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Bergman M, Manco M, Satman I, Chan J, Inês Schmidt M, Sesti G, Vanessa Fiorentino T, Abdul-Ghani M, Jagannathan R, Kumar Thyparambil Aravindakshan P, Gabriel R, Mohan V, Buysschaert M, Bennakhi A, Pascal Kengne A, Dorcely B, Nilsson PM, Tuomi T, Battelino T, Hussain A, Ceriello A, Tuomilehto J. International Diabetes Federation Position Statement on the 1-hour post-load plasma glucose for the diagnosis of intermediate hyperglycaemia and type 2 diabetes. Diabetes Res Clin Pract 2024; 209:111589. [PMID: 38458916 DOI: 10.1016/j.diabres.2024.111589] [Citation(s) in RCA: 0] [Impact Index Per Article: 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: 03/10/2024]
Abstract
Many individuals with intermediate hyperglycaemia (IH), including impaired fasting glycaemia (IFG) and impaired glucose tolerance (IGT), as presently defined, will progress to type 2 diabetes (T2D). There is confirmatory evidence that T2D can be prevented by lifestyle modification and/or medications, in people with IGT diagnosed by 2-h plasma glucose (PG) during a 75-gram oral glucose tolerance test (OGTT). Over the last 40 years, a wealth of epidemiological data has confirmed the superior value of 1-h plasma glucose (PG) over fasting PG (FPG), glycated haemoglobin (HbA1c) and 2-h PG in populations of different ethnicity, sex and age in predicting diabetes and associated complications including death. Given the relentlessly rising prevalence of diabetes, a more sensitive, practical method is needed to detect people with IH and T2D for early prevention or treatment in the often lengthy trajectory to T2D and its complications. The International Diabetes Federation (IDF) Position Statement reviews findings that the 1-h post-load PG ≥ 155 mg/dL (8.6 mmol/L) in people with normal glucose tolerance (NGT) during an OGTT is highly predictive for detecting progression to T2D, micro- and macrovascular complications, obstructive sleep apnoea, cystic fibrosis-related diabetes mellitus, metabolic dysfunction-associated steatotic liver disease, and mortality in individuals with risk factors. The 1-h PG of 209 mg/dL (11.6 mmol/L) is also diagnostic of T2D. Importantly, the 1-h PG cut points for diagnosing IH and T2D can be detected earlier than the recommended 2-h PG thresholds. Taken together, the 1-h PG provides an opportunity to avoid misclassification of glycaemic status if FPG or HbA1c alone are used. The 1-h PG also allows early detection of high-risk people for intervention to prevent progression to T2D which will benefit the sizeable and growing population of individuals at increased risk of T2D. Using a 1-h OGTT, subsequent to screening with a non-laboratory diabetes risk tool, and intervening early will favourably impact the global diabetes epidemic. Health services should consider developing a policy for screening for IH based on local human and technical resources. People with a 1-h PG ≥ 155 mg/dL (8.6 mmol/L) are considered to have IH and should be prescribed lifestyle intervention and referred to a diabetes prevention program. People with a 1-h PG ≥ 209 mg/dL (11.6 mmol/L) are considered to have T2D and should have a repeat test to confirm the diagnosis of T2D and then referred for further evaluation and treatment. The substantive data presented in the Position Statement provides strong evidence for redefining current diagnostic criteria for IH and T2D by adding the 1-h PG.
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Affiliation(s)
- Michael Bergman
- NYU Grossman School of Medicine, Departments of Medicine and of Population Health, Division of Endocrinology, Diabetes and Metabolism, VA New York Harbor Healthcare System, New York, NY, USA.
| | - Melania Manco
- Predictive and Preventive Medicine Research Unit, Bambino Gesù Children's Hospital IRCCS, Rome, Italy
| | - Ilhan Satman
- Istanbul University Faculty of Medicine, Department of Internal Medicine, Division of Endocrinology and Metabolism, Istanbul, Turkey
| | - Juliana Chan
- The Chinese University of Hong Kong, Faculty of Medicine, Department of Medicine and Therapeutics, Hong Kong Institute of Diabetes and Obesity, Hong Kong, China
| | - Maria Inês Schmidt
- Postgraduate Program in Epidemiology, School of Medicine and Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil
| | - Giorgio Sesti
- Department of Clinical and Molecular Medicine, University of Rome-Sapienza, 00189 Rome, Italy
| | - Teresa Vanessa Fiorentino
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy
| | - Muhammad Abdul-Ghani
- Division of Diabetes, University of Texas Health Science Center at San Antonio, San Antonio Texas, USA
| | - Ram Jagannathan
- Hubert Department of Global Health Rollins, School of Public Health, Emory University, Atlanta, GA, USA
| | | | - Rafael Gabriel
- Department of International Health, National School of Public Health, Instituto de Salud Carlos III, Madrid, Spain
| | - Viswanathan Mohan
- Dr. Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Martin Buysschaert
- Department of Endocrinology and Diabetology, Université Catholique de Louvain, University, Clinic Saint-Luc, Brussels, Belgium
| | - Abdullah Bennakhi
- Dasman Diabetes Institute Office of Regulatory Affairs, Ethics Review Committee, Kuwait
| | - Andre Pascal Kengne
- South African Medical Research Council, Francie Van Zijl Dr, Parow Valley, Cape Town, 7501, South Africa
| | - Brenda Dorcely
- NYU Grossman School of Medicine, Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, New York, NY, USA
| | - Peter M Nilsson
- Department of Clinical Sciences and Lund University Diabetes Centre, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Tiinamaija Tuomi
- Folkhälsan Research Center, Helsinki, Finland; Abdominal Center, Endocrinology, Helsinki University Central Hospital, Research Program for Diabetes and Obesity, Center of Helsinki, Helsinki, Finland
| | | | - Akhtar Hussain
- Faculty of Health Sciences, Nord University, Bodø, Norway; Faculty of Medicine, Federal University of Ceará (FAMED-UFC), Brazil; International Diabetes Federation (IDF), Brussels, Belgium; Diabetes in Asia Study Group, Post Box: 752, Doha-Qatar; Centre for Global Health Research, Diabetic Association of Bangladesh, Dhaka, Bangladesh
| | | | - Jaakko Tuomilehto
- Department of International Health, National School of Public Health, Instituto de Salud Carlos III, Madrid, Spain; Public Health Promotion Unit, Finnish Institute for Health and Welfare, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Saudi Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
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Dorcely B, DeBermont J, Gujral A, Reid M, Vanegas SM, Popp CJ, Verano M, Jay M, Schmidt AM, Bergman M, Goldberg IJ, Alemán JO. Continuous glucose monitoring captures glycemic variability in obesity after sleeve gastrectomy: A prospective cohort study. Obes Sci Pract 2024; 10:e729. [PMID: 38187121 PMCID: PMC10768733 DOI: 10.1002/osp4.729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/26/2023] [Accepted: 11/28/2023] [Indexed: 01/09/2024] Open
Abstract
Objective HbA1c is an insensitive marker for assessing real-time dysglycemia in obesity. This study investigated whether 1-h plasma glucose level (1-h PG) ≥155 mg/dL (8.6 mmol/L) during an oral glucose tolerance test (OGTT) and continuous glucose monitoring (CGM) measurement of glucose variability (GV) better reflected dysglycemia than HbA1c after weight loss from metabolic and bariatric surgery. Methods This was a prospective cohort study of 10 participants with type 2 diabetes compared with 11 participants with non-diabetes undergoing sleeve gastrectomy (SG). At each research visit; before SG, and 6 weeks and 6 months post-SG, body weight, fasting lipid levels, and PG and insulin concentrations during an OGTT were analyzed. Mean amplitude of glycemic excursions (MAGE), a CGM-derived GV index, was analyzed. Results The 1-h PG correlated with insulin resistance markers, triglyceride/HDL ratio and triglyceride glucose index in both groups before surgery. At 6 months, SG caused 22% weight loss in both groups. Despite a reduction in HbA1c by 3.0 ± 1.3% in the diabetes group (p < 0.01), 1-h PG, and MAGE remained elevated, and the oral disposition index, which represents pancreatic β-cell function, remained reduced in the diabetes group when compared to the non-diabetes group. Conclusions Elevation of GV markers and reduced disposition index following SG-induced weight loss in the diabetes group underscores persistent β-cell dysfunction and the potential residual risk of diabetes complications.
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Affiliation(s)
- Brenda Dorcely
- Laboratory of Translational Obesity ResearchNYU Langone HealthNew YorkNew YorkUSA
- Division of Endocrinology, Diabetes and MetabolismNYU Langone HealthNew YorkNew YorkUSA
| | - Julie DeBermont
- Division of Endocrinology, Diabetes and MetabolismNYU Langone HealthNew YorkNew YorkUSA
| | - Akash Gujral
- Comprehensive Program in Obesity ResearchNYU Langone HealthNew YorkNew YorkUSA
| | - Migdalia Reid
- Laboratory of Translational Obesity ResearchNYU Langone HealthNew YorkNew YorkUSA
- Division of Endocrinology, Diabetes and MetabolismNYU Langone HealthNew YorkNew YorkUSA
| | - Sally M. Vanegas
- Laboratory of Translational Obesity ResearchNYU Langone HealthNew YorkNew YorkUSA
- Comprehensive Program in Obesity ResearchNYU Langone HealthNew YorkNew YorkUSA
| | - Collin J. Popp
- Department of Population HealthNYU Langone HealthNew YorkNew YorkUSA
| | - Michael Verano
- Laboratory of Translational Obesity ResearchNYU Langone HealthNew YorkNew YorkUSA
- Division of Endocrinology, Diabetes and MetabolismNYU Langone HealthNew YorkNew YorkUSA
| | - Melanie Jay
- Comprehensive Program in Obesity ResearchNYU Langone HealthNew YorkNew YorkUSA
| | - Ann Marie Schmidt
- Division of Endocrinology, Diabetes and MetabolismNYU Langone HealthNew YorkNew YorkUSA
| | - Michael Bergman
- Division of Endocrinology, Diabetes and MetabolismNYU Langone HealthNew YorkNew YorkUSA
| | - Ira J. Goldberg
- Division of Endocrinology, Diabetes and MetabolismNYU Langone HealthNew YorkNew YorkUSA
| | - José O. Alemán
- Laboratory of Translational Obesity ResearchNYU Langone HealthNew YorkNew YorkUSA
- Division of Endocrinology, Diabetes and MetabolismNYU Langone HealthNew YorkNew YorkUSA
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Bergman M, Dorcely B. Remission of prediabetes via lifestyle intervention. Lancet Diabetes Endocrinol 2023; 11:784-785. [PMID: 37769678 DOI: 10.1016/s2213-8587(23)00258-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 10/03/2023]
Affiliation(s)
- Michael Bergman
- Department of Medicine and Population Health, Veterans Affairs New York Harbor Healthcare System, Division of Endocrinology, Diabetes and Metabolism, Grossman School of Medicine, New York University, New York, NY 10010, USA.
| | - Brenda Dorcely
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Grossman School of Medicine, New York University, New York, NY 10010, USA
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Bergman M, Xiao X, Hall CK. In Silico Design and Analysis of Plastic-Binding Peptides. J Phys Chem B 2023; 127:8370-8381. [PMID: 37735840 PMCID: PMC10591858 DOI: 10.1021/acs.jpcb.3c04319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
Peptides that bind to inorganic materials can be used to functionalize surfaces, control crystallization, or assist in interfacial self-assembly. In the past, inorganic-binding peptides have been found predominantly through peptide library screening. While this method has successfully identified peptides that bind to a variety of materials, an alternative design approach that can intelligently search for peptides and provide physical insight for peptide affinity would be desirable. In this work, we develop a computational, physics-based approach to design inorganic-binding peptides, focusing on peptides that bind to the common plastics polyethylene, polypropylene, polystyrene, and poly(ethylene terephthalate). The PepBD algorithm, a Monte Carlo method that samples peptide sequence and conformational space, was modified to include simulated annealing, relax hydration constraints, and an ensemble of conformations to initiate design. These modifications led to the discovery of peptides with significantly better scores compared to those obtained using the original PepBD. PepBD scores were found to improve with increasing van der Waals interactions, although strengthening the intermolecular van der Waals interactions comes at the cost of introducing unfavorable electrostatic interactions. The best designs are enriched in amino acids with bulky side chains and possess hydrophobic and hydrophilic patches whose location depends on the adsorbed conformation. Future work will evaluate the top peptide designs in molecular dynamics simulations and experiment, enabling their application in microplastic pollution remediation and plastic-based biosensors.
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Affiliation(s)
- Michael Bergman
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, 27606, USA
| | - Xingqing Xiao
- Department of Chemistry, School of Science, Hainan University, Longhua District, Haikou, Hainan, 571101, China
| | - Carol K. Hall
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, 27606, USA
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Ha J, Chung ST, Bogardus C, Jagannathan R, Bergman M, Sherman AS. One-hour glucose is an earlier marker of dysglycemia than two-hour glucose. Diabetes Res Clin Pract 2023; 203:110839. [PMID: 37482221 PMCID: PMC10592221 DOI: 10.1016/j.diabres.2023.110839] [Citation(s) in RCA: 0] [Impact Index Per Article: 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: 05/15/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 07/25/2023]
Abstract
AIMS The timing of increase in 1-hour PG and its utility as an earlier predictor of both prediabetes (PreDM) and type 2 diabetes (T2D) compared to 2-hour PG (2 h-PG) are unknown. To evaluate the timing of crossing of the 1 h-PG ≥ 155 mg/dl (8.6 mmol/L) for PreDM and 209 mg/dl (11.6 mmol/L) for T2D and respective current 2 h-PG thresholds of 140 mg/dl (7.8 mmol/L) and 200 mg/dl (11.1 mmol/L). METHODS Secondary analysis of 201 Southwest Native Americans who were followed longitudinally for 6-10 years and had at least 3 OGTTs. RESULTS We identified a subset of 43 individuals who first developed PreDM by both 1 h-PG and 2 h-PG criteria during the study. For most (32/43,74%), 1 h-PG ≥ 155 mg/dl was observed before 2 h-PG reached 140 mg/dl (median [IQR]: 1.7 [-0.25, 4.59] y; mean ± SEM: 5.3 ± 1.9 y). We also identified a subset of 33 individuals who first developed T2D during the study. For most (25/33, 75%), 1 h-PG reached 209 mg/dl earlier (median 1.0 [-0.56, 2.02] y; mean ± SEM: 1.6 ± 0.8 y) than 2 h-PG reached 200 mg/dl, diagnostic of T2D. CONCLUSIONS 1 h-PG ≥ 155 mg/dl is an earlier marker of elevated risk for PreDM and T2D than 2 h-PG ≥ 140 mg/dl.
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Affiliation(s)
- Joon Ha
- Department of Mathematics, Howard University, Washington, DC, USA
| | - Stephanie T Chung
- Section on Pediatric Diabetes, Obesity, and Metabolism, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Clifton Bogardus
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, 445 N 5th Street, Phoenix, AZ 85004, USA
| | - Ram Jagannathan
- Hubert Department of Global Health, Emory University School of Public Health Atlanta, GA, USA
| | - Michael Bergman
- NYU Grossman School of Medicine, Departments of Medicine and Population Health, Division of Endocrinology, Diabetes, Metabolism, VA New York Harbor Healthcare System, Manhattan Campus, New York, NY 10010, USA
| | - Arthur S Sherman
- Laboratory of Biological Modeling, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA.
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Kharmats AY, Popp C, Hu L, Berube L, Curran M, Wang C, Pompeii ML, Li H, Bergman M, St-Jules DE, Segal E, Schoenthaler A, Williams N, Schmidt AM, Barua S, Sevick MA. A randomized clinical trial comparing low-fat with precision nutrition-based diets for weight loss: impact on glycemic variability and HbA1c. Am J Clin Nutr 2023; 118:443-451. [PMID: 37236549 PMCID: PMC10447469 DOI: 10.1016/j.ajcnut.2023.05.026] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 05/16/2023] [Accepted: 05/23/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Recent studies have demonstrated considerable interindividual variability in postprandial glucose response (PPGR) to the same foods, suggesting the need for more precise methods for predicting and controlling PPGR. In the Personal Nutrition Project, the investigators tested a precision nutrition algorithm for predicting an individual's PPGR. OBJECTIVE This study aimed to compare changes in glycemic variability (GV) and HbA1c in 2 calorie-restricted weight loss diets in adults with prediabetes or moderately controlled type 2 diabetes (T2D), which were tertiary outcomes of the Personal Diet Study. METHODS The Personal Diet Study was a randomized clinical trial to compare a 1-size-fits-all low-fat diet (hereafter, standardized) with a personalized diet (hereafter, personalized). Both groups received behavioral weight loss counseling and were instructed to self-monitor diets using a smartphone application. The personalized arm received personalized feedback through the application to reduce their PPGR. Continuous glucose monitoring (CGM) data were collected at baseline, 3 mo and 6 mo. Changes in mean amplitude of glycemic excursions (MAGEs) and HbA1c at 6 mo were assessed. We performed an intention-to-treat analysis using linear mixed regressions. RESULTS We included 156 participants [66.5% women, 55.7% White, 24.1% Black, mean age 59.1 y (standard deviation (SD) = 10.7 y)] in these analyses (standardized = 75, personalized = 81). MAGE decreased by 0.83 mg/dL per month for standardized (95% CI: 0.21, 1.46 mg/dL; P = 0.009) and 0.79 mg/dL per month for personalized (95% CI: 0.19, 1.39 mg/dL; P = 0.010) diet, with no between-group differences (P = 0.92). Trends were similar for HbA1c values. CONCLUSIONS Personalized diet did not result in an increased reduction in GV or HbA1c in patients with prediabetes and moderately controlled T2D, compared with a standardized diet. Additional subgroup analyses may help to identify patients who are more likely to benefit from this personalized intervention. This trial was registered at clinicaltrials.gov as NCT03336411.
