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Metabolomic-based identification of clusters that reflect dietary patterns

Overview of attention for article published in Molecular Nutrition & Food Research, July 2017
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (56th percentile)

Mentioned by

twitter
10 tweeters

Citations

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19 Dimensions

Readers on

mendeley
87 Mendeley
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Title
Metabolomic-based identification of clusters that reflect dietary patterns
Published in
Molecular Nutrition & Food Research, July 2017
DOI 10.1002/mnfr.201601050
Pubmed ID
Authors

Helena Gibbons, Eibhlin Carr, Breige A. McNulty, Anne P. Nugent, Janette Walton, Albert Flynn, Michael J. Gibney, Lorraine Brennan

Abstract

Classification of subjects into dietary patterns generally relies on self-reporting dietary data which are prone to error. The aim of the present study was to develop a model for objective classification of people into dietary patterns based on metabolomic data. Dietary and urinary metabolomic data from the National Adult Nutrition Survey (NANS) was used in the analysis (n = 567). Two-step cluster analysis was applied to the urinary data to identify clusters. The subsequent model was used in an independent cohort to classify people into dietary patterns. Two distinct dietary patterns were identified. Cluster 1 was characterized by significantly higher intakes of breakfast cereals, low fat and skimmed milks, potatoes, fruit and fish, fish dishes (p < 0.05) representing a "healthy" cluster. Cluster 2 had significantly higher intakes of chips/processed potatoes, meat products, savory snacks and high-energy beverages (p < 0.05) representing an "unhealthy cluster". Classification was supported by significant differences in nutrient status (p < 0.05). Validation in an independent group revealed that 94% of subjects were correctly classified. The model developed was capable of classifying individuals into dietary patterns based on metabolomics data. Future applications of this approach could be developed for rapid and objective assignment of subjects into dietary patterns. This article is protected by copyright. All rights reserved.

Twitter Demographics

The data shown below were collected from the profiles of 10 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 87 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 1%
Switzerland 1 1%
Unknown 85 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 20%
Student > Ph. D. Student 16 18%
Student > Master 10 11%
Student > Doctoral Student 7 8%
Student > Bachelor 7 8%
Other 10 11%
Unknown 20 23%
Readers by discipline Count As %
Medicine and Dentistry 19 22%
Agricultural and Biological Sciences 16 18%
Nursing and Health Professions 8 9%
Chemistry 4 5%
Engineering 3 3%
Other 12 14%
Unknown 25 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 September 2017.
All research outputs
#5,476,757
of 20,781,426 outputs
Outputs from Molecular Nutrition & Food Research
#897
of 2,393 outputs
Outputs of similar age
#70,768
of 239,730 outputs
Outputs of similar age from Molecular Nutrition & Food Research
#11
of 25 outputs
Altmetric has tracked 20,781,426 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 2,393 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.8. This one has gotten more attention than average, scoring higher than 62% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 239,730 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.
We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.