29 results on '"Forgie, Ian M."'
Search Results
2. Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: descriptive characteristics of the epidemiological studies within the IMI DIRECT Consortium
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Koivula, Robert W., Forgie, Ian M., Kurbasic, Azra, Viñuela, Ana, Heggie, Alison, Giordano, Giuseppe N., Hansen, Tue H., Hudson, Michelle, Koopman, Anitra D. M., Rutters, Femke, Siloaho, Maritta, Allin, Kristine H., Brage, Søren, Brorsson, Caroline A., Dawed, Adem Y., De Masi, Federico, Groves, Christopher J., Kokkola, Tarja, Mahajan, Anubha, Perry, Mandy H., Rauh, Simone P., Ridderstråle, Martin, Teare, Harriet J. A., Thomas, E. Louise, Tura, Andrea, Vestergaard, Henrik, White, Tom, Adamski, Jerzy, Bell, Jimmy D., Beulens, Joline W., Brunak, Søren, Dermitzakis, Emmanouil T., Froguel, Philippe, Frost, Gary, Gupta, Ramneek, Hansen, Torben, Hattersley, Andrew, Jablonka, Bernd, Kaye, Jane, Laakso, Markku, McDonald, Timothy J., Pedersen, Oluf, Schwenk, Jochen M., Pavo, Imre, Mari, Andrea, McCarthy, Mark I., Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Franks, Paul W., and for the IMI DIRECT Consortium
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- 2019
- Full Text
- View/download PDF
3. Inferring causal pathways between metabolic processes and liver fat accumulation: an IMI DIRECT study
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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
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- 2021
- Full Text
- View/download PDF
4. Profiles of Glucose Metabolism in Different Prediabetes Phenotypes, Classified by Fasting Glycemia, 2-Hour OGTT, Glycated Hemoglobin, and 1-hour OGTT: An IMI DIRECT Study
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Tura, Andrea, primary, Grespan, Eleonora, primary, Göbl, Christian S., primary, Koivula, Robert W., primary, Franks, Paul W., primary, Pearson, Ewan R., primary, Walker, Mark, primary, Forgie, Ian M., primary, Giordano, Giuseppe N., primary, Pavo, Imre, primary, Ruetten, Hartmut, primary, Dermitzakis, Emmanouil T., primary, McCarthy, Mark I., primary, Pedersen, Oluf, primary, Schwenk, Jochen M., primary, Adamski, Jerzy, primary, Masi, Federico De, primary, Tsirigos, Konstantinos D., primary, Brunak, Søren, primary, Viñuela, Ana, primary, Mahajan, Anubha, primary, McDonald, Timothy J., primary, Kokkola, Tarja, primary, Vangipurapu, Jagadish, primary, Cederberg, Henna, primary, Laakso, Markku, primary, Rutters, Femke, primary, Elders, Petra J.M., primary, Koopman, Anitra D.M., primary, Beulens, Joline W., primary, Ridderstråle, Martin, primary, Hansen, Tue H., primary, Allin, Kristine H., primary, Hansen, Torben, primary, Vestergaard, Henrik, primary, Mari, Andrea, primary, and Consortium, IMI DIRECT, primary
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- 2021
- Full Text
- View/download PDF
5. Correction to: The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study (Diabetologia, (2020), 63, 4, (744-756), 10.1007/s00125-019-05083-6)
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Koivula, Robert W., Atabaki-Pasdar, Naeimeh, Giordano, Giuseppe N., White, Tom, Adamski, Jerzy, Bell, Jimmy D., Beulens, Joline, Brage, S. ren, Brunak, S. ren, de Masi, Federico, Dermitzakis, Emmanouil T., Forgie, Ian M., Frost, Gary, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew, Kokkola, Tarja, Kurbasic, Azra, Laakso, Markku, Mari, Andrea, McDonald, Timothy J., Pedersen, Oluf, Rutters, Femke, Schwenk, Jochen M., Teare, Harriet J. A., Thomas, E. Louise, Vinuela, Ana, Mahajan, Anubha, McCarthy, Mark I., Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Pavo, Imre, Franks, Paul W., Epidemiology and Data Science, ACS - Diabetes & metabolism, APH - Health Behaviors & Chronic Diseases, APH - Aging & Later Life, and ACS - Heart failure & arrhythmias
- Abstract
Unfortunately, ‘Present address’ was omitted from one of the addresses provided for Mark I. McCarthy (#26). The corrected address details are given on the following page.
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- 2021
6. Profiles of glucose metabolism in different prediabetes phenotypes, classified by fasting glycemia, 2-hour OGTT, glycated hemoglobin, and 1-hour OGTT:An IMI DIRECT study
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Tura, Andrea, Grespan, Eleonora, Göbl, Christian S, Koivula, Robert W, Franks, Paul W, Pearson, Ewan R, Walker, Mark, Forgie, Ian M, Giordano, Giuseppe N, Pavo, Imre, Ruetten, Hartmut, Dermitzakis, Emmanouil T, McCarthy, Mark I, Pedersen, Oluf, Schwenk, Jochen M, Adamski, Jerzy, De Masi, Federico, Tsirigos, Konstantinos D, Brunak, Søren, Viñuela, Ana, Mahajan, Anubha, McDonald, Timothy J, Kokkola, Tarja, Vangipurapu, Jagadish, Cederberg, Henna, Laakso, Markku, Rutters, Femke, Elders, Petra J M, Koopman, Anitra D M, Beulens, Joline W, Ridderstråle, Martin, Hansen, Tue H, Allin, Kristine H, Hansen, Torben, Vestergaard, Henrik, Mari, Andrea, Tura, Andrea, Grespan, Eleonora, Göbl, Christian S, Koivula, Robert W, Franks, Paul W, Pearson, Ewan R, Walker, Mark, Forgie, Ian M, Giordano, Giuseppe N, Pavo, Imre, Ruetten, Hartmut, Dermitzakis, Emmanouil T, McCarthy, Mark I, Pedersen, Oluf, Schwenk, Jochen M, Adamski, Jerzy, De Masi, Federico, Tsirigos, Konstantinos D, Brunak, Søren, Viñuela, Ana, Mahajan, Anubha, McDonald, Timothy J, Kokkola, Tarja, Vangipurapu, Jagadish, Cederberg, Henna, Laakso, Markku, Rutters, Femke, Elders, Petra J M, Koopman, Anitra D M, Beulens, Joline W, Ridderstråle, Martin, Hansen, Tue H, Allin, Kristine H, Hansen, Torben, Vestergaard, Henrik, and Mari, Andrea
- Abstract
Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants (N=2111) underwent 2h-75g OGTT at baseline and 48 months. HbA1c was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose, IFG; impaired glucose tolerance, IGT; HbA1c-prediabetes, IA1c), two defects (IFG+IGT, IFG+IA1c, IGT+IA1c), or all defects (IFG+IGT+IA1c). Beta-cell function (BCF) and insulin sensitivity (IS) were assessed from OGTT. At baseline, when pooling participants with isolated defects, they showed impairment in both BCF and IS compared to healthy controls. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, IGT showed lower IS, insulin secretion at reference glucose (ISRr), and insulin secretion potentiation (p<0.002). Conversely, IA1c showed higher IS and ISRr (p<0.0001). Among groups with two defects, we similarly found differences in both BCF and IS. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio >2, p<0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared to the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence.
