Aiding the discovery of new treatments for dementia by uncovering unknown benefits of existing medications

Incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease in patients with multiple sclerosis initiating disease-modifying therapies: Retrospective cohort study using a frequentist model averaging statistical framework

Researchers are increasingly using insights derived from large-scale, electronic healthcare data to inform drug development and provide human validation of novel treatment pathways and aid in drug repurposing/repositioning. The objective of this study was to determine whether treatment of patients with multiple sclerosis with dimethyl fumarate, an activator of the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, results in a change in incidence of type 2 diabetes and its complications. This retrospective cohort study used administrative claims data to derive four cohorts of adults with multiple sclerosis initiating dimethyl fumarate, teriflunomide, glatiramer acetate or fingolimod between January 2013 and December 2018. A causal inference frequentist model averaging framework based on machine learning was used to compare the time to first occurrence of a composite endpoint of type 2 diabetes, cardiovascular disease or chronic kidney disease, as well as each individual outcome, across the four treatment cohorts. There was a statistically significantly lower risk of incidence for dimethyl fumarate versus teriflunomide for the composite endpoint (restricted hazard ratio [95% confidence interval] 0.70 [0.55, 0.90]) and type 2 diabetes (0.65 [0.49, 0.98]), myocardial infarction (0.59 [0.35, 0.97]) and chronic kidney disease (0.52 [0.28, 0.86]). No differences for other individual outcomes or for dimethyl fumarate versus the other two cohorts were observed. This study effectively demonstrated the use of an innovative statistical methodology to test a clinical hypothesis using real-world data to perform early target validation for drug discovery. Although there was a trend among patients treated with dimethyl fumarate towards a decreased incidence of type 2 diabetes, cardiovascular disease and chronic kidney disease relative to other disease-modifying therapies-which was statistically significant for the comparison with teriflunomide-this study did not definitively support the hypothesis that Nrf2 activation provided additional metabolic disease benefit in patients with multiple sclerosis.

Nucleoside Reverse Transcriptase Inhibitor Exposure Is Associated with Lower Alzheimer's Disease Risk: A Retrospective Cohort Proof-of-Concept Study

Brain somatic gene recombination (SGR) and the endogenous reverse transcriptases (RTs) that produce it have been implicated in the etiology of Alzheimer's disease (AD), suggesting RT inhibitors as novel prophylactics or therapeutics. This retrospective, proof-of-concept study evaluated the incidence of AD in people with human immunodeficiency virus (HIV) with or without exposure to nucleoside RT inhibitors (NRTIs) using de-identified medical claims data. Eligible participants were aged ≥60 years, without pre-existing AD diagnoses, and pursued medical services in the United States from October 2015 to September 2016. Cohorts 1 (N = 46,218) and 2 (N = 32,923) had HIV. Cohort 1 had prescription claims for at least one NRTI within the exposure period; Cohort 2 did not. Cohort 3 (N = 150,819) had medical claims for the common cold without evidence of HIV or antiretroviral therapy. The cumulative incidence of new AD cases over the ensuing 2.75-year observation period was lowest in patients with NRTI exposure and highest in controls. Age- and sex-adjusted hazard ratios showed a significantly decreased risk for AD in Cohort 1 compared with Cohorts 2 (HR 0.88, p < 0.05) and 3 (HR 0.84, p < 0.05). Sub-grouping identified a decreased AD risk in patients with NRTI exposure but without protease inhibitor (PI) exposure. Prospective clinical trials and the development of next-generation agents targeting brain RTs are warranted.

Assessment of cognitive games to improve the quality of life of Parkinson's and Alzheimer's patients

Objectives

The core objectives of this study centre on enhancing the quality of life and well-being of individuals diagnosed with Parkinson's and Alzheimer's diseases. Our aim is to facilitate the monitoring of patient information, benefiting both caregivers and healthcare professionals.

