The relationship between diet and chronic disease is complex. We consume foods and nutrients in different combinations and as part of meals. Thus, evaluating diet as a whole on the basis of dietary patterns is a complementary approach to the study of single nutrients or foods to understand the relationship between diet and disease( Reference Newby and Tucker 1 ). When investigating associations between diet and disease, the findings are in most cases based on self-reported dietary intake. Previous research has revealed extensive misreporting, especially under-reporting, of self-reported dietary intake( Reference Schoeller 2 – Reference Jonnalagadda, Mitchell and Smiciklas-Wright 5 ). The misreporting can be general under-reporting of food intake, or under- or over-reporting of certain food groups related to social desirability( Reference Connor Gorber and Tremblay 6 – Reference Scagliusi, Polacow and Artioli 8 ). Under- or over-reporting of certain food groups may distort dietary patterns, and such distortion could result in erroneous conclusions regarding the associations between dietary patterns and disease.
The doubly labelled water (DLW) technique has been looked upon as a gold standard in the evaluation of reported energy intake (EI). Unfortunately, the DLW method is technically challenging and extremely expensive, and therefore not possible to implement in most studies. The more simple method developed by Goldberg et al. ( Reference Goldberg, Black and Jebb 9 ) and later revised by Black( Reference Black 10 ) has been proposed as an alternative to identify potential misreporters of EI. By using the level of discrepancy between the ratio of EI:estimated BMR and the presumed physical activity level (PAL) of the population, individuals can be classified as likely to be low energy, plausible or high energy reporters( Reference Goldberg, Black and Jebb 9 , Reference Black 10 ).
A few studies have investigated the effect of under-reporting on empirically derived dietary patterns( Reference Funtikova, Gomez and Fito 11 – Reference Pryer, Nichols and Elliott 18 ); four studies have reported that the composition of food groups that significantly contributed to the dietary patterns remained relatively unchanged after removal of low energy reporters( Reference Funtikova, Gomez and Fito 11 , Reference Bailey, Mitchell and Miller 13 – Reference Martikainen, Brunner and Marmot 15 ). One study found that the number of dietary patterns differed between plausible reporters and all reporters( Reference Shaneshin, Jessri and Rashidkhani 12 ). In all these studies, cluster analysis was used to define dietary patterns. The distribution of low energy reporters across clusters was not uniform, and whether the highest proportion of low energy reporters were found in the healthy or unhealthy clusters differed between the studies( Reference Funtikova, Gomez and Fito 11 , Reference Shaneshin, Jessri and Rashidkhani 12 , Reference Winkvist, Hornell and Hallmans 14 – Reference Pryer, Nichols and Elliott 18 ). The cluster analysis assigns the study subjects to one of a number of discrete clusters or dietary patterns. When deriving dietary patterns by principal component analysis (PCA) an individual’s diet is characterised using a continuous score for each of the derived patterns; thus, this method has the advantage that it looks at more than one dimension of variation in the diet( Reference Crozier, Robinson and Borland 19 ). Recently, a study among Swedish adults investigated the effect of excluding low energy reporters on dietary patterns derived by PCA( Reference Ax, Warensjo Lemming and Becker 20 ) and found that the patterns were largely consistent. That study is, to the best of our knowledge, the only study that has investigated the effect of under-reporting of EI on dietary patterns derived by PCA.
The aims of the present study were to investigate the effect of under-reporting of EI, by excluding low energy reporters from the study sample, on (a) the dietary patterns derived by PCA and (b) the association between the dietary patterns and self-reported chronic disease.
Methods
Study sample
The Norwegian Breast Cancer Screening Program is a government-funded national screening programme administered by the Cancer Registry of Norway( Reference Hofvind, Wang and Thoresen 21 ). All Norwegian women aged 50–69 years are invited to a bilateral two-view mammogram biennially. The participation rate is 77 %( Reference Hofvind, Geller and Vacek 22 ), with about 250 000 women invited/year. In 2006/2007, the Norwegian Breast Cancer Screening Program’s invitation letter for mammographic screening included a question on willingness to complete a dietary questionnaire. A total of 67 527 women agreed to participate. In 2008, a consent form and a FFQ were sent to a random sample of 10 000 of these women living all over Norway. A total of 6974 returned the FFQ, and 676 women were excluded because of the following reasons: the FFQ were not filled in (n 46); missing data on height and/or weight (n 158), age (n 5), smoking status (n 41), education (n 79), physical activity (n 104); height<125 cm (n 7) and weight <30 or >170 kg (n 13); age not within the range 50–69 years (n 15); BMI<18·5 or ≥40 kg/m2 (n 98); or EI<2100 or >15 000 kJ/d (n 204). This left us with a total sample of 6204 women.
