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.

Alzheimer's disease classification using cluster-based labelling for graph neural network on heterogeneous data

Biomarkers for Alzheimer's disease (AD) diagnosis do not always correlate reliably with cognitive symptoms, making clinical diagnosis inconsistent. In this study, the performance of a graphical neural network (GNN) classifier based on data-driven diagnostic classes from unsupervised clustering on heterogeneous data is compared to the performance of a classifier using clinician diagnosis as an outcome. Unsupervised clustering on tau-positron emission tomography (PET) and cognitive and functional assessment data was performed. Five clusters embedded in a non-linear uniform manifold approximation and project (UMAP) space were identified. The individual clusters revealed specific feature characteristics with respect to clinical diagnosis of AD, gender, family history, age, and underlying neurological risk factors (NRFs). In particular, one cluster comprised mainly diagnosed AD cases. All cases within this cluster were re-labelled AD cases. The re-labelled cases are characterized by high cerebrospinal fluid amyloid beta (CSF Aβ) levels at a younger age, even though Aβ data was not used for clustering. A GNN model was trained using the re-labelled data with a multiclass area-under-the-curve (AUC) of 95.2%, higher than the AUC of a GNN trained on clinician diagnosis (91.7%; p = 0.02). Overall, our work suggests that more objective cluster-based diagnostic labels combined with GNN classification may have value in clinical risk stratification and diagnosis of AD.

Multi-dimensional relationships among dementia, depression and prescribed drugs in England and Wales hospitals

Background

 Dementia is a group of symptoms that largely affects older people. The majority of patients face behavioural and psychological symptoms (BPSD) during the course of their illness. Alzheimer’s disease (AD) and vascular dementia (VaD) are two of the most prevalent types of dementia. Available medications provide symptomatic benefits and provide relief from BPSD and associated health issues. However, it is unclear how specific dementia, antidepressant, antipsychotic, antianxiety, and mood stabiliser drugs, used in the treatment of depression and dementia subtypes are prescribed in hospital admission, during hospital stay, and at the time of discharge. To address this, we apply multi-dimensional data analytical approaches to understand drug prescribing practices within hospitals in England and Wales.

Methods

We made use of the UK National Audit of Dementia (NAD) dataset and pre-processed the dataset. We evaluated the pairwise Pearson correlation of the dataset and selected key data features which are highly correlated with dementia subtypes. After that, we selected drug prescribing behaviours (e.g. specific medications at the time of admission, during the hospital stay, and upon discharge), drugs and disorders. Then to shed light on the relations across multiple features or dimensions, we carried out multiple regression analyses, considering the number of dementia, antidepressant, antipsychotic, antianxiety, mood stabiliser, and antiepileptic/anticonvulsant drug prescriptions as dependent variables, and the prescription of other drugs, number of patients with dementia subtypes (AD/VaD), and depression as independent variables.

Results

In terms of antidepressant drugs prescribed in hospital admission, during stay and discharge, the number of sertraline and venlafaxine prescriptions were associated with the number of VaD patients whilst the number of mirtazapine prescriptions was associated with frontotemporal dementia patients. During admission, the number of lamotrigine prescriptions was associated with frontotemporal dementia patients, and with the number of valproate and dosulepin prescriptions. During discharge, the number of mirtazapine prescriptions was associated with the number of donepezil prescriptions in conjunction with frontotemporal dementia patients. Finally, the number of prescriptions of donepezil/memantine at admission, during hospital stay and at discharge exhibited positive association with AD patients.

Conclusion

Our analyses reveal a complex, multifaceted set of interactions among prescribed drug types, dementia subtypes, and depression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-01892-9.

Distinguishing Lewy Body Dementia from Alzheimer's Disease using Machine Learning on Heterogeneous Data: A Feasibility Study

Dementia with Lewy Bodies (DLB) is the second most common form of dementia, but diagnostic markers for DLB can be expensive and inaccessible, and many cases of DLB are undiagnosed. This work applies machine learning techniques to determine the feasibility of distinguishing DLB from Alzheimer's Disease (AD) using heterogeneous data features. The Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm was first applied using a Leave-One-Out Cross-Validation protocol to a dataset comprising DLB and AD cases. Then, interpretable association rule-based diagnostic classifiers were obtained for distinguishing DLB from AD. The various diagnostic classifiers generated by this process had high accuracy over the whole dataset (mean accuracy of 94%). The mean accuracy in classifying their out-of-sample case was 80.5%. Every classifier generated consisted of very simple structure, each using 1-2 classification rules and 1-3 data features. As a group, the classifiers were heterogeneous and used several different data features. In particular, some of the classifiers used very simple and inexpensive diagnostic features, yet with high diagnostic accuracy. This work suggests that opportunities may exist for incorporating accessible diagnostic assessments while improving diagnostic rate for DLB. Clinical Relevance- Simple and interpretable high-performing machine learning algorithms identified a variety of readily available clinical assessments for differential diagnosis of dementia offering the opportunities to incorporate various simple and inexpensive screening tests for DLB and addressing the problem of DLB underdiagnosis.

