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Flannery DD, Gouma S, Dhudasia MB, Mukhopadhyay S, Pfeifer MR, Woodford EC, Briker SM, Triebwasser JE, Gerber JS, Morris JS, Weirick ME, McAllister CM, Hensley SE, Puopolo KM. Comparison of Maternal and Neonatal Antibody Levels After COVID-19 Vaccination vs SARS-CoV-2 Infection. JAMA Netw Open 2022; 5:e2240993. [PMID: 36350652 PMCID: PMC9647482 DOI: 10.1001/jamanetworkopen.2022.40993] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
IMPORTANCE Pregnant persons are at an increased risk of severe COVID-19 from SARS-CoV-2 infection, and COVID-19 vaccination is currently recommended during pregnancy. OBJECTIVE To ascertain the association of vaccine type, time from vaccination, gestational age at delivery, and pregnancy complications with placental transfer of antibodies to SARS-CoV-2. DESIGN, SETTING, AND PARTICIPANTS This cohort study was conducted in Pennsylvania Hospital in Philadelphia, Pennsylvania, and included births at the study site between August 9, 2020, and April 25, 2021. Maternal and cord blood serum samples were available for antibody level measurements for maternal-neonatal dyads. EXPOSURES SARS-CoV-2 infection vs COVID-19 vaccination. MAIN OUTCOMES AND MEASURES IgG antibodies to the receptor-binding domain of the SARS-CoV-2 spike protein were measured by quantitative enzyme-linked immunosorbent assay. Antibody concentrations and transplacental transfer ratios were measured after SARS-CoV-2 infection or receipt of COVID-19 vaccines. RESULTS A total of 585 maternal-newborn dyads (median [IQR] maternal age, 31 [26-35] years; median [IQR] gestational age, 39 [38-40] weeks) with maternal IgG antibodies to SARS-CoV-2 detected at the time of delivery were included. IgG was detected in cord blood from 557 of 585 newborns (95.2%). Among 169 vaccinated persons without SARS-CoV-2 infection, the interval from first dose of vaccine to delivery ranged from 12 to 122 days. The geometric mean IgG level among 169 vaccine recipients was significantly higher than that measured in 408 persons after infection (33.88 [95% CI, 27.64-41.53] arbitrary U/mL vs 2.80 [95% CI, 2.50-3.13] arbitrary U/mL). Geometric mean IgG levels were higher after vaccination with the mRNA-1273 (Moderna) vaccine compared with the BNT162b2 (Pfizer/BioNTech) vaccine (53.74 [95% CI, 40.49-71.33] arbitrary U/mL vs 25.45 [95% CI, 19.17-33.79] arbitrary U/mL; P < .001). Placental transfer ratios were lower after vaccination compared with after infection (0.80 [95% CI, 0.68-0.93] vs 1.06 [95% CI, 0.98-1.14]; P < .001) but were similar between the mRNA vaccines (mRNA-1273: 0.70 [95% CI, 0.55-0.90]; BNT162b2: 0.85 [95% CI, 0.69-1.06]; P = .25). Time from infection or vaccination to delivery was associated with transfer ratio in models that included gestational age at delivery and maternal hypertensive disorders, diabetes, and obesity. Placental antibody transfer was detectable as early as 26 weeks' gestation. Transfer ratio that was higher than 1.0 was present for 48 of 51 (94.1%) births at 36 weeks' gestation or later by 8 weeks after vaccination. CONCLUSIONS AND RELEVANCE This study found that maternal and cord blood IgG antibody levels were higher after COVID-19 vaccination compared with after SARS-CoV-2 infection, with slightly lower placental transfer ratios after vaccination than after infection. The findings suggest that time from infection or vaccination to delivery was the most important factor in transfer efficiency.
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Affiliation(s)
- Dustin D. Flannery
- Division of Neonatology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia
- Clinical Futures, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Sigrid Gouma
- Department of Microbiology, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Miren B. Dhudasia
- Division of Neonatology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
- Clinical Futures, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Sagori Mukhopadhyay
- Division of Neonatology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia
- Clinical Futures, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Madeline R. Pfeifer
- Division of Neonatology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Emily C. Woodford
- Division of Neonatology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Sara M. Briker
- Division of Neonatology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | | | - Jeffrey S. Gerber
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia
- Clinical Futures, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
- Division of Infectious Diseases, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Jeffrey S. Morris
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Madison E. Weirick
- Department of Microbiology, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | | | - Scott E. Hensley
- Department of Microbiology, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Karen M. Puopolo
- Division of Neonatology, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia
- Clinical Futures, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Mukhopadhyay S, Briker SM, Flannery DD, Dhudasia MB, Coggins SA, Woodford E, Walsh EM, Li S, Puopolo KM, Kuzniewicz MW. Time to positivity of blood cultures in neonatal late-onset bacteraemia. Arch Dis Child Fetal Neonatal Ed 2022; 107:583-588. [PMID: 35273079 PMCID: PMC9465986 DOI: 10.1136/archdischild-2021-323416] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 02/21/2022] [Indexed: 01/26/2023]
Abstract
OBJECTIVE To determine the time to positivity (TTP) of blood cultures among infants with late-onset bacteraemia and predictors of TTP >36 hours. DESIGN Retrospective cohort study. SETTING 16 birth centres in two healthcare systems. PATIENTS Infants with positive blood cultures obtained >72 hours after birth. OUTCOME The main outcome was TTP, defined as the time interval from specimen collection to when a neonatal provider was notified of culture growth. TTP analysis was restricted to the first positive culture per infant. Patient-specific and infection-specific factors were analysed for association with TTP >36 hours. RESULTS Of 10 235 blood cultures obtained from 3808 infants, 1082 (10.6%) were positive. Restricting to bacterial pathogens and the first positive culture, the median TTP (25th-75th percentile) for 428 cultures was 23.5 hours (18.4-29.9); 364 (85.0%) resulted in 36 hours. Excluding coagulase-negative staphylococci (CoNS), 275 of 294 (93.5%) cultures were flagged positive by 36 hours. In a multivariable model, CoNS isolation and antibiotic pretreatment were significantly associated with increased odds of TTP >36 hours. Projecting a 36-hour empiric duration at one site and assuming that all negative evaluations were associated with an empiric course of antibiotics, we estimated that 1164 doses of antibiotics would be avoided in 629 infants over 10 years, while delaying a subsequent antibiotic dose in 13 infants with bacteraemia. CONCLUSIONS Empiric antibiotic administration in late-onset infection evaluations (not targeting CoNS) can be stopped at 36 hours. Longer durations (48 hours) should be considered when there is pretreatment or antibiotic therapy is directed at CoNS.
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Affiliation(s)
- Sagori Mukhopadhyay
- Division of Neonatology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Center for Pediatric Clinical Effectiveness, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Sara M Briker
- Division of Neonatology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Dustin D Flannery
- Division of Neonatology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Center for Pediatric Clinical Effectiveness, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Miren B Dhudasia
- Division of Neonatology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Center for Pediatric Clinical Effectiveness, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Sarah A Coggins
- Division of Neonatology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Emily Woodford
- Division of Neonatology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Eileen M Walsh
- Division of Research, Kaiser Permanente Northern California, San Francisco, California, USA
| | - Sherian Li
- Division of Research, Kaiser Permanente Northern California, San Francisco, California, USA
| | - Karen M Puopolo
- Division of Neonatology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Center for Pediatric Clinical Effectiveness, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Michael W Kuzniewicz
