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Olawore O, Turner LE, Evans MD, Johnson SG, Huling JD, Bramante CT, Buse JB, Stürmer T. Risk of Post-Acute Sequelae of SARS-CoV-2 Infection (PASC) Among Patients with Type 2 Diabetes Mellitus on Anti-Hyperglycemic Medications. Clin Epidemiol 2024; 16:379-393. [PMID: 38836048 PMCID: PMC11149650 DOI: 10.2147/clep.s458901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 05/17/2024] [Indexed: 06/06/2024] Open
Abstract
Background Observed activity of metformin in reducing the risk of severe COVID-19 suggests a potential use of the anti-hyperglycemic in the prevention of post-acute sequelae of SARS-CoV-2 infection (PASC). We assessed the 3-month and 6-month risk of PASC among patients with type 2 diabetes mellitus (T2DM) comparing metformin users to sulfonylureas (SU) or dipeptidyl peptidase-4 inhibitors (DPP4i) users. Methods We used de-identified patient level electronic health record data from the National Covid Cohort Collaborative (N3C) between October 2021 and April 2023. Participants were adults ≥ 18 years with T2DM who had at least one outpatient healthcare encounter in health institutions in the United States prior to COVID-19 diagnosis. The outcome of PASC was defined based on the presence of a diagnosis code for the illness or using a predicted probability based on a machine learning algorithm. We estimated the 3-month and 6-month risk of PASC and calculated crude and weighted risk ratios (RR), risk differences (RD), and differences in mean predicted probability. Results We identified 5596 (mean age: 61.1 years; SD: 12.6) and 1451 (mean age: 64.9 years; SD 12.5) eligible prevalent users of metformin and SU/DPP4i respectively. We did not find a significant difference in risk of PASC at 3 months (RR = 0.86 [0.56; 1.32], RD = -3.06 per 1000 [-12.14; 6.01]), or at 6 months (RR = 0.81 [0.55; 1.20], RD = -4.91 per 1000 [-14.75, 4.93]) comparing prevalent users of metformin to prevalent users of SU/ DPP4i. Similar observations were made for the outcome definition using the ML algorithm. Conclusion The observed estimates in our study are consistent with a reduced risk of PASC among prevalent users of metformin, however the uncertainty of our confidence intervals warrants cautious interpretations of the results. A standardized clinical definition of PASC is warranted for thorough evaluation of the effectiveness of therapies under assessment for the prevention of PASC.
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Affiliation(s)
- Oluwasolape Olawore
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Lindsey E Turner
- Division of Biostatistics and Health Data Science, University of Minnesota School of Public Health, Minneapolis, MN, USA
- Clinical and Translational Science Institute, University of Minnesota, Minneapolis, MN, USA
| | - Michael D Evans
- Clinical and Translational Science Institute, University of Minnesota, Minneapolis, MN, USA
| | - Steven G Johnson
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Jared D Huling
- Division of Biostatistics and Health Data Science, University of Minnesota School of Public Health, Minneapolis, MN, USA
| | - Carolyn T Bramante
- Division of General Internal Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, MN, USA
| | - John B Buse
- Division of Endocrinology, Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Til Stürmer
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - On behalf of the N3C Consortium
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Division of Biostatistics and Health Data Science, University of Minnesota School of Public Health, Minneapolis, MN, USA
- Clinical and Translational Science Institute, University of Minnesota, Minneapolis, MN, USA
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
- Division of General Internal Medicine, Department of Medicine, University of Minnesota Medical School, Minneapolis, MN, USA
- Division of Endocrinology, Department of Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
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Yang X, Huang K, Yang D, Zhao W, Zhou X. Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review. GLOBAL CHALLENGES (HOBOKEN, NJ) 2024; 8:2300163. [PMID: 38223896 PMCID: PMC10784210 DOI: 10.1002/gch2.202300163] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 09/20/2023] [Indexed: 01/16/2024]
Abstract
The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.
