Alzheimer Disease: Aging Brain Study
Abstract: Alzheimer disease has become a common disease in aging, which accounts for 60 to 80 percent of dementia cases (Qiu, Kivipelto & Strauss, 2009). β-Amyloid deposition in brain is associated with cognitive decline in aging, which is associated with the risk of developing Alzheimer Disease. Low-density lipoprotein (LDL) cholesterol is collected in the walls of blood vessels, which can cause blockages and cerebral amyloidosis if the LDL level becomes higher. Since many patients with cerebral amyloidosis or Alzheimer Disease had LDL cholesterol concentrations in the normal range for the general population, we used the LDL subfractions classified by LDL particle size and density to examine which kind of the LDL can be the best predictors of cerebral amyloidosis, which would be a good marker of Alzheimer Disease. Cross-sectional analysis of LDL subfractions levels together with measures of amyloid deposition (Pittsburgh Compound B index), Apolipoprotein C-III (ApoC-III) glycoforms, hippocampal brain volume, and cognitive testing scores was among 58 participants (40 women and 18 men) from the Aging Brain Study. In this study, we find that
…show more content…
LDL.IVa and LDL.I can be the best predictors of amyloid deposition, and the ApoC-III-I ratio was strongly associated with cognition and hippocampal volume, the region affected in Alzheimer Disease. These findings suggest an important role for LDL (especially LDL.IVa and LDL.I) metabolism in brain amyloid deposition, which is a signal of Alzheimer Disease.
Key words: LDL subfractions, Apolipoprotein C-III; Alzheimer Disease; cognitive performance; hippocampal brain volume; cerebral amyloidosis.
1. Introduction
Alzheimers’ disease is currently a worldwide epidemic, leading to complete loss of memory and ability to function independently. According to the World Alzheimer Report 2016, over 46.8 million people are living with dementia, and by 2050 this number will have risen to 131.5 million, which requires our immediate attention (Prince et al., 2016).
Cerebral amyloidosis is the symptom caused by the deposition of β-amyloid (Aβ) in the media and adventitia of the cerebral cortex, which is always regarded as a signal of the development of Alzheimer Disease (Felmeden et al., 2003; Krauss, 2010).
ApoC-III is a component of very low-density lipoprotein (VLDL), and many studies have demonstrated that it is an Aβ-binding protein which can be seen an early marker of Alzheimer Disease (Tsai et al., 2014). ApoC-III protein is a small protein containing 79 amino acids, which can regulate plasma triglycerides metabolism (Vaith, Assmann & Uhlenbruck, 1978). Previous reports have suggested an important role for ApoC-III in promoting the assembly and secretion of LDL particles from hepatic cells, under the rich-lipid condition (Sundaram et al., 2010). Because of the higher abundance and potential functional importance of the sialylations, analysis of ApoC-III was largely restricted to its glycoforms (Yassine et al., 2015). As a result, we measured ApoC-III glycoforms by triplexed mass spectrometric immunoassay (MSIA) in this study.
LDL is one of the five major groups of lipoprotein divided by density, and it is the main carrier of plasma cholesterol and the major component of atherosclerotic plaque. Elevating level of LDL cholesterol can increase the risk of coronary events and Aβ deposition (Rajman, Eacho, Chowienczyk & Ritte, 1999). However, in previous studies, we found many patients with coronary artery disease or Alzheimer Disease had LDL cholesterol level in the normal range as healthy people (Musliner & Krauss, 1988). To study on the further analysis of the effect of LDL cholesterol on cerebral amyloidosis or Alzheimer Disease, we classified the LDL cholesterol into 8 subfractions by its particle size and density from small to large and measured their levels separately in participants’ plasma. Our goal was to examine the effect of LDL cholesterol on developing Alzheimer Disease and to determine which part of LDL cholesterol would be the best predictor of cerebral amyloidosis among subjects participating in the Aging Brain Study.
