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Patricio Reyes M.D., F.A.N.N.
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Barrow Neurological Institute
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"2 NEW THERAPIES FOR ALZHEIMER'S"
Patricio Reyes M.D., F.A.N.N.
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St. Joseph's Hospital and Medical Center



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Patricio Reyes M.D.
Director, Traumatic Brain Injury, Alzheimer's Disease & Cognitive Disorders Clinics; Phoenix, AZ; Chief Medical Officer, Retired NFL Players Association

Barrow Neurological Institute

St. Joseph's Hospital and Medical Center
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Thursday, May 25, 2017

 

Biomarkers in Saliva May Spot Alzheimer's Early


























Results of a pilot study suggest that biomarkers in saliva may help identify people at risk for developing Alzheimer's disease (AD).

"Our results demonstrate that there are significant differences in the concentrations of a large number of salivary metabolites in patients with AD and mild cognitive impairment (MCI) relative to unaffected controls," they report.
The study was published online May 11 in the Journal of Alzheimer's Disease. 
"We used metabolomics, a newer technique, to study molecules involved in metabolism," Stewart Graham, PhD, from Oakland University–William Beaumont School of Medicine, Beaumont Health in Royal Oak, Michigan, explains in a statement. "Our goal was to find unique patterns of molecules in the saliva of our study participants that could be used to diagnose Alzheimer's disease in the earliest stages, when treatment is considered most effective."

The research team biochemically profiled saliva samples collected from 12 healthy controls, 8 individuals with MCI, and 9 with AD. "We accurately identified significant concentration changes in 22 metabolites in the saliva of MCI and AD patients compared to controls," they report.  
The metabolites galactose, imidazole, and acetone; creatine and 5-aminopentanoate; and propionate and acetone were used to distinguish controls vs MCI, MCI vs AD, and controls vs AD, respectively.

Some of the observed variances in these biomarkers were "relatively large," the researchers say. Using logistic regression modeling, they achieved statistically significant prediction of MCI and AD from controls and MCI from AD.
The regression model with the greatest predictive ability was generated when controls were separated from MCI by using the concentrations of galactose, imidazole, and acetone with an area under the receiver-operating characteristic curve (AUC) of 0.826 (95% confidence interval [CI], 0.634 - 1.00) and with a sensitivity and specificity of 0.909 and 0.889, respectively.

When the concentration values of creatinine and 5-aminopentanoate from MCI and AD patients were analyzed by using logistic regression, an AUC value of 0.897 (95% CI, 0.707 - 1.000) was achieved, with a sensitivity and specificity of 0.900 and 0.944, respectively.

The logistic regression model based on the concentration values of propionate and acetone for separating controls from patients with AD produced an AUC of 0.871 (95% CI, 0.689 - 1.000) with 0.909 and 0.842 sensitivity and specificity values, respectively.

Of all the logistic regression models created, controls vs MCI was the weakest. "This could be the result of the heterogeneous nature of MCI; 10% of the MCI sufferers progress to AD while a small percentage regress to being considered healthy controls," the researchers note.


Story Source: The above story is based on materials provided by MEDSCAPE
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