Cerebral circulation - cognition and behavior最新文献

Worldwide, the incidence of neurodegenerative diseases especially dementia is steadily increasing due to the aging population. Abundant research emerges on the probability of combating or preventing the degeneration process, with the most established one being to tackle the existence of oxidative stress and free radicals production due to their nature of aggravating dementia. Astaxanthin, a marine carotenoid, was proven to be a protective agent of cerebral ischemia through many animal model clinical trials. This review summarizes the evidence of Astaxanthin's benefits for cognitive function across clinical trials done in older age. The results are of interest as its supplementation does not exhibit unwanted issues on the consumer based on physical and laboratory examinations. Despite not being supported statistically, however, subjective and objective cognitive amelioration were reported according to the majority of this review's trial subjects. Although there is no clear and direct mechanism for cognitive improvement by Astaxanthin activity in the body systems, the encouragement of Astaxanthin supplementation should be considered as the elderly with dementia may highly benefit from the improved cognitive function.

Brain health means optimal physiological brain function across the normal life-course. It encompasses not only healthy brain aging but also brain diseases, their diagnosis and treatment. In all these areas, molecular science has advanced our understanding.

This multi-disciplinary review combines viewpoints from laboratory science, clinical medicine and the bioscience industry. First, we review the advances that molecular science has brought to brain health in the past twenty years. These include therapeutic antibodies for CNS diseases (multiple sclerosis, Alzheimer disease) and the dramatic introduction of RNA-targeted therapeutics. Second, we highlight areas where greater molecular understanding is needed. Salient examples are the relation of brain structure to cognitive symptoms, and molecular biomarkers for diagnosis, target discovery and testing of interventions. Finally, we speculate on aspects of molecular science that are likely to advance brain health in the next twenty years. These include: cell senescence and chronobiology; gene editing (notably, CRISPR) and RNA targeting (RNA interference, miRNA manipulation); brain-immune interactions; novel drug targets (AQP4, HIF1, Toll-like receptors); and novel chemistry to make new drugs (molecular machines, quantum molecular modelling and “click” chemistry). Early testing of the relationships between molecular pathways and clinical manifestations will drive much-needed breakthroughs in neurology and psychiatry.

Introduction

Two of the main causes for dementia are Alzheimer's disease (AD) pathology and vascular pathology. Plasma biomarkers for AD pathology have recently emerged, including amyloid-beta, p-tau, neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). Vascular pathology can be assessed on MRI via white matter hyperintensities (WMH) and infarcts. Our aim was to estimate the relationship between plasma AD biomarkers and MRI markers of vascular pathology and neurodegeneration in non-demented individuals with manifest arterial disease.

Methods

Data from 594 individuals (mean (SD) age: 64 (8) years; 17% female) were included from the SMART-MR Study, a prospective cohort study from the UMC Utrecht in the Netherlands. Vascular and neurodegenerative MRI markers included WMH volume, presence of infarcts (yes/no), total brain volume (TBV), and hippocampal volume (HV) assessed on 1.5T MRI. AD plasma markers (amyloid-beta 42/40 ratio, ptau-181, NfL, and GFAP) were assessed using Single Molecular Array (Simoa; Quanterix) assays. Linear regressions were performed for each plasma marker with WMH volume, TBV, and HV, correcting for age, sex, education, and ICV. Additionally, logistic regressions were performed for the presence of lacunar and cortical infarcts. Plasma AD levels were converted to z-scores.

Results

Higher ptau-181 was associated with larger WMH volume (β=0.16, 95% CI=0.06; 0.26, p=0.001). Higher NfL (β=-5.63, 95% CI=-8.95; -2.31, p=0.001) was associated with lower TBV. Higher NfL levels (.R=1.58, 95% CI=1.20; 2.08, p=0.001) and higher GFAP levels (OR=1.45, 95% CI=1.09; 1.92, p=0.010) were associated with cortical infarcts.

Discussion

In our sample of patients with manifest arterial disease, NfL was related to both brain volume and infarcts. Further, an association between ptau-181 and WMH was found, as well as between GFAP and cortical infarcts. Plasma biomarkers offer the potential to easily measure a wider range of pathophysiological processes related to cognitive decline.

Introduction

Cerebral small vessel disease (SVD) contributes to 45% of dementia cases worldwide. Only a minority of SVD patients develop dementia, yet we lack a reliable model for predicting incident dementia in SVD. Most attempts to date have relied on traditional statistical approaches, whereas machine learning (ML) methods are increasingly used for clinical prediction in other settings.

Methods

We investigated whether ML methods improved prediction of incident dementia in SVD over traditional statistical. We included three cohorts with varying SVD severity (RUN DMC, n=503; SCANS, n=121; HARMONISATION, n=265). Baseline demographics, vascular risk factors, cognitive scores, and MRI features of SVD were used for prediction. We conducted both survival analysis and classification analysis predicting 3-year dementia risk. For each analysis, several ML methods were evaluated against standard Cox or logistic regression. Finally, we compared the feature importance ranking by different models.

Results

We included 789 participants without missing data in the survival analysis, among whom 108 (13.7%) developed dementia during a median (IQR) follow-up period of 5.4 (4.1, 8.7) years. After excluding those censored before three years, we included 750 participants in the classification analysis, among whom 48 (6.4%) developed dementia by year 3. Comparing statistical and ML models, only the regularised Cox/logistic regression models outperformed their statistical counterparts overall, but not significantly so in survival analysis. Baseline cognitive scores were highly predictive, and all methods ranked global cognition as the most important feature.

Discussion

ML survival or classification models brought little improvement over traditional statistical approaches in predicting incident dementia in SVD. ML approaches may be better suited to prediction problems using a larger number of input variables.