Browsing by Author "Frohnert, Brigitte I."

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    Consensus guidance for monitoring individuals with islet autoantibody-positive pre-stage 3 type 1 diabetes
    (Springer, 2024-09) Phillip, Moshe; Achenbach, Peter; Addala, Ananta; Albanese-O'Neill, Anastasia; Battelino, Tadej; Bell, Kirstine J.; Besser, Rachel E. J.; Bonifacio, Ezio; Colhoun, Helen M.; Couper, Jennifer J.; Craig, Maria E.; Danne, Thomas; de Beaufort, Carine; Dovc, Klemen; Driscoll, Kimberly A.; Dutta, Sanjoy; Ebekozien, Osagie; Elding Larsson, Helena; Feiten, Daniel J.; Frohnert, Brigitte I.; Gabbay, Robert A.; Gallagher, Mary P.; Greenbaum, Carla J.; Griffin, Kurt J.; Hagopian, William; Haller, Michael J.; Hendrieckx, Christel; Hendriks, Emile; Holt, Richard I. G.; Hughes, Lucille; Ismail, Heba M.; Jacobsen, Laura M.; Johnson, Suzanne B.; Kolb, Leslie E.; Kordonouri, Olga; Lange, Karin; Lash, Robert W.; Lernmark, Åke; Libman, Ingrid; Lundgren, Markus; Maahs, David M.; Marcovecchio, M. Loredana; Mathieu, Chantal; Miller, Kellee M.; O'Donnell, Holly K.; Oron, Tal; Patil, Shivajirao P.; Pop-Busui, Rodica; Rewers, Marian J.; Rich, Stephen S.; Schatz, Desmond A.; Schulman-Rosenbaum, Rifka; Simmons, Kimber M.; Sims, Emily K.; Skyler, Jay S.; Smith, Laura B.; Speake, Cate; Steck, Andrea K.; Thomas, Nicholas P. B.; Tonyushkina, Ksenia N.; Veijola, Riitta; Wentworth, John M.; Wherrett, Diane K.; Wood, Jamie R.; Ziegler, Anette-Gabriele; DiMeglio, Linda A.; Pediatrics, School of Medicine
    Given the proven benefits of screening to reduce diabetic ketoacidosis (DKA) likelihood at the time of stage 3 type 1 diabetes diagnosis, and emerging availability of therapy to delay disease progression, type 1 diabetes screening programmes are being increasingly emphasised. Once broadly implemented, screening initiatives will identify significant numbers of islet autoantibody-positive (IAb+) children and adults who are at risk of (confirmed single IAb+) or living with (multiple IAb+) early-stage (stage 1 and stage 2) type 1 diabetes. These individuals will need monitoring for disease progression; much of this care will happen in non-specialised settings. To inform this monitoring, JDRF in conjunction with international experts and societies developed consensus guidance. Broad advice from this guidance includes the following: (1) partnerships should be fostered between endocrinologists and primary-care providers to care for people who are IAb+; (2) when people who are IAb+ are initially identified there is a need for confirmation using a second sample; (3) single IAb+ individuals are at lower risk of progression than multiple IAb+ individuals; (4) individuals with early-stage type 1 diabetes should have periodic medical monitoring, including regular assessments of glucose levels, regular education about symptoms of diabetes and DKA, and psychosocial support; (5) interested people with stage 2 type 1 diabetes should be offered trial participation or approved therapies; and (6) all health professionals involved in monitoring and care of individuals with type 1 diabetes have a responsibility to provide education. The guidance also emphasises significant unmet needs for further research on early-stage type 1 diabetes to increase the rigour of future recommendations and inform clinical care.
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    Correction to: Consensus guidance for monitoring individuals with islet autoantibody‑positive pre‑stage 3 type 1 diabetes
    (Springer, 2024) Phillip, Moshe; Achenbach, Peter; Addala, Ananta; Albanese-O'Neill, Anastasia; Battelino, Tadej; Bell, Kirstine J.; Besser, Rachel E. J.; Bonifacio, Ezio; Colhoun, Helen M.; Couper, Jennifer J.; Craig, Maria E.; Danne, Thomas; de Beaufort, Carine; Dovc, Klemen; Driscoll, Kimberly A.; Dutta, Sanjoy; Ebekozien, Osagie; Elding Larsson, Helena; Feiten, Daniel J.; Frohnert, Brigitte I.; Gabbay, Robert A.; Gallagher, Mary P.; Greenbaum, Carla J.; Griffin, Kurt J.; Hagopian, William; Haller, Michael J.; Hendrieckx, Christel; Hendriks, Emile; Holt, Richard I. G.; Hughes, Lucille; Ismail, Heba M.; Jacobsen, Laura M.; Johnson, Suzanne B.; Kolb, Leslie E.; Kordonouri, Olga; Lange, Karin; Lash, Robert W.; Lernmark, Åke; Libman, Ingrid; Lundgren, Markus; Maahs, David M.; Marcovecchio, M. Loredana; Mathieu, Chantal; Miller, Kellee M.; O'Donnell, Holly K.; Oron, Tal; Patil, Shivajirao P.; Pop-Busui, Rodica; Rewers, Marian J.; Rich, Stephen S.; Schatz, Desmond A.; Schulman-Rosenbaum, Rifka; Simmons, Kimber M.; Sims, Emily K.; Skyler, Jay S.; Smith, Laura B.; Speake, Cate; Steck, Andrea K.; Thomas, Nicholas P. B.; Tonyushkina, Ksenia N.; Veijola, Riitta; Wentworth, John M.; Wherrett, Diane K.; Wood, Jamie R.; Ziegler, Anette-Gabriele; DiMeglio, Linda A.; Pediatrics, School of Medicine
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    Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation
    (Springer Nature, 2021) Nakayasu, Ernesto S.; Gritsenko, Marina; Piehowski, Paul D.; Gao, Yuqian; Orton, Daniel J.; Schepmoes, Athena A.; Fillmore, Thomas L.; Frohnert, Brigitte I.; Rewers, Marian; Krischer, Jeffrey P.; Ansong, Charles; Suchy-Dicey, Astrid M.; Evans-Molina, Carmella; Qian, Wei-Jun; Webb-Robertson, Bobbie-Jo M.; Metz, Thomas O.; Pediatrics, School of Medicine
    Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.