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Affiliation(s)
- Xue Yang
- Department of Pancreatic Surgery and West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu610041China
| | - Kexin Huang
- Department of Pancreatic Surgery and West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu610041China
| | - Dewei Yang
- College of Advanced Manufacturing EngineeringChongqing University of Posts and TelecommunicationsChongqingChongqing400000China
| | - Weiling Zhao
- Center for Systems MedicineSchool of Biomedical InformaticsUTHealth at HoustonHoustonTX77030USA
| | - Xiaobo Zhou
- Center for Systems MedicineSchool of Biomedical InformaticsUTHealth at HoustonHoustonTX77030USA
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Hsieh TYJ, Chang R, Yong SB, Liao PL, Hung YM, Wei JCC. 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] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 09/06/2023] [Indexed: 10/19/2023]
Abstract
OBJECTIVE Previous studies have indicated a bidirectional correlation between diabetes and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, no investigation has comprehensively explored the potential of coronavirus disease 2019 (COVID-19) vaccination to reduce the risk of new-onset diabetes in infected individuals. RESEARCH DESIGN AND METHODS In the first of 2 cohorts, we compared the risk of new-onset diabetes between individuals infected with SARS-CoV-2 and noninfected individuals (N = 1,562,606) using the TriNetX database to validate findings in prior literature. For the second cohort, we identified 83,829 vaccinated and 83,829 unvaccinated COVID-19 survivors from the same period. Diabetes, antihyperglycemic drug use, and a composite of both were defined as outcomes. We conducted Cox proportional hazard regression analysis for the estimation of hazard ratios (HRs) and 95% CIs. Kaplan-Meier analysis was conducted to calculate the incidence of new-onset diabetes. Subgroup analyses based on age (18-44, 45-64, ≥65 years), sex (female, male), race (White, Black or African American, Asian), and BMI categories (<19.9, 20-29, 30-39, ≥40), sensitivities analyses, and a dose-response analysis were conducted to validate the findings. RESULTS The initial cohort of patients infected with SARS-CoV-2 had a 65% increased risk (HR 1.65; 95% CI 1.62-1.68) of developing new-onset diabetes relative to noninfected individuals. In the second cohort, we observed that vaccinated patients had a 21% lower risk of developing new-onset diabetes in comparison with unvaccinated COVID-19 survivors (HR 0.79; 95% CI 0.73-0.86). Subgroup analyses by sex, age, race, and BMI yielded similar results. These findings were consistent in sensitivity analyses and cross-validation with an independent data set from TriNetX. CONCLUSIONS In conclusion, this study validates a 65% higher risk of new-onset diabetes in SARS-CoV-2-infected individuals compared to noninfected counterparts. Furthermore, COVID-19 survivors who received COVID-19 vaccinations experienced a reduced risk of new-onset diabetes, with a dose-dependent effect. Notably, the protective impact of COVID-19 vaccination is more pronounced among the Black/African American population than other ethnic groups. These findings emphasize the imperative of widespread vaccination to mitigate diabetes risk and the need for tailored strategies for diverse demographic groups to ensure equitable protection.
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Affiliation(s)
- Tina Yi Jin Hsieh
- Department of Bioinformatics, Harvard Medical School, Boston, MA
- Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston, MA
| | - Renin Chang
- Department of Emergency Medicine, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan
- Department of Recreation and Sports Management, Tajen University, Pintung, Taiwan
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Su-Boon Yong
- Department of Allergy and Immunology, China Medical University Children's Hospital, Taichung, Taiwan
| | - Pei-Lun Liao
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Center for Health Data Science, Department of Medical Research, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Yao-Min Hung
- Department of Internal Medicine, Division of Nephrology, Taipei Veterans General Hospital Taitung Branch, Taitung, Taiwan
- Master Program in Biomedicine, College of Science and Engineering, National Taitung University, Taitung, Taiwan
- College of Health and Nursing, Meiho University, Pingtung, Taiwan
| | - James Cheng-Chung Wei
- Department of Allergy, Immunology and Rheumatology, Chung Shan Medical University Hospital, Taichung, Taiwan
- Department of Nursing, Chung Shan Medical University, Taichung, Taiwan
- Graduate Institute of Integrated Medicine, China Medical University, Taichung, Taiwan
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
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Hill EL, Mehta HB, Sharma S, Mane K, Singh SK, Xie C, Cathey E, Loomba J, Russell S, Spratt H, DeWitt PE, Ammar N, Madlock-Brown C, Brown D, McMurry JA, Chute CG, Haendel MA, Moffitt R, Pfaff ER, Bennett TD. Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study. BMC Public Health 2023; 23:2103. [PMID: 37880596 PMCID: PMC10601201 DOI: 10.1186/s12889-023-16916-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 10/05/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis. METHODS This was a retrospective case-control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system and COVID index date within ± 45 days of the corresponding case's earliest COVID index date. Measurements of risk factors included demographics, comorbidities, treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. RESULTS Among 8,325 individuals with PASC, the majority were > 50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30 + days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls. CONCLUSIONS This national study identified important risk factors for PASC diagnosis such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.