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Affiliation(s)
- Anna Y Kharmats
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Collin Popp
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Lu Hu
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Lauren Berube
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States.
| | - Margaret Curran
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Chan Wang
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Mary Lou Pompeii
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Huilin Li
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Michael Bergman
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States; Division of Endocrinology, Diabetes and Metabolism, New York University Grossman School of Medicine, New York, NY, United States
| | - David E St-Jules
- Department of Nutrition, University of Nevada, Reno, Reno, NV, United States
| | - Eran Segal
- Department of Computer Science and Applied Math, Weizmann Institute of Science, Rehovot, Israel
| | - Antoinette Schoenthaler
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Natasha Williams
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States
| | - Ann Marie Schmidt
- Diabetes Research Program, Department of Medicine, New York University Langone Health, New York, NY, United States
| | - Souptik Barua
- Division of Precision Medicine, Department of Medicine, New York University Langone Health, New York, NY, United States
| | - Mary Ann Sevick
- Center for Healthful Behavior Change, Institute for Excellence in Health Equity, New York University Langone Health, New York, NY, United States; Department of Population Health, New York University Grossman School of Medicine, New York, NY, United States; Division of Endocrinology, Diabetes and Metabolism, New York University Grossman School of Medicine, New York, NY, United States
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Golovaty I, Bergman M, Moin T. Response to the letter to the editor: "Two decade of diabetes prevention efforts: A call to innovate and revitalize our approach to lifestyle change". Diabetes Res Clin Pract 2023; 201:110681. [PMID: 37105399 DOI: 10.1016/j.diabres.2023.110681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 04/29/2023]
Affiliation(s)
- Ilya Golovaty
- Division of General Internal Medicine, University of Washington School of Medicine, Seattle, WA, USA; General Medicine Service, VA Puget Sound Health Care System, Seattle, WA, USA.
| | - Michael Bergman
- Division of Endocrinology and Metabolism, Department of Medicine and of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Tannaz Moin
- David Geffen School of Medicine, University of California, Los Angeles, CA, USA; VA Greater Los Angeles Health System and HSR&D Center for the Study of Healthcare Innovation, Implementation & Policy, Los Angeles, CA, USA
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Brar PC, Mehta S, Brar A, Pierce KA, Albano A, Bergman M. Value of 1-Hour Plasma Glucose During an Oral Glucose Tolerance Test in a Multiethnic Cohort of Obese Children and Adolescents. Clin Med Insights Endocrinol Diabetes 2023; 16:11795514231177206. [PMID: 37323220 PMCID: PMC10262663 DOI: 10.1177/11795514231177206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [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: 09/22/2022] [Accepted: 05/04/2023] [Indexed: 06/17/2023] Open
Abstract
One hour plasma glucose (1-hr PG) concentration during an oral glucose tolerance test (OGTT) is steadily emerging as an independent predictor of type 2 diabetes (T2D). Methods We applied the current cut off thresholds reported in the pediatric literature for the 1-hr PG, 132.5 (7.4 mmol/l) and 155 mg/dL (8.6 mmol/l) during an OGTT, to report abnormal glucose tolerance (AGT) using ROC curve analyses. We determined the empirical optimal cut point for 1-hr PG for our multi ethnic cohort using the Youden Index. Results About 1-hour and 2-hours plasma glucose showed the highest predictive potential based on Areas under the curve (AUC) values of 0.91 [CI: 0.85, 0.97] and 1 [CI: 1, 1], respectively. Further comparison of the ROC curves of the 1-hour and 2-hour PG measurements as predictors of an abnormal OGTT showed that their associated AUCs differed significantly (X2(1) = 9.25, P < .05). Using 132.5 mg/dL as the cutoff point for plasma glucose at 1-hour yielded a ROC curve with an AUC of 0.796, a sensitivity of 88%, and a specificity of 71.2%. Alternatively, the cutoff point of 155 mg/dL resulted in a ROC AUC of 0.852, a sensitivity of 80%, and a specificity of 90.4%. Conclusion Our cross-sectional study affirms that the 1-hr PG can identify obese children and adolescents at increased risk for prediabetes and/or T2D with almost the same accuracy as a 2-hr PG. In our multi-ethnic cohort, a 1-hr PG ⩾ 155 mg/dL (8.6 mmol/l) serves as an optimal cut-point, using the estimation of the Youden index with AUC of 0.86 and sensitivity of 80%.We support the petition to consider the 1-hr PG as integral during an OGTT, as this adds value to the interpretation of the OGTT beyond the fasting and 2-hr PG.
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Affiliation(s)
- Preneet Cheema Brar
- Division of Endocrinology and Diabetes, Department of Pediatrics, New York University Grossman School of Medicine, New York, USA
| | - Shilpa Mehta
- Division of Endocrinology and Diabetes, Department of Pediatrics, New York Medical College, Valhalla, New York, USA
| | - Ajay Brar
- Biology and Public Health, College of Arts and Science, New York University, New York, USA
| | - Kristyn A Pierce
- Department of Pediatrics, New York University Grossman School of Medicine
| | | | - Michael Bergman
- Departments of Medicine and Population Health, Division of Endocrinology, Diabetes and Metabolism, New York University Grossman School of Medicine, New York, USA
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Neves JS, Buysschaert M, Bergman M. Editorial: Prediabetes: new insights on the diagnosis, risk stratification, comorbidites, cardiovascular disease, microvascular complications, and treatment. Front Endocrinol (Lausanne) 2023; 14:1214479. [PMID: 37251678 PMCID: PMC10210134 DOI: 10.3389/fendo.2023.1214479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Accepted: 05/04/2023] [Indexed: 05/31/2023] Open
Affiliation(s)
- João Sérgio Neves
- Department of Endocrinology, Diabetes and Metabolism, Centro Hospitalar Universitário de São João, Porto, Portugal
- Cardiovascular Research and Development Center, Department of Surgery and Physiology, Faculty of Medicine, University of Porto, Porto, Portugal
| | - Martin Buysschaert
- Department of Endocrinology and Diabetology, Université Catholique de Louvain, University Clinic Saint-Luc, Brussels, Belgium
| | - Michael Bergman
- Division of Endocrinology, Diabetes and Metabolism, New York University Grossman School of Medicine, New York, NY, United States
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Bergman M, Buysschaert M, Ceriello A, Hussain A, Mohan V, Sesti G, Tuomilehto J. Current diagnostic criteria identify risk for type 2 diabetes too late. Lancet Diabetes Endocrinol 2023; 11:224-226. [PMID: 36803366 DOI: 10.1016/s2213-8587(23)00039-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/29/2023] [Accepted: 01/30/2023] [Indexed: 02/18/2023]
Affiliation(s)
- Michael Bergman
- Division of Endocrinology, Diabetes and Metabolism, New York University Grossman School of Medicine, New York, NY 10010, USA.
| | - Martin Buysschaert
- Department of Endocrinology and Diabetology, Université Catholique de Louvain, University Clinic Saint-Luc, Brussels, Belgium
| | | | - Akhtar Hussain
- Faculty of Health Sciences, Nord University, Bodø, Norway; Faculty of Medicine, Federal University of Ceará, Fortaleza, Brazil; International Diabetes Federation, Brussels, Belgium; Diabetes in Asia Study Group, Doha, Qatar; Centre for Global Health Research, Diabetic Association of Bangladesh, Dhaka, Bangladesh
| | - Viswanathan Mohan
- Dr Mohan's Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, India
| | - Giorgio Sesti
- Department of Clinical and Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Jaakko Tuomilehto
- Public Health Promotion Unit, Finnish Institute for Health and Welfare, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Saudi Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia; Department of International Health, National School of Public Health, Instituto de Salud Carlos III, Madrid, Spain
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11
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Golovaty I, Ritchie ND, Tuomilehto J, Mohan V, Ali MK, Gregg EW, Bergman M, Moin T. Two decades of diabetes prevention efforts: A call to innovate and revitalize our approach to lifestyle change. Diabetes Res Clin Pract 2023; 198:110195. [PMID: 36470316 PMCID: PMC10079599 DOI: 10.1016/j.diabres.2022.110195] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [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: 07/26/2022] [Revised: 11/07/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022]
Abstract
The impact of global diabetes prevention efforts has been modest despite the promise of landmark diabetes prevention trials nearly twenty years ago. While national and regional initiatives show potential, challenges remain to adapt large-scale strategies in the real-world that fits individuals and their communities. Additionally, the sedentary lifestyle changes during the COVID-19 pandemic and guidelines that now call for earlier screening (e.g., US Preventative Task Force) will increase the pool of eligible adults worldwide. Thus, a more adaptable, person-centered approach that expands the current toolkit is urgently needed to innovate and revitalize our approach to diabetes prevention. This review identifies key priorities to optimize the population-level delivery of diabetes prevention based on a consensus-based evaluation of the current evidence among experts in global translational programs; key priorities identified include (1) participant eligibility, (2) intervention intensity, (3) delivery components, (4) behavioral economics, (5) technology, and (6) the role of pharmacotherapy. We offer a conceptual framework for a broader, person-centered approach to better address an individual's risk, readiness, barriers, and digital competency.
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Affiliation(s)
- Ilya Golovaty
- Division of General Internal Medicine, University of Washington School of Medicine, Seattle, WA, USA; General Medicine Service, VA Puget Sound Health Care System, Seattle, WA, USA.
| | - Natalie D Ritchie
- Office of Research, Denver Health and Hospital Authority, Denver, CO. Department of Psychiatry, University of Colorado School of Medicine, Aurora, CO. University of Colorado College of Nursing, Aurora, CO, USA
| | - Jaakko Tuomilehto
- Public Health Promotion Unit, Finnish Institute for Health and Welfare, Helsinki, Finland; Department of Public Health, University of Helsinki, Helsinki, Finland; Saudi Diabetes Research Group, King Abdulaziz University Jeddah, Saudi Arabia; Department of International Health, National School of Public Health, Instituto de Salud Carlos III. Madrid, Spain
| | - Viswanathan Mohan
- Madras Diabetes Research Foundation & Chairman, Dr. Mohan's Diabetes Specialties Centre, Chennai, India
| | - Mohammed K Ali
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA; Department of Family and Preventive Medicine, School of Medicine, Emory University, Atlanta, GA, USA; Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Edward W Gregg
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
| | - Michael Bergman
- Division of Endocrinology and Metabolism, Department of Medicine and of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Tannaz Moin
- David Geffen School of Medicine, University of California, Los Angeles, CA, USA; VA Greater Los Angeles Health System and HSR&D Center for the Study of Healthcare Innovation, Implementation & Policy, Los Angeles, CA, USA
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12
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Gupta A, Hu J, Huang S, Diaz L, Gore R, Levy N, Bergman M, Tanner M, Sherman SE, Islam N, Schwartz MD. Implementation fidelity to a behavioral diabetes prevention intervention in two New York City safety net primary care practices. BMC Public Health 2023; 23:575. [PMID: 36978071 PMCID: PMC10045092 DOI: 10.1186/s12889-023-15477-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 03/20/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND It is critical to assess implementation fidelity of evidence-based interventions and factors moderating fidelity, to understand the reasons for their success or failure. However, fidelity and fidelity moderators are seldom systematically reported. The study objective was to conduct a concurrent implementation fidelity evaluation and examine fidelity moderators of CHORD (Community Health Outreach to Reduce Diabetes), a pragmatic, cluster-randomized, controlled trial to test the impact of a Community Health Workers (CHW)-led health coaching intervention to prevent incident type 2 Diabetes Mellitus in New York (NY). METHODS We applied the Conceptual Framework for Implementation Fidelity to assess implementation fidelity and factors moderating it across the four core intervention components: patient goal setting, education topic coaching, primary care (PC) visits, and referrals to address social determinants of health (SDH), using descriptive statistics and regression models. PC patients with prediabetes receiving care from safety-net patient-centered medical homes (PCMHs) at either, VA NY Harbor or at Bellevue Hospital (BH) were eligible to be randomized into the CHW-led CHORD intervention or usual care. Among 559 patients randomized and enrolled in the intervention group, 79.4% completed the intake survey and were included in the analytic sample for fidelity assessment. Fidelity was measured as coverage, content adherence and frequency of each core component, and the moderators assessed were implementation site and patient activation measure. RESULTS Content adherence was high for three components with nearly 80.0% of patients setting ≥ 1 goal, having ≥ 1 PC visit and receiving ≥ 1 education session. Only 45.0% patients received ≥ 1 SDH referral. After adjusting for patient gender, language, race, ethnicity, and age, the implementation site moderated adherence to goal setting (77.4% BH vs. 87.7% VA), educational coaching (78.9% BH vs. 88.3% VA), number of successful CHW-patient encounters (6 BH vs 4 VA) and percent of patients receiving all four components (41.1% BH vs. 25.7% VA). CONCLUSIONS The fidelity to the four CHORD intervention components differed between the two implementation sites, demonstrating the challenges in implementing complex evidence-based interventions in different settings. Our findings underscore the importance of measuring implementation fidelity in contextualizing the outcomes of randomized trials of complex multi-site behavioral interventions. TRIAL REGISTRATION The trial was registered with ClinicalTrials.gov on 30/12/2016 and the registration number is NCT03006666 .
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Affiliation(s)
- Avni Gupta
- School of Global Public Health, New York University, 708 Broadway, New York, NY, 10003, USA.
| | - Jiyuan Hu
- Department of Population Health, NYU Grossman School of Medicine, 180 Madison Ave 2F Rm 222, New York, NY, 10016, USA
| | - Shengnan Huang
- Department of Population Health, NYU Grossman School of Medicine, 180 Madison Ave, 2Nd Floor, New York, NY, 10016, USA
| | - Laura Diaz
- Department of Population Health, NYU Grossman School of Medicine, 180 Madison Ave, 9-43A, New York, NY, 10016, USA
| | - Radhika Gore
- Department of Population Health, NYU Grossman School of Medicine, 180 Madison Ave, New York, NY, 10016, USA
| | - Natalie Levy
- Department of Medicine, NYU Grossman School of Medicine, 462 First Avenue, Area 2d, New York, NY, 10016, USA
| | - Michael Bergman
- Department of Population Health, NYU Grossman School of Medicine, 180 Madison Ave, 2Nd Floor, New York, NY, 10016, USA
- Department of Medicine, NYU Grossman School of Medicine, 423 East 23Rd Street, Room 16049C, New York, NY, 10010, USA
- VA New York Harbor Healthcare System, 423 East 23Rd Street, Room 16049C, New York, NY, 10010, USA
| | - Michael Tanner
- Department of Medicine, NYU Grossman School of Medicine, 462 1St Ave, New York, NY, 10016, USA
| | - Scott E Sherman
- Department of Population Health, NYU Grossman School of Medicine, 180 Madison Avenue, New York, NY, 10016, USA
- VA New York Harbor Healthcare System, 180 Madison Avenue, New York, NY, 10016, USA
| | - Nadia Islam
- Department of Population Health, NYU Grossman School of Medicine, 180 Madison Avenue, New York, NY, 10016, USA
| | - Mark D Schwartz
- Department of Population Health, NYU Grossman School of Medicine, 180 Madison Avenue, Suite 955, New York, NY, 10016, USA
- VA New York Harbor Healthcare System, 180 Madison Avenue, Suite 955, New York, NY, 10016, USA
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13
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Bergman M, Deravi L. Manipulating polydispersity of LLENS β-crystallins using divalent cations demonstrates evidence of calcium regulation. Biophys J 2023; 122:191a. [PMID: 36782916 DOI: 10.1016/j.bpj.2022.11.1171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023] Open
Affiliation(s)
- Michael Bergman
- Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
| | - Leila Deravi
- Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA
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14
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Bang RS, Bergman M, Li T, Mukherjee F, Alshehri AS, Abbott NL, Crook NC, Velev OD, Hall CK, You F. An integrated chemical engineering approach to understanding microplastics. AIChE J 2023. [DOI: 10.1002/aic.18020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Affiliation(s)
- Rachel S. Bang
- Department of Chemical and Biomolecular Engineering North Carolina State University Raleigh NC USA
| | - Michael Bergman
- Department of Chemical and Biomolecular Engineering North Carolina State University Raleigh NC USA
| | - Tianyu Li
- Department of Chemical and Biomolecular Engineering North Carolina State University Raleigh NC USA
| | - Fiona Mukherjee
- Robert F. Smith School of Chemical and Biomolecular Engineering Cornell University Ithaca NY USA
- Department of Chemistry and Chemical Biology Cornell University Ithaca NY USA
| | - Abdulelah S. Alshehri
- Robert F. Smith School of Chemical and Biomolecular Engineering Cornell University Ithaca NY USA
- Department of Chemical Engineering, College of Engineering King Saud University Riyadh Saudi Arabia
| | - Nicholas L. Abbott
- Robert F. Smith School of Chemical and Biomolecular Engineering Cornell University Ithaca NY USA
- Department of Chemistry and Chemical Biology Cornell University Ithaca NY USA
| | - Nathan C. Crook
- Department of Chemical and Biomolecular Engineering North Carolina State University Raleigh NC USA
| | - Orlin D. Velev
- Department of Chemical and Biomolecular Engineering North Carolina State University Raleigh NC USA
| | - Carol K. Hall
- Department of Chemical and Biomolecular Engineering North Carolina State University Raleigh NC USA
| | - Fengqi You
- Robert F. Smith School of Chemical and Biomolecular Engineering Cornell University Ithaca NY USA
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15
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Andellini M, Manco M, Esposito MT, Tozzi AE, Bergman M, Ritrovato M. A simulation model estimates lifetime health and economic outcomes of screening prediabetes using the 1-h plasma glucose. Acta Diabetol 2023; 60:9-17. [PMID: 36127565 DOI: 10.1007/s00592-022-01963-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/22/2022] [Indexed: 01/07/2023]
Abstract
AIMS The current method to diagnose impaired glucose tolerance (IGT) is based on the 2-h plasma glucose (2-hPG) value during a 75-g oral glucose tolerance test (OGTT). Robust evidence demonstrates that the 1-h post-load plasma glucose (1-hPG) ≥ 8.6 mmol/L in those with normal glucose tolerance is highly predictive of type 2 diabetes (T2D), micro and macrovascular complications and mortality. The aim of this study was to conduct a health economic analysis to estimate long-term cost-effectiveness of using the 1-hPG compared to the 2-hPG for screening and assessing the risk of diabetes over 35 years. The main outcome was cost per quality-adjusted life year (QALY) gained. METHODS A Monte Carlo-based Markov simulation model was developed to forecast long-term effects of two screening strategies with regards to clinical and cost-effectiveness outcomes. The base case model included 20,000 simulated patients over 35-years follow-up. Transition probabilities on disease progression, mortality, effects on preventive treatments and complications were retrieved from landmark diabetes studies. Direct medical costs were sourced from published literature and inflated to 2019 Euros. RESULTS In the lifetime analysis, the 1-hPG was projected to increase the number of years free from disease (2 years per patient); to delay the onset of T2D (1 year per patient); to reduce the incidence of T2D complications (0·6 RR-Relative Risk per patient) and to increase the QALY gained (0·58 per patient). Even if the 1-hPG diagnostic method resulted in higher initial costs associated with preventive treatment, long-term diabetes-related costs as well as complications costs were reduced leading to a lifetime saving of - 31225719.82€. The incremental cost-effectiveness ratio was - 8214.7€ per each QALY gained for the overall population. CONCLUSIONS Screening prediabetes with the 1-hPG is feasible and cost-effective resulting in reduced costs per QALY. Notwithstanding, the higher initial costs of testing with the 1-hPG compared to the 2-hPG due to incremental preventive intervention, long-term diabetes and complications costs were reduced projecting an overall cost saving of - 8214.7€ per each QALY gained.
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Affiliation(s)
- Martina Andellini
- Health Technology Assessment Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Melania Manco
- Research Area for Multifactorial Diseases and Complex Phenotypes. Bambino Gesù Children's Hospital, IRCCS, Via F. Baldelli 38, 00146, Rome, Italy.
| | - Maria Teresa Esposito
- Health Technology Assessment Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Alberto Eugenio Tozzi
- Research Area for Multifactorial Diseases and Complex Phenotypes. Bambino Gesù Children's Hospital, IRCCS, Via F. Baldelli 38, 00146, Rome, Italy
| | - Michael Bergman
- NYU Grossman School of Medicine, NYU Diabetes Prevention Program, Division of Endocrinology, Diabetes, Metabolism, VA New York Harbor Healthcare System, Manhattan Campus, New York, NY, 10010, USA
| | - Matteo Ritrovato
- Health Technology Assessment Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
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16
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Sundaralingam A, Aujayeb A, Akca B, Tiedeman C, George V, Carling M, Brown J, Banka R, Addala D, Bedawi EO, Hallifax RJ, Iqbal B, Denniston P, Tsakok MT, Kanellakis NI, Vafai-Tabrizi F, Bergman M, Funk GC, Benamore RE, Wrightson JM, Rahman NM. Achieving Molecular Profiling in Pleural Biopsies: A Multicenter, Retrospective Cohort Study. Chest 2022; 163:1328-1339. [PMID: 36410492 DOI: 10.1016/j.chest.2022.11.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 11/03/2022] [Accepted: 11/12/2022] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Pleural biopsy findings offer greater diagnostic sensitivity in malignant pleural effusions compared with pleural fluid. The adequacy of pleural biopsy techniques in achieving molecular marker status has not been studied, and such information (termed "actionable" histology) is critical in providing a rational, efficient, and evidence-based approach to diagnostic investigation. RESEARCH QUESTION What is the adequacy of various pleural biopsy techniques at providing adequate molecular diagnostic information to guide treatment in malignant pleural effusions? STUDY DESIGN AND METHODS This study analyzed anonymized data on 183 patients from four sites across three countries in whom pleural biopsy results had confirmed a malignant diagnosis and molecular profiling was relevant for the diagnosed cancer type. The primary outcome measure was adequacy of pleural biopsy for achieving molecular marker status. Secondary outcomes included clinical factors predictive of achieving a molecular diagnosis. RESULTS The median age of patients was 71 years (interquartile range, 63-78 years), with 92 of 183 (50%) male. Of the 183 procedures, 105 (57%) were local anesthetic thoracoscopies (LAT), 12 (7%) were CT scan guided, and 66 (36%) were ultrasound guided. Successful molecular marker analysis was associated with mode of biopsy, with LAT having the highst yield and ultrasound-guided biopsy the lowest (LAT vs CT scan guided vs ultrasound guided: LAT yield, 95%; CT scan guided, 86%; and ultrasound guided, 77% [P = .004]). Biopsy technique and size of biopsy sample were independently associated with successful molecular marker analysis. LAT had an adjusted OR for successful diagnosis of 30.16 (95% CI, 3.15-288.56; P = .003) and biopsy sample size an OR of 1.18 (95% CI, 1.02-1.37) per millimeter increase in tissue sample size (P < .03). INTERPRETATION Although previous studies have shown comparable overall diagnostic yields, in the modern era of targeted therapies, this study found that LAT offers far superior results to image-guided techniques at achieving molecular profiling and remains the optimal diagnostic tool.
