Recent studies defined a potentially important role of the microbiome in modulating pancreatic ductal adenocarcinoma (PDAC) and responses to therapies. We hypothesized that antibiotic usage may predict outcomes in patients with PDAC.We retrospectively analyzed clinical data of patients with resectable or metastatic PDAC seen at MD Anderson Cancer from 2003 to 2017. Demographic, chemotherapy regimen and antibiotic use, duration, type, and reason for indication were recorded.p = 0.0001) and PFS . In multivariate analysis, the impact of ATB remained significant for PFS  and borderline statistically significant for OS . When we analyzed by chemotherapy regimen, we found that patients who received gemcitabine‐based chemotherapy as first‐line therapy (n = 118) had significantly prolonged OS  and PFS  if they received antibiotics, while those receiving 5FU‐based chemotherapy (n = 98) had only prolonged PFS .A total of 580 patients with PDAC were studied, 342 resected and 238 metastatic patients, selected retrospectively from our database. Antibiotic use, for longer than 48 hrs, was detected in 209 resected patients (61%) and 195 metastatic ones (62%). On resectable patients, we did not find differences in overall survival (OS) or progression‐free survival (PFS), based on antibiotic intake. However, in the metastatic cohort, antibiotic consumption was associated with a significantly longer OS (13.3 months vs. 9.0 months, HR 0.48, 95% CI 0.34–0.7, Antibiotics‐associated modulation of the microbiome is associated with better outcomes in patients with metastatic PDAC. We have analyzed the effect of antibiotics’ intake on two cohorts of patients with pancreatic adenocarcinoma, resectable, and metastatic. We have found that on the metastatic cohort, antibiotics use was significantly associated with better outcomes, particularly, on patients that received gemcitabine based‐chemotherapy as the first line. The 5‐year survival rate of PDAC is 9.3% making it is the third most common cause of cancer‐related death in the United States.The presence of specific microbes within tumors is linked to resistance to gemcitabine as a consequence of the metabolism of this chemotherapeutic agent.Antibiotics can alter the gut microbiota diversity and composition, leading to modified responses to chemotherapeutic agents and immunotherapy regimens.Antibiotics have been linked to lower efficacy of immunotherapy in several solid tumors, but none of these studies specifically examined PDAC.22.1We studied 580 patients with evaluable chemotherapy data belonging to the following two groups: (1) Patients with localized/borderline disease that underwent resection (n = 342) and (2) Patients with metastatic disease (n = 238). Patients with resectable or metastatic disease diagnosed and/or treated at MD Anderson Cancer Center between 2003–2015 and 2009–2017, respectively, were included in the analysis.We reviewed patients’ medical records and collected data on patient demographics, antibiotic use, duration, class and indication, disease and treatment characteristics, and survival data including progression, death, and last follow‐up dates. Antibiotic use was assessed from diagnosis to death or the date of the last follow‐up for metastatic cohort, and diagnosis to surgical resection for the resected cohort. All patients were followed up until death or data lock (February 2019 for both cohorts). Antibiotic use was defined as any use of antibiotics for longer than 48 hrs, which excludes the usual one‐time antibiotic given before or during procedure or surgery. Information regarding the class of the antibiotics and indication of use were also provided. This study was approved by the institutional review board.2.2Patients characteristics were described according to antibiotics intake and compared using Fisher or chi‐squared test for categorical data and Wilcoxon rank‐sum test for continuous data. Frequencies and percentages were reported for categorical variables. Summary statistics such as median, minimum, and maximum were provided for continuous data (such as age). Overall survival (OS) was defined as the time from diagnosis to death from any cause. Patients were censored to the date of the last follow‐up. Progression‐free survival (PFS) was defined as the time from surgery to progression or death, whichever occurred first, in the resected patients; and as the time from first‐line chemotherapy to progression or death, whichever occurred first, in the metastatic group.Univariate and multivariable Cox proportional hazard models were used to determine the effects of potential risk factors and antibiotic status variables on OS and PFS. Clinically and statistically important variables were included in the multivariable models. Hazard ratios and 95% confidence intervals were provided. Kaplan–Meier curves were estimated for the survival distributions by antibiotic status. The Log‐rank test was used to test the difference in survival distributions between treatment groups. All tests were two‐sided. P‐values less than 0.05 are considered statistically significant. All analyses were conducted using SAS 9.4  and S‐Plus 8.0  software.33.1A total of 580 patients were studied: 342 patients with resected PDAC and 238 patients with metastatic PDAC. All patients were seen between 2003 and 2017 at a single institution: University of Texas MD Anderson Cancer Center.3.1.1The median age was 64 years (range of 34–85 years), including 147 (43%) females and 195 (57%) males. A total of 284 (83%) patients in the cohort were White followed by 33 (10%) Hispanic, 10 (3%) Black, 8 (2%) Asian, and 7 (2%) from other racial backgrounds. Most patients had PDAC Stage IIB , followed by IIA , Stage 0/I , and 9 (3%) patients were found to be stage III/IV during surgical exploration  for the patients in this group, including 117 (49%) females and 121 (51%) males. A total of 176 (74%) patients in the cohort were White, 22 (9%) Black, 11 (5%) Asian, 10 (4%) Hispanic, and 8 (3%) from other races. The most common location for the primary tumor was the head of pancreas in 111(47%) patients, followed by body and neck in 76 (32%) patients, with 51 (21%) patients having the primary tumor in the tail . Single dose of antibiotics administered prior to operation or ancillary procedures was not considered for this analysis. While 133 patients (39%) did not receive any antibiotics, 209 patients (61%) received antibiotics prior to surgery. Within the group received antibiotics , 62 patients (18%) received less than 7 days, 147 patients (43%) received 7 days or more duration of antibiotics. A total of 206 (60%) patients received gemcitabine‐based chemotherapy (mostly gemcitabine/Nab‐paclitaxel), while 44 (13%) received 5‐fluorouracil‐based chemotherapy, and 7 (2%) both chemotherapies. Eighty‐five patients (25%) did not receive chemotherapy prior to surgical exploration and resection Table .3.2.2The frequency of antibiotic use from diagnosis to death or the date of the last follow‐up was 82% n = 195). Forty‐three patients (18%) did not receive any antibiotics. Among 195 patients (82%) that received antibiotics, 35 patients (15%) received antibiotics less than 7 days, and 160 patients (67%) received antibiotics 7 days and more. A total of 118 (50%) patients received gemcitabine‐based chemotherapy, while 98 (41%) received 5‐Fluorouracil‐based chemotherapy, and 22 (9%) received other chemotherapeutic regimens in the first‐line setting , while for OS antibiotics usage was associated with borderline statistically significant OS . On univariate analysis respiratory and skin infections were associated with an improved outcome. Furthermore, on multivariate analysis respiratory and skin infections independently remained associated with an improved OS and PFS . Antibiotics use remained independently associated with significantly prolonged PFS  and OS ; (Table p = 0.003), but did not have a significantly different OS   and 5‐FU based chemotherapy (n = 98). After applying the multivariate modeling in the gemcitabine‐based subgroup, patients who received antibiotics had a significantly improved PFS ; Table . For the9) Table , D.4The present study reports the impact of antibiotic usage on overall survival and progression‐free survival in PDAC patients, contrasting results in early resected patients and those with metastatic disease. To the best of our knowledge, this is the first study that systemically examined the effect of ATB usage in a large population of pancreatic cancer patients with resectable and metastatic disease. Use of antibiotics in patients with metastatic PDAC was associated with better outcomes: increased PFS and OS and only PFS after multivariate analysis. Furthermore, subgroup analysis revealed that antibiotic usage in patients who received gemcitabine independently prolonged OS and PFS. In the 5FU‐based chemotherapy group, patients who had received ATB had a significantly prolonged PFS but no differences in OS.Antibiotics are known to reduce microbial diversity and have been associated with worse outcomes in the setting of immunotherapy for other tumor types.Immune response modulation by infections, especially chronic infections, and tumors has similarities Use of antibiotics in resectable patients was not associated with better outcome, which may be explained by the fact that surgery‐related outcomes may play a more important role with other prognostic factors in resected patients .Our study has several limitations. First, the study was a single‐center study of retrospective nature. Second, the analysis did not take into consideration additional factors which could alter the microbiome composition such as diet, country of origin, other medications or supplements. We conducted a multivariate and subgroup analysis to avoid confounding factors within our data set that replicated similar results after adjustment with age, gender, initial treatment type and the reason for antibiotic use. It would be worth mentioning here, there has been increasing evidence that probiotics could possibly prolong survival in PDAC by altering the microbiome.Finally, recent studies have described differences in the microbiome of individuals of different races and ethnicity.In conclusion, antibiotic use among patients with metastatic PDAC is associated with an increased progression‐free and overall survival, even more marked on patients who received concomitant gemcitabine‐based chemotherapy. These findings need to be validated within a larger group of patients within the context of a prospective randomized clinical trial that is being planned now (MTL).No conflict of interests reported by authors.CM, MH, and FM designed, extracted data, performed analysis, and wrote the manuscript. FM supervised the study. WD performed statistical analysis. JR, LP, SB, and JM had worked on data tabulation and extraction. MO, GV, RW, MJ, DF, MK, MK, SP, CWT, and FM contributed with patients’ data, study revision, analysis, and writing revision. ML contributed by the critical analysis of manuscript and contribution in resubmission write up.Table S1Click here for additional data file.


