Modeling human heterogeneity of obesity with diversity outbred mice reveals a fat mass‐dependent therapeutic window for resolvin E1

Abstract

Resolvin E1 (RvE1), a specialized pro‐resolving mediator (SPM), improves glucose homeostasis in inbred mouse models of obesity. However, an impediment toward translation is that obesity is a highly heterogenous disease in which individuals will respond very differently to interventions such as RvE1. Thus, there is a need to study SPMs in the context of modeling the heterogeneity of obesity that is observed in humans. We investigated how RvE1 controls the concentration of key circulating metabolic biomarkers using diversity outbred (DO) mice, which mimic human heterogeneity. We first demonstrate that weights of DO mice can be classified into distinct distributions of fat mass (i.e., modeling differing classes of obesity) in response to a high‐fat diet and in the human population when examining body composition. Next, we show RvE1 administration based on body weight for four consecutive days after giving mice a high‐fat diet led to approximately half of the mice responding positively for serum total gastric inhibitory polypeptide (GIP), glucagon, insulin, glucose, leptin, and resistin. Interestingly, RvE1 improved hyperleptinemia most effectively in the lowest class of fat mass despite adjusting the dose of RvE1 with increasing adiposity. Furthermore, leptin levels after RvE1 treatment were the lowest in those mice that were also RvE1 positive responders for insulin and resistin. Collectively, these results suggest a therapeutic fat mass‐dependent window for RvE1, which should be considered in future clinical trials. Moreover, the data underscore the importance of studying SPMs with heterogenous mice as a step toward precision SPM administration in humans.

The Impact of Obesity on Diabetes Onset and Neovascularization in Mouse Models of Metabolic Stress

“…When more than two treatment groups were performed, one-way ANOVA was used across all the groups. If significant, pairs of focused groups were tested with two-sample t-tests, and p values were adjusted with Benjamini Krieger and Yekutieli's method [37]. For continuous variables that failed Shiparo-Wilk normality tests, the statistical significance of differences between the two groups was determined using the Mann-Whitney U test.…”

“…For continuous variables that failed Shiparo-Wilk normality tests, the statistical significance of differences between the two groups was determined using the Mann-Whitney U test. In blood perfusion curve data, the two groups were tested using multiple Mann-Whitney U tests with Benjamini Krieger and Yekutieli's adjustment [37]. When more than two groups of treatments were performed, the Kruskal-Wallis test was applied across all the groups, and if significant, the pairs of primary interest, based on scientific rationale, were assessed using the Mann-Whitney U test with Benjamini Krieger and Yekutieli's adjustment for multiple comparisons [37].…”

“…In blood perfusion curve data, the two groups were tested using multiple Mann-Whitney U tests with Benjamini Krieger and Yekutieli's adjustment [37]. When more than two groups of treatments were performed, the Kruskal-Wallis test was applied across all the groups, and if significant, the pairs of primary interest, based on scientific rationale, were assessed using the Mann-Whitney U test with Benjamini Krieger and Yekutieli's adjustment for multiple comparisons [37]. The significant differences that came from post-hoc comparisons of groups were noted.…”

Heterogeneity in the response to n-3 polyunsaturated fatty acids

“…At a rodent level, the use of collaborative cross and diversity outbred mice and other mouse models will allow us to identify genetic factors that contribute toward positive/negative responders and nonresponders [36,37,38]. As an example, positive/negative responders and nonresponders to a metabolite of EPA were identified with diversity outbred mice [39 ]. The use of multiple omics approaches (RNA sequencing, metabolomics, lipidomics) combined with artificial intelligence and other state-of-the-art statistical approaches seems like an obvious approach to move the field forward identifying new sources of heterogeneity that can then be replicated, tested in diverse populations, and further studied to understand their mechanism of action.…”