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Study Finds People Have Short-Lived Immunity to Seasonal Coronaviruses

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Microscopic view of Coronavirus
Caption: Artistic rendering of coronaviruses. Credit: iStock/Naeblys

A key metric in seeking to end the COVID-19 pandemic is the likely duration of acquired immunity, which is how long people infected with SARS-CoV-2, the novel coronavirus that causes COVID-19, are protected against reinfection. The hope is that acquired immunity from natural infection—or from vaccines—will be long-lasting, but data to confirm that’s indeed the case won’t be in for many months or years.

In the meantime, a useful place to look for clues is in long-term data on reinfections with other seasonal coronaviruses. Could the behavior of less life-threatening members of the coronavirus family give us some insight into what to expect from SARS-CoV-2?

A new study, published in the journal Nature Medicine, has taken exactly this approach. The researchers examined blood samples collected continuously from 10 healthy individuals since the 1980s for evidence of infections—and reinfections—with four common coronaviruses. Unfortunately, it’s not particularly encouraging news. The new data show that immunity to other coronaviruses tends to be short-lived, with reinfections happening quite often about 12 months later and, in some cases, even sooner.

Prior to the discovery of SARS-CoV-2, six coronaviruses were known to infect humans. Four are responsible for relatively benign respiratory illnesses that regularly circulate to cause the condition we recognize as the common cold. The other two are more dangerous and, fortunately, less common: SARS-CoV-1, the virus responsible for outbreaks of Severe Acute Respiratory Syndrome (SARS), which ended in 2004; and MERS-CoV, the virus that causes the now rare Middle East Respiratory Syndrome (MERS).

In the new study, a team led by Lia van der Hoek, University of Amsterdam, the Netherlands, set out to get a handle on reinfections with the four common coronaviruses: HCoV-NL63, HCoV-229E, HCoV-OC43, and HCoV-HKU1. This task isn’t as straightforward as it might sound. That’s because, like SARS-CoV-2, infections with such viruses don’t always produce symptoms that are easily tracked. So, the researchers looked instead to blood samples from 10 healthy individuals enrolled for decades in the Amsterdam Cohort Studies on HIV-1 Infection and AIDS.

To detect coronavirus reinfections, they measured increases in antibodies to a particular portion of the nucleocapsid of each coronavirus. The nucleocapsid is a protein shell that encapsulates a coronavirus’ genetic material and serves as important targets for antibodies. An increase in antibodies targeting the nucleocapsid indicated that a person was fighting a new infection with one of the four coronaviruses.

All told, the researchers examined a total of 513 blood samples collected at regular intervals—every 3 to 6 months. In those samples, the team’s analyses uncovered 3 to 17 coronavirus infections per study participant over more than 35 years. Reinfections occurred every 6 to 105 months. But reinfections happened most frequently about a year after a previous infection.

Not surprisingly, they also found that blood samples collected in the Netherlands during the summer months—June, July, August, and September—had the lowest rate of infections for all four seasonal coronaviruses, indicating a higher frequency of infections in winter in temperate countries. While it remains to be seen, it’s possible that SARS-CoV-2 ultimately may share the same seasonal pattern after the pandemic.

These findings show that annual reinfections are a common occurrence for all other common coronaviruses. That’s consistent with evidence that antibodies against SARS-CoV-2 decrease within two months of infection [2]. It also suggests that similar patterns of reinfection may emerge for SARS-CoV-2 in the coming months and years.

At least three caveats ought to be kept in mind when interpreting these data. First, the researchers tracked antibody levels but didn’t have access to information about actual illness. It’s possible that a rise in antibodies to a particular coronavirus might have provided exactly the response needed to convert a significant respiratory illness to a mild case of the sniffles or no illness at all.

Second, sustained immunity to viruses will always be disrupted if the virus is undergoing mutational changes and presenting a new set of antigens to the host; the degree to which that might have contributed to reinfections is not known. And, third, the role of cell-based immunity in fighting off coronavirus infections is likely to be significant, but wasn’t studied in this retrospective analysis.

