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54. Long-Term Outcomes after Severe Childhood Malnutrition (LOSCM Study): Growth and Clinical Catch-Up in an Adolescent Cohort

Author

Kirolos, Amir, primary, Harawa, Philliness Prisca, additional, Chimowa, Takondwa, additional, Divala, Oscar, additional, Freyne, Bridget, additional, Jones, Angus G., additional, Lelijveld, Natasha, additional, Lissauer, Samantha, additional, Maleta, Kenneth, additional, Gladstone, Melissa J., additional, Kerac, Marko, additional, and Group, CHANGE Study Collaborators, additional

Published
2023
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55. Pharmacogenomics of GLP-1 receptor agonists: a genome-wide analysis of observational data and large randomised controlled trials

Author

Dawed, Adem Y, primary, Mari, Andrea, additional, Brown, Andrew, additional, McDonald, Timothy J, additional, Li, Lin, additional, Wang, Shuaicheng, additional, Hong, Mun-Gwan, additional, Sharma, Sapna, additional, Robertson, Neil R, additional, Mahajan, Anubha, additional, Wang, Xuan, additional, Walker, Mark, additional, Gough, Stephen, additional, Hart, Leen M ‘t, additional, Zhou, Kaixin, additional, Forgie, Ian, additional, Ruetten, Hartmut, additional, Pavo, Imre, additional, Bhatnagar, Pallav, additional, Jones, Angus G, additional, Pearson, Ewan R, additional, 't Hart, L.M., additional, Abdalla, M., additional, Adam, J., additional, Adamski, J., additional, Adragni, K., additional, Allin, K.H., additional, Arumugam, M., additional, Atabaki Pasdar, N., additional, Baltauss, T., additional, Banasik, K.B., additional, Baum, P., additional, Bell, J.D., additional, Bergstrom, M., additional, Beulens, J.W., additional, Bianzano, S., additional, Bizzotto, R., additional, Bonneford, A., additional, Brorsson, C.A.B., additional, Brown, A.A., additional, Brunak, S.B., additional, Cabrelli, L., additional, Caiazzo, R., additional, Canouil, M., additional, Dale, M., additional, Davtian, D., additional, Dawed, A.Y., additional, De Masi, F.M., additional, de Preville, N., additional, Dekkers, K.F., additional, Dermitzakis, E.T., additional, Deshmukh, H.A., additional, Dings, C., additional, Donnelly, L., additional, Dutta, A., additional, Ehrhardt, B., additional, Elders, P.J.M., additional, Engel Thomas, C.E.T., additional, Engelbrechtsen, L., additional, Eriksen, R.G., additional, Eriksen, R.E., additional, Fan, Y., additional, Fernandez, J., additional, Ferrer, J., additional, Fitipaldi, H., additional, Forgie, I.M., additional, Forman, A., additional, Franks, P.W., additional, Frau, F., additional, Fritsche, A., additional, Froguel, P., additional, Frost, G., additional, Gassenhuber, J., additional, Giordano, G.N., additional, Giorgino, T., additional, Gough, S., additional, Graefe-Mody, U., additional, Grallert, H., additional, Grempler, R., additional, Groeneveld, L., additional, Groop, L., additional, Gudmundsdóttir, V.G., additional, Gupta, R.G., additional, Haid, M., additional, Hansen, T., additional, Hansen, T.H., additional, Hattersley, A.T., additional, Haussler, R.S., additional, Heggie, A.J., additional, Hennige, A.M., additional, Hill, A.V., additional, Holl, R.W., additional, Hong, M.-G., additional, Hudson, M., additional, Jablonka, B., additional, Jennison, C., additional, Jiao, J., additional, Johansen, J.J., additional, Jones, A.G., additional, Jonsson, A., additional, Karaderi, T.K., additional, Kaye, J., additional, Klintenberg, M., additional, Koivula, R.W., additional, Kokkola, T., additional, Koopman, A.D.M., additional, Kurbasic, A, additional, Kuulasmaa, T., additional, Laakso, M., additional, Lehr, T., additional, Loftus, H., additional, Lundbye Allesøe, R.L.A, additional, Mahajan, A., additional, Mari, A., additional, Mazzoni, G.M., additional, McCarthy, M.I., additional, McDonald, T.J., additional, McEvoy, D., additional, McRobert, N., additional, McVittie, I., additional, Mourby, M., additional, Musholt, P., additional, Mutie, P, additional, Nice, R., additional, Nicolay, C., additional, Nielsen, A.M.N., additional, Nilsson, B.N., additional, Palmer, C.N., additional, Pattou, F., additional, Pavo, I., additional, Pearson, E.R., additional, Pedersen, O., additional, Pedersen, H.K.P., additional, Perry, M.H., additional, Pomares-Millan, H., additional, Ramisch, A., additional, Rasmussen, S.R., additional, Raverdi, V., additional, Ridderstrale, M., additional, Robertson, N., additional, Roderick, R.C., additional, Rodriquez, M., additional, Ruetten, H., additional, Rutters, F., additional, Sackett, W., additional, Scherer, N., additional, Schwenk, J.M., additional, Shah, N., additional, Sharma, S., additional, Sihinevich, I., additional, Sondertoft, N.B., additional, Staerfeldt, H., additional, Steckel-Hamann, B., additional, Teare, H., additional, Thomas, M.K., additional, Thomas, E.L., additional, Thomsen, H.S., additional, Thorand, B., additional, Thorne, C.E., additional, Tillner, J., additional, Troen Lundgaard, A.T.L., additional, Troll, M., additional, Tsirigos, K.D.T., additional, Tura, A., additional, Uhlen, M., additional, van Leeuwen, N., additional, van Oort, S., additional, Verkindt, H., additional, Vestergaard, H., additional, Viñuela, A., additional, Vogt, J.K, additional, Wad Sackett, P.W.S, additional, Wake, D., additional, Walker, M., additional, Wesolowska-Andersen, A., additional, Whitcher, B., additional, White, M.W., additional, and Wu, H., additional