Improved understanding of the interconnectedness of structural remodeling processes in atrial fibrillation (AF) in patients could identify targets for future therapies.n = 64). RNA expression levels were validated in the same and an independent cohort with qPCR. Biological processes were assessed with histological and immunohistochemical analyses.We present transcriptome sequencing of atrial tissues of patients without AF, with paroxysmal AF, and persistent AF  and mesenchymal cell gene expression. Immunohistochemistry demonstrated thickening of the epicardium and an increased proportion of (myo)fibroblast‐like cells in the myocardium, supporting enhanced EMT in AF. We furthermore report an upregulation of endothelial cell proliferation, angiogenesis, and endothelial signaling. EMT and endothelial cell proliferation concurred with increased interstitial (myo)fibroblast‐like cells and extracellular matrix gene expression including enhanced tenascin‐C, thrombospondins, biglycan, and versican. Morphological analyses discovered increased and redistributed glycosaminoglycans and collagens in the atria of AF patients. Signaling pathways, including cell–matrix interactions, PI3K‐AKT, and Notch signaling that could regulate mesenchymal cell activation, were upregulated.Our results suggest that EMT and endothelial cell proliferation work in concert and characterize the (myo)fibroblast recruitment and ECM remodeling of AF. These processes could guide future research toward the discovery of targets for AF therapy. Various signaling pathways are upregulated in atrial fibrillation, which can activate epithelial‐to‐mesenchymal transition and endothelial cell proliferation and differentiation. These processes are associated with increased interstitial fibroblasts and increased and altered extracellular matrix deposits including glycoproteins and glycosaminoglycans. EC, endothelial cell; ECM, extracellular matrix; EMT, epithelial‐to‐mesenchymal transition; FB, fibroblast; MSC, mesenchymal cells; PVC, perivascular cell. With respect to studies in patients and human materials, these are commonly faced with practical restrictions. First, materials derived from cardiothoracic surgery for other conditions than AF are commonly used. As a consequence, there are relatively many reports on, for example, mitral stenosis‐related AF, although this condition is clinically uncommon in the developed world.In the current study, we profiled left atrial tissue samples of patients undergoing thoracoscopic surgery for AF as their primary diagnosis. As controls, we used left atrial tissues from patients without AF undergoing unrelated cardiothoracic surgery. By profiling a large population of clinically well‐defined patients including both paroxysmal and persistent AF patients, we aimed to distinguish processes that play a role throughout the course of the disease from those that are disease stage specific.We used transcriptome sequencing to examine the changes in non‐cardiomyocyte mesenchymal gene expression and changes in ECM components beyond collagens. We sought to describe how multiple structural remodeling processes coexist and interconnect in patients with AF at different stages of the disease.22.1We included 22 patients without a history of AF (non‐AF), 22 patients with paroxysmal AF (par‐AF), 18 patients with persistent AF (pers‐AF), and two longstanding persistent AF (lspers‐AF) patients. Paroxysmal, persistent, and longstanding persistent AF were defined according to current guidelines,Atrial structural remodeling in atrial fibrillation is accompanied by a widespread activation of non‐cardiomyocyte mesenchymal cells found in the epicardium and perivascular niche.Atrial extracellular matrix remodeling not only consists of collagen synthesis, but involves glycoproteins, proteoglycans, and increased extracellular matrix turnover.Atrial fibrillation is accompanied by hypoxic signaling, angiogenesis, and increased microvessel density.Numerous signaling pathways involved in epithelial‐to‐mesenchymal transition and structural remodeling are upregulated in human atrial fibrillation, which suggests a complex interplay of parallel pathways.2DS2‐VASc score of ≥2 and were aged 18–80 years.Control subjects without a history of AF participated in the previously described PREDICT‐AF study .Patients with AF participated in the AFACT trial (NCT01091389) and MARK AF registry.All subjects were clinically characterized, underwent preoperative screening and echocardiography prior to surgery. All patients provided informed consent. Tissue removal and tissue storage were conducted in accordance with the protocol. The protocol was approved by both the Institutional Review Board and the Biobank Review Board of the Amsterdam University Medical Center location AMC and was in accordance with the Declaration of Helsinki.2.2Upon excision, LAAs were directly washed in ice‐cold modified Tyrode's solution before one half was fixated in formaldehyde and the other half was snap‐frozen in liquid nitrogen in the operating theater and stored at −80°C. We extracted RNA from 50–100 mg of snap frozen whole tissues using Trizol . The quality of the RNA samples used for whole transcriptome sequencing was confirmed by Bioanalyzer . Samples were rRNA depleted and used for paired end sequencing (100 bp) on the Illumina NovaSeq 6000  . For further analyses, we selected protein coding genes only.2.3Voom). Genes with a false discovery rate (FDR) adjusted p < .05 (Benjamini–Hochberg) were considered to be differentially expressed genes (DEG) unless otherwise specified. Displayed fold‐changes (FCs) are log2 transformed as are reported counts per million (CPM). We focused on the processes that were continuously increasing or decreasing from non‐AF to par‐AF to pers‐AF. If not otherwise indicated, the FCs and FDRs that were reported resulted from comparing pers‐AF to non‐AF.Reads were mapped toward the human reference genome (GRCh38) using HiSAT2 v2.1.0 and counted with HTSeq v0.11. The differential expression analysis compared three study groups: control, par‐AF, and pers‐AF, and was corrected for age and gender  was performed for all three comparisons using a ranked gene list: sign(log2FC)*−log10(FDR). Details regarding the strategy for GSEA have been described previously.2.5n = 30) with similar clinical characteristics. Baseline characteristics of the independent cohort are described in Table Gene expression results were validated by qPCR in the study cohort and an independent cohort including non‐AF, par‐AF, and pers‐AF patients  that had been identified from literature and had been assessed for their expression in the sequencing data.cDNA was synthesized from the same pool of total RNA as used for RNA sequencing. For biological validation, RNA was extracted from tissue samples of the independent cohort. cDNA was synthesized from 500 ng of total RNA with SuperScript II reverse transcriptase . Real‐time PCR quantification was performed on the LightCycler 480 (Roche) with the SYBR Green PCR Kit . Starting concentrations of each gene were calculated using LinRegPCR.2.6In 61 out of 64 patients, left atrial tissue was available for histological analyses. Transmural paraffin sections were prepared at a thickness of 5 μm.Immunohistochemistry of CD31 , vimentin , and αSMA  was performed with the Ventana Benchmark Ultra autostainer. Immunohistochemistry of fibroblast specific protein , SNAIL , TWIST , NFAT2 , WT1 , and CD44  was performed manually and visualized using DAB (Immunologic BrightDAB BS04). Immunohistochemistry of type I collagen  was visualized with NovaRed (Vector Laboratories Inc. #SK‐4800) and subsequently digitized, stripped (β‐mercaptoehtanol in Tris‐HCl), and re‐stained with type III collagen . In a similar manner, sections were stained successively with CD31, αSMA, and vimentin, and with αSMA, FSP1, and vimentin.Sections were stained with alcian blue (Alfa Aesar J60122 lotnr Q24F022) and nuclear fast red (Clin‐Tech 642420 lotnr C153‐22‐06) and with picrosirius red .All sections were digitized at a 40× magnification . Random fields from qualitatively good stainings were selected for quantitative analyses. The endocardial layer, epicardial layer, and major vessels were manually excluded prior to analyses in imageJ (Color Deconvolution).Sections stained on the autostainer  were quantified by normalizing the positively stained area for the interstitial cardiomyocyte count (nuclei). FSP1 was quantified by normalizing the positively stained area against the total area selected for analysis.Alcian blue stainings were quantified by dividing the glycosaminoglycan area (blue) by the total area selected for analysis. The interstitial distribution of proteoglycans was categorized by two independent reviewers as “none,” “moderate,” “extensive” endocardial localization.Picrosirius red staining was quantified by dividing the area of collagen (red) by the area of cardiomyocytes (yellow). A two‐step approach was applied to quantify perivascular collagens, by quantifying sections prior to and after the manual removal of larger vessels.2.7p‐value <.05 was considered statistically significant (R version 3.2.3).Histological and qPCR data were compared with ANOVA for normally distributed data, and Kruskal–Wallis test for non‐normally distributed data. We compared non‐AF, par‐AF, and pers‐AF patient groups. All performed tests were two‐sided and a 33.1n = 22), paroxysmal AF (n = 22), and persistent AF (n = 20) patients were used for transcriptome analysis. The baseline characteristics of the patients are displayed in Table The atrial tissues from non‐AF  .CDH1) was moderately decreased . Downregulation of EPC genes was corroborated by qPCR in the same patients and in an independent study cohort  or, more specifically, the epicardial cell layer  Figure .26, 2) Figure , includit Figure .cardiac EMT and Hallmark EMT , expressed by MSC, was validated with qPCR to be highly increased in par‐AF and pers‐AF  Immunohistochemistry of type I and type III collagen and the merged image of the two stainings illustrate the relation between the epicardial cells, the fibrous subepicardium, and epicardial adipose tissue. The subepicardium is highly enriched with collagen 1 and collagen III fibers. Type I collagen is found directly lining the epicardial monolayer. (B) Vimentin‐stained sections identify the epicardial monolayer and  myocardial vimentin+ cells. In AF, there is thickening of the epicardium and a loss of epicardial cell organization. (C) αSMA+ cells (arrows) were found in the epicardium of non‐AF and AF patients. (D) WT1+ cells were found in the epicardial monolayer and subepicardial fibrotic layer in both non‐AF and pers‐AF patients. Absolute number of WT+ appeared decreased in pers‐AF (compare a decrease in Vim+ cells in J). Ratio of cells residing in the subepicardium (arrows) compared to the epicardial monolayer increased in pers‐AF. (E) NFATC1 was found in the epicardium and subepicardium of AF patients. Note the clusters of subepicardial NFATC1+ cells in pers‐AF (arrow). Almost no signals were observed in non‐AF. (F) Number of SNAIL+ cells increased in AF patients along with thickening of the epicardial monolayer. Arrows indicate SNAIL+ nuclei localized to the apical layer, suggesting a loss of polarization. (G) TWIST was lower expressed and cytoplasmic (arrows) in AF patients. (H) Quantification of the area of the fibrous subepicardium identified a tended increase in pers‐AF. (I) EAT area did not differ between the study groups, although the spread in pers‐AF patients was remarkable. (J) A tended decrease in subepicardial vimentin+ area fraction was found in par‐AF and pers‐AF. (K and L) Interstitial myocardial vimentin+ area fraction increased in pers‐AF. (M and N) Sections stained with the fibroblast‐specific protein (FSP1) demonstrated an increased FSP1+ area fraction in AF. CM, cardiomyocyte; EAT, epicardial adipose tissue; EPC, epicardial cell genes; MSC, mesenchymal cells; Vim, vimentin. Boxplots depict range and interquartile rangeWT1 was lower in AF patients, which was confirmed by immunohistochemistry  and the expression of regulators of endothelial cell proliferation were increased  was the most significantly upregulated gene in pers‐AF versus non‐AF   Figure . The expR Figure . MicroveR Figure .KLF2, KLF4, KLF15) and hypoxia‐induced factors  .LAMA2, LAMA5, LAMB2, LAMC3, COL4A1‐4, COL18A1, HSPG2, NID1)  Figure .34With immunohistochemistry, an increase in perivascular αSMA‐positive cells was found in AF Figure . The αSM3.6TNC, THBS1, THBS2) , was increased , polymerization (CHPF2), transferase enzymes , hyaluronic acid synthase (HAS2), and hyaluronic and proteoglycan link proteins   Figure . Furtherd Figure . The upr) Figure .FIGURE 6ECM proteoglycans (Reactome) and Naba proteoglycans (MSigDB). The largest fold changes can be found between par‐AF versus non‐AF. (C) qPCR validated increased expression of glycoproteins and proteoglycans in par‐AF and pers‐AF in the study cohort and an independent cohort. (D) Typical examples of interstitial glycosaminoglycans (GAGs) visualized with alcian blue. Contrasts were enhanced for visualization only. (E) GAGs showed an increase in par‐AF and pers‐AF. (F) Typical examples of alcian blue stainings showing that GAGs were most abundant in the endocardium and myocardium adjacent to the endocardium, and fewer proteoglycans were observed in or toward the epicardium. Contrasts were enhanced for visualization only. (G) The hyaluronic acid receptor CD44 could be seen in the epicardial basal layer, the fibrotic subepicardial layer, the endocardium, and surrounding small arterioles. There was no clear difference in CD44 between non‐AF and AF patients. (H and I) Quantification of interstitial collagens on picrosirius red stainings found increased interstitial collagen fractions in par‐AF and pers‐AF patients. AB, alcian blue; EAT, epicardial adipose tissue; PSR, picrosirius red. Boxplots depict range and interquartile rangeProteoglycan and glycoprotein expression is increased in atrial