The Journal of Prevention of Alzheimer's Disease最新文献

Until recently, accurate early detection of clinical symptoms associated with Alzheimer's disease (AD) and related dementias (ADRD) has been difficult. Digital technologies have created new opportunities to capture cognitive and other AD/ADRD related behaviors with greater sensitivity and specificity. Speech captured through digital recordings has shown recent promise at feasible levels of scalability because of the widespread penetration of smartphones. One such study is described in detail to illustrate the depth in which artificial intelligence (AI) analytic approaches can be used to amplify the value of audio recordings. Another modality that has also attracted research interest are ocular scans that have near term potential for validation as a digital biomarker and a point of entry for clinical care workflows. Single modality measures, however, are rapidly giving way to multi-modality sensors that are embedded in all smartphones and other internet-of-things connected devices. Artificial intelligence (AI) driven analytic approaches are able to divine clinical signals from these high dimensional digital data streams. These data driven findings are setting the stage for a future state in which AD/ADRD detection will be possible at the earliest possible stage of the neurodegenerative process and enable interventions that would significantly attenuate or alter the trajectory, preventing disease from reaching the clinical diagnosis threshold.

To effectively combat dementia onset and progression, lifestyle-based interventions targeting multiple risk factors and disease mechanisms through a multidomain approach - tailored and implemented early in the disease process - have emerged as promising. Electronic databases and relevant websites (clinicaltrials.gov, euclinicaltrials.eu, PubMed and EMBASE) were systematically searched for randomized controlled trials (RCTs) testing the combination of multidomain lifestyle and pharmacological interventions. Studies were included if 1) lifestyle intervention was multimodal (≥2 domains); 2) it was combined with drugs, supplements, or medical food; 3) the study population was within the Alzheimer's disease (AD) and related dementias continuum, including cognitively normal individuals at-risk for dementia, people with subjective cognitive decline (SCD), mild cognitive impairment (MCI), or prodromal AD; 4) outcomes included cognitive or dementia-related measure(s), and 5) intervention lasted at least 6 months. Twelve combination RCTs were identified, incorporating 2 to 7 lifestyle domains (physical exercise, cognitive training, dietary guidance, social activities, sleep hygiene, cardiovascular/metabolic risk management, psychoeducation or stress management), combined with pharmacological components (e.g., Omega-3, Tramiprosate, vitamin D, BBH-1001, epigallocatechin gallate, Souvenaid, and metformin). Seven RCTs targeted participants with prodromal AD, MCI or early dementia, five focused on at risk individuals or SCD. Additionally, 2 studies adopted a precision medicine approach by enriching populations with APOE-ε4 carriers. Findings suggest that well-designed interventions - tailored to the right individuals, implemented at the optimal time - may effectively improve cognition. However, further refinement of the RCT methodology is warranted, for better alignment with the multifaceted nature of dementia prevention and management.

Background: Elevated pulse pressure (PP), indicative of arterial stiffness, has been implicated in cognitive impairment and Alzheimer's disease (AD) pathology. However, its role in preclinical AD and interactions with genetic risk factors like apolipoprotein E ε4 (APOE4) remain unclear.

Objectives: To investigate the association between baseline PP and AD biomarkers (amyloid-beta (Aβ) and tau) and cognitive decline, and to determine whether APOE4 carrier status moderates these relationships.

Design: Prospective cohort study and secondary analysis of the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) randomized clinical trial SETTING: Multicenter observational cohort and randomized clinical trial conducted at 67 sites across the United States, Canada, Australia, and Japan.

Participants: This study included 1690 cognitively unimpaired older adults from the A4 and Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) studies. Participants underwent baseline PP assessment, Aβ and tau PET imaging, and cognitive testing with longitudinal follow-up over 240 weeks.

Measurements: Blood pressure was measured at baseline, with PP calculated as the difference between systolic and diastolic pressures. AD pathologies were assessed through Aβ PET imaging using 18F-Florbetapir, and regional tau deposition in inferior temporal and meta-temporal regions using 18F-Flortaucipir PET imaging. Cognitive performance was measured using the Preclinical Alzheimer Cognitive Composite (PACC).

Results: Higher baseline PP was significantly associated with increased Aβ (β = 0.078; p = 0.001), inferior temporal tau (β = 0.110; p = 0.032), and meta-temporal tau deposition (β = 0.116; p = 0.022). In longitudinal analyses, elevated PP predicted greater decline in PACC scores (β = -0.020; p < 0.001). APOE4 status moderated these associations, with significant effects of PP on tau deposition and cognitive decline observed exclusively among APOE4 carriers. Mediation analysis indicated that tau deposition significantly mediated the association between PP and cognitive decline (indirect effect β = -0.068; 95 % CI [-0.126, -0.011]).

Conclusions: Elevated PP is associated with increased AD biomarker burden and accelerated cognitive decline in cognitively unimpaired older adults, particularly among APOE4 carriers. Our study suggests that arterial stiffness may contribute to AD pathogenesis and progression via tau pathology. These results highlight the potential of vascular health management as an early intervention target for AD prevention, especially in genetically at-risk populations.

