Meal patterns associated with energy intake in people with obesity

Abstract It is widely assumed that people with obesity have several common eating patterns, including breakfast-skipping (1), eating during the night (2) and high fast-food consumption (3). However, differences in individual meal and dietary patterns may be crucial to optimizing obesity treatment. Therefore, we investigated the inter-individual variation in eating patterns, hypothesizing that individuals with obesity show different dietary and meal patterns, and that these associate with self-reported energy intake (rEI) and/or anthropometric measures. Cross-sectional data from 192 participants (aged 20–55 years) with obesity, including 6 days of weighed food records, were analyzed. Meal patterns and dietary patterns were derived using exploratory hierarchical cluster analysis and k-means cluster analysis, respectively. Five clear meal patterns were found based on the time-of-day with the highest mean rEI. The daily rEI (mean ± SD kcal) was highest among “midnight-eaters” (2550 ± 550), and significantly (p < 0.05) higher than “dinner-eaters” (2060 ± 550), “lunch-eaters” (2080 ± 520), and “supper-eaters” (2100 ± 460), but not “regular-eaters” (2330 ± 650). Despite differences of up to 490 kcal between meal patterns, there were no significant differences in anthropometric measures or physical activity level (PAL). Four dietary patterns were also found with significant differences in intake of specific food groups, but without significant differences in anthropometry, PAL, or rEI. Our data highlight meal timing as a determinant of individual energy intake in people with obesity. The study supports the importance of considering a person’s specific meal pattern, with possible implications for more person-focused guidelines and targeted advice.

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Comparison of cluster and principal component analysis techniques to derive dietary patterns in Irish adults

British Journal Of Nutrition ◽

10.1017/s0007114508014128 ◽

2008 ◽

Vol 101 (4) ◽

pp. 598-608 ◽

Cited By ~ 69

Author(s):

Áine P. Hearty ◽

Michael J. Gibney

Keyword(s):

Principal Component Analysis ◽

Cluster Analysis ◽

Energy Intake ◽

Dietary Patterns ◽

Principal Component ◽

Food Group ◽

Food Groups ◽

Healthy Foods ◽

Percentage Contribution ◽

Group Variable

The aims of the present study were to examine and compare dietary patterns in adults using cluster and factor analyses and to examine the format of the dietary variables on the pattern solutions (i.e. expressed as grams/day (g/d) of each food group or as the percentage contribution to total energy intake). Food intake data were derived from the North/South Ireland Food Consumption Survey 1997–9, which was a randomised cross-sectional study of 7 d recorded food and nutrient intakes of a representative sample of 1379 Irish adults aged 18–64 years. Cluster analysis was performed using thek-means algorithm and principal component analysis (PCA) was used to extract dietary factors. Food data were reduced to thirty-three food groups. For cluster analysis, the most suitable format of the food-group variable was found to be the percentage contribution to energy intake, which produced six clusters: ‘Traditional Irish’; ‘Continental’; ‘Unhealthy foods’; ‘Light-meal foods & low-fat milk’; ‘Healthy foods’; ‘Wholemeal bread & desserts’. For PCA, food groups in the format of g/d were found to be the most suitable format, and this revealed four dietary patterns: ‘Unhealthy foods & high alcohol’; ‘Traditional Irish’; ‘Healthy foods’; ‘Sweet convenience foods & low alcohol’. In summary, cluster and PCA identified similar dietary patterns when presented with the same dataset. However, the two dietary pattern methods required a different format of the food-group variable, and the most appropriate format of the input variable should be considered in future studies.

