Neuroinflammatory biomarkers in neurodegenerative disease: Insights from the ONDRI Cohort
Background Neuroinflammation (NI) has been implicated in both the pathogenesis of and neuroprotection against neurodegenerative diseases (NDs)(Psenicka et al., 2021). Plasma glial fibrillary acidic protein (GFAP), and Neurofilament light (NFL) are measures of astrogliosis and neurodegeneration, resp...
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| creator | Sumra, Vishaal Dilliott, Allison Ann Frank, Andrew R Lang, Anthony E Roberts, Angela C Troyer, Angela Levine, Brian Arnott, Stephen R. Tan, Brian Fischer, Corinne E. Marras, Connie Kwan, Donna Munoz, Douglas Tang‐Wai, David F. Finger, Elizabeth Rogaeva, Ekaterina Orange, Joseph B Ramirez, Joel Sunderland, Kelly M Zinman, Lorne Binns, Malcolm Borrie, Michael Masellis, Mario Freedman, Morris Montero‐Odasso, Manuel Ozzoude, Miracle Bartha, Robert Swartz, Richard H. Tartaglia, Carmela |
| description | Background
Neuroinflammation (NI) has been implicated in both the pathogenesis of and neuroprotection against neurodegenerative diseases (NDs)(Psenicka et al., 2021). Plasma glial fibrillary acidic protein (GFAP), and Neurofilament light (NFL) are measures of astrogliosis and neurodegeneration, respectively. Amyloid beta (Aß)42/40 ratio (Aß42 concentration to total Aß concentration) below 0.068 is associated with AD pathology (Baldeiras et al., 2018). Neuroimaging‐based inflammatory biomarkers have been proposed, including free‐water diffusion (FWD)(Pasternak et al., 2009). Here we investigated FWD as a candidate biomarker for NI in AD compared to non‐AD dementia using Aß42/40 ratio in a subset of data from the Ontario Neurodegenerative Disease Research Initiative (ONDRI).
Method
FWD maps were generated in 370 subjects (126 non‐AD and 244 AD). MRI processing included ICVmapp3r for brain extraction and bias field correction, Synb0, Topup and Eddy for dMRI preprocessing and MATLAB for freewater mapping. Plasma Aß42, Aß40, GFAP and NFL were measured using the Simoa Human Neurology 4‐Plex E assay, and cognition was estimated using the Montreal Cognitive Assessment (MoCA). Linear regression was used to estimate the ability for FWD in the left (LcGM) and right (RcGM) cortical grey matter to predict GFAP, NFL and MoCA score in AD and nonAD based on Aß42/40 threshold of 0.068.
Result
FWD correlated with GFAP (LcGM; R = 0.4, p = 0.0013 and RcGM; R = 0.37, p = 0.0069), and MoCA total score (LcGM; R = ‐0.29, p = 0.001 and RcGM; R = ‐0.27, p = 0.001), but not with NFL across the whole group. This relationship was largely driven by the AD group wherein FWD predicted GFAP (LcGM: R = 0.12, p = 0.02, RcGM approaching significance R = 0.1, p = 0.06), and MoCA Total score (LcGM: R = ‐0.33, p |
| doi_str_mv | 10.1002/alz.081848 |
| format | Article |
