Total energy consumption and activity-related energy expenditure (AREE) estimates that have been calibrated using biomarkers to correct for measurement error were simultaneously associated with the risks of cardiovascular disease, cancer, and diabetes among postmenopausal women who were enrolled in the Women's Health Initiative at 40 US clinical centers and followed from 1994 to the present. Calibrated energy consumption was found to be positively related, and AREE inversely related, to the risks of various cardiovascular diseases, cancers, and diabetes. These associations were not evident in most corresponding analyses that did not correct for measurement error. However, an important analytical caveat relates to the role of body mass index (BMI) (weight (kg)/height (m)2). In the calibrated variable analyses, BMI was regarded, along with self-reported data, as a source of information on energy consumption and physical activity, and BMI was otherwise excluded from the disease risk models. This approach cannot be fully justified with available data, and the analyses herein imply a need for improved dietary and physical activity assessment methods and for longitudinal self-reported and biomarker data to test and relax modeling assumptions. Estimated hazard ratios for 20% increases in total energy consumption and AREE, respectively, were as follows: 1.49 (95% confidence interval: 1.18, 1.88) and 0.80 (95% confidence interval: 0.69, 0.92) for total cardiovascular disease; 1.43 (95% confidence interval: 1.17, 1.73) and 0.84 (95% confidence interval: 0.73, 0.96) for total invasive cancer; and 4.17 (95% confidence interval: 2.68, 6.49) and 0.60 (95% confidence interval: 0.44, 0.83) for diabetes.
body mass index; cancer; cardiovascular disease; diabetes; energy consumption; hazard ratio; measurement error; physical activity
Regression calibration using biomarkers provides an attractive approach to strengthening nutritional epidemiology. We consider this approach to assessing the relationship of fat and total energy consumption with postmenopausal breast cancer. In analyses that included fat density data, biomarker-calibrated total energy was positively associated with postmenopausal breast cancer incidence in cohorts of the US Women's Health Initiative from 1994–2010. The estimated hazard ratio for a 20% increment in calibrated food frequency questionnaire (FFQ) energy was 1.22 (95% confidence interval (CI): 1.15, 1.30). This association was not evident without biomarker calibration, and it ceased to be apparent following control for body mass index (weight (kg)/height (m)2), suggesting that the association is mediated by body fat deposition over time. The hazard ratio for a corresponding 40% increment in FFQ fat density was 1.05 (95% CI: 1.00, 1.09). A stronger fat density association, with a hazard ratio of 1.19 (95% CI: 1.00, 1.41), emerged from analyses that used 4-day food records for dietary assessment. FFQ-based analyses were also carried out by using a second dietary assessment in place of the biomarker for calibration. This type of calibration did not correct for systematic bias in energy assessment, but may be able to accommodate the “noise” component of dietary measurement error. Implications for epidemiologic applications more generally are described.
bias; biological markers; breast cancer; dietary assessment; dietary energy; dietary fat; postmenopausal women
The respiratory quotient (RQ), defined as the ratio of carbon dioxide exhaled to oxygen uptake, reflects substrate utilization when energy is expended. Fat and alcohol have RQ values of about 0.7, compared to 1.0 for carbohydrate, and about 0.8 for protein. Here, the association between RQ and postmenopausal breast cancer risk is studied.
Paired RQ measurements were obtained, separated by about 6 months, for women in the reliability subset of a Women’s Health Initiative (WHI) Nutrition and Physical Activity Assessment Study. Linear regression of the average of the paired log RQ assessments on a corresponding log food quotient (FQ) average and other study subject characteristics, including age, body mass index, race, and education, yielded calibration equations for predicting RQ.
Calibration equations, using any of food frequency, food record, or dietary recall data, explained an appreciable fraction of measured log RQ variation, and these were used to compute calibrated RQ estimates throughout WHI cohorts. Calibrated RQ estimates using four-day food record data related inversely (P=0.004) to (invasive) breast cancer risk in the WHI Dietary Modification trial comparison group, and corresponding RQ estimates using food frequency data related inversely (P=0.002) to breast cancer incidence in this cohort combined with the larger WHI Observational Study.
Though preliminary, these analyses suggest a substantially higher postmenopausal breast cancer risk among women having relatively low RQ.
