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bioRxiv · 10.1101/2025.01.10.632396

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

Johnson, H.·Longden, J.·Cameron, G.·Waiter, G. D.·Waldron, F. M.·Gregory, J. M.·Spence, H.
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Abstract

BackgroundSeveral modifiable risk factors for dementia and related neurodegenerative diseases have been identified including education level, socio-economic status, and environmental exposures - however, how these population-level risks relate to individual risk remains elusive. To address this, we assess over 450 potential risk factors in one deeply clinically and demographically phenotyped cohort using random forest classifiers to determine predictive markers of poor cognitive function. This study aims to understand early risk factors for dementia by identifying predictors of poor cognitive performance amongst a comprehensive battery of imaging, blood, atmospheric pollutant and socio-economic measures. MethodsRandom forest modelling was used to determine significant predictors of poor cognitive performance in a cohort of 324 individuals (age 61.6 {+/-} 4.8 years; 150 males, 174 females) without extant neurological disease. 457 features were assessed including brain imaging measures of volume and iron deposition, blood measures of anaemia, inflammation, and heavy metal levels, social deprivation indicators and atmospheric pollution exposure. ResultsRoutinely assessed markers of anaemia including mean corpuscular haemoglobin concentration were identified as robust predictors of poor general cognition, where both extremes (low and high) were associated with poor cognitive performance. The strongest, most consistent predictors of poor cognitive performance were environmental 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 highlighting the potential of routinely assessed blood tests as a predictive measure of pollution-dependent cognitive functioning, at an individual level. ConclusionsTaken together, these data demonstrate how routine, inexpensive medical testing and local authority initiatives could help to identify and protect at-risk individuals. These findings highlight the potential to identify individuals for targeted, cost effective medical and social interventions to improve population cognitive health. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=66 SRC="FIGDIR/small/632396v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1cda09org.highwire.dtl.DTLVardef@2e9243org.highwire.dtl.DTLVardef@1849bfcorg.highwire.dtl.DTLVardef@11cf9aa_HPS_FORMAT_FIGEXP M_FIG C_FIG

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Johnson, H., Longden, J., Cameron, G., Waiter, G. D., Waldron, F. M., Gregory, J. M., Spence, H.. 2025-01-13. Machine learning identifies routine blood tests as accurate predictive measures of pollution-dependent poor cognitive function. https://doi.org/10.1101/2025.01.10.632396

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