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 regional ethics committee and the Norwegian Data Protection Authority. Written informed consent was obtained from all subjects.
Dietary assessment
The 16-page, 253-item FFQ was designed to measure the habitual food intake among Norwegian adults the preceding year. The questionnaire had an extra focus on fruits, vegetables and other antioxidant-rich foods and beverages, and thus the foods accounting for the variation in antioxidant intake in a population could be investigated( Reference Qureshi, Lund and Veierod 23 ). The 253-item FFQ has been described in detail earlier( Reference Markussen, Veierod and Sakhi 24 ). In short, it was based on a previously validated 180-item FFQ designed to measure total EI in the Norwegian population( Reference Andersen, Solvoll and Johansson 25 ), which later was expanded to a 270-item FFQ to cover most of the antioxidant-rich foods and beverages in Norway( Reference Carlsen, Lillegaard and Karlsen 26 ). The energy and food intakes estimated from the 270-item FFQ have been validated( Reference Carlsen, Lillegaard and Karlsen 26 , Reference Carlsen, Karlsen and Lillegaard 27 ). The EI was compared with independent measures of energy expenditure (EE) using the ActiReg® system (motion detection)( Reference Berntsen, Hageberg and Aandstad 28 ), whereas 7-d weighed food records were used to study the relative validity of food and nutrient intakes. The correlation coefficient between EI and EE was 0·54( Reference Carlsen, Lillegaard and Karlsen 26 ). Correlations between FFQ and the weighed food records were 0·41 for berries, 0·61 for fruits and 0·38 for vegetables( Reference Carlsen, Lillegaard and Karlsen 26 ). This FFQ has also been validated for ranking individuals according to their usual intakes of fruits, juices and vegetables using the method of triads with two independent and specific biomarkers of fruits and vegetables and 7-d weighed food records( Reference Carlsen, Karlsen and Lillegaard 27 ). Using the method of triads, the validity correlation was found to range from 0·60 to 0·94. The 253-item FFQ used in this study was revised from the original 270-item FFQ by removing seventeen items that were seldom or never eaten (curly kale, red cabbage, mushroom, globe artichoke, sundried tomatoes, tofu, cumin, turmeric, ginger powder, caraway, cloves, piri piri, sage, rosehip tea, organic blueberry juice, organic blackcurrant juice and crowberry juice). The questionnaire also collected information about dietary supplements, age, height, weight, smoking status, physical activity, chronic diseases (present or previous) and use of medication. Daily intake of energy, nutrients and foods were computed using the food database AE-07 and KBS software system (KBS, version 4.9, 2008) developed at the Department of Nutrition, University of Oslo, Norway. The food database AE-07 is based on the 2006 edition of the Norwegian Food Composition Table (www.norwegianfoodcomp.no). Intakes from dietary supplements were included in the calculations.
The 253 food items were categorised into forty-nine food groups on the basis of similarity in ingredients, nutrient profile or culinary usage (online Supplementary Table S1).
Disease assessment
In the FFQ, the participants were asked whether they had currently or previously been diagnosed with one or more of the following diseases: asthma, joint inflammation, muscle or skeletal disorder, chronic gastrointestinal disease, chronic respiratory disease, depression or psychiatric disorder, stroke, heart attack or angina, hypertension and diabetes (type 1 or type 2). We defined six disease groups: total chronic disease (composed of all of the following disease groups), CVD (stroke, heart attack, angina and hypertension), diabetes (type 1 and 2), chronic respiratory disease (asthma and chronic respiratory disease), cancer and joint/muscle/skeletal disorders (joint inflammation and muscle and skeletal disorders). A participant was identified to belong to a disease group if she had been diagnosed with at least one of the diseases in the group.
