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. Author manuscript; available in PMC: 2024 Nov 6.
Published in final edited form as: JPEN J Parenter Enteral Nutr. 2024 Jun 25;48(8):906–916. doi: 10.1002/jpen.2662

Association between malnutrition and post–acute COVID-19 sequelae: A retrospective cohort study

Jana Ponce 1,2, A Jerrod Anzalone 3, Makayla Schissel 4, Kristina Bailey 5,6, Harlan Sayles 4, Megan Timmerman 1,2, Mariah Jackson 1, Jonathan Tefft 7, Corrine Hanson 1; National COVID Cohort Collaborative (N3C) Consortium
PMCID: PMC11537834  NIHMSID: NIHMS2018252  PMID: 38924100

Abstract

Background:

Long coronavirus disease consists of health problems people experience after being infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These can be severe and include respiratory, neurological, and gastrointestinal symptoms, with resulting detrimental impacts on quality of life. Although malnutrition has been shown to increase risk of severe disease and death during acute infection, less is known about its influence on post–acute COVID-19 outcomes. We addressed this critical gap in knowledge by evaluating malnutrition’s impact on post–COVID-19 sequelae.

Methods:

This study leveraged the National COVID Cohort Collaborative to identify a cohort of patients who were at least 28 days post–acute COVID-19 infection. Multivariable Cox proportional hazard models evaluated the impact of malnutrition on the following postacute sequelae of SARS-CoV-2: (1) death, (2) long COVID diagnosis, (3) COVID-19 reinfection, and (4) other phenotypic abnormalities. A subgroup analysis evaluated these outcomes in a cohort of hospitalized patients with COVID-19 with hospital-acquired (HAC) malnutrition.

Results:

The final cohort included 4,372,722 individuals, 78,782 (1.8%) with a history of malnutrition. Individuals with malnutrition had a higher risk of death (adjusted hazard ratio [aHR]: 2.10; 95% CI: 2.04–2.17) and SARS-CoV-2 reinfection (aHR: 1.52; 95% CI: 1.43–1.61) in the postacute period than those without malnutrition. In the subgroup, those with HAC malnutrition had a higher risk of death and long COVID diagnosis.

Conclusion:

Nutrition screening for individuals with acute SARS-CoV-2 infection may be a crucial step in mitigating life-altering, negative postacute outcomes through early identification and intervention of patients with malnutrition.

Keywords: COVID-19, long COVID, malnutrition, postacute COVID-19 syndrome

INTRODUCTION

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in >100 million cases and >1 million deaths to date in the United States.1 However, a second pandemic has dramatically emerged with the recognition of post–acute COVID-19, a syndrome characterized by persistent symptoms and/or delayed or long-term complications. Post-COVID conditions consist of a wide range of new, returning, or ongoing health problems people can experience ≥4 weeks after first being infected with the virus that causes COVID-19 and may ultimately result in death. Collectively, these symptoms have been labeled “long COVID,” but diagnosis is often hindered because no established criteria for post–acute COVID exist.2 Because millions of people have been infected, and more will continue to be infected, the number of new conditions, chronic diseases, and fatalities caused by long-term manifestations of COVID-19 is dramatically increasing. This can potentially add a major burden to an already stressed healthcare system and negatively impact health and quality of life for those afflicted. Because these symptoms can be long lasting and difficult to eradicate, the management of COVID-19 shifts from the acute care model to a chronic disease management model,3 which focuses on prevention and treatment. However, little is known about which individuals develop long COVID and why.

Malnutrition has been associated with adverse outcomes in patients with acute COVID-19. For example, in adults hospitalized with COVID-19, the presence of malnutrition increased the odds of mortality by nearly three times compared with those who are well-nourished.4 Despite this, there is a dearth of evidence describing the impact of malnutrition in long COVID. As the population of patients recovering from COVID-19 grows, it is paramount to establish an understanding of the healthcare issues and risk factors for adverse outcomes surrounding them. To address this knowledge gap, we aimed to evaluate the impact of malnutrition on postacute sequelae of COVID-19 (PASC), including death, long COVID diagnosis, SARS-CoV-2 reinfection, and the development of other phenotypic abnormalities. As a secondary aim, we sought to investigate the effect of hospital-acquired (HAC) malnutrition on PASC.

To accomplish our objective, we performed a retrospective cohort study of adult patients who were ≥28 days post–acute COVID-19 infection from the National Institutes of Health National COVID Cohort Collaborative (N3C) data enclave, which systematically collects longitudinal data derived from the electronic health record (EHR) of patients diagnosed with COVID-19. This resource offers a rich infrastructure to critically expand our understanding of the role of malnutrition on PASC after acute infection. This robust data set allowed us to test the hypothesis that malnutrition will increase the risk of negative outcomes in a population of patients who survived acute SARS-CoV-2 infection.

METHODS

This study used data from 66 health centers across the US that reported data to the N3C, including EHR data from January 1, 2018, to December 13, 2023, or the latest health center data submission, for historical medical context for all individuals. The N3C, described in detail by Haendel et al.5 operates under the authority of the National Institutes of Health, with Johns Hopkins University School of Medicine as the Central Institutional Review Board (#IRB00249128). The N3C approved a data use agreement completed by the University of Nebraska Medical Center, an N3C contributing partner, and a data use request completed by this team of investigators (RP-B3442B). The University of Nebraska Medical Center Institutional Review Board approved this study protocol (#0176–21-EP).

The final data extraction was completed on December 14, 2023 (N3C Release v153), in the Observational Medical Outcomes Partnership (OMOP) Common Data Model version 5.3.1. Given that reporting practices by N3C data partners vary, a data analysis plan was developed to determine the minimum fact reporting per patient across key domains. We included those diagnosed with COVID-19 between April 1, 2020, and March 31, 2023, with pre–COVID-19 visit history (at least one visit documented before COVID-19) who survived to day 28 postacute COVID-19 and were either not admitted or discharged by day 28. We excluded patients with missing sex or age and sites with limited long COVID or death reporting, which were primary outcomes (Figure S1). This follows a similar approach used by the four source data models, which all rely on data quality dashboards to enhance site reporting for inclusion in network studies: OMOP6 Accrual to Clinical Trials (ACT),7 TriNetX,8 and the Patient-Centered Clinical Research Network (PCORnet).9

All clinical concept sets were created collaboratively within the N3C Enclave, with at least one informatician and one clinical subject matter expert reviewing each relevant concept set. An overview of the ingestion and harmonization process, sampling approaches, and overall structure of the N3C Enclave, concept set definitions, and computable phenotypes used can be found in the Methods S1.