2. Methods
Participants
Participants were recruited from the Aging Brain Study, a multisite research program designed to determine the contributions of vascular risk factors to cognitive impairment, with and without Alzheimer Disease. We recruited individuals with no or mild cognitive impairment from the clinic and community. Inclusion criteria have previously been described in detail (Reed et al., 2007). There were 58 participants in this study, and all of them underwent cognitive testing, Pittsburgh Compound B–positron emission tomography (PiB-PET), and magnetic resonance imaging (MRI). The study was approved by the institutional review boards at all participating institutions. Written informed consent was obtained from all participants following IRB-approved protocols.
PET imaging Cerebral β-Amyloid (Aβ) was measured with positron emission tomography (PET) using the tracer C-labeled Pittsburgh Compound B (PiB), which is specifically retained in fibrillar Aβ plaques.
The PiB radiotracer was synthesized at Lawrence Berkeley National Laboratory using a previously published protocol (Mathis et al., 2003). PiB-PET imaging was conducted using a Siemens ECAT HR scanner in 3-D acquisition mode, which can provide direct insight into cerebral amyloidosis. Cerebral Aβ was measured using the PiB index that averages PiB signal in brain regions with amyloidosis (Marchant et al., 2014). Distribution volume ratio values were extracted from regions of interest vulnerable to Aβ deposition. PiB index can be used as a marker in diagnosing and monitoring disease progression in Alzheimer Disease (Lockhart et al.,
2007).
Cognitive Testing
All participants received a standardized neuropsychological test battery from which linear measures of global cognition, verbal memory, non-verbal memory, and executive function were derived using item-response theory (Reed et al., 2007). Scale development used 400 elderly individuals with cognitive function ranging from normal to demented. Detailed information have been described in previous studies (Yassine et al., 2016). Each scale was transformed to a mean of 100 and a standard deviation of 15.
Measures of Brain Volume by MRI All participants underwent MRI using a 3-T scanner (Magnetom Trio System; Siemens) with an 8-channel head coil. Acquired images included a T1-weighted, volumetric, magnetization-prepared rapid gradient-echo (MPRAGE) image and a fluid-attenuated inversion recovery (FLAIR) image. Regional brain areas were normalized to measures of total intracranial volumes. Hippocampal volumes passed quality control measures in 46 of the 58 participants.
LDL subfractions Prior to ion mobility (IM) fractionation, lipoproteins were isolated by dextran sulfate precipitation. Plasma was treated with 17% ethanol which removed >97% of fibrinogen, and lipoproteins were then precipitated with dextran sulfate (2 mg/mL) and calcium (0.15 M). Precipitated lipoproteins were harvested on paramagnetic particles, washed to remove free salt and proteins and then resuspended in 25 mM ammonium acetate for analysis by ion mobility. Following isolation, the lipoproteins were fractionated and quantitated in a single scan using gas-phase electrophoresis (ion mobility). The intra-assay variation was <0.7% for the LDL peak particle size.
ApoC-III glycoform analysis Measurements of ApoC-III glycoforms were performed using triplexed mass spectrometric immunoassay (MSIA) (Wada, Kadoya & Okamoto, 2012). Firstly, affinity pipettes were complexed with corresponding antibodies. Samples were diluted in PBS, 0.1%Tween, and then a Multimek 96-channel robot was used to capture all ApoC glycoforms from samples by repeated pipette tip. Non-specifically bound proteins were rinsed away, whereas captured apolipoproteins were eluted directly onto a 96-well formatted MALDI target using a sinapinic acid matrix. Using an Ultraflex III MALDI-TOF instrument, linear mass spectra were acquired from each sample spot. On average, 5000 laser shots mass spectra were saved for each sample, and further processed with Flex Analysis 3.0 software. All peaks representing apolipoproteins and their glycoforms were integrated baseline-to-baseline using Zebra 1.0 software, and the obtained peak area values were tabulated. Detailed protocols have been described in previous studies (Yassine et al., 2015). The abundance of each ApoC-III glycoform was calculated as relative percentage of the total ApoC-III. MSIA can detect a total of 12 ApoC-III glycoforms, which present with variations in the content of galactose (Gal), N-acteyl galactosamine (GalNAc), fucose, alanine truncations or sialic acid residues. Since some of ApoC-III glycoforms had low signals there, we focused on four ApoC-III glycoforms in this study (ApoC-III native, var1, var2 and var3).