RQ elevation could provide a novel target for breast cancer risk reduction.
breast cancer; dietary assessment; food quotient; indirect calorimetry; postmenopausal women; respiratory quotient; substrate utilization
On June 20, 2013, the American Journal of Epidemiology sponsored a symposium at the Society for Epidemiologic Research's 46th Annual Meeting in Boston, Massachusetts, entitled, “What Is the Role of Epidemiology in the Era of Molecular Biology and Genomics?” The future of epidemiology depends on innovation in generating interesting and important testable hypotheses that are relevant to population health. These new strategies will depend on new technology, both in measurement of agents and environment and in the fields of pathophysiology and outcomes, such as cellular epidemiology and molecular pathology. The populations to be studied, sample sizes, and study designs should be selected based on the hypotheses to be tested and include case-control, cohort, and clinical trials. Developing large mega cohorts without attention to specific hypotheses is inefficient, will fail to address many associations with high-quality data, and may well produce spurious results.
immunology; pathology; study design
The principal findings are briefly reviewed from the Women's Health Initiative (WHI) trials of the most commonly used postmenopausal hormone regimens in the US, conjugated equine estrogens and these same estrogens plus medroxyprogesterone acetate. A more detailed review is presented for three major clinical outcomes: coronary heart disease, the primary trial outcome for which a major benefit was hypothesized; invasive breast cancer, the primary safety outcome for which some adverse effect was expected; and stroke which surfaced as an important adverse effect with both regimens, and one that is influential in decisions concerning the continued use of postmenopausal estrogens alone. The review for these outcomes includes an update on interactions of treatment effects with study subject characteristics and exposures and with pre-randomization biomarker levels. It also includes a focus on timing issues that are important to the understanding of treatment effects. Specifically, with combined estrogen plus progestin coronary heart disease risk was elevated early with the elevation dissipating after a few years of treatment, whereas breast cancer elevations increased during the treatment period, and climbed to about a 3-fold increase following 5 years of adherence. Importantly, breast cancer risk elevations appear to be higher among women who initiate treatment at the menopause, or soon thereafter, compared to women having a longer gap time. Stroke effects, on the other hand didn't seem to vary appreciably with these timing issues. The adverse effect was evidently localized to ischemic strokes, for which there was an approximate 50% increase with either regimen. The rather limited knowledge concerning the biomarkers and biological pathways that mediate the hormone therapy effects on these diseases is also briefly reviewed.
breast cancer; coronary heart disease; postmenopausal hormone therapy; randomized controlled trial; stroke
Reports from nutritional epidemiology studies lack reliability if based solely on self-reported dietary consumption estimates. Consumption biomarkers are available for some components of diet. These can be collected in subsets of study cohorts, along with corresponding self-report assessments. Linear regression of (log-transformed) biomarker values on corresponding self-report values and other pertinent study subject characteristics yields calibration equations for dietary consumption, from which calibrated consumption estimates can be calculated throughout study cohorts. Nutritional epidemiology disease association studies of enhanced reliability can be expected from analyses that relate disease risk to calibrated consumption estimates. Applications to the study of energy and protein consumption in relation to cardiovascular diseases, type 2 diabetes, and cancer in the Women’s Health Initiative will be briefly summarized. Also, challenges related to variables that may either mediate or confound associations of interest will be described, along with the need for longitudinal biomarker and self-report data, and the need for additional nutritional biomarkers development.
biomarker; calibration; cardiovascular disease; epidemiology; nutrition; measurement error
During the intervention phase in the Women's Health Initiative (WHI) clinical trial, use of estrogen plus progestin reduced the colorectal cancer diagnosis rate, but the cancers were found at a substantially higher stage. To assess the clinical relevance of the findings, analyses of the influence of combined hormone therapy on colorectal cancer incidence and colorectal cancer mortality were conducted after extended follow-up.
Patients and Methods
The WHI study was a randomized, double-blind, placebo-controlled clinical trial involving 16,608 postmenopausal women with an intact uterus who were randomly assigned to daily 0.625 mg conjugated equine estrogen plus 2.5 mg medroxyprogesterone acetate (n = 8,506) or matching placebo (n = 8,102). Colorectal cancer diagnosis rates and colorectal cancer mortality were assessed.
