Candidate biomarkers of physical frailty in heart failure: an exploratory cross-sectional study

Diagnostic and predictive abilities of myokines in patients with heart failure

Myokines are defined as a heterogenic group of numerous cytokines, peptides and metabolic derivates, which are expressed, synthesized, produced, and released by skeletal myocytes and myocardial cells and exert either auto- and paracrine, or endocrine effects. Previous studies revealed that myokines play a pivotal role in mutual communications between skeletal muscles, myocardium and remote organs, such as brain, vasculature, bone, liver, pancreas, white adipose tissue, gut, and skin. Despite several myokines exert complete divorced biological effects mainly in regulation of skeletal muscle hypertrophy, residential cells differentiation, neovascularization/angiogenesis, vascular integrity, endothelial function, inflammation and apoptosis/necrosis, attenuating ischemia/hypoxia and tissue protection, tumor growth and malignance, for other occasions, their predominant effects affect energy homeostasis, glucose and lipid metabolism, adiposity, muscle training adaptation and food behavior. Last decade had been identified 250 more myokines, which have been investigating for many years further as either biomarkers or targets for heart failure management. However, only few myokines have been allocated to a promising tool for monitoring adverse cardiac remodeling, ischemia/hypoxia-related target-organ dysfunction, microvascular inflammation, sarcopenia/myopathy and prediction for poor clinical outcomes among patients with HF. This we concentrate on some most plausible myokines, such as myostatin, myonectin, brain-derived neurotrophic factor, muslin, fibroblast growth factor 21, irisin, leukemia inhibitory factor, developmental endothelial locus-1, interleukin-6, nerve growth factor and insulin-like growth factor-1, which are suggested to be useful biomarkers for HF development and progression.

Hitting the (bio)mark part 2: analyzing, interpreting, and reporting biomarker data in cardiovascular research

Incorporating biomarkers into cardiovascular studies, including nursing research, is a common approach when identifying underlying mechanisms and providing targets for intervention. However, effective utilization of biomarker data demands careful consideration. In the analysis, interpretation, and reporting phase, there are many facets to consider, including non-normality of the data, normalization procedures, and potential confounding influences of other clinical data. Furthermore, as many studies focus on patient-reported outcomes, it is important that the analysis and interpretation of biomarkers in relation to patient-reported outcomes is rigorous and reproducible. In this article, part 2 of 2, we provide an overview of considerations for the analysis, interpretation, and reporting phases of biomarker studies. We also provide an example of these steps.

Hitting the (bio)mark part 1: selecting and measuring biomarkers in cardiovascular research

Cardiovascular studies, including nursing research, frequently integrate biomarkers for diagnostic, prognostic, monitoring and therapeutic insights. However, effective utilization of biomarker data demands careful consideration. In the study design phase, researchers must select biomarkers that align with study objectives while considering resources and logistical factors. Additionally, a nuanced understanding of disease pathophysiology and biomarker characteristics is needed. During data collection, suitable experimental conditions and assays need to be defined. Whether researchers opt to manage these steps internally or outsource some, a comprehensive understanding of biomarker selection and experiments remains crucial. In this article, part 1 of 2, we provide an overview of considerations for the design to measurement phases of biomarker studies.

Sex differences in the impact of physical frailty on outcomes in heart failure

BACKGROUND

Physical frailty is strongly related to adverse outcomes in heart failure (HF), and women are more likely to be physically frail than men; however, it is unknown if this sex difference affects outcomes.

OBJECTIVES

To determine if there are sex differences in the associations between physical frailty and health-related quality of life (HRQOL) and clinical outcomes in HF.

METHODS

We conducted a prospective study of adults with HF. Physical frailty was assessed using the Frailty Phenotype Criteria. HRQOL was assessed using the Minnesota Living with HF Questionnaire. One-year clinical events (all-cause death or cardiovascular hospitalization or emergency department visit) were ascertained. We used generalized linear modeling to quantify associations between physical frailty and HRQOL, and Cox proportional hazards modeling to quantify associations between physical frailty and clinical events, adjusting for Seattle HF Model scores.

RESULTS

The sample (n = 115) was 63.5 ± 15.7 years old and 49% women. Physical frailty was associated with significantly worse total HRQOL among women (p = 0.005) but not men (p = 0.141). Physical frailty was associated with worse physical HRQOL among both women (p < 0.001) and men (p = 0.043). There was a 46% higher clinical event risk for every one-point increase in physical frailty score among men (p = 0.047) but not women (p = 0.361).

CONCLUSIONS

Physical frailty is associated with worse overall HRQOL among women and higher clinical event risk among men, indicating a need to better understand contributors to sex-specific health differences associated with physical frailty in HF.

Survival analysis 101: an easy start guide to analysing time-to-event data

Survival analysis, also called time-to-event analysis, is a common approach to handling event data in cardiovascular nursing and health-related research. Survival analysis is used to describe, explain, and/or predict the occurrence and timing of events. There is a specific language used and methods designed to handle the unique nature of event data. In this methods paper, we provide an 'easy start guide' to using survival analysis by (i) providing a step-by-step guide and (ii) applying the steps with example data. Specifically, we analyse cardiovascular event data over 6 months in a sample of patients with heart failure.

The importance of biological sex in cardiac cachexia

Cardiac cachexia is a catabolic muscle wasting syndrome observed in approximately 1 in 10 heart failure patients. Increased skeletal muscle atrophy leads to frailty and limits mobility which impacts quality of life, exacerbates clinical care, and is associated with higher rates of mortality. Heart failure is known to exhibit a wide range of prevalence and severity when examined across individuals of different ages and with co-morbidities related to diabetes, renal failure and pulmonary dysfunction. It is also recognized that men and women exhibit striking differences in the pathophysiology of heart failure as well as skeletal muscle homeostasis. Given that both skeletal muscle and heart failure physiology are in-part sex dependent, the diagnosis and treatment of cachexia in heart failure patients may depend on a comprehensive examination of how these organs interact. In this review we explore the potential for sex-specific differences in cardiac cachexia. We summarize advantages and disadvantages of clinical methods used to measure muscle mass and function and provide alternative measurements that should be considered in preclinical studies. Additionally, we summarize sex-dependent effects on muscle wasting in preclinical models of heart failure, disuse, and cancer. Lastly, we discuss the endocrine function of the heart and outline unanswered questions that could directly impact patient care.