Cell-free RNA signatures predict Alzheimer's disease.

There is a need for affordable, scalable, and specific blood-based biomarkers for Alzheimer's disease that can be applied to a population level. We have developed and validated disease-specific cell-free transcriptomic blood-based biomarkers composed by a scalable number of transcripts that capture AD pathobiology even in the presymptomatic stages of the disease. Accuracies are in the range of the current CSF and plasma biomarkers, and specificities are high against other neurodegenerative diseases.

Genome-wide association study stratified by MAPT haplotypes identifies potential novel loci in Parkinson's disease.

Objective: To identify genetic factors that may modify the effects of the MAPT locus in Parkinson's disease (PD). Methods: We used data from the International Parkinson's Disease Genomics Consortium (IPDGC) and the UK biobank (UKBB). We stratified the IPDGC cohort for carriers of the H1/H1 genotype (PD patients n=8,492 and controls n=6,765) and carriers of the H2 haplotype (with either H1/H2 or H2/H2 genotypes, patients n=4,779 and controls n=4,849) to perform genome-wide association studies (GWASs). Then, we performed replication analyses in the UKBB data. To study the association of rare variants in the new nominated genes, we performed burden analyses in two cohorts (Accelerating Medicines Partnership - Parkinson Disease and UKBB) with a total sample size PD patients n=2,943 and controls n=18,486. Results: We identified a novel locus associated with PD among MAPT H1/H1 carriers near EMP1 (rs56312722, OR=0.88, 95%CI= 0.84-0.92, p= 1.80E-08), and a novel locus associated with PD among MAPT H2 carriers near VANGL1 (rs11590278, OR=1.69 95%CI=1.40-2.03, p=2.72E-08). Similar analysis of the UKBB data did not replicate these results and rs11590278 near VANGL1 did have similar effect size and direction in carriers of H2 haplotype, albeit not statistically significant (OR= 1.32, 95%CI= 0.94-1.86, p=0.17). Rare EMP1 variants with high CADD scores were associated with PD in the MAPT H2 stratified analysis (p=9.46E-05), mainly driven by the p.V11G variant. Interpretation: We identified several loci potentially associated with PD stratified by MAPT haplotype and larger replication studies are required to confirm these associations.

A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study.

The development of tools that provide early triage of COVID-19 patients with minimal use of diagnostic tests, based on easily accessible data, can be of vital importance in reducing COVID-19 mortality rates during high-incidence scenarios. This work proposes a machine learning model to predict mortality and risk of hospitalization using both 2 simple demographic features and 19 comorbidities obtained from 86,867 electronic medical records of COVID-19 patients, and a new method (LR-IPIP) designed to deal with data imbalance problems. The model was able to predict with high accuracy (90-93%, ROC-AUC = 0.94) the patient's final status (deceased or discharged), while its accuracy was medium (71-73%, ROC-AUC = 0.75) with respect to the risk of hospitalization. The most relevant characteristics for these models were age, sex, number of comorbidities, osteoarthritis, obesity, depression, and renal failure. Finally, to facilitate its use by clinicians, a user-friendly website has been developed ( https://alejandrocisterna.shinyapps.io/PROVIA ).