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BACKGROUND: The associations of red and processed meat with chronic disease risk remain to be clarified, in part because of measurement error in self-reported diet. OBJECTIVES: We sought to develop metabolomics-based biomarkers for red and processed meat, and to evaluate associations of biomarker-calibrated meat intake with chronic disease risk among postmenopausal women. METHODS: Study participants were women who were members of the Women's Health Initiative (WHI) study cohorts. These participants were postmenopausal women aged 50–79 y when enrolled during 1993–1998 at 40 US clinical centers with embedded human feeding and nutrition biomarker studies. Literature reports of metabolomics correlates of meat consumption were used to develop meat intake biomarkers from serum and 24-h urine metabolites in a 153-participant feeding study (2010–2014). Resulting biomarkers were used in a 450-participant biomarker study (2007–2009) to develop linear regression calibration equations that adjust FFQ intakes for random and systematic measurement error. Biomarker-calibrated meat intakes were associated with cardiovascular disease, cancer, and diabetes incidence among 81,954 WHI participants (1993–2020). RESULTS: Biomarkers and calibration equations meeting prespecified criteria were developed for consumption of red meat and red plus processed meat combined, but not for processed meat consumption. Following control for nondietary confounding factors, hazard ratios were calculated for a 40% increment above the red meat median intake for coronary artery disease (HR: 1.10; 95% CI: 1.07, 1.14), heart failure (HR: 1.26; 95% CI: 1.20, 1.33), breast cancer (HR: 1.10; 95% CI: 1.07, 1.13) for, total invasive cancer (HR: 1.07; 95% CI: 1.05, 1.09), and diabetes (HR: 1.37; 95% CI: 1.34, 1.39). HRs for red plus processed meat intake were similar. HRs were close to the null, and mostly nonsignificant following additional control for dietary potential confounding factors, including calibrated total energy consumption. CONCLUSIONS: A relatively high-meat dietary pattern is associated with somewhat higher chronic disease risks. These elevations appear to be largely attributable to the dietary pattern, rather than to consumption of red or processed meat per se.

BackgroundIdentification of nutrients of public health concern has been a hallmark of the Dietary Guidelines for Americans (DGA); however, a formal systematic process for identifying them has not been published.ObjectivesWe aimed to propose a framework for identifying "nutrients or food components" (NFCs) of public health relevance to inform the DGA.MethodsThe proposed framework consists of 1) defining terminology; 2) establishing quantitative thresholds to identify NFCs; and 3) examining national data. The proposed framework utilizes available data from 3 key data sources or "prongs": 1) dietary intakes; 2) biological endpoints; and 3) clinical health consequences such as prevalence of health conditions, directly or indirectly through validated surrogate markers.ResultsIn identifying potential NFCs of public health concern, the 2020 DGA Committee developed a decision-tree framework with suggestions for combining the 3 prongs. The identified NFCs of public health concern for Americans ≥1y old included fiber, calcium (≥2y old), vitamin D, and potassium for low intakes and sodium, added sugars, and saturated fats (≥2y old) for high intakes that were associated with adverse health consequences. Iron was identified among infants ages 6-12mo fed human milk. For reproductive-aged and pregnant females, iron (all trimesters) and folate (first trimester) were identified for low intake, based on dietary and biomarker data (iron) or the severity of the consequence (folic acid and neural tube defects). Among pregnant women, low iodine was of potential public health concern based on biomarker data. Other NFCs that were underconsumed, overconsumed, and pose special challenges were identified across the life course.ConclusionsThe proposed decision-tree framework was intended to streamline and add transparency to the work of this and future Dietary Guidelines Advisory Committees to identify NFCs that need to be encouraged or discouraged in order to help reduce risk of chronic disease and promote health and energy balance in the population.

BACKGROUND: Multiple methods of correcting nutrient intake for misreported energy intake have been proposed but have not been extensively compared. The availability of the Women's Health Initiative (WHI) data set, which includes several objective recovery biomarkers, offers an opportunity to compare these corrections with respect to protein intake. OBJECTIVE: We compared 5 energy-correction methods for self-reported dietary protein against urinary nitrogen–derived protein intake. METHODS: As part of the WHI Nutritional Biomarkers Study (NBS) 544 participants (50- to 80-y-old women) completed a FFQ and biomarker assessments using doubly labeled water (DLW) for total energy expenditure (TEE) and 24-h urinary nitrogen. Correction methods evaluated were as follows: 1) DLW-TEE; 2) the Institute of Medicine's (IOM's) estimated energy requirement (EER) TEE prediction equation based on sex, height, weight, and age; 3) published NBS total energy TEE prediction (WHI-NBS-TEE) using age, BMI, race, and income; 4) reported protein versus reported energy linear regression–based residual method; and 5) a Goldberg cutoff to exclude subjects reporting energy intakes

