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Original Research
13 August 2024

Differentiation of Prior SARS-CoV-2 Infection and Postacute Sequelae by Standard Clinical Laboratory Measurements in the RECOVER CohortFREE

Authors: Kristine M. Erlandson, MD, MSc https://orcid.org/0000-0003-0808-6729, Linda N. Geng, MD, PhD https://orcid.org/0000-0002-9891-5705, Caitlin A. Selvaggi, MS, Tanayott Thaweethai, PhD https://orcid.org/0000-0003-0613-4176, Peter Chen, MD https://orcid.org/0000-0002-5330-1718, Nathan B. Erdmann, MD, PhD, Jason D. Goldman, MD, MPH https://orcid.org/0000-0002-3825-6832, … Show All … , Timothy J. Henrich, MD, MMSc, Mady Hornig, MD, MA https://orcid.org/0000-0001-7572-3092, Elizabeth W. Karlson, MD, MS https://orcid.org/0000-0001-5455-7443, Stuart D. Katz, MD, MS https://orcid.org/0000-0003-2340-2021, C. Kim, BS https://orcid.org/0000-0002-3881-1592, Sushma K. Cribbs, MD, MSc https://orcid.org/0000-0001-6248-474X, Adeyinka O. Laiyemo, MD, MPH https://orcid.org/0000-0001-9699-4879, Rebecca Letts, BA https://orcid.org/0009-0009-4747-7918, Janet Y. Lin, MD, MPH, MBA https://orcid.org/0000-0002-4347-5060, Jai Marathe, MBBS https://orcid.org/0000-0002-6084-7145, Sairam Parthasarathy, MD, Thomas F. Patterson, MD https://orcid.org/0000-0002-9513-7127, Brittany D. Taylor, MPH https://orcid.org/0009-0009-4037-5708, Elizabeth R. Duffy, MA https://orcid.org/0000-0002-8953-6308, Monika Haack, PhD https://orcid.org/0000-0001-6214-4294, Boris Julg, MD, PhD https://orcid.org/0000-0002-4687-9626, Gabrielle Maranga, MPH https://orcid.org/0000-0001-7180-9358, Carla Hernandez, RN, Nora G. Singer, MD https://orcid.org/0000-0001-7041-723X, Jenny Han, MD, MSc, Priscilla Pemu, MD, MS, Hassan Brim, PhD, Hassan Ashktorab, PhD https://orcid.org/0000-0002-4048-4666, Alexander W. Charney, MD, PhD, Juan Wisnivesky, MD, Jenny J. Lin, MD, MPH https://orcid.org/0000-0001-7104-8480, Helen Y. Chu, MD, MPH https://orcid.org/0000-0001-8502-9600, Minjoung Go, MD https://orcid.org/0000-0002-6865-5308, Upinder Singh, MD https://orcid.org/0000-0003-0630-0306, Emily B. Levitan, ScD https://orcid.org/0000-0002-5429-3852, Paul A. Goepfert, MD https://orcid.org/0000-0001-8441-5737, Janko Ž. Nikolich, MD, PhD, Harvey Hsu, MD, PhD https://orcid.org/0009-0004-0671-4626, Michael J. Peluso, MD, MHS, J. Daniel Kelly, MD, PhD https://orcid.org/0000-0002-7616-0321, Megumi J. Okumura, MD, MAS, Valerie J. Flaherman, MD, MPH, John G. Quigley, MD https://orcid.org/0000-0003-3116-4545, Jerry A. Krishnan, MD, PhD https://orcid.org/0000-0001-5525-4778, Mary Beth Scholand, MD https://orcid.org/0000-0001-6513-8893, Rachel Hess, MD, MS https://orcid.org/0000-0003-2545-8504, Torri D. Metz, MD, MS, Maged M. Costantine, MD https://orcid.org/0000-0003-4076-469X, Dwight J. Rouse, MD, Barbara S. Taylor, MD https://orcid.org/0000-0003-2471-9629, Mark P. Goldberg, MD https://orcid.org/0000-0003-3534-6979, Gailen D. Marshall, MD, Jeremy Wood, PhD https://orcid.org/0000-0001-5487-852X, David Warren, PhD https://orcid.org/0000-0003-0539-2587, Leora Horwitz, MD, MHS https://orcid.org/0000-0003-1800-6040, Andrea S. Foulkes, PhD https://orcid.org/0000-0002-9520-0501, and Grace A. McComsey, MD https://orcid.org/0000-0003-2690-8888 ; on behalf of the RECOVER-Adult CohortAuthor, Article, & Disclosure Information
Publication: Annals of Internal Medicine
Volume 177, Number 9
https://doi.org/10.7326/M24-0737
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Visual Abstract. Differentiation of Prior SARS-CoV-2 Infection and Postacute Sequelae by Standard Clinical Laboratory Measurements in the RECOVER Cohort
This study evaluated whether SARS-CoV-2 infection led to persistent changes in common clinical laboratory tests in people with prior infection compared with those without prior infection and whether people with postacute sequelae of SARS-CoV-2 infection (PASC) have persistent laboratory changes compared with those who are unlikely to have PASC.

Abstract

Background:

There are currently no validated clinical biomarkers of postacute sequelae of SARS-CoV-2 infection (PASC).

Objective:

To investigate clinical laboratory markers of SARS-CoV-2 and PASC.

Design:

Propensity score–weighted linear regression models were fitted to evaluate differences in mean laboratory measures by prior infection and PASC index (≥12 vs. 0). (ClinicalTrials.gov: NCT05172024)

Setting:

83 enrolling sites.

Participants:

RECOVER-Adult cohort participants with or without SARS-CoV-2 infection with a study visit and laboratory measures 6 months after the index date (or at enrollment if >6 months after the index date). Participants were excluded if the 6-month visit occurred within 30 days of reinfection.

Measurements:

Participants completed questionnaires and standard clinical laboratory tests.

Results:

Among 10 094 participants, 8746 had prior SARS-CoV-2 infection, 1348 were uninfected, 1880 had a PASC index of 12 or higher, and 3351 had a PASC index of zero. After propensity score adjustment, participants with prior infection had a lower mean platelet count (265.9 × 109 cells/L [95% CI, 264.5 to 267.4 × 109 cells/L]) than participants without known prior infection (275.2 × 109 cells/L [CI, 268.5 to 282.0 × 109 cells/L]), as well as higher mean hemoglobin A1c (HbA1c) level (5.58% [CI, 5.56% to 5.60%] vs. 5.46% [CI, 5.40% to 5.51%]) and urinary albumin–creatinine ratio (81.9 mg/g [CI, 67.5 to 96.2 mg/g] vs. 43.0 mg/g [CI, 25.4 to 60.6 mg/g]), although differences were of modest clinical significance. The difference in HbA1c levels was attenuated after participants with preexisting diabetes were excluded. Among participants with prior infection, no meaningful differences in mean laboratory values were found between those with a PASC index of 12 or higher and those with a PASC index of zero.

Limitation:

Whether differences in laboratory markers represent consequences of or risk factors for SARS-CoV-2 infection could not be determined.

Conclusion:

Overall, no evidence was found that any of the 25 routine clinical laboratory values assessed in this study could serve as a clinically useful biomarker of PASC.

Primary Funding Source:

National Institutes of Health.
Postacute sequelae of SARS-CoV-2 infection (PASC), also known as long COVID, has been reported in millions of people worldwide and is a major public health burden (1–3). “PASC” is generally accepted as an umbrella term encompassing a wide spectrum of symptoms and health conditions that persist after acute SARS-CoV-2 infection, resulting in a major impact on quality of life (4–12). Although potential models of pathogenesis have been postulated, including immune dysregulation, viral persistence, organ injury, endothelial dysfunction, and gut dysbiosis (13–17), there are currently no validated clinical biomarkers of PASC.
As part of the National Institutes of Health’s RECOVER (Researching COVID to Enhance Recovery) Initiative (https://recovercovid.org), we previously examined prospectively collected data from nearly 10 000 people in the RECOVER-Adult cohort with and without SARS-CoV-2 infection and identified 12 symptoms that best distinguish those with prior infection from those who are uninfected (8) (Supplement Table 1). We developed a PASC index based on these 12 symptoms, with 23% of the cohort with prior SARS-CoV-2 infection meeting this research threshold. We further identified multiple clusters or subphenotypes of PASC. This framework does not encompass all people experiencing PASC but permits exploration of clinical laboratory features among those meeting the PASC threshold.
Routine clinical laboratory tests that accurately distinguish people with PASC from those without PASC might be useful in the diagnosis, prognosis, prevention, and treatment of PASC or its subtypes. Laboratory tests might also identify those who have persistent organ damage but minimal or no symptoms. Studies have found potential biomarkers associated with PASC using mostly research-focused assays, but results have been inconsistent, perhaps due to different PASC study definitions; use of only selected biomarkers; choice of comparison groups, if any (people who have recovered from PASC or healthy control participants); duration of symptoms; types of symptoms or phenotypes; and patient population features, such as sex, age, race, vaccination status, comorbidities, and severity of initial infection (18–20). Systematic reviews have identified candidate biomarker categories, including inflammatory (for example, higher levels of C-reactive protein [CRP], leukocytes), coagulation (for example, international normalized ratio), and hematologic (for example, lower levels of hemoglobin), which likely reflect multifaceted pathophysiology and phenotypes of PASC (18, 19, 21). Autoimmune (22, 23), hormonal (24), viral (25, 26), and other (20) biomarkers associated with PASC phenotypes have also been described, although many studies have been limited by small sample sizes, limited follow-up, and/or lack of appropriate controls. Early small-cohort studies failed to find routine clinical biomarkers (27). There is a paucity of large studies examining the utility of standardized laboratory tests obtained in routine clinical care.
Accordingly, we conducted a study to determine whether SARS-CoV-2 infection led to persistent changes in results of common clinical laboratory tests, regardless of symptoms, in people with prior infection compared with those without prior infection. If so, laboratory studies could be used to augment symptom-based definitions of PASC. Second, we aimed to determine whether those with PASC have persistent laboratory changes compared with those unlikely to have PASC. If so, we might be able to identify specific physiologic abnormalities driving symptomatic PASC. Therefore, we analyzed 25 routinely used and standardized laboratory tests that were selected on the basis of availability across different institutions, prior literature, and clinical experience (28). These tests were prospectively done in Clinical Laboratory Improvement Amendments (CLIA)–certified laboratories with samples from 10 094 RECOVER-Adult participants representing a diverse cohort from across the United States. We compared results 1) between participants with and without prior SARS-CoV-2 infection at 6 months or later after an index infection; 2) between participants with and without PASC, defined by an index of 12 or greater or zero, respectively; and 3) between participants with each of 4 previously characterized PASC symptom phenotypes and those unlikely to have PASC.