Multiple Cost Optimisation for Alzheimer's Disease Diagnosis

Current machine learning techniques for dementia diagnosis often do not take into account real-world practical constraints, which may include, for example, the cost of diagnostic assessment time and financial budgets. In this work, we built on previous cost-sensitive feature selection approaches by generalising to multiple cost types, while taking into consideration that stakeholders attempting to optimise the dementia care pathway might face multiple non-fungible budget constraints. Our new optimisation algorithm involved the searching of cost-weighting hyperparameters while constrained by total budgets. We then provided a proof of concept using both assessment time cost and financial budget cost. We showed that budget constraints could control the feature selection process in an intuitive and practical manner, while adjusting the hyperparameter increased the range of solutions selected by feature selection. We further showed that our budget-constrained cost optimisation framework could be implemented in a user-friendly graphical user interface sandbox tool to encourage non-technical users and stakeholders to adopt and to further explore and audit the model - a humans-in-the-loop approach. Overall, we suggest that setting budget constraints initially and then fine tuning the cost-weighting hyperparameters can be an effective way to perform feature selection where multiple cost constraints exist, which will in turn lead to more realistic optimising and redesigning of dementia diagnostic assessments. Clinical Relevance-By optimising diagnostic accuracy against various costs (e.g. assessment administration time and financial budget) predictive yet practical dementia diagnostic assessments can be redesigned to suit clinical use.

Simvastatin is associated with superior lipid and glycaemic control to atorvastatin and reduced levels of incident Type 2 diabetes, in men and women, in the UK Biobank

INTRODUCTION

Cardiovascular disease (CVD) is the leading cause of mortality in people with Type 2 diabetes mellitus (T2DM). Statins reduce low-density lipoproteins and positively affect CVD outcomes. Statin type and dose have differential effects on glycaemia and risk of incident T2DM; however, the impact of gender, and of individual drugs within the statin class, remains unclear.

AIM

To compare effects of simvastatin and atorvastatin on lipid and glycaemic control in men and women with and without T2DM, and their association with incident T2DM.

METHODS

The effect of simvastatin and atorvastatin on lipid and glycaemic control was assessed in the T2DM DiaStrat cohort. Prescribed medications, gender, age, BMI, diabetes duration, blood lipid profile and HbA1c were extracted from Electronic Care Record, and compared in men and women prescribed simvastatin and atorvastatin. Analyses were replicated in the UKBiobank in those with and without T2DM. The association of simvastatin and atorvastatin with incident T2DM was also investigated in the UKBiobank. Cohorts where matched for age, BMI and diabetes duration in men and women, in the UKBioBank analysis, where possible.

RESULTS

Simvastatin was associated with better LDL (1.6 ± 0.6 vs 2.1 ± 0.9 mmol/L, p < .01) and total cholesterol (3.6 ± 0.7 vs 4.2 ± 1.0 mmol/L, p < .05), and glycaemic control (62 ± 17 vs 67 ± 19 mmol/mol, p < .059) than atorvastatin specifically in women in the DiaStrat cohort. In the UKBiobank, both men and women prescribed simvastatin had better LDL (Women: 2.6 ± 0.6 vs 2.6 ± 0.7 mmol/L, p < .05; Men: 2.4 ± 0.6 vs 2.4 ± 0.6, p < .01) and glycaemic control (Women:54 ± 14 vs 56 ± 15mmol/mol, p < .05; Men, 54 ± 14 vs 55 ± 15 mmol/mol, p < .01) than those prescribed atorvastatin. Simvastatin was also associated with reduced risk of incident T2DM in both men and women (p < .0001) in the UKBiobank.

CONCLUSIONS

Simvastatin is associated with superior lipid and glycaemic control to atorvastatin in those with and without T2DM, and with fewer incident T2DM cases. Given the importance of lipid and glycaemic control in preventing secondary complications of T2DM, these findings may help inform prescribing practices.

The impact of hearing impairment and hearing aid use on progression to mild cognitive impairment in cognitively healthy adults: An observational cohort study

INTRODUCTION

We assessed the association of self-reported hearing impairment and hearing aid use with cognitive decline and progression to mild cognitive impairment (MCI).

METHODS

We used a large referral-based cohort of 4358 participants obtained from the National Alzheimer's Coordinating Center. The standard covariate-adjusted Cox proportional hazards model, the marginal structural Cox model with inverse probability weighting, standardized Kaplan-Meier curves, and linear mixed-effects models were applied to test the hypotheses.

RESULTS

Hearing impairment was associated with increased risk of MCI (standardized hazard ratio [HR] 2.58, 95% confidence interval [CI: 1.73 to 3.84], P = .004) and an accelerated rate of cognitive decline (P < .001). Hearing aid users were less likely to develop MCI than hearing-impaired individuals who did not use a hearing aid (HR 0.47, 95% CI [0.29 to 0.74], P = .001). No difference in risk of MCI was observed between individuals with normal hearing and hearing-impaired adults using hearing aids (HR 0.86, 95% CI [0.56 to 1.34], P = .51).

DISCUSSION

Use of hearing aids may help mitigate cognitive decline associated with hearing loss.

Practical Strategies for Extreme Missing Data Imputation in Dementia Diagnosis

Accurate computational models for clinical decision support systems require clean and reliable data but, in clinical practice, data are often incomplete. Hence, missing data could arise not only from training datasets but also test datasets which could consist of a single undiagnosed case, an individual. This work addresses the problem of extreme missingness in both training and test data by evaluating multiple imputation and classification workflows based on both diagnostic classification accuracy and computational cost. Extreme missingness is defined as having ~50% of the total data missing in more than half the data features. In particular, we focus on dementia diagnosis due to long time delays, high variability, high attrition rates and lack of practical data imputation strategies in its diagnostic pathway. We identified and replicated the extreme missingness structure of data from a real-world memory clinic on a larger open dataset, with the original complete data acting as ground truth. Overall, we found that computational cost, but not accuracy, varies widely for various imputation and classification approaches. Particularly, we found that iterative imputation on the training dataset combined with a reduced-feature classification model provides the best approach, in terms of speed and accuracy. Taken together, this work has elucidated important factors to be considered when developing a predictive model for a dementia diagnostic support system.

