A longstanding goal of dietary surveillance has been to estimate the proportion of the population with intakes above or below a target, such as a recommended level of intake. However, until now, ...statistical methods for assessing the alignment of food intakes with recommendations have been lacking. The purposes of this study were to demonstrate the National Cancer Institute's method of estimating the distribution of usual intake of foods and determine the proportion of the U.S. population who does not meet federal dietary recommendations. Data were obtained from the 2001-2004 NHANES for 16,338 persons, aged 2 y and older. Quantities of foods reported on 24-h recalls were translated into amounts of various food groups using the MyPyramid Equivalents Database. Usual dietary intake distributions were modeled, accounting for sequence effect, weekend/weekday effect, sex, age, poverty income ratio, and race/ethnicity. The majority of the population did not meet recommendations for all of the nutrient-rich food groups, except total grains and meat and beans. Concomitantly, overconsumption of energy from solid fats, added sugars, and alcoholic beverages ("empty calories") was ubiquitous. Over 80% of persons age ≥71 y and over 90% of all other sex-age groups had intakes of empty calories that exceeded the discretionary calorie allowances. In conclusion, nearly the entire U.S. population consumes a diet that is not on par with recommendations. These findings add another piece to the rather disturbing picture that is emerging of a nation's diet in crisis.
Quantitative research depends on using measures to collect data that are valid (ie, reflect well the phenomena of interest) and perform equivalently across contexts. Demonstrating validity and ...cross-context equivalence requires specifically designed studies, but many such studies have problems that have limited their usefulness. This article explains validity and cross-context equivalence of measures (and important related concepts) and clarifies how to establish them. Validation is the process of determining whether a measure or indicator is suitable for providing useful analytical measurement for a given purpose and context. Cross-context equivalence means that a measure performs comparably across contexts. Four types of equivalence are construct, item, measurement, and scalar. Establishing validity and cross-context equivalence requires representing mathematically the errors (ie, imprecision, undependability, and inaccuracy) of a measure and using appropriate statistical methods to quantify these errors. Studies aiming to provide evidence about the validity of a measure need to clarify the purpose and context for use of that measure. Choose one of the two conceptual systems for validation; obtain data to establish the extent to which the measure is well constructed, reliable, and accurate; and use analytic methods beyond simple correlations to provide a basis for making reasoned judgment about whether the measure provides useful analytic measurement for the particular purpose(s) and context. Establishing accuracy of a measure requires having available other measures known to be accurate as comparators; in the case that no other measure understood to be more accurate is available, then the study will be able to establish agreement rather than validity.
Background: Advanced glycation end products (AGEs) are a heterogeneous group of compounds present in uncooked foods as well as in foods cooked at high temperatures. AGEs have been associated with ...insulin resistance, oxidative stress, and chronic inflammation in patients with diabetes. Dietary AGEs are an important contributor to the AGE pool in the body. Nϵ-(carboxymethyl)lysine (CML) AGE is one of the major biologically and chemically well-characterized AGE markers. The consumption of red meat, which is CML-AGE rich, has been positively associated with pancreatic cancer in men.Objectives: With the use of a published food CML-AGE database, we estimated the consumption of CML AGE in the prospective NIH-AARP Diet and Health Study and evaluated the association between CML-AGE consumption and pancreatic cancer and the mediating effect of CML AGE on the association between red meat consumption and pancreatic cancer.Design: Multivariate Cox proportional hazard regression models were used to estimate HRs and 95% CIs for pancreatic cancer.Results: During an average of 10.5 y of follow-up, we identified 2193 pancreatic cancer cases (1407 men and 786 women) from 528,251 subjects. With the comparison of subjects in the fifth and the first quintiles of CML-AGE consumption, we observed increased pancreatic cancer risk in men (HR: 1.43; 95% CI: 1.06, 1.93, P-trend = 0.003) but not women (HR: 1.14; 95% CI: 0.76, 1.72, P-trend = 0.42). Men in the highest quintile of red meat consumption had higher risk of pancreatic cancer (HR: 1.35; 95% CI: 1.07, 1.70), which attenuated after adjustment for CML-AGE consumption (HR: 1.20; 95% CI: 0.95, 1.53).Conclusion: Dietary CML-AGE consumption was associated with modestly increased risk of pancreatic cancer in men and may partially explain the positive association between red meat and pancreatic cancer. The NIH-AARP Diet and Health Study was registered at clinicaltrials.gov as NCT00340015.