fibrillation. (A) The top 15 upregulated biological processes in the comparison pers‐AF versus non‐AF. Bold, processes are directly related to ECM production. (B) DEG included in the upregulated biological processes The increased gene expression of glycoproteins and proteoglycans was validated with qPCR in the same and in an independent cohort Figure . Atrial COL16A1, COL21A1, COL9A3) and basement membrane collagens  in AF  were increased in AF  genes was only modestly and nonsignificantly increased in AF , and a disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS) families  were decreased. At the same time, the tissue inhibitor of metalloproteinase 1 (TIMP1) was found downregulated. Together, these data suggest a wide‐ranging increase of ECM turnover.In addition to ECM gene upregulation, the expression of ECM degrading constituents was increased in par‐AF and pers‐AF, such as hyaluronidases , and intracellular integrin signaling pathways were upregulated such as integrin signaling, integrin cell surface interactions, and integrins in angiogenesis  Figure .PIK3R3 regulatory subunit was among the most significantly increased genes . Furthermore, multiple proinflammatory pathways were upregulated, including cytokines and immune cells such as TNFa via NFkB signaling , interleukin signaling , and leukocyte chemotaxis .Figure Detailed information regarding the individual nodes and complex signaling that gave rise to Figure 4SNAI1 and ZEB1 expression, consistent with both EMT and angiogensisis.We describe a contextual framework of structural remodeling in human AF, depicting a highly dynamic system involving EMT, endothelial cell proliferation, and ECM remodeling . Transcriptome sequencing identified gene expression signatures that predominantly followed an ordinal trend from no to paroxysmal to persistent AF. These continuous processes of AF were the focus of this study. We found a downregulation of epicardial cell markers and cell–cell junction genes in AF in contrast to an increase in mesenchymal cell gene expression and an upregulation of biological processes related to EMT. EMT was supported by immunohistochemistry showing changes in EMT regulatory TFs such as SNAIL and TWIST, and disorganization and thickening of the epicardial monolayer in AF patients. Within the subepicardium of AF patients, there were less MSC cells, whereas the interstitial MSC proportion increased. These observations may indicate MSC migration from the subepicardium into the myocardium.SNAI1 and ZEB1, and by upregulated signaling pathways such as Notch, PI3K‐AKT, and cell–matrix interactions.Decreased EPC gene expression and epicardial morphological changes imply re‐activation of the epicardial layer in AF. We lately reported that signs of atrial EMT may even precede the first occurrence of incident AF.Contrary to the findings of Suffee et al., we did not find increased numbers of confined interstitial myofibroblasts.LOXL2 in exosomes.We found endothelial cell proliferation, angiogenesis, and microvessel density continuously upregulated along the comparisons of no, paroxysmal, and persistent AF, corresponding to a previous report.HIF1A and HIF3A gene expression, may drive EMT and endothelial cell proliferation and differentiation.HIF1A, HIF3A, ZEB1, or SNAI1 and obtain (myo)fibroblasts‐like phenotypes and thereby contribute to structural remodeling.Persistent tissue hypoxia, implicated in this study by increased The changes in epicardial, endothelial, and perivascular cell gene expression signatures in our study characterize the plasticity of MSC.Here, we found indications for re‐activation of MSC in the epicardial monolayer and possibly the perivascular niche.SNAI1, HIF1A, HIF3A, Notch signaling, inflammation).LTBP1, LTBP2).Angiogenesis, EndMT, and EMT are regulated by overlapping TFs and pathways with significant cross‐talk and feedback mechanisms  in AF. Increased GAGs in AF were predominantly found toward the endocardium, whereas collagens increased in the interstitium, perivascularly, and in the subepicardial layer. We recently reported that an upregulation of glycoproteins and proteoglycans may even precede the first onset of AF.It needs to be determined how glycoproteins and proteoglycans could contribute to increased arrhythmogenicity. Glycoproteins and proteoglycans can modify an inflammatory response, mesenchymal cell proliferation, migration, and signaling.HSPG2, NID1, COL18A1) can function as both pro‐ and anti‐angiogenic factors by binding to and regulating the amount of growth factors in proximity to cell‐surface receptors.Proteoglycans are present in MSC niches and may play an important regulatory role in EMT. For example, the hyaluronic acid receptor CD44, which was found to activate EMT after injury in zebrafish,WT1 expression being the result of complex balanced signaling or completed EMT. It is important to note that the data presented are associative only and due to the aforementioned spatiotemporal regulation of the transcriptome, gene expression changes are not always equivalent to functional changes. For a full understanding of the physiology underlying structural remodeling in AF, it is essential that our findings are confirmed in functional models, including epicardial cell cultures of non‐AF and AF patients, to study the relevant signaling pathways and cellular response  upon stimulation.Overall, the complex nature of the discovered processes precludes us from pointing at one specific regulatory network or one specific origin of MSCs. mRNA sequencing of bulk tissue does not provide data regarding miRNA regulation, single‐cell gene expression, or protein expression. EMT is a process that is significantly regulated by miRNAs.5Our mostly descriptive data allowed us to draw up a contextual framework for structural remodeling in human AF. Our study provides evidence demonstrating that human structural remodeling in AF involves a mesenchymal cell response that arises from the epicardium and perivascular niche through EMT and involves endothelial cell proliferation and differentiation. The mesenchymal cell activation was not the result of a single activated regulatory pathway. Resulting (myo)fibroblast‐like cells may provide paracrine signaling and may orchestrate the complex structural remodeling in AF, which encompasses not only collagen biosynthesis but also glycoprotein, proteoglycan, and glycosaminoglycan deposits. Our results suggest that EMT and endothelial cell proliferation work in concert and characterize (myo)fibroblast recruitment and ECM remodeling. These results may help guide future functional research toward the discovery of targets for AF therapies.SUPPORTING INFORMATIONClick here for additional data file.SUPPORTING INFORMATIONClick here for additional data file.


Reduced muscular strength, as measured by absolute grip strength, has been associated with increased risk of some site‐specific cancers. The ability of grip strength to predict other diseases may be affected by whether it is expressed in absolute or relative terms, but the evidence for cancer is scarce. This study compared the associations of absolute and relative grip strength with all‐cause and 15 site‐specific cancers.A prospective cohort study was undertaken using data from the UK Biobank. The exposure variable was grip strength, in absolute form (kilogrammes) and relative to weight, body mass index (BMI), height. and body fat mass. The outcome was incident cancer, at 15 sites and overall. Cox proportional hazard models were performed to study the associations.P value <0.001], gallbladder , liver , kidney , and breast , as well as all‐cause cancer . Eight cancer sites were inversely associated with HGS relative to weight and BMI: endometrium, liver, gallbladder, kidney, oesophagus, pancreas, colorectal, breast, and all‐cause cancer. Compared with absolute grip strength, grip strength relative to body fat mass had better discriminatory power for head and neck and breast cancer. Grip strength relative to BMI was marginally better than absolute grip strength in predicting stomach cancer.This study included 445 552 participants, where 53.8% of the participants were women, with a mean (SD) age of 56.3 (8.11) years. During a median of 8.8 years follow‐up period, 48 886 (11.0%) patients were diagnosed with cancer. After adjusting for sociodemographic and lifestyle factors, as well as multiple testing, absolute grip strength was inversely and linearly associated with endometrial [hazard ratio (HR): 0.74, 95% confidence interval (CI): 0.69; 0.79, Grip strength was associated with risk of several site‐specific cancers and all‐cause cancer. Head and neck and breast cancers might be better predicted by relative grip strength. There were 19.3 million new cancer cases in 2020,One of the most common muscle strength markers, in clinical and research settings, is handgrip strength (HGS) as it correlates well with overall strength.et al., the authors concluded that the association between absolute HGS and all‐cause cancer mortality was less consistent than other diseases.et al., in a meta‐analysis that included 42 studies, did not find an association between HGS and overall cancer .A meta‐analysis published in 2018, which included 309 413 participants and 9787 cases, found no association between HGS and overall cancer mortality. However, the categorization of strength and adjustment for covariates was heterogeneous between studies, and there was no differentiation between sites of cancer.Studies have shown that relative HGS might be a better indicator for muscle weakness,n = 41 406), and with missing data for the exposure and covariates (n = 15 534), our sample was restricted to the 445 552 participants who had full data available.Between April 2007 and December 2010, UK Biobank recruited approximately 502 000 participants, aged 37–73 years from the general population.http://www.ukbiobank.ac.uk).Hospital admissions were identified via record linkage to Health Episode Statistics records for England (1 June 2020) and Wales (31 March 2017) and to Scottish Morbidity Records for Scotland (31 March 2017). The International Classification of Diseases, 10th revision (ICD‐10) was used to define the following 15 cancers: all cancers , and oral (C00‐C14), oesophageal (C15), stomach (C16), colorectal , liver (C22), gallbladder (C23), pancreatic (C25), lung (C34), kidney (C64‐C65), bladder (C67), breast (C50), endometrial (C54), cervical (C53), ovarian (C56), and prostate (C61) cancer. Of these, 10 cancer sites were used for men and women; one site was specific to men (prostate) and four to women . Potential confounders were identified a priori based on established relationships with cancer and muscular strength. Area‐based socioeconomic status was derived from postcode of residence, using the Townsend score.The HGS was assessed using a Jamar J00105 hydraulic hand dynamometer , and the mean of the right and left hand values, expressed as kilogramme, was used in the analysis, as reported elsewhere.P values (denoted as Padj) using Holm's method controlling family‐wise error rate.Continuous variables were summarized using mean and standard deviation, and categorical variables using frequencies and percentages. Non‐linear associations between HGS and cancer sites were visually explored using multivariable penalized cubic splines in Cox‐proportional hazard models.We calculated Harrell's C‐index (which estimates the probability of concordance between observed and predicted responses) to compare the discriminatory power of HGS markers.No patients were involved in setting the research question or the outcome measures.TableThere were 445 552 participants included in the analysis. The median follow‐up period was 8.8 years [IQR 7.9–9.6]. During the follow‐up period, 48 886 11.0%) people developed cancer. 1.0% peopP value <0.001), gallbladder , liver , kidney , and breast , as well as all‐cause cancer   was previously associated with lung, breast and colorectal cancer.et al. found a 19% decrease in overall cancer risk per 1‐SD increase of HGS,et al. did not find the same association for cancer mortality .To date, all studies have focused on absolute HGS, with equivocal results with most evidence relating to all‐cause cancer.The HGS has been suggested as a good risk marker for other diseases, such as cardiovascular disease, irrespective of which HGS marker is used.The main finding of the current study was that when comparing numerous different ways to express HGS—absolute and relative to height, weight, BMI, and BFM—relative HGS only showed a modestly improvement in prediction of two groups of cancers. These findings could have important public health implications in terms of the operationalization of HGS in predicting cancer risk, which warrants further studies.UK Biobank is not representative of the general population in terms of deprivation and lifestyle.HGS was associated with a higher risk of several cancer sites and all‐cause cancer. HGS expressed in relative terms modestly improved the prediction of head and neck and breast cancers. Therefore, expressing grips strength in it most simple unit (kg) appears adequate for predicting most cancer outcomes.None to declare.UK Biobank was established and funded by the Wellcome Trust, Medical Research Council, Department of Health, Scottish Government, and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government and the British Heart Foundation. All authors had final responsibility for submission for publication. SPS was funded by the Chilean Government PhD scholarship programme.Table S1. Association between HGS z‐scores and cancer incidenceFigure S1. Association between absolute HGS and cancer incidenceFigure S2. Association between HGS relative to body weight and cancer incidenceFigure S3. Association between HGS relative to height and cancer incidenceFigure S4. Association between HGS relative to body mass index and cancer incidenceFigure S5. Association between HGS relative to body fat mass and cancer incidenceClick here for additional data file.