| fullrecord | <record><control><sourceid>wiley_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1002_alz_081848</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>ALZ081848</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1138-cca96dc6cf427a60a8346ff236287e66a567dce8c18bee52e0307bb3716e1c8a3</originalsourceid><addsrcrecordid>eNp9kMFKxDAQhoMouK5efIKcha5J2qZZb0t1tVB2QfSgl5Kmk220bSSpSn16u3Tx6GkG5psf_g-hS0oWlBB2LZufBRFUROIIzWgcsyBmyfL4b-fkFJ15_0ZINGLxDL1s4NNZ0-lGtq3srRtwaWwr3Ts4j02Hu_29gh104GRvvgBXxoP0cIOzzptd3XusnW1xXwPebm4fM5za2rr-HJ1o2Xi4OMw5el7fPaUPQb69z9JVHihKQxEoJZe8UlzpiCWSEynCiGvNQs5EApzLmCeVAqGoKAFiBiQkSVmGCeVAlZDhHF1NucpZ7x3o4sOZscBQUFLspRSjlGKSMsJ0gr9NA8M_ZLHKXw8_v0a4ZbU</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Neuroinflammatory biomarkers in neurodegenerative disease: Insights from the ONDRI Cohort</title><source>Access via Wiley Online Library</source><creator>Sumra, Vishaal ; Dilliott, Allison Ann ; Frank, Andrew R ; Lang, Anthony E ; Roberts, Angela C ; Troyer, Angela ; Levine, Brian ; Arnott, Stephen R. ; Tan, Brian ; Fischer, Corinne E. ; Marras, Connie ; Kwan, Donna ; Munoz, Douglas ; Tang‐Wai, David F. ; Finger, Elizabeth ; Rogaeva, Ekaterina ; Orange, Joseph B ; Ramirez, Joel ; Sunderland, Kelly M ; Zinman, Lorne ; Binns, Malcolm ; Borrie, Michael ; Masellis, Mario ; Freedman, Morris ; Montero‐Odasso, Manuel ; Ozzoude, Miracle ; Bartha, Robert ; Swartz, Richard H. ; Tartaglia, Carmela</creator><creatorcontrib>Sumra, Vishaal ; Dilliott, Allison Ann ; Frank, Andrew R ; Lang, Anthony E ; Roberts, Angela C ; Troyer, Angela ; Levine, Brian ; Arnott, Stephen R. ; Tan, Brian ; Fischer, Corinne E. ; Marras, Connie ; Kwan, Donna ; Munoz, Douglas ; Tang‐Wai, David F. ; Finger, Elizabeth ; Rogaeva, Ekaterina ; Orange, Joseph B ; Ramirez, Joel ; Sunderland, Kelly M ; Zinman, Lorne ; Binns, Malcolm ; Borrie, Michael ; Masellis, Mario ; Freedman, Morris ; Montero‐Odasso, Manuel ; Ozzoude, Miracle ; Bartha, Robert ; Swartz, Richard H. ; Tartaglia, Carmela</creatorcontrib><description>Background
Neuroinflammation (NI) has been implicated in both the pathogenesis of and neuroprotection against neurodegenerative diseases (NDs)(Psenicka et al., 2021). Plasma glial fibrillary acidic protein (GFAP), and Neurofilament light (NFL) are measures of astrogliosis and neurodegeneration, respectively. Amyloid beta (Aß)42/40 ratio (Aß42 concentration to total Aß concentration) below 0.068 is associated with AD pathology (Baldeiras et al., 2018). Neuroimaging‐based inflammatory biomarkers have been proposed, including free‐water diffusion (FWD)(Pasternak et al., 2009). Here we investigated FWD as a candidate biomarker for NI in AD compared to non‐AD dementia using Aß42/40 ratio in a subset of data from the Ontario Neurodegenerative Disease Research Initiative (ONDRI).
Method
FWD maps were generated in 370 subjects (126 non‐AD and 244 AD). MRI processing included ICVmapp3r for brain extraction and bias field correction, Synb0, Topup and Eddy for dMRI preprocessing and MATLAB for freewater mapping. Plasma Aß42, Aß40, GFAP and NFL were measured using the Simoa Human Neurology 4‐Plex E assay, and cognition was estimated using the Montreal Cognitive Assessment (MoCA). Linear regression was used to estimate the ability for FWD in the left (LcGM) and right (RcGM) cortical grey matter to predict GFAP, NFL and MoCA score in AD and nonAD based on Aß42/40 threshold of 0.068.
