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

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

Nutrients ◽

10.3390/nu13041335 ◽

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.

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Maternal dietary patterns and associated nutrient intakes during each trimester of pregnancy

Public Health Nutrition ◽

10.1017/s1368980012000997 ◽

2012 ◽

Vol 16 (1) ◽

pp. 97-107 ◽

Cited By ~ 44

Author(s):

Ciara A McGowan ◽

Fionnuala M McAuliffe

Keyword(s):

Cluster Analysis ◽

Pregnant Women ◽

Dietary Patterns ◽

Dietary Pattern ◽

Dietary Intervention ◽

First Trimester ◽

Dietary Guidelines ◽

Food Groups ◽

Food Diary ◽

Nutrient Intakes

AbstractObjectiveTo determine the main dietary patterns of pregnant women during each of the three trimesters of pregnancy and to examine associated nutrient intakes.DesignParticipants completed a 3 d food diary during each trimester of pregnancy. Thirty-six food groups were created and dietary patterns were derived using k-means cluster analysis.SettingNational Maternity Hospital, Dublin, Ireland.SubjectsTwo hundred and eighty-five healthy pregnant women aged between 20 and 41 years.ResultsTwo dietary patterns were identified at each time point. They were labelled ‘Unhealthy’ (n 143, 150 and 155 at trimester 1, 2 and 3, respectively) and ‘Health Conscious’ (n 142, 135 and 130 at trimester 1, 2 and 3, respectively). Women in the ‘Health Conscious’ cluster were significantly older, had lower BMI and were higher educated than those in the ‘Unhealthy’ cluster. Of those in the ‘Unhealthy’ cluster in the first trimester (n 143), 103 (72·0 %) continued in this dietary pattern into trimester 2 and eighty-one (56·6 %) continued into trimester 3. Of those in the ‘Health Conscious’ cluster in trimester 1 (n 142), ninety-five (66·9 %) continued in this dietary pattern into trimester 2 and sixty-nine (48·6 %) continued into trimester 3.ConclusionsCluster analysis produced two clearly defined dietary patterns at each stage of pregnancy. Knowledge of maternal dietary patterns is important for the development of pregnancy-specific dietary guidelines. Identifying women with an ‘Unhealthy’ dietary pattern in early pregnancy affords the opportunity for a dietary intervention which may positively impact both maternal and infant health.

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

Scientific Reports ◽

10.1038/s41598-021-95185-2 ◽

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.

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Comparing two methods for deriving dietary patterns associated with risk of metabolic syndrome among middle-aged and elderly Taiwanese adults with impaired kidney function

10.21203/rs.3.rs-29635/v2 ◽

2020 ◽

Author(s):

Adi Lukas Kurniawan ◽

Chien-Yeh Hsu ◽

Hsiu-An Lee ◽

Hsiao-Hsien Rau ◽

Rathi Paramastri ◽

...

Keyword(s):

Metabolic Syndrome ◽

Kidney Function ◽

Dietary Patterns ◽

Dietary Pattern ◽

Density Lipoprotein ◽

Principal Component ◽

Dietary Guidelines ◽

Food Groups ◽

Reduced Rank Regression ◽

Sugary Drinks

Abstract Background: Dietary patterns were associated with the risk of chronic disease development and outcome-related diseases. In this study, we aimed to compare the correlation between dietary patterns and metabolic syndrome (MetS) using two methods for identifying dietary patterns.Methods: The participants (n = 25,569) aged ≥ 40 years with impaired kidney function were retrieved from Mei Jau (MJ) Health Screening database from 2008 to 2010. Dietary patterns were identified by principal component analysis (PCA) and reduced rank regression (RRR) from twenty-two food groups using PROC FACTOR and PROC PLS functions.Results: We identified two similar dietary pattern characteristics (high intakes of deep fried foods, preserved or processed foods, dipping sauce, meat, sugary drinks, organ meats, jam/honey, fried rice/flour products, instant noodles and eggs) derived by PCA and RRR. Logistic regression analysis revealed that RRR-derived dietary pattern scores were positively associated with an odds ratio (OR = 1.70, 95% CI: 1.56, 1.86) of having MetS than PCA-derived dietary pattern scores (OR = 1.38, 95% CI: 1.27, 1.51). The correlations between RRR-derived dietary pattern scores and elevated systolic and diastolic blood pressure (OR = 1.30 for both) or low high density lipoprotein cholesterol in women (OR = 1.32) were statistically significant but not significant in PCA-derived dietary pattern scores.Conclusions: Our findings suggest that RRR gives better results when studying behavior related dietary patterns in association with MetS. RRR may be more preferable to provide dietary information for developing dietary guidelines among people with MetS. Further studies with prospective measurements are needed to verify whether RRR is a useful analytic tool for the association between dietary patterns and other chronic diseases.

