A population-based study of 92 clinically recognized risk factors for heart failure: co-occurrence, prognosis and preventive potential

Diabetes and Heart Failure: A Literature Review, Reflection and Outlook

Heart failure (HF) is a complex clinical syndrome caused by structural or functional dysfunction of the ventricular filling or blood supply. Diabetes mellitus (DM) is an independent predictor of mortality for HF. The increase in prevalence, co-morbidity and hospitalization rates of both DM and HF has further fueled the possibility of overlapping disease pathology between the two. For decades, antidiabetic drugs that are known to definitively increase the risk of HF are the thiazolidinediones (TZDs) and saxagliptin in the dipeptidyl peptidase-4 (DPP-4) inhibitor, and insulin, which causes sodium and water retention, and whether metformin is effective or safe for HF is not clear. Notably, sodium-glucose transporter 2 (SGLT2) inhibitors and partial glucagon-like peptide-1 receptor agonists (GLP-1 RA) all achieved positive results for HF endpoints, with SGLT2 inhibitors in particular significantly reducing the composite endpoint of cardiovascular mortality and hospitalization for heart failure (HHF). Further understanding of the mutual pathophysiological mechanisms between HF and DM may facilitate the detection of novel therapeutic targets to improve the clinical outcome. This review focuses on the association between HF and DM, emphasizing the efficacy and safety of antidiabetic drugs and HF treatment. In addition, recent therapeutic advances in HF and the important mechanisms by which SGLT2 inhibitors/mineralocorticoid receptor antagonist (MRA)/vericiguat contribute to the benefits of HF are summarized.

Recurrent heart failure hospitalizations in heart failure with preserved ejection fraction: an analysis of TOPCAT trial

AIMS

Recurrent heart failure hospitalization (HFH) is an important feature of the progression of heart failure (HF). In the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist (TOPCAT) trial, we analysed risk factors for recurrent HFH events in HF patients with preserved ejection fraction (HFpEF) and developed a risk prediction model for recurrent HFH.

METHODS AND RESULTS

This analysis focused on the subset of TOPCAT participants enrolled in the Americas (n = 1767). Recurrent HFH was defined as two or more hospitalizations for HF during the follow-up period. Lasso regression and multivariate logistic regression were used to screen the risk factors, and the risk prediction model of recurrent HFH was established. During a median follow-up period of 3.4 (95% confidence interval: 3.3-3.6) years, 72.2% (542 of 751 total hospitalizations) of HFH events occurred in 9.4% (n = 163) of patients with recurrent HFHs. Patients in the recurrent HFH group had higher cardiovascular mortality rate [6.2 per 100 patient-years (PY) vs. 3.8 per 100 PY, P = 0.016] and all-cause mortality rate (10.0 per 100 PY vs. 6.8 per 100 PY, P = 0.015) than those in the non-recurrent HFH group. The model consisting of nine predictors has moderate predictive power for recurrent HFH events in patients with HFpEF (AUC = 0.75, Brier score = 0.08). Decision curve analysis showed a net clinical benefit from the application of the prediction model.

CONCLUSIONS

In patients with HFpEF, the majority of HFHs occur in a small proportion of patients with repeated hospitalizations, who typically have more comorbidities and are at higher risk of death. The predictive model developed in this study helps to identify patients at high risk of recurrent HFH.

Risk factors, outcomes and healthcare utilisation in individuals with multimorbidity including heart failure, chronic kidney disease and type 2 diabetes mellitus: a national electronic health record study

BACKGROUND

Heart failure (HF), type 2 diabetes (T2D) and chronic kidney disease (CKD) commonly coexist. We studied characteristics, prognosis and healthcare utilisation of individuals with two of these conditions.

METHODS

We performed a retrospective, population-based linked electronic health records study from 1998 to 2020 in England to identify individuals diagnosed with two of: HF, T2D or CKD. We described cohort characteristics at time of second diagnosis and estimated risk of developing the third condition and mortality using Kaplan-Meier and Cox regression models. We also estimated rates of healthcare utilisation in primary care and hospital settings in follow-up.

FINDINGS

We identified cohorts of 64 226 with CKD and HF, 82 431 with CKD and T2D, and 13 872 with HF and T2D. Compared with CKD and T2D, those with CKD and HF and HF and T2D had more severe risk factor profile. At 5 years, incidence of the third condition and all-cause mortality occurred in 37% (95% CI: 35.9%, 38.1%%) and 31.3% (30.4%, 32.3%) in HF+T2D, 8.7% (8.4%, 9.0%) and 51.6% (51.1%, 52.1%) in HF+CKD, and 6.8% (6.6%, 7.0%) and 17.9% (17.6%, 18.2%) in CKD+T2D, respectively. In each of the three multimorbid groups, the order of the first two diagnoses was also associated with prognosis. In multivariable analyses, we identified risk factors for developing the third condition and mortality, such as age, sex, medical history and the order of disease diagnosis. Inpatient and outpatient healthcare utilisation rates were highest in CKD and HF, and lowest in CKD and T2D.