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Affiliation(s)
- Anand Sundaralingam
- Oxford Pleural Unit, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Oxford Centre for Respiratory Medicine, and Department of Radiology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| | - Avinash Aujayeb
- Respiratory Department, Northumbria Healthcare NHS Trust, Newcastle, UK
| | - Baki Akca
- Karl Landsteiner Institute for Lung Research and Pulmonary Oncology, Klinik Ottakring, Vienna, Austria
| | - Clare Tiedeman
- Department of Respiratory and Sleep Medicine, John Hunter Hospital, NSW, Australia
| | - Vineeth George
- Department of Respiratory and Sleep Medicine, John Hunter Hospital, NSW, Australia
| | - Michael Carling
- Respiratory Department, Northumbria Healthcare NHS Trust, Newcastle, UK
| | - Jennifer Brown
- Department of Histopathology, Nuffield Orthopaedic Centre, Oxford, UK
| | - Radhika Banka
- PD Hinduja National Hospital and Medical Research Centre
| | - Dinesh Addala
- Oxford Pleural Unit, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Oxford Centre for Respiratory Medicine, and Department of Radiology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Eihab O Bedawi
- Oxford Respiratory Trials Unit, University of Oxford, Oxford, UK
| | - Rob J Hallifax
- Oxford Pleural Unit, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Oxford Respiratory Trials Unit, University of Oxford, Oxford, UK
| | - Beenish Iqbal
- Oxford Pleural Unit, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Poppy Denniston
- Oxford Pleural Unit, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Maria T Tsakok
- Oxford Centre for Respiratory Medicine, and Department of Radiology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Nikolaos I Kanellakis
- Nuffield Department of Medicine, Chinese Academy of Medical Sciences (CAMS) Oxford Institute (COI), University of Oxford, Oxford, UK; Nuffield Department of Medicine, Laboratory of Pleural and Lung Cancer Translational Research, University of Oxford, Oxford, UK; Nuffield Department of Medicine, and the National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Florian Vafai-Tabrizi
- Karl Landsteiner Institute for Lung Research and Pulmonary Oncology, Klinik Ottakring, Vienna, Austria
| | - Michael Bergman
- Karl Landsteiner Institute for Lung Research and Pulmonary Oncology, Klinik Ottakring, Vienna, Austria
| | - Georg-Christian Funk
- Karl Landsteiner Institute for Lung Research and Pulmonary Oncology, Klinik Ottakring, Vienna, Austria
| | - Rachel E Benamore
- Oxford Centre for Respiratory Medicine, and Department of Radiology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - John M Wrightson
- Oxford Pleural Unit, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Najib M Rahman
- Oxford Pleural Unit, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Oxford Respiratory Trials Unit, University of Oxford, Oxford, UK; Nuffield Department of Medicine, Chinese Academy of Medical Sciences (CAMS) Oxford Institute (COI), University of Oxford, Oxford, UK; Nuffield Department of Medicine, Laboratory of Pleural and Lung Cancer Translational Research, University of Oxford, Oxford, UK; Nuffield Department of Medicine, and the National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, UK
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17
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Buysschaert M, Bergman M, Valensi P. 1-h post-load plasma glucose for detecting early stages of prediabetes. Diabetes Metab 2022; 48:101395. [PMID: 36184047 DOI: 10.1016/j.diabet.2022.101395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/28/2022] [Accepted: 09/28/2022] [Indexed: 06/16/2023]
Abstract
Prediabetes is a very prevalent condition associated with an increased risk of developing diabetes and/or other chronic complications, in particular cardiovascular disorders. Early detection is therefore mandatory since therapeutic interventions may limit the development of these complications. Diagnosis of prediabetes is currently based on glycemic criteria (fasting plasma glucose (PG), and/or glycemia at 120 min during a 75 g oral glucose tolerance test (OGTT) and/or glycated hemoglobin (HbA1c). Accumulating longitudinal evidence suggests that a 1-hour PG ≥155 mg/dl (8.6 mmol/l) during the OGTT is an earlier marker of prediabetes than fasting PG, 2-h post-load PG, or HbA1c. There is substantial evidence demonstrating that the 1-h post-load PG is a more sensitive predictor of type 2 diabetes, cardiovascular disease, microangiopathy and mortality compared with conventional glucose criteria. The aim of this review is to highlight the paramount importance of detecting prediabetes early in its pathophysiological course. Accordingly, as recommended by an international panel in a recent petition, 1-h post-load PG could replace current criteria for diagnosing early stages of "prediabetes" before prediabetes evolves as conventionally defined.
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Affiliation(s)
- M Buysschaert
- Service d'Endocrinologie et Nutrition, Cliniques universitaires UCLouvain Saint-Luc, B-1200 Brussels, Belgium.
| | - M Bergman
- NYU Grossman School of Medicine, Division of Endocrinology, Diabetes and Metabolism, New York, NY, USA
| | - P Valensi
- Unit of Endocrinology-Diabetology-Nutrition. Jean Verdier Hospital, APHP, Paris 13 University, Sorbonne Paris Cité, CINFO, CRNH-IdF. Bondy, France
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18
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Huang JF, Tan QC, Bai H, Wang J, Bergman M, Wu Z. Bone mineral density, osteopenia and osteoporosis among US adults with cancer. QJM 2022; 115:653-660. [PMID: 35092293 DOI: 10.1093/qjmed/hcac015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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] [Received: 01/01/2022] [Revised: 01/13/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Bone mineral deficits are one of the most common complications in cancer survivors. However, there are no studies evaluating bone mineral density (BMD) and the prevalence of osteopenia and osteoporosis among patients with different types of cancers. AIM The objective was to assess BMD and evaluate the prevalence of osteopenia and osteoporosis among US adults with cancer. DESIGN A cross-section propensity score matching study. METHODS We extracted data from National Health and Nutrition Examination Survey database from 2005 to 2018. We compared BMD in participants with and without cancer which was further analyzed according to cancer type. We conducted logistic regression to evaluate adjusted odds ratios of osteopenia and osteoporosis and determine risk factors for their development. RESULTS We found that BMD was significantly higher in participants without cancer than cancer patients. Furthermore, the median BMD of patients with breast cancer or skin cancer (including melanoma) was significantly lower than participants without cancer. People with breast, lung, genitourinary and skin cancers were more likely to incur osteopenia/osteoporosis than those without cancer. CONCLUSIONS BMD differs depending upon type in survivors. Individuals with a history of cancer have a poor understanding of osteoporosis and its risk factors. Understanding risk factors in patients with cancers identified in our study may be helpful for preventing osteoporosis and fractures and the development of screening guidelines.
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Affiliation(s)
- J-F Huang
- Department of Orthopaedics, Xijing Hospital, The Air Force Medical University, No. 17 Changle Xi Road, Xi'an, Shaanxi Province 710032, China
| | - Q-C Tan
- Department of Orthopaedics, Xijing Hospital, The Air Force Medical University, No. 17 Changle Xi Road, Xi'an, Shaanxi Province 710032, China
| | - H Bai
- Department of Orthopaedics, Xijing Hospital, The Air Force Medical University, No. 17 Changle Xi Road, Xi'an, Shaanxi Province 710032, China
| | - J Wang
- Department of Orthopaedics, Xijing Hospital, The Air Force Medical University, No. 17 Changle Xi Road, Xi'an, Shaanxi Province 710032, China
| | - M Bergman
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, VA New York Harbor Healthcare System, NYU Grossman School of Medicine, 423 East 23rd Street, New York, NY 10010, USA
| | - Z Wu
- Department of Orthopaedics, Xijing Hospital, The Air Force Medical University, No. 17 Changle Xi Road, Xi'an, Shaanxi Province 710032, China
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Popp CJ, Hu L, Kharmats AY, Curran M, Berube L, Wang C, Pompeii ML, Illiano P, St-Jules DE, Mottern M, Li H, Williams N, Schoenthaler A, Segal E, Godneva A, Thomas D, Bergman M, Schmidt AM, Sevick MA. Effect of a Personalized Diet to Reduce Postprandial Glycemic Response vs a Low-fat Diet on Weight Loss in Adults With Abnormal Glucose Metabolism and Obesity: A Randomized Clinical Trial. JAMA Netw Open 2022; 5:e2233760. [PMID: 36169954 PMCID: PMC9520362 DOI: 10.1001/jamanetworkopen.2022.33760] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
IMPORTANCE Interindividual variability in postprandial glycemic response (PPGR) to the same foods may explain why low glycemic index or load and low-carbohydrate diet interventions have mixed weight loss outcomes. A precision nutrition approach that estimates personalized PPGR to specific foods may be more efficacious for weight loss. OBJECTIVE To compare a standardized low-fat vs a personalized diet regarding percentage of weight loss in adults with abnormal glucose metabolism and obesity. DESIGN, SETTING, AND PARTICIPANTS The Personal Diet Study was a single-center, population-based, 6-month randomized clinical trial with measurements at baseline (0 months) and 3 and 6 months conducted from February 12, 2018, to October 28, 2021. A total of 269 adults aged 18 to 80 years with a body mass index (calculated as weight in kilograms divided by height in meters squared) ranging from 27 to 50 and a hemoglobin A1c level ranging from 5.7% to 8.0% were recruited. Individuals were excluded if receiving medications other than metformin or with evidence of kidney disease, assessed as an estimated glomerular filtration rate of less than 60 mL/min/1.73 m2 using the Chronic Kidney Disease Epidemiology Collaboration equation, to avoid recruiting patients with advanced type 2 diabetes. INTERVENTIONS Participants were randomized to either a low-fat diet (<25% of energy intake; standardized group) or a personalized diet that estimates PPGR to foods using a machine learning algorithm (personalized group). Participants in both groups received a total of 14 behavioral counseling sessions and self-monitored dietary intake. In addition, the participants in the personalized group received color-coded meal scores on estimated PPGR delivered via a mobile app. MAIN OUTCOMES AND MEASURES The primary outcome was the percentage of weight loss from baseline to 6 months. Secondary outcomes included changes in body composition (fat mass, fat-free mass, and percentage of body weight), resting energy expenditure, and adaptive thermogenesis. Data were collected at baseline and 3 and 6 months. Analysis was based on intention to treat using linear mixed modeling. RESULTS Of a total of 204 adults randomized, 199 (102 in the personalized group vs 97 in the standardized group) contributed data (mean [SD] age, 58 [11] years; 133 women [66.8%]; mean [SD] body mass index, 33.9 [4.8]). Weight change at 6 months was -4.31% (95% CI, -5.37% to -3.24%) for the standardized group and -3.26% (95% CI, -4.25% to -2.26%) for the personalized group, which was not significantly different (difference between groups, 1.05% [95% CI, -0.40% to 2.50%]; P = .16). There were no between-group differences in body composition and adaptive thermogenesis; however, the change in resting energy expenditure was significantly greater in the standardized group from 0 to 6 months (difference between groups, 92.3 [95% CI, 0.9-183.8] kcal/d; P = .05). CONCLUSIONS AND RELEVANCE A personalized diet targeting a reduction in PPGR did not result in greater weight loss compared with a low-fat diet at 6 months. Future studies should assess methods of increasing dietary self-monitoring adherence and intervention exposure. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT03336411.
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Affiliation(s)
- Collin J. Popp
- Institute for Excellence in Health Equity, Center for Healthful Behavior Change, Department of Population Health, NYU Langone Health, New York, New York
| | - Lu Hu
- Institute for Excellence in Health Equity, Center for Healthful Behavior Change, Department of Population Health, NYU Langone Health, New York, New York
| | - Anna Y. Kharmats
- Institute for Excellence in Health Equity, Center for Healthful Behavior Change, Department of Population Health, NYU Langone Health, New York, New York
| | - Margaret Curran
- Institute for Excellence in Health Equity, Center for Healthful Behavior Change, Department of Population Health, NYU Langone Health, New York, New York
| | - Lauren Berube
- Institute for Excellence in Health Equity, Center for Healthful Behavior Change, Department of Population Health, NYU Langone Health, New York, New York
| | - Chan Wang
- Division of Biostatistics, Department of Population Health, NYU Langone Health, New York, New York
| | - Mary Lou Pompeii
- Institute for Excellence in Health Equity, Center for Healthful Behavior Change, Department of Population Health, NYU Langone Health, New York, New York
| | - Paige Illiano
- Institute for Excellence in Health Equity, Center for Healthful Behavior Change, Department of Population Health, NYU Langone Health, New York, New York
| | | | - Meredith Mottern
- Institute for Excellence in Health Equity, Center for Healthful Behavior Change, Department of Population Health, NYU Langone Health, New York, New York
| | - Huilin Li
- Division of Biostatistics, Department of Population Health, NYU Langone Health, New York, New York
| | - Natasha Williams
- Institute for Excellence in Health Equity, Center for Healthful Behavior Change, Department of Population Health, NYU Langone Health, New York, New York
| | - Antoinette Schoenthaler
- Institute for Excellence in Health Equity, Center for Healthful Behavior Change, Department of Population Health, NYU Langone Health, New York, New York
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - Anastasia Godneva
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - Diana Thomas
- Department of Mathematical Sciences, United States Military Academy, West Point, New York
| | - Michael Bergman
- Institute for Excellence in Health Equity, Center for Healthful Behavior Change, Department of Population Health, NYU Langone Health, New York, New York
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, NYU Langone Health, New York, New York
| | - Ann Marie Schmidt
- Diabetes Research Program, Department of Medicine, NYU Langone Health, New York, New York
| | - Mary Ann Sevick
- Institute for Excellence in Health Equity, Center for Healthful Behavior Change, Department of Population Health, NYU Langone Health, New York, New York
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, NYU Langone Health, New York, New York
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20
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Harris FR, Sikes ML, Bergman M, Goller CC, Hasley AO, Sjogren CA, Ramirez MV, Gordy CL. Hands-on immunology: Engaging learners of all ages through tactile teaching tools. Front Microbiol 2022; 13:966282. [PMID: 36090062 PMCID: PMC9453673 DOI: 10.3389/fmicb.2022.966282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 08/11/2022] [Indexed: 11/13/2022] Open
Abstract
Ensuring the public has a fundamental understanding of human–microbe interactions, immune responses, and vaccines is a critical challenge in the midst of a pandemic. These topics are commonly taught in undergraduate- and graduate-level microbiology and immunology courses; however, creating engaging methods of teaching these complex concepts to students of all ages is necessary to keep younger students interested when science seems hard. Building on the Tactile Teaching Tools with Guided Inquiry Learning (TTT-GIL) method we used to create an interactive lac operon molecular puzzle, we report here two TTT-GIL activities designed to engage diverse learners from middle schoolers to masters students in exploring molecular interactions within the immune system. By pairing physical models with structured activities built on the constructivist framework of Process-Oriented Guided Inquiry Learning (POGIL), TTT-GIL activities guide learners through their interaction with the model, using the Learning Cycle to facilitate construction of new concepts. Moreover, TTT-GIL activities are designed utilizing Universal Design for Learning (UDL) principles to include all learners through multiple means of engagement, representation, and action. The TTT-GIL activities reported here include a web-enhanced activity designed to teach concepts related to antibody–epitope binding and specificity to deaf and hard-of-hearing middle and high school students in a remote setting and a team-based activity that simulates the evolution of the Major Histocompatibility Complex (MHC) haplotype of a population exposed to pathogens. These activities incorporate TTT-GIL to engage learners in the exploration of fundamental immunology concepts and can be adapted for use with learners of different levels and educational backgrounds.
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Affiliation(s)
- Felix R. Harris
- Department of Biological Sciences, North Carolina State University, Raleigh, NC, United States
| | - Michael L. Sikes
- Department of Biological Sciences, North Carolina State University, Raleigh, NC, United States
| | - Michael Bergman
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, United States
| | - Carlos C. Goller
- Department of Biological Sciences, North Carolina State University, Raleigh, NC, United States
- Biotechnology Program, North Carolina State University, Raleigh, NC, United States
| | - Andrew O. Hasley
- Biotechnology Program, North Carolina State University, Raleigh, NC, United States
| | - Caroline A. Sjogren
- Biotechnology Program, North Carolina State University, Raleigh, NC, United States
| | - Melissa V. Ramirez
- Department of Biological Sciences, North Carolina State University, Raleigh, NC, United States
| | - Claire L. Gordy
- Department of Biological Sciences, North Carolina State University, Raleigh, NC, United States
- *Correspondence: Claire L. Gordy,
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21
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Neves JS, Newman C, Bostrom JA, Buysschaert M, Newman JD, Medina JL, Goldberg IJ, Bergman M. Management of dyslipidemia and atherosclerotic cardiovascular risk in prediabetes. Diabetes Res Clin Pract 2022; 190:109980. [PMID: 35787415 DOI: 10.1016/j.diabres.2022.109980] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 06/26/2022] [Accepted: 06/29/2022] [Indexed: 11/03/2022]
Abstract
Prediabetes affects at least 1 in 3 adults in the U.S. and 1 in 5 in Europe. Although guidelines advocate aggressive management of lipid parameters in diabetes, most guidelines do not address treatment of dyslipidemia in prediabetes despite the increased atherosclerotic cardiovascular disease (ASCVD) risk. Several criteria are used to diagnose prediabetes: impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and HbA1c of 5.7-6.4%. Individuals with prediabetes have a greater risk of diabetes, a higher prevalence of dyslipidemia with a more atherogenic lipid profile and an increased risk of ASCVD. In addition to calculating ASCVD risk using traditional methods, an OGTT may further stratify risk. Those with 1-hour plasma glucose ≥8.6 mmol/L (155 mg/dL) and/or 2-hour ≥7.8 mmol/L (140 mg/dL) (IGT) have a greater risk of ASCVD. Diet and lifestyle modification are fundamental in prediabetes. Statins, ezetimibe and PCSK9 inhibitors are recommended in people requiring pharmacotherapy. Although high-intensity statins may increase risk of diabetes, this is acceptable because of the greater reduction of ASCVD. The LDL-C goal in prediabetes should be individualized. In those with IGT and/or elevated 1-hour plasma glucose, the same intensive approach to dyslipidemia as recommended for diabetes should be considered, particularly if other ASCVD risk factors are present.
