Abstract
Background:
The menopause transition (MT) could trigger low-grade chronic inflammation which may modify high-density lipoproteins (HDL) and lead to additional inflammatory responses contributing to atherosclerosis development.
Objective:
To test whether complement proteins C3 and C4 increase around the final menstrual period (FMP), and whether changes in HDL subclasses and lipid content associate with C3 and C4 levels over time in midlife women.
Methods:
The study included 471 women (at baseline: age 50.2(2.7) years; 87.3% pre or perimenopausal) who had nuclear magnetic resonance spectroscopy HDL subclasses, lipid content, and C3 and C4 measured up to 5 times over the MT.
Results:
Adjusted annual changes in C3 and C4 varied by time segments relative to FMP with significant increases, steeper for C3, only observed within 1 year before to 2 years after the FMP. Greater decreases in large HDL particles (HDL-P), HDL size, and HDL-phospholipids, and greater increases in small HDL-P and HDL-Triglycerides were associated with higher C3 and C4 over time, although associations with C4 were weaker than those with C3.
Conclusion:
Complement proteins C3 and C4 significantly rise around menopause with C3 showing the steepest rise. Changes in HDL subclasses, overall size, and lipid content, over the MT may play a role in modulating inflammation responses known to be related to atherosclerosis. These results raise the possibility that novel therapeutic agents focusing on HDL might contribute to CVD protection by modulating inflammation.
Keywords: Menopause, estradiol, complement, high-density lipoproteins, lipid
Introduction
In addition to its well-recognized cardioprotective feature involving lipid metabolism, high-density lipoprotein (HDL) may contribute to inflammatory and immune responses.1 This novel role has been attributed to the heterogenous proteome carried by HDL particles.2 Interestingly, HDL proteome changes in response to inflammation.3,4 Moreover, recent research reported that changes in HDL proteome may play a role in modulating immune responses. For example, it has been noted that the HDL of critically ill COVID-19 subjects is enriched in proteins related to inflammation and immune response.5 Thus, the HDL proteome may play a role in modulating immune response in addition to be altered as a result of inflammation.
HDL subclasses vary in their proteome composition and thus may modulate the immune response differently.4,6 Complement proteins C3 and C4, which play critical roles in the activation of the complement system and the release of pro-inflammatory mediators,7 are particularly linked to HDL biology.6 HDL carries multiple proteins with direct roles in complement activation and innate immune response including C3 and C4.7 Interestingly, small HDL particles from coronary artery disease patients and pro-inflammatory HDL particles from rheumatoid arthritis patients are significantly enriched with C3 protein.4,6
The menopause transition (MT) is a critical stage of women’s lives accompanied by the accumulation of adverse changes in sex hormones, fat distribution, and lipid/lipoprotein profile,8 that place women at higher risk of chronic inflammation potentially altering HDL proteome composition and impacting HDL functionality. As women transition through menopause, they experience significant declines in large HDL particles (HDL-P) and overall HDL particle size, but increases in small HDL-P. The triglyceride content of HDL (HDL-Tg) increases while that of phospholipids (HDL-PL) decreases. Moreover, the per particle ability of HDL to mediate cholesterol efflux capacity declines over midlife in women.9 Interestingly, C3 levels are higher in postmenopausal women compared to premenopausal women, and may be related to blood clots via their associations with hemostatic markers.10 Thus, C3 might be a possible pathway through which postmenopausal women are at higher risk of atherosclerosis and cardiovascular related events.8
Taken together, the MT could trigger low-grade chronic inflammation which may modify HDL subclasses and their lipid content, which in turn may trigger complement activation, and lead to additional inflammatory responses and the development of atherosclerosis in midlife women. The Study of Women’s Health Across the Nation (SWAN) HDL ancillary study provides a unique opportunity to better understand the contribution of the MT and HDL metrics to complement proteins during midlife. Our objectives were to test whether C3 and C4 increase during the MT, and whether changes in HDL subclasses, lipid content and overall size over the MT associate with C3 and C4 levels in midlife women.
