Evaluation of a glomerular filtration term in the DISST model to capture the glucose pharmacodynamics of an insulin resistant cohort

Transcription

1 Evaluation of a glomerular filtration term in the DISST model to capture the glucose pharmacodynamics of an insulin resistant cohort Paul D Docherty J Geoffrey Chase Thomas F Lotz Jeremy D Krebs

2 Study design Study: 24 week Atkins-diet based intervention Weeks 1-12 weight loss stage. Weeks weight maintenance Significant carbohydrate limit ~2g/day target energy composition: 2-25% via carbohydrate; 3% via protein; and 45-5% via fats Participants: 12 individuals with established type 2 diabetes Duration of diabetes.5-12 years BMI range 34-46kg/m 2 Exclusion criteria: No other major illness or recent weight change

3 Study Evaluation Tests: Week, 12 and 24 Bodyweight and composition Fasting assays lipids, HbA1c, Creatinine Insulin sensitivity (SI) test SI test protocol: GM Ward et al. (Metabolism 21 5(5)).2g/kg Glucose bolus (t=) 3.5mU kg -1 min -1 insulin ( to 2);.5mU kg -1 min -1 (7 to 17);.25mU kg -1 min -1 (17 to 5).1mU kg -1 min -1 (5 to 3) 33 samples, 5+ hours - Samples at t=-1, -5, -1,, 1, 2, 3, 4, 5, 6, 8, 1, 12.5, 15, 2, 25, 3, 35, 4, 5, 6, 7, 8, 9, 1, 12, 14, 16, 18, 21, 24, 27, 3 minutes Assayed for glucose and insulin

4 SI test evaluation Model Based: Standard DISST model (McAuley et al. 211 Metabolism) DISST model modified with a glomerular filtration (GFR) term DISTq (Docherty et al. 29 Open Med. Inf. J. 3) Minimal Model Simple Measures: Area Under the Curve (AUC) glucose (AUC G ), and insulin (AUC I ) HOMA Basal assays: glucose (G B ), insulin (I B ), HbA1c and triglyceride (TAG)

5 DISST model Exogenous insulin bolus Post hepatic β-cell secretion Endogenous glucose production Exogenous glucose bolus Plasma Insulin Interstitial Insulin Glucose SI Hepatic clearance Renal clearance Binding to cells Insulin mediated clearance Nonmediated clearance

6 DISST validation Sparsely-sampled, low-dose, short duration IM-IVGTT protocol Concurrent model-based assessment of SI and endogenous insulin production High intra-patient repeatability (11-13% variation) High gold standard correlation (R=.81), absolute ( median =-1.6%) and diagnostic equivalence (ROC c-unit=.96) ISI clamp [mg/kg/(mu/l)/min] Important note: DISST protocol was not undertaken results of this study not indicative of DISST test ISI DISST [mg/kg/(mu/l)/min]

7 Glomerular filtration term Glomerular filtration (GFR) [mmol.min -1.kg -1 ] From Arleth et al. (2 CMPB 62(3)) Initial trace of clearance at ~1 mmol/l glucose Linear clearance at ~22 mmol/l glucose Smooth transition Glucose concentration (G) [mmol.l -1 ]

8 Parameter identification The iterative integral method (IIM): Robust not dependent on starting estimations Does not locate local minima Computationally quick recent studies have exhibited a 3 to 35x faster convergence than Levenburg-Marquardt algorithms. Does not explicitly map error surface does not become unstable due to over-sized convergence steps Docherty et al. published in application (Open Med. Inf. J 29 3) - validation paper in review (Med. Biol. Eng. Comput.)

9 IIM method 1. Simulate species: G=interpolate G data 2. Evaluate eqn coefficients at sample times: LHS=G i -G +p G G(t)-G dt i CSI=- G(t)Q(t)-G Q dt i CV G = P X (t)-gfr(t)*bwdt i 3. Evaluate matrix representation: {LHS i } T =[CSI i, CV G i ]{SI, 1/V G } T where i is the sample times 4. Re-simulate G(t) as function of SI and V G outcomes, re-simulate GFR(t) if relevant 5. Repeat steps 2 to 4 until convergence

10 Minimal Model ID Minimal model is re-arranged to allow identification with IIM: Governing eqns:,, Can be rearranged: And enable the matrix formulation using the following Coefficients: LHS;, RHS;,, and

11 Comparison of IIM and Levenberg-Marquardt (L-M) L-M G sim -G data RESIDUALS.75 L-M [s].5.25 ID TIME Residuals of IIM comparable to L-M L-M much faster than IIM due to indirect identification of parameters in IIM IIM G -G sim data IIM [s] (Minimal model identified using full dataset in IIM and L-M parameter identification methods) Not ideal application of IIM but it worked nonetheless!

12 Comparison between DISST, DISST+GFR and Minimal model MM [L.mU -1.min -1 ] MM vs DISST insulin sensitivity DISST [1-4 L.mU -1.min -1 ] DISST+GFR [1-4 L.mU -1.min -1 ] DISST+GFR vs DISST insulin sensitivity DISST [1-4 L.mU -1.min -1 ] Minimal model SI was very different to DISST SI DISST SI very similar to DISST+GFR SI

13 Residuals DISST+GFR DISST Minimal model solutions were generally more adherent to the measured data Minimal Model Time [mins] Time [mins] DISST and DISST+GFR were not able to characterise 3 minute profile, but were similar to MM over -1 to 6mins

14 Investigation outcomes MM S G MM S I DISST DISST +GFR DISTq HOMA AUC I I B TAG AUC G G B Compliance to the conditions of the dietary intervention trial was generally good Weight loss was observed across the trial HbA1c Excess weight -2% -1% % 1% 2%

15 Model-based outcomes MM S G MM S I DISST DISST +GFR DISTq HOMA AUC I I B TAG AUC G G B HbA1c Excess weight Minimal model parameter identification frequently reached bounds unusable results DISST measured realistic results DISST+GFR modulated range in more realistic direction DISTq failed to estimate insulin concentration, thus SI accurately -2% -1% % 1% 2%

16 Simple metric outcomes MM S G MM S I DISST DISST +GFR Fasting assays (HbA1c, G B, I B, TAG) generally improved DISTq HOMA AUC I I B TAG AUC G G B HbA1c Excess weight AUC G was indistinct to G B (ρ=.9) obsolete AUC I was indistinct to I B (ρ=.69) obsolete HOMA similar to I B (ρ=.94) -2% -1% % 1% 2%

17 Study outcomes Regarding model-based metrics: Minimal model unstable in this IR cohort previously reported DISST stable, but unable to characterise longer-term steadystate condition. DISTq stable but inaccurate. Adding variable model parameters can aid residuals but can also obscure results or disable stable parameter estimation The addition of the GFR term modulated the glucose clearance in some IFG participants and had a positive contribution to the study outcomes. However the effect was too small to assess

18 Study outcomes Regarding simple metrics: Simple, single blood-test HOMA observed very large, possibly excessive changes HbA1c, G B, I B and weight all exhibited positive outcomes at a cohort level G B described the same changes as AUC G clinical protocol was not necessary if AUC G is intended I B described the same changes as AUC I clinical protocol was not necessary if AUC I is intended TAG exhibited reasonable behaviour but had a significant number of confounders

19 Questions?