Abstract
Aim
To evaluate longitudinal associations of Long COVID with incident cardiometabolic and respiratory outcomes among adults.
Methods
We used the Michigan COVID-19 Recovery Surveillance Study, a population-based longitudinal study of adults with PCR-confirmed COVID-19 in Michigan. We included adults with COVID-19 who responded to the baseline (data collection: 06/2020-12/2022) and follow-up survey (data collection: 01/2022-11/2023) and were free of each outcome at baseline. Long COVID was defined as recovery taking ≥90 days after infection or no recovery. We evaluated four self-reported incident outcomes: 1) diabetes, 2) hypertension, 3) heart disease, and 4) asthma. We conducted modified Poisson models to examine longitudinal exposures-outcomes association separately, overall and stratified by sex.
Results
Long COVID was associated with higher past-year incidence of heart disease and asthma in multivariable models, when we stratified the models by sex, we observed statistically significant associations for females only between Long COVID and all measured outcomes, except hypertension (Females = diabetes: Incidence Risk Ratio (IRR) = 2.33 95% CI 1.15,4.73; heart disease: IRR = 1.98 95% CI 1.10,3.57; asthma: IRR = 2.99 95% CI 1.60,5.57).
Conclusion
Study findings reinforce the importance of preventing Long COVID and to monitor sequelae including incident disease outcomes for those who are experiencing Long COVID.
Keywords: Long COVID, Diabetes incidence, Asthma incidence, Heart disease incidence, Hypertension incidence
Highlights
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Long COVID was associated with higher incidence of heart disease and asthma.
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Among females only, Long COVID was also associated with diabetes.
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Findings highlight the need to prevent Long COVID and monitor health sequelae.
1. Introduction
Long COVID is a public health problem in the United States, with an estimated 8.4% of US adults reporting ever experiencing Long COVID and 3.6% reporting a current Long COVID condition in the U.S adult population in 2023 (Vahratian et al., 2024). Moreover, both ever experiencing Long COVID and currently experiencing Long COVID are more common among women than men (National Center for Health Statistics, 2024; National Sciences of Engineering Medicine, 2024a). Long COVID manifests in multiple ways affecting many systems of the body (Davis et al., 2021). Long COVID patients report various persistent symptoms, with the most common including fatigue, loss of smell or change in taste, shortness of breath, cough, and headache (Woodrow et al., 2023; Hirschtick et al., 2023; Crook et al., 2021). In addition, Long COVID could trigger the development of new diseases or conditions, or exacerbate pre-existing conditions, potentially affecting any organ or system of the body (National Sciences of Engineering Medicine, 2024a).
A small set of studies have examined the relationship between COVID-19 illness and the onset of chronic diseases, but little is known about the relationship between Long COVID and incident chronic disease. Regarding COVID-19 illness and diabetes, several longitudinal studies using mostly medical record data, with follow-up periods ranging from 120 days to one year, found that COVID-19 was associated with new onset diabetes mellitus (hereafter, diabetes) (Rathmann et al., 2022; Birabaharan et al., 2022; Daugherty et al., 2021; Xie and Al-Aly, 2022).One study found a higher risk of incident diabetes among men but not women (Wander et al., 2022). Other prospective studies that used medical records found that COVID-19 illness increased the risk of cardiovascular outcomes at follow-up (range at follow up time 1–3 years), such as risk of cerebrovascular, ischemic heart disease, pericarditis, myocarditis, heart failure, and thromboembolic disease (Xie et al., 2022; Hilser et al., 2024; Raman et al., 2022). COVID-19 illness has also been associated with an increase in new onset of asthma among adults in a clinic-based sample (Lee et al., 2023). Despite the growing evidence base of the relationship between COVID-19 illness with new onset of diabetes, cardiovascular diseases, and asthma, these studies all compare people with COVID-19 illness to those without COVID-19 illness (National Center for Health Statistics, 2024; Rathmann et al., 2022; Birabaharan et al., 2022; Xie and Al-Aly, 2022; Wander et al., 2022; Xie et al., 2022; Raman et al., 2022; Steenblock et al., 2022; Zhang et al., 2022). Evaluating the link between Long COVID and incident chronic diseases is important to provide additional evidence for long-term consequences of Long COVID and to provide adequate medical care to patients with Long COVID. Moreover, given the higher prevalence of Long COVID among women compared to men (National Center for Health Statistics, 2024; National Sciences of Engineering Medicine, 2024a), and because recent research has identified some biomarkers related to Long COVID that are more frequent among women (Swank et al., 2024), it is important to understand if these associations between Long COVID and incident chronic diseases vary by sex.
The 2024 National Sciences of Engineering Medicine (NASEM) report on Long COVID highlighted the need to build upon the current understanding of Long COVID by measuring the long-term effects of Long COVID at the population-level using longitudinal data, including understanding the risk of developing new chronic diseases (National Sciences of Engineering Medicine, 2024a; National Sciences of Engineering Medicine, 2024b). Our paper responds to this need by evaluating the association between Long COVID and incident chronic diseases using longitudinal data from a population-based probability sample of adults with confirmed SARS-CoV-2 in Michigan. Because Long COVID illness can affect any organ of the human body (National Sciences of Engineering Medicine, 2024a; National Sciences of Engineering Medicine, 2024b), we investigated whether Long COVID at baseline was associated with a higher incidence of new conditions such as diabetes, hypertension, cardiovascular disease, and asthma at follow-up. We expected a higher incidence of metabolic, cardiovascular, and respiratory diseases among adults with Long COVID at baseline compared to adults who did have Long COVID. Moreover, we hypothesized these associations would be stronger for female than male adults.