Materials and Methods
Study participants.
SWAN is an ongoing, community-based longitudinal study on midlife women as they traverse the MT. During 1996 and 1997, the SWAN study recruited 3,302 women of different racial/ethnicities between 42 and 52 years old from 7 different clinical sites in the United States. The complete description of the SWAN study has been published previously.11 Eligible women were recruited if they had an intact uterus and at least one ovary, were not pregnant, had at least one menstrual period within the 3 months before recruitment, and were not using hormone therapy in the last 3 months before recruitment time.
The SWAN HDL study is an ancillary study to SWAN that focuses on characterizing changes in HDL content and function during ovarian aging and how these changes can impact the cardioprotective function of HDL in women over the MT. In the SWAN HDL study, a total of 558 women were selected from the SWAN cohort if they had no history of cardiovascular disease, at least one visit before their final menstrual period (FMP), and two visits after the FMP with available blood samples (a total of 1,461 specimens). HDL metrics were measured on SWAN HDL visits that coincident with SWAN visit 1, follow-up visits 3-9, and visit 12. Participants had 2 to 5 measurements during the MT. For the current analysis, we excluded 87 women due to missing FMP date, leaving a total of 471 women (1,288 observations) for final analysis. The included women had lower levels of C3 and total cholesterol compared with excluded women. All the participants were provided with informed consent before the SWAN study enrollment. SWAN study protocol has received approval from the institutional review boards at all SWAN sites.
Blood measurements.
Blood samples were collected after at least 10 hours of overnight fasting. Participants were scheduled to have blood draws within day 2 to day 5 of their menstrual cycle. When scheduled blood collection was not possible due to less regular menstrual cycles, the sample was taken randomly within 90 days of the scheduled SWAN follow up visit. The collected samples were stored at −80° C and only thawed for SWAN HDL assay use for maximum validity.
C3 and C3 measurements:
Complement protein C3 and C4 were analyzed with the Alfa-Wasserman ACE analyzer using the K-ASSAY ((KAMIYA BIOMEDICAL COMPANY) complement C3 and C4 reagents separately by immunoturbidimetric assay. Complement sample reacts with the anti-complement sensitized latex particles to form antigen-antibody complexes that agglutinate the latex particles. The increase in absorbance is directly proportional to the amount of complement in the sample. The measurable range of the assay for complement C3 was 30-350 mg/dL, and the measurable range for complement C4 was 8 to 80 mg/dL. The intra-assay and inter-assay coefficients of variance for C3 ranged from 1.94% to 3.33% and 1.48% to 3.63%, respectively, and for C4 ranged from 3.38% to 5.17%, and 2.10% to 3.72%, respectively.
Nuclear Magnetic Resonance (NMR) Spectroscopy:
HDL subclasses were analyzed by an automated Vantera Clinical Analyzer (a 400 MHz NMR spectroscopy platform) at LabCorp (Morrisville, NC) by the NMR Spectroscopy LipoProfile-3 algorithm.12 Each subpopulation of subclasses has NMR signals with unique frequencies and shapes. The larger the number of participles that release the signals, the higher the amplitude of the signals. The line shape of the signal envelope was modeled as the total of all lipoprotein signals to obtain the amplitude of each subpopulation of subclasses. Conversion factors were used to quantify the concentrations from the areas of different subpopulations, and the concentrations were categorized into small, medium, or large HDL subclasses. The sum of the concentrations of all subclasses resulted in the total HDL particle concentration, and the average size of the HDL particles was obtained by summing the relative mass percentage adjusted diameter of each subclass from the NMR signal amplitude. HDL subclasses were grouped by size into small (7.3-8.2 nm), medium (8.2-9.4 nm), and large (9.4-14 nm) HDL particles (HDL-Ps). The coefficients of variation for HDL-P concentrations and size for the intra- and inter-assay were 0.6% to 3.7% and 1.5% to 4.0%, respectively.
