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Othman NA, Azhar MAAS, Damanhuri NS, Mahadi IA, Abbas MH, Shamsuddin SA, Chase JG. Optimization of identifying insulinaemic pharmacokinetic parameters using artificial neural network. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 236:107566. [PMID: 37186981 DOI: 10.1016/j.cmpb.2023.107566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 04/09/2023] [Accepted: 04/21/2023] [Indexed: 05/17/2023]
Abstract
BACKGROUND AND OBJECTIVE The identification of insulinaemic pharmacokinetic parameters using the least-squares criterion approach is easily influenced by outlying data due to its sensitivity. Furthermore, the least-squares criterion has a tendency to overfit and produce incorrect results. Hence, this research proposes an alternative approach using the artificial neural network (ANN) with two hidden layers to optimize the identifying of insulinaemic pharmacokinetic parameters. The ANN is selected for its ability to avoid overfitting parameters and its faster speed in processing data. METHODS 18 voluntarily participants were recruited from the Canterbury and Otago region of New Zealand to take part in a Dynamic Insulin Sensitivity and Secretion Test (DISST) clinical trial. A total of 46 DISST data were collected. However, due to ambiguous and inconsistency, 4 data had to be removed. Analysis was done using MATLAB 2020a. RESULTS AND DISCUSSION Results show that, with 42 gathered dataset, the ANN generates higher gains, ∅P = 20.73 [12.21, 28.57] mU·L·mmol-1·min-1 and ∅D = 60.42 [26.85, 131.38] mU·L·mmol-1 as compared to the linear least square method, ∅P = 19.67 [11.81, 28.02] mU·L·mmol-1 ·min-1 and ∅D = 46.21 [7.25, 116.71] mU·L·mmol-1. The average value of the insulin sensitivity (SI) of ANN is lower with, SI = 16 × 10-4 L·mU-1 ·min-1 than the linear least square, SI = 17 × 10-4 L·mU-1 ·min-1. CONCLUSION Although the ANN analysis provided a lower SI value, the results were more dependable than the linear least square model because the ANN approach yielded a better model fitting accuracy than the linear least square method with a lower residual error of less than 5%. With the implementation of this ANN architecture, it shows that ANN able to produce minimal error during optimization process particularly when dealing with outlying data. The findings may provide extra information to clinicians, allowing them to gain a better knowledge of the heterogenous aetiology of diabetes and therapeutic intervention options.
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Affiliation(s)
- Nor Azlan Othman
- Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia.
| | - Muhammad Amirul Aizad Shaharul Azhar
- Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia
| | - Nor Salwa Damanhuri
- Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia
| | - Iqmal Ammar Mahadi
- Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia
| | - Mohd Hussaini Abbas
- Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia
| | - Sarah Addyani Shamsuddin
- Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, Pulau Pinang 13500, Malaysia
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8041, New Zealand
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Lam N, Murray R, Docherty PD, Te Morenga L, Chase JG. The Effects of Additional Local-Mixing Compartments in the DISST Model-Based Assessment of Insulin Sensitivity. J Diabetes Sci Technol 2022; 16:1196-1207. [PMID: 34116618 PMCID: PMC9445349 DOI: 10.1177/19322968211021602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The identification of insulin sensitivity in glycemic modelling can be heavily obstructed by the presence of outlying data or unmodelled effects. The effect of data indicative of local mixing is especially problematic with models assuming rapid mixing of compartments. Methods such as manual removal of data and outlier detection methods have been used to improve parameter ID in these cases, but modelling data with more compartments is another potential approach. METHODS This research compares a mixing model with local depot site compartments with an existing, clinically validated insulin sensitivity test model. The Levenberg-Marquardt (LM) parameter identification method was implemented alongside a modified version (aLM) capable of operator-independent omission of outlier data in accordance with the 3 standard deviation rule. Three cases were tested: LM where data points suspected to be affected by incomplete mixing at the depot site were removed, aLM, and LM with the more complex mixing model. RESULTS While insulin parameters identified in the mixing model differed greatly from those in the DISST model, there were strong Spearman correlations of approximately 0.93 for the insulin sensitivity values identified across all 3 methods. The 2 models also showed comparable identification stability in insulin sensitivity estimation through a Monte Carlo analysis. However, the mixing model required modifications to the identification process to improve convergence, and still failed to converge to feasible parameters on 5 of the 212 trials. CONCLUSIONS The mixing compartment model effectively captured the dynamics of mixing behavior, but with no significant improvement in insulin sensitivity identification.
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Affiliation(s)
- Nicholas Lam
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Rua Murray
- School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand
| | - Paul D. Docherty
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
- Institute of Technical Medicine, Furtwangen University, Villingen-Schwenningen, Baden-Württemberg, Germany
- Paul Docherty, Department of Mechanical Engineering, University of Canterbury, Private Bag 4800 Christchurch, 8041, New Zealand.
| | - Lisa Te Morenga
- Faculty of Health, Victoria University of Wellington, New Zealand
| | - J. Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
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McHugh AD, Chase JG, Knopp JL, Ormsbee JJ, Kulawiec DG, Merry TL, Murphy R, Shepherd PR, Burden HJ, Docherty PD. The Impact of Exogenous Insulin Input on Calculating Hepatic Clearance Parameters. J Diabetes Sci Technol 2022; 16:945-954. [PMID: 33478257 PMCID: PMC9264438 DOI: 10.1177/1932296820986878] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
OBJECTIVE Model-based metabolic tests require accurate identification of subject-specific parameters from measured assays. Insulin assays are used to identify insulin kinetics parameters, such as general and first-pass hepatic clearances. This study assesses the impact of intravenous insulin boluses on parameter identification precision. METHOD Insulin and C-peptide data from two intravenous glucose tolerance test (IVGTT) trials of healthy adults (N = 10 × 2; denoted A and B), with (A) and without (B) insulin modification, were used to identify insulin kinetics parameters using a grid search. Monte Carlo analysis (N = 1000) quantifies variation in simulation error for insulin assay errors of 5%. A region of parameter values around the optimum was identified whose errors are within variation due to assay error. A smaller optimal region indicates more precise practical identifiability. Trial results were compared to assess identifiability and precision. RESULTS Trial B, without insulin modification, has optimal parameter regions 4.7 times larger on average than Trial A, with 1-U insulin bolus modification. Ranges of optimal parameter values between trials A and B increase from 0.04 to 0.12 min-1 for hepatic clearance and from 0.07 to 0.14 for first-pass clearance on average. Trial B's optimal values frequently lie outside physiological ranges, further indicating lack of distinct identifiability. CONCLUSIONS A small 1-U insulin bolus improves identification of hepatic clearance parameters by providing a smaller region of optimal parameter values. Adding an insulin bolus in metabolic tests can significantly improve identifiability and outcome test precision. Assay errors necessitate insulin modification in clinical tests to ensure identifiability and precision.
