Abstract
Background:
A greater frequency of vasomotor symptoms (VMS) has been associated with higher low-density lipoprotein cholesterol (LDL-C), but the association with high-density lipoprotein cholesterol (HDL-C) remains unclear. Endogenous estradiol (E2) levels are associated with both VMS and lipid levels, and thus, may confound such associations.
Objectives:
To assess the relationship of VMS frequency with HDL-C, LDL-C and lipoprotein concentrations (HDL and LDL particles [HDL-P; LDL-P]) and lipoprotein sizes in midlife women and to evaluate whether these associations are explained by E2.
Methods:
Participants were from the Study of Women’s Health Across the Nation (SWAN) HDL ancillary study who had both Nuclear Magnetic Resonance (NMR) spectroscopy lipoprotein subclass metrics and self-reported frequency of VMS measured 2–5 times over the menopause transition. VMS frequency was categorized into none, 1–5 days (infrequent) or ≥6 days (frequent) within the past 2 weeks.
Results:
We evaluated 522 women [at baseline: mean age 50.3 (SD: 2.8) years; infrequent VMS: 29.8%, frequent VMS:16.5%]. Adjusting for potential confounders except E2, frequent VMS was associated with smaller HDL size [β(SE): −0.06 (0.03); p= 0.04] and higher concentrations of LDL-C [β(SE): 3.58 (1.77); p=0.04] and intermediate LDL-P [β(SE): 0.09 (0.05); p=0.04] than no VMS. These associations were largely explained by E2, all p’s >0.05.
Conclusions:
Frequent VMS were associated with smaller HDL size and higher concentrations of LDL-C and intermediate LDL-P. These associations were explained by endogenous E2. Whether treating frequent VMS with exogenous E2 could simultaneously improve lipids/lipoproteins profile should be assessed in future studies.
Keywords: Vasomotor symptoms, lipoproteins, nuclear magnetic resonance spectroscopy, menopause, estrogen
Introduction:
Hot flashes and night sweats, jointly known as vasomotor symptoms (VMS), are the cardinal symptoms of the menopause transition. A potential link between VMS and cardiovascular disease (CVD) has been suggested[1,2]. VMS has been linked to a worse subclinical CVD profile including higher carotid intima media thickness[1] and aortic calcification[2], and lower flow-mediated dilation[2], as well as to worse CVD outcome[3].
The pathophysiological mechanisms that link VMS to higher CVD risk are not well understood. Potential associations observed between VMS and conventional lipid measures may contribute; however, results of studies that evaluated these associations have not been consistent, where some studies reported associations between high-density lipoprotein cholesterol (HDL-C) or low-density lipoprotein cholesterol (LDL-C) and frequency and severity of VMS (with inconsistent directions), while others failed to find any relationships[4–7].
Endogenous estradiol (E2) level declines as women transition through menopause[8]. This decline in E2 may be a critical contributor to the increased risk of CVD observed after the age of 50 in women[9]. Randomized clinical trials on hormone therapy (HT), however, have failed to show a direct protective effect of HT on CVD risk in menopausal women[10,11], with evolving results suggesting a potential timing effect implying possible cardio-protective effect if HT is initiated <10 years after menopause[12,13]. Several reports, nonetheless, have shown that HT use may alter the lipid profile in postmenopausal women, as evident by lower LDL-C and higher HDL-C in HT users compared to non-users [14,15]. Since the timing of E2 decline during the menopause transition has been linked to the occurrence of VMS[16], and the use of HT is an effective regimen to treat VMS[17], E2 may play a role in the relationship between VMS and lipids.
Previous observational studies have reported that higher HDL-C levels provides protection against cardiovascular disease, but randomized clinical trials which failed to reduce CVD risk after raising HDL-C levels challenged this idea[18,19]. The direction of change of HDL-C around menopause has been inconsistent among women[20–22]. In addition, studies have shown that higher HDL-C after menopause may be associated with higher risk of CVD[23,24] suggesting a dysfunctionality of HDL as women traverse menopause. In one study, however, higher HDL-C, but not measures of HDL function, has been shown to be associated with higher carotid plaques in postmenopausal women[25]. Nonetheless, novel metrics of lipoprotein subclasses may be better markers of disease risk[26], especially because they have shown stronger associations with CVD beyond conventional lipids[27,28]. Studies have suggested that HDL and LDL particle concentrations (HDL-P and LDL-P, respectively) and size are distinctively associated with CVD risk factors and outcomes. Smaller HDL size and higher concentrations of total LDL particles have been linked to a higher CVD risk, while higher concentrations of total and large HDL particles have been linked to a lower CVD risk[29,30].
