Habitual short sleepers with pre-existing medical conditions are at higher risk of Long COVID
ABSTRACT
Study Objectives:
Preliminary evidence suggests that the risk of Long COVID is higher among people with pre-existing medical conditions. Based on its proven adjuvant role in immunity, habitual sleep duration may alter the risk of developing Long COVID. The objective of this study was to determine whether the odds of Long COVID are higher among those with pre-existing medical conditions, and whether the strength of this association varies by habitual sleep duration.
Methods:
Using data from 13,461 respondents from 16 countries who participated in the 2021 survey-based International COVID Sleep Study II (ICOSS II), we studied the associations between habitual sleep duration, pre-existing medical conditions, and Long COVID.
Results:
Of 2,508 individuals who had COVID-19, 61% reported at least 1 Long COVID symptom. Multivariable logistic regression analysis showed that the risk of having Long COVID was 1.8-fold higher for average-length sleepers (6-9 h/night) with pre-existing medical conditions compared with those without pre-existing medical conditions (adjusted odds ratio [aOR] 1.84 [1.18–2.90]; P = .008). The risk of Long COVID was 3-fold higher for short sleepers with pre-existing medical conditions (aOR 2.95 [1.04–8.4]; P = .043) and not significantly higher for long sleepers with pre-existing conditions (aOR 2.11 [0.93–4.77]; P = .073) compared with average-length sleepers without pre-existing conditions.
Conclusions:
Habitual short nighttime sleep duration exacerbated the risk of Long COVID in individuals with pre-existing conditions. Restoring nighttime sleep to average duration represents a potentially modifiable behavioral factor to lower the odds of Long COVID for at-risk patients.
Citation:
Berezin L, Waseem R, Merikanto I, et al. Habitual short sleepers with pre-existing medical conditions are at higher risk of long COVID. J Clin Sleep Med. 2024;20(1):111–119.
BRIEF SUMMARY
Current Knowledge/Study Rationale: Preliminary evidence suggests that the risk of Long COVID is higher among people with pre-existing medical conditions. Based on its proven adjuvant role in immunity, habitual sleep duration may also alter the risk of Long COVID. We aimed to study the associations between habitual sleep duration, pre-existing conditions, and the risk of Long COVID.
Study Impact: This study found that habitual short nighttime sleep duration exacerbated the risk of Long COVID in individuals with pre-existing conditions. Restoring nighttime sleep to average duration represents a potentially modifiable behavioral factor to lower the odds of Long COVID for at-risk patients.
INTRODUCTION
As of August 9, 2023, there have been more than 769 million laboratory-confirmed coronavirus disease 2019 (COVID-19) cases and 6.9 million deaths documented globally.1 While there has been significant emphasis on the acute phase of COVID-19, less attention has been given to the persisting symptoms. Individuals with COVID-19 are considered recovered after 2 weeks or after a negative COVID-19 test, but emerging data suggest that many continue to experience long-term sequelae. This constellation of persisting symptoms is known as post–COVID-19 condition, postacute sequelae of COVID-19 or “Long COVID”. Long COVID is defined by the World Health Organization (WHO) as the condition that occurs in people who have a history of probable or confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, usually within 3 months from the onset of COVID-19, with symptoms and effects that last for at least 2 months.2,3
While most individuals fully recover, the global prevalence of Long COVID is estimated to be approximately 40%.4,5 Long COVID consists of diverse symptoms that include, but are not limited to, fatigue, dysgeusia, dyspnea, asthenia, persistent cough, muscle aches, depression, anxiety, cognitive dysfunction, and sleep problems.2,6–10 Long COVID is also associated with greater sleep alterations (sleep quality, daytime sleepiness, sleep inertia, naps, insomnia, sleep apnea, and nightmares).6 There is a greater incidence of Long COVID in individuals with greater body mass index (BMI), older age, female sex, pre-existing medical conditions, and severe COVID-19.8,11,12 The most frequent pre-existing conditions that have been linked to Long COVID include chronic obstructive pulmonary disease, fibromyalgia, anxiety, depression, migraine, multiple sclerosis, heart disease, hypertension, and diabetes.11–13
Habitual sleep duration has been shown to impact health outcomes, all-cause mortality, and immunity in adults. There exists a clear U-shaped relationship between sleep duration and mortality, whereby both individuals with short (< 6 h/night) and long (> 9 h/night) habitual sleep duration have an increased risk of adverse outcomes.14–16 Long habitual sleep duration is associated with increased cardiovascular-related mortality, diabetes, all-cause mortality, and poorer mental health.17 It is also associated with increased levels of systemic inflammation and proinflammatory biomarkers, including C-reactive protein and interleukin-6.17 Similarly, individuals with short habitual sleep duration are at increased risk of mortality, diabetes, hypertension, cardiovascular disease, obesity, and respiratory infections.15 Habitual sleep duration is known to play a large role in adaptive immunity.18–20 These associations suggest that habitual sleep duration may play an important role in the development and pathogenesis of Long COVID, but whether the risk of developing Long COVID varies by nighttime sleep duration is unknown. It is possible that patients with pre-existing medical conditions and habitual short or long sleep duration are more prone to developing persisting COVID-19 symptoms compared with those with average sleep duration.