Methods

Study Design

The RECOVER-Adult study design has been described previously (28). Briefly, participants were recruited at 83 sites in 33 states plus Washington, DC, and Puerto Rico. Adults aged 18 years or older were eligible to enroll regardless of prior infection with SARS-CoV-2. All participants completed a baseline set of surveys, a minimal physical examination, and standard laboratory test sample collection at enrollment. Participants were followed prospectively with survey completion every 3 months and laboratory sample collection at enrollment and 6, 12, 24, 36, and 48 months after infection or the date of a negative test result (index date).

Participants

Participants were enrolled 0 days to 3 years after the index date and followed a visit schedule based on time from the index date. All participants had laboratory tests at the 6-month visit, or at the enrollment visit if enrollment was more than 6 months after the index date (Supplement Table 2); however, after this 6-month or enrollment visit, laboratory tests were repeated only if the prior result was abnormal. Analysis was therefore based on the 6-month visit (or the enrollment visit if enrollment was >6 months after the index date), and subsequent visits were not considered in order to minimize bias. The index date was defined as the date of the first infection (suspected, probable, or confirmed SARS-CoV-2 infection as defined by World Health Organization criteria or positive SARS-CoV-2 infection–specific antibody testing) or the date of a negative SARS-CoV-2 test result for participants who were never infected. Participants were excluded if the 6-month visit was within 30 days of reinfection, if they did not start the protocol, or if they had no symptom survey data. Participants who had no prior SARS-CoV-2 infection at entry but reported infection within 6 months were reclassified as having SARS-CoV-2 infection, with a new index date defined as the date of infection. Uninfected participants reporting an on-study infection more than 6 months after the index date were considered uninfected and the 6-month visit was used in the analysis. Participants who were pregnant at the time of infection were included in the analysis.

Exposures and Outcomes

The primary outcome was laboratory measurements at 6 months after the index date, or at enrollment if it was more than 6 months after the index date. The 2 primary exposures were history of SARS-CoV-2 infection and PASC classification at the same time point as laboratory measurements.
All laboratory measures were done locally at each site’s CLIA-certified clinical laboratory. Samples were drawn as part of the study visit if they had not been obtained via routine clinical care within 30 days of the index date or 90 days before the 6-month follow-up. Laboratory studies that were done were complete blood count with differential, complete metabolic panel, international normalized ratio, D-dimer, lipid panel, 25-hydroxyvitamin D, thyroid-stimulating hormone, free thyroxine, hemoglobin A1c (HbA1c), high-sensitivity CRP (hsCRP), cystatin C, N-terminal pro–B-type natriuretic peptide, troponin, urinalysis, and urinary albumin–creatinine ratio (uACR). These tests were selected on the basis of their routine availability and standardized use across CLIA-certified laboratories, prior literature, and clinical expertise of the RECOVER investigators.
The definition of PASC was based on our prior work (8). Briefly, we identified 12 symptoms that best distinguished between people with and without prior infection, assigned each symptom a certain number of points based on the degree of difference, and summed all points corresponding to the symptoms present in an individual to construct an index (Supplement Table 1). A total of 12 or more was considered the optimal threshold above which participants were likely to have PASC. Comparisons were made to participants with a PASC index of zero. A PASC index of zero does not necessarily mean that the participant has no symptoms or no PASC; however, they would be unlikely to have PASC. Subphenotypes of PASC were previously defined as cluster 1, representing high frequency of impairments in smell and taste; cluster 2, representing high frequency of postexertional malaise (PEM) (defined as worsening of symptoms after even minor physical or mental effort) and fatigue; cluster 3, representing high frequency of brain fog, PEM, and fatigue; and cluster 4, representing high frequency of fatigue, PEM, dizziness, brain fog, gastrointestinal symptoms, and palpitations (8).

Statistical Analysis

Demographic characteristics of the cohort were reported overall, by history of SARS-CoV-2 infection, and by PASC status. In the primary analysis, participants with and without a history of SARS-CoV-2 infection were compared to determine whether SARS-CoV-2 infection led to persistent laboratory abnormalities regardless of symptoms. In the secondary analysis, participants with prior infection with a PASC index of 12 or higher were compared with participants with a PASC index of zero. In all analyses, separate propensity score–weighted linear regression models were fitted for each laboratory measurement to evaluate between-group differences in means. Model-based estimated means and corresponding 95% CIs were reported.
Propensity scores were calculated based on age, sex, race and ethnicity, SARS-CoV-2 variant era, referral source, vaccination status at the index date, comorbidities before the index date, homelessness, employment status, insurance status, income, level of difficulty in covering expenses, last visit to a physician, and food insecurity to balance on these factors between exposure groups. A complete list of adjustment variables is provided in Table 1. Multiple imputation was used to handle missing data. Propensity score–weighted means, 95% CIs, medians, and 10th and 90th percentiles were reported for each laboratory measurement. The target populations for estimated model-based mean differences in the propensity score model were participants with prior infection in the primary analysis and those who were positive for PASC in secondary analyses. Stabilized weights were applied in all analyses, and the top 1% of weights were trimmed in secondary analyses to address extreme values in the weights.
Table 1. Demographic Characteristics Overall and by Prior SARS-CoV-2 Infection Status
CharacteristicPrior Infection (n = 8746)No Prior Infection (n = 1348)Overall (n = 10 094)
Age at infection   
 Mean (SD), y46 (15)52 (15)47 (15)
 Median (IQR), y45 (34–59)54 (39–64)47 (34–60)
 Missing, n516
    
Race/ethnicity, n (%)    
 Non-Hispanic White5080 (58)847 (63)5927 (59)
 Non-Hispanic Black1214 (14)204 (15)1418 (14)
 Non-Hispanic Asian462 (5)88 (7)550 (5)
 Hispanic1523 (17)156 (12)1679 (17)
 Mixed race/other400 (5)43 (3)443 (4)
 Missing671077
    
Sex assigned at birth, n (%)    
 Female6329 (73)911 (68)7240 (72)
 Male2392 (27)432 (32)2824 (28)
 Intersex4 (0)0 (0)4 (0)
 Missing21526
    
Enrollment group and era, n (%)    
 Pre-Omicron3362 (38)317 (24)3679 (36)
 Omicron, acute2963 (34)548 (41)3511 (35)
 Omicron, postacute 2421 (28)483 (36)2904 (29)
    
Referral type, n (%)    
 Self-referral/community outreach/long COVID4377 (50)960 (71)5337 (53)
 Another referral4364 (50)388 (29)4752 (47)
 Missing505
    
Hospitalized during acute phase of first infection, n (%)    
 No7738 (91)NA7738 (91)
 Yes723 (9)NA723 (9)
 Missing285NA285
    
Vaccinated at first infection, n (%)    
 Unvaccinated3042 (35)182 (14)3224 (32)
 Partially vaccinated or date of last dose unknown516 (6)85 (6)601 (6)
 Fully vaccinated5144 (59)1068 (80)6212 (62)
 Missing441357
    
Visit month, n (%)    
 6 mo5000 (57)952 (71)5952 (59)
 ≥9 mo3746 (43)396 (29)4142 (41)
    
Marital status at enrollment, n (%)    
 Divorced, widowed, separated, or never married3177 (37)531 (40)3708 (38)
 Married or living with partner5376 (63)783 (60)6159 (62)
 Missing19334227
    
Experiencing homelessness at enrollment, n (%)    
 Yes197 (2)30 (2)227 (2)
 No8422 (98)1294 (98)9716 (98)
 Missing12724151
    
Disabled at enrollment, n (%)    
 Yes319 (4)80 (6)399 (4)
 No8271 (96)1233 (94)9504 (96)
 Missing15635191
    
Unemployed at enrollment, n (%)    
 Yes245 (3)47 (4)292 (3)
 No8345 (97)1266 (96)9611 (97)
 Missing15635191
    
Medicaid at enrollment, n (%)    
 Yes1303 (15)176 (13)1479 (15)
 No7196 (85)1132 (87)8328 (85)
 Missing24740287
    
Uninsured at enrollment, n (%)    
 Yes273 (3)38 (3)311 (3)
 No8226 (97)1270 (97)9496 (97)
 Missing24740287
    
Lost insurance due to pandemic, n (%)    
 Yes274 (3)23 (2)297 (3)
 No8256 (97)1279 (98)9535 (97)
 Missing21646262
    
Difficulty in covering expenses in the month before enrollment, n (%)    
 Not at all difficult5105 (62)933 (73)6038 (63)
 Somewhat difficult2288 (28)258 (20)2546 (27)
 Very difficult889 (11)92 (7)981 (10)
 Missing46465529
    
Most recent physician visit before index date, n (%)    
 Within past 5 y8373 (99)1290 (99)9663 (99)
 >5 y before82 (1)15 (1)97 (1)
 Missing29143334
    
Skipped care in 12 mo before index date, n (%)    
 Yes480 (6)58 (4)538 (5)
 No8035 (94)1258 (96)9293 (95)
 Missing23132263
    
2019 household income, n (%)    
 <$25 0001319 (16)218 (18)1537 (17)
 $25 000–$49 9991279 (16)149 (12)1428 (15)
 ≥$50 0005403 (68)856 (70)6259 (68)
 Missing745125870
    
Food insecurity in 12 mo before index date, n (%)    
 Yes1233 (14)156 (12)1389 (14)
 No7310 (86)1161 (88)8471 (86)
 Missing20331234
NA = not applicable.
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Sensitivity analyses were performed that excluded participants with diabetes (for HbA1c) and an immunocompromising condition (for platelet count). Additional exploratory between-group comparisons were participants with prior infection within each PASC cluster versus those with a PASC index of zero.

