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CD4+ T cells are essential for protection against viruses, including SARS-CoV-2. The sensitivity of CD4+ T cells to mutations in SARS-CoV-2 variants of concern (VOCs) is poorly understood. Here, we isolated 159 SARS-CoV-2-specific CD4+ T cell clones from healthcare workers previously infected with wild-type SARS-CoV-2 (D614G) and defined 21 epitopes in spike, membrane and nucleoprotein. Lack of CD4+ T cell cross-reactivity between SARS-CoV-2 and endemic beta-coronaviruses suggested these responses arose from naïve rather than pre-existing cross-reactive coronavirus-specific T cells. Of the 17 epitopes located in the spike protein, 10 were mutated in VOCs and CD4+ T cell clone recognition of 7 of them was impaired, including 3 of the 4 epitopes mutated in omicron. Our results indicated that broad targeting of epitopes by CD4+ T cells likely limits evasion by current VOCs. However, continued genomic surveillance is vital to identify new mutations able to evade CD4+ T cell immunity.

B-cell-depleting agents are among the most commonly used drugs to treat haemato-oncological and autoimmune diseases. They rapidly induce a state of peripheral B-cell aplasia with the potential to interfere with nascent vaccine responses, particularly to novel antigens. We have examined the relationship between B-cell reconstitution and SARS-CoV-2 vaccine responses in two cohorts of patients previously exposed to B-cell-depleting agents: a cohort of patients treated for haematological B-cell malignancy and another treated for rheumatological disease. B-cell depletion severely impairs vaccine responsiveness in the first 6 months after administration: SARS-CoV-2 antibody seroprevalence was 42.2% and 33.3% in the haemato-oncological patients and rheumatology patients, respectively and 22.7% in patients vaccinated while actively receiving anti-lymphoma chemotherapy. After the first 6 months, vaccine responsiveness significantly improved during early B-cell reconstitution; however, the kinetics of reconstitution was significantly faster in haemato-oncology patients. The AstraZeneca ChAdOx1 nCoV-19 vaccine and the Pfizer BioNTech 162b vaccine induced equivalent vaccine responses; however, shorter intervals between vaccine doses (36 m previously), vaccine non-responsiveness was independent of peripheral B-cell reconstitution. The findings have important implications for primary vaccination and booster vaccination strategies in individuals clinically vulnerable to SARS-CoV-2., B cell depleting drugs including rituximab are commonly used to treat haematological malignancy and autoimmune diseases but may impair the immunological response to vaccination. This study investigates SARS-CoV-2 vaccine responses in individuals with haematological and rheumatological disease with previously exposure to B cell depleting agents. Vaccination within the first 6 months of B cell depletion is associated with significant impairment of vaccine responsiveness; however, rheumatology and haemato-oncological patients display different kinetics of B cell reconstitution corresponding to differential vaccine responsiveness over time., Graphical Abstract Graphical Abstract

Heart failure (HF) is a leading cause of morbidity and mortality worldwide1. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained2–4. We report the largest GWAS meta-analysis of HF to-date, comprising 47,309 cases and 930,014 controls. We identify 12 independent variant associations with HF at 11 genomic loci, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function suggesting shared genetic aetiology. Expression quantitative trait analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homeostasis (BAG3), and cellular senescence (CDKN1A). Using Mendelian randomisation analysis we provide new evidence supporting previously equivocal causal roles for several HF risk factors identified in observational studies, and demonstrate CAD-independent effects for atrial fibrillation, body mass index, hypertension and triglycerides. These findings extend our knowledge of the genes and pathways underlying HF and may inform the development of new therapeutic approaches.

Prevalence of heart failure is increasing, especially in the elderly population. Noncardiac comorbidities complicate heart failure care and are increasingly common in elderly patients with reduced or preserved ejection fraction heart failure, owing to prolongation of patient's lives by advances in chronic heart failure (CHF) management. Common comorbidities include respiratory disease, renal dysfunction, anemia, arthritis, obesity, diabetes mellitus, cognitive dysfunction, and depression. These conditions contribute to the progression of the disease and may alter the response to treatment, partly as polypharmacy is inevitable in these patients. Cardiologists and other physicians caring for patients with CHF need to be vigilant to comorbid conditions that complicate the care of these patients. There is now more guidance on management of noncardiac comorbidities in heart failure, and this article contains a comprehensive review of the most recent updates on management of noncardiac comorbidities in CHF.