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Affiliation(s)
- Alexander D. McHugh
- Centre for Bioengineering, Department of
Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
- Alexander D. McHugh, BE(Hons),
Centre for Bioengineering, Department of Mechanical Engineering,
University of Canterbury, Level 5 Civil/Mechanical Building, Private Bag 4800,
Christchurch, 8140, New Zealand.
| | - J. Geoffrey Chase
- Centre for Bioengineering, Department of
Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Jennifer L. Knopp
- Centre for Bioengineering, Department of
Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Jennifer J. Ormsbee
- Centre for Bioengineering, Department of
Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Diana G. Kulawiec
- Department of Biomedical Engineering,
Rochester Institute of Technology, Rochester, NY, USA
| | - Troy L. Merry
- Discipline of Nutrition, Faculty of
Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular
Biodiscovery, University of Auckland, Auckland, New Zealand
| | - Rinki Murphy
- Discipline of Nutrition, Faculty of
Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Peter R. Shepherd
- Discipline of Nutrition, Faculty of
Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Hannah J. Burden
- Discipline of Nutrition, Faculty of
Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Paul D. Docherty
- Centre for Bioengineering, Department of
Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
- Institute for Technical Medicine,
Furtwangen University, Villingen-Schwenningen, Germany
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Ormsbee JJ, Burden HJ, Knopp JL, Chase JG, Murphy R, Shepherd PR, Merry T. Variability in Estimated Modelled Insulin Secretion. J Diabetes Sci Technol 2022; 16:732-741. [PMID: 33588609 PMCID: PMC9294570 DOI: 10.1177/1932296821991120] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
BACKGROUND The ability to measure insulin secretion from pancreatic beta cells and monitor glucose-insulin physiology is vital to current health needs. C-peptide has been used successfully as a surrogate for plasma insulin concentration. Quantifying the expected variability of modelled insulin secretion will improve confidence in model estimates. METHODS Forty-three healthy adult males of Māori or Pacific peoples ancestry living in New Zealand participated in an frequently sampled, intravenous glucose tolerance test (FS-IVGTT) with an average age of 29 years and a BMI of 33 kg/m2. A 2-compartment model framework and standardized kinetic parameters were used to estimate endogenous pancreatic insulin secretion from plasma C-peptide measurements. Monte Carlo analysis (N = 10 000) was then used to independently vary parameters within ±2 standard deviations of the mean of each variable and the 5th and 95th percentiles determined the bounds of the expected range of insulin secretion. Cumulative distribution functions (CDFs) were calculated for each subject for area under the curve (AUC) total, AUC Phase 1, and AUC Phase 2. Normalizing each AUC by the participant's median value over all N = 10 000 iterations quantifies the expected model-based variability in AUC. RESULTS Larger variation is found in subjects with a BMI > 30 kg/m2, where the interquartile range is 34.3% compared to subjects with a BMI ≤ 30 kg/m2 where the interquartile range is 24.7%. CONCLUSIONS Use of C-peptide measurements using a 2-compartment model and standardized kinetic parameters, one can expect ~±15% variation in modelled insulin secretion estimates. The variation should be considered when applying this insulin secretion estimation method to clinical diagnostic thresholds and interpretation of model-based analyses such as insulin sensitivity.
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Affiliation(s)
- Jennifer J. Ormsbee
- Department of Mechanical Engineering,
Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
- Jennifer J. Ormsbee, MSc, University of
Canterbury, Level 5 Civil/Mechanical Building, Private Bag 4800, Christchurch,
Canterbury 8140, New Zealand.
| | - Hannah J. Burden
- Discipline of Nutrition, Faculty of
Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Jennifer L. Knopp
- Department of Mechanical Engineering,
Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - J. Geoffrey Chase
- Department of Mechanical Engineering,
Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - Rinki Murphy
- Discipline of Nutrition, Faculty of
Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Peter R. Shepherd
- Discipline of Nutrition, Faculty of
Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular
Biodiscovery, The University of Auckland, Auckland, New Zealand
| | - Troy Merry
- Discipline of Nutrition, Faculty of
Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular
Biodiscovery, The University of Auckland, Auckland, New Zealand
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McHugh AD, Chase JG, Knopp JL, Zhou T, Holder-Pearson L. Determining Losses in Jet Injection Subcutaneous Insulin Delivery: A Model-Based Approach. J Diabetes Sci Technol 2022:19322968221085032. [PMID: 35343255 DOI: 10.1177/19322968221085032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
OBJECTIVE Accurate, safe glycemic management requires reliable delivery of insulin doses. Insulin can be delivered subcutaneously for action over a longer period of time. Needle-free jet injectors provide subcutaneous (SC) delivery without requiring needle use, but the volume of insulin absorbed varies due to losses associated with the delivery method. This study employs model-based methods to determine the expected proportion of active insulin present from a needle-free SC dose. METHODS Insulin, C-peptide, and glucose assay data from a frequently sampled insulin-modified oral glucose tolerance test trial with 2U SC insulin delivery, paired with a well-validated metabolic model, predict metabolic outcomes for N = 7 healthy adults. Subject-specific nonlinear hepatic clearance profiles are modeled over time using third-order basis splines with knots located at assay times. Hepatic clearance profiles are constrained within a physiological rate of change, and relative to plasma glucose profiles. Insulin loss proportions yielding optimal insulin predictions are then identified, quantifying delivery losses. RESULTS Optimal parameter identification suggests losses of up to 22% of the nominal 2U SC dose. The degree of loss varies between subjects and between trials on the same subject. Insulin fit accuracy improves where loss greater than 5% is identified, relative to where delivery loss is not modeled. CONCLUSIONS Modeling shows needle-free SC jet injection of a nominal dose of insulin does not necessarily provide metabolic action equivalent to total dose, and this availability significantly varies between trials. By quantifying and accounting for variability of jet injection insulin doses, better glycemic management outcomes using SC jet injection may be achieved.
