University of Groningen
Circulating factors in heart failure
Meijers, Wouter
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Publication date: 2019
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Meijers, W. (2019). Circulating factors in heart failure: Biomarkers, markers of co-morbidities and disease factors. Rijksuniversiteit Groningen.
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Chapter 11
General discussion and future perspectives
Biomarker standard
Heart failure (HF) is a complex syndrome that is associated with substantial morbidity and mortality. There is a clear need to improve treatment for HF patients, to ensure a lower burden of disease and increase life expectancy in HF.
HF is a systemic disease, and is characterized by cardiac dysfunction, but also by sev-eral systemic manifestations and co-morbidities. Numerous circulating factors can be detected in plasma and urine from HF patients, that reflect a pathophysiogical change in the heart, or in one of the HF-associated neurohormonal systems, or other affected organs. In this thesis, we study such markers, and construed three layers. The first layer addresses the potential new roles biomarkers may play in patient management. The second part describes several pertinent issues that must be addressed in proper and correct interpretation of biomarkers, their levels, and relation with co-morbidities. The last part embarks on an entirely novel potential role of HF-derived circulating factors, i.e. if such factors may be endocrine proteins affecting or causing other diseases.
Biomarkers may be helpful in clinical decision making. As first stated by Morrow and de Lemos, a biomarker needs to fulfill a certain set of criteria to be clinically useful: “First, accurate, repeated measurements must be available to the clinician at a reasonable cost and with short turnaround times; second, the biomarker must provide information that is not already available from a careful clinical assessment; and finally, knowing the measured level should aid in medical decision making.”1 These criteria were discussed in different chapters of this thesis. Nevertheless, some criteria might be worthwhile considering, in an effort to better integrate biomarkers into modern day medicine. First, biomarkers are measured at different stages of a patients’ disease; therefore, sim-ply comparing one biomarker result to another is usually impossible; interpretation by specialized and experienced clinicians is needed. Furthermore, it could be argued that a selection of biomarkers that reflect different classes (stretch, injury, remodeling, inflam-mation, renal dysfunction, neuro-hormonal and oxidative stress), measured at different time points, would better phenotype patients and allow better clinical decision making. To extend the last criterion, clinicians should be familiar with the indices of biological variation. Lastly, if a biomarker of a non-organ specific disease domain is elevated, and no clear cardiac phenotype is defined, other organ specialists may be consulted for the assessment of extra-cardiac disease. In this thesis, these criteria (Figure 1) are discussed within the framework of the various chapters.
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examples of utilities of biomarkers in patients with heart failure
Biomarkers – clinically important?
In Chapter 2, we addressed the difficulty to predict near term hospitalization after an HF hospitalization. In particular, the potential of galectin-3 with respect to predicting near-term clinical outcomes is described, defined as HF rehospitalisation (30, 60, 90, 120 days) and/or all-cause mortality. Since the incidence of acute HF readmission is as high as 25 to 30% within the 30- to 90-day post-discharge period, awareness is needed for those at high risk for near-term events.2,3
We pooled data from three independent clinical cohorts of acute HF patients that were enrolled at discharge, collectively comprising 902 HF patients, and assessed the value of galectin-3 in predicting at 30, 60, 90, and 120 days post-discharge. The main finding in this chapter was that galectin-3 independently and strongly predicted near-term HF rehospitalisation and mortality. A significant number of patients was adequately reclas-sified on top of a clinical model, from low to high risk and vice versa. Since physicians seek more accurate risk stratification tools, with a goal to potentially reduce near-term HF rehospitalisation, these data provide a rationale to use galectin-3 to this aim. In Chapter 3a, we examined a broad spectrum of biomarkers as a tool to predict low risk of an adverse event for HF patients. Risk prediction generally focuses on high risk patients, but these generally are already optimally treated, or cannot tolerate all medica-tion, so clinical equipoise of targeting this subgroup is minimal. We focused on patients at low risk, a category of which we have very few data. Out of this large panel biomark-ers, galectin-3 proved to yield the best prognostic value regarding low risk compared to biomarkers that reflect other classes, such as hemodynamic loading (NT-proBNP) and inflammation (interleukin-6, C-reactive protein). Galectin-3 accurately identified those patients who did not suffer from mortal events after 30 or 180 days, were not
Biomarker - criteria
Repeated measurements available at reasonable cost/short turn-around; Provide information not available from clinical assessment;
Aid in medical decision making; Interpretation by specialised clinicians; Measure biomarkers from different classes; Take indices of biological variation into account;
Consult other specialist(s) if changes in biomarker levels seem non-cardiac related.
rehospitalised within 30 days and had maximally one rehospitalisation 180 days after discharge. We postulate that these findings can be explained as cardiac remodeling in these patients likely will not (yet) have progressed to a state associated with elevated galectin-3, as seen in irreversible remodeling. Therefore, patients with reversible disease may be identified by low galectin-3 levels. Alternatively, a high galectin-3 level may not only be a signal, but rather a “phenotype” that should prompt doctors to request ad-ditional tests (renal function, albuminuria and liver tests for steatosis), and to possibly act on these results. Natriuretic peptides performed less well in this respect. The data in this chapter could be directly helpful in daily care for HF patients by assessing low risk with galectin-3 levels, and an editorial by Prof. Richards provides additional thoughts and perspectives on our findings in Chapter 3b. He describes overlap with our findings in the management of patients with chest pain at the emergency department, using troponins in ruling out acute coronary syndrome. Biomarkers may serve as a strategy to identify patients at low risk for adverse events who are admitted with a suspicion for HF, to identify those suitable for early discharge. Biomarkers could be implemented in future algorithms to this aim.
