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Verma A, Huffman JE, Gao L, Minnier J, Wu WC, Cho K, Ho YL, Gorman BR, Pyarajan S, Rajeevan N, Garcon H, Joseph J, McGeary JE, Suzuki A, Reaven PD, Wan ES, Lynch JA, Petersen JM, Meigs JB, Freiberg MS, Gatsby E, Lynch KE, Zekavat SM, Natarajan P, Dalal S, Jhala DN, Arjomandi M, Bonomo RA, Thompson TK, Pathak GA, Zhou JJ, Donskey CJ, Madduri RK, Wells QS, Gelernter J, Huang RDL, Polimanti R, Chang KM, Liao KP, Tsao PS, Sun YV, Wilson PWF, O’Donnell CJ, Hung AM, Gaziano JM, Hauger RL, Iyengar SK, Luoh SW. Association of Kidney Comorbidities and Acute Kidney Failure With Unfavorable Outcomes After COVID-19 in Individuals With the Sickle Cell Trait. JAMA Intern Med 2022; 182:796-804. [PMID: 35759254 PMCID: PMC9237798 DOI: 10.1001/jamainternmed.2022.2141] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Importance Sickle cell trait (SCT), defined as the presence of 1 hemoglobin beta sickle allele (rs334-T) and 1 normal beta allele, is prevalent in millions of people in the US, particularly in individuals of African and Hispanic ancestry. However, the association of SCT with COVID-19 is unclear. Objective To assess the association of SCT with the prepandemic health conditions in participants of the Million Veteran Program (MVP) and to assess the severity and sequelae of COVID-19. Design, Setting, and Participants COVID-19 clinical data include 2729 persons with SCT, of whom 353 had COVID-19, and 129 848 SCT-negative individuals, of whom 13 488 had COVID-19. Associations between SCT and COVID-19 outcomes were examined using firth regression. Analyses were performed by ancestry and adjusted for sex, age, age squared, and ancestral principal components to account for population stratification. Data for the study were collected between March 2020 and February 2021. Exposures The hemoglobin beta S (HbS) allele (rs334-T). Main Outcomes and Measures This study evaluated 4 COVID-19 outcomes derived from the World Health Organization severity scale and phenotypes derived from International Classification of Diseases codes in the electronic health records. Results Of the 132 577 MVP participants with COVID-19 data, mean (SD) age at the index date was 64.8 (13.1) years. Sickle cell trait was present in 7.8% of individuals of African ancestry and associated with a history of chronic kidney disease, diabetic kidney disease, hypertensive kidney disease, pulmonary embolism, and cerebrovascular disease. Among the 4 clinical outcomes of COVID-19, SCT was associated with an increased COVID-19 mortality in individuals of African ancestry (n = 3749; odds ratio, 1.77; 95% CI, 1.13 to 2.77; P = .01). In the 60 days following COVID-19, SCT was associated with an increased incidence of acute kidney failure. A counterfactual mediation framework estimated that on average, 20.7% (95% CI, -3.8% to 56.0%) of the total effect of SCT on COVID-19 fatalities was due to acute kidney failure. Conclusions and Relevance In this genetic association study, SCT was associated with preexisting kidney comorbidities, increased COVID-19 mortality, and kidney morbidity.