Left ventricular (LV) hypertrophy (LVH) is a heritable trait that is common in type 2 diabetes and is associated with the development of heart failure. The transcriptional factor Kruppel like factor 15 (KLF15) is expressed in the heart and acts as a repressor of cardiac hypertrophy in experimental models. This study investigated if KLF15 gene variants were associated with LVH in type 2 diabetes. In stage 1 of a 2-stage approach, patients with type 2 diabetes and no known cardiac disease were prospectively recruited for a transthoracic echocardiographic assessment (Melbourne Diabetes Heart Cohort) (n = 318) and genotyping of two KLF15 single nucleotide polymorphisms (SNPs) (rs9838915, rs6796325). In stage 2, the association of KLF15 SNPs with LVH was investigated in the Genetics of Diabetes Audit and Research in Tayside Scotland (Go-DARTS) type 2 diabetes cohort (n = 5631). The KLF15 SNP rs9838915 A allele was associated in a dominant manner with LV mass before (P = 0.003) and after (P = 0.001) adjustment for age, gender, body mass index (BMI) and hypertension, and with adjusted septal (P, Highlights • KLF15 SNP rs9838915 A allele is associated with increased LV mass in patients with 2 diabetes. • KLF15 SNP rs9838915 predicts incident heart failure hospitalization. • Genotyping KLF15 SNP rs9838915 allowed more precise stratification of the risk of heart failure hospitalization. Left ventricular hypertrophy (LVH) is a heritable trait that is common in patients with diabetes. The Kruppel like factor 15 (KLF15) is expressed in the heart and acts as a repressor of cardiac hypertrophy and fibrosis. Our study provides evidence that genetic variation in KLF15 is associated with LVH in patients with type 2 diabetes and these findings were then replicated in an independent cohort of patients with type 2 diabetes. The KLF15 genetic variant was also associated with first heart failure hospitalization. These findings add to our understanding of the molecular mechanisms that contribute to increased LV mass.

Chronic lymphocytic leukemia (B-CLL) and small lymphocytic lymphoma (SLL) are part of the same disease classification but are defined by differential distribution of tumor cells. B-CLL is characterized by significant immune suppression and dysregulation but this is not typical of patients with SLL. Natural killer cells (NK) are important mediators of immune function but have been poorly studied in patients with B-CLL/SLL. Here we report for the first time the NK cell phenotype and function in patients with B-CLL and SLL alongside their transcriptional profile. We show for the first time impaired B-CLL NK cell function in a xenograft model with reduced activating receptor expression including NKG2D, DNAM-1 and NCRs in-vitro. Importantly, we show these functional differences are associated with transcriptional downregulation of cytotoxic pathway genes, including activating receptors, adhesion molecules, cytotoxic molecules and intracellular signalling molecules, which remain intact in patients with SLL. In conclusion, NK cell function is markedly influenced by the anatomical site of the tumor in patients with B-CLL/SLL and lymphocytosis leads to marked impairment of NK cell activity. These observations have implications for treatment protocols which seek to preserve immune function by limiting the exposure of NK cells to tumor cells within the peripheral circulation.

Heart failure is a common disease with high levels of morbidity and mortality. Current treatment comprises β-blockers, ACE inhibitors, aldosterone antagonists and diuretics. Variation in clinical response seen in patients begs the question of whether there is a pharmacogenetic component yet to be identified. To date, the genes most studied involve the β-1, β-2, α-2 adrenergic receptors and the renin-angiotensin-aldosterone pathway, mainly focusing on SNPs. However results have been inconsistent. Genome-wide association studies and next-generation sequencing are seen as alternative approaches to discovering genetic variations influencing drug response. Hopefully future research will lay the foundations for genotype-led drug management in these patients with the ultimate aim of improving their clinical outcome.

