Genetically Determined High Levels of Iron Parameters Are Protective for Coronary Artery Disease

The observation that premenopausal women have a relatively low incidence of heart disease led in the nineteen eighties to the hypothesis that iron deficiency protects against heart diseases. These early observations were followed-up by conflicting epidemiological data. To confer causal relationships from epidemiological data is challenging as results can be influenced by residual confounding or reverse causation. For bias reduction, an alternative analysis strategy utilizing single-nucleotide polymorphisms (SNPs) as instrumental variables (Mendelian Randomization) has been developed. A recent study using 3 iron status associated SNPs suggested a protective effect of a higher iron status on the development of CAD.3 With a larger set of SNPs covering different components of iron metabolism, we aimed to provide a reliable answer to this lingering question

Iron deficiency in worsening heart failure is associated with reduced estimated protein intake, fluid retention, inflammation and antiplatelet use

AIMS: Iron deficiency (ID) is common in heart failure (HF) patients and negatively impacts symptoms and prognosis. The aetiology of ID in HF is largely unknown. We studied determinants and the biomarker profile of ID in a large international HF cohort. METHODS AND RESULTS: We studied 2357 worsening HF patients from the BIOSTAT-CHF cohort. ID was defined as transferrin saturation <20%. Univariable and multivariable logistic regression models were constructed to identify determinants for ID. We measured 92 cardiovascular markers (Olink Cardiovascular III) to establish a biomarker profile of ID. The primary endpoint was the composite of all-cause mortality and first HF rehospitalization. Mean age (±standard deviation) of all patients was 69 ± 12.0 years, 26.1% were female and median N-terminal pro B-type natriuretic peptide levels (+interquartile range) were 4305 (2360-8329) ng/L. Iron deficiency was present in 1453 patients (61.6%), with highest prevalence in females (71.1% vs. 58.3%; P < 0.001). Independent determinants of ID were female sex, lower estimated protein intake, higher heart rate, presence of peripheral oedema and orthopnoea, chronic kidney disease, lower haemoglobin, higher C-reactive protein levels, lower serum albumin levels, and P2Y12 inhibitor use (all P < 0.05). None of these determinants were sex-specific. The biomarker profile of ID largely consisted of pro-inflammatory markers, including paraoxonase 3 (PON3) and tartrate-resistant acid phosphatase type 5. In multivariable Cox proportional hazard regression analyses, ID was associated to worse outcome, independently of predictors of ID (hazard ratio 1.25, 95% confidence interval 1.06-1.46; P = 0.007). CONCLUSION: Our data suggest that the aetiology of ID in worsening HF is complex, multifactorial and seems to consist of a combination of reduced iron uptake (malnutrition, fluid overload), impaired iron storage (inflammation, chronic kidney disease), and iron loss (antiplatelets)

Tumor biomarkers:association with heart failure outcomes

Background: There is increasing recognition that heart failure (HF) and cancer are conditions with a number of shared characteristics. Objectives: To explore the association between tumor biomarkers and HF outcomes. Methods: In 2,079 patients of BIOSTAT-CHF cohort, we measured six established tumor biomarkers: CA125, CA15-3, CA19-9, CEA, CYFRA 21-1, and AFP. Results: During a median follow-up of 21 months, 555 (27%) patients reached the primary endpoint of all-cause mortality. CA125, CYFRA 21-1, CEA, and CA19-9 levels were positively correlated with NT-proBNP quartiles (all P&lt;0.001, P for trend &lt;0.001), and were respectively associated with a hazard ratio of 1.17 (95% CI 1.12 – 1.23; P&lt;0.0001), 1.45 (95% CI 1.30 – 1.61; P&lt;0.0001), 1.19 (95% CI 1.09 – 1.30; P =0.006), and 1.10 (95% CI 1.05 – 1.16; P&lt;0.001)for all-cause mortality after correction for BIOSTAT risk model (age, BUN, NT-proBNP, hemoglobin, and beta-blocker). All tumor biomarkers (except AFP) had significant associations with secondary endpoints (composite of all-cause mortality and HF hospitalization, HF hospitalization, cardiovascular (CV) mortality, and non-CV mortality). ROC curves showed the AUC of CYFRA 21-1 (0.64) had a non-inferior AUC compared to NT-proBNP (0.68) for all-cause mortality (P =0.08). A combination of CYFRA 21-1 and NT-proBNP (AUC =0.71) improved the predictive value of the model for all-cause mortality (P =0.0002 compared to NT-proBNP). Conclusions: Several established tumor biomarkers showed independent associations with indices of severity of HF and independent prognostic value for HF outcomes. This demonstrates that pathophysiological pathways sensed by these tumor biomarkers are also dysregulated in HF