Association of the use of hearing aids with the conversion from mild cognitive impairment to dementia and progression of dementia: A longitudinal retrospective study

INTRODUCTION

Hearing aid usage has been linked to improvements in cognition, communication, and socialization, but the extent to which it can affect the incidence and progression of dementia is unknown. Such research is vital given the high prevalence of dementia and hearing impairment in older adults, and the fact that both conditions often coexist. In this study, we examined for the first time the effect of the use of hearing aids on the conversion from mild cognitive impairment (MCI) to dementia and progression of dementia.

METHODS

We used a large referral-based cohort of 2114 hearing-impaired patients obtained from the National Alzheimer's Coordinating Center. Survival analyses using multivariable Cox proportional hazards regression model and weighted Cox regression model with censored data were performed to assess the effect of hearing aid use on the risk of conversion from MCI to dementia and risk of death in hearing-impaired participants. Disease progression was assessed with Clinical Dementia Rating Sum of Boxes (CDR-SB) scores. Three types of sensitivity analyses were performed to validate the robustness of the results.

RESULTS

MCI participants that used hearing aids were at significantly lower risk of developing all-cause dementia compared to those not using hearing aids (hazard ratio [HR] 0.73, 95% confidence interval [CI], 0.61 to 0.89; false discovery rate [FDR] P = 0.004). The mean annual rate of change (standard deviation) in CDR-SB scores for hearing aid users with MCI was 1.3 (1.45) points and significantly lower than for individuals not wearing hearing aids with a 1.7 (1.95) point increase in CDR-SB per year (P = 0.02). No association between hearing aid use and risk of death was observed. Our findings were robust subject to sensitivity analyses.

DISCUSSION

Among hearing-impaired adults, hearing aid use was independently associated with reduced dementia risk. The causality between hearing aid use and incident dementia should be further tested.

An Early Stage Researcher's Primer on Systems Medicine Terminology

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

Shaping a data-driven era in dementia care pathway through computational neurology approaches

BACKGROUND

Dementia is caused by a variety of neurodegenerative diseases and is associated with a decline in memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The complexity of dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly in clinical decision-making.

MAIN BODY

Despite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment. However, the dementia care pathway is currently suboptimal. We propose that through computational approaches, understanding of dementia aetiology could be improved, and dementia assessments could be more standardised, objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of data-driven computational neurology approaches and the development of practical clinical decision support systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany such implementations.

CONCLUSION

The data-driven era for dementia research has arrived with the potential to transform the healthcare system, creating a more efficient, transparent and personalised service for dementia.

Multi-time-point data preparation robustly reveals MCI and dementia risk factors

Introduction

Conflicting results on dementia risk factors have been reported across studies. We hypothesize that variation in data preparation methods may partially contribute to this issue.

Methods

We propose a comprehensive data preparation approach comparing individuals with stable diagnosis over time to those who progress to mild cognitive impairment (MCI)/dementia. This was compared to the often-used "baseline" analysis. Multivariate logistic regression was used to evaluate both methods.

Results

The results obtained from sensitivity analyses were consistent with those from our multi-time-point data preparation approach, exhibiting its robustness. Compared to analysis using only baseline data, the number of significant risk factors identified in progression analyses was substantially lower. Additionally, we found that moderate depression increased healthy-to-MCI/dementia risk, while hypertension reduced MCI-to-dementia risk.

Discussion

Overall, multi-time-point-based data preparation approaches may pave the way for a better understanding of dementia risk factors, and address some of the reproducibility issues in the field.

Opportunities for multiscale computational modelling of serotonergic drug effects in Alzheimer's disease

Alzheimer's disease (AD) is an age-specific neurodegenerative disease that compromises cognitive functioning and impacts the quality of life of an individual. Pathologically, AD is characterised by abnormal accumulation of beta-amyloid (Aβ) and hyperphosphorylated tau protein. Despite research advances over the last few decades, there is currently still no cure for AD. Although, medications are available to control some behavioural symptoms and slow the disease's progression, most prescribed medications are based on cholinesterase inhibitors. Over the last decade, there has been increased attention towards novel drugs, targeting alternative neurotransmitter pathways, particularly those targeting serotonergic (5-HT) system. In this review, we focused on 5-HT receptor (5-HTR) mediated signalling and drugs that target these receptors. These pathways regulate key proteins and kinases such as GSK-3 that are associated with abnormal levels of Aβ and tau in AD. We then review computational studies related to 5-HT signalling pathways with the potential for providing deeper understanding of AD pathologies. In particular, we suggest that multiscale and multilevel modelling approaches could potentially provide new insights into AD mechanisms, and towards discovering novel 5-HTR based therapeutic targets.

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Alzheimer's disease (AD) drug treatment is limited, and alternatives are needed.

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Serotonin (5-HT) mediated signalling pathways may regulate Aβ and tau levels.

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5-HT based drugs have the potential to provide as novel therapeutics for AD.

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Complex 5-HT signalling mechanisms for AD and related drugs hinder understanding.

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Multiscale models may offer insights into mechanisms and therapeutic targets.

A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R ² = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.

Early Stage Glycosylation Biomarkers in Alzheimer's Disease

Alzheimer's disease (AD) is of great cause for concern in our ageing population, which currently lacks diagnostic tools to permit accurate and timely diagnosis for affected individuals. The development of such tools could enable therapeutic interventions earlier in the disease course and thus potentially reducing the debilitating effects of AD. Glycosylation is a common, and important, post translational modification of proteins implicated in a host of disease states resulting in a complex array of glycans being incorporated into biomolecules. Recent investigations of glycan profiles, in a wide range of conditions, has been made possible due to technological advances in the field enabling accurate glycoanalyses. Amyloid beta (Aβ) peptides, tau protein, and other important proteins involved in AD pathogenesis, have altered glycosylation profiles. Crucially, these abnormalities present early in the disease state, are present in the peripheral blood, and help to distinguish AD from other dementias. This review describes the aberrant glycome in AD, focusing on proteins implicated in development and progression, and elucidates the potential of glycome aberrations as early stage biomarkers of AD.

