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- Patra, Kalicharan, et al.
(author)
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Assessment of kallikrein 6 as a cross-sectional and longitudinal biomarker for Alzheimer's disease
- 2018
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In: Alzheimer's Research & Therapy. - : Springer Science and Business Media LLC. - 1758-9193. ; 10
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Journal article (peer-reviewed)abstract
- Background: Kallikrein 6 (KLK6) is known to be an age-related protease expressed at high levels in the central nervous system. It was previously shown to be involved in proteolysis of extracellular proteins implicated in neurodegenerative diseases such as Alzheimer's disease (AD), prompting validation of KLK6 as a potential biomarker of disease. However, analyses of both plasma and cerebrospinal fluid (CSF) levels of KLK6 in patients with AD have been inconclusive. We present a detailed analysis of KLK6 in plasma and CSF in two separate cohorts in a cross-sectional and a longitudinal clinical setting. Methods: The cross-sectional cohort included control subjects without dementia and patients with AD, and the longitudinal cohort included patients with MCI and patients with AD followed over a 2-year period. Plasma and CSF levels of KLK6 were quantified by use of a previously developed and validated enzyme-linked immunosorbent assay. Statistical analyses were performed to compare KLK6 levels between diagnostic groups and to identify potential associations between KLK6 level, age, apolipoprotein E (APOE) genotype, total apoE level and the classical CSF AD biomarkers. Results: In the cross-sectional setting, KLK6 levels in plasma but not in CSF were significantly higher in the AD group than in control subjects. CSF but not plasma KLK6 levels were positively correlated with age in both the cross-sectional and longitudinal settings. In both cohorts, the CSF KLK6 levels were significantly and positively correlated with the CSF levels of core AD biomarkers. Total plasma and CSF apoE levels were positively associated with KLK6 in the cross-sectional study. Finally, during the 2-year monitoring period of the longitudinal cohort, CSF KLK6 levels increased with disease progression over time in the investigated patient groups. Conclusions: In two separate cohorts we have confirmed the previously reported correlation between age and CSF levels of KLK6. Increased plasma KLK6 levels in patients with AD with a more advanced disease stage suggest KLK6 as a potential biomarker in patients with AD with more severe dementia. Significant correlations between KLK6 levels and core CSF AD biomarkers suggest molecular links between KLK6 and AD-related pathological processes.
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| 2. |
- Kruczyk, Marcin, et al.
(author)
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Monte Carlo feature selection and rule-based models to predict Alzheimer's disease in mild cognitive impairment.
- 2012
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In: Journal of neural transmission (Vienna, Austria : 1996). - : Springer Science and Business Media LLC. - 1435-1463 .- 0300-9564. ; 119:7, s. 821-31
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Journal article (peer-reviewed)abstract
- The objective of the present study was to evaluate a Monte Carlo feature selection (MCFS) and rough set Rosetta pipeline for generating rule-based models as a tool for comprehensive risk estimates for future Alzheimer's disease (AD) in individual patients with mild cognitive impairment (MCI). Risk estimates were generated on the basis of age, gender, Mini-Mental State Examination scores, apolipoprotein E (APOE) genotype and the cerebrospinal fluid (CSF) biomarkers total tau (T-tau), phospho-tau(181) (P-tau) and the 42 amino acid form of amyloid β (Aβ42) in two sets of longitudinally followed MCI patients (n = 217 in total). The predictive model was created in Rosetta, evaluated with the standard tenfold cross-validation approach and tested on an external set. Features were ranked and selected by the MCFS algorithm. Using the combined pipeline of MCFS and Rosetta, it was possible to predict AD among patients with MCI with an area under the receiver operating characteristics curve of 0.92. Risk estimates were produced for the individual patients and showed good correlation with actual diagnosis in cross validation, and on an external dataset from a new study. Analysis of the importance of attributes showed that the biochemical CSF markers contributed the most to the predictions, and that added value was gained by combining several biochemical markers. Despite a correlation with the biochemical markers, the genetic marker APOE ε4 did not contribute to the predictive power of the model.
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