Mechanisms of Risk Reduction in the Clinical Practice of Alzheimer's Disease Prevention

Impact of mechanical ventilation on clinical outcomes in ICU-admitted Alzheimer's disease patients: a retrospective cohort study

Background

Alzheimer's disease (AD) is increasingly recognized as a pressing global public health issue, demanding urgent development of scientific AD management strategies. In recent years, the proportion of AD patients in Intensive Care Units (ICU) has been on the rise. Simultaneously, the use of mechanical ventilation (MV) is becoming more prevalent among this specific patient group. Considering the pathophysiological characteristics of AD, the application of MV in AD patients may lead to different outcomes. However, due to insufficient research data, the significant impact of MV on the prognosis of AD patients in the ICU remains unclear. Therefore, we conducted this study to comprehensively evaluate the potential influence of MV on the survival rate of AD patients in the ICU.

Methods

We obtained data from the MIMIC-IV database for patients diagnosed with AD. Using propensity score matching (PSM), we paired patients who received MV treatment with those who did not receive treatment. Next, we conducted Cox regression analysis to evaluate the association between MV and in-hospital mortality, 7-day mortality, 28-day mortality, 90-day mortality, 4-year mortality, length of hospital stay, and ICU stay.

Results

The data analysis involved a cohort of 641 AD patients spanning from 2008 to 2019, inclusive. Following a 1:2 propensity score matching (PSM) procedure, 300 patients were successfully paired, comprising 123 individuals who underwent MV treatment and 177 who did not. MV demonstrated an association with an elevated risk of in-hospital mortality (HR 5.782; 95% CI 2.981-11.216; p < 0.001), 7-day mortality (HR 6.353; 95% CI 3.014-13.392; p < 0.001), 28-day mortality (HR 3.210; 95% CI 1.977-5.210; p < 0.001), 90-day mortality (HR 2.334; 95% CI 1.537-3.544; p < 0.001), and 4-year mortality (HR 1.861; 95% CI 1.370-2.527; p < 0.001). Furthermore, it was associated with a prolonged length of ICU stay [3.6(2.2,5.8) vs. 2.2(1.6,3.7); p = 0.001]. In the subgroup analysis, we further confirmed the robustness of the results obtained from the overall population. Additionally, we observed a significant interaction (p-interaction <0.05) between age, admission type, aspirin use, statin use, and the use of MV.

Conclusion

In patients with AD who are receiving treatment in the ICU, the use of MV has been linked to higher short-term, medium-term, and long-term mortality rates, as well as prolong ICU stays. Therefore, it is crucial to break away from conventional thinking and meticulously consider both the medical condition and personal preferences of these vulnerable patients. Personalized treatment decisions, comprehensive communication between healthcare providers and patients, formulation of comprehensive treatment plans, and a focus on collaboration between the ICU and community organizations become imperative.

Combining pathological and cognitive tests scores: A novel data analytics process to improve dementia prediction models1

BACKGROUND

The term 'dementia' covers a range of progressive brain diseases from which many elderly people suffer. Traditional cognitive and pathological tests are currently used to detect dementia, however, applications using Artificial Intelligence (AI) methods have recently shown improved results from improved detection accuracy and efficiency.

OBJECTIVE

This research paper investigates the efficacy of one type of data analytics called supervised learning to detect Alzheimer's disease (AD) - a common dementia condition.

METHODS

The aim is to evaluate cognitive tests and common biological markers (biomarkers) such as cerebrospinal fluid (CSF) to develop predictive classification systems for dementia detection.

RESULTS

A data analytics process has been proposed, implemented, and tested against real data obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) repository.

CONCLUSION

The models showed good power in predicting AD levels, notably from specified cognitive tests' scores and tauopathy related features.