Published
2023
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57. Additional file 1 of Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine

Author

Venkatasubramaniam, Ashwini, Mateen, Bilal A., Shields, Beverley M., Hattersley, Andrew T., Jones, Angus G., Vollmer, Sebastian J., and Dennis, John M.

Abstract

Additional file 1: sFlowchart. A) CANTATA D and D2 trials. B) CPRD patient flow and inclusion criteria. sTable 1. Additional baseline clinical characteristics by initiated drug class in CPRD. sFigure 1. Distribution of the predicted individualized treatment effect of SGLT2-inhibitor treatment compared to DPP4-inhibitor treatment in the RCT derivation data. a) Penalized regression. sTable 2. Comparison of model performance for the penalised ridge regression model with lasso regression. sTable 3. Sensitivity analysis testing variation in predicted treatment effects varying the number of trees in the causal forest algorithm. sFigure 2. Treatment selection model performance for A) Penalized regression and B) Causal forest in CPRD validation data using double robust estimation incorporating inverse probability of treatment weighting and adjustment for all predictors and confounders in the outcome model. sFigure 3. Treatment selection model performance for A) Penalized regression and B) Causal forest in CPRD validation data using double robust estimation incorporating inverse probability of treatment weighting, inverse probability of censoring weighting, and adjustment for all predictors and confounders in the outcome model. sFigure 4. Treatment selection model performance for A) Penalized regression and B) Causal forest in CPRD validation data, in patients initiating second-line therapy only. TRIPOD Checklist.

Published
2023
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59. Comment on Garvey et al. Association of Baseline Factors With Glycemic Outcomes in GRADE: A Comparative Effectiveness Randomized Clinical Trial. Diabetes Care 2024;47:562–570.

Author

Cardoso, Pedro, Young, Katie G., Hattersley, Andrew T., Shields, Beverley M., Jones, Angus G., and Dennis, John M.

Subjects
GLUCAGON-like peptide 1, TREATMENT effect heterogeneity, SODIUM-glucose cotransporter 2 inhibitors, TYPE 2 diabetes, CD26 antigen
Abstract

The article discusses a recent analysis of the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE) randomized controlled trial. The study compares the glycemic outcomes of four commonly used drug classes for type 2 diabetes: sulfonylureas, dipeptidyl peptidase 4 (DPP4) inhibitors, glucagon-like peptide 1 receptor agonists (GLP-1RA), and insulin. The findings indicate that there is differential glycemic response among these drug classes, with sex and age being identified as important factors in treatment effectiveness. The article also addresses some points of clarification regarding the study's findings. Overall, the study contributes to the growing evidence of treatment effect heterogeneity in noninsulin type 2 diabetes therapies. [Extracted from the article]

Published
2024
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62. Routine islet autoantibody testing in clinically diagnosed adult-onset type 1 diabetes can help identify misclassification and the possibility of successful insulin cessation.

Author

Eason, Russel J, primary, Thomas, Nicholas J., primary, Hill, Anita V, primary, Knight, Bridget A, primary, Carr, Alice, primary, T. Hattersley, Andrew, primary, McDonald, Timothy J., primary, M. Shields, Beverley, primary, G. Jones, Angus, primary, and group, the StartRight Study, primary

Published
2022
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63. TriMaster: randomised double-blind crossover trial of a DPP4-inhibitor, SGLT2-inhibitor and thiazolidinedione to evaluate differential glycaemic response to therapy based on obesity and renal function

Author

Hattersley, Andrew, primary, Shields, Beverley, additional, Dennis, John, additional, Angwin, Catherine, additional, Warren, Fiona, additional, Henley, William, additional, Farmer, Andrew, additional, Sattar, Naveed, additional, Holman, Rury, additional, Jones, Angus, additional, and Pearson, Ewan, additional

