Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population

Background We sought to define a cutoff for β-amyloid 1–42 in cerebrospinal fluid (CSF), a key marker for Alzheimer’s disease (AD), with data-driven Gaussian mixture modeling in a memory clinic population. Methods We performed a combined cross-sectional and prospective cohort study. We selected 2462...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Bertens, Daniela [verfasserIn] Tijms, Betty M. Scheltens, Philip Teunissen, Charlotte E. Visser, Pieter Jelle

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Alzheimer’s disease MCI Cerebrospinal fluid Diagnosis

Anmerkung:	© The Author(s). 2017

Übergeordnetes Werk:	Enthalten in: Alzheimer's research & therapy - London : BioMed Central, 2009, 9(2017), 1 vom: 14. Feb.
Übergeordnetes Werk:	volume:9 ; year:2017 ; number:1 ; day:14 ; month:02

Links:	Volltext

DOI / URN:	10.1186/s13195-016-0233-7

Katalog-ID:	SPR030815460

Internformat


LEADER	01000caa a22002652 4500
001	SPR030815460
003	DE-627
005	20230519203601.0
007	cr uuu---uuuuu
008	201007s2017 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1186/s13195-016-0233-7 \|2 doi
035			\|a (DE-627)SPR030815460
035			\|a (SPR)s13195-016-0233-7-e
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
100	1		\|a Bertens, Daniela \|e verfasserin \|4 aut
245	1	0	\|a Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population
264		1	\|c 2017
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
500			\|a © The Author(s). 2017
520			\|a Background We sought to define a cutoff for β-amyloid 1–42 in cerebrospinal fluid (CSF), a key marker for Alzheimer’s disease (AD), with data-driven Gaussian mixture modeling in a memory clinic population. Methods We performed a combined cross-sectional and prospective cohort study. We selected 2462 subjects with subjective cognitive decline, mild cognitive impairment, AD-type dementia, and dementia other than AD from the Amsterdam Dementia Cohort. We defined CSF β-amyloid 1–42 cutoffs by data-driven Gaussian mixture modeling in the total population and in subgroups based on clinical diagnosis, age, and apolipoprotein E (APOE) genotype. We investigated whether abnormal β-amyloid 1–42 as defined by the data-driven cutoff could better predict progression to AD-type dementia than abnormal β-amyloid 1–42 defined by a clinical diagnosis-based cutoff using Cox proportional hazards regression. Results In the total group of patients, we found a cutoff for abnormal CSF β-amyloid 1–42 of 680 pg/ml (95% CI 660–705 pg/ml). Similar cutoffs were found within diagnostic and APOE genotype subgroups. The cutoff was higher in elderly subjects than in younger subjects. The data-driven cutoff was higher than our clinical diagnosis-based cutoff and had a better predictive accuracy for progression to AD-type dementia in nondemented subjects (HR 7.6 versus 5.2, p < 0.01). Conclusions Mixture modeling is a robust method to determine cutoffs for CSF β-amyloid 1–42. It might better capture biological changes that are related to AD than cutoffs based on clinical diagnosis.
650		4	\|a Alzheimer’s disease \|7 (dpeaa)DE-He213
650		4	\|a MCI \|7 (dpeaa)DE-He213
650		4	\|a Cerebrospinal fluid \|7 (dpeaa)DE-He213
650		4	\|a Diagnosis \|7 (dpeaa)DE-He213
700	1		\|a Tijms, Betty M. \|4 aut
700	1		\|a Scheltens, Philip \|4 aut
700	1		\|a Teunissen, Charlotte E. \|4 aut
700	1		\|a Visser, Pieter Jelle \|0 (orcid)0000-0001-8008-9727 \|4 aut
773	0	8	\|i Enthalten in \|t Alzheimer's research & therapy \|d London : BioMed Central, 2009 \|g 9(2017), 1 vom: 14. Feb. \|w (DE-627)605683557 \|w (DE-600)2506521-X \|x 1758-9193 \|7 nnns
773	1	8	\|g volume:9 \|g year:2017 \|g number:1 \|g day:14 \|g month:02
856	4	0	\|u https://dx.doi.org/10.1186/s13195-016-0233-7 \|z kostenfrei \|3 Volltext
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_SPRINGER
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 9 \|j 2017 \|e 1 \|b 14 \|c 02