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Affiliation(s)
- João Sérgio Neves
- Department of Endocrinology, Diabetes and Metabolism, São João University Hospital Center, Porto, Portugal; Cardiovascular Research and Development Center, Department of Surgery and Physiology, Faculty of Medicine, University of Porto, Porto, Portugal.
| | - Connie Newman
- Division of Endocrinology, Diabetes and Metabolism, New York University Grossman School of Medicine, New York, NY, USA
| | - John A Bostrom
- Section of Cardiovascular Medicine, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Martin Buysschaert
- Department of Endocrinology and Diabetology, Université Catholique de Louvain, University Clinic Saint-Luc, Brussels, Belgium
| | - Jonathan D Newman
- Division of Cardiology and the Center for the Prevention of Cardiovascular Disease, New York University Grossman School of Medicine, New York, NY, USA
| | | | - Ira J Goldberg
- Division of Endocrinology, Diabetes and Metabolism, New York University Grossman School of Medicine, New York, NY, USA
| | - Michael Bergman
- Division of Endocrinology, Diabetes and Metabolism, New York University Grossman School of Medicine, New York, NY, USA; Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
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22
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Dorcely B, Sifonte E, Popp C, Divakaran A, Katz K, Musleh S, Jagannathan R, Curran M, Sevick MA, Aleman JO, Goldberg IJ, Bergman M. Continuous glucose monitoring and 1-h plasma glucose identifies glycemic variability and dysglycemia in high-risk individuals with HbA1c < 5.7%: a pilot study. Endocrine 2022; 77:403-407. [PMID: 35729471 PMCID: PMC9212201 DOI: 10.1007/s12020-022-03109-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 06/05/2022] [Indexed: 12/04/2022]
Affiliation(s)
- Brenda Dorcely
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, NYU Grossman School of Medicine, New York, NY, 10016, USA.
| | - Eliud Sifonte
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Collin Popp
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Anjana Divakaran
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Karin Katz
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Sarah Musleh
- Department of Endocrinology, Diabetes & Metabolism and Internal Medicine, Hawaii Permanente Medical Group, Honolulu, HI, 96814, USA
| | - Ram Jagannathan
- Division of Hospital Medicine, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Margaret Curran
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Mary Ann Sevick
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, NYU Grossman School of Medicine, New York, NY, 10016, USA
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - José O Aleman
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Ira J Goldberg
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, NYU Grossman School of Medicine, New York, NY, 10016, USA
| | - Michael Bergman
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, NYU Grossman School of Medicine, New York, NY, 10016, USA
- Department of Population Health, NYU Grossman School of Medicine, New York, NY, 10016, USA
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23
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Jagannathan R, Fiorentino TV, Marini MA, Sesti G, Bergman M. One-hour post-load glucose is associated with severity of hepatic fibrosis risk. Diabetes Res Clin Pract 2022; 189:109977. [PMID: 35772586 DOI: 10.1016/j.diabres.2022.109977] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 06/20/2022] [Accepted: 06/24/2022] [Indexed: 01/03/2023]
Abstract
AIM Individuals with high 1-hour post-load glucose (1-h PG > 155 mg/dl; 8.6 mmol/l) during an oral glucose tolerance test are at increased risk of type 2 diabetes (T2D) and cardiovascular complications, hepatic steatosis, and mortality. However,the clinical relevance of 1-h PG for the severity of hepatic fibrosis risk remains undefined. METHODS Cross-sectional data of the CATAMERI study (n = 2335) were analyzed. Participants underwent anthropometric measurements, liver enzyme determinations, cardiometabolic profiling, and a75-gram oral glucose tolerance test, including fasting, 1-h and 2-h PG determinations and measurement of FIB-4 score to assess degree of hepatic fibrosis. Multivariable logistic regression analysis was performed to evaluate risk of advanced hepatic fibrosis with worsening glycemic status. RESULTS We stratifiedthe study group into 6 categories based on glycemic status: normal glucose tolerance (NGT) 1h-PG Low, NGT 1h-PG High, iIFG 1h-PG Low, iIFG 1h-PG High, IGT, and newly detected T2D. Anthropometric and cardiometabolic profiles worsened gradually with glycemic status. Moreover, compared to NGT-1h-PG Low group, worsening glycemic status was significantly associated with the severity of fibrosis, independent of other significant clinical risk factors. CONCLUSIONS 1-PG is a valuable tool for stratifying subjects with NGT or IFG at heightened risk of hepatic fibrosis requiring further evaluation with elastography.
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Affiliation(s)
- Ram Jagannathan
- Department of Medicine, Division of Hospital Medicine, Emory University School of Medicine, Atlanta, GA, USA.
| | | | | | - Giorgio Sesti
- Department of Clinical and Molecular Medicine, Sapienza University of Rome, Italy
| | - Michael Bergman
- NYU Grossman School of Medicine, NYU Diabetes Prevention Program, Division of Endocrinology, Diabetes, Metabolism, VA New York Harbor Healthcare System, Manhattan Campus, New York, NY 10010, USA
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24
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Mottern M, Kharmats A, Curran M, Berube L, Popp C, Hu L, Vanegas S, Bergman M, Pompeii ML, St-Jules D, Sevick MA. Impact of the COVID-19 Pandemic on Dietary Counseling Session Attendance and Self-Monitoring Adherence Dur034 a Behavioral Weight Loss Intervention. Curr Dev Nutr 2022. [PMCID: PMC9193975 DOI: 10.1093/cdn/nzac048.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Objectives
To assess the impact of the COVID-19 pandemic on participants’ intervention counseling session attendance and dietary self-monitoring adherence during the Personal Diet Study, a remote behavioral weight loss intervention for individuals with overweight and obesity with pre-diabetes and moderately controlled type 2 diabetes.
Methods
Participants (n = 200) were instructed to complete four in-person measurement visits, enter their meals daily in a smartphone application, and attend 14 virtual group nutrition counseling sessions over a 6-month intervention period. Due to COVID-19, the assessments were modified to be conducted remotely. We stratified participants into 3 categories: a) all study measures and intervention occurred before the start of the COVID-19 pandemic (BEFORE, n = 106) b) a portion of the intervention or follow-up measures occurred after the start of the pandemic (MIXED, n = 54), and 3) all study measures and intervention took place after the start of the pandemic (AFTER, n = 40). Attendance was defined as percentage of counseling intervention sessions attended. Dietary self-monitoring adherence was measured as percentage of days participants entered at least 50% of their daily caloric goal in a smart phone application. Between-group differences were assessed using linear regression models.
Results
Mean [SD] counseling session attendance for the MIXED (72.6%, [28.9%]) and AFTER (73.8% [28.1%]) groups did not differ from the BEFORE group (64.5% [31.8%]), p = 0.26 and 0.22 respectively. Adherence to dietary self-monitoring was lower for the MIXED group (25.5% [30.55]) compared to BEFORE group (36.0% [34.8%], p = 0.03), but did not differ between the AFTER (44.5% [35.8%]) and BEFORE groups (p = 0.288).
Conclusions
Intervention counseling attendance did not change substantially due to the COVID-19 pandemic. The MIXED group had lower self-monitoring adherence rates than the BEFORE grouip, which may be due to disruptions in daily life and habits that occurred in the early months of the COVID-19 pandemic. Virtual weight loss counseling methods are a practical way of circumventing program disruptions without compromising protocol adherence.
Funding Sources
This research was supported by the American Heart Association.
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Popp C, Hu L, Wang C, Curran M, Li H, Kharmats A, Thomas L, Pompeii ML, Mottern M, Polyn A, Schoenthaler A, St-Jules D, Williams N, Godnev A, Segal E, Bergman M, Sevick A. A Randomized Clinical Trial to Compare a Precision Nutrition Intervention Targeting a Reduction in Postprandial Glycemic Response to Meals With a Low-Fat Diet for Weight Loss. Curr Dev Nutr 2022. [PMCID: PMC9193517 DOI: 10.1093/cdn/nzac078.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Objectives The primary aim is to compare the effects of a low-fat diet vs a personalized diet on % weight loss at 6-months. Secondary outcomes include body composition (fat mass [FM] and fat free mass [FFM]), resting energy expenditure (REE) and adaptive thermogenesis (AT). Methods The Personal Diet Study was a 6-month, single-center, randomized clinical trial in adults with pre-diabetes and moderately controlled type 2 diabetes who were overweight or obese. Participants were randomized to follow either a hypocaloric low-fat diet, with < 25% energy intake from total fat (Standardized), or a hypocaloric personalized diet determined by a machine learning algorithm which predicts PPGR to meals (Personalized). Participants in both arms received behavioral counseling and logged dietary intake and physical activity into a smartphone app. Participants in the Personalized arm received real-time feedback as color-coded scores based on pre-consumed meals entered into the smartphone app. T-tests were used to assess group differences. Results A total of 200 adults (Standardized: n = 97 vs. Personalized: n = 103) contributed data (mean [SD]: age, 58 [11] years; 67% female; BMI, 34.0 [4.8] kg/m2; HbA1c, 5.8 [0.6]%; Metformin use, 21.0%). There were no significant group differences in mean % weight loss (Standardized: −4.4 [4.8]% vs Personalized: −3.3 [5.4]%; p = 0.19), mean absolute change in FM (Standardized: −2.7 [3.4] kg vs. Personalized: −1.6 [3.5] kg; p = 0.18), and AT between the two arms (Standardized: −54.7 [177] kcal/d vs. Personalized: 26.2 [199] kcal/d; p = 0.078). However, the Standardized arm lost significantly more FFM (−1.4 [1.6] kg vs. −0.45 [2.0] kg; p = 0.03) and had a greater decrease in REE (−111.0 [195.0] kcal/d vs. 1.93 [215.0] kcal/d; p = 0.02) compared to Personalized. Conclusions A personalized diet to minimize PPGR had no greater effect on % weight loss compared to a low-fat diet at 6-months. Future precision nutrition trials may require deeper phenotyping of individuals or the development of body weight-specific algorithms. Funding Sources Supported by grants from the American Heart Association 17SFRN33590133.
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Affiliation(s)
| | - Lu Hu
- New York University Langone Health
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Affiliation(s)
- Michael Bergman
- Division of Endocrinology, Diabetes and Metabolism, New York University Grossman School of Medicine, New York, NY, USA.
| | - Martin Buysschaert
- Department of Endocrinology and Diabetology, Université Catholique de Louvain, University Clinic Saint Luc, Brussels, Belgium
| | | | - Jaakko Tuomilehto
- Public Health Promotion Unit, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Saudi Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of International Health, National School of Public Health, Instituto de Salud Carlos III, Madrid, Spain
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Bergman M, Patel J, Saffore C, Mcdearmon-Blondell E, Topuria I, Cavanaugh C. POS1064 CLINICAL AND ECONOMIC BURDEN OF PATIENTS WITH PSORIATIC ARTHRITIS WITH AND WITHOUT AXIAL INVOLVEMENT. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
BackgroundAxial involvement affects about 12% to 50% of patients (pts) with psoriatic arthritis (PsA),1,2 and these pts report worse pain and function than pts without axial involvement.3 Limited evidence exists quantifying the clinical and economic impact of axial involvement and pain in pts with PsA.ObjectivesTo examine the clinical and economic burden of pts with PsA with and without axial involvement and assess the relationship between pain and frequency/costs of healthcare resource utilization (HCRU).MethodsThis cross-sectional study was conducted using de-identified linked electronic medical record and administrative claims data from the OM1 PsA Registry, a subset of the OM1 Real-World Data Cloud (OM1, Inc, Boston, MA, US). Adults aged ≥18 years with PsA (ICD-10 codes: L40.5x except for L40.53) were divided into two cohorts based on the presence or absence of the diagnosis code for axial involvement (ICD-10: L40.53) during 2019. Demographic and clinical characteristics between pts with and without axial involvement were compared with t-tests or Chi-square tests. Poisson regression models were used to assess the association of pain with HCRU. Mean costs per HCRU encounter (inpatient and emergency department [ED] visits) in 2019 were obtained from Optum’s de-identified Clinformatics Data Mart Database (2007-2019) and multiplied by the mean annual rate of HCRU encounters to generate per patient per year (PPPY) costs.ResultsOf 11,531 pts with PsA, 1,118 (10%) were diagnosed as having axial involvement. The two cohorts were similar in age, Charlson comorbidity score, and biologic disease-modifying antirheumatic drug (DMARD) use (Table 1). More pts with vs without axial involvement were commercially insured, had higher pain, and used opioids. Higher mean annual rates of inpatient (9 vs 5 per 100 pts) and ED (19 vs 14 per 100 pts) visits were seen in pts with vs without axial involvement, respectively, which translated to higher mean annual inpatient ($1,899 vs $1,055) and ED ($222 vs $164) visit costs PPPY (Figure 1). A 1-point higher pain score was associated with a higher likelihood of inpatient (52% vs 11%) and ED (20% vs 10%) visits (Table 1) and additional mean annual inpatient ($987 vs $116) and ED ($44 vs $16) visit costs PPPY (Figure 1) in pts with and without axial involvement, respectively.Table 1.Demographics, treatment utilization, and healthcare resource utilizationMean (SD), unless otherwise specifiedPsA pts without axial involvement n=10,413PsA pts with axial involvement n=1,118p-valuesAge, years56.7 (13.0)56.8 (14.0)0.8948Female, n (%)6,401 (61%)653 (58%)0.0494Insurance, n (%)<0.0001 Commercial3,285 (62%)414 (73%) Medicaid110 (2%)18 (3%) Medicare1,618 (30%)103 (18%)Charlson comorbidity score0.4 (1.0)0.4 (1.0)0.9900Pain, VAS (0–10)4.2 (2.6)a4.5 (2.6)b0.0422bDMARD use, n (%)6,871 (66%)753 (67%)0.3762tsDMARD use, n (%)1,117 (11%)91 (8%)0.0072Opioid use, n (%)1,722 (17%)224 (20%)0.0034Inpatient visits/100 pts5 (32)9 (35)0.0021ED visits/100 pts14 (63)19 (72)0.0168Association of pain and HCRU, IRR (95% CI)cInpatient visits1.11 (1.08–1.15)*1.52 (1.13–2.03)**ED visits1.10 (1.07–1.13)*1.20 (1.05–1.38)**bDMARD, biologic DMARD; CI, confidence interval; IRR, incidence rate ratio; MTX, methotrexate; NSAIDs, non-steroidal anti-inflammatory drugs; SD, standard deviation; tsDMARD, targeted synthetic DMARD; VAS, visual analog scale.*p<0.0001 and **p<0.01 for association between 1-point increase in pain and HCRU.an=9,981bn=320cBased on Poisson regression model adjusted for age, sex, race, insurance type, Charlson comorbidity score, and PsA treatments (b/tsDMARDs, MTX, and NSAIDs).ConclusionAxial involvement in PsA was associated with an increased clinical and economic burden. Higher pain was associated with higher HCRU and costs in pts with vs without axial involvement.References[1]Baraliakos X, et al. Clin Exp Rheumatic. 2015;33:S31–5.[2]Ogdie A, et al. J Rheumatol. 2021;48:698–706.[3]Mease PJ, et al. J Rheumatol. 2018;45:1389–96.AcknowledgementsAbbVie funded this study and participated in the study design, research, analysis, data collection, interpretation of data, review, and approval of the abstract. No honoraria or payments were made for authorship. Medical writing support was provided by Julia Zolotarjova, MSc, MWC, of AbbVie.Disclosure of InterestsMartin Bergman Shareholder of: JNJ (parent of Janssen) and Merck, Speakers bureau: AbbVie, Amgen, BMS, Janssen, Merck, Novartis, Pfizer, Sanofi, and Sandoz, Consultant of: AbbVie, Amgen, BMS, Janssen, Merck, Novartis, Pfizer, Sanofi, and Sandoz, Jayeshkumar Patel Shareholder of: May own AbbVie stock or options, Employee of: AbbVie, Christopher Saffore Shareholder of: May own AbbVie stock or options, Employee of: AbbVie, Erin McDearmon-Blondell Shareholder of: May own AbbVie stock or options, Employee of: AbbVie, Ia Topuria Employee of: OM1, Cristi Cavanaugh Employee of: OM1
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Ogdie A, Coates L, Acayaba DE Toledo R, Biljan A, Jones H, Tacelosky K, Yue C, Padilla B, Bergman M. AB0905 Routine Assessment of Patient Index Data 3 (RAPID3) in Patients With Active Psoriatic Arthritis (PsA) After Inadequate Response or Intolerance to DMARDs: Pooled Results From the Phase 3, Randomized, Double-Blind KEEPsAKE 1 and 2 Trials. Ann Rheum Dis 2022. [DOI: 10.1136/annrheumdis-2022-eular.2913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
