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More than 50 Long-term effects of COVID-19: a systematic review and meta-analysis

Affiliations.

1 Drug Development, Novartis Pharmaceuticals, New Jersey, USA.
2 Instituto Nacional de Cancerología, Subdirección de Investigación básica, Ciudad de México, México.
3 National Autonomous University of Mexico, SOMEDICyT, RedMPC, México.
4 Harvard T.H. Chan School of Public Health Boston, Massachusetts, USA.
5 Divison of Infectious Diseases, Emory University School of Medicine, Atlanta, Georgia, USA.
6 Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.
7 Center for Infectious Medicine, Department of Medicine Huddinge, Karolinska Institute, Stockholm, Sweden.
8 Department of Neurosurgery, Center for Neuroregeneration, Houston Methodist Research Institute, Houston, Texas, USA.
9 Department of Neuroscience in Neurological Surgery, Weill Cornell Medical College, New York, USA.
PMID: 33532785
PMCID: PMC7852236
DOI: 10.1101/2021.01.27.21250617
More than 50 long-term effects of COVID-19: a systematic review and meta-analysis. Lopez-Leon S, Wegman-Ostrosky T, Perelman C, Sepulveda R, Rebolledo PA, Cuapio A, Villapol S. Lopez-Leon S, et al. Sci Rep. 2021 Aug 9;11(1):16144. doi: 10.1038/s41598-021-95565-8. Sci Rep. 2021. PMID: 34373540 Free PMC article.

COVID-19, caused by SARS-CoV-2, can involve sequelae and other medical complications that last weeks to months after initial recovery, which has come to be called Long-COVID or COVID long-haulers. This systematic review and meta-analysis aims to identify studies assessing long-term effects of COVID-19 and estimates the prevalence of each symptom, sign, or laboratory parameter of patients at a post-COVID-19 stage. LitCOVID (PubMed and Medline) and Embase were searched by two independent researchers. All articles with original data for detecting long-term COVID-19 published before 1 st of January 2021 and with a minimum of 100 patients were included. For effects reported in two or more studies, meta-analyses using a random-effects model were performed using the MetaXL software to estimate the pooled prevalence with 95% CI. Heterogeneity was assessed using I 2 statistics. This systematic review followed Preferred Reporting Items for Systematic Reviewers and Meta-analysis (PRISMA) guidelines, although the study protocol was not registered. A total of 18,251 publications were identified, of which 15 met the inclusion criteria. The prevalence of 55 long-term effects was estimated, 21 meta-analyses were performed, and 47,910 patients were included. The follow-up time ranged from 14 to 110 days post-viral infection. The age of the study participants ranged between 17 and 87 years. It was estimated that 80% (95% CI 65-92) of the patients that were infected with SARS-CoV-2 developed one or more long-term symptoms. The five most common symptoms were fatigue (58%), headache (44%), attention disorder (27%), hair loss (25%), and dyspnea (24%). All meta-analyses showed medium (n=2) to high heterogeneity (n=13). In order to have a better understanding, future studies need to stratify by sex, age, previous comorbidities, severity of COVID-19 (ranging from asymptomatic to severe), and duration of each symptom. From the clinical perspective, multi-disciplinary teams are crucial to developing preventive measures, rehabilitation techniques, and clinical management strategies with whole-patient perspectives designed to address long COVID-19 care.

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Conflict of interest statement

Conflict of interest statement SLL is an employee of Novartis Pharmaceutical Company; the statements presented in the paper do not necessarily represent the position of the company. The remaining authors have no competing interests to declare.

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Figure 2.. Long-term effects of coronavirus disease 2019 (COVID-19).

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The Risk Factors For Long COVID Have Finally Been Revealed

For many, catching SARS-CoV-2 means an unpleasant few weeks of aches, coughs, and fatigue. In roughly one in every five cases, however, the discomfort endures for months on end.

What puts some individuals at greater risk of an acute infection lingering as long COVID has been far from clear.

A team of experts from across the US analyzed the records of 4,708 US adults infected by SARS-CoV-2 between April 2020 and February 2023. Around one in five still had difficulties with COVID-19 after three months – the threshold for long COVID.

Long COVID was found to be more common in women, and those with previous cardiovascular disease issues. It was also less common in those who had been vaccinated, and in people with the less severe Omicron variant of the infection.

"Our study underscores the important role that vaccination against COVID has played, not just in reducing the severity of an infection but also in reducing the risk of long COVID," says Elizabeth Oelsner, an epidemiologist at the Columbia University Irving Medical Center.

While some health conditions like chronic obstructive pulmonary disease and a history of smoking were linked to longer recovery times, these became insignificant once other risk factors were also factored in.

Severe infections and longer recovery times were also found to be more common in American Indian and Alaska Native participants, adding to what we already know about racial and ethnic disparities with COVID-19.

Some of these risk factors, including a higher long COVID risk for females and a lower risk for vaccinated individuals, have been reported before . However, in this sample the researchers didn't find any significant link to mental health issues – even though long COVID results in some major changes in the brain .

"Although studies have suggested that many patients with long COVID experience mental health challenges, we did not find that depressive symptoms prior to SARS-CoV-2 infection were a major risk factor for long COVID," says Oelsner.

With a better knowledge of who is most at risk from long COVID, it becomes easier for researchers to figure out why it's happening in certain people – and from there what sort of treatments might be effective against the condition.

As most of the world tries to move on from the pandemic , millions worldwide with persisting COVID-19 symptoms and society at large stand to benefit from ongoing research on the disease .

"Our study clearly establishes that long COVID poses a substantial personal and societal burden," says Oelsner.

"By identifying who was likely to have experienced a lengthy recovery, we have a better understanding of who should be involved in ongoing studies of how to lessen or prevent the long-term effects of SARS-CoV-2 infection."

The research has been published in JAMA Network Open .

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Long COVID: Long-Term Effects of COVID-19

Featured Experts:

Tae Chung, M.D.

Megan Hosey Mastalerz, Ph.D.

Amanda Kole Morrow, M.D.

Arun Venkatesan, M.D., Ph.D.

Ann Parker, M.D., Ph.D.

Mild or moderate COVID-19 lasts about two weeks for most people. But in some others, long-term effects of COVID-19 can cause lingering health problems and wreak havoc for months.

Tae Chung, M.D., a specialist in neurology and physical medicine and rehabilitation; Megan Hosey, Ph.D. , an expert in rehabilitation psychology; Arun Venkatesan, M.D., Ph.D. , a specialist in neurology; Amanda Morrow, M.D. , an expert in pediatric rehabilitation medicine; and Ann M. Parker, M.D., Ph.D. , who specializes in lung disease and critical care, discuss long-term COVID-19, what symptoms are most common and what those affected by them can expect.

How long does COVID last?

Mild or moderate COVID-19 lasts about two weeks for most people. But others experience lingering health problems even after the fever and cough go away and they are no longer testing positive for the illness.

Parker notes that the World Health Organization has developed a definition for post-COVID-19 condition (the WHO’s term for long COVID) as coronavirus symptoms that persist or return three months after a person becomes ill from infection with SARS CoV-2, the coronavirus that causes COVID-19. Those symptoms can include:

Shortness of breath
Cognitive problems (thinking and memory)

The symptoms can come and go, but have an impact on the person’s everyday functioning, and cannot be explained by another health problem.

What causes post-COVID syndrome?

While it’s clear that people with certain risk factors (including high blood pressure, smoking, diabetes, obesity and other conditions) are more likely to have a serious bout of COVID-19, there isn’t a clear link between these risk factors and long-term problems. In fact, long COVID can happen in people who have mild symptoms, although patients with more severe initial illness seem to be more likely to have long-term impairments.

More studies will shed light on why these stubborn health problems persist in some people.

What causes symptoms in COVID long haulers?

SARS-CoV-2 can attack the body in a range of ways, causing damage to the lungs, heart, nervous system, kidneys, liver and other organs. Mental health problems can arise from grief and loss, unresolved pain or fatigue, or from post-traumatic stress disorder (PTSD) after treatment in the intensive care unit (ICU).

Doctors are seeing a spectrum of symptoms after acute COVID-19, some of which would be expected after other critical illnesses. Some are minor, but other people may need continuing care and even readmission to the hospital.

Similar, lingering problems can affect patients with other serious illnesses. But it is notable that post-COVID-19 syndrome is not just afflicting people who were very sick with the coronavirus: Some patients who were never severely ill with COVID-19 are experiencing long-term symptoms.

Do COVID vaccines prevent long COVID?

Getting vaccinated for COVID-19 lowers the risks of COVID infection. While breakthrough infections are possible, being fully vaccinated and boosted is effective in reducing the risk of hospitalization and death due to COVID. Research is ongoing about how long COVID affects people who had breakthrough COVID, but it is likely that being vaccinated reduces the risk.

Breathing Issues after COVID-19

A bad case of COVID-19 can produce scarring and other permanent problems in the lungs , but even mild infections can cause persistent shortness of breath — getting winded easily after even light exertion.

Lung recovery after COVID-19 is possible, but takes time. Experts say it can take months for a person’s lung function to return to pre-COVID-19 levels. Breathing exercises and respiratory therapy can help.

Heart Problems in COVID Long Haulers

SARS-CoV-2 infection can leave some people with heart problems , including inflammation of the heart muscle. In fact, one study showed that 60% of people who recovered from COVID-19 had signs of ongoing heart inflammation, which could lead to the common symptoms of shortness of breath, palpitations and rapid heartbeat. This inflammation appeared even in those who had had a mild case of COVID-19 and who had no medical issues before they got sick.

Kidney Damage from COVID-19

If the coronavirus infection caused kidney damage , this can raise the risk of long-term kidney disease and the need for dialysis.

Loss of Taste and Smell after COVID-19

The senses of smell and taste are related, and because the coronavirus can affect cells in the nose, having COVID-19 can result in lost or distorted senses of smell (anosmia) or taste. Before and after people become ill with COVID-19, they might lose their sense of smell or taste entirely, or find that familiar things smell or taste bad, strange or different.

For about a quarter of people with COVID-19 who have one or both of these symptoms, the problem resolves in a couple of weeks. But for most, these symptoms persist. Though not life-threatening, prolonged distortion of these senses can be devastating and can lead to lack of appetite, anxiety and depression. Some studies suggest that there’s a 60% to 80% chance that these people will see improvement in their sense of smell within a year.

Neurologic Problems in Long COVID

Neurologist Arun Venkatesan, M.D., Ph.D. , says, “Some individuals develop medium to long-term symptoms following COVID infection, including brain fog, fatigue, headaches and dizziness. The cause of these symptoms is unclear but is an active area of investigation.”

Cognitive Problems and Mental Health after COVID-19

Can COVID-19 increase a person’s risk for anxiety, depression and cognitive issues? A study of COVID-19’s impact on mental and emotional well-being conducted by Johns Hopkins experts in psychiatry, cognition (thinking, reasoning and remembering) and mental health found that these problems were common among a diverse sample of COVID-19 survivors.

Cognitive impairment after acute coronavirus infection can have a severe impact on a person’s life. Long-haul COVID patients may experience changes in the way they think, concentrate, speak and remember, and these symptoms can affect their ability to work or even maintain activities of daily living.

After recovering from the coronavirus, some people are left with lingering anxiety, depression and other post-COVID mental health issues . Physical changes such as pain and weakness can be complicated by long periods of isolation, stress from job loss and financial difficulties, and grief from the deaths of loved ones and the loss of good health.

Post-Intensive Care Syndrome

Patients who were hospitalized for COVID-19 treatment have a particularly challenging recovery. Experts note that p ost-intensive care syndrome, or PICS , puts COVID-19 survivors and other people who have spent time in the ICU at a higher risk for problems with mental health, cognition and physical recovery.

Megan Hosey, Ph.D., a rehabilitation psychologist, says that prolonged time in the ICU can cause delirium. The strange surroundings, multiple mind-altering medications, isolation and loss of control can leave patients with lasting and recurrent sensations of terror or dread, including post-traumatic stress disorder (PTSD).

“Many patients have hallucinations where they believe that medical providers are trying to harm them,” Hosey says. “We've had patients tell us things like ‘I thought I was being buried alive’ when they were being put into an MRI.”

Learn more about depression and anxiety associated with COVID-19 .

POTS and Insomnia after COVID-19

Postural orthostatic tachycardia syndrome, or POTS , is a condition that affects blood circulation, and people who have survived COVID-19 may be more vulnerable to it. Tae Chung, M.D. , who specializes in physical medicine and rehabilitation, says “POTS can leave survivors with other neurologic symptoms , including continuing headache, fatigue, brain fog, difficulties in thinking or concentrating, and insomnia.

Persistent post-COVID-19 insomnia, or “COVID-somnia” is an increasingly common complaint among COVID-19 survivors and can be a typical symptom of POTS.

Diabetes after COVID-19

The relationship between COVID-19 and diabetes, especially type 2 diabetes, is complex. Type 2 diabetes is a risk factor for serious cases of COVID-19, and some survivors of the illness seem to be developing type 2 diabetes signs after they recover from COVID-19.

Long COVID Symptoms in Children and Teens

It’s not yet known whether children who have had COVID-19 are more or less likely than adults to experience continuing symptoms. But long-term COVID-19 in children is a possibility, showing up as fatigue, headaches, difficulty with school work, mood concerns, shortness of breath and other long-hauler symptoms.

Amanda Morrow, M.D. , a specialist in physical medicine and rehabilitation, is part of the multidisciplinary team at Kennedy Krieger Institute’s Pediatric Post COVID-19 Rehabilitation Clinic , which addresses lingering coronavirus symptoms in children and teens. She says it isn't clear why long COVID-19 symptoms affect some children and not others.

“We are seeing patients who are often very high-functioning, healthy children who did not have any previous illnesses or medical conditions,” she says, noting that many of the kids being treated at the clinic only had mild bouts of COVID-19.

Heart inflammation after COVID-19 is a concern, especially among young athletes returning to their sports after a mild or even asymptomatic case of the coronavirus. They should be screened for any signs of heart damage to ensure it is safe for them to resume activity.

Kids who have experienced the uncommon but serious complication of COVID-19 called multisystem inflammatory syndrome in children, or MIS-C , can be left with serious heart damage, and should be followed by a pediatric cardiologist.

Long-term COVID-19 problems challenge health care, too

Brigham says that the sheer scale of caring for patients with lingering COVID-19 symptoms is a serious challenge. She notes that clinicians saw post-viral symptoms in patients affected by two other coronavirus diseases — severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) .

But, she says, outbreaks of those diseases were limited. Millions more people have had COVID-19 than SARS or MERS, so the potential problem of lingering health problems is huge, particularly in the context of the pandemic, with isolation, economic disadvantage, lack of access and changed daily routines further compounding the complexities of long-term COVID-19 care.

Long Covid | Johns Hopkins Post Acute Covid-19 Team (PACT)

People with long COVID, or “long-haulers,” are COVID-19 survivors but they have persistent symptoms such as shortness of breath, fatigue, headaches, palpitations, and impairments in mental health and cognition. At Johns Hopkins, the Post-Acute COVID-19 Team works with patients to help them return to previous life. Learn more at hopkinsmedicine.org/coronavirus/pact/.

What is the treatment for long-haul COVID?

Doctors and therapists can work with you to address symptoms. The Johns Hopkins Post-Acute COVID-19 Team (JH PACT) is a special multidisciplinary clinic to support the recovery of people who have had COVID-19, and similar clinics are emerging at other hospitals.

Breathing exercises, physical therapy, medications and other treatments appear to be helpful.

Many clinical trials are being planned to test various drugs and interventions for long-haul COVID. For example, a clinical trial on a novel immune-modulating drug will be launched for patients who developed POTS as a post-COVID syndrome (PI: Tae Chung, MD) at Johns Hopkins POTS Clinic Program in the summer of 2022. More information will be updated in Johns Hopkins websites.

How do I prevent long COVID-19?