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- 2021
7. Processes Underlying Glycemic Deterioration in Type 2 Diabetes:An IMI DIRECT Study
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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
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- 2021
8. The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study
- Author
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Koivula, Robert W, Atabaki-Pasdar, Naeimeh, Giordano, Giuseppe N, White, Tom, Adamski, Jerzy, Bell, Jimmy D, Beulens, Joline, Brage, Søren, Brunak, Søren, De Masi, Federico, Dermitzakis, Emmanouil T, Forgie, Ian M, Frost, Gary, Hansen, Torben, Hansen, Tue H, Hattersley, Andrew, Kokkola, Tarja, Kurbasic, Azra, Laakso, Markku, Mari, Andrea, McDonald, Timothy J, Pedersen, Oluf, Rutters, Femke, Schwenk, Jochen M, Teare, Harriet JA, Thomas, E Louise, Vinuela, Ana, Mahajan, Anubha, McCarthy, Mark I, Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Pavo, Imre, Franks, Paul W, IMI DIRECT Consortium, Koivula, Robert W [0000-0002-1646-4163], and Apollo - University of Cambridge Repository
- Subjects
Blood Glucose ,Male ,Denmark ,Glycemic Control ,Ectopic fat ,Cohort Studies ,Glycaemic control ,Homeostasis ,Humans ,Exercise ,Finland ,Aged ,Netherlands ,Sweden ,Physical activity ,Beta cell function ,Type 2 diabetes ,Glucose Tolerance Test ,Middle Aged ,Insulin sensitivity ,Cross-Sectional Studies ,Diabetes Mellitus, Type 2 ,Structural equation modelling ,Female ,Insulin Resistance ,Energy Metabolism ,Prediabetes - Abstract
AIMS/HYPOTHESIS: It is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle. We aimed to examine this notion in the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) Consortium cohorts comprised of participants with normal or impaired glucose regulation (cohort 1: N ≤ 920) or with recently diagnosed type 2 diabetes (cohort 2: N ≤ 435). METHODS: We defined a structural equation model that describes the TC and fitted this within the IMI DIRECT dataset. A second model, twin-cycle plus physical activity (TC-PA), to assess the extent to which the effects of physical activity in glycaemic regulation are mediated by components in the twin-cycle, was also fitted. Beta cell function, insulin sensitivity and glycaemic control were modelled from frequently sampled 75 g OGTTs (fsOGTTs) and mixed-meal tolerance tests (MMTTs) in participants without and with diabetes, respectively. Abdominal fat distribution was assessed using MRI, and physical activity through wrist-worn triaxial accelerometry. Results are presented as standardised beta coefficients, SE and p values, respectively. RESULTS: The TC and TC-PA models showed better fit than null models (TC: χ2 = 242, p = 0.004 and χ2 = 63, p = 0.001 in cohort 1 and 2, respectively; TC-PA: χ2 = 180, p = 0.041 and χ2 = 60, p = 0.008 in cohort 1 and 2, respectively). The association of physical activity with glycaemic control was primarily mediated by variables in the liver fat cycle. CONCLUSIONS/INTERPRETATION: These analyses partially support the mechanisms proposed in the twin-cycle model and highlight mechanistic pathways through which insulin sensitivity and liver fat mediate the association between physical activity and glycaemic control.
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- 2020
9. Predicting and elucidating the etiology of fatty liver disease:A machine learning modeling and validation study in the IMI DIRECT cohorts
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Atabaki-Pasdar, Naeimeh, Ohlsson, Mattias, Viñuela, Ana, Frau, Francesca, Pomares-Millan, Hugo, Haid, Mark, Jones, Angus G, Thomas, E Louise, Koivula, Robert W, Kurbasic, Azra, Mutie, Pascal M, Fitipaldi, Hugo, Fernandez, Juan, Dawed, Adem Y, Giordano, Giuseppe N, Forgie, Ian M, McDonald, Timothy J, Rutters, Femke, Cederberg, Henna, Chabanova, Elizaveta, Dale, Matilda, Masi, Federico De, Thomas, Cecilia Engel, Allin, Kristine H., Hansen, Tue H, Heggie, Alison, Hong, Mun-Gwan, Elders, Petra J M, Kennedy, Gwen, Kokkola, Tarja, Pedersen, Helle Krogh, Mahajan, Anubha, McEvoy, Donna, Pattou, Francois, Raverdy, Violeta, Häussler, Ragna S, Sharma, Sapna, Thomsen, Henrik S, Vangipurapu, Jagadish, Vestergaard, Henrik, Adamski, Jerzy, Musholt, Petra B, Brage, Søren, Brunak, Søren, Dermitzakis, Emmanouil, Frost, Gary, Hansen, Torben, Laakso, Markku, and Pedersen, Oluf
- 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 (CONCLUSIONS: In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community.TRIAL REGISTRATION: ClinicalTrials.gov NCT03814915.
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- 2020
10. The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes:an IMI DIRECT study
- Author
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Koivula, Robert W, Atabaki-Pasdar, Naeimeh, Giordano, Giuseppe N, White, Tom, Adamski, Jerzy, Bell, Jimmy D, Beulens, Joline, Brage, Søren, Brunak, Søren, De Masi, Federico, Dermitzakis, Emmanouil T, Forgie, Ian M, Frost, Gary, Hansen, Torben, Hansen, Tue H, Hattersley, Andrew, Kokkola, Tarja, Kurbasic, Azra, Laakso, Markku, Mari, Andrea, McDonald, Timothy J, Pedersen, Oluf, Rutters, Femke, Schwenk, Jochen M, Teare, Harriet J A, Thomas, E Louise, Vinuela, Ana, Mahajan, Anubha, McCarthy, Mark I, Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Pavo, Imre, Franks, Paul W, Koivula, Robert W, Atabaki-Pasdar, Naeimeh, Giordano, Giuseppe N, White, Tom, Adamski, Jerzy, Bell, Jimmy D, Beulens, Joline, Brage, Søren, Brunak, Søren, De Masi, Federico, Dermitzakis, Emmanouil T, Forgie, Ian M, Frost, Gary, Hansen, Torben, Hansen, Tue H, Hattersley, Andrew, Kokkola, Tarja, Kurbasic, Azra, Laakso, Markku, Mari, Andrea, McDonald, Timothy J, Pedersen, Oluf, Rutters, Femke, Schwenk, Jochen M, Teare, Harriet J A, Thomas, E Louise, Vinuela, Ana, Mahajan, Anubha, McCarthy, Mark I, Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Pavo, Imre, and Franks, Paul W
- Abstract
AIMS/HYPOTHESIS: It is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle. We aimed to examine this notion in the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) Consortium cohorts comprised of participants with normal or impaired glucose regulation (cohort 1: N ≤ 920) or with recently diagnosed type 2 diabetes (cohort 2: N ≤ 435).METHODS: We defined a structural equation model that describes the TC and fitted this within the IMI DIRECT dataset. A second model, twin-cycle plus physical activity (TC-PA), to assess the extent to which the effects of physical activity in glycaemic regulation are mediated by components in the twin-cycle, was also fitted. Beta cell function, insulin sensitivity and glycaemic control were modelled from frequently sampled 75 g OGTTs (fsOGTTs) and mixed-meal tolerance tests (MMTTs) in participants without and with diabetes, respectively. Abdominal fat distribution was assessed using MRI, and physical activity through wrist-worn triaxial accelerometry. Results are presented as standardised beta coefficients, SE and p values, respectively.RESULTS: The TC and TC-PA models showed better fit than null models (TC: χ2 = 242, p = 0.004 and χ2 = 63, p = 0.001 in cohort 1 and 2, respectively; TC-PA: χ2 = 180, p = 0.041 and χ2 = 60, p = 0.008 in cohort 1 and 2, respectively). The association of physical activity with glycaemic control was primarily mediated by variables in the liver fat cycle.CONCLUSIONS/INTERPRETATION: These analyses partially
- Published
- 2020
11. The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study
- Author
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Koivula, Robert W., Atabaki-Pasdar, Naeimeh, Giordano, Giuseppe N., White, Tom, Adamski, Jerzy, Bell, Jimmy D., Beulens, Joline, Brage, Søren, Brunak, Søren, De Masi, Federico, Dermitzakis, Emmanouil T., Forgie, Ian M., Frost, Gary, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew, Kokkola, Tarja, Kurbasic, Azra, Laakso, Markku, Mari, Andrea, McDonald, Timothy J., Pedersen, Oluf, Rutters, Femke, Schwenk, Jochen M., Teare, Harriet J.A., Thomas, E. Louise, Vinuela, Ana, Mahajan, Anubha, McCarthy, Mark I., Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Pavo, Imre, Franks, Paul W., Koivula, Robert W., Atabaki-Pasdar, Naeimeh, Giordano, Giuseppe N., White, Tom, Adamski, Jerzy, Bell, Jimmy D., Beulens, Joline, Brage, Søren, Brunak, Søren, De Masi, Federico, Dermitzakis, Emmanouil T., Forgie, Ian M., Frost, Gary, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew, Kokkola, Tarja, Kurbasic, Azra, Laakso, Markku, Mari, Andrea, McDonald, Timothy J., Pedersen, Oluf, Rutters, Femke, Schwenk, Jochen M., Teare, Harriet J.A., Thomas, E. Louise, Vinuela, Ana, Mahajan, Anubha, McCarthy, Mark I., Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Pavo, Imre, and Franks, Paul W.
