Lawrence Tabak, D.D.S., Ph.D.
Posted on by Lawrence Tabak, D.D.S., Ph.D.
As we get older, unfortunately our chances of having a stroke rise. While there’s obviously no way to turn back the clock on our age, fortunately there are ways to lower our risk of a stroke and that includes staying physically active. Take walks, ride a bike, play a favorite sport. According to our current exercise guidelines for American adults, the goal is to get in at least two and a half hours each week of moderate-intensity physical activity as well as two days of muscle-strengthening activity .
But a new study, published in the journal JAMA Network Open, shows that reducing the chances of a stroke as we get older doesn’t necessarily require heavy aerobic exercise or a sweat suit . For those who are less mobile or less interested in getting out to exercise, the researchers discovered that just spending time doing light-intensity physical activity—such as tending to household chores—“significantly” protects against stroke.
The study also found you don’t have to dedicate whole afternoons to tidying up around the house to protect your health. It helps to just get up out of your chair for five or 10 minutes at a time throughout the day to straighten up a room, sweep the floor, fold the laundry, step outside to water the garden, or just take a leisurely stroll.
That may sound simple, but consider that the average American adult now spends on average six and a half hours per day just sitting . That comes to nearly two days per week on average, much to the detriment of our health and wellbeing. Indeed, the study found that middle-aged and older people who were sedentary for 13 hours or more hours per day had a 44 percent increased risk of stroke.
These latest findings come from Steven Hooker, San Diego State University, CA, and his colleagues on the NIH-supported Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. Launched in 2003, REGARDS continues to follow over time more than 30,000 Black and white participants aged 45 and older.
Hooker and colleagues wanted to know more about the amount and intensity of exercise required to prevent a stroke. Interestingly, the existing data were relatively weak, in part because prior studies looking at the associations between physical activity and stroke risk relied on self-reported data, which don’t allow for precise measures. What’s more, the relationship between time spent sitting and stroke risk also remained unknown.
To get answers, Hooker and team focused on 7,607 adults enrolled in the REGARDS study. Rather than relying on self-reported physical activity data, team members asked participants to wear a hip-mounted accelerometer—a device that records how fast people move—during waking hours for seven days between May 2009 and January 2013.
The average age of participants was 63. Men and women were represented about equally in the study, while about 70 percent of participants were white and 30 percent were Black.
Over the more than seven years of the study, 286 participants suffered a stroke. The researchers then analyzed all the accelerometer data, including the amount and intensity of their physical activity over the course of a normal week. They then related those data to their risk of having a stroke over the course of the study.
The researchers found, as anticipated, that adults who spent the most time doing moderate-to-vigorous intensity physical activity were less likely to have a stroke than those who spent the least time physically active. But those who spent the most time sitting also were at greater stroke risk, whether they got their weekly exercise in or not.
Those who regularly sat still for longer periods—17 minutes or more at a time—had a 54 percent increase in stroke risk compared to those who more often sat still for less than eight minutes. After adjusting for the time participants spent sitting, those who more often had shorter periods of moderate-to-vigorous activity—less than 10 minutes at a time—still had significantly lower stroke risk. But, once the amount of time spent sitting was taken into account, longer periods of more vigorous activity didn’t make a difference.
While high blood pressure, diabetes, and myriad other factors also contribute to a person’s cumulative risk of stroke, the highlighted paper does bring some good actionable news. For each hour spent doing light-intensity physical activity instead of sitting, a person can reduce his or her stroke risk.
The bad news, of course, is that each extra hour spent sitting per day comes with an increased risk for stroke. This bad news shouldn’t be taken lightly. In the U.S., almost 800,000 people have a stroke each year. That’s one person every 40 seconds with, on average, one death every four minutes. Globally, stroke is the second most common cause of death and third most common cause of disability in people, killing more than 6.5 million each year.