Baseline Profiles of Drug Prescriptions Prior to Diagnosis of Mild Cognitive Impairment (MCI) Obtained by Latent Class Analysis (LCA), and Assessment of Their Association with Conversion to Dementia

Polypharmacy has been linked to cognitive decline. However, interventions targeting modifiable risk factors, some of which are targets of the most commonly used drugs, could reduce the prevalence of dementia. Our aim was to determine the drug prescription regimen at baseline, prior to the diagnosis of mild cognitive impairment (MCI), and its possible association with progression to dementia. Data were collected from the electronic medical records of 342 MCI outpatients diagnosed during 2006-2017 at their first neurology consultation. We followed the classical three-step method of statistical analysis, starting with a Latent Class Analysis (LCA) to discover subgroups of drug prescription probability. Half of the patients were under polypharmacy (≥5 drugs), 17.5% had no recorded medication, 33.3% progressed to dementia (94.7% in ≤5 years), and 84.1% of them to Alzheimer's disease (AD). According to the LCA and based on 20 therapeutic indicators obtained from 240 substances and regrouped according the Anatomical Therapeutic Chemical Classification, we identified a four-profile model: (1) low (35.7% of patients); (2) mixed (28.7%); (3) cardio-metabolic (19.3%); and (4) psychotropic (16.4%). The binomial regression logistic model showed that profiles 2 and 3 (and 4 for AD), with a higher drug prescription conditioned probability against classic risk factors, were protective than profile 1 (OR = 0.421, p = 0.004; OR = 0.278, p = 0.000; OR = 0.457, p = 0.040, respectively), despite polypharmacy being significant in profiles 2 and 3 (mean > 7 drugs) vs. profile 1 (1.4 ± 1.6) (p = 0.000). Patients in the latter group were not significantly older, although being aged 65-79 years old quadrupled (OR = 4.217, p = 000) and being >79 tripled (OR = 2.945, p = 0.010) the conversion risk compared to patients <65 years old. According to the proposed analytical model, profiling the heterogeneous association of risk factors, which were taken prior to diagnosis, could be explored as an indicator of prior care and a predictor of conversion to dementia.

Diet and lifestyle impact the development and progression of Alzheimer's dementia

Dementia is a growing public health concern, with an estimated prevalence of 57 million adults worldwide. Alzheimer's disease (AD) accounts for 60-80% of the cases. Clinical trials testing potential drugs and neuroprotective agents have proven futile, and currently approved drugs only provide symptomatic benefits. Emerging epidemiological and clinical studies suggest that lifestyle changes, including diet and physical activity, offer an alternative therapeutic route for slowing and preventing cognitive decline and dementia. Age is the single most common risk factor for dementia, and it is associated with slowing cellular bioenergetics and metabolic processes. Therefore, a nutrient-rich diet is critical for optimal brain health. Furthermore, type 2 diabetes (T2D) is a risk factor for AD, and diets that reduce the risk of T2D may confer neuroprotection. Foods predominant in Mediterranean, MIND, and DASH diets, including fruits, leafy green vegetables, fish, nuts, and olive oil, may prevent or slow cognitive decline. The mechanisms by which these nutrients promote brain health, however, are not yet completely understood. Other dietary approaches and eating regimes, including ketogenic and intermittent fasting, are also emerging as beneficial for brain health. This review summarizes the pathophysiology, associated risk factors, and the potential neuroprotective pathways activated by several diets and eating regimes that have shown promising results in promoting brain health and preventing dementia.

The LEISURE Study: A Longitudinal Randomized Controlled Trial Protocol for a Multi-Modal Lifestyle Intervention Study to Reduce Dementia Risk in Healthy Older Adults

Dementia is understood to arise from a mixed etiology, enveloping chronic inflammatory and vascular impacts on the brain, driven by a constellation of modifiable risk factors which are largely mediated by lifestyle-related behaviors. These risk factors manifest over a prolonged preclinical period and account for up to 40% of the population attributable risk for dementia, representing viable targets for early interventions aimed at abating disease onset and progression. Here we outline the protocol for a 12-week randomized control trial (RCT) of a multimodal Lifestyle Intervention Study for Dementia Risk Reduction (LEISURE), with longitudinal follow-up at 6-months and 24-months post-intervention. This trial integrates exercise, diet, sleep, and mindfulness to simultaneously target multiple different etiopathogenetic mechanisms and their interplay in a healthy older adult population (aged 50-85 years), and assesses dementia risk reduction as the primary endpoint. The LEISURE study is located in the Sunshine Coast region of Australia, which has one of the nation's highest proportions of adults aged over 50 years (36.4%), and corresponding dementia prevalence. This trial is novel in its inclusion of mindfulness and sleep as multidomain lifestyle targets, and in its comprehensive suite of secondary outcomes (based on psychological, physical health, sleep activity, and cognitive data) as well as exploratory neuroimaging (magnetic resonance imaging and electroencephalography) and molecular biology measures. These measures will provide greater insights into the brain-behavioral underpinnings of dementia prevention, as well as the predictors and impacts of the proposed lifestyle intervention. The LEISURE study was prospectively registered (ACTRN12620000054910) on 19 January 2020.