Physical activity assessment
Physical activity was assessed using a modified version( Reference Qureshi, Ellingjord-Dale and Hofvind 29 ) of the physical activity questionnaire used in the California Teachers Study( Reference Dallal, Sullivan-Halley and Ross 30 ). Subjects were asked to assess habitual weekly physical activity and report all physical activity lasting at least 10 min/session. They were provided examples of light activities (defined as walking or cross-country skiing at a slow pace), moderate activities (defined as activities where some effort is required and that cause increased breathing, such as bicycling, swimming or cross-country skiing at a moderate pace, jogging at a slow pace, dancing) and strenuous activities (defined as activities that require hard effort and causes substantial increased breathing, such as aerobics, running, cross-country skiing or bicycling at a brisk pace). The subjects were asked to estimate their mean hours per week (none, <0·5, 0·5–1, 1·5–2, 2·5–3·5, 4–6, ≥7 h) of participation at each level of activity. We created separate light, moderate and strenuous activity variables in minutes per week by summing up hours per week for each level of activity multiplied with 60. We calculated EE as the number of hours of each physical activity multiplied by its estimated metabolic cost( Reference Ainsworth, Haskell and Whitt 31 ) and expressed this variable in metabolic equivalent task (h/week).
Definition of low energy reporters
Low energy reporters were determined using the revised Goldberg cut-off method( Reference Black 10 ). This method is based on the principle that EI equals EE when weight is stable (equation (1): EI=EE). EE can also be expressed as multiples of BMR and PAL, and replacing EE in equation (1) with BMR×PAL gives equation (2): EI/BMR=PAL. The idea by Goldberg et al. was that the ratio EI:BMR can be derived from a dietary assessment method and then be evaluated against an expected PAL for a population.
The revised Goldberg cut-off values( Reference Black 10 ) used in the present study are based on estimated 95 % confidence limits (cut-offs) for the plausible EI. The values of these cut-off points vary according to PAL, number of days of food recording and whether the evaluation of EI/BMR is at the individual or group level. Subjects are defined as plausible, low energy or high energy reporters from their ratio of EI:BMR according to whether this ratio is within, below or above the 95 % confidence limits calculated, respectively.
A PAL for sedentary lifestyle (1·55)( 32 , 33 ) was applied for all participants, because of lack of an objective measure of total physical activity in the present study. We have used the lower 95 % confidence limits published by Black( Reference Black 10 ) to identify low energy reporters, which is based on a PAL of 1·55 and infinity number of days of food recording (habitual intake measured by a FFQ) at the individual level (n 1). Black calculated the value of this cut-off point to be 1·10, and all women with EI/BMR<1·10 were classified as low energy reporters in this study.
In the present study, BMR was calculated from the following equations( Reference Henry 34 ):
$${\rm BMR}\,{\rm women}\,{\rm 31}\!-\!{\rm 60}\,{\rm years \colon\,}\,{\rm 0\!\cdot\!0433}\,{\rm W}{\plus}{\rm 2\!\cdot\!57 H}\!-\!{\rm 1\!\cdot\!180}$$
and
$$\quad{\rm BMR}\,{\rm women}\,{\rm 61}\!-\!{\rm 70}\,{\rm years\colon\,}\,{\rm 0}\!\cdot\!{\rm 0342}\,{\rm W}{\plus}{\rm 2}\!\cdot\!{\rm 10}\,{\rm H}\!-\!{\rm 0}\!\cdot\!{\rm 0486}{\rm .}$$
Statistical methods
We divided the study sample into all and plausible reporters, and each of these subsamples was stratified by BMI: 18·5 kg/m2≤BMI<25 kg/m2 (normal weight) and 25 kg/m2≤BMI<40 kg/m2 (overweight/obese). Differences between under-reporters and plausible reporters and between normal weight and overweight/obese subjects were studied by the two-sample t test for continuous variables and the χ 2 test for categorical variables. Physical activity and alcohol intake data were loge transformed in these analyses.
