Menopausal hormone therapy (MHT) increases risk of coronary heart disease (CHD) in older women with elevated low-density lipoprotein (LDLC) levels. The endogenous estrogen receptor antagonist 27-hydroxycholesterol (27OHC) is correlated with LDLC levels and may block beneficial effects of estrogen on the cardiovascular system.
Methods and Results
We conducted a nested case-control study in the Women’s Health Initiative trials of 350 CHD cases and 813 matched controls to explore potential mediation by 27OHC of the dependence of the CHD risk elevation with MHT on LDLC. Baseline levels of 27OHC were not associated with CHD risk when LDLC was included in the multivariable models. The odds ratio for CHD associated with increased LDLC was 1.15 (95% confidence interval 1.08, 1.23) and was unchanged at 1.14 (1.07, 1.22) when 27OHC was added to the model. Baseline 27OHC did not interact with MHT on CHD risk (p = 0.81). In contrast, LDLC levels modified the effect of MHT on CHD risk (p for interaction = 0.02), and adding 27OHC did not affect this result. Using log scales the MHT effect on CHD increased linearly with increasing level of baseline LDLC, with a transition from no risk to increased risk at approximately 3.36 mmol/L (130 mg/dl).
27OHC does not independently increase risk of CHD, does not modify the increased risk of CHD due to MHT, and does not mediate the interaction of LDLC with MHT. Measuring blood lipids may aid in counseling individual women about initiating MHT and cardiovascular risk mitigation.
acute coronary syndrome; atherosclerosis; estrogen; low-density lipoprotein (LDL)-cholesterol; pathophysiology
The food frequency questionnaire approach to dietary assessment is ubiquitous in nutritional epidemiology research. Food records and recalls provide approaches that may also be adaptable for use in large epidemiologic cohorts, if warranted by better measurement properties. The authors collected (2007–2009) a 4-day food record, three 24-hour dietary recalls, and a food frequency questionnaire from 450 postmenopausal women in the Women’s Health Initiative prospective cohort study (enrollment, 1994–1998), along with biomarkers of energy and protein consumption. Through comparison with biomarkers, the food record is shown to provide a stronger estimate of energy and protein than does the food frequency questionnaire, with 24-hour recalls mostly intermediate. Differences were smaller and nonsignificant for protein density. Food frequencies, records, and recalls were, respectively, able to “explain” 3.8%, 7.8%, and 2.8% of biomarker variation for energy; 8.4%, 22.6%, and 16.2% of biomarker variation for protein; and 6.5%, 11.0%, and 7.0% of biomarker variation for protein density. However, calibration equations that include body mass index, age, and ethnicity substantially improve these numbers to 41.7%, 44.7%, and 42.1% for energy; 20.3%, 32.7%, and 28.4% for protein; and 8.7%, 14.4%, and 10.4% for protein density. Calibration equations using any of the assessment procedures may yield suitable consumption estimates for epidemiologic study purposes.
bias (epidemiology); biological markers; diet; energy intake; epidemiologic methods; measurement error; nutrition assessment
Triple-negative breast cancer is a particularly aggressive and lethal breast cancer subtype that is more likely to be interval-detected rather than screen-detected. The purpose of this study is to discover and initially validate novel early detection biomarkers for triple-negative breast cancer using preclinical samples. Plasma samples collected up to 17 months prior to diagnosis from 28 triple-negative cases and 28 matched controls from the Women’s Health Initiative Observational Study were equally divided into a training set and a test set and interrogated using a customized antibody array. Data were available on 889 antibodies, and in the training set statistically significant differences in case vs. control signals were observed for 93 (10.5%) antibodies at p<0.05. Of these 93 candidates, 29 were confirmed in the test set at p<0.05. Areas under the curve for these candidates ranged from 0.58 to 0.79. With specificity set at 98%, sensitivity ranged from 4% to 68% with ≥20 candidates having a sensitivity 20% and 6 having a sensitivity ≥40%. In an analysis of KEGG gene sets, the pyrimidine metabolism gene set was upregulated in cases compared to controls (p=0.004 in the testing set) and the JAK/Stat signaling pathway gene set was downregulated (p=0.003 in the testing set). Numerous potential early detection biomarkers specific to triple-negative breast cancer in multiple pathways were identified. Further research is required to follow-up on promising candidates in larger sample sizes and to better understand their potential biological importance as our understanding of the etiology of triple-negative breast cancer continues to grow.