Other blood measures:
E2 was measured via the Bayer Diagnostics ACS:180 instrument (SWAN baseline visit and visit 3 to 9) or via the ADVIA Centaur (SWAN visit 12). The results obtained from ADVIA Centaur were calibrated to become comparable to results from the Bayer Diagnostics. Total cholesterol and fasting triglycerides were measured either using the Hitachi 747-200 clinical analyzer at Medical Research Lab (MRL, Lexington, KY) (SWAN baseline visit and visit 3 to 8), or by using the ADVIA assay at the University of Michigan (UM, Ann Arbor, MI) (SWAN visits 9 and 12). University of Michigan results were calibrated to become comparable to those values produced by the Medical Research Laboratories. The LDL-C values were calculated using the Friedewald equation when the triglycerides values (<400mg/dL for valid measurements).13 The separation of fasting HDL-C using heparin-2M manganese chloride was conducted either at MRL (SWAN baseline visit to visit 7) or at UM (SWAN visits 9 and 12).14,15 UM results were calibrated so that values were comparable to those produced by MRL.
Study covariates.
Race/ethnicity and education were self-reported at the baseline SWAN visit. For every SWAN visit, data was collected on age, body mass index (BMI), estradiol (E2), menopausal status, alcohol consumption, smoking status, physical activity, triglyceride, total cholesterol, and LDL-C. The reported race/ethnicity categories included White, Black, Chinese, Hispanic, and Japanese. The educational levels included post-graduate degree, college degree, some college degree, high school degree or less. Age was calculated by subtracting visit date and birth date. FMP was the reported last menstrual period in the visit prior to the visit that women were first classified as postmenopausal. Age at FMP was calculated by subtracting the date of the final menstrual period and birth date. BMI (Kg/m2) was calculated as the ratio of the measured weight (Kg) and measured height2 (m2). Alcohol use was obtained from participant self-assessed questionnaires and dichotomized into < one-time alcohol consumption per month or ≥ one-time alcohol consumption per month. Smoking status was based on participant self-report and categorized as current vs. past/never. Physical activity was measured as scores using the modified Kaiser Permanente Health Plan Activity Survey.13
Menopause status was also based on participants’ self-reports as well as the frequency and pattern of the menstrual periods during the past 12 months before each visit. The menopause status was categorized into 5 categories as premenopausal (no change in menstrual bleeding and cycles), early perimenopausal (at least one menstrual bleeding in the last three months and some changes in the intervals of the menstrual cycles), later perimenopausal (at least one menstrual cycle within the past 12 months but no bleeding within the past 3 months), postmenopausal (no menstrual cycle within the past 12 months due to natural menopause), or unknown (menopausal status unknown due to hormone therapy).
Statistical Analysis.
To characterize changes in complement protein C3 and C4 over the MT, non-parametric LOESS (locally weighted regression with scatter smoothing) plots were used to determine if trajectories of C3/C4 over the MT were linear or had inflection points around the FMP. Inflection points were identified from LOESS plots. Piecewise linear mixed-effects models were then used to estimate the yearly changes in each LOESS-identified time segments for C3 and C4. Models were adjusted for study site, race/ethnicity and age at FMP. To determine the associations of changes in each HDL metric and C3 or C4 over the MT, linear mixed effect models were used. We modeled repeated measurements of C3 and C4 (separately) as functions of baseline level (corresponding in this analysis to first available visit in SWAN HDL) and change since baseline in each HDL (separate models for each HDL metric). Models were adjusted for study site, race/ethnicity and time-varying: age, BMI, smoking status, physical activity, alcohol consumption, E2 and cycle day of the blood draw.
Results
Participants characteristics at first visit of SWAN HDL are presented in Table 1. On average, women were 50.2(SD=2.7) years old, and more than 87.3% were pre- or early peri-menopausal and about 50% White.
