Wearables, Genetics Reveal Early Health Issues

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9 May 2019. Genetic and personal health data, including from wearable devices, from more than 100 healthy volunteers for several years revealed many early signs of disease. A report of this project appears in yesterday’s issue of the journal Nature Medicine.

A team from Stanford University in California, led by genetics professor Michael Snyder and medical school professor Francois Haddad, are assessing the value of so-called big data collection and analysis of ostensibly healthy individuals, to both the understanding of human biology, as well as the personal health of the participants. Snyder notes in a university statement that today’s general focus on studying disease is misplaced, with more emphasis needed on defining healthy. “We generally study people when they’re sick, rarely when they’re healthy,” says Snyder, “and it means we don’t really know what ‘healthy’ looks like at an individual biochemical level.”

The Stanford team recruited 109 individuals considered healthy and followed their health conditions for up to 8 years, with half or more of the group tracked for at least 2.8 years. The initial focus of the study known as iPOP, was the risk of type 2 diabetes, but the researchers used the opportunity to gather a wide range of health data. The team collected quarterly blood and stool samples, family medical histories, stress levels, and asked participants to wear smartwatches or personal health trackers during this time.

With these data, the researchers assembled detailed profiles of their genomes and related gene expression and protein compositions, as well as metabolic and microbial profiles. These detailed pictures of the individuals’ body chemistries was then associated with their health conditions and traits, using a number of algorithms to sort through and analyze the large mass of data to highlight overall trends, as well as identify personal health risks in the participants.

The results identified 67 actionable health issues among the 109 individuals in the study. Genetic sequencing identified 13 health risks in mutations, including 2 genetic variations associated with heart disease. In one of these cases, the person discovered heart disease in later cardiac tests. In most of these instances, however, changes from the individuals’ overall baseline health profiles triggered warnings to physicians.

“For instance,” says Snyder, “we caught 9 people with diabetes as it was developing by continuously monitoring their glucose and insulin levels.” In addition, say the researchers, 18 participants found they had high blood pressure, 2 participants were found with early signs of cancer, and 6 individuals discovered they had plaque accumulations in their arteries.

Haddad adds that following a person’s heart rate offers an indicator of infection. “As an infectious state sets in,” says Haddad, “heart rate often increases, so if we know a person’s baseline heart rate, an elevated heart rate that should otherwise be normal can tell us that an infection is brewing.” And in many cases, the participants initiated lifestyle changes on their own when informed of the findings.

In addition, the team identified a set of new biomarkers or indicators for metabolic and cardiovascular disorders, as well as cancer, and developed prediction models from the data for insulin resistance, which the researchers say could replace current tests. But the main benefit could be a new tool kit for patients and their physicians in keeping people healthy. “Right now,” says Snyder, “we’re pretty much in the dark until we profile people, but this approach could provide us a much better view of people’s norms, what it means to be healthy, and what it means when people deviate from that.”

In addition to his academic work, Snyder is a serial entrepreneur who founded several companies related to his research in genetics. Among his companies are Personalis in Menlo Park, California developing genomic sequencing tools for cancer immunotherapy, and Sensomics, based in Hangzhou, China, creating genetic analysis tools to detect childhood diseases, including autism.

More from Science & Enterprise:

• A.I. Speeds Infant Genetic Disease Diagnostics
• Precision Medicine Tested for Combination Cancer Therapies
• Genetic Risk Analysis Shows Ethnic Bias
• Genomics, A.I. Calculate Immune Health
• Population Data Show Genetics-Health Links

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