Showing papers by "Ross L. Prentice published in 2011"

Evaluation and Comparison of Food Records, Recalls, and Frequencies for Energy and Protein Assessment by Using Recovery Biomarkers

Abstract: 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.

Reproductive History and Oral Contraceptive Use in Relation to Risk of Triple-Negative Breast Cancer

Abstract: Results Reproductive history was differentially associated with risk of triple-negative and ER+ breast cancers. Nulliparity was associated with decreased risk of triple-negative breast cancer (HR = 0.61, 95% confidence interval [CI] = 0.37 to 0.97) but increased risk of ER+ breast cancer (HR = 1.35, 95% CI = 1.20 to 1.52). Age-adjusted absolute rates of triple-negative breast cancer were 2.71 and 1.54 per 10 000 person-years in parous and nulliparous women, respectively; by comparison, rates of ER+ breast cancer were 21.10 and 28.16 per 10 000 person-years in the same two groups. Among parous women, the number of births was positively associated with risk of triple-negative disease (HR for three births or more vs one birth = 1.46, 95% CI = 0.82 to 2.63) and inversely associated with risk of ER+ disease (HR = 0.88, 95% CI = 0.74 to 1.04). Ages at menarche and menopause were modestly associated with risk of ER+ but not triple-negative breast cancer; breastfeeding and oral contraceptive use were not associated with either subtype. Conclusion The association between parity and breast cancer risk differs appreciably for ER+ and triple-negative breast cancers. These findings require further confirmation because the biological mechanisms underlying these differences are uncertain.

Body Size, Physical Activity, and Risk of Triple-Negative and Estrogen Receptor–Positive Breast Cancer

Abstract: Background: Triple-negative breast cancer, characterized by a lack of hormone receptor and HER2 expression, is associated with a particularly poor prognosis. Focusing on potentially modifiable breast cancer risk factors, we examined the relationship between body size, physical activity, and triple-negative disease risk. Methods: Using data from 155,723 women enrolled in the Women's Health Initiative (median follow-up, 7.9 years), we assessed associations between baseline body mass index (BMI), BMI in earlier adulthood, waist and hip circumference, waist–hip ratio, recreational physical activity, and risk of triple-negative ( n = 307) and estrogen receptor–positive (ER+, n = 2,610) breast cancers. Results: Women in the highest versus lowest BMI quartile had 1.35-fold (95% CI, 0.92–1.99) and 1.39-fold (95% CI, 1.22–1.58) increased risks of triple-negative and ER+ breast cancers, respectively. Waist and hip circumferences were positively associated with risk of ER+ breast cancer ( P trend = 0.01 for both measures) but were not associated with triple-negative breast cancer. Compared with women who reported no recreational physical activity, women in the highest activity tertile had similarly lower risks of triple-negative and ER+ breast cancers (HR = 0.77; 95% CI, 0.51–1.13; and HR = 0.85; 95% CI, 0.74–0.98, respectively). Conclusions: Despite biological and clinical differences, triple-negative and ER+ breast cancers are similarly associated with BMI and recreational physical activity in postmenopausal women. The biological mechanisms underlying these similarities are uncertain and these modest associations require further investigation. Impact: If confirmed, these results suggest potential ways postmenopausal women might modify their risk of both ER+ and triple-negative breast cancers. Cancer Epidemiol Biomarkers Prev; 20(3); 454–63. ©2011 AACR .

A Regularized Hotelling’s T2 Test for Pathway Analysis in Proteomic Studies

Abstract: 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 nonparametric testing procedure, which effectively controls the Type I error rate and maintains ...

Arthritis Increases the Risk for Fractures---Results from the Women’s Health Initiative

Abstract: Objective. To examine the relationship between arthritis and fracture. Methods. Women were classified into 3 self-reported groups at baseline: no arthritis (n = 83,295), osteoarthritis (OA; n = 63,402), and rheumatoid arthritis (RA; n = 960). Incident fractures were self-reported throughout followup. Age-adjusted fracture rates by arthritis category were generated, and the Cox proportional hazards model was used to test the association between arthritis and fracture. Results. After an average of 7.80 years, 24,137 total fractures were reported including 2559 self-reported clinical spinal fractures and 1698 adjudicated hip fractures. For each fracture type, age-adjusted fracture rates were highest in the RA group and lowest in the nonarthritic group. After adjustment for several covariates, report of arthritis was associated with increased risk for spine, hip, and any clinical fractures. Compared to the nonarthritis group, the risk of sustaining any clinical fracture in the OA group was HR 1.09 (95% CI 1.05, 1.13; p Conclusion. The increase in fracture risk confirms the importance of fracture prevention in patients with RA and OA.