BackgroundPsA is a chronic, systemic inflammatory disease with diverse clinical manifestations that can impact a patients’ quality of life. Risankizumab (RZB), a humanized immunoglobulin G1 monoclonal antibody that specifically inhibits interleukin 23 by binding to its p19 subunit, is approved for the treatment of active PsA in adults. In the phase 3 KEEPsAKE 1 and 2 studies, RZB treatment resulted in significantly greater improvements in signs and symptoms of active PsA compared with placebo (PBO).1,2 RAPID3 is frequently used in clinical practice to evaluate PsA disease activity and consists of 3 key patient-reported measures (physical function, pain, and patient’s global assessment of disease activity [PtGA]).3ObjectivesTo evaluate short- (24 week) and long-term (52 week) improvements in RAPID3 scores and achievement of RAPID3 minimal clinically important difference (MCID) across the RZB KEEPsAKE 1 and 2 clinical program.MethodsIn KEEPsAKE 1 (NCT03675308) and KEEPsAKE 2 (NCT03671148), patients with active PsA who experienced inadequate response or intolerance to ≥ 1 csDMARD (KEEPsAKE 1) and/or ≤ 2 biological therapies (KEEPsAKE 2) were randomized to PBO or RZB 150 mg from baseline to week (W) 24; from W28–W52, all patients received open-label RZB 150 mg. At W16, nonresponders could add or modify rescue therapy. This post hoc analysis assessed the mean change from baseline to W24 and W52 in RAPID3 scores and the proportion of patients who achieved a RAPID3 MCID (defined as a decrease of ≥3.8 points4). Modified RAPID3 scores (range: 0–30) were calculated using pain scores, PtGA, and HAQ-DI, each rescaled to 0–10 and summed together.3ResultsA total of 961 and 443 patients were included from KEEPsAKE 1 and 2, respectively. At baseline, mean RAPID3 scores were 15.3 in both treatment arms of KEEPsAKE 1 (PBO n = 479, RZB n = 482) and 15.1 (PBO n = 219) and 14.8 (RZB n = 224) in KEEPsAKE 2. From W4 to W24, RAPID3 scores were significantly reduced with RZB treatment compared with PBO in both KEEPsAKE 1 (mean change from baseline at W24 of −5.3 vs −2.4, respectively, P <.001) and KEEPsAKE 2 (−3.8 vs −1.6, P <.001; Figure 1 A, B), and a significantly greater proportion of patients achieved MCID at W24 with RZB than with PBO in KEEPsAKE 1 (57.0% vs 36.4%, P <.001) and KEEPsAKE 2 (48.8% vs 32.8%, P <.001; Table 1). At W52 among patients who received RZB from W0–W52, mean change from baseline was −7.0 (KEEPsAKE 1) and −5.2 (KEEPsAKE 2; Figure 1 C, D), and MCID was achieved by 67.5% (KEEPsAKE 1) and 56.5% (KEEPsAKE 2) of patients. Patients who switched from PBO to RZB at W24 experienced similar and substantial improvements in RAPID3 scores by W52.Table 1.Proportion of Patients Achieving a Minimal Clinically Important Difference From Baseline in RAPID3 (AO).Patients, % (n/N) [95% CI]KEEPsAKE 1KEEPsAKE 2PBORZB 150 mgPBORZB 150 mgW2436.4 (166/456) [32.0, 40.8]57.0 (262/460) [52.4, 61.5]***32.8 (64/195) [26.2, 39.4]48.8 (104/213) [42.1, 55.5]***PBO to RZB 150 mgaRZB 150 mgPBO to RZB 150 mgaRZB 150 mgW5259.8 (260/435) [55.2, 64.4]67.5 (297/440) [63.1, 71.9]57.4 (105/183) [50.2, 64.5]56.5 (109/193) [49.5, 63.5]aPatients randomized to PBO at W0 switched to open-label RZB 150 mg at W24.***, P < .001 vs PBO.AO, as observed; PBO, placebo; RAPID3, Routine Assessment of Patient Index Data 3; RZB, risankizumab; W, week.Figure 1.Mean Change From Baseline in RAPID3 Scores During KEEPsAKE 1 and 2.**, P < .01; ***, P < .001 vs PBO.AO, as observed; LS, least squares; MMRM, mixed-effect model repeated measurement; PBO, placebo; RAPID3, Routine Assessment of Patient Index Data 3; RZB, risankizumab.ConclusionRZB 150 mg was associated with improvement in RAPID3 total scores over 24–52 weeks of treatment in patients with active PsA in KEEPsAKE 1 and 2.References[1]Kristensen LE, et al. Ann Rheum Dis. 2022;81:225–231.[2]Östör A, et al. Ann Rheum Dis. 2021;annrheumdis-2021-221048.[3]Coates LC, et al. Arthritis Care Res (Hoboken). 2018;70:1198–1205.[4]Ward MM, et al. J Rheumatol. 2019;46:27–30.AcknowledgementsAbbVie Inc. participated in the study design; study research; collection, analysis, and interpretation of data; and writing, reviewing, and approving of this abstract for submission. All authors had access to the data; participated in the development, review, and approval of and in the decision to submit this abstract to EULAR 2022 for consideration as a poster or oral presentation. No honoraria or payments were made for authorship. AbbVie and the authors thank all study investigators for their contributions and the patients who participated in this study. AbbVie funded the research for this study and provided writing support for this abstract.Medical writing assistance, funded by AbbVie, was provided by Callie A. S. Corsa, PhD, of JB Ashtin.Disclosure of InterestsAlexis Ogdie Consultant of: AO has received consulting fees and/or honoraria from AbbVie, Amgen, Bristol Myers Squibb, Celgene, CorEvitas, Gilead, Janssen, Eli Lilly, Novartis, Pfizer, and UCB, Grant/research support from: AO has received grants from AbbVie, Novartis, and Pfizer to the trustees of University of Pennsylvania, and from Amgen to Forward., Laura Coates Speakers bureau: LCC has been paid as a speaker for AbbVie, Amgen, Biogen, Celgene, Eli Lilly, Galapagos, Gilead, GSK, Janssen, Medac, Novartis, Pfizer and UCB., Consultant of: LCC has worked as a paid consultant for AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Galapagos, Janssen, Moonlake, Novartis, Pfizer and UCB, Grant/research support from: LCC has received grants/research support from AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer and UCB, RICARDO ACAYABA DE TOLEDO Speakers bureau: RAT has received honoraria as a speaker/consultant for Abbvie, Celltrion, Janssen, Novartis, Pfizer, and UCB, Consultant of: RAT has received honoraria as a speaker/consultant for Abbvie, Celltrion, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: RAT has received grants as an investigator from Abbvie, GSK, Novartis, and Pfizer., Ana Biljan Shareholder of: AB may hold AbbVie stock or stock options., Employee of: AB is a full-time employee of AbbVie., Heather Jones Shareholder of: HJ may hold AbbVie stock or stock options., Employee of: HJ is a full-time employee of AbbVie., Kristin Tacelosky Shareholder of: KT may hold AbbVie stock or stock options., Employee of: KT is a full-time employee of AbbVie., Cuiyong Yue Shareholder of: CY may hold AbbVie stock or stock options., Employee of: CY is a full-time employee of AbbVie., Byron Padilla Shareholder of: BP may hold AbbVie stock or stock options., Employee of: BP is a full-time employee of AbbVie., Martin Bergman Shareholder of: MB is a stock holder of Johnson & Johnson and Merck., Speakers bureau: MB has received honoraria as a speaker/consultant for Abbvie, Amgen, GSK, Janssen, Novartis, Pfizer, Sanofi, and Scipher, Consultant of: MB has received honoraria as a speaker/consultant for Abbvie, Amgen, GSK, Janssen, Novartis, Pfizer, Sanofi, and Scipher
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Popp CJ, Zhou B, Manigrasso MB, Li H, Curran M, Hu L, St-Jules DE, Alemán JO, Vanegas SM, Jay M, Bergman M, Segal E, Sevick MA, Schmidt AM. Soluble Receptor for Advanced Glycation End Products (sRAGE) Isoforms Predict Changes in Resting Energy Expenditure in Adults with Obesity during Weight Loss. Curr Dev Nutr 2022; 6:nzac046. [PMID: 35542387 PMCID: PMC9071542 DOI: 10.1093/cdn/nzac046] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/17/2022] [Accepted: 03/24/2022] [Indexed: 01/05/2023] Open
Abstract
Background Accruing evidence indicates that accumulation of advanced glycation end products (AGEs) and activation of the receptor for AGEs (RAGE) play a significant role in obesity and type 2 diabetes. The concentrations of circulating RAGE isoforms, such as soluble RAGE (sRAGE), cleaved RAGE (cRAGE), and endogenous secretory RAGE (esRAGE), collectively sRAGE isoforms, may be implicit in weight loss and energy compensation resulting from caloric restriction. Objectives We aimed to evaluate whether baseline concentrations of sRAGE isoforms predicted changes (∆) in body composition [fat mass (FM), fat-free mass (FFM)], resting energy expenditure (REE), and adaptive thermogenesis (AT) during weight loss. Methods Data were collected during a behavioral weight loss intervention in adults with obesity. At baseline and 3 mo, participants were assessed for body composition (bioelectrical impedance analysis) and REE (indirect calorimetry), and plasma was assayed for concentrations of sRAGE isoforms (sRAGE, esRAGE, cRAGE). AT was calculated using various mathematical models that included measured and predicted REE. A linear regression model that adjusted for age, sex, glycated hemoglobin (HbA1c), and randomization arm was used to test the associations between sRAGE isoforms and metabolic outcomes. Results Participants (n = 41; 70% female; mean ± SD age: 57 ± 11 y; BMI: 38.7 ± 3.4 kg/m2) experienced modest and variable weight loss over 3 mo. Although baseline sRAGE isoforms did not predict changes in ∆FM or ∆FFM, all baseline sRAGE isoforms were positively associated with ∆REE at 3 mo. Baseline esRAGE was positively associated with AT in some, but not all, AT models. The association between sRAGE isoforms and energy expenditure was independent of HbA1c, suggesting that the relation was unrelated to glycemia. Conclusions This study demonstrates a novel link between RAGE and energy expenditure in human participants undergoing weight loss.This trial was registered at clinicaltrials.gov as NCT03336411.
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Affiliation(s)
- Collin J Popp
- Center for Healthful Behavior Change, Department of Population Health, New York University Langone Health, New York, NY, USA
| | - Boyan Zhou
- Division of Biostatistics, Department of Population Health, New York University Langone Health, New York, NY, USA
| | - Michaele B Manigrasso
- Diabetes Research Program, Department of Medicine, New York University Langone Health, New York, NY, USA
| | - Huilin Li
- Division of Biostatistics, Department of Population Health, New York University Langone Health, New York, NY, USA
| | - Margaret Curran
- Center for Healthful Behavior Change, Department of Population Health, New York University Langone Health, New York, NY, USA
| | - Lu Hu
- Center for Healthful Behavior Change, Department of Population Health, New York University Langone Health, New York, NY, USA
| | - David E St-Jules
- Department of Nutrition, University of Nevada, Reno, Reno, NV, USA
| | - José O Alemán
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, New York University Langone Health, New York, NY, USA
| | - Sally M Vanegas
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, New York University Langone Health, New York, NY, USA
| | - Melanie Jay
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, New York University Langone Health, New York, NY, USA
| | - Michael Bergman
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, New York University Langone Health, New York, NY, USA
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - Mary A Sevick
- Center for Healthful Behavior Change, Department of Population Health, New York University Langone Health, New York, NY, USA
- Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, New York University Langone Health, New York, NY, USA
| | - Ann M Schmidt
- Diabetes Research Program, Department of Medicine, New York University Langone Health, New York, NY, USA
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Bergman M, Tundia N, Bryant A, Topuria I, Brecht T, Dunlap K, Gibofsky A. POS0436 PATIENT CHARACTERISTICS AND OUTCOMES IN PATIENTS WITH RHEUMATOID ARTHRITIS TREATED WITH UPADACITINIB: THE OM1 RA REGISTRY. Ann Rheum Dis 2021. [DOI: 10.1136/annrheumdis-2021-eular.172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Background:Upadacitinib (UPA) has demonstrated efficacy in the treatment of rheumatoid arthritis (RA) in randomized controlled trials,1-6 but there are limited data available on its real-world use and effectiveness in patients with RA.Objectives:To describe the characteristics and clinical outcomes at 3 months among real-world patients with RA initiating UPA.Methods:The data source for this study was the OM1 RA Registry, a subset of the OM1 Real-World Data Cloud (OM1, Inc, Boston, MA, US), a large, linked clinical and administrative dataset derived from medical and pharmacy claims, electronic medical record data, and death data. This analysis includes data collected in patients who initiated UPA during or after August 2019. Patients had ≥1 prescription for UPA (index date was first UPA prescription), were ≥18 years of age at index date, had ≥6 months of available data in the OM1 RA Registry prior to index date (ie, baseline period), ≥1 baseline disease activity measure, and ≥1 follow-up disease activity measure (3 or 6 months post-index). Disease activity was based on RAPID3 or CDAI. Multivariate analyses were conducted using a mixed-effects linear model adjusting for age, sex, and baseline scores. Outcomes were also assessed by therapy status (monotherapy or combination therapy) and targeted immunomodulator (TIM) use (naïve vs experienced).Results:Inclusion criteria were met by 1,102 patients, of whom 620 were on monotherapy and 482 were on combination therapy at index. Mean age was 57.7 years, 83% were female, 75% had prior treatment with a biologic, and 47% had prior treatment with a Janus kinase inhibitor. Of 651 patients with known disease activity category, 113 (17%) were in low disease activity (LDA)/remission. At baseline, overall mean±SD scores were 19.9±12.3 for CDAI, 4.5±2.4 for RAPID3, 5.7±2.8 for pain, 5.2±3.0 for fatigue, 3.1±2.7 for MDHAQ Physician Global Assessment (PGA), 5.2±2.8 for MDHAQ Patient Global Assessment (PtGA), and 3.1±2.3 for MDHAQ Functional Index. At 3 months post-UPA initiation, mean (95% CI) change in CDAI was –5.1 (–7.5 to –2.7) in the monotherapy group and –5.9 (–8.7 to –3.0) in the combination group. At 3 months, 29% (109/374) of patients were in LDA/remission and 32% (120/374) of patients showed improvement in disease activity. Of 94 patients with moderate disease at baseline, 34 (36%) were in LDA/remission at 3 months. Of 215 patients with high disease at baseline, 30 (14%) were in LDA/remission and 49 (23%) had moderate disease at 3 months. RAPID3 and other outcomes also improved at 3 months in the monotherapy and combination therapy groups (Figure 1). Improvements in disease activity were observed at 3 months and maintained at 6 months post-UPA initiation. Of 1,102 patients, 16% were TIM naïve and 84% TIM experienced. Both TIM-naïve and TIM-experienced patients achieved significant mean changes in CDAI (–5.7 [–10.8 to –0.6] and–5.0 [–7.0 to –3.0], respectively) and RAPID3 (–1.0 [–1.6 to –0.4] and –0.5 [–0.8 to –0.1]) at 3 months (Table 1). Improvements in clinical outcomes were maintained at 6 months in both TIM-naïve and TIM-experienced patients.Conclusion:Significant improvements in disease activity were consistently observed at 3 months and maintained at 6 months post-UPA initiation regardless of monotherapy, combination therapy, or prior TIM use.References:[1]Fleischmann R. Arthritis Rheumatol. 2019;71:1788–800.[2]Smolen JS. Lancet. 2019;393:2303–11.[3]Burmester GR. Lancet. 2018;382:2505–12.[4]Genovese MC. Lancet. 2018;391:2513–24.[5]van Vollenhoven R. Arthritis Rheumatol. 2020;72:1607–20.[6]Rubbert-Roth A. N Engl J Med. 2020;383:1511–21.Table 1.Change in clinical outcomes from baseline at 3 months: TIM-naïve and TIM-experienced groupsTIM naïve(N=179)TIM experienced(N=923)nMean changenMean changeCDAI36–5.7*160–5.0*RAPID367–1.0*189–0.5*Pain (VAS)76–1.5*237–0.9*Fatigue46–0.7149–0.5MDHAQ PGA65–0.7*251–0.7*MDHAQ PtGA97–0.6*383–0.3MDHAQ Functional Index72–0.7*215–0.2*Statistically significant change from baseline (P<0.05).Acknowledgements:Funding statement: Financial support for the study was provided by AbbVie. AbbVie participated in the interpretation of data, review, and approval of the abstract. All authors contributed to the development of the publication and maintained control over the final content.Acknowledgment:Medical writing services were provided by Joann Hettasch of Fishawack Facilitate Ltd, part of Fishawack Health, and funded by AbbVie.Disclosure of Interests:Martin Bergman Shareholder of: JNJ (parent of Janssen), Speakers bureau: AbbVie, Amgen, BMS, Genentech, Gilead, Janssen, Merck, Novartis, Pfizer, Regeneron, Sanofi, Sandoz, Consultant of: AbbVie, Amgen, BMS, Genentech, Gilead, Janssen, Merck, Novartis, Pfizer, Regeneron, Sanofi, Sandoz, Namita Tundia Shareholder of: AbbVie, Employee of: AbbVie, Allison Bryant: None declared, Ia Topuria: None declared, Tom Brecht: None declared, Kendall Dunlap Shareholder of: AbbVie, Employee of: AbbVie, Allan Gibofsky Shareholder of: AbbVie, Amgen, Horizon, J&J, Pfizer, Regeneron, Speakers bureau: AbbVie, Acquist, Amgen, Lilly, Merck, Pfizer, Sandoz, Samumed, Consultant of: AbbVie, Acquist, Amgen, Lilly, Merck, Pfizer, Sandoz, Samumed
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Bergman M, Buch MH, Tanaka Y, Citera G, Bahlas S, Wong E, Song Y, Tundia N, Suboticki J, Strand V. POS0670 ROUTINE ASSESSMENT OF PATIENT INDEX DATA 3 (RAPID3) IN PATIENTS WITH RHEUMATOID ARTHRITIS TREATED WITH LONG-TERM UPADACITINIB THERAPY. Ann Rheum Dis 2021. [DOI: 10.1136/annrheumdis-2021-eular.2090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Background:Routine Assessment of Patient Index Data 3 (RAPID3) is a pooled index of 3 patient-reported measures: patient global assessment, pain, and physical function. RAPID3 was shown to correlate with other composite measures of disease activity1 and is recommended by the American College of Rheumatology for use in clinical practice.2Objectives:To evaluate the impact of upadacitinib (UPA) versus comparators on RAPID3 over 60 weeks, as well as the correlation of RAPID3 scores with other disease measures in the UPA phase 3 SELECT clinical program.Methods:This post hoc analysis included placebo-controlled (SELECT-NEXT, -BEYOND, and -COMPARE) and active comparator-controlled (SELECT-EARLY, -MONOTHERAPY, and -COMPARE) trials. Patients received UPA as monotherapy or in combination with conventional synthetic disease-modifying antirheumatic drugs (csDMARDs). Mean change from baseline in RAPID3 and the proportion of patients reporting RAPID3 remission (≤3), low (LDA, >3 to ≤6), moderate (MDA, >6 to ≤12), and high disease activity (HDA, >12) were assessed. Correlations between absolute scores for RAPID3 and Clinical Disease Activity Index (CDAI), Simplified Disease Activity Index (SDAI), and 28-joint Disease Activity Score with C-reactive protein (DAS28[CRP]) were assessed using Spearman correlation coefficients. All data are as observed.Results:A total of 661, 498, 648, 1629, and 945 patients were included from SELECT-NEXT, -BEYOND, -MONOTHERAPY, -COMPARE, and -EARLY. At baseline, the majority of patients across all studies were in RAPID3 HDA (mean baseline RAPID3 [across all studies], 17.2–19.2) (Table 1 and Figure 1). Improvements from baseline in RAPID3 were observed with UPA 15 mg and 30 mg through Week 60, with numerically greater improvements observed with UPA compared with active comparators (Table 1). Across studies, mean improvements in RAPID3 exceeded the minimal clinically important difference (MCID) with UPA and adalimumab (ADA) treatment (MCID=3.83). By Week 60, approximately one-half of UPA-treated patients were in RAPID3 remission or LDA, with only 10–25% remaining in HDA, except for the more refractory population in SELECT-BEYOND, in which ~38% of patients remained in HDA (Figure 1). RAPID3 scores moderately to strongly correlated with CDAI (ρ=0.69–0.83), SDAI (ρ=0.69–0.82), and DAS28(CRP) (ρ=0.58–0.77), across all studies, at Week 60 (all p<0.001).Conclusion:UPA, as monotherapy or in combination with csDMARDs, was associated with improvements in patient-reported disease activity, pain, and physical function, as assessed by RAPID3 over 60 weeks in the phase 3 SELECT clinical program. RAPID3 continues to be an important tool in clinical practice to assess disease activity, as it was shown to correlate to other disease activity measures and allows for rapid scoring.References:[1]Pincus T, et al. Arthritis Care Res (Hoboken) 2010;62:181–9.[2]England BR, et al. Arthritis Care Res (Hoboken) 2019;71:1540–55.[3]Ward MM, et al. J Rheumatol 2019;46:27–30.Table 1.Change from BL in RAPID3 at Week 60 (as observed)Phase 3 studyGroupnaMean (SD) BL scoreMean (SD) change from BLbSELECT-EARLYc(MTX-naïve)MTX23618.5 (5.6)−9.6 (7.5)UPA 15 mg QD26918.9 (5.6)−12.0 (7.6)UPA 30 mg QD25318.2 (5.6)−13.4 (7.2)SELECT-NEXT(csDMARD-IR)UPA 15 mg QD17217.7 (5.1)−11.1 (7.3)UPA 30 mg QD17217.6 (5.3)−10.4 (6.8)SELECT-MONOTHERAPY(MTX-IR)UPA 15 mg QD17217.4 (5.8)−9.6 (7.4)UPA 30 mg QD18017.2 (5.9)−10.6 (7.2)SELECT-COMPAREc(MTX-IR)UPA 15 mg QD55218.5 (5.5)−10.2 (7.1)ADA 40 mg EOW26418.7 (5.4)−8.8 (6.7)SELECT-BEYOND(bDMARD-IR)UPA 15 mg QD13319.2 (5.1)−8.6 (6.8)UPA 30 mg QD11818.5 (5.3)−9.3 (7.3)b, biologic; BL, baseline; EOW, every other week; IR, inadequate response; MTX, methotrexate; QD, once daily; SD, standard deviationaNumber of patients with RAPID3 values at both BL and Week 60. bNegative values indicate improvement from BL. cObserved data include patients rescued to UPA and/or ADA; treatment effect may include both the randomized and switch treatments in these patientsAcknowledgements:AbbVie funded this study; contributed to its design; participated in data collection, analysis, and interpretation of the data; and participated in the writing, review, and approval of the abstract. No honoraria or payments were made for authorship. Medical writing support was provided by Grant Kirkpatrick, MSc, of 2 the Nth (Cheshire, UK), and was funded by AbbVie.Disclosure of Interests:Martin Bergman Shareholder of: Johnson & Johnson, Speakers bureau: AbbVie, Celgene, GSK, MSD, Novartis, Pfizer, and Sanofi/Regeneron, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Genentech/Roche, Gilead, Horizon, Janssen, MSD, Novartis, Pfizer, Sandoz, Sanofi/Regeneron, and Scipher, Maya H Buch Consultant of: AbbVie, Eli Lilly, Merck-Serono, Pfizer, Sandoz, and Sanofi, Grant/research support from: Pfizer, Roche, and UCB, Yoshiya Tanaka Speakers bureau: AbbVie, Asahi Kasei, Astellas, Bristol-Myers Squibb, Chugai, Daiichi Sankyo, Eisai, Eli Lilly, GSK, Janssen, Mitsubishi Tanabe, Novartis, Pfizer, Sanofi, Takeda, UCB, and YL Biologics, Grant/research support from: AbbVie, Astellas, Bristol-Myers Squibb, Chugai, Daiichi Sankyo, Eisai, Mitsubishi Tanabe, MSD, Ono, Taisho Toyama, and Takeda, Gustavo Citera Consultant of: AbbVie, Bristol-Myers Squibb, Eli Lilly, Genzyme, Pfizer, and Roche, Sami Bahlas: None declared, Ernest Wong Consultant of: AbbVie, Chugai, Eli Lilly, MSD, Novartis, Pfizer, Roche, and UCB, Grant/research support from: AbbVie, Chugai, Novartis, and UCB, Yanna Song Shareholder of: May own stock or options in AbbVie, Employee of: AbbVie, Namita Tundia Shareholder of: May own stock or options in AbbVie, Employee of: AbbVie, Jessica Suboticki Shareholder of: May own stock or options in AbbVie, Employee of: AbbVie, Vibeke Strand Consultant of: AbbVie, Amgen, Arena, AstraZeneca, Bayer, Bristol-Myers Squibb, Boehringer Ingelheim, Celltrion, Eli Lilly, Gilead, Ichnos, Inmedix, Janssen, Kiniksa, MSD, Myriad Genetics, Novartis, Pfizer, Regeneron, Samsung, Sandoz, Sanofi, Scipher, Setpoint, and UCB.
