Diabetes status-related differences in risk factors and mediators of heart failure in the general population: results from the MORGAM/BiomarCaRE consortium

Abstract Background The risk of heart failure among diabetic individuals is high, even under tight glycemic control. The correlates and mediators of heart failure risk in individuals with diabetes need more elucidation in large population-based cohorts with long follow-up times and a wide panel of biologically relevant biomarkers. Methods In a population-based sample of 3834 diabetic and 90,177 non-diabetic individuals, proportional hazards models and mediation analysis were used to assess the relation of conventional heart failure risk factors and biomarkers with incident heart failure. Results Over a median follow-up of 13.8 years, a total of 652 (17.0%) and 5524 (6.1%) cases of incident heart failure were observed in participants with and without diabetes, respectively. 51.4% were women and the mean age at baseline was 48.7 (standard deviation [SD] 12.5) years. The multivariable-adjusted hazard ratio (HR) for heart failure among diabetic individuals was 2.70 (95% confidence interval, 2.49–2.93) compared to non-diabetic participants. In the multivariable-adjusted Cox models, conventional cardiovascular disease risk factors, such as smoking (diabetes: HR 2.07 [1.59–2.69]; non-diabetes: HR 1.85 [1.68–2.02]), BMI (diabetes: HR 1.30 [1.18–1.42]; non-diabetes: HR 1.40 [1.35–1.47]), baseline myocardial infarction (diabetes: HR 2.06 [1.55–2.75]; non-diabetes: HR 2.86 [2.50–3.28]), and baseline atrial fibrillation (diabetes: HR 1.51 [0.82–2.80]; non-diabetes: HR 2.97 [2.21–4.00]) had the strongest associations with incident heart failure. In addition, biomarkers for cardiac strain (represented by nT-proBNP, diabetes: HR 1.26 [1.19–1.34]; non-diabetes: HR 1.43 [1.39–1.47]), myocardial injury (hs-TnI, diabetes: HR 1.10 [1.04–1.16]; non-diabetes: HR 1.13 [1.10–1.16]), and inflammation (hs-CRP, diabetes: HR 1.13 [1.03–1.24]; non-diabetes: HR 1.29 [1.25–1.34]) were also associated with incident heart failure. In general, all these associations were equally strong in non-diabetic and diabetic individuals. However, the strongest mediators of heart failure in diabetes were the direct effect of diabetes status itself (relative effect share 43.1% [33.9–52.3] and indirect effects (effect share 56.9% [47.7-66.1]) mediated by obesity (BMI, 13.2% [10.3–16.2]), cardiac strain/volume overload (nT-proBNP, 8.4% [-0.7–17.4]), and hyperglycemia (glucose, 12.0% [4.2–19.9]). Conclusions The findings suggest that the main mediators of heart failure in diabetes are obesity, hyperglycemia, and cardiac strain/volume overload. Conventional cardiovascular risk factors are strongly related to incident heart failure, but these associations are not stronger in diabetic than in non-diabetic individuals. Active measurement of relevant biomarkers could potentially be used to improve prevention and prediction of heart failure in high-risk diabetic patients.

Download Full-text

Differential Effect of Metabolic Health and Obesity on Incident Heart Failure: A Nationwide Population-Based Cohort Study

Frontiers in Endocrinology ◽

10.3389/fendo.2021.625083 ◽

2021 ◽

Vol 12 ◽

Author(s):

Hwi Seung Kim ◽

Jiwoo Lee ◽

Yun Kyung Cho ◽

Joong-Yeol Park ◽

Woo Je Lee ◽

...

