Model 2: baseline lumen diameter, age, race, BMI, leisure time physical activity, β-agonist meds, parity, and anxiety. Model 3: Model 2 plus education, SBP, HOMA, LDL-C, triglycerides, lipid-lowering medications, blood pressure-lowering medications, and diabetes medications. Abbreviation: SBP, systolic blood pressure. a P < 0.05. b P < 0.001. c P < 0.01. d FT estimated using ensemble allostery models. View Large Table 2. Relations Between Sex Hormones, SHBG, and FMD FMD β(SE) Model 1 Model 2 Model 3 E1 0.60 (0.36) 0.69 (0.35)a 0.77 (0.37)a E2 0.06 (0.15) 0.11 (0.15) 0.14 (0.16) T 0.15 (0.69) –0.08 (0.65) –0.05 (0.67) SHBG 1.27 (0.36)b 1.12 (0.36)c 1.31 (0.40)c FTd −1.41 (0.47)c −1.42 (0.47)c −1.58 (0.52)c FMD β(SE) Model 1 Model 2 Model 3 E1 0.60 (0.36) 0.69 (0.35)a 0.77 (0.37)a E2 0.06 (0.15) 0.11 (0.15) 0.14 (0.16) T 0.15 (0.69) –0.08 (0.65) –0.05 (0.67) SHBG 1.27 (0.36)b 1.12 (0.36)c 1.31 (0.40)c FTd −1.41 (0.47)c −1.42 (0.47)c −1.58 (0.52)c Hormones log-transformed. Model 1: unadjusted. Model 2: baseline lumen diameter, age, race, BMI, leisure time physical activity, β-agonist meds, parity, and anxiety. Model 3: Model 2 plus education, SBP, HOMA, LDL-C, triglycerides, lipid-lowering medications, blood pressure-lowering medications, and diabetes medications. Abbreviation: SBP, systolic blood pressure. a P < 0.05. b P < 0.001. c P < 0.01. d FT estimated using ensemble allostery models. View Large We next considered carotid indices. Higher E2 was associated with lower IAD, indicating less adverse vascular remodeling (Table 3). Higher SHBG and lower FT were each related to higher IAD and carotid plaque presence in multivariable models, indicating greater remodeling and plaque. Neither E1 nor T were related to carotid indices. Associations persisted with multivariable adjustment. Table 3. Relations Between Hormones and Carotid IMT, Adventitial Diameter, and Plaque Mean IMT IAD Plaque Presence β(SE) β(SE) OR (95% CI) Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 E1 0.008 (0.009) 0.01 (0.009) 0.02 (0.05) –0.003 (0.05) 1.14 (0.82–1.59) 1.31 (0.88–1.95) E2 –0.0003 (0.004) 0.001 (0.004) –0.04 (0.02)a –0.06 (0.02)b 1.01 (0.88–1.16) 1.10 (0.93–1.32) T 0.016 (0.018) 0.007 (0.016) –0.006 (0.10) –0.06 (0.10) 0.84 (0.43–1.64) 0.91 (0.44–1.89) SHBG –0.01 (0.009) 0.01 (0.01) 0.02 (0.05) 0.15 (0.06)b 1.27 (0.89–1.79) 1.84 (1.16–2.91)c FTd 0.02 (0.01) –0.013 (0.01) –0.03 (0.07) –0.19 (0.08)b 0.69 (0.43–1.09) 0.49 (0.28–0.88)b Mean IMT IAD Plaque Presence β(SE) β(SE) OR (95% CI) Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 E1 0.008 (0.009) 0.01 (0.009) 0.02 (0.05) –0.003 (0.05) 1.14 (0.82–1.59) 1.31 (0.88–1.95) E2 –0.0003 (0.004) 0.001 (0.004) –0.04 (0.02)a –0.06 (0.02)b 1.01 (0.88–1.16) 1.10 (0.93–1.32) T 0.016 (0.018) 0.007 (0.016) –0.006 (0.10) –0.06 (0.10) 0.84 (0.43–1.64) 0.91 (0.44–1.89) SHBG –0.01 (0.009) 0.01 (0.01) 0.02 (0.05) 0.15 (0.06)b 1.27 (0.89–1.79) 1.84 (1.16–2.91)c FTd 0.02 (0.01) –0.013 (0.01) –0.03 (0.07) –0.19 (0.08)b 0.69 (0.43–1.09) 0.49 (0.28–0.88)b Hormones log-transformed. Model 1: unadjusted. Model 2: age, race (white/nonwhite), BMI, education, SBP, DBP, HOMA, LDL-C, HDL-C, triglycerides, lipid-lowering medications, blood pressure-lowering medications, and diabetes medications. Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure. a P < 0.10. b P < 0.05. c P < 0.01. d FT estimated using ensemble allostery models. View Large Table 3. Relations Between Hormones and Carotid IMT, Adventitial Diameter, and Plaque Mean IMT IAD Plaque Presence β(SE) β(SE) OR (95% CI) Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 E1 0.008 (0.009) 0.01 (0.009) 0.02 (0.05) –0.003 (0.05) 1.14 (0.82–1.59) 1.31 (0.88–1.95) E2 –0.0003 (0.004) 0.001 (0.004) –0.04 (0.02)a –0.06 (0.02)b 1.01 (0.88–1.16) 1.10 (0.93–1.32) T 0.016 (0.018) 0.007 (0.016) –0.006 (0.10) –0.06 (0.10) 0.84 (0.43–1.64) 0.91 (0.44–1.89) SHBG –0.01 (0.009) 0.01 (0.01) 0.02 (0.05) 0.15 (0.06)b 1.27 (0.89–1.79) 1.84 (1.16–2.91)c FTd 0.02 (0.01) –0.013 (0.01) –0.03 (0.07) –0.19 (0.08)b 0.69 (0.43–1.09) 0.49 (0.28–0.88)b Mean IMT IAD Plaque Presence β(SE) β(SE) OR (95% CI) Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 E1 0.008 (0.009) 0.01 (0.009) 0.02 (0.05) –0.003 (0.05) 1.14 (0.82–1.59) 1.31 (0.88–1.95) E2 –0.0003 (0.004) 0.001 (0.004) –0.04 (0.02)a –0.06 (0.02)b 1.01 (0.88–1.16) 1.10 (0.93–1.32) T 0.016 (0.018) 0.007 (0.016) –0.006 (0.10) –0.06 (0.10) 0.84 (0.43–1.64) 0.91 (0.44–1.89) SHBG –0.01 (0.009) 0.01 (0.01) 0.02 (0.05) 0.15 (0.06)b 1.27 (0.89–1.79) 1.84 (1.16–2.91)c FTd 0.02 (0.01) –0.013 (0.01) –0.03 (0.07) –0.19 (0.08)b 0.69 (0.43–1.09) 0.49 (0.28–0.88)b Hormones log-transformed. Model 1: unadjusted. Model 2: age, race (white/nonwhite), BMI, education, SBP, DBP, HOMA, LDL-C, HDL-C, triglycerides, lipid-lowering medications, blood pressure-lowering medications, and diabetes medications. Abbreviations: DBP, diastolic blood pressure; SBP, systolic blood pressure. a P < 0.10. b P < 0.05. c P < 0.01. d FT estimated using ensemble allostery models. View Large We conducted several additional sensitivity analyses. We considered SHBG and E2 or SHBG and T together in multivariable models in relation to outcomes (FMD, IAD, IMT, and plaque). Findings were comparable to that of each hormone considered separately (data not shown). We considered additional covariates of menopause stage and hot flashes; findings were unchanged (data not shown). Analyses were repeated excluding women with very high T (>52 ng/dL, n = 7) or low E2 values (<2.0 pg/mL, n = 40); findings were unchanged. We considered alternate methods to analyze FMD (32), and conclusions were unchanged (data not shown). Moreover, we examined interactions by menopause stage, given evidence that (exogenous) estrogens may exert menopause state-specific effects (3). Menopause stage was a modifier of select associations, with higher E1 and T related to a more favorable vascular profile (lower IAD, higher FMD) among perimenopausal women, whereas lower SHBG was related to poorer endothelial function (lower FMD) among postmenopausal women (Supplemental Table 1). Discussion Using highly accurate and sensitive LC-MS/MS assays for the measurement of E1, E2, and T, we show here that among nonsmoking midlife women free of clinical CVD, higher E1 levels are related to indicators of better endothelial function; in contrast, higher E2 levels were associated with less carotid remodeling. Higher SHBG and lower FT were associated with better endothelial function, yet greater carotid remodeling and plaque. Taken together, these findings suggest a unique potential physiologic role of E1 in endothelial function and of E2 in vascular remodeling and a complex role of SHBG and free androgen that depends on the specific aspect of the vasculature under study. The vascular endothelium is well known to be sensitive to estrogens (3), which upregulate endothelial nitric oxide synthase expression and activity (33). However, existing work has largely focused on exogenous E2. The vascular impact of other endogenous estrogens, especially E1, has received little attention. E1 is produced mostly by aromatization of adrenal androgen precursors in peripheral tissues, particularly body fat. The present findings point to a unique, potential beneficial relation of endogenous E1 to endothelial function. Although E1 is a weaker estrogen than E2 in some bioassays, circulating E1 levels in perimenopausal and postmenopausal women are higher than those of E2. The association of E1, but not E2, with endothelial function may be related to the differential activity of these two ligands in estrogen receptor (ER) subtypes. Whether E1 exerts additional nongenomic effects on endothelial function is not known. This work indicated that higher E2 was related to less adverse vascular remodeling, or lower IAD. Our finding is consistent with the limited prior work on E2 and carotid remodeling. The Study of Women’s Health Across the Nation (SWAN) showed higher endogenous E2 associated with lower IAD with adjustment for multiple risk factors (34). Many automated immunoassays have limited accuracy <30 pg/mL (7). Notably, 90% of the women in our study had values that fell below 30 pg/mL. Thus, here we consider these relations with state-of-the-art measurements of E2, with a lower limit of quantitation of 2.5 pg/mL, sensitive to the low E2 levels of postmenopausal women. The ER subtype specificity of various ER ligands may contribute to their differential physiologic effects. This work highlights the potential importance of SHBG. SHBG is a protein produced in the liver. Its classical role is as a carrier protein that binds the steroid hormones and regulates their bioavailability (25). However, SHBG is now being recognized to have additional physiologic significance as a risk factor for diabetes and