Novel aspects of heart failure biomarkers Suthahar, Navin
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10.33612/diss.135383104
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46. Frankel DS, Vasan RS, D’Agostino RB, Benjamin EJ, Levy D, Wang TJ, et al. Resistin, Adiponectin, and Risk of Heart Failure. J Am Coll Cardiol. 2009;53:754–62.
47. Oh A, Okazaki R, Sam F, Valero-Muñoz M. Heart Failure With Preserved Ejection Fraction and Adipose Tissue: A Story of Two Tales. Front Cardiovasc Med. 2019;6. 48. Davis JM, Roger VL, Crowson CS, Kremers HM, Therneau TM, Gabriel SE. The
presentation and outcome of heart failure in patients with rheumatoid arthritis differs from that in the general population. Arthritis Rheum. 2008;58:2603–11.
49. Shi C, Wal HH, Silljé HHW, Dokter MM, den Berg F, Huizinga L, et al. Tumour biomarkers: association with heart failure outcomes. J Intern Med. 2020;288:207–18. 50. Piek A, Meijers WC, Schroten NF, Gansevoort RT, de Boer RA, Silljé HHW. HE4 Serum
Levels Are Associated with Heart Failure Severity in Patients With Chronic Heart Failure. J Card Fail. 2017;23:12–9.
51. Packer M. Leptin-Aldosterone-Neprilysin Axis. Circulation. 2018;137:1614–31.
52. Raphael R, Purushotham D, Gastonguay C, Chesnik MA, Kwok W-M, Wu H-E, et al. Combining patient proteomics and in vitro cardiomyocyte phenotype testing to identify potential mediators of heart failure with preserved ejection fraction. J Transl Med. 2016;14:18.
53. Cheng M-L, Wang C-H, Shiao M-S, Liu M-H, Huang Y-Y, Huang C-Y, et al. Metabolic Disturbances Identified in Plasma Are Associated With Outcomes in Patients With Heart Failure. J Am Coll Cardiol. 2015;65:1509–20.
54. Wong LL, Zou R, Zhou L, Lim JY, Phua DCY, Liu C, et al. Combining Circulating MicroRNA and NT-proBNP to Detect and Categorize Heart Failure Subtypes. J Am Coll Cardiol. 2019;73:1300–13.
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GENERAL DISCUSSION AND
FUTURE PRESPECTIVES
Novel Aspects of
Heart Failure Biomarkers
238
reventing heart failure (HF) development is an important public health goal. Indeed, preventing cardiovascular risk factor development (i.e. diabetes mellitus, obesity and hypertension) by leading a “heart-healthy” lifestyle, and addressing risk factors once they develop, would be the first and foremost steps towards realizing this goal. Nevertheless, HF prevention, in its fullest sense, may often not be an achievable goal1 – particularly in individuals with pre-existing
cardiac disease such as myocardial infarction, or in the elderly, where myocardial structural and functional changes are almost universally observed. Rather, postponing HF development i.e. increasing the time spent without HF (after clearly defining HF), and increasing the transit time between various stages of HF (after clearly defining these stages) would be more realistic goals.
According to current guidelines, first-line pharmacological HF therapies such as angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers and beta blockers are initiated only when a patient is already in stage B or C of HF. A more effective preventative strategy, however, would be to identify patients with stage A HF and prolong progress to stage B HF. To this end, besides promoting a heart-healthy lifestyle and promptly treating prevalent cardiovascular risk factors, it would also be necessary to i) develop novel HF therapeutic agents targeting causative
pathophophysiological mechanisms – particularly fibro-inflammatory activation and ii) refine our understanding on inexpensive, yet accurate methods to identify individuals at higher risk for HF (e.g. optimising the utility of biomarkers in HF risk prediction and in early diagnosis of HF), so as to derive maximal clinical and economic benefit.
Therefore, in Chapter 1, we reviewed the role of fibro-inflammatory axis in HF
and its precursors.2 We highlighted that inflammation and fibrosis are key
pathophysiological mechanisms operating in HF, and myocardial damage can be a cause as well as a consequence of fibro-inflammatory activation. We also provided an overview of emerging therapeutic options that are being developed to target systemic and myocardial fibro-inflammatory pathways. In Chapter 2, we reviewed
the role of fibrosis biomarker, galectin-3, with HF and cardiovascular disease (CVD), and described galectin-3 from a binding perspective.3 We discussed
mechanisms leading to bio-activation of galectin-3, and focussed on various types of galectin-3 multimerization, including lattice formation. We also provided an overview of available galectin-3 inhibitors, highlighting their lack of selectivity. Overall, regulating the fibroinflammatory axis by inhibiting galectin-3 activation using selective bio-engineered molecules appears to be an exciting avenue, and
P
could potentially emerge as an attractive HF therapeutic strategy in the comingdecade.
