Alzheimer's Disease Drug Research Articles

Nanotechnology-based delivery of therapeutics through the intranasal pathway and the blood-brain barrier for Alzheimer's disease treatment.

Background: drugs for Alzheimer's disease (AD) fail to exhibit efficacy in clinical trials for a number of reasons, a major one being blood-brain barrier (BBB) permeability. Meanwhile, the increasing incidence of this disease emphasizes the need for effective therapeutics. Herein, we discuss novel nanoplatform technologies developed for the effective delivery of AD drugs by traversing the BBB. Main text: the interfacial and surface chemistry of nanomaterials is utilized in several industries, including pharmaceutical, and has drawn considerable attention in the field of nanotechnology. Various reports have suggested the potential of nanotechnology for AD treatment, describing unique drug carriers that improve drug stability and solubility while maintaining therapeutic dosages. These nanotechnologies are harnessed for the transport of drugs across the BBB, with or without surface modifications. We also discuss the transfer of drugs via the nose-to-brain pathway, as intranasal delivery enables direct drug distribution in the brain. In addition, nanomaterial modifications that prolong drug delivery and improve safety following intranasal administration are addressed. Conclusion: although several studies have yielded promising results, limited efforts have been undertaken to translate research findings into clinical contexts. Nevertheless, nanomaterials hold considerable potential for the development of novel effective therapeutic solutions against AD.

Epidemiological characteristics and prevention of pressure injury in older inpatients of Zhuang and Han ethnicity in Guangxi, China: a cross-sectional study.

Pressure injury (PI) is a significant health concern among older inpatients, particularly in regions with diverse ethnic populations. Understanding the epidemiological characteristics and preventive measures is crucial for improving patient outcomes. To analyze the epidemiological characteristics, prevention status, and influencing factors of pressure injury (PI) in older inpatients of Zhuang and Han nationality in Guangxi, China. A total of 2206 inpatients age 60 years or older in 2 class III grade A general hospitals in the Guangxi Zhuang Autonomous Region between April 1, 2021, and May 27, 2021, were included in this cross-sectional study. Epidemiological characteristics-including age, sex, educational background, race, ethnicity, and hospitalization information-were collected using a general information questionnaire designed by the researchers. The PI risk factors were evaluated using the Braden Scale. Prevention status was assessed using the Epidemiology and Prevention Skin Injuries in the Elderly Scale and Skin Injury Survey Scale. Of the total 2206 patients included in the study, 555 (25.16%) were of Zhuang nationality and 1651 (74.84%) were of Han nationality. The overall PI incidence was 2.58%, with PI prevalence of 1.80% and 2.85% in Zhuang and Han patients, respectively. The main influencing factor for PI in Zhuang patients was caregivers (P < .05), whereas in Han patients the main influencing factors were urinary conditions, Alzheimer disease, sedatives, and antihypertensive drugs (P < .05). The PI prevalence rates were similar in both ethnic groups. Health care staff in high-risk departments for PI must remain vigilant and take appropriate action.

Current Progress on Central Cholinergic Receptors as Therapeutic Targets for Alzheimer's Disease.

Acetylcholine (ACh) is ubiquitously present in the nervous system and has been involved in the regulation of various brain functions. By modulating synaptic transmission and promoting synaptic plasticity, particularly in the hippocampus and cortex, ACh plays a pivotal role in the regulation of learning and memory. These procognitive actions of ACh are mediated by the neuronal muscarinic and nicotinic cholinergic receptors. The impairment of cholinergic transmission leads to cognitive decline associated with aging and dementia. Therefore, the cholinergic system has been of prime focus when concerned with Alzheimer's disease (AD), the most common cause of dementia. In AD, the extensive destruction of cholinergic neurons occurs by amyloid-β plaques and tau protein-rich neurofibrillary tangles. Amyloid-β also blocks cholinergic receptors and obstructs neuronal signaling. This makes the central cholinergic system an important target for the development of drugs for AD. In fact, centrally acting cholinesterase inhibitors like donepezil and rivastigmine are approved for the treatment of AD, although the outcome is not satisfactory. Therefore, identification of specific subtypes of cholinergic receptors involved in the pathogenesis of AD is essential to develop future drugs. Also, the identification of endogenous rescue mechanisms to the cholinergic system can pave the way for new drug development. In this article, we discussed the neuroanatomy of the central cholinergic system. Further, various subtypes of muscarinic and nicotinic receptors involved in the cognition and pathophysiology of AD are described in detail. The article also reviewed primary neurotransmitters that regulate cognitive processes by modulating basal forebrain cholinergic projection neurons.