insulin resistance (11, 12). Multiple studies have linked lower SHBG to poorer vascular health, even after adjustment for CVD risk factors in some work (14, 16), but not in other work (15, 35, 36). BMI is particularly important to consider in these analyses given its close relation to SHBG and its role as a strong risk factor for CVD. Notably, little work has considered SHBG in relation to endothelial function. We show higher SHBG, measured via two-site directed immunometric assay, associated with higher FMD (better endothelial function), yet when considering carotid indices, higher SHBG was associated with carotid remodeling and plaque. Thus, SHBG was associated with better endothelial function, yet poorer carotid health. As relations between SHBG and vascular outcomes remained significant even after adjusting for E2 or T, they are unlikely driven by the steroid hormone itself. The reasons for this divergent pattern of associations of SHBG with different vascular indices are unclear, yet other studies have shown similar findings. MESA showed higher SHBG associated with lower carotid IMT, yet higher coronary artery calcification (CAC) (15). Together, findings raise the potential of complex relations of SHBG to different vessels and aspects of vascular health that warrant further attention. The LC-MS/MS measurement of T is particularly important in women, as most automated T immunoassays have poor sensitivity and precision below 100 ng/dL (5) or even 300 ng/dL (8), levels far above that of most women. Notably, 99% and 100% of the women in this study had values that fell below 100 ng/dL and 300 ng/dL, respectively. Existing work with older androgen assays yields mixed findings, with several studies finding no associations between T and vascular indices (IMT, CAC) (13, 14), others showing higher T associated with better vascular health (lower IMT) (16, 37), and still others showing higher androgens associated with poorer cardiovascular health [FMD (38), IMT (39), and coronary heart disease (40)]. MESA shows mixed results, with higher T and bioavailable T related to higher IMT, yet less CAC (15). We found no significant relation between total T and vascular outcomes. In contrast, higher FT was related to poorer endothelial function, yet less carotid remodeling and plaque. Thus, our results suggest distinctly different relations of T, E1, and E2 with markers of endothelial function, vascular remodeling, and plaque formation. It should be noted that the similar yet inverse effects of SHBG and FT in relation to outcomes would be anticipated given the binding physiology of T to SHBG and determination of FT, which incorporates SHBG. To further understand these findings, future work should consider the independent effects of each of T, estrogens, and SHBG on the underlying physiological mechanisms driving FMD (e.g., nitric oxide–mediated vasodilatation, etc.) vs vascular remodeling and plaque formation (e.g., collagen/elastin changes, lipoprotein-driven inflammation, smooth muscle cell proliferation, etc.). These findings underscore the importance of ongoing investigation of T in relation to women’s vascular health. Findings were robust to sensitivity analyses, such as inclusion of additional covariates, exclusion of extreme T and E2 values, and alternate methods of analyzing FMD. Several interactions emerged for menopause stage; evidence suggested that higher E1 and T were associated with a more favorable vascular profile (lower IAD or higher FMD) among perimenopausal women, whereas lower SHBG was related to poorer endothelial function principally among postmenopausal women. These post hoc interactions with a limited number of perimenopausal women should be interpreted with caution. However, stage-specific actions warrant consideration in future work. There were several study limitations. T was measured, yet FT was calculated using the ensemble allostery model, which has been shown to provide FT values that match closely those measured using the equilibrium dialysis. Future work should consider more direct measures of free or bioavailable T. The sample size was smaller than epidemiologic studies, yet the rigor and expense of LC-MS/MS steroid hormone assays are often prohibitive for larger samples. E2 assays were state of the art, yet there were women who had values below the limit of quantitation of our assay, underscoring the need for continued