Evidence map›Paper›PMID 41122496›Full record

ArticleJournal of the American Statistical Association2025

The Effect of Alcohol Intake on Brain White Matter Microstructural Integrity: A New Causal Inference Framework for Incomplete Phenomic Data.

Chixiang Chen, Shuo Chen, Zhenyao Ye, Xu Shi, Tianzhou Ma, Michelle Shardell

Abstract read
In one paragraph

Article in Journal of the American Statistical Association, 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. Article
  3. Article
  4. 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

6 authors.

Chixiang ChenDivision of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD.
Shuo ChenDivision of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD.
Zhenyao YeDivision of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD.
Xu ShiDepartment of Biostatistics, University of Michigan, Ann Arbor, MI.ORCID 0000-0001-8566-9552
Tianzhou MaDepartment of Epidemiology and Biostatistics, University of Maryland, College Park, MD.
Michelle ShardellDivision of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD.ORCID 0000-0002-5283-2082

Funding

University of Maryland Claude D. Pepper Older Americans Independence Center (UM-OAIC)P30AG028747 · NIA · UNIVERSITY OF MARYLAND BALTIMORE · PI Stephen L. Seliger · 2006 to 2026
$28.9M
Statistical methods for vitamin D targets for functional outcomes in older adultsR01AG048069 · NIA · UNIVERSITY OF MARYLAND BALTIMORE · PI SHARDELL, MICHELLE DENISE · 2015 to 2025
$4.3M
A Multivariate Mediation and Deep Learning Framework for Genome-Connectome -Substance Use ResearchDP1DA048968 · NIDA · UNIVERSITY OF MARYLAND BALTIMORE · PI CHEN, SHUO · 2019 to 2023
$2.3M
Methods to Test Biomarkers of Aging as Shared Determinants of Alzheimers Disease and Related Dementias and Physical DisabilityR01AG079854 · NIA · UNIVERSITY OF MARYLAND BALTIMORE · PI SHARDELL, MICHELLE DENISE · 2023 to 2025
$2.3M
Statistical Models and Mechanisms Linking Biomarkers of Aging to Cognitive-Physical Decline and DementiaRF1NS128360 · NINDS · UNIVERSITY OF MARYLAND BALTIMORE · PI SHARDELL, MICHELLE DENISE · 2022 to 2022
$2.2M
Developing a Novel Analytical Toolbox to Tackle Multifaceted Statistical Challenges in Analyzing Post-Fracture Recovery Trajectories in Older Adults with ADRDR01AG089377 · NIA · UNIVERSITY OF MARYLAND BALTIMORE · PI Chixiang Chen · 2024 to 2026
$1.8M
NIA NIH HHS P30 AG028747NIA NIH HHS R01 AG048069NIA NIH HHS R01 AG079854NIA NIH HHS R01 AG089377NIDA NIH HHS DP1 DA048968NINDS NIH HHS RF1 NS128360
6 · The paper itself

Abstract

Although substance use, such as alcohol intake, is known to be associated with cognitive decline during aging, its direct influence on the central nervous system remains incompletely understood. In this study, we investigate the influence of alcohol intake frequency on reduction of brain white matter microstructural integrity in the fornix, a brain region considered a promising marker of age-related microstructural degeneration, using a large UK Biobank (UKB) cohort with extensive phenomic data reflecting a comprehensive lifestyle profile. Two major challenges arise: (a) potentially nonlinear confounding effects from phenomic variables and (b) a limited proportion of participants with complete phenomic data. To address these challenges, we develop a novel ensemble learning framework tailored for robust causal inference and introduce a data integration step to incorporate information from UKB participants with incomplete phenomic data, improving estimation efficiency. Our analysis reveals that daily alcohol intake may significantly reduce fractional anisotropy, a neuroimaging-derived measure of white matter structural integrity, in the fornix and increase systolic and diastolic blood pressure levels. Moreover, extensive numerical studies demonstrate the superiority of our method over competing approaches in terms of estimation bias, while outcome regression-based estimators may be preferred when minimizing mean squared error is prioritized. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Indexed as

Brain agingData integrationEnsemble learningPhenomicsRobustness

Identifiers

PMID41122496
PMCPMC12536561

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.