Association Between Coronary Vascular …circ.ahajournals.org/content/early/2012/08/23/CIRCULATIONAHA.112...Association Between Coronary Vascular Dysfunction and Cardiac Mortality

DOI: 10.1161/CIRCULATIONAHA.112.120402

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Association Between Coronary Vascular Dysfunction and Cardiac Mortality

in Patients with and without Diabetes Mellitus

Running title: Murthy et al.; Coronary vascular function in diabetes

Venkatesh L. Murthy, MD, PhD1,2; Masanao Naya, MD, PhD1; Courtney R. Foster, BS, CNMT3;

Mariya Gaber, MLA3; Jon Hainer, BSc3; Josh Klein, BA3; Sharmila Dorbala, MD, MPH1,3;

Ron Blankstein, MD1,2; Marcelo F. Di Carli, MD1,2,

1Noninvasive Cardiovascular Imaging Program, Depts of Medicine and Radiology; 2Division of

Cardiovascular Medicine; 3Division of Nuclear Medicine and Molecular Imaging,

Brigham and Women’s Hospital, Boston, MA

Address for Correspondence:

Marcelo F. Di Carli, M.D.

Brigham & Women’s Hospital

ASB-L1 037C

75 Francis St

Boston, MA 02115

Tel: (617) 732-6291

Fax: (617) 582-6056

E-mail:[email protected]

Journal Subject Codes: [7] Chronic ischemic heart disease; [32] Nuclear cardiology and PET; [87] Coronary circulation; [190] Type 2 diabetes

Ron Blankstein, MD1,2; Marcelo F. Di Carli, MD1,2,

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Abstract:

Background - Diabetes increases the risk of adverse cardiac outcomes and is considered a

coronary artery disease (CAD) equivalent. We examined whether coronary vascular dysfunction,

an early manifestation of CAD, accounts for increased risk among patients with diabetes

compared to non-diabetics.

Methods and Results - 2783 consecutive patients (1172 diabetics and 1611 non-diabetics)

underwent quantification of coronary flow reserve (CFR=stress divided by rest myocardial blood

flow) by PET and were followed for a median of 1.4 years (Q1-Q3: 0.7-3.2). The primary

endpoint was cardiac death. Impaired CFR (below the median) was associated with an adjusted

3.2 and 4.9-fold increase in the rate of cardiac death for diabetics and non-diabetics, respectively

(p=0.0004). Addition of CFR to clinical and imaging risk models improved risk discrimination

both diabetics and non-diabetics (c-index: 0.77 to 0.79, p=0.04, and 0.82 to 0.85, p=0.03,

respectively). Diabetic patients without known CAD with impaired CFR experienced a rate of

cardiac death comparable to that for non-diabetic patients with known CAD (2.8 vs 2.0%/year,

P=0.33). Conversely, diabetics without known CAD and preserved CFR had very low annualized

cardiac mortality, which was similar to patients without known CAD or diabetes and normal

stress perfusion and systolic function (0.3 vs. 0.5%/year, P=0.65).

Conclusions - Coronary vasodilator dysfunction is a powerful, independent correlate of cardiac

mortality among both diabetics and non-diabetics and provides meaningful incremental risk

stratification. Among diabetic patients without CAD, those with impaired CFR have event rates

comparable to patients with prior CAD while those with preserved CFR have event rates

comparable to non-diabetics.

Key words: coronary disease; diabetes mellitus; imaging; myocardial perfusion imaging

endpoint was cardiac death. Impaired CFR (below the median) was associated witth h anan aadjjussteted

3.2 and 4.9-fold increase in the rate of cardiac death for diabetics and non-diabeteticicics,s,, rrresesspepepectctctivivivelele yyy

p=0.0004). Addition of CFR to clinical and imaging risk models improved risk discrimination

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eeespppectively).) Diiaiabbbeticcc pppatieenenttsts witithohouuut kknownwnwn CADADAD wwititith h h imimimppaiirreedd CFRFRFR exxpxpeeriencncceedd aaa rraate ofoff

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Introduction

Despite advances in medical therapy, cardiovascular disease remains the leading cause of

mortality among patients with diabetes mellitus1. Indeed, diabetes has been classified as a

coronary heart disease equivalent2. For any degree of myocardial ischemia on non-invasive

testing, diabetics are at considerably higher risk of cardiac mortality than those without diabetes3.

This may be due in part to a higher prevalence of high-risk coronary anatomy among diabetics4.

However, the absence of myocardial ischemia on noninvasive testing in patients with diabetes

does not necessarily identify a lower risk cohort5. This may be related, at least in part, to the

observation that diffuse coronary vascular dysfunction in diabetes precedes overt

atherosclerosis6. Abnormalities of vascular dysfunction may help identify additional high-risk

populations for therapy who are missed by current risk stratification methods. Indeed, there is

growing, consistent evidence that impaired coronary vascular function is associated with adverse

prognosis7–10. However, the link between coronary vascular dysfunction and adverse outcomes

has been established in predominantly non-diabetic populations. Whether the strength of these

associations is maintained in the setting of diabetes is unknown.

This study was designed to test the hypothesis that the presence of impaired coronary

vasodilator function helps explain the observed excess risk of cardiac mortality among patients

with diabetes and to compare the strength of this association with non-diabetics.

Methods

Study Population

All patients referred for rest/stress cardiac PET at the Brigham & Women’s Hospital (Boston,

MA) between January 1, 2006 and June 30, 2010 were included in this study, excluding those

whose images were missing or uninterpretable due to poor image quality (n=254). In cases of

atherosclerosis6. Abnormalities of vascular dysfunction may help identify additiiooonalala hhhigiggh-h-h riririssksk

populations for therapy who are missed by current risk stratification methods. Indeed, there is

grgrowowwinining,g, ccconono ssisttenenent t evidence that impaired corononnararyy vascular funnccctionn iiiss s associated with adverse

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repeat PET scans during the study period, only the earliest evaluable study was included. Patients

with diabetes were identified by interview, medical records and laboratory results (hemoglobin

A1c 6.5% or fasting plasma glucose 126 mg/dl). A combined analysis of all of the diabetic

and non-diabetic patients in this study was previously published11. The study was approved by

the Partners Healthcare Institutional Review Board and conducted in accordance with

institutional guidelines.

Risk Factor Assessment

Demographic factors and key elements of the patients’ history including risk factors and

medication use were ascertained at the time of the study by patient interview and review of

medical records. Diabetic nephropathy was identified based on medical records and laboratory

results (urine total protein 500 mg/dl, spot urine albumin/creatine ratio 30 mcg/mg or 24 hour

urine albumin 30 mg). Microalbuminuria was identified based on spot urine albumin/creatine

ratio 30 mcg/mg or 24 hour urine albumin 30 mg). Diabetic neuropathy and retinopathy were

identified from medical records.

Positron Emission Tomographic Imaging

Patients were studied using a whole body PET-CT scanner (Discovery RX or STE LightSpeed

64, GE Healthcare, Milwaukee, WI) after an overnight fast. Patients refrained from caffeine and

methylxanthine containing substances and drugs for 24 hours prior to their scans. Myocardial

blood flow (MBF) was measured during rest and peak stress using 82Rubidium as a perfusion

tracer, as described previously12. Briefly, after transmission imaging and beginning with the

intravenous bolus administration of 82Rubidium (1,480-2,200 MBq), list mode images were

acquired for seven minutes. Then, a standard intravenous infusion of dipyridamole, adenosine,

regadenoson or dobutamine was given. At peak stress, a second dose of 82Rubidium was injected

medical records. Diabetic nephropathy was identified based on medical records aaand dd lalaaboboborararatototoryry

esults (urine total protein 500 mg/dl, spot urine albumin/creatine ratio 30 mcg/mg or 24 hour

urrininnee e alalalbububumimiminnn 303030 mmg). Microalbuminuria was iiidddennntified based ononn spopoott t uuurine albumin/creatine

aatioo o 30 mcgg/m/mmg orr 2224 4 hohohoururur uuuririnenee aaalblbumummin 330 mmmggg). DiDiDiababeteticici nnneuuuroopapaathhhy y anananddd reretitit nononoppaathhhyy wewewere

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and images were recorded in the same manner. The average radiation exposure per study was 4.6

mSv13,14. Heart rate, blood pressure, and 12-lead electrocardiogram were recorded at baseline

and every minute during and after pharmacological stress.