To date, most research on Long COVID has focused on previously hospitalized patients with severe illness, but little is known about Long COVID in the general population and its association with habitual sleep duration and pre-existing medical conditions. Using a large, diverse multinational sample, the objective of our study was to examine whether habitual sleep duration alters the risk of Long COVID and its interaction with pre-existing medical conditions. We hypothesized that individuals with pre-existing comorbidities would have a higher probability of Long COVID, with exacerbation by short and long habitual sleep duration.
METHODS
Global COVID-19 survey
The research protocol and standardized survey questionnaire have been published previously.21 The survey was designed by the International COVID Sleep Study (ICOSS) II group.22 All countries/regions obtained ethical approval or exemptions in keeping with national research governance and regulations (Table S1 in the supplemental material). The multinational cross-sectional survey was conducted between May and December 2021 in the general adult population in 16 countries in their native language (Austria, Brazil, Bulgaria, Canada, Hong Kong/China, Croatia, Finland, France, Germany, Israel, Italy, Japan, Norway, Portugal, Sweden, United States).21 The survey was administered online by sharing a link on national newspapers, social media sites, and university or hospital webpages. Participants aged 18 years or older, anonymously, and voluntarily, took part in the self-administered online survey. The most used survey platforms for administration were Redcap (Research Electronic Data Capture, Vanderbilt University, Nashville, Tennessee) and Qualtrics (Qualtrics, Provo, Utah).
The survey included sociodemographic variables (age, sex, BMI) and pre-existing medical conditions. Participants were asked if they had tested positive for COVID-19. We considered COVID-19 cases as those who responded “yes” to both having had COVID-19 and testing positive for COVID-19. We considered COVID-19–negative control cases as those who reported not having had COVID-19 and did not report positive results from a COVID-19 test. Those who responded “yes” only for either having had COVID-19 or testing positive for COVID-19 were excluded. Participants were asked whether they have persistent symptoms related to COVID-19. We used the following definition of Long COVID, defined according to a Delphi consensus by the WHO: individuals with a history of confirmed or probable SARS-CoV-2 infection with at least 1 symptom lasting for over 3 months.2
This research was conducted according to the Declaration of Helsinki, and all countries obtained ethical approval or exemptions in keeping with national research governance and regulations.
Pre-existing medical conditions
Pre-existing medical conditions were grouped into relevant categories: (1) chronic cardiac diseases, including arterial hypertension, atrial fibrillation, heart failure, other heart conditions, and stroke; (2) chronic respiratory diseases, including chronic obstructive pulmonary disease and asthma; (3) chronic neurological conditions, including cognitive impairment and problems of movement (tremor, rigidity, bradykinesia, and gait difficulty); (4) autoimmune conditions and cancer, such as autoimmune disease, use of cytostatic medications, immunosuppressive treatment, and allergy (atopy, seasonal allergy, other allergies); (5) endocrine disorders including type 1 and 2 diabetes; (6) chronic pain, including chronic pain syndromes, and migraine or headache; and (7) psychiatric conditions, including depression, anxiety, or panic disorder. Pre-existing medical conditions were defined as medical conditions existing prior to COVID-19. Those pre-existing conditions developed during or because of COVID-19 were excluded.
