Evidence mapPaperPMID 40401627Full record

ArticleJournal of the American Heart Association2025

Development and Validation of Models to Estimate the Incident Risk of Cognitive Impairment and Atherosclerotic Cardiovascular Disease in Older Adults.

Michael G Nanna, Daniel Wojdyla, Eric D Peterson, Ann Marie Navar, Jeff D Williamson, Lisandro D Colantonio, Stephen Y Wang, Yasser Jamil, Alain G Bertoni, Musarrat Nahid and 5 more

Abstract readValidation Study
In one paragraph

Article in Journal of the American Heart Association, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Cardiovascular Risk Prediction in Older Adults.Current atherosclerosis reports · 2025
    Review
  3. 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

15 authors.

Michael G NannaSection of Cardiovascular Medicine, Yale School of Medicine New Haven CT USA.ORCID 0000-0001-5745-1636
Daniel WojdylaDuke Clinical Research Institute Durham NC USA.
Eric D PetersonDivision of Cardiology UT Southwestern Medical Center Dallas TX USA.ORCID 0000-0002-5415-4721
Ann Marie NavarDivision of Cardiology UT Southwestern Medical Center Dallas TX USA.ORCID 0000-0002-6197-9860
Jeff D WilliamsonSection on Gerontology and Geriatric Medicine and the Sticht Center for Healthy Aging and Alzheimer's Prevention at Wake Forest School of Medicine Winston-Salem NC USA.
Lisandro D ColantonioDepartment of Epidemiology, School of Public Health University of Alabama at Birmingham Birmingham AL USA.ORCID 0000-0001-8742-1788
Stephen Y WangDivision of Cardiology The CardioVascular Center, Tufts Medical Center Boston MA USA.ORCID 0000-0003-3547-7688
Yasser JamilSection of Cardiovascular Medicine Department of Internal Medicine, Inova Schar Heart and Vascular Institute Falls Church VA USA.
Alain G BertoniSection of Epidemiology and Prevention, Wake Forest School of Medicine Winston-Salem NC USA.ORCID 0000-0002-7503-6273
Musarrat NahidDivision of General Internal Medicine, Weill Cornell Medicine New York NY USA.
Abdulla A DamlujiInova Center of Outcomes Research Falls Church VA USA.ORCID 0000-0002-8774-6416
Parag GoyalProgram for the Care and Study of the Aging Heart, Department of Medicine, Weill Cornell Medicine New York NY USA.ORCID 0000-0001-7474-3737
Sarwat I ChaudhryDepartment of Internal Medicine, Yale School of Medicine New Haven CT USA.ORCID 0000-0002-5614-8157
Thomas M GillDivision of Geriatrics Yale School of Medicine New Haven CT USA.ORCID 0000-0002-6450-0368
Karen P AlexanderDuke Clinical Research Institute Durham NC USA.ORCID 0000-0003-4418-1424

Funding

TREATMENT GOALS FOR PERSONS WITH MULTIFACTORIAL GERIATRIC HEALTH CONDITIONSP30AG021342 · YALE UNIVERSITY · 2002 to 2025
$8.0M
Physical Rehabilitation for Older Patients with Acute HFpEF-The REHAB-HFpEF TrialR01AG078153 · WAKE FOREST UNIVERSITY HEALTH SCIENCES · 2025 to 2025
$6.8M
Technological Assessment and Solutions Core - RC4P30AG021334 · JOHNS HOPKINS UNIVERSITY · 2003 to 2025
$6.1M
Exceptional aging: 12 year trajectories to functionR01AG023629 · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2004 to 2005
$2.6M
CHS Events Follow-up StudyU01HL080295 · UNIVERSITY OF WASHINGTON · 2005 to 2005
$1.1M
Effectiveness of Strategies to Improve Outcomes after Hospitalization for Acute Myocardial Infarction in Older AdultsR01HL160822 · NHLBI · YALE UNIVERSITY · PI Sarwat I Chaudhry · 2022 to 2022
$785k
Estimating the treatment effect of cardiovascular medications to modify the risk for future cognitive decline in older adultsK76AG088428 · YALE UNIVERSITY · 2025 to 2025
$243k
CORONARY HEART DISEASE &STROKE IN PEOPLE AGED 65 TO 84N01HC085079 · UNIVERSITY OF WASHINGTON · 1988 to 2005
NHLBI NIH HHS HHSN268200800007CNHLBI NIH HHS HHSN268201200036CNHLBI NIH HHS K23 HL153771NHLBI NIH HHS N01 HC085079NHLBI NIH HHS R01 HL160822NHLBI NIH HHS U01 HL080295NIA NIH HHS K76 AG088428NIA NIH HHS P30 AG021334NIA NIH HHS P30 AG021342NIA NIH HHS R01 AG023629NIA NIH HHS R01 AG078153NIA NIH HHS R03 AG074067
6 · The paper itself

Abstract

backgroundGuidelines emphasize using atherosclerotic cardiovascular disease (ASCVD) risk prediction models for treatment decisions, but risk of cognitive impairment is an equally important concern in older adults. Current ASCVD risk prediction models were derived in younger adults and do not include holistic measures of health or predict cognitive impairment.

methodsWe utilized data from the Framingham, Framingham Offspring, CHS (Cardiovascular Health Study), and ARIC (Atherosclerosis Risk in Communities) cohorts to derive and validate 2 Selective Functional Prediction models to estimate an older person's (aged ≥75 years) risk within 5 years of developing incident: (1) cognitive impairment; and (2) ASCVD, while accounting for the competing risk of death. Variable selection, including functional status, was based on the least absolute shrinkage and selection operator method. The cognitive impairment (N=3466) and ASCVD (N=4403) model populations were split into derivation and validation cohorts with external validation, then performed in MESA (Multi-Ethnic Study of Atherosclerosis).

resultsIn the derivation and external validation cohorts (median age, 79 years), 579 (16.7%) and 67 (15.3%) participants developed incident cognitive impairment, respectively; 748 (17.0%) and 80 (8.4%), respectively, experienced an ASCVD event. The cognitive impairment model (baseline Mini-Mental State Examination (MMSE), atrial fibrillation, antidepressant use, mobility impairment, and dependence for grocery shopping) had good discrimination in the internal and external validation cohorts (C index 0.75 and 0.73, respectively). The ASCVD model (employment status, MMSE, aspirin, lipid-lowering medications, blood pressure medications, systolic blood pressure, general health status, high-density lipoprotein cholesterol, triglycerides, creatinine, and mobility impairment) had satisfactory discrimination (C index 0.67) on internal validation and outperformed the pooled cohort equations, but had modest discrimination (C index 0.59) on external validation. Although both models were well calibrated in the internal validation cohorts, they overpredicted risk in the external validation cohort.

conclusionsAccurate prediction of an older person's risk of developing cognitive impairment is possible, but predicting future ASCVD events remains more challenging.

Indexed as

AtherosclerosisCognitionCognitive DysfunctionAgedAged, 80 and overAge FactorsFemaleHumansIncidenceMaleReproducibility of ResultsRisk AssessmentRisk FactorsUnited Statescardiovascular diseasecognitive impairmentgeriatricolder adultspreventionrisk prediction

Identifiers

PMID40401627
PMCPMC12229195

What Socratic holds

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Registered trials

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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.