The best way to avoid post-COVID-19 complications is to prevent infection with the coronavirus in the first place. Practicing coronavirus precautions and staying up to date with COVID-19 vaccines and boosters are effective ways to avoid getting COVID-19.

When should I see a doctor about post-COVID-19 symptoms?

Long-term COVID-19 symptoms can be similar to signs of other disease, so it is important to see your doctor and rule out other problems, such as cardiac issues or lung disease.

Don’t ignore loss of smell, depression, anxiety or insomnia, or write these off as unimportant or “all in your head.” Any symptom that interferes with your daily life is worth a call to your doctor, who can help you address these problems and improve the quality of your life.

If you experience new chest pain, difficulty breathing, bluish lips or any other sign of a life-threatening problem, call 911 or emergency services right away.

More information will emerge on long-term effects of COVID

SARS-CoV-2, the virus that causes COVID-19, was identified in December 2019. There is still a lot to learn about it, but our understanding of the virus and COVID-19 is evolving by the day.

Researchers will learn more about how and why the coronavirus affects different people in such a variety of ways, and why some people experience no symptoms at all while others have life-threatening organ damage or lasting disability. New insights will provide avenues for therapies and hope for people living with long-term COVID-19 effects.

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Coronavirus: Younger Adults Are at Risk, Too

NIH builds large nationwide study population of tens of thousands to support research on long-term effects of COVID-19

The National Institutes of Health awarded nearly $470 million to build a national study population of diverse research volunteers and support large-scale studies on the long-term effects of COVID-19. The NIH REsearching COVID to Enhance Recovery (RECOVER) Initiative made the parent award to New York University (NYU) Langone Health, New York City, which will make multiple sub-awards to more than 100 researchers at more than 30 institutions and serves as the RECOVER Clinical Science Core. This major new award to NYU Langone supports new studies of COVID-19 survivors and leverages existing long-running large cohort studies with an expansion of their research focus. This combined population of research participants from new and existing cohorts, called a meta-cohort, will comprise the RECOVER Cohort. This funding was supported by the American Rescue Plan.

NIH launched the RECOVER Initiative to learn why some people have prolonged symptoms (referred to as long COVID) or develop new or returning symptoms after the acute phase of infection from SARS-CoV-2, the virus that causes COVID-19. The most common symptoms include pain, headaches, fatigue, “brain fog,” shortness of breath, anxiety, depression, fever, chronic cough, and sleep problems.

“We know some people have had their lives completely upended by the major long-term effects of COVID-19,” said NIH Director Francis S. Collins, M.D., Ph.D. “These studies will aim to determine the cause and find much needed answers to prevent this often-debilitating condition and help those who suffer move toward recovery.”

Data from the RECOVER Cohort will include clinical information, laboratory tests, and analyses of participants in various stages of recovery following SARS-CoV-2 infection. With immediate access to data from existing, diverse study populations, it is anticipated researchers will be able to accelerate the timeline for this important research.

“This scientifically rigorous approach puts into place a collaborative and multidisciplinary research community inclusive of diverse research participants that are critical to informing the treatment and prevention of the long-term effects of COVID-19,” said Gary H. Gibbons, M.D., director of NIH’s National Heart, Lung, and Blood Institute and one of the co-chairs of the RECOVER Initiative.

Researchers, people affected by long COVID, and representatives from advocacy organizations worked together to develop the RECOVER master protocols that use standardized trial designs and research methods to enable uniform evaluation of study populations across studies and the ability to quickly pivot the research focus depending on what findings show. This approach allows for data harmonization across research studies and study populations. Data harmonization allows data to be compared and analyzed, which will facilitate the research process and provide more robust findings.

Studies will include adult, pregnant, and pediatric populations; enroll patients during the acute as well as post-acute phases of the SARS-CoV-2 infection; evaluate tissue pathology; analyze data from millions of electronic health records; and use mobile health technologies, such as smartphone apps and wearable devices, which will gather real-world data in real time. Together, these studies are expected to provide insights over the coming months into many important questions including the incidence and prevalence of long-term effects from SARS-CoV-2 infection, the range of symptoms, underlying causes, risk factors, outcomes, and potential strategies for treatment and prevention.

“Given the range of symptoms that have been reported, intensive research using all available tools is necessary to understand what happens to stall recovery from this terrible virus. Importantly, the tissue pathology studies in RECOVER will enable in depth studies of the virus’s effects on all body systems” said Walter J. Koroshetz, M.D., director of NIH’s National Institute of Neurological Disorders and Stroke and one of the RECOVER co-chairs.

Research opportunity announcements were issued in February 2021 and awards to launch the RECOVER Clinical Science Core and Data Resource Core were announced in June . An award in support of a RECOVER Biorepository Core has also been made to the Mayo Clinic for approximately $40 million to collect, curate, and distribute comprehensive source of clinical samples for additional research studies. The Cores provide coordination and infrastructure for the RECOVER Initiative, including supporting the activities of the investigator consortium and ensuring that all data are harmonized and shared among researchers. In May and June, short-term awards were provided to more than 30 institutions to develop the master protocols.

These awards pave the path to gaining greater understanding of the long-term effects of SARS-CoV-2 infection and enabling researchers to identify potential interventions and preventive strategies.

About the National Institute of Neurological Disorders and Stroke (NINDS): NINDS is the nation’s leading funder of research on the brain and nervous system. The mission of NINDS is to seek fundamental knowledge about the brain and nervous system and to use that knowledge to reduce the burden of neurological disease. For more information, visit www.ninds.nih.gov .

About the National Heart, Lung, and Blood Institute (NHLBI): NHLBI is the global leader in conducting and supporting research in heart, lung, and blood diseases and sleep disorders that advances scientific knowledge, improves public health, and saves lives. For more information, visit www.nhlbi.nih.gov .

About the National Institutes of Health (NIH): NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit www.nih.gov .

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CDC Science and the public health approach to Long COVID

Establishing a comprehensive understanding of SARS-CoV-2 infection and Long COVID helps inform current and future public health strategies.
Public health professionals should promote awareness of Long COVID, help combat the stigma that patients with Long COVID encounter and emphasize prevention of Long COVID by getting an updated COVID-19 vaccine.

Long COVID is an infection-associated chronic condition that occurs after SARS-CoV-2 infection and is present for at least 3 months as a continuous, relapsing and remitting, or progressive disease state that affects one or more organ systems. Long COVID affects millions of people across the United States and increases healthcare needs. Health departments play a crucial role in surveillance, communication, and education to increase awareness, reduce stigma, and improve care.

CDC and its partners are conducting research on Long COVID in a variety of populations and settings and are actively working to:

Disseminate clinical guidance and share epidemiologic and surveillance data to inform public health policy and action.
Create and increase access to Long COVID educational and communication resources.
Support clinical capacity by connecting clinicians and public health professionals to Long COVID clinical guidance, resources and educational opportunities .

Key findings

Long COVID estimates vary due to different study criteria, symptoms investigated, patient populations, and timing of when symptoms are assessed. CDC collects and analyzes data through several public health surveys . In 2022, 6.9% of adults and 1.3% of children (roughly 17 million and 1 million, respectively) in the United States ever reported experiencing Long COVID. (4-5)

While Long COVID can occur in anyone who gets a SARS-CoV-2 infection, some people or groups of people are at higher risk of developing Long COVID. These include women, people with underlying conditions, people who experienced more severe outcomes of COVID-19, and people of Hispanic ethnicity. (4,9,10) Approximately 1 in 4 adults with Long COVID reported experiencing significant limitations in their daily activity. (6-8)

More than 200 Long COVID symptoms have been identified. However, fatigue, brain fog, and exhaustion (post-exertional malaise) are among the commonly reported symptoms of Long COVID. (11,12)

COVID-19 vaccination is the best available tool to reduce the risk of Long COVID. Research shows that COVID-19 vaccination prior to SARS-CoV-2 infection reduces the risk of developing Long COVID among both children and adults. (1-3)

Public health approach to Long COVID

Collaboration between federal agencies and public health partners is important to address the serious nature of Long COVID. CDC has identified key areas of support that clinicians and public health professionals need to empower their efforts to address Long COVID. (13)

Surveillance and epidemiology

Using a variety of data sources to better understand and estimate the:

Incidence and prevalence of Long COVID
Economic, social, and clinical burden of Long COVID
Impact of Long COVID in certain populations who are disproportionately affected
Role of COVID-19 vaccination and other mitigation measures in preventing Long COVID

Communication and public education

Creating and increasing access to educational and communication resources to inform people about:

Long COVID as a serious infection–associated chronic condition
Prevention and management of Long COVID
Connecting individuals to existing resources and support groups to combat stigma and feelings of isolation, and to provide validation

Clinical capacity

Supporting clinician efforts to effectively diagnose and manage Long COVID by:

Connecting clinicians to continuing education opportunities and webinars focused on Long COVID
Sharing information and increasing access to clinical resources and guidance for Long COVID

What CDC is doing

CDC continues to collaborate with clinicians, public health partners, and other federal agencies to better understand and address the impacts of Long COVID. CDC supports these goals by:

Monitoring the burden and impact of Long COVID on various populations through TrackPCC , a study tracking Post-COVID Conditions
Sharing data and research aimed at better understanding COVID-19 and Long COVID
Supporting free clinician education through CDC’s Clinician Outreach and Communication Activity (COCA) calls

Explore CDC data

CDC’s research and data on Long COVID can be accessed through:

Scientific publications
Data briefs

Prevalence data can be readily accessed from the National Center for Health Statistics rapid survey systems . Inclusion of data and analyses of Long COVID by race/ethnicity, age, sex, and other factors are a CDC priority.

A Long COVID Definition: A Chronic, Systemic Disease State with Profound Consequences | The National Academies Press
Long-Term Health Effects of COVID-19: Disability and Function Following SARS-CoV-2 Infection | The National Academies Press
Implementation of the Government-wide Response to Long COVID
National Research Action Plan
Services and Supports for Longer-Term Impacts of COVID-19
Health+ Long Covid Human-Centered Design Report
Whole-Health-System-Approach-to-Long-COVID_080122_FINAL.pdf
CSTE-STLT-Long-COVID-Surveillance-August-2023.pdf

Toolkits and additional resources

Education and Practice-Based Resources | AAFP
The AHC Health-Related Social Needs Screening Tool
Long Covid (ca.gov)
Long COVID (PASC) Resources

Continuing education and webinars

Long-COVID webinars – Institute for Learning, Education and Development
The EveryONE Project™: COVID-19 and Health Equity
Continuing Education|Clinician Outreach and Communication Activity (COCA)

Echo Program

Public Program | iECHO
Long-COVID ECHO | College of Health Solutions
Exploring Clinical Practice and Research

Select CDC Publications

O’Laughlin KN, Thompson M, Hota B, Gottlieb M, Plumb ID, Chang AM, Wisk LE, Hall AJ, Wang RC, Spatz ES, Stephens KA, Huebinger RM, McDonald SA, Venkatesh A, Gentile N, Slovis BH, Hill M, Saydah S, Idris AH, Rodriguez R, Krumholz HM, Elmore JG, Weinstein RA, Nichol G; INSPIRE Investigators. Study protocol for the Innovative Support for Patients with SARS-COV-2 Infections Registry (INSPIRE): A longitudinal study of the medium and long-term sequelae of SARS-CoV-2 infection. PLoS One. 2022 Mar 3;17(3) .
Han JH, Womack KN, Tenforde MW, Files DC, Gibbs KW, Shapiro NI, Prekker ME, Erickson HL, Steingrub JS, Qadir N, Khan A, Hough CL, Johnson NJ, Ely EW, Rice TW, Casey JD, Lindsell CJ, Gong MN, Srinivasan V, Lewis NM, Patel MM, Self WH; Influenza and Other Viruses in the Acutely Ill (IVY) Network. Associations between persistent symptoms after mild COVID-19 and long-term health status, quality of life, and psychological distress. Influenza Other Respir Viruses. 2022 Mar 28 .
Gottlieb M, Wang RC, Yu H, Spatz ES, Montoy JCC, Rodriguez RM, Chang AM, Elmore JG, Hannikainen PA, Hill M, Huebinger RM, Idris AH, Lin Z, Koo K, McDonald S, O’Laughlin KN, Plumb ID, Santangelo M, Saydah S, Willis M, Wisk LE, Venkatesh A, Stephens KA, Weinstein RA; Innovative Support for Patients with SARS-CoV-2 Infections Registry (INSPIRE) Group. Severe Fatigue and Persistent Symptoms at 3 Months Following Severe Acute Respiratory Syndrome Coronavirus 2 Infections During the Pre-Delta, Delta, and Omicron Time Periods: A Multicenter Prospective Cohort Study. Clin Infect Dis. 2023 Jun 8;76(11):1930-1941. doi: 1093/cid/ciad045 . PMID: 36705268; PMCID: PMC10249989.
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Vahratian A, Adjaye-Gbewonyo D, Lin JS, Saydah S. Long COVID in children: United States, 2022. NCHS Data Brief, no 479. Hyattsville, MD: National Center for Health Statistics. 2023. DOI:  https://dx.doi.org/10.15620/cdc:132416 .
Ford ND, Slaughter D, Edwards D, Dalton A, Perrine C, Vahratian A, Saydah S. Long COVID and Significant Activity Limitation Among Adults, by Age – United States, June 1-13, 2022, to June 7-19, 2023. MMWR Morb Mortal Wkly Rep. 2023 Aug 11;72(32):866-870. doi: 15585/mmwr.mm7232a3 . PMID: 37561665; PMCID: PMC10415000.
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Bai F, Tomasoni D, Falcinella C, Barbanotti D, Castoldi R, Mulè G, Augello M, Mondatore D, Allegrini M, Cona A, Tesoro D, Tagliaferri G, Viganò O, Suardi E, Tincati C, Beringheli T, Varisco B, Battistini CL, Piscopo K, Vegni E, Tavelli A, Terzoni S, Marchetti G, Monforte AD. Female gender is associated with long COVID syndrome: a prospective cohort study. Clin Microbiol Infect. 2022 Apr;28(4):611.e9-611.e16. doi: 1016/j.cmi.2021.11.002 . Epub 2021 Nov 9. PMID: 34763058; PMCID: PMC8575536.
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Looking forward: Understanding the long-term effects of COVID-19

A woman covering her face wearing facial medical blue mask,

NHLBI-funded researchers tackle big questions with large study of patients hospitalized with COVID-19

The doctors on the front lines of the coronavirus pandemic may be aware of the health impacts that face survivors of mass shootings or natural disasters, but the highly contagious virus—one of the deadliest in history—is forcing them to grapple with a new question: if people who have beaten the disease should worry about their long-term health.

It is a question that’s front and center not just for doctors caring for patients, but also for researchers, many of whom are working frantically to understand the long-term effects of SARS-CoV-2—the virus that causes the coronavirus disease 2019 (COVID-19)—even as they try to develop effective therapies to beat it.

No small charge, but NHLBI is forging a way to help.

Enter the COVID-19 Observational Study, or the CORAL study, which will launch this month by NHLBI’s Prevention and Early Treatment of Acute Lung Injury (PETAL) Clinical Trials Network. The network is also behind a clinical trial to evaluate the safety and effectiveness of hydroxychloroquine to treat adults hospitalized with COVID-19 .

For the CORAL study, researchers across approximately 50 participating PETAL network hospitals plan to enroll 3,000 adult patients admitted to a hospital who had, or currently have, a laboratory confirmed case of COVID-19. The hope is that researchers can use the data and samples of materials they collect on patients hospitalized with COVID-19 to be able to better characterize the disease, understand the underlying biology, predict health outcomes, describe the long-term outcomes, and come to a fuller understanding of the overall impact of the coronavirus on the health system.