Dementia Prevention in Clinical Practice

Over 55 million people globally are living with dementia and, by 2050, this number is projected to increase to 131 million. This poses immeasurable challenges for patients and their families and a significant threat to domestic and global economies. Given this public health crisis and disappointing results from disease-modifying trials, there has been a recent shift in focus toward primary and secondary prevention strategies. Approximately 40% of Alzheimer's disease (AD) cases, which is the most common form of dementia, may be prevented or at least delayed. Success of risk reduction studies through addressing modifiable risk factors, in addition to the failure of most drug trials, lends support for personalized multidomain interventions rather than a "one-size-fits-all" approach. Evolving evidence supports early intervention in at-risk patients using individualized interventions directed at modifiable risk factors. Comprehensive risk stratification can be informed by emerging principals of precision medicine, and include expanded clinical and family history, anthropometric measurements, blood biomarkers, neurocognitive evaluation, and genetic information. Risk stratification is key in differentiating subtypes of dementia and identifies targetable areas for intervention. This article reviews a clinical approach toward dementia risk stratification and evidence-based prevention strategies, with a primary focus on AD.

A Comprehensive Survey on the Detection, Classification, and Challenges of Neurological Disorders

Neurological disorders (NDs) are becoming more common, posing a concern to pregnant women, parents, healthy infants, and children. Neurological disorders arise in a wide variety of forms, each with its own set of origins, complications, and results. In recent years, the intricacy of brain functionalities has received a better understanding due to neuroimaging modalities, such as magnetic resonance imaging (MRI), magnetoencephalography (MEG), and positron emission tomography (PET), etc. With high-performance computational tools and various machine learning (ML) and deep learning (DL) methods, these modalities have discovered exciting possibilities for identifying and diagnosing neurological disorders. This study follows a computer-aided diagnosis methodology, leading to an overview of pre-processing and feature extraction techniques. The performance of existing ML and DL approaches for detecting NDs is critically reviewed and compared in this article. A comprehensive portion of this study also shows various modalities and disease-specified datasets that detect and records images, signals, and speeches, etc. Limited related works are also summarized on NDs, as this domain has significantly fewer works focused on disease and detection criteria. Some of the standard evaluation metrics are also presented in this study for better result analysis and comparison. This research has also been outlined in a consistent workflow. At the conclusion, a mandatory discussion section has been included to elaborate on open research challenges and directions for future work in this emerging field.

Mass spectrometry-based urinary metabolomics for exploring the treatment effects of Radix ginseng-Schisandra chinensis herb pair on Alzheimer's disease in rats

Herb pairs are the unique combinations of two relatively fixed herbs, intrinsically convey the basic idea of traditional Chinese medicine prescriptions. The compatibility of Radix ginseng and Schisandra chinensis has been used in traditional Chinese medicine for treating Alzheimer's disease for many years. However, there are few studies on Radix ginseng-Schisandra chinensis herb pair, and the underlying action mechanism is still unclear. In this study, the mechanism of Radix ginseng-Schisandra chinensis herb pair on Alzheimer's disease was investigated by using the mass spectrometry-based urinary metabolomics method. Sixteen urinary endogenous metabolites were identified as potential biomarkers. Meanwhile, 10 biomarkers were quantified with tandem mass spectrometry. The study result showed that the brain pathologic symptoms of model rats were improved and the potential biomarkers were adjusted backward significantly after the herb pair administration. The metabolic pathways linked to the herb pair-regulated endogenous biomarkers included phenylalanine and tyrosine metabolism, tryptophan metabolism, purine metabolism, and so on. The above metabolic pathways reflected that Radix ginseng-Schisandra chinensis herb pair mainly regulates abnormal energy metabolism, reduces inflammation, and regulates gut microbiota and neurotransmitters in the treatment of Alzheimer's disease.

The Role of Nutrition in Individualized Alzheimer's Risk Reduction

PURPOSE OF REVIEW

Decades of research suggests nutritional interventions can be an effective tool for reducing risk of Alzheimer's disease (AD), especially as part of an individualized clinical management plan. This review aims to emphasize new findings examining how specific dietary changes may delay or possibly prevent AD onset, and highlight how interventions can be adopted in clinical practice based on emerging principles of precision medicine.