To our knowledge, the relationships between VMS and lipids/lipoproteins metrics, such as subclasses concentrations and overall size, and the role that endogenous E2 may play in these associations have not been investigated. With the availability of lipoprotein subclass measures over the menopause transition, the SWAN HDL ancillary study provides a unique opportunity to evaluate these associations. The purpose of this research study was to investigate the relationships between VMS frequency and concentrations and sizes of lipids/lipoprotein subclasses, as measured by Nuclear Magnetic Resonance spectroscopy (NMR), and to assess whether these associations are explained by concurrent endogenous E2 levels in women transitioning through menopause. We hypothesized that greater VMS frequency will be associated with higher concentrations of smaller HDL and LDL particles and smaller particle sizes, and these associations will be explained by concurrent E2 concentrations.
Materials and Methods:
Study Participants:
The Study of Women’s Health Across the Nation (SWAN) is an ongoing, community-based, multiethnic, multisite, longitudinal study of the physiological and psychological changes in women as they progress through the menopause transition. Details of SWAN study design were previously described[31]. Briefly, 3,302 women aged between 42 and 52 years were recruited between 1996 and 1997 at 7 sites across the United States (Pittsburgh, PA, Chicago, IL, Boston, MA, Newark, NJ, Detroit, MI, Los Angeles, CA and Oakland, CA). Eligibility criteria for recruitment were (i) having an intact uterus and at least one ovary, (ii) not being pregnant or breastfeeding at time of recruitment, (iii) having at least one menstrual period within the last 3 months, (iv) not using HT and (v) identifying themselves as either White, Black, Hispanic, Chinese or Japanese.
SWAN HDL is an ancillary study to SWAN, designed to characterize high-density lipoprotein (HDL) composition and function across the menopause transition and to test the role of changes in HDL novel metrics on cardiovascular disease risk in midlife women. Five-hundred fifty-eight (n=558) women from SWAN were selected to be part of SWAN HDL ancillary study if they had at least one visit before and two visits after the final menstrual period (FMP) with available stored blood specimens on the selected visits. Lipoproteins subclasses were measured on stored samples 2–5 times over the menopause transition for each participant (coincident with SWAN visit 1, follow-up visits 3–9, and visit 12). Women were included in this analysis if they had at least one visit with VMS data concurrent with lipoproteins measurements (n=556). Eight women were excluded due to their unknown menopausal status (either due to HT use or hysterectomy), and 26 postmenopausal women due to use of menopausal HT which could impact their endogenous level of E2. This resulted in a final study sample of 522 women with 1,333 observations for the longitudinal analysis (1 visit: 52 women; 2 visits: 171 women; 3 visits: 263 women; 4 visits: 30 women; 5 visits: 6 women). Out of all women included in this analysis, 510 women had VMS data at first available visit (the baseline visit in the analysis).
Written informed consent was provided by all participant prior to enrollment, and study protocols were approved by the institutional review boards at each SWAN site.
Blood Assays:
Phlebotomy was performed after a minimum of 10-hour overnight fast. This was scheduled 2–5 days after a spontaneous menstrual cycle when possible. When menstrual cycles were less predictable, blood was collected randomly within 90 days of the annual SWAN visit. Cycle day of blood draw was categorized as either within day 2–5 of the menstrual cycle or not.
Lipoprotein Assessment:
Fasting stored serum samples were used to quantify lipoprotein subclasses. HDL and LDL subclasses were measured at LabCorp (Morrisville, NC, USA) by the Nuclear Magnetic Resonance Spectroscopy (NMR spectroscopy) LipoProfile-3 algorithm[32], by the Vantera Clinical Analyzer, an automated, 400 MHz NMR spectroscopy platform. In brief, lipoprotein particle quantification by NMR utilizes composite signal envelopes at 0.8 ppm, which contain the signals emitted by terminal methyl group protons of the phospholipids, unesterified cholesterol, cholesterol ester and triglycerides that are carried in each lipoprotein particle (HDL, LDL, VLDL). Deconvolution of the composite signal produces signal amplitudes that contribute to the composite plasma signal. Different lipoprotein subclasses produce NMR signals that have distinct frequencies and shapes, and the amplitude of the signals is proportional to the number of subclass particles releasing the signal. Modeling of the line shape of the signal envelope as a sum of all lipoprotein signals results in the amplitude of each subpopulation. The areas of the subpopulations are then multiplied by conversion factors to give the concentrations, which are grouped into either small, medium or large HDL or LDL[32]. Total particle concentration is obtained by summing the concentrations of the subclasses, and the average size of particles is calculated by adding the diameter of each subclass multiplied by its relative mass percentage from NMR signal amplitude[32]. HDL subclasses included large (9.4–14 nm), medium (8.2–9.4 nm) and small (7.3–8.2 nm) HDL particles (HDL-Ps); LDL subclasses included intermediate (23–29 nm), large (20.5–23 nm) and small (18–20.5 nm) LDL particles (LDL-Ps). Due to the magnetic property of lipoproteins which produces signals of different shapes and frequencies for different lipoproteins, NMR spectroscopy does not require the separation of lipoprotein subclasses as is required by electrophoresis or ultracentrifugation.
