Multiomics Blood Test for Increasing Asymptomatic AD Patients Proportion in Preventive Clinical Trials

Abstract

AbstractBackgroundWith the first positive phase III trials for anti‐amyloid approaches in MCI participants, the drug evaluation during the asymptomatic Alzheimer’s disease (AD) stage is now more relevant than ever. Currently, cognitively unimpaired participants are included in preventive trials based on risk factors such as amyloid positivity. The lifetime risks for an individual without cognitive impairment does not exceed 30% for a 65‐year‐old with amyloidosis alone (Brookmeyer et al., 2018). Based on this risk factor, most participants included will never develop AD and therefore will not respond to anti‐AD therapies. There is a need to develop new biomarkers with high specificity predicting which individuals in the cognitively unimpaired population will exhibit AD symptoms and which will not to conduct effective preventive clinical trials.MethodTargeted mass spectrometry assays were developed for 81 blood biomarkers (45 proteins and 36 metabolites) pre‐identified in AAV‐AD rats (Audrain et al. 2018). 287 participants were collected in plasma at baseline and followed clinically from 1 to 18 years. Participants’ diagnosis (AD, or Healthy Controls) was established at the last clinical visit based on reference diagnosis standard. 73.9% of participants had available amyloid status, 42.9% were determined as amyloid positive. Predictive machine learning models for asymptomatic AD participants (n = 48, followed for an average of 6.1 years) among individuals without cognitive decline (HC, n = 239, followed for an average of 4.6 years) was developed. The training dataset (70%) aimed to select the biomarkers subset, train the algorithm, and define the cut‐off. External test dataset (30%) aimed to validate in blind the locked predictive model.ResultWith 20 blood biomarkers and 2 covariates (age and gender), the model predicted asymptomatic AD (n = 16) from HC participants (n = 72) with 81.9% specificity and 56.3% sensitivity during external validation (AUROC = 69.1%, p = 0.02). When current amyloid tests (CSF or PET) and this predictive ML model were applied in series, and participants positive for both were predicted as asymptomatic AD, 90.9% specificity and 54.5% sensitivity were achieved.ConclusionMultiomics blood peripheral biomarkers predict asymptomatic AD patients among cognitively unimpaired individuals with high specificity. This could constitute an additional tool for the inclusion of participants in preventive clinical trials.