Keyword(s):

Heart Failure ◽

National Health ◽

Population Based ◽

Metabolic Health ◽

Metabolically Healthy Obese ◽

Obesity Status ◽

Increased Risk ◽

Metabolically Healthy ◽

Incident Heart Failure

BackgroundMetabolically healthy obese (MHO) individuals and their association with cardiometabolic diseases have remained controversial. We aimed to explore the risk of incident heart failure (HF) based on the baseline metabolic health and obesity status as well as their transition over 2 years.MethodsThe Korean National Health Insurance Service-National Health Screening Cohort data of 514,886 participants were analyzed. Obesity was defined as BMI ≥25 kg/m2 according to the Korean Centers for Disease Control and Prevention. The metabolic health and obesity status were evaluated at baseline and after two years. Study participants were followed to either the date of newly diagnosed HF or the last follow-up visit, whichever occurred first.ResultsThe MHO group comprised 9.1% of the entire population and presented a better baseline metabolic profile than the metabolically unhealthy non-obese (MUNO) and metabolicavlly unhealthy obese (MUO) groups. During the median 71.3 months of follow-up, HF developed in 5,406 (1.5%) participants. The adjusted hazard ratios [HRs (95% CI)] of HF at baseline compared with the metabolically healthy non-obese (MHNO) group were 1.29 [1.20–1.39], 1.37 [1.22–1.53], and 1.63 [1.50–1.76] for MUNO, MHO, and MUO groups, respectively. With the stable MHNO group as reference, transition into metabolically unhealthy status (MUNO and MUO) increased the risk of HF, regardless of the baseline status. Subjects who were obese at both baseline and follow-up showed an increased risk of HF, regardless of their metabolic health status.ConclusionsMetabolic health and obesity status and their transition can predict the risk of incident HF. Losing metabolic health in baseline non-obese and obese individuals and remaining obese in baseline obese individuals showed a significantly increased risk of incident HF. Maintaining good metabolic health and a lean body may prevent the development of HF.

Download Full-text

Risk factors for incident heart failure in age‐ and sex‐specific strata: a population‐based cohort using linked electronic health records

European Journal of Heart Failure ◽

10.1002/ejhf.1350 ◽

2019 ◽

Vol 21 (10) ◽

pp. 1197-1206 ◽

Cited By ~ 16

Author(s):

Alicia Uijl ◽

Stefan Koudstaal ◽

Kenan Direk ◽

Spiros Denaxas ◽

Rolf H. H. Groenwold ◽

...

Keyword(s):

Risk Factors ◽

Heart Failure ◽

Electronic Health Records ◽

Population Based ◽

Health Records ◽

Incident Heart Failure ◽

Electronic Health ◽

Age And Sex

Download Full-text

Fasting Plasma Glucose and Incident Heart Failure Risk: A Population-Based Cohort Study and New Meta-analysis

Journal of Cardiac Failure ◽

10.1016/j.cardfail.2014.05.011 ◽

2014 ◽

Vol 20 (8) ◽

pp. 584-592 ◽

Cited By ~ 11

Author(s):

Hassan Khan ◽

Setor K. Kunutsor ◽

Jussi Kauhanen ◽

Sudhir Kurl ◽

Eiran Z. Gorodeski ◽

...

Keyword(s):

Heart Failure ◽

Cohort Study ◽

Plasma Glucose ◽

Fasting Plasma Glucose ◽

Meta Analysis ◽

Population Based ◽

Failure Risk ◽

Fasting Plasma ◽

Incident Heart Failure

Download Full-text

Baseline functional capacity in patients with heart failure risk factors: association with heart failure symptoms and events at 1 year follow-up

Heart Lung and Circulation ◽

10.1016/j.hlc.2015.06.206 ◽

2015 ◽

Vol 24 ◽

pp. S198-S199

Author(s):

H. Yang ◽

K. Negishi ◽

M. Nolan ◽

Y. Wang ◽

M. Saito ◽

...

