Risks of catheter ablation for atrial fibrillation and flutter assessed in retrospective studies, registries, and controlled trials may underestimate 'real world' conditions.
To assess complications ...in a nationwide approach, we included all cases undergoing catheter ablation for atrial fibrillation and atrial flutter in Germany in 2014, using ICD-10-GM-based German Diagnosis Related Group (G-DRG) codes and the well differentiated German Operation and Procedure Classification (OPS) analysing 33 353 in-hospital cases. For left atrial ablations (19 514 cases), the overall complication rate ranged from a mean of 11.7% to 13.8% depending on type and site of applied energy, including major complications ranging from 3.8% to 7.2%. Whereas overall complication rates were lower for atrial flutter ablations (13 871 cases, 10.5%; P < 0.001), interestingly, major complications occurred more frequently (7.4%; P < 0.001). Particularly, in-hospital death was four-times more common following right than following left atrial ablations (47 vs. 18 cases, 0.34% vs. 0.09%; P < 0.001). Stratified by centre ablation volume, significantly fewer overall complications occurred in centres performing >100 vs. ≤100 left atrial ablations annually (12.7% vs. 16.4%; P < 0.002).
Administrative data of all atrial fibrillation ablations in Germany in 2014 revealed higher overall and major complication rates than previously reported. Few patients were treated in low volume centres, but were exposed to a higher overall complication risk. Atrial flutter ablations were associated with surprisingly high rates of life-threatening complications. Advanced age combined with highly prevalent cardiac, pulmonary and, vascular comorbidities likely play a major role. In addition, individual-level clinical studies need to address the safety and benefits of catheter ablation in an elderly, diseased population.
It is still unclear how genetic information, provided as single‐nucleotide polymorphisms (SNPs), can be most effectively integrated into risk prediction models for coronary heart disease (CHD) to add ...significant predictive value beyond clinical risk models. For the present study, a population‐based case‐cohort was used as a trainingset (451 incident cases, 1488 noncases) and an independent cohort as testset (160 incident cases, 2749 noncases). The following strategies to quantify genetic information were compared: A weighted genetic risk score including Metabochip SNPs associated with CHD in the literature (GRSMetabo); selection of the most predictive SNPs among these literature‐confirmed variants using priority‐Lasso (PLMetabo); validation of two comprehensive polygenic risk scores: GRSGola based on Metabochip data, and GRSKhera (available in the testset only) based on cross‐validated genome‐wide genotyping data. We used Cox regression to assess associations with incident CHD. C‐index, category‐free net reclassification index (cfNRI) and relative integrated discrimination improvement (IDIrel) were used to quantify the predictive performance of genetic information beyond Framingham risk score variables. In contrast to GRSMetabo and PLMetabo, GRSGola significantly improved the prediction (delta C‐index 95% confidence interval: 0.0087 0.0044, 0.0130; IDIrel: 0.0509 0.0131, 0.0894; cfNRI improved only in cases: 0.1761 0.0253, 0.3219). GRSKhera yielded slightly worse prediction results than GRSGola.
The low five-year survival rate of pancreatic ductal adenocarcinoma (PDAC) and the low diagnostic rate of early-stage PDAC via imaging highlight the need to discover novel biomarkers and improve the ...current screening procedures for early diagnosis. Familial pancreatic cancer (FPC) describes the cases of PDAC that are present in two or more individuals within a circle of first-degree relatives. Using innovative high-throughput proteomics, we were able to quantify the protein profiles of individuals at risk from FPC families in different potential pre-cancer stages. However, the high-dimensional proteomics data structure challenges the use of traditional statistical analysis tools. Hence, we applied advanced statistical learning methods to enhance the analysis and improve the results' interpretability.
We applied model-based gradient boosting and adaptive lasso to deal with the small, unbalanced study design via simultaneous variable selection and model fitting. In addition, we used stability selection to identify a stable subset of selected biomarkers and, as a result, obtain even more interpretable results. In each step, we compared the performance of the different analytical pipelines and validated our approaches via simulation scenarios.
In the simulation study, model-based gradient boosting showed a more accurate prediction performance in the small, unbalanced, and high-dimensional datasets than adaptive lasso and could identify more relevant variables. Furthermore, using model-based gradient boosting, we discovered a subset of promising serum biomarkers that may potentially improve the current screening procedure of FPC.
Advanced statistical learning methods helped us overcome the shortcomings of an unbalanced study design in a valuable clinical dataset. The discovered serum biomarkers provide us with a clear direction for further investigations and more precise clinical hypotheses regarding the development of FPC and optimal strategies for its early detection.
