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Identification of unique adaptive immune system signature in acute coronary syndromes
564
Letters to the Editor
Identification of unique adaptive immune system signature in acute coronary syndromes☆ Giovanna Liuzzo ⁎,1, Rocco A. Montone 1, Mario Gabriele 1, Daniela Pedicino 1, Ada F. Giglio 1, Francesco Trotta 1, Vincenzo A. Galiffa 1, Marco Previtero 1, Anna Severino 1, Luigi M. Biasucci 1, Filippo Crea 1 Institute of Cardiology, Catholic University, Rome, Italy
a r t i c l e
i n f o
Article history: Received 19 December 2012 Accepted 13 January 2013 Available online 5 February 2013 Keywords: Acute coronary syndromes Adaptive immunity Pathogenesis Prognosis
Several studies have consistently demonstrated that inflammation plays a key role in the pathogenesis of acute coronary syndromes (ACS) [1,2]. More recent studies have highlighted the importance of adaptive immunity in ACS [3,4]. In particular, profound abnormalities have been observed in specific subsets of T-cells, including CD4+CD28nullT-cells, a subset of cytotoxic CD4+T-lymphocytes producing large amount of interferon-γ (IFN-γ) [5–7], naturally occurring regulatory T-cells (Treg) [8,9] and interleukin (IL)-17-producing T-cells (Th17) [10–12]. Our group systematically investigated the clinical relevance of adaptive immunity alterations, in a sizeable population of patients with non-ST elevation (NSTE)-ACS (n =95), as compared with patients presenting chronic stable angina (SA) (n= 80) and individuals without overt cardiovascular diseases (controls) (n =70). Characteristics of study population are reported in Table 1. All patients gave their written informed consent. The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki. The Ethics Committee of the Catholic University of Rome approved the study. The author(s) of this article have certified that they comply with the Principles of Ethical Publishing in the International Journal of Cardiology. Venous blood samples were taken at the time of patient enrollment. In ACS patients, blood samples were taken within 24-hours from symptom onset. We examined T-cell subset frequency (CD4+CD28nullTcells, Th17, and Treg defined as CD4+Foxp3+T-cells) by flow-cytometry, as previously described [5,10,13]. Cut-off values ≥4% and ≥2.0% (90th percentile of distribution in our control group) and ≤5% (10th percentile of distribution in our control group) were chosen to define patients with high frequency of CD4+CD28nullT-cells and Th17 and with low frequency of Treg, respectively. Data distribution was assessed by Shapiro Wilk test. Comparisons between groups were done by 1-way ANOVA for repeated measures, with Bonferroni correction for multiple pairwise comparisons, or Kruskal Wallis test, with Dunn's test for multiple pairwise comparison, as appropriate. Comparisons of two-related-samples within groups were done by Wilcoxon signed-rank test. Spearman's rank test was used for correlations. Proportions were compared using Chi-square test. The two step cluster analysis procedure, by the Bayesian Information Criterion (BIC), was applied to identify natural groupings of ACS and SA patients according to single T-cell subsets and their ratios. ☆ There are no relationships with industry. ⁎ Corresponding author at: Cardiology, Catholic University, Largo A. Gemelli, 8-00168 Rome, Italy. Tel.: +39 06 30154187; fax: +39 06 3055535. E-mail address: [email protected] (G. Liuzzo). 1 This author takes responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.
