• Lutz Frölich, Oliver Peters, Piotr Lewczuk, Oliver Gruber, Stefan J Teipel, Hermann J Gertz, Holger Jahn, Frank Jessen, Alexander Kurz, Christian Luckhaus, Michael Hüll, Johannes Pantel, Friedel M Reischies, Johannes Schröder, Michael Wagner, Otto Rienhoff, Stefanie Wolf, Chris Bauer, Johannes Schuchhardt, Isabella Heuser, Eckart Rüther, Fritz Henn, Wolfgang Maier, Jens Wiltfang, and Johannes Kornhuber.
• Department of Geriatric Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Zentralinstitut für Seelische Gesundheit, Quadrat J5, D-68159, Mannheim, Germany. lutz.froelich@zi-mannheim.de.
• Alzheimers Res Ther. 2017 Oct 10; 9 (1): 84.

BackgroundThe 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 (Aβ42), amyloid-beta1-40 (Aβ40) levels, the ratio of Aβ42/Aβ40, phosphorylated tau, and total tau (t-Tau) levels in the CSF best predicted a short-term conversion from MCI to AD dementia.MethodsWe 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%.ResultsOut 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 Aβ40. 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.ConclusionA 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.

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