Breast cancer; triple-negative; biomarkers; early detection
Recent proteomic studies have identified proteins related to specific
phenotypes. In addition to marginal association analysis for individual
proteins, analyzing pathways (functionally related sets of proteins) may yield
additional valuable insights. Identifying pathways that differ between
phenotypes can be conceptualized as a multivariate hypothesis testing problem:
whether the mean vector μ of a
p-dimensional random vector X is
μ0. Proteins within the same biological
pathway may correlate with one another in a complicated way, and type I error
rates can be inflated if such correlations are incorrectly assumed to be absent.
The inflation tends to be more pronounced when the sample size is very small or
there is a large amount of missingness in the data, as is frequently the case in
proteomic discovery studies. To tackle these challenges, we propose a
regularized Hotelling’s T2
(RHT) statistic together with a non-parametric
testing procedure, which effectively controls the type I error rate and
maintains good power in the presence of complex correlation structures and
missing data patterns. We investigate asymptotic properties of the
RHT statistic under pertinent assumptions and compare
the test performance with four existing methods through simulation examples. We
apply the RHT test to a hormone therapy proteomics data
set, and identify several interesting biological pathways for which blood serum
concentrations changed following hormone therapy initiation.
proteomics; pathway analysis; regularization; Hotelling’s T2
In this article, the authors propose to simultaneously test for marginal genetic association and gene-environment interaction to discover single nucleotide polymorphisms that may be involved in gene-environment or gene-treatment interaction. The asymptotic independence of the marginal association estimator and various interaction estimators leads to a simple and flexible way of combining the 2 tests, allowing for exploitation of gene-environment independence in estimating gene-environment interaction. The proposed test differs from the 2-df test proposed by Kraft et al. (Hum Hered. 2007;63(2):111–119) in two respects. First, for the genetic association component, it tests for marginal association, which is often the primary objective in inference, rather than the main effect in a model with gene-environment interaction. Second, the gene-environment testing component can easily exploit putative gene-environment independence using either the case-only estimator or the empirical Bayes estimator, depending on whether the goal is gene-treatment interaction in a randomized trial or gene-environment interaction in an observational study. The use of the proposed joint test is illustrated through simulations and a genetic study (1993–2005) from the Women's Health Initiative.
association; empirical Bayes; genetic epidemiology; genetics; gene-environment interaction; two-stage procedure
This paper summarizes the results of a Nutrient Biomarker Study in the Women’s Health Initiative, and its application to studies of the association between energy and protein consumption and the risk of major cancers and cardiovascular diseases. The presentation emphasizes measurement error modeling and related data analysis methods, since addressing measurement issues appears to be central to these topics and to progress in nutritional epidemiology more generally. The manner in which body mass index is modeled in disease association analysis is particularly challenging, since it could serve as a mediator or as a confounder of the association, and at the same time contributes valuably to energy and protein consumption assessment. A hazard ratio parameter estimation procedure that acknowledges body mass index as a possible mediating variable is described and applied. Some aspects of the future nutritional epidemiology research agenda are briefly discussed, including an ongoing human feeding study to develop biomarkers for additional dietary components.