“This study is critical to understand the epidemiology of hospitalized COVID-19 patients in the United States and their recovery,” said James P. Kiley, Ph.D., director of NHLBI’s Division of Lung Diseases. “CORAL is uniquely positioned to leverage the expertise of PETAL and is deliberately designed to work with global efforts to understand the clinical course, and outcomes of these patients.”

The current scientific literature documenting COVID-19 cases in the U.S. is scattered. But one large report from China, issued in late February, described roughly 44,000 people with confirmed cases of COVID-19. Although most had mild symptoms of the disease, nearly 20 percent were critically ill with lung injury that made breathing difficult. Among the critically ill, many experienced cardiomyopathy and catastrophic arrythmias , prompting a call for more research to study the outcomes and long-term health problems that many patients will face.

The CORAL study is taking charge by looking at two sets of patients: those who were admitted to a hospital and were since discharged, and those who are currently admitted. To learn about eligible participants already discharged from the hospital, researchers will access their electronic medical records for clinical data, chest radiographs, and CT scans. Data and chest imaging on patients currently admitted to the hospital will also be collected so that researchers can document how COVID-19 takes hold of air sacs in both lungs and fills them with fluid—a major hallmark of the disease and the culprit behind the inability to breathe.

To help better understand how COVID-19 impacts more than just the lungs, researchers will also collect information on patient’s cardiac function, and important laboratory tests for markers of inflammation, coagulation, injury to the heart, liver and kidneys, and immunity. Prior reports have documented that patients with poor outcomes, particularly the oldest and most severely ill, were more likely to have abnormal findings in these areas, as well as a higher risk of death.

“This study will help us better understand the different ways that COVID19 affects patients and what factors influence patient outcomes in both the short and long-term,” said Lora Reineck, M.D., M.S., program director of Acute Lung Injury/Critical Care Program in NHLBI’s Division of Lung Diseases.

For patients enrolled in CORAL, researchers will collect samples of blood, urine, as well as sputum and fluid from the lung.

“We hope that collecting and testing these samples will allow investigators to better characterize the illness by comparing the clinical characteristics with corresponding biological responses in acutely ill patients, ” said Neil Aggarwal, M.D., branch chief of the Lung Biology and Disease Branch in NHLBI’s Division of Lung Diseases. “That should give the research community and health care providers a better understanding of what biological factors predict severe illness and which patients may benefit from being treated early.”

Participants who survive hospitalization may additionally be contacted by phone up to six months after hospital discharge for a follow-up study. The assessment will measure the levels and rates of recovery, including a special focus on heart and lungs, in an effort to determine which risk factors are tied to poor outcomes.

Data collected in CORAL will be deposited into the World Health Organization’s International Severe Acute Respiratory and Emerging Infections Consortium registry, which aims to advance global efforts in understanding COVID-19. Researchers can expect early data sharing to accelerate their knowledge of the disease.

Kiley said the CORAL study effort came together out of urgency. “This is a rapidly evolving area where more data is critically needed,” he said. “This study plans to not only provide desperately needed insights to help patients and health care providers, but also serve as a resource for all researchers.”

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http://orcid.org/0009-0007-9331-6790 Mads Bloch-Ibenfeldt 1 , 2 ,
Anne Theil Gates 1 , 2 ,
Karoline Karlog 1 ,
Naiara Demnitz 3 ,
Michael Kjaer 1 , 4 ,
Carl-Johan Boraxbekk 1 , 4 , 5
1 Department of Orthopedic Surgery M81 , Institute of Sports Medicine Copenhagen (ISMC), Bispebjerg Hospital , Copenhagen , Denmark
2 Center for Healthy Aging , Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen , Denmark
3 Danish Research Centre for Magnetic Resonance (DRCMR), Centre for Functional and Diagnostic Imaging and Research , Copenhagen University Hospital - Amager and Hvidovre , Copenhagen , Denmark
4 Institute for Clinical Medicine , Faculty of Health and Medical Sciences, University of Copenhagen , Copenhagen , Denmark
5 Department of Neurology , Copenhagen University Hospital - Bispebjerg and Frederiksberg , Copenhagen , Denmark
Correspondence to Mads Bloch-Ibenfeldt; mads.bloch-ibenfeldt{at}regionh.dk

Objectives Muscle function and size decline with age, but long-term effects of resistance training in older adults are largely unknown. Here, we explored the long-lasting (3 years) effects of 1 year of supervised resistance training with heavy loads.

Methods The LIve active Successful Ageing (LISA) study was a parallel group randomised controlled trial at a university hospital in Denmark. Older adults (n=451) at retirement age were randomised to 1 year of heavy resistance training (HRT), moderate-intensity training (MIT) or a non-exercising control group (CON). Primary outcome measure was leg extensor power. Secondary outcomes included maximal isometric quadriceps torque (isometric leg strength) and body composition (dual-energy X-ray absorptiometry (DXA)). Participants completed test procedures at baseline, following the 1-year intervention, and 2 and 4 years post study start.

Conclusion In well-functioning older adults at retirement age, 1 year of HRT may induce long-lasting beneficial effects by preserving muscle function.

Trial registration number NCT02123641 .

Body composition
Skeletal muscle

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

https://doi.org/10.1136/bmjsem-2024-001899

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Worldwide, the ageing population is growing. Unfortunately, skeletal muscle function and autonomy decrease with increased age. Thus, a challenge for society is to promote a healthy lifespan without age-related diseases and loss of autonomy.

WHAT THIS STUDY ADDS

Despite relatively healthy and well-functioning participants, 1 year of heavy resistance training at retirement age resulted in maintained strength 4 years after the study started. We propose that higher load resistance training may play an important role to induce long-lasting adaptations.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

This study provides evidence that resistance training with heavy loads at retirement age can have long-term effects over several years. The results, therefore, provide means for practitioners and policy-makers to encourage older individuals to engage in heavy resistance training.

Introduction

Skeletal muscle function declines with advancing age. 1–3 Although resistance training may partly counteract loss of muscle mass and function, shorter training studies (6–9 months duration) only show somewhat preserved muscle mass and function at 6–12 months follow-up. 4 5 Unfortunately, long-term follow-ups are sparse. 6 In one study, strength gains following high-intensity resistance training, and not low-intensity training, were preserved after 48 weeks of detraining. 7 The LIve active Successful Ageing (LISA) study, a large-scale randomised controlled trial (n=451), showed that strength can be maintained over 12 months following 1 year of heavy resistance training (HRT), but not after moderate training. 8 Thus, to gain long-lasting effects of resistance training in ageing one could speculate that high intensity or heavy loads are required. Here, we investigated whether there would be long-lasting effects of a 1-year supervised resistance training regimen with heavy loads, 3 years following the training in older individuals at retirement age.

Intervention

The current manuscript is an interim analysis of the LISA study, and additional follow-ups are planned (7-year and 10-year follow-ups). For details of intervention, recruitment and power calculations, see previous publications. 9 10 Briefly, 451 older adults were stratified according to sex, body mass index (BMI) and chair-rise test performance and randomised to 1 year of training with either heavy loads (HRT, n=149), moderate-intensity training (MIT, n=154) or a control condition (CON, n=148). At a commercial gym, HRT performed a supervised full body programme three times per week, with 6–8 weeks of initial habituation. The periodisation programme was machine based and each exercise included 3 sets of 6–12 repetitions at ~70%–85% of 1 RM, which was estimated using the prediction equation according to methods by Brzycki. 11 12 The moderate training in MIT was performed as circuit training with body weight and resistance bands once per week at the hospital and two times per week at home. Exercises in MIT progressed with the load of resistance bands (TheraBand, Akron, Ohio, USA) and mimicked the exercises in HRT but were performed with 3 sets of 10–18 repetitions at ~50%–60% of 1 RM. Both training programmes were created to comply with recommended guidelines 13 and included nine exercises—see published study protocol for full details. 10 Individuals in CON were encouraged to maintain their habitual physical activity level and were invited to regular cultural and social activities. In general, participants did not receive advice on healthy behaviour but were aware of the study timeline and planned follow-ups.

Test procedures

Day 1 included a health screening. On day 2, participants were dual-energy X-ray absorptiometry (DXA) scanned. Visceral fat mass was estimated by scanner software (Lunar iDXA, GE HealthCare—enCORE software V.16). Isometric leg strength (quadriceps) was assessed in a Good Strength chair (Bluetooth V.3.14, Metitur) and maximal isometric quadriceps torque (Newton metres) was measured during a minimum of 3 attempts per leg. 14 15 Day 3 included MRI of the brain and thigh (two-dimensional T1-weighted, 3.0 Tesla Phillips Achieva). Blinded assessors determined CSA of m. vastus lateralis using JIM software (Xinapse systems).

Daily physical activity was assessed as daily step count between days 2 and 3, by an accelerometer (activPAL micro, PAL Technologies) worn by the participants for five consecutive days. The test procedures were performed at baseline, postintervention (year 1) and at 2-year and 4-year follow-ups.

Patient and public involvement

Participants were informed of study progress through newsletters, and received overviews of personal results after tests at each time point. Additionally, participants were invited to an information evening, where the general study results at the time were presented.

Statistical analysis

Statistical analyses were performed in R V.4.1.1 and Rstudio 2021.09.0 using ‘psych’, 16 ‘emmeans’ 17 and ‘sjstats’ 18 packages. Figures were created in GraphPad Prism V.10.0.3.

At year 4, 369 participants attended follow-up assessments (HRT, n=128; MIT, n=126; CON, n=115). 82 older adults dropped out primarily due to lack of motivation or severe illness. These individuals had higher body weight, BMI and waist circumference at baseline compared with participants who were still part of the study at year 4. However, there was no difference in the response to the intervention in all outcomes at year 1 assessments between participants and individuals subsequently lost to follow-up. On average, participants were 71 years old (range: 64–75 years), 61% women and still active based on the daily physical activity ( table 1 ). There was no difference in sample characteristics between groups at baseline or at follow-up.

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Sample characteristics (mean±SD), n=369 unless otherwise specified

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(A–C) Isometric strength (mean±SEM) across 4 years for the different groups (heavy resistance training, HRT, moderate-intensity training, MIT and control group, CON). (A) (n=353) Isometric leg strength (Nm) trajectories for all time points separated by group. (B) Baseline and 4-year follow-up data (n=362), each group shown separately. (C) Individual data points showing the distribution of change from baseline to year four separated by group. *Significantly different from baseline (A): HRT 1 year, p<0.001; MIT 1 year, p=0.01; HRT 2 years, p<0.001; CON 4 years, p<0.001) (B): MIT 4 years, p=0.01; CON 4 years, p<0.001). #Change from baseline significantly different from change in MIT (A): HRT 4 years, p=0.003) (B): HRT 4 years, p=0.03). $Change from baseline significantly different from change in CON (A): HRT 4 years, p<0.001) (B): HRT 4 years, p<0.001).

In the change from baseline to year 4 ( figure 1B ), muscle strength was decreased in MIT (t(122)=1.98, p=0.01) and in CON (t(113)=1.98, p<0.001), whereas it was maintained in HRT (t(124)=1.98, p=0.37).

(A–B) Lean body mass and visceral fat (mean±SEM) across 4 years for the different groups (heavy resistance training, HRT, moderate-intensity training, MIT and control group, CON). (A) (n=365) Lean body mass (kg) trajectories for all time points separated by group. (B) Visceral fat (g) trajectories (n=365), for all time points separated by group. *Significantly different from baseline (A): HRT 1 year, p<0.001; MIT 4 years, p<0.001; CON 4 years, p=0.003) (B): HRT 1 year, p=0.01; CON 4 years, p=0.04).

Significant group×time interactions for CSA of m. vastus lateralis and the percentage of total body fat ( table 2 ) were driven by 1-year and 2-year changes, which have been reported previously. 8 9

Outcome variables (mean±SD) at baseline and at 4 years separated by group

For leg extensor power, handgrip strength and lean leg mass, there was a main effect of time, with decreases over the 4 years across all groups, but no interaction effects or significant group differences for the Δchange over 4 years ( table 2 ).

Resistance training with heavy loads induced long-lasting beneficial effects on muscle strength in a sample of older adults. We observed a difference between groups in leg strength, whereas handgrip strength, a measure of overall muscle strength, 19 was not influenced by any of the training regimes. Notably, benefits in leg strength were present despite lowered leg lean mass. Neural adaptations influence the response to resistance training. 20 21 The present results suggest that these adaptations might play a role even as lean leg mass and thigh CSA decrease. This is in line with a recent report showing that prolonged training across the lifespan is associated with permanently elevated acetylcholine receptors and improved neuromuscular function. 22 Resistance training may, therefore, be beneficial for function beyond the influence of muscle size itself.

Despite no group effects in lean leg mass, HRT maintained total lean mass, yet differences were minor. Interestingly, leg muscle strength was maintained from baseline in HRT, indicating that among individuals who already seemed to have a high physical activity level but were previously resistance training naive, implementing resistance training with heavy loads for 1 year may at group-level induce long-term health effects. Considering that muscle strength has been shown to predict mortality in apparently healthy populations, 23 these results may be of particular relevance. It is somewhat surprising that there was no muscular effect of the moderate training at year 4, as the intervention improved both lean mass and function in MIT, although to a lesser extent than HRT.

Interestingly, the amount of visceral fat was maintained from baseline to year 4 in both training groups, implying that some parameters may not be load-dependent or intensity-dependent in the long term. Recent research suggests that visceral fat is positively affected by resistance training. 24 Like visceral fat, the decrease over time in leg extensor power (primary outcome measure) was in line with our previous studies. 8 9

The present study benefited from its large sample size, long intervention and multiple follow-ups. Further, study attendance remained high (82% at year 4). Of note, with almost 10 000 daily steps, the study sample is likely to be healthier and more active than the average ageing population. Even so, ≈80% of the participants had at least one chronic medical disease. 9 In age-matched older individuals living in residential care facilities, high-intensity functional training has proven effective in improving independence in activities of daily living. Although over 4 months, these results show further evidence of the effectiveness of high-intensity training in older adults. 25

In conclusion, we showed that in a group of well-functioning older adults around retirement age, 1 year of HRT may induce long-lasting beneficial effects by preserving muscle function.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by Regional ethics committee: Capital Region, Copenhagen, Denmark, No. H-3-2014-017. Participants gave informed consent to participate in the study before taking part.

Acknowledgments

The authors thank Sussi Larsen, Kenneth Hudlebusch Mertz, Christian Skou Eriksen and Andreas Kraag Ziegler for helping with data collection.

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Supplementary materials

Press release

Contributors Conceptualisation: MK; Methodology: MK and C-JB; Validation: MB-I, ATG, KK, ND, MK and C-JB; Formal analysis: MB-I and ND; Investigation: MB-I, ATG and KK; Visualisation: MB-I; Supervision: MK and C-JB; Project administration: MB-I and ATG; Writing–original draft: MB-I; Writing–review and editing: MB-I, ATG, KK, ND, MK and C-JB. All authors approved the final manuscript to be published.

Funding Lundbeck Foundation (R380-2021-1269) and supported by Nordea Foundation (Grant from Center for Healthy Aging, University of Copenhagen, Denmark).

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

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Leicester team behind major study into the long-term impacts of COVID-19 is highly commended

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A Leicester team behind a major UK study into the long-term health impacts of COVID-19 on hospitalised patients has been recognised by the Medical Research Council (MRC).

Led by the National Institute for Health Research (NIHR) Leicester Biomedical Research Centre (a partnership between the University of Leicester and the University Hospitals of Leicester NHS Trust), the PHOSP-COVID study has drawn on expertise from a consortium of leading researchers and clinicians from across the UK to assess the impact of COVID-19 on patients’ physical and mental health, and their recovery.

Now the team, co-led by Dr Rachael Evans, Clinical Associate Professor and Honorary Consultant Respiratory Physician, Professor of Respiratory Research, Louise Wain, and NIHR Senior Investigator and Clinical Professor in Respiratory Medicine, Chris Brightling, has seen its work to understand the long-term health implications of COVID-19 highly commended for the Outstanding Team Impact Award at the MRC’s Impact Prize .