PCA was used in order to reduce the dimensionality of the dietary data. Before the PCA, the 253 food items were categorised into forty-nine food groups (g/d) according to similarity in ingredients, nutrient profile or culinary usage. In the PCA, the food groups are aggregated in linear combinations called principal components, according to the degree to which the food groups are correlated to each other( Reference Newby and Tucker 1 ). Before extracting components, the suitability for using PCA was assessed by the Kaiser–Meyer–Olkin measure of sampling adequacy and the Bartlett’s test of sphericity, which tests whether our correlation matrix is significantly different from an identity matrix( Reference Tabachnick and Fidell 35 ). The Kaiser–Meyer–Olkin value was 0·76 for both all and plausible reporters, which is above the suggested minimum of 0·50( Reference Hutcheson and Sofroniou 36 ), and Bartlett’s test of sphericity was statistically significant (P<0·001), supporting the suitability of the data for PCA. The input variables were standardised using the correlation matrix of the forty-nine food group variables in the PCA, and not the covariance matrix. To determine the number of meaningful components or dietary patterns to retain, we considered the eigenvalue-one criterion, the scree test, the proportion of variance accounted for and the interpretability of the patterns( Reference Michels and Schulze 37 ). After extraction of the dietary patterns, a rotation method is usually applied to improve interpretation( Reference Newby and Tucker 1 ). We investigated unrotated, orthogonal (varimax) rotation and oblique (oblimin) rotation. The unrotated dietary patterns were less interpretable than the rotated ones. The orthogonal (varimax) and oblique (oblimin) rotation methods gave the same result, and we chose to apply the orthogonal (varimax) rotation method in order to achieve a simpler structure of the dietary patterns with greater interpretability. The varimax rotation is the most common rotation method applied in dietary pattern analysis( Reference Newby and Tucker 1 , Reference Dekker, van Dam and Snijder 38 – Reference Hearty and Gibney 54 ) and leads to uncorrelated dietary patterns. Food groups with a factor loading ≥0·3 (absolute value) were considered to be important contributors to a component. Factor loadings can be interpreted as correlation coefficients between food groups and dietary patterns( Reference Schulze, Hoffmann and Kroke 55 ). We labelled the dietary patterns according to the more or less healthy combinations of food groups and according to the influence of international cuisines. Finally, individual scores were calculated for each of the retained components. The overall dietary pattern of a participant is represented by her factor scores on all the identified dietary patterns. A high factor score for a given dietary pattern indicated high intake of food groups constituting that dietary pattern, whereas a low score indicated low intake of those food groups.
Owing to the high prevalence of outcomes, a generalised linear regression with a log link and binomial distribution (log-binomial regression)( Reference Barros and Hirakata 56 ) was used to estimate the association between dietary pattern scores and the prevalence of self-reported chronic diseases among all and plausible reporters. Women with self-reported chronic diseases were compared with those reporting not having a disease. The dietary pattern scores were categorised into tertiles, and we estimated adjusted prevalence ratios (PR) and 95 % CI for each tertile compared with the lowest tertile of each dietary pattern. We analysed trends across tertiles of dietary pattern scores by treating the variable as a continuous variable in the regression analysis. We consider the succeeding variables as potential confounders and adjusted for them as follows: age (50–60, 61–69 years), education (≤upper secondary school, academy/college/university ≥4 years), smoking status (yes, no), BMI (18·5–24·9, 25–39·9 kg/m2), physical activity (continuous) and EI (continuous). Wald’s test was used to test for interaction between BMI and dietary pattern scores. No significant interactions were found.
All tests were two sided, and P<0·05 was considered to be statistically significant. The analyses were conducted using SPSS version 20.0 (IBM Corp.).
Results
A total of 1133 (18·3 %) of the 6204 women were defined as low energy reporters (Table 1). Low energy reporters had significantly lower EI, higher BMI, lower physical activity, lower alcohol intake and lower educational level than plausible reporters (P≤0·02). Moreover, there was an indication of a higher proportion of smokers among low energy reporters compared with plausible reporters (P=0·09). The prevalence of self-reported total chronic disease, CVD, diabetes and joint/muscle/skeletal disorders was significantly higher (P≤0·04) among low energy reporters compared with plausible reporters. Overweight/obesity was more common in low energy reporters than plausible reporters (63 and 46 %, respectively).
Table 1 Selected characteristics and prevalence of disease of all reporters, under-reporters and plausible reporters (Mean values and standard deviations; numbers; percentages; medians and 25th and 75th percentiles (P))
MET, metabolic equivalent task.
* Comparison of under-reporters and plausible reporters: two-sample t test for continuous variables and χ test for categorical variables.
† Physical activity and alcohol were loge transformed for the comparison of BMI groups.
‡ Disease group composed of CVD, diabetes, chronic respiratory disease, cancer and joint inflammation and muscle and skeletal disorder.
