A novel and robust method for testing bimodality and characterizing porcine adipocytes of adipose tissue of 5 purebred lines of pig

Isolation method and characterization of adipocytes as a tool for equine obesity research - In vitro study

Adipose tissue functions as an endocrine organ; however, excessive lipid accumulation can lead to obesity and metabolic disorders, such as Equine Metabolic Syndrome (EMS), characterized by insulin resistance, fat deposition, and increased inflammation. Despite the growing prevalence of obesity in horses, knowledge of equine adipocytes and their metabolic functions remains limited. The main objective of the study was to develop and optimize a method for isolating equine adipocytes and to characterize their metabolic activity. Using slaughterhouse-derived horse visceral adipose tissue, we developed a protocol to isolate mature adipocytes. Metabolic activity of cells was assessed by examining their sensitivity to lipolytic factors: isoproterenol (0.001-10 µM), epinephrine (0.001-1 µM), and forskolin (0.001-1 µM)-and lipogenesis intensity after stimulation with insulin. We obtained mature equine adipocytes with diameters ranging from 50 to 160 µm. These cells demonstrated full metabolic functionality, responding to lipolytic factors such as isoproterenol (all doses: p < 0.001), epinephrine (0.01 µM: p < 0.05; 0.1-1 µM: p < 0.0001), and forskolin (0.001 µM: p < 0.0001). The adipocytes also responded to insulin from all tested species, with effects being dose- and time-dependent (after 2 h human insulin 10 nM, p < 0.05; bovine 10, 100 nM p < 0.05 and after 8 h all doses p < 0.05). The presented method for isolating mature equine adipocytes is effective, yielding metabolically functional cells, which can serve as a valuable in vitro model for studying the effects of various factors on adipocyte function, contributing to a better understanding of equine adipose tissue dysfunction, particularly in the context of metabolic disorders.

'Statistical Irreproducibility' Does Not Improve with Larger Sample Size: How to Quantify and Address Disease Data Multimodality in Human and Animal Research

Poor study reproducibility is a concern in translational research. As a solution, it is recommended to increase sample size (N), i.e., add more subjects to experiments. The goal of this study was to examine/visualize data multimodality (data with >1 data peak/mode) as cause of study irreproducibility. To emulate the repetition of studies and random sampling of study subjects, we first used various simulation methods of random number generation based on preclinical published disease outcome data from human gut microbiota-transplantation rodent studies (e.g., intestinal inflammation and univariate/continuous). We first used unimodal distributions (one-mode, Gaussian, and binomial) to generate random numbers. We showed that increasing N does not reproducibly identify statistical differences when group comparisons are repeatedly simulated. We then used multimodal distributions (>1-modes and Markov chain Monte Carlo methods of random sampling) to simulate similar multimodal datasets A and B (t-test-p = 0.95; N = 100,000), and confirmed that increasing N does not improve the ‘reproducibility of statistical results or direction of the effects’. Data visualization with violin plots of categorical random data simulations with five-integer categories/five-groups illustrated how multimodality leads to irreproducibility. Re-analysis of data from a human clinical trial that used maltodextrin as dietary placebo illustrated multimodal responses between human groups, and after placebo consumption. In conclusion, increasing N does not necessarily ensure reproducible statistical findings across repeated simulations due to randomness and multimodality. Herein, we clarify how to quantify, visualize and address disease data multimodality in research. Data visualization could facilitate study designs focused on disease subtypes/modes to help understand person–person differences and personalized medicine.