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IGFBP7 and GDF-15, but not P1NP, are associated with cardiac alterations and 10-year outcome in an elderly community-based study

Abstract

Background

Little is known about the clinical value of Insulin-like growth factor-binding protein-7 (IGFBP7), a cellular senescence marker, in an elderly general population with multiple co-morbidities and high prevalence of asymptomatic cardiovascular ventricular dysfunction. Inflammation and fibrosis are hallmarks of cardiac aging and remodelling. Therefore, we assessed the clinical performance of IGFBP7 and two other biomarkers reflecting these pathogenic pathways, the growth differentiation factor-15 (GFD-15) and amino-terminal propeptide of type I procollagen (P1NP), for their association with cardiac phenotypes and outcomes in the PREDICTOR study.

Methods

2001 community-dwelling subjects aged 65–84 years who had undergone centrally-read echocardiography, were selected through administrative registries. Atrial fibrillation (AF) and 4 echocardiographic patterns were assessed: E/e’ (> 8), enlarged left atrial area, left ventricular hypertrophy (LVH) and reduced midwall circumference shortening (MFS). All-cause and cardiovascular mortality and hospitalization were recorded over a median follow-up of 10.6 years.

Results

IGFBP7 and GDF-15, but not P1NP, were independently associated with prevalent AF and echocardiographic variables after adjusting for age and sex. After adjustment for clinical risk factors and cardiac patterns or NT-proBNP and hsTnT, both IGFBP7 and GDF-15 independently predicted all-cause mortality, hazard ratios 2.13[1.08–4.22] and 2.03[1.62–2.56] per unit increase of Ln-transformed markers, respectively.

Conclusions

In a community-based elderly cohort, IGFBP7 and GDF-15 appear associated to cardiac alterations as well as to 10-year risk of all-cause mortality.

Peer Review reports

Background

Cardiovascular remodelling in the elderly is a complex phenomenon, which involves different pathways including hypertrophy, fibrosis, and cell death. Players are not limited to cardiac myocytes, but include fibroblasts, endothelial and smooth-muscle vascular cells. Several biomarkers have been made available over the recent years as possible readouts of the different processes involved [1].

In particular, insulin-like growth factor-binding protein-7 (IGFBP7) has been identified as a cellular senescence marker [2]. IGFBP7 is associated with cardiac structural and functional abnormalities, including hypertrophy, diastolic dysfunction and poor prognosis in patients with heart failure (HF) with and without atrial fibrillation (AF) [3,4,5]. These observations have been recently extended to insulin resistance and metabolic syndrome [6, 7]. Little is known on the clinical value of IGFBP7 in elderly general population with multiple co-morbidities and high prevalence of asymptomatic cardiovascular ventricular dysfunction.

Since inflammation and fibrosis are hallmarks of cardiac aging and remodelling, we compared the clinical performance of IGFBP7 with 2 other biomarkers reflecting these pathogenic pathways, i.e. the growth differentiation factor-15 (GDF-15) [8] and amino-terminal pro-peptide of type I procollagen (P1NP) [9]. GDF-15 is a member of the transforming growth factor-beta superfamily. Several studies have shown the strong prognostic value of GDF-15 in the general population [10, 11] and its relation with cardiac remodelling [12]. P1NP is a circulating marker of bone turnover, used to monitor evolution of osteoporosis [13]; P1NP was found altered in patients with HF indicating the activation of pro-fibrotic signalling and their prognostic relevance [14].

The PREDICTOR cohort of 2001 community-dwelling subjects aged 65–84 years was chosen since all subjects had undergone echocardiography, centrally read, and long-term outcome data were made available through administrative registries [15]. Taking the opportunity of the good characterization of the population and of the long-term follow-up, the present study was conducted with two complementary aims:

  • To assess the relation of IGFBP7, GDF-15 and P1NP with atrial fibrillation, left ventricular hypertrophy (LVH), reduced mid-wall circumference fraction shortening (MFS), E/e’, and enlarged left atrium in an elderly community dwelling cohort;

  • To estimate the long-term prognostic value for fatal and non-fatal clinical events.

Methods

Study population

PREDICTOR (Valutazione della PREvalenza di DIsfunzione Cardiaca asinTOmatica e di scompenso caRdiaco) was a cross-sectional, population-based study of the prevalence of asymptomatic LV dysfunction and heart failure (HF) in 2001 elderly people aged 65–84 years resident in the Lazio region of Italy.

The design of the PREDICTOR study has been described in detail [15]. A random sample of 5940 residents, 65–84 years old, from four cities (Rome, Civitavecchia, Frosinone, and Viterbo) in the Lazio region was identified based on the Regional Health Registry. Between June 2007 and January 2010, a total of 2001 subjects provided written informed consent. Participants were referred to eight cardiology centres in the Lazio region for clinical examination, blood tests, electrocardiography, comprehensive Doppler echocardiography and blood sampling to measure circulating biomarkers.