- Abstract
Aims/hypothesis: It is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle. We aimed to examine this notion in the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) Consortium cohorts comprised of participants with normal or impaired glucose regulation (cohort 1: N ≤ 920) or with recently diagnosed type 2 diabetes (cohort 2: N ≤ 435). Methods: We defined a structural equation model that describes the TC and fitted this within the IMI DIRECT dataset. A second model, twin-cycle plus physical activity (TC-PA), to assess the extent to which the effects of physical activity in glycaemic regulation are mediated by components in the twin-cycle, was also fitted. Beta cell function, insulin sensitivity and glycaemic control were modelled from frequently sampled 75 g OGTTs (fsOGTTs) and mixed-meal tolerance tests (MMTTs) in participants without and with diabetes, respectively. Abdominal fat distribution was assessed using MRI, and physical activity through wrist-worn triaxial accelerometry. Results are presented as standardised beta coefficients, SE and p values, respectively. Results: The TC and TC-PA models showed better fit than null models (TC: χ2 = 242, p = 0.004 and χ2 = 63, p = 0.001 in cohort 1 and 2, respectively; TC-PA: χ2 = 180, p = 0.041 and χ2 = 60, p = 0.008 in cohort 1 and 2, respectively). The association of physical activity with glycaemic control was primarily mediated by variables in the liver fat cycle. Conclusions/interpretation: These analyses partially suppo
- Published
- 2020
12. Processes Underlying Glycemic Deterioration in Type 2 Diabetes: An IMI DIRECT Study
- Author
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Bizzotto, Roberto, primary, Jennison, Christopher, primary, Jones, Angus G, primary, Kurbasic, Azra, primary, Tura, Andrea, primary, Kennedy, Gwen, primary, Bell, Jimmy D, primary, Thomas, Elizabeth L, primary, Frost, Gary, primary, Eriksen, Rebeca, primary, Koivula, Robert W, primary, Brage, Soren, primary, Kaye, Jane, primary, Hattersley, Andrew T, primary, Heggie, Alison, primary, McEvoy, Donna, primary, Hart, Leen M ’t, primary, Beulens, Joline W, primary, Elders, Petra, primary, Musholt, Petra B, primary, Ridderstråle, Martin, primary, Hansen, Tue H, primary, Allin, Kristine H, primary, Hansen, Torben, primary, Vestergaard, Henrik, primary, Lundgaard, Agnete T, primary, Thomsen, Henrik S, primary, Masi, Federico De, primary, Tsirigos, Konstantinos D, primary, Brunak, Søren, primary, Viñuela, Ana, primary, Mahajan, Anubha, primary, McDonald, Timothy J, primary, Kokkola, Tarja, primary, Forgie, Ian M, primary, Giordano, Giuseppe N, primary, Pavo, Imre, primary, Ruetten, Hartmut, primary, Dermitzakis, Emmanouil, primary, McCarthy, Mark I, primary, Pedersen, Oluf, primary, Schwenk, Jochen M, primary, Adamski, Jerzy, primary, Franks, Paul W, primary, Walker, Mark, primary, Pearson, Ewan R, primary, Mari, Andrea, primary, and consortium, the IMI DIRECT, primary
- Published
- 2020
- Full Text
- View/download PDF
13. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts
- Author
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Atabaki-Pasdar, Naeimeh, primary, Ohlsson, Mattias, additional, Viñuela, Ana, additional, Frau, Francesca, additional, Pomares-Millan, Hugo, additional, Haid, Mark, additional, Jones, Angus G., additional, Thomas, E. Louise, additional, Koivula, Robert W., additional, Kurbasic, Azra, additional, Mutie, Pascal M., additional, Fitipaldi, Hugo, additional, Fernandez, Juan, additional, Dawed, Adem Y., additional, Giordano, Giuseppe N., additional, Forgie, Ian M., additional, McDonald, Timothy J., additional, Rutters, Femke, additional, Cederberg, Henna, additional, Chabanova, Elizaveta, additional, Dale, Matilda, additional, Masi, Federico De, additional, Thomas, Cecilia Engel, additional, Allin, Kristine H., additional, Hansen, Tue H., additional, Heggie, Alison, additional, Hong, Mun-Gwan, additional, Elders, Petra J. M., additional, Kennedy, Gwen, additional, Kokkola, Tarja, additional, Pedersen, Helle Krogh, additional, Mahajan, Anubha, additional, McEvoy, Donna, additional, Pattou, Francois, additional, Raverdy, Violeta, additional, Häussler, Ragna S., additional, Sharma, Sapna, additional, Thomsen, Henrik S., additional, Vangipurapu, Jagadish, additional, Vestergaard, Henrik, additional, ‘t Hart, Leen M., additional, Adamski, Jerzy, additional, Musholt, Petra B., additional, Brage, Soren, additional, Brunak, Søren, additional, Dermitzakis, Emmanouil, additional, Frost, Gary, additional, Hansen, Torben, additional, Laakso, Markku, additional, Pedersen, Oluf, additional, Ridderstråle, Martin, additional, Ruetten, Hartmut, additional, Hattersley, Andrew T., additional, Walker, Mark, additional, Beulens, Joline W. J., additional, Mari, Andrea, additional, Schwenk, Jochen M., additional, Gupta, Ramneek, additional, McCarthy, Mark I., additional, Pearson, Ewan R., additional, Bell, Jimmy D., additional, Pavo, Imre, additional, and Franks, Paul W., additional
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- 2020
- Full Text
- View/download PDF
14. Predicting and elucidating the etiology of fatty liver disease using a machine learning-based approach: an IMI DIRECT study
- Author