Cerebrospinal fluid proteomics in patients with Alzheimer’s disease reveals five molecular subtypes with distinct genetic risk profiles

Alzheimer’s disease (AD) is heterogenous at the molecular level. Understanding this heterogeneity is critical for AD drug development. Here we define AD molecular subtypes using mass spectrometry proteomics in cerebrospinal fluid, based on 1,058 proteins, with different levels in individuals with AD (n = 419) compared to controls (n = 187). These AD subtypes had alterations in protein levels that were associated with distinct molecular processes: subtype 1 was characterized by proteins related to neuronal hyperplasticity; subtype 2 by innate immune activation; subtype 3 by RNA dysregulation; subtype 4 by choroid plexus dysfunction; and subtype 5 by blood–brain barrier impairment. Each subtype was related to specific AD genetic risk variants, for example, subtype 1 was enriched with TREM2 R47H. Subtypes also differed in clinical outcomes, survival times and anatomical patterns of brain atrophy. These results indicate molecular heterogeneity in AD and highlight the need for personalized medicine.

ADME profiling, molecular docking, DFT, and MEP analysis reveal cissamaline, cissamanine, and cissamdine from Cissampelos capensis L.f. as potential anti-Alzheimer's agents.

The current pharmacotherapies for Alzheimer's disease (AD) demonstrate limited efficacy and are associated with various side effects, highlighting the need for novel therapeutic agents. Natural products, particularly from medicinal plants, have emerged as a significant source of potential neuroprotective compounds. In this context, Cissampelos capensis L.f., renowned for its medicinal properties, has recently yielded three new proaporphine alkaloids; cissamaline, cissamanine, and cissamdine. Despite their promising bioactive profiles, the biological targets of these alkaloids in the context of AD have remained unexplored. This study undertakes a comprehensive in silico examination of the binding affinity and molecular interactions of these alkaloids with human protein targets implicated in AD. The drug likeness and ADME analyses indicate favorable pharmacokinetic profiles for these compounds, suggesting their potential efficacy in targeting the central nervous system. Molecular docking studies indicate that cissamaline, cissamanine, and cissamdine interact with key AD-associated proteins. These interactions are comparable to, or in some aspects slightly less potent than, those observed with established AD drugs, highlighting their potential as novel therapeutic agents for Alzheimer's disease. Crucially, Density Functional Theory (DFT) calculations offer deep insights into the electronic and energetic characteristics of these alkaloids. These calculations reveal distinct electronic properties, with differences in total energy, binding energy, HOMO-LUMO gaps, dipole moments, and electrophilicity indices. Such variations suggest unique reactivity profiles and molecular stability, pertinent to their pharmacological potential. Moreover, Molecular Electrostatic Potential (MEP) analyses provide visual representations of the electrostatic characteristics of these alkaloids. The analyses highlight areas prone to electrophilic and nucleophilic attacks, indicating their potential for specific biochemical interactions. This combination of DFT and MEP results elucidates the intricate electronic, energetic, and electrostatic properties of these compounds, underpinning their promise as AD therapeutic agents. The in silico findings of this study shed light on the promising potential of cissamaline, cissamanine, and cissamdine as agents for AD treatment. However, further in vitro and in vivo studies are necessary to validate these theoretical predictions and to understand the precise mechanisms through which these alkaloids may exert their therapeutic effects.

Genome-Wide Mendelian Randomization Identifies Ferroptosis-Related Drug Targets for Alzheimer's Disease.

