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

Background: Cognitive impairment is a growing public health concern, particularly in aging populations. While trends in CI prevalence in China were studied up to 2018, no previous research has explored how the COVID-19 pandemic has affected these trends.

Objectives: This study aims to extend the analysis to 2022, examining the impact of the pandemic on cognitive impairment prevalence.

Participants: The Chinese Longitudinal Healthy Longevity Survey (CLHLS) data across multiple waves (2002 to 2022) was used (n=64,872).

Measurements: Cognitive impairment was assessed using a Chinese version of the Mini-Mental State Examination (MMSE). The rural/urban-sex-single age-specific prevalence of cognitive impairment across different waves were estimated using the DemoRates R package. Cognitive impairment trends before and after the onset of the COVID-19 pandemic were compared to identify any significant changes.

Results: In 2018 and previous waves, an average of 16,191 participants per wave were surveyed (four waves), with a cognitive impairment prevalence of 4.3%. In 2022, post-COVID-19, the survey included 14,022 participants and showed a significant increase in CI prevalence to 6.8%. The observed trends were independent of gender, age group, and residential environment (P-trend < 0.001). However, a significant decrease in mean calf circumference, increase in proportion of overweight participants, and increases in daily fruit and vegetable intake and regular physical activity were notable after the pandemic.

Conclusion: The study suggests that the COVID-19 pandemic may have contributed to the observed increase in cognitive impairment prevalence in China, underscoring the importance of further research into the long-term cognitive effects of global health crises. These findings highlight the need to strengthen healthcare systems to support cognitive health in an aging population, while considering both pandemic-related and ongoing factors in the management of cognitive impairment.

Lecanemab and donanemab are the first anti-Aβ treatments to receive approval in Europe. Eligibility criteria are strict, eg., APOE ε4/4 carriers are excluded. Successful implementation in public healthcare hinges on accurate estimates of eligibility rates in settings which will be the first to roll out the treatments (specialized memory clinics with early disease stages). We applied the appropriate use recommendations (AUR) to assess treatment eligibility in a Swedish tertiary memory clinic where Aβ and APOE assessments are routinely performed. Of the full cohort (N = 410), 26 and 25 patients met the AUR criteria for lecanemab and donanemab, respectively (6 %; partial overlap between the groups). After excluding APOE ε4/4 carriers in line with the European guidelines, only 14 and 13 patients remained eligible (3 %). In clinics with younger populations, a significant percentage of potentially eligible patients are likely to have the APOE ε4/4 genotype. These findings are important to inform the implementation of anti-Aβ treatments.

Background: The diet-gut-microbiota-brain axis is critical for maintaining brain health. The Dietary Index for Gut Microbiota (DI-GM), comprising beneficial and unfavorable components, may serve as a proxy for this connection, yet its association with cognition remains underexplored.

Methods: This study examined the relationship between DI-GM, its components, and cognitive function in older adults using data from the National Health and Nutrition Examination Survey (NHANES). Findings were validated in an independent Hong Kong osteoporosis cohort (OS cohort) with gut metagenomic data to assess of microbiota's mediating role in diet-cognition relationship. Cognitive assessment in NHANES utilized the Consortium to Establish a Registry for Alzheimer's Disease (CERAD), Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST), while the OS cohort employed the Hong Kong version of the Montreal Cognitive Assessment (HK-MoCA). DI-GM was calculated from 24-hour dietary recalls. The diet-cognition associations were assessed by weighted multivariate regressions, supplemented by restricted cubic spline (RCS), subgroup, correlation network, and mediation analyses.

Results: Higher DI-GM was significantly associated with better performance on DSST (OR=0.90; 95 % CI: 0.82, 0.99; p = 0.033). The beneficial-to-gut-microbiota score (BGMS) associated with lower psychometric mild cognitive impairment (p-MCI) risk (OR=0.88; 95 % CI: 0.80, 0.98; p = 0.022) and better CERAD immediate and delayed recall and DSST (all p < 0.05). The beneficial-to-gut-microbiota components like dietary fiber demonstrated protective effects across cognitive domains, while refined grains was associated with poorer cognition. In the OS cohort, higher dietary fiber intake correlated with higher HK-MoCA score (p < 0.05) and increased abundance of fermenting bacteria. Among these species, Eubacterium ventriosum mediated the beneficial effect of dietary fiber intake on dementia risk reduction, with an indirect effect of -0.014 (95 % CrI: -0.040, -0.001), accounting for approximately 12.7 % of the total effect.

