A review of the mechanisms underlying selected comorbidities in Alzheimer's disease

Development of the Dardashna Checklist for Identifying Triggers of Behavioral Change in Individuals With Dementia: A Qualitative Study

The purpose of the current qualitative study was to describe the development of the Dardashna Checklist to clinically identify behavior change triggers in individuals with dementia. Semi-structured, in-depth, face-to-face interviews were conducted with four physicians and four experienced caregivers involved in the care of individuals with dementia. From analysis of participants' interviews, themes extracted included Triggers of Behavioral Change in Individuals With Dementia and Types of Behavioral Changes, using the checklist structure as a guide. The information gathered by this checklist conveys important messages to experienced physicians or caregivers who want to help less experienced caregivers or individuals with dementia. In this case, physicians' prescriptions and the responses of other experienced caregivers will be more targeted and useful. This checklist will help facilitate clinical care decisions, improve quality of life, reduce expenses and side effects of medications, and improve communication among persons with dementia, their caregivers, and health care providers. [Journal of Gerontological Nursing, 50(1), 37-46.].

Alzheimer`s disease mortality in the United States: Cross-sectional analysis of county-level socio-environmental factors

BACKGROUND

Geographical disparities in mortality among Alzheimer`s disease (AD) patients have been reported and complex sociodemographic and environmental determinants of health (SEDH) may be contributing to this variation. Therefore, we aimed to explore high-risk SEDH factors possibly associated with all-cause mortality in AD across US counties using machine learning (ML) methods.

METHODS

We performed a cross-sectional analysis of individuals ≥65 years with any underlying cause of death but with AD in the multiple causes of death certificate (ICD-10,G30) between 2016 and 2020. Outcomes were defined as age-adjusted all-cause mortality rates (per 100,000 people). We analyzed 50 county-level SEDH and Classification and Regression Trees (CART) was used to identify specific county-level clusters. Random Forest, another ML technique, evaluated variable importance. CART`s performance was validated using a "hold-out" set of counties.

RESULTS

Overall, 714,568 individuals with AD died due to any cause across 2,409 counties during 2016-2020. CART identified 9 county clusters associated with an 80.1% relative increase of mortality across the spectrum. Furthermore, 7 SEDH variables were identified by CART to drive the categorization of clusters, including High School Completion (%), annual Particulate Matter 2.5 Level in Air, live births with Low Birthweight (%), Population under 18 years (%), annual Median Household Income in US dollars ($), population with Food Insecurity (%), and houses with Severe Housing Cost Burden (%).

CONCLUSION

ML can aid in the assimilation of intricate SEDH exposures associated with mortality among older population with AD, providing opportunities for optimized interventions and resource allocation to reduce mortality among this population.

DapBCH: a disease association prediction model Based on Cross-species and Heterogeneous graph embedding

The study of comorbidity can provide new insights into the pathogenesis of the disease and has important economic significance in the clinical evaluation of treatment difficulty, medical expenses, length of stay, and prognosis of the disease. In this paper, we propose a disease association prediction model DapBCH, which constructs a cross-species biological network and applies heterogeneous graph embedding to predict disease association. First, we combine the human disease-gene network, mouse gene-phenotype network, human-mouse homologous gene network, and human protein-protein interaction network to reconstruct a heterogeneous biological network. Second, we apply heterogeneous graph embedding based on meta-path aggregation to generate the feature vector of disease nodes. Finally, we employ link prediction to obtain the similarity of disease pairs. The experimental results indicate that our model is highly competitive in predicting the disease association and is promising for finding potential disease associations.

Multimorbidity Pattern and Risk for Mortality Among Patients With Dementia: A Nationwide Cohort Study Using Latent Class Analysis

OBJECTIVE

Individuals with dementia are at a substantially elevated risk for mortality; however, few studies have examined multimorbidity patterns and determined the inter-relationship between these comorbidities in predicting mortality risk.

METHODS

This is a prospective cohort study. Data from 6,556 patients who were diagnosed with dementia between 1997 and 2012 using the Taiwan National Health Insurance Research Database were analyzed. Latent class analysis was performed using 16 common chronic conditions to identify mortality risk among potentially different latent classes. Logistic regression was performed to determine the adjusted association of the determined latent classes with the 5-year mortality rate.

