Non-cardiac comorbidities in heart failure with preserved ejection fraction
Streng, Koen Wouter
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Non-cardiac Comorbidities in Heart
Failure with Preserved Ejection
Fraction
Focussing on obesity and renal dysfunction
Koen W. Streng
Groningen for the publication of this thesis is gratefully acknowledged. Lay-out and printing by: Optima Grafische Communicatie
Focussing on obesity and renal dysfunction
Proefschrift
ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen
op gezag van de
rector magnificus prof. dr. E. Sterken en volgens besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op woensdag 12 juni 2019 om 14.30 uur
door
Koen Wouter Streng
geboren op 5 november 1988 te Apeldoorn
2
Non-cardiac Comorbidities in Heart
Failure with Preserved Ejection
Fraction
Focussing on obesity and renal dysfunction
Proefschrift
ter verkrijging van de graad van doctor aan de Rijksuniversiteit Groningen
op gezag van de
rector magnificus prof. dr. E. Sterken en volgens besluit van het College voor Promoties.
De openbare verdediging zal plaatsvinden op woensdag 12 juni 2019 om 14.30 uur
door
Koen Wouter Streng
geboren op 5 november 1988Prof. dr. A.A. Voors Prof. dr. J.L. Hillege Copromotor Dr. K. Damman Beoordelingscommissie Prof. dr. M.C. Verhaar Prof. dr. R.A. de Boer Prof. dr. M.P. van den Berg
Mw. B.T. Santema Dhr. J.F. Nauta
Financial support by the Dutch Heart Foundation for the publication of this thesis is gratefully acknowledged. The research described in this thesis was supported by a grant of the Dutch Heart Foundation (2014B011 CVON RECONNECT)
Chapter 1 Introduction 9
Chapter 2 Non-cardiac comorbidities in heart failure with reduced,
mid-range and preserved ejection fraction
19
Chapter 3 Associations of body mass index with laboratory and
biomarkers in patients with acute heart failure
41 Circ Heart Fail 2017;10:e003350
Chapter 4 Waist-to-hip ratio and mortality in heart failure 67
Chapter 5 Clinical implications of low estimated protein intake in patients
with heart failure
89 Submitted
Chapter 6 Renin-angiotensin system inhibition, worsening renal function,
and outcome in heart failure patients with reduced and preserved ejection fraction: a meta-analysis of published study data
109
Circ Heart Fail 2017;10:e003588
Chapter 7 Urinary marker profiles in heart failure with reduced and
preserved ejection fraction
133 To be submitted
Chapter 8 Summary and future perspectives 153
Appendices
Dutch summary | Nederlandse samenvatting 169
Acknowledgements | Dankwoord 177
Bibliography 183
Chapter 1
1
INTRODUCTION
Heart failure is a clinical syndrome with symptoms of dyspnoea, fatigue and impaired exercise tolerance caused by structural or functional abnormality of the heart. The prevalence of heart failure is still rising, and is currently prevalent in around 1-2 per cent in Western countries. The estimated incidence of heart failure in the elderly population in the Netherlands raises up to 12.4 per 1000 persons aged above 85 years. Due to its rising prevalence and its high mortality and hospitalization rates, heart failure is a major
burden for the patients but also for our health care costs.1,2
Heart failure can be categorized into three groups based on left ventricular ejection fraction (LVEF). Heart failure with reduced ejection fraction (HFrEF) is considered to be the classical form of heart failure. In the beginning of this millennium, it became clear that there are patients who presented with identical signs and symptoms of heart failure, but had a normal LVEF. These patients were classified as patients with heart failure with preserved ejection fraction (HFpEF). However, there was a grey area present within this classification, since patients with a moderately reduced ejection fraction were nei-ther HFrEF patients nor HFpEF patients. In 2016 the European Society of Cardiology presented new heart failure guidelines, and provided distinct cut-offs for LVEF; HFrEF was defined as an LVEF below 40%, HFpEF as equal or above 50%, and patients in the former grey area were classified as heart failure with mid-range ejection fraction
(HFmrEF) with an LVEF between 40 and 49%.3 Categorizing these groups is essential
since patient characteristics in these groups are very different, and where there are treatment options available for reducing mortality and hospitalization in patients with reduced ejection fraction, these therapies are not available for patients with heart failure with preserved ejection fraction. Instead, patients with HFpEF are mainly treated for their symptoms by prescribing diuretics.
HFpEF currently accounts for over half of all heart failure cases, and the population
burden is only expected to rise in the upcoming decades.4 These patients are
pre-dominantly elderly women and are often accompanied by a number of concurrent diseases, referred to as comorbidities. Recent data suggest that around 75% of the heart failure patients has at least one comorbidity, of which renal disease, anaemia
and diabetes mellitus are most common.5,6 These comorbidities often interfere with the
current treatment for heart failure patients. For example renal dysfunction limits the use of ACE-inhibitors.
A major contributing factor for the high prevalence of comorbidities could be the higher age in patients with HFpEF. However, comorbidities themselves could also contribute
to the onset and progression of HFpEF, resulting in a more systemic disease. A number of studies have shown that the phenotype of HFpEF is complex, and at least partially
defined by accompanying comorbidities.7-9 Some have even suggested that HFpEF is
not more than a collection of comorbidities.10 Within the comorbidities, we differentiate
between cardiac comorbidities and non-cardiac comorbidities. This thesis will focus on two important non-cardiac comorbidities: Obesity and renal dysfunction.
The first aim of this thesis is to assess the prevalence of non-cardiac comorbidities in patients with HFrEF, HFmrEF and HFpEF and the associations between these comor-bidities and clinical outcome
Non-cardiac comorbidities are often associated with higher mortality and hospitalization
rates, and are highly prevalent in patients with heart failure.11,12 However, the association
between each of the individual comorbidities in patients with heart failure is unknown, as well as their association with quality of life in these patients. Quality of life is often underestimated, with treatment options focussing on reducing mortality and hospital-ization and not on softer endpoints. However, quality of life is of great importance to patients, and identifying comorbidities which influence quality of life could therefore be meaningful.
In Chapter 2, we study the prevalence of eight different non-cardiac comorbidities in
patients with HFrEF, HFmrEF and HFpEF in two combined large heart failure cohorts. Furthermore we assess their individual association with quality of life, mortality and hospitalization.
One of the highly prevalent non-cardiac comorbidities in patients with HFpEF is obesity,
often defined as a body mass index equal or above 30 kg/m2. Obesity is an
ongo-ing burden to society with a growongo-ing prevalence, especially in developed countries. Over one-third of the adults in the United Stated has obesity, and this trend shows an
epidemic spread around the rest of the world.13 Obesity is a risk factor for the onset of
heart failure, partly due to its relation with hypertension, diabetes mellitus and coronary
artery disease.14,15 However, obesity could also interfere with the diagnosis of heart
failure. Physical examination could be impeded, where pulmonary auscultation is difficult, and echocardiographic images are often of poor quality. Furthermore, brain natriuretic peptides (BNP) are important for the diagnosis of heart failure, but obesity is
also known to negatively influence levels of BNP.16 However, if this holds true for other
biomarkers, and if body mass index influences the prognostic value of these markers,
1
different laboratory and biomarkers in acute heart failure, and we investigate whether this influences the predictive value of these biomarkers on mortality.