2. Methods
2.1. Study design and population
We used data from the Michigan COVID-19 Recovery Surveillance Study (MI CReSS) longitudinal, a population-based longitudinal study. At baseline, sixteen sequential probability samples were drawn from the Michigan Disease Surveillance System records of PCR-confirmed SARS-CoV-2 infections from March 2020 through May 2022 among noninstitutionalized adults with valid phone numbers and zip codes in Michigan. Baseline surveys were completed online in English or by telephone in English, Spanish, or Arabic between June 2020 and December 2022, with a median of 4.4 months after COVID-19 onset [Interquartile Range (IQR) 3.4–5.7]. Follow-up surveys were completed between January 2022 and November 2023, with a median of 18.4 months [IQR: 15.0–21.3] after COVID-19 onset. A total of 5521 adults completed surveys at baseline, for a response rate of 32.1% (American Association for Public Opinion Research, 2016; Hirschtick et al., 2025). A total of 4100 completed surveys at follow-up, for a response rate of 74.3.%. The time between baseline and follow-up survey administrations was a median of 13.3 months [IQR 11.0–15.7]. We constructed four analytic samples, one for each outcome variable, by excluding respondents with missing data on the exposure, outcome, or covariates. We also restricted each disease-specific analytic sample to respondents who did not report the outcome at baseline. The four final analytic samples for this study consisted of N = 3259 for the diabetes analysis; N = 2602 for the hypertension analysis; N = 3284 for the heart disease or other cardiovascular disease analysis; and N = 3088 for the asthma analysis (Supplementary Fig. 1). The University of Michigan Institutional Review Board deemed this study exempt due to the use of secondary, de-identified data.
2.2. Measures
2.2.1. Exposure
We aligned our Long COVID definition with the NASEM report of a length of recovery from COVID-19 to usual state of health of 90+ days (Hirschtick et al., 2023; National Sciences of Engineering Medicine, 2024b). At baseline, individuals were asked if they recovered to their usual state of health following their COVID-19 diagnosis. Individuals who reported either still recovering at the time of the survey (if it had been at least 90 days from onset), or who reported a recovery time of 90+ days were classified as having Long COVID.
2.2.2. Outcomes
We evaluated the outcomes at follow-up using the question, “Over the last year, have you been told by a doctor or other health professional that you have any of the following conditions,” with conditions including 1) “diabetes,” 2) “hypertension,” 3) “heart disease or other cardiovascular disease,” and 4) “asthma.” Individuals who responded “yes” were considered to have an incident outcome since we excluded those who reported the outcome at baseline from each analytic sample.
2.2.3. Covariates
We included the following baseline covariates in regression models: age categorical (18–34, 35–54, and 55+ years); sex (male and female); race and ethnicity (Non-Hispanic White, Non-Hispanic Black, Hispanic, Another race or ethnicity group); imputed annual household income (less than $35,000, $35,000–$74,999, ≥$75,000); and COVID-19 vaccination prior to COVID-19 illness (yes, no). We also controlled for baseline current combustible tobacco use (current use of cigarettes, cigars, pipes, or hookah) and body mass index (BMI; under/normal <25 kg/m2, overweight 25–30 kg/m2, obesity>30 kg/m2), as well as survey mode (telephone, online) and pandemic phase of infection (3/01/20-9/30/20, 10/01/20-9/30/21, 10/01/21-5/31/22). Additionally, we adjusted for the total number of physical pre-existing conditions at baseline, including cardiovascular, cerebrovascular, diabetes, liver disease, cancer, and autoimmune conditions. We categorized the variable into: none, one, or two or more preexisting conditions.
2.3. Statistical analysis
First, we described sample characteristics of the analytic sample for each outcome. Second, we estimated the crude incidence of diabetes, hypertension, heart disease, and asthma overall and by Long COVID status. Third, we conducted bivariate and adjusted modified Poisson regression models to examine each prospective exposure-outcome association separately. In addition, due to differences in Long COVID by sex, we stratified the models. Finally, given that the data collection was conducted over a broad range of time and the question asked about the outcome in the past 12 months, we further split the sample into two groups: adults who responded to the follow-up survey within 12 months of the baseline survey administration and adults who responded more than 12 months after baseline. We examined the incidence of the chronic disease outcomes by Long COVID status, in both subsamples. Additionally, to control for differences in timing between survey administrations, we conducted a sensitivity analysis that included a time variable between baseline and follow-up (≤12 months vs. >12 months); we were unable to stratify by this variable due to convergence problems due to small sample sizes. In this sensitivity analysis, we also controlled for health insurance status (private insurance/Medicare/Medicaid/another type (yes) vs none) as a proxy for access to health services. All analyses adjusted for the complex sample design and longitudinal weights that take into account the original sampling design and attrition. All analyses were conducted in Stata 18.
3. Results
3.1. Descriptive statistics
Table 1 shows the sample characteristics for the four analytic samples, which were similar, with some small differences. For example, obesity was slightly lower in the hypertension (34.5%) sample compared to the diabetes (38.5%), cardiovascular disease (41.1%), and asthma samples (42.2%). About 55% of respondents were female adults and more than 70% were non-Hispanic White, with about 9% non-Hispanic Black, 7% Hispanic, and 11.5% from another race or ethnicity group (Table 1). Approximately 28% of each sample had an annual household income lower than $35,000 and about 40% had an annual household income of $75,000 or more.
Table 1.
Characteristics for the four analytic samples of adults with COVID-19 in Michigan who completed the baseline and follow-up survey, 2022–2023.
