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Prediction of fast decline in amyloid positive mild cognitive impairment patients using multimodal biomarkers

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dc.contributor.authorJang, Hyemin-
dc.contributor.authorPark, Jongyun-
dc.contributor.authorWoo, Sookyoung-
dc.contributor.authorKim, Seonwoo-
dc.contributor.authorKim, Hee Jin-
dc.contributor.authorNa, Duk L.-
dc.contributor.authorLockhart, Samuel N.-
dc.contributor.authorKim, Yeshin-
dc.contributor.authorKim, Ko Woon-
dc.contributor.authorCho, Soo Hyun-
dc.contributor.authorKim, Seung Joo-
dc.contributor.authorSeong, Joon-Kyung-
dc.contributor.authorSeo, Sang Won-
dc.date.accessioned2021-09-01T22:41:48Z-
dc.date.available2021-09-01T22:41:48Z-
dc.date.created2021-06-19-
dc.date.issued2019-
dc.identifier.issn2213-1582-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/68894-
dc.description.abstractIt may be possible to classify patients with A beta positive (+) mild cognitive impairment (MCI) into fast and slow decliners according to their biomarker status. In this study, we aimed to develop a risk prediction model to predict fast decline in the A beta + MCI population using multimodal biomarkers. We included 186 A beta + MCI patients who underwent florbetapir PET, brain MRI, cerebrospinal fluid (CSF) analyses, and FDG PET at baseline. We defined conversion to dementia within 3 years (= fast decline) as the outcome. The associations of potential covariates (MCI stage, APOE4 genotype, corrected hippocampal volume (HV), FDG PET SUVR, AV45 PET SUVR, CSF A beta, total tau (t-tau), and phosphorylated tau (p-tau)) with the outcome were tested and nomograms were constructed using logistic regression models in the training dataset (n=124, n of fast decliners = 52). The model was internally validated with the testing dataset (n= 62, n of fast decliners = 22). The multivariable analysis (including CSF t-tau) showed that MCI stage (late MCI vs. early MCI; OR 15.88, 95% CI 4.59, 54.88), APOE4 (OR 5.65, 95% CI 1.52, 20.98), corrected HV*1000 (OR 0.22, 95% CI 0.09, 0.57), FDG SUVR*10 (OR 0.43, 95% CI 0.27, 0.71), and log(e) CSF t-tau (OR 6.20, 95% CI 1.48, 25.96) were associated with being fast decliners. In the second model including CSF p-tau instead of t-tau, the above associations remained the same, with a significant association between log(e) CSF p-tau (OR 4.53, 95% CI 1.26, 16.31) and fast decline. The constructed nomograms showed excellent predictive performance (90%) on validation with the testing dataset. Among A beta + MCI patients, our findings suggested that multimodal AD biomarkers are significantly associated with being classified as fast decliners. A nomogram incorporating these biomarkers might be useful in early treatment decisions or stratified enrollment of this population into clinical trials.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherELSEVIER SCI LTD-
dc.subjectCSF-PHOSPHORYLATED-TAU-
dc.subjectALZHEIMERS-DISEASE-
dc.subjectDEMENTIA RISK-
dc.subjectCONVERSION-
dc.subjectMCI-
dc.subjectPET-
dc.subjectAD-
dc.titlePrediction of fast decline in amyloid positive mild cognitive impairment patients using multimodal biomarkers-
dc.typeArticle-
dc.contributor.affiliatedAuthorSeong, Joon-Kyung-
dc.identifier.doi10.1016/j.nicl.2019.101941-
dc.identifier.scopusid2-s2.0-85069913619-
dc.identifier.wosid000504663800102-
dc.identifier.bibliographicCitationNEUROIMAGE-CLINICAL, v.24-
dc.relation.isPartOfNEUROIMAGE-CLINICAL-
dc.citation.titleNEUROIMAGE-CLINICAL-
dc.citation.volume24-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaNeurosciences & Neurology-
dc.relation.journalWebOfScienceCategoryNeuroimaging-
dc.subject.keywordPlusCSF-PHOSPHORYLATED-TAU-
dc.subject.keywordPlusALZHEIMERS-DISEASE-
dc.subject.keywordPlusDEMENTIA RISK-
dc.subject.keywordPlusCONVERSION-
dc.subject.keywordPlusMCI-
dc.subject.keywordPlusPET-
dc.subject.keywordPlusAD-
dc.subject.keywordAuthorAmyloid-
dc.subject.keywordAuthorMild cognitive impairment-
dc.subject.keywordAuthorAlzheimer&apos-
dc.subject.keywordAuthors disease-
dc.subject.keywordAuthorMultimodal biomarkers-
dc.subject.keywordAuthorNomogram-
dc.subject.keywordAuthorConversion to dementia-
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