Nevertheless, obesity in heart failure is not only negatively portrayed in the literature. In patients with established heart failure there is a so-called obesity paradox. This paradox refers to the phenomenon that a higher body mass index is related to worse cardio-vascular outcomes in non-heart failure patients, while in patients with heart failure a higher body mass index is related to better cardiovascular outcomes. The best survival
rates are seen in patients with a body mass index between 25 and 35 kg/m2, and their
outcome is better than underweight patients with heart failure or those with a normal
body mass index (between 20-25 kg/m2).17-20 However, this paradox is mainly described
using body mass index to define obesity, which only gives limited information about fat distribution in the body, and therefore poorly distinguishes between fitness and fatness
in a patient.21,22 The obesity paradox remains an incomplete understood phenomenon,
which might be partially caused by the limitations of solely assessing body mass index in these patients. A more comprehensive assessment of the patient by additional mea-surements using other indices, such as waist and/or hip circumference, could provide more information about fat distribution in the patient.
Therefore the second aim of this thesis is to study the association of obesity and nutri-tional status in patients with heart failure and clinical outcome.
In Chapter 4 we study the association of waist-to-hip ratio and mortality in heart failure
patients, and show the additive value of different assessments besides body mass index in these patients. Besides fat distribution, nutrition could also play an important role in patients with heart failure. Proteins are an essential part of a healthy diet, and are present in a variety of foods. Proteins are used to form amino acids and building up muscle mass, and are known to be beneficial in the general population. However, if pro-tein intake has any association with mortality in a heart failure population is unknown.
Therefore Chapter 5 sets out to assess the association of estimated protein intake
through a urinary marker in a large heart failure cohort, and identifies whether protein intake is associated with mortality in patients with heart failure.
Another highly prevalent non-cardiac comorbidity in patients with heart failure is renal
dysfunction, being prevalent in around half of the patients with heart failure.6,23 Renal
dysfunction and heart failure often co-exist, and is commonly referred to as the cardio-renal syndrome. Renal dysfunction may worsen heart failure, or vice versa heart failure may worsen renal dysfunction via low cardiac output and increased central venous pressure. Furthermore, shared risk factors like diabetes and hypertension may play a
role in the onset or progression of cardiac and renal dysfunction. Renal dysfunction in heart failure has been extensively studied over the past years. However, the majority of studies have been performed in patients with HFrEF. Much less is known about the mechanisms related to cardiac and renal failure in patients with HFpEF. In patients with HFpEF the pathophysiology behind renal dysfunction might be different from patients with HFrEF. For example, endothelial dysfunction and chronic low grade state of inflammation have been recently linked to renal dysfunction and HFpEF. However,
these mechanisms have not been fully understood.24 In the second part of this thesis
we therefore aimed to provide more insight in renal dysfunction in patients with HFpEF and HFrEF.
The third aim of this thesis is to use established and novel renal markers to explore the pathophysiology behind renal dysfunction in patients with HFrEF and HFpEF.
Chapter 6 is a meta-analysis regarding the use of renin-angiotensin aldosterone system
(RAAS) inhibitors in patients with HFrEF and patients with HFpEF, and assesses the association of a RAAS inhibitor induced worsening renal function and the association
with mortality in these patients. In Chapter 7, we examine several urinary markers in a
large chronic heart failure cohort, to study the pathophysiology behind the differences in renal dysfunction between patients with HFrEF and patients with HFpEF. Via several urinary markers we aimed to investigate the potential differences in nephron segment damage in patients with HFrEF and HFpEF.
Finally, Chapter 8 discusses the main findings and the relevance of this thesis, and
1
References
1. Braunschweig F, Cowie MR, Auricchio A. What are the costs of heart failure? Europace 2011;13 Suppl 2:ii13-7.
2. Conrad N, Judge A, Tran J, Mohseni H, Hedgecott D, Crespillo AP, Allison M, Hemingway H, Cleland JG, McMurray JJV, Rahimi K. Temporal trends and patterns in heart failure incidence: a population-based study of 4 million individuals. Lancet 2018;391:572-580. 3. Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JG, Coats AJ, Falk V,
Gonzalez-Juanatey JR, Harjola VP, Jankowska EA, Jessup M, Linde C, Nihoyannopoulos P, Parissis JT, Pieske B, Riley JP, Rosano GM, Ruilope LM, Ruschitzka F, Rutten FH, van der Meer P, Authors/Task Force Members. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC)Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur Heart J 2016;37:2129-2200.
4. Dunlay SM, Roger VL, Redfield MM. Epidemiology of heart failure with preserved ejection fraction. Nat Rev Cardiol 2017;
5. Mentz RJ, Kelly JP, von Lueder TG, Voors AA, Lam CS, Cowie MR, Kjeldsen K, Jankowska EA, Atar D, Butler J, Fiuzat M, Zannad F, Pitt B, O’Connor CM. Noncardiac comorbidi-ties in heart failure with reduced versus preserved ejection fraction. J Am Coll Cardiol 2014;64:2281-2293.
6. van Deursen VM, Urso R, Laroche C, Damman K, Dahlstrom U, Tavazzi L, Maggioni AP, Voors AA. Co-morbidities in patients with heart failure: an analysis of the European Heart Failure Pilot Survey. Eur J Heart Fail 2014;16:103-111.
7. Mentz RJ, Kelly JP, von Lueder TG, Voors AA, Lam CS, Cowie MR, Kjeldsen K, Jankowska EA, Atar D, Butler J, Fiuzat M, Zannad F, Pitt B, O’Connor CM. Noncardiac comorbidi-ties in heart failure with reduced versus preserved ejection fraction. J Am Coll Cardiol 2014;64:2281-2293.
8. Paulus WJ, Tschope C. A novel paradigm for heart failure with preserved ejection fraction: comorbidities drive myocardial dysfunction and remodeling through coronary microvascular endothelial inflammation. J Am Coll Cardiol 2013;62:263-271.
9. Shah SJ, Kitzman DW, Borlaug BA, van Heerebeek L, Zile MR, Kass DA, Paulus WJ. Phenotype-Specific Treatment of Heart Failure With Preserved Ejection Fraction: A Multior-gan Roadmap. Circulation 2016;134:73-90.
10. Packer M. Can brain natriuretic peptide be used to guide the management of patients with heart failure and a preserved ejection fraction? The wrong way to identify new treatments for a nonexistent disease. Circ Heart Fail 2011;4:538-540.
11. van Deursen VM, Damman K, van der Meer P, Wijkstra PJ, Luijckx GJ, van Beek A, van Veldhuisen DJ, Voors AA. Co-morbidities in heart failure. Heart Fail Rev 2014;19:163-172. 12. Baldi I, Azzolina D, Berchialla P, Gregori D, Scotti L, Corrao G. Comorbidity-adjusted
relative survival in newly hospitalized heart failure patients: A population-based study. Int J Cardiol 2017;243:385-388.
13. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011-2012. JAMA 2014;311:806-814.
14. Kenchaiah S, Sesso HD, Gaziano JM. Body mass index and vigorous physical activity and the risk of heart failure among men. Circulation 2009;119:44-52.
15. Kenchaiah S, Evans JC, Levy D, Wilson PW, Benjamin EJ, Larson MG, Kannel WB, Vasan RS. Obesity and the risk of heart failure. N Engl J Med 2002;347:305-313.
16. Madamanchi C, Alhosaini H, Sumida A, Runge MS. Obesity and natriuretic peptides, BNP and NT-proBNP: mechanisms and diagnostic implications for heart failure. Int J Cardiol 2014;176:611-617.
17. Lavie CJ, Alpert MA, Arena R, Mehra MR, Milani RV, Ventura HO. Impact of obesity and the obesity paradox on prevalence and prognosis in heart failure. JACC Heart Fail 2013;1:93-102.
18. Horwich TB, Fonarow GC, Hamilton MA, MacLellan WR, Woo MA, Tillisch JH. The re-lationship between obesity and mortality in patients with heart failure. J Am Coll Cardiol 2001;38:789-795.
19. Lavie CJ, Sharma A, Alpert MA, De Schutter A, Lopez-Jimenez F, Milani RV, Ventura HO. Update on Obesity and Obesity Paradox in Heart Failure. Prog Cardiovasc Dis 2016;58:393-400.
20. Shah R, Gayat E, Januzzi JL,Jr, Sato N, Cohen-Solal A, diSomma S, Fairman E, Har-jola VP, Ishihara S, Lassus J, Maggioni A, Metra M, Mueller C, Mueller T, Parenica J, Pascual-Figal D, Peacock WF, Spinar J, van Kimmenade R, Mebazaa A, GREAT (Global Research on Acute Conditions Team) Network. Body mass index and mortality in acutely decompensated heart failure across the world: a global obesity paradox. J Am Coll Cardiol 2014;63:778-785.
21. Piepoli MF, Corra U, Veglia F, Bonomi A, Salvioni E, Cattadori G, Metra M, Lombardi C, Sinagra G, Limongelli G, Raimondo R, Re F, Magri D, Belardinelli R, Parati G, Mina C, Scar-dovi AB, Guazzi M, Cicoira M, Scrutinio D, Di Lenarda A, Bussotti M, Frigerio M, Correale M, Villani GQ, Paolillo S, Passino C, Agostoni P, MECKI Score Research Group. Exercise tolerance can explain the obesity paradox in patients with systolic heart failure: data from the MECKI Score Research Group. Eur J Heart Fail 2016;18:545-553.
22. Lavie CJ, De Schutter A, Milani RV. Healthy obese versus unhealthy lean: the obesity paradox. Nat Rev Endocrinol 2015;11:55-62.
23. Damman K, Valente MA, Voors AA, O’Connor CM, van Veldhuisen DJ, Hillege HL. Renal impairment, worsening renal function, and outcome in patients with heart failure: an up-dated meta-analysis. Eur Heart J 2014;35:455-469.
24. Ter Maaten JM, Damman K, Verhaar MC, Paulus WJ, Duncker DJ, Cheng C, van Heere-beek L, Hillege HL, Lam CS, Navis G, Voors AA. Connecting heart failure with preserved ejection fraction and renal dysfunction: the role of endothelial dysfunction and inflammation. Eur J Heart Fail 2016;18:588-598.
Chapter 2
Non-cardiac comorbidities in heart
failure with reduced, mid-range and
preserved ejection fraction
Koen W. Streng
Jan F. Nauta
Hans L. Hillege
Stefan D. Anker
John G. Cleland
Kenneth Dickstein
Gerasimos Filippatos
Chim C. Lang
Marco Metra
Leong L. Ng
Piotr Ponikowski
Nilesh J. Samani
Dirk J. van Veldhuisen
Aeilko H. Zwinderman
Faiez Zannad
Kevin Damman
Peter van der Meer
Adriaan A. Voors
ABSTRACT
Background
Comorbidities play a major role in heart failure. Whether prevalence and prognostic importance of comorbidities differ between heart failure with preserved ejection fraction (HFpEF), mid-range (HFmrEF) or reduced ejection fraction (HFrEF) is unknown.
Methods
Patients from index (n=2516) and validation cohort (n=1738) of The BIOlogy Study to TAilored Treatment in Chronic Heart Failure (BIOSTAT-CHF) were pooled. Eight non-cardiac comorbidities were assessed; diabetes mellitus, thyroid dysfunction, obesity, anaemia, chronic kidney disease (CKD, estimated glomerular filtration rate < 60 mL/ min/1.73m2), COPD, stroke and peripheral arterial disease. Patients were classified based on ejection fraction. The association of each comorbidity with quality of life (QoL), all-cause mortality and hospitalisation was evaluated.
Results
Patients with complete comorbidity data were included (n=3499). Most prevalent co-morbidity was CKD (50%). All comorbidities showed the highest prevalence in HFpEF, except for stroke. Prevalences of HFmrEF were in between the other entities. COPD was the comorbidity associated with the greatest reduction in QoL. In HFrEF, almost all were associated with a significant reduction in QoL, while in HFpEF only CKD and obesity were associated with a reduction. Most comorbidities in HFrEF were associated with an increased mortality risk, while in HFpEF only CKD, anaemia and COPD were associated with higher mortality risks.
Conclusions
The highest prevalence of comorbidities was seen in patients with HFpEF. Overall, co-morbidities were associated with a lower QoL, but this was more pronounced in patients with HFrEF. Most comorbidities were associated with higher mortality risks, although the associations with diabetes were only present in patients with HFrEF.
2
INTRODUCTION
Heart failure (HF) is often accompanied by one or multiple non-cardiac comorbidities, making diagnosis and management of HF more complicated. These comorbidities
are often associated with worse outcomes and higher hospitalisation rates.1-3 It is
known that HF and comorbidities such as chronic kidney disease (CKD, defined as glomerular filtration rate (GFR) < 60 mL/min/1.73m2), chronic obstructive pulmonary disease (COPD), diabetes mellitus, stroke and anaemia are often present in HF, and
that CKD is associated with an increased mortality risk.4,5 Studies have shown that
comorbidities are more prevalent, are associated with a higher mortality risk and with more physical impairment in patients with heart failure with preserved ejection fraction
(HFpEF) compared with patients with a reduced ejection fraction (HFrEF).6-8 However,
the association of each of the separate comorbidities with mortality in patients with HF is currently unknown. Secondly, little is known about the association of individual non-cardiac comorbidities with quality of life (QoL) in patients with HFrEF, HFpEF and heart failure with mid-range ejection fraction (HFmrEF).
This study therefore aimed to investigate the associations between individual non-cardiac comorbidities and QoL and their association with mortality in patients with HFrEF, HFmrEF and HFpEF.