Conclusion: Higher adherence to beneficial-to-gut-microbiota dietary patterns, as reflected by DI-GM, was associated with better cognitive function in older adults. These findings highlight the importance of a gut-microbiota-targeted diet in maintaining cognitive health.

Three anti-amyloid monoclonal antibodies (MABs) including aducanumab, lecanemab, and donanemab have been approved by the FDA and lecanemab and donanemab are available in the US market and a variety of other national markets. The increasing use of anti-amyloid MABs to treat early AD will require that development of novel agents occur as add-on treatment to MABs. There is limited experience with add-on therapy to anti-amyloid agents. In most cases, it is prudent to initiate novel agents after at least six-months exposure to the MAB at the highest dose. Agents with extensive data on pharmacokinetics and pharmacodynamics and well-known safety may employ alternative approaches. Anti-amyloid MABs have different mechanisms of action, titration, and side effect profiles suggesting that add-on trials include only one type of MAB if possible. Demonstration of clinical benefit with add-on therapy will require showing additional slowing beyond that provided by the anti-amyloid MAB. Anti-amyloid therapies have profound effects on biomarkers including amyloid positron emission tomography and plasma p-tau and plasma GFAP measures. Definition of the biomarker profile of a novel agent prior to initiation of add-on therapies, inclusion of target engagement biomarkers specific to the novel intervention, assessment of biomarkers not known to be affected by anti-amyloid MABs, and interrogation of the magnitude, timing, and trajectory of biomarker change in the add-on context compared to monotherapy with MABs will provide insight into the biological impact of the novel therapy on AD. Patient convenience in terms of formulation and timing of add-on therapies will be important to successful clinical implementation. Add-on therapies are an important step in addressing the complexity of AD and optimizing patient outcomes.

Despite substantial investment in biomedical and pharmaceutical research over the past two decades, the global prevalence of Alzheimer's disease (AD) and AD-related dementias (AD/ADRD) is still rising. This underscores the significant unmet need for identifying effective disease-modifying therapies. Here, we provide a critical perspective on the application of data science and artificial intelligence (AI) to the rational design of drug combinations in AD and ADRD, addressing their potential to transform therapeutic development. We examine AI's current and prospective capabilities in therapeutic discovery, identify areas where AI-driven strategies can enhance drug combination development, and outline how multidisciplinary professionals in the field, including clinical trialists, neuropsychiatrists, pharmacologists, medicinal chemists, and computational scientists, can leverage these tools to address therapeutic gaps. We also highlight AI's role in synthesizing the rapidly growing amount of biomedical data in the field of AD/ADRD, especially clinical trials, biomarkers, multi-omics data (genomics, transcriptomics, proteomics, metabolomics, interactomics, and radiomics), and real-world patient data. We further explore AI's utility in prioritizing potential drug combination regimens and estimating clinical effect size in combination therapy trials for AD/ADRD. Lastly, we emphasize AI-powered network medicine methodologies for prioritizing drug combinations targeting AD/ADRD co-pathologies and summarize the challenges of their translation to clinical practice.

Background: A new era of Alzheimer's disease (AD) research is beginning with multiple approved anti-amyloid monoclonal antibodies (mABs). These drugs are currently not widely used, but may be soon, especially at clinical trial sites. Putative disease-modifying therapies (DMTs) may alter the progression rate, potentially reducing our ability to detect effects on top of mABs. Co-administration of amyloid-targeted agents may diminish benefit (antagonism, due to the overlapping mechanism of action); alternatively, complementary treatment mechanisms may increase benefit (synergy).

Method: We consider several clinical trial design scenarios: a 2-arm trial added-on to a mAB, a 2-arm combination compared to double placebo, and a 4-arm full factorial trial. We calculate the required sample sizes for the shortest practical study for secondary prevention (prevention of AD clinical diagnosis in biomarker positive individuals, 2-year study), early AD (18-months), and mild-to-moderate AD (1-year). We consider additivity, antagonism, and synergy.

Result: The expected interaction between investigational and mAB treatment can have a large effect on power and study design. Antagonistic treatment effects often require double the sample size of synergistic effects. The 4-arm scenario required ∼10-fold increase compared to a 2-arm combination study.

Conclusion: Studies evaluating investigational therapies as add-on to mABs are complex, and their cost will depend on the interaction between treatments. An inescapable fact in add-on trials is the slower progression of the control arm; and it is difficult to further slow already slow progression. Treatments that are likely to work better with amyloid removal will be easier to study due to their complementary MOA. Symptomatic treatments may require fewer additional subjects than disease-modifying treatments since they are less affected by the presence or absence of mABs.