Statistics
Means (SDs) for participant baseline characteristics and differences among PiB index tertiles (1, 2 &3) were computed. Pearson coefficients for non-normally distributed data were used to correlate LDL subfractions levels with PiB index and ApoC-III glycoforms, and also between ApoC-III glycoforms and cognitive scores, hippocampal brain colume. The groups were also compared using a linear regression model to allow adjusting for age, sex, years of education, and other potential confounders. All analyses were conducted using SAS statistical software (University Edition). A 2-tailed level of .05 was used to determine statistical significance.
3. Results
To get better analyses, we divided PiB index into three groups equally from low to high (1 to 3), called “PiB tertiles”. The baseline characteristics, including demographic data, cholesterol levels and cognitive scores of participants stratified by PiB tertiles were presented in Table 1. Among the 58 participants (40 females and 18 males), sex ratio (female/male) was statistically significantly differed by PiB tertiles (p=0.0066), which was not compatible with the previous results (Yassine et al., 2016). The mean (±SD) age of the participants was 77.8 (±6.3) years and ranges from 67 to 90 years and the average (±SD) years of education were 14.4 (±2.6) years. Since the Aging Brain Study focused on vascular contributions to cognitive decline, many participants were overweight with pre-diabetes or treated diabetes (Yassine et al., 2016). As can be seen, participants displayed a relative higher Body Mass Index (BMI) with the mean (±SD) of 28.4 (±5.6) kg/m2, compared with the range of 18.5 to 24.9 kg/m2 in the normal population (Razak et al., 2007). The mean (±SD) of LDL cholesterol of the participants was 90.1 (±30.1) mg/dL, which seems normal. More analyses about LDL subfractions would be assessed next. We also found that LDL levels were statistically significantly differed in PiB tertiles groups (p=0.0321), indicating that LDL cholesterol is statistically significantly associated with cerebral amyloidosis, which is compatible with the previous studies (Yassine et al., 2016). Blood pressures, cognitive scores, HDL cholesterol and glucose level seem not significantly related to PiB index there.
LDL.IVa and LDL.I can be the best predictors of amyloid deposition, and the ApoC-III-I ratio was strongly associated with cognition and hippocampal volume, the region affected in Alzheimer Disease. These findings suggest an important role for LDL (especially LDL.IVa and LDL.I) metabolism in brain amyloid deposition, which is a signal of Alzheimer Disease.
Key words: LDL subfractions, Apolipoprotein C-III; Alzheimer Disease; cognitive performance; hippocampal brain volume; cerebral amyloidosis.
1. Introduction
Alzheimers’ disease is currently a worldwide epidemic, leading to complete loss of memory and ability to function independently. According to the World Alzheimer Report 2016, over 46.8 million people are living with dementia, and by 2050 this number will have risen to 131.5 million, which requires our immediate attention (Prince et al., 2016).
Cerebral amyloidosis is the symptom caused by the deposition of β-amyloid (Aβ) in the media and adventitia of the cerebral cortex, which is always regarded as a signal of the development of Alzheimer Disease (Felmeden et al., 2003; Krauss, 2010).
