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European Journal of Nuclear Medicine and Molecular Imaging

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Open Access 14-07-2022 | Alzheimer's Disease | Original Article

Introducing a gatekeeping system for amyloid status assessment in mild cognitive impairment

Authors: E. Doering, M. C. Hoenig, G. N. Bischof, K. P. Bohn, L. M. Ellingsen, T. van Eimeren, A. Drzezga, for the Alzheimer’s Disease Neuroimaging Initiative

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 13/2022

Abstract

Background

In patients with mild cognitive impairment (MCI), enhanced cerebral amyloid-β plaque burden is a high-risk factor to develop dementia with Alzheimer’s disease (AD). Not all patients have immediate access to the assessment of amyloid status (A-status) via gold standard methods. It may therefore be of interest to find suitable biomarkers to preselect patients benefitting most from additional workup of the A-status. In this study, we propose a machine learning–based gatekeeping system for the prediction of A-status on the grounds of pre-existing information on APOE-genotype ¹⁸F-FDG PET, age, and sex.

Methods

Three hundred and forty-two MCI patients were used to train different machine learning classifiers to predict A-status majority classes among APOE-ε4 non-carriers (APOE4-nc; majority class: amyloid negative (Aβ-)) and carriers (APOE4-c; majority class: amyloid positive (Aβ +)) from ¹⁸F-FDG-PET, age, and sex. Classifiers were tested on two different datasets. Finally, frequencies of progression to dementia were compared between gold standard and predicted A-status.

Results

Aβ- in APOE4-nc and Aβ + in APOE4-c were predicted with a precision of 87% and a recall of 79% and 51%, respectively. Predicted A-status and gold standard A-status were at least equally indicative of risk of progression to dementia.

Conclusion

We developed an algorithm allowing approximation of A-status in MCI with good reliability using APOE-genotype, ¹⁸F-FDG PET, age, and sex information. The algorithm could enable better estimation of individual risk for developing AD based on existing biomarker information, and support efficient selection of patients who would benefit most from further etiological clarification. Further potential utility in clinical routine and clinical trials is discussed.

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Albert MS, DeKosky ST, Dickson D, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 2011;7:270–9.CrossRef

Jack CR, Albert MS, Knopman DS, McKhann GM, Sperling RA, Carrillo MC, Thies B, Phelps CH. Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 2011;7:257–62.CrossRef

Haeberlein SB, Hehn C von, Tian Y, et al (2019) EMERGE and ENGAGE topline results: two phase 3 studies to evaluate aducanumab in patients with early Alzheimer’s disease. San Diego, CA

Salloway S, Chalkias S, Barkhof F, et al. Amyloid-related imaging abnormalities in 2 phase 3 studies evaluating aducanumab in patients with early Alzheimer disease. JAMA Neurol. 2022. https://doi.org/10.1001/jamaneurol.2021.4161.CrossRefPubMed

Benedict C, Blennow K, Zetterberg H, Cedernaes J. Effects of acute sleep loss on diurnal plasma dynamics of CNS health biomarkers in young men. Neurology. 2020. https://doi.org/10.1212/WNL.0000000000008866.CrossRefPubMedPubMedCentral

Jack CR, Bennett DA, Blennow K, et al. A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016. https://doi.org/10.1212/WNL.0000000000002923.CrossRefPubMedPubMedCentral

Chételat G, Arbizu J, Barthel H, et al. Amyloid-PET and 18F-FDG-PET in the diagnostic investigation of Alzheimer’s disease and other dementias. Lancet Neurol. 2020;19:951–62.CrossRef

Mosconi L, Tsui WH, Herholz K, et al. Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias. J Nucl Med. 2008;49:390–8.CrossRef

Smailagic N, Lafortune L, Kelly S, Hyde C, Brayne C. 18F-FDG PET for prediction of conversion to Alzheimer’s disease dementia in people with mild cognitive impairment: an updated systematic review of test accuracy. J Alzheimer’s Dis. 2018;64:1175–94.CrossRef

10.

Carbonell F, Zijdenbos AP, Mclaren DG, Iturria-Medina Y, Bedell BJ. Modulation of glucose metabolism and metabolic connectivity by β-amyloid. J Cereb Blood Flow Metab. 2016. https://doi.org/10.1177/0271678X16654492.CrossRefPubMedPubMedCentral

11.

Dukart J, Mueller K, Barthel H, Villringer A, Sabri O, Schroeter ML. Meta-analysis based SVM classification enables accurate detection of Alzheimer’s disease across different clinical centers using FDG-PET and MRI. Psychiatry Res - Neuroimaging. 2013;212:230–6.CrossRef

12.

Yuan Y, Gu ZX, Wei WS. Fluorodeoxyglucose-positron-emission tomography, single-photon emission tomography, and structural MR imaging for prediction of rapid conversion to alzheimer disease in patients with mild cognitive impairment: a meta-analysis. Am J Neuroradiol. 2009;30:404–10.CrossRef

13.

