Brain metabolic patterns in patients with suspected non-Alzheimer’s pathophysiology (SNAP) and Alzheimer’s disease (AD): is [18F] FDG a specific biomarker in these patients?

Purpose The present study was conducted to compare the pattern of brain [18F] FDG uptake in suspected non-Alzheimer’s pathophysiology (SNAP), AD, and healthy controls using 2-deoxy-2-[18F]fluoroglucose ([18F] FDG) positron emission tomography imaging. Cerebrospinal fluid (CSF) biomarkers amyloid-β1-...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Chiaravalloti, Agostino [verfasserIn] Barbagallo, Gaetano Martorana, Alessandro Castellano, Anna Elisa Ursini, Francesco Schillaci, Orazio

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	[ F] FDG Alzheimer, SNAP PET/CT Biomarkers

Anmerkung:	© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Übergeordnetes Werk:	Enthalten in: European journal of nuclear medicine and molecular imaging - Heidelberg [u.a.] : Springer-Verl., 2002, 46(2019), 9 vom: 14. Juni, Seite 1796-1805
Übergeordnetes Werk:	volume:46 ; year:2019 ; number:9 ; day:14 ; month:06 ; pages:1796-1805

Links:	Volltext

DOI / URN:	10.1007/s00259-019-04379-4

Katalog-ID:	SPR003170403

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245	1	0	\|a Brain metabolic patterns in patients with suspected non-Alzheimer’s pathophysiology (SNAP) and Alzheimer’s disease (AD): is [18F] FDG a specific biomarker in these patients?
264		1	\|c 2019
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520			\|a Purpose The present study was conducted to compare the pattern of brain [18F] FDG uptake in suspected non-Alzheimer’s pathophysiology (SNAP), AD, and healthy controls using 2-deoxy-2-[18F]fluoroglucose ([18F] FDG) positron emission tomography imaging. Cerebrospinal fluid (CSF) biomarkers amyloid-β1-42 peptide (Aβ1-42) and tau were used in order to differentiate AD from SNAP. Methods The study included 43 newly diagnosed AD patients (female = 23; male = 20) according to the NINCDS-ADRDA criteria, 15 SNAP patients (female = 12; male =3), and a group of 34 healthy subjects that served as the control group (CG), who were found to be normal at neurological evaluation (male = 20; female = 14). A battery of neuropsychological tests was administrated in AD and SNAP subjects; cerebrospinal fluid assay was conducted in both AD and SNAP as well. Brain PET/CT acquisition was started 30 ± 5 min after [18F] FDG injection in all subjects. SPM12 [statistical parametric mapping] implemented in MATLAB 2018a was used for the analysis of PET scans in this study. Results As compared to SNAP, AD subjects showed significant hypometabolism in a wide cortical area involving the right frontal, parietal, and temporal lobes. As compared to CG, AD subjects showed a significant reduction in [18F] FDG uptake in the parietal, limbic, and frontal cortex, while a more limited reduction in [18F] FDG uptake in the same areas was found when comparing SNAP to CG. Conclusions SNAP subjects show milder impairment of brain [18F] FDG uptake as compared to AD. The partial overlap of the metabolic pattern between SNAP and AD limits the use of [18F] FDG PET/CT in effectively discriminating these clinical entities.