Heart failure is a complex clinical syndrome that occurs at the end stage of heart disease. Despite advances in therapy for heart failure, improvement of clinical outcomes remains a challenge for physicians. The identification of treatment response early in the course of disease would be useful to improve management of these patients. The aim of this study was to identify novel biomarkers in plasma that could predict treatment response in patients with heart failure.Patients with heart failure who met inclusion and exclusion criteria according to the guidelines of the European Society of Cardiology were recruited. Uptitration of angiotensin-converting enzyme inhibitors and β blockers was performed over 6 months. Patients were followed up for clinical events within the next 24 months. Plasma proteins in patients who responded to standard treatment (responders) were compared with patients who died or were re-admitted for heart failure (non-responders). Plasma samples were depleted of 14 high abundance proteins with a multiple affinity removal system column (MARS). Then plasma samples were analysed on two-dimensional liquid chromatography coupled to a tandem mass spectrometry (2D LC-ESI-MS/MS) in high definition mode (HDMS(E)) to identify and quantify the different expression of proteins in plasma. Finally, ELISA was used to verify candidate biomarkers.Participants were 100 patients with heart failure matched for sex and age (50 responders [25 women], 50 non-responders [25 women], mean age 76·6 years [SD 8·1]). Of the non-responders, 18 died and 32 were re-admitted to hospital. 2D LC-ESI-MS/MS showed that the expression of neurotrimin (NTM) was highly upregulated, by 26·5 times (p0·0001), in the responder group compared with the non-responder group. ELISA in the verification phase showed that the concentrations of NTM in plasma were significantly higher in the responders and lower in the non-responders (mean 4·73 log10 relative light units [SD 0·07] vs 4·70 [0·08], p=0·036). When ANOVA with Bonferroni post-hoc comparisons was used in three outcome subgroups (responders, patients re-admitted to hospital, and deaths), NTM concentrations were significantly different between death and the other groups (higher in responder vs death group, p0·0001; higher in re-admission vs death group, p=0·001).Our findings suggest that NTM as a novel biomarker in heart failure will not only add information to understand the pathophysiological mechanisms of heart failure better, but also might provide a more accurate prediction of treatment response to guide medical therapy. In addition, a novel therapeutic target could be identified for design of drugs to improve outcomes. Futher work is required in larger populations to confirm this biomarker.European Union's Seventh Framework Programme (BIOSTAT-CHF), John and Lucille van Geest Foundation.

Controversy exists regarding the importance of glycaemic control in patients with type 2 diabetes mellitus (T2DM) and chronic heart failure (CHF) based on conflicting previous reports that had used a single baseline HbA1c. HbA1c variability has also been linked to vascular complications in T2DM. We

Background— Type 2 diabetes mellitus is an independent risk factor for heart failure development, but the relationship between incident heart failure and antecedent glycemia has not been evaluated. Methods and Results— The Genetics of Diabetes Audit and Research in Tayside Study study holds data for 8683 individuals with type 2 diabetes mellitus. Dispensed prescribing, hospital admission data, and echocardiography reports were linked to extract incident heart failure cases from December 1998 to August 2011. All available HbA1c measures until heart failure development or end of study were used to model HbA1c time-dependently. Individuals were observed from study enrolment until heart failure development or end of study. Proportional hazard regression calculated heart failure development risk associated with specific HbA1c ranges accounting for comorbidities associated with heart failure, including blood pressure, body mass index, and coronary artery disease. Seven hundred and one individuals with type 2 diabetes mellitus (8%) developed heart failure during follow up (mean 5.5 years, ±2.8 years). Time-updated analysis with longitudinal HbA1c showed that both HbA1c P value 10% (hazard ratio =1.80; 95% confidence interval, 1.60–2.16; P value Conclusions— Both high and low HbA1c predicted heart failure development in our cohort, forming a U-shaped relationship.