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Atabaki-Pasdar, Naeimeh, primary, Ohlsson, Mattias, additional, Viñuela, Ana, additional, Frau, Francesca, additional, Pomares-Millan, Hugo, additional, Haid, Mark, additional, Jones, Angus G, additional, Thomas, E Louise, additional, Koivula, Robert W, additional, Kurbasic, Azra, additional, Mutie, Pascal M, additional, Fitipaldi, Hugo, additional, Fernandez, Juan, additional, Dawed, Adem Y, additional, Giordano, Giuseppe N, additional, Forgie, Ian M, additional, McDonald, Timothy J, additional, Rutters, Femke, additional, Cederberg, Henna, additional, Chabanova, Elizaveta, additional, Dale, Matilda, additional, De Masi, Federico, additional, Thomas, Cecilia Engel, additional, Allin, Kristine H, additional, Hansen, Tue H, additional, Heggie, Alison, additional, Hong, Mun-Gwan, additional, Elders, Petra JM, additional, Kennedy, Gwen, additional, Kokkola, Tarja, additional, Pedersen, Helle Krogh, additional, Mahajan, Anubha, additional, McEvoy, Donna, additional, Pattou, Francois, additional, Raverdy, Violeta, additional, Häussler, Ragna S, additional, Sharma, Sapna, additional, Thomsen, Henrik S, additional, Vangipurapu, Jagadish, additional, Vestergaard, Henrik, additional, ‘t Hart, Leen M, additional, Adamski, Jerzy, additional, Musholt, Petra B, additional, Brage, Soren, additional, Brunak, Søren, additional, Dermitzakis, Emmanouil, additional, Frost, Gary, additional, Hansen, Torben, additional, Laakso, Markku, additional, Pedersen, Oluf, additional, Ridderstråle, Martin, additional, Ruetten, Hartmut, additional, Hattersley, Andrew T, additional, Walker, Mark, additional, Beulens, Joline WJ, additional, Mari, Andrea, additional, Schwenk, Jochen M, additional, Gupta, Ramneek, additional, McCarthy, Mark I, additional, Pearson, Ewan R, additional, Bell, Jimmy D, additional, Pavo, Imre, additional, and Franks, Paul W, additional
- Published
- 2020
- Full Text
- View/download PDF
15. Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes:descriptive characteristics of the epidemiological studies within the IMI DIRECT Consortium
- Author
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Koivula, Robert W, Forgie, Ian M, Kurbasic, Azra, Viñuela, Ana, Heggie, Alison, Giordano, Giuseppe N, Hansen, Tue H., Hudson, Michelle, Koopman, Anitra D M, Rutters, Femke, Siloaho, Maritta, Allin, Kristine H., Brage, Søren, Brorsson, Caroline A, Dawed, Adem Y, De Masi, Federico, Groves, Christopher J, Kokkola, Tarja, Mahajan, Anubha, Perry, Mandy H, Rauh, Simone P, Ridderstråle, Martin, Teare, Harriet J A, Thomas, E Louise, Tura, Andrea, Vestergaard, Henrik, White, Tom, Adamski, Jerzy, Bell, Jimmy D, Beulens, Joline W, Brunak, Søren, Dermitzakis, Emmanouil T, Froguel, Philippe, Frost, Gary, Gupta, Ramneek, Hansen, Torben, Hattersley, Andrew, Jablonka, Bernd, Kaye, Jane, Laakso, Markku, McDonald, Timothy J, Pedersen, Oluf, Schwenk, Jochen M, Pavo, Imre, Mari, Andrea, McCarthy, Mark I, Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Franks, Paul W, Koivula, Robert W, Forgie, Ian M, Kurbasic, Azra, Viñuela, Ana, Heggie, Alison, Giordano, Giuseppe N, Hansen, Tue H., Hudson, Michelle, Koopman, Anitra D M, Rutters, Femke, Siloaho, Maritta, Allin, Kristine H., Brage, Søren, Brorsson, Caroline A, Dawed, Adem Y, De Masi, Federico, Groves, Christopher J, Kokkola, Tarja, Mahajan, Anubha, Perry, Mandy H, Rauh, Simone P, Ridderstråle, Martin, Teare, Harriet J A, Thomas, E Louise, Tura, Andrea, Vestergaard, Henrik, White, Tom, Adamski, Jerzy, Bell, Jimmy D, Beulens, Joline W, Brunak, Søren, Dermitzakis, Emmanouil T, Froguel, Philippe, Frost, Gary, Gupta, Ramneek, Hansen, Torben, Hattersley, Andrew, Jablonka, Bernd, Kaye, Jane, Laakso, Markku, McDonald, Timothy J, Pedersen, Oluf, Schwenk, Jochen M, Pavo, Imre, Mari, Andrea, McCarthy, Mark I, Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, and Franks, Paul W
- Abstract
AIMS/HYPOTHESIS: Here, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up).METHODS: From a sampling frame of 24,682 adults of European ancestry enrolled in population-based cohorts across Europe, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm (based on age, BMI, waist circumference, use of antihypertensive medication, smoking status and parental history of type 2 diabetes) and enrolled into a prospective cohort study (n = 2127) (cohort 1, prediabetes risk). We also recruited people from clinical registries with type 2 diabetes diagnosed 6-24 months previously (n = 789) into a second cohort study (cohort 2, diabetes). Follow-up examinations took place at ~18 months (both cohorts) and at ~48 months (cohort 1) or ~36 months (cohort 2) after baseline examinations. The cohorts were studied in parallel using matched protocols across seven clinical centres in northern Europe.RESULTS: Using ADA 2011 glycaemic categories, 33% (n = 693) of cohort 1 (prediabetes risk) had normal glucose regulation and 67% (n = 1419) had impaired glucose regulation. Seventy-six per cent of participants in cohort 1 was male. Cohort 1 participants had the following characteristics (mean ± SD) at baseline: age 62 (6.2) years; BMI 27.9 (4.0) kg/m2; fasting glucose 5.7 (0.6) mmol/l; 2 h glucose 5.9 (1.6) mmol/l. At the final follow-up examination the participants' clinical characteristics were as follows: fasting glucose 6.0 (0.6) mmol/l; 2 h OGTT glucose 6.5 (2.0) mmol/l. In cohort 2 (diabetes), 66% (n = 517) were treated by lifestyle modification and 34% (n = 272) were treated with metformin plus lifestyle modification at enrolment. Fifty-eight per cent of participants in cohort 2 was male. Cohort 2 participants had the following characteristics at
- Published
- 2019
16. Profiles of Glucose Metabolism in Different Prediabetes Phenotypes, Classified by Fasting Glycemia, 2-Hour OGTT, Glycated Hemoglobin, and 1-Hour OGTT: An IMI DIRECT Study.