| Diabetes (3259) |
Hypertension (n = 2602) |
Heart Disease (n = 3284) |
Asthma (n = 3088) |
|||||
|---|---|---|---|---|---|---|---|---|
| % | CI 95% | % | CI 95% | % | CI 95% | % | CI 95% | |
| Long COVID at baseline | ||||||||
| No | 84.2 | 82.8,85.5 | 85.7 | 84.2,87.1 | 84.0 | 82.6,85.3 | 84.5 | 83.1,85.8 |
| Yes | 15.8 | 14.5,17.2 | 14.3 | 12.9,15.8 | 16.0 | 14.7,17.4 | 15.5 | 14.2,16.9 |
| Sex | ||||||||
| Male | 44.9 | 43.0,46.9 | 42.3 | 40.1,44.5 | 44.3 | 42.4,46.3 | 46.4 | 44.4,48.4 |
| Female | 55.1 | 53.1,57.0 | 57.7 | 55.5,59.9 | 55.7 | 53.7,57.6 | 53.6 | 51.6,55.6 |
| Age | ||||||||
| 18–34 years | 39.0 | 37.1,41.0 | 44.7 | 42.6,46.9 | 38.1 | 36.2,40.0 | 35.2 | 33.2,37.2 |
| 35–54 years | 35.8 | 34.0,37.6 | 36.5 | 34.4,38.5 | 36.8 | 35.0,38.7 | 35.1 | 33.2,37.0 |
| 55+ years | 25.2 | 23.7,26.8 | 18.8 | 17.3,20.4 | 25.1 | 23.6,26.7 | 29.8 | 28.1,31.5 |
| Race and Ethnicity | ||||||||
| Non-Hispanic White | 72.0 | 70.1,73.7 | 71.7 | 69.6,73.7 | 70.3 | 68.,4,72.1 | 71.5 | 69.6,73.4 |
| Non-Hispanic Black | 9.5 | 8.5,10.7 | 8.3 | 7.2,9.6 | 10.4 | 9.3,11.6 | 9.7 | 8.6,10.9 |
| Hispanic | 7.3 | 6.2,8.5 | 7.5 | 6.3,8.9 | 7.5 | 6.4,8.7 | 7.4 | 6.3,8.7 |
| Non-Hispanic Other | 11.2 | 10.0,12.6 | 12.5 | 11.0,14.1 | 11.9 | 10.6,13.2 | 11.3 | 10.1,12.8 |
| Income | ||||||||
| Less than $35,000 | 28.1 | 26.3,29.9 | 28.1 | 26.1,30.1 | 28.9 | 27.2,30.8 | 28.1 | 26.3,30.0 |
| $35,000–$75,000 | 29.9 | 28.2,31.7 | 29.0 | 27.1,31.0 | 29.7 | 27.9,31.4 | 29.8 | 28.0,31.6 |
| $75,000 or more | 42.0 | 40.1,43.9 | 42.9 | 40.8,45.1 | 41.4 | 39.5,43.3 | 42.1 | 40.2,44.0 |
| Body Mass Index (BMI) | ||||||||
| Normal/Underweight (<25 kg/m2) | 28.8 | 27.1,30.6 | 32.4 | 30.4,34.4 | 27.3 | 25.6,29.1 | 28.1 | 26.3,29.9 |
| Overweight (25–30 kg/m2) | 32.7 | 30.9,34.5 | 33.1 | 31.1,35.2 | 31.6 | 29.8,33.4 | 29.7 | 28.0,31.6 |
| Obesity >30 kg/m2 | 38.5 | 36.7,40.4 | 34.5 | 32.5,36.6 | 41.1 | 39.2,43.0 | 42.2 | 40.2,44.1 |
| Current combustible tobacco use | ||||||||
| No | 89.7 | 88.4,90.9 | 89.7 | 88.3,91.0 | 89.8 | 88.5,91.0 | 90.2 | 88.8,91.3 |
| Yes | 10.3 | 9.1,11.6 | 10.3 | 9.0-,1.7 | 10.2 | 9.0,11.5 | 9.8 | 8.7,11.2 |
| Number of preexisting conditions | ||||||||
| None | 48.4 | 46.5,50.4 | 59.4 | 57.3,61.4 | 48.1 | 46.1,50.0 | 51.5 | 49.5,53.5 |
| One | 29.1 | 27.4,30.8 | 27.0 | 25.2,28.9 | 28.7 | 27.0,30.4 | 24.9 | 23.3,26.7 |
| Two or more | 22.5 | 21.0,24.1 | 13.6 | 12.3,15.1 | 23.3 | 21.7,24.9 | 23.6 | 22.0,25.2 |
| Phase of the pandemic | ||||||||
| 03/01/2020-09/3/02020 | 26.2 | 24.5,28.0 | 26.2 | 24.3,28.2 | 26.8 | 25.1,28.6 | 27.2 | 25.5,29.1 |
| 10/01/2020-09/30/2021 | 42.4 | 40.5,44.3 | 42.7 | 40.6,44.8 | 41.6 | 39.7,43.5 | 41.6 | 39.6,43.5 |
| 10/01/2021-05/31/2022 | 31.4 | 29.6,33.2 | 31.1 | 29.2,33.1 | 31.6 | 29.8,33.4 | 31.2 | 29.4,33.1 |
| Survey mode | ||||||||
| Phone | 33.0 | 31.2,34.9 | 31.6 | 29.6,33.7 | 33.4 | 31.6,35.3 | 34.3 | 32.4,36.2 |
| Online | 67.0 | 65.1,68.8 | 68.4 | 66.3,70.4 | 66.6 | 64.7,68.4 | 65.7 | 63.8,67.6 |
| Covid Vaccination Prior illness’ | ||||||||
| No | 73.9 | 72.2,75.5 | 74.1 | 72.2,75.9 | 74.2 | 72.6,75.9 | 73.5 | 71.7,75.2 |
| Yes | 26.1 | 24.5,27.8 | 25.9 | 24.1,27.8 | 25.8 | 24.1,27.4 | 26.5 | 24.8,28.3 |
Table 2 shows the results for past-year incidence of diabetes. The crude incidence of diabetes was higher among adults with Long COVID (3.8%) compared to adults without Long COVID (1.4%). Overall, with data from both women and men combined, Long COVID was not associated with a higher incidence of diabetes in the adjusted model (Incidence Risk Ratio (IRR) = 1.74, 95% CI 0.97,3.12). In the stratified models, we found that Long COVID was associated with higher incidence of diabetes at follow-up among women (IRR = 2.33, 95% CI 1.15,4.73), but not among men (IRR = 1.35, 95% CI 0.49,3.72) (Table 2 and Fig. 1).
Table 2.