Table 1.
Demographics and clinical characteristics of study participants at first available visit
| Variable | N=471 |
|---|---|
| Demographics | |
| Age, years, mean (SD) | 50.19(2.67) |
| Race/ethnicity, n(%) | |
| White | 244(51.80%) |
| Black | 130(27.60%) |
| Chinese | 52(11.04%) |
| Hispanic | 3(0.64%) |
| Japanese | 42(8.92%) |
| Education, n(%) | |
| <High School | 13(2.77%) |
| High School | 70(14.93%) |
| Some college | 143(30.49%) |
| College | 106(22.60%) |
| Post-Graduate | 137(29.21%) |
| Menopausal Status, n(%) | |
| Premenopausal | 59(12.53%) |
| Early Perimenopausal | 315(66.88%) |
| Late Perimenopausal | 37(7.86%) |
| Postmenopausal | 51(10.83%) |
| Unknown | 9(1.91%) |
| Time before FMP, median (Q1, Q3) | 2.08(3.54, 0.78) |
| Clinical characteristics | |
| BMI, kg/m2, median (Q1, Q3) | 26.54(22.95, 31.71) |
| Alcohol use, n(%) | |
| <1 drink/month | 239 (51.86%) |
| ≥1 drink/month | 221 (48.04%) |
| Smoking Status, n(%) | |
| Past\Never smoked | 419 (89.53%) |
| Current | 49 (10.47%) |
| Physical Activity score, mean (SD) | 8.00 (1.80) |
| E2, pg/mL, median (Q1, Q3) | 37.90(20.25, 100.00) |
| C3, mg/dL, mean (SD) | 125.01(29.24) |
| C4, mg/dL, mean (SD) | 26.68(8.62) |
| Total Cholesterol, mg/dL, mean (SD) | 193.93(34.06) |
| LDL-C, mg/dL, mean (SD) | 112.40(31.03) |
| HDL-C, mg/dL, mean (SD) | 59.32(14.22) |
| Triglycerides, mg/dL, median (Q1, Q3) | 112.55(78.69) |
| Total HDL-P, umol/L, mean (SD) | 34.67(5.97) |
| Large HDL-P, umol/L, mean (SD) | 8.49(3.59) |
| Medium HDL-P, umol/L, mean (SD) | 11.19(6.19) |
| Small HDL-P, umol/L, mean (SD) | 14.99(7.03) |
| HDL Size, nm, mean (SD) | 9.54(0.54) |
| HDL-PL, mg/dL, mean (SD) | 54.15 (10.26) |
| HDL-Tg, mg/dL, median (Q1, Q3) | 17.00 (14.00, 21.00) |
BMI: body mass index; C3: complement component 3; C4: complement component 4; E2: estradiol; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol; HDL-P: high density lipoprotein particles; HDL-PL: HDL phospholipids; HDL-Tg: HDL triglycerides.
C3 and C4 trajectories over the menopause transition
Adjusted annual changes in C3 and C4 varied by time segments relative to FMP with significant increases only observed within 1 year before to 2 years after the FMP, Figure 1. Increases reported in the second segment, which encompasses time within 1 year before to 2 years after the FMP, were significantly greater than changes in the other 2 time segments, Table 2. Results did not change after adjusting for age at FMP, study site and race/ethnicity, Table 2.
Figure 1. Spline for C3 and C4 levels over the menopause transition.
First segment: One year or more before FMP, Second segment: One year before FMP to two years after FMP, Third segment: Two years or more after FMP
Table 2.