After a mean of 5.6 years (standard deviation [SD], 1.03 years) of intervention and 11.6 years (SD, 3.1 years) of total follow-up, fewer colorectal cancers were diagnosed in the combined hormone therapy group compared with the placebo group (diagnoses/year, 0.12% v 0.16%; hazard ratio [HR], 0.72; 95% CI, 0.56 to 0.94; P = .014). Bowel screening examinations were comparable between groups throughout. Cancers in the combined hormone therapy group more commonly had positive lymph nodes (50.5% v 28.6%; P < .001) and were at higher stage (regional or distant, 68.8% v 51.4%; P = .003). Although not statistically significant, there was a higher number of colorectal cancer deaths in the combined hormone therapy group (37 v 27 deaths; 0.04% v 0.03%; HR, 1.29; 95% CI, 0.78 to 2.11; P = .320).
The findings, suggestive of diagnostic delay, do not support a clinically meaningful benefit for combined hormone therapy on colorectal cancer.
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
We pooled data from 5 large validation studies of dietary self-report instruments that used recovery biomarkers as references to clarify the measurement properties of food frequency questionnaires (FFQs) and 24-hour recalls. The studies were conducted in widely differing US adult populations from 1999 to 2009. We report on total energy, protein, and protein density intakes. Results were similar across sexes, but there was heterogeneity across studies. Using a FFQ, the average correlation coefficients for reported versus true intakes for energy, protein, and protein density were 0.21, 0.29, and 0.41, respectively. Using a single 24-hour recall, the coefficients were 0.26, 0.40, and 0.36, respectively, for the same nutrients and rose to 0.31, 0.49, and 0.46 when three 24-hour recalls were averaged. The average rate of under-reporting of energy intake was 28% with a FFQ and 15% with a single 24-hour recall, but the percentages were lower for protein. Personal characteristics related to under-reporting were body mass index, educational level, and age. Calibration equations for true intake that included personal characteristics provided improved prediction. This project establishes that FFQs have stronger correlations with truth for protein density than for absolute protein intake, that the use of multiple 24-hour recalls substantially increases the correlations when compared with a single 24-hour recall, and that body mass index strongly predicts under-reporting of energy and protein intakes.
24-hour recall; attenuation factors; calibration equations; dietary measurement error; food frequency questionnaire; under-reporting
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
Accurate and individualized risk prediction is critical for population control of chronic diseases such as cancer and cardiovascular disease. Large cohort studies provide valuable resources for building risk prediction models, as the risk factors are collected at the baseline and subjects are followed over time until disease occurrence or termination of the study. However, for rare diseases the baseline risk may not be estimated reliably based on cohort data only, due to sparse events. In this paper, we propose to make use of external information to improve efficiency for estimating time-dependent absolute risk. We derive the relationship between external disease incidence rates and the baseline risk, and incorporate the external disease incidence information into estimation of absolute risks, while allowing for potential difference of disease incidence rates between cohort and external sources. The asymptotic properties, namely, uniform consistency and weak convergence, of the proposed estimators are established. Simulation results show that the proposed estimator for absolute risk is more efficient than that based on the Breslow estimator, which does not utilize external disease incidence rates. A large cohort study, the Women’s Health Initiative Observational Study, is used to illustrate the proposed method.
absolute risk; attributable risk; cohort data; colorectal cancer; external disease incidence rate 1
We investigated measurement error in the self-reported diets of US Hispanics/Latinos, who are prone to obesity and related comorbidities, by background (Central American, Cuban, Dominican, Mexican, Puerto Rican, and South American) in 2010–2012. In 477 participants aged 18–74 years, doubly labeled water and urinary nitrogen were used as objective recovery biomarkers of energy and protein intakes. Self-report was captured from two 24-hour dietary recalls. All measures were repeated in a subsample of 98 individuals. We examined the bias of dietary recalls and their associations with participant characteristics using generalized estimating equations. Energy intake was underestimated by 25.3% (men, 21.8%; women, 27.3%), and protein intake was underestimated by 18.5% (men, 14.7%; women, 20.7%). Protein density was overestimated by 10.7% (men, 11.3%; women, 10.1%). Higher body mass index and Hispanic/Latino background were associated with underestimation of energy (P < 0.05). For protein intake, higher body mass index, older age, nonsmoking, Spanish speaking, and Hispanic/Latino background were associated with underestimation (P < 0.05). Systematic underreporting of energy and protein intakes and overreporting of protein density were found to vary significantly by Hispanic/Latino background. We developed calibration equations that correct for subject-specific error in reporting that can be used to reduce bias in diet-disease association studies.
biological markers; calibration equations; dietary measurement error; Hispanics/Latinos; 24-hour dietary recall; nutrition assessment
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.