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Nordström T, Discacciati A, Bergman M, Aly M, Annerstedt M, Glaessgen A, Carlsson S, Jäderling F, Eklund M, Grönberg H. Prostate cancer screening using prostate-specific antigen, a multiplex blood-test, magnetic resonance imaging and targeted prostate biopsies: The STHLM3MRI trial. Eur Urol 2021. [DOI: 10.1016/s0302-2838(21)01387-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Ahuja V, Aronen P, Pramodkumar TA, Looker H, Chetrit A, Bloigu AH, Juutilainen A, Bianchi C, La Sala L, Anjana RM, Pradeepa R, Venkatesan U, Jebarani S, Baskar V, Fiorentino TV, Timpel P, DeFronzo RA, Ceriello A, Del Prato S, Abdul-Ghani M, Keinänen-Kiukaanniemi S, Dankner R, Bennett PH, Knowler WC, Schwarz P, Sesti G, Oka R, Mohan V, Groop L, Tuomilehto J, Ripatti S, Bergman M, Tuomi T. Erratum. Accuracy of 1-Hour Plasma Glucose During the Oral Glucose Tolerance Test in Diagnosis of Type 2 Diabetes in Adults: A Meta-analysis. Diabetes Care 2021;44:1062-1069. Diabetes Care 2021; 44:1457. [PMID: 33931489 PMCID: PMC8247490 DOI: 10.2337/dc21-er06c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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Mease PJ, Kavanaugh A, Ogdie A, Wells AF, Bergman M, Gladman DD, Behrens F, Klyachkin Y, Richter S, Teng L, Smolen JS. AB0553 BASELINE DISEASE ACTIVITY AS A PREDICTOR FOR ACHIEVING cDAPSA TREATMENT TARGETS WITH APREMILAST IN DMARD-NAIVE PATIENTS WITH MANIFESTATIONS OF ACTIVE PSORIATIC ARTHRITIS. Ann Rheum Dis 2021. [DOI: 10.1136/annrheumdis-2021-eular.2224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Background:In PALACE 4, DMARD-naive patients (pts) with moderately active (ModDA) psoriatic arthritis (PsA) at baseline (BL) were more likely to achieve Clinical Disease Activity Index for PsA (cDAPSA) treatment targets (cDAPSA remission [REM] or low disease activity [LDA]) at Week 52 with continued apremilast 30 mg BID (APR) treatment than pts with high disease activity (HDA) at BL. Pts who achieved cDAPSA treatment targets also had no or mild articular and extra-articular disease activity by Week 52. Whether specific PsA manifestations other than arthritis impact the achievement of cDAPSA treatment targets in this population is unknown.Objectives:To assess the predictive value of BL clinical disease status on achieving cDAPSA treatment targets in DMARD-naive pts in PALACE 4 with PsA in ModDA or HDA who exhibited manifestations of skin involvement, enthesitis, and/or dactylitis at BL.Methods:This post hoc analysis included APR-treated pts in ModDA or HDA with available cDAPSA data at BL and Week 52 who exhibited any of the PsA manifestations at BL, including skin-involved body surface area (BSA) ≥3%, Maastricht Ankylosing Spondylitis Entheses Score (MASES) >0, or dactylitis count >0. Pts were divided into 4 subgroups based on number of manifestations: ≥1, only 1, any 2, or all 3. The proportions of pts who shifted across ModDA (>13 to ≤27) and HDA (>27) cDAPSA categories at BL to REM (≤4) and LDA (>4 to ≤13) treatment targets at Week 52 were calculated (data as observed).Results:In 176 PALACE 4 pts with PsA receiving APR, 165 had involvement in ≥1 PsA manifestation in addition to peripheral arthritis (ie, skin/enthesitis/dactylitis) at BL. This population had a mean age of 48.8 years, PsA duration of 3.6 years, Psoriasis Area and Severity Index (PASI) score of 6.6, MASES of 3.8, and dactylitis count of 3.5 (Table 1). Within this subgroup, 32.7% had only 1 of these non-arthritic PsA manifestations, 50.9% had any 2, and 16.4% had all 3. In pts with ≥1 manifestation, a greater proportion in ModDA achieved REM/LDA at Week 52 than those in HDA (66.7% vs 32.2%; risk difference: 0.34) (Figure 1). Similarly, greater rates of treatment target achievement were observed in subgroups of pts in ModDA vs HDA and only 1 (72.2% vs 39.1%; risk difference: 0.33), any 2 (57.1% vs 28.6%; risk difference: 0.29), or all 3 (75.0% vs 33.3%; risk difference: 0.42) PsA manifestations (Figure 1).Conclusion:In DMARD-naive pts exhibiting various non-arthritic manifestations of active PsA (ie, skin/enthesitis/dactylitis), those in ModDA at BL were more likely to achieve cDAPSA REM or LDA at Week 52 of APR treatment than pts in HDA. This observation was consistent whether pts had only 1 or multiple manifestations. These findings are consistent with the probability of achieving treatment targets demonstrated in the overall population in PALACE 4 (61.7% ModDA vs 28.2% HDA).Table 1.BL Demographics and Disease Characteristics in Pts With ≥1 Manifestations of PsA (Skin Involvement, Enthesitis, and/or Dactylitis) Treated With APR (N = 165)Age*, years48.8 (12.5)Women, n (%)87 (52.7)BMI*, kg/m229.9 (6.5)Duration of PsA*, years3.6 (5.0)Duration of psoriasis*, years15.5 (13.3)cDAPSA (0-154)*39.4 (19.7)Swollen joint count (0-66)*10.3 (7.7)Tender joint count (0-68)*18.5 (12.9)Pt’s Assessment of Pain (VAS 0-100 mm)*52.8 (21.5)Pt’s Global Assessment (VAS 0-100 mm)*53.8 (20.1)Physician’s Global Assessment (VAS 0-100 mm)*52.2 (17.6)PASI score (0-72)*,†6.6 (5.1)MASES (0-13)*,‡3.8 (3.0)Dactylitis count (0-20)*,§3.5 (3.3)Corticosteroid use, n (%)13 (7.9)NSAID use, n (%)126 (76.4)*Mean (SD).†In pts with BSA ≥3% at BL.‡In pts with enthesitis at BL.§In pts with dactylitis at BL.Acknowledgements:This study was funded by Celgene. Additional analyses were funded by Amgen Inc. Writing support was funded by Amgen Inc. and provided by Kristin Carlin, RPh, MBA, of Peloton Advantage, LLC, an OPEN Health company.Figure 1.Disclosure of Interests:Philip J Mease Speakers bureau: AbbVie, Amgen Inc., Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen Inc., Boehringer Ingelheim, BMS, Celgene, Eli Lilly, Galapagos, GSK, Novartis, Pfizer, Sun, and UCB, Grant/research support from: AbbVie, Amgen Inc., Boehringer Ingelheim, BMS, Celgene, Eli Lilly, Galapagos, GSK, Novartis, Pfizer, Sun, and UCB, Arthur Kavanaugh Grant/research support from: AbbVie, Amgen Inc., AstraZeneca, BMS, Celgene, Centocor-Janssen, Pfizer, Roche, and UCB, Alexis Ogdie Consultant of: AbbVie, Amgen Inc., BMS, Celgene, Corrona, Eli Lilly, Gilead, Novartis, Pfizer, and UCB, Grant/research support from: Novartis and Pfizer, Alvin F. Wells Speakers bureau: AbbVie, Alexion, Amgen Inc., BMS, Celgene, Horizon, Lilly, Novartis, and UCB, Consultant of: AbbVie, Alexion, Amgen Inc., BMS, Celgene, Horizon, Lilly, Novartis, and UCB, Grant/research support from: AbbVie, Celgene, and Lilly, Martin Bergman Shareholder of: Johnson & Johnson, Speakers bureau: AbbVie, Amgen Inc., Novartis, Pfizer, and Sanofi, Consultant of: AbbVie, BMS, Celgene, Genentech, Janssen, Merck, Novartis, Pfizer, and Sanofi, Dafna D Gladman Consultant of: AbbVie, Amgen, BMS, Celgene Corporation, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Amgen, BMS, Celgene Corporation, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, and UCB, Frank Behrens Speakers bureau: AbbVie, Biotest, Boehringer Ingelheim, Celgene, Chugai, Eli Lilly, Genzyme, Janssen, Novartis, Pfizer, Roche, and UCB, Grant/research support from: AbbVie, Chugai, Janssen, Roche, and Pfizer, Yuri Klyachkin Employee of: Amgen Inc., Sven Richter Employee of: Amgen Inc., Lichen Teng Employee of: Amgen Inc., Josef S. Smolen Speakers bureau: AbbVie, Amgen Inc., AstraZeneca, Astro, Celgene, Celtrion, Eli Lilly, Glaxo, ILTOO, Janssen, Medimmune, MSD, Novartis, Pfizer, Roche, Samsung, Sanofi, and UCB, Consultant of: AbbVie, Amgen Inc., AstraZeneca, Astro, Celgene, Celtrion, Eli Lilly, Glaxo, ILTOO, Janssen, Medimmune, MSD, Novartis, Pfizer, Roche, Samsung, Sanofi, and UCB, Grant/research support from: AbbVie, Eli Lilly, Janssen, MSD, Medimmune, Pfizer, and Roche.
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Ahuja V, Aronen P, Pramodkumar TA, Looker H, Chetrit A, Bloigu AH, Juutilainen A, Bianchi C, La Sala L, Anjana RM, Pradeepa R, Venkatesan U, Jebarani S, Baskar V, Fiorentino TV, Timpel P, DeFronzo RA, Ceriello A, Del Prato S, Abdul-Ghani M, Keinänen-Kiukaanniemi S, Dankner R, Bennett PH, Knowler WC, Schwarz P, Sesti G, Oka R, Mohan V, Groop L, Tuomilehto J, Ripatti S, Bergman M, Tuomi T. Accuracy of 1-Hour Plasma Glucose During the Oral Glucose Tolerance Test in Diagnosis of Type 2 Diabetes in Adults: A Meta-analysis. Diabetes Care 2021; 44:1062-1069. [PMID: 33741697 PMCID: PMC8578930 DOI: 10.2337/dc20-1688] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 01/11/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE One-hour plasma glucose (1-h PG) during the oral glucose tolerance test (OGTT) is an accurate predictor of type 2 diabetes. We performed a meta-analysis to determine the optimum cutoff of 1-h PG for detection of type 2 diabetes using 2-h PG as the gold standard. RESEARCH DESIGN AND METHODS We included 15 studies with 35,551 participants from multiple ethnic groups (53.8% Caucasian) and 2,705 newly detected cases of diabetes based on 2-h PG during OGTT. We excluded cases identified only by elevated fasting plasma glucose and/or HbA1c. We determined the optimal 1-h PG threshold and its accuracy at this cutoff for detection of diabetes (2-h PG ≥11.1 mmol/L) using a mixed linear effects regression model with different weights to sensitivity/specificity (2/3, 1/2, and 1/3). RESULTS Three cutoffs of 1-h PG, at 10.6 mmol/L, 11.6 mmol/L, and 12.5 mmol/L, had sensitivities of 0.95, 0.92, and 0.87 and specificities of 0.86, 0.91, and 0.94 at weights 2/3, 1/2, and 1/3, respectively. The cutoff of 11.6 mmol/L (95% CI 10.6, 12.6) had a sensitivity of 0.92 (0.87, 0.95), specificity of 0.91 (0.88, 0.93), area under the curve 0.939 (95% confidence region for sensitivity at a given specificity: 0.904, 0.946), and a positive predictive value of 45%. CONCLUSIONS The 1-h PG of ≥11.6 mmol/L during OGTT has a good sensitivity and specificity for detecting type 2 diabetes. Prescreening with a diabetes-specific risk calculator to identify high-risk individuals is suggested to decrease the proportion of false-positive cases. Studies including other ethnic groups and assessing complication risk are warranted.
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Affiliation(s)
- Vasudha Ahuja
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland
| | - Pasi Aronen
- Biostatistics Unit, Faculty of Medicine, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - T A Pramodkumar
- Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialties Centre, ICMR Centre for Advanced Research on Diabetes and IDF Centre of Excellence in Diabetes, Chennai, India
| | - Helen Looker
- Phoenix Epidemiology and Clinical Research Branch, National Institute for Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Angela Chetrit
- Unit for Cardiovascular Epidemiology, Gertner Institute for Epidemiology and Health Policy Research, Ramat Gan, Israel
| | - Aini H Bloigu
- Center for Life Course Health Research, University of Oulu, Oulu, Finland
| | - Auni Juutilainen
- University of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
| | - Cristina Bianchi
- Section of Diabetes and Metabolic Diseases, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
| | - Lucia La Sala
- Department of Cardiovascular and Dysmetabolic Diseases, IRCCS MultiMedica, Milan, Italy
| | - Ranjit Mohan Anjana
- Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialties Centre, ICMR Centre for Advanced Research on Diabetes and IDF Centre of Excellence in Diabetes, Chennai, India
| | - Rajendra Pradeepa
- Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialties Centre, ICMR Centre for Advanced Research on Diabetes and IDF Centre of Excellence in Diabetes, Chennai, India
| | - Ulagamadesan Venkatesan
- Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialties Centre, ICMR Centre for Advanced Research on Diabetes and IDF Centre of Excellence in Diabetes, Chennai, India
| | - Sarvanan Jebarani
- Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialties Centre, ICMR Centre for Advanced Research on Diabetes and IDF Centre of Excellence in Diabetes, Chennai, India
| | - Viswanathan Baskar
- Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialties Centre, ICMR Centre for Advanced Research on Diabetes and IDF Centre of Excellence in Diabetes, Chennai, India
| | - Teresa Vanessa Fiorentino
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Patrick Timpel
- Department of Medicine III, Technical University of Dresden, Dresden, Germany
| | - Ralph A DeFronzo
- Division of Diabetes, University of Texas Health Science Center at San Antonio, San Antonio, TX
| | - Antonio Ceriello
- Department of Cardiovascular and Dysmetabolic Diseases, IRCCS MultiMedica, Milan, Italy
| | - Stefano Del Prato
- Section of Diabetes and Metabolic Diseases, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
| | - Muhammad Abdul-Ghani
- Division of Diabetes, University of Texas Health Science Center at San Antonio, San Antonio, TX
| | - Sirkka Keinänen-Kiukaanniemi
- Center for Life Course Health Research, University of Oulu, Oulu, Finland.,Healthcare and Social Services of Selänne, Pyhäjärvi, Finland
| | - Rachel Dankner
- Unit for Cardiovascular Epidemiology, Gertner Institute for Epidemiology and Health Policy Research, Ramat Gan, Israel.,Department of Epidemiology and Preventive Medicine, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Peter H Bennett
- Phoenix Epidemiology and Clinical Research Branch, National Institute for Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - William C Knowler
- Phoenix Epidemiology and Clinical Research Branch, National Institute for Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Peter Schwarz
- Department of Medicine III, Technical University of Dresden, Dresden, Germany.,Paul Langerhans Institute of the Helmholtz Zentrum München at the University Hospital Carl Gustav Carus and the Medical Faculty of TU Dresden (PLID), Dresden, Germany.,German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Giorgio Sesti
- Department of Clinical and Molecular Medicine, Sapienza University of Rome, Rome, Italy
| | - Rie Oka
- Department of Internal Medicine, Hokuriku Central Hospital, Toyama, Japan
| | - Viswanathan Mohan
- Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialties Centre, ICMR Centre for Advanced Research on Diabetes and IDF Centre of Excellence in Diabetes, Chennai, India
| | - Leif Groop
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.,Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Jaakko Tuomilehto
- Public Health Promotion Unit, Finnish Institute for Health and Welfare, Helsinki, Finland.,Department of Public Health, University of Helsinki, Helsinki, Finland.,Saudi Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.,Department of Public Health, Clinicum, University of Helsinki, Helsinki, Finland.,Broad Institute of the Massachusetts Institute of Technology and Harvard University, Cambridge, MA
| | - Michael Bergman
- Division of Endocrinology and Metabolism, Department of Medicine and Department of Population Health, and NYU Langone Diabetes Prevention Program, NYU Grossman School of Medicine, New York, NY
| | - Tiinamaija Tuomi
- Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.,Lund University Diabetes Centre, Lund University, Malmö, Sweden.,Abdominal Centre, Endocrinology, Helsinki University Hospital, and Folkhalsan Research Centre, Biomedicum, and Research Program Unit, Clinical and Molecular Medicine, University of Helsinki, Helsinki, Finland
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Lu JE, Bergman M, Burnstine MA. Technique for modified transantral orbital decompression for improved cosmesis in stable thyroid eye disease. Int J Oral Maxillofac Surg 2021; 50:1440-1442. [PMID: 33658150 DOI: 10.1016/j.ijom.2021.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 11/28/2020] [Accepted: 02/03/2021] [Indexed: 10/22/2022]
Abstract
Functional and aesthetic rehabilitation of exophthalmos in stable thyroid eye disease (TED) can be achieved with a variety of surgical approaches. This article illustrates modifications of the classic transantral technique to provide a graded orbital decompression and achieve improved cosmesis. A retrospective chart review was performed of stable TED patients who elected to undergo the modified transantral decompression; illustrative cases are described. This modified transantral orbital decompression allows for graded orbital decompression surgery, adding to the range of treatment options for stable TED patients.
