White matter hyperintensity patterns: associations with comorbidities, amyloid, and cognition

Background White matter hyperintensities (WMHs) are often measured globally, but spatial patterns of WMHs could underlie different risk factors and neuropathological and clinical correlates. We investigated the spatial heterogeneity of WMHs and their association with comorbidities, Alzheimer’s disease (AD) risk factors, and cognition. Methods In this cross-sectional study, we studied 171 cognitively unimpaired (CU; median age: 65 years, range: 50 to 89) and 51 mildly cognitively impaired (MCI; median age: 72, range: 53 to 89) individuals with available amyloid (18F-flutementamol) PET and FLAIR-weighted images. Comorbidities were assessed using the Cumulative Illness Rating Scale (CIRS). Each participant’s white matter was segmented into 38 parcels, and WMH volume was calculated in each parcel. Correlated principal component analysis was applied to the parceled WMH data to determine patterns of WMH covariation. Adjusted and unadjusted linear regression models were used to investigate associations of component scores with comorbidities and AD-related factors. Using multiple linear regression, we tested whether WMH component scores predicted cognitive performance. Results Principal component analysis identified four WMH components that broadly describe FLAIR signal hyperintensities in posterior, periventricular, and deep white matter regions, as well as basal ganglia and thalamic structures. In CU individuals, hypertension was associated with all patterns except the periventricular component. MCI individuals showed more diverse associations. The posterior and deep components were associated with renal disorders, the periventricular component was associated with increased amyloid, and the subcortical gray matter structures was associated with sleep disorders, endocrine/metabolic disorders, and increased amyloid. In the combined sample (CU + MCI), the main effects of WMH components were not associated with cognition but predicted poorer episodic memory performance in the presence of increased amyloid. No interaction between hypertension and the number of comorbidities on component scores was observed. Conclusion Our study underscores the significance of understanding the regional distribution patterns of WMHs and the valuable insights that risk factors can offer regarding their underlying causes. Moreover, patterns of hyperintensities in periventricular regions and deep gray matter structures may have more pronounced cognitive implications, especially when amyloid pathology is also present. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Bachmann, Dario [verfasserIn] von Rickenbach, Bettina Buchmann, Andreas Hüllner, Martin Zuber, Isabelle Studer, Sandro Saake, Antje Rauen, Katrin Gruber, Esmeralda Nitsch, Roger M. Hock, Christoph Treyer, Valerie Gietl, Anton

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Alzheimer’s disease Small vessel disease Amyloid-beta Cognitive performance Risk factors Healthy aging Clusters Copathology

Anmerkung:	© The Author(s) 2024

Übergeordnetes Werk:	Enthalten in: Alzheimer's research & therapy - BioMed Central, 2009, 16(2024), 1 vom: 01. Apr.
Übergeordnetes Werk:	volume:16 ; year:2024 ; number:1 ; day:01 ; month:04

Links:	Volltext

DOI / URN:	10.1186/s13195-024-01435-6

Katalog-ID:	SPR055375235

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allfields	10.1186/s13195-024-01435-6 doi (DE-627)SPR055375235 (SPR)s13195-024-01435-6-e DE-627 ger DE-627 rakwb eng 610 VZ Bachmann, Dario verfasserin aut White matter hyperintensity patterns: associations with comorbidities, amyloid, and cognition 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background White matter hyperintensities (WMHs) are often measured globally, but spatial patterns of WMHs could underlie different risk factors and neuropathological and clinical correlates. We investigated the spatial heterogeneity of WMHs and their association with comorbidities, Alzheimer’s disease (AD) risk factors, and cognition. Methods In this cross-sectional study, we studied 171 cognitively unimpaired (CU; median age: 65 years, range: 50 to 89) and 51 mildly cognitively impaired (MCI; median age: 72, range: 53 to 89) individuals with available amyloid (18F-flutementamol) PET and FLAIR-weighted images. Comorbidities