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Remote communication in Scrum teams – a COVID-19 preventive measure or work time optimisation?

e-mentor ◽

10.15219/em92.1546 ◽

2021 ◽

Vol 92 (5) ◽

pp. 81-90

Author(s):

Kamil Brodnicki ◽

Keyword(s):

Principal Component Analysis ◽

Cluster Analysis ◽

Principal Component ◽

Preventive Measure ◽

Remote Work ◽

Remote Communication ◽

Work Time ◽

Research Material ◽

And Cluster Analysis ◽

The Impact

The article presents the impact of remote work, resulting from the COVID-19 pandemic, on the functioning of Scrum teams. Attempts have been made to analyse the positive and negative aspects of remote work. The article also looks at the impact of remote work on the level of communication and efficiency of Scrum teams. For this purpose, the author conducted research on a sample of 40 organisations that declared to use Scrum methodology, using 187 questionnaires as the research material. The study was carried out at the turn of April and May 2021 and was carried out using the CAWI technique. The obtained results were analysed using the Principal Component Analysis and Cluster Analysis methods, and enable defining a picture of an organisation’s readiness to work remotely. In addition, they also allowed for an assessment of how the infrastructure used for remote work communication translates into the organisation of Sprint meetings. This paper presents conclusions aimed at counteracting the observed irregularities detected during the tests. At the end, the author proposes solutions that could improve communication within Scrum teams, with remote work in mind.

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La Niña weather impacts dietary patterns and dietary diversity among children in the Peruvian Amazon

Public Health Nutrition ◽

10.1017/s1368980020003705 ◽

2020 ◽

pp. 1-11

Author(s):

Ramya Ambikapathi ◽

Margaret N Kosek ◽

Gwenyth O Lee ◽

Maribel Paredes Olortegui ◽

Benjamin Zaitchik ◽

...

Keyword(s):

Dietary Patterns ◽

Dietary Diversity ◽

Southern Oscillation ◽

La Niña ◽

Birth Cohort Study ◽

Peruvian Amazon ◽

Food Groups ◽

Social Strategies ◽

La Nina ◽

The Impact

Abstract Objective: In 2011–2012, severe El Niño Southern Oscillation (ENSO) conditions (La Niña) led to massive flooding and temporarily displacement in the Peruvian Amazon. Our aims were to examine the impact of this ENSO exposure on child diets, in particular: (1) frequency of food consumption patterns, (2) the amount of food consumed (g/d), (3) dietary diversity (DD), (4) consumption of donated foods, among children aged 9–36 months living in the outskirts of City of Iquitos in the Amazonian Peru. Design: This was a longitudinal study that used quantitative 24-h recall dietary data collection from children aged 9–36 months from 2010 to 2014 as part of the MAL-ED birth cohort study. Setting: Iquitos, Loreto, Peru. Participants: Two hundred and fifty-two mother–child dyads. Results: The frequency of grains, rice, dairy and sugar in meals reduced by 5–7 %, while the frequency of plantain in meals increased by 24 % after adjusting for covariates. ENSO exposure reduced girl’s intake of plantains and sugar. Despite seasonal fluctuations in the availability of fruits, vegetables and fish, DD remained constant across seasons and as children aged. However, DD was significantly reduced under moderate La Niña conditions by 0·32 (P < 0·05) food groups. Adaptive social strategies such as consumption of donated foods were significantly higher among households with girls. Conclusions: This is the first empirical study to show differential effect of the ENSO on the dietary patterns of children, highlighting differences by gender. Public health nutrition programmes should be climate- and gender-sensitive in their efforts to safeguard the diets of vulnerable populations.

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Clustering of Dietary Patterns and Lifestyles Among Spanish Children in the EsNuPI Study †

Nutrients ◽

10.3390/nu12092536 ◽

2020 ◽

Vol 12 (9) ◽

pp. 2536

Author(s):

Julio Plaza-Díaz ◽

Esther Molina-Montes ◽

María José Soto-Méndez ◽

Casandra Madrigal ◽

Ángela Hernández-Ruiz ◽

...

Keyword(s):

Dietary Patterns ◽

Dietary Pattern ◽

Parental Education ◽

Dietary Habits ◽

Pediatric Population ◽

Age Groups ◽

Principal Component ◽

Lifestyle Factors ◽

Study Cohort ◽

Food Groups

Dietary patterns (DPs) are known to be tied to lifestyle behaviors. Understanding DPs and their relationships with lifestyle factors can help to prevent children from engaging in unhealthy dietary practices. We aimed to describe DPs in Spanish children aged 1 to <10 years and to examine their associations with sociodemographic and lifestyle variables. The consumption of toddler and young children milk formulas, enriched and fortified milk within the Spanish pediatric population is increasing, and there is a lack of evidence whether the consumption of this type of milk is causing an impact on nutrient intakes and if they are helping to reach the nutrient recommendations. Within the Nutritional Study in the Spanish Pediatric Population (EsNuPI), we considered two study cohorts and three different age groups in three year-intervals in each of them. The study cohort included 740 children in a representative sample of the urban non-vegan Spanish population and 772 children in a convenience cohort of adapted milk consumers (AMS) (including follow-on formula, toddler’s milk, growing up milk, and fortified and enriched milks) who provided information about sociodemographics, lifestyle, and dietary habits; a food frequency questionnaire was used for the latter. Principal component analysis was performed to identify DPs from 18 food groups. Food groups and sociodemographic/lifestyle variables were combined through a hierarchical cluster algorithm. Three DPs predominated in every age group and study sample: a palatable energy-dense food dietary pattern, and two Mediterranean-like DPs. However, children from the AMS showed a predominant dietary pattern markedly related to the Mediterranean diet, with high consumption of cereals, fruits and vegetables, as well as milk and dairy products. The age of children and certain lifestyle factors, namely level of physical activity, parental education, and household income, correlated closely with the dietary clusters. Thus, the findings provide insight into designing lifestyle interventions that could reverse the appearance of unhealthy DPs in the Spanish child population.

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