RECENT FINDINGS

Specific dietary patterns and varied nutrient combinations can have a protective effect on brain health, promote cognitive function, and mediate the comorbidity of chronic conditions associated with increased AD risk. Individuals at risk for AD may see a greater impact of evidence-based dietary changes when initiated earlier in the AD spectrum. Depending on individual clinical profiles, incorporation of nutrition strategies is an essential component of an AD risk reduction plan in clinical practice.

Pre-Clinical Neuroprotective Evidences and Plausible Mechanisms of Sulforaphane in Alzheimer's Disease

Sulforaphane, a potent dietary bioactive agent obtainable from cruciferous vegetables, has been extensively studied for its effects in disease prevention and therapy. Sulforaphane potently induces transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2)-mediated expression of detoxification, anti-oxidation, and immune system-modulating enzymes, and possibly acts as an anti-carcinogenic agent. Several clinical trials are in progress to study the effect of diverse types of cruciferous vegetables and sulforaphane on prostate cancer, breast cancer, lung cancer, atopic asthmatics, skin aging, dermatitis, obesity, etc. Recently, the protective effects of sulforaphane on brain health were also considerably studied, where the studies have further extended to several neurological diseases, including Alzheimer’s disease (AD), Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, multiple sclerosis, autism spectrum disorder, and schizophrenia. Animal and cell studies that employ sulforaphane against memory impairment and AD-related pre-clinical biomarkers on amyloid-β, tau, inflammation, oxidative stress, and neurodegeneration are summarized, and plausible neuroprotective mechanisms of sulforaphane to help prevent AD are discussed. The increase in pre-clinical evidences consistently suggests that sulforaphane has a multi-faceted neuroprotective effect on AD pathophysiology. The anti-AD-like evidence of sulforaphane seen in cells and animals indicates the need to pursue sulforaphane research for relevant biomarkers in AD pre-symptomatic populations.

Feasibility of Using a Wearable Biosensor Device in Patients at Risk for Alzheimer's Disease Dementia

BACKGROUND

Alzheimer's disease (AD) is the most common and most costly chronic neurodegenerative disease globally. AD develops over an extended period prior to cognitive symptoms, leaving a "window of opportunity" for targeted risk-reduction interventions. Further, this pre-dementia phase includes early physiological changes in sleep and autonomic regulation, for which wearable biosensor devices may offer a convenient and cost-effective method to assess AD-risk.

METHODS

Patients with a family history of AD and no or minimal cognitive complaints were recruited from the Alzheimer's Prevention Clinic at Weill Cornell Medicine and New York-Presbyterian. Of the 40 consecutive patients screened, 34 (85%) agreed to wear a wearable biosensor device (WHOOP). One subject (2.5%) lost the device prior to data collection. Of the remaining subjects, 24 were classified as normal cognition and were asymptomatic, 6 were classified as subjective cognitive decline, and 3 were amyloid-positive (one with pre-clinical AD, one with pre-clinical Lewy-Body Dementia, and one with mild cognitive impairment due to AD). Sleep-cycle, autonomic (heart rate variability [HRV]) and activity measures were collected via WHOOP. Blood biomarkers and neuropsychological testing sensitive to cognitive changes in pre-clinical AD were obtained. Participants completed surveys assessing their sleep-patterns, exercise habits, and attitudes towards WHOOP. The goal of this prospective observational study was to determine the feasibility of using a wrist-worn biosensor device in patients at-risk for AD dementia. Unsupervised machine learning was performed to first separate participants into distinct phenotypic groups using the multivariate biometric data. Additional statistical analyses were conducted to examine correlations between individual biometric measures and cognitive performance.

RESULTS

27 (81.8%) participants completed the follow-up surveys. Twenty-four participants (88.9%) were satisfied with WHOOP after six months, and twenty-three (85.2%) wanted to continue wearing WHOOP. K-means clustering separated participants into two groups. Group 1 was older, had lower HRV, and spent more time in slow-wave sleep (SWS) than Group 2. Group 1 performed better on two cognitive tests assessing executive function: Flanker Inhibitory Attention/Control (FIAC) (p=.031), and Dimensional Change Card Sort (DCCS) (p=.061). In Group 1, DCCS was correlated with SWS (ρ=.68, p=0.024) and HRV (ρ=.6, p=0.019). In Group 2, DCCS was correlated with HRV (ρ=.55, p=0.018). There were no significant differences in blood biomarkers between the two groups.

