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Abstract Alzheimer's disease (AD) treatment is freely available in the Brazilian public health system. However, the prescription pattern and its associated factors have been poorly studied in our country. We reviewed all granted requests for AD treatment in the public health system in October 2021 in the Rio Grande do Sul (RS) state, Southern Brazil. We performed a spatial autocorrelation analysis with the population-adjusted patients receiving any AD medication as the outcome and correlated it with several socioeconomic variables. 2382 patients with AD were being treated during the period analyzed. The distribution of the outcome variable was not random (Moran's I 0.17562, P

Abstract Background Risk factors for dementia have distinct frequency and impact in relation to race. Our aim was to identify differences in modifiable risk factors of dementia related to races and estimate their population attributable fraction (PAF). Methods An epidemiological cohort was used to estimate the prevalence of 10 modifiable risk factors for dementia among five races—White, Black, Brown, Asian, and Indigenous. Sample weighting was used to estimate the prevalence and PAF of each risk factor in each race. Results A total of 9070 individuals were included. Overall adjusted PAF was the lowest in Indigenous (38.9%), and Asian individuals (41.2%). Race‐related prevalence of individual risk factors was widely variable in our population, but hearing loss was the most important contributor to the overall PAF in all races. Conclusions Public policies aiming to reduce preventable risk factors for dementia should take into consideration the race of the target populations. HIGHLIGHTS Preventable risk factors for dementia vary according to race. Hearing loss presented the highest prevalence among all races studied. Indigenous and Asian individuals presented the lowest population attributable fractions. Black and Brown individuals were more vulnerable to social determinants.

Abstract Background Changes in soluble amyloid-beta (Aβ) levels in cerebrospinal fluid (CSF) are detectable at early preclinical stages of Alzheimer’s disease (AD). However, whether Aβ levels can predict downstream AD pathological features in cognitively unimpaired (CU) individuals remains unclear. With this in mind, we aimed at investigating whether a combination of soluble Aβ isoforms can predict tau pathology (T+) and neurodegeneration (N+) positivity. Methods We used CSF measurements of three soluble Aβ peptides (Aβ1 –38, Aβ1 –40 and Aβ1 –42) in CU individuals (n = 318) as input features in machine learning (ML) models aiming at predicting T+ and N+. Input data was used for building 2046 tuned predictive ML models with a nested cross-validation technique. Additionally, proteomics data was employed to investigate the functional enrichment of biological processes altered in T+ and N+ individuals. Results Our findings indicate that Aβ isoforms can predict T+ and N+ with an area under the curve (AUC) of 0.929 and 0.936, respectively. Additionally, proteomics analysis identified 17 differentially expressed proteins (DEPs) in individuals wrongly classified by our ML model. More specifically, enrichment analysis of gene ontology biological processes revealed an upregulation in myelinization and glucose metabolism-related processes in CU individuals wrongly predicted as T+. A significant enrichment of DEPs in pathways including biosynthesis of amino acids, glycolysis/gluconeogenesis, carbon metabolism, cell adhesion molecules and prion disease was also observed. Conclusions Our results demonstrate that, by applying a refined ML analysis, a combination of Aβ isoforms can predict T+ and N+ with a high AUC. CSF proteomics analysis highlighted a promising group of proteins that can be further explored for improving T+ and N+ prediction.

Abstract Introduction Alzheimer's disease consensus recommends biomarker dichotomization, a practice with well‐described clinical strengths and methodological limitations. Although neuroimaging studies have explored alternative biomarker interpretation strategies, a formally defined three‐range approach and its prognostic impact remains under‐explored for cerebrospinal fluid (CSF) biomarkers . Methods With two‐graph receiver‐operating characteristics based on different reference schemes, we derived three‐range cut‐points for CSF Elecsys biomarkers. According to baseline CSF status, we assessed the prognostic utility of this in predicting risk of clinical progression and longitudinal trajectories of cognitive decline and amyloid–beta (Aβ) positron emission tomography (PET) accumulation in non‐demented individuals (Alzheimer's Disease Neuroimaging Initiative [ADNI]; n = 1246). In all analyses, we compared herein‐derived three‐range CSF cut‐points to previously described binary ones. Results In our main longitudinal analyses, we highlight CSF p‐tau181/Aβ1‐42 three‐range cut‐points derived based on the cognitively normal Aβ‐PET negative versus dementia Aβ‐PET positive reference scheme for best depicting a prognostically relevant biomarker abnormality range. Longitudinally, our approach revealed a divergent intermediate cognitive trajectory undetected by dichotomization and a clearly abnormal group at higher risk for cognitive decline, with power analyses suggesting the latter group as potential trial enrichment candidates. Furthermore, we demonstrate that individuals with intermediate‐range CSF status have similar rates of Aβ deposition to those in the clearly abnormal group. Discussion The proposed approach can refine clinico‐biological prognostic assessment and potentially enhance trial recruitment, as it captures faster biomarker‐related cognitive decline in comparison to binary cut‐points. Although this approach has implications for trial recruitment and observational studies, further discussion is needed regarding clinical practice applications.

Proteomics and metabolomics are two emerging fields that hold promise to shine light on the molecular mechanisms causing neurodegenerative diseases. Research in this area may reveal and quantify specific metabolites and proteins that can be targeted by therapeutic interventions intended at halting or reversing the neurodegenerative process. This review aims at providing a general overview on the current status of proteomic and metabolomic profiling in neurodegenerative diseases. We focus on the most common neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. We discuss the relevance of state-of-the-art metabolomics and proteomics approaches and their potential for biomarker discovery. We critically review advancements made so far, highlighting how metabolomics and proteomics may have a significant impact in future therapeutic and biomarker development. Finally, we further outline technologies used so far as well as challenges and limitations, placing the current information in a future-facing context.