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Affiliation(s)
- Elaine L Hill
- Department of Public Health Sciences, University of Rochester Medical Center, 265 Crittenden Boulevard Box 420644, Rochester, NY, 14642, USA.
| | - Hemalkumar B Mehta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, 21205, USA.
| | - Suchetha Sharma
- School of Data Science, University of Virginia, 3 Elliewood Ave, Charlottesville, VA, 22903, USA
| | - Klint Mane
- Department of Economics, University of Rochester, 1232 Mount Hope Ave, Rochester, NY, 14620, USA
| | - Sharad Kumar Singh
- Goergen Institute for Data Science, University of Rochester, 1209 Wegmans Hall, Rochester, NY, 14627, USA
| | - Catherine Xie
- CMC BOX 275184, University of Rochester, 500 Joseph C. Wilson Blvd, Rochester, NY, 14627-5184, USA
| | - Emily Cathey
- Ivy Foundations Building, Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, 560 Ray C Hunt Drive RM 2153, Charlottesville, VA, 22903, USA
| | - Johanna Loomba
- Ivy Foundations Building, Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, 560 Ray C Hunt Drive RM 2153, Charlottesville, VA, 22903, USA
| | - Seth Russell
- Department of Pediatrics, University of Colorado School of Medicine, 1890 N. Revere Court, Mail Stop 600, Aurora, CO, 80045, USA
| | - Heidi Spratt
- Department of Biostatistics and Data Science, Medical Branch, University of Texas, 301 University Blvd, Galveston, TX, 77555-1148, USA
| | - Peter E DeWitt
- Department of Pediatrics, University of Colorado School of Medicine, 1890 N. Revere Court, Mail Stop 600, Aurora, CO, 80045, USA
| | - Nariman Ammar
- Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, 50 N Dunlap St., Memphis, TN, 38103, USA
| | - Charisse Madlock-Brown
- Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, 930 Madison Avenue 6Th Floor, Memphis, TN, 38163, USA
| | - Donald Brown
- Integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, 151 Engineer's Way Olsson Hall Rm. 102E, PO Box 400747, Charlottesville, VA, USA
| | - Julie A McMurry
- Center for Health AI, University of Colorado School of Medicine, 12800 East 19Th Avenue, Aurora, CO, 80045, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, 2024 E Monument St. , Baltimore, MD, 21287, USA
| | - Melissa A Haendel
- Center for Health AI, University of Colorado School of Medicine, East 17Th Place Campus Box C290, Aurora, CO, 1300180045, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, and Stony Brook Cancer Center, Stony Brook, NY, MART L7 081011794, USA
| | - Emily R Pfaff
- Department of Medicine, North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, 160 N Medical Drive, Chapel Hill, NC, 27599, USA
| | - Tellen D Bennett
- Department of Biomedical Informatics, University of Colorado School of Medicine, 1890 N. Revere Court, Mail Stop 600, Aurora, CO, 80045, USA
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Zakaria NI, Tehranifar P, Laferrère B, Albrecht SS. Racial and Ethnic Disparities in Glycemic Control Among Insured US Adults. JAMA Netw Open 2023; 6:e2336307. [PMID: 37796503 PMCID: PMC10556965 DOI: 10.1001/jamanetworkopen.2023.36307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 08/24/2023] [Indexed: 10/06/2023] Open
Abstract
Importance Poor access to care and lack of health insurance are important contributors to disparities in glycemic control. However expanding health insurance coverage may not be enough to fully address the high burden of poor glycemic control for some groups. Objective To characterize racial and ethnic disparities in glycemic control among adults with private and public insurance in the US over a 15-year timeframe and to evaluate whether social, health care, and behavioral or health status factors attenuate estimates of disparities. Design, Setting, and Participants This cross-sectional study used data from the National Health and Nutrition Examination Survey from 2003 to 2018. Participants included Hispanic or Latino, non-Hispanic Black, and non-Hispanic White adults aged 25 to 80 years with self-reported diabetes and health insurance. Data were analyzed from January 15 to August 23, 2023. Exposure Participants self-identified as Hispanic or Latino, non-Hispanic Black, or non-Hispanic White. Main Outcomes and Measures The main outcome, poor glycemic control, was defined as glycated hemoglobin A1c (HbA1c) of 7.0% or greater. Information about social (education, food security, and nativity), health care (insurance type, routine place for health care, insurance gap in past year, and use of diabetes medications), and behavioral or health status (years with diabetes, waist circumference, and smoking) factors were collected via questionnaires. Results A total of 4070 individuals (weighted mean [SE] age, 61.4 [0.27] years; 1970 [weighted proportion, 49.3%] were women) were included, representing 16 337 362 US adults, including 1146 Hispanic or Latino individuals (weighted proportion, 13.2%), 1196 non-Hispanic Black individuals (weighted proportion, 15.7%), and 1728 non-Hispanic White individuals (weighted proportion, 71.1%). In models adjusted for age, sex, and survey year, Hispanic or Latino and non-Hispanic Black individuals had significantly higher odds of poor glycemic control than non-Hispanic White individuals (Hispanic or Latino: odds ratio [OR], 1.46; 95% CI, 1.16-1.83; Black: OR, 1.28; 95% CI, 1.04-1.57). There was some attenuation after adjustment for social factors, especially food security (Hispanic or Latino: OR, 1.39; 95% CI, 1.08-1.81); Black: OR, 1.39; 95% CI, 1.08-1.81). However, accounting for health care and behavioral or health status factors increased disparities, especially for Hispanic or Latino individuals (OR, 1.63; 95% CI, 1.24-2.16), with racial and ethnic disparities persisting even among those with private insurance (OR, 1.66; 95% CI, 1.10-2.52). Conclusions and Relevance In this cross-sectional study of insured adults with diabetes in the US, disparities in poor glycemic control persisted despite adjustment for social, health care, and behavioral factors. Research is needed to identify the barriers contributing to poor control even in populations with access to care.
