Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals
Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and validated machine learning models to quantify the d...
Gespeichert in:
| Veröffentlicht in: | Nature communications Jg. 16; H. 1; S. 2724 - 12 |
|---|---|
| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
London
Nature Publishing Group UK
19.03.2025
Nature Publishing Group Nature Portfolio |
| Schlagworte: | |
| ISSN: | 2041-1723, 2041-1723 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and validated machine learning models to quantify the distinct spatial patterns of atrophy and white matter hyperintensities related to hypertension, hyperlipidemia, smoking, obesity, and type-2 diabetes mellitus at the patient level. Using harmonized MRI data from 37,096 participants (45–85 years) in a large multinational dataset of 10 cohort studies, we generated five in silico severity markers that: i) outperformed conventional structural MRI markers with a ten-fold increase in effect sizes, ii) captured subtle patterns at sub-clinical CVM stages, iii) were most sensitive in mid-life (45–64 years), iv) were associated with brain beta-amyloid status, and v) showed stronger associations with cognitive performance than diagnostic CVM status. Integrating personalized measurements of CVM-specific brain signatures into phenotypic frameworks could guide early risk detection and stratification in clinical studies.
Cardiovascular and metabolic risk factors (CVM) impact brain structure and increase dementia risk. Here, the authors developed and validated machine learning models to measure the neuroanatomical changes in people with cardiovascular and metabolic diseases that are cognitively unimpaired. |
|---|---|
| AbstractList | Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and validated machine learning models to quantify the distinct spatial patterns of atrophy and white matter hyperintensities related to hypertension, hyperlipidemia, smoking, obesity, and type-2 diabetes mellitus at the patient level. Using harmonized MRI data from 37,096 participants (45–85 years) in a large multinational dataset of 10 cohort studies, we generated five in silico severity markers that: i) outperformed conventional structural MRI markers with a ten-fold increase in effect sizes, ii) captured subtle patterns at sub-clinical CVM stages, iii) were most sensitive in mid-life (45–64 years), iv) were associated with brain beta-amyloid status, and v) showed stronger associations with cognitive performance than diagnostic CVM status. Integrating personalized measurements of CVM-specific brain signatures into phenotypic frameworks could guide early risk detection and stratification in clinical studies.
Cardiovascular and metabolic risk factors (CVM) impact brain structure and increase dementia risk. Here, the authors developed and validated machine learning models to measure the neuroanatomical changes in people with cardiovascular and metabolic diseases that are cognitively unimpaired. Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and validated machine learning models to quantify the distinct spatial patterns of atrophy and white matter hyperintensities related to hypertension, hyperlipidemia, smoking, obesity, and type-2 diabetes mellitus at the patient level. Using harmonized MRI data from 37,096 participants (45–85 years) in a large multinational dataset of 10 cohort studies, we generated five in silico severity markers that: i) outperformed conventional structural MRI markers with a ten-fold increase in effect sizes, ii) captured subtle patterns at sub-clinical CVM stages, iii) were most sensitive in mid-life (45–64 years), iv) were associated with brain beta-amyloid status, and v) showed stronger associations with cognitive performance than diagnostic CVM status. Integrating personalized measurements of CVM-specific brain signatures into phenotypic frameworks could guide early risk detection and stratification in clinical studies.Cardiovascular and metabolic risk factors (CVM) impact brain structure and increase dementia risk. Here, the authors developed and validated machine learning models to measure the neuroanatomical changes in people with cardiovascular and metabolic diseases that are cognitively unimpaired. Abstract Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and validated machine learning models to quantify the distinct spatial patterns of atrophy and white matter hyperintensities related to hypertension, hyperlipidemia, smoking, obesity, and type-2 diabetes mellitus at the patient level. Using harmonized MRI data from 37,096 participants (45–85 years) in a large multinational dataset of 10 cohort studies, we generated five in silico severity markers that: i) outperformed conventional structural MRI markers with a ten-fold increase in effect sizes, ii) captured subtle patterns at sub-clinical CVM stages, iii) were most sensitive in mid-life (45–64 years), iv) were associated with brain beta-amyloid status, and v) showed stronger associations with cognitive performance than diagnostic CVM status. Integrating personalized measurements of CVM-specific brain signatures into phenotypic frameworks could guide early risk detection and stratification in clinical studies. Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and validated machine learning models to quantify the distinct spatial patterns of atrophy and white matter hyperintensities related to hypertension, hyperlipidemia, smoking, obesity, and type-2 diabetes mellitus at the patient level. Using harmonized MRI data from 37,096 participants (45–85 years) in a large multinational dataset of 10 cohort studies, we generated five in silico severity markers that: i) outperformed conventional structural MRI markers with a ten-fold increase in effect sizes, ii) captured subtle patterns at sub-clinical CVM stages, iii) were most sensitive in mid-life (45–64 years), iv) were associated with brain beta-amyloid status, and v) showed stronger associations with cognitive performance than diagnostic CVM status. Integrating personalized measurements of CVM-specific brain signatures into phenotypic frameworks could guide early risk detection and stratification in clinical studies. Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and validated machine learning models to quantify the distinct spatial patterns of atrophy and white matter hyperintensities related to hypertension, hyperlipidemia, smoking, obesity, and type-2 diabetes mellitus at the patient level. Using harmonized MRI data from 37,096 participants (45-85 years) in a large multinational dataset of 10 cohort studies, we generated five in silico severity markers that: i) outperformed conventional structural MRI markers with a ten-fold increase in effect sizes, ii) captured subtle patterns at sub-clinical CVM stages, iii) were most sensitive in mid-life (45-64 years), iv) were associated with brain beta-amyloid status, and v) showed stronger associations with cognitive performance than diagnostic CVM status. Integrating personalized measurements of CVM-specific brain signatures into phenotypic frameworks could guide early risk detection and stratification in clinical studies.Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and validated machine learning models to quantify the distinct spatial patterns of atrophy and white matter hyperintensities related to hypertension, hyperlipidemia, smoking, obesity, and type-2 diabetes mellitus at the patient level. Using harmonized MRI data from 37,096 participants (45-85 years) in a large multinational dataset of 10 cohort studies, we generated five in silico severity markers that: i) outperformed conventional structural MRI markers with a ten-fold increase in effect sizes, ii) captured subtle patterns at sub-clinical CVM stages, iii) were most sensitive in mid-life (45-64 years), iv) were associated with brain beta-amyloid status, and v) showed stronger associations with cognitive performance than diagnostic CVM status. Integrating personalized measurements of CVM-specific brain signatures into phenotypic frameworks could guide early risk detection and stratification in clinical studies. |
| ArticleNumber | 2724 |
| Author | Wolk, David A. Abdulkadir, Ahmed LaMontagne, Pamela Bilgel, Murat Fripp, Jurgen Erus, Guray Maruff, Paul Nasrallah, Ilya M. Resnick, Susan M. Albert, Marilyn S. Shou, Haochang Pomponio, Raymond Bryan, R. Nick Melhem, Randa Sotiras, Aristeidis Govindarajan, Sindhuja Tirumalai Masters, Colin L. Mamourian, Elizabeth Doshi, Jimit Tosun, Duygu Benzinger, Tammie L. S. Launer, Lenore J. Davatzikos, Christos Habes, Mohamad Johnson, Sterling C. Morris, John C. Marcus, Daniel S. An, Yang Espeland, Mark A. |
| Author_xml | – sequence: 1 givenname: Sindhuja Tirumalai orcidid: 0000-0003-1741-0906 surname: Govindarajan fullname: Govindarajan, Sindhuja Tirumalai email: sindhuja.tirumalaigovindarajan@pennmedicine.upenn.edu organization: Center for Biomedical Image Computing and Analytics, University of Pennsylvania – sequence: 2 givenname: Elizabeth orcidid: 0000-0001-8581-4887 surname: Mamourian fullname: Mamourian, Elizabeth organization: Center for Biomedical Image Computing and Analytics, University of Pennsylvania – sequence: 3 givenname: Guray orcidid: 0000-0001-6633-4861 surname: Erus fullname: Erus, Guray organization: Center for Biomedical Image Computing and Analytics, University of Pennsylvania – sequence: 4 givenname: Ahmed surname: Abdulkadir fullname: Abdulkadir, Ahmed organization: Centre for Artificial Intelligence, ZHAW School of Engineering – sequence: 5 givenname: Randa surname: Melhem fullname: Melhem, Randa organization: Center for Biomedical Image Computing and Analytics, University of Pennsylvania – sequence: 6 givenname: Jimit