Fructose Rich Diet-Induced High Plasminogen Activator Inhibitor-1 (PAI-1) Production in the Adult Female Rat: Protective Effect of Progesterone.

The effect of progesterone (P4) on fructose rich diet (FRD) intake-induced metabolic, endocrine and parametrial adipose tissue (PMAT) dysfunctions was studied in the adult female rat. Sixty day-old rats were i.m. treated with oil alone (control, CT) or containing P4 (12 mg/kg). Rats ate Purina chow-diet ad libitum throughout the entire experiment and, between 100 and 120 days of age drank ad libitum tap water alone (normal diet; CT-ND and P4-ND) or containing fructose (10% w/v; CT-FRD and P4-FRD). At age 120 days, animals were subjected to a glucose tolerance test or decapitated. Plasma concentrations of various biomarkers and PMAT gene abundance were monitored. P4-ND (vs. CT-ND) rats showed elevated circulating levels of lipids. CT-FRD rats displayed high (vs. CT-ND) plasma concentrations of lipids, leptin, adiponectin and plasminogen activator inhibitor-1 (PAI-1). Lipidemia and adiponectinemia were high (vs. P4-ND) in P4-FRD rats. Although P4 failed to prevent FRD-induced hyperleptinemia, it was fully protective on FRD-enhanced plasma PAI-1 levels. PMAT leptin and adiponectin mRNAs were high in CT-FRD and P4-FRD rats. While FRD enhanced PMAT PAI-1 mRNA abundance in CT rats, this effect was absent in P4 rats. Our study supports that a preceding P4-enriched milieu prevented the enhanced prothrombotic risk induced by FRD-elicited high PAI-1 production.

Robust Estimators of the Generalized Log-Gamma Distribution

We propose robust estimators of the generalized log-gamma distribution and, more generally, of location-shape-scale families of distributions. A (weighted) Q tau estimator minimizes a tau scale of the differences between empirical and theoretical quantiles. It is n(1/2) consistent; unfortunately, it is not asymptotically normal and, therefore, inconvenient for inference. However, it is a convenient starting point for a one-step weighted likelihood estimator, where the weights are based on a disparity measure between the model density and a kernel density estimate. The one-step weighted likelihood estimator is asymptotically normal and fully efficient under the model. It is also highly robust under outlier contamination. Supplementary materials are available online.

Robust Estimates of the Negative Binomial Model

We consider robust parametric procedures for univariate discrete distributions, focusing on the negative binomial model. The procedures are based on three steps: ?First, a very robust, but possibly inefficient, estimate of the model parameters is computed. ?Second, this initial model is used to identify outliers, which are then removed from the sample. ?Third, a corrected maximum likelihood estimator is computed with the remaining observations. The final estimate inherits the breakdown point (bdp) of the initial one and its efficiency can be significantly higher. Analogous procedures were proposed in [1], [2], [5] for the continuous case. A comparison of the asymptotic bias of various estimates under point contamination points out the minimum Neyman's chi-squared disparity estimate as a good choice for the initial step. Various minimum disparity estimators were explored by Lindsay [4], who showed that the minimum Neyman's chi-squared estimate has a 50% bdp under point contamination; in addition, it is asymptotically fully efficient at the model. However, the finite sample efficiency of this estimate under the uncontaminated negative binomial model is usually much lower than 100% and the bias can be strong. We show that its performance can then be greatly improved using the three step procedure outlined above. In addition, we compare the final estimate with the procedure described in