Published
2022
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64. Islet Autoantibody Level Distribution in Type 1 Diabetes and Their Association With Genetic and Clinical Characteristics

Author

Grace, Sian Louise, primary, Bowden, Jack, additional, Walkey, Helen C, additional, Kaur, Akaal, additional, Misra, Shivani, additional, Shields, Beverley M, additional, McKinley, Trevelyan J, additional, Oliver, Nick S, additional, McDonald, Timothy J, additional, Johnston, Desmond G, additional, Jones, Angus G, additional, and Patel, Kashyap A, additional

Published
2022
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65. Age of Diagnosis Does Not Alter the Presentation or Progression of Robustly Defined Adult-Onset Type 1 Diabetes.

Author

Thomas, Nicholas J., Hill, Anita V., Dayan, Colin M., Oram, Richard A., McDonald, Timothy J., Shields, Beverley M., Jones, Angus G., StartRight Study Group, Simon, Godwin, Ramos, Angelo, Norris, Andrea, Tan, Kai, Narendran, Parth, Ramtoola, Shenaz, Ali, Amar, Banerjee, Moulinath, Brooks, Augustin, Chakera, Ali, Johnson, Andrew, and Tatovic, Danijela

Abstract

OBJECTIVE: To determine whether presentation, progression, and genetic susceptibility of robustly defined adult-onset type 1 diabetes (T1D) are altered by diagnosis age. RESEARCH DESIGN AND METHODS: We compared the relationship between diagnosis age and presentation, C-peptide loss (annual change in urine C-peptide–creatinine ratio [UCPCR]), and genetic susceptibility (T1D genetic risk score [GRS]) in adults with confirmed T1D in the prospective StartRight study, 1,798 adults with new-onset diabetes. T1D was defined in two ways: two or more positive islet autoantibodies (of GAD antibody, IA-2 antigen, and ZnT8 autoantibody) irrespective of clinical diagnosis (n = 385) or one positive islet autoantibody and a clinical diagnosis of T1D (n = 180). RESULTS: In continuous analysis, age of diagnosis was not associated with C-peptide loss for either definition of T1D (P > 0.1), with mean (95% CI) annual C-peptide loss in those diagnosed before and after 35 years of age (median age of T1D defined by two or more positive autoantibodies): 39% (31–46) vs. 44% (38–50) with two or more positive islet autoantibodies and 43% (33–51) vs. 39% (31–46) with clinician diagnosis confirmed by one positive islet autoantibody (P > 0.1). Baseline C-peptide and T1D GRS were unaffected by age of diagnosis or T1D definition (P > 0.1). In T1D defined by two or more autoantibodies, presentation severity was similar in those diagnosed before and after 35 years of age: unintentional weight loss, 80% (95% CI 74–85) vs. 82% (76–87); ketoacidosis, 24% (18–30) vs. 19% (14–25); and presentation glucose, 21 mmol/L (19–22) vs. 21 mmol/L (20–22) (all P ≥ 0.1). Despite similar presentation, older adults were less likely to be diagnosed with T1D, insulin-treated, or admitted to hospital. CONCLUSIONS: When adult-onset T1D is robustly defined, the presentation characteristics, progression, and T1D genetic susceptibility are not altered by age of diagnosis. [ABSTRACT FROM AUTHOR]

Published
2023
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66. Diagnosing Type 1 diabetes in adults:Guidance from the UK T1D Immunotherapy consortium

Author

Tatovic, Danijela, Jones, Angus G., Evans, Carol, Long, Anna E., Gillespie, Kathleen, Besser, Rachel E. J., Leslie, Richard David, and Dayan, Colin M.

Subjects
Adult, Endocrinology, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Endocrinology, Diabetes and Metabolism, Internal Medicine, Humans, Genetic Predisposition to Disease, Immunotherapy, United Kingdom
Published
2022

67. 985-P: Prediction Models Combining Clinical Measures Identify Participants with Youth-Onset Diabetes Who Maintain Insulin Secretion

Author

JONES, ANGUS, primary, SHIELDS, BEVERLEY, additional, ORAM, RICHARD A., additional, DABELEA, DANA, additional, HAGOPIAN, WILLIAM, additional, LUSTIGOVA, EVA, additional, SHAH, AMY S., additional, MOTTL, AMY K., additional, DAGOSTINO, RALPH, additional, WILLIAMS, ADRIENNE H., additional, MARCOVINA, SANTICA M., additional, PIHOKER, CATHERINE, additional, DIVERS, JASMIN, additional, and REDONDO, MARIA J., additional

Published
2022
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75. Continuous glucose monitoring demonstrates low risk of clinically significant hypoglycemia associated with sulphonylurea treatment in an African type 2 diabetes population: results from the OPTIMAL observational multicenter study

Author

Niwaha, Anxious J, primary, Rodgers, Lauren R, additional, Carr, Alice L J, additional, Balungi, Priscilla A, additional, Mwebaze, Raymond, additional, Hattersley, Andrew T, additional, Shields, Beverley M, additional, Nyirenda, Moffat J, additional, and Jones, Angus G, additional

Published
2022
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80. The impact of population-level HbA1c screening on reducing diabetes diagnostic delay in middle-aged adults: a UK Biobank analysis.