Comorbidities in Heart Failure

Comorbidities frequently accompany chronic heart failure (HF), contributing to increased morbidity and mortality, and an impaired quality of life. We describe the prevalence of several high-impact comorbidities in chronic HF patients and their impact on morbidity and mortality. Furthermore, we try to explain the underlying pathophysiological processes and the complex interaction between chronic HF and specific comorbidities. Although common risk factors are likely to contribute, it is reasonable to believe that factors associated with HF might cause other comorbidities and vice versa. Potential factors are inflammation, neurohormonal activation, and hemodynamic changes

Pharmacotherapy for co-morbidities in chronic heart failure:a focus on hematinic deficiencies, diabetes mellitus and hyperkalemia

INTRODUCTION: Chronic heart failure (HF) is frequently accompanied by one or more co-morbidities. The presence of co-morbidities in chronic HF is strongly correlated to HF severity and impaired outcome. AREAS COVERED: This review will address several co-morbidities with high prevalence and/or high impact in patients with chronic HF, including diabetes, anemia, hematinic deficiencies, and hyperkalemia. The background and subsequent pharmacotherapeutic options of these co-morbidities will be discussed. For this review, a MEDLINE search was performed. EXPERT OPINION: Heart failure is increasingly considered a multimorbid syndrome, including metabolic derangements and chronic inflammation. Persistent metabolic derangements and low-grade inflammation might lead to progression of HF and the development of co-morbidities. Although several co-morbidity-specific drugs became available in the past decade, most of these therapies are studied in relatively small cohorts using surrogate end-points. Therefore, larger studies are needed to address whether treating these co-morbidities will improve patient outcome in chronic HF

Definition of Iron Deficiency Based on the Gold Standard of Bone Marrow Iron Staining in Heart Failure Patients

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Validity and Predictive Value of a Portable Two-Channel Sleep-Screening Tool in the Identification of Sleep Apnea in Patients With Heart Failure

Background: Sleep apnea is an important comorbidity in heart failure (HF) and is associated with an adverse outcome. Diagnosing sleep apnea is difficult, and polysomnography, considered to be the criterion standard, is not widely available. We assessed the validity of a portable 2-channel sleep-screening tool for the identification of sleep apnea in patients with HF. Methods and Results: One hundred patients with stable HF had simultaneous recordings of home-based polysomnography and the screening tool (Apnealink) To compare the apnea-hypopnea index of the screening tool with polysomnography, intraclass correlation (ICC), sensitivity, and specificity were calculated, and a Bland-Altman plot and receiver operating characteristic (ROC) curves were constructed. Ninety valid measurements with the screening tool were obtained (mean age 65.5 +/- 11.0 y, 72% male, mean left ventricular ejection fraction 34.6 +/- 11.0%). Agreement between the screening tool and polysomnography was high (ICC 0.85). The optimal cutoff value was apnea-hypopnea index >= 15/h (area under the ROC curve 0.94). Sensitivity and specificity were 92.9% and 91.9%, respectively. Conclusions: The screening tool is useful in excluding the presence of sleep apnea in HF patients to refer only high-risk patients for more extensive polysomnography. This method may potentially reduce the need for the more expensive polysomnography