We examined whether co-consumption of red and processed meat with key foods items and food constituents recommended for cancer prevention (vegetables and fruit, whole grains, and fiber) mitigates ...cancer incidence. In a prospective cohort of 26,218 adults aged 35-69 years at baseline, dietary intake was collected through 124-item past-year food frequency questionnaire. Incidence of all-cause and 15 cancers previously linked to red and processed meat intake was obtained through data linkage with a cancer registry (average follow-up 13.5 years). Competing risk Cox Proportional Hazard models estimated cancer risk and Accelerated Failure Time models estimated time-to-cancer occurrence for different combinations of intake levels while considering mortality from vital statistics and established confounders. Co-consumption of low vegetables and fruit intake with high processed meat was associated with higher incidence of all-cause and 15 cancers (men: HR = 1.85, 1.91; women: HR = 1.44, 1.49) and accelerated time-to-cancer occurrence (men: 6.5 and 7.1 years and women: 5.6 and 6.3 years, respectively), compared to high vegetables and fruit with low processed meat intake. Less pronounced and less consistent associations were observed for whole grains and fiber and for red meat. The findings provide initial evidence toward refining existing cancer prevention recommendations to optimize the intake and combination of foods in the general adult population.
All self-reported dietary intake data are characterized by measurement error, and validation studies indicate that the estimation of energy intake (EI) is particularly affected.
Using self-reported ...food frequency and physical activity data from Alberta's Tomorrow Project participants (n = 9847 men 16,241 women), we compared the revised-Goldberg and the predicted total energy expenditure methods in their ability to identify misreporters of EI. We also compared dietary patterns derived by k-means clustering under different scenarios where misreporters are included in the cluster analysis (Inclusion); excluded prior to completing the cluster analysis (ExBefore); excluded after completing the cluster analysis (ExAfter); and finally, excluded before the cluster analysis but added to the ExBefore cluster solution using the nearest neighbor method (InclusionNN).
The predicted total energy expenditure method identified a significantly higher proportion of participants as EI misreporters compared to the revised-Goldberg method (50% vs. 47%, p < 0.0001). k-means cluster analysis identified 3 dietary patterns: Healthy, Meats/Pizza and Sweets/Dairy. Among both men and women, participants assigned to dietary patterns changed substantially between ExBefore and ExAfter and also between the Inclusion and InclusionNN scenarios (Hubert and Arabie's adjusted Rand Index, Kappa and Cramer's V statistics < 0.8).
Different scenarios used to account for EI misreporters influenced cluster analysis and hence the composition of the dietary patterns. Continued efforts are needed to explore and validate methods and their ability to identify and mitigate the impact of EI misestimation in nutritional epidemiology.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
Dietary patterns derived by cluster analysis are commonly reported with little information describing how decisions are made at each step of the analytical process. Using food frequency questionnaire ...data obtained in 2001-2007 on Albertan men (n = 6,445) and women (n = 10,299) aged 35-69 years, the authors explored the use of statistical approaches to diminish the subjectivity inherent in cluster analysis. Reproducibility of cluster solutions, defined as agreement between 2 cluster assignments, by 3 clustering methods (Ward's minimum variance, flexible beta, K means) was evaluated. Ratios of between- versus within-cluster variances were examined, and health-related variables across clusters in the final solution were described. K means produced cluster solutions with the highest reproducibility. For men, 4 clusters were chosen on the basis of ratios of between- versus within-cluster variances, but for women, 3 clusters were chosen on the basis of interpretability of cluster labels and descriptive statistics. In comparison with those in other clusters, men and women in the "healthy" clusters by greater proportions reported normal body mass index, smaller waist circumference, and lower energy intakes. The authors' approach appeared helpful when choosing the clustering method for both sexes and the optimal number of clusters for men, but additional analyses are required to understand why it performed differently for women.
Sedentary behavior has been proposed as a risk factor for obesity that is distinct from physical inactivity. This study aimed to examine the association between occupational sedentary behavior and ...obesity, and to determine if this association is independent of leisure-time physical activity (LTPA).