- Author
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Tura, Andrea, Grespan, Eleonora, Göbl, Christian S., Koivula, Robert W., Franks, Paul W., Pearson, Ewan R., Walker, Mark, Forgie, Ian M., Giordano, Giuseppe N., Pavo, Imre, Ruetten, Hartmut, Dermitzakis, Emmanouil T., McCarthy, Mark I., Pedersen, Oluf, Schwenk, Jochen M., Adamski, Jerzy, De Masi, Federico, Tsirigos, Konstantinos D., Brunak, Søren, and Viñuela, Ana
- Subjects
GLYCOSYLATED hemoglobin ,GLUCOSE metabolism ,INSULIN sensitivity ,PREDIABETIC state ,TYPE 2 diabetes - Abstract
Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants (N = 2,111) underwent a 2-h 75-g oral glucose tolerance test (OGTT) at baseline and 48 months. HbA1c was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose [IFG], impaired glucose tolerance [IGT], or HbA1c indicative of prediabetes [IA1c]), two defects (IFG+IGT, IFG+IA1c, or IGT+IA1c), or all defects (IFG+IGT+IA1c). β-Cell function (BCF) and insulin sensitivity were assessed from OGTT. At baseline, in pooling of participants with isolated defects, they showed impairment in both BCF and insulin sensitivity compared with healthy control subjects. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, those with IGT showed lower insulin sensitivity, insulin secretion at reference glucose (ISRr), and insulin secretion potentiation (P < 0.002). Conversely, those with IA1c showed higher insulin sensitivity and ISRr (P < 0.0001). Among groups with two defects, we similarly found differences in both BCF and insulin sensitivity. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio >2, P < 0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared with the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
17. Etiology of acute lower respiratory tract infections in children in a rural community in The Gambia
- Author
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FORGIE, IAN M., primary, CAMPBELL, HARRY, additional, LLOYD-EVANS, NELLIE, additional, LEINONEN, MAIJA, additional, OʼNEILL, KEVIN P., additional, SAIKKU, PEKKA, additional, WHITTLE, HILTON C., additional, and GREENWOOD, BRIAN M., additional
- Published
- 1992
- Full Text
- View/download PDF
18. Etiology of acute lower respiratory tract infections in Gambian children
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FORGIE, IAN M., primary, OʼNEILL, KEVIN P., additional, LLOYD-EVANS, NELLIE, additional, LEINONEN, MAIJA, additional, CAMPBELL, HARRY, additional, WHITTLE, HILTON C., additional, and GREENWOOD, BRIAN M., additional
- Published
- 1991
- Full Text
- View/download PDF
19. Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: descriptive characteristics of the epidemiological studies within the IMI DIRECT Consortium
- Author
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Koivula, Robert W, Forgie, Ian M, Kurbasic, Azra, Viñuela, Ana, Heggie, Alison, Giordano, Giuseppe N, Hansen, Tue H, Hudson, Michelle, Koopman, Anitra DM, Rutters, Femke, Siloaho, Maritta, Allin, Kristine H, Brage, Søren, Brorsson, Caroline A, Dawed, Adem Y, De Masi, Federico, Groves, Christopher J, Kokkola, Tarja, Mahajan, Anubha, Perry, Mandy H, Rauh, Simone P, Ridderstråle, Martin, Teare, Harriet JA, Thomas, E Louise, Tura, Andrea, Vestergaard, Henrik, White, Tom, Adamski, Jerzy, Bell, Jimmy D, Beulens, Joline W, Brunak, Søren, Dermitzakis, Emmanouil T, Froguel, Philippe, Frost, Gary, Gupta, Ramneek, Hansen, Torben, Hattersley, Andrew, Jablonka, Bernd, Kaye, Jane, Laakso, Markku, McDonald, Timothy J, Pedersen, Oluf, Schwenk, Jochen M, Pavo, Imre, Mari, Andrea, McCarthy, Mark I, Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Franks, Paul W, and IMI DIRECT Consortium
- Subjects
Blood Glucose ,Male ,Genome ,Physical activity ,Insulin secretion ,Type 2 diabetes ,Glucose Tolerance Test ,Middle Aged ,Insulin sensitivity ,Ectopic fat ,Metformin ,3. Good health ,Diet ,Cohort Studies ,Prediabetic State ,Glucose ,Diabetes Mellitus, Type 2 ,Glycaemic control ,Humans ,Personalised medicine ,Female ,Prospective Studies ,Prediabetes ,Biomarkers ,Aged - Abstract
AIMS/HYPOTHESIS: Here, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up). METHODS: From a sampling frame of 24,682 adults of European ancestry enrolled in population-based cohorts across Europe, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm (based on age, BMI, waist circumference, use of antihypertensive medication, smoking status and parental history of type 2 diabetes) and enrolled into a prospective cohort study (n = 2127) (cohort 1, prediabetes risk). We also recruited people from clinical registries with type 2 diabetes diagnosed 6-24 months previously (n = 789) into a second cohort study (cohort 2, diabetes). Follow-up examinations took place at ~18 months (both cohorts) and at ~48 months (cohort 1) or ~36 months (cohort 2) after baseline examinations. The cohorts were studied in parallel using matched protocols across seven clinical centres in northern Europe. RESULTS: Using ADA 2011 glycaemic categories, 33% (n = 693) of cohort 1 (prediabetes risk) had normal glucose regulation and 67% (n = 1419) had impaired glucose regulation. Seventy-six per cent of participants in cohort 1 was male. Cohort 1 participants had the following characteristics (mean ± SD) at baseline: age 62 (6.2) years; BMI 27.9 (4.0) kg/m2; fasting glucose 5.7 (0.6) mmol/l; 2 h glucose 5.9 (1.6) mmol/l. At the final follow-up examination the participants' clinical characteristics were as follows: fasting glucose 6.0 (0.6) mmol/l; 2 h OGTT glucose 6.5 (2.0) mmol/l. In cohort 2 (diabetes), 66% (n = 517) were treated by lifestyle modification and 34% (n = 272) were treated with metformin plus lifestyle modification at enrolment. Fifty-eight per cent of participants in cohort 2 was male. Cohort 2 participants had the following characteristics at baseline: age 62 (8.1) years; BMI 30.5 (5.0) kg/m2; fasting glucose 7.2 (1.4) mmol/l; 2 h glucose 8.6 (2.8) mmol/l. At the final follow-up examination, the participants' clinical characteristics were as follows: fasting glucose 7.9 (2.0) mmol/l; 2 h mixed-meal tolerance test glucose 9.9 (3.4) mmol/l. CONCLUSIONS/INTERPRETATION: The IMI DIRECT cohorts are intensely characterised, with a wide-variety of metabolically relevant measures assessed prospectively. We anticipate that the cohorts, made available through managed access, will provide a powerful resource for biomarker discovery, multivariate aetiological analyses and reclassification of patients for the prevention and treatment of type 2 diabetes.
20. The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study
- Author
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Koivula, Robert W, Atabaki-Pasdar, Naeimeh, Giordano, Giuseppe N, White, Tom, Adamski, Jerzy, Bell, Jimmy D, Beulens, Joline, Brage, Søren, Brunak, Søren, De Masi, Federico, Dermitzakis, Emmanouil T, Forgie, Ian M, Frost, Gary, Hansen, Torben, Hansen, Tue H, Hattersley, Andrew, Kokkola, Tarja, Kurbasic, Azra, Laakso, Markku, Mari, Andrea, McDonald, Timothy J, Pedersen, Oluf, Rutters, Femke, Schwenk, Jochen M, Teare, Harriet JA, Thomas, E Louise, Vinuela, Ana, Mahajan, Anubha, McCarthy, Mark I, Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Pavo, Imre, Franks, Paul W, and IMI DIRECT Consortium
- Subjects
Blood Glucose ,Male ,Denmark ,Glycemic Control ,Ectopic fat ,Cohort Studies ,Glycaemic control ,Homeostasis ,Humans ,Exercise ,Finland ,Aged ,Netherlands ,Sweden ,Physical activity ,Beta cell function ,Type 2 diabetes ,Glucose Tolerance Test ,Middle Aged ,Insulin sensitivity ,3. Good health ,Cross-Sectional Studies ,Diabetes Mellitus, Type 2 ,Structural equation modelling ,Female ,Insulin Resistance ,Energy Metabolism ,Prediabetes - Abstract
AIMS/HYPOTHESIS: It is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle. We aimed to examine this notion in the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) Consortium cohorts comprised of participants with normal or impaired glucose regulation (cohort 1: N ≤ 920) or with recently diagnosed type 2 diabetes (cohort 2: N ≤ 435). METHODS: We defined a structural equation model that describes the TC and fitted this within the IMI DIRECT dataset. A second model, twin-cycle plus physical activity (TC-PA), to assess the extent to which the effects of physical activity in glycaemic regulation are mediated by components in the twin-cycle, was also fitted. Beta cell function, insulin sensitivity and glycaemic control were modelled from frequently sampled 75 g OGTTs (fsOGTTs) and mixed-meal tolerance tests (MMTTs) in participants without and with diabetes, respectively. Abdominal fat distribution was assessed using MRI, and physical activity through wrist-worn triaxial accelerometry. Results are presented as standardised beta coefficients, SE and p values, respectively. RESULTS: The TC and TC-PA models showed better fit than null models (TC: χ2 = 242, p = 0.004 and χ2 = 63, p = 0.001 in cohort 1 and 2, respectively; TC-PA: χ2 = 180, p = 0.041 and χ2 = 60, p = 0.008 in cohort 1 and 2, respectively). The association of physical activity with glycaemic control was primarily mediated by variables in the liver fat cycle. CONCLUSIONS/INTERPRETATION: These analyses partially support the mechanisms proposed in the twin-cycle model and highlight mechanistic pathways through which insulin sensitivity and liver fat mediate the association between physical activity and glycaemic control.
21. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts
- Author
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Atabaki-Pasdar, Naeimeh, Ohlsson, Mattias, Viñuela, Ana, Frau, Francesca, Pomares-Millan, Hugo, Haid, Mark, Jones, Angus G, Thomas, E Louise, Koivula, Robert W, Kurbasic, Azra, Mutie, Pascal M, Fitipaldi, Hugo, Fernandez, Juan, Dawed, Adem Y, Giordano, Giuseppe N, Forgie, Ian M, McDonald, Timothy J, Rutters, Femke, Cederberg, Henna, Chabanova, Elizaveta, Dale, Matilda, Masi, Federico De, Thomas, Cecilia Engel, Allin, Kristine H, Hansen, Tue H, Heggie, Alison, Hong, Mun-Gwan, Elders, Petra JM, Kennedy, Gwen, Kokkola, Tarja, Pedersen, Helle Krogh, Mahajan, Anubha, McEvoy, Donna, Pattou, Francois, Raverdy, Violeta, Häussler, Ragna S, Sharma, Sapna, Thomsen, Henrik S, Vangipurapu, Jagadish, Vestergaard, Henrik, 'T Hart, Leen M, Adamski, Jerzy, Musholt, Petra B, Brage, Soren, Brunak, Søren, Dermitzakis, Emmanouil, Frost, Gary, Hansen, Torben, Laakso, Markku, Pedersen, Oluf, Ridderstråle, Martin, Ruetten, Hartmut, Hattersley, Andrew T, Walker, Mark, Beulens, Joline WJ, Mari, Andrea, Schwenk, Jochen M, Gupta, Ramneek, McCarthy, Mark I, Pearson, Ewan R, Bell, Jimmy D, Pavo, Imre, and Franks, Paul W
- Subjects
Diabetes Complications ,Fatty Liver ,Machine Learning ,Male ,Models, Statistical ,Humans ,Reproducibility of Results ,Female ,Prospective Studies ,Middle Aged ,Risk Assessment ,3. Good health - 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 (
22. The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study
- Author
-
Koivula, Robert W., Atabaki-Pasdar, Naeimeh, Giordano, Giuseppe N., White, Tom, Adamski, Jerzy, Bell, Jimmy D., Beulens, Joline, Brage, Søren, Brunak, Søren, De Masi, Federico, Dermitzakis, Emmanouil T., Forgie, Ian M., Frost, Gary, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew, Kokkola, Tarja, Kurbasic, Azra, Laakso, Markku, Mari, Andrea, McDonald, Timothy J., Pedersen, Oluf, Rutters, Femke, Schwenk, Jochen M., Teare, Harriet J. A., Thomas, E. Louise, Vinuela, Ana, Mahajan, Anubha, McCarthy, Mark I., Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Pavo, Imre, and Franks, Paul W.
- Subjects
Physical activity ,Structural equation modelling ,Glycaemic control ,Beta cell function ,Type 2 diabetes ,16. Peace & justice ,Insulin sensitivity ,Prediabetes ,Article ,Ectopic fat ,3. Good health - Abstract
Aims/hypothesis: It is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle. We aimed to examine this notion in the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) Consortium cohorts comprised of participants with normal or impaired glucose regulation (cohort 1: N ≤ 920) or with recently diagnosed type 2 diabetes (cohort 2: N ≤ 435). Methods: We defined a structural equation model that describes the TC and fitted this within the IMI DIRECT dataset. A second model, twin-cycle plus physical activity (TC-PA), to assess the extent to which the effects of physical activity in glycaemic regulation are mediated by components in the twin-cycle, was also fitted. Beta cell function, insulin sensitivity and glycaemic control were modelled from frequently sampled 75 g OGTTs (fsOGTTs) and mixed-meal tolerance tests (MMTTs) in participants without and with diabetes, respectively. Abdominal fat distribution was assessed using MRI, and physical activity through wrist-worn triaxial accelerometry. Results are presented as standardised beta coefficients, SE and p values, respectively. Results: The TC and TC-PA models showed better fit than null models (TC: χ2 = 242, p = 0.004 and χ2 = 63, p = 0.001 in cohort 1 and 2, respectively; TC-PA: χ2 = 180, p = 0.041 and χ2 = 60, p = 0.008 in cohort 1 and 2, respectively). The association of physical activity with glycaemic control was primarily mediated by variables in the liver fat cycle. Conclusions/interpretation: These analyses partially support the mechanisms proposed in the twin-cycle model and highlight mechanistic pathways through which insulin sensitivity and liver fat mediate the association between physical activity and glycaemic control.
23. Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts
- Author
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Atabaki-Pasdar, Naeimeh, Ohlsson, Mattias, Viñuela, Ana, Frau, Francesca, Pomares-Millan, Hugo, Haid, Mark, Jones, Angus G., Thomas, E. Louise, Koivula, Robert W., Kurbasic, Azra, Mutie, Pascal M., Fitipaldi, Hugo, Fernandez, Juan, Dawed, Adem Y., Giordano, Giuseppe N., Forgie, Ian M., McDonald, Timothy J., Rutters, Femke, Cederberg, Henna, Chabanova, Elizaveta, Dale, Matilda, Masi, Federico De, Thomas, Cecilia Engel, Allin, Kristine H., Hansen, Tue H., Heggie, Alison, Hong, Mun-Gwan, Elders, Petra J. M., Kennedy, Gwen, Kokkola, Tarja, Pedersen, Helle Krogh, Mahajan, Anubha, McEvoy, Donna, Pattou, Francois, Raverdy, Violeta, Häussler, Ragna S., Sharma, Sapna, Thomsen, Henrik S., Vangipurapu, Jagadish, Vestergaard, Henrik, ‘T Hart, Leen M., Adamski, Jerzy, Musholt, Petra B., Brage, Soren, Brunak, Søren, Dermitzakis, Emmanouil, Frost, Gary, Hansen, Torben, Laakso, Markku, Pedersen, Oluf, Ridderstråle, Martin, Ruetten, Hartmut, Hattersley, Andrew T., Walker, Mark, Beulens, Joline W. J., Mari, Andrea, Schwenk, Jochen M., Gupta, Ramneek, McCarthy, Mark I., Pearson, Ewan R., Bell, Jimmy D., Pavo, Imre, and Franks, Paul W.