Although emotional audiovisual integration has been investigated previously, whether emotional audiovisual integration is affected by the spatial allocation of visual attention is currently unknown. To examine this question, a variant of the exogenous spatial cueing paradigm was adopted, in which stimuli varying by facial expressions and nonverbal affective prosody were used to express six basic emotions  via a visual, an auditory, or an audiovisual modality. The emotional stimuli were preceded by an unpredictive cue that was used to attract participants’ visual attention. The results showed significantly higher accuracy and quicker response times in response to bimodal audiovisual stimuli than to unimodal visual or auditory stimuli for emotional perception under both valid and invalid cue conditions. The auditory facilitation effect was stronger than the visual facilitation effect under exogenous attention for the six emotions tested. Larger auditory enhancement was induced when the target was presented at the expected location than at the unexpected location. For emotional perception, happiness shared the biggest auditory enhancement among all six emotions. However, the influence of exogenous cueing effect on emotional perception seemed to be absent. Specifically, video games give players not only auditory emotional feedback but also visual emotional feedback, and players combine this information into a coherent percept, thus enabling a deeper immersive experience than visual or auditory emotional feedback presented alone. Under such circumstances, the following question will arise: Can spatially allocated attention have an effect on emotional audiovisual integration? If the answer is yes, then the emotional audiovisual integration might be facilitated by setting an exogenous attentional cue in interface systems, therefore enhancing the emotional experience of users. Attention can be classified as either endogenous or exogenous. Endogenous attention, also called goal-driven or voluntary attention, involves a more purposeful orienting process driven by an individual’s goals and intentions  information but also information from the integration of different modalities. By combining information from different sensory channels, multisensory integration can make better use of the available information and reduce interference in the sensory system . Evidence shows one-way crossmodal dependence in exogenous orienting from audition to vision  as well However, studies on whether exogenous attention can have an impact on emotional audiovisual integration perception are limited. Most of the research has focused on the relationship between emotion and exogenous attention. In terms of the effects of emotions on exogenous attention, a study applied a color-flanker task to investigate the effects of emotion and color congruency on attentional allocation . In the As illustrated earlier, previous studies have demonstrated the interplay between exogenous attention and crossmodal integration, and the biases between different modalities as well as in spatial perception for emotional audiovisual integration. In the past, both scientists and layfolk have considered that the integration of facial and vocal emotional expressions is an automatic process that is independent of attention. This is because we often intuitively, effortlessly, and unconsciously integrate emotional audiovisual information, and previous studies seem to espouse this intuition . Under st test with a medium expected effect size of dz = 0.5 was performed. That analysis yielded a required sample size of N = 54. All participants provided written informed consent to participate in this study, which was previously approved by the Ethics Committee of Hubei University. All participants received payment for their time.Fifty-four participants , all right-handed and with normal or corrected-to-normal vision and hearing capabilities, participated in this experiment. To calculate the required sample size, a simple power analysis for a repeated-measures A black box  was used as an exogenous attention cue presented on the left or right side of the computer screen and was spatially congruent  or incongruent  with the target location. Visual emotional stimuli were selected from the Chinese Facial Emotional Pictures collection . All facial pictures were black and white, 260 mm × 300 mm, and 24 bits. We selected pictures portraying six emotions, including happiness, sadness, disgust, surprise, fear, and anger. For each emotion, there were four pictures . The selection of visual stimuli was based on the dimensions of affective identity and affective intensity. The emotional pictures were chosen to have similar affective intensity scores (approximately 5) and a high identity rate (more than 70%). The rationale of choosing high identity rate pictures was to make sure the facial picture expressed the corresponding emotion, and the choice of similar intensity score pictures was to make sure a similar level of arousal was induced.For auditory stimuli, students  who were performance artists from Sinan Troupe at Hubei University were asked to say six Chinese interjections  with happy, sad, disgusted, surprised, fearful, and angry facial expressions. While talking, the speakers were required to generate consistent facial expressions and imitate the expressions of emotional pictures to ensure the validity of the audio stimuli. Before the recording session, the speakers practiced until they could convey the specified emotion with simultaneous facial and vocal expressions. The recording session was conducted in a bright and quiet room, with a smart phone used to record the sound. The samples were standardized to 44.1 kHz, 32 bits, and stereo, with a duration of 500 ms using Adobe Audition CC.For audiovisual stimuli, auditory stimuli were paired with the facial expressions for each emotion with the same valence and presented in the same direction (left or right). To avoid interactions of speaker sex and emotionality in stimulus pairs, only tokens from same-sex speakers were combined.t tests were conducted to compare the value of the consistency score with 5. The result showed that the consistency score of all the selected stimuli was significantly higher than 5 .In the evaluation test, we tested the self-made auditory stimuli and the retested chosen visual stimuli on affective identity to ensure the validity of all the selected stimuli. Twenty-four college students were recruited to rate the affective identity of previous selected stimuli on a scale of 1 to 9. Each participant needed to complete a session for auditory stimuli evaluation as well as a session for visual stimuli evaluation. The sequence of the previous sessions was counterbalanced across participants. In both evaluation sessions, trials began with a fixation cross presented at the center of the screen for 500 ms; an emotional cue word  was then presented to suggest the type of emotion that the subsequent stimulus expressed. The duration of the emotional cue word was 1,000 ms, and the subsequent visual or auditory stimulus was presented for 500 ms. Participants needed to report the degree of consistency between the category shown on the screen and the type of emotion that visual or auditory stimuli expressed. Results were showed in Table S1 and Table S2. After collecting the consistency score of each stimulus, one-sample For the experiment, a 2 cue  × 6 emotion  × 3 modality  within-group design was employed. The experiment was conducted in a quiet and bright room . Visual stimuli were generated and displayed with E-prime 2.0 and presented on a monitor . The auditory stimuli were presented by means of two loudspeaker cones located on either side of the computer monitor. Participants sat at a distance of 50 cm from the screen and were asked to complete an emotional discrimination task to identify the emotion portrayed on each trial. At the beginning of each trial, a black fixation point appeared on a white background screen for 500 ms, and then a black box appeared  that was spatially congruent  or incongruent  with the target’s location for 50 ms before target presentation . Then, pFirst, to investigate whether the factors  had a general influence on accuracy (ACC) and response times (RTs), we performed a 2 × 6 × 3 repeated-measures analysis of variance in each presentation condition, with cue validity ; emotion ; and modality  serving as within-subject factors. Effect sizes are reported as partial eta-squared , which reflects the benefit gained from adding an additional visual stimulus to an auditory stimulus, and auditory enhancement (AE), which reflects the benefit gained from adding an additional auditory stimulus to a visual stimulus. The measures of VE and AE have been widely used in investigation of audiovisual performance . The forp) of a particular RT is higher in the multisensory condition than the joint probability of the unisensory responses, that is, p(AV) > p(A + V) – p(A*V), for that given RTs. Significant violations of the race model  indicate multisensory integration.Finally, to examine whether the RTs obtained under the crossmodal condition exceeded the statistical facilitation predicted by the race model , the amoF = 517.889, p < .001, F = 187.661, p < .001, p < .001). Importantly, we found significant main effect of cue validity on ACC and RTs. Participants performed less accurately and more slowly under invalid cue than valid cue—72.7 ± 1.4% versus 73.8 ± 1.4%, F = 8.908, p = .004, F = 18.348, p < .001, F = 22.265, p < .001, F = 12.308, p < .001, F = 25.921, p < .001, F = 21.938, p < .001, F = 0.550, p = 0.509, F = 1.495, p = 0.234, F = 1.591, p = 0.163, F = 1.368, p = 0.253, F = 1.120, p = 0.345, F = 1.125, p = 0.341, ps < .001). The accuracy for audiovisual stimuli was significantly higher than that for visual stimuli in anger (p < .001), disgust (p = .001), fear (p < .001), sadness (p < .001), and surprise (p = .001) emotional conditions. The RT for audiovisual stimuli was significantly faster than that for visual stimuli in anger (p < .001), disgust (p < .001), fear (p = .003), happiness (p < .001), and sadness (p < .001) emotional conditions. Happiness induced the highest accuracy and the fastest RTs for visual and audiovisual stimuli among all six emotions.ACC and RTs for all conditions are presented in sons see  further F = 405.762, p < .001, F = 188.043, p < .001, F = 8.004, p = .007, F = 1.822, p = .183, F = 35.410, p < .001, F = 16.723, p < .001, F = 53.691, p < .001, F =33.709, p < .001, F = 8.216, p = 0.006, F = 0.500, p = .775, F = 0.500, p = .775, F = 1.591, p = .163, F = 1.69, p = .137, F = 0.895, p = .492, F = 0.586, p = .711, ps < .05; ps < .05; p = .002; ps < .001) and ACC .To demonstrate the level of crossmodal enhancement for accuracy and RTs under exogenous attention conditions, VE and AE were calculated separately. We found significant main effect of modality on ACC, t tests at each time bin to determine whether the observed audiovisual RT probabilities, p(AV), were higher than the joint probability of the unisensory responses, p(A + V) – p(A * V). The CDF values for RTs to audiovisual conditions were significantly larger (p < .05) than the CDF values of the joint probability of the unisensory responses for each time bin from 110 ms to 130 ms under invalid conditions and from 100 ms to 120 ms under validly conditions, which indicated audiovisual integration.To test