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Combinatorial markers of Mild Cognitive Impairment conversion to Alzheimer’s Disease – cytokines and MRI measures together predict disease progression

Research output: Contribution to journalArticle

Simon Furney, Deborah Kronenberg-Versteeg, Andrew Simmons, Andreas Guentert, Richard Dobson, Petra Proitsi, Lars-Olof Wahlund, Iwona Kloszewska, Patrizia Mecocci, Hilkka Soininen, Magda Tsolaki, Bruno Vellas, Christian Spenger, Simon Lovestone

Original languageEnglish
Pages (from-to)395-405
Number of pages11
JournalJOURNAL OF ALZHEIMERS DISEASE
Volume26
Issue numberS3
DOIs
Publication statusPublished - 2011

King's Authors

Abstract

Progression of people presenting with Mild Cognitive Impairment (MCI) to dementia is not certain and it is not possible for clinicians to predict which people are most likely to convert. The inability of clinicians to predict progression limits the use of MCI as a syndrome for treatment in prevention trials and, as more people present with this syndrome in memory clinics, and as earlier diagnosis is a major goal of health services, this presents an important clinical problem. Some data suggest that CSF biomarkers and functional imaging using PET might act as markers to facilitate prediction of conversion. However, both techniques are costly and not universally available. The objective of our study was to investigate the potential added benefit of combining biomarkers that are more easily obtained in routine clinical practice to predict conversion from MCI to Alzheimer's disease. To explore this we combined automated regional analysis of structural MRI with analysis of plasma cytokines and chemokines and compared these to measures of APOE genotype and clinical assessment to assess which best predict progression. In a total of 205 people with MCI, 77 of whom subsequently converted to Alzheimer's disease, we find biochemical markers of inflammation to be better predictors of conversion than APOE genotype or clinical measures (Area under the curve (AUC) 0.65, 0.62, 0.59 respectively). In a subset of subjects who also had MRI scans the combination of serum markers of inflammation and MRI automated imaging analysis provided the best predictor of conversion (AUC 0.78). These results show that the combination of imaging and cytokine biomarkers provides an improvement in prediction of MCI to AD conversion compared to either datatype alone, APOE genotype or clinical data and an accuracy of prediction that would have clinical utility.

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