Dietary Patterns and Their Sociodemographic and Lifestyle Determinants in Switzerland: Results from the National Nutrition Survey menuCH

Abstract

From a public health perspective, determinants of diets are crucial to identify, but they remain unclear in Switzerland. Hence, we sought to define current dietary patterns and their sociodemographic and lifestyle determinants using the national nutrition survey menuCH (2014–2015, n = 2057). First, we applied multiple factorial analysis and hierarchical clustering on the energy-standardised daily consumption of 17 food categories. Four dietary patterns were identified (“Swiss traditional”: high intakes of dairy products and chocolate, n = 744; “Western 1”: soft drinks and meat, n = 383; “Western 2”: alcohol, meat and starchy, n = 444; and “Prudent”: n = 486). Second, we used multinomial logistic regression to examine the determinants of the four dietary patterns: ten sociodemographic or lifestyle factors (sex, age, body mass index, language region, nationality, marital status, income, physical activity, smoking status, and being on a weight-loss diet) were significantly associated with the dietary patterns. Notably, belonging to the French- and Italian-speaking regions of Switzerland increased the odds of following a “Prudent” diet (Odds ratio [95% confidence interval]: 1.92 [1.45–2.53] and 1.68 [0.98–2.90], respectively) compared to the German-speaking regions. Our findings highlight the influence of sociodemographic and lifestyle parameters on diet and the particularities of the language regions of Switzerland. These results provide the basis for public health interventions targeted for population subgroups.

Dietary patterns and their associations with gestational weight gain in the United Arab Emirates: results from the MISC cohort

“…On the other hand, the "Western" pattern consisted of sweets, sugar-sweetened beverages, fast foods, and added sugars. A common finding to most studies investigating dietary patterns is the identification of a western pattern in addition to other 'ethnic/ traditional/or Prudent' patterns [32][33][34][35][36][37]. Though the foods constituting these patterns would vary from one study to the other, a few characteristic foods/food groups are repeatedly reported as defining each of these two patterns.…”

Combining Recent Nutritional Data with Prospective Cohorts to Quantify the Impact of Modern Dietary Patterns on Disability–Adjusted Life Years: A Feasibility Study

“…This approach was motivated by the availability of a nation-wide nutrition survey allowing for the definition of dietary patterns in the Swiss population [11,22,25] and evidence of shifts in dietary patterns over 20 years in Switzerland [10]. For this, we combined two chronologically distinct data sources, i.e., the recent cross-sectional study menuCH to characterize the currently-consumed dietary patterns [22,25] and the census-linked cohort study NRP-MONICA, to characterize participants who consumed a diet similar to the currently-consumed dietary patterns. The main strength of our approach is to investigate the health impacts of modern dietary patterns by looking at the DALYs associated with individuals who consumed similar diets in the past.…”

“…The software GloboDiet ® (formerly EPIC-Soft ® , version CH-2016.4.10, International Agency for Research on Cancer (IARC), Lyon, France) [20,21], adapted to Switzerland (GloboDiet ® trilingual databases dated 12 December, 2016; IARC, Lyon, France, and Federal Food Safety and Veterinary Office, Bern, Switzerland) was used to record dietary intakes. A sample of 2057 participants completed two 24HDR, and their dietary data were used to derive a posteriori dietary patterns [22]. Multiple factorial analysis and hierarchical clustering were applied on the energy-standardized daily consumption of 17 food categories, and four dietary patterns were identified ("Prudent" n = 486; "Swiss traditional" n = 744; "Western 1-soft drinks and meat" n = 383; "Western 2-alcohol, meat and starchy" n = 444) [22].…”

“…A sample of 2057 participants completed two 24HDR, and their dietary data were used to derive a posteriori dietary patterns [22]. Multiple factorial analysis and hierarchical clustering were applied on the energy-standardized daily consumption of 17 food categories, and four dietary patterns were identified ("Prudent" n = 486; "Swiss traditional" n = 744; "Western 1-soft drinks and meat" n = 383; "Western 2-alcohol, meat and starchy" n = 444) [22].…”

“…First, when menuCH food categories originally used by Krieger et al [22] did not directly match the available NRP-MONICA food categories, we used menuCH food subcategories (defined in GloboDiet ® ; Table S1). Second, we dichotomized the four menuCH dietary patterns on the basis of the z-scores of energy-standardized food consumption in the menuCH survey; all z-scores >0.1 were transformed into yes, others in no.…”