In Chapter 3, we studied 7953 individuals from the Prevention of Renal and
Vascular End-stage Disease (PREVEND) cohort, and used HF biomarkers to identify common pathophysiologic mechanisms linking incident type-2 diabetes mellitus (DM) with new-onset HF.4 Our results showed that despite the close
association between DM and HF,5,6 myocardial injury mechanisms did not relate
with the risk of developing DM. Inflammatory mechanisms, however, were strongly associated with the risk of developing DM as well as HF. Modulating the inflammatory axis may therefore be useful in preventing or prolonging the onset of both these disorders. However, as pointed out earlier, this may not be straightforward. For instance, although anti-inflammatory medications such as statins substantially reduce the risk of developing CVD (JUPITER trial), they are known to increase the risk of developing new-onset DM.6,7 Similarly, NSAIDS
(which are commonly used anti-inflammatory class of drugs) are known to reduce blood glucose levels, but regular NSAID usage is associated with an increased risk of developing cardiovascular outcomes.7–9 There is certainly a need to conduct
further research aimed at developing immunomodulatory agents that reduce the risk of developing DM as well as CVD, and also have minimal side-effects.
In Chapter 4, we included data of 8213 community-dwelling individuals from the
PREVEND cohort we found that plasma concentrations of several cardiovascular biomarkers were positively or negatively influenced by obesity. Among a vast array of cardiovascular biomarkers, only cardiac natriuretic peptides (NT-proBNP, MR-proANP) and cardiac troponin-T were strongly associated with incident HF, and BMI did not modify these associations. The biomarker cutpoint in which HF risk became apparent did not also substantially differ in lean, overweight and obese individuals. These data indicate that while using cardiac natriuretic peptides or troponins to estimate future HF risk in the community, a single cutpoint would be sufficient in lean, overweight and obese individuals.
In Chapter 5, we evaluated associations of fat distribution and NT-proBNP in
8260 participants from the PREVEND cohort.10 As fat distribution is profoundly
different in men and women, we chose to perform analyses sex-specifically. Interestingly, we found that the so called “U-shaped relationship of natriuretic peptides with obesity” was, in great part, the effect of sex confounding the effect of obesity.
Age was another major factor affecting NT-proBNP levels, and obesity appeared to be positively associated with NT-proBNP levels if confounding effects of age
reventing heart failure (HF) development is an important public health goal. Indeed, preventing cardiovascular risk factor development (i.e. diabetes mellitus, obesity and hypertension) by leading a “heart-healthy” lifestyle, and addressing risk factors once they develop, would be the first and foremost steps towards realizing this goal. Nevertheless, HF prevention, in its fullest sense, may often not be an achievable goal1 – particularly in individuals with pre-existing
cardiac disease such as myocardial infarction, or in the elderly, where myocardial structural and functional changes are almost universally observed. Rather, postponing HF development i.e. increasing the time spent without HF (after clearly defining HF), and increasing the transit time between various stages of HF (after clearly defining these stages) would be more realistic goals.
According to current guidelines, first-line pharmacological HF therapies such as angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers and beta blockers are initiated only when a patient is already in stage B or C of HF. A more effective preventative strategy, however, would be to identify patients with stage A HF and prolong progress to stage B HF. To this end, besides promoting a heart-healthy lifestyle and promptly treating prevalent cardiovascular risk factors, it would also be necessary to i) develop novel HF therapeutic agents targeting causative
pathophophysiological mechanisms – particularly fibro-inflammatory activation and ii) refine our understanding on inexpensive, yet accurate methods to identify individuals at higher risk for HF (e.g. optimising the utility of biomarkers in HF risk prediction and in early diagnosis of HF), so as to derive maximal clinical and economic benefit.
