Predictive Value of Biomarkers of Fibrosis for Development ... · Predictive Value of Biomarkers of Fibrosis for Development of Hypertension and Future Cardiovascular & Noncardiovascular

Predictive Value of Biomarkers of Fibrosis for Development of Hypertension

and Future Cardiovascular & Noncardiovascular Disease

Daniel Duprez,MD, PhD

Donald and Patricia Garofalo Chair in Preventive Cardiology

University of Minnesota

Agenda• Collagen biomarkers and extracellular matrix (ECM)

• Collagen biomarkers and new onset of hypertension

• Collagen biomarkers and future heart failure (HFpEF vs HFrEF)

• Collagen biomarkers and future atrial fibrillation

• Collagen biomarkers and total death, CVD outcome, noncardiovascular disease events (ChrIRD) and cancer

• Future

Agenda

• Collagen biomarkers and extracellular matrix (ECM)





• Future

ArteriesExtracellular Matrix: Elastin vs Collagen

Images, University Dundee, UK

Remodeling of extracellular matrix (ECM)

• Remodeling of extracellular matrix (ECM) occurs as a functional

adaptation, response to injury or inflammation.

• Sustained ECM remodeling can compromise tissue function.

• Fibrosis, the excessive accumulation of ECM, is the result of

severe or repetitive injury.

• Both acute and persistent low-grade inflammatory reactions are

important in triggering fibrosis in many organ systems.

Collagen BiomarkersICTP and PIIINP

Fan D et al. BMC Fibrogenesis & Tissue Repair 2012;5:15

Significance of collagen biomarkers in CVD and non-CVD prediction in asymptomatic subjects?

• Circulating biomarkers of collagen synthesis and degradation may provide a reliable, noninvasive assessment of fibrosis.

• Type I and III collagens are the major fibrillar collagens in both normal and diseased tissue.

• The predictive value of collagen biomarkers for prognosis in patients with myocardial infarction and heart failure has been a focus or research.

• The predictive value of collagen biomarkers in asymptomatic subjects free of overt CVD for hypertension, CVD morbidity and mortality is largely unknown.

Study Sample – MESA Study

• The Multi-ethnic Study of Atherosclerosis (MESA): investigate the prevalence, correlates, and progression of subclinical CVD in people initially free of overt clinical CVD, radiation or chemotherapy-treated cancer, other serious major illness, or cognitive impairment.

• Between 2000 and 2002, 6,814 men and women of white, black, Hispanic, or Chinese race/ethnicity, aged 45–84 years were enrolled.

• Collagen turnover marker study: participants, all with adjudicated CVD prior to 2011 plus a 56% random sample of all remaining participants. All included participants had assessment of both PIIINP and ICTP at baseline.

Agenda√ Collagen biomarkers and extracellular matrix (ECM)





• Future

Collagen biomarkers markers predict new onset of hypertension in normotensive subjects?

Background

• It is well known that the aorta stiffens in arterial hypertension and that a stiff aorta may accelerate an increase in blood pressure.

• Experimental and human data demonstrate that structural and functional blood vessel abnormalities predate the development of overt hypertension.

• Medial fibrosis is more prevalent at autopsy in several arteries among those who had been hypertensive in life, compared to those who had been normotensive.

Methods

• Subjects: BP measured and not hypertensive at the baseline visit - defined as having a systolic/diastolic BP <130/85 mmHg and not using any antihypertensive medication.

• Blood Pressure: 3 BP measurements were obtained 5 min apart in the seated position by using an automated oscillometricsphygmomanometer following a standardized protocol. The mean of the second 2 measurements was used for analysis.

• Collagen biomarkers: PIIINP and ITCP at baseline.

• Follow-up: BP and medication use were assessed during follow-up MESA exams after medians of 1.6, 3.1, 4.8, and 9.4 years.

• Incident hypertension: defined as a systolic/diastolic BP of ≥140/90 mmHg or the use of medication for hypertension during the follow-up.

Lab Measurements

• Markers of Collagen Turnover

- Blood was drawn following a 12 hour fast, and stored at −70°C.