Nighttime sleep duration
Sleep duration was assessed by self-report questions, where participants were asked, “How many hours per night did you sleep on average before the pandemic” and “How many hours per night did you sleep on average during the pandemic, prior to having COVID-19?” Self-reported sleep duration was used as an indicator for habitual sleep duration. Participants were classified into the average sleep duration category if they reported sleeping an average of 6 to 9 hours per night, short-duration sleepers if they slept, on average, less than 6 hours per night, and long-duration sleepers if they slept, on average, more than 9 hours per night.
Clusters of Long COVID symptoms
The Long COVID symptoms were grouped into clusters relevant to the specific symptoms according to a recent systematic review and meta-analysis.4 Cardiac cluster symptoms included fatigue, dyspnea, palpitations, cardiac arrhythmia, tachycardia, postexertional malaise referring to prolonged weakness or poor functionality after exertion, such as muscle weakness, and difficulties walking long distances. Neurological cluster symptoms included attention or concentration problems, brain fog, cognitive dysfunction, memory problems, loss of smell or taste, hallucinations, and psychotic symptoms. Pain cluster symptoms included muscle pain or ache, joint pain, migraine, or headache. Dysautonomia cluster symptoms included dizziness, hypotension, urinary symptoms, sweating problem, heat or cold intolerance, abdominal pain or colic, diarrhea, and nausea or vomiting.4 In this study, sleep-related symptoms were not considered in the definition of Long COVID to avoid collinearity, since we studied the effect of sleep duration.
Statistical analysis
Statistical analysis was conducted using Stata version 14.2 (StataCorp, College Station, TX). The demographic data were summarized using means and standard deviations. The categorical variables were described using frequencies and percentages. Baseline demographic characteristics of participants with no COVID, Short COVID, and Long COVID, as well as habitual average, short, and long-duration sleep were compared using 1-way analysis of variance (ANOVA) and chi-square tests. Multivariable logistic regression analyses were conducted to examine the association between Long COVID with habitual sleep duration and pre-existing medical conditions, adjusting for age, sex, BMI, vaccination status, ethnicity, marital status, type of geographic area (rural vs urban), and exercise. Variables were chosen based on clinical significance and statistical significance in unadjusted analysis. Both adjusted (adjusted odds ratio [aOR]) and unadjusted odds ratios were reported. A P value < .05 was considered statistically significant (2-sided). All analyses were weighted by joint distribution of age and sex by country.
RESULTS
In total, 16,899 participants completed the survey and 13,461 answered the specific questions about COVID-19 diagnosis. There were 10,953 participants with no prior COVID-19, 2,508 who had COVID-19, including 966 who did not report Long COVID symptoms (referred to as “Short COVID”). Among 1,542 participants with Long COVID, 1,505 also reported whether they had pre-existing medical conditions and their habitual sleep duration. Among them, 945 participants reported having pre-existing medical conditions prior to the pandemic (Figure 1). The numbers of habitual average-length sleepers, short, and long sleepers with and without pre-existing medical conditions are shown in Figure 1.
There were significant differences between the demographic characteristics of the participants with Long COVID with average (6–9 hours), short (< 6 hours), and long habitual sleep duration (> 9 hours) (Table 1). Long sleepers were significantly younger (36.0 ± 14.7 years, P < .001) compared with short sleepers (44.5 ± 13.8 years) and those with average sleep duration (43.9 ± 13.5 years). A greater proportion of long sleepers and average-duration sleepers were of White ethnicity (90% and 86.6%, respectively) compared with short sleepers (76.1%). There were significantly more unvaccinated participants who reported habitual long sleep duration compared with average sleepers and short sleepers (38.9% vs 30.0% vs 21.4%, P = .049). In comparison to average and long sleepers, short sleepers had a significantly greater number of individuals with asymptomatic COVID-19 (6.3% vs 6.1% vs 9.6%, P = .002) and as well as those with severe or life-threatening COVID-19 (8.5% vs 6.1% vs 12.2%).
