Adherence to the 2015 Dietary Guidelines for Americans (DGA) and Risk of Healthy and Unhealthy Obesity among Canadian Adults.
Bibliographic record
Abstract
Recently, the scientific community has recognized the importance of differentiating between obesity phenotypes; however, less attention has been given to this issue in nutritional epidemiology. The objective of this study was to examine whether closer adherence to the recommendations proposed in the Scientific Report of the 2015 Dietary Guidelines for Americans (DGA), as measured by an updated 2015 DGA Adherence Index (DGAI), is differentially associated with risk of obesity with and without an accompanying chronic disease, including diabetes, hypertension and heart disease (i.e., healthy and unhealthy obesity). Weighted multinomial logistic regression‐GLM was used to examine the associations between the a priori 19‐score DGAI and healthy and unhealthy obesity among 11,748 participants aged≥18 years in the Canadian Community Health Survey 2.2. All models were additionally adjusted for the energy misreporting status to account for this systematic bias. The mean 2015 DGAI score was 8.82 (±0.051)(Possible Max:19) and its two subscores (“food intake” and “healthy choice”) were 3.92(±0.038) (Possible Max:11) and 4.90 (±0.028) (Possible Max:8), respectively, which indicates that our population was adherent to less than 50% of recommendations. After adjusting for age, sex and energy misreporting, adherence to the 2015 DGA recommendations decreased the odds of being unhealthy obese monotonically from Odds Ratio (OR): 2.413 (1.732–3.361) in quartile 1 (poorest diet), to 2.088 (1.505–2.898) in quartile 2, and 1.412 (1.01–1.973) in the third quartile of the 2015 DGAI score, compared to the fourth quartile category (healthiest diet) (p‐trend<0.0001). The odds of being obese without a chronic disease (healthy obese) also decreased from 2.29 (1.482–3.539) in quartile 1, to 2.328 (1.485–3.649) in quartile 2, and 1.629 (0.932–2.849) in the third quartile category of the 2015 DGAI, as compared to the fourth quartile (p‐trend<0.0001). In the same model, individuals in the first quartile of the 2015 DGAI (poorest diets) showed 1.424 (1.031–1.966) times higher risk of being lean with at least one accompanying chronic disease, compared to those in the fourth quartile (healthiest diet). Our findings suggest that individuals with the obesity phenotype coupled with an accompanying metabolic disorder may benefit the most from following the 2015 DGA, even though unhealthy lean and healthy obese phenotypes are also reduced markedly as a result of closer compliance to the 2015 DGA, albeit not as strongly as unhealthy obesity phenotype. Therefore, differentiating unhealthy obesity from healthy obesity has important implications for public health and clinical management of obesity. Even though none of the participants met all the 2015 DGA recommendations, our results support the value of compliance to the 2015 DGA for reducing the cumulative prevalence of chronic diseases at the population level. Owing to the similarity of the North American dietary recommendations, these findings suggest that the 2015 DGA may be adapted to improve dietary behaviours in the Canadian context. Support or Funding Information This research was supported by funds to the Canadian Research Data Centre Network from the Social Science and Humanities Research Council, the Canadian Institute for Health Research (CIHR), the Canadian Foundation for Innovation and Statistics Canada. M. J. is supported by the Canadian Institute of Health Research (CIHR) Vanier Canada Graduate Scholarship, the CIHR/Cancer Care Ontario (CCO) Population Intervention for Chronic Disease Prevention (PICDP): a Pan‐Canadian Fellowship, and the Ontario Graduate Scholarship (OGS). M. L. is the Earle W. McHenry professor and is supported by the chair‐endowed unrestricted research funds, University of Toronto.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".