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Affiliation(s)
- Anurag Verma
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
- Perelman School of Medicine, Department of Medicine, University of Pennsylvania, Philadelphia
| | | | - Lina Gao
- Knight Cancer Institute, Biostatistics Shared Resource, Oregon Health & Science University, Portland
- VA Portland Health Care System, Portland, Oregon
| | - Jessica Minnier
- VA Portland Health Care System, Portland, Oregon
- OHSU-PSU School of Public Health, Oregon Health & Science University, Portland
- Knight Cancer Institute, Biostatistics Shared Resource, Oregon Health & Science University, Portland
| | - Wen-Chih Wu
- Department of Medicine, Cardiology, Providence VA Healthcare System, Providence, Rhode Island
- Alpert Medical School & School of Public Health, Brown University, Providence, Rhode Island
| | - Kelly Cho
- MAVERIC, VA Boston Healthcare System, Boston, Massachusetts
- Medicine, Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Yuk-Lam Ho
- MAVERIC, VA Boston Healthcare System, Boston, Massachusetts
| | | | - Saiju Pyarajan
- VA Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Nallakkandi Rajeevan
- Yale Center for Medical Informatics, Yale School of Medicine, New Haven, Connecticut
- Clinical Epidemiology Research Center (CERC), VA Connecticut Healthcare System, West Haven
| | - Helene Garcon
- MAVERIC, VA Boston Healthcare System, Boston, Massachusetts
| | - Jacob Joseph
- Department of Medicine, VA Boston Healthcare System, Boston, Massachusetts
- Brigham & Women’s Hospital, Boston, Massachusetts
| | - John E. McGeary
- Department of Psychiatry and Human Behavior, Providence VA Medical Center, Providence, Rhode Island
- Brown University Medical School, Providence, Rhode Island
| | - Ayako Suzuki
- Department of Medicine, Gastroenterology, Durham VA Medical Center, Durham, North Carolina
- Department of Medicine, Gastroenterology, Duke University, Durham, North Carolina
| | - Peter D. Reaven
- Department of Medicine, Phoenix VA Healthcare System, Phoenix, Arizona
- University of Arizona, Phoenix
| | - Emily S. Wan
- Department of Medicine, Pulmonary, Critical Care, Sleep, and Allergy Section, VA Boston Healthcare System, Boston, Massachusetts
- Channing Division of Network Medicine, Brigham & Women’s Hospital, Boston, Massachusetts
| | - Julie A. Lynch
- VA Informatics & Computing Infrastructure, VA Salt Lake City Utah & University of Utah, School of Medicine, Salt Lake City
| | - Jeffrey M. Petersen
- Pathology and Laboratory Medicine, Corporal Michael Crescenz VA Medical Center, Philadelphia, Pennsylvania
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - James B. Meigs
- Medicine, General Internal Medicine, Massachusetts General Hospital, Boston
| | | | - Elise Gatsby
- VA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Healthcare System, Salt Lake City, Utah
| | - Kristine E. Lynch
- VA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Healthcare System, Salt Lake City, Utah
- Internal Medicine, Epidemiology, University of Utah School of Medicine, Salt Lake City
| | - Seyedeh Maryam Zekavat
- Computational Biology & Bioinformatics, Yale School of Medicine, New Haven, Connecticut
- Program in Medical and Population Genetics, Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
| | - Pradeep Natarajan
- Program in Medical and Population Genetics, Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
- Cardiovascular Research Center, Massachusetts General Hospital, Boston
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Clinical Data Science Research Group, ORD, Portland VA Medical Center, Portland, Oregon
| | - Sharvari Dalal
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Pathology and Laboratory Medicine, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
| | - Darshana N. Jhala
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
- Pathology and Laboratory Medicine, Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
| | - Mehrdad Arjomandi
- Medicine, Pulmonary and Critical Care, San Francisco VA Healthcare System, San Francisco, California
- University of California San Francisco
| | - Robert A. Bonomo
- Cleveland VA Medical Center, Cleveland, Ohio
- Medicine, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | | | - Gita A. Pathak
- Department of Psychiatry, Division of Human Genetics, Yale School of Medicine, New Haven, Connecticut
- VA Connecticut Healthcare System, West Haven
| | - Jin J. Zhou
- Medicine, University of California, Los Angeles
- Epidemiology and Biostatistics, University of Arizona, Phoenix
| | - Curtis J. Donskey
