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Nutrients 2019, 11(11), 2614; https://doi.org/10.3390/nu11112614

Article
Strategies to Address Misestimation of Energy Intake Based on Self-Report Dietary Consumption in Examining Associations Between Dietary Patterns and Cancer Risk
1
Cancer Research & Analytics, Alberta Health Services, 1820 Richmond Rd SW, Calgary, AB T2T 5C7, Canada
2
School of Public Health and Health Systems, University of Waterloo; 200 University Avenue West, LHN 1713, Waterloo, ON N2L 3G1, Canada
3
Cancer Research & Analytics and the Cancer Strategic Clinical Network, Alberta Health Services, Sun Life Place, 15th floor, 10123 99 Street NW, Edmonton, AB T5J 3C6, Canada
*
Author to whom correspondence should be addressed.
Received: 27 September 2019 / Accepted: 29 October 2019 / Published: 1 November 2019

Abstract

:
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 (n = 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 p < 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.
Keywords:
dietary patterns; energy misestimation; Alberta’s Tomorrow Project; revised Goldberg method; cancer incidence; diet-disease associations

1. Introduction

Cancer continues to exert a large toll on morbidity and mortality globally [1]. Cancer prevention recommendations emphasize the importance of behaviors such as tobacco cessation, physical activity, and healthy eating [2]. With regard to characterizing healthy eating, there is a growing emphasis on moving beyond single dietary components to a more holistic approach that embraces overall eating patterns [3]. The relationship between diet and disease is complex: foods and beverages are consumed in different combinations that allow for countless interactions between nutrients and other dietary components such as phytochemicals, making attributing health effects to a single dietary component difficult [4]. Examining dietary patterns and their associations with cancer risk acknowledges this complexity and could lead to improved estimates of diet-cancer associations [5], as well as clearer recommendations for promoting health and reducing disease risk.
Epidemiological studies investigating associations between eating patterns and disease risk are typically reliant on self-reported intake captured using tools such as food frequency questionnaires (FFQ) [6]. However, all self-report dietary intake data are characterized by measurement error, exhibited by differences between observed and true intake values [7]. Multiple factors contribute to measurement error, including imperfect recall of intake over long time periods (leading, for example, to omission of consumed foods or beverages or inaccurate portion size estimates), social desirability biases, and characteristics of the tools themselves, such as incomplete food lists and portion size options within FFQs [8]. Measurement error can obscure associations that truly exist, leading to inconsistencies in estimated associations between eating patterns and disease risk [9].
Evidence from validation studies indicates that estimation of energy intake (EI) is particularly affected by measurement error [10]. Given that almost all foods and beverages contribute energy, even small errors in reporting of individual foods and beverages can compound to result in energy misestimation [7]. Misestimation of EI occurs when there is a discrepancy between reported EI and energy expenditure (EE), assuming that an individual has a relatively stable body weight [11]. The optimal method for measuring EE is the doubly labeled water (DLW) technique. However, DLW—a recovery marker able to provide unbiased estimates of true EI—can usually be administered only in small samples due to the cost and participant burden [12]. Thus, while DLW is very useful for validation and calibration studies, it is not feasible for large-scale studies. Nonetheless, its use in biomarker-based validation studies has provided evidence that the difference between true and reported EI among adults based on FFQs may be substantial (in the range of 28%) [13] and larger than that observed for other dietary components for which biomarkers are available, including protein and potassium [13,14].
For the purpose of population-based research, alternative methods have been developed to assess the plausibility of reported EI derived from self-reported food and beverage consumption in relation to EE based on basal metabolic rate (BMR) and physical activity level (PAL) [11]. For example, the revised Goldberg method [15] uses an equation to predict total EE [16,17]. Assuming that changes in body weight can be ignored at the group level, observed EI should equal total EE [15]. Cut-offs can then be used to classify participants based on the plausibility of their EI compared to their estimated EE. Tooze et al. [18] reported that, compared to DLW, the revised Goldberg method had a sensitivity of >92% for identifying participants whose EI estimates were affected by misreporting based on FFQ data.
Once energy misestimation is characterized, researchers must determine how to handle it in their analyses. Excluding individuals determined to have implausible EI estimates is not recommended but it has been suggested that analyses are stratified based on energy reporting status [19] or that the EI:EE ratio be included in statistical models to account for energy misestimation [20,21,22]. There has been relatively little attention to the impact of strategies for addressing EI misestimation in analyses seeking to examine associations between dietary patterns and disease risk [3]. Thus the objective of this study was to determine the influence of different strategies for addressing EI misestimation on observed associations between dietary patterns, determined using k-means clustering, and risk of all cancers, a subgroup of cancers with strong evidence of association to diet, and digestive system cancers, among adults.

2. Materials and Methods

2.1. Data Source

Alberta’s Tomorrow Project (ATP) is a prospective cohort of ~55,000 Albertans established in 2000 to facilitate studies into the etiology of cancer and chronic diseases. Recruitment, enrollment, and data collection methods are described in detail elsewhere [23,24,25]. Briefly, Albertans aged 35–69 years at enrollment, with no history of cancer except non-melanoma skin cancer, were recruited by telephone-based random digit dialing which facilitated balanced recruitment across the province. Eligible participants were mailed a consent form and a Health and Lifestyle Questionnaire (HLQ), followed by a past-year FFQ (Canadian Diet History Questionnaire-I; CDHQ-I), and the Past-Year Total Physical Activity Questionnaire (PYTPAQ). Participants had the opportunity to consent to linkage with the Alberta Cancer Registry (ACR) and provided personal health numbers. All questionnaires were sent via postal mail to participants who returned completed questionnaires in pre-paid envelopes.
Inclusion in the current study was limited to participants who consented to administrative data linkage and completed the HLQ, PYTPAQ, and CDHQ-I. Participants were excluded if they resided outside of Alberta (n = 29), had a prior cancer diagnosis, except for non-melanoma skin cancer, assessed via ACR linkage (n = 71), were recruited as the second ATP member in their household (n = 342) (due to potential intra-class correlations among members of the same household), were pregnant (n = 63), or were characterized as underweight (body mass index (BMI) <18.5) based on self-reported heights and weights (n = 18) (due to potential association between underweight and increased risk of disease [26]). Additionally, participants with missing height or weight measures (n = 70) were excluded since these values are required to calculate BMR for the purpose of the revised Goldberg method. The final sample sizes were n = 9,847 men and n = 16,241 women.

2.2. Dietary Intake Assessment

The CDHQ-I is a 257-item past-year FFQ based on the Diet History Questionnaire developed by the U.S. National Cancer Institute [27] and modified to reflect food availability, brand names, nutrition composition and food fortification in Canada [28,29]. Responses to the CDHQ-I were analyzed using Diet Calc software (version 1.4.2; National Cancer Institute, MD, USA) and a nutrient database tailored to the CDHQ-I, resulting in data on intake of energy, 66 nutrients, and 284 single foods. On the basis of similarities in macronutrient composition and culinary use, the 284 single foods were categorized into 55 food groups [30]. The percentage of daily total EI contributed by each of the 55 food groups was calculated by dividing daily EI provided by each food group by daily total EI.

2.3. Physical Activity Assessment

The PYTPAQ collects domain-specific (transportation, occupational, household and recreational) information on frequency, duration, and intensity of physical activity in the past 12 months [31]. The PYTPAQ has been evaluated relative to accelerometer data, showing acceptable reliability (0.64) and validity (0.41) for measurement of past-year physical activity [31].

2.4. Energy Intake Estimation

For EI estimation, participants were classified as EI under-reporters, plausible-reporters, or over-reporters using the revised Goldberg method [15,32]. Briefly, the plausibility of total reported energy intake (rEI) was determined based on the 95% confidence limits of agreement (cut-offs) between the ratio of total rEI to BMR and the ratio of total EE to BMR (PAL). BMR was calculated based on the participant’s age, sex, body weight, and standing height using the Mifflin equation [33]. EE was calculated based on BMR, physical activity (sum of all domains from the PYTPAQ), and body weight [34]. To account for skewness in the distribution of rEI, the rEI to BMR ratio was transformed to a logarithmic scale. Individuals with rEI:BMR to PAL values below the lower Goldberg cut off, above the upper Goldberg cut off, and within Goldberg cut-offs were identified as under-reporters, over-reporters, and plausible-reporters, respectively. The Goldberg cut-offs were: lower = 0.75270, upper = 2.07586 for sedentary, lower = 0.90324, upper = 2.49103 for low active, lower = 1.05378, upper = 2.90620 for active and lower = 1.32475, upper = 3.65351 for very active.

2.5. Cancer Incidence and Sub-Groups

Primary incident cancer cases (All-Cancers, except non-melanoma skin cancer) were obtained by linkage to ACR in July 2017. The International Classification of Diseases for Oncology 3rd edition (ICD-O-3) was used to identify individual cancers. A subgroup of 21 primary cancers were identified based on a matrix from the World Cancer Research Fund/American Institute for Cancer Research Continuous Update Project (WCRF/AICR CUP) reporting on dietary components with convincing or probable evidence for increased or decreased risk of cancer [35] (Dietary-Cancers; Table 1). Another subgroup of 11 primary cancers were chosen based on the World Health Organization (WHO) classification of digestive system cancers [36] (Digestive-Cancers; Table 1).
Follow-up time was calculated from the age at enrollment to the age at cancer diagnosis or at ACR linkage for participants who remained cancer-free during the follow-up period. All age variables were expressed with up to 2 decimal places for precision. To account for competing risk during follow-up due to death in participants who were cancer-free, vital statistics data were obtained from Alberta Health Services Data Integration, Measurement and Reporting (DIMR). In participants who remained cancer-free but died before linkage to ACR, follow-up time was calculated from age at enrollment to age at death.
Linkage with the ACR identified 2276 primary cancer cases (All-Cancers; 982 men and 1294 women) over 33,6524.5 person-years follow-up (median (IQR)= 13.1 (5.0) years). For Dietary-Cancers and Digestive-Cancers, there were 1169 (264 men and 905 women) and 392 cases (191 men and 204 women), respectively.