CONCLUSIONS

Wearable biosensor devices may be a feasible tool to assess AD-related physiological changes. Longitudinal collection of sleep and HRV data may potentially be a non-invasive method for monitoring cognitive changes related to pre-clinical AD. Further study is warranted in larger populations.

Interactions between Curcumin Derivatives and Amyloid-β Fibrils: Insights from Molecular Dynamics Simulations

The aggregation of amyloid-β (Aβ) peptides into senile plaques is a hallmark of Alzheimer's disease (AD) and is hypothesized to be the primary cause of AD related neurodegeneration. Previous studies have shown the ability of curcumin to both inhibit the aggregation of Aβ peptides into oligomers or fibrils and reduce amyloids in vivo. Despite the promise of curcumin and its derivatives to serve as diagnostic, preventative, and potentially therapeutic AD molecules, the mechanism by which curcumin and its derivatives bind to and inhibit Aβ fibrils' formation remains elusive. Here, we investigated curcumin and a set of curcumin derivatives in complex with a hexamer peptide model of the Aβ1-42 fibril using nearly exhaustive docking, followed by multi-ns molecular dynamics simulations, to provide atomistic-detail insights into the molecules' binding and inhibitory properties. In the vast majority of the simulations, curcumin and its derivatives remain firmly bound in complex with the fibril through primarily three different principle binding modes, in which the molecules interact with residue domain 17LVFFA21, in line with previous experiments. In a small subset of these simulations, the molecules partly dissociate the outermost peptide of the Aβ1-42 fibril by disrupting β-sheets within the residue domain 12VHHQKLVFF20. A comparison between binding modes leading or not leading to partial dissociation of the outermost peptide suggests that the latter is attributed to a few subtle key structural and energetic interaction-based differences. Interestingly, partial dissociation appears to be either an outcome of high affinity interactions or a cause leading to high affinity interactions between the molecules and the fibril, which could partly serve as a compensation for the energy loss in the fibril due to partial dissociation. In conjunction with this, we suggest a potential inhibition mechanism of Αβ1-42 aggregation by the molecules, where the partially dissociated 16KLVFF20 domain of the outermost peptide could either remain unstructured or wrap around to form intramolecular interactions with the same peptide's 29GAIIG33 domain, while the molecules could additionally act as a patch against the external edge of the second outermost peptide's 16KLVFF20 domain. Thereby, individually or concurrently, these could prohibit fibril elongation.

Individualized clinical management of patients at risk for Alzheimer's dementia

INTRODUCTION

Multidomain intervention for Alzheimer's disease (AD) risk reduction is an emerging therapeutic paradigm.

METHODS

Patients were prescribed individually tailored interventions (education/pharmacologic/nonpharmacologic) and rated on compliance. Normal cognition/subjective cognitive decline/preclinical AD was classified as Prevention. Mild cognitive impairment due to AD/mild-AD was classified as Early Treatment. Change from baseline to 18 months on the modified Alzheimer's Prevention Cognitive Composite (primary outcome) was compared against matched historical control cohorts. Cognitive aging composite (CogAging), AD/cardiovascular risk scales, and serum biomarkers were secondary outcomes.

RESULTS

One hundred seventy-four were assigned interventions (age 25-86). Higher-compliance Prevention improved more than both historical cohorts (P = .0012, P < .0001). Lower-compliance Prevention also improved more than both historical cohorts (P = .0088, P < .0055). Higher-compliance Early Treatment improved more than lower compliance (P = .0007). Higher-compliance Early Treatment improved more than historical cohorts (P < .0001, P = .0428). Lower-compliance Early Treatment did not differ (P = .9820, P = .1115). Similar effects occurred for CogAging. AD/cardiovascular risk scales and serum biomarkers improved.

DISCUSSION

Individualized multidomain interventions may improve cognition and reduce AD/cardiovascular risk scores in patients at-risk for AD dementia.

Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data

Deep learning, a state-of-the-art machine learning approach, has shown outstanding performance over traditional machine learning in identifying intricate structures in complex high-dimensional data, especially in the domain of computer vision. The application of deep learning to early detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention, as rapid progress in neuroimaging techniques has generated large-scale multimodal neuroimaging data. A systematic review of publications using deep learning approaches and neuroimaging data for diagnostic classification of AD was performed. A PubMed and Google Scholar search was used to identify deep learning papers on AD published between January 2013 and July 2018. These papers were reviewed, evaluated, and classified by algorithm and neuroimaging type, and the findings were summarized. Of 16 studies meeting full inclusion criteria, 4 used a combination of deep learning and traditional machine learning approaches, and 12 used only deep learning approaches. The combination of traditional machine learning for classification and stacked auto-encoder (SAE) for feature selection produced accuracies of up to 98.8% for AD classification and 83.7% for prediction of conversion from mild cognitive impairment (MCI), a prodromal stage of AD, to AD. Deep learning approaches, such as convolutional neural network (CNN) or recurrent neural network (RNN), that use neuroimaging data without pre-processing for feature selection have yielded accuracies of up to 96.0% for AD classification and 84.2% for MCI conversion prediction. The best classification performance was obtained when multimodal neuroimaging and fluid biomarkers were combined. Deep learning approaches continue to improve in performance and appear to hold promise for diagnostic classification of AD using multimodal neuroimaging data. AD research that uses deep learning is still evolving, improving performance by incorporating additional hybrid data types, such as-omics data, increasing transparency with explainable approaches that add knowledge of specific disease-related features and mechanisms.

N-of-1 Clinical Trials in Nutritional Interventions Directed at Improving Cognitive Function

Longer life expectancy has led to an increase in the prevalence of age-related cognitive decline and dementia worldwide. Due to the current lack of effective treatment for these conditions, preventive strategies represent a research priority. A large body of evidence suggests that nutrition is involved in the pathogenesis of age-related cognitive decline, but also that it may play a critical role in slowing down its progression. At a population level, healthy dietary patterns interventions, such as the Mediterranean and the MIND diets, have been associated with improved cognitive performance and a decreased risk of neurodegenerative disease development. In the era of evidence-based medicine and patient-centered healthcare, personalized nutritional recommendations would offer a considerable opportunity in preventing cognitive decline progression. N-of-1 clinical trials have emerged as a fundamental design in evidence-based medicine. They consider each individual as the only unit of observation and intervention. The aggregation of series of N-of-1 clinical trials also enables population-level conclusions. This review provides a general view of the current scientific evidence regarding nutrition and cognitive decline, and critically states its limitations when translating results into the clinical practice. Furthermore, we suggest methodological strategies to develop N-of-1 clinical trials focused on nutrition and cognition in an older population. Finally, we evaluate the potential challenges that researchers may face when performing studies in precision nutrition and cognition.

Alzheimer's "Prevention" vs. "Risk Reduction": Transcending Semantics for Clinical Practice

The terms "prevention" and "risk reduction" are often used interchangeably in medicine. There is considerable debate, however, over the use of these terms in describing interventions that aim to preserve cognitive health and/or delay disease progression of Alzheimer's disease (AD) for patients seeking clinical care. Furthermore, it is important to distinguish between Alzheimer's disease prevention and Alzheimer's dementia prevention when using these terms. While prior studies have codified research-based criteria for the progressive stages of AD, there are no clear clinical consensus criteria to guide the use of these terms for physicians in practice. A clear understanding of the implications of each term will help guide clinical practice and clinical research. The authors explore the semantics and appropriate use of the terms "prevention" and "risk reduction" as they relate to AD in clinical practice.

The clinical practice of risk reduction for Alzheimer's disease: A precision medicine approach

Like virtually all age-related chronic diseases, late-onset Alzheimer's disease (AD) develops over an extended preclinical period and is associated with modifiable lifestyle and environmental factors. We hypothesize that multimodal interventions that address many risk factors simultaneously and are individually tailored to patients may help reduce AD risk. We describe a novel clinical methodology used to evaluate and treat patients at two Alzheimer's Prevention Clinics. The framework applies evidence-based principles of clinical precision medicine to tailor individualized recommendations, follow patients longitudinally to continually refine the interventions, and evaluate N-of-1 effectiveness (trial registered at ClinicalTrials.gov NCT03687710). Prior preliminary results suggest that the clinical practice of AD risk reduction is feasible, with measurable improvements in cognition and biomarkers of AD risk. We propose using these early findings as a foundation to evaluate the comparative effectiveness of personalized risk management within an international network of clinician researchers in a cohort study possibly leading to a randomized controlled trial.