7 research outputs found
Synaptic proteins and phospholipids are increased in gerbil brain by administering uridine plus docosahexaenoic acid orally
The synthesis of brain phosphatidy1choline may utilize three circulating precursors: choline; a pyrimidine (e.g., uridine, converted via UTP to brain CTP); and a PUFA (e.g., docosahexaenoic acid); phosphatidylethanolamine may utilize two of these, a pyrimidine and a PUFA. We observe that consuming these precursors can substantially increase membrane phosphatide and synaptic protein levels in gerbil brains. (Pyrimidine metabolism in gerbils, but not rats, resembles that in humans.) Animals received, daily for 4 weeks, a diet containing choline chloride and UMP (a uridine source) and/or DHA by gavage. Brain phosphatidy1choline rose by 13-22% with uridine and choline alone, or DHA alone, or by 45% with the combination, phosphatidylethanolamine and the other phosphatides increasing by 39-74%. Smaller elevations occurred after 1-3 weeks. The combination also increased the vesicular protein Synapsin-1 by 41%, the postsynaptic protein PSD-95 by 38% and the neurite neurofibrillar proteins NF-70 and NF-M by up to 102% and 48%, respectively. However, it had no effect on the cytoskeletal protein beta-tubulin. Hence, the quantity of synaptic membrane probably increased. The precursors act by enhancing the substrate saturation of enzymes that initiate their incorporation into phosphatidylcholine and phosphatidylethanolamine and by UTP-mediated activation of P2Y receptors. Alzheimer's disease brains contain fewer and smaller synapses and reduced levels of synaptic proteins, membrane phosphatides, choline and DHA. The three phosphatide precursors might thus be useful in treating this disease..United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Mental Health (NIMH) - R01MH028783, R37MH028783United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Mental Health (NIMH) - MH 2878
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Predicting missing biomarker data in a longitudinal study of Alzheimer disease
Objective:To investigate predictors of missing data in a longitudinal study of Alzheimer disease (AD).Methods:The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a clinic-based, multicenter, longitudinal study with blood, CSF, PET, and MRI scans repeatedly measured in 229 participants with normal cognition (NC), 397 with mild cognitive impairment (MCI), and 193 with mild AD during 2005–2007. We used univariate and multivariable logistic regression models to examine the associations between baseline demographic/clinical features and loss of biomarker follow-ups in ADNI.Results:CSF studies tended to recruit and retain patients with MCI with more AD-like features, including lower levels of baseline CSF Aβ42. Depression was the major predictor for MCI dropouts, while family history of AD kept more patients with AD enrolled in PET and MRI studies. Poor cognitive performance was associated with loss of follow-up in most biomarker studies, even among NC participants. The presence of vascular risk factors seemed more critical than cognitive function for predicting dropouts in AD.Conclusion:The missing data are not missing completely at random in ADNI and likely conditional on certain features in addition to cognitive function. Missing data predictors vary across biomarkers and even MCI and AD groups do not share the same missing data pattern. Understanding the missing data structure may help in the design of future longitudinal studies and clinical trials in AD
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Predicting missing biomarker data in a longitudinal study of Alzheimer disease
Objective:To investigate predictors of missing data in a longitudinal study of Alzheimer disease (AD).Methods:The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a clinic-based, multicenter, longitudinal study with blood, CSF, PET, and MRI scans repeatedly measured in 229 participants with normal cognition (NC), 397 with mild cognitive impairment (MCI), and 193 with mild AD during 2005–2007. We used univariate and multivariable logistic regression models to examine the associations between baseline demographic/clinical features and loss of biomarker follow-ups in ADNI.Results:CSF studies tended to recruit and retain patients with MCI with more AD-like features, including lower levels of baseline CSF Aβ42. Depression was the major predictor for MCI dropouts, while family history of AD kept more patients with AD enrolled in PET and MRI studies. Poor cognitive performance was associated with loss of follow-up in most biomarker studies, even among NC participants. The presence of vascular risk factors seemed more critical than cognitive function for predicting dropouts in AD.Conclusion:The missing data are not missing completely at random in ADNI and likely conditional on certain features in addition to cognitive function. Missing data predictors vary across biomarkers and even MCI and AD groups do not share the same missing data pattern. Understanding the missing data structure may help in the design of future longitudinal studies and clinical trials in AD