To ensure valid results, stored samples that have been frozen at −80°C and never been thawed were used. The intra- and inter-assay coefficients of variation for HDL-P concentrations and size ranged from 0.6% to 3.7% (intra-assay) and 1.5% to 4.0% (inter-assay)[32]; and for LDL-P concentrations and size ranged from 0.5% to 11.9% (intra-assay) and 0.6% to 13.2% (inter-assay)[32].
Other Blood Assays:
Lipid fractions were determined from EDTA-treated plasma[33,34]. Fasting HDL-C was separated with heparin-2M manganese chloride (Medical Research Laboratory, Lexington, KY for SWAN baseline visit – follow-up visit 7; University of Michigan Pathology, Ann Arbor MI for SWAN follow up visits 9 and 12)[33,34]. HDL-C was calibrated by converting the University of Michigan results to equivalent Medical Research Laboratory values. Fasting LDL-C was calculated by the Friedewald equation[35].
E2 was performed on the Bayer Diagnostics ACS:180 instrument (SWAN baseline visit – follow-up visit 9) or the ADVIA Centaur (SWAN visit 12). Calibration equations were developed to convert the hormone results obtained from the Centaur to equivalent ACS:180 values with an R-squared values of 92.4%. Prior to SWAN visit 12, the lower limit of detection (LLD) of E2 was <25.7 pg/ml, and for visit 12 onward, the LLD of E2 was <45.5 pg/ml. In this analysis, 79 observations below LLD were excluded in a sensitivity analysis to detect whether this would affect the results.
Vasomotor Symptoms:
Hot flashes and night sweats were self-reported and assessed as the number of days a woman had experienced hot flashes and/or night sweats over the past two weeks prior to a study visit. VMS was defined as the presence of either hot flashes and/or night sweats, and was categorized as none, infrequent (experienced for 1–5 days) or frequent (experienced ≥6 days) over the past 2 weeks[6]. VMS was measured repeatedly at each study visit.
Study Covariates:
Race/ethnicity and education level were self-reported at the initial visit. Age, menopausal status, alcohol use, physical activity, and body mass index (BMI) were ascertained at every visit. Age was calculated as the difference between date of visit and date of birth. Menopausal status was self-reported and based on menstrual bleeding frequency and pattern within the past 12 months. Menopausal status was categorized as premenopausal (no changes in menstrual bleeding within the last three months), early perimenopause (at least one menstrual bleed within the last three months with perceived changes in cycle intervals), late perimenopause (no menstrual bleed within the last 3 months but at least one cycle within the last 12 months) or postmenopausal (no menstrual bleed within the last 12 months, either due to natural menopause (n=64) or bilateral salpingo-oophorectomy (n=5)). Frequency of alcohol use was acquired from self-administered questionnaires and dichotomized into either fewer than once/month versus more than once/month. Physical activity score was obtained at baseline and follow-up SWAN visits by the modified Kaiser Permanente Health Plan Activity Survey[36]. BMI was calculated as measured weight/ measured height2 (in kg/m2).
Statistical Analysis
Baseline characteristics of participants at the first available SWAN HDL study visit were compared between the three VMS frequency groups. Continuous variables were compared by ANOVA for normally distributed variables or the Kruskal-Wallis test for skewed data, and multiple comparison adjustments were performed for pairwise comparisons after applying Bonferroni corrections. Categorical variables were compared by Chi-square or Fisher’s exact test as appropriate. Skewed variables (total LDL-P, intermediate LDL-P, large LDL-P, small LDL-P, LDL size and E2) were log-transformed when used in regression models.
Longitudinal associations between VMS frequency and each lipid/lipoprotein metric (HDL-C, total HDL-P, large HDL-P, medium HDL-P, small HDL-P, HDL size, LDL-C, log-total LDL-P, log- intermediate LDL-P, log- large LDL-P, log- small LDL-P, and log- LDL size) over the menopause transition were evaluated using mixed-effect models with random intercepts. Repeated measures for each dependent variable (each lipid/lipoprotein metric, separately) were modeled as a function of repeated measures of VMS frequency at concurrent visits; mixed models generate unbiased results in the presence of missing observations[37]. Linear trends were tested by treating the ordered categories of the VMS as continuous values (e.g.1,2,3). Models were then adjusted for age, race/ethnicity, study site, education level, menopausal status, alcohol consumption, physical activity, and BMI. Additional adjustment for log- E2 and cycle day of blood draw was then performed. Age, menopausal status, alcohol consumption, physical activity, BMI, log- E2 and cycle day of blood draw were treated as time-varying covariates. To visually present the association between VMS groups and lipids/lipoproteins metrics, model based-means of lipoproteins by VMS categories were estimated for metrics whose associations were explained by E2.