Research has looked into a variety of issues associated with COVID-19, including patient recovery outcomes; lung damage; breathlessness; organ abnormalities and the role of blood clots in cognitive problems.

Dr Evans said: “The event was really enjoyable - a very special day spent with a fantastic team to celebrate the achievements of the PHOSP collaboration for people living with Long Covid. Recognition of the team's hard work through a highly commended team impact award from the MRC is highly valued."

The PHOSP-COVID study launched in April 2020. Within five months, it had established a national consortium and research platform to understand and improve long-term outcomes for survivors following hospitalisation.

PHOSP-COVID combines expertise from doctors, nurses, allied health professionals, sociologists, scientists, statisticians, and data scientists across 24 universities and 83 hospitals together with 13 charities and patient groups.

The study has recruited more than 7,900 participants from 83 hospitals to obtain 16m data points and over 100,000 samples.

Detailed analysis of this data has led to research outputs which have been influential in informing treatment for people with long-COVID and the actions of policy-makers including the Department of Health and Social Care long-COVID task force, the Chief Medical Officers’ long-COVID group and the Scientific Advisory Group for Emergencies (SAGE).

The team’s ongoing work looking into mechanisms that drive the long-term effects of COVID-19 on all organs should help in the development of new tests, new treatments, and improved outcomes for people living with long COVID.

The Medical Research Council (MRC) Impact Prize launched in 2022 to recognise individuals or teams who have made outstanding contributions in medical research.

Awards and prizes
Coronavirus
Human health

Short- and long-term neuropsychiatric outcomes in long COVID in South Korea and Japan

Sunyoung Kim 1 na1 ,
Hayeon Lee ORCID: orcid.org/0009-0000-2403-6241 2 , 3 na1 na2 ,
Jinseok Lee ORCID: orcid.org/0000-0002-8580-490X 2 ,
Seung Won Lee ORCID: orcid.org/0000-0001-5632-5208 4 ,
Rosie Kwon 3 ,
Min Seo Kim ORCID: orcid.org/0000-0003-2115-7835 5 ,
Ai Koyanagi 6 ,
Lee Smith ORCID: orcid.org/0000-0002-5340-9833 7 ,
Guillaume Fond ORCID: orcid.org/0000-0003-3249-2030 8 ,
Laurent Boyer 8 ,
Masoud Rahmati ORCID: orcid.org/0000-0003-4792-027X 8 , 9 , 10 ,
Guillermo F. López Sánchez ORCID: orcid.org/0000-0002-9897-5273 11 ,
Elena Dragioti 12 , 13 ,
Samuele Cortese 14 , 15 , 16 , 17 , 18 ,
Ju-Young Shin 19 ,
Ahhyung Choi 19 ,
Hae Sun Suh 20 , 21 ,
Sunmi Lee 22 ,
Marco Solmi ORCID: orcid.org/0000-0003-4877-7233 23 , 24 , 25 , 26 ,
Chanyang Min 3 ,
Jae Il Shin ORCID: orcid.org/0000-0003-2326-1820 27 , 28 na2 ,
Dong Keon Yon ORCID: orcid.org/0000-0003-1628-9948 3 , 20 , 21 , 29 na2 &
Paolo Fusar-Poli 30 , 31 , 32 , 33

Nature Human Behaviour ( 2024 ) Cite this article

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Epidemiology
Risk factors

We investigated whether SARS-CoV-2 infection is associated with short- and long-term neuropsychiatric sequelae. We used population-based cohorts from the Korean nationwide cohort (discovery; n = 10,027,506) and the Japanese claims-based cohort (validation; n = 12,218,680) to estimate the short-term (<30 days) and long-term (≥30 days) risks of neuropsychiatric outcomes after SARS-CoV-2 infection compared with general population groups or external comparators (people with another respiratory infection). Using exposure-driven propensity score matching, we found that both the short- and long-term risks of developing neuropsychiatric sequelae were elevated in the discovery cohort compared with the general population and those with another respiratory infection. A range of conditions including Guillain-Barré syndrome, cognitive deficit, insomnia, anxiety disorder, encephalitis, ischaemic stroke and mood disorder exhibited a pronounced increase in long-term risk. Factors such as mild severity of COVID-19, increased vaccination against COVID-19 and heterologous vaccination were associated with reduced long-term risk of adverse neuropsychiatric outcomes. The time attenuation effect was the strongest during the first six months after SARS-CoV-2 infection, and this risk remained statistically significant for up to one year in Korea but beyond one year in Japan. The associations observed were replicated in the validation cohort. Our findings contribute to the growing evidence base on long COVID by considering ethnic diversity.

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Long COVID prevalence and impact on quality of life 2 years after acute COVID-19

Features of acute COVID-19 associated with post-acute sequelae of SARS-CoV-2 phenotypes: results from the IMPACC study

A nationwide questionnaire study of post-acute symptoms and health problems after SARS-CoV-2 infection in Denmark

Data availability.

The datasets analysed during the current study are available from the NHIS, South Korea ( https://nhiss.nhis.or.kr/bd/ab/bdaba000eng.do ) and the JMDC, Japan ( https://www.jmdc.co.jp/en/jmdc-claims-database/ ). This protects the confidentiality of the data and ensures that information governance is robust. Applications to access health data in South Korea should be submitted to the NHIS, South Korea. Information can be found at https://nhiss.nhis.or.kr/bd/ab/bdaba000eng.do . Applications to access health data in Japan should be submitted to the JMDC, Japan. Information can be found at https://www.jmdc.co.jp/en/jmdc-claims-database/ .

Code availability

This study did not generate new or customized code or algorithms. The statistical analyses were performed using SAS (version 9.4; SAS Institute Inc.) for big-data analysis. The code used in the analysis is available from the corresponding author upon request.

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Acknowledgements

This study used the database of the KDCA and the NHIS for policy and academic research. The research number of this study is KDCA-NHIS-2022-1-632 in South Korea and PHP-00002201-04 in Japan. This research was supported by a grant from the National Research Foundation of Korea funded by the Korean government (MSIT; no. RS-2023-00248157; D.K.Y.) and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea (no. HI22C1976; D.K.Y.). The research was supported by a grant (no. 21153MFDS601; D.K.Y.) from the Ministry of Food and Drug Safety in 2024. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. S.C., NIHR Research Professor (NIHR303122) is funded by the NIHR for this research project. The views expressed in this publication are those of the author(s) and not necessarily those of the NIHR, NHS or the UK Department of Health and Social Care. S.C. is also supported by NIHR grants NIHR203684, NIHR203035, NIHR130077, NIHR128472, RP-PG-0618-20003 and by grant 101095568-HORIZONHLTH- 2022-DISEASE-07-03 from the European Research Executive Agency.

Author information

These authors contributed equally: Sunyoung Kim, Hayeon Lee.

These authors jointly supervised this work: Hayeon Lee, Jae Il Shin, Dong Keon Yon.

Authors and Affiliations

Department of Family Medicine, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea

Sunyoung Kim

Department of Biomedical Engineering, Kyung Hee University, Yongin, South Korea

Hayeon Lee & Jinseok Lee

Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine, Seoul, South Korea

Hayeon Lee, Rosie Kwon, Chanyang Min & Dong Keon Yon

Department of Precision Medicine, Sungkyunkwan University School of Medicine, Suwon, South Korea

Seung Won Lee

Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA

Min Seo Kim

Research and Development Unit, Parc Sanitari Sant Joan de Deu, Barcelona, Spain

Ai Koyanagi

Centre for Health, Performance and Wellbeing, Anglia Ruskin University, Cambridge, UK

Research Centre on Health Services and Quality of Life, Assistance Publique-Hôpitaux de Marseille, Aix Marseille University, Marseille, France

Guillaume Fond, Laurent Boyer & Masoud Rahmati

Department of Physical Education and Sport Sciences, Faculty of Literature and Human Sciences, Lorestan University, Khoramabad, Iran

Masoud Rahmati

Department of Physical Education and Sport Sciences, Faculty of Literature and Humanities, Vali-E-Asr University of Rafsanjan, Rafsanjan, Iran

Division of Preventive Medicine and Public Health, Department of Public Health Sciences, School of Medicine, University of Murcia, Murcia, Spain

Guillermo F. López Sánchez

Pain and Rehabilitation Centre, and Department of Medical and Health Sciences, Linköping University, Linköping, Sweden

Elena Dragioti

Research Laboratory Psychology of Patients, Families and Health Professionals, Department of Nursing, School of Health Sciences, University of Ioannina, Ioannina, Greece

Centre for Innovation in Mental Health, School of Psychology, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK

Samuele Cortese

Clinical and Experimental Sciences (CNS and Psychiatry), Faculty of Medicine, University of Southampton, Southampton, UK

Solent NHS Trust, Southampton, UK

Child Study Center, Hassenfeld Children’s Hospital at NYU Langone, NYU Langone, New York, NY, USA

Department of Precision and Regenerative Medicine and Jonic Area, University of Bari ‘Aldo Moro’, Bari, Italy

School of Pharmacy, Sungkyunkwan University, Suwon, South Korea

Ju-Young Shin & Ahhyung Choi

Department of Regulatory Science, Kyung Hee University Graduate School, Seoul, South Korea

Hae Sun Suh & Dong Keon Yon

Institute of Regulatory Innovation through Science, Kyung Hee University College of Pharmacy, Seoul, South Korea

Department of Applied Mathematics, Kyung Hee University, Yongin, South Korea

Department of Psychiatry, SCIENCES lab, University of Ottawa, Ottawa, Ontario, Canada

Marco Solmi

On Track: The Champlain First Episode Psychosis Program, Department of Mental Health, Ottawa Hospital, Ottawa, Ontario, Canada

Ottawa Hospital Research Institute Clinical Epidemiology Program, University of Ottawa, Ottawa, Ontario, Canada

Department of Child and Adolescent Psychiatry, Charité Universitätsmedizin, Berlin, Germany

Department of Pediatrics, Yonsei University College of Medicine, Seoul, South Korea

Jae Il Shin

Severance Underwood Meta-Research Center, Institute of Convergence Science, Yonsei University, Seoul, South Korea

Department of Pediatrics, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, South Korea

Dong Keon Yon

Early Psychosis: Interventions and Clinical-Detection Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

Paolo Fusar-Poli

Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy

Outreach and Support in South-London Service, South London and Maudlsey NHS Foundation Trust, London, UK

Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University of Munich, Munich, Germany

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Contributions

D.K.Y. had full access to all of the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. All authors approved the final version before submission. Study concept and design: S.K., H.L., C.M., J.I.S. and D.K.Y. Acquisition, analysis or interpretation of data: S.K., H.L., C.M., J.I.S. and D.K.Y. Drafting of the paper: S.K., H.L., C.M., J.I.S. and D.K.Y. Critical revision of the paper for important intellectual content: S.K., H.L., J.L., S.W.L., R.K., M.S.K., A.K., L.S., G.F., L.B., M.R., G.F.L.S., E.D., S.C., J.-Y.S., A.C., H.S.S., S.L., M.S., C.M., J.I.S., D.K.Y. and P.F.-P. Statistical analysis: S.K., H.L., C.M., J.I.S. and D.K.Y. Study supervision: D.K.Y. and P.F.-P. P.F.-P and D.K.Y. are the senior authors. H.L., J.I.S. and D.K.Y. contributed equally as corresponding authors. S.K. and H.L. contributed equally as first authors. D.K.Y. is the guarantor for this study. The corresponding authors attest that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Corresponding authors

Correspondence to Hayeon Lee , Jae Il Shin or Dong Keon Yon .

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Competing interests.

M.S. received honoraria or has been a consultant for AbbVie, Angelini, Lundbeck and Otsuka. P.F.-P. is supported by #NEXTGENERATIONEU, funded by the Ministry of University and Research, National Recovery and Resilience Plan, project MNESYS (PE0000006)—A Multiscale Integrated Approach to the Study of the Nervous System in Health and Disease (DN. 1553 11.10.2022). S.C. has declared reimbursement for travel and accommodation expenses from the Association for Child and Adolescent Central Health (ACAMH) in relation to lectures delivered for ACAMH, the Canadian AADHD Alliance Resource, the British Association of Psychopharmacology, and from Healthcare Convention for educational activity on ADHD, and has received honoraria from Medice. The other authors declare no competing interests.

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Kim, S., Lee, H., Lee, J. et al. Short- and long-term neuropsychiatric outcomes in long COVID in South Korea and Japan. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01895-8

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Published: 20 June 2024

EEG signatures of cognitive decline after mild SARS-CoV-2 infection: an age-dependent study

Yike Sun 1 na1 ,
Jingnan Sun 1 na1 ,
Xiaogang Chen 2 ,
Yijun Wang 3 &
Xiaorong Gao 1

BMC Medicine volume 22 , Article number: 257 ( 2024 ) Cite this article

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Current research on the neurological impact of SARS-CoV-2 primarily focuses on the elderly or severely ill individuals. This study aims to explore the diverse neurological consequences of SARS-CoV-2 infection, with a particular focus on mildly affected children and adolescents.

A cohort study was conducted to collect pre- and post-infection resting-state electroencephalogram (EEG) data from 185 participants and 181 structured questionnaires of long-term symptoms across four distinct age groups. The goal was to comprehensively evaluate the impact of SARS-CoV-2 infection on these different age demographics. The study analyzed EEG changes of SARS-CoV-2 by potential biomarkers across age groups using both spatial and temporal approaches.

Spatial analysis indicated that children and adolescents exhibit smaller changes in brain network and microstate patterns post-infection, implying a milder cognitive impact. Sequential linear analyses showed that SARS-CoV-2 infection is associated with a marked rise in low-complexity, synchronized neural activity within low-frequency EEG bands. This is evidenced by a significant increase in Hjorth activity within the theta band and Hjorth mobility in the delta band. Sequential nonlinear analysis indicated a significant reduction in the Hurst exponent across all age groups, pointing to increased chaos and complexity within the cognitive system following infection. Furthermore, linear regression analysis based on questionnaires established a significant positive relationship between the magnitude of changes in these neural indicators and the persistence of long-term symptoms post-infection.

Conclusions

The findings underscore the enduring neurological impacts of SARS-CoV-2 infection, marked by cognitive decline and increased EEG disarray. Although children and adolescents experienced milder effects, cognitive decline and heightened low-frequency electrical activity were evident. These observations might contribute to understanding potential anxiety, insomnia, and neurodevelopmental implications.

Peer Review reports

The eruption of the SARS-CoV-2 pandemic has instigated a global public health crisis, posing significant threats to respiratory health [ 1 , 2 , 3 ]. Significantly, this crisis has not only posed a substantial menace to the respiratory system [ 4 , 5 ] but has also sparked concerns regarding its impact on the central nervous system [ 6 , 7 , 8 ]. A wealth of empirical research has confirmed that SARS-CoV-2 can induce a range of neurological issues, notably affecting cognitive functions [ 9 , 10 ]. Amidst various methodologies employed for cognitive function assessment, electroencephalography (EEG) techniques emerge as pivotal tools [ 11 ] for evaluating cognitive function and quantifying the detrimental effects of SARS-CoV-2 infection on cognitive performance [ 12 , 13 ].

However, existing research predominantly focuses on EEG studies involving elderly and severely affected patients [ 10 , 14 , 15 , 16 ]. Recent shifts in focus explore the effects on younger, more diverse populations. For instance, in 2024, researchers employed EEG to analyze sleep patterns in children post-SARS-CoV-2 infection [ 17 ]. Although numerous comparative EEG studies have targeted younger demographics [ 18 , 19 , 20 ], these investigations often involve limited participant numbers and age ranges. Therefore, it is critical to expand EEG studies to more comprehensively assess the long-term cognitive impacts of SARS-CoV-2.