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Indexfelder

author_variant	a c ac g b gb a m am a e c ae aec f u fu o s os
matchkey_str	article:16197089:2019----::rimtblcatrsnainsihupcennlhiesahpyilgsaadlhiesiesai1f
hierarchy_sort_str	2019
publishDate	2019
allfields	10.1007/s00259-019-04379-4 doi (DE-627)SPR003170403 (SPR)s00259-019-04379-4-e DE-627 ger DE-627 rakwb eng Chiaravalloti, Agostino verfasserin (orcid)0000-0002-8017-8858 aut Brain metabolic patterns in patients with suspected non-Alzheimer’s pathophysiology (SNAP) and Alzheimer’s disease (AD): is [18F] FDG a specific biomarker in these patients? 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Purpose The present study was conducted to compare the pattern of brain [18F] FDG uptake in suspected non-Alzheimer’s pathophysiology (SNAP), AD, and healthy controls using 2-deoxy-2-[18F]fluoroglucose ([18F] FDG) positron emission tomography imaging. Cerebrospinal fluid (CSF) biomarkers amyloid-β1-42 peptide (Aβ1-42) and tau were used in order to differentiate AD from SNAP. Methods The study included 43 newly diagnosed AD patients (female = 23; male = 20) according to the NINCDS-ADRDA criteria, 15 SNAP patients (female = 12; male =3), and a group of 34 healthy subjects that served as the control group (CG), who were found to be normal at neurological evaluation (male = 20; female = 14). A battery of neuropsychological tests was administrated in AD and SNAP subjects; cerebrospinal fluid assay was conducted in both AD and SNAP as well. Brain PET/CT acquisition was started 30 ± 5 min after [18F] FDG injection in all subjects. SPM12 [statistical parametric mapping] implemented in MATLAB 2018a was used for the analysis of PET scans in this study. Results As compared to SNAP, AD subjects showed significant hypometabolism in a wide cortical area involving the right frontal, parietal, and temporal lobes. As compared to CG, AD subjects showed a significant reduction in [18F] FDG uptake in the parietal, limbic, and frontal cortex, while a more limited reduction in [18F] FDG uptake in the same areas was found when comparing SNAP to CG. Conclusions SNAP subjects show milder impairment of brain [18F] FDG uptake as compared to AD. The partial overlap of the metabolic pattern between SNAP and AD limits the use of [18F] FDG PET/CT in effectively discriminating these clinical entities. [ (dpeaa)DE-He213 F] FDG (dpeaa)DE-He213 Alzheimer, SNAP (dpeaa)DE-He213 PET/CT (dpeaa)DE-He213 Biomarkers (dpeaa)DE-He213 Barbagallo, Gaetano aut Martorana, Alessandro aut Castellano, Anna Elisa aut Ursini, Francesco aut Schillaci, Orazio aut Enthalten in European journal of nuclear medicine and molecular imaging Heidelberg [u.a.] : Springer-Verl., 2002 46(2019), 9 vom: 14. Juni, Seite 1796-1805 (DE-627)359787258 (DE-600)2098375-X 1619-7089 nnns volume:46 year:2019 number:9 day:14 month:06 pages:1796-1805 https://dx.doi.org/10.1007/s00259-019-04379-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2019 9 14 06 1796-1805
spelling	10.1007/s00259-019-04379-4 doi (DE-627)SPR003170403 (SPR)s00259-019-04379-4-e DE-627 ger DE-627 rakwb eng Chiaravalloti, Agostino verfasserin (orcid)0000-0002-8017-8858 aut Brain metabolic patterns in patients with suspected non-Alzheimer’s pathophysiology (SNAP) and Alzheimer’s disease (AD): is [18F] FDG a specific biomarker in these patients? 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Purpose The present study was conducted to compare the pattern of brain [18F] FDG uptake in suspected non-Alzheimer’s pathophysiology (SNAP), AD, and healthy controls using 2-deoxy-2-[18F]fluoroglucose ([18F] FDG) positron emission tomography imaging. Cerebrospinal fluid (CSF) biomarkers amyloid-β1-42 peptide (Aβ1-42) and tau were used in order to differentiate AD from SNAP. Methods The study included 43 newly diagnosed AD patients (female = 23; male = 20) according to the NINCDS-ADRDA criteria, 15 SNAP patients (female = 12; male =3), and a group of 34 healthy subjects that served as the control group (CG), who were found to be normal at neurological evaluation (male = 20; female = 14). A battery of neuropsychological tests was