1. Annapoorna Kini, MD, MRCP, FACC
YELLOW II: Relationship of Serial Change in Plaque Morphology of Obstructive Non-Culprit Lesions to HDL Efflux, Inflammation and Transcriptome Perturbations in Response to High- Dose Statin Therapy
YELLOW II (Reduction in Coronary Yellow Plaque, Lipids and Vascular Inflammation by Aggressive Lipid Lowering)
Annapoorna Kini, MD, MRCP, FACC
Director, Cardiac Catheterization Lab
Director, Structural Heart Intervention Program
Director, Interventional Cardiology Fellowship Program
Zena and Michael A. Wiener Professor of Medicine
Cardiovascular Institute
Mount Sinai Hospital, New York, NY

2. Disclosure Statement of Financial Interest
This research was conducted with support from the Investigator-Sponsored Study Program of AstraZeneca, partial support from Infraredx and Mount Sinai catheterization laboratory endowment funds
Good Afternoon Dr King and Dr. Narula. Thank you for the opportunity to present this study at TCT. The study was partial funded by AZ, Infrarex and Mountsinai cathlab

3. Hypothesis Primary End-points: Secondary End-point:
Plaque morphology can be altered by high-dose statin therapy and the changes will be related to alterations in LDL-C, HDL-C, Apo-AI, and HDL functionality
Primary End-points:
To examine changes in lipid content of the obstructive non culprit lesion (NCL) measured by NIRS and plaque morphology assessed by OCT.
To compare the changes in lipid content and plaque morphology to the changes in LDL-C, HDL-C, Apo-AI.
Secondary End-point:
Correlations between the changes in plaque morphology, cholesterol efflux capacity (CEC) and perturbations in PBMC transcriptome.

4. maxLCBI4mm < 150 -> EXCLUDE
Methods
Two/Three Vessel CAD
After culprit vessel PCI,
NCL underwent NIRS/IVUS
maxLCBI4mm ≥ 150 -> OCT
maxLCBI4mm < 150 -> EXCLUDE
Rosuvastatin 40 mg daily
(8-12 weeks)
We enrolled The final study population consisted of 85 patients with both BL and FU paired images available
Imaging data analysis, CEC assessment and PBMC microarray by independent core labs
Follow-up Cath and PCI of NCL N=85
(angiogram, OCT and NIRS/IVUS)
Clinical follow-up at 1, 6, 12 month

5. Baseline characteristics
Baseline (n=85)
Age, years
62.4 ± 11.2
Male gender
58 (68)
Hypertension
76 (89)
Hypercholesterolemia
75 (88)
Diabetes mellitus
37 (44)
BMI
29.6 ± 5.2
Current smoking
12 (14)
History of smoking
26 (31)
Prior MI
Prior PCI
24 (28)
Statin use
69 (81)
Coronary vessel
LAD
36 (42)
LCX
23 (27)
RCA
Baseline demographic characteristics, concomitant medications and treated vessel information for the study patients

6. Chemical parameters and CEC
Results
Chemical parameters and CEC
Baseline (n=85)
Follow-up (n=85)
P value
Total cholesterol, mg/dl
153.3 ± 44.9
115.0 ± 29.9
<0.001
LDL cholesterol , mg/dl
86.8 ± 39.6
50.6 ± 25.0
HDL cholesterol , mg/dl
41.2 ± 12.7
42.2 ± 13.1
0.41
Triglyceride, mg/dl
128.6 ± 111.8
107.8 ± 66.7
0.04
ApoB, mg/dl
79.6 ± 28.0
57.4 ± 17.5
Apo-AI, mg/dl
± 25.6
± 23.3
0.004
hs-CRP, mg/l
3.5 ± 5.5
2.7 ± 4.2
CEC
0.81 ± 0.14
0.84 ± 0.14
0.003
At the follow-up, there was a significant reduction of LDL-C and hs-CRC levels and increase in cholesterol efflux capacity

