Employing biochemical biomarkers for building decision tree models to predict bipolar disorder from major depressive disorder
Conventional biochemical parameters may have predictive values for use in clinical identification between bipolar disorder (BD) and major depressive disorder (MDD). This study enrolled 2470 hospitalized patients with BD (n = 1333) or MDD (n = 1137) at reproductive age from 2009 to 2018 in China. We...
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| Veröffentlicht in: | Journal of affective disorders 2022-07, Vol.308, p.190-198 |
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| creator | Zhu, Yuncheng Wu, Xiaohui Liu, Hongmei Niu, Zhiang Zhao, Jie Wang, Fan Mao, Ruizhi Guo, Xiaoyun Zhang, Chen Wang, Zuowei Chen, Jun Fang, Yiru |
| description | Conventional biochemical parameters may have predictive values for use in clinical identification between bipolar disorder (BD) and major depressive disorder (MDD).
This study enrolled 2470 hospitalized patients with BD (n = 1333) or MDD (n = 1137) at reproductive age from 2009 to 2018 in China. We extracted 8 parameters, uric acid (UA), direct bilirubin (DBIL), indirect bilirubin (IDBIL), lactic dehydrogenase (LDH), free triiodothyronine (FT3), thyroid-stimulating hormone (TSH), high-density lipoprotein (HDL) and prealbumin of male, patients and 12 parameters, UA, DBIL, IBIL, LDH, FT3, TSH, glutamic-pyruvic transaminase (GPT), white blood cell (WBC), alkaline phosphatase (ALP), fasting blood glucose (FBG), triglyceride and low-density lipoprotein (LDL) of female patients. Backward stepwise multivariate regression analysis and the Chi-Square Automatic Interaction Detection (CHAID) segmentation analysis via SPSS Decision Tree were implemented to define the discrimination of BD and MDD.
DBIL was extracted as the first splitting variable, with LDH and IBIL as the second, TSH and prealbumin as the third in the model of male patients (p-value |
| doi_str_mv | 10.1016/j.jad.2022.03.080 |
| format | Article |
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This study enrolled 2470 hospitalized patients with BD (n = 1333) or MDD (n = 1137) at reproductive age from 2009 to 2018 in China. We extracted 8 parameters, uric acid (UA), direct bilirubin (DBIL), indirect bilirubin (IDBIL), lactic dehydrogenase (LDH), free triiodothyronine (FT3), thyroid-stimulating hormone (TSH), high-density lipoprotein (HDL) and prealbumin of male, patients and 12 parameters, UA, DBIL, IBIL, LDH, FT3, TSH, glutamic-pyruvic transaminase (GPT), white blood cell (WBC), alkaline phosphatase (ALP), fasting blood glucose (FBG), triglyceride and low-density lipoprotein (LDL) of female patients. Backward stepwise multivariate regression analysis and the Chi-Square Automatic Interaction Detection (CHAID) segmentation analysis via SPSS Decision Tree were implemented to define the discrimination of BD and MDD.
DBIL was extracted as the first splitting variable, with LDH and IBIL as the second, TSH and prealbumin as the third in the model of male patients (p-value < .05). For the model of female patients, DBIL was also extracted as the first splitting variable, with UA, LDH, and IBIL as the second, triglyceride and FT3 as the third (p-value < .05). The predictive accuracies of the Decision Tree and multiple logistic regression models were similar (74.9% vs 76.9% in males; 74.4% vs 79.5% in females).
This study suggests the value of the Decision Tree models, which employ biochemical parameters as diagnostic predictors for BD and MDD. The CHAID Decision Tree identified that patients with concomitantly increased LDH, IBIL, and decreased DBIL could be in the group that showed the highest risk of being diagnosed as BD.