surname: Doshi fullname: Doshi, Jimit organization: Center for Biomedical Image Computing and Analytics, University of Pennsylvania – sequence: 7 givenname: Raymond surname: Pomponio fullname: Pomponio, Raymond organization: Center for Biomedical Image Computing and Analytics, University of Pennsylvania – sequence: 8 givenname: Duygu orcidid: 0000-0001-8644-7724 surname: Tosun fullname: Tosun, Duygu organization: Department of Radiology and Biomedical Imaging, University of California, San Francisco – sequence: 9 givenname: Murat orcidid: 0000-0001-5042-7422 surname: Bilgel fullname: Bilgel, Murat organization: Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health – sequence: 10 givenname: Yang surname: An fullname: An, Yang organization: Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health – sequence: 11 givenname: Aristeidis orcidid: 0000-0003-0795-8820 surname: Sotiras fullname: Sotiras, Aristeidis organization: Department of Radiology, Washington University School of Medicine – sequence: 12 givenname: Daniel S. surname: Marcus fullname: Marcus, Daniel S. organization: Department of Radiology, Washington University School of Medicine – sequence: 13 givenname: Pamela orcidid: 0000-0002-6752-8518 surname: LaMontagne fullname: LaMontagne, Pamela organization: Department of Radiology, Washington University School of Medicine – sequence: 14 givenname: Tammie L. S. orcidid: 0000-0002-8114-0552 surname: Benzinger fullname: Benzinger, Tammie L. S. organization: Department of Radiology, Washington University School of Medicine – sequence: 15 givenname: Mark A. surname: Espeland fullname: Espeland, Mark A. organization: Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Department of Biostatistics and Data Science, Wake Forest School of Medicine – sequence: 16 givenname: Colin L. surname: Masters fullname: Masters, Colin L. organization: Florey Institute, The University of Melbourne – sequence: 17 givenname: Paul surname: Maruff fullname: Maruff, Paul organization: Florey Institute, The University of Melbourne – sequence: 18 givenname: Lenore J. orcidid: 0000-0002-3238-7612 surname: Launer fullname: Launer, Lenore J. organization: Neuroepidemiology Section, Intramural Research Program, National Institute on Aging – sequence: 19 givenname: Jurgen surname: Fripp fullname: Fripp, Jurgen organization: CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO – sequence: 20 givenname: Sterling C. orcidid: 0000-0002-8501-545X surname: Johnson fullname: Johnson, Sterling C. organization: Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health – sequence: 21 givenname: John C. surname: Morris fullname: Morris, John C. organization: Knight Alzheimer Disease Research Center, Washington University in St. Louis – sequence: 22 givenname: Marilyn S. surname: Albert fullname: Albert, Marilyn S. organization: Department of Neurology, Johns Hopkins University School of Medicine – sequence: 23 givenname: R. Nick surname: Bryan fullname: Bryan, R. Nick organization: Department of Radiology, University of Pennsylvania – sequence: 24 givenname: Susan M. orcidid: 0000-0003-1115-7145 surname: Resnick fullname: Resnick, Susan M. organization: Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health – sequence: 25 givenname: Mohamad orcidid: 0000-0001-9447-5805 surname: Habes fullname: Habes, Mohamad organization: Biggs Alzheimer’s Institute, University of Texas San Antonio Health Science Center – sequence: 26 givenname: Haochang orcidid: 0000-0002-3043-047X surname: Shou fullname: Shou, Haochang organization: Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania – sequence: 27 givenname: David A. surname: Wolk fullname: Wolk, David A. organization: Department of Neurology, University of Pennsylvania – sequence: 28 givenname: Ilya M. orcidid: 0000-0003-2346-7562 surname: Nasrallah fullname: Nasrallah, Ilya M. organization: Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Department of Radiology, University of Pennsylvania – sequence: 29 givenname: Christos orcidid: 0000-0002-1025-8561 surname: Davatzikos fullname: Davatzikos, Christos email: Christos.davatzikos@pennmedicine.upenn.edu organization: Center for Biomedical Image Computing and Analytics, University of Pennsylvania |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40108173$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9ks1u1DAQxyNURD_oC3BAlrhwCfgrsXNCqAJaqYgLnK2JPUm9SuzFTlbqM_DSeLultBzwZTye3_xnbM9pdRRiwKp6xeg7RoV-nyWTraopb-pG6bJTz6oTTiWrmeLi6NH-uDrPeUPLEh3TUr6ojiVlVDMlTqpfX8He-IBkQkjBh5Ek3CFMmTifFx_sQgKuKUKAJc7ewkSyH4uzJswkDsRCcj7uINt1gkQgODLjAn2cvN1rIOQC-kBsHINf_A6nW7IGP2_BJ3Ql4vzOu7WUfFk9H4rB83t7Vv34_On7xWV9_e3L1cXH69rKTi41MjroBqWzw2ARBddtA84p4NLxQXNn21aqTnZMcst7x7AX4DpLB6RaUinOqquDrouwMdvkZ0i3JoI3dwcxjQbS4u2Ehjf9IFoumNSNVK6HQbuedk63TjXFLVofDlrbtZ_RWQxLgumJ6NNI8DdmjDvDWMcFlW1ReHuvkOLPFfNiZp8tThMEjGs2gqmOy7ZluqBv_kE3cU2hvNWe0p2Sjdpf7_Xjlh56-fPpBeAHwKaYc8LhAWHU7IfLHIbLlOEyd8NlVEkSh6Rc4DBi-lv7P1m_AaiR1f4 |
