scholarly journals Data-Driven Dietary Patterns, Nutrient Intake and Body Weight Status in a Cross-Section of Singaporean Children Aged 6–12 Years

Nutrients ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 1335
Author(s):  
Michelle Jie Ying Choy ◽  
Iain Brownlee ◽  
Aoife Marie Murphy
Keyword(s):  
Cluster Analysis ◽  
Total Energy ◽  
Dietary Patterns ◽  
Weight Status ◽  
Disease Risk ◽  
Noncommunicable Disease ◽  
Dietary Habit ◽  
Food Groups ◽  
Dietary Recall ◽  
Western Group

Pattern analysis of children’s diet may provide insights into chronic disease risk in adolescence and adulthood. This study aimed to assess dietary patterns of young Singaporean children using cluster analysis. An existing dataset included 15,820 items consumed by 561 participants (aged 6–12 years) over 2 days of dietary recall. Thirty-seven food groups were defined and expressed as a percentage contribution of total energy. Dietary patterns were identified using k-means cluster analysis. Three clusters were identified, “Western”, “Convenience” and “Local/hawker”, none of which were defined by more prudent dietary choices. The “Convenience” cluster group had the lowest total energy intake (mean 85.8 ± SD 25.3% of Average Requirement for Energy) compared to the other groups (95.4 ± 25.9% for “Western” and 93.4 ± 25.3% for “Local/hawker”, p < 0.001) but also had the lowest calcium intake (66.3 ± 34.7% of Recommended Dietary Allowance), similar to intake in the “Local/hawker” group (69.5 ± 38.9%) but less than the “Western” group (82.8 ± 36.1%, p < 0.001). These findings highlight the need for longitudinal analysis of dietary habit in younger Singaporeans in order to better define public health messaging targeted at reducing risk of major noncommunicable disease.

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

2011 ◽  
Vol 16 (5) ◽  
pp. 848-857 ◽  
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.

scholarly journals The food patterns of a multicenter cohort of Brazilian nulliparous pregnant women

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Maria J. Miele ◽  
Renato T. Souza ◽  
Iracema M. Calderon ◽  
Francisco E. Feitosa ◽  
Débora F. Leite ◽  
...  
Keyword(s):  
Pregnant Women ◽  
Dietary Patterns ◽  
Principal Component ◽  
Good Representation ◽  
Processed Foods ◽  
Food Groups ◽  
Dietary Recall ◽  
Complex Process ◽  
Nutrient Consumption ◽  
Main Components

AbstractAssessment of human nutrition is a complex process, in pregnant women identify dietary patterns through mean nutrient consumption can be an opportunity to better educate women on how to improve their overall health through better eating. This exploratory study aimed to identify a posteriori dietary patterns in a cohort of nulliparous pregnant women. The principal component analysis (PCA) technique was performed, with Varimax orthogonal rotation of data extracted from the 24-h dietary recall, applied at 20 weeks of gestation. We analysed 1.145 dietary recalls, identifying five main components that explained 81% of the dietary pattern of the sample. Dietary patterns found were: Obesogenic, represented by ultra-processed foods, processed foods, and food groups rich in carbohydrates, fats and sugars; Traditional, most influenced by natural, minimally processed foods, groups of animal proteins and beans; Intermediate was similar to the obesogenic, although there were lower loads; Vegetarian, which was the only good representation of fruits, vegetables and dairy products; and Protein, which best represented the groups of proteins (animal and vegetable). The obesogenic and intermediate patterns represented over 37% of the variation in food consumption highlighting the opportunity to improve maternal health especially for women at first mothering.

scholarly journals Early development of dietary patterns: transitions in the contribution of food groups to total energy—Feeding Infants and Toddlers Study, 2008

BMC Nutrition ◽  
2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Kathleen C. Reidy ◽  
Denise M. Deming ◽  
Ronette R. Briefel ◽  
Mary Kay Fox ◽  
Jose M. Saavedra ◽  
...  
Keyword(s):  
Total Energy ◽  
Early Development ◽  
Dietary Patterns ◽  
Food Groups ◽  
Infants And Toddlers

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

2012 ◽  
Vol 109 (11) ◽  
pp. 2050-2058 ◽  
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.

scholarly journals The reproducibility and stability of dietary patterns

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.

scholarly journals Analysis of patterns of food intake in nutritional epidemiology: food classification in principal components analysis and the subsequent impact on estimates for endometrial cancer

2001 ◽  
Vol 4 (5) ◽  
pp. 989-997 ◽  
Author(s):  
Susan E McCann ◽  
James R Marshall ◽  
John R Brasure ◽  
Saxon Graham ◽  
Jo L Freudenheim
Keyword(s):  
New York ◽  
Endometrial Cancer ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Dietary Patterns ◽  
Disease Risk ◽  
Nutritional Epidemiology ◽  
Food Groups ◽  
Diet Study ◽  
Components Analysis

AbstractObjective:To assess the effect of different methods of classifying food use on principal components analysis (PCA)-derived dietary patterns, and the subsequent impact on estimation of cancer risk associated with the different patterns.Methods:Dietary data were obtained from 232 endometrial cancer cases and 639 controls (Western New York Diet Study) using a 190-item semi-quantitative food-frequency questionnaire. Dietary patterns were generated using PCA and three methods of classifying food use: 168 single foods and beverages; 56 detailed food groups, foods and beverages; and 36 less-detailed groups and single food items.Results:Classification method affected neither the number nor character of the patterns identified. However, total variance explained in food use increased as the detail included in the PCA decreased (~8%, 168 items to ~17%, 36 items). Conversely, reduced detail in PCA tended to attenuate the odds ratio (OR) associated with the healthy patterns (OR 0.55, 95% confidence interval (CI) 0.35–0.84 and OR 0.77, 95% CI 0.49–1.20, 168 and 36 items, respectively) but not the high-fat patterns (OR 0.95, 95% CI 0.57–1.58 and OR 0.85, 0.51–1.40, 168 and 36 items, respectively).Conclusions:Greater detail in food-use information may be desirable in determination of dietary patterns for more precise estimates of disease risk.