METHODS
Study population
For the current study population we have combined both the index cohort (n=2516) and validation cohort (n=1738) of the BIOSTAT-CHF (A systems BIOlogy Study to Tailored
Treatment in Chronic Heart Failure), a multicentre, prospective observational study.9 In
the index cohort primary inclusion criteria were an objective cardiac dysfunction, defined by either a left ventricular ejection fraction (LVEF) <40% or plasma N-terminal pro-brain natriuretic peptide (NT-proBNP) of >2000 pg/ml and be treated with at least 40mg of furosemide or equivalent, and were on sub-optimal dose of angiotensin-converting enzyme inhibitors and/or angiotensin receptor blockers. Main inclusion criteria in the validation cohort were documented HF and patients had to be treated with at least 20mg furosemide or equivalent per day and were anticipated to be up titrated with ACE inhibitors/ARBs and/or beta-blockers. Institutional review board approved the study, and all patients gave written informed consent, A full list of inclusion and exclusion
cri-teria has been previously published.9 Patients were divided based on LVEF into HFrEF
recent ESC HF guidelines.10 Patients who had full data available for the 8 non-cardiac comorbidities stated below and who had available LVEF were included (n=3499).
Non-cardiac comorbidities
Eight non-cardiac comorbidities were included in this analysis. Comorbidities included were diabetes mellitus ( type I and type II diabetes), obesity (defined as a body mass index above or equal to 30 kg/m2), thyroid dysfunction (both hypo- and hyperthyroid disease), chronic kidney disease (CKD) (defined as an estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2) measured at baseline, a history of stroke, chronic obstructive pulmonary disease (COPD), peripheral arterial disease (PAD) and anae-mia (defined as a haemoglobin below 12 g/dL in woman and below 13 g/dL in men,
measured at baseline).11 The presence of COPD, stroke, thyroid dysfunction, PAD or
diabetes was assessed by the treating physicians, based on information available on the patients’ medical history and during inclusion of the study.
Statistical analysis
Normally distributed data are presented as means and standard deviation, not normally distributed data as medians and 25th until 75th percentile, and categorical variables as percentages and frequencies. Intergroup differences between variables were tested using one-way ANOVA for normal distributed data; skewed data was tested using Chi-squared test or Kruskal-Wallis test depending on whether the data was continuous or nominal. Post-hoc analysis was performed to calculate differences between the groups. Prevalence of each of the comorbidities was also standardized for age. Age groups were created per 10 years, starting at 20 years up to 100 years. Per age group, the age specific prevalence in each of the HF subgroups was assessed, and multiplied by the total number of patients in that age category. This was done for each of the age groups, after which the sum of all the age groups was divided by the total number of patients. QoL was assessed by using the Kansas City Cardiomyopathy Questionnaire
(KCCQ) and EuroQol five dimensions questionnaire (EQ-5D).12,13 A difference of ≥5
points between mean scores was considered to be minimally clinically important.13,14
To evaluate the association of the comorbidities with KCCQ overall score, univariable and multivariable linear regression analysis was performed. Cox proportional hazard analysis was performed to analyze the different hazard ratios with 95% confidence interval (CI) per comorbidity. These were depicted in a forest plot combined with a p-value for interaction. All hazard ratios were corrected for age, sex, NYHA class and physical limitation score.
2
All analyses were performed using IBM SPSS Statistics version 23 and R: a Language and Environment for Statistical Computing, version 3.0.2. (R Foundation for Statistical Computing, Vienna, Austria).
RESULTS
Baseline characteristics
A total of 3499 patients were included in our current study. Baseline characteristics are shown in Table 1. We included 2309 patients with HFrEF (66%), 634 with HFmrEF (18%) and 556 patients with HFpEF (16%). Patients with HFpEF were older, more often women, and had higher systolic blood pressures (P<0.001). Patients with HFrEF were less likely to have a history of hypertension (57%). CKD was present in 50% of patients, anaemia in 36%, obesity and diabetes mellitus in 33%, COPD in 18%, and stroke and thyroid dysfunction in 13% of the patients (Figure 1). In general, the prevalences of comorbidities were greater in HFpEF, compared with HFmrEF and HFrEF. CKD (56%) and anaemia (46%) had the highest prevalence in HFpEF (respectively P=0.002 and P<0.001). COPD was present in 24% of HFpEF patients and 17% in patients with HFrEF (P<0.001). A history of stroke was found more often in HFmrEF (17%). The prevalence of diabetes differed between HFpEF and HFrEF, where the prevalence within HFmrEF was in between HFpEF and HFrEF, but not significantly different. The prevalences of the other comorbidities are shown in Table 1. The number of comorbidities in patients with HFrEF, HFmrEF and HFpEF differed significantly (P<0.001). Patients with HFpEF had the highest number of comorbidities, while patients with HFrEF had the lowest number of comorbidities (Supplementary Figure 1). At least 1 comorbidity was found in 84% of the patients with HFrEF, while this was 87% in HFmrEF patients and 94% in patients with HFpEF (P<0.001). Age-standardized prevalence for the comorbidities is depicted in Supplementary Figure 2.