Role of the Funding Source

The funding sources had no role in the study design; collection, analysis, or interpretation of the data; writing of the manuscript; or the decision to submit the manuscript for publication.

Results

Participants

A total of 10 094 participants (8746 with prior infection and 1348 who were uninfected) met study criteria (Figure 1). Seventy-two percent (7240 of 10 068) were female, 17% (1679 of 10 017) were Hispanic or Latino, 14% (1418 of 10 017) were non-Hispanic Black, 62% (6212 of 10 037) were fully vaccinated on the index date, and the median age was 47 years (IQR, 34 to 60 years) (Table 1). Among participants with prior SARS-CoV-2 infection, 21.5% (1880 of 8746) had a PASC index of 12 or higher and 38.3% (3351 of 8746) had a PASC index of zero (Table 2). There were no significant differences in demographic and clinical characteristics between participants included and those not included in the analysis cohort (Supplement Table 2).
Figure 1. CONSORT (Consolidated Standards of Reporting Trials) flow diagram.
PASC = postacute sequelae of SARS-CoV-2 infection.
* Reinfection window for exclusion is 30 days before and 7 days after the visit.
† The enrollment visit was defined as the first visit if it was available or the enrollment date if the visit date was not available.
‡ Participants who completed the visit without reaching the end of the visit window.
Table 2. Demographic Characteristics Overall and by PASC Index Among People With a Prior SARS-CoV-2 Infection
CharacteristicPASC Index ≥12 (n = 1880)PASC Index of 0 (n = 3351)Overall (n = 5231)
Age at infection   
 Mean (SD), y48 (14)47 (16)47 (15)
 Median (IQR), y47 (37–58)45 (33–60)47 (34–59)
 Missing, n123
    
Race/ethnicity, n (%)    
 Non-Hispanic White1209 (65)1813 (54)3022 (58)
 Non-Hispanic Black198 (11)541 (16)739 (14)
 Non-Hispanic Asian58 (3)219 (7)277 (5)
 Hispanic296 (16)615 (18)911 (18)
 Mixed race/other104 (6)141 (4)245 (5)
 Missing152237
    
Sex assigned at birth, n (%)    
 Female1456 (78)2242 (67)3698 (71)
 Male419 (22)1099 (33)1518 (29)
 Intersex1 (0)2 (0)3 (0)
 Missing4812
    
Enrollment group and era, n (%)    
 Pre-Omicron1203 (64)909 (27)2112 (40)
 Omicron, acute317 (17)1398 (42)1715 (33)
 Omicron, postacute 360 (19)1044 (31)1404 (27)
    
Referral type, n (%)    
 Self-referral/community outreach/long COVID1180 (63)1509 (45)2689 (51)
 Another referral700 (37)1840 (55)2540 (49)
 Missing022
    
Hospitalized during acute phase of first infection, n (%)    
 No1577 (85)3011 (94)4588 (91)
 Yes272 (15)189 (6)461 (9)
 Missing31151182
    
Vaccinated at first infection, n (%)    
 Unvaccinated1058 (56)856 (26)1914 (37)
 Partially vaccinated or date of last dose unknown105 (6)220 (7)325 (6)
 Fully vaccinated711 (38)2254 (68)2965 (57)
 Missing62127
    
Visit month, n (%)    
 6 mo682 (36)2218 (66)2900 (55)
 ≥9 mo1198 (64)1133 (34)2331 (45)
    
Marital status at enrollment, n (%)    
 Divorced, widowed, separated, or never married732 (40)1161 (36)1893 (37)
 Married or living with partner1111 (60)2103 (64)3214 (63)
 Missing3787124
    
Experiencing homelessness at enrollment, n (%)    
 Yes49 (3)69 (2)118 (2)
 No1813 (97)3223 (98)5036 (98)
 Missing185977
    
Disabled at enrollment, n (%)    
 Yes130 (7)80 (2)210 (4)
 No1729 (93)3192 (98)4921 (96)
 Missing2179100
    
Unemployed at enrollment, n (%)    
 Yes61 (3)85 (3)146 (3)
 No1798 (97)3187 (97)4985 (97)
 Missing2179100
    
Medicaid at enrollment, n (%)    
 Yes341 (18)436 (13)777 (15)
 No1505 (82)2796 (87)4301 (85)
 Missing34119153
    
Uninsured at enrollment, n (%)    
 Yes61 (3)105 (3)166 (3)
 No1785 (97)3127 (97)4912 (97)
 Missing34119153
    
Lost insurance due to pandemic, n (%)    
 Yes136 (7)55 (2)191 (4)
 No1706 (93)3206 (98)4912 (96)
 Missing3890128
    
Difficulty in covering expenses in the month before enrollment, n (%)    
 Not at all difficult824 (46)2211 (70)3035 (61)
 Somewhat difficult603 (34)745 (24)1348 (27)
 Very difficult355 (20)213 (7)568 (11)
 Missing98182280
    
Most recent physician visit before index date, n (%)    
 Within past 5 y1826 (99)3174 (99)5000 (99)
 >5 y before16 (1)35 (1)51 (1)
 Missing38142180
    
Skipped care in 12 mo before index date, n (%)    
 Yes180 (10)110 (3)290 (6)
 No1660 (90)3149 (97)4809 (94)
 Missing4092132
    
2019 household income, n (%)    
 <$25 000299 (17)494 (16)793 (17)
 $25 000–$49 999313 (18)455 (15)768 (16)
 ≥$50 0001130 (65)2086 (69)3216 (67)
 Missing138316454
    
Food insecurity in 12 mo before index date, n (%)    
 Yes348 (19)409 (13)757 (15)
 No1501 (81)2854 (87)4355 (85)
 Missing3188119
PASC = postacute sequelae of SARS-CoV-2 infection.
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Laboratory Measurements Associated With Prior SARS-CoV-2 Infection

Differences in mean values between participants with and without prior infection were observed for platelet count, HbA1c level, and uACR (Table 3 and Figure 2) based on propensity score–weighted models. Covariate balance was achieved after propensity score weighting (Supplement Figure 1). Participants with prior infection had a lower mean platelet count (265.9 × 109 cells/L [95% CI, 264.5 to 267.4 × 109 cells/L]) than participants without known prior infection (275.2 × 109 cells/L [CI, 268.5 to 282.0 × 109 cells/L]) as well as a higher mean HbA1c level (5.58% [CI, 5.56% to 5.60%] vs. 5.46% [CI, 5.40% to 5.51%]) and uACR (81.9 mg/g [CI, 67.5 to 96.2 mg/g] vs. 43.0 mg/g [CI, 25.4 to 60.6 mg/g]). Box plots and empirical cumulative distribution functions of laboratory measurements for HbA1c, platelets, and uACR by infection status are provided in Supplement Figure 2.
Figure 2. Differences in laboratory measurements between groups after propensity score weighting.
The figure shows comparisons for people with prior SARS-CoV-2 infection vs. those without prior infection (top) and people with a PASC index ≥12 vs. those with a PASC index of zero (bottom). HbA1c = hemoglobin A1c; hsCRP = high-sensitivity C-reactive protein; PASC = postacute sequelae of SARS-CoV-2 infection.
Table 3. Estimated Mean Laboratory Measurements by Group After Propensity Score Weighting
AnalyteEstimated Mean Laboratory Measurement (95% CI)
Prior SARS-CoV-2 InfectionNo Prior InfectionPASC Index ≥12PASC Index of 0
Sodium, mmol/L139.1 (139.1–139.2)139.2 (139.0–139.4)139.2 (139.1–139.3)139.2 (139.0–139.4)
Potassium, mmol/L4.16 (4.15–4.16)4.16 (4.12–4.19)4.16 (4.14–4.18)4.16 (4.13–4.19)
Chloride, mmol/L103.4 (103.3–103.5)103.6 (103.3–103.9)103.2 (103.0–103.3)103.3 (103.1–103.6)
Bicarbonate, mmol/L25.2 (25.2–25.3)25.1 (24.9–25.4)25.2 (25.1–25.3)25.0 (24.7–25.2)
Serum creatinine    
 μmol/L77.5 (76.2–78.7)75.4 (72.5–78.3)78.4 (75.3–81.5)76.7 (73.8–79.6)
 mg/dL0.876 (0.862–0.890)0.853 (0.820–0.886)0.887 (0.852–0.922)0.868 (0.835–0.900)
Calcium    
 mmol/L2.35 (2.35–2.36)2.36 (2.35–2.37)2.36 (2.35–2.36)2.35 (2.34–2.36)
 mg/dL9.43 (9.42–9.44)9.47 (9.44–9.51)9.45 (9.43–9.47)9.43 (9.39–9.46)
Alanine aminotransferase, U/L22.6 (22.2–22.9)22.1 (20.6–23.6)23.3 (22.5–24.2)22.8 (21.4–24.1)
Aspartate aminotransferase, U/L21.8 (21.5–22.1)21.9 (19.9–23.8)22.0 (21.3–22.6)22.2 (21.1–23.3)
Total bilirubin    
 μmol/L9.56 (9.44–9.67)9.45 (9.06–9.84)9.09 (8.87–9.32)9.13 (8.76–9.51)
 mg/dL0.559 (0.552–0.565)0.553 (0.530–0.575)0.532 (0.519–0.545)0.534 (0.512–0.556)
Albumin, g/L43.9 (43.8–44.0)44.2 (43.8–44.5)44.0 (43.9–44.2)43.7 (43.4–44.0)
Leukocyte count, × 109 cells/L6.58 (6.53–6.63)6.63 (6.42–6.84)6.90 (6.80–7.00)6.88 (6.62–7.15)
Absolute lymphocyte count, × 109 cells/L195.2 (193.3–197.1)198.1 (189.3–207.0)198.3 (194.9–201.8)198.3 (189.7–206.9)
Absolute neutrophil count, × 109 cells/L392.5 (388.7–396.2)394.5 (379.1–410.0)421.0 (412.1–429.9)410.1 (392.8–427.3)
Hemoglobin, g/L136.1 (135.8–136.4)137.2 (135.9–138.5)136.4 (135.7–137.1)136.3 (135.1–137.6)
Platelet count, × 109 cells/L265.9 (264.5–267.4)*275.2 (268.5–282.0)*272.7 (269.4–276.0)266.3 (259.9–272.7)
International normalized ratio1.02 (1.02–1.03)1.02 (1.01–1.03)1.02 (1.01–1.03)1.02 (0.99–1.05)
D-dimer, μg/L517.6 (497.8–537.4)536.8 (481.6–591.9)518.6 (476.2–561.1)580.0 (471.8–688.3)
hsCRP, mg/L4.08 (3.93–4.23)3.65 (3.18–4.12)5.01 (4.62–5.39)4.23 (3.70–4.76)
Cystatin C, mg/L0.895 (0.886–0.904)0.887 (0.863–0.911)0.918 (0.899–0.937)0.933 (0.884–0.981)
HbA1c, %5.58 (5.56–5.60)*5.46 (5.40–5.51)*5.63 (5.59–5.67)5.67 (5.59–5.74)
HDL cholesterol    
 mmol/L1.50 (1.49–1.51)1.49 (1.45–1.52)1.47 (1.45–1.49)1.47 (1.43–1.50)
 mg/dL57.9 (57.5–58.2)57.4 (56.0–58.8)56.9 (56.1–57.6)56.7 (55.4–57.9)
Non-HDL cholesterol    
 mmol/L3.38 (3.36–3.40)3.33 (3.24–3.41)3.52 (3.47–3.57)3.49 (3.40–3.59)
 mg/dL130.7 (129.8–131.5)128.6 (125.4–131.8)136.2 (134.3–138.0)135.0 (131.3–138.8)
Thyroid-stimulating hormone, μIU/mL1.95 (1.91–1.99)2.04 (1.85–2.23)2.00 (1.90–2.10)2.00 (1.86–2.15)
NT-proBNP, pg/mL83.7 (76.4–91.1)80.8 (64.3–97.2)83.1 (67.1–99.0)124.6 (51.1–198.1)
Urinary albumin–creatinine ratio, mg/g81.9 (67.5–96.2)*43.0 (25.4–60.6)*67.0 (43.5–90.5)72.2 (42.7–101.7)
HbA1c = hemoglobin A1c; HDL = high-density lipoprotein; hsCRP = high-sensitivity C-reactive protein; NT-proBNP = N-terminal pro–B-type natriuretic peptide; PASC = postacute sequelae of SARS-CoV-2 infection.
* Nonoverlapping 95% CI between groups.
Download table
Quantiles of propensity score–weighted laboratory values (Supplement Table 3) suggest that the right tails of the distributions of HbA1c and uACR were higher in participants with versus those without prior infection, and the entirety of the distribution of platelet counts was shifted lower in participants with versus those without prior infection (Supplement Table 4).
The difference in mean HbA1c level was attenuated after 867 participants with preexisting diabetes were excluded (5.40% [CI, 5.39% to 5.42%] among those with prior infection vs. 5.37% [CI, 5.33% to 5.41%] among those without known infection). The small difference in mean platelet count remained after 640 participants with a preexisting immunocompromising condition were excluded (267.0 × 109 cells/L [CI, 265.6 to 268.5 × 109 cells/L] among those with prior infection vs. 277.4 × 109 cells/L [CI, 270.3 to 284.6 × 109 cells/L] among those without prior infection).