The Hospital Information System (HIS) provided data on hospitalizations occurred after the PREDICTOR baseline visit, while the Regional Mortality Registry provided mortality status with cause of death. Data from these two sources were linked through standardized methods based on a unique, anonymous, personal identifier, as reported elsewhere [16, 17]. A complete list of participating centres and investigators has been published [15]. Approval for this study was obtained from the local ethics committee.

Participants were followed-up from mid-2007 until 31st of December 2019 for all-cause mortality and hospitalization. Cause-specific mortality and hospitalization data was also available until 31 December, 2017. The ICD9 (mortality data) and ICD9CM (hospitalization data) codes 390–459 were classified as ‘cardiovascular’ and ICD9 code 428.x was classified as heart failure [18].

Circulating biomarkers

Venous blood samples from fasted subjects were collected with participants resting in the supine position for at least 15 min. Blood samples were collected in tubes containing ethylendiamine tetraacetic acid tripotassium salt (EDTA). Blood was centrifuged at 2000 g at 4 °C within 10 min and aliquots of plasma were immediately frozen and subsequently transported on dry ice to a central laboratory. Samples were stored at − 70 °C until they were assayed. Plasma concentrations of all biomarkers were assayed in a central laboratory by personnel blinded to the identity of each sample. NT-proBNP, hs-cTnT, GDF-15, P1NP, were measured by electrochemiluminescence immunoassay using commercial reagents (cobas Elecsys® 2010, Roche Diagnostics GmbH, Mannheim). The cardiac biomarkers, hs-cTnT and NT-proBNP, were used as benchmark.

IGFBP7 was measured using a preclinical research-use only assay on an automated platform (Roche Diagnostics GmbH, Penzberg, Germany). The detection method for IGFBP7 was a sandwich immunoassay developed on the Elecsys® platform for electrochemiluminescence detection (Roche Diagnostics GmbH, Mannheim, Germany). Mouse monoclonal antibodies were generated and screened for specific detection of IGFBP7. Precision within-run coefficient of variation for IGFBP7 was 2%, the limit of detection was 0.01 ng/mL.

Echocardiography and cardiac phenotype

Color Doppler echocardiography was performed in participating centres using commercially available machines, according to a predefined acquisition protocol, and centrally read [15]. Details on echocardiographic methods used for LV function and mass, and staging of heart failure have been reported [8, 12], and are summarized for convenience in Additional file 1: Supplemental Material.

Atrial fibrillation was diagnosed at 12-lead ECG at study entry.

The following cardiac phenotypes were defined based on echocardiographic exam at study entry:

  • Left ventricular hypertrophy (LVH): sex-specific LVH was defined as Left Ventricular Mass/Body Surface Area > 95 g/m2 for women and > 115 g/m2 for men.

  • Mid-wall circumference fraction shortening (MFS): Reduced MFS was defined as < 15%.

  • Diastolic dysfunction defined as E/e’ > 8. [19

  • Enlarged left atrium, defined as left atrial area (LAA) > 20 cm2/m2 based on the recommendations of the American society of echocardiography) [20].

In addition, 1715 participants were matched with data from the Hospital Information System (HIS) until 31 December 2019, allowing a median follow-up of 10.6 years [2 months to 12.5 years] for all-cause mortality, but of 2 years less for cause-specific mortality. The following long term outcomes were assessed:

  • All-cause mortality

  • Cardiovascular mortality, (data available until 31/12/17);

  • All-cause hospitalization

  • Cardiovascular hospitalization

Statistical methods

Baseline characteristics are reported by means of descriptive statistics. Categorical variables are presented as proportions. Normally distributed continuous variables are expressed as mean (SD) and compared by means of ANOVA while non-parametric variables are expressed as median [Q1–Q3] and compared by Kruskal–Wallis. Proportions were compared by means of Fisher’s exact test. The correlations between the biomarkers (IGFBP7, GDF-15, P1NP, hs-cTnT and NT-proBNP) were assessed by means of Spearman Rank non-parametric test. Binary logistic univariate and multivariable regression models adjusted for age and sex were used to assess the association between ln-transformed biomarkers and the cardiac phenotypes. P1NP was not included in the analysis due to its lack of relation with clinical outcomes. Kaplan–Meier curves were constructed for the tertiles of IGFBP7 and GDF-15. Cox-proportional hazard models were used to assess predictive value of ln-transformed IGFBP7 or GDF-15 for clinical outcomes, adjusted for those variables found to different between tertiles of biomarker univariate. SPSS v26.0 (IBM SPSS, Armonk, NY, USA) was used for statistical analysis. A p value of < 0.05 was considered statistically significant.