Distinguishing normal brain aging from the development of Alzheimer's disease: inflammation, insulin signaling and cognition

As populations age, prevalence of Alzheimer's disease (AD) is rising. Over 100 years of research has provided valuable insights into the pathophysiology of the disease, for which age is the principal risk factor. However, in recent years, a multitude of clinical trial failures has led to pharmaceutical corporations becoming more and more unwilling to support drug development in AD. It is possible that dependence on the amyloid cascade hypothesis as a guide for preclinical research and drug discovery is part of the problem. Accumulating evidence suggests that amyloid plaques and tau tangles are evident in non-demented individuals and that reducing or clearing these lesions does not always result in clinical improvement. Normal aging is associated with pathologies and cognitive decline that are similar to those observed in AD, making differentiation of AD-related cognitive decline and neuropathology challenging. In this mini-review, we discuss the difficulties with discerning normal, age-related cognitive decline with that related to AD. We also discuss some neuropathological features of AD and aging, including amyloid and tau pathology, synapse loss, inflammation and insulin signaling in the brain, with a view to highlighting cognitive or neuropathological markers that distinguish AD from normal aging. It is hoped that this review will help to bolster future preclinical research and support the development of clinical tools and therapeutics for AD.

Sustained high-fat diet modulates inflammation, insulin signalling and cognition in mice and a modified xenin peptide ameliorates neuropathology in a chronic high-fat model

AIMS

To demarcate pathological events in the brain as a result of short-term to chronic high-fat-diet (HFD) feeding, which leads to cognitive impairment and neuroinflammation, and to assess the efficacy of Xenin-25[Lys(13)PAL] in chronic HFD-fed mice.

METHODS

C57BL/6 mice were fed an HFD or a normal diet for 18 days, 34 days, 10 and 21 weeks. Cognition was assessed using novel object recognition and the Morris water maze. Markers of insulin signalling and inflammation were measured in brain and plasma using immunohistochemistry, quantitative PCR and multi-array technology. Xenin-25[Lys(13)PAL] was also administered for 5 weeks in chronic HFD-fed mice to assess therapeutic potential at a pathological stage.

RESULTS

Recognition memory was consistently impaired in HFD-fed mice and spatial learning was impaired in 18-day and 21-week HFD-fed mice. Gliosis, oxidative stress and IRS-1 pSer⁶¹⁶ were increased in the brain on day 18 in HFD-fed mice and were reduced by Xenin-25[Lys(13)PAL] in 21-week HFD-fed mice. In plasma, HFD feeding elevated interleukin (IL)-6 and chemokine (C-X-C motif) ligand 1 at day 34 and IL-5 at week 10. In the brain, HFD feeding reduced extracellular signal-regulated kinase 2 (ERK2), mechanistic target of rapamycin (mTOR), NF-κB1, protein kinase C (PKC)θ and Toll-like receptor 4 (TLR4) mRNA at week 10 and increased expression of glucacon-like peptide-1 receptor, inhibitor of NF-κB kinase β, ERK2, mTOR, NF-κB1, PKCθ and TLR4 at week 21, elevations that were abrogated by Xenin-25[Lys(13)PAL].

CONCLUSIONS

HFD feeding modulates cognitive function, synapse density, inflammation and insulin resistance in the brain. Xenin-25[Lys(13)PAL] ameliorated markers of inflammation and insulin signalling dysregulation and may have therapeutic potential in the treatment of diseases associated with neuroinflammation or perturbed insulin signalling in the brain.

Alzheimer's disease-like pathology has transient effects on the brain and blood metabolome

The pathogenesis of Alzheimer's disease (AD) is complex involving multiple contributing factors. The extent to which AD pathology affects the metabolome is still not understood nor is it known how disturbances change as the disease progresses. For the first time, we have profiled longitudinally (6, 8, 10, 12, and 18 months) both the brain and plasma metabolome of APPswe/PS1deltaE9 double transgenic and wild-type mice. A total of 187 metabolites were quantified using a targeted metabolomic methodology. Multivariate statistical analysis produced models that distinguished APPswe/PS1deltaE9 from wild-type mice at 8, 10, and 12 months. Metabolic pathway analysis found perturbed polyamine metabolism in both brain and blood plasma. There were other disturbances in essential amino acids, branched-chain amino acids, and also in the neurotransmitter serotonin. Pronounced imbalances in phospholipid and acylcarnitine homeostasis were evident in 2 age groups. AD-like pathology, therefore, affects greatly on both the brain and blood metabolomes, although there appears to be a clear temporal sequence whereby changes to brain metabolites precede those in blood.

Restoration of cerebral and systemic microvascular architecture in APP/PS1 transgenic mice following treatment with Liraglutide™

OBJECTIVE

Cerebral microvascular impairments occurring in AD may reduce Aβ peptide clearance and impact upon circulatory ultrastructure and function. We hypothesized that microvascular pathologies occur in organs responsible for systemic Aβ peptide clearance in a model of AD and that Liraglutide (Victoza(®)) improves vessel architecture.

METHODS

Seven-month-old APP/PS1 and age-matched wild-type mice received once-daily intraperitoneal injections of either Liraglutide or saline (n = 4 per group) for eight weeks. Casts of cerebral, splenic, hepatic, and renal microanatomy were analyzed using SEM.