In prostate adenocarcinoma (PCa), distinction between indolent and aggressive disease is challenging. Around 50% of PCa are characterized by TMPRSS2‐ERG (T2E)‐fusion oncoproteins defining two ...molecular subtypes (T2E‐positive/negative). However, current prognostic tests do not differ between both molecular subtypes, which might affect outcome prediction. To investigate gene‐signatures associated with metastasis in T2E‐positive and T2E‐negative PCa independently, we integrated tumor transcriptomes and clinicopathological data of two cohorts (total n = 783), and analyzed metastasis‐associated gene‐signatures regarding the T2E‐status. Here, we show that the prognostic value of biomarkers in PCa critically depends on the T2E‐status. Using gene‐set enrichment analyses, we uncovered that metastatic T2E‐positive and T2E‐negative PCa are characterized by distinct gene‐signatures. In addition, by testing genes shared by several functional gene‐signatures for their association with event‐free survival in a validation cohort (n = 272), we identified five genes (ASPN, BGN, COL1A1, RRM2 and TYMS)—three of which are included in commercially available prognostic tests—whose high expression was significantly associated with worse outcome exclusively in T2E‐negative PCa. Among these genes, RRM2 and TYMS were validated by immunohistochemistry in another validation cohort (n = 135), and several of them proved to add prognostic information to current clinicopathological predictors, such as Gleason score, exclusively for T2E‐negative patients. No prognostic biomarkers were identified exclusively for T2E‐positive tumors. Collectively, our study discovers that the T2E‐status, which is per se not a strong prognostic biomarker, crucially determines the prognostic value of other biomarkers. Our data suggest that the molecular subtype needs to be considered when applying prognostic biomarkers for outcome prediction in PCa.
What's new?
Genetic rearrangements involving androgen‐regulated transmembrane protease serine 2 and genes from the ETS transcription factor family (T2E), most commonly ERG and ETV1, occur in half of prostate cancers but are currently not considered in risk predictions. The authors integrate clinical and transcriptomic data from multiple studies and show that the prognostic value of biomarkers critically depends on the T2E‐status. They identify five biomarkers that predict negative outcome exclusively in T2E‐negative prostate cancers, which has implications for outcome prediction based on the molecular subtype.
Fifteen percent of atopic dermatitis (AD) liability-scale heritability could be attributed to 31 susceptibility loci identified by using genome-wide association studies, with only 3 of them (IL13, ...IL-6 receptor IL6R, and filaggrin FLG) resolved to protein-coding variants.
We examined whether a significant portion of unexplained AD heritability is further explained by low-frequency and rare variants in the gene-coding sequence.
We evaluated common, low-frequency, and rare protein-coding variants using exome chip and replication genotype data of 15,574 patients and 377,839 control subjects combined with whole-transcriptome data on lesional, nonlesional, and healthy skin samples of 27 patients and 38 control subjects.
An additional 12.56% (SE, 0.74%) of AD heritability is explained by rare protein-coding variation. We identified docking protein 2 (DOK2) and CD200 receptor 1 (CD200R1) as novel genome-wide significant susceptibility genes. Rare coding variants associated with AD are further enriched in 5 genes (IL-4 receptor IL4R, IL13, Janus kinase 1 JAK1, JAK2, and tyrosine kinase 2 TYK2) of the IL13 pathway, all of which are targets for novel systemic AD therapeutics. Multiomics-based network and RNA sequencing analysis revealed DOK2 as a central hub interacting with, among others, CD200R1, IL6R, and signal transducer and activator of transcription 3 (STAT3). Multitissue gene expression profile analysis for 53 tissue types from the Genotype-Tissue Expression project showed that disease-associated protein-coding variants exert their greatest effect in skin tissues.
Our discoveries highlight a major role of rare coding variants in AD acting independently of common variants. Further extensive functional studies are required to detect all potential causal variants and to specify the contribution of the novel susceptibility genes DOK2 and CD200R1 to overall disease susceptibility.
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Phytosterol serum concentrations are under tight genetic control. The relationship between phytosterols and coronary artery disease (CAD) is controversially discussed. We perform a genome-wide ...meta-analysis of 32 phytosterol traits reflecting resorption, cholesterol synthesis and esterification in six studies with up to 9758 subjects and detect ten independent genome-wide significant SNPs at seven genomic loci. We confirm previously established associations at ABCG5/8 and ABO and demonstrate an extended locus heterogeneity at ABCG5/8 with different functional mechanisms. New loci comprise HMGCR, NPC1L1, PNLIPRP2, SCARB1 and APOE. Based on these results, we perform Mendelian Randomization analyses (MR) revealing a risk-increasing causal relationship of sitosterol serum concentrations and CAD, which is partly mediated by cholesterol. Here we report that phytosterols are polygenic traits. MR add evidence of both, direct and indirect causal effects of sitosterol on CAD.
