ArticleFrontiers in bioengineering and biotechnology2020
Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 42 citations in OpenAlex.
- The inflammatory proteome, obesity, and medical weight loss and regain in humans.Obesity (Silver Spring, Md.) · 2023Trial
- A machine learning approach to conglomerate multi-domain features of cardiac aging.European heart journal. Digital health · 2026Article
- Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges.Chronic diseases and translational medicine · 2025Review
- Identification of novel hypertension biomarkers using explainable AI and metabolomics.Metabolomics : Official journal of the Metabolomic Society · 2024Article
- Artificial Intelligence in Metabolomics: A Current Review.Trends in analytical chemistry : TRAC · 2024Article
- Predicting metabolomic profiles from microbial composition through neural ordinary differential equations.Nature machine intelligence · 2023Article
- Benchmarking of Machine Learning classifiers on plasma proteomic for COVID-19 severity prediction through interpretable artificial intelligence.Artificial intelligence in medicine · 2023Review
- A machine learning model for visualization and dynamic clinical prediction of stroke recurrence in acute ischemic stroke patients: A real-world retrospective study.Frontiers in neuroscience · 2023Article
- Metabolomics and modelling approaches for systems metabolic engineering.Metabolic engineering communications · 2022Review
- Precision Medicine Approaches with Metabolomics and Artificial Intelligence.International journal of molecular sciences · 2022Review
- Machine Learning Data Analysis Highlights the Role ofBiomedicines · 2022Article
- Metabolite patterns associated with individual response to supervised exercise therapy in patients with intermittent claudication.JVS-vascular science · 2022Article
- Targeted plasma metabolomics combined with machine learning for the diagnosis of severe acute respiratory syndrome virus type 2.Frontiers in microbiology · 2022Article
- Lipidomic Signatures of Changes in Adiposity: A Large Prospective Study of 5849 Adults from the Australian Diabetes, Obesity and Lifestyle Study.Metabolites · 2021Article
- GeneCloudOmics: A Data Analytic Cloud Platform for High-Throughput Gene Expression Analysis.Frontiers in bioinformatics · 2021Article
- Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering.Metabolic engineering communications · 2020Review
- Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Weight gain is a metabolic disorder that often culminates in the development of obesity and other comorbidities such as diabetes. Obesity is characterized by the development of a chronic, subclinical systemic inflammation, and is regarded as a remarkably important factor that contributes to the development of such comorbidities. Therefore, laboratory methods that allow the identification of subjects at higher risk for severe weight-associated morbidity are of utter importance, considering the health, and safety of populations. This contribution analyzed the plasma of 180 Brazilian individuals, equally divided into a eutrophic control group and case group, to assess the presence of biomarkers related to weight gain, aiming at characterizing the phenotype of this population. Samples were analyzed by mass spectrometry and most discriminant features were determined by a machine learning approach using Random Forest algorithm. Five biomarkers related to the pathogenesis and chronicity of inflammation in weight gain were identified. Two metabolites of arachidonic acid were upregulated in the case group, indicating the presence of inflammation, as well as two other molecules related to dysfunctions in the cycle of nitric oxide (NO) and increase in superoxide production. Finally, a fifth case group marker observed in this study may indicate the trigger for diabetes in overweight and obesity individuals. The use of mass spectrometry combined with machine learning analyses to prospect and characterize biomarkers associated with weight gain will pave the way for elucidating potential therapeutic and prognostic targets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.