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Affiliation(s)
- J E Lu
- Department of Ophthalmology, Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - M Bergman
- Department of Ophthalmology, Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Eyesthetica, Oculofacial and Cosmetic Surgery Associates, Los Angeles, CA, USA
| | - M A Burnstine
- Department of Ophthalmology, Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Eyesthetica, Oculofacial and Cosmetic Surgery Associates, Los Angeles, CA, USA.
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Abstract
Evidence from populations at risk for type 1 diabetes, type 2 diabetes or gestational diabetes substantiates the 1-hour plasma glucose as a sensitive alternative marker for identifying high-risk individuals when ß-cell function is relatively more functional. An elevated 1-hour plasma glucose could therefore diagnose dysglycemia and risk for complications across the glycemic spectrum. Reducing the 2-hour oral glucose tolerance test to 1-hour would reduce the burden on patients, likely reduce costs, and enhance its accessibility in practice.
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Jagannathan R, DuBose CW, Mabundo LS, Chung ST, Ha J, Sherman A, Bergman M, Sumner AE. The OGTT is highly reproducible in Africans for the diagnosis of diabetes: Implications for treatment and protocol design. Diabetes Res Clin Pract 2020; 170:108523. [PMID: 33153960 PMCID: PMC7578647 DOI: 10.1016/j.diabres.2020.108523] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [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: 08/31/2020] [Revised: 10/08/2020] [Accepted: 10/14/2020] [Indexed: 01/20/2023]
Abstract
Whether an OGTT reproducibly detects either type 2 diabetes (T2D) or prediabetes in Africans in unknown. Therefore, 131 Africans had two OGTT. Diagnostic reproducibility for T2D was excellent (κ = 0.84), but only moderate for prediabetes (κ = 0.51). A single OGTT positive for T2D may be sufficient to guide clinical care and inform epidemiologic study design. ClinicalTrials.gov Identifier: NCT00001853.
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Affiliation(s)
- Ram Jagannathan
- Department of Medicine, Emory University School of Medicine, Atlanta, Georgia, United States
| | - Christopher W DuBose
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, United States
| | - Lilian S Mabundo
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, United States
| | - Stephanie T Chung
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, United States
| | - Joon Ha
- Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Arthur Sherman
- Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, United States
| | - Michael Bergman
- Division of Endocrinology and Metabolism, Department of Medicine and of Population Health, New York University School of Medicine, New York, NY, United States
| | - Anne E Sumner
- Section on Ethnicity and Health, Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, United States; National Institute of Minority Health and Health Disparities, National Institutes of Health, Bethesda, MD, United States.
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Grinshpun SA, Corey J, Yermakov M, Wu B, Strickland KT, Bergman M, Zhuang Z. New respirator performance monitor (RePM) for powered air-purifying respirators. J Occup Environ Hyg 2020; 17:538-545. [PMID: 32941118 PMCID: PMC10065132 DOI: 10.1080/15459624.2020.1814491] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Powered air-purifying respirators (PAPRs) that offer protection from particulates are deployed in different workplace environments. Usage of PAPRs by healthcare workers is rapidly increasing; these respirators are often considered the best option in healthcare settings, particularly during public health emergency situations, such as outbreaks of pandemic diseases. At the same time, lack of user training and certain vigorous work activities may lead to a decrease in a respirator's performance. There is a critical need for real-time performance monitoring of respiratory protective devices, including PAPRs. In this effort, a new robust and low-cost real-time performance monitor (RePM) capable of evaluating the protection offered by a PAPR against aerosol particles at a workplace was developed. The new device was evaluated on a manikin and on human subjects against a pair of condensation nuclei counters (P-Trak) used as the reference protection measurement system. The outcome was expressed as a manikin-based protection factor (mPF) and a Simulated Workplace Protection Factor (SWPF) determined while testing on subjects. For the manikin-based testing, the data points collected by the two methods were plotted against each other; a near-perfect correlation was observed with a correlation coefficient of 0.997. This high correlation is particularly remarkable since RePM and condensation particle counter (CPC) measure in different particle size ranges. The data variability increased with increasing mPF. The evaluation on human subjects demonstrated that RePM prototype provided an excellent Sensitivity (96.3% measured on human subjects at a response time of 60 sec) and a Specificity of 100%. The device is believed to be the first of its kind to quantitatively monitor PAPR performance while the wearer is working; it is small, lightweight, and does not interfere with job functions.
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Affiliation(s)
- Sergey A Grinshpun
- Center for Health-Related Aerosol Studies, Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, Ohio, USA
| | - Jonathan Corey
- Center for Health-Related Aerosol Studies, Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, Ohio, USA
| | - Michael Yermakov
- Center for Health-Related Aerosol Studies, Department of Environmental and Public Health Sciences, University of Cincinnati, Cincinnati, Ohio, USA
| | - Bingbing Wu
- National Personal Protective Technology Laboratory, National Institute for Occupational Safety and Health (NIOSH), Pittsburgh, Pennsylvania, USA
| | - Kevin T Strickland
- National Personal Protective Technology Laboratory, National Institute for Occupational Safety and Health (NIOSH), Pittsburgh, Pennsylvania, USA
| | - Michael Bergman
- National Personal Protective Technology Laboratory, National Institute for Occupational Safety and Health (NIOSH), Pittsburgh, Pennsylvania, USA
| | - Ziqing Zhuang
- National Personal Protective Technology Laboratory, National Institute for Occupational Safety and Health (NIOSH), Pittsburgh, Pennsylvania, USA
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Jagannathan R, Neves JS, Dorcely B, Chung ST, Tamura K, Rhee M, Bergman M. The Oral Glucose Tolerance Test: 100 Years Later. Diabetes Metab Syndr Obes 2020; 13:3787-3805. [PMID: 33116727 PMCID: PMC7585270 DOI: 10.2147/dmso.s246062] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 09/24/2020] [Indexed: 12/15/2022] Open
Abstract
For over 100 years, the oral glucose tolerance test (OGTT) has been the cornerstone for detecting prediabetes and type 2 diabetes (T2DM). In recent decades, controversies have arisen identifying internationally acceptable cut points using fasting plasma glucose (FPG), 2-h post-load glucose (2-h PG), and/or HbA1c for defining intermediate hyperglycemia (prediabetes). Despite this, there has been a steadfast global consensus of the 2-h PG for defining dysglycemic states during the OGTT. This article reviews the history of the OGTT and recent advances in its application, including the glucose challenge test and mathematical modeling for determining the shape of the glucose curve. Pitfalls of the FPG, 2-h PG during the OGTT, and HbA1c are considered as well. Finally, the associations between the 30-minute and 1-hour plasma glucose (1-h PG) levels derived from the OGTT and incidence of diabetes and its complications will be reviewed. The considerable evidence base supports modifying current screening and diagnostic recommendations with the use of the 1-h PG. Measurement of the 1-h PG level could increase the likelihood of identifying high-risk individuals when the pancreatic ß-cell function is substantially more intact with the added practical advantage of potentially replacing the conventional 2-h OGTT making it more acceptable in the clinical setting.
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Affiliation(s)
- Ram Jagannathan
- Division of Hospital Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - João Sérgio Neves
- Department of Surgery and Physiology, Cardiovascular Research and Development Center, Faculty of Medicine, University of Porto, Porto, Portugal
- Department of Endocrinology, Diabetes and Metabolism, Sa˜o Joa˜ o University Hospital Center, Porto, Portugal
| | - Brenda Dorcely
- NYU Grossman School of Medicine, Division of Endocrinology, Diabetes, Metabolism, New York, NY10016, USA
| | - Stephanie T Chung
- Diabetes, Obesity, and Endocrinology Branch, National Institute of Diabetes & Digestive & Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Kosuke Tamura
- Social Determinants of Obesity and Cardiovascular Risk Laboratory, Cardiovascular Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD20892, USA
| | - Mary Rhee
- Emory University School of Medicine, Department of Medicine, Division of Endocrinology, Metabolism, and Lipids, Atlanta VA Health Care System, Atlanta, GA30322, USA
| | - Michael Bergman
- NYU Grossman School of Medicine, NYU Diabetes Prevention Program, Endocrinology, Diabetes, Metabolism, VA New York Harbor Healthcare System, Manhattan Campus, New York, NY10010, USA
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Bergman M, Jagannathan R, Sesti G. The contribution of unrecognized factors to the diabetes epidemic. Diabetes Metab Res Rev 2020; 36:e3315. [PMID: 32223051 DOI: 10.1002/dmrr.3315] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 03/03/2020] [Accepted: 03/13/2020] [Indexed: 12/16/2022]
Affiliation(s)
- Michael Bergman
- NYU School of Medicine, NYU Diabetes Prevention Program, Endocrinology, Diabetes, Metabolism, VA New York Harbor Healthcare System, New York, New York, USA
| | | | - Giorgio Sesti
- Department of Clinical and Molecular Medicine, University of Rome Sapienza, Rome, Italy
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Bergman M, Abdul-Ghani M, Neves JS, Monteiro MP, Medina JL, Dorcely B, Buysschaert M. Pitfalls of HbA1c in the Diagnosis of Diabetes. J Clin Endocrinol Metab 2020; 105:dgaa372. [PMID: 32525987 PMCID: PMC7335015 DOI: 10.1210/clinem/dgaa372] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [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: 03/28/2020] [Accepted: 06/08/2020] [Indexed: 02/06/2023]
Abstract
Many health care providers screen high-risk individuals exclusively with an HbA1c despite its insensitivity for detecting dysglycemia. The 2 cases presented describe the inherent caveats of interpreting HbA1c without performing an oral glucose tolerance test (OGTT). The first case reflects the risk of overdiagnosing type 2 diabetes (T2D) in an older African American male in whom HbA1c levels, although variable, were primarily in the mid-prediabetes range (5.7-6.4% [39-46 mmol/mol]) for many years although the initial OGTT demonstrated borderline impaired fasting glucose with a fasting plasma glucose of 102 mg/dL [5.7 mmol/L]) without evidence for impaired glucose tolerance (2-hour glucose ≥140-199 mg/dl ([7.8-11.1 mmol/L]). Because subsequent HbA1c levels were diagnostic of T2D (6.5%-6.6% [48-49 mmol/mol]), a second OGTT performed was normal. The second case illustrates the risk of underdiagnosing T2D in a male with HIV having normal HbA1c levels over many years who underwent an OGTT when mild prediabetes (HbA1c = 5.7% [39 mmol/mol]) developed that was diagnostic of T2D. To avoid inadvertent mistreatment, it is therefore essential to perform an OGTT, despite its limitations, in high-risk individuals, particularly when glucose or fructosamine and HbA1c values are discordant. Innate differences in the relationship between fructosamine or fasting glucose to HbA1c are demonstrated by the glycation gap or hemoglobin glycation index.
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Affiliation(s)
- Michael Bergman
- NYU School of Medicine, Director, NYU Diabetes Prevention Program, Section Chief, Endocrinology, Diabetes, Metabolism, VA New York Harbor Healthcare System, Manhattan Campus, New York, New York
| | - Muhammad Abdul-Ghani
- Division of Diabetes, University of Texas Health Science Center at San Antonio, San Antonio, Texas
| | - João Sérgio Neves
- Department of Surgery and Physiology, Cardiovascular Research Center, Faculty of Medicine, University of Porto, Porto, Portugal
- Department of Endocrinology, Diabetes and Metabolism, São João University Hospital Center, Porto, Portugal
| | - Mariana P Monteiro
- Endocrine, Cardiovascular & Metabolic Research, Unit for Multidisciplinary Research in Biomedicine (UMIB), University of Porto, Porto, Portugal
- Institute of Biomedical Sciences Abel Salazar (ICBAS), University of Porto, Porto, Portugal
| | | | - Brenda Dorcely
- NYU Grossman School of Medicine, Division of Endocrinology, Diabetes, Metabolism, New York, New York
| | - Martin Buysschaert
- Department of Endocrinology and Diabetology, Université Catholique de Louvain, University Clinic Saint-Luc, Brussels, Belgium
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Abstract
Quality Improvement Success Stories are published by the American Diabetes Association in collaboration with the American College of Physicians, Inc., and the National Diabetes Education Program. This series is intended to highlight best practices and strategies from programs and clinics that have successfully improved the quality of care for people with diabetes or related conditions. Each article in the series is reviewed and follows a standard format developed by the editors of Clinical Diabetes. The following article describes the establishment of a Diabetes Prevention Clinic for veterans with prediabetes.
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Affiliation(s)
| | | | - Craig Tenner
- New York University School of Medicine, New York, NY
| | - Karin Katz
- New York University School of Medicine, New York, NY
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Bergman M, Abdul-Ghani M, DeFronzo RA, Manco M, Sesti G, Fiorentino TV, Ceriello A, Rhee M, Phillips LS, Chung S, Cravalho C, Jagannathan R, Monnier L, Colette C, Owens D, Bianchi C, Del Prato S, Monteiro MP, Neves JS, Medina JL, Macedo MP, Ribeiro RT, Filipe Raposo J, Dorcely B, Ibrahim N, Buysschaert M. Review of methods for detecting glycemic disorders. Diabetes Res Clin Pract 2020; 165:108233. [PMID: 32497744 PMCID: PMC7977482 DOI: 10.1016/j.diabres.2020.108233] [Citation(s) in RCA: 91] [Impact Index Per Article: 22.8] [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: 05/19/2020] [Accepted: 05/19/2020] [Indexed: 02/07/2023]
Abstract
Prediabetes (intermediate hyperglycemia) consists of two abnormalities, impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) detected by a standardized 75-gram oral glucose tolerance test (OGTT). Individuals with isolated IGT or combined IFG and IGT have increased risk for developing type 2 diabetes (T2D) and cardiovascular disease (CVD). Diagnosing prediabetes early and accurately is critical in order to refer high-risk individuals for intensive lifestyle modification. However, there is currently no international consensus for diagnosing prediabetes with HbA1c or glucose measurements based upon American Diabetes Association (ADA) and the World Health Organization (WHO) criteria that identify different populations at risk for progressing to diabetes. Various caveats affecting the accuracy of interpreting the HbA1c including genetics complicate this further. This review describes established methods for detecting glucose disorders based upon glucose and HbA1c parameters as well as novel approaches including the 1-hour plasma glucose (1-h PG), glucose challenge test (GCT), shape of the glucose curve, genetics, continuous glucose monitoring (CGM), measures of insulin secretion and sensitivity, metabolomics, and ancillary tools such as fructosamine, glycated albumin (GA), 1,5- anhydroglucitol (1,5-AG). Of the approaches considered, the 1-h PG has considerable potential as a biomarker for detecting glucose disorders if confirmed by additional data including health economic analysis. Whether the 1-h OGTT is superior to genetics and omics in providing greater precision for individualized treatment requires further investigation. These methods will need to demonstrate substantially superiority to simpler tools for detecting glucose disorders to justify their cost and complexity.
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Affiliation(s)
- Michael Bergman
- NYU School of Medicine, NYU Diabetes Prevention Program, Endocrinology, Diabetes, Metabolism, VA New York Harbor Healthcare System, Manhattan Campus, 423 East 23rd Street, Room 16049C, NY, NY 10010, USA.
| | - Muhammad Abdul-Ghani
- Division of Diabetes, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA.
| | - Ralph A DeFronzo
- Division of Diabetes, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA.
| | - Melania Manco
- Research Area for Multifactorial Diseases, Bambino Gesù Children Hospital, Rome, Italy.
| | - Giorgio Sesti
- Department of Clinical and Molecular Medicine, University of Rome Sapienza, Rome 00161, Italy
| | - Teresa Vanessa Fiorentino
- Department of Medical and Surgical Sciences, University Magna Græcia of Catanzaro, Catanzaro 88100, Italy.
| | - Antonio Ceriello
- Department of Cardiovascular and Metabolic Diseases, Istituto Ricerca Cura Carattere Scientifico Multimedica, Sesto, San Giovanni (MI), Italy.
| | - Mary Rhee
- Emory University School of Medicine, Department of Medicine, Division of Endocrinology, Metabolism, and Lipids, Atlanta VA Health Care System, Atlanta, GA 30322, USA.
| | - Lawrence S Phillips
- Emory University School of Medicine, Department of Medicine, Division of Endocrinology, Metabolism, and Lipids, Atlanta VA Health Care System, Atlanta, GA 30322, USA.
| | - Stephanie Chung
- Diabetes Endocrinology and Obesity Branch, National Institutes of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Celeste Cravalho
- Diabetes Endocrinology and Obesity Branch, National Institutes of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Ram Jagannathan
- Emory University School of Medicine, Department of Medicine, Division of Endocrinology, Metabolism, and Lipids, Atlanta VA Health Care System, Atlanta, GA 30322, USA.
| | - Louis Monnier
- Institute of Clinical Research, University of Montpellier, Montpellier, France.
| | - Claude Colette
- Institute of Clinical Research, University of Montpellier, Montpellier, France.
| | - David Owens
- Diabetes Research Group, Institute of Life Science, Swansea University, Wales, UK.
| | - Cristina Bianchi
- University Hospital of Pisa, Section of Metabolic Diseases and Diabetes, University Hospital, University of Pisa, Pisa, Italy.
| | - Stefano Del Prato
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
| | - Mariana P Monteiro
- Endocrine, Cardiovascular & Metabolic Research, Unit for Multidisciplinary Research in Biomedicine (UMIB), University of Porto, Porto, Portugal; Institute of Biomedical Sciences Abel Salazar (ICBAS), University of Porto, Porto, Portugal.
| | - João Sérgio Neves
- Department of Surgery and Physiology, Cardiovascular Research and Development Center, Faculty of Medicine, University of Porto, Porto, Portugal; Department of Endocrinology, Diabetes and Metabolism, São João University Hospital Center, Porto, Portugal.
| | | | - Maria Paula Macedo
- CEDOC-Centro de Estudos de Doenças Crónicas, NOVA Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisboa, Portugal; APDP-Diabetes Portugal, Education and Research Center (APDP-ERC), Lisboa, Portugal.
| | - Rogério Tavares Ribeiro
- Institute for Biomedicine, Department of Medical Sciences, University of Aveiro, APDP Diabetes Portugal, Education and Research Center (APDP-ERC), Aveiro, Portugal.
| | - João Filipe Raposo
- CEDOC-Centro de Estudos de Doenças Crónicas, NOVA Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisboa, Portugal; APDP-Diabetes Portugal, Education and Research Center (APDP-ERC), Lisboa, Portugal.
| | - Brenda Dorcely
- NYU School of Medicine, Division of Endocrinology, Diabetes, Metabolism, NY, NY 10016, USA.
| | - Nouran Ibrahim
- NYU School of Medicine, Division of Endocrinology, Diabetes, Metabolism, NY, NY 10016, USA.
| | - Martin Buysschaert
- Department of Endocrinology and Diabetology, Université Catholique de Louvain, University Clinic Saint-Luc, Brussels, Belgium.