Biomarker-calibrated Energy and Protein Consumption and Cardiovascular Disease Risk Among Postmenopausal Women

Abstract: The food frequency questionnaire (FFQ) has been ubiquitous in nutritional epidemiology research for the past 25 years. Its self-administered and machine-readable features make it practical for application to large study cohorts. An early report comparing FFQ consumption estimates to 28 days of food records showed moderate to high correlations for calorie-adjusted nutrient consumption, but generally weak correlations for absolute consumption estimates.1 More recent evaluations of FFQ measurement properties have, instead, made comparisons with urinary recovery biomarkers.2 Recovery biomarkers have measurement errors that are plausibly unrelated to corresponding self-report measurement errors or to study subject characteristics such as body mass index (BMI), age, and ethnicity. This measurement-error independence is crucial to the adequacy of measurement-error correction procedures. The National Cancer Institute’s OPEN study (Observing Protein & Energy Nutrition) reported3, 4 a correlation of only 0.098 between log-transformed FFQ and log-transformed biomarker energy consumption as determined using a doubly-labeled water procedure.5 Utilizing a urinary nitrogen biomarker6 for protein assessment, the corresponding correlations were 0.298 for protein and 0.346 for protein density (percent of energy from protein). Energy was found to be underreported overall, and there was greater underreporting among overweight and obese persons. We conducted a similar Nutrient Biomarker Study among 544 postmenopausal women enrolled in the Women’s Health Initiative (WHI) Dietary Modification Trial during 2004–2005.7 In addition to overall energy underreporting, we found various sources of systematic bias in the FFQ energy and protein assessments. Groups who underreported to a greater extent included women with higher body mass index, younger women, and racial/ethnic minorities.8 By simple linear regression of log-transformed biomarker values on log-FFQ assessments and other subject characteristics, we developed calibration equations that yield “calibrated” consumption estimates thatinclude corrections for these systematic biases) for energy, protein, and protein density.8 The FFQ assessments in conjunction with data on study subject characteristics explained a substantial fraction of the variation among women in the log-biomarker measurements, supporting study of the association between calibrated consumption estimates and clinical outcomes of interest. There are few epidemiologic data relating energy consumption to cardiovascular disease risk. The joint WHO/FAQ expert consultation that summarized the world literature on diet, nutrition, and the prevention of chronic diseases9 does not list energy consumption among the factors that are convincingly, probably, or possibly associated with cardiovascular disease risk. Rather, overweight is described as convincingly associated with increased risk, and regular physical activity is convincingly associated with reduced risk. Similarly, an expert panel reviewing the pertinent literature on food, nutrition, and the prevention of cancer writes10 that, “In the view of the panel, the effect of energy intake on cancer is best assessed by examining the data on related factors: rate of growth, body mass, and physical activity.” Hence, it seems that energy consumption per se has not been carefully studied in relation to cardiovascular disease risk, presumably because of uncertainties concerning self-reported energy consumption estimates. Note that log-transformed FFQ energy was not clearly associated with body mass index in our Nutrient Biomarker Study (correlation 0.07), whereas corresponding biomarker-derived log-energy consumption was strongly correlated with BMI (correlation 0.81).8 Reliable information on energy consumption and such major cardiovascular diseases as coronary heart disease (CHD) and stroke is needed to inform dietary recommendation and guidelines, in the context of our national and international obesity epidemic. Here, we report analyses to relate biomarker-calibrated energy consumption to these diseases in WHI cohorts. Epidemiologic reports on protein consumption in relation to cardiovascular disease almost exclusively focus on protein density, or more generally on protein consumption with some form of total energy adjustment. For example, energy-adjusted protein has been reported to be inversely associated with CHD risk in women,11, 12 whereas no association was found with stroke risk in men.13 In addition to examining the association of absolute protein consumption with these diseases, we provide analyses of biomarker-calibrated protein density in relation to cardiovascular disease incidence in WHI cohorts, for comparison with these earlier reports.

Serum Selenium, Genetic Variation in Selenoenzymes, and Risk of Colorectal Cancer: Primary Analysis from the Women's Health Initiative Observational Study and Meta-analysis