| Cites_doi | 10.1212/WNL.0000000000207806 10.1007/s11886-012-0304-8 10.1093/brain/aww008 10.1161/HYPERTENSIONAHA.121.17608 10.1016/j.neuroimage.2020.117248 10.1016/j.neurobiolaging.2010.04.007 10.1212/WNL.0000000000200551 10.1371/journal.pone.0060515 10.1007/978-3-030-33330-0_4 10.1016/j.jalz.2014.04.521 10.3389/fnagi.2018.00408 10.1210/clinem/dgab135 10.1001/jamaneurol.2016.0194 10.1016/j.neurobiolaging.2015.05.021 10.1093/brain/awp091 10.1001/jamaneurol.2021.0178 10.1111/obr.12799 10.1186/s13195-021-00819-2 10.3389/fnagi.2017.00345 10.4324/9780203771587 10.1038/s41398-019-0401-1 10.1212/WNL.0b013e31826c1b9d 10.1186/s13195-021-00830-7 10.1212/WNL.0000000000006955 10.1212/WNL.0b013e31826846de 10.1093/cercor/bhr271 10.1016/j.neuroimage.2019.116450 10.1001/jamanetworkopen.2022.19672 10.1016/j.neurobiolaging.2021.02.002 10.1111/1753-0407.12646 10.1111/j.1532-5415.2005.53360.x 10.2967/jnumed.111.090340 10.1002/alz.12178 10.1016/S0140-6736(20)30367-6 10.1212/WNL.0000000000000550 10.1002/ana.21610 10.2337/db08-0586 10.5281/zenodo.14872923 10.48550/arXiv.1907.02110 10.1016/j.neuroimage.2015.11.073 10.2337/dc14-1196 10.1097/HJH.0b013e32836184b5 10.1016/j.jalz.2012.10.007 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2025 2025. The Author(s). Copyright Nature Publishing Group 2025 The Author(s) 2025 2025 |
| Copyright_xml | – notice: The Author(s) 2025 – notice: 2025. The Author(s). – notice: Copyright Nature Publishing Group 2025 – notice: The Author(s) 2025 2025 |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7QL 7QP 7QR 7SN 7SS 7ST 7T5 7T7 7TM 7TO 7X7 7XB 88E 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI C1K CCPQU DWQXO FR3 FYUFA GHDGH GNUQQ H94 HCIFZ K9. LK8 M0S M1P M7P P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS RC3 SOI 7X8 5PM DOA |
| DOI | 10.1038/s41467-025-57867-7 |
| DatabaseName | Springer Nature OA Free Journals (WRLC) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Bacteriology Abstracts (Microbiology B) Calcium & Calcified Tissue Abstracts Chemoreception Abstracts Ecology Abstracts Entomology Abstracts (Full archive) Environment Abstracts Immunology Abstracts Industrial and Applied Microbiology Abstracts (Microbiology A) Nucleic Acids Abstracts Oncogenes and Growth Factors Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection ProQuest Hospital Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials - QC Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection Environmental Sciences and Pollution Management ProQuest One ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student AIDS and Cancer Research Abstracts SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Biological Science Collection ProQuest Health & Medical Collection Medical Database Biological Science Database (ProQuest) Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Genetics Abstracts Environment Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student Oncogenes and Growth Factors Abstracts ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials Nucleic Acids Abstracts SciTech Premium Collection ProQuest Central China Environmental Sciences and Pollution Management ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection Chemoreception Abstracts Industrial and Applied Microbiology Abstracts (Microbiology A) ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database Ecology Abstracts ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts Entomology Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic Calcium & Calcified Tissue Abstracts ProQuest One Academic (New) Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Genetics Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Bacteriology Abstracts (Microbiology B) AIDS and Cancer Research Abstracts ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest Medical Library Immunology Abstracts Environment Abstracts ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database CrossRef MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2041-1723 |
| EndPage | 12 |
| ExternalDocumentID | oai_doaj_org_article_25bf3623148547dbaf8db09d86d75dba PMC11923046 40108173 10_1038_s41467_025_57867_7 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: The iSTAGING study is a multi-institutional effort funded by the National Institute on Aging (NIA) by RF1 AG054409 (C. Davatzikos). Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf. ADNI is funded by the NIA, the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica; Biogen; Bristol-Myers Squibb; CereSpir; Cogstate; Eisai; Elan Pharmaceuticals; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche and its affiliated company Genentech; Fujirebio; GE Healthcare; IXICO; Janssen Alzheimer Immunotherapy Research & Development; Johnson & Johnson Pharmaceutical Research & Development; Lumosity; Lundbeck; Merck & Co; Meso Scale Diagnostics; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Data used in the preparation of this article was obtained from the Australian Imaging Biomarkers and Lifestyle flagship study of ageing (AIBL) funded by the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which was made available at the ADNI database (www.loni.usc.edu/ADNI). The AIBL researchers contributed data but did not participate in analysis or writing of this report. AIBL researchers are listed at www.aibl.csiro.au. The BIOCARD study is partly supported by NIH grant U19-AG033655 (M.S. Albert). The BLSA neuroimaging study is funded by the Intramural Research Program, NIA, National Institutes of Health (NIH), and by HHSN271201600059C (S. M. Resnick, M. Bilgel, Y. An). CARDIA study is conducted and supported by the NHLBI in collaboration with the University of Alabama at Birmingham (HHSN268201300025C and HHSN268201300026C), Northwestern University (HHSN268201300027C), University of Minnesota (HHSN268201300028C), Kaiser Foundation Research Institute (HHSN268201300029C), and Johns Hopkins University School of Medicine (HHSN268200900041C). CARDIA is also partially supported by the Intramural Research Program of the National Institute on Aging (NIA) and an intra-agency agreement between NIA and NHLBI (AG0005) (L.J. Launer). Data used in the preparation of this article was obtained from the OASIS study funded in part by grants P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382 for OASIS-1, P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382 for OASIS-2, and NIH P30 AG066444, P50 AG00561, P30 NS09857781, P01 AG026276, P01 AG003991, R01 AG043434, UL1 TR000448, R01 EB009352 for OASIS-3 (T. Benzinger, D. Marcus, J. Morris, P. LaMontagne). Data used in the preparation of this article was obtained at Penn Alzheimer’s Disease Research Center funded in part by grant P30 AG072979 (D.A. Wolk). Data used in the preparation of this article was obtained from the UK Biobank Resource under application number 35148. The Women’s Health Initiative was funded by the National Heart, Lung and Blood Institute of the NIH, US Department of Health and Human Services. Contracts HHSN268200464221C and N01-WH-4-4221 provided additional support. The WHIMS (M.A. Espeland) was funded in part by Wyeth Pharmaceuticals. The WRAP study was supported by grants: NIH R01AG027161 and R01AG054047 (S.C. Johnson). The authors would like to acknowledge the clinical and neuropathology diagnostic support provided by the Wisconsin ADRC’s Clinical, Neuropathology and Biomarkers Cores, and biostatistical support provided by the Data Management and Biostatistics Core. S.T. Govindarajan was partly supported by the Alzheimer’s Association Research Fellowship AARFD-23-1151286. A. Abdulkadir was funded through grants 191026 and 206795 awarded by the Swiss National Science Foundation. M. Habes was supported by grant 1R01AG080821 from the National Institutes of Health. – fundername: U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) grantid: RF1 AG054409 funderid: https://doi.org/10.13039/100000049 – fundername: NIA NIH HHS grantid: RF1 AG054409 – fundername: NIA NIH HHS grantid: R01 AG085571 – fundername: NIA NIH HHS grantid: R01 AG083865 – fundername: NIA NIH HHS grantid: R01 AG067103 – fundername: NIA NIH HHS grantid: R01 AG080821 |
| GroupedDBID | --- 0R~ 39C 53G 5VS 70F 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ AAHBH AAJSJ AASML ABUWG ACGFO ACGFS ACIWK ACMJI ACPRK ADBBV ADFRT ADMLS ADRAZ AENEX AEUYN AFKRA AFRAH AHMBA AJTQC ALIPV ALMA_UNASSIGNED_HOLDINGS AMTXH AOIJS ARAPS ASPBG AVWKF AZFZN BBNVY BCNDV BENPR BGLVJ BHPHI BPHCQ BVXVI C6C CCPQU DIK EBLON EBS EE. EMOBN F5P FEDTE FYUFA GROUPED_DOAJ HCIFZ HMCUK HVGLF HYE HZ~ KQ8 LGEZI LK8 LOTEE M1P M7P M~E NADUK NAO NXXTH O9- OK1 P2P P62 PHGZT PIMPY PQQKQ PROAC PSQYO RNS RNT RNTTT RPM SV3 TSG UKHRP AAYXX AFFHD CITATION PHGZM PJZUB PPXIY PQGLB SNYQT CGR CUY CVF ECM EIF NPM 3V. 7QL 7QP 7QR 7SN 7SS 7ST 7T5 7T7 7TM 7TO 7XB 8FD 8FK AZQEC C1K DWQXO FR3 GNUQQ H94 K9. M48 P64 PKEHL PQEST PQUKI PRINS RC3 SOI 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c494t-e10f85e4dcffcee32865add7a24d2f82dc6647949142c2bd1eb3ad9c0fe084043 |
| IEDL.DBID | M7P |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001469480300024&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2041-1723 |
| IngestDate | Tue Nov 25 02:48:40 EST 2025 Tue Nov 04 02:03:36 EST 2025 Fri Sep 05 09:33:35 EDT 2025 Tue Oct 07 07:09:53 EDT 2025 Wed Jul 23 01:47:21 EDT 2025 Sat Nov 29 08:07:30 EST 2025 Thu Mar 20 02:10:44 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | 2025. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c494t-e10f85e4dcffcee32865add7a24d2f82dc6647949142c2bd1eb3ad9c0fe084043 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0002-8114-0552 0000-0002-6752-8518 0000-0003-1741-0906 0000-0001-8644-7724 0000-0001-8581-4887 0000-0002-3043-047X 0000-0002-1025-8561 0000-0001-6633-4861 0000-0002-8501-545X 0000-0001-5042-7422 0000-0003-2346-7562 0000-0003-0795-8820 0000-0002-3238-7612 0000-0003-1115-7145 0000-0001-9447-5805 |