2.6. Statistical Analysis

k-means cluster analyses [37] were performed to characterize dietary patterns. Individuals whose EI was determined to be affected by misestimation (EI under-reporters and over-reporters, henceforth collectively grouped as EI misreporters because over—reporters comprised only 1% of the study sample—and could not be assessed separately) were accounted for in the cluster analyses using four methods: 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 neighbour method (Inclusion-NN) [38] (Figure 1). The nearest neighbour method (k = 1) is a pattern classification method that measures the Euclidean distance between a test example (i.e., participant) and the data set and assigns the test example to the cluster of the nearest neighbour [38].
All analyses were stratified by sex as self-reported by participants. The percentages of total rEI contributed by each of the 55 food groups were used as input variables. The k-means cluster analyses method started with the researcher selecting k initial clusters (a positive integer representing the number of clusters) and initial cluster seeds (a random positive integer representing the initial number of participants to be assigned to each cluster). Subsequently, each additional participant was automatically assigned to the nearest cluster on the basis of Euclidean distance, forming temporary clusters. Seeds were then replaced by the centroid of each temporary cluster, with the “centroid” referring to the mean observation of a cluster. Each participant was then reassigned to the nearest centroid, updating the location of the centroids. The process was repeated until centroids did not significantly change location. For these analyses, between two and seven cluster solutions were tested to balance feasibility and robustness. To reduce the impact of local optima [39], cluster analyses were run 10 times with different random starting seeds for each cluster solution. In both men and women, the cluster solution that provided the minimum total within-cluster sum of squares distance was selected. For all selected cluster solutions (2 to 7), the between- and within-cluster variances for each food group were calculated. Then, the natural log-transformed ratios of the between- versus within-cluster variances were calculated to compare heterogeneity between and within clusters. The further apart the clusters, the larger the ratio; therefore, the optimal number of clusters is given by the cluster solution that has many food groups with large ratios. Dietary patterns were established by including each food group in the cluster to which it contributed the highest rEI. As such, food groups included in each of the three dietary patterns are mutually exclusive.
Before cluster analysis, each input variable was standardized by subtracting the minimum input value and then dividing by the range. This standardization method, known as the range method, has been reported to give consistently better recovery of cluster structure in different error conditions, separation distances, clustering methods, and coverage levels when compared with other standardization methods, such as the z score [40].
Multivariable Cox proportional hazard regression models were used to assess the associations between observed dietary patterns and cancer risk, including All-Cancers, Dietary-Cancers, and Digestive-Cancers. Adjusted hazard ratios (AHR) were estimated in comparison to the association of a reference pattern with cancer outcomes. Competing risk analysis was performed, with the standard multivariable Cox proportional hazard regression model applied to the cause-specific hazard of interest and competing events treated as censored observations [41]. Regression models were adjusted for age (modelled on a continuous scale), BMI (modelled on a continuous scale), leisure-time physical activity (MET hours/week; modelled on a continuous scale), marital status, educational attainment, smoking status, family history of cancer, and personal history of chronic disease. In models for women only, menopausal status and hormone replacement therapy usage were included.
Means and SD are presented for continuous variables, and counts and percentages for categorical variables. For interpretation purposes, comparisons examined whether associations would be considered significant based on the often used p-value threshold of <0.05, though the consistency of estimates across methods of accounting for EI misestimation is also considered more holistically given that this p-value threshold is arbitrary [42]. All analyses were performed using SAS statistical software (version 9.2-Linux, SAS Institute, INC., Cary, North Carolina, USA).

3. Results

3.1. Participant Baseline Sociodemographic Characteristics

Three dietary patterns, or clusters, were identified for both men and women: Healthy, Meats/Pizza, and Sweets/Dairy. Baseline sociodemographic characteristics stratified by dietary pattern and EI reporting status are presented in Table 2. Higher proportions of men and women assigned to the Meats/Pizza pattern were affected by obesity (BMI ≥ 30), while lower proportions had BMI < 25, compared to participants in both the Healthy and Sweets/Dairy patterns. Men and women in the Healthy pattern had higher reported leisure-time physical activity values compared to their counterparts in the Sweets/Dairy and Meats/Pizza patterns. The highest proportions of current smokers for both men and women were in the Meats/Pizza pattern. In men, the proportion who reported a personal history of chronic disease was highest in the Healthy pattern while in women, the proportion who reported a personal history of chronic disease was very similar across dietary patterns. For both men and women and across all dietary patterns, higher proportions of misreporters were affected by obesity, while lower proportions had BMI < 25, compared to plausible reporters. The proportions of EI misreporters were very similar between men (47.9%) and women (46.8%) and across all cancer cases and non-cases in men (49.0% vs. 47.8%) and in women (48.6% vs. 46.7%).

3.2. Dietary Patterns in Relation to Methods for Accounting for Misestimation of Energy Intake

The greatest contributors to total rEI in each dietary pattern across different methods of accounting for EI misreporters is summarized in Table 3. With few exceptions, the majority of the food groups in all three dietary patterns were common across the different methods in both men and women. However, Other Breads was not included in the Meats/Pizza pattern within the ExBefore and InclusionNN methods among men and ExBefore method in women. The percentage contribution of food groups in all three dietary patterns were very similar across different methods of accounting for EI misreporting. For the Inclusion method, fruits, high-fiber breakfast cereal, fruit juices, rice and nuts contributed the greatest proportions of energy for men within the Healthy pattern. For women in the Healthy pattern under the Inclusion method, fruit, regular-fat dairy products, lean fat poultry, nuts and rice were the largest contributors to total EI. Men assigned to the Meats/Pizza pattern with Inclusion had the highest total rEI contribution from meats, pasta/pizza, beer, regular soda and chips; while women in the Meats/Pizza pattern had similar intakes except for beer. Men and women assigned to the Sweets/Dairy pattern with Inclusion had high total rEI of low-fat dairy products and wholemeal (whole-grain) bread, and several sweets such as cakes, jams and ice cream. Mean intakes of plausible reporters in the ExBefore and ExAfter methods were similar in both men and women. Women in the Sweets/Dairy pattern ExBefore had only 3 food groups with the highest percentage contribution of total rEI compared to 7 food groups in the ExAfter. The largest contributors of rEI were similar between ExBefore and InclusionNN in both men and women. The mean intake of some food groups varied across different methods for accounting for potential misreporting of EI, this changed the ranking of food groups but the overall dietary patterns remained the same.

3.3. Association between Dietary Patterns and Cancer Risk

For All-Cancers, no significant associations were observed between dietary patterns and cancer risk in men, regardless of the method used to account for misestimation of EI. However, the point estimates for the Sweets/Dairy and Meats/Pizza patterns and All-Cancer were higher in the ExAfter and Inclusion methods, respectively, compared to the other methods of accounting for EI misreporting. In women, a significant increased cancer risk was associated with the Meats/Pizza pattern in the InclusionNN (AHR (95%CI): 1.14 (1.00–1.30)) method and in the Sweets/Dairy pattern for the ExBefore (AHR (95%CI): 1.28 (1.04–1.58)) method (Table 4). Among women, the point estimate for the Meats/Pizza pattern under the Inclusion method was very similar to the estimate for InclusionNN, but the former would not be considered a statistically significant association if applying a p-value threshold of <0.05.
For Dietary-Cancers, the Meats/Pizza pattern was associated with increased cancer risk among men under both the Inclusion (AHR (95%CI): 1.42 (1.00–2.02)) and ExAfter (AHR (95%CI): 1.92 (1.12–3.29)) methods (Table 5). Also among men, the Sweets/Dairy pattern was associated with increased cancer risk under the InclusionNN (AHR (95%CI): 1.45 (1.07–1.97)) and ExBefore (AHR (95%CI): 1.74 (1.12–2.72)) methods (Table 5). In women, no significant associations were observed for this subset of cancers (Table 5).
For Digestive-Cancers, no significant associations were observed with dietary patterns among men. Among women, a significantly increased risk of digestive cancers was observed for the Meats/Pizza pattern under the InclusionNN method (AHR (95%CI): 1.43 (1.02–2.01)) and the Sweets/Dairy pattern under the ExBefore method (AHR (95%CI): 1.73 (1.03–2.89)) (Table 6).
Competing risk analysis to account for deaths before ACR linkage date in participants who were cancer-free during follow-up did not significantly change the observed hazard ratios (Supplementary Materials: Tables S1–S3).