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Affiliation(s)
- Alexander D McHugh
- Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - J Geoffrey Chase
- Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Jennifer L Knopp
- Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Tony Zhou
- Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
| | - Lui Holder-Pearson
- Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand
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Abstract
BACKGROUND Stress-induced hyperglycemia is frequently experienced by critically ill patients and the use of glycemic control (GC) has been shown to improve patient outcomes. For model-based approaches to GC, it is important to understand and quantify model parameter assumptions. This study explores endogenous glucose production (EGP) and the use of a population-based parameter value in the intensive care unit context. METHOD Hourly insulin sensitivity (SI) was fit to clinical data from 145 patients on the Specialized Relative Insulin and Nutrition Titration GC protocol for at least 24 hours. Constraint of SI at a lower bound was used to explore likely EGP variability due to stress response. Minimum EGP was estimated during times when the model SI was constrained, and time and duration of events were examined. RESULTS Constrained events occur for 1.6% of patient hours. About 70% of constrained events occur in the first 12 hours and most events (~80%) occur when there is no exogenous nutrition given. Enhanced EGP values ranged from 1.16 mmol/min (current population value) to 2.75 mmol/min, with most being below 1.5 mmol/min (21% increase). CONCLUSION The frequency of constrained events is low and the current population value of 1.16 mmol/min is sufficient for more than 98% of patient hours, however, some patients experience significantly raised EGP probably due to an extreme stress response early in patient stay.
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Affiliation(s)
- Jennifer J. Ormsbee
- Department of Mechanical Engineering, Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - Jennifer L. Knopp
- Department of Mechanical Engineering, Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - J. Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
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Uyttendaele V, Chase JG, Knopp JL, Gottlieb R, Shaw GM, Desaive T. Insulin sensitivity in critically ill patients: are women more insulin resistant? Ann Intensive Care 2021; 11:12. [PMID: 33475909 PMCID: PMC7818291 DOI: 10.1186/s13613-021-00807-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 01/12/2021] [Indexed: 02/07/2023] Open
Abstract
Background Glycaemic control (GC) in intensive care unit is challenging due to significant inter- and intra-patient variability, leading to increased risk of hypoglycaemia. Recent work showed higher insulin resistance in female preterm neonates. This study aims to determine if there are differences in inter- and intra-patient metabolic variability between sexes in adults, to gain in insight into any differences in metabolic response to injury. Any significant difference would suggest GC and randomised trial design should consider sex differences to personalise care. Methods Insulin sensitivity (SI) levels and variability are identified from retrospective clinical data for men and women. Data are divided using 6-h blocks to capture metabolic evolution over time. In total, 91 male and 54 female patient GC episodes of minimum 24 h are analysed. Hypothesis testing is used to determine whether differences are significant (P < 0.05), and equivalence testing is used to assess whether these differences can be considered equivalent at a clinical level. Data are assessed for the raw cohort and in 100 Monte Carlo simulations analyses where the number of men and women are equal. Results Demographic data between females and males were all similar, including GC outcomes (safety from hypoglycaemia and high (> 50%) time in target band). Females had consistently significantly lower SI levels than males, and this difference was not clinically equivalent. However, metabolic variability between sexes was never significantly different and always clinically equivalent. Thus, inter-patient variability was significantly different between males and females, but intra-patient variability was equivalent. Conclusion Given equivalent intra-patient variability and significantly greater insulin resistance, females can receive the same benefit from safe, effective GC as males, but may require higher insulin doses to achieve the same glycaemia. Clinical trials should consider sex differences in protocol design and outcome analyses.
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Affiliation(s)
- Vincent Uyttendaele
- GIGA-In silico Medicine,, University of Liège, Allée du 6 Août 19, Bât. B5a, 4000, Liège, Belgium. .,Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Jennifer L Knopp
- GIGA-In silico Medicine,, University of Liège, Allée du 6 Août 19, Bât. B5a, 4000, Liège, Belgium
| | - Rebecca Gottlieb
- Medtronic Diabetes, 18000 Devonshire St, Northridge, CA, 91325, USA
| | - Geoffrey M Shaw
- Christchurch Hospital, Dept of Intensive Care, Christchurch, New Zealand and University of Otago, School of Medicine, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA-In silico Medicine,, University of Liège, Allée du 6 Août 19, Bât. B5a, 4000, Liège, Belgium
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8
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Uyttendaele V, Chase JG, Knopp JL, Gottlieb R, Shaw GM, Desaive T. Insulin sensitivity in critically ill patients: are women more insulin resistant? Ann Intensive Care 2021. [PMID: 33475909 DOI: 10.1186/s13613-021-00807-7.] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Glycaemic control (GC) in intensive care unit is challenging due to significant inter- and intra-patient variability, leading to increased risk of hypoglycaemia. Recent work showed higher insulin resistance in female preterm neonates. This study aims to determine if there are differences in inter- and intra-patient metabolic variability between sexes in adults, to gain in insight into any differences in metabolic response to injury. Any significant difference would suggest GC and randomised trial design should consider sex differences to personalise care. METHODS Insulin sensitivity (SI) levels and variability are identified from retrospective clinical data for men and women. Data are divided using 6-h blocks to capture metabolic evolution over time. In total, 91 male and 54 female patient GC episodes of minimum 24 h are analysed. Hypothesis testing is used to determine whether differences are significant (P < 0.05), and equivalence testing is used to assess whether these differences can be considered equivalent at a clinical level. Data are assessed for the raw cohort and in 100 Monte Carlo simulations analyses where the number of men and women are equal. RESULTS Demographic data between females and males were all similar, including GC outcomes (safety from hypoglycaemia and high (> 50%) time in target band). Females had consistently significantly lower SI levels than males, and this difference was not clinically equivalent. However, metabolic variability between sexes was never significantly different and always clinically equivalent. Thus, inter-patient variability was significantly different between males and females, but intra-patient variability was equivalent. CONCLUSION Given equivalent intra-patient variability and significantly greater insulin resistance, females can receive the same benefit from safe, effective GC as males, but may require higher insulin doses to achieve the same glycaemia. Clinical trials should consider sex differences in protocol design and outcome analyses.
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Affiliation(s)
- Vincent Uyttendaele
- GIGA-In silico Medicine,, University of Liège, Allée du 6 Août 19, Bât. B5a, 4000, Liège, Belgium. .,Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand.
