Coronary Artery Calcification in Type 1 Diabetes (CACTI) Study:

Six-Year Follow-up Results

Marian Rewers, MD, PhDProfessor & Clinical Director

University of Colorado Denver, School of Medicine

Duality of Interest DeclarationI have no conflict of interest in this presentation

but I’d rather be there …

Improving survival among T1 DM patientsAllegheny County IDDM Registry 1965-1999

Nishmura R, et al. Diabetes Care 2001

Duration of diabetes (yrs)

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30 35

1975-79 SMR =235

1970-74 SMR =367

1965-69 SMR =497

Duration of diabetes (yrs)

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30 35

1975-79 SMR =235

1970-74 SMR =367

1965-69 SMR =497

General Population SMR =100

0

10

20

30

20 y 25 y 30 y

1950-59 1960-64 1965-69 1970-74 1975-80

CAD %

Cumulative incidence of Coronary Artery Disease is not improving in T1D

patients diagnosed:

Duration of diabetesPittsburgh EDC Study

0

10

20

30

20 y 25 y 30 y

1950-59 1960-64 1965-69 1970-74 1975-80

CAD %

Cumulative incidence of Coronary Artery Disease is not improving in T1D

patients diagnosed:

Duration of diabetes

Rates should be decreasingif prevention effective

Pittsburgh EDC Study

•

The prevalence of T1D peaks ~50 yrs of age in the U.S.

•

Survival has improved, due to better control of hyperglycemia, hypertension and prevention of ESRD and acute complications.

•

Coronary artery disease rates are not declining and CAD has become the leading cause of death in people with T1D

•

Diabetic women have 10-30 times higher risk of CAD, and diabetic men have 4-10 times higher risk, compared to the general population.

Background

Coronary Artery Calcification (CAC)A Non-invasive Measure of the Extent

& Progression of Coronary Atherosclerosis

35-40 slices through the heart, each 0.1 s, ECG gated

Calcium Score (Agatston) = sum of the area x density of each calcification in all coronary arteries in all slices

Calcium Volume = total volume of calcifications in all coronary arteries

Imatron

C-150 Ultrafast CT

Coronary artery calcification

• tightly correlated with all CVD

risk factors

•

predicts myocardial infarction in short-term follow-up studies

Pletcher MJ, Arch Intern Med 2004Reaven PD, Diabetologia 2005

Budoff MJ et al. A Scientific Statement From the American Heart Association Committee. Circulation 2006, 114:1761-91

Coronary Artery Calcification in Type 1D Study

Baseline examination (N=1,416)

652 T1D pts, at least 10 yr diabetes duration764 non-diabetic controls (spouses, friends)

age 38±9 yr, 52% women, all asymptomatic for CAD

3-yr follow-up examination (N=1,215)

2000-02

2003-04

6-yr follow-up examination (N=1,173)2006-07

Men WomenT1DM300

Controls382

T1DM352

Controls382

HbA1c (%)LDL-c (mg/dl)HDL-c (mg/dl)Age (yr)BMI (kg/m²)Ever smoker

7.8*105*51*3727

28%

5.5 122434027

30%

7.9* 98* 60 3626

31%

5.3105583725

30%

Coronary Artery Calcification in Type 1 Diabetes (CACTI) Baseline Characteristics

Distribution of CAC scores at the baseline

0%

20%

40%

60%

80%

0 0.1-10 10.1-100 100-1000 >1000

T1DM Controls

Agatston units

Prevalence of Coronary Artery Calcification CAC>0

0%20%40%60%80%

100%

20-29 30-39 40-49 50+

Type 1 DM Controls

Age20-29 30-39 40-49 50+

women menage-adjusted OR=4.2 (2.4-7.5) OR=2.3 (1.5-3.7)

Dabelea D, et al. CACTI. Diabetes 2003

Female, asymptomaticDM diagnosis age 8CACTI EBT age 26

Coronary artery calcification (EBT)in a young woman with T1 DM and premature CAD

Coronary Artery Calcification score >400

Coronary Artery Calcification score >400

Female, asymptomatic

Plaque definitionAngiography - lumenIVUS - wall

Coronary artery calcification (EBT)in a young woman with T1 DM and premature CAD

Myocardial Perfusion Evaluation

StressStress

Rest

RMPRLAD = .76CX = .73RCA = 1.1

Basal Mid Apical VLA

Same CACTI patient as above; Reversible perfusion defect present

CACTI definition of CAC progression •

OR 2.5 unit increase in square root transformed calcium volume:0

6 mm3

10

32 mm3

320 %

100

156 mm3

56 %

1000 1164 mm3

16 %

•

Valid across the full range of scores

•

Increase is <1% likely to be due to a measurement error

•

Based on EBT repeated 5 min apart in 1,074 subjects

Hokanson J, MacKenzie , et al. Am J Roentgenol 2004:1327-32

CAC progressors

CAC non-progressors

Duration of follow-up

Cumulative mortality30%

20%

10%

0 1000 2000 3000 4000 5000 days

Dramatic relationship between CAC progression and total mortality (N=4,252; UCLA)