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Relationship between Eating Patterns and Body Composition among Young Females in Qatar

University of the Future: Re-Imagining Research and Higher Education ◽

10.29117/quarfe.2020.0219 ◽

2020 ◽

Author(s):

Fatma Elsayed ◽

Aram Alhammadi ◽

Alanood Alahmad ◽

Zahra Babiker ◽

Abdelhamid Kerkadi

Keyword(s):

Body Composition ◽

Dietary Patterns ◽

Fast Food ◽

Dietary Habits ◽

Communicable Disease ◽

Normal Weight ◽

Female Students ◽

Eating Patterns ◽

Young Females ◽

Significant Difference

The prevalence of obesity has been increased in Qatar, with the transition from healthy to unhealthy dietary habits. Behavioral factors that are associated with obesity are, long-term imbalanced energy intake, high screen time, skipping breakfast and physical inactivity. Changes in body composition and percent body fat (PBF) increase the risk of non-communicable disease. This study is the first study conducted in Qatar to investigate the relationship between dietary patterns and body composition among young females at Qatar University. This cross-sectional study consisted of 766 healthy female students Qatari and non-Qatari aged from 18-26 years randomly selected from different colleges at Qatar University. A validate questionnaire was used in order to collect data about healthy and unhealthy dietary patterns. Anthropometric measurements involved body weight, height, waist-to-height ratio (WHtR), waist circumference (WC), body mass index (BMI) and body composition using “Seca285”, “Seca203” and “InbodyBiospace 720”. Dietary patterns were identified by using factor loading. Linear regression was used to estimate confidence intervals and regression coefficient. More than half of the participants had a normal weight (65.1%), whereas 22.8 % and 12.0% were overweight and obese, respectively. Fat mass, BMI and PBF were slightly increased with age, but there was no significant difference. Factor analysis identified two dietary patterns: unhealthy patterns and healthy patterns. The frequent intake of vegetables and fruits was significant among high PBF female students (p=0.045 and p=0.001, respectively). The frequent intake of fast food was higher for overweight female students but there was no significant difference (p=0.289), whereas, the frequent intake of sweetened beverages was associated with higher significant rate of normal weight among female students (p = 0.009). No significant relation was found between dietary patterns, BMI and PBF. In conclusion, body composition is not significantly associated with healthy and unhealthy eating patterns among young females.

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Strategies to Address Misestimation of Energy Intake Based on Self-Report Dietary Consumption in Examining Associations Between Dietary Patterns and Cancer Risk

Nutrients ◽

10.3390/nu11112614 ◽

2019 ◽

Vol 11 (11) ◽

pp. 2614

Author(s):

Solbak ◽

Al Rajabi ◽

Akawung ◽

Lo Siou ◽

Kirkpatrick ◽

...

Keyword(s):

Cluster Analysis ◽

Cancer Risk ◽

Energy Intake ◽

Dietary Patterns ◽

Nearest Neighbor ◽

Self Report ◽

Cox Proportional Hazard ◽

Disease Associations ◽

Cox Proportional Hazard Regression ◽

Risk Of Cancer

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.

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Dietary patterns in Irish adolescents: a comparison of cluster and principal component analyses

Public Health Nutrition ◽

10.1017/s1368980011002473 ◽

2011 ◽

Vol 16 (5) ◽

pp. 848-857 ◽

Cited By ~ 15

Author(s):

Áine P Hearty ◽

Michael J Gibney

Keyword(s):

Cluster Analysis ◽

Total Energy ◽

Dietary Patterns ◽

Principal Component ◽

Food Groups ◽

Food Diary ◽

Breakfast Cereal ◽

Percentage Contribution ◽

Main Meal ◽

The Impact

AbstractObjectivePattern analysis of adolescent diets may provide an important basis for nutritional health promotion. The aims of the present study were to examine and compare dietary patterns in adolescents using cluster analysis and principal component analysis (PCA) and to examine the impact of the format of the dietary variables on the solutions.DesignAnalysis was based on the Irish National Teens Food Survey, in which food intake data were collected using a semi-quantitative 7 d food diary. Thirty-two food groups were created and were expressed as either g/d or percentage contribution to total energy. Dietary patterns were identified using cluster analysis (k-means) and PCA.SettingRepublic of Ireland, 2005–2006.SubjectsA representative sample of 441 adolescents aged 13–17 years.ResultsFive clusters based on percentage contribution to total energy were identified, ‘Healthy’, ‘Unhealthy’, ‘Rice/Pasta dishes’, ‘Sandwich’ and ‘Breakfast cereal & Main meal-type foods’. Four principal components based on g/d were identified which explained 28 % of total variance: ‘Healthy foods’, ‘Traditional foods’, ‘Sandwich foods’ and ‘Unhealthy foods’.ConclusionsA ‘Sandwich’ and an ‘Unhealthy’ pattern are the main dietary patterns in this sample. Patterns derived from either cluster analysis or PCA were comparable, although it appears that cluster analysis also identifies dietary patterns not identified through PCA, such as a ‘Breakfast cereal & Main meal-type foods’ pattern. Consideration of the format of the dietary variable is important as it can directly impact on the patterns obtained for both cluster analysis and PCA.