If you’re already meeting the current exercise guidelines for adults, keep up the good work. If not, this paper shows you can still do something to lower your stroke risk. Make a habit throughout the day of getting up out of your chair for a mere five or 10 minutes to straighten up a room, sweep the floor, fold the laundry, step outside to water the garden, or take a leisurely stroll. It could make a big difference to your health as you age.
 How much physical activity do adults need? Centers for Disease Control and Prevention. June 2, 2022.
 Association of accelerometer-measured sedentary time and physical activity with risk of stroke among US adults. Hooker SP, Diaz KM, Blair SN, Colabianchi N, Hutto B, McDonnell MN, Vena JE, Howard VJ. JAMA Netw Open. 2022 Jun 1;5(6):e2215385.
 Trends in sedentary behavior among the US population, 2001-2016. Yang L, Cao C, Kantor ED, Nguyen LH, Zheng X, Park Y, Giovannucci EL, Matthews CE, Colditz GA, Cao Y. JAMA. 2019 Apr 23;321(16):1587-1597.
Stroke (National Institute of Neurological Disorders and Stroke/NIH)
REGARDS Study (University of Alabama at Birmingham)
NIH Support: National Institute of Neurological Disorders and Stroke; National Institute on Aging
Posted on by Lawrence Tabak, D.D.S., Ph.D.
The COVID-19 pandemic continues to present considerable public health challenges in the United States and around the globe. One of the most puzzling is why many people who get over an initial and often relatively mild COVID illness later develop new and potentially debilitating symptoms. These symptoms run the gamut including fatigue, shortness of breath, brain fog, anxiety, and gastrointestinal trouble.
People understandably want answers to help them manage this complex condition referred to as Long COVID syndrome. But because Long COVID is so variable from person to person, it’s extremely difficult to work backwards and determine what these people had in common that might have made them susceptible to Long COVID. The variability also makes it difficult to identify all those who have Long COVID, whether they realize it or not. But a recent study, published in the journal Lancet Digital Health, shows that a well-trained computer and its artificial intelligence can help.
Researchers found that computers, after scanning thousands of electronic health records (EHRs) from people with Long COVID, could reliably make the call. The results, though still preliminary and in need of further validation, point the way to developing a fast, easy-to-use computer algorithm to help determine whether a person with a positive COVID test is likely to battle Long COVID.
In this groundbreaking study, NIH-supported researchers led by Emily Pfaff, University of North Carolina, Chapel Hill, and Melissa Haendel, the University of Colorado Anschutz Medical Campus, Aurora, relied on machine learning. In machine learning, a computer sifts through vast amounts of data to look for patterns. One reason machine learning is so powerful is that it doesn’t require humans to tell the computer which features it should look for. As such, machine learning can pick up on subtle patterns that people would otherwise miss.
In this case, Pfaff, Haendel, and team decided to “train” their computer on EHRs from people who had reported a COVID-19 infection. (The records are de-identified to protect patient privacy.) The researchers found just what they needed in the National COVID Cohort Collaborative (N3C), a national, publicly available data resource sponsored by NIH’s National Center for Advancing Translational Sciences. It is part of NIH’s Researching COVID to Enhance Recovery (RECOVER) initiative, which aims to improve understanding of Long COVID.
The researchers defined a group of more than 1.5 million adults in N3C who either had been diagnosed with COVID-19 or had a record of a positive COVID-19 test at least 90 days prior. Next, they examined common features, including any doctor visits, diagnoses, or medications, from the group’s roughly 100,000 adults.
They fed that EHR data into a computer, along with health information from almost 600 patients who’d been seen at a Long COVID clinic. They developed three machine learning models: one to identify potential long COVID patients across the whole dataset and two others that focused separately on people who had or hadn’t been hospitalized.
All three models proved effective for identifying people with potential Long-COVID. Each of the models had an 85 percent or better discrimination threshold, indicating they are highly accurate. That’s important because, once researchers can identify those with Long COVID in a large database of people such as N3C, they can begin to ask and answer many critical questions about any differences in an individual’s risk factors or treatment that might explain why some get Long COVID and others don’t.
