- Email: [email protected]
Correlation between metabolic and CSF biomarkers in Alzheimer's disease patients with early cognitive decline
P56
Poster Presentations: IC-P
P1-157) and predicted conversion from mild cognitive impairment (MCI) to AD (AAIC 2013, P3-085). However, it is not known which biological phenomena of the disease process texture reflect. The purpose of this study was to investigate a potential relationship between texture and tau-mediated neuronal injury as reflected by reduced glucose metabolism in FDG-PET. Methods: The study dataset consisted of the 215 baseline T1-weighted structural MRI scans from the “complete annual year 2 visit” 1.5T standardized Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset [62 normal controls (NC), 101 MCI subjects, and 52 AD patients] that had an associated FDG-PET measurement of hippocampus metabolic rate of glucose (MRglc) available from the Center for Brain Health, NYU School of Medicine, New York with a maximum of 60 days between scans. MRI analysis consisted of segmenting the hippocampi using cross-sectional FreeSurfer (v.5.1.0), computation of the hippocampal fraction (HF, hippocampal volume divided by intra-cranial volume), and texture scoring of the hippocampus using our in-house method. Left and right hippocampal MRglc were downloaded directly from the ANDI website and subsequently averaged to obtain a single score. Results: A scatter plot revealed a linear relationship between texture and MRglc (see figure). Three linear regression models with different combinations of MRI biomarkers as covariates were fitted, all with hippocampal MRglc as outcome, and with age and sex as additional covariates (see table). Both HF (model 1) and texture (model 2) explained hippocampal MRglc, and the model using texture resulted in a better overall fit. Adding both HF and texture as covariates (model 3) resulted in an even better overall model fit, and both texture and HF had a significant contribution. Conclusions: There was a significant relationship between hippocampal MRI texture and hippocampal glucose metabolism demonstrating that texture potentially is related to the synaptic dysfunction that accompanies neurodegeneration in AD. The relationship persisted when controlling for volume, i.e., texture carries additional information. Interestingly, the combination of volume and texture resulted in an even better overall model fit.
Table 1 Linear regression with hippocampal MRglc in FDG-PET as outcome. model 1: HF Covariate Age (yr) Sex (1: male, 2: female) HF (%) Texture (pos: “AD-like”) Model fit R2 AIC
0.0026* 0.0444** 0.9114***
model 2: texture 0.0027* 0.0292* 0.0697***
0.3730 -354.2560
0.4110 -367.7701
model 3: HF + texture 0.0022* 0.0353* 0.4268** 0.0470*** 0.4390 -376.3692
* p-value < 0.05; ** p-value < 0.01; *** p-value < 0.001. Abbreviations: AIC, Akaike information criterion; FDG-PET, fluorodeoxyglucose-positron emission tomography; MRglc, metabolic rate of glucose metabolism; R2, coefficient of determination. IC-P-078
CORRELATION BETWEEN METABOLIC AND CSF BIOMARKERS IN ALZHEIMER’S DISEASE PATIENTS WITH EARLY COGNITIVE DECLINE
Luka Jensterle1, Petra Tomse1, Uros Rot1, Andreja Emersic1, Zvezdan Pirtosek1, Chris C. Tang2, David Eidelberg2, Maja Trost1, 1 University Medical Centre Ljubljana, Ljubljana, Slovenia; 2The Feinstein Institute for Medical Research, Manhasset, NY, USA. Contact e-mail: maja. [email protected] Background: Early diagnosis of Alzheimer’s dementia may be diffi-
cult based on clinical presentation. Evaluation of different biomarkers can improve the diagnostic accuracy. We have recently utilized spatial covariance analysis with FDG/PET to identify a specific Alzheimer’s disease-related metabolic pattern (ADRP; attached Figure 1) that differentiates AD patients from healthy subjects (Mattis et al., 2015; manuscript in preparation). In this study, we aim to further evaluate the ability of ADRP expression to differentiate between patients with early cognitive disorder and normal controls (NC) and to correlate ADRP expression in individual subjects with their CSF biomarkers for Alzheimer’s disease. Methods: We studied 49 patients with early cognitive impairment (mean age 71.4 69.4 years, MMSE score 23.9 65.4, MoCA score 20.9 64.4) recruted from Center for cognitive disorders and 14 NC (mean age 64. 66.9 years, MMSE score 28.6 61.3, MoCA score 27.1 61.9). All subjects underwent lumbar puncture (amyloid b42 and p-tau were measured) as well as FDG/ PET brain scan. An automated computational procedure was used to compute ADRP expression in the scans of individual subjects. ADRP scores were then used to separate between the AD and NC groups as well as correlate with amyloid beta/p-tau ratio. Results: The expression of ADRP was significantly increased in AD patients with cognitive impairment compared to normal controls (p¼0.003; Figure 2). ADRP expresion in individual subjects correlated significantly with amyloid b42 /p-tau (R2¼0.16, p¼0.001; Figure 3). Conclusions: ADRP has shown to be a reliable metabolic biomarker of AD as its expression significantly differentiates cognitively impaired patients from normal subjects. Correlation of ADRP expression with CSF biomarker indicates that this metabolic biomarker is a sensitive measure of Alzheimer’s pathology in individual patients with early cognitive decline.
