Evidence map›Paper›PMID 39773296›Full record

ArticleAlzheimer's research & therapy2025

Variables associated with cognitive function: an exposome-wide and mendelian randomization analysis.

Yong-Li Zhao, Yi-Ning Hao, Yi-Jun Ge, Yi Zhang, Lang-Yu Huang, Yan Fu, Dan-Dan Zhang, Ya-Nan Ou, Xi-Peng Cao, Jian-Feng Feng and 3 more

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In one paragraph

Article in Alzheimer's research & therapy, 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. Article
  2. Review
  3. Article
  4. Review
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

13 authors.

Yong-Li Zhao *Department of Neurology, Institute of Neurology, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical College, Huashan Hospital, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.
Yi-Ning Hao *Department of Neurology, Institute of Neurology, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical College, Huashan Hospital, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.
Yi-Jun GeDepartment of Neurology, Institute of Neurology, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical College, Huashan Hospital, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.
Yi ZhangDepartment of Neurology, Institute of Neurology, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical College, Huashan Hospital, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.
Lang-Yu HuangDepartment of Neurology, Qingdao Municipal Hospital, Qingdao University, No. 5 Donghai Middle Road, Qingdao, 266071, China.
Yan FuDepartment of Neurology, Qingdao Municipal Hospital, Qingdao University, No. 5 Donghai Middle Road, Qingdao, 266071, China.
Dan-Dan ZhangDepartment of Neurology, Qingdao Municipal Hospital, Qingdao University, No. 5 Donghai Middle Road, Qingdao, 266071, China.
Ya-Nan OuDepartment of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, 266000, China.
Xi-Peng CaoClinical Research Centre, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), No. 5 Donghai Middle Road, Qingdao, 266071, China.
Jian-Feng FengInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.
Wei ChengDepartment of Neurology, Institute of Neurology, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical College, Huashan Hospital, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China.
Lan TanDepartment of Neurology, Qingdao Municipal Hospital, Qingdao University, No. 5 Donghai Middle Road, Qingdao, 266071, China. dr.tanlan@163.com.
Jin-Tai YuDepartment of Neurology, Institute of Neurology, State Key Laboratory of Medical Neurobiology and MOE Frontier Center for Brain Science, Shanghai Medical College, Huashan Hospital, Fudan University, 12th Wulumuqi Zhong Road, Shanghai, 200040, China. jintai_yu@fudan.edu.cn.

Funding

Shanghai Municipal Science and Technology Major Project No.2018SHZDZX01the National Key R&D Program of China 2018YFC1314702the National Natural Science Foundation of China 82071201the Science and Technology Innovation 2030 Major Projects 2022ZD0211600
6 · The paper itself

Abstract

backgroundEvidence indicates that cognitive function is influenced by potential environmental factors. We aimed to determine the variables influencing cognitive function.

methodsOur study included 164,463 non-demented adults (89,644 [54.51%] female; mean [SD] age, 56.69 [8.14] years) from the UK Biobank who completed four cognitive assessments at baseline. 364 variables were finally extracted for analysis through a rigorous screening process. We performed univariate analyses to identify variables significantly associated with each cognitive function in two equal-sized split discovery and replication datasets. Subsequently, the identified variables in univariate analyses were further assessed in a multivariable model. Additionally, for the variables identified in multivariable model, we explored the associations with longitudinal cognitive decline. Moreover, one- and two- sample Mendelian randomization (MR) analyses were conducted to confirm the genetic associations. Finally, the quality of the pooled evidence for the associations between variables and cognitive function was evaluated.

results252 variables (69%) exhibited significant associations with at least one cognitive function in the discovery dataset. Of these, 231 (92%) were successfully replicated. Subsequently, our multivariable analyses identified 41 variables that were significantly associated with at least one cognitive function, spanning categories such as education, socioeconomic status, lifestyle factors, body measurements, mental health, medical conditions, early life factors, and household characteristics. Among these 41 variables, 12 were associated with more than one cognitive domain, and were further identified in all subgroup analyses. And LASSO, rigde, and principal component analysis indicated the robustness of the primary results. Moreover, among these 41 variables, 12 were significantly associated with a longitudinal cognitive decline. Furthermore, 22 were supported by one-sample MR analysis, and 5 were further confirmed by two-sample MR analysis. Additionally, the quality of the pooled evidence for the associations between 10 variables and cognitive function was rated as high. Based on these 10 identified variables, adopting a more favorable lifestyle was significantly associated with 38% and 34% decreased risks of dementia and Alzheimer's disease (AD).

conclusionOverall, our study constructed an evidence database of variables associated with cognitive function, which could contribute to the prevention of cognitive impairment and dementia.

Indexed as

CognitionCognitive DysfunctionExposomeMendelian Randomization AnalysisAgedFemaleHumansMaleMiddle AgedUnited KingdomAssessmentCognitive functionDementiaEWASMRRisk factors

Identifiers

PMID39773296
PMCPMC11706180

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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.