Keyword(s):

Risk Factors ◽

Heart Failure ◽

Functional Capacity ◽

Failure Risk ◽

Patients With Heart Failure

Download Full-text

Bioimpedance and new onset heart failure: A longitudinal study of ~500,000 individuals from the general population

10.1101/183483 ◽

2017 ◽

Author(s):

Daniel Lindholm ◽

Eri Fukaya ◽

Nicholas J. Leeper ◽

Erik Ingelsson

Keyword(s):

Machine Learning ◽

Risk Factors ◽

Myocardial Infarction ◽

Heart Failure ◽

Cohort Study ◽

Population Based ◽

Heart Failure Hospitalization ◽

Predictive Capacity ◽

History Of ◽

Incident Heart Failure

AbstractImportanceHeart failure constitutes a high burden on patients and society, but although lifetime risk is high, it is difficult to predict without costly or invasive testing. Knowledge about novel risk factors could enable early diagnosis and possibly preemptive treatment.ObjectiveTo establish new risk factors for heart failure.DesignWe applied supervised machine learning in UK Biobank in an agnostic search of risk factors for heart failure. Novel predictors were then subjected to several in-depth analyses, including multivariable Cox models of incident heart failure, and assessment of discrimination and calibration.SettingPopulation-based cohort study.Participants500,451 individuals who volunteered to participate in the UK Biobank cohort study, excluding those with prevalent heart failure.Exposure3646 variables reflecting different aspects of lifestyle, health and disease-related factors.Main OutcomeIncident heart failure hospitalization.ResultsMachine learning confirmed many known and putative risk factors for heart failure, and identified several novel candidates. Mean reticulocyte volume appeared as one novel factor, and leg bioimpedance another; the latter appearing as the most important new factor. Leg bioimpedance was significantly lower in those who developed heart failure (p=1.1x10-72) during up to 9.8-year follow-up. When adjusting for known heart failure risk factors, leg bioimpedance was inversely related to heart failure (hazard ratio [95%CI], 0.60 [0.48–0.73]) and 0.75 [0.59–0.94], in age- and sex-adjusted and fully adjusted models, respectively, comparing the upper vs. lower quartile). A model including leg bioimpedance, age, sex, and self-reported history of myocardial infarction showed good predictive capacity of future heart failure hospitalization (C-index=0.82) and good calibration.Conclusions and RelevanceLeg bioimpedance is inversely associated with heart failure incidence in the general population. A simple model of exclusively non-invasive measures, combining leg bioimpedance with history of myocardial infarction, age, and sex provides accurate predictive capacity.Key pointsQuestionWhich are the most important risk factors for incident heart failure?FindingsIn this population-based cohort study of ~500,000 individuals, machine learning identified well-established risk factors, but also several novel factors. Among the most important were leg bioimpedance and mean reticulocyte volume. There was a strong inverse relationship between leg bioimpedance and incident heart failure, also in adjusted analyses. A model entailing leg bioimpedance, age, sex, and self-reported history of myocardial infarction showed good predictive capacity of heart failure hospitalization and good calibration.MeaningLeg bioimpedance appears to be an important new factor associated with incident heart failure.

Download Full-text

Age dependent associations of risk factors with heart failure: pooled population based cohort study

BMJ ◽

10.1136/bmj.n461 ◽

2021 ◽

pp. n461

Author(s):

Jasper Tromp ◽

Samantha M A Paniagua ◽

Emily S Lau ◽

Norrina B Allen ◽

Michael J Blaha ◽

...

Keyword(s):

