Evidence map›Paper›PMID 38807092›Full record

ArticleBMC medicine2024

Specification curve analysis to identify heterogeneity in risk factors for dementia: findings from the UK Biobank.

Renhao Luo, Dena Zeraatkar, Maria Glymour, Randall J Ellis, Hossein Estiri, Chirag J Patel

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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 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

6 authors.

Renhao LuoDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Dena ZeraatkarDepartment of Anesthesia and Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Canada.
Maria GlymourDepartment of Epidemiology, School of Public Health, Boston University, Boston, MA, USA.
Randall J EllisDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Hossein EstiriDepartment of Medicine, Harvard Medical School, Boston, MA, USA.
Chirag J PatelDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. chirag_patel@hms.harvard.edu.

Funding

The confluence of extreme heat cold on the health and longevity of an Aging Population with Alzheimers and related DementiaRF1AG074372 · NIA · HARVARD MEDICAL SCHOOL · PI DOMINICI, FRANCESCA, ESTIRI, HOSSEIN · 2022 to 2023
$2.6M
NIA NIH HHS RF1 AG074372
6 · The paper itself

Abstract

backgroundIn 2020, the Lancet Commission identified 12 risk factors as priorities for prevention of dementia, and other studies identified APOE e4/e4 genotype and family history of Alzheimer's disease strongly associated with dementia outcomes; however, it is unclear how robust these relationships are across dementia subtypes and analytic scenarios. Specification curve analysis (SCA) is a new tool to probe how plausible analytical scenarios influence outcomes.

methodsWe evaluated the heterogeneity of odds ratios for 12 risk factors reported from the Lancet 2020 report and two additional strong associated non-modifiable factors (APOE e4/e4 genotype and family history of Alzheimer's disease) with dementia outcomes across 450,707 UK Biobank participants using SCA with 5357 specifications across dementia subtypes (outcomes) and analytic models (e.g., standard demographic covariates such as age or sex and/or 14 correlated risk factors).

resultsSCA revealed variable dementia risks by subtype and age, with associations for TBI and APOE e4/e4 robust to model specification; in contrast, diabetes showed fluctuating links with dementia subtypes. We found that unattributed dementia participants had similar risk factor profiles to participants with defined subtypes.

conclusionsWe observed heterogeneity in the risk of dementia, and estimates of risk were influenced by the inclusion of a combination of other modifiable risk factors; non-modifiable demographic factors had a minimal role in analytic heterogeneity. Future studies should report multiple plausible analytic scenarios to test the robustness of their association. Considering these combinations of risk factors could be advantageous for the clinical development and evaluation of novel screening models for different types of dementia.

Indexed as

Biological Specimen BanksDementiaAgedAged, 80 and overFemaleHumansMaleMiddle AgedRisk FactorsUK BiobankUnited KingdomAll-cause dementiaAlzheimer’s diseaseDementiaFrontotemporal dementiaRisk factorsSpecification curve analysisVascular dementia

Identifiers

PMID38807092
PMCPMC11134914

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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.