were assessed using the Cumulative Illness Rating Scale (CIRS). Each participant’s white matter was segmented into 38 parcels, and WMH volume was calculated in each parcel. Correlated principal component analysis was applied to the parceled WMH data to determine patterns of WMH covariation. Adjusted and unadjusted linear regression models were used to investigate associations of component scores with comorbidities and AD-related factors. Using multiple linear regression, we tested whether WMH component scores predicted cognitive performance. Results Principal component analysis identified four WMH components that broadly describe FLAIR signal hyperintensities in posterior, periventricular, and deep white matter regions, as well as basal ganglia and thalamic structures. In CU individuals, hypertension was associated with all patterns except the periventricular component. MCI individuals showed more diverse associations. The posterior and deep components were associated with renal disorders, the periventricular component was associated with increased amyloid, and the subcortical gray matter structures was associated with sleep disorders, endocrine/metabolic disorders, and increased amyloid. In the combined sample (CU + MCI), the main effects of WMH components were not associated with cognition but predicted poorer episodic memory performance in the presence of increased amyloid. No interaction between hypertension and the number of comorbidities on component scores was observed. Conclusion Our study underscores the significance of understanding the regional distribution patterns of WMHs and the valuable insights that risk factors can offer regarding their underlying causes. Moreover, patterns of hyperintensities in periventricular regions and deep gray matter structures may have more pronounced cognitive implications, especially when amyloid pathology is also present. Alzheimer’s disease (dpeaa)DE-He213 Small vessel disease (dpeaa)DE-He213 Amyloid-beta (dpeaa)DE-He213 Cognitive performance (dpeaa)DE-He213 Risk factors (dpeaa)DE-He213 Healthy aging (dpeaa)DE-He213 Clusters (dpeaa)DE-He213 Copathology (dpeaa)DE-He213 von Rickenbach, Bettina aut Buchmann, Andreas aut Hüllner, Martin aut Zuber, Isabelle aut Studer, Sandro aut Saake, Antje aut Rauen, Katrin aut Gruber, Esmeralda aut Nitsch, Roger M. aut Hock, Christoph aut Treyer, Valerie aut Gietl, Anton aut Enthalten in Alzheimer's research & therapy BioMed Central, 2009 16(2024), 1 vom: 01. Apr. (DE-627)605683557 (DE-600)2506521-X 1758-9193 nnns volume:16 year:2024 number:1 day:01 month:04 https://dx.doi.org/10.1186/s13195-024-01435-6 kostenfrei Volltext SYSFLAG_0 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 16 2024 1 01 04
spelling	10.1186/s13195-024-01435-6 doi (DE-627)SPR055375235 (SPR)s13195-024-01435-6-e DE-627 ger DE-627 rakwb eng 610 VZ Bachmann, Dario verfasserin aut White matter hyperintensity patterns: associations with comorbidities, amyloid, and cognition 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background White matter hyperintensities (WMHs) are often measured globally, but spatial patterns of WMHs could underlie different risk factors and neuropathological and clinical correlates. We investigated the spatial heterogeneity of WMHs and their association with comorbidities, Alzheimer’s disease (AD) risk factors, and cognition. Methods In this cross-sectional study, we studied 171 cognitively unimpaired (CU; median age: 65 years, range: 50 to 89) and 51 mildly cognitively impaired (MCI; median age: 72, range: 53 to 89) individuals with available amyloid (18F-flutementamol) PET and FLAIR-weighted images. Comorbidities were assessed using the Cumulative Illness Rating Scale (CIRS). Each participant’s white matter was segmented into 38 parcels, and WMH volume was calculated in each parcel. Correlated principal component analysis was applied to the parceled WMH data to determine patterns of WMH covariation. Adjusted and unadjusted linear regression models were used to investigate associations of component scores with comorbidities and AD-related factors. Using multiple linear regression, we tested whether WMH component scores predicted cognitive performance. Results Principal component analysis identified four WMH components that broadly describe FLAIR signal hyperintensities in posterior, periventricular, and deep white