Evidence map›Paper›PMID 40675984›Full record

Articlenpj aging2025

Modifiable traits and genetic associations with grey matter volume in mid-to-late adulthood: a population-based study in the UK biobank.

Guoqing Pan, Yi Zhang, Ju-Jiao Kang, Yuchao Jiang, Wei Zhang, Peng Ren, Jia You, Weikang Gong, Jin-Tai Yu, Jian-Feng Feng and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in npj aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Guoqing PanInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Yi ZhangDepartment of Neurology and National Center for Neurological Disorders, Fudan University, Shanghai, China.
Ju-Jiao KangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Yuchao JiangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Wei ZhangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Peng RenInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Jia YouInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Weikang GongSchool of Data Science, Fudan University, Shanghai, China.
Jin-Tai YuDepartment of Neurology and National Center for Neurological Disorders, Fudan University, Shanghai, China.
Jian-Feng FengInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
Xuejuan ZhangSchool of Mathematical Sciences, Zhejiang Normal University, Jinhua, Zhejiang Province, China.
Wei ChengInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China. wcheng.fdu@gmail.com.
Linbo WangInstitute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China. linbowang@fudan.edu.cn.

Funding

Higher Education Discipline Innovation Project B18015Key Technologies Research and Development Program 2023YFC3605400National Natural Science Foundation of China 82071997National Natural Science Foundation of China 82302312Natural Science Foundation of Shanghai 23ZR1406000Shanghai Rising-Star Program 21QA1408700Shanghai Science and Technology Commission Program 23JS1410100
6 · The paper itself

Abstract

Given the growing global elderly population and the accelerating decrease in grey matter volume (GMV) with age, understanding healthy brain aging is increasingly important. This study investigates whether variations in modifiable traits can account for differences in GMV and whether these traits can inform strategies to mitigate risks of future brain disorders. We identified 66 traits significantly associated with total GMV. Further, we examined the joint contributions of different domain traits to the GMV variance, finding that blood biomarkers and physical measurements accounted for the largest proportion of GMV variance. Some traits mediated the relationship between the genetic risk for brain disorders and GMV. Moreover, the identified traits divided the population into two subgroups, with significant differences in GMV and incidences of brain disorders. Our findings underscore the importance of modifiable traits in supporting healthy brain aging and reducing the risk of brain disorders, suggesting potential targets for intervention.

Identifiers

PMID40675984
PMCPMC12271401

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.