Image Analysis

Semiquantitative Analysis of Myocardial Perfusion

Semi-quantitative 17-segment visual interpretation of the gated myocardial perfusion images was

performed by experienced observers using a standard 5-point scoring system15,16. Summed rest

(SRS) and stress scores (SSS) were calculated as the sum of individual segmental scores on the

respective images, and their difference was recorded as summed difference score (SDS). These

were converted to percentages of left ventricular myocardium by dividing by the maximum

score, i.e. 68. For each of these variables, higher scores reflect larger areas of myocardial

ischemia and/or scar.

Left Ventricular Systolic Function

Rest and stress LV ejection fraction (LVEF) were calculated from gated myocardial perfusion

images using commercially available software. Left ventricular ejection fraction reserve was

considered present when LVEF increased from rest to stress.

Quantitative Myocardial Blood Flow and Flow Reserve

Absolute MBF (in ml/g/min) was computed from the dynamic rest and stress imaging series

using commercially available software (Corridor4DM; Ann Arbor, Michigan) and previously

validated methods12,17. Automated factor analysis was used to generate blood pool (arterial input

function) and tissue time-activity curves18. Regional and global rest and peak stress MBF were

calculated by fitting the 82Rubidium time-activity curves to a two-compartment tracer kinetic

model as described previously17. Per-patient global coronary flow reserve (CFR) was calculated

were converted to percentages of left ventricular myocardium by t dividing by the e mamaaxixiimumumum m m

core, i.e. 68. For each of these variables, higher scores reflect larger areas of myocardial

sschchhemememiiaia aaandndnd//or r scscscarar.

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as the ratio of absolute MBF at stress over rest for the entire left ventricle. Quantitation of MBF

was performed post hoc by four operators in randomly allocated blocks of approximately fifty

patients. Prior to flow quantification, the intra-class correlation coefficient for CFR among these

four readers on a training set was 0.94 (95% CI 0.88-0.98), indicating excellent reproducibility.

Assessment of Outcomes

The primary outcome was death from any cardiac cause. Patients who died from non-cardiac

causes were censored. Vital status of all patients was ascertained by integrating data from the

Social Security Death Index, the National Death Index and the Partners Healthcare Research

Patient Data Registry. Cause of death was determined by blinded adjudication of hospital records

and death certificates. Early revascularization (within 90 days) was ascertained from the Partners

Healthcare Research Patient Data Registry and hospital records. Mortality from any cause was

used as a secondary endpoint.

Statistical Analysis

Statistical significance was assessed using Wilcoxon tests, Fisher exact and chi-square tests for

continuous, dichotomous and categorical variables, respectively. Two sided p-values < 0.05 were

considered significant. All statistical analyses were performed using SAS 9.3 (SAS Institute Inc.,

Cary, NC).

Multivariable Modeling

The Cox proportional hazards model19 was used to assess the impact of CFR on cardiac mortality

after controlling for the effects of critical covariates. A series of models were developed starting

with the Duke Clinical Score, an index of CAD likelihood and prognosis based on clinical

covariates20. Rest LVEF, combined extent and severity of scar and ischemia, stress-induced

LVEF augmentation (LVEF reserve) and CFR (dichotomized separately at the median values for

and death certificates. Early revascularization (within 90 days) was ascertained frfrrommm tthehehe PPPararartntnere s

Healthcare Research Patient Data Registry and hospital records. Mortality from any cause was

ussededd aaasss aa sesesecocoondddararary y endpoint.

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diabetics, 1.6, and non-diabetics, 1.9) were then sequentially incorporated into the model. In

order to investigate the effects of absolute peak stress MBF, we generated additional models

containing absolute stress MBF instead of CFR. The models were examined for the validity of

the proportional hazards assumption (using time-dependent covariates, standardized score

process plots and the Kolmogorov-type supremum test) and for additive value, taking care to

avoid over-fitting. Survival was plotted using direct adjusted survival probabilities21 from the

Cox survival model.

To assess for biases introduced by early revascularization, analyses were repeated

censoring all patients who underwent early revascularization22. In an exploratory analysis, we

considered the effect of any revascularization, including those >90 days after the PET scan, as a

time-dependent covariate.

Assessment of Incremental Value

Incremental prognostic value of CFR was assessed with the likelihood ratio test to determine the

improvement in prediction power of each sequential Cox model. The Harrell c-index23 and Nam-

D’Agostino calibration statistic24 were calculated for each model. The potential impact of CFR

on risk stratification was assessed by net reclassification improvement (NRI)25 at 2-years using

threshold annual rates of cardiac mortality of 1% and 3%, derived from the ACC/AHA

guidelines for management of chronic stable angina26.

Analysis of Annualized Event Rates

In order to assess the relative prognostic impact of diabetes with that of prior CAD, four groups

were constructed: (1) patients with known prior CAD (history of revascularization or myocardial

infarction) but free of diabetes, (2) patients with diabetes without history of CAD with impaired

CFR, (3) patients with diabetes without history of CAD with preserved CFR and (4) patients

considered the effect of any revascularization, including those >90 days after thehe PPETETET ssscacacan,n,n, aas s a

ime-dependent covariate.

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without diabetes or CAD with normal scans (no perfusion abnormality at stress and rest LVEF

50%). Poisson regression was performed to compute annualize cardiac mortality rates adjusted

for Duke clinical risk score, combined extent and severity of scar and ischemia, rest LVEF and

early revascularization. The hypothesis that diabetes carries CAD equivalent risk only among

patients with decreased CFR was evaluated by comparing annualized cardiac mortality for

groups 1 versus 2, 1 versus 3, and 3 versus 4.

Results

Patient Characteristics

A total of 2783 consecutive patients (1172 with and 1611 without diabetes) met inclusion and

exclusion criteria during the study period and were followed for a median of 1.4 years (first and

third quartiles: 0.7-3.2 years). Baseline characteristics are given in Table 1. The most common

indications for testing were evaluation for chest pain, dyspnea, or their combination.

Approximately half of all studies were normal by semi-quantitative visual analysis.

Patient Outcomes

Mortality from any cause occurred in 279 patients, of which 137 (49.1%) were due to cardiac

causes (Table 2). Kaplan-Meier estimated three-year cardiac mortality was 11.6 and 5.8% for

patients with and without diabetes, respectively (p=0.0003).

The annualized rate of cardiac death increased with increasing extent and severity of

perfusion abnormalities (Figure 1) and was consistently higher for diabetics than non-diabetics

(P=0.02). Furthermore, regardless of ischemia and scar extent, or LVEF, impaired vs. preserved

CFR separated higher and lower risk subgroups in both diabetics and non-diabetics, including

among those with visually normal PET scans (Figure 2).

A total of 2783 consecutive patients (1172 with and 1611 without diabetes) met t inininclclclusssioioion nn ananandd d

exclusion criteria during the study period and were followed for a median of 1.4 years (first and

hhirirrddd quququararrtititilelelesss: 00 7.7.7-3-3.2 years). Baseline characteeriririststiics are given ininn Taablblblee e 1. The most common

nndiiicac tions for r teteestttinngg wewewererere eeevavvaluluatata iioionn ffoor chchheeest ppaaiinn, dydydyspspnnenea,a, ooor ththeieiirrr ccoombmbmbinininatatioioionn.n.

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Patient Outctcomomomeseses



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Unadjusted Correlates of Cardiac Mortality Among Diabetics

Impaired CFR was associated with 6.0-fold (95%CI 3.2-11.0, p<0.0001) and 8.9-fold (95%CI

3.8-20.8, p<0.0001) increased rates of cardiac death among diabetics and non-diabetics,

respectively. Other significant correlates of increased rate of cardiac death included age, male

gender, BMI and prior CAD. Chest pain as a reason for testing and obesity were associated with

a decreased cardiac mortality, possibly reflecting confounding, although other explanations have

also been proposed27. As in prior studies, dyspnea was associated with increased cardiac

mortality among diabetics28, perhaps in part due to a slightly lower LVEF among diabetics with

dyspnea, 54% [Q1-Q3: 40-65%], compared to those without, 56% [Q1-Q3:47-64%], (p=0.053).

