Association of anticoagulant-related bleeding events with cancer detection in atrial fibrillation: A systematic review and meta-analysis

BackgroundA bleeding episode may herald cancer in the general population. Oral anticoagulants (OACs), the mainstay treatment for atrial fibrillation (AF), are known to increase the risk of bleeding, and may thus promote an earlier diagnosis of cancer. Data regarding the association of bleeding episodes with cancer in patients with AF on OACs are scarce.MethodsIn this systematic review and meta-analysis, we searched electronic databases (Medline, Scopus, and Central) and gray literature sources for studies of patients with nonvalvular AF under any OAC, from inception until 14 October 2020. The primary outcome was the association of bleeding occurrences with the detection of cancer. A subgroup analysis was performed according to OAC type [NOAC (non-vitamin K oral anticoagulant) versus VKA (vitamin K antagonist)].ResultsOverall, 4 studies were included, accounting for a total of 144,362 patients with AF receiving OAC. During follow-up, 816 (0.57%) cases of cancer were confirmed. The presence of a bleeding event, either major or minor, was associated with a higher risk for cancer detection (odds ratio [OR] 8.79, 95% confidence interval [CI] 4.98-15.51, and I² 85%). Heterogeneity was explained after studies were stratified by the type of OAC (NOACs: OR 6.12, 95% CI 4.47-8.37, I² 0%, VKAs: OR 15.16, 95% CI 12.61-18.23, and I² 0%).ConclusionThe detection of a bleeding event could be an alerting sign of cancer in patients with AF on OACs, particularly in patients receiving VKAs.Registration number (DOI)available in https://doi.org/10.17605/OSF.IO/3948R, DOI: 10.17605/OSF.IO/3948R.     

Diabetic cardiorenal syndrome: insights into its pathogenesis

A subset of diabetic patients develops renal or cardiovascular complications: the cardiorenal syndrome. A constellation of risk factors coexist and appear to interact. Raised arterial pressure, poor metabolic control and dyslipidaemia are already present at the stage of microalbuminuria, which in itself is associated with significant glomerular pathology. Family histories of diabetic nephropathy, hypertension or cardiovascular disease are all significantly related to the development of proteinuria in the diabetic proband. The presence of elevated red cell sodium-lithium countertransport increases the risk of proteinuria by approximately fourfold and is related to reduced insulin sensitivity, a phenomenon that itself is linked to cardiovascular risk in the general population. Susceptibility factors, most probably genetic, which are associated with a familial disposition to cardiovacular disease therefore appear crucial for the development of the cardiorenal syndrome of diabetes.     

Comprehensive assessment of cancer missense mutation clustering in protein structures

