Discovery of novel CSF biomarkers to predict progression in dementia using machine learning

Dea Gogishvili*, Eleonora M. Vromen, Sascha Koppes-den Hertog, Afina W. Lemstra, Yolande A. L. Pijnenburg, Pieter Jelle Visser, Betty M. Tijms, Marta Del Campo, Sanne Abeln, Charlotte E. Teunissen, Lisa Vermunt

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Providing an accurate prognosis for individual dementia patients remains a challenge since they greatly differ in rates of cognitive decline. In this study, we used machine learning techniques with the aim to identify cerebrospinal fluid (CSF) biomarkers that predict the rate of cognitive decline within dementia patients. First, longitudinal mini-mental state examination scores (MMSE) of 210 dementia patients were used to create fast and slow progression groups. Second, we trained random forest classifiers on CSF proteomic profiles and obtained a well-performing prediction model for the progression group (ROC–AUC = 0.82). As a third step, Shapley values and Gini feature importance measures were used to interpret the model performance and identify top biomarker candidates for predicting the rate of cognitive decline. Finally, we explored the potential for each of the 20 top candidates in internal sensitivity analyses. TNFRSF4 and TGF
β
-1 emerged as the top markers, being lower in fast-progressing patients compared to slow-progressing patients. Proteins of which a low concentration was associated with fast progression were enriched for cell signalling and immune response pathways. None of our top markers stood out as strong individual predictors of subsequent cognitive decline. This could be explained by small effect sizes per protein and biological heterogeneity among dementia patients. Taken together, this study presents a novel progression biomarker identification framework and protein leads for personalised prediction of cognitive decline in dementia.
Original languageEnglish
Article number6531
Number of pages13
JournalScientific Reports
Volume13
Issue number1
DOIs
Publication statusPublished - Dec 2023
Externally publishedYes

Keywords

  • Care
  • Cognitive decline
  • Expression
  • Gene
  • Mental-state-examination
  • Receptors
  • Tnf

Fingerprint

Dive into the research topics of 'Discovery of novel CSF biomarkers to predict progression in dementia using machine learning'. Together they form a unique fingerprint.

Cite this