Therefore, in Chapter 1, we reviewed the role of fibro-inflammatory axis in HF
and its precursors.2 We highlighted that inflammation and fibrosis are key
pathophysiological mechanisms operating in HF, and myocardial damage can be a cause as well as a consequence of fibro-inflammatory activation. We also provided an overview of emerging therapeutic options that are being developed to target systemic and myocardial fibro-inflammatory pathways. In Chapter 2, we reviewed
the role of fibrosis biomarker, galectin-3, with HF and cardiovascular disease (CVD), and described galectin-3 from a binding perspective.3 We discussed
mechanisms leading to bio-activation of galectin-3, and focussed on various types of galectin-3 multimerization, including lattice formation. We also provided an overview of available galectin-3 inhibitors, highlighting their lack of selectivity. Overall, regulating the fibroinflammatory axis by inhibiting galectin-3 activation using selective bio-engineered molecules appears to be an exciting avenue, and
P
could potentially emerge as an attractive HF therapeutic strategy in the comingdecade.
In Chapter 3, we studied 7953 individuals from the Prevention of Renal and
Vascular End-stage Disease (PREVEND) cohort, and used HF biomarkers to identify common pathophysiologic mechanisms linking incident type-2 diabetes mellitus (DM) with new-onset HF.4 Our results showed that despite the close
association between DM and HF,5,6 myocardial injury mechanisms did not relate
with the risk of developing DM. Inflammatory mechanisms, however, were strongly associated with the risk of developing DM as well as HF. Modulating the inflammatory axis may therefore be useful in preventing or prolonging the onset of both these disorders. However, as pointed out earlier, this may not be straightforward. For instance, although anti-inflammatory medications such as statins substantially reduce the risk of developing CVD (JUPITER trial), they are known to increase the risk of developing new-onset DM.6,7 Similarly, NSAIDS
(which are commonly used anti-inflammatory class of drugs) are known to reduce blood glucose levels, but regular NSAID usage is associated with an increased risk of developing cardiovascular outcomes.7–9 There is certainly a need to conduct
further research aimed at developing immunomodulatory agents that reduce the risk of developing DM as well as CVD, and also have minimal side-effects.
In Chapter 4, we included data of 8213 community-dwelling individuals from the
PREVEND cohort we found that plasma concentrations of several cardiovascular biomarkers were positively or negatively influenced by obesity. Among a vast array of cardiovascular biomarkers, only cardiac natriuretic peptides (NT-proBNP, MR-proANP) and cardiac troponin-T were strongly associated with incident HF, and BMI did not modify these associations. The biomarker cutpoint in which HF risk became apparent did not also substantially differ in lean, overweight and obese individuals. These data indicate that while using cardiac natriuretic peptides or troponins to estimate future HF risk in the community, a single cutpoint would be sufficient in lean, overweight and obese individuals.
In Chapter 5, we evaluated associations of fat distribution and NT-proBNP in
8260 participants from the PREVEND cohort.10 As fat distribution is profoundly
different in men and women, we chose to perform analyses sex-specifically. Interestingly, we found that the so called “U-shaped relationship of natriuretic peptides with obesity” was, in great part, the effect of sex confounding the effect of obesity.
Age was another major factor affecting NT-proBNP levels, and obesity appeared to be positively associated with NT-proBNP levels if confounding effects of age
240
prevalent HF (as opposed to HF patients), obesity-related lowering of NT-proBNP is rather subtle, and the impact of sex and age on NT-proBNP levels is much stronger than that of obesity. Indeed, only after accounting for age did the inverse association of obesity with NT-proBNP become apparent in both sexes. Interestingly, we observed that this effect, i.e. inverse association of NT-proBNP with BMI was more pronounced in women than men. We realized that these sex-related differences could arise partly due to the fact that BMI reflects fat mass more accurately in women than men.11 However, similar trends were also observed with
WC i..e., the inverse association of NTproBNP with WC was also stronger in women than men. On further examination, we found that abdominal adiposity, independent of the effect of BMI, was associated with a linear decline of NTproBNP levels in men, compared with U-shaped relationship in men. We speculated that this may be due to the effect of “androgen excess” associated with abdominal adiposity in women. However, as mentioned earlier, this is a hypothesis that needs to be tested in mechanistic models. An editorial based on Chapter 5,
highlighted the controversy associated with this topic i.e. overlapping effects of sex and obesity on NT-proBNP levels.12 This also led us to review the impact of sex
and obesity on HF biomarkers. In Chapter 6, we summarized literature on
sex-differences in HF biomarkers highlighting the overlap between sex and obesity.13
We observed that sex-related differences in biomarker levels were more prominent in community-dwelling individuals compared with HF patients. On the other hand,
obesity-related effects on biomarker levels (particularly natriuretic peptides) may be more relevant in HF patients. We also formulated several clinical pointers and developed potential research questions which may particularly be useful for practicing physicians, cardiology residents and students interested in HF research.
