Evidence map›Paper›PMID 37840484›Full record

ArticleJournal of Alzheimer's disease : JAD2023

The Construction of a Multidomain Risk Model of Alzheimer's Disease and Related Dementias.

Igor Akushevich, Arseniy Yashkin, Svetlana Ukraintseva, Anatoliy I Yashin, Julia Kravchenko

Open access · bronzeAbstract read
In one paragraph

Article in Journal of Alzheimer's disease : JAD, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.2field-weighted citation impact, top 12% of its field
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

8 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Lower Hippocampal Volume Partly Mediates the Association Between rs6859 in themedRxiv : the preprint server for health sciences · 2025
    Article
  4. Article
  5. Article
  6. Article
  7. Journal of Alzheimer's disease reports · 2024
    Article
  8. Article
4 · The record

Corrections and comments

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

5 authors at 2 institutions in 1 country.

Igor AkushevichBiodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, USA.
Arseniy YashkinBiodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, USA.
Svetlana UkraintsevaBiodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, USA.
Anatoliy I YashinBiodemography of Aging Research Unit, Social Science Research Institute, Duke University, Durham, NC, USA.
Julia KravchenkoDepartment of Surgery, Duke University Medical Center, Durham, NC, USA.
Social Science Research Council · USDuke Medical Center · US

Funding

HRS Yrs29-34: Y33 SSA CoFundingU01AG009740 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jessica Faul, KENNETH M LANGA · 1990 to 2026
$555.8M
Racial and Geographic Disparities in Risk and Survival of Alzheimer's Disease and Related DementiasR01AG066133 · NIA · DUKE UNIVERSITY · PI AKUSHEVICH, IGOR · 2019 to 2023
$3.7M
Understanding Alzheimer's Disease in the Context of the AgingR01AG062623 · NIA · DUKE UNIVERSITY · PI OUKRAINTSEVA, SVETLANA V. · 2019 to 2023
$3.6M
YASHIN Admin Supp: Time Trends in Alzheimer's Disease: Addressing the Changing Role of Risk Factors and Instrument ValidityR01AG046860 · NIA · DUKE UNIVERSITY · PI YASHIN, ANATOLIY I · 2014 to 2018
$3.1M
Leveraging population-based human data to uncover mechanisms connecting Alzheimer's disease and common infections and facilitate vaccines repurposing for AD preventionR01AG076019 · NIA · DUKE UNIVERSITY · PI OUKRAINTSEVA, SVETLANA V. · 2021 to 2025
$3.1M
Determinants of geographic disparities in mortality and multimorbidity in the U.S.R01AG057801 · NIA · DUKE UNIVERSITY · PI AKUSHEVICH, IGOR, KRAUCHANKA, YULIYA · 2019 to 2023
$2.1M
NIA NIH HHS R01 AG046860NIA NIH HHS R01 AG057801NIA NIH HHS R01 AG062623NIA NIH HHS R01 AG066133NIA NIH HHS R01 AG076019NIA NIH HHS U01 AG009740
6 · The paper itself

Abstract

backgroundAlzheimer's disease (AD) and related dementia (ADRD) risk is affected by multiple dependent risk factors; however, there is no consensus about their relative impact in the development of these disorders.

objectiveTo rank the effects of potentially dependent risk factors and identify an optimal parsimonious set of measures for predicting AD/ADRD risk from a larger pool of potentially correlated predictors.

methodsWe used diagnosis record, survey, and genetic data from the Health and Retirement Study to assess the relative predictive strength of AD/ADRD risk factors spanning several domains: comorbidities, demographics/socioeconomics, health-related behavior, genetics, and environmental exposure. A modified stepwise-AIC-best-subset blanket algorithm was then used to select an optimal set of predictors.

resultsThe final predictive model was reduced to 10 features for AD and 19 for ADRD; concordance statistics were about 0.85 for one-year and 0.70 for ten-year follow-up. Depression, arterial hypertension, traumatic brain injury, cerebrovascular diseases, and the APOE4 proxy SNP rs769449 had the strongest individual associations with AD/ADRD risk. AD/ADRD risk-related co-morbidities provide predictive power on par with key genetic vulnerabilities.

conclusionResults confirm the consensus that circulatory diseases are the main comorbidities associated with AD/ADRD risk and show that clinical diagnosis records outperform comparable self-reported measures in predicting AD/ADRD risk. Model construction algorithms combined with modern data allows researchers to conserve power (especially in the study of disparities where disadvantaged groups are often grossly underrepresented) while accounting for a high proportion of AD/ADRD-risk-related population heterogeneity stemming from multiple domains.

Indexed as

Alzheimer DiseaseDementiaHypertensionComorbidityHumansMedicareUnited StatesAlzheimer’s diseaseenvironmental exposuregenetic factorsMedicarepolygenic risk scorespredictive modelsrisk factors

Identifiers

PMID37840484
PMCPMC10657690
OpenAlexW4387503962

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.