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Jennifer L Knopp
- GIGA-In silico Medicine,, University of Liège, Allée du 6 Août 19, Bât. B5a, 4000, Liège, Belgium
| | - Rebecca Gottlieb
- Medtronic Diabetes, 18000 Devonshire St, Northridge, CA, 91325, USA
| | - Geoffrey M Shaw
- Christchurch Hospital, Dept of Intensive Care, Christchurch, New Zealand and University of Otago, School of Medicine, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA-In silico Medicine,, University of Liège, Allée du 6 Août 19, Bât. B5a, 4000, Liège, Belgium
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Mirzaee A, Dehghani M, Mohammadi M. Robust LPV control design for blood glucose regulation considering daily life factors. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2019.101830] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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10
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van Noorden B, Knopp JL, Chase JG. A subcutaneous insulin pharmacokinetic model for insulin Detemir. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 178:1-9. [PMID: 31416537 DOI: 10.1016/j.cmpb.2019.06.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 05/16/2019] [Accepted: 06/07/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Type 2 diabetes (T2D) is rapidly increasing in incidence and has significant social and economic costs. Given the increasing cost of complications, even relatively short delays in the onset of T2D can significantly reduce long-term complications and costs. Equally, recent studies have shown the onset of T2D can be delayed by use of long-acting insulin, despite the risk and concomitant low adherence. Thus, there is a strong potential motivation to develop models of long-acting insulin analogues to enable safe, effective use in model-based dosing systems. In particular, there are no current models of long-acting insulin Detemir and its unique action for model-based control. The objective of this work is to develop a first model of insulin Detemir and its unique action, and validate it against existing data in the literature. METHODS This study develops a detailed compartment model for insulin Detemir. Model specific parameters are identified using data from a range of published clinical studies on the pharmacokinetic of insulin Detemir. Model validity and robustness are assessed by identifying the model for each study and using average identified parameters over several dose sizes and study cohorts. Comparisons to peak concentration, time of peak concentration and overall error versus measured plasma concentrations are used to assess model accuracy and validity. RESULTS Almost all studies and cohorts fit literature data to within one standard deviation of error, even when using averaged identified model parameters. However, there appears to be a noticeable dose dependent dynamic not included in this first model, nor reported in the literature studies. CONCLUSIONS A first model of insulin Detemir including its unique albumin binding kinetics is derived and provisionally validated against clinical pharmacokinetic data. The pharmacokinetic curves are suitable for model-based control and general enough for use. While there are limitations in the studies used for validation that prevent a more complete understanding, the results provide an effective first model and justify the design and implementation of further, more precise human trials.
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Affiliation(s)
- Ben van Noorden
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - Jennifer L Knopp
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand
| | - J Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Christchurch, New Zealand.
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11
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Othman NA, Docherty PD, Krebs JD, Bell DA, Chase JG. The Need to Calculate Target Glucose Levels When Measuring Changes in Insulin Sensitivity During Interventions for Individuals With Type 2 Diabetes. J Diabetes Sci Technol 2018; 12:665-672. [PMID: 29295634 PMCID: PMC6154237 DOI: 10.1177/1932296817750402] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Physiological models that are used with dynamic test data to assess insulin sensitivity (SI) assume that the metabolic target glucose concentration ( GTARGET) is equal to fasting glucose concentration ( G0). However, recent research has implied that irregularities in G0 in diabetes may cause erroneous SI values. This study quantifies the magnitude of these errors. METHODS A clinically validated insulin/glucose model was used to calculate SI with the standard fasting assumption (SFA) G0 = GTARGET. Then GTARGET was treated as a variable in a second analysis (VGT). The outcomes were contrasted across twelve participants with established type 2 diabetes mellitus that were recruited to take part in a 24-week dietary intervention. Participants underwent three insulin-modified intravenous glucose tolerance tests (IM-IVGTT) at 0, 12, and 24 weeks. RESULTS SIVGT had a median value of 3.36×10-4 L·mU-1·min-1 (IQR: 2.30 - 4.95×10-4) and were significantly lower ( P < .05) than the median SISFA (6.38×10-4 L·mU-1·min-1, IQR: 4.87 - 9.39×10-4). The VGT approach generally yielded lower SI values in line with expected participant physiology and more effectively tracked changes in participant state over the 24-week trial. Calculated GTARGET values were significantly lower than G0 values (median GTARGET = 5.48 vs G0 = 7.16 mmol·L-1 P < .001) and were notably higher in individuals with longer term diabetes. CONCLUSIONS Typical modeling approaches can overestimate SI when GTARGET does not equal G0. Hence, calculating GTARGET may enable more precise SI measurements in individuals with type 2 diabetes, and could imply a dysfunction in diabetic metabolism.
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Affiliation(s)
- Nor Azlan Othman
- Faculty of Electrical Engineering,
Universiti Teknologi MARA (UiTM), Permatang Pauh, Penang, Malaysia
| | - Paul D. Docherty
- Department of Mechanical Engineering,
Centre for Bio-Engineering, University of Canterbury, Christchurch, New
Zealand
- Paul D. Docherty, BE (hons), PhD, Department
of Mechanical Engineering, University of Canterbury, Private bag 4800,
Christchurch 8140, New Zealand.
| | - Jeremy D. Krebs
- Department of Medicine, University of
Otago, Wellington, New Zealand
| | - Damon A. Bell
- School of Medicine and Pharmacology
Royal Perth Hospital Unit, The University of Western Australia, Perth, Western
Australia, Australia
| | - J. Geoffrey Chase
- Department of Mechanical Engineering,
Centre for Bio-Engineering, University of Canterbury, Christchurch, New
Zealand
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Muhd Shukeri WFW, Mat-Nor MB, Jamaludin UK, Suhaimi F, Abd Razak NN, Ralib AM. Levels and Diagnostic Value of Model-based Insulin Sensitivity in Sepsis: A Preliminary Study. Indian J Crit Care Med 2018; 22:402-407. [PMID: 29962739 PMCID: PMC6020646 DOI: 10.4103/ijccm.ijccm_92_18] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Background and Aims: Currently, there is a lack of real-time metric with high sensitivity and specificity to diagnose sepsis. Insulin sensitivity (SI) may be determined in real-time using mathematical glucose-insulin models; however, its effectiveness as a diagnostic test of sepsis is unknown. Our aims were to determine the levels and diagnostic value of model-based SI for identification of sepsis in critically ill patients. Materials and Methods: In this retrospective, cohort study, we analyzed SI levels in septic (n = 18) and nonseptic (n = 20) patients at 1 (baseline), 4, 8, 12, 16, 20, and 24 h of their Intensive Care Unit admission. Patients with diabetes mellitus Type I or Type II were excluded from the study. The SI levels were derived by fitting the blood glucose levels, insulin infusion and glucose input rates into the Intensive Control of Insulin-Nutrition-Glucose model. Results: The median SI levels were significantly lower in the sepsis than in the nonsepsis at all follow-up time points. The areas under the receiver operating characteristic curve of the model-based SI at baseline for discriminating sepsis from nonsepsis was 0.814 (95% confidence interval, 0.675–0.953). The optimal cutoff point of the SI test was 1.573 × 10−4 L/mu/min. At this cutoff point, the sensitivity was 77.8%, specificity was 75%, positive predictive value was 73.7%, and negative predictive value was 78.9%. Conclusions: Model-based SI ruled in and ruled out sepsis with fairly high sensitivity and specificity in our critically ill nondiabetic patients. These findings can be used as a foundation for further, prospective investigation in this area.