A sub-analysis with hot flashes or night sweats separately as the main independent variable and a sensitivity analysis excluding observations with E2 levels below the LLD were conducted. All analyses were run using SAS v9.4 (SAS Institute, Cary, NC).
Results:
Mean (SD) age of participants at first SWAN HDL ancillary study visit was 50.3 (2.8) years old; 53.6% were White, 27.8% Black, 10.0% Chinese, 8.1% Japanese and <1% Hispanic. At baseline, 29.8% of women reported infrequent VMS and 16.5% reported frequent VMS; 11.5% were premenopausal, 67.1% were early-perimenopausal, 7.9% were late-perimenopausal and 13.6% were postmenopausal, Table 1. Median (Q1, Q3) duration of follow up, defined as the difference between the last and first available visits, was 6.2 (2.1, 10.0) years.
Table 1:
Characteristics of study participants at baseline (n=510a) by VMS frequency categories
| VMS Frequency Categories | |||||
|---|---|---|---|---|---|
| Total (n=510) | None (n=274; 53.7% ) | Infrequent (n=152; 29.8%) | Frequent(n=84; 16.5%) | p | |
| Age, years | 50.3 (2.8) | 50.2 (2.7) | 50.1 (2.7) | 51.1 (3.2)b, c | 0.02 |
| Race, n(%) | 0.0006 | ||||
| White | 277 (54.3%) | 155 (56.6%) | 78 (51.3%) | 44 (52.4%) | |
| Black | 137 (26.7%) | 58 (21.2%) | 43 (28.3%) | 36 (42.9%) | |
| Chinese | 52 (10.2%) | 32 (11.7%) | 18 (11.8%) | 2 (2.4%) | |
| Hispanic | 2 (0.4%) | 2 (0.7%) | 0 (0%) | 0 (0%) | |
| Japanese | 42 (8.2%) | 27 (9.9%) | 13 (8.6%) | 2 (2.4%) | |
| Education, n(%) | <0.0001 | ||||
| ≤High School | 90 (17.7%) | 40 (14.7%) | 34 (22.5%) | 16 (19.1%) | |
| Some college | 156 (30.7%) | 63 (23.1%) | 58 (38.4%) | 35 (41.7%) | |
| College/Post-Graduate | 262 (51.6%) | 170 (62.3%) | 59 (39.1%) | 33 (39.3%) | |
| Menopausal Status, n(%) | <0.0001 | ||||
| Postmenopausal (BSO/Natural) | 69 (13.5%) | 26 (9.5%) | 17 (11.2%) | 26 (31.0%) | |
| Late Perimenopausal | 41 (8.0%) | 17 (6.2%) | 10 (6.6%) | 14 (16.7%) | |
| Early Perimenopausal | 340 (66.7%) | 189 (69.0%) | 113 (74.3%) | 38 (45.2%) | |
| Premenopausal | 60 (11.8%) | 42 (15.3%) | 12 (7.9%) | 6 (7.1%) | |
| BMI, kg/m2 | 26.7 (23.0, 31.7) | 26.1 (22.2, 31.4) | 26.4 (23.8, 31.7) | 29.6 (25.4, 32.7)b | 0.002 |
| Physical Activity Score | 7.90 (1.68) | 8.01 (1.66) | 7.85 (1.70) | 7.66 (1.72) | 0.23 |
| Alcohol consumption, n(%) | 0.12 | ||||
| None- ≤1 drink/month | 252 (49.8%) | 146 (53.9%) | 71 (46.7%) | 35 (42.2%) | |
| >1 drink/month | 254 (50.2%) | 125 (46.1%) | 81 (53.3%) | 48 (57.8%) | |
| E2, pmol/L | 128.9 (71.6, 334.8) | 334.8 (82.6, 379.6) | 150.2 (78.6, 350.6) | 72.0 (44.4, 119.0)b, c | <0.0001 |
| HDL-C, mg/dLd | 59.1 (14.1) | 59.9 (14.8) | 58.4 (13.2) | 57.7 (13.3) | 0.36 |
| Total HDL-P, umol/L | 34.7 (6.0) | 34.7 (6.0) | 34.5 (5.8) | 35.0 (6.2) | 0.78 |
| Large HDL-P, umol/L | 8.4 (3.5) | 8.8 (3.6) | 8.4 (3.5) | 7.2 (3.3)b, c | 0.001 |
| Medium HDL-P, umol/L | 11.3 (6.3) | 11.1 (6.2) | 11.9 (6.2) | 11.2 (6.6) | 0.46 |
| Small HDL-P, umol/L | 15.0 (7.0) | 14.9 (7.0) | 14.2 (6.9) | 16.7 (7.0)c | 0.03 |
| HDL Size, nm | 9.5 (0.5) | 9.6 (0.5) | 9.5 (0.5) | 9.3 (0.5) b, c | 0.004 |
| LDL-Cd, mg/dL | 114.2 (31.8) | 111.6 (31.1) | 113.3 (30.9) | 124.6 (34.0)b, c | 0.006 |
| Total LDL-P, nmol/L | 1102.0 (888.5, 1386.5) | 1106.5 (858.0, 1359.0) | 1070.0 (904.5, 1317.0) | 1213.5 (965.0, 1506.5)b, c | 0.03 |
| Intermediate LDL-P, nmol/L | 261.0 (170.5, 349.5) | 260.5 (166.0, 341.0) | 262.5 (179.0, 345.0) | 256.0 (1630, 394.0) | 0.99 |
| Large LDL-P, nmol/L | 360.0 (224.0, 513.5) | 352.5 (220.0, 515.5) | 364.5 (223.5, 500.5) | 374.5 (260.0, 534.5) | 0.37 |
