Multimorbidity in Incident Heart Failure: Characterisation and Impact on 1-Year Outcomes.

Background/Objectives: Heart failure (HF) is usually accompanied by other comorbidities, which, altogether, have a major impact on patients and healthcare systems. Our aim was to analyse the demographic and clinical characteristics of incident HF patients and the effect of comorbidities on one-year health outcomes. Methods: This was an observational, retrospective, population-based study of incident HF patients between 2014 and 2018 in the EpiChron Cohort, Spain. The included population contained all primary and hospital care patients with a diagnosis of HF. All chronic diseases in their electronic health records were pooled into three comorbidity clusters (cardiovascular, mental, other physical). These comorbidity groups and the health outcomes were analysed until 31 December 2018. A descriptive analysis was performed. Cox regression models and survival curves were calculated to determine the hazard risk (HR) of all-cause mortality, all-cause and HF-related hospital admissions, hospital readmissions, and emergency room visits for each comorbidity group. Results: In total, 13,062 incident HF patients were identified (mean age = 82.0 years; 54.8% women; 93.7% multimorbid; mean of 4.52 ± 2.06 chronic diseases). After one-year follow-up, there were 3316 deaths (25.3%) and 4630 all-cause hospitalisations (35.4%). After adjusting by gender, age, and inpatient/outpatient status, the mental cluster was associated (HR; 95% confidence interval) with a higher HR of death (1.08; 1.01-1.16) and all-cause hospitalisation (1.09; 1.02-1.16). Conclusions: Cardiovascular comorbidities are the most common and studied ones in HF patients; however, they are not the most strongly associated with negative impacts on health outcomes in these patients. Our findings suggest the importance of a holistic and integral approach in the care of HF patients and the need to take into account the entire spectrum of comorbidities for improving HF management in clinical practice.

Multimorbidity patterns and trajectories in young and middle-aged adults: a large-scale population-based cohort study.

Introduction: The presence of multiple chronic conditions, also referred to as multimorbidity, is a common finding in adults. Epidemiologic research can help identify groups of individuals with similar clinical profiles who could benefit from similar interventions. Many cross-sectional studies have revealed the existence of different multimorbidity patterns. Most of these studies were focused on the older population. However, multimorbidity patterns begin to form at a young age and can evolve over time following distinct multimorbidity trajectories with different impact on health. In this study, we aimed to identify multimorbidity patterns and trajectories in adults 18-65 years old. Methods: We conducted a retrospective longitudinal epidemiologic study in the EpiChron Cohort, which includes all inhabitants of Aragón (Spain) registered as users of the Spanish National Health System, linking, at the patient level, information from electronic health records from both primary and specialised care. We included all 293,923 patients 18-65 years old with multimorbidity in 2011. We used cluster analysis at baseline (2011) and in 2015 and 2019 to identify multimorbidity patterns at four and eight years of follow-up, and we then created alluvial plots to visualise multimorbidity trajectories. We performed age- and sex-adjusted logistic regression analysis to study the association of each pattern with four- and eight-year mortality. Results: We identified three multimorbidity patterns at baseline, named dyslipidaemia & endocrine-metabolic, hypertension & obesity, and unspecific. The hypertension & obesity pattern, found in one out of every four patients was associated with a higher likelihood of four- and eight-year mortality (age- and sex-adjusted odds ratio 1.11 and 1.16, respectively) compared to the unspecific pattern. Baseline patterns evolved into different patterns during the follow-up. Discussion: Well-known preventable cardiovascular risk factors were key elements in most patterns, highlighting the role of hypertension and obesity as risk factors for higher mortality. Two out of every three patients had a cardiovascular profile with chronic conditions like diabetes and obesity that are linked to low-grade systemic chronic inflammation. More studies are encouraged to better characterise the relatively large portion of the population with an unspecific disease pattern and to help design and implement effective and comprehensive strategies towards healthier ageing.

Proteomic analysis of postprandial high-density lipoproteins in healthy subjects.

The relationship between the functionality and composition of high-density lipoproteins (HDL) is yet not fully studied, and little is known about the influence of the diet in HDL proteome. Therefore, the aim of this research was to elucidate the HDL proteome associated to postprandial hyperlipidemia. Male volunteers were recruited for an interventional study with high fatty acid-based meals. Blood samples were collected before the intake (baseline), and 2-3 (postprandial peak) and 5-6 (postprandial post peak) hours later. HDL were purified and the protein composition was quantified by LC-MS/MS. Statistical analysis was performed by lineal models (amica) and by ANOVA and multi-t-test of the different conditions (MetaboAnalyst). Additionally, a clustering of the expression profiles of each protein was done with coseq R package (RStudio). Initially, 320 proteins were identified but only 119 remained after the filtering. APOM, APOE, APOB, and APOA2, proteins previously identified in the HDL proteome, were the only proteins with a statistically significant altered expression in postprandial hyperlipidemia when compared to baseline (p values <0.05 and logFC >1). In conclusion, we have been able to describe several behaviors of the whole HDL proteome during the postprandial hyperlipidemic metabolism.