According to the World Health Organization, since December 2019, over 760 million humans have contracted the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), resulting in Coronavirus Disease 2019 (COVID-19), with a recorded global death toll of 6.9 million attributed to the infection [1]. A meta-analysis indicates that approximately 43% of individuals diagnosed with COVID-19 have reported experiencing lingering symptoms or health issues following the acute phase of infection, also named long COVID [2]. Numerous medications have been introduced to mitigate the severe and long-lasting health effects associated with COVID-19, including antiviral agents such as remdesivir [3]. However, vaccinations against SARS-CoV-2, particularly utilizing the recently developed mRNA vaccines, have emerged as the most potent measure in combating the virus [4]. For instance, mRNA vaccines have been associated with a reduced likelihood of contracting SARS-CoV-2, being hospitalized, or dying from COVID-19 [5]. Furthermore, they may lower the risk of experiencing long COVID symptoms, such as chest pain, breathing difficulties, ageusia, or anosmia [6].

People with obesity face an increased risk of developing a more severe course of COVID-19. For instance, in a study of COVID-19 cases among patients aged 18 years and younger, individuals with obesity had a 3.1 times higher risk of hospitalization and a 1.4 times higher risk of severe illness when hospitalized, which includes admission to the intensive care unit, need for invasive mechanical ventilation, or death [7]. Moreover, obesity has been identified as a risk factor for persistent symptoms following SARS-CoV-2 infection [8,9,10,11]. Given that mRNA vaccination against SARS-CoV-2 reduces both mortality and morbidity of COVID-19 [4, 5], including long COVID [6], it may be particularly relevant for people with obesity. However, a recent study suggests that two doses of SARS-CoV-2 mRNA vaccine may provide less protection in people with obesity. In this study, it was observed that breakthrough infections occurred more frequently and developed more rapidly in individuals with obesity who had received two doses of mRNA vaccine, compared to those with normal weight [12]. However, it remains unclear whether this weight-related effect also influences the risk of experiencing long COVID symptoms in individuals who have received two doses of the SARS-CoV-2 mRNA vaccine.

In our present multinational anonymous survey study, we included 5919 adults who had completed a two-dose regimen of SARS-CoV-2 mRNA vaccination. Our primary objective was to investigate whether individuals with obesity have a higher odds ratio (OR) of experiencing symptoms resembling long COVID, thus rejecting the null hypothesis that BMI status does not affect the likelihood of experiencing such symptoms. Given that only a small proportion of respondents reported a positive COVID test result, the assessment of long COVID likelihood relied on the Post-Acute Sequelae of SARS-CoV-2 (PASC) score [13]. This scoring system incorporates various typical symptoms associated with long COVID to ascertain its presence. Additionally, we explored the potential impact of gender on the relationship between BMI status and long COVID-like symptoms, considering gender-based differences in long COVID risk [14].

Notably, research indicates a link between obesity and sleep, encompassing issues such as insomnia [15], obstructive sleep apnea (OSA; [16]), and presence of short sleep duration [17]. Given sleep’s pivotal role in managing viral infections [18, 19], including SARS-CoV-2 [20,21,22,23], it is reasonable to hypothesize that individuals with obesity may be more prone to experiencing symptoms akin to long COVID compared to those with normal weight, owing to heightened sleep burden. Consequently, we investigated whether the occurrence of insomnia, OSA, and both short and long sleep durations is more prevalent among participants with probable long COVID and obesity.

Study design and statistical methods

Study population

In this study, self-reported data were collected from 5919 participants aged 18 to 89, with 63% of participants being women. The data were obtained through the second wave of the International COVID Sleep Study survey (ICOSS-2), as described in detail in reference [24]. The survey was conducted anonymously between May and December 2021 and was accessible through various online platforms, including RedCap and Qualtrics. To reach a diverse audience, the survey was advertised on university web pages, newspapers, television, Facebook, and Twitter (since 2023 named X). Additionally, it was made available in multiple languages, including German, Portuguese, Brazilian Portuguese, English, French, Bulgarian, Croatian, Chinese, Finnish, Hebrew, Italian, Japanese, Norwegian, and Swedish. After applying the exclusion criteria outlined in Supplemental Table S1, 5919 participants who reported receiving two doses of mRNA SARS-CoV-2 vaccine (Moderna and BioNTech/Pfizer) had complete data for analysis. During the survey period in 2021, a third dose of SARS-CoV-2 mRNA vaccine was not yet standard. Therefore, we only inquired about participants receiving up to two doses of SARS-CoV-2 mRNA vaccine.