| Variables | Average Sleep Duration (6–9 h) (n = 1,186) | Short Sleep Duration (< 6 h) (n = 131) | Long Sleep Duration (> 9 h) (n = 108) | P |
|---|---|---|---|---|
| Age, y | 43.9 ± 13.5 | 44.5 ± 13.8 | 36.0 ± 14.7 | <.001 |
| Sex, female | 961 (81.0) | 101 (77.1) | 91 (84.3) | .363 |
| Geophragical region, urban | 717 (78.3) | 69 (74.2) | 51 (72.9) | .414 |
| Ethnicity | ||||
| White | 789 (86.6) | 70 (76.1) | 63 (90.0) | .001 |
| Asian | 35 (3.8) | 6 (6.5) | 3 (4.3) | |
| Black | 6 (0.7) | 3 (3.3) | 0 | |
| Hispanic | 23 (2.5) | 9 (9.8) | 2 (2.9) | |
| Other | 58 (6.4) | 4 (4.4) | 2 (2.9) | |
| Marital status | ||||
| Single | 224 (24.5) | 19(20.4) | 38 (54.3) | <.001 |
| Relations | 609 (66.5) | 69 (74.2) | 28 (40.0) | |
| Divorce/separated | 66 (7.2) | 4 (4.3) | 3 (4.3) | |
| Widowed | 17 (1.9) | 1 (1.1) | 1 (1.4) | |
| No. of vaccinations | ||||
| 0 | 356 (30.0) | 28 (21.4) | 42 (38.9) | .049 |
| 1 | 412 (34.8) | 49 (37.4) | 29 (26.9) | |
| 2 | 417 (35.2) | 54 (41.2) | 37 (34.3) | |
| COVID-19 severity | ||||
| No marked symptoms | 40 (6.3) | 66 (9.6) | 6 (6.1) | .002 |
| Mild | 352 (55.4) | 322 (46.6) | 63 (64.3) | |
| Moderate | 190 (29.9) | 219 (31.7) | 23 (23.5) | |
| Severe/life threatening | 54 (8.5) | 84 (12.2) | 6 (6.1) | |
| No. of pre-existing conditions | ||||
| 0 | 129 (28.5) | 13 (26.5) | 10 (23.3) | .740 |
| ≥ 1 | 323 (71.5) | 36 (73.5) | 33 (76.7) | |
| Pre-existing conditions | ||||
| Cardiac | 193 (18.7) | 39 (33.6)* | 7 (6.6) | <.001 |
| Respiratory | 150 (13.3) | 19 (15.3) | 14 (14.0) | .819 |
| Endocrine | 41 (3.5) | 10 (7.8) | 3 (2.8) | .050 |
| Neurological | 97 (13.9) | 18 (22.0) | 13 (21.0) | .069 |
| Autoimmune and cancer | 154 (13.5) | 12 (9.5) | 9 (8.7) | .188 |
| Chronic pain | 220 (30.9) | 30 (39.0) | 20 (34.5) | .325 |
| Depression/anxiety | 222 (25.2) | 15 (16.0) | 27 (32.1) | .098 |
There were also significantly more individuals with pre-existing cardiac conditions among habitual short sleepers compared with average and long sleepers (33.6% vs 18.7% vs 6.6%, P < .001), but no differences between other types of pre-existing conditions, including respiratory, endocrine, neurological, autoimmune, or psychiatric pre-existing comorbidities. There were no significant differences among sexes and number of pre-existing medical conditions.
Multivariable logistic regression analyses on the association of Long COVID with habitual sleep duration and presence of pre-existing medical conditions are shown in Table 2. The probability of Long COVID for average, short, and long sleepers with or without pre-existing medical conditions is shown in Figure 2. The model was adjusted for significant variables including sex, age, BMI, vaccination status, ethnicity, marital status, geophragical region, and exercise. Multivariable logistic regression analysis showed that the risk of having Long COVID was 1.8- fold higher for average-length sleepers with pre-existing medical conditions than those without pre-existing medical conditions (aOR 1.84 [1.18–2.90], P = .008) (Table 2, Figure 2). The risk of Long COVID was 3-fold higher for habitual short sleepers with pre-existing medical conditions (aOR 2.95 [1.04–8.4], P = .043) but not significantly higher for habitual long sleepers with pre-existing conditions (aOR 2.11 [0.93–4.77], P = .073) than the average sleepers without pre-existing conditions (Table 2, Figure 2).
