Binary Regression in Truncated Samples, with Application to Comparing Dietary Instruments in a Large Prospective Study

Midthune, Douglas; Kipnis, Victor; Freedman, Laurence S.; Carroll, Raymond J.

doi:10.1111/j.1541-0420.2007.00833.x

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…A third limitation of this study was that the FFQ may be a flawed measure of food intake(62), and even if it did measure intake, it did not address hunger and cravings, which have been previously associated with sleep loss(7). Future studies should measure actual food intake and these dimensions to obtain a clearer picture of the relationship between sleep and diet.…”

Section: Discussionmentioning

confidence: 99%

Relationships among dietary nutrients and subjective sleep, objective sleep, and napping in women

et al. 2010

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Objective-To describe which dietary nutrient variables are related to subjective and objective habitual sleep and subjective and objective napping.Methods-Participants were 459 postmenopausal women enrolled in the Women's Health Initiative. Objective sleep was estimated using one week of actigraphy. Subjective sleep was prospectively estimated with a daily sleep diary. Dietary nutrients were calculated from food frequency questionnaires.Results-The most significant correlations were with subjective napping, including (from strongest to weakest): total fat,

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Section: Discussionmentioning

confidence: 99%

Relationships among dietary nutrients and subjective sleep, objective sleep, and napping in women

et al. 2010

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“…Iron in red meat may increase oxidative damage and increase the formation of N -nitroso-compounds 37-40. Furthermore, meat is a major source of saturated fat, which has been positively associated with breast41-43 and colorectal cancer 44…”

Section: Discussionmentioning

confidence: 99%

Meat Intake and Mortality

Sinha

Cross²,

Graubard³

et al. 2009

Arch Intern Med

473

156

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Context High intakes of red or processed meat may increase risk of mortality. Objective Determine the relations of red, white and processed meat intakes to risk for total, and cause-specific mortality. Design, Setting, and Participants The NIH-AARP Diet and Health Study cohort of half a million people aged 50-71 years at baseline. Meat intake was estimated from a food frequency questionnaire administered at baseline. Cox proportional hazards regression estimated hazard ratios (HRs) and 95% confidence intervals (CIs) within quintiles of meat intake. The covariates included in the models were: age; education; marital status; family history of cancer (yes/no) (cancer mortality only); race; body mass index; 31-level smoking history; physical activity; energy intake; alcohol intake; vitamin supplement use; fruit consumption; vegetable consumption; and menopausal hormone therapy among women. Main Outcome Measure Total mortality, deaths due to cancer, CVD, accidents, and other causes. Results There were 47,976 male deaths and 23,276 female deaths during 10 years of follow-up. Men and women in the highest versus lowest quintile of red (HR 1.31, 95% CI 1.27-1.35; HR 1.36, 95% CI 1.30-1.43, respectively) and processed meat intake (HR 1.16, 95% CI 1.12-1.20; HR 1.25, 95% 1.20-1.31, respectively) had elevated risks for overall mortality. Regarding cause-specific mortality, men and women had elevated risks for cancer mortality for red (HR 1.22, 95% CI 1.16-1.29; HR 1.20, 95% CI 1.12-1.30, respectively) and processed meats (HR 1.12, 95% CI 1.06-1.19; HR 1.11, 95% CI 1.04-1.19, respectively). Furthermore, CVD risk was elevated for men and women in the highest quintile of red (HR 1.27, 95% CI 1.20-1.35; HR 1.50, 95% CI 1.37-1.65, respectively) and processed meat (HR 1.09, 95% CI 1.03-1.15; HR 1.38, 95% CI 1.26-1.51, respectively). When comparing the highest to the lowest quintile of white meat intake, there was an inverse association for total mortality, and cancer mortality, as well as all other deaths for both men and women. Conclusion Red and processed meat intakes were associated with modest increases in total mortality, cancer mortality and CVD mortality.

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“…Despite this history, relatively few useful markers -other than direct intake markers such as carotenoids in blood, double-labeled water, and urinary nitrogen -have been identified and are in widespread use for dietary status assessment [34,[58][59][60]. Attempts to identify biomarkers of direct dietary intake have been limited by many factors, including both analytical and biological issues [34,[61][62][63][64][65][66][67][68]. In this report, we present evidence that biomarker profiles reflecting two extremes of caloric intake in rodents can be adapted for use in humans.…”

Section: Discussionmentioning

confidence: 99%

Biological Variability Dominates and Influences Analytical Variance in HPLC‐ECD Studies of the Human Plasma Metabolome

et al. 2006

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Background: Biomarker-based assessments of biological samples are widespread in clinical, preclinical, and epidemiological investigations. We previously developed serum metabolomic profiles assessed by HPLC-separations coupled with coulometric array detection that can accurately identify ad libitum fed and caloric-restricted rats. These profiles are being adapted for human epidemiology studies, given the importance of energy balance in human disease.Methods: Human plasma samples were biochemically analyzed using HPLC separations coupled with coulometric electrode array detection. Results:We identified these markers/metabolites in human plasma, and then used them to determine which human samples represent blinded duplicates with 100% accuracy (N = 30 of 30). At least 47 of 61 metabolites tested were sufficiently stable for use even after 48 hours of exposure to shipping conditions. Stability of some metabolites differed between individuals (N = 10 at 0, 24, and 48 hours), suggesting the influence of some biological factors on parameters normally considered as analytical. Conclusion:Overall analytical precision (mean median CV, ~9%) and total between-person variation (median CV, ~50-70%) appear well suited to enable use of metabolomics markers in human clinical trials and epidemiological studies, including studies of the effect of caloric intake and balance on long-term cancer risk. BackgroundAfter tobacco, over-nutrition is, arguably, the major cause of excess morbidity in developed countries, affecting a broad spectrum of diseases including cancer, cardio-/cere-brovascular disease, and type II diabetes. This association may be seen in both broad demographic groups, such as the American Cancer Society study group (900,000 U.S. adults) [1] and in more narrowly defined demographic

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Binary Regression in Truncated Samples, with Application to Comparing Dietary Instruments in a Large Prospective Study

Cited by 5 publications

References 18 publications

Relationships among dietary nutrients and subjective sleep, objective sleep, and napping in women

Relationships among dietary nutrients and subjective sleep, objective sleep, and napping in women

Meat Intake and Mortality

Biological Variability Dominates and Influences Analytical Variance in HPLC‐ECD Studies of the Human Plasma Metabolome

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