This study adhered to the principles outlined in the Helsinki Declaration and obtained either ethical approval or waivers in all participating countries, in accordance with their respective national research governance and regulations. Notably, ethical approval was not mandated by national law in Austria, Brazil, Finland, France, Norway, and Sweden due to the anonymous nature of the survey collection. The anonymity of the data was preserved throughout the study. Prior to accessing the questionnaire, participants were required to provide their consent to participate, with a minimum age requirement of 18 years. No monetary compensation was provided to participants. Further details regarding ethical approval in each country can be found in Supplemental Table S2.

Definition of BMI cut-off points

The BMI ranges used to define normal weight, overweight, and obesity vary for Asians compared to other ethnicities [25]. Hence, we employed the following BMI thresholds for non-Asians: normal weight (reference group) <25 kg/m2, overweight 25-29.9 kg/m2, and obesity ≥30 kg/m2. Meanwhile, for participants of Asian ethnicity, their BMI status was determined as follows: normal weight <23 kg/m2, overweight ≥23 to <27.5 kg/m2, and obesity ≥27.5 kg/m2.

Definition of composite risk score for long COVID

A recent study [13] identified 12 symptoms that persist for at least six months post-infection with SARS-CoV-2 that can be used to assess the likelihood of experiencing long COVID, using a PASC score. Specifically, each symptom was assigned a score based on its predictive ability for long COVID: loss of or change in smell or taste (8 points), post-exertional malaise (7 points), chronic cough (4 points), brain fog (3 points), thirst (3 points), palpitations (2 points), chest pain (2 points), fatigue (1 point), dizziness (1 point), gastrointestinal tract symptoms (1 point), changes in sexual desire or capacity (1 point), and abnormal movements (1 point).

We included eight symptoms that lasted for at least six months at the time of the survey to calculate the PASC score for each participant. These symptoms were: loss of or change in smell or taste, post-exertional malaise, brain fog, palpitations, chest pain, fatigue, dizziness, and gastrointestinal tract symptoms. Based on a previous study’s proposal [13], participants with a PASC score of 12 or higher were classified as having a high likelihood of experiencing long COVID, regardless of whether they reported a previous SARS-CoV-2 infection.

To address the potential bias of misclassifying participants as unlikely to suffer from probable long COVID due to the absence of survey questions about chronic cough, thirst, changes in sexual desire or capacity, and abnormal movements, a sensitivity analysis was performed (see statistical section for more details).

Assessment of sleep

Based on participants’ sleep duration reports, we evaluated if participants usually slept less than six or more than nine hours per night (in the following, referred to as short or long sleep duration, respectively). The literature often uses these thresholds to discriminate short and long sleep duration from normal sleep duration [26, 27]. Participants’ insomnia risk was determined through the Insomnia Severity Index (ISI) [28], a validated questionnaire of seven items assessing the severity of insomnia symptoms and their impact on daily functioning. A score greater than 14 is indicative of moderate-to-severe insomnia. To assess the presence of OSA, we used the STOP scale [29]. Specifcally, participants were asked to respond on a 5-point Likert scale to the following four questions: (a) Do you snore loudly, surpassing the volume of talking or being audible through closed doors? Response options ranged from “Not at all” to “Every night/almost every night.” (b) Do you frequently experience daytime tiredness, fatigue, or excessive sleepiness? Response options spanned from “Not at all” to “Every day/almost every day.” (c) Has anyone ever witnessed you ceasing to breathe or choking during your sleep? Response options varied from “No, never” to “Every day/almost every day.” Participants’ answers were dichotomized into two categories for the first three questions: 0 = Less than three nights per week and 1 = Three nights per week or more. (d) We also surveyed whether participants currently had or received treatment for high blood pressure. A “Yes”-response was counted as one score. The risk of OSA was considered high if participants scored two or greater on the STOP scale.

Statistical analysis

Data are presented as mean ± SD unless otherwise specified. Group characteristics were compared using the Chi-Square test for categorical variables and generalized linear models for continuous variables. Logistic regression analyses were conducted to examine the associations between BMI group as a predictor and probable long COVID status as a dependent variable using SPSS 28.0 (IBM Corp., Armonk, NY, USA).

In addition to conducting an unadjusted logistic regression analysis, we employed one additional regression model to examine the robustness of the association between BMI status and probable long COVID. The adjusted analysis incorporated self-reported positive SARS-CoV-2 test, age, sex, race/ethnicity, smoking status, the time elapsed since the first vaccination ( ≤ six vs. > six months), urbanicity, and weekly physical activity level score (ranging from 0 to 7; higher score indicating higher physical activity; for more details, see [22]). We additionally considered a medical history encompassing hypertension, type 2 diabetes, depression, and attention deficit hyperactivity disorder. These were defined as instances where individuals had been diagnosed with or received treatment for these conditions either prior to or at the time of the survey. This inclusion was motivated by the recognition that each of these conditions commonly co-occurs with obesity [30,31,32,33].