In Chapter 7, we studied 8226 individuals from the PREVEND cohort –
focussing on sex-related differences in associations of cardiac troponin-T (cTnT) with HF and CVD.14 We found that associations of cTnT with cardiovascular
outcomes were generally stronger in women than men. Importantly, the cTnT threshold in which the cardiovascular risk became apparent was lower in women than men i.e. a lower cutpoint (<3ng/L) identified future cardiovascular risk in women compared to a relatively higher cutpoint in men (≥5ng/L). In a simultaneous study examining associations of cTnI with incident HF using data from 48,455 individuals, similar trends were observed: optimal predictive cutpoints for incident HF was 2.6 ng/L in women compared with 4.2 ng/L in men.15 These
findings may help design future trials examining the value of cTns as CVD screening tools in high-risk individuals (akin to the STOP-HF trial),16 and it would
particularly be interesting to investigate whether instilling sex-specific cutpoints may improve outcomes in women.
In Chapter 8, we pooled individual-level data from the Framingham Heart Study
(FHS), the PREVEND study, the Multi-Ethnic Study of Atherosclerosis (MESA), and the Cardiovascular Health Study (CHS), and investigated whether cardiovascular risk factors and biomarkers differentially related with the risk of developing HF in men versus women.17 We found that although the prevalence of
CV risk factors differed between men and women, their association with future HF was broadly comparable in both sexes. Likewise, baseline plasma biomarker concentrations were different between men and women, but this did not translate to pronounced sex-related differences in their association with incident HF i.e. elevated biomarker levels were similarly associated with future HF in both men and women. Our findings highlight the similar importance of preventing cardiovascular risk factor development and having an optimal biomarker profile in preventing HF development in both sexes.18 These results should, however, be viewed along with
the fact that HF risk is not (yet) similar across sexes: i.e. baseline HF risk is lower in women than in men. In this context, we hypothesize that accumulative burden of CV risk factors would be greater in men than women, which will be explored in future studies. Next, we found that addition of individual biomarkers did not substantially improve HF risk prediction beyond the clinical model in both sexes. However, minor sex-related differences in predictive value of individual biomarkers were observed: cTns and urinary-albumin-to-creatinine ratio (UACR) improved HF risk prediction in both sexes, but cardiac natriuretic peptides improved HF risk prediction only in men and not in women. Although subtle, these findings appear to
be quite interesting and rather controversial, and should be studied further.
In Chapter 9, we build upon the discussion from previous chapter, and highlight
the inadequacy of cardiac natriuretic peptides in HFpEF diagnosis. Indeed, from a holistic and futuristic point of view - there are several biomarkers that provide information on ‘non-cardiac components’ of the HFpEF syndrome. Although, at present, these biomarkers do not directly aid in the diagnosis of HFpEF, they would still be useful in the classification of HFpEF phenotypes/endotypes - which may aid patient selection in HFpEF trials, and in optimizing therapeutic strategies. We also pointed out that some of the non-cardiac biomarkers, including novel markers of inflammation and fibrosis, may also serve as biotargets in the treatment of
prevalent HF (as opposed to HF patients), obesity-related lowering of NT-proBNP is rather subtle, and the impact of sex and age on NT-proBNP levels is much stronger than that of obesity. Indeed, only after accounting for age did the inverse association of obesity with NT-proBNP become apparent in both sexes. Interestingly, we observed that this effect, i.e. inverse association of NT-proBNP with BMI was more pronounced in women than men. We realized that these sex-related differences could arise partly due to the fact that BMI reflects fat mass more accurately in women than men.11 However, similar trends were also observed with
WC i..e., the inverse association of NTproBNP with WC was also stronger in women than men. On further examination, we found that abdominal adiposity, independent of the effect of BMI, was associated with a linear decline of NTproBNP levels in men, compared with U-shaped relationship in men. We speculated that this may be due to the effect of “androgen excess” associated with abdominal adiposity in women. However, as mentioned earlier, this is a hypothesis that needs to be tested in mechanistic models. An editorial based on Chapter 5,
highlighted the controversy associated with this topic i.e. overlapping effects of sex and obesity on NT-proBNP levels.12 This also led us to review the impact of sex
and obesity on HF biomarkers. In Chapter 6, we summarized literature on
sex-differences in HF biomarkers highlighting the overlap between sex and obesity.13
We observed that sex-related differences in biomarker levels were more prominent in community-dwelling individuals compared with HF patients. On the other hand,
obesity-related effects on biomarker levels (particularly natriuretic peptides) may be more relevant in HF patients. We also formulated several clinical pointers and developed potential research questions which may particularly be useful for practicing physicians, cardiology residents and students interested in HF research.
