1. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 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. Hsu 1, K. Patton 2, J. Liang 3, R. Okabe 4, J. Aberg 5, N. Fineberg 2 Institute(s): 1 New York University Medical Center, Internal Medicine, New York, United States, 2 University of Alabama at Birmingham, Biostatistics, Birmingham, United States, 3 New York University, New York, United States, 4 New York University, School of Medicine, New York, United States, 5 New York University Medical Center, Infectious Diseases, New York, United States WEAB0206

2. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 Carotid Intimal Thickness (CIMT) predicts CAD and helps risk-stratify patients for cardiovascular events 1,2. HIV+ patients have greater and more rapid progression of CIMT than HIV- patients 3. Advantages include: – Low cost – No radiation – Insurance Coverage (1 CRF was required for study enrollment including HIV) Background 1 Ruijter, H., “Common Carotid Intima-Media Thickness Measurements in Cardiovascular Risk Prediction, A Meta-analysis”, JAMA 2012; 308(8) 796-803. 2 Nambi, 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; 33- 183-190. 3 Hsue, P., et al., “progression of atherosclerosis as assessed by carotid intima-media thickness in patients with HIV infection, Circulation, 109:1603-1608.

3. www.ias2013.org 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

4. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 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. Methods

5. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 HIV Neg. (N=128) HIV Pos. (N=179) P-value HIV+ vs. HIV- Differences Demographics Age50.6±11.854.8±8.20.0013 3.8 yrs. older Gender89.06% (M)97.77% (M)0.0014 More Men Medical History Smoking (0=never, 1=former, 2=current) 50.78% (0), 21.9% (1), 27.3% (2) 54.78% (0), 19.6% (1), 25.7% (2) 0.7812 DM (diabetes mellitus)7.8% (Y)8.9% (Y)0.7268 HTN21.9% (Y)26.2% (Y)0.3783 HTN Medications15.6% (Y)27.4% (Y)0.0150 12% higher Family history of heart disease 22.7% (Y)29.6% (Y)0.1746 History of MI/stroke (myocardial infarction) 4.7% (Y)5.6% (Y)0.7267 Heart age53.6±14.1 (N=125)60.3±12.9< 0.0001 Heart Age 6.7 years older Framingham Risk Score10.1%±0.0714.6%±0.09< 0.0001 4.5% Higher Demographics/PMH

6. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 HIV Neg. (N=128)HIV Pos. (N=179)P-value HIV+ vs. HIV- Medications Statins18.0% (Y)27.4% (Y)0.0551 Niacin, fish oil, fibrates7.8% (Y)40.8 (Y)< 0.0001 33% Higher Use Aspirin25.0% (Y)27.4% (Y)0.6416 Anticoagulants**1.6% (Y)5.0% (Y)0.1072 Carotid Intimal Medial Thickness CCA 0.75±0.34 (n=124) 0.84±0.34 (N=168) 0.039 Higher CCA plaque ICA (incl bulb) 1.00±0.49 (N=124) 1.13±0.52 (N=168) 0.049 Higher ICA plaque HIV + Patients Only (Median Values) CD4-609.5140±257.7024 609cells/mL Nadir-249.5587±182.8723 250cells/mL Viral Load-1910.82±13000.18 91% PCR <200c/mL Duration Infection-18.1453±6.9031 18 yrs. infection D:A:D Risk Score-0.0535±0.0591 5.4% Demographics/PMH

7. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 HIV Neg. (N=128)HIV Pos. (N=179)P-value HIV+ vs. HIV- Systolic BP121.1±13.4769122.8±12.24130.2555 Total cholesterol197.4±40.6608182.3±36.43130.0007 Lower HDL53.8047±14.476745.7542±15.6957< 0.0001 Lower Large HDL Particle5.7846±4.5039 (N=107)3.8168±4.4569 (N=113)0.0013 Lower LDL121.8±39.2706 (N=124)109.0±32.3485 (N=172)0.0031 Lower Small LDL Particle #662.4±461.3 (N=99)868.3±503.4 (N=106)0.0026 Higher LDL Size21.1253±1.0153 (N=99)20.7336±0.6287 (N=107)0.0012 Smaller Triglycerides142.5±88.5995 (N=124)168.9±99.7843 (N=170)0.0198 Higher IL-63.7791±2.3717 (N=108)3.1127±1.8583 (N=145)0.0165 Lower D-dimer0.5826±1.5991 (N=78)0.2936±0.4686 (N=115)0.1244 hsCRP (C-Reactive Protein) 2.3300±4.3044 (N=110)2.8153±5.1219 (N=150)0.4207 Homocysteine10.0037±3.1431 (N=108)10.6336±7.1259 (N=146)0.3430 LpPLA 2149.5±45.4270 (N=99)140.7±41.7291 (N=108)0.1358 LP(a)-c 25.3110±27.8481 (N=100) 23.3646±27.5353(N=113 ) 0.6091 ApoB/A1 ratio1.4327±7.0970 (N=101)0.7750±0.3224 (N=111)0.3543 Laboratory Data

8. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 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. RESULTS: Univariate Correlates with CIMT

9. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 HIV +HIV -Compar ison Spearm an P-valueSpearm an P-value CCAHDL-0.08670.24860.18370.04120.0209 Large HDL Particle -0.14050.13780.22020.02400.0076 ICALDL-0.13620.07480.18970.03720.0057 LDL Size-0.19460.0446-0.14110.16580.6972 RESULTS: Multivariate Regression

10. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 RESULTS: Multivariate Regression HIV +HIV -Compar ison Spearm an P-valueSpearm an P-value CCAFramingham0.19850.00770.28960.00110.4116 DAD0.14810.0479--- ICAFramingham0.3666<0.00010.19230.03180.1081 DAD0.3418<0.0001--- Abacavir and duration of lopinavir/r or indinavir use was not correlated with CIMT

11. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 RESULTS: Multivariate Regression Table 4HIV +HIV - PredictorPar. Est.St. ErrorP-ValuePredictorPar. Est.St. ErrorP-Value CCAHeart Age0.00350.00160.0353Age0.00480.07390.0017 Hypertension0.19630.06710.0048 Hyp meds-0.34450.08640.0003 MI/Stroke History0.35340.10290.0011 ICAIL-6-0.03410.01320.0123Age0.00560.00150.0005 Diabetes-0.16360.07260.0277SBP0.00270.00130.0480 Hyp meds0.12100.04370.0074HDL0.00810.00220.0007 DAD1.35850.48180.0064Large HDL-0.02510.00760.0020 Triglycerides-0.00050.00020.0280 hsCRP0.01910.0039<0.0001 Age0.00560.00150.0005 SBP0.00270.00130.0480 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

12. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 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. Conclusions

13. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 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. Conclusions

14. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 Retrospective analysis of data Sample Size Skewed Sex of this Patient Population (predominantly male) Study Limitations

15. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 Re-stratification of cardiovascular risk by CIMT, lipid sub- particles, 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. Part II of Study

16. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 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. Case Example 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) CHD or CHD Risk Equivalents (10-year risk >20%) <100  100  130 (100–129: drug optional) 2+ Risk Factors (10-year risk  20%) <130  130 10-year risk 10–20%:  130 10-year risk <10%:  160 ORIGINAL FRAMINGHAM 0–1 Risk Factor (10-year risk <10%) <160  160  190 (160–189: LDL- lowering drug optional)

17. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 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. Case Example 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) RE-STRATIFIED FRAMINGHAM CHD Risk (10-year risk >20%) <100  100  130 (100–129: drug optional) 2+ Risk Factors (10-year risk 10-20%) <130  130 10-year risk 10–20%:  130 10-year risk <10%:  160 ORIGINAL FRAMINGHAM 0–1 Risk Factor (10-year risk <10%) <160  160  190 (160–189: LDL- lowering drug optional)

18. www.ias2013.org Kuala Lumpur, Malaysia, 30 June - 3 July 2013 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 Acknowledgements