matter regions, as well as basal ganglia and thalamic structures. In CU individuals, hypertension was associated with all patterns except the periventricular component. MCI individuals showed more diverse associations. The posterior and deep components were associated with renal disorders, the periventricular component was associated with increased amyloid, and the subcortical gray matter structures was associated with sleep disorders, endocrine/metabolic disorders, and increased amyloid. In the combined sample (CU + MCI), the main effects of WMH components were not associated with cognition but predicted poorer episodic memory performance in the presence of increased amyloid. No interaction between hypertension and the number of comorbidities on component scores was observed. Conclusion Our study underscores the significance of understanding the regional distribution patterns of WMHs and the valuable insights that risk factors can offer regarding their underlying causes. Moreover, patterns of hyperintensities in periventricular regions and deep gray matter structures may have more pronounced cognitive implications, especially when amyloid pathology is also present. Alzheimer’s disease (dpeaa)DE-He213 Small vessel disease (dpeaa)DE-He213 Amyloid-beta (dpeaa)DE-He213 Cognitive performance (dpeaa)DE-He213 Risk factors (dpeaa)DE-He213 Healthy aging (dpeaa)DE-He213 Clusters (dpeaa)DE-He213 Copathology (dpeaa)DE-He213 von Rickenbach, Bettina aut Buchmann, Andreas aut Hüllner, Martin aut Zuber, Isabelle aut Studer, Sandro aut Saake, Antje aut Rauen, Katrin aut Gruber, Esmeralda aut Nitsch, Roger M. aut Hock, Christoph aut Treyer, Valerie aut Gietl, Anton aut Enthalten in Alzheimer's research & therapy BioMed Central, 2009 16(2024), 1 vom: 01. Apr. (DE-627)605683557 (DE-600)2506521-X 1758-9193 nnns volume:16 year:2024 number:1 day:01 month:04 https://dx.doi.org/10.1186/s13195-024-01435-6 kostenfrei Volltext SYSFLAG_0 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 16 2024 1 01 04
allfields_unstemmed	10.1186/s13195-024-01435-6 doi (DE-627)SPR055375235 (SPR)s13195-024-01435-6-e DE-627 ger DE-627 rakwb eng 610 VZ Bachmann, Dario verfasserin aut White matter hyperintensity patterns: associations with comorbidities, amyloid, and cognition 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background White matter hyperintensities (WMHs) are often measured globally, but spatial patterns of WMHs could underlie different risk factors and neuropathological and clinical correlates. We investigated the spatial heterogeneity of WMHs and their association with comorbidities, Alzheimer’s disease (AD) risk factors, and cognition. Methods In this cross-sectional study, we studied 171 cognitively unimpaired (CU; median age: 65 years, range: 50 to 89) and 51 mildly cognitively impaired (MCI; median age: 72, range: 53 to 89) individuals with available amyloid (18F-flutementamol) PET and FLAIR-weighted images. Comorbidities were assessed using the Cumulative Illness Rating Scale (CIRS). Each participant’s white matter was segmented into 38 parcels, and WMH volume was calculated in each parcel. Correlated principal component analysis was applied to the parceled WMH data to determine patterns of WMH covariation. Adjusted and unadjusted linear regression models were used to investigate associations of component scores with comorbidities and AD-related factors. Using multiple linear regression, we tested whether WMH component scores predicted cognitive performance. Results Principal component analysis identified four WMH components that broadly describe FLAIR signal hyperintensities in posterior, periventricular, and deep white matter regions, as well as basal ganglia and thalamic structures. In CU individuals, hypertension was associated with all patterns except the periventricular component. MCI individuals showed more diverse associations. The posterior and deep components were associated with renal disorders, the periventricular component was associated with increased amyloid, and the subcortical gray matter structures was associated with sleep disorders, endocrine/metabolic disorders, and increased amyloid. In the combined sample (CU + MCI), the main effects of WMH components were not associated with cognition but predicted poorer episodic memory performance in the presence of increased amyloid. No interaction