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Affiliation(s)
- Wan Fadzlina Wan Muhd Shukeri
- Department of Anaesthesiology and Intensive Care, Kulliyyah of Medicine, International Islamic University Malaysia, Kuantan, Malaysia.,Department of Anaesthesiology and Intensive Care, School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia
| | - Mohd Basri Mat-Nor
- Department of Anaesthesiology and Intensive Care, Kulliyyah of Medicine, International Islamic University Malaysia, Kuantan, Malaysia
| | | | - Fatanah Suhaimi
- Advanced Medical and Dental Institute, Universiti Sains Malaysia, Pulau Pinang, Malaysia
| | - Normy Norafiza Abd Razak
- Department of Mechanical Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang, Selangor, Malaysia
| | - Azrina Md Ralib
- Department of Anaesthesiology and Intensive Care, Kulliyyah of Medicine, International Islamic University Malaysia, Kuantan, Malaysia
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Langdon R, Docherty PD, Mansell EJ, Chase JG. Accurate and precise prediction of insulin sensitivity variance in critically ill patients. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.08.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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14
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Te Morenga L, Docherty P, Williams S, Mann J. The Effect of a Diet Moderately High in Protein and Fiber on Insulin Sensitivity Measured Using the Dynamic Insulin Sensitivity and Secretion Test (DISST). Nutrients 2017; 9:nu9121291. [PMID: 29186908 PMCID: PMC5748742 DOI: 10.3390/nu9121291] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2017] [Revised: 11/24/2017] [Accepted: 11/24/2017] [Indexed: 12/13/2022] Open
Abstract
Evidence shows that weight loss improves insulin sensitivity but few studies have examined the effect of macronutrient composition independently of weight loss on direct measures of insulin sensitivity. We randomised 89 overweight or obese women to either a standard diet (StdD), that was intended to be low in fat and relatively high in carbohydrate (n = 42) or to a relatively high protein (up to 30% of energy), relatively high fibre (>30 g/day) diet (HPHFib) (n = 47) for 10 weeks. Advice regarding strict adherence to energy intake goals was not given. Insulin sensitivity and secretion was assessed by a novel method—the Dynamic Insulin Sensitivity and Secretion Test (DISST). Although there were significant improvements in body composition and most cardiometabolic risk factors on HPHFib, insulin sensitivity was reduced by 19.3% (95% CI: 31.8%, 4.5%; p = 0.013) in comparison with StdD. We conclude that the reduction in insulin sensitivity after a diet relatively high in both protein and fibre, despite cardiometabolic improvements, suggests insulin sensitivity may reflect metabolic adaptations to dietary composition for maintenance of glucose homeostasis, rather than impaired metabolism.
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Affiliation(s)
- Lisa Te Morenga
- Department of Human Nutrition, University of Otago, PO Box 56, Dunedin 9054, New Zealand.
- Edgar Diabetes and Obesity Research Centre, University of Otago, Dunedin 9054, New Zealand.
- Riddet Institute, University of Otago, PO Box 56, Dunedin 9054, New Zealand.
| | - Paul Docherty
- Department of Mechanical Engineering, University of Canterbury, Christchurch 8140, New Zealand.
| | - Sheila Williams
- Department of Preventive and Social Medicine, University of Otago, Dunedin 9054, New Zealand.
| | - Jim Mann
- Department of Human Nutrition, University of Otago, PO Box 56, Dunedin 9054, New Zealand.
- Edgar Diabetes and Obesity Research Centre, University of Otago, Dunedin 9054, New Zealand.
- Riddet Institute, University of Otago, PO Box 56, Dunedin 9054, New Zealand.
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15
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Evaluation of pharmacokinetic model designs for subcutaneous infusion of insulin aspart. J Pharmacokinet Pharmacodyn 2017; 44:477-489. [DOI: 10.1007/s10928-017-9535-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Accepted: 08/11/2017] [Indexed: 10/19/2022]
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Uyttendaele V, Dickson JL, Shaw GM, Desaive T, Chase JG. Untangling glycaemia and mortality in critical care. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2017. [PMID: 28645302 PMCID: PMC5482947 DOI: 10.1186/s13054-017-1725-y] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Background Hyperglycaemia is associated with adverse outcomes in the intensive care unit, and initial studies suggested outcome benefits of glycaemic control (GC). However, subsequent studies often failed to replicate these results, and they were often unable to achieve consistent, safe control, raising questions about the benefit or harm of GC as well as the nature of the association of glycaemia with mortality and clinical outcomes. In this study, we evaluated if non-survivors are harder to control than survivors and determined if glycaemic outcome is a function of patient condition and eventual outcome or of the glycaemic control provided. Methods Clinically validated, model-based, hour-to-hour insulin sensitivity (SI) and its hour-to-hour variability (%ΔSI) were identified over the first 72 h of therapy in 145 patients (119 survivors, 26 non-survivors). In hypothesis testing, we compared distributions of SI and %ΔSI in 6-hourly blocks for survivors and non-survivors. In equivalence testing, we assessed if differences in these distributions, based on blood glucose measurement error, were clinically significant. Results SI level was never equivalent between survivors and non-survivors (95% CI of percentage difference in medians outside ±12%). Non-survivors had higher SI, ranging from 9% to 47% higher overall in 6-h blocks, and this difference became statistically significant as glycaemic control progressed. %ΔSI was equivalent between survivors and non-survivors for all 6-hourly blocks (95% CI of difference in medians within ±12%) and decreased in general over time as glycaemic control progressed. Conclusions Whereas non-survivors had higher SI levels, variability was equivalent to that of survivors over the first 72 h. These results indicate survivors and non-survivors are equally controllable, given an effective glycaemic control protocol, suggesting that glycaemia level and variability, and thus the association between glycaemia and outcome, are essentially determined by the control provided rather than by underlying patient or metabolic condition. Electronic supplementary material The online version of this article (doi:10.1186/s13054-017-1725-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Vincent Uyttendaele
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand. .,GIGA - In Silico Medicine, University of Liège, Allée du 6 Août 19, bâtiment B5a, 4000, Liège, Belgium.