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The under-reporting of energy intake influences the dietary pattern reported by obese women in the waiting list for bariatric surgery

Revista de Nutrição ◽

10.1590/1678-98652018000200009 ◽

2018 ◽

Vol 31 (2) ◽

pp. 235-249 ◽

Cited By ~ 1

Author(s):

Michele Novaes RAVELLI ◽

Maria Márcia Pereira SARTORI ◽

José Eduardo CORRENTE ◽

Irineu RASERA JUNIOR ◽

Noa Pereira Prada de SOUZA ◽

...

Keyword(s):

Physical Activity ◽

Bariatric Surgery ◽

Energy Intake ◽

Dietary Patterns ◽

Activity Level ◽

Waiting List ◽

Obese Women ◽

Nutrient Intakes ◽

Study Results

ABSTRACT Objective To verify the interference of the energy intake under-reporting in the determination of the dietary patterns and nutrient intakes reported by obese women in the waiting list for bariatric surgery. Methods The study included 412 women aged 20 to 45 years with a body mass index ranging from 35 to 60kg/m2 who were on waiting list for bariatric surgery. Data from three reported food intake and physical activity, body weight, and height were used for estimating the reported energy intake, physical activity level, and resting energy expenditure. Subsequently, it was checked the biological plausibility of the reported energy intakes, classifying all participants as plausible reporters or under-reporters. Exploratory factor analysis was used to determine the participants’ dietary patterns. The Mann-Whitney test assessed the reported energy and nutrient intakes between plausible reporters and under-reporters groups. The Z-test assessed the variables of plausible reporters or under-reporters in relation to all participants of the study. Results Six dietary patterns were determined for all participants of study. After excluding information from under-reporting women, only two dietary patterns remained similar to those of all participants, while three other dietary patterns presented different conformations from food subgroups to plausible reporters. The reported energy intake did not present difference for the subgroups of fruits, leaf vegetables and vegetables. However, the energetic value reported for the other food subgroups was higher for the plausible reporters. Conclusion The under-reporting of energy intake influenced the determination of dietary patterns of obese women waiting for bariatric surgery.

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Longitudinal comparisons of dietary patterns derived by cluster analysis in 7- to 13-year-old children

British Journal Of Nutrition ◽

10.1017/s0007114512004072 ◽

2012 ◽

Vol 109 (11) ◽

pp. 2050-2058 ◽

Cited By ~ 35

Author(s):

Kate Northstone ◽

Andrew D. A. C. Smith ◽

P. K. Newby ◽

Pauline M. Emmett

Keyword(s):

Cluster Analysis ◽

Dietary Patterns ◽

Maternal Education ◽

Average Weight ◽

Food Groups ◽

Number Of Children ◽

High Consumption ◽

Parents And Children ◽

Over Time ◽

Time Point

Little is known about changes in dietary patterns over time. The present study aims to derive dietary patterns using cluster analysis at three ages in children and track these patterns over time. In all, 3 d diet diaries were completed for children from the Avon Longitudinal Study of Parents and Children at 7, 10 and 13 years. Children were grouped based on the similarities between average weight consumed (g/d) of sixty-two food groups using k-means cluster analysis. A total of four clusters were obtained at each age, with very similar patterns being described at each time point: Processed (high consumption of processed foods, chips and soft drinks), Healthy (high consumption of high-fibre bread, fruit, vegetables and water), Traditional (high consumption of meat, potatoes and vegetables) and Packed Lunch (high consumption of white bread, sandwich fillings and snacks). The number of children remaining in the same cluster at different ages was reasonably high: 50 and 43 % of children in the Healthy and Processed clusters, respectively, at age 7 years were in the same clusters at age 13 years. Maternal education was the strongest predictor of remaining in the Healthy cluster at each time point – children whose mothers had the highest level of education were nine times more likely to remain in that cluster compared to those with the lowest. Cluster analysis provides a simple way of examining changes in dietary patterns over time, and similar underlying patterns of diet at two ages during late childhood, that persisted through to early adolescence.