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Affiliation(s)
- Nora I. Zakaria
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York
| | - Parisa Tehranifar
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York
| | - Blandine Laferrère
- Department of Medicine, Division of Endocrinology, Diabetes Research Center, Columbia University Irving Medical Center, New York, New York
| | - Sandra S. Albrecht
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, New York
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Szczerbiński Ł, Okruszko MA, Szabłowski M, Sołomacha S, Sowa P, Kiszkiel Ł, Gościk J, Krętowski AJ, Moniuszko-Malinowska A, Kamiński K. Long-term effects of COVID-19 on the endocrine system - a pilot case-control study. Front Endocrinol (Lausanne) 2023; 14:1192174. [PMID: 37790604 PMCID: PMC10544976 DOI: 10.3389/fendo.2023.1192174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 09/01/2023] [Indexed: 10/05/2023] Open
Abstract
Background Coronavirus disease 2019 (COVID-19) has permanently changed the world. Despite having been a pandemic for nearly 3 years, the mid- and long-term complications of this disease, including endocrine disorders, remain unclear. Our study aimed to evaluate the lasting effects of COVID-19 on the endocrine system 6 months after initial infection. Methods We compared patients who underwent COVID-19 to age- and sex-matched subjects from a population-based study conducted before the pandemic. We evaluated differences in multiple parameters related to metabolism and the endocrine system including fasting glucose, insulin, lipids, body composition, thyroid stimulating hormone (TSH), free thyroxine (fT4), free triiodothyronine (fT3), anti-thyroglobulin (aTG) and anti-thyroid peroxidase (aTPO) antibodies, prolactin, cortisol, testosterone, and estradiol. Results We found significantly lower levels of fT3 and fT4, accompanied by higher levels of TSH and aTPO antibodies, in COVID-19 survivors. Moreover, we found that patients who underwent SARS-CoV2 infection had higher levels of prolactin and lower levels of testosterone than controls. Interestingly, differences in testosterone levels were observed only in male subjects. We did not detect significant differences in body composition or metabolic and glycemic parameters between cases and controls, except for significantly higher values of the HOMA2-B index in COVID-19 survivors. Conclusion Our study indicates that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection might have long-term consequences on the endocrine system, including the suppressed function of the thyroid gland, prolactin, and male sex hormone secretion. Moreover, we showed that in a 6-month follow-up, COVID-19 had no consequences on glycemic parameters, lipid profiles, liver function, body composition, cortisol levels, and estradiol levels.
