1. Gina LaRocca, MD, MHSc, FACC, FASE, FSCCT
Fibrosis, as Measured by the Biomarker TIMP-1, Predicts Mortality in the AGES-Reykjavik Study Gina LaRocca, MD, MHSc1,5, Thor Aspelund, PhD3,4, Anders M. Greve, MD, PhD1, Gudny Eiriksdottir, MSc3, Gudmundur Thorgeirsson, MD, PhD4, Tamara B. Harris, MD, MS2, Lenore J. Launer, PhD2, Vilmundur Gudnason3,4, MD, PhD, Andrew E. Arai, MD1
Gina LaRocca, MD, MHSc, FACC, FASE, FSCCT
National Institutes of Health / National Heart, Lung, Blood Institute / DHHS
Icahn School of Medicine / Mount Sinai Medical Center

2. Disclosures & Funding The authors report no conflicts of interest.
This work was supported by:
NHLBI and NIA of the National Institutes of Health
Hjartavernd (the Icelandic Heart Association)
the Althingi (the Icelandic Parliament)

3. TIMP-1 inhibits MMP-9 and thus promotes fibrosis
Potential Circulating Biomarkers:
MMP-9 TIMP-1
hsCRP eGFR
Inflammation can lead to tissue
repair or fibrosis
TIMP-1 inhibits MMP-9
and thus promotes fibrosis
One general concept behind this study is that TIMPs inhibit MMPs and thus promote fibrosis.

4. The prospective Uppsala Longitudinal Study of Adult Men (ULSAM) cohort
MMP-9
TIMP-1
Higher levels of circulating MMP-9 or TIMP-1 were associated with increased risk of death.
Higher TIMP-1 levels associated with higher risk of stroke and cardiovascular mortality.
Random population base study of 1,082 / 71-year-old men
(Cox proportional hazard ratio [HR] per standard deviation 1.10, 95% confidence interval [CI] 1.03–1.19; and 1.11, 1.02–1.20; respectively).
Hansson et al. PLoS ONE. 2011

5. AGES-Reykjavik Study AGES-Reykjavik Study Reykjavik Study
5764 community dwelling subjects >68 years of age.
All were survivors of the original Reykjavik study.
Reykjavik Study
19,381 men and women 1967 and 1996
Jonsdottir LS. et al. Eur Heart J 1998;19:
Sigurdsson A, et al. Eur Heart J 1993;14:
Harris TB, et al. American J Epidemiology 2007;165:
The AGES-Reykjavik Study is an interesting population to study fibrosis due to the Age and high mortality in this cohort.

6. Aims / Hypothesis of the Study
Aim 1: To understand the prognostic significance of fibrotic processes in the AGES-Reykjavik cohort of older community-dwelling subjects.
Aim 2: To assess the incremental value of the biomarkers for predicting mortality.

We hypothesized that the novel biomarkers TIMP-1 and MMP-9 provide prognostic information over risk factors and hsCRP and eGFR.

7. AGES-Reykjavik Subject Selection & Biomarker Measurements
5511 participants (analysis cohort) after excluding subjects missing biomarkers or baseline characteristics.
Blood was drawn at the initial AGES-Reykjavik study and stored at -80⁰ C.
MMP-9 and TIMP-1 measured by ELISA assay kits.
hsCRP measured with Roche Diagnostics assay.
Serum creatinine used to estimate glomerular filtration rate (eGFR).

8. The Baseline Multivariable Model was based on Clinical Variables without Biomarkers
Framingham Risk Score Variables
Age
Gender
Type 2 Diabetes
Smoking Status
Treated and untreated Systolic Blood Pressure
Total Cholesterol
HDL Cholesterol
+ BMI
+ Statin Use
Additional Clinical Variables
Framingham Risk Score variables and the four biomarkers with all-cause mortality, and with CVD and cancer as causes of death.

9. Demographics & Mortality in our Study
Mean age was 76.8 years (range 68 to 98 years)
57% were female
Type 2 diabetes mellitus was present in 13%
Mean systolic blood pressure in treated patients was 144 mmHg
12% were current smokers
Mean body mass index was 27 kg/m2
All-cause mortality:
16% (886 deaths) at 5-year follow-up
41% (2263 deaths) at 10-year follow-up

10. 10-year Multivariable Analysis: Baseline Model + 4 Biomarkers
Wald Chi Square
Hazard Ratio
(95% Confidence Interval)
P value
Age (per 5 years)
798.7
1.79 ( )
<0.0001
TIMP-1 (1 SD)
125.2
1.28 (1.22 – 1.33)
Current Smoker
58.1
1.67 ( )
Gender
41.4
0.72 ( )
Type 2 Diabetes Mellitus
29.2
1.39 ( )
Body Mass Index (kg/m2)
25.6
0.97 ( )
Log-eGFR (1 SD)
16.7
0.91 (0.87 – 0.95)
Log-hsCRP (1 SD)
11.3
1.08 (1.03 – 1.12)
0.0008
MMP-9 (1 SD)
1.1
1.02 (0.98 – 1.06)
0.31
Cox proportional Hazards regression model was used for survival analysis.
MMP-9 was not specific for predicting 10 year mortality. We will not focus on MMP-9 for the remainder of the presentation. Details can be seen in the publication.