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The reproducibility and stability of dietary patterns

Proceedings of The Nutrition Society ◽

10.1017/s0029665120004644 ◽

2020 ◽

Vol 79 (OCE2) ◽

Author(s):

Orla Prendiville ◽

Aoife E. McNamara ◽

Lorraine Brennan

Keyword(s):

Cluster Analysis ◽

Dietary Patterns ◽

Dietary Pattern ◽

Food Group ◽

Multiple Time ◽

Food Groups ◽

Energy Contribution ◽

Dietary Analysis ◽

Breakfast Cereals ◽

Time Points

AbstractA person's dietary intake consists of multiple foods eaten as part of a meal as opposed to any one single food/nutrient. Therefore, it is important to understand the interactions between foods and how they affect diet-disease associations. As a result, dietary patterns have emerged as important tools in nutrition research. The objective of the current study is to assess the reproducibility and stability of dietary patterns across four different time-points. Anthropometric measurements were taken from a subset of participants of a free-living cohort study (n = 94), followed by the administration of a 24-hour dietary recall once a month, for four months. The dietary data was entered into dietary analysis software, Nutritics, by two researchers independently, and cross-checked. Foods were assigned to one of 33 predefined food groups, which were further collapsed to 18 food groups based on previous research. Statistical analysis was then performed on the final dataset. Intra-class correlation coefficients were derived to assess the reproducibility of each food group across the four time-points. Variables were standardized using z-scores and dietary patterns were derived using K-means cluster analysis. Stability was assessed by coding participants into one of six groups based on their dietary pattern transition between visit one and four. Analysis of this sub cohort revealed that the intake of food groups (% energy contribution) was reproducible across the time-points. The majority had good to very good agreement, with vegetables and vegetable dishes having the strongest agreement (ICC = 0.831) followed by milk and yogurts (ICC = 0.773), fruit and fruit dishes (ICC = 0.729), and breakfast cereals (ICC = 0.680). Two distinct dietary patterns were identified at each time-point; a ‘Healthy’ and an ‘Unhealthy’ dietary pattern. The ‘Healthy’ dietary pattern was characterized by a significantly higher energy contribution (p < 0.05) from the following food groups – vegetables and vegetable dishes; fruit and fruit dishes; milk and yogurts; breakfast cereals; butter, spreading fats and oils. The analysis on stability demonstrated 42% of participants remained in the same dietary pattern, while 58% transitioned from one dietary pattern to the other. Our results to date demonstrate that two distinct dietary patterns can be derived across multiple time-points using cluster analysis and the food group composition of these dietary patterns can be considered reproducible. Future work will explore these dietary patterns further incorporating the entire cohort and linking stability to health parameters.

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The effect of different methods to identify, and scenarios used to address energy intake misestimation on dietary patterns derived by cluster analysis

Nutrition Journal ◽

10.1186/s12937-021-00696-3 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Geraldine Lo Siou ◽

Alianu K. Akawung ◽

Nathan M. Solbak ◽

Kathryn L. McDonald ◽

Ala Al Rajabi ◽

...

Keyword(s):

