Development, Application, and Results from a Precision-medicine Platform that Personalizes Multi-modal Treatment Plans for Mild Alzheimer's Disease and At-risk Individuals

Stratification of the Gut Microbiota Composition Landscape across the Alzheimer's Disease Continuum in a Turkish Cohort

Alzheimer's disease (AD) is a heterogeneous disorder that spans a continuum with multiple phases, including preclinical, mild cognitive impairment, and dementia. Unlike for most other chronic diseases, human studies reporting on AD gut microbiota in the literature are very limited. With the scarcity of approved drugs for AD therapies, the rational and precise modulation of gut microbiota composition using diet and other tools is a promising approach to the management of AD. Such an approach could be personalized if an AD continuum can first be deconstructed into multiple strata based on specific microbiota features by using single or multiomics techniques. However, stratification of AD gut microbiota has not been systematically investigated before, leaving an important research gap for gut microbiota-based therapeutic approaches. Here, we analyze 16S rRNA amplicon sequencing of stool samples from 27 patients with mild cognitive impairment, 47 patients with AD, and 51 nondemented control subjects by using tools compatible with the compositional nature of microbiota. To stratify the AD gut microbiota community, we applied four machine learning techniques, including partitioning around the medoid clustering and fitting a probabilistic Dirichlet mixture model, the latent Dirichlet allocation model, and we performed topological data analysis for population-scale microbiome stratification based on the Mapper algorithm. These four distinct techniques all converge on Prevotella and Bacteroides stratification of the gut microbiota across the AD continuum, while some methods provided fine-scale resolution in stratifying the community landscape. Finally, we demonstrate that the signature taxa and neuropsychometric parameters together robustly classify the groups. Our results provide a framework for precision nutrition approaches aiming to modulate the AD gut microbiota. IMPORTANCE The prevalence of AD worldwide is estimated to reach 131 million by 2050. Most disease-modifying treatments and drug trials have failed, due partly to the heterogeneous and complex nature of the disease. Recent studies demonstrated that gut dybiosis can influence normal brain function through the so-called "gut-brain axis." Modulation of the gut microbiota, therefore, has drawn strong interest in the clinic in the management of the disease. However, there is unmet need for microbiota-informed stratification of AD clinical cohorts for intervention studies aiming to modulate the gut microbiota. Our study fills in this gap and draws attention to the need for microbiota stratification as the first step for microbiota-based therapy. We demonstrate that while Prevotella and Bacteroides clusters are the consensus partitions, the newly developed probabilistic methods can provide fine-scale resolution in partitioning the AD gut microbiome landscape.

Heterogeneity and Factorial Structure in Alzheimer's Disease: A Cognitive Perspective

BACKGROUND

Alzheimer's disease (AD) patients show heterogeneous cognitive profiles which suggest the existence of cognitive subgroups. A deeper comprehension of this heterogeneity could contribute to move toward a precision medicine perspective.

OBJECTIVE

In this study, we aimed 1) to investigate AD cognitive heterogeneity as a product of the combination of within- (factors) and between-patients (sub-phenotypes) components, and 2) to promote its assessment in clinical practice by defining a small set of critical tests for this purpose.

METHODS

We performed factor mixture analysis (FMA) on neurocognitive assessment results of N = 230 patients with a clinical diagnosis of AD. This technique allowed to investigate the structure of cognitive heterogeneity in this sample and to characterize the core features of cognitive sub-phenotypes. Subsequently, we performed a tests selection based on logistic regression to highlight the best tests to detect AD patients in our sample. Finally, the accuracy of the same tests in the discrimination of sub-phenotypes was evaluated.

RESULTS

FMA revealed a structure characterized by five latent factors and four groups, which were identifiable by means of a few cognitive tests and were mainly characterized by memory deficits with visuospatial difficulties ("Visuospatial AD"), typical AD cognitive pattern ("Typical AD"), less impaired memory ("Mild AD"), and language/praxis deficits with relatively spared memory ("Nonamnestic AD").

CONCLUSION

The structure of cognitive heterogeneity in our sample of AD patients, as studied by FMA, could be summarized by four sub-phenotypes with distinct cognitive characteristics easily identifiable in clinical practice. Clinical implications under the precision medicine framework are discussed.