whether race model predictions are met or violated, the RT data were plotted as cumulative distribution functions (CDFs). We divided the RT interval from 90 ms to 1,800 ms into 10 ms bins and calculated the likelihood that a response occurred at a given RT or faster. The CDFs under valid cue conditions and invalid cue conditions are plotted in The purpose of the present study was to investigate whether exogenous attention was able to affect emotional audiovisual integration. Visual , auditory , and audiovisual  stimuli expressing six basic emotions  were adopted to examine the multisensory nature of emotion processing under exogenous attention. In this study, our results have shown the processing advantage of audiovisual modality over unimodal visual and auditory modalities. We also found higher accuracy and a shorter RT in categorizing emotional stimuli when an onset cue correctly predicts the following target location. Moreover, larger AE was induced when the target was presented at the expected location than at the unexpected location. For emotional perception, happiness indicated the biggest AE among all six emotions. However, exogenous attention and emotional perception seemed to work independently and showed no interactions. One potential limitation of the current study is that the cue we used to attract participants’ attention was a visual cue (a black square); it is possible that the cue worked more effectively for attracting visual attention. Thus, it would be helpful to perform a similar study with an auditory cue in the future. Combination of electroencephalogram or functional magnetic resonance imaging could also be used to further establish the corresponding neural correlates of these effects.First, we observed higher accuracy and shorter RTs in categorizing audiovisual emotional stimuli than in categorizing unimodal visual or auditory stimuli. Analysis of the race model revealed violations under exogenously invalid conditions (110–130 ms) and valid conditions (100–120 ms), which indicated that the faster responses during audiovisual conditions were likely due to an interaction of the two unisensory information channels and not simply the result of two redundant signals . Our resFurthermore, we observed faster behavioral responses and higher accuracy for target emotional discrimination in valid than invalid conditions for all manipulations, demonstrating that the allocation of visual attention can be successfully manipulated by an onset visual cue (a black square). Our results indicated the existence of an exogenous cueing effect during the perception of peripheral information and was in accordance with that of Click here for additional data file.Supplemental material, sj-pdf-1-ipe-10.1177_20416695211018714 for The Modulation of Exogenous Attention on Emotional Audiovisual Integration by Yueying Li, Zimo Li, Aihui Deng, Hewu Zheng, Jianxin Chen, Yanna Ren and Weiping Yang in i-Perception


Weight gain is routinely monitored to assess hydration and growth during treatment of children with complicated severe acute malnutrition (SAM). However, changes in weight and mid-upper arm circumference (MUAC) gain velocities over time are scarcely described. We assessed weight and MUAC gain velocities in 6–59 mo-old children with complicated SAM by treatment phase and edema status.ISRCTN16454889). Weight and MUAC gain velocities were assessed by treatment phase and edema at admission using linear mixed-effects models.This was a prospective study, nested in a randomized/probiotic trial (P > 0.9). During outpatient therapeutic care (OTC), overall, weight gain velocity showed a decreasing trend over time (from 5 to 2 g/kg/d), [difference between edema and non-edema groups at week 2 was 2.1  g/kg/d]. MUAC gain velocity results mirrored those of weight gain velocity [differences were − 2.30  mm/week at week 1 in ITC and 0.65  mm/week at week 2 in OTC].Among 400 children enrolled, the median (IQR) age was 15.0  months, 58% were males, and 65% presented with edema. During inpatient therapeutic care (ITC), children with edema vs no edema at admission had negative weight gain velocity in the stabilization phase . This gradually changed into positive weight gain velocity in transition and eventually peaked at 12 g/kg/d early in the rehabilitation phase, with no difference by edema status (Weight and MUAC gain velocities among Ugandan children with complicated SAM showed an increasing trend during transition and early in the rehabilitation phase, and a decreasing trend thereafter, but, overall, catch-up growth was prolonged. Further research to establish specific cut-offs to assess weight and MUAC gain velocities during different periods of rehabilitation is needed.The online version contains supplementary material available at 10.1186/s40795-021-00428-0. Severe acute malnutrition (SAM) affects an estimated 14.3 million children under-five globally . ChildreDuring hospitalization, attained weight and weight gain velocity are monitored to track progress and identify children with failure to respond –8. AttaiSeveral studies have reported overall average weight gain velocities ranging from 1.8 to 8.9 g/kg/d –20 for cSome studies have looked at the role of edema in overall weight gain velocity in children with SAM but the results remain inconsistent , 19, 21.Mid-upper arm circumference (MUAC) has been accepted as an independent admission and discharge criteria , and is In this study, we assessed the changes in weight and MUAC gain velocities among children with complicated SAM by treatment phase and edema at admission.ISRCTN16454889) as detailed elsewhere , and categorical variables were presented as percentages (n). To evaluate whether median duration of treatment phases differed by edema at admission, we used Mann-Whitney U Test.P-values below 0.05 were considered statistically significant.Linear mixed-effects models (with restricted maximum likelihood estimation) were used to investigate the changes in weight and MUAC gain velocities between time points during ITC and OTC, adjusted for a priori potential confounders . The models included child-specific random intercepts and robust SEs were used. Because weight gain differs by edema at admission , 7, the Among 400 children with SAM enrolled into the parent ProbiSAM trial, the median (IQR) age was 15.0  months, 58% were males, and 65% presented with edema  duration of stay was longer in the stabilization phase  but shorter in the transition phase  among children with edema compared to those without edema at admission, whereas there were no differences in the rehabilitation phase in ITC, in hospitalization and in OTC, P > 0.08.The overall median (IQR) duration of stay in the stabilization, transition and rehabilitation phases of ITC and, hospitalization was 7 5; 11), 3 , 4  and 17  days, respectively (data not shown). In OTC, it was 56  days, but was pre-defined by the parent trial. As shown in Supplemental Table 1, 3 , during ITC, children with edema had negative daily weight gain velocities throughout the stabilization phase and the initial 2 days of transition. This was followed by positive weight gain velocities, peaking at 12 g/kg/d three days into the rehabilitation phase. Among children without edema the mean weight gain velocities were variable, but mainly positive during the stabilization phase, but were then similar to what was seen for children with edema during the transition and rehabilitation phases. During OTC , among children with edema at admission, the mean weight gain velocities declined from around 5 g/kg/d during the first 2 weeks to around 2 g/kg/d from 6 to 8 weeks. For children without edema at admission, the weight gain velocities were slightly lower during the first 2 weeks and peaked after 4 weeks.As seen in Fig. 2The weight gain velocities were, however, highly variable for each child over time and between children at a given time as shown in Supplemental Fig. P = 0.315).In Table The estimates did not change after age and sex adjustments. Also, further adjustment for HIV status did not change the estimates by much, hence it was not included in the final models. There were no differences in weight gain velocities by edema at admission in the transition and rehabilitation phases. In OTC, a 2 g/kg/d higher weight gain velocity was seen in edematous vs non-edematous children up to 2 weeks, whereas no differences were seen afterwards., panel A), among children with edema, the mean MUAC gain velocity was negative during the first 1 week but then increased steadily, peaking at around 4 mm/week after 4 weeks. Among children without edema, the mean MUAC gain velocities declined from around 2 to around 1 mm/week after 2 weeks, but increased to around 3.5 mm/week after 4 weeks, similar to what was seen for children with edema. During OTC , mean MUAC gain velocities declined from around 3 mm/week after 2 weeks to < 1 mm/week from 6 to 8 weeks, irrespective of edema at admission.Figure 3Table In ITC, a 2 mm/week lower MUAC gain velocity was seen in children with edema vs without edema at admission up to 1 week, whereas no differences were seen afterwards. In OTC, there was a marginally higher mean difference in MUAC gain velocities after 2 weeks and similar but lower mean differences after 4, 6, and 8 weeks in edematous vs non-edematous children. These differences were however not statistically significant.For MUAC and weight gain velocities for children who stayed longer than 4 weeks in ITC and 8 weeks in OTC, supplemental Figs. The striking variability in daily weight gain velocity, particularly in the stabilization phase, may be due to considerable biological variation as well as technical error. Diarrhoea and vomiting are common among children with SAM and cause dehydration , 37, 38.As expected, a mean negative weight gain velocity was found among children with edema compared to those without edema at admission in the stabilization phase. This is related to weight loss due to edema resolution during initial feeding with a low-calorie, specialized formula, F-75. This finding is consistent with those of an old study of hospitalized young Jamaican children with SAM, indicating that individual mean weight gain velocity during the acute phase when children were receiving a “maintenance” diet varied between − 10.9 to − 7.3 g/kg/d . The autA positive weight gain velocity was observed during transition and rehabilitation phases with no differences between edematous and non- edematous children (P for interaction > 0.9). Weight gain velocity ranged from around 2 g/kg/d to around 12 g/kg/d from day 1 to day 3, and from around 12 g/kg/d to around 8 g/kg/d from day 3 to day 5 of the rehabilitation phase. These results indicate that there is accelerated catch-up growth in the first few days of rehabilitation phase and later on, the catch-up growth is gradual, as expected. This is explained by the change in diet from F-75 to RUTF or F-100, which are higher energy and protein “catch-up” formulae intended to rebuild wasted tissues . Our finP = 0.002 for interaction between edema and time), suggesting accelerated catch-up growth in those with edema at admission. Kwashiorkor is often considered an acute dysadaptation; after disappearance of edema, children achieve accelerated growth and later on it is gradual. In contrast, marasmus is believed to be a chronic reductive adaptation, thus, children have a gradual onset of catch up. These findings are also