Poster Presentations: IC-P
P57
Background: Cerebral blood flow (CBF) is lower in patient with Alzheimer’s disease (AD) compared to the general elderly population. We aimed to investigate whether lower CBF was associated with a more rapid cognitive decline in patients with AD. Methods: We included 88 AD patients from the Amsterdam dementia cohort, with an arterial spin labeling (ASL) scan at baseline and at least one year of clinical follow-up. 3T pseudo-continuous (PC-)ASL was used to determine whole brain and regional (parietal, frontal, occipital, temporal and cerebellar) CBF (ml/100gr/min). For statistical purposes, CBF was inverted (i.e., higher is worse) and standardized. Linear mixed models were used to determine associations between CBF and cognitive decline over time as measured with the MMSE. We adjusted for age, sex, education and normalized brain volume, and in a separate model additionally for white matter hyperintensities and lacunes. Results: Patients were 6567 years old, 44 (50%) were female and mean baseline MMSE was 2264. Mean follow-up of 2.160.8 years and patients declined with -2.263.0 MMSE points per year. Linear mixed models revealed no associations between whole brain or regional CBF with baseline MMSE (table 1). One standard deviation (SD) lower whole brain CBF was associated with a steeper decline of 0.5 MMSE points per year (Model 1: b[SE] -0.50[0.25], p¼0.05). In particular, lower parietal CBF was associated with steeper annual decline on the MMSE (Model 1: b[SE] -0.59[0.25], p<0.05). Lower occipital CBF tended to be associated with cognitive decline as well (Model 1: b[SE] - 0.47[0.25], p¼0.06). Additional adjustment for white matter hyperintensities and lacunes did not change these results. CBF in frontal, temporal and cerebellar regions was not associated with cognitive decline. Conclusions: Lower CBF, in particular in the parietal lobes, is associated with a steeper decline on the MMSE. These findings indicate that CBF as measured with ASL may have value as prognostic marker for cognitive decline in patients with AD.
Table 1 Associations of CBF with baseline MMSE and with annual change in MMSE. Model 1
Cerebral blood flowx Whole brain Parietal Frontal Temporal Occipital Cerebellum
IC-P-079
LOWER CEREBRAL BLOOD FLOW IS ASSOCIATED WITH COGNITIVE DECLINE IN PATIENTS WITH ALZHEIMER’S DISEASE
Marije Benedictus, Annebet Leeuwis, Joost Kuijer, Philip Scheltens, Frederik Barkhof, Wiesje M. van der Flier, Niels Prins, VU University Medical Center, Amsterdam, Netherlands. Contact e-mail: m.benedictus@ vumc.nl
Model 2
Estimated baseline MMSE
Estimated Estimated annual change baseline in MMSE MMSE
Estimated annual change in MMSE
-0.3260.39 -0.5060.41 -0.0260.43 -0.2060.39 -0.2960.38 0.2660.39
-0.50+0.25U -0.59+0.25* -0.1460.26 -0.4660.25 -0.47+0.25* -0.1860.26
-0.4910.25U -0.59+0.25* -0.1260.26 -0.4760.25 -0.47+0.25* -0.1760.26
-0.2860.39 -0.45+0.41 0.1260.43 -0.17+0.39 -0.2360.39 0.3060.40
Data are represented as (b6SE. Linear mixed models were used to investigate associations between CBF and change in MMSE. A random intercept and random slope for time (in years) were assumed. The model includes terms for the CBF measure, time, the interaction between the CBF measure and time and covariates. The given b; in each first column represents the difference in baseline MMSE.In each second column, the b represents the difference in annual MMSE change. Negative values indicate that a lower CBF is associated with a decline in MMSE. Model 1: adjusted forage, sex, education and NBV Model 2: additional adjustment for WMH and lacunes *: p< 0.05 U p¼0.05 # : p¼0.06 x :CBF was inverted (i.e., higher is worse) and given per SD deviation increase (worsening)
Poster Presentations: IC-P
P1-157) and predicted conversion from mild cognitive impairment (MCI) to AD (AAIC 2013, P3-085). However, it is not known which biological phenomena of the disease process texture reflect. The purpose of this study was to investigate a potential relationship between texture and tau-mediated neuronal injury as reflected by reduced glucose metabolism in FDG-PET. Methods: The study dataset consisted of the 215 baseline T1-weighted structural MRI scans from the “complete annual year 2 visit” 1.5T standardized Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset [62 normal controls (NC), 101 MCI subjects, and 52 AD patients] that had an associated FDG-PET measurement of hippocampus metabolic rate of glucose (MRglc) available from the Center for Brain Health, NYU School of Medicine, New York with a maximum of 60 days between scans. MRI analysis consisted of segmenting the hippocampi using cross-sectional FreeSurfer (v.5.1.0), computation of the hippocampal fraction (HF, hippocampal volume divided by intra-cranial volume), and texture scoring of the hippocampus using our in-house method. Left and right hippocampal MRglc were downloaded directly from the ANDI website and subsequently averaged to obtain a single score. Results: A scatter plot revealed a linear relationship between texture and MRglc (see figure). Three linear regression models with different combinations of MRI biomarkers as covariates were fitted, all with hippocampal MRglc as outcome, and with age and sex as additional covariates (see table). Both HF (model 1) and texture (model 2) explained hippocampal MRglc, and the model using texture resulted in a better overall fit. Adding both HF and texture as covariates (model 3) resulted in an even better overall model fit, and both texture and HF had a significant contribution. Conclusions: There was a significant relationship between hippocampal MRI texture and hippocampal glucose metabolism demonstrating that texture potentially is related to the synaptic dysfunction that accompanies neurodegeneration in AD. The relationship persisted when controlling for volume, i.e., texture carries additional information. Interestingly, the combination of volume and texture resulted in an even better overall model fit.
Table 1 Linear regression with hippocampal MRglc in FDG-PET as outcome. model 1: HF Covariate Age (yr) Sex (1: male, 2: female) HF (%) Texture (pos: “AD-like”) Model fit R2 AIC
0.0026* 0.0444** 0.9114***
model 2: texture 0.0027* 0.0292* 0.0697***
0.3730 -354.2560
0.4110 -367.7701
model 3: HF + texture 0.0022* 0.0353* 0.4268** 0.0470*** 0.4390 -376.3692
* p-value < 0.05; ** p-value < 0.01; *** p-value < 0.001. Abbreviations: AIC, Akaike information criterion; FDG-PET, fluorodeoxyglucose-positron emission tomography; MRglc, metabolic rate of glucose metabolism; R2, coefficient of determination. IC-P-078
CORRELATION BETWEEN METABOLIC AND CSF BIOMARKERS IN ALZHEIMER’S DISEASE PATIENTS WITH EARLY COGNITIVE DECLINE
Luka Jensterle1, Petra Tomse1, Uros Rot1, Andreja Emersic1, Zvezdan Pirtosek1, Chris C. Tang2, David Eidelberg2, Maja Trost1, 1 University Medical Centre Ljubljana, Ljubljana, Slovenia; 2The Feinstein Institute for Medical Research, Manhasset, NY, USA. Contact e-mail: maja. [email protected] Background: Early diagnosis of Alzheimer’s dementia may be diffi-
cult based on clinical presentation. Evaluation of different biomarkers can improve the diagnostic accuracy. We have recently utilized spatial covariance analysis with FDG/PET to identify a specific Alzheimer’s disease-related metabolic pattern (ADRP; attached Figure 1) that differentiates AD patients from healthy subjects (Mattis et al., 2015; manuscript in preparation). In this study, we aim to further evaluate the ability of ADRP expression to differentiate between patients with early cognitive disorder and normal controls (NC) and to correlate ADRP expression in individual subjects with their CSF biomarkers for Alzheimer’s disease. Methods: We studied 49 patients with early cognitive impairment (mean age 71.4 69.4 years, MMSE score 23.9 65.4, MoCA score 20.9 64.4) recruted from Center for cognitive disorders and 14 NC (mean age 64. 66.9 years, MMSE score 28.6 61.3, MoCA score 27.1 61.9). All subjects underwent lumbar puncture (amyloid b42 and p-tau were measured) as well as FDG/ PET brain scan. An automated computational procedure was used to compute ADRP expression in the scans of individual subjects. ADRP scores were then used to separate between the AD and NC groups as well as correlate with amyloid beta/p-tau ratio. Results: The expression of ADRP was significantly increased in AD patients with cognitive impairment compared to normal controls (p¼0.003; Figure 2). ADRP expresion in individual subjects correlated significantly with amyloid b42 /p-tau (R2¼0.16, p¼0.001; Figure 3). Conclusions: ADRP has shown to be a reliable metabolic biomarker of AD as its expression significantly differentiates cognitively impaired patients from normal subjects. Correlation of ADRP expression with CSF biomarker indicates that this metabolic biomarker is a sensitive measure of Alzheimer’s pathology in individual patients with early cognitive decline.