4. Discussion

The findings of this study suggest that misestimation of EI, ascertained using a prediction equation and self-reported physical activity and body weight and height, was prevalent among adults whose dietary intake was characterized using a FFQ within the context of a cohort study. Further, differing methods to account for this misestimation appear to impact observed associations between dietary patterns and cancer risk. Among men, there were no significant associations between dietary patterns and risk of all cancers regardless of the method of handling EI misestimation. However, the point estimates for All-cancers risk associated with the Sweets/Dairy and Meats/Pizza patterns were higher in ExAfter and Inclusion methods, respectively, compared to the other methods of accounting for EI misreporting. Among women, the Meats/Pizza pattern was associated with a 14% increased risk of all cancers in the method that included all participants regardless of EI misestimation (similar to that observed in the InclusionNN method). The Sweets/Dairy pattern was associated with a 28% increased risk of all cancers in the method that excluded women whose EI estimates were deemed to be affected by misestimation following the cluster analyses. Similarly, associations between dietary patterns and risk differed based on how EI misestimation was addressed for the subgroup of primary cancers for which there is evidence of the influence of dietary risk factors (men and women) and for digestive cancers (women). However, given that there is no marker of true dietary patterns, it is not possible to ascertain which method for accounting for EI misestimation results in observed associations that are the closest to truth.
Other studies have similarly suggested that analytical approaches used to account for potential EI misestimation can impact observed associations between dietary intake and disease outcomes among adults. A cross-sectional study of Norwegian women aged 50–69 years [44], which used an FFQ, found that self-reported CVD was significantly positively associated with “Western” dietary pattern scores among plausible reporters but not among all reporters. A prospective cohort study of Swedish adults [45] which used an interview-based diet history method, reported an increased risk of breast cancer with high alcohol intakes, with stronger risk estimates among plausible reporters compared with all reporters. A prospective cohort study of US adults [46] investigated the effect on the association between risk of breast, colon, endometrial and kidney cancer with reported EI calibrated to DLW data. Calibrated energy consumption was positively associated with risk of breast, colon, endometrial and kidney cancer, while uncalibrated energy was not. However, these studies reported lower proportions of misreporters (e.g., Norwegian 18%, Swedish 18% in men and 12% in women) compared to the current study (50% in both men and women). This could be due to the different equations used for calculating BMR. In the current study, BMR was calculated using the Mifflin equation while the Schofield and the Oxford equations were used in the Norwegian and Swedish studies respectively. In a study conducted with Korean adults [47], energy under-estimation was estimated to affect 14% of men and 23% of women, lower than the proportions observed in this study. This may be attributed to the use of a 24-hour recall in the Korean study as opposed to an FFQ in the current study.
Despite slight differences in methodology and design, the findings of this study are in line with previously published results indicating that estimated diet-disease associations can be influenced by measurement error [44]. Associations between dietary patterns and cancer risk varied depending on the methods used to account for misestimation of EI. Importantly, comparisons of findings based on different methods within and between studies are affected by considerations of what constitutes significant differences. For example, for women, the hazards ratios for cancer associated with the Meats/Pizza pattern were almost identical under two methods of accounting for energy misestimation, but under the conventional practice of applying a threshold of p < 0.05, only one of the two would be interpreted as significant. Thus, the findings highlight the need to consider not only how EI misestimation is accounted for across studies, but also to improve the reporting and interpretation of findings within nutritional epidemiology [42].
Prior analyses have highlighted the importance of considering measurement error and identified the need for caution in terms of the interpretation of diet-disease associations that have not been, at least partially, corrected for this error [45,46,48]. For example, regression calibration approaches are well developed and can make use of reference data, such as those collected using biomarkers or a less-biased tool such as 24-hour recalls in a subsample, to somewhat mitigate the impact of measurement error on diet-disease associations in large cohort studies in which an FFQ is the main tool [49]. Given that data from recalls have been shown to be affected by systematic measurement error to a lesser extent than data from FFQ [13], cohort studies administering recalls as the main assessment tool may be helpful for advancing our understanding of dietary intake and health. This is particularly true in the context of patterns since recalls provide comprehensive data including details on eating occasions and foods and beverages consumed in combination [7]. The use of recalls in cohort studies has become increasingly feasible with technological advances, such as online and mobile device-based tools [50]. Using such tools, cohort studies of the future can potentially take advantage of multiple modes of dietary assessment to dampen measurement error and its implications for observed diet-disease associations [51].
However, many current sources of data on diet and disease outcomes, with sufficient time elapsed from baseline data collection for cases of cancer and other conditions to accrue, may not provide opportunities for regression calibration. In this study, no reference data are available and we opted to use the revised Goldberg method to attempt to account for measurement error exhibited as EI misestimation. However, this method has challenges. The use of EI/BMR for evaluating EI depends on knowledge of energy requirements or EE [15]. For the purposes of the calculations, self-reported physical activity and anthropometric data were used—these data also undoubtedly contain measurement error, potentially resulting in misclassification of individuals based on their energy reporting status. Furthermore, the Goldberg method pertains to misestimation of energy only. It is known that misreporting is differential among different types of foods, beverages, and dietary components. For example, based on recovery biomarker-based studies, protein and potassium are less affected by misestimation than is energy [13]. This may be because errors in EI accumulate over many foods and beverages [7] but it may also be because energy-dense items are less accurately reported than other foods due to social desirability biases [52]. Studies based on observation and weighing have shown that different types of foods and beverages may be reported with differing levels of accuracy [53]. This is particularly relevant to studies of dietary patterns given that interest is inherently in combinations of foods and beverages consumed and the implications for health and disease risk.
Several statistical techniques are available for identifying dietary patterns and the choice of method depends largely on the research question at hand [54]. For example, cluster analysis may be useful for identifying mutually exclusive groups which differ according to their reported diet [54,55,56] and, as such, may help identify those at greater risk for developing specific cancers [57] or other chronic diseases. Alternatively, cluster analysis may group together those who tend to misreport their food and beverage consumption in similar ways, for example, due to social desirability biases. In this study, 55 food groups were created from the original 284 items in the FFQ while other studies have used smaller [54,55,56,58,59,60,61] or larger [57,62,63,64] numbers of food groups, potentially influencing the findings. The k-means method has limitations, including the need to pre-specify the number of clusters to retain, sensitivity to initial cluster seeds [65], and challenges posed by the existence of clusters of different size or shapes or those that may be nonspherical or occur across several subspaces [66]. Other studies have used principal component analysis [44,67,68,69], which aggregates food groups in linear combinations called principal components according to the extent to which they are correlated with each other. Studies using both k-means clustering and principal components analysis have observed similar patterns to those observed here. For example, Maree et al. [70] reported three dietary patterns in Australian men and women using k-means cluster analysis, with two of the clusters similar to the Healthy and Meats/Pizza patterns observed in the current study. Also using k-means cluster analysis, Freitas-Vilela et al. [71] also reported three dietary patterns, labelled Fruits and Vegetables, Meat and Potatoes and White Bread and Coffee, among pregnant women. Despite differences in naming, the three patterns are similar to those observed here. Further, using principal components analysis, Markussen et al. [44] identified similar patterns, named Prudent, Western and Continental, among both plausible and all reporters in a sample of women aged 50–60 years. Repeatability of dietary pattern analysis is often critiqued, since each cohort study can produce different patterns due to large variation between studies and their participants. However, the use of principal component analysis or cluster analysis appears to result in somewhat similar named dietary patterns.
This study made use of an existing cohort with a large sample size and careful validation of data and few missing values [24]. One exception was household income, which was characterized by a high degree of missingness and was not included in the Cox regression analysis despite evidence that socioeconomic status is associated with several types of cancers [72,73]. Cancer outcomes were ascertained via linkage with an accredited cancer registry (ACR), providing a more accurate diagnosis of the disease compared to self-report [44]. However, for privacy reasons, ATP does not release exact date of events and age at cancer diagnosis, given up to two decimal places, was used as an approximation for date of event. Thus, for the Cox regression analysis, precise follow-up times could not be calculated and therefore, hazard ratios might not have been precisely estimated. Due to the arbitrary nature of cluster analysis used in this study, the assignment of dietary patterns for an individual participant could have been different across methods of accounting for EI misreporting. This could also explain why differing methods of accounting for misestimation appear to impact observed associations between dietary patterns and cancer risk. For the k-means cluster analysis, total rEI was chosen as the input variable because EI is the foundation of the diet. All other nutrients must be provided within the quantity of food consumed to fulfill energy requirements. Therefore, if total EI is misreported, other dietary components may also be mis-estimated, albeit to differing degrees [74]. Other studies have used different measures such as the daily intake frequencies [70] and the average weight of food consumed per day [75]. These different measures may impact the results of the cluster analysis and hence the estimated diet-disease association. Finally, in addition to measurement error affecting the FFQ data, other variables, including physical activity, heights, and weights, are also subject to reporting error, potentially impacting the characterization of energy misestimation and the observed associations [76].

5. Conclusions

The results of this study suggest that observed associations between dietary patterns and health outcomes vary in relation to strategies for addressing EI misestimation. It is possible cohort studies that include the administration of biomarkers such as DLW in a subset of participants can shed light on misreporting of different dietary components and optimal strategies for accounting for it. Advances are also needed to enable improved characterization of dietary patterns, which inherently involve intake of many different foods and beverages that may be reported with different levels of accuracy. In the meantime, researchers should carefully consider how misestimation and other sources and symptoms of measurement error are characterized and accounted for and carefully report these details to enable appropriate interpretation of their findings.

Supplementary Materials

The following are available online at https://www.mdpi.com/2072-6643/11/11/2614/s1. Tables S1–S3: Competing risk hazard ratios for All-Cancers, Dietary-Cancers, and Digestive-Cancers.

Author Contributions

Formulating the research question: N.M.S., A.A.R., G.L.S., and P.J.R.; designing the study: N.M.S., A.A.R., G.L.S., and P.J.R.; analyzing the data: A.K.A. and G.L.S.; writing and/or revising the manuscript: N.M.S., A.K.A., A.A.R., G.L.S., P.J.R., and S.I.K.

Funding

Alberta’s Tomorrow Project is funded by the Alberta Cancer Foundation, the Canadian Partnership Against Cancer, the Alberta Cancer Prevention Legacy Fund (administered by the Government of Alberta), the University of Toronto and substantial in-kind funding from Alberta Health Services. Although funding has been provided by several organizations, the analyses and interpretation of the data presented in this paper are those of the authors alone.

Acknowledgments

Alberta’s Tomorrow Project was made possible because of the commitment of its research participants and its staff. Cancer registry data was obtained through linkage with Surveillance & Reporting, C-MORE Cancer Control Alberta.

Conflicts of Interest

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript and in the decision to publish the results.

Ethics of Human Participation

This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were approved by the former Alberta Cancer Board’s Research Ethics Committee and the Health Research Ethics Board of Alberta Cancer Committee. Written informed consent was obtained from all participants.