administrated in AD and SNAP subjects; cerebrospinal fluid assay was conducted in both AD and SNAP as well. Brain PET/CT acquisition was started 30 ± 5 min after [18F] FDG injection in all subjects. SPM12 [statistical parametric mapping] implemented in MATLAB 2018a was used for the analysis of PET scans in this study. Results As compared to SNAP, AD subjects showed significant hypometabolism in a wide cortical area involving the right frontal, parietal, and temporal lobes. As compared to CG, AD subjects showed a significant reduction in [18F] FDG uptake in the parietal, limbic, and frontal cortex, while a more limited reduction in [18F] FDG uptake in the same areas was found when comparing SNAP to CG. Conclusions SNAP subjects show milder impairment of brain [18F] FDG uptake as compared to AD. The partial overlap of the metabolic pattern between SNAP and AD limits the use of [18F] FDG PET/CT in effectively discriminating these clinical entities. [ (dpeaa)DE-He213 F] FDG (dpeaa)DE-He213 Alzheimer, SNAP (dpeaa)DE-He213 PET/CT (dpeaa)DE-He213 Biomarkers (dpeaa)DE-He213 Barbagallo, Gaetano aut Martorana, Alessandro aut Castellano, Anna Elisa aut Ursini, Francesco aut Schillaci, Orazio aut Enthalten in European journal of nuclear medicine and molecular imaging Heidelberg [u.a.] : Springer-Verl., 2002 46(2019), 9 vom: 14. Juni, Seite 1796-1805 (DE-627)359787258 (DE-600)2098375-X 1619-7089 nnns volume:46 year:2019 number:9 day:14 month:06 pages:1796-1805 https://dx.doi.org/10.1007/s00259-019-04379-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2019 9 14 06 1796-1805
allfields_unstemmed	10.1007/s00259-019-04379-4 doi (DE-627)SPR003170403 (SPR)s00259-019-04379-4-e DE-627 ger DE-627 rakwb eng Chiaravalloti, Agostino verfasserin (orcid)0000-0002-8017-8858 aut Brain metabolic patterns in patients with suspected non-Alzheimer’s pathophysiology (SNAP) and Alzheimer’s disease (AD): is [18F] FDG a specific biomarker in these patients? 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Purpose The present study was conducted to compare the pattern of brain [18F] FDG uptake in suspected non-Alzheimer’s pathophysiology (SNAP), AD, and healthy controls using 2-deoxy-2-[18F]fluoroglucose ([18F] FDG) positron emission tomography imaging. Cerebrospinal fluid (CSF) biomarkers amyloid-β1-42 peptide (Aβ1-42) and tau were used in order to differentiate AD from SNAP. Methods The study included 43 newly diagnosed AD patients (female = 23; male = 20) according to the NINCDS-ADRDA criteria, 15 SNAP patients (female = 12; male =3), and a group of 34 healthy subjects that served as the control group (CG), who were found to be normal at neurological evaluation (male = 20; female = 14). A battery of neuropsychological tests was administrated in AD and SNAP subjects; cerebrospinal fluid assay was conducted in both AD and SNAP as well. Brain PET/CT acquisition was started 30 ± 5 min after [18F] FDG injection in all subjects. SPM12 [statistical parametric mapping] implemented in MATLAB 2018a was used for the analysis of PET scans in this study. Results As compared to SNAP, AD subjects showed significant hypometabolism in a wide cortical area involving the right frontal, parietal, and temporal lobes. As compared to CG, AD subjects showed a significant reduction in [18F] FDG uptake in the parietal, limbic, and frontal cortex, while a more limited reduction in [18F] FDG uptake in the same areas was found when comparing SNAP to CG. Conclusions SNAP subjects show milder impairment of brain [18F] FDG uptake as compared to AD. The partial overlap of the metabolic pattern between SNAP and AD limits the use of [18F] FDG PET/CT in effectively discriminating these clinical entities. [ (dpeaa)DE-He213 F] FDG (dpeaa)DE-He213 Alzheimer, SNAP (dpeaa)DE-He213 PET/CT (dpeaa)DE-He213 Biomarkers (dpeaa)DE-He213 Barbagallo, Gaetano aut Martorana, Alessandro aut Castellano, Anna Elisa aut Ursini, Francesco aut Schillaci, Orazio aut Enthalten in European journal of nuclear medicine and molecular imaging Heidelberg [u.a.] : Springer-Verl., 2002 46(2019), 9 vom: 14. Juni, Seite 1796-1805 (DE-627)359787258 (DE-600)2098375-X 1619-7089 nnns volume:46 year:2019 number:9 day:14 month:06 pages:1796-1805 https://dx.doi.org/10.1007/s00259-019-04379-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2019 9 14 06 1796-1805
