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Record W4407625864 · doi:10.1001/jamaneurol.2024.5263

Temporal Dynamics and Biological Variability of Alzheimer Biomarkers

2025· article· en· W4407625864 on OpenAlexaff
Jihwan Yun, Daeun Shin, Eun Hye Lee, Jun Pyo Kim, Hongki Ham, Yuna Gu, Min Young Chun, Sung Hoon Kang, Duk L. Na, Chi‐Hun Kim, Ko Woon Kim, Si Eun Kim, Yeshin Kim, Jaeho Kim, Na‐Yeon Jung, Yeo Jin Kim, Soo Hyun Cho, Henrik Zetterberg, Kaj Blennow, Fernando González‐Ortiz, Nicholas J. Ashton, Joseph Therriault, Nesrine Rahmouni, Pedro Rosa‐Neto, Michael Weiner, Sang Won Seo, Hyemin Jang, Young-Soo Kim, Sun-Ho Han, Joon-Kyung Seong, Junkyu Choi, Eek‐Sung Lee, Tak-Kyeong Lee, Juhee Chin, Hee Jin Kim, Haesook Bok, Hang-Rai Kim, Seung Joo Kim, Seunghee Na, Geon Ha Kim, Jin San Lee, Hanna Cho, Byeong C. Kim, Dong Young Lee, So Young Moon, Min Soo Byun, Dahyun Yi, Han Na Lee, Jae‐Won Jang, Jee Hyang Jeong, Young Hee Jung, Jong Hun Kim, Young‐Ju Kim, Bo Kyoung Cheon, Jin‐Kyu Seo, Young Noh, Y. Ha, Hae-Eun Shin, Kyunghun Kang, Ki Young Shin, Yeongshin Kim, Ji Sung Jang, Do Kyung Lee, Yu Hyun Park, Soo-Jong Kim, Byung-Hyun Byun, Yejoo Choi, Na Kyung Lee, Hong-Hee Won, Minyoung Cho, Sang‐Hyuk Jung, Dong Hyun Lee, Beomsu Kim, Paul Aisen, Ronald Petersen, Clifford R. Jack, William Jagust, John Q. Trojanowki, Arthur W. Toga, Laurel Beckett, Robert C. Green, Andrew J. Saykin, John C. Morris, Leslie M. Shaw, Zaven S. Khachaturian, Greg Sorensen, María C. Carrillo, Lew Kuller, Marc Raichle, Steven M. Paul, Peter J. Davies, Howard Fillit, Franz Hefti, David M. Holtzman, Marek-Marsel Mesulam, William C. Potter, Peter Snyder, Veronika Logovinsky, Tom Montine, Gustavo Jiménez, Michael Donohue, Devon Gessert, Kelly Harless, Jennifer Salazar, Yuliana Cabrera, Sarah Walter, Lindsey Hergesheimer, Danielle Harvey, Matt A. Bernstein, Nick J. Fox, Paul Thompson, Norbert Schuff, Charles DeCarli, Bret Borowski, Jeff Gunter, Matthew L. Senjem, Prashanthi Vemuri, David Jones, Kejal Kantarchi, Chad Ward, Robert A. Koeppe, Norm Foster, Eric M. Reiman, Kewei Chen, Susan Landau, Nigel J. Cairns, Erin Franklin, Lisa Taylor‐Reinwald, Virginia M.‐Y. Lee, Magdalena Korecka, Michal Figurski, Karen Crawford, Scott Neu, Tatiana M. Foroud, Steven Potkin, Li Shen, Kelley Faber, Sungeun Kim, Kwangsik Nho, Lean Thal, Neil Buckholtz, Marilyn Albert, R.T. Frank, John Hsiao, Cécile Tissot, Gleb Bezgin, Stijn Servaes, Jenna Stevenson, Serge Gauthier, Paolo Vitali

Bibliographic record

VenueJAMA Neurology · 2025
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill University
FundersNational Institute on Aging
KeywordsConcordanceBiomarkerPositron emission tomographyMedicineInternal medicineDementiaOncologyPet imagingCohortStandardized uptake valueDiseasePathologyNuclear medicine

Abstract

fetched live from OpenAlex

Importance: Understanding the characteristics of discordance between plasma biomarkers and positron emission tomography (PET) results in Alzheimer disease (AD) is crucial for accurate interpretation of the findings. Objective: To compare (1) medical comorbidities affecting plasma biomarker concentrations, (2) imaging and clinical features, and (3) cognitive changes between plasma biomarker and PET discordant and concordant cases. Design, Setting, and Participants: This multicenter cohort study, conducted between 2016 and 2023, included individuals with unimpaired cognition, mild cognitive impairment, or Alzheimer-type dementia, who had both amyloid β (Aβ) PET imaging and plasma biomarkers. A subset of participants also underwent tau PET imaging. Exposures: Participants were categorized into 4 groups based on their plasma and PET biomarker results: plasma-/PET-, plasma+/PET-, plasma-/PET+, and plasma+/PET+. Main Outcomes and Measures: Clinical characteristics were compared between the 4 groups, focusing on the discordant groups. Results: A total of 2611 participants (mean [SD] age was 71.2 [8.7] years; 1656 female [63.4%]), of whom 124 additionally underwent tau PET, were included. Among the plasma biomarkers, phosphorylated tau (p-tau) 217 exhibited the highest concordance rate with Aβ (2326 of 2571 [90.5%]) and tau (100 of 120 [83.3%]) PET. The p-tau217+/Aβ PET- group was older (mean [SD] age, 75.8 [7.2] years vs 70.0 [8.8] years; P < .001) with a higher prevalence of hypertension (56 of 152 [36.8%] vs 266 of 1073 [25.0%]), diabetes (40 of 152 [26.3%] vs 156 of 1059 [14.7%]), and chronic kidney disease (17 of 152 [11.2%] vs 21 of 1073 [2.0%]) compared with the p-tau217-/Aβ PET- group (P < .001 for all). Body mass index was higher in p-tau217-/Aβ PET+ than in p-tau217+/Aβ PET+ (mean [SD], 24.1 [2.8] vs 23.1 [3.1], respectively; P = .001; calculated as weight in kilograms divided by height in meters squared). The p-tau217+/Aβ PET- group had lower hippocampal volume (mean [SD], 2555.4 [576.9] vs 2979.1 [545.8]; P < .001) and worse clinical trajectory compared with p-tau217-/Aβ PET- (β = -0.53; P < .001). In contrast, tau PET discordant cases did not show significant differences in medical comorbidities or clinical outcomes compared with the p-tau217-/tau PET- group. Only the p-tau 217+/tau PET+ group demonstrated faster cognitive deterioration compared with the p-tau 217-/tau PET- group (β = -1.66; P < .001). Conclusions and Relevance: Results of this cohort study suggest that the mechanisms underlying the discordance between plasma biomarkers and PET findings may be multifaceted, underscoring the need to consider the temporal dynamics and biological variability of plasma biomarkers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.320
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2025
Admission routes1
Has abstractyes

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