consistent with those of a study of Indian children admitted with SAM, showing that those with non-edematous SAM started gaining weight earlier than their edematous counter parts: mean (SD) 4.4 (3.9) days vs 11.4 [The observed decreasing trend of weight gain velocity during OTC is not surprising since during recovery from SAM, a rapid weight gain velocity is seen initially when children have low WHZ, but then it falls to 1–2 g/kg/d when a normal WHZ is approached , 44. Stu vs 11.4  days [22A constant weight gain velocity expressed in g/kg/d reflects an exponential growth, which is never seen in multicellular organisms. Hence, it is bound to decrease over time and it is not surprising that we see this in all studies including this one. This has important implications when weight gain velocity is used to assess catch-up growth as part of a national or international health policy. The WHO guidelines , 11 mentDuring ITC, a negative trend of MUAC was seen among children with edema at admission, whereas a declining trend was seen among children without edema at admission in the early period. This is similar to what was observed for weight gain velocity during the stabilization phase and is mainly explained by edema resolution. From 2 weeks onwards (during catch-up), MUAC gain velocity is positive and shows an increasing trend in both groups, with no difference by edema at admission, similar to what was observed for weight gain velocity during transition and rehabilitation phases. During OTC, the observed decreasing trend of MUAC gain velocity over time mirrors that of weight gain velocity, suggesting a decreasing trend in MUAC growth. This finding is consistent with those of other studies , 29. A rIn the current study, we found less variability in MUAC gain velocity than seen in weight gain velocity. This finding is consistent with those of Mwangome et al.  showing Overall, these results suggest that recovery continues throughout treatment in OTC, similar to findings by Kangas et al. . This obThis is among the few studies that describe the changes in both weight and MUAC gain velocities among children with complicated SAM by treatment phase and edema at admission. The limitations include measurement of weight using a weighing scale with a low precision (100 g) and taking single measurements on most days. However, body weight measurements were taken at the same time (i.e. one hour after feeding) by trained nutritionists and, daily single measurements were replaced with means of triple weekly measurements when available. Moreover, missing data could have introduced bias, but we used linear interpolation to impute the missing values for daily weight data using values before and after the “missing value” in order to yield a more complete data set .Despite the huge variability in weight gain velocity in the stabilization phase, we find evidence of an increasing trend during transition and early in the rehabilitation phase in ITC followed by a decreasing trend, but, overall, there is elongated catch-up growth among Ugandan children with complicated SAM. The study also reveals that children with edematous SAM have a higher weight gain velocity over the first 2 weeks in OTC compared to those with non-edematous SAM, which is suggestive of accelerated catch-up growth among those with edema at admission. Further, changes in MUAC gain velocity over time mirrored those of weight gain velocity but with less variability. The results support the WHO guidelines , 11 thatAdditional file 1: Supplemental Table 1. Duration of stay and outcome of children admitted with severe acute malnutrition by edema at admission a.Additional file 2: Supplemental Figure 1. Spaghetti plot showing variability in weight gain velocity among four selected children with severe acute malnutrition by treatment phase. Data are presented as absolute values (g/kg/d). Child 1, 16 months old non-breastfed, HIV positive with oedema and diarrhoea at admission; child 2, 51 months old, non-breastfed, HIV negative and, with oedema and diarrhoea at admission; child 3, 11 months old, breastfed, HIV exposed and, with no diarrhoea or oedema at admission; child 4, 23 months old, non-breastfed, HIV exposed, with diarrhoea at admission and no oedema at admission.Additional file 3: Supplemental Figure 2. Mid-upper arm circumference (MUAC) gain velocity presented as mean (standard error) mm/week for children with severe acute malnutrition who stayed longer than 4 weeks in inpatient therapeutic care and 8 weeks in outpatient therapeutic care by edema at admission and treatment phase.Additional file 4: Supplemental Figure 3. Weight gain velocity presented as mean (standard error) g/kg/d for children with severe acute malnutrition who stayed longer than 8 weeks in outpatient therapeutic care by edema at admission.


The single‐arm DAWN trial (NCT01779791) of ibrutinib monotherapy in patients with relapsed/refractory follicular lymphoma (FL) showed an overall response rate (ORR) of 20.9% and a median response duration of 19.4 months. This biomarker analysis of the DAWN dataset sought to determine genetic classifiers for prediction of response to ibrutinib treatment.Whole exome sequencing was performed on baseline tumor samples. Potential germline variants were excluded; a custom set of 1216 cancer‐related genes was examined. Responder‐ versus nonresponder‐associated variants were identified using Fisher's exact test. Classifiers with increasing numbers of genes were created using a greedy algorithm that repeatedly selected genes, adding the most nonresponders to the existing “predicted nonresponders” set and were evaluated with 10‐fold cross‐validation.FANCA, HISTH1B, ANXA6, BTG1, and PARP10, highlighting the importance of functions outside of B‐cell receptor signaling, including epigenetic processes, DNA damage repair, cell cycle/proliferation, and cell motility/invasiveness. While nonresponder‐associated genes included well‐known TP53 and CARD11, genetic classifiers developed using nonresponder‐associated genes included ATP6AP1, EP400, ARID1A, SOCS1, and TBL1XR1, suggesting resistance to ibrutinib may be related to broad biological functions connected to epigenetic modification, telomere maintenance, and cancer‐associated signaling pathways .Exome data were generated from 88 patient samples and 13,554 somatic mutation variants were inferred. Response data were available for 83 patients . Each sample showed 100 to >500 mutated genes, with greater variance across nonresponders. The overall variant pattern was consistent with previous FL studies; 75 genes had mutations in >10% of patients, including genes previously reported as associated with FL. Univariate analysis yielded responder‐associated genes The results from univariate and genetic classifier analyses provide insights into genes associated with response or resistance to ibrutinib in FL and identify a classifier developed using nonresponder‐associated genes, which warrants further investigation.Trial registration: NCT01779791. n = 17) or lack of response  to ibrutinib monotherapy in relapsed/refractory follicular lymphoma. Genetic classifiers were developed using nonresponder‐associated genes, and the top five genes associated with no response to ibrutinib included ATP6AP1, EP400, ARID1A, SOCS1, and TBL1XR1, suggesting that resistance to ibrutinib might be related to broad biological functions outside Bruton tyrosine kinase signaling.Using patient samples from the single‐arm DAWN trial (NCT01779791), we performed univariate and genetic classifier analyses to gain insights into genes associated with response (responders,  BCL2) are often detected in t.KMT2D/MLL2, CREBBP, EP300), histone genes , vacuolar ATPase genes , and components of the BCR or CXCR4 signaling pathway .CARD11, which constitutively activates NF‐kB signaling downstream of BTK, may be associated with inferior response to ibrutinib in FL.Previous analyses have revealed that FL is a heterogeneous disease with varying genetic alterations underlying its pathobiology. The t/Given the heterogeneity of FL and differential responses to treatment, identifying biomarkers that can potentially predict therapeutic benefit may improve clinical outcomes for specific subsets of patients. Here, we present the results of a biomarker analysis in which tumor samples from the DAWN study were examined to detect somatic mutations that could be used to identify patients with FL who are responsive to ibrutinib.22.1DAWN was a multicenter, open‐label, single‐arm, phase 2 study evaluating ibrutinib in relapsed/refractory FL. The study was conducted in accordance with International Conference on Harmonisation Good Clinical Practice guidelines and was approved by an independent institutional review board. All patients provided written informed consent. Detailed methodology for this trial is published elsewhere.2.2Formalin‐fixed, paraffin‐embedded (FFPE) tumor samples were collected at baseline for whole exome sequencing. Exome enrichment was performed using Nimblegen kits (Roche Sequencing Solutions), and sequencing libraries were created using KAPA construction kits (Roche Sequencing Solutions). Sequencing was performed using the HiSeq2500 platform (Illumina), achieving a mean target coverage of 60.7×.2.3Results of the sequencing analyses were visually examined by generating histograms illustrating variant allele frequency (VAF). This visual approach was used to qualitatively assess the degree to which somatic versus germline variants were present in the data by determining whether (i) low‐VAF variants were sufficiently represented in the results and (ii) variants with VAF values near 0.5 and 1.0 were sufficiently rare.To improve the variant selection, an exome analysis pipeline was run on DNAnexus using raw FASTQ sequence data files (DNAnexus), and somatic variants were selected using multiple filters in the R software environment.Because the analysis was performed without matching normal samples, multiple filters were applied to nonsynonymous coding variants to rule out sequencing artifacts and germline variants. Detailed filtering criteria, imposed using base R functions on GEMINI outputs, are shown in Figure Variant frequencies were compared using Fisher's exact test to identify genes associated with responders (CR + PR) versus nonresponders (stable disease + progressive disease). Classifiers with increasing numbers of genes were developed using nonresponder‐associated genes, beginning with one gene and adding a single gene each time. Genes were added to classifiers based on their ranking by a greedy algorithm, which chose genes that allowed, in each iteration, the inference of the most additional nonresponders until all nonresponders were covered. During this process, ties were broken at random and a penalty was enforced for mutations in responders, such that the selected gene could add the most nonresponders after removing a proportion of patients equivalent to twice the proportion of responder patients with a mutation in the same gene. The performance of the classifiers was assessed by 10‐fold cross‐validation within the DAWN dataset, in which patients were binned as nonresponders if they harbored putative somatic mutations in any of the selected genes.33.1EZH2‐Tyr646 and STAT6‐Asp419, two known somatic FL‐associated mutations,In total, 88 out of 110 patients enrolled in the DAWN study had FFPE tumor biopsy samples (one sample per patient) available for sequencing