Relationship Between the Japanese Version of the Montreal Cognitive Assessment and PET Imaging in Subjects with Mild Cognitive Impairment

Lower GLUT1 and unchanged MCT1 in Alzheimer's disease cerebrovasculature

The brain is a highly demanding organ, utilizing mainly glucose but also ketone bodies as sources of energy. Glucose transporter-1 (GLUT1) and monocarboxylates transporter-1 (MCT1) respectively transport glucose and ketone bodies across the blood-brain barrier. While reduced glucose uptake by the brain is one of the earliest signs of Alzheimer's disease (AD), no change in the uptake of ketone bodies has been evidenced yet. To probe for changes in GLUT1 and MCT1, we performed Western immunoblotting in microvessel extracts from the parietal cortex of 60 participants of the Religious Orders Study. Participants clinically diagnosed with AD had lower cerebrovascular levels of GLUT1, whereas MCT1 remained unchanged. GLUT1 reduction was associated with lower cognitive scores. No such association was found for MCT1. GLUT1 was inversely correlated with neuritic plaques and cerebrovascular β-secretase-derived fragment levels. No other significant associations were found between both transporters, markers of Aβ and tau pathologies, sex, age at death or apolipoprotein-ε4 genotype. These results suggest that, while a deficit of GLUT1 may underlie the reduced transport of glucose to the brain in AD, no such impairment occurs for MCT1. This study thus supports the exploration of ketone bodies as an alternative energy source for the aging brain.

Predicting positron emission tomography brain amyloid positivity using interpretable machine learning models with wearable sensor data and lifestyle factors

BACKGROUND

Developing a screening method for identifying individuals at higher risk of elevated brain amyloid burden is important to reduce costs and burden to patients in clinical trials on Alzheimer's disease or the clinical setting. We developed machine learning models using objectively measured lifestyle factors to predict elevated brain amyloid burden on positron emission tomography.

METHODS

Our prospective cohort study of non-demented, community-dwelling older adults aged ≥ 65 years was conducted from August 2015 to September 2019 in Usuki, Oita Prefecture, Japan. One hundred and twenty-two individuals with mild cognitive impairment or subjective memory complaints (54 men and 68 women, median age: 75.50 years) wore wearable sensors and completed self-reported questionnaires, cognitive test, and positron emission tomography imaging at baseline. Moreover, 99 individuals in the second year and 61 individuals in the third year were followed up. In total, 282 eligible records with valid wearable sensors, cognitive test results, and amyloid imaging and data on demographic characteristics, living environments, and health behaviors were used in the machine learning models. Amyloid positivity was defined as a standardized uptake value ratio of ≥ 1.4. Models were constructed using kernel support vector machine, Elastic Net, and logistic regression for predicting amyloid positivity. The mean score among 10 times fivefold cross-validation repeats was utilized for evaluation.

RESULTS

In Elastic Net, the mean area under the receiver operating characteristic curve of the model using objectively measured lifestyle factors alone was 0.70, whereas that of the models using wearable sensors in combination with demographic characteristics and health and life environment questionnaires was 0.79. Moreover, 22 variables were common to all machine learning models.

CONCLUSION

Our machine learning models are useful for predicting elevated brain amyloid burden using readily-available and noninvasive variables without the need to visit a hospital.

TRIAL REGISTRATION

This prospective study was conducted in accordance with the Declaration of Helsinki and was approved by the local ethics committee of Oita University Hospital (UMIN000017442). A written informed consent was obtained from all participants. This research was performed based on the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline.

Montreal Cognitive Assessment in Mild Cognitive Impairment: Relationship with Cerebrospinal Fluid Biomarkers and Conversion to Dementia

BACKGROUND

Mild cognitive impairment (MCI) is considered a prodromal state of dementia. Abnormal values of cerebrospinal fluid Alzheimer's disease biomarkers (CSF-AD-b) have been associated with a higher risk of conversion to dementia (due to Alzheimer's disease), but studies evaluating the ability of Montreal Cognitive Assessment (MoCA) in this task are lacking.

OBJECTIVE

This study aims to investigate the relationship between MoCA and CSF-AD-b, as well as the ability of those tools to predict conversion to dementia.