Abstract: Background: Selenium may prevent colorectal cancer. However, several previous studies are small and few investigated the association between selenium and colorectal cancer among women whose selenium metabolism may differ from men. Furthermore, genetic variants in selenoenzymes may be associated with colorectal cancer risk. Methods: This nested case–control study investigated whether serum selenium concentration and genetic variants in five selenoenzymes (glutathione peroxidase 1–4 and selenoprotein P) were associated with colorectal cancer risk in 804 colorectal cancer cases and 805 matched controls from the Women's Health Initiative (WHI) Observational Study. A meta-analysis was conducted to compare the WHI result with previous studies including 12 observational studies and two clinical trials on selenium. Results: Within the WHI, selenium concentrations were relatively high (mean = 135.6 μg/L) and were not associated with colorectal cancer risk ( P trend = 0.10); the adjusted OR comparing the fifth with first quintile was 1.26 (95% CI, 0.91–1.73). Moreover, genetic variants in selenoenzymes were not significantly associated with colorectal cancer risk. Consistent with the finding in WHI, our meta-analysis showed no association between selenium and colorectal tumor risk in women (OR = 0.97; 95% CI, 0.79–1.18) comparing the highest quantile with the lowest); however, in men, there was a significant inverse association (OR = 0.68; 95% CI, 0.57–0.82) ( P = 0.01). Conclusion: Consistent with previous studies, we observed no protective effect of selenium on colorectal cancer among women. Impact: Our analyses suggest that a population with relatively high selenium concentrations, especially women, would not benefit from increasing selenium intake. Cancer Epidemiol Biomarkers Prev; 20(9); 1822–30. ©2011 AACR .

Measurement error modeling and nutritional epidemiology association analyses.

Abstract: 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.

Higher Biomarker-Calibrated Protein Intake Is Not Associated with Impaired Renal Function in Postmenopausal Women

Abstract: With aging, renal function tends to decline, as evidenced by reduced glomerular filtration rate. High-protein intake may further stress the kidneys by causing sustained hyperfiltration. To investigate whether dietary protein is associated with impaired renal function, we used data from 2 nested case-control studies within the Women's Health Initiative Observational Study (n = 2419). We estimated protein intake using a FFQ and estimated glomerular filtration rate (eGFR) from cystatin C. To account for the original study designs, inverse probability weights were applied. Self-reported energy and protein were calibrated using biomarkers of energy and protein intake. Associations between protein intake and renal function were estimated by weighted linear and logistic regression models. Average calibrated protein intake (mean ± SD) was 1.1 ± 0.2 g/(kg body weight·d).Twelve percent (n = 292) of women had impaired renal function. The odds of impaired renal function, defined as eGFR <60 mL/(min·1.73m(2)), was not associated with calibrated protein intake. When eGFR was modeled continuously, there was no association with calibrated protein when protein was expressed in absolute (g/d) or relative to energy (protein % energy/d), but protein relative to body weight [g/(kg body weight·d)] was associated with higher eGFR. There was no evidence for effect modification by age, BMI, or general health status. These data suggest higher protein intake is not associated with impaired renal function among postmenopausal women without a diagnosis of chronic kidney disease.

Genetic variants in the MRPS30 region and postmenopausal breast cancer risk

Abstract: Background Genome-wide association studies have identified several genomic regions that are associated with breast cancer risk, but these provide an explanation for only a small fraction of familial breast cancer aggregation. Genotype by environment interactions may contribute further to such explanation, and may help to refine the genomic regions of interest.

Estimation of the 2-sample hazard ratio function using a semiparametric model

Abstract: 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.

Cohorts and consortia conference: A summary report (Banff, Canada, June 17-19, 2009)

Abstract: Epidemiologic studies have adapted to the genomics era by forming large international consortia to overcome issues of large data volume and small sample size. Whereas both cohort and well-conducted case–control studies can inform disease risk from genetic susceptibility, cohort studies offer the additional advantages of assessing lifestyle and environmental exposure–disease time sequences often over a life course. Consortium involvement poses several logistical and ethical issues to investigators, some of which are unique to cohort studies, including the challenge to harmonize prospectively collected lifestyle and environmental exposures validly across individual studies. An open forum to discuss the opportunities and challenges of large-scale cohorts and their consortia was held in June 2009 in Banff, Canada, and is summarized in this report.

Empirical Evaluation of Gene and Environment Interactions: Methods and Potential

Abstract: In this issue of the Journal, Campa et al. (1) empirically examine interactions between single-nucleotide polymorphisms (SNPs) that have been associated with disease incidence and certain risk factors, for breast cancer. The authors analyzed data from 8576 breast cancer case subjects and 11 892 control subjects from the Breast and Prostate Cancer Cohort Consortium (BPC3), more than 80% of whom are of European ancestry, for interactions between 17 SNPs that were “strongly and statistically significantly” associated with breast cancer risk in previous studies, and nine established breast cancer risk factors. They summarize that “this study does not support the hypothesis that known common breast cancer susceptibility loci strongly modify the association between established risk factors and cancer,” and they argue that “these findings are important given the size, prospective design, and comprehensive approach of the study.”

Invited commentary on Pearl and principal stratification.

