TY  - JOUR
PB  - Biomed Central
IS  - 84
UR  - https://archiv.ub.uni-heidelberg.de/volltextserver/23593/
AV  - public
SN  - 1758-9193
A1  - Frölich, Lutz
A1  - Peters, Oliver
A1  - Lewczuk, Piotr
A1  - Gruber, Oliver
A1  - Teipel, Stefan J.
A1  - Gertz, Hermann J.
A1  - Jahn, Holger
A1  - Jessen, Frank
A1  - Kurz, Alexander
A1  - Luckhaus, Christian
A1  - Hüll, Michael
A1  - Pantel, Johannes
A1  - Reischies, Friedel M.
A1  - Schröder, Johannes
A1  - Wagner, Michael
A1  - Rienhoff, Otto
A1  - Wolf, Stefanie
A1  - Bauer, Chris
A1  - Schuchhardt, Johannes
A1  - Heuser, Isabella
A1  - Rüther, Eckart
A1  - Henn, Fritz
A1  - Maier, Wolfgang
A1  - Wiltfang, Jens
A1  - Kornhuber, Johannes
CY  - London
VL  - 9
JF  - Alzheimer's Research & Therapy
TI  - Incremental value of biomarker combinations to predict progression of mild cognitive impairment to Alzheimer?s dementia
Y1  - 2017///
SP  - 1
EP  - 15
N2  - Background The progression of mild cognitive impairment (MCI) to Alzheimer?s disease (AD) dementia can be predicted by cognitive, neuroimaging, and cerebrospinal fluid (CSF) markers. Since most biomarkers reveal complementary information, a combination of biomarkers may increase the predictive power. We investigated which combination of the Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR)-sum-of-boxes, the word list delayed free recall from the Consortium to Establish a Registry of Dementia (CERAD) test battery, hippocampal volume (HCV), amyloid-beta1?42 (A?42), amyloid-beta1?40 (A?40) levels, the ratio of A?42/A?40, phosphorylated tau, and total tau (t-Tau) levels in the CSF best predicted a short-term conversion from MCI to AD dementia.   Methods We used 115 complete datasets from MCI patients of the ?Dementia Competence Network?, a German multicenter cohort study with annual follow-up up to 3 years. MCI was broadly defined to include amnestic and nonamnestic syndromes. Variables known to predict progression in MCI patients were selected a priori. Nine individual predictors were compared by receiver operating characteristic (ROC) curve analysis. ROC curves of the five best two-, three-, and four-parameter combinations were analyzed for significant superiority by a bootstrapping wrapper around a support vector machine with linear kernel. The incremental value of combinations was tested for statistical significance by comparing the specificities of the different classifiers at a given sensitivity of 85%.   Results Out of 115 subjects, 28 (24.3%) with MCI progressed to AD dementia within a mean follow-up period of 25.5 months. At baseline, MCI-AD patients were no different from stable MCI in age and gender distribution, but had lower educational attainment. All single biomarkers were significantly different between the two groups at baseline. ROC curves of the individual predictors gave areas under the curve (AUC) between 0.66 and 0.77, and all single predictors were statistically superior to A?40. The AUC of the two-parameter combinations ranged from 0.77 to 0.81. The three-parameter combinations ranged from AUC 0.80?0.83, and the four-parameter combination from AUC 0.81?0.82. None of the predictor combinations was significantly superior to the two best single predictors (HCV and t-Tau). When maximizing the AUC differences by fixing sensitivity at 85%, the two- to four-parameter combinations were superior to HCV alone.   Conclusion A combination of two biomarkers of neurodegeneration (e.g., HCV and t-Tau) is not superior over the single parameters in identifying patients with MCI who are most likely to progress to AD dementia, although there is a gradual increase in the statistical measures across increasing biomarker combinations. This may have implications for clinical diagnosis and for selecting subjects for participation in clinical trials.
ID  - heidok23593
ER  -