Differential risk of Alzheimer's disease in MCI subjects with elevated Abeta
People with elevated beta amyloid have different risk and progress speed to Alzheimer's disease. The research is to validate the risk classification of AD developed in the Shanghai mild cognitive impairment (MCI) cohort study using ADNI data. The risk classification of AD in MCI was based on se...
Saved in:
Published in | Journal of the neurological sciences Vol. 467; p. 123319 |
---|---|
Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Netherlands
Elsevier B.V
15.12.2024
|
Subjects | |
Online Access | Get full text |
ISSN | 0022-510X 1878-5883 1878-5883 |
DOI | 10.1016/j.jns.2024.123319 |
Cover
Loading…
Abstract | People with elevated beta amyloid have different risk and progress speed to Alzheimer's disease.
The research is to validate the risk classification of AD developed in the Shanghai mild cognitive impairment (MCI) cohort study using ADNI data.
The risk classification of AD in MCI was based on several optimal cut-off points of a novel parameter Cog_Vol.
In total, 843 subjects with MCI were included, of whom 220 had elevated PET beta amyloid. 273 (32.3 %) and 70 (31.8 %) progressed to AD in all subjects and in those with elevated PET beta amyloid, respectively. The risk of AD in subjects whose Cog_Vol >340 was very low, while the risk for those with Cog_Vol less than 101 indicated a super high within 4 years of follow-up.
Risk classification using Cog_Vol at an optimal value was able to detect subjects among those with PET-amyloid-elevated MCI were at greater risk of developing AD and were unlikely to develop AD within 4 years of follow-up.
•Using hippocampus volume and cognition test ADAScog13 we created one new parameter Cog_Vol.•The parameter Cog_Vol can detect super high risk group (conversion rate >90%) and low risk group of AD in subjects with Aβ+ MCI•The method simple and practicable in identifying subjects when considering prevention clinical trial design of AD. |
---|---|
AbstractList | People with elevated beta amyloid have different risk and progress speed to Alzheimer's disease.
The research is to validate the risk classification of AD developed in the Shanghai mild cognitive impairment (MCI) cohort study using ADNI data.
The risk classification of AD in MCI was based on several optimal cut-off points of a novel parameter Cog_Vol.
In total, 843 subjects with MCI were included, of whom 220 had elevated PET beta amyloid. 273 (32.3 %) and 70 (31.8 %) progressed to AD in all subjects and in those with elevated PET beta amyloid, respectively. The risk of AD in subjects whose Cog_Vol >340 was very low, while the risk for those with Cog_Vol less than 101 indicated a super high within 4 years of follow-up.
Risk classification using Cog_Vol at an optimal value was able to detect subjects among those with PET-amyloid-elevated MCI were at greater risk of developing AD and were unlikely to develop AD within 4 years of follow-up. People with elevated beta amyloid have different risk and progress speed to Alzheimer's disease. The research is to validate the risk classification of AD developed in the Shanghai mild cognitive impairment (MCI) cohort study using ADNI data. The risk classification of AD in MCI was based on several optimal cut-off points of a novel parameter Cog_Vol. In total, 843 subjects with MCI were included, of whom 220 had elevated PET beta amyloid. 273 (32.3 %) and 70 (31.8 %) progressed to AD in all subjects and in those with elevated PET beta amyloid, respectively. The risk of AD in subjects whose Cog_Vol >340 was very low, while the risk for those with Cog_Vol less than 101 indicated a super high within 4 years of follow-up. Risk classification using Cog_Vol at an optimal value was able to detect subjects among those with PET-amyloid-elevated MCI were at greater risk of developing AD and were unlikely to develop AD within 4 years of follow-up. •Using hippocampus volume and cognition test ADAScog13 we created one new parameter Cog_Vol.•The parameter Cog_Vol can detect super high risk group (conversion rate >90%) and low risk group of AD in subjects with Aβ+ MCI•The method simple and practicable in identifying subjects when considering prevention clinical trial design of AD. People with elevated beta amyloid have different risk and progress speed to Alzheimer's disease.BACKGROUNDSPeople with elevated beta amyloid have different risk and progress speed to Alzheimer's disease.The research is to validate the risk classification of AD developed in the Shanghai mild cognitive impairment (MCI) cohort study using ADNI data.PURPOSEThe research is to validate the risk classification of AD developed in the Shanghai mild cognitive impairment (MCI) cohort study using ADNI data.The risk classification of AD in MCI was based on several optimal cut-off points of a novel parameter Cog_Vol.METHODSThe risk classification of AD in MCI was based on several optimal cut-off points of a novel parameter Cog_Vol.In total, 843 subjects with MCI were included, of whom 220 had elevated PET beta amyloid. 