To ensure the robustness of the hypothesized association between BMI status and probable long COVID, we conducted several sensitivity analyses:

1. We examined individuals who reported testing positive for SARS-CoV-2 before the survey (n = 515).

2. We separately analyzed data for men and women because previous findings suggest that the risk of long COVID is higher among women than men [14]. In this context, we assessed multiplicative interactions between BMI status and sex.

3. Participants from the USA were excluded from the analysis as they were significantly younger than participants from other countries (Supplemental Table S3).

4. We excluded individuals whose PASC score fell between 3 and 11, as our study did not survey four of the twelve symptoms used to calculate the PASC score in a previous study [13]. As mentioned earlier, if experienced for at least six months, the total sum of these symptoms corresponds to 9 points.

5. We excluded participants (n = 412) who were underweight from the normal weight reference group. Underweight was defined as having a BMI < 18.5 kg/m2 across all ethnicities.

To assess the potential variability in the risk of inadequate sleep associated with BMI and probable long COVID status, we conducted logistic regression analyses, both unadjusted and adjusted. Our binary outcome variables included moderate-to-severe insomnia, a high risk of OSA, short nighttime sleep duration (less than 6 hours), and long nighttime sleep duration (more than 9 hours). In the adjusted regression model, both BMI group and probable long COVID status were entered together to account for mutual adjustment. Additionally, for the sleep outcomes, we assessed multiplicative interactions between BMI group and probable long COVID status. Overall, a P value less than 0.05 was considered significant.


Cohort characteristics

A comprehensive overview of characteristics, categorized by BMI status, is provided in Table 1. Compared to participants with normal weight, those with obesity exhibited several distinctive features. Specifically, they were more likely to be White/Caucasian and reside in urban areas. Additionally, they were more likely to report a history of type 2 diabetes, hypertension, depression, and attention deficit hyperactivity disorder. Additionally, they more frequently met the criteria for OSA and moderate-to-severe insomnia and reported shorter sleep durations. Finally, participants in the obesity group had significantly higher PASC scores, indicating a greater likelihood of experiencing persistent symptoms commonly associated with a prior COVID-19 infection. The participant’s country of origin is specified in Supplemental Table S3.

Table 1 Characteristics of ICOSS-2 participants categorized by body mass index status (N = 5919).

Association between obesity and long COVID status

The frequency of PASC symptoms among double-vaccinated ICOSS-2 participants, categorized by BMI group alone, as well as by combined BMI group and probable long COVID status, is presented in Supplemental Table S4 and Supplemental Table S5, respectively. As depicted in Fig. 1, participants with obesity had 1.55 times higher OR of having a PASC score ≥12 [95% CI: 1.05, 2.28], compared to those with normal weight (adjusted P = 0.028). However, there were no significant differences in OR of having a PASC score ≥12 between participants with overweight and those with normal weight, both in the unadjusted (P = 0.188) and the adjusted analyses (P = 0.650; Fig. 1).

Fig. 1: Association of body mass index with probable long COVID status.
figure 1

OR [95% CI] indicates odds ratio [95% Confidence Interval]. NW participants with normal weight, OW participants with overweight; and OB, participants with obesity.

In the subgroup analysis of ICOSS-2 participants who indicated that they had tested positive for SARS-CoV-2 before the survey, the unadjusted OR of having a PASC score ≥12 was 2.61 times higher in participants with obesity (n = 100) compared to the normal weight reference group (n = 270; [95% CI: 1.49, 4.58], P < 0.001). However, the unadjusted OR of having a PASC score ≥12 between participants with overweight (n = 145) and those with normal weight did not significantly differ (OR: 1.01 [95% CI: 0.56, 1.84], P = 0.968). After adjusting for potential confounders, the OR of having a PASC score ≥12 did not significantly differ among the BMI groups (P = 0.808 for obesity vs. normal weight and P = 0.381 for overweight vs. normal weight, respectively).

As summarized in Supplemental Table S6, the association between obesity and a having a PASC score ≥12 was present in both men and women in the unadjusted analysis (P < 0.001). Following adjustment, OR of having a PASC score ≥12 were only significantly higher among women (adjusted P = 0.032). However, no significant multiplicative interaction between participants’ sex and BMI status was found (adjusted P = 0.518).

Ultimately, the correlation between obesity and an increased OR of having a PASC score ≥12 remained significant even after excluding participants from the USA (Supplemental Table S7), those from the group without probable long COVID whose PASC score ranged from 3 to 11 (Supplemental Table S8), and individuals who were underweight (Supplemental Table S9).