Methods

As part of the PROCare4Life platform sensorial ecosystem, a web application with six engaging cognitive games focusing on developing cognitive training and stimulating brain activity are developed. A set of metrics calculated by the application feed machine learning predictive models to evaluate the cognitive status and evolution over time. Long-term analysis of the daily cognitive ability information is used to generate high-level outcomes and identify deviations for each patient from the multimodal fusion engine. And based on these results, a recommender system provides a set of personalized notifications.

Results

A 3-month pilot study that took place in five different countries shows the results obtained from 93 patients. An average of 22.4 games were completed per day and the recommender system generated a total of 260 game notifications, 37.7% of them were marked as read by the patients. The Cognitive State Score and the Deviations in Cognitive Abilities measurement, calculated by the multimodal fusion engine, when used in conjunction present a good overview of the patient's current state and potential deviations.

Conclusion

The cognitive games application was well-received by elderly individuals who took part in the study. This tool can be valuable for caregivers and healthcare providers in assessing the cognitive function of patients through engaging in cognitive games.

Dementia risk predictions from German claims data using methods of machine learning

INTRODUCTION

We examined whether German claims data are suitable for dementia risk prediction, how machine learning (ML) compares to classical regression, and what the important predictors for dementia risk are.

METHODS

We analyzed data from the largest German health insurance company, including 117,895 dementia-free people age 65+. Follow-up was 10 years. Predictors were: 23 age-related diseases, 212 medical prescriptions, 87 surgery codes, as well as age and sex. Statistical methods included logistic regression (LR), gradient boosting (GBM), and random forests (RFs).

RESULTS

Discriminatory power was moderate for LR (C-statistic = 0.714; 95% confidence interval [CI] = 0.708-0.720) and GBM (C-statistic = 0.707; 95% CI  = 0.700-0.713) and lower for RF (C-statistic = 0.636; 95% CI  = 0.628-0.643). GBM had the best model calibration. We identified antipsychotic medications and cerebrovascular disease but also a less-established specific antibacterial medical prescription as important predictors.

DISCUSSION

Our models from German claims data have acceptable accuracy and may provide cost-effective decision support for early dementia screening.

Identification of risk genes for Alzheimer's disease by gene embedding

Most disease-gene association methods do not account for gene-gene interactions, even though these play a crucial role in complex, polygenic diseases like Alzheimer's disease (AD). To discover new genes whose interactions may contribute to pathology, we introduce GeneEMBED. This approach compares the functional perturbations induced in gene interaction network neighborhoods by coding variants from disease versus healthy subjects. In two independent AD cohorts of 5,169 exomes and 969 genomes, GeneEMBED identified novel candidates. These genes were differentially expressed in post mortem AD brains and modulated neurological phenotypes in mice. Four that were differentially overexpressed and modified neurodegeneration in vivo are PLEC, UTRN, TP53, and POLD1. Notably, TP53 and POLD1 are involved in DNA break repair and inhibited by approved drugs. While these data show proof of concept in AD, GeneEMBED is a general approach that should be broadly applicable to identify genes relevant to risk mechanisms and therapy of other complex diseases.

Potential long-term effect of tumor necrosis factor inhibitors on dementia risk: A propensity score matched retrospective cohort study in US veterans

INTRODUCTION

Tumor necrosis factor (TNF) inhibitors are widely used to treat rheumatoid arthritis (RA) and their potential to retard Alzheimer's disease (AD) progression has been reported. However, their long-term effects on the dementia/AD risk remain unknown.

METHODS

A propensity scored matched retrospective cohort study was conducted among 40,207 patients with RA within the US Veterans Affairs health-care system from 2000 to 2020.

RESULTS

A total of 2510 patients with RA prescribed TNF inhibitors were 1:2 matched to control patients. TNF inhibitor use was associated with reduced dementia risk (hazard ratio [HR]: 0.64, 95% confidence interval [CI]: 0.52-0.80), which was consistent as the study period increased from 5 to 20 years after RA diagnosis. TNF inhibitor use also showed a long-term effect in reducing the risk of AD (HR: 0.57, 95% CI: 0.39-0.83) during the 20 years of follow-up.