Dyspnea was not associated with increased cardiac mortality among non-diabetics. In addition, a

decrease in rest LVEF, as well as increasing burden of scar, ischemia or their combination on

semi-quantitative visual analysis were all significantly associated with increased cardiac

mortality in both patient cohorts.

Multivariable Survival Analysis and Incremental prognostic Value

A series of multivariable models were then constructed to assess the incremental value of CFR

after adjustment for critical covariates known to be associated with increased risk of cardiac

mortality for diabetics and non-diabetics (Table 3). Among diabetics, addition of CFR to a

model including the clinical risk, early revascularization, rest LVEF, a history of nephropathy

and retinopathy, LVEF reserve and the combined extent of ischemia and scar was associated

with a significant increase in global 2 and decrease in Akaike information criterion, indicating

improved model fit and a significant increase in the c-index from 0.77 to 0.79 (p=0.04).

Compared to those with preserved CFR, the fully-adjusted hazard ratio of impaired for cardiac

death was 3.2 (95% CI 1.7-6.2, p=0.0004) (Figure 3). Although the inclusion of peak stress

Dyspnea was not associated with increased cardiac mortality among non-diabeticiccs. IIn adadaddididitititionono , a

decrease in rest LVEF, as well as increasing burden of scar, ischemia or their combination on

eemimimi-q-q-quuauantntntitititaaativve ee vvivisual analysis were all signifficicicananntly associatedd wwith h h ininincrc eased cardiac

mmorrtrtality in bototh hh pppatitiieeent t t cocoohohohorrtrts.s

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MBF alone added incremental prognostic information, the use of CFR resulted in a significantly

better model fit (global 2 of 97.3 vs. 110.6, respectively).

Similarly, for non-diabetics, addition of CFR to a model containing the clinical risk, early

revascularization, rest LVEF, LVEF reserve and the combined extent of ischemia and scar

improved model fit and c-statistic (0.82 to 0.85, P=0.03). The fully adjusted hazard ratio for

cardiac death of impaired CFR was 4.9 (95%CI 2.0-11.5, p=0.0004).

Risk Reclassification

For both diabetics and non-diabetics, addition of CFR to the model resulted in the

reclassification of approximately 1 in 3 patients across clinically relevant categories of risk

(Tables 4-5, NRI 0.171 and 0.214, respectively). More than half of patients at intermediate risk,

between 1 and 3% annual cardiac mortality, were reclassified (NRI of 0.657 and 0.897 for

diabetics and non-diabetics, respectively). Importantly, diabetic and non-diabetic patients who

were downward reclassified from intermediate risk experienced 0.2 and 0.0% annualized cardiac

mortality, respectively (Supplemental Figures 1&2). Improvements in risk reclassification were

also noted after addition of CFR among patients considered low and high risk on the basis of

clinical risk and traditional stress imaging findings.

All-Cause Mortality

Analyses were repeated using mortality from any cause as a secondary outcome and the results

were similar. After adjustment for clinical risk and traditional stress imaging findings, impaired

CFR remained a significant correlate of mortality for both diabetics and non-diabetics and was

associated with 2.0 and 3.4-fold increased rates of death, respectively (p<0.001). Addition of

CFR improved c-indices for both diabetics (p=0.008) and non-diabetics (p=0.03) and with

favorable risk reclassification (Supplemental Table 1).

Tables 4-5, NRI 0.171 and 0.214, respectively). More than half of patients at inntttermrmmededediaiaatetete rrrisisk,,

between 1 and 3% annual cardiac mortality, were reclassified (NRI of 0.657 and 0.897 for

diiababbeteteticicicss ananandd d noon-n-n-didid abetics, respectively). Impororrtatat nnntly, diabetic aandnn nnononon-d-diabetic patients who

wwerrere downwararddd reeecllasasssiiifififiededed fffrrorom m ininntetermrmmediiaattee rissksk expppeererieiencncceded 0.22 aandnn 00.0.00%%% anannunnualalalizizizeddd ccaraardddiac

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Comparison of patients with and without Diabetes mellitus

We sought to determine whether the presence of preserved CFR could separate diabetic patients

without known CAD with favorable prognosis (i.e. comparable to patients without CAD or

diabetes and with normal myocardial perfusion and systolic function) from those with

unfavorable prognosis (i.e. comparable to patients with known CAD with or without diabetes).

Adjusted annualized cardiac mortality was highest in patients with known CAD and diabetes and

lowest among patients with neither diabetes nor known CAD (Figure 4). Diabetic patients

without known CAD showed different annual cardiac mortality rates depending on their CFR.

Those with preserved CFR had a very low annual cardiac mortality that was comparable to

patients without diabetes or CAD with normal stress perfusion and systolic function (0.3 vs.

0.5%/year, respectively, p=0.65) and markedly lower than patients with known CAD (0.3 vs.

2.0%/year, respectively, p=0.015). In contrast, adjusted annualized cardiac mortality in diabetics

without known CAD who exhibited impaired CFR was comparable to that for non-diabetic

patients with known CAD (2.8 vs. 2.0%/year, p=0.33).

Discussion

This study demonstrates that the presence of coronary vascular dysfunction, as assessed by PET,

is an independent correlate of cardiac and all-cause mortality among patients with diabetes

mellitus as well as non-diabetics. We observed that inability to appropriately augment

myocardial blood flow in response to stress identified diabetics and non-diabetics with

substantially higher cardiac mortality (7.6 vs. 1.3%/year and 4.2 vs. 0.4%/year, respectively, both

p<0.0001). Furthermore, identification of coronary vasodilatory dysfunction improved risk

stratification beyond comprehensive clinical assessment, LV systolic function and semi-

patients without diabetes or CAD with normal stress perfusion and systolic funcctitiionnn (0.0.0.3 3 3 vsvsvs..

0.5%/year, respectively, p=0.65) and markedly lower than patients with known CAD (0.3 vs.

2..0%0%%/y/y/yeaear,rr, rreespeeectctctivively, p=0.015). In contrast, adadadjujusted annualizeed dd caarrdididiaac mortality in diabetics

wwithhhouo t knowwn n CACACAD D whwhwhoo exexexhihihibibiteteedd d imimmpppaireeed CFRRR wwaass ccomommppapararaablble too thhaat t fofofor r nonon-n-n dididiababa etetetiicic

papatititienenntstst wwititith h h knknowowown n CACACAD D (222.8.8. vvvsss. 222.0.0%/%/%/yeyeyeaaar, p=p=p 000.3333)).).



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quantitative measures of myocardial ischemia and scar. Indeed, quantitative estimation of

coronary vasodilator reserve in this cohort was able to improve risk stratification in more than

half of both diabetic and non-diabetic patients with intermediate risk based on clinical risk

factors and traditional stress imaging findings. Importantly, diabetic patients without known

CAD with impaired coronary vascular function experienced a rate of cardiac death comparable

to, and possibly higher than that for non-diabetic patients with known CAD. Conversely, the rate

of cardiac death in diabetic patients without known CAD was very low in the presence of

relatively preserved coronary vascular function. These findings may, in part, account for the

inconsistent relationship between diabetes and cardiac risk reported in the literature30–33.

Noninvasive measures of coronary vasodilator reserve integrate the hemodynamic effects

of focal epicardial coronary stenosis, the fluid dynamic effects of diffuse atherosclerosis and the

presence of coronary microvascular dysfunction. As a result, the observed relationship between

impaired coronary flow reserve and prognosis may be due to any or all of these factors

combined. Patients with diabetes may be more likely to have advanced multi-vessel epicardial

coronary disease34. Additionally, diffuse, albeit non-obstructive, atherosclerosis seen in diabetics

is known to be associated with vascular dysfunction35. Finally, microvascular dysfunction is

more prevalent among those with diabetes6. The increased prevalence of all three of these

factors, namely multi-vessel epicardial disease, diffuse disease and microvascular dysfunction,

among diabetics may account, in part, for the relatively worse prognosis of impaired CFR among

diabetics compared with non-diabetics.