§ Disease group composed of stroke, heart attack, angina and hypertension.
|| Disease group composed of diabetes type 1 and type 2.
¶ Disease group composed of asthma and chronic respiratory inflammation.
Among all reporters, EI was significantly higher for the normal weight group than for the overweight/obese group (P=0·02) (Table 2). By removal of the low energy reporters, this changed to the opposite (P=0·001). In both all and plausible reporters, the overweight/obese group was slightly older, had lower physical activity, lower alcohol intake, were less likely to smoke, were less educated and had a higher prevalence of chronic diseases than the normal weight group (P<0·001).
Table 2 Selected characteristics and prevalence of disease of all v. plausible reporters stratified by BMI (Mean values and standard deviations; numbers; percentages; medians and 25th and 75th percentiles (P))
MET, metabolic equivalent task.
* Comparison of BMI groups: two-sample t test for continuous variables and χ test for categorical variables.
† Physical activity and alcohol were loge transformed for the comparison of BMI groups.
‡ Disease group composed of CVD, diabetes, chronic respiratory disease, cancer and joint inflammation and muscle and skeletal disorder.
§ Disease group composed of stroke, heart attack, angina and hypertension.
|| Disease group composed of diabetes type 1 and type 2.
¶ Disease group composed of asthma and chronic respiratory inflammation.
We identified three major dietary patterns for both all and plausible reporters, all with eigenvalues≥2·0. The point at which the slope of the graph in the scree plot showed a change, and the interpretation of the components, justified retaining three dietary patterns. Table 3 presents the three dietary patterns for all and plausible reporters, with food groups having factor loadings with absolute values ≥0·30. The three dietary patterns accounted for 17·4 and 16·7 % of the total variance among all and plausible reporters, respectively. Among all reporters, the dietary pattern labelled ‘Prudent’ was characterised by high positive loadings for vegetables, fish as dinner, fruits, herbs and spices, berries, nuts and seeds, legumes, meat dishes, salad dressings, poultry, vegetarian food, soup and tea. Although the ‘Prudent’ pattern derived for the plausible reporters was substantially similar to that of all reporters, differences were noted for three food groups: vegetarian food, tea and salad dressings had factor loadings <0·30 among plausible reporters. Furthermore, among all reporters the ‘Prudent’ pattern was the pattern explaining most of the variance in the dietary data. After excluding the low energy reporters from the study sample, the ‘Prudent’ pattern explained the lowest amount of variance in the dietary data. Among all reporters, the ‘Western’ dietary pattern was characterised by high loadings for potatoes, sauce, refined grains, processed meat, cakes and desserts, margarine, sweet spreads, red meat and game as well as high negative loadings for wine and herbs and spices. For plausible reporters, a similar ‘Western’ pattern was found, but this pattern also showed a high negative loading for vegetarian food. The ‘Western’ pattern explained the highest total variance among the plausible reporters. The third pattern was labelled ‘Continental’, because several of the food groups contributing significantly to this pattern were influenced by international cuisine. Among all reporters, it was characterised by high loadings for tomato sauce, pasta, processed meat, fat-rich potatoes, salty snacks, pizza, salad dressings, rice, poultry, mustard, sweets and wine. We found a similar ‘Continental’ pattern among plausible reporters, but here soya sauce was also among the highly loaded food groups (0·31), and wine had a slightly lower loading (0·29) than in the ‘Continental’ pattern among all reporters.
Table 3 Factor loadings for the three dietary patterns found in the principal component analysis for all (n 6204) and plausible (n 5071) reporters
* Factor loadings with an absolute value≥0·30.
Table 4 presents the adjusted PR of self-reported chronic disease by tertiles of the dietary pattern scores among all and plausible reporters. Self-reported CVD was significantly positively associated with ‘Western’ pattern scores among plausible reporters but not among all reporters (PR for highest v. lowest tertile: PRall reporters 1·05; 95 % CI 0·94, 1·18; P trend=0·40; PRplausible reporters 1·15; 95 % CI 1·02, 1·31; P trend=0·03). Self-reported CVD was also significantly positively associated with ‘Prudent’ pattern scores among both all and plausible reporters, but the association was slightly stronger among plausible reporters (PR for highest v. lowest tertile: PRall reporters 1·27; 95 % CI 1·14, 1·43; P trend<0·001; PRplausible reporters 1·37; 95 % CI 1·20, 1·56; P trend<0·001). The largest differences between all and plausible reporters were found for the association between self-reported diabetes and the ‘Prudent’ pattern, where a stronger association was found among the plausible reporters, although with wide CI (PR for highest v. lowest tertile: PRall reporters 2·16; 95 % CI 1·50, 3·13; P trend<0·001; PRplausible reporters 2·86; 95 % CI 1·81, 4·51; P trend<0·001). In addition, a significant positive association between self-reported chronic respiratory disease and ‘Prudent’ pattern scores was found among plausible reporters, but not among all reporters (PR for highest v. lowest tertile: PRall reporters 1·18; 95 % CI 0·98, 1·42; P trend=0·12; PRplausible reporters 1·33; 95 % CI 1·08, 1·63; P trend=0·007). Finally, we found a significant inverse association between self-reported joint/muscle/skeletal disorders and the ‘Continental’ pattern and a significant positive association between these disorders and the ‘Prudent’ pattern. However, there were no differences in the effect estimates between all and plausible reporters.
Table 4 Relationship between prevalence of self-reported chronic disease and tertiles (T) of dietary pattern scores among all and plausible reporters (Numbers; prevalence ratio (PR)Footnote * and 95 % confidence intervals)
* Adjusted for age (50–60, 61–69 years), education (≤upper secondary school, academy/college/university ≥4 years), smoking status (yes, no), physical activity (continuous), BMI (18·5≤BMI<25, 25≤BMI<40 kg/m2) and energy (continuous).
† Disease group composed of CVD, diabetes, chronic respiratory disease, cancer and joint inflammation and muscle and skeletal disorder.
‡ Disease group composed of stroke, heart attack, angina and hypertension.
§ Disease group composed of diabetes type 1 and type 2.
|| Disease group composed of asthma and chronic respiratory inflammation.
The online Supplementary Table S2 shows the effect of including the covariates one by one in the log-binomial regression model of the relationship between the tertiles of dietary pattern score and self-reported total chronic disease among all and plausible reporters. Only minor differences were found between the effect estimates in the different models.
Discussion
We identified almost one-fifth of the women to be low energy reporters based on the revised Goldberg cut-off method( Reference Black 10 ). The majority of food groups contributing significantly to the ‘Prudent’, ‘Western’ and ‘Continental’ patterns were consistently found for both all and plausible reporters, differing only with a few food groups. The PR expressing the associations between the ‘Western’ and ‘Prudent’ dietary pattern scores and self-reported chronic diseases were consistently highest among plausible reporters except for joint/muscle/skeletal disorders.
Studies using the DLW method have clearly shown that all dietary assessment methods tend to underestimate EI to various degrees( Reference Hill and Davies 57 – Reference Schoeller and Schoeller 59 ). Previous studies have reported prevalence of low energy reporting ranging from 10 to 60 % depending on the dietary assessment method, the reference method used to identify low energy reporters and the characteristics of the study population( Reference Subar, Kipnis and Troiano 4 , Reference Funtikova, Gomez and Fito 11 , Reference Livingstone and Black 58 , Reference Black, Goldberg and Jebb 60 – Reference Luhrmann, Herbert and Neuhauser-Berthold 72 ). In the revised Goldberg cut-off equations( Reference Black 10 ), the individual’s physical activity is taken into account. To increase sensitivity, Black( Reference Black 10 ) recommended collecting more information about home or occupational and leisure-time physical activity, to be able to assign subjects to low, medium and high activity categories; three different cut-offs can then be calculated for the subjects belonging to the different activity categories. This would probably have resulted in a higher prevalence of low energy reporters in our study sample. Unfortunately, the physical activity questionnaires used in the present study did not give enough information about the individuals’ total amount of physical activity. Therefore, we used a PAL of 1·55, which is the value defined by FAO/WHO/United Nations University( 33 ), representing a sedentary level of EE( Reference Livingstone and Black 58 ), in order not to overestimate the extent of under-reporting. Nevertheless, it could be criticised to be a very conservative PAL value for this population, and misclassifications of more active participants could exist. We found a prevalence of 18·3 % low energy reporters in our study sample. Other studies using the revised Goldberg cut-off method( Reference Black 10 ) have found a prevalence of low energy reporters of 21–33 %( Reference Murakami and Livingstone 73 – Reference Johansson, Solvoll and Bjorneboe 78 ). The differences between studies in the prevalence of low energy reporters could be due to differences in the accuracy of reporting or it might be due to the differences in criteria used to identify low energy reporters or the way the dietary data are collected and calculated. It is important to take into account that the confidence limits calculated by Black are wide, and only extreme degrees of misreporting can be identified( Reference Black 10 ).