Annual changes of C3 and C4 by time relative to the final menstrual period
| Segment 1a β(SE) | p-value | Segment 2a β(SE) | p-value | Segment 3a β(SE) | p-value | 1st vs 2nd Segment | 2nd vs. 3rd Segment | 1st vs 3nd Segment | |
|---|---|---|---|---|---|---|---|---|---|
| C3, mg/dL | |||||||||
| Unadjusted | −1.77 (0.93) | 0.06 | 4.56 (0.78) | <.0001 | −0.38 (0.38) | 0.32 | 0.0002 | <.0001 | 0.23 |
| Adjusted b | −1.19(0.92) | 0.19 | 4.11(0.77) | <.0001 | −0.18 (0.38) | 0.64 | 0.0005 | <.0001 | 0.90 |
| C4, mg/dL | |||||||||
| Unadjusted | −0.22 (0.21) | 0.28 | 0.86 (0.18) | <.0001 | 0.18 (0.09) | 0.04 | 0.002 | 0.005 | 0.09 |
| Adjusted b | −0.16 (0.21) | 0.45 | 0.81(0.18) | <.0001 | 0.19(0.09) | 0.03 | 0.005 | 0.01 | 0.19 |
Segment 1: One year or more before FMP, Segment 2: One year before FMP to two years after FMP, Segment 3: Two years or more after FMP. The beta coefficients signify change in Y per 1 unit of X. Y is modeled as the repeated measure of each HDL metric (separately) and X is a time segment in year from a piecewise model.
Adjusted for Age at FMP, study site, race/ethnicity
Univariate associations of study variables with complement proteins over time
Table 3 presents beta coefficients for C3 and C4 in mg/dL per 1 SD unit increment of each covariate. In general, patterns of associations of study covariates with each of C3 and C4 were similar with beta coefficients being stronger for C3 compared to C4. Higher BMI, physical activity score, E2 level, HDL-C, large HDL-P, HDL-PL and larger HDL size were associated with lower levels of C3 and C4 over time. On the other hand, higher total cholesterol, triglycerides, LDL-C, small HDL-P and greater HDL-Tg were associated with higher C3 and C4 levels over time. Smoking status, alcohol consumption, total HDL-P and medium HDL-P were not related to either C3 or C4.
Table 3.
Univariate associations of study variables with C3 and C4 over time
| C3, mg/dl | C4, mg/dl | |||
|---|---|---|---|---|
| β(SE)a | p-value | β(SE)a | p-value | |
| BMI | 17.79(0.87) | <.0001 | 3.61(0.29) | <.0001 |
| Smoking Status (Current vs Past/Never) | −1.77(3.24) | 0.59 | 0.04(0.87) | 0.96 |
| Physical activity score | −2.72(0.76) | 0.0004 | −0.65(0.19) | 0.0006 |
| Alcohol consumption (<1 vs ≥ 1) | −2.00(1.70) | 0.24 | −0.88(0.44) | 0.05 |
| E2 a | −2.55(0.59) | <.0001 | −0.73(0.14) | <.0001 |
| Total Cholesterol | 6.46(0.77) | <.0001 | 1.39(0.19) | <.0001 |
| Triglycerides a | 8.22(0.80) | <.0001 | 0.79(0.21) | 0.0002 |
| LDL-C | 7.70(0.81) | <.0001 | 1.76(0.21) | <.0001 |
| HDL-C | −7.77(0.88) | <.0001 | −0.72(0.24) | 0.003 |
| Total HDL-P | −0.04(0.81) | 0.96 | 0.27(0.20) | 0.20 |
| Large HDL-P | −11.76(0.83) | <.0001 | −1.48(0.23) | <.0001 |
| Medium HDL-P | −1.21(0.73) | 0.1 | −0.17(0.18) | 0.36 |
| Small HDL-P | 5.99(0.77) | <.0001 | 0.94(0.19) | <.0001 |
| HDL Size | −11.75(0.83) | <.0001 | −2.04(0.23) | <.0001 |
| HDL-PL | −6.50(0.85) | <.0001 | −0.94(0.23) | <.0001 |
| HDL-TG a | 5.28(0.75) | <.0001 | 0.76(0.19) | <.0001 |
All independent variables are standardized; thus, beta coefficients are per 1 SD unit. Triglycerides, HDL-Tg and E2 are log transformed
Adjusted associations of changes in HDL metrics over the menopause transition and levels of C3 and C4 during midlife
Adjusting for baseline values of HDL metrics and study site, race/ethnicity, and time varying age, BMI, smoking status, physical activity, alcohol consumption, E2 and cycle day of the blood draw, greater decreases in HDL-C, large HDL-P, HDL size, and HDL-PL and greater increases in small HDL-P and HDL-Tg associated with higher C3 over time. Similar independent associations, but with weaker strengths, were observed for C4 except for greater decreases in HDL-C and greater increases in small HDL-P and HDL-Tg, which were not significant. Changes in total HDL-P and medium HDL-P were not related to either C3 or C4.
