Genotypic-Phenotypic Analysis, Metabolic Profiling and Clinical Correlations in Parkinson's Disease Patients from Tamil Nadu Population, India

Parkinson's disease (PD) is an ageing disorder caused by dopaminergic neuron depletion with age. Growing research in the field of metabolomics is expected to play a major role in PD diagnosis, prognosis and therapeutic development. In this study, we looked at how SNCA and GBA1 gene mutations, as well as metabolomic abnormalities of kynurenine and cholesterol metabolites, were linked to alpha-synuclein (α-syn) and clinical characteristics in three different PD age groups. In all three age groups, a metabolomics analysis revealed an increased amount of 27-hydroxycholesterol (27-OHC) and a lower level of kynurenic acid (KYNA). The effect of 27-OHC on SNCA and GBA1 modifications was shown to be significant (P < 0.05) only in the A53T variant of the SNCA gene in late-onset and early-onset PD groups, whereas GBA1 variants were not. Based on the findings, we observed that the increase in 27-OHC would have elevated α-syn expression, which triggered the changes in the SNCA gene but not in the GBA1 gene. Missense variations in the SNCA and GBA1 genes were investigated using the sequencing technique. SNCA mutation A53T has been linked to increased PD symptoms, but there is no phenotypic link between GBA1 and PD. As a result of the data, we hypothesise that cholesterol and kynurenine metabolites play an important role in PD, with the metabolite 27-OHC potentially serving as a PD biomarker. These findings will aid in the investigation of pathogenic causes as well as the development of therapeutic and preventative measures for PD.

Clinical decision support system for early detection of Alzheimer's disease using an enhanced gradient boosted decision tree classifier

Alzheimer's disease (AD) is one of the most common forms of dementia contributing to more than 70% of the cases. The factors accounting for the cause and progression of neurodegenerative diseases like AD are primarily genetic, in addition to life style and environmental factors. Early and accurate diagnoses of AD empower practitioners to take timely clinical decisions and preventive actions. This being the motivation, the work proposes a novel pattern matching and scoring method on genetic material towards devising an effective classifier. We propose a distinctive disease causing gene sequence pattern identification using suffix trees as a base detection model with an accuracy of 91.5% in linear time complexity. A scoring mechanism is implemented to assign scores to genes based on the severity of the disease causing and disease resistant Single Nucleotide Polymorphisms associated with the genes. These scores are then used as a remarkable feature in the gradient boosted decision tree classifier to enhance the classification of AD versus healthy control. The efficiency of the proposed gene powered EGBDT classifier is evaluated on ADNI benchmark data set with the prediction accuracy of 94.16% and is found to be efficient compared to the recent works in the literature.

The association between multiple risk factors, clinical correlations and molecular insights in Parkinson's disease patients from Tamil Nadu population, India

Parkinson's disease (PD) is a neurodegenerative disease with multifactorial aetiology that influences the quality of life. However, the association of possible factors with PD is need to be investigated in Indian population, hence we aimed to determine the association of lifestyle, environmental factors, biochemical parameters and genetic insights of MT-ND1 gene in PD patients. Using a standardised questionnaire, PD patients and control group of about 146 subjects were interviewed on demographic, lifestyle and environmental factors. The subjects includes n = 73 Parkinson's patients [juvenile (n = 4); early-onset (n = 8); late-onset (n = 61)] with equal number of age and sex matched controls, further we had obtained institutional ethical clearance and informed consent from study participants. Biomarker investigations and MT-ND1 alterations were investigated by appropriate molecular techniques. During the average follow-up years of 5.1, significant association was observed among smoking, alcohol, caffeinated drinks, surgery, pesticide exposure at p < 0.05 in varied PD age groups. Occupational exposure to agriculture and industry showed an increased risk among the late-onset group. The biomarkers uric acid (UA) and dopamine (DA) were significant at p < 0.05 in all the three PD age groups. The MT-ND1 alteration with A3843 G variant was significant at p < 0.05 for AG allele in all the three PD groups but the highest prevalence was observed in late-onset group. From our study, smoking, alcohol, caffeinated drinks, occupational influence of agriculture and industry and pesticide exposure had more association with PD occurrence. Hence, to the best of our knowledge, this is the first kind of study in Tamil Nadu population, India to validate the various factors with PD. Therefore we suggest that further research is mandatory to detect other possible associations among PD, using comprehensive larger sample size.