Fully employed participants enrolled between 2001 and 2008 to Alberta's Tomorrow Project, a prospective cohort study in Alberta, Canada, were studied (n = 12,409). Associations between occupational sedentary behavior and waist circumference (WC), waist-to-hip ratio (WHR), and body mass index (BMI) were examined using multiple binary and multinomial logistic regressions.
In men, a positive association was observed between daily occupational sedentary hours and WC, WHR, BMI, and with high risk profiles that incorporated both BMI and WC (P < .01). Controlling for vigorous-intensity LTPA in all models strengthened associations between sedentary behavior and measures of obesity. In contrast, inverse associations were observed for occupational sedentary hours and WHR for women (P < .05).
In fully employed men, occupational sedentary behavior was positively associated with obesity risk that was not attenuated by physical activity. In women, an increase in obesity risk was not observed with sedentary behavior. Gender differences in the health effects of sedentary behavior require further study.
Knowledge of adult activity patterns across domains of physical activity is essential for the planning of population-based strategies that will increase overall energy expenditure and reduce the risk ...of obesity and related chronic diseases. We describe domain-specific hours of activity and energy expended among participants in a prospective cohort in Alberta, Canada.
The Past Year Total Physical Activity Questionnaire was completed by 15,591 Tomorrow Project® participants, between 2001 and 2005 detailing physical activity type, duration, frequency and intensity. Domain-specific hours of activity and activity-related energy expenditure, expressed as a percent of total energy expenditure (TEE) (Mean (SD); Median (IQR)) are reported across inactive (<1.4), low active (1.4 to 1.59), active (1.6 to 1.89) and very active (≥ 1.9) Physical Activity Level (PAL = TEE:REE) categories.
In very active women and amongst all men except those classified as inactive, activity-related energy expenditure comprised primarily occupational activity. Amongst inactive men and women in active, low active and inactive groups, activity-related energy expenditure from household activity was comparable to, or exceeded that for occupational activity. Leisure-time activity-related energy expenditure decreased with decreasing PAL categories; however, even amongst the most active men and women it accounted for less than 10 percent of TEE. When stratified by employment status, leisure-time activity-related energy expenditure was greatest for retired men mean (SD): 10.8 (8.5) percent of TEE, compared with those who were fully employed, employed part-time or not employed. Transportation-related activity was negligible across all categories of PAL and employment status.
For the inactive portion of this population, active non-leisure activities, specifically in the transportation and occupational domains, need to be considered for inclusion in daily routines as a means of increasing population-wide activity levels. Environmental and policy changes to promote active transport and workplace initiatives could increase overall daily energy expenditure through reducing prolonged sitting time.
Current cancer prevention recommendations advise limiting red meat intake to <500 g/week and avoiding consumption of processed meat, but do not differentiate the source of processed meat. We examined ...the associations of processed meat derived from red v. non-red meats with cancer risk in a prospective cohort of 26 218 adults who reported dietary intake using the Canadian Diet History Questionnaire. Incidence of cancer was obtained through data linkage with Alberta Cancer Registry with median follow-up of 13·3 (interquartile range (IQR) 5·1) years. Multivariable Cox proportional hazards regression models were adjusted for covariates and stratified by age and sex. The median consumption (g/week) of red meat, processed meat from red meat and processed meat from non-red meat was 267·9 (IQR 269·9), 53·6 (IQR 83·3) and 11·9 (IQR 31·8), respectively. High intakes (4th Quartile) of processed meat from red meat were associated with increased risk of gastrointestinal cancer adjusted hazard ratio (AHR): 1·68 (95 % CI 1·09, 2·57) and colorectal cancers AHR: 1·90 (95 % CI 1·12, 3·22), respectively, in women. No statistically significant associations were observed for intakes of red meat or processed meat from non-red meat. Results suggest that the carcinogenic effect associated with processed meat intake may be limited to processed meat derived from red meats. The findings provide preliminary evidence towards refining cancer prevention recommendations for red and processed meat intake.
To determine the extent to which differences in sociodemographic, dietary and lifestyle characteristics exist between users of different types of dietary supplements and supplement non-users.
