Practical cut-offs for visual rating scales of medial temporal, frontal and posterior atrophy in Alzheimer's disease and mild cognitive impairment.

BACKGROUND: Atrophy in the medial temporal lobe, frontal lobe and posterior cortex can be measured with visual rating scales such as the medial temporal atrophy (MTA), global cortical atrophy - frontal subscale (GCA-F) and posterior atrophy (PA) scales, respectively. However, practical cut-offs are urgently needed, especially now that different presentations of Alzheimer's disease (AD) are included in the revised diagnostic criteria. AIMS: The aim of this study was to generate a list of practical cut-offs for the MTA, GCA-F and PA scales, for both diagnosis of AD and determining prognosis in mild cognitive impairment (MCI), and to evaluate the influence of key demographic and clinical factors on these cut-offs. METHODS: AddNeuroMed and ADNI cohorts were combined giving a total of 1147 participants (322 patients with AD, 480 patients with MCI and 345 control subjects). The MTA, GCA-F and PA scales were applied and a broad range of cut-offs was evaluated. RESULTS: The MTA scale showed better diagnostic and predictive performances than the GCA-F and PA scales. Age, apolipoprotein E (ApoE) ε4 status and age at disease onset influenced all three scales. For the age ranges 45-64, 65-74, 75-84 and 85-94 years, the following cut-offs should be used. MTA: ≥1.5, ≥1.5, ≥2 and ≥2.5; GCA-F, ≥1, ≥1, ≥1 and ≥1; and PA, ≥1, ≥1, ≥1 and ≥1, respectively, with an adjustment for early-onset ApoE ε4 noncarrier AD patients (MTA: ≥2, ≥2, ≥3 and ≥3; and GCA-F: ≥1, ≥1, ≥2 and ≥2, respectively). CONCLUSIONS: If successfully validated in clinical settings, the list of practical cut-offs proposed here might be useful in clinical practice. Their use might also (i) promote research on atrophy subtypes, (ii) increase the understanding of different presentations of AD, (iii) improve diagnosis and prognosis and (iv) aid population selection and enrichment for clinical trials.

Classification and prediction of clinical diagnosis of Alzheimer's disease based on MRI and plasma measures of α-/γ-tocotrienols and γ-tocopherol.

BACKGROUND: The aim of this study was to evaluate the accuracy of combined structural magnetic resonance imaging (MRI) measures and plasma levels of vitamin E forms, including all eight natural vitamin E congeners (four tocopherols and four tocotrienols) and markers of vitamin E oxidative/nitrosative damage, in differentiating individuals with Alzheimer's disease (AD) and mild cognitive impairment (MCI) from cognitively intact control (CTL) subjects. METHODS: Overall, 81 patients with AD, 86 with MCI and 86 CTL individuals were enrolled from the longitudinal multicentre AddNeuroMed study. MRI and plasma vitamin E data were acquired at baseline. MRI scans were analysed using Freesurfer, an automated segmentation scheme which generates regional volume and cortical thickness measures. Orthogonal partial least squares to latent structures (OPLS), a multivariate data analysis technique, was used to analyse MRI and vitamin E measures in relation to AD and MCI diagnosis. RESULTS: The joint evaluation of MRI and plasma vitamin E measures enhanced the accuracy of differentiating individuals with AD and MCI from CTL subjects: 98.2% (sensitivity 98.8%, specificity 97.7%) for AD versus CTL, and 90.7% (sensitivity 91.8%, specificity 89.5%) for MCI versus CTL. This combination of measures also identified 85% of individuals with MCI who converted to clinical AD at follow-up after 1 year. CONCLUSIONS: Plasma levels of tocopherols and tocotrienols together with automated MRI measures can help to differentiate AD and MCI patients from CTL subjects, and to prospectively predict MCI conversion into AD. Our results suggest the potential role of nutritional biomarkers detected in plasma-tocopherols and tocotrienols-as indirect indicators of AD pathology, and the utility of a multimodality approach.

An MRI-based index to measure the severity of Alzheimer's disease-like structural pattern in subjects with mild cognitive impairment.

BACKGROUND: Structural magnetic resonance imaging (MRI) is sensitive to neurodegeneration and can be used to estimate the risk of converting to Alzheimer's disease (AD) in individuals with mild cognitive impairment (MCI). Brain changes in AD and prodromal AD involve a pattern of widespread atrophy. The use of multivariate analysis algorithms could enable the development of diagnostic tools based on structural MRI data. In this study, we investigated the possibility of combining multiple MRI features in the form of a severity index. METHODS: We used baseline MRI scans from two large multicentre cohorts (AddNeuroMed and ADNI). On the basis of volumetric and cortical thickness measures at baseline with AD cases and healthy control (CTL) subjects as training sets, we generated an MRI-based severity index using the method of orthogonal projection to latent structures (OPLS). The severity index tends to be close to 1 for AD patients and 0 for CTL subjects. Values above 0.5 indicate a more AD-like pattern. The index was then estimated for subjects with MCI, and the accuracy of classification was investigated. RESULTS: Based on the data at follow-up, 173 subjects converted to AD, of whom 112 (64.7%) were classified as AD-like and 61 (35.3%) as CTL-like. CONCLUSION: We found that joint evaluation of multiple brain regions provided accurate discrimination between progressive and stable MCI, with better performance than hippocampal volume alone, or a limited set of features. A major challenge is still to determine optimal cut-off points for such parameters and to compare their relative reliability.