RESULTS

Casts from wild-type mice showed regularly spaced microvasculature with smooth lumenal profiles, whereas APP/PS1 mice revealed evidence of microangiopathies including cerebral microanuerysms, intracerebral microvascular leakage, extravasation from renal glomerular microvessels, and significant reductions in both splenic sinus density (p = 0.0286) and intussusceptive microvascular pillars (p = 0.0412). Quantification of hepatic vascular ultrastructure in APP/PS1 mice revealed that vessel parameters (width, length, branching points, intussusceptive pillars and microaneurysms) were not significantly different from wild-type mice. Systemic administration of Liraglutide reduced the incidence of cerebral microanuerysms and leakage, restored renal microvascular architecture and significantly increased both splenic venous sinus number (p = 0.0286) and intussusceptive pillar formation (p = 0.0129).

CONCLUSION

Liraglutide restores cerebral, splenic, and renal architecture in APP/PS1 mice.

Lixisenatide, a drug developed to treat type 2 diabetes, shows neuroprotective effects in a mouse model of Alzheimer's disease

Type 2 diabetes is a risk factor for developing Alzheimer's disease (AD). In the brains of AD patients, insulin signalling is desensitised. The incretin hormone Glucagon-like peptide-1 (GLP-1) facilitates insulin signalling, and analogues such as liraglutide are on the market as treatments for type 2 diabetes. We have previously shown that liraglutide showed neuroprotective effects in the APPswe/PS1ΔE9 mouse model of AD. Here, we test the GLP-1 receptor agonist lixisenatide in the same mouse model and compare the effects to liraglutide. After ten weeks of daily i.p. injections with liraglutide (2.5 or 25 nmol/kg) or lixisenatide (1 or 10 nmol/kg) or saline of APP/PS1 mice at an age when amyloid plaques had already formed, performance in an object recognition task was improved in APP/PS1 mice by both drugs at all doses tested. When analysing synaptic plasticity in the hippocampus, LTP was strongly increased in APP/PS1 mice by either drug. Lixisenatide (1 nmol/kg) was most effective. The reduction of synapse numbers seen in APP/PS1 mice was prevented by the drugs. The amyloid plaque load and dense-core Congo red positive plaque load in the cortex was reduced by both drugs at all doses. The chronic inflammation response (microglial activation) was also reduced by all treatments. The results demonstrate that the GLP-1 receptor agonists liraglutide and lixisenatide which are on the market as treatments for type 2 diabetes show promise as potential drug treatments of AD. Lixisenatide was equally effective at a lower dose compared to liraglutide in some of the parameters measured.

A novel retro-inverso peptide inhibitor reduces amyloid deposition, oxidation and inflammation and stimulates neurogenesis in the APPswe/PS1ΔE9 mouse model of Alzheimer's disease

Previously, we have developed a retro-inverso peptide inhibitor (RI-OR2, rGffvlkGr) that blocks the in vitro formation and toxicity of the Aβ oligomers which are thought to be a cause of neurodegeneration and memory loss in Alzheimer’s disease. We have now attached a retro-inverted version of the HIV protein transduction domain ‘TAT’ to RI-OR2 to target this new inhibitor (RI-OR2-TAT, Ac-rGffvlkGrrrrqrrkkrGy-NH₂) into the brain. Following its peripheral injection, a fluorescein-labelled version of RI-OR2-TAT was found to cross the blood brain barrier and bind to the amyloid plaques and activated microglial cells present in the cerebral cortex of 17-months-old APPswe/PS1ΔE9 transgenic mice. Daily intraperitoneal injection of RI-OR2-TAT (at 100 nmol/kg) for 21 days into 10-months-old APPswe/PS1ΔE9 mice resulted in a 25% reduction (p<0.01) in the cerebral cortex of Aβ oligomer levels, a 32% reduction (p<0.0001) of β-amyloid plaque count, a 44% reduction (p<0.0001) in the numbers of activated microglial cells, and a 25% reduction (p<0.0001) in oxidative damage, while the number of young neurons in the dentate gyrus was increased by 210% (p<0.0001), all compared to control APPswe/PS1ΔE9 mice injected with vehicle (saline) alone. Our data suggest that oxidative damage, inflammation, and inhibition of neurogenesis are all a downstream consequence of Aβ aggregation, and identify a novel brain-penetrant retro-inverso peptide inhibitor of Aβ oligomer formation for further testing in humans as a potential disease-modifying treatment for Alzheimer’s disease.

An anti-diabetes agent protects the mouse brain from defective insulin signaling caused by Alzheimer's disease- associated Aβ oligomers

Defective brain insulin signaling has been suggested to contribute to the cognitive deficits in patients with Alzheimer's disease (AD). Although a connection between AD and diabetes has been suggested, a major unknown is the mechanism(s) by which insulin resistance in the brain arises in individuals with AD. Here, we show that serine phosphorylation of IRS-1 (IRS-1pSer) is common to both diseases. Brain tissue from humans with AD had elevated levels of IRS-1pSer and activated JNK, analogous to what occurs in peripheral tissue in patients with diabetes. We found that amyloid-β peptide (Aβ) oligomers, synaptotoxins that accumulate in the brains of AD patients, activated the JNK/TNF-α pathway, induced IRS-1 phosphorylation at multiple serine residues, and inhibited physiological IRS-1pTyr in mature cultured hippocampal neurons. Impaired IRS-1 signaling was also present in the hippocampi of Tg mice with a brain condition that models AD. Importantly, intracerebroventricular injection of Aβ oligomers triggered hippocampal IRS-1pSer and JNK activation in cynomolgus monkeys. The oligomer-induced neuronal pathologies observed in vitro, including impaired axonal transport, were prevented by exposure to exendin-4 (exenatide), an anti-diabetes agent. In Tg mice, exendin-4 decreased levels of hippocampal IRS-1pSer and activated JNK and improved behavioral measures of cognition. By establishing molecular links between the dysregulated insulin signaling in AD and diabetes, our results open avenues for the investigation of new therapeutics in AD.

Val(8)GLP-1 rescues synaptic plasticity and reduces dense core plaques in APP/PS1 mice

Diabetes is a risk factor for Alzheimer's disease. We tested the effects of Val(8)GLP-1, an enzyme-resistant analogue of the incretin hormone glucagon-like peptide 1 originally developed to treat diabetes in a mouse model of Alzheimer's disease that expresses mutated amyloid precursor protein (APP) and presenilin-1. We tested long term potentiation (LTP) of synaptic plasticity, inflammation response, and plaque formation. Val(8)GLP-1 crosses the blood-brain barrier when administered via intraperitoneal injection. Val(8)GLP-1 protected LTP in 9- and 18-month-old Alzheimer's disease mice when given for 3 weeks at 25 nmol/kg intraperitoneally. LTP was also enhanced in 18-month-old wild type mice, indicating that Val(8)GLP-1 also ameliorates age-related synaptic degenerative processes. Paired-pulse facilitation was also enhanced. The number of beta-amyloid plaques and microglia activation in the cortex increased with age but was not reduced by Val(8)GLP-1. In 18-month-old mice, however, the number of Congo red positive dense-core amyloid plaques was reduced. Treatment with Val(8)GLP-1 might prevent or delay neurodegenerative processes.

Metabolic effects of sustained activation of the GLP-1 receptor alone and in combination with background GIP receptor antagonism in high fat-fed mice

AIM

Enzyme-resistant glucagon-like peptide-1 (GLP-1) receptor agonists and GIP receptor antagonists have been proposed to have therapeutic potential for the treatment of type 2 diabetes. Such benefits are based on actions mediated primarily through stimulation of insulin secretion or alleviation of insulin resistance respectively. This study examined the long-term actions of the stable GLP-1 receptor agonist (D-Ala(8))GLP-1 and the GIP receptor antagonist (Pro(3))GIP alone and in combination in high fat-fed mice.

METHODS

Mice on high-fat diet for 155 days were injected once daily with (D-Ala(8))GLP-1 or (Pro(3))GIP (25 nmol/kg body weight) for 24 days. In the following 24-day period, half of the (Pro(3))GIP-treated mice were administered an additional dose of (D-Ala(8))GLP-1 (25 nmol/kg body weight), while the remaining mice continued their original treatment regimes.

RESULTS

Daily intraperitoneal injections of (D-Ala(8))GLP-1 or (Pro(3))GIP restored glycaemic control to normal levels and significantly (p < 0.05) improved glucose tolerance compared with high-fat controls by day 24. Food intake and body weights were not affected. On day 48, all treatment groups displayed significantly improved glucose tolerance (p < 0.05) and insulin sensitivity (p < 0.001) compared with high-fat controls on day 48. HDL cholesterol levels were significantly increased in mice treated with (D-Ala(8))GLP-1 alone (p < 0.05) or in combination with (Pro(3))GIP (p < 0.01) compared with normal chow-fed controls.

CONCLUSIONS

These results illustrate efficacy of (Pro(3))GIP and (D-Ala(8))GLP-1 for treatment of glucose intolerance and insulin resistance caused by high-fat feeding. Combination therapy appeared to have little benefit over either treatment alone.

Active immunisation against gastric inhibitory polypeptide (GIP) improves blood glucose control in an animal model of obesity-diabetes

Recent research suggests that long-term ablation of gastric inhibitory polypeptide (GIP) receptor signalling can reverse or prevent many of the metabolic abnormalities associated with dietary and genetically induced obesity-diabetes. The present study was designed to assess the sub-chronic effects of passive or active immunisation against GIP in ob/ob mice. Initial acute administration of GIP antibody together with oral glucose in ob/ob mice significantly increased the glycaemic excursion compared to controls (p<0.05). This was associated with a significant reduction (p<0.05) in the overall glucose-mediated insulin response. However, sub-chronic passive GIP immunisation was not associated with any changes in body weight, food intake or metabolic control. In contrast, active immunisation against GIP for 56 days in young ob/ob mice resulted in significantly (p<0.05) reduced circulating plasma glucose concentrations on day 56 compared to controls. There was a tendency for decreased circulating insulin in GIP immunised mice. The glycaemic response to intraperitoneal glucose was correspondingly improved (p<0.05) in mice immunised against GIP. Glucose-stimulated insulin levels were not significantly different from controls. Furthermore, insulin sensitivity was similar in mice immunised against GIP and respective controls. Overall, the results reveal that active, as opposed to passive, immunisation against GIP improves blood glucose control ob/ob mice.

Daily administration of the GIP-R antagonist (Pro3)GIP in streptozotocin-induced diabetes suggests that insulin-dependent mechanisms are critical to anti-obesity-diabetes actions of (Pro3)GIP

AIM

Glucose-dependent insulinotropic polypeptide-receptor (GIP-R) antagonism using (Pro3)GIP improves glucose tolerance and ameliorates insulin resistance and abnormalities of islet structure and function in a commonly used model of obesity-diabetes, namely ob/ob mice. The effect of GIP-R antagonism in a streptozotocin (STZ)-induced model of insulin deficiency has not been evaluated. The present study has investigated the effects of daily administration of (Pro(3))GIP to STZ-treated mice.

METHODS

Swiss TO mice received once-daily injection of (Pro3)GIP (25 nmol/kg body weight) or saline 4 days prior to and 16 days after injection of STZ, and effects on metabolic parameters and islet architecture were assessed.

