Estimating Risk of Cardiovascular Disease Among Long-Term Colorectal Cancer Survivors: A Nationwide Cohort Study

BackgroundConcerns about a growing number of colorectal cancer survivors have emerged regarding cardiovascular disease (CVD) risks. However, there is not yet a predictive tool that can estimate CVD risk and support the management of healthcare as well as disease prevention in terms of CVD risk among long-term colorectal cancer survivors.AimTo develop predictive tools to estimate individualized overall and each subtype of CVD risk using a nationwide cohort in South Korea.Methods and ResultsA total of 4,709 newly diagnosed patients with colorectal cancer who survived at least 5 years in the National Health Insurance System were analyzed. Cox proportional hazard regression was used for the identification of independent risk factors for the derivation of predictive nomograms, which were validated in an independent cohort (n = 3,957). Age, fasting serum glucose, γ-glutamyl transpeptidase, Charlson comorbidity index, household income, body mass index, history of chemotherapy, cigarette smoking, and alcohol consumption were identified as independent risk factors for either overall CVD or each subtype of CVD subtype. Based on the identified independent risk factors, six independent nomograms for each CVD category were developed. Validation by an independent cohort demonstrated a good calibration with a median C-index of 0.687. According to the nomogram-derived median score, relative risks of 2.643, 1.821, 4.656, 2.629, 4.248, and 5.994 were found for overall CVD, ischemic heart disease, myocardial infarction, total stroke, ischemic stroke, and hemorrhage stroke in the validation cohort.ConclusionsThe predictive tools were developed with satisfactory accuracy. The derived nomograms may support the estimation of overall and individual CVD risk for long-term colorectal cancer survivors.

An ensemble prediction model for COVID-19 mortality risk

Background: It's critical to identify COVID-19 patients with a higher death risk at early stage to give them better hospitalization or intensive care. However, thus far, none of the machine learning models has been shown to be successful in an independent cohort. We aim to develop a machine learning model which could accurately predict death risk of COVID-19 patients at an early stage in other independent cohorts. Methods: We used a cohort containing 4711 patients whose clinical features associated with patient physiological conditions or lab test data associated with inflammation, hepatorenal function, cardiovascular function and so on to identify key features. To do so, we first developed a novel data preprocessing approach to clean up clinical features and then developed an ensemble machine learning method to identify key features. Results: Finally, we identified 14 key clinical features whose combination reached a good predictive performance of AUC 0.907. Most importantly, we successfully validated these key features in a large independent cohort containing 15,790 patients. Conclusions: Our study shows that 14 key features are robust and useful in predicting the risk of death in patients confirmed SARS-CoV-2 infection at an early stage, and potentially useful in clinical settings to help in making clinical decisions.

A Differential Signature of Circulating miRNAs and Cytokines Between COVID-19 and Community-Acquired Pneumonia Uncovers Novel Physiopathological Mechanisms of COVID-19

Coronavirus Disease 2019 (COVID-19) pneumonia is a life-threatening infectious disease, especially for elderly patients with multiple comorbidities. Despite enormous efforts to understand its underlying etiopathogenic mechanisms, most of them remain elusive. In this study, we compared differential plasma miRNAs and cytokines profiles between COVID-19 and other community-acquired pneumonias (CAP). A first screening and subsequent validation assays in an independent cohort of patients revealed a signature of 15 dysregulated miRNAs between COVID-19 and CAP patients. Additionally, multivariate analysis displayed a combination of 4 miRNAs (miR-106b-5p, miR-221-3p, miR-25-3p and miR-30a-5p) that significantly discriminated between both pathologies. Search for targets of these miRNAs, combined with plasma protein measurements, identified a differential cytokine signature between COVID-19 and CAP that included EGFR, CXCL12 and IL-10. Significant differences were also detected in plasma levels of CXCL12, IL-17, TIMP-2 and IL-21R between mild and severe COVID-19 patients. These findings provide new insights into the etiopathological mechanisms underlying COVID-19.