Results

Clinical correlates of plasma concentrations of the 3 circulating biomarkers

The demographic, clinical and echocardiographic characteristics of participants according to tertiles of IGFBP7, GDF-15 and P1NP concentrations are shown in Tables 1, 2 and 3. The median [Q1–Q3] concentrations of IGFBP7, GFD-15 and P1NP in the overall population were 166 [151–184] ng/mL, 1468 [1168–1984] pg/mL and 35.2 [26.3–46.0] ng/mL, respectively. Both GDF-15 and P1NP were above the reference normal values. IGFBP7 and GDF-15 were correlated with each other (r = 0.474) and with hsTnT (IGFBP7: r = 0.394; GDF15: r = 0.433) and NT-proBNP (IGFBP7: r = 0.337; GDF15: r = 0.305), all correlations had p < 0.0001.

Table 1 Demographic, clinical and echocardiographic characteristics according to tertiles of IGF BP 7
Table 2 Demographic, clinical and echocardiographic characteristics according to tertiles of GDF-15
Table 3 Demographic, clinical and echocardiographic characteristics of the participants according to tertiles of P1NP

Subjects with IGFBP7 in the tertile 3 were older, less frequently females or smokers, and with decreased renal function and more often cardiovascular risk factors and disorders. Tertile 3 of IGFBP7 was associated with higher concentrations of all circulating biomarkers, in particular GDF-15, hsTnT and NT-proBNP. In multiple linear regression analyses the strongest variables independently associated with higher concentrations of IGFBP7 were creatinine, age and heart failure (all p < 0.0001).

Tertiles 2 and 3 of GDF-15 were older, had significantly higher creatinine levels as well as a larger proportion males and smokers. Higher GDF-15 was associated with more patients with diabetes, angina pectoris, myocardial infarction, atrial fibrillation and COPD. Tertile 3 of GDF-15 was associated with higher concentrations of all circulating biomarkers except for P1NP. In addition, higher GDF-15 were associated with higher proportions of altered echocardiographic patterns (Table 2). Age, creatinine, diabetes and atrial fibrillation were strongly, independently associated with GDF-15 in multivariate regression model (all p < 0.0001).

P1NP was higher in females, in non-diabetics and in non-smokers (Table 3). Upon including these variables in multivariable regression, only diabetes, sex creatinine and age were associated with higher P1NP.

Relationship between concentrations of the 3 biomarkers and echocardiographic variables

Atrial fibrillation and four echocardiographic patterns were dichotomized by presence vs absence: increased E/e’, enlarged LAA, left ventricular hypertrophy, and reduced mid-wall circumference fraction shortening. Ln-transformed IGFBP7 was associated with all variables after adjustment for age and sex. All echocardiographic characteristics, except for enlarged LAA, were independently associated to Ln-transformed GDF-15, whereas Ln-transformed P1NP was not associated with any echocardiographic characteristic (Table 4).

Table 4 Results of logistic regression models

Prognostic value of IGFBP7 and GDF-15

During a median of 10.6 years of follow-up, 526 patients (26.3%) died, and 1365 (68.2%) were admitted to hospital for any reason. Both IGFBP7 and GDF-15 had significantly higher all-cause and cardiovascular mortality in their highest tertiles as well as a significantly increased all-cause and cardiovascular hospitalization. On the contrary, P1NP appeared totally unrelated to study outcomes (data not shown).

Figure 1 shows the Kaplan–Meier survival curves for these outcomes (all-cause and cardiovascular mortality and hospitalization). In general, patients in tertile 3 had worse outcomes, as evident from Tables 1 and 2. In univariate Cox analyses, increased concentrations of IGFBP7 and GDF-15 predicted mortality and hospitalization either all-cause or cardiovascular (Table 5). After adjustment for clinical variables, the association with all-cause and cardiovascular mortality remained significant for both IGFBP7 and GFD-15: HR 2.13[1.08–4.22] and 2.03[1.62–2.56] per unit increase of Ln-transformed markers, respectively. Upon adjusting for echocardiographic variables (e.g. MFS and enlarged LAA) or biomarkers (e.g. NT-proBNP and hsTnT), IGFBP7 and GDF-15 no longer independently predicted hospitalizations but only mortality.

Fig. 1
figure1

Kaplan–Meier curves for: all-cause and cardiovascular mortality, all-cause-, cardiovascular- and heart failure hospitalization split by tertiles of IGFBP7 and GDF-15. p value for log-rank test for the comparison of Kaplan–Meier estimates. Blue—lowest tertile, red- middle tertile, green—highest tertile

Table 5 Results of Cox proportional hazard regression models

In addition, IGFBP7 and more so GDF-15 predicted cancer mortality (147 events, 27.9% of all deaths). Death rates for cancer in the upper tertile of GDF-15 and IGFBP7 were 11.5% and 9.7%, compared to 3.9% and 6.0% in the lower tertile (p < 0.0001 and p = 0.011, respectively). In addition, Cox proportional hazard regression analyses, adjusted for age, sex, systolic blood pressure, diabetes, COPD, alcohol consumption, atrial fibrillation, heart failure, smoking, dyslipidemia, history of ischemic heart disease, LVEF and LV mass/BSA, showed significant results for both IGFBP7 (HR:3.97 [95% CI 1.16–13.58], p = 0.028) and GDF15 (HR: 2.04 [1.30–3.19], p = 0.002).

