Validating novel neurodegenerative targets for future dementia therapeutics

Population-based proteomics offers a groundbreaking avenue to predict future disease risks, enhance our understanding of disease mechanisms, and discover novel therapeutic targets and biomarkers. The role of plasma proteins in dementia, however, requires further exploration. This study investigated 276 protein-dementia associations in 229 incident all-cause dementia, 89 Alzheimer's disease, and 41 vascular dementia among 3249 participants (55% women, 97.2% white ethnicity) from the English Longitudinal Study of Ageing (ELSA) over a median 9.8-year follow-up. We used Cox proportional hazard regression for the analysis. Receiver operating characteristic analyses were conducted to assess the precision of the identified proteins from the fully adjusted Cox regression models in predicting incident all-cause dementia, both individually and in combination with demographic predictors, APOE genotype, and memory score, to estimate the area under the curve. Additionally, the eXtreme Gradient Boosting machine learning algorithm was used to identify the most important features predictive of future all-cause dementia onset. These associations were then validated in 1506 incident all-cause dementia, 732 Alzheimer's disease, 281 vascular dementia, and 111 frontotemporal dementia cases among 52 745 individuals (53.9% women, 93.3% White ethnicity) from the UK Biobank over a median 13.7-year follow-up. Two-sample bi-directional Mendelian randomization and drug target Mendelian randomization were further employed to determine the causal direction between protein concentration and dementia. NEFL (hazard ratio [HR] [95% confidence intervals (CIs)]: 1.54 [1.29, 1.84]) and RPS6KB1 (HR [95% CI]: 1.33 [1.16, 1.52]) were robustly associated with incident all-cause dementia; MMP12 (HR [95% CI]: 2.06 [1.41, 2.99]) was associated with vascular dementia in ELSA, after correcting for multiple testing. Additional markers EDA2R and KIM1 were identified from subgroup and sensitivity analyses. Combining NEFL and RPS6KB1 with other predictors yielded high predictive accuracy (area under the curve = 0.871) for incident all-cause dementia. The eXtreme Gradient Boosting machine learning algorithm also identified RPS6KB1, NEFL, and KIM1 as the most important protein features for predicting future all-cause dementia. Sex difference was evident for the association between RPS6KB1 and all-cause dementia, with stronger association in men (P for interaction = 0.037). Replication in the UK Biobank confirmed the associations between the identified proteins and various dementia subtypes. The results from Mendelian randomization in the reverse direction indicated that several proteins serve as early markers for dementia, rather than being direct causes of the disease. These findings provide insights into putative mechanisms for dementia. Future studies are needed to validate the findings on RPS6KB1 in relation to dementia risk.

Introduction Subjective cognitive decline (SCD) is an early marker of neurodegenerative disease and a target for preventative interventions. With advances in smartphone-based clinical interventions and understanding of modifiable risk factors for cognitive decline, this simulation study aimed to estimate the potential benefits of a prescription digital therapeutic (PDT) with multi-risk-factor modification on the cognitive trajectory of individuals with SCD. We constructed a Monte Carlo simulation to model progression to mild cognitive impairment (MCI) over a five-year period. Methods A virtual cohort of 10,000 patients with SCD was simulated over five years. Baseline annual risk of progression to MCI was set at 10%. A PDT was assumed to yield a 30% relative risk reduction, modulated by adherence levels (70% full, 20% partial, 10% none). Additionally, 14 modifiable dementia risk factors were modeled based on the 2024 Lancet Commission update, with each risk factor assigned a prevalence, modifiability flag, relative risk reduction, and effectiveness rate. Risk adjustments were applied multiplicatively, and outcomes were tracked annually. Results After five years, 6,487 (65%) of patients remained cognitively stable, 2,496 (25%) progressed to MCI, and 1,017 (10%) dropped out. These results represent a significant improvement over the expected 41% progression rate in untreated SCD populations. Reductions in physical inactivity, hypertension, hearing loss, and social isolation contributed substantially to outcome improvements, particularly when multiple risk factors were addressed concurrently. Conclusion The incorporation of a PDT with systematic modification of multiple modifiable dementia risk factors demonstrates a meaningful reduction in cognitive decline risk among individuals with SCD. These findings highlight the potential of integrated digital-first strategies to meaningfully delay cognitive decline and may inform future PDT trials and dementia prevention programs.Validation in prospective clinical trials is warranted to confirm these simulation-based findings.