Result
FWD correlated with GFAP (LcGM; R = 0.4, p = 0.0013 and RcGM; R = 0.37, p = 0.0069), and MoCA total score (LcGM; R = ‐0.29, p = 0.001 and RcGM; R = ‐0.27, p = 0.001), but not with NFL across the whole group. This relationship was largely driven by the AD group wherein FWD predicted GFAP (LcGM: R = 0.12, p = 0.02, RcGM approaching significance R = 0.1, p = 0.06), and MoCA Total score (LcGM: R = ‐0.33, p<0.0001), RcGM: R = ‐0.25, p<0.0001). The nonAD group did not show this relationship. FWD did not predict NFL in the AD and nonAD group.
Conclusion
In patients with Aß42/40<0.068, suggestive of AD, FWD in cGM was more strongly related to GFAP than NFL, and predicted cognition, a pattern that was not observed in nonAD patients. Our results suggest distinct patterns of NI in AD compared with nonAD that can be detected with FWD and with a multi‐modal approach could further understanding of differences in pathophysiology across NDs.</description><identifier>ISSN: 1552-5260</identifier><identifier>EISSN: 1552-5279</identifier><identifier>DOI: 10.1002/alz.081848</identifier><language>eng</language><ispartof>Alzheimer's & dementia, 2023-12, Vol.19 (S10), p.n/a</ispartof><rights>2023 the Alzheimer's Association.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Falz.081848$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Falz.081848$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,27924,27925,45574,45575</link.rule.ids></links><search><creatorcontrib>Sumra, Vishaal</creatorcontrib><creatorcontrib>Dilliott, Allison Ann</creatorcontrib><creatorcontrib>Frank, Andrew R</creatorcontrib><creatorcontrib>Lang, Anthony E</creatorcontrib><creatorcontrib>Roberts, Angela C</creatorcontrib><creatorcontrib>Troyer, Angela</creatorcontrib><creatorcontrib>Levine, Brian</creatorcontrib><creatorcontrib>Arnott, Stephen R.</creatorcontrib><creatorcontrib>Tan, Brian</creatorcontrib><creatorcontrib>Fischer, Corinne E.</creatorcontrib><creatorcontrib>Marras, Connie</creatorcontrib><creatorcontrib>Kwan, Donna</creatorcontrib><creatorcontrib>Munoz, Douglas</creatorcontrib><creatorcontrib>Tang‐Wai, David F.</creatorcontrib><creatorcontrib>Finger, Elizabeth</creatorcontrib><creatorcontrib>Rogaeva, Ekaterina</creatorcontrib><creatorcontrib>Orange, Joseph B</creatorcontrib><creatorcontrib>Ramirez, Joel</creatorcontrib><creatorcontrib>Sunderland, Kelly M</creatorcontrib><creatorcontrib>Zinman, Lorne</creatorcontrib><creatorcontrib>Binns, Malcolm</creatorcontrib><creatorcontrib>Borrie, Michael</creatorcontrib><creatorcontrib>Masellis, Mario</creatorcontrib><creatorcontrib>Freedman, Morris</creatorcontrib><creatorcontrib>Montero‐Odasso, Manuel</creatorcontrib><creatorcontrib>Ozzoude, Miracle</creatorcontrib><creatorcontrib>Bartha, Robert</creatorcontrib><creatorcontrib>Swartz, Richard H.</creatorcontrib><creatorcontrib>Tartaglia, Carmela</creatorcontrib><title>Neuroinflammatory biomarkers in neurodegenerative disease: Insights from the ONDRI Cohort</title><title>Alzheimer's & dementia</title><description>Background
Neuroinflammation (NI) has been implicated in both the pathogenesis of and neuroprotection against neurodegenerative diseases (NDs)(Psenicka et al., 2021). Plasma glial fibrillary acidic protein (GFAP), and Neurofilament light (NFL) are measures of astrogliosis and neurodegeneration, respectively. Amyloid beta (Aß)42/40 ratio (Aß42 concentration to total Aß concentration) below 0.068 is associated with AD pathology (Baldeiras et al., 2018). Neuroimaging‐based inflammatory biomarkers have been proposed, including free‐water diffusion (FWD)(Pasternak et al., 2009). Here we investigated FWD as a candidate biomarker for NI in AD compared to non‐AD dementia using Aß42/40 ratio in a subset of data from the Ontario Neurodegenerative Disease Research Initiative (ONDRI).