273 (32.3 %) and 70 (31.8 %) progressed to AD in all subjects and in those with elevated PET beta amyloid, respectively. The risk of AD in subjects whose Cog_Vol >340 was very low, while the risk for those with Cog_Vol less than 101 indicated a super high within 4 years of follow-up.RESULTSIn total, 843 subjects with MCI were included, of whom 220 had elevated PET beta amyloid. 273 (32.3 %) and 70 (31.8 %) progressed to AD in all subjects and in those with elevated PET beta amyloid, respectively. The risk of AD in subjects whose Cog_Vol >340 was very low, while the risk for those with Cog_Vol less than 101 indicated a super high within 4 years of follow-up.Risk classification using Cog_Vol at an optimal value was able to detect subjects among those with PET-amyloid-elevated MCI were at greater risk of developing AD and were unlikely to develop AD within 4 years of follow-up.DISCUSSIONRisk classification using Cog_Vol at an optimal value was able to detect subjects among those with PET-amyloid-elevated MCI were at greater risk of developing AD and were unlikely to develop AD within 4 years of follow-up. AbstractBackgroundsPeople with elevated beta amyloid have different risk and progress speed to Alzheimer's disease. PurposeThe research is to validate the risk classification of AD developed in the Shanghai mild cognitive impairment (MCI) cohort study using ADNI data. MethodsThe risk classification of AD in MCI was based on several optimal cut-off points of a novel parameter Cog_Vol. ResultsIn total, 843 subjects with MCI were included, of whom 220 had elevated PET beta amyloid. 273 (32.3 %) and 70 (31.8 %) progressed to AD in all subjects and in those with elevated PET beta amyloid, respectively. The risk of AD in subjects whose Cog_Vol >340 was very low, while the risk for those with Cog_Vol less than 101 indicated a super high within 4 years of follow-up. DiscussionRisk classification using Cog_Vol at an optimal value was able to detect subjects among those with PET-amyloid-elevated MCI were at greater risk of developing AD and were unlikely to develop AD within 4 years of follow-up. |
ArticleNumber | 123319 |
Author | Zhou, Bin Fukushima, Masanori |
AuthorAffiliation | Foundation for Learning Health Society Institute, Nagoya, Aichi 450-0003, Japan |
AuthorAffiliation_xml | – name: Foundation for Learning Health Society Institute, Nagoya, Aichi 450-0003, Japan |
Author_xml | – sequence: 1 givenname: Bin surname: Zhou fullname: Zhou, Bin email: bin.shu928@lhsi.jp – sequence: 2 givenname: Masanori surname: Fukushima fullname: Fukushima, Masanori |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39612639$$D View this record in MEDLINE/PubMed |
BookMark | eNqFkU1v1DAQhi3Uim4LP4AL8o1esnhsJ7GFhLRaviot4gBI3CzbmahOs06xk6Ly65toCwck2tNcnndG7zOn5CgOEQl5AWwNDKrX3bqLec0Zl2vgQoB-QlagalWUSokjsmKM86IE9uOEnObcMcYqpfRTciJ0BbwSekV270LbYsI4BtvTFPIVHVq66X9fYthjepVpEzLajDRE-nl7QfPkOvRjpr_CeEmxxxs7YkM3Dkf7jBy3ts_4_H6eke8f3n_bfip2Xz5ebDe7wktgY6EaVQrtG_DonGt4xSSzumx5ray2XDjeSgVOSXRS1xzqStY414BWaWhrJc7I-WHvdRp-TphHsw_ZY9_biMOUjQAhZx0Sqhl9eY9Obo-NuU5hb9Ot-WNgBuAA-DTknLD9iwAzi2XTmdmyWSybg-U58-aQwbnkTcBksg8YPTYhzW5MM4QH02__Sfs-xOBtf4W3mLthSnG2Z8Bkbpj5unxxeSKXjMmyXDrp_y945Pgd-wGqGQ |
Cites_doi | 10.1016/j.jalz.2018.02.018 10.1159/000487852 10.1186/s13024-021-00503-x 10.1155/2020/7029642 10.1016/j.neurobiolaging.2011.02.022 10.1109/JBHI.2020.2984355 10.1186/s13195-021-00900-w 10.1093/braincomms/fcac231 10.1186/s13195-022-01150-0 10.3233/JAD-210092 10.1186/s13024-019-0320-x 10.1016/j.neuroimage.2012.01.021 10.3389/fnagi.2017.00329 10.2174/1567205020666230914161034 10.3390/ijms21228661 10.1007/s00401-014-1269-z 10.3233/JAD-190818 10.1016/S0006-8993(00)03082-1 10.4172/2161-0460.1000224 10.1056/NEJMoa2212948 10.3389/fnagi.2019.00220 10.2967/jnumed.114.148981 10.1038/s41591-021-01348-z 10.2967/jnumed.119.230797 10.3233/JAD-201438 10.1007/s11682-020-00366-8 10.1016/j.neurobiolaging.2021.09.017 10.1001/jamaneurol.2021.4654 10.1016/j.nicl.2020.102387 10.3233/JAD-200906 |
ContentType | Journal Article |
