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Ware J, Boughton CK, Allen JM, Wilinska ME, Tauschmann M, Denvir L, Thankamony A, Campbell FM, Wadwa RP, Buckingham BA, Davis N, DiMeglio LA, Mauras N, Besser REJ, Ghatak A, Weinzimer SA, Hood KK, Fox DS, Kanapka L, Kollman C, Sibayan J, Beck RW, Hovorka R, Hovorka R, Acerini CL, Thankamony A, Allen JM, Boughton CK, Dovc K, Dunger DB, Ware J, Musolino G, Tauschmann M, Wilinska ME, Hayes JF, Hartnell S, Slegtenhorst S, Ruan Y, Haydock M, Mangat J, Denvir L, Kanthagnany SK, Law J, Randell T, Sachdev P, Saxton M, Coupe A, Stafford S, Ball A, Keeton R, Cresswell R, Crate L, Cripps H, Fazackerley H, Looby L, Navarra H, Saddington C, Smith V, Verhoeven V, Bratt S, Khan N, Moyes L, Sandhu K, West C, Wadwa RP, Alonso G, Forlenza G, Slover R, Towers L, Berget C, Coakley A, Escobar E, Jost E, Lange S, Messer L, Thivener K, Campbell FM, Yong J, Metcalfe E, Allen M, Ambler S, Waheed S, Exall J, Tulip J, Buckingham BA, Ekhlaspour L, Maahs D, Norlander L, Jacobson T, Twon M, Weir C, Leverenz B, Keller J, Davis N, Kumaran A, Trevelyan N, Dewar H, Price G, Crouch G, Ensom R, Haskell L, Lueddeke LM, Mauras N, Benson M, Bird K, Englert K, Permuy J, Ponthieux K, Marrero-Hernandez J, DiMeglio LA, Ismail H, Jolivette H, Sanchez J, Woerner S, Kirchner M, Mullen M, Tebbe M, Besser REJ, Basu S, London R, Makaya T, Ryan F, Megson C, Bowen-Morris J, Haest J, Law R, Stamford I, Ghatak A, Deakin M, Phelan K, Thornborough K, Shakeshaft J, Weinzimer SA, Cengiz E, Sherr JL, Van Name M, Weyman K, Carria L, Steffen A, Zgorski M, Sibayan J, Beck RW, Borgman S, Davis J, Rusnak J, Hellman A, Cheng P, Kanapka L, Kollman C, McCarthy C, Chalasani S, Hood KK, Hanes S, Viana J, Lanning M, Fox DS, Arreaza-Rubin G, Eggerman T, Green N, Janicek R, Gabrielson D, Belle SH, Castle J, Green J, Legault L, Willi SM, Wysham C. Cambridge hybrid closed-loop algorithm in children and adolescents with type 1 diabetes: a multicentre 6-month randomised controlled trial. Lancet Digit Health 2022; 4:e245-e255. [PMID: 35272971 DOI: 10.1016/s2589-7500(22)00020-6] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 12/10/2021] [Accepted: 01/25/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND Closed-loop insulin delivery systems have the potential to address suboptimal glucose control in children and adolescents with type 1 diabetes. We compared safety and efficacy of the Cambridge hybrid closed-loop algorithm with usual care over 6 months in this population. METHODS In a multicentre, multinational, parallel randomised controlled trial, participants aged 6-18 years using insulin pump therapy were recruited at seven UK and five US paediatric diabetes centres. Key inclusion criteria were diagnosis of type 1 diabetes for at least 12 months, insulin pump therapy for at least 3 months, and screening HbA1c levels between 53 and 86 mmol/mol (7·0-10·0%). Using block randomisation and central randomisation software, we randomly assigned participants to either closed-loop insulin delivery (closed-loop group) or to usual care with insulin pump therapy (control group) for 6 months. Randomisation was stratified at each centre by local baseline HbA1c. The Cambridge closed-loop algorithm running on a smartphone was used with either (1) a modified Medtronic 640G pump, Medtronic Guardian 3 sensor, and Medtronic prototype phone enclosure (FlorenceM configuration), or (2) a Sooil Dana RS pump and