| Variables | Unadjusted OR | P | Adjusted OR | P |
|---|---|---|---|---|
| Sex, female | 1.84 (1.42–2.37) | <.001 | 1.62 (1.05–2.50) | .029 |
| Age | ||||
| < 30 y | Reference | |||
| 30–39 y | 2.45 (1.72–3.48) | <.001 | 2.72 (1.38–5.37) | .004 |
| 40–49 y | 3.48 (2.51–4.84) | <.001 | 2.64 (1.42–4.91) | .002 |
| 50–59 y | 3.50 (2.43–3.40) | <.001 | 2.50 (1.22–5.18) | .012 |
| 60–69 y | 2.10 (1.35–3.40) | .001 | 2.17 (0.77–6.13) | .144 |
| ≥ 70 y | 3.06 (1.63–5.74) | <.001 | 1.20 (0.25–5.77) | .820 |
| BMI > 30 kg/m2 | 1.76 (1.2–2.57) | .004 | 1.32 (0.74–2.34) | .029 |
| No. of vaccinations | ||||
| 0 | Reference | |||
| 1 | 1.91 (1.41–-2.58) | <.001 | 1.98 (1.13–3.46) | .017 |
| > 1 | 1.29 (0.94–-1.77) | .011 | 1.65 (1.00–2.73) | .049 |
| Marital status | ||||
| Single | Reference | |||
| Married | 3.46 (2.56–-4.68) | <.001 | 1.76 (1.05–2.96) | .032 |
| Divorced | 9.81 (4.23–-22.70) | <.001 | 4.35 (1.27–14.90) | .020 |
| Widowed | 9.10 (2.94–-28.10) | <.001 | 0.46 (0.09–2.49) | .369 |
| Urban area | 0.71 (0.53–0.97) | .034 | 0.96 (0.62–1.49) | .851 |
| Vigorous intensity exercise | Reference | |||
| < 1 h | 0.50 (0.32–0.80) | .003 | 0.84 (0.44–1.61) | .601 |
| 1–1.5 h | 1.01 (0.62–1.64) | .959 | 1.04 (0.55–1.97) | .893 |
| 2–3 h | 0.42 (0.27–0.67) | <.001 | 0.99 (0.55–1.78) | .982 |
| > 4 h | 0.20 (0.12–0.36) | <.001 | 0.73 (0.35–1.51) | .394 |
| Ethnicity | ||||
| White | Reference | |||
| Asian | 0.38 (0.23–0.62) | <.001 | 0.27 (0.12–0.62) | .002 |
| Black | 0.15 (0.67–0.36) | <.001 | 0.23 (0.06–0.90) | .035 |
| Hispanic | 1.21 (0.54–2.68) | .644 | 0.83 (0.25–2.71) | .752 |
| Other | 1.03 (0.54–1.99) | .919 | 1.01 (0.38–2.70) | .980 |
| Average sleepers and no pre-existing medical conditions | Reference | |||
| Short sleepers and no pre-existing medical conditions | 1.34 (0.54–3.34) | .53 | 0.79 (0.17–3.60) | .759 |
| Long sleepers and no pre-existing medical conditions | 1.18 (0.31–4.47) | .81 | 0.94 (0.30–3.00) | .923 |
| Average sleepers and pre-existing medical conditions | 2.03 (1.39–3.00) | <.001 | 1.84 (1.18–2.90) | .008 |
| Short sleepers and pre-existing medical conditions | 2.60 (1.21–5.60) | .014 | 2.95 (1.04–8.40) | .043 |
| Long sleepers and pre-existing medical conditions | 2.55 (1.16–5.59) | .020 | 2.11 (0.93–4.77) | .073 |
Normal sleepers refer to average-length sleepers.
