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Your search keyword '"McEvoy, Donna"' showing total 2 results
Start Over Author "McEvoy, Donna" Topic insulin sensitivity
2 results on '"McEvoy, Donna"'

1. 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, and Musholt, Petra B.

Subjects
TYPE 2 diabetes, GLUCAGON-like peptide 1, INSULIN sensitivity, RECEIVER operating characteristic curves, GLUCAGON-like peptides, LIVER enzymes
Abstract

Objective: We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D).Research Design and Methods: A 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), β-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 HbA1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression.Results: Faster HbA1c 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 (R2 = 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.Conclusions: Deteriorating insulin sensitivity and β-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, β-cell function, and insulin clearance may be relevant to prevent progression. [ABSTRACT FROM AUTHOR]

Published
2021
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2. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts

Author

Atabaki-Pasdar, N, Ohlsson, M, Vinuela, A, Frau, F, Pomares-Millan, H, Haid, M, Jones, AG, Thomas, EL, Koivula, RW, Kurbasic, A, Mutie, PM, Fitipaldi, H, Fernandez, J, Dawed, AY, Giordano, GN, Forgie, IM, McDonald, TJ, Rutters, F, Cederberg, H, Chabanova, E, Dale, M, Masi, FD, Thomas, CE, Allin, KH, Hansen, TH, Heggie, A, Hong, M-G, Elders, PJM, Kennedy, G, Kokkola, T, Pedersen, HK, Mahajan, A, McEvoy, D, Pattou, F, Raverdy, V, Haussler, RS, Sharma, S, Thomsen, HS, Vangipurapu, J, Vestergaard, H, 't Hart, LM, Adamski, J, Musholt, PB, Brage, S, Brunak, S, Dermitzakis, E, Frost, G, Hansen, T, Laakso, M, Pedersen, O, Ridderstrale, M, Ruetten, H, Hattersley, AT, Walker, M, Beulens, JWJ, Mari, A, Schwenk, JM, Gupta, R, McCarthy, MI, Pearson, ER, Bell, JD, Pavo, I, Franks, PW, Epidemiology and Data Science, General practice, APH - Health Behaviors & Chronic Diseases, Amsterdam Reproduction & Development (AR&D), ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, APH - Aging & Later Life, Atabaki-Pasdar, Naeimeh [0000-0001-7229-1888], Ohlsson, Mattias [0000-0003-1145-4297], Viñuela, Ana [0000-0003-3771-8537], Pomares-Millan, Hugo [0000-0001-9245-4576], Haid, Mark [0000-0001-6118-1333], Jones, Angus G. [0000-0002-0883-7599], Thomas, E. Louise [0000-0003-4235-4694], Koivula, Robert W. [0000-0002-1646-4163], Kurbasic, Azra [0000-0002-1910-2619], Fitipaldi, Hugo [0000-0001-5352-2134], Dawed, Adem Y. [0000-0003-0224-2428], Forgie, Ian M. [0000-0002-8800-6145], Cederberg, Henna [0000-0003-2901-9373], Dale, Matilda [0000-0002-5788-7744], Masi, Federico De [0000-0003-4859-4170], Thomas, Cecilia Engel [0000-0001-6201-6380], Allin, Kristine H. [0000-0002-6880-5759], Hansen, Tue H. [0000-0001-5948-8993], Elders, Petra J. M. [0000-0002-5907-7219], Kennedy, Gwen [0000-0002-9856-3236], Kokkola, Tarja [0000-0002-3303-3912], Pedersen, Helle Krogh [0000-0001-9609-7377], Mahajan, Anubha [0000-0001-5585-3420], McEvoy, Donna [0000-0003-1546-5567], Häussler, Ragna S. [0000-0003-1664-8875], Vangipurapu, Jagadish [0000-0001-6657-2659], Vestergaard, Henrik [0000-0003-3090-269X], ‘t Hart, Leen M. [0000-0003-4401-2938], Brage, Soren [0000-0002-1265-7355], Frost, Gary [0000-0003-0529-6325], Hansen, Torben [0000-0001-8748-3831], Hattersley, Andrew T. [0000-0001-5620-473X], Mari, Andrea [0000-0002-1436-5591], Schwenk, Jochen M. [0000-0001-8141-8449], Gupta, Ramneek [0000-0001-6841-6676], McCarthy, Mark I. [0000-0002-4393-0510], Pearson, Ewan R. [0000-0001-9237-8585], Bell, Jimmy D. [0000-0003-3804-1281], Franks, Paul W. [0000-0002-0520-7604], Apollo - University of Cambridge Repository, HUS Abdominal Center, Clinicum, Department of Medicine, Endokrinologian yksikkö, Jones, Angus G [0000-0002-0883-7599], Thomas, E Louise [0000-0003-4235-4694], Koivula, Robert W [0000-0002-1646-4163], Dawed, Adem Y [0000-0003-0224-2428], Forgie, Ian M [0000-0002-8800-6145], Allin, Kristine H [0000-0002-6880-5759], Hansen, Tue H [0000-0001-5948-8993], Elders, Petra JM [0000-0002-5907-7219], Häussler, Ragna S [0000-0003-1664-8875], 't Hart, Leen M [0000-0003-4401-2938], Hattersley, Andrew T [0000-0001-5620-473X], Schwenk, Jochen M [0000-0001-8141-8449], McCarthy, Mark I [0000-0002-4393-0510], Pearson, Ewan R [0000-0001-9237-8585], Bell, Jimmy D [0000-0003-3804-1281], Franks, Paul W [0000-0002-0520-7604], and IMI