References

  1. Murray, C.J.L.; Lopez, A.D. Measuring the global burden of disease. N. Engl. J. Med. 2013, 369, 448–457. [Google Scholar] [CrossRef] [PubMed]
  2. World Cancer Research Fund/American Institute for Cancer Research. Diet, Nutrition, Physical Activity and Cancer: A Global Perspective. Continuous Update Project Expert Report; World Cancer Research Fund: London, UK; American Institute for Cancer Research: Washington, DC, USA, 2018. [Google Scholar]
  3. Reedy, J.; Subar, A.F.; George, S.M.; Krebs-Smith, S.M. Extending Methods in Dietary Patterns Research. Nutrients 2018, 10, 571. [Google Scholar] [CrossRef] [PubMed]
  4. Schulze, M.B.; Hoffmann, K. Methodological approaches to study dietary patterns in relation to risk of coronary heart disease and stroke. Br. J. Nutr. 2006, 95, 860–869. [Google Scholar] [CrossRef] [PubMed]
  5. Grosso, G.; Bella, F.; Godos, J.; Sciacca, S.; Del Rio, D.; Ray, S.; Galvano, F.; Giovannucci, E.L. Possible role of diet in cancer: Systematic review and multiple meta-analyses of dietary patterns, lifestyle factors, and cancer risk. Nutr. Rev. 2017, 75, 405–419. [Google Scholar] [CrossRef]
  6. Illner, A.-K.; Freisling, H.; Boeing, H.; Huybrechts, I.; Crispim, S.P.; Slimani, N. Review and evaluation of innovative technologies for measuring diet in nutritional epidemiology. Int. J. Epidemiol. 2012, 41, 1187–1203. [Google Scholar] [CrossRef]
  7. Subar, A.F.; Freedman, L.S.; Tooze, J.A.; Kirkpatrick, S.I.; Boushey, C.; Neuhouser, M.L.; Thompson, F.E.; Potischman, N.; Guenther, P.M.; Tarasuk, V.; et al. Addressing Current Criticism Regarding the Value of Self-Report Dietary Data. J. Nutr. 2015, 145, 2639–2645. [Google Scholar] [CrossRef]
  8. Freedman, L.S.; Schatzkin, A.; Midthune, D.; Kipnis, V. Dealing with dietary measurement error in nutritional cohort studies. J. Natl. Cancer Inst. 2011, 103, 1086–1092. [Google Scholar] [CrossRef]
  9. Mayne, S.T.; Playdon, M.C.; Rock, C.L. Diet, nutrition, and cancer: Past, present and future. Nat. Rev. Clin. Oncol. 2016, 13, 504–515. [Google Scholar] [CrossRef]
  10. Watanabe, D.; Nanri, H.; Sagayama, H.; Yoshida, T.; Itoi, A.; Yamaguchi, M.; Yokoyama, K.; Watanabe, Y.; Goto, C.; Ebine, N.; et al. Estimation of Energy Intake by a Food Frequency Questionnaire: Calibration and Validation with the Doubly Labeled Water Method in Japanese Older People. Nutrients 2019, 11, 1546. [Google Scholar] [CrossRef]
  11. Banna, J.C.; McCrory, M.A.; Fialkowski, M.K.; Boushey, C. Examining Plausibility of Self-Reported Energy Intake Data: Considerations for Method Selection. Front. Nutr. 2017, 4, 45. [Google Scholar] [CrossRef]
  12. Park, J.; Kazuko, I.-T.; Kim, E.; Kim, J.; Yoon, J. Estimating free-living human energy expenditure: Practical aspects of the doubly labeled water method and its applications. Nutr. Res. Pract. 2014, 8, 241–248. [Google Scholar] [CrossRef] [PubMed]
  13. Freedman, L.S.; Commins, J.M.; Moler, J.E.; Arab, L.; Baer, D.J.; Kipnis, V.; Midthune, D.; Moshfegh, A.J.; Neuhouser, M.L.; Prentice, R.L.; et al. Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for energy and protein intake. Am. J. Epidemiol. 2014, 180, 172–188. [Google Scholar] [CrossRef] [PubMed]
  14. Freedman, L.S.; Commins, J.M.; Moler, J.E.; Willett, W.; Tinker, L.F.; Subar, A.F.; Spiegelman, D.; Rhodes, D.; Potischman, N.; Neuhouser, M.L.; et al. Pooled results from 5 validation studies of dietary self-report instruments using recovery biomarkers for potassium and sodium intake. Am. J. Epidemiol. 2015, 181, 473–487. [Google Scholar] [CrossRef] [PubMed]
  15. Black, A. Critical evaluation of energy intake using the Goldberg cut-off for energy intake:basal metabolic rate. A practical guide to its calculation, use and limitations. Int. J. Obes. 2000, 24, 1119–1130. [Google Scholar] [CrossRef] [PubMed]
  16. McCrory, M.A.; Hajduk, C.L.; Roberts, S.B. Procedures for screening out inaccurate reports of dietary energy intake. Public Health Nutr. 2002, 5, 873–882. [Google Scholar] [CrossRef] [PubMed]
  17. Huang, T.T.-K.; Roberts, S.B.; Howarth, N.C.; Mccrory, M.A. Diet and Physical Activity Effect of Screening Out Implausible Energy Intake Reports on Relationships between Diet and BMI. Obes. Res. 2005, 13, 1205–1217. [Google Scholar] [CrossRef] [PubMed]
  18. Tooze, J.; Krebs-Smith, S.; Troiano, R.; Subar, A. The accuracy of the Goldberg method for classifying misreporters of energy intake on a food frequency questionnaire and 24-h recalls: Comparison with doubly labeled water. Eur. J. Clin. Nutr. 2012, 66, 569–576. [Google Scholar] [CrossRef]
  19. Tooze, J.A.; Freedman, L.S.; Carroll, R.J.; Midthune, D.; Kipnis, V. The impact of stratification by implausible energy reporting status on estimates of diet-health relationships. Biom. J. 2016, 58, 1538–1551. [Google Scholar] [CrossRef]
  20. McNaughton, S.A.; Mishra, G.D.; Brunner, E.J. Food patterns associated with blood lipids are predictive of coronary heart disease: The Whitehall II study. Br. J. Nutr. 2009, 102, 619–624. [Google Scholar] [CrossRef]
  21. McNaughton, S.A.; Mishra, G.D.; Brunner, E.J. Dietary patterns, insulin resistance, and incidence of type 2 diabetes in the Whitehall II Study. Diabetes Care 2008, 31, 1343–1348. [Google Scholar] [CrossRef]
  22. Brunner, E.J.; Mosdøl, A.; Witte, D.R.; Martikainen, P.; Stafford, M.; Shipley, M.J.; Marmot, M.G. Dietary patterns and 15-y risks of major coronary events, diabetes, and mortality. Am. J. Clin. Nutr. 2008, 87, 1414–1421. [Google Scholar] [CrossRef] [PubMed]
  23. Bryant, H.; Robson, P.J.; Ullman, R.; Friedenreich, C.; Dawe, U. Population-based cohort development in Alberta, Canada: A feasibility study. Chronic Dis. Can. 2006, 27, 51–59. [Google Scholar] [PubMed]
  24. Robson, P.J.; Solbak, N.M.; Haig, T.R.; Whelan, H.K.; Vena, J.E.; Akawung, A.K.; Rosner, W.K.; Brenner, D.R.; Cook, L.S.; Csizmadi, I.; et al. Design, methods and demographics from phase I of Alberta’s Tomorrow Project cohort: A prospective cohort profile. CMAJ Open 2016, 4, E515–E527. [Google Scholar] [CrossRef] [PubMed]
  25. Ye, M.; Robson, P.J.; Eurich, D.T.; Vena, J.E.; Xu, J.-Y.; Johnson, J.A. Cohort Profile: Alberta’s Tomorrow Project. Int. J. Epidemiol. 2017, 46, 1097–1098l. [Google Scholar] [CrossRef] [PubMed]
  26. Dobner, J.; Kaser, S. Body mass index and the risk of infection—From underweight to obesity. Clin. Microbiol. Infect. 2018, 24, 24–28. [Google Scholar] [CrossRef]
  27. National Institutes of Health Diet History Questionnaire; NIH: Bethesda, MD, USA, 2007.
  28. Csizmadi, I.; Kahle, L.; Ullman, R.; Dawe, U.; Zimmerman, T.P.; Friedenreich, C.M.; Bryant, H.; Subar, A.F. Adaptation and evaluation of the National Cancer Institute’s Diet History Questionnaire and nutrient database for Canadian populations. Public Health Nutr. 2007, 10, 88–96. [Google Scholar] [CrossRef]
  29. Csizmadi, I.; Boucher, B.A.; Lo Siou, G.; Massarelli, I.; Rondeau, I.; Garriguet, D.; Koushik, A.; Elenko, J.; Subar, A.F. Using national dietary intake data to evaluate and adapt the US Diet History Questionnaire: The stepwise tailoring of an FFQ for Canadian use. Public Health Nutr. 2016, 19, 3247–3255. [Google Scholar] [CrossRef]
  30. Lo Siou, G.; Yasui, Y.; Csizmadi, I.; McGregor, S.E.; Robson, P.J. Exploring statistical approaches to diminish subjectivity of cluster analysis to derive dietary patterns: The Tomorrow Project. Am. J. Epidemiol. 2011, 173, 956–967. [Google Scholar] [CrossRef]
  31. Friedenreich, C.M.; Courneya, K.S.; Neilson, H.K.; Matthews, C.E.; Willis, G.; Irwin, M.; Troiano, R.; Ballard-Barbash, R. Reliability and validity of the Past Year Total Physical Activity Questionnaire. Am. J. Epidemiol. 2006, 163, 959–970. [Google Scholar] [CrossRef]
