The relationship between CSF biomarkers and cerebral metabolism in early-onset Alzheimer's disease

Glucose metabolism in posterior cingulate cortex has supplementary value to predict the progression of cognitively unimpaired to dementia due to Alzheimer's disease: an exploratory study of 18F-FDG-PET

Amyloid-β (Aβ) and tau are important biomarkers to predict the progression of cognitively unimpaired (CU) to dementia due to Alzheimer's disease (AD), according to the diagnosis framework from the US National Institute on Aging and the Alzheimer's Association (NIA-AA). However, it is clinically difficult to predict those subjects who were already with Aβ positive (A +) or tau positive (T +). As a typical characteristic of neurodegeneration in the diagnosis framework, the hypometabolism of the posterior cingulate cortex (PCC) has significant clinical value in the early prediction and prevention of AD. In this paper, we proposed the glucose metabolism in the PCC as a biomarker supplement to Aβ and tau biomarkers. First, we calculated the standard uptake value ratio (SUVR) of PCC based on fluorodeoxyglucose positron emission computed tomography (FDG PET) imaging. Secondly, we performed Kaplan-Meier (KM) survival analyses to explore the predictive performance of PCC SUVR, and the hazard ratio (HR) was calculated. Finally, we performed Pearson correlation analyses to explore the physiological significance of PCC SUVR. As a result, the PCC SUVR showed a consistent downward trend along the AD continuum. KM analyses showed better predictive performance when we combined PCC SUVR with cerebro-spinal fluid (CSF) Aβ42 (from HR = 2.56 to 3.00 within 5 years; from HR = 2.76 to 4.20 within 10 years) and ptau-181 (from 2.83 to 3.91 within 5 years; from HR = 2.32 to 4.17 within 10 years). There was a slight correlation between Aβ42/Aβ40 and PCC SUVR (r = 0.14, p = 0.02). In addition, several cognition scales were also correlated to PCC SUVR (from r = -0.407 to 0.383, p < 0.05). Our results showed that glucose metabolism in PCC may be a potential biomarker supplement to the Aβ and tau biomarkers to predict the progression of CU to AD.

Identify Biomarkers of Alzheimer's Disease Based on Multi-task Canonical Correlation Analysis and Regression Model

Imaging genetics using imaging technology is regarded as a neuroanatomical phenotype to evaluate gene single nucleotide polymorphisms and their effects on the structure and function of different brain regions. It plays a vital role in bridging the initial understanding of the genetic basis of brain structure and dysfunction. Sparse canonical correlation analysis (SCCA) has become a widespread technique in this field because of its powerful ability to identify bivariate relationships and feature selection. Since most traditional SCCA algorithms assume that the input features are independent, this method obviously cannot be used to analyze genetic image data. The MT-SCCA model is unsupervised and cannot identify the genotype-phenotype associations for diagnostic guidance. Meanwhile, a single biological clinical index cannot fully reflect the physiological process of a comprehensive disease. Therefore, it is necessary to find biomarkers that can reflect Alzheimer's disease and physiological functions that can more comprehensively reflect the development of the disease. This article uses a multi-task sparse canonical correlation analysis and regression (MT-SCCAR) model to combine the annual depression level total score (GDSCALE), clinical dementia assessment scale (GLOBAL CDR), functional activity questionnaire (FAQ), and neuropsychiatric Symptom Questionnaire (NPI-Q) in this paper. These four clinical data are used as compensation information and embedded in the algorithm in a linear regression manner. It also reflects its superiority and robustness compared to traditional correlation analysis methods on actual and simulated data. Meanwhile, compared with MT-SCCA, the model utilized in this paper obtains a higher gene-ROI weight and identifies clearer biomarkers, which provides a practical basis for the study of complex human disease pathology.

Tau Biomarkers in Dementia: Positron Emission Tomography Radiopharmaceuticals in Tauopathy Assessment and Future Perspective

Abnormal accumulation of Tau protein is closely associated with neurodegeneration and cognitive impairment and it is a biomarker of neurodegeneration in the dementia field, especially in Alzheimer’s disease (AD); therefore, it is crucial to be able to assess the Tau deposits in vivo. Beyond the fluid biomarkers of tauopathy described in this review in relationship with the brain glucose metabolic patterns, this review aims to focus on tauopathy assessment by using Tau PET imaging. In recent years, several first-generation Tau PET tracers have been developed and applied in the dementia field. Common limitations of first-generation tracers include off-target binding and subcortical white-matter uptake; therefore, several institutions are working on developing second-generation Tau tracers. The increasing knowledge about the distribution of first- and second-generation Tau PET tracers in the brain may support physicians with Tau PET data interpretation, both in the research and in the clinical field, but an updated description of differences in distribution patterns among different Tau tracers, and in different clinical conditions, has not been reported yet. We provide an overview of first- and second-generation tracers used in ongoing clinical trials, also describing the differences and the properties of novel tracers, with a special focus on the distribution patterns of different Tau tracers. We also describe the distribution patterns of Tau tracers in AD, in atypical AD, and further neurodegenerative diseases in the dementia field.

Plasma tau predicts cerebral vulnerability in aging

Identifying cerebral vulnerability in late life may help prevent or slow the progression of aging-related chronic diseases. However, non-invasive biomarkers aimed at detecting subclinical cerebral changes in the elderly are lacking. Here, we have examined the potential of plasma total tau (t-tau) for identifying cerebral and cognitive deficits in normal elderly subjects. Patterns of cortical thickness and cortical glucose metabolism were used as outcomes of cerebral vulnerability. We found that increased plasma t-tau levels were associated with widespread reductions of cortical glucose uptake, thinning of the temporal lobe, and memory deficits. Importantly, tau-related reductions of glucose consumption in the orbitofrontal cortex emerged as a determining factor of the relationship between cortical thinning and memory loss. Together, these results support the view that plasma t-tau may serve to identify subclinical cerebral and cognitive deficits in normal aging, allowing detection of individuals at risk for developing aging-related neurodegenerative conditions.