alexa Biomarkers for Identifying Individuals at Risk of Alzheimer Disease | OMICS International| Abstract
ISSN: 1522-4821

International Journal of Emergency Mental Health and Human Resilience
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  • Int J Emerg Ment Health 2018, Vol 20(1): 390
  • DOI: 10.4172/1522-4821.1000390

Biomarkers for Identifying Individuals at Risk of Alzheimer Disease

Winnie Thomas1,2, Vemula Ramana Sreekanth3,4, Pratibha Nallari5, Altaf Ali5, Kodati VijayaLakshmi2, Yog Raj Ahuja2 and Qurratulain Hasan1,6*
1Department of Biotechnology, , Hyderabad Science Society, Mehdipatnam, Hyderabad, Telangana, India
2Department of Genetics and Molecular Medicine, , Vasavi Medical and Research Center, Khairatabad, Telangana, India
3Department of Neurology, Apollo hospital, , Jubilee hills, Hyderabad- 500033, Telangana, India
4Department of Neurology, Lucid diagnostics, , road no.2, Banjara hills, Hyderabad-500034, Telangan, India
5Department of Genetics, Osmania University, Amberpet, Hyderabad- 500007, Telangana, India
6Department of Genetics and Molecular Medicine, Kamineni hospital, , L.B.nagar, Hyderabad - 500 068, Telangana, India
*Corresponding Author : Qurratulain Hasan, Department of Genetics and Molecular Medicine, Kamineni Hospital, L.B. Nagar, Hyderabad,Telangana, India, Email: [email protected]

Received Date: Jan 01, 1970 / Accepted Date: Jan 01, 1970 / Published Date: Mar 30, 2018


Background: Alzheimer Disease (AD), the most common form of dementia, is a progressive and irreversible neurodegenerative disorder. Promising preventative strategies includes identification of potential modifiable risk factors for AD that could help identify individual who are at risk of AD. This study focuses on identifying biochemical factors associated with non- familial AD.
: One hundred and ten individuals which included 55 AD patients and 55 healthy controls were recruited for the study. Patients clinically diagnosed by a neurologist as AD and controls with no clinical or family history of any neurological disease were subjected to Mini-Mental State Examination (MMSE) were evaluated for fourteen relevant biochemical markers using commercial kits. MDR analysis was carried out which is considered a basic machine learning tool for understanding the role of interaction and combination of the factors towards the outcome. PCA is performed to support the MDR interpretation. Through clustering analysis the probably causative factors towards the disease can be identified.
: MDR analysis revealed that the overall best fit model included 10 factors which had a maximal testing accuracy of 61%, cross-validation consistency of 8/10. PCA analysis has further reduced the factors to Iron, TSAT, HDL, VitB12, FA, and Hcy which are important in disease initiation/progression. Apart from the cases, 9% of the controls who had lower MMSE also had low Iron, TSAT, HDL, VitB12, FA, and high Hcy.
: As per the results obtained, we would suggest a medical practice where, screening individuals above the age of 55 years with both MMSE and selected biochemical parameters (Iron, TSAT, HDL, VitB12, FA, and Hcy) should be carried out to identify those at risk of developing AD. Higher risk individuals can be suggested for modifications in diet/life style, enhancing certain nutritional components which may constitute promising strategies in postponing, slowing, and/or preventing cognitive decline in AD.

Keywords: Alzheimer disease; Folic acid; Vitamin B12; High-density-lipoprotein

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