  32. Goldberg, G.R.; Black, A.E.; Jebb, S.A.; Cole, T.J.; Murgatroyd, P.R.; Coward, W.A.; Prentice, A.M. Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording. Eur. J. Clin. Nutr. 1991, 45, 569–581. [Google Scholar]
  33. Mifflin, M.D.; St Jeor, S.T.; Hill, L.A.; Scott, B.J.; Daugherty, S.A.; Koh, Y.O. A new predictive equation for resting energy expenditure in healthy individuals. Am. J. Clin. Nutr. 1990, 51, 241–247. [Google Scholar] [CrossRef] [PubMed]
  34. Csizmadi, I.; Lo Siou, G.; Friedenreich, C.M.; Owen, N.; Robson, P.J. Hours spent and energy expended in physical activity domains: Results from the Tomorrow Project cohort in Alberta, Canada. Int. J. Behav. Nutr. Phys. Act. 2011, 8, 110. [Google Scholar] [CrossRef] [PubMed]
  35. World Cancer Research Fund/American Institute for Cancer Research. Continuous Update Project: Diet, Nutrition, Physical Activity and the Prevention of Cancer. Summary of Strong Evidence; World Cancer Research Fund: London, UK; American Institute for Cancer Research: Washington, DC, USA, 2018. [Google Scholar]
  36. Hamilton, S.R.; Aaltonen, L. World Health Organization Classification of Tumours. Pathology and Genetics of Tumours of the Digestive System; IARC Press: Lyon, France, 2000. [Google Scholar]
  37. Forgy, E. Cluster analysis of Multivariate Data: Efficiency Versus Interpretability of Classifications. Biometrics 1965, 21, 768–769. [Google Scholar]
  38. Hu, L.-Y.; Huang, M.-W.; Ke, S.-W.; Tsai, C.-F. The distance function effect on k-nearest neighbor classification for medical datasets. Springerplus 2016, 5, 1304. [Google Scholar] [CrossRef] [PubMed]
  39. Sauvageot, N.; Schritz, A.; Leite, S.; Alkerwi, A.; Stranges, S.; Zannad, F.; Streel, S.; Hoge, A.; Donneau, A.-F.; Albert, A.; et al. Stability-based validation of dietary patterns obtained by cluster analysis. Nutr. J. 2017, 16, 4. [Google Scholar] [CrossRef] [PubMed]
  40. Cooper, M.C.; Milligan, G.W. A study of standardization of variables in cluster analysis. J. Classif. 1988, 5, 181–204. [Google Scholar]
  41. Prentice, R.L.; Kalbfleisch, J.D.; Peterson, A.V.; Flournoy, N.; Farewell, V.T.; Breslow, N.E. The analysis of failure times in the presence of competing risks. Biometrics 1978, 34, 541–554. [Google Scholar] [CrossRef]
  42. Greenland, S.; Senn, S.J.; Rothman, K.J.; Carlin, J.B.; Poole, C.; Goodman, S.N.; Altman, D.G. Statistical tests, P values, confidence intervals, and power: A guide to misinterpretations. Eur. J. Epidemiol. 2016, 31, 337–350. [Google Scholar] [CrossRef]
  43. Health Canada. Canadian guidelines for body weight classification in adults. Available online: http://www.hc-sc.gc.ca/fn-an/nutrition/weights-poids/guide-ld-adult/index-eng.php (accessed on 28 November 2016).
  44. Markussen, M.S.; Veierød, M.B.; Ursin, G.; Andersen, L.F. The effect of under-reporting of energy intake on dietary patterns and on the associations between dietary patterns and self-reported chronic disease in women aged 50-69 years. Br. J. Nutr. 2016, 116, 547–558. [Google Scholar] [CrossRef]
  45. Mattisson, I.; Wirfält, E.; Aronsson, C.A.; Wallström, P.; Sonestedt, E.; Gullberg, B.; Berglund, G. Misreporting of energy: Prevalence, characteristics of misreporters and influence on observed risk estimates in the Malmö Diet and Cancer cohort. Br. J. Nutr. 2005, 94, 832–842. [Google Scholar] [CrossRef]
  46. Prentice, R.L.; Shaw, P.A.; Bingham, S.A.; Beresford, S.A.A.; Caan, B.; Neuhouser, M.L.; Patterson, R.E.; Stefanick, M.L.; Satterfield, S.; Thomson, C.A.; et al. Biomarker-calibrated energy and protein consumption and increased cancer risk among postmenopausal women. Am. J. Epidemiol. 2009, 169, 977–989. [Google Scholar] [CrossRef] [PubMed]
  47. Kye, S.; Kwon, S.O.; Lee, S.Y.; Lee, J.; Kim, B.H.; Suh, H.J.; Moon, H.K. Under-reporting of energy intake from 24-hour dietary recalls in the korean national health and nutrition examination survey. Osong Public Heal. Res. Perspect. 2014, 5, 85–91. [Google Scholar] [CrossRef] [PubMed]
  48. Heerstrass, D.W.; Ocké, M.C.; Bueno-de-Mesquita, H.B.; Peeters, P.H.; Seidell, J.C. Underreporting of energy, protein and potassium intake in relation to body mass index. Int. J. Epidemiol. 1998, 27, 186–193. [Google Scholar] [CrossRef] [PubMed]
  49. Freedman, L.S.; Midthune, D.; Carroll, R.J.; Tasevska, N.; Schatzkin, A.; Mares, J.; Tinker, L.; Potischman, N.; Kipnis, V. Using regression calibration equations that combine self-reported intake and biomarker measures to obtain unbiased estimates and more powerful tests of dietary associations. Am. J. Epidemiol. 2011, 174, 1238–1245. [Google Scholar] [CrossRef]
  50. Shim, J.-S.; Oh, K.; Kim, H.C. Dietary assessment methods in epidemiologic studies. Epidemiol. Health 2014, 36, e2014009. [Google Scholar] [CrossRef]
  51. Carroll, R.J.; Midthune, D.; Subar, A.F.; Shumakovich, M.; Freedman, L.S.; Thompson, F.E.; Kipnis, V. Taking advantage of the strengths of 2 different dietary assessment instruments to improve intake estimates for nutritional epidemiology. Am. J. Epidemiol. 2012, 175, 340–347. [Google Scholar] [CrossRef]
  52. Hebert, J.R.; Hurley, T.G.; Peterson, K.E.; Resnicow, K.; Thompson, F.E.; Yaroch, A.L.; Ehlers, M.; Midthune, D.; Williams, G.C.; Greene, G.W.; et al. Social Desirability Trait Influences on Self-Reported Dietary Measures among Diverse Participants in a Multicenter Multiple Risk Factor Trial. J. Nutr. 2008, 138, 226S–234S. [Google Scholar] [CrossRef]
  53. Cook, A.; Pryer, J.; Shetty, P. The problem of accuracy in dietary surveys. Analysis of the over 65 UK National Diet and Nutrition Survey. J. Epidemiol. Community Health 2000, 54, 611–616. [Google Scholar] [CrossRef]
  54. Hearty, A.P.; Gibney, M.J. Comparison of cluster and principal component analysis techniques to derive dietary patterns in Irish adults. Br. J. Nutr. 2009, 101, 598–608. [Google Scholar] [CrossRef]
  55. Bamia, C.; Orfanos, P.; Ferrari, P.; Overvad, K.; Hundborg, H.H.; Tjønneland, A.; Olsen, A.; Kesse, E.; Boutron-Ruault, M.-C.; Clavel-Chapelon, F.; et al. Dietary patterns among older Europeans: The EPIC-Elderly study. Br. J. Nutr. 2005, 94, 100–113. [Google Scholar] [CrossRef]
  56. Wirfält, E.; Mattisson, I.; Gullberg, B.; Berglund, G. Food patterns defined by cluster analysis and their utility as dietary exposure variables: A report from the Malmö Diet and Cancer Study. Public Health Nutr. 2000, 3, 159–173. [Google Scholar] [CrossRef] [PubMed]
  57. Reedy, J.; Wirfält, E.; Flood, A.; Mitrou, P.N.; Krebs-Smith, S.M.; Kipnis, V.; Midthune, D.; Leitzmann, M.; Hollenbeck, A.; Schatzkin, A.; et al. Comparing 3 dietary pattern methods—Cluster analysis, factor analysis, and index analysis—With colorectal cancer risk: The NIH-AARP Diet and Health Study. Am. J. Epidemiol. 2010, 171, 479–487. [Google Scholar] [CrossRef] [PubMed]
  58. Newby, P.K.; Muller, D.; Tucker, K.L. Associations of empirically derived eating patterns with plasma lipid biomarkers: A comparison of factor and cluster analysis methods. Am. J. Clin. Nutr. 2004, 80, 759–767. [Google Scholar] [CrossRef] [PubMed]
  59. Costacou, T.; Bamia, C.; Ferrari, P.; Riboli, E.; Trichopoulos, D.; Trichopoulou, A. Tracing the Mediterranean diet through principal components and cluster analyses in the Greek population. Eur. J. Clin. Nutr. 2003, 57, 1378–1385. [Google Scholar] [CrossRef] [PubMed]
  60. Newby, P.K.; Muller, D.; Hallfrisch, J.; Qiao, N.; Andres, R.; Tucker, K.L. Dietary patterns and changes in body mass index and waist circumference in adults. Am. J. Clin. Nutr. 2003, 77, 1417–1425. [Google Scholar] [CrossRef] [PubMed]
