To estimate a regional progression pattern of amyloid deposition from cross-sectional amyloid-sensitive PET data and evaluate its potential for in vivo staging of an individual's amyloid pathology.
Abstract Multifactorial mechanisms underlying late-onset Alzheimer’s disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system’s integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions.
Abstract Background The association between subclinical cardiovascular disease (CVD) and cognitive decline in hypertensive adults and the underlying brain pathologies remain unclear. It is also undetermined whether intensifying blood pressure (BP) treatment slows down cognitive decline associated with subclinical CVD. Methods We conducted a post hoc analysis of the Systolic Blood Pressure Intervention Trial. Subclinical CVD at baseline was identified by elevated levels of high-sensitivity cardiac troponin T (hs-cTnT≥14 ng/L) and N-terminal pro-B-type natriuretic peptide (NT-proBNP≥125 pg/mL). Global cognitive function and domain-specific measures (memory, processing speed, language, and executive function) were assessed at baseline and follow-up (years 2, 4, and 6) in 2733 participants. White matter lesions, cerebral blood flow, and brain tissue volume were assessed by MRI at baseline and years 4 in a subset of 639 participants. Results Both elevated hs-cTnT and NT-proBNP levels at baseline were associated with accelerated cognitive decline across all domains after adjusting for potential confounding factors. The group with elevated levels of both cardiac biomarkers showed the fastest decline, with a larger annual decline rate of 0.033 (95% CI: 0.024-0.041) in the z-score of global cognitive function compared to the group with normal levels. Elevated levels of both biomarkers were also associated with a faster progression in white matter lesions, but not with changes in total brain tissue volume or cerebral blood flow. Intensive BP treatment did not attenuate these associations compared to standard treatment. Conclusions Subclinical CVD may contribute to faster white matter lesion progression and accelerated cognitive decline in patients with hypertension, regardless of intensive BP treatment.
The two most common methods for generating normative scores are stratification tables and linear regression. An issue with both of these methods is that the scores are based on an underlying assumption of normality; however, most cognitive tests scores are not normally distributed. In a normally distributed variable, we expect a certain proportion of the data to fall below a specific z-score, but when a variable is skewed this proportion can be over or under estimated. Consequently these tests are prone to error when identifying individuals at risk for decline. We compared normative values derived from multinomial logistic regression (which does not rely on normality) to the traditional methods for evaluating cognitive decline. We obtained a sample of 2,857 cognitively normal longitudinally studied subjects from the National Alzheimer's Coordinating Center (NACC). There were 361 subjects who had a follow up diagnosis of Mild Cognitive Impairment or Alzheimer's disease (NL-decliners). The remaining subjects were randomly divided into a normative (training) group (n=1261) and a control group (n=1235). The training group was used to derive stratified norms, linear regression based norms, and norms from a multinomial logistic model. The control group and the NL-decliners were then used to test the three sets of norms in predicting future clinical decline. The control group was also used to examine if the norms biased the prediction of impairment in certain subgroups. Norms were derived for the mini-mental state exam (MMSE) as it is commonly used and has a known negative skew. The three methods were comparable in the overall identification of future decliners (AUC range 59-60%). However, the stratification and linear regression methods overestimated poor performance in non-Caucasian and non-native English speakers), where the multinomial logistic regression method did not. This was due to the subgroups having less skewed z-scores (and therefore a higher proportion of subjects below the mean) than the more negatively skewed control group. Using multinomial logistic regression for norm derivation produces norms that are less susceptible to cultural biases than norms derived from stratification or linear regression which overestimate impairment in minority groups.
