Progressive Mild Cognitive Impairment Subjects Research Articles

Blood Neuroexosomal Mitochondrial Proteins Predict Alzheimer Disease in Diabetes.

There is accumulating evidence that mitochondrial dysfunction is associated with the contribution of diabetes to Alzheimer disease (AD) progression. Neuronal mitochondrial proteins are found in plasma neuronal-derived exosomes (NDEs) at levels that reflect those in brain neurons. Here, we tested the performance of mitochondrial proteins in plasma NDEs to predict cognitive decline and brain injury in participants with diabetes. The study participants with type 2 diabetes mellitus (T2DM) included 41 cognitively normal control subjects, 97 individuals with mild cognitive impairment (MCI) (68 individuals with stable MCI; 29 individuals with progressive MCI), and 36 patients with AD dementia. Plasma neuroexosomal proteins were measured by ELISA kits. Spearman correlation was used to test associations between plasma neuroexosomal mitochondrial proteins and other core biomarkers of AD. Diagnostic accuracy for progressive MCI and AD was obtained for mitochondrial proteins using receiver operating characteristic curve analyses. The associations of mitochondrial proteins with the conversion from MCI to AD were assessed by Cox proportional hazard regression analysis. Plasma levels of neuroexosomal NADH ubiquinone oxidoreductase core subunit S3 (NDUFS3) and succinate dehydrogenase complex subunit B (SDHB) were significantly lower in patients with T2DM with AD dementia and progressive MCI than in cognitively normal subjects (P < 0.001 for both groups). We also found that plasma neuroexosomal NDUFS3 and SDHB levels were lower in progressive MCI subjects than in stable MCI subjects. Both plasma neuroexosomal NDUFS3 and SDHB offer diagnostic utility for AD. Low plasma neuroexosomal SDHB levels significantly predicted conversion from MCI to AD. In addition, low mitochondrial protein levels were associated with the rate of hippocampal and gray matter atrophy and reduced AD signature cortical thickness in progressive MCI over the follow-up period. These data suggest that both plasma neuroexosomal NDUFS3 and SDHB are already increased at the early clinical stage of AD, and indicate the promise of plasma neuroexosomal mitochondrial proteins as diagnostic and prognostic biomarkers for the earliest symptomatic stage of AD in participants with diabetes.

Improved Classification of Alzheimer's Disease Data via Removal of Nuisance Variability

Diagnosis of Alzheimer's disease is based on the results of neuropsychological tests and available supporting biomarkers such as the results of imaging studies. The results of the tests and the values of biomarkers are dependent on the nuisance features, such as age and gender. In order to improve diagnostic power, the effects of the nuisance features have to be removed from the data. In this paper, four types of interactions between classification features and nuisance features were identified. Three methods were tested to remove these interactions from the classification data. In stratified analysis, a homogeneous subgroup was generated from a training set. Data correction method utilized linear regression model to remove the effects of nuisance features from data. The third method was a combination of these two methods. The methods were tested using all the baseline data from the Alzheimer's Disease Neuroimaging Initiative database in two classification studies: classifying control subjects from Alzheimer's disease patients and discriminating stable and progressive mild cognitive impairment subjects. The results show that both stratified analysis and data correction are able to statistically significantly improve the classification accuracy of several neuropsychological tests and imaging biomarkers. The improvements were especially large for the classification of stable and progressive mild cognitive impairment subjects, where the best improvements observed were 6% units. The data correction method gave better results for imaging biomarkers, whereas stratified analysis worked well with the neuropsychological tests. In conclusion, the study shows that the excess variability caused by nuisance features should be removed from the data to improve the classification accuracy, and therefore, the reliability of diagnosis making.