Percepta Genomic Sequencing Classifier and decision-making in patients with high-risk lung nodules: a decision impact study

Background Incidental and screening-identified lung nodules are common, and a bronchoscopic evaluation is frequently nondiagnostic. The Percepta Genomic Sequencing Classifier (GSC) is a genomic classifier developed in current and former smokers which can be used for further risk stratification in these patients. Percepta GSC has the capability of up-classifying patients with a pre-bronchoscopy risk that is high (> 60%) to “very high risk” with a positive predictive value of 91.5%. This prospective, randomized decision impact survey was designed to test the hypothesis that an up-classification of risk of malignancy from high to very high will increase the rate of referral for surgical or ablative therapy without additional intervening procedures while increasing physician confidence. Methods Data were collected from 37 cases from the Percepta GSC validation cohort in which the pre-bronchoscopy risk of malignancy was high (> 60%), the bronchoscopy was nondiagnostic, and the patient was up-classified to very high risk by Percepta GSC. The cases were randomly presented to U.S pulmonologists in three formats: a pre-post cohort where each case is presented initially without and then with a GSG result, and two independent cohorts where each case is presented either with or without with a GSC result. Physicians were surveyed with respect to subsequent management steps and confidence in that decision. Results One hundred and one survey takers provided a total of 1341 evaluations of the 37 patient cases across the three different cohorts. The rate of recommendation for surgical resection was significantly higher in the independent cohort with a GSC result compared to the independent cohort without a GSC result (45% vs. 17%, p < 0.001) In the pre-post cross-over cohort, the rate increased from 17 to 56% (p < 0.001) following the review of the GSC result. A GSC up-classification from high to very high risk of malignancy increased Pulmonologists’ confidence in decision-making following a nondiagnostic bronchoscopy. Conclusions Use of the Percepta GSC classifier will allow more patients with early lung cancer to proceed more rapidly to potentially curative therapy while decreasing unnecessary intervening diagnostic procedures following a nondiagnostic bronchoscopy.

Integrative blood-derived epigenetic and transcriptomic analysis reveals the potential regulatory role of DNA methylation in ankylosing spondylitis

Background The currently known risk loci could explain a small proportion of the heritability of ankylosing spondylitis (AS). Epigenetics might account for the missing heritability. We aimed to seek more novel AS-associated DNA methylation alterations and delineate the regulatory effect of DNA methylation and gene expression with integrated analysis of methylome and transcriptome. Methods Epigenome-wide DNA methylation and mRNA expression were profiled in peripheral blood mononuclear cells (PBMCs) from 45 individuals (AS: health controls (HCs) = 30:15) with high-throughput array. The methylome was validated in an independent cohort (AS: HCs = 12:12). Pearson correlation analysis and causal inference tests (CIT) were conducted to determine potentially causative regulatory effects of methylation on mRNA expression. Results A total of 4794 differentially methylated positions (DMPs) were identified associated with AS, 2526 DMPs of which were validated in an independent cohort. Both cohorts highlighted T cell receptor (TCR) signaling and Th17 differentiation pathways. Besides, AS patients manifested increased DNA methylation variability. The methylation levels of 158 DMPs were correlated with the mRNA expression levels of 112 genes, which formed interconnected network concentrated on Th17 cell differentiation and TCR signaling pathway (LCK, FYN, CD3G, TCF7, ZAP70, CXCL12, and PLCG1). We also identified several cis-acting DNA methylation and gene expression changes associated with AS risk, which might regulate the cellular mechanisms underlying AS. Conclusions Our studies outlined the landscapes of epi-signatures of AS and several methylation-gene expression-AS regulatory axis and highlighted the Th17 cell differentiation and TCR signaling pathway, which might provide innovative molecular targets for therapeutic interventions for AS.

