Gut Microbiota Composition Is Related to AD Pathology

IntroductionSeveral studies have reported alterations in gut microbiota composition of Alzheimer’s disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD).Materials and MethodsWe included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE.ResultsThe machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of [Clostridium] leptum and lower abundance of [Eubacterium] ventriosum group spp., Lachnospiraceae spp., Marvinbryantia spp., Monoglobus spp., [Ruminococcus] torques group spp., Roseburia hominis, and Christensenellaceae R-7 spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of Lachnospiraceae spp., Lachnoclostridium spp., Roseburia hominis and Bilophila wadsworthia and higher odds of positive p-tau status.ConclusionsGut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Barbara J. H. Verhaar [verfasserIn] Heleen M. A. Hendriksen [verfasserIn] Francisca A. de Leeuw [verfasserIn] Astrid S. Doorduijn [verfasserIn] Mardou van Leeuwenstijn [verfasserIn] Charlotte E. Teunissen [verfasserIn] Frederik Barkhof [verfasserIn] Philip Scheltens [verfasserIn] Robert Kraaij [verfasserIn] Cornelia M. van Duijn [verfasserIn] Max Nieuwdorp [verfasserIn] Majon Muller [verfasserIn] Wiesje M. van der Flier [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	gut microbiota microbiome Alzheimer’s disease amyloid beta P-tau MRI

Übergeordnetes Werk:	In: Frontiers in Immunology - Frontiers Media S.A., 2011, 12(2022)
Übergeordnetes Werk:	volume:12 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fimmu.2021.794519

Katalog-ID:	DOAJ06160240X

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520			\|a IntroductionSeveral studies have reported alterations in gut microbiota composition of Alzheimer’s disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD).Materials and MethodsWe included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE.ResultsThe machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of [Clostridium] leptum and lower abundance of [Eubacterium] ventriosum group spp., Lachnospiraceae spp., Marvinbryantia spp., Monoglobus spp., [Ruminococcus] torques group spp., Roseburia hominis, and Christensenellaceae R-7 spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of Lachnospiraceae spp., Lachnoclostridium spp., Roseburia hominis and Bilophila wadsworthia and higher odds of positive p-tau status.ConclusionsGut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status.
650		4	\|a gut microbiota
650		4	\|a microbiome
650		4	\|a Alzheimer’s disease
650		4	\|a amyloid beta
650		4	\|a P-tau
650		4	\|a MRI
653		0	\|a Immunologic diseases. Allergy
700	0		\|a Barbara J. H. Verhaar \|e verfasserin \|4 aut
700	0		\|a Barbara J. H. Verhaar \|e verfasserin \|4 aut
700	0		\|a Heleen M. A. Hendriksen \|e verfasserin \|4 aut
700	0		\|a Francisca A. de Leeuw \|e verfasserin \|4 aut
700	0		\|a Astrid S. Doorduijn \|e verfasserin \|4 aut
700	0		\|a Mardou van Leeuwenstijn \|e verfasserin \|4 aut
700	0		\|a Charlotte E. Teunissen \|e verfasserin \|4 aut
700	0		\|a Frederik Barkhof \|e verfasserin \|4 aut
700	0		\|a Frederik Barkhof \|e verfasserin \|4 aut
700	0		\|a Philip Scheltens \|e verfasserin \|4 aut
700	0		\|a Robert Kraaij \|e verfasserin \|4 aut
700	0		\|a Cornelia M. van Duijn \|e verfasserin \|4 aut
700	0		\|a Cornelia M. van Duijn \|e verfasserin \|4 aut
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700	0		\|a Majon Muller \|e verfasserin \|4 aut
700	0		\|a Wiesje M. van der Flier \|e verfasserin \|4 aut
700	0		\|a Wiesje M. van der Flier \|e verfasserin \|4 aut
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allfields	10.3389/fimmu.2021.794519 doi (DE-627)DOAJ06160240X (DE-599)DOAJa5d22258812d4abcb86aeffa17454e82 DE-627 ger DE-627 rakwb eng RC581-607 Barbara J. H. Verhaar verfasserin aut Gut Microbiota Composition Is Related to AD Pathology 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionSeveral studies have reported alterations in gut microbiota composition of Alzheimer’s disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD).Materials and MethodsWe included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE.ResultsThe machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of [Clostridium] leptum and lower abundance of [Eubacterium] ventriosum group spp., Lachnospiraceae spp., Marvinbryantia spp., Monoglobus spp., [Ruminococcus] torques group spp., Roseburia hominis, and Christensenellaceae R-7 spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of Lachnospiraceae spp., Lachnoclostridium spp., Roseburia hominis and Bilophila wadsworthia and higher odds of positive p-tau status.ConclusionsGut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status. gut microbiota microbiome Alzheimer’s disease amyloid beta P-tau MRI Immunologic diseases. Allergy Barbara J. H. Verhaar verfasserin aut Barbara J. H. Verhaar verfasserin aut Heleen M. A. Hendriksen verfasserin aut Francisca A. de Leeuw verfasserin aut Astrid S. Doorduijn