In Chapter 7, we studied 8226 individuals from the PREVEND cohort –
focussing on sex-related differences in associations of cardiac troponin-T (cTnT) with HF and CVD.14 We found that associations of cTnT with cardiovascular
outcomes were generally stronger in women than men. Importantly, the cTnT threshold in which the cardiovascular risk became apparent was lower in women than men i.e. a lower cutpoint (<3ng/L) identified future cardiovascular risk in women compared to a relatively higher cutpoint in men (≥5ng/L). In a simultaneous study examining associations of cTnI with incident HF using data from 48,455 individuals, similar trends were observed: optimal predictive cutpoints for incident HF was 2.6 ng/L in women compared with 4.2 ng/L in men.15 These
findings may help design future trials examining the value of cTns as CVD screening tools in high-risk individuals (akin to the STOP-HF trial),16 and it would
particularly be interesting to investigate whether instilling sex-specific cutpoints may improve outcomes in women.
In Chapter 8, we pooled individual-level data from the Framingham Heart Study
(FHS), the PREVEND study, the Multi-Ethnic Study of Atherosclerosis (MESA), and the Cardiovascular Health Study (CHS), and investigated whether cardiovascular risk factors and biomarkers differentially related with the risk of developing HF in men versus women.17 We found that although the prevalence of
CV risk factors differed between men and women, their association with future HF was broadly comparable in both sexes. Likewise, baseline plasma biomarker concentrations were different between men and women, but this did not translate to pronounced sex-related differences in their association with incident HF i.e. elevated biomarker levels were similarly associated with future HF in both men and women. Our findings highlight the similar importance of preventing cardiovascular risk factor development and having an optimal biomarker profile in preventing HF development in both sexes.18 These results should, however, be viewed along with
the fact that HF risk is not (yet) similar across sexes: i.e. baseline HF risk is lower in women than in men. In this context, we hypothesize that accumulative burden of CV risk factors would be greater in men than women, which will be explored in future studies. Next, we found that addition of individual biomarkers did not substantially improve HF risk prediction beyond the clinical model in both sexes. However, minor sex-related differences in predictive value of individual biomarkers were observed: cTns and urinary-albumin-to-creatinine ratio (UACR) improved HF risk prediction in both sexes, but cardiac natriuretic peptides improved HF risk prediction only in men and not in women. Although subtle, these findings appear to
be quite interesting and rather controversial, and should be studied further.
In Chapter 9, we build upon the discussion from previous chapter, and highlight
the inadequacy of cardiac natriuretic peptides in HFpEF diagnosis. Indeed, from a holistic and futuristic point of view - there are several biomarkers that provide information on ‘non-cardiac components’ of the HFpEF syndrome. Although, at present, these biomarkers do not directly aid in the diagnosis of HFpEF, they would still be useful in the classification of HFpEF phenotypes/endotypes - which may aid patient selection in HFpEF trials, and in optimizing therapeutic strategies. We also pointed out that some of the non-cardiac biomarkers, including novel markers of inflammation and fibrosis, may also serve as biotargets in the treatment of
242
IMPORTANT TAKE HOME MESSAGES
1. Fibro-inflammatory mechanisms link diabetes mellitus, obesity and heart failure (HF). Novel HF therapeutic strategies, although still in their nascent stage, have the potential to revolutionize HF management by targeting fibro-inflammatory activation.
2. HF biomarker concentrations can be affected by obesity, and also by sex. These effects often overlap with each other.
3. In the general population, lower baseline natriuretic peptide (NP) levels in heavier individuals is better explained by male sex than by obesity. However, both sex as well as obesity do not substantially modify associations of NPs with incident HF. Furthermore, the NP threshold in which HF risk becomes apparent does not differ among lean, overweight and obese individuals. Future studies should examine the need for sex-specific cutpoints to predict incident HF. 4. Cardiac troponins (cTns) are powerful predictors of HF,
cardiovascular (CV) disease and premature death in both sexes. However, the cTn threshold in which CV risk becomes apparent is lower in women than in men. Therefore, even minor cTn elevations should be taken seriously – particularly in women.