Alzheimer's disease (AD) currently lacks effective disease-modifying treatments. Recent research suggests that ferroptosis could be a potential therapeutic target. Mendelian randomization (MR) is a widely used method for identifying novel therapeutic targets. Employ genetic information to evaluate the causal impact of ferroptosis-related genes on the risk of AD. 564 ferroptosis-related genes were obtained from FerrDb. We derived genetic instrumental variables for these genes using four brain quantitative trait loci (QTL) and two blood QTL datasets. Summary-data-based Mendelian randomization (SMR) and two-sample MR methods were applied to estimate the causal effects of ferroptosis-related genes on AD. Using extern transcriptomic datasets and triple-transgenic mouse model of AD (3xTg-AD) to further validate the gene targets identified by the MR analysis. We identified 17 potential AD risk gene targets from GTEx, 13 from PsychENCODE, and 22 from BrainMeta (SMR p < 0.05 and HEIDI test p > 0.05). Six overlapping ferroptosis-related genes associated with AD were identified, which could serve as potential therapeutic targets (PEX10, CDC25A, EGFR, DLD, LIG3, and TRIB3). Additionally, we further pinpointed risk genes or proteins at the blood tissue and pQTL levels. Notably, EGFR demonstrated significant dysregulation in the extern transcriptomic datasets and 3xTg-AD models. This study provides genetic evidence supporting the potential therapeutic benefits of targeting the six druggable genes for AD treatment, especially for EGFR (validated by transcriptome and 3xTg-AD), which could be useful for prioritizing AD drug development in the field of ferroptosis.

Recent Advances on Early Diagnosis and Immunotherapy Drug for Alzheimer's Disease

Recent Advances on Early Diagnosis and Immunotherapy Drug for Alzheimer's Disease

Biomaterials-based anti-inflammatory treatment strategies for Alzheimer's disease.

The current therapeutic drugs for Alzheimer's disease only improve symptoms, they do not delay disease progression. Therefore, there is an urgent need for new effective drugs. The underlying pathogenic factors of Alzheimer's disease are not clear, but neuroinflammation can link various hypotheses of Alzheimer's disease; hence, targeting neuroinflammation may be a new hope for Alzheimer's disease treatment. Inhibiting inflammation can restore neuronal function, promote neuroregeneration, reduce the pathological burden of Alzheimer's disease, and improve or even reverse symptoms of Alzheimer's disease. This review focuses on the relationship between inflammation and various pathological hypotheses of Alzheimer's disease; reports the mechanisms and characteristics of small-molecule drugs (e.g., nonsteroidal anti-inflammatory drugs, neurosteroids, and plant extracts); macromolecule drugs (e.g., peptides, proteins, and gene therapeutics); and nanocarriers (e.g., lipid-based nanoparticles, polymeric nanoparticles, nanoemulsions, and inorganic nanoparticles) in the treatment of Alzheimer's disease. The review also makes recommendations for the prospective development of anti-inflammatory strategies based on nanocarriers for the treatment of Alzheimer's disease.

Efficacy and safety of chinese herbal medicines combined with chemical drugs for alzheimer's disease: A systematic review and meta-analysis

Objective: To evaluate the efficacy and safety of Chinese herbal medicines (CHMs) combined with chemical drugs for the treatment of Alzheimer's disease (AD). Materials and Methods: Databases were searched to retrieve randomized controlled trials (RCTs) of CHMs combined with tacrine, galantamine, rivastigmine, donepezil, or memantine, following strict inclusion and exclusion criteria. Only research papers published in English, Chinese, and Japanese were considered. The primary outcome was the mini-mental state examination (MMSE) score and the secondary outcomes were AD assessment scale cognitive subscale (ADAS-Cog) score and safety evaluation. Meta-analysis was carried out using RevMan 5.3 and subgroup analysis was conducted to identify the sources of heterogeneity. Results: A total of 15 RCTs with 1386 participants were included in this study. Only donepezil was used in the retrieved literature. Meta-analyses showed that the combination of CHMs with donepezil led to significant improvement in the MMSE score (Z = 9.45; P &lt; 0.00001; weighted mean difference [WMD] =2.68, 95% confidence interval [CI]: 2.12–3.24) and a significant decrease in the ADAS-Cog score (Z = 5.53; P &lt; 0.00001; WMD = −3.79; 95% CI: −5.13–−2.44). Safety evaluation demonstrated that these combination treatments relieved adverse events such as insomnia (risk ratio [RR] =0.20, 95% CI: 0.06–0.68; P = 0.01) and hive (RR = 0.10; 95% CI: 0.01–0.73; P = 0.02). Conclusions: The combination of CHMs with a chemical drug like donepezil led to significant improvements in patient cognition as well as a better safety profile when compared to the application of a chemical drug alone.