Laboratory Measurements Associated With a PASC Index of 12 or Higher Versus Zero

When the analysis was restricted to participants with prior infection, in propensity score–weighted models that balanced on all covariates (Supplement Figure 3), we found no clinically meaningful differences (Table 3) in mean laboratory values between those with a PASC index of 12 or higher versus zero.

Laboratory Measurements Associated With PASC Subphenotypes Versus a PASC Index of Zero

In exploratory propensity score–weighted models (Supplement Figure 3) restricted to participants with prior infection (Supplement Tables 5 and 6), we observed a higher mean hsCRP level in cluster 1 (smell or taste impairments), a lower mean sodium level and a higher mean calcium level in cluster 2 (high frequency of PEM and fatigue), no differences in cluster 3 (high frequency of brain fog, PEM, and fatigue), and a higher mean hsCRP level in cluster 4 (high frequency of fatigue, PEM, dizziness, brain fog, gastrointestinal symptoms, and palpitations) compared with participants with a PASC index of zero. Box plots and empirical cumulative distribution functions of laboratory measurements by cluster are provided in Supplement Figure 4.

Discussion

In a cohort study of more than 10 000 participants with and without prior SARS-CoV-2 infection, we found no evidence that any of 25 routine clinical laboratory values provide a reliable biomarker of prior infection, PASC, or the specific type of PASC cluster. We did identify small differences in some laboratory values between those with and those without prior infection; specifically, we found that prior infection was associated with modest increases in HbA1c level and uACR and small decreases in platelet count. Within PASC symptom clusters, hsCRP level was slightly higher in cluster 1 (impairments in smell and taste) and cluster 4 (high frequency of fatigue, PEM, dizziness, brain fog, gastrointestinal symptoms, and palpitations) and sodium level was lower and calcium level was higher in cluster 2 (high frequency of PEM and fatigue) compared with participants with a PASC index of zero. These results may have been due to chance given multiple comparisons and were largely not clinically meaningful. Thus, although clinicians should rule out treatable causes of PASC symptoms with appropriate diagnostic testing, routine laboratory tests are not useful biomarkers for PASC. Earlier, smaller studies have found similar results (27). Although these small differences are not useful for PASC diagnosis, they may suggest potential pathways in the pathogenesis of PASC and PASC clusters.
First, an association between severity of acute COVID-19 and diabetes or glucose intolerance has been recognized since early in the pandemic. SARS-CoV-2 itself may contribute to the development of diabetes, and previously undiagnosed diabetes or receipt of high doses of corticosteroids during a hospital stay may play a role in the severity of initial disease (29). The association between diabetes and PASC has been less well studied and is often limited to electronic health record data of patients with variably defined PASC and shorter follow-up after initial infection. In a cohort of more than 5 million patients with and without prior SARS-CoV-2 infection, prior infection was associated with increased risk for subsequent diabetes (30). A study of nearly 10 million veterans found that those with a SARS-CoV-2 diagnosis had an increased risk for incident diabetes and antihyperglycemic use in the 12 months after diagnosis; however, 15% of the veterans in the cohort were missing HbA1c values, and there may have been underdiagnosis of antecedent diabetes, particularly in the control groups (31). Our results are consistent with both of these large cohort studies, with the advantage of systematic HbA1c collection and comprehensive, standardized symptom collection. Of note, we did not detect a difference in HbA1c levels in the sensitivity analysis that excluded people with preexisting diabetes. Furthermore, we compared HbA1c by PASC index severity. Although, in theory, SARS-CoV-2 infection may increase risk for diabetes, diabetes may also worsen PASC symptoms, including microvascular complications (32). Despite our large cohort, we did not detect a difference in HbA1c level between people with a PASC index of 12 or higher and those with a PASC index of zero, suggesting that SARS-CoV-2 may contribute to glucose dysregulation independent of symptoms.
A second finding distinguishing participants with and those without prior SARS-CoV-2 infection was the decrease in platelet count, although our detectable platelet differences were likely of minimal clinical relevance and most participants were well within the clinically normal range. In contrast, among participants with a PASC index of 12 or higher, both platelet count and hsCRP level tended to be higher than among those with a PASC index of zero, suggesting an ongoing inflammatory state, which is consistent with prior literature (33–36). Platelet and clotting abnormalities are more complex than simply the total number of platelets, and abnormalities have been widely recognized during acute COVID-19, including in vitro studies showing internalization of SARS-CoV-2 virion by platelets and rapid cell death (37). Some evidence supports a role of clotting and platelet abnormalities in PASC (38), although other studies have not found convincing evidence of microclots (39, 40). A low platelet count seen in association with prior infection may be multifactorial, reflecting platelet destruction, marrow suppression, or consumption, as seen with other viral infections, such as HIV and Epstein–Barr virus. Indeed, one study found downregulation of platelet genes among patients with long COVID (41). Other studies have demonstrated markers of platelet activation in PASC (42). Among a subset of 80 symptomatic participants in a long COVID/PASC registry, microthrombi and platelet abnormalities (hyperactivity) were seen in nearly all participants (38, 43). Platelet abnormalities have also been described among patients with myalgic encephalomyelitis/chronic fatigue syndrome or postural orthostatic tachycardia syndrome (44, 45). Clinical quantitation of platelet count is not a reliable biomarker for PASC, and more specific markers of platelet biology will likely be needed to detect platelet dysfunction related to PASC.
The other difference among participants with and without prior SARS-CoV-2 was a higher uACR without differences in cystatin C or creatinine (46). Smaller case–control studies have found increased uACR either acutely or several months after SARS-CoV-2 infection compared with those without infection (47, 48). Other larger observational studies have shown declines in renal function in the year after SARS-CoV-2 infection, with a slightly larger proportion of people with moderately or severely increased uACR from 3 to 12 months, although no statistical comparisons were provided (46, 49). These findings may reflect greater comorbidity among people with prior infection or consequences of severe initial infection; other studies have suggested a direct effect of SARS-CoV-2 on renal tubules and parenchyma, resulting in renal dysfunction (50, 51). Beyond the direct association with renal function, elevated uACR (even in the microalbuminuria range) has been associated with increased cardiovascular disease risk (52, 53). In HIV, albuminuria is associated with increased mortality, cardiovascular disease, and heart failure (54) through endothelial dysfunction (55, 56), a known contributor to PASC-related cardiovascular risk (57) that will be assessed in future RECOVER analyses.
The association with hsCRP and cluster 1 (smell and taste disturbances) is consistent with a recent study that evaluated the ultrastructural changes of olfactory bulbs and tracts in SARS-CoV-2 infection (58). The authors suggested that anosmia is likely due to inflammation from the virus rather than direct viral invasion. The association with hsCRP and smell or taste disturbance conflicts with a recent systematic review of 11 studies in which patients with chemosensory disturbances tended to have lower overall inflammation, including hsCRP, compared with those without chemosensory disturbances (59). This discrepancy may reflect the newer variants captured in our cohort in comparison to many studies with earlier variants where chemosensory disturbances were often associated with lesser disease severity (60).
Our study has many strengths as the first large study to prospectively assess a broad battery of clinical laboratory biomarkers with standardized systematic evaluation for symptoms, which reduced ascertainment bias. We had large and robust control groups, and our participants were diverse in terms of demographic characteristics, geographic distribution, SARS-CoV-2 variant, and vaccination status. We used a rigorously derived, concrete, and reproducible research definition of PASC, enabling the cohort to be well characterized. However, our study also has important limitations. Laboratory studies were done at local CLIA-certified laboratories whose assays may differ slightly from each other. Samples were not always drawn after fasting. We did not have preinfection laboratory results for most participants, impeding the ability to study pre–post infection changes and limiting the distinction between abnormalities predisposing to infection and those resulting from infection. We considered each measure independently and did not evaluate multivariate laboratory profiles. The analysis combined laboratory values and PASC status assessed between a 6-month visit (180 ± 45 days) and up to 3 years after the index date. Finally, other clinical laboratory studies not captured here (such as cortisol and fibrinogen) warrant further study.
Although clinicians should perform routine clinical tests to rule out other treatable causes of PASC symptoms, we found no evidence that 25 routine clinical laboratory values offer clinical utility as biomarkers of PASC, and they are therefore not useful as part of a definition. We did find slightly lower platelet counts, higher HbA1c levels, and higher uACR among participants with prior SARS-CoV-2 infection compared with those without prior infection. Whether these differences represent consequences of or risk factors for initial acquisition or severity of SARS-CoV-2 infection cannot be determined without preinfection evaluations. Furthermore, differences were small, likely had minimal clinical relevance, and may have been due to chance. Differences in laboratory values within PASC subphenotypes suggest ongoing inflammation (hsCRP) as a potential mechanism underlying PASC. Future work within the RECOVER cohort will evaluate whether clusters of biomarkers can help refine a research-based or clinically relevant PASC diagnosis.
In summary, our findings suggest that even highly symptomatic PASC may have no clinically observable objective findings on routine laboratory testing. Understanding the basic biological underpinnings of persistent symptoms after SARS-CoV-2 infection will likely require a rigorous focus on investigations beyond routine clinical laboratory studies (for example, transcriptomics, proteomics, metabolomics) to identify novel biomarkers.

