Citations

AbstractBackground/ObjectivesThough maculopapular cutaneous mastocytosis is the most common form of pediatric mastocytosis, it remains unclear which patients will experience severe symptoms. We sought to better define the presentation and the cutaneous and systemic signs and symptoms in patients with maculopapular cutaneous mastocytosis.MethodsWe analyzed retrospective data on 227 patients diagnosed with maculopapular cutaneous mastocytosis prior to age 15 years from five US clinical sites. We collected data on signs, symptoms, age of onset, and laboratory testing.ResultsMedian age of onset of maculopapular cutaneous mastocytosis was 3 months, with 94% of patients presenting prior to age 2 (range 0‐15 years). Patients presenting before age 2 had significantly lower serum tryptase level (P = .019). Greater number of skin lesions (P = .006), number of reported skin signs and symptoms (P < .001), and higher tryptase levels (P < .001) were associated with more systemic symptoms.ConclusionChildren with maculopapular cutaneous mastocytosis, who have greater skin involvement, higher serum tryptase level, and more skin signs and symptoms, are more likely to have systemic symptoms.

AbstractBackgroundObstructive sleep apnea (OSA) is a common disease associated with worse structural and functional impairment of the heart in patients with hypertrophic obstructive cardiomyopathy (HOCM).HypothesisThe presence and severity of OSA can decrease the right ventricular ejection fraction (RVEF) in patients with HOCM.MethodsIn total, 151 consecutive patients with a confirmed diagnosis of HOCM at Fuwai Hospital between September 2017 and September 2018 were included. Polysomnography and cardiac magnetic resonance imaging were performed in all patients.ResultsOverall, 84 (55.6%) patients were diagnosed with OSA. The RVEF significantly decreased with the severity of OSA (none, mild, moderate‐severe: 46.1 ± 8.2 vs 42.9 ± 7.5 vs 41.4 ± 7.4, P = .009). The apnea‐hypopnea index (AHI) was significantly high in patients with RVEF<40% among the different OSA groups (mild, moderate:7.7 ± 2.4 vs 9.6 ± 2.9, P = .03; 24.4 ± 9.0 vs 36.3 ± 18.0, P = .01). In the multiple linear regression model, the right ventricular end‐systolic volume (β = −0.28, P < .001), AHI (β = −0.09, P = .02), and oxygen desaturation index (β = −0.11, P = .04) were independently associated with a decrease in RVEF (adjusted R2 = 0.347, P < .001). Furthermore, the prevalence of RVEF<40% was high in patients with OSA. Compared with RVEF>40%, RVEF<40% was associated with more symptoms, mainly chest pain, chest distress, NYHA class III or IV, pulmonary hypertension, and moderate or severe mitral regurgitation.ConclusionIn patients with HOCM, the presence and severity of OSA is independently associated with a lower RVEF. In addition, compared with patients with RVEF>40%, those with RVEF<40% had more symptoms, including chest pain, chest distress, and NYHA class III or IV.

Abstract Pancreatic ductal adenocarcinoma (PDAC) is one of most lethal malignancies with few known risk factors and biomarkers. Identification of disease biomarkers is critical for understanding the pathogenesis of this cancer and identifying high risk individuals for close surveillance. Several blood protein biomarkers have been linked to PDAC in previous studies, but these studies have assessed only a limited number of biomarkers usually in small samples. To identify novel circulating protein biomarkers of PDAC, we studied 8,280 cases and 6,728 controls of European descent from the Pancreatic Cancer Cohort Consortium and the Pancreatic Cancer Case-Control Consortium, by using genetic instruments. Protein quantitative trait loci (pQTLs) for 1,226 plasma proteins identified in a large INTERVAL study of 3,301 healthy European descendants were used as instruments to evaluate associations between genetically predicted protein levels and PDAC. For proteins showing a significant association, we further conducted conditional analysis with adjustments for previously identified risk variants to assess whether the observed associations between genetically predicted protein concentrations and PDAC risk were independent of the risk variants identified in genome-wide association studies (GWAS). Furthermore, for the proteins that were associated with PDAC risk, we performed an enrichment analysis of the genes encoding these proteins to examine whether they are enriched in specific pathways, functions or networks. We observed associations between predicted concentrations of 38 proteins and PDAC risk at a false discovery rate of &lt; 0.05, including those of 23 proteins that showed a significant association even after Bonferroni correction (4.08 × 10−5). These include Histo-blood group ABO system transferase encoded by ABO, which has been previously implicated as a potential target gene of PDAC risk variant identified in GWAS. Eight of the identified proteins (Beta-crystallin B2, Dedicator of cytokinesis protein 9, VIP36-like protein, Erythrocyte band 7 integral membrane protein, Tensin-2, Transmembrane protease serine 11D, Alcohol dehydrogenase 1B, and C-X-C motif chemokine 10) were associated with PDAC risk after conditioning on previously reported pancreatic cancer risk variants (odds ratios ranged from 0.79 to 1.52, P-values from 1.28 × 10−3 to 6.47 × 10−4). Pathway enrichment analysis showed that the encoding genes for the implicated proteins were significantly enriched in cancer-related pathways, such as STAT3 and IL-15 production. In conclusion, we identified 38 protein biomarker candidates for PDAC risk, which if validated by additional studies, may contribute to the etiological understanding of PDAC tumor development. Citation Format: Jingjing Zhu, Xiang Shu, Xingyi Guo, Duo Liu, Jiandong bao, Roger Milne, Graham G Giles, Chong Wu, Mengmeng Du, Emily White, Harvey A Risch, Nuria Malats, Eric J. Duell, Phyllis J. Goodman, Donghui Li, Paige Bracci, Verena Katzke, Rachel E Neale, Steven Gallinger, Stephen Van Den Eeden, Alan Arslan, Federico Canzian, Charles Kooperberg, Brian Wolpin, Laura Beane-Freeman, Ghislaine Scelo, Kala Visvanatha, Christopher A. Haiman, Loïc Le Marchand, Herbert Yu, Gloria M Petersen, Rachael Stolzenberg-Solomon, Alison P Klein, Laufey T Amundadottir, Qiuyin Cai, Jirong Long, Xiao-Ou Shu, Wei Zheng, Lang Wu. Associations between genetically predicted blood protein biomarkers and pancreatic ductal adenocarcinoma risk [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1200.