analyses. Sequencing generated 974,686 nonsynonymous coding variants and after filtering out potential errors and possible germline mutations, the variant count was 12,890. Following in‐house reprocessing and variant selection, the final VAF histogram showed a substantially increased somatic versus germline ratio for the variants Figure . The VAFCREBBP, BCL2, KMT2D; Figure Of 88 patients with exome data, 83 had response data per independent review committee assessment (17 responders and 66 nonresponders) and were included in this analysis. The background demographic and disease characteristics summarized in Table FANCA, HISTH1B, ANXA6, BTG1, DIAPH1, PARP10, PBRM1, PRDM1, RAD50, and RECQL4 , are summarized in Table Univariate analysis of variants from the selected genes in responders versus nonresponders is summarized in Table n = 9) with a non‐CR group, including patients with a PR (n = 8), as well as all nonresponders . Gene mutations significantly enriched in patients with a CR (vs. non‐CR) are presented in Table HISTH1B, ANXA6, BTG1, PARP10, PBRM1; Table FANCA mutation, which was found in two patients with a PR and in one patient with a CR. A direct comparison between responders with a CR versus those with a PR was inconclusive because of small sample size , while DIAPH1, PKD1, and TNFAIP3 mutations occurred only in patients who achieved a PR (two each). Mutations in several genes, including ANXA6, BTG1, and PARP10, were found only in patients with CR (two each), but none of these results reached statistical significance  were targeted for classifier development. Development started with a single gene that was ranked most informative, and subsequent classifiers were created by adding, one at a time, another gene in the order of decreasing new information. For the selected panel, 17 classifier models were developed including variants in B Figure . Each te3.3ATP6AP1, EP400, ARID1A, SOCS1, and TBL1XR1 ,KMT2D and TNFRSF14 mutations were higher than those seen in this analysis, but a large proportion of the mutations was represented by indels, which were not assessed in the current study and explains the discrepancy. In addition, the rates of BCL2 and MUC4 mutations were higher in the present analysis, but the previous study showed a higher number of somatic mutations called in these genes when a matched normal sample was not present. Taking these factors into account, the overall results of the two studies compare favorably.By using whole exome sequencing and a custom variant selection scheme, this analysis identified somatic gene mutations potentially associated with response/nonresponse to ibrutinib in patients with relapsed/refractory FL. Comparison to previous genetic studies in FL established the validity of the variant selection scheme developed in this study, as the overall pattern of variant frequencies identified here was generally comparable to the earlier studies,HIST1H1B is a histone H1 gene that was significantly mutated in patients with FL, with all known alterations being missense mutations.PBRM1, a gene also found mutated in FL, is a subunit of the chromatin‐remodeling SWI/SNF complexes.PARP10 has a role in the regulation of chromatin and gene transcription and cell proliferation,PARP10 knockdown results in genomic instability and DNA damage hypersensitivity.FANCA and RAD50, with three and two mutations each, respectively, are associated with DNA damage repair, and mutations in these genes are known to sensitize tumors to chemotherapeutics.BTG1 gene, mutated exclusively in two responders with a CR in this analysis, was previously characterized as a negative regulator of cell cycle progression and cell proliferation,ANXA6 gene (mutated only in two patients with a CR) is required for membrane localization of activated EGFR and persistent activation of MAP kinaseERK1/2 and PI3K/Akt pathways in invasive breast cancer cells. Depletion of ANXA6 expression in these cells leads to degradation of activated EGFR, inhibition of cell motility and invasiveness, and increased sensitivity to the EGFR‐targeted tyrosine kinase inhibitors.In the univariate analysis presented herein, many responder‐associated gene mutations have shown functions outside of BCR signaling. Mutations in histone genes and epigenetic modifiers have been frequently reported in studies of FL,TP53 and CARD11,ATP6AP1, EP400, ARID1A, SOCS1, and TBL1XR1) has suggested potential mechanisms underlying resistance to ibrutinib. In ATP6AP1, a v‐ATPase complex mediating mTORC1 activation,ARID1A mutations have been reported in many types of human cancers,ARID1A showed a distribution pattern consistent with a previous analysis in FL.ARID1A is a negative regulator of TERS, loss of its expression caused by inactivating mutations would enhance TERT transcription, conferring a survival advantage for tumor cells by maintaining their telomeres.SOCS1 is a negative regular of JAK/STAT signaling. Mutations in SOCS1, including those in the SH2 domain, were reported in B‐cell malignancies such as diffuse large B‐cell lymphoma and FL.SOCS1 to JAK, leading to the subsequent inactivation of the JAK/STAT signaling, mutations in this domain detected in our analysis may prolong the activation of the JAK/STAT pathway.TBL1XR1, a transducin β‐like 1 X‐linked receptor 1, is a tumor‐suppressor gene with E3 ubiquitin ligase activity, mediating transcriptional repression through NF‐κB and WNT signaling pathways.TBL1XR1 somatic mutations were found in nonresponders, indicating that mutations in this gene lead to alternative, BTK‐independent NF‐κB‐activation and, therefore, resistance to ibrutinib, as reported in certain MCL cell lines.The univariate analysis did not identify any significant nonresponder‐associated gene mutations, which otherwise would be of special interest as they may activate survival mechanisms that bypass BTK, including the mTOR and JAK/STAT pathways, and confer resistance to ibrutinib.CARD11 was identified as the primary “nonresponder” gene, with eight mutations reported in five patients (n = 31), all of whom were nonresponders.CARD11 mutations in six patients, five of whom were nonresponders (mutations were mostly in the coiled‐coil domain). One CARD11 mutation  was found in a responder who achieved a PR after 5.5 months of ibrutinib treatment. This patient was a 53‐year‐old white female with stage II FL and low‐risk Follicular Lymphoma International Prognostic Index score. The C49Y CARD11 variant has been reported in diffuse large B‐cell lymphoma and was identified in an unbiased screen for gain‐of‐function CARD11 mutants capable of activating NF‐κB and promoting human diffuse large B‐cell lymphoma tumor growth in vitro.CARD11 germline mutations present with relatively mild BENTA, suggesting that the gain‐of‐function effect of a C49Y CARD11 variant is weaker than that of a mutation in the coiled‐coil or LATCH domain, making patient outcome highly context dependent.CARD11 activation, but nonetheless responded to ibrutinib due to other factors, such as relatively young age and overall low‐risk disease.In a previous phase 2 consortium study of ibrutinib in FL, ATP6AP1 mutations may overcome BTK inhibition by activating mTOR signaling,TBL1XR1 activates NF‐κB, which could trigger resistance to ibrutinib treatment.SOCS1, a regulator of the JAK/STAT pathway, acts not only as an intracellular pathway component, but also as a modulator of the tumor microenvironment.CARD11, which is associated with lack of response to ibrutinib,ATP6AP1, have not been previously reported as major drivers of FL, and somatic mutations in the main genes known to be associated with FL did not appear to effectively predict the response to ibrutinib. These observations may have revealed additional molecular mechanisms contributing to FL pathogenesis or unique aspects of ibrutinib's activity in FL patients, which warrants further investigation.In summary, the genes found to be most useful for predicting lack of response to ibrutinib in the DAWN dataset demonstrate a variety of biological functions. Some of these genes act directly through the BTK‐NF‐κB pathway. Among the top‐ranked nonresponder genes, The ORR with ibrutinib monotherapy in relapsed/refractory FL ranged from 20.9% in the DAWN study to 62.5% in the long‐term follow‐up phase 1 trial, with wide variation potentially attributed to different response assessment approaches, patient populations, and follow‐up periods.The main limitations of this study include an unbalanced analysis cohort with more nonresponders than responders; the absence of normal matching samples; the classifier gene selection strategy may have resulted in “overfitting” of the model; and the lack of an independent cohort for external validation.In conclusion, this analysis developed a viable approach to identify somatic mutations associated with response to ibrutinib in FL. Our results suggest that the pattern of response to ibrutinib in FL may be linked to a variety of non‐BTK‐related pathways and microenvironmental interactions. The genetic data suggest that resistance to ibrutinib in FL may be related to epigenetic modification and telomere maintenance, in addition to cancer‐associated signaling pathways . However, these results need to be validated in additional patient datasets/prospective studies to fully determine their clinical predictive value.SB: Employment: Janssen Research & Development; Equity ownership: Janssen Research & Development. BH, MS, SS, SD, and JV: Employment: Janssen Research & Development. SJS: Consultancy and research funding: Novartis, Pharmacyclics, Celgene; Research funding: Gilead, Janssen Research & Development, Hoffmann‐La Roche, Merck; Board of directors or advisory committees: Nordic Nanovector; Consultancy: Genentech, Acerta. NHF: Consulting or advisory role: Pharmacyclics, Janssen; Research funding: Pharmacyclics, Janssen. JT: Research funding to institution: Janssen, PCYC, Roche, BeiGene, Takeda, Celgene; Cooperative group funding: Janssen; Advisory role (unremunerated): Janssen, Roche, Celgene, Takeda. GH: Research support: Roche, Pfizer, Celgene, CTI; Consultant: Roche, Pfizer, Janssen, Celgene, AbbVie; Honoraria: Roche, Pfizer, Janssen, Celgene, AbbVie; Advisory board: Roche, Pfizer, Janssen, Celgene, AbbVie. BDC: Advisory board: Acerta, AbbVie, Roche‐Genentech, Celgene, TG Therapeutics; Research funding to institution: Gilead, Pharmacyclics, Acerta, TG Therapeutics, AbbVie. GS: Research funding: Roche, Celgene; Consultancy: Novartis; Honoraria: AbbVie, Acerta, Amgen, Celgene, Epizyme, Gilead, Janssen, Merck, Morphosys, Novartis, Pfizer, Roche, Servier, Takeda, Celgene, Mundipharma. AKG: Research funding: Merck, Janssen, Spectrum, Takeda, Bristol Myers Squibb, Pfizer, Seattle Genetics, Gilead; Donations: Frank and Betty Vandermeer; Consulting fees: Seattle Genetics, Gilead, Janssen, Brim, Aptevo, Genzyme.J Clin Oncol. 2018;36(23):2405–2412.)This secondary analysis did not require ethical approval. The primary phase 2 study DAWN (NCT01779791) was conducted in accordance with International Conference on Harmonisation Good Clinical Practice guidelines and was approved by an independent institutional review board. All patients provided written informed consent (Gopal AK, Schuster SJ, Fowler NH, et al. Ibrutinib as treatment for patients with relapsed/refractory follicular lymphoma: results from the open‐label, multicenter, phase II DAWN study. Supplementary MaterialClick here for additional data file.