Author

Young, Katherine G., McGovern, Andrew P., Barroso, Inês, Hattersley, Andrew T., Jones, Angus G., Shields, Beverley M., Thomas, Nicholas J., and Dennis, John M.

Abstract

Aims/hypothesis: Screening programmes can detect cases of undiagnosed diabetes earlier than symptomatic or incidental diagnosis. However, the improvement in time to diagnosis achieved by screening programmes compared with routine clinical care is unclear. We aimed to use the UK Biobank population-based study to provide the first population-based estimate of the reduction in time to diabetes diagnosis that could be achieved by HbA1c-based screening in middle-aged adults. Methods: We studied UK Biobank participants aged 40–70 years with HbA1c measured at enrolment (but not fed back to participants/clinicians) and linked primary and secondary healthcare data (n=179,923) and identified those with a pre-existing diabetes diagnosis (n=13,077, 7.3%). Among the remaining participants (n=166,846) without a diabetes diagnosis, we used an elevated enrolment HbA1c level (≥48 mmol/mol [≥6.5%]) to identify those with undiagnosed diabetes. For this group, we used Kaplan–Meier analysis to assess the time between enrolment HbA1c measurement and subsequent clinical diabetes diagnosis up to 10 years, and Cox regression to identify clinical factors associated with delayed diabetes diagnosis. Results: In total, 1.0% (1703/166,846) of participants without a diabetes diagnosis had undiagnosed diabetes based on calibrated HbA1c levels at UK Biobank enrolment, with a median HbA1c level of 51.3 mmol/mol (IQR 49.1–57.2) (6.8% [6.6–7.4]). These participants represented an additional 13.0% of diabetes cases in the study population relative to the 13,077 participants with a diabetes diagnosis. The median time to clinical diagnosis for those with undiagnosed diabetes was 2.2 years, with a median HbA1c at clinical diagnosis of 58.2 mmol/mol (IQR 51.0–80.0) (7.5% [6.8–9.5]). Female participants with lower HbA1c and BMI measurements at enrolment experienced the longest delay to clinical diagnosis. Conclusions/interpretation: Our population-based study shows that HbA1c screening in adults aged 40–70 years can reduce the time to diabetes diagnosis by a median of 2.2 years compared with routine clinical care. The findings support the use of HbA1c screening to reduce the time for which individuals are living with undiagnosed diabetes. [ABSTRACT FROM AUTHOR]

Published
2023
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81. Routine Islet Autoantibody Testing in Clinically Diagnosed Adult-Onset Type 1 Diabetes Can Help Identify Misclassification and the Possibility of Successful Insulin Cessation.

Author

Eason, Russell J., Thomas, Nicholas J., Hill, Anita V., Knight, Bridget A., Carr, Alice, Hattersley, Andrew T., McDonald, Timothy J., Shields, Beverley M., Jones, Angus G., SimonGodwinRamosAngeloNorrisAndreaTanKaiNarendranParthRamtoolaShenazAliAmarBanerjeeMoulinathBrooksAugustinChakeraAliJohnsonAndrewTatovicDanijelaBallavChitrabhanuDayanColinNairSunilGameFrancesJonesAngusBeamesSusanRaymanGerrySnellMarieButlerSusieBeckSarahBe, Simon, Godwin, Ramos, Angelo, Norris, Andrea, Tan, Kai, Narendran, Parth, Ramtoola, Shenaz, Ali, Amar, Banerjee, Moulinath, Brooks, Augustin, and Chakera, Ali