Impaired vasomotor function among diabetics may be due to the adverse effects of

hyperglycemia6 and insulin resistance36 on vascular endothelium. Additionally, diabetes

promotes inflammation which also has adverse effects on vascular health37. Similarly, autonomic

Noninvasive measures of coronary vasodilator reserve integrate the hemomoodydydynanamimimicc c efefeffeffects

of focal epicardial coronary stenosis, the fluid dynamic effects of diffuse atherosclerosis and the

prresessenenenccece ooof f f cooroonananaryry microvascular dysfunctionnn.. AsAs a result, the oboo seervrvveeded relationship between

mmppapaired corononararryy flflowow rrresesesererrvevve aandndnd pprrrogggnosssisss maaayy be dduueue ttto ananyyy ooror aallllll oof f ththhesesee fafafactcttororo sss

coombmbmbininineded.. PaPaPatitienenntsss wwiitithh h didiabbbeteteteseses mmmayay bbe ee momomoreee llikikkelelly y totot hhhavavave e adada vvavancncncededd mmmululltitit -v-vvesessesesell l epeppicccararddiialll

coronary disseaeaasesee343434.. AdAdAddidid tionononalaa lyyy,,, dididifffff ususse,e,e aaalblblbeieie t t t nononon-n-n obobstststruruructctctivivve,e,e, aaththhererrosososclclclerererosossisisis ssseeeeeen n n in diabeticsss



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13

dysfunction has been associated with both increased risk38 and impaired coronary vascular

function39. Coronary flow-reserve measures integrate the adverse effects on the vasculature due

to all of these pathways which may also be relevant to non-diabetics.

Among diabetics without apparent myocardial ischemia or scar on visual evaluation of

myocardial perfusion images, 63% had preserved CFR. Among these patients, cardiac mortality

occurred at an extremely low rate (0.4%/year), comparable to rates for non-diabetic patients with

visually normal scans in our study and previously reported in the literature. Thus the excess

cardiac mortality seen in diabetic patients with visually normal stress testing is due to a relatively

small subgroup of these patients who also have severely impaired coronary vasodilator function.

Conversely, the extremely high cardiac mortality rates (3.5%/year) seen in those diabetics

despite the absence of overt ischemia or scar, suggests that patients with diffuse epicardial

atherosclerosis and/or microvascular dysfunction carry a prognosis comparable to those with

obstructive epicardial stenosis. This observation was confirmed by comparing adjusted

annualized cardiac mortality among all diabetics without history of CAD who had preserved

CFR, including those with abnormal scans, with non-diabetics without CAD, myocardial scar,

ischemia or systolic dysfunction, showing that diabetes itself in the absence of vasodilator

dysfunction is not associated with excess cardiac mortality. This finding has implications for the

classification of diabetes as a coronary disease risk equivalent2. Specifically, only among

diabetics with impaired vascular function is prognosis comparable to non-diabetic patients with

known CAD. Differing levels of vascular health among previously studied cohorts may account

for inconsistencies in the relative mortality rates of diabetics without CAD and non-diabetics

with CAD30–33. The therapeutic implications of the observation that diabetics with impaired CFR

have “CAD-equivalent” rates of cardiac death while those diabetics with preserved CFR have

Conversely, the extremely high cardiac mortality rates (3.5%/year) seen in those e ddidiababa eteteticicics ss

despite the absence of overt ischemia or scar, suggests that patients with diffuse epicardial

attheheerororosscsclelelerororosisis s ananndd/d/or microvascular dysfunctiononn caarry a prognossisisi comommppaparable to those with

obbststtrur ctive eppicicaaardddialall steteenononosisisiss.s. TThhihiss s obobbsseervaaatiioon wwwaaas cccononnfifirmrmrmeded bbyyy cocompmpmpararininingg g adaddjujujuststs ededed

annnnunualalalizizi ededd cccaarardidiacacc mmooorttaalilityy aammmonononggg alalll l dididiabababetticicics s wiwiwiththouououttt hihihistttororo y y y ofoff CCCADADAD wwhohoho hhadadd pppreesseserrvrvededd rrr

CFR, includiingngng tthohohoseses wwwiti h h abababnonoormrmrmalalal scacacansnsns,, wiwiwiththth nnnononon-d-diaiaabebebetititicscscs wwwiti hohohoututut CCCADADAD,, mymymyocococararardid al scar,



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14

extremely favorable prognosis is uncertain and deserves further investigation. Specifically,

whether impaired CFR can identify diabetics who will benefit from aspirin or other medical

interventions with conflicting evidence among diabetics may warrant further exploration.

The current study is a single-center, non-randomized, observational study and carries all

of the inherent limitations of that study design. As such, it is likely that some amount of residual

confounding remains, despite careful adjustment for clinically relevant covariates. On the other

hand, compared to data derived from patients selectively enrolled in a randomized trial, these

data, with very limited exclusion criteria, may be more representative of patients seen in routine

clinical practice. Follow-up was relatively limited and with longer observation periods, the

favorable prognosis of those with preserved CFR may not prove to be durable. Finally, we are

not able to evaluate the downstream impact of CFR on patient management decisions as referring

clinicians were not informed of CFR results in clinical reports. However, this reduces bias in

estimates of CFR effect size introduced by subsequent treatment decisions based on this result.

Conclusion

In summary, among both patients with and without diabetes, assessment of coronary vasodilator

function provides incremental risk stratification beyond routine measures of clinical risk,

including estimates of LV systolic function and the extent and severity of myocardial ischemia

and scar, and results in a meaningful risk reclassification of 1 in 3 patients with known or

suspected CAD. Furthermore, the nearly two thirds of diabetic patients without overt myocardial

ischemia or scar who also have relatively preserved coronary vasodilator capacity have an

extremely low rate of cardiac mortality (0.4%/year). The presence of abnormal CFR identified

diabetic patients without overt CAD who experience a rate of cardiac death at least as high as

favorable prognosis of those with preserved CFR may not prove to be durable. FFiininalala lylyy,, wewewe aaarere

not able to evaluate the downstream impact of CFR on patient management decisions as referring

cllininniciciciaiaiansns wwwerre nononottt ini formed of CFR results in ccclllinini iiical reports. HoHooweveveerr,r, this reduces bias in

estiimmam tes of CCFRFRFR eeefffffeecct sisisizeze iiinntntrrododduucuceedd bby ssusubbbseqqquuenttt ttrrereatattmmementntt dddeccisisi ioi nsns bbbaasaseded ooon n n thththis rrresesululu tt.t

Conclusion



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(and possibly higher than) that for non-diabetic patients with known CAD. These findings may

provide a pathophysiologic explanation for the inconsistencies in studies comparing mortality

rates of diabetics without CAD and non-diabetics with CAD.

Funding Sources: The study was funded in part by grants from the National Institutes of Health

(RC1 HL101060-01 and T32 HL094301-01A1).

Conflict of Interest Disclosures: Dr. Di Carli receives research grant support from Toshiba.

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3663 . QuQQ iñoness MJJJ, HHerrnanaanddeezez--P-Pamampapalooonnii M,, SSScheeelbbbert t H,H,H, BBBuullnees-s-EEnriquququezez I, , Jiimeeeneneez z X,X, HeHeernrnnanana dedez z G,G,G, DDee LLaLa RRRoososa a R,R,R, CCChohohonn n YY,Y, YYYanannggg H,H,H, NNNicchchooolaasas SSSB,B,, MMMododdililleveve skskky y TT,T, YYYu u K,K,K, VVaanan HHererrleee KKCCaststellellalalaninini LLLW,W, EEEllalashshhoffoffff RR,R HHssueueuehh h WAWA. CoCoCorororonanaryry vvasasomommototoror aabnnorormamalililitititieses iin n inininssusulilin-n-rere isisi tststananttt ndididi iividddu llals. AAnAnn. III tntern n MMMedd.d. 2220000004;4;4;1414140:0:0:70707000-0-70707088.8.