Multi-template tensor-based morphometry: Application to analysis of Alzheimer's disease

In this paper methods for using multiple templates in tensor-based morphometry (TBM) are presented and compared to the conventional single-template approach. TBM analysis requires non-rigid registrations which are often subject to registration errors. When using multiple templates and, therefore, multiple registrations, it can be assumed that the registration errors are averaged and eventually compensated. Four different methods are proposed for multi-template TBM. The methods were evaluated using magnetic resonance (MR) images of healthy controls, patients with stable or progressive mild cognitive impairment (MCI), and patients with Alzheimer's disease (AD) from the ADNI database (N=772). The performance of TBM features in classifying images was evaluated both quantitatively and qualitatively. Classification results show that the multi-template methods are statistically significantly better than the single-template method. The overall classification accuracy was 86.0% for the classification of control and AD subjects, and 72.1% for the classification of stable and progressive MCI subjects. The statistical group-level difference maps produced using multi-template TBM were smoother, formed larger continuous regions, and had larger t-values than the maps obtained with single-template TBM.

Analysis of regional MRI volumes and thicknesses as predictors of conversion from mild cognitive impairment to Alzheimer's disease

We determined predictors of conversion to Alzheimer's disease (AD) from mild cognitive impairment (MCI) with automated magnetic resonance imaging (MRI) regional cortical volume and thickness measures. One hundred amnestic MCI subjects, 118 AD patients, and 94 age-matched healthy controls were selected from AddNeuroMed study. Twenty-four regional cortical volumes and 34 cortical thicknesses were measured with automated image processing software at baseline. Twenty-one subjects converted from MCI to AD determined with the cognitive tests at baseline and 1 year later. The hippocampus, amygdala, and caudate volumes were significantly smaller in progressive MCI subjects than in controls and stable MCI subjects. The cortical volumes achieved higher predictive accuracy than did cognitive tests or cortical thickness. Combining the volumes, thicknesses, and cognitive tests did not improve the accuracy. The volume of amygdala and caudate were independent variables in predicting conversion from MCI to AD. We conclude that regional cortical volume measures are more powerful than those common cognitive tests we used in identifying AD patients at the very earliest stage of the disease.

Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve

Brain atrophy measured by magnetic resonance structural imaging has been proposed as a surrogate marker for the early diagnosis of Alzheimer's disease. Studies on large samples are still required to determine its practical interest at the individual level, especially with regards to the capacity of anatomical magnetic resonance imaging to disentangle the confounding role of the cognitive reserve in the early diagnosis of Alzheimer's disease. One hundred and thirty healthy controls, 122 subjects with mild cognitive impairment of the amnestic type and 130 Alzheimer's disease patients were included from the ADNI database and followed up for 24 months. After 24 months, 72 amnestic mild cognitive impairment had converted to Alzheimer's disease (referred to as progressive mild cognitive impairment, as opposed to stable mild cognitive impairment). For each subject, cortical thickness was measured on the baseline magnetic resonance imaging volume. The resulting cortical thickness map was parcellated into 22 regions and a normalized thickness index was computed using the subset of regions (right medial temporal, left lateral temporal, right posterior cingulate) that optimally distinguished stable mild cognitive impairment from progressive mild cognitive impairment. We tested the ability of baseline normalized thickness index to predict evolution from amnestic mild cognitive impairment to Alzheimer's disease and compared it to the predictive values of the main cognitive scores at baseline. In addition, we studied the relationship between the normalized thickness index, the education level and the timeline of conversion to Alzheimer's disease. Normalized thickness index at baseline differed significantly among all the four diagnosis groups (P < 0.001) and correctly distinguished Alzheimer's disease patients from healthy controls with an 85% cross-validated accuracy. Normalized thickness index also correctly predicted evolution to Alzheimer's disease for 76% of amnestic mild cognitive impairment subjects after cross-validation, thus showing an advantage over cognitive scores (range 63–72%). Moreover, progressive mild cognitive impairment subjects, who converted later than 1 year after baseline, showed a significantly higher education level than those who converted earlier than 1 year after baseline. Using a normalized thickness index-based criterion may help with early diagnosis of Alzheimer's disease at the individual level, especially for highly educated subjects, up to 24 months before clinical criteria for Alzheimer's disease diagnosis are met.