Indexfelder

author_variant	d b db b m t bm bmt p s ps c e t ce cet p j v pj pjv
matchkey_str	article:17589193:2017----::nisdsiaeocrbopnlliayod4ctfsnlre
hierarchy_sort_str	2017
publishDate	2017
allfields	10.1186/s13195-016-0233-7 doi (DE-627)SPR030815460 (SPR)s13195-016-0233-7-e DE-627 ger DE-627 rakwb eng Bertens, Daniela verfasserin aut Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 Background We sought to define a cutoff for β-amyloid 1–42 in cerebrospinal fluid (CSF), a key marker for Alzheimer’s disease (AD), with data-driven Gaussian mixture modeling in a memory clinic population. Methods We performed a combined cross-sectional and prospective cohort study. We selected 2462 subjects with subjective cognitive decline, mild cognitive impairment, AD-type dementia, and dementia other than AD from the Amsterdam Dementia Cohort. We defined CSF β-amyloid 1–42 cutoffs by data-driven Gaussian mixture modeling in the total population and in subgroups based on clinical diagnosis, age, and apolipoprotein E (APOE) genotype. We investigated whether abnormal β-amyloid 1–42 as defined by the data-driven cutoff could better predict progression to AD-type dementia than abnormal β-amyloid 1–42 defined by a clinical diagnosis-based cutoff using Cox proportional hazards regression. Results In the total group of patients, we found a cutoff for abnormal CSF β-amyloid 1–42 of 680 pg/ml (95% CI 660–705 pg/ml). Similar cutoffs were found within diagnostic and APOE genotype subgroups. The cutoff was higher in elderly subjects than in younger subjects. The data-driven cutoff was higher than our clinical diagnosis-based cutoff and had a better predictive accuracy for progression to AD-type dementia in nondemented subjects (HR 7.6 versus 5.2, p < 0.01). Conclusions Mixture modeling is a robust method to determine cutoffs for CSF β-amyloid 1–42. It might better capture biological changes that are related to AD than cutoffs based on clinical diagnosis. Alzheimer’s disease (dpeaa)DE-He213 MCI (dpeaa)DE-He213 Cerebrospinal fluid (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213 Tijms, Betty M. aut Scheltens, Philip aut Teunissen, Charlotte E. aut Visser, Pieter Jelle (orcid)0000-0001-8008-9727 aut Enthalten in Alzheimer's research & therapy London : BioMed Central, 2009 9(2017), 1 vom: 14. Feb. (DE-627)605683557 (DE-600)2506521-X 1758-9193 nnns volume:9 year:2017 number:1 day:14 month:02 https://dx.doi.org/10.1186/s13195-016-0233-7 kostenfrei 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2017 1 14 02
spelling	10.1186/s13195-016-0233-7 doi (DE-627)SPR030815460 (SPR)s13195-016-0233-7-e DE-627 ger DE-627 rakwb eng Bertens, Daniela verfasserin aut Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 Background We sought to define a cutoff for β-amyloid 1–42 in cerebrospinal fluid (CSF), a key marker for Alzheimer’s disease (AD), with data-driven Gaussian mixture modeling in a memory clinic population. Methods We performed a combined cross-sectional and prospective cohort study. We selected 2462 subjects with subjective cognitive decline, mild cognitive impairment, AD-type dementia, and dementia other than AD from the Amsterdam Dementia Cohort. We defined CSF β-amyloid 1–42 cutoffs by data-driven Gaussian mixture modeling in the total population and in subgroups based on clinical diagnosis, age, and apolipoprotein E (APOE) genotype. We investigated whether abnormal β-amyloid 1–42 as defined by the data-driven cutoff could better predict progression to AD-type dementia than abnormal β-amyloid 1–42 defined by a clinical diagnosis-based cutoff using Cox proportional hazards regression. Results In the total group of patients, we found a cutoff for abnormal CSF β-amyloid 1–42 of 680 pg/ml (95% CI 660–705 pg/ml). Similar cutoffs were found within diagnostic and APOE genotype subgroups. The cutoff was higher in elderly subjects than in younger subjects. The data-driven cutoff was higher than our clinical diagnosis-based cutoff and had a better predictive accuracy for progression to AD-type dementia in nondemented subjects (HR 7.6 versus 5.2, p < 0.01). Conclusions Mixture modeling is a robust method to determine cutoffs for CSF β-amyloid 1–42. It might better capture biological changes that are related to AD than cutoffs based on clinical diagnosis. Alzheimer’s disease (dpeaa)DE-He213 MCI (dpeaa)DE-He213 Cerebrospinal fluid (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213 Tijms, Betty M. aut Scheltens, Philip aut Teunissen, Charlotte E. aut Visser, Pieter Jelle (orcid)0000-0001-8008-9727 aut Enthalten in Alzheimer's research & therapy London : BioMed Central, 2009 9(2017), 1 vom: 14. Feb. (DE-627)605683557 (DE-600)2506521-X 1758-9193 nnns volume:9 year:2017 number:1 day:14 month:02 https://dx.doi.org/10.1186/s13195-016-0233-7 kostenfrei 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2017 1 14 02