between hypertension and the number of comorbidities on component scores was observed. Conclusion Our study underscores the significance of understanding the regional distribution patterns of WMHs and the valuable insights that risk factors can offer regarding their underlying causes. Moreover, patterns of hyperintensities in periventricular regions and deep gray matter structures may have more pronounced cognitive implications, especially when amyloid pathology is also present. Alzheimer’s disease (dpeaa)DE-He213 Small vessel disease (dpeaa)DE-He213 Amyloid-beta (dpeaa)DE-He213 Cognitive performance (dpeaa)DE-He213 Risk factors (dpeaa)DE-He213 Healthy aging (dpeaa)DE-He213 Clusters (dpeaa)DE-He213 Copathology (dpeaa)DE-He213 von Rickenbach, Bettina aut Buchmann, Andreas aut Hüllner, Martin aut Zuber, Isabelle aut Studer, Sandro aut Saake, Antje aut Rauen, Katrin aut Gruber, Esmeralda aut Nitsch, Roger M. aut Hock, Christoph aut Treyer, Valerie aut Gietl, Anton aut Enthalten in Alzheimer's research & therapy BioMed Central, 2009 16(2024), 1 vom: 01. Apr. (DE-627)605683557 (DE-600)2506521-X 1758-9193 nnns volume:16 year:2024 number:1 day:01 month:04 https://dx.doi.org/10.1186/s13195-024-01435-6 kostenfrei Volltext SYSFLAG_0 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 16 2024 1 01 04
allfieldsGer	10.1186/s13195-024-01435-6 doi (DE-627)SPR055375235 (SPR)s13195-024-01435-6-e DE-627 ger DE-627 rakwb eng 610 VZ Bachmann, Dario verfasserin aut White matter hyperintensity patterns: associations with comorbidities, amyloid, and cognition 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background White matter hyperintensities (WMHs) are often measured globally, but spatial patterns of WMHs could underlie different risk factors and neuropathological and clinical correlates. We investigated the spatial heterogeneity of WMHs and their association with comorbidities, Alzheimer’s disease (AD) risk factors, and cognition. Methods In this cross-sectional study, we studied 171 cognitively unimpaired (CU; median age: 65 years, range: 50 to 89) and 51 mildly cognitively impaired (MCI; median age: 72, range: 53 to 89) individuals with available amyloid (18F-flutementamol) PET and FLAIR-weighted images. Comorbidities were assessed using the Cumulative Illness Rating Scale (CIRS). Each participant’s white matter was segmented into 38 parcels, and WMH volume was calculated in each parcel. Correlated principal component analysis was applied to the parceled WMH data to determine patterns of WMH covariation. Adjusted and unadjusted linear regression models were used to investigate associations of component scores with comorbidities and AD-related factors. Using multiple linear regression, we tested whether WMH component scores predicted cognitive performance. Results Principal component analysis identified four WMH components that broadly describe FLAIR signal hyperintensities in posterior, periventricular, and deep white matter regions, as well as basal ganglia and thalamic structures. In CU individuals, hypertension was associated with all patterns except the periventricular component. MCI individuals showed more diverse associations. The posterior and deep components were associated with renal disorders, the periventricular component was associated with increased amyloid, and the subcortical gray matter structures was associated with sleep disorders, endocrine/metabolic disorders, and increased amyloid. In the combined sample (CU + MCI), the main effects of WMH components were not associated with cognition but predicted poorer episodic memory performance in the presence of increased amyloid. No interaction between hypertension and the number of comorbidities on component scores was observed. Conclusion Our study underscores the significance of understanding the regional distribution patterns of WMHs and the valuable insights that risk factors can offer regarding their underlying causes. Moreover, patterns of hyperintensities in periventricular regions and deep gray matter structures may have more pronounced cognitive implications, especially when amyloid pathology is also present. Alzheimer’s disease (dpeaa)DE-He213 Small vessel disease (dpeaa)DE-He213 Amyloid-beta (dpeaa)DE-He213 Cognitive performance (dpeaa)DE-He213 Risk factors (dpeaa)DE-He213 Healthy aging (dpeaa)DE-He213 Clusters (dpeaa)DE-He213 Copathology (dpeaa)DE-He213 von Rickenbach, Bettina aut Buchmann, Andreas aut Hüllner, Martin aut Zuber, Isabelle aut Studer, Sandro aut Saake, Antje aut Rauen, Katrin aut Gruber, Esmeralda aut Nitsch, Roger M. aut Hock, Christoph aut Treyer, Valerie aut Gietl, Anton aut Enthalten in Alzheimer's research & therapy BioMed Central, 2009 16(2024), 1 vom: 01. Apr. (DE-627)605683557 (DE-600)2506521-X 1758-9193 nnns volume:16 year:2024 number:1 day:01 month:04 https://dx.doi.org/10.1186/s13195-024-01435-6 kostenfrei Volltext SYSFLAG_0 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 16 2024 1 01 04
