Transcript Powerpoint

WEAB0206
Independent Predictors of Carotid Intimal Thickness Differ
Between HIV+ and HIV- Patients with Respect to
Traditional Cardiac Risk Factors, Risk Calculators, Lipid
Subfractions, and Inflammatory Markers
Author(s): R. Hsu1, K. Patton2, J. Liang3, R. Okabe4, J. Aberg5, N. Fineberg2
1New York University Medical Center, Internal Medicine, New
Institute(s):
York, United States, 2University of Alabama at Birmingham, Biostatistics,
Birmingham, United States, 3New York University, New York, United States, 4New
York University, School of Medicine, New York, United States, 5New York University
Medical Center, Infectious Diseases, New York, United States
www.ias2013.org
Kuala Lumpur, Malaysia , 30 June - 3 July 2013
Background
• Carotid Intimal Thickness (CIMT) predicts CAD
and helps risk-stratify patients for cardiovascular
events1,2. HIV+ patients have greater and more
rapid progression of CIMT than HIV- patients3.
• Advantages include:
– Low cost
– No radiation
– Insurance Coverage (1 CRF was required for study
enrollment including HIV)
1Ruijter,
H., “Common Carotid Intima-Media Thickness Measurements in Cardiovascular
Risk Prediction, A Meta-analysis”, JAMA 2012; 308(8) 796-803.
2Nambi, V., et al., “Common carotid artery intima-media thickness is as good as carotid
intima-media thickness of all carotid artery segments in improving prediction of coronary
heart disease risk in the Atherosclerosis Risk in Communities (ARIC) study”, 2012, Jun; 33183-190.
3Hsue, P., et al., “progression of atherosclerosis as assessed by carotid intima-media
thickness in patients with HIVwww.ias2013.org
infection, Circulation, 109:1603-1608.
Kuala Lumpur, Malaysia , 30 June - 3 July 2013
Background
• CIMT (as a surrogate marker for atherosclerosis) was then
correlated with Testable Predictors of CIMT with the results
differentiated between HIV+ and HIV- patients.
• These Clinically Testable predictors include:
1. Traditional risk factor assessment
•
Hypertension, smoking, hyperlipidemia, diabetes, family history, and
prior cardiac events, HIV (if positive)
2. Lipids and Lipid sub-particles
•
Total cholesterol, triglycerides, direct HDL-C, direct LDL-C, LDL-P (# of
particles), small LDL-P (# of particles), HDL-P (# of particles), LPa-C,
ApoB/A-1 ratio
3. Framingham, D:A:D (if HIV+) Risk calculators, Heart Age
4. Inflammatory markers (all commercially available)
•
hsCRP, D-dimer, IL-6, homocysteine, Lp-PLA2
www.ias2013.org
Kuala Lumpur, Malaysia , 30 June - 3 July 2013
Methods
• 307 patients (179 HIV+,128 HIV-) had their maximal CIMT
determined at the CCA and ICA (including bulb).
• Heart Age, traditional risk factors, Framingham and D:A:D
Risk (HIV+), Lipids and sub-particles (Total Cholesterol,
LDL, HDL, TG, LDL#, small LDL, Large HDL#, LP(a)-c,
ApoB/A1 ratio), and inflammatory indices (d-dimer, IL-6,
hsCRP, LPPLA2, homocysteine) were measured in each
patient.
• Differences in demographics and these testable risk
factors were determined between HIV+ and HIV- patients
and were retrospectively analyzed with Mann Whitney
and Chi-square testing to determine correlations with
CIMT. Stepwise multiple regression analysis determined
which variables were independently correlated with
CIMT.
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Kuala Lumpur, Malaysia , 30 June - 3 July 2013
Demographics/PMH
HIV Neg.
(N=128)
HIV Pos.