5. The prevalence of CV risk factors and risk of developing HF are
generally lower in women than in men. Once an individual risk factor or manifestation of heart disease (such as obesity, diabetes mellitus, hypertension or myocardial infarction) develops, however, the increase in HF risk is comparable across sexes. Likewise, although baseline biomarker levels differ between men and women, equal increases (e.g. 2-fold change) in biomarker levels are associated with similar increases in the risk of HF in both sexes.
Limitations: A major limitation in all our studies is that we used a single-time
point biomarker approach i.e. we measured biomarker concentrations only at baseline. More precise information about pathophysiological mechanisms can be obtained using serial biomarker measurements. We also acknowledge that biomarkers included in this thesis were limited to those biomarkers that were preselected using a priori knowledge.
Future directions: In order to identify novel HF biomarkers, an unbiased,
high-throughput screening approach may be needed. In this regard, biomarkers need not be limited to circulating proteins, but can be extended to urinary biomarkers (which are relatively under-utilized), and genomic and imaging biomarkers. Future studies using a multimarker, muti-time point approach in apparently healthy men and women may better identify the clinical course of HF, and unravel (sex-specific) pathophysiological mechanisms leading to HF. A biomarker-only approach to identify sub-clinical HF phenotypes in men and in women could also potentially be an exciting avenue to explore in the near future.
On a concluding note, I envisage that in the coming decade, a systems epidemiology approach to study health and disease at the human population level would take “center-stage.” This would integrate various population-level omic-metrics including the phenome, metabolome, proteome, transcriptome, genome as well as various environmental factors and their interactions – and provide us with a more comprehensive understanding of mechanisms leading to HF, which would eventually drive therapeutics and clinical care.
IMPORTANT TAKE HOME MESSAGES
1. Fibro-inflammatory mechanisms link diabetes mellitus, obesity and heart failure (HF). Novel HF therapeutic strategies, although still in their nascent stage, have the potential to revolutionize HF management by targeting fibro-inflammatory activation.
2. HF biomarker concentrations can be affected by obesity, and also by sex. These effects often overlap with each other.
3. In the general population, lower baseline natriuretic peptide (NP) levels in heavier individuals is better explained by male sex than by obesity. However, both sex as well as obesity do not substantially modify associations of NPs with incident HF. Furthermore, the NP threshold in which HF risk becomes apparent does not differ among lean, overweight and obese individuals. Future studies should examine the need for sex-specific cutpoints to predict incident HF. 4. Cardiac troponins (cTns) are powerful predictors of HF,
cardiovascular (CV) disease and premature death in both sexes. However, the cTn threshold in which CV risk becomes apparent is lower in women than in men. Therefore, even minor cTn elevations should be taken seriously – particularly in women.
5. The prevalence of CV risk factors and risk of developing HF are
generally lower in women than in men. Once an individual risk factor or manifestation of heart disease (such as obesity, diabetes mellitus, hypertension or myocardial infarction) develops, however, the increase in HF risk is comparable across sexes. Likewise, although baseline biomarker levels differ between men and women, equal increases (e.g. 2-fold change) in biomarker levels are associated with similar increases in the risk of HF in both sexes.
Limitations: A major limitation in all our studies is that we used a single-time
point biomarker approach i.e. we measured biomarker concentrations only at baseline. More precise information about pathophysiological mechanisms can be obtained using serial biomarker measurements. We also acknowledge that biomarkers included in this thesis were limited to those biomarkers that were preselected using a priori knowledge.
Future directions: In order to identify novel HF biomarkers, an unbiased,
high-throughput screening approach may be needed. In this regard, biomarkers need not be limited to circulating proteins, but can be extended to urinary biomarkers (which are relatively under-utilized), and genomic and imaging biomarkers. Future studies using a multimarker, muti-time point approach in apparently healthy men and women may better identify the clinical course of HF, and unravel (sex-specific) pathophysiological mechanisms leading to HF. A biomarker-only approach to identify sub-clinical HF phenotypes in men and in women could also potentially be an exciting avenue to explore in the near future.
On a concluding note, I envisage that in the coming decade, a systems epidemiology approach to study health and disease at the human population level would take “center-stage.” This would integrate various population-level omic-metrics including the phenome, metabolome, proteome, transcriptome, genome as well as various environmental factors and their interactions – and provide us with a more comprehensive understanding of mechanisms leading to HF, which would eventually drive therapeutics and clinical care.
244
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