Poster Presentations: IC-P
P57
Background: Cerebral blood flow (CBF) is lower in patient with Alzheimer’s disease (AD) compared to the general elderly population. We aimed to investigate whether lower CBF was associated with a more rapid cognitive decline in patients with AD. Methods: We included 88 AD patients from the Amsterdam dementia cohort, with an arterial spin labeling (ASL) scan at baseline and at least one year of clinical follow-up. 3T pseudo-continuous (PC-)ASL was used to determine whole brain and regional (parietal, frontal, occipital, temporal and cerebellar) CBF (ml/100gr/min). For statistical purposes, CBF was inverted (i.e., higher is worse) and standardized. Linear mixed models were used to determine associations between CBF and cognitive decline over time as measured with the MMSE. We adjusted for age, sex, education and normalized brain volume, and in a separate model additionally for white matter hyperintensities and lacunes. Results: Patients were 6567 years old, 44 (50%) were female and mean baseline MMSE was 2264. Mean follow-up of 2.160.8 years and patients declined with -2.263.0 MMSE points per year. Linear mixed models revealed no associations between whole brain or regional CBF with baseline MMSE (table 1). One standard deviation (SD) lower whole brain CBF was associated with a steeper decline of 0.5 MMSE points per year (Model 1: b[SE] -0.50[0.25], p¼0.05). In particular, lower parietal CBF was associated with steeper annual decline on the MMSE (Model 1: b[SE] -0.59[0.25], p<0.05). Lower occipital CBF tended to be associated with cognitive decline as well (Model 1: b[SE] - 0.47[0.25], p¼0.06). Additional adjustment for white matter hyperintensities and lacunes did not change these results. CBF in frontal, temporal and cerebellar regions was not associated with cognitive decline. Conclusions: Lower CBF, in particular in the parietal lobes, is associated with a steeper decline on the MMSE. These findings indicate that CBF as measured with ASL may have value as prognostic marker for cognitive decline in patients with AD.
Table 1 Associations of CBF with baseline MMSE and with annual change in MMSE. Model 1
Cerebral blood flowx Whole brain Parietal Frontal Temporal Occipital Cerebellum
IC-P-079
LOWER CEREBRAL BLOOD FLOW IS ASSOCIATED WITH COGNITIVE DECLINE IN PATIENTS WITH ALZHEIMER’S DISEASE
Marije Benedictus, Annebet Leeuwis, Joost Kuijer, Philip Scheltens, Frederik Barkhof, Wiesje M. van der Flier, Niels Prins, VU University Medical Center, Amsterdam, Netherlands. Contact e-mail: m.benedictus@ vumc.nl
Model 2
Estimated baseline MMSE
Estimated Estimated annual change baseline in MMSE MMSE
Estimated annual change in MMSE
-0.3260.39 -0.5060.41 -0.0260.43 -0.2060.39 -0.2960.38 0.2660.39
-0.50+0.25U -0.59+0.25* -0.1460.26 -0.4660.25 -0.47+0.25* -0.1860.26
-0.4910.25U -0.59+0.25* -0.1260.26 -0.4760.25 -0.47+0.25* -0.1760.26
-0.2860.39 -0.45+0.41 0.1260.43 -0.17+0.39 -0.2360.39 0.3060.40
Data are represented as (b6SE. Linear mixed models were used to investigate associations between CBF and change in MMSE. A random intercept and random slope for time (in years) were assumed. The model includes terms for the CBF measure, time, the interaction between the CBF measure and time and covariates. The given b; in each first column represents the difference in baseline MMSE.In each second column, the b represents the difference in annual MMSE change. Negative values indicate that a lower CBF is associated with a decline in MMSE. Model 1: adjusted forage, sex, education and NBV Model 2: additional adjustment for WMH and lacunes *: p< 0.05 U p¼0.05 # : p¼0.06 x :CBF was inverted (i.e., higher is worse) and given per SD deviation increase (worsening)