Copyright | 2024 The Authors The Authors Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved. |
Copyright_xml | – notice: 2024 The Authors – notice: The Authors – notice: Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved. |
CorporateAuthor | The Alzheimer's Disease Neuroimaging Initiative Alzheimer's Disease Neuroimaging Initiative |
CorporateAuthor_xml | – name: The Alzheimer's Disease Neuroimaging Initiative – name: Alzheimer's Disease Neuroimaging Initiative |
DBID | 6I. AAFTH AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 |
DOI | 10.1016/j.jns.2024.123319 |
DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
DatabaseTitleList | MEDLINE MEDLINE - Academic |
Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Medicine |
EISSN | 1878-5883 |
EndPage | 123319 |
ExternalDocumentID | 39612639 10_1016_j_jns_2024_123319 S0022510X24004556 1_s2_0_S0022510X24004556 |
Genre | Journal Article |
GeographicLocations | China |
GeographicLocations_xml | – name: China |
GroupedDBID | --- --K --M .1- .FO .~1 0R~ 1B1 1P~ 1RT 1~. 1~5 4.4 457 4G. 5GY 5RE 5VS 7-5 71M 8P~ 9JM AABNK AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AATTM AAXKI AAXLA AAXUO AAYWO ABBQC ABCQJ ABFNM ABFRF ABGSF ABIVO ABJNI ABLJU ABMAC ABMZM ABTEW ABUDA ACDAQ ACGFO ACGFS ACIEU ACIUM ACRLP ACVFH ADBBV ADCNI ADEZE ADUVX AEBSH AEFWE AEHWI AEIPS AEKER AENEX AEUPX AEVXI AFJKZ AFPUW AFRHN AFTJW AFXIZ AGCQF AGHFR AGUBO AGWIK AGYEJ AHHHB AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX APXCP AXJTR BKOJK BLXMC BNPGV CS3 EBS EFJIC EFKBS EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA IHE J1W KOM L7B LX8 M29 M2V M41 MO0 MOBAO N9A O-L O9- OAUVE OP~ OZT P-8 P-9 P2P PC. Q38 ROL RPZ SAE SCC SDF SDG SDP SEL SES SEW SPCBC SSH SSN SSU SSZ T5K Z5R ~G- .55 .GJ 29L 53G AACTN AAQXK ABWVN ABXDB ACRPL ADMUD ADNMO AFCTW AFKWA AGRDE AJOXV AKRLJ AMFUW ASPBG AVWKF AZFZN EJD FEDTE FGOYB G-2 HDW HMK HMO HMQ HVGLF HZ~ R2- RIG SNS WUQ X7M ZGI ZXP 6I. AAFTH AAYXX AGQPQ AGRNS CITATION CGR CUY CVF ECM EIF NPM 7X8 |
ID | FETCH-LOGICAL-c410t-8d8539cd1cebbbd26040a95f278a9a23b2f481b84eb497217647e5101f891f783 |
IEDL.DBID | .~1 |
ISSN | 0022-510X 1878-5883 |
IngestDate | Fri Jul 11 11:07:45 EDT 2025 Wed Feb 19 02:18:00 EST 2025 Tue Jul 01 00:51:33 EDT 2025 Sat Dec 21 16:01:23 EST 2024 Tue Feb 25 20:00:48 EST 2025 Tue Aug 26 16:33:42 EDT 2025 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | true |
IsScholarly | true |
Keywords | PET beta amyloid Mild cognitive impariment Alzheimer's disease Risk assessment |
Language | English |
License | This is an open access article under the CC BY-NC-ND license. Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved. |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c410t-8d8539cd1cebbbd26040a95f278a9a23b2f481b84eb497217647e5101f891f783 |
Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
OpenAccessLink | https://www.sciencedirect.com/science/article/pii/S0022510X24004556 |
PMID | 39612639 |
PQID | 3134331416 |
PQPubID | 23479 |
PageCount | 1 |
ParticipantIDs | proquest_miscellaneous_3134331416 pubmed_primary_39612639 crossref_primary_10_1016_j_jns_2024_123319 elsevier_sciencedirect_doi_10_1016_j_jns_2024_123319 elsevier_clinicalkeyesjournals_1_s2_0_S0022510X24004556 elsevier_clinicalkey_doi_10_1016_j_jns_2024_123319 |
ProviderPackageCode | CITATION AAYXX |
PublicationCentury | 2000 |
PublicationDate | 2024-12-15 |
PublicationDateYYYYMMDD | 2024-12-15 |
PublicationDate_xml | – month: 12 year: 2024 text: 2024-12-15 day: 15 |
PublicationDecade | 2020 |
PublicationPlace | Netherlands |
PublicationPlace_xml | – name: Netherlands |
PublicationTitle | Journal of the neurological sciences |
PublicationTitleAlternate | J Neurol Sci |
PublicationYear | 2024 |
Publisher | Elsevier B.V |
Publisher_xml | – name: Elsevier B.V |
References | Zhou, Zhao, Kojima, Ding, Fukushima, Hong (bb0080) 2016; 6 Zhou, Fukushima (bb0160) 2020; 21 van Dyck, Swanson, Aisen, Bateman, Chen, Gee, Kanekiyo, Li, Reyderman, Cohen, Froelich, Katayama, Sabbagh, Vellas, Watson, Dhadda, Irizarry, Kramer, Iwatsubo (bb0120) 2023; 388 Nagaraj, Duong (bb0030) 2021; 80 Beyer, Brendel, Scheiwein, Sauerbeck, Hosakawa, Alberts, Shi, Bartenstein, Ishii, Seibyl, Cumming, Rominger (bb0020) 2020; 74 Josephs, Murray, Whitwell, Parisi, Petrucelli, Jack, Petersen, Dickson (bb0125) 2014; 127 Blazhenets, Ma, Sörensen, Schiller, Rücker, Eidelberg, Frings, Meyer (bb0015) 2020; 61 Arai, Yamazaki, Mori, Muramatsu, Asano, Katayama (bb0135) 2001; 888 Shim, Kang, Youn, An, Kim (bb0140) 2022; 14 Twohig, Nielsen (bb0145) 2019; 14 König, Linz, Tröger, Wolters, Alexandersson, Robert (bb0110) 2018; 45 