The incidence of colorectal cancer (CRC) in patients under age 50 is rising for unclear reasons. We examined the effects of socioeconomic factors on outcomes for patients with early‐onset CRC compared to late‐onset CRC.Patients with CRC from 2004 to 2015 in the National Cancer Database were included and categorized by age (under or over 50 years). Differences in demographic and socioeconomic factors, disease characteristics, and survival outcomes between early‐onset versus late‐onset CRC patients were assessed by Chi‐squared test and Cox models.p < 0.0001). Early‐onset CRC patients were more likely to be Black (15.1% vs. 11.3%) or Hispanic (8.6% vs. 4.6%) and to present with stage 4 disease (24.9% vs. 17.0%), p < 0.0001 for all. Black patients had the worst median OS (58.3 months) compared to White (67.0 months), Hispanic (91.6 months), or Asian (104.9 months) patients, p < 0.0001. Within the subgroup of early‐onset CRC patients with private insurance, Black patients had worse OS compared to White patients, even in communities with higher income and education status.The study population included 1,061,204 patients, 108,058 (10.2%) of whom were under age 50. The proportion of patients diagnosed under age 50 increased over time: 9.4% in 2004–2006, 10.1% in 2007–2009, 10.5% in 2010–2012, and 10.7% in 2013–2015 (Early‐onset CRC continues to increase. Patients with early‐onset CRC are more likely to be Black or Hispanic and to present with stage 4 cancer. Early‐onset Black patients showed worse OS compared to White patients in all income subgroups, even with private insurance. Patients with early‐onset colorectal cancer are more likely to be Black or Hispanic and to present with stage 4 cancer. Within the subgroup of early‐onset CRC patients with private insurance, Black patients had worse OS compared to White patients, even in communities with higher income and education status. The number of people under age 50 that are diagnosed with colorectal cancer continues to increase for unknown reasons. Patients with early‐onset colorectal cancer are more likely to be Black or Hispanic and to present with stage 4 cancer. Black patients with early‐onset colorectal cancer have worse outcomes and shorter survival compared to White patients, even with private insurance and in communities with higher income and education status.1Colorectal cancer (CRC) is the third most common cancer and second leading cause of cancer death in the United States.Early‐onset CRC has notable differences in epidemiology, tumor characteristics, and prognosis from late‐onset CRC (loCRC). Early‐onset CRC appears to affect non‐White patients at higher rates compared to White patients.The cause of the rise in incidence of eoCRC remains unknown, but likely many factors from environmental exposures and tumor biology differences play a role. Early‐onset CRC patients are more likely to be Black or Hispanic, two groups that experience socioeconomic barriers and health disparities in cancer care.2The study cohort was obtained from the NCDB, a joint program of the Commission on Cancer (CoC) of the American College of Surgeons and the American Cancer Society. The NCDB is a hospital‐based, prospectively collected outcomes database comprising approximately 70% of all new invasive cancer diagnoses and more than 1500 CoC‐accredited facilities in the United States. Data collection is standardized based on the Facility Oncology Registry Data Standards (FORDS) and data were generated using the Participant Use File program. For this study, adult patients diagnosed with CRC from 2004 to 2015 were included. As the dataset is de‐identified and publically available, this study was granted exempt status by the institutional review board of the Cleveland Clinic Taussig Cancer Institute.Data abstracted included age, sex, race, ethnicity, community median income, insurance status, community education level, county urbanization, year of diagnosis, American Joint Committee on Cancer (AJCC) stage, tumor grade, cancer center type, and treatment modalities used. Patient self‐reported race is defined and categorized in the NCDB as non‐Hispanic White, Black, Asian, and Other/unknown. The NCDB categorizes patient self‐reported ethnicity as either of Hispanic origin or not. For our analyses, race and ethnicity were evaluated as one variable. The NCDB measures community education level by the percentage of people in a community that have not completed at least a high school degree, which is categorized as <7.0%, 7.0%–12.9%, 13.0%–20.9%, and >21.0%. Level of comorbidity was measured using the Charlson–Deyo score. Patients were then categorized as eoCRC (age <50 years) and loCRC (age ≥50 years) based on age at CRC diagnosis.Statistical analyses evaluated associations between patient, socioeconomic, and disease attributes with overall survival. Baseline characteristics were summarized using percentages for categorical variables and medians for continuous variables. Overall survival was estimated using the Kaplan–Meier method. Baseline characteristics between eoCRC and loCRC patients were compared using the Chi‐squared test for categorical variables and the Wilcoxon rank sum test for continuous variables. The Cox proportional hazards model was used for univariate and multivariate regression analyses of overall survival, which included all baseline characteristics and age <50 years versus age ≥50 years at diagnosis as variables. There were significant interaction effects on OS between race/ethnicity and income, insurance status, and education, therefore, multivariate Cox modeling was performed to assess the effect of race/ethnicity on overall survival within both age groups after subgrouping by community median income, insurance status, and community education level. All tests were two‐sided and subgroup analyses used Bonferroni correction to control overall type I error rate at 5%. Data analyses were performed using SAS Studio 3.7  and R version 4 (R Foundation).3The study population was comprised of 1,061,204 patients, 108,058 (10.2%) of whom had eoCRC. The median age at diagnosis for all patients was 68 years. In the total study population, 48.9% of patients were female and 51.1% were male. In terms of race, 79.1% of patients were White, 11.6% of patients were Black, and 2.7% of patients were Asian. In terms of ethnicity, 5.0% of patients self‐reported as Hispanic. The majority of patients had government insurance (60.5%) and lived in a metropolitan area (82.2%). Government insurance included all forms of Medicaid and Medicare. In terms of stage, 68.4% of patients presented with stages I–III cancer, 17.8% had stage IV cancer, and 13.9% had an unknown stage. These and other baseline characteristics are summarized in Table p < 0.0001). Compared to loCRC patients, a higher proportion of eoCRC patients were Black (15.1% vs. 11.3%), Hispanic (8.6% vs. 4.6%), or Asian (4.0% vs. 2.6%) while a smaller proportion were White (70.2% vs. 80.1%), p < 0.0001 for all. A higher proportion of eoCRC patients were diagnosed with stage IV CRC compared to loCRC patients . The majority of eoCRC patients had private insurance (71.1%) while the majority of loCRC patients had government insurance (65.4%), p < 0.0001. The eoCRC patients were also more likely to be uninsured compared to loCRC patients . These data and comparisons of other socioeconomic and cancer‐specific factors are shown in Table p < 0.0001 for both comparisons).The proportion of patients diagnosed with eoCRC increased over time: 9.4% from 2004 to 2006, 10.1% from 2007 to 2009, 10.5% from 2010 to 2012, and 10.7% from 2013 to 2015 (p < 0.0001). As expected, earlier stage at diagnosis, lower tumor grade, younger age, lower Charlson–Deyo comorbidity score, private insurance status, receiving care at an academic center, higher income, and higher community education level were all associated with improved overall survival. These data and others are summarized in Figure Compared to loCRC patients, eoCRC patients had significantly longer median overall survival . Conversely, patients of Asian, Hispanic, or Other origins had better overall survival compared to White patients . The interactions between race/ethnicity and treatment status were not statistically significant, indicating the effects of treatment on OS were the same across racial and ethnic groups.In the total study population, Black patients experienced worse median overall survival compared to White patients . Black patients experienced worse overall survival compared to White patients in communities with median income between $38,000–$63,000 and 7.0%–12.9% of patients without a high school degree . In communities with median income >$63,000 (highest income bracket) and 7.0%–12.9% of patients without a high school degree, Black patients still experienced worse overall survival compared to White patients . In communities with median income >$63,000 (highest income bracket) and <7.0% of patients without a high school degree , the disparity in survival for Black patients was mitigated and did not reach statistical significance . These data and other statistically significant multivariable Cox models comparing different community income and education levels by race or ethnicity are shown in Table Within the subgroup of eoCRC patients with private insurance, multivariable Cox models showed Black patients had worse OS compared to White patients at multiple community income and education levels, after adjusting for gender, residence area, comorbidity, stage, tumor grade, treatment facility type, and treatments given. In communities with median income <$38,000 and more than 21% of patients without a high school degree, Black patients had worse overall survival compared to White patients  versus health maintenance organization (HMO) found that patients with HMO insurance were less likely to receive chemotherapy and had greater mortality compared to patients with FFS plans. The results were independent of stage at diagnosis, patient comorbidities, and treatment modalities used.This study also found that lower community education status was associated with worse outcomes, even in communities with the same median income. This is consistent with prior studies that have shown that less educated patients are less likely to be offered colorectal cancer screening or to receive chemotherapy as part of their treatment.Unconscious bias among health professionals may inadvertently contribute to worse outcomes for Black patients. Past and present systemic racism embedded in the healthcare system leading to diminished trust among Black patients may also contribute to the reduced benefits of private insurance, higher income, and better education for young Black patients compared to young White patients.This study has several limitations. First, the NCDB is hospital‐based and not population‐based, meaning potential bias in types of hospitals included could bias our findings. However, given that the NCDB includes a large number of hospitals accounting for approximately 70% of all new cancer diagnoses, it is likely these results can be generalized to the broader population. Second, NCDB data does not include specific comorbidities such as obesity, chemotherapy regimens, or tumor genomics, which could vary by race or ethnicity and lead to disparate outcomes. Third, this study included many subgroup analyses, which could skew results compared to the total population. However, the large size of the NCDB dataset ensures even subgroups contain a large number of patients. The extensive subgroup analyses also ensure that the conclusions drawn in this study are robust and that the observed differences in outcomes can reasonably be attributed to racial and ethnic differences. Finally, the NCDB data on income and education status are based on geographic location, which may not account for racial/ethnic differences in income or education within each community.In conclusion, the rising incidence of eoCRC disproportionately affects Black, Hispanic, and Asian patients. Black patients experience worse outcomes at any age, though the disparity is most pronounced in the early‐onset population. Alarmingly, Black patients with eoCRC showed worse OS compared to White patients in all income subgroups, even with private insurance. More work is needed to better understand and address these health disparities, particularly for younger patients.Suneel Kamath––consulting or advisory role: Exelixis, Tempus. Nataly Torrejon––No disclosures. Wei Wei––No disclosures. Katherine Tullio––No disclosures. Kanika G. Nair––No disclosures. David Liska––No disclosures. Smitha S. Krishnamurthi––Consulting or Advisory Role––Array BioPharma, Research Funding––Abbvie (Inst); Bristol‐Myers Squibb; Celgene (Inst). Alok A. Khorana––has been paid honoraria directly by Janssen, Halozyme, Pfizer, Bayer, Nektar, and Medscape, currently or during the past 2 years. Dr. Khorana has been paid for any consulting or advisory role by Janssen, Halozyme, Bayer, Pfizer, Pharmacyte Biotech, Pharmacyclics, and Seattle Genetics.As the dataset is de‐identified and publically available, this study was granted exempt status by the institutional review board of the Cleveland Clinic Taussig Cancer Institute.Patients with early‐onset colorectal cancer are more likely to be Black or Hispanic and to present with stage 4 cancer.Within the subgroup of early‐onset CRC patients with private insurance, Black patients had worse OS compared to White patients, even in communities with higher income and education status.Table S1Click here for additional data file.