There was no significant increase in the risk of Long COVID for both long and short sleepers without pre-existing conditions. When average-length sleepers with pre-existing medical conditions are used as the reference group, the risk of Long COVID is significantly reduced for average sleepers with no pre-existing medical conditions (aOR 0.54 [0.35–0.90], P = .008) (Table 2, Figure 2). There were no significant differences in the risk of Long COVID for short and long sleepers with or without pre-existing medical conditions when compared with average sleepers with pre-existing medical conditions (Table S1). Female sex, age between 30 and 59 years, BMI over 30 kg/m2, COVID-19 vaccination, and married or divorced marital status were all significantly associated with an increased risk of developing Long COVID. Asian and Black ethnicity and male sex were found to decrease the risk of developing Long COVID.
DISCUSSION
We studied the associations between Long COVID, habitual sleep duration, and pre-existing medical conditions based on a multinational, cross-sectional survey. We found that prior-infection habitual short sleepers with pre-existing medical conditions had a higher likelihood of Long COVID. Notably, the risk of Long COVID was 3-fold higher for short sleepers with pre-existing medical conditions than average-duration sleepers without pre-existing conditions. Average-duration sleepers with pre-existing medical conditions had a 1.8-fold higher risk of developing Long COVID compared with those without pre-existing medical conditions. Long-duration sleepers were not found to have a significantly higher risk of developing Long COVID compared with those with or without pre-existing medical conditions. Thus, our results suggest that pre-existing medical conditions play a greater role in the development of Long COVID than habitual sleep duration. These associations persisted even after adjustment for demographic and socioeconomic covariates, as well as BMI and vaccination status.
Our findings suggest that habitual short sleep duration may modulate the relationship between Long COVID and pre-existing medical conditions, and the interplay between sleep duration and pre-existing medical conditions may play an important role in the pathogenesis of Long COVID. Prior studies have shown that, among individuals double-vaccinated for COVID-19, those with habitual short duration sleep before the pandemic, but not long sleep duration, had a higher risk of Long COVID (aOR 1.56 [1.29–1.88] vs 1.18 [0.70–1.97]).23
A study of over 400,000 participants using genetic variants associated with short and long sleep durations showed that genetically predicted short sleep duration had causal associations with 5 of 12 cardiovascular diseases, while genetically predicted long sleep duration lacked associations with any cardiovascular diseases.24 A genetic predisposition to short sleep duration may influence cardiovascular health through common pathophysiological mechanisms identified in insomnia, including sympathetic nervous system dysfunction, accelerated atherosclerosis, increased inflammation, and cardiovascular dysfunction.24 These same pathophysiological pathways may exacerbate the risk of Long COVID in short sleepers with pre-existing medical conditions.
Our finding of increased Long COVID risk among habitual short sleepers with pre-existing medical conditions adds to the growing literature suggesting that short sleep duration impairs immune function. Associations have previously been found between habitual short sleep duration and increased COVID-19 severity, along with worse prognosis.25 Habitual short sleep duration may be associated with detrimental effects on immunological responses through disrupted circadian rhythmicity, impaired cytokine production, increased release of inflammatory factors, and impaired immunity against pathogens. Habitual sleep duration during vaccination against viral pathogens may play a role in the adaptive immune system response.19,20,26,27 For instance, 10 days following influenza vaccination, individuals who were immunized after 4 consecutive nights of 4-hour sleep duration had 50% lower levels of immunoglobulin (Ig) G (IgG) antibody titers than individuals with average sleep duration at the time of vaccination.19 We suspect that habitual short sleep duration may reduce vaccination efficacy and impair the body’s immune response to both the COVID-19 vaccine and initial infection, thereby predisposing these individuals to Long COVID through impaired adaptive immunity.23 Short sleep duration may reduce neutrophil function, impairing the body’s rapid innate host defense against the COVID-19 pathogen.28
We found no association between preinfection habitual long sleep duration and increased Long COVID risk in those with or without pre-existing medical conditions. This is supported by the fact that there is a lack of experimental evidence to suggest a link between habitual long-duration sleep and impaired immune function. However, habitual long sleep duration may represent a surrogate marker for poor health status, rather than a causative factor. Habitual long sleep duration has been closely linked with numerous risk factors for poor health, such as depression, diabetes, hypertension, obesity, and chronic inflammation.29–31 Many of these conditions can also lead to longer sleep duration and may reflect a reverse causality or confounding effect.24 There may be a bidirectional effect between habitual long sleep duration and pre-existing medical disorders, resulting in a positive feedback loop that further perpetuates disordered sleep.29 For example, a bidirectional relationship between sleep issues and depression has been well documented, whereby insomnia serves as both an independent risk factor and secondary manifestation of depression.28 Long sleep duration may therefore represent a symptom, rather than a causative factor, of Long COVID.
