TY - JOUR
T1 - Discovery of novel CSF biomarkers to predict progression in dementia using machine learning
AU - Gogishvili, Dea
AU - Vromen, Eleonora M
AU - Koppes-den Hertog, Sascha
AU - Lemstra, Afina W
AU - Pijnenburg, Yolande A L
AU - Visser, Pieter Jelle
AU - Tijms, Betty M
AU - Del Campo, Marta
AU - Abeln, Sanne
AU - Teunissen, Charlotte E
AU - Vermunt, Lisa
N1 - © 2023. The Author(s).
PY - 2023/4/21
Y1 - 2023/4/21
N2 - 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 [Formula: see text]-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.
AB - 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 [Formula: see text]-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.
KW - Alzheimer Disease/diagnosis
KW - Amyloid beta-Peptides/cerebrospinal fluid
KW - Biomarkers/cerebrospinal fluid
KW - Cognitive Dysfunction/diagnosis
KW - Disease Progression
KW - Humans
KW - Machine Learning
KW - Proteomics
KW - tau Proteins/cerebrospinal fluid
UR - http://www.scopus.com/inward/record.url?scp=85153543563&partnerID=8YFLogxK
U2 - https://doi.org/10.1038/s41598-023-33045-x
DO - https://doi.org/10.1038/s41598-023-33045-x
M3 - Article
C2 - 37085545
SN - 2045-2322
VL - 13
SP - 6531
JO - Scientific reports
JF - Scientific reports
IS - 1
M1 - 6531
ER -