- Subjects
Medicine and health sciences ,Research and analysis methods ,FOS: Computer and information sciences ,Computer and information sciences ,Biology and life sciences ,3. Good health ,Research Article - Abstract
Funder: Henning och Johan Throne-Holsts, Funder: Hans Werthén, Funder: Swedish Foundation for Strategic Research, Funder: NIHR clinical senior lecturer fellowship, Funder: Wellcome Trust Senior Investigator, Funder: NIHR Exeter Clinical Research Facility, Funder: Science for Life Laboratory (Plasma Profiling Facility), Funder: Knut and Alice Wallenberg Foundation (Human Protein Atlas), Funder: Erling-Persson Foundation (KTH Centre for Precision Medicine), 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 (
24. The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study
- Author
-
Koivula, Robert W., Atabaki-Pasdar, Naeimeh, Giordano, Giuseppe N., White, Tom, Adamski, Jerzy, Bell, Jimmy D., Beulens, Joline, Brage, Søren, Brunak, Søren, De Masi, Federico, Dermitzakis, Emmanouil T., Forgie, Ian M., Frost, Gary, Hansen, Torben, Hansen, Tue H., Hattersley, Andrew, Kokkola, Tarja, Kurbasic, Azra, Laakso, Markku, Mari, Andrea, McDonald, Timothy J., Pedersen, Oluf, Rutters, Femke, Schwenk, Jochen M., Teare, Harriet J. A., Thomas, E. Louise, Vinuela, Ana, Mahajan, Anubha, McCarthy, Mark I., Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, Pavo, Imre, and Franks, Paul W.
- Subjects
Physical activity ,Structural equation modelling ,Glycaemic control ,Beta cell function ,Type 2 diabetes ,Insulin sensitivity ,Prediabetes ,Article ,Ectopic fat ,3. Good health - Abstract
Aims/hypothesis: It is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle. We aimed to examine this notion in the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) Consortium cohorts comprised of participants with normal or impaired glucose regulation (cohort 1: N ≤ 920) or with recently diagnosed type 2 diabetes (cohort 2: N ≤ 435). Methods: We defined a structural equation model that describes the TC and fitted this within the IMI DIRECT dataset. A second model, twin-cycle plus physical activity (TC-PA), to assess the extent to which the effects of physical activity in glycaemic regulation are mediated by components in the twin-cycle, was also fitted. Beta cell function, insulin sensitivity and glycaemic control were modelled from frequently sampled 75 g OGTTs (fsOGTTs) and mixed-meal tolerance tests (MMTTs) in participants without and with diabetes, respectively. Abdominal fat distribution was assessed using MRI, and physical activity through wrist-worn triaxial accelerometry. Results are presented as standardised beta coefficients, SE and p values, respectively. Results: The TC and TC-PA models showed better fit than null models (TC: χ2 = 242, p = 0.004 and χ2 = 63, p = 0.001 in cohort 1 and 2, respectively; TC-PA: χ2 = 180, p = 0.041 and χ2 = 60, p = 0.008 in cohort 1 and 2, respectively). The association of physical activity with glycaemic control was primarily mediated by variables in the liver fat cycle. Conclusions/interpretation: These analyses partially support the mechanisms proposed in the twin-cycle model and highlight mechanistic pathways through which insulin sensitivity and liver fat mediate the association between physical activity and glycaemic control.
25. Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: descriptive characteristics of the epidemiological studies within the IMI DIRECT Consortium
- Author
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Koivula, Robert W., Forgie, Ian M., Kurbasic, Azra, Viñuela, Ana, Heggie, Alison, Giordano, Giuseppe N., Hansen, Tue H., Hudson, Michelle, Koopman, Anitra D. M., Rutters, Femke, Siloaho, Maritta, Allin, Kristine H., Brage, Søren, Brorsson, Caroline A., Dawed, Adem Y., De Masi, Federico, Groves, Christopher J., Kokkola, Tarja, Mahajan, Anubha, Perry, Mandy H., Rauh, Simone P., Ridderstråle, Martin, Teare, Harriet J. A., Thomas, E. Louise, Tura, Andrea, Vestergaard, Henrik, White, Tom, Adamski, Jerzy, Bell, Jimmy D., Beulens, Joline W., Brunak, Søren, Dermitzakis, Emmanouil T., Froguel, Philippe, Frost, Gary, Gupta, Ramneek, Hansen, Torben, Hattersley, Andrew, Jablonka, Bernd, Kaye, Jane, Laakso, Markku, McDonald, Timothy J., Pedersen, Oluf, Schwenk, Jochen M., Pavo, Imre, Mari, Andrea, McCarthy, Mark I., Ruetten, Hartmut, Walker, Mark, Pearson, Ewan, and Franks, Paul W.
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Genome ,Physical activity ,Insulin secretion ,Glycaemic control ,Personalised medicine ,Type 2 diabetes ,16. Peace & justice ,Insulin sensitivity ,Prediabetes ,Article ,Ectopic fat ,3. Good health ,Diet - Abstract
Aims/hypothesis: Here, we describe the characteristics of the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) epidemiological cohorts at baseline and follow-up examinations (18, 36 and 48 months of follow-up). Methods: From a sampling frame of 24,682 adults of European ancestry enrolled in population-based cohorts across Europe, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm (based on age, BMI, waist circumference, use of antihypertensive medication, smoking status and parental history of type 2 diabetes) and enrolled into a prospective cohort study (n = 2127) (cohort 1, prediabetes risk). We also recruited people from clinical registries with type 2 diabetes diagnosed 6–24 months previously (n = 789) into a second cohort study (cohort 2, diabetes). Follow-up examinations took place at ~18 months (both cohorts) and at ~48 months (cohort 1) or ~36 months (cohort 2) after baseline examinations. The cohorts were studied in parallel using matched protocols across seven clinical centres in northern Europe. Results: Using ADA 2011 glycaemic categories, 33% (n = 693) of cohort 1 (prediabetes risk) had normal glucose regulation and 67% (n = 1419) had impaired glucose regulation. Seventy-six per cent of participants in cohort 1 was male. Cohort 1 participants had the following characteristics (mean ± SD) at baseline: age 62 (6.2) years; BMI 27.9 (4.0) kg/m2; fasting glucose 5.7 (0.6) mmol/l; 2 h glucose 5.9 (1.6) mmol/l. At the final follow-up examination the participants’ clinical characteristics were as follows: fasting glucose 6.0 (0.6) mmol/l; 2 h OGTT glucose 6.5 (2.0) mmol/l. In cohort 2 (diabetes), 66% (n = 517) were treated by lifestyle modification and 34% (n = 272) were treated with metformin plus lifestyle modification at enrolment. Fifty-eight per cent of participants in cohort 2 was male. Cohort 2 participants had the following characteristics at baseline: age 62 (8.1) years; BMI 30.5 (5.0) kg/m2; fasting glucose 7.2 (1.4) mmol/l; 2 h glucose 8.6 (2.8) mmol/l. At the final follow-up examination, the participants’ clinical characteristics were as follows: fasting glucose 7.9 (2.0) mmol/l; 2 h mixed-meal tolerance test glucose 9.9 (3.4) mmol/l. Conclusions/interpretation: The IMI DIRECT cohorts are intensely characterised, with a wide-variety of metabolically relevant measures assessed prospectively. We anticipate that the cohorts, made available through managed access, will provide a powerful resource for biomarker discovery, multivariate aetiological analyses and reclassification of patients for the prevention and treatment of type 2 diabetes.