Survival analysis based on Kaplan–Meier curves and log-rank tests were used to assess the event free-survival between ACS patients according to T-cell distribution. Cox proportional hazards regression analysis was applied to identify the variables associated with long-term outcome in ACS patients. A two-tailed P value b0.05 was considered statistically significant. Statistical analysis was performed with SPSS 18.0 software (SPSS Inc., Chicago, Illinois). CD4+CD28nullT-cell frequency was significantly higher and Treg frequency lower in ACS patients as compared to SA and controls (P b 0.001 for all comparisons, Fig. 1, Panels A and B). To define an alteration of the balance, we calculated the CD4+CD28null/Treg cell percentage ratio in each subject, choosing a cut-off value ≥1.1 (99th percentile of distribution in our control group) and finding a strikingly higher CD4+CD28null/Treg ratio in ACS patients than in SA patients and in controls (P b 0.001 for all comparisons). Moreover, we observed a statistically significant inverse correlation between CD4+CD28nullT-cell frequency and Treg (R = −0.27; P = 0.019). In sharp contrast, in SA patients a positive correlation was observed between these T-cell subsets (R = 0.28; P = 0.014), suggesting that when aggressive T-cell responses emerge in this group of patients, an adequate Treg response might intervene to maintain immune homeostasis, while this regulatory response is not observed in ACS patients. The trends of Th17 paralleled those observed for CD4+CD28nullT-cells, with a frequency significantly higher in ACS than in SA and in controls (Pb 0.05 for both comparisons) (Fig. 1, Panel C), a negative correlation between Th17 and Treg cells in ACS patients (R=−0.25; Pb 0.001) versus a direct correlation in SA patient (R=0.33; P=0.004) and a Th17/Treg ratio significantly higher in ACS patients than in SA patients and in controls (Pb 0.001 for both comparisons). The functional profile of CD4+CD28nullT-cells and Treg was assessed by INF-γ and IL-10 production respectively upon stimulation, as previously described [5]. The functional profile of CD4+CD28nullT-cells and Treg paralleled their number, as the percentage of CD4+CD28nullTcells producing IFN-γ was higher (P b 0.001) and the percentage of Treg producing IL-10 was lower (P = 0.009) in ACS than in SA and in controls. Moreover, in ACS a statistically significant negative correlation was detected between CD4+CD28nullT-cells producing INF-γ and Treg producing IL-10 (r = −0.46; P = 0.010). In 30 ACS patients without recurrence of coronary events, we reassessed CD4+CD28nullT-cell/Treg balance at 1-month follow-up. Interestingly, CD4+CD28nullT-cell frequency significantly decreased and Treg frequency significantly increased (P= 0.003 and P b 0.001, respectively) as compared to baseline values. Furthermore, a striking decrease of CD4+CD28null/Treg ratio was observed as compared to baseline value (P b 0.001). Thus, in ACS perturbation of adaptive immunity was extremely dynamic. Of note, a previous study in a smaller number of ACS patients reported that about 2 months following ACS the frequency of Treg increased, thus confirming that Treg dysfunction can be transient [14]. To further confirm whether the altered balance of T-cell subsets during the acute phase of the disease tends to be associated in the single patient, and to investigate whether this may be relevant for the outcome, a two step cluster analysis procedure was used. In the overall population of 245 individuals, two clusters of homogeneous patients were identified according to frequencies of CD4+CD28nullT-cells, Treg, and CD4+CD28null/Treg ratio. Cluster-1 (42% of ACS, 4% of SA, 0% of
Letters to the Editor
565
Table 1 Clinical characteristics of study population. ACS
SA
Controls
P-value
Number of patients Sex (M/F) Age (mean ± SD)
95 75/20 66 ± 12
80 68/12 64 ± 10