CONCLUSION

TNF inhibitor use is associated with lower long-term risk of dementia/AD among US veterans with RA.

Revealing Unknown Benefits of Existing Medications to Aid the Discovery of New Treatments for Post‐Traumatic Stress Disorder

Objective

To systematically identify novel pharmacological strategies for preventing or treating post‐traumatic stress disorder (PTSD) by leveraging large‐scale analysis of real‐world observational data.

Methods

Using a self‐controlled study design, the association between 1399 medications and the incidence of PTSD across four US insurance claims databases covering commercially insured, Medicare eligible, and Medicaid patients was examined. A validated algorithm for identifying PTSD in claims data was used, and medications were identified by their RxNorm ingredient. Medications used to treat PTSD or its symptoms (e.g., antidepressants, antipsychotics) were excluded. Medications associated with ≥30% reduction in risk of PTSD in ≥2 databases were identified.

Results

A total of 137,182,179 individuals were included in the analysis. Fifteen medications met the threshold criteria for a potential protective effect on PTSD; six were categorized as “primary signals” while the remaining nine were considered “potential signals”. The primary signals include a beta blocker that has been previously studied for PTSD, and five medications used to treat attention‐deficit/hyperactivity disorder. The potential signals include four medications used to treat substance use disorders and five medications used to treat sleep disorders.

Discussion

The medications identified in this analysis provide targets for further research in studies that are designed to examine specific hypotheses regarding these medications and the incidence of PTSD. This work may aid in discovering novel therapeutic approaches to treat PTSD, wherein new and effective treatments are badly needed.

Four large US‐based administrative claims databases were used to analyze the association between all marketed prescription medications and the outcome of incident post‐traumatic stress disorder (PTSD)

Of the 1399 medications examined, there were 15 that met the strict filtering criteria for showing consistent, moderate‐to‐strong, protective effects against the outcome

Medications fell into four main classes: (1) a beta blocker (propranolol), (2) five medications used to treat attention‐deficit/hyperactivity disorder (ADHD), (3) four medications used to treat substance use disorders and (4) five medications used to treat sleep disorders

These findings identify rational starting points for future hypothesis‐driven research to explore these associations in greater detail

Treatment with TNF-α inhibitors versus methotrexate and the association with dementia and Alzheimer's disease

INTRODUCTION

Peripheral inhibition of tumor necrosis factor (TNF)-α, outside of the central nervous system, may result in clinical improvement of Alzheimer's disease (AD) outcomes. TNF-α inhibitors (TNFIs) are effective treatments for various autoimmune conditions and may be effective for preventing and/or treating AD. The objective of this study was to compare the risk of dementia and AD in patients initiating methotrexate versus those initiating TNFIs.

METHODS

Insurance claims data from databases of commercially insured and Medicare-eligible patients were used to estimate the risk of dementia and AD within patients with rheumatoid arthritis (RA) initiating a TNFI versus initiation of methotrexate. A sensitivity analysis included all patients without the RA diagnosis requirement. The at-risk period spanned from the index date until a diagnosis of the outcome, loss-to-follow-up, or receipt of the comparator drug. Patients were matched 1-to-1 using propensity scores. A Cox proportional hazards model was used to estimate the hazard ratio (HR). Negative controls were used to calibrate the results.

RESULTS

A total of 11,092 new TNFI patients and 44,023 new methotrexate patients were identified, and 8925 from each group were matched. The outcome of dementia occurred in 1.4% of patients in both groups. The calibrated results from the Cox regression found no difference between the two groups (commercially insured database: calibrated HR = 0.69, 95% confidence interval = 0.45 to 1.05; Medicare-only database: 1.14, 0.66 to 1.96). Results were similar in all sensitivity analyses: outcome of AD and including patients without RA.