•A big data study using the conventional biochemical indexes for clinical identification between BD and MDD.•We have good reason to believe that an easy way is found to identify BD and MDD with good accuracy.•CHAID model is easily accessible in comparison with neuroinflammation or genetic measures shown only in lab experiments.</description><identifier>ISSN: 0165-0327</identifier><identifier>EISSN: 1573-2517</identifier><identifier>DOI: 10.1016/j.jad.2022.03.080</identifier><identifier>PMID: 35439462</identifier><language>eng</language><publisher>Netherlands: Elsevier B.V</publisher><subject>Bilirubin ; Biomarkers ; Bipolar disorder ; Bipolar Disorder - diagnosis ; Decision tree ; Decision Trees ; Depressive Disorder, Major - diagnosis ; Female ; Humans ; Major depressive disorder ; Male ; Menstrual cycle ; Neurogenic inflammation ; Oxidative stress ; Prealbumin ; Thyrotropin ; Triglycerides ; Uric Acid</subject><ispartof>Journal of affective disorders, 2022-07, Vol.308, p.190-198</ispartof><rights>2022 Elsevier B.V.</rights><rights>Copyright © 2022 Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c353t-daebbb397cbc4cf80fa658f99ae0ea90959677ebbe791142e251e67f77f27d303</citedby><cites>FETCH-LOGICAL-c353t-daebbb397cbc4cf80fa658f99ae0ea90959677ebbe791142e251e67f77f27d303</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S016503272200413X$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35439462$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhu, Yuncheng</creatorcontrib><creatorcontrib>Wu, Xiaohui</creatorcontrib><creatorcontrib>Liu, Hongmei</creatorcontrib><creatorcontrib>Niu, Zhiang</creatorcontrib><creatorcontrib>Zhao, Jie</creatorcontrib><creatorcontrib>Wang, Fan</creatorcontrib><creatorcontrib>Mao, Ruizhi</creatorcontrib><creatorcontrib>Guo, Xiaoyun</creatorcontrib><creatorcontrib>Zhang, Chen</creatorcontrib><creatorcontrib>Wang, Zuowei</creatorcontrib><creatorcontrib>Chen, Jun</creatorcontrib><creatorcontrib>Fang, Yiru</creatorcontrib><title>Employing biochemical biomarkers for building decision tree models to predict bipolar disorder from major depressive disorder</title><title>Journal of affective disorders</title><addtitle>J Affect Disord</addtitle><description>Conventional biochemical parameters may have predictive values for use in clinical identification between bipolar disorder (BD) and major depressive disorder (MDD).
This study enrolled 2470 hospitalized patients with BD (n = 1333) or MDD (n = 1137) at reproductive age from 2009 to 2018 in China. We extracted 8 parameters, uric acid (UA), direct bilirubin (DBIL), indirect bilirubin (IDBIL), lactic dehydrogenase (LDH), free triiodothyronine (FT3), thyroid-stimulating hormone (TSH), high-density lipoprotein (HDL) and prealbumin of male, patients and 12 parameters, UA, DBIL, IBIL, LDH, FT3, TSH, glutamic-pyruvic transaminase (GPT), white blood cell (WBC), alkaline phosphatase (ALP), fasting blood glucose (FBG), triglyceride and low-density lipoprotein (LDL) of female patients. Backward stepwise multivariate regression analysis and the Chi-Square Automatic Interaction Detection (CHAID) segmentation analysis via SPSS Decision Tree were implemented to define the discrimination of BD and MDD.
DBIL was extracted as the first splitting variable, with LDH and IBIL as the second, TSH and prealbumin as the third in the model of male patients (p-value < .05). For the model of female patients, DBIL was also extracted as the first splitting variable, with UA, LDH, and IBIL as the second, triglyceride and FT3 as the third (p-value < .05). The predictive accuracies of the Decision Tree and multiple logistic regression models were similar (74.9% vs 76.9% in males; 74.4% vs 79.5% in females).
This study suggests the value of the Decision Tree models, which employ biochemical parameters as diagnostic predictors for BD and MDD. The CHAID Decision Tree identified that patients with concomitantly increased LDH, IBIL, and decreased DBIL could be in the group that showed the highest risk of being diagnosed as BD.