Past-year incidence of diabetes at follow-up among adults with COVID-19 diagnosis in Michigan, 2022–2023 (n = 3259).
| Crude Incidence |
Bivariate Model |
Multivariate Model |
Stratified by sex female (n = 1994) |
Stratified by sex (men, n = 1265) |
|||||
|---|---|---|---|---|---|---|---|---|---|
| IRR | [95% CI] | IRR | [95% CI] | IRR | [95% CI] | IRR | [95% CI] | ||
| Long COVID at baseline | |||||||||
| No | 1.4 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Yes | 3.8 | 2.99 | 1.71,5.25 | 1.74 | 0.97,3.12 | 2.33 | 1.15,4.73 | 1.35 | 0.49,3.72 |
| Sex | |||||||||
| Male | 1.7 | 1.00 | 1.00 | ||||||
| Female | 1.7 | 1.01 | 0.58,1.75 | 0.93 | 0.54,1.63 | ||||
| Age | |||||||||
| 18–34 years | 0.4 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| 35–54 years | 2.6 | 7.05 | 2.80,17.60 | 6.22 | 2.32,16.64 | 9.29 | 1.93,44.70 | 3.36 | 0.89,12.71 |
| 55+ years | 2.4 | 6.31 | 2.50,16.10 | 6.87 | 2.27,20.80 | 8.35 | 1.44,48.27 | 4.76 | 1.09,20.90 |
| Race | |||||||||
| Non-Hispanic White | 1.3 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Non-Hispanic Black | 2.8 | 2.11 | 1.06,4.19 | 1.37 | 0.62,3.02 | 2.91 | 1.21,7.00 | 0.29 | 0.03,2.43 |
| Hispanic | 2.4 | 1.79 | 0.64,4.99 | 1.65 | 0.58,4.68 | 3.04 | 0.83,11.17 | 0.89 | 0.17,4.75 |
| Non-Hispanic Other | 2.4 | 1.77 | 0.84,3.73 | 1.91 | 0.84,4.35 | 3.11 | 1.08,88.96 | 1.06 | 0.30,3.77 |
| Income | |||||||||
| Less than $35,000 | 2.5 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| $35,000 to $75,000 | 1.6 | 0.64 | 0.33,1.24 | 0.58 | 0.29,1.14 | 0.48 | 0.20,1.17 | 0.57 | 0.20,1.65 |
| $75,000 or more | 1.2 | 0.47 | 0.25,0.91 | 0.51 | 0.25,1.08 | 0.60 | 0.25,1.42 | 0.42 | 0.14,1.26 |
| Body Mass Index (BMI) | |||||||||
| Normal/Underweight (<25 kg/m2) | 0.4 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Overweight (25–30 kg/m2) | 0.8 | 1.84 | 0.57,5.99 | 1.32 | 0.41,4.28 | 1.63 | 0.40,6.56 | 0.88 | 0.10,7.64 |
| Obesity >30 kg/m2 | 3.4 | 7.92 | 2.81–22.29 | 5.09 | 1.78,14.54 | 4.05 | 1.15,14.3 | 6.20 | 0.82,46.62 |
| Current combustible tobacco use | |||||||||
| No | 1.5 | 1.00 | 1.00 | 1.00,1.50 | 1.00 | 1.00 | |||
| Yes | 2.9 | 1.88 | 0.92,3.83 | 1.65 | 0.75,3.65 | 1.72 | 0.66,4.44 | 1.50 | 0.38,6.01 |
| Number of preexisting conditions | |||||||||
| None | 0.9 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| One | 2.1 | 2.28 | 1.12,4.63 | 1.44 | 0.70,2.96 | 1.68 | 0.70,4.03 | 1.37 | 0.42,4.43 |
| Two or more | 2.8 | 3.05 | 1.50,6.17 | 1.32 | 0.56,3.11 | 1.27 | 0.45,3.57 | 1.54 | 0.40,5.89 |
| Phase of the pandemic | |||||||||
| 03/01/2020-09/3/02020 | 2.5 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| 10/01/2020-09/30/2021 | 1.7 | 0.68 | 0.37,1.25 | 0.89 | 0.48,1.66 | 0.86 | 0.38,1.91 | 0.95 | 0.37,2.43 |
| 10/01/2021-05/31/2022 | 1.0 | 0.42 | 0.20,0.89 | 0.68 | 0.27,1.71 | 0.87 | 0.27,2.84 | 0.44 | 0.09,2.04 |
| Survey mode | |||||||||
| Phone | 2.4 | 1.00 | 1.00 | 1.00 | |||||
| Online | 1.3 | 0.54 | 0.31,0.92 | 1.03 | 0.60,1.77 | 1.12 | 0.57,2.21 | 1.07 | 0.46,2.50 |
| Covid Vaccination Prior illness | |||||||||
| No | 2.0 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Yes | 0.8 | 0.42 | 0.20,0.88 | 0.67 | 0.26,1.69 | 1.18 | 0.38,3.64 | 0.23 | 0.03,1.58 |
Fig. 1.
Incidence Risk Ratio of Diabetes, Hypertension, Heart Disease and Asthma for adults with Long COVID vs adults who did not report Long COVID. Michigan, 2022–2023.
Table 3 shows the results for past-year incidence of hypertension. The crude incidence of hypertension was higher among adults with Long COVID (9.1%) compared to adults without Long COVID (5.4%). In the adjusted model, with data from both sexes, Long COVID was not statistically associated with higher incidence of hypertension (IRR = 1.39 95% CI 0.90,2.90). Similarly, in the stratified models, we found that Long COVID was not statistically associated with higher incidence of hypertension among women (IRR = 1.69 95% CI 0.98,2.90) or men (IRR = 0.82 95% CI 0.36,1.86) (Table 3 and Fig. 1).
Table 3.