DISCUSSION

No significant difference for the risk of dementia or AD was seen between patients initiating a TNFI versus methotrexate. Although this study cannot conclude whether use of TNFIs is protective against dementia and AD compared with receiving no treatment, there was no evidence that it is more protective than the active comparator methotrexate.

Amyloid-β: a potential link between epilepsy and cognitive decline

People with epilepsy - in particular, late-onset epilepsy of unknown aetiology - have an elevated risk of dementia, and seizures have been detected in the early stages of Alzheimer disease (AD), supporting the concept of an epileptic AD prodrome. However, the relationship between epilepsy and cognitive decline remains controversial, with substantial uncertainties about whether epilepsy drives cognitive decline or vice versa, and whether shared pathways underlie both conditions. Here, we review evidence that amyloid-β (Aβ) forms part of a shared pathway between epilepsy and cognitive decline, particularly in the context of AD. People with epilepsy show an increased burden of Aβ pathology in the brain, and Aβ-mediated epileptogenic alterations have been demonstrated in experimental studies, with evidence suggesting that Aβ pathology might already be pro-epileptogenic at the soluble stage, long before plaque deposition. We discuss the hypothesis that Aβ mediates - or is at least a major determinant of - a continuum spanning epilepsy and cognitive decline. Serial cognitive testing and assessment of Aβ levels might be worthwhile to stratify the risk of developing dementia in people with late-onset epilepsy. If seizures are a clinical harbinger of dementia, people with late-onset epilepsy could be an ideal group in which to implement preventive or therapeutic strategies to slow cognitive decline.

Application of Real-World Data and the REWARD Framework to Detect Unknown Benefits of Memantine and Identify Potential Disease Targets for New NMDA Receptor Antagonists

BACKGROUND

Observational data may inform novel drug development programs by identifying previously unappreciated, clinical benefits of existing drugs. Several preclinical and clinical studies have suggested emergent therapeutic utility of drugs acting on the N-methyl-D-aspartate (NMDA) receptor, a subtype of glutamate receptors, including the antidementia drug memantine.

METHODS

Using a self-controlled cohort study design, the association of exposure to the NMDA receptor antagonist memantine with the incidence of all observed disease outcomes in four US administrative claims databases, spanning from January 2000 through January 2019, was assessed. The databases used in this study were the IBM MarketScan^® Commercial Database (CCAE), the IBM MarketScan^® Multi-State Medicaid Database (MDCD), the IBM MarketScan^® Medicare Supplemental Database (MDCR), and the Optum^© De-Identified Clinformatics^® Data Mart Database. Outcomes were defined according to the unique Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) classification system codes and required a diagnosis on two or more distinct dates. Of 20,953 outcomes assessed, only those for which memantine was associated with a ≥ 50% reduction in risk in two or more databases were included. A meta-analysis with random effects was used to pool data across the databases.

RESULTS

Overall, 312,336 patients were exposed to memantine during the study. After removing conditions related to dementia and memory loss, 60 outcomes met the threshold criteria. Results fell into five disease categories: mental disorders, substance use disorders, pain, gastrointestinal and colon disorders, and demyelinating disease. The bulk of findings fell into the first two groups, with 28 outcomes related to mental disorders and 24 related to substance use disorders.

CONCLUSION

The present results confirm that NMDA receptor antagonism may have broader therapeutic utility than previously recognized. Further observational and clinical research may be warranted to explore the therapeutic benefit of NMDA antagonists for the outcomes found in this study.