RESULTS

With adjustment for age, a three-class model was identified, with 42.7% of participants classified as "low comorbidity class (cluster 1)", 44.2% as "cardiometabolic multimorbidity class (cluster 2)", and 13.1% as "FRINGED class (cluster 3, characterized by FRacture, Infection, NasoGastric feeding, and bleEDing over upper gastrointestinal tract)." The incidence of 5-year mortality was 17.6% in cluster 1, 26.7% in cluster 2, and 59.6% in cluster 3. Compared with cluster 1, the odds ratio for mortality was 9.828 (95% confidence interval [CI]=6.708-14.401; p<0.001) in cluster 2 and 1.582 (95% CI=1.281-1.953; p<0.001) in cluster 3.

CONCLUSION

Among patients with dementia, the risk for 5-year mortality was highest in the subpopulation characterized by fracture, urinary and pulmonary infection, upper gastrointestinal bleeding, and nasogastric intubation, rather than cancer or cardiometabolic comorbidities. These findings may improve decision-making and advance care planning for patients with dementia.

Prevalence of treated patients with Alzheimer's disease: current trends and COVID-19 impact

BACKGROUND

There are few updated studies on the prevalence and management of Alzheimer's disease (AD), which could be underdiagnosed or undertreated. The COVID-19 pandemic may have worsened the deficiencies in the diagnosis and treatment of these patients. Electronic medical records (EMR) offer an opportunity to assess the impact and management of medical processes and contingencies in the population.

OBJECTIVE

To estimate AD prevalence in Spain over a 6-year period, based on treated patients, according to usual clinical practice. Additionally, to describe the management of AD-treated patients and the evolution of that treatment during the 2020 COVID-19 pandemic.

METHODS

Retrospective study using the Spanish IQVIA EMR database. Patients treated with donepezil, galantamine, rivastigmine, and/or memantine were included in the study. Annual AD prevalence (2015-2020) was estimated and extrapolated to the national population level. Most frequent treatments and involved specialties were described. To assess the effect of COVID-19, the incidence of new AD cases in 2020 was calculated and compared with newly diagnosed cases in 2019.

RESULTS

Crude AD prevalence (2015-2020) was estimated at 760.5 per 100,000 inhabitants, and age-standardized prevalence (2020) was 664.6 (male 595.7, female 711.0). Monotherapy was the most frequent way to treat AD (86.2%), in comparison with dual therapy (13.8%); rivastigmine was the most prescribed treatment (37.3%), followed by memantine (36.4%) and donepezil (33.0%). Rivastigmine was also the most utilized medication in newly treated patients (46.7%), followed by donepezil (29.8%), although donepezil persistence was longer (22.5 vs. 20.6 months). Overall, donepezil 10 mg, rivastigmine 9.5 mg, and memantine 20 mg were the most prescribed presentations. The incidence rate of AD decreased from 148.1/100,000 (95% confidence interval [CI] 147.0-149.2) in 2019 to 118.4/100,000 (95% CI 117.5-119.4) in 2020.

CONCLUSIONS

The obtained prevalence of AD-treated patients was consistent with previous face-to-face studies. In contrast with previous studies, rivastigmine, rather than donepezil, was the most frequent treatment. A decrease in the incidence of AD-treated patients was observed during 2020 in comparison with 2019, presumably due to the significant impact of the COVID-19 pandemic on both diagnosis and treatment. EMR databases emerge as valuable tools to monitor in real time the incidence and management of medical conditions in the population, as well as to assess the health impact of global contingencies and interventions.