allfieldsSound	10.1186/s13195-024-01435-6 doi (DE-627)SPR055375235 (SPR)s13195-024-01435-6-e DE-627 ger DE-627 rakwb eng 610 VZ Bachmann, Dario verfasserin aut White matter hyperintensity patterns: associations with comorbidities, amyloid, and cognition 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2024 Background White matter hyperintensities (WMHs) are often measured globally, but spatial patterns of WMHs could underlie different risk factors and neuropathological and clinical correlates. We investigated the spatial heterogeneity of WMHs and their association with comorbidities, Alzheimer’s disease (AD) risk factors, and cognition. Methods In this cross-sectional study, we studied 171 cognitively unimpaired (CU; median age: 65 years, range: 50 to 89) and 51 mildly cognitively impaired (MCI; median age: 72, range: 53 to 89) individuals with available amyloid (18F-flutementamol) PET and FLAIR-weighted images. Comorbidities were assessed using the Cumulative Illness Rating Scale (CIRS). Each participant’s white matter was segmented into 38 parcels, and WMH volume was calculated in each parcel. Correlated principal component analysis was applied to the parceled WMH data to determine patterns of WMH covariation. Adjusted and unadjusted linear regression models were used to investigate associations of component scores with comorbidities and AD-related factors. Using multiple linear regression, we tested whether WMH component scores predicted cognitive performance. Results Principal component analysis identified four WMH components that broadly describe FLAIR signal hyperintensities in posterior, periventricular, and deep white matter regions, as well as basal ganglia and thalamic structures. In CU individuals, hypertension was associated with all patterns except the periventricular component. MCI individuals showed more diverse associations. The posterior and deep components were associated with renal disorders, the periventricular component was associated with increased amyloid, and the subcortical gray matter structures was associated with sleep disorders, endocrine/metabolic disorders, and increased amyloid. In the combined sample (CU + MCI), the main effects of WMH components were not associated with cognition but predicted poorer episodic memory performance in the presence of increased amyloid. No interaction between hypertension and the number of comorbidities on component scores was observed. Conclusion Our study underscores the significance of understanding the regional distribution patterns of WMHs and the valuable insights that risk factors can offer regarding their underlying causes. Moreover, patterns of hyperintensities in periventricular regions and deep gray matter structures may have more pronounced cognitive implications, especially when amyloid pathology is also present. Alzheimer’s disease (dpeaa)DE-He213 Small vessel disease (dpeaa)DE-He213 Amyloid-beta (dpeaa)DE-He213 Cognitive performance (dpeaa)DE-He213 Risk factors (dpeaa)DE-He213 Healthy aging (dpeaa)DE-He213 Clusters (dpeaa)DE-He213 Copathology (dpeaa)DE-He213 von Rickenbach, Bettina aut Buchmann, Andreas aut Hüllner, Martin aut Zuber, Isabelle aut Studer, Sandro aut Saake, Antje aut Rauen, Katrin aut Gruber, Esmeralda aut Nitsch, Roger M. aut Hock, Christoph aut Treyer, Valerie aut Gietl, Anton aut Enthalten in Alzheimer's research & therapy BioMed Central, 2009 16(2024), 1 vom: 01. Apr. (DE-627)605683557 (DE-600)2506521-X 1758-9193 nnns volume:16 year:2024 number:1 day:01 month:04 https://dx.doi.org/10.1186/s13195-024-01435-6 kostenfrei Volltext SYSFLAG_0 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 16 2024 1 01 04
language	English
source	Enthalten in Alzheimer's research & therapy 16(2024), 1 vom: 01. Apr. volume:16 year:2024 number:1 day:01 month:04
