Potential Biomarkers for Predicting Depression in Diabetes Mellitus

Objective: To identify the potential biomarkers for predicting depression in diabetes mellitus using support vector machine to analyze routine biochemical tests and vital signs between two groups: subjects with both diabetes mellitus and depression, and subjects with diabetes mellitus alone.Methods:...

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Autores principales:	Xiuli Song, Qiang Zheng, Rui Zhang, Miye Wang, Wei Deng, Qiang Wang, Wanjun Guo, Tao Li, Xiaohong Ma
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	diabetes mellitus depression support vector machine biomarkers machine learning method Psychiatry RC435-571
Acceso en línea:	https://doaj.org/article/fd72b1817b4b4162b8b759939b47cdf5
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spelling	oai:doaj.org-article:fd72b1817b4b4162b8b759939b47cdf52021-12-01T13:33:55ZPotential Biomarkers for Predicting Depression in Diabetes Mellitus1664-064010.3389/fpsyt.2021.731220https://doaj.org/article/fd72b1817b4b4162b8b759939b47cdf52021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpsyt.2021.731220/fullhttps://doaj.org/toc/1664-0640Objective: To identify the potential biomarkers for predicting depression in diabetes mellitus using support vector machine to analyze routine biochemical tests and vital signs between two groups: subjects with both diabetes mellitus and depression, and subjects with diabetes mellitus alone.Methods: Electronic medical records upon admission and biochemical tests and vital signs of 135 patients with both diabetes mellitus and depression and 187 patients with diabetes mellitus alone were identified for this retrospective study. After matching on factors of age and sex, the two groups (n = 72 for each group) were classified by the recursive feature elimination-based support vector machine, of which, the training data, validation data, and testing data were split for ranking the parameters, determine the optimal parameters, and assess classification performance. The biomarkers were identified by 10-fold cross validation.Results: The experimental results identified 8 predictive biomarkers with classification accuracy of 78%. The 8 biomarkers are magnesium, cholesterol, AST/ALT, percentage of monocytes, bilirubin indirect, triglyceride, lactic dehydrogenase, and diastolic blood pressure. Receiver operating characteristic curve analysis was also adopted with area under the curve being 0.72.Conclusions: Some biochemical parameters may be potential biomarkers to predict depression among the subjects with diabetes mellitus.Xiuli SongXiuli SongQiang ZhengRui ZhangMiye WangWei DengQiang WangWanjun GuoTao LiXiaohong MaFrontiers Media S.A.articlediabetes mellitusdepressionsupport vector machinebiomarkersmachine learning methodPsychiatryRC435-571ENFrontiers in Psychiatry, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	diabetes mellitus depression support vector machine biomarkers machine learning method Psychiatry RC435-571
spellingShingle	diabetes mellitus depression support vector machine biomarkers machine learning method Psychiatry RC435-571 Xiuli Song Xiuli Song Qiang Zheng Rui Zhang Miye Wang Wei Deng Qiang Wang Wanjun Guo Tao Li Xiaohong Ma Potential Biomarkers for Predicting Depression in Diabetes Mellitus
description	Objective: To identify the potential biomarkers for predicting depression in diabetes mellitus using support vector machine to analyze routine biochemical tests and vital signs between two groups: subjects with both diabetes mellitus and depression, and subjects with diabetes mellitus alone.Methods: Electronic medical records upon admission and biochemical tests and vital signs of 135 patients with both diabetes mellitus and depression and 187 patients with diabetes mellitus alone were identified for this retrospective study. After matching on factors of age and sex, the two groups (n = 72 for each group) were classified by the recursive feature elimination-based support vector machine, of which, the training data, validation data, and testing data were split for ranking the parameters, determine the optimal parameters, and assess classification performance. The biomarkers were identified by 10-fold cross validation.Results: The experimental results identified 8 predictive biomarkers with classification accuracy of 78%. The 8 biomarkers are magnesium, cholesterol, AST/ALT, percentage of monocytes, bilirubin indirect, triglyceride, lactic dehydrogenase, and diastolic blood pressure. Receiver operating characteristic curve analysis was also adopted with area under the curve being 0.72.Conclusions: Some biochemical parameters may be potential biomarkers to predict depression among the subjects with diabetes mellitus.
format	article
author	Xiuli Song Xiuli Song Qiang Zheng Rui Zhang Miye Wang Wei Deng Qiang Wang Wanjun Guo Tao Li Xiaohong Ma
author_facet	Xiuli Song Xiuli Song Qiang Zheng Rui Zhang Miye Wang Wei Deng Qiang Wang Wanjun Guo Tao Li Xiaohong Ma
author_sort	Xiuli Song
title	Potential Biomarkers for Predicting Depression in Diabetes Mellitus
title_short	Potential Biomarkers for Predicting Depression in Diabetes Mellitus
title_full	Potential Biomarkers for Predicting Depression in Diabetes Mellitus
title_fullStr	Potential Biomarkers for Predicting Depression in Diabetes Mellitus
title_full_unstemmed	Potential Biomarkers for Predicting Depression in Diabetes Mellitus
title_sort	potential biomarkers for predicting depression in diabetes mellitus
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/fd72b1817b4b4162b8b759939b47cdf5
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Potential Biomarkers for Predicting Depression in Diabetes Mellitus

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