Evidence map›Paper›PMID 38883759›Full record

ArticlemedRxiv : the preprint server for health sciences2024

Multi-organ imaging-derived polygenic indexes for brain and body health.

Xiaochen Yang, Patrick F Sullivan, Bingxuan Li, Zirui Fan, Dezheng Ding, Juan Shu, Yuxin Guo, Peristera Paschou, Jingxuan Bao, Li Shen and 6 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. 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

16 authors.

Xiaochen YangDepartment of Statistics, Purdue University, West Lafayette, IN 47907, USA.
Patrick F SullivanDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Bingxuan LiUCLA Samueli School of Engineering, Los Angeles, CA 90095, USA.
Zirui FanDepartment of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA.
Dezheng DingDepartment of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
Juan ShuDepartment of Statistics, Purdue University, West Lafayette, IN 47907, USA.
Yuxin GuoDepartment of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA.
Peristera PaschouDepartment of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA.
Jingxuan BaoDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Li ShenDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Marylyn D RitchieDepartment of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA.
Gideon NaveMarketing Department, University of Pennsylvania, Philadelphia, PA 19104, USA.
Michael L PlattMarketing Department, University of Pennsylvania, Philadelphia, PA 19104, USA.
Tengfei LiDepartment of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Hongtu ZhuDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Bingxin ZhaoDepartment of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA.

Funding

Mapping the Causal Genetic-Imaging-Clinical Pathway for Alzheimer's DiseaseRF1AG082938 · NIA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Bingxin Zhao, Hongtu Zhu · 2023 to 2026
$3.6M
NIA NIH HHS RF1 AG082938
6 · The paper itself

Abstract

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.

Indexed as

Abdominal MRIAlzheimer’s diseaseBrain MRICardiovascular magnetic resonanceDisease risk assessmentGenetic predictionOCT imaging

Identifiers

PMID38883759
PMCPMC11177904

What OpenQuestion holds

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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.