- EDTA plasma PIIINP (µg/L) and ICTP (µg/L) assays were performed in the Molecular Epidemiology and Biomarker Research Laboratory (University of Minnesota, Myron Gross, PhD) using competitive radioimmunoassay kits (UNIQ #06099, ICTP and UniQ #06098 PIIINP, Orion Diagnostica, Espoo, Finland).

• Lipids and Inflammatory Markers

- Variables analyzed included total cholesterol, high density lipoprotein (HDL) cholesterol, triglycerides, high sensitivity CRP (hsCRP), interleukin 6 (IL6), D-Dimer.

Results

Parameter Mean or % SD

Incident Hypertension, % (dependent variable) 35.9

PIIINP, µg/L 5.39 1.95

ICTP, µg/L 3.18 1.39

Age, years 58.1 12.4

Male, % 48.7

eGFR, ml/min.m1.73 93.8 18.8

Heart rate, beats/min 62.4 11.3

Systolic blood pressure, mmHg 110.9 14

Diastolic blood pressure, mmHg 67.9 10.4

BMI, kg/m2 27.1 6.7

Former smoking, % 33.2

Current smoking, % 15.3

Parameter Mean or % SD

Cholesterol, mg/dL 195.8 47.2

LDL-cholesterol, mg/dL 119.4 41.4

HDL-cholesterol, mg/dL 51.3 19.2

Triglycerides, mg/dL 124.7 94

Cholesterol medication, % 9.5

Diabetes mellitus, % 6.3

Hs-CRP, mg/L 3.14 6.15

IL-6, pg/mL 1.37 1.42

D-dimer, µg/mL 0.3 0.91

Results

Relative Incidence Density of New Onset of Hypertension by PIIINP and ICTP, per SD

Rel. Incidence Dens. (95% CI) P

model 1 ln(PIIINP) 1.16 (1.06, 1.28) 0.0017

model 2 ln(PIIINP) 1.28 (1.15, 1.42) <.0001

model 1 ln(ICTP) 1.20 (1.08, 1.33) 0.0008

model 2 ln(ICTP) 1.29 (1.15, 1.44) <.0001

Model 1: adjusted for age, race and sex

Model 2: Model 1 and adjusted cardiovascular risk factors (systolic and diastolic BP, total chol, HDL-chol, triglycerides, smoking (current, past), renal function (eGFR), inflammation factors (hs-CRP, Interleukin-6 (Il-6), D-dimer)

PIIINP: Procollagen Type III N-Terminal PropeptideICTP: Collagen Type I CarboxyTerminal Telopeptide

0.25

0.3

0.35

0.4

0.45

0.5

0 2 4 6 8

Inci

de

nce

de

nsi

ty p

er

10

y

PIIINP µg/L

PIIINP age, race, sex

PIIINP + age,race, sex, + riskfactors, inflammation

Incidence density of hypertension according to PIIINP quartiles

0.25

0.3

0.35

0.4

0.45

0.5

0 1 2 3 4 5 6

Inci

de

nce

de

nsi

ty p

er

10

y

ICTP µg/L

ICTP age, race, sex

ICTP + age,race, sex, + riskfactors, inflammation

Incidence density of hypertension according to ICTP quartiles


√ Collagen biomarkers and new onset of hypertension



• Collagen biomarkers and and total death, CVD outcome, noncardiovascular disease events (ChrIRD) and cancer

• Future

Interstitial fibrosis in normal (A) and hypertrophic heart (B,C,D)

Predictive Value of Collagen Biomarkers for Heart Failure (HF) with (HFpEF) and without (HFrEF) Preserved Ejection Fraction

• Changes in the quantity and quality of the ECM, involving the collagen

network and associated myocardial remodeling, ultimately lead to

deterioration of left ventricular (LV) function and facilitate the

development of HF.

• Such changes differ to the different pathophysiological mechanisms

leading to the two major subtypes of HF, HFrEF and HFpEF.

