Metabolic Brain Changes Can Predict the Underlying Pathology in Neurodegenerative Brain Disorders: A Case Report of Sporadic Creutzfeldt–Jakob Disease with Concomitant Parkinson’s Disease
Abstract
1. Background
2. Case Report
3. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Kovacs, G.G.; Alafuzoff, I.; Al-Sarraj, S.; Arzberger, T.; Bogdanovic, N.; Capellari, S.; Ferrer, I.; Gelpi, E.; Kövari, V.; Kretzschmar, H.; et al. Mixed brain pathologies in dementia: The BrainNet Europe consortium experience. Dement. Geriatr. Cogn. Disord. 2008, 26, 343–350. [Google Scholar] [CrossRef] [Scilit]
- Rahimi, J.; Kovacs, G.G. Prevalence of mixed pathologies in the aging brain. Alzheimers Res. Ther. 2014, 6, 82. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Villemagne, V.L.; Fodero-Tavoletti, M.T.; Masters, C.L.; Rowe, C.C. Tau imaging: Early progress and future directions. Lancet Neurol. 2015, 14, 114–124. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Parobkova, E.; van der Zee, J.; Dillen, L.; Van Broeckhoven, C.; Rusina, R.; Matej, R. Sporadic Creutzfeldt-Jakob Disease and Other Proteinopathies in Comorbidity. Front. Neurol. 2020, 11, 596108. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Rossi, M.; Kai, H.; Baiardi, S.; Bartoletti-Stella, A.; Carlà, B.; Zenesini, C.; Capellari, S.; Kitamoto, T. The characterization of AD/PART co-pathology in CJD suggests independent pathogenic mechanisms and no cross-seeding between misfolded Aβ and prion proteins. Acta Neuropathol. Commun. 2019, 7, 53. [Google Scholar] [CrossRef] [Scilit]
- Fernández-Vega, I.; Ruiz-Ojeda, J.; Juste, R.A.; Geijo, M.; Zarranz, J.J.; Menoyo, J.L.S.; Vicente-Etxenausia, I.; Mediavilla-García, J.; Guerra-Merino, I. Coexistence of mixed phenotype Creutzfeldt-Jakob disease, Lewy body disease and argyrophilic grain disease plus histological features of possible Alzheimer’s disease: A multi-protein disorder in an autopsy case. Neuropathology 2015, 35, 56–63. [Google Scholar] [CrossRef] [Scilit]
- Kubo S ichiro Matsubara, T.; Taguchi, T.; Sengoku, R.; Takeuchi, A.; Saito, Y. Parkinson’s disease with a typical clinical course of 17 years overlapped by Creutzfeldt–Jakob disease: An autopsy case report. BMC Neurol. 2021, 21, 480. [Google Scholar] [CrossRef] [Scilit]
- Klotz, S.; König, T.; Erdler, M.; Ulram, A.; Nguyen, A.; Ströbel, T.; Zimprich, A.; Stögmann, E.; Regelsberger, G.; Höftberger, R.; et al. Co-incidental C9orf72 expansion mutation-related frontotemporal lobar degeneration pathology and sporadic Creutzfeldt-Jakob disease. Eur. J. Neurol. 2021, 28, 1009–1015. [Google Scholar] [CrossRef] [Scilit]
- Guedj, E.; Varrone, A.; Boellaard, R.; Albert, N.L.; Barthel, H.; van Berckel, B.; Brendel, M.; Cecchin, D.; Ekmekcioglu, O.; Garibotto, V.; et al. EANM procedure guidelines for brain PET imaging using [18F]FDG, version 3. Eur. J. Nucl. Med. Mol. Imaging 2022, 49, 632. [Google Scholar] [CrossRef] [Scilit]
- Friston, K.; Ashburner, J.; Kiebel, S.; Nichols, T.; Penny, W. (Eds.) Statistical Parametric Mapping: The Analysis of Functional Brain Images; Elsevier: Amsterdam, The Netherlands, 2007. [Google Scholar]