| OpenAccessLink | https://www.proquest.com/docview/3178974574?pq-origsite=%requestingapplication% |
| PMID | 40108173 |
| PQID | 3178974574 |
| PQPubID | 546298 |
| PageCount | 12 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_25bf3623148547dbaf8db09d86d75dba pubmedcentral_primary_oai_pubmedcentral_nih_gov_11923046 proquest_miscellaneous_3179246618 proquest_journals_3178974574 pubmed_primary_40108173 crossref_primary_10_1038_s41467_025_57867_7 springer_journals_10_1038_s41467_025_57867_7 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-03-19 |
| PublicationDateYYYYMMDD | 2025-03-19 |
| PublicationDate_xml | – month: 03 year: 2025 text: 2025-03-19 day: 19 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Nature communications |
| PublicationTitleAbbrev | Nat Commun |
| PublicationTitleAlternate | Nat Commun |
| PublicationYear | 2025 |
| Publisher | Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
| References | H Jang (57867_CR23) 2024; 102 57867_CR7 O Trofimova (57867_CR14) 2021; 102 G Livingston (57867_CR2) 2020; 396 IM Nasrallah (57867_CR31) 2021; 78 M Lee (57867_CR3) 2022; 5 H Muqtadar (57867_CR30) 2012; 14 G Erus (57867_CR4) 2015; 38 M Habes (57867_CR37) 2021; 17 D Srinivasan (57867_CR34) 2020; 223 WV Borelli (57867_CR1) 2022; 11 MJ Herrmann (57867_CR11) 2019; 20 S Villeneuve (57867_CR22) 2014; 83 N Cherbuin (57867_CR17) 2012; 79 O Beauchet (57867_CR10) 2013; 31 KA Johnson (57867_CR45) 2013; 9 X Shang (57867_CR20) 2021; 78 57867_CR36 I Lopes Alves (57867_CR43) 2021; 13 57867_CR35 BS Ye (57867_CR24) 2015; 11 57867_CR33 CC Rowe (57867_CR41) 2010; 31 CA Lane (57867_CR21) 2021; 13 M Noale (57867_CR29) 2020; 1216 F Fang (57867_CR12) 2018; 10 F Morys (57867_CR13) 2021; 106 TJ Hohman (57867_CR27) 2015; 36 M Ewers (57867_CR40) 2012; 22 C Davatzikos (57867_CR6) 2009; 132 C Moran (57867_CR15) 2019; 92 M Habes (57867_CR5) 2016; 139 RJ Kamil (57867_CR42) 2018; 10 A Soldan (57867_CR39) 2016; 73 AD Joshi (57867_CR44) 2012; 53 LM Shaw (57867_CR38) 2009; 65 JW Davis (57867_CR9) 2011; 70 R Pomponio (57867_CR8) 2020; 208 W Xu (57867_CR19) 2009; 58 J Doshi (57867_CR32) 2016; 127 BJ Neth (57867_CR16) 2017; 9 AB Newman (57867_CR28) 2005; 53 JH Lee (57867_CR18) 2013; 8 RY Lo (57867_CR26) 2012; 79 JS Rabin (57867_CR25) 2022; 99 |
| References_xml | – volume: 70 start-page: 209 year: 2011 ident: 57867_CR9 publication-title: Hawaii Med. J. – volume: 102 start-page: e207806 year: 2024 ident: 57867_CR23 publication-title: Neurology doi: 10.1212/WNL.0000000000207806 – volume: 14 start-page: 732 year: 2012 ident: 57867_CR30 publication-title: Curr. Cardiol. Rep. doi: 10.1007/s11886-012-0304-8 – volume: 139 start-page: 1164 year: 2016 ident: 57867_CR5 publication-title: Brain doi: 10.1093/brain/aww008 – volume: 78 start-page: 1463 year: 2021 ident: 57867_CR20 publication-title: Hypertension doi: 10.1161/HYPERTENSIONAHA.121.17608 – volume: 223 start-page: 117248 year: 2020 ident: 57867_CR34 publication-title: NeuroImage doi: 10.1016/j.neuroimage.2020.117248 – volume: 31 start-page: 1275 year: 2010 ident: 57867_CR41 publication-title: Neurobiol. Aging doi: 10.1016/j.neurobiolaging.2010.04.007 – volume: 99 start-page: e270 year: 2022 ident: 57867_CR25 publication-title: Neurology doi: 10.1212/WNL.0000000000200551 – volume: 8 start-page: e60515 year: 2013 ident: 57867_CR18 publication-title: PLoS ONE doi: 10.1371/journal.pone.0060515 – volume: 1216 start-page: 29 year: 2020 ident: 57867_CR29 publication-title: Adv. Exp. Med Biol. doi: 10.1007/978-3-030-33330-0_4 – volume: 11 start-page: 494 year: 2015 ident: 57867_CR24 publication-title: Alzheimers Dement. doi: 10.1016/j.jalz.2014.04.521 – volume: 11 start-page: 100256 year: 2022 ident: 57867_CR1 publication-title: Lancet Reg. Health Am. – volume: 10 start-page: 408 year: 2018 ident: 57867_CR42 publication-title: Front. Aging Neurosci. doi: 10.3389/fnagi.2018.00408 – volume: 106 start-page: e4260 year: 2021 ident: 57867_CR13 publication-title: J. Clin. Endocrinol. Metab. doi: 10.1210/clinem/dgab135 – volume: 73 start-page: 698 year: 2016 ident: 57867_CR39 publication-title: JAMA Neurol. doi: 10.1001/jamaneurol.2016.0194 – volume: 36 start-page: 2501 year: 2015 ident: 57867_CR27 publication-title: Neurobiol. Aging doi: 10.1016/j.neurobiolaging.2015.05.021 – volume: 132 start-page: 2026 year: 2009 ident: 57867_CR6 publication-title: Brain doi: 10.1093/brain/awp091 – volume: 78 start-page: 568 year: 2021 ident: 57867_CR31 publication-title: JAMA Neurol. doi: 10.1001/jamaneurol.2021.0178 – volume: 20 start-page: 464 year: 2019 ident: 57867_CR11 publication-title: Obes. Rev. doi: 10.1111/obr.12799 – volume: 13 start-page: 82 year: 2021 ident: 57867_CR43 publication-title: Alzheimer’s Res. Ther. doi: 10.1186/s13195-021-00819-2 – volume: 9 start-page: 345 year: 2017 ident: 57867_CR16 publication-title: Front. Aging Neurosci. doi: 10.3389/fnagi.2017.00345 – ident: 57867_CR36 doi: 10.4324/9780203771587 – ident: 57867_CR7 doi: 10.1038/s41398-019-0401-1 – volume: 79 start-page: 1349 year: 2012 ident: 57867_CR26 publication-title: Neurology doi: 10.1212/WNL.0b013e31826c1b9d – volume: 13 start-page: 91 year: 2021 ident: 57867_CR21 publication-title: Alzheimer’s Res. Ther. doi: 10.1186/s13195-021-00830-7 – volume: 92 start-page: e823 year: 2019 ident: 57867_CR15 publication-title: Neurology doi: 10.1212/WNL.0000000000006955 – volume: 79 start-page: 1019 year: 2012 ident: 57867_CR17 publication-title: Neurology doi: 10.1212/WNL.0b013e31826846de – volume: 22 start-page: 1993 year: 2012 ident: 57867_CR40 publication-title: Cereb. Cortex doi: 10.1093/cercor/bhr271 – volume: 208 start-page: 116450 year: 2020 ident: 57867_CR8 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2019.116450 – volume: 5 start-page: e2219672 year: 2022 ident: 57867_CR3 publication-title: JAMA Netw. Open doi: 10.1001/jamanetworkopen.2022.19672 – volume: 102 start-page: 50 year: 2021 ident: 57867_CR14 publication-title: Neurobiol. Aging doi: 10.1016/j.neurobiolaging.2021.02.002 – volume: 10 start-page: 625 year: 2018 ident: 57867_CR12 publication-title: J. Diab. doi: 10.1111/1753-0407.12646 – volume: 53 start-page: 1101 year: 2005 ident: 57867_CR28 publication-title: J. Am. Geriatr. Soc. doi: 10.1111/j.1532-5415.2005.53360.x – volume: 53 start-page: 378 year: 2012 ident: 57867_CR44 publication-title: J. Nucl. Med. doi: 10.2967/jnumed.111.090340 – volume: 17 start-page: 89 year: 2021 ident: 57867_CR37 publication-title: Alzheimers Dement. doi: 10.1002/alz.12178 – volume: 396 start-page: 413 year: 2020 ident: 57867_CR2 publication-title: Lancet doi: 10.1016/S0140-6736(20)30367-6 – volume: 83 start-page: 40 year: 2014 ident: 57867_CR22 publication-title: Neurology doi: 10.1212/WNL.0000000000000550 – volume: 65 start-page: 403 year: 2009 ident: 57867_CR38 publication-title: Ann. Neurol. doi: 10.1002/ana.21610 – volume: 58 start-page: 71 year: 2009 ident: 57867_CR19 publication-title: Diabetes doi: 10.2337/db08-0586 – ident: 57867_CR35 doi: 10.5281/zenodo.14872923 – ident: 57867_CR33 doi: 10.48550/arXiv.1907.02110 – volume: 127 start-page: 186 year: 2016 ident: 57867_CR32 publication-title: Neuroimage doi: 10.1016/j.neuroimage.2015.11.073 – volume: 38 start-page: 97 year: 2015 ident: 57867_CR4 publication-title: Diabetes Care doi: 10.2337/dc14-1196 – volume: 31 start-page: 1502 year: 2013 ident: 57867_CR10 publication-title: J. Hypertens. doi: 10.1097/HJH.0b013e32836184b5 – volume: 9 start-page: S72 year: 2013 ident: 57867_CR45 publication-title: Alzheimers Dement. doi: 10.1016/j.jalz.2012.10.007 |
| SSID | ssj0000391844 |
| Score | 2.468621 |
| Snippet | Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic... Abstract Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic... |
| SourceID | doaj pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Publisher |
| StartPage | 2724 |
| SubjectTerms | 59/57 631/114/1305 631/378 692/308/53/2421 692/499 692/617/375/132 Aged Aged, 80 and over Alzheimer's disease Anatomy Atrophy Brain Brain - diagnostic imaging Brain - pathology Brain architecture Cardiovascular diseases Cardiovascular Diseases - diagnostic imaging Cardiovascular Diseases - epidemiology Cardiovascular Diseases - pathology Cognition - physiology Cognitive ability Cohort Studies Dementia Dementia disorders Diabetes mellitus Diabetes Mellitus, Type 2 Female Humanities and Social Sciences Humans Hyperlipidemia Hypertension Learning algorithms Machine Learning Magnetic Resonance Imaging Male Metabolic Diseases - diagnostic imaging Metabolic Diseases - pathology Metabolic disorders Middle Aged multidisciplinary Neuroimaging Risk Factors Science Science (multidisciplinary) Signatures Substantia alba White Matter - diagnostic imaging White Matter - pathology β-Amyloid |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Ni9YwEB5kUfAi6_rVdZUI3rRs2qRNclRx2YuLB4W9hTQfWljzyravsL_BP-0k7Vu3fuDFW9uEEjLPTGaYyTMAzyvlg8WTraw84yU3QZRGOVM2wrWtZYmxzOVmE-LsTJ6fq_fXWn2lmrCJHnjauOO66QIaWYZue8OF60yQrqPK4T9Eg6_J-lKhrgVT2QYzhaELn2_JUCaPB55tQureiiDFJ7E6iTJh_5-8zN-LJX_JmOaD6GQf7sweJHk1rfwu3PDxAG5NPSWv7sH3d7k80pO5H8QnkkiaEGTEJXWOdiSZw9JEDLczVwBJNRyZ33Mgm0DsqkKVmOjIFz8iVi56S-Z8zkD6SJbKo4srso3puiWaT0f65YrXcB8-nrz98Oa0nDsulJYrPpa-okE2njsbAp6eLF1bRQMoTM1dHWTtbNsmSnpV8drWnaswFDdOWRo8lYmn5wHsxU30j4AwabxCc6Ia0_E2GIPDNbUiOTydp66AF7vd118nYg2dE-JM6klWGmWls6y0KOB1EtAyM5Fi5w8IFT1DRf8LKgUc7cSrZ00dNPpPEmOqRvACni3DqGMpcWKi32zzHAxT0ZORBTyc0LCsBONT9KoEK0CucLJa6nok9p8zj3eVvGvK2wJe7iD1c11_34vD_7EXj-F2nXQhVSaqI9gbL7f-Cdy038Z-uHyalekH0qInfA priority: 102 providerName: Directory of Open Access Journals |
| Title | Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals |
| URI | https://link.springer.com/article/10.1038/s41467-025-57867-7 https://www.ncbi.nlm.nih.gov/pubmed/40108173 https://www.proquest.com/docview/3178974574 https://www.proquest.com/docview/3179246618 https://pubmed.ncbi.nlm.nih.gov/PMC11923046 https://doaj.org/article/25bf3623148547dbaf8db09d86d75dba |
| Volume | 16 |
| WOSCitedRecordID | wos001469480300024&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2041-1723 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000391844 issn: 2041-1723 databaseCode: DOA dateStart: 20150101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2041-1723 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000391844 issn: 2041-1723 databaseCode: M~E dateStart: 20100101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2041-1723 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000391844 issn: 2041-1723 databaseCode: P5Z dateStart: 20100101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 2041-1723 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000391844 issn: 2041-1723 databaseCode: M7P dateStart: 20100101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 2041-1723 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000391844 issn: 2041-1723 databaseCode: 7X7 dateStart: 20100101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2041-1723 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000391844 issn: 2041-1723 databaseCode: BENPR dateStart: 20100101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2041-1723 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000391844 issn: 2041-1723 databaseCode: PIMPY dateStart: 20100101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB7RFiQuvB-BsjISN4iahxM7J0RRKzh0FSGQFi6R40cbqWTLZhepv4E_zYyTTRVeFy5REudgZ96e8TcAL-LCOo2WLYxtykOunAhVYVSYCZPnOiXEMuObTYj5XC4WRTlsuHVDWeVWJ3pFbZaa9sgP0M5J9H0zwV9ffAupaxRlV4cWGjuwRygJiS_dK8c9FkI_l5wPZ2WiVB503GsG6uGKrIp3YmKPPGz_n3zN30smf8mbenN0fPt_F3IHbg2OKHvTc85duGbbe3Cjb015eR9-nPgqS8uGthKnjLCekFeZIa3Q6jXzUJiqxajdQw4wKgXxMKEdWzqmJ4WuTLWGfbVrZLnzRrMhLdSxpmVjAdP5Jdu0dGoTtbBhzXhSrHsAn46PPr59Fw6NG0LNC74ObRw5mVlutHNohFM6_Yp6VKiEm8TJxOg8J2T7IuaJTmoTY0SvTKEjZyNJcD8PYbddtvYxsFQqW6BWKjJV89wphcNJpAX5TbWNTAAvt-SrLnp8jsrn1VNZ9cSukNiVJ3YlAjgkCo9fEra2f7FcnVaDqFZJVjs06ykGihkXplZOmjoqDHKtyPAxgP0tYatB4LvqiqoBPB-HUVQp_6Jau9z4bzDaRYdIBvCoZ6dxJhjmonMm0gDkhNEmU52OtM2ZhwOPyUmPeB7Aqy1PXs3r7__iyb-X8RRuJiQmVLpY7MPuerWxz-C6_r5uutUMdsRC-Kucwd7h0bz8MPPbGTMvgXgtsy84Ur4_KT__BBTMPEE |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Jb9QwFH4qUxBc2JdAASPBCaJmcWLngBBb1VE7ozkUqZxcx3bKSCUpkwxofgP_hd_Is7NUw3brgdtkHEUez_e9JX7-HsDTMDOFQs_mhyamPpUF82WmpZ8wnaYqtopl2jWbYNMpPzzMZhvwoz8LY8sqe5voDLWulH1Hvo1-jmPsmzD66vSLb7tG2d3VvoVGC4s9s_qGKVv9cvwO_99nUbTz_uDtrt91FfAVzWjjmzAoeGKoVkWBHiK2RzOR5ExGVEcFj7RKUyu7noU0UlGuQ0w3pc5UUJiAWy0afO4F2KQW7CPYnI0ns4_DWx2rt84p7U7nBDHfrqmzRbZrLJIDP7E1D-gaBfwpuv29SPOXnVrnAHeu_W9Ldx2udqE2ed1y4wZsmPImXGqbb65uwfeJqyM1pGuccUysmhWykWhr90rVECf2KUvZVE5UgdhiFyeEWpOqIGqtlJfIUpPPpkFSncwV6Ta-ajIvyVCidbIiy9KeS0U_o8l8OAtX34YP57ISd2BUVqW5ByTm0mRod7NE5jQtpMThKFDMRoa5CbQHz3u4iNNWgUS4yoGYixZcAsElHLgE8-CNRdRwp1UPd19Ui2PRGSMRJXmBgUuMqXBCmc5lwXUeZBp5yRK89GCrB5LoTFotzlDkwZNhGI2R3WGSpamW7h7M5zHk4x7cbeE7zAQTeQw_WewBXwP22lTXR8r5Jyd4Hto0JKCpBy96DpzN6-9rcf_fP-MxXN49mOyL_fF07wFciSxFbaFmtgWjZrE0D-Gi-trM68WjjuUEjs6bHT8BWlCVmw |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Jb9QwFH4qZREX9iVQwEhwgmiyOLFzQAgoFVVhNAeQKi6u46WMVJIyyYDmN_CP-HU8O0s1bLceuE3GUeTxvO8t8efvATyKC2MVRrYwNikNqbQslIWWYcZ0nqvUKZZp32yCTad8f7-YbcCP4SyMo1UOPtE7al0r9458gnGOY-6bMTqxPS1itr3z_PhL6DpIuZ3WoZ1GZyJ7ZvUNy7fm2e42_tePk2Tn9ftXb8K-w0CoaEHb0MSR5ZmhWlmL0SJ1xzQR8EwmVCeWJ1rluZNgL2KaqKTUMZaeUhcqsibiTpcGn3sGzjKsMR2dcJZ9HN_vOOV1Tml_TidK-aSh3iu5_rEIE_zE1mKhbxnwpzz3d7rmL3u2PhTuXP6fF_EKXOoTcPKiQ8xV2DDVNTjfteRcXYfv7zy71JC-ncYhcRpXiFGinTesVEu8BKisZFt7qQXiKDBeHrUhtSVqjeBLZKXJZ9Mi1I7mivTbYQ2ZV2Qkbh2tyLJyp1Ux-mgyH0_INTfgw6msxE3YrOrK3AaScmkK9MZFJkuaWylxOIkUc_liaSIdwJPBdMRxp0siPJ8g5aIzNIGGJryhCRbAS2dd451OU9x_US8ORe-iRJKVFtOZFAvkjDJdSst1GRUa0coyvAxgazAq0Tu6RpxYVAAPx2F0UW7fSVamXvp7sMrHRJAHcKsz5XEmWN5jUsrSAPiaka9NdX2kmn_yMuixK04imgfwdMDDybz-vhZ3_v0zHsAFhIR4uzvduwsXE4dWx94stmCzXSzNPTinvrbzZnHfw53AwWlD4yfMF5z- |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Machine+learning+reveals+distinct+neuroanatomical+signatures+of+cardiovascular+and+metabolic+diseases+in+cognitively+unimpaired+individuals&rft.jtitle=Nature+communications&rft.au=Govindarajan%2C+Sindhuja+Tirumalai&rft.au=Mamourian%2C+Elizabeth&rft.au=Erus%2C+Guray&rft.au=Abdulkadir%2C+Ahmed&rft.date=2025-03-19&rft.issn=2041-1723&rft.eissn=2041-1723&rft.volume=16&rft.issue=1&rft_id=info:doi/10.1038%2Fs41467-025-57867-7&rft.externalDBID=n%2Fa&rft.externalDocID=10_1038_s41467_025_57867_7 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2041-1723&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2041-1723&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2041-1723&client=summon |