Association between obesity, probable long COVID, and inadequate sleep

As presented in Table 2, a significant correlation between overweight and obesity and elevated adjusted ORs ranging from 1.33 to 5.12 [95% CI range: 1.11, 6.70] for experiencing moderate-to-severe insomnia, OSA, and short sleep duration was found (adjusted P ≤ 0.002). Furthermore, participants with obesity had lower OR of sleeping more than 9 h (OR: 0.60 [95% CI: 0.37, 0.98], P = 0.042). Additionally, irrespective of their BMI status, participants with probable long COVID exhibited a heightened likelihood of experiencing moderate-to-severe insomnia, OSA, and prolonged sleep duration (adjusted ORs ranging from 1.98 to 2.88 [95% CI range: 1.32, 4.78]; adjusted P ≤ 0.008).

Table 2 Association between probable long COVID, body mass index, and sleep in ICOSS-2.

A separate logistic regression analysis, incorporating the multiplicative interaction term between BMI and probable long COVID status, indicated that the association between participants’ probable long COVID status and sleep outcomes remained consistent across different BMI statuses (adjusted P ≥ 0.141 for the multiplicative interaction between BMI and probable long COVID status).


In our international survey study, encompassing 5919 participants aged 18 to 89 years who had received two doses of SARS-CoV-2 mRNA vaccine, we found that participants with obesity exhibited a higher likelihood of experiencing multiple probable long COVID symptoms, as determined by a composite risk score for long COVID, compared to those with normal weight. This finding challenges the null hypothesis, indicating that BMI status indeed influences the probability of experiencing long COVID symptoms.

While our survey study did not directly investigate the mechanisms underlying the association between obesity and experiencing probable long COVID, despite being double-vaccinated with SARS-CoV-2 mRNA vaccine, several potential explanations exist. For instance, a previous study [12] revealed that vaccinated participants with severe obesity had a 76% higher likelihood of experiencing hospitalization or death from COVID-19. This finding may be attributed to the fact that 55% of participants with severe obesity had undetectable levels of neutralizing antibodies against SARS-CoV-2, compared to only 12% of those with normal BMI six months after their second vaccine dose. Additionally, the neutralizing capacity of SARS-CoV-2-specific antibodies was lower in participants with obesity compared to those with a BMI in the normal weight range.

In our study, participants with probable long COVID exhibited elevated odds of experiencing sleep disorders, including moderate-to-severe insomnia and a high risk for OSA, as well as prolonged sleep durations, compared to participants with a low likelihood of long COVID. It is noteworthy that participants with obesity presented with a heightened sleep burden, manifesting as a higher likelihood of suffering from insomnia, OSA, and short sleep duration. However, associations between sleep outcomes and probable long COVID did not vary by BMI status. This suggests that although improving sleep in patients with persistent COVID symptoms may hold promise for reducing the frequency, duration, and severity of such symptoms - given that sleep enhances innate and adaptive immunity [18, 19, 34] - the heightened sleep burden due to probable long COVID may occur regardless of a participant’s BMI status.


When interpreting our results, it is important to consider several factors. Firstly, we relied on self-reported data in this study, which could have introduced recall bias. Secondly, to mitigate the risk of misclassifying participants experiencing potential long COVID symptoms, our survey study utilized the PASC score, incorporating a cluster of post-COVID infection symptoms documented in the literature [13]. However, concerns remain regarding participants with a PASC score of 12 or higher, especially if previous COVID infection has not been confirmed, as this could stem from conditions beyond those considered in our analysis. Thus, comprehensive studies, which include rigorous testing for COVID infections such as antibody assays and polymerase chain reaction testing, are necessary to confirm our findings. Another limitation is that various factors such as limited access to SARS-CoV-2 tests, timing issues related to test sensitivity, and reluctance among respondents to undergo testing may contribute to the fact that only 9% of participants reported a positive SARS-CoV-2 result. Additionally, previous research has highlighted that a significant number of SARS-CoV-2 cases go undetected [35], with some individuals potentially unaware of their infection. It is also worth mentioning that our analysis focused exclusively on SARS-CoV-2 mRNA vaccine, as the majority of respondents received this type of vaccine. Finally, future studies could enhance their comprehensiveness by incorporating additional long COVID symptoms, as demonstrated in previous research [36]. This approach would contribute to a deeper understanding of how BMI status may influence both the risk and severity of long COVID symptoms despite vaccination against SARS-CoV-2.


Participants with obesity may face an increased likelihood of experiencing multiple symptoms attributed to long COVID, even after receiving two doses of the SARS-CoV-2 mRNA vaccine, compared to individuals with normal weight. This finding from our study aligns well with previous results suggesting that participants with obesity exhibit a faster decline in immunity against SARS-CoV-2 following vaccination compared to those with normal weight [12]. The association between probable long COVID and disrupted sleep, irrespective of BMI, may be significant from a therapeutic standpoint, as sleep can enhance immunity [18, 19, 34, 37] and thus aid in the recovery from long COVID. However, given the limitations of our study, such as reliance on self-reported data and potential confounding factors, our findings should be viewed as hypothesis-generating rather than definitive conclusions.