# Serum and urinary biomarkers of collagen type-I turnover predict prognosis in patients with heart failure

Tianlin He, Jesus Melgarejo, Andrew Clark, Yu-ling Yu, Lutgarde Thijs, Javier Díez, Begoña López, Arantxa González, John Cleland, Joost Schanstra, et al.

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Tianlin He, Jesus Melgarejo, Andrew Clark, Yu-ling Yu, Lutgarde Thijs, et al.. Serum and urinary biomarkers of collagen type-I turnover predict prognosis in patients with heart failure. Clinical and Translational Medicine, 2021, 11 (1), pp.1-11. 10.1002/ctm2.267. hal-03268305

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# Serum and urinary biomarkers of collagen type-I turnover predict prognosis in patients with heart failure

Dear Editor,

Previous studies indicated an association of biomarkers reflecting collagen turnover with heart failure.¹,² However, their value in predicting unfavorable outcomes is unknown. In the myocardium, the collagen matrix forms a scaffold that maintains the anatomical integrity and function of the heart. Collagen I is dominant among the four primary types of cardiac collagen.³ The abundance of collagen is the net result of the balance between synthesis and degradation.⁴,⁵ Collagen degradation releases fragments to the circulation and urine. Here, we investigate if specific changes in collagen I turnover, detected based on urinary peptide fragments and circulating biomarkers, are associated with heart failure and subsequent clinical events.

Our study includes all 354 participants with suspected heart failure from the HOMAGE-HULL subcohort⁶,⁷ recruited between 2010 and 2014, whose baseline serum and urine samples were available. Table S1 shows their demographic and clinical data. We used capillary electrophoresis coupled with mass spectrometry (CE-MS) to quantify type I alpha-1 (COL1A1) fragments in urine, confining the analyses to 293 fragments present in at least 30% of subjects. For serum samples, we used ELISA Kit to quantify the collagen synthesis biomarkers carboxy-terminal propeptide of procollagen type I (PICP),⁸ collagen degradation biomarkers carboxy-terminal telopeptide of collagen type I (CITP),⁹ and matrix metalloproteinase (MMP-1)¹⁰, which are known as good indicators of collagen turnover (Figure S1). The primary outcome is heart failure death. The rank-normalized biomarker abundance was regressed with the time of survival in a Cox proportional hazard model corrected for six confounders: age, sex, body mass index, history of ischemic disease, heart rate, and serum creatinine. Significant biomarkers were combined into a classifier, using the support vector machine-based MosaCluster software.² Cox regression was used to determine if prognosis via the classifier was independent of the six confounders plus NT-proBNP. All analyses were

performed using Python 3.7 and were considered significant if $P < .05$.

There were 33 heart failure deaths during a median follow-up of 6.4 years (interquartile range 4.9-7.0 years). Figure 1 shows the -log10(P) plot of heart failure death with the 293 urinary COL1A1 fragments, in which eight were significant. Three of them were associated with an increased risk of heart failure death, all close to either the N- or C- terminus of mature collagen I. Five were associated with a decreased risk of heart failure death, all located in the central part of COL1A1. Table S2 presents the relative risk associated with a unitary change in fragment abundances and start amino acid positions on COL1A1 of the fragments. In serum, we detected a significantly positive association between heart failure death and CITP (HR 1.74, 95% CI 1.09-2.78, $P = .019$) and MMP-1 (HR 1.43, 95% CI 1.00-2.04; $P = .048$), but not PICP (HR 0.99, 95% CI 0.69-1.41; $P = .96$). Figure S2 displays the correlation between the eight urinary biomarkers and the three serum biomarkers. (HR = hazard ratio, CI = confidence interval of the hazard ratio)

These biomarkers were combined into a “urinary” and a “serum” classifier, respectively, whose components are listed in Table S3. We first verified that the association between the two classifiers and adverse outcome was independent of the other, and neither classifier was associated with noncardiovascular death (Table S4). There was no overlap between fragments associated with heart failure death and those associated with noncardiovascular death (Figure S3). We stratified the patients into quartiles by classifier scores. In analyses adjusted for the six confounders and NT-proBNP, increasing classifier score was associated with a higher incidence of heart failure death (Figure 2). The incidence of heart failure death in the highest quartile was eightfold higher than that in the lowest quartile (20.3% vs 2.53%, $P < .001$) for the urinary classifier, and fourfold higher for the serum classifier (14.0% vs 3.44%, $P = .022$).

