The Experts below are selected from a list of 13191 Experts worldwide ranked by ideXlab platform
Jiangtao Gou - One of the best experts on this subject based on the ideXlab platform.
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application of a new Dietary Pattern Analysis method in nutritional epidemiology
BMC Medical Research Methodology, 2018Co-Authors: Fengqing Zhang, Tinashe M Tapera, Jiangtao GouAbstract:Background Diet plays an important role in chronic disease, and the use of Dietary Pattern Analysis has grown rapidly as a way of deconstructing the complexity of nutritional intake and its relation to health. Pattern Analysis methods, such as principal component Analysis (PCA), have been used to investigate various dimensions of diet. Existing analytic methods, however, do not fully utilize the predictive potential of Dietary assessment data. In particular, these methods are often suboptimal at predicting clinically important variables.
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application of a new Dietary Pattern Analysis method in nutritional epidemiology
BMC Medical Research Methodology, 2018Co-Authors: Fengqing Zhang, Tinashe M Tapera, Jiangtao GouAbstract:Diet plays an important role in chronic disease, and the use of Dietary Pattern Analysis has grown rapidly as a way of deconstructing the complexity of nutritional intake and its relation to health. Pattern Analysis methods, such as principal component Analysis (PCA), have been used to investigate various dimensions of diet. Existing analytic methods, however, do not fully utilize the predictive potential of Dietary assessment data. In particular, these methods are often suboptimal at predicting clinically important variables. We propose a new Dietary Pattern Analysis method using the advanced LASSO (Least Absolute Shrinkage and Selection Operator) model to improve the prediction of disease-related risk factors. Despite the potential advantages of LASSO, this is the first time that the model has been adapted for Dietary Pattern Analysis. Hence, the systematic evaluation of the LASSO model as applied to Dietary data and health outcomes is highly innovative and novel. Using Food Frequency Questionnaire data from NHANES 2005–2006, we apply PCA and LASSO to identify Dietary Patterns related to cardiovascular disease risk factors in healthy US adults (n = 2609) after controlling for confounding variables (e.g., age and BMI). Both analyses account for the sampling weights. Model performance in terms of prediction accuracy is evaluated using an independent test set. PCA yields 10 principal components (PCs) that together account for 65% of the variation in the data set and represent distinct Dietary Patterns. These PCs are then used as predictors in a regression model to predict cardiovascular disease risk factors. We find that LASSO better predicts levels of triglycerides, LDL cholesterol, HDL cholesterol, and total cholesterol (adjusted R2 = 0.861, 0.899, 0.890, and 0.935 respectively) than does the traditional, linear-regression-based, Dietary Pattern Analysis method (adjusted R2 = 0.163, 0.005, 0.235, and 0.024 respectively) when the latter is applied to components derived from PCA. The proposed method is shown to be an appropriate and promising statistical means of deriving Dietary Patterns predictive of cardiovascular disease risk. Future studies, involving different diseases and risk factors, will be necessary before LASSO’s broader usefulness in nutritional epidemiology can be established.
Ute Nothlings - One of the best experts on this subject based on the ideXlab platform.
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advances in Dietary Pattern Analysis in nutritional epidemiology
European Journal of Nutrition, 2021Co-Authors: Christinaalexandra Schulz, Kolade Oluwagbemigun, Ute NothlingsAbstract:It used to be a common practice in the field of nutritional epidemiology to analyze separate nutrients, foods, or food groups. However, in reality, nutrients and foods are consumed in combination. The introduction of Dietary Patterns (DP) and their Analysis has revolutionized this field, making it possible to take into account the synergistic effects of foods and to account for the complex interaction among nutrients and foods. Three approaches of DP Analysis exist: (1) the hypothesis-based approach (based on prior knowledge regarding the current understanding of Dietary components and their health relation), (2) the exploratory approach (solely relying on Dietary intake data), and (3) the hybrid approach (a combination of both approaches). During the recent past, complementary approaches for DP Analysis have emerged both conceptually and methodologically. We have summarized the recent developments that include incorporating the Treelet transformation method as a complementary exploratory approach in a narrative review. Uses, peculiarities, strengths, limitations, and scope of recent developments in DP Analysis are outlined. Next, the narrative review gives an overview of the literature that takes into account potential relevant Dietary-related factors, specifically the metabolome and the gut microbiome in DP Analysis. Then the review deals with the aspect of data processing that is needed prior to DP Analysis, particularly when Dietary data arise from assessment methods other than the long-established food frequency questionnaire. Lastly, potential opportunities for upcoming DP Analysis are summarized in the outlook. Biological factors like the metabolome and the microbiome are crucial to understand diet-disease relationships. Therefore, the inclusion of these factors in DP Analysis might provide deeper insights.
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Dietary Pattern Analysis and biomarkers of low grade inflammation a systematic literature review
Nutrition Reviews, 2013Co-Authors: Matthias B Schulze, Ute Nothlings, Janett Barbaresko, Manja KochAbstract:The purpose of the present literature review was to investigate and summarize the current evidence on associations between Dietary Patterns and biomarkers of inflammation, as derived from epidemiological studies. A systematic literature search was conducted using PubMed, Web of Science, and EMBASE, and a total of 46 studies were included in the review. These studies predominantly applied principal component Analysis, factor Analysis, reduced rank regression Analysis, the Healthy Eating Index, or the Mediterranean Diet Score. No prospective observational study was found. Patterns identified by reduced rank regression as being statistically significantly associated with biomarkers of inflammation were almost all meat-based or "Western" Patterns. Studies using principal component Analysis or a priori-defined diet scores found that meat-based or "Western-like" Patterns tended to be positively associated with biomarkers of inflammation, predominantly C-reactive protein, while vegetable- and fruit-based or "healthy" Patterns tended to be inversely associated. While results of the studies were inconsistent, interventions with presumed healthy diets resulted in reductions of almost all investigated inflammatory biomarkers. In conclusion, prospective studies are warranted to confirm the reported findings and further analyze associations, particularly by investigating Dietary Patterns as risk factors for changes in inflammatory markers over time.