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

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.

Abstract MP52: Increased Intake of Non-Dairy Sugars in Foods and Beverages is Positively Associated with Annual Increases in Waist Circumference among Overweight and Obese Adolescent Females

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Alexandra Lee ◽  
Ritam Chowdhury ◽  
Jean Welsh
Keyword(s):  
Waist Circumference ◽  
Total Energy ◽  
Weight Status ◽  
Disease Risk ◽  
Cardiovascular Disease Risk ◽  
Health Study ◽  
Continuous Change ◽  
Sugar Consumption ◽  
Obese Adolescent ◽  
Multiple Observations

Introduction: Waist circumference (WC) is an important measure of adiposity that predicts cardiovascular disease risk in adolescents. Previous studies have shown a positive association between sugar-sweetened beverages and WC in adolescents but it is unknown if this effect differs when sugars are consumed in foods vs. beverages. Hypothesis: Increased sugar consumption from both foods and beverages is associated with increased WC. Methods: NHLBI’s Growth and Health Study was a 10-year cohort study of Caucasian (n=1,166) and African-American (n=1,213) girls aged 9 and 10 at baseline who were recruited from three sites in the US in 1987 and 1988. Minimum WC was measured annually except at baseline. Diet was assessed using a three-day food record in eight of ten years; nutrient content was determined using the Nutrient Data System for Research. Girls were grouped by weight status (normal vs. overweight or obese). One-year changes in WC and in consumption of liquid and solid non-dairy sugars were calculated by subtracting each year’s measure from its value for the subsequent year. A linear mixed model was used to accommodate multiple observations per individual. After modeling change in sugar consumption as quintiles, a linear trend was observed and further modeling was conducted using continuous change in sugars consumption. Model I adjusted for race, age, puberty stage, initial WC, initial BMI, change in height, dieting status, initial and change in physical activity, initial and change in % energy from fat and % energy from carbohydrates, and initial consumption of sugars. Model II additionally adjusted for initial and change in total energy. Results: Among normal weight girls using Model I, significant increases in WC were observed with each additional teaspoon of liquid, 0.19mm (0.05, 0.33) (p=0.006), but not solid sugars 0.11mm (-0.02, 0.24) (p=0.11). These increases were no longer significant after adjusting for total energy in Model II. Among overweight and obese girls in Model I, increases in WC were observed with each additional teaspoon of solid sugars, 0.42 mm (0.22, 0.62) (p<0.0001) and liquid sugars, 0.26 mm (0.09, 0.44) (p=0.004). This association was slightly attenuated but still significant after controlling for total energy with WC increases of 0.31 mm (0.10, 0.52) (p=0.005) with each teaspoon of solid sugars and 0.19 mm (0.005, 0.37) (p=0.049) with each teaspoon of liquid sugars. Conclusions: Increases in non-dairy sugars from both foods and beverage were associated with increased WC among overweight and obese adolescents, both before and after adjusting for total energy intake.

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

2008 ◽  
Vol 101 (4) ◽  
pp. 598-608 ◽  
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.

scholarly journals Defining a Healthy Diet: Evidence for the Role of Contemporary Dietary Patterns in Health and Disease

Nutrients ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 334 ◽  
Author(s):  
Hellas Cena ◽  
Philip C. Calder
Keyword(s):  
Dietary Patterns ◽  
Fruits And Vegetables ◽  
Disease Risk ◽  
Eating Habits ◽  
Epidemiological Studies ◽  
Healthy Diet ◽  
Total Evidence ◽  
Food Groups ◽  
Health And Disease ◽  
Definition Of

The definition of what constitutes a healthy diet is continually shifting to reflect the evolving understanding of the roles that different foods, essential nutrients, and other food components play in health and disease. A large and growing body of evidence supports that intake of certain types of nutrients, specific food groups, or overarching dietary patterns positively influences health and promotes the prevention of common non-communicable diseases (NCDs). Greater consumption of health-promoting foods and limited intake of unhealthier options are intrinsic to the eating habits of certain regional diets such as the Mediterranean diet or have been constructed as part of dietary patterns designed to reduce disease risk, such as the Dietary Approaches to Stop Hypertension (DASH) or Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diets. In comparison with a more traditional Western diet, these healthier alternatives are higher in plant-based foods, including fresh fruits and vegetables, whole grains, legumes, seeds, and nuts and lower in animal-based foods, particularly fatty and processed meats. To better understand the current concept of a “healthy diet,” this review describes the features and supporting clinical and epidemiologic data for diets that have been shown to prevent disease and/or positively influence health. In total, evidence from epidemiological studies and clinical trials indicates that these types of dietary patterns reduce risks of NCDs including cardiovascular disease and cancer.

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