Psychological disorders and dietary patterns by reduced-rank regression

2019 ◽  
Vol 73 (3) ◽  
pp. 408-415 ◽  
Author(s):  
Mahdieh Hosseinzadeh ◽  
Mohammad-Reza Vafa ◽  
Ahmad Esmaillzadeh ◽  
Awat Feizi ◽  
Reza Majdzadeh ◽  
...  
Keyword(s):  
Dietary Patterns ◽  
Psychological Disorders ◽  
Reduced Rank Regression ◽  
Rank Regression ◽  
Reduced Rank

Dietary patterns, approaches, and multicultural perspectiveThis is one of a selection of papers published in the CSCN–CSNS 2009 Conference, entitled Can we identify culture-specific healthful dietary patterns among diverse populations undergoing nutrition transition?

2010 ◽  
Vol 35 (2) ◽  
pp. 211-218 ◽  
Author(s):  
Katherine L. Tucker
Keyword(s):  
Dietary Patterns ◽  
The United States ◽  
Healthy Eating Index ◽  
United States Department ◽  
Diverse Populations ◽  
Reduced Rank Regression ◽  
Linear Combinations ◽  
Rank Regression ◽  
Reduced Rank ◽  
Nutrition Research

Nutrition research has traditionally focused on single nutrients in relation to health. However, recent appreciation of the complex synergistic interactions among nutrients and other food constituents has led to a growing interest in total dietary patterns. Methods of measurement include summation of food or nutrient recommendations met, such as the United States Department of Agriculture Healthy Eating Index; data-driven approaches — principal components (PCA) and cluster analyses — which describe actual intake patterns in the population; and, most recently, reduced rank regression, which defines linear combinations of food intakes that maximally explain intermediate markers of disease. PCA, a form of factor analysis, derives linear combinations of foods based on their intercorrelations. Cluster analysis groups individuals into maximally differing eating patterns. These approaches have now been used in diverse populations with good reproducibility. In contrast, because it is based on associations with outcomes rather than on coherent behavioral patterns, reduced rank regression may be less reproducible, but more research is needed. However, it is likely to yield useful information for hypothesis generation. Together, the focus on dietary patterns has been fruitful in demonstrating the powerful protective associations of healthy or prudent dietary patterns, and the higher risk associations of Western or meat and refined grains patterns. The field, however, has not fully addressed the effects of diet in subpopulations, including ethnic minorities. Depending on food group coding, subdietary patterns may be obscured or artificially separated, leading to potentially misleading results. Further attention to the definition of the dietary patterns of different populations is critical to providing meaningful results. Still, dietary pattern research has great potential for use in nutrition policy, particularly as it demonstrates the importance of total diet in health promotion.

scholarly journals Relationship Between Dietary Patterns Derived by Reduced Rank Regression and Risk of Depression in American Adults

2021 ◽  
Vol 5 (Supplement_2) ◽  
pp. 464-464
Author(s):  
Peng Zhao ◽  
Yemian Li ◽  
Jingxian Wang ◽  
Yuhui Yang ◽  
Danmeng Liu ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Dietary Patterns ◽  
Dietary Pattern ◽  
The United States ◽  
Reduced Rank Regression ◽  
Eating Pattern ◽  
Rank Regression ◽  
Reduced Rank ◽  
High Adherence ◽  
Response Variables

Abstract Objectives Depression is one of the most serious mental disorder worldwide. Published studies indicated that nutrients such as folic acid and magnesium may provide a protective effect against it. The purpose of this study was to analyze whether dietary patterns defined by nutrients are associated with the risk of depression. Methods Research data content of 23 464 adults was obtained from the NHANES database. Dietary data were assessed with a valid food frequency questionnaire. Dietary patterns were derived by reduced rank regression with EPA + DHA, folate, Mg and Zn as response variables. The Patient Health Questionnaire was used to assess depressive symptoms (cutoff = 10). We applied logistic regression analyses to test the association between dietary patterns and depressive symptoms. Finally, all samples were divided into three groups: low, medium and high adherence to dietary patterns according to the trinomial score of dietary patterns, and the differences of depression risk among the three groups were compared. Results In total, 3 020 cases with depression were observed. We identified a dietary pattern that was strongly associated with EPA + DHA, folate, Mg and Zn (response variables) intake, which was also characterized by the consumption of vegetables, grains, meat, nuts, beans, peas, and lentils, milk, cheese, oils and solid fats. After adjustment for confounders, a statistically significant association was observed (OR = 0.42, 95%CI: 0.36,0.50; P < 0.001). In addition, compared with the low-adherence group, increasing adherence to this dietary pattern significantly reduced the risk of depression (medium-adherence: OR = 0.62, 95%CI: 0.55,0.71; high-adherence: OR = 0.43, 95%CI: 0.36,0.51; P < 0.001). Conclusions Adults living in the United States have been linked to a lower risk of depression with a high-nutrient eating pattern. Funding Sources National Natural Science Foundation of China and National Key R&D Program of China.

scholarly journals Can dietary patterns help us detect diet–disease associations?