It is well-established that protein-energy malnutrition decreases serum insulin-like growth factor (IGF-I) levels, and supplementation of 30 grams of whey protein daily increased serum IGF-1 levels by 8% after 2 years in a clinical trial(1). Cohort studies provide the opportunity to assess associations between dietary protein intake and the IGF-axis under more typical eating conditions. We studied the associations of circulating IGF-axis protein levels (ELISA, Diagnostic Systems Laboratories) with total biomarker-calibrated protein intake, as well as dairy and milk intake, among postmenopausal women enrolled in the Women's Health Initiative (n=747). Analyses were conducted using multivariate linear regression models that adjusted for age, BMI, race/ethnicity, education, biomarker-calibrated energy, alcohol, smoking, physical activity, and hormone therapy use. There was a positive association between milk intake and free-IGF-1. A 3 serving increase in milk intake per day (~30 grams of protein) was associated with an estimated average 18.6% higher increase in free IGF-1 (95% CI 0.9% to 39.3%). Total IGF-I and IGFBP-3, however, were not associated with milk consumption, nor were there associations between biomarker-calibrated protein intake, biomarker-calibrated energy, and free IGF-I, total IGF-I, or IGFBP-3. This study of postmenopausal women is consistent with clinical trial data suggesting a specific relationship between milk consumption and serum IGF-I levels; albeit, in our dataset, this association was only statistically significant for free, but not total, IGF-I nor IGFBP-3.
Research comparing hormone therapy (HT) doses, regimens, and routes of delivery in relation to cardiovascular disease (CVD) outcomes have been limited. This study directly compared different estrogen doses, routes of delivery, and HT formulations in postmenopausal women in relation to the risk of coronary heart disease (CHD), stroke, CVD mortality, total CVD, and all-cause mortality.
The Women’s Health Initiative Observational Study (WHI-OS) is a multi-center prospective cohort study conducted at 40 US sites. Analyses included 93,676 postmenopausal women, aged 50-79 years at study entry and recruited September 1994 - December 1998, with annual follow-up through August 14, 2009.
Average follow-up was 10.4 years. In direct comparisons, oral estradiol was associated with lower hazard ratios (HRs) for stroke than oral conjugated equine estrogens (CEE) (HR 0.64; 95% CI 0.40, 1.02), but statistical power was limited. Similarly, transdermal estradiol was associated with a moderate but non-significant lower risk of CHD compared to oral CEE (HR 0.63; 95% CI 0.37, 1.06). For other outcomes, comparisons revealed no appreciable differences by estrogen doses, formulations, or routes of delivery. Absolute risks of CVD events and all-cause mortality were markedly lower in younger, compared to older, women.
In direct comparisons, various HT doses and regimens were associated with similar rates of cardiovascular events and all-cause mortality. However, oral estradiol may be associated with a lower risk of stroke and transdermal estradiol with a lower risk of CHD, compared to conventional-dose oral CEE. Additional research is needed to confirm these hypotheses.
Menopause Hormone Therapy; Cardiovascular disease; Stroke
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
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
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
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
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 Women’s Health Initiative randomized controlled trial found a trend (p = 0.09) toward a lower breast cancer risk among women assigned to daily 0.625-mg conjugated equine estrogens (CEEs) compared with placebo, in contrast to an observational literature that mostly reports a moderate increase in risk with estrogenalone preparations. In 1993–2004 at 40 US clinical centers, breast cancer hazard ratio estimates for this CEE regimen were compared between the Women’s Health Initiative clinical trial and observational study toward understanding this apparent discrepancy and refining hazard ratio estimates. After control for prior use of postmenopausal hormone therapy and for confounding factors, CEE hazard ratio estimates were higher from the observational study compared with the clinical trial by 43% (p = 0.12). However, after additional control for time from menopause to first use of postmenopausal hormone therapy, the hazard ratios agreed closely between the two cohorts (p = 0.82). For women who begin use soon after menopause, combined analyses of clinical trial and observational study data do not provide clear evidence of either an overall reduction or an increase in breast cancer risk with CEEs, although hazard ratios appeared to be relatively higher among women having certain breast cancer risk factors or a low body mass index.
breast neoplasms; clinical trial; cohort studies; estrogens; hormone replacement therapy; postmenopause