A FinnGen pilot clinical recall study for Alzheimer's disease

Successful development of novel therapies requires that clinical trials are conducted in patient cohorts with the highest benefit-to-risk ratio. Population-based biobanks with comprehensive health and genetic data from large numbers of individuals hold promise to facilitate identification of trial participants, particularly when interventions need to start while symptoms are still mild, such as for Alzheimer's disease (AD). This study describes a process for clinical recall studies from FinnGen. We demonstrate the feasibility to systematically ascertain customized clinical data from FinnGen participants with ICD10 diagnosis of AD or mild cognitive disorder (MCD) in a single-center cross-sectional study testing blood-based biomarkers and cognitive functioning in-person, computer-based and remote. As a result, 19% (27/140) of a pre-specified FinnGen subcohort were successfully recalled and completed the study. Hospital records largely validated registry entries. For 8/12 MCD patients, other reasons than AD were identified as underlying diagnosis. Cognitive measures correlated across platforms, with highest consistencies for dementia screening (r = 0.818) and semantic fluency (r = 0.764), respectively, for in-person versus telephone-administered tests. Glial fibrillary acidic protein (GFAP) (p < 0.002) and phosphorylated-tau 181 (pTau-181) (p < 0.020) most reliably differentiated AD from MCD participants. We conclude that informative, customized clinical recall studies from FinnGen are feasible.

Clinical evidence of human pathogens implicated in Alzheimer's disease pathology and the therapeutic efficacy of antimicrobials: an overview

A wealth of pre-clinical reports and data derived from human subjects and brain autopsies suggest that microbial infections are relevant to Alzheimer's disease (AD). This has inspired the hypothesis that microbial infections increase the risk or even trigger the onset of AD. Multiple models have been developed to explain the increase in pathogenic microbes in AD patients. Although this hypothesis is well accepted in the field, it is not yet clear whether microbial neuroinvasion is a cause of AD or a consequence of the pathological changes experienced by the demented brain. Along the same line, the gut microbiome has also been proposed as a modulator of AD. In this review, we focus on human-based evidence demonstrating the elevated abundance of microbes and microbe-derived molecules in AD hosts as well as their interactions with AD hallmarks. Further, the direct-purpose and potential off-target effects underpinning the efficacy of anti-microbial treatments in AD are also addressed.

Diagnostic groups of hospital stays and outpatient visits during 10 years before Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD) is a major determinant of healthcare costs and increase in the healthcare service use occur already before the AD diagnosis. However, little is known how the different diagnosis categories contribute to this increase in healthcare use. We investigated how the hospitalizations and specialized healthcare outpatient visits from different diagnosis categories, based on the International Classification of Diseases (ICD-10) chapters, contribute to increased specialized healthcare service use during ten-year period preceding AD diagnosis.

METHODS

A register-based nationwide cohort of 42,934 community-dwelling persons who received clinically verified AD diagnosis in between 2008 and 2011 in Finland and 1:1 age, sex and hospital district- matched comparison cohort were included. Hospitalizations and specialized healthcare visits were categorized by the main diagnosis, according to the ICD-10 chapters. AD and dementia were separated to their own category. The number of persons with visits and stays was calculated for every 6 months, irrespective of the frequency of visits/stays individual had during that time window. Furthermore, the relative distribution of the diagnosis categories was computed.

RESULTS

AD cohort was more likely to have visits and stays during the 10-year period (OR 1.19, 95% CI 1.17-1.21). The number of persons with visits and stays peaked in AD cohort from 1.5 years before the diagnosis when the differences in relative distribution of different diagnosis categories also became evident. The largest differences were observed for visits/stays with cognitive disorders, symptoms of unspecified diseases and psychiatric disorders diagnoses, and those with missing diagnosis codes in the last time window before AD diagnosis.

CONCLUSIONS AND IMPLICATIONS

Increased healthcare service use before AD diagnosis does not seem to arise from differences in specific diagnosis categories of ICD-10 such as diseases of the circulatory system, but from the higher frequency of visits and stays among persons with AD across diagnosis categories. Based on the relative distribution of diagnosis categories, the steep increase in healthcare service use just before and during the diagnostic process is likely due to prodromal symptoms and visits related to cognition.

Alzheimer's Disease: A Brief History of Immunotherapies Targeting Amyloid β

Alzheimer's disease (AD) is the most common form of dementia and may contribute to 60-70% of cases. Worldwide, around 50 million people suffer from dementia and the prediction is that the number will more than triple by 2050, as the population ages. Extracellular protein aggregation and plaque deposition as well as accumulation of intracellular neurofibrillary tangles, all leading to neurodegeneration, are the hallmarks of brains with Alzheimer's disease. Therapeutic strategies including active and passive immunizations have been widely explored in the last two decades. Several compounds have shown promising results in many AD animal models. To date, only symptomatic treatments are available and because of the alarming epidemiological data, novel therapeutic strategies to prevent, mitigate, or delay the onset of AD are required. In this mini-review, we focus on our understanding of AD pathobiology and discuss current active and passive immunomodulating therapies targeting amyloid-β protein.