This new study is also an excellent example of N3C’s goal to assemble data from EHRs that enable researchers around the world to get rapid answers and seek effective interventions for COVID-19, including its long-term health effects. It’s also made important progress toward the urgent goal of the RECOVER initiative to identify people with or at risk for Long COVID who may be eligible to participate in clinical trials of promising new treatment approaches.
Long COVID remains a puzzling public health challenge. Another recent NIH study published in the journal Annals of Internal Medicine set out to identify people with symptoms of Long COVID, most of whom had recovered from mild-to-moderate COVID-19 . More than half had signs of Long COVID. But, despite extensive testing, the NIH researchers were unable to pinpoint any underlying cause of the Long COVID symptoms in most cases.
So if you’d like to help researchers solve this puzzle, RECOVER is now enrolling adults and kids—including those who have and have not had COVID—at more than 80 study sites around the country.
 Identifying who has long COVID in the USA: a machine learning approach using N3C data. Pfaff ER, Girvin AT, Bennett TD, Bhatia A, Brooks IM, Deer RR, Dekermanjian JP, Jolley SE, Kahn MG, Kostka K, McMurry JA, Moffitt R, Walden A, Chute CG, Haendel MA; N3C Consortium. Lancet Digit Health. 2022 May 16:S2589-7500(22)00048-6.
 A longitudinal study of COVID-19 sequelae and immunity: baseline findings. Sneller MC, Liang CJ, Marques AR, Chung JY, Shanbhag SM, Fontana JR, Raza H, Okeke O, Dewar RL, Higgins BP, Tolstenko K, Kwan RW, Gittens KR, Seamon CA, McCormack G, Shaw JS, Okpali GM, Law M, Trihemasava K, Kennedy BD, Shi V, Justement JS, Buckner CM, Blazkova J, Moir S, Chun TW, Lane HC. Ann Intern Med. 2022 May 24:M21-4905.
COVID-19 Research (NIH)
National COVID Cohort Collaborative (N3C) (National Center for Advancing Translational Sciences/NIH)
Emily Pfaff (University of North Carolina, Chapel Hill)
Melissa Haendel (University of Colorado, Aurora)
NIH Support: National Center for Advancing Translational Sciences; National Institute of General Medical Sciences; National Institute of Allergy and Infectious Diseases
Posted on by Lawrence Tabak, D.D.S., Ph.D.
There are 37 trillion or so cells in our bodies that work together to give us life. But it may surprise you that we still haven’t put a good number on how many distinct cell types there are within those trillions of cells.
That’s why in 2016, a team of researchers from around the globe launched a historic project called the Human Cell Atlas (HCA) consortium to identify and define the hundreds of presumed distinct cell types in our bodies. Knowing where each cell type resides in the body, and which genes each one turns on or off to create its own unique molecular identity, will revolutionize our studies of human biology and medicine across the board.
Since its launch, the HCA has progressed rapidly. In fact, it has already reached an important milestone with the recent publication in the journal Science of four studies that, together, comprise the first multi-tissue drafts of the human cell atlas. This draft, based on analyses of millions of cells, defines more than 500 different cell types in more than 30 human tissues. A second draft, with even finer definition, is already in the works.
Making the HCA possible are recent technological advances in RNA sequencing. RNA sequencing is a topic that’s been mentioned frequently on this blog in a range of research areas, from neuroscience to skin rashes. Researchers use it to detect and analyze all the messenger RNA (mRNA) molecules in a biological sample, in this case individual human cells from a wide range of tissues, organs, and individuals who voluntarily donated their tissues.
By quantifying these RNA messages, researchers can capture the thousands of genes that any given cell actively expresses at any one time. These precise gene expression profiles can be used to catalogue cells from throughout the body and understand the important similarities and differences among them.