Predictors of an Immunogenic Response to the BNT162b2 mRNA COVID-19 Vaccination in Patients with Autoimmune Inflammatory Rheumatic Diseases Treated with Rituximab: A Multicenter Study

BackgroundTreatment with rituximab (RTX) blunts SARS-CoV-2 vaccination-induced humoral response. We sought to identify predictors of a positive immunogenic response to the BNT162b2 mRNA vaccine in patients with autoimmune inflammatory rheumatic diseases (AIIRD) treated with RTX (AIIRD-RTX).MethodsWe analyzed 108 AIIRD-RTX patients and 122 immunocompetent controls immunized with BNT162b2 mRNA vaccine participating in a multicenter vaccination study. Immunogenicity was defined by positive anti-SARS-CoV-2 S1/S2 IgG measured at 2 to 6 weeks after the second vaccine dose. We used a stepwise backward multiple logistic regression to identify predicting factors for a positive immunogenic response to vaccination and develop a predicting calculator, further validated in an independent cohort of AIIRD-RTX patients (n=48) immunized with the BNT162b2 mRNA vaccine.Results AIIRD-RTX patients who mounted a seropositive immunogenic response significantly differed from non-responders by lower number of RTX courses (median (range) 3 (1-10) vs 5 (1-15), p=0.007; lower cumulative RTX dose 6943.11±5975.74 vs 9780.95±7240.12 mg, p=0.033; higher IgG level prior to last RTX course (mean ± SD), 1189.78±576.28 vs. 884.33±302.31 mg/dL, p=0.002, and extended interval between RTX treatment and vaccination, 469.82±570.39 vs 162.08±160.12 days, p=0.0009, respectively. Patients with ANCA-associated vasculitis and inflammatory myositis had a low likelihood of a seropositive immunogenic response compared to patients with rheumatoid arthritis, odds ratio (OR) 0.209, 95% confidence interval (CI) 0.046-0.96, p=0.044 and OR 0.189, 95% CI 0.036-0.987, p=0.048, respectively. Based on these findings, we constructed a calculator predicting the probability of a seropositive immunogenic response following BNT162b2 mRNA vaccination which performed with 90.5% sensitivity, 59.3% specificity, 63.3% positive and 88.9% negative predictive values.ConclusionsThe predicting calculator might guide clinicians for optimal timing of BNT162b2 mRNA vaccination in AIIRD-RTX patients.

Genes and Weightlifting Performance

A recent case-control study identified 28 DNA polymorphisms associated with strength athlete status. However, studies of genotype-phenotype design are required to support those findings. The aim of the present study was to investigate both individually and in combination the association of 28 genetic markers with weightlifting performance in Russian athletes and to replicate the most significant findings in an independent cohort of Japanese athletes. Genomic DNA was collected from 53 elite Russian (31 men and 22 women, 23.3 ± 4.1 years) and 100 sub-elite Japanese (53 men and 47 women, 21.4 ± 4.2 years) weightlifters, and then genotyped using PCR or micro-array analysis. Out of 28 DNA polymorphisms, LRPPRC rs10186876 A, MMS22L rs9320823 T, MTHFR rs1801131 C, and PHACTR1 rs6905419 C alleles positively correlated (p < 0.05) with weightlifting performance (i.e., total lifts in snatch and clean and jerk in official competitions adjusted for sex and body mass) in Russian athletes. Next, using a polygenic approach, we found that carriers of a high (6–8) number of strength-related alleles had better competition results than carriers of a low (0–5) number of strength-related alleles (264.2 (14.7) vs. 239.1 (21.9) points; p = 0.009). These findings were replicated in the study of Japanese athletes. More specifically, Japanese carriers of a high number of strength-related alleles were stronger than carriers of a low number of strength-related alleles (212.9 (22.6) vs. 199.1 (17.2) points; p = 0.0016). In conclusion, we identified four common gene polymorphisms individually or in combination associated with weightlifting performance in athletes from East European and East Asian geographic ancestries.