  61. Schulze, M.B.; Hoffmann, K.; Kroke, A.; Boeing, H. Dietary patterns and their association with food and nutrient intake in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study. Br. J. Nutr. 2001, 85, 363–373. [Google Scholar] [CrossRef]
  62. Wirfält, E.; Midthune, D.; Reedy, J.; Mitrou, P.; Flood, A.; Subar, A.F.; Leitzmann, M.; Mouw, T.; Hollenbeck, A.R.; Schatzkin, A.; et al. Associations between food patterns defined by cluster analysis and colorectal cancer incidence in the NIH-AARP diet and health study. Eur. J. Clin. Nutr. 2009, 63, 707–717. [Google Scholar] [CrossRef]
  63. Berg, C.M.; Lappas, G.; Strandhagen, E.; Wolk, A.; Torén, K.; Rosengren, A.; Aires, N.; Thelle, D.S.; Lissner, L. Food patterns and cardiovascular disease risk factors: The Swedish INTERGENE research program. Am. J. Clin. Nutr. 2008, 88, 289–297. [Google Scholar] [CrossRef]
  64. Martikainen, P.; Brunner, E.; Marmot, M. Socioeconomic differences in dietary patterns among middle-aged men and women. Soc. Sci. Med. 2003, 56, 1397–1410. [Google Scholar] [CrossRef]
  65. Milligan, G.W. An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika 1980, 45, 325–342. [Google Scholar] [CrossRef]
  66. Gan, G.; Wu, J. Subspace clustering for high dimensional categorical data. ACM SIGKDD Explor. Newsl. 2004, 6, 87. [Google Scholar] [CrossRef]
  67. Smith, A.D.A.C.; Emmett, P.M.; Newby, P.K.; Northstone, K. Dietary patterns obtained through principal components analysis: The effect of input variable quantification. Br. J. Nutr. 2013, 109, 1881–1891. [Google Scholar] [CrossRef] [PubMed]
  68. Shrestha, A.; Koju, R.P.; Beresford, S.A.A.; Gary Chan, K.C.; Karmacharya, B.M.; Fitzpatrick, A.L. Food patterns measured by principal component analysis and obesity in the Nepalese adult. Heart Asia 2016, 8, 46–53. [Google Scholar] [CrossRef]
  69. Mullie, P.; Clarys, P. Relation between dietary pattern analysis (principal component analysis) and body mass index: A 5-year follow-up study in a Belgian military population. J. R. Army Med. Corps 2016, 162, 23–29. [Google Scholar] [CrossRef] [PubMed]
  70. Thorpe, M.G.; Milte, C.M.; Crawford, D.; McNaughton, S.A. A comparison of the dietary patterns derived by principal component analysis and cluster analysis in older Australians. Int. J. Behav. Nutr. Phys. Act. 2016, 13, 30. [Google Scholar] [CrossRef] [PubMed]
  71. Freitas-Vilela, A.A.; Smith, A.D.A.C.; Kac, G.; Pearson, R.M.; Heron, J.; Emond, A.; Hibbeln, J.R.; Castro, M.B.T.; Emmett, P.M. Dietary patterns by cluster analysis in pregnant women: Relationship with nutrient intakes and dietary patterns in 7-year-old offspring. Matern. Child Nutr. 2017, 13. [Google Scholar] [CrossRef]
  72. Wilkins, R.; Berthelot, J.M.; Ng, E. Trends in Mortality by Neighbourhood Income in Urban Canada from 1971 to 1996; Health Reports; Toronto Public Library: Toronto, ON, Canada, 2002; pp. 45–72. [Google Scholar]
  73. Parkin, D.; Muir, C.; Whelan, S. Cancer Incidence in Five Continents; WHO: Geneva, Switzerland, 2012; pp. 128–153. [Google Scholar]
  74. Livingstone, M.B.E.; Black, A.E. Markers of the validity of reported energy intake. J. Nutr. 2003, 133, 895S–920S. [Google Scholar] [CrossRef]
  75. Pérez-Rodrigo, C.; Gil, Á.; González-Gross, M.; Ortega, R.M.; Serra-Majem, L.; Varela-Moreiras, G.; Aranceta-Bartrina, J. Clustering of dietary patterns, lifestyles, and overweight among Spanish children and adolescents in the ANIBES study. Nutrients 2015, 8, 11. [Google Scholar] [CrossRef]
  76. Whelan, H.K.; Xu, J.-Y.; Vaseghi, S.; Lo Siou, G.; McGregor, S.E.; Robson, P.J. Alberta’s Tomorrow Project: Adherence to cancer prevention recommendations pertaining to diet, physical activity and body size. Public Health Nutr. 2017, 20, 1143–1153. [Google Scholar] [CrossRef]
Figure 1. Flow chart illustrating different methods for accounting for potential misreporting of energy intake.
Figure 1. Flow chart illustrating different methods for accounting for potential misreporting of energy intake.
Nutrients 11 02614 g001
Table 1. Summary of primary cancers used in subgroup survival analyses.
Table 1. Summary of primary cancers used in subgroup survival analyses.
Cancer locationICD CodeMorphology Code c
Dietary-Cancers a
 MouthC1-C6, C9
 PharynxC10, C11, C13
 LarynxC32
 Esophagus-squamous cell carcinomaC15Include only 8051,
8070, 8074, 8083
 LungC34
 StomachC16
 LiverC22
 ColonC18, C26.0
 Rectosigmoid and rectumC19, C20
 BreastC50
 EndometriumC54.1
 KidneyC64
Digestive-Cancers b
 EsophagusC15
 StomachC16
 Small IntestineC17
 ColonC18, C26.0
 Rectosigmoid and rectumC19, C20
 Anus, anal canal and anorectumC21
 Liver and intrahepatic bile ductsC22
 Gall bladder and extrahepatic bile ductsC23-24
 Exocrine pancreasC25Include only 8500,
8480, 8490, 8560,
8020, 8035,8154,
8441, 8470,8453,
8550, 8551, 8154,
8971, 8452
a Diet-related cancers based on World Cancer Research Fund/American Institute for Cancer Research Continuous Update Project; b Digestive system cancers based on World Health Organization classification; c For both Dietary-Cancers and Digestive-Cancers, cases excluded morphology codes 9050–9055, 9140, 9590–9992.
Table 2. Baseline sociodemographic characteristics by EI reporting status and dietary pattern.
Table 2. Baseline sociodemographic characteristics by EI reporting status and dietary pattern.
Reporting StatusDietary Pattern
HealthySweets & DairyMeats & Pizza
TotalPlausible ReportersMisreportersTotalPlausible ReportersMisreportersTotalPlausible ReportersMisreporters
Men
n = 2690n = 1205n = 1485n = 3233n = 1758n = 1475n = 3924n = 2165n = 1759
Age at enrollment, median (IQR)52.0 (15.3)51.5 (15.9)52.3 (14.8)52.4 (15.6)52.3 (16.1)52.6 (15.0)48.3 (12.4)48.2 (12.9)48.3 (12.1)
Body mass indexb, %
 <25.026.733.621.025.330.319.518.822.913.8
 25.0–29.949.649.349.850.149.550.848.249.646.6
 ≥30.023.817.129.224.620.329.833.027.539.7
Leisure-time physical activity(MET hrs/week) median (IQR)27.5 (36.6)26.5 (34.1)28.6 (38.4)17.9 (28.2)18.0 (26.9)17.8 (19.7)18.0 (28.8)17.8 (28.8)18.2 (29.0)
Marital status, %
 Married/with partner82.883.881.984.085.182.782.782.483.1
 Single7.17.17.15.96.45.36.46.56.2
 Divorced/separated/widowed10.19.111.010.18.511.910.911.110.7
Education, %
 Post-secondary complete66.069.563.254.757.551.451.954.049.2
 Some post-secondary17.916.219.317.916.119.919.017.420.9
 High school complete8.97.610.014.914.615.318.416.820.2
 High school not complete7.26.87.512.611.813.410.811.79.7
Annual household income, %
 <$50,00020.921.220.529.529.929.021.322.719.6
$50,000–$99,99942.041.042.844.444.843.945.744.747.0
 ≥$100,00036.036.235.824.523.725.431.631.132.3
Smoking status, %
 Never smoked51.252.150.541.641.042.336.135.636.7
 Former smoker41.640.942.240.940.441.537.936.339.8
 Current smoker7.17.07.317.518.516.225.928.023.3
Family history of cancer, %
 No50.250.050.047.940.047.851.151.350.9
 Yes49.950.050.052.160.052.248.948.749.1
Personal history of chronic disease a, %
 None48.850.947.152.552.352.854.556.751.7
 One29.528.430.428.628.928.328.727.729.9
 Two or more21.620.822.418.818.818.916.815.618.3
Women
n = 4808n = 2239n = 2469n = 4790n = 2667n = 2123n = 6643n = 3621n = 3022
Age at enrollment, median (IQR)51.9 (14.0)52.6 (14.2)51.3 (13.8)51.9 (16.0)52.4 (16.7)51.6 (15.2)47.6 (13.4)47.8 (13.4)47.5 (13.3)
Body mass index b, %
 <25.043.451.136.242.749.933.635.741.129.3
 25.0–29.934.632.736.433.232.034.833.233.033.5
 ≥30.022.016.327.424.118.231.531.025.937.2
Leisure-time physical activity
(MET hrs/week) median (IQR)
