Predicting Depression Risk in Physically Inactive Older Adults Using Dietary Antioxidants and Machine Learning: A SHAP-Interpretable Analysis of NHANES.
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| Title: | Predicting Depression Risk in Physically Inactive Older Adults Using Dietary Antioxidants and Machine Learning: A SHAP-Interpretable Analysis of NHANES. |
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| Authors: | ShangGuan Y; Changzhou Maternal and Child Health Care Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, China.; Department of Exercise Physiology, Kunsan National University, Gunsan, Republic of Korea., Wu K; Department of Exercise Physiology, Kunsan National University, Gunsan, Republic of Korea., Li D; School of Physical Education and Health, Zhaoqing University, Zhaoqing, China., Sim YJ; Department of Exercise Physiology, Kunsan National University, Gunsan, Republic of Korea., Zhang C; Department of Global Sports Industry, Hanyang University, Seoul, Republic of Korea., Lin Z; Department of Exercise Physiology, Kunsan National University, Gunsan, Republic of Korea., Yan L; Changzhou Maternal and Child Health Care Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, China.; Department of Articular Orthopedics, The First People's Hospital of Changzhou, The Third Affiliated Hospital of Soochow University, Changzhou, China. |
| Source: | CNS neuroscience & therapeutics [CNS Neurosci Ther] 2026 Jun; Vol. 32 (6), pp. e70961. |
| Publication Type: | Journal Article; Research Support, Non-U.S. Gov't |
| Journal Info: | Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 101473265 Publication Model: Print Cited Medium: Internet ISSN: 1755-5949 (Electronic) Linking ISSN: 17555930 NLM ISO Abbreviation: CNS Neurosci Ther Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| Header | DbId: mdl DbLabel: MEDLINE Ultimate An: 42216694 AccessLevel: 2 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Predicting Depression Risk in Physically Inactive Older Adults Using Dietary Antioxidants and Machine Learning: A SHAP-Interpretable Analysis of NHANES. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AU" term="%22ShangGuan+Y%22">ShangGuan Y</searchLink>; Changzhou Maternal and Child Health Care Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, China.; Department of Exercise Physiology, Kunsan National University, Gunsan, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Wu+K%22">Wu K</searchLink>; Department of Exercise Physiology, Kunsan National University, Gunsan, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Li+D%22">Li D</searchLink>; School of Physical Education and Health, Zhaoqing University, Zhaoqing, China.<br /><searchLink fieldCode="AU" term="%22Sim+YJ%22">Sim YJ</searchLink>; Department of Exercise Physiology, Kunsan National University, Gunsan, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Zhang+C%22">Zhang C</searchLink>; Department of Global Sports Industry, Hanyang University, Seoul, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Lin+Z%22">Lin Z</searchLink>; Department of Exercise Physiology, Kunsan National University, Gunsan, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Yan+L%22">Yan L</searchLink>; Changzhou Maternal and Child Health Care Hospital, Changzhou Medical Center, Nanjing Medical University, Changzhou, China.; Department of Articular Orthopedics, The First People's Hospital of Changzhou, The Third Affiliated Hospital of Soochow University, Changzhou, China. – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22101473265%22">CNS neuroscience & therapeutics</searchLink> [CNS Neurosci Ther] 2026 Jun; Vol. 32 (6), pp. e70961. – Name: TypePub Label: Publication Type Group: TypPub Data: Journal Article; Research Support, Non-U.S. Gov't – Name: TitleSource Label: Journal Info Group: Src Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Wiley-Blackwell%22">Wiley-Blackwell </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>101473265 <i>Publication Model: </i>Print <i>Cited Medium: </i>Internet <i>ISSN: </i>1755-5949 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2217555930%22">17555930 </searchLink><i>NLM ISO Abbreviation: </i>CNS Neurosci Ther <i>Subsets: </i>MEDLINE |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=42216694 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/cns.70961 Languages: – Code: eng Text: English PhysicalDescription: Pagination: StartPage: e70961 Titles: – TitleFull: Predicting Depression Risk in Physically Inactive Older Adults Using Dietary Antioxidants and Machine Learning: A SHAP-Interpretable Analysis of NHANES. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: ShangGuan Y – PersonEntity: Name: NameFull: Wu K – PersonEntity: Name: NameFull: Li D – PersonEntity: Name: NameFull: Sim YJ – PersonEntity: Name: NameFull: Zhang C – PersonEntity: Name: NameFull: Lin Z – PersonEntity: Name: NameFull: Yan L IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: 2026 Jun Type: published Y: 2026 Identifiers: – Type: issn-electronic Value: 1755-5949 Numbering: – Type: volume Value: 32 – Type: issue Value: 6 Titles: – TitleFull: CNS neuroscience & therapeutics Type: main |
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