Table 1; Baseline characteristics N =
HFrEF HFmrEF HFpEF Total P-value
2309 634 556 3499
Demographics
Sex (% male) 1744 (75.5) 416 (65.6) 300 (54.0) 2460 (70.3) <0.001
Age (years) 69±12.2 75±11.1 78±9.8 71±12 <0.001
Systolic Blood Pressure (mmHg) 123±21 129±22 130±23 125±22 <0.001
Diastolic Blood Pressure (mmHg) 74±13 72±14 69±14 72±13 <0.001
Heart Rate (beats/min) 79±19 75±19 76±18 78±19 <0.001
NT-proBNP (ng/L) 3054
Table 1; Baseline characteristics (continued) N =
HFrEF HFmrEF HFpEF Total P-value
2309 634 556 3499 Non-cardiac comorbidities Diabetes mellitus (%) 722 (31.3) 221 (34.9) 198 (35.6) 1141 (32.6) 0.060 Thyroid dysfunction (%) 252 (10.9) 87 (13.7) 97 (17.4) 436 (12.5) <0.001 Stroke (%) 256 (11.1) 107 (16.9) 91 (16.4) 454 (13.0) <0.001 COPD (%) 384 (16.6) 103 (16.2) 132 (23.7) 619 (17.7) <0.001 CKD (%) 1115 (48.3) 334 (52.7) 312 (56.1) 1761 (50.3) 0.002 Anaemia (%) 758 (32.8) 254 (40.1) 253 (45.5) 1265 (36.2) <0.001 Obesity (%) 679 (29.4) 233 (36.8) 235 (42.3) 1147 (32.8) <0.001
Peripheral arterial disease (%) 323 (14.0) 127 (20.0) 135 (24.3) 585 (16.7) <0.001
Number of comorbidities 1.8±1.3 2.1±1.4 2.4±1.3 2.0±1.3 <0.001 Medical History Hypertension (%) 1303 (56.5) 443 (70.0) 386 (69.4) 2132 (60.9) <0.001 Myocardial infarction (%) 998 (43.2) 303 (47.8) 181 (32.6) 1482 (42.4) <0.001 PCI (%) 471 (20.4) 128 (20.2) 89 (16.0) 688 (19.7) 0.044 CABG (%) 398 (17.2) 129 (20.3) 77 (13.9) 604 (17.3) 0.022 Atrial Fibrillation (%) 996 (43.2) 313 (49.5) 275 (49.6) 1584 (45.3) <0.001 NYHA Class <0.001 I 135 (5.8) 34 (5.4) 16 (2.9) 185 (5.3) II 1074 (46.5) 270 (42.7) 194 (34.9) 1538 (44.0) III 770 (33.3) 239 (37.8) 235 (42.3) 1244 (35.6) IV 130 (5.6) 60 (9.5) 89 (16.0) 279 (8.0) Quality of life KCCQ Physical limitation 54 [29-79] 50 [25-75] 42 [21-67] 50 [29-75] <0.001 Symptom stability 52 [25-75] 50 [25-75] 50 [25-75] 50 [25-75] 0.008 Symptom Frequency 50 [25-70] 45 [25-70] 35 [15-60] 45 [25-70] <0.001 Symptom burden 42 [27-60] 40 [20-60] 33 [20-53] 40 [20-60] <0.001 Self-efficacy score 75 [50-88] 75 [50-88] 75 [50-88] 75 [50-88] <0.001 Quality of life 42 [25-67] 50 [25-67] 42 [25-67] 42 [25-67] 0.134 Social limitation 35 [15-60] 30 [10-55] 25 [5-50] 35 [15-55] <0.001 Overall score 47 [31-64] 43 [30-59] 38 [24-53] 44 [30-61] <0.001 EQ-5D VAS score 55 [40-70] 59 [48-70] 52 [45-70] 55 [45-70] 0.359
Values are given as means ± standard deviation, median (25th to 75th percentiles) or percentage and frequency HFrEF = Heart failure with reduced ejection fraction; HFmrEF = Heart failure with mid-range ejection fraction; HF-pEF = Heart failure with preserved ejection fraction; NTpro-BNP = N-terminal pro brain natriuretic peptide; COPD = Chronic obstructive pulmonary disease; CKD = Chronic kidney disease; PCI = Percutaneous coronary intervention; CABG = Coronary artery bypass graft; NYHA = New York Heart Association; KCCQ = Kansas city Cardiomyopathy Questionnaire
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Non-cardiac comorbidities and quality of life
Overall, QoL was lower in HFpEF compared with HFmrEF and HFrEF. When assess-ing the different domains within the KCCQ, patients with HFpEF had more physical limitations, more symptom frequency and burden, and had the most social limitations (all P<0.001). Most comorbidities were associated with a significant decline in mean KCCQ score (all P<0.001), but the decline in mean overall KCCQ score was larger in patients with HFrEF and HFmrEF, compared with patients with HFpEF (Table 2). In patients with HFrEF each comorbidity, except for thyroid dysfunction, was associated with a significant decline in mean KCCQ score, while in patients with HFpEF COPD (P=0.002), obesity (P=0.048) and thyroid dysfunction (P=0.017) were associated with a decline in QoL. Other comorbidities did not yield a significant difference in mean KCCQ score. Supplementary Figure 3 shows the difference in overall mean KCCQ score between the subgroups. A difference of ≥5 points was considered to be minimal clinically important. In the total cohort, each of the comorbidities had a minimal clinically important difference, where a decrease of 10 points in mean KCCQ score was seen in patients with COPD. However, in patients with HFrEF, obesity and thyroid dysfunction are no longer associated with a difference in QoL, while the same was true for CKD in HFmrEF. In contrast to the other HF groups, the only comorbidities with a minimal clinical important difference in patients with HFpEF were COPD and thyroid dysfunc-Figure 1; Prevalence of non-cardiac comorbidities in heart failure groups
tion. To evaluate the association of comorbidities with QoL in the different HF groups, linear regression was performed (Supplementary Table 1). Overall, each non-cardiac comorbidity was associated with a lower KCCQ overall score (all P<0.001, except for thyroid dysfunction (P=0.035)). Consistent in each of the HF groups, both COPD and obesity were significantly associated with a lower KCCQ score. However, diabetes was only associated with a lower KCCQ score in HFrEF (P<0.001), but not in HFmrEF and HFpEF. Both CKD and anaemia were not associated with KCCQ score in HFpEF (respectively P=0.987 and P=0.293).
The differences between the groups were less pronounced when using the EQ-5D scale. When assessing the Visual Analog Scale (VAS) score used in the EQ-5D in the total cohort, the presence of each comorbidity significantly lowers the VAS scale, except for obesity (P=0.115). In patients with HFrEF, the presence of COPD (P<0.001), stroke (P=0.039), diabetes (P<0.001), CKD (P=0.003) and anaemia (P<0.001) lowers the VAS score. In HFmrEF patients, only anaemia (P=0.003) is associated with a lower VAS, while in HFpEF only patients with COPD (P=0.002) or thyroid dysfunction (P=0.009) had a significantly lower VAS score.
Non-cardiac comorbidities and outcome
In the overall cohort, all comorbidities were associated with increased risk for all-cause mortality, except for stroke. Mean follow up was 25 months. Figure 2 shows a forest plot with hazard ratios for all-cause mortality and for hospitalisation per HF subgroup. For hospitalisation, the only comorbidity with an increased hazard ratio in HFpEF was thy-roid dysfunction, while in HFmrEF CKD, diabetes mellitus, thythy-roid dysfunction, COPD and anaemia were significantly associated with increased hospitalisation risks. HFrEF showed similar results as in HFmrEF.
Furthermore, in all HF subgroups the presence of CKD was associated with increased risk of mortality (HFpEF Hazard ratio (HR) 1.39, 95% CI 1.03 to 1.87, P=0.032, HFmrEF HR 1.79, 95% CI 1.32 to 2.43, P<0.001 and HFrEF HR 1.49, 95% CI 1.25 to 1.77, P<0.001, respectively). In HFrEF, diabetes mellitus, anaemia and COPD were all as-sociated with significantly higher event rates. In HFmrEF, besides anaemia (P<0.001) no other comorbidities were significantly related with higher mortality rates. In HFpEF, COPD and thyroid dysfunction were both associated with significantly increased event rates. For obesity, a decreased mortality risk was seen in HFpEF (HR 0.60, 95% CI 0.44 to 0.80, P<0.001) and in HFmrEF (HR 0.66, 95% CI 0.48 to 0.89, P=0.008). A significant interaction between comorbidity and LVEF as a continuous variable were seen for diabetes mellitus (P=0.031) and anaemia (P=0.043). Diabetes and anaemia had a stronger association with poor outcomes in HFrEF and HFmrEF, compared with HFpEF.
