Version 1
: Received: 23 June 2020 / Approved: 23 June 2020 / Online: 23 June 2020 (11:35:38 CEST)
How to cite:
Bonifacio, K.; Maes, M.; de Farias, C.; Matsumoto, A.; Tomereli, C.; Pereira da Silva, D.; Cyrino, E.; Nixdorf, S.; Venturini, D.; Barbosa, D. Increased Aldehyde Formation as a Key Component of the Metabolic Syndrome in Adolescents: A Machine Learning Study. Preprints2020, 2020060283. https://doi.org/10.20944/preprints202006.0283.v1
Bonifacio, K.; Maes, M.; de Farias, C.; Matsumoto, A.; Tomereli, C.; Pereira da Silva, D.; Cyrino, E.; Nixdorf, S.; Venturini, D.; Barbosa, D. Increased Aldehyde Formation as a Key Component of the Metabolic Syndrome in Adolescents: A Machine Learning Study. Preprints 2020, 2020060283. https://doi.org/10.20944/preprints202006.0283.v1
Bonifacio, K.; Maes, M.; de Farias, C.; Matsumoto, A.; Tomereli, C.; Pereira da Silva, D.; Cyrino, E.; Nixdorf, S.; Venturini, D.; Barbosa, D. Increased Aldehyde Formation as a Key Component of the Metabolic Syndrome in Adolescents: A Machine Learning Study. Preprints2020, 2020060283. https://doi.org/10.20944/preprints202006.0283.v1
APA Style
Bonifacio, K., Maes, M., de Farias, C., Matsumoto, A., Tomereli, C., Pereira da Silva, D., Cyrino, E., Nixdorf, S., Venturini, D., & Barbosa, D. (2020). Increased Aldehyde Formation as a Key Component of the Metabolic Syndrome in Adolescents: A Machine Learning Study. Preprints. https://doi.org/10.20944/preprints202006.0283.v1
Chicago/Turabian Style
Bonifacio, K., Danielle Venturini and Decio Barbosa. 2020 "Increased Aldehyde Formation as a Key Component of the Metabolic Syndrome in Adolescents: A Machine Learning Study" Preprints. https://doi.org/10.20944/preprints202006.0283.v1
Abstract
Purpose: To investigate the alterations in nitro-oxidative stress (OS) and antioxidant status in adolescents with metabolic syndrome (MetS) and whether these alterations occur independently from effects of overweight or obesity.Methods: Blood was collected in 47 adolescents with MetS and 94 adolescents without MetS as assessed with the International Diabetes Federation criteria. The International Obesity Task Force (IOTF) criteria were used to classify the subjects into those with overweight or obesity. We measured nitro-oxidative biomarkers including nitric oxide metabolites (NOx), lipid hydroperoxides (LOOH), and malondialdehyde (MDA), and antioxidant biomarkers, i.e. total radical-trapping antioxidant parameter (TRAP), paraoxonase (PON)-1 activity, thiol (SH-) groups, as well as tumor necrosis factor-α, glucose, insulin, triglycerides, uric acid and high-density lipoprotein cholesterol (HDL-C).Results: Logistic regression analysis showed that increased MDA and NOx and a lowered TRAP/uric acid ratio were associated with MetS. Machine learning including soft independent modeling of class analogy (SIMCA) showed that the top-3 most important features of MetS were increased glucose and MDA and lowered HDL-C. Support vector machine using MDA, glucose, insulin, HDL-C, triglycerides and body mass index as input variables yielded a 10-fold cross-validated accuracy of 89.8% when discriminating MetS from controls. The association between MetS and increased MDA was independent from the effects of overweight-obesity. glucose, insulin, triglycerides and HDL-C.Conclusion: In adolescents, increased MDA formation is a key component of MetS, indicating that increased production of reactive oxygen species with consequent lipid peroxidation and aldehyde formation participate in the development of MetS.
Medicine and Pharmacology, Endocrinology and Metabolism
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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