To further explore the value of low biomarker levels, we studied if low levels compared to high levels of natriuretic peptides in patients with HF with preserved ejection (HF-pEF) fraction would be associated with differential clinical characteristics in Chapter 4. We assessed baseline characteristics, HF symptoms, biomarker levels, quality of life measurements, and outcome parameters. It appeared that both groups (low and high) had similar clinical characteristics, an equal number and frequency of co-morbidities, equally severe HF symptoms with impaired quality of life and the same poor outcome. The only difference between the groups was a higher body mass index in those with low natriuretic peptide levels. We conclude that these patients still suffer from HFpEF de-spite their (pseudo) lower biomarker levels. Of note, the European Society of Cardiology - Heart Failure Guidelines require an elevated natriuretic peptide level for diagnosing HF, in addition to the presence of HF symptoms.4 Thus, we describe a patient pool where diagnosis of HF is a challenge for daily practice. We believe that these patients may likely benefit from exercise treatment and referral to a rehabilitation center. Weight loss may paradoxically lead to an increase in natriuretic peptide levels yet a better clinical out-come. These chapters demonstrate that biomarkers may be useful in the setting of low risk, but also highlight the challenges - low biomarker levels do not always correspond with low risk.
In the first part of this thesis, multiple potential clinical applications in which biomarkers may be important were demonstrated. There is a need to design prospective clinical
304 Chapter 11
studies using biomarker algorithms and to prospectively test hypotheses generated by these studies.
Biomarkers – Interpretation is key
Galectin-3 is strongly linked to renal dysfunction, and this connection confounds the relation with adverse prognosis. In several post-hoc studies in HF cohorts, the predictive value of galectin-3 was shown to diminish when adjusted for renal function.5–7 However, the precise relation remained obscure, and we investigated the renal handling of galec-tin-3 in Chapter 5. Our translational study investigated whether galecgalec-tin-3 was excreted by the kidneys, and whether patients without kidney function had higher levels. Using recombinant human galectin-3, we endeavored to achieve a steady state with continu-ous infusion in rats. From the experiments we concluded that galectin-3 can be cleared by the kidney, albeit incompletely. In selected human cohorts (healthy controls, HF and hemodialysis patients), we observed an impressive increase in galectin-3 plasma levels in patient samples. The lowest galectin-3 levels were found in controls, while extremely high levels were observed in hemodialysis patients (individuals with no renal clearance). We assessed renal handling of galectin-3 and showed that HF patients had a reduced galectin-3 excretion rate compared to controls. Therefore, hemodialysis patients might have extreme levels of galectin-3 because - at least in part - of an inability to filter the blood and clear galectin-3 in the urine.
In Chapter 6, we studied various assays for measuring galectin-3. Several galectin-3-related publications are difficult to interpret and compare because they used different assays in which the crude levels differ. We believe automated assays will open up a new era of biomarker testing. They will allow for quick, easy and reliable measurement of plasma and/or serum biomarker levels. Chapter 7 described the biological variation of markers in patients with HF. For prognostic purposes, it has been postulated that serial measures provide superior power over a single measurement.8,9 Ideally, changes over time should reflect clinical improvement or disease progression. However, proper inter-pretation as to whether these temporal changes are clinically relevant is complex, and published data were restricted to healthy controls or small series. In this study, we aimed to assess the conjoint analytical and biological variation of clinically established, novel and neuro-hormonal biomarkers and to compare these levels with healthy controls. Although the crude levels of the biomarkers significantly differed between healthy sub-jects and stable patients with HF, we observed a clearly comparable biological variation. We propose that certain indices of biological variation should be used in any clinical study with serial biomarker measurements. These indices include the reference change value and the coefficient of variation within an individual. These indices are more suit-able for biomarker-targeted strategy programs.
Biomarker targeted therapy might be another tool to treat patients with HF. In chapter 8 we reviewed the current literature about galectin-3 activation and inhibition. Further re-search is needed to unravel the complete interplay of galectin-3 and its binding agents. Nevertheless, studies are emerging of anti-galectin-3 agents which result in reduced fibrosis in different organs.10,11 Anti-fibrotic treatment might become a new pillar in treating patients with HF.
Heart failure and cancer… does the heart speak to tumors?
Circulating factor may be biomarkers (signaling disease), but they might also confer deleterious signals to peripheral organs. Co-morbidities have received wide attention in the study of HF, but the most lethal co-morbidity, cancer, has received very little at-tention. In Chapter 9a, we investigated a new paradigm in HF, namely whether a causal relationship between HF and cancer development exists.