Subjects
Male, Proteomics, Oral Glucose Suppression Test, Biochemistry, Machine Learning, Fats, Database and Informatics Methods, Endocrinology, Medicine and Health Sciences, Insulin, Prospective Studies, 11 Medical and Health Sciences, GLOBAL EPIDEMIOLOGY, INSULIN SENSITIVITY, Proteomic Databases, Liver Diseases, Middle Aged, Lipids, Medicine, Female, Life Sciences & Biomedicine, Research Article, Computer and Information Sciences, Endocrine Disorders, BIOMARKERS, Gastroenterology and Hepatology, Research and Analysis Methods, Risk Assessment, Diabetes Complications, Medicine, General & Internal, SDG 3 - Good Health and Well-being, Artificial Intelligence, General & Internal Medicine, NAFLD, Diabetes Mellitus, Humans, Metabolomics, Diabetic Endocrinology, Pharmacology, Science & Technology, Models, Statistical, Reproducibility of Results, Biology and Life Sciences, ALCOHOLIC STEATOHEPATITIS, Hormones, Pharmacologic-Based Diagnostics, Fatty Liver, Metabolism, Biological Databases, 3121 General medicine, internal medicine and other clinical medicine, Metabolic Disorders
Abstract

Background Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Methods and findings We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (, In a modelling study, Naeimeh Atabaki-Pasdar and colleagues apply machine learning techniques to develop models to predict non-alcoholic fatty liver disease diagnosis using multi-omic and clinical data from individuals with and without type 2 diabetes in the IMI DIRECT cohorts., Author summary Why was this study done? Globally, about 1 in 4 adults have non-alcoholic fatty liver disease (NAFLD), which adversely affects energy homeostasis (in particular blood glucose concentrations), blood detoxification, drug metabolism, and food digestion. Although numerous noninvasive tests to detect NAFLD exist, these typically include inaccurate blood-marker tests or expensive imaging methods. The purpose of this work was to develop accurate noninvasive methods to aid in the clinical prediction of NAFLD. What did the researchers do and find? The analyses applied machine learning methods to data from the deep-phenotyped IMI DIRECT cohorts (n = 1,514) to identify sets of highly informative variables for the prediction of NAFLD. The criterion measure was liver fat quantified from MRI. We developed a total of 18 prediction models that ranged from very inexpensive models of modest accuracy to more expensive biochemistry- and/or omics-based models with high accuracy. We found that models using measures commonly collected in either clinical settings or research studies proved adequate for the prediction of NAFLD. The addition of detailed omics data significantly improved the predictive utility of these models. We also found that of all omics markers, proteomic markers yielded the highest predictive accuracy when appropriately combined. What do these findings mean? We envisage that these new approaches to predicting fatty liver may be of clinical value when screening at-risk populations for NAFLD. The identification of specific molecular features that underlie the development of NAFLD provides novel insights into the disease’s etiology, which may lead to the development of new treatments.

Published
2020
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