70 43/27 59 ± 9
0.002a 0.002a
Risk factors – Hypercholesterolemia, n (%) – Hypertension, n (%) – Smoke, n (%) – Obesity, n (%) – Family history of IHD, n (%) – Diabetes, n (%)
56 (59) 75 (79) 52 (55) 15 (16) 33 (35) 25 (26)
59 (74) 58 (73) 41 (47) 3 (4) 43 (53) 26 (32)
22 (32) 32 (52) 34 (30) 4 (6) 21 (20) 6 (9)
b0.001ab b0.001a 0.730 0.011b 0.006ab 0.002a
Previous history – ACS, n (%) – Previous PCI/CABG, n (%)
19 (20) 16 (17)
25 (31) 27 (34)
NA NA
0.033b 0.005b
Medications – Aspirin, n (%) – Ticlopidin/clopidogrel, n (%) – Low molecular weight heparin, n (%) – Glycoprotein IIb/IIIa inhibitors, n (%) – β-Blockers, n (%) – ACE-inhibitors/ARBs, n (%) – Statins, n (%) – Insulin, n (%) – Oral antidiabetic drugs, n (%)
95 (100) 84 (88) 95 (100) 52 (55) 62 (65) 74 (78) 43 (45) 10 (11) 15 (16)
74 (93) 34 (43) NA NA 53 (66) 59 (74) 54 (68) 8 (10) 18 (22)
27 (39) 2 (3) NA NA 28 (40) 44 (63) 11 (16) 1 (1) 3 (4)
b0.001a b0.001ab – – 0.001a 0.096 b0.001ab 0.064 0.007a
In-hospital management – cTnT N 0.01 ng/mL, n (%) – LVEF (mean ± SD) – Multi-vessel disease, n (%) – PCI/CABG for the index event, n (%)
69 (73) 50 ± 8 55 (58) 87 (92)
NA 55 ± 7 44 (55) 65 (81)
NA NA NA NA
– 0.89 0.11 0.037b
Follow-up events – Non-ST-elevation ACS (%) – ST-elevation acute MI (%) – Cardiac death (%) – Total events (%)
12 (13) 2 (2) 2 (2) 16 (17)
4 (5) 1 (1) 0 5 (6)
– – – –
– – – b0.001b
Laboratory assay (mean ± SD) – Total Cholesterol (mg/dL) – LDL (mg/dL) – HDL (mg/dL) – Triglycerides (mg/dL) – Lymphocyte count (109/L) – Total CD4+ T-cell frequency (%) – hs-CRP (mg/L), median (range)
188 ± 50 119 ± 40 43 ± 10 160 ± 105 2 ± 0.9 50.3 ± 20.2 5.5 (0.3–96.4)
180 ± 52 99 ± 49 50 ± 13 130 ± 70 2 ± 0.8 50.5 ± 19.7 2.0 (0.2–49.2)
183 ± 49 114 ± 38 49 ± 14 150 ± 101 2 ± 0.5 48.4 ± 24.3 1.1 (0.2–8.9)
0.68 0.10 0.001b 0.33 0.70 0.40 b0.001c
2.2 (0.2–35.0) 7.0 (0.9–12.0) 1.3 (0.1–4.5) 0.35 (0.02–8.7) 0.30 (0.03–1.73)
1.5 (0.1–8.0) 7.8 (4.1–12.3) 1.0 (0.2–2.3) 0.22 (0.02–1.3) 0.12 (0.02–0.50)
b0.001d b0.001e 0.003f b0.001g b0.001g
Frequency of different T-cell subsets Expressed as percentage of the entire CD4+ T-cell population, median (range) 7.0 (0.2–39.0) – CD4+CD28null T-cells (%) – CD4+Foxp3+ T-cells (Treg) (%) 3.1 (0.2–12.1) – CD4+IL17+ T-cells (Th17) (%) 1.9 (0.4–6.6) + null – CD4 CD28 /Treg ratio 2.9 (0.2–69.6) – Th17/Treg ratio 0.81 (0.08–31.5)
ACS = acute coronary syndromes; DM = diabetes mellitus; IHD = ischemic heart disease; PCI = percutaneous coronary intervention; CABG = coronary artery by-pass graft; cTnT = cardiac troponin T; LVEF = left ventricular ejection fraction; MI = myocardial infarction; hs-CRP = high-sensitivity C-reactive protein. a Controls were younger, had a better risk factor profile, and took less medications at the time of blood sampling as compared with both ACS and SA patients. b ACS and SA patients differed regarding risk factors, treatment and recurrence of new acute coronary events at 12-months follow-up. c hs-CRP levels were higher in ACS, than in SA and controls. d CD4+CD28null T-cells frequency was higher in ACS than in SA and in controls, and it was higher in SA than in controls. e Treg frequency was lower in ACS than in SA and in controls, and it was similar in SA and controls. f Th17 frequency was higher in ACS than in SA and in controls, and it was similar in SA and controls. g CD4+CD28null/Treg ratio and Th17/Treg ratio were higher in ACS than in SA and in controls, and they were higher in SA than in controls.
controls) was characterized by CD4+CD28nullT-cells ≥4%, Treg ≤5%, and CD4+CD28null/Treg ratio ≥1.1. Cluster-2 (15% of ACS, 62% of SA, 80% of controls) was characterized by CD4+CD28nullT-cells b4%, Treg N5%, and CD4+CD28null/Treg ratio b1.1 (an immune profile opposite to the one observed in Cluster-1). Cluster-3 included the remaining patients, heterogeneous according to their immune profile (Fig. 2, Panel A).