sourceStr	Enthalten in Alzheimer's research & therapy 16(2024), 1 vom: 01. Apr. volume:16 year:2024 number:1 day:01 month:04
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Alzheimer’s disease Small vessel disease Amyloid-beta Cognitive performance Risk factors Healthy aging Clusters Copathology
dewey-raw	610
isfreeaccess_bool	true
container_title	Alzheimer's research & therapy
authorswithroles_txt_mv	Bachmann, Dario @@aut@@ von Rickenbach, Bettina @@aut@@ Buchmann, Andreas @@aut@@ Hüllner, Martin @@aut@@ Zuber, Isabelle @@aut@@ Studer, Sandro @@aut@@ Saake, Antje @@aut@@ Rauen, Katrin @@aut@@ Gruber, Esmeralda @@aut@@ Nitsch, Roger M. @@aut@@ Hock, Christoph @@aut@@ Treyer, Valerie @@aut@@ Gietl, Anton @@aut@@
publishDateDaySort_date	2024-04-01T00:00:00Z
hierarchy_top_id	605683557
dewey-sort	3610
id	SPR055375235
language_de	englisch
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We investigated the spatial heterogeneity of WMHs and their association with comorbidities, Alzheimer’s disease (AD) risk factors, and cognition. Methods In this cross-sectional study, we studied 171 cognitively unimpaired (CU; median age: 65 years, range: 50 to 89) and 51 mildly cognitively impaired (MCI; median age: 72, range: 53 to 89) individuals with available amyloid (18F-flutementamol) PET and FLAIR-weighted images. Comorbidities were assessed using the Cumulative Illness Rating Scale (CIRS). Each participant’s white matter was segmented into 38 parcels, and WMH volume was calculated in each parcel. Correlated principal component analysis was applied to the parceled WMH data to determine patterns of WMH covariation. Adjusted and unadjusted linear regression models were used to investigate associations of component scores with comorbidities and AD-related factors. Using multiple linear regression, we tested whether WMH component scores predicted cognitive performance. Results Principal component analysis identified four WMH components that broadly describe FLAIR signal hyperintensities in posterior, periventricular, and deep white matter regions, as well as basal ganglia and thalamic structures. In CU individuals, hypertension was associated with all patterns except the periventricular component. MCI individuals showed more diverse associations. The posterior and deep components were associated with renal disorders, the periventricular component was associated with increased amyloid, and the subcortical gray matter structures was associated with sleep disorders, endocrine/metabolic disorders, and increased amyloid. In the combined sample (CU + MCI), the main effects of WMH components were not associated with cognition but predicted poorer episodic memory performance in the presence of increased amyloid. No interaction between hypertension and the number of comorbidities on component scores was observed. Conclusion Our study underscores the significance of understanding the regional distribution patterns of WMHs and the valuable insights that risk factors can offer regarding their underlying causes. 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author	Bachmann, Dario
spellingShingle	Bachmann, Dario ddc 610 misc Alzheimer’s disease misc Small vessel disease misc Amyloid-beta misc Cognitive performance misc Risk factors misc Healthy aging misc Clusters misc Copathology White matter hyperintensity patterns: associations with comorbidities, amyloid, and cognition
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topic_title	610 VZ White matter hyperintensity patterns: associations with comorbidities, amyloid, and cognition Alzheimer’s disease (dpeaa)DE-He213 Small vessel disease (dpeaa)DE-He213 Amyloid-beta (dpeaa)DE-He213 Cognitive performance (dpeaa)DE-He213 Risk factors (dpeaa)DE-He213 Healthy aging (dpeaa)DE-He213 Clusters (dpeaa)DE-He213 Copathology (dpeaa)DE-He213
topic	ddc 610 misc Alzheimer’s disease misc Small vessel disease misc Amyloid-beta misc Cognitive performance misc Risk factors misc Healthy aging misc Clusters misc Copathology
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title	White matter hyperintensity patterns: associations with comorbidities, amyloid, and cognition
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title_full	White matter hyperintensity patterns: associations with comorbidities, amyloid, and cognition