Large-scale tumor sequencing projects enabled the identification of many new cancer gene candidates through computational approaches. Here, we describe a general method to detect cancer genes based on significant 3D clustering of mutations relative to the structure of the encoded protein products. The approach can also be used to search for proteins with an enrichment of mutations at binding interfaces with a protein, nucleic acid, or small molecule partner. We applied this approach to systematically analyze the PanCancer compendium of somatic mutations from 4,742 tumors relative to all known 3D structures of human proteins in the Protein Data Bank. We detected significant 3D clustering of missense mutations in several previously known oncoproteins including HRAS, EGFR, and PIK3CA. Although clustering of missense mutations is often regarded as a hallmark of oncoproteins, we observed that a number of tumor suppressors, including FBXW7, VHL, and STK11, also showed such clustering. Beside these known cases, we also identified significant 3D clustering of missense mutations in NUF2, which encodes a component of the kinetochore, that could affect chromosome segregation and lead to aneuploidy. Analysis of interaction interfaces revealed enrichment of mutations in the interfaces between FBXW7-CCNE1, HRAS-RASA1, CUL4B-CAND1, OGT-HCFC1, PPP2R1A-PPP2R5C/PPP2R2A, DICER1-Mg²⁺, MAX-DNA, SRSF2-RNA, and others. Together, our results indicate that systematic consideration of 3D structure can assist in the identification of cancer genes and in the understanding of the functional role of their mutations.To elucidate the genetic basis of cancer, efforts have been initiated to sequence the exomes or genomes of many human tumors. Among them are large-scale efforts such as The Cancer Genome Atlas (TCGA) (1) and the International Cancer Genome Consortium (ICGC) (2), as well as many smaller-scale projects. These efforts have collectively found millions of somatic mutations in virtually all human genes (the vast majority of which are nonfunctional or “passenger” mutations) across thousands of tumor samples (3). The amounts of available cancer sequencing data are growing rapidly and will continue to grow in the foreseeable future. We and others have developed computational methods to detect cancer-associated genes and functional mutations from such data, based on a significant overall burden of mutations or on significant positional clustering of mutations in the one-dimensional (1D) gene sequences, corresponding to mutational hotspots (3–6).For some cancer proteins, it has been observed that, although mutations may be distributed along the linear amino acid sequence, they tend to cluster in certain regions of the 3D structure, such as active sites. A clear example is KRAS, where particular missense mutations at the active site are positively selected in cancer because they disable the GTPase activity of the protein, locking it in its GTP-bound, active state, which promotes proliferation. As a result, recurrently mutated residues (e.g., G12, G13, I36, A59, Q61, K117, A146) tend to occur around the substrate-binding pocket of KRAS (Fig. 1). This and other individual examples of proteins showing 3D clustering of cancer missense mutations are sometimes used in the literature as supporting evidence for the involvement of those proteins in the disease or as a basis for functional hypotheses about the clustered mutations [e.g., EGFR (8), PIK3CA (9), DIS3 (10), SPOP (11), MRE11 (12), ERCC2 (13)]. Stehr et al. (14) and Ryslik et al. (15) assessed the structural clustering of missense mutations in 29 and 131 proteins, respectively, and demonstrated that taking into account 3D structural information can be helpful for identifying mutation hotspots in known cancer proteins or in new candidates.Open in a separate windowFig. 1.Spatial mutation clustering in KRAS. (A) Protein sequence of KRAS (Isoform 2B; UniProt: P01116-2) with mutated residues from the PanCancer data set (3) shown in red. Recurrent mutations (at least three samples) are shown in larger font and are annotated with position and number of samples with such mutations. Gray arcs between such residues are shown if their centroids are located closer than 13 Å between each other in the protein structure; arc width and label show the spatial distance in these cases (wider arcs corresponding to shorter distances). The C-terminal part of the protein sequence not covered by the structure in B is shown in smaller, gray font. (B) 3D structure of KRAS (gray) with substrate GDP (blue) bound to its active site (PDB ID code 4LUC) (7). Mutated residues are shown in red (recurrent mutations: sticks, nonrecurrent mutations: thin lines) and color intensity scales with the number of mutations per residue.Here, we seek to undertake comprehensive studies of 3D clustering of somatic missense mutations in cancer across all human proteins with available protein structures. Such integrative analysis may help to identify new cancer proteins that have been missed by other methods. In addition, it can help explain the functional roles of individual mutations based on their spatial location in the protein; for example, mutations that cluster at protein interaction interfaces may perturb key molecular interactions (16).     

Familial aggregation in inflammatory bowel disease：Is it genes or environment？

Inflammatory bowel disease (IBD) develops in genetically susceptible individuals due to the influence of environmental factors, leading to an abnormal recognition of microbiota antigens by the innate immune system which triggers an exaggerated immune response and subsequent bowel tissue damage. IBD has been more frequently found in families, an observation that could be due to either genetic, environmental or both types of factors present in these families. In addition to expanding our knowledge on IBD path...     

Clustering of cardiovascular risk factors in type 2 diabetes mellitus: prognostic significance and tracking