verfasserin aut Mardou van Leeuwenstijn verfasserin aut Charlotte E. Teunissen verfasserin aut Frederik Barkhof verfasserin aut Frederik Barkhof verfasserin aut Philip Scheltens verfasserin aut Robert Kraaij verfasserin aut Cornelia M. van Duijn verfasserin aut Cornelia M. van Duijn verfasserin aut Max Nieuwdorp verfasserin aut Majon Muller verfasserin aut Wiesje M. van der Flier verfasserin aut Wiesje M. van der Flier verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 12(2022) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:12 year:2022 https://doi.org/10.3389/fimmu.2021.794519 kostenfrei https://doaj.org/article/a5d22258812d4abcb86aeffa17454e82 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2021.794519/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2014 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 12 2022
spelling	10.3389/fimmu.2021.794519 doi (DE-627)DOAJ06160240X (DE-599)DOAJa5d22258812d4abcb86aeffa17454e82 DE-627 ger DE-627 rakwb eng RC581-607 Barbara J. H. Verhaar verfasserin aut Gut Microbiota Composition Is Related to AD Pathology 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionSeveral studies have reported alterations in gut microbiota composition of Alzheimer’s disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD).Materials and MethodsWe included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE.ResultsThe machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of [Clostridium] leptum and lower abundance of [Eubacterium] ventriosum group spp., Lachnospiraceae spp., Marvinbryantia spp., Monoglobus spp., [Ruminococcus] torques group spp., Roseburia hominis, and Christensenellaceae R-7 spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of Lachnospiraceae spp., Lachnoclostridium spp., Roseburia hominis and Bilophila wadsworthia and higher odds of positive p-tau status.ConclusionsGut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status. gut microbiota microbiome Alzheimer’s disease amyloid beta P-tau MRI Immunologic diseases. Allergy Barbara J. H. Verhaar verfasserin aut Barbara J. H. Verhaar verfasserin aut Heleen M. A. Hendriksen verfasserin aut Francisca A. de Leeuw verfasserin aut Astrid S. Doorduijn verfasserin aut Mardou van Leeuwenstijn verfasserin aut Charlotte E. Teunissen verfasserin aut Frederik Barkhof verfasserin aut Frederik Barkhof verfasserin aut Philip Scheltens verfasserin aut Robert Kraaij verfasserin aut Cornelia M. van Duijn verfasserin aut Cornelia M. van Duijn verfasserin aut Max Nieuwdorp verfasserin aut Majon Muller verfasserin aut Wiesje M. van der Flier verfasserin aut Wiesje M. van der Flier verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 12(2022) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:12 year:2022 https://doi.org/10.3389/fimmu.2021.794519 kostenfrei https://doaj.org/article/a5d22258812d4abcb86aeffa17454e82 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2021.794519/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2014 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 12 2022
allfields_unstemmed	10.3389/fimmu.2021.794519 doi (DE-627)DOAJ06160240X (DE-599)DOAJa5d22258812d4abcb86aeffa17454e82 DE-627 ger DE-627 rakwb eng RC581-607 Barbara J. H. Verhaar verfasserin aut Gut Microbiota Composition Is Related to AD Pathology 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionSeveral studies have reported alterations in gut microbiota composition of Alzheimer’s disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD).Materials and MethodsWe included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE.ResultsThe machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of [Clostridium] leptum and lower abundance of [Eubacterium] ventriosum group spp., Lachnospiraceae spp., Marvinbryantia spp., Monoglobus spp., [Ruminococcus] torques group spp., Roseburia hominis, and Christensenellaceae R-7 spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of Lachnospiraceae spp., Lachnoclostridium spp., Roseburia hominis and Bilophila wadsworthia and higher odds of positive p-tau status.ConclusionsGut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status. gut microbiota microbiome Alzheimer’s disease amyloid beta P-tau MRI Immunologic diseases. Allergy Barbara J. H. Verhaar verfasserin aut Barbara J. H. Verhaar verfasserin aut Heleen M. A. Hendriksen verfasserin aut Francisca A. de Leeuw verfasserin aut Astrid S. Doorduijn verfasserin aut Mardou van Leeuwenstijn verfasserin aut Charlotte E. Teunissen verfasserin aut Frederik Barkhof verfasserin aut Frederik Barkhof verfasserin aut Philip Scheltens verfasserin aut Robert Kraaij verfasserin aut Cornelia M. van Duijn verfasserin aut Cornelia M. van Duijn verfasserin aut Max Nieuwdorp verfasserin aut Majon Muller verfasserin aut Wiesje M. van der Flier verfasserin aut Wiesje M. van der Flier verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 12(2022) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:12 year:2022 https://doi.org/10.3389/fimmu.2021.794519 kostenfrei https://doaj.org/article/a5d22258812d4abcb86aeffa17454e82 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2021.794519/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2014 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 12 2022