allfields_unstemmed	10.1186/s13195-016-0233-7 doi (DE-627)SPR030815460 (SPR)s13195-016-0233-7-e DE-627 ger DE-627 rakwb eng Bertens, Daniela verfasserin aut Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 Background We sought to define a cutoff for β-amyloid 1–42 in cerebrospinal fluid (CSF), a key marker for Alzheimer’s disease (AD), with data-driven Gaussian mixture modeling in a memory clinic population. Methods We performed a combined cross-sectional and prospective cohort study. We selected 2462 subjects with subjective cognitive decline, mild cognitive impairment, AD-type dementia, and dementia other than AD from the Amsterdam Dementia Cohort. We defined CSF β-amyloid 1–42 cutoffs by data-driven Gaussian mixture modeling in the total population and in subgroups based on clinical diagnosis, age, and apolipoprotein E (APOE) genotype. We investigated whether abnormal β-amyloid 1–42 as defined by the data-driven cutoff could better predict progression to AD-type dementia than abnormal β-amyloid 1–42 defined by a clinical diagnosis-based cutoff using Cox proportional hazards regression. Results In the total group of patients, we found a cutoff for abnormal CSF β-amyloid 1–42 of 680 pg/ml (95% CI 660–705 pg/ml). Similar cutoffs were found within diagnostic and APOE genotype subgroups. The cutoff was higher in elderly subjects than in younger subjects. The data-driven cutoff was higher than our clinical diagnosis-based cutoff and had a better predictive accuracy for progression to AD-type dementia in nondemented subjects (HR 7.6 versus 5.2, p < 0.01). Conclusions Mixture modeling is a robust method to determine cutoffs for CSF β-amyloid 1–42. It might better capture biological changes that are related to AD than cutoffs based on clinical diagnosis. Alzheimer’s disease (dpeaa)DE-He213 MCI (dpeaa)DE-He213 Cerebrospinal fluid (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213 Tijms, Betty M. aut Scheltens, Philip aut Teunissen, Charlotte E. aut Visser, Pieter Jelle (orcid)0000-0001-8008-9727 aut Enthalten in Alzheimer's research & therapy London : BioMed Central, 2009 9(2017), 1 vom: 14. Feb. (DE-627)605683557 (DE-600)2506521-X 1758-9193 nnns volume:9 year:2017 number:1 day:14 month:02 https://dx.doi.org/10.1186/s13195-016-0233-7 kostenfrei 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2017 1 14 02
allfieldsGer	10.1186/s13195-016-0233-7 doi (DE-627)SPR030815460 (SPR)s13195-016-0233-7-e DE-627 ger DE-627 rakwb eng Bertens, Daniela verfasserin aut Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 Background We sought to define a cutoff for β-amyloid 1–42 in cerebrospinal fluid (CSF), a key marker for Alzheimer’s disease (AD), with data-driven Gaussian mixture modeling in a memory clinic population. Methods We performed a combined cross-sectional and prospective cohort study. We selected 2462 subjects with subjective cognitive decline, mild cognitive impairment, AD-type dementia, and dementia other than AD from the Amsterdam Dementia Cohort. We defined CSF β-amyloid 1–42 cutoffs by data-driven Gaussian mixture modeling in the total population and in subgroups based on clinical diagnosis, age, and apolipoprotein E (APOE) genotype. We investigated whether abnormal β-amyloid 1–42 as defined by the data-driven cutoff could better predict progression to AD-type dementia than abnormal β-amyloid 1–42 defined by a clinical diagnosis-based cutoff using Cox proportional hazards regression. Results In the total group of patients, we found a cutoff for abnormal CSF β-amyloid 1–42 of 680 pg/ml (95% CI 660–705 pg/ml). Similar cutoffs were found within diagnostic and APOE genotype subgroups. The cutoff was higher in elderly subjects than in younger subjects. The data-driven cutoff was higher than our clinical diagnosis-based cutoff and had a better predictive accuracy for progression to AD-type dementia in nondemented subjects (HR 7.6 versus 5.2, p < 0.01). Conclusions Mixture modeling is a robust method to determine cutoffs for CSF β-amyloid 1–42. It might better capture biological changes that are related to AD than cutoffs based on clinical diagnosis. Alzheimer’s disease (dpeaa)DE-He213 MCI (dpeaa)DE-He213 Cerebrospinal fluid (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213 Tijms, Betty M. aut Scheltens, Philip aut Teunissen, Charlotte E. aut Visser, Pieter Jelle (orcid)0000-0001-8008-9727 aut Enthalten in Alzheimer's research & therapy London : BioMed Central, 2009 9(2017), 1 vom: 14. Feb. (DE-627)605683557 (DE-600)2506521-X 1758-9193 nnns volume:9 year:2017 number:1 day:14 month:02 https://dx.doi.org/10.1186/s13195-016-0233-7 kostenfrei 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2017 1 14 02