| | - Jennifer L Dickson
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
| | - Geoffrey M Shaw
- Department of Intensive Care, Christchurch Hospital, Private Bag 4710, Christchurch, New Zealand
| | - Thomas Desaive
- GIGA - In Silico Medicine, University of Liège, Allée du 6 Août 19, bâtiment B5a, 4000, Liège, Belgium
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
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17
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Mansell EJ, Docherty PD, Chase JG. Shedding light on grey noise in diabetes modelling. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.06.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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18
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A modified approach to objective surface generation within the Gauss-Newton parameter identification to ignore outlier data points. Biomed Signal Process Control 2016. [DOI: 10.1016/j.bspc.2016.06.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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19
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Incorporating bolus and infusion pharmacokinetics into the ICING insulin model. Math Biosci 2016; 281:1-8. [PMID: 27580690 DOI: 10.1016/j.mbs.2016.08.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2015] [Revised: 08/04/2016] [Accepted: 08/17/2016] [Indexed: 12/25/2022]
Abstract
The ICING model has been successfully used to guide clinical decisions on insulin administration in critical illness. However, insulin pharmacokinetics in the ICING model can be improved to better describe both intravenous (IV) bolus and infusion insulin administration. Patient data from 217 Dynamic Insulin Sensitivity and Secretion Tests (DISST) and 36 Intravenous Glucose Tolerance Tests (IVGTT) from independent dietary intervention studies was used to fit model parameters to a model structure that conforms to known behaviour. The DISST tests measured both endogenous and exogenous IV insulin bolus responses, while the IVGTT measured exogenous IV insulin infusion dynamics. Unidentifiable parameters were given physiologically justified values, with knowledge on relative insulin clearance rates used to constrain parameter values. The resulting whole-cohort description was able to simultaneously describe both IV bolus and infusion dynamics, and improves ICING model descriptive capability. Improved infusion dynamics will allow better description of subcutaneous insulin, the insulin administration route favoured in outpatient care of diabetes.
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Mansell EJ, Docherty PD, Fisk LM, Chase JG. Estimation of secondary effect parameters in glycaemic dynamics using accumulating data from a virtual type 1 diabetic patient. Math Biosci 2015; 266:108-17. [DOI: 10.1016/j.mbs.2015.06.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2014] [Revised: 06/08/2015] [Accepted: 06/09/2015] [Indexed: 11/25/2022]
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21
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Othman NA, Docherty PD, Krebs JD, Bell DA, Chase JG. The necessity of identifying the basal glucose set-point in the IVGTT for patients with Type 2 Diabetes. Biomed Eng Online 2015; 14:18. [PMID: 25881031 PMCID: PMC4350631 DOI: 10.1186/s12938-015-0015-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2014] [Accepted: 02/16/2015] [Indexed: 12/16/2022] Open
Abstract
Background The model-based dynamic insulin sensitivity and secretion test (DISST) uses fasting glucose (G0) as the basal glucose (GB) concentration when assessing insulin sensitivity (SI). However, this model was developed in a healthy, normoglycaemic cohort. We sought to determine the suitability the DISST model has for individuals with established type 2 diabetes (T2D). Methods 14 participants with established T2D were recruited to take part in a dietary intervention study. Insulin-modified intravenous glucose tolerance tests (IM-IVGTT) were undertaken at week 0, 12 and 24 and were used with DISST model to identify GB. A total of 36 tests were conducted across 12 participants throughout the study. Measured G0 and identified GB values were compared using a Kolmogorov-Smirnov (KS) and signed rank (RS) test for the cohort. Results There were significant differences between the G0 and identified GB values in this cohort (prs and pks < 0.0001), although both values were well correlated (R = 0.70). The residual plot demonstrates that the modified model captures the behaviour of the participants more accurately than the original model. Conclusions This analysis has shown that GB is an important variable for modelling the glycaemic behaviour in T2D. These findings suggest that the original DISST model, while appropriate for normoglycaemic cohorts, needs to model basal glucose level as a variable for assessing individuals with established T2D.
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Affiliation(s)
- Nor Azlan Othman
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, 8054, New Zealand.
| | - Paul D Docherty
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, 8054, New Zealand.
| | - Jeremy D Krebs
- Department of Medicine, University of Otago, Wellington, 6242, New Zealand.
| | - Damon A Bell
- School of Medicine and Pharmacology Royal Perth Hospital Unit, The University of Western Australia, Perth, Western Australia, 6009, Australia.
| | - J Geoffrey Chase
- Department of Mechanical Engineering, Centre for Bio-Engineering, University of Canterbury, Private Bag 4800, Christchurch, 8054, New Zealand.
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22
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Davidson SM, Docherty PD, Chase JG. Use of the DISST model to estimate the HOMA and Matsuda indexes using only a basal insulin assay. J Diabetes Sci Technol 2014; 8:815-20. [PMID: 24876431 PMCID: PMC4764219 DOI: 10.1177/1932296814532490] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
It is hypothesized that early detection of reduced insulin sensitivity (SI) could prompt intervention that may reduce the considerable financial strain type 2 diabetes mellitus (T2DM) places on global health care. Reduction of the cost of already inexpensive SI metrics such as the Matsuda and HOMA indexes would enable more widespread, economically feasible use of these metrics for screening. The goal of this research was to determine a means of reducing the number of insulin samples and therefore the cost required to provide an accurate Matsuda Index value. The Dynamic Insulin Sensitivity and Secretion Test (DISST) model was used with the glucose and basal insulin measurements from an Oral Glucose Tolerance Test (OGTT) to predict patient insulin responses. The insulin response to the OGTT was determined via population based regression analysis that incorporated the 60-minute glucose and basal insulin values. The proposed method derived accurate and precise Matsuda Indices as compared to the fully sampled Matsuda (R = .95) using only the basal assay insulin-level data and 4 glucose measurements. Using a model employing the basal insulin also allows for determination of the 1-day HOMA value. The DISST model was successfully modified to allow for the accurate prediction an individual's insulin response to the OGTT. In turn, this enabled highly accurate and precise estimation of a Matsuda Index using only the glucose and basal insulin assays. As insulin assays account for the majority of the cost of the Matsuda Index, this model offers a significant reduction in assay cost.