Evaluating User Experiences of Alzheimer’s Drugs from Online Reviews Using Text Mining and Sentiment Analysis

Text mining is a new technology that attempts to find useful patterns, trends, patterns and rules from unstructured text data. One of the most commonly used techniques in Text Mining is Sentiment Analysis. Sentiment analysis is the most widely used classification tool to explore an author's attitude. It explores whether the author's attitude is positive, negative or impartial by means of a text. As most of the information in the internet age is found as text, the importance and usage areas of Sentiment analysis are increasing day by day. Sentiment analysis, which is frequently used in social media, can be used to expose users' ideas about a particular topic or product. The aim of this study is to transform drug reviews on websites into meaningful information. This information can help users in decision-making. In this study, personal data obtained from a social platform with Alzheimer's drug reviews of 78 users were evaluated. In particular, the selection of Alzheimer's drugs, unlike other drugs, allows the observations of the patients and relatives of the patient to be evaluated together. The 3723 people who read the review and found it useful strengthens the effect of the comment. In the implementation phase, polarity values of user comments were calculated with Sentiment analysis and Alzheimer's drugs were ranked with the formula developed. In this way, the satisfaction levels of consumers according to the drugs were determined.

Research progress on the targets and therapies of Alzheimer’s disease

Alzheimer 's disease (AD) is a progressive neurodegenerative disease. Most of the patients have the following characteristics, such as abnormalities in memory, language, and behavioral ability. It is very common in dementia in the elderly. The pathological features are mainly the plaques formed by Aβ protein deposition and the neurofibrillary tangles. The causes of AD are complex, and there are many pathological hypotheses. The current clinical therapeutic drugs for AD are mainly cholinesterase inhibitors developed based on the cholinergic hypothesis and receptor antagonists targeting NMDA receptors. Although there are some research results on the targets based on other hypotheses, further research is needed for clinical application. Nevertheless, in recent years, there have been many new development directions, such as combination therapy. Multi-target combination therapy is expected to achieve better efficacy than traditional single-target therapy. This makes it even more important to develop drugs with different potential targets. This article introduces three current mainstream therapeutic targets and theories, and introduces the drugs based on them. At the same time, we also introduced 3 targets and therapies with good potential in the future, and introduced some current stage results and current drugs based on their research and development.

Machine learning-based q-RASAR approach for the in silico identification of novel multi-target inhibitors against Alzheimer's disease

In the present research, we present the application of a novel approach, termed the Machine Learning (ML)-Based q-RASAR (quantitative read-across structure-activity relationship) method, for the identification of potential multi-target inhibitors against Alzheimer's disease (AD). The q-RASAR effectively combines the principles of both read-across and 2D QSAR approaches. As a result, it is imperative to take into account similarity-related aspects in the process of developing q-RASAR models. In this investigation, we have implemented ML-based q-RASAR modeling against seven major targets (AChE, BuChE, BACE1, 5-HT6, CDK-5 enzymes, Amyloid precursor protein, and Tau aggregation) of AD using the initially selected features in 2D-QSAR models for the identifications of novel multitarget inhibitors. The models were individually used to check the applicability domain of a pool of 407270 natural products (NPs) obtained from the COCONUT database (https://coconut.naturalproducts.net/download) and provided prioritized compounds for experimental detection of their performance as anti-Alzheimer's drugs. Furthermore, we have also developed the q-RASAAR (quantitative read-across structure-activity-activity relationship) and selectivity-based q-RASAR models to explore the most important features contributing to the dual inhibition against the respective targets. Furthermore, we have applied seven distinct machine learning algorithms to check their influence on the predictive abilities of q-RASAR and q-RASAAR models. Moreover, we have also developed the univariate q-RASAR model, with the RA function as the primary independent variable. Additionally, molecular docking experiments have been conducted to gain insights into the atomic-level molecular interactions between ligands and enzymes. These observations are then juxtaposed with the structural characteristics obtained from models that elucidate the mechanistic aspects of binding events. These proposed models may serve as valuable tools for pinpointing crucial molecular attributes when designing potential drugs for Alzheimer's therapy through the rational design of multi-target inhibitors.