7. Intravascular image findings
OCT
Baseline (n=85)
Follow-up (n=85)
P value
Reference lumen CSA, mm2
6.8 ± 2.2
6.6 ± 2.1
0.07
Minimum lumen CSA, mm2
1.80 ± 0.69
1.84 ± 0.68
0.26
Area stenosis, %
71.9 ± 8.5
70.1 ± 9.0
0.24
Lipid rich plaque
75 (88.2)
72 (84.7)
0.50
Lipid arc maximum,°
147.2 ± 80.1
139.2 ± 79.4
0.20
Lipid length, mm
5.8 ± 5.2
4.9 ± 4.0
0.03
Lipid volume index,° x mm
663.7 ± 668.6
586.8 ± 616.5
0.16
Minimum cap thickness, µm
100.9 ± 41.7
108.6 ± 39.6
<0.001
TCFA
17 (20.0)
6 (7.1)
0.003
Macrophages
83 (97.6)
80 (94.1)
Macrophage arc (max),°
136.2 ± 66.6
129.1 ± 60.5
0.11
Macrophage length, mm
9.8 ± 5.4
8.8 ± 5.1
Thrombus
15 (17.6)
12 (14.1)
0.51
Microvessel
75.3
77.6
0.63
Calcium deposition
74 (87.1)
0.71
Calcium arc (max),°
124.4 ± 85.6
127.6 ± 86.2
0.05
IVUS
EEM volume, mm3
298.2 ± 147.3
297.4 ± 148.8
0.73
TAV, mm3
182.3 ± 94.5
182.7 ± 95.5
0.82
PAV, %
60.71 ± 7.52
60.97 ± 7.57
0.30
Plaque Burden, %
75.93 ± 7.07
75.79 ± 7.96
0.78
Plaque + media, mm2
7.67 ± 3.32
7.75 ± 3.44
0.52
NIRS
maxLCBI4mm
416.6 ± 172.9
± 180.4
0.43
By intravascular imaging, OCT-verified minimal FCT increased significantly and the prevalence of TCFA substantially reduced, while lipid and macrophage length decreased
IVUS and LCBI did not demonstrate any significant change at follow up

8. Correlates of the change in FCT
Results
Correlates of the change in FCT
Beta coefficient (95% CI)
P value
∆CEC
0.30 (0.07 to 0.54)
0.01
∆LDL-C
0.09 (-0.14 to 0.33)
0.15
∆HDL-C
0.04 (-0.27 to 0.20)
0.97
∆ hs-CRP
-0.27 (-0.46 to )
0.02
∆ TG
-0.14 (-0.34 to 0.09)
0.37
Age
-0.01 (-0.26 to 0.24)
Gender (Female)
0.11 (-0.13 to 0.36)
Baseline FCT
-0.27 (-0.54 to -0.06)
There was a significant correlation between the increase in FCT, increase in efflux and reduction in hs-CRP levels after adjusting for age, gender, and changes in lipid levels.
The minimal cap thickness at baseline was another independent predictor of the change in FCT after high-dose statin therapy.

9. Intravascular imaging
Baseline
Follow-up
In this representative image, the minimal FCT was 50 μm at baseline and 90 µm after intensive statin therapy, there was no change in IVUS plaque burden (86%) and a decrease in LCBI