• HFrEF is a consequence of cardiomyocyte loss from ischemic or other

insults, which is accompanied by replacement fibrosis.

• HFpEF is associated with cardiomyocyte preservation but increased

diffuse interstitial fibrosis.

Characteristics of MESA Subjects (n = 3187)with no CVD and collagen biomarkers at baseline with 13 years follow-up

and newly diagnosed HFpEF and HFrEF during this period

No HF HFpEF HFrEFn = 2984 n = 107 n = 96

Mean ± SE Mean ± SE Mean ± SEP diff: no HF

vs HFpEFP diff: no HF vs vs HFrEF

ICTP (µg/ml) 3.38±0.02 3.68±0.15 3.68±0.16 0.05 0.06

PIIINP (µg/ml) 5.47±0.03 5.89±0.18 5.55±0.19 0.03 0.68

Age (year) 62.2±0.19 69.3±1.21 67.2±1.27 <.0001 <.0001

SBP (mmHg) 126.5±0.35 137.4±2.32 129.3±2.42 <.0001 0.27

DBP (mmHg) 71.9±0.17 74.3±1.13 72.6±1.18 0.04 0.58

Hypertension (%)

45.1 67.4 53 <.0001 0.17

BP Rx (%) 33.4 52.6 37.8 0.0004 0.43

Prediction of incident HFpEF from octiles of ICTPand PIIINP

Duprez DA et al., J Am Heart Assoc 2018 (in press)

Prediction of incident HFrEF from octiles of ICTP and PIIINP

Duprez DA et al., J Am Heart Assoc 2018 (in press)



√ Collagen biomarkers and future heart failure (HFpEF vs HFrEF)



• Future

Collagen biomarkers and atrial fibrillation• AF was identified based on assignment of an ICD-9 diagnosis

code for AF (427.31) or atrial flutter (427.32) in any position at hospital discharge or (for those enrolled in fee-for-service Medicare) in inpatient or outpatient Medicare claims through 2011.

• AF was identified based in a 12-lead ECG at Exam 5 (10 years after baseline).

• It is recognized both that age is a strong risk factor for AF and that inclusion of Medicare outpatient claims provides more complete AF case-finding in ages ≥65 years than in younger people.

• Baseline age-specific rates were 4.4% (72 AF cases) in ages 45-64 years, vs 19.68% (285 AF cases) in ages 65-84 years.

Incidence densities per 1000 person yrs for atrial fibrillation according to 1 µg/L interval categories of PIIINP and of ICTP

0

5

10

15

20

25

0 2 4 6 8 10 12

Inci

de

nce

de

nsi

ty

PIIINP (µg/L)

A. Incidence density per 1000 person years of atrial fibrillation by equal concentration

interval PIIINP categories

adjusted for age, race/ethnicity, sex

plus SBP, Ht, BMI, smoker, eGFR<60

0

5

10

15

20

25

0 2 4 6 8 10 12

Inci

de

nce

de

nsi

ty

ICTP (µg/L)

B. Incidence density per 1000 person years of atrial fibrillation by equal concentration

interval ICTP categories

adjusted for age, race/ethnicity, sex

plus SBP, Ht, BMI, smoker, eGFR<60



√ Collagen biomarkers and future heart failure (HFpEF vs HFrEF)

√ Collagen biomarkers and future atrial fibrillation


• Future

Collagen biomarkers and Outcome Events: Total death, CVD, ChrIRD, Total Cancer (13 yrs FU)

• Participants were contacted at 9 to 12-month intervals to identify deaths and

hospitalizations, with further reference to the death certificate and the National Death

Index.

• Cardiovascular disease events (CVD) events were adjudicated by physicians based on medical records. CVD event was defined as myocardial infarction, angina, stroke, transient ischemic attack, heart failure, and peripheral arterial disease and CVD death.

• Chronic inflammatory related disease (ChrIRD) events were adjudicated based

on death and hospital records to classify ChrIRD. The following pathologic conditions

contributed to ChrIRD: severe infectious diseases, chronic endocrine, nutritional, and

metabolic disease, nervous system disorders, respiratory system diseases, digestive

system diseases, skin diseases, musculoskeletal and connective tissue disorders,

genitourinary diseases, and blood disorders.