Background While there is a long history of measuring death and disability from injuries, modern research methods must account for the wide spectrum of disability that can occur in an injury, and must provide estimates with sufficient demographic, geographical and temporal detail to be useful for policy makers. The Global Burden of Disease (GBD) 2017 study used methods to provide highly detailed estimates of global injury burden that meet these criteria. Methods In this study, we report and discuss the methods used in GBD 2017 for injury morbidity and mortality burden estimation. In summary, these methods included estimating cause-specific mortality for every cause of injury, and then estimating incidence for every cause of injury. Non-fatal disability for each cause is then calculated based on the probabilities of suffering from different types of bodily injury experienced. Results GBD 2017 produced morbidity and mortality estimates for 38 causes of injury. Estimates were produced in terms of incidence, prevalence, years lived with disability, cause-specific mortality, years of life lost and disability-adjusted life-years for a 28-year period for 22 age groups, 195 countries and both sexes. Conclusions GBD 2017 demonstrated a complex and sophisticated series of analytical steps using the largest known database of morbidity and mortality data on injuries. GBD 2017 results should be used to help inform injury prevention policy making and resource allocation. We also identify important avenues for improving injury burden estimation in the future.

Abstract Cellular senescence restrains the expansion of neoplastic cells through several layers of regulation, including epigenetic decoration of chromatin structure and functional modulation of bioactive components. Here we report that expression of the histone H3-specific demethylase KDM4 is upregulated in human stromal cells upon cellular senescence. In clinical oncology, upregulated KDM4 and diminished H3K9/H3K36 methylation are correlated with adverse survival of cancer patients post-chemotherapy. Global chromatin accessibility mapping via ATAC-seq and expression profiling through RNA-seq reveal extensive reorganization of chromosomes and spatiotemporal reprogramming of the transcriptomic landscape, events responsible for development of the senescence-associated secretory phenotype (SASP). Selectively targeting KDM4 dampens the SASP of senescent stromal cells and enhances the apoptotic index of cancer cells in the treatment-damaged tumor microenvironment (TME), together prolonging overall survival of experimental animals. Our study supports the dynamic change of H3K9/H3K36 methylation marks during cellular senescence, identifies an unusually permissive chromatin state, unmasks KDM4 as a key modulator of the SASP, and presents a novel therapeutic avenue to manipulate cellular senescence and curtail age-related pathologies.

AbstractEpilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.

Background: Asymptomatic recurrences of atrial fibrillation (AF) are common after ablation of AF. Objective: We aimed to analyze the performance of the mobile ECG device using artificial intelligence (AI) algorithm in detection of AF after ablation. Method: A randomized controlled trial of AF screening using a handheld single-lead ECG monitor (BigThumb®) or a traditional follow-up strategy was conducted in patients with non-valvular AF after catheter ablation. Consecutive patients were randomized to either BigThumb Group (BT Group) or Traditional Follow-up Group (TF Group). Monitoring data was collected and analyzed. The ECGs collected by BigThumb were compared using the automated AF detection algorithm, AI algorithm and cardiologists’ manual review. Subsequent changes in adherence on oral anticoagulation of patients were also recorded. Result: We studied 218 patients (109 in BT Group, 109 in TF Group). After a follow-up of 345.4±60.2 days, AF-free survival rate was 64.2% in BT Group and 78.9% in TF Group (P=0.0163), with more adherence on oral anticoagulation in BT Group (P=0.0052). The participants in the BT Group recorded 26133 ECGs during the follow-up, among which 3299 (12.6%) were diagnosed as AF by cardiologists’ manual review. The sensitivity and specificity of the AI algorithm were 94.4% and 98.5% respectively, which are significantly higher than the automated AF detection algorithm (90.7% and 96.2%). Conclusion: We found that follow-up after AF ablation using BigThumb leads to a more frequent detection of AF recurrence and more adherence on oral anticoagulation. Artificial intelligence algorithm improves the accuracy of ECG diagnosis.