The low energy reporters in this study reported higher BMI, lower physical activity, lower alcohol intake and lower educational level than the plausible reporters (Table 1). This is in line with previous studies investigating characteristics of low energy reporters( Reference Bailey, Mitchell and Miller 13 , Reference Livingstone and Black 58 , Reference Park, Lee and Kuller 79 ).
We have previously discussed the dietary patterns derived from this study in detail( Reference Markussen, Veierod and Kristiansen 80 ). In the current analyses, we wanted to investigate whether the measurement errors introduced by under-reporting distorted the food groups defining the dietary patterns. We found three major dietary patterns among both all and plausible reporters that were not vastly different from each other, differing only with a few food groups in each pattern. Other studies have also identified relatively similar patterns after removal of low energy reporters from the analysis compared with the total sample( Reference Bailey, Mitchell and Miller 13 – Reference Martikainen, Brunner and Marmot 15 ). Interestingly, the dietary pattern explaining the highest extent of variance in the dietary intake differed between all and plausible reporters, with the ‘Prudent’ pattern explaining the highest extent of variance among all reporters and the ‘Western’ pattern explaining the highest extent of variance among plausible reporters. This may be related to the fact that low energy reporters tend to over-report foods perceived as healthy or/and under-report foods perceived as unhealthy( Reference Scagliusi, Polacow and Artioli 8 , Reference Rasmussen, Matthiessen and Biltoft-Jensen 63 , Reference Lafay, Mennen and Basdevant 81 , Reference Krebs-Smith, Graubard and Kahle 82 ). The identification of the first principal component as a prudent dietary pattern among all reporters is comparable with other studies investigating dietary patterns derived by PCA( Reference Crozier, Robinson and Borland 19 , Reference Link, Canchola and Bernstein 46 , Reference Chocano-Bedoya, O’Reilly and Lucas 48 , Reference Williams, Prevost and Whichelow 83 , Reference Herber-Gast and Mishra 84 ). Some, but not all( Reference Pryer, Nichols and Elliott 18 ), studies investigating the association between dietary patterns derived by cluster analysis and under-reporting observed more severe under-reporting among subjects in healthy dietary pattern clusters( Reference Scagliusi, Ferriolli and Pfrimer 16 , Reference Wirfalt, Mattisson and Gullberg 85 , Reference Hornell, Winkvist and Hallmans 86 ).
The implications of under-reporting might be distortion of the associations between diet and disease. Most studies in nutritional epidemiology have excluded subjects with implausible high or low EI using cut-off values for plausible EI, usually <2100 and >15 000 kJ/d( Reference Anderson, Harris and Houston 87 – Reference Tucker, Chen and Hannan 92 ); however, this does not account for all the misreporting. In our study, the associations between dietary patterns and self-reported chronic disease were somewhat stronger among plausible reporters (additional exclusion of low energy reporters as defined by the revised Goldberg cut-off method( Reference Black 10 )) compared with all reporters (excluding those with implausible EI<2100 and >15 000 kJ/d). Specifically, the associations between the ‘Prudent’ pattern and self-reported chronic diseases were strengthened. The positive relationship between the ‘Prudent’ pattern and several of the chronic diseases indicated that the participants tried to eat healthy in order to reduce either the symptoms of their condition or reduce the likelihood of possible detrimental consequences. A positive relationship between a healthy dietary pattern and disease has also been reported in a Swedish study, where the highest prevalence of previously known health problems was observed in the healthy ‘fruit and vegetables’ cluster among women( Reference Wirfalt, Mattisson and Gullberg 85 ).