(N=179)
P-value
HIV+ vs. HIVDifferences
50.6±11.8
54.8±8.2
0.0013
3.8 yrs. older
89.06% (M)
97.77% (M)
0.0014
More Men
50.78% (0), 21.9% (1),
27.3% (2)
7.8% (Y)
54.78% (0), 19.6% (1),
25.7% (2)
8.9% (Y)
0.7812
HTN
21.9% (Y)
26.2% (Y)
0.3783
HTN Medications
15.6% (Y)
27.4% (Y)
0.0150
Family history of heart
disease
History of MI/stroke
(myocardial infarction)
Heart age
22.7% (Y)
29.6% (Y)
0.1746
4.7% (Y)
5.6% (Y)
0.7267
53.6±14.1 (N=125)
60.3±12.9
< 0.0001
Heart Age
6.7 years older
Framingham Risk Score
10.1%±0.07
14.6%±0.09
< 0.0001
4.5% Higher
Demographics
Age
Gender
Medical History
Smoking (0=never,
1=former, 2=current)
DM (diabetes mellitus)
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0.7268
12% higher
Kuala Lumpur, Malaysia , 30 June - 3 July 2013
Demographics/PMH
HIV Neg. (N=128)
HIV Pos. (N=179)
P-value
Statins
18.0% (Y)
27.4% (Y)
0.0551
Niacin, fish oil, fibrates
7.8% (Y)
40.8 (Y)
< 0.0001
Aspirin
25.0% (Y)
27.4% (Y)
0.6416
Anticoagulants**
1.6% (Y)
5.0% (Y)
0.1072
0.75±0.34
(n=124)
1.00±0.49
(N=124)
0.84±0.34
(N=168)
1.13±0.52
(N=168)
(Median Values)
0.039
Higher CCA plaque
0.049
Higher ICA plaque
CD4
-
609.5140±257.7024
Nadir
-
249.5587±182.8723
Viral Load
-
1910.82±13000.18
Duration Infection
-
18.1453±6.9031
D:A:D Risk Score
-
0.0535±0.0591
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HIV+ vs. HIV-
Medications
Carotid Intimal Medial
Thickness
CCA
ICA (incl bulb)
HIV + Patients Only
33% Higher Use
609cells/mL
250cells/mL
91% PCR <200c/mL
18 yrs. infection
Kuala Lumpur, Malaysia , 30 5.4%
June - 3 July 2013
Laboratory Data
HIV Neg. (N=128)
HIV Pos. (N=179)
P-value
Systolic BP
121.1±13.4769
122.8±12.2413
0.2555
Total cholesterol
197.4±40.6608
182.3±36.4313
0.0007
53.8047±14.4767
45.7542±15.6957
< 0.0001
Large HDL Particle
5.7846±4.5039 (N=107)
3.8168±4.4569 (N=113)
0.0013
LDL
121.8±39.2706 (N=124)
109.0±32.3485 (N=172)
0.0031
662.4±461.3 (N=99)
868.3±503.4 (N=106)
0.0026
LDL Size
21.1253±1.0153 (N=99)
20.7336±0.6287 (N=107)
0.0012
Triglycerides
142.5±88.5995 (N=124)
168.9±99.7843 (N=170)
0.0198
IL-6
3.7791±2.3717 (N=108)
3.1127±1.8583 (N=145)
0.0165
D-dimer
0.5826±1.5991 (N=78)
0.2936±0.4686 (N=115)
0.1244
hsCRP (C-Reactive
Protein)
Homocysteine
2.3300±4.3044 (N=110)
2.8153±5.1219 (N=150)
0.4207
10.0037±3.1431 (N=108) 10.6336±7.1259 (N=146)
0.3430
HDL
Small LDL Particle #
LpPLA 2
149.5±45.4270 (N=99)
140.7±41.7291 (N=108)
0.1358
LP(a)-c
25.3110±27.8481
(N=100)
1.4327±7.0970 (N=101)
23.3646±27.5353(N=113
)
0.7750±0.3224
(N=111)
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0.6091
ApoB/A1 ratio
HIV+ vs. HIVLower
Lower
Lower
Lower
Higher
Smaller
Higher
Lower
0.3543Kuala Lumpur, Malaysia , 30 June - 3 July 2013
RESULTS:
Univariate Correlates with CIMT
• HIV+ Patients: Heart age, IL-6, Diabetes,
Hypertensive Medications, Framingham
Risk, and D:A:D.