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Table 1. Patient Characteristics

Variable Non-Diabetics (N=1611)

Diabetics (N=1172)

All Patients (N=2783)

p-Value

Demographics Age (y) 64.3 [55.3-76] 65.4 [57.3-74.1] 64.8 [56.1-75.2] 0.63 Male gender 747 (46.4) 586 (50) 1333 (47.9) 0.06 Hispanic 134 (8.3) 169 (14.4) 303 (10.9) <0.0001 Race <0.0001 White 1134 (70.4) 628 (53,6) 1762 (63.3) Black 208 (12.9) 245 (20.9) 453 (16.3) Other/Unknown 269 (16.7) 299 (25.5) 568 (20.41) Risk Factors BMI (kg/m2) 27.5 [24.4-32.3] 30.9 [26.5-37.6] 28.8 [25.1-34.4] <0.0001 BMI 30 kg/m2 548 (34) 656 (56) 1204 (43.3) <0.0001 Hypertension 1207 (74.9) 1064 (90.8) 2271 (81.6) <0.0001 Dyslipidemia 966 (60) 887 (75.7) 1853 (66.6) <0.0001 Family history of CAD 471 (29.2) 285 (24.3) 756 (27.2) 0.004 Tobacco Use 175 (10.9) 119 (10.2) 294 (10.6) 0.57 Duke Clinical Risk (%) 37.8 [14-74.4] 61.7 [28-86.7] 47.9 [18.7-81.1] <0.0001 Diabetes Complications Retinopathy 0 (0) 85 (7.3) 85 (3.1) <0.0001 Nephropathy 0 (0) 178 (15.2) 178 (6.4) <0.0001 Microalbuminuria 53 (3.3) 206 (17.6) 259 (9.3) <0.0001 Neuropathy 0 (0) 157 (13.4) 157 (5.6) <0.0001 Diabetes Control HbA1c Unknown 1356 (84.2) 577 (49.2) 1933 (69.5) <0.0001 HbA1c 5.8 [5.6-6.1] 7.1 [6.4-8.4] 6.5 [5.9-7.7] <0.0001 Medications Aspirin 911 (56.5) 794 (67.7) 1705 (61.3) <0.0001 -adrenergic blockers 940 (58.3) 822 (70.1) 1762 (63.3) <0.0001 Cholesterol agents 880 (54.6) 843 (71.9) 1723 (61.9) <0.0001 Insulin 0 (0) 443 (37.8) 443 (15.9) <0.0001 Oral hypoglycemic agents 3 (0.2) 281 (24) 284 (10.2) <0.0001 Ca-channel blockers 303 (18.8) 318 (27.1) 621 (22.3) <0.0001 ACE inhibitors 514 (31.9) 590 (50.3) 1104 (39.7) <0.0001 Nitrates 160 (9.9) 196 (16.7) 356 (12.8) <0.0001 Diuretics 476 (29.5) 549 (46.8) 1025 (36.8) <0.0001 Indications Chest Pain 753 (46.7) 553 (47.2) 1306 (46.9) 0.82 Dyspnea 457 (28.4) 395 (33.7) 852 (30.6) 0.003 Post-MI 136 (8.4) 115 (9.8) 251 (9) 0.23 Pre-operative 251 (15.6) 157 (13.4) 408 (14.7) 0.12 Cardiovascular History Any prior CAD 569 (35.3) 606 (51.7) 1175 (42.2) <0.0001 Recent MI ( 30 days) 156 (9.7) 160 (13.7) 316 (11.4) 0.001 Remote MI (>30 days) 248 (15.4) 263 (22.4) 511 (18.4) <0.0001 Prior PCI 286 (17.8) 326 (27.8) 612 (22) <0.0001 Prior CABG 160 (9.9) 209 (17.8) 369 (13.3) <0.0001 Cerebrovascular Disease 82 (5.1) 88 (7.5) 170 (6.1) 0.01 Peripheral Vascular Disease 83 (5.2) 98 (8.4) 181 (6.5) 0.0008 Early Revascularization ( 90 days post-PET)

120 (7.4) 115 (9.8) 235 (8.4) 0.03

Duke Clinical Risk (%) 37.8 [14-74.4] 61.7 [28-86.7] 47.9 [18.7-81.1] <<0.0.00000101 Diabetes Complications Retinopathy 0 (0) 85 (7.3) 85 (3.1) <0<0<0.00.0000000111 Nephropathy 0 (0) 178 (15.2) 178 (6.4) <0.0001 Microalbuminuria 53 (3.3) 206 (17.6) 259 (9.3) <0.0001

NNeueuroroopapapathhy y y 0 (0) 157 (13.4) 11157575 (5.6) <0.0001 DiDiiabababeetetese CCConono ttroll

HHHbbA1c Unknkknoownwnwn 1313565656 (((8484 2.2.2)) ) 577777 (4(4(499.9.2)2 1193939333 (669.9.5)5)5) <<<0.0..00000001010 HHHbA1c 5.5..8 8 [[5..66-6.111] 7.11 [6[6[6.44--8- .44. ]] 6.66.5 5 [[5.999-7-7-7.7.7]] <0<00.0.00000 111

MeMeMedid cations AAAspspspiririn 9199111 (5(556.6.5)5)5) 7799494 ((67677.77))) 171770055 ((6616 .33.3) ) <<0<0.0.00000111

-aa-adrdrdrenenenererergigic c blblblococockkekersrsrs 94940 0 (5(5(588.8.3)3)3) 88222222 (((77070 11.1))) 171717626262 ((63633 3.3.3)) ) <0<0<0.0.00000000111CCChohoholelelestststerererololol aaagegegentntntsss 888888000 (5(5(5444.6)6)6) 888434343 (((717171 99.9))) 171717232323 (((616161 99.9))) <0<0<0 00.0000000111

Insulin 000 (0(0(0))) 4444 333 (3(3(37.7.7.8)8)8 444434343 (((15151 .9.99))) <0.0001 OO ll hh ll ii t 33 (0(0 22)) 282811 (2(24)4) 228484 ((1010 22)) 0<0 00000011



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Imaging Parameters Rest LVEF 60 [50-67] 56 [45-64] 58 [48-66] <0.0001 Stress-induced LVEF 1283 (79.6) 864 (73.7) 2147 (77.1) 0.0003 Ischemia + Scar (%) 0 [0-8.8] 2.9 [0-14.7] 0 [0-10.3] <0.0001 Ischemia (%) 0 [0-2.9] 0 [0-7.4] 0 [0-5.9] <0.0001 Scar (%) 0 [0-1.5] 0 [0-4.4] 0 [0-2.9] <0.0001 Global CFR 1.87 [1.43-2.35] 1.58 [1.24-2] 1.73 [1.34-2.22] <0.0001 Stress Global MBF (ml/g/min)

1.97 [1.37-2.73] 1.6 [1.12-2.19] 1.8 [1.23-2.52] <0.0001

Rest Global MBF (ml/g/min) 1.04 [0.81-1.35] 0.96 [0.74-1.31] 1.01 [0.78-1.33] <0.0001 Impaired CFR 852 (52.9) 600 (51.2) 1452 (52.2) 0.4

Continuous variables are presented as median (first and third quartiles). Dichotomous variables are presented as number (%). Patients whose LVEF at stress was greater than that at rest were considered to have positive stress-induced increase in LVEF. BMI = body mass index. ACE = angiotensin converting enzyme. MI = myocardial infarction. CAD = coronary artery disease. HbA1c = Hemoglobin A1c. PCI = percutaneous coronary intervention. CABG = coronary artery bypass graft. LVEF = left ventricular ejection fraction. CFR = coronary flow reserve. MBF = myocardial blood flow.