allfieldsSound	10.1186/s13195-016-0233-7 doi (DE-627)SPR030815460 (SPR)s13195-016-0233-7-e DE-627 ger DE-627 rakwb eng Bertens, Daniela verfasserin aut Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 Background We sought to define a cutoff for β-amyloid 1–42 in cerebrospinal fluid (CSF), a key marker for Alzheimer’s disease (AD), with data-driven Gaussian mixture modeling in a memory clinic population. Methods We performed a combined cross-sectional and prospective cohort study. We selected 2462 subjects with subjective cognitive decline, mild cognitive impairment, AD-type dementia, and dementia other than AD from the Amsterdam Dementia Cohort. We defined CSF β-amyloid 1–42 cutoffs by data-driven Gaussian mixture modeling in the total population and in subgroups based on clinical diagnosis, age, and apolipoprotein E (APOE) genotype. We investigated whether abnormal β-amyloid 1–42 as defined by the data-driven cutoff could better predict progression to AD-type dementia than abnormal β-amyloid 1–42 defined by a clinical diagnosis-based cutoff using Cox proportional hazards regression. Results In the total group of patients, we found a cutoff for abnormal CSF β-amyloid 1–42 of 680 pg/ml (95% CI 660–705 pg/ml). Similar cutoffs were found within diagnostic and APOE genotype subgroups. The cutoff was higher in elderly subjects than in younger subjects. The data-driven cutoff was higher than our clinical diagnosis-based cutoff and had a better predictive accuracy for progression to AD-type dementia in nondemented subjects (HR 7.6 versus 5.2, p < 0.01). Conclusions Mixture modeling is a robust method to determine cutoffs for CSF β-amyloid 1–42. It might better capture biological changes that are related to AD than cutoffs based on clinical diagnosis. Alzheimer’s disease (dpeaa)DE-He213 MCI (dpeaa)DE-He213 Cerebrospinal fluid (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213 Tijms, Betty M. aut Scheltens, Philip aut Teunissen, Charlotte E. aut Visser, Pieter Jelle (orcid)0000-0001-8008-9727 aut Enthalten in Alzheimer's research & therapy London : BioMed Central, 2009 9(2017), 1 vom: 14. Feb. (DE-627)605683557 (DE-600)2506521-X 1758-9193 nnns volume:9 year:2017 number:1 day:14 month:02 https://dx.doi.org/10.1186/s13195-016-0233-7 kostenfrei 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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2017 1 14 02
language	English
source	Enthalten in Alzheimer's research & therapy 9(2017), 1 vom: 14. Feb. volume:9 year:2017 number:1 day:14 month:02
sourceStr	Enthalten in Alzheimer's research & therapy 9(2017), 1 vom: 14. Feb. volume:9 year:2017 number:1 day:14 month:02
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Alzheimer’s disease MCI Cerebrospinal fluid Diagnosis
isfreeaccess_bool	true
container_title	Alzheimer's research & therapy
authorswithroles_txt_mv	Bertens, Daniela @@aut@@ Tijms, Betty M. @@aut@@ Scheltens, Philip @@aut@@ Teunissen, Charlotte E. @@aut@@ Visser, Pieter Jelle @@aut@@
publishDateDaySort_date	2017-02-14T00:00:00Z
hierarchy_top_id	605683557
id	SPR030815460
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR030815460</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519203601.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2017 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13195-016-0233-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR030815460</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13195-016-0233-7-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bertens, Daniela</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s). 