- Infectious Disease Section, Louis Stokes Cleveland VA, Cleveland, Ohio
- Case Western Reserve University, Cleveland, Ohio
| | - Ravi K. Madduri
- Data Science and Learning, Argonne National Laboratory, Lemont, Illinois
| | - Quinn S. Wells
- Departments of Medicine, Biomedical Informatics, and Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Joel Gelernter
- VA Connecticut Healthcare System, West Haven
- Psychiatry, Human Genetics, Yale University School of Medicine, West Haven, Connecticut
| | | | - Renato Polimanti
- Departments of Medicine, Biomedical Informatics, and Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee
- Psychiatry, Human Genetics, Yale University School of Medicine, West Haven, Connecticut
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
- Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Katherine P. Liao
- Medicine, Rheumatology, VA Boston Healthcare System, Boston, Massachusetts
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital, Boston, Massachusetts
- Department of Medicine & Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Philip S. Tsao
- Precision Medicine, VA Palo Alto Health Care System, Palo Alto, California
| | - Yan V. Sun
- Epidemiology, Emory University School of Public Health, Atlanta, Georgia
- Atlanta VA Health Care System, Decatur, Georgia
| | - Peter W. F. Wilson
- Atlanta VA Health Care System, Decatur, Georgia
- Emory University School of Medicine, Atlanta, Georgia
| | | | - Adriana M. Hung
- Vanderbilt University Medical Center, Nashville, Tennessee
- Nashville VA Medical Center, Nashville, Tennessee
| | - J. Michael Gaziano
- VA Boston Health Care System, Boston, Massachusetts
- Medicine, Harvard Medical School, Boston, Massachusetts
| | - Richard L. Hauger
- Center of Excellence for Stress & Mental Health, VA San Diego Healthcare System, San Diego, California
- Center for Behavioral Genetics of Aging, University of California, San Diego, La Jolla
| | - Sudha K. Iyengar
- Departments of Population and Quantitative Health Sciences, Ophthalmology and Visual Sciences and Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio
- Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio
| | - Shiuh-Wen Luoh
- VA Portland Health Care System, Portland, Oregon
- Division of Hematology and Medical Oncology, Knight Cancer Institute, Oregon Health and Science University, Portland
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Zhang T, Rayamajhi S, Meng G, Zhang Q, Liu L, Wu H, Gu Y, Wang Y, Zhang S, Wang X, Zhang J, Li H, Thapa A, Sun S, Wang X, Zhou M, Jia Q, Song K, Niu K. Edible mushroom consumption and incident hyperuricemia: results from the TCLSIH cohort study. Food Funct 2021; 12:9178-9187. [PMID: 34606546 DOI: 10.1039/d1fo00650a] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Background: Basic studies have found that xanthine oxidase inhibitors extracted from mushrooms have inhibitory effects on hyperuricemia. However, the association between mushroom consumption and hyperuricemia is unknown in humans. Objective: We therefore designed a large-scale cohort study to examine whether mushroom consumption is a protective factor for developing hyperuricemia in adults. Methods: This prospective cohort study investigated 19 830 participants (mean age: 39.4 years; and 9906 [50.0%] men) who were free of hyperuricemia, cardiovascular disease, and cancer at the baseline. Mushroom consumption was measured at the baseline using a validated food frequency questionnaire. Hyperuricemia is defined as serum uric acid levels >420 μmol L-1 in men and >350 μmol L-1 in women. Cox proportional hazards regression models were used to examine the association of mushroom consumption with incident hyperuricemia. Restricted cubic spline regression was used to estimate the dose-response relationship between mushroom consumption and risk of hyperuricemia. Results: A total of 4260 first incident cases of hyperuricemia occurred during 61 421 person-years of follow-up (median follow-up of 4.2 years). After adjusting for demographic characteristics, lifestyle factors, dietary intake, and inflammatory markers, the multivariable hazard ratios (95% confidence intervals) for incident hyperuricemia were 1.00(reference) for <1.76 g per 1000 kcal per day, 0.93(0.86, 1.01) for 1.76-2.84 g per 1000 kcal per day, 0.93(0.85, 1.01) for 2.85-5.52 g per 1000 kcal per day, and 0.88 (0.80, 0.96) for >5.52 g per 1000 kcal per day, respectively (P for trend = 0.007). Conclusions: This population-based prospective cohort study has firstly demonstrated that higher mushroom consumption is significantly associated with lower incidence of hyperuricemia among general adults.