23.1 (30.0)22.1 (29.4)23.8 (30.3)16.3 (23.7)16.0 (22.9)16.9 (24.8)13.7 (22.2)13.5 (22.0)14.1 (22.3)
Marital status, %
 Married/with partner73.274.472.174.277.270.578.981.375.9
 Single6.46.16.85.24.75.74.84.55.1
 Divorced/separated/widowed20.319.521.120.618.023.816.414.219.0
Education, %
 Post-secondary complete53.755.551.949.251.346.543.844.542.9
 Some post-secondary21.420.222.421.020.721.222.922.423.4
 High school complete17.717.318.120.019.021.323.723.523.9
 High school not complete7.26.97.59.88.919.39.79.59.9
Annual household income, %
 <$50,00031.831.831.739.037.640.934.533.435.9
$50,000–$99,99938.336.939.737.938.936.740.240.040.4
 ≥$100,00026.927.926.020.020.619.322.623.721.2
Smoking status, %
 Never smoked49.750.449.151.453.748.640.841.539.9
 Former smoker40.640.440.834.733.036.834.533.535.7
 Current smoker9.69.210.013.813.314.424.725.024.3
Family history of cancer, %
 No45.347.043.745.445.045.947.147.546.8
 Yes54.752.956.354.655.054.052.952.653.2
Personal history of chronic disease a
 None57.258.156.457.259.454.660.161.658.3
 One28.228.128.328.827.829.927.025.928.2
 Two or more14.513.715.314.012.815.512.912.513.5
Menopausal status, %
 Pre-menopause58.959.558.459.459.159.951.850.653.3
 Post-menopause40.740.141.440.040.539.347.849.146.2
Hormone replacement therapy use, %
 Never used84.883.386.282.882.982.886.286.985.4
 Ever used15.016.513.616.816.816.913.512.814.4
a Self-reported personal history of one or more of the following: high blood pressure, diabetes, ulcerative colitis, Crohn’s disease, angina, high cholesterol, heart attack, stroke, hepatitis, and cirrhosis of the liver. b Body mass index was categorized based on Health Canada’s classification scheme [43].
Table 3. Largest contributor based on percentage of food groups to daily total energy intake across dietary patterns and different methods to account for misreporting of energy intake.
Table 3. Largest contributor based on percentage of food groups to daily total energy intake across dietary patterns and different methods to account for misreporting of energy intake.
Men
Healthy Pattern
Inclusion a (n = 2690)ExBefore b (n = 1780)ExAfter c (n = 1205)InclusionNN d (n = 3468)
Food groupsMean e (SD)Food groupsMean e (SD)Food groupsMean e (SD)Food groupsMean e (SD)
Fruit9.9 (5.4)Fruit7.8 (5.0)Fruit9.3 (5.2)Fruit8.1 (5.4)
Breakfast cereal4.6 (4.1)Low-fat dairy6.0 (6.7)Fruit juice4.6 (5.7)Low-fat dairy5.9 (6.8)
Fruit juice4.5 (5.4)Fruit juice4.5 (5.6)Breakfast cereal4.2 (3.5)Fruit juice4.4 (5.4)
Rice3.6 (6.0)Breakfast cereal4.2 (3.4)Rice4.0 (6.4)Breakfast cereal4.4 (3.8)
Nuts3.1 (5.0)Rice3.3 (5.7)Nuts3.7 (5.5)Rice3.1 (5.5)
Poultry no skin3.0 (3.5)Nuts3.2 (4.9)Poultry no skin3.2 (3.7)Nuts2.7 (4.6)
Regular fat dairy2.7 (3.2)Poultry no skin2.9 (3.4)Regular fat dairy2.6 (2.9)Poultry no skin2.7 (3.3)
Cooked vegetables1.9 (1.7)Regular fat dairy2.1 (2.6)Cooked vegetables2.0 (1.8)Regular fat dairy2.2 (2.9)
Soup1.8 (2.1)Soup1.7 (1.9)Soup1.8 (2.1)Soup1.7 (2.0)
Fish1.6 (1.6)Cooked vegetables1.7 (1.6)Fish1.6 (1.6)Cooked vegetables1.6 (1.5)
Wine1.5 (3.3)Fish1.4 (1.5)Wine1.5 (3.5)Fish1.4 (1.4)
Legumes1.2 (1.6)Wine1.4 (3.4)Meal replacement1.5 (5.3)Wine1.4 (3.3)
Meats/Pizza Pattern
Inclusion a (n =3924)ExBefore b (n = 2127)ExAfter c (n = 2165)InclusionNN d (n = 3760)
Food groupsMean e (SD)Food groupsMean e (SD)Food groupsMean e (SD)Food groupsMean e (SD)
Meat11.6 (5.4)Meat10.6 (5.4)Meat11.6 (5.4)Meat10.3 (5.4)
Pasta/pizza6.8 (4.7)Pasta/pizza6.8 (4.8)Pasta/pizza6.9 (4.9)Pasta/pizza6.7 (4.6)
Beer5.6 (11.0)Beer5.2 (10.8)Beer5.8 (11.1)Beer5.0 (11.0)
Regular soda4.3 (6.4)Regular soda5.0 (7.2)Regular soda4.5 (6.7)Regular soda4.7 (6.9)
Chips3.6 (3.6)Chips3.9 (3.7)Chips3.6 (3.5)Chips3.8 (3.8)
Other breads3.5 (3.7)Processed meat3.4 (2.6)Other bread3.5 (3.8)Processed meat3.3 (2.6)
Processed meat3.5 (2.6)Regular fat cheese2.6 (2.8)Processed meat3.5 (2.6)Regular fat cheese2.4 (2.7)
Regular fat cheese2.4 (2.8)French fries2.2 (2.0)Regular fat cheese2.5 (2.8)French fries2.1 (2.1)
French fries2.3 (2.2)Confectionary2.2 (3.0)French fries2.3 (2.1)Confectionary2.1 (2.9)
Eggs2.2 (2.1)Liquor1.9 (5.3)Eggs2.0 (1.8)Liquor1.9 (5.1)
Liquor1.9 (5.0)Regular fat salad dressing1.5 (1.9)Liquor1.9 (5.1)Regular fat salad dressing1.5 (1.9)
Regular fat salad dressing1.5 (2.0)Mexican1.2 (1.6)Regular fat salad dressing1.5 (1.9)Mexican1.3 (1.6)
Sweets/Dairy Pattern
Inclusion a (n = 3233)ExBefore b (n = 1221)ExAfter c (n = 1758)InclusionNN d (n =2619)
Food groupsMean e (SD)Food groupsMean e (SD)Food groupsMean e (SD)Food groupsMean e (SD)
Low fat dairy7.3 (7.5)Jam5.0 (4.7)Low fat dairy7.2 (7.3)Jam4.5 (4.6)
Wholemeal bread5.0 (4.9)Wholemeal bread4.8 (4.6)Cake5.1 (4.6)Wholemeal bread4.5 (4.8)
Jam4.8 (4.5)Cake3.9 (4.1)Wholemeal bread4.9 (4.5)Cake3.5 (3.7)
Cake4.7 (4.3)Other bread3.5 (4.2)Jam4.8 (4.5)Other bread3.4 (4.1)
Cooked potatoes3.1 (2.6)Cooked potatoes3.2 (2.3)Cooked potatoes2.9 (2.3)Cooked potatoes3.2 (2.6)
Dessert2.2 (2.3)Margarine2.5 (2.4)Confectionary2.3 (3.4)Margarine2.1 (2.3)
Confectionary2.2 (3.2)Eggs2.2 (2.0)Dessert2.2 (2.3)Eggs2.3 (2.3)
Margarine1.8 (2.1)Dessert1.9 (1.9)Ice cream1.9 (2.6)Dessert1.8 (1.9)
Ice cream1.8 (2.6)Coffee1.8 (0.8)Margarine1.9 (2.1)Coffee2.1 (1.2)
Coffee1.3 (1.2)Ice cream1.6 (2.4)Coffee1.0 (0.9)Ice cream1.5 (2.3)
Mayonnaise0.7 (1.1)High fat dairy1.6 (3.9)Mayonnaise0.7 (1.1)High fat dairy1.4 (3.7)
Women
Healthy Pattern
Inclusion a (n = 4808)ExBefore b (n = 2919)ExAfter c (n = 2239)InclusionNN d (n = 5633)
Food groupsMean e (SD)Food groupsMean e (SD)Food groupsMean e (SD)Food groupsMean e (SD)
Fruit13.3 (6.3)Fruit11.6 (6.0)Fruit12.9 (6.0)Fruit11.6 (6.5)
Regular fat dairy5.1 (4.6)Regular fat dairy4.4 (3.9)Regular fat dairy4.9 (4.1)Regular fat dairy4.4 (4.3)
Poultry no skin4.6 (4.6)Poultry no skin4.3 (4.2)Poultry no skin4.6 (4.4)Poultry no skin4.3 (4.4)
Nuts3.5 (5.5)Nuts4.2 (6.1)Nuts4.4 (6.3)Nuts3.4 (5.3)
Rice3.0 (3.7)Wholemeal bread3.2 (3.2)Rice3.2 (3.9)Wholemeal bread3.2 (3.3)
Cooked vegetables2.6 (2.3)Rice3.1 (3.8)Cooked vegetables2.6 (2.4)Rice3.0 (3.9)
Fish1.9 (2.2)Cooked vegetables2.4 (2.3)Fish1.9 (2.1)Cooked vegetables2.4 (2.2)
Soup1.9 (2.2)Soup1.9 (2.1)Soup1.9 (2.0)Soup2.0 (2.3)
Wine1.7 (3.4)Fish1.9 (2.0)Wine1.7 (3.6)Fish1.9 (2.1)
Legumes1.5 (1.6)Wine1.8 (3.7)Legumes1.5 (1.6)Wine1.7 (3.6)
Raw vegetables1.5 (1.1)Legumes 1.5 (1.5) Legumes 1.5 (1.6)
Cabbage1.3 (1.6)Raw vegetables1.4 (0.9) Raw vegetables1.4 (1.1)
Meats/Pizza Pattern
Inclusion a (n = 6643)ExBefore b (n = 3835)ExAfter c (n = 3621)InclusionNN d (n = 7049)
Food groupsMean e (SD)Food groupsMean e (SD)Food groupsMean e (SD)Food groupsMean e (SD)
Meat9.2 (4.8)Meat8.6 (4.7)Meat9.2 (4.7)Meat8.4 (4.8)
Pasta/pizza6.5 (4.4)Pasta/pizza6.2 (4.2)Pasta/pizza6.4 (4.3)Pasta/pizza6.2 (4.3)
Chips3.8 (4.0)Chips3.8 (4.0)Chips3.9 (4.1)Chips3.7 (4.0)
Regular soda3.5 (6.6)Regular soda3.5 (6.7)Regular soda3.6 (6.7)Regular soda3.4 (6.6)
Other bread3.4 (3.6)Cake3.3 (3.4)Other bread3.3 (3.4)Cake3.1 (3.2)
Cooked potatoes2.8 (2.2)Other bread3.1 (3.2)Cooked potatoes2.7 (2.0)Other bread3.1 (3.4)
Regular fat cheese2.7 (3.3)Jam2.8 (2.9)Regular fat cheese2.7 (3.2)Jam2.7 (3.0)
Processed meat2.5 (1.9)Regular fat cheese2.7 (3.2)Confectionary2.6 (3.9)Regular fat cheese2.6 (3.2)
Confectionary2.5 (3.7)Cooked potatoes2.7 (2.0)Processed meat2.5 (1.9)Cooked potatoes2.8 (2.2)
Eggs2.2 (2.4)Confectionary2.7 (4.0)Eggs2.1 (2.1)Confectionary2.5 (3.8)
Regular fat salad dressing2.1 (2.7)Processed meat2.4 (1.8)Regular fat salad dressing2.1 (2.6)Processed meat2.4 (1.9)
Dessert1.7 (1.9)Eggs2.1 (2.1)Dessert1.8 (1.9)Eggs2.1 (2.3)
Sweets/Dairy Pattern
Inclusion a (n = 4790)ExBefore b (n = 1873)ExAfter c (n = 2667)InclusionNN c (n = 3559)