Primary exposure

Malnutrition before acute COVID-19 diagnosis date served as the primary exposure in this study, as defined by the presence of one or more of the following diagnostic codes (Table S1) within a patient’s medical record: malnutrition, severe protein-calorie malnutrition, malnutrition of moderate degree, moderate protein-energy malnutrition, moderate protein-calorie malnutrition, mild protein-calorie malnutrition, malnutrition of mild degree, malnutrition following gastrointestinal surgery, starvation, semistarvation, undernutrition, deficiency of macronutrients, nutrition deficiency disorder, nutrition wasting, wasting disease, marasmic kwashiorkor, and kwashiorkor. The use of specific diagnostic codes to identify patients with malnutrition is consistent with previously published work.4

Subgroup analysis

In a subgroup analysis, we limited our sample to persons without a history of malnutrition who were hospitalized within 14 days of COVID-19 diagnosis for a minimum of 5 days. This secondary aim subgroup was stratified by whether they acquired malnutrition during their COVID-19 hospitalization to determine if outcomes differed in patients with HAC malnutrition. HAC malnutrition (yes/no) was defined as new-onset malnutrition (using the above-mentioned diagnostic codes) among patients hospitalized with COVID-19 between days 5 and 27 of hospitalization.

Outcomes

The primary outcomes of this study include the following postacute (days 28–180) outcomes in survivors of SARS-CoV-2 infection: (1) death, (2) diagnosis of long COVID, and (3) COVID-19 reinfection >90 days after initial infection. A long COVID clinical diagnosis was defined by the addition of the long COVID International Classification of Diseases Tenth Revision (ICD-10) diagnostic code (U09.9) or a B94.8 ICD-10 diagnosis, which was used as a proxy for long COVID before the ICD-10 diagnostic code was created in October 2021.10 Secondary outcomes included the development of other phenotypic abnormalities in the postacute period. We defined other phenotypic abnormalities using a combination of physical, medical, or cognitive manifestations of COVID-19 during the postacute phase. We clustered sequelae by phenotypic abnormalities at the organ system level (eg, circulatory or digestive) as classified by the Human Phenotype Ontology11 mapped to the OMOP Common Data Model using a validated approach. The detailed definitions for the other phenotypical abnormalities can be found in Methods S1. Conditions were attributed to post–acute COVID-19 only if they emerged as new onset during days 28–180 following infection and were absent in the patient’s medical history in the year before contracting COVID-19.

Statistical analysis

Descriptive statistics (medians with interquartile ranges and counts with percentages) were used to summarize the demographic and clinical characteristics of the patients by malnutrition status (history of malnutrition vs no history of malnutrition) on or before their acute COVID-19 index date. Groups were then compared using Wilcoxon rank sum tests for continuous measures or chi-square tests for categorical variables. Multivariable Cox proportional hazard models were used to evaluate adjusted hazard ratios (aHRs) of PASC, including death, long COVID diagnosis, COVID-19 reinfection >90 days after initial infection, and other relevant PASC. Adjusted models were controlled with the following covariates: sex, age group (<30, 30–49, 50–64, 65–74, and ≥75 years), race and ethnicity, obesity, COVID-19 variant dominant period (prevaccination [before December 10, 2020], pre-Delta [December 10, 2020–June 14, 2021], Delta [June 15, 2021–December 21, 2021], or Omicron [on or after December 22, 2021]), Charlson comorbidity index (CCI),12,13 COVID-19 severity (nonhospitalized, hospitalized, and hospitalized requiring oxygen support or ventilation), and healthcare use before COVID-19 diagnosis. A sensitivity analysis was performed on hospitalized patients with no history of malnutrition before their acute COVID-19 diagnosis to assess to role of HAC malnutrition on the described outcomes. An additional sensitivity analysis was performed on the overall cohort as well as the HAC cohort, including data partner ID (center) as a random intercept within the models. To account for multiple comparisons, all confidence intervals from adjusted models were Bonferroni corrected by using a significance level of 0.003 (0.05/16 models). All statistical analyses were performed in R v4.1.3 within the N3C platform. P < 0.05 was considered statistically significant. All P values presented are for two-sided tests.

RESULTS

The final cohort included 4,372,722 individuals with COVID-19, of whom 78,782 (1.8%) had a history of malnutrition (Table 1). The cohort was predominantly White and non-Hispanic (66%) women (60%) without a documented COVID-19 vaccination before SARS-CoV-2 infection (78%). Compared with those without malnutrition, individuals with malnutrition were older (median age: 64 vs 48 years), had a higher CCI (median CCI: 5 vs, 0) and had a higher severity of COVID-19 illness (P < 0.001 for all). Individuals with malnutrition had more healthcare interaction with a significantly higher median number of visits before (number of visits: 63 vs 17) and after (number of visits: 26 vs 12) SARS-CoV-2 infection (P < 0.001).

TABLE 1.

Characteristics of patients with SARS-CoV-2 infection by malnutrition.

Characteristic Overall (N = 4,372,722) No malnutrition (n = 4,293,940) Malnutrition (n = 78,782) P valuea
Sex, n (%) <0.001
 Female 2,623,226 (60) 2,579,932 (60) 43,294 (55)
 Male 1,749,496 (40) 1,714,008 (40) 35,488 (45)
Age at COVID-19 diagnosis, median (IQR), years 49 (34–63) 48 (34–63) 64 (50–75) <0.001
Race/ethnicity, n (%) <0.001
 White non-Hispanic 2,892,602 (66) 2,842,914 (66) 49,688 (63)
 Black or African American non-Hispanic 553,805 (13) 538,994 (13) 14,811 (19)
 Hispanic or Latino any race 481,497 (11) 473,885 (11) 7612 (9.7)
 Other 233,551 (5.3) 229,476 (5.3) 4075 (5.2)
 Unknown/missing 211,267 (4.8) 208,671 (4.9) 2596 (3.3)
Vaccination status before SARS-CoV-2 infection, n (%) <0.001
 No documented COVID-19 vaccination 3,421,554 (78) 3,361,415 (78) 60,139 (76)
 Primary vaccination series 532,409 (12) 522,824 (12) 9585 (12)
 Primary and vaccination series 418,759 (9.6) 409,701 (9.5) 9058 (11)
COVID-19 epoch, n (%) <0.001
 Prevaccination (before December 10, 2020) 890,143 (20) 878,310 (20) 11,833 (15)
 Pre-Delta (December 10, 2020–June 14, 2021) 707,610 (16) 697,434 (16) 10,176 (13)
 Delta (June 15, 2021–December 21, 2021) 765,437 (18) 754,679 (18) 10,758 (14)
 Omicron (at or after December 22, 2021) 2,009,532 (46) 1,963,517 (46) 46,015 (58)
Acute COVID-19 severity, n (%) <0.001
 Mild: nonhospitalized 3,963,714 (91) 3,917,428 (91) 46,286 (59)
 Moderate: hospitalized 300,128 (6.9) 279,305 (6.5) 20,823 (26)
 Severe: supplemental oxygen in the hospital 108,880 (2.5) 97,207 (2.3) 11,673 (15)
CCI, median (IQR) 0 (0–2) 0 (0–2) 5 (2–8) <0.001
Obesity, n (%) 1,718,964 (39) 1,684,750 (39) 34,214 (43) <0.001
Healthcare interaction, median (IQR)
 Number of visits before COVID-19 18 (6–45) 17 (6–44) 63 (25–133) <0.001
 Observation period before COVID-19 1022 (585–1375) 1019 (581–1372) 1193 (824–1485) <0.001
 Number of visits after COVID-19 13 (4–31) 12 (4–30) 26 (9–63) <0.001
 Observation period after COVID-19 487 (268–724) 488 (269–726) 422 (212–654) <0.001
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Abbreviations: CCI, Charlson comorbidity index; COVID-19, coronavirus disease 2019; IQR, interquartile range; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

a

Statistical tests performed: Wilcoxon rank sum test and chi-square test of independence.