All ACS were followed-up for 12 months. The 17% of ACS patients developed new acute coronary events (Table 1). The 40 ACS patients in Cluster-1 had a significantly higher incidence of new acute coronary events as compared with the remaining patients (12/40, 30% versus 4/ 55, 7%; P =0.005); in particular, no events occurred in the 14 ACS patients in Cluster-2. Survival analysis based on Kaplan–Meier curves
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Fig. 1. Frequencies of T-cell subsets in different groups. Frequencies of CD4+CD28nullT-cells (Panel A), CD4+Foxp3+ T-cells (Treg) (Panel B) and CD4+IL17+T-cells (Th17) (Panel C) were determined by two-color flow-cytometry, and expressed as percentage of total CD4+T-cells. CD4+CD28null and Th17 frequencies were significantly higher, and Treg significantly lower, in ACS than in SA and in controls. Data are presented as single data points. Dotted lines indicate cut-off values: CD4+CD28nullT-cell frequency ≥4% (Panel A); Treg ≤5% (Panel B); Th17 cells ≥ 2% (Panel C).
and log-rank tests according to clusters (Fig. 2, Panels C and D) confirmed the association between altered immune balance and outcome. In Cox proportional hazards regression analysis Cluster-1 (HR = 3.82, 95% CI 1.13–12.76; P = 0.032) was the only variable independently associated with the outcome. These findings confirm and expand our previous observations that Tcell perturbation in ACS impacts on prognosis [6,7], providing a more comprehensive assessment of T-cell subsets. A plethora of previous studies have showed that ACS associated with raised levels of soluble markers of inflammation exhibit a worse outcome [1,2]. A change of focus from soluble markers of inflammation to markers of adaptive immunity activation might prove to be more rewarding, as T-cells are the main conductors of immune response and their assessment might allow the identification of new therapeutic targets in the subset of patients in whom an inflammatory outburst is the likely cause of coronary instability [15]. In our study, Treg were defined as Foxp3+ cells, and this might represent a limitation [14]; however, this transcription factor is crucial to Treg development and function [13]. Moreover, T-cell count and function in peripheral blood not necessarily reflect what happens in the microenvironment of the unstable atherosclerotic plaque; yet, we have previously shown that CD4+CD28nullT-cells infiltrate unstable coronary plaques where they undergo clonal expansion and Foxp3+Treg represent a small minority of T-cell population in vulnerable atherosclerotic plaques. Finally, the clustering of patients as
performed in the present study remains exploratory in nature; therefore, our findings suggest an association between an altered immune profile and a worse outcome of ACS patients, but do not resolve the question of causality and they need to be validated in a separate independent prospective study. In conclusion, our data show for the first time that about half of patients with ACS exhibit a unique immune profile, associated with a worse outcome at 1-year follow-up, which is very rarely found in SA and never in healthy controls. In this subset of ACS patients, the failure to mount a counter regulatory response to the activation of aggressive Tcells might play a key pathogenetic role and might represent an attractive therapeutic target [16]. The remaining half of ACS patients consisted of a small subset with an immune profile similar to that found in SA and in controls, and of a larger subset exhibiting a heterogeneous immune profile. In these subsets, coronary instability is unlikely to be caused by an inflammatory outburst.
References [1] Liuzzo G, Biasucci LM, Gallimore JR, et al. The prognostic value of C-reactive protein and serum amyloid a protein in severe unstable angina. N Engl J Med 1994;331:417–24. [2] Blake GJ, Ridker PM. C-reactive protein and other inflammatory risk markers in acute coronary syndromes. J Am Coll Cardiol 2003;41(4 Suppl S):37S–42S. [3] Caligiuri G, Paulsson G, Nicoletti A, Maseri A, Hansson GK. Evidence for antigen-driven T-cell response in unstable angina. Circulation 2000;102:1114–9.