Supplemental Material

Supplemental Material

References

1.
Wulf Hanson S, Abbafati C, Aerts JG, et al; Global Burden of Disease Long COVID Collaborators. Estimated global proportions of individuals with persistent fatigue, cognitive, and respiratory symptom clusters following symptomatic COVID-19 in 2020 and 2021. JAMA. 2022;328:1604-1615. [PMID: 36215063] doi: 10.1001/jama.2022.18931
Crossref
PubMed
Google Scholar
2.
Xie Y, Bowe B, Al-Aly Z. Burdens of post-acute sequelae of COVID-19 by severity of acute infection, demographics and health status. Nat Commun. 2021;12:6571. [PMID: 34772922] doi: 10.1038/s41467-021-26513-3
Crossref
PubMed
Google Scholar
3.
National Center for Health Statistics. U.S. Census Bureau, Household Pulse Survey, 2022–2024. Long COVID. Accessed at www.cdc.gov/nchs/covid19/pulse/long-covid.htm on 2 January 2024.
Google Scholar
4.
Reese JT, Blau H, Casiraghi E, et al; RECOVER Consortium. Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes. EBioMedicine. 2023;87:104413. [PMID: 36563487] doi: 10.1016/j.ebiom.2022.104413
Crossref
PubMed
Google Scholar
5.
Davis HE, Assaf GS, McCorkell L, et al. Characterizing long COVID in an international cohort: 7 months of symptoms and their impact. EClinicalMedicine. 2021;38:101019. [PMID: 34308300] doi: 10.1016/j.eclinm.2021.101019
Crossref
PubMed
Google Scholar
6.
Huang L, Yao Q, Gu X, et al. 1-year outcomes in hospital survivors with COVID-19: a longitudinal cohort study. Lancet. 2021;398:747-758. [PMID: 34454673] doi: 10.1016/S0140-6736(21)01755-4
Crossref
PubMed
Google Scholar
7.
Nalbandian A, Sehgal K, Gupta A, et al. Post-acute COVID-19 syndrome. Nat Med. 2021;27:601-615. [PMID: 33753937] doi: 10.1038/s41591-021-01283-z
Crossref
PubMed
Google Scholar
8.
Thaweethai T, Jolley SE, Karlson EW, et al; RECOVER Consortium. Development of a definition of postacute sequelae of SARS-CoV-2 infection. JAMA. 2023;329:1934-1946. [PMID: 37278994] doi: 10.1001/jama.2023.8823
Crossref
PubMed
Google Scholar
9.
Lopez-Leon S, Wegman-Ostrosky T, Perelman C, et al. More than 50 long-term effects of COVID-19: a systematic review and meta-analysis. Sci Rep. 2021;11:16144. [PMID: 34373540] doi: 10.1038/s41598-021-95565-8
Crossref
PubMed
Google Scholar
10.
Bailey J, Lavelle B, Miller J, et al; Northwestern Medicine Comprehensive COVID Center Investigators. Multidisciplinary center care for long COVID syndrome—a retrospective cohort study. Am J Med. 2023. [PMID: 37220832] doi: 10.1016/j.amjmed.2023.05.002
Crossref
PubMed
Google Scholar
11.
Ogungbe O, Slone S, Alharthi A, et al. "Living like an empty gas tank with a leak”: mixed methods study on post-acute sequelae of COVID-19. PLoS One. 2022;17:e0279684. [PMID: 36584125] doi: 10.1371/journal.pone.0279684
Crossref
PubMed
Google Scholar
12.
Peluso MJ, Kelly JD, Lu S, et al. Persistence, magnitude, and patterns of postacute symptoms and quality of life following onset of SARS-CoV-2 infection: cohort description and approaches for measurement. Open Forum Infect Dis. 2022;9:ofab640. [PMID: 35106317] doi: 10.1093/ofid/ofab640
Crossref
PubMed
Google Scholar
13.
Davis HE, McCorkell L, Vogel JM, et al. Long COVID: major findings, mechanisms and recommendations. Nat Rev Microbiol. 2023;21:133-146. [PMID: 36639608] doi: 10.1038/s41579-022-00846-2
Crossref
PubMed
Google Scholar
14.
Sherif ZA, Gomez CR, Connors TJ, et al; RECOVER Mechanistic Pathway Task Force. Pathogenic mechanisms of post-acute sequelae of SARS-CoV-2 infection (PASC). Elife. 2023;12:e86002. [PMID: 36947108] doi: 10.7554/eLife.86002
Crossref
PubMed
Google Scholar
15.
Proal AD, VanElzakker MB. Long COVID or post-acute sequelae of COVID-19 (PASC): an overview of biological factors that may contribute to persistent symptoms. Front Microbiol. 2021;12:698169. [PMID: 34248921] doi: 10.3389/fmicb.2021.698169
Crossref
PubMed
Google Scholar
16.
Mohandas S, Jagannathan P, Henrich TJ, et al; RECOVER Mechanistic Pathways Task Force. Immune mechanisms underlying COVID-19 pathology and post-acute sequelae of SARS-CoV-2 infection (PASC). Elife. 2023;12:e86014. [PMID: 37233729] doi: 10.7554/eLife.86014
Crossref
PubMed
Google Scholar
17.
Chen B, Julg B, Mohandas S, et al; RECOVER Mechanistic Pathways Task Force. Viral persistence, reactivation, and mechanisms of long COVID. Elife. 2023;12:e86015. [PMID: 37140960] doi: 10.7554/eLife.86015
Crossref
PubMed
Google Scholar
18.
Yong SJ, Halim A, Halim M, et al. Inflammatory and vascular biomarkers in post-COVID-19 syndrome: a systematic review and meta-analysis of over 20 biomarkers. Rev Med Virol. 2023;33:e2424. [PMID: 36708022] doi: 10.1002/rmv.2424
Crossref
PubMed
Google Scholar
19.
Lai YJ, Liu SH, Manachevakul S, et al. Biomarkers in long COVID-19: a systematic review. Front Med (Lausanne). 2023;10:1085988. [PMID: 36744129] doi: 10.3389/fmed.2023.1085988
Crossref
PubMed
Google Scholar
20.
Tsilingiris D, Vallianou NG, Karampela I, et al. Laboratory findings and biomarkers in long COVID: what do we know so far? Insights into epidemiology, pathogenesis, therapeutic perspectives and challenges. Int J Mol Sci. 2023;24:10458. [PMID: 37445634] doi: 10.3390/ijms241310458
Crossref
PubMed
Google Scholar
21.
Yin J-X, Agbana YL, Sun Z-S, et al. Increased interleukin-6 is associated with long COVID-19: a systematic review and meta-analysis. Infect Dis Poverty. 2023;12:43. [PMID: 37095536] doi: 10.1186/s40249-023-01086-z
Crossref
PubMed
Google Scholar
22.
Son K, Jamil R, Chowdhury A, et al. Circulating anti-nuclear autoantibodies in COVID-19 survivors predict long COVID symptoms. Eur Respir J. 2023;61:2200970. doi: 10.1183/13993003.00970-2022
Crossref
PubMed
Google Scholar
23.
Rojas M, Rodríguez Y, Acosta-Ampudia Y, et al. Autoimmunity is a hallmark of post-COVID syndrome. J Transl Med. 2022;20:129. [PMID: 35296346] doi: 10.1186/s12967-022-03328-4
Crossref
PubMed
Google Scholar
24.
Klein J, Wood J, Jaycox JR, et al. Distinguishing features of long COVID identified through immune profiling. Nature. 2023;623:139-148. [PMID: 37748514] doi: 10.1038/s41586-023-06651-y
Crossref
PubMed
Google Scholar
25.
Peluso MJ, Deveau T-M, Munter SE, et al. Chronic viral coinfections differentially affect the likelihood of developing long COVID. J Clin Invest. 2023;133:e163669. [PMID: 36454631] doi: 10.1172/JCI163669
Crossref
PubMed
Google Scholar
26.
Zubchenko S, Kril I, Nadizhko O, et al. Herpesvirus infections and post-COVID-19 manifestations: a pilot observational study. Rheumatol Int. 2022;42:1523-1530. [PMID: 35650445] doi: 10.1007/s00296-022-05146-9
Crossref
PubMed
Google Scholar
27.
Sneller MC, Marques AR, Liang CJ, et al. A longitudinal study of COVID-19 sequelae and immunity. Ann Intern Med. 2022;175:W153. [PMID: 36315947] doi: 10.7326/L22-0331
Link
Google Scholar
28.
Horwitz LI, Thaweethai T, Brosnahan SB, et al. Researching COVID to Enhance Recovery (RECOVER) adult study protocol: rationale, objectives, and design. PLoS One. 2023;18:e0286297. [PMID: 38128008] doi: 10.1371/journal.pone.0285351
Crossref
PubMed
Google Scholar
29.
Aluganti Narasimhulu C, Singla DK. Mechanisms of COVID-19 pathogenesis in diabetes. Am J Physiol Heart Circ Physiol. 2022;323:H403-H420. [PMID: 35776683] doi: 10.1152/ajpheart.00204.2022
Crossref
PubMed
Google Scholar
30.
Hsieh TYJ, Chang R, Yong SB, et al. COVID-19 vaccination prior to SARS-CoV-2 infection reduced risk of subsequent diabetes mellitus: a real-world investigation using U.S. electronic health records. Diabetes Care. 2023;46:2193-2200. [PMID: 37851392] doi: 10.2337/dc23-0936
Crossref
PubMed
Google Scholar
31.
Xie Y, Al-Aly Z. Risks and burdens of incident diabetes in long COVID: a cohort study. Lancet Diabetes Endocrinol. 2022;10:311-321. [PMID: 35325624] doi: 10.1016/S2213-8587(22)00044-4
Crossref
PubMed
Google Scholar
32.