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Affiliation(s)
- Shaun M Davidson
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - Paul D Docherty
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
| | - J Geoffrey Chase
- Centre for Bioengineering, University of Canterbury, Christchurch, New Zealand
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23
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Docherty PD, Chase JG, Te Morenga L, Fisk LM. A novel hierarchal-based approach to measure insulin sensitivity and secretion in at-risk populations. J Diabetes Sci Technol 2014; 8:807-14. [PMID: 24876451 PMCID: PMC4764222 DOI: 10.1177/1932296814536511] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The pathogenesis of type 2 diabetes is characterized by insulin resistance and insulin secretory dysfunction. Few existing metabolic tests measure both characteristics, and no such tests are inexpensive enough to enable widespread use. A hierarchical approach uses 2 down-sampled tests in the dynamic insulin sensitivity and secretion test (DISST) family to first determine insulin sensitivity (SI) using 4 glucose measurements. Second the insulin secretion is determined for only participants with reduced SI using 3 C-peptide measurements from the original test. The hierarchical approach is assessed via its ability to classify 214 individual test responses of 71 females with an elevated risk of type 2 diabetes into 5 bins with equivalence to the fully sampled DISST. Using an arbitrary SI cut-off, 102 test responses were reassayed for C-peptide and unique insulin secretion characteristics estimated. The hierarchical approach correctly classified 84.5% of the test responses and 94.4% of the responses of individuals with increased fasting glucose. The hierarchical approach is a low-cost methodology for measuring key characteristics of type 2 diabetes. Thus the approach could provide an economical approach to studying the pathogenesis of type 2 diabetes, or in early risk screening. As the higher cost test uses the same clinical protocol as the low-cost test, the cost of the additional information is limited to the assay cost of C-peptide, and no additional procedures or callbacks are required.
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Docherty PD, Chase JG. An in-silico proof-of-concept investigation of a combined glucose-insulin bolus quick dynamic insulin sensitivity test. Biomed Signal Process Control 2014. [DOI: 10.1016/j.bspc.2012.12.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Docherty P, Gray R, Mansell E. Reducing the Effect of Outlying Data on the Identification of Insulinaemic Pharmacokinetic Parameters with an Adapted Gauss-Newton Approach. ACTA ACUST UNITED AC 2014. [DOI: 10.3182/20140824-6-za-1003.01351] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Docherty PD, Berkeley JE, Lotz TF, Te Morenga L, Fisk LM, Shaw GM, McAuley KA, Mann JI, Chase JG. Clinical validation of the quick dynamic insulin sensitivity test. IEEE Trans Biomed Eng 2012; 60:1266-72. [PMID: 23232364 DOI: 10.1109/tbme.2012.2232667] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The quick dynamic insulin sensitivity test (DISTq) can yield an insulin sensitivity result immediately after a 30-min clinical protocol. The test uses intravenous boluses of 10 g glucose and 1 U insulin at t = 1 and 11 min, respectively, and measures glucose levels in samples taken at t = 0, 10, 20, and 30 min. The low clinical cost of the protocol is enabled via robust model formulation and a series of population-derived relationships that estimate insulin pharmacokinetics as a function of insulin sensitivity (SI). Fifty individuals underwent the gold standard euglycaemic clamp (EIC) and DISTq within an eight-day period. SI values from the EIC and two DISTq variants (four-sample DISTq and two-sample DISTq30) were compared with correlation, Bland-Altman and receiver operator curve analyses. DISTq and DISTq30 correlated well with the EIC [R = 0.76 and 0.75, and receiver operator curve c-index = 0.84 and 0.85, respectively]. The median differences between EIC and DISTq/DISTq30 SI values were 13% and 22%, respectively. The DISTq estimation method predicted individual insulin responses without specific insulin assays with relative accuracy and thus high equivalence to EIC SI values was achieved. DISTq produced very inexpensive, relatively accurate immediate results, and can thus enable a number of applications that are impossible with established SI tests.
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Affiliation(s)
- Paul D Docherty
- Centre for Bioengineering, University of Canterbury, Christchurch 8140, New Zealand.
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Schranz C, Docherty PD, Chiew YS, Möller K, Chase JG. Iterative integral parameter identification of a respiratory mechanics model. Biomed Eng Online 2012; 11:38. [PMID: 22809585 PMCID: PMC3460758 DOI: 10.1186/1475-925x-11-38] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2012] [Accepted: 06/19/2012] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Patient-specific respiratory mechanics models can support the evaluation of optimal lung protective ventilator settings during ventilation therapy. Clinical application requires that the individual's model parameter values must be identified with information available at the bedside. Multiple linear regression or gradient-based parameter identification methods are highly sensitive to noise and initial parameter estimates. Thus, they are difficult to apply at the bedside to support therapeutic decisions. METHODS An iterative integral parameter identification method is applied to a second order respiratory mechanics model. The method is compared to the commonly used regression methods and error-mapping approaches using simulated and clinical data. The clinical potential of the method was evaluated on data from 13 Acute Respiratory Distress Syndrome (ARDS) patients. RESULTS The iterative integral method converged to error minima 350 times faster than the Simplex Search Method using simulation data sets and 50 times faster using clinical data sets. Established regression methods reported erroneous results due to sensitivity to noise. In contrast, the iterative integral method was effective independent of initial parameter estimations, and converged successfully in each case tested. CONCLUSION These investigations reveal that the iterative integral method is beneficial with respect to computing time, operator independence and robustness, and thus applicable at the bedside for this clinical application.
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Affiliation(s)
- Christoph Schranz
- Institute of Technical Medicine, Furtwangen University, Jakob-Kienzle-Str. 17, Villingen-Schwenningen, Germany
| | - Paul D Docherty
- Department of Mechanical Engineering, University of Canterbury, Private Bag 8140, Christchurch, New Zealand
| | - Yeong Shiong Chiew
- Department of Mechanical Engineering, University of Canterbury, Private Bag 8140, Christchurch, New Zealand
| | - Knut Möller
- Institute of Technical Medicine, Furtwangen University, Jakob-Kienzle-Str. 17, Villingen-Schwenningen, Germany
| | - J Geoffrey Chase
- Department of Mechanical Engineering, University of Canterbury, Private Bag 8140, Christchurch, New Zealand
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Docherty PD, Chase JG, David T. Characterisation of the iterative integral parameter identification method. Med Biol Eng Comput 2011; 50:127-34. [PMID: 22205574 DOI: 10.1007/s11517-011-0851-y] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2011] [Accepted: 12/07/2011] [Indexed: 11/26/2022]
Abstract
Parameter identification methods are used to find optimal parameter values to fit models to measured data. The single integral method was defined as a simple and robust parameter identification method. However, the method did not necessarily converge to optimum parameter values. Thus, the iterative integral method (IIM) was developed. IIM will be compared to a proprietary nonlinear-least-squares-based Levenberg-Marquardt parameter identification algorithm using a range of reasonable starting values. Performance is assessed by the rate and accuracy of convergence for an exemplar two parameters insulin pharmacokinetic model, where true values are known a priori. IIM successfully converged to within 1% of the true values in all cases with a median time of 1.23 s (IQR 0.82-1.55 s; range 0.61-3.91 s). The nonlinear-least-squares method failed to converge in 22% of the cases and had a median (successful) convergence time of 3.29 s (IQR 2.04-4.89 s; range 0.42-44.9 s). IIM is a stable and relatively quick parameter identification method that can be applied in a broad variety of model configurations. In contrast to most established methods, IIM is not susceptible to local minima and is thus, starting point and operator independent.