METHODS

Taking advantage of our MCI cohort with biological characterization on longitudinal follow-up (180 patients followed for 62.6 months during which 41.3% converted), we computed MoCA and MMSE z-scores, using Portuguese normative data. The performance in MoCA z-score was correlated with CSF-AD-b and the relative time to conversion and risk according to baseline characteristics were analyzed using Kaplan-Meier analysis and Cox regression models.

RESULTS

MoCA z-scores were correlated with Aβ42 (p = 0.026), t-tau (p = 0.033), and p-tau (p = 0.01). Impaired MMSE (p < 0.001) and MoCA z-scores (p = 0.019), decreased Aβ42 (p < 0.001) and increased t-tau (p < 0.001) and p-tau (p < 0.001) were associated with shorter estimated time of conversion. Aβ42 (p < 0.001) and MMSE z-scores (p = 0.029) were independent predictors of conversion. For those with at least 9 years of education, MoCA z-score (p = 0.004) (but not MMSE) was an independent predictor of conversion as well as Aβ42.

CONCLUSIONS

This study confirms the role of CSF-AD-b, namely Aβ42, in predicting conversion from MCI to dementia and suggests the utility of MoCA in predicting conversion in highly educated subjects, supporting its use in the evaluation of MCI patients.

Is comprehensiveness critical? Comparing short and long format cognitive assessments in preclinical Alzheimer disease

BACKGROUND

Comprehensive testing of cognitive functioning is standard practice in studies of Alzheimer disease (AD). Short-form tests like the Montreal Cognitive Assessment (MoCA) use a "sampling" of measures, administering key items in a shortened format to efficiently assess cognition while reducing time requirements, participant burden, and administrative costs. We compared the MoCA to a commonly used long-form cognitive battery in predicting AD symptom onset and sensitivity to AD neuroimaging biomarkers.

METHODS

Survival, area under the receiver operating characteristic (ROC) curve (AUC), and multiple regression analyses compared the MoCA and long-form measures in predicting time to symptom onset in cognitively normal older adults (n = 6230) from the National Alzheimer's Coordinating Center (NACC) cohort who had, on average, 2.3 ± 1.2 annual assessments. Multiple regression models in a separate sample (n = 416) from the Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC) compared the sensitivity of the MoCA and long-form measures to neuroimaging biomarkers including amyloid PET, tau PET, and cortical thickness.

RESULTS

Hazard ratios suggested that both the MoCA and the long-form measures are similarly and modestly efficacious in predicting symptomatic conversion, although model comparison analyses indicated that the long-form measures slightly outperformed the MoCA (HRs > 1.57). AUC analyses indicated no difference between the measures in predicting conversion (DeLong's test, Z = 1.48, p = 0.13). Sensitivity to AD neuroimaging biomarkers was similar for the two measures though there were only modest associations with tau PET (rs = - 0.13, ps < 0.02) and cortical thickness in cognitively normal participants (rs = 0.15-0.16, ps < 0.007).

CONCLUSIONS

Both test formats showed weak associations with symptom onset, AUC analyses indicated low diagnostic accuracy, and biomarker correlations were modest in cognitively normal participants. Alternative assessment approaches are needed to improve how clinicians and researchers monitor cognitive changes and disease progression prior to symptom onset.

Association between Matrix Metalloproteinases, Their Tissue Inhibitor and White Matter Lesions in Mild Cognitive Impairment

BACKGROUND

White matter lesions are frequently found in mild cognitive impairments and Alzheimer's disease. Matrix metalloproteinases and the tissue inhibitor of metalloproteinases are implicated in amyloid-β catabolism and blood brain barrier permeability. However, it remains unclear whether they are associated with white matter lesions in Alzheimer's disease.

OBJECTIVE

The aim of this study was to examine the association of matrix metalloproteinases and tissue inhibitor of metalloproteinases with white matter degeneration in subjects with amyloid-positive mild cognitive impairment.