Abstract

OBJECTIVE: Recent joint American Diabetes Association and European Association for the Study of Diabetes guidelines recommend routine islet autoantibody testing in all adults newly diagnosed with type 1 diabetes. We aimed to assess the impact of routine islet autoantibody testing in this population. RESEARCH DESIGN AND METHODS: We prospectively assessed the relationship between islet autoantibody status (GADA, IA-2A, and ZNT8A), clinical and genetic characteristics, and progression (annual change in urine C-peptide–to–creatinine ratio [UCPCR]) in 722 adults (≥18 years old at diagnosis) with clinically diagnosed type 1 diabetes and diabetes duration <12 months. We also evaluated changes in treatment and glycemia over 2 years after informing participants and their clinicians of autoantibody results. RESULTS: Of 722 participants diagnosed with type 1 diabetes, 24.8% (179) were autoantibody negative. This group had genetic and C-peptide characteristics suggestive of a high prevalence of nonautoimmune diabetes: lower mean type 1 diabetes genetic risk score (islet autoantibody negative vs. positive: 10.85 vs. 13.09 [P < 0.001] [type 2 diabetes 10.12]) and lower annual change in C-peptide (UCPCR), −24% vs. −43% (P < 0.001). After median 24 months of follow-up, treatment change occurred in 36.6% (60 of 164) of autoantibody-negative participants: 22.6% (37 of 164) discontinued insulin, with HbA1c similar to that of participants continuing insulin (57.5 vs. 60.8 mmol/mol [7.4 vs. 7.7%], P = 0.4), and 14.0% (23 of 164) added adjuvant agents to insulin. CONCLUSIONS: In adult-onset clinically diagnosed type 1 diabetes, negative islet autoantibodies should prompt careful consideration of other diabetes subtypes. When routinely measured, negative antibodies are associated with successful insulin cessation. These findings support recent recommendations for routine islet autoantibody assessment in adult-onset type 1 diabetes. [ABSTRACT FROM AUTHOR]

Published
2022
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83. Derivation and validation of a type 2 diabetes treatment selection algorithm for SGLT2-inhibitor and DPP4-inhibitor therapies based on glucose-lowering efficacy: cohort study using trial and routine clinical data

Author

Dennis, John M, primary, Young, Katherine G, additional, McGovern, Andrew P, additional, Mateen, Bilal A, additional, Vollmer, Sebastian J, additional, Simpson, Michael D, additional, Henley, William E, additional, Holman, Rury R, additional, Sattar, Naveed, additional, Pearson, Ewan R, additional, Hattersley, Andrew, additional, Jones, Angus G, additional, and Shields, Beverley, additional

Published
2021
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84. Adult-Onset Type 1 Diabetes: Current Understanding and Challenges

Author

Leslie, R. David, primary, Evans-Molina, Carmella, additional, Freund-Brown, Jacquelyn, additional, Buzzetti, Raffaella, additional, Dabelea, Dana, additional, Gillespie, Kathleen M., additional, Goland, Robin, additional, Jones, Angus G., additional, Kacher, Mark, additional, Phillips, Lawrence S., additional, Rolandsson, Olov, additional, Wardian, Jana L., additional, and Dunne, Jessica L., additional

Published
2021
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85. Inferring causal pathways between metabolic processes and liver fat accumulation: an IMI DIRECT study

Author

Atabaki-Pasdar, Naeimeh, primary, Pomares-Millan, Hugo, additional, Koivula, Robert W, additional, Tura, Andrea, additional, Brown, Andrew, additional, Viñuela, Ana, additional, Agudelo, Leandro, additional, Coral, Daniel, additional, van Oort, Sabine, additional, Allin, Kristine, additional, Chabanova, Elizaveta, additional, Cederberg, Henna, additional, De Masi, Federico, additional, Elders, Petra, additional, Tajes, Juan Fernandez, additional, Forgie, Ian M, additional, Hansen, Tue H, additional, Heggie, Alison, additional, Jones, Angus, additional, Kokkola, Tarja, additional, Mahajan, Anubha, additional, McDonald, Timothy J, additional, McEvoy, Donna, additional, Tsirigos, Konstantinos, additional, Teare, Harriet, additional, Vangipurapu, Jagadish, additional, Vestergaard, Henrik, additional, Adamski, Jerzy, additional, Beulens, Joline WJ, additional, Brunak, Søren, additional, Dermitzakis, Emmanouil, additional, Hansen, Torben, additional, Hattersley, Andrew T, additional, Laakso, Markku, additional, Pedersen, Oluf, additional, Ridderstråle, Martin, additional, Ruetten, Hartmut, additional, Rutters, Femke, additional, Schwenk, Jochen M, additional, Walker, Mark, additional, Giordano, Giuseppe N, additional, Ohlsson, Mattias, additional, Gupta, Ramneek, additional, Mari, Andrea, additional, McCarthy, Mark I, additional, Thomas, E Louise, additional, Bell, Jimmy D, additional, Pavo, Imre, additional, Pearson, Ewan R, additional, and Franks, Paul W, additional

Published
2021
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86. HbA1c performs well in monitoring glucose control even in populations with high prevalence of medical conditions that may alter its reliability: the OPTIMAL observational multicenter study

Author

Niwaha, Anxious J, primary, Rodgers, Lauren R, additional, Greiner, Rosamund, additional, Balungi, Priscilla A, additional, Mwebaze, Raymond, additional, McDonald, Timothy J, additional, Hattersley, Andrew T, additional, Shields, Beverley M, additional, Nyirenda, Moffat J, additional, and Jones, Angus G, additional

Published
2021
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87. Evaluating associations between the benefits and risks of drug therapy in type 2 diabetes: a joint modeling approach

Author

Dennis,John M., Shields,Beverley M., Jones,Angus G., Pearson,Ewan R., Hattersley,Andrew T., and Henley,William E.