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author_browse	Bachmann, Dario von Rickenbach, Bettina Buchmann, Andreas Hüllner, Martin Zuber, Isabelle Studer, Sandro Saake, Antje Rauen, Katrin Gruber, Esmeralda Nitsch, Roger M. Hock, Christoph Treyer, Valerie Gietl, Anton
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title_sort	white matter hyperintensity patterns: associations with comorbidities, amyloid, and cognition
title_auth	White matter hyperintensity patterns: associations with comorbidities, amyloid, and cognition
abstract	Background White matter hyperintensities (WMHs) are often measured globally, but spatial patterns of WMHs could underlie different risk factors and neuropathological and clinical correlates. We investigated the spatial heterogeneity of WMHs and their association with comorbidities, Alzheimer’s disease (AD) risk factors, and cognition. Methods In this cross-sectional study, we studied 171 cognitively unimpaired (CU; median age: 65 years, range: 50 to 89) and 51 mildly cognitively impaired (MCI; median age: 72, range: 53 to 89) individuals with available amyloid (18F-flutementamol) PET and FLAIR-weighted images. Comorbidities were assessed using the Cumulative Illness Rating Scale (CIRS). Each participant’s white matter was segmented into 38 parcels, and WMH volume was calculated in each parcel. Correlated principal component analysis was applied to the parceled WMH data to determine patterns of WMH covariation. Adjusted and unadjusted linear regression models were used to investigate associations of component scores with comorbidities and AD-related factors. Using multiple linear regression, we tested whether WMH component scores predicted cognitive performance. Results Principal component analysis identified four WMH components that broadly describe FLAIR signal hyperintensities in posterior, periventricular, and deep white matter regions, as well as basal ganglia and thalamic structures. In CU individuals, hypertension was associated with all patterns except the periventricular component. MCI individuals showed more diverse associations. The posterior and deep components were associated with renal disorders, the periventricular component was associated with increased amyloid, and the subcortical gray matter structures was associated with sleep disorders, endocrine/metabolic disorders, and increased amyloid. In the combined sample (CU + MCI), the main effects of WMH components were not associated with cognition but predicted poorer episodic memory performance in the presence of increased amyloid. No interaction between hypertension and the number of comorbidities on component scores was observed. Conclusion Our study underscores the significance of understanding the regional distribution patterns of WMHs and the valuable insights that risk factors can offer regarding their underlying causes. Moreover, patterns of hyperintensities in periventricular regions and deep gray matter structures may have more pronounced cognitive implications, especially when amyloid pathology is also present. © The Author(s) 2024
abstractGer	Background White matter hyperintensities (WMHs) are often measured globally, but spatial patterns of WMHs could underlie different risk factors and neuropathological and clinical correlates. We investigated the spatial heterogeneity of WMHs and their association with comorbidities, Alzheimer’s disease (AD) risk factors, and cognition. Methods In this cross-sectional study, we studied 171 cognitively unimpaired (CU; median age: 65 years, range: 50 to 89) and 51 mildly cognitively impaired (MCI; median age: 72, range: 53 to 89) individuals with available amyloid (18F-flutementamol) PET and FLAIR-weighted images. Comorbidities were assessed using the Cumulative Illness Rating Scale (CIRS). Each participant’s white matter was segmented into 38 parcels, and WMH volume was calculated in each parcel. Correlated principal component analysis was applied to the parceled WMH data to determine patterns of WMH covariation. Adjusted and unadjusted linear regression models were used to investigate associations of component scores with comorbidities and AD-related factors. Using multiple linear regression, we tested whether WMH component scores predicted cognitive performance. Results Principal component analysis identified four WMH components that broadly describe