Childhood maltreatment is associated with changes in mitochondrial bioenergetics in maternal, but not in neonatal immune cells.

Childhood maltreatment (CM) comprises experiences of abuse and neglect during childhood. CM causes psychological as well as biological alterations in affected individuals. In humans, it is hardly explored whether these CM consequences can be transmitted directly on a biological level to the next generation. Here, we investigated the associations between maternal CM and mitochondrial bioenergetics (mitochondrial respiration and intracellular mitochondrial density) in immune cells of mothers and compared them with those of their newborns. In n = 102 healthy mother-newborn dyads, maternal peripheral blood mononuclear cells and neonatal umbilical cord blood mononuclear cells were collected and cryopreserved shortly after parturition to measure mitochondrial respiration and intracellular mitochondrial density with high-resolution respirometry and spectrophotometric analyses, respectively. Maternal CM was assessed with the Childhood Trauma Questionnaire Maternal and neonatal mitochondrial bioenergetics were quantitatively comparable and positively correlated. Female newborns showed higher mitochondrial respiration compared to male newborns. Maternal CM load was significantly and positively associated with mitochondrial respiration and density in mothers, but not with mitochondrial respiration in newborns. Although maternal and neonatal mitochondrial bioenergetics were positively correlated, maternal CM only had a small effect on mitochondrial density in newborns, which was not significant in this study after adjustment for multiple comparisons. The biological relevance of our finding and its consequences for child development need further investigation in future larger studies. This study reports data on mitochondrial bioenergetics of healthy mother-newborn dyads with varying degrees of CM.

The consequences of neglected confounding and interactions in mixed-effects meta-regression: An illustrative example.

Analysts seldom include interaction terms in their meta-regression model, which can introduce bias if an interaction is present. We illustrate this by reanalysing a meta-regression study in acute heart failure. Based on a total of 285 studies, the 1-year mortality rate related to acute heart failure is considered and the connection to the study-level covariates year of recruitment and average age of study participants are of interest. We show that neglecting a possibly confounding variable and an interaction term might lead to erroneous inference and conclusions. Based on our results and accompanying simulations, we recommend to include possible confounders and interaction terms, whenever they are plausible, in mixed-effects meta-regression models.

Fisher transformation based confidence intervals of correlations in fixed- and random-effects meta-analysis.

Meta-analyses of correlation coefficients are an important technique to integrate results from many cross-sectional and longitudinal research designs. Uncertainty in pooled estimates is typically assessed with the help of confidence intervals, which can double as hypothesis tests for two-sided hypotheses about the underlying correlation. A standard approach to construct confidence intervals for the main effect is the Hedges-Olkin-Vevea Fisher-z (HOVz) approach, which is based on the Fisher-z transformation. Results from previous studies (Field, 2005, Psychol. Meth., 10, 444; Hafdahl and Williams, 2009, Psychol. Meth., 14, 24), however, indicate that in random-effects models the performance of the HOVz confidence interval can be unsatisfactory. To this end, we propose improvements of the HOVz approach, which are based on enhanced variance estimators for the main effect estimate. In order to study the coverage of the new confidence intervals in both fixed- and random-effects meta-analysis models, we perform an extensive simulation study, comparing them to established approaches. Data were generated via a truncated normal and beta distribution model. The results show that our newly proposed confidence intervals based on a Knapp-Hartung-type variance estimator or robust heteroscedasticity consistent sandwich estimators in combination with the integral z-to-r transformation (Hafdahl, 2009, Br. J. Math. Stat. Psychol., 62, 233) provide more accurate coverage than existing approaches in most scenarios, especially in the more appropriate beta distribution simulation model.

A simulation study to compare robust tests for linear mixed-effects meta-regression.

The explanation of heterogeneity when synthesizing different studies is an important issue in meta-analysis. Besides including a heterogeneity parameter in the statistical model, it is also important to understand possible causes of between-study heterogeneity. One possibility is to incorporate study-specific covariates in the model that account for between-study variability. This leads to linear mixed-effects meta-regression models. A number of alternative methods have been proposed to estimate the (co)variance of the estimated regression coefficients in these models, which subsequently drives differences in the results of statistical methods. To quantify this, we compare the performance of hypothesis tests for moderator effects based upon different heteroscedasticity consistent covariance matrix estimators and the (untruncated) Knapp-Hartung method in an extensive simulation study. In particular, we investigate type 1 error and power under varying conditions regarding the underlying distributions, heterogeneity, effect sizes, number of independent studies, and their sample sizes. Based upon these results, we give recommendations for suitable inference choices in different scenarios and highlight the danger of using tests regarding the study-specific moderators based on inappropriate covariance estimators.