Method
FWD maps were generated in 370 subjects (126 non‐AD and 244 AD). MRI processing included ICVmapp3r for brain extraction and bias field correction, Synb0, Topup and Eddy for dMRI preprocessing and MATLAB for freewater mapping. Plasma Aß42, Aß40, GFAP and NFL were measured using the Simoa Human Neurology 4‐Plex E assay, and cognition was estimated using the Montreal Cognitive Assessment (MoCA). Linear regression was used to estimate the ability for FWD in the left (LcGM) and right (RcGM) cortical grey matter to predict GFAP, NFL and MoCA score in AD and nonAD based on Aß42/40 threshold of 0.068.
Result
FWD correlated with GFAP (LcGM; R = 0.4, p = 0.0013 and RcGM; R = 0.37, p = 0.0069), and MoCA total score (LcGM; R = ‐0.29, p = 0.001 and RcGM; R = ‐0.27, p = 0.001), but not with NFL across the whole group. This relationship was largely driven by the AD group wherein FWD predicted GFAP (LcGM: R = 0.12, p = 0.02, RcGM approaching significance R = 0.1, p = 0.06), and MoCA Total score (LcGM: R = ‐0.33, p<0.0001), RcGM: R = ‐0.25, p<0.0001). The nonAD group did not show this relationship. FWD did not predict NFL in the AD and nonAD group.
Conclusion
In patients with Aß42/40<0.068, suggestive of AD, FWD in cGM was more strongly related to GFAP than NFL, and predicted cognition, a pattern that was not observed in nonAD patients. Our results suggest distinct patterns of NI in AD compared with nonAD that can be detected with FWD and with a multi‐modal approach could further understanding of differences in pathophysiology across NDs.</description><issn>1552-5260</issn><issn>1552-5279</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9kMFKxDAQhoMouK5efIKcha5J2qZZb0t1tVB2QfSgl5Kmk220bSSpSn16u3Tx6GkG5psf_g-hS0oWlBB2LZufBRFUROIIzWgcsyBmyfL4b-fkFJ15_0ZINGLxDL1s4NNZ0-lGtq3srRtwaWwr3Ts4j02Hu_29gh104GRvvgBXxoP0cIOzzptd3XusnW1xXwPebm4fM5za2rr-HJ1o2Xi4OMw5el7fPaUPQb69z9JVHihKQxEoJZe8UlzpiCWSEynCiGvNQs5EApzLmCeVAqGoKAFiBiQkSVmGCeVAlZDhHF1NucpZ7x3o4sOZscBQUFLspRSjlGKSMsJ0gr9NA8M_ZLHKXw8_v0a4ZbU</recordid><startdate>202312</startdate><enddate>202312</enddate><creator>Sumra, Vishaal</creator><creator>Dilliott, Allison Ann</creator><creator>Frank, Andrew R</creator><creator>Lang, Anthony E</creator><creator>Roberts, Angela C</creator><creator>Troyer, Angela</creator><creator>Levine, Brian</creator><creator>Arnott, Stephen R.</creator><creator>Tan, Brian</creator><creator>Fischer, Corinne E.</creator><creator>Marras, Connie</creator><creator>Kwan, Donna</creator><creator>Munoz, Douglas</creator><creator>Tang‐Wai, David F.</creator><creator>Finger, Elizabeth</creator><creator>Rogaeva, Ekaterina</creator><creator>Orange, Joseph B</creator><creator>Ramirez, Joel</creator><creator>Sunderland, Kelly M</creator><creator>Zinman, Lorne</creator><creator>Binns, Malcolm</creator><creator>Borrie, Michael</creator><creator>Masellis, Mario</creator><creator>Freedman, Morris</creator><creator>Montero‐Odasso, Manuel</creator><creator>Ozzoude, Miracle</creator><creator>Bartha, Robert</creator><creator>Swartz, Richard H.