allfieldsGer	10.1007/s00259-019-04379-4 doi (DE-627)SPR003170403 (SPR)s00259-019-04379-4-e DE-627 ger DE-627 rakwb eng Chiaravalloti, Agostino verfasserin (orcid)0000-0002-8017-8858 aut Brain metabolic patterns in patients with suspected non-Alzheimer’s pathophysiology (SNAP) and Alzheimer’s disease (AD): is [18F] FDG a specific biomarker in these patients? 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Purpose The present study was conducted to compare the pattern of brain [18F] FDG uptake in suspected non-Alzheimer’s pathophysiology (SNAP), AD, and healthy controls using 2-deoxy-2-[18F]fluoroglucose ([18F] FDG) positron emission tomography imaging. Cerebrospinal fluid (CSF) biomarkers amyloid-β1-42 peptide (Aβ1-42) and tau were used in order to differentiate AD from SNAP. Methods The study included 43 newly diagnosed AD patients (female = 23; male = 20) according to the NINCDS-ADRDA criteria, 15 SNAP patients (female = 12; male =3), and a group of 34 healthy subjects that served as the control group (CG), who were found to be normal at neurological evaluation (male = 20; female = 14). A battery of neuropsychological tests was administrated in AD and SNAP subjects; cerebrospinal fluid assay was conducted in both AD and SNAP as well. Brain PET/CT acquisition was started 30 ± 5 min after [18F] FDG injection in all subjects. SPM12 [statistical parametric mapping] implemented in MATLAB 2018a was used for the analysis of PET scans in this study. Results As compared to SNAP, AD subjects showed significant hypometabolism in a wide cortical area involving the right frontal, parietal, and temporal lobes. As compared to CG, AD subjects showed a significant reduction in [18F] FDG uptake in the parietal, limbic, and frontal cortex, while a more limited reduction in [18F] FDG uptake in the same areas was found when comparing SNAP to CG. Conclusions SNAP subjects show milder impairment of brain [18F] FDG uptake as compared to AD. The partial overlap of the metabolic pattern between SNAP and AD limits the use of [18F] FDG PET/CT in effectively discriminating these clinical entities. [ (dpeaa)DE-He213 F] FDG (dpeaa)DE-He213 Alzheimer, SNAP (dpeaa)DE-He213 PET/CT (dpeaa)DE-He213 Biomarkers (dpeaa)DE-He213 Barbagallo, Gaetano aut Martorana, Alessandro aut Castellano, Anna Elisa aut Ursini, Francesco aut Schillaci, Orazio aut Enthalten in European journal of nuclear medicine and molecular imaging Heidelberg [u.a.] : Springer-Verl., 2002 46(2019), 9 vom: 14. Juni, Seite 1796-1805 (DE-627)359787258 (DE-600)2098375-X 1619-7089 nnns volume:46 year:2019 number:9 day:14 month:06 pages:1796-1805 https://dx.doi.org/10.1007/s00259-019-04379-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2019 9 14 06 1796-1805
allfieldsSound	10.1007/s00259-019-04379-4 doi (DE-627)SPR003170403 (SPR)s00259-019-04379-4-e DE-627 ger DE-627 rakwb eng Chiaravalloti, Agostino verfasserin (orcid)0000-0002-8017-8858 aut Brain metabolic patterns in patients with suspected non-Alzheimer’s pathophysiology (SNAP) and Alzheimer’s disease (AD): is [18F] FDG a specific biomarker in these patients? 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag GmbH Germany, part of Springer Nature 2019 Purpose The present study was conducted to compare the pattern of brain [18F] FDG uptake in suspected non-Alzheimer’s pathophysiology (SNAP), AD, and healthy controls using 2-deoxy-2-[18F]fluoroglucose ([18F] FDG) positron emission tomography imaging. Cerebrospinal fluid (CSF) biomarkers amyloid-β1-42 peptide (Aβ1-42) and tau were used in order to differentiate AD from SNAP. Methods The study included 43 newly diagnosed AD patients (female = 23; male = 20) according to the NINCDS-ADRDA criteria, 15 SNAP patients (female = 12; male =3), and a group of 34 healthy subjects that served as the control group (CG), who were found to be normal at neurological evaluation (male = 20; female = 14). A battery of neuropsychological tests was administrated in AD and SNAP