cancer; cardiovascular disease; diet; epidemiology; failure time data; measurement error
Longitudinal blood collections from cohort studies provide the means to search for proteins associated with disease prior to clinical diagnosis. We investigated plasma samples from the Women’s Health Initiative (WHI) cohort to determine quantitative differences in plasma proteins between subjects subsequently diagnosed with colorectal cancer (CRC) and matched controls that remained cancer free during the period of follow-up. Proteomic analysis of WHI samples collected prior to diagnosis of CRC resulted in the identification of six proteins with significantly (p <0.05) elevated concentrations in cases compared to controls. Proteomic analysis of two colorectal cancer cell lines showed 5 of the 6 proteins were produced by cancer cells. MAPRE1, IGFBP2, LRG1 and CEA were individually assayed by enzyme linked immunosorbent assay (ELISA) in 58 pairs of newly diagnosed CRC samples and controls and yielded significant elevations (p <0.05) among cases relative to controls. A combination of these four markers resulted in an ROC with an AUC=0.841 and 57% sensitivity at 95% specificity. This combination rule was tested in an independent set of WHI samples collected within 7 months prior to diagnosis from cases and matched controls resulting in 41% sensitivity at 95% specificity. A panel consisting of CEA, MAPRE1, IGFBP2 and LRG1 has predictive value in pre-diagnostic colorectal cancer plasmas.
colorectal cancer; risk markers; Pre-Diagnostic samples
The authors further analyzed results from the Women's Health Initiative randomized trials (1993–2004) of conjugated equine estrogens, with or without medroxyprogesterone acetate, focusing on health benefits versus risks among women who initiated hormone therapy soon after menopause. Data from the Women's Health Initiative observational study (1993–2004) were included in some analyses for additional precision. Results are presented here for incident coronary heart disease, stroke, venous thromboembolism, breast cancer, colorectal cancer, endometrial cancer, or hip fracture; death from other causes; a summary global index; total cancer; and total mortality. Hazard ratios for breast cancer and total cancer were comparatively higher (P < 0.05) among women who initiated hormone therapy soon after menopause, for both regimens. Among these women, use of conjugated equine estrogens appeared to produce elevations in venous thromboembolism and stroke and a reduction in hip fracture. Estrogen plus progestin results among women who initiated use soon after menopause were similar for venous thromboembolism, stroke, and hip fracture but also included evidence of longer-term elevations in breast cancer, total cancer, and the global index. These analyses provide little support for the hypothesis of favorable effects among women who initiate postmenopausal estrogen use soon after menopause, either for coronary heart disease or for health benefits versus risk indices considered.
clinical trial; cohort studies; estrogens; estrogen replacement therapy; hormone replacement therapy; medroxyprogesterone 17-acetate; postmenopause; progestins
The authors previously reported equations, derived from the Nutrient Biomarker Study within the Women's Health Initiative, that produce calibrated estimates of energy, protein, and percentage of energy from protein consumption from corresponding food frequency questionnaire estimates and data on other factors, such as body mass index, age, and ethnicity. Here, these equations were applied to yield calibrated consumption estimates for 21,711 women enrolled in the Women's Health Initiative dietary modification trial comparison group and 59,105 women enrolled in the observational study. These estimates were related prospectively to total and site-specific invasive cancer incidence (1993–2005). In combined cohort analyses that do not control for body mass, uncalibrated energy was not associated with total cancer incidence or site-specific cancer incidence for most sites, whereas biomarker-calibrated energy was positively associated with total cancer (hazard ratio = 1.18, 95% confidence interval: 1.10, 1.27, for 20% consumption increase), as well as with breast, colon, endometrial, and kidney cancer (respective hazard ratios of 1.24, 1.35, 1.83, and 1.47). Calibrated protein was weakly associated, and calibrated percentage of energy from protein was inversely associated, with total cancer. Calibrated energy and body mass index associations were highly interdependent. Implications for the interpretation of nutritional epidemiology studies are described.
bias (epidemiology); biological markers; diet; energy intake; epidemiologic methods; neoplasms; nutrition assessment; proteins
Publications that compare randomized controlled trial and cohort study results on the effects of postmenopausal estrogen plus progestin therapy are reviewed. The 2 types of studies agree in identifying an early elevation in coronary heart disease (CHD) risk, and a later — developing elevation in breast cancer risk. Effects among women who begin hormone therapy within a few years following the menopause may be comparatively more favorable for CHD and less favorable for breast cancer. These analyses illustrate the potential of modern data analysis methods to enhance the reliability and interpretation of epidemiologic data.