Raveendran AV, Misra A. Post COVID-19 syndrome (“long COVID”) and diabetes: challenges in diagnosis and management. Diabetes Metab Syndr. 2021;15:102235. [PMID: 34384972] doi: 10.1016/j.dsx.2021.102235
Crossref
PubMed
Google Scholar
33.
Garcia-Larragoiti N, Cano-Mendez A, Jimenez-Vega Y, et al. Inflammatory and prothrombotic biomarkers contribute to the persistence of sequelae in recovered COVID-19 patients. Int J Mol Sci. 2023;24:17468. [PMID: 38139298] doi: 10.3390/ijms242417468
Crossref
PubMed
Google Scholar
34.
Al-Hakeim HK, Al-Rubaye HT, Almulla AF, et al. Chronic fatigue, depression and anxiety symptoms in long COVID are strongly predicted by neuroimmune and neuro-oxidative pathways which are caused by the inflammation during acute infection. J Clin Med. 2023;12:511. [PMID: 36675440] doi: 10.3390/jcm12020511
Crossref
PubMed
Google Scholar
35.
Sanhueza S, Vidal MA, Hernandez MA, et al. Clinical and pulmonary function analysis in long-COVID revealed that long-term pulmonary dysfunction is associated with vascular inflammation pathways and metabolic syndrome. Front Med (Lausanne). 2023;10:1271863. [PMID: 37869162] doi: 10.3389/fmed.2023.1271863
Crossref
PubMed
Google Scholar
36.
Laubscher GJ, Lourens PJ, Venter C, et al. TEG®, microclot and platelet mapping for guiding early management of severe COVID-19 coagulopathy. J Clin Med. 2021;10:5381. [PMID: 34830660] doi: 10.3390/jcm10225381
Crossref
PubMed
Google Scholar
37.
Koupenova M, Corkrey HA, Vitseva O, et al. SARS-CoV-2 initiates programmed cell death in platelets. Circ Res. 2021;129:631-646. [PMID: 34293929] doi: 10.1161/CIRCRESAHA.121.319117
Crossref
PubMed
Google Scholar
38.
Kell DB, Laubscher GJ, Pretorius E. A central role for amyloid fibrin microclots in long COVID/PASC: origins and therapeutic implications. Biochem J. 2022;479:537-559. [PMID: 35195253] doi: 10.1042/BCJ20220016
Crossref
PubMed
Google Scholar
39.
Appelman B, Charlton BT, Goulding RP, et al. Muscle abnormalities worsen after post-exertional malaise in long COVID. Nat Commun. 2024;15:17. [PMID: 38177128] doi: 10.1038/s41467-023-44432-3
Crossref
PubMed
Google Scholar
40.
Fox T, Hunt BJ, Ariens RA, et al. Plasmapheresis to remove amyloid fibrin(ogen) particles for treating the post-COVID-19 condition. Cochrane Database Syst Rev. 2023;7:CD015775. [PMID: 37491597] doi: 10.1002/14651858.CD015775
Crossref
PubMed
Google Scholar
41.
Ryan FJ, Hope CM, Masavuli MG, et al. Long-term perturbation of the peripheral immune system months after SARS-CoV-2 infection. BMC Med. 2022;20:26. [PMID: 35027067] doi: 10.1186/s12916-021-02228-6
Crossref
PubMed
Google Scholar
42.
Martins-Gonçalves R, Campos MM, Palhinha L, et al. Persisting platelet activation and hyperactivity in COVID-19 survivors. Circ Res. 2022;131:944-947. [PMID: 36268747] doi: 10.1161/CIRCRESAHA.122.321659
Crossref
PubMed
Google Scholar
43.
Cervia-Hasler C, Brüningk SC, Hoch T, et al. Persistent complement dysregulation with signs of thromboinflammation in active long Covid. Science. 2024;383:eadg7942. [PMID: 38236961] doi: 10.1126/science.adg7942
Crossref
PubMed
Google Scholar
44.
Nunes JM, Kruger A, Proal A, et al. The occurrence of hyperactivated platelets and fibrinaloid microclots in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Pharmaceuticals (Basel). 2022;15:931. [PMID: 36015078] doi: 10.3390/ph15080931
Crossref
PubMed
Google Scholar
45.
Gunning WT, Kramer PM, Cichocki JA, et al. Platelet storage pool deficiency and elevated inflammatory biomarkers are prevalent in postural orthostatic tachycardia syndrome. Cells. 2022;11:774. [PMID: 35269395] doi: 10.3390/cells11050774
Crossref
PubMed
Google Scholar
46.
Atiquzzaman M, Thompson JR, Shao S, et al. Long-term effect of COVID-19 infection on kidney function among COVID-19 patients followed in post-COVID-19 recovery clinics in British Columbia, Canada. Nephrol Dial Transplant. 2023;38:2816-2825. [PMID: 37349962] doi: 10.1093/ndt/gfad121
Crossref
PubMed
Google Scholar
47.
Gameil MA, Marzouk RE, Elsebaie AH, et al. Long-term clinical and biochemical residue after COVID-19 recovery. Egypt Liver J. 2021;11:74. [PMID: 34777873] doi: 10.1186/s43066-021-00144-1
Crossref
PubMed
Google Scholar
48.
Hong X-W, Chi Z-P, Liu G-Y, et al. Characteristics of renal function in patients diagnosed with COVID-19: an observational study. Front Med (Lausanne). 2020;7:409. [PMID: 32754610] doi: 10.3389/fmed.2020.00409
Crossref
PubMed
Google Scholar
49.
Obrișcă B, Mocanu V, Vornicu A, et al. The impact of SARS-CoV-2 infection on renal function in patients with biopsy-proven kidney diseases. PLoS One. 2023;18:e0296168. [PMID: 38134019] doi: 10.1371/journal.pone.0296168
Crossref
PubMed
Google Scholar
50.
Martinez-Rojas MA, Vega-Vega O, Bobadilla NA. Is the kidney a target of SARS-CoV-2? Am J Physiol Renal Physiol. 2020;318:F1454-F1462. [PMID: 32412303] doi: 10.1152/ajprenal.00160.2020
Crossref
PubMed
Google Scholar
51.
Moledina DG, Simonov M, Yamamoto Y, et al. The association of COVID-19 with acute kidney injury independent of severity of illness: a multicenter cohort study. Am J Kidney Dis. 2021;77:490-499.e1. [PMID: 33422598] doi: 10.1053/j.ajkd.2020.12.007
Crossref
PubMed
Google Scholar
52.
Mahemuti N, Zou J, Liu C, et al. Urinary albumin-to-creatinine ratio in normal range, cardiovascular health, and all-cause mortality. JAMA Netw Open. 2023;6:e2348333. [PMID: 38113044] doi: 10.1001/jamanetworkopen.2023.48333
Crossref
PubMed
Google Scholar
53.
Lin X, Song W, Zhou Y, et al. Elevated urine albumin creatinine ratio increases cardiovascular mortality in coronary artery disease patients with or without type 2 diabetes mellitus: a multicenter retrospective study. Cardiovasc Diabetol. 2023;22:203. [PMID: 37563647] doi: 10.1186/s12933-023-01907-3
Crossref
PubMed
Google Scholar
54.
Choi AI, Li Y, Deeks SG, et al. Association between kidney function and albuminuria with cardiovascular events in HIV-infected persons. Circulation. 2010;121:651-658. [PMID: 20100969] doi: 10.1161/CIRCULATIONAHA.109.898585
Crossref
PubMed
Google Scholar
55.
Pirro M, Mannarino MR, Francisci D, et al. Urinary albumin-to-creatinine ratio is associated with endothelial dysfunction in HIV-infected patients receiving antiretroviral therapy. Sci Rep. 2016;6:28741. [PMID: 27353425] doi: 10.1038/srep28741
Crossref
PubMed
Google Scholar
56.
Bartz SK, Caldas MC, Tomsa A, et al. Urine albumin-to-creatinine ratio: a marker of early endothelial dysfunction in youth. J Clin Endocrinol Metab. 2015;100:3393-3399. [PMID: 26176802] doi: 10.1210/JC.2015-2230
Crossref
PubMed
Google Scholar
57.
Puntmann VO, Shchendrygina A, Bolanos CR, et al. Cardiac involvement due to COVID-19: insights from imaging and histopathology. Eur Cardiol. 2023;18:e58. [PMID: 37942208] doi: 10.15420/ecr.2023.02
Crossref
PubMed
Google Scholar
58.
Mahajan S, Sen D, Sunil A, et al. Knockout of angiotensin converting enzyme-2 receptor leads to morphological aberrations in rodent olfactory centers and dysfunctions associated with sense of smell. Front Neurosci. 2023;17:1180868. [PMID: 37404465] doi: 10.3389/fnins.2023.1180868
Crossref
PubMed
Google Scholar
59.
de Melo EGM, Andrade RM, de Abreu de Vasconcellos SJ, et al. Association between chemosensory dysfunctions and inflammatory biomarkers in patients with SARS-CoV-2 infection: a systematic review and meta-analysis. Inflammopharmacology. 2022;30:2079-2087. [PMID: 36097300] doi: 10.1007/s10787-022-01066-z
Crossref
PubMed
Google Scholar
60.
Porta-Etessam J, Núñez-Gil IJ, González García N, et al. COVID-19 anosmia and gustatory symptoms as a prognosis factor: a subanalysis of the HOPE COVID-19 (Health Outcome Predictive Evaluation for COVID-19) registry. Infection. 2021;49:677-684. [PMID: 33646505] doi: 10.1007/s15010-021-01587-9
Crossref
PubMed
Google Scholar