Background: Hypertension is a modifiable risk factor for dementia, potentially influencing Alzheimer's disease (AD) pathology. Understanding this relationship is essential for developing interventions to reduce dementia risk.

Objectives: We investigated cross-sectional and longitudinal associations between blood pressure and AD biomarkers in cerebrospinal fluid (CSF) and amyloid (Aβ) positron emission tomography (PET) in cognitively unimpaired adults.

Design: Prospective observational study.

Setting: We analyzed data from cognitively unimpaired participants from three observational prospective European studies: ALFA+ (NCT02485730), EPAD-LCS (NCT02804789), and AMYPAD PNHS (EudraCT: 2018-002,277-22).

Measurements: ALFA+ participants had either CSF biomarkers (CSF Aβ42, Aβ40, p-tau181, t-tau) and/or Aβ PET data. EPAD participants had CSF biomarkers (CSF Aβ42, p-tau181, t-tau), and AMYPAD participants had Aβ PET data. All participants had available data about diabetes, use of hypertensive medication, and waist-to-hip ratio. Multivariable linear regression models were used to analyze cross-sectional associations between systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP) with CSF biomarkers or Aβ PET (Centiloid units, CL); longitudinal associations were tested by means of delta CL scores between baseline and follow-up to assess Aβ PET changes over time.

Results: We included 405 participants from ALFA+ (mean age 61.1 years; 60 % female), 1104 from EPAD (mean age 64.8 years; 59.1 % female), and 340 from AMYPAD (mean age 71.8 years; 60 % female). In ALFA+, DBP was negatively associated with CSF Aβ40 (p = 0.016) and p-tau181 (p = 0.050), while there was a non-significant trend towards a positive association between SBP and CL over time (p = 0.058). In EPAD, DBP was negatively associated with CSF Aβ42 (p < 0.001) and p-tau181 (p = 0.014), while PP was positively associated with CSF Aβ42 (p = 0.024). In AMYPAD, SBP (p = 0.002) and PP (p = 0.003) were positively associated with CL at baseline, with a similar non-significant trend being found for DBP (p = 0.089). Higher DBP (p = 0.042) was significantly associated to increased CL over time, with a similar non-significant trend being found for SBP (p = 0.072). We did not find significant associations between blood pressure and longitudinal changes in CSF biomarkers.

Conclusions: Elevated blood pressure was associated with increased Aβ PET accumulation in cognitively unimpaired individuals. Further research is warranted to elucidate potential mechanisms underlying the negative associations between DBP and CSF biomarkers, which do not reflect the typical AD molecular signature. These findings highlight the relevance of high blood pressure as a potential risk factor for cognitive decline.

Background: Donanemab significantly slowed clinical progression in participants with early symptomatic Alzheimer's disease (AD) during the 76-week placebo-controlled period of TRAILBLAZER-ALZ 2.

Methods: Participants who completed the placebo-controlled period were eligible for the 78-week, double-blind, long-term extension (LTE). Early-start participants were randomized to donanemab in the placebo-controlled period. Delayed-start participants (randomized to placebo) started donanemab in the LTE. Participants who met amyloid treatment course completion criteria were switched to placebo. An external control cohort comprised participants from the AD Neuroimaging Initiative (ADNI).

Results: At 3 years, donanemab slowed disease progression on the Clinical Dementia Rating Scale (CDR)-Sum of Boxes (CDR-SB) in early-start participants versus a weighted ADNI control (-1.2 points; 95 % confidence interval [CI], -1.7, -0.7). Seventy-six weeks after initiating donanemab, delayed-start participants also demonstrated slower CDR-SB progression versus a weighted ADNI control (-0.8 points; 95 % CI, -1.3, -0.3). Participants who completed treatment by 52 weeks demonstrated similar slowing of CDR-SB progression at 3 years. Compared with delayed-start participants, early-start participants demonstrated a significantly lower risk of disease progression on the CDR-Global over 3 years (hazard ratio=0.73; p < 0.001). In both groups, >75 % of participants assessed by positron emission tomography 76 weeks after starting donanemab achieved amyloid clearance (<24.1 Centiloids). The addition of LTE data to prior modeling predicted a median reaccumulation rate of 2.4 Centiloids/year. No new safety signals were observed compared to the established donanemab safety profile.

Conclusions: Over 3 years, donanemab-treated participants with early symptomatic AD demonstrated increasing clinical benefits and a consistent safety profile, with limited-duration dosing.

Trial registration: ClinicalTrials.gov identifier NCT04437511.