A nomogram for predicting risk of death during hospitalization in elderly patients with Alzheimer's disease at the time of admission

Background and objectives

Elderly patients with Alzheimer's disease (AD) often have multiple underlying disorders that lead to frequent hospital admissions and are associated with adverse outcomes such as in-hospital mortality. The aim of our study was to develop a nomogram to be used at hospital admission for predicting the risk of death in patients with AD during hospitalization.

Methods

We established a prediction model based on a dataset of 328 patients hospitalized with AD -who were admitted and discharged from January 2015 to December 2020. A multivariate logistic regression analysis method combined with a minimum absolute contraction and selection operator regression model was used to establish the prediction model. The identification, calibration, and clinical usefulness of the predictive model were evaluated using the C-index, calibration diagram, and decision curve analysis. Internal validation was evaluated using bootstrapping.

Results

The independent risk factors included in our nomogram were diabetes, coronary heart disease (CHD), heart failure, hypotension, chronic obstructive pulmonary disease (COPD), cerebral infarction, chronic kidney disease (CKD), anemia, activities of daily living (ADL) and systolic blood pressure (SBP). The C-index and AUC of the model were both 0.954 (95% CI: 0.929-0.978), suggesting that the model had accurate discrimination ability and calibration. Internal validation achieved a good C-index of 0.940.

Conclusion

The nomogram including the comorbidities (i.e., diabetes, CHD, heart failure, hypotension, COPD, cerebral infarction, anemia and CKD), ADL and SBP can be conveniently used to facilitate individualized identification of risk of death during hospitalization in patients with AD.

Early cognitive comorbidities before disease onset: A common symptom towards prevention of related brain diseases?

Brain diseases are very heterogeneous; however they also display multiple common risk factors and comorbidities. With a paucity of disease-modifying therapies, prevention became a health priority. Towards prevention, one strategy is to focus on similar symptoms of brain diseases occurring before disease onset. Cognitive deficits are a promising candidate as they occur across brain diseases before disease onset. Based on recent research, this review highlights the similarity of brain diseases and discusses how early cognitive deficits can be exploited to tackle disease prevention. After briefly introducing common risk factors, I review common comorbidities across brain diseases, with a focus on cognitive deficits before disease onset, reporting both experimental and clinical findings. Next, I describe network abnormalities associated with early cognitive deficits and discuss how these abnormalities can be targeted to prevent disease onset. A scenario on brain disease etiology with the idea that early cognitive deficits may constitute a common symptom of brain diseases is proposed.

A large-scale genome-wide cross-trait analysis reveals shared genetic architecture between Alzheimer's disease and gastrointestinal tract disorders

Consistent with the concept of the gut-brain phenomenon, observational studies suggest a relationship between Alzheimer's disease (AD) and gastrointestinal tract (GIT) disorders; however, their underlying mechanisms remain unclear. Here, we analyse several genome-wide association studies (GWAS) summary statistics (N = 34,652-456,327), to assess the relationship of AD with GIT disorders. Findings reveal a positive significant genetic overlap and correlation between AD and gastroesophageal reflux disease (GERD), peptic ulcer disease (PUD), gastritis-duodenitis, irritable bowel syndrome and diverticulosis, but not inflammatory bowel disease. Cross-trait meta-analysis identifies several loci (P_{meta-analysis} < 5 × 10^-8) shared by AD and GIT disorders (GERD and PUD) including PDE4B, BRINP3, ATG16L1, SEMA3F, HLA-DRA, SCARA3, MTSS2, PHB, and TOMM40. Colocalization and gene-based analyses reinforce these loci. Pathway-based analyses demonstrate significant enrichment of lipid metabolism, autoimmunity, lipase inhibitors, PD-1 signalling, and statin mechanisms, among others, for AD and GIT traits. Our findings provide genetic insights into the gut-brain relationship, implicating shared but non-causal genetic susceptibility of GIT disorders with AD's risk. Genes and biological pathways identified are potential targets for further investigation in AD, GIT disorders, and their comorbidity.