Heart failure is a common disease with high levels of morbidity and mortality. A large body of evidence guiding treatment shows prognostic benefit with beta-blockers and angiotensin-converting enzyme inhibitors, while diuretics are commonly prescribed for symptomatic benefit. Wide variation in drug response between clinically similar patients is a significant problem. Evidence suggests this may have a genetic component. Variation in candidate genes including the beta-1, beta-2, and alpha-2 adrenergic receptors, the renin-angiotensin-aldosterone pathway and genes involved in renal electrolyte handling with diuretics may be important. Single-nucleotide polymorphisms (SNPs) potentially influencing drug response include the Arg 389 Gly variant and the Ser 49 Gly variant in the beta-1 adrenergic receptor, the Arg 16 Gly, Gln 27 Glu, and Thr 164 Ile polymorphisms within the beta-2 adrenergic receptor, an insertion at the 287th base pair in the angiotensin-converting enzyme and the Gly 264 Ala mutation in the sodium chloride co-transporter. However, research addressing the clinical significance of these polymorphisms has yielded conflicting results that have had no influence on clinical practice. Genome-wide association studies may provide an alternative approach to discovering genetic variations influencing drug response, a relatively unchartered area in heart failure management. If future work in this area produces a strong case that variation in drug response has a specific and clinically meaningful genetic component, this could be used to guide drug dosing based on genotype; a step forward in the journey toward personally tailored medicine.

In diabetes, hyperglycemia increases the risk of cardiovascular events. However, intensive glycemic control predicted worse outcome in recent studies. We assessed the link between glycemic control and chronic heart failure (CHF) development. We studied the diabetic population of Go-DARTS (n=8 890)

Introduction Heart failure is a major health problem in western countries with high costs and poor outcome despite advances in therapy. 50% of patients die within 4 years of presentation, and 40% of patients admitted are dead or rehospitalised within 1 year. We searched for biomarkers in plasma that could predict treatment response in patients with heart failure, using bottom-up proteomics (liquid chromatography tandem mass spectrometry). Methods Heart failure patients were recruited and their treatment with ACE-inhibitors and β–blockers was optimised over 6 months. Major adverse events (death or heart failure hospitalisation) were recorded over the next 24 months. Plasma proteins in 50 patients who responded to standard treatment (responders) were compared to 50 patients who did not respond (non-responders). Plasma samples were pooled, depleted of 14 high abundance proteins and then trypsinised to peptides. Peptides were analysed on 2-dimensional liquid chromatography coupled to electrospray high-definition ion mobility tandem mass spectrometry. Results 220 proteins were similar between responders and non-responders. A further 220 plasma proteins were identified which were over or under-expressed in responders, with 144 up-regulated and 76 down-regulated. Several of these proteins have the potential to become new biomarkers for predicting treatment response in patients with heart failure which are classified according to 5 pathobiological processes in heart failure: (1) Neurohormones: Hepatocyte growth factor like protein and Insulin like growth factor II; (2) Vascular system: Intercellular adhesion molecule 2, Sex hormone binding globulin and Prostaglandin H2 D isomerase; (3) Inflammation: C reactive protein and Mannose binding protein C; (4) Matrix and cellular remodelling: Retinol binding protein 4; (5) Cardio-renal system: Cystatin C. Implications The discovery of new biomarkers for predicting treatment response in this study will lead to improved understanding of the basis of this response, as well as the development of a more personalised treatment by giving guidance to medical therapy. For example, in heart failure patients with abnormally elevated levels of a marker for matrix and cellular remodelling as Retinol binding protein 4 may benefit from a drug for reducing collagen deposition. In this way, the unnecessary prescription of therapy to non-responders may be avoided and novel therapeutic targets could be identified for design of therapies that may improve outcomes.