Sarica, Cerasa, Quattrone (bb0065) 2017; 9 Zhou B, Tanabe K, Kojima S, Teramukai S, Fukushima M, Alzheimer Disease Neuroimaging Initiative Alzheimer disease neuroimaging initiative. protective factors modulate the risk of beta amyloid in Alzheimer's disease. Behav. Neurol. (2020):7029642. doi Platero, Tobar, Alzheimer’s Disease Neuroimaging Initiative (bb0050) 2021; 15 Fristed, Skirrow, Meszaros, Lenain, Meepegama, Papp, Ropacki, Weston (bb0105) 2022; 4 Grueso, Viejo-Sobera (bb0055) 2021; 13 Jack, Bennett, Blennow, Carrillo, Dunn, Haeberlein, Holtzman, Jagust, Jessen, Karlawish, Liu, Molinuevo, Montine, Phelps, Rankin, Rowe, Scheltens, Siemers, Snyder, Sperling, Contributors. (bb0005) 2018; 14 Zhou, Zhao, Kojima, Ding, Higashide, Fukushima, Hong (bb0075) 2023; 20 de Souza, Chupin, Lamari, Jardel, Leclercq, Colliot, Lehéricy, Dubois, Sarazin (bb0150) 2012; 33 Li, Zhang, Riphagen, Yochim, Li, Liu, Salat, Alzheimer’s Disease Neuroimaging Initiative (bb0045) 2020; 28 Fischl (bb0095) 2012; 62 . Leuzy, Smith, Cullen, Strandberg, Vogel, Binette, Borroni, Janelidze, Ohlsson, Jögi, Ossenkoppele, Palmqvist, Mattsson-Carlgren, Klein, Stomrud, Hansson (bb0025) 2022; 79 ADNI (bb0090) 2024 Prakash, Abdelaziz, Zhang, Strange, Tohka, Alzheimer’s Disease Neuroimaging Initiative (bb0035) 2021; 79 Wisse, Xie, Das, de Flores, Hansson, Habes, Doshi, Davatzikos, Yushkevich, Wolk, Alzheimers Disease Neuroimaging Initiative (bb0155) 2022; 109 Landau, Fero, Baker, Koeppe, Mintun, Chen, Reiman, Jagust (bb0085) 2015; 56 Palmqvist, Tideman, Cullen, Zetterberg, Blennow, Alzheimer’s Disease Neuroimaging Initiative, Dage, Stomrud, Janelidze, Mattsson-Carlgren, Hansson (bb0070) 2021; 27 ADNI4 (bb0115) 2024 Darmanthé, Tabatabaei-Jafari, Cherbuin, Alzheimer’s Disease Neuroimaging Initiative (bb0040) 2021; 82 Jo, Nho, Saykin (bb0060) 2019; 11 Eke, Jammeh, Li, Carroll, Pearson, Ifeachor (bb0100) 2021; 25 Meneses, Koga, O’Leary, Dickson, Bu, Zhao (bb0130) 2021; 16 Shim (10.1016/j.jns.2024.123319_bb0140) 2022; 14 Palmqvist (10.1016/j.jns.2024.123319_bb0070) 2021; 27 Arai (10.1016/j.jns.2024.123319_bb0135) 2001; 888 König (10.1016/j.jns.2024.123319_bb0110) 2018; 45 Platero (10.1016/j.jns.2024.123319_bb0050) 2021; 15 Fristed (10.1016/j.jns.2024.123319_bb0105) 2022; 4 Li (10.1016/j.jns.2024.123319_bb0045) 2020; 28 Zhou (10.1016/j.jns.2024.123319_bb0160) 2020; 21 Meneses (10.1016/j.jns.2024.123319_bb0130) 2021; 16 Sarica (10.1016/j.jns.2024.123319_bb0065) 2017; 9 Zhou (10.1016/j.jns.2024.123319_bb0080) 2016; 6 Grueso (10.1016/j.jns.2024.123319_bb0055) 2021; 13 Wisse (10.1016/j.jns.2024.123319_bb0155) 2022; 109 Eke (10.1016/j.jns.2024.123319_bb0100) 2021; 25 Jack (10.1016/j.jns.2024.123319_bb0005) 2018; 14 Beyer (10.1016/j.jns.2024.123319_bb0020) 2020; 74 Fischl (10.1016/j.jns.2024.123319_bb0095) 2012; 62 Twohig (10.1016/j.jns.2024.123319_bb0145) 2019; 14 ADNI4 (10.1016/j.jns.2024.123319_bb0115) Josephs (10.1016/j.jns.2024.123319_bb0125) 2014; 127 Darmanthé (10.1016/j.jns.2024.123319_bb0040) 2021; 82 van Dyck (10.1016/j.jns.2024.123319_bb0120) 2023; 388 Nagaraj (10.1016/j.jns.2024.123319_bb0030) 2021; 80 Zhou (10.1016/j.jns.2024.123319_bb0075) 2023; 20 de Souza (10.1016/j.jns.2024.123319_bb0150) 2012; 33 Leuzy (10.1016/j.jns.2024.123319_bb0025) 2022; 79 10.1016/j.jns.2024.123319_bb0010 Jo (10.1016/j.jns.2024.123319_bb0060) 2019; 11 Blazhenets (10.1016/j.jns.2024.123319_bb0015) 2020; 61 Prakash (10.1016/j.jns.2024.123319_bb0035) 2021; 79 Landau (10.1016/j.jns.2024.123319_bb0085) 2015; 56 ADNI (10.1016/j.jns.2024.123319_bb0090) |
References_xml | – volume: 74 start-page: 101 year: 2020 end-page: 112 ident: bb0020 article-title: Alzheimer’s Disease Neuroimaging Initiative improved risk stratification for progression from mild cognitive impairment to Alzheimer’s Disease with a multi-analytical evaluation of amyloid-β positron emission tomography publication-title: J. Alzheimers Dis. – volume: 80 start-page: 1079 year: 2021 end-page: 1090 ident: bb0030 article-title: Deep learning and risk score classification of mild cognitive impairment and Alzheimer’s Disease publication-title: J. Alzheimers Dis. – volume: 33 start-page: 1253 year: 2012 end-page: 1257 ident: bb0150 article-title: CSF tau markers are correlated with hippocampal volume in Alzheimer’s disease publication-title: Neurobiol. Aging – year: 2024 ident: bb0090 – volume: 13 start-page: 162 year: 2021 ident: bb0055 article-title: Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review publication-title: Alzheimers Res. Ther. – volume: 15 start-page: 1728 year: 2021 end-page: 1738 ident: bb0050 article-title: Predicting Alzheimer’s conversion in mild cognitive impairment patients using longitudinal neuroimaging and clinical markers publication-title: Brain Imaging Behav. – volume: 56 start-page: 567 year: 2015 end-page: 574 ident: bb0085 article-title: Measurement of longitudinal β-amyloid change with 18F-florbetapir PET and standardized uptake value ratios publication-title: J. Nucl. Med. – volume: 82 start-page: 951 year: 2021 end-page: 964 ident: bb0040 article-title: Combination of plasma Neurofilament light chain and mini-mental state examination score predicts progression from mild cognitive impairment to Alzheimer’s disease within 5 years publication-title: J. Alzheimers Dis. – volume: 61 start-page: 597 year: 2020 end-page: 603 ident: bb0015 article-title: Alzheimer disease neuroimaging initiative; predictive value of 18F-Florbetapir and 18F-FDG PET for conversion from mild cognitive impairment to Alzheimer dementia publication-title: J. Nucl. Med. – volume: 20 start-page: 431 year: 2023 end-page: 439 ident: bb0075 article-title: Early detection of dementia using risk classification in MCI: outcomes of Shanghai mild cognitive impairment cohort study publication-title: Curr. Alzheimer Res. – reference: .. – volume: 14 start-page: 23 year: 2019 ident: bb0145 article-title: Alpha-synuclein in the pathophysiology of Alzheimer’s disease publication-title: Mol. Neurodegener. – volume: 21 start-page: 8661 year: 2020 ident: bb0160 article-title: Clinical utility of the pathogenesis-related proteins in Alzheimer’s disease publication-title: Int. J. Mol. Sci. – volume: 888 start-page: 287 year: 2001 end-page: 296 ident: bb0135 article-title: Alpha-synuclein-positive structures in cases with sporadic Alzheimer’s disease: morphology and its relationship to tau aggregation publication-title: Brain Res. – volume: 109 start-page: 135 year: 2022 end-page: 144 ident: bb0155 article-title: Tau pathology mediates age effects on medial temporal lobe structure publication-title: Neurobiol. Aging – volume: 127 start-page: 811 year: 2014 end-page: 824 ident: bb0125 article-title: TDP-43 is a key player in the clinical features associated with Alzheimer’s disease publication-title: Acta Neuropathol. – volume: 45 start-page: 198 year: 2018 end-page: 209 ident: bb0110 article-title: Fully automatic speech-based analysis of the semantic verbal fluency task publication-title: Dement. Geriatr. Cogn. Disord. – year: 2024 ident: bb0115 – volume: 27 start-page: 1034 year: 2021 end-page: 1042 ident: bb0070 article-title: Prediction of future Alzheimer’s disease dementia using plasma phospho-tau combined with other accessible measures publication-title: Nat. Med. – volume: 62 start-page: 774 year: 2012 end-page: 781 ident: bb0095 article-title: FreeSurfer publication-title: Neuroimage – volume: 388 start-page: 9 year: 2023 end-page: 21 ident: bb0120 article-title: Lecanemab in early Alzheimer’s disease publication-title: N. Engl. J. Med. – volume: 9 start-page: 329 year: 2017 ident: bb0065 article-title: Random forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: a systematic review publication-title: Front. Aging Neurosci. – volume: 25 start-page: 218 year: 2021 end-page: 226 ident: bb0100 article-title: Early detection of Alzheimer’s disease with blood plasma proteins using support vector machines publication-title: IEEE J. Biomed. Health Inform. – volume: 79 start-page: 149 year: 2022 end-page: 158 ident: bb0025 article-title: Biomarker-based prediction of longitudinal tau positron emission tomography in Alzheimer Disease publication-title: JAMA Neurol. – volume: 79 start-page: 1533 year: 2021 end-page: 1546 ident: bb0035 article-title: Quantitative longitudinal predictions of Alzheimer’s disease by multi-modal predictive learning publication-title: J. Alzheimers Dis. – volume: 16 start-page: 84 year: 2021 ident: bb0130 article-title: TDP-43 pathology in Alzheimer’s disease publication-title: Mol. Neurodegener. – volume: 11 start-page: 220 year: 2019 ident: bb0060 article-title: Deep learning in Alzheimer’s Disease: diagnostic classification and prognostic prediction using neuroimaging data publication-title: Front. Aging Neurosci. – volume: 4 year: 2022 ident: bb0105 article-title: Leveraging speech and artificial intelligence to screen for early Alzheimer’s disease and amyloid beta positivity publication-title: Brain Commun. – volume: 6 start-page: 224 year: 2016 ident: bb0080 article-title: Shanghai cohort study on mild cognitive impairment: study design and baseline characteristics publication-title: J, Alzheimers Dis. Parkinsonism – volume: 14 start-page: 201 year: 2022 ident: bb0140 article-title: Alpha-synuclein: a pathological factor with Aβ and tau and biomarker in Alzheimer’s disease publication-title: Alzheimers Res. Ther. – volume: 28 year: 2020 ident: bb0045 article-title: Prediction of clinical and biomarker conformed Alzheimer’s disease and mild cognitive impairment from multi-feature brain structural MRI using age-correction from a large independent lifespan sample publication-title: Neuroimage Clin. – volume: 14 start-page: 535 year: 2018 end-page: 562 ident: bb0005 article-title: NIA-AA research framework: toward a biological definition of Alzheimer’s disease publication-title: Alzheimers Dement. – reference: Zhou B, Tanabe K, Kojima S, Teramukai S, Fukushima M, Alzheimer Disease Neuroimaging Initiative Alzheimer disease neuroimaging initiative. protective factors modulate the risk of beta amyloid in Alzheimer's disease. Behav. Neurol. (2020):7029642. doi: – volume: 14 start-page: 535 issue: 4 year: 2018 ident: 10.1016/j.jns.2024.123319_bb0005 article-title: NIA-AA research framework: toward a biological definition of Alzheimer’s disease publication-title: Alzheimers Dement. doi: 10.1016/j.jalz.2018.02.018 – volume: 45 start-page: 198 issue: 3–4 year: 2018 ident: 10.1016/j.jns.2024.123319_bb0110 article-title: Fully automatic speech-based analysis of the semantic verbal fluency task publication-title: Dement. Geriatr. Cogn. Disord. doi: 10.1159/000487852 – volume: 16 start-page: 84 year: 2021 ident: 10.1016/j.jns.2024.123319_bb0130 article-title: TDP-43 pathology in Alzheimer’s disease publication-title: Mol. Neurodegener. doi: 10.1186/s13024-021-00503-x – ident: 10.1016/j.jns.2024.123319_bb0010 doi: 10.1155/2020/7029642 – volume: 33 start-page: 1253 issue: 7 year: 2012 ident: 10.1016/j.jns.2024.123319_bb0150 article-title: CSF tau markers are correlated with hippocampal volume in Alzheimer’s disease publication-title: Neurobiol. Aging doi: 10.1016/j.neurobiolaging.2011.02.022 – volume: 25 start-page: 218 issue: 1 year: 2021 ident: 10.1016/j.jns.2024.123319_bb0100 article-title: Early detection of Alzheimer’s disease with blood plasma proteins using support vector machines publication-title: IEEE J. Biomed. Health Inform. doi: 10.1109/JBHI.2020.2984355 – volume: 13 start-page: 162 issue: 1 year: 2021 ident: 10.1016/j.jns.2024.123319_bb0055 article-title: Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review publication-title: Alzheimers Res. Ther. doi: 10.1186/s13195-021-00900-w – volume: 4 issue: 5 year: 2022 ident: 10.1016/j.jns.2024.123319_bb0105 article-title: Leveraging speech and artificial intelligence to screen for early Alzheimer’s disease and amyloid beta positivity publication-title: Brain Commun. doi: 10.1093/braincomms/fcac231 – volume: 14 start-page: 201 issue: 1 year: 2022 ident: 10.1016/j.jns.2024.123319_bb0140 article-title: Alpha-synuclein: a pathological factor with Aβ and tau and biomarker in Alzheimer’s disease publication-title: Alzheimers Res. Ther. doi: 10.1186/s13195-022-01150-0 – volume: 82 start-page: 951 issue: 3 year: 2021 ident: 10.1016/j.jns.2024.123319_bb0040 article-title: Combination of plasma Neurofilament light chain and mini-mental state examination score predicts progression from mild cognitive impairment to Alzheimer’s disease within 5 years publication-title: J. Alzheimers Dis. doi: 10.3233/JAD-210092 – volume: 14 start-page: 23 year: 2019 ident: 10.1016/j.jns.2024.123319_bb0145 article-title: Alpha-synuclein in the pathophysiology of Alzheimer’s disease publication-title: Mol. Neurodegener. doi: 10.1186/s13024-019-0320-x – volume: 62 start-page: 774 issue: 2 year: 2012 ident: 10.1016/j.jns.2024.123319_bb0095 article-title: FreeSurfer publication-title: Neuroimage doi: 10.1016/j.neuroimage.2012.01.021 – volume: 9 start-page: 329 year: 2017 ident: 10.1016/j.jns.2024.123319_bb0065 article-title: Random forest algorithm for the classification of neuroimaging data in Alzheimer’s disease: a systematic review publication-title: Front. Aging Neurosci. doi: 10.3389/fnagi.2017.00329 – volume: 20 start-page: 431 issue: 6 year: 2023 ident: 10.1016/j.jns.2024.123319_bb0075 article-title: Early detection of dementia using risk classification in MCI: outcomes of Shanghai mild cognitive impairment cohort study publication-title: Curr. Alzheimer Res. doi: 10.2174/1567205020666230914161034 – volume: 21 start-page: 8661 year: 2020 ident: 10.1016/j.jns.2024.123319_bb0160 article-title: Clinical utility of the pathogenesis-related proteins in