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Related Experiment Video

Updated: Jun 23, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Machine learning identifies routine blood tests as accurate predictive measures of pollution-dependent poor cognitive

Hamish Johnson1, James Longden2, Gary Cameron3

  • 1School of Geosciences, University of Aberdeen, UK.

Biorxiv : the Preprint Server for Biology
|January 27, 2025
PubMed
Summary

Routine blood tests and environmental monitoring can predict cognitive decline. Low hemoglobin and high pollution exposure, like lead and particulate matter, are key indicators, enabling early interventions for brain health.

Keywords:
Atmospheric PollutionBiomarkersBrain ImagingCognitionDementiaMachine LearningRandom ForestRisk FactorsSocial Deprivation

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Area of Science:

  • Neuroscience
  • Environmental Health
  • Public Health

Background:

  • Identified modifiable risk factors for dementia (education, SES, environment) but individual risk remains unclear.
  • Assessed over 450 potential risk factors in a phenotyped cohort to find predictive markers of poor cognitive function.
  • Aimed to understand early dementia risk factors by analyzing imaging, blood, pollutant, and socio-economic data.

Purpose of the Study:

  • To identify predictors of poor cognitive performance in a cohort without neurological disease.
  • To link population-level risks to individual cognitive health.
  • To explore the relationship between environmental exposures, blood markers, and cognitive function.

Main Methods:

  • Utilized random forest modeling on a cohort of 324 individuals (mean age 61.6 years).
  • Assessed 457 features including brain imaging, blood markers (anemia, heavy metals), social deprivation, and atmospheric pollution.
  • Analyzed predictors of poor general cognition.

Main Results:

  • Routinely assessed anemia markers, specifically mean corpuscular hemoglobin concentration (MCHC), predicted poor general cognition at both low and high extremes.
  • Environmental measures of atmospheric pollution (lead, carbon monoxide, particulate matter) were the strongest predictors of poor cognitive performance.
  • A negative relationship was found between low MCHC and high atmospheric pollutant levels, suggesting pollution-dependent cognitive effects.

Conclusions:

  • Routine, inexpensive medical tests and local initiatives can identify and protect individuals at risk of cognitive decline.
  • Findings suggest a potential for using blood tests to predict pollution-related cognitive functioning at an individual level.
  • Highlights opportunities for targeted, cost-effective interventions to improve population cognitive health.