11. Kaplan Meier Survival Analysis
TIMP-1 THIS WAS THE STRONGEST BIOMARKER IN THIS STUDY
WORSE SURVIVAL FOR THE HIGHEST PERCENTILE (>95%)
1.17 ( ) ( ) ( )
Kaplan Meier analysis of the biomarkers when stratified by percentiles showed that the highest TIMP-1 percentile category (>95th percentile) had the worst survival for all biomarkers at both 5 and 10 years.
HR 2.32 at 10 years
The percent survival for subjects with TIMP-1 in the highest percentile category was 54% at 5 years and 19% at 10 years.
MMP-9
THE LOWEST 3 PERCENTILES ARE SUPER-IMPOSED OVER 10 YEAR FOLLOW UP.
ONLY >95TH PERCENTILE SEPARATED FROM THE REFERENT GROUP
0.93 ( ) 1.03 ( ) 1.02 ( )

12. Kaplan Meier Survival Analysis
For hsCRP, the percent survival was 72% and 57% at 5 and 10 years for the highest percentile category and the hazard ratio was 1.29
hsCRP 1.06 ( ) 1.27 ( ) 1.29 ( )
PROVIDED BETTER DISCRIMINATION THAN MMP-9 BUT THE HAZARD RATIO WAS WEAKER THAN TIMP-1

13. Cause of Death Analysis: 10-year Multivariable Model including Biomarkers
Cardiovascular Death
Cancer Death
Other Death
Variable χ2
Age
513.7
49.0
310.4
TIMP-1
67.9
Smoker
39.3
Diabetes
29.0
Gender
38.9
37.7
22.7
eGFR
25.2
hsCRP
11.7
BMI
18.1
21.2
10.2
Cholesterol
15.0
7.7
8.1
Statins
10.4
6.8
6.6
5.6
6.3
All statistically significant variables and biomarkers are shown.
Relative strength of Framingham variables and the biomarkers for cause-specific death as assessed in multivariable Cox regression models.

14. Continuous NRI and IDI Analysis of TIMP-1
5-Year Mortality
10-Year Mortality
Index
95% C.I.
P value
NRI
0.28
0.21 – 0.36
<0.0000
0.19
0.14 – 0.26
NRI for events
0.02
-0.04 – 0.08
0.01
-0.03 – 0.04
NRI for non-events
0.27
0.22 – 0.30
0.15 – 0.23
IDI
0.027
0.019 –
0.01 – 0.02
NRI and IDI statistics for individual biomarkers over Framingham risk variables, BMI and Statin Use
TIMP-1 and hsCRP had the highest continuous net reclassification improvement indices when added to the baseline model for 5-year survival (NRI 0.28 and 0.19 respectively, both p<0.0001) and for 10-year survival (NRI 0.19 and 0.11 respectively, TIMP-1 p< and hsCRP p = 0.013).
The integrated discrimination improvement index (IDI) was used as a tool to evaluate the relative strength of variables to predict an outcome of interest. TIMP-1 had the highest IDI indices when added to the baseline model for 5-year survival (IDI 0.027, p<0.0001, table 6) and for 10-year survival (IDI 0.017, p<0.0001) but these were relatively weak changes

15. Conclusions Concerning TIMP-1
TIMP-1 is a biomarker of fibrosis and mortality.
TIMP-1 is the strongest predictor of all-cause mortality and cardiovascular death in this study after age.
TIMP-1 is a stronger predictor of mortality than hsCRP.

16. Importance of Fibrotic Pathways
The metabolic pathways regulating homeostasis and fibrosis in the extracellular matrix are pathologically relevant.
Considering the long time scale on the Kaplan Meier survival curves, there may be a significant opportunity for therapeutic intervention.

17. Fibrosis: A Reversible process?
Fibrosis is a key pathological outcome in many disease states.
The extracellular matrix (ECM) is a highly dynamic structure, constantly undergoing remodeling.
Abnormal ECM dynamics play a role in deregulated cell proliferation and in excessive tissue fibrosis.
Fibrosis contributes to permanent scarring, organ malfunction, heart failure, and death.
25 potential treatments to modulate fibrosis and some are in clinical trials.
Inhibitors of IL-1β (as studied in the CANTOS study) are modulators of inflammation and therefore fibrosis.
Wynn TA et al. Nat Med. 2012;18(7):
Lu P et al. Cold Spring Harb Perspect Biol. 2011;3(12).

18. academic.oup.com/eurheartj
doi: /eurheartj/ehx510
academic.oup.com/eurheartj
Available 8/29/2017
Acknowledgements
Valentin Fuster, MD, PhD, MACC
Jagut Narula, MD, PhD, MACC
Jonathan L. Halperin, MD, MSVM, FACC, FAHA