The Potential of a Stratified Approach to Drug Repurposing in Alzheimer's Disease.

Alzheimer's disease (AD) is a complex neurodegenerative condition that is characterized by the build-up of amyloid-beta plaques and neurofibrillary tangles. While multiple theories explaining the aetiology of the disease have been suggested, the underlying cause of the disease is still unknown. Despite this, several modifiable and non-modifiable factors that increase the risk of developing AD have been identified. To date, only eight AD drugs have ever gained regulatory approval, including six symptomatic and two disease-modifying drugs. However, not all are available in all countries and high costs associated with new disease-modifying biologics prevent large proportions of the patient population from accessing them. With the current patient population expected to triple by 2050, it is imperative that new, effective, and affordable drugs become available to patients. Traditional drug development strategies have a 99% failure rate in AD, which is far higher than in other disease areas. Even when a drug does reach the market, additional barriers such as high cost and lack of accessibility prevent patients from benefiting from them. In this review, we discuss how a stratified medicine drug repurposing approach may address some of the limitations and barriers that traditional strategies face in relation to drug development in AD. We believe that novel, stratified drug repurposing studies may expedite the discovery of alternative, effective, and more affordable treatment options for a rapidly expanding patient population in comparison with traditional drug development methods.

Lecanemab: More Questions Than Answers!

The approval of lecanemab by the US Food and Drug Administration has been touted as a defining moment in the treatment of Alzheimer's disease. Lecanemab, an anti-amyloid beta monoclonal antibody, is the first Alzheimer's disease drug targeting amyloid beta that has shown statistically significant cognitive benefits in phase III trials. However, there have been many questions raised over the clinical relevance of the otherwise minimal cognitive improvements. Furthermore, its rapid approval has been mired in controversy, in addition to the reports of adverse events such as amyloid-related imaging abnormalities and several deaths of participants in the lecanemab trials. Here, we analyze the evidence supporting lecanemab as an amyloid beta therapy and also discuss the concerns raised about its efficacy and safety.

Queuine as a potential multi-target drug for alzheimer’s disease: insights from protein dynamics

Alzheimer’s disease (AD) is a neurodegenerative disorder with a complex pathogenesis. One promising approach to treating AD is simultaneously targeting multiple aspects of the disease using a multi-target drug (MTD). In this study, multi-target drug (MTD) potential of the nutraceutical molecule Queuine was explored using molecular docking and molecular dynamics (MD) simulations with five different protein targets engaged in AD: AChE, beta-site amyloid precursor protein cleaving enzyme-1 (BACE-1), N-methyl-D-aspartate receptor (NMDAR), monoamine oxidase A (MAO-A), and Synapsin III. Queuine revealed significant binding affinities, the docking scores being −10.1, −5.97, −5.63, −8.40, and −10.56 kcal/mol for AChE, BACE-1, NMDAR, MAO-A, and Synapsin III, respectively. MD simulations showed that Queuine formed stable complexes and preserved its stability throughout the simulation, the backbone fluctuations remaining within 2.5 Å specifically in the case of the BACE-1. Elastic network model simulations and principal component analysis were carried out to illustrate the dynamics of the protein systems. Significant hinge-bending and twisting-type motions that may be relevant to function were observed around the dimerization interfaces or binding sites. Structural clustering based on PCA analysis and cross-correlation maps demonstrated that Queuine binding altered the protein dynamics more drastically in the case of highly mobile proteins NMDAR and MAO-A. We propose that the neuroprotective effect of Queuine may stem from its prominent inhibitory action on enzymes BACE-1 and AChE. Our results suggest that Queuine may serve as a promising MTD candidate for the treatment of AD. Communicated by Ramaswamy H. Sarma

High-throughput target trial emulation for Alzheimer’s disease drug repurposing with real-world data

Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer’s disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer’s patients.

