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

ABSTRACT: 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.

SUBMITTER: Diebner HH

PROVIDER: S-EPMC8421519 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Presently, for patients presenting with suspected Normal Pressure Hydrocephalus (NPH) who undergo temporary drainage of cerebrospinal fluid (CSF) there is no defined model to differentiate chance improvement form clinical significance change at the individual patient level. To address this lack of information we computed standard regression based clinical change models for the 10 Meter Walk Test, Timed Up & Go, Dual Timed Up & Go, 6-Minute Walk Test, Mini-Balance Evaluation Systems Test, Montreal Cognitive Assessment, and Symbol Digit Modalities using data from patients with suspected NPH that underwent temporary drainage of CSF. These clinically significant change modes can classify clinically significant improvement following temporary drainage of CSF at the individual patient level. This allows for physicians to differentiate a clinically significant improvement in symptoms from chance improvement. METHODS:Data was collected from 323 patients, over the age of 60, with suspected NPH that underwent temporary drainage of CSF with corresponding gait and cognitive testing. McSweeney Standardized Regression Based Clinical Change Models were computed for standard gait and cognitive measures: Timed Up & Go, Dual Timed Up & Go, 10 Meter Walk Test, MiniBESTest, 6-Minute Walk Test, Montreal Cognitive Assessment, and Symbol Digit Modalities Test. To assess the discriminate validity of the measures we used correlations, Chi2, and regression analyses. RESULTS:The clinical change models explained 69-91.8% of the variability in post-drain performance (p?<? 0.001). As patient scores became more impaired, the percent change required for improvement to be clinically significant increased for all measures. We found that the measures were not discriminate, the Timed Up & Go was highly related to the 10 Meter Walk Test (r?=?0.85, R2?=?0.769-0.738, p?<? 0.001), MiniBESTest (r?=?-?0.67, R2?=?0.589-0.734, p?<? 0.001), and 6?Minute Walk Test (r?=?-?0.77, R2?=?0.71-0.734, p?<? 0.001). CONCLUSION:Standardized Regression Based Clinically Significant Change Models allow for physicians to use an evidence-based approach to differentiate clinically significant change from chance improvement at the individual patient level. The Timed Up & Go was shown to be predictive of detailed measures of gait velocity, balance, and endurance.

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

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