Discussion

In a cross-sectional epidemiological study including almost 2000 community-dwelling elderly persons (65–84 years) living in the region of Rome, Italy, and followed up for 10 years, the novel biomarker IGFBP7 was found to be associated with cardiac characteristics related to aging, such as LV hypertrophy and mild LV systolic dysfunction. Atrial fibrillation, enlarged LAA and E/e’ > 8 were also associated with higher concentrations of IGFBP7. IGFBP7 was also independently associated with mortality, all-cause as well as cardiovascular.

Similarly, GDF-15 was found to be associated with echocardiographic variables such as LVH and MFS, atrial fibrillation and E/e’ > 8. In addition, after adjusting for clinical characteristics, GDF-15 was predictive for mortality (both all-cause and cardiovascular). On the other side, P1NP, a marker of fibrosis, did not show any association with cardiac phenotypes or with outcomes (data not shown). If other circulating markers of collagen turnover, such as PIIINP, PICP had been assayed, more encouraging results may have been obtained [21]. In general, the concentrations of IGFBP7 and GDF-15 were lower than those reported in other studies, focused on patients with cardiovascular disease. Indeed, in the present elderly cohort, the prevalence of heart failure and of history of myocardial infarction was very low, respectively 6.3% and 6.1%.

IGFBP7 and the other 2 biomarkers, GDF-15 and P1NP, were chosen since they covered different aspects of cardiac diseases, such as inflammation, apoptosis, fibrosis, and were described as specifically linked to one or more cardiac phenotypes. IGFBP7, a novel prognostic biomarker for heart failure, has been suggested also as a marker for diastolic dysfunction in patients with heart failure with preserved EF, at risk of disease progression [6, 22, 23]. In PREDICTOR, IGFBP7 showed the best association with all cardiac phenotypes. This finding in community-dwelling elder individuals is in agreement with previous studies in patients [13,14,15].

Some features are worth mentioning. IGFBP7 and GDF-15 markedly increased with age, while this trend for P1NP was weaker, with a borderline statistical significance (p = 0.035). Females were significantly less frequent in the highest tertile of IGFBP7 and GDF-15, while the opposite was true for P1NP. This last finding is attributable to the loss of estrogen production due to menopause [24].

Serum creatinine, and consequently eGFR, significantly increased over tertiles of IGFBP7 and of GDF-15, but not of P1NP, which was unrelated to serum creatinine. The presence of diabetes mellitus was strongly associated with higher levels of GDF-15 and to a lesser extent of IGFBP7. In a cohort of 4360 Swedish non-diabetic individuals, GDF-15 was shown to be a strong independent predictor of risk of incident diabetes [25]. While the authors reported that the predictive power of GDF-15 was lost beyond 60 years of age, in PREDICTOR, a cohort with a mean age of 73, higher concentrations of GDF-15 were strongly associated with the presence diabetes mellitus.

The trend for P1NP goes in the opposite direction: the prevalence of diabetes mellitus is significantly higher in the lower tertile of P1NP. Indeed, it has been consistently shown that insulin resistance [26] and overt diabetes mellitus decrease circulating concentrations of markers of bone turnover such as P1NP [27, 28].

Unexpectedly, smokers were significantly more frequent in the lower tertile of concentrations of IGFBP7; however, the statistical significance disappeared in the multivariable analysis: younger age of smokers may well explain this univariate association. On the other side, the markedly higher prevalence of smokers in the highest tertile of GDF-15 has been reported in a Framingham cohort of subjects without overt cardiovascular disease [29]. Abundant evidence exists on association of GDF-15 with impaired endothelial function, arterial stiffness [30], carotid plaques [31], and higher coronary calcium scores [32]. The higher prevalence of history of MI, angina pectoris and atrial fibrillation in particular, goes along the same line of evidence on GDF-15, a cytokine produced in cardiovascular cells under the effect of inflammation and oxidative stress.

In a cohort of 228 patients with HFpEF, IGFBP7 and GDF-15 were found to be related to LV structure, function, and to the burden of comorbidities [33]. The results of PREDICTOR confirm the association of both biomarkers with LV structure and function, in particular with an impaired LV filling. In addition, IGFBP7 and GDF-15 showed for the first time to predict 10-year risk of CV and non-CV major events.

Altogether, the evidence on the three circulating biomarkers assayed in the PREDICTOR community dwelling elderly individuals confirms and extends the findings of previous studies, while it supports a comprehensive assessment of the novel molecule IGFBP7 in relation to cardiac function and clinical outcomes.