We ...analysed cross-sectional data obtained from self-administered questionnaires completed at baseline by participants in The Tomorrow Project; a prospective cohort study in Alberta, Canada. Participants who used at least one type of dietary supplement at least weekly in the year prior to questionnaire completion were defined as supplement users, while the remainder were classified as non-users. Seven discrete user categories were created: multivitamins (+/- minerals) only, specific nutritional supplements only, herbal/other supplements only, and all possible combinations. Differences in sociodemographic, dietary and lifestyle characteristics between different groups of supplement users and non-users were analysed using Rao-Scott chi2 tests and multinomial logistic regression.
Subjects were 5,067 men and 7,439 women, aged 35-69 years, recruited by random digit dialling throughout Alberta.
Supplement use was extensive in this study population (69.8 %). Users of herbal/other supplements only, and women who used multivitamins only, tended to report dietary and lifestyle characteristics that were not significantly different from non-users. In contrast, those who reported using a combination of multivitamins, specific nutritional and herbal/other supplements were more likely than non-users to report behaviours and characteristics consistent with current health guidelines.
Dichotomizing participants as supplement users or non-users is likely to mask further differences in sociodemographic, dietary and lifestyle characteristics among users of different types of supplements. This may have implications for analysis and interpretation of observational studies.
The objective of this study was to determine the influence of strategies of handling misestimation of energy intake (EI) on observed associations between dietary patterns and cancer risk. Data from ...Alberta's Tomorrow Project participants (
= 9,847 men and 16,241 women) were linked to the Alberta Cancer Registry. The revised-Goldberg method was used to characterize EI misestimation. Four strategies assessed the influence of EI misestimation: Retaining individuals with EI misestimation in the cluster analysis (Inclusion), excluding before (ExBefore) or after cluster analysis (ExAfter), or reassigning into ExBefore clusters using the nearest neighbor method (InclusionNN). Misestimation of EI affected approximately 50% of participants. Cluster analysis identified three patterns: Healthy, Meats/Pizza and Sweets/Dairy. Cox proportional hazard regression models assessed associations between the risk of cancer and dietary patterns. Among men, no significant associations (based on an often-used threshold of
< 0.05) between dietary patterns and cancer risk were observed. In women, significant associations were observed between the Sweets/Dairy and Meats/Pizza patterns and all cancer risk in the ExBefore (HR (95% CI): 1.28 (1.04-1.58)) and InclusionNN (HR (95% CI): 1.14 (1.00-1.30)), respectively. Thus, strategies to address misestimation of EI can influence associations between dietary patterns and disease outcomes. Identifying optimal approaches for addressing EI misestimation, for example, by leveraging biomarker-based studies could improve our ability to characterize diet-disease associations.
Objectives To examine the 12-year trend, in Alberta and Canada, of five modifiable lifestyle risk factors for cancer, and their associations with sociodemographic factors. Methods Six surveys ...collected data from Canadians aged >=12 years. The prevalence, trends, and sociodemographic association of five lifestyle risk factors (smoking, inactivity, excessive drinking, overweight/obesity, and insufficient fruit/vegetable intake) were examined. Results Smoking and inactivity decreased significantly: by 5.4% and 2.7% (Alberta men) and 4.9% and 12.1% (Alberta women); by 7.5% and 8.5% (Canada men) and 7.7% and 11.9% (Canada women). Excessive drinking increased significantly: by 3.6% (men) and 0.9% (women), Alberta; by 2.5% (men) and 0.9% (women), Canada. Overweight/obesity significantly increased by 6.0% (Alberta) and 4.1% (Canada) in women. Being female, single, highly educated, or having higher income decreased the likelihood of exposure to multiple lifestyle risk factors; being middle aged, widowed/separated/divorced, or in poor health condition increased the likelihood. Conclusions The downward trends for smoking and physical inactivity were in a direction that may help reduce cancer burden. The excessive drinking and overweight/obesity trends did not change in desired direction and deserve attention. The clustering of the lifestyle risk factors in specific social groups provides useful information for future intervention planning.
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