Consumption of eggs is associated with a lower risk of being diagnosed with Alzheimer's Disease for those 65 years and older, according to researchers at Loma Linda University Health

 

Study shows that lead iodide can support phonon-polaritons, allowing radiation to be confined on a nanoscale and enabling new data transmission devices.

Alzheimer’s disease develops over many years before symptoms appear, with underlying processes such as amyloid plaque buildup, tau pathology, neuroinflammation, and neuronal loss. A key turning point occurs when amyloid-β accumulation triggers tau phosphorylation, leading to neuronal damage and eventual cognitive decline. Historically, this early stage has been difficult to study in living patients. In this Perspective, De Strooper and Zetterberg highlight a newly developed blood-based biomarker, phosphorylated tau 217 (pTau217), which allows detection of early pathological changes associated with Alzheimer’s disease without invasive procedures. According to the authors, the identification and validation of pTau217 enables highly specific, in vivo detection of the transition from amyloid accumulation to tau-driven neuronal injury, making it possible to study and identify this previously inaccessible early disease stage. “The next challenge will be to develop therapies that directly interrupt the induction and propagation of tau pathology captured by plasma pTau217,” write the authors. “Ultimately, however, the impact of this diagnostic progress will depend on whether these biomarkers can be deployed in ways that meaningfully improve patient outcomes once effective preventive therapies become available.”

A new study published today in the Proceedings of the National Academy of Sciences (PNAS), an international research team led jointly by scientists from the Weizmann Institute of Science and Washington University School of Medicine in St. Louis presents the first use of CAR-T in the treatment of Alzheimer’s disease. 

Samples of plasma and cerebrospinal fluid can yield widely diverging clues about proteins associated with neurological disorders such as Alzheimer’s disease, according to a new study. The work underscores the limitations of relying on plasma samples for protein discovery and drug development efforts, and stresses the importance of a more holistic, tissue context-relevant approach. Many neurological diseases are associated with genetic variants and with shifts in the expression of certain proteins, and scientists can use these signatures to develop new diagnostics and therapies. Some of these molecular clues are called protein quantitative trait loci (pQTLs), which are regions in the genome that are linked to the abundance of specific proteins. However, most existing knowledge about pQTLs in neurological disease has been gathered from plasma samples, although human plasma might be less relevant to the central nervous system. This could mislead or delay drug discovery efforts, but there is a paucity of research about pQTLs in other types of fluid sampling. To bridge this gap, Western et al. compared the number and strength of pQTLs derived using plasma samples and samples of cerebrospinal fluid. They examined an assortment of 3,506 CSF samples and 2,238 plasma samples from control donors, patients with Alzheimer’s disease, Parkinson’s disease, and undiagnosed dementia. They then integrated the pQTL data with publicly available genome-wide association study data for 14 different neurological diseases, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, and schizophrenia. This analysis identified almost 5,000 pQTLs, but fewer than 30% were shared by both fluid types.  Furthermore, the team identified 249 previously unknown associations between proteins and neurological traits. They also found that protein analysis of CSF samples could capture cell death and immune response signatures in Alzheimer’s disease and multiple sclerosis better than plasma sampling. “In a drug development landscape where costs are rapidly expanding, these analyses provide a framework for prioritizing disease-relevant proteins and selecting the most informative biological context for downstream validation,” the authors conclude.