Postacute outcomes in survivors of SARS-CoV-2 infection

Table 2 shows the univariate and multivariate analyses of the primary and secondary outcomes by malnutrition group. Following adjustment for confounders, individuals with malnutrition were 2.10 times (aHR: 2.10; 95% CI: 2.04–2.17) more likely to die in the postacute (days 28–180) period than those without malnutrition (Figure 1). Patients with malnutrition were also 1.52 times (aHR: 1.52; 95% CI: 1.43–1.61) more likely to have a SARS-CoV-2 reinfection >90 days after initial infection than individuals without malnutrition. The malnutrition group’s risk of many other phenotypic abnormalities, including abnormality of the digestive system (aHR: 1.51; 95% CI: 1.48–1.54), abnormality of blood and blood forming tissues (aHR: 1.32; 95% CI: 1.30–1.35), and abnormality of the respiratory system (aHR: 1.16; 95% CI: 1.14–1.17) were likewise increased. There was slight protective association between malnutrition status and long COVID diagnosis (aHR: 0.92; 95% CI: 0.86–0.98). Full unadjusted, full adjusted, and multiplicity-corrected model specifications are available in Tables S2 and S3.

TABLE 2.

Unadjusted and adjusted HRs of postacute (28–180 days) outcomes in survivors of SARS-CoV-2 infection by malnutrition group.

Characterstic Unadjusted HR (95% CI) Adjusted HRa (95% CI)
Death
 No malnutrition Reference Reference
 Malnutrition 13.4 (13.0–13.8) 2.10 (2.04–2.17)
Long COVID-19
 No malnutrition Reference Reference
 Malnutrition 2.05 (1.93–2.17) 0.92 (0.86–0.98)
SARS-CoV-2 reinfection
 No malnutrition Reference Reference
 Malnutrition 2.15 (2.03–2.27) 1.52 (1.43–1.61)
Abnormality of the digestive system
 No malnutrition Reference Reference
 Malnutrition 3.54 (3.48–3.59) 1.51 (1.48–1.54)
Abnormality of blood and blood-forming tissues
 No malnutrition Reference Reference
 Malnutrition 2.70 (2.65–2.74) 1.32 (1.30–1.35)
Abnormality of the respiratory system
 No malnutrition Reference Reference
 Malnutrition 2.57 (2.53–2.61) 1.16 (1.14–1.17)
Abnormality of the nervous system
 No malnutrition Reference Reference
 Malnutrition 2.28 (2.24–2.31) 1.21 (1.19–1.23)
Abnormality of the musculoskeletal system
 No malnutrition Reference Reference
 Malnutrition 2.46 (2.42–2.50) 1.17 (1.15–1.19)
Abnormality of the integument
 No malnutrition Reference Reference
 Malnutrition 2.54 (2.49–2.59) 1.15 (1.13–1.18)
Abnormality of the immune system
 No malnutrition Reference Reference
 Malnutrition 2.70 (2.66–2.75) 1.26 (1.24–1.29)
Abnormality of the genitourinary system
 No malnutrition Reference Reference
 Malnutrition 2.37 (2.33–2.42) 1.13 (1.11–1.15)
Abnormality of the endocrine system
 No malnutrition Reference Reference
 Malnutrition 2.58 (2.52–2.64) 1.15 (1.12–1.18)
Abnormality of the cardiovascular system
 No malnutrition Reference Reference
 Malnutrition 2.83 (2.79–2.87) 1.15 (1.14–1.17)
Abnormality of metabolism homeostasis
 No malnutrition Reference Reference
 Malnutrition 2.60 (2.57–2.64) 1.21 (1.19–1.23)
Neoplasm
 No malnutrition Reference Reference
 Malnutrition 3.17 (3.08–3.27) 1.15 (1.11–1.19)
Constitutional symptom
 No malnutrition Reference Reference
 Malnutrition 2.07 (2.04–2.10) 1.13 (1.11–1.15)
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Abbreviations: COVID-19, coronavirus disease 2019; HR, hazard ratio; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

a

The adjusted model was controlled with the following covariates: sex, age group (<30, 30–49, 50–64, 65–74, and ≥75 years), race/ethnicity, obesity, COVID-19 variant dominant period (prevaccination [before December 10, 2020], pre-Delta [December 10, 2020–June 14, 2021], Delta [June 15, 2021–December 21, 2021], and Omicron [at or after December 22, 2021]), Charlson comorbidity index, COVID-19 severity (nonhospitalized, hospitalized, and hospitalized requiring oxygen support), and number of visits before COVID.

FIGURE 1.

FIGURE 1

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Forest plot showing the aHRs for postacute outcomes in survivors of SARS-CoV-2 infection with malnutrition. aHR, adjusted hazard ratio; CI, Confidence Interval; COVID-19, coronavirus disease 2019; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

Subgroup analysis

The subgroup analysis consisted of 163,098 acute COVID-19 survivors hospitalized within 14 days of original COVID-19 infection for a minimum of 5 days and a maximum of 27 days with no prior history of malnutrition. Of this subgroup, 4212 (2.6%) survivors were diagnosed with malnutrition between hospital days 5 and 27. The subgroup was largely White and non-Hispanic (62%), equally male and female, and without a documented COVID-19 vaccination (88%) before infection (see Table S4). Those with HAC malnutrition were again older (median age of 71 vs 66) with a higher median CCI (4 vs 3), more severe COVID-19 illness (P < 0.001 for all), and higher median numbers of healthcare visits before (number of visits: 28 vs 21) but not after (number of visits: 17 vs 17) SARS-CoV-2 infection (P < 0.001).

Comparisons of primary and secondary outcomes between HAC malnutrition groups are shown inTable 3. In adjusted analyses, patients in the HAC malnutrition group were 2.31 times (aHR: 2.31; 95% CI: 2.15–2.49) more likely to die in the 28–180-day postacute period and 1.56 times (aHR: 1.56; 95% CI: 1.33–1.82) more likely to be diagnosed with long COVID (Table 3). No difference in SARS-CoV-2 reinfection >90 days after initial infection was observed between groups. However, the risk of many other phenotypic abnormalities were significantly increased in the HAC malnutrition group (Figure 2). Full unadjusted, full adjusted, and multiplicity-corrected model specifications are available in Tables S5 and S6.