Background: Multiple diet indexes have been developed to capture the Dietary Approaches to Stop Hypertension (DASH) dietary pattern and examine relations with health outcomes but have not been ...compared within the same study population to our knowledge.Objective: We compared 4 established DASH indexes and examined associations with colorectal cancer.Design: Scores were generated from a food-frequency questionnaire in the NIH-AARP Diet and Health Study (n = 491,841). Separate indexes defined by Dixon (7 food groups, saturated fat, and alcohol), Mellen (9 nutrients), Fung (7 food groups and sodium), and Günther (8 food groups) were used. HRs and 95% CIs for colorectal cancer were generated by using Cox proportional hazard models.Results: From 1995 through 2006, 6752 incident colorectal cancer cases were ascertained. In men, higher scores were associated with reduced colorectal cancer incidence by comparing highest to lowest quintiles for all indexes as follows: Dixon (HR: 0.77; 95% CI: 0.69, 0.87), Mellen (HR: 0.78; 95% CI: 0.71, 0.86), Fung (HR: 0.75; 95% CI: 0.68, 0.83), and Günther (HR: 0.81; 95% CI: 0.74, 0.90). Higher scores in women were inversely associated with colorectal cancer incidence by using methods defined by Mellen (HR: 0.79; 95% CI: 0.68, 0.91), Fung (HR: 0.84; 95% CI: 0.73, 0.96), and Günther (HR: 0.84; 95% CI: 0.73.0.97) but not Dixon (HR: 1.01; 95% CI: 0.80, 1.28).Conclusion: The consistency in findings, particularly in men, suggests that all indexes capture an underlying construct inherent in the DASH dietary pattern, although the specific index used can affect results.
Rates of esophageal adenocarcinoma and gastric cardia adenocarcinoma have increased, while rates of esophageal squamous cell carcinoma (ESCC) and gastric noncardia adenocarcinoma have decreased, ...suggesting distinct etiologies. The authors prospectively investigated the associations of alcohol and tobacco with these cancers in 474,606 US participants using Cox models adjusted for potential confounders. Between 1995/1996 and 2000, 97 incident cases of ESCC, 205 of esophageal adenocarcinoma, 188 of gastric cardia, and 187 of gastric noncardia cancer occurred. Compared with nonsmokers, current smokers were at increased risk for ESCC (hazard ratio (HR) = 9.27, 95% confidence interval (CI): 4.04, 21.29), esophageal adenocarcinoma (HR = 3.70, 95% CI: 2.20, 6.22), gastric cardia (HR = 2.86, 95% CI: 1.73, 4.70), and gastric noncardia (HR = 2.04, 95% CI: 1.32, 3.16). Assuming causality, ever smoking had population attributable risks of 77% (95% CI: 0.55, 0.89) for ESCC, 58% (95% CI: 0.38, 0.72) for esophageal adenocarcinoma, 47% (95% CI: 0.27, 0.63) for gastric cardia, and 19% (95% CI: 0.00, 0.37) for gastric noncardia. For drinkers of more than three alcoholic beverages per day, compared with those whose intake was up to one drink per day, the authors found significant associations between alcohol intake and ESCC risk (HR = 4.93, 95% CI: 2.69, 9.03) but not risk for esophageal, gastric cardia, or gastric noncardia adenocarcinoma.
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.
Healthy dietary patterns that conform to national dietary guidelines are related to lower chronic disease incidence and longer life span. However, the precise mechanisms involved are unclear. ...Identifying biomarkers of dietary patterns may provide tools to validate diet quality measurement and determine underlying metabolic pathways influenced by diet quality.
