Long-term quality of life after liver donation in the adult to adult living donor liver transplantation cohort study (A2ALL)

BACKGROUND AND AIMS. There are few long-term studies of health-related quality of life (HRQOL) in living liver donors. This study aimed to characterize donor HRQOL in the Adult to Adult Living Donor Liver Transplantation Study (A2ALL) up to 11 years post-donation. METHODS. Between 2004-2013, HRQOL was assessed at evaluation, and 3 months and yearly post-donation in prevalent liver donors using the Short Form survey (SF-36), which provides a physical (PCS) and a mental component summary (MCS). RESULTS. Of the 458 donors enrolled in A2ALL, 374 (82%) had SF-36 data. Mean age at evaluation was 38 (range 18-63), 47% were male, 93% white, and 43% had a bachelor’s degree or higher. MCS and PCS means were above the US population at all time points. However, at every time point there were some donors who reported poor scores (>1/2 standard deviation below the age and sex adjusted mean) (PCS: 5.3%-26.8%, MCS: 10.0%-25.0%). Predictors of poor PCS and MCS scores included recipient death within the two years prior to the survey and education less than a bachelor’s degree; poor PCS scores were also predicted by time since donation, Hispanic ethnicity, and at the 3-month post-donation time point. CONCLUSIONS. In summary, most living donors maintain above average HRQOL up to 11 years prospectively supporting the notion that living donation does not negatively affect HRQOL. However, targeted support for donors at risk for poor HRQOL may improve overall HRQOL outcomes for living liver donors

Answer ALS, a large-scale resource for sporadic and familial ALS combining clinical and multi-omics data from induced pluripotent cell lines.

Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical-molecular-biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics