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Multidimensional dietary patterns and their joint associations with intersecting sociodemographic characteristics among adults in Canada: a cross-sectional study
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Background: Dietary patterns consist of multiple interrelated components, while individuals have numerous characteristics that may jointly influence dietary patterns. Studies to assess associations between sociodemographic characteristics and dietary patterns typically do not consider this complexity. Objective: The objective of this study was to examine joint relationships between dietary patterns and sociodemographic characteristics among adults in Canada. Methods: 24-hour dietary recall data for adults ≥18 years were drawn from the 2015 Canadian Community Health Survey Nutrition (n=14 097). Three mixed graphical models were developed to explore networks of sociodemographic characteristics, dietary components, and sociodemographic characteristics and dietary components together. Networks included 30 log-transformed food groups (grams), sex, age, household food security status, income, employment status, education, geographic region, and smoking status. Results are expressed as (edge weight; [95% CI]). Results: The strongest pairwise relationships were observed among dietary components and among sociodemographic characteristics. Positive linear relationships were observed among vegetable groupings; for example, between green and orange vegetables (0.12; [0,08, 0.16]). Negative relationships were observed among subgroups of each of animal foods, beverages, and grains; for example, between refined and whole grains (-0.30; [-0.33, -0.26]). In the model including dietary components and sociodemographic characteristics, age was associated with grains (other) (-0.12; [-0.16, -0.09]), coffee/tea (0.21; 95% CI [0.17, 0.24]), and whole grains (0.12; [0.08, 0.15]). Sex was associated with sweet beverages (0.11; [0.06, 0.17]), alcohol (0.18; [0.13, 0.24]), cured meat (0.20; [0.15, 0.26]), and red meat (0.16; [0.11, 0.21]). Conclusions: In some cases, pairwise relationships between dietary components suggest displacement, for example, of whole grains by refined grains. Age and sex were the characteristics most strongly connected to dietary components. Statement of significance: Exploring joint relationships between intersecting sociodemographic characteristics and multidimensional dietary patterns can assist with better understanding dietary heterogeneity to inform policies and programs that support healthy eating.
Title: Multidimensional dietary patterns and their joint associations with intersecting sociodemographic characteristics among adults in Canada: a cross-sectional study
Description:
Background: Dietary patterns consist of multiple interrelated components, while individuals have numerous characteristics that may jointly influence dietary patterns.
Studies to assess associations between sociodemographic characteristics and dietary patterns typically do not consider this complexity.
Objective: The objective of this study was to examine joint relationships between dietary patterns and sociodemographic characteristics among adults in Canada.
Methods: 24-hour dietary recall data for adults ≥18 years were drawn from the 2015 Canadian Community Health Survey Nutrition (n=14 097).
Three mixed graphical models were developed to explore networks of sociodemographic characteristics, dietary components, and sociodemographic characteristics and dietary components together.
Networks included 30 log-transformed food groups (grams), sex, age, household food security status, income, employment status, education, geographic region, and smoking status.
Results are expressed as (edge weight; [95% CI]).
Results: The strongest pairwise relationships were observed among dietary components and among sociodemographic characteristics.
Positive linear relationships were observed among vegetable groupings; for example, between green and orange vegetables (0.
12; [0,08, 0.
16]).
Negative relationships were observed among subgroups of each of animal foods, beverages, and grains; for example, between refined and whole grains (-0.
30; [-0.
33, -0.
26]).
In the model including dietary components and sociodemographic characteristics, age was associated with grains (other) (-0.
12; [-0.
16, -0.
09]), coffee/tea (0.
21; 95% CI [0.
17, 0.
24]), and whole grains (0.
12; [0.
08, 0.
15]).
Sex was associated with sweet beverages (0.
11; [0.
06, 0.
17]), alcohol (0.
18; [0.
13, 0.
24]), cured meat (0.
20; [0.
15, 0.
26]), and red meat (0.
16; [0.
11, 0.
21]).
Conclusions: In some cases, pairwise relationships between dietary components suggest displacement, for example, of whole grains by refined grains.
Age and sex were the characteristics most strongly connected to dietary components.
Statement of significance: Exploring joint relationships between intersecting sociodemographic characteristics and multidimensional dietary patterns can assist with better understanding dietary heterogeneity to inform policies and programs that support healthy eating.
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