PONE-D-21-05281

Using partial least squares to identify a dietary pattern associated with obesity in a nationally-representative sample of Canadian adults: results from the Canadian Community Health Survey – Nutrition 2015

PLOS ONE

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Reviewers' comments:

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Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: I Don't Know

Reviewer #2: N/A

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

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Reviewer #1: Dietary patterns in industrialized countries are characterized by overconsumption of fats, sugars, and processed meats in the total caloric availability, leading to a rise of several non-communicable diseases, including obesity. Considering the complex challenge that represents dietary intake evaluation and its consequences, this study proposes an original way to study diet-disease relationships in a large sample of Canadian adults.

- Data from the Candian Health Survey Nutrition 2015 were used, providing a large and recent study population (20,487), but analyses were performed on a sample of 13,598 adults. The authors may justifiy why they didn’t use entire cohort. Also, It would be appreciable to describe the population briefly (age, sex…).

- The weighted partial least squares wPLS is a hybrid method previously used by Jessri et al. (2017) to derive an energy-dense, high-fat and low-fiber density dietary pattern. It would have been interesting to include high sugar intake. Indeed, the previously validated Dietary Guidelines for Americans Adherence Index (DGAI) is a diet quality index focused on overconsumption and energy density. Eleven index items assess the energy-specific food intake recommendations, including "Added sugar," and 9 assess the healthy choice nutrient recommendations. Dietary assessment is a classical one 24-hour recall. Even if all individuals were guided through the 24-hr dietary recall, it is well-known that total sugar intake is difficult to evaluate precisely.

- Then, the authors used a simplified diet score (SDS) constructed from the results of the wPLS regression using the methodology of Schulze et al., (2003). Thus, the authors chose a model based on energy density, total fat intake, and fiber density to define "obesogenic" dietary patterns, while excessive sugar intake would also be considered. Nevertheless, the authors assume study limitations (single day of dietary recall, BMI as obesity marker).

- This study represents a great update of the previous cycle of CCHS nutrition data (CCHS 2004) where wPLS was used, allowing comparison. In this way, despite the magnitude of some predictor variables changing, the association's direction has generally stayed the same. Interestingly, this article identifies some key food predictors that have changed since 2004 (an increase of whole grains, legumes, and soy & salty snacks and a decrease in yogurt). Others like tea, shellfish and vegetable oils or eggs, whole-fat dairy products, and alcohol have not changed since 2004 whereas these food groups have been inconsistently associated with either contributing to or being protective against obesity in the scientific literature.

Finally, even if the authors were unable to elucidate the differential effect of diet on one’s obesity progression in this study, this article contributes to evidence on the relationship between diet, obesity, and chronic diseases, an area that still lacks longitudinal data.

Reviewer #2: The outputs of this research published before.

Ng, A., Jessri, M., & L'Abbé, M. (2020). Identification of an Obesogenic Dietary Pattern Using Partial Least Squares in a Nationally-Representative Sample of Canadian Adults. Current Developments in Nutrition, 4(Supplement_2), 552-552

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Reviewer #1: No

Reviewer #2: No

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Using partial least squares to identify a dietary pattern associated with obesity in a nationally-representative sample of Canadian adults: results from the Canadian Community Health Survey – Nutrition 2015

PONE-D-21-05281R1

Dear Dr. L'Abbe,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

David Meyre

Academic Editor

PLOS ONE

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