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Affiliation(s)
- Łukasz Szczerbiński
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
- Department of Endocrinology, Diabetology and Internal Diseases, Medical University of Bialystok, Bialystok, Poland
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology (MIT) and Harvard, Cambridge, MA, United States
| | - Michał Andrzej Okruszko
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
- Doctoral School at the Medical University of Bialystok, Bialystok, Poland
| | - Maciej Szabłowski
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Sebastian Sołomacha
- Doctoral School at the Medical University of Bialystok, Bialystok, Poland
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Białystok, Poland
| | - Paweł Sowa
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Białystok, Poland
| | - Łukasz Kiszkiel
- Society and Cognition Unit, University of Bialystok, Bialystok, Poland
| | - Joanna Gościk
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Adam Jacek Krętowski
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
- Department of Endocrinology, Diabetology and Internal Diseases, Medical University of Bialystok, Bialystok, Poland
| | - Anna Moniuszko-Malinowska
- Department of Infectious Diseases and Neuroinfections, Medical University of Bialystok, Białystok, Poland
| | - Karol Kamiński
- Department of Population Medicine and Lifestyle Diseases Prevention, Medical University of Bialystok, Białystok, Poland
- Department of Cardiology, University Hospital of Bialystok, Białystok, Poland
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Veronese-Araújo A, de Lucena DD, Aguiar-Brito I, Cristelli MP, Tedesco-Silva H, Medina-Pestana JO, Rangel ÉB. Sex Differences among Overweight/Obese Kidney Transplant Recipients Requiring Oxygen Support Amid the COVID-19 Pandemic. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1555. [PMID: 37763674 PMCID: PMC10535294 DOI: 10.3390/medicina59091555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 08/19/2023] [Accepted: 08/25/2023] [Indexed: 09/29/2023]
Abstract
Background and Objectives: Overweight/obesity puts individuals at greater risk for COVID-19 progression and mortality. We aimed to evaluate the impact of overweight/obesity on oxygen (O2) requirement outcomes of male and female kidney transplant recipients (KTRs) during the COVID-19 pandemic. Materials and Methods: We conducted a retrospective analysis of a cohort of KTRs diagnosed with COVID-19. Participants were stratified based on BMI categories, and data on the need for O2 therapy outcome were collected and analyzed separately for male and female KTRs. Results: In total, 284 KTRs (97 males and 187 females) were included in the study. Overweight/obesity was observed in 60.6% of male KTRs and 71% of female KTRs. Strikingly, overweight/obese women had a significantly higher requirement for supplemental O2 (63.3% vs. 41.7%, OR = 2.45, p = 0.03), particularly among older individuals (OR = 1.05, p = 0.04), smokers (OR = 4.55, p = 0.03), those with elevated lactate dehydrogenase (LDH) levels (OR = 1.01, p = 0.006), and those with lower admission and basal estimated glomerular filtration rate (eGFR) levels. Within this cohort, the necessity for O2 supplementation was correlated with more unfavorable outcomes. These included heightened mortality rates, transfers to the intensive care unit, employment of invasive mechanical ventilation, and the emergence of acute kidney injury requiring hemodialysis. On the other hand, although overweight/obese male KTRs had a higher prevalence of hypertension and higher fasting blood glucose levels, no significant association was found with COVID-19-related outcomes when compared to lean male KTRs. Conclusions: Overweight/obesity is highly prevalent in KTRs, and overweight/obese women demonstrated a higher need for supplemental O2. Therefore, the early identification of factors that predict a worse outcome in overweight/obese female KTRs affected by COVID-19 contributes to risk stratification and guides therapeutic decisions.
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Affiliation(s)
- Alexandre Veronese-Araújo
- Nephrology Division, Department of Medicine, Federal University of São Paulo, São Paulo 04038-031, SP, Brazil; (A.V.-A.); (D.D.d.L.); (I.A.-B.); (H.T.-S.); (J.O.M.-P.)
| | - Débora D. de Lucena
- Nephrology Division, Department of Medicine, Federal University of São Paulo, São Paulo 04038-031, SP, Brazil; (A.V.-A.); (D.D.d.L.); (I.A.-B.); (H.T.-S.); (J.O.M.-P.)
- Hospital do Rim, São Paulo 04038-002, SP, Brazil;
| | - Isabella Aguiar-Brito
- Nephrology Division, Department of Medicine, Federal University of São Paulo, São Paulo 04038-031, SP, Brazil; (A.V.-A.); (D.D.d.L.); (I.A.-B.); (H.T.-S.); (J.O.M.-P.)
| | | | - Hélio Tedesco-Silva
- Nephrology Division, Department of Medicine, Federal University of São Paulo, São Paulo 04038-031, SP, Brazil; (A.V.-A.); (D.D.d.L.); (I.A.-B.); (H.T.-S.); (J.O.M.-P.)
- Hospital do Rim, São Paulo 04038-002, SP, Brazil;
| | - José O. Medina-Pestana
- Nephrology Division, Department of Medicine, Federal University of São Paulo, São Paulo 04038-031, SP, Brazil; (A.V.-A.); (D.D.d.L.); (I.A.-B.); (H.T.-S.); (J.O.M.-P.)
- Hospital do Rim, São Paulo 04038-002, SP, Brazil;
| | - Érika B. Rangel
- Nephrology Division, Department of Medicine, Federal University of São Paulo, São Paulo 04038-031, SP, Brazil; (A.V.-A.); (D.D.d.L.); (I.A.-B.); (H.T.-S.); (J.O.M.-P.)