| Small LDL-P, nmol/L | 469.5 (307.0, 674.0) | 454.0 (288.5, 686.5) | 456.0 (308.0, 638.0) | 513.0 (354.0, 729.5) | 0.16 |
| LDL size, nm | 21.1 (20.5, 21.4) | 21.0 (20.5, 21.4) | 21.1 (20.6, 21.5) | 21.1 (20.5, 21.5) | 0.55 |
BMI: Body Mass Index; E2: Estradiol; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol. Normally distributed data (age, physical activity score, HDL-C, Total HDL-P, Large HDL-P, medium HDL-P, small HDL-P, HDL size and LDL-C) presented as mean (SD), skewed data (BMI, E2, total LDL-P, intermediate LDL-P, large LDL-P, small LDL-P and LDL size) presented as median (Q1, Q3).
Missing VMS or other variable (education level, BMI, physical activity score, alcohol consumption, HDL-C, LDL-C, HDL particles, LDL particles, HDL size or LDL size) at baseline
Significantly different from no VMS
Significantly different from infrequent VMS
HDL-C: 1 mg/dL= 0.02586 mmol/L; LDL-C: 1mg/dL=0.02586 mmol/L
Women with frequent VMS were more likely to be Black and late peri- or post-menopausal compared to women with no VMS at baseline. Women with frequent VMS were older, had lower levels of E2, higher concentrations of LDL-C, total LDL-P, and HDL-P, and smaller HDL size than no and infrequent VMS groups. Further, women with frequent VMS had higher BMI than no VMS group and higher concentrations of small HDL-P than infrequent VMS group, Table 1.
Association between frequency of vasomotor symptoms and lipids/lipoproteins metrics
The results for the relationships of VMS frequency categories and HDL lipids/lipoproteins metrics are presented in Table 2. In the unadjusted model (Table 2, Model 1), women with frequent VMS had smaller HDL size compared to no VMS group. After adjusting for age, site, race/ethnicity, menopausal status, physical activity, alcohol consumption and BMI (Table 2, Model 2), frequent VMS was still significantly associated with smaller HDL size. Additional adjustment for log-E2 explained this association (Table 3, Model 3; Figure 1 (A)).
Table 2:
Longitudinal associations of HDL metrics, separate models, with VMS frequency
| HDL-C β (SE) |
Total HDL-P β (SE) |
Large HDL-P β (SE) |
Medium HDL-P β (SE) |
Small HDL-P β (SE) |
HDL-Size β (SE) |
|
|---|---|---|---|---|---|---|
| Model 1a | ||||||
| Infrequent VMS | 0.03 (0.61) | 0.34 (0.32) | −0.02 (0.16) | 0.29 (0.35) | 0.12 (0.37) | −0.03 (0.02) |
| Frequent VMS | 0.23 (0.73) | 0.57 (0.38) | −0.35 (0.19) | 0.22 (0.41) | 0.80 (0.44) | −0.08 (0.03)d |
| Trend p | 0.77 | 0.12 | 0.09 | 0.51 | 0.09 | 0.002 |
| Model 2 b | ||||||
| Infrequent VMS | 0.22 (0.59) | 0.33 (0.31) | 0.08 (0.15) | 0.20 (0.36) | 0.14 (0.37) | −0.02 (0.02) |
| Frequent VMS | 0.32 (0.73) | 0.32 (0.39) | −0.19 (0.19) | 0.22 (0.43) | 0.45 (0.45) | −0.06 (0.03)d |
| Trend p | 0.64 | 0.34 | 0.43 | 0.56 | 0.33 | 0.04 |
| Model 3c | ||||||
| Infrequent VMS | −0.06 (0.59) | 0.20 (0.31) | 0.06 (0.15) | 0.19 (0.36) | 0.05 (0.37) | −0.02 (0.02) |
| Frequent VMS | −0.08 (0.74) | 0.05 (0.39) | −0.19 (0.19) | 0.13 (0.44) | 0.26 (0.46) | −0.04 (0.03) |
| Trend p | 0.91 | 0.80 | 0.43 | 0.70 | 0.59 | 0.11 |
Model 1: Unadjusted Model
Model 2: Adjusted for age, site, race/ethnicity, education, menopausal status, physical activity, alcohol use and BMI
Model 3: Model 2, log-E2 and menstrual cycle day of blood collection
p<0.05 compared to reference group
Table 3:
Longitudinal associations of LDL metrics, separate models, with VMS frequency
| LDL-C β (SE) |
Total LDL-Pe β (SE) |
