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. 2014 Aug 28;10(8):e1003798.
doi: 10.1371/journal.pcbi.1003798. eCollection 2014 Aug.

Possible role of interleukin-1β in type 2 diabetes onset and implications for anti-inflammatory therapy strategies

Gang Zhao  1 , Gitanjali Dharmadhikari  2 , Kathrin Maedler  2 , Michael Meyer-Hermann  3
Affiliations

Possible role of interleukin-1β in type 2 diabetes onset and implications for anti-inflammatory therapy strategies

Gang Zhao et al. PLoS Comput Biol. .

Abstract

Increasing evidence of a role of chronic inflammation in type 2 diabetes progression has led to the development of therapies targeting the immune system. We develop a model of interleukin-1β dynamics in order to explain principles of disease onset. The parameters in the model are derived from in vitro experiments and patient data. In the framework of this model, an IL-1β switch is sufficient and necessary to account for type 2 diabetes onset. The model suggests that treatments targeting glucose bear the potential of stopping progression from pre-diabetes to overt type 2 diabetes. However, once in overt type 2 diabetes, these treatments have to be complemented by adjuvant anti-inflammatory therapies in order to stop or decelerate disease progression. Moreover, the model suggests that while glucose-lowering therapy needs to be continued all the way, dose and duration of the anti-inflammatory therapy needs to be specifically controlled. The model proposes a framework for the discussion of clinical trial outcomes.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Model scheme: IL-1Ra (A) and IL-1β (L) compete for IL-1 receptors giving rise to a fraction of IL-1β bound receptors (F) which determines subsequent signalling in β-cells (B).
These control insulin (I) release and, via the influence of insulin resistance, blood glucose level (G). Arrows: activation effect, line with bar end: inhibition effect. Kinetic terms corresponding to each interaction are labelled. Variables evolving on different time scales are marked by different colours.
Figure 2
Figure 2. Parameter fit from experimental data.
A: Data are reproduced from Fig. 4A in . The function g(G) in Eq. (4) is fitted to these data. Note a = 1 is used in the model, since the constant could be merged with k 5. B: Data were reproduced from Fig. 4B in . The function l(F) in Eq. (5) is fitted to these data. Note a = 1 is used in the model. C: Data are reproduced from Fig. 1E in . Eq. (6) is fitted to these data. Note the interchanged axes and a = 1 is used in the model. D: Data are reproduced from Fig. 1C in . The function a(F) in Eq. (13) is fitted to these data. E: Data are reproduced from Fig. 1B in . The function p(F) in Eq. (14) is fitted to these data.
Figure 3
Figure 3. Parameter determined by scanning.
A: Random points are selected in the L b-A b plane (normalised with L c and A c) and used in the steady state equations (Eq. (2,3)) to determine k 1–6. If k 1–6 are non-negative, points either induce an IL-1β switch (red) or do not (blue). B: The IL-1β level L d/L c after the switch is shown as a function of IL-1Ra concentration at the bifurcation point.
Figure 4
Figure 4. Fitting the model to the fasting glucose history to determine m and τ.
Shown are the best fit results of incident diabetes cases before T2D diagnosis and the data (black line) (red line cross) and non-diabetic controls (red line) (A), interpolated insulin resistance as model input (B), corresponding behaviour of β-cell mass (C) and IL-1β (D).
Figure 5
Figure 5. There exist multiple reasonable fits (shadow) of incident diabetes cases before T2D diagnosis and the data (black line) and non-diabetic controls (red line) (A).
Fitted parameters exhibit correlations (B).
Figure 6
Figure 6. A: The bifurcation diagram of the IL-1β-IL-1Ra subsystem as glucose (G) varies.
This subsystem exhibits three stable (full lines) and two unstable (dotted blue lines) steady states. Above a critical glucose level (5.84 mM) the system loses stability of the pre-diabetic state (green line) and progresses to a high IL-1β state (red line), which is stable across the whole physiological glucose range. A third stable state (black line) with even higher IL-1β exists, which, in the hyperglycaemia range, may be associated with advanced T2D. B: A transition from five to three steady states is found that corresponds to the transition from pre-diabetes to overt T2D. Steady states appear as crossing points of the nullclines. The nullclines of IL-1β (red) and IL-1Ra (black) cross in five points at normal glucose level (4.5 mM). When glucose increases, the IL-1β nullcline raises its minimum (inset, transition from red to blue to green curve) such that the low IL-1β and low IL-1Ra steady state vanishes. The system is forced to switch to a high IL-1β state which may be associated with overt T2D. The corresponding glucose level is 4.5, 5.84 and 6.84 mM for the red, blue and green IL-1β nullcline in the inset, respectively.
Figure 7
Figure 7. T2D therapy strategies and their effect on β-cell mass.
A: Glucose-lowering therapy. The pre-diabetic steady state is restored at the IL-1β (red line) and IL-1Ra (black line) nullcline intersection (red circle). But the system stays in the stable T2D steady state (black square). B: Permanent IL-1Ra therapy. The IL-1β (red line) and IL-1Ra (black dotted line) nullclines do not intersect at low IL-1β and the system starts in the stable overt T2D steady state (black square). Clamping IL-1Ra to levels below the peak of the IL-1β nullcline (lower blue line) reduces IL-1β (red cross). Clamping IL-1Ra to levels above the peak (upper blue line) makes IL-1β jump to low levels (green triangle). C: Combined IL-1Ra and glucose-lowering therapy. Above threshold clamp of IL-1Ra (dashed dotted line) induces a substantial IL-1β reduction (red dot). Simultaneous control of glucose restores a stable steady state at low IL-1β (red and black nullclines intersect at the blue circle). A release of IL-1Ra clamp induces a transition to this steady state. D: Effect of the different treatments on β-cell mass. Therapy as in A (black squares), B (red crosses for below threshold IL-1Ra clamp; green triangles for above threshold IL-1Ra clamp), C (red dots for combined IL-1Ra and glucose-lowering therapy; blue circles after release of IL-1Ra clamp). No treatment (black line).
Figure 8
Figure 8. The dose-effect relationship of IL-1Ra therapy for mild (red circle) and strong T2D (black dot) in silico are shown.
Following the protocol of the clinical trial, glycaemia improvement after 90 days of therapy versus control, is used as a measure. The IL-1Ra threshold predicted by the model is reflected in the jump of the resulting glucose level for both T2D groups. The optimal IL1-Ra dose for strong T2D is substantially larger because of the lower endogenous level of IL-1Ra. The best achieved glucose improvement in response to the therapy is three-fold higher in the case of strong than in mild T2D. The starting glucose level is 11.96 and 18.02 mM for mild and strong T2D, respectively. The final glucose level of the both control groups is 12.21 and 18.82 mM, respectively.
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This work was supported by the Helmholtz Association cross-program activity “Metabolic Dysfunction and Human Disease”. MMH was supported by the BMBF GerontoSys initiative GerontoShield, by the HFSP, and by the BMBF eMED SYSIMIT and SysStomach project. KM is supported by Competence network diabetes mellitus funded by the Federal Ministry of Education and Research (BMBF) and the European Research Council (ERC 260336). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.