The association between fibrinogen levels and severity of coronary artery disease and long-term prognosis following percutaneous coronary intervention in patients with type 2 diabetes mellitus

BackgroundFibrinogen is a potential risk factor for the prognosis of CAD and is associated with the complexity of CAD. There is limited research specifically investigating the predictive role of fibrinogen in determining the severity of CAD among patients with T2DM, as well as its impact on the prognosis following PCI.MethodsThe study included 675 T2DM patients who underwent PCI at the Third People’s Hospital of Chengdu between April 27, 2018, and February 5, 2021, with 540 of them remaining after exclusions. The complexity of CAD was assessed using the SYNTAX score. The primary endpoint of the study was the incidence of MACCEs.ResultsAfter adjusting for multiple confounding factors, fibrinogen remained a significant independent risk factor for mid/high SYNTAX scores (SYNTAX score > 22, OR 1.184, 95% CI 1.022-1.373, P = 0.025). Additionally, a dose-response relationship between fibrinogen and the risk of complicated CAD was observed (SYNTAX score > 22; nonlinear P = 0.0043). The area under the receiver operating characteristic curve(AUROC) of fibrinogen for predicting mid/high SYNTAX score was 0.610 (95% CI 0.567–0.651, P = 0.0002). The high fibrinogen group (fibrinogen > 3.79 g/L) had a higher incidence of calcified lesions and an elevated trend of more multivessel disease and chronic total occlusion. A total of 116 patients (21.5%) experienced MACCEs during the median follow-up time of 18.5 months. After adjustment, multivariate Cox regression analysis confirmed that fibrinogen (HR, 1.138; 95% CI 1.010-1.284, P = 0.034) remained a significant independent risk factor for MACCEs. The AUROC of fibrinogen for predicting MACCEs was 0.609 (95% CI 0.566-0.650, P = 0.0002). Individuals with high fibrinogen levels (fibrinogen > 4.28 g/L) had a higher incidence of acute myocardial infarction (P < 0.001), MACCEs (P < 0.001), all-cause death (P < 0.001), stroke (P = 0.030), and cardiac death (P = 0.002). Kaplan-Meier analysis revealed a higher incidence of MACCEs in the high fibrinogen group (Log-Rank test: P < 0.001).ConclusionsElevated fibrinogen levels were associated with increased coronary anatomical complexity (as quantified by the SYNTAX score) and a higher incidence of MACCEs after PCI in patients with T2DM

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Abstract Background Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Funding GMP, PN, and CW are supported by NHLBI R01HL127564. GMP and PN are supported by R01HL142711. AG acknowledge support from the Wellcome Trust (201543/B/16/Z), European Union Seventh Framework Programme FP7/2007–2013 under grant agreement no. HEALTH-F2-2013–601456 (CVGenes@Target) & the TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation’s Grant number NNF15CC0018486. JMM is supported by American Diabetes Association Innovative and Clinical Translational Award 1–19-ICTS-068. SR was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant No 312062), the Finnish Foundation for Cardiovascular Research, the Sigrid Juselius Foundation, and University of Helsinki HiLIFE Fellow and Grand Challenge grants. EW was supported by the Finnish innovation fund Sitra (EW) and Finska Läkaresällskapet. CNS was supported by American Heart Association Postdoctoral Fellowships 15POST24470131 and 17POST33650016. Charles N Rotimi is supported by Z01HG200362. Zhe Wang, Michael H Preuss, and Ruth JF Loos are supported by R01HL142302. NJT is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215–2001) and the MRC Integrative Epidemiology Unit (MC_UU_00011), and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A19169). Ruth E Mitchell is a member of the MRC Integrative Epidemiology Unit at the University of Bristol funded by the MRC (MC_UU_00011/1). Simon Haworth is supported by the UK National Institute for Health Research Academic Clinical Fellowship. Paul S. de Vries was supported by American Heart Association grant number 18CDA34110116. Julia Ramierz acknowledges support by the People Programme of the European Union’s Seventh Framework Programme grant n° 608765 and Marie Sklodowska-Curie grant n° 786833. Maria Sabater-Lleal is supported by a Miguel Servet contract from the ISCIII Spanish Health Institute (CP17/00142) and co-financed by the European Social Fund. Jian Yang is funded by the Westlake Education Foundation. Olga Giannakopoulou has received funding from the British Heart Foundation (BHF) (FS/14/66/3129). CHARGE Consortium cohorts were supported by R01HL105756. Study-specific acknowledgements are available in the Additional file 32: Supplementary Note. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services.Peer reviewedPublisher PD

