Clinical trials

Wellderly Brain is a randomized, direct-to-participant trial evaluating whether a virtually delivered, multidomain lifestyle coaching intervention can favorably impact plasma biomarkers of Alzheimer's disease (AD) in adults aged 60-80 with APOE4 positivity or elevated polygenic risk. Participants are recruited through 23andMe and enrolled via the MyDataHelps platform. Following genetic eligibility screening, 1,200 participants will be randomized to either a digital lifestyle coaching arm (UCardia) or an education-only control arm. The intervention consists of 16 virtual coaching sessions delivered over 52 weeks. All participants will wear an Oura Ring for continuous health monitoring and provide dried blood samples at baseline, 6 months, and 12 months for plasma p-tau217 and proteomic profiling via the NULISAseq CNS Disease Panel. Saliva samples will be collected for epigenetic aging analysis. The study duration is 14-16 months per participant.

To further characterize Long COVID-19 by collecting data from individuals who already own wearable devices or are provided with a wearable device along with basic and enhanced educational materials to determine if both can improve Long COVID-19 symptom management and post-exertional malaise.

This study seeks to incorporate genetic testing into the postmortem examination of cases of sudden unexplained death.

With the completion of the human genome project, investigators can now explore new questions in human biology. Previously human genetics focused on highly penetrant, Mendelian traits; however, now rare and common variants can be discovered that affect "common" diseases that have multi-gene architecture with variable penetrance such as breast cancer, diabetes mellitus, and coronary artery disease. This change took place because investigators now have the tools to illuminate the whole genome at once to discover the genetic variants responsible for different disease phenotypes through statistical differences between populations. Besides disease phenotypes, health can be considered a human phenotype that can be studied. Health is not merely the absence of disease but may be viewed as a dynamic ongoing interplay between the environment and the genome to maintain homeostasis. Individuals often attempt to optimize environmental conditions according to ones genome to maximize their health. All individuals possess potentially beneficial and harmful variants depending on the environment. How this dynamic interplay occurs between the genome and environment requires understanding the boundary conditions of the genetic architecture of health and disease and then modeling the system to simulate the observed data. The aging process also affects health. Aging involves a loss of the normal coping responses to internal and external environmental stressors or signals. Investigators now have the tools to uncover from the bottom up the mechanisms involved in maintaining the ability to overcome environmental conditions that can affect health. Against this genomic breakthrough of whole genome association studies, the demographics in the United States are quickly changing. The older population (age \> 65 years) in 2030 is projected to be twice as large as in 2000 representing nearly 20 percent of the total US population. The first baby boomers turn 65 in 2011 and will challenge all facets of health care in the coming decades. The demographic changes underscore the need to understand the mechanisms that promote health and disease in this cohort. Genomic discoveries will help individuals and may reduce medical costs and benefit society. In summary, the objective of this study is to obtain blood and/or saliva samples in order to help model health and disease phenotypes through population genomics. The blood and/or saliva samples may allow for participants' entire genomes to be sequenced if such comprehensive analysis becomes feasible and economical.

This is a digital health study in which participants are recruited to collect sleep and activity data from digital activity trackers. We are also collecting survey/questionnaire data on baseline health and sleep characteristics as well as bi-weekly assessments of sleep quality and mood. Overall, we aim to examine how sleep relates to physical and mental health in a large population of activity tracker users.

Many conditions affecting health are caused by a combination of environment, behaviors, and genes. While individuals can alter some factors in their lives to reduce the chances of developing different diseases (e.g., not smoking cigarettes), the contribution from genetic risk encoded by DNA remains with people throughout their lives. Scientists are still trying to determine the entirety of genetic factors that influence disease, but for some conditions it has been shown that the factors identified thus far can begin to identify people at high to low genetic risk. Looking across the genome, scientists can calculate a cumulative genetic risk score - which can be used to rank genetic risk compared to other worldwide populations. The goal of this study is to determine how genetic risk influences health decisions and other things that can be controlled in life. The first genetic risk score is calculated for coronary heart disease (CAD). CAD ultimately leads to heart attacks, heart failure and sometimes sudden cardiac death and is the main reason heart disease remains as the number one cause of death worldwide. Other researchers have shown that this genetic risk score can be used to identify people with low, intermediate, and high risk for coronary heart disease. It has also been shown that the use of statins (cholesterol lowering drugs) provides greater benefit and protection against heart attack for people with high genetic risk for coronary artery disease. Leveraging the Apple ResearchKit and the ResearchKit linked 23andMe API, customers of 23andMe are able to provide researchers access to their genomic data. Participants will use the ResearchKit app to provide consent, view study information, answer surveys, and contact the study team. Participants will be asked to complete 3 surveys. One before viewing genetic risk scores, one immediately after viewing scores, and one 6 months after viewing scores.

This study will take advantage of the open source framework of ResearchKit developed by Apple to bring research directly to participants. Using the ResearchKit platform as well as a stand alone app available for Android and HTML, it makes it easier to enroll large numbers of participants and carry out real-world health research to answer questions important to a broad population.

This study is enrolling pregnant persons treated at Rady Children's Hospital fetal cardiology program with a prenatal diagnosis of congenital heart disease to look for genetic disorders in the fetus or unborn baby. Congenital heart disease (CHD) is a group of structural differences to the heart that represent the most common birth defect among liveborn infants world-wide. CHD is the leading cause of birth-defect associated infant death. Prenatal detection allows for delivery planning, postnatal repair, specialized medications, and detailed counseling for parents. Up to one in three fetuses with CHD may have a genetic cause. In babies, knowing about genetic diseases helps patients and doctors provide the best care for their babies. If identified prenatally, this same knowledge may help participants prepare for their location of delivery, meet with specialists, and consider specialized treatments and medications that may be appropriate. The diagnostic yield and clinical utility of whole genome sequencing (WGS) in fetuses with prenatally detected congenital heart disease (CHD) will be compared to routine clinical testing in patients choosing amniocentesis or chorionic villus sampling. DNA will be obtained from fetal samples and biological parent blood samples and analyzed according to standard clinical interpretation guidelines. Results will be reported to healthcare providers and patients and measures of clinical utility will be collected. Additionally, measures of stress, anxiety, depression, and perceived utility of information will be assessed by validated survey tools. A historical cohort of patients electing for diagnostic procedures will be used as a comparison population.

This study will investigate the role of polygenic risk scores (PRS) in preventive health.