allfieldsGer	10.3389/fimmu.2021.794519 doi (DE-627)DOAJ06160240X (DE-599)DOAJa5d22258812d4abcb86aeffa17454e82 DE-627 ger DE-627 rakwb eng RC581-607 Barbara J. H. Verhaar verfasserin aut Gut Microbiota Composition Is Related to AD Pathology 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionSeveral studies have reported alterations in gut microbiota composition of Alzheimer’s disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD).Materials and MethodsWe included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE.ResultsThe machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of [Clostridium] leptum and lower abundance of [Eubacterium] ventriosum group spp., Lachnospiraceae spp., Marvinbryantia spp., Monoglobus spp., [Ruminococcus] torques group spp., Roseburia hominis, and Christensenellaceae R-7 spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of Lachnospiraceae spp., Lachnoclostridium spp., Roseburia hominis and Bilophila wadsworthia and higher odds of positive p-tau status.ConclusionsGut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status. gut microbiota microbiome Alzheimer’s disease amyloid beta P-tau MRI Immunologic diseases. Allergy Barbara J. H. Verhaar verfasserin aut Barbara J. H. Verhaar verfasserin aut Heleen M. A. Hendriksen verfasserin aut Francisca A. de Leeuw verfasserin aut Astrid S. Doorduijn verfasserin aut Mardou van Leeuwenstijn verfasserin aut Charlotte E. Teunissen verfasserin aut Frederik Barkhof verfasserin aut Frederik Barkhof verfasserin aut Philip Scheltens verfasserin aut Robert Kraaij verfasserin aut Cornelia M. van Duijn verfasserin aut Cornelia M. van Duijn verfasserin aut Max Nieuwdorp verfasserin aut Majon Muller verfasserin aut Wiesje M. van der Flier verfasserin aut Wiesje M. van der Flier verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 12(2022) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:12 year:2022 https://doi.org/10.3389/fimmu.2021.794519 kostenfrei https://doaj.org/article/a5d22258812d4abcb86aeffa17454e82 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2021.794519/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2014 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 12 2022
allfieldsSound	10.3389/fimmu.2021.794519 doi (DE-627)DOAJ06160240X (DE-599)DOAJa5d22258812d4abcb86aeffa17454e82 DE-627 ger DE-627 rakwb eng RC581-607 Barbara J. H. Verhaar verfasserin aut Gut Microbiota Composition Is Related to AD Pathology 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier IntroductionSeveral studies have reported alterations in gut microbiota composition of Alzheimer’s disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD).Materials and MethodsWe included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE.ResultsThe machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of [Clostridium] leptum and lower abundance of [Eubacterium] ventriosum group spp., Lachnospiraceae spp., Marvinbryantia spp., Monoglobus spp., [Ruminococcus] torques group spp., Roseburia hominis, and Christensenellaceae R-7 spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of Lachnospiraceae spp., Lachnoclostridium spp., Roseburia hominis and Bilophila wadsworthia and higher odds of positive p-tau status.ConclusionsGut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status. gut microbiota microbiome Alzheimer’s disease amyloid beta P-tau MRI Immunologic diseases. Allergy Barbara J. H. Verhaar verfasserin aut Barbara J. H. Verhaar verfasserin aut Heleen M. A. Hendriksen verfasserin aut Francisca A. de Leeuw verfasserin aut Astrid S. Doorduijn verfasserin aut Mardou van Leeuwenstijn verfasserin aut Charlotte E. Teunissen verfasserin aut Frederik Barkhof verfasserin aut Frederik Barkhof verfasserin aut Philip Scheltens verfasserin aut Robert Kraaij verfasserin aut Cornelia M. van Duijn verfasserin aut Cornelia M. van Duijn verfasserin aut Max Nieuwdorp verfasserin aut Majon Muller verfasserin aut Wiesje M. van der Flier verfasserin aut Wiesje M. van der Flier verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 12(2022) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:12 year:2022 https://doi.org/10.3389/fimmu.2021.794519 kostenfrei https://doaj.org/article/a5d22258812d4abcb86aeffa17454e82 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2021.794519/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 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_2014 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 12 2022
language	English
source	In Frontiers in Immunology 12(2022) volume:12 year:2022
sourceStr	In Frontiers in Immunology 12(2022) volume:12 year:2022
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	gut microbiota microbiome Alzheimer’s disease amyloid beta P-tau MRI Immunologic diseases. Allergy
isfreeaccess_bool	true
container_title	Frontiers in Immunology
authorswithroles_txt_mv	Barbara J. H. Verhaar @@aut@@ Heleen M. A. Hendriksen @@aut@@ Francisca A. de Leeuw @@aut@@ Astrid S. Doorduijn @@aut@@ Mardou van Leeuwenstijn @@aut@@ Charlotte E. Teunissen @@aut@@ Frederik Barkhof @@aut@@ Philip Scheltens @@aut@@ Robert Kraaij @@aut@@ Cornelia M. van Duijn @@aut@@ Max Nieuwdorp @@aut@@ Majon Muller @@aut@@ Wiesje M. van der Flier @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	657998354
id	DOAJ06160240X
language_de	englisch
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H. Verhaar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Gut Microbiota Composition Is Related to AD Pathology</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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="520" ind1=" " ind2=" "><subfield code="a">IntroductionSeveral studies have reported alterations in gut microbiota composition of Alzheimer’s disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD).Materials and MethodsWe included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE.ResultsThe machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of [Clostridium] leptum and lower abundance of [Eubacterium] ventriosum group spp., Lachnospiraceae spp., Marvinbryantia spp., Monoglobus spp., [Ruminococcus] torques group spp., Roseburia hominis, and Christensenellaceae R-7 spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of Lachnospiraceae spp., Lachnoclostridium spp., Roseburia hominis and Bilophila wadsworthia and higher odds of positive p-tau status.ConclusionsGut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">gut microbiota</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">microbiome</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Alzheimer’s disease</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">amyloid beta</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">P-tau</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MRI</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Immunologic diseases. Allergy</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Barbara J. H. Verhaar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Barbara J. H. Verhaar</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Heleen M. A. Hendriksen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Francisca A. de Leeuw</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="0" ind2=" "><subfield code="a">Astrid S. 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callnumber-first	R - Medicine
author	Barbara J. H. Verhaar
spellingShingle	Barbara J. H. Verhaar misc RC581-607 misc gut microbiota misc microbiome misc Alzheimer’s disease misc amyloid beta misc P-tau misc MRI misc Immunologic diseases. Allergy Gut Microbiota Composition Is Related to AD Pathology
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topic_title	RC581-607 Gut Microbiota Composition Is Related to AD Pathology gut microbiota microbiome Alzheimer’s disease amyloid beta P-tau MRI
topic	misc RC581-607 misc gut microbiota misc microbiome misc Alzheimer’s disease misc amyloid beta misc P-tau misc MRI misc Immunologic diseases. Allergy
topic_unstemmed	misc RC581-607 misc gut microbiota misc microbiome misc Alzheimer’s disease misc amyloid beta misc P-tau misc MRI misc Immunologic diseases. Allergy
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title	Gut Microbiota Composition Is Related to AD Pathology
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title_full	Gut Microbiota Composition Is Related to AD Pathology
author_sort	Barbara J. H. Verhaar
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author_browse	Barbara J. H. Verhaar Heleen M. A. Hendriksen Francisca A. de Leeuw Astrid S. Doorduijn Mardou van Leeuwenstijn Charlotte E. Teunissen Frederik Barkhof Philip Scheltens Robert Kraaij Cornelia M. van Duijn Max Nieuwdorp Majon Muller Wiesje M. van der Flier
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title_sort	gut microbiota composition is related to ad pathology
callnumber	RC581-607
title_auth	Gut Microbiota Composition Is Related to AD Pathology
abstract	IntroductionSeveral studies have reported alterations in gut microbiota composition of Alzheimer’s disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD).Materials and MethodsWe included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE.ResultsThe machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of [Clostridium] leptum and lower abundance of [Eubacterium] ventriosum group spp., Lachnospiraceae spp., Marvinbryantia spp., Monoglobus spp., [Ruminococcus] torques group spp., Roseburia hominis, and Christensenellaceae R-7 spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of Lachnospiraceae spp., Lachnoclostridium spp., Roseburia hominis and Bilophila wadsworthia and higher odds of positive p-tau status.ConclusionsGut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status.
