Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing 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.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
6 authors.
Daniel ArnoldGraduate Program in Biological Sciences: Pharmacology and Therapeutics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil.
Rodrigo C BarrosGraduate Program in Computer Science (PPGCC), School of Technology, Pontifical Catholic University of Rio Grande do Sul (PUCRS), Porto Alegre, Rio Grande do Sul, Brazil.
João Pedro Ferrari-SouzaGraduate Program in Biological Sciences: Biochemistry, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil.
Marco Antonio de BastianiGraduate Program in Biological Sciences: Pharmacology and Therapeutics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil.
Eduardo R ZimmerGraduate Program in Biological Sciences: Pharmacology and Therapeutics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil.
Wyllians Vendramini BorelliGraduate Program in Biological Sciences: Pharmacology and Therapeutics, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre, Rio Grande do Sul, Brazil.
Funding
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 Gil Dan Rabinovici · 2019 to 2026
$36.9M
Research Education ComponentP30AG062421 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI Teresa Gomez-Isla · 2019 to 2026
$36.5M
UCSD Shiley-Marcos Alzheimer's Disease Research Center P30P30AG062429 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Mark W Bondi · 2019 to 2026
$34.9M
Wisconsin Alzheimer's Disease Research CenterP30AG062715 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI Barbara Brigitta Bendlin · 2019 to 2026
$34.5M
Research Education ComponentP30AG062677 · NIA · MAYO CLINIC ROCHESTER · PI NILUFER ERTEKIN-TANER · 2019 to 2026
$33.5M
Research Education ComponentP30AG066514 · NIA · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Carolyn Wei Zhu · 2020 to 2026
$31.0M
Yale Alzheimer Disease Research CenterP30AG066508 · NIA · YALE UNIVERSITY · PI STEPHEN M STRITTMATTER, CHRISTOPHER H VAN DYCK · 2020 to 2026
$30.2M
Research Education CoreP30AG066462 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Miguel Arce · 2020 to 2026
$30.1M
Research Education ComponentP30AG066468 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI C. Elizabeth Shaaban · 2020 to 2026
$29.4M
Research Education ComponentP30AG066507 · NIA · JOHNS HOPKINS UNIVERSITY · PI MARILYN S. ALBERT · 2020 to 2026
$29.3M
University of Washington Alzheimer's Disease Research CenterP30AG066509 · NIA · UNIVERSITY OF WASHINGTON · PI Thomas J. Grabowski · 2020 to 2026
$29.0M
Alzheimer's Association AARFD-23-1148735ARDBrazilian National Institute of ScienceCNPQConselho Nacional de Desenvolvimento Científico e Tecnológico 16/2551- 0000475-7Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul 21/2551- 0000673-0Instituto Serrapilheira R-2401-47242Instituto Serrapilheira Serra-1912-31365National Academy of Neuropsychology AARFD-23-1148735National Academy of Neuropsychology ALZ-NAN-22-928381NIA 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 U24 AG072122PRONEX 16/2551- 0000475-7Technology in Excitotoxicity and Neuroprotection 465671/2014-4the Alzheimer's Association 312306/2021-0the Alzheimer's Association 312410/2018-2the Alzheimer's Association 409066/2022-2the Alzheimer's Association 435642/2018- 9the Alzheimer's Association AACSFD-22-928689the FAPERGS 21/2551- 0000673-0the FAPERGS AARGD-21- 850670
6 · The paper itself
Abstract
introductionMost older adults present multimorbidity, but dementia risk factors are typically analyzed individually. Direct methodological comparisons evaluating simultaneous multiple risk factors are essential to provide the realistic effects of multimorbidity. We aimed to compare hypothesis- and data-driven approaches for dementia risk stratification in a real-world cohort.
methodsWe analyzed 9606 participants from the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (2005-2023) using machine learning with interpretability analysis and survival models to simultaneously evaluate 13 risk factors for incident dementia conversion.
resultsA total of 877 participants (9%) developed dementia over (mean ± SD, 6 ± 4.2) years of follow-up. Both approaches consistently identified four key predictors: age, depression, low education, and body mass index (protective). Convergent findings across methodologies demonstrated robust factor identification despite different analytical paradigms. DISCUSSION: In this direct methodological comparison, age, depression, and low education emerge as major dementia risk factors regardless of analytical approach. Convergent interpretability of these approaches support simultaneous multifactorial risk assessment in clinical practice. HIGHLIGHTS: Data- and hypothesis-driven approaches identified convergent key risk factors Age, depression, and low education are major risk factors for dementia Higher body mass index was unexpectedly protective against dementia conversion Multimorbidity requires simultaneous evaluation of multiple risk factors Real-world analysis reveals complex interactions between dementia risks.
Indexed as
DementiaAgedAged, 80 and overAge FactorsBody Mass IndexCohort StudiesDepressionEducational StatusFemaleHumansMachine LearningMaleRisk AssessmentRisk FactorsAlzheimer's diseasecognitive impairmentmachine learningprognosisSHAP
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.
Stratifying dementia risk factors: A prediction model and hypothesis-driven analysis. · full record | OpenQuestion