Annapaola Prestia1, Anna Caroli2, Sara K Wade3, Wiesjie M van der Flier4, Rik Ossenkoppele5, Bart Van Berckel6, Frederik Barkhof6, Charlotte E Teunissen7, Anders Wall8, Stephen F Carter9, Michael Schöll9, Il Han Choo10, Agneta Nordberg11, Philip Scheltens12, Giovanni B Frisoni13. 1. Laboratory of Epidemiology and Neuroimaging, IRCCS Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy. 2. Medical Imaging Unit, Biomedical Engineering Department, IRCCS Mario Negri Institute for Pharmacological Research, Bergamo, Italy. 3. Department of Engineering, University of Cambridge, Cambridge, UK; Department of Decision Science, Bocconi University, Milan, Italy. 4. Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, The Netherlands; Department of Epidemiology & Biostatistics, VU University Medical Center, Amsterdam, The Netherlands. 5. Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, The Netherlands; Department of Radiology and Nuclear Medicine and PET research, VU University Medical Center, Amsterdam, The Netherlands. 6. Department of Radiology and Nuclear Medicine and PET research, VU University Medical Center, Amsterdam, The Netherlands. 7. Department of Clinical Chemistry, VU University Medical Center, Amsterdam, The Netherlands. 8. PET-Center, Section of Nuclear Medicine & PET, Department of Radiology, Oncology and Radiation Sciences, Uppsala University, Uppsala, Sweden. 9. Karolinska Institutet, Alzheimer Neurobiology Center, Stockholm, Sweden. 10. Karolinska Institutet, Alzheimer Neurobiology Center, Stockholm, Sweden; Department of Neuropsychiatry, School of Medicine, Chosun University, Gwangju, Republic of Korea. 11. Karolinska Institutet, Alzheimer Neurobiology Center, Stockholm, Sweden; Department of Geriatric Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden. 12. Alzheimer Center and Department of Neurology, VU University Medical Center, Amsterdam, The Netherlands. 13. Laboratory of Epidemiology and Neuroimaging, IRCCS Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy; Departments of Internal Medicine and Psychiatry, University Hospitals and University of Geneva, Geneve, Switzerland. Electronic address: gfrisoni@fatebenefratelli.it.
Abstract
INTRODUCTION: Proposed diagnostic criteria (international working group and National Institute on Aging and Alzheimer's Association) for Alzheimer's disease (AD) include markers of amyloidosis (abnormal cerebrospinal fluid [CSF] amyloid beta [Aβ]42) and neurodegeneration (hippocampal atrophy, temporo-parietal hypometabolism on [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET), and abnormal CSF tau). We aim to compare the accuracy of these biomarkers, individually and in combination, in predicting AD among mild cognitive impairment (MCI) patients. METHODS: In 73 MCI patients, followed to ascertain AD progression, markers were measured. Sensitivity and specificity, positive (LR+) and negative (LR-) likelihood ratios, and crude and adjusted hazard ratios were computed. RESULTS: Twenty-nine MCI patients progressed and 44 remained stable. Positivity to any marker achieved the lowest LR- (0.0), whereas the combination Aβ42 plus FDG-PET achieved the highest LR+ (6.45). In a survival analysis, positivity to any marker was associated with 100% conversion rate, whereas negativity to all markers was associated with 100% stability. DISCUSSION: The best criteria combined amyloidosis and neurodegeneration biomarkers, whereas the individual biomarker with the best performance was FDG-PET.
INTRODUCTION: Proposed diagnostic criteria (international working group and National Institute on Aging and Alzheimer's Association) for Alzheimer's disease (AD) include markers of amyloidosis (abnormal cerebrospinal fluid [CSF] amyloid beta [Aβ]42) and neurodegeneration (hippocampal atrophy, temporo-parietal hypometabolism on [18F]-fluorodeoxyglucose-positron emission tomography (FDG-PET), and abnormal CSF tau). We aim to compare the accuracy of these biomarkers, individually and in combination, in predicting AD among mild cognitive impairment (MCI) patients. METHODS: In 73 MCI patients, followed to ascertain AD progression, markers were measured. Sensitivity and specificity, positive (LR+) and negative (LR-) likelihood ratios, and crude and adjusted hazard ratios were computed. RESULTS: Twenty-nine MCI patients progressed and 44 remained stable. Positivity to any marker achieved the lowest LR- (0.0), whereas the combination Aβ42 plus FDG-PET achieved the highest LR+ (6.45). In a survival analysis, positivity to any marker was associated with 100% conversion rate, whereas negativity to all markers was associated with 100% stability. DISCUSSION: The best criteria combined amyloidosis and neurodegeneration biomarkers, whereas the individual biomarker with the best performance was FDG-PET.
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