Cluster Analysis ◽

Energy Expenditure ◽

Total Energy ◽

Energy Intake ◽

Dietary Patterns ◽

Total Energy Expenditure ◽

Nutritional Epidemiology ◽

Adjusted Rand Index ◽

Cluster Solution ◽

The Impact

Abstract Background All self-reported dietary intake data are characterized by measurement error, and validation studies indicate that the estimation of energy intake (EI) is particularly affected. Methods Using self-reported food frequency and physical activity data from Alberta’s Tomorrow Project participants (n = 9847 men 16,241 women), we compared the revised-Goldberg and the predicted total energy expenditure methods in their ability to identify misreporters of EI. We also compared dietary patterns derived by k-means clustering under different scenarios where misreporters are included in the cluster analysis (Inclusion); excluded prior to completing the cluster analysis (ExBefore); excluded after completing the cluster analysis (ExAfter); and finally, excluded before the cluster analysis but added to the ExBefore cluster solution using the nearest neighbor method (InclusionNN). Results The predicted total energy expenditure method identified a significantly higher proportion of participants as EI misreporters compared to the revised-Goldberg method (50% vs. 47%, p < 0.0001). k-means cluster analysis identified 3 dietary patterns: Healthy, Meats/Pizza and Sweets/Dairy. Among both men and women, participants assigned to dietary patterns changed substantially between ExBefore and ExAfter and also between the Inclusion and InclusionNN scenarios (Hubert and Arabie’s adjusted Rand Index, Kappa and Cramer’s V statistics < 0.8). Conclusions Different scenarios used to account for EI misreporters influenced cluster analysis and hence the composition of the dietary patterns. Continued efforts are needed to explore and validate methods and their ability to identify and mitigate the impact of EI misestimation in nutritional epidemiology.

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An Analysis of Contributors to Energy Intake Among Middle Aged and Elderly Adults

Current Research in Nutrition and Food Science Journal ◽

10.12944/crnfsj.4.special-issue-elderly-november.02 ◽

2016 ◽

Vol 4 (Special-Issue-November) ◽

pp. 08-18 ◽

Cited By ~ 1

Author(s):

Natasha Hurree ◽

Rajesh Jeewon

Keyword(s):

Energy Intake ◽

Eating Habits ◽

Economic Status ◽

High Body Mass Index ◽

Activity Level ◽

Food Groups ◽

Middle Aged ◽

Unhealthy Eating ◽

Increased Risk ◽

Food Commodities

Adulthood and middle age is widely recognized as the time of life when unhealthy eating habits may develop. Data from various studies have demonstrated that changes in eating habits may also occur during old age. It is essential to acknowledge that a high consumption of certain food groups such as sweetened beverages, meat and eggs may contribute to an increased energy intake. This obviously results in high body mass index (BMI) and consequently an increased risk of non-communicable diseases (NCDs) and obesity. Energy intake among middle aged and elderly individuals may be influenced by socio demographic factors (for example: age, gender, socio economic status), social factors (for example: marital status), environmental factors like access to food commodities as well as nutrition knowledge and physical activity level. The present review highlights eating habits, contribution of specific food groups to energy intake and the influence of several factors on energy intake among the middle aged and elderly population.

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A comparison of three statistical methods applied in the identification of eating patterns

Cadernos de Saúde Pública ◽

10.1590/s0102-311x2010001100015 ◽

2010 ◽

Vol 26 (11) ◽

pp. 2138-2148 ◽

Cited By ~ 16

Author(s):

Diana Barbosa Cunha ◽

Renan Moritz Varnier Rodrigues de Almeida ◽

Rosângela Alves Pereira

Keyword(s):

Cluster Analysis ◽

Factor Analysis ◽

Statistical Methods ◽

Dietary Patterns ◽

Metropolitan Region ◽

Eating Patterns ◽

Reduced Rank Regression ◽

Cross Sectional Survey ◽

Cross Sectional ◽

Traditional Patterns

This work aimed to compare the results of three statistical methods applied in the identification of dietary patterns. Data from 1,009 adults between the ages of 20 and 65 (339 males and 670 females) were collected in a population-based cross-sectional survey in the Metropolitan Region of Rio de Janeiro, Brazil. Information on food consumption was obtained using a semi-quantitative food frequency questionnaire. A factor analysis, cluster analysis, and reduced rank regression (RRR) analysis were applied to identify dietary patterns. The patterns identified by the three methods were similar. The factor analysis identified "mixed", "Western", and "traditional" eating patterns and explained 35% of the data variance. The cluster analysis identified "mixed" and "traditional" patterns. In the RRR, the consumption of carbohydrates and lipids were included as response variables and again "mixed" and "traditional" patterns were identified. Studies comparing these methods can help to inform decisions as to which procedures best suit a specific research scenario.

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