Abstract
Importance Biomarkers do not determine conversion to Alzheimer disease (AD) perfectly, and criteria do not specify how to take patient characteristics into account. Consequently, biomarker use may be challenging for clinicians, especially in patients with mild cognitive impairment (MCI).
Objective To construct biomarker-based prognostic models that enable determination of future AD dementia in patients with MCI.
Design, Setting, and Participants This study is part of the Alzheimer’s Biomarkers in Daily Practice (ABIDE) project. A total of 525 patients with MCI from the Amsterdam Dementia Cohort (longitudinal cohort, tertiary referral center) were studied. All patients had their baseline visit to a memory clinic from September 1, 1997, through August 31, 2014. Prognostic models were constructed by Cox proportional hazards regression with patient characteristics (age, sex, and Mini-Mental State Examination [MMSE] score), magnetic resonance imaging (MRI) biomarkers (hippocampal volume, normalized whole-brain volume), cerebrospinal fluid (CSF) biomarkers (amyloid-β1-42, tau), and combined biomarkers. Data were analyzed from November 1, 2015, to October 1, 2016.
Main Outcomes and Measures Clinical end points were AD dementia and any type of dementia after 1 and 3 years.
Results Of the 525 patients, 210 (40.0%) were female, and the mean (SD) age was 67.3 (8.4) years. On the basis of age, sex, and MMSE score only, the 3-year progression risk to AD dementia ranged from 26% (95% CI, 19%-34%) in younger men with MMSE scores of 29 to 76% (95% CI, 65%-84%) in older women with MMSE scores of 24 (1-year risk: 6% [95% CI, 4%-9%] to 24% [95% CI, 18%-32%]). Three- and 1-year progression risks were 86% (95% CI, 71%-95%) and 27% (95% CI, 17%-41%) when MRI results were abnormal, 82% (95% CI, 73%-89%) and 26% (95% CI, 20%-33%) when CSF test results were abnormal, and 89% (95% CI, 79%-95%) and 26% (95% CI, 18%-36%) when the results of both tests were abnormal. Conversely, 3- and 1-year progression risks were 18% (95% CI, 13%-27%) and 3% (95% CI, 2%-5%) after normal MRI results, 6% (95% CI, 3%-9%) and 1% (95% CI, 0.5%-2%) after normal CSF test results, and 4% (95% CI, 2%-7%) and 0.5% (95% CI, 0.2%-1%) after combined normal MRI and CSF test results. The prognostic value of models determining any type of dementia were in the same order of magnitude although somewhat lower. External validation in Alzheimer’s Disease Neuroimaging Initiative 2 showed that our models were highly robust.
Conclusions and Relevance This study provides biomarker-based prognostic models that may help determine AD dementia and any type of dementia in patients with MCI at the individual level. This finding supports clinical decision making and application of biomarkers in daily practice.
Introduction
Alzheimer disease (AD) has a long predementia phase that is often referred to as mild cognitive impairment (MCI). The cumulative progression incidence from MCI to dementia is approximately 50% over 3 years.1,2 This finding simultaneously implies that the other half of patients with MCI will remain clinically stable or return to a normal state. Therefore, there is an urgent need for individualized risk assessments in patients with MCI.3
Identification of abnormal biomarkers in patients with MCI helps to identify individuals at high risk of progression to AD dementia.4 Atrophy on brain magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) concentrations of amyloid-β1-42 (Aβ1-42) and tau protein are among the most widely used AD biomarkers and are associated with an increased risk of AD dementia at follow-up.5- 10 These findings resulted in the National Institute on Aging and Alzheimer Association (NIA-AA) criteria, stating that biomarker evidence enhances the pathologic specificity of the diagnosis of AD dementia and MCI due to AD dementia, facilitating an accurate and early diagnosis.3,11,12 However, these criteria do not specify how to deal with conflicting or borderline biomarker results and how to take patient characteristics into account. Moreover, although MRI and CSF are increasingly used in clinical practice, their diagnostic and prognostic value is not perfect. Therefore, optimal use of these biomarkers in daily clinical practice is challenging.3,13 We aimed to construct prognostic models based on MRI measures and CSF biomarkers for patients with MCI, taking into account patient characteristics to obtain individualized probabilities of progression.
JAMA Neurology 2017
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