Intermediate LDL-Pe β (SE) |
Large LDL-Pe β (SE) |
Small LDL-Pe β (SE) |
LDL-Sizee β (SE) |
|
|---|---|---|---|---|---|---|
| Model 1a | ||||||
| Infrequent VMS | 2.77 (1.52) | 0.03 (0.01) | 0.07 (0.04) | −0.04 (0.04) | 0.04(0.05) | 0.001 (0.002) |
| Frequent VMS | 6.30 (1.82)d | 0.05 (0.02)d | 0.09 (0.05)d | 0.03 (0.05) | 0.08 (0.06) | −0.001 (0.002) |
| Trend p | 0.0005 | 0.003 | 0.03 | 0.78 | 0.13 | 0.76 |
| Model 2b | ||||||
| Infrequent VMS | 2.09 (1.43) | 0.02 (0.01) | 0.06 (0.04) | −0.05 (0.05) | 0.05 (0.05) | 0.00004 (0.002) |
| Frequent VMS | 3.58 (1.77)d | 0.03 (0.02) | 0.09 (0.05) | −0.02 (0.06) | 0.07 (0.06) | −0.003 (0.002) |
| Trend p | 0.04 | 0.06 | 0.04 | 0.55 | 0.20 | 0.18 |
| Model 3c | ||||||
| Infrequent VMS | 1.59 (1.42) | 0.02 (0.01) | 0.05 (0.04) | −0.05 (0.05) | 0.05 (0.05) | −0.0001 (0.002) |
| Frequent VMS | 1.97 (1.78) | 0.02 (0.02) | 0.07 (0.05) | −0.03 (0.06) | 0.07 (0.06) | −0.004 (0.002) |
| Trend p | 0.23 | 0.14 | 0.14 | 0.49 | 0.20 | 0.14 |
Model 1: Unadjusted Model
Model 2: Adjusted for age, site, race/ethnicity, education, menopausal status, physical activity, alcohol use and BMI
Model 3: Model 2 + log-E2 and menstrual cycle day of blood collection
p<0.05 compared to reference group
Log- Transformed
Figure 1.
Adjusted means of HDL size, LDL-C and log-transformed intermediate LDL-P by VMS categories. (i) Unadjusted model; (ii) Model adjusted for age, site, race/ethnicity, education, menopausal status, physical activity, alcohol use and BMI; (iii) Model adjusted for age, site, race/ethnicity, education, menopausal status, physical activity, alcohol use, BMI, log-E2 and menstrual cycle day of blood collection. Trend p-values presented in figures. (A) HDL size; (B) LDL-C; (C) Log-transformed Intermediate LDL-P
The associations of VMS frequency categories with LDL lipids/lipoproteins metrics are presented in Table 3. Frequent VMS was associated with higher LDL-C, log-intermediate LDL-P, and log-total LDL-P compared to no VMS (Table 3, Model 1). After adjusting for age, site, race/ethnicity, menopausal status, physical activity, alcohol consumption and BMI (Table 3, Model 2), frequent VMS was still associated with LDL-C, and the linear trends were persistently significant for LDL-C and log- intermediate LDL-P. Additional adjustment for log-E2 explained all associations (Table 3, Model 3; Figure 1 (B, C)).
The results persisted in the sensitivity analysis after excluding E2 levels below the LLD (data not shown).
Sub-Analyses
We then tested the association between hot flashes or night sweats, separately with lipids/lipoproteins metrics. Frequent hot flashes were associated with higher total LDL-P and smaller LDL size after adjusting for all covariates including log-E2, whereas the association between frequent hot flashes and each of total HDL-P, HDL size, and LDL-C was explained after adjusting for log-E2 (Supplementary Tables 1 and 2). Night sweats were not related, suggesting that the reported associations for VMS may be driven by hot flashes more than night sweats.
Discussion
The current study provided novel findings on associations between VMS frequency and lipids/lipoproteins metrics in women transitioning through menopause. We found that frequent VMS was associated with smaller HDL particle size, and higher LDL-C and intermediate LDL-P concentrations over time. Endogenous E2 levels appeared to explain these associations to non-significance, suggesting a critical contribution of E2 levels in this relationship.
