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.
BACKGROUND:Although metabolomic profiling offers promise for the prediction of coronary heart disease (CHD), and metabolic risk factors are more strongly associated with CHD in women than men, ...limited data are available for women.
METHODS:We applied a liquid chromatography–tandem mass spectrometry metabolomics platform to measure 371 metabolites in a discovery set of postmenopausal women (472 incident CHD cases, 472 controls) with validation in an independent set of postmenopausal women (312 incident CHD cases, 315 controls).
RESULTS:Eight metabolites, primarily oxidized lipids, were significantly dysregulated in cases after the adjustment for matching and CHD risk factors in both the discovery and validation data sets. One oxidized phospholipid, C34:2 hydroxy-phosphatidylcholine, remained associated with CHD after further adjustment for other validated metabolites. Subjects with C34:2 hydroxy-phosphatidylcholine levels in the highest quartile had a 4.7-fold increase in CHD odds in comparison with the lowest quartile; C34:2 hydroxy-phosphatidylcholine also significantly improved the area under the curve (P<0.01) for CHD. The C34:2 hydroxy-phosphatidylcholine findings were replicated in a third replication data set of 980 men and women (230 cardiovascular events) with a stronger association observed in women.
CONCLUSIONS:These data replicate known metabolite predictors, identify novel markers, and support the relationship between lipid oxidation and subsequent CHD.
Nutrition influences health throughout the life course. Good nutrition increases the probability of good pregnancy outcomes, proper childhood development, and healthy aging, and it lowers the ...probability of developing common diet-related chronic diseases, including obesity, cardiovascular disease, cancer, and type 2 diabetes. Despite the importance of diet and health, studying these exposures is among the most challenging in population sciences research. US and global food supplies are complex; eating patterns have shifted such that half of meals are eaten away from home, and there are thousands of food ingredients with myriad combinations. These complexities make dietary assessment and links to health challenging both for population sciences research and for public health policy and practice. Furthermore, most studies evaluating nutrition and health usually rely on self-report instruments prone to random and systematic measurement error. Scientific advances involve developing nutritional biomarkers and then applying these biomarkers as stand-alone nutritional exposures or for calibrating self-reports using specialized statistics.
We pooled data from 5 large validation studies (1999-2009) of dietary self-report instruments that used recovery biomarkers as referents, to assess food frequency questionnaires (FFQs) and 24-hour ...recalls (24HRs). Here we report on total potassium and sodium intakes, their densities, and their ratio. Results were similar by sex but were heterogeneous across studies. For potassium, potassium density, sodium, sodium density, and sodium:potassium ratio, average correlation coefficients for the correlation of reported intake with true intake on the FFQs were 0.37, 0.47, 0.16, 0.32, and 0.49, respectively. For the same nutrients measured with a single 24HR, they were 0.47, 0.46, 0.32, 0.31, and 0.46, respectively, rising to 0.56, 0.53, 0.41, 0.38, and 0.60 for the average of three 24HRs. Average underreporting was 5%-6% with an FFQ and 0%-4% with a single 24HR for potassium but was 28%-39% and 4%-13%, respectively, for sodium. Higher body mass index was related to underreporting of sodium. Calibration equations for true intake that included personal characteristics provided improved prediction, except for sodium density. In summary, self-reports capture potassium intake quite well but sodium intake less well. Using densities improves the measurement of potassium and sodium on an FFQ. Sodium:potassium ratio is measured much better than sodium itself on both FFQs and 24HRs.
Poor diet quality is thought to be a leading risk factor for years of life lost. We examined how scores on 4 commonly used diet quality indices-the Healthy Eating Index 2010 (HEI), the Alternative ...Healthy Eating Index 2010 (AHEI), the Alternate Mediterranean Diet (aMED), and the Dietary Approaches to Stop Hypertension (DASH)-are related to the risks of death from all causes, cardiovascular disease (CVD), and cancer among postmenopausal women. Our prospective cohort study included 63,805 participants in the Women's Health Initiative Observational Study (from 1993-2010) who completed a food frequency questionnaire at enrollment. Cox proportional hazards models were fit using person-years as the underlying time metric. We estimated multivariate hazard ratios and 95% confidence intervals for death associated with increasing quintiles of diet quality index scores. During 12.9 years of follow-up, 5,692 deaths occurred, including 1,483 from CVD and 2,384 from cancer. Across indices and after adjustment for multiple covariates, having better diet quality (as assessed by HEI, AHEI, aMED, and DASH scores) was associated with statistically significant 18%-26% lower all-cause and CVD mortality risk. Higher HEI, aMED, and DASH (but not AHEI) scores were associated with a statistically significant 20%-23% lower risk of cancer death. These results suggest that postmenopausal women consuming a diet in line with a priori diet quality indices have a lower risk of death from chronic disease.