abstractGer	IntroductionSeveral studies have reported alterations in gut microbiota composition of Alzheimer’s disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD).Materials and MethodsWe included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE.ResultsThe machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of [Clostridium] leptum and lower abundance of [Eubacterium] ventriosum group spp., Lachnospiraceae spp., Marvinbryantia spp., Monoglobus spp., [Ruminococcus] torques group spp., Roseburia hominis, and Christensenellaceae R-7 spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of Lachnospiraceae spp., Lachnoclostridium spp., Roseburia hominis and Bilophila wadsworthia and higher odds of positive p-tau status.ConclusionsGut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status.
abstract_unstemmed	IntroductionSeveral studies have reported alterations in gut microbiota composition of Alzheimer’s disease (AD) patients. However, the observed differences are not consistent across studies. We aimed to investigate associations between gut microbiota composition and AD biomarkers using machine learning models in patients with AD dementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD).Materials and MethodsWe included 170 patients from the Amsterdam Dementia Cohort, comprising 33 with AD dementia (66 ± 8 years, 46%F, mini-mental state examination (MMSE) 21[19-24]), 21 with MCI (64 ± 8 years, 43%F, MMSE 27[25-29]) and 116 with SCD (62 ± 8 years, 44%F, MMSE 29[28-30]). Fecal samples were collected and gut microbiome composition was determined using 16S rRNA sequencing. Biomarkers of AD included cerebrospinal fluid (CSF) amyloid-beta 1-42 (amyloid) and phosphorylated tau (p-tau), and MRI visual scores (medial temporal atrophy, global cortical atrophy, white matter hyperintensities). Associations between gut microbiota composition and dichotomized AD biomarkers were assessed with machine learning classification models. The two models with the highest area under the curve (AUC) were selected for logistic regression, to assess associations between the 20 best predicting microbes and the outcome measures from these machine learning models while adjusting for age, sex, BMI, diabetes, medication use, and MMSE.ResultsThe machine learning prediction for amyloid and p-tau from microbiota composition performed best with AUCs of 0.64 and 0.63. Highest ranked microbes included several short chain fatty acid (SCFA)-producing species. Higher abundance of [Clostridium] leptum and lower abundance of [Eubacterium] ventriosum group spp., Lachnospiraceae spp., Marvinbryantia spp., Monoglobus spp., [Ruminococcus] torques group spp., Roseburia hominis, and Christensenellaceae R-7 spp., was associated with higher odds of amyloid positivity. We found associations between lower abundance of Lachnospiraceae spp., Lachnoclostridium spp., Roseburia hominis and Bilophila wadsworthia and higher odds of positive p-tau status.ConclusionsGut microbiota composition was associated with amyloid and p-tau status. We extend on recent studies that observed associations between SCFA levels and AD CSF biomarkers by showing that lower abundances of SCFA-producing microbes were associated with higher odds of positive amyloid and p-tau status.
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title_short	Gut Microbiota Composition Is Related to AD Pathology
url	https://doi.org/10.3389/fimmu.2021.794519 https://doaj.org/article/a5d22258812d4abcb86aeffa17454e82 https://www.frontiersin.org/articles/10.3389/fimmu.2021.794519/full https://doaj.org/toc/1664-3224
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author2	Barbara J. H. Verhaar Heleen M. A. Hendriksen Francisca A. de Leeuw Astrid S. Doorduijn Mardou van Leeuwenstijn Charlotte E. Teunissen Frederik Barkhof Philip Scheltens Robert Kraaij Cornelia M. van Duijn Max Nieuwdorp Majon Muller Wiesje M. van der Flier
author2Str	Barbara J. H. Verhaar Heleen M. A. Hendriksen Francisca A. de Leeuw Astrid S. Doorduijn Mardou van Leeuwenstijn Charlotte E. Teunissen Frederik Barkhof Philip Scheltens Robert Kraaij Cornelia M. van Duijn Max Nieuwdorp Majon Muller Wiesje M. van der Flier
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