Discussion
In this longitudinal assessment of a well-characterized sample of women transitioning through menopause, complement proteins C3 and C4 significantly rose around FMP; with C3 showing a steeper rise than C4. Moreover, greater decreases in HDL-C, large HDL-P, HDL size, and HDL-PL, and greater increases in small HDL-P and HDL-Tg were associated with higher levels of C3 and C4 over time, with associations with C4 being weaker than those with C3, suggesting a potentially stronger role of HDL on C3 compared to C4. These findings suggest that changes in HDL subclasses, overall size, and lipid content over the MT may play a role in modulating complement system inflammation responses known to be related to atherosclerosis. As such, therapeutic agents targeting HDL metrics may also protect from atherosclerosis through modulating complement protein components.
In a previous cross-sectional analysis of 100 women (50 postmenopausal and 50 premenopausal) from SWAN, C3 but not C4 was significantly higher in postmenopausal women compared to premenopausal women independent of age, race and BMI.10 Our current findings are consistent with earlier findings from SWAN of a link between menopausal status and complement protein C3, but also C4, however to a lesser degree. In addition, the current findings showed a sharp increase in C3 starting 1 year before to 2 years after menopause supporting that this increase was more driven by the MT rather than chronological aging. Thus, complement proteins C3 and C4 could be possible pathways by which midlife women are at higher risk of atherosclerosis and cardiovascular related events.8 C3 is the central component of the complement system known for its cardinal function in immune response.17 C3 is high in patients with cardiometabolic health conditions and CVD.18 Among 756 (55% women; 39% having metabolic syndrome and 11.5% CHD) subjects with mean age 52±11 years, a cut-off point of ≥160 mg/dL of C3 indicated a significant 1.9-fold likelihood of coronary heart disease (CHD) compared with individuals with a C3 <160 mg/dL, independent of sex, age and standard risk factors.19
The current results of changes in multiple HDL metrics associated with levels of C3 and C4 over time in women transition through menopause support the notion that HDL may play a role in modulating immune response in midlife women. Previous studies assessed associations of C3 and/or C4 with lipids/lipoproteins were mainly cross-sectional, not adjusted for covariates, and not specific to midlife women.19,20 In a cohort of 756 unselected adults, 39% of whom had the metabolic syndrome, lower HDL-C associated with higher C3.19 Another cross-sectional study of 1016 subjects aged 70 also reported a negative association between HDL-C and C3.20 Very limited studies assessed associations between complement proteins and other metrics of HDL and these studies were also cross-sectional and not specific to women transition through menopause. In a cross-sectional study of 523 participants (59.6(6.9) years, 60.8% men) from a Caucasian cohort with a moderately increased risk of cardiometabolic disease, C3 was evaluated as a predictor of lipoproteins measured via NMR. Higher C3 concentrations associated with lower concentrations of large HDL-P, and higher concentrations of small HDL-P with smaller mean particle size for HDL. Interestingly, higher plasma C3 was associated with lower absolute concentrations of PL in larger HDL lipoproteins and with amount of TG in small HDL. C3 was not related to medium HDL-P. There were no significant interactions of sex with complement components on the cross-sectional associations with lipoprotein characteristics.21 The findings from the current study of a contribution of HDL subclasses to complement proteins C3 and C4 are in agreement with this one study 21 and extend the associations to longitudinal changes in HDL subclasses, HDL particle size and HDL triglycerides and phospholipid content in midlife women.