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Affiliation(s)
- Paul D Docherty
- Department of Mechanical Engineering, University of Canterbury, PO Box:4800, Christchurch 8140, New Zealand.
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McAuley KA, Berkeley JE, Docherty PD, Lotz TF, Te Morenga LA, Shaw GM, Williams SM, Chase JG, Mann JI. The dynamic insulin sensitivity and secretion test--a novel measure of insulin sensitivity. Metabolism 2011; 60:1748-56. [PMID: 21704347 DOI: 10.1016/j.metabol.2011.05.009] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2011] [Revised: 05/05/2011] [Accepted: 05/09/2011] [Indexed: 10/18/2022]
Abstract
The objective was to validate the methodology for the dynamic insulin sensitivity and secretion test (DISST) and to demonstrate its potential in clinical and research settings. One hundred twenty-three men and women had routine clinical and biochemical measurements, an oral glucose tolerance test, and a DISST. For the DISST, participants were cannulated for blood sampling and bolus administration. Blood samples were drawn at t = 0, 10, 15, 25, and 35 minutes for measurement of glucose, insulin, and C-peptide. A 10-g bolus of intravenous glucose at t = 5 minutes and 1 U of intravenous insulin immediately after the t = 15 minute sample were given. Fifty participants also had a hyperinsulinemic-euglycemic clamp. Relationships between DISST insulin sensitivity (SI) and the clamp, and both DISST SI and secretion and other metabolic variables were measured. A Bland-Altman plot showed little bias in the comparison of DISST with the clamp, with DISST underestimating the glucose clamp by 0.1·10(-2)·mg·L·kg(-1)·min(-1)·pmol(-1) (90% confidence interval, -0.2 to 0). The correlation between SI as measured by DISST and the clamp was 0.82; the c unit for the receiver operating characteristic curve analysis for the 2 tests was 0.96. Metabolic variables showed significant correlations with DISST SI and the second phase of insulin release. The DISST also appears able to distinguish different insulin secretion patterns in individuals with identical SI values. The DISST is a simple, dynamic test that compares favorably with the clamp in assessing SI and allows simultaneous assessment of insulin secretion. The DISST has the potential to provide even more information about the pathophysiology of diabetes than more complicated tests.
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Affiliation(s)
- Kirsten A McAuley
- Edgar National Centre for Diabetes and Obesity Research, University of Otago, Dunedin 9054, New Zealand.
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Docherty PD, Chase JG, Morenga LT, Lotz TF, Berkeley JE, Shaw GM, McAuley KA, Mann JI. A spectrum of dynamic insulin sensitivity test protocols. J Diabetes Sci Technol 2011; 5:1499-508. [PMID: 22226272 PMCID: PMC3262721 DOI: 10.1177/193229681100500626] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Numerous tests have been developed to estimate insulin sensitivity (SI). However, most of the established tests are either too expensive for widespread application or do not yield reliable results. The dynamic insulin sensitivity and secretion test (DISST) uses assays of glucose, insulin, and C-peptide from nine samples to quantify SI and endogenous insulin secretion (UN) at a comparatively low cost. The quick dynamic insulin sensitivity test has shown that the DISST SI values are robust to significant assay omissions. METHODS Eight DISST-based variations of the nine-sample assay regimen are proposed to investigate the effects of assay omission within the DISST-based framework. SI and UN were identified using the fully-sampled DISST and data from 218 nine-sample tests undertaken in 74 female individuals with elevated diabetes risk. This same data was then used with appropriate assay omissions to identify SI and UN with the eight DISST-based assay variations. RESULTS Median intraprocedure proportional difference between SI values from fully-sampled DISST and the DISST-based variants was in the range of -17.9 to 7.8%. Correlations were in the range of r = 0.71 to 0.92 with the highest correlations between variants with the greatest commonality with the nine-sample DISST. Metrics of UN correlated relatively well between tests when C-peptide was assayed (r = 0.72 to 1) but were sometimes not well estimated when samples were not assayed for C-peptide (r = -0.14 to 0.75). CONCLUSIONS The DISST-based spectrum offers a series of tests with very distinct compromises of information yield, accuracy, assay cost, and clinical intensity. Thus, the spectrum of tests has the potential to enable researchers to better allocate funds by selecting an optimal test configuration for their particular application.
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Affiliation(s)
- Paul D Docherty
- Department of Mechanical Engineering, University of Canterbury, Christchurch, New Zealand.
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Docherty PD, Chase JG, Lotz TF, Desaive T. A graphical method for practical and informative identifiability analyses of physiological models: a case study of insulin kinetics and sensitivity. Biomed Eng Online 2011; 10:39. [PMID: 21615928 PMCID: PMC3129319 DOI: 10.1186/1475-925x-10-39] [Citation(s) in RCA: 80] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2011] [Accepted: 05/26/2011] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Derivative based a-priori structural identifiability analyses of mathematical models can offer valuable insight into the identifiability of model parameters. However, these analyses are only capable of a binary confirmation of the mathematical distinction of parameters and a positive outcome can begin to lose relevance when measurement error is introduced. This article presents an integral based method that allows the observation of the identifiability of models with two-parameters in the presence of assay error. METHODS The method measures the distinction of the integral formulations of the parameter coefficients at the proposed sampling times. It can thus predict the susceptibility of the parameters to the effects of measurement error. The method is tested in-silico with Monte Carlo analyses of a number of insulin sensitivity test applications. RESULTS The method successfully captured the analogous nature of identifiability observed in Monte Carlo analyses of a number of cases including protocol alterations, parameter changes and differences in participant behaviour. However, due to the numerical nature of the analyses, prediction was not perfect in all cases. CONCLUSIONS Thus although the current method has valuable and significant capabilities in terms of study or test protocol design, additional developments would further strengthen the predictive capability of the method. Finally, the method captures the experimental reality that sampling error and timing can negate assumed parameter identifiability and that identifiability is a continuous rather than discrete phenomenon.
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Affiliation(s)
- Paul D Docherty
- Centre for Bioengineering, Department of Mechanical Engineering, University of Canterbury, New Zealand, Private Bag 4800, Christchurch, New Zealand.
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