Human Longevity’s Largest Study of Its Kind Shows Early Detection of Disease and Disease Risks in Adults

On January 27, 2020 Human Longevity, Inc. (HLI), an innovator in providing data-driven health intelligence and precision health to physicians and patients, reported the publication of a groundbreaking study in the journal Proceedings of the National Academy of Sciences (PNAS) (Press release, Human Longevity, JAN 27, 2020, View Source [SID1234553593]). The study titled, "Precision medicine integrating whole-genome sequencing, comprehensive metabolomics, and advanced imaging," showed that by integrating whole-genome sequencing with advanced imaging and blood metabolites, clinicians identified adults at risk for key health conditions. Data from 1190 self-referred individuals evaluated with HLI’s multi-modal precision health platform, Health Nucleus, show clinically significant findings associated with age-related chronic conditions including cancer, heart disease, diabetes, chronic liver disease, and neurological disorders — leading causes of premature mortality in adults.

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"The goal of precision medicine is to provide a path to assist physicians in achieving disease prevention and implementing accurate treatment strategies," said C. Thomas Caskey, MD, FACP, FACMG, FRSC, chief medical officer for Human Longevity, Inc., lead author of the study, and a member of the National Academy of Sciences. "Our study showed that by employing a holistic and data-driven health assessment for each individual, we are able to achieve early disease detection in adults."

Study highlights include:

Approximately 1 in 6 adult individuals (17.3%) had at least one pathogenic genetic variant, and when integrated with deep phenotyping (imaging, blood test, etc.), 1 in 9 (11.9%) had genotype and phenotype associations, supporting the clinical diagnosis of a genetic disorder.
Additional highly actionable findings in this self-referred cohort, most of which were not previously known, include:
Insulin resistance and/or impaired glucose tolerance (34.2%)
Elevated liver fat (29.2%)
Cardiac structure or function abnormalities such as valvular disorders (16.2%)
Significant calcified coronary artery plaque (calcium score > 100) (11.4%)
Elevated liver iron (9.3%)
Cardiac arrhythmias such as atrial fibrillation (6.1%)
Cardiac conduction disorders (4.8%)
Early stage tumors, most malignant (1.7%)
A lack of phenotype and genotype associations were observed in 5.8% of individuals with pathogenic genetic variants, further suggesting that the identification of pathogenic genetic variant(s) by sequencing alone is not sufficient for a definitive diagnosis, highlighting the importance of a multi-modal assessment.
Genomics and metabolomics associations revealed 5.1% of heterozygous carriers with phenotype manifestations, affecting serum metabolite levels, suggesting that some genetic carriers may not be completely asymptomatic.
"This study shows that the definition of ‘healthy’ may not be what we think it is and depends upon a comprehensive health evaluation," said J. Craig Venter, PhD, founder, Human Longevity, Inc. and a member of the National Academy of Sciences. "The data underscore Human Longevity’s innovative approach to helping clinicians with early detection and personalized treatments, potentially achieving better health outcomes for patients."

"Our traditional approach to the annual health assessment has been very superficial and will need to be replaced by data-driven measures that will be made possible as costs continue to decline for whole-genome sequencing, advanced imaging, especially MRI, and specialized blood analytics," said David Karow, MD, PhD, president and chief innovation officer, Human Longevity, Inc.

ABOUT THE STUDY

The study cohort was composed of 1190 self-referred participants who enrolled at Health Nucleus with a median age of 54 y (range 20 to 89+ y, 33.8% female, 70.6% European). A multidisciplinary team, including cardiologists, radiologists, primary care physicians, clinical geneticists, genetic counselors, and research scientists, integrated deep phenotype data with genome data for each study participant. Participants were enrolled in the study between September 2015 and March 2018.