RESULTS

(Pro3)GIP treatment had no significant effect on hyperphagia or body weight loss. However, hyperglycaemia and glycated haemoglobin were worsened, glucose tolerance further decreased and insulin sensitivity was impaired by (Pro3)GIP. These effects were observed on an STZ-induced background characterized by severe reductions of circulating insulin, beta-cell mass and pancreatic insulin stores.

CONCLUSIONS

These data indicate that the beneficial actions of the GIP-R antagonist, (Pro3)GIP, in obesity-diabetes appear to be largely mediated through insulin-dependent mechanisms that merit further investigation.

GIP receptor antagonism reverses obesity, insulin resistance, and associated metabolic disturbances induced in mice by prolonged consumption of high-fat diet

The gut hormone gastric inhibitory polypeptide (GIP) plays a key role in glucose homeostasis and lipid metabolism. This study investigated the effects of administration of a stable and specific GIP receptor antagonist, (Pro(3))GIP, in mice previously fed a high-fat diet for 160 days to induce obesity and related diabetes. Daily intraperitoneal injection of (Pro(3))GIP over 50 days significantly decreased body weight compared with saline-treated controls, with a modest increase in locomotor activity but no change of high-fat diet intake. Plasma glucose, glycated hemoglobin, and pancreatic insulin were restored to levels of chow-fed mice, and circulating triglyceride and cholesterol were significantly decreased. (Pro(3))GIP treatment also significantly decreased circulating glucagon and corticosterone, but concentrations of GLP-1, GIP, resistin, and adiponectin were unchanged. Adipose tissue mass, adipocyte hypertrophy, and deposition of triglyceride in liver and muscle were significantly decreased. These changes were accompanied by significant improvement of insulin sensitivity, meal tolerance, and normalization of glucose tolerance in (Pro(3))GIP-treated high-fat-fed mice. (Pro(3))GIP concentrations peaked rapidly and remained elevated 24 h after injection. These data indicate that GIP receptor antagonism using (Pro(3))GIP provides an effective means of countering obesity and related diabetes induced by consumption of a high-fat, energy-rich diet.

Comparison of the anti-diabetic effects of GIP- and GLP-1-receptor activation in obese diabetic (ob/ob) mice: studies with DPP IV resistant N-AcGIP and exendin(1-39)amide

BACKGROUND

The two major incretin hormones, glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) are being actively explored as anti-diabetic agents because they lower blood glucose through multiple mechanisms. The rapid inactivation of GIP and GLP-1 by the ubiquitous enzyme, dipeptidyl peptidase IV (DPP IV) makes their biological actions short-lived, but stable agonists such as N-acetylated GIP (N-AcGIP) and exendin(1-39)amide have been advocated as stable and specific GIP and GLP-1 analogues.

METHODS

The present study examined the sub-chronic (14 days) anti-diabetic actions of single daily doses of N-AcGIP and exendin(1-39)amide given alone or in combination to obese diabetic (ob/ob) mice over a 14-day period.

RESULTS

Initial experiments confirmed the potent anti-hyperglycaemic and insulinotropic properties of N-AcGIP and exendin(1-39)amide. Sub-chronic administration of N-AcGIP alone or in combination with exendin(1-39)amide significantly decreased non-fasting plasma glucose and improved glucose tolerance compared to control ob/ob mice. This was associated with a significant enhancement of the insulin response to glucose and a notable improvement of insulin sensitivity. Combined treatment with N-AcGIP and exendin(1-39)amide also significantly decreased glycated haemoglobin. Exendin(1-39)amide alone had no significant effect on any of the metabolic parameters monitored. In addition, no significant effects were observed on body weight and food intake in any of the treatment groups.

CONCLUSIONS

The results illustrate significant anti-diabetic potential of N-AcGIP alone and in combination with exendin(1-39)amide.

Chemical gastric inhibitory polypeptide receptor antagonism protects against obesity, insulin resistance, glucose intolerance and associated disturbances in mice fed high-fat and cafeteria diets

AIMS/HYPOTHESIS

Gastric inhibitory polypeptide (GIP) receptor antagonism with (Pro(3))GIP improves glucose tolerance and ameliorates insulin resistance and abnormalities of islet structure/function in ob/ob mice. This study examined the ability of (Pro(3))GIP to counter the development of obesity, insulin resistance and diabetes in mice fed high-fat and cafeteria diets.

MATERIALS AND METHODS

Young Swiss TO mice on standard chow or high-fat, cafeteria or high-carbohydrate diets received daily injections of either saline or (Pro(3))GIP (25 nmol kg(-1)day(-1)) over 16 weeks. Food intake, body weight, and circulating glucose and insulin were measured frequently. At 16 weeks, glucose tolerance, insulin sensitivity, HbA(1c), circulating hormones and plasma lipids were assessed. Adipose tissue, liver and muscle were excised and weighed, and their histology and triacylglycerol content were further examined.

RESULTS

(Pro(3))GIP significantly reduced body weight, enhanced locomotor activity, and improved HbA(1c), glucose tolerance, beta cell responsiveness and insulin sensitivity in mice fed high-fat and cafeteria diets (p < 0.05 to p < 0.01). Similarly, (Pro(3))GIP significantly reduced plasma corticosterone and triacylglycerols (p < 0.05 to p < 0.001), while glucagon, resistin and adiponectin were unchanged. (Pro(3))GIP decreased adipose tissue mass (p < 0.01) and the triacylglycerol content of liver, muscle and adipose tissue (p < 0.01 to p < 0.001). Adipocyte size and liver morphology were partially normalised. (Pro(3))GIP did not significantly affect any of these parameters in mice fed a high-carbohydrate diet.