Both short and long habitual sleep duration are associated with an increase in markers of systemic inflammation.32 For instance, long habitual sleep duration has been associated with elevations in C-reactive protein and interleukin-6 level, while short sleep duration has been found to have elevated tumor necrosis alpha levels.31 This suggests that extremes of sleep duration are associated with activation of proinflammatory pathways, which may represent a mechanism by which sleep duration affects predisposition to Long COVID in habitual short-duration sleepers.
Interestingly, among those with no pre-existing conditions, sleep duration did not modify the risk of Long COVID. Prior studies have shown that underlying medical conditions shape the immune response associated with severe COVID-19.33 For example, patients with metabolic syndrome, immunological disorders, and chronic kidney disease have characteristic altered immune signatures, which may additively increase immune dysregulation.33 This suggests that underlying medical conditions may impair the immune response to viral infection. Short sleep duration may exacerbate this underlying immune dysregulation, thereby further increasing the risk of Long COVID. A study of over 480,000 adults with Long COVID found that the comorbidities with increased risk of Long COVID symptoms were chronic obstructive pulmonary disease, fibromyalgia, anxiety, depression, migraine, and multiple sclerosis.11 In contrast, Sudre et al12 found that asthma was the only pre-existing comorbidity significantly associated with the development of Long COVID. Other frequently cited pre-existing medical conditions that have been linked to Long COVID include heart disease, hypertension, and diabetes.11,13 Our study found a higher incidence of pre-existing cardiac conditions among habitual short sleepers, but no significant differences with respiratory, endocrine, neurological, autoimmune, or psychiatric conditions. The association between various pre-existing medical conditions and Long COVID warrants analysis of the pathophysiological drivers underlying the risk of Long COVID.
Recent studies provide evidence of mechanisms and predictors for the risk of Long COVID. In a cohort of 309 patients with COVID-19, a longitudinal multi-omic analysis identified 4 risk factors, including pre-existing type 2 diabetes, high levels of SARS-CoV-2 RNAemia, Epstein-Barr virus reactivation during acute infection, and specific autoantibodies, which may predict sustained disease.34 Patients with respiratory symptoms exhibited repressed levels of cortisol and cortisone, while patients reporting neurological symptoms had higher levels of proteins associated with negative regulation of the circadian sleep/wake cycle.34 These biomarkers may suggest distinct pathophysiological origins of various Long COVID symptom clusters. Another study identified a distinct SARS-CoV-2–specific immunoglobulin signature, with reduced IgM and IgG3 titers, in individuals with Long COVID.35 This immunoglobulin signature may be related to a reduction in the production of type 1 interferons (IFNs), resulting in a failure of isotype switching. Studies have also shown that persistently positive antinuclear antibodies present up to 1 year after COVID-19 infection, which were associated with persisting Long COVID symptoms.36 Long COVID may result from direct tissue damage due to the virus, excessive inflammation, or thrombotic events.35 This suggests that a combination of host and virus factors, including potential persistence of viral antigen, inflammation, autoimmunological dysregulation, and host comorbidities, may contribute to the risk of Long COVID.