• HIV- Patients: Heart Age, Hypertension,
Hypertensive meds, MI/Stroke history, HDL,
Large HDL, Triglycerides, Framingham Risk
scores, and hsCRP were all correlated with
increased CIMT measurements.
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Kuala Lumpur, Malaysia , 30 June - 3 July 2013
RESULTS: Multivariate Regression
HIV +
Compar
ison
Spearm P-value Spearm P-value P-value
an
an
-0.0867 0.2486 0.1837 0.0412 0.0209
-0.1405 0.1378 0.2202 0.0240 0.0076
CCA
HDL
Large
HDL
Particle
ICA
LDL
-0.1362
LDL Size -0.1946
0.0748
0.0446
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HIV -
0.1897
-0.1411
0.0372
0.1658
0.0057
0.6972
Kuala Lumpur, Malaysia , 30 June - 3 July 2013
RESULTS: Multivariate Regression
HIV +
HIV -
Compar
ison
Spearm P-value Spearm P-value P-value
an
an
CCA Framingham 0.1985
DAD
0.1481
0.0077
0.0479
0.2896
-
0.0011
-
0.4116
-
ICA Framingham 0.3666 <0.0001 0.1923
DAD
0.3418 <0.0001
-
0.0318
-
0.1081
-
• Abacavir and duration of lopinavir/r or indinavir use was not correlated with CIMT
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Kuala Lumpur, Malaysia , 30 June - 3 July 2013
RESULTS: Multivariate Regression
Table 4
CCA
ICA
HIV +
HIV -
Predictor
Par. Est.
St. Error
P-Value
Predictor
Par. Est.
St. Error
P-Value
Heart Age
0.0035
0.0016
0.0353
Age
0.0048
0.0739
0.0017
Hypertension
0.1963
0.0671
0.0048
Hyp meds
MI/Stroke
History
-0.3445
0.0864
0.0003
0.3534
0.1029
0.0011
IL-6
-0.0341
0.0132
0.0123
Age
0.0056
0.0015
0.0005
Diabetes
-0.1636
0.0726
0.0277
SBP
0.0027
0.0013
0.0480
Hyp meds
0.1210
0.0437
0.0074
HDL
0.0081
0.0022
0.0007
DAD
1.3585
0.4818
0.0064
Large HDL
-0.0251
0.0076
0.0020
Triglycerides
-0.0005
0.0002
0.0280
hsCRP
0.0191
0.0039
<0.0001
Age
SBP
0.0056
0.0027
0.0015
0.0013
0.0005
0.0480
• At the CCA, heart age was the only significant independent predictor for HIV+ pts.
• At the ICA, IL-6 emerged as an independent predictor for HIV+ patients
• At the ICA, Large HDL# and hsCRP were additional predictors for HIV- patients
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Kuala Lumpur, Malaysia , 30 June - 3 July 2013
Conclusions
• Today, with HIV suppression, lipid, and hypertension
control, HIV+ patients continue to have a
disproportionately greater CIMT and calculated heart
age than HIV- comparators.
• Although HIV+ patients generally had lower HDL than
their HIV- counterparts, HDL was not an independent
predictor of atherosclerosis in HIV+ patients, in contrast
to the HIV- cohort.
• In the context of LDL control in this HIV+ patient
population, LDL size was predictive of ICA CIMT. In the
comparator HIV- population, HDL and Large HDL Particle
number was predictive at the CCA CIMT, while LDL
number only was predictive at the ICA CIMT.
www.ias2013.org
Kuala Lumpur, Malaysia , 30 June - 3 July 2013
Conclusions
• Postulated inflammatory markers like LPPLA2 and
homocysteine were not predictive of CIMT. Only IL-6 was
associated with ICA CIMT in HIV+ patients, whereas hsCRP
was associated with ICA in HIV- patients. This contrast in
observation from markers associated with cardiovascular
mortality in the SMART study (IL-6, d-dimer, hsCRP) may be
partially explained by the reduction of inflammatory markers
in the context of HIV suppression in this patient population
and the use of standard citrate assays.