INTERPRETATION

HF, CKD and T2D carry significant mortality and healthcare burden in combination. Compared with other disease pairs, individuals with CKD and HF had the most severe risk factor profile, prognosis and healthcare utilisation. Service planning, policy and prevention must take into account and monitor data across conditions.

Identifying subtypes of heart failure from three electronic health record sources with machine learning: an external, prognostic, and genetic validation study

BACKGROUND

Machine learning has been used to analyse heart failure subtypes, but not across large, distinct, population-based datasets, across the whole spectrum of causes and presentations, or with clinical and non-clinical validation by different machine learning methods. Using our published framework, we aimed to discover heart failure subtypes and validate them upon population representative data.

METHODS

In this external, prognostic, and genetic validation study we analysed individuals aged 30 years or older with incident heart failure from two population-based databases in the UK (Clinical Practice Research Datalink [CPRD] and The Health Improvement Network [THIN]) from 1998 to 2018. Pre-heart failure and post-heart failure factors (n=645) included demographic information, history, examination, blood laboratory values, and medications. We identified subtypes using four unsupervised machine learning methods (K-means, hierarchical, K-Medoids, and mixture model clustering) with 87 of 645 factors in each dataset. We evaluated subtypes for (1) external validity (across datasets); (2) prognostic validity (predictive accuracy for 1-year mortality); and (3) genetic validity (UK Biobank), association with polygenic risk score (PRS) for heart failure-related traits (n=11), and single nucleotide polymorphisms (n=12).

FINDINGS

We included 188 800, 124 262, and 9573 individuals with incident heart failure from CPRD, THIN, and UK Biobank, respectively, between Jan 1, 1998, and Jan 1, 2018. After identifying five clusters, we labelled heart failure subtypes as (1) early onset, (2) late onset, (3) atrial fibrillation related, (4) metabolic, and (5) cardiometabolic. In the external validity analysis, subtypes were similar across datasets (c-statistics: THIN model in CPRD ranged from 0·79 [subtype 3] to 0·94 [subtype 1], and CPRD model in THIN ranged from 0·79 [subtype 1] to 0·92 [subtypes 2 and 5]). In the prognostic validity analysis, 1-year all-cause mortality after heart failure diagnosis (subtype 1 0·20 [95% CI 0·14-0·25], subtype 2 0·46 [0·43-0·49], subtype 3 0·61 [0·57-0·64], subtype 4 0·11 [0·07-0·16], and subtype 5 0·37 [0·32-0·41]) differed across subtypes in CPRD and THIN data, as did risk of non-fatal cardiovascular diseases and all-cause hospitalisation. In the genetic validity analysis the atrial fibrillation-related subtype showed associations with the related PRS. Late onset and cardiometabolic subtypes were the most similar and strongly associated with PRS for hypertension, myocardial infarction, and obesity (p<0·0009). We developed a prototype app for routine clinical use, which could enable evaluation of effectiveness and cost-effectiveness.

INTERPRETATION

Across four methods and three datasets, including genetic data, in the largest study of incident heart failure to date, we identified five machine learning-informed subtypes, which might inform aetiological research, clinical risk prediction, and the design of heart failure trials.

FUNDING

European Union Innovative Medicines Initiative-2.

The Prevalence and Risk Factors of Chronic Heart Failure in the Mongolian Population

BACKGROUND

The prevalence of heart failure in the Mongolian population is unknown. Thus, in this study, we aimed to define the prevalence of heart failure in the Mongolian population and to identify significant risk factors for heart failure among Mongolian adults.

METHODS

This population-based study included individuals 20 years and older from seven provinces as well as six districts of the capital city of Mongolia. The prevalence of heart failure was based on the European Society of Cardiology diagnostic criteria.

RESULTS

In total, 3480 participants were enrolled, of which 1345 (38.6%) participants were males, and the median age was 41.0 years (IQR 30-54 years). The overall prevalence of heart failure was 4.94%. Patients with heart failure had significantly higher body mass index, heart rate, oxygen saturation, respiratory rate, and systolic/diastolic blood pressure than patients without heart failure. In the logistic regression analysis, hypertension (OR 4.855, 95% CI 3.127-7.538), previous myocardial infarction (OR 5.117, 95% CI 3.040-9.350), and valvular heart disease (OR 3.872, 95% CI 2.112-7.099) were significantly correlated with heart failure.

CONCLUSIONS

This is the first report on the prevalence of heart failure in the Mongolian population. Among the cardiovascular diseases, hypertension, old myocardial infarction, and valvular heart disease were identified as the three foremost risk factors in the development of heart failure.

