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P3‐173: Apolipoproteins and Apolipoprotein Subtypes in Human Cerebrospinal Fluid and Plasma
Author(s) -
Koch Manja,
Furtado Jeremy D.,
Falk Kim K.,
Leypoldt Frank,
Mukamal Kenneth J.,
Jensen Majken K.
Publication year - 2016
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1016/j.jalz.2016.06.1833
Subject(s) - apolipoprotein e , cerebrospinal fluid , apolipoprotein b , medicine , endocrinology , blood plasma , cholesterol , disease
pean, longitudinal controlled study for the evaluation of a new blood-based in vitro diagnostic test for AD. All participants provided written informed consent, and the protocols were in accordance with the Declaration of Helsinki (1975) and approved by the Ethics Committees. The study is aiming at recruiting 140 controls, 140 AD patients and 80 mild cognitive impairment (MCI). At this intermediate analysis, we recruited 133 controls, 15 MCI and 76 AD patients. For further analysis, MCI and AD patients were pooled into a single group with cognitive dysfunction. To identify a classification rule, an iterative model building approach was applied. A robust cross-validation method consisted of 100 random samples using a 70% sample for the training dataset and 30% sample of data for validation. Results:Data from our intermediate analysis indicated that the most consistent plasma metabolomics markers in APOE e4 carriers are related to lipid metabolism dysfunction (glycerophospholipids and phosphatidylcholines). Opposite, in APOE e4 non-carriers we observed a clear over-representation of amino acids involved in the urea cycle, as well as acylcarnitines that are classical markers of mitochondrial dysfunction. Conclusions: APOE e4 stratification of Alzheimer’s disease patients defines two distinct sets of metabolomics markers related to lipid (APOE e4 carriers) or mitochondrial (APOE e4 non-carriers) metabolism dysfunction. Stratification according to APOE e4 status may help to identify specific subsets of metabolomics markers for a more accurate risk prediction diagnosis and disease diagnosis.