Summary
Aim   Little attention has been paid to the prognostic significance and tracking effect of risk factor clusters characteristic of type 2 diabetes mellitus. We studied the clustering of eight cardiovascular risk factors (smoking, high body mass index, elevated systolic blood pressure, high serum, low density lipoprotein (LDL) cholesterol, high serum LDL triglycerides, low serum, high density lipoprotein (HDL) cholesterol, high fasting blood glucose and high plasma insulin concentration) and their effect on the prognosis and the tracking effect.
Methods   This study is a population-based prospective follow-up of newly diagnosed type 2 diabetic subjects (n = 133, aged 45–64 years) in Eastern Finland. The following end points were used: all-cause mortality, cardiovascular mortality, and incidences of first myocardial infarction and first stroke. Furthermore, we studied the 'tracking effect' of the risk factor clusters during the 10-year follow-up period.
Results   When the clustering of risk factors typical of type 2 diabetes mellitus was taken into account, all-cause mortality increased from 28.6% to 50.0% (p < 0.05) and cardiovascular disease mortality increased from 14.3% to 50.0% (p < 0.01) depending on the number of risk factors present. The incidence of first myocardial infarction increased from 0% to 40.0% (p < 0.05) as the number of risk factors increased from 0 to 5. In survivors, the proportion of individuals with no risk factors decreased and the proportion on individuals with three to four risk factors increased during the 10-year follow-up period despite the high mortality among the group with many risk factors.
Conclusions   The risk factor clusters among type 2 diabetic subjects are of great predictive value and when not aggressively treated, show a relentless increase despite selective mortality.     

某养老机构新型冠状病毒肺炎聚集性疫情流行病学特征分析

目的对某养老机构发生一起新型冠状病毒肺炎(COVID-19)聚集性疫情进行回顾性流行病学调查和分析。方法采用"新型冠状病毒肺炎防控方案(第三版)"推荐的个案调查表和依照"新型冠状病毒肺炎聚集性疫情流行病学调查指南(试行第一版)"进行。结果某养老机构有8名护理人员和住院老人感染新冠肺炎,年龄中位为59岁,男、女比例1∶7。于末次暴露后4~15 d确诊。排查出225名密切接触者,无三代院内续发传播;不排除无症状感染存在传染的可能性;病亡1例。结论本次聚集性疫情的感染来源于新冠肺炎确诊病例。建议防控策略应关注养老机构、监管场所等重点部门、重点人群的管控与监测。     

Clustering of albumin excretion rate abnormalities in Caucasian patients with NIDDM

Summary Proteinuria and nephropathy have been found to cluster in families of non-insulin-dependent diabetic (NIDDM) Pima Indian, and in Caucasian insulin-dependent diabetic (IDDM) patients. No information is at present available for Caucasian NIDDM patients. The aim of the present study was to determine whether micro-macroalbuminuria (AER + ) is associated with albumin excretion rate abnormalities in diabetic and non-diabetic siblings of probands with NIDDM and AER + . We identified 169 Caucasian families with one NIDDM proband (the patient with longest known NIDDM duration) (101 families with only NIDDM siblings, 33 families with both NIDDM and non-NIDDM siblings and 35 families with only non-NIDDM siblings). Of the probands 56 had AER + [Prob-NIDDM-(AER + )], 78 had AER– [Prob-NIDDM-(AER–)], 74 siblings of Prob-NIDDM-(AER + ), and 113 siblings of Prob-NIDDM-(AER–) also had NIDDM. Data on albuminuria and retinopathy from multiple sibling pairs when the size of the sibship was more than two was adjusted according to a weighting factor. The odds ratio for AER + , in siblings of Prob-NIDDM-(AER + ) adjusted for age, hypertension, glycated haemoglobin A_{1 c} and other confounding variables was 3.94 (95 % confidence intervals: 1.93–9.01) as compared to siblings of Prob-NIDDM-(AER–). The 74 siblings of Prob-NIDDM-(AER + ) had higher prevalence of proliferative retinopathy than siblings of Prob-NIDDM-(AER–) (14 vs 2 %; p < 0.01). We also identified 66 non-diabetic siblings of 41 NIDDM probands with AER + and 36 non-diabetic siblings of 27 NIDDM probands with AER–. Albumin excretion was two times higher, although still within the normal range, in the non-diabetic siblings of Prob-NIDDM-(AER + ) than in siblings of Prob-NIDDM-(AER–) [median = 13.5 (range 0.5–148) vs 6.6 (range 1–17) μg/min (p < 0.05)]. In conclusion higher rates of albumin excretion aggregate in Caucasian families with NIDDM. Proliferative retinopathy is more frequently observed in families showing a clustering of AER + and NIDDM. These findings suggest that familial factors play a role in the pathogenesis of renal and retinal complications in NIDDM. [Diabetologia (1997) 40: 816–823] Received: 6 November 1996 and in revised form: 17 February 1997