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Background: Evidence of an inverse association between serum vitamin D and mortality from epidemiological studies has prompted efforts to reduce mortality by vitamin D supplementation, either in targeted interventions for people with vitamin D insufficiency or deficiency, or in untargeted interventions regardless of baseline vitamin D status.Objective: We aimed to assess the expected impact of the 2 different approaches on effect sizes and power of intervention studies.Methods: Serum concentrations of 25-hydroxyvitamin D [25(OH)D] were measured in 9579 participants aged 50-75 y in the German Epidemiologische Studie zu Chancen der Verhütung, Früherkennung und optimierten Therapie chronischer Erkrankungen in der älteren Bevölkerung (ESTHER) study who were followed for mortality for a median of 12.4 y. We estimated adjusted HRs of mortality from all causes, cardiovascular disease, and cancer for defined increases in serum 25(OH)D by Cox regression, both across the full range of 25(OH)D concentrations and for those with vitamin D insufficiency [30 nmol/L ≤ 25(OH)D < 50 nmol/L] or deficiency [25(OH)D <30 nmol/L] only, and we calculated the power of intervention studies achieving those effect sizes.Results: An inverse association between serum 25(OH)D and mortality was observed only for participants with vitamin D insufficiency or deficiency and was strongest for the latter. Accordingly, the expected effects were much stronger and the expected power was much higher for interventions that targeted these groups than for untargeted interventions. For example, a targeted intervention study with 10,000 older adults (age 50-75 y) with serum 25(OH)D <50 nmol/L that increases serum 25(OH)D concentrations by 20 nmol/L in the intervention group (n = 5000) would be expected to yield a 26% reduction of all-cause mortality that could be detected with 89% power within 5 y of follow-up compared with a 10% mortality reduction and 20% power in an untargeted intervention study of the same size.Conclusions: Vitamin D supplementation trials aimed at reducing mortality in older adults have much higher power when focused on those with low serum 25(OH)D concentrations. [ABSTRACT FROM AUTHOR]

Helicobacter pylori colonizes the gastric mucosa. H. pylori infection is the main cause of peptic ulcer and gastric malignancy. This review gives an overview on the epidemiology of H. pylori infection and H. pylori-associated diseases in the developed world, mainly Europe, and it discusses very recent developments with regard to the prevalence and its future implications for H. pylori-associated diseases. [Copyright &y& Elsevier]

The research aimed to identify previously published CpG-methylation-based prognostic biomarkers and prediction models for colorectal cancer (CRC) prognosis and validate them in a large external cohort. A systematic search was conducted, analyzing 298 unique CpGs and 12 CpG-based prognostic models from 28 studies. After adjustment for clinical variables, 48 CpGs and five prognostic models were confirmed to be associated with survival. However, the discrimination ability of the models was insufficient, with area under the receiver operating characteristic curves ranging from 0.53 to 0.62. Calibration accuracy was mostly poor, and no significant added prognostic value beyond traditional clinical variables was observed. All prognostic models were rated at high risk of bias. While a fraction of CpGs showed potential clinical utility and generalizability, the CpG-based prognostic models performed poorly and lacked clinical relevance. [Display omitted] • A systematic review retrieved 298 CpG-methylation biomarkers and 12 CpG-based prediction models for colorectal cancer (CRC) prognosis. • Our external validation confirmed the prognostic value of 48 CpG-biomarkers and 5 prediction models. • Our analysis finds inadequate external model performance and a high risk of bias of published CpG-based prognostic models. [ABSTRACT FROM AUTHOR]

DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis. We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 2 January 2023. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from 'A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST)' and from the 'Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). Different ML methods and workflows used in included studies were summarized and visualized by a sunburst chart, a bubble chart, and Sankey diagrams, respectively. Eighty-three studies were included in this review. Three major types of ML-based workflows were identified. 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques. There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. In theory, most existing workflows could not handle the high multi-collinearity and potentially non-linearity interactions in epigenome-wide DNA methylation data. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed. • This is the first review to systematically summarize current use of machine learning (ML) in epigenome-wide studies for cancer prognosis. • We classified the ML-based workflows used in previous studies into clustering-based, supervised feature selection-based, and deep learning-based workflows • There is a lack of benchmarking studies comparing the relative performance of existing ML-based workflows. • We found common methodological and reporting flaws in existing studies and discuss required improvements and research gaps. [ABSTRACT FROM AUTHOR]

Summary Endothelial cells (ECs) provide angiocrine factors orchestrating tumor progression. Here, we show that activated Notch1 receptors (N1ICD) are frequently observed in ECs of human carcinomas and melanoma, and in ECs of the pre-metastatic niche in mice. EC N1ICD expression in melanoma correlated with shorter progression-free survival. Sustained N1ICD activity induced EC senescence, expression of chemokines and the adhesion molecule VCAM1. This promoted neutrophil infiltration, tumor cell (TC) adhesion to the endothelium, intravasation, lung colonization, and postsurgical metastasis. Thus, sustained vascular Notch signaling facilitates metastasis by generating a senescent, pro-inflammatory endothelium. Consequently, treatment with Notch1 or VCAM1-blocking antibodies prevented Notch-driven metastasis, and genetic ablation of EC Notch signaling inhibited peritoneal neutrophil infiltration in an ovarian carcinoma mouse model. [ABSTRACT FROM AUTHOR]

: ObjectivesWe sought to investigate the association of CD14 genotype and plasma levels of soluble (s)CD14 with risk of stable coronary artery disease (CAD), chronic infections and sensitive markers of systemic inflammation.: BackgroundIt has been suggested that genetic variation of the CD14 receptor with increased CD14 gene expression might play a role in atherogenesis. A mechanistic link would consist in its contribution to the inflammatory response seen in this disease.: MethodsWe measured levels of sCD14 (μg/ml; ELISA) in 312 patients with angiographically proven CAD and stable angina pectoris, and in 477 age- and gender-matched healthy blood donors. CD14 genotype was determined by polymerase chain reaction. In addition, seropositivity to Chlamydia pneumoniae and Helicobacter pylori, a complete lipid profile and various sensitive systemic markers of inflammation were measured.: ResultsCD14 C(-260)→T genotype was not independently associated with increased risk of CAD after multivariable adjustments (odds ratio [OR] 1.34; 95% confidence interval [CI] 0.84 to 2.16). However, sCD14 plasma levels were higher in subjects with TT genotype compared with those with CT or CC genotype (p = 0.005). Plasma levels were not different between cases and controls (4.2 ± 1.3 μg/ml vs. 4.3 ± 1.3 μg/ml, p = NS). In multivariable logistic regression, the OR for the presence of CAD was 1.11 (95% CI, 0.65 to 1.91) if the top quintile of the sCD14 distribution was compared with the bottom quintile. There was no consistent association between seropositivity to either C. pneumoniae or H. pylori, or both, and sCD14 levels and between sCD14 levels or CD14 genotype and the various markers of inflammation.: ConclusionsThese results do not confirm an independent relationship between CD14 genotypes or plasma levels of sCD14 and risk of stable CAD in this population. [Copyright &y& Elsevier]