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Bergman M, Zhou L, Patel P, Sawant R, Clewell J, Tundia N. THU0546 HEALTHCARE COSTS OF NOT ACHIEVING REMISSION IN PATIENTS WITH RHEUMATOID ARTHRITIS. Ann Rheum Dis 2020. [DOI: 10.1136/annrheumdis-2020-eular.955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Background:Guidelines recommend sustained remission as a treatment goal for patients with rheumatoid arthritis (RA). However, only one-third of patients are known to achieve this goal with current treatments. A few studies have evaluated the impact of remission in a real-world setting, but evidence is limited to the elderly population.Objectives:To understand the impact of remission on healthcare costs by comparing overall and RA-related direct healthcare costs and resource use in patients with RA who maintain vs those who do not maintain remission using a real-world database.Methods:Data for this retrospective cohort study were derived from Optum electronic health records linked to claims from commercial and Medicare Advantage health plans in the United States. Patients with ≥2 diagnoses for RA, ≥1 Disease Activity Score 28 (DAS28-CRP/ESR) or Routine Assessment of Patient Index Data 3 (RAPID3) measurement, and continuous medical and pharmacy coverage 6 months before and 1 year after the index date were included. Two cohorts were created: remission and non-remission. Remission was defined as DAS28 <2.6 or RAPID3 ≤3.0. In the remission cohort, the index date was defined as the first date remission was achieved. In the non-remission cohort, the index date was defined as the first date of DAS28 or RAPID3 measurement. Outcomes were all-cause and RA-related total, medical, and prescription costs; healthcare resource use (number of inpatient, emergency department [ED], outpatient, and other visits); and number of prescriptions within 1 year of index date. A weighted generalized linear model and binomial regression were used to estimate adjusted annual direct costs and healthcare resource use, respectively. Confounding between cohorts due to age, sex, race and comorbidities using the Elixhauser index was controlled for in the models.Results:A total of 335 patients with RA (remission cohort: 125; non-remission cohort: 210) met the study inclusion criteria. Annual all-cause total direct costs in the remission cohort were significantly less than in the non-remission cohort ($30,427 vs $38,645, respectively; cost ratio (CR)=0.79; 95% CI: 0.63, 0.99). All-cause medical costs were significantly lower in the remission cohort than in the non-remission cohort (Figure 1); furthermore, among all-cause medical costs, outpatient visit costs were significantly lower in the remission than in the non-remission cohort. All-cause resource use (mean number of visits) was less in the remission vs non-remission cohort: inpatient (0.23 vs 0.63; visit ratio (VR)=0.36; 95% CI: 0.19, 0.70), ED (0.36 vs 0.77; VR=0.47; 95% CI: 0.30, 0.74), and outpatient visits (20.7 vs 28.5; VR=0.73; 95% CI: 0.62, 0.86). Annual RA-related total direct costs were similar in both cohorts (Figure 2); however, RA-related medical costs were numerically lower in the remission vs non-remission cohort ($8,594 vs $10,002, respectively; CR=0.86; 95% CI: 0.59, 1.25). RA-related resource use (mean number of visits) was less in the remission vs non-remission cohort: inpatient (0.15 vs 0.22; VR=0.67; 95% CI: 0.35, 1.30), ED (0.04 vs 0.13; VR=0.31; 95% CI: 0.10, 0.95), and outpatient visits (5.4 vs 7.4; VR=0.72; 95% CI: 0.58, 0.91).Conclusion:Significant economic burden was associated with patients who did not maintain remission compared with those who maintained remission. Although outpatient visits were the driver of medical costs in both groups studied in this analysis, the contribution of outpatient visits was greater among those who did not maintain remission.Acknowledgments:Financial support for the study was provided by AbbVie. AbbVie participated in the interpretation of data, review, and approval of the abstract. All authors contributed to the development of the publication and maintained control over the final content. Medical writing services were provided by Joann Hettasch of JK Associates Inc., a member of the Fishawack Group of Companies, and funded by AbbVie.Disclosure of Interests:Martin Bergman Shareholder of: Johnson & Johnson – stockholder, Consultant of: AbbVie, BMS, Celgene Corporation, Genentech, Janssen, Merck, Novartis, Pfizer, Sanofi – consultant, Speakers bureau: AbbVie, Celgene Corporation, Novartis, Pfizer, Sanofi – speakers bureau, Lili Zhou Shareholder of: AbbVie, Employee of: AbbVie, Pankaj Patel Shareholder of: AbbVie, Employee of: AbbVie, Ruta Sawant Shareholder of: AbbVie, Employee of: AbbVie, Jerry Clewell Shareholder of: AbbVie, Employee of: AbbVie, Namita Tundia Shareholder of: AbbVie, Employee of: AbbVie
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Abstract
Individuals with non-communicable diseases (NCDs) such as diabetes are susceptible to communicable diseases (CDs) as the current COVID-19 pandemic illustrates. The co-occurrence of diabetes as well as other co-morbid conditions with COVID-19 augurs greater risk for severe outcomes and mortality. Hence, NCD and CD pandemics are closely linked and require global efforts to thwart and disrupt their nexus before the next viral outbreaks occurs. This will require steadfast dedication and resolve to address NCDs previously committed to by the global community.
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Affiliation(s)
- Michael Bergman
- NYU Grossman School of Medicine, Department of Endocrinology, Diabetes, Metabolism and of Population Health, NYU Langone Diabetes Prevention Program, VA New York Harbor Healthcare System - Manhattan Campus, 423 East 23rd Street, Room 16049C, New York, NY 10010 USA.
| | | | - K M Venkat Narayan
- Rollins School of Public Health, Emory University, Atlanta, GA 30322 USA
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Schroeder K, Pincus T, Bergman M. AB1194 STRIKING DIFFERENCES IN THE COURSE OF OSTEOARTHRITIS (OA) COMPARED TO RHEUMATOID ARTHRITIS (RA) OVER THE FIRST 24 MONTHS OF RHEUMATOLOGY CARE AT ONE PRIVATE PRACTICE SETTING. Ann Rheum Dis 2020. [DOI: 10.1136/annrheumdis-2020-eular.3840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Background:Recent reports indicate that disease burden in osteoarthritis (OA) is similar to or greater than in rheumatoid arthritis (RA) when an identical measure is used to assess patients with either disease, generally an MDHAQ/RAPID3 (multidimensional health assessment questionnaire/routine assessment of patient index data). The data suggest that a traditional view that RA is more severe than OA no longer is valid at this time. One concern is that similar disease burdens in OA vs RA may result entirely from superior treatments for RA, and RA may be considerably more severe than OA at initial presentation.Objectives:To analyze MDHAQ disease burden in patients with OA vs RA at initial visit and at 24-month follow-up in routine care at a single solo-rheumatologist private practice setting.Methods:All patients at this setting complete an MDHAQ at each visit in the waiting area, prior to seeing the rheumatologist. The MDHAQ includes three 0-10 scores for physical function, pain visual numeric scale (VNS), and patient global VNS, which may be compiled into a 0–30 RAPID3, as well as a 0-10 fatigue VNS, and 0-16 rheumatoid arthritis disease activity index (RADAI) self-report painful joint count. Mean MDHAQ scores were analyzed for all 73 OA and 116 RA patients seen for an initial visit between 2011 and 2017. Mean scores at initial and 24-month visits were compared for all 25 OA and 63 RA patients seen at 24 month (21-27 month) follow-up visits, using paired t tests.Results:Mean MDHAQ scores at first visit were similar for all 73 OA and 116 RA patients, and also for 25 OA and 63 RA patients who were also seen 24 months later, e.g., mean RAPID3 was 12.0-14.2. However, mean changes over 2 years were strikingly different in OA versus RA patients (Table). Almost all mean scores in OA were somewhat higher, while all mean scores in RA were clinically and statistically significantly improved at 24 months, e.g., mean RAPID3 worsened from 13.0 to 15.2 (+2.2 units, 17%) in OA patients, compared to improvement from 12.5 to 8.2 (-4.3 units, -34%) in RA patients. The smallest mean change in RA patients involved the joint count (7.7 to 6.1, -21%) (Table), suggesting possible control of inflammation, but continued damage to specific joints. An important limitation is that the data do not include follow-up on patients not seen over the 24 month “window,” because of substantially better or poorer status, joint surgery, or other reasons, although the data present an accurate characterization of one rheumatology practice setting.Mean values of patient MDHAQ scores in patients with OA or RA at first visit and 24-month follow-upMDHAQ score:OA first visit of those seen at 24 months(n=25)OA 24- month visit (n=25)% change, over 24 monthsRA first visit of those seen at 24 months(n=63)RA 24- month visit (n=63)% change, over 24 monthsRAPID313.015.2+2.2, +17%12.58.2-4.3, -34%Function0.810.77-0.04, -5%0.710.50-0.21, -29%Pain5.26.4+1.2, +23%5.13.2-1.9, -37%Patient global5.15.9+0.8, +16%5.13.3-1.8, -35%Fatigue4.14.4+0.3, +7%4.83.5-1.3, -27%Pt joint count7.57.8+0.3, +4%7.76.1-1.6, -21%Abbreviations: MDHAQ=multidimensional health assessment questionnaire, OA=osteoarthritis, RA=rheumatoid arthritis, RAPID3=routine assessment of patient index data.In change data, negative numbers indicate improvement, positive numbers indicate worsening.Conclusion:Mean MDHAQ/RAPID3 scores were similar in RA or OA at the initial visit. Over 24 months, scores worsened slightly in OA and improved considerably in RA, resulting in considerably poorer status in OA versus RA, likely reflecting superior treatments for RA vs OA. At an individual level, patients with primary OA may have better or poorer status than patients with primary RA. Nonetheless, at a group level, the severity of disease burden in OA appears similar to RA, and becomes greater over the next 24 months, likely as a result of better treatments. The severity of OA is underrated, suggesting a need for increased resources for research toward better treatments for OA.Disclosure of Interests:Kyle Schroeder: None declared, Theodore Pincus Shareholder of:Dr. Pincus holds a copyright and trademark on MDHAQ and RAPID3 for which he receives royalties and license fees from profit-making organizations, all of which are used to support further development of quantitative clinical measures for patients and health professionals., Martin Bergman Shareholder of: Johnson & Johnson – stockholder, Consultant of: AbbVie, BMS, Celgene Corporation, Genentech, Janssen, Merck, Novartis, Pfizer, Sanofi – consultant, Speakers bureau: AbbVie, Celgene Corporation, Novartis, Pfizer, Sanofi – speakers bureau
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Mease PJ, Kavanaugh A, Ogdie A, Wells AF, Bergman M, Gladman DD, Behrens F, Richter S, Brunori M, Teng L, Guerette B, Smolen JS. FRI0352 PROBABILITY OF ACHIEVING LOW DISEASE ACTIVITY OR REMISSION WITH APREMILAST TREATMENT AMONG DMARD-NAIVE SUBJECTS WITH ACTIVE PSORIATIC ARTHRITIS. Ann Rheum Dis 2020. [DOI: 10.1136/annrheumdis-2020-eular.1530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Background:Apremilast (APR) is associated with comparable ACR response rates in DMARD-naive vs DMARD-experienced patients (pts) with psoriatic arthritis (PsA).1,2A question that remains is if DMARD-naive pts treated with APR have greater chances of achieving treatment targets than DMARD-experienced pts. cDAPSA is a commonly used treatment target.Objectives:To assess the predictive value of baseline (BL) clinical disease status on achieving long-term cDAPSA treatment targets at Wk 52 among DMARD-naive subjects in PALACE 4; to compare these findings vs those recently reported from the PALACE 1-3 studies in subjects with prior exposure to DMARDs; and to provide further evidence that at a group level, achievement of cDAPSA disease targets with APR is associated with no or mild articular and extra-articular disease activity by Wk 52.Methods:This post hoc analysis included subjects assigned to APR 30 mg twice daily at BL who had available cDAPSA data at BL. We calculated the probabilities of shifting across different cDAPSA categories (remission [REM]: ≤4; low disease activity [LDA]: >4 to ≤13; moderate disease activity [Mod]: >13 to ≤27; high disease activity [HDA]: >273) from BL to Wk 52. Mean values of articular and non-articular variables (e.g., PASI, SJC/TJC, MASES, dactylitis) from BL to Wk 52 were assessed by cDAPSA category achieved at Wk 52 to determine the association between achievement of targets and control of articular and non-articular manifestations. Results from the current analyses were compared with the previously reported results from PALACE 1-3.Results:A total of 175 subjects receiving APR were included; at BL, 66.3% were in HDA, 31.4% in Mod, and 2.3% were in LDA. Overall, subjects who achieved treatment targets (LDA or REM) by Wk 52 had lower levels of disease activity at BL, as shown by a lower number of swollen and tender joints and lower presence of enthesitis and dactylitis. Higher prevalence of psoriasis-involved body surface area ≥3% at BL was observed. Subjects in Mod at BL were estimated to be more than twice as likely to achieve REM or LDA at Wk 52 vs subjects in HDA at BL; for subjects in LDA at BL, the estimated probability of achieving cDAPSA treatment targets was 100% (Figure). PALACE 4 subjects with LDA and Mod at BL exhibited higher estimated probabilities of achieving treatment targets (100.0% and 61.7%, respectively) than those observed in the DMARD-experienced population of PALACE 1-3 (71.1% and 46.9%). Subjects in PALACE 4 who achieved REM or LDA by Wk 52 showed no or mild articular and extra-articular disease activity by Wk 52, similar to what was observed in the PALACE 1-3 population.4Conclusion:DMARD-naive subjects in PALACE 4 who had LDA or Mod at BL had the highest likelihood of achieving treatment targets (cDAPSA REM or LDA) by Wk 52 with continued APR treatment. Results from the current probability analyses revealed higher probability rates than those observed in the DMARD-experienced PALACE 1-3 population; control of articular and extra-articular manifestations was observed in the DMARD-naive and DMARD-experienced populations.References:[1]Wells AF, et al. Rheumatology. 2018;57:1253-63. 2. Kavanaugh A, et al. Arthritis Res Ther. 2019;21:118. 3. Machado PM. Ann Rheum Dis. 2016;75:787-90. 4. Mease PJ, et al. Arthritis Care Res. 2020 Jan 7.Disclosure of Interests:Philip J Mease Grant/research support from: Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB – grant/research support, Consultant of: Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB – consultant, Speakers bureau: Abbott, Amgen, Biogen Idec, BMS, Eli Lilly, Genentech, Janssen, Pfizer, UCB – speakers bureau, Arthur Kavanaugh Grant/research support from: Abbott, Amgen, AstraZeneca, BMS, Celgene Corporation, Centocor-Janssen, Pfizer, Roche, UCB – grant/research support, Alexis Ogdie Grant/research support from: Novartis, Pfizer – grant/research support, Consultant of: AbbVie, BMS, Eli Lilly, Novartis, Pfizer, Takeda – consultant, Alvin F. Wells Grant/research support from: AbbVie, Celgene Corporation, Lilly – grant/research support, Consultant of: AbbVie, Alexion, Amgen, BMS, Celgene Corporation, Horizon, Lilly, Novartis, UCB – consultant, Speakers bureau: AbbVie, Alexion, Amgen, BMS, Celgene Corporation, Horizon, Lilly, Novartis, UCB – speakers bureau, Martin Bergman Shareholder of: Johnson & Johnson – stockholder, Consultant of: AbbVie, BMS, Celgene Corporation, Genentech, Janssen, Merck, Novartis, Pfizer, Sanofi – consultant, Speakers bureau: AbbVie, Celgene Corporation, Novartis, Pfizer, Sanofi – speakers bureau, Dafna D Gladman Grant/research support from: AbbVie, Amgen Inc., BMS, Celgene Corporation, Janssen, Novartis, Pfizer, UCB – grant/research support, Consultant of: AbbVie, Amgen Inc., BMS, Celgene Corporation, Janssen, Novartis, Pfizer, UCB – consultant, Frank Behrens Grant/research support from: AbbVie, Chugai, Janssen, Roche, Pfizer – grant/research support, Consultant of: AbbVie Biotest, Boehringer Ingelheim, Celgene Corporation, Chugai, Eli Lilly, Genzyme, Janssen, Novartis, Pfizer, Roche, UCB – consultant, Speakers bureau: AbbVie, Biotest, BMS, Celgene Corporation, Chugai, Eli Lilly, Genzyme, Janssen, Merck Sharp & Dohme, Novartis, Pfizer, Roche, Sandoz, UCB - speaker, Sven Richter Employee of: Amgen Inc. – employment; Celgene Corporation – employment at the time of study conduct, Michele Brunori Employee of: Amgen Inc. – employment; Celgene Corporation – employment at the time of study conduct, Lichen Teng Employee of: Amgen Inc. – employment; Celgene Corporation – employment at the time of study conduct, Benoit Guerette Employee of: Amgen Inc. – employment; Celgene Corporation – employment at the time of study conduct, Josef S. Smolen Grant/research support from: AbbVie, Eli Lilly, Janssen, Merck Sharp & Dohme, Pfizer, Roche – grant/research support, Consultant of: AbbVie, Amgen Inc., AstraZeneca, Astro, Celgene Corporation, Celtrion, Eli Lilly, Glaxo, ILTOO, Janssen, Medimmune, Merck Sharp & Dohme, Novartis, Pfizer, Roche, Samsung, Sanofi, UCB – consultant, Speakers bureau: AbbVie, Amgen Inc., AstraZeneca, Astro, Celgene Corporation, Celtrion, Eli Lilly, Glaxo, ILTOO, Janssen, Medimmune, Merck Sharp & Dohme, Novartis, Pfizer, Roche, Samsung, Sanofi, UCB – speaker
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Vo E, Horvatin M, Bergman M, Wu B, Zhuang Z. A technique to measure respirator protection factors against aerosol particles in simulated workplace settings using portable instruments. J Occup Environ Hyg 2020; 17:231-242. [PMID: 32243774 PMCID: PMC9494708 DOI: 10.1080/15459624.2020.1735640] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
The aim of this study was to develop a new method to measure respirator protection factors for aerosol particles using portable instruments while workers conduct their normal work. The portable instruments, including a set of two handheld condensation particle counters (CPCs) and two portable aerosol mobility spectrometers (PAMSs), were evaluated with a set of two reference scanning mobility particle sizers (SMPSs). The portable instruments were mounted to a tactical load-bearing vest or backpack and worn by the test subject while conducting their simulated workplace activities. Simulated workplace protection factors (SWPFs) were measured using human subjects exposed to sodium chloride aerosols at three different steady state concentration levels: low (8x103 particles/cm3), medium (5x104 particles/cm3), and high (1x105 particles/cm3). Eight subjects were required to pass a quantitative fit test before beginning a SWPF test for the respirators. Each SWPF test was performed using a protocol of five exercises for 3 min each: (1) normal breathing while standing; (2) bending at the waist; (3) a simulated laboratory-vessel cleaning motion; (4) slow walking in place; and (5) deep breathing. Two instrument sets (one portable instrument {CPC or PAMS} and one reference SMPS for each set) were used to simultaneously measure the aerosol concentrations outside and inside the respirator. The SWPF was calculated as a ratio of the outside and inside particles. Generally, the overall SWPFs measured with the handheld CPCs had a relatively good agreement with those measured with the reference SMPSs, followed by the PAMSs. Under simulated workplace activities, all handheld CPCs, PAMSs, and the reference SMPSs showed a similar GM SWPF trend, and their GM SWPFs decreased when simulated workplace movements increased. This study demonstrated that the new design of mounting two handheld CPCs in the tactical load-bearing vest or mounting one PAMS unit in the backpack permitted subjects to wear it while performing the simulated workplace activities. The CPC shows potential for measuring SWPFs based on its light weight and lack of major instrument malfunctions.
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Affiliation(s)
- Evanly Vo
- National Personal Protective Technology Laboratory, National Institute for Occupational Safety and Health, Pittsburgh, Pennsylvania
| | | | - Michael Bergman
- National Personal Protective Technology Laboratory, National Institute for Occupational Safety and Health, Pittsburgh, Pennsylvania
| | - Bingbing Wu
- National Personal Protective Technology Laboratory, National Institute for Occupational Safety and Health, Pittsburgh, Pennsylvania
| | - Ziqing Zhuang
- National Personal Protective Technology Laboratory, National Institute for Occupational Safety and Health, Pittsburgh, Pennsylvania
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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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