Progress in understanding the complexity of Alzheimer's disease informs the search for combination therapies that can successfully prevent or substantially slow the progression of the disease. Anti-amyloid monoclonal antibodies are the first approved disease targeted therapies; they slow disease progression by approximately 30 %. Building on these agents in add-on therapies is one avenue to designing combination treatments. Development of combination drugs consisting of two or more novel interventions is an alternate pathway for combination treatment development. Combination therapies can involve small molecule drugs, biological agents, devices, stem cells, gene therapies, lifestyle interventions, or cognitive training. Nonclinical assessment of drug combinations may involve animal models or new approach methodologies such as induced pluripotent stem cells or organoids. Phase 1 trials are required to characterize each member of a novel combination. Phase 2 trials may use a 2-by-2 factorial design comparing each drug to placebo and the drug combination. In Phase 3, comparison of the novel combination to standard of care may be sufficient or more complex designs may be required. Targets for combination therapies beyond amyloid-related processes include tau abnormalities, inflammation, neurodegeneration, and co-pathologies such as alpha-synuclein and TDP-43. The choice of combination therapies will depend on the strength of the information regarding the target, biomarkers to guide clinical trials, and a candidate agent with the appropriate mechanism of action. Computational strategies based on network analysis of disease and drugs, validation in non-clinical models, and use of real-world data may facilitate prioritization of candidates for combination treatments.

Statistical methods in clinical research tend to become entrenched. Innovations threaten the status quo. The "right way" becomes frozen in lore. This is so even when the "right way" is not best. "Statistical significance" and the associated requirement of "high power" is an example. This attitude is an impediment to efficient design. Willingness to address some design issues with moderate power enables building highly informative and highly efficient clinical trials. This article considers several types of clinical trials, including dose-finding, combinations, and factorial designs. Bayesian adaptive methods are used to show that trials can be made more efficient and more informative. Surprisingly, the approach is consistent with many attitudes of the widely regarded "Father of Modern Statistics," R.A. Fisher. Fisher was anti-Bayesian in rejecting its subjective interpretations. But Fisher and Bayes come to the same conclusion in many applied matters. Fisher invented factorial design. Its principal attraction for him was enabling addressing two or more questions with a single experiment. He complained about attitudes that hindered progress: "No aphorism is more frequently repeated in connection with field trials [and clinical trials], than that we must ask Nature few questions, or, ideally, one question at a time… this view is wholly mistaken." Fisher's primary analysis required modeling and making assumptions. For example, his first analysis in a factorial setting assumed no interactions among the factors. He investigated possibilities of interactions but he did not see the need for doing so with high power.

Decades of advances unfolding in parallel across diverse domains have delivered to science rapid rises in the scale of multiplexing, population-level cohort sizes, global computational capacity, massive-scale artificial intelligence (AI) models, and advanced human cellular modeling capabilities. These have generated unprecedented volumes of data, allowing researchers to explore Alzheimer's disease (AD) biology at a depth and scale never before possible. The explosion of multi-omics datasets and computational power heralds an era in which the complexity of AD can be meaningfully dissected and reconstructed leveraging AI. These can be applied to advance our understanding of the root causes of disease, fundamentally a forward problem, tracing how dysfunction emergence from interactions across genes, cells and environments over time. On the other hand, therapeutic discovery requires addressing the inverse problem, working back from the diseased state to pinpoint upstream interventions that restore health. Human induced pluripotent stem cells (iPSCs) and other human cell models play a pivotal role in this process, naturally computing the mapping from perturbation to phenotype at scale. By recreating human-relevant biology, this cellular intelligence enables validation of targets predicted by AI and testing of interventions that drive therapeutic progress. We look to the next horizon in Alzheimer's research as a collaboration, a convergence of three forms of intelligence: human, artificial and cellular. In unison, these complementary forces will shape a new frontier for AD research where scientific innovation and human ingenuity work together bringing hope for meaningful advances and new therapies.

Despite major advances in Alzheimer's disease and related diseases (ADRD) research, the translation of discoveries into impactful clinical interventions remains slow. Overwhelming data complexity, fragmented knowledge, and prolonged research cycles hinder progress in understanding and treating neurodegenerative diseases. Artificial intelligence (AI) offers a promising path forward, particularly when developed as a scientist-in-the-loop system that collaborates with researchers throughout the scientific discovery process. This paper introduces the concept of an AI Biomedical Scientist, an intelligent platform designed to support literature synthesis, hypothesis generation, experimental design, and data interpretation. This platform aims to function as a holistic scientific partner, integrating diverse biomedical data and expert reasoning to accelerate discovery. We review commercial and academic efforts and introduce targeted Minimum Viable Products (MVPs) needed for general biomedical research lab utilization of AI, such as robust and accurate tools for literature and data analysis, negative data models, and virtual peer review, with a longer-term vision of foundation models trained directly on biomedical datasets. In AD and neurodegeneration research, such tools are anticipated to deliver efficiency gains ranging from modest improvements in specific research tasks to potential multi-fold accelerations in discovery workflows as systems mature and scale. This review examines the technical foundations, challenges, and anticipated impacts of AI and aims to inform and engage researchers in utilizing these systems to transform biomedical discovery, starting with AD and extending to other complex conditions.