Background— Pulse wave velocity (PWV), a noninvasive index of central arterial stiffness, is a potent predictor of cardiovascular mortality and morbidity. Heritability and linkage studies have pointed toward a genetic component affecting PWV. We conducted a genome-wide association study to identify single-nucleotide polymorphisms (SNPs) associated with PWV. Methods and Results— The study cohort included participants from the SardiNIA study for whom PWV measures were available. Genotyping was performed in 4221 individuals, using either the Affymetrix 500K or the Affymetrix 10K mapping array sets (with imputation of the missing genotypes). Associations with PWV were evaluated using an additive genetic model that included age, age2, and sex as covariates. The findings were tested for replication in an independent internal Sardinian cohort of 1828 individuals, using a custom chip designed to include the top 43 nonredundant SNPs associated with PWV. Of the loci that were tested for association with PWV, the no...

Background: Accurate prediction of the disease severity of patients with COVID-19 would greatly improve care delivery and resource allocation and thereby reduce mortality risks, especially in less developed countries. Many patient-related factors, such as pre-existing comorbidities, affect disease severity and can be used to aid this prediction. Objective: Because rapid automated profiling of peripheral blood samples is widely available, we aimed to investigate how data from the peripheral blood of patients with COVID-19 can be used to predict clinical outcomes. Methods: We investigated clinical data sets of patients with COVID-19 with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, k-nearest neighbor, and deep learning methods. Results: Our work revealed that several clinical parameters that are measurable in blood samples are factors that can discriminate between healthy people and COVID-19–positive patients, and we showed the value of these parameters in predicting later severity of COVID-19 symptoms. We developed a number of analytical methods that showed accuracy and precision scores >90% for disease severity prediction. Conclusions: We developed methodologies to analyze routine patient clinical data that enable more accurate prediction of COVID-19 patient outcomes. With this approach, data from standard hospital laboratory analyses of patient blood could be used to identify patients with COVID-19 who are at high risk of mortality, thus enabling optimization of hospital facilities for COVID-19 treatment.