The following features of PREDICTOR study strengthen the results presented: identification of subjects through the registry of the National Health Service [15], long-term assessment of outcomes through the same administrative registry, central reading of all echocardiographic exams in a single core laboratory under blind conditions, assay of all anonymized plasma samples for the circulating biomarkers in a central laboratory (Roche Diagnostics GmbH, Penzberg, Germany). A limitation of this study is that the predictive analyses have only been performed for the 1715 patients who could be matched to the Hospital Information System. The remaining 14.3% of the cases were not matched, which may introduce some selection bias. Upon comparing the clinical characteristics of the patients with and without FU available, we found that those with FU available had more often a history of atrial fibrillation and a high AHA/ACC class. In addition, we estimated the statistical power to detect any modest effects on clinical endpoints. For IGFBP7, at an alpha of 0.05, we had at least 80% power for all-cause mortality in the full sample if the true HR was 1.40. For GDF-15, we had at least 80% power for all-cause mortality in the full sample if the true HR was 1.35 with an alpha of 0.05. For all cause hospitalization, for both IGFPB7 and GDF15, with an alpha of 0.05 we had at least 80% power if the true HR was 1.20.

The specificity of a biomarker for a defined cardiac phenotype remains to be elucidated. In fact, there is no such thing as pure/isolated fibrosis without some cardiac myocyte injury and inflammatory activation; on the other hand, myocardial hypertrophy coexists with some interstitial fibrosis. Accordingly, IGFBP7 and GDF-15, two molecules found to be related with cancer and not only with cardiovascular disease states, in the PREDICTOR cohort were found to predict not only all-cause mortality, but also the probability of cancer death after full adjustments (Table 5). This evidence is consistent with other community-dwelling elderly studies [34, 35]

Conclusions

In conclusion, the peculiar feature of the present study is the comparative evaluation of three circulating biomarkers in about 2000 community-dwelling elderly with a follow-up of over 10 years. The significant association of GDF-15 with structural and functional cardiac alterations confirms previous results from the Framingham population [36]. However, similar findings for IGFBP7 in a community based elderly cohort is novel. In addition, the 10-year follow-up allowed to show the independent predictive power of both IGFBP7 and GDF-15, which was markedly reduced if not cancelled by the presence of NT-proBNP and hs-cTnT in the multivariable models. Both IGFBP7 and GDF15 independently predict mortality and hospitalization for heart failure. This suggests that either these two molecules are directly related to outcomes (quite unlikely) or that their effects on outcomes is mediated, at least in part, by unidentified processes. This is consistent with what already found for NT-proBNP and hs-cTnT [15, 37], both reliable readouts of cardiac injury and dysfunction, but not playing causal roles.

Availability of data and materials

The data are stored at the Department of Epidemiology-Regional Health Service in Rome, at the S. Giovanni-Addolorata Hospital in Rome, and at the Mario Negri Institute for Pharmacological Research in Milan. For privacy policies of the PREDICTOR study, data sharing is not possible.

Abbreviations

AF:

Atrial fibrillation

AHA/ACC:

American Heart Association/American College of Cardiologists

BSA:

Body surface area

COPD:

Chronic obstructive pulmonary disease

CV:

Cardiovascular

EDTA:

Ethylendiamine tetraacetic acid tripotassium salt

EF:

Ejection fraction

eGFR:

Estimated glomerular filtration rate

FU:

Follow-up

GDF15:

Growth differentiation factor-15

HF:

Heart failure

HFpEF:

Heart failure with preserved ejection fraction

HIS:

Hospital information system

HR:

Hazard ratio

hs-cTnT:

High sensitive cardiac Troponin-T

IGFBP7:

Insulin-like growth factor-binding protein-7

LAA:

Left atrial area

LV:

Left ventricle

LVH:

Left ventricular hypertrophy

MFS:

Midwall circumference shortening

MI:

Myocardial infarct

NT-proBNP:

N-terminal brain natriuretic peptide

P1NP:

Propeptide of type I procollagen

PICP:

Procollagen type I carboxy-terminal propeptide

PIIINP:

Amino-terminal propeptide type III procollagen

PREDICTOR:

Valutazione della PREvalenza di DIsfunzione Cardiaca asinTOmatica e di scompenso caRdiaco

References

  1. 1.

    Biernacka A, Frangogiannis NG. Aging and cardiac fibrosis. Aging Dis. 2011;2(2):158–73.

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Chugh S, Ouzounian M, Lu Z, Mohamed S, Li W, Bousette N, et al. Pilot study identifying myosin heavy chain 7, desmin, insulin-like growth factor 7, and annexin A2 as circulating biomarkers of human heart failure. Proteomics. 2013;13(15):2324–34.

    CAS  Article  Google Scholar 

  3. 3.