BACKGROUND: The carbon isotope ratio (CIR) is a proposed biomarker for added sugar (AS) intake in the United States; however, because the CIR is also associated with meat intake in most populations the need for specificity remains. The CIR of amino acids (AAs) has the potential to differentiate sugars from meat intakes, because essential AAs must derive from dietary protein whereas certain nonessential AAs can be synthesized from sugars. OBJECTIVES: We tested whether serum CIR-AAs in combination with participant characteristics could meet a prespecified biomarker criterion for AS intake in the Nutrition and Physical Activity Assessment Study Feeding Study (NPAAS-FS) of the Women's Health Initiative, a population in which the whole-serum CIR was not associated with AS intake. METHODS: Postmenopausal women (n = 145) from Seattle, WA, were provided with individualized diets that approximated their habitual food intakes for 2 wk. Dietary intakes from consumed foods were characterized over the feeding period using the Nutrition Data System for Research. The CIR of 7 AAs—Ala, Gly, Val, Leu, Ile, Pro, and Phe—were measured in fasting serum collected at the end of the 2-wk feeding period, using gas chromatography–combustion isotope ratio mass spectrometry. Biomarker models were evaluated using regression R(2) ≥ 0.36 as a major biomarker criterion, based on the benchmark R(2) values of well-established recovery biomarkers in the NPAAS-FS. RESULTS: AS intake was associated with CIR-Ala (ρ = 0.32; P

Coronary heart disease (CHD) is the leading cause of death in women. A nested case-control study tested whether dietary patterns predicted CHD events among 1224 participants in the Women's Health Initiative-Observational Study (WHI-OS) with centrally confirmed CHD, fatal or nonfatal myocardial infarct compared to 1224 WHI-OS controls matched for age, enrollment date, race/ethnicity, and absence of CHD at baseline or follow-up. The first six principal components explained >75% of variation in dietary intakes and K-mean analysis based on these six components produced three clusters. Diet cluster 1 was rich in carbohydrate, vegetable protein, fiber, dietary vitamin K, folate, carotenoids, α-linolenic acid [18:3(n-3)], linoleic acid [18:2(n-6)], and supplemental calcium and vitamin D. Diet cluster 2 was rich in total and animal protein, arachidonic acid [20:4(n-6)], DHA [22:6(n-3)], vitamin D, and calcium. Diet cluster 3 was rich in energy, total fat, and trans fatty acids (all P < 0.01). Conditional logistic regression analysis demonstrated diet cluster 1 was associated with lower CHD risk than diet cluster 2 (reference group) adjusted for smoking, education, and physical activity [OR = 0.79 (95% CI = 0.64, 0.99); P = 0.038]. This difference was not significant after adjustment for BMI and systolic blood pressure. Diet cluster 3 was associated with higher CHD risk than diet cluster 2 [OR = 1.28 (95% CI = 1.04, 1.57); P = 0.019], but this difference did not remain significant after adjustment for smoking, education, and physical activity. Within this WHI-OS cohort, distinct dietary patterns may be associated with subsequent CHD outcomes.

Consumption of foods with a high glycemic index (GI) or glycemic load (GL) is hypothesized to contribute to insulin resistance, which is associated with increased risk of diabetes mellitus, obesity, cardiovascular disease, and some cancers. However, dietary assessment of GI and GL is difficult because values are not included in standard food composition databases. Our objective was to develop a database of GI and GL values that could be integrated into an existing dietary database used for the analysis of FFQ. Food GI values were obtained from published human experimental studies or imputed from foods with a similar carbohydrate and fiber content. We then applied the values to the Women's Health Initiative (WHI) FFQ database and tested the output in a random sample of previously completed WHI FFQs. Of the 122 FFQ line items (disaggregated into 350 foods), 83% had sufficient carbohydrate (>5 g/serving) for receipt of GI and GL values. The foods on the FFQ food list with the highest GL were fried breads, potatoes, pastries, pasta, and soft drinks. The fiber content of foods had very little influence on calculated GI or GL estimates. The augmentation of this FFQ database with GI and GL values will enable etiologic investigations of GI and GL with numerous disease outcomes in the WHI and other epidemiologic studies that utilize this FFQ.

An educational program at the University of Iowa has been designed to provide medical students with the opportunity to do a self-assessment of their personal risk factors for developing cardiovascular disease. The University of Iowa Hospital and Clinic's General Clinical Research Center (GCRC) provided the personnel and resources to work with students in small groups to allow them to experience the diagnostic testing that is a standard part of cardiovascular assessment procedures. This report presents preliminary data from the first 88 students to participate in the program.

We evaluated the ability of a biological marker (nitrogen excretion expressed as protein) to accurately reflect the protein intake of 12 healthy subjects consuming a low protein diet (0.6 g protein/kg standard body wt). In this crossover study, protein intake was confirmed by chemically analyzing a duplicate of the constant diet each subject consumed for 3 d and by calculating protein content of self-selected diets recorded during two additional 3-d periods. Diet analysis matched excretion (difference 0.03 ± 0.04 g protein/kg standard body wt, means ± SEM). Self-selected intake manually calculated by subjects using educational materials matched the prescription [0.60 (0.42, 0.86) g protein/kg standard body wt, median (range)], but underestimated excretion by 0.18 ± 0.02 g protein/kg standard body wt (means ± SEM). Self-selected intake recalculated by the authors using a computerized database was only +0.05 (-0.08, +0.44) g protein/kg standard body wt higher than subjects' calculations, suggesting that discrepancies between databases and/or subject calculation errors only partially accounted for how greatly self-selected intake underestimated excretion. In a secondary analysis of self-selected intake, the three dietitian subjects consumed more energy and excreted less protein than nondietitians (137 ± 4.9 vs. 94 ± 3.5 kJ/standard body wt ; 0.72 ± 0.02 vs. 0.83 ± 0.02 g protein/kg standard body wt), suggesting that adequate energy intake and/or additional training might improve agreement between intake and excretion. Thus, discrepancies between protein excretion and reported intake may reflect factors other than willful noncompliance. J. Nutr. 125 : 2333-2340, 1995.