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Table 2; Quality of life in HF subgroups
HFrEF P-value HFmrEF P-value HFpEF P-value Total P-value Comorbidity present? No Yes No Yes No Yes No Yes
KCCQ overall score COPD
50 [32-65] 37 [25-53] <0.001 45 [31-61] 36 [22-47] <0.001 41 [27-56] 30 [19-43] <0.001 46 [31-63] 36 [24-49] <0.001 Stroke 48 [31-64] 41 [24-60] <0.001 44 [30-60] 39 [29-51] 0.098 39 [24-54] 34 [23-52] 0.362 45 [30-63] 39 [25-55] <0.001 Diabetes 50 [33-66] 41 [26-58] <0.001 45 [31-61] 39 [28-56] 0.039 38 [24-55] 37 [22-51] 0.350 47 [31-64] 40 [26-57] <0.001 Obesity 48 [32-64] 44 [28-62] 0.01 1 46 [32-62] 39 [27-56] 0.004 40 [24-56] 36 [24-50] 0.048 46 [31-63] 41 [27-58] <0.001 Thyroid dysfunction 47 [31-64] 44 [29-60] 0.202 45 [31-60] 34 [24-53] 0.002 39 [24-55] 34 [21-48] 0.017 45 [30-63] 40 [25-56] <0.001 CKD 51 [33-67] 43 [28-60] <0.001 46 [31-64] 41 [28-56] 0.010 37 [25-52] 38 [23-54] 0.767 48 [31-65] 42 [28-58] <0.001 Anaemia 49 [33-66] 42 [27-58] <0.001 46 [32-64] 39 [28-51] <0.001 38 [24-56] 37 [24-52] 0.676 47 [31-65] 41 [27-56] <0.001 PAD 48 [31-64] 42 [27-58] 0.001 45 [31-61] 37 [28-52] 0.016 39 [24-53] 36 [22-52] 0.472 45 [30-63] 40 [26-55] <0.001 EQ-5D V AS score COPD 60 [45-70] 50 [40-65] <0.001 60 [49-70] 50 [43-65] 0.078 58 [50-70] 50 [40-60] 0.002 60 [45-70] 50 [40-65] <0.001 Stroke 56 [43-70] 50 [40-70] 0.039 60 [49-70] 50 [40-70] 0.088 55 [45-70] 50 [40-65] 0.191 56 [45-70] 50 [40-70] 0.004 Diabetes 60 [45-70] 50 [40-70] <0.001 60 [49-70] 55 [45-70] 0.204 52 [40-70] 53 [50-70] 0.839 60 [45-70] 50 [40-70] 0.001 Obesity 55 [43-70] 59 [40-70] 0.737 60 [48-70] 55 [47-70] 0.1 18 59 [45-70] 50 [40-70] 0.106 55 [45-70] 55 [40-70] 0.1 15 Thyroid dysfunction 55 [40-70] 55 [40-70] 0.708 60 [49-70] 50 [40-70] 0.220 55 [45-70] 50 [41-60] 0.009 58 [45-70] 50 [40-70] 0.044 CKD 60 [45-70] 52 [40-70] 0.003 59 [49-70] 59 [46-70] 0.492 55 [43-70] 51 [46-69] 0.370 60 [45-70] 52 [40-70] 0.002 Anaemia 60 [45-70] 50 [40-70] <0.001 60 [50-71] 50 [45-70] 0.003 55 [45-70] 50 [45-68] 0.145 60 [45-70] 50 [40-70] <0.001 PAD 55 [41-70] 50 [40-70] 0.266 60 [47-70] 50 [48-70] 0.179 55 [45-70] 50 [43-70] 0.748 55 [45-70] 50 [40-70] 0.1 17 Values are given as median [25th to 75th percentiles] HFrEF = Heart failure with reduced ejection fraction; HFmrEF = Heart failure with mid-range ejection fraction; HFpEF = Heart failure with preserved ejection fraction; KCCQ =Kansas city cardiomyopathy questionnaire; COPD = Chronic obstructive pulmonary disease; CKD = Chronic kidney disease; PAD =
Figure 2; Forest plot with hazard ratios for all-cause mortality (top) and hospitalisation (bottom) and each comorbidity; corrected for age, sex, NYHA class and physical limitation. On the right is P-value for interac-tion with Heart Failure group
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DISCUSSION
We studied 8 non-cardiac comorbidities in a broad cohort of patients with HF. Comor-bidities with the greatest prevalence were the presence of CKD, anaemia, diabetes and obesity. For all comorbidities, except for stroke, the prevalence was the highest in patients with HFpEF. We have further shown that most comorbidities were associated with lower QoL, although the difference compared with not having the comorbidity was generally larger in patients with HFrEF compared with patients with HFmrEF or HFpEF. Furthermore, most comorbidities were associated with an increased risk of mortality, although the presence of diabetes was only associated with higher mortality risks in HFrEF.
Prevalence of non-cardiac comorbidities
The most common comorbidities in this cohort were CKD and anaemia. These findings are in line with previous studies, where a prevalence of CKD in different cohorts of
patients with HF is seen, ranging from 28% up to 55%.1,4,15 Prevalence of anaemia
var-ies widely in the literature, with numbers ranging from 5 to 60%, in concordance with our
study.16,17 Obesity was present in 33% of our cohort, and its prevalence was particularly
high in patients with HFpEF. Obesity is more often seen in patients with HFpEF, and
could trouble the diagnosis of HF in these patients.18 However in our study, patients
with HFpEF also had increased levels of NT-proBNP, making misdiagnosis of HF much more unlikely. Diabetes was present in 33% of patients, which is similar to previous
studies which report a prevalence ranging from 22% up to 45%.7,19,20 Novel findings
were the prevalences of non-cardiac comorbidities in patients with HFmrEF. To the best of our knowledge, this has not been described before. Prevalences of comorbidities showed a gradual increase from HFrEF to HFmrEF to HFpEF. One of our consistent findings was the fact that comorbidities were more prevalent in patients with HFpEF. Although two previous studies have depicted that non-cardiac comorbidities were more
prevalent in patients with HFpEF 6,7, our study additionally focussed on the individual
association of each of the comorbidities with QoL and all-cause mortality. To assess whether the higher prevalence of comorbidities in patients with HFpEF was driven by age, we calculated age-standardized prevalences, showing largely similar results. Only for CKD, the similarity in prevalences among HF phenotypes could be argued to be at least partly age driven, as after age adjustment CKD was more frequently observed in patients with HFrEF. For all other comorbidities the prevalence remained greater in patients with HFpEF. Furthermore, multi-morbidity was a common finding in our cohort, especially in patients with HFpEF. Braunstein et al previously showed that nearly 40%
of chronic HF patients had 5 or more comorbidities.2 The prevalence of (multiple)
multiple comorbidities were more often present in patients with HFpEF. This could partly be due to an older age, however precise mechanisms behind non-cardiac comorbidities and HF are still unclear. However, they do seem an important target for a more holistic
approach in the treatment of HFpEF patients.24
Novel findings in our study also regard the prevalence and associations of comorbidities within HFmrEF. This entity is often referred to as the middle child, which holds true in our cohort for the prevalence of the different comorbidities. The prevalence for each co-morbidity was in between HFpEF and HFrEF. A recent review on HFmrEF studies found that HFmrEF might be more similar to HFrEF, especially with regard to the prevalence
of ischemic heart disease.25 We also found that hazard ratio’s for the different
comor-bidities for HFmrEF showed a more similar pattern to HFrEF compared with HFpEF.