Dexcom G6 sensor (CamAPS FX configuration). The primary endpoint was change in HbA1c at 6 months combining data from both configurations. The primary analysis was done in all randomised patients (intention to treat). Trial registration ClinicalTrials.gov, NCT02925299. FINDINGS Of 147 people initially screened, 133 participants (mean age 13·0 years [SD 2·8]; 57% female, 43% male) were randomly assigned to either the closed-loop group (n=65) or the control group (n=68). Mean baseline HbA1c was 8·2% (SD 0·7) in the closed-loop group and 8·3% (0·7) in the control group. At 6 months, HbA1c was lower in the closed-loop group than in the control group (between-group difference -3·5 mmol/mol (95% CI -6·5 to -0·5 [-0·32 percentage points, -0·59 to -0·04]; p=0·023). Closed-loop usage was low with FlorenceM due to failing phone enclosures (median 40% [IQR 26-53]), but consistently high with CamAPS FX (93% [88-96]), impacting efficacy. A total of 155 adverse events occurred after randomisation (67 in the closed-loop group, 88 in the control group), including seven severe hypoglycaemia events (four in the closed-loop group, three in the control group), two diabetic ketoacidosis events (both in the closed-loop group), and two non-treatment-related serious adverse events. There were 23 reportable hyperglycaemia events (11 in the closed-loop group, 12 in the control group), which did not meet criteria for diabetic ketoacidosis. INTERPRETATION The Cambridge hybrid closed-loop algorithm had an acceptable safety profile, and improved glycaemic control in children and adolescents with type 1 diabetes. To ensure optimal efficacy of the closed-loop system, usage needs to be consistently high, as demonstrated with CamAPS FX. FUNDING National Institute of Diabetes and Digestive and Kidney Diseases.
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Redondo MJ, Geyer S, Steck AK, Sharp S, Wentworth JM, Weedon MN, Antinozzi P, Sosenko J, Atkinson M, Pugliese A, Oram RA, Antinozzi P, Atkinson M, Battaglia M, Becker D, Bingley P, Bosi E, Buckner J, Colman P, Gottlieb P, Herold K, Insel R, Kay T, Knip M, Marks J, Moran A, Palmer J, Peakman M, Philipson L, Pugliese A, Raskin P, Rodriguez H, Roep B, Russell W, Schatz D, Wherrett D, Wilson D, Winter W, Ziegler A, Benoist C, Blum J, Chase P, Clare-Salzler M, Clynes R, Eisenbarth G, Fathman C, Grave G, Hering B, Kaufman F, Leschek E, Mahon J, Nanto-Salonen K, Nepom G, Orban T, Parkman R, Pescovitz M, Peyman J, Roncarolo M, Simell O, Sherwin R, Siegelman M, Steck A, Thomas J, Trucco M, Wagner J, Greenbaum ,CJ, Bourcier K, Insel R, Krischer JP, Leschek E, Rafkin L, Spain L, Cowie C, Foulkes M, Krause-Steinrauf H, Lachin JM, Malozowski S, Peyman J, Ridge J, Savage P, Skyler JS, Zafonte SJ, Kenyon NS, Santiago I, Sosenko JM, Bundy B, Abbondondolo M, Adams T, Amado D, Asif I, Boonstra M, Bundy B, Burroughs C, Cuthbertson D, Deemer M, Eberhard C, Fiske S, Ford J, Garmeson J, Guillette H, Browning G, Coughenour T, Sulk M, Tsalikan E, Tansey M, Cabbage J, Dixit N, Pasha S, King M, Adcock K, Geyer S, Atterberry H, Fox L, Englert K, Mauras N, Permuy J, Sikes K, Berhe T, Guendling B, McLennan L, Paganessi L, Hays B, Murphy C, Draznin M, Kamboj M, Sheppard S, Lewis V, Coates L, Moore W, Babar G, Bedard J, Brenson-Hughes D, Henderson C, Cernich J, Clements M, Duprau R, Goodman S, Hester L, Huerta-Saenz L, Karmazin A, Letjen T, Raman S, Morin D, Henry M, Bestermann