Risk Factors ◽

Heart Failure ◽

Cohort Study ◽

Absolute Risk ◽

Population Based ◽

Attributable Risk ◽

Smoking History ◽

Middle Aged ◽

Current Smoking ◽

Incident Heart Failure

Abstract Objective To assess age differences in risk factors for incident heart failure in the general population. Design Pooled population based cohort study. Setting Framingham Heart Study, Prevention of Renal and Vascular End-stage Disease Study, and Multi-Ethnic Study of Atherosclerosis. Participants 24 675 participants without a history of heart failure stratified by age into young (<55 years; n=11 599), middle aged (55-64 years; n=5587), old (65-74 years; n=5190), and elderly (≥75 years; n=2299) individuals. Main outcome measure Incident heart failure. Results Over a median follow-up of 12.7 years, 138/11 599 (1%), 293/5587 (5%), 538/5190 (10%), and 412/2299 (18%) of young, middle aged, old, and elderly participants, respectively, developed heart failure. In young participants, 32% (n=44) of heart failure cases were classified as heart failure with preserved ejection fraction compared with 43% (n=179) in elderly participants. Risk factors including hypertension, diabetes, current smoking history, and previous myocardial infarction conferred greater relative risk in younger compared with older participants (P for interaction <0.05 for all). For example, hypertension was associated with a threefold increase in risk of future heart failure in young participants (hazard ratio 3.02, 95% confidence interval 2.10 to 4.34; P<0.001) compared with a 1.4-fold risk in elderly participants (1.43, 1.13 to 1.81; P=0.003). The absolute risk for developing heart failure was lower in younger than in older participants with and without risk factors. Importantly, known risk factors explained a greater proportion of overall population attributable risk for heart failure in young participants (75% v 53% in elderly participants), with better model performance (C index 0.79 v 0.64). Similarly, the population attributable risks of obesity (21% v 13%), hypertension (35% v 23%), diabetes (14% v 7%), and current smoking (32% v 1%) were higher in young compared with elderly participants. Conclusions Despite a lower incidence and absolute risk of heart failure among younger compared with older people, the stronger association and greater attributable risk of modifiable risk factors among young participants highlight the importance of preventive efforts across the adult life course.

Download Full-text

Faculty Opinions recommendation of Risk factors for incident open-angle glaucoma: a population-based 20-year follow-up study.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717963019.793464997 ◽

2012 ◽

Author(s):

Albert Alm

Keyword(s):

Risk Factors ◽

Open Angle Glaucoma ◽

Population Based ◽

Open Angle ◽

Follow Up Study

Download Full-text

External validation of a heart failure risk prediction model in a remote monitoring cohort submitted to cardiac resynchronization therapy

European Heart Journal ◽

10.1093/ehjci/ehaa946.0817 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

I Cardoso ◽

M Coutinho ◽

G Portugal ◽

A Valentim ◽

A.S Delgado ◽

...

Keyword(s):

Heart Failure ◽

Heart Rate ◽

High Risk ◽

Remote Monitoring ◽

Hospital Admissions ◽

External Validation ◽

Left Ventricular Ejection ◽

National Database ◽

Failure Risk

Abstract Background Patients (P) submitted to cardiac ressynchronization therapy (CRT) are at high risk of heart failure (HF) events during follow-up. Continuous analysis of various physiological parameters, as reported by remote monitoring (RM), can contribute to point out incident HF admissions. Tailored evaluation, including multi-parameter modelling, may further increase the accuracy of such algorithms. Purpose Independent external validation of a commercially available algorithm (“Heart Failure Risk Status” HFRS, Medtronic, MN USA) in a cohort submitted to CRT implantation in a tertiary center. Methods Consecutive P submitted to CRT implantation between January 2013 and September 2019 who had regular RM transmissions were included. The HFRS algorithm includes OptiVol (Medtronic Plc., MN, USA), patient activity, night heart rate (NHR), heart rate variability (HRV), percentage of CRT pacing, atrial tachycardia/atrial fibrillation (AT/AF) burden, ventricular rate during AT/AF (VRAF), and detected arrhythmia episodes/therapy delivered. P were classified as low, medium or high risk. Hospital admissions were systematically assessed by use of a national database (“Plataforma de Dados de Saúde”). Accuracy of the HFRS algorithm was evaluated by random effects logistic regression for the outcome of unplanned hospital admission for HF in the 30 days following each transmission episode. Results 1108 transmissions of 35 CRT P, corresponding to 94 patient-years were assessed. Mean follow-up was 2.7 yrs. At implant, age was 67.6±9.8 yrs, left ventricular ejection fraction 28±7.8%, BNP 156.6±292.8 and NYHA class >II in 46% of the P. Hospital admissions for HF were observed within 30 days in 9 transmissions. Stepwise increase in HFRS was significantly associated with higher risk of HF admission (odds ratio 12.7, CI 3.2–51.5). HFRS had good discrimination for HF events with receiving-operator curve AUC 0.812. Conclusions HFRS was significantly associated with incident HF admissions in a high-risk cohort. Prospective use of this algorithm may help guide HF therapy in CRT recipients. Funding Acknowledgement Type of funding source: None

Download Full-text