- Division of Research, Kaiser Permanente Northern California, San Francisco, California, USA
- Department of Pediatrics, Kaiser Permanente Northern California, San Francisco, California, USA
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Briker SM, Hormenu T, DuBose CW, Mabundo LS, Chung ST, Ha J, Sherman A, Tulloch-Reid MK, Bergman M, Sumner AE. Metabolic characteristics of Africans with normal glucose tolerance and elevated 1-hour glucose: insight from the Africans in America study. BMJ Open Diabetes Res Care 2020; 8:8/1/e000837. [PMID: 31958302 PMCID: PMC7039615 DOI: 10.1136/bmjdrc-2019-000837] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 11/19/2019] [Accepted: 12/10/2019] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION Risk of insulin resistance, dyslipidemia, diabetes and cardiac death is increased in Asians and Europeans with normal glucose tolerance (NGT) and 1-hour glucose ≥8.6 mmol/L. As African descent populations often have insulin resistance but a normal lipid profile, the implications for Africans with NGT and glucose ≥8.6 mmol/L (NGT-1-hour-high) are unknown. OBJECTIVE We performed oral glucose tolerance tests (OGTTs) in 434 African born-blacks living in Washington, DC (male: 66%, age 38±10 years (mean±SD)) and determined in the NGT group if either glucometabolic or lipid profiles varied according to a 1-hour-glucose threshold of 8.6 mmol/L. METHODS Glucose tolerance category was defined by OGTT criteria. NGT was subdivided into NGT-1-hour-high (glucose ≥8.6 mmol/L) and NGT-1-hour-normal (glucose <8.6 mmol/L). Second OGTT were performed in 27% (119/434) of participants 10±7 days after the first. Matsuda Index and Oral Disposition Index measured insulin resistance and beta-cell function, respectively. Lipid profiles were obtained. Comparisons were by one-way analysis of variance with Bonferonni corrections for multiple comparisons. Duplicate tests were assessed by к-statistic. RESULTS One-hour-glucose ≥8.6 mmol/L occurred in 17% (47/272) with NGT, 72% (97/134) with pre-diabetes and in 96% (27/28) with diabetes. Both insulin resistance and beta-cell function were worse in NGT-1-hour-high than in NGT-1-hour-normal. Dyslipidemia occurred in both the diabetes and pre-diabetes groups but not in either NGT group. One-hour glucose concentration ≥8.6 mmol/L showed substantial agreement for the two OGTTs (к=0.628). CONCLUSIONS Although dyslipidemia did not occur in either NGT group, insulin resistance and beta-cell compromise were worse in NGT-1 hour-high. Subdividing the NGT group at a 1-hour glucose threshold of 8.6 mmol/L may stratify risk for diabetes in Africans.
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Affiliation(s)
- Sara M Briker
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), Bethesda, Maryland, USA
| | - Thomas Hormenu
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), Bethesda, Maryland, USA
| | - Christopher W DuBose
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), Bethesda, Maryland, USA
| | - Lilian S Mabundo
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), Bethesda, Maryland, USA
| | - Stephanie T Chung
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), Bethesda, Maryland, USA
| | - Joon Ha
- Laboratory of Biological Modeling Medicine, National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), Bethesda, Maryland, USA
| | - Arthur Sherman
- Laboratory of Biological Modeling Medicine, National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), Bethesda, Maryland, USA
| | | | - Michael Bergman
- Division of Endocrinology and Metabolism, Department of Medicine and of Population Health, New York University School of Medicine, New York city, New York, USA
| | - Anne E Sumner
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK), National Institutes of Health (NIH), Bethesda, Maryland, USA
- National Institute of Minority Health and Health Disparities, National Institutes of Health (NIH), Bethesda, Maryland, USA
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Mugeni R, Aduwo JY, Briker SM, Hormenu T, Sumner AE, Horlyck-Romanovsky MF. A Review of Diabetes Prediction Equations in African Descent Populations. Front Endocrinol (Lausanne) 2019; 10:663. [PMID: 31632346 PMCID: PMC6779831 DOI: 10.3389/fendo.2019.00663] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 09/12/2019] [Indexed: 12/27/2022] Open
Abstract