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Fig. 2. T-cell cluster analysis and outcome in ACS patients. Panel A: in the overall population of 245 individuals, two clusters of homogeneous patients were identified according to frequencies of CD4+CD28nullT-cells, CD4+Foxp3+T-cells (Treg), and CD4+CD28null/Treg ratio. Cluster 1: characterized by a harmful immune profile, with CD4+CD28nullT-cells ≥ 4%, Treg ≤5%, and CD4+CD28null/Treg ratio ≥ 1.1; Cluster 2: characterized by a favorable immune profile, with CD4+CD28nullT-cells b 4%, Treg N5%, and CD4+CD28null/Treg ratio b1.1; Cluster 3: heterogeneous population including 43% of ACS, 34% of SA, and 20% of controls. Panel B: hs-CRP levels were similar in ACS patients belonging to different clusters. Data are presented as single data points. Dotted lines indicate cut-off values: hs-CRP ≥3 mg/L and ≥10 mg/L, respectively. Panel C: unadjusted Kaplan–Meier cumulative survival plot in ACS patients according to clusters. The 12-months event-free survival from cardiovascular events was significantly lower in ACS patients belonging to Cluster-1 (red line) than in ACS patients belonging to Cluster-2 (green line) or Cluster-3 (blue line), according to log-rank tests. In particular, no events occurred in the 14 ACS patients in Cluster-2. Clusters are defined as in Panel A. Panel D: Kaplan–Meier cumulative survival plot in ACS patients according to clusters (Cluster-1, red line versus Cluster-2 + 3, blue line), adjusted for age, sex, diabetes, previous ACS, cTnT (N 0.01 ng/mL) and CRP levels (≥3 mg/L). Cluster-1 (HR 3.82, 95% CI 1.13–12.76; P = 0.032) was the only variable independently associated with the outcome, by Cox proportional hazards regression analysis. Clusters are defined as in Panel A.
[4] De Palma R, Del Galdo F, Abbate G, et al. Patients with acute coronary syndrome show oligoclonal T-cell recruitment within unstable plaque: evidence for a local, intracoronary immunologic mechanism. Circulation 2006;113:640–6. [5] Liuzzo G, Kopecky SL, Frye RL, et al. Perturbation of the T-cell repertoire in patients with unstable angina. Circulation 1999;100:2135–9. [6] Liuzzo G, Biasucci LM, Trotta G, et al. Unusual CD4+ CD28null T-lymphocytes and recurrence of acute coronary events. J Am Coll Cardiol 2007;50:1450–8. [7] Giubilato S, Liuzzo G, Brugaletta S, et al. Expansion of CD4 +CD28null T-lymphocytes in diabetic patients: exploring new pathogenetic mechanisms of increased cardiovascular risk in diabetes mellitus. Eur Heart J 2011;32:1214–26. [8] Mor A, Luboshits G, Planer D, Keren G, George J. Altered status of CD4(+)CD25(+) regulatory T cells in patients with acute coronary syndromes. Eur Heart J 2006;27:2530–7. [9] Han S, Liu P, Zhang W, et al. The opposite-direction modulation of CD4+ CD25+ Tregs and T helper 1 cells in acute coronary syndromes. Clin Immunol 2007;124:90–7. [10] Cheng X, Yu X, Ding YJ, et al. The Th17/Treg imbalance in patients with acute coronary syndrome. Clin Immunol 2008;127:89–97. 0167-5273/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ijcard.2013.01.009
[11] Li Q, Wang Y, Chen K, et al. The role of oxidized low-density lipoprotein in breaking peripheral Th17/Treg balance in patients with acute coronary syndrome. Biochem Biophys Res Commun 2010;394:836–42. [12] Zhao Z, Wu Y, Cheng M, et al. Activation of Th17/Th1 and Th1, but not Th17, is associated with the acute cardiac event in patients with acute coronary syndrome. Atherosclerosis 2011;217:518–24. [13] Sakaguchi S, Miyara M, Costantino CM, Hafler DA. FOXP3+ regulatory T cells in the human immune system. Nat Rev Immunol 2010;10:490–500. [14] Ammirati E, Cianflone D, Banfi M, et al. Circulating CD4+CD25hiCD127low regulatory T-Cell levels do not reflect the extent or severity of carotid and coronary atherosclerosis. Arterioscler Thromb Vasc Biol 2010;30:1832–41. [15] Crea F, Liuzzo G. Pathogenesis of acute coronary syndromes. J Am Coll Cardiol 2013;61:1–11. [16] Lahoute C, Herbin O, Mallat Z, Tedgui A. Adaptive immunity in atherosclerosis: mechanisms and future therapeutic targets. Nat Rev Cardiol 2011;8:348–58.
