Language Use Decreases In Young Children And Caregivers When Television Is On, Study Finds
June 1, 2009: In a new study, young children and their adult caregivers uttered fewer vocalizations, used fewer words and engaged in fewer conversations when in the presence of audible television. The population-based study is the first of its kind completed in the home environment, guided by lead researcher Dimitri A. Christakis, MD, MPH, director of the Center for Child Health, Behavior and Development at Seattle Children"s Research Institute and professor of pediatrics at the University of Washington School of Medicine. "Audible Television and Decreased Adult Words, Infant Vocalizations, and Conversational Turns" was published in the June 2009 issue of Archives of Pediatrics & Adolescent Medicine.
Endocrinology
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Alzheimer's Prediction And Diagnosis May Be Improved By Brain Imaging And Proteins In Spinal Fluid
sampling of many brain regions, including the hippocampus. Matthews and her team collaborated with NYU to apply the automated method to 250 subjects from the ADNI database (78 female/172 male, age 59-88; 79 healthy, 111 MCI, 60 Alzheimer"s). Using the automated approach, rCMglc was measured by PET in 32 brain regions. Participants were divided into seven subgroups across normal, MCI, and AD categories, based upon their initial diagnosis and results of subsequent memory and thinking tests up to 3 years after the scan. The researchers observed a significant correlation between rCMglc in several brain regions and the progression from "stable normal" to "normal with subsequent clinical decline", to subcategories of MCI and Alzheimer"s. They also found that HIP rCMglc was a sensitive predictor of decline and discriminator between disease stages. As compared to people considered "stable normal," HIP rCMglc was reduced by 5% in "normal with subsequent clinical decline", 12% in "stable MCI," 14% in "MCI with subsequent clinical decline" (p About ICAD 2009 The 2009 Alzheimer"s Association International Conference on Alzheimer"s Disease (ICAD 2009) brings together more than 5,000 researchers from 60 countries to share groundbreaking research and information on the cause, diagnosis, treatment and prevention of Alzheimer"s disease and related disorders. As a part of the Association"s research program, ICAD 2009 serves as a catalyst for generating new knowledge about dementia and fostering a vital, collegial research community. ICAD 2009 is being held in Vienna, Austria at Messe Wien Exhibition and Congress Center from July 1. About the Alzheimer"s Association The Alzheimer"s Association is the leading voluntary health organization in Alzheimer care, support and research. Our mission is to eliminate Alzheimer"s disease through the advancement of research, to provide and enhance care and support for all affected, and to reduce the risk of dementia through the promotion of brain health. Our vision is a world without Alzheimer"s. For more information, visit http://www.alz.org. * A list of ADNI sponsors is here. * Michael Ewers, et al - Biomarker Based Diagnosis Of Very Mild Alzheimer"s Disease: A Multicenter Study (Funders: Science Foundation Ireland (SFI), the Health Service Executive (HSE), the Health Research Board (HRB) of Ireland, National Institute on Aging, Foundation for the National Institutes of Health, National Institutes of Health, Evelyn F. McKnight Brain Institute of the University of Arizona, State of Arizona and Arizona Department of Health Services, Pfizer, Eisai, Janssen, Novartis, Lilly, Astra Zeneca, Sanofi, Canadian Institutes of health Research, Alzheimer Society of Canada, Michael Smith Health Research Foundation.) * Susan Landau, et al - Comparing predictors of conversion: Data from the Alzheimer"s Disease Neuroimaging Initiative (Funders: National Institute on Aging, ADNI Partnership) * Dawn C. Matthews, et al - Hippocampal glucose metabolism predicts cognitive decline and correlates to disease progression in the ADNI population (Funder: Abiant, Inc.) Biomarker Based Diagnosis Of Very Mild Alzheimer"s Disease: A Multicenter Study Michael Ewers1,2, Cathal Walsh3, John Q. Trojanowski4, Leslie M. Shaw5, Philip Sheltens6, Arun LW Bokde1,2, Howard Feldman7, Gene E. Alexander8, Bruno Dubois9, Harald Hampel1,2 1Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience (TCIN), Trinity College, University of Dublin & Adelaide and Meath Hospital Incorporating the National Children"s Hospital (AMNCH), Dublin, Ireland; 2Dementia Research Section and Memory Clinic, Alzheimer Memorial Center, Department of Psychiatry, Ludwig Maximilian University, Munich, Germany; 3Department of Statistics, Trinity College, University of Dublin, Dublin, Ireland; 4Institute on Aging, Alzheimer"s Disease Core Center, Center for Neurodegenerative Disease and Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philapelphia, PA; 5Institute on Aging, Alzheimer"s Disease Core Center, Center for Neurodegenerative Disease and Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA,; 6VU Medical Center, Amsterdam, Netherlands; 7Division of Neurology, University of British Columbia and Vancouver Coastal Health, Vancouver, BC, Canada; 8The Department of Psychology and Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, 9Hö´pital de la Salpöªtriö¨re et Universitö© Pierre et Marie