Background: Sarcopenia and its underlying physiology in patients with heart failure (HF) may cause abnormal cardiac structure/function especially in those with so-called ‘cardiac cachexia’.
Objectives
· To characterise HF-sarcopenia phenotype using multivariate molecular, imaging, and functional assessment biomarkers, and test this for HF-rehospitalization risk.
· UK Biobank data analysis, to investigation the risk factors associated with HF common comorbidities.
Novelty
Combination of multivariate biomarkers is superior to single markers in terms of sensitivity, specificity, and reliability.
Timelines
Months 1-12: (a)obtain UK Biobank data, (b)apply for ethics (c) start with patient recruitment.
Months 12-24: (d)complete recruitment, (e) complete patient assessments, (f) finalize and submit UKB data analysis.
Months 24-36: (g) complete the data analysis from clinical study (f) submit the findings for publications and thesis write up.
Experimental Approach
Study centres: Liverpool University Hospital Foundation Trust, and Liverpool Heart and Chest
Inclusion criteria: patients with reduced ejection fraction HF (EG <35%), aged >60 years. Muscle strength (handgrip and chair stand), functional test (walking-test), and segmental-muscle-mass (with BIA), quadriceps volume and cross-sectional area by ultrasound (cm3) will be assessed to define sarcopenia based on EWGSOP. Plasma samples will be collected for semi-targeted metabolomic analysis.
Sample size: group 1: HF with sarcopenia (n=30), group 2: HF without sarcopenia (n=30), and group 3: older adults with sarcopenia and no HF. The latter allows HF and/or sarcopenia comparisons and would make results more robust.
Metabolomics: semi-targeted metabolomics to determine metabolites associated with HF and sarcopenia.
Clinical data: Nutritional status by Food Frequency Questionnaire, physical activity level by IPAQ, medical history, medications, hospitalization, and mortality will be recorded for 24 months.
Data analysis: We will perform univariate analysis for each of the features using the appropriate statistical tests based on the statistical properties and distribution of each feature (including plasma metabolites, muscle mass, muscle function and clinical data) between the three groups. Based on the resulting statistical significance of the univariate analysis, the likelihood of a feature being associated to ‘HF-Sarcopenia’ is assessed.
UK Biobank: A total of 5221 cases of HF were recorded by April 2019 in the UKB dataset, with data available on sarcopenia index. Study objective is to assess sarcopenia risk factors in HF including medications, lifestyle factors (e.g., diet, physical activity, sleep), and genome-wide effect of Genetic Interleukin-6 and IGF-1 on ‘HF-Sarcopenia’ phenotype and will utilize the NMR metabolomics. Primary supervisor obtained access.
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