scholarly journals Gaussian Graphical Models Identify Networks of Dietary Intake in a German Adult Population

2016 ◽  
Vol 146 (3) ◽  
pp. 646-652 ◽  
Author(s):  
Khalid Iqbal ◽  
Brian Buijsse ◽  
Janine Wirth ◽  
Matthias B Schulze ◽  
Anna Floegel ◽  
...  
PLoS ONE ◽  
2018 ◽  
Vol 13 (8) ◽  
pp. e0202936 ◽  
Author(s):  
Carolina Schwedhelm ◽  
Sven Knüppel ◽  
Lukas Schwingshackl ◽  
Heiner Boeing ◽  
Khalid Iqbal

2021 ◽  
pp. 003329412110252
Author(s):  
Alex Bacadini França ◽  
Clarissa Trzesniak ◽  
Patrícia Waltz Schelini ◽  
Gerson Hiroshi Yoshinari Junior ◽  
Luciano Magalhães Vitorino

Our study aimed to examine the symptoms that might play a role in the co-occurrence of 9 DSM-5 symptom criteria of major depression among Brazil's adult population and healthcare professionals after three months of detecting the new coronavirus in Brazil. We estimated regularized Gaussian graphical models for both samples and compared the network structures. Depressed mood was the most central symptom in the general population network compared to the healthcare professional network. The findings revealed some individual symptoms showed a differential association between the general population and healthcare professionals. Those symptoms may be valuable targets for future research and treatment.


Biometrics ◽  
2019 ◽  
Vol 75 (4) ◽  
pp. 1288-1298
Author(s):  
Gwenaël G. R. Leday ◽  
Sylvia Richardson

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vincent Bessonneau ◽  
Roy R. Gerona ◽  
Jessica Trowbridge ◽  
Rachel Grashow ◽  
Thomas Lin ◽  
...  

AbstractGiven the complex exposures from both exogenous and endogenous sources that an individual experiences during life, exposome-wide association studies that interrogate levels of small molecules in biospecimens have been proposed for discovering causes of chronic diseases. We conducted a study to explore associations between environmental chemicals and endogenous molecules using Gaussian graphical models (GGMs) of non-targeted metabolomics data measured in a cohort of California women firefighters and office workers. GGMs revealed many exposure-metabolite associations, including that exposures to mono-hydroxyisononyl phthalate, ethyl paraben and 4-ethylbenzoic acid were associated with metabolites involved in steroid hormone biosynthesis, and perfluoroalkyl substances were linked to bile acids—hormones that regulate cholesterol and glucose metabolism—and inflammatory signaling molecules. Some hypotheses generated from these findings were confirmed by analysis of data from the National Health and Nutrition Examination Survey. Taken together, our findings demonstrate a novel approach to discovering associations between chemical exposures and biological processes of potential relevance for disease causation.


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