Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model
The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people. A total of 302 adult healthy su...
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| Veröffentlicht in: | Lipids in health and disease 2017-02, Vol.16 (1), p.42-42, Article 42 |
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| container_title | Lipids in health and disease |
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| creator | Ma, Jing Yu, Jiong Hao, Guangshu Wang, Dan Sun, Yanni Lu, Jianxin Cao, Hongcui Lin, Feiyan |
| description | The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people.
A total of 302 adult healthy subjects and 273 overweight subjects were enrolled in this study. The levels of fasting indexes of TG (fs-TG), TC (fs-TC), blood glucose, liver function, and kidney function were measured and analyzed by correlation analysis and multiple linear regression (MRL). The back propagation artificial neural network (BP-ANN) was applied to develop prediction models of fs-TG and fs-TC.
The results showed there was significant difference in biochemical indexes between healthy people and overweight people. The correlation analysis showed fs-TG was related to weight, height, blood glucose, and indexes of liver and kidney function; while fs-TC was correlated with age, indexes of liver function (P |
| doi_str_mv | 10.1186/s12944-017-0434-5 |
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| fullrecord | <record><control><sourceid>gale_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_5319080</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A481900108</galeid><sourcerecordid>A481900108</sourcerecordid><originalsourceid>FETCH-LOGICAL-c527t-c0b6f3221d84069f6107c1436bfe462ccdedd855cb4da237eaaefeb4ad2371623</originalsourceid><addsrcrecordid>eNqNUk1v1DAQjRCIloUfwAVZ4sIlxeM4dvaCtKr4kipxAYmb5diTrCvHXuykVX8A_xtHW0qLOCAfbM-89zTz9KrqJdAzgE68zcC2nNcUZE15w-v2UXUKXIq6Bfj--N77pHqW8yWljEohnlYnrGOw5Q2cVj93OWPOE4aZxIHMyY3-xmByFokOlph99JhnTNETF0i8wnSNbtzP5IDx4JH0OqMlMZBp8bNbK94F1IkkHFNRdqW1Cuk0u8EZp1edGb13IwaDZIoW_fPqyaB9xhe396b69uH91_NP9cWXj5_Pdxe1aZmca0N7MTSMge04FdtBAJUGeCP6Ablgxli0tmtb03OrWSNRaxyw59qWDwjWbKp3R93D0k9oTdk6aa8OyU063aionXrYCW6vxnil2ga2tKNF4M2tQIo_lmKMmlw2ZRsdMC5ZQSdl10reyf-BUsG3lK7Q139BL-OSQnFiRXEmoIHuD2rUHpULQywjmlVU7XhX5qNAV9TZP1DlWJyciQEHV-oPCHAkmBRzTjjc2QFUrTFTx5ipEjO1xqyYsale3ffxjvE7V80vdsLQKA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1874261318</pqid></control><display><type>article</type><title>Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model</title><source>MEDLINE</source><source>DOAJ Directory of Open Access Journals</source><source>SpringerLink Journals</source><source>PubMed Central Open Access</source><source>Springer Nature OA Free Journals</source><source>EZB-FREE-00999 freely available EZB journals</source><source>PubMed Central</source><source>Free Full-Text Journals in Chemistry</source><creator>Ma, Jing ; Yu, Jiong ; Hao, Guangshu ; Wang, Dan ; Sun, Yanni ; Lu, Jianxin ; Cao, Hongcui ; Lin, Feiyan</creator><creatorcontrib>Ma, Jing ; Yu, Jiong ; Hao, Guangshu ; Wang, Dan ; Sun, Yanni ; Lu, Jianxin ; Cao, Hongcui ; Lin, Feiyan</creatorcontrib><description>The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people.
A total of 302 adult healthy subjects and 273 overweight subjects were enrolled in this study. The levels of fasting indexes of TG (fs-TG), TC (fs-TC), blood glucose, liver function, and kidney function were measured and analyzed by correlation analysis and multiple linear regression (MRL). The back propagation artificial neural network (BP-ANN) was applied to develop prediction models of fs-TG and fs-TC.
The results showed there was significant difference in biochemical indexes between healthy people and overweight people. The correlation analysis showed fs-TG was related to weight, height, blood glucose, and indexes of liver and kidney function; while fs-TC was correlated with age, indexes of liver function (P < 0.01). The MRL analysis indicated regression equations of fs-TG and fs-TC both had statistic significant (P < 0.01) when included independent indexes. The BP-ANN model of fs-TG reached training goal at 59 epoch, while fs-TC model achieved high prediction accuracy after training 1000 epoch.