In one of the published studies, funded in part by the NIH, a team co-led by Aviv Regev, a founding co-chair of the consortium at the Broad Institute of MIT and Harvard, Cambridge, MA, established a framework for multi-tissue human cell atlases . (Regev is now on leave from the Broad Institute and MIT and has recently moved to Genentech Research and Early Development, South San Francisco, CA.)
Among its many advances, Regev’s team optimized single-cell RNA sequencing for use on cell nuclei isolated from frozen tissue. This technological advance paved the way for single-cell analyses of the vast numbers of samples that are stored in research collections and freezers all around the world.
Using their new pipeline, Regev and team built an atlas including more than 200,000 single-cell RNA sequence profiles from eight tissue types collected from 16 individuals. These samples were archived earlier by NIH’s Genotype-Tissue Expression (GTEx) project. The team’s data revealed unexpected differences among cell types but surprising similarities, too.
For example, they found that genetic profiles seen in muscle cells were also present in connective tissue cells in the lungs. Using novel machine learning approaches to help make sense of their data, they’ve linked the cells in their atlases with thousands of genetic diseases and traits to identify cell types and genetic profiles that may contribute to a wide range of human conditions.
By cross-referencing 6,000 genes previously implicated in causing specific genetic disorders with their single-cell genetic profiles, they identified new cell types that may play unexpected roles. For instance, they found some non-muscle cells that may play a role in muscular dystrophy, a group of conditions in which muscles progressively weaken. More research will be needed to make sense of these fascinating, but vital, discoveries.
The team also compared genes that are more active in specific cell types to genes with previously identified links to more complex conditions. Again, their data surprised them. They identified new cell types that may play a role in conditions such as heart disease and inflammatory bowel disease.
Two of the other papers, one of which was funded in part by NIH, explored the immune system, especially the similarities and differences among immune cells that reside in specific tissues, such as scavenging macrophages [2,3] This is a critical area of study. Most of our understanding of the immune system comes from immune cells that circulate in the bloodstream, not these resident macrophages and other immune cells.
These immune cell atlases, which are still first drafts, already provide an invaluable resource toward designing new treatments to bolster immune responses, such as vaccines and anti-cancer treatments. They also may have implications for understanding what goes wrong in various autoimmune conditions.
Scientists have been working for more than 150 years to characterize the trillions of cells in our bodies. Thanks to this timely effort and its advances in describing and cataloguing cell types, we now have a much better foundation for understanding these fundamental units of the human body.
But the latest data are just the tip of the iceberg, with vast flows of biological information from throughout the human body surely to be released in the years ahead. And while consortium members continue making history, their hard work to date is freely available to the scientific community to explore critical biological questions with far-reaching implications for human health and disease.
 Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Eraslan G, Drokhlyansky E, Anand S, Fiskin E, Subramanian A, Segrè AV, Aguet F, Rozenblatt-Rosen O, Ardlie KG, Regev A, et al. Science. 2022 May 13;376(6594):eabl4290.
 Cross-tissue immune cell analysis reveals tissue-specific features in humans. Domínguez Conde C, Xu C, Jarvis LB, Rainbow DB, Farber DL, Saeb-Parsy K, Jones JL,Teichmann SA, et al. Science. 2022 May 13;376(6594):eabl5197.
 Mapping the developing human immune system across organs. Suo C, Dann E, Goh I, Jardine L, Marioni JC, Clatworthy MR, Haniffa M, Teichmann SA, et al. Science. 2022 May 12:eabo0510.
Ribonucleic acid (RNA) (National Human Genome Research Institute/NIH)
Studying Cells (National Institute of General Medical Sciences/NIH)
Regev Lab (Broad Institute of MIT and Harvard, Cambridge, MA)
NIH Support: Common Fund; National Cancer Institute; National Human Genome Research Institute; National Heart, Lung, and Blood Institute; National Institute on Drug Abuse; National Institute of Mental Health; National Institute on Aging; National Institute of Allergy and Infectious Diseases; National Institute of Neurological Disorders and Stroke; National Eye Institute