Past-year incidence of hypertension at follow-up among adults with COVID-19 diagnosis in Michigan, 2022–2023 (n = 2602).
| Crude Incidence |
Bivariate Model |
Multivariate Model |
Stratified by sex female (n = 1994) |
Stratified by sex (men, n = 1265) |
|||||
|---|---|---|---|---|---|---|---|---|---|
| IRR | [95% CI] | IRR | [95% CI] | IRR | [95% CI] | IRR | [95% CI] | ||
| Long COVID at baseline | |||||||||
| No | 5.4 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Yes | 9.1 | 1.69 | 1.14,2.51 | 1.39 | 0.90,2.17 | 1.69 | 0.98-,2.90 | 0.82 | 0.36,1.86 |
| Sex | |||||||||
| Male | 6.9 | 1.00 | 1.00 | ||||||
| Female | 5.2 | 0.75 | 0.54,1.05 | 0.71 | 0.50,1.00 | ||||
| Age | |||||||||
| 18–34 years | 3.3 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| 35–54 years | 7.2 | 2.18 | 1.38,3.45 | 2.22 | 1.39,3.56 | 2.82 | 1.44,5.52 | 1.74 | 0.86,3.50 |
| 55+ years | 9.7 | 2.96 | 1.86,4.71 | 3.16 | 1.93,5.15 | 3.41 | 1.67,6.96 | 3.01 | 1.50,6.01 |
| Race | |||||||||
| Non-Hispanic White | 5.1 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Non-Hispanic Black | 10.2 | 1.98 | 1.23,3.19 | 1.60 | 0.99,2.60 | 1.43 | 0.81,2.53 | 2.11 | 0.94,4.73 |
| Hispanic | 6.4 | 1.25 | 0.61,2.58 | 1.15 | 0.55,2.39 | 1.30 | 0.54,3.18 | 1.12 | 0.34,3.67 |
| Non-Hispanic Other | 7.2 | 1.41 | 0.84,2.36 | 1.47 | 0.87,2.49 | 0.81 | 0.36,1.81 | 2.23 | 1.11,4.46 |
| Income | |||||||||
| Less than $35,000 | 6.8 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| $35,000–$75,000 | 6.7 | 0.99 | 0.64,1.52 | 0.93 | 0.61,1.43 | 0.72 | 0.43,1.20 | 1.34 | 0.68,2.65 |
| $75,000 or more | 4.8 | 0.71 | 0.47,1.06 | 0.64 | 0.42,0.97 | 0.62 | 0.35,1.10 | 0.76 | 0.40,1.47 |
| Body Mass Index (BMI) | |||||||||
| Normal/Underweight (<25 kg/m2) | 3.7 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Overweight (25–30 kg/m2) | 5.6 | 1.52 | 0.94,2.45 | 1.14 | 0.69,1.89 | 1.66 | 0.92,3.01 | 0.69 | 0.30,1.56 |
| Obesity >30 kg/m2 | 8.3 | 2.25 | 1.46,3.49 | 1.81 | 1.14,2.86 | 1.44 | 0.80,2.58 | 2.05 | 0.98,4.29 |
| Current combustible tobacco use | |||||||||
| No | 5.6 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Yes | 8.3 | 1.46 | 0.92,2.35 | 1.37 | 0.87,2.16 | 1.64 | 0.91,2.95 | 1.12 | 0.52,2.38 |
| Number of preexisting conditions | |||||||||
| None | 5.0 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| One | 7.9 | 1.58 | 1.08,2.28 | 1.29 | 0.89,1.87 | 1.25 | 0.76,2.04 | 1.37 | 0.78,2.40 |
| Two or more | 6.0 | 1.20 | 0.74,1.94 | 0.78 | 0.47,1.27 | 0.67 | 0.35,1.28 | 0.92 | 0.44,1.92 |
| Phase of the pandemic | |||||||||
| 03/01/2020-09/3/02020 | 7.0 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| 10/01/2020-09/30/2021 | 4.7 | 0.67 | 0.44,1.02 | 0.72 | 0.47,1.10 | 0.61 | 0.36,1.05 | 0.82 | 0.42,1.60 |
| 10/01/2021-05/31/2022 | 6.7 | 0.96 | 0.64,1.45 | 0.88 | 0.50,1.55 | 0.81 | 0.40,1.61 | 0.95 | 0.40,2.23 |
| Survey mode | |||||||||
| Phone | 7.9 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Online | 5.0 | 0.64 | 0.45,0.89 | 0.77 | 0.54,1.11 | 0.72 | 0.46,1.11 | 0.84 | 0.47,1.50 |
| Covid Vaccination Prior illness | |||||||||
| No | 5.4 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Yes | 7.3 | 1.34 | 0.94,1.92 | 1.59 | 0.96,2.64 | 1.46 | 0.73,2.93 | 1.72 | 0.81,3.65 |
Table 4 displays results for past-year incidence of heart disease. We found that the crude incidence of heart disease was 3.5 times higher among adults with Long COVID (5.3%) compared to adults without Long COVID (1.4%). In the adjusted models, with data from both women and men combined, Long COVID was associated with a higher incidence of heart disease (IRR = 1.97 95% CI 1.19,3.25). In the stratified models, we found that Long COVID was associated with a higher incidence of heart disease among female adults (IRR = 1.98 95% CI 1.10,3.96). Although results for men were in the same direction, they did not reach statistical significance (IRR = 1.76 95% CI 0.79,3.96) (Table 4 and Fig. 1).
Table 4.