Recent studies that have investigated novel metrics of lipoproteins in midlife women have shown that a more atherogenic lipoprotein profile may be defined by smaller HDL and LDL particle sizes, when quantified by NMR, compared to larger particles[29,30,38]. A study conducted in postmenopausal women showed that smaller HDL size and lower concentrations of total LDL-P were associated with incident cardiovascular events[30]. Another study in obese postmenopausal women found that higher concentrations of total LDL-P and lower levels of total and medium HDL-P were associated with higher risk of incident coronary heart disease[29]. Higher concentrations of small LDL-P and smaller LDL size were found to be positively associated with higher odds of coronary artery calcification[38]. However, the relation between HDL and LDL lipoproteins and CVD risk may differ by method of assessment; for instance, results from ion mobility have shown that larger HDL-P was associated with higher cIMT [26,39], and more studies are needed to confirm the utility of each measure in CVD risk assessment. Thus, the current findings showing frequent VMS to be associated with smaller HDL size and higher concentrations of intermediate LDL-P as measured by NMR and LDL-C compared to no VMS suggested frequent VMS to be related to a more atherogenic lipoprotein profile.
High density and low density lipoproteins are complex macromolecules composed of varying concentrations of triglycerides, phospholipids, proteins and cholesterol. Different HDL and LDL particles vary in size, density and composition [26,40]. Due to the complexity of the HDL and LDL particles, it has been suggested that there should be more focus on evaluating lipoprotein subclasses and their functionality rather than their total cholesterol which may not be reflective of risk associated with the lipoproteins [26]. We are not aware of other studies that assessed associations between VMS frequency and lipoprotein subclasses over the menopause transition. However, a few previous studies have assessed the associations between frequency or severity of VMS and conventional lipid measures (HDL-C and LDL-C), but results have not been consistent. In a cross-sectional study of Korean women (mean age 54 years; 58% reporting any VMS), presence of VMS was associated with higher odds of increased HDL-C[41]. Conversely, Gast el al. (mean age 56.3 years; 55% reporting VMS, 31% reporting sweating only) reported no associations between presence of VMS and lipids[4], but presence of sweating was associated with higher LDL-C, independent of E2 levels. Compared to our baseline study population, those participants were older and more likely to be postmenopausal, and VMS was defined as presence versus absence of symptoms. Tuomikoski et al.[7] reported no difference in HDL-C and LDL-C levels with more frequent hot flashes quality (n=150; mean age 52.4 years), whereas Sassarini et al.[5] reported lower HDL-C in women with hot flashes (n=46). Both studies; however, were limited to postmenopausal women, and evaluated the severity of symptoms. SWAN had previously reported that the presence of hot flashes was associated with higher HDL-C and LDL-C concentrations after adjusting for E2[6]. The women included in the previous SWAN study were younger at baseline (45.9 (2.7) years) and were all premenopausal or early perimenopausal at baseline. The median level of E2 at baseline in women included in our analysis was lower than the previous study (35.1 pg/ml vs 55.3 pg/ml). These factors could explain the difference in the results observed between the two studies.
The exact mechanism that explains the association observed between VMS and lipids/lipoproteins metrics in this study is not well understood. The changes in the hormonal environment during the menopause transition overlap with the occurrence of VMS symptoms, and has been linked to changes in the lipoprotein profile[42]. We have reported here that E2 level explains the relationship between lipoprotein subclasses and VMS, a finding that was not described in other studies[4,6] and may help explain the relationship. However, these prior studies have only measured lipids by conventional metrics and not the lipoprotein subclass metrics. Another potential mechanism that could potentially link VMS to lipoproteins is through an inflammatory process. In previous studies, women with VMS have exhibited elevated inflammatory markers compared to women who do not report VMS[43]. Increased inflammation has been linked to an altered lipoprotein profile, particularly smaller lipoprotein particles[44,45]. Adding high sensitivity C-reactive protein to our models did not affect our results (results not shown), suggesting other pathways. The menopause transition is also accompanied by gain in body weight and fat redistribution, particularly deposition of abdominal fat[46]. Higher BMI is associated with increased vasomotor symptom frequency prior to menopause[47], and abdominal adiposity is linked to more hot flashes[48]. Moreover, increased abdominal fat, particularly visceral fat, is associated with poorer adipokines profile, such as lower adiponectin and higher leptin and monocyte chemoattractant protein-1 (MCP-1)[49], which have been linked to increased hot flashes in midlife women[49]. The increase in BMI, abdominal adiposity and adipokine profile have been linked to an unfavorable lipid profiles in midlife women[22,50,51]. Our results in this analysis persisted with adjustment for BMI, but we did not adjust for other measures of adiposity. Future studies should assess this hypothesis. Moreover, it has been suggested previously that estrogen deficiency may alter endothelial-dependent vasodilation through the accumulation of LDL particles[44], and reduce the anti-oxidative effect of E2[26]. The changes in hormones that accompany the menopausal transition, such as the drop in E2, may impact the lipolysis, which could influence the remodeling of HDL molecule, and shift the distribution of HDL subclasses[52]. For instance, E2 is known to reduce the activity of hepatic lipase leading to increase in the size and proportion of large HDLs [52]; thus, the change in E2 levels could shift the HDL pool to a smaller size.