Background: Predicting undiagnosed diabetes is a critical step toward addressing the diabetes epidemic in populations of African descent worldwide. Objective: To review characteristics of equations developed, tested, or modified to predict diabetes in African descent populations. Methods: Using PubMed, Scopus, and Embase databases, a scoping review yielded 585 research articles. After removal of duplicates (n = 205), 380 articles were reviewed. After title and abstract review 328 articles did not meet inclusion criteria and were excluded. Fifty-two articles were retained. However, full text review revealed that 44 of the 52 articles did not report findings by AROC or C-statistic in African descent populations. Therefore, eight articles remained. Results: The 8 articles reported on a total of 15 prediction equation studies. The prediction equations were of two types. Prevalence prediction equations (n = 9) detected undiagnosed diabetes and were based on non-invasive variables only. Non-invasive variables included demographics, blood pressure and measures of body size. Incidence prediction equations (n = 6) predicted risk of developing diabetes and used either non-invasive variables or both non-invasive and invasive. Invasive variables required blood tests and included fasting glucose, high density lipoprotein-cholesterol (HDL), triglycerides (TG), and A1C. Prevalence prediction studies were conducted in the United States, Africa and Europe. Incidence prediction studies were conducted only in the United States. In all these studies, the performance of diabetes prediction equations was assessed by area under the receiver operator characteristics curve (AROC) or the C-statistic. Therefore, we evaluated the efficacy of these equations based on standard criteria, specifically discrimination by either AROC or C-statistic were defined as: Poor (0.50 - 0.69); Acceptable (0.70 - 0.79); Excellent (0.80 - 0.89); or Outstanding (0.90 - 1.00). Prediction equations based only on non-invasive variables reported to have poor to acceptable detection of diabetes with AROC or C-statistic 0.64 - 0.79. In contrast, prediction equations which were based on both non-invasive and invasive variables had excellent diabetes detection with AROC or C-statistic 0.80 - 0.82. Conclusion: Equations which use a combination of non-invasive and invasive variables appear to be superior in the prediction of diabetes in African descent populations than equations that rely on non-invasive variables alone.
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Affiliation(s)
- Regine Mugeni
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
- National Institute of Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, United States
| | - Jessica Y. Aduwo
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Sara M. Briker
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Thomas Hormenu
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Anne E. Sumner
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
- National Institute of Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, United States
| | - Margrethe F. Horlyck-Romanovsky
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
- Brooklyn College, City University of New York, Brooklyn, NY, United States
- *Correspondence: Margrethe F. Horlyck-Romanovsky
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Briker SM, Aduwo JY, Mugeni R, Horlyck-Romanovsky MF, DuBose CW, Mabundo LS, Hormenu T, Chung ST, Ha J, Sherman A, Sumner AE. A1C Underperforms as a Diagnostic Test in Africans Even in the Absence of Nutritional Deficiencies, Anemia and Hemoglobinopathies: Insight From the Africans in America Study. Front Endocrinol (Lausanne) 2019; 10:533. [PMID: 31447780 PMCID: PMC6692432 DOI: 10.3389/fendo.2019.00533] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Accepted: 07/18/2019] [Indexed: 12/16/2022] Open
Abstract
Introduction: To improve detection of undiagnosed diabetes in Africa, there is movement to replace the OGTT with A1C. The performance of A1C in the absence of hemoglobin-related micronutrient deficiencies, anemia and heterozygous hemoglobinopathies is unknown. Therefore, we determined in 441 African-born blacks living in America [male: 65% (281/441), age: 38 ± 10 y (mean ± SD), BMI: 27.5 ± 4.4 kg/m2] (1) nutritional and hematologic profiles and (2) glucose tolerance categorization by OGTT and A1C. Methods: Hematologic and nutritional status were assessed. Hemoglobin <11 g/dL occurred in 3% (11/441) of patients and led to exclusion. A1C and OGTT were performed in the remaining 430 participants. ADA thresholds for A1C and OGTT were used. Diagnosis by A1C required meeting either A1C-alone or A1C&OGTT criteria. Diagnosis by OGTT-alone required detection by OGTT and not A1C. Results: Hemoglobin, mean corpuscular volume and red blood cell distribution width were 14.0 ± 1.3 g/dL, 85.5 ± 5.3 fL, and 13.2 ± 1.2% respectively. B12, folate, and iron deficiency occurred in 1% (5/430), 0% (0/430), and 4% (12/310), respectively. Heterozygous hemoglobinopathy prevalence was 18% (78/430). Overall, diabetes prevalence was 7% (32/430). A1C detected diabetes in 32% (10/32) but OGTT-alone detected 68% (22/32). Overall prediabetes prevalence was 41% (178/430). A1C detected 57% (102/178) but OGTT-alone identified 43% (76/178). After excluding individuals with heterozygous hemoglobinopathies, the rate of missed diagnosis by A1C of abnormal glucose tolerance did not change (OR: 0.99, 95% CI: 0.61, 1.62). Conclusions: In nutritionally replete Africans without anemia or heterozygous hemoglobinopathy, if only A1C is used, ~60% with diabetes and ~40% with prediabetes would be undiagnosed. Clinical Trial Registration:: www.ClinicalTrials.gov, Identifier: NCT00001853.