Few strong and consistent associations have arisen from observational studies of dietary consumption in relation to chronic disease risk. Measurement error in self-reported dietary assessment may be obscuring many such associations. Attempts to correct for measurement error have mostly used a second self-report assessment in a subset of a study cohort to calibrate the self-report assessment used throughout the cohort, under the dubious assumption of uncorrelated measurement errors between the two assessments. The use, instead, of objective biomarkers of nutrient consumption to produce calibrated consumption estimates provides a promising approach to enhance study reliability. As summarized here, we have recently applied this nutrient biomarker approach to examine energy, protein, and percent of energy from protein, in relation to disease incidence in Women’s Health Initiative cohorts, and find strong associations that are not evident without biomarker calibration. A major bottleneck for the broader use of a biomarker-calibration approach is the rather few nutrients for which a suitable biomarker has been developed. Some methodologic approaches to the development of additional pertinent biomarkers, including the possible use of a respiratory quotient from indirect calorimetry for macronutrient biomarker development, and the potential of human feeding studies for the evaluation of a range of urine- and blood-based potential biomarkers, will briefly be described.
Biological markers; epidemiologic methods; human feeding study; indirect calorimetry; nutrition; measurement error; regression calibration
The hazard ratio provides a natural target for assessing a treatment effect with survival data, with the Cox proportional hazards model providing a widely used special case. In general, the hazard ratio is a function of time and provides a visual display of the temporal pattern of the treatment effect. A variety of nonproportional hazards models have been proposed in the literature. However, available methods for flexibly estimating a possibly time-dependent hazard ratio are limited. Here, we investigate a semiparametric model that allows a wide range of time-varying hazard ratio shapes. Point estimates as well as pointwise confidence intervals and simultaneous confidence bands of the hazard ratio function are established under this model. The average hazard ratio function is also studied to assess the cumulative treatment effect. We illustrate corresponding inference procedures using coronary heart disease data from the Women's Health Initiative estrogen plus progestin clinical trial.
Clinical trial; Empirical process; Gaussian process; Hazard ratio; Simultaneous inference; Survival analysis; Treatment–time interaction
Nutritional epidemiology cohort studies primarily use food frequency questionnaires (FFQs). In part because FFQs are more reliable for nutrient densities than for absolute nutrient consumption, reports from association studies typically present only nutrient density measures in relation to disease risk.
We used objective biomarkers to correct FFQ assessments for measurement error, and examined absolute energy and protein consumption in relation to cardiovascular disease incidence. FFQs and subsequent physician-adjudicated cardiovascular disease incidence were assessed for 80,370 postmenopausal women in the age range 50–79 years at enrollment in the comparison group of the Dietary Modification Trial or the prospective Observational Study in the Women’s Health Initiative. Urinary recovery biomarkers of energy and protein were obtained from a subsample of 544 women, with concurrent FFQ information.
Following biomarker correction, energy consumption was positively associated with coronary heart disease incidence (hazard ratio = 1.18 [95% confidence interval = 1.04–1.33], for 20% consumption increment) and protein density was inversely associated (0.85 [0.75–0.97]). The positive energy association appeared to be mediated by body fat accumulation. Ischemic stroke incidence was inversely associated with energy and protein consumption, but not with protein density.
A positive association between energy and coronary heart disease risk can be attributed to body mass accumulation. Ischemic stroke risk is inversely associated with energy and protein consumption, possibly due to correlations between consumption and physical activity.