CONCLUSIONS/INTERPRETATION

(Pro(3))GIP protects against obesity, insulin resistance, glucose intolerance and associated disturbances in mice fed high-fat and cafeteria diets. This highlights chemical GIP receptor antagonism as a new possibility for the treatment of obesity and associated metabolic disturbances.

Early administration of the glucose-dependent insulinotropic polypeptide receptor antagonist (Pro3)GIP prevents the development of diabetes and related metabolic abnormalities associated with genetically inherited obesity in ob/ob mice

AIMS/HYPOTHESIS

Ablation of gastric inhibitory polypeptide (GIP) receptor action is reported to protect against obesity and associated metabolic abnormalities. The aim of this study was to use prediabetic ob/ob mice to examine whether 60 days of chemical GIP receptor ablation with (Pro(3))GIP is able to counter the development of genetic obesity-related diabetes.

MATERIALS AND METHODS

Young (5-7 weeks) ob/ob mice received once daily i.p. injections of either saline vehicle or (Pro(3))GIP (25 nmol kg(-1) day(-1)) over a 60 day period. Food intake, body weight and circulating glucose and insulin were measured at frequent intervals. At 60 days, glucose tolerance, response to native GIP, postprandial responses, insulin sensitivity, HbA(1c), circulating hormones and plasma lipids were assessed.

RESULTS

Body weight and food intake in (Pro(3))GIP-treated mice did not differ from ob/ob controls. GIP receptor blockade significantly improved non-fasting glucose (p < 0.001), HbA(1c) (p < 0.05), glucose tolerance (p < 0.001), meal tolerance (p < 0.001) and insulin sensitivity (p < 0.05). Remarkably, (Pro(3))GIP treatment prevented the age-related development of diabetes, as none of these parameters differed significantly between treated ob/ob mice and normal age-matched lean controls. Circulating levels of glucagon, corticosterone, adiponectin and total cholesterol were unchanged by (Pro(3))GIP, while levels of triacylglycerol, LDL-cholesterol and resistin were decreased (p < 0.05) compared with those in control ob/ob mice. Plasma and pancreatic insulin concentrations were generally lower after (Pro(3))GIP treatment than in control ob/ob mice (p < 0.01), but plasma insulin levels remained substantially raised (p < 0.001) compared with those observed in lean controls.

CONCLUSIONS/INTERPRETATION

These data indicate that sustained GIP receptor antagonism provides an effective means of preventing the development of many of the metabolic abnormalities of obesity-driven diabetes.

Effects of subchronic treatment with the long-acting glucose-dependent insulinotropic polypeptide receptor agonist, N-AcGIP, on glucose homeostasis in streptozotocin-induced diabetes

OBJECTIVES

N-AcGIP is a potent and dipeptidylpeptidase IV-resistant analogue of glucose-dependent insulinotropic polypeptide with significantly improved antidiabetic actions in type 2 diabetes. The present study investigated the effects of subchronic treatment with N-AcGIP on glucose homeostasis in a type 1 model, namely, streptozotocin (STZ)-induced diabetic mice.

METHODS

Swiss TO mice given a single intraperitoneal injection of STZ (150 mg/kg body weight) received once-daily injection of N-AcGIP (25 nmol/kg body weight) or saline for 20 days and effects on metabolic parameters and islet architecture assessed.

RESULTS

Daily injection of N-AcGIP for 20 days did not significantly alter the characteristic STZ-induced changes of pancreatic insulin content, body weight, food intake, glucose, and glycated hemoglobin levels. Glucose tolerance and insulin sensitivity were also unchanged by N-AcGIP treatment. Circulating insulin was undetectable, and the number of intact islets and insulin expression was greatly reduced in both groups. Some proliferative activity was identified by 5-bromo-2-deoxyuridine staining in the pancreas, but this and expression of glucagon and somatostatin were similar in the 2 groups.

CONCLUSIONS

These data indicate that subchronic treatment with the long-acting glucose-dependent insulinotropic polypeptide receptor agonist, N-AcGIP, does not have beneficial effects in insulin-deficient STZ-diabetic mice. This supports the primary antidiabetic action of this analogue in type 2 diabetes as stimulation of beta-cell function and insulin secretion.

Comparison of the subchronic antidiabetic effects of DPP IV–resistant GIP and GLP-1 analogues in obese diabetic (ob/ob) mice

Glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) are the two key incretin hormones released from the gastrointestinal tract that regulate blood glucose homeostasis through potent insulin secretion. The rapid degradation of GIP and GLP-1 by the ubiquitous enzyme dipeptidyl peptidase IV (DPP IV) renders both peptides noninsulinotropic. However, DPP IV stable agonists, such as N-AcGIP and (Val8)GLP-1, have now been developed. The present study has examined and compared the metabolic effects of subchronic administration of daily i.p. injections of N-AcGIP, (Val8) GLP-1 and a combination of both peptides (all at 25 nmol/kg bw) in obese diabetic (ob/ob) mice. Initial in vitro experiments confirmed the potent insulinotropic properties of N-AcGIP and (Val8)GLP-1 in the clonal pancreatic BRIN BD11 cell line. Subchronic administration of N-AcGIP, (Val8)GLP-1 or combined peptide administration had no significant effects on the body weight, food intake and plasma insulin concentrations. However, all treatment groups had significantly (p < 0.05) decreased plasma glucose levels and improved glucose tolerance by day 14. The effectiveness of the peptide groups was similar, and glucose concentrations were substantially reduced following injection of insulin to assess insulin sensitivity compared to control. These results provide evidence for an improvement of glucose homeostasis following treatment with enzyme-resistant GIP and GLP-1 analogues.