10. Gene expression profiling
Results
Gene expression profiling
A total of 20,819 genes were assayed using microarray; differential expression profile identified 117 genes with 78 upregulated and 39 downregulated genes
SQLE - catalyzes the first oxygenation step in sterol biosynthesis, one of the crucial rate-limiting enzymes in this pathway (Howe et al. J Biol Chem 2015;290: )
DHCR24 - terminal enzyme in the cholesterol synthesis and a mediator of vascular inflammation inhibition by lipid-free ApoAI (Luu et al. J Lipid Res 2014; Wu et al. Circ Res 2013)
FADS1 - regulation of fatty acids unsaturation
LDLR - cellular cholesterol uptake
ABCA1, ABCG1 - cholesterol efflux
After analyzing more than 21K transcriptomes, 117 were found to be relevant, of which 6 emerged as most defining upon a stricter scrutiny.
These include SQLE, DHCR24, FADS1, LDLR, ABCA1 and ABCG1
These transcriptome perturbations are closely associated with cholesterol metabolism, transport, inflammation and efflux
The transcriptome phenotypically correlated with plaque stabilization suggested by increased FCT and reduced inflammation suggested by decreased hsCRP

11. Heat map of 117 differentially expressed genes
Results
Heat map of 117 differentially expressed genes
SQLE
This is diagram showing the cholesterol metabolism pathway where statin is inhibiting HMGCA reductase and Squalene is an one of the crucial rate-limiting enzymes in this pathway. We think the up regulation of squalene is indicator of statin therapy effectiveness.
These transcriptomic changes in the peripheral blood may have potential for being developed as a biomarker to predict patients likely to favorably respond to maximization of statins.
Such prediction is of obvious clinical importance as we are well aware that not more than 30% of patients started on high doses of statins achieve the desirable clinical effect
These transcriptomic changes in the peripheral blood may have potential for being developed as a biomarker to predict patients who will favorably respond to maximum dose of statins

12. CONCLUSIONS
We detected a significant increase in FCT of obstructive coronary lesions by OCT, enhancement of CEC, reduction in CRP levels and significant changes in PBMC transcriptome after 8-12 weeks of rosuvastatin 40 mg daily.
Improved macrophage CEC and reduced CRP contributed to plaque stabilization independently of changes in serum cholesterol.
The significant transcriptomic changes related to cholesterol synthesis (SQLE), regulation of fatty acid unsaturation (FADS1), cellular cholesterol uptake (LDLR), efflux (ABCA1, ABCG1) and inflammation (DHCR24) may co-operate in determining the beneficial effects of statin on plaque stabilization.
In patients with stable CAD, ...

13. Limitations
Lack of a randomized design precludes causal inferences between the use of high-intensity statins and the changes we observed in various morphologic, functional and genetic parameters.
Short duration of follow-up was resulted in small changes in plaque morphology. A prospectively designed randomized study in the next phase should compare the continuation of statin therapy with maximization of the dose on these important morphologic and mechanistic endpoints.
Unlike our previous YELLOW study, the present experiment did not demonstrate significant reductions in NIRS-based LCBI, a null result that may be attributable to a NIRS-based inclusion criterion in the YELLOW II protocol and the lack of a comparative standard dose arm.

14. Thank you
Mount Sinai Hospital and Icahn School of Medicine at Mount Sinai, New York, NY:
Vengrenyuk Y, Purushothaman M, Yoshimura T, Aquino M, Haider N, Feig J, Krishnan P, Sweeny J, Mahajan M, Moreno M, Mehran R, Kovacic J, Baber U, Narula J, Sharma S Shameer K, Johnson K, Readhead B, Kidd B, Dudley J Maehara A, Matsumura M
Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY:
Collaborative effort of multiple groups from MSH, Genomics institute and CRF
Columbia University Medical Center and CRF, New York, NY:

