Influence of a Concurrent Exercise Training Intervention during Pregnancy on Maternal and Arterial and Venous Cord Serum Cytokines: The GESTAFIT Project

The aim of the present study was to analyze the influence of a supervised concurrent exercise-training program, from the 17th gestational week until delivery, on cytokines in maternal (at 17th and 35th gestational week, and at delivery) and arterial and venous cord serum. Fifty-eight Caucasian pregnant women (age: 33.5 +/- 4.7 years old, body mass index: 23.6 +/- 4.1kg/m(2)) from the GESTAFIT Project (exercise (n = 37) and control (n = 21) groups) participated in this quasi-experimental study (per-protocol basis). The exercise group followed a 60-min 3 days/week concurrent (aerobic-resistance) exercise-training from the 17th gestational week to delivery. Maternal and arterial and venous cord serum cytokines (fractalkine, interleukin (IL)-1 beta, IL-6, IL-8, IL-10, interferon (IFN)-gamma, and tumor necrosis factor (TNF)-alpha) were assessed using Luminex xMAP technology. In maternal serum (after adjusting for the baseline values of cytokines), the exercise group decreased TNF-alpha (from baseline to 35th week, p = 0.02), and increased less IL-1 beta (from baseline to delivery, p = 0.03) concentrations than controls. When adjusting for other potential confounders, these differences became non-significant. In cord blood, the exercise group showed reduced arterial IL-6 and venous TNF-alpha (p = 0.03 and p = 0.001, respectively) and higher concentrations of arterial IL-1 beta (p = 0.03) compared to controls. The application of concurrent exercise-training programs could be a strategy to modulate immune responses in pregnant women and their fetuses. However, future research is needed to better understand the origin and clearance of these cytokines, their role in the maternal-placental-fetus crosstalk, and the influence of exercise interventions on them

Polymorphisms in the circadian expressed genes PER3 and ARNTL2 are associated with diurnal preference and GNβ3 with sleep measures

Sleep and circadian rhythms are intrinsically linked, with several sleep traits, including sleep timing and duration, influenced by both sleep homeostasis and the circadian phase. Genetic variation in several circadian genes has been associated with diurnal preference (preference in timing of sleep), although there has been limited research on whether they are associated with other sleep measurements. We investigated whether these genetic variations were associated with diurnal preference (Morningness-Eveningness Questionnaire) and various sleep measures, including: the global Pittsburgh Sleep Quality index score; sleep duration; and sleep latency and sleep quality. We genotyped 10 polymorphisms in genes with circadian expression in participants from the G1219 sample (n = 966), a British longitudinal population sample of young adults. We conducted linear regressions using dominant, additive and recessive models of inheritance to test for associations between these polymorphisms and the sleep measures. We found a significant association between diurnal preference and a polymorphism in period homologue 3 (PER3) (P < 0.005, recessive model) and a novel nominally significant association between diurnal preference and a polymorphism in aryl hydrocarbon receptor nuclear translocator-like 2 (ARNTL2) (P < 0.05, additive model). We found that a polymorphism in guanine nucleotide binding protein beta 3 (GNβ3) was associated significantly with global sleep quality (P < 0.005, recessive model), and that a rare polymorphism in period homologue 2 (PER2) was associated significantly with both sleep duration and quality (P < 0.0005, recessive model). These findings suggest that genes with circadian expression may play a role in regulating both the circadian clock and sleep homeostasis, and highlight the importance of further studies aimed at dissecting the specific roles that circadian genes play in these two interrelated but unique behaviours

Relationships between estimated autozygosity and complex traits in the UK Biobank

