The prognostic value of amyloid imaging.
ABSTRACT Mild cognitive impairment is characterized by a decline in cognitive performance without interference with activities of daily living. The amnestic subtype of mild cognitive impairment progresses to Alzheimer's disease at a rate of 10-15% per year and in the majority the neuropathology is intermediate between the neuropathological changes of typical ageing and Alzheimer's disease. Amyloid deposition occurs over a decade before the development of noticeable cognitive symptoms in a continuous process that starts in healthy elderly individuals. Newly developed PET amyloid imaging agents provide noninvasive biomarkers for the early in vivo detection of Alzheimer's pathology in healthy elderly individuals and those with mild cognitive impairment. Exclusion of amyloid pathology should allow a more accurate prognosis to be given and ensure appropriate recruitment into clinical trials testing the efficacy of new putative antiamyloid agents at an earlier disease stage. The development of (18)F-labelled amyloid imaging agents has increased the availability of this new technology for clinical and research use since they can be used in PET centres where a cyclotron and radiochemistry are not available. This review discusses the role of PET imaging for assessing the amyloid load in cognitively normal elderly subjects and subjects with mild cognitive impairment at risk of conversion to Alzheimer's disease.
SourceAvailable from: Massimo Buscema[Show abstract] [Hide abstract]
ABSTRACT: Alzheimer's disease (AD) is the most common form of dementia, while mild cognitive impairment (MCI) causes a slight but measurable decline in cognitive abilities. A person with MCI has an increased risk of developing AD or another dementia. Thus, it is of medical interest to develop predictive tools to assess this risk. A growing awareness exists that pro-oxidative state and neuro-inflammation are both involved in AD. However, the extent of this relationship is still a matter of debate. Due to the expected non-linear correlations between oxidative and inflammatory markers, traditional statistics is unsuitable to dissect their relationship with the disease. Artificial neural networks (ANNs) are computational models inspired by central nervous system networks, capable of machine learning and pattern recognition. The aim of this work was to disclose the relationship between immunological and oxidative stress markers in AD and MCI by the application of ANNs. Through a machine learning approach, we were able to construct an algorithm to classify MCI and AD with high accuracy. Such an instrument, requiring a small amount of immunological and oxidative-stress parameters, would be useful in the clinical practice. Moreover, applying an innovative non-linear mathematical technique, a global immune deficit was shown to be associated with cognitive impairment. Surprisingly, both adaptive and innate immunity were peripherally defective in AD and MCI patients. From this study, new pathogenetic aspects of these diseases could emerge.Journal of Alzheimer's disease: JAD 09/2014; DOI:10.3233/JAD-141116 · 3.61 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: Background: Affective disorders are associated with an increased occurrence of cognitive deficits and have been linked to cognitive impairment and Alzheimer's disease. The putative molecular mechanisms involved in these associations are however not clear. The aim of this systematic review was to explore clinically founded evidence for amyloid-beta peptides in cerebrospinal fluid and blood as putative biomarkers for affective disorders. Method: Systematic searches in Embase and PubMed databases yielded 23 eligible, observational studies. Results: Despite inconsistencies that were partly ascribed to the application of different assay formats, study results indicate a potentially altered amyloid-beta metabolism in affective disorder. Limitations: Since most studies used a cross-sectional design, causality is difficult to establish. Moreover, methodological rigor of included studies varied and several studies were limited by very low sample numbers. Finally, different assays for amyloid-beta were utilized in the different studies, thus hampering comparisons. Conclusion: To unravel possible risk relations and causalities between affective disorder and Alzheimer's disease and to determine how amyloid-beta concentrations change over time and are associated with cognition as well as affective symptomatology, future research should include prospective, longitudinal studies, implemented in large study populations, where peripheral and central amyloid-p ratios are quantified concomitantly and continuously across various affective phases. Also, to enable inter survey comparisons, the use of standardized pre-analytical/analytical procedures is crucial.Journal of Affective Disorders 07/2014; 168C:167-183. DOI:10.1016/j.jad.2014.06.050 · 3.71 Impact Factor
[Show abstract] [Hide abstract]
ABSTRACT: To explore gender, age-related, and regional differences of magnetization transfer ratio (MTR) of brain cortical and subcortical gray matter (GM).Journal of Magnetic Resonance Imaging 08/2014; 40(2). DOI:10.1002/jmri.24355 · 2.79 Impact Factor