Predicting the rate of cognitive decline in aging and early Alzheimer disease

Objectives: To determine prognostic factors affecting the course of Alzheimer disease (AD) and to determine the role of region-specific brain volumes as predictors of cognitive decline. Methods: Longitudinal data from 166 normal elderly individuals and 59 early AD patients were analyzed. Brain volumes were extracted from MRI scans using semiautomated recursive segmentation methods. Prognostic factors were considered significant if they had a significant effect on the rate of cognitive decline. Results: In multivariate analysis, higher Clinical Dementia Rating Scale (CDR) score at entry was a significant prognostic factor for an increased rate of cognitive decline. Significant prognostic factors within the baseline CDR = 0 group were base rate of progression and percent total high signal intensity (HSI), percent ventricular, and percent CSF volumes. Base rate of progression, family history, and percent ventricular volume were significant prognostic factors within the CDR = 0.5 group and APOE had a marginally significant effect on the rate of cognitive decline in the CDR = 1 group. Conclusions: Percent total HSI, ventricular, and total CSF volume measures can independently predict the rate of cognitive decline and improve the predictive power of statistical models that use only clinical data. Brain volumetric measures from MRI can be used to estimate the rate of cognitive decline even among normal elderly individuals and thus may aid in the prediction of time of onset of disease.

[1]  V. Barnett,et al.  Applied Linear Statistical Models , 1975 .

[2]  S. Folstein,et al.  "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. , 1975, Journal of psychiatric research.

[3]  G. Fillenbaum,et al.  The development, validity, and reliability of the OARS multidimensional functional assessment questionnaire. , 1981, Journal of gerontology.

[4]  J. Ware,et al.  Random-effects models for longitudinal data. , 1982, Biometrics.

[5]  N. Cook,et al.  Design and analysis methods for longitudinal research. , 1983, Annual review of public health.

[6]  M. Folstein,et al.  Clinical diagnosis of Alzheimer's disease , 1984, Neurology.

[7]  J. Langston,et al.  The Neurobehavioral Cognitive Status Examination: a brief but quantitative approach to cognitive assessment. , 1987, Annals of internal medicine.

[8]  R. Mohs,et al.  Consortium to establish a registry for Alzheimer's disease (CERAD) clinical and neuropsychological assessment of Alzheimer's disease. , 2002, Psychopharmacology bulletin.

[9]  A. Heyman,et al.  The Consortium to Establish a Registry for Alzheimer's Disease (CERAD) , 1993, Neurology.

[10]  J. Morris The Clinical Dementia Rating (CDR) , 1993, Neurology.

[11]  J. Kaye,et al.  Neurologic function in the optimally healthy oldest old , 1993, Neurology.

[12]  M. Folstein,et al.  Population-based norms for the Mini-Mental State Examination by age and educational level. , 1993, JAMA.

[13]  P. Tiraboschi,et al.  Rate of Progression and Prognostic Factors in Alzheimer's Disease: A Prospective Study , 1993, Journal of the American Geriatrics Society.

[14]  S. Edland,et al.  in Alzheimer's Disease , 1994 .

[15]  J A Yesavage,et al.  'How far' vs 'how fast' in Alzheimer's disease. The question revisited. , 1994, Archives of neurology.

[16]  R. Mayeux,et al.  Influence of education and occupation on the incidence of Alzheimer's disease. , 1994, JAMA.

[17]  Jan de Leeuw,et al.  Questioning Multilevel Models , 1995 .

[18]  J. Growdon,et al.  Rate of progression of Alzheimer's disease is associated with genetic risk. , 1995, Archives of neurology.

[19]  E B Larson,et al.  Cognitive decline in Alzheimer's disease: a longitudinal investigation of risk factors for accelerated decline. , 1995, The journals of gerontology. Series A, Biological sciences and medical sciences.

[20]  S H Ferris,et al.  A family intervention to delay nursing home placement of patients with Alzheimer disease. A randomized controlled trial. , 1996, JAMA.

[21]  J. Growdon,et al.  Risk of dementia among relatives of Alzheimer's disease patients in the MIRAGE study , 1996, Neurology.

[22]  J. Kaye,et al.  A prospective study of cognitive health in the elderly (Oregon Brain Aging Study): effects of family history and apolipoprotein E genotype. , 1997, American journal of human genetics.

[23]  J. Kaye,et al.  Volume loss of the hippocampus and temporal lobe in healthy elderly persons destined to develop dementia , 1997, Neurology.

[24]  M. Albert,et al.  Predicting time to nursing home care and death in individuals with Alzheimer disease. , 1997, JAMA.

[25]  J. Kaye,et al.  Brain volume preserved in healthy elderly through the eleventh decade , 1998, Neurology.

[26]  C. Jack,et al.  Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment , 1999, Neurology.

[27]  H. Gertz,et al.  Do white matter changes contribute to the subsequent development of dementia in patients with mild cognitive impairment? A longitudinal study , 2000, International journal of geriatric psychiatry.

[28]  S. Edland,et al.  Mixed effect models of longitudinal Alzheimer's disease data: a cautionary note. , 2000, Statistics in medicine.

[29]  F. Bellavance,et al.  Tracking Cognitive Decline in Alzheimer's Disease Using the Mini-Mental State Examination: A Meta-Analysis , 2000, International Psychogeriatrics.

[30]  D. Gelb,et al.  Measurement of progression in Alzheimer's disease: a clinician's perspective. , 2000, Statistics in medicine.

[31]  J. Ashford,et al.  Modelling mini mental state examination changes in Alzheimer's disease. , 2000, Statistics in medicine.

[32]  A. Convit,et al.  Atrophy of the medial occipitotemporal, inferior, and middle temporal gyri in non-demented elderly predict decline to Alzheimer’s disease☆ , 2000, Neurobiology of Aging.

[33]  R. Doody,et al.  A method for estimating progression rates in Alzheimer disease. , 2001, Archives of neurology.

[34]  D. Bennett,et al.  MRI-derived entorhinal and hippocampal atrophy in incipient and very mild Alzheimer’s disease☆ ☆ This research was supported by grants P01 AG09466 and P30 AG10161 from the National Institute on Aging, National Institutes of Health. , 2001, Neurobiology of Aging.

[35]  C. DeCarli,et al.  The role of neuroimaging in dementia. , 2001, Clinics in geriatric medicine.

[36]  A. Dale,et al.  Whole Brain Segmentation Automated Labeling of Neuroanatomical Structures in the Human Brain , 2002, Neuron.

[37]  M. Albert,et al.  MRI measures of entorhinal cortex vs hippocampus in preclinical AD , 2002, Neurology.

[38]  J Philip Miller,et al.  Rates of progression in mild cognitive impairment and early Alzheimer’s disease , 2002, Neurology.

[39]  Richard Camicioli,et al.  Independent predictors of cognitive decline in healthy elderly persons. , 2002, Archives of neurology.

[40]  Erik Meijer,et al.  MLA. Software for multilevel analysis of data with two levels. User's guide for version 4.1 , 2005 .