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Information & Authors

Information

Published In

cover image Annals of Internal Medicine
Annals of Internal Medicine
Volume 177 • Number 9 • September 2024
Pages: 1209 - 1221

History

Published online: 13 August 2024
Published in issue: September 2024

Keywords

  1. Clinical laboratories
  2. Cohort studies
  3. COVID-19
  4. Fatigue
  5. HbA1c
  6. Infectious diseases
  7. Inflammation
  8. Long COVID
  9. Platelets
  10. Research laboratories

Copyright

© 2024 American College of Physicians.

Authors

Affiliations

Kristine M. Erlandson, MD, MSc* https://orcid.org/0000-0003-0808-6729
Department of Medicine, Division of Infectious Diseases, University of Colorado, Anschutz Medical Campus, Aurora, Colorado (K.M.E.)
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Linda N. Geng, MD, PhD* https://orcid.org/0000-0002-9891-5705
Department of Medicine, Stanford University, Stanford, California (L.N.G., M.G., U.S.)
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Caitlin A. Selvaggi, MS
Massachusetts General Hospital Biostatistics, Boston, Massachusetts (C.A.S., T.T., A.S.F.)
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Tanayott Thaweethai, PhD https://orcid.org/0000-0003-0613-4176
Massachusetts General Hospital Biostatistics, Boston, Massachusetts (C.A.S., T.T., A.S.F.)
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Peter Chen, MD https://orcid.org/0000-0002-5330-1718
Department of Medicine, Division of Pulmonary and Critical Care Medicine, Cedars-Sinai Medical Center, and Women’s Guild Lung Institute at Cedars-Sinai Medical Center, New York, New York (P.C.)
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Nathan B. Erdmann, MD, PhD
Department of Medicine, Division of Infectious Diseases, University of Alabama at Birmingham, Birmingham, Alabama (N.B.E., P.A.G.)
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Jason D. Goldman, MD, MPH https://orcid.org/0000-0002-3825-6832
Swedish Center for Research and Innovation, Providence Swedish Medical Center, and Division of Allergy and Infectious Diseases, University of Washington, Seattle, Washington (J.D.G.)
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Timothy J. Henrich, MD, MMSc
Division of Experimental Medicine, University of California San Francisco, San Francisco, California (T.J.H.)
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Mady Hornig, MD, MA https://orcid.org/0000-0001-7572-3092
CORe Community Inc., and Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York (M.H.)
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Elizabeth W. Karlson, MD, MS https://orcid.org/0000-0001-5455-7443
Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts (E.W.K.)
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Stuart D. Katz, MD, MS https://orcid.org/0000-0003-2340-2021
Department of Medicine, NYU Grossman School of Medicine, New York, New York (S.D.K.)
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C. Kim, BS https://orcid.org/0000-0002-3881-1592
RECOVER Initiative, New York, New York (C.K., R.L.)
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Sushma K. Cribbs, MD, MSc https://orcid.org/0000-0001-6248-474X
Department of Medicine, Emory University School of Medicine, and Atlanta Veterans Affairs Medical Center, Atlanta, Georgia (S.K.C.)
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Adeyinka O. Laiyemo, MD, MPH https://orcid.org/0000-0001-9699-4879
Department of Medicine, Division of Gastroenterology, Howard University College of Medicine, Washington, DC (A.O.L.)
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Rebecca Letts, BA https://orcid.org/0009-0009-4747-7918
RECOVER Initiative, New York, New York (C.K., R.L.)
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Janet Y. Lin, MD, MPH, MBA https://orcid.org/0000-0002-4347-5060
Department of Emergency Medicine, University of Illinois Chicago, Chicago, Illinois (J.Y.L.)
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Jai Marathe, MBBS https://orcid.org/0000-0002-6084-7145
Department of Medicine, Division of Infectious Diseases, Boston University Medical Campus, Boston, Massachusetts (J.M.)
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Sairam Parthasarathy, MD
Department of Medicine, University of Arizona, Tucson, Arizona (S.P.)
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Thomas F. Patterson, MD https://orcid.org/0000-0002-9513-7127
Department of Medicine, University of Texas Health San Antonio, San Antonio, Texas (T.F.P., B.S.T.)
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Brittany D. Taylor, MPH https://orcid.org/0009-0009-4037-5708
RECOVER Initiative, New York, New York, and American Heart Association, Health Strategies, Atlanta, Georgia (B.D.T.)
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Elizabeth R. Duffy, MA https://orcid.org/0000-0002-8953-6308
Boston Medical Center, Boston, Massachusetts (E.R.D.)
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Monika Haack, PhD https://orcid.org/0000-0001-6214-4294
Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts (M.H.)
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Boris Julg, MD, PhD https://orcid.org/0000-0002-4687-9626
Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology, and Harvard University, Boston, Massachusetts (B.J.)
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Gabrielle Maranga, MPH https://orcid.org/0000-0001-7180-9358
Department of Population Health, NYU Grossman School of Medicine, New York, New York (G.M.)
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Carla Hernandez, RN
Departments of Pediatrics and Medicine, Case Western Reserve University, Cleveland, Ohio (C.H.)
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Nora G. Singer, MD https://orcid.org/0000-0001-7041-723X
Departments of Pediatrics and Medicine and Division of Rheumatology, Case Western Reserve University, Cleveland, Ohio (N.G.S.)
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Jenny Han, MD, MSc
Department of Medicine, Emory University School of Medicine, and Grady Hospital, Atlanta, Georgia (J.H.)
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Priscilla Pemu, MD, MS
Department of Medicine, Morehouse School of Medicine, Atlanta, Georgia (P.P.)
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Hassan Brim, PhD
Department of Pathology, Howard University, Washington, DC (H.B.)
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Hassan Ashktorab, PhD https://orcid.org/0000-0002-4048-4666
Department of Medicine, Howard University, Washington, DC (H.A.)
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Alexander W. Charney, MD, PhD
Icahn School of Medicine at Mount Sinai Hospital, New York, New York (A.W.C., J.W., J.L.)
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Juan Wisnivesky, MD
Icahn School of Medicine at Mount Sinai Hospital, New York, New York (A.W.C., J.W., J.L.)
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Jenny J. Lin, MD, MPH https://orcid.org/0000-0001-7104-8480
Icahn School of Medicine at Mount Sinai Hospital, New York, New York (A.W.C., J.W., J.L.)
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Helen Y. Chu, MD, MPH https://orcid.org/0000-0001-8502-9600
Division of Global Health, University of Washington, Seattle, Washington (H.Y.C.)
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Minjoung Go, MD https://orcid.org/0000-0002-6865-5308
Department of Medicine, Stanford University, Stanford, California (L.N.G., M.G., U.S.)
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Upinder Singh, MD https://orcid.org/0000-0003-0630-0306
Department of Medicine, Stanford University, Stanford, California (L.N.G., M.G., U.S.)
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Emily B. Levitan, ScD https://orcid.org/0000-0002-5429-3852
Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama (E.B.L.)
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Paul A. Goepfert, MD https://orcid.org/0000-0001-8441-5737
Department of Medicine, Division of Infectious Diseases, University of Alabama at Birmingham, Birmingham, Alabama (N.B.E., P.A.G.)
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Janko Ž. Nikolich, MD, PhD
Department of Immunobiology, University of Arizona College of Medicine-Tucson, and Arizona Center on Aging, Tucson, Arizona (J.ŽN.)
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Harvey Hsu, MD, PhD https://orcid.org/0009-0004-0671-4626
Banner University Medical Center, Tucson, Arizona (H.H.)
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Michael J. Peluso, MD, MHS
Department of Medicine, Division of Infectious Diseases, University of California San Francisco, San Francisco, California (M.J.P., J.D.K.)
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J. Daniel Kelly, MD, PhD https://orcid.org/0000-0002-7616-0321
Department of Medicine, Division of Infectious Diseases, University of California San Francisco, San Francisco, California (M.J.P., J.D.K.)