- Perovnik, M.; Rus, T.; Schindlbeck, K.A.; Eidelberg, D. Functional brain networks in the evaluation of patients with neurodegenerative disorders. Nat. Rev. Neurol. 2023, 19, 73–90. [Google Scholar] [CrossRef] [Scilit]
- Rus, T.; Mlakar, J.; Ležaić, L.; Vo, A.; Nguyen, N.; Tang, C.; Fiorini, M.; Prieto, E.; Marti-Andres, G.; Arbizu, J.; et al. Sporadic Creutzfeldt-Jakob disease is associated with reorganization of metabolic connectivity in a pathological brain network. Eur. J. Neurol. 2023, 30, 1035–1047. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Rus, T.; Perovnik, M.; Vo, A.; Nguyen, N.; Tang, C.; Jamšek, J.; Popović, K.Š.; Grimmer, T.; Yakushev, I.; Diehl-Schmid, J.; et al. Disease specific and nonspecific metabolic brain networks in behavioral variant of frontotemporal dementia. Hum. Brain Mapp. 2023, 44, 1079–1093. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Teune, L.K.; Strijkert, F.; Renken, R.J.; Gerbrand, J.I.; Jeroen, J.d.V.; Marcel, S.; Jos, B.T.M.R.; Rudi, A.J.O.D.; Klaus, J.L. The Alzheimer’s disease-related glucose metabolic brain pattern. Curr. Alzheimer Res. 2014, 11, 725–732. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Rus, T.; Tomše, P.; Jensterle, L.; Grmek, M.; Pirtošek, Z.; Eidelberg, D.; Tang, C.; Trošt, M. Differential diagnosis of parkinsonian syndromes: A comparison of clinical and automated-metabolic brain patterns’ based approach. Eur. J. Nucl. Med. Mol. Imaging 2020, 47, 2901–2910. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Watson, N.; Hermann, P.; Ladogana, A.; Denouel, A.; Baiardi, S.; Colaizzo, E.; Giaccone, G.; Glatzel, M.; Green, A.J.E.; Haik, S. Validation of Revised International Creutzfeldt-Jakob Disease Surveillance Network Diagnostic Criteria for Sporadic Creutzfeldt-Jakob Disease. JAMA Netw. Open 2022, 5, e2146319. [Google Scholar] [CrossRef] [Scilit]
- Spetsieris, P.G.; Eidelberg, D. Scaled subprofile modeling of resting state imaging data in Parkinson’s disease: Methodological issues. Neuroimage 2011, 54, 2899–2914. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Tomše, P.; Jensterle, L.; Grmek, M.; Zaletel, K.; Pirtošek, Z.; Dhawan, V.; Peng, S.; Eidelberg, D.; Ma, Y. Abnormal metabolic brain network associated with Parkinson’s disease: Replication on a new European sample. Neuroradiology 2017, 59, 507–515. [Google Scholar] [CrossRef] [Scilit]
- Renard, D.; Castelnovo, G.; Collombier, L.; Thouvenot, E.; Boudousq, V. FDG-PET in Creutzfeldt-Jakob disease: Analysis of clinical-PET correlation. Prion 2017, 11, 440–453. [Google Scholar] [CrossRef] [Scilit]
- Prieto, E.; Domínguez-Prado, I.; Riverol, M.; Ortega-Cubero, S.; Ribelles, M.J.; Luquin, M.R.; de Castro, P. Metabolic patterns in prion diseases: An FDG PET voxel-based analysis. Eur. J. Nucl. Med. Mol. Imaging 2015, 42, 1522–1529. [Google Scholar] [CrossRef] [Scilit]
- Berti, V.; Mosconi, L.; Pupi, A. Brain: Normal Variations and Benign Findings in FDG PET/CT imaging. PET Clin. 2014, 9, 129. [Google Scholar] [CrossRef] [Scilit]