Past-year incidence of heart disease at follow-up among adults with COVID-19 diagnosis in Michigan, 2022–2023 (n = 3284).
| Crude Incidence |
Bivariate Model |
Multivariate Model |
Stratified by sex female (n = 1994) |
Stratified by sex (men, n = 1265) |
|||||
|---|---|---|---|---|---|---|---|---|---|
| (IRR) | [95% CI] | IRR | [95% CI] | IRR | [95% CI] | IRR | [95% CI] | ||
| Long COVID at baseline | |||||||||
| No | 1.4 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Yes | 5.3 | 3.77 | 2.36,6.03 | 1.97 | 1.19,3.25 | 1.98 | 1.10,3.57 | 1.76 | 0.79,3.96 |
| Sex | |||||||||
| Male | 1,7 | 1.00 | 1.00 | ||||||
| Female | 2.3 | 1.39 | 0.84,2.30 | 1.27 | 0.77,2.09 | ||||
| Age | |||||||||
| 18–34 years | 0.2 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| 35–54 years | 1.4 | 6.77 | 2.06,22.2 | 4.75 | 1.37,16.52 | 5.15 | 1.20,22.08 | 3.30 | 0.32,34.35 |
| 55+ years | 5.6 | 26.40 | 8.60,81.3 | 14.06 | 4.14,47.73 | 11.47 | 2.66,49.43 | 23.78 | 3.07,184.26 |
| Race | |||||||||
| Non-Hispanic White | 2.1 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Non-Hispanic Black | 3.3 | 1.59 | 0.83,3.05 | 0.77 | 0.40,1.49 | 0.77 | 0.35,1.67 | 0.90 | 0.27,3.03 |
| Hispanic | 0.4 | 0.20 | 0.03,1.43 | 0.14 | 0.02,1.01 | 0.30 | 0.04,2.04 | 0.00 | 0.00,0.00 |
| Non-Hispanic Other | 1.3 | 0.62 | 0.25,1.59 | 0.71 | 0.28,1.78 | 1.04 | 0.38,2.84 | 0.30 | 0.04,2.25 |
| Income | |||||||||
| Less than $35,000 | 2.2 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| $35,000–$75,000 | 2.3 | 1.06 | 0.59,1.91 | 1.06 | 0.60,1.88 | 1.04 | 0.51,2.10 | 0.91 | 0.32,2.57 |
| $75,000 or more | 1.7 | 0.77 | 0.43,1.36 | 0.89 | 0.49,1.60 | 1.12 | 0.56,2.25 | 0.54 | 0.18,1.62 |
| Body Mass Index (BMI) | |||||||||
| Normal/Underweight (<25 kg/m2) | 1.2 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Overweight (25–30 kg/m2) | 1.9 | 1.63 | 0.80,3.29 | 1.14 | 0.58,2.24 | 1.18 | 0.56,2.50 | 0.98 | 0.24,3.95 |
| Obesity >30 kg/m2 | 2.7 | 2.34 | 1.23,4.44 | 1.22 | 0.64,2.34 | 0.98 | 0.47,2.02 | 1.76 | 0.45,6.82 |
| Current combustible tobacco use | |||||||||
| No | 2.0 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Yes | 2.4 | 1.20 | 0.62,2.30 | 1.63 | 0.84,3.18 | 1.70 | 0.80,3.61 | 1.43 | 0.30,6.88 |
| Number of preexisting conditions | |||||||||
| None | 0.6 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| One | 1.8 | 2.96 | 1.40,6.25 | 1.66 | 0.77,3.59 | 1.04 | 0.43,2.56 | 3.85 | 0.65,22.66 |
| Two or more | 5.1 | 8.27 | 4.26,16.1 | 2.57 | 1.23,5.36 | 2.32 | 1.05,5.10 | 3.04 | 0.47,19.61 |
| Phase of the pandemic | |||||||||
| 03/01/2020-09/3/02020 | 3.9 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| 10/01/2020-09/30/2021 | 1.3 | 0.33 | 0.19,0.58 | 0.42 | 0.24,0.73 | 0.42 | 0.21,0.84 | 0.29 | 0.11,0.77 |
| 10/01/2021-05/31/2022 | 1.3 | 0.34 | 0.19,0.61 | 0.43 | 0.19,0.95 | 0.54 | 0.24,1.24 | 0.26 | 0.06,1.18 |
| Survey mode | |||||||||
| Phone | 3.6 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Online | 1.2 | 0.33 | 0.21,0.53 | 0.54 | 0.33,0.88 | 0.73 | 0.41,1.30 | 0.28 | 0.11,0.73 |
| Covid Vaccination Prior illness | |||||||||
| No | 2.3 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Yes | 1.3 | 0.56 | 0.31,1.02 | 1.07 | 0.49,2.36 | 0.57 | 0.23,1.38 | 2.44 | 0.77,7.75 |
Table 5 displays the results for past-year incidence asthma. The crude incidence of asthma was higher among adults with Long COVID (5.6%) compared to adults without Long COVID adults (1.9%). In the adjusted model that combines data for both sexes, we found that Long COVID was associated with higher incidence of asthma (IRR = 2.50 95% CI 1.46,4.28). In the adjusted models stratified by sex, we found that Long COVID was associated with a higher incidence of asthma among women (IRR = 2.99 95% CI 1.60,5.57), but not among men (IRR = 1.72 95% CI 0.43,6.87) (Table 5 and Fig. 1).
Table 5.
Past-year incidence of asthma at follow-up among adults with COVID-19 diagnosis in Michigan, 2022–2023 (n = 3088).