TABLE 3.

Unadjusted and adjusted HRs of postacute (28–180 days) outcomes in survivors of SARS-CoV-2 infection requiring hospitalization by HAC malnutrition group.

Characteristic Unadjusted HR (95% CI) Adjusted HRa (95% CI)
Death
 No malnutrition Reference Reference
 HAC malnutrition 3.07 (2.86–3.29) 2.31 (2.15–2.49)
Long COVID-19
 No malnutrition Reference Reference
 HAC malnutrition 1.75 (1.50–2.05) 1.56 (1.33–1.82)
SARS-CoV-2 reinfection
 No malnutrition Reference Reference
 HAC malnutrition 1.27 (0.94–1.70) 1.17 (0.87–1.57)
Abnormality of the digestive system
 No malnutrition Reference Reference
 HAC malnutrition 2.68 (2.53–2.83) 2.38 (2.25–2.52)
Abnormality of blood and blood-forming tissues
 No malnutrition Reference Reference
 HAC malnutrition 1.71 (1.62–1.82) 1.58 (1.49–1.67)
Abnormality of the respiratory system
 No malnutrition Reference Reference
 HAC malnutrition 1.73 (1.64–1.82) 1.56 (1.48–1.65)
Abnormality of the nervous system
 No malnutrition Reference Reference
 HAC malnutrition 1.73 (1.64–1.83) 1.58 (1.50–1.67)
Abnormality of the musculoskeletal system
 No malnutrition Reference Reference
 HAC malnutrition 1.60 (1.50–1.71) 1.44 (1.35–1.54)
Abnormality of the integument
 No malnutrition Reference Reference
 HAC malnutrition 1.63 (1.50–1.76) 1.49 (1.37–1.61)
Abnormality of the immune system
 No malnutrition Reference Reference
 HAC malnutrition 1.83 (1.73–1.94) 1.66 (1.56–1.76)
Abnormality of the genitourinary system
 No malnutrition Reference Reference
 HAC malnutrition 1.71 (1.60–1.84) 1.51 (1.41–1.62)
Abnormality of the endocrine system
 No malnutrition Reference Reference
 HAC malnutrition 1.67 (1.53–1.83) 1.54 (1.40–1.68)
Abnormality of the cardiovascular system
 No malnutrition Reference Reference
 HAC malnutrition 1.60 (1.52–1.68) 1.43 (1.36–1.51)
Abnormality of metabolism homeostasis
 No malnutrition Reference Reference
 HAC malnutrition 1.73 (1.64–1.82) 1.59 (1.51–1.68)
Neoplasm
 No malnutrition Reference Reference
 HAC malnutrition 1.91 (1.68–2.19) 1.61 (1.41–1.84)
Constitutional symptom
 No malnutrition Reference Reference
 HAC malnutrition 1.63 (1.53–1.73) 1.50 (1.41–1.60)
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Abbreviations: COVID-19, coronavirus disease 2019; HAC, hospital acquired; HR, hazard ratio; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

a

The adjusted model was controlled with the following covariates: sex, age group (<30, 30–49, 50–64, 65–74, and ≥75 years), race/ethnicity, obesity, COVID-19 variant dominant period (prevaccination [before December 10, 2020], pre-Delta [December 10, 2020–June 14, 2021], Delta [June 15, 2021–December 21, 2021], and Omicron [at or after December 22, 2021]), Charlson comorbidity index, COVID-19 severity (nonhospitalized, hospitalized, and hospitalized requiring oxygen support), and number of visits before COVID.

FIGURE 2.

FIGURE 2

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Forest plot showing the aHRs for postacute outcomes in survivors of SARS-CoV-2 infection requiring hospitalization with hospital-acquired malnutrition. aHR, adjusted hazard ratio; CI, Confidence Interval; COVID-19, coronavirus disease 2019; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.

DISCUSSION

To the best of our knowledge, this is the first study to investigate the association between malnutrition and PASC. We show that patients with malnutrition are 2.10 times more likely to die and 1.52 times more likely to become reinfected with SARs-CoV-2 in the 28–180-day postacute period than those without malnutrition. Those with malnutrition are also at a significantly higher risk of developing many other relevant PASC.

Malnutrition and PASC

Our group4 and others14,15 have found that malnutrition significantly increases the odds of mortality during acute SARS-CoV-2 infection. However, to the best of our knowledge, no data are currently available regarding the impact of malnutrition on mortality in the post–acute phase of COVID-19. Previous studies have found that patients with severe COVID-19 have a significantly increased risk of mortality compared with those with mild/moderate COVID-1916 and individuals who are aged ≥65 years are at increased risk of both severe disease and death.17 Further, malnutrition impacts the predisposition to severe COVID-19 and is dependent on age.18 This study is the first to show that a history of malnutrition or HAC malnutrition, independent of COVID-19 severity, age, and other confounding variables, significantly increases mortality risk in the postacute period.

Understanding risk factors that may predict SARS-CoV-2 reinfection is crucial because each time a person is infected or reinfected with the virus, they risk developing PASC. Although individuals who recovered from a SARS-CoV-2 infection may benefit from a short duration of immunity, protection against reinfection is not universal. In the present study, the risk of COVID-19 reinfection was significantly higher in patients with a history of malnutrition (aHR: 1.52; 95% CI: 1.43–1.61), even with the malnutrition group having a higher number of patients who received a COVID-19 vaccination (Table 1). Nutrition deficiencies are closely associated with impaired innate and adaptive immune response and loss of host resistance to infection.19 This is seen specifically in respiratory illnesses in which malnutrition decreases immunity and increases susceptibility to influenza in animal models.20 Malnutrition is a major risk factor for several infectious diseases, including influenza, in epidemiological research.21 Patients with chronic malnutrition may be at an increased risk for multiple macronutrient and micronutrient deficiencies and may represent a cohort of COVID-19 survivors with very short-term immunity following acute infection who require increased monitoring and protective measures to prevent reinfection.