Antibodies targeting merozoites induce natural killer cell degranulation and interferon gamma secretion and are associated with immunity against malaria

Natural killer cells are potent immune effectors that can be activated via antibody mediated Fc receptor engagement. Using multiparameter flow cytometry, we found that natural killer (NK) cells degranulate and release IFNγ upon stimulation with antibody-opsonized Plasmodium falciparum merozoites. Antibody-dependent NK activity (Ab-NK) was largely strain-transcending and enhanced the inhibition of invasion into erythrocytes. Ab-NK was associated with the successful control of parasitemia following experimental malaria challenge in African adults. In an independent cohort study in children, Ab-NK increased with age, was boosted by concurrent falciparum infections and associated with a lower risk of clinical episodes of malaria. Nine of 14 vaccine candidates tested induced Ab-NK including some less well-studied antigens - P41, P113, MSP11, RHOPH3, and Pf_11363200. These data highlight an important role for ab-NK in immunity against malaria and provide a new mechanism for the evaluation of vaccine candidates.

Cerebral Volumetric Correlates of Apathy in Alzheimer’s Disease and Cognitively Normal Older Adults: Meta-Analysis, Label-Based Review, and Study of an Independent Cohort

Background: Affecting nearly half of the patients with Alzheimer’s disease (AD), apathy is associated with higher morbidity and reduced quality of life. Basal ganglia and cortical atrophy have been implicated in apathy. However, the findings have varied across studies and left unclear whether subdomains of apathy may involve distinct neuroanatomical correlates. Objective: To identify neuroanatomical correlates of AD-associated apathy. Methods: We performed a meta-analysis and label-based review of the literature. Further, following published routines of voxel-based morphometry, we aimed to confirm the findings in an independent cohort of 19 patients with AD/mild cognitive impairment and 25 healthy controls evaluated with the Apathy Evaluation Scale. Results: Meta-analysis of 167 AD and 56 healthy controls showed convergence toward smaller basal ganglia gray matter volume (GMV) in apathy. Label-based review showed anterior cingulate, putamen, insula, inferior frontal gyrus (IFG) and middle temporal gyrus (MTG) atrophy in AD apathy. In independent cohort, with small-volume-correction, right putamen and MTG showed GMVs in negative correlation with Apathy Evaluation Scale total, behavioral, and emotional scores, and right IFG with emotional score (p < 0.05 family-wise error (FWE)-corrected), controlling for age, education, intracranial volume, and depression. With the Mini-Mental State Examination scores included as an additional covariate, the correlation of right putamen GMV with behavioral and emotional score, right MTG GMV with total and emotional score, and right IFG GMV with emotional score were significant. Conclusion: The findings implicate putamen, MTG and IFG atrophy in AD associated apathy, potentially independent of cognitive impairment and depression, and suggest potentially distinct volumetric correlates of apathy.

Venous tortuosity as a novel biomarker of rupture risk in arteriovenous malformations: ARI score

BackgroundRisk of rupture in arteriovenous malformations (AVMs) varies considerably among series. Hemodynamic factors, especially within the venous side of the circuit, seem to be responsible but are not yet well defined. We analyzed tortuosity in the draining vein as a potential new marker of rupture in AVMs, and propose a simple index to predict AVM bleeding.MethodsA retrospective analysis of the venous angioarchitecture of brain AVMs was carried out at our center from 2013 to 2021, with special attention to venous tortuosity. After univariate analysis, the features of interest were combined to construct several predictive models using multivariate logistic regression. The best model proposed was the new AVM rupture index (ARI), which was then validated in an independent cohort.Results68 AVMs were included in the first step and 32 in the validation cohort. Venous tortuosity, expressed as at least one curve >180°, was a significant predictor of rupture (p=0.023). The proposed bleeding index consisted of: venous tortuosity (any curve of >180°), single draining vein, and paraventricular/infratentorial location. It seems to be a robust evaluation tool, with an area under the receiver operating characteristic (AUROC) curve of 0.806 (95% CI 0.714 to 0.899), consistently replicated in the independent sample (AUROC 0.759 (95% CI 0.607 to 0.911)), and with an inter-rater kappa coefficient of 0.81 .ConclusionsVenous tortuosity may serve as a predictor of bleeding in AVMs that warrants further investigation. This likely new marker was one of the three elements of the proposed ARI. ARI outperformed the predictive accuracy of previous scores, and remained consistent in an independent cohort.