Table 2. Causes of Death

Cause of Death Impaired CFR (n=600)

Preserved CFR (n=572)

All Patients (n=1172) p-Value

Diabetics Cardiac, n (%/year) 66 (7.6) 12 (1.3) 78 (4.3) <0.0001 Any Cause, n (%/year) 104 (11.9) 34 (3.5) 138 (7.5) <0.0001

Non-Diabetics Impaired CFR (n=852)



Cardiac, n (%/year) 53 (4.2) 6 (0.4) 59 (2.3) <0.0001 Any Cause, n (%/year) 117 (9.3) 24 (1.8) 141 (5.4) <0.0001

Table 2. Causes of Death

Cause of Death Impaired CFR (n=600)



Diababeteteticicics s s CCCararardidiaca , n n (%(%(%/yyeaear)r) 66 (7.6) ) 12 ((1.3) 778 8 (4.3) ) <0.0001 AAAnny Causee, nn (%(%(%/y/y/yeaeaar)r)r) 101004 4 4 (1(1(11.9)9)9) 334 44 (3(3(3 5.5.5) ) ) 13131388 (77.5.55))) <0<0<0 0.0.0000000111

NoNoNon-n--DiDiD aba eticcss s Immmpapap iireedd CFRFRFR (n(n==8=8525252)))

PPrPresessererervveveddd CCCFRR (n(n( =7=7=759559) )

AAlll PaPattiiennntss (((n==1=1616111)1)1) p--VaValuluue

CCarardididiac, , n n (%%/y/yea )r)r) 53 (((4.44.222)) 66 (0(0(0.4.4))) 5959 (((2.2 3)3) <0.0.00000101 AAAny CCCause,, n (%(%(%/y/y/year)r)r) 11111177 7 (9(9(9 33.3))) 242424 (((111.8)8)8) 111414141 (((555.4)4)4) <<000.000000010101



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Table 3A. Multivariable Survival Analysis in Diabetics. Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Fit Statistic p-value Fit Statistic p-value Fit Statistic p-value Fit Statistic p-value Fit Statistic p-value Fit Statistic p-value Global 2 43.88 ref 47.81 <.0001 81.89 <.0001 94.69 <.0001 95.74 n/s 110.62 <.0001

AIC 988.47 ref 986.54 <.0001 954.45 <.0001 943.66 <.0001 944.61 n/s 931.73 <.0001

Adj Calibration 2 16.57 0.06 15.32 0.08 4.87 0.85 9.41 0.4 3.57 0.94 5.74 0.77

c-index 0.70 (0.64-0.76) ref 0.71

(0.65-0.77) 0.57 0.76 (0.71-0.81) 0.02 0.77

(0.71-0.83) 0.6 0.77 (0.71-0.83) 0.98 0.79

(0.74-0.85) 0.04

Covariate HR p-value HR p-value HR p-value HR p-value HR p-value HR p-value Duke Clinical Risk (%)

1.01 (1.01-1.02) 0.001 1.01

(1.01-1.02) 0.0009 1.01 (1.00-1.02) 0.1 1.00

(0.99-1.01) 0.42 1.00 (0.99-1.01) 0.46 1.00

(0.99-1.01) 0.45

BMI (kg/m2) 0.92 (0.89-0.96) <0.0001 0.92

(0.89-0.96) <0.0001 0.94 (0.91-0.98) 0.003 0.94

(0.90-0.97) 0.001 0.94 (0.90-0.97) 0.001 0.94

(0.91-0.98) 0.003

Early Revascularization

1.09 (0.57-2.07) 0.8 1.11

(0.58-2.12) 0.75 0.97 (0.51-1.85) 0.93 0.66

(0.33-1.29) 0.23 0.64 (0.33-1.27) 0.2 0.60

(0.30-1.18) 0.14

Nephropathy/ Retinopathy 1.70

(1.03-2.81) 0.04 1.74 (1.05-2.88) 0.03 1.86

(1.12-3.09) 0.02 1.91 (1.14-3.18) 0.01 1.62

(0.96-2.72) 0.07

Rest LVEF (%) 0.96 (0.94-0.97) <0.0001 0.97

(0.95-0.98) 0.0002 0.97 (0.95-0.98) 0.0001 0.97

(0.95-0.99) 0.0005

Scar+Ischemia (%) 1.03 (1.01-1.05) 0.0002 1.03

(1.01-1.05) 0.0006 1.02 (1.01-1.04) 0.01

LVEF Reserve 0.78 (0.49-1.25) 0.3 0.91

(0.56-1.47) 0.69

Impaired CFR 3.23 (1.68-6.20) 0.0004

(0.64-0.76) (0.65-0.77) (0.71-0.81) (0.71-0.83) (0.71-0.83) (0(0.7.74-4-0.0 85)

HR p-value HR p-value HR p-value HR p-value HR pp-v-valala ueueue HHHRR R al Risk 1.01

(1.01-1.02) 0.001 1.01 (1.01-1.02) 0.0009 1.01

(1.00-1.02) 0.1 1.00 (0.99-1.01) 0.42 1.00

(0.99-1.01) 0.46 1.00 (0.99-1.01)

2) 0.92 (0( .89-0.0 969 ) <0.0001 0.92

(0.89-0.96) <0.0001 0.94 (0.91-0.98) ) 0.003 0.94

(0.90-0.97) 0.001 0.94 (0.90-0.97) 0.001 0.94

(0.91-0.98)

zzatattioioionn111.090909

(0. 75757-2-2- 00.07) 0.0 8 1.11 (0.58-2.12) 0.75 0.97

(0.51-1.85) ) 0.93 0.66 (0.33-1.29) 0.2323 0.64

(0.33-1.27) 0.2 0.60 (0.30-1.18)

hhy/y/y/ y y 1.1.700

(1(1( 0.03-3-3 2.2.2.818181)) ) 0.0.040404 1.1.7474 (1(1 0.0.05-55 ..2 8888) ) 0.03 1.1 86

(1(1(1.1. 2-2-2-3.090909))) 0.00 02022 1.1.9191 (1.1.14-4-4-3.3.3.181818)) ) 0.0.01011 1.1.6262

(0(0(0.9.96-6-2.2 72)

(%(( )) 0.00 6696 (0.94-44 ..0 97) 0<0< .0 000111 0.97979

(((0.0.0 95-0- .9 ))8 0.00 220 0..97979 0(0.9. -5-5-0.98) .0 000 100 0.0 7979

(0 99.95-0.00 99)

imimia a a %%(%) ) 1.0300 (1(1(1.0.0 -1-1.1.1 05050 ))) 0..0000 220 1.0300

(1(1 0.0.01-1 1.1.0505) ) 0.0.0 00 600 1.1 2020 (1(1 0.0.01- .11 040 )

rve 00.787878 ((0.49-1.25)5) 0.0.3 3 000.919191

((0.0 565 -1.447)7

FR 3.23 (1.68-6. 020))



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Table 3B. Multivariable Survival Analysis in Non-Diabetics. Model 1 Model 2 Model 3 Model 4 Model 5 Fit Statistic p-value Fit Statistic p-value Fit Statistic p-value Fit Statistic p-value Fit Statistic p-value Global 2 48.08 ref 86.44 <0.0001 93.99 0.006 93.99 0.96 112.11 <0.0001 AIC 781.22 ref 744.86 <0.0001 739.31 0.02 741.31 N/S 725.19 <0.0001 Adj Calibration 2 9.90 0.36 5.61 0.78 12.49 0.19 13.31 0.15 8.84 0.45 c-index 0.75 ref 0.80 0.1 0.82 0.02 0.82 0.4 0.85 0.03 Covariate HR p-value HR p-value HR p-value HR p-value HR p-value Duke Clinical Risk (%)

1.02 (1.02-1.03) <0.0001

1.02 (1.01-1.03) 0.0007

1.01 (1.00-1.02) 0.01

1.01 (1.00-1.02) 0.02

1.01 (1.00-1.02) 0.03

BMI (kg/m2) 0.93

(0.88-0.98) 0.004 0.95

(0.91-1.00) 0.05 0.95

(0.90-1.00) 0.05 0.95

(0.90-1.00) 0.05 0.95

(0.90-1.00) 0.05 Early Revascularization

0.96 (0.43-2.15) 0.92

0.71 (0.32-1.59) 0.4

0.50 (0.21-1.17) 0.11

0.50 (0.21-1.17) 0.11

0.48 (0.21-1.12) 0.09

Rest LVEF (%) 0.95

(0.94-0.97) <0.0001 0.96

(0.94-0.98) <0.0001 0.96

(0.94-0.98) <0.0001 0.97

(0.95-0.99) 0.0006

Scar+Ischemia (%) 1.03

(1.01-1.04) 0.006 1.03

(1.01-1.04) 0.008 1.02

(1.00-1.04) 0.01

LVEF Reserve

0.99

(0.55-1.77) 0.96 1.06

(0.59-1.88) 0.85

Impaired CFR

4.85

(2.04-11.54) 0.0004 Summary of characteristics of nested models. *P-values for fit statistics are for comparison of each model to the next simpler model (e.g. model 5 vs. model 4). C-indices and calibration statistics are calculated for 2-year event data. Global 2 = likelihood ratio chi-squared statistic for the entire model. AIC = Akaike Information Criterion. LVEF = left ventricular ejection fraction. CFR = coronary flow reserve.

