The role of AT1 receptor autoantibodies in the pathophysiology of preeclampsia

Preeclampsia is a disease that affects 3–5% of all pregnancies. The cause is unknown and there is currently no treatment. The disease poses significant health risks to both the mother and the fetus. To date, research on the topic has not produced a convincing cause for the development of the hallmark symptoms of preeclampsia. The hypothesis of an agonistic autoimmune response to the AT1 receptor is presented. Immunoglobulin fractions from normotensive and preeclampsia patients were prepared for experimental tests. Model systems were tested in three categories to determine if AT 1 receptor specific activation and receptor-ligand interaction was caused by a suspected autoantibody. Activation was found in rat neonatal cardiornyocytes that caused an increased contraction rate. This activity was found in preeclampsia patients, absent in normotensive patients. The activation was antagonized by losartan, an AT1 receptor antagonist, and by epitope peptide competition of the receptor-ligand type interaction. This epitope was the 7 amino acid peptide fragment, AFHYESQ, a sequence present in the second extracellular loop of the AT1 receptor. The patterns of AT1 receptor activation were also found in a human trophoblast cell line, HTR8, with an effect on Pai-1 secretion, a factor that plays a role in preventing hypercoagulation. In human mesangial cells, the AT1 receptor autoantibody present in the immunoglobulin fraction from preeclampsia patients was found to stimulate the secretion of Pai-1, and IL-6, a factor that plays a role in the activation of an inflammatory response. This activity was found in samples from preeclampsia patients, but absent in normotensive patients. Tests including losartan, AFHYESQ, and a non-competitive peptide demonstrated that the secretion of Pai-1 and IL-6 met the criteria for AT1 receptor activation by the suspected agonistic autoantibody. These three model systems address relevant pathophysiology for preeclampsia patients, including increased cardiac output, abnormal placentation, and renal damage. The AT1 receptor agonistic autoantibody is potentially a key player in the development of the pathology and symptoms of preeclampsia. ^

Motivational interviewing and feedback for college drinkers: Handling missing values with complete case analysis and multiple imputation

Objective: In this secondary data analysis, three statistical methodologies were implemented to handle cases with missing data in a motivational interviewing and feedback study. The aim was to evaluate the impact that these methodologies have on the data analysis. ^ Methods: We first evaluated whether the assumption of missing completely at random held for this study. We then proceeded to conduct a secondary data analysis using a mixed linear model to handle missing data with three methodologies (a) complete case analysis, (b) multiple imputation with explicit model containing outcome variables, time, and the interaction of time and treatment, and (c) multiple imputation with explicit model containing outcome variables, time, the interaction of time and treatment, and additional covariates (e.g., age, gender, smoke, years in school, marital status, housing, race/ethnicity, and if participants play on athletic team). Several comparisons were conducted including the following ones: 1) the motivation interviewing with feedback group (MIF) vs. the assessment only group (AO), the motivation interviewing group (MIO) vs. AO, and the intervention of the feedback only group (FBO) vs. AO, 2) MIF vs. FBO, and 3) MIF vs. MIO.^ Results: We first evaluated the patterns of missingness in this study, which indicated that about 13% of participants showed monotone missing patterns, and about 3.5% showed non-monotone missing patterns. Then we evaluated the assumption of missing completely at random by Little's missing completely at random (MCAR) test, in which the Chi-Square test statistic was 167.8 with 125 degrees of freedom, and its associated p-value was p=0.006, which indicated that the data could not be assumed to be missing completely at random. After that, we compared if the three different strategies reached the same results. For the comparison between MIF and AO as well as the comparison between MIF and FBO, only the multiple imputation with additional covariates by uncongenial and congenial models reached different results. For the comparison between MIF and MIO, all the methodologies for handling missing values obtained different results. ^ Discussions: The study indicated that, first, missingness was crucial in this study. Second, to understand the assumptions of the model was important since we could not identify if the data were missing at random or missing not at random. Therefore, future researches should focus on exploring more sensitivity analyses under missing not at random assumption.^