Immune Response at the Crossroads of Atherosclerosis and Alzheimer's Disease

Alzheimer's disease (AD) and cardiovascular disease (CVD) are pathologies that are characterized by common signatures of vascular dysfunction and chronic inflammation that are accelerated with aging. Importantly, epidemiological studies report an independent interaction between AD and CVD and data suggest that chronic inflammation in CVD may accelerate AD development. Atherosclerosis affects most large to medium sized arteries including those supplying the cerebral circulation. Vascular dysfunction caused by atherosclerosis results in blood brain barrier breakdown, inflammation, an impaired clearance of amyloid-beta (Aβ), and finally ends with neurovascular dysfunction. Numerous data indicate that innate and adaptive immune responses shape atherogenesis and increasing evidence suggests an implication of the immune response in AD progression. Currently, mechanisms by which these two diseases are interconnected with each other are not well-defined. In this review, we discuss the recent advances in our understanding of the intertwined role of the immune response in atherosclerosis and AD and the implications of these findings for human health.

Novel Cyclopentaquinoline and Acridine Analogs as Multifunctional, Potent Drug Candidates in Alzheimer's Disease

A series of new cyclopentaquinoline derivatives with 9-acridinecarboxylic acid and a different alkyl chain length were synthesized, and their ability to inhibit cholinesterases was evaluated. All designed compounds, except derivative 3f, exhibited a selectivity for butyrylcholinesterase (BuChE) with IC₅₀ values ranging from 103 to 539 nM. The 3b derivative revealed the highest inhibitory activity towards BuChE (IC₅₀ = 103.73 nM) and a suitable activity against AChE (IC₅₀ = 272.33 nM). The 3f derivative was the most active compound to AChE (IC₅₀ = 113.34 nM) with satisfactory activity towards BuChE (IC₅₀ = 203.52 nM). The potential hepatotoxic effect was evaluated for both 3b and 3f compounds. The 3b and 3f potential antioxidant activity was measured using the ORAC-FL method. The 3b and 3f derivatives revealed a significantly higher antioxidant potency, respectively 35 and 25 higher than tacrine. Theoretical, physicochemical, and pharmacokinetic properties were calculated using ACD Labs Percepta software. Molecular modeling and kinetic study were used to reveal the mechanism of cholinesterase inhibition in the most potent compounds: 3b and 3f.

Stress-Induced Depression and Alzheimer's Disease: Focus on Astrocytes

Neurodegenerative diseases and depression are multifactorial disorders with a complex and poorly understood physiopathology. Astrocytes play a key role in the functioning of neurons in norm and pathology. Stress is an important factor for the development of brain disorders. Here, we review data on the effects of stress on astrocyte function and evidence of the involvement of astrocyte dysfunction in depression and Alzheimer's disease (AD). Stressful life events are an important risk factor for depression; meanwhile, depression is an important risk factor for AD. Clinical data indicate atrophic changes in the same areas of the brain, the hippocampus and prefrontal cortex (PFC), in both pathologies. These brain regions play a key role in regulating the stress response and are most vulnerable to the action of glucocorticoids. PFC astrocytes are critically involved in the development of depression. Stress alters astrocyte function and can result in pyroptotic death of not only neurons, but also astrocytes. BDNF-TrkB system not only plays a key role in depression and in normalizing the stress response, but also appears to be an important factor in the functioning of astrocytes. Astrocytes, being a target for stress and glucocorticoids, are a promising target for the treatment of stress-dependent depression and AD.

Genetically modified mice for research on human diseases: A triumph for Biotechnology or a work in progress?