    Gandhi PU, Gaggin HK, Redfield MM, Chen HH, Stevens SR, Anstrom KJ, et al. Insulin-like growth factor-binding protein-7 as a biomarker of diastolic dysfunction and functional capacity in heart failure with preserved ejection fraction: results from the RELAX trial. JACC Heart Fail. 2016;4(11):860–9.

    Article  Google Scholar 

  4. 4.

    Kalayci A, Peacock WF, Nagurney JT, Hollander JE, Levy PD, Singer AJ, et al. Echocardiographic assessment of insulin-like growth factor binding protein-7 and early identification of acute heart failure. ESC Heart Fail. 2020.

  5. 5.

    Ibrahim NE, Afilalo M, Chen-Tournoux A, Christenson RH, Gaggin HK, Hollander JE, et al. Diagnostic and prognostic utilities of insulin-like growth factor binding protein-7 in patients with dyspnea. JACC Heart Fail. 2020;8(5):415–22.

    Article  Google Scholar 

  6. 6.

    Januzzi JL, Packer M, Claggett B, Liu J, Shah AM, Zile MR, et al. IGFBP7 (insulin-like growth factor-binding protein-7) and neprilysin inhibition in patients with heart failure. Circ Heart Fail. 2018;11(10):e005133.

    CAS  Article  Google Scholar 

  7. 7.

    Blum S, Aeschbacher S, Meyre P, Kühne M, Rodondi N, Beer JH, et al. Insulin-like growth factor-binding protein 7 and risk of congestive heart failure hospitalization in patients with atrial fibrillation. Heart Rhythm. 2020.

  8. 8.

    George M, Jena A, Srivatsan V, Muthukumar R, Dhandapani V. GDF 15—a novel biomarker in the offing for heart failure. Curr Cardiol Rev. 2016;12(1):37–46.

    CAS  Article  Google Scholar 

  9. 9.

    Zile MR, O’Meara E, Claggett B, Prescott MF, Solomon SD, Swedberg K, et al. Effects of sacubitril/valsartan on biomarkers of extracellular matrix regulation in patients with HFrEF. J Am Coll Cardiol. 2019;73(7):795–806.

    CAS  Article  Google Scholar 

  10. 10.

    Wollert KC, Kempf T, Wallentin L. Growth differentiation factor 15 as a biomarker in cardiovascular disease. Clin Chem. 2017;63(1):140–51.

    CAS  Article  Google Scholar 

  11. 11.

    Daniels LB, Clopton P, Laughlin GA, Maisel AS, Barrett-Connor E. Growth-differentiation factor-15 is a robust, independent predictor of 11-year mortality risk in community-dwelling older adults: the Rancho Bernardo Study. Circulation. 2011;123(19):2101–10.

    Article  Google Scholar 

  12. 12.

    Hijazi Z, Verdecchia P, Oldgren J, Andersson U, Reboldi G, Di Pasquale G, et al. Cardiac biomarkers and left ventricular hypertrophy in relation to outcomes in patients with atrial fibrillation: experiences from the RE—LY trial. J Am Heart Assoc. 2019;8(2):e010107.

    CAS  Article  Google Scholar 

  13. 13.

    Garnero P, Vergnaud P, Hoyle N. Evaluation of a fully automated serum assay for total N-terminal propeptide of type I collagen in postmenopausal osteoporosis. Clin Chem. 2008;54(1):188–96.

    CAS  Article  Google Scholar 

  14. 14.

    Dupuy AM, Kuster N, Curinier C, Huet F, Plawecki M, Solecki K, et al. Exploring collagen remodeling and regulation as prognosis biomarkers in stable heart failure. Clin Chim Acta. 2019;490:167–71.

    CAS  Article  Google Scholar 

  15. 15.

    Mureddu GF, Agabiti N, Rizzello V, Forastiere F, Latini R, Cesaroni G, et al. Prevalence of preclinical and clinical heart failure in the elderly. A population-based study in Central Italy. Eur J Heart Fail. 2012;14(7):718–29.

    Article  Google Scholar 

  16. 16.

    Cesaroni G, Agabiti N, Forastiere F, Perucci CA. Socioeconomic differences in stroke incidence and prognosis under a universal healthcare system. Stroke. 2009;40(8):2812–9.

    Article  Google Scholar 

  17. 17.

    Kirchmayer U, Di Martino M, Agabiti N, Bauleo L, Fusco D, Belleudi V, et al. Effect of evidence-based drug therapy on long-term outcomes in patients discharged after myocardial infarction: a nested case–control study in Italy. Pharmacoepidemiol Drug Saf. 2013;22(6):649–57.

    Article  Google Scholar 

  18. 18.

    Huang HD, Turner M, Lederle F. Abstract 320: Accuracy of ICD-9 Codes for Identifying Acute Heart Failure Hospitalizations. Circul Cardiovasc Qual Outcomes 2015;8(suppl_2):A320–A320.