Subjects
HbA1c, precision medicine, ADOPT, hypoglycemia, type 2, diabetes mellitus, sulfonylurea compounds, Clinical Epidemiology, drug-related side effects, metformin, thiazolidinediones, edema, Original Research, joint model
Abstract

John M Dennis,1 Beverley M Shields,2 Angus G Jones,2 Ewan R Pearson,3 Andrew T Hattersley,2 William E Henley1 On behalf of the MASTERMIND consortium 1Health Statistics Group, University of Exeter Medical School, Exeter, UK; 2National Institute for Health Research Exeter Clinical Research Facility, University of Exeter Medical School, Exeter, UK; 3Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK Objective: Precision medicine drug therapy seeks to maximize efficacy and minimize harm for individual patients. This will be difficult if drug response and side effects are positively associated, meaning that patients likely to respond best are at increased risk of side effects. We applied joint longitudinal–survival models to evaluate associations between drug response (longitudinal outcome) and the risk of side effects (survival outcome) for patients initiating type 2 diabetes therapy. Study design and setting: Participants were randomized to metformin (MFN), sulfonylurea(SU), or thiazolidinedione (TZD) therapy in the A Diabetes Outcome Progression Trial (ADOPT) drug efficacy trial (n=4,351). Joint models were parameterized for 1) current HbA1c response (change from baseline in HbA1c) and 2) cumulative HbA1c response (total HbA1c change). Results: With MFN, greater HbA1c response did not increase the risk of gastrointestinal events (HR per 1% absolute greater current response 0.82 [95% CI 0.67, 1.01]; HR per 1% higher cumulative response 0.90 [95% CI 0.81, 1.00]). With SU, greater current response was associated with an increased risk of hypoglycemia (HR 1.41 [95% CI 1.04, 1.91]). With TZD, greater response was associated with an increased risk of edema (current HR 1.45 [95% CI 1.05, 2.01]; cumulative 1.22 [95% CI 1.07, 1.38]) but not fracture. Conclusion: Joint modeling provides a useful framework to evaluate the association between response to a drug and the risk of developing side effects. There may be great potential for widespread application of joint modeling to evaluate the risks and benefits of both new and established medications. Keywords: diabetes mellitus, type 2, drug-related side effects, HbA1c, hypoglycemia, joint model, precision medicine, thiazolidinediones, metformin, sulfonylurea compounds, ADOPT, edema

Published
2018

90. Islet autoantibody level distributions in type 1 diabetes and their association with genetic and clinical characteristics

Author

Grace, Sian Louise, primary, Bowden, Jack, additional, Walkey, Helen C., additional, Kaur, Akaal, additional, Misra, Shivani, additional, Shields, Beverley M., additional, McKinley, Trevelyan J., additional, Oliver, Nick S, additional, McDonald, Timothy, additional, Johnston, Desmond G., additional, Jones, Angus G., additional, and Patel, Kashyap Amratial, additional

Published
2021
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94. Additional file 5 of Choice of HbA1c threshold for identifying individuals at high risk of type 2 diabetes and implications for diabetes prevention programmes: a cohort study

Author

Rodgers, Lauren R., Hill, Anita V., Dennis, John M., Craig, Zoe, May, Benedict, Hattersley, Andrew T., McDonald, Timothy J., Andrews, Rob C., Jones, Angus, and Shields, Beverley M.

Subjects
endocrine system diseases, nutritional and metabolic diseases
Abstract

Additional file 5. Kaplan-Meier survival plot by HbA1c category.

Published
2021
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95. Additional file 3 of Choice of HbA1c threshold for identifying individuals at high risk of type 2 diabetes and implications for diabetes prevention programmes: a cohort study

Author

Rodgers, Lauren R., Hill, Anita V., Dennis, John M., Craig, Zoe, May, Benedict, Hattersley, Andrew T., McDonald, Timothy J., Andrews, Rob C., Jones, Angus, and Shields, Beverley M.

Subjects
health care economics and organizations
Abstract

Additional file 3. Sensitivity analysis excluding data after the launch of the UK diabetes prevention programme.

Published
2021
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97. Additional file 4 of Choice of HbA1c threshold for identifying individuals at high risk of type 2 diabetes and implications for diabetes prevention programmes: a cohort study

Author

Rodgers, Lauren R., Hill, Anita V., Dennis, John M., Craig, Zoe, May, Benedict, Hattersley, Andrew T., McDonald, Timothy J., Andrews, Rob C., Jones, Angus, and Shields, Beverley M.