<div><p>Inbreeding increases the risk of certain Mendelian disorders in humans but may also reduce fitness through its effects on complex traits and diseases. Such inbreeding depression is thought to occur due to increased homozygosity at causal variants that are recessive with respect to fitness. Until recently it has been difficult to amass large enough sample sizes to investigate the effects of inbreeding depression on complex traits using genome-wide single nucleotide polymorphism (SNP) data in population-based samples. Further, it is difficult to infer causation in analyses that relate degree of inbreeding to complex traits because confounding variables (e.g., education) may influence both the likelihood for parents to outbreed and offspring trait values. The present study used runs of homozygosity in genome-wide SNP data in up to 400,000 individuals in the UK Biobank to estimate the proportion of the autosome that exists in autozygous tracts—stretches of the genome which are identical due to a shared common ancestor. After multiple testing corrections and controlling for possible sociodemographic confounders, we found significant relationships in the predicted direction between estimated autozygosity and three of the 26 traits we investigated: age at first sexual intercourse, fluid intelligence, and forced expiratory volume in 1 second. Our findings corroborate those of several published studies. These results may imply that these traits have been associated with Darwinian fitness over evolutionary time. However, some of the autozygosity-trait relationships were attenuated after controlling for background sociodemographic characteristics, suggesting that alternative explanations for these associations have not been eliminated. Care needs to be taken in the design and interpretation of ROH studies in order to glean reliable information about the genetic architecture and evolutionary history of complex traits.</p></div

Spike-and-Slab Priors for Function Selection in Structured Additive Regression Models

Structured additive regression provides a general framework for complex Gaussian and non-Gaussian regression models, with predictors comprising arbitrary combinations of nonlinear functions and surfaces, spatial effects, varying coefficients, random effects and further regression terms. The large flexibility of structured additive regression makes function selection a challenging and important task, aiming at (1) selecting the relevant covariates, (2) choosing an appropriate and parsimonious representation of the impact of covariates on the predictor and (3) determining the required interactions. We propose a spike-and-slab prior structure for function selection that allows to include or exclude single coefficients as well as blocks of coefficients representing specific model terms. A novel multiplicative parameter expansion is required to obtain good mixing and convergence properties in a Markov chain Monte Carlo simulation approach and is shown to induce desirable shrinkage properties. In simulation studies and with (real) benchmark classification data, we investigate sensitivity to hyperparameter settings and compare performance to competitors. The flexibility and applicability of our approach are demonstrated in an additive piecewise exponential model with time-varying effects for right-censored survival times of intensive care patients with sepsis. Geoadditive and additive mixed logit model applications are discussed in an extensive appendix

Circulating miRNAs as predictive biomarkers of type 2 diabetes mellitus development in coronary heart disease patients fromt he CORDIOPREV study

Circulating microRNAs (miRNAs) have been proposed as type 2 diabetes biomarkers, and they may be a more sensitive way to predict development of the disease than the currently used tools. Our aim was to identify whether circulating miRNAs, added to clinical and biochemical markers, yielded better potential for predicting type 2 diabetes. The study included 462 non-diabetic patients at baseline in the CORDIOPREV study. After a median follow-up of 60 months, 107 of them developed type 2 diabetes. Plasma levels of 24 miRNAs were measured at baseline by qRT-PCR, and other strong biomarkers to predict diabetes were determined. The ROC analysis identified 9 miRNAs, which, added to HbA1c, have a greater predictive value in early diagnosis of type 2 diabetes (AUC = 0.8342) than HbA1c alone (AUC = 0.6950). The miRNA and HbA1cbased model did not improve when the FINDRISC was included (AUC = 0.8293). Cox regression analyses showed that patients with low miR-103, miR-28-3p, miR-29a, and miR-9 and high miR-30a-5p and miR-150 circulating levels have a higher risk of disease (HR = 11.27; 95% CI = 2.61–48.65). Our results suggest that circulating miRNAs could potentially be used as a new tool for predicting the development of type 2 diabetes in clinical practice

Generalized Functional Additive Mixed Models

We propose a comprehensive framework for additive regression models for non-Gaussian functional responses, allowing for multiple (partially) nested or crossed functional random effects with flexible correlation structures for, e.g., spatial, temporal, or longitudinal functional data as well as linear and nonlinear effects of functional and scalar covariates that may vary smoothly over the index of the functional response. Our implementation handles functional responses from any exponential family distribution as well as many others like Beta- or scaled non-central $t$-distributions. Development is motivated by and evaluated on an application to large-scale longitudinal feeding records of pigs. Results in extensive simulation studies as well as replications of two previously published simulation studies for generalized functional mixed models demonstrate the good performance of our proposal. The approach is implemented in well-documented open source software in the "pffr()" function in R-package "refund"