• Cancer was based on ICD codes.

Duprez DA, et. Clin Chem. 2017;63(7):1237-1247

Predictor

Total Death

Model Q1 Q2 Q3 Q4

Rel. Inc. Dens.

Per SD p trend

PIIINP

(µg/L) 1 (min.) 1

0.87

(0.71-1.08)

0.95

(0.77-1.16)

1.20

(0.98-1.47)

1.20

(1.09-1.32)0.0002

2 (risk factors) 1

0.88

(0.71-1.09)

0.98

(0.79-1.2)

1.24

(1.01-1.52)

1.21

(1.1-1.34) <.0001

3 (inflam.) 1

0.89

(0.72-1.11)

0.96

(0.78-1.18)

1.25

(1.02-1.54)

1.21

(1.1-1.33) 0.0001

ICTP

(µg/L) 1 1

1.02

(0.8-1.29)

1.13

(0.9-1.42)

1.12

(0.88-1.43)

1.13

(1.02-1.26)0.02

2 1

1.04

(0.82-1.32)

1.11

(0.88-1.4)

1.12

(0.88-1.44)

1.12

(1.01-1.25) 0.03

3 1

1.08

(0.85-1.37)

1.12

(0.89-1.42)

1.09

(0.85-1.39)

1.10

(0.98-1.22) 0.10

Total Death (n = 571) occurrence during median 13 years of FU according to PIIINP and ICTP

-Model 1 (minimal): Outcome variable regressed on baseline ICTP or PIIINP adjusted for baseline age, race/ethnicity, sex, eGFR

-Model 2 (risk factor): Model 1 plus additional general risk covariates (height, heart rate, systolic and diastolic blood pressures, antihypertensive medication use

, BMI, smoking status, total cholesterol, HDL-C, triglycerides, lipid lowering medication use, and diabetes

-Model 3 (inflammatory): Model 2 plus baseline inflammation-related biomarkers (hsCRP, IL-6, D-dimer, GlycA, and the sum of small plus medium HDLparticles)

ChrIRD

Model Q1 Q2 Q3 Q4

Rel. Inc. Den.

Per SD P trend

PIIINP

(µg/L) (min.) 1

0.94

(0.78-1.14)

1.03

(0.86-1.24)

1.34

(1.12-1.59)

1.27

(1.17-1.38)<.0001

(risk factors) 1

0.92

(0.76-1.11)

1.01

(0.84-1.21)

1.27

(1.06-1.52)

1.23

(1.13-1.34)<.0001

(inflam.) 1

0.93

(0.77-1.12)

1.00

(0.83-1.20)

1.29

(1.07-1.54)

1.23

(1.12-1.34)<.0001

ICTP

(µg/L) 1 1

1.27

(1.04-1.56)

1.39

(1.14-1.70)

1.55

(1.26-1.91)

1.31

(1.2-1.44)<.0001

2 1

1.28

(1.04-1.56)

1.31

(1.07-1.6)

1.43

(1.16-1.77)

1.25

(1.14-1.38)<.0001

3 1

1.31

(1.07-1.61)

1.33

(1.09-1.64)

1.42

(1.14-1.76)

1.23

(1.12-1.35)<.0001

Chronic Inflammatory Related Disease (ChrIRD, n = 726)

occurrence during median 13 yrs FU according to PIIINP and ICTP

CVD

Model Q1 Q2 Q3 Q4

Rel. Inc. Den.

