Gene expression profiling in developing human hippocampus

The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence

Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.

Serum peptides as candidate biomarkers for dementia with Lewy bodies

OBJECTIVE

For diagnosis of dementia with Lewy bodies (DLB), we tried to find blood biomarkers for the disease.

METHODS

Serum peptides were comprehensively detected by mass spectrometry. Peptides of interest were identified by tandem mass spectrometry.

RESULTS

One hundred forty-six peptides were detected in a training set consisting of 30 DLB patients, 30 patients with Alzheimer's disease (AD), and 28 healthy control (HC) subjects. Multivariate analysis for discriminating the DLB group from the non-DLB (AD and HC) group using ion intensity of four peptides (2898, 4052, 4090, and 5002 m/z) showed sensitivity of 93.3% and specificity of 87.9% (DLB/nonDLB-4P model). In a testing set consisting of 20 DLB patients, 30 AD patients, and 14 HC subjects, this model showed sensitivity of 90.0% and specificity of 88.6%. DLB/nonDLB-4P model detected 86.7% and 90.0% of the AD patients as non-DLB in the training and testing sets, respectively, and discriminated all the 15 patients with amnestic mild cognitive impairment as non-DLB. Notably, a combination of two peptides (1737 and 5002 m/z) showed sensitivity of 95.0% and specificity of 93.3% for discriminating the DLB group from the AD group (DLB/nonDLB-2P model) in the testing set. The peptides used in these models included fragments from complement 4b, Wnt-2b, and lipopolysaccharide-binding protein, which were reported to be involved in the pathology of DLB or Parkinson's disease and hippocampal neurogenesis.

CONCLUSIONS

Serum peptide profiles would provide useful DLB biomarker candidates, which may be implicated in the pathophysiology of the disease.