M. Budoff & J. Hokanson, 2009

HR=9.5 (6.9-13)

0

2

4

6

8

10

Baseline 3-Year 6-Year Visit

Squa

re R

oot T

rans

form

ed

CVS

Men T1D WomenT1D Men nonDM Women nonDM

Calcium Volume Score By Diabetes and Sex Age-adjusted, CACTI 2009

CVDevents

Person-years offollow up

Incidence/1,000 person-yrs

2-Year Follow-up

T1D 19 1160 16.3

Non-DM 3 1195 2.5

6-YearFollow-up

T1D 36 3955 9.1

Non-DM 9 4668 1.9

10-YearFollow-up (est)

T1D 69 5995c 11.3

Non-DM 16 7178c 2.2

The number of CVD eventsperson-years at risk, and incidence density

Predictors of progression of coronary calcium volume (n=109) Multivariate logistic regression “best model” Variable Standardized

RR (95% CI) p-value

Age 1.7 (0.7 - 4.6) 0.27 Gender (male vs. female) 3.9 (0.8 - 19) 0.09 Diabetes duration 3.2 (1.3 - 8.3) 0.02 Glycemic control (HbA1c > 7.5%) 7.1 (1.4 - 36) 0.02 Baseline CAC > 0 9.7 (1.8 - 51) 0.01 BMI x insulin dose interaction (RR per .2 unit increase in insulin)

0.02

BMI < 23 0.3 (0.1 - 1.4) 0.13 BMI 23-24 2.4 (0.4 - 13) 0.32 BMI 25-27 6.6 (0.6 - 73) 0.13 BMI >27 7.7 (1.0 - 58) 0.048

Snell-Bergeon et al. Diabetes Care 2003

0.0

0.1

1.0

10.0

100.0

< 6.0 6- 6.8 6.8-7.5 >7.5

HbA1c

OR

for p

rogr

essi

on o

f CA

C

1

10

<7% 7.1-7.7% 7-8-8.4% >8.4%

HbA1c

RR

Relationship between average HbA1c levels and 3-yr CAC progression. Type 1 diabetic patients (N=510)

Adjusted for baseline coronary calcium volume, age, duration of diabetes, gender, waist circumference and hypertension.

2

34

Predictors of rapid progression of coronary calcification Best fitting logistic regression models

all variables simultaneously in the model

TYPE 1 DIABETIC PATIENTS

Predictor RR 95%CI pBaseline CVS 1.1

1.1-1.2 <0.0001

Gender (male) 1.8

1.1-3.0 0.03 Age 1.0

1.0-1.1

0.02

Diabetes duration

1.1

1.0-1.1

0.002Waist circumference 1.0

1.0-1.0

0.07

Hypertension 2.1

1.3-3.5 0.004HbA1c (>8.4% vs. else)

2.4 1.4-4.2 0.002

Apo A-IV 360His

2.3 1.3-4.2

0.006

1

10

<5.3% 5.3-5.4% 5.5-5-6% >5.6%HbA1c

RR

Relationship between average HbA1c levels and 3-yr CAC progression. Non-diabetic controls (N=636):

Adjusted for baseline coronary calcium score, age, gender, BMI.

2

3

T1D patients Non-diabetic participants

Estimate (95% CI) p-value Estimate (95% CI) p-value

Age (yrs) 0.97 (0.50 , 1.44 ) .0001 0.60 (0.37 , 0.84 ) .0000

Male - 0.85 (0.36 , 1.35 ) .0008Diabetes Duration 1.10 (0.63 , 1.57 ) .0000 n/a

Hypertension 1.74 (0.94 , 2.53 ) .0000 1.02 (0.41 , 1.63 ) .0011

HDL Cholesterol - -0.33 (-0.60 , -0.06 ) .0152HbA1c 0.54 (0.08 , 1.01 ) .0224 1.33 (0.48 , 2.19 ) .0023