Per SDP trend

PIIINP

(µg/L) 1 (min) 1

0.98

(0.80-1.21)

1.05

(0.86-1.29)

1.09

(0.89-1.34)

1.06

(0.96-1.17) 0.28

2 (risk factors) 1

0.95

(0.77-1.17)

1.02

(0.83-1.26)

1.02

(0.83-1.26)

1.01

(0.92-1.12) 0.79

3 (inflam.) 1

0.96

(0.78-1.18)

1.01

(0.82-1.25)

1.04

(0.84-1.29)

1.02

(0.92-1.13) 0.74

ICTP

(µg/L) 1 1

1.05

(0.85-1.3)

0.87

(0.70-1.08)

0.92

(0.73-1.16)

0.95

(0.85-1.06) 0.33

2 1

1.07

(0.86-1.32)

0.80

(0.64-1.00)

0.86

(0.68-1.09)

0.89

(0.80-1.00) 0.05

3 1

1.09

(0.88-1.35)

0.78

(0.62-0.97)

0.82

(0.64-1.04)

0.87

(0.78-0.98) 0.02

Cardiovascular Disease (CVD, n = 697) events

occurrence during median 13 years FU according

PIIINP and ICTP

Model 1 (minimal): Outcome variable regressed on baseline ICTP or PIIINP adjusted for baseline age, race/ethnicity, sex, eGFR

Model 2 (risk factor): Model 1 plus additional general risk covariates (height, heart rate, systolic and diastolic blood pressures, antihypertensive medication use, BMI,

smoking status, total cholesterol, HDL-C, triglycerides, lipid lowering medication use, and diabetes

Model 3 (inflammatory): Model 2 plus baseline inflammation-related biomarkers (hsCRP, IL-6, D-dimer, GlycA, and the sum of small plus medium HDL particles)

Total cancer (n = 327) occurrence during median 13 years follow-up according to PIIINP and ICTP

Total Cancer

Model Q1 Q2 Q3 Q4

Rel. Inc. Dens.

Per SDP trend

PIIINP

(µg/L) 1 (min.) 1

1.11

(0.85-1.44)

1.02

(0.78-1.33)

1.31

(1.01-1.70)

1.20

(1.07-1.36)

0.003

2 (risk factors) 1

1.09

(0.84-1.41)

1.01

(0.77-1.32)

1.33

(1.02-1.73)

1.22

(1.08-1.38)

0.002

3 (inflam.) 1

1.11

(0.86-1.45)

1.03

(0.79-1.35)

1.39

(1.06-1.81)

1.23

(1.08-1.4)

0.001

ICTP

(µg/L) M1 1

1.22

(0.92-1.61)

1.29

(0.98-1.71)

1.49

(1.11-1.99)

1.12

(0.98-1.28)

0.11

M2 1

1.22

(0.92-1.61)

1.27

(0.96-1.69)

1.47

(1.09-1.98)

1.11

(0.97-1.27)

0.14

M3 1

1.26

(0.95-1.67)

1.31

(0.99-1.74)

1.47

(1.08-1.98)

1.10

(0.96-1.26)

0.17

Model 1 (minimal): Outcome variable regressed on baseline ICTP or PIIINP adjusted for baseline age, race/ethnicity, sex, eGFR

Model 2 (risk factor): Model 1 plus additional general risk covariates (height, heart rate, systolic and diastolic blood pressures, antihypertensive medication use,

BMI, smoking status, total cholesterol, HDL-C, triglycerides, lipid lowering medication use, and diabetes

Model 3 (inflammatory): Model 2 plus baseline inflammation-related biomarkers (hsCRP, IL-6, D-dimer, GlycA, and the sum of small plus medium HDL particles)

Future

• Collagen markers and coronary artery calcification

• Collagen markers and lung fibrosis and function

• Collagen markers and kidney

Collagen Biomarkers (PIIINP and ICTP) and Fibrosis:Effect on Death and Disease

Collagen

degradationCollagen

Synthesis

Fibroblast

Myofibroblast

Local mechanical Systemic hemodynamic

humoral & inflammatory humoral & inflammatory

factors factors

Death Cancer CVD Chronic Inflammatory

severe disease (ChrIRD)

PIIINP and ICTP

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Predictive Value of Biomarkers of Fibrosis for Development ... · Predictive Value of Biomarkers of Fibrosis for Development of Hypertension and Future Cardiovascular & Noncardiovascular