development of increasingly sensitive methods for postmenopausal women. Women were not characterized with respect to polycystic ovary syndrome, and its role in study findings is unclear. However, analyses excluding women with elevated T (>52 ng/dL) yielded unchanged conclusions. Our study sample, reflecting the demographics of the geographic area, included primarily non-Hispanic white and African American women. Future studies should include additional ethnic groups. We used a range of subclinical CVD indices, but did not include CAC. As this sample of women were nonsmoking midlife women free of clinical CVD, the choice of outcome measures was informed by the typically low prevalence of CAC among nonsmoking midlife women (41). This study had several strengths. We used gold-standard, state-of-the-art measures of steroid hormones. We considered a wide range of hormonal exposures, including E1, an understudied hormone in relation to vascular outcomes. FT was estimated using the unique ensemble allostery model that takes into account the complex, nonlinear allosteric binding of T to SHBG and provides values that closely match those derived from equilibrium dialysis (18). We included several well-validated subclinical CVD measures, including indices of endothelial function, carotid atherosclerosis, and vascular remodeling. We considered a wide range of confounders and potential explanatory factors, including a full array of CVD risk factors. Associations persisted with adjustment for these CVD risk factors. The implications of this work are multiple. First, E1, a previously underexamined hormone, is likely important to women’s endothelial health and requires further investigation. Second, E2 appears important to vascular remodeling. Third, an androgenic milieu may not have exclusively adverse implications for women’s health. Finally, further research into the physiologic actions of SHBG and its impact on vascular health is critical to understand the role of reproductive hormones and this protein carrier in women’s cardiovascular health. These data underscore the importance of endogenous reproductive hormones to women’s vascular health as they age. Abbreviations: Abbreviations: BMI body mass index CAC coronary artery calcification CCA common carotid artery CVD cardiovascular disease E1 estrone E2 estradiol ER estrogen receptor FMD flow-mediated dilation FT free testosterone HDL-C high-density lipoprotein cholesterol HOMA homeostatic model assessment IAD interadventitial diameter IMT intima media thickness LC-MS/MS liquid chromatography–tandem mass spectrometry LDL-C low-density lipoprotein cholesterol RSD relative SD T testosterone Acknowledgments We thank Samar El Khoudary for providing critical feedback on this manuscript. Financial Support: This work was supported by the National Institutes of Health (NIH), National Heart Lung and Blood Institute (R01HL105647, K24123565 to R.C.T.) and the University of Pittsburgh Clinical and Translational Science Institute (NIH Grant UL1TR000005). Allosteric framework for free testosterone determination is supported by the National Institutes of Health, National Institute on Aging (R43 AG045011 and R44 AG045011 to R.J.). Assay development and validation research was supported by the National Institutes of Health, National Institute on Aging (R01AG31206 to S.B.). Additional support to S.B. was provided by the Boston Claude D. Pepper Older Americans Independence Center grant P30AG031679 from the National Institute on Aging and by a grant from the CDC Foundation. This project used the University of Pittsburgh Small Molecule Biomarker Core (NIH Grant S10RR023461). Disclosure Summary: R.C.T. consults for MAS Innovation and Guidepoint. S.B. has received research grants from the National Institute on Aging, the National Institute of Nursing Research, the Patient Centered Outcomes Research Institute, the Foundation for the National Institutes of Health, AbbVie, Alivegen, MIB, Althea Biosciences, and Transition Therapeutics; these research grants are managed by his institution. S.B. has received consulting fees from AbbVie, Novartis, and Opko. S.B. holds an equity interest in FPT, LLC. N.S. consults for Astellas, serves on the scientific advisory board for Menogenix, and owns stock options in Menogenix. The remaining authors have nothing to disclose. References 1. 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