Abstract: Pearl (2011) posed the question of whether confinement of clinical trial analyses involving post-randomization variables to the principal stratum "framework" of Frangakis and Rubin (2002) unduly restricts the scientific questions that can be asked. Frangakis and Rubin illustrated their proposal through examples involving compliance, mediation, and surrogacy. Here the utility of the principal stratum framework, and the potential outcomes formulation from which it derives, are considered for these topics in the specific setting of the Women's Health Initiative randomized, placebo controlled trials of postmenopausal hormone therapy. It is argued that the essential issues related to study reliability and causal interpretation involve the avoidance of context-specific biases that are typically not closely related to whether or not treatment effects have a representation in terms of potential outcomes contrasts. Also, while the questions posed within principal strata may be of interest, some key questions in the hormone therapy setting would not be addressed if restricted to contrasts within principal strata.

Biomarker-calibrated dietary energy and protein intake associations with diabetes risk among postmenopausal women from the Women's

Abstract: Background: Self-report of dietary energy and protein intakes has been shown to be systematically and differentially underreported. Objective: We assessed and compared the association of diabetes among postmenopausal women with biomarker-calibrated and uncalibrated dietary energy and protein intakes from food-frequency questionnaires (FFQs). Design: The analyses were performed for 74,155 participants of various race-ethnicities from the Women’s Health Initiative. Uncalibrated and calibrated energy and protein intakes from FFQs were assessed for associations with incident diabetes by using HR estimates based on Cox regression. Results: A 20% increment in uncalibrated energy consumption was associated with increased diabetes risk (HR) of 1.03 (95% CI: 1.01, 1.05), 2.41 (95% CI: 2.06, 2.82) with biomarker calibration, and 1.30 (95% CI: 0.96, 1.76) after adjustment for BMI. A 20% increment in uncalibrated protein (g/d) resulted in an HR of 1.05 (95% CI: 1.03, 1.07), 1.82 (95% CI: 1.56, 2.12) with calibration, and 1.16 (95% CI: 1.05, 1.28) with adjustment for BMI. A 20% increment in uncalibrated protein density (% of energy from protein) resulted in an HR of 1.13 (95% CI: 1.09, 1.17), 1.01 (95% CI: 0.75, 1.37) with calibration, and 1.19 (95% CI: 1.07, 1.32) with adjustment for BMI. Conclusions: Higher protein and total energy intakes (calibrated) appear to be associated with a substantially increased diabetes risk that may be mediated by an increase in body mass over time. Dietdisease associations without correction of self-reported measurement error should be viewed with caution. This trial is registered at clinicaltrials.gov as NCT00000611. Am J Clin Nutr 2011;94:

Biomarkers and the risk of stroke in the Women's Health Initiative.

Abstract: ISSN 1745-5057 Women's Health (2011) 7(3), 269–273 10.2217/WHE.11.17 © 2011 Future Medicine Ltd trials. A total of 10,639 women who were posthysterectomy were randomized to 0.625 mg/day conjugated equine estrogens (Premarin) or placebo, and 16,608 women with a uterus were randomized to this same estrogen regimen plus 0.625 mg/day medroxyprogesterone acetate (Prempro) or placebo. These preparations were used by approximately 8 million and 6 million women, respectively, in the USA when the WHI trials began in 1993. All women were postmenopausal and in the age range of 50–79 years at randomization. Both HT trials were stopped prematurely; the estrogen plus progestin (E + P) trial in 2002 when it was judged that health risks exceeded benefits over a 5.6 year average follow-up period [1], and the estrogen-alone (E-alone) trial in 2004 after an average 7.1 years of follow-up, largely because of an elevation in stroke incidence of similar magnitude to that seen for E + P [2]. Specifically, the estimated hazard ratio (95% CI) for stroke was 1.31 (95% CI: 1.02–1.68) for women assigned to E + P compared with placebo [3], and 1.37 (95% CI: 1.09–1.73) for women assigned to E-alone versus placebo [4]. The elevated risk appeared to be confined to ischemic strokes, which constituted approximately 80% of incident strokes, with no evidence of hemorrhagic stroke risk elevation. The excess stroke risk appeared to be present in all subsets of women considered, including younger and more recently postmenopausal women. The WHI also included a prospective cohort study among 93,676 postmenopausal women in the same age range, drawn from the same catchment populations as the clinical trials, with much commonality in data collection and outcome ascertainment. Specifically, data collection included baseline HT history ascertainment by trained interviewers assisted by a structured questionnaire and chart displaying colored photographs of various hormone pre parations, followed by annual updates. These data provided an excellent opportunity to compare HT hazard ratios from a high-quality observational study to The Editors of this special focus issue on stroke in women have asked me to provide a perspective on the role and potential of biomarkers in stroke risk assessment and prevention. This invitation undoubtedly arises from our studies of stroke in relation to postmenopausal hormone therapy (HT), among other exposures, in the context of the NIH-sponsored Women’s Health Initiative (WHI), for which I have been a principal investigator of the Clinical Coordinating Center since the inception of the program in 1992.