- Hospital do Rim, São Paulo 04038-002, SP, Brazil;
- Hospital Israelita Albert Einstein, São Paulo 05652-900, SP, Brazil
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Wong R, Lam E, Bramante CT, Johnson SG, Reusch J, Wilkins KJ, Yeh HC. Does COVID-19 Infection Increase the Risk of Diabetes? Current Evidence. Curr Diab Rep 2023; 23:207-216. [PMID: 37284921 PMCID: PMC10244847 DOI: 10.1007/s11892-023-01515-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/18/2023] [Indexed: 06/08/2023]
Abstract
PURPOSE OF REVIEW Multiple studies report an increased incidence of diabetes following SARS-CoV-2 infection. Given the potential increased global burden of diabetes, understanding the effect of SARS-CoV-2 in the epidemiology of diabetes is important. Our aim was to review the evidence pertaining to the risk of incident diabetes after COVID-19 infection. RECENT FINDINGS Incident diabetes risk increased by approximately 60% compared to patients without SARS-CoV-2 infection. Risk also increased compared to non-COVID-19 respiratory infections, suggesting SARS-CoV-2-mediated mechanisms rather than general morbidity after respiratory illness. Evidence is mixed regarding the association between SARS-CoV-2 infection and T1D. SARS-CoV-2 infection is associated with an elevated risk of T2D, but it is unclear whether the incident diabetes is persistent over time or differs in severity over time. SARS-CoV-2 infection is associated with an increased risk of incident diabetes. Future studies should evaluate vaccination, viral variant, and patient- and treatment-related factors that influence risk.
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Affiliation(s)
- Rachel Wong
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY USA
- Health Science Center, Stony Brook Medical Center, Level 3, Room 45101 Nicolls Road, Stony Brook, NY 11794 USA
| | - Emily Lam
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY USA
| | - Carolyn T. Bramante
- Division of General Internal Medicine, University of Minnesota Medical School, Minneapolis, MN USA
| | - Steven G. Johnson
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN USA
| | - Jane Reusch
- Division of Endocrinology, Metabolism & Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO 80045 USA
| | - Kenneth J. Wilkins
- Biostatistics Program/Office of Clinical Research Support, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD USA
| | - Hsin-Chieh Yeh
- Department of Medicine, Johns Hopkins University, Baltimore, MD USA
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD USA
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9
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Casiraghi E, Wong R, Hall M, Coleman B, Notaro M, Evans MD, Tronieri JS, Blau H, Laraway B, Callahan TJ, Chan LE, Bramante CT, Buse JB, Moffitt RA, Stürmer T, Johnson SG, Raymond Shao Y, Reese J, Robinson PN, Paccanaro A, Valentini G, Huling JD, Wilkins KJ. A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative. J Biomed Inform 2023; 139:104295. [PMID: 36716983 PMCID: PMC10683778 DOI: 10.1016/j.jbi.2023.104295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 01/16/2023] [Accepted: 01/21/2023] [Indexed: 02/01/2023]
Abstract
Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.
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Affiliation(s)
- Elena Casiraghi
- AnacletoLab, Department of Computer Science "Giovanni degli Antoni", Università degli Studi di Milano, Milan, Italy; CINI, Infolife National Laboratory, Roma, Italy; Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Rachel Wong
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Margaret Hall
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Ben Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Marco Notaro
- AnacletoLab, Department of Computer Science "Giovanni degli Antoni", Università degli Studi di Milano, Milan, Italy; CINI, Infolife National Laboratory, Roma, Italy
| | - Michael D Evans
- Biostatistical Design and Analysis Center, Clinical and Translational Science Institute, University of Minnesota, Minneapolis, MN, USA
| | - Jena S Tronieri
- Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Hannah Blau
- The Jackson Laboratory for Genomic Medicine, Farmington, USA
| | - Bryan Laraway
- University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | | | - Lauren E Chan
- College of Public Health and Human Sciences, Oregon State University, Corvallis, USA
| | - Carolyn T Bramante
- Division of General Internal Medicine, University of Minnesota, Minneapolis, MN, USA
| | - John B Buse
- NC Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Division of Endocrinology, Department of Medicine, University of North Carolina School of Medicine, USA
| | - Richard A Moffitt
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Til Stürmer
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Steven G Johnson
- Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA
| | - Yu Raymond Shao
- Harvard-MIT Division of Health Sciences and Technology (HST), 260 Longwood Ave, Boston, USA; Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, USA
| | - Justin Reese
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
| | - Alberto Paccanaro
- School of Applied Mathematics (EMAp), Fundação Getúlio Vargas, Rio de Janeiro, Brazil; Department of Computer Science, Royal Holloway, University of London, Egham, UK
| | - Giorgio Valentini
- AnacletoLab, Department of Computer Science "Giovanni degli Antoni", Università degli Studi di Milano, Milan, Italy; CINI, Infolife National Laboratory, Roma, Italy
| | - Jared D Huling
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Kenneth J Wilkins
- Biostatistics Program, Office of the Director, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
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10