ApoC-III is a component of very low-density lipoprotein (VLDL), and many studies have demonstrated that it is an Aβ-binding protein which can be seen an early marker of Alzheimer Disease (Tsai et al., 2014). ApoC-III protein is a small protein containing 79 amino acids, which can regulate plasma triglycerides metabolism (Vaith, Assmann & Uhlenbruck, 1978). Previous reports have suggested an important role for ApoC-III in promoting the assembly and secretion of LDL particles from hepatic cells, under the rich-lipid condition (Sundaram et al., 2010). Because of the higher abundance and potential functional importance of the sialylations, analysis of ApoC-III was largely restricted to its glycoforms (Yassine et al., 2015). As a result, we measured ApoC-III glycoforms by triplexed mass spectrometric immunoassay (MSIA) in this study.
LDL is one of the five major groups of lipoprotein divided by density, and it is the main carrier of plasma cholesterol and the major component of atherosclerotic plaque. Elevating level of LDL cholesterol can increase the risk of coronary events and Aβ deposition (Rajman, Eacho, Chowienczyk & Ritte, 1999). However, in previous studies, we found many patients with coronary artery disease or Alzheimer Disease had LDL cholesterol level in the normal range as healthy people (Musliner & Krauss, 1988). To study on the further analysis of the effect of LDL cholesterol on cerebral amyloidosis or Alzheimer Disease, we classified the LDL cholesterol into 8 subfractions by its particle size and density from small to large and measured their levels separately in participants’ plasma. Our goal was to examine the effect of LDL cholesterol on developing Alzheimer Disease and to determine which part of LDL cholesterol would be the best predictor of cerebral amyloidosis among subjects participating in the Aging Brain Study.
2. Methods
Participants
Participants were recruited from the Aging Brain Study, a multisite research program designed to determine the contributions of vascular risk factors to cognitive impairment, with and without Alzheimer Disease. We recruited individuals with no or mild cognitive impairment from the clinic and community. Inclusion criteria have previously been described in detail (Reed et al., 2007). There were 58 participants in this study, and all of them underwent cognitive testing, Pittsburgh Compound B–positron emission tomography (PiB-PET), and magnetic resonance imaging (MRI). The study was approved by the institutional review boards at all participating institutions. Written informed consent was obtained from all participants following IRB-approved protocols.
PET imaging Cerebral β-Amyloid (Aβ) was measured with positron emission tomography (PET) using the tracer C-labeled Pittsburgh Compound B (PiB), which is specifically retained in fibrillar Aβ plaques.
The PiB radiotracer was synthesized at Lawrence Berkeley National Laboratory using a previously published protocol (Mathis et al., 2003). PiB-PET imaging was conducted using a Siemens ECAT HR scanner in 3-D acquisition mode, which can provide direct insight into cerebral amyloidosis. Cerebral Aβ was measured using the PiB index that averages PiB signal in brain regions with amyloidosis (Marchant et al., 2014). Distribution volume ratio values were extracted from regions of interest vulnerable to Aβ deposition. PiB index can be used as a marker in diagnosing and monitoring disease progression in Alzheimer Disease (Lockhart et al.,
2007).
Cognitive Testing
All participants received a standardized neuropsychological test battery from which linear measures of global cognition, verbal memory, non-verbal memory, and executive function were derived using item-response theory (Reed et al., 2007). Scale development used 400 elderly individuals with cognitive function ranging from normal to demented. Detailed information have been described in previous studies (Yassine et al., 2016). Each scale was transformed to a mean of 100 and a standard deviation of 15.
Measures of Brain Volume by MRI All participants underwent MRI using a 3-T scanner (Magnetom Trio System; Siemens) with an 8-channel head coil. Acquired images included a T1-weighted, volumetric, magnetization-prepared rapid gradient-echo (MPRAGE) image and a fluid-attenuated inversion recovery (FLAIR) image. Regional brain areas were normalized to measures of total intracranial volumes. Hippocampal volumes passed quality control measures in 46 of the 58 participants.