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Megumi J. Okumura, MD, MAS
Departments of Medicine and Pediatrics, University of California San Francisco, San Francisco, California (M.O.)
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Valerie J. Flaherman, MD, MPH
Department of Pediatrics, University of California San Francisco, San Francisco, California (V.J.F.)
View all articles by this author
John G. Quigley, MD https://orcid.org/0000-0003-3116-4545
Department of Medicine, Division of Hematology/Oncology, University of Illinois Chicago, Chicago, Illinois (J.G.Q.)
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Jerry A. Krishnan, MD, PhD https://orcid.org/0000-0001-5525-4778
Department of Medicine, University of Illinois Chicago, Chicago, Illinois (J.A.K.)
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Mary Beth Scholand, MD https://orcid.org/0000-0001-6513-8893
Department of Medicine, Spencer Fox Eccles School of Medicine at the University of Utah, Salt Lake City, Utah (M.B.S., R.H.)
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Rachel Hess, MD, MS https://orcid.org/0000-0003-2545-8504
Department of Medicine, Spencer Fox Eccles School of Medicine at the University of Utah, Salt Lake City, Utah (M.B.S., R.H.)
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Torri D. Metz, MD, MS
Department of Obstetrics and Gynecology, University of Utah, Salt Lake City, Utah (T.D.M.)
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Maged M. Costantine, MD https://orcid.org/0000-0003-4076-469X
Division of Maternal Fetal Medicine, The Ohio State University, Columbus, Ohio (M.M.C.)
View all articles by this author
Dwight J. Rouse, MD
Department of Obstetrics and Gynecology, Brown University, Providence, Rhode Island (D.J.R.)
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Barbara S. Taylor, MD https://orcid.org/0000-0003-2471-9629
Department of Medicine, University of Texas Health San Antonio, San Antonio, Texas (T.F.P., B.S.T.)
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Mark P. Goldberg, MD https://orcid.org/0000-0003-3534-6979
Department of Neurology, University of Texas Health San Antonio, San Antonio, Texas (M.P.G.)
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Gailen D. Marshall, MD
Department of Medicine, University of Mississippi Medical Center, Jackson, Mississippi (G.D.M.)
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Jeremy Wood, PhD https://orcid.org/0000-0001-5487-852X
The Gill Heart and Vascular Institute and Saha Cardiovascular Research Center, University of Kentucky, Lexington, Kentucky (J.W.)
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David Warren, PhD https://orcid.org/0000-0003-0539-2587
Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, Nebraska (D.W.)
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Leora Horwitz, MD, MHS† https://orcid.org/0000-0003-1800-6040
Department of Population Health, NYU Grossman School of Medicine, and Center for Healthcare Innovation and Delivery Science, NYU Langone Health, New York, New York (L.H.)
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Andrea S. Foulkes, PhD† https://orcid.org/0000-0002-9520-0501
Massachusetts General Hospital Biostatistics, Boston, Massachusetts (C.A.S., T.T., A.S.F.)
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Grace A. McComsey, MD† https://orcid.org/0000-0003-2690-8888
Departments of Pediatrics and Medicine, Case Western Reserve University, and University Hospitals Cleveland Medical Center, Cleveland, Ohio (G.A.M.).
View all articles by this author
Note: This study is part of the National Institutes of Health’s RECOVER Initiative, which seeks to understand, treat, and prevent PASC. More information on RECOVER is available at https://recovercovid.org.
Disclaimer: The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Acknowledgment: The authors thank the National Community Engagement Group; all patients, caregivers, and community representatives; and all participants enrolled in the RECOVER Initiative.
Grant Support: This research was funded by the National Institutes of Health Agreements OTA OT2HL161847, OT2HL161841, and OT2HL156812 as part of the RECOVER Research Initiative and R01 HL162373.
Reproducible Research Statement: Study protocol: Available at https://studies.recovercovid.org/pdf/RECOVER-Adult-Protocol-v10.0.pdf. Statistical code: Not available. Data set: Available at https://biodatacatalyst.nhlbi.nih.gov/recover.
Corresponding Author: Kristine M. Erlandson, MD, MSc, University of Colorado, Anschutz Medical Campus, 12700 East 19th Avenue, Mail Stop B168, Aurora, CO 80045; e-mail, [email protected].
Author Contributions: Conception and design: K.M. Erlandson, L.N. Geng, T. Thaweethai, N.B. Erdmann, T.J. Henrich, S.D. Katz, R. Letts, S. Parthasarathy, T.F. Patterson, G. Maranga, H. Brim, J. Wisnivesky, U. Singh, J.D. Kelly, J.A. Krishnan, R. Hess, T.D. Metz, L. Horwitz, A.S. Foulkes, G.A. McComsey.
Analysis and interpretation of the data: K.M. Erlandson, L.N. Geng, C.A. Selvaggi, T. Thaweethai, N.B. Erdmann, M. Hornig, S.D. Katz, S.K. Cribbs, A.O. Laiyemo, R. Letts, S. Parthasarathy, T.F. Patterson, M. Haack, G. Maranga, J. Han, H. Brim, U. Singh, P.A. Goepfert, J.A. Krishnan, M.B. Scholand, R. Hess, B.S. Taylor, M.P. Goldberg, L. Horwitz, A.S. Foulkes, G.A. McComsey.
Drafting of the article: K.M. Erlandson, L.N. Geng, T.J. Henrich, E.W. Karlson, S.D. Katz, S.K. Cribbs, A.O. Laiyemo, B.D. Taylor, J. Han, H. Hsu, D. Warren, L. Horwitz, A.S. Foulkes, G.A. McComsey.
Critical revision for important intellectual content: K.M. Erlandson, L.N. Geng, C.A. Selvaggi, T. Thaweethai, P. Chen, N.B. Erdmann, J.D. Goldman, T.J. Henrich, M. Hornig, E.W. Karlson, S.D. Katz, C. Kim, S.K. Cribbs, A.O. Laiyemo, R. Letts, J.Y. Lin, J. Marathe, T.F. Patterson, B. Julg, G. Maranga, N.G. Singer, J. Han, H. Brim, J. Wisnivesky, J.J. Lin, U. Singh, E.B. Levitan, J.Ž. Nikolich, M.J. Peluso, J.D. Kelly, M.J. Okumura, J.G. Quigley, J.A. Krishnan, R. Hess, T.D. Metz, M.M. Costantine, B.S. Taylor, G.D. Marshall, L. Horwitz, A.S. Foulkes, G.A. McComsey.
Final approval of the article: K.M. Erlandson, L.N. Geng, C.A. Selvaggi, T. Thaweethai, P. Chen, N.B. Erdmann, J.D. Goldman, T.J. Henrich, M. Hornig, E.W. Karlson, S.D. Katz, C. Kim, S.K. Cribbs, A.O. Laiyemo, R. Letts, J.Y. Lin, J. Marathe, S. Parthasarathy, T.F. Patterson, B.D. Taylor, E.R. Duffy, M. Haack, B. Julg, G. Maranga, C. Hernandez, N.G. Singer, J. Han, P. Pemu, H. Brim, H. Ashktorab, A.W. Charney, J. Wisnivesky, J.J. Lin, H.Y. Chu, M. Go, U. Singh, E.B. Levitan, P.A. Goepfert, J.Ž. Nikolich, H. Hsu, M.J. Peluso, J.D. Kelly, M.J. Okumura, V.J. Flaherman, J.G. Quigley, J.A. Krishnan, M.B. Scholand, R. Hess, T.D. Metz, M.M. Costantine, D.J. Rouse, B.S. Taylor, M.P. Goldberg, G.D. Marshall, J. Wood, D. Warren, L. Horwitz, A.S. Foulkes, G.A. McComsey.
Provision of study materials or patients: K.M. Erlandson, P. Chen, N.B. Erdmann, J.D. Goldman, A.O. Laiyemo, S. Parthasarathy, T.F. Patterson, G. Maranga, C. Hernandez, N.G. Singer, J. Han, P. Pemu, H. Brim, J. Wisnivesky, J.J. Lin, H.Y. Chu, U. Singh, P.A. Goepfert, J.G. Quigley, J.A. Krishnan, M.B. Scholand, R. Hess, T.D. Metz, B.S. Taylor, M.P. Goldberg, G.D. Marshall, G.A. McComsey.
Statistical expertise: C.A. Selvaggi, T. Thaweethai, B.S. Taylor, A.S. Foulkes.
Obtaining of funding: K.M. Erlandson, E.W. Karlson, S.D. Katz, T.F. Patterson, P. Pemu, A.W. Charney, J. Wisnivesky, P.A. Goepfert, V.J. Flaherman, J.A. Krishnan, R. Hess, T.D. Metz, B.S. Taylor, D. Warren, L. Horwitz, A.S. Foulkes, G.A. McComsey.
Administrative, technical, or logistic support: K.M. Erlandson, L.N. Geng, N.B. Erdmann, B.D. Taylor, G. Maranga, C. Hernandez, P. Pemu, J. Wisnivesky, J.D. Kelly, V.J. Flaherman, J.A. Krishnan, M.B. Scholand, R. Hess, D.J. Rouse, B.S. Taylor, M.P. Goldberg, G.D. Marshall, L. Horwitz.
Collection and assembly of data: K.M. Erlandson, N.B. Erdmann, J.D. Goldman, E.W. Karlson, S.K. Cribbs, T.F. Patterson, E.R. Duffy, B. Julg, G. Maranga, C. Hernandez, J. Han, P. Pemu, H. Brim, H. Ashktorab, J. Wisnivesky, H.Y. Chu, M. Go, U. Singh, E.B. Levitan, M.J. Peluso, J.D. Kelly, V.J. Flaherman, J.A. Krishnan, M.B. Scholand, T.D. Metz, M.M. Costantine, B.S. Taylor, J. Wood, L. Horwitz, A.S. Foulkes, G.A. McComsey.
This article was published at Annals.org on 13 August 2024.
*
Drs. Erlandson and Geng share first authorship.
†
Drs. Horwitz, Foulkes, and McComsey share senior authorship.
‡
For the RECOVER consortium list, see the Supplement.

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Kristine M. Erlandson, Linda N. Geng, Caitlin A. Selvaggi, et al. Differentiation of Prior SARS-CoV-2 Infection and Postacute Sequelae by Standard Clinical Laboratory Measurements in the RECOVER Cohort. Ann Intern Med.2024;177:1209-1221. [Epub 13 August 2024]. doi:10.7326/M24-0737

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