subjects; cerebrospinal fluid assay was conducted in both AD and SNAP as well. Brain PET/CT acquisition was started 30 ± 5 min after [18F] FDG injection in all subjects. SPM12 [statistical parametric mapping] implemented in MATLAB 2018a was used for the analysis of PET scans in this study. Results As compared to SNAP, AD subjects showed significant hypometabolism in a wide cortical area involving the right frontal, parietal, and temporal lobes. As compared to CG, AD subjects showed a significant reduction in [18F] FDG uptake in the parietal, limbic, and frontal cortex, while a more limited reduction in [18F] FDG uptake in the same areas was found when comparing SNAP to CG. Conclusions SNAP subjects show milder impairment of brain [18F] FDG uptake as compared to AD. The partial overlap of the metabolic pattern between SNAP and AD limits the use of [18F] FDG PET/CT in effectively discriminating these clinical entities. [ (dpeaa)DE-He213 F] FDG (dpeaa)DE-He213 Alzheimer, SNAP (dpeaa)DE-He213 PET/CT (dpeaa)DE-He213 Biomarkers (dpeaa)DE-He213 Barbagallo, Gaetano aut Martorana, Alessandro aut Castellano, Anna Elisa aut Ursini, Francesco aut Schillaci, Orazio aut Enthalten in European journal of nuclear medicine and molecular imaging Heidelberg [u.a.] : Springer-Verl., 2002 46(2019), 9 vom: 14. Juni, Seite 1796-1805 (DE-627)359787258 (DE-600)2098375-X 1619-7089 nnns volume:46 year:2019 number:9 day:14 month:06 pages:1796-1805 https://dx.doi.org/10.1007/s00259-019-04379-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 46 2019 9 14 06 1796-1805
language	English
source	Enthalten in European journal of nuclear medicine and molecular imaging 46(2019), 9 vom: 14. Juni, Seite 1796-1805 volume:46 year:2019 number:9 day:14 month:06 pages:1796-1805
sourceStr	Enthalten in European journal of nuclear medicine and molecular imaging 46(2019), 9 vom: 14. Juni, Seite 1796-1805 volume:46 year:2019 number:9 day:14 month:06 pages:1796-1805
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	[ F] FDG Alzheimer, SNAP PET/CT Biomarkers
isfreeaccess_bool	false
container_title	European journal of nuclear medicine and molecular imaging
authorswithroles_txt_mv	Chiaravalloti, Agostino @@aut@@ Barbagallo, Gaetano @@aut@@ Martorana, Alessandro @@aut@@ Castellano, Anna Elisa @@aut@@ Ursini, Francesco @@aut@@ Schillaci, Orazio @@aut@@
publishDateDaySort_date	2019-06-14T00:00:00Z
hierarchy_top_id	359787258
id	SPR003170403
language_de	englisch
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Cerebrospinal fluid (CSF) biomarkers amyloid-β1-42 peptide (Aβ1-42) and tau were used in order to differentiate AD from SNAP. Methods The study included 43 newly diagnosed AD patients (female = 23; male = 20) according to the NINCDS-ADRDA criteria, 15 SNAP patients (female = 12; male =3), and a group of 34 healthy subjects that served as the control group (CG), who were found to be normal at neurological evaluation (male = 20; female = 14). A battery of neuropsychological tests was administrated in AD and SNAP subjects; cerebrospinal fluid assay was conducted in both AD and SNAP as well. Brain PET/CT acquisition was started 30 ± 5 min after [18F] FDG injection in all subjects. SPM12 [statistical parametric mapping] implemented in MATLAB 2018a was used for the analysis of PET scans in this study. Results As compared to SNAP, AD subjects showed significant hypometabolism in a wide cortical area involving the right frontal, parietal, and temporal lobes. As compared to CG, AD subjects showed a significant reduction in [18F] FDG uptake in the parietal, limbic, and frontal cortex, while a more limited reduction in [18F] FDG uptake in the same areas was found when comparing SNAP to CG. Conclusions SNAP subjects show milder impairment of brain [18F] FDG uptake as compared to AD. 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author	Chiaravalloti, Agostino
spellingShingle	Chiaravalloti, Agostino misc [ misc F] FDG misc Alzheimer, SNAP misc PET/CT misc Biomarkers Brain metabolic patterns in patients with suspected non-Alzheimer’s pathophysiology (SNAP) and Alzheimer’s disease (AD): is [18F] FDG a specific biomarker in these patients?
