Incretin Therapies Do Not Expand ß-Cell Mass or Alter Pancreatic Histology in Young Male Mice.

The impact of incretins upon pancreatic ß-cell expansion remains extremely controversial. Multiple studies indicate that incretin-based therapies can increase ß-cell proliferation, and incretins have been hypothesized to expand ß-cell mass. However, disagreement exists on whether incretins increase ß-cell mass. Moreover, some reports indicate that incretins may cause metaplastic changes in pancreatic histology. To resolve these questions, we treated a large cohort of mice with incretin-based therapies and carried out a rigorous analysis of ß-cell turnover and pancreatic histology using high-throughput imaging. Young mice received exenatide via osmotic pump, des-fluoro-sitagliptin, or glipizide compounded in diet for 2 weeks (short-term) on a low-fat diet (LFD) or 4.5 months (long-term) on a LFD or high-fat diet (HFD). Pancreata were quantified for ß-cell turnover and mass. Slides were examined for gross anatomical and microscopic changes to exocrine pancreas. Short-term des-fluoro-sitagliptin increased serum insulin and induced modest ß-cell proliferation but no change in ß-cell mass. Long-term incretin therapy in HFD-fed mice resulted in reduced weight gain, improved glucose homeostasis, and abrogated ß-cell mass expansion. No evidence for rapidly dividing progenitor cells was found in islets or pancreatic parenchyma, indicating that incretins do not induce islet neogenesis or pancreatic metaplasia. Contrasting prior reports, we found no evidence of ß-cell mass expansion after acute or chronic incretin therapy. Chronic incretin administration was not associated with histological abnormalities in pancreatic parenchyma; mice did not develop tumors, pancreatitis, or ductal hyperplasia. We conclude that incretin therapies do not generate ß-cells or alter pancreatic histology in young mice.

Modern methods for longitudinal data analysis, capabilities, caveats and cautions.

Longitudinal studies are used in mental health research and services studies. The dominant approaches for longitudinal data analysis are the generalized linear mixed-effects models (GLMM) and the weighted generalized estimating equations (WGEE). Although both classes of models have been extensively published and widely applied, differences between and limitations about these methods are not clearly delineated and well documented. Unfortunately, some of the differences and limitations carry significant implications for reporting, comparing and interpreting research findings. In this report, we review both major approaches for longitudinal data analysis and highlight their similarities and major differences. We focus on comparison of the two classes of models in terms of model assumptions, model parameter interpretation, applicability and limitations, using both real and simulated data. We discuss caveats and cautions when applying the two different approaches to real study data.