Genetically modified mice are engineered as models for human diseases. These mouse models include inbred strains, mutants, gene knockouts, gene knockins, and ‘humanized’ mice. Each mouse model is engineered to mimic a specific disease based on a theory of the genetic basis of that disease. For example, to test the amyloid theory of Alzheimer’s disease, mice with amyloid precursor protein genes are engineered, and to test the tau theory, mice with tau genes are engineered. This paper discusses the importance of mouse models in basic research, drug discovery, and translational research, and examines the question of how to define the “best” mouse model of a disease. The critiques of animal models and the caveats in translating the results from animal models to the treatment of human disease are discussed. Since many diseases are heritable, multigenic, age-related and experience-dependent, resulting from multiple gene-gene and gene-environment interactions, it will be essential to develop mouse models that reflect these genetic, epigenetic and environmental factors from a developmental perspective. Such models would provide further insight into disease emergence, progression and the ability to model two-hit and multi-hit theories of disease. The summary examines the biotechnology for creating genetically modified mice which reflect these factors and how they might be used to discover new treatments for complex human diseases such as cancers, neurodevelopmental and neurodegenerative diseases.

Improving mouse models for the study of Alzheimer's disease

Alzheimer's disease (AD) is a complex neurodegenerative disease whose risk is influenced by genetic and environmental factors. Although a number of pathological hallmarks have been extensively studied over the last several decades, a complete picture of disease initiation and progression remains unclear. We now understand that numerous cell types and systems are involved in AD pathogenesis, and that this cellular profile may present differently for each individual, making the creation of relevant mouse models challenging. However, with increasingly diverse data made available by genome-wide association studies, we can identify and examine new genes and pathways involved in genetic risk for AD, many of which involve vascular health and inflammation. When developing mouse models, it is critical to assess (1) an aging timeline that represents onset and progression in humans, (2) genetic variants and context, (3) environmental factors present in human populations that result in both neuropathological and functional changes-themes that we address in this chapter.

Sceletium tortuosum: A review on its phytochemistry, pharmacokinetics, biological, pre-clinical and clinical activities

ETHNOPHARMACOLOGICAL RELEVANCE

Sceletium tortuosum (L.) N.E.Br., the most sought after and widely researched species in the genus Sceletium is a succulent forb endemic to South Africa. Traditionally, this medicinal plant is mainly masticated or smoked and used for the relief of toothache, abdominal pain, as a mood-elevator, analgesic, hypnotic, anxiolytic, thirst and hunger suppressant, and for its intoxicating/euphoric effects. Sceletium tortuosum is currently of widespread scientific interest due to its clinical potential in treating anxiety and depression, relieving stress in healthy individuals, and enhancing cognitive functions. These pharmacological actions are attributed to its phytochemical constituents referred to as mesembrine-type alkaloids.

AIM OF THE REVIEW

The aim of this review was to comprehensively summarize and critically evaluate recent research advances on the phytochemistry, pharmacokinetics, biological, pre-clinical and clinical activities of the medicinal plant S. tortuosum. Additionally, current ongoing research and future perspectives are also discussed.

METHODS

All relevant scientific articles, books, MSc and Ph.D. dissertations on botany, behavioral pharmacology, traditional uses, and phytochemistry of S. tortuosum were retrieved from different databases (including Science Direct, PubMed, Google Scholar, Scopus and Web of Science). For pharmacokinetics and pharmacological effects of S. tortuosum, the focus fell on relevant publications published between 2009 and 2021.

RESULTS

Twenty-five alkaloids belonging to four structural classes viz: mesembrine, Sceletium A4, joubertiamine, and tortuosamine, have been identified from S. tortuosum, of which the mesembrine class is predominant. The crude extracts and commercially available standardized extracts of S. tortuosum have displayed a wide spectrum of biological activities (e.g. antimalarial, anti-oxidant, neuromodulatory, immunomodulatory, anti-HIV, neuroprotection) in in vitro or in vivo studies. While the plant has been studied in clinical populations, this has only been in healthy subjects, so that further study in pathological states remains to be done. Nevertheless, the aforementioned studies have demonstrated that S. tortuosum has potential for enhancing cognitive function and managing anxiety and depression.

CONCLUSION

As an important South African medicinal plant, S. tortuosum has garnered many research advances on its phytochemistry and biological activities over the last decade. These scientific studies have shown that S. tortuosum has various bioactivities. The findings have further established the link between the phytochemistry and pharmacological application, and support the traditional use of S. tortuosum in the indigenous medicine of South Africa.