15. Correlates of change in FCT stratified by use of statin at baseline
Beta coefficient (95% CI)
P value
∆CEC
0.75 (0.30 to 1.21)
0.001
∆LDL-C
0.17 (-0.01to 0.43)
0.17
∆HDL-C
0.01 (-0.22 to 0.25)
0.91
∆ hs-CRP
-0.26 (-0.45 to )
0.02
∆ TG
-0.10 (-0.30 to 0.12)
0.40
Age
-0.02 (-0.26 to 0.22)
0.87
Gender (Female)
0.15 (-0.09 to 0.40)
0.20
Baseline FCT
-0.27 (-0.53 to -0.06)
Baseline statin
0.06(-0.30 to 0.18)
0.64
∆CEC* Baseline statin
-0.53(-1.1 to -0.09)
Baseline statin therapy was not an independent covariate of the change in FCT (P=0.64).
The correlation between the change in FCT and CEC was higher in statin naïve patients compared to patient exposed to statins at baseline (R = 0.739, P = vs , P =0.15) (Figure S3A). Statin therapy at baseline had a significant dampening effect (β, =-0.53; 95%CI, -1.1 to -0.09; P=0.02) on the association between the change in FCT and CEC (Figure S3B).

16. QRT-PCR validation * * * * *
A panel of six genes (SQLE, DHCR24, FADS1, LDLR, ABCA1, and ABCG1) with the lowest P values was used for RT-PCR assay.
The observed perturbations were confirmed by RT-PCR * *

17. Reverse cholesterol transport by HDL
Overview of reverse cholesterol transport by HDL.
The flux of cholesterol through this pathway is termed ‘reverse cholesterol transport’, as it removes cholesterol from tissue
1. Lipid-free or lipid-poor pre–b-HDL containing apoA-I is the initial acceptor of the cholesterol exported by ABCA1 (Fig. 1). However,
the factors that control levels of poorly lipidated HDL in humans are poorly understood. One important factor may be the rates at which
the liver and intestine synthesize apoA-I. Atherosclerosis is enhanced in mice and rabbits deficient in apoA-I, whereas transgenic
mice expressing high amounts of the protein are protected from vascular disease2.
2. HDL that has accepted cholesterol from artery-wall macrophages reenters the circulation, where it transports cholesterol back to the liver for excretion in the bile (Fig. 1).
Cholesteryl ester is removed from HDL by a membrane protein on hepatocytes, SR-BI. It is converted back to cholesterol and then bile
acids, which are secreted into the bile for excretion.
(Heinecke, Nature Med 2012)

18. Integrated molecular network of modules and biochemical functions mediating CEC and FCT
The increase in CEC was associated with the Midnight Blue module of 65 genes.
Genes in this module were enriched for immune and inflammatory response pathways, interferon gamma signaling and regulation of lipid metabolic processes
Change in FCT was associated with two modules, Grey60 (n=40 genes) and Light Green (n=37 genes).
Genes in grey60 modules were localized to the collagen catabolism, extracellular matrix organization and angiofenesis
Graphical representation of network relationship of modules (circles), clinical traits (triangles) and enrichment terms (squares).

19. Projection of WGCNA module genes associated with CEC to human pathways
Weighted gene coexpression network analysis (WGCNA) was used to discover gene modules significantly correlating to clinical phenotypes
There is a wide variety of biochemical pathways resulting from genes which are associated with cholesterol efflux capacity. These pathways are especially involved in signal transduction and gene expression

20. Projection of WGCNA module genes associated with FCT to human pathways
Here, we can see that again there are a great number of biochemical pathways resulting from genes associated with FCT-especially enriched areas are the immune function, transmembrane molecular transport, and extracellular matrix organization

21. Methods Total number of patients screened: N = 962
Generally/clinically excluded: N = 31
Renal insufficiency, participating in another study
Angiographically excluded: N = 834
Normal coronaries, non-obstructive or 1 vessel CAD, ISR, CTO, vein graft
Patients excluded based on NIRS: N = 6
Study lesion maxLCBI4mm <150
Lost to follow-up: N = 6
Final study population: N = 85

22. Clinical events Events (n = 85) Death from cardiovascular causes
0 (0.0)
Myocardial infarction
3 (3.5)
Urgent revascularization
13 (15.3)
Nonfatal stroke
Any bleeding
Periprocedural complication
2 (2.4)
Statin discontinuation
Statin reduction
6 (7.1)
Hospitalization for chest pain
15 (17.6)