Fengqing Zhang - One of the best experts on this subject based on the ideXlab platform.
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application of a new Dietary Pattern Analysis method in nutritional epidemiology
BMC Medical Research Methodology, 2018Co-Authors: Fengqing Zhang, Tinashe M Tapera, Jiangtao GouAbstract:Background Diet plays an important role in chronic disease, and the use of Dietary Pattern Analysis has grown rapidly as a way of deconstructing the complexity of nutritional intake and its relation to health. Pattern Analysis methods, such as principal component Analysis (PCA), have been used to investigate various dimensions of diet. Existing analytic methods, however, do not fully utilize the predictive potential of Dietary assessment data. In particular, these methods are often suboptimal at predicting clinically important variables.
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application of a new Dietary Pattern Analysis method in nutritional epidemiology
BMC Medical Research Methodology, 2018Co-Authors: Fengqing Zhang, Tinashe M Tapera, Jiangtao GouAbstract:Diet plays an important role in chronic disease, and the use of Dietary Pattern Analysis has grown rapidly as a way of deconstructing the complexity of nutritional intake and its relation to health. Pattern Analysis methods, such as principal component Analysis (PCA), have been used to investigate various dimensions of diet. Existing analytic methods, however, do not fully utilize the predictive potential of Dietary assessment data. In particular, these methods are often suboptimal at predicting clinically important variables. We propose a new Dietary Pattern Analysis method using the advanced LASSO (Least Absolute Shrinkage and Selection Operator) model to improve the prediction of disease-related risk factors. Despite the potential advantages of LASSO, this is the first time that the model has been adapted for Dietary Pattern Analysis. Hence, the systematic evaluation of the LASSO model as applied to Dietary data and health outcomes is highly innovative and novel. Using Food Frequency Questionnaire data from NHANES 2005–2006, we apply PCA and LASSO to identify Dietary Patterns related to cardiovascular disease risk factors in healthy US adults (n = 2609) after controlling for confounding variables (e.g., age and BMI). Both analyses account for the sampling weights. Model performance in terms of prediction accuracy is evaluated using an independent test set. PCA yields 10 principal components (PCs) that together account for 65% of the variation in the data set and represent distinct Dietary Patterns. These PCs are then used as predictors in a regression model to predict cardiovascular disease risk factors. We find that LASSO better predicts levels of triglycerides, LDL cholesterol, HDL cholesterol, and total cholesterol (adjusted R2 = 0.861, 0.899, 0.890, and 0.935 respectively) than does the traditional, linear-regression-based, Dietary Pattern Analysis method (adjusted R2 = 0.163, 0.005, 0.235, and 0.024 respectively) when the latter is applied to components derived from PCA. The proposed method is shown to be an appropriate and promising statistical means of deriving Dietary Patterns predictive of cardiovascular disease risk. Future studies, involving different diseases and risk factors, will be necessary before LASSO’s broader usefulness in nutritional epidemiology can be established.
R Estruch - One of the best experts on this subject based on the ideXlab platform.
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Dietary Patterns and the risk of obesity type 2 diabetes mellitus cardiovascular diseases asthma and neurodegenerative diseases
Critical Reviews in Food Science and Nutrition, 2018Co-Authors: Alexander Medinaremon, Richard Kirwan, Rosa M Lamuelaraventos, R EstruchAbstract:Diet and lifestyle play a significant role in the development chronic diseases; however the full complexity of this relationship is not yet understood. Dietary Pattern investigation, which reflects the complexity of Dietary intake, has emerged as an alternative and complementary approach for examining the association between diet and chronic diseases. Literature on this association has largely focused on individual nutrients, with conflicting outcomes, but individuals consume a combination of foods from many groups that form Dietary Patterns. Our objective was to systematically review the current findings on the effects of Dietary Patterns on chronic diseases. In this review, we describe and discuss the relationships between Dietary Patterns, such as the Mediterranean, the Dietary Approach to Stop Hypertension, Prudent, Seventh-day Adventists, and Western, with risk of obesity, type-2 diabetes mellitus, cardiovascular diseases, asthma, and neurodegenearive diseases. Evidence is increasing from both observational and clinical studies that plant-based Dietary Patterns, which are rich in fruits, vegetables, and whole grains, are valuable in preventing various chronic diseases, whereas a diet high in red and processed meat, refined grains and added sugar seems to increase said risk. Dietary Pattern Analysis might be especially valuable to the development and evaluation of food-based Dietary guidelines.
Paul F Jacques - One of the best experts on this subject based on the ideXlab platform.
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invited commentary Dietary Pattern Analysis
American Journal of Epidemiology, 2011Co-Authors: Fumiaki Imamura, Paul F JacquesAbstract:The analytic approaches used in nutritional epidemiology for Dietary Pattern analyses share common characteristics with those of genetic epidemiology. In this issue of the Journal, Gorst-Rasmussen et al. (Am J Epidemiol. 2011;173(10):1097–1104) discuss one such approach. Application of methods used in genetic Pattern analyses to nutritional epidemiology could prove valuable but raises important issues that need to be considered because Dietary and genetic studies often address different types of questions in analyzing interrelated variables. These different aims require statistical methods that assume different characteristics of the underlying Patterns. The authors briefly describe such differences to facilitate interpretation and applications of previous and future Pattern studies. diet; myocardial infarction; statistics