Complement C3, C4 and C9 are present on HDL6 supporting a link between the complement system and metabolism and/or function of circulating HDL. Previous studies hypothesized a role of complement system in lipid metabolism. In particular, experimental data showed that the alternative pathway of the complement system may causally affect lipid metabolism. Male mice that are lacking C3 showed delayed postprandial Tg clearance and increased fasting and postprandial free fatty acid levels.22 Moreover, C3adesarg, the degradation product of C3, stimulated Tg synthesis in adipocytes.23 However, lipids/lipoproteins may also play a role in immune responses and the activation of the complement system. HDL has antioxidant and anti-inflammatory effects.24 The molecular mechanism by which HDL mediates its anti-inflammatory effects has been poorly understood. HDL surface associated proteins (apolipoproteins) may play a role.25,26 For instance, compared to no treatment, a treatment with HDL slowed liposome-induced drop of systemic arterial pressure (e.g. a manifestation of complement activation-related pseudoallergy ),25 suggesting that HDL associated proteins may have lessened the adverse hemodynamic changes, possibly as a consequence of suppressing complement activation in vivo. Alternately, HDL may impact inflammation and immune response genetically. HDL was shown to induce expression of ATF3, a transcriptional negative regulator of several proinflammatory genes.27
The current findings suggest that therapeutic options impacting HDL might also impact complement proteins. Indeed, increases in HDL-C as a result of niacin administration significantly correlated with decreases in C3 among 64 subjects in an open-label clinical trial of extended-release niacin to 2 grams daily, starting with 500 mg and adding 500 mg/week for the next 3 weeks, and returning after the 4th week.28 Additionally, reconstituted HDL (rHDL) composed of lipoprotein apoA-I with phosphatidylcholine bound to cholesterol crystals, that accumulate in plaques and initiate atherosclerosis, and impairs the ability of these crystals to activate complement, and consequently attenuates the related inflammatory responses.29 As such, the rHDL effect on complement activation could be of significant interest for management of atherosclerosis. Future study should additionally consider potential approaches to modify HDL proteome including complement factors.
The current study has multiple strengths including focusing on midlife women transition through menopause for whom changes in HDL and complement proteins have been linked to the MT.9,10 We evaluated a well characterized sample of midlife women, which allowed us to thoroughly adjust for potential confounders. We used a strong study design of repeated measures of HDL metrics and complement proteins over the MT, which enable optimal characterization of changes over time. Main limitations include the likelihood that our findings can only be generalized to similar populations of midlife women transition through menopause. Whether changes in HDL metrics during midlife are relevant to men and could modulate the immune response is not known. Additionally, since accelerated lipoprotein turnover might enhance the synthesis of several liver produced plasma enzymes and proteins including C3,30 accounting for liver synthetic capacity might be necessary to better understand the independent contribution of the MT to the reported changes in C3. Unfortunately, such data were not collected as part of the SWAN study.
In conclusion, complement proteins C3 and C4 significantly rise around menopause with C3 showing the steepest rise. Changes in HDL subclasses and overall size over the MT may play a role in modulating inflammation responses known to be related to atherosclerosis.31,32 These findings support the need to investigate how therapeutic agents impacting HDL metrics might also impact immune response and possibility its associated CVD risk in midlife women.
Table 4.