Among those with confirmed COVID-19, several risk factors were associated with the risk of Long COVID, including female sex, age 30 to 59 years, BMI over 30 kg/m2, married or divorced marital status, and vaccination status. Similarly, previous studies have suggested that female sex, severity of COVID-19, and lower socioeconomic status increase the risk for Long COVID.8,12,13,37
Limitations
There are several limitations to our study. Given the retrospective nature of the study, there is a potential for recall bias when participants answer questions regarding times prior to the pandemic. Our assessment of sleep duration was based on a single self-report question rather than objective measurement, which may result in misclassification bias with both false positives and false negatives secondary to under- and overestimation of sleep duration. Prior work has shown poor-to-moderate agreement between self-reported short sleep duration and actigraphy in the older population and those with pre-existing comorbidities, such as congestive heart failure and hypersomnia.38–40 Conversely, other studies showed no significant differences between estimates of sleep duration based on both actigraphy and subjective sleep assessment.41 Nevertheless, self-reported sleep duration provides a method for estimating sleep duration in a large heterogenous sample of adults across the world where retrospective objective data collection is not practically attainable during the COVID-19 pandemic. Due to very few participants self-reporting having sleep-disordered breathing, we were unable to analyze its effect on Long COVID. Also, self-selection existed as those with Long COVID may be more likely to participate in the survey. The web survey may limit generalizability and exclude participants who lacked internet access or had low digital literacy. Last, the mean age of short, average, and long habitual sleepers was 44.5, 43.9, and 36.0 years, respectively. The significant age differences among the cohorts may influence one’s predisposition for Long COVID and is an important limitation of categorizing participants into groups of short, average, and long sleepers. However, our findings are consistent with age-dependent sleep-duration norms.42
CONCLUSIONS
Based on a large, multinational, cross-sectional survey of individuals with confirmed COVID-19, we found that, compared with average-duration sleepers, habitual short sleepers were at significantly increased risk of Long COVID in the presence of pre-existing medical conditions. Our data suggest that having nighttime sleep of average duration (6–9 h/night) may represent an important potentially modifiable behavior to reduce the risk of Long COVID in those with pre-existing medical conditions. Further work is needed to understand the pathophysiology of Long COVID to target prevention and treatment strategies.
DISCLOSURE STATEMENT
All authors have seen and approved this manuscript. Work for this study was performed at the Department of Anesthesia and Pain Medicine, Toronto Western Hospital, University Health Network, University of Toronto. Dr. Chung reports grants from Ontario Ministry of Health Innovation Grant, grants from ResMed Foundation and University Health Network Foundation, and consultation fees from Takeda Pharma, outside the submitted work. Dr. Espie reports grants from the Wellcome Trust and the National Institute for Health and Care Research (Health Technology Assessment) and is a co-founder and a shareholder in Big Health, outside of the submitted work. Dr. Inoue reports personal fees and other from Astellas Pharma, personal fees from Eisai, other from Idorsia Pharmaceuticals Japan, grants from Koike Medical, personal fees from Otsuka Pharmaceutical, and grants from Philips Japan, outside the submitted work. Dr Landtblom reports personal fees from Takeda, Jazz Pharmaceuticals, and UCBPharma, as well as a research grant from Aoporphan Drugs. Dr. Leger reports grants from Philips (Netherlands), grants from Vanda (USA), grants from Sanofi, grants from VitalAire International, grants from Merck, and grants from Janssen, Jazz, and TYTHM, outside the submitted work. Dr. Matsui reports personal fees from Eisai, Meiji Seika Pharma, Mochida, MSD, Otsuka Pharmaceutical, and Yoshitomi Pharmaceutical, outside the submitted work. Dr. Morin reports research grants from the Canadian Institutes for Health Research, Eisai, Idorsia, and Lallemand Health; consulting fees from Eisai, Idorsia, Pear Therapeutics, and Sunovion; and royalties from Mapi Research Trust, all outside the submitted work. Dr. Partinen reports personal fees and other from Bioprojet, other from Jazz Pharmaceuticals, personal fees from UCBPharma, personal fees from GSK, personal fees from Takeda, personal fees and other from MSD, personal fees from Orion, and personal fees and other from Umecrine, outside the submitted work; and a grant from Signe & Ane Gyllenberg Foundation. Dr. Penzel reports research grants from Cidelec, Löwenstein Medical, and Novartis, and consulting fees from Bayer Healthcare, Bioprojet, Cerebra, Idorsia, Jazz Pharmaceuticals, Neuwirth, Philips, and Sleepimage, outside the submitted work. Dr. Wing reported personal fees for delivering a lecture, Eisai Co. Ltd., and personal fees from Sponsorship from Lundbeck HK Ltd., outside the submitted work. Dr. Bjorvatn reports that he has served as consultant for F. Hoffmann-La Roche Ltd. and received honoraria for lectures from AGB-Pharma AB.
ABBREVIATIONS
| aOR | adjusted odds ratio |
| BMI | body mass index |
| COVID-19 | coronavirus disease 2019 |
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