HR p-value HR p-value HR p-value HR p-value e HRHRH pp-v

cal Risk 1.02 (1.02-1.03) <0.0001

1.02 (1.01-1.03) 0.0007

1.01 (1.00-1.02) 0.01

1.01 (1.00-1.02) 0.02

1.1.010101 (1.00-1.02) 0.

2)0.93

(0.88-0.98) 0.0040.95

(0.91-1.00) 0.050.95

(0.90-1.00) 0.050.95

(0.90-1.00) 0.050.95

(0.90-1.00) 0.

izzatatioioionn0.0.969

(((0.0.0.43434 -2- .1 )5)5) 0.92 0.71

(0.32-1.59) 0.40.0 505050

(0 2.2.21-11 .1 17) 0.11 0.500

(0(0.2. 1-1.1.171717)) ) 0.11 0.48

(0.21-1.12) 0.

(%(( ) ) 0.0.9595

(0(0( .9.94-4-4-0.0.0.979797))) <0<0<0.0.0.000001110.0 696

(0(0.9 -44 .0 989 ) <0<0<0 0.0.000000 10.9696

(0.00.949494-0-0 9.9.98)8)8) <<0.0..000000010101 0.979

((0.0.959595 0-0-0.9.99)9)9) 0.0

mimm a (%( ) 1.0. 33

(1.0 -11 .1 040 ) 00.0.0000 6 61.0303

1(1.0. 1-1.0400 ) .0 000 8 .1.1 202

((1.1 0000-1 0. 4) 0.

errveveve 0.99

0(0.5.55-5-5 1.1.7777 ) 0.96969 ..1 606

(0( .59-99 .1 8888) ) 0.

CFRR 44.4.858585

(22 0.04-4 111 5.54)4) 00.00characteristics of neststed models. *P-values for fit statistics are for r coc mpparison of each model ttoo ththe next simplp er momodel (e.gg. model 5 vs. model 4)). C-indices and ca iil bcalculated for 2-yeararr eeveveventntnt dddatatata.a.a GGGloolobabab ll l 2 = lllikikikelelelihihi oodd d raraatitio o chchi-sqsqquauau rered d d sts atatisistitic c fofofor r ththhe e enenentit ere mmmododod lell. . AIAIA C CC == AkA aiaikekeke IIInfnfnforro mmmatatatioioion n CrCrittiterere iioion.n. LLVEVEVEF F F = left ventricular eeej

RR == cocororonanaryry ffloloww rereseservrvee



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Table 4. Risk Reclassification.

Model without CFR Model with CFR Diabetics <1% Annual Risk 1-3% Annual Risk >3% Annual Risk Total Patients with Cardiac Death <1% Annual Risk 1 (23.2) 3.3 (76.8) 0 (0) 4.3 1-3% Annual Risk 1 (5.4) 8.7 (46.4) 9 (48.1) 18.7 >3% Annual Risk 0 (0) 6.2 (8.8) 64 (91.2) 70.2 Total 2 18.2 73 93.2 Patients without Cardiac Death <1% Annual Risk 197 (75.9) 62.7 (24.1) 0 (0) 259.7 1-3% Annual Risk 177 (39.3) 191.3 (42.5) 82 (18.2) 450.3 >3% Annual Risk 0 (0) 86.8 (23.5) 282 (76.5) 368.8 Total 374 340.8 364 1078.8 Non-Diabetics <1% Annual Risk 1-3% Annual Risk >3% Annual Risk Total Patients with Cardiac Death <1% Annual Risk 7.4 (32.2) 15.5 (67.8) 0 (0) 22.9 1-3% Annual Risk 2.4 (8.8) 17.2 (62.7) 7.8 (28.5) 27.4 >3% Annual Risk 0 (0) 2.3 (8.6) 24.9 (91.4) 27.3 Total 9.8 35.1 32.7 77.6 Patients without Cardiac Death <1% Annual Risk 890.6 (78.3) 246.5 (21.7) 0 (0) 1137.1 1-3% Annual Risk 106.6 (34.2) 149.8 (48.1) 55.2 (17.7) 311.6 >3% Annual Risk 1.0 (1.2) 14.7 (17.3) 69.1 (81.5) 84.7 Total 998.2 410.9 124.3 1533.4 Reclassification table for censored data using method of Steyerberg and Pencina29 from 2-year event data. Parentheses indicate percentages of each pre-test category reclassified to each post-stress category. CFR=coronary flow reserve. Table 5. Comparison of Performance of CFR in Diabetics and Non-Diabetics

Diabetics (N=1172) Non-Diabetics (N=1611) Cardiac Mortality Annualized Cardiac Mortality 4.3% 2.3% HR for CFR 3.23 (1.68-6.20) 4.85 (2.04-11.54) C-Index 0.794 (0.740-0.849) 0.852 (0.804-0.900) Continuous NRI 0.611 (0.367-0.830) 0.822 (0.637-0.992) NRI (1 and 3%/yr) 0.171 (0.034-0.312) 0.214 (0.050-0.375) NRI (1 and 3%/yr), Intermediate

Risk Stratum 0.657 (0.293, 1.029) 0.897 (0.561,1.222)

IDI 0.016 (0.005-0.027) 0.019 (0.005-0.034) Relative IDI 0.130 (0.039-0.217) 0.154 (0.043-0.268) Comparison of prognostic performance of CFR. Estimates for HR, c-index, NRI and IDI are adjusted for Duke clinical risk score, BMI, nephropathy/retinopathy (diabetics only), rest LVEF, combined extent and severity of scar and ischemia and LVEF reserve. CFR=coronary flow reserve. HR=hazard ratio. NRI=net reclassification improvement. IDI=integrated discrimination improvement. Similar data for all-cause mortality are available in the Supplemental Table 1.

( ) ( ) ( )1-3% Annual Risk 2.4 (8.8) 17.2 (62.7) 7.8 (2(2288.8.5)5)5) 22277.4>3% Annual Risk 0 (0) 2.3 (8.6) 24.99 (9(9(91.11.4)4)4) 22277.7 333Total 9.8 35.1 32.7 77.6

Patients without Cardiac Death <<<1%1%1% AAAnnnuauaall RiR sk 890.6 (78.3))) 246.5 (21.7) 0 (0) 1137.1-33% %% AAAnnunualal RRisisk k 101 6.6.6 6 (3(34.4.2)2) 114949.8 ((48488.11) ) 5555.2.2 ((1717.77) ) 31311.6>3%% AAnnnnnuauaual RiRiRisksk 11.0.0 ((11.1 22)2) 1144.4 77 7 (1(117.77 3)3)) 66699.9.11 (8(8(81.1.1 5)5)5) 8884.7Total 998.2 4110.999 1244.3. 1155333.

ReReclclclasasssisis fificaatitiononon ttabablle fforor cccennnsosorer d d d daaatatata uuusisisingng mmmeetethohooddd oofof SSteteeyeyeerbrberere g g g ananndd PePePencnccininaaa29 ffrroomm m 2-2-yyeyeaarar eeveveentntnt daatta.. PPareentnthehehesesesesss iinindidicacattete pperercece tntntagagees oof f eaeaachchch ppre-teeststst cccatategege oory y rere lclclasassisiifififi deded tto o eaeachhh pposost-t- tststreressss ccatategeggorororyy. CCFRFRFR=c=cororononararyy lllowowow rrreseseserererveveve.



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Figure Legends:

Figure 1. Effect of Diabetes and Perfusion Abnormalities on Cardiac Mortality. Unadjusted

annualized cardiac mortality in categories of total extent of myocardial ischemia and scar for

patients with and without diabetes. Even after accounting for the extent and severity of ischemia

and scar, patients with diabetes experienced higher rates of cardiac mortality than those without

diabetes.

