Summary: A new blood test can help identify your body’s precise internal time clock in relation to external time. Researchers say the test could help examine the impact of misaligned circadian clocks in a wide range of diseases.
Source: Northwestern University.
The first simple blood test to identify your body’s precise internal time clock as compared to the external time has been developed by Northwestern Medicine scientists.
The test, TimeSignature — which requires only two blood draws – can tell physicians and researchers the time in your body despite the time in the external world. For instance, even if it’s 8 a.m. in the external world, it might be 6 a.m. in your body.
“This is a much more precise and sophisticated measurement than identifying whether you are a morning lark or a night owl,” said lead author Rosemary Braun, assistant professor of preventive medicine (biostatistics) at Northwestern University Feinberg School of Medicine. “We can assess a person’s biological clock to within 1.5 hours.
“Various groups have tried to get at internal circadian time from a blood test, but nothing has been as accurate or as easy to use as TimeSignature,” Braun said.
Previously, measurements this precise could only be achieved through a costly and laborious process of taking samples every hour over a span of multiple hours.
The paper will be published Sept. 10 in the journal PNAS.
Processes in nearly every tissue and organ system in the body are orchestrated by an internal biological clock, which directs circadian rhythm, such as the sleep-wake cycle. Some individuals’ internal clocks are in sync with external time but others are out of sync and considered misaligned.
The new test for the first time will offer researchers the opportunity to easily examine the impact of misaligned circadian clocks in a range of diseases from heart disease to diabetes and Alzheimer’s disease. When the blood test eventually becomes clinically available, it also will provide doctors with a measurement of an individual’s internal biological clock to guide medication dosing at the most effective time for his or her body.
The software and algorithm are available for free to other researchers so they can assess physiological time in a person’s body. Northwestern has filed for a patent on the blood test.
“This is really an integral part of personalized medicine,” said coauthor Dr. Phyllis Zee, chief of sleep medicine in neurology at Feinberg and a Northwestern Medicine neurologist. “So many drugs have optimal times for dosing. Knowing what time it is in your body is critical to getting the most effective benefits. The best time for you to take the blood pressure drug or the chemotherapy or radiation may be different from somebody else.”
Zee also is the Benjamin and Virginia T. Boshes Professor of Neurology.
The test measures 40 different gene expression markers in the blood and can be taken any time of day, regardless of whether the patient had a good night’s sleep or was up all night with a baby. It is based on an algorithm developed by Braun and colleagues by drawing subjects’ blood every two hours and examining which genes were higher or lower at certain times of day. Scientists also used gene expression data from studies conducted at four other centers.
The scientists then developed a novel machine-learning method that was used to train a computer to predict the time of day based on patterns in these gene expression measurements. Out of about 20,000 genes measured, these 40 emerged with the strongest signal.
“Timing is everything,” said study coauthor Ravi Allada, a professor of neurobiology at Northwestern’s Weinberg College of Arts and Sciences. “We know if you have disruption of your internal clock, it can predispose you to a range of diseases. Virtually every tissue and organ system are governed by circadian rhythm.
“Before we didn’t have a clinically feasible way of assessing the clock in healthy people and people with disease. Now we can see if a disrupted clock correlates with various diseases and, more importantly, if it can predict who is going to get sick.”
A link between circadian misalignment and diabetes, obesity, depression, heart disease and asthma has been identified in preclinical research by scientist Joe Bass, chief of endocrinology, metabolism and molecular medicine at Feinberg.
Down the road, Zee envisions improving health and treating disease by aligning people’s circadian clocks that are out of sync with external time.
“Circadian timing is a modifiable risk factor for improving cognitive health, but if we can’t measure it, it’s difficult to know if we’ve made the right diagnosis,” Zee said. “Now we can measure it just like a lipid level.”
Funding: The research was supported by Scholar Award 220020394 from the James S. McDonnell Foundation and Defense Advanced Research Projects Agency (DARPA) Grant D15AP00027. Additional funding was provided by the Northwestern Center for Circadian and Sleep Medicine.
Source: Marla Paul – Northwestern University
Publisher: Organized by NeuroscienceNews.com.
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Original Research: Open access research for “A Universal Method for Robust Detection of Circadian State from Gene Expression.” by Rosemary Braun, William L. Kath, Marta Iwanaszko, Elzbieta Kula-Eversole, Sabra M. Abbott, Kathryn J. Reid, Phyllis C. Zee, and Ravi Allada in PNAS. Published September 10 2018.
A Universal Method for Robust Detection of Circadian State from Gene Expression.
Circadian clocks play a key role in regulating a vast array of biological processes, with significant implications for human health. Accurate assessment of physiological time using transcriptional biomarkers found in human blood can significantly improve diagnosis of circadian disorders and optimize the delivery time of therapeutic treatments. To be useful, such a test must be accurate, minimally burdensome to the patient, and readily generalizable to new data. A major obstacle in development of gene expression biomarker tests is the diversity of measurement platforms and the inherent variability of the data, often resulting in predictors that perform well in the original datasets but cannot be universally applied to new samples collected in other settings. Here, we introduce TimeSignature, an algorithm that robustly infers circadian time from gene expression. We demonstrate its application in data from three independent studies using distinct microarrays and further validate it against a new set of samples profiled by RNA-sequencing. Our results show that TimeSignature is more accurate and efficient than competing methods, estimating circadian time to within 2 h for the majority of samples. Importantly, we demonstrate that once trained on data from a single study, the resulting predictor can be universally applied to yield highly accurate results in new data from other studies independent of differences in study population, patient protocol, or assay platform without renormalizing the data or retraining. This feature is unique among expression-based predictors and addresses a major challenge in the development of generalizable, clinically useful tests.