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Funding Information: GMP, PN, and CW are supported by NHLBI R01HL127564. GMP and PN are supported by R01HL142711. AG acknowledge support from the Wellcome Trust (201543/B/16/Z), European Union Seventh Framework Programme FP7/2007–2013 under grant agreement no. HEALTH-F2-2013–601456 (CVGenes@Target) & the TriPartite Immunometabolism Consortium [TrIC]-Novo Nordisk Foundation’s Grant number NNF15CC0018486. JMM is supported by American Diabetes Association Innovative and Clinical Translational Award 1–19-ICTS-068. SR was supported by the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant No 312062), the Finnish Foundation for Cardiovascular Research, the Sigrid Juselius Foundation, and University of Helsinki HiLIFE Fellow and Grand Challenge grants. EW was supported by the Finnish innovation fund Sitra (EW) and Finska Läkaresällskapet. CNS was supported by American Heart Association Postdoctoral Fellowships 15POST24470131 and 17POST33650016. Charles N Rotimi is supported by Z01HG200362. Zhe Wang, Michael H Preuss, and Ruth JF Loos are supported by R01HL142302. NJT is a Wellcome Trust Investigator (202802/Z/16/Z), is the PI of the Avon Longitudinal Study of Parents and Children (MRC & WT 217065/Z/19/Z), is supported by the University of Bristol NIHR Biomedical Research Centre (BRC-1215–2001) and the MRC Integrative Epidemiology Unit (MC_UU_00011), and works within the CRUK Integrative Cancer Epidemiology Programme (C18281/A19169). Ruth E Mitchell is a member of the MRC Integrative Epidemiology Unit at the University of Bristol funded by the MRC (MC_UU_00011/1). Simon Haworth is supported by the UK National Institute for Health Research Academic Clinical Fellowship. Paul S. de Vries was supported by American Heart Association grant number 18CDA34110116. Julia Ramierz acknowledges support by the People Programme of the European Union’s Seventh Framework Programme grant n° 608765 and Marie Sklodowska-Curie grant n° 786833. Maria Sabater-Lleal is supported by a Miguel Servet contract from the ISCIII Spanish Health Institute (CP17/00142) and co-financed by the European Social Fund. Jian Yang is funded by the Westlake Education Foundation. Olga Giannakopoulou has received funding from the British Heart Foundation (BHF) (FS/14/66/3129). CHARGE Consortium cohorts were supported by R01HL105756. Study-specific acknowledgements are available in the Additional file : Supplementary Note. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe

The Predictive Value of Different Nutritional Indices Combined with the GRACE Score in Predicting the Risk of Long-Term Death in Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention

Nutritional status is associated with prognosis in acute coronary syndrome (ACS) patients. Although the Global Registry of Acute Coronary Events (GRACE) risk score is regarded as a relevant risk predictor for the prognosis of ACS patients, nutritional variables are not included in the GRACE score. This study aimed to compare the prognostic ability of the Geriatric Nutritional Risk Index (GNRI) and Prognostic Nutritional Index (PNI) in predicting long-term all-cause death in ACS patients undergoing percutaneous coronary intervention (PCI) and to determine whether the GNRI or PNI could improve the predictive value of the GRACE score. A total of 799 patients with ACS who underwent PCI from May 2018 to December 2019 were included and regularly followed up. The performance of the PNI in predicting all-cause death was better than that of the GNRI [C-index, 0.677 vs. 0.638, p = 0.038]. The addition of the PNI significantly improved the predictive value of the GRACE score for all-cause death [increase in C-index from 0.722 to 0.740; IDI 0.006; NRI 0.095; p < 0.05]. The PNI was superior to the GNRI in predicting long-term all-cause death in ACS patients undergoing PCI. The addition of the PNI to the GRACE score could significantly improve the prediction of long-term all-cause death

Evaluation of an artificial intelligence-based clinical trial matching system in Chinese patients with hepatocellular carcinoma: a retrospective study

Abstract Background Artificial intelligence (AI)-assisted clinical trial screening is a promising prospect, although previous matching systems were developed in English, and relevant studies have only been conducted in Western countries. Therefore, we evaluated an AI-based clinical trial matching system (CTMS) that extracts medical data from the electronic health record system and matches them to clinical trials automatically. Methods This study included 1,053 consecutive inpatients primarily diagnosed with hepatocellular carcinoma who were referred to the liver tumor center of an academic medical center in China between January and December 2019. The eligibility criteria extracted from two clinical trials, patient attributes, and gold standard were decided manually. We evaluated the performance of the CTMS against the established gold standard by measuring the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and run time required. Results The manual reviewers demonstrated acceptable interrater reliability (Cohen’s kappa 0.65–0.88). The performance results for the CTMS were as follows: accuracy, 92.9–98.0%; sensitivity, 51.9–83.5%; specificity, 99.0–99.1%; PPV, 75.7–85.1%; and NPV, 97.4–98.9%. The time required for eligibility determination by the CTMS and manual reviewers was 2 and 150 h, respectively. Conclusions We found that the CTMS is particularly reliable in excluding ineligible patients in a significantly reduced amount of time. The CTMS excluded ineligible patients for clinical trials with good performance, reducing 98.7% of the work time. Thus, such AI-based systems with natural language processing and machine learning have potential utility in Chinese clinical trials

Triglyceride-glucose index is associated with recurrent revascularization in patients with type 2 diabetes mellitus after percutaneous coronary intervention

Abstract Background The Triglyceride-glucose (TyG) index, as a surrogate marker of insulin resistance, is independently associated with the severity of coronary artery lesions and the prognosis of coronary heart disease. The investigation aimed to explore the relationship between the TyG index and recurrent revascularization in individuals with type 2 diabetes mellitus (T2DM) resulting from the progression of lesions or in-stent restenosis (ISR) after percutaneous coronary intervention (PCI). Method A total of 633 patients who met the inclusion and exclusion criteria were enrolled and divided into three groups based on the tertiles of the TyG index. The primary endpoint was recurrent revascularization resulting from the progression of lesions or ISR. All-cause death was considered as the competing risk event. Competing risk analysis and Cox regression analysis for predicting recurrent revascularization after PCI were conducted stepwise. Variables were standardized to make the hazard ratio (HR), subdistribution hazard ratio (SHR) and corresponding 95% CI more consistent prior to being used for fitting the multivariate risk model. The predictive ability of the TyG index was evaluated using several measures, including the ROC curve, likelihood ratio test, Akaike’s information criteria, category-free continuous net reclassification improvement (cNRI > 0), and integrated discrimination improvement (IDI). Internal validation was conducted through bootstrapping with 1000 resamples. Results During a median follow-up period of 18.33 months, a total of 64 (10.11%) patients experienced recurrent revascularization, including 55 cases of lesion progression and 9 cases of in-stent restenosis. After controlling for competitive risk events, the TyG index was independently associated with a higher risk of recurrent revascularization [SHR:1.4345, (95% CI 1.1458–1.7959), P = 0.002]. The likelihood ratio test and Akaike’s information criteria showed that the TyG index significantly improves the prognostic ability. Additionally, adding the TyG index improved the ability of the established risk model in predicting recurrent revascularization, indicated by a C-index of 0.759 (95% CI 0.724–0.792, P  0 of 0.170 (95% CI 0.023–0.287, P < 0.05), and an IDI of 0.024 (95% CI 0.009–0.039, P = 0.002). These results remained consistent when the models containing TyG index were confirmed using an internal bootstrap validation method. Conclusion The findings highlight the potential of the TyG index as a predictor of recurrent revascularization. Lesion progression emerged as the primary contributor to recurrent revascularization instead of in-stent restenosis. The incorporation of the TyG index into risk prediction models is likely to be beneficial for accurate risk stratification in order to improve prognosis

Mendelian randomization analysis does not support causal associations of birth weight with hypertension risk and blood pressure in adulthood

International audienceEpidemiology studies suggested that low birthweight was associated with a higher risk of hypertension in later life. However, little is known about the causality of such associations. In our study, we evaluated the causal association of low birthweight with adulthood hypertension following a standard analytic protocol using the study-level data of 183,433 participants from 60 studies (CHARGE-BIG consortium), as well as that with blood pressure using publicly available summary-level genome-wide association data from EGG consortium of 153,781 participants, ICBP consortium and UK Biobank cohort together of 757,601 participants. We used seven SNPs as the instrumental variable in the study-level analysis and 47 SNPs in the summary-level analysis. In the study-level analyses, decreased birthweight was associated with a higher risk of hypertension in adults (the odds ratio per 1 standard deviation (SD) lower birthweight, 1.22; 95% CI 1.16 to 1.28), while no association was found between genetically instrumented birthweight and hypertension risk (instrumental odds ratio for causal effect per 1 SD lower birthweight, 0.97; 95% CI 0.68 to 1.41). Such results were consistent with that from the summary-level analyses, where the genetically determined low birthweight was not associated with blood pressure measurements either. One SD lower genetically determined birthweight was not associated with systolic blood pressure (β = − 0.76, 95% CI − 2.45 to 1.08 mmHg), 0.06 mmHg lower diastolic blood pressure (β = − 0.06, 95% CI − 0.93 to 0.87 mmHg), or pulse pressure (β = − 0.65, 95% CI − 1.38 to 0.69 mmHg, all p > 0.05). Our findings suggest that the inverse association of birthweight with hypertension risk from observational studies was not supported by large Mendelian randomization analyses

Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Publisher Copyright: © 2022, The Author(s).Background: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. Results: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3–5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. Conclusions: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.Peer reviewe