| Crude Incidence |
Bivariate Model |
Multivariate Model |
Stratified by sex female (n = 1994) |
Stratified by sex (men, n = 1265) |
|||||
|---|---|---|---|---|---|---|---|---|---|
| IRR | [95% CI] | IRR | [95% CI] | IRR | [95% CI] | IRR | [95% CI] | ||
| Long COVID at baseline | |||||||||
| No | 1.9 | 1.00 | 1.00 | 1.00 | 1.00 | ||||
| Yes | 5.6 | 3.00 | 1.82,4.97 | 2.50 | 1.46,4.28 | 2.99 | 1.60,5.57 | 1.73 | 0.43,6.87 |
| Sex | |||||||||
| Male | 1.6 | 1.00 | |||||||
| Female | 3.2 | 2.06 | 1.14,3.70 | 1.77 | 0.98,3.20 | ||||
| Age | |||||||||
| 18–34 years | 2.6 | 1.00 | |||||||
| 35–54 years | 2.8 | 1.09 | 0.61,1.98 | 0.91 | 0.49,1.70 | 1.08 | 0.52,2.24 | 0.69 | 0.18,2.62 |
| 55+ years | 1.8 | 0.72 | 0.37,1.37 | 0.46 | 0.25,0.87 | 0.46 | 0.22,0.99 | 0.56 | 0.17,1.85 |
| Race | |||||||||
| Non-Hispanic White | 2.2 | 1.00 | |||||||
| Non-Hispanic Black | 3.9 | 1.78 | 0.91,3.47 | 1.12 | 0.55,2.26 | 1.38 | 0.64,2.95 | 0.43 | 0.05,3.74 |
| Hispanic | 2.0 | 0.91 | 0.29,2.81 | 0.66 | 0.20,2.20 | 0.49 | 0.08,2.86 | 1.18 | 0.20,6.86 |
| Non-Hispanic Other | 3.1 | 1.42 | 0.68,2.95 | 1.18 | 0.56,2.52 | 1.07 | 0.44,2.57 | 1.47 | 0.30,7.14 |
| Income | |||||||||
| Less than $35,000 | 3.7 | 1.00 | |||||||
| $35,000–$75,000 | 2.5 | 0.68 | 0.38,1.22 | 0.76 | 0.41,1.42 | 0.54 | 0.29,1.02 | 1.61 | 0.33,7.89 |
| $75,000 or more | 1.6 | 0.42 | 0.23,0.78 | 0.56 | 0.29,1.08 | 0.49 | 0.24,0.99 | 0.70 | 0.16,3.10 |
| Body Mass Index (BMI) | |||||||||
| Normal/Underweight (<25 kg/m2) | 2.1 | 1.00 | |||||||
| Overweight (25–30 kg/m2) | 1.7 | 0.82 | 0.39,1.73 | 0.90 | 0.40,2.02 | 1.43 | 0.58,3.53 | 0.38 | 0.07,2.01 |
| Obesity >30 kg/m2 | 3.3 | 1.60 | 0.85,2.98 | 1.40 | 0.70,2.79 | 1.93 | 0.85,4.36 | 0.74 | 0.20,2.75 |
| Current combustible tobacco use | |||||||||
| No | 2.3 | 1.00 | |||||||
| Yes | 3.5 | 1.51 | 0.72,3.18 | 1.17 | 0.54,2.55 | 1.00 | 0.43,2.29 | 1.44 | 0.34,6.11 |
| Number of preexisting conditions | |||||||||
| None | 2.1 | 1.00 | |||||||
| One | 2.0 | 0.98 | 0.50,1.91 | 0.97 | 0.49,1.93 | 1.61 | 0.74,3.53 | 0.07 | 0.01,0.57 |
| Two or more | 3.6 | 1.76 | 1.00,3.08 | 1.74 | 0.95,3.18 | 2.17 | 1.04,4.54 | 1.15 | 0.43,3.06 |
| Phase of the pandemic | |||||||||
| 03/01/2020-09/3/02020 | 2.9 | 1.00 | |||||||
| 10/01/2020-09/30/2021 | 2.3 | 0.80 | 0.44,1.46 | 0.88 | 0.46,1.67 | 0.70 | 0.36,1.38 | 1.69 | 0.37,7.76 |
| 10/01/2021-05/31/2022 | 2.1 | 0.72 | 0.38,1.34 | 0.70 | 0.29,1.68 | 0.51 | 0.21,1.23 | 1.61 | 0.19,13.74 |
| Survey mode | |||||||||
| Phone | 3.2 | 1.00 | |||||||
| Online | 2.1 | 0.65 | 0.40,1.07 | 0.77 | 0.44,1.35 | 1.11 | 0.58,2.13 | 0.37 | 0.11,1.28 |
| Covid Vaccination Prior illness | |||||||||
| No | 2.5 | 1.00 | |||||||
| Yes | 2.1 | 0.84 | 0.48,1.47 | 1.46 | 0.67,3.19 | 1.89 | 0.79,4.54 | 1.10 | 0.22,5.35 |
Results from the sensitivity analyses that included the time variable between baseline and follow-up (≤12 months vs. 12 months) and the health insurance variable (Appendix 2a-d) as additional covariates were similar in direction and statistical significance as the main findings for all outcomes.
4. Discussion
Using data from a population-based longitudinal study in Michigan, we found that adults with Long COVID at baseline were more likely to develop heart disease and asthma over a nearly two-year period compared to adults who did not have Long COVID at baseline. When we examined differences by sex, we found that Long COVID was associated with incident diabetes, heart disease, and asthma among female, but not male, adults, although the association between Long COVID and diabetes and heart disease were in the same direction for men and women, albeit not statistically significant for men.
Previous studies on the long-term health effects of COVID-19 have found that COVID-19 was associated with higher incidence of cardiometabolic outcomes (Xie et al., 2022; Raman et al., 2022). However, these studies compared people with COVID-19 illness to people with no COVID-19 illness. Our study contributes to the literature by using a longitudinal, population-based sample to compare adults with Long COVID to adults without Long COVID, all of whom had a confirmed diagnosis of COVID-19. Moreover, we found that the incidences of diabetes (3.8%) and asthma (5.4%) among adults with Long COVID in our study are higher than the U.S. adult annual estimates of diabetes and asthma incidence, which are each lower than 1% (Centers for Disease Control, 2026a; Centers for Disease Control, 2026b). This further highlights the importance of following people with Long COVID for long-term chronic disease sequelae. Our sample also reported a higher prevalence of Long COVID compared to NHIS estimates (Vahratian et al., 2024), perhaps indicating differences in the methodology or study population.
