Identification of mutations at familial loci for amyotrophic lateral sclerosis (ALS) has provided novel insights into the aetiology of this rapidly progressing fatal neurodegenerative disease. However, genome-wide association studies (GWAS) of the more common (∼90%) sporadic form have been less successful with the exception of the replicated locus at 9p21.2. To identify new loci associated with disease susceptibility, we have established the largest association study in ALS to date and undertaken a GWAS meta-analytical study combining 3959 newly genotyped Italian individuals (1982 cases and 1977 controls) collected by SLAGEN (Italian Consortium for the Genetics of ALS) together with samples from Netherlands, USA, UK, Sweden, Belgium, France, Ireland and Italy collected by ALSGEN (the International Consortium on Amyotrophic Lateral Sclerosis Genetics). We analysed a total of 13 225 individuals, 6100 cases and 7125 controls for almost 7 million single-nucleotide polymorphisms (SNPs). We identified a novel locus with genome-wide significance at 17q11.2 (rs34517613 with P = 1.11 × 10(-8); OR 0.82) that was validated when combined with genotype data from a replication cohort (P = 8.62 × 10(-9); OR 0.833) of 4656 individuals. Furthermore, we confirmed the previously reported association at 9p21.2 (rs3849943 with P = 7.69 × 10(-9); OR 1.16). Finally, we estimated the contribution of common variation to heritability of sporadic ALS as ∼12% using a linear mixed model accounting for all SNPs. Our results provide an insight into the genetic structure of sporadic ALS, confirming that common variation contributes to risk and that sufficiently powered studies can identify novel susceptibility loci.

VGF mRNA and its precursor-derived products are selectively expressed in certain neurons and promptly respond to neurotrophins and to neural ⁄ electrical activity. Proteomic studies have previously revealed a reduction in some VGF peptides in the cerebrospinal fluid of patients affected by Alzheimer's disease and other conditions, suggesting their potential diagnostic and clinical significance. As the presence of VGF peptides within the human cortex has been somewhat elucidated, they were studied postmortem in the frontal, parietal, and temporal cortex areas of control subjects and patients affected by Parkinson's disease, and in parietal cortex samples from patients with Alzheimer's disease. We raised antibodies to the C-⁄ N-terminal portions of the proVGF precursor protein, to the TPGH and TLQP sequences and to the neuroendocrine regulatory peptide (NERP)-1, all used for enzyme-linked immunosorbent assay coupled with gel chromatography and for immunohistochemistry. In the control brain samples, the levels of TPGH and C-terminus peptides were about 130-200 and 700-2000 pmol g)1 , respectively, the N-terminus and NERP-1 peptides were less represented (about 10-30 and 4-20 pmol g)1 , respectively), and the TLQP peptides were below detection limits. Upon gel chromatography, the VGF antisera mainly revealed small molecular weight forms (i.e. about 0.8-1.3 kDa), whereas VGF immunolocalisation was found within different types of neuron in rat and bovine brain cortices. In the Parkinson's disease samples, a clear-cut decrease was revealed in the parietal cortex only, exclusively for TPGH and NERP-1 peptides, whereas in the Alzheimer's disease samples, a reduction in all of the VGF peptides was shown. The results suggest the involvement of VGF in the physiological or pathophysiological mechanisms occurring in the parietal cortex of patients with Parkinson's and Alzheimer's diseases.

BackgroundThe main objective of this paper is to develop and test the ability of the Leap Motion controller (LMC) to assess the motor dysfunction in patients with Parkinson disease (PwPD) based on the MDS-UPDRSIII exercises. Four exercises (thumb forefinger tapping, hand opening/closing, pronation/supination, postural tremor) were used to evaluate the characteristics described in MDS-UPDRSIII. Clinical ratings according to the MDS/UPDRS-section III items were used as target. For that purpose, 16 participants with PD and 12 healthy people were recruited in Ospedale Cisanello, Pisa, Italy. The participants performed standardized hand movements with camera-based marker. Time and frequency domain features related to velocity, angle, amplitude, and frequency were derived from the LMC data.ResultsDifferent machine learning techniques were used to classify the PD and healthy subjects by comparing the subjective scale given by neurologists against the predicted diagnosis from the machine learning classifiers. Feature selection methods were used to choose the most significant features. Logistic regression (LR), naive Bayes (NB), and support vector machine (SVM) were trained with tenfold cross validation with selected features. The maximum obtained classification accuracy with LR was 70.37%; the average area under the ROC curve (AUC) was 0.831. The obtained classification accuracy with NB was 81.4%, with AUC of 0.811. The obtained classification accuracy with SVM was 74.07%, with AUC of 0.675.ConclusionsResults revealed that the system did not return clinically meaningful data for measuring postural tremor in PwPD. In addition, it showed limited potential to measure the forearm pronation/supination. In contrast, for finger tapping and hand opening/closing, the derived parameters showed statistical and clinical significance. Future studies should continue to validate the LMC as updated versions of the software are developed. The obtained results support the fact that most of the set of selected features contributed significantly to classify the PwPD and healthy subjects.

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