Applying advanced proteomic technologies to prospectively collected specimens from large studies is one means of identifying preclinical changes in plasma proteins that are potentially relevant to the early detection of diseases like breast cancer. We conducted fourteen independent quantitative proteomics experiments comparing pooled plasma samples collected from 420 estrogen receptor positive (ER+) breast cancer patients ≤17 months prior to their diagnosis and matched controls. Based on the over 3.4 million tandem mass spectra collected in the discovery set, 503 proteins were quantified of which 57 differentiated cases from controls with a p-value<0.1. Seven of these proteins, for which quantitative ELISA assays were available, were assessed in an independent validation set. Of these candidates, epidermal growth factor receptor (EGFR) was validated as a predictor of breast cancer risk in an independent set of preclinical plasma samples for women overall [odds ratio (OR)=1.44, p-value=0.0008], and particularly for current users of estrogen plus progestin (E+P) menopausal hormone therapy (OR=2.49, p-value=0.0001). Among current E+P users EGFR's sensitivity for breast cancer risk was 31% with 90% specificity. While EGFR's sensitivity and specificity are insufficient for a clinically useful early detection biomarker, this study suggests that proteins that are elevated preclinically in women who go on to develop breast cancer can be discovered and validated using current proteomic technologies. Further studies are warranted to both examine the role of EGFR and to discover and validate other proteins that could potentially be used for breast cancer early detection.
Breast cancer; epidermal growth factor receptor; menopausal hormone therapy
To evaluate the association between protein intake and incident frailty.
Prospective cohort study.
Subset of the Women’s Health Initiative Observational Study conducted at 40 clinical centers.
24,417 women aged 65-79 years who were free of frailty at baseline with plausible self-reported energy intakes (600-5000 kcal/day) by Food Frequency Questionnaire (FFQ)
Baseline protein intake was estimated from FFQ. Calibrated estimates of energy and protein intake were corrected for measurement error using regression calibration equations estimated from objective measures of total energy expenditure (doubly labeled water) and dietary protein (24-hr urinary nitrogen). After three-years of follow-up, frailty was defined as having at least three of the following components: low physical function (measured by Rand-36 questionnaire), exhaustion, low physical activity, and unintended weight loss. Multinomial logistic regression models estimated associations for both uncalibrated and calibrated protein intake.
Among the 24,417 eligible women, 3,298 (13.5%) developed frailty over three years. After adjustment for confounders, a 20% increase in uncalibrated protein intake (%kcal) was associated with a 12% (95% Confidence Interval (CI)= 8% to 16%) lower risk of frailty, while a 20% increase in calibrated protein was associated with a 32% (95% CI= 23% to 50%) lower risk of frailty.
Higher protein consumption, as a fraction of energy, is associated with a strong, independent, dose-responsive lower risk of incident frailty in older women. Using uncalibrated measures underestimated the strength of the association. Incorporating more protein into the diet may be an intervention target for frailty prevention.
frailty, protein, calibration; essential amino acids; measurement error
Differences in disease outcomes between users and non-users of hormone therapy (HT) and between users of estrogen alone (ET) and users of estrogen plus progesterone therapy (EPT) may relate to differences in serum hormone concentrations between these populations. In this study, we examine the response of serum hormone levels in healthy post-menopausal women after one year of HT.
A representative sub-sample of 200 healthy adherent participants from the active and placebo groups of the Women's Health Initiative randomized, controlled clinical trials of ET (conjugated equine estrogen 0.625 mg daily) or EPT (ET plus medroxyprogesterone actetate 2.5 mg daily) were selected for determination of selected sex hormone levels at baseline and one year after randomization.
In participants receiving active ET intervention compared to placebo, estrogenic hormone levels increased from baseline to year 1 by 3.6-fold for total estrone, 2.7-fold for total estradiol, and 1.8-fold for bioavailable and free estradiol concentrations. Serum SHBG concentrations also increased 2.5-fold. In contrast, progesterone levels decreased slightly in women taking exogenous EPT. The response of serum estrogens and SHBG did not differ substantially with the addition of progesterone. In subgroup analyses, hormone response varied by age, ethnicity, BMI, smoking, vasomotor symptoms, and baseline hormone levels.