LDL subfractions Prior to ion mobility (IM) fractionation, lipoproteins were isolated by dextran sulfate precipitation. Plasma was treated with 17% ethanol which removed >97% of fibrinogen, and lipoproteins were then precipitated with dextran sulfate (2 mg/mL) and calcium (0.15 M). Precipitated lipoproteins were harvested on paramagnetic particles, washed to remove free salt and proteins and then resuspended in 25 mM ammonium acetate for analysis by ion mobility. Following isolation, the lipoproteins were fractionated and quantitated in a single scan using gas-phase electrophoresis (ion mobility). The intra-assay variation was <0.7% for the LDL peak particle size.
ApoC-III glycoform analysis Measurements of ApoC-III glycoforms were performed using triplexed mass spectrometric immunoassay (MSIA) (Wada, Kadoya & Okamoto, 2012). Firstly, affinity pipettes were complexed with corresponding antibodies. Samples were diluted in PBS, 0.1%Tween, and then a Multimek 96-channel robot was used to capture all ApoC glycoforms from samples by repeated pipette tip. Non-specifically bound proteins were rinsed away, whereas captured apolipoproteins were eluted directly onto a 96-well formatted MALDI target using a sinapinic acid matrix. Using an Ultraflex III MALDI-TOF instrument, linear mass spectra were acquired from each sample spot. On average, 5000 laser shots mass spectra were saved for each sample, and further processed with Flex Analysis 3.0 software. All peaks representing apolipoproteins and their glycoforms were integrated baseline-to-baseline using Zebra 1.0 software, and the obtained peak area values were tabulated. Detailed protocols have been described in previous studies (Yassine et al., 2015). The abundance of each ApoC-III glycoform was calculated as relative percentage of the total ApoC-III. MSIA can detect a total of 12 ApoC-III glycoforms, which present with variations in the content of galactose (Gal), N-acteyl galactosamine (GalNAc), fucose, alanine truncations or sialic acid residues. Since some of ApoC-III glycoforms had low signals there, we focused on four ApoC-III glycoforms in this study (ApoC-III native, var1, var2 and var3).
Statistics
Means (SDs) for participant baseline characteristics and differences among PiB index tertiles (1, 2 &3) were computed. Pearson coefficients for non-normally distributed data were used to correlate LDL subfractions levels with PiB index and ApoC-III glycoforms, and also between ApoC-III glycoforms and cognitive scores, hippocampal brain colume. The groups were also compared using a linear regression model to allow adjusting for age, sex, years of education, and other potential confounders. All analyses were conducted using SAS statistical software (University Edition). A 2-tailed level of .05 was used to determine statistical significance.
3. Results
To get better analyses, we divided PiB index into three groups equally from low to high (1 to 3), called “PiB tertiles”. The baseline characteristics, including demographic data, cholesterol levels and cognitive scores of participants stratified by PiB tertiles were presented in Table 1. Among the 58 participants (40 females and 18 males), sex ratio (female/male) was statistically significantly differed by PiB tertiles (p=0.0066), which was not compatible with the previous results (Yassine et al., 2016). The mean (±SD) age of the participants was 77.8 (±6.3) years and ranges from 67 to 90 years and the average (±SD) years of education were 14.4 (±2.6) years. Since the Aging Brain Study focused on vascular contributions to cognitive decline, many participants were overweight with pre-diabetes or treated diabetes (Yassine et al., 2016). As can be seen, participants displayed a relative higher Body Mass Index (BMI) with the mean (±SD) of 28.4 (±5.6) kg/m2, compared with the range of 18.5 to 24.9 kg/m2 in the normal population (Razak et al., 2007). The mean (±SD) of LDL cholesterol of the participants was 90.1 (±30.1) mg/dL, which seems normal. More analyses about LDL subfractions would be assessed next. We also found that LDL levels were statistically significantly differed in PiB tertiles groups (p=0.0321), indicating that LDL cholesterol is statistically significantly associated with cerebral amyloidosis, which is compatible with the previous studies (Yassine et al., 2016). Blood pressures, cognitive scores, HDL cholesterol and glucose level seem not significantly related to PiB index there.