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Affiliation(s)
- Sara M. Briker
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Jessica Y. Aduwo
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Regine Mugeni
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
- National Institute of Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, United States
| | - Margrethe F. Horlyck-Romanovsky
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Christopher W. DuBose
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Lilian S. Mabundo
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Thomas Hormenu
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Stephanie T. Chung
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Joon Ha
- Laboratory of Biological Modeling Medicine, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Arthur Sherman
- Laboratory of Biological Modeling Medicine, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Anne E. Sumner
- Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
- National Institute of Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, United States
- *Correspondence: Anne E. Sumner
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Kabakambira JD, Baker RL, Briker SM, Courville AB, Mabundo LS, DuBose CW, Chung ST, Eckel RH, Sumner AE. Do current guidelines for waist circumference apply to black Africans? Prediction of insulin resistance by waist circumference among Africans living in America. BMJ Glob Health 2018; 3:e001057. [PMID: 30364383 PMCID: PMC6195140 DOI: 10.1136/bmjgh-2018-001057] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Revised: 08/19/2018] [Accepted: 08/20/2018] [Indexed: 02/06/2023] Open
Abstract
Background To lower the risk of diabetes and heart disease in Africa, identification of African-centred thresholds for inexpensive biomarkers of insulin resistance (IR) is essential. The waist circumference (WC) thresholds that predicts IR in African men and women have not been established, but investigations recently conducted in Africa using indirect measures of IR suggest IR is predicted by WC of 80–95 cm in men and 90–99 cm in women. These WC cannot be used for guidelines until validated by direct measurements of IR and visceral adipose tissue (VAT). Therefore, we determined in a group of African-born black people living in America (A) the WC, which predicts IR and (B) the influence of abdominal fat distribution on IR. Methods The 375 participants (age 38±10 years (mean±SD), 67% men) had IR determined by HOMA-IR and Matsuda index. VAT and subcutaneous adipose tissue (SAT) were measured by abdominal CT scans. Optimal WC for the prediction of IR was determined in sex-specific analyses by area under the receiver operating characteristic (AUC-ROC) and Youden index. Results Women had more SAT (203±114 vs 128±74 cm2) and less VAT than men (63±48 vs 117±72 cm2, p<0.001). Optimal WC for prediction of IR in men and women were: 91 cm (AUC-ROC: 0.80±0.03 (mean±SE)) and 96 cm (AUC-ROC: 0.81±0.08), respectively. Regression analyses revealed a significant sex–VAT interaction (p<0.001). Therefore, for every unit increase in VAT, women had a 0.94 higher unit increase in SAT and 0.07 higher unit increase in WC than men. Conclusion Working with a group of African-born black people living in America, we accessed technology, which validated observations made in Africa. Higher SAT at every level of VAT explained why the WC that predicted IR was higher in women (96 cm) than men (91 cm). For Africans to benefit from WC measurements, convening a panel of experts to develop evidence-based African-centred WC guidelines may be the way forward.
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Affiliation(s)
- J Damascene Kabakambira
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, Bethesda, Maryland, USA.,National Institute of Minority Health and Health Disparities, Bethesda, Maryland, USA
| | - Rafeal L Baker
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, Bethesda, Maryland, USA
| | - Sara M Briker
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, Bethesda, Maryland, USA
| | - Amber B Courville
- Clinical Center, National Institutes of Health, Bethesda, Maryland, USA
| | - Lilian S Mabundo
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, Bethesda, Maryland, USA
| | - Christopher W DuBose
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, Bethesda, Maryland, USA
| | - Stephanie T Chung
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, Bethesda, Maryland, USA
| | - Robert H Eckel
- University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Anne E Sumner
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive and Kidney Diseases, Bethesda, Maryland, USA.,National Institute of Minority Health and Health Disparities, Bethesda, Maryland, USA
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