These data provide a reference point for the serum hormone response to HT, and demonstrate that response of serum estrogens is similar for ET and EPT. The implications of the slight decrease in serum progesterone levels with EPT therapy are uncertain. Potential treatment interactions for estrogenic hormones were identified, which suggest a larger response to HT in women with low endogenous levels.
Hormone therapy; estrogen; progesterone; estradiol; hormone levels; sex hormone binding globulin; Women's Health Initiative
We examine the measurement properties of pooled DNA odds ratio estimates for 7357 single nucleotide polymorphisms (SNPs) genotyped in a genome-wide association study of postmenopausal breast cancer. This study involved DNA pools formed from 125 cases or 125 matched controls. Individual genotyping for these SNPs subsequently came available for a substantial majority of women included in seven pool pairs, providing the opportunity for a comparison of pooled DNA and individual odds ratio estimates and their variances. We find that the ‘per minor allele’ odds ratio estimates from the pooled DNA comparisons agree fairly well with those from individual genotyping. Furthermore, the log-odds ratio variance estimates support a pooled DNA measurement model that we previously described, though with somewhat greater extra-binomial variation than was hypothesized in project design. Implications for the role of pooled DNA comparisons in the future genetic epidemiology research agenda are discussed.
Breast Cancer; Case-Control Studies; DNA pooling; GWAS; Odds Ratio; SNP
Genome-wide association studies (GWAS) provide an important approach for identifying common genetic variants that predispose to human disease. However, odds ratio (OR) estimates for the reported findings from GWAS discovery data are typically affected by a bias away from the null sometimes referred to the “winner's curse”. Also standard confidence intervals (CIs) may have far from the desired coverage rates. We applied a bias reduction method to GWAS findings from several major complex human diseases, including breast cancer, colorectal cancer, lung cancer, prostate cancer, type I diabetes and type II diabetes. We found the simple bias correction procedure allows one to estimate bias-adjusted ORs that have substantial consistency with ORs from subsequent replication studies; and that corresponding selection-adjusted CIs appear to help quantify the uncertainty of the findings. Selection-adjusted ORs and CIs can provide a reliable summary of GWAS data, and can help to choose single nucleotide polymorphisms (SNPs) for subsequent validation studies.
bias correction; odds ratio; confidence interval; genomewide association study; complex human disease
The Women’s Health Initiative (WHI) dietary modification (DM) trial provided suggestive evidence of a benefit of a low-fat dietary pattern on breast cancer risk, with stronger evidence among women whose baseline diet was high in fat. Single nucleotide polymorphisms (SNPs) in the FGFR2 gene relate strongly to breast cancer risk, and could influence intervention effects.
Participants and Methods
All 48,835 trial participants were postmenopausal and aged 50–79 at enrollment (1993–98). We interrogated eight SNPs in intron 2 of the FGFR2 gene for 1676 women who developed breast cancer during trial follow-up (1993–2005). Case-only analyses were used to estimate odds ratios for the DM intervention in relation to SNP genotype.
Odds ratios for the DM intervention did not vary significantly with the genotype for any of the eight FGFR2 SNPs (p≥0.18). However, odds ratios varied (p<0.05) with the genotype of six of these SNPs, among women having baseline percent of energy from fat in the upper quartile (≥36.8%). This variation is most evident for SNP rs3750817, with odds ratios (95% confidence intervals) for the DM intervention at 0, 1, and 2 minor SNP alleles of 1.06 (0.80, 1.41), 0.53 (0.38, 0.74), and 0.62 (0.33, 1.15). The nominal significance level for this interaction is p=0.005, and is p=0.03 following multiple testing adjustment, with most evidence deriving from hormone receptor-positive tumors.