2005 ◽  
Vol 18 (2) ◽  
pp. 241-248 ◽  
Author(s):  
Karin B. Michels ◽  
Matthias B. Schulze
Keyword(s):  
Dietary Patterns ◽  
Data Driven ◽  
Dietary Composition ◽  
Reduced Rank Regression ◽  
Rank Regression ◽  
Reduced Rank ◽  
Disease Associations ◽  
Factor And Cluster Analysis ◽  
And Cluster Analysis

The role of diet in promoting health and preventing disease is difficult to elucidate due to its complex network of foods and nutrients. Besides total energy intake, dietary composition is probably the most important discriminator within and between populations. Dietary composition is reflected in dietary patterns, which have recently gained popularity. The present paper reviews the most commonly applied methods to identify dietary patterns, data-driven methods such as factor and cluster analysis, investigator-driven methods such as indices and score, and methods combining the two, namely reduced rank regression. We describe the techniques and their application, discuss strengths and limitations, and discuss the usefulness of dietary pattern analyses.

scholarly journals The association between dietary patterns derived by reduced rank regression and depressive symptoms over time: the Invecchiare in Chianti (InCHIANTI) study

2016 ◽  
Vol 115 (12) ◽  
pp. 2145-2153 ◽  
Author(s):  
Esther Vermeulen ◽  
Karien Stronks ◽  
Marjolein Visser ◽  
Ingeborg A. Brouwer ◽  
Aart H. Schene ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Olive Oil ◽  
Dietary Patterns ◽  
Dietary Pattern ◽  
Processed Meat ◽  
Inverse Association ◽  
Reduced Rank Regression ◽  
Rank Regression ◽  
Reduced Rank ◽  
Red And Processed Meat

AbstractThis study aimed to identify dietary patterns using reduced rank regression (RRR) and to explore their associations with depressive symptoms over 9 years in the Invecchiare in Chianti study. At baseline, 1362 participants (55·4 % women) aged 18–102 years (mean age 68 (sd 15·5) years) were included in the study. Baseline data collection started in 1998 and was repeated after 3, 6 and 9 years. Dietary intake information was obtained using a country-specific, validated FFQ with 188 food items. For baseline diet, dietary pattern scores in quartiles (Q) were derived using RRR with the nutrients EPA+DHA, folate, Mg and Zn as response variables. Continuous depression scores from the Centre for Epidemiologic Studies Depression (CES-D) scale were used for assessing depressive symptoms. The derived dietary pattern was rich in vegetables, olive oil, grains, fruit, fish and moderate in wine and red and processed meat, and was labelled as ‘typical Tuscan dietary pattern’. After full adjustment, an inverse association was observed between this dietary pattern and depressive symptoms at baseline (Q1 v. Q4, B −2·77; 95 % CI −4·55, −0·98). When examining the relationship between the above-mentioned dietary pattern at baseline and depressive symptoms over 9 years, a similar association was found after full adjustment for confounding factors (Q1 v. Q4, B −1·78; 95 % CI −3·17, −0·38). A diet rich in vegetables, olive oil, grains, fruits, fish and moderate in wine and red and processed meat was consistently associated with lower CES-D scores over a 9-year period in the Tuscan population.

scholarly journals Identifying cardiovascular risk factor–related dietary patterns with reduced rank regression and random forest in the EPIC-NL cohort

2015 ◽  
Vol 102 (1) ◽  
pp. 146-154 ◽  
Author(s):  
Sander Biesbroek ◽  
Daphne L van der A ◽  
Marinka CC Brosens ◽  
Joline WJ Beulens ◽  
WM Monique Verschuren ◽  
...  
Keyword(s):  
Risk Factor ◽  
Cardiovascular Risk ◽  
Random Forest ◽  
Cardiovascular Risk Factor ◽  
Dietary Patterns ◽  
Reduced Rank Regression ◽  
Rank Regression ◽  
Reduced Rank

scholarly journals Maternal Dietary Patterns by Reduced Rank Regression Are Associated With Low Birth Weight Outcomes in Mothers and Children's Environmental Health Cohort

2021 ◽  
Vol 5 (Supplement_2) ◽  
pp. 1044-1044
Author(s):  
Jiyoung Hwang ◽  
Dayeon Shin ◽  
Hyesook Kim ◽  
Oran Kwon
Keyword(s):  
Birth Weight ◽  
Dietary Patterns ◽  
Dietary Pattern ◽  
Lower Risk ◽  
Food Groups ◽  
Reduced Rank Regression ◽  
Rank Regression ◽  
Reduced Rank ◽  
Low Weight ◽  
Children’S Environmental Health

Abstract Objectives This study aims to identify the associations between maternal dietary patterns and the risks of low birth weight (≤ 10th percentile). Methods A total of 1,751 mothers and their newborns recruited for the Mothers and Children's Environmental Health cohort study between 2006 and 2010. A semiquantitative FFQ for nutrient intakes was collected and dietary patterns were derived using Reduced Rank Regression (RRR). A total of 138 food items were categorized into 40 pre-defined food groups. In this study, log transformed maternal intakes of folate, iron, and zinc were selected as the intermediate response variables based on the associations with birth weight. Associations were assessed by logistic regression with adjustment for confounding factors. Results All of energy and nutrient intakes of dietary pattern 1, characterized by high intakes of grain, green/yellow, and light-colored vegetables, legumes, fruits, red meat, poultry, eggs, fishes, seaweeds, tofu/soymilk, yogurt, and nuts significantly increased as the from quartile one to quartile four. Biochemical marker levels such as triglyceride, C-reactive protein, and malondialdehyde levels were significantly decreased from quartile one to quartile four in pattern 1. Pregnant women, who adhered to pattern 1 had a lower risk of low weight at birth in the highest quartile compared to the lowest quartile (adjusted odds ratio 0.35, 95% confidence interval 0.32–0.95). No association was observed for pattern 2(green/yellow vegetables, light-colored vegetables, kimchi, and seaweeds) and 3(grains, milk, and yogurt) with low weight at birth. Conclusions Mothers who practiced good nutrition such as various food groups were likely to have a lower risk of low weight at birth. This study was the first to use a birth cohort to investigate the association between maternal dietary pattern and low weight at birth using RRR method, which highlights the important role of whole foods or quality of nutrients during pregnancy. Funding Sources This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education(MOE, Korea) and National Research Foundation of Korea(NRF).

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