Subjects
endocrine system diseases, nutritional and metabolic diseases
Abstract

Additional file 4. Absolute 5 year risk of developing Type 2 diabetes given baseline HbA1c.

Published
2021
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98. Adult-Onset Type 1 Diabetes : Current Understanding and Challenges

Author

Leslie, R. David, Evans-Molina, Carmella, Freund-Brown, Jacquelyn, Buzzetti, Raffaella, Dabelea, Dana, Gillespie, Kathleen M., Goland, Robin, Jones, Angus G., Kacher, Mark, Phillips, Lawrence S., Rolandsson, Olov, Wardian, Jana L., Dunne, Jessica L., Leslie, R. David, Evans-Molina, Carmella, Freund-Brown, Jacquelyn, Buzzetti, Raffaella, Dabelea, Dana, Gillespie, Kathleen M., Goland, Robin, Jones, Angus G., Kacher, Mark, Phillips, Lawrence S., Rolandsson, Olov, Wardian, Jana L., and Dunne, Jessica L.

Abstract

Recent epidemiological data have shown that more than half of all new cases of type 1 diabetes occur in adults. Key genetic, immune, and metabolic differences exist between adult- and childhood-onset type 1 diabetes, many of which are not well understood. A substantial risk of misclassification of diabetes type can result. Notably, some adults with type 1 diabetes may not require insulin at diagnosis, their clinical disease can masquerade as type 2 diabetes, and the consequent misclassification may result in inappropriate treatment. In response to this important issue, JDRF convened a workshop of international experts in November 2019. Here, we summarize the current understanding and unanswered questions in the field based on those discussions, highlighting epidemiology and immunogenetic and metabolic characteristics of adult-onset type 1 diabetes as well as disease-associated comorbidities and psychosocial challenges. In adult-onset, as compared with childhood-onset, type 1 diabetes, HLA-associated risk is lower, with more protective genotypes and lower genetic risk scores; multiple diabetes-associated autoantibodies are decreased, though GADA remains dominant. Before diagnosis, those with autoantibodies progress more slowly, and at diagnosis, serum C-peptide is higher in adults than children, with ketoacidosis being less frequent. Tools to distinguish types of diabetes are discussed, including body phenotype, clinical course, family history, autoantibodies, comorbidities, and C-peptide. By providing this perspective, we aim to improve the management of adults presenting with type 1 diabetes.

Published
2021
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99. Genome-Wide Association Analysis of Pancreatic Beta-Cell Glucose Sensitivity

Author

Deshmukh, Harshal A., Madsen, Anne Lundager, Vinuela, Ana, Have, Christian Theil, Grarup, Niels, Tura, Andrea, Mahajan, Anubha, Heggie, Alison J., Koivula, Robert W., De Masi, Federico, Tsirigos, Konstantinos K., Linneberg, Allan, Drivsholm, Thomas, Pedersen, Oluf, Sorensen, Thorkild I. A., Astrup, Arne, Gjesing, Anette A. P., Pavo, Imre, Wood, Andrew R., Ruetten, Hartmut, Jones, Angus G., Koopman, Anitra D. M., Cederberg, Henna, Rutters, Femke, Ridderstrale, Martin, Laakso, Markku, McCarthy, Mark, I, Frayling, Tim M., Ferrannini, Ele, Franks, Paul W., Pearson, Ewan R., Mari, Andrea, Hansen, Torben, Walker, Mark, Deshmukh, Harshal A., Madsen, Anne Lundager, Vinuela, Ana, Have, Christian Theil, Grarup, Niels, Tura, Andrea, Mahajan, Anubha, Heggie, Alison J., Koivula, Robert W., De Masi, Federico, Tsirigos, Konstantinos K., Linneberg, Allan, Drivsholm, Thomas, Pedersen, Oluf, Sorensen, Thorkild I. A., Astrup, Arne, Gjesing, Anette A. P., Pavo, Imre, Wood, Andrew R., Ruetten, Hartmut, Jones, Angus G., Koopman, Anitra D. M., Cederberg, Henna, Rutters, Femke, Ridderstrale, Martin, Laakso, Markku, McCarthy, Mark, I, Frayling, Tim M., Ferrannini, Ele, Franks, Paul W., Pearson, Ewan R., Mari, Andrea, Hansen, Torben, and Walker, Mark