Chemical, Biochemical, Cellular, and Physiological Characterization of Leucettinib-21, a Down Syndrome and Alzheimer's Disease Drug Candidate.

Leucettinibs are substituted 2-aminoimidazolin-4-ones (inspired by the marine sponge natural product Leucettamine B) developed as pharmacological inhibitors of DYRK1A (dual-specificity, tyrosine phosphorylation-regulated kinase 1A), a therapeutic target for indications such as Down syndrome and Alzheimer's disease. Leucettinib-21 was selected as a drug candidate following extensive structure/activity studies and multiparametric evaluations. We here report its physicochemical properties (X-ray powder diffraction, differential scanning calorimetry, stability, solubility, crystal structure) and drug-like profile. Leucettinib-21's selectivity (analyzed by radiometric, fluorescence, interaction, thermal shift, residence time assays) reveals DYRK1A as the first target but also some "off-targets" which may contribute to the drug's biological effects. Leucettinib-21 was cocrystallized with CLK1 and modeled in the DYRK1A structure. Leucettinib-21 inhibits DYRK1A in cells (demonstrated by direct catalytic activity and phosphorylation levels of Thr286-cyclin D1 or Thr212-Tau). Leucettinib-21 corrects memory disorders in the Down syndrome mouse model Ts65Dn and is now entering safety/tolerance phase 1 clinical trials.

Systematic review and meta‑analysis of observational studies to check the protective role of non‑steroidal anti‑inflammatory drugs in Alzheimer's disease.

Alzheimer's disease (AD) is a major neurodegenerative disease, affecting more than two third cases of dementia in the world. Non‑steroidal anti‑inflammatory drugs (NSAIDs) are widely used anti‑inflammatory analgesic agents representing 7.7% of worldwide prescriptions of which 90% are in patients over 65 years old. Based on mixed findings a systematic review and meta‑analysis were conducted to develop a better understanding of the protective role of NSAIDs in AD. We used three database PubMed, Web of Science, and Embase to identify the literatures. The studies following cohort and case‑control design were investigated separately to check the effect of NSAIDs on AD, by the using their fundamental indicators (relative risk and odds ratio). The fixed effect or random effects model were used to estimate the pooled relative risk and pooled odds ratio separately for both the study design, based on magnitude of heterogeneity. A total of 14 studies were selected for meta‑analysis. Eight studies were following cohort study design, whereas, six studies were following case‑control study design. In meta‑analysis of cohort studies, the pooled relative risk was 0.67 with 95% C.I 0.39 to 1.15, which was statistically insignificant. In meta‑analysis of case‑control studies, the pooled odds ratio was 0.71 with 95% C.I 0.46 to 1.10, which was statistically insignificant. NSAIDs do not act as a protective factor for Alzheimer's disease. Additionally, methodologically sound randomized controlled trials are required to produce a robust result.

Characterize demographics, comorbidities and co‐medications in newly diagnosed United States (US) Alzheimer’s Disease patients using Medicare claims