W, Morawski E, White J, Brockmyer A, Bays R, Campbell S, Stapleton A, Stone N, Donoho A, Everett H, Heyman K, Hensley H, Johnson M, Marshall C, Skirvin N, Taylor P, Williams R, Ray L, Wolverton C, Nickels D, Dothard C, Hsiao B, Speiser P, Pellizzari M, Bokor L, Izuora K, Abdelnour S, Cummings P, Paynor S, Leahy M, Riedl M, Shockley S, Karges C, Saad R, Briones T, Casella S, Herz C, Walsh K, Greening J, Hay F, Hunt S, 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Trunnel S, Transue D, Surhigh J, Bezzaire D, Moltz K, Zacharski E, Henske J, Desai S, Frizelis K, Khan F, Sjoberg R, Allen K, Manning P, Hendry G, Taylor B, Jones S, Couch R, Danchak R, Lieberman D, Strader W, Bencomo M, Bailey T, Bedolla L, Roldan C, Moudiotis C, Vaidya B, Anning C, Bunce S, Estcourt S, Folland E, Gordon E, Harrill C, Ireland J, Piper J, Scaife L, Sutton K, Wilkins S, Costelloe M, Palmer J, Casas L, Miller C, Burgard M, Erickson C, Hallanger-Johnson J, Clark P, Taylor W, Galgani J, Banerjee S, Banda C, McEowen D, Kinman R, Lafferty A, Gillett S, Nolan C, Pathak M, Sondrol L, Hjelle T, Hafner S, Kotrba J, Hendrickson R, Cemeroglu A, Symington T, Daniel M, Appiagyei-Dankah Y, Postellon D, Racine M, Kleis L, Barnes K, Godwin S, McCullough H, Shaheen K, Buck G, Noel L, Warren M, Weber S, Parker S, Gillespie I, Nelson B, Frost C, Amrhein J, Moreland E, Hayes A, Peggram J, Aisenberg J, Riordan M, Zasa J, Cummings E, Scott K, Pinto T, Mokashi A, McAssey K, Helden E, Hammond P, Dinning L, Rahman S, Ray S, Dimicri C, Guppy S, Nielsen H, Vogel C, Ariza C, Morales L, Chang Y, Gabbay R, Ambrocio L, Manley L, Nemery R, Charlton W, Smith P, Kerr L, Steindel-Kopp B, Alamaguer M, Tabisola-Nuesca E, Pendersen A, Larson N, Cooper-Olviver H, Chan D, Fitz-Patrick D, Carreira T, Park Y, Ruhaak R, Liljenquist D. A Type 1 Diabetes Genetic Risk Score Predicts Progression of Islet Autoimmunity and Development of Type 1 Diabetes in Individuals at Risk. Diabetes Care 2018; 41:1887-1894. [PMID: 30002199 PMCID: PMC6105323 DOI: 10.2337/dc18-0087] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 06/06/2018] [Indexed: 02/03/2023]
Abstract
OBJECTIVE We tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk individuals. RESEARCH DESIGN AND METHODS We studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients' relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2-51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial-Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables. RESULTS Higher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06-1.6; P = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS >0.295, 95% CI 1.47-3.51; P = 0.0002). CONCLUSIONS The T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.
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Affiliation(s)
- Maria J. Redondo
- Texas Children’s Hospital, Baylor College of Medicine, Houston, TX
| | | | - Andrea K. Steck
- Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, CO
| | - Seth Sharp
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, U.K
| | - John M. Wentworth
- Walter and Eliza Hall Institute of Medical Research and Royal Melbourne Hospital, Parkville, Victoria, Australia
| | - Michael N. Weedon
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, U.K
| | | | | | | | | | - Richard A. Oram
- Institute of Biomedical and Clinical Science, University of Exeter, Exeter, U.K
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