Drzezga A, Grimmer T, Henriksen G, et al. Effect of APOE genotype on amyloid plaque load and gray matter volume in Alzheimer disease. Neurology. 2009. https://doi.org/10.1212/WNL.0b013e3181a2e8d0.CrossRefPubMed

14.

Jansen WJ, Ossenkoppele R, Knol DL, et al. Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis. JAMA - J Am Med Assoc. 2015. https://doi.org/10.1001/jama.2015.4668.CrossRef

15.

Lehmann M, Ghosh PM, Madison C, Karydas A, Coppola G, O’Neil JP, Huang Y, Miller BL, Jagust WJ, Rabinovici GD. Greater medial temporal hypometabolism and lower cortical amyloid burden in ApoE4-positive AD patients. J Neurol Neurosurg Psychiatry. 2014. https://doi.org/10.1136/jnnp-2013-305858.CrossRefPubMed

16.

Drzezga A, Grimmer T, Riemenschneider M, Lautenschlager N, Siebner H, Alexopoulus P, Minoshima S, Schwaiger M, Kurz A. Prediction of individual clinical outcome in MCI by means of genetic assessment and 18F-FDG PET. J Nucl Med 2005;46.

17.

Jung YH, Lee H, Kim HJ, Na DL, Han HJ, Jang H. Seo SW (2020) Prediction of amyloid β PET positivity using machine learning in patients with suspected cerebral amyloid angiopathy markers. Sci Reports. 2020;101(10):1–10.

18.

Kim JP, Kim J, Jang H, et al. Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach. Sci Rep. 2021;11:1–9.

19.

Lee JH, Byun MS, Yi D, Sohn BK, Jeon SY, Lee Y, Lee JY, Kim YK, Lee YS, Lee DY. Prediction of cerebral amyloid with common information obtained from memory clinic practice. Front Aging Neurosci. 2018. https://doi.org/10.3389/fnagi.2018.00309.CrossRefPubMedPubMedCentral

20.

Kim SE, Woo S, Kim SW, et al. A nomogram for predicting amyloid PET positivity in amnestic mild cognitive impairment. J Alzheimer’s Dis. 2018;66:681–91.CrossRef

21.

Kim S, Lee P, Oh KT, et al. Deep learning-based amyloid PET positivity classification model in the Alzheimer’s disease continuum by using 2-[18F]FDG PET. EJNMMI Res. 2021;11:1–14.CrossRef

22.

Grimmer T, Wutz C, Drzezga A, Forster S, Forstl H, Ortner M, Perneczky R, Kurz A. The usefulness of amyloid imaging in predicting the clinical outcome after two years in subjects with mild cognitive impairment. Curr Alzheimer Res. 2013;10:82–5.PubMed

23.

Winblad B, Palmer K, Kivipelto M, et al. Mild cognitive impairment - beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. In: J. Intern. Med. J Intern Med, 2004, pp 240–246

24.

Landau S, Koeppe R, Jagust W (2011) Florbetaben processing and positivity threshold derivation Motivation for changing the threshold.

25.

Landau S, Jagust W. Florbetapir processing methods. 2011.

26.

Jagust WJ, Landau SM, Shaw LM, et al. Relationships between biomarkers in aging and dementia. Neurology. 2009;73:1193–9.CrossRef

27.

Jagust WJ, Bandy D, Chen K, Foster NL, Landau SM, Mathis CA, Price JC, Reiman EM, Skovronsky D, Koeppe RA. The Alzheimer’s Disease Neuroimaging Initiative positron emission tomography core. Alzheimer’s Dement. 2010. https://doi.org/10.1016/j.jalz.2010.03.003.CrossRef

28.

Ye BS, Kim HJ, Kim YJ, et al. Longitudinal outcomes of amyloid positive versus negative amnestic mild cognitive impairments: a three-year longitudinal study. Sci Rep. 2018;8:5557.CrossRef

29.

Saykin AJ, Shen L, Foroud TM, et al. Alzheimer’s Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: genetics core aims, progress, and plans. Alzheimer’s Dement. 2010;6:265–73.CrossRef

30.

Verger A, Doyen M, Campion JY, Guedj E. The pons as reference region for intensity normalization in semi-quantitative analysis of brain 18FDG PET: application to metabolic changes related to ageing in conventional and digital control databases. EJNMMI Res. 2021;11:1–7.CrossRef

31.

Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002. https://doi.org/10.1006/nimg.2001.0978.CrossRefPubMed

32.

Molnar C. Permutation feature importance. Interpret. Mach. Learn. - A Guid. Mak. Blackbox Model. Explain. 2021.

33.

McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of ALZHEIMER’s disease: report of the NINCDS-ADRDA work group⋆ under the auspices of Department Of Health and Human Services Task Force on Alzheimer’s Disease. Neurology. 1984. https://doi.org/10.1212/wnl.34.7.939.CrossRefPubMed

34.