• Finally, there was no association with atherosclerosis as
measured by CIMT with the use of abacavir, or duration of
lopinavir or indinavir use, and the D:A:D cardiovascular risk
equation, although predictive of CIMT, was shown to be less
predictive than the Framingham risk equation in this HIV+
population.
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Kuala Lumpur, Malaysia , 30 June - 3 July 2013
Study Limitations
• Retrospective analysis of data
• Sample Size
• Skewed Sex of this Patient Population
(predominantly male)
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Kuala Lumpur, Malaysia , 30 June - 3 July 2013
Part II of Study
• Re-stratification of cardiovascular risk by CIMT, lipid subparticles, and/or inflammatory markers found significant in
Multivariable Regression analysis will be performed, and
determined if predictive of atherosclerotic regression as
measured by CIMT 1 year later.
• All patients with 1, 2, or 3 S.D.’s above the norm CIMT will
be re-stratified by 1, 2, or 3 Framingham Risk categories,
respectively, to achieve their new LDL goals with lipid
lowering agents and also to start aspirin (option to decline).
• Additional new markers of monocyte immune activation
like sCD14+ and sCD163+, markers correlated with unstable
CVD plaque formation will also be measured before and
after intervention.
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Kuala Lumpur, Malaysia , 30 June - 3 July 2013
Case Example
• 44 year-old Caucasian Male, HIV+, T cells 490, VL <50, BP 135/85, Tchol 180,
HDL 30, no DM, non-smoker, no hypertension. Framingham Risk Score 3%.
CIMT performed showing 1.3mm CIMT at Rt. and Lft. Carotid bulbs, IL-6 level 8
• Patient would be moved from Low Framingham Risk to High Risk Based on his
IL6 level and CIMT 1.6 S.D. above Median values.
Risk Category
CHD or CHD Risk
Equivalents
(10-year risk >20%)
2+ Risk Factors
(10-year risk 20%)
LDL Goal
(mg/dL)
LDL Level at Which to
Initiate Therapeutic
Lifestyle Changes
(TLC) (mg/dL)
LDL Level at Which
to Consider
Drug Therapy
(mg/dL)
100
130
(100–129: drug
optional)
<100
10-year risk 10–20%:
130
130
<130
10-year risk <10%:
160
ORIGINAL
FRAMINGHAM
0–1 Risk Factor
(10-year risk <10%)
160
<160
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190
(160–189: LDLlowering drug
optional)
Kuala Lumpur, Malaysia , 30 June - 3 July 2013
Case Example
•
•
Patient would be Re-stratified two categories higher from Low Framingham Risk to High
Framingham Risk based on his IL6 level and CIMT 1.6 S.D. above normal
Patient to Start ASA 81mg qD and attempt to reach LDL goal of <100, with re-assessment of
Lipid sub-particles, Inflammatory Markers, monocyte activation markers and measurement
of CIMT 1 year later to assess if atherosclerotic regression occurs.
Risk Category
LDL Goal
(mg/dL)
LDL Level at Which to
Initiate Therapeutic
Lifestyle Changes
(TLC) (mg/dL)
LDL Level at Which
to Consider
Drug Therapy
(mg/dL)
100
130
(100–129: drug
optional)
RE-STRATIFIED
FRAMINGHAM
CHD Risk
<100
(10-year risk >20%)
2+ Risk Factors
(10-year risk 10-20%)
10-year risk 10–20%:
130
130
<130
10-year risk <10%:
160
ORIGINAL
FRAMINGHAM
0–1 Risk Factor
(10-year risk <10%)
160
<160
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190
(160–189: LDLlowering drug
optional)
Kuala Lumpur, Malaysia , 30 June - 3 July 2013
Acknowledgements
• Naomi Fineberg and Kyle Patton
– University of Alabama at Birmingham, Division
of Biostatistics, Birmingham, United States
• Judy Aberg and Hui Zhan
– New York University Medical Center,
Department of Infectious Diseases, New York,
United States
• Rachel Okabe and Jennifer Liang
– New York University School of Medicine and
New York University, New York, United States
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Kuala Lumpur, Malaysia , 30 June - 3 July 2013