Evidence map›Paper›PMID 39482675›Full record

ArticleBMC medicine2024

A single risk assessment for the most common diseases of ageing, developed and validated on 10 cohort studies.

Md Hamidul Huque, Scherazad Kootar, Kim M Kiely, Craig S Anderson, Martin van Boxtel, Henry Brodaty, Perminder S Sachdev, Michelle Carlson, Annette L Fitzpatrick, Rachel A Whitmer and 9 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 pooled it
–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

7 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
  6. Review
  7. 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

19 authors.

Md Hamidul HuqueSchool of Psychology, University of New South Wales, Kensington, NSW, Australia.
Scherazad KootarThe Sydney Children's Hospital Network, Sydney, Australia.
Kim M KielySchool of Mathematics and Applied Statistics, and, School of Health and Society , University of Wollongong, Wollongong, NSW, Australia.
Craig S AndersonFaculty of Medicine, The George Institute for Global Health, University of New South Wales, Sydney, NSW, Australia.
Martin van BoxtelDepartment of Psychiatry and Neuropsychology, Maastricht University, Maastricht, The Netherlands.
Henry BrodatyCentre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia.
Perminder S SachdevCentre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia.
Michelle CarlsonJohns Hopkins Center On Aging and Health, Johns Hopkins University, Baltimore, USA.
Annette L FitzpatrickDepartments of Family Medicine and Epidemiology, University of Washington, Seattle, WA, USA.
Rachel A WhitmerDepartment of Public Health Sciences, University of California, Davis, CA, USA.
Miia KivipeltoDivision of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
Louisa JormCentre for Big Data Research in Health, School of Medicine and Health, University of New South Wales, Sydney, NSW, Australia.
Sebastian KöhlerDepartment of Psychiatry and Neuropsychology, Maastricht University, Maastricht, The Netherlands.
Nicola T LautenschlagerDepartment of Psychiatry, University of Melbourne, Melbourne, VIC, Australia.
Oscar L LopezDepartments of Neurology and Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
Jonathan E ShawDepartment of Clinical Diabetes and Epidemiology, Baker Heart and Diabetes Institute, Melbourne, Australia.
Fiona E MatthewsPopulation Health Sciences Institute, Newcastle University, Newcastle, UK.
Ruth PetersUniversity of New South Wales Ageing Futures Institute, University of NSW, Kensington, NSW, Australia.
Kaarin J AnsteySchool of Psychology, University of New South Wales, Kensington, NSW, Australia. k.anstey@unsw.edu.au.

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
UC Davis Alzheimer's Disease Research CenterP30AG072972 · NIA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI Rachel A Whitmer · 2021 to 2026
$25.2M
Exceptional Survival: Trajectories to Functional Aging (CHS All Stars)R01AG023629 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI NEWMAN, ANNE B. · 2004 to 2016
$9.5M
CARDIOVASCULAR HEALTH STUDY (CHS) - TASK AREA C, STUDY CLOSEOUT75N92021D00006 · NHLBI · UNIVERSITY OF WASHINGTON · PI PSATY, BRUCE · 2021 to 2024
$8.4M
CHS research resources for the cardiovascular health of older adultsU01HL130114 · NHLBI · UNIVERSITY OF WASHINGTON · PI BURKE, GREGORY L, KRONMAL, RICHARD A · 2016 to 2019
$6.6M
CHS Events Follow-up StudyU01HL080295 · NHLBI · UNIVERSITY OF WASHINGTON · PI PSATY, BRUCE M · 2005 to 2008
$4.6M
CORONARY HEART DISEASE &STROKE IN PEOPLE AGED 65-84N01HC085083 · HC · UNIVERSITY OF CALIFORNIA DAVIS · PI ROBBINS, JOHN A · 1988 to 1999
–
CENTRAL BLOOD ANALYSIS LABORATORY FOR CHSN01HC085086 · HC · UNIVERSITY OF VERMONT &ST AGRIC COLLEGE · PI TRACY, RUSSELL P · 1988 to 2000
–
CORONARY HEART DISEASE &STROKE IN PEOPLE AGED 65-84N01HC085081 · HC · JOHNS HOPKINS UNIVERSITY · PI FRIED, LINDA P · 1988 to 2000
–
CORONARY HEART DISEASE &STROKE IN THE ELDERLYN01HC085080 · HC · WAKE FOREST UNIVERSITY · PI BURKE, GREGORY L · 1988 to 2000
–
CORONARY HEART DISEASE &STROKE IN PEOPLE AGED 65 TO 84N01HC085079 · HC · UNIVERSITY OF WASHINGTON · 1988 to 2005
–
CORONARY HEART DISEASE AND STROKE IN PEOPLE AGED 65-84N01HC085082 · HC · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI KULLER, LEWIS H · 1988 to 2000
–
NHLBI NIH HHS 75N92021D00006NHLBI NIH HHS HHSN268200800007CNHLBI NIH HHS HHSN268201200036CNHLBI NIH HHS HHSN268201800001CNHLBI NIH HHS N01 HC055222NHLBI NIH HHS N01 HC085079NHLBI NIH HHS N01 HC085080NHLBI NIH HHS N01 HC085081NHLBI NIH HHS N01 HC085082NHLBI NIH HHS N01 HC085083NHLBI NIH HHS N01 HC085086NHLBI NIH HHS U01 HL080295NHLBI NIH HHS U01 HL130114NIA NIH HHS P30 AG072972NIA NIH HHS R01 AG023629NIA NIH HHS U01 AG009740
6 · The paper itself

Abstract

backgroundWe aimed to develop risk tools for dementia, stroke, myocardial infarction (MI), and diabetes, for adults aged ≥ 65 years using shared risk factors.

methodsData were obtained from 10 population-based cohorts (N = 41,755) with median follow-up time (years) for dementia, stroke, MI, and diabetes of 6.2, 7.0, 6.8, and 7.4, respectively. Disease-free participants at baseline were included, and 22 risk factors (sociodemographic, medical, lifestyle, laboratory biomarkers) were evaluated. Two risk tools (DemNCD and DemNCD-LR based on Fine and Gray sub-distribution and logistic regression [LR], respectively) were developed and validated. Predictive accuracies of these risk tools were assessed using Harrel's C-statistics and area under the curve (AUC) and 95% confidence interval (CI). Model calibration was conducted using Hosmer-Lemeshow goodness of fit test along calibration plots.

resultsBoth the DemNCD and DemNCD-LR resulted in similar predictive accuracy for each outcome. The overall AUC (95% CI) for dementia, stroke, MI, and diabetes risk tool were 0·68 (0·65, 0·70), 0·58 (0·54, 0·61), 0·65 (0·61, 0·68), and 0·68 (0·64, 0·72), respectively, for males. For females, these figures were 0·65 (0·63, 0·67), 0·55 (0·52, 0·57), 0·65 (0·62, 0·68), and 0·61 (0·57, 0·65).

conclusionsThe DemNCD is the first tool to predict both dementia and multiple cardio-metabolic diseases using comprehensive risk factors and provided similar predictive accuracy to existing risk tools. It has similar predictive accuracy as tools designed for single outcomes in this age-group. DemNCD has the potential to be used in community and clinical settings as it includes self-reported and routinely available clinical measures.

Indexed as

DementiaDiabetes MellitusAgedAged, 80 and overAgingCohort StudiesFemaleHumansMaleMyocardial InfarctionRisk AssessmentRisk FactorsStrokeDementiaDiabetesHeart attackPrimary preventionRisk factorsRisk predictionRisk toolStroke

Identifiers

PMID39482675
PMCPMC11526665

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.