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Affiliation(s)
- Tingjing Zhang
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
| | - Sabina Rayamajhi
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
| | - Ge Meng
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China. .,Department of Toxicology and Sanitary Chemistry, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Qing Zhang
- Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China
| | - Li Liu
- Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China
| | - Hongmei Wu
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
| | - Yeqing Gu
- Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Yawen Wang
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
| | - Shunming Zhang
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
| | - Xuena Wang
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
| | - Juanjuan Zhang
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
| | - Huiping Li
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
| | - Amrish Thapa
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China.
| | - Shaomei Sun
- Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China
| | - Xing Wang
- Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China
| | - Ming Zhou
- Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China
| | - Qiyu Jia
- Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China
| | - Kun Song
- Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China
| | - Kaijun Niu
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China. .,Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China.,Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.,Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China.,Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China
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5
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Zhang T, Rayamajhi S, Meng G, Zhang Q, Liu L, Wu H, Gu Y, Wang Y, Zhang S, Wang X, Zhang J, Li H, Thapa A, Sun S, Wang X, Zhou M, Jia Q, Song K, Niu K. Dietary patterns and risk for hyperuricemia in the general population: Results from the TCLSIH cohort study. Nutrition 2021; 93:111501. [PMID: 34717108 DOI: 10.1016/j.nut.2021.111501] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 09/04/2021] [Accepted: 09/19/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Prospective cohort studies linking dietary patterns and hyperuricemia (HUA) are limited, especially in Asian populations. The aim of this study was to prospectively investigate the association between various dietary patterns and risk for HUA in a general adult population. METHOD We used data from the TCLSIH (Tianjin Chronic Low-grade Systemic Inflammation and Health) cohort study of 20 766 men and women who were free from HUA, cancer, and cardiovascular disease at baseline. Dietary patterns at baseline were identified with factor analysis based on responses to a validated 81-item food frequency questionnaire. HUA was defined as serum uric acid levels >420 μmol/L in men and >350 μmol/L in women. Cox proportional hazards regression models were used to examine the association of dietary patterns with incident HUA. RESULTS In all, 4389 first incident cases of HUA occurred during 73 822 person-years of follow-up (median follow-up of 4.2 y). Three main dietary patterns were extracted. They were the vegetable, sweet food, and animal food patterns. After adjusting for demographic characteristics, lifestyle factors, other dietary pattern scores, and inflammatory markers, comparing the highest with the lowest quartiles of dietary pattern scores, the multivariable hazard ratios (95% confidence interval) of HUA were 0.79 (0.72-0.87; Ptrend < 0.0001) for the vegetable pattern, 1.22 (1.12-1.33; Ptrend < 0.0001) for the sweet food pattern, and 1.24 (1.13-1.37; Ptrend < 0.0001) for the animal food pattern. CONCLUSIONS Dietary patterns rich in animal or sweet foods were positively associated with a higher risk for HUA, whereas the vegetable pattern was negatively associated.
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Affiliation(s)
- Tingjing Zhang
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Sabina Rayamajhi
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Ge Meng
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China; Department of Toxicology and Sanitary Chemistry, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Qing Zhang
- Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China
| | - Li Liu
- Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China
| | - Hongmei Wu
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Yeqing Gu
- Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Yawen Wang
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Shunming Zhang
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Xuena Wang
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Juanjuan Zhang
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Huiping Li
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Amrish Thapa
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China
| | - Shaomei Sun
- Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China
| | - Xing Wang
- Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China
| | - Ming Zhou
- Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China
| | - Qiyu Jia
- Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China
| | - Kun Song
- Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China
| | - Kaijun Niu
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China; Health Management Centre, Tianjin Medical University General Hospital, Tianjin, China; Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China; Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China; Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China.
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