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2021 The Authors. *Clinical and Translational Medicine* published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics
<figure><img src="image_3.png" /><figcaption>FIG URE 1 Log10(p) probability of urinary COL1A1 fragments associated with heart failure death in the multivariable Cox regression model in 354 patients. There are 33 heart failure deaths. The model was adjusted for age, sex, body mass index, history of ischemic disease, heart rate, and serum creatinine. The fragments were allocated according to their start position on COL1A1. The two horizontal lines denote the significance thresholds at *P* = .01 and *P* = .05, respectively. Only significant fragments (*P* < .05) were colored and annotated. Fragments that were increased in heart failure deaths were colored in red, otherwise in blue</figcaption></figure>

To the best of knowledge, our study is the first one inves-
tigating a large number of collagen I fragments (N = 293)
with a focus on collagen degradation. We observed that
patients who died from heart failure exhibited (i) spe-
cific changes in urinary COL1A1 fragments including an
increase in abundance of terminal fragments and, con-
versely, a decrease in central fragments and (ii) an increase
in serum collagen degradation biomarkers, but no signif-
icant change in the collagen synthesis biomarker. Both
urinary collagen I fragments and serum collagen I degra-
dation biomarkers can independently predict adverse car-
diovascular outcomes.

We hypothesize that the observed pattern (Figure 1) is a
consequence of differential degradation between termini
and the central part of the collagen I molecule. Imme-
diately after procollagen synthesis, collagen I undergoes
chemical cross-linking and modifications. Cross-linking
stabilizes collagen by forming insoluble fibers with
increased thickness and stiffness.⁵ Cross-linking increases
over time as a result of chemical modification with

aging, inflammation, and cardiomyopathies,¹² rendering
collagen fibers more resistant to degradation.¹³ Since
the impact of modification is expected to be more pro-
nounced in the central, rigid part and reduced in the
terminal regions (due to increased terminal flexibility),
collagen fragments from the central part of COL1A1 are
reduced in the group with poor cardiovascular outcomes,
indicating reduced degradation of this part of the col-
lagen molecule as a result of increased cross-linking.
This hypothesis also fits with the observed upregulation
of MMP-1: to counteract the attenuation of collagen
degradation by cross-linking, protease activity is upreg-
ulated. Study limitations include that we could not test
the observation and the classifiers in an independent
cohort. Additionally, due to lack of power, we could
not account for the multiple testing of 293 fragments or
assess the presence of other fibrotic diseases that might
affect collagen levels. However, it should be noted that
there were no reported signs of fibrotic diseases in the
participants.
<figure><img src="image_4.png" /><figcaption>FIGURE 2 Cumulative incidence of heart failure deaths stratified by the quartiles of (A) urinary classifier scores and (B) serum classifier scores. Among the 354 patients included, 33 heart failure deaths occurred. The cumulative incidence was adjusted for age, sex, body mass index, history of ischemic disease, heart rate, and serum creatinine. Q1 to Q4 represent the highest quartile to the lowest quartile of the classifier score. P-values for trend were derived by Cox proportional hazards regression. The corresponding number of subjects at risk and the number of HF endpoints at yearly intervals are shown below the figure</figcaption></figure>

In conclusion, our results indicate that attenuation of collagen degradation may be responsible for progressive fibrosis in heart failure, with cross-linking of collagen fibers potentially being a major cause. Urinary markers of collagen I turnover might be a valuable prognostic tool for fibrotic heart disease, possibly enabling the monitoring of treatments targeting fibrosis.