The primary aim of this study is to bridge the gap in understanding the cognitive effects of SARS-CoV-2 in individuals presenting mild symptoms, with a focus on EEG patterns across different age groups, especially in children and adolescents. We gathered resting EEG data from a diverse cohort of 185 individuals who experienced mild symptoms related to SARS-CoV-2, both before infection and after full recovery. Utilizing advanced analytical techniques such as source connectivity and microstate analysis, this study explores the subtle cognitive changes induced by SARS-CoV-2, analyzing both spatial and temporal aspects.

Against the backdrop of the globally reported tally of more than 770 million confirmed cases of SARS-CoV-2 infection as of September 29, 2023 [ 21 ], it is of paramount importance to fathom the cognitive implications wrought by SARS-CoV-2 infection upon the substantial proportion of individuals who exhibit mild symptoms. Such an endeavor is indispensable not only for enhanced comprehension of the virus itself but also for the formulation of healthcare strategies and support systems, with a specific focus on the child and adolescent demographics alongside other vulnerable segments of the population. Our investigation serves to elucidate the intricacies surrounding the cognitive ramifications of SARS-CoV-2 infection in mildly symptomatic populations across varying age groups, thereby contributing to the foundation of rehabilitation strategies geared towards ameliorating the afflictions of SARS-CoV-2 and mitigating the challenges posed by long COVID or post-COVID-19 syndrome [ 22 ].

Study design

The data elucidated in this investigation emanate from a comprehensive longitudinal EEG study, tracking EEG recordings across diverse age cohorts. Initially, the scope of the research was not aligned with clinical objectives. Nevertheless, an unforeseen opportunity arose due to a pivotal shift in China’s public health policy after 2022. Consequentially, a significant proportion of the participants contracted the SARS-CoV-2 virus within a markedly narrow timeframe—specifically, not exceeding a 1-week variance—and uniformly achieved recovery within 4 weeks. All participants in the study were clinically classified as having mild manifestations of the disease and were experiencing their first infection. This unique circumstance allowed us to capture and analyze the EEG data from these individuals’ pre-infection and post-recovery, providing an invaluable comparative perspective on the neurophysiological impact of SARS-CoV-2.

EEG recordings before infection were taken 1 to 2 months before the participants tested positive for SARS-CoV-2 via nasal or throat swab tests. Follow-up EEG recordings were performed 1 to 2 months after the participants tested negative. During the data collection phases, participants were placed in a controlled environment—a small, brightly-lit room devoid of any visual stimuli that might influence the EEG results. Participants were instructed to remain seated, avoid bodily or eye movements, and keep their eyes open throughout the recording session.

Participants

This study was enhanced by administering a structured questionnaire to a group of 181 participants, consisting of 88 males and 93 females. The data curation and validation process yielded 185 reliable EEG recordings after excluding data affected by noise or interference. It is essential to note that the subset of participants providing EEG data did not completely overlap with those responding to the questionnaire. The participants were divided into four age categories: child (under 10 years), adolescent (10 to 20 years), young adult (20 to 27 years), and adult (over 27 years), with group sizes of 63, 28, 39, and 55, respectively. All subjects had prior exposure to long-term EEG studies, which acquainted them with the EEG recording procedure. Consequently, sequential effects were minimized in this study, though they could not be entirely disregarded.

For the EEG analysis, the groups included adults ( n = 55), young adults ( n = 39), adolescents ( n = 28), and children ( n = 63). The mean age of the adult group was 31.64 years (SD = 5.61), comprising 58% females; the young adult group had a mean age of 24.36 years (SD = 1.48), with 77% females; the adolescent group’s mean age was 15.07 years (SD = 1.03), with 29% females; and the child group’s mean age was 7.49 years (SD = 1.47), with 30% females. All participants resided in North China, were diagnosed with mild clinical conditions, and had no neurological lesions attributed to SARS-CoV-2 (Table 1 ).

EEG data preprocessing

The EEG dataset analyzed in this research encompasses eye-open resting-state data acquired using a saline electrode device recorded at a sampling rate of 100 Hz (JBZH-16–1, BRAINNEWLIFE, 16-channel system). All lead positions are arranged according to the 10–20 standard. A preprocessing protocol was implemented to maintain data integrity. During the recording phase, a specialist flagged any segments where significant body movements caused electrode dislodgement. To mitigate the impact of ocular and muscular artifacts, the independent component analysis (ICA) was employed. Additionally, direct current (DC) and instrumental frequency (IF) interferences were eliminated using a bandpass filter ranging from 0.5 to 45 Hz. These preprocessing steps were critical to ensuring the reliability and validity of the study’s findings.

In the analytical phase, the EEG data was segregated into six distinct frequency bands using a specialized filter bank, covering the full frequency spectrum: full band (0.5–45 Hz), delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), and gamma (30–45 Hz). An averaging reference operation was subsequently applied across all datasets to ensure analytical consistency and accuracy.

Brain network source connectivity analysis in the spatial domain

Source connectivity analysis is a critical technique using neuroimaging data for examining complex interactions between brain regions [ 23 ]. Its main goal is to identify functional or effective linkages among cerebral sources that reflect cognitive shifts in conditions like depression and schizophrenia [ 24 , 25 ]. Among various functional connectivity metrics, coherence is a key measure, calculating the linear correlation between two signals in the frequency domain. However, coherence measurements can be affected by volumetric conduction, causing misleading pseudo-coherent values [ 26 , 27 ].

In response, several effective connectivity measures, such as the directional transfer function (DTF) and partially directional coherence (PDC), have been proposed [ 28 ]. The direct directed transfer function (dDTF), a modification of the DTF method, is especially notable. It incorporates Granger causality principles and allows distinguishing between direct and indirect connections [ 29 ]. This study employs the dDTF method for source linkage analysis.

In this research, we initially computed the connectivity data from EEG recordings taken prior to infection as well as from data collected post-infection and during recovery. These results were then subjected to statistical testing. Ultimately, we highlighted findings demonstrating statistically significant reductions, along with their respective differences, within the study.

Microstate analysis in the spatial domain

EEG microstate analysis is a key methodology in neuroscience, providing deep insights into spontaneous brain activity [ 30 ]. It assumes EEG stability over short time periods, segments EEG signals into brief, stable scalp electrical topographies, and uses cluster analysis to reveal potential functional changes [ 31 , 32 , 33 ]. Microstate analysis is widely used in diverse neuroscience studies, including examinations of brain states in neuropsychiatric disorders and normative aging and developmental processes [ 34 , 35 ]. This study applies the KMeans clustering method for microstate analysis of EEG data and uses the MNE-Python toolkit for visualizing microstate topographic data [ 36 ]. It is important to note, however, that due to variations in sampling frequency and timing in this study compared to most other studies, the resulting microstate topography maps differ. Nevertheless, since this study focuses on comparing the relative relationship between pre- and post-intervention states, this discrepancy is considered justifiable.

Linear analysis in time sequence

For an in-depth analysis of temporal variation in EEG signals, we have chosen the Hjorth parameter and Kolmogorov complexity as key metrics. The Hjorth parameter, commonly used in EEG analysis and epilepsy detection studies, consists of three elements: activity (HA), mobility (HM), and complexity (HC) [ 37 , 38 , 39 ]. HA measures signal power, reflecting brain activity or arousal levels. HM quantifies mean frequency, providing insights into the synchronization of brain activity and dynamic neural processes. HC gauges frequency change in an EEG signal, reflecting the regularity or irregularity of brain activity [ 40 ].

Kolmogorov complexity, on the other hand, examines the shortest algorithmic length of a string [ 41 ]. In our analysis, it is used to characterize the minimum representation length of an EEG signal, with higher values indicating more intricate activity [ 42 , 43 ]. To compute Kolmogorov complexity, we used a threshold at 0 for binarization, enabling an effective evaluation of complexity metrics in the EEG data.

Nonlinear analysis in time sequence

Our methodology uses nonlinear approaches to assess EEG signal changes pre- and post-SARS-CoV-2 infection. Sample entropy, an improvement over approximate entropy, measures time series complexity and pattern generation likelihood [ 44 , 45 , 46 ]. Due to its computational independence from data length and enhanced consistency, it becomes a robust measure for assessing EEG’s nonlinear processes [ 47 , 48 ].

The Hurst index identifies the long-term memory of a time series, providing insight into brain activity and function [ 49 ]. It also highlights differences in EEG signals across brain regions, age groups, and mental states [ 50 ]. We use detrended fluctuation analysis (DFA) to calculate the Hurst index, which effectively eliminates potential spurious long-range correlations due to the non-smoothness of temporal order in EEG signals, revealing intrinsic long-range correlations in complex systems [ 51 ].

Statistical test methods

In this study, statistical analyses were conducted to ensure the robustness and validity of the findings. First and foremost, normality testing was performed using the Shapiro–Wilk normality test, a fundamental step in validating the assumptions underlying parametric statistical methods. Subsequently, for datasets adhering to a normal distribution ( P > 0.05), a paired t -test was employed, a method widely acknowledged for its appropriateness in comparing means under normal conditions. In cases where the data did not conform to a normal distribution ( P ≤ 0.05), the analysis was conducted using the Wilcoxon signed-rank test, a non-parametric test known for its effectiveness in evaluating differences between paired samples without relying on the assumptions of normality. All statistical procedures were executed with the SCIPY.STATS toolkit for Python.

Spatial biomarkers: source connectivity analysis

To evaluate the impact of SARS-CoV-2 infection on cognitive processes over time, we conducted a source connectivity analysis using EEG data, collected before infection and after recovery. We employed the dDTF, known for its effectiveness in reducing signal interference caused by the volumetric conductor effect in EEG studies. Our analysis revealed statistically significant reductions in connectivity, as illustrated in Fig. 1 . Notably, the reduction in connectivity was particularly evident around the T5 region, which is closely linked to memory, language, and emotion processing. Previous studies have suggested that decreased connectivity in this region is associated with cognitive changes observed in conditions such as attention deficit hyperactivity disorder (ADHD) and mild cognitive impairment (MCI) [ 52 , 53 ]. Our findings suggest that SARS-CoV-2 infection could potentially lead to noticeable cognitive decline.

This figure displays chordal plots representing brain network source connectivity analysis outcomes. The colors in the plots designate outgoing source leads: red for T-region, orange for O-region, pink for P-region, blue for F-region, and dark blue for FP-region. Each subplot ( a to d ) represents different age groups: child, adolescent, young adult, and adult. Subplots I to VI depict results across various frequency bands: full, delta, theta, alpha, beta, and gamma

Moreover, the majority of the interactions showing significant declines were from the T-region to the F-region, which are areas typically associated with task execution and memory/decision-making, respectively. This pattern suggests that the infection may result in long-term deficits in cognitive and decision-making functions.

A key observation was that statistically significant reductions in connectivity were mainly intra-hemispheric (left in odd leads, right in even leads), indicating that the cognitive impact of SARS-CoV-2 might be limited in scope. However, the effects on higher cognitive functions appear more pronounced, as evidenced by significant decreases at higher frequencies.

Age-related differences in the impact of SARS-CoV-2 were also apparent. Young adults showed the most significant cognitive impact, followed by adults and adolescents, while children under 10 exhibited the least effect, with significantly fewer link reductions compared to young adults. These findings suggest that the cognitive resilience varies with age, with the brain networks of young adults being notably more vulnerable to disruption by SARS-CoV-2. This vulnerability could be influenced by factors such as the stage of brain development, lifestyle, or pre-existing health conditions. Adults and adolescents displayed moderate resilience, while the minimal impact on children could indicate more robust brain networks or compensatory mechanisms that protect against connectivity loss.

Spatial biomarkers: microstate analysis

A comprehensive clustering analysis of EEG microstates, conducted before and after SARS-CoV-2 infection, revealed distinct patterns. Following this, a distance analysis was performed on the central microstate patterns identified by the clustering, using Euclidean distance as the metric for differentiation. The results, along with the EEG topographies of these microstates, are presented in Fig. 2 .

This figure illustrates the changes in microstate analysis before and after SARS-CoV-2 infection. Part I displays the four microstates before infection (arranged by decreasing frequency from left to right), while part II shows the results following recovery. The numerical values between parts I and II indicate the mean Euclidean distances for the four microstates pre- and post-infection. Specifically, a represents the clustered outcomes for the entire population, whereas b , c , d , and e show the results for the child, adolescent, young adult, and adult groups, respectively

In the aggregate, the disparity in EEG microstates before and after infection manifested as 1.65. This deviation was discerned as 1.34 for the child group, 1.55 for the adolescent group, 1.84 for the young adult group, and 1.66 for the adult group. This numerical representation is posited to encapsulate the alteration in microstate patterns, with a higher deviation indicative of a more substantial shift in cognitive patterns. Benchmarking against the deviation value across all groups, it is discerned that the cognitive pattern alteration for the child and adolescent groups is below the population average. Conversely, the adult group exhibits a marginally higher cognitive pattern change, while the young adult group demonstrates the most considerable alteration, surpassing all other age cohorts.

This analysis leads us to conclude that the young adult group experienced the most substantial impact from SARS-CoV-2 infection, with the adult group also significantly affected. The child and adolescent groups, however, seemed to maintain more stable cognitive patterns post-infection.

It is important to note, however, that these results were not compared against a control group of uninfected individuals. Therefore, we cannot entirely exclude the potential influence of external factors such as social pressure. Unfortunately, it is now challenging to find uninfected control subjects for such studies. Therefore, these findings should be interpreted with caution. Nonetheless, given the short intervals between signal acquisitions, significant changes in cognitive patterns were unlikely. The minimal change observed in the fastest-developing child and adolescent groups further supports the notion that the adult and young adult groups were more significantly affected.

Sequence biomarkers: linear analysis

In the analysis of EEG time sequences, our initial focus was on quantifying energy changes. However, these outcomes were omitted from the narrative due to the absence of statistically significant alterations in energy levels before and after SARS-CoV-2 infection. This absence of discernible energy shifts implies that the impact of SARS-CoV-2 on EEG may not attain a pathological magnitude, thereby implying that cognitive changes resulting from SARS-CoV-2 infection may not reach pathological thresholds. In light of this, we computed HA, HM, HC, and KC parameters.

HA parameter intricately linked to EEG energy changes. As shown in Fig. 3 a, our statistical analysis unveiled a noteworthy surge in theta (50.96 percentage points; 95% CI, − 316.53 to 418.46 percentage points; P = 0.0096 < 0.01) and alpha (52.84 percentage points; 95% CI, − 360.17 to 465.84 percentage points; P = 0.008 < 0.01) bands following recovery from SARS-CoV-2 infection. This compellingly indicates heightened EEG activation in theta and alpha bands across all demographics post-infection and recovery.

This figure presents bilateral violin plots illustrating the distribution of linear analysis sequence biomarkers across four age cohorts before infection and after recovery. a to d detail the outcomes associated with HA, HM, HC, and KC parameters, respectively, across all frequency bands: full, delta, theta, alpha, beta, and gamma. e to h focus on the theta band for each age group: child, adolescent, young adult, and adult. Each panel contrasts the pre-infection EEG parameters (gray area) against the post-recovery parameters (red area), with asterisks indicating statistically significant results

Upon this foundation, a thorough examination of the HM parameters in conjunction with the HC parameters was conducted, as depicted visually in Fig. 3 b and c for each frequency band across all populations. The comparison between pre-infection EEG and post-recovery EEG reveals a discernible trend. Significantly, the HM parameters demonstrated statistically noteworthy elevations in both the delta band (3.15 percentage points; 95% CI, − 15.13 to 21.42 percentage points; P = 0.0001 < 0.001) and theta band (0.48 percentage points; 95% CI, − 2.49 to 3.44 percentage points; P = 0.000045 < 0.001) after SARS-CoV-2 infection. This finding implies that, following infection and subsequent recovery from SARS-CoV-2, the population displayed heightened alterations in frequency within the delta and theta bands, accompanied by a discernible degree of synchronization in brain activity.