FLAIR signal hyperintensities in posterior, periventricular, and deep white matter regions, as well as basal ganglia and thalamic structures. In CU individuals, hypertension was associated with all patterns except the periventricular component. MCI individuals showed more diverse associations. The posterior and deep components were associated with renal disorders, the periventricular component was associated with increased amyloid, and the subcortical gray matter structures was associated with sleep disorders, endocrine/metabolic disorders, and increased amyloid. In the combined sample (CU + MCI), the main effects of WMH components were not associated with cognition but predicted poorer episodic memory performance in the presence of increased amyloid. No interaction between hypertension and the number of comorbidities on component scores was observed. Conclusion Our study underscores the significance of understanding the regional distribution patterns of WMHs and the valuable insights that risk factors can offer regarding their underlying causes. Moreover, patterns of hyperintensities in periventricular regions and deep gray matter structures may have more pronounced cognitive implications, especially when amyloid pathology is also present. © The Author(s) 2024
abstract_unstemmed	Background White matter hyperintensities (WMHs) are often measured globally, but spatial patterns of WMHs could underlie different risk factors and neuropathological and clinical correlates. We investigated the spatial heterogeneity of WMHs and their association with comorbidities, Alzheimer’s disease (AD) risk factors, and cognition. Methods In this cross-sectional study, we studied 171 cognitively unimpaired (CU; median age: 65 years, range: 50 to 89) and 51 mildly cognitively impaired (MCI; median age: 72, range: 53 to 89) individuals with available amyloid (18F-flutementamol) PET and FLAIR-weighted images. Comorbidities were assessed using the Cumulative Illness Rating Scale (CIRS). Each participant’s white matter was segmented into 38 parcels, and WMH volume was calculated in each parcel. Correlated principal component analysis was applied to the parceled WMH data to determine patterns of WMH covariation. Adjusted and unadjusted linear regression models were used to investigate associations of component scores with comorbidities and AD-related factors. Using multiple linear regression, we tested whether WMH component scores predicted cognitive performance. Results Principal component analysis identified four WMH components that broadly describe FLAIR signal hyperintensities in posterior, periventricular, and deep white matter regions, as well as basal ganglia and thalamic structures. In CU individuals, hypertension was associated with all patterns except the periventricular component. MCI individuals showed more diverse associations. The posterior and deep components were associated with renal disorders, the periventricular component was associated with increased amyloid, and the subcortical gray matter structures was associated with sleep disorders, endocrine/metabolic disorders, and increased amyloid. In the combined sample (CU + MCI), the main effects of WMH components were not associated with cognition but predicted poorer episodic memory performance in the presence of increased amyloid. No interaction between hypertension and the number of comorbidities on component scores was observed. Conclusion Our study underscores the significance of understanding the regional distribution patterns of WMHs and the valuable insights that risk factors can offer regarding their underlying causes. Moreover, patterns of hyperintensities in periventricular regions and deep gray matter structures may have more pronounced cognitive implications, especially when amyloid pathology is also present. © The Author(s) 2024
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title_short	White matter hyperintensity patterns: associations with comorbidities, amyloid, and cognition
url	https://dx.doi.org/10.1186/s13195-024-01435-6
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author2	von Rickenbach, Bettina Buchmann, Andreas Hüllner, Martin Zuber, Isabelle Studer, Sandro Saake, Antje Rauen, Katrin Gruber, Esmeralda Nitsch, Roger M. Hock, Christoph Treyer, Valerie Gietl, Anton
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