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Record W3030738693 · doi:10.1093/cdn/nzaa046_052

Identification of an Obesogenic Dietary Pattern Using Partial Least Squares in a Nationally-Representative Sample of Canadian Adults

2020· article· en· W3030738693 on OpenAlexaffabout
Alena Ng, Mahsa Jessri, Mary R. L’Abbé

Bibliographic record

VenueCurrent Developments in Nutrition · 2020
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsUniversity of OttawaUniversity of Toronto
Fundersnot available
KeywordsQuartilePartial least squares regressionObesityNutritional epidemiologyFood groupLogistic regressionMultivariate statisticsEnvironmental healthFood scienceMedicineMathematicsEpidemiologyStatisticsBiologyConfidence interval

Abstract

fetched live from OpenAlex

Hybrid methods of dietary patterns analysis have emerged as a unique and informative way to study diet-disease relationships in nutritional epidemiology research. The objectives of this research were to identify an obesogenic dietary pattern using weighted PLS in nationally-representative Canadian survey data, and to identify key foods and/or beverages associated with the defined obesogenic pattern. Data from one 24-hr dietary recall data from the cross-sectional Canadian Community Health Survey-Nutrition (CCHS) 2015 (n = 12,110 adults) were used. Weighed partial least squares (wPLS) was used to identify an obesogenic dietary pattern from 40 standardized food and/or beverage categories using the variables energy density, fibre density, and total fat as outcomes. The association between the derived dietary pattern and likelihood of obesity was examined using weighted multivariate logistic regression. Key dietary components highly associated with the derived pattern were identified. Compared to quartile one (i.e., those least adherent to an obesogenic dietary pattern), those in quartile four had a 2.40-fold increased odds of being obese (OR = 2.40, 95% CI = 1.91, 3.02, P-trend < 0.0001) with a monotonically increasing trend. Using a factor loading significance cut-off of ≥|0.17|, three food/beverage categories loaded positively for the derived obesogenic dietary pattern: fast food, carbonated drinks and salty snacks. Seven food/beverage categories loaded negatively (i.e., in the protective direction): consumption of whole fruits, orange vegetables, “other” vegetables (including vegetable juice), whole grains, dark green vegetables, legumes and soy, and pasta and rice. This study pinpoints key dietary components that are associated with obesity and consumed among a nationally-representative sample of Canadians adults. Compared to a similarly-defined obesogenic diet identified by our research group in 2004, the top contributors to a Canadian-specific obesogenic diet in 2015 have remained consistent. This evidence may aid in developing targeted policies and dietary interventions for obesity and chronic disease prevention. Supported by grants from the Burroughs Wellcome Fund Innovation in Regulatory Science Award and the Canadian Institutes of Health Research.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.074
GPT teacher head0.336
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2020
Admission routes2
Has abstractyes

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