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topic_title	Brain metabolic patterns in patients with suspected non-Alzheimer’s pathophysiology (SNAP) and Alzheimer’s disease (AD): is [18F] FDG a specific biomarker in these patients? (dpeaa)DE-He213 F] FDG (dpeaa)DE-He213 Alzheimer, SNAP (dpeaa)DE-He213 PET/CT (dpeaa)DE-He213 Biomarkers (dpeaa)DE-He213
topic	misc [ misc F] FDG misc Alzheimer, SNAP misc PET/CT misc Biomarkers
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title	Brain metabolic patterns in patients with suspected non-Alzheimer’s pathophysiology (SNAP) and Alzheimer’s disease (AD): is [18F] FDG a specific biomarker in these patients?
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title_full	Brain metabolic patterns in patients with suspected non-Alzheimer’s pathophysiology (SNAP) and Alzheimer’s disease (AD): is [18F] FDG a specific biomarker in these patients?
author_sort	Chiaravalloti, Agostino
journal	European journal of nuclear medicine and molecular imaging
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author_browse	Chiaravalloti, Agostino Barbagallo, Gaetano Martorana, Alessandro Castellano, Anna Elisa Ursini, Francesco Schillaci, Orazio
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author-letter	Chiaravalloti, Agostino
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title_sort	brain metabolic patterns in patients with suspected non-alzheimer’s pathophysiology (snap) and alzheimer’s disease (ad): is [18f] fdg a specific biomarker in these patients?
title_auth	Brain metabolic patterns in patients with suspected non-Alzheimer’s pathophysiology (SNAP) and Alzheimer’s disease (AD): is [18F] FDG a specific biomarker in these patients?
abstract	Purpose The present study was conducted to compare the pattern of brain [18F] FDG uptake in suspected non-Alzheimer’s pathophysiology (SNAP), AD, and healthy controls using 2-deoxy-2-[18F]fluoroglucose ([18F] FDG) positron emission tomography imaging. Cerebrospinal fluid (CSF) biomarkers amyloid-β1-42 peptide (Aβ1-42) and tau were used in order to differentiate AD from SNAP. Methods The study included 43 newly diagnosed AD patients (female = 23; male = 20) according to the NINCDS-ADRDA criteria, 15 SNAP patients (female = 12; male =3), and a group of 34 healthy subjects that served as the control group (CG), who were found to be normal at neurological evaluation (male = 20; female = 14). A battery of neuropsychological tests was administrated in AD and SNAP subjects; cerebrospinal fluid assay was conducted in both AD and SNAP as well. Brain PET/CT acquisition was started 30 ± 5 min after [18F] FDG injection in all subjects. SPM12 [statistical parametric mapping] implemented in MATLAB 2018a was used for the analysis of PET scans in this study. Results As compared to SNAP, AD subjects showed significant hypometabolism in a wide cortical area involving the right frontal, parietal, and temporal lobes. As compared to CG, AD subjects showed a significant reduction in [18F] FDG uptake in the parietal, limbic, and frontal cortex, while a more limited reduction in [18F] FDG uptake in the same areas was found when comparing SNAP to CG. Conclusions SNAP subjects show milder impairment of brain [18F] FDG uptake as compared to AD. The partial overlap of the metabolic pattern between SNAP and AD limits the use of [18F] FDG PET/CT in effectively discriminating these clinical entities. © Springer-Verlag GmbH Germany, part of Springer Nature 2019