Contrary to our initial hypothesis, there was little protective association with a history of malnutrition and long COVID diagnosis (aHR: 0.92; 95% CI: 0.86–0.8) compared with those without malnutrition. However, patients with HAC malnutrition had 1.56 times higher risk of being diagnosed with long COVID (aHR: 1.56; 95% CI: 1.33–1.82). Current research suggests long COVID may be caused by long-term tissue damage and pathological inflammation,22 which may explain, in part, why patients with COVID-19 who developed HAC malnutrition were more likely to develop the condition. Acute malnutrition is associated with a pronounced inflammatory response and intestinal dysbiosis, which can amplify the already massive inflammatory cascade, resulting in cell death and tissue damage.23

In the subgroup analysis, patients in this study with HAC malnutrition were not found to have an increased risk of COVID-19 reinfection in the postacute period. However, the risk of many other phenotypic abnormalities were significantly increased in the group. One potential explanation for the difference could be due to essential and conditionally essential amino acid deficiencies associated with acute malnutrition24 and the upregulation of arginine25 and glutamine26 metabolism in COVID-19. Deficiencies of conditionally essential amino acids, such as arginine and glutamine, play a pivotal role in the pathogenesis of a growing number of diseases caused by endothelial and/or T-cell dysfunction. Interestingly, the risk of developing diseases of the digestive system (aHR: 2.38; 95% CI, 2.25–2.52) and of blood and blood-forming tissues (aHR: 1.58; 95% CI: 1.49–1.67) were among the highest of the other phenotypic abnormalities.

Screening patients with COVID-19 for malnutrition may be an effective strategy to identify patients who may be at increased risk for negative postacute outcomes, such as death, SARs-CoV-2 reinfection, and long COVID. Validated and reliable tools, such as the Malnutrition Screening Tool27 and the Nutrition Universal Screening Tool 2022,28 assist in detecting at-risk individuals who may benefit from an in-depth nutrition assessment. The implementation of these tools and timely response to results by the healthcare team can facilitate early nutrition interventions that have the potential to decrease the risk of PASC.

LIMITATIONS

The following limitations to the research must be considered. Firstly, these data are retrospective and used diagnostic codes that rely highly on provider documentation of malnutrition and may vary considerably among providers and across COVID-19 epochs. Because of the restrictions of working with discrete data fields, we are unable to identify persons with resolved, exacerbated, or worsened malnutrition, which would only be possible with chart review. The standard of care would be to identify characteristics recommended for identifying and documenting adult malnutrition based on the Academy of Nutrition and Dietetics and American Society for Parenteral and Enteral Nutrition guidelines.29 N3C contains EHR data from sites with diverse patient populations and differences in data reporting that may result in the misclassification of comorbid conditions and malnutrition reporting based on the degree of hospital interaction in the period leading up to SARs-CoV-2 infection. Finally, malnutrition relies on provider identification, which is known to be universally low across most care settings.30 Although we anticipate nondifferential misclassification and likely a significant underestimation of the impact of malnutrition because of under-reporting, we acknowledge that all comparisons are made with patients lacking documented malnutrition rather than reflecting a true absence of malnutrition.

CONCLUSION

Both pre-existing and HAC malnutrition have significant associations with post–COVID-19 sequelae. Early and consistent nutrition screening for all individuals with acute SARS-CoV-2 infection may be a crucial step in mitigating life-altering, negative postacute outcomes through early identification and intervention of patients with, or at risk for malnutrition.

Supplementary Material

Supplemental Figure 1
Supplemental Table 6
Supplemental Table 5
Supplemental Table 3
Supplemental Table 4
Supplemental Table 2
Supplemental Table 1
Supplemental Methods

ACKNOWLEDGMENTS

We gratefully acknowledge the following core contributors to N3C: Adam B. Wilcox, Adam M. Lee, Alexis Graves, Alfred (Jerrod) Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E. Williams, Andrew Southerland, Andrew T. Girvin, Anita Walden, Anjali A. Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, Caleb Alexander, Carolyn Bramante, Cavin Ward-Caviness, Charisse Madlock-Brown, Christine Suver, Christopher Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A. Eichmann, Diego Mazzotti, Don Brown, Eilis Boudreau, Elaine Hill, Elizabeth Zampino, Emily Carlson Marti, Emily R. Pfaff, Evan French, Farrukh M. Koraishy, Federico Mariona, Fred Prior, George Sokos, Greg Martin, Harold Lehmann, Heidi Spratt, Hemalkumar Mehta, Hongfang Liu, Hythem Sidky, J. W. Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Islam, Jin Ge, Joel Gagnier, Joel H. Saltz, Joel Saltz, Johanna Loomba, John Buse, Jomol Mathew, Joni L. Rutter, Julie A. McMurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Kellie M. Walters, Ken Wilkins, Kenneth R. Gersing, Kenrick Dwain Cato, Kimberly Murray, Kristin Kostka, Lavance Northington, Lee Allan Pyles, Leonie Misquitta, Lesley Cottrell, Lili Portilla, Mariam Deacy, Mark M. Bissell, Marshall Clark, Mary Emmett, Mary Morrison Saltz, Matvey B. Palchuk, Melissa A. Haendel, Meredith Adams, Meredith Temple-O’Connor, Michael G. Kurilla, Michele Morris, Nabeel Qureshi, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A. Francis, Penny Wung Burgoon, Peter Robinson, Philip R. O. Payne, Rafael Fuentes, Randeep Jawa, Rebecca Erwin-Cohen, Rena Patel, Richard A. Moffitt, Richard L. Zhu, Rishi Kamaleswaran, Robert Hurley, Robert T. Miller, Saiju Pyarajan, Sam G. Michael, Samuel Bozzette, Sandeep Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T. O’Neil, Soko Setoguchi, Stephanie S. Hong, Steve Johnson, Tellen D. Bennett, Tiffany Callahan, Umit Topaloglu, Usman Sheikh, Valery Gordon, Vignesh Subbian, Warren A. Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, and Xiaohan Tanner Zhang. The details of the contributions are available at https://covid.cd2h.org/contributors/.

Funding information

The project described was supported by the National Institute of General Medical Sciences, Grant/Award Numbers: U54GM104942, U54GM115458; The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Other support for this project was provided by the NIAAA, Grant/Award Numbers: R25AA020818, R24AA019661; The Department of Veterans Affairs, Grant/Award Number: I01CX001714

Footnotes

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

DISCLOSURE

Disclaimers: The N3C Publication committee confirmed that this manuscript msid (1733.373) is in accordance with N3C data use and attribution policies; however, this content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the N3C program.

The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave https://covid.cd2h.org and N3C Attribution & Publication Policy v 1.2–2020-08–25b supported by NCATS U24 TR002306, Axle Informatics Subcontract: NCATS-P00438-B, and (insert additional funding agencies or sources and reference numbers as declared by the contributors in their form response above). This research was possible because of the patients whose information is included within the data and the organizations (https://ncats.nih.gov/n3c/resources/data-contribution/data-transfer-agreement-signatories) and scientists who have contributed to the ongoing development of this community resource (doi:10.1093/jamia/ocaa196).

SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.