  19. 19.

    Paulus WJ, Tschöpe C, Sanderson JE, Rusconi C, Flachskampf FA, Rademakers FE, et al. How to diagnose diastolic heart failure: a consensus statement on the diagnosis of heart failure with normal left ventricular ejection fraction by the Heart Failure and Echocardiography Associations of the European Society of Cardiology. Eur Heart J. 2007;28(20):2539–50.

    Article  Google Scholar 

  20. 20.

    Nagueh SF, Smiseth OA, Appleton CP, Byrd BF, Dokainish H, Edvardsen T, et al. Recommendations for the evaluation of left ventricular diastolic function by echocardiography: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. J Am Soc Echocardiogr. 2016;29(4):277–314.

    Article  Google Scholar 

  21. 21.

    López B, González A, Querejeta R, Zubillaga E, Larman M, Díez J. Galectin-3 and histological, molecular and biochemical aspects of myocardial fibrosis in heart failure of hypertensive origin. Eur J Heart Fail. 2015;17(4):385–92.

    Article  Google Scholar 

  22. 22.

    Gandhi PU, Gaggin HK, Sheftel AD, Belcher AM, Weiner RB, Baggish AL, et al. Prognostic usefulness of insulin-like growth factor-binding protein 7 in heart failure with reduced ejection fraction: a novel biomarker of myocardial diastolic function? Am J Cardiol. 2014;114(10):1543–9.

    CAS  Article  Google Scholar 

  23. 23.

    Barroso MC, Kramer F, Greene SJ, Scheyer D, Köhler T, Karoff M, et al. Serum insulin-like growth factor-1 and its binding protein-7: potential novel biomarkers for heart failure with preserved ejection fraction. BMC Cardiovasc Disord. 2016;16(1):199.

    Article  Google Scholar 

  24. 24.

    Jørgensen NR, Møllehave LT, Hansen YBL, Quardon N, Lylloff L, Linneberg A. Comparison of two automated assays of BTM (CTX and P1NP) and reference intervals in a Danish population. Osteoporos Int. 2017;28(7):2103–13.

    Article  Google Scholar 

  25. 25.

    Bao X, Borné Y, Muhammad IF, Nilsson J, Lind L, Melander O, et al. Growth differentiation factor 15 is positively associated with incidence of diabetes mellitus: the Malmö Diet and Cancer-Cardiovascular Cohort. Diabetologia. 2019;62(1):78–86.

    CAS  Article  Google Scholar 

  26. 26.

    Fuglsang-Nielsen R, Rakvaag E, Vestergaard P, Hartmann B, Holst JJ, Hermansen K, et al. Consumption of nutrients and insulin resistance suppress markers of bone turnover in subjects with abdominal obesity. Bone. 2020;133:115230.

    CAS  Article  Google Scholar 

  27. 27.

    An Y, Liu S, Wang W, Dong H, Zhao W, Ke J, et al. Low serum levels of bone turnover markers are associated with the presence and severity of diabetic retinopathy in patients with type 2 diabetes mellitus. J Diabetes. 2020

  28. 28.

    Hunt HB, Miller NA, Hemmerling KJ, Koga M, Lopez KA, Taylor EA, et al. Bone tissue composition in post-menopausal women varies with glycemic control from normal glucose tolerance to type 2 diabetes mellitus. J Bone Miner Res. 2020

  29. 29.

    Ho JE, Mahajan A, Chen M-H, Larson MG, McCabe EL, Ghorbani A, et al. Clinical and genetic correlates of growth differentiation factor 15 in the community. Clin Chem. 2012;58(11):1582–91.

    CAS  Article  Google Scholar 

  30. 30.

    Andersson C, Enserro D, Sullivan L, Wang TJ, Januzzi JL, Benjamin EJ, et al. Relations of circulating GDF-15, soluble ST2, and troponin-I concentrations with vascular function in the community: the Framingham Heart Study. Atherosclerosis. 2016;248:245–51.

    CAS  Article  Google Scholar 

  31. 31.

    Lind L, Wallentin L, Kempf T, Tapken H, Quint A, Lindahl B, et al. Growth-differentiation factor-15 is an independent marker of cardiovascular dysfunction and disease in the elderly: results from the Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) Study. Eur Heart J. 2009;30(19):2346–53.

    CAS  Article  Google Scholar 

  32. 32.

    Rohatgi A, Patel P, Das SR, Ayers CR, Khera A, Martinez-Rumayor A, et al. Association of growth differentiation factor-15 with coronary atherosclerosis and mortality in a young, multiethnic population: observations from the Dallas Heart Study. Clin Chem. 2012;58(1):172–82.

    CAS  Article  Google Scholar 

  33. 33.

