Evidence map›Paper›PMID 41332861›Full record

ArticlemedRxiv : the preprint server for health sciences2025

Development of Alzheimer's Disease Risk Score for Future Primary Care: A White-Box Approach.

Yumiko Wiranto, Devin R Setiawan, Amber Watts, Arian Ashourvan

Abstract readPreprint
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Yumiko WirantoDepartment of Psychology, University of Kansas, Lawrence, Kansas, USA.ORCID 0009-0001-4127-6690
Devin R SetiawanDepartment of Electrical Engineering and Computer Science, University of Kansas, Lawrence, Kansas, USA.
Amber WattsDepartment of Psychology, University of Kansas, Lawrence, Kansas, USA.
Arian AshourvanDepartment of Psychology, University of Kansas, Lawrence, Kansas, USA.

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
National Alzheimer's Coordinating CenterU24AG072122 · NIA · UNIVERSITY OF WASHINGTON · PI STEPHENS, KARI A · 2021 to 2025
$45.8M
Research Education ComponentP30AG062422 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Serggio Carlo Lanata · 2019 to 2026
$36.9M
Research Education ComponentP30AG062421 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI Christine S Ritchie · 2019 to 2026
$36.5M
UCSD Shiley-Marcos Alzheimer's Disease Research Center P30P30AG062429 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Steven Dyal Edland · 2019 to 2026
$34.9M
Wisconsin Alzheimer's Disease Research CenterP30AG062715 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI AMY J. KIND · 2019 to 2026
$34.5M
Research Education ComponentP30AG062677 · NIA · MAYO CLINIC ROCHESTER · PI Walter K. Kremers · 2019 to 2026
$33.5M
Research Education ComponentP30AG066514 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI NIKOLAOS K ROBAKIS · 2020 to 2026
$31.0M
Yale Alzheimer Disease Research CenterP30AG066508 · NIA · YALE UNIVERSITY · PI STEPHEN M STRITTMATTER · 2020 to 2026
$30.2M
Research Education CoreP30AG066462 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Andrew Franklin Teich · 2020 to 2026
$30.1M
Research Education ComponentP30AG066468 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI HOWARD J AIZENSTEIN · 2020 to 2026
$29.4M
Research Education ComponentP30AG066507 · NIA · JOHNS HOPKINS UNIVERSITY · PI Corinne Pettigrew · 2020 to 2026
$29.3M
NIA NIH HHS P20 AG068082NIA NIH HHS P30 AG062421NIA NIH HHS P30 AG062422NIA NIH HHS P30 AG062429NIA NIH HHS P30 AG062677NIA NIH HHS P30 AG062715NIA NIH HHS P30 AG066444NIA NIH HHS P30 AG066462NIA NIH HHS P30 AG066468NIA NIH HHS P30 AG066506NIA NIH HHS P30 AG066507NIA NIH HHS P30 AG066508NIA NIH HHS P30 AG066509NIA NIH HHS P30 AG066511NIA NIH HHS P30 AG066512NIA NIH HHS P30 AG066514NIA NIH HHS P30 AG066515NIA NIH HHS P30 AG066518NIA NIH HHS P30 AG066519NIA NIH HHS P30 AG066530NIA NIH HHS P30 AG066546NIA NIH HHS P30 AG072931NIA NIH HHS P30 AG072946NIA NIH HHS P30 AG072947NIA NIH HHS P30 AG072958NIA NIH HHS P30 AG072959NIA NIH HHS P30 AG072972NIA NIH HHS P30 AG072973NIA NIH HHS P30 AG072975NIA NIH HHS P30 AG072976NIA NIH HHS P30 AG072977NIA NIH HHS P30 AG072978NIA NIH HHS P30 AG072979NIA NIH HHS P30 AG086401NIA NIH HHS P30 AG086404NIA NIH HHS R01 AG079280NIA NIH HHS U01 AG024904NIA NIH HHS U24 AG072122NIGMS NIH HHS P20 GM152280
6 · The paper itself

Abstract

Importance: Interpretable scoring system can contribute to bridge the gap between the timeliness and complexity of diagnosing Alzheimer's disease (AD) and promote early intervention at non-specialist settings. Objective: To develop a risk score to predict the likelihood of AD with interpretable machine learning using variables that are obtainable at integrated primary care settings. Design: A secondary data analysis including cohort studies from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the National Alzheimer's Coordinating Center (NACC) extracted in August 2023 and March 2024. Setting: The ADNI and NACC are multi-site cohort studies in North America. Participants: Participants with normal cognition or mild cognitive impairment at baseline visit were identified. Participants with the same diagnosis overtime were assigned to the stable group, and those converted to AD were placed in the progressive group. Main Outcomes and Measures: Cognitive tests and daily functioning measured with Functional Assessment Questionnaire (FAQ) at baseline visit. Results: A total of 676 participants from ADNI and 4592 participants from NACC were identified. After removing incomplete data, 665 ADNI (mean age [SD]: 73.44 [6.90]; 293 [44.1%] female; 374 stable and 291 progressive) and 3657 NACC participants (mean age [SD]: 70.96 [10.03]; 2405 [65.8%] female; 2445 stable and 1212 progressive) remained. Combinations of 4 measures were selected to generate 10 scorecards using FasterRisk algorithm, showing strong performance (area under the curve [AUC] = 0.868-0.892) in ADNI and remaining robust when validated in NACC (AUC = 0.795). The features were Category Animal ≤ 20 (2 points), Trail Making Test B ≤ 143 (-3 points), Logical Memory Delayed ≤ 3 (4 points), Logical Memory Delayed ≤ 8 (3 points), and FAQ ≤ 2 (-5 points). The probable AD risk corresponded to total points: 7.4% (-8), 25.3% (-4), 50% (-1), 74.7% (2), and > 90% (≥ 6). We refer to this model as the (F)unctioning, (LA)nguage, (M)emory, and (E)xecutive functioning or FLAME scorecard. Conclusions and Relevance: Our findings highlight the potential to predict AD development using obtainable information, allowing for applicability at integrated primary care. While our scope centers on AD, this foundation paves the way for other dementia types.

Indexed as

Alzheimer’s DiseaseCognitionIntegrated Primary CareInterpretable Machine Learning

Identifiers

PMID41332861
PMCPMC12668042

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.