DATA AVAILABILITY STATEMENT

The following institutions have data that are released or pending. Available: Advocate Health Care Network (UL1TR002389, The Institute for Translational Medicine [ITM]); Aurora Health Care Inc (UL1TR002373, Wisconsin Network For Health Research); Boston University Medical Campus (UL1TR001430, Boston University Clinical and Translational Science Institute); Brown University (U54GM115677, Advance Clinical Translational Research [Advance-CTR]); Carilion Clinic (UL1TR003015, iTHRIV Integrated Translational health Research Institute of Virginia); Case Western Reserve University (UL1TR002548, The Clinical & Translational Science Collaborative of Cleveland [CTSC]); Charleston Area Medical Center (U54GM104942, West Virginia Clinical and Translational Science Institute [WVCTSI]); Children’s Hospital Colorado (UL1TR002535, Colorado Clinical and Translational Sciences Institute); Columbia University Irving Medical Center (UL1TR001873, Irving Institute for Clinical and Translational Research); Dartmouth College (none [voluntary]); Duke University (UL1TR002553, Duke Clinical and Translational Science Institute); George Washington Children’s Research Institute (UL1TR001876, Clinical and Translational Science Institute at Children’s National [CTSA-CN]); George Washington University (UL1TR001876, Clinical and Translational Science Institute at Children’s National [CTSA-CN]); Harvard Medical School (UL1TR002541, Harvard Catalyst); Indiana University School of Medicine (UL1TR002529, Indiana Clinical and Translational Science Institute); Johns Hopkins University (UL1TR003098, Johns Hopkins Institute for Clinical and Translational Research); Louisiana Public Health Institute (none (voluntary); Loyola Medicine (Loyola University Medical Center); Loyola University Medical Center (UL1TR002389, The Institute for Translational Medicine [ITM]); Maine Medical Center (U54GM115516, Northern New England Clinical & Translational Research [NNE-CTR]); Mary Hitchcock Memorial Hospital & Dartmouth Hitchcock Clinic (none [voluntary]); Massachusetts General Brigham (UL1TR002541, Harvard Catalyst); Mayo Clinic Rochester (UL1TR002377, Mayo Clinic Center for Clinical and Translational Science [CCaTS]); Medical University of South Carolina (UL1TR001450, South Carolina Clinical & Translational Research Institute [SCTR]); MITRE Corporation (none [voluntary]); Montefiore Medical Center (UL1TR002556, Institute for Clinical and Translational Research at Einstein and Montefiore); Nemours (U54GM104941, Delaware CTR ACCEL Program); NorthShore University HealthSystem (UL1TR002389, The Institute for Translational Medicine [ITM]); Northwestern University at Chicago (UL1TR001422, Northwestern University Clinical and Translational Science Institute [NUCATS]); OCHIN (INV-018455, Bill and Melinda Gates Foundation grant to Sage Bionetworks); Oregon Health & Science University (UL1TR002369, Oregon Clinical and Translational Research Institute); Penn State Health Milton S. Hershey Medical Center (UL1TR002014, Penn State Clinical and Translational Science Institute); Rush University Medical Center (UL1TR002389, The Institute for Translational Medicine [ITM]); Rutgers, The State University of New Jersey (UL1TR003017, New Jersey Alliance for Clinical and Translational Science); Stony Brook University (U24TR002306, National Center for Advancing Translational Sciences); The Alliance at the University of Puerto Rico, Medical Sciences Campus (U54GM133807, Hispanic Alliance for Clinical and Translational Research [The Alliance]); The Ohio State University (UL1TR002733, Center for Clinical and Translational Science); The State University of New York at Buffalo (UL1TR001412, Clinical and Translational Science Institute); The University of Chicago (UL1TR002389, The Institute for Translational Medicine [ITM]); The University of Iowa (UL1TR002537, Institute for Clinical and Translational Science); The University of Miami Leonard M. Miller School of Medicine (UL1TR002736, University of Miami Clinical and Translational Science Institute); The University of Michigan at Ann Arbor (UL1TR002240, Michigan Institute for Clinical and Health Research); The University of Texas Health Science Center at Houston (UL1TR003167, Center for Clinical and Translational Sciences [CCTS]); The University of Texas Medical Branch at Galveston (UL1TR001439, The Institute for Translational Sciences); The University of Utah (UL1TR002538, Uhealth Center for Clinical and Translational Science); Tufts Medical Center (UL1TR002544, Tufts Clinical and Translational Science Institute); Tulane University (UL1TR003096, Center for Clinical and Translational Science); The Queens Medical Center (none [voluntary]); University Medical Center New Orleans (U54GM104940, Louisiana Clinical and Translational Science [LA CaTS] Center); University of Alabama at Birmingham (UL1TR003096, Center for Clinical and Translational Science); University of Arkansas for Medical Sciences (UL1TR003107, UAMS Translational Research Institute); University of Cincinnati (UL1TR001425, Center for Clinical and Translational Science and Training); University of Colorado Denver, Anschutz Medical Campus (UL1TR002535, Colorado Clinical and Translational Sciences Institute); University of Illinois at Chicago (UL1TR002003, UIC Center for Clinical and Translational Science); University of Kansas Medical Center (UL1TR002366, Frontiers: University of Kansas Clinical and Translational Science Institute); University of Kentucky (UL1TR001998, UK Center for Clinical and Translational Science); University of Massachusetts Medical School Worcester (UL1TR001453, The UMass Center for Clinical and Translational