Curie, Paris, France. Disclosure Block: M. Ewers, None. Background: Biomarker-supported diagnosis of Alzheimer"s disease has been proposed to enhance early clinical detection of AD (Dubois et al., Lancet Neurology, 2007). Objective: To develop a hypothesis-driven multi-biomarker classification algorithm for the detection of very mild AD. Methods: A total of 345 subjects including 81 patients with AD diagnosed according to the standard clinical NINCDS-ADRDA criteria, 163 amnestic mild cognitive impairment (MCI) patients and 101 elderly healthy controls (HC) were assessed in the Alzheimer"s Disease Neuroimaging Initiative (ADNI). Predictor variables included 1) cerebrospinal fluid (CSF)-concentration and ratios of total tau, p-tau181, and Aí²1-42 2) MRI volumetric measures of the left and right hippocampus and entorhinal cortex, and 3) Rey Auditory Verbal Learning test (RAVLT) and ADAS subtest scores of memory. These measures were obtained only for research purposes at baseline. Multiple logistic regression analyses, validated by random-split resampling, were performed. Results: 50 out of 163 amnestic MCI patients had converted to AD (MCI-AD) after a mean follow-up interval of 1.5 years. Significant neuropsychological predictors were ADAS delayed recall, and RAVLT total-immediate and 30-min.-delayed recall (classification accuracy = 89.9%) for the discrimination between MCI-AD and HC. In combination with these memory tests, unilateral hippocampus volume (left or right) improved differentiation between MCI-AD converters vs. HC most robustly, reaching a resampling validated sensitivity of 88.9% and the specificity of 96.76% (classification accuracy = 94.09%). The optimal cut-off value for the left hippocampus volume in this model was 2929 mm2. Cross-validation of the model by application to the classification of HC against AD reached a classification accuracy of 95.69%. The ratio of CSF concentration of total tau/Aí²1-42 added independently to the prediction accuracy, although less robustly when compared to the hippocampus-only model. Similarly to the CSF-ratio of total tau/Aí²1-42, a recently in autopsy-confirmed AD and HC cases established AD-biomarker signature including CSF-tau, Aí²1-42, and ApoE genotype contributed marginally to the left hippocampus volume plus memory model, with the extended model reaching an overall classification accuracy of 95.6%. Conclusion: A combination of hippocampus volume and episodic memory performance shows a robust and clinically significant diagnostic accuracy of AD at a very early stage. Comparing predictors of conversion: Data from the Alzheimer"s Disease Neuroimaging Initiative Susan M. Landau1, Danielle Harvey2, Cindee Madison1, Normal L. Foster3, Eric M. Reiman4, Leslie M. Shaw5, John Q. Trojanowski5, Ronald C. Petersen6, Michael W. Weiner7, William J. Jagust1,8 1UC Berkeley, Berkeley, CA, USA; 2UC Davis School of Medicine, Davis, CA, USA; 3University of Utah, Salt Lake City, UT, USA; 4Banner Alzheimer"s Institute and Banner Good Samaritan PET Center, Phoenix, AZ, USA; 5University of Pennsylvania School of Medicine, Philadelphia, PA, USA; 6Mayo Clinic College of Medicine, Rochester, MN, USA; 7Department of Veterans Affairs Medical Center, San Francisco, CA, USA; 8Lawrence Berkeley National Laboratory, Berkeley, CA, USA. Disclosure Block: S.M. Landau, None; D. Harvey, None; C. Madison, None; N.L. Foster, Myriad Pharmaceuticals, GE Healthcare, Employee; co-investigator in clinical trials funded by Myriad Pharmaceuticals, Merck& Co., Elan Pharmaceuticals, Eisai, and Eli Lilly, Other financial or material support; E.M. Reiman, None; L.M. Shaw, None; J.Q. Trojanowski, None; R.C. Petersen, Elan Pharmaceuticals, Wyeth Pharmaceuticals, Consultant; M.W. Weiner, Avid, Grants/Research Support; Neurochem, Eli Lilly, Nestle, Bristol Meyers, Eisai, Pfizer, Genentech, Forest, Consultant; Elan Pharmaceuticals, Synarc, Other financial or material support; W.J. Jagust, Synarc, Elan Pharmaceuticals, Genentech, Ceregene, Schering- Plough, Merck & Co., Consultant. Background: A variety of measures (genetic, cerebrospinal fluid (CSF), brain glucose metabolism, gray matter volume) have shown promise in predicting conversion from mild cognitive impairment (MCI) to Alzheimer"s disease (AD). However, the difficulty of obtaining all measurements in the same subjects has prevented comparisons across candidate predictors. The Alzheimer"s Disease Neuroimaging Initiative (ADNI) is a large multisite study designed to improve detection of longitudinal decline in AD and MCI. Here, we compared the utility of baseline measurements for predicting cognitive decline in MCI and conversion from MCI to AD. Methods: Candidate predictors of decline included hippocampal volume, relative rates of glucose metabolism in a prespecified composite region of interest (FDG-ROI) that included angular, temporal, and posterior cingulate areas; number of apolipoprotein E4 (ApoE4) alleles; and CSF measurement of Abeta42, total tau (t-Tau), and tau phosphorylated in the 181 threonine position (p-Tau181p). Independent and outcome measures were analyzed in continuous and dichotomous forms. Models with dichotomous outcomes assessed conversion from MCI to AD, whereas models with continuous outcomes assessed cognitive decline over 2 years. Analyses were carried out in MCI subjects using all available data (N = 66Pages: 1 [2] 3