Evidence of the association between daily variation in air pollution and risk of stroke is inconsistent, potentially due to the heterogeneity in stroke etiology.
To estimate the associations between ...daily variation in ambient air pollution and risk of stroke and its subtypes among participants of the Women's Health Initiative, a large prospective cohort study in the United States.
We used national-scale, log-normal ordinary kriging models to estimate daily concentrations of fine particulate matter (PM2.5), respirable particulate matter (PM10), nitrogen dioxide (NO2), nitrogen oxides (NOx), sulphur dioxide, and ozone at participant addresses. Stroke was adjudicated by trained neurologists and classified as ischemic or hemorrhagic. Ischemic strokes were further classified according to the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification. We used a time-stratified case-crossover approach to estimate the odds ratio (OR) of the risk of stroke associated with an interquartile range (IQR) increase in concentrations of each air pollutant. We performed stratified analysis to examine whether associations varied across subgroups defined by age at stroke onset, US census region, smoking status, body mass index, and prior history of diabetes mellitus, hypertension, heart or circulation problems, or arterial fibrillation at enrollment.
Among 5417 confirmed strokes between 1993 and 2012, 4300 (79.4%) were classified as ischemic and 924 (17.1%) as hemorrhagic. No association was observed between day-to-day variation in any pollutant and risk of total stroke, ischemic stroke, or specific etiologies of ischemic stroke. We observed a positive association between risk of hemorrhagic stroke and NO2 and NOx in the 3 days prior to stroke with OR of 1.24 (95% CI: 1.01, 1.52) and 1.18 (95% CI: 1.03, 1.34) per IQR increase, respectively. The observed associations with hemorrhagic stroke were more pronounced among non-obese participants.
In this large cohort of post-menopausal US women, daily NO2 and NOx were associated with higher risk of hemorrhagic stroke, but ambient levels of four other air pollutants were not associated with higher risk of total stroke, ischemic stroke, or ischemic stroke subtypes.
•We examined associations between air pollution and stroke subtypes among US women.•Stroke cases were adjudicated by trained neurologists based on brain imaging.•We observed no associations between daily air pollution exposures and ischemic stroke and its subtypes.•We observed a positive association of NO2 and NO with haemorrhagic stroke.
Abstract
Improving estimates of individuals’ dietary intakes is key to obtaining more reliable evidence for diet-health relationships from nutritional cohort studies. One approach to improvement is ...combining information from different self-report instruments. Previous work evaluated the gains obtained from combining information from a food frequency questionnaire (FFQ) and multiple 24-hour recalls (24HRs), based on assuming that 24HRs provide unbiased measures of individual intakes. Here we evaluate the same approach of combining instruments but base it on the better assumption that recovery biomarkers provide unbiased measures of individual intakes. Our analysis uses data from the 5 large validation studies included in the Validation Studies Pooling Project: the Observing Protein and Energy Nutrition Study (1999–2000), the Automated Multiple-Pass Method validation study (2002–2004), the Energetics Study (2006–2009), the Nutrition Biomarker Study (2004–2005), and the Nutrition and Physical Activity Assessment Study (2007–2009). The data included intakes of energy, protein, potassium, and sodium. Under a time-varying usual-intake model analysis, the combination of an FFQ with 4 24HRs improved correlations with true intake for predicted protein density, potassium density, and sodium density (range, 0.39–0.61) in comparison with use of a single FFQ (range, 0.34–0.50). Absolute increases in correlation ranged from 0.02 to 0.26, depending on nutrient and sex, with an average increase of 0.14. Based on unbiased recovery biomarker evaluation for these nutrients, we confirm that combining an FFQ with multiple 24HRs modestly improves the accuracy of estimates of individual intakes.