## ACKNOWLEDGMENT

The work was supported by the EU Commission via the HOMAGE project [HEALTH-FP7- 305507], Marie Skłodowska-Curie Actions [MSCA-ITN-2017-Project ID: 764474] and German Research Foundation [SFB/TRR219] Project-ID 322900939) to V.J. (subproject S-03).

## CONFLICT OF INTEREST

Harald Mischak is the co-founder and co-owner of Mosaiques Diagnostics GmbH. Tianlin He and Agnieszka Latosinska are employed by Mosaiques Diagnostics GmbH. The Research Institute Alliance for the Promotion of Preventive Medicine (JAS) received a nonbinding grant from Omron Healthcare Inc. Ltd., Kyoto, Japan. All other authors declared no conflict of interest.

## ETHICS APPROVAL AND CONSENT TO PARTICIPATE

The study complied with the Helsinki declaration for research in humans. Participants provided informed written consent for their data and biological samples to

be stored and analyzed. The Ethics Committee of the University of Aachen approved the secondary use of anonymized data and the biomarker measurements (number EK163/19).

Tianlin He<fnref n="1" />,<fnref n="2" /> ID

Jesus D. Melgarejo<fnref n="3" />

Andrew L. Clark⁴

Yu-Ling Yu<fnref n="3" />

Lutgarde Thijs<fnref n="3" />

Javier Díez⁵,⁶,⁷

Begoña López⁵,⁶,⁷

Arantxa González⁵,⁶,⁷

John G. Cleland⁸

Joost P. Schanstra⁹,¹⁰

Antonia Vlahou¹¹

Agnieszka Latosinska<fnref n="1" /> ID

Harald Mischak<fnref n="1" />,¹²

Jan A. Staessen<fnref n="3" />,¹³

Zhen-Yu Zhang<fnref n="3" />

Vera Jankowski<fnref n="2" />

<footnote n="1">¹ Mosaiques Diagnostics GmbH, Hannover, Germany</footnote>

<footnote n="2">² Institute for Molecular Cardiovascular Research (IMCAR), RWTH Aachen University, Aachen, Germany</footnote>

<footnote n="3">³ Studies Coordinating Centre, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium</footnote>
⁴ Department of Academic Cardiology, Castle Hill Hospital,
University of Hull, Nottingham, UK

⁵ Program of Cardiovascular Diseases, Centre for Applied
Medical, University of Navarra and IdisNA, Pamplona,
Spain

⁶ CIBERCV, Carlos III Institute of Health, Madrid, Spain

⁷ Departments of Cardiology and Cardiac Surgery and of
Nephrology, University of Navarra Clinic, Pamplona, Spain

⁸ Robertson Centre for Biostatistics and Clinical Trials,
Institute of Health and Wellbeing, University of Glasgow,
Glasgow, UK

⁹ Institute of Cardiovascular and Metabolic Disease,
INSERM, Toulouse, France

¹⁰ Université Toulouse III Paul-Sabatier, Toulouse, France

¹¹ Biotechnology Laboratory, Centre of Basic Research,
Biomedical Research Foundation of the Academy of Athens,
Athens, Greece

¹² BHF Glasgow Cardiovascular Research Centre,
University of Glasgow, Glasgow, UK

¹³ NPA Alliance for the Promotion of Preventive Medicine
(APPREMED), Mechelen, Belgium

**Correspondence**

Vera Jankowski, Institute for Molecular Cardiovascular
Research (IMCAR), RWTH Aachen University,
Pauwelsstrasse 30, D-52074 Aachen.
Email: vjankowski@ukaachen.de

**ORCID**

Tianlin He https://orcid.org/0000-0001-7806-4846

Agnieszka Latosinska https://orcid.org/0000-0001-8917-2412

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**SUPPORTING INFORMATION**

Additional supporting information may be found online in the Supporting Information section at the end of the article.