2017</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background We sought to define a cutoff for β-amyloid 1–42 in cerebrospinal fluid (CSF), a key marker for Alzheimer’s disease (AD), with data-driven Gaussian mixture modeling in a memory clinic population. Methods We performed a combined cross-sectional and prospective cohort study. We selected 2462 subjects with subjective cognitive decline, mild cognitive impairment, AD-type dementia, and dementia other than AD from the Amsterdam Dementia Cohort. We defined CSF β-amyloid 1–42 cutoffs by data-driven Gaussian mixture modeling in the total population and in subgroups based on clinical diagnosis, age, and apolipoprotein E (APOE) genotype. We investigated whether abnormal β-amyloid 1–42 as defined by the data-driven cutoff could better predict progression to AD-type dementia than abnormal β-amyloid 1–42 defined by a clinical diagnosis-based cutoff using Cox proportional hazards regression. Results In the total group of patients, we found a cutoff for abnormal CSF β-amyloid 1–42 of 680 pg/ml (95% CI 660–705 pg/ml). Similar cutoffs were found within diagnostic and APOE genotype subgroups. The cutoff was higher in elderly subjects than in younger subjects. The data-driven cutoff was higher than our clinical diagnosis-based cutoff and had a better predictive accuracy for progression to AD-type dementia in nondemented subjects (HR 7.6 versus 5.2, p < 0.01). Conclusions Mixture modeling is a robust method to determine cutoffs for CSF β-amyloid 1–42. It might better capture biological changes that are related to AD than cutoffs based on clinical diagnosis.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Alzheimer’s disease</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MCI</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cerebrospinal fluid</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Diagnosis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tijms, Betty M.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Scheltens, Philip</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Teunissen, Charlotte E.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Visser, Pieter Jelle</subfield><subfield code="0">(orcid)0000-0001-8008-9727</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Alzheimer's research & therapy</subfield><subfield code="d">London : BioMed Central, 2009</subfield><subfield code="g">9(2017), 1 vom: 14. Feb.</subfield><subfield code="w">(DE-627)605683557</subfield><subfield code="w">(DE-600)2506521-X</subfield><subfield code="x">1758-9193</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:9</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:1</subfield><subfield code="g">day:14</subfield><subfield code="g">month:02</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s13195-016-0233-7</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">9</subfield><subfield code="j">2017</subfield><subfield code="e">1</subfield><subfield code="b">14</subfield><subfield code="c">02</subfield></datafield></record></collection>
author	Bertens, Daniela
spellingShingle	Bertens, Daniela misc Alzheimer’s disease misc MCI misc Cerebrospinal fluid misc Diagnosis Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population
authorStr	Bertens, Daniela
ppnlink_with_tag_str_mv	@@773@@(DE-627)605683557
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut
collection	springer
remote_str	true
illustrated	Not Illustrated
issn	1758-9193
topic_title	Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population Alzheimer’s disease (dpeaa)DE-He213 MCI (dpeaa)DE-He213 Cerebrospinal fluid (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213
topic	misc Alzheimer’s disease misc MCI misc Cerebrospinal fluid misc Diagnosis
topic_unstemmed	misc Alzheimer’s disease misc MCI misc Cerebrospinal fluid misc Diagnosis
topic_browse	misc Alzheimer’s disease misc MCI misc Cerebrospinal fluid misc Diagnosis
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Alzheimer's research & therapy
hierarchy_parent_id	605683557
hierarchy_top_title	Alzheimer's research & therapy
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)605683557 (DE-600)2506521-X
title	Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population
ctrlnum	(DE-627)SPR030815460 (SPR)s13195-016-0233-7-e
title_full	Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population
author_sort	Bertens, Daniela
journal	Alzheimer's research & therapy
journalStr	Alzheimer's research & therapy
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2017
contenttype_str_mv	txt
author_browse	Bertens, Daniela Tijms, Betty M. Scheltens, Philip Teunissen, Charlotte E. Visser, Pieter Jelle
container_volume	9
format_se	Elektronische Aufsätze
author-letter	Bertens, Daniela
doi_str_mv	10.1186/s13195-016-0233-7
normlink	(ORCID)0000-0001-8008-9727
normlink_prefix_str_mv	(orcid)0000-0001-8008-9727
title_sort	unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population
title_auth	Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population