Abstract People with Down syndrome (DS) are at an increased risk for Alzheimer's disease (AD). After 60 years of age, >50% of DS subjects acquire dementia. Nevertheless, the age of onset is highly variable possibly because of both genetic and environmental factors. Genetics cannot be modified, but environmental risk factors present a potentially relevant intervention for DS persons at risk for AD. Among them, inflammation, important in AD of DS type, is potential target. Consistent with this hypothesis, chronic peripheral inflammation and infections may contribute to AD pathogenesis in DS. People with DS have an aggressive form of periodontitis characterized by rapid progression, significant bacterial and inflammatory burden, and an onset as early as 6 years of age. This review offers a hypothetical mechanistic link between periodontitis and AD in the DS population. Because periodontitis is a treatable condition, it may be a readily modifiable risk factor for AD.
A great deal of current research is aiming to better understand what happens to brains affected by Alzheimer's disease, both structurally and metabolically. However, little is known about how the brain ages in the cognitively normal adult population. There is also a limited understanding as to how glucose metabolism and brain atrophy interact in the normal human brain. To our knowledge, this study is the first to longitudinally analyze measures of atrophy and metabolism in both MRI and FDG-PET in cognitively normal subjects. 45 cognitively normal subjects were studied longitudinally over a span of at least 1.5 years (average = 5.99 years). Each had at least two structural MRIs and 2 FDG-PET scans. Free Surfer was used to analyze ventricle and intracranial volume. This was turned into an intracranial volume ratio to get a normalized picture of brain atrophy. A hippocampal masking technique (HipMask) was used to analyze the FDG-PET scans for changes in metabolic rates in regions normally affected by Alzheimer's disease (precuneus/posterior cingulate, hippocampus, inferior parietal lobe), as well as regions affected by normal aging (prefrontal cortex and cerebellum). Mixed model analysis indicates that ventricle enlargement occurs in the cognitively normal adult brain as early as adult middle age (t = 7.050, p < .001), with a particular acceleration after the age of 65 (t = 2.878, p = .004). However, the mixed model results did not show any evidence of a concomitant decrease in rate with age, nor any acceleration, in regional metabolism after controlling for brain atrophy. Our results show that brain atrophy increases over the adult lifespan. These changes appear to accelerate over time. However, metabolically, we did not find any significant results with regard to change over time. This is in contrast to Alzheimer's disease, where literature has shown metabolic decreases in frontal and parietal regions, particularly in precuneus/posterior cingulate. Our results also illustrate the importance of controlling for structural atrophy in FDG-PET.
Cross-sectional MRI and CSF measures have separately been used to detect early Alzheimer's disease (AD) in individuals with mild cognitive impairment (MCI). We examined the combination of these two modalities at baseline in the prediction of decline from MCI to AD. 23 individuals with MCI were studied with MRI and lumbar puncture at baseline and after 2 years. CSF total and phosphorylated tau (T-tau, P-tau231), amyloid beta Aβ42/Aβ40 ratio, and isoprostanes (IP) were measured at baseline and follow-up. MRI measures of gray matter concentrations were measured using voxel based morphometry and longitudinal atrophy was measured with boundary shift analysis. ANCOVAs with age, gender, and apoE as a covariate were performed to examine cross sectional group differences. Logistic regression analyses were performed to examine predictors of decline. 15 individuals remained stable (MCI-MCI) and 8 declined to AD (MCI-AD). At baseline MCI-AD showed significant GMC reductions in the right and left hippocampus, left parahippocampal gyrus, and the right inferior temporal gyrus compared to the MCI-MCI. Significantly higher level of T-Tau, P-Tau231, and IP were found in the MCI-AD group compared to the MCI-MCI group. The Aβ42/Aβ40 ratio was significantly reduced in MCI-AD compared to MCI-MCI. GMC of the left parahippocampus was the only MRI measure that predicted decline (75%, p < .05) whereas all CSF measures predicted decline with equal accuracy (81%, p < .05). In a Logistic Regression analysis IP significantly added to the prediction of decline beyond that of the parahippocampal GMC (χ2 = 10.23, p = .001), increasing the overall accuracy from 75% to 88%. CSF and MRI measures independently predict decline to AD 2 years in advance with measures of IP concentrations significantly adding to prediction.
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