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.