Conversely, the analysis of HC parameters unveiled notable alterations in the delta band (− 0.93 percentage points; 95% CI, − 13.49 to 11.64 percentage points; P = 0.022 < 0.05) and gamma band (− 0.98 percentage points; 95% CI, − 10.13 to 8.17 percentage points; P = 0.0027 < 0.01) before and after infection. In stark contrast to the observed augmentation in HM parameters, the HC parameters exhibited a reduction. This discrepancy suggests that the EEG signal manifests a diminished rate of frequency change in both delta and gamma bands post-SARS-CoV-2 infection and recovery. Consequently, this distinction implies a diminished occurrence of perturbations and changes in the EEG signal following SARS-CoV-2 infection and recovery, accompanied by a decrease in the complexity of the time domain.

Having scrutinized the enhanced EEG activity through parametric analysis of HM and HC, a subsequent step involved binarizing the EEG signals and conducting a comprehensive analysis of signal activity complexity using the KC parameters, as illustrated in Fig. 3 d. The resultant figure unequivocally demonstrates a substantial elevation in the KC parameter within the delta band post-SARS-CoV-2 infection compared to the pre-infection state (1.97 percentage points; 95% CI, − 11.48 to 15.42 percentage points; P = 0.00067 < 0.001). This observed phenomenon implies a noteworthy increase in the length of the shortest algorithm describing the EEG, indicative of heightened pattern changes in EEG dynamics after infection with SARS-CoV-2 and recovery.

From the findings of the aforementioned analysis, it is evident that following infection with and recovery from SARS-CoV-2, there is a discernible augmentation in the extent of low-complexity activity in the EEG. However, the overall complexity of the EEG registers a decline owing to the escalated prevalence of low-complexity activity, consequently resulting in an elevation of the HM parameter and a concomitant reduction in the HC parameter. The upsurge in the KC parameter signifies an augmentation in low-complexity synchronized activity that was nonexistent before the viral infection, constituting an entirely novel pattern of neural activity. We posit that the SARS-CoV-2 infection precipitates an influx of novel low-complexity synchronized activity in the EEG, reminiscent to some extent of the abnormal discharge activity observed in epilepsy, albeit with a considerably diminished degree of variability.

Our investigation unveils a notable concentration of alterations within the theta band, compelling an exploration of this specific frequency range. As depicted in Fig. 3 e to h, a discernible trend in the association between HM, KC, and decreasing HC across all age groups and leads is evident.

For HA analysis in theta band, only the young adult group exhibits a simultaneous increase across the whole brain regions (58.71 percentage points; 95% CI, − 196.22 to 313.64 percentage points; P = 0.012 < 0.05), as well as in the prefrontal, frontal, central, and parietal areas. And the adult cohort manifested statistically significant variations in prefrontal zone leads (0.85 percentage points; 95% CI, − 4.65 to 6.35 percentage points; P = 0.045 < 0.05) concerning the HM parameter. Simultaneously, the adolescent cohort also exhibited significant alterations in prefrontal zone leads (1.45 percentage points; 95% CI, − 4.18 to 7.08 percentage points; P = 0.029 < 0.05). Furthermore, the child cohort and adult cohort did not exhibit statistical significance. Concerning the HC parameters, only the adolescent cohort exhibited a statistically significant alteration in frontal areas (0.25 percentage points; 95% CI, − 0.73 to 1.23 percentage points; P = 0.017 < 0.05). This alteration may imply the emergence of a greater number of novel EEG patterns within the occipital lobe region. Contrastingly, for the KC parameters, no significant changes were observed in any of the four age groups.

Notably, these findings substantiate the proposition that SARS-CoV-2 infection may impact perceptual awareness, with observed changes predominantly localized in the prefrontal and frontal region. In the aggregate, in terms of P values, the young adult cohort attained the highest significance, followed by the adolescent cohort, the adult cohort, and the child cohort in descending order. It is noteworthy that the changes in linear analysis sequence biomarkers attributable to SARS-CoV-2 infection were more conspicuous in the young adult and adult cohorts than in other cohorts.

Sequence biomarkers: nonlinear analysis

In the examination of nonlinearity, the initial step involved the computation of sample entropy, with the outcomes graphically depicted in Fig. 4 a to d. In comparison to the pre-infection state, the sample entropy of EEG after SARS-CoV-2 infection and recovery demonstrated an ascending tendency. However, none of these alterations attained statistical significance, except for the young adult cohort, wherein a noteworthy increase in the delta band was observed (2.43 percentage points; 95% CI, − 11.01 to 15.86 percentage points; P = 0.027 < 0.05). This observation signifies a discernible augmentation in nonlinear activity within the delta frequency band among young adults. Notably, this frequency band is widely acknowledged for its association with the underlying neural processes of sleep and mood regulation. Consequently, the discerned escalation in the delta band suggests a predisposition of the young adult cohort to post-infection cognitive disorders related to sleep and mood.

This figure presents bilateral violin plots illustrating the distribution of nonlinear analysis sequence biomarkers before infection and after recovery across four age cohorts. a through d display the sample entropy analysis results for the child, adolescent, young adult, and adult groups, respectively. Each panel details the outcomes for six frequency bands: full band, delta, theta, alpha, beta, and gamma. e provides a focused view of the sample entropy analysis within the delta band across different cerebral leads. f depicts the results of the Hurst index analysis conducted via the DFA method, detailing findings across all age groups. The labels ALL, FP, F, C, P, O, and T represent full-lead averaged results, and results for the prefrontal, frontal, central, parietal, occipital, and temporal areas, respectively

The findings of the region-specific analysis for each age group are delineated in Fig. 4 e to undertake a more granular examination of the variations within the delta frequency band. Evidently, within the young adult cohort, a statistically significant elevation in sample entropy is evident in both the frontal and parietal regions. These cerebral regions are integral to diverse cognitive functions, encompassing attention, memory, decision-making, and sensory integration. Consequently, this outcome posits a plausible influence of SARS-CoV-2 infection on attentional processes within the young adult demographic. The discerned effects were most pronounced within the young adult cohort, with relatively diminished impacts observed in the remaining age groups, particularly among children and adolescents.

We conducted a comprehensive examination of the Hurst index to scrutinize the long-term memory characteristics inherent in the EEG time series. The outcomes of this analysis, presented in Fig. 4 f through a full band exploration, unveil noteworthy findings. Specifically, within the child cohort, a substantial reduction in the prefrontal (− 3.44 percentage points; 95% CI, − 34.25 to 27.37 percentage points; P = 0.034 < 0.05) and parietal (− 3.2 percentage points; 95% CI, − 41.01 to 34.61 percentage points; P = 0.028 < 0.05) regions is evident. Analogously, the adolescent group manifests a comparable noteworthy decrease in the prefrontal regions (− 5.03 percentage points; 95% CI, − 28.66 to 18.59 percentage points; P = 0.036 < 0.05). The young adult group exhibits a simultaneous decline across the whole brain regions (− 3.66 percentage points; 95% CI, − 28.43 to 21.12 percentage points; P = 0.028 < 0.05), as well as in the prefrontal (− 4.68 percentage points; 95% CI, − 31.14 to 21.79 percentage points; P = 0.013 < 0.05), frontal (− 3.55 percentage points; 95% CI, − 29.1 to 22 percentage points; P = 0.0296 < 0.05), central (− 5.78 percentage points; 95% CI, − 36.01 to 24.46 percentage points; P = 0.028 < 0.05), and parietal (− 3.59 percentage points; 95% CI, − 38.63 to 31.45 percentage points; P = 0.036 < 0.05) areas. In the case of the adult group, the reduction in significance extends to the prefrontal (− 4.31 percentage points; 95% CI, − 36.11 to 27.49 percentage points; P = 0.032 < 0.05) and parietal (− 4.38 percentage points; 95% CI, − 41.36 to 32.59 percentage points; P = 0.025 < 0.05) regions.

In interpreting the results, we observe a noticeable decline in the Hurst index following SARS-CoV-2 infection and subsequent recovery. This trend suggests a reduction in the long-term regularity of EEG signals, indicative of increased randomness in brain activity. However, it is crucial to consider that this decrease in the Hurst index might not solely reflect changes in cognitive processes. Factors such as alterations in cognitive function and variations in sleep–wake states, which are not directly measured in this study, could also influence these results. Therefore, while the data suggest an increase in the chaotic and complex nature of the cognitive system, potentially leading to higher anxiety levels, these interpretations should be approached with caution. The impact appears most pronounced in the young adult group, followed by adults, children, and adolescents, as inferred from the analysis of respective P values. Future studies should aim to disentangle the effects of cognitive and sleep–wake changes from those directly related to viral infection to better understand the mechanisms underlying these observations.

Behavioral questionnaire results and regression analysis

After acquiring the EEG data, a supplementary questionnaire was administered to the participants with the primary objective of scrutinizing potential cognitive symptoms such as insomnia, mood disorders, and memory impairments. The outcomes depicted in Fig. 5 a reveal distinctive patterns among age groups. During the survey process, participants reported their symptoms, marking “1” if they perceived the symptom and “0” if they did not. Significantly, the young adult group demonstrated the highest prevalence of cognitive dysfunctions, closely followed by the adult cohort. In contrast, the adolescent and child groups showed a lower probability of exhibiting cognitive-related symptoms. This pattern is consistent with the insights obtained from the comprehensive analyses conducted previously.

To enhance the robustness of the association between the identified potential biomarkers and symptomatology delineated in the preceding analysis, we operationalized the questionnaire responses into discrete scores. Each of the ten symptoms enumerated was assigned a corresponding score based on participant responses, yielding an aggregate score with a potential range from 0 to 10. Subsequently, we normalized the transformation magnitude across the various biomarker indices to fall within a unified spectrum of 0 to 1 and computed their mean to ascertain the average biomarker alteration.

It is imperative to note that we calculated the change in biomarker levels as an absolute value, given that the correlation between these indicators and symptomatology is not presupposed to be linear. The regression analysis outcomes, depicted in Fig. 5 b to e, illustrate our findings across four distinct age cohorts. It is evident from these results that—except for the child age group, where the link between the linear series of biomarkers and symptoms did not reach statistical significance—the remaining age groups exhibited a notable positive correlation. This correlation signifies that as the degree of deviation in the three categories of biomarkers escalates, there is a concomitant intensification of cognitive and psychiatric symptoms.

a shows the outcomes of the questionnaire through a heatmap. The graph’s horizontal axis represents four distinct age groups, while the vertical axis denotes potential symptoms relevant to cognition. The color intensity conveys the ratio of the number of people with specific symptoms to the total number of people within a given age group. Red hues signify a higher rate of occurrence, whereas blue indicates a lower rate of the corresponding symptom manifesting in that age group. b to e represent the results of regression analyses of spatial biomarkers, linear biomarkers, and nonlinear biomarkers against questionnaire scores for each of the four age groups, where the equation represents the expression of the fitted line

The outcomes of this study distinctly highlight the amplified susceptibility of young adults to cognitive deficits following a SARS-CoV-2 infection, a demographic that has traditionally not been considered as high risk. This is predominantly observable in the significant decrement in EEG source connectivity, particularly within the region of the temporal lobe, a key area for the functions of memory, language, and emotional processing. Such modifications could potentially result in cognitive deterioration, displaying patterns akin to those observed in cases of ADHD and MCI. Our findings propose a more profound impact of SARS-CoV-2 on young adults in comparison to adolescents and children. This insight can potentially steer the formulation of rehabilitation strategies tailored for long COVID patients.

The diminished connectivity in specific brain regions, such as electrode T5, which in temporal lobe, may reflect disruptions in neural networks that are crucial for cognitive functions [ 54 ]. This aligns with existing studies that link changes in brain connectivity to various cognitive impairments [ 55 ]. The persistence of connectivity reductions primarily within hemispheres further underscores the targeted impact of SARS-CoV-2 on brain function. The increase in the HA parameter within the theta band post-infection in adults suggests subtle yet discernible changes in EEG activity, potentially reflecting alterations in cognitive states. The heightened complexity in EEG patterns post-recovery, particularly in the delta band, might indicate a compensatory neural mechanism or an altered state of brain activity in response to the infection.

The observed concentration of alterations within the delta frequency band presents a pioneering insight, proposing that this band may be particularly susceptible to the neurological impacts of SARS-CoV-2 [ 56 ]. Traditionally, it is recognized that delta wave activity is diminished when the eyes are open. However, the findings of this study suggest that delta waves can also reflect changes in subject states to a certain degree. This assertion is supported by the use of ICA to eliminate electromyographic and oculomotor noise, potentially influencing the observed effects. Furthermore, the isolated analysis of the delta wave through filtering techniques underscores the sensitivity of this frequency band. Such findings could be pivotal for future EEG studies focusing on COVID-19 patients, particularly for elucidating alterations in brain activity. Previous research has associated low-frequency energy with long-range communication across brain regions [ 57 ]. The modifications in low-frequency activity observed in this study may indicate a substantial impact of the infection on the nervous system. Moreover, the results concerning complexity and entropy imply an increase in the chaotic nature of the neural system post-infection. Although none of the participants in this study was clinically diagnosed with “brain fog,” the EEG changes noted bear resemblance to those associated with “brain fog,” hinting at a potential underlying neurological impact of the infection [ 58 ].

Results indicate a gradation in susceptibility to cognitive impacts post-SARS-CoV-2 infection across different age groups. The most substantial cognitive changes were observed in young adults, a demographic that is not typically considered at high risk for severe COVID-19 implications. While previous studies have also shown that infection has a greater impact on young adults [ 59 ], the results of the present study provide additional evidence at the electrophysiological level for this conclusion. Warranting further investigates the long-term consequences of SARS-CoV-2 in younger populations. Notably, children also showed significant changes in HC and HM parameters, but this may be related to their rapid neurological development.

While this study has made discoveries regarding the impact of the coronavirus on the nervous system, it is not without its limitations and shortcomings. We endeavored to include as broad a population as possible, yet our study did not encompass all age groups, particularly the elderly. This omission means that the effects of the coronavirus on the neurological systems of older individuals remain unknown, given that some studies suggest this demographic may be more susceptible to such impacts [ 60 ]. Furthermore, our research did not involve continuous longitudinal tracking of the infected population, omitting long-term comparative data. The acquisition of such longitudinal information would be highly valuable and meaningful for understanding the full spectrum of the virus’s impact over time.

In essence, this research furthers the existing knowledge on the neurological implications of SARS-CoV-2, underscoring the urgent requirement for a more profound understanding of the virus’s enduring effects on cognition. Particularly, it focuses on its impact on younger demographics, encompassing children and adolescents. The results intimate that the influence of SARS-CoV-2 is amplified within the younger populace. Although children and adolescents were relatively less affected, they exhibited noteworthy neurophysiological markers of abnormality, suggesting possible risk. This study, therefore, serves as a groundwork for more extensive research into potential therapeutic interventions and strategies to alleviate these cognitive alterations.

Availability of data and materials

This study constitutes a collaborative effort with BRAINNEWLIFE for data acquisition. As per the stipulations outlined in the agreement, the authors of this paper are bound by the obligation to safeguard the confidentiality of the underlying dataset, particularly the confidential and sensitive information embedded within. Nonetheless, all pertinent data metrics employed for the analysis explicated in this paper are delineated within the manuscript. Consequently, these metrics are readily available for replication and in-depth scrutiny. For researchers seeking access to pertinent anonymized data, we encourage the submission of formal requests to the corresponding author via email.

Abbreviations

Attention deficit hyperactivity disorder

Direct current

Direct directed transfer function

Directional transfer function

Electroencephalography

Hjorth activity

Hjorth complexity

Hjorth mobility

Independent component analysis

Instrumental frequency

Mild cognitive impairment

Partially directional coherence

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Acknowledgements

The authors express their heartfelt thanks to all the adults, children, and their guardians who participated in the study.

This study received funding from the National Natural Science Foundation of China under Grant U2241208, which supported the data collection, analysis, and interpretation.