</creator><creator>Tartaglia, Carmela</creator><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>202312</creationdate><title>Neuroinflammatory biomarkers in neurodegenerative disease: Insights from the ONDRI Cohort</title><author>Sumra, Vishaal ; Dilliott, Allison Ann ; Frank, Andrew R ; Lang, Anthony E ; Roberts, Angela C ; Troyer, Angela ; Levine, Brian ; Arnott, Stephen R. ; Tan, Brian ; Fischer, Corinne E. ; Marras, Connie ; Kwan, Donna ; Munoz, Douglas ; Tang‐Wai, David F. ; Finger, Elizabeth ; Rogaeva, Ekaterina ; Orange, Joseph B ; Ramirez, Joel ; Sunderland, Kelly M ; Zinman, Lorne ; Binns, Malcolm ; Borrie, Michael ; Masellis, Mario ; Freedman, Morris ; Montero‐Odasso, Manuel ; Ozzoude, Miracle ; Bartha, Robert ; Swartz, Richard H. ; Tartaglia, Carmela</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1138-cca96dc6cf427a60a8346ff236287e66a567dce8c18bee52e0307bb3716e1c8a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sumra, Vishaal</creatorcontrib><creatorcontrib>Dilliott, Allison Ann</creatorcontrib><creatorcontrib>Frank, Andrew R</creatorcontrib><creatorcontrib>Lang, Anthony E</creatorcontrib><creatorcontrib>Roberts, Angela C</creatorcontrib><creatorcontrib>Troyer, Angela</creatorcontrib><creatorcontrib>Levine, Brian</creatorcontrib><creatorcontrib>Arnott, Stephen R.</creatorcontrib><creatorcontrib>Tan, Brian</creatorcontrib><creatorcontrib>Fischer, Corinne E.</creatorcontrib><creatorcontrib>Marras, Connie</creatorcontrib><creatorcontrib>Kwan, Donna</creatorcontrib><creatorcontrib>Munoz, Douglas</creatorcontrib><creatorcontrib>Tang‐Wai, David F.</creatorcontrib><creatorcontrib>Finger, Elizabeth</creatorcontrib><creatorcontrib>Rogaeva, Ekaterina</creatorcontrib><creatorcontrib>Orange, Joseph B</creatorcontrib><creatorcontrib>Ramirez, Joel</creatorcontrib><creatorcontrib>Sunderland, Kelly M</creatorcontrib><creatorcontrib>Zinman, Lorne</creatorcontrib><creatorcontrib>Binns, Malcolm</creatorcontrib><creatorcontrib>Borrie, Michael</creatorcontrib><creatorcontrib>Masellis, Mario</creatorcontrib><creatorcontrib>Freedman, Morris</creatorcontrib><creatorcontrib>Montero‐Odasso, Manuel</creatorcontrib><creatorcontrib>Ozzoude, Miracle</creatorcontrib><creatorcontrib>Bartha, Robert</creatorcontrib><creatorcontrib>Swartz, Richard H.</creatorcontrib><creatorcontrib>Tartaglia, Carmela</creatorcontrib><collection>CrossRef</collection><jtitle>Alzheimer's & dementia</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sumra, Vishaal</au><au>Dilliott, Allison Ann</au><au>Frank, Andrew R</au><au>Lang, Anthony E</au><au>Roberts, Angela C</au><au>Troyer, Angela</au><au>Levine, Brian</au><au>Arnott, Stephen R.</au><au>Tan, Brian</au><au>Fischer, Corinne E.</au><au>Marras, Connie</au><au>Kwan, Donna</au><au>Munoz, Douglas</au><au>Tang‐Wai, David F.</au><au>Finger, Elizabeth</au><au>Rogaeva, Ekaterina</au><au>Orange, Joseph B</au><au>Ramirez, Joel</au><au>Sunderland, Kelly M</au><au>Zinman, Lorne</au><au>Binns, Malcolm</au><au>Borrie, Michael</au><au>Masellis, Mario</au><au>Freedman, Morris</au><au>Montero‐Odasso, Manuel</au><au>Ozzoude, Miracle</au><au>Bartha, Robert</au><au>Swartz, Richard H.