Evidence mapPaperPMID 35816468Full record

ArticleThe Journal of nutrition2022

A Clustering Approach to Meal-Based Analysis of Dietary Intakes Applied to Population and Individual Data.

Cathal O'Hara, Aifric O'Sullivan, Eileen R Gibney

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Article in The Journal of nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Cathal O'HaraInsight Centre for Data Analytics, University College Dublin, Dublin, Ireland.ORCID 0000-0001-7703-7897
Aifric O'SullivanUCD Institute of Food and Health, University College Dublin, Dublin, Ireland.ORCID 0000-0002-7441-1983
Eileen R GibneyInsight Centre for Data Analytics, University College Dublin, Dublin, Ireland.ORCID 0000-0001-9465-052X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExamination of meal intakes can elucidate the role of individual meals or meal patterns in health not evident by examining nutrient and food intakes. To date, meal-based research has been limited to focus on population rather than individual intakes, without considering portions or nutrient content when characterizing meals.

objectivesWe aimed to characterize meals commonly consumed, incorporating portions and nutritional content, and to determine the accuracy of nutrient intake estimates using these meals at both population and individual levels.

methodsThe 2008-2010 Irish National Adult Nutrition Survey (NANS) data were used. A total of 1500 participants, with a mean ± SD age of 44.5 ± 17.0 y and BMI of 27.1 ± 5.0 kg/m2, recorded their intake using a 4-d weighed food diary. Food groups were identified using k-means clustering. Partitioning around the medoids clustering was used to categorize similar meals into groups (generic meals) based on their Nutrient Rich Foods Index (NRF9.3) score and the food groups that they contained. The nutrient content for each generic meal was defined as the mean content of the grouped meals. Seven standard portion sizes were defined for each generic meal. Mean daily nutrient intakes were estimated using the original and the generic data.

resultsThe 27,336 meals consumed were aggregated to 63 generic meals. Effect sizes from the comparisons of mean daily nutrient intakes (from the original compared with generic meals) were negligible or small, with P values ranging from <0.001 to 0.941. When participants were classified according to nutrient-based guidelines (high, adequate, or low), the proportion of individuals who were classified into the same category ranged from 55.3% to 91.5%.

conclusionsA generic meal-based method can estimate nutrient intakes based on meal rather than food intake at the sample population and individual levels. Future work will focus on incorporating this concept into a meal-based dietary intake assessment tool.

Indexed as

Energy IntakeMealsAdultCluster AnalysisDietDiet RecordsEatingHumansclusteringdietary assessmentfood combinationsgeneric mealsmeal patterns

Identifiers

PMID35816468
PMCPMC9535445

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.