26. Role of human plasma metabolites in prediabetes and type 2 diabetes from the IMI-DIRECT study.
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Sharma S, Dong Q, Haid M, Adam J, Bizzotto R, Fernandez-Tajes JJ, Jones AG, Tura A, Artati A, Prehn C, Kastenmüller G, Koivula RW, Franks PW, Walker M, Forgie IM, Giordano G, Pavo I, Ruetten H, Dermitzakis M, McCarthy MI, Pedersen O, Schwenk JM, Tsirigos KD, De Masi F, Brunak S, Viñuela A, Mari A, McDonald TJ, Kokkola T, Adamski J, Pearson ER, and Grallert H
- Abstract
Aims/hypothesis: Type 2 diabetes is a chronic condition that is caused by hyperglycaemia. Our aim was to characterise the metabolomics to find their association with the glycaemic spectrum and find a causal relationship between metabolites and type 2 diabetes., Methods: As part of the Innovative Medicines Initiative - Diabetes Research on Patient Stratification (IMI-DIRECT) consortium, 3000 plasma samples were measured with the Biocrates AbsoluteIDQ p150 Kit and Metabolon analytics. A total of 911 metabolites (132 targeted metabolomics, 779 untargeted metabolomics) passed the quality control. Multivariable linear and logistic regression analysis estimates were calculated from the concentration/peak areas of each metabolite as an explanatory variable and the glycaemic status as a dependent variable. This analysis was adjusted for age, sex, BMI, study centre in the basic model, and additionally for alcohol, smoking, BP, fasting HDL-cholesterol and fasting triacylglycerol in the full model. Statistical significance was Bonferroni corrected throughout. Beyond associations, we investigated the mediation effect and causal effects for which causal mediation test and two-sample Mendelian randomisation (2SMR) methods were used, respectively., Results: In the targeted metabolomics, we observed four (15), 34 (99) and 50 (108) metabolites (number of metabolites observed in untargeted metabolomics appear in parentheses) that were significantly different when comparing normal glucose regulation vs impaired glucose regulation/prediabetes, normal glucose regulation vs type 2 diabetes, and impaired glucose regulation vs type 2 diabetes, respectively. Significant metabolites were mainly branched-chain amino acids (BCAAs), with some derivatised BCAAs, lipids, xenobiotics and a few unknowns. Metabolites such as lysophosphatidylcholine a C17:0, sum of hexoses, amino acids from BCAA metabolism (including leucine, isoleucine, valine, N-lactoylvaline, N-lactoylleucine and formiminoglutamate) and lactate, as well as an unknown metabolite (X-24295), were associated with HbA
1c progression rate and were significant mediators of type 2 diabetes from baseline to 18 and 48 months of follow-up. 2SMR was used to estimate the causal effect of an exposure on an outcome using summary statistics from UK Biobank genome-wide association studies. We found that type 2 diabetes had a causal effect on the levels of three metabolites (hexose, glutamate and caproate [fatty acid (FA) 6:0]), whereas lipids such as specific phosphatidylcholines (PCs) (namely PC aa C36:2, PC aa C36:5, PC ae C36:3 and PC ae C34:3) as well as the two n-3 fatty acids stearidonate (18:4n3) and docosapentaenoate (22:5n3) potentially had a causal role in the development of type 2 diabetes., Conclusions/interpretation: Our findings identify known BCAAs and lipids, along with novel N-lactoyl-amino acid metabolites, significantly associated with prediabetes and diabetes, that mediate the effect of diabetes from baseline to follow-up (18 and 48 months). Causal inference using genetic variants shows the role of lipid metabolism and n-3 fatty acids as being causal for metabolite-to-type 2 diabetes whereas the sum of hexoses is causal for type 2 diabetes-to-metabolite. Identified metabolite markers are useful for stratifying individuals based on their risk progression and should enable targeted interventions., (© 2024. The Author(s).)- Published
- 2024
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27. Processes Underlying Glycemic Deterioration in Type 2 Diabetes: An IMI DIRECT Study.
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Bizzotto R, Jennison C, Jones AG, Kurbasic A, Tura A, Kennedy G, Bell JD, Thomas EL, Frost G, Eriksen R, Koivula RW, Brage S, Kaye J, Hattersley AT, Heggie A, McEvoy D, 't Hart LM, Beulens JW, Elders P, Musholt PB, Ridderstråle M, Hansen TH, Allin KH, Hansen T, Vestergaard H, Lundgaard AT, Thomsen HS, De Masi F, Tsirigos KD, Brunak S, Viñuela A, Mahajan A, McDonald TJ, Kokkola T, Forgie IM, Giordano GN, Pavo I, Ruetten H, Dermitzakis E, McCarthy MI, Pedersen O, Schwenk JM, Adamski J, Franks PW, Walker M, Pearson ER, and Mari A
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- Blood Glucose, Cholesterol, HDL, Humans, Insulin, Diabetes Mellitus, Type 2, Insulin Resistance, Insulin-Secreting Cells
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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 HbA
1c 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., (© 2020 by the American Diabetes Association.)- Published
- 2021
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28. Correction to: The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study.
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Koivula RW, Atabaki-Pasdar N, Giordano GN, White T, Adamski J, Bell JD, Beulens J, Brage S, Brunak S, De Masi F, Dermitzakis ET, Forgie IM, Frost G, Hansen T, Hansen TH, Hattersley A, Kokkola T, Kurbasic A, Laakso M, Mari A, McDonald TJ, Pedersen O, Rutters F, Schwenk JM, Teare HJA, Thomas EL, Vinuela A, Mahajan A, McCarthy MI, Ruetten H, Walker M, Pearson E, Pavo I, and Franks PW
- Abstract
Unfortunately, 'Present address' was omitted from one of the addresses provided for Mark I. McCarthy (#26).
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- 2021
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29. The role of physical activity in metabolic homeostasis before and after the onset of type 2 diabetes: an IMI DIRECT study.
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Koivula RW, Atabaki-Pasdar N, Giordano GN, White T, Adamski J, Bell JD, Beulens J, Brage S, Brunak S, De Masi F, Dermitzakis ET, Forgie IM, Frost G, Hansen T, Hansen TH, Hattersley A, Kokkola T, Kurbasic A, Laakso M, Mari A, McDonald TJ, Pedersen O, Rutters F, Schwenk JM, Teare HJA, Thomas EL, Vinuela A, Mahajan A, McCarthy MI, Ruetten H, Walker M, Pearson E, Pavo I, and Franks PW
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- Aged, Blood Glucose metabolism, Cohort Studies, Cross-Sectional Studies, Denmark epidemiology, Diabetes Mellitus, Type 2 blood, Diabetes Mellitus, Type 2 epidemiology, Diabetes Mellitus, Type 2 therapy, Female, Finland epidemiology, Glucose Tolerance Test, Glycemic Control, Humans, Insulin Resistance, Male, Middle Aged, Netherlands epidemiology, Sweden epidemiology, Diabetes Mellitus, Type 2 metabolism, Energy Metabolism physiology, Exercise physiology, Homeostasis physiology
- Abstract
Aims/hypothesis: It is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle. We aimed to examine this notion in the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) Consortium cohorts comprised of participants with normal or impaired glucose regulation (cohort 1: N ≤ 920) or with recently diagnosed type 2 diabetes (cohort 2: N ≤ 435)., Methods: We defined a structural equation model that describes the TC and fitted this within the IMI DIRECT dataset. A second model, twin-cycle plus physical activity (TC-PA), to assess the extent to which the effects of physical activity in glycaemic regulation are mediated by components in the twin-cycle, was also fitted. Beta cell function, insulin sensitivity and glycaemic control were modelled from frequently sampled 75 g OGTTs (fsOGTTs) and mixed-meal tolerance tests (MMTTs) in participants without and with diabetes, respectively. Abdominal fat distribution was assessed using MRI, and physical activity through wrist-worn triaxial accelerometry. Results are presented as standardised beta coefficients, SE and p values, respectively., Results: The TC and TC-PA models showed better fit than null models (TC: χ
2 = 242, p = 0.004 and χ2 = 63, p = 0.001 in cohort 1 and 2, respectively; TC-PA: χ2 = 180, p = 0.041 and χ2 = 60, p = 0.008 in cohort 1 and 2, respectively). The association of physical activity with glycaemic control was primarily mediated by variables in the liver fat cycle., Conclusions/interpretation: These analyses partially support the mechanisms proposed in the twin-cycle model and highlight mechanistic pathways through which insulin sensitivity and liver fat mediate the association between physical activity and glycaemic control.- Published
- 2020
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