To our knowledge, this is the first study to investigate the relation between VMS frequency and HDL and LDL subclasses and sizes in midlife women. The major strengths of this study were the method of VMS collection, where the symptoms were collected yearly and were obtained on the previous 2 weeks before a visit. This reduces the magnitude of recall and misclassification bias that can ensue when collecting measures of menopausal symptoms. The data was collected longitudinally which allowed for assessing this relation as women progress through menopause. We adjusted for multiple confounders that may impact this association. However, this analysis has certain limitations that are worth mentioning. First, we did not adjust for multiple comparisons in this analysis. Thus, the results of this study should be interpreted with care. Even though our study was longitudinal in nature, we cannot establish causality and the direction of the associations. We also did not adjust for other potential confounders such as smoking; however, a small number of women were smokers in this study. Adding smoking to the models did not change the results (data not reported). Moreover, our results are limited to the NMR method, and need to be replicated using other lipoprotein subclass methods, such as ion mobility, ultracentrifugation and gel electrophoresis.
Conclusions
In conclusion, VMS frequency may impact HDL and LDL subclasses, and E2 may play a crucial role in in this association in midlife women. Future studies are necessary to investigate potential mechanistic pathways through which endogenous E2 might influence how VMS frequency are related to the lipoprotein profile in women as they progress through menopause. Since previous randomized clinical trials have reported that exogenous estrogen therapy in women may alter lipid levels[53], the role of HT use on lipoprotein subclasses in women suffering from VMS should be further investigated.
Supplementary Material
Highlights:
Frequent VMS is linked to smaller HDL size and higher LDL-C and intermediate LDL-P.
Endogenous E2 could be a pathway linking frequent VMS with lipoprotein subclasses.
Studies should assess effect of exogenous E2 on lipoproteins in women with VMS.
Acknowledgements:
Clinical Centers: University of Michigan, Ann Arbor – Siobán Harlow, PI 2011 – present, MaryFran Sowers, PI 1994–2011; Massachusetts General Hospital, Boston, MA – Joel Finkelstein, PI 1999 – present; Robert Neer, PI 1994 – 1999; Rush University, Rush University Medical Center, Chicago, IL – Howard Kravitz, PI 2009 – present; Lynda Powell, PI 1994 – 2009; University of California, Davis/Kaiser – Ellen Gold, PI ; University of California, Los Angeles – Gail Greendale, PI ; Albert Einstein College of Medicine, Bronx, NY – Carol Derby, PI 2011 – present, Rachel Wildman, PI 2010 – 2011; Nanette Santoro, PI 2004 – 2010; University of Medicine and Dentistry – New Jersey Medical School, Newark – Gerson Weiss, PI 1994 – 2004; and the University of Pittsburgh, Pittsburgh, PA – Karen Matthews, PI.
NIH Program Office: National Institute on Aging, Bethesda, MD – Chhanda Dutta 2016-present; Winifred Rossi 2012–2016; Sherry Sherman 1994 – 2012; Marcia Ory 1994 – 2001; National Institute of Nursing Research, Bethesda, MD – Program Officers.
Central Laboratory: University of Michigan, Ann Arbor – Daniel McConnell (Central Ligand Assay Satellite Services).
SWAN Repository: University of Michigan, Ann Arbor – Siobán Harlow 2013 - Present; Dan McConnell 2011 – 2013; MaryFran Sowers 2000 – 2011
Coordinating Center: University of Pittsburgh, Pittsburgh, PA – Maria Mori Brooks, PI 2012 - present; Kim Sutton-Tyrrell, PI 2001 – 2012; New England Research Institutes, Watertown, MA - Sonja McKinlay, PI 1995 – 2001.
Steering Committee: Susan Johnson, Current Chair; Chris Gallagher, Former Chair
We thank the study staff at each site and all the women who participated in SWAN.
Financial Support:
Funding: This work was funded by the National Institutes of Health (NIH), DHHS, through the National Institute on Aging (NIA), the National Institute of Nursing Research (NINR) and the NIH Office of Research on Women’s Health (ORWH) (Grants U01NR004061; U01AG012505, U01AG012535, U01AG012531, U01AG012539, U01AG012546, U01AG012553, U01AG012554, U01AG012495). The SWAN Repository (U01AG017719).
The Study of Women’s Health Across the Nation (SWAN) HDL ancillary study has grant support from National Institute on Aging (NIA) AG058690.
The content of this abstract is solely the responsibility of the authors and does not necessarily represent the official views of the NIA, NINR, ORWH or the NIH.
Footnotes
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