Science [UMCCTS]); University Medical Center of Southern Nevada (none [voluntary]); University of Minnesota (UL1TR002494, Clinical and Translational Science Institute); University of Mississippi Medical Center (U54GM115428, Mississippi Center for Clinical and Translational Research [CCTR]); University of Nebraska Medical Center (U54GM115458, Great Plains IDeA [Clinical & Translational Research]); University of North Carolina at Chapel Hill (UL1TR002489, North Carolina Translational and Clinical Science Institute); University of Oklahoma Health Sciences Center (U54GM104938, Oklahoma Clinical and Translational Science Institute [OCTSI]); University of Pittsburgh (UL1TR001857, The Clinical and Translational Science Institute [CTSI]); University of Pennsylvania (UL1TR001878, Institute for Translational Medicine and Therapeutics); University of Rochester (UL1TR002001, UR Clinical & Translational Science Institute); University of Southern California (UL1TR001855, The Southern California Clinical and Translational Science Institute (SC CTSI); University of Vermont (U54GM115516, Northern New England Clinical & Translational Research [NNE-CTR] Network); University of Virginia (UL1TR003015, iTHRIV Integrated Translational health Research Institute of Virginia); University of Washington (UL1TR002319, Institute of Translational Health Sciences); University of Wisconsin (Madison (UL1TR002373, UW Institute for Clinical and Translational Research); Vanderbilt University Medical Center (UL1TR002243, Vanderbilt Institute for Clinical and Translational Research); Virginia Commonwealth University (UL1TR002649, C. Kenneth and Dianne Wright Center for Clinical and Translational Research); Wake Forest University Health Sciences (UL1TR001420, Wake Forest Clinical and Translational Science Institute); Washington University in St. Louis (UL1TR002345, Institute of Clinical and Translational Sciences); Weill Medical College of Cornell University (UL1TR002384, Weill Cornell Medicine Clinical and Translational Science Center); and West Virginia University (U54GM104942, West Virginia Clinical and Translational Science Institute [WVCTSI]). Submitted: Icahn School of Medicine at Mount Sinai (UL1TR001433, ConduITS Institute for Translational Sciences); The University of Texas Health Science Center at Tyler (UL1TR003167, Center for Clinical and Translational Sciences [CCTS]); University of California, Davis (UL1TR001860, UCDavis Health Clinical and Translational Science Center); University of California, Irvine (UL1TR001414, The UC Irvine Institute for Clinical and Translational Science [ICTS]); University of California, Los Angeles (UL1TR001881, UCLA Clinical Translational Science Institute); University of California, San Diego (UL1TR001442, Altman Clinical and Translational Research Institute); and University of California, San Francisco (UL1TR001872, UCSF Clinical and Translational Science Institute). Pending: Arkansas Children’s Hospital (UL1TR003107, UAMS Translational Research Institute); Baylor College of Medicine (none [voluntary]); Children’s Hospital of Philadelphia (UL1TR001878, Institute for Translational Medicine and Therapeutics); Cincinnati Children’s Hospital Medical Center (UL1TR001425, Center for Clinical and Translational Science and Training); Emory University (UL1TR002378, Georgia Clinical and Translational Science Alliance); HonorHealth (none [voluntary]); Loyola University Chicago (UL1TR002389, The Institute for Translational Medicine [ITM]); Medical College of Wisconsin (UL1TR001436, Clinical and Translational Science Institute of Southeast Wisconsin); MedStar Health Research Institute (none [voluntary]); Georgetown University (UL1TR001409, Georgetown-Howard Universities Center for Clinical and Translational Science [GHUCCTS]); MetroHealth (none [voluntary]); Montana State University (U54GM115371, American Indian/Alaska Native CTR); NYU Langone Medical Center (UL1TR001445, Langone Health’s Clinical and Translational Science Institute); Ochsner Medical Center (U54GM104940, Louisiana Clinical and Translational Science [LA CaTS] Center); Regenstrief Institute (UL1TR002529, Indiana Clinical and Translational Science Institute); Sanford Research (none [voluntary]); Stanford University (UL1TR003142, Spectrum: The Stanford Center for Clinical and Translational Research and Education); The Rockefeller University (UL1TR001866, Center for Clinical and Translational Science); The Scripps Research Institute (UL1TR002550, Scripps Research Translational Institute); University of Florida (UL1TR001427, UF Clinical and Translational Science Institute); University of New Mexico Health Sciences Center (UL1TR001449, University of New Mexico Clinical and Translational Science Center); University of Texas Health Science Center at San Antonio (UL1TR002645, Institute for Integration of Medicine and Science); and the Yale New Haven Hospital (UL1TR001863, Yale Center for Clinical Investigation).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Figure 1
NIHMS2018252-supplement-Supplemental_Figure_1.png (48.8KB, png)
Supplemental Table 6
NIHMS2018252-supplement-Supplemental_Table_6.docx (55.7KB, docx)
Supplemental Table 5
NIHMS2018252-supplement-Supplemental_Table_5.docx (63.6KB, docx)
Supplemental Table 3
NIHMS2018252-supplement-Supplemental_Table_3.docx (56.4KB, docx)
Supplemental Table 4
NIHMS2018252-supplement-Supplemental_Table_4.docx (16.5KB, docx)
Supplemental Table 2
NIHMS2018252-supplement-Supplemental_Table_2.docx (65KB, docx)
Supplemental Table 1
NIHMS2018252-supplement-Supplemental_Table_1.docx (16.1KB, docx)
Supplemental Methods
NIHMS2018252-supplement-Supplemental_Methods.docx (51.1KB, docx)