To prepare for COVID-19 this winter, it’s essential to understand how likely a person who has recovered from the illness will be re-infected and potentially spread the virus to other people. While much more study is needed, the evidence suggests it will be prudent to proceed carefully and with caution when it comes to long-term immunity, whether achieved through naturally acquired infections or vaccination.

While we await a COVID-19 vaccine, the best way to protect yourself, your family, and your community is to take simple steps all of us can do today: maintain social distancing, wear a mask, avoid crowded indoor gatherings, and wash your hands.

References:

[1] Seasonal coronavirus protective immunity is short-lasting. Edridge AWD, Kaczorowska J, Hoste ACR, Bakker M, Klein M, Loens K, Jebbink MF, Matser A, Kinsella CM, Rueda P, Ieven M, Goossens H, Prins M, Sastre P, Deijs M, van der Hoek L. Nat Med. 2020 Sep 14. doi: 10.1038/s41591-020-1083-1. [Published online ahead of print.]

[2] Rapid decay of anti-SARS-CoV-2 antibodies in persons with mild Covid-19. Ibarrondo FJ, Fulcher JA, Goodman-Meza D, Elliott J, Hofmann C, Hausner MA, Ferbas KG, Tobin NH, Aldrovandi GM, Yang OO. N Engl J Med. 2020 Sep 10;383(11):1085-1087.

Links:

Coronavirus (COVID-19) (NIH)

Lia van der hoek (University of Amsterdam, the Netherlands)


Big Data Study Reveals Possible Subtypes of Type 2 Diabetes

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Computational model

Caption: Computational model showing study participants with type 2 diabetes grouped into three subtypes, based on similarities in data contained in their electronic health records. Such information included age, gender (red/orange/yellow indicates females; blue/green, males), health history, and a range of routine laboratory and medical tests.
Credit: Dudley Lab, Icahn School of Medicine at Mount Sinai, New York

In recent years, there’s been a lot of talk about how “Big Data” stands to revolutionize biomedical research. Indeed, we’ve already gained many new insights into health and disease thanks to the power of new technologies to generate astonishing amounts of molecular data—DNA sequences, epigenetic marks, and metabolic signatures, to name a few. But what’s often overlooked is the value of combining all that with a more mundane type of Big Data: the vast trove of clinical information contained in electronic health records (EHRs).

In a recent study in Science Translational Medicine  [1], NIH-funded researchers demonstrated the tremendous potential of using EHRs, combined with genome-wide analysis, to learn more about a common, chronic disease—type 2 diabetes. Sifting through the EHR and genomic data of more than 11,000 volunteers, the researchers uncovered what appear to be three distinct subtypes of type 2 diabetes. Not only does this work have implications for efforts to reduce this leading cause of death and disability, it provides a sneak peek at the kind of discoveries that will be made possible by the new Precision Medicine Initiative’s national research cohort, which will enroll 1 million or more volunteers who agree to share their EHRs and genomic information.


Bold Blueprint for Precision Medicine Initiative’s Research Cohort

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Twitter Chat

Caption: #PMINetwork Twitter chat with @NIHDirector Francis Collins, NIH Media Branch’s @RenateMyles, and, in background, PMI Cohort Program Acting Director @NCCIH_Josie Briggs.
Credit: @KathyHudsonNIH

Readers of this blog know how excited I am about the potential of precision medicine for revolutionizing efforts to treat disease and improve human health. So, it stands to reason that I’m delighted by the positive reactions of researchers, health professionals, and the public to a much-anticipated report from the Precision Medicine Initiative (PMI) Working Group of the Advisory Committee to the NIH Director. Topping the report’s list of visionary recommendations? Build a national research cohort of 1 million or more Americans over the next three to four years to expand knowledge and practice of precision medicine.

When the President announced PMI during his 2015 State of the Union address, he envisioned a precise new era in medicine in which every patient receives the right treatment at the right time—an era in which health care professionals have the resources at hand to take into account individual differences in genes, environments, and lifestyles that contribute to disease. To achieve this, PMI’s national research cohort would tap into recent advances in science, technology, and research participation policies to build the knowledge base needed to develop individualized care for all diseases and conditions.