Long-term maintenance of healthy lifestyle practices in DPP translation: Evaluation of adherence, barriers, and strategies

The Diabetes Prevention Program (DPP) demonstrated that type 2 diabetes can be prevented or delayed through behavioral lifestyle modification. Due to the success of the DPP lifestyle intervention, multiple effective translation efforts have been completed in the community; however long-term data regarding healthy lifestyle practices after such intervention is lacking. The current study aimed to assess long-term adherence and barriers to healthy lifestyle goals among participants who had completed a DPP translation study. In addition, participants’ perception regarding strategies for long term maintenance of healthy lifestyle practices was assessed. A total of 156 individuals who attended ≥ 4/16 of the adapted DPP program core sessions and had not formally withdrawn from the study were contacted and asked to complete a brief survey; 73 (47%) individuals completed the survey and 65 (42%) had data for all assessment time-points (baseline, 6MO, 12MO and follow-up 24-36MO). Mean weight loss for this group at 6-months from baseline was 6.2 kg (-6.6%), and at 12-months was 6.1 kg (-6.4%); self-reported weight at follow up was 4.8 kg (-5.1%). Approximately 62% of participants reported increased physical activity levels at 6-months from baseline (+95.0 min/week (60.0, 135.0), 60% at 12-months (+90.0 min/week (60.0, 150.0), and 48% at follow-up (+90 min/week (45.0, 225.0). Frequently reported barriers for maintaining or reaching healthy eating and physical activity goals were self-motivation and time/scheduling issues; injury/illness was another frequently reported barrier for physical activity. The strategies reported most frequently as useful for long-term maintenance of healthy practices included in-person meetings and self-monitoring of fat/calorie intake. These results suggest that although weight loss and increased physical activity continued to be observed over time in this group, some weight regain and decrease in physical activity occurred. The current study is important from a public health perspective as it is the first DPP-based translation follow-up study to provide long-term information about adherence and perceived barriers in individuals who have completed an adapted DPP intervention 24-36 months from baseline. This information and the strategies identified will aid in development of programs to promote long-term healthy lifestyle practices after completing a community DPP intervention

Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data

Antimicrobial peptides (AMPs) are a heterogeneous group of short polypeptides that target not only microorganisms but also viruses and cancer cells. Due to their lower selection for resistance compared with traditional antibiotics, AMPs have been attracting the ever-growing attention from researchers, including bioinformaticians. Machine learning represents the most cost-effective method for novel AMP discovery and consequently many computational tools for AMP prediction have been recently developed. In this article, we investigate the impact of negative data sampling on model performance and benchmarking. We generated 660 predictive models using 12 machine learning architectures, a single positive data set and 11 negative data sampling methods; the architectures and methods were defined on the basis of published AMP prediction software. Our results clearly indicate that similar training and benchmark data set, i.e. produced by the same or a similar negative data sampling method, positively affect model performance. Consequently, all the benchmark analyses that have been performed for AMP prediction models are significantly biased and, moreover, we do not know which model is the most accurate. To provide researchers with reliable information about the performance of AMP predictors, we also created a web server AMPBenchmark for fair model benchmarking. AMP Benchmark is available at http://BioGenies.info/AMPBenchmark

Extrapolation of Survival Data Using a Bayesian Approach:A Case Study Leveraging External Data from Cilta-Cel Therapy in Multiple Myeloma

Introduction: Extrapolating long-term overall survival (OS) from shorter-term clinical trial data is key to health technology assessment in oncology. However, extrapolation using conventional methods is often subject to uncertainty. Using ciltacabtagene autoleucel (cilta-cel), a chimeric antigen receptor T-cell therapy for multiple myeloma, we used a flexible Bayesian approach to demonstrate use of external longer-term data to reduce the uncertainty in long-term extrapolation. Methods: The pivotal CARTITUDE-1 trial (NCT03548207) provided the primary efficacy data for cilta-cel, including a 12-month median follow-up snapshot of OS. Longer-term (48-month median follow-up) survival data from the phase I LEGEND-2 study (NCT03090659) were also available. Twelve-month CARTITUDE-1 OS data were extrapolated in two ways: (1) conventional survival models with standard parametric distributions (uninformed), and (2) Bayesian survival models whose shape prior was informed from 48-month LEGEND-2 data. For validation, extrapolations from 12-month CARTITUDE-1 data were compared with observed 28-month CARTITUDE-1 data. Results: Extrapolations of the 12-month CARTITUDE-1 data using conventional uninformed parametric models were highly variable. Using informative priors from the 48-month LEGEND-2 dataset, the ranges of projected OS at different timepoints were consistently narrower. Area differences between the extrapolation curves and the 28-month CARTITUDE-1 data were generally lower in informed Bayesian models, except for the uninformed log-normal model, which had the lowest difference. Conclusions: Informed Bayesian survival models reduced variation of long-term projections and provided similar projections as the uninformed log-normal model. Bayesian models generated a narrower and more plausible range of OS projections from 12-month data that aligned with observed 28-month data. Trial Registration: CARTITUDE-1 ClinicalTrials.gov identifier, NCT03548207. LEGEND-2 ClinicalTrials.gov identifier, NCT03090659, registered retrospectively on 27 March 2017, and ChiCTR-ONH-17012285

Systemic response to rupture of intracranial aneurysms involves expression of specific gene isoforms

Abstract Background Rupture of an intracranial aneurysm (IA) causes a systemic response that involves an immune/inflammatory reaction. Our previous study revealed a downregulation of genes related to T lymphocytes and an upregulation of genes related to monocytes and neutrophils after IA rupture. It remains unknown whether that resulted from alterations in transcription or cell count. We sought to characterize the systemic response to IA rupture through analysis of transcript expression profiles in peripheral blood cells. We also investigated effects of IA rupture on the composition of mononuclear cells in peripheral blood. Methods We included 19 patients in the acute phase of IA rupture (RAA, first 72 h), 20 patients in the chronic phase (RAC, 3–15 months), and 20 controls. Using deep transcriptome sequencing, we analyzed the expression of protein-coding and noncoding RNAs. Expression levels, transcript biotypes, alternative splicing and other features of the regulated transcripts were studied. A functional analysis was performed to determine overrepresented ontological groups among gene expression profiles. Flow cytometry was used to analyze alterations in the level of mononuclear leukocyte subpopulations. Results Comparing RAA and controls, we identified 491 differentially expressed transcripts (303 were downregulated, and 188 were upregulated in RAA). The results indicate that the molecular changes in response to IA rupture occur at the level of individual transcripts. Functional analysis revealed that the most impacted biological processes are related to regulation of lymphocyte activation and toll-like receptor signaling pathway. Differences between RAC and controls were less prominent. Analysis of leukocyte subsets revealed a significantly decreased number of CD4+ lymphocytes and increase of classical and intermediate monocytes in RAA patients compared to controls. Conclusions IA rupture in the acute phase strongly influences the transcription profiles of peripheral blood cells as well as the composition of mononuclear cells. A specific pattern of gene expression alteration was found, suggesting a depression of lymphocyte response and enhancement of monocyte activity