Data Availability Statement

The following institutions have data that are released or pending. Available: Advocate Health Care Network (UL1TR002389, The Institute for Translational Medicine [ITM]); Aurora Health Care Inc (UL1TR002373, Wisconsin Network For Health Research); Boston University Medical Campus (UL1TR001430, Boston University Clinical and Translational Science Institute); Brown University (U54GM115677, Advance Clinical Translational Research [Advance-CTR]); Carilion Clinic (UL1TR003015, iTHRIV Integrated Translational health Research Institute of Virginia); Case Western Reserve University (UL1TR002548, The Clinical & Translational Science Collaborative of Cleveland [CTSC]); Charleston Area Medical Center (U54GM104942, West Virginia Clinical and Translational Science Institute [WVCTSI]); Children’s Hospital Colorado (UL1TR002535, Colorado Clinical and Translational Sciences Institute); Columbia University Irving Medical Center (UL1TR001873, Irving Institute for Clinical and Translational Research); Dartmouth College (none [voluntary]); Duke University (UL1TR002553, Duke Clinical and Translational Science Institute); George Washington Children’s Research Institute (UL1TR001876, Clinical and Translational Science Institute at Children’s National [CTSA-CN]); George Washington University (UL1TR001876, Clinical and Translational Science Institute at Children’s National [CTSA-CN]); Harvard Medical School (UL1TR002541, Harvard Catalyst); Indiana University School of Medicine (UL1TR002529, Indiana Clinical and Translational Science Institute); Johns Hopkins University (UL1TR003098, Johns Hopkins Institute for Clinical and Translational Research); Louisiana Public Health Institute (none (voluntary); Loyola Medicine (Loyola University Medical Center); Loyola University Medical Center (UL1TR002389, The Institute for Translational Medicine [ITM]); Maine Medical Center (U54GM115516, Northern New England Clinical & Translational Research [NNE-CTR]); Mary Hitchcock Memorial Hospital & Dartmouth Hitchcock Clinic (none [voluntary]); Massachusetts General Brigham (UL1TR002541, Harvard Catalyst); Mayo Clinic Rochester (UL1TR002377, Mayo Clinic Center for Clinical and Translational Science [CCaTS]); Medical University of South Carolina (UL1TR001450, South Carolina Clinical & Translational Research Institute [SCTR]); MITRE Corporation (none [voluntary]); Montefiore Medical Center (UL1TR002556, Institute for Clinical and Translational Research at Einstein and Montefiore); Nemours (U54GM104941, Delaware CTR ACCEL Program); NorthShore University HealthSystem (UL1TR002389, The Institute for Translational Medicine [ITM]); Northwestern University at Chicago (UL1TR001422, Northwestern University Clinical and Translational Science Institute [NUCATS]); OCHIN (INV-018455, Bill and Melinda Gates Foundation grant to Sage Bionetworks); Oregon Health & Science University (UL1TR002369, Oregon Clinical and Translational Research Institute); Penn State Health Milton S. Hershey Medical Center (UL1TR002014, Penn State Clinical and Translational Science Institute); Rush University Medical Center (UL1TR002389, The Institute for Translational Medicine [ITM]); Rutgers, The State University of New Jersey (UL1TR003017, New Jersey Alliance for Clinical and Translational Science); Stony Brook University (U24TR002306, National Center for Advancing Translational Sciences); The Alliance at the University of Puerto Rico, Medical Sciences Campus (U54GM133807, Hispanic Alliance for Clinical and Translational Research [The Alliance]); The Ohio State University (UL1TR002733, Center for Clinical and Translational Science); The State University of New York at Buffalo (UL1TR001412, Clinical and Translational Science Institute); The University of Chicago (UL1TR002389, The Institute for Translational Medicine [ITM]); The University of Iowa (UL1TR002537, Institute for Clinical and Translational Science); The University of Miami Leonard M. Miller School of Medicine (UL1TR002736, University of Miami Clinical and Translational Science Institute); The University of Michigan at Ann Arbor (UL1TR002240, Michigan Institute for Clinical and Health Research); The University of Texas Health Science Center at Houston (UL1TR003167, Center for Clinical and Translational Sciences [CCTS]); The University of Texas Medical Branch at Galveston (UL1TR001439, The Institute for Translational Sciences); The University of Utah (UL1TR002538, Uhealth Center for Clinical and Translational Science); Tufts Medical Center (UL1TR002544, Tufts Clinical and Translational Science Institute); Tulane University (UL1TR003096, Center for Clinical and Translational Science); The Queens Medical Center (none [voluntary]); University Medical Center New Orleans (U54GM104940, Louisiana Clinical and Translational Science [LA CaTS] Center); University of Alabama at Birmingham (UL1TR003096, Center for Clinical and Translational Science); University of Arkansas for Medical Sciences (UL1TR003107, UAMS Translational Research Institute); University of Cincinnati (UL1TR001425, Center for Clinical and Translational Science and Training); University of Colorado Denver, Anschutz Medical Campus (UL1TR002535, Colorado Clinical and Translational Sciences Institute); University of Illinois at Chicago (UL1TR002003, UIC Center for Clinical and Translational Science); University of Kansas Medical Center (UL1TR002366, Frontiers: University of Kansas Clinical and Translational Science Institute); University of Kentucky (UL1TR001998, UK Center for Clinical and Translational Science); University of Massachusetts Medical School Worcester (UL1TR001453, The UMass Center for Clinical and Translational Science [UMCCTS]); University Medical Center of Southern Nevada (none [voluntary]); University of Minnesota (UL1TR002494, Clinical and Translational Science Institute); University of Mississippi Medical Center (U54GM115428, Mississippi Center for Clinical and Translational Research [CCTR]); University of Nebraska Medical Center (U54GM115458, Great Plains IDeA [Clinical & Translational Research]); University of North Carolina at Chapel Hill (UL1TR002489, North Carolina Translational and Clinical Science Institute); University of Oklahoma Health Sciences Center (U54GM104938, Oklahoma Clinical and Translational Science Institute [OCTSI]); University of Pittsburgh (UL1TR001857, The Clinical and Translational Science Institute [CTSI]); University of Pennsylvania (UL1TR001878, Institute for Translational Medicine and Therapeutics); University of Rochester (UL1TR002001, UR Clinical & Translational Science Institute); University of Southern California (UL1TR001855, The Southern California Clinical and Translational Science Institute (SC CTSI); University of Vermont (U54GM115516, Northern New England Clinical & Translational Research [NNE-CTR] Network); University of Virginia (UL1TR003015, iTHRIV Integrated Translational health Research Institute of Virginia); University of Washington (UL1TR002319, Institute of Translational Health Sciences); University of Wisconsin (Madison (UL1TR002373, UW Institute for Clinical and Translational Research); Vanderbilt University Medical Center (UL1TR002243, Vanderbilt Institute for Clinical and Translational Research); Virginia Commonwealth University (UL1TR002649, C. Kenneth and Dianne Wright Center for Clinical and Translational Research); Wake Forest University Health Sciences (UL1TR001420, Wake Forest Clinical and Translational Science Institute); Washington University in St. Louis (UL1TR002345, Institute of Clinical and Translational Sciences); Weill Medical College of Cornell University (UL1TR002384, Weill Cornell Medicine Clinical and Translational Science Center); and West Virginia University (U54GM104942, West Virginia Clinical and Translational Science Institute [WVCTSI]). Submitted: Icahn School of Medicine at Mount Sinai (UL1TR001433, ConduITS Institute for Translational Sciences); The University of Texas Health Science Center at Tyler (UL1TR003167, Center for Clinical and Translational Sciences [CCTS]); University of California, Davis (UL1TR001860, UCDavis Health Clinical and Translational Science Center); University of California, Irvine (UL1TR001414, The UC Irvine Institute for Clinical and Translational Science [ICTS]); University of California, Los Angeles (UL1TR001881, UCLA Clinical Translational Science Institute); University of California, San Diego (UL1TR001442, Altman Clinical and Translational Research Institute); and University of California, San Francisco (UL1TR001872, UCSF Clinical and Translational Science Institute). Pending: Arkansas Children’s Hospital (UL1TR003107, UAMS Translational Research Institute); Baylor College of Medicine (none [voluntary]); Children’s Hospital of Philadelphia (UL1TR001878, Institute for Translational Medicine and Therapeutics); Cincinnati Children’s Hospital Medical Center (UL1TR001425, Center for Clinical and Translational Science and Training); Emory University (UL1TR002378, Georgia Clinical and Translational Science Alliance); HonorHealth (none [voluntary]); Loyola University Chicago (UL1TR002389, The Institute for Translational Medicine [ITM]); Medical College of Wisconsin (UL1TR001436, Clinical and Translational Science Institute of Southeast Wisconsin); MedStar Health Research Institute (none [voluntary]); Georgetown University (UL1TR001409, Georgetown-Howard Universities Center for Clinical and Translational Science [GHUCCTS]); MetroHealth (none [voluntary]); Montana State University (U54GM115371, American Indian/Alaska Native CTR); NYU Langone Medical Center (UL1TR001445, Langone Health’s Clinical and Translational Science Institute); Ochsner Medical Center (U54GM104940, Louisiana Clinical and Translational Science [LA CaTS] Center); Regenstrief Institute (UL1TR002529, Indiana Clinical and Translational Science Institute); Sanford Research (none [voluntary]); Stanford University (UL1TR003142, Spectrum: The Stanford Center for Clinical and Translational Research and Education); The Rockefeller University (UL1TR001866, Center for Clinical and Translational Science); The Scripps Research Institute (UL1TR002550, Scripps Research Translational Institute); University of Florida (UL1TR001427, UF Clinical and Translational Science Institute); University of New Mexico Health Sciences Center (UL1TR001449, University of New Mexico Clinical and Translational Science Center); University of Texas Health Science Center at San Antonio (UL1TR002645, Institute for Integration of Medicine and Science); and the Yale New Haven Hospital (UL1TR001863, Yale Center for Clinical Investigation).

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