Inflammation and heart failure: a two-sample Mendelian randomization study

BACKGROUND

It is hypothesized that inflammation leads to heart failure. Results from observational studies thus far have been inconsistent and it is unclear whether inflammation is causally associated with new-onset heart failure. Mendelian randomization analyses are less prone to biases common in observational studies such as reverse causation and unmeasured confounding. The aim of this study was to investigate the causal relation between various inflammatory biomarkers with risk of new-onset heart failure by using a two-sample Mendelian randomization approach.

METHODS

Ten inflammatory biomarkers with available genome-wide association studies (GWAS) among individuals of European ancestry were identified and included C-reactive protein (CRP), immunoglobulin E, tumour necrosis factor (TNF), toll-like receptor 4, interleukin 1 receptor antagonist, interleukin 2 receptor subunit α, interleukin 6 receptor subunit α, interleukin 16, 17 and 18. For the associations between the identified SNPs and heart failure, we used the largest GWAS meta-analysis performed by the Heart Failure Molecular Epidemiology for Therapeutic Targets Consortium with 47 309 participants with heart failure and 930 014 controls. For our main analyses, we used the inverse-variance weighted method.

RESULTS

We included 63 SNPs. CRP, TNF, interleukin 2, 16 and 18 were not associated with heart failure with odds ratios (ORs) of 1.01 [95% confidence interval (95% CI: 0.94-1.09), 1.11 (95% CI: 0.80-1.48), 0.97 (95% CI: 0.93-1.02), 0.99 (95% CI: 0.96-1.03) and 1.01 (95% CI: 0.97-1.06), respectively. The other biomarkers were also not associated with the risk of heart failure and suffered from weak instrument bias.

CONCLUSION

This Mendelian randomization study could not determine a causal relationship between inflammation and risk of heart failure. However, some biomarkers suffered from weak instrument bias.

The non-linear association between ascending aorta diameter and risk of 12-month mortality in Chinese patients with heart failure: A retrospective cohort study

Background

There is no conclusive proven link between ascending aorta diameter (AoD) and the risk of death from heart failure (HF). As a result, a retrospective cohort analysis was carried out to determine whether AoD is associated with 12-month mortality in Chinese HF patients.

Methods

From January 2017 to March 2020, we collected data on 575 Chinese patients with HF. The exposure and outcome variables were baseline AoD and 12-month risk of mortality (all-cause + cardiac origin), respectively. Data on demographics, drug usage, clinical characteristics, recognized indicators of HF, and comorbidities were included as covariates. To investigate the independent relationships of AoD with the risk of 12-month death, binary logistic regression and two-piecewise linear models were utilized.

Results

Our findings imply that there was a non-linear relationship between AoD and the risk of 12-month mortality. For the AoD range of 23 to 37, there was no association with the risk of cardiac mortality [odds ratio (OR) 0.78, 95% confidence interval (CI), 0.62–1.04]. In the AoD range of 37–49, however, the risk of 12-month cardiac death increased by approximately 70% for every 1 mm increase in AoD (OR 1.70, 95% CI, 1.13–2.55). When all-cause death was chosen as the outcome, the same outcome was shown.

Conclusion

An AoD larger than 37 mm is a hazardous threshold for Chinese HF patients. Beyond this limit increased the risk of cardiac death by 70% for every 1 mm increase in AoD.

Baseline Electrocardiographic and Echocardiographic Assessment May Help Predict Survival in Lung Cancer Patients-A Prospective Cardio-Oncology Study

Cardiovascular disease and cancer coexist and lead to exertional dyspnea. The aim of the study was to determine the prognostic significance of cardiac comorbidities, ECG and baseline echocardiography in lung cancer patients with varying degrees of reduced performance status. This prospective study included 104 patients with histopathologically confirmed lung cancer, pre-qualified for systemic treatment due to metastatic or locally advanced malignancy but not eligible for thoracic surgery. The patients underwent a comprehensive cardio-oncological evaluation. Overall survival negative predictors included low ECOG 2 (Eastern Cooperative Oncology Group) performance status, stage IV (bone or liver/adrenal metastases in particular), pleural effusion, the use of analgesics and among cardiac factors, two ECG parameters: atrial fibrillation (HR = 2.39) and heart rate >90/min (HR = 1.67). Among echocardiographic parameters, RVSP > 39 mmHg was a negative predictor (HR = 2.01), while RVSP < 21 mmHg and RV free wall strain < −30% were positive predictors (HR = 0.36 and HR = 0.56, respectively), whereas RV GLS < −25.5% had a borderline significance (HR = 0.59; p = 0.05). Logistical regression analysis showed ECOG = 2 significantly correlated with the following echocardiographic parameters: increasing RVSP, RV GLS, RV free wall strain and decreasing ACT, FAC (p < 0.05). Selected echocardiographic parameters may be helpful in predicting poor performance in lung cancer patients and, supplemented with ECG evaluation, broaden the possibilities of prognostic evaluation.