It is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of... Show moreIt is increasingly recognized that Alzheimer’s disease (AD) exists before dementia is present and that shifts in amyloid beta occur long before clinical symptoms can be detected. Early detection of these molecular changes is a key aspect for the success of interventions aimed at slowing down rates of cognitive decline. Recent evidence indicates that of the two established methods for measuring amyloid, a decrease in cerebrospinal fluid (CSF) amyloid β1−42 (Aβ1−42) may be an earlier indicator of Alzheimer’s disease risk than measures of amyloid obtained from Positron Emission Tomography (PET). However, CSF collection is highly invasive and expensive. In contrast, blood collection is routinely performed, minimally invasive and cheap. In this work, we develop a blood-based signature that can provide a cheap and minimally invasive estimation of an individual’s CSF amyloid status using a machine learning approach. We show that a Random Forest model derived from plasma analytes can accurately predict subjects as having abnormal (low) CSF Aβ1−42 levels indicative of AD risk (0.84 AUC, 0.78 sensitivity, and 0.73 specificity). Refinement of the modeling indicates that only APOEε4 carrier status and four plasma analytes (CGA, Aβ1−42, Eotaxin 3, APOE) are required to achieve a high level of accuracy. Furthermore, we show across an independent validation cohort that individuals with predicted abnormal CSF Aβ1−42 levels transitioned to an AD diagnosis over 120 months significantly faster than those with predicted normal CSF Aβ1−42 levels and that the resulting model also validates reasonably across PET Aβ1−42 status (0.78 AUC). This is the first study to show that a machine learning approach, using plasma protein levels, age and APOEε4 carrier status, is able to predict CSF Aβ1−42 status, the earliest risk indicator for AD, with high accuracy. Show less
Mahmoudian Dehkordi, S.; Arnold, M.; Nho, K.; Ahmad, S.; Jia, W.; Xie, G.; ... ; Kaddurah-Daouk, R. 2018
Introduction: Increasing evidence suggests a role for the gut microbiome in central nervous system disorders and a specific role for the gut-brain axis in neurodegeneration. Bile acids (BAs),... Show moreIntroduction: Increasing evidence suggests a role for the gut microbiome in central nervous system disorders and a specific role for the gut-brain axis in neurodegeneration. Bile acids (BAs), products of cholesterol metabolism and clearance, are produced in the liver and are further metabolized by gut bacteria. They have major regulatory and signaling functions and seem dysregulated in Alzheimer's disease (AD).Methods: Serum levels of 15 primary and secondary BAs and their conjugated forms were measured in 1464 subjects including 370 cognitively normal older adults, 284 with early mild cognitive impairment, 505 with late mild cognitive impairment, and 305 AD cases enrolled in the AD Neuroimaging Initiative. We assessed associations of BA profiles including selected ratios with diagnosis, cognition, and AD-related genetic variants, adjusting for confounders and multiple testing.Results: In AD compared to cognitively normal older adults, we observed significantly lower serum concentrations of a primary BA (cholic acid [CA]) and increased levels of the bacterially produced, secondary BA, deoxycholic acid, and its glycine and taurine conjugated forms. An increased ratio of deoxycholic acid: CA, which reflects 7 alpha-dehydroxylation of CA by gut bacteria, strongly associated with cognitive decline, a finding replicated in serum and brain samples in the Rush Religious Orders and Memory and Aging Project. Several genetic variants in immune response-related genes implicated in AD showed associations with BA profiles.Discussion: We report for the first time an association between altered BA profile, genetic variants implicated in AD, and cognitive changes in disease using a large multicenter study. These findings warrant further investigation of gut dysbiosis and possible role of gut-liver-brain axis in the pathogenesis of AD. (C) 2018 The Authors. Published by Elsevier Inc. on behalf of the Alzheimer's Association. Show less
Nho, K.; Kueider-Paisley, A.; Mahmoudian Dekhordi, S.; Arnold, M.; Risacher, S.L.; Louie, G.; ... ; Kaddurah-Daouk, R. 2018
Carboxylesterase 1 (CES1) metabolizes methylphenidate and other drugs. CES1 gene variation only partially explains pharmacokinetic (PK) variability. Biomarkers predicting the PKs of drugs... Show moreCarboxylesterase 1 (CES1) metabolizes methylphenidate and other drugs. CES1 gene variation only partially explains pharmacokinetic (PK) variability. Biomarkers predicting the PKs of drugs metabolized by CES1 are needed. We identified lipids in plasma from 44 healthy subjects that correlated with CES1 activity as determined by PK parameters of methylphenidate including a ceramide (q value = 0.001) and a phosphatidylcholine (q value = 0.005). Carriers of the CES1 143E allele had decreased methylphenidate metabolism and altered concentration of this phosphatidylcholine (q value = 0.040) and several high polyunsaturated fatty acid lipids (PUFAs). The half‐maximal inhibitory concentration (IC50) values of chenodeoxycholate and taurocholate were 13.55 and 19.51 μM, respectively, consistent with a physiological significance. In silico analysis suggested that bile acid inhibition of CES1 involved both binding to the active and superficial sites of the enzyme. We initiated identification of metabolites predicting PKs of drugs metabolized by CES1 and suggest lipids to regulate or be regulated by this enzyme. Show less