Invasive breast cancer odds ratios for a low-fat dietary pattern, among women whose usual diets are high in fat, appear to vary with SNP rs3750817 in the FGFR2 gene.
breast cancer; genotype; gene-intervention interaction; low fat dietary pattern; randomized controlled trial
The Gail model is widely used for the assessment of risk of invasive breast cancer based on recognized clinical risk factors. In recent years, a substantial number of single-nucleotide polymorphisms (SNPs) associated with breast cancer risk have been identified. However, it remains unclear how to effectively integrate clinical and genetic risk factors for risk assessment.
Seven SNPs associated with breast cancer risk were selected from the literature and genotyped in white non-Hispanic women in a nested case–control cohort of 1664 case patients and 1636 control subjects within the Women’s Health Initiative Clinical Trial. SNP risk scores were computed based on previously published odds ratios assuming a multiplicative model. Combined risk scores were calculated by multiplying Gail risk estimates by the SNP risk scores. The independence of Gail risk and SNP risk was evaluated by logistic regression. Calibration of relative risks was evaluated using the Hosmer–Lemeshow test. The performance of the combined risk scores was evaluated using receiver operating characteristic curves. The net reclassification improvement (NRI) was used to assess improvement in classification of women into low (<1.5%), intermediate (1.5%–2%), and high (>2%) categories of 5-year risk. All tests of statistical significance were two-sided.
The SNP risk score was nearly independent of Gail risk. There was good agreement between predicted and observed SNP relative risks. In the analysis for receiver operating characteristic curves, the combined risk score was more discriminating, with area under the curve of 0.594 compared with area under the curve of 0.557 for Gail risk alone (P < .001). Classification also improved for 5.6% of case patients and 2.9% of control subjects, showing an NRI value of 0.085 (P = 1.0 × 10−5). Focusing on women with intermediate Gail risk resulted in an improved NRI of 0.195 (P = 8.6 × 10−5).
Combining validated common genetic risk factors with clinical risk factors resulted in modest improvement in classification of breast cancer risks in white non-Hispanic postmenopausal women. Classification performance was further improved by focusing on women at intermediate risk.
Breast cancer concern is a major reason for the recent marked reduction in use of postmenopausal hormone therapy, though equally effective means of controlling menopausal symptoms are lacking. Single nucleotide polymorphisms (SNPs) in the fibroblast growth factor receptor two (FGFR2) gene are substantially associated with postmenopausal breast cancer risk, and could influence hormone therapy effects.
Participants and Methods
We interrogated eight SNPs in intron 2 of the FGFR2 gene for 2166 invasive breast cancer cases from the Women's Health Initiative clinical trial and one-to-one matched controls, to confirm an association with breast cancer risk. We used case-only analyses to examine the dependence of estrogen plus progestin and estrogen-alone odds ratios on SNP genotype.
Seven FGFR2 SNPs, including six in a single linkage disequilibrium region, were found to associate strongly (p<10−7) with breast cancer risk. SNP rs3750817 (minor allele T with frequency 0.37) had an estimated `per minor allele' odds ratio of 0.78, and was not in such strong linkage disequilibrium with the other SNPs. The genotype of this SNP related significantly (p<0.05) to hormone therapy odds ratios. For estrogen plus progestin, the odds ratios (95% confidence intervals) at 0, 1, and 2 minor SNP alleles were 1.52 (1.14, 2.02), 1.33 (1.01, 1.75), and 0.69 (0.41, 1.17), while corresponding values for estrogen-alone were 0.74 (0.51, 1.09), 0.99 (0.68, 1.44), and 0.34 (0.15, 0.76).
