ArticleScientific reports2021
Associated factors of white matter hyperintensity volume: a machine-learning approach.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06836141 (Silent Brain Infarcts in Spontaneous Intracerebral Hemorrhage as a Prognostic Biomarker for Vascular Contributions to Cognitive Impairment and Dementia), which is not on this map. Cited by 16 papers.
What it found
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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.
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.
Silent Brain Infarcts in Spontaneous Intracerebral Hemorrhage as a Prognostic Biomarker for Vascular Contributions to Cognitive Impairment and Dementia (VCID)
Who cites it
16 citing papers in PubMed, 28 citations in OpenAlex.
- Exploring the link between body physiology and cognition: the role of the brain and aging.npj aging · 2026Article
- Effect of taxaneZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Article
- Association between traffic-related air pollution and brain morphology, assessing the olfactory pathway as a mediator.Brain communications · 2026Article
- Distinct spatiotemporal patterns of white matter hyperintensity progression.Nature communications · 2025Article
- Deep Learning Analysis of White Matter Hyperintensity and its Association with Comprehensive Vascular Factors in Two Large General Populations.Journal of imaging informatics in medicine · 2025Article
- Predicting White Matter Hyperintensity: Leveraging Portable MRI for Accessible Brain Health Screening.AJNR. American journal of neuroradiology · 2025Article
- Association between household size and risk of incident dementia in the UK Biobank study.Scientific reports · 2024Article
- Age-Accelerated Increase of White Matter Hyperintensity Volumes Is Exacerbated by Heavy Alcohol Use in People Living With HIV.Biological psychiatry · 2024Article
- The volume and the distribution of premorbid white matter hyperintensities: Impact on post-stroke aphasia.Human brain mapping · 2024Article
- The spine-brain axis: is spinal anatomy associated with brain volume?Brain communications · 2024Article
- Characterization of MRI White Matter Signal Abnormalities in the Pediatric Population.Children (Basel, Switzerland) · 2023Article
- Association between central obesity and incident diabetes mellitus among Japanese: a retrospective cohort study using propensity score matching.Scientific reports · 2022Article
- White matter hyperintensity volume in pre-diabetes, diabetes and normoglycemia.BMJ open diabetes research & care · 2021Article
- The Impact of Common Epidemiological Factors on Gray and White Matter Volumes in Magnetic Resonance Imaging-Is Prevention of Brain Degeneration Possible?Frontiers in neurology · 2021Article
- Leukoaraiosis and Gray Matter Volume Alteration in Older Adults: The PROOF Study.Frontiers in neuroscience · 2021Article
- Risk factors and cognitive correlates of white matter hyperintensities in ethnically diverse populations without dementia: The COSMIC consortium.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
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Authors and funding
19 authors at 11 institutions in 3 countries.
Funding
Abstract
To identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we used a data-driven machine-learning approach. We analysed two independent cohorts (KORA and SHIP). WMH volumes were derived from cMRI-images (FLAIR). 90 (KORA) and 34 (SHIP) potential determinants of WMH including measures of diabetes, blood-pressure, medication-intake, sociodemographics, life-style factors, somatic/depressive-symptoms and sleep were collected. Elastic net regression was used to identify relevant predictor covariates associated with WMH volume. The ten most frequently selected variables in KORA were subsequently examined for robustness in SHIP. The final KORA sample consisted of 370 participants (58% male; age 55.7 ± 9.1 years), the SHIP sample comprised 854 participants (38% male; age 53.9 ± 9.3 years). The most often selected and highly replicable parameters associated with WMH volume were in descending order age, hypertension, components of the social environment (i.e. widowed, living alone) and prediabetes. A systematic machine-learning based analysis of two independent, population-based cohorts showed, that besides age and hypertension, prediabetes and components of the social environment might play important roles in the development of WMH. Our results enable personal risk assessment for the development of WMH and inform prevention strategies tailored to the individual patient.
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Registered trials
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.