    Sanders-van Wijk Sandra, Tromp Jasper, Beussink-Nelson Lauren, Hage Camilla, Svedlund Sara, Saraste Antti, et al. Proteomic Evaluation of the Comorbidity-Inflammation Paradigm in Heart Failure with Preserved Ejection Fraction: Results from the PROMIS-HFpEF Study. Circulation [Internet]. [cited 2020 Oct 27];0(0). https://doi.org/10.1161/CIRCULATIONAHA.120.045810

  34. 34.

    Wallentin L, Zethelius B, Berglund L, Eggers KM, Lind L, Lindahl B, et al. GDF-15 for prognostication of cardiovascular and cancer morbidity and mortality in men. PLoS ONE. 2013;8(12):e78797.

    Article  Google Scholar 

  35. 35.

    Doerstling S, Hedberg P, Öhrvik J, Leppert J, Henriksen E. Growth differentiation factor 15 in a community-based sample: age-dependent reference limits and prognostic impact. Ups J Med Sci. 2018;123(2):86–93.

    Article  Google Scholar 

  36. 36.

    Xanthakis V, Larson MG, Wollert KC, Aragam J, Cheng S, Ho J, Coglianese E, Levy D, Colucci WS, Michael Felker G, Benjamin EJ, Januzzi JL, Wang TJ, Vasan RS. Association of novel biomarkers of cardiovascular stress with left ventricular hypertrophy and dysfunction: implications for screening. J Am Heart Assoc. 2013;2(6):e000399. https://doi.org/10.1161/JAHA.113.000399.PMID:24200688;PMCID:PMC3886765.

    Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Masson S, Latini R, Mureddu GF, Agabiti N, Miceli M, Cesaroni G, Forastiere F, Wienhues-Thelen UH, Block D, Zaugg C, Vago T, Boccanelli A. PREDICTOR study. High-sensitivity cardiac troponin T for detection of subtle abnormalities of cardiac phenotype in a general population of elderly individuals. J Intern Med. 2013;273(3):306–17. https://doi.org/10.1111/joim.12023.

    CAS  Article  PubMed  Google Scholar 

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Acknowledgements

The PREDICTOR Study Group: A. Boccanelli, G. Cacciatore, G.F. Mureddu, V.Rizzello (S. Giovanni-Addolorata Hospital, Roma); N. Agabiti, G. Cesaroni, F.Forastiere, C.A. Perucci, M. Davoli (Department of Epidemiology, ASL Roma 1,Roma); F. Colivicchi, M. Santini (S. Filippo Neri Hospital, Roma); R. Latini (Mario Negri Institute, Milano); M. Uguccioni (A. Alesini Hospital,Roma); M. Iacomelli, M. Di Gennaro (S. Paolo Hospital, Civitavecchia); F. Qualandri, V. Paniccia (Umberto I Hospital, Frosinone); F. Ammirati, R. Donati, R.Fiaschetti (GB Grassi Hospital, Ostia); G. Barbato, T.A. Gaspardone (S. EugenioHospital, Roma); G. Vitaliani, F. Catalano (S. Giacomo Hospital, Roma); A. Achilli (Belcolle Hospital Viterbo).

Funding

The PREDICTOR study was funded by Takeda Italia Farmaceutici S.p.A, Rome, Italy; the funders had no role in the concept or design of the study, or in the data reporting.

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All authors adhere to the four ICMJE authorship criteria and had full access to the data in the study and take complete responsibility for the integrity and accuracy of the data analysis. RL and SM conceived the idea and designed the study in collaboration with AB and GC. GFM, NA, PK, DN, UHWT and GC were responsible for the acquisition of the data. JMTAM was responsible for undertaking the data analysis with the help of LIS, GB and MLOF. RL, JMTAM, MM and LIS contributed to the interpretation of the results. The manuscript was drafted by RL with the help of JMTAM and GC and then shared with all authors for critical revision. All authors have read and approved the final manuscript.

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Correspondence to Roberto Latini.

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Ethics approval was obtained from the S. Giovanni-Addolorata Hospital Ethics Committee (Prot N. 5177/72 on April 5, 2007). Each participant provided a written informed consent.

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Not applicable.

Competing interests

UHWT, PMK and SM are employees at Roche Diagnostics. Roche diagnostics had no role in data analysis, but carefully revised the manuscript and contributed to the interpretation of findings of this study. JMTAM, GC, GFM, AB, MLOF, DN, GB, MM, NA, LS and RL have no competing interests to report.

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Meessen, J.M.T.A., Cesaroni, G., Mureddu, G.F. et al. IGFBP7 and GDF-15, but not P1NP, are associated with cardiac alterations and 10-year outcome in an elderly community-based study. BMC Cardiovasc Disord 21, 328 (2021). https://doi.org/10.1186/s12872-021-02138-8

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Keywords

  • IGFBP7
  • GDF-15
  • P1NP
  • Cardiac remodelling
  • Community-dwelling elderly