Food groupsMean e (SD)Food groupsMean e (SD)Food groupsMean e (SD)Food groupsMean e (SD)
Low-fat dairy10.3 (8.1)Low-fat dairy14.3 (6.5)Low-fat dairy10.3 (7.6)Low-fat dairy13.3 (7.7)
Breakfast cereal5.1 (4.2)Breakfast cereal5.0 (3.7)Breakfast cereal4.6 (3.5)Breakfast cereal5.2 (4.3)
Wholemeal bread4.5 (4.3)Fruit juice3.8 (4.7)Wholemeal bread4.5 (4.0)Fruit juice3.7 (4.7)
Fruit juice4.2 (5.6) Fruit juice4.3 (5.5)
Cake3.4 (3.4) Cake3.7 (3.7)
Jam2.9 (2.9) Jam3.0 (2.8)
Ice cream1.1 (1.9) Ice cream1.2 (2.0)
a Inclusion reports on all participants. Misreporters were included in the k-means cluster analysis. b ExBefore reports on plausible reporters; however, exclusion of misreporters identified using the revised-Goldberg method was completed before k-means cluster analysis. c ExAfter reports on plausible reporters; however, exclusion of misreporters identified using the revised-Goldberg method was completed after k-means cluster analysis; d InclusionNN reports on all participants; however, misreporters identified using the revised-Goldberg method excluded before the cluster analysis but added to the ExBefore cluster solution using the nearest neighbour method; e Mean percentage contribution by each food group.
Table 4. Multivariable Cox proportional hazards ratio of the incidence of All-Cancers for dietary patterns identified by cluster analysis and stratified by four methods to account for misreporting.
Table 4. Multivariable Cox proportional hazards ratio of the incidence of All-Cancers for dietary patterns identified by cluster analysis and stratified by four methods to account for misreporting.
Men
Accounting for MisreportersDietary PatternnCancer Cases a% of Cases MisreportCancer Risk–HR (95%) b
InclusionHealthy269025757.21.00
Sweets/Dairy323338446.61.13 (0.96–1.33)
Meats/Pizza392434145.61.10 (0.93–1.30)
InclusionNNHealthy346834947.01.00
Sweets/Dairy261933647.51.11 (0.95–1.30)
Meats/Pizza376029752.40.95 (0.81–1.11)
ExBeforeHealthy1780185 1.00
Sweets/Dairy1221160 1.08 (0.87–1.35)
Meats/Pizza2127156--0.85 (0.68–1.06)
ExAfterHealthy1205110 1.00
Sweets/Dairy1758209 1.17 (0.93–1.48)
Meats/Pizza2165182 1.08 (0.84–1.39)
Women
Accounting for MisreportersDietary PatternnCancer Cases a% of Cases MisreportCancer Risk–HR (95%) c
InclusionHealthy480834754.21.00
Sweets/Dairy479041948.71.11 (0.96–1.28)
Meats/Pizza664352843.91.14 (0.99–1.32)
InclusionNNHealthy563342651.91.00
Sweets/Dairy355928749.01.10 (0.94–1.28)
Meats/Pizza704958142.91.14 (1.00–1.30)
ExBeforeHealthy2919205 1.00
Sweets/Dairy1873164 1.28 (1.04–1.58)
Meats/Pizza3835296 1.12 (0.93–1.35)
ExAfterHealthy2239159 1.00
Sweets/Dairy2667235 1.17 (0.96–1.44)
Meats/Pizza3621271 1.12 (0.91–1.38)
a All primary cancer cases except non-melanoma skin cancer. b Adjusted for age (modelled on a continuous scale), BMI (modelled on a continuous scale), leisure-time physical activity (MET hours/week; modelled on a continuous scale), marital status, educational attainment, smoking status, family history of cancer, and personal history of chronic disease. c Adjusted for age (modelled on a continuous scale), BMI (modelled on a continuous scale), leisure-time physical activity (MET hours/week; modelled on a continuous scale), marital status, educational attainment, smoking status, family history of cancer, and personal history of chronic disease, menopausal status and hormone replacement therapy usage.
Table 5. Multivariable Cox proportional hazards ratio of the incidence of Dietary-Cancers a stratified by four methods to account for misreporting.
Table 5. Multivariable Cox proportional hazards ratio of the incidence of Dietary-Cancers a stratified by four methods to account for misreporting.
Men
Accounting for MisreportersDietary PatternnCancer Cases a% of Cases MisreportCancer Risk-HR(95%) b
InclusionHealthy26905263.51.00
Sweets/Dairy323310750.51.34 (0.96–1.89)
Meats/Pizza392410538.11.42 (1.00–2.02)
InclusionNNHealthy34687353.41.00
Sweets/Dairy261911048.21.45 (1.07–1.97)
Meats/Pizza37608143.21.13 (0.82–1.57)
ExBeforeHealthy178034 1.00
Sweets/Dairy122157 1.74 (1.12–2.72)
Meats/Pizza212746 1.23 (0.77–1.95)
ExAfterHealthy120519 1.00
Sweets/Dairy175853 1.50 (0.88–2.56)
Meats/Pizza216565 1.92 (1.12–3.29)
Women
Accounting for MisreportersDietary PatternnCancer Cases a% of Cases MisreportCancer Risk-HR (95%) c
InclusionHealthy480824152.71.00
Sweets/Dairy479028443.71.05 (0.88–1.25)
Meats/Pizza664338050.01.13 (0.96–1.34)
InclusionNNHealthy563330352.11.00
Sweets/Dairy355919143.51.00 (0.84–1.21)
Meats/Pizza704941148.71.10 (0.94–1.28)
ExBeforeHealthy2919145 1.00
Sweets/Dairy1873108 1.17 (0.91–1.50)
Meats/Pizza3835211 1.07 (0.85–1.34)
ExAfterHealthy2239114 1.00
Sweets/Dairy2667160 1.09 (0.86–1.40)
Meats/Pizza3621190 1.02 (0.79–1.30)
a Diet-related cancers based on World Cancer Research Fund/American Institute for Cancer Research Continuous Update Project report. b Adjusted for age (modelled on a continuous scale), BMI (modelled on a continuous scale), leisure-time physical activity (MET hours/week; modelled on a continuous scale), marital status, educational attainment, smoking status, family history of cancer, and personal history of chronic disease. c Adjusted for age (modelled on a continuous scale), BMI (modelled on a continuous scale), leisure-time physical activity (MET hours/week; modelled on a continuous scale), marital status, educational attainment, smoking status, family history of cancer, and personal history of chronic disease, menopausal status and hormone replacement therapy usage.
Table 6. Multivariable cox proportional hazards ratio of the incidence of Digestive-Cancers a stratified by four methods to account for misreporting.
Table 6. Multivariable cox proportional hazards ratio of the incidence of Digestive-Cancers a stratified by four methods to account for misreporting.
Men
Accounting for MisreportersDietary PatternnCancer Cases a% of Cases MisreportCancer Risk-HR (95%) b
InclusionHealthy26903857.91.00
Sweets/Dairy32337651.31.43 (0.96–2.13)
Meats/Pizza39247740.31.45 (0.96–2.17)
InclusionNNHealthy34685844.81.00
Sweets/Dairy26196956.51.23 (0.86–1.76)
Meats/Pizza37606442.21.10 (0.77–1.60)
ExBeforeHealthy178032 1.00
Sweets/Dairy122130 1.08 (0.64–1.82)
Meats/Pizza212737 1.01 (0.61–1.67)
ExAfterHealthy120516 1.00
Sweets/Dairy175837 1.37 (0.76–2.49)
Meats/Pizza216546 1.62 (0.89–2.95)
Women
Accounting for MisreportersDietary PatternnCancer Cases a% of Cases MisreportCancer Risk-HR (95%) c
InclusionHealthy48085152.91.00
Sweets/Dairy47906934.81.17 (0.81–1.69)
Meats/Pizza66438150.61.22 (0.84–1.77)
InclusionNNHealthy56336051.71.00
Sweets/Dairy35594634.81.25 (0.84–1.84)
Meats/Pizza70499849.01.43 (1.02–2.01)
ExBeforeHealthy291929 1.00
Sweets/Dairy187330 1.73 (1.03–2.89)
Meats/Pizza383550 1.43 (0.88–2.33)
ExAfterHealthy223924 1.00
Sweets/Dairy266745 1.42 (0.86–2.35)
Meats/Pizza362140 1.13 (0.66–1.93)
a Digestive system cancers based on World Health Organization classification. b Adjusted for age (modelled on a continuous scale), BMI (modelled on a continuous scale), leisure-time physical activity (MET hours/week; modelled on a continuous scale), marital status, educational attainment, smoking status, family history of cancer, and personal history of chronic disease. c Adjusted for age (modelled on a continuous scale), BMI (modelled on a continuous scale), leisure-time physical activity (MET hours/week; modelled on a continuous scale), marital status, educational attainment, smoking status, family history of cancer, and personal history of chronic disease, menopausal status and hormone replacement therapy usage.
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