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

Abstract Objectives 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. Methods 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. Results 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. Conclusions 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. Funding Sources Supported by grants from the Burroughs Wellcome Fund Innovation in Regulatory Science Award and the Canadian Institutes of Health Research.

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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

PLoS ONE ◽

10.1371/journal.pone.0255415 ◽

2021 ◽

Vol 16 (8) ◽

pp. e0255415

Author(s):

Alena (Praneet) Ng ◽

Mahsa Jessri ◽

Mary R. L’Abbe

Keyword(s):

Community Health ◽

Least Squares ◽

Partial Least Squares ◽

Health Survey ◽

Dietary Pattern ◽

Representative Sample ◽

Canadian Community Health Survey ◽

Community Health Survey ◽

Dietary Components ◽

Nationally Representative

Background Hybrid methods of dietary patterns analysis have emerged as a unique and informative way to study diet-disease relationships in nutritional epidemiology research. Objective To identify an obesogenic dietary pattern using weighted partial least squares (wPLS) in nationally representative Canadian survey data, and to identify key foods and/or beverages associated with the defined dietary pattern. Design Data from one 24-hr dietary recall data from the cross-sectional Canadian Community Health Survey-Nutrition (CCHS) 2015 (n = 12,049) were used. wPLS was used to identify an obesogenic dietary pattern from 40 standardized food and 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. Results Compared to quartile one (i.e. those least adherent to an obesogenic dietary pattern), those in quartile four had 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 (+0.32), carbonated drinks (including energy drinks, sports drinks and vitamin water) (+0.30), and salty snacks (+0.19). Seven categories loaded negatively (i.e. in the protective direction): whole fruits (-0.40), orange vegetables (-0.32), “other” vegetables (-0.32), whole grains (-0.26), dark green vegetables (-0.22), legumes and soy (-0.18) and pasta and rice (-0.17). Conclusion This is the first study to apply weighted partial least squares to CCHS 2015 data to derive a dietary pattern associated with obesity. The results from this study pinpoint key dietary components that are associated with obesity and consumed among a nationally representative sample of Canadians adults.

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Abstract P114: Sparse Partial Least Squares Regression: A Promising Technique To Identify Heart-healthy Dietary Patterns In The Multi-Ethnic Study Of Atherosclerosis (MESA)

Circulation ◽

10.1161/circ.143.suppl_1.p114 ◽

2021 ◽

Vol 143 (Suppl_1) ◽

Author(s):

Natalie Gasca ◽

Robyn McClelland

Keyword(s):

Heart Disease ◽

Variable Selection ◽

Least Squares ◽

Partial Least Squares ◽

Dietary Patterns ◽

Survival Outcome ◽

Nutritional Epidemiology ◽

Model Parameters ◽

Ethnic Study ◽

C Statistic

Most nutritional epidemiology studies investigating trends between diet and heart disease use outcome-independent dimension reduction methods, like principal component analysis, to create dietary patterns. While these methods construct patterns that describe important aspects of food consumption, these patterns are not inherently related to heart disease. Incorporating disease data into the pattern construction offers the possibility of more concisely summarizing the most disease-related foods. Sparse partial least squares (SPLS), one such method, was found to have favorable interpretation and prediction properties in the continuous outcome setting; while selecting a subset of relevant foods, it constructed a few dietary patterns that were correlated with BMI while also capturing variation in diet composition. These results were validated with simulated data. We propose incorporating SPLS into the Cox proportional hazards model to analyze a right-censored survival outcome. We hypothesized that this method would inherit the beneficial parsimony properties seen in the continuous setting, and we assessed whether this proposed method could use the most relevant covariates to create a few patterns that were associated with a survival outcome. While the proposed method targets covariate-level sparsity (i.e. variable selection), one competitor method exists that integrates pattern-level parsimony and partial least squares (PLS) in the Cox model, but it imposes more model parameters than the proposed method. We compared the variable selection, pattern selection, and predictive performance of four survival methods (Lasso, PLS, competitor sparse PLS, and proposed SPLS) via a simulation study. Simulation settings were informed in part by the Multi-Ethnic Study of Atherosclerosis (MESA), which has detailed food frequency questionnaire data on a large multi-ethnic population-based sample (6814 participants aged 45-84), as well as subsequent cardiovascular disease follow-up for over 15 years. In most studied simulation settings, the proposed method selected all 9 relevant predictors and the fewest number of irrelevant predictors (of 15) while creating a similar number of patterns and maintaining predictive ability of the outcome. In the setting most comparable to MESA, PLS chose all 24 predictors (by default) and 3.4 patterns (C-statistic=0.90), the competitor SPLS selected 21.1 predictors and 4.4 patterns (C-statistic=0.91), Lasso chose 16.4 predictors (C-statistic=0.91), and the proposed SPLS selected 11.7 predictors and 4.3 patterns (C-statistic=0.91), on average. We will also present an analysis of a coronary event in MESA using these four survival methods. In conclusion, we propose that using methods like SPLS to summarize food intake can create more heart disease-tailored dietary patterns that can complement the current nutritional epidemiology literature.

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Pengaruh Kualitas Layanan dan Produk terhadap Kepuasan serta Loyalitas Pelanggan KFC di Tangerang Selatan

Jurnal Manajemen dan Organisasi ◽

10.29244/jmo.5.3.183-199 ◽

2016 ◽

Vol 5 (3) ◽

pp. 183

Author(s):

Yesenia Yesenia ◽

Edward Halomoan Siregar

Keyword(s):

Performance Analysis ◽

Customer Satisfaction ◽

Least Squares ◽

Partial Least Squares ◽

Fast Food ◽

Satisfaction Index ◽

Customer Satisfaction Index

PT. Fast Food Indonesia adalah pemegang lisensi tunggal restoran cepat saji terkemuka di dunia yaitu Kentucky Fried Chicken (KFC). Tujuan penelitian ini adalah menganalisis atribut-atribut kualitas pelayanan dan produk yang dapat mempengaruhi kepuasan dan loyalitas pelanggan KFC. Hasil penelitian dengan Important Performance Analysis (IPA) dan Customer Satisfaction Index (CSI) menunjukan tingkat kepuasan pelanggan KFC sebesar 81.26% yang mengindikasikan pelanggan merasa sangat puas. Berdasarkan hasil analisis Partial Least Squares (PLS), Kualitas Layanan tidak berpengaruh signifikan terhadap kepuasan namun berpengaruh signifikan terhadap loyalitas pelanggan pada model total pengunjung, pelajar dan non pelajar. Kualitas produk berpengaruh signifikan terhadap kepuasan pada semua model. Kualitas produk tidak berpengaruh signifikan terhadap loyalitas pelanggan pada model total pengunjung namun kualitas produk berpengaruh signifikan terhadap loyalitas pada model pelajar dan non pelajar, serta kepuasan berpengaruh signifikan terhadap loyalitas pada seluruh model.Kata kunci: customer satisfaction index, kentucky fried chicken, important performance analysis, loyalty, satisfaction, partial least squares

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