Jack CR, Bennett DA, Blennow K, et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimer’s Dement. 2018. https://doi.org/10.1016/j.jalz.2018.02.018.CrossRef

35.

Wolz R, Schwarz AJ, Gray KR, Yu P, Hill DLG. Enrichment of clinical trials in MCI due to AD using markers of amyloid and neurodegeneration. Neurology. 2016. https://doi.org/10.1212/WNL.0000000000003126.CrossRefPubMedPubMedCentral

36.

Drzezga A, Lautenschlager N, Siebner H, Riemenschneider M, Willoch F, Minoshima S, Schwaiger M, Kurz A. Cerebral metabolic changes accompanying conversion of mild cognitive impairment into Alzheimer’s disease: a PET follow-up study. Eur J Nucl Med Mol Imaging. 2003. https://doi.org/10.1007/s00259-003-1194-1.PubMed

37.

Loewenstein DA, Barker WW, Chang JY, Apicella A, Yoshii F, Kothari P, Levin B, Duara R. Predominant left hemisphere metabolic dysfunction in dementia. Arch Neurol. 1989. https://doi.org/10.1001/archneur.1989.00520380046012.CrossRefPubMed

38.

Weise CM, Chen K, Chen Y, Kuang X, Savage CR, Reiman EM. Left lateralized cerebral glucose metabolism declines in amyloid-β positive persons with mild cognitive impairment. NeuroImage Clin. 2018;20:286–96.CrossRef

39.

Pfeil J, Hoenig MC, Doering E, van Eimeren T, Drzezga A, Bischof GN. Unique regional patterns of amyloid burden predict progression to prodromal and clinical stages of Alzheimer’s disease. Neurobiol Aging. 2021;106:119–29.CrossRef

40.

Sanabria-Diaz G, Martínez-Montes E, Melie-Garcia L. Glucose metabolism during resting state reveals abnormal brain networks organization in the Alzheimer’s disease and mild cognitive impairment. PLoS ONE. 2013. https://doi.org/10.1371/journal.pone.0068860.CrossRefPubMedPubMedCentral

41.

Arnemann KL, Stöber F, Narayan S, Rabinovici GD, Jagust WJ. Metabolic brain networks in aging and preclinical Alzheimer’s disease. NeuroImage Clin. 2018. https://doi.org/10.1016/j.nicl.2017.12.037.CrossRefPubMed

42.

Hammes J, Bischof GN, Bohn KP, et al. One stop shop: flortaucipir PET differentiates amyloid positive and negative forms of neurodegenerative diseases. J Nucl Med. 2020.

43.

Rowe CC, Ellis KA, Rimajova M, et al. Amyloid imaging results from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging. Neurobiol Aging. 2010. https://doi.org/10.1016/j.neurobiolaging.2010.04.007

44.

Duara R, Loewenstein DA, Lizarraga G, et al. Effect of age, ethnicity, sex, cognitive status and APOE genotype on amyloid load and the threshold for amyloid positivity. NeuroImage Clin. 2019. https://doi.org/10.1016/j.nicl.2019.101800.CrossRefPubMedPubMedCentral

45.

Ashford MT, Raman R, Miller G, et al. Screening and enrollment of underrepresented ethnocultural. Alzheimer’s Dement. 2022. https://doi.org/10.1002/alz.12640.CrossRef

46.

Mattsson N, Groot C, Jansen WJ, et al. Prevalence of the apolipoprotein E ε4 allele in amyloid β positive subjects across the spectrum of Alzheimer’s disease. Alzheimer’s Dement. 2018. https://doi.org/10.1016/j.jalz.2018.02.009.CrossRef

47.

Yokeş MB, Emre M, Harmanci H, Gürvit H, Hanaǧasi H, Şahin H, Bilgiç B, Başak AN. The apolipoprotein E (APOE) genotype in a Turkish population with Alzheimer’s disease. Balk. J Med Genet. 2005;8.

Title: Introducing a gatekeeping system for amyloid status assessment in mild cognitive impairment
Authors: E. Doering
M. C. Hoenig
G. N. Bischof
K. P. Bohn
L. M. Ellingsen
T. van Eimeren
A. Drzezga
for the Alzheimer’s Disease Neuroimaging Initiative
Publication date: 14-07-2022
Publisher: Springer Berlin Heidelberg
Keywords: Alzheimer's Disease
Dementia
Positron Emission Tomography
Alzheimer's Disease
Dementia
Mild Cognitive Impairment
Published in: European Journal of Nuclear Medicine and Molecular Imaging / Issue 13/2022
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI: https://doi.org/10.1007/s00259-022-05879-6

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Introducing a gatekeeping system for amyloid status assessment in mild cognitive impairment

Abstract

Background

Methods

Results

Conclusion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

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