Influence of comorbidities on quality of life
Comorbidities could influence QoL in several ways.26,27 The majority of these
non-cardiac comorbidities require the use of medication, and polypharmacy is associated
with a decrease in functional status of the patient.28 Furthermore, the majority of these
comorbidities are accompanied by a variety of (physical) symptoms, such as fatigue, decrease in general condition and/or shortness of breath. These factors not only limit the patients in functional status, but could also influence their social status and with that an even further decline in QoL. Here, we indeed showed that comorbidities were associated with a lower QoL. In a multivariable analysis, there were more individual comorbidities that were independently associated with overall KCCQ score in HFrEF compared with HFpEF patients. Since comorbidities had a higher prevalence in patients with HFpEF, analyses were repeated within a matched cohort for number of comor-bidities with HFrEF. This did not yield any significant difference. A plausible explanation could be that the non-cardiac comorbidities were already present before the onset of HFpEF, while in HFrEF the comorbidities were a consequence of the HF itself. Although this cannot be concluded based on these data, Paulus et al have previously postulated that comorbidities in HFpEF induce a pro-inflammatory state, resulting in alterations in myocardial structure and functions. Consequently, the comorbidity itself might be
the cause -or deteriorating factor- in HFpEF.29 Our findings might be supportive of this
theory.
One of the comorbidities consistently associated with QoL in all 3 HF groups was COPD. HF and COPD often co-exist, with a reported prevalence of approximately 20% within patients with HF. COPD is known to be characterized by a low-grade state of inflammation, and may thus be associated with more frequent cardiovascular events
2
Influence of comorbidities on outcome
We found a consistent and strong association between the presence of non-cardiac comorbidities and outcome. This finding is consistent with previous studies in patients
with chronic HF.4,31 Overall, CKD and anaemia were associated with the highest risks of
all-cause mortality. In patients with HFrEF, the presence of diabetes mellitus or COPD was significantly associated with a worse outcome. The presence of COPD may be associated with higher mortality risk in HF, which could partially be due to the fact that patients with COPD are less likely to receive treatment with a beta-blocker and have a
reduced exercise capacity.32,33 However, there are common shared denominators such
as inflammation, smoking and/or chronic illness which are known to cause both HF and
comorbidities such as COPD.34
Although in our cohort the association was borderline non-significant, the association between a history of stroke and higher mortality risk was previously shown in a cohort
of patients with HFrEF.35 One reason for this association could be the shared risk factor
of atherosclerosis, or the development of thromboembolic events in patients with very
low ejection fractions.36
The precise mechanisms behind the increased mortality risks are still unclear, however there could be several factors involved in the increased mortality risk observed in pa-tients with (multiple) comorbidities. First of all, HF could result in more comorbidities. Due to fatigue and shortness of breath, patients are more inactive which could play a part in the development of for example diabetes and obesity. Furthermore, patients with multiple comorbidities often represent a more severe HF and are therefore associated with higher mortality risks and higher hospitalisation rates. Lastly, comorbidities may cause worsening HF via medication used to treat these comorbidities, or comorbidities may influence the use of HF medication, influencing their effect on outcome in these patients.
The optimal treatment for both HF and the accompanying comorbidities is a clinical challenge. Especially in HFpEF, HF treatment options are very limited. Therefore opti-mizing treatment of the separate comorbidities might at least improve the QoL of these patients. This hypothesis will be investigated in a clinical trial, OPTIMIZE-HFPEF, which aims to randomize patients to usual care or intensive treatment of several common
comorbidities in HFpEF.37 It has been depicted in previous research that, especially in
HFpEF patients, a more targeted approach might be necessary and therefore treating different phenotypes of HFpEF by not only focussing on the symptoms of HF but also
Study limitations
This was a retrospective, post-hoc study, combining two large HF cohorts. In this study in- and exclusion criteria were used, which might result in a more selected population. The majority of patients was recruited in-hospital, which might bias the QoL compared with outpatients included. Another limitation concerns possible underreporting of comorbidities, since they were assessed by the treating physician and/or based on their reported medical history. For COPD, no confirming spirometry was performed which could also result in a false reporting of the comorbidity. Finally, the choice of comorbidities analysed in our study was arbitrary, although this selection allowed us to focus on specific non-cardiac comorbidities. Some comorbidities were not assessed (for example obstructive sleep apnoea syndrome, malignancy, depression and hepatic disease) since data on these comorbidities were not complete.
CONCLUSION
We have studied 8 non-cardiac comorbidities in a broad cohort of patients with HF. The most prevalent non-cardiac comorbidities were CKD, anaemia, diabetes and obesity. The highest prevalence of comorbidities was seen in patients with HFpEF, whereas the prevalence in HFmrEF was consistently in between HFpEF and HFrEF. While in the overall group most of the comorbidities were associated with a lower QoL, this associa-tion was more pronounced in patients with HFrEF compared with patients with HFmrEF or HFpEF. Most comorbidities were associated with higher mortality risks, however the associations with diabetes were only present in patients with HFrEF in contrast to patients with HFmrEF or HFpEF.
2
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35. Di Tullio MR, Qian M, Thompson JL, Labovitz AJ, Mann DL, Sacco RL, Pullicino PM, Freudenberger RS, Teerlink JR, Graham S, Lip GY, Levin B, Mohr JP, Buchsbaum R, Estol CJ, Lok DJ, Ponikowski P, Anker SD, Homma S, WARCEF Investigators. Left Ventricular
Ejection Fraction and Risk of Stroke and Cardiac Events in Heart Failure: Data From the Warfarin Versus Aspirin in Reduced Ejection Fraction Trial. Stroke. 2016; 47: 2031-2037. 36. Gallino A, Aboyans V, Diehm C, Cosentino F, Stricker H, Falk E, Schouten O, Lekakis J,
Amann-Vesti B, Siclari F, Poredos P, Novo S, Brodmann M, Schulte KL, Vlachopoulos C, De Caterina R, Libby P, Baumgartner I, European Society of Cardiology Working Group on Peripheral Circulation. Non-coronary atherosclerosis. Eur Heart J. 2014; 35: 1112-1119. 37. Fu M, Zhou J, Thunstrom E, Almgren T, Grote L, Bollano E, Schaufelberger M, Johansson
MC, Petzold M, Swedberg K, Andersson B. Optimizing the Management of Heart Failure With Preserved Ejection Fraction in the Elderly by Targeting Comorbidities (OPTIMIZE-HFPEF). J Card Fail. 2016; 22: 539-544.