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Yike Sun and Jingnan Sun contributed equally to this work.

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The School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China

Yike Sun, Jingnan Sun & Xiaorong Gao

Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300192, China

Xiaogang Chen

Institute of Semiconductor, Chinese Academy of Sciences, Beijing, 100083, China

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X.G. and Y.W. formulated the research design; Y.S. and J.S. executed the data analysis and interpretation; Y.S. and X.C. were responsible for the manuscript’s composition.

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Correspondence to Xiaorong Gao .

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Sun, Y., Sun, J., Chen, X. et al. EEG signatures of cognitive decline after mild SARS-CoV-2 infection: an age-dependent study. BMC Med 22 , 257 (2024). https://doi.org/10.1186/s12916-024-03481-1

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Nat Sci Sleep

Short- and long-term health consequences of sleep disruption

Goran medic.

1 Market Access, Horizon Pharma B.V., Utrecht

2 Unit of Pharmacoepidemiology & Pharmacoeconomics, Department of Pharmacy, University of Groningen, Groningen, The Netherlands

Micheline Wille

Michiel eh hemels.

Sleep plays a vital role in brain function and systemic physiology across many body systems. Problems with sleep are widely prevalent and include deficits in quantity and quality of sleep; sleep problems that impact the continuity of sleep are collectively referred to as sleep disruptions. Numerous factors contribute to sleep disruption, ranging from lifestyle and environmental factors to sleep disorders and other medical conditions. Sleep disruptions have substantial adverse short- and long-term health consequences. A literature search was conducted to provide a nonsystematic review of these health consequences (this review was designed to be nonsystematic to better focus on the topics of interest due to the myriad parameters affected by sleep). Sleep disruption is associated with increased activity of the sympathetic nervous system and hypothalamic–pituitary–adrenal axis, metabolic effects, changes in circadian rhythms, and proinflammatory responses. In otherwise healthy adults, short-term consequences of sleep disruption include increased stress responsivity, somatic pain, reduced quality of life, emotional distress and mood disorders, and cognitive, memory, and performance deficits. For adolescents, psychosocial health, school performance, and risk-taking behaviors are impacted by sleep disruption. Behavioral problems and cognitive functioning are associated with sleep disruption in children. Long-term consequences of sleep disruption in otherwise healthy individuals include hypertension, dyslipidemia, cardiovascular disease, weight-related issues, metabolic syndrome, type 2 diabetes mellitus, and colorectal cancer. All-cause mortality is also increased in men with sleep disturbances. For those with underlying medical conditions, sleep disruption may diminish the health-related quality of life of children and adolescents and may worsen the severity of common gastrointestinal disorders. As a result of the potential consequences of sleep disruption, health care professionals should be cognizant of how managing underlying medical conditions may help to optimize sleep continuity and consider prescribing interventions that minimize sleep disruption.

Introduction

Sleep is a biologic process that is essential for life and optimal health. Sleep plays a critical role in brain function and systemic physiology, including metabolism, appetite regulation, and the functioning of immune, hormonal, and cardiovascular systems. 1 , 2 Normal healthy sleep is characterized by sufficient duration, good quality, appropriate timing and regularity, and the absence of sleep disturbances and disorders. 3 Despite the importance of sleep, up to 70 million people in the US and ~45 million people in Europe have a chronic sleep disorder that impacts daily functioning and health. 2 , 4 For example, ~20% of the serious injuries that result from car accidents can be associated with driver sleepiness, independent of the effects of alcohol. 2 Lifestyle and environmental factors, psychosocial issues, and medical conditions all contribute to sleep problems. 2 There are ~100 sleep disorder classifications; however, they are typically manifested in one of the following three ways: failure to obtain the necessary amount or quality of sleep (sleep deprivation), an inability to maintain sleep continuity (disrupted sleep, also called sleep fragmentation, difficulty maintaining sleep, and middle insomnia), and events that occur during sleep (eg, sleep apnea, restless legs syndrome). 2 The effects of sleep disorders on the body are numerous and widely varied across multiple body systems. This review focuses on the clinical consequences, both short term and long term, that result from disrupted sleep (not including short sleep duration) in adults, adolescents, and children who are otherwise healthy and in those who have an underlying medical condition. Information on basic science and mechanisms of these effects are included to provide background for the clinical outcomes, but are not thoroughly reviewed. Several recent reviews provide detailed information on the science and mechanisms of sleep disruption. 5 – 7

Methodology

In order to better focus on the topics of interest among the myriad parameters affected by sleep, this review of the literature was designed to be nonsystematic. A search of English-language publications in the PubMed database was conducted in March and April 2016. Search terms were “caregiver AND sleep”, “caregiver AND drug administration”, “insomnia”, “middle insomnia”, “restless leg[s] syndrome”, “sleep AND drug administration”, “sleep apnea”, “sleep continuity”, “sleep deprivation”, “sleep disorder”, “sleep disruption”, “sleep disturbance”, “sleep fragmentation”, and “sleep maintenance”. Together, these search terms generated over 60,000 hits. For each individual search, we reviewed the most recent articles to identify those that specifically discussed the consequences of disrupted sleep, rather than those of short sleep duration or other sleep problems. For topics that were not adequately covered by recent literature (previous ~5–10 years), we looked slightly further back in the literature. Other publications were identified by examining the reference lists of publications included in the literature searches. The websites of the American Academy of Sleep Medicine, Sleep Research Society, and the European Sleep Research Society were also searched for additional publications. This nonsystematic review pulled information from a total of 97 references.

Characteristics of normal sleep

The stages of sleep have historically been divided into one stage of rapid eye movement (REM) sleep and four stages (Stages 1–4) of non-rapid eye movement (NREM) sleep that are characterized by increasing sleep depth. 2 , 8 The deeper sleep stages (Stages 3 and 4) are collectively referred to as slow-wave sleep (SWS), which is believed to be the most restorative type of sleep and typically occurs during the first one-third of the night. 2 , 8 , 9 In contrast, REM sleep increases as the night progresses and is longest in the last one-third of a sleep episode. 2 REM and NREM sleep are characterized by numerous, yet different, physiologic changes, including brain activity, heart rate, blood pressure (BP), sympathetic nervous system activity, muscle tone, blood flow to the brain, respiration, airway resistance, renal function, endocrine function, body temperature, and sexual arousal. 2 For example, during NREM sleep, heart rate, BP, blood flow to the brain, and respiration are decreased compared with wakeful periods. During REM sleep, these processes are increased compared with NREM sleep. Brain activity decreases from wakefulness during NREM sleep; activity levels are similar during REM sleep, except for increases in motor and sensory areas. 2

A newer sleep classification system developed by the American Academy of Sleep Medicine has only three stages of NREM sleep: lighter sleep (Stages N1 and N2) and deeper sleep (or SWS; Stage N3). 10 The major changes with the newer classification system are focused on electroencephalogram (EEG) derivations and the merging of Stages 3 and 4 into Stage N3. 11 In a comparison of the two sleep classifications, only minor differences were noted for total sleep time, sleep efficiency, and REM sleep, but the choice of classification impacted the measurement of wake after sleep onset and the distribution of NREM sleep stages. 11

The two-process model describes the interplay between the sleep-promoting process (process S) and the maintenance of wakefulness system (process C). 2 The balance between these processes shifts throughout the course of the day, leading to regulation of the sleep–wake cycle. This sleep–wake cycle is controlled by daily rhythms of physiology and behavior, called circadian rhythms. 2 Circadian rhythms also control metabolic activity through physical activity and food consumption, as well as body temperature, heart rate, muscle tone, and hormone secretion. 2 The sleep process is regulated by neurons in the hypothalamus, which turn off the arousal systems in order to allow sleep to occur. 2 Insomnia results from the loss of these neurons. Other brain regions are also involved in sleep disruption, including the brain stem and cognitive areas of the forebrain. Over the course of the night, neurons in the pons switch between NREM and REM sleep by sending outputs to the brain stem and spinal cord, causing muscle atonia and chaotic autonomic activity; to the forebrain; and to the thalamus via cholinergic pathways. 2

The circadian rhythms work to synchronize sleep with the external day–night cycle, via the suprachiasmatic nucleus (SCN) that receives direct input from nerve cells in the retina acting as brightness detectors. 2 , 12 Light travels from the retina to the SCN, which signals the pineal gland to control the secretion of melatonin. This neurohormone acts to synchronize the circadian rhythms with the environment and the body through melatonin receptors in nearly all tissues. The SCN also works with a series of clock genes to synchronize the peripheral tissues, giving rise to daily patterns of activity.

Overview of sleep disruption

Disruption of sleep is widespread. A 2014 survey conducted by the National Sleep Foundation reported that 35% of American adults rated their sleep quality as “poor” or “only fair”. 13 Trouble falling asleep at least one night per week was reported by 45% of respondents. 13 In addition, 53% of respondents had trouble staying asleep on at least one night of the previous week, and 23% of respondents had trouble staying asleep on five or more nights. 13 Snoring was reported by 40% of respondents, 13 and 17% of respondents had been told by a physician that they have a sleep disorder, the majority (68%) of which was sleep apnea. 13 Relatively few studies have looked at sleep disruption in children. In a study that included a random sample of Chinese children aged 5–12 years, the overall prevalence of chronic sleep disruption was 9.8% (boys, 10.0%; girls, 8.9%). 14

Risk factors for sleep disruption are vast and involve a combination of biologic, psychologic, genetic, and social factors ( Table 1 ). 2 , 6 , 15 – 39 Lifestyle factors include consuming excessive amounts of caffeine 15 and drinking alcohol. 16 Performing shift work 20 or being a college student 2 is also a risk factor for sleep disruption. Exposure to excessive nighttime light pollution and underexposure to daytime sunlight can lead to disruption of circadian rhythms. 19 Stressful life circumstances, such as being the parent of a young infant 21 or serving as a caregiver for a family member with a chronic, life-threatening, or terminal illness, 22 – 25 are also contributors to sleep problems. In addition to the stress and worry associated with caregiving, caregivers of patients with complex medication schedules may experience sleep disruption due to the requirement to wake themselves during the night to administer medication. 25

Risk factors contributing to sleep deprivation and disruption

Category	Risk factors
Lifestyle	• Consuming excessive amounts of caffeine
	• Drinking alcohol
	• Drug abuse
	• Shift work
	• Attending university
	• Jet lag
Environmental	• Excessive noise, such as industrial wind turbines
	• Excessive light
Psychosocial	• Anxiety, worry, and rumination
	• Parents of young children
	• Caregivers to a family member with a chronic, life-threatening, or terminal illness
Sleep disorder	• Insomnia
	• Obstructive sleep apnea
	• Restless leg syndrome
	• Narcolepsy
	• Circadian rhythm disorders
Medical conditions	• Pain
	• Restrictive lung disease
	• Chronic kidney disease
	• Diabetes
	• Neurodegenerative diseases
	• Psychiatric disorders
	• Use of certain medications

Note: Data from the following references. 2 , 6 , 15 – 19

Sleep disruption is frequently attributable to a sleep disorder, such as obstructive sleep apnea 26 , 27 and restless legs syndrome, which is related to altered dopamine and iron metabolism; >50% of idiopathic cases of restless leg syndrome have a positive family history. 28 , 29 Many major medical conditions have been associated with sleep disruption, particularly those that require nighttime medical monitoring (eg, continuous glucose monitoring for individuals with diabetes) 38 or hospitalization, especially in an intensive or critical care unit. 39 , 40

Sleep deprivation studies and studies of insomniacs have identified the primary mechanisms by which sleep disruption is believed to exert its detrimental short- and long-term health effects ( Figure 1 ). 41 – 44 During both brief and extended arousals during sleep, increased metabolism is evidenced by increased oxygen consumption and carbon dioxide production. 43 Levels of catecholamine, norepinephrine, and epinephrine have been correlated with fragmented sleep. 44 In addition, chronic persistent insomnia is associated with increased secretion of adrenocorticotropic hormone and cortisol, which is present throughout a 24-hour sleep–wake cycle. 42 These findings suggest that activations of the sympathetic nervous system, the sympathoadrenal system, and the hypothalamic–pituitary–adrenal axis are involved in the health consequences of sleep disruption. 41 – 44 In addition, suppression of SWS was associated with decreased insulin sensitivity that did not result in an increase in insulin release; these findings may explain the increased risk of type 2 diabetes mellitus (T2DM) in patients with poor sleep quality. 9 Other metabolic changes include decreased leptin and increased ghrelin that may contribute to increased appetite. 45 Sleep abnormalities affect immune function in a reciprocal manner, leading to changes in proinflammatory cytokines, such as tumor necrosis factor, interleukins 1 and 6, and C-reactive protein. 12 , 46 The multitude of systems that react to sleep loss suggest effects beyond the central nervous system and include total body functioning. 5

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Proposed mechanisms by which sleep disruption is thought to exert its detrimental short- and long-term effects.

Notes: ↑, increase; ↓, decrease. Data from the following references. 9 , 12 , 41 – 45

Abbreviations: ACTH, adrenocorticotropic hormone; CO 2 , carbon dioxide; TNF, tumor necrosis factor; IL, interleukin; CRP, C-reactive protein; T2DM, type 2 diabetes mellitus.

These wide-ranging effects of sleep disruption are often interrelated and bidirectional. For example, the distress associated with sleep loss can create additional stress to maximize sleep, which, in turn, contributes to worsening (rather than improving) sleep disruption. 23 The current research suggests that the mechanisms of short- and long-term health consequences are similar but are affected by time. In chronic sleep deprivation, the body’s ability to compensate for physiologic changes is diminished, leading to gradually accumulating effects and basal changes. 47 Insomniacs have been shown to have increased EEG activity, abnormal hormone secretion, increased metabolic activity, and increased sympathetic nervous system activity throughout the day and night. Over time, this heightened and abnormal activity, resulting from the lack of appropriate body rest, can lead to the development of disease and chronic conditions. 45 Further, insufficient sleep may contribute to alterations in the neuroendocrine stress response system, ultimately leading to stress-related disorders such as mood disorders and depression. 47

Short-term health consequences of sleep disruption

As a result of the physiologic changes associated with sleep disruption, numerous health consequences have been reported. Short-term consequences of sleep disruption include increased stress responsivity; somatic problems; reduced quality of life (QoL); emotional distress; mood disorders and other mental health problems; cognition, memory, and performance deficits; and behavior problems in otherwise healthy individuals. Sleep disruption may also diminish the health-related quality of life (HRQoL) of children and adolescents with underlying medical conditions.

Short-term consequences in otherwise healthy individuals

Increased stress responsivity.

Increased autonomic sympathetic activation is a consequence of fragmented and interrupted sleep. 47 Results of experimental studies suggest that the extent of increased sympathetic activation was related more to the disruption and discontinuity of sleep than to the duration of sleep deprivation or the amount of sleep that was lost. 44 , 48 , 49 Sympathoadrenal activation produces a combination of transient hemodynamic, vasoconstrictive, and prothrombotic processes associated with a stress response. 48 These effects of sleep disruption on nocturnal regulation of sympathetic activity may offer a connection between sleep disruption and cardiovascular disease (CVD) as well as psychiatric conditions. 48 By affecting stress hormones, sleep disruption may directly affect functionality, including cognition and mood.

Somatic problems

A study of adolescents in two Finnish communities found that the 6-month prevalence of weekly sleep problems was 27% and that sleep problems were strongly associated with weekly headache and abdominal pain. 50 Girls had more symptoms than boys, and an increasing frequency of pain and sleep problems was associated with psychosocial difficulties, such as psychiatric symptoms and substance use. Bidirectional associations between somatic problems and sleep disorders are expected, and these associations may be related to common background factors, such as personality and adverse life events. 50 During clinical examination, when one symptom is reported, screening for related symptoms should be considered.