Figure 2. Effects of CFR and Traditional MPI Findings on Cardiac Mortality. Unadjusted

annualized cardiac mortality for patients with diabetes (panels A-D) and without (panels E-H)

by in categories of total extent of myocardial ischemia and scar (panels A&E), total extent of

myocardial ischemia (panels B&F), total extent of myocardial scar (panel C&G) or left

ventricular ejection fraction (panels E&H) and impaired (red) versus preserved CFR (blue). The

annual rate of cardiac death increased with increasing extent of ischemia and scar, decreasing

LVEF and CFR. Importantly, lower CFR was consistently associated with higher rates of cardiac

mortality regardless of the level of ischemia, scar extent or LVEF.

Figure 3. Cardiac Mortality Incidence of cardiac mortality for patients with diabetes (panels

A&B) and without diabetes (panels C&D), with impaired (red) or preserved (blue) coronary

flow reserve (CFR) presented in Kaplan-Meier form (panel A&C) showing significantly

increased cardiac mortality with impaired CFR (p<0.0001) which continued after adjustment21

for Duke clinical risk score, BMI, nephropathy/retinopathy, early revascularization, rest left

annualized cardiac mortality for patients with diabetes (panels A-D) and withoututt (papp nenenelslss EEE-H--H) )

by in categories of total extent of myocardial ischemia and scar (f panels A&E), total extent of

mymyyoococararardididialalal iiissscheheemmimiaa (panels B&F), total extent tt ofofo myocardial sccaraa (papaannenel C&G) or left

vvenntntrir cular ejecectititionnn fffrraactcttioioionn n (((ppapaneneelslss EE&&&H) anannd immmpppairrrededd ((rrredd)d) vvverrsuuss pprpresesererrvevved d CFCFCFR R R ((b( lululue))e).. TTThe

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25

ventricular ejection fraction (LVEF), extent of myocardial ischemia and scar and LVEF reserve

(panel B; p=0.0004). HR = hazard ratio.

Figure 4. Annualized Cardiac Mortality Among Patients with Diabetes or CAD. Adjusted

cardiac mortality among patients with coronary artery disease (CAD,i.e. history of coronary

revascularization or myocardial infarction) without diabetes (orange), diabetic patients without

CAD who have impaired CFR (red), diabetic patients without CAD who have preserved CFR

(blue) and patients without diabetes or CAD with normal scans (no scar, ischemia or left

ventricular dysfunction, green) presented as survival curves (panel A) and annualized cardiac

mortality rates (panel B). Data for patients with CAD and diabetes are also presented for

comparison (purple).

mortality rates (panel B). Data for patients with CAD and diabetes are also presseneenteteed d fofofor rr

comparison (purple).



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Sharmila Dorbala, Ron Blankstein and Marcelo F. Di CarliVenkatesh L. Murthy, Masanao Naya, Courtney R. Foster, Mariya Gaber, Jon Hainer, Josh Klein,

and without Diabetes MellitusAssociation Between Coronary Vascular Dysfunction and Cardiac Mortality in Patients with

Print ISSN: 0009-7322. Online ISSN: 1524-4539 Copyright © 2012 American Heart Association, Inc. All rights reserved.

is published by the American Heart Association, 7272 Greenville Avenue, Dallas, TX 75231Circulation published online August 23, 2012;Circulation.

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SUPPLEMENTAL MATERIAL

ASSOCIATION BETWEEN CORONARY VASCULAR DYSFUNCTION AND CARDIAC MORTALITY IN PATIENTS WITH AND WITHOUT DIABETES MELLITUS Venkatesh L. Murthy, M.D., Ph.D.; Masanao Naya, M.D., Ph.D.; Courtney R. Foster, B.S., C.N.M.T; Mariya Gaber, M.L.A.; Jon Hainer, B.Sc.; Josh Klein, B.A., Sharmila Dorbala, M.D., M.P.H.; Ron Blankstein, M.D.; Marcelo F. Di Carli, M.D.

From the (1) Noninvasive Cardiovascular Imaging Program (VLM, MN, SD, RB, MDC), Departments of Medicine and Radiology; (2) Division of Cardiovascular Medicine (VLM, RB, MDC), (3) Division of Nuclear Medicine and Molecular Imaging (CRF, MG, JH, JK, SD, MDC), Brigham and Women’s Hospital, Boston, MA

Short Title: Murthy, et al. Coronary vascular function in diabetes Correspondence to: Marcelo F. Di Carli, M.D. Brigham & Women’s Hospital ASB-L1 037C 75 Francis St Boston, MA 02115 Email: [email protected] Phone: (617) 732-6291 Fax: (617) 582-6056

Supplement to Murthy, et al. Coronary vascular function in diabetes

CIRCULATIONAHA/2012/120402/R1

SUPPLEMENTAL TABLE 1. MULTIVARIABLE SURVIVAL ANALYSIS (NON-DIABETICS) Diabetics (N=1172) Non-Diabetics

(N=1611) All-Cause Mortality Annualized Mortality 7.5% 5.4% HR for CFR 2.03 (1.34-3.08) 3.42 (2.17-5.38) C-Index 0.778 (0.736-0.819) 0.775 (0.736-0.814) Continuous NRI 0.503 (0.312-0.701) 0.678 (0.519-0.826) NRI (2 and 6%/yr) 0.126 (0.052-0.207) 0.197 (0.110-0.288) NRI (2 and 6%/yr), Intermediate

Risk Stratum 0.535 (0.263-0.822) 0.406 (0.185-0.628)

IDI 0.012 (0.004-0.020) 0.029 (0.021-0.038) Relative IDI 0.089 (0.028-0.146) 0.286 (0.197-0.387)

Comparison of prognostic performance of CFR. Estimates for HR, c-index, NRI and IDI are adjusted for Duke clinical risk score, BMI, nephropathy/retinopathy (diabetics only), rest LVEF, combined extent and severity of scar and ischemia and LVEF reserve. CFR=coronary flow reserve. HR=hazard ratio. NRI=net reclassification improvement. IDI=integrated discrimination improvement.


CIRCULATIONAHA/2012/120402/R1 3

SUPPLEMENT FIGURE 1. RISK RECLASSIFICATION FOR DIABETICS

Illustration of risk reclassification by addition of coronary flow reserve (CFR) to a model containing clinical risk factors, left ventricular ejection fraction (LVEF), LVEF reserve and combined extent of myocardial ischemia and scar. The height of each bar is proportional to the number of patients in each pre-CFR risk category (<1, 1-3 and >3% per year risk of cardiac death) as estimated by a model containing clinical risk factors, rest LVEF, LVEF reserve and extent of myocardial ischemia and scar (Model 5, Table 3A, Main Text). Each of these bars is subdivided proportionate to the number of patients reclassified as <1 (green), 1-3 (blue) and >3% (red) per year risk of cardiac death categories after the addition of CFR to the risk model (Model 6, Table 3A, Main Text). The horizontal bar charts at right represent the observed annualized rates of cardiac mortality in each of the post-CFR risk categories.


CIRCULATIONAHA/2012/120402/R1 4

SUPPLEMENT FIGURE 2. RISK RECLASSIFICATION FOR NON-DIABETICS

Illustration of risk reclassification by addition of coronary flow reserve (CFR) to a model containing clinical risk factors, left ventricular ejection fraction (LVEF), LVEF reserve and combined extent of myocardial ischemia and scar. The height of each bar is proportional to the number of patients in each pre-CFR risk category (<1, 1-3 and >3% per year risk of cardiac death) as estimated by a model containing clinical risk factors, rest LVEF, LVEF reserve and extent of myocardial ischemia and scar (Model 4, Table 3B, Main Text). Each of these bars is subdivided proportionate to the number of patients reclassified as <1 (green), 1-3 (blue) and >3% (red) per year risk of cardiac death categories after the addition of CFR to the risk model (Model 5, Table 3B, Main Text). The horizontal bar charts at right represent the observed annualized rates of cardiac mortality in each of the post-CFR risk categories.

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