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dc.contributor.authorLee, Yu-Chi
dc.contributor.authorChristensen, Jacob J
dc.contributor.authorParnell, Laurence D
dc.contributor.authorSmith, Caren E
dc.contributor.authorShao, Jonathan
dc.contributor.authorMcKeown, Nicola M
dc.contributor.authorOrdovás, José M
dc.contributor.authorLai, Chao-Qiang
dc.date.accessioned2023-03-17T12:15:06Z
dc.date.available2023-03-17T12:15:06Z
dc.date.issued2021
dc.identifier.citationFront Genet. 2022 Jan 3;12:783845es_ES
dc.identifier.issn1664-8021es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/15669
dc.description.abstractObesity is associated with many chronic diseases that impair healthy aging and is governed by genetic, epigenetic, and environmental factors and their complex interactions. This study aimed to develop a model that predicts an individual's risk of obesity by better characterizing these complex relations and interactions focusing on dietary factors. For this purpose, we conducted a combined genome-wide and epigenome-wide scan for body mass index (BMI) and up to three-way interactions among 402,793 single nucleotide polymorphisms (SNPs), 415,202 DNA methylation sites (DMSs), and 397 dietary and lifestyle factors using the generalized multifactor dimensionality reduction (GMDR) method. The training set consisted of 1,573 participants in exam 8 of the Framingham Offspring Study (FOS) cohort. After identifying genetic, epigenetic, and dietary factors that passed statistical significance, we applied machine learning (ML) algorithms to predict participants' obesity status in the test set, taken as a subset of independent samples (n = 394) from the same cohort. The quality and accuracy of prediction models were evaluated using the area under the receiver operating characteristic curve (ROC-AUC). GMDR identified 213 SNPs, 530 DMSs, and 49 dietary and lifestyle factors as significant predictors of obesity. Comparing several ML algorithms, we found that the stochastic gradient boosting model provided the best prediction accuracy for obesity with an overall accuracy of 70%, with ROC-AUC of 0.72 in test set samples. Top predictors of the best-fit model were 21 SNPs, 230 DMSs in genes such as CPT1A, ABCG1, SLC7A11, RNF145, and SREBF1, and 26 dietary factors, including processed meat, diet soda, French fries, high-fat dairy, artificial sweeteners, alcohol intake, and specific nutrients and food components, such as calcium and flavonols. In conclusion, we developed an integrated approach with ML to predict obesity using omics and dietary data. This extends our knowledge of the drivers of obesity, which can inform precision nutrition strategies for the prevention and treatment of obesity. Clinical Trial Registration: [www.ClinicalTrials.gov], the Framingham Heart Study (FHS), [NCT00005121].es_ES
dc.description.sponsorshipThis research was funded by the United States Department of Agriculture (USDA), Agriculture Research Service (ARS) under agreement no. 8050-51000-107-000D. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the USDA.es_ES
dc.language.isoenges_ES
dc.publisherFrontiers Media es_ES
dc.type.hasVersionVoRes_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleUsing Machine Learning to Predict Obesity Based on Genome-Wide and Epigenome-Wide Gene-Gene and Gene-Diet Interactions.es_ES
dc.typejournal articlees_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.identifier.pubmedID35047011es_ES
dc.format.volume12es_ES
dc.format.page783845es_ES
dc.identifier.doi10.3389/fgene.2021.783845es_ES
dc.contributor.funderUnited States Department of Agriculture es_ES
dc.description.peerreviewedes_ES
dc.relation.publisherversion10.3389/fgene.2021.783845es_ES
dc.identifier.journalFrontiers in geneticses_ES
dc.repisalud.institucionCNICes_ES
dc.rights.accessRightsopen accesses_ES


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Atribución 4.0 Internacional
Este Item está sujeto a una licencia Creative Commons: Atribución 4.0 Internacional