•A big data study using the conventional biochemical indexes for clinical identification between BD and MDD.•We have good reason to believe that an easy way is found to identify BD and MDD with good accuracy.•CHAID model is easily accessible in comparison with neuroinflammation or genetic measures shown only in lab experiments.</description><subject>Bilirubin</subject><subject>Biomarkers</subject><subject>Bipolar disorder</subject><subject>Bipolar Disorder - diagnosis</subject><subject>Decision tree</subject><subject>Decision Trees</subject><subject>Depressive Disorder, Major - diagnosis</subject><subject>Female</subject><subject>Humans</subject><subject>Major depressive disorder</subject><subject>Male</subject><subject>Menstrual cycle</subject><subject>Neurogenic inflammation</subject><subject>Oxidative stress</subject><subject>Prealbumin</subject><subject>Thyrotropin</subject><subject>Triglycerides</subject><subject>Uric Acid</subject><issn>0165-0327</issn><issn>1573-2517</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kMFO3DAQhi1UVBbaB-il8pFLwthO4lg9IUQLEhIXerYce0ydJuvUziJx4N3xaoFjTzPSfP8vzUfINwY1A9ZdjPVoXM2B8xpEDT0ckQ1rpah4y-QnsilMW4Hg8oSc5jwCQKckfCYnom2Eajq-IS_X8zLF57B9pEOI9g_OwZppv88m_cWUqY-JDrswuT3j0IYc4pauCZHO0eGU6RrpktAFu5bcEieTqAs5JoeJ-hRnOpuxlDgsVM7hCT_OX8ixN1PGr2_zjPz-ef1wdVPd3f-6vbq8q6xoxVo5g8MwCCXtYBvre_Cma3uvlEFAo0C1qpOyMCgVYw3H8j920kvpuXQCxBk5P_QuKf7bYV71HLLFaTJbjLusedfyvmt61ReUHVCbYs4JvV5SKC6eNQO9t65HXazrvXUNQhfrJfP9rX43zOg-Eu-aC_DjABRd-BQw6WwDbm2RltCu2sXwn_pXdKOV3w</recordid><startdate>20220701</startdate><enddate>20220701</enddate><creator>Zhu, Yuncheng</creator><creator>Wu, Xiaohui</creator><creator>Liu, Hongmei</creator><creator>Niu, Zhiang</creator><creator>Zhao, Jie</creator><creator>Wang, Fan</creator><creator>Mao, Ruizhi</creator><creator>Guo, Xiaoyun</creator><creator>Zhang, Chen</creator><creator>Wang, Zuowei</creator><creator>Chen, Jun</creator><creator>Fang, Yiru</creator><general>Elsevier B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20220701</creationdate><title>Employing biochemical biomarkers for building decision tree models to predict bipolar disorder from major depressive disorder</title><author>Zhu, Yuncheng ; Wu, Xiaohui ; Liu, Hongmei ; Niu, Zhiang ; Zhao, Jie ; Wang, Fan ; Mao, Ruizhi ; Guo, Xiaoyun ; Zhang, Chen ; Wang, Zuowei ; Chen, Jun ; Fang, Yiru</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c353t-daebbb397cbc4cf80fa658f99ae0ea90959677ebbe791142e251e67f77f27d303</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Bilirubin</topic><topic>Biomarkers</topic><topic>Bipolar disorder</topic><topic>Bipolar Disorder - diagnosis</topic><topic>Decision tree</topic><topic>Decision Trees</topic><topic>Depressive Disorder, Major - diagnosis</topic><topic>Female</topic><topic>Humans</topic><topic>Major depressive disorder</topic><topic>Male</topic><topic>Menstrual cycle</topic><topic>Neurogenic inflammation</topic><topic>Oxidative stress</topic><topic>Prealbumin</topic><topic>Thyrotropin</topic><topic>Triglycerides</topic><topic>Uric Acid</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhu, Yuncheng</creatorcontrib><creatorcontrib>Wu, Xiaohui</creatorcontrib><creatorcontrib>Liu, Hongmei</creatorcontrib><creatorcontrib>Niu, Zhiang</creatorcontrib><creatorcontrib>Zhao, Jie</creatorcontrib><creatorcontrib>Wang, Fan</creatorcontrib><creatorcontrib>Mao, Ruizhi</creatorcontrib><creatorcontrib>Guo, Xiaoyun</creatorcontrib><creatorcontrib>Zhang, Chen</creatorcontrib><creatorcontrib>Wang, Zuowei</creatorcontrib><creatorcontrib>Chen, Jun</creatorcontrib><creatorcontrib>Fang, Yiru</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of affective disorders</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhu, Yuncheng</au><au>Wu, Xiaohui</au><au>Liu, Hongmei</au><au>Niu, Zhiang</au><au>Zhao, Jie</au><au>Wang, Fan</au><au>Mao, Ruizhi</au><au>Guo, Xiaoyun</au><au>Zhang, Chen</au><au>Wang, Zuowei</au><au>Chen, Jun</au><au>Fang, Yiru</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Employing biochemical biomarkers for building decision tree models to predict bipolar disorder from major depressive disorder</atitle><jtitle>Journal of affective disorders</jtitle><addtitle>J Affect Disord</addtitle><date>2022-07-01</date><risdate>2022</risdate><volume>308</volume><spage>190</spage><epage>198</epage><pages>190-198</pages><issn>0165-0327</issn><eissn>1573-2517</eissn><abstract>Conventional biochemical parameters may have predictive values for use in clinical identification between bipolar disorder (BD) and major depressive disorder (MDD).
This study enrolled 2470 hospitalized patients with BD (n = 1333) or MDD (n = 1137) at reproductive age from 2009 to 2018 in China. We extracted 8 parameters, uric acid (UA), direct bilirubin (DBIL), indirect bilirubin (IDBIL), lactic dehydrogenase (LDH), free triiodothyronine (FT3), thyroid-stimulating hormone (TSH), high-density lipoprotein (HDL) and prealbumin of male, patients and 12 parameters, UA, DBIL, IBIL, LDH, FT3, TSH, glutamic-pyruvic transaminase (GPT), white blood cell (WBC), alkaline phosphatase (ALP), fasting blood glucose (FBG), triglyceride and low-density lipoprotein (LDL) of female patients. Backward stepwise multivariate regression analysis and the Chi-Square Automatic Interaction Detection (CHAID) segmentation analysis via SPSS Decision Tree were implemented to define the discrimination of BD and MDD.
DBIL was extracted as the first splitting variable, with LDH and IBIL as the second, TSH and prealbumin as the third in the model of male patients (p-value < .05). For the model of female patients, DBIL was also extracted as the first splitting variable, with UA, LDH, and IBIL as the second, triglyceride and FT3 as the third (p-value < .05). The predictive accuracies of the Decision Tree and multiple logistic regression models were similar (74.9% vs 76.9% in males; 74.4% vs 79.5% in females).
This study suggests the value of the Decision Tree models, which employ biochemical parameters as diagnostic predictors for BD and MDD. The CHAID Decision Tree identified that patients with concomitantly increased LDH, IBIL, and decreased DBIL could be in the group that showed the highest risk of being diagnosed as BD.
•A big data study using the conventional biochemical indexes for clinical identification between BD and MDD.•We have good reason to believe that an easy way is found to identify BD and MDD with good accuracy.•CHAID model is easily accessible in comparison with neuroinflammation or genetic measures shown only in lab experiments.</abstract><cop>Netherlands</cop><pub>Elsevier B.V</pub><pmid>35439462</pmid><doi>10.1016/j.jad.2022.03.080</doi><tpages>9</tpages></addata></record> |
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| subjects | Bilirubin Biomarkers Bipolar disorder Bipolar Disorder - diagnosis Decision tree Decision Trees Depressive Disorder, Major - diagnosis Female Humans Major depressive disorder Male Menstrual cycle Neurogenic inflammation Oxidative stress Prealbumin Thyrotropin Triglycerides Uric Acid |
| title | Employing biochemical biomarkers for building decision tree models to predict bipolar disorder from major depressive disorder |
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