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Stevens Point, CA, United States

Shankle W.R.,Shankle Clinic | Shankle W.R.,Newport Corporation | Shankle W.R.,Hoag Neurosciences Institute | Shankle W.R.,University of California at Irvine | And 6 more authors.
Alzheimer's and Dementia | Year: 2013

Identifying disease-modifying treatment effects in earlier stages of Alzheimer's disease (AD)- when changes are subtle-will require improved trial design and more sensitive analytical methods. We applied hierarchical Bayesian analysis with cognitive processing (HBCP) models to the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and MCI (mild cognitive impairment) Screen word list memory task data from 14 Alzheimer's disease AD patients of the Myriad Pharmaceuticals' phase III clinical trial of Flurizan (a g-secretase modulator) versus placebo. The original analysis of 1649 patients found no treatment group differences. HBCP analysis and the original ADAS-Cog analysis were performed on the small sample. HBCP analysis detected impaired memory storage during delayed recall, whereas the original ADAS-Cog analytical method did not. The HBCP model identified a harmful treatment effect in a small sample, which has been independently confirmed from the results of other g-secretase inhibitor. The original analytical method applied to the ADAS-Cog data did not detect this harmful treatment effect on either the full or the small sample. These findings suggest that HBCP models can detect treatment effects more sensitively than currently used analytical methods required by the Food and Drug Administration, and they do so using small patient samples. © 2013 The Alzheimer's Association. All rights reserved.

Shankle W.R.,Shankle Clinic | Shankle W.R.,Hoag Neurosciences Institute | Shankle W.R.,Medical Care Corporation | Shankle W.R.,University of California at Irvine | And 7 more authors.
Alzheimer Disease and Associated Disorders | Year: 2013

Determining how cognition affects functional abilities is important in Alzheimer disease and related disorders. A total of 280 patients (normal or Alzheimer disease and related disorders) received a total of 1514 assessments using the functional assessment staging test (FAST) procedure and the MCI Screen. A hierarchical Bayesian cognitive processing model was created by embedding a signal detection theory model of the MCI Screen-delayed recognition memory task into a hierarchical Bayesian framework. The signal detection theory model used latent parameters of discriminability (memory process) and response bias (executive function) to predict, simultaneously, recognition memory performance for each patient and each FAST severity group. The observed recognition memory data did not distinguish the 6 FAST severity stages, but the latent parameters completely separated them. The latent parameters were also used successfully to transform the ordinal FAST measure into a continuous measure reflecting the underlying continuum of functional severity. Hierarchical Bayesian cognitive processing models applied to recognition memory data from clinical practice settings accurately translated a latent measure of cognition into a continuous measure of functional severity for both individuals and FAST groups. Such a translation links 2 levels of brain information processing and may enable more accurate correlations with other levels, such as those characterized by biomarkers. © 2013 by Lippincott Williams & Wilkins.

Tsuruoka M.,Keio University | Hara J.,Shankle Clinic | Hirayama A.,Keio University | Sugimoto M.,Keio University | And 5 more authors.
Electrophoresis | Year: 2013

Despite increasing global prevalence, the precise pathogenesis and terms for objective diagnosis of neurodegenerative dementias remain controversial, and comprehensive understanding of the disease remains lacking. Here, we conducted metabolomic analysis of serum and saliva obtained from patients with neurodegenerative dementias (n = 10), including Alzheimer's disease, frontotemporal lobe dementia, and Lewy body disease, as well as from age-matched healthy controls (n = 9). Using CE-TOF-MS, six metabolites in serum (β-alanine, creatinine, hydroxyproline, glutamine, iso-citrate, and cytidine) and two in saliva (arginine and tyrosine) were significantly different between dementias and controls. Using multivariate analysis, serum was confirmed as a more efficient biological fluid for diagnosis compared to saliva; additionally, 45 metabolites in total were identified as candidate markers that could discriminate at least one pair of diagnostic groups from the healthy control group. These metabolites possibly provide an objective method for diagnosing dementia-type by multiphase screening. Moreover, diagnostic-type-dependent differences were observed in several tricarboxylic acid cycle compounds detected in serum, indicating that some pathways in glucose metabolism may be altered in dementia patients. This pilot study revealed novel alterations in metabolomic profiles between various neurodegenerative dementias, which would contribute to etiological investigations. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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