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LPS is frequently used to induce experimental endotoxic shock, representing a standard model of acute inflammation in mice. The resulting inflammatory response leads to hypothermia of the experimental animals, which in turn can be used as surrogate for the severity of systemic inflammation. Although increasingly applied as a humane endpoint in murine studies, differences between obtained temperature-time curves are typically evaluated at a single time point with  t -tests or ANOVA analyses. We hypothesized that analyses of the entire temperature-time curves using a kinetic response model could fit the data, which show a temperature decrease followed by a tendency to return to normal temperature, and could increase the statistical power. Using temperature-time curves obtained from LPS stimulated mice, we derived a biologically motivated kinetic response model based on a differential equation. The kinetic model includes four parameters: (i) normal body temperature ( T   n  ), (ii) a coefficient related to the force of temperature autoregulation ( r ), (iii) damage strength ( p  0 ), and (iv) clearance rate ( k ). Kinetic modeling of temperature-time curves obtained from LPS stimulated mice is feasible and leads to a high goodness-of-fit. Here, modifying key enzymes of inflammatory cascades induced a dominant impact of genotypes on the damage strength and a weak impact on the clearance rate. Using a likelihood-ratio test to compare modeled curves of different experimental groups yields strongly enhanced statistical power compared to pairwise  t -tests of single temperature time points. Taken together, the kinetic model presented in this study has several advantages compared to simple analysis of individual time points and therefore may be used as a standard method for assessing inflammation-triggered hypothermic response curves in mice.

A Kinetic Response Model for Standardized Regression Analyses of Inflammation-Triggered Hypothermic Body Temperature-Time Courses in Mice