To assess the extent of error present in self-reported weight data in the Women's Health Initiative, variables that may be associated with error, and to develop methods to reduce any identified ...error.
Prospective cohort study.
Forty clinical centres in the USA.ParticipantsWomen (n 75 336) participating in the Women's Health Initiative Observational Study (WHI-OS) and women (n 6236) participating in the WHI Long Life Study (LLS) with self-reported and measured weight collected about 20 years later (2013-2014).
The correlation between self-reported and measured weights was 0·97. On average, women under-reported their weight by about 2 lb (0·91 kg). The discrepancies varied by age, race/ethnicity, education and BMI. Compared with normal-weight women, underweight women over-reported their weight by 3·86 lb (1·75 kg) and obese women under-reported their weight by 4·18 lb (1·90 kg) on average. The higher the degree of excess weight, the greater the under-reporting of weight. Adjusting self-reported weight for an individual's age, race/ethnicity and education yielded an identical average weight to that measured.
Correlations between self-reported and measured weights in the WHI are high. Discrepancies varied by different sociodemographic characteristics, especially an individual's BMI. Correction of self-reported weight for individual characteristics could improve the accuracy of assessment of obesity status in postmenopausal women.
Dietary biomarkers measured in biospecimens can play an important role in correcting for random and systematic measurement error in self-reported nutrient intake when assessing diet–disease ...associations. To date, high-quality biomarkers for calibrating self-reported dietary intake have only been developed for a few nutrients.
To investigate new study designs and regression calibration approaches for calibrating self-reported nutrient intake for use in disease association analyses.
We studied 3 regression calibration approaches: 1) an existing approach built on a calibration cohort assuming the existence of an objective biomarker (i.e., biomarker with random independent measurement error), 2) a proposed approach using a biomarker development cohort, and 3) a proposed 2-stage approach using both cohorts. We conducted simulation studies to compare the performance of different study designs/methods for estimating diet–disease associations and applied suitable methods to examine the association of sodium and potassium intake with cardiovascular disease (CVD) risk in Women's Health Initiative cohorts.
Simulation studies showed that the first approach can lead to biased association estimation when the objective biomarker assumption is violated; the second and third proposed approaches obviate the need for such an objective biomarker. Precision for estimating the association depends critically on sample size of the biomarker development cohort and the strength of the self-reported nutrient intake. Analyses based on the second and third approaches support previously reported significant findings using the first approach about associations of the ratio of sodium to potassium intake with CVD risk while providing efficiency gain for some outcomes.
Self-reported dietary intake needs to be calibrated for measurement error correction in diet–disease association analyses. When there are no existing objective biomarkers that can be used for calibration purpose, controlled feeding studies can be used to develop new biomarkers for use in calibration or can be used to calibrate self-reported dietary intake directly.
Measurement error arises commonly in clinical research settings that rely on data from electronic health records or large observational cohorts. In particular, self‐reported outcomes are typical in ...cohort studies for chronic diseases such as diabetes in order to avoid the burden of expensive diagnostic tests. Dietary intake, which is also commonly collected by self‐report and subject to measurement error, is a major factor linked to diabetes and other chronic diseases. These errors can bias exposure‐disease associations that ultimately can mislead clinical decision‐making. We have extended an existing semiparametric likelihood‐based method for handling error‐prone, discrete failure time outcomes to also address covariate error. We conduct an extensive numerical study to compare the proposed method to the naive approach that ignores measurement error in terms of bias and efficiency in the estimation of the regression parameter of interest. In all settings considered, the proposed method showed minimal bias and maintained coverage probability, thus outperforming the naive analysis which showed extreme bias and low coverage. This method is applied to data from the Women's Health Initiative to assess the association between energy and protein intake and the risk of incident diabetes mellitus. Our results show that correcting for errors in both the self‐reported outcome and dietary exposures leads to considerably different hazard ratio estimates than those from analyses that ignore measurement error, which demonstrates the importance of correcting for both outcome and covariate error.
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