METHODS

Thirty subjects with amnestic mild cognitive impairment (14 men and 16 women; mean age, 75.6 ± 5.8 years) underwent magnetic resonance imaging, ¹¹C-Pittsburgh Compound B positron emission tomography, and ¹⁸F-fluorodeoxyglucose positron emission tomography. Levels of plasma matrix metalloproteinases and tissue inhibitor of metalloproteinases were measured using multiplex assays. All subjects had an abnormal brain amyloid burden. Subjects were divided into two groups according to the presence of white matter lesions using the Fazekas scale. Cognitive function testing results i.e., mean ¹¹C-Pittsburgh Compound B and ¹⁸F-fluorodeoxyglucose uptake, concentrations of matrix metalloproteinases and tissue inhibitor of metalloproteinases, and matrix metalloproteinases/tissue inhibitor of metalloproteinases ratios were compared between the groups. Correlation analysis was conducted to investigate the association between Fazekas scale score and clinical and neuroimaging variables as well as concentrations of matrix metalloproteinases and tissue inhibitor of metalloproteinases.

RESULTS

Matrix metalloproteinases-2, -8, and -9 levels, matrix metalloproteinases-2/ tissue inhibitor of metalloproteinases-2, matrix metalloproteinases-8/ tissue inhibitor of metalloproteinases-1, and matrix metalloproteinases-9/tissue inhibitor of metalloproteinases-1 significantly increased and tissue inhibitor of metalloproteinases-1 and-2 levels significantly decreased in the group with white matter lesions compared with the group without white matter lesions. Matrix metalloproteinases-2, -8, and -9 levels correlated positively and tissue inhibitor of metalloproteinases-1 and -2 levels correlated negatively with Fazekas scale score.

CONCLUSION

Plasma matrix metalloproteinases-2, -8, -9 and tissue inhibitor of metalloproteinases-1 and -2 levels are associated with white matter lesions in the mild cognitive impairment stage of Alzheimer's disease.

Association of Modifiable Lifestyle Factors With Cortical Amyloid Burden and Cerebral Glucose Metabolism in Older Adults With Mild Cognitive Impairment

IMPORTANCE

Understanding the association of lifestyle factors with mild cognitive impairment enables the development of evidence-based interventions for delaying cognitive impairment.

OBJECTIVE

To explore whether objectively measured lifestyle factors, such as physical activity, conversation, and sleep, are associated with cortical amyloid burden and cerebral glucose metabolism in older adults with mild cognitive impairment.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included 855 community-dwelling adults in Usuki, Oita Prefecture, Japan, aged 65 years or older. Data were collected from August 2015 to December 2017. Participants were reviewed to examine risk and protective lifestyle factors for dementia. Data analysis was conducted in June 2019.

EXPOSURES

Wearable sensors, carbon-11 labeled Pittsburgh compound B positron emission tomography images, and fluorine-18 fluorodeoxyglucose positron emission tomography images.

MAIN OUTCOMES AND MEASURES

Wearable sensor data, such as walking steps, conversation time, and sleep, were collected from August 2015 to October 2017, and positron emission tomography images were collected from October 2015 to December 2017. A multiple regression model and change-point regression model were used to examine the association of lifestyle factors with mean amyloid or fluorodeoxyglucose uptake, assessed on the basis of a standardized uptake value ratio of the frontal lobes, temporoparietal lobes, and posterior cingulate gyrus with the cerebellar cortex as the reference region. The bootstrap method was used to obtain nonparametric 95% CIs on the associations of lifestyle factors with cognitive decline.

RESULTS

Of the 855 adults in the study, 118 (13.8%) were diagnosed with mild cognitive impairment, with a mean (SD) age of 75.7 (5.8) years and 66 (55.9%) women. Total sleep time was inversely associated with fluorodeoxyglucose uptake after adjusting for covariates (β = -0.287; 95% CI, -0.452 to -0.121, P < .001). Change-point regression showed an inverse association between total sleep time and mean amyloid uptake when sleep duration was longer than 325 minutes (B = -0.0018; 95% CI, -0.0031 to -0.0007).

CONCLUSIONS AND RELEVANCE

To our knowledge, this is the first study to demonstrate that total sleep time was associated with brain function in older adults with mild cognitive impairment. Sleep duration is a potentially modifiable risk factor for dementia at the mild cognitive impairment stage.