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Russell CD, Lone NI, Baillie JK. Comorbidities, multimorbidity and COVID-19. Nat Med 2023; 29:334-343. [PMID: 36797482 DOI: 10.1038/s41591-022-02156-9] [Citation(s) in RCA: 99] [Impact Index Per Article: 99.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 11/25/2022] [Indexed: 02/18/2023]
Abstract
The influence of comorbidities on COVID-19 outcomes has been recognized since the earliest days of the pandemic. But establishing causality and determining underlying mechanisms and clinical implications has been challenging-owing to the multitude of confounding factors and patient variability. Several distinct pathological mechanisms, not active in every patient, determine health outcomes in the three different phases of COVID-19-from the initial viral replication phase to inflammatory lung injury and post-acute sequelae. Specific comorbidities (and overall multimorbidity) can either exacerbate these pathological mechanisms or reduce the patient's tolerance to organ injury. In this Review, we consider the impact of specific comorbidities, and overall multimorbidity, on the three mechanistically distinct phases of COVID-19, and we discuss the utility of host genetics as a route to causal inference by eliminating many sources of confounding. Continued research into the mechanisms of disease-state interactions will be crucial to inform stratification of therapeutic approaches and improve outcomes for patients.
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Affiliation(s)
- Clark D Russell
- Centre for Inflammation Research, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh BioQuarter, Edinburgh, UK
| | - Nazir I Lone
- Usher Institute, University of Edinburgh, Edinburgh BioQuarter, Edinburgh, UK.
- Intensive Care Unit, Royal Infirmary of Edinburgh, Little France Crescent, Edinburgh, UK.
| | - J Kenneth Baillie
- Intensive Care Unit, Royal Infirmary of Edinburgh, Little France Crescent, Edinburgh, UK.
- Baillie Gifford Pandemic Science Hub, Centre for Inflammation Research, University of Edinburgh, Edinburgh BioQuarter, Edinburgh, UK.
- Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, UK.
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11
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Abstract
The multifaceted interaction between coronavirus disease 2019 (COVID-19) and the endocrine system has been a major area of scientific research over the past two years. While common endocrine/metabolic disorders such as obesity and diabetes have been recognized among significant risk factors for COVID-19 severity, several endocrine organs were identified to be targeted by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). New-onset endocrine disorders related to COVID-19 were reported while long-term effects, if any, are yet to be determined. Meanwhile, the "stay home" measures during the pandemic caused interruption in the care of patients with pre-existing endocrine disorders and may have impeded the diagnosis and treatment of new ones. This review aims to outline this complex interaction between COVID-19 and endocrine disorders by synthesizing the current scientific knowledge obtained from clinical and pathophysiological studies, and to emphasize considerations for future research.
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Affiliation(s)
- Seda Hanife Oguz
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Hacettepe University School of Medicine, Ankara, Turkey;
| | - Bulent Okan Yildiz
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Hacettepe University School of Medicine, Ankara, Turkey;
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Krawczyk N, Rivera BD, Basaraba C, Corbeil T, Allen B, Schultebraucks K, Henry BF, Pincus HA, Levin FR, Martinez D. COVID-19 complications among patients with opioid use disorder: a retrospective cohort study across five major NYC hospital systems. Addiction 2022; 118:857-869. [PMID: 36459420 PMCID: PMC9878119 DOI: 10.1111/add.16105] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 11/18/2022] [Indexed: 12/05/2022]
Abstract
BACKGROUND AND AIMS Individuals with opioid use disorder (OUD) suffer disproportionately from COVID-19. To inform clinical management of OUD patients, research is needed to identify characteristics associated with COVID-19 progression and death among this population. We aimed to investigate the role of OUD and specific comorbidities on COVID-19 progression among hospitalized OUD patients. DESIGN Retrospective cohort study of merged electronic health records (EHR) from five large private health systems. SETTING New York City, New York, USA, 2011-21. PARTICIPANTS Adults with a COVID-19 encounter and OUD or opioid overdose diagnosis between March 2020 and February 2021. MEASUREMENTS Primary exposure included diagnosis of OUD/opioid overdose. Risk factors included age, sex, race/ethnicity and common medical, substance use and psychiatric comorbidities known to be associated with COVID-19 severity. Outcomes included COVID-19 hospitalization and subsequent intubation, acute kidney failure, severe sepsis and death. FINDINGS Of 110 917 COVID-19+ adults, 1.17% were ever diagnosed with OUD/opioid overdose. OUD patients had higher risk of COVID-19 hospitalization [adjusted risk ratio (aRR) = 1.40, 95% confidence interval (CI) = 1.33, 1.47], intubation [adjusted odds ratio (aOR) = 2.05, 95% CI = 1.74, 2.42], kidney failure (aRR = 1.51, 95% CI = 1.34, 1.70), sepsis (aRR = 2.30, 95% CI = 1.88, 2.81) and death (aRR = 2.10, 95% CI = 1.84, 2.40). Among hospitalized OUD patients, risks for worse COVID-19 outcomes included being male; older; of a race/ethnicity other than white, black or Hispanic; and having comorbid chronic kidney disease, diabetes, obesity or cancer. Protective factors included having asthma, hepatitis-C and chronic pain. CONCLUSIONS Opioid use disorder patients appear to have a substantial risk for COVID-19-associated morbidity and mortality, with particular comorbidities and treatments moderating this risk.