abstractGer	Purpose The present study was conducted to compare the pattern of brain [18F] FDG uptake in suspected non-Alzheimer’s pathophysiology (SNAP), AD, and healthy controls using 2-deoxy-2-[18F]fluoroglucose ([18F] FDG) positron emission tomography imaging. Cerebrospinal fluid (CSF) biomarkers amyloid-β1-42 peptide (Aβ1-42) and tau were used in order to differentiate AD from SNAP. Methods The study included 43 newly diagnosed AD patients (female = 23; male = 20) according to the NINCDS-ADRDA criteria, 15 SNAP patients (female = 12; male =3), and a group of 34 healthy subjects that served as the control group (CG), who were found to be normal at neurological evaluation (male = 20; female = 14). A battery of neuropsychological tests was administrated in AD and SNAP subjects; cerebrospinal fluid assay was conducted in both AD and SNAP as well. Brain PET/CT acquisition was started 30 ± 5 min after [18F] FDG injection in all subjects. SPM12 [statistical parametric mapping] implemented in MATLAB 2018a was used for the analysis of PET scans in this study. Results As compared to SNAP, AD subjects showed significant hypometabolism in a wide cortical area involving the right frontal, parietal, and temporal lobes. As compared to CG, AD subjects showed a significant reduction in [18F] FDG uptake in the parietal, limbic, and frontal cortex, while a more limited reduction in [18F] FDG uptake in the same areas was found when comparing SNAP to CG. Conclusions SNAP subjects show milder impairment of brain [18F] FDG uptake as compared to AD. The partial overlap of the metabolic pattern between SNAP and AD limits the use of [18F] FDG PET/CT in effectively discriminating these clinical entities. © Springer-Verlag GmbH Germany, part of Springer Nature 2019
abstract_unstemmed	Purpose The present study was conducted to compare the pattern of brain [18F] FDG uptake in suspected non-Alzheimer’s pathophysiology (SNAP), AD, and healthy controls using 2-deoxy-2-[18F]fluoroglucose ([18F] FDG) positron emission tomography imaging. Cerebrospinal fluid (CSF) biomarkers amyloid-β1-42 peptide (Aβ1-42) and tau were used in order to differentiate AD from SNAP. Methods The study included 43 newly diagnosed AD patients (female = 23; male = 20) according to the NINCDS-ADRDA criteria, 15 SNAP patients (female = 12; male =3), and a group of 34 healthy subjects that served as the control group (CG), who were found to be normal at neurological evaluation (male = 20; female = 14). A battery of neuropsychological tests was administrated in AD and SNAP subjects; cerebrospinal fluid assay was conducted in both AD and SNAP as well. Brain PET/CT acquisition was started 30 ± 5 min after [18F] FDG injection in all subjects. SPM12 [statistical parametric mapping] implemented in MATLAB 2018a was used for the analysis of PET scans in this study. Results As compared to SNAP, AD subjects showed significant hypometabolism in a wide cortical area involving the right frontal, parietal, and temporal lobes. As compared to CG, AD subjects showed a significant reduction in [18F] FDG uptake in the parietal, limbic, and frontal cortex, while a more limited reduction in [18F] FDG uptake in the same areas was found when comparing SNAP to CG. Conclusions SNAP subjects show milder impairment of brain [18F] FDG uptake as compared to AD. The partial overlap of the metabolic pattern between SNAP and AD limits the use of [18F] FDG PET/CT in effectively discriminating these clinical entities. © Springer-Verlag GmbH Germany, part of Springer Nature 2019
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container_issue	9
title_short	Brain metabolic patterns in patients with suspected non-Alzheimer’s pathophysiology (SNAP) and Alzheimer’s disease (AD): is [18F] FDG a specific biomarker in these patients?
url	https://dx.doi.org/10.1007/s00259-019-04379-4
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author2	Barbagallo, Gaetano Martorana, Alessandro Castellano, Anna Elisa Ursini, Francesco Schillaci, Orazio
author2Str	Barbagallo, Gaetano Martorana, Alessandro Castellano, Anna Elisa Ursini, Francesco Schillaci, Orazio
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doi_str	10.1007/s00259-019-04379-4
up_date	2024-07-03T17:47:20.890Z
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Brain metabolic patterns in patients with suspected non-Alzheimer’s pathophysiology (SNAP) and Alzheimer’s disease (AD): is [18F] FDG a specific biomarker in these patients?

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