How to face the aging world - lessons from dementia research

A continuous rise in life expectancy has led to an increase in the number of senior citizens, now amounting to a fifth of the global population, and to a dramatic increase in the prevalence of diseases of the elderly. This review discusses the threat of dementia, a disease that imposes enormous financial burden on health systems and warrants efficient therapeutic solutions. What we learned from numerous failed clinical trials is that we have to immediately take into account two major elements: early detection of dementia, much before the onset of symptoms, and personalized (precision) medicine treatment approach. We also discuss some of the most promising therapeutic directions, including stem cells, exosomes, electromagnetic fields, and ozone.

Perceptions of precision medicine among diverse dementia caregivers and professional providers

Introduction

Underrepresented groups experience health disparities and a history of exploitation by researchers and the health-care system that may contribute to distrust of new treatments and technologies. This study aims to understand how diverse family caregivers and health-care professionals view the benefits and risks of precision medicine as well as cultural dimensions to consider when developing and implementing precision medicine interventions in dementia care.

Methods

Eight focus group sessions and one individual interview were conducted over a 6-month period. Fifty-four focus group participants included African-American, American Indian, rural Caucasian, Latino, and West African caregivers and health professionals. The majority of participants were female (73%) and were of Hispanic/Latino ethnicity (68%). About a third of participants identified their race as white. Participants were presented with four hypothetical scenarios related to precision medicine diagnostic and treatment approaches in dementia care: (1) genetic testing for dementia risk, (2) health-care informatics to determine individualized medication dosages based on health and family history, (3) a smartphone application providing dementia caregiving tips, and (4) remote activity monitoring technology in the home. Focus groups' responses were coded using thematic analysis.

Results

Participants indicated skepticism regarding the use of precision medicine in their communities. Concerns included cost of precision medicine and insurance coverage; lack of alignment with cultural norms; fraught relationships between communities, health professionals, and researchers; data ownership and privacy; and the trade-off between knowing risk and treatment benefit.

Discussion

Establishing relationships with underserved communities is crucial to advancing precision medicine in dementia care. Appropriate engagement with diverse racial, ethnic, and geographic communities may require significant investment but is necessary to deliver precision medicine effectively.

Polypharmacy in an Elderly Population: Enhancing Medication Management Through the Use of Clinical Decision Support Software Platforms

INTRODUCTION

Polypharmacy is a growing problem in the United States. The use of multiple medications increases the likelihood that a patient will experience potential drug interactions and adverse drug reactions (ADRs). Those individuals with dementia or Alzheimer's disease (AD) are at greater risk, due to age, comorbidities, and an increased likelihood of being on a greater number of neuroactive medications.

METHODS

uMETHOD Health (uMH) has developed a precision medicine platform to address dementia and mild AD through the creation of personalized, multidomain treatment plans. Many interactions and ADRs may be observed, such as drug-drug interactions (DDIs), drug-gene interactions (DGIs), anticholinergic cognitive burden (ACB), and depression-inducing drugs (DIDs). uMH's algorithms can parse these interactions, rate them based on input from open-source databases, and then record all these interactions in a generated treatment plan. A total of 295 individuals aged 65 and older were included in this analysis.

RESULTS

Of 295 individuals, 97.59% were on at least one medication, with an overall mean of 11.5 medications per person; 83.66% were on five or more medications. A total of 102 DGIs, 3642 DDIs, and one high-priority DDI were found in this population. There was a significant increase in the number of DDIs as medications per person increased (P value < 0.0001). Of the population, 65.86% were on one or more anticholinergic drugs. There was a significant difference in the ACB score between individuals with cognitive decline and those without. In total, 60.98% of the overall population were on DIDs, with a mean of 1.19 medications per person.

CONCLUSIONS

The results of this work show that older populations have a high medication burden. With the growing elderly and AD populations, medication management for polypharmacy is a need that grows direr every year. uMH's platform was able to identify a multitude of polypharmacy problems that individuals are currently facing.

FUNDING

uMETHOD Health.