Multivariable adjusted associations of changes in HDL metrics and repeated measures of C3 and C4 over the menopause transition
| HDL metrics | C3, mg/dL | C4, mg/dL | ||
|---|---|---|---|---|
| β(SE) a | p-value | β(SE) a | p-value | |
| HDL-C | ||||
| Unadjusted | −5.08(1.16) | <.0001 | −0.29(0.28) | 0.29 |
| Adjusted b | −4.39(1.07) | <.0001 | −0.50(0.27) | 0.06 |
| Total HDL-P | ||||
| Unadjusted | 1.18(0.89) | 0.19 | 0.44(0.21) | 0.04 |
| Adjusted b | 0.61(0.82) | 0.46 | −0.09(0.20) | 0.66 |
| Large HDL-P | ||||
| Unadjusted | −10.03(1.07) | <.0001 | −1.20(0.26) | <.0001 |
| Adjusted b | −7.69(0.98) | <.0001 | −0.87(0.25) | 0.0005 |
| Medium HDL-P | ||||
| Unadjusted | −0.15(0.75) | 0.84 | −0.16(0.18) | 0.37 |
| Adjusted b | 0.44(0.67) | 0.51 | −0.11(0.16) | 0.51 |
| Small HDL-P | ||||
| Unadjusted | 4.59(0.83) | <.0001 | 0.93(0.20) | <.0001 |
| Adjusted b | 2.73(0.76) | 0.0004 | 0.34(0.19) | 0.07 |
| HDL size | ||||
| Unadjusted | −10.37(1.07) | <.0001 | −1.95(0.26) | <.0001 |
| Adjusted b | −6.93(1.00) | <.0001 | −0.90(0.25) | 0.0004 |
| HDL-PL | ||||
| Unadjusted | −3.58(1.01) | 0.0004 | −0.59(0.24) | 0.02 |
| Adjusted b | −1.96(0.92) | 0.03 | −0.62(0.23) | 0.006 |
| HDL-TG | ||||
| Unadjusted | 5.17(0.79) | <.0001 | 0.82(0.19) | <.0001 |
| Adjusted b | 3.96(0.73) | <.0001 | 0.35(0.18) | 0.05 |
All independent variables are standardized; thus, beta coefficients are per 1 SD unit.
Adjusted for study site, race/ethnicity, baseline level of HDL metric and time-varying age, BMI, menopause status, smoking status, physical activity, alcohol consumption, E2 and cycle day of blood draw
Highlights.
Complement proteins C3 and C4 rise around the menopause transition (MT).
Changes in HDL over the MT may modulate inflammation.
Therapies impacting HDL may also impact immune response.
Acknowledgments
The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the NIA, NINR, ORWH or the NIH.
Clinical Centers: University of Michigan, Ann Arbor – Carrie Karvonen-Gutierrez, PI 2021 – present, Siobán Harlow, PI 2011 – 2021, MaryFran Sowers, PI 1994-2011; Massachusetts General Hospital, Boston, MA – Sherri-Ann Burnett-Bowie, PI 2020 – Present; Joel Finkelstein, PI 1999 – 2020; Robert Neer, PI 1994 – 1999; Rush University, Rush University Medical Center, Chicago, IL – Imke Janssen, PI 2020 – Present; Howard Kravitz, PI 2009 – 2020; Lynda Powell, PI 1994 – 2009; University of California, Davis/Kaiser – Elaine Waetjen and Monique Hedderson, PIs 2020 – Present; Ellen Gold, PI 1994 - 2020; University of California, Los Angeles – Arun Karlamangla, PI 2020 – Present; Gail Greendale, PI 1994 - 2020; 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 – Rebecca Thurston, PI 2020 – Present; Karen Matthews, PI 1994 - 2020.
NIH Program Office: National Institute on Aging, Bethesda, MD – Rosaly Correa-de-Araujo 2020 - present; 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.
Funding Sources:
The Study of Women’s Health Across the Nation (SWAN) has grant support from 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, and U19AG063720) and 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.
Footnotes
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Declaration of interests:
Samar R El Khoudary: None
Xirun Chen: None
Dan McConnell: None
Maria M Brooks: None
Jeff Billheimer: None
Trevor Orchard: None
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