Background Left ventricular hypertrophy has multiple aetiologies including diabetes and genetic factors. We aimed to identify genetic variants predicting left ventricular hypertrophy in diabetic individuals. Methods Demographic, echocardiographic, prescribing, morbidity, mortality and genotyping databases connected with the Genetics of Diabetes Audit and Research in Tayside, Scotland project were accurately linked using a patient-specific identifier. Left ventricular hypertrophy cases were identified using echocardiographic data. Genotyping data from 973 cases and 1443 non-left ventricular hypertrophy controls were analysed, investigating whether single nucleotide polymorphisms associated with left ventricular hypertrophy in previous Genome Wide Association Studies predicted left ventricular hypertrophy in our population of individuals with type 2 diabetes. Meta-analysis assessed overall significance of these single nucleotide polymorphisms, which were also used to create gene scores. Logistic regression assessed whether these scores predicted left ventricular hypertrophy. Results Two single nucleotide polymorphisms previously associated with left ventricular hypertrophy were significant: rs17132261: OR 2.03, 95% CI 1.10-3.73, p-value 0.02 and rs2292462: OR 0.82, 95% CI 0.73-0.93 and p-value 2.26x10-3. Meta-analysis confirmed rs17132261 and rs2292462 were associated with left ventricular hypertrophy (p=1.03x10-8 and p=5.86x10-10 respectively) and one single nucleotide polymorphisms in IGF1R (rs4966014) became genome wide significant upon meta-analysis although was not significant in our study. Gene scoring based on published single nucleotide polymorphisms also predicted left ventricular hypertrophy in our study. Rs17132261, within SLC25A46, encodes a mitochondrial phosphate transporter, implying abnormal myocardial energetics contribute to left ventricular hypertrophy development. Rs2292462 lies within the obesity-implicated neuromedin B gene. Rs4966014 lies within the IGF1R1 gene. IGF1 signalling is an established factor in cardiac hypertrophy. Conclusions We created a resource to study genetics of left ventricular hypertrophy in diabetes and validated our left ventricular hypertrophy phenotype in replicating single nucleotide polymorphisms identified by previous genome wide association studies investigating left ventricular hypertrophy.

Background As IT dominates cardiology, the ability of centres to link clinical databases to perform outcome based research has increased significantly. Good quality research relies on the ability to accurately identify and characterise the disease of interest in the population. Heart Failure is one such disease that is often challenging to define from datasets. Uniquely we have the ability to link the Tayside echocardiography dataset to other regional datasets including dispensed prescription and hospitalisation data. Echocardiography reports are commonly comprised of structured, usually numerical values, and a free text component to store overall conclusions or impressions. We therefore sought to develop a computer algorithm to determine LV function from the free text and subsequently to validate the ability to define systolic HF based upon LVSD and loop diuretic therapy. Methods We iteratively the algorithm to process the free text component the reports and determine the degree of impairment. The algorithm was comprised of a lexicon of words and phrases and applied with negation detection. This was repetitively enhanced by recurrent processing of a subset of the data. The final algorithm was subsequently applied to the full dataset and was validated, first, against blinded manual review of a subset of reports and second by blinded review of the stored images. The data were then linked using a unique patient identifier to the dispensed prescribing data to determine loop diuretic use. The specificity of diagnosis of systolic heart failure was examined by blinded case note review. Results The database contained 153 836 reports on 63 309 individuals. The lexicon comprised 488 keywords or phrases. When applied to the data 145 525 reports were classified (94.4%), while 8584 remained unclassified. (5969 (70%) contained no information in the free text fields, and the remainder provided either insufficient data on left ventricular function or severe spelling or typographical errors, preventing matching.) 19 758 were classified as having LVSD (5378 (27%) mild, 818 (4%) mild to moderate, 4646 (24%) moderate, 583 (3%) moderate to severe and 8333 (42%) severe). The validation of 1000 reports reviewed for the presence or absence of LVSD found concurrence with the algorithm in 980 (98%) cases. Blinded review of the stored movies and images revealed a 90% concordance for the presence or absence of LVSD. Record linkage with the dispensed prescription dataset identified 9875 individuals with LVSD who also received loop diuretic therapy. Validation, by case note review, demonstrated a 91% concordance with a clinical diagnosis of systolic HF. Conclusion A computer algorithm can quickly and accurately identify the degree of LVSD from the free text component on an echocardiogram report and the presence of LVSD and combined with loop diuretic use is specific for a diagnosis of systolic heart failure.