Effects of under-reporting on diet–disease associations have been reported in some studies. A Swedish study investigated the effect of under-reporting on the association between risk of breast cancer and alcohol intake( Reference Mattisson, Wirfalt and Aronsson 93 ). The researchers reported an increased risk of breast cancer with high alcohol intakes, and the risk estimates were strengthened among the plausible reporters compared with all reporters. A study in the US( Reference Prentice, Shaw and Bingham 94 ) investigated the use of calibrated EI to account for under-reporting and the effect on the association between risk of breast, colon, endometrial and kidney cancer. They produced the calibrated consumption estimates based on calibration equations developed in a substudy among 544 women where DLW was used to estimate total EE and urinary N was used as the recovery biomarker for protein( Reference Neuhouser, Tinker and Shaw 95 ). The researchers found calibrated energy consumption to be positively associated with the risk of breast, colon, endometrial and kidney cancer, while uncalibrated energy was not. In a few studies, the investigators have adjusted for under-reporting of EI in their analyses in order to avoid biased conclusions( Reference McNaughton, Mishra and Brunner 96 – Reference Brunner, Mosdol and Witte 98 ). Our results and those of other studies show that it is important to consider under-reporting in dietary studies and the effect this might have on associations between dietary patterns and health outcomes.
The extensive information on diet, lifestyle and self-reported chronic diseases and the large study sample from different parts of the country are important strengths of the present study. However, there are some limitations. First, it might be that the women responding to the FFQ were healthier and/or more health conscious than those not responding. Moreover, as the FFQ had an extra focus on fruits and vegetables, these food items may have been overestimated. Furthermore, by using PCA to derive dietary patterns, many subjective decisions were made that can impact the number and type of dietary patterns( Reference Newby and Tucker 1 , Reference Smith, Emmett and Newby 52 , Reference Hu 99 , Reference Northstone, Ness and Emmett 100 ). The DLW method is the best method to measure EE, and therefore the best method to evaluate the reported EI, but it is too expensive and impractical for application to large-scale epidemiological studies. Therefore, in this study, the reported EI was evaluated against presumed energy requirements as proposed by Black( Reference Black 10 ). The questionnaire used in the present study was designed for a study on diet and breast cancer and focused on recreational physical activity. Therefore, recreational light, moderate and vigorous physical activities were assessed, but not occupational or household physical activities, which are important contributors to total EE. Owing to the lack of information about total physical activity, the PAL value for a sedentary lifestyle was used for our sample of middle-aged and old women. Using this PAL value may have resulted in misclassification of more physically active individuals as plausible reporters. In this cross-sectional study, the measurement of exposure and disease was made at the same time and it was impossible to determine which came first. In addition, we do not have information on whether the participant actually had the disease at the time the questionnaire was filled in. Therefore, we could not adjust for co-morbidity in the analyses. We attempted to adjust for potential confounding in the statistical analyses; however, as there is always a chance of measurement errors in the confounders or unmeasured confounders, we cannot rule out the possibility of residual confounding.
In conclusion, in this large sample of women aged 50–69 years, we identified three dietary patterns: ‘Prudent’, ‘Western’ and ‘Continental’ for both all and plausible reporters. The food groups significantly contributing to the dietary patterns were quite similar for both all and plausible reporters; however, the pattern contributing most to the explanation of variances in the dietary data was the ‘Prudent’ pattern among all reporters and the ‘Western’ pattern among plausible reporters. We also found that under-reporting of EI attenuated the associations between dietary patterns and self-reported chronic diseases. Our findings suggest that under-reporting of food items can result in measurement errors of dietary patterns, which may affect the association between dietary patterns and disease. Investigators should consider reporting effect estimates for the associations between dietary patterns and disease for all individuals as well as restricting analyses to those with plausible intake.
Acknowledgements
This project was supported by the Research Council of Norway (G. U., grant number 196999). The Research Council of Norway had no role in the design, analysis or writing of this article.
M. S. M. carried out the calculations of the daily intakes of energy, nutrients and foods, the statistical analyses and drafted the manuscript. M. B. V. contributed to the statistical analysis, interpretation of the data and the revisions of the manuscript. G. U. designed the study, obtained the funding, planned and executed the data collection process and participated in the discussion of results and development of the manuscript. L. F. A. contributed to the interpretation of the data and the revisions of the manuscript. All authors read and approved the final version of the manuscript.
None of the authors have any financial or other interests concerning the outcomes of the investigation or any conflicts of interest to declare.
Supplementary Material
For supplementary material/s referred to in this article, please visit http://dx.doi.org/doi:10.1017/S000711451600218X