Harnessing the paradoxical phenotypes of APOE ɛ2 and APOE ɛ4 to identify genetic modifiers in Alzheimer's disease

The strongest genetic risk factor for idiopathic late‐onset Alzheimer's disease (LOAD) is apolipoprotein E (APOE) ɛ4, while the APOE ɛ2 allele is protective. However, there are paradoxical APOE ɛ4 carriers who remain disease‐free and APOE ɛ2 carriers with LOAD. We compared exomes of healthy APOE ɛ4 carriers and APOE ɛ2 Alzheimer's disease (AD) patients, prioritizing coding variants based on their predicted functional impact, and identified 216 genes with differential mutational load between these two populations. These candidate genes were significantly dysregulated in LOAD brains, and many modulated tau‐ or β42‐induced neurodegeneration in Drosophila. Variants in these genes were associated with AD risk, even in APOE ɛ3 homozygotes, showing robust predictive power for risk stratification. Network analyses revealed involvement of candidate genes in brain cell type‐specific pathways including synaptic biology, dendritic spine pruning and inflammation. These potential modifiers of LOAD may constitute novel biomarkers, provide potential therapeutic intervention avenues, and support applying this approach as larger whole exome sequencing cohorts become available.

Brain cell somatic gene recombination and its phylogenetic foundations

A new form of somatic gene recombination (SGR) has been identified in the human brain that affects the Alzheimer's disease gene, amyloid precursor protein (APP). SGR occurs when a gene sequence is cut and recombined within a single cell's genomic DNA, generally independent of DNA replication and the cell cycle. The newly identified brain SGR produces genomic complementary DNAs (gencDNAs) lacking introns, which integrate into locations distinct from germline loci. This brief review will present an overview of likely related recombination mechanisms and genomic cDNA-like sequences that implicate evolutionary origins for brain SGR. Similarities and differences exist between brain SGR and VDJ recombination in the immune system, the first identified SGR form that now has a well-defined enzymatic machinery. Both require gene transcription, but brain SGR uses an RNA intermediate and reverse transcriptase (RT) activity, which are characteristics shared with endogenous retrotransposons. The identified gencDNAs have similarities to other cDNA-like sequences existing throughout phylogeny, including intron-less genes and inactive germline processed pseudogenes, with likely overlapping biosynthetic processes. gencDNAs arise somatically in an individual to produce multiple copies; can be functional; appear most frequently within postmitotic cells; have diverse sequences; change with age; and can change with disease state. Normally occurring brain SGR may represent a mechanism for gene optimization and long-term cellular memory, whereas its dysregulation could underlie multiple brain disorders and, potentially, other diseases like cancer. The involvement of RT activity implicates already Food and Drug Administration-approved RT inhibitors as possible near-term interventions for managing SGR-associated diseases and suggest next-generation therapeutics targeting SGR elements.

Increased Levels of Coagulation Factor XI in Plasma Are Related to Alzheimer's Disease Diagnosis

BACKGROUND

Alzheimer's disease is a complex disorder of unclear etiology that develops in the elderly population. It is a debilitating, progressive neurodegeneration for which disease-modifying therapies do not exist. Previous studies have suggested that, for a subset of patients, dysregulation in hemostasis might be one of the molecular mechanisms that ultimately leads to the development of neurodegeneration resulting in cognitive decline that represents the most prominent symptomatic characteristic of Alzheimer's disease.

OBJECTIVE

To examine a relationship between factors that are part of coagulation and anticoagulation pathways with cognitive decline that develops during Alzheimer's disease.

METHODS

SOMAscan assay was used to measure levels of coagulation/anticoagulation factors V, VII, IX, X, Xa, XI, antithrombin III, protein S, protein C, and activated protein C in plasma samples obtained from three groups of subjects: 1) subjects with stable cognitively healthy function, 2) subjects with stable mild cognitive impairment, and 3) subjects diagnosed with probable Alzheimer's disease.

RESULTS

Our results show that protein levels of coagulation factor XI are significantly increased in patients who are diagnosed with probable Alzheimer's disease compared with cognitively healthy subjects or patients diagnosed with mild cognitive impairment. Furthermore, our results demonstrate that significant predictors of Alzheimer's-type diagnosis are factors IX and XI-an increase in both factors is associated with a reduction in cognitive function.

CONCLUSION

Our study justifies further investigations of biological pathways involving coagulation/anticoagulation factors in relation to dementia, including dementia resulting from Alzheimer's-type neurodegeneration.