In conclusions, there was high relationship of fs-TG and fs-TC with weight, height, age, blood glucose, indexes of liver function and kidney function. Based on related variables, the indexes of fs-TG and fs-TC can be predicted by BP-ANN models in overweight people.</description><identifier>ISSN: 1476-511X</identifier><identifier>EISSN: 1476-511X</identifier><identifier>DOI: 10.1186/s12944-017-0434-5</identifier><identifier>PMID: 28219431</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Adult ; Artificial intelligence ; Case-Control Studies ; Cholesterol ; Cholesterol - blood ; Fasting ; Health aspects ; Humans ; Hyperlipidemia ; Linear Models ; Middle Aged ; Models, Biological ; Neural Networks (Computer) ; Overweight - blood ; Overweight persons ; Risk factors ; Triglycerides ; Triglycerides - blood</subject><ispartof>Lipids in health and disease, 2017-02, Vol.16 (1), p.42-42, Article 42</ispartof><rights>COPYRIGHT 2017 BioMed Central Ltd.</rights><rights>Copyright BioMed Central 2017</rights><rights>The Author(s). 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c527t-c0b6f3221d84069f6107c1436bfe462ccdedd855cb4da237eaaefeb4ad2371623</citedby><cites>FETCH-LOGICAL-c527t-c0b6f3221d84069f6107c1436bfe462ccdedd855cb4da237eaaefeb4ad2371623</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319080/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319080/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28219431$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ma, Jing</creatorcontrib><creatorcontrib>Yu, Jiong</creatorcontrib><creatorcontrib>Hao, Guangshu</creatorcontrib><creatorcontrib>Wang, Dan</creatorcontrib><creatorcontrib>Sun, Yanni</creatorcontrib><creatorcontrib>Lu, Jianxin</creatorcontrib><creatorcontrib>Cao, Hongcui</creatorcontrib><creatorcontrib>Lin, Feiyan</creatorcontrib><title>Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model</title><title>Lipids in health and disease</title><addtitle>Lipids Health Dis</addtitle><description>The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people.
A total of 302 adult healthy subjects and 273 overweight subjects were enrolled in this study. The levels of fasting indexes of TG (fs-TG), TC (fs-TC), blood glucose, liver function, and kidney function were measured and analyzed by correlation analysis and multiple linear regression (MRL). The back propagation artificial neural network (BP-ANN) was applied to develop prediction models of fs-TG and fs-TC.
The results showed there was significant difference in biochemical indexes between healthy people and overweight people. The correlation analysis showed fs-TG was related to weight, height, blood glucose, and indexes of liver and kidney function; while fs-TC was correlated with age, indexes of liver function (P < 0.01). The MRL analysis indicated regression equations of fs-TG and fs-TC both had statistic significant (P < 0.01) when included independent indexes. The BP-ANN model of fs-TG reached training goal at 59 epoch, while fs-TC model achieved high prediction accuracy after training 1000 epoch.
In conclusions, there was high relationship of fs-TG and fs-TC with weight, height, age, blood glucose, indexes of liver function and kidney function. Based on related variables, the indexes of fs-TG and fs-TC can be predicted by BP-ANN models in overweight people.</description><subject>Adult</subject><subject>Artificial intelligence</subject><subject>Case-Control Studies</subject><subject>Cholesterol</subject><subject>Cholesterol - blood</subject><subject>Fasting</subject><subject>Health aspects</subject><subject>Humans</subject><subject>Hyperlipidemia</subject><subject>Linear Models</subject><subject>Middle Aged</subject><subject>Models, Biological</subject><subject>Neural Networks (Computer)</subject><subject>Overweight - blood</subject><subject>Overweight persons</subject><subject>Risk factors</subject><subject>Triglycerides</subject><subject>Triglycerides - blood</subject><issn>1476-511X</issn><issn>1476-511X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNUk1v1DAQjRCIloUfwAVZ4sIlxeM4dvaCtKr4kipxAYmb5diTrCvHXuykVX8A_xtHW0qLOCAfbM-89zTz9KrqJdAzgE68zcC2nNcUZE15w-v2UXUKXIq6Bfj--N77pHqW8yWljEohnlYnrGOw5Q2cVj93OWPOE4aZxIHMyY3-xmByFokOlph99JhnTNETF0i8wnSNbtzP5IDx4JH0OqMlMZBp8bNbK94F1IkkHFNRdqW1Cuk0u8EZp1edGb13IwaDZIoW_fPqyaB9xhe396b69uH91_NP9cWXj5_Pdxe1aZmca0N7MTSMge04FdtBAJUGeCP6Ablgxli0tmtb03OrWSNRaxyw59qWDwjWbKp3R93D0k9oTdk6aa8OyU063aionXrYCW6vxnil2ga2tKNF4M2tQIo_lmKMmlw2ZRsdMC5ZQSdl10reyf-BUsG3lK7Q139BL-OSQnFiRXEmoIHuD2rUHpULQywjmlVU7XhX5qNAV9TZP1DlWJyciQEHV-oPCHAkmBRzTjjc2QFUrTFTx5ipEjO1xqyYsale3ffxjvE7V80vdsLQKA</recordid><startdate>20170220</startdate><enddate>20170220</enddate><creator>Ma, Jing</creator><creator>Yu, Jiong</creator><creator>Hao, Guangshu</creator><creator>Wang, Dan</creator><creator>Sun, Yanni</creator><creator>Lu, Jianxin</creator><creator>Cao, Hongcui</creator><creator>Lin, Feiyan</creator><general>BioMed