abstract	Background We sought to define a cutoff for β-amyloid 1–42 in cerebrospinal fluid (CSF), a key marker for Alzheimer’s disease (AD), with data-driven Gaussian mixture modeling in a memory clinic population. Methods We performed a combined cross-sectional and prospective cohort study. We selected 2462 subjects with subjective cognitive decline, mild cognitive impairment, AD-type dementia, and dementia other than AD from the Amsterdam Dementia Cohort. We defined CSF β-amyloid 1–42 cutoffs by data-driven Gaussian mixture modeling in the total population and in subgroups based on clinical diagnosis, age, and apolipoprotein E (APOE) genotype. We investigated whether abnormal β-amyloid 1–42 as defined by the data-driven cutoff could better predict progression to AD-type dementia than abnormal β-amyloid 1–42 defined by a clinical diagnosis-based cutoff using Cox proportional hazards regression. Results In the total group of patients, we found a cutoff for abnormal CSF β-amyloid 1–42 of 680 pg/ml (95% CI 660–705 pg/ml). Similar cutoffs were found within diagnostic and APOE genotype subgroups. The cutoff was higher in elderly subjects than in younger subjects. The data-driven cutoff was higher than our clinical diagnosis-based cutoff and had a better predictive accuracy for progression to AD-type dementia in nondemented subjects (HR 7.6 versus 5.2, p < 0.01). Conclusions Mixture modeling is a robust method to determine cutoffs for CSF β-amyloid 1–42. It might better capture biological changes that are related to AD than cutoffs based on clinical diagnosis. © The Author(s). 2017
abstractGer	Background We sought to define a cutoff for β-amyloid 1–42 in cerebrospinal fluid (CSF), a key marker for Alzheimer’s disease (AD), with data-driven Gaussian mixture modeling in a memory clinic population. Methods We performed a combined cross-sectional and prospective cohort study. We selected 2462 subjects with subjective cognitive decline, mild cognitive impairment, AD-type dementia, and dementia other than AD from the Amsterdam Dementia Cohort. We defined CSF β-amyloid 1–42 cutoffs by data-driven Gaussian mixture modeling in the total population and in subgroups based on clinical diagnosis, age, and apolipoprotein E (APOE) genotype. We investigated whether abnormal β-amyloid 1–42 as defined by the data-driven cutoff could better predict progression to AD-type dementia than abnormal β-amyloid 1–42 defined by a clinical diagnosis-based cutoff using Cox proportional hazards regression. Results In the total group of patients, we found a cutoff for abnormal CSF β-amyloid 1–42 of 680 pg/ml (95% CI 660–705 pg/ml). Similar cutoffs were found within diagnostic and APOE genotype subgroups. The cutoff was higher in elderly subjects than in younger subjects. The data-driven cutoff was higher than our clinical diagnosis-based cutoff and had a better predictive accuracy for progression to AD-type dementia in nondemented subjects (HR 7.6 versus 5.2, p < 0.01). Conclusions Mixture modeling is a robust method to determine cutoffs for CSF β-amyloid 1–42. It might better capture biological changes that are related to AD than cutoffs based on clinical diagnosis. © The Author(s). 2017
abstract_unstemmed	Background We sought to define a cutoff for β-amyloid 1–42 in cerebrospinal fluid (CSF), a key marker for Alzheimer’s disease (AD), with data-driven Gaussian mixture modeling in a memory clinic population. Methods We performed a combined cross-sectional and prospective cohort study. We selected 2462 subjects with subjective cognitive decline, mild cognitive impairment, AD-type dementia, and dementia other than AD from the Amsterdam Dementia Cohort. We defined CSF β-amyloid 1–42 cutoffs by data-driven Gaussian mixture modeling in the total population and in subgroups based on clinical diagnosis, age, and apolipoprotein E (APOE) genotype. We investigated whether abnormal β-amyloid 1–42 as defined by the data-driven cutoff could better predict progression to AD-type dementia than abnormal β-amyloid 1–42 defined by a clinical diagnosis-based cutoff using Cox proportional hazards regression. Results In the total group of patients, we found a cutoff for abnormal CSF β-amyloid 1–42 of 680 pg/ml (95% CI 660–705 pg/ml). Similar cutoffs were found within diagnostic and APOE genotype subgroups. The cutoff was higher in elderly subjects than in younger subjects. The data-driven cutoff was higher than our clinical diagnosis-based cutoff and had a better predictive accuracy for progression to AD-type dementia in nondemented subjects (HR 7.6 versus 5.2, p < 0.01). Conclusions Mixture modeling is a robust method to determine cutoffs for CSF β-amyloid 1–42. It might better capture biological changes that are related to AD than cutoffs based on clinical diagnosis. © The Author(s). 2017
