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A Single Baseline Amyloid Positron Emission Tomography Could Be Sufficient for Predicting Alzheimer’s Disease Conversion in Mild Cognitive Impairment

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주제분류정신과학

초록

Objective Baseline amyloid burden in mild cognitive impairment (MCI) has been linked to conversion to Alzheimer’s disease (AD), but the comparison of baseline and longitudinal changes in amyloid burden for predicting AD remains unresolved. The objectives of this study aimed to compare the prognostic ability of baseline and longitudinal changes in amyloid burden in MCI patients.Methods Seventy-five individuals with MCI were recruited and examined annually by clinical interviews for a mean follow-up of 24 months (range, 11.6–42.0). [18F]Florbetaben positron emission tomography (PET) scans were performed. T1-weighted 3D volumes were acquired for co-registration, and to define regions of interest. We examined whether baseline and longitudinal amyloid burden changes can improve AD conversion by Cox proportional hazard model analysis and receiver operating characteristic (ROC) curve analysis.Results Cox proportional hazards model analysis showed that baseline amyloid burden was significantly associated with increased risk of conversion to AD (hazard ratio [HR]=10.0; 95% confidence interval [CI], 1.15–85.39; p=0.04), but longitudinal amyloid burden changes was not (HR=0.2; 95% CI, 0.02–1.18; p=0.07). When predicting AD, longitudinal amyloid burden changes had better ROC accuracy of 65.2% (95% CI, 48.4–82.0) than baseline amyloid burden of 59.6% (95% CI, 40.3–79.0), without statistical significance in pairwise comparison.Conclusion A single baseline amyloid PET could be sufficient in the prediction of AD conversion in MCI.

Abstract

Objective Baseline amyloid burden in mild cognitive impairment (MCI) has been linked to conversion to Alzheimer’s disease (AD), but the comparison of baseline and longitudinal changes in amyloid burden for predicting AD remains unresolved. The objectives of this study aimed to compare the prognostic ability of baseline and longitudinal changes in amyloid burden in MCI patients.Methods Seventy-five individuals with MCI were recruited and examined annually by clinical interviews for a mean follow-up of 24 months (range, 11.6–42.0). [18F]Florbetaben positron emission tomography (PET) scans were performed. T1-weighted 3D volumes were acquired for co-registration, and to define regions of interest. We examined whether baseline and longitudinal amyloid burden changes can improve AD conversion by Cox proportional hazard model analysis and receiver operating characteristic (ROC) curve analysis.Results Cox proportional hazards model analysis showed that baseline amyloid burden was significantly associated with increased risk of conversion to AD (hazard ratio [HR]=10.0; 95% confidence interval [CI], 1.15–85.39; p=0.04), but longitudinal amyloid burden changes was not (HR=0.2; 95% CI, 0.02–1.18; p=0.07). When predicting AD, longitudinal amyloid burden changes had better ROC accuracy of 65.2% (95% CI, 48.4–82.0) than baseline amyloid burden of 59.6% (95% CI, 40.3–79.0), without statistical significance in pairwise comparison.Conclusion A single baseline amyloid PET could be sufficient in the prediction of AD conversion in MCI.

참고문헌 (24)

  1. Petersen RC, "Mild cognitive impairment: transition between aging and Alzheimer’s disease", Neurologia, 15, pp.93~101 (2000)
  2. Okello A, "Conversion of amyloid positive and negative MCI to AD over 3years: an 11C-PIB PET study", Neurology, 73, pp.754~760 (2009)
  3. Nordberg A, "A European multicentre PET study of fibrillar amyloid in Alzheimer’s disease", Eur J Nucl Med Mol Imaging, 40, pp.104~114 (2013)
  4. Rowe CC, "Predicting Alzheimer disease with β-amyloid imaging: results from the Australian imaging, biomarkers, and lifestyle study of ageing", Ann Neurol, 74, pp.905~913 (2013)
  5. Villemagne VL, "Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study", Lancet Neurol, 12, pp.357~367 (2013)
  6. Kemppainen NM, "Five-year follow-up of 11C-PIB uptake in Alzheimer’s disease and MCI", Eur J Nucl Med Mol Imaging, 41, pp.283~289 (2014)
  7. Ong KT, "Aβ imaging with 18F-florbetaben in prodromal Alzheimer’s disease: a prospective outcome study", J Neurol Neurosurg Psychiatry, 86, pp.431~436 (2015)
  8. Hatashita S, "Amyloid-β deposition and long-term progression in mild cognitive impairment due to Alzheimer’s disease defined with amyloid PET imaging", J Alzheimers Dis, 57, pp.765~773 (2017)
  9. -PIB across the spectrum of Alzheimer’s disease, "Front Aging Neurosci", 2019, 251~
  10. Morris JC, "The clinical dementia rating (CDR): current version and scoring rules", Neurology, 43, pp.2412~2414 (1993)
  11. Petersen RC, "Mild cognitive impairment as a diagnostic entity", J Intern Med, 256, pp.183~194 (2004)
  12. Fazekas F, "MR signal abnormalities at 1.5 T in Alzheimer’s dementia and normal aging", AJR Am J Roentgenol, 149, pp.351~356 (1987)
  13. McKhann GM, "The diagnosis of dementia due to Alzheimer’s disease:recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease", Alzheimers Dement, 7, pp.263~269 (2011)
  14. In-Woong Kang, "Accuracy of Korean-Mini-Mental Status Examination Based on Seoul Neuro-Psychological Screening Battery II Results", Korean Journal of Family Medicine, 37(3), pp.177~181 (2016), 대한가정의학회
  15. Tzourio-Mazoyer N, "Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI singlesubject brain", Neuroimage, 15, pp.273~289 (2002)
  16. Reiman EM, "Fibrillar amyloid-beta burden in cognitively normal people at 3 levels of genetic risk for Alzheimer’s disease", Proc Natl Acad Sci U S A, 106, pp.6820~6825 (2009)
  17. DeLong ER, "Comparing the areas under two or more correlated receiver operating characteristic curves:a nonparametric approach", Biometrics, 44, pp.837~845 (1988)
  18. Farias ST, "Progression of mild cognitive impairment to dementia in clinic- vs community-based cohorts", Arch Neurol, 66, pp.1151~1157 (2009)
  19. Iaccarino L, "A cross-validation of FDG-and amyloid-PET biomarkers in mild cognitive impairment for the risk prediction to dementia due to Alzheimer’s disease in a clinical setting", J Alzheimers Dis, 59, pp.603~614 (2017)
  20. Ye BS, "Longitudinal outcomes of amyloid positive versus negative amnestic mild cognitive impairments:a three-year longitudinal study", Sci Rep, 8, pp.5557~ (2018)
  21. Palmqvist S, "Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity", Nat Commun, 8, pp.1214~ (2017)
  22. Collij LE, "Multitracer model for staging cortical amyloid deposition using PET imaging", Neurology, 95, pp.e1538~e1553 (2020)
  23. Thomas KR, "Objective subtle cognitive difficulties predict future amyloid accumulation and neurodegeneration", Neurology, 94, pp.e397~e406 (2020)
  24. Lopes Alves I, "Strategies to reduce sample sizes in Alzheimer’s disease primary and secondary prevention trials using longitudinal amyloid PET imaging", Alzheimers Res Ther, 13, pp.82~ (2021)

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