38. Senni M, Paulus WJ, Gavazzi A, Fraser AG, Diez J, Solomon SD, Smiseth OA, Guazzi M, Lam CS, Maggioni AP, Tschope C, Metra M, Hummel SL, Edelmann F, Ambrosio G, Stewart Coats AJ, Filippatos GS, Gheorghiade M, Anker SD, Levy D, Pfeffer MA, Stough WG, Pieske BM. New strategies for heart failure with preserved ejection fraction: the importance of targeted therapies for heart failure phenotypes. Eur Heart J. 2014; 35: 2797-2815.
2
SUPPLEMENTARY MATERIAL
Supplementary Figure 1; Number of comorbidities per heart failure group
Supplementary Figure 3; Mean difference between presence of specific comorbidity in overall KCCQ score. Mean difference of 5 or more points represents a minimal clinical difference
Supplementary Table 1; Linear regression with overall KCCQ score*
HFpEF HFmrEF HFrEF Total
KCCQ overall
score [95% CI] P-valueβ [95% CI] P-valueβ [95% CI] P-valueβ [95% CI] P-valueβ CKD [-3.69-3.75] 0.9870.03 [-7.02--0.12] 0.043-3.57 [-6.67--2.91] <0.001-4.79 [-5.17--2.05] <0.001-3.61 Diabetes [-6.82-0.80] 0.121-3.01 [-7.00-0.14] 0.060-3.43 [-7.81--4.01] <0.001-5.91 [-6.66--3.58] <0.001-5.12 Thyroid dysfunction [-8.45-1.03] 0.125-3.71 [-9.85--0.02] 0.049-4.94 [-3.56-2.20] 0.644-0.68 [-4.64--0.17] 0.035-2.40 Stroke [-7.68-2.01] 0.252-2.83 [-7.50-1.55] 0.198-2.97 [-7.95--2.26] <0.001-5.11 [-6.58--2.25] <0.001-4.41 COPD [-14.3--5.84] <0.001-10.1 [-13.9--3.96] <0.001-8.43 [-11.7--7.03] <0.001-9.38 [-11.47--7.74] <0.001-9.60 Anaemia [-5.61-1.70] 0.293-1.96 [-10.6--3.69] <0.001-7.16 [-7.47--3.68] <0.001-5.57 [-7.04--3.99] <0.001-5.51 Obesity [-8.36--0.97] 0.013-4.66 [-8.53--1.45] 0.006-4.99 [-4.99--1.07] 0.002-3.03 [-5.76--2.66] <0.001-4.21 PAD [-5.94-2.71] 0.464-1.62 [-9.75--1.18] 0.012-5.47 [-7.30--1.89] <0.001-4.59 [-6.16--2.11] <0.001-4.13
Chapter 3
Associations of Body Mass Index With
Laboratory and Biomarkers in Patients
With Acute Heart Failure
Koen W. Streng
Jozine M. ter Maaten
John G. Cleland
Christopher M. O’Connor
Beth A. Davison
Marco Metra
Michael M. Givertz
John R. Teerlink
Piotr Ponikowski
Daniel M. Bloomfield
Howard C. Dittrich
Hans L. Hillege
Dirk J. van Veldhuisen
Adriaan A. Voors
Peter van der Meer
ABSTRACT
Background
Plasma concentrations of natriuretic peptides decline with obesity in patients with heart failure. Whether this is true for other biomarkers is unknown. We investigated a wide range of biomarker profiles in acute heart failure across the body mass index (BMI) spectrum.
Methods
A total of 48 biomarkers, assessing multiple pathophysiological pathways, were mea-sured in 2033 patients included in PROTECT; a trial comparing the effects of rolofylline to placebo in patients with acute heart failure. Patients were classified into four groups
according to BMI (<25, 25-30, 30-35 and >35 kg/m2).
Results
Of 2003 patients with known weight and height, mean age was 70±12 years and 67% were men. Patients with a higher BMI (>35 kg/m²) had higher blood pressures, were younger and more often women. Median levels of BNP were 550 pg/ml in patients with
a BMI <25 kg/m2 and 319 pg/ml in patients with a BMI >35 kg/m2 (p<0.001).
Multivari-able regression revealed that BNP (β=-0.250, p<0.001) and RAGE (β=-0.095, p<0.007) were inversely correlated to BMI, whereas higher levels of uric acid (β=0.164, p<0.001), proADM (β=0.171, p<0.001), creatinine (β=0.118, p=0.003), sodium (β=0.101, p=0.006) and bicarbonate (β=0.094, p=0.009) were associated with higher BMI. No significant interaction was seen between these seven biomarkers and BMI on 180-day mortality.
Conclusions
The plasma concentration of several biomarkers are either positively or negatively influenced by BMI. These findings suggest that these markers should be interpreted with caution in obese patients. Though concentrations differ, their prognostic value for mortality up to 180 days did not differ.
3
INTRODUCTION
Biomarkers play an important role in the diagnosis and management of heart failure
(HF).1-4 There are a variety of biomarkers available for HF, reflecting several biological
processes such as oxidative stress, myocardial stretch or injury, remodelling,
inflam-mation, renal function or neurohumoral activation.5 One of the most frequently used
biomarkers for the diagnosis and prognosis of HF is (NT-pro) Brain Natriuretic Peptide (BNP), of which levels show a positive association with left ventricle systolic dysfunction and mortality. Serum levels of BNP are known to be lower in obese patients, though the underlying severity of HF does not differ. BNP is cleared by type C clearance recep-tors. Adipose tissue is known to contain more natriuretic peptide clearance receptors-C
(NPR-C), which possibly leads to more degradation of circulating BNP.6 However,
obe-sity is also related with lower circulating levels of NT-proBNP, precursor of BNP, despite the fact NT-proBNP is not degraded through NPR-C. A more likely explanation for the lower levels in obese patients is suggested by Bartels et al. They hypothesize that the expression of BNP in impaired in obese patients due to lipid accumulation, suggesting
a link between the fat metabolism and BNP expression.7 Lower circulating levels have
led to the suggestion different cut-off points should be used in obese patients.8 With the
rising prevalence of obesity worldwide, HF in obese patients is a growing problem. In contrast to BNP, to date it is unknown how other (cardiac) biomarkers behave across the BMI spectrum. Little is known about a variety of clinical used markers such as Troponin, or more novel marker for HF such as Galectin-3 or GDF-15. The association between BMI and these markers could influence their interpretation in patients with a higher BMI in contrast to patients with a lower BMI.
Therefore we aimed to study biomarker levels in obese patients with AHF and their behavioural patterns across the BMI spectrum.
METHODS
Study population
The study population consisted of 2033 patients originating from the PROTECT trial (a placebo-controlled randomized study of the selective A1 adenosine receptor antagonist Rolofylline for patients hospitalized with acute decompensated heart failure and volume overload to assess treatment effect on congestion and renal function) which had neutral
results.9-11 The local ethics committee at each participating center approved the trial,
and all patients provided written informed consent. Key inclusion criteria were dyspnea at rest or at minimal exertion, BNP level ≥500 pg/mL or NT-proBNP ≥2000 pg/mL and