Our findings that Long COVID is associated with higher incidence of diabetes, heart disease, and asthma among women, with similar albeit not statistically significant findings for diabetes and heart disease for men, are consistent with previous studies that suggest Long COVID illness could lead to persistent immune dysregulation, inflammation (Steenblock et al., 2022), endothelial disfunction (Al-Aly et al., 2024), coagulopathies (Al-Aly et al., 2024), and mitochondrial disfunction, thereby leading to a higher incidence of metabolic and cardiovascular diseases (National Sciences of Engineering Medicine, 2024a). One potential reason for the differences by sex for diabetes and heart disease is that we may have not enough statistical power to detect statistical associations in the stratified models for men (female diabetes sample: n = 1994, male diabetes sample: n = 1265; female heart disease sample: n = 2040, male heart disease sample: n = 1244). Additionally, the short follow-up period may exclude eventual disease outcomes that would have been identified with a longer follow-up period. However, results for asthma show a 3 times higher risk for female adults, but a null association for male adults, reflecting a more distinct pattern. One alternative explanation of the different results by sex is that female adults with Long COVID are seeking healthcare more frequently than male adults with Long COVID, and therefore are more likely to receive an incident diagnosis (Ye and Ren, 2022). Furthermore, recent findings suggest that women with Long COVID are more likely than men to have higher levels of a SARS-CoV2 protein that may be linked with long-term consequences of Long COVID (Swank et al., 2024). Other potential mechanisms could include differences by gender in exposure to financial and social stressors, which could lead to persistent biological reactions such as higher cortisol levels, immune dysregulation, neuroinflammation, and endothelial disfunction that could lead to development of new diseases (National Sciences of Engineering Medicine, 2024a).
Future research should explore the biological mechanisms that may cause people with Long COVID to develop chronic health conditions and isolate the differences attributed to biological sex. In the meantime, there is a need to develop and promote healthcare access to Long COVID patients. It is also vital to implement interventions to train health care professionals to be aware of the long-term health consequences of Long COVID and mitigate health disparities by sex.
Our study has several limitations. We used self-reported measures of persistent COVID-19 symptoms at baseline to construct our measure of Long COVID, and self-reported measures of the four outcomes, which may introduce information bias. However, there are no current clinical diagnostic tests used ubiquitously to identify Long COVID (Ely et al., 2024), and our Long COVID definition is consistent with national and international standardized definitions developed by NASEM (National Sciences of Engineering Medicine, 2024b). Another limitation is that our survey asks about diabetes in general, but did not distinguish between Type 1, Type 2, or diabetes insipidus. However, Type 2 diabetes is the most diagnosed type of metabolic disorder in adulthood (Galicia-Garcia et al., 2020), so the misclassification is expected to be very small. In addition, it is possible that the relationships we see are due to detection bias if adults with Long COVID were more likely to visit a health care provider and subsequently receive a chronic disease diagnosis than participants who did not experience Long COVID. To partially address this limitation, we conducted a sensitivity analysis in which we adjusted by insurance status as a proxy for access to healthcare, and the results did not change. Another potential limitation for longitudinal studies is attrition. We overcame this by creating weights that adjusted for the loss to follow-up. An additional limitation is that the baseline and follow-up surveys were both fielded over broad time periods, which resulted in variation in the time between surveys for participants that spanned several months. Because the survey questions on the outcome variables ask about the outcome in the past year, we recognize that this could lead to a potential misclassification bias in our study. For example, adults who responded to the survey after 12 months have more time to develop the incident outcome compared to those who responded in less than 12 months. On the other hand, it is possible that adults who responded to the follow-up survey more than 12 months after the baseline survey responded “no” if their new diagnosis occurred more than 12 months prior to follow-up survey administration. In our study, we observed that there were more incident outcomes reported among participants with >12 months of follow-up compared to participants with ≤12 months of follow-up (Appendix 1). However, because the incidence of the outcomes was higher among participants with Long COVID than among those without Long COVID for participants with both ≤12 months of follow-up and those with >12 months of follow-up, it is likely that the misclassification is non-differential. Moreover, results from sensitivity analysis show that our results did not differ from the main results when we adjusted the models by time of follow-up. Despite these limitations, our study is one of the first to characterize the association of Long COVID with metabolic, cardiovascular, and respiratory diseases using data from a population-based longitudinal study.
5. Conclusion
Using a population-based longitudinal study, we found that Long COVID increases the risk of diabetes, heart disease, and asthma among female adults, with associations for diabetes and heart disease in a similar direction (albeit not statistically significant) among male adults. Our findings show the need to implement health policies to prevent onset of Long COVID, such as keeping free vaccine programs for COVID-19 (Schwartz, 2025) and improving access to health care for people experiencing Long COVID. Moreover, our results highlight the importance of evaluating Long COVID and health-related outcomes in specific populations that are disproportionately affected by Long COVID.
CRediT authorship contribution statement
Luis Zavala Arciniega: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Lynda Lisabeth: Writing – review & editing, Writing – original draft, Conceptualization. Elizabeth M. Slocum: Writing – review & editing, Writing – original draft. Robert C. Orellana: Writing – review & editing, Writing – original draft. Nancy L. Fleischer: Writing – review & editing, Writing – original draft, Validation, Supervision, Resources, Project administration, Investigation, Funding acquisition, Conceptualization.
Funding sources
The Michigan COVID-19 Recovery Surveillance Study has received funding from the Michigan Department of Health and Human Services, the Michigan Public Health Institute, the University of Michigan Institute for Data Science, the University of Michigan Rogel Cancer Center, and the University of Michigan Epidemiology Department. This manuscript is supported by the Centers for Disease Control and Prevention of the U.S. Department of Health and Human Services (HHS) funded by CDC/HHS through grant number 6NU50CK000510-02-04. The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement, by CDC/HHS, or the U.S. Government.
Declaration of competing interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Nancy L. Fleischer reports financial support was provided by CDC foundation. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
The authors thank the Michigan COVID-19 Recovery Surveillance Study participants and interviewers for making this study possible, as well as the study's Community Advisory Committee, including Ghada Aziz, Rev. Sarah Bailey, Vicki Dobbins, Carlton Evans, Adnan Hammad, Chuqui King, Marta Larson, Roquesha O'Neal, and LaKila Shea Salter.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.pmedr.2026.103449.
Appendix A. Supplementary data
Supplementary material 1
Supplementary material 2
Data availability
Please contact, Dr. Nancy Fleischer, the PI study to request access to the data. Currently the data is not publicily available
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary material 1
Supplementary material 2
Data Availability Statement
Please contact, Dr. Nancy Fleischer, the PI study to request access to the data. Currently the data is not publicily available