AbstractBackgroundAccess to Medicare claims provides a unique opportunity to broadly characterize diseases affecting US adults 65+ years of age. This can provide insights into real world populations and a basis of comparison to those in clinical trial populations. To this end, this study characterizes demographics, comorbidities and co‐medications in patients newly diagnosed with Alzheimer’s Disease (AD).MethodsThis study utilized Medicare Part A and B claims (Fee‐For‐Service and Medicare Advantage) limited to patients with at least one 2019 medical encounter. The study’s source population was limited to those 65+ years of age with Medicare Part D coverage and 1‐year pre‐encounter continuous enrollment. From this source population, three newly diagnosed AD cohorts were compiled; cohort 1 utilized the 1‐year Bynum‐Standard Alzheimer’s disease and related dementia (ADRD) claims algorithm, cohort 2 adapted the ADRD algorithm to include only specific AD diagnoses, and cohort 3 was selected based on new use of cholinesterase inhibitors (irrespective of ADRD diagnosis). Demographics, comorbidities, and co‐medications present in the 1‐year pre‐cohort qualification are described.ResultsFrom 53,890,958 patients with at least one 2019 medical encounter, 33,315,038 qualified for the source population. Of these patients, 861,727 (2.6%) were newly diagnosed with ADRD (cohort 1), 373,798 patients (1.1%) were newly diagnosed with AD (cohort 2), and 395,319 patients (1.2%) were new users of cholinesterase inhibitors (cohort 3). The cohorts were not mutually exclusive. Overall, the three cohorts were similar in terms of demographics (table 1) with 75.1‐85.3% of patients aged 75+ and 61.5‐66.4% female. Common comorbidities (table 2) across the 3 cohorts include cardiovascular (CV) comorbidities such as myocardial infarction (34.1‐41.9%) and heart failure (39.8‐51.4%), diabetes (38.5‐41.6%), arthritis (44.0‐46.6%), stroke (28.9‐34.4%) and anxiety/depression (48.6‐54.5%). Co‐medications received (table 3) such as anti‐hypertensives, lipid modifying drugs, anti‐diabetics, systemic corticosteroids, and anti‐depressants reflect these conditions.ConclusionExtensive understanding of a drug’s real‐world target population can inform generalizability of clinical trial results and highlight unaddressed risk. This study stresses the importance of studying new AD drugs in populations with a history of CV risk, diabetes and stroke as well as those that may be immunocompromised due to treatments with systemic corticosteroids.

Comparison of diffusion metrics in dementia

AbstractBackgroundThe recent FDA approval of a disease‐modifying drug for Alzheimer’s disease has stressed the need for a test which can be used at scale to detect neurodegeneration. MRI diffusion shows great promise (Douaud et al., 2013), and recently a multiplicity of analysis methods have been developed. We look at which diffusion analysis method might be most sensitive.MethodWe worked with data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) (Weber et al., 2021). Participants with Alzheimer’s disease (AD) and a group of age‐matched healthy controls (HC) were identified. T1 images were processed with FreeSurfer v7 (Fischl, 2012), providing tissue segmentations and cortical parcellations, as well as regional volumes. Diffusion images were preprocessed using the PreQual pipeline in native space (including FSL’s Topup and Eddy) (Cai et al, 2021), and analysed with Diffusion Tensor Imaging (DTI), Neurite Orientation Dispersion and Density Imaging (NODDI) (Zhang, Schneider, et al, 2012), Apparent Measures Using Reduced Acquisitions (AMURA) (Aja‐Fernández et al, 2022), Diffusion Orientation Complexity (DOC) (Haroon et al, 2009) and Probabilistic Index of Connectivity (PICo) (Parker et al, 2003). Regional values were extracted in FreeSurfer regions (grey and white matter).Pairwise Mann‐Whitney U‐tests were carried out between AD and HC regional measures. Regions with the most statistically significant volume differences were selected; diffusion‐based metrics for these regions were calculated using weighted values and compared. The 10 metrics with the smallest p‐values were used to find the most significant regions, and these were compared with volumes.ResultParticipants: 100 AD (38 female, age 74.5±7.9 years) and 207 HC (132 female, age 72.0±7.4 years).We selected the 25 most significant regions from the volumetric analyses. From these regions, the most effective diffusion metrics were DTI‐derived mean, axial and radial diffusivities, and 5 of the AMURA metrics.Given the 10 most significant metrics, of the 15 most significant volumes, 8 were also in the top diffusion regions.ConclusionThis study demonstrates that DTI metrics are most sensitive at differentiating HC from AD. If confirmed this has clinical significance, as these scans are widely available.