Cutaneous lupus erythematosus (CLE) is a chronic autoimmune disease of the skin with typical clinical manifestations. Here, we genotyped 906 600 single nucleotide polymorphisms (SNPs) in 183 CLE ...cases and 1288 controls of Central European ancestry. Replication was performed for 13 SNPs in 219 case subjects and 262 controls from Finland. Association was particularly pronounced at 4 loci, all with genomewide significance (P < 5 × 10−8): rs2187668 (PGWAS = 1.4 × 10−12), rs9267531 (PGWAS = 4.7 × 10−10), rs4410767 (PGWAS = 1.0 × 10−9) and rs3094084 (PGWAS = 1.1 × 10−9). All mentioned SNPs are located within the major histocompatibility complex (MHC) region of chromosome 6 and near genes of known immune functions or associations with other autoimmune diseases such as HLA‐DQ alpha chain 1 (HLA‐DQA1), MICA, MICB, MSH5, TRIM39 and RPP21. For example, TRIM39/RPP21 read through transcript is a known mediator of the interferon response, a central pathway involved in the pathogenesis of CLE and systemic lupus erythematosus (SLE). Taken together, this genomewide analysis of disease association of CLE identified candidate genes and genomic regions that may contribute to pathogenic mechanisms in CLE via dysregulated antigen presentation (HLA‐DQA1), apoptosis regulation, RNA processing and interferon response (MICA, MICB, MSH5, TRIM39 and RPP21).
Epidemiological studies suggested an association between atopic dermatitis (AD) and cardiovascular disease. Therefore, we investigate associations and potential underlying pathways of AD and ...cardiovascular disease in large cohort studies: the AOK PLUS cohort (n = 1.2 Mio), the GINIplus/LISAplus birth cohorts (n = 2,286), and the Cooperative Health Research in the Region of Augsburg (KORA) F4 cohort (n = 2,990). In addition, metabolomics in KORA F4 and established cardiovascular risk loci in genome-wide data on 10,788 AD cases and 30,047 controls were analyzed. Longitudinal analysis of patients with AD in AOK PLUS showed slightly increased risk for incident angina pectoris (adjusted risk ratio 1.17 95% confidence interval 1.12–1.23), hypertension (1.04 1.02–1.06), and peripheral arterial disease (1.15 1.11–1.19) but not for myocardial infarction (1.05 0.99–1.12) and stroke (1.02 0.98–1.07). In KORA F4 and GINIplus/LISAplus, AD was not associated with cardiovascular risk factors and no differences in metabolite levels were detected. There was no robust evidence for shared genetic risk variants of AD and cardiovascular disease. This study indicates only a marginally increased risk for angina pectoris, hypertension, and peripheral arterial disease and no increased risk for myocardial infarction or stroke in patients with AD. Relevant associations of AD with cardiovascular risk factors reported in US populations could not be confirmed. Likewise, patients with AD did not have increased genetic risk factors for cardiovascular disease.
To investigate the role of epigenetics in statins' diabetogenic effect comparing DNA methylation (DNAm) between statin users and nonusers in an epigenome-wide association study in blood.
Five cohort ...studies' participants (
= 8,270) were classified as statin users when they were on statin therapy at the time of DNAm assessment with Illumina 450K or EPIC array or noncurrent users otherwise. Associations of DNAm with various outcomes like incident type 2 diabetes, plasma glucose, insulin, and insulin resistance (HOMA of insulin resistance HOMA-IR) as well as with gene expression were investigated.
Discovery (
= 6,820) and replication (
= 1,450) phases associated five DNAm sites with statin use: cg17901584 (1.12 × 10
), cg10177197 (3.94 × 10
), cg06500161 (2.67 × 10
), cg27243685 (6.01 × 10
), and cg05119988 (7.26 × 10
). Two sites were associated with at least one glycemic trait or type 2 diabetes. Higher cg06500161 methylation was associated with higher fasting glucose, insulin, HOMA-IR, and type 2 diabetes (odds ratio 1.34 95% CI 1.22, 1.47). Mediation analyses suggested that
methylation partially mediates the effect of statins on high insulin and HOMA-IR. Gene expression analyses showed that statin exposure and
methylation were associated with
downregulation, suggesting epigenetic regulation of
expression. Further, outcomes insulin and HOMA-IR were significantly associated with
expression.
This study sheds light on potential mechanisms linking statins with type 2 diabetes risk, providing evidence on DNAm partially mediating statins' effects on insulin traits. Further efforts shall disentangle the molecular mechanisms through which statins may induce DNAm changes, potentially leading to
epigenetic regulation.
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