Alzheimer’s disease publication-title: Int. J. Mol. Sci. doi: 10.3390/ijms21228661 – volume: 127 start-page: 811 issue: 6 year: 2014 ident: 10.1016/j.jns.2024.123319_bb0125 article-title: TDP-43 is a key player in the clinical features associated with Alzheimer’s disease publication-title: Acta Neuropathol. doi: 10.1007/s00401-014-1269-z – volume: 74 start-page: 101 issue: 1 year: 2020 ident: 10.1016/j.jns.2024.123319_bb0020 article-title: Alzheimer’s Disease Neuroimaging Initiative improved risk stratification for progression from mild cognitive impairment to Alzheimer’s Disease with a multi-analytical evaluation of amyloid-β positron emission tomography publication-title: J. Alzheimers Dis. doi: 10.3233/JAD-190818 – volume: 888 start-page: 287 issue: 2 year: 2001 ident: 10.1016/j.jns.2024.123319_bb0135 article-title: Alpha-synuclein-positive structures in cases with sporadic Alzheimer’s disease: morphology and its relationship to tau aggregation publication-title: Brain Res. doi: 10.1016/S0006-8993(00)03082-1 – volume: 6 start-page: 224 year: 2016 ident: 10.1016/j.jns.2024.123319_bb0080 article-title: Shanghai cohort study on mild cognitive impairment: study design and baseline characteristics publication-title: J, Alzheimers Dis. Parkinsonism doi: 10.4172/2161-0460.1000224 – volume: 388 start-page: 9 issue: 1 year: 2023 ident: 10.1016/j.jns.2024.123319_bb0120 article-title: Lecanemab in early Alzheimer’s disease publication-title: N. Engl. J. Med. doi: 10.1056/NEJMoa2212948 – volume: 11 start-page: 220 year: 2019 ident: 10.1016/j.jns.2024.123319_bb0060 article-title: Deep learning in Alzheimer’s Disease: diagnostic classification and prognostic prediction using neuroimaging data publication-title: Front. Aging Neurosci. doi: 10.3389/fnagi.2019.00220 – volume: 56 start-page: 567 issue: 4 year: 2015 ident: 10.1016/j.jns.2024.123319_bb0085 article-title: Measurement of longitudinal β-amyloid change with 18F-florbetapir PET and standardized uptake value ratios publication-title: J. Nucl. Med. doi: 10.2967/jnumed.114.148981 – volume: 27 start-page: 1034 issue: 6 year: 2021 ident: 10.1016/j.jns.2024.123319_bb0070 article-title: Prediction of future Alzheimer’s disease dementia using plasma phospho-tau combined with other accessible measures publication-title: Nat. Med. doi: 10.1038/s41591-021-01348-z – volume: 61 start-page: 597 issue: 4 year: 2020 ident: 10.1016/j.jns.2024.123319_bb0015 article-title: Alzheimer disease neuroimaging initiative; predictive value of 18F-Florbetapir and 18F-FDG PET for conversion from mild cognitive impairment to Alzheimer dementia publication-title: J. Nucl. Med. doi: 10.2967/jnumed.119.230797 – ident: 10.1016/j.jns.2024.123319_bb0115 – volume: 80 start-page: 1079 issue: 3 year: 2021 ident: 10.1016/j.jns.2024.123319_bb0030 article-title: Deep learning and risk score classification of mild cognitive impairment and Alzheimer’s Disease publication-title: J. Alzheimers Dis. doi: 10.3233/JAD-201438 – volume: 15 start-page: 1728 issue: 4 year: 2021 ident: 10.1016/j.jns.2024.123319_bb0050 article-title: Predicting Alzheimer’s conversion in mild cognitive impairment patients using longitudinal neuroimaging and clinical markers publication-title: Brain Imaging Behav. doi: 10.1007/s11682-020-00366-8 – volume: 109 start-page: 135 year: 2022 ident: 10.1016/j.jns.2024.123319_bb0155 article-title: Tau pathology mediates age effects on medial temporal lobe structure publication-title: Neurobiol. Aging doi: 10.1016/j.neurobiolaging.2021.09.017 – volume: 79 start-page: 149 issue: 2 year: 2022 ident: 10.1016/j.jns.2024.123319_bb0025 article-title: Biomarker-based prediction of longitudinal tau positron emission tomography in Alzheimer Disease publication-title: JAMA Neurol. doi: 10.1001/jamaneurol.2021.4654 – volume: 28 year: 2020 ident: 10.1016/j.jns.2024.123319_bb0045 article-title: Prediction of clinical and biomarker conformed Alzheimer’s disease and mild cognitive impairment from multi-feature brain structural MRI using age-correction from a large independent lifespan sample publication-title: Neuroimage Clin. doi: 10.1016/j.nicl.2020.102387 – ident: 10.1016/j.jns.2024.123319_bb0090 – volume: 79 start-page: 1533 issue: 4 year: 2021 ident: 10.1016/j.jns.2024.123319_bb0035 article-title: Quantitative longitudinal predictions of Alzheimer’s disease by multi-modal predictive learning publication-title: J. Alzheimers Dis. doi: 10.3233/JAD-200906 |
SSID | ssj0006889 |
Score | 2.441612 |
Snippet | People with elevated beta amyloid have different risk and progress speed to Alzheimer's disease.
