Association Between Dipeptidyl Peptidase-4 Inhibitor Use and Cognitive Functions, Brain-Derived Neurotrophic Factor, and Pentraxin-3 Levels in Patients With Type 2 Diabetes.

Background Diabetes mellitus is an important risk factor for dementia, Alzheimer's disease, and other neurodegenerative diseases. Recent findings have made the relationship between the inhibition of the dipeptidyl peptidase-4 (DPP-4) enzyme and cognitive functions an important research topic. Objective This study aimed to evaluate the association between DPP-4 inhibitor use and cognitive functions, serum brain-derived neurotrophic factor (BDNF), and pentraxin-3 (PTX-3) levels in patients with type 2 diabetes, compared with the patients who only use metformin treatment. Design, patients, and methods A total of 50 patients with type 2 diabetes (hemoglobin A1c levels at ≤%7.5) who were under treatment with metformin±DPP-4 inhibitor (n=25) or only metformin (n=25) were included in this cross-sectional study. Serum BDNF and PTX-3 levels were assessed using an enzyme-linked immunosorbent assay. A standardized mini-mental test (sMMSE) was used to evaluate cognitive functions. Results There were no significant differences in the characteristics of the study groups. The mean sMMSE score of the patients receiving DPP-4±metformin treatment was statistically higher when compared with patients receiving only metformin treatment (27.16±1.95 vs. 25.40±3.07; p=0.041). The BDNF levels of the patients receiving DPP-4±metformin treatment were considerably higher than the patients receiving only metformin treatment (394.51±205.66 ng/ml vs. 180.63±297.94 ng/ml; p=0.001). The difference in PTX-3 levels between study groups was not statistically significant (5.47±3.44 vs. 3.79±2.53; p=0.055). Conclusion When compared to metformin alone, the use of DPP-4 inhibitors in the treatment of patients with type 2 diabetes was associated with increased serum BDNF levels and improved cognitive functions.

Can the Gut Microbiota Serve as a Guide to the Diagnosis and Treatment of Childhood Epilepsy?

BACKGROUND: To investigate the activity of the gut-brain axis in the pathogenesis of childhood epilepsy and to define biomarkers capable of assisting with determining new strategies in that context. METHODS: Twenty children with epilepsy of "unknown etiology" and seven healthy controls in the same age group were included in the study. The groups were compared using a questionnaire. Stool samples were stored in tubes containing DNA/RNA Shield (Zymo Research) with a sterile swab. Sequencing was carried out using the MiSeq System (Illumina). The 16S rRNA sequencing of samples using next-generation sequencing involved V4 variable region polymerase chain reaction amplification concluded by 2 × 250-bp paired-end sequencing of amplicons and at least 50,000 reads (>Q30) per sample. DNA sequences were classified at the genus level using the Kraken program. Bioinformatics and statistical analysis were then performed. RESULTS: Individuals' gut microbiota relative abundance values differed between the groups at the genus, order, class, family, and phylum levels. Flavihumibacter, Niabella, Anoxybacillus, Brevundimonas, Devosia, and Delftia were seen only in the control group, whereas Megamonas and Coriobacterium were observed only in the epilepsy group. The linear discriminant analysis effect size method identified 33 taxa as important in differentiating the groups. CONCLUSIONS: We think that bacterial varieties (such as Megamonas and Coriobacterium) that differ between the two groups can be employed as useful biomarkers in the diagnosis and follow-up of epileptic patients. We also predict that, in addition to epilepsy treatment protocols, the restoration of eubiotic microbiota may increase the success of treatment.

Lowering Risk for Significant Behavior Problems Through Cognitive-Behavioral Intervention: Effects of the Tools for Getting Along Curriculum 2 Years Following Implementation.

To address the needs of students at risk for significant behavior problems, educators need efficient, effective, and feasible preventive classroom interventions that increase students' ability to regulate their own behavior. Tools for Getting Along is a universally delivered cognitive-behavioral curriculum designed to address early emotional and behavioral risk among fourth and fifth grade students within the general classroom setting. We used latent growth model statistical methodology to investigate the effects of Tools for Getting Along 2 years following treatment cessation on students who evidenced baseline risk relative to peers. We followed an average of 455 students across measure-specific baseline risk groups at pretest, posttest, 1-year post-treatment, and 2-years post-treatment. Growth models fit data for four (behavior regulation, metacognition, aggression, and behavioral adjustment) of the eight factors used to assess emotional and behavioral outcomes. Findings indicated a long-term positive treatment effect for students at baseline risk on behavior regulation and general behavioral adjustment. We discuss how findings related to long-term treatment benefits add to prior research on Tools for Getting Along and to the evaluation of preventive treatment effects on emotional and behavioral risk over time.