- Moreno-Ajona, D.; Prieto, E.; Grisanti, F.; Esparragosa, I.; Sánchez Orduz, L.; Gállego Pérez-Larraya, J.; Arbizu, J.; Riverol, M. 18F-FDG-PET Imaging Patterns in Autoimmune Encephalitis: Impact of Image Analysis on the Results. Diagnostics 2020, 10, 356. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Liu, X.; Shan, W.; Zhao, X.; Ren, J.; Ren, G.; Chen, C.; Shi, W.; Lv, R.; Li, Z.; Liu, Y.; et al. The Clinical Value of 18F-FDG-PET in Autoimmune Encephalitis Associated With LGI1 Antibody. Front. Neurol. 2020, 11, 418. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Capuron, L.; Pagnoni, G.; Demetrashvili, M.; Lawson, D.; Fornwalt, F.; Woolwine, B.; Berns, G.; Nemeroff, C. Basal Ganglia Hypermetabolism and Symptoms of Fatigue during Interferon-α Therapy. Neuropsychopharmacology 2007, 32, 2384–2392. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Rus, T.; Schindlbeck, K.A.; Tang, C.C.; Vo, A.; Dhawan, V.; Trošt, M.; Eidelberg, D. Stereotyped relationship between motor and cognitive metabolic networks in Parkinson’s disease. Mov. Disord. 2022, 37, 2247–2256. [Google Scholar] [CrossRef] [Scilit]
- Braak, H.; Tredici, K.; Del Rüb, U.; de Vos, R.A.I.; Jansen Steur, E.N.H.; Braak, E. Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol. Aging 2003, 24, 197–211. [Google Scholar] [CrossRef] [Scilit]
- Burke, R.E.; Dauer, W.T.; Vonsattel, J.P.G. A Critical Evaluation of The Braak Staging Scheme for Parkinson’s Disease. Ann. Neurol. 2008, 64, 485. [Google Scholar] [CrossRef] [Scilit]
- Kovacs, G.G. Molecular pathology of neurodegenerative diseases: Principles and practice. J. Clin. Pathol. 2019, 72, 725–735. [Google Scholar] [CrossRef] [Scilit]



Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Rus, T.; Mlakar, J.; Jamšek, J.; Trošt, M. Metabolic Brain Changes Can Predict the Underlying Pathology in Neurodegenerative Brain Disorders: A Case Report of Sporadic Creutzfeldt–Jakob Disease with Concomitant Parkinson’s Disease. Int. J. Mol. Sci. 2023, 24, 13081. https://doi.org/10.3390/ijms241713081
Rus T, Mlakar J, Jamšek J, Trošt M. Metabolic Brain Changes Can Predict the Underlying Pathology in Neurodegenerative Brain Disorders: A Case Report of Sporadic Creutzfeldt–Jakob Disease with Concomitant Parkinson’s Disease. International Journal of Molecular Sciences. 2023; 24(17):13081. https://doi.org/10.3390/ijms241713081
Chicago/Turabian StyleRus, Tomaž, Jernej Mlakar, Jan Jamšek, and Maja Trošt. 2023. "Metabolic Brain Changes Can Predict the Underlying Pathology in Neurodegenerative Brain Disorders: A Case Report of Sporadic Creutzfeldt–Jakob Disease with Concomitant Parkinson’s Disease" International Journal of Molecular Sciences 24, no. 17: 13081. https://doi.org/10.3390/ijms241713081
APA StyleRus, T., Mlakar, J., Jamšek, J., & Trošt, M. (2023). Metabolic Brain Changes Can Predict the Underlying Pathology in Neurodegenerative Brain Disorders: A Case Report of Sporadic Creutzfeldt–Jakob Disease with Concomitant Parkinson’s Disease. International Journal of Molecular Sciences, 24(17), 13081. https://doi.org/10.3390/ijms241713081

