Evidence map›Paper›PMID 40135150›Full record

ArticleAlzheimer's & dementia (Amsterdam, Netherlands)

Machine learning to discover factors predicting volume of white matter hyperintensities: Insights from the UK Biobank.

Yigizie Yeshaw, Iqbal Madakkatel, Anwar Mulugeta, Amanda Lumsden, Elina Hypponen

Abstract read
In one paragraph

Article in Alzheimer's & dementia (Amsterdam, Netherlands). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Yigizie YeshawAustralian Centre for Precision Health University of South Australia Adelaide South Australia Australia.ORCID https://orcid.org/0000-0003-4587-7925
Iqbal MadakkatelAustralian Centre for Precision Health University of South Australia Adelaide South Australia Australia.ORCID https://orcid.org/0000-0003-2339-5917
Anwar MulugetaAustralian Centre for Precision Health University of South Australia Adelaide South Australia Australia.ORCID https://orcid.org/0000-0002-8018-3454
Amanda LumsdenAustralian Centre for Precision Health University of South Australia Adelaide South Australia Australia.ORCID https://orcid.org/0000-0002-0214-6498
Elina HypponenAustralian Centre for Precision Health University of South Australia Adelaide South Australia Australia.ORCID https://orcid.org/0000-0003-3670-9399

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionBrain white matter hyperintensities (WMHs) reflect the risks of stroke, dementia, and overall mortality.

methodsWe used a hypothesis-free gradient boosting decision tree (GBDT) approach and conventional statistical methods to discover risk factors associated with volume of WMHs. The GBDT models considered data on 2891 input features, collected ∼10 years prior to volume of WMH measurements from 44,053 participants. Top 3% of features, ranked by Shapley values, were taken forward to epidemiological analyses using linear regression.

resultsAdiposity, lung function, and indicators of metabolic health (eg, glycated hemoglobin, hypertension, alkaline phosphatase, microalbumin, and urate) contribute to WMH prediction. Of lifestyle factors, smoking had the strongest association. Time spent outdoors, creatinine, and several red blood cell indices were among the identified less-known predictors of WMHs.

conclusionsObesity, high blood pressure, lung function, metabolic abnormalities, and lifestyle are key contributors to WMHs, providing opportunities to prevent or reduce their development. Highlights: Obesity and related metabolic abnormalities were linked with WMHs.Associations with time spent outdoors, creatinine, some red blood cell indices and height were among the less-known risk factors identified.Action on blood pressure, metabolic abnormalities, and adequate oxygenation may help to prevent WMHs.Biomarker links may suggest simple blood tests could aid in early dementia prediction.

Indexed as

machine learningrisk factorsUK Biobankwhite matter hyperintensities

Identifiers

PMID40135150
PMCPMC11934216

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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.