Letters to the Editor
Identification of unique adaptive immune system signature in acute coronary syndromes☆ Giovanna Liuzzo ⁎,1, Rocco A. Montone 1, Mario Gabriele 1, Daniela Pedicino 1, Ada F. Giglio 1, Francesco Trotta 1, Vincenzo A. Galiffa 1, Marco Previtero 1, Anna Severino 1, Luigi M. Biasucci 1, Filippo Crea 1 Institute of Cardiology, Catholic University, Rome, Italy
a r t i c l e
i n f o
Article history: Received 19 December 2012 Accepted 13 January 2013 Available online 5 February 2013 Keywords: Acute coronary syndromes Adaptive immunity Pathogenesis Prognosis
Several studies have consistently demonstrated that inflammation plays a key role in the pathogenesis of acute coronary syndromes (ACS) [1,2]. More recent studies have highlighted the importance of adaptive immunity in ACS [3,4]. In particular, profound abnormalities have been observed in specific subsets of T-cells, including CD4+CD28nullT-cells, a subset of cytotoxic CD4+T-lymphocytes producing large amount of interferon-γ (IFN-γ) [5–7], naturally occurring regulatory T-cells (Treg) [8,9] and interleukin (IL)-17-producing T-cells (Th17) [10–12]. Our group systematically investigated the clinical relevance of adaptive immunity alterations, in a sizeable population of patients with non-ST elevation (NSTE)-ACS (n =95), as compared with patients presenting chronic stable angina (SA) (n= 80) and individuals without overt cardiovascular diseases (controls) (n =70). Characteristics of study population are reported in Table 1. All patients gave their written informed consent. The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki. The Ethics Committee of the Catholic University of Rome approved the study. The author(s) of this article have certified that they comply with the Principles of Ethical Publishing in the International Journal of Cardiology. Venous blood samples were taken at the time of patient enrollment. In ACS patients, blood samples were taken within 24-hours from symptom onset. We examined T-cell subset frequency (CD4+CD28nullTcells, Th17, and Treg defined as CD4+Foxp3+T-cells) by flow-cytometry, as previously described [5,10,13]. Cut-off values ≥4% and ≥2.0% (90th percentile of distribution in our control group) and ≤5% (10th percentile of distribution in our control group) were chosen to define patients with high frequency of CD4+CD28nullT-cells and Th17 and with low frequency of Treg, respectively. Data distribution was assessed by Shapiro Wilk test. Comparisons between groups were done by 1-way ANOVA for repeated measures, with Bonferroni correction for multiple pairwise comparisons, or Kruskal Wallis test, with Dunn's test for multiple pairwise comparison, as appropriate. Comparisons of two-related-samples within groups were done by Wilcoxon signed-rank test. Spearman's rank test was used for correlations. Proportions were compared using Chi-square test. The two step cluster analysis procedure, by the Bayesian Information Criterion (BIC), was applied to identify natural groupings of ACS and SA patients according to single T-cell subsets and their ratios. ☆ There are no relationships with industry. ⁎ Corresponding author at: Cardiology, Catholic University, Largo A. Gemelli, 8-00168 Rome, Italy. Tel.: +39 06 30154187; fax: +39 06 3055535. E-mail address: [email protected] (G. Liuzzo). 1 This author takes responsibility for all aspects of the reliability and freedom from bias of the data presented and their discussed interpretation.