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Affiliation(s)
- Noa Krawczyk
- Center for Opioid Epidemiology and Policy, Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | - Bianca D Rivera
- Center for Opioid Epidemiology and Policy, Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | - Cale Basaraba
- Area Mental Health Data Science, New York State Psychiatric Institute, New York, NY, USA
| | - Thomas Corbeil
- Area Mental Health Data Science, New York State Psychiatric Institute, New York, NY, USA
| | - Bennett Allen
- Center for Opioid Epidemiology and Policy, Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA
| | | | - Brandy F Henry
- College of Education, The Pennsylvania State University, University Park, PA, USA
| | - Harold A Pincus
- Department of Psychiatry and Irving Institute for Clinical and Translational Research, Columbia University and New York State Psychiatric Institute, New York, NY, USA
| | - Frances R Levin
- Department of Psychiatry and Irving Institute for Clinical and Translational Research, Columbia University and New York State Psychiatric Institute, New York, NY, USA
| | - Diana Martinez
- Department of Psychiatry and Irving Institute for Clinical and Translational Research, Columbia University and New York State Psychiatric Institute, New York, NY, USA
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13
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Hill E, Mehta H, Sharma S, Mane K, Xie C, Cathey E, Loomba J, Russell S, Spratt H, DeWitt PE, Ammar N, Madlock-Brown C, Brown D, McMurry JA, Chute CG, Haendel MA, Moffitt R, Pfaff ER, Bennett TD. Risk Factors Associated with Post-Acute Sequelae of SARS-CoV-2 in an EHR Cohort: A National COVID Cohort Collaborative (N3C) Analysis as part of the NIH RECOVER program. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2022:2022.08.15.22278603. [PMID: 36032983 PMCID: PMC9413724 DOI: 10.1101/2022.08.15.22278603] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Background More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). Objective To identify risk factors associated with PASC/long-COVID. Design Retrospective case-control study. Setting 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). Patients 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system. Measurements Risk factors included demographics, comorbidities, and treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. Results Among 8,325 individuals with PASC, the majority were >50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30+ days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls. Conclusions This national study identified important risk factors for PASC such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.
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Affiliation(s)
- Elaine Hill
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | - Hemal Mehta
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Suchetha Sharma
- School of Data Science, University of Virginia, Charlottesville, VA, USA
| | - Klint Mane
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | - Catherine Xie
- Department of Public Health Sciences, University of Rochester Medical Center, Rochester, NY, USA
| | - Emily Cathey
- integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA
| | - Johanna Loomba
- integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA
| | - Seth Russell
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Heidi Spratt
- Department of Biostatistics and Data Science, University of Texas Medical Branch, Galveston, TX, USA
| | - Peter E DeWitt
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
| | - Nariman Ammar
- Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Charisse Madlock-Brown
- Department of Diagnostic and Health Sciences, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Donald Brown
- integrated Translational Health Research Institute of Virginia (iTHRIV), University of Virginia, Charlottesville, VA, USA
| | - Julie A McMurry
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD, USA
| | - Melissa A Haendel
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA
| | - Richard Moffitt
- Department of Biomedical Informatics, Stony Brook University, and Stony Brook Cancer Center, Stony Brook, NY, USA
| | - Emily R Pfaff
- Department of Medicine, North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tellen D Bennett
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA
- Center for Health AI, University of Colorado School of Medicine, Aurora, CO, USA
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