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

Background and objectives: Estimated glucose disposal rate (eGDR) is a novel and reliable marker of insulin resistance (IR), yet its association with white matter hyperintensities (WMH) remains unclear. This study investigates the relationship between eGDR and WMH in a cohort from the UK Biobank.

Methods: We included 34,789 participants without a history of stroke or dementia at baseline. WMH volume was estimated from T2-FLAIR brain magnetic resonance imaging (MRI) scans acquired in 2014, normalized to intracranial volume, and log-transformed. Multiple linear regression models were used to examine the association between eGDR and WMH volume. Additionally, restricted cubic spline (RCS) analysis was employed to explore the dose-response relationship between eGDR and WMH volume.

Results: Each 1-SD increase in eGDR was significantly associated with a reduction in WMH volume (β = -0.057; 95% CI: -0.062 to -0.051; p < 0.001). Compared to participants in the lowest eGDR quartile (Q1), those in quartiles Q2, Q3, and Q4 exhibited progressively lower WMH volumes, with β coefficients of -0.068 (95% CI: -0.097 to -0.039), -0.199 (95% CI: -0.228 to -0.169), and -0.295 (95% CI: -0.330 to -0.259), respectively (p for trend < 0.001). RCS analysis demonstrated a significant linear inverse relationship between eGDR and WMH volume (p for nonlinearity > 0.05). Subgroup analyses indicated consistent associations across most predefined groups.

Conclusion: Lower eGDR levels are associated with a greater burden of WMH, suggesting that eGDR may serve as a potential marker for predicting WMH burden in future clinical practice.

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.

Importance: Despite the emergence of anti-amyloid therapies for Alzheimer's disease, targeting modifiable risk factors remains the most effective primary prevention strategy for dementia. While cognitive benefits of multimodal lifestyle interventions have been demonstrated, the underlying effects on brain structure remain unclear, likely due to heterogeneity in brain structure among at-risk individuals.

Objective: To investigate how distinct subgroups of at-risk individuals, defined by cortical and subcortical grey matter (GM) patterns, differ in their response to the FINGER intervention, as well as in their demographic, vascular, and lifestyle profiles.

Design: Observational study employing unsupervised clustering of MRI-based cortical thickness and subcortical volume metrics, followed by longitudinal assessment of a lifestyle intervention.

Setting: The FINGER randomized controlled trial (RCT), a population-based, multidomain lifestyle intervention targeting older adults (aged 60-77) with elevated cardiovascular risk (CAIDE score ≥ 6) and average to slightly below-average cognitive performance.

Participants: A total of 120 participants (61 intervention, 59 control) with available baseline MRI data.

Intervention: Participants were randomly assigned (1:1, double-blind) to a 2-year multidomain lifestyle intervention group - targeting diet, physical activity, cognitive training, social engagement, and metabolic and vascular risk management - or to a control group receiving standard health advice.

Main outcomes and measures: Sociodemographic, vascular, and lifestyle factors, medical comorbidities, and cognitive performance, were assessed at baseline (pre-intervention). Additionally, brain structural outcomes (mean cortical thickness, Alzheimer's disease and resilience-related cortical signatures, hippocampal volume), and cognition (global, executive function, processing speed, memory) were analysed post-intervention using hierarchical linear models stratified by GM cluster.

Results: Clusters with diffuse or frontal-predominant cortical thinning, but with more favourable vascular profiles, characterized by lower blood pressure and reduced obesity, showed significantly less cortical thinning (mean thickness, AD-signature, and resilience-signature regions; all p < 0.05) following the intervention.

Conclusions and relevance: Stratifying at-risk individuals by GM patterns and vascular risk revealed differential brain responses to the FINGER intervention. These findings underscore the value of brain-based subtyping to optimize personalized dementia prevention strategies in heterogeneous at-risk populations.

Trial registration: ClinicalTrials.gov Identifier: NCT01041989.

Background: This study aimed to evaluate cortical mean diffusivity (cMD) as a sensitive biomarker for early neurodegenerative changes in familial frontotemporal lobar degeneration (FTLD) associated with C9orf72, GRN, and MAPT mutations. We compared cMD with cortical thickness (cTH) in detecting subtle microstructural alterations and examined its association with clinical severity and neurofilament light chain (NFL) concentrations.

Methods: We analyzed data from 322 participants, including symptomatic carriers of C9orf72 (n = 85), GRN (n = 56), and MAPT (n = 58) mutations, along with 123 healthy controls. Cortical microstructure was assessed using both cTH and cMD. Clinical severity was evaluated with the CDR plus NACC FTLD scale, and plasma NFL was measured as a marker of neuroaxonal injury.

Results: C9orf72 carriers exhibited the most widespread cortical thinning and increased cMD, while GRN and MAPT carriers showed more regionally restricted alterations. Across all mutation groups, cMD demonstrated higher sensitivity than cTH in detecting early changes. Furthermore, cMD values were significantly correlated with CDR plus NACC FTLD scores and NFL concentrations, underscoring its relevance to disease progression.

Conclusion: Cortical mean diffusivity outperforms cortical thickness in detecting early microstructural changes in familial FTLD. Its strong association with both clinical severity and neurodegeneration biomarkers highlights its potential utility for early diagnosis, disease monitoring, and individualized therapeutic strategies in FTLD.