Abstract

Context: Pancreatic beta-cell glucose sensitivity is the slope of the plasma glucose-insulin secretion relationship and is a key predictor of deteriorating glucose tolerance and development of type 2 diabetes. However, there are no large-scale studies looking at the genetic determinants of beta-cell glucose sensitivity. Objective: To understand the genetic determinants of pancreatic beta-cell glucose sensitivity using genome-wide meta-analysis and candidate gene studies. Design: We performed a genome-wide meta-analysis for beta-cell glucose sensitivity in subjects with type 2 diabetes and nondiabetic subjects from 6 independent cohorts (n = 5706). Beta-cell glucose sensitivity was calculated from mixed meal and oral glucose tolerance tests, and its associations between known glycemia-related single nucleotide polymorphisms (SNPs) and genome-wide association study (GWAS) SNPs were estimated using linear regression models. Results: Beta-cell glucose sensitivity was moderately heritable (h2 ranged from 34% to 55%) using SNP and family-based analyses. GWAS meta-analysis identified multiple correlated SNPs in the CDKAL1 gene and GIPR-QPCTL gene loci that reached genome-wide significance, with SNP rs2238691 in GIPR-QPCTL (P value = 2.64 × 10−9) and rs9368219 in the CDKAL1 (P value = 3.15 × 10−9) showing the strongest association with beta-cell glucose sensitivity. These loci surpassed genome-wide significance when the GWAS meta-analysis was repeated after exclusion of the diabetic subjects. After correction for multiple testing, glycemia-associated SNPs in or near the HHEX and IGF2B2 loci were also associated with beta-cell glucose sensitivity. Conclusion: We show that, variation at the GIPR-QPCTL and CDKAL1 loci are key determinants of pancreatic beta-cell glucose sensitivity.

Published
2021
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100. Processes Underlying Glycemic Deterioration in Type 2 Diabetes:An IMI DIRECT Study

Author

Bizzotto, Roberto, Jennison, Christopher, Jones, Angus G., Kurbasic, Azra, Tura, Andrea, Kennedy, Gwen, Bell, Jimmy D., Thomas, E. Louise, Frost, Gary, Eriksen, Rebeca, Koivula, Robert W., Brage, Soren, Kaye, Jane, Hattersley, Andrew T., Heggie, Alison, McEvoy, Donna, 't Hart, Leen M., Beulens, Joline W., Elders, Petra, Musholt, Petra B., Ridderstrale, Martin, Hansen, Tue H., Allin, Kristine H., Hansen, Torben, Vestergaard, Henrik, Lundgaard, Agnete T., Thomsen, Henrik S., De Masi, Federico, Tsirigos, Konstantinos D., Brunak, Søren, Vinuela, Ana, Mahajan, Anubha, McDonald, Timothy J., Kokkola, Tarja, Forgie, Ian M., Giordano, Giuseppe N., Pavo, Imre, Ruetten, Hartmut, Dermitzakis, Emmanouil, McCarthy, Mark I., Pedersen, Oluf, Schwenk, Jochen M., Adamski, Jerzy, Franks, Paul W., Walker, Mark, Pearson, Ewan R., Mari, Andrea, Bizzotto, Roberto, Jennison, Christopher, Jones, Angus G., Kurbasic, Azra, Tura, Andrea, Kennedy, Gwen, Bell, Jimmy D., Thomas, E. Louise, Frost, Gary, Eriksen, Rebeca, Koivula, Robert W., Brage, Soren, Kaye, Jane, Hattersley, Andrew T., Heggie, Alison, McEvoy, Donna, 't Hart, Leen M., Beulens, Joline W., Elders, Petra, Musholt, Petra B., Ridderstrale, Martin, Hansen, Tue H., Allin, Kristine H., Hansen, Torben, Vestergaard, Henrik, Lundgaard, Agnete T., Thomsen, Henrik S., De Masi, Federico, Tsirigos, Konstantinos D., Brunak, Søren, Vinuela, Ana, Mahajan, Anubha, McDonald, Timothy J., Kokkola, Tarja, Forgie, Ian M., Giordano, Giuseppe N., Pavo, Imre, Ruetten, Hartmut, Dermitzakis, Emmanouil, McCarthy, Mark I., Pedersen, Oluf, Schwenk, Jochen M., Adamski, Jerzy, Franks, Paul W., Walker, Mark, Pearson, Ewan R., and Mari, Andrea

Abstract

OBJECTIVEWe investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D).RESEARCH DESIGN AND METHODSA total of 732 recently diagnosed patients with T2D from the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) study were extensively phenotyped over 3 years, including measures of insulin sensitivity (OGIS), beta-cell glucose sensitivity (GS), and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA(1c) deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression.RESULTSFaster HbA(1c) progression was independently associated with faster deterioration of OGIS and GS and increasing CLIm; visceral or liver fat, HDL-cholesterol, and triglycerides had further independent, though weaker, roles (R-2 = 0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from area under the receiver operating characteristic = 0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS, and CLIm was relatively stable (odds ratios 0.07-0.09). T2D polygenic risk score and baseline pancreatic fat, glucagon-like peptide 1, glucagon, diet, and physical activity did not show an independent role.CONCLUSIONSDeteriorating insulin sensitivity and beta-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of patients with T2D in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, beta-cell function, and insulin clearance may be relevant to prevent

Published
2021

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