</au><au>Tartaglia, Carmela</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Neuroinflammatory biomarkers in neurodegenerative disease: Insights from the ONDRI Cohort</atitle><jtitle>Alzheimer's & dementia</jtitle><date>2023-12</date><risdate>2023</risdate><volume>19</volume><issue>S10</issue><epage>n/a</epage><issn>1552-5260</issn><eissn>1552-5279</eissn><abstract>Background
Neuroinflammation (NI) has been implicated in both the pathogenesis of and neuroprotection against neurodegenerative diseases (NDs)(Psenicka et al., 2021). Plasma glial fibrillary acidic protein (GFAP), and Neurofilament light (NFL) are measures of astrogliosis and neurodegeneration, respectively. Amyloid beta (Aß)42/40 ratio (Aß42 concentration to total Aß concentration) below 0.068 is associated with AD pathology (Baldeiras et al., 2018). Neuroimaging‐based inflammatory biomarkers have been proposed, including free‐water diffusion (FWD)(Pasternak et al., 2009). Here we investigated FWD as a candidate biomarker for NI in AD compared to non‐AD dementia using Aß42/40 ratio in a subset of data from the Ontario Neurodegenerative Disease Research Initiative (ONDRI).
Method
FWD maps were generated in 370 subjects (126 non‐AD and 244 AD). MRI processing included ICVmapp3r for brain extraction and bias field correction, Synb0, Topup and Eddy for dMRI preprocessing and MATLAB for freewater mapping. Plasma Aß42, Aß40, GFAP and NFL were measured using the Simoa Human Neurology 4‐Plex E assay, and cognition was estimated using the Montreal Cognitive Assessment (MoCA). Linear regression was used to estimate the ability for FWD in the left (LcGM) and right (RcGM) cortical grey matter to predict GFAP, NFL and MoCA score in AD and nonAD based on Aß42/40 threshold of 0.068.
Result
FWD correlated with GFAP (LcGM; R = 0.4, p = 0.0013 and RcGM; R = 0.37, p = 0.0069), and MoCA total score (LcGM; R = ‐0.29, p = 0.001 and RcGM; R = ‐0.27, p = 0.001), but not with NFL across the whole group. This relationship was largely driven by the AD group wherein FWD predicted GFAP (LcGM: R = 0.12, p = 0.02, RcGM approaching significance R = 0.1, p = 0.06), and MoCA Total score (LcGM: R = ‐0.33, p<0.0001), RcGM: R = ‐0.25, p<0.0001). The nonAD group did not show this relationship. FWD did not predict NFL in the AD and nonAD group.
Conclusion
In patients with Aß42/40<0.068, suggestive of AD, FWD in cGM was more strongly related to GFAP than NFL, and predicted cognition, a pattern that was not observed in nonAD patients. Our results suggest distinct patterns of NI in AD compared with nonAD that can be detected with FWD and with a multi‐modal approach could further understanding of differences in pathophysiology across NDs.</abstract><doi>10.1002/alz.081848</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| title | Neuroinflammatory biomarkers in neurodegenerative disease: Insights from the ONDRI Cohort |
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