Psychosocial issues

Studies have identified a range of psychosocial issues associated with sleep disruption in adults, from emotional distress and mood disorders to cognitive, memory, and performance deficits.

In a qualitative interview-based study by Neu et al, 23 mothers of children who were receiving maintenance treatment for acute lymphoblastic leukemia routinely experienced sleep disruption because their children awoke and needed assistance or because of worries related to the child’s illness. The mothers reported being irritable, impatient, and less productive than before the illness. In a longitudinal, community-based study of midlife women who had a history of depression and/or anxiety but were not currently ill, sleep disturbance was significantly associated with reduced HRQoL, as measured by the 36-item Short Form Health Survey (SF-36). Odds ratios (ORs) ranged from 2.04 to 2.96, with P < 0.05 across all HRQoL domains. 51 A study of 61 maternal caregivers of young children with bronchopulmonary dysplasia showed that 80% of mothers had clinically disturbed sleep (based on self-report using the Pittsburgh Sleep Quality Index [PSQI]). 25 This sleep disturbance may be due to the need to administer medication and provide other care during the night, as well as worry about the child’s condition. Disrupted sleep was associated with diminished QoL in this study, as assessed using the World Health Organization’s Quality of Life Brief. Sleep quality emerged as the only independent variable to significantly predict QoL.

A recent review by Meerlo et al 52 surveyed the evidence that showed that disrupted sleep is a major causal factor in the development of depression. An experimental study that compared the effects of forced nocturnal awakenings with restricted sleep opportunity and uninterrupted sleep showed that partial sleep loss from sleep continuity disruption was more detrimental to positive mood than partial sleep loss from delaying bedtime. 53 Adult subjects experiencing forced awakenings had significantly less SWS after the first night of sleep deprivation than other participants. Furthermore, in adults who completed the Personality Assessment Inventory, self-reports of recurring sleep problems were associated with symptoms of depression and anxiety. 54 The reported frequency of sleep disturbance was closely linked with the severity of the self-reported symptoms. Among primary care physicians, disrupted sleep was associated with high burnout levels. 55

Sleep disruption alters cognition and performance in many domains, including attention/vigilance, executive function, emotional reactivity, memory formation, decision-making, risk-taking behavior, and judgment. 56 An experimental study showed that SWS disruption resulted in slower or impaired information processing, impaired sustained attention, less precise motor control, and erroneous implementation of well-practiced actions. 57 Younger, middle-aged, and older adults were similarly affected by SWS disruption. In another study, poor sleep quality negatively affected the emotional valence of memories. 58

Across these various studies, the interrelationships between sleep disruption, life events (such as illness of a child), and increased stress responsivity confound the physiologic response. These associations are bidirectional, as anxiety and depression are associated with sleep disruption, and thus make it challenging to separate cause from consequence. 47 Despite this difficulty, sleep disruption impacts psychosocial functioning in adults and may contribute to psychological conditions that require appropriate intervention.

Adolescents

Later bedtimes and an inadequate amount of sleep are well-documented changes in sleep patterns associated with adolescence. 59 A systematic review including 76 studies of the functional consequences of sleep problems in adolescents showed that sleep disruption had a negative effect on psychosocial health, school performance, and risk-taking behaviors, particularly use of nicotine and marijuana. 59 Studies assessing the relationships between sleep and psychosocial health measures found that sleep disruption was associated with new onset of poor mental health status, 60 loneliness, 61 worry, 62 anxiety, 61 , 63 and depression. 63 In a study of 1,629 adolescents, those with excellent academic performance had earlier bedtimes and longer sleep on weekdays with less severe daytime sleepiness than those with poor grades. 64 Other studies showed an association between sleep quality and sleep deprivation with poor academic performance. 65 , 66 Adolescent risk behaviors associated with sleep disruption included cigarette smoking, 67 , 68 drinking alcohol, 68 , 69 illicit drug use, 68 and aggressive behaviors, including driving while intoxicated, considering suicide, and having unprotected sex. 59 , 62 , 68

Psychosocial outcomes such as depression and mood disturbances, risk-taking behavior, and academic performance appear to be the primary factors affected by sleep disruption in adolescents. Taken together, causal bidirectional relationships are apparent between sleep and psychosocial health as noted earlier for adults. 59 These findings must be interpreted with caution, however, as many studies of sleep disturbance in adolescents group together the effects of short sleep duration (a common complaint among adolescents) and sleep disruption.

In a real-world study of 135 healthy children, diminished performance on neurobehavioral functioning measures (particularly those associated with more complex tasks, such as a continuous performance test and a symbol-digit substitution test) were found in children with fragmented sleep. 70 Parents of these children also rated them as having more behavioral problems than those with continuous sleep. Other reported issues include psychiatric symptoms, 71 social problems, 72 externalizing symptoms, 71 and self-harm behaviors. 73

Short-term consequences in individuals with underlying medical conditions

Reduced qol.

Of 159 children and adolescents with chronic kidney disease (pre-dialysis, dialysis, and transplant patients), 58.5% had symptoms of sleep disturbance, as measured by the Epworth Sleepiness Scale. 34 The presence of a sleep disturbance was most frequent in the dialysis group compared to the other groups, 34 while sleep disturbance was associated with a significant decrease in the overall total QoL score on the Pediatric Quality of Life Inventory (PedsQL) Version 4.0 Generic Core Scales for pre-dialysis and transplant subjects ( P = 0.002 and P = 0.001, respectively). A study of 47 pediatric liver transplant recipients investigated the impact of sleep problems (as assessed by the Pediatric Sleep Questionnaire) on HRQoL, as measured using the PedsQL. 74 Sleep-related breathing disorders and excessive daytime sleepiness were prevalent, affectinĝ23% and 40% of children in the study, respectively. 74 According to the parent proxy and child self-report, ~40% of participants had a substandard HRQoL. The physical manifestations of chronic diseases, such as chemical imbalances in dialysis patients, along with medications that may adversely affect sleep, play a role in sleep disruption and require comprehensive management to allow for effective sleep. 34 , 74

Long-term health consequences of sleep disruption

Long-term consequences of sleep disruption in otherwise healthy individuals include hypertension, dyslipidemia, CVD, weight-related issues, metabolic syndrome, and T2DM. Evidence suggests that sleep disruption may increase the risk of certain cancers and death. Sleep disruption may also worsen the symptoms of some gastrointestinal disorders.

Long-term consequences in otherwise healthy individuals

Cardiovascular.

The increased activity of the sympathetic nervous system that is associated with sleep deprivation has substantial long-term consequences for adults and adolescents. 45 , 47 , 75 – 79 Adults who experienced sleep disruption had elevated BP 70 and an increased risk of developing hypertension. 76 – 78 A meta-analysis of data from four prospective cohort studies found that the relative risk of incident hypertension was 1.20 (95% confidence interval [CI], 1.06–1.36) in adults with sleep continuity disturbance, with equal effects in men and women. 45 In adolescents, higher sleep disturbance scores on the PSQI were associated with higher cholesterol, higher body mass index (BMI), higher systolic BP, and an increased risk of hypertension. 79 Two large, population-based studies assessed the association between CVD and sleep disruption. 76 , 80 In the prospective, population-based Atherosclerosis Risk in Communities (ARIC) Study, incident CVD was observed in patients who experienced sleep continuity disturbance in combination with difficulty falling asleep and nonrestorative sleep (OR, 1.5; 95% CI, 1.1–2.0). 76 An association between difficulty maintaining sleep or short sleep duration and incident myocardial infarction was observed in middle-aged women who participated in the MONICA/KORA Augsburg Cohort Study. 80 Despite differences in study design and populations enrolled, these studies extend the literature to suggest that the effects of sleep disruption on sympathetic activity, glucose metabolism, and possibly inflammation may lead to adverse cardiovascular effects. 80

A recent review by Cedernaes et al 81 described a variety of molecular and behavioral factors that may lead to an association between sleep disruption and metabolic disorders, including obesity and T2DM. Sleep loss appears to affect energy metabolism primarily by impairing insulin sensitivity and increasing food intake. 81 Disrupted sleep has been associated with weight gain and other weight-related issues in both adults 82 , 83 and adolescents. 79 A 5-year ancillary study nested within the Coronary Artery Risk Development in Young Adults (CARDIA) study showed that sleep fragmentation was strongly associated with increases in BMI. 82 A common cause of sleep disruption is shift work, which has been implicated in high BP and increased stress. 20 A 14-year longitudinal study in male Japanese workers showed that alternating shift work increased the rate of everyday drinking, smoking, and absence of habitual exercise and also heightened the risk of increasing BMI. 83 In adolescents, sleep disruption was associated with a high BMI z -score, being overweight, and having a high waist circumference percentile. 79

The results of experimental studies in healthy volunteers suggest that, independent of sleep duration, sleep fragmentation can alter glucose homeostasis. 9 In an experimental study in healthy young adults, sleep disruption (characterized by three nights of SWS suppression) resulted in decreased insulin sensitivity, which was similar to that reported for populations at high risk of T2DM, and reduced glucose tolerance. 9 Other experimental studies showed that sleep fragmentation resulted in reduced insulin sensitivity, reduced glucose effectiveness (defined as the ability of glucose to mobilize itself independent of an insulin response), and increased cortisol levels. 84 , 85 Large longitudinal studies have shown that sleep disruption is associated with an increased risk of developing T2DM. 78 , 86 – 89 A meta-analysis of four of these studies 86 – 89 found that the overall relative risk of developing T2DM was 1.84 (95% CI, 1.39–2.43; P < 0.0001) in adults who experienced difficulty maintaining sleep. 90

The coexistence of obesity, elevated BP and glucose levels, and low levels of high-density lipoprotein cholesterol defines the metabolic syndrome. 91 An observational, cross-sectional study compared global scores on the PSQI with concurrently collected measures of metabolic syndrome components. 91 Poor global sleep-quality scores on the PSQI were related significantly to the presence of metabolic syndrome, and the PSQI global sleep-quality score was significantly related to waist circumference, BMI, percentage of body fat, serum levels of insulin and glucose, and estimated insulin resistance.

The accumulating evidence points to the importance of regular sleep for normal metabolic functioning and prevention of the metabolic syndrome. 81 The metabolic effects of sleep disruption appear to manifest in both the brain and peripheral organs. The effects of sleep disruption on appetite, glucose metabolism, and diabetes risk are critical to understanding the epidemic of obesity and metabolic disease. It has even been suggested that sleep may be an appropriate therapeutic target for treatment and prevention of obesity and diabetes. 81

Disruption of circadian rhythm and sleep deprivation have been shown to accelerate tumor formation 12 and may increase the risk of cancer. 12 , 92 Exposure to light at night decreases production of melatonin, which may lead to increased production of reproductive hormones. 93 Melatonin has other important properties, including DNA repair, inhibition of tumor growth, and acting as a potent free-radical scavenger. 92 , 94 A study in mice subjected to suprachiasmatic nuclei destruction showed that disruption of circadian coordination accelerated malignant growth, which suggests that the host circadian clock controls tumor progression 95 and provides a potential mechanistic reason for this association.

With regard to clinical data, night shift work has been associated with an increased risk of cancer. In the Nurses’ Health Study, 602 incident cases of colorectal cancer were documented among 78,586 women who were followed over 10 years. 93 Compared with women who never worked rotating night shifts, women who worked 1–14 years or ≥15 years on rotating night shifts had multivariate relative risks of colorectal cancer of 1.00 (95% CI, 0.84–1.19) and 1.35 (95% CI, 1.03–1.77), respectively ( P trend = 0.04). These data suggest that extended night shift work may increase the risk of colorectal cancer. Moreover, men who suffered from severe problems of falling and staying asleep were about twice as likely to develop prostate cancer as those without insomnia. 92

A recent large nested case–control study from Taiwan determined an increased risk of cancer among patients with sleep disorders compared with those without sleep disorders. 96 In this study, sleep disorders were separated into three categories: insomnia, parasomnia, and obstructive sleep apnea, all of which can contribute to sleep disruption. The risk of breast cancer was increased for patients with each of these types of disorder (adjusted hazard ratio 1.73 [95% CI, 1.57–1.90] for insomnia, 2.76 [95% CI, 1.53–5.00] for parasomnia, 2.10 [95% CI, 1.16–3.80] for obstructive sleep apnea). There was also a higher risk of nasal cancer and prostate cancer in patients with obstructive sleep apnea compared with those without sleep disruptions.

The mechanisms responsible for carcinogenesis in sleep-disrupted individuals are not clear, and much of the work is focused on nighttime light exposure and decreased melatonin levels. 92 , 93 Additional research is required to determine the effect and etiology of sleep disruption on cancer risk.

In the GAZEL cohort study that assessed sleep disturbances using the 5-item sleep dimension from the Nottingham Health Profile, sleep disturbance was associated with a higher all-cause risk of mortality in men ( P = 0.005), but not in women ( P = 0.33). In particular, men who reported sleep disruption on the Nottingham Health Profile (“I sleep badly at night”) had a higher all-cause mortality risk compared with those who did not report sleep disruption (hazard ratio, 1.69; 95% CI, 1.25–2.31). 78 In a study in which the family and friends of adolescent suicide completers reported sleep disturbances for the deceased, history of sleep disturbances, including middle insomnia, was significantly associated with suicide compared with matched community controls. 97 The effect remained significant when controlling for current affective disorders and severity of depressive symptoms.

The high correlation between sleep disturbances, depression, and suicidal ideation may play a role in identifying an increased risk of mortality in these studies. Other studies have linked sleep disorders to mortality through an increase in cardiovascular deaths, which have also been related to sleep disruption. Additional studies are needed in larger cohorts and controlling for confounding factors. Importantly, hypertension and diabetes may not explain death in younger individuals with sleep disruption, but the association of sleep disruption with these factors is a risk factor for mortality in later life. 78

Long-term consequences in individuals with underlying medical conditions

The interdependent relationship between sleep and the immune system may be a factor in the effect of sleep abnormalities on common gastrointestinal disorders. Sleep disruption may worsen symptoms of inflammatory bowel disease, irritable bowel syndrome, and gastroesophageal reflux disease. 12 Conversely, these same gastrointestinal disorders can also contribute to sleep disruption. As seen with many other consequences of sleep disruption, the bidirectional interplay between sleep disruption and gastrointestinal disorders provides the opportunity for clinicians to treat both conditions for improved patient outcomes.

Disrupted sleep is a pervasive problem, with numerous contributing factors from lifestyle and environmental factors to psychosocial issues and iatrogenic effects. Sleep is vital to most major physiologic processes, and, as such, sleep disruption has vast potential for adverse short- and long-term health consequences in otherwise healthy individuals as well as those with underlying medical conditions. In healthy individuals, short-term consequences include a heightened stress response; pain; depression; anxiety; and cognition, memory, and performance deficits. In adolescents and children, disrupted sleep can lead to poor school performance and behavior problems. Reduced QoL may be a short-term consequence of sleep disruption in otherwise healthy individuals and those with an underlying medical condition. Long-term consequences for otherwise healthy individuals include hypertension, dyslipidemia, CVD, weight gain, metabolic syndrome, and T2DM. There is also evidence that sleep disruption may increase the risk of certain cancers and death in males and suicidal adolescents. Long-term sleep disruption may also worsen the symptoms of a variety of gastrointestinal disorders.

Ultimately, it has been suggested that the physiologic consequences of disrupted sleep may be just as damaging as those of short sleep duration. 5 Given the detrimental impact of disrupted sleep, it is important for health care professionals to effectively treat symptoms of underlying medical conditions to optimize sleep continuity. In addition, when possible, health care providers should consider prescribing interventions that minimize disruptions to sleep continuity, 25 such as medications with a long dosing interval.

Acknowledgments

Medical writing assistance for this manuscript was provided by Katie Gersh, PhD, of MedErgy and was funded by Horizon Pharma.

All authors are employees of Horizon Pharma, which funded medical writing assistance for this manuscript. The authors report no other conflicts of interest in this work.

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