collection_details	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_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	1
title_short	Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population
url	https://dx.doi.org/10.1186/s13195-016-0233-7
remote_bool	true
author2	Tijms, Betty M. Scheltens, Philip Teunissen, Charlotte E. Visser, Pieter Jelle
author2Str	Tijms, Betty M. Scheltens, Philip Teunissen, Charlotte E. Visser, Pieter Jelle
ppnlink	605683557
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1186/s13195-016-0233-7
up_date	2024-07-03T20:16:53.226Z
_version_	1803590370050703361
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR030815460</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519203601.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2017 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13195-016-0233-7</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR030815460</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13195-016-0233-7-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bertens, Daniela</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2017</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s). 2017</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background We sought to define a cutoff for β-amyloid 1–42 in cerebrospinal fluid (CSF), a key marker for Alzheimer’s disease (AD), with data-driven Gaussian mixture modeling in a memory clinic population. Methods We performed a combined cross-sectional and prospective cohort study. We selected 2462 subjects with subjective cognitive decline, mild cognitive impairment, AD-type dementia, and dementia other than AD from the Amsterdam Dementia Cohort. We defined CSF β-amyloid 1–42 cutoffs by data-driven Gaussian mixture modeling in the total population and in subgroups based on clinical diagnosis, age, and apolipoprotein E (APOE) genotype. We investigated whether abnormal β-amyloid 1–42 as defined by the data-driven cutoff could better predict progression to AD-type dementia than abnormal β-amyloid 1–42 defined by a clinical diagnosis-based cutoff using Cox proportional hazards regression. Results In the total group of patients, we found a cutoff for abnormal CSF β-amyloid 1–42 of 680 pg/ml (95% CI 660–705 pg/ml). Similar cutoffs were found within diagnostic and APOE genotype subgroups. The cutoff was higher in elderly subjects than in younger subjects. The data-driven cutoff was higher than our clinical diagnosis-based cutoff and had a better predictive accuracy for progression to AD-type dementia in nondemented subjects (HR 7.6 versus 5.2, p < 0.01). Conclusions Mixture modeling is a robust method to determine cutoffs for CSF β-amyloid 1–42. It might better capture biological changes that are related to AD than cutoffs based on clinical diagnosis.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Alzheimer’s disease</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MCI</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cerebrospinal fluid</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Diagnosis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tijms, Betty M.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Scheltens, Philip</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Teunissen, Charlotte E.</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Visser, Pieter Jelle</subfield><subfield code="0">(orcid)0000-0001-8008-9727</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Alzheimer's research & therapy</subfield><subfield code="d">London : BioMed Central, 2009</subfield><subfield code="g">9(2017), 1 vom: 14. Feb.</subfield><subfield code="w">(DE-627)605683557</subfield><subfield code="w">(DE-600)2506521-X</subfield><subfield code="x">1758-9193</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:9</subfield><subfield code="g">year:2017</subfield><subfield code="g">number:1</subfield><subfield code="g">day:14</subfield><subfield code="g">month:02</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s13195-016-0233-7</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">9</subfield><subfield code="j">2017</subfield><subfield code="e">1</subfield><subfield code="b">14</subfield><subfield code="c">02</subfield></datafield></record></collection>
score	7.4022093

Nicht das Richtige dabei?

Schreiben Sie uns!

Unbiased estimates of cerebrospinal fluid β-amyloid 1–42 cutoffs in a large memory clinic population

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?