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<p>Several modifiable risk factors for dementias have been identified including socio-economic status and environmental exposures – however, how these population-level risks relate to individual risk remains elusive. To address this, we use random forest modelling to determine significant predictors of poor cognitive performance in on deeply phenotyped cohort of 324 individuals (age 61.6 ± 4.8 years; 150 males, 174 females) without extant neurological disease. 457 features were assessed including a comprehensive battery of imaging, blood, atmospheric pollutant and socio-economic measures. This approach where brain imaging, blood, socio-economic and environmental exposure measures were used to model cognitive performance was able to classify general cognition tertiles with 0.70 overall accuracy. Routinely assessed markers of anaemia including mean corpuscular haemoglobin were identified as predictors of poor general cognition and both extremes (low and high) of mean corpuscular haemoglobin concentration were associated with poor immediate recall (p = 0.0253). The predictors of poor cognition which consistently improved model performance across all models were measures of atmospheric pollution, in particular, lead, carbon monoxide, and particulate matter. Feature analysis demonstrated a significant negative relationship between low mean corpuscular haemoglobin concentration and high levels of atmospheric pollutants (p &lt; 0.05) highlighting the potential of routinely assessed blood tests as predictive measures of pollution-dependent cognitive functioning, at an individual level. These data demonstrate how routine medical testing and local authority initiatives could identify at-risk individuals, highlighting the potential for targeted, cost-effective medical and social interventions to improve population cognitive health. </p>

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Machine learning identifies routine blood tests as accurate predictive measures of pollution-dependent poor cognitive function

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月29日

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  • Hamish Johnson,
  • James Longden,
  • Gary Cameron,
  • Gordon D. Waiter,
  • Fergal M. Waldron,
  • Jenna M. Gregory,
  • Holly Spence

摘要

Several modifiable risk factors for dementias have been identified including socio-economic status and environmental exposures – however, how these population-level risks relate to individual risk remains elusive. To address this, we use random forest modelling to determine significant predictors of poor cognitive performance in on deeply phenotyped cohort of 324 individuals (age 61.6 ± 4.8 years; 150 males, 174 females) without extant neurological disease. 457 features were assessed including a comprehensive battery of imaging, blood, atmospheric pollutant and socio-economic measures. This approach where brain imaging, blood, socio-economic and environmental exposure measures were used to model cognitive performance was able to classify general cognition tertiles with 0.70 overall accuracy. Routinely assessed markers of anaemia including mean corpuscular haemoglobin were identified as predictors of poor general cognition and both extremes (low and high) of mean corpuscular haemoglobin concentration were associated with poor immediate recall (p = 0.0253). The predictors of poor cognition which consistently improved model performance across all models were measures of atmospheric pollution, in particular, lead, carbon monoxide, and particulate matter. Feature analysis demonstrated a significant negative relationship between low mean corpuscular haemoglobin concentration and high levels of atmospheric pollutants (p < 0.05) highlighting the potential of routinely assessed blood tests as predictive measures of pollution-dependent cognitive functioning, at an individual level. These data demonstrate how routine medical testing and local authority initiatives could identify at-risk individuals, highlighting the potential for targeted, cost-effective medical and social interventions to improve population cognitive health.