Baseline CAC>0 3.39 (2.53 , 4.25 ) .0000 2.17 (1.65 , 2.70 ) .0000

Mixed-effects models for 6-yr progression of CACbest models by diabetes status

Novel Predictors of 3-Year Progression of CAC98 progressors vs. 173 controls

Adjusting for age, gender, diabetes and baseline CAC

OR 95% CI p-valuefor doubling of level

AdiponectinsIL-2R

0.34 0.20-0.60 0.00022.09 1.07-4.08 0.03

Maahs D, et al. Circulation 2005; Wadwa P, et al. 2005

Independent of BMI, hypertension, LDL-ch, HDL-ch, smoking, AERCRP, fibrinogen, HbA1c, homocysteine, PAI-1, CD40L

Multiple linear regression model

Beta estimate p-value

IL-6 -0.06 0.003IL-18 0.09 0.014IL-1 ra 0.03 0.046sTNF-α

rII -0.55 <0.001OPG -0.13 0.004MMP3 -0.11 <0.001Uric Acid -0.33 <0.001

Predictors of cysGFR at baseline examination

Multiple Logistic Regression Analysis of Presence of CAC at Year-3 Visit

Odds Ratio (95%CI) p-value

Vitamin D deficiency 2.8 (1.4-5.3) 0.003

Age (per 10 years) 2.3 (1.8-2.9) <0.001

T1D 2.6 (1.6-4.5) <0.001

Male sex 2.7(1.4-5.3) <0.001

* adjusted for season (summer vs. winter) (p=0.21). Adjustment for FoKI and BsmI genotypes did not change these findings; neither polymorphism was associated with presence of CAC

R. Naik

et al. 2009

Predictors of CAC Progression from the 3-yr to the 6-yr visit

Odds Ratio (95% CI) P-value

Vitamin D DeficiencyNo CAC at 3-year visitCAC present at 3-year visit

2.6 (0.97-6.7)0.5 (0.2-1.4)

0.050.54

Age (years) 1.1 (1.04-1.1) <0.001

T1D 2.3 (1.3-4.3) 0.006

Male sex 1.7 (1.1-2.7) 0.02

BMI (kg/m2) 1.1 (1.0-1.1) 0.038

VDR FoKITT vs. CCCT vs. CC

0.53 (0.27-1.03)0.61 (0.38-0.99)

0.0610.048

R. Naik

et al. 2009

Hypertension and dyslipidemia remain poorly controlled in patients with T1D, CACTI, 2000-2002 (n=652)

7%10%

30%

53%

Normal Treated, controlledTreated, uncontrolled Untreated

6%11%

16%67%

Hypertension DyslipidemiaMaahs D, Diabetes Care 2005 Wadwa P, Diabetes Care 2005

• Coronary calcification is 2-4 x more frequent in T1D, compared to non-diabetic controls

• Hyperglycemia, hypertension and abdominal obesity independently predict progression of CAC

• Hyperglycemia, hypertension, dyslipidemia, vit. D status: (A, B, C, D) are not optimally controlled in many patients

• Inflammatory markers - low adiponectin and elevated sIL-2R - are also predictive; CRP levels are of a limited predictive value

Summary of CACTI results

Practical implications• Take care of the ABCD factors:

A1c, Blood pressure, Cholesterol, vit. D

• Screen all asymptomatic diabetic patients older than 30 for increased plaque burden:

high or rapidly increasing CAC score

• Scores higher than 400 (100?) should by followed with myocardial perfusion tests

• Perfusion defect and/or symptoms -> angiography and IVUS

University of ColoradoBarbara Davis Center:Marian Rewers, P.I., Janet Snell-BergeonDavid Maahs, Franziska BishopGreg Kinney, Paul WadwaRam Naik, Satish GargNicole Gendelman, Katherine Pratte

Colorado School of Public Health:John Hokanson, Lorri OgdenDana Dabelea, Kim McFann

Medicine: Robert EckelRobert Quaife, Marcus ChenIrene Schauer, Bryan Bergman

Colorado Heart Imaging: James Ehrlich

Roche Molecular Systems: Suzanne Cheng, Henry Erlich

Univ. Bialystok: Adam KretowskiUniv. Vermont: Russell TracyWake Forest Univ. Ronald PrineasPorto Alegre Univ. Ticiana RodiriguesUniv. de Chile: Rossana RomanUCLA: Matthew BudoffUniv. Pittsburgh: Trevor Orchard

Tina Costacou