On the relationships between epilepsy, sleep, and Alzheimer's disease: A narrative review

Epilepsy, sleep, and Alzheimer's disease (AD) are tightly and potentially causally interconnected. The aim of our review was to investigate current research directions on these relationships. Our hope is that they may indicate preventive measures and new treatment options for early neurodegeneration. We included articles that assessed all three topics and were published during the last ten years. We found that this literature corroborates connections on various pathophysiological levels, including sleep-stage-related epileptiform activity in AD, the negative consequences of different sleep disorders on epilepsy and cognition, common biochemical pathways as well as network dysfunctions. Here we provide a detailed overview of these topics and we discuss promising diagnostic and therapeutic consequences.

Five Major Psychiatric Disorders and Alzheimer’s Disease: A Bidirectional Mendelian Randomization Study

Background: Extensive studies put forward the association between Alzheimer’s disease (AD) and psychiatric disorders; however, it remains unclear whether these associations are causal. Objective: We aimed to assess the potential causal relationship between major psychiatric disorders and AD. Methods: A bidirectional two-sample Mendelian randomization (MR) was applied to evaluate potential causality between five psychiatric disorders and AD by selecting the single-nucleotide polymorphisms from the genome-wide association studies as instrumental variables. Inverse-variance weighted (IVW) method was used as the main analyzing approach to estimate possible causal effects, alternative methods including MR-Egger, the MR pleiotropy residual sum and outlier, and leave-one-out analysis method were implemented as sensitivity analyzing approaches to ensure the robustness of results. Results: All forward and reverse MR analyses consistently suggested absent causal relations between psychiatric disorders and AD risk [forward IVW: ORADHD, 1.030, 95% CI, 0.908–1.168, p = 0.674; ORanxiety disorders, 0.904, 95% CI, 0.722–1.131, p = 0.377; ORASD, 0.973, 95% CI, 0.746–1.272, p = 0.846; ORBIP, 1.033, 95% CI, 0.925–1.153, p = 0.564; and ORschizophrenia, 1.039, 95% CI, 0.986–1.095, p = 0.156; reverse IVW: ORADHD, 0.993, 95% CI, 0.954–1.034, p = 0.746; ORanxiety disorders, 1.000, 95% CI, 0.999–1.000, p = 0.898; ORASD, 1.001, 95% CI, 0.962–1.042, p = 0.949; ORBIP, 0.997, 95% CI, 0.966–1.028, p = 0.831; and ORschizophrenia, 1.013, 95% CI, 0.978–1.051, p = 0.466]. Conclusion: There is no significant evidence supporting the causal association between the five major psychiatric disorders and AD.

Etiology and management of Alzheimer's disease: Potential role of gut microbiota modulation with probiotics supplementation

Alzheimer's disease (AD) is the leading type of dementia in aging people and is a progressive condition that causes neurodegeneration, resulting in confusion, memory loss, and deterioration of mental functions. AD happens because of abnormal twisting of the microtubule tau protein in neurons into a tangled neurofibrillary structure. Different factors responsible for AD pathogenesis include heavy metals, aging, cardiovascular disease, and environmental and genetic factors. Market available drugs for AD have several side effects that include hepato-toxicity, accelerated cognitive decline, worsened neuropsychiatric symptoms, and triggered suicidal ideation. Therefore, an emerging alternative therapeutic approach is probiotics, which can improve AD by modulating the gut-brain axis. Probiotics modulate different neurochemical pathways by regulating the signalling pathways associated with inflammation, histone deacetylation, and microglial cell activation and maturation. In addition, probiotics-derived metabolites (i.e., short-chain fatty acid, neurotransmitters, and antioxidants) have shown ameliorative effects against AD. Probiotics also modulate gut microbiota, with a beneficial impact on neural signalling and cognitive activity, which can attenuate AD progression. Therefore, the current review describes the etiology and mechanism of AD progression as well as various treatment options with a focus on the use of probiotics. PRACTICAL APPLICATIONS: In an aging population, dementia concerns are quite prevalent globally. AD is one of the most commonly occurring cognition disorders, which is linked to diminished brain functions. Scientific evidence supports the findings that probiotics and gut microbiota can regulate/modulate brain functions, one of the finest strategies to alleviate such disorders through the gut-brain axis. Thus, gut microbiota modulation, especially through probiotic supplementation, could become an effective solution to ameliorate AD.