Postmenopausal women having TT genotype for SNP rs3750817 have a reduced breast cancer risk, and appear to experience comparatively favorable effects of postmenopausal hormone therapy.
breast cancer; genotype; gene-treatment interaction; randomized controlled trial; postmenopausal hormone therapy
Coronary heart disease (CHD) and stroke were key outcomes in the Women's Health Initiative (WHI) randomized trials of postmenopausal estrogen and estrogen plus progestin therapy. We recently reported a large number of changes in blood protein concentrations in the first year following randomization in these trials using an in-depth quantitative proteomics approach. However, even though many affected proteins are in pathways relevant to the observed clinical effects, the relationships of these proteins to CHD and stroke risk among postmenopausal women remains substantially unknown.
The same in-depth proteomics platform was applied to plasma samples, obtained at enrollment in the WHI Observational Study, from 800 women who developed CHD and 800 women who developed stroke during cohort follow-up, and from 1-1 matched controls. A plasma pooling strategy, followed by extensive fractionation prior to mass spectrometry, was used to identify proteins related to disease incidence, and the overlap of these proteins with those affected by hormone therapy was examined. Replication studies, using enzyme-linked-immunosorbent assay (ELISA), were carried out in the WHI hormone therapy trial cohorts.
Case versus control concentration differences were suggested for 37 proteins (nominal P < 0.05) for CHD, with three proteins, beta-2 microglobulin (B2M), alpha-1-acid glycoprotein 1 (ORM1), and insulin-like growth factor binding protein acid labile subunit (IGFALS) having a false discovery rate < 0.05. Corresponding numbers for stroke were 47 proteins with nominal P < 0.05, three of which, apolipoprotein A-II precursor (APOA2), peptidyl-prolyl isomerase A (PPIA), and insulin-like growth factor binding protein 4 (IGFBP4), have a false discovery rate < 0.05. Other proteins involved in insulin-like growth factor signaling were also highly ranked. The associations of B2M with CHD (P < 0.001) and IGFBP4 with stroke (P = 0.005) were confirmed using ELISA in replication studies, and changes in these proteins following the initiation of hormone therapy use were shown to have potential to help explain hormone therapy effects on those diseases.
In-depth proteomic discovery analysis of prediagnostic plasma samples identified B2M and IGFBP4 as risk markers for CHD and stroke respectively, and provided a number of candidate markers of disease risk and candidate mediators of hormone therapy effects on CHD and stroke.
Clinical Trials Registration
ClinicalTrials.gov identifier: NCT00000611
Colorectal cancer incidence was reduced among women assigned to active treatment in the Women's Health Initiative (WHI) estrogen plus progestin randomized trial, but the interpretation was obscured by an associated later stage of diagnosis. In contrast the estrogen-alone trial showed no incidence reduction or differential stage at diagnosis. Here, data from the WHI observational study are considered, in conjunction with colorectal cancer mortality data from the hormone therapy trials, in an attempt to clarify postmenopausal hormone therapy effects.
Participants and Methods
Postmenopausal women aged 50−79 at WHI enrollment. Estrogen-alone analyses include 21,552 and 10,739 women who were post-hysterectomy from the observational study and clinical trial respectively. Estrogen plus progestin analyses include 32,084 and 16,608 observational study and clinical trial women with uterus. Colorectal cancers were verified by central medical and pathology report review.
Hazard ratios (95% confidence intervals) from the WHI observational study were 0.80 (0.53 to 1.20) for estrogen and 1.15 (0.74 to 1.79) for estrogen plus progestin, with respectively 168 and 175 women diagnosed with colorectal cancer. Delayed diagnosis with estrogen plus progestin is not evident in the observational study. No protective effect on colorectal cancer mortality in the estrogen plus progestin trial is seen over an 8-year intervention and follow-up period.
Hazard ratio patterns in the WHI clinical trial and observational study do not provide strong evidence of a clinically important colorectal cancer benefit with either estrogen-alone or estrogen plus progestin over 7−8 years of treatment and follow-up.
cohort study; colorectal cancer; randomized controlled trial; postmenopausal hormone therapy