First, we observed that a substantial proportion of patients with HF developed cancer and died from the consequences. In the contemporary treatment and work-up for HF, cancer surveillance plays no role as currently, HF and cancer are seen as two different diseases. However, both consist of similarities and possible interactions. Recent epide-miological and case-control studies showed that patients with prevalent HF were more prone to develop incident cancer.12,13 In this study, we explored our hypothesis by com-bining a murine cancer model, prone to develop precancerous polyps, and a cardiology model of MI-induced HF. This combination resulted in increased tumor formation and accelerated tumor growth in mice with HF. To further corroborate our findings, we used a heterotopic murine heart transplantation model and validated our initial results while ruling out hemodynamic impairment as a cause. To pin down which factors could be responsible for these observations, we conducted a literature search for targets excreted by the heart and previously associated with new-onset colorectal cancer. These possible targets were tested in vitro, in vivo and in human studies, and we identified alpha-1-antichymotrypsin (SERPINA3) as the most robust and promising target. In addition, we provided human validation by showing that cardiac markers, including natriuretic peptides and inflammatory markers, were associated with prediction of new-onset can-cer. Chapter 9b is the editorial comment written by Prof. Kitsis and colleagues. In this comment, he states that compelling data are shown in chapter 9a with regards to the hypothesis that HF might be considered as a risk factor for new-onset cancer, a finding that could open the gates of a new scientific research field. He also raises important questions that arise from our novel findings. Can these observations be extrapolated to other precancerous or cancerous lesions? Would HF of non-ischemic origin elicit the same response? Does HF also promote metastasis? At this point we cannot answer these questions, but future research will provide further insights. In Chapter 10, we discussed
306 Chapter 11
possible mechanisms and knowledge gaps in the field of onco-cardiology. We described epidemiology of incident cancer in HF and provided an explanation for excess risk for cancer in HF. This hazard could be due to shared risk factors, shared pathophysiology or possibly cardiovascular drug interventions. Lastly, we propose that the heart ‘com-municates’ with different organs via plasma factors, including biomarkers. Chapter 9a is a prime example of this hypothesis.
future perspective
The position of biomarkers in HF is changing over time. The first evidence of the useful-ness of biomarkers is mostly derived from diagnostic studies. Almost immediately after these publications prognostic studies emerged to further strengthen the importance of biomarkers in clinical practice. Nevertheless, implementation in daily practice has proven to be challenging. Supported by pre-clinical evidence, and insights into the pathophysiogical mechanisms, biomarkers could be better placed in different classes and implemented in different clinical settings. Biomarker-guided therapy is currently conduced for example with NT-proBNP. Furthermore, prospective studies are designed to investigate inhibitors of certain biomarkers, like galectin-3, to investigate the effects on cardiac function.
Further studies should focus on pathway analyses and biomarker interactions with dif-ferent organs, diseases and with specific interest in cancer. HF related biomarkers might orchestrate downstream effects in a different set of organs. Vice versa would also be a possibility. In this perspective, new biomarkers need to be used as a tool to assess the disease state in patients with HF. For example, what is the cardiac status, which co-morbidities are present and is an interaction with other organs expected. Proteomics analyses of the cardiac secretome of different HF etiologies and in the presence of differ-ent co-morbidities will be needed to answer which biomarkers are important in which patient. Further, pharmaceutical investigations to directly target biomarkers are needed to take biomarker research from bench to bedside. Table 1 summarizes the tool-set to determine a patients’ disease status based upon biomarkers.
Early identification of those at risk to develop HF should be a main goal to reduce the burden of HF. Asymptomatic patients should be treated based upon their cardiovascular risk and biomarkers might be the key to identify those at risk. Proper phenotyping of a patient starts with integration of biomarker measurements early in the disease process. Background information, supported by biomarker data, could result in better tailored medicine. We could select certain subjects at risk, based upon the biomarker profile, and start treatment earlier, and therefore even prevent the initial cardiac event, which may prevent HF development.
Currently, HF specialists are given the difficult task to integrate biomarkers in an end-stage disease. Multiple aspects, like age, co-morbidities and disease progression influence the biomarker levels measured. Therefore, it is challenging to correctly interpret biomarker levels and changes over time. Future biomarker studies, focused on algorithms, targeted therapy and unravelling of the pathophysiological mechanisms are needed. In the com-ing years, we will work hard to solve the heart failure puzzle uscom-ing biomarkers to reduce the burden of heart failure in people we all work so hard to help – patients.
Table 1. Biomarkers as a tool-set to determine a patients’ disease status
Heart failure status Heart failure &
peripheral organ Interaction Diagnostic - Cardiac specific
- Not influenced by, age, sex and co-morbidities
- Determine which co-morbidity/ other diseases are likely to develop
- Determine which diagnostic test to order
Prognostic - Independent predictor of outcome in heart failure or in heart failure with organ interactions
Therapeutic - Target for therapy
- Reflects current treatment strategy
- Target to prevent peripheral organ deterioration
Monitoring Outpatient:
- Tailor therapy
- Indicates improvement/ deterioration over time
In hospital:
- Determines when admission is needed; and discharge is safe to prevent near-term rehospitalization
- Monitoring by other medical specialists depending on organ interaction
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