Survival analysis based on Kaplan–Meier curves and log-rank tests were used to assess the event free-survival between ACS patients according to T-cell distribution. Cox proportional hazards regression analysis was applied to identify the variables associated with long-term outcome in ACS patients. A two-tailed P value b0.05 was considered statistically significant. Statistical analysis was performed with SPSS 18.0 software (SPSS Inc., Chicago, Illinois). CD4+CD28nullT-cell frequency was significantly higher and Treg frequency lower in ACS patients as compared to SA and controls (P b 0.001 for all comparisons, Fig. 1, Panels A and B). To define an alteration of the balance, we calculated the CD4+CD28null/Treg cell percentage ratio in each subject, choosing a cut-off value ≥1.1 (99th percentile of distribution in our control group) and finding a strikingly higher CD4+CD28null/Treg ratio in ACS patients than in SA patients and in controls (P b 0.001 for all comparisons). Moreover, we observed a statistically significant inverse correlation between CD4+CD28nullT-cell frequency and Treg (R = −0.27; P = 0.019). In sharp contrast, in SA patients a positive correlation was observed between these T-cell subsets (R = 0.28; P = 0.014), suggesting that when aggressive T-cell responses emerge in this group of patients, an adequate Treg response might intervene to maintain immune homeostasis, while this regulatory response is not observed in ACS patients. The trends of Th17 paralleled those observed for CD4+CD28nullT-cells, with a frequency significantly higher in ACS than in SA and in controls (Pb 0.05 for both comparisons) (Fig. 1, Panel C), a negative correlation between Th17 and Treg cells in ACS patients (R=−0.25; Pb 0.001) versus a direct correlation in SA patient (R=0.33; P=0.004) and a Th17/Treg ratio significantly higher in ACS patients than in SA patients and in controls (Pb 0.001 for both comparisons). The functional profile of CD4+CD28nullT-cells and Treg was assessed by INF-γ and IL-10 production respectively upon stimulation, as previously described [5]. The functional profile of CD4+CD28nullT-cells and Treg paralleled their number, as the percentage of CD4+CD28nullTcells producing IFN-γ was higher (P b 0.001) and the percentage of Treg producing IL-10 was lower (P = 0.009) in ACS than in SA and in controls. Moreover, in ACS a statistically significant negative correlation was detected between CD4+CD28nullT-cells producing INF-γ and Treg producing IL-10 (r = −0.46; P = 0.010). In 30 ACS patients without recurrence of coronary events, we reassessed CD4+CD28nullT-cell/Treg balance at 1-month follow-up. Interestingly, CD4+CD28nullT-cell frequency significantly decreased and Treg frequency significantly increased (P= 0.003 and P b 0.001, respectively) as compared to baseline values. Furthermore, a striking decrease of CD4+CD28null/Treg ratio was observed as compared to baseline value (P b 0.001). Thus, in ACS perturbation of adaptive immunity was extremely dynamic. Of note, a previous study in a smaller number of ACS patients reported that about 2 months following ACS the frequency of Treg increased, thus confirming that Treg dysfunction can be transient [14]. To further confirm whether the altered balance of T-cell subsets during the acute phase of the disease tends to be associated in the single patient, and to investigate whether this may be relevant for the outcome, a two step cluster analysis procedure was used. In the overall population of 245 individuals, two clusters of homogeneous patients were identified according to frequencies of CD4+CD28nullT-cells, Treg, and CD4+CD28null/Treg ratio. Cluster-1 (42% of ACS, 4% of SA, 0% of
Letters to the Editor
565
Table 1 Clinical characteristics of study population. ACS
SA
Controls
P-value
Number of patients Sex (M/F) Age (mean ± SD)
95 75/20 66 ± 12
80 68/12 64 ± 10
70 43/27 59 ± 9
0.002a 0.002a
Risk factors – Hypercholesterolemia, n (%) – Hypertension, n (%) – Smoke, n (%) – Obesity, n (%) – Family history of IHD, n (%) – Diabetes, n (%)
56 (59) 75 (79) 52 (55) 15 (16) 33 (35) 25 (26)
59 (74) 58 (73) 41 (47) 3 (4) 43 (53) 26 (32)
22 (32) 32 (52) 34 (30) 4 (6) 21 (20) 6 (9)
b0.001ab b0.001a 0.730 0.011b 0.006ab 0.002a
Previous history