The research is to validate the risk classification of AD... AbstractBackgroundsPeople with elevated beta amyloid have different risk and progress speed to Alzheimer's disease. PurposeThe research is to validate the risk... People with elevated beta amyloid have different risk and progress speed to Alzheimer's disease.BACKGROUNDSPeople with elevated beta amyloid have different... |
SourceID | proquest pubmed crossref elsevier |
SourceType | Aggregation Database Index Database Publisher |
StartPage | 123319 |
SubjectTerms | Aged Aged, 80 and over Alzheimer Disease - diagnosis Alzheimer Disease - epidemiology Alzheimer's disease Amyloid beta-Peptides - metabolism China - epidemiology Cognitive Dysfunction - diagnosis Cognitive Dysfunction - epidemiology Cohort Studies Disease Progression Female Humans Male Middle Aged Mild cognitive impariment Neurology PET beta amyloid Positron-Emission Tomography Risk assessment |
Title | Differential risk of Alzheimer's disease in MCI subjects with elevated Abeta |
URI | https://www.clinicalkey.com/#!/content/1-s2.0-S0022510X24004556 https://www.clinicalkey.es/playcontent/1-s2.0-S0022510X24004556 https://dx.doi.org/10.1016/j.jns.2024.123319 https://www.ncbi.nlm.nih.gov/pubmed/39612639 https://www.proquest.com/docview/3134331416 |
Volume | 467 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3dS9xAEB_EgvRFWrX1_GKFQkE4TbKTj308zsppq08K97bsZjf0RHNi7l764N_uzGVjKfUDfEzIkmS-9rc7v5kF-OYdYiRT0kCFtEApM3KprCj7npYfiSMPw5ZtcZGNrvBsnI6XYNjVwjCtMsT-NqYvonW4cxSkeXQ3mXCNL9liHI2ZBYlpym23uXsd2fThw1-aR1YUqusYzk93mc0Fx-u65o7dCR5S_JbcbOf5uekl7LmYg04-wWoAj2LQft9nWPL1Gqych_T4Ovw6DsedkNveCGaNi2klBjd_fvvJrb__3oiQjxGTWpwPT0Uzt7wP0wjejhVcak7Q04mB9TOzAVcnPy6Ho344LqFfYhzN-oWjqVeVLi69tdbRQgUjo9IqyQujTCItqYNAaoHeIvfsyTPMPbtkVai4ygv5BZbrae03QaCJ0KJVlXMKpa2McTL1qXJ81lFuTQ8OOkHpu7Yrhu7oYteapKpZqrqVag-STpS6K_ekAKUpZr82KH9ukG-CizU61k2iI_2fGfQAn0b-Y0lvvXC_07ImD-O0ian9dN5oGUsuKyPk2oOvrfqffloqQogE8rbe99Jt-MhXTI-J0x1Ynt3P_S6BnJndW1jxHnwYnP4cXTwCDjf31w |
linkProvider | Elsevier |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1La9tAEB5SB9peSt9108cWCoWCG0k7euzROA12Y_uUgG_LrnZFHFI5RPYlvz4z1spQkrTQq8Sw0rz2250XwFfvECOZkgQqpANKmZFJZUU58HT8SBxZGLbZFvNsfIa_FuliD0ZdLQynVQbf3_r0rbcOTw4DNw-vlkuu8SVdjKMFZ0FimmaPYJ-7U2EP9oeTk_F855CzolBd03Am6IKb2zSvi5qbdif4g1y45H47929PD8HP7TZ0_ByeBfwohu0nvoA9X7-Ex7MQIX8F06Mw8YQs91Jw4rhYVWJ4eXPul7_99bdGhJCMWNZiNpqIZmP5KqYRfCMruNqc0KcTQ-vX5jWcHf88HY0HYWLCoMQ4Wg8KR7uvKl1cemuto7MKRkalVZIXRplEWpII4dQCvUVu25NnmHu2yqpQcZUX8g306lXt34FAE6FFqyrnFEpbGeNk6lPleNxRbk0fvneM0ldtYwzdZYxdaOKqZq7qlqt9SDpW6q7ik3yUJrf9N6L8PiLfBCtrdKybREf6jib0AXeUfyjTvxb80klZk5Fx5MTUfrVptIwlV5YReO3D21b8u5-WikAi4bz3_7foZ3gyPp1N9XQyPzmAp_yGs2Xi9AP01tcb_5Ewz9p-Cjp9C5GO-og |
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=Differential+risk+of+Alzheimer%27s+disease+in+MCI+subjects+with+elevated+Abeta&rft.jtitle=Journal+of+the+neurological+sciences&rft.au=Zhou%2C+Bin&rft.au=Fukushima%2C+Masanori&rft.date=2024-12-15&rft.issn=0022-510X&rft.volume=467&rft.spage=123319&rft_id=info:doi/10.1016%2Fj.jns.2024.123319&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_jns_2024_123319 |
thumbnail_m | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=https%3A%2F%2Fcdn.clinicalkey.com%2Fck-thumbnails%2F0022510X%2Fcov200h.gif |