The objective of this study was to examine the correlation of 4 diet quality indexes the Healthy Eating Index (HEI) 2010, the Alternate Mediterranean Diet Score (aMED), the WHO Healthy Diet Indicator (HDI), and the Baltic Sea Diet (BSD) with serum metabolites.
We evaluated dietary patterns and metabolites in male Finnish smokers (n = 1336) from 5 nested case-control studies within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study cohort. Participants completed a validated food-frequency questionnaire and provided a fasting serum sample before study randomization (1985-1988). Metabolites were measured with the use of mass spectrometry. We analyzed cross-sectional partial correlations of 1316 metabolites with 4 diet quality indexes, adjusting for age, body mass index, smoking, energy intake, education, and physical activity. We pooled estimates across studies with the use of fixed-effects meta-analysis with Bonferroni correction for multiple comparisons, and conducted metabolic pathway analyses.
The HEI-2010, aMED, HDI, and BSD were associated with 23, 46, 23, and 33 metabolites, respectively (17, 21, 11, and 10 metabolites, respectively, were chemically identified; r-range: -0.30 to 0.20; P = 6 × 10
to 8 × 10
). Food-based diet indexes (HEI-2010, aMED, and BSD) were associated with metabolites correlated with most components used to score adherence (e.g., fruit, vegetables, whole grains, fish, and unsaturated fat). HDI correlated with metabolites related to polyunsaturated fat and fiber components, but not other macro- or micronutrients (e.g., percentages of protein and cholesterol). The lysolipid and food and plant xenobiotic pathways were most strongly associated with diet quality.
Diet quality, measured by healthy diet indexes, is associated with serum metabolites, with the specific metabolite profile of each diet index related to the diet components used to score adherence. This trial was registered at clinicaltrials.gov as NCT00342992.
This paper describes the Observing Protein and Energy Nutrition (OPEN) Study, conducted from September 1999 to March 2000. The purpose of the study was to assess dietary measurement error using two ...self-reported dietary instruments—the food frequency questionnaire (FFQ) and the 24-hour dietary recall (24HR)—and unbiased biomarkers of energy and protein intakes: doubly labeled water and urinary nitrogen. Participants were 484 men and women aged 40–69 years from Montgomery County, Maryland. Nine percent of men and 7% of women were defined as underreporters of both energy and protein intake on 24HRs; for FFQs, the comparable values were 35% for men and 23% for women. On average, men underreported energy intake compared with total energy expenditure by 12–14% on 24HRs and 31–36% on FFQs and underreported protein intake compared with a protein biomarker by 11–12% on 24HRs and 30–34% on FFQs. Women underreported energy intake on 24HRs by 16–20% and on FFQs by 34–38% and underreported protein intake by 11–15% on 24HRs and 27–32% on FFQs. There was little underreporting of the percentage of energy from protein for men or women. These findings have important implications for nutritional epidemiology and dietary surveillance.
With the advent of Internet-based 24-hour recall (24HR) instruments, it is now possible to envision their use in cohort studies investigating the relation between nutrition and disease. Understanding ...that all dietary assessment instruments are subject to measurement errors and correcting for them under the assumption that the 24HR is unbiased for usual intake, here the authors simultaneously address precision, power, and sample size under the following 3 conditions: 1) 1-12 24HRs; 2) a single calibrated food frequency questionnaire (FFQ); and 3) a combination of 24HR and FFQ data. Using data from the Eating at America's Table Study (1997-1998), the authors found that 4-6 administrations of the 24HR is optimal for most nutrients and food groups and that combined use of multiple 24HR and FFQ data sometimes provides data superior to use of either method alone, especially for foods that are not regularly consumed. For all food groups but the most rarely consumed, use of 2-4 recalls alone, with or without additional FFQ data, was superior to use of FFQ data alone. Thus, if self-administered automated 24HRs are to be used in cohort studies, 4-6 administrations of the 24HR should be considered along with administration of an FFQ.
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