Evidence map›Paper›PMID 40099367›Full record

ArticleJournal of the American Geriatrics Society2025

Integrating Social Determinants of Health and Established Risk Factors to Predict Cardiovascular Disease Risk Among Healthy Older Adults.

Achamyeleh Birhanu Teshale, Htet Lin Htun, Mor Vered, Alice J Owen, Joanne Ryan, Kevan R Polkinghorne, Monique F Kilkenny, Andrew Tonkin, Rosanne Freak-Poli

Abstract read
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In one paragraph

Article in Journal of the American Geriatrics Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. 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

9 authors.

Achamyeleh Birhanu TeshaleSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Htet Lin HtunSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.ORCID 0000-0003-2307-0610
Mor VeredDepartment of Data Science and AI, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia.
Alice J OwenSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.ORCID 0000-0002-4807-6069
Joanne RyanSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Kevan R PolkinghorneSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Monique F KilkennyStroke and Ageing Research, Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
Andrew TonkinSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Rosanne Freak-PoliSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.

Funding

ASPirin in Reducing Events in the ElderlyU01AG029824 · NIA · HENNEPIN HEALTHCARE RESEARCH INSTITUTE · PI MCNEIL, JOHN JAMES, MURRAY, ANNE M · 2009 to 2018
$64.6M
Main Administrative CoreU19AG062682 · NIA · HENNEPIN HEALTHCARE RESEARCH INSTITUTE · PI CHAN, ANDREW T, MCNEIL, JOHN JAMES · 2019 to 2023
$42.9M
ANZ TrusteesMason FoundationMonash International Tuition Scholarship and Monash Graduate ScholarshipMonash UniversityNational Health and Medical Research Council 1127060National Health and Medical Research Council 334047National Heart Foundation of Australia 105737NCI NIH HHS U19AG062682NHMRC Investigator Grant Leadership Level 1 2016438NIA NIH HHS U01 AG029824NIA NIH HHS U01AG029824NIA NIH HHS U19 AG062682Victorian Cancer AgencyWicking Trust
6 · The paper itself

Abstract

backgroundRecent evidence underscores the significant impact of social determinants of health (SDoH) on cardiovascular disease (CVD). However, available CVD risk assessment tools often neglect SDoH. This study aimed to integrate SDoH with traditional risk factors to predict CVD risk.

methodsThe data was sourced from the ASPirin in Reducing Events in the Elderly (ASPREE) longitudinal study, and its sub-study, the ASPREE Longitudinal Study of Older Persons (ALSOP). The study included 12,896 people (5884 men and 7012 women) aged 70 or older who were initially free of CVD, dementia, and independence-limiting physical disability. The participants were followed for a median of eight years. CVD risk was predicted using state-of-the-art machine learning (ML) and deep learning (DL) models: Random Survival Forest (RSF), Deepsurv, and Neural Multi-Task Logistic Regression (NMTLR), incorporating both SDoH and traditional CVD risk factors as candidate predictors. The permutation-based feature importance method was further utilized to assess the predictive potential of the candidate predictors.

resultsAmong men, the RSF model achieved relatively good performance (C-index = 0.732, integrated brier score (IBS) = 0.071, 5-year and 10-year AUC = 0.657 and 0.676 respectively). For women, DeepSurv was the best-performing model (C-index = 0.670, IBS = 0.042, 5-year and 10-year AUC = 0.676 and 0.677 respectively). Regarding the contribution of the candidate predictors, for men, age, urine albumin-to-creatinine ratio, and smoking, along with SDoH variables, were identified as the most significant predictors of CVD. For women, SDoH variables, such as social network, living arrangement, and education, predicted CVD risk better than the traditional risk factors, with age being the exception.

conclusionSDoH can improve the accuracy of CVD risk prediction and emerge among the main predictors for CVD. The influence of SDoH was greater for women than for men, reflecting gender-specific impacts of SDoH.

Indexed as

Cardiovascular DiseasesSocial Determinants of HealthAgedAged, 80 and overFemaleHeart Disease Risk FactorsHumansLongitudinal StudiesMachine LearningMaleRisk AssessmentRisk Factorsartificial intelligencecardiovascular diseasedeep learningmachine learningsocial determinants of health

Identifiers

PMID40099367
PMCPMC12205292

What OpenQuestion 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 OpenQuestion graph.