Central Ltd</general><general>BioMed Central</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>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20170220</creationdate><title>Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model</title><author>Ma, Jing ; Yu, Jiong ; Hao, Guangshu ; Wang, Dan ; Sun, Yanni ; Lu, Jianxin ; Cao, Hongcui ; Lin, Feiyan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c527t-c0b6f3221d84069f6107c1436bfe462ccdedd855cb4da237eaaefeb4ad2371623</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adult</topic><topic>Artificial intelligence</topic><topic>Case-Control Studies</topic><topic>Cholesterol</topic><topic>Cholesterol - blood</topic><topic>Fasting</topic><topic>Health aspects</topic><topic>Humans</topic><topic>Hyperlipidemia</topic><topic>Linear Models</topic><topic>Middle Aged</topic><topic>Models, Biological</topic><topic>Neural Networks (Computer)</topic><topic>Overweight - blood</topic><topic>Overweight persons</topic><topic>Risk factors</topic><topic>Triglycerides</topic><topic>Triglycerides - blood</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ma, Jing</creatorcontrib><creatorcontrib>Yu, Jiong</creatorcontrib><creatorcontrib>Hao, Guangshu</creatorcontrib><creatorcontrib>Wang, Dan</creatorcontrib><creatorcontrib>Sun, Yanni</creatorcontrib><creatorcontrib>Lu, Jianxin</creatorcontrib><creatorcontrib>Cao, Hongcui</creatorcontrib><creatorcontrib>Lin, Feiyan</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Lipids in health and disease</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ma, Jing</au><au>Yu, Jiong</au><au>Hao, Guangshu</au><au>Wang, Dan</au><au>Sun, Yanni</au><au>Lu, Jianxin</au><au>Cao, Hongcui</au><au>Lin, Feiyan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model</atitle><jtitle>Lipids in health and disease</jtitle><addtitle>Lipids Health Dis</addtitle><date>2017-02-20</date><risdate>2017</risdate><volume>16</volume><issue>1</issue><spage>42</spage><epage>42</epage><pages>42-42</pages><artnum>42</artnum><issn>1476-511X</issn><eissn>1476-511X</eissn><abstract>The prevalence of high hyperlipemia is increasing around the world. Our aims are to analyze the relationship of triglyceride (TG) and cholesterol (TC) with indexes of liver function and kidney function, and to develop a prediction model of TG, TC in overweight people.
A total of 302 adult healthy subjects and 273 overweight subjects were enrolled in this study. The levels of fasting indexes of TG (fs-TG), TC (fs-TC), blood glucose, liver function, and kidney function were measured and analyzed by correlation analysis and multiple linear regression (MRL). The back propagation artificial neural network (BP-ANN) was applied to develop prediction models of fs-TG and fs-TC.
The results showed there was significant difference in biochemical indexes between healthy people and overweight people. The correlation analysis showed fs-TG was related to weight, height, blood glucose, and indexes of liver and kidney function; while fs-TC was correlated with age, indexes of liver function (P < 0.01). The MRL analysis indicated regression equations of fs-TG and fs-TC both had statistic significant (P < 0.01) when included independent indexes. The BP-ANN model of fs-TG reached training goal at 59 epoch, while fs-TC model achieved high prediction accuracy after training 1000 epoch.
In conclusions, there was high relationship of fs-TG and fs-TC with weight, height, age, blood glucose, indexes of liver function and kidney function. Based on related variables, the indexes of fs-TG and fs-TC can be predicted by BP-ANN models in overweight people.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>28219431</pmid><doi>10.1186/s12944-017-0434-5</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; DOAJ Directory of Open Access Journals; SpringerLink Journals; PubMed Central Open Access; Springer Nature OA Free Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adult Artificial intelligence Case-Control Studies Cholesterol Cholesterol - blood Fasting Health aspects Humans Hyperlipidemia Linear Models Middle Aged Models, Biological Neural Networks (Computer) Overweight - blood Overweight persons Risk factors Triglycerides Triglycerides - blood |
| title | Assessment of triglyceride and cholesterol in overweight people based on multiple linear regression and artificial intelligence model |
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