Comparison of model- and design-based approaches to detect the treatment effect and covariate by treatment interactions in three-level models for multisite cluster-randomized trials.

In this study, we evaluated the estimation of three important parameters for data collected in a multisite cluster-randomized trial (MS-CRT): the treatment effect, and the treatment by covariate interactions at Levels 1 and 2. The Level 1 and Level 2 interaction parameters are the coefficients for the products of the treatment indicator, with the covariate centered on its Level 2 expected value and with the Level 2 expected value centered on its Level 3 expected value, respectively. A comparison of a model-based approach to design-based approaches was performed using simulation studies. The results showed that both approaches produced similar treatment effect estimates and interaction estimates at Level 1, as well as similar Type I error rates and statistical power. However, the estimate of the Level 2 interaction coefficient for the product of the treatment indicator and an arithmetic mean of the Level 1 covariate was severely biased in most conditions. Therefore, applied researchers should be cautious when using arithmetic means to form a treatment by covariate interaction at Level 2 in MS-CRT data.

The Effects of Including Observed Means or Latent Means as Covariates in Multilevel Models for Cluster Randomized Trials.

We investigated methods of including covariates in two-level models for cluster randomized trials to increase power to detect the treatment effect. We compared multilevel models that included either an observed cluster mean or a latent cluster mean as a covariate, as well as the effect of including Level 1 deviation scores in the model. A Monte Carlo simulation study was performed manipulating effect sizes, cluster sizes, number of clusters, intraclass correlation of the outcome, patterns of missing data, and the squared correlations between Level 1 and Level 2 covariates and the outcome. We found no substantial difference between models with observed means or latent means with respect to convergence, Type I error rates, coverage, and bias. However, coverage could fall outside of acceptable limits if a latent mean is included as a covariate when cluster sizes are small. In terms of statistical power, models with observed means performed similarly to models with latent means, but better when cluster sizes were small. A demonstration is provided using data from a study of the Tools for Getting Along intervention.

Preventing risk for significant behavior problems through a cognitive-behavioral intervention: effects of the tools for getting along curriculum at one-year follow-up.

Efficient and effective social-emotional learning programs increase the likelihood of success in school for all students, and particularly for those who may develop emotional or behavior problems. In this study, we followed a sub-sample of students 1 year after their participation in a randomized controlled trial of the effects of the Tools for Getting Along (TFGA) curriculum. TFGA is a universally delivered, preventive cognitive-behavioral curricular intervention designed to improve upper elementary school students' emotional and behavioral self-regulation. To determine effects at 1-year follow-up, we assessed 720 out of the 1,296 original students across TFGA and control conditions on measures of curricular knowledge, teacher-rated executive function and behavior, and student-reported anger and social problem solving. Findings indicated a continued positive effect on curricular knowledge for students taught TFGA relative to controls. We also found significant pretest by condition interaction effects on teacher reports of skills associated with executive function, including inhibitory control and shift (cognitive flexibility), and on teacher reported internalizing and externalizing behavior. Specifically, students with poorer scores on these measures at pretest benefited from TFGA at follow-up relative to comparable students in the control condition. Finally, we found marginally significant pretest by condition interaction effects on proactive aggression, outward expressions of anger, and the executive function related skills of initiating activities and using working memory. Counter to expectations, we found negative TFGA effects on student-reported trait anger and anger control.

The Consequences of Ignoring Variability in Measurement Occasions Within Data Collection Waves in Latent Growth Models.

In longitudinal data collection, it is common that each wave of collection spans several months. However, researchers using latent growth models commonly ignore variability in data collection occasions within a wave. In this study, we investigated the consequences of ignoring within-wave variability in measurement occasions using a Monte Carlo simulation and an empirical study. The results of the simulation study showed that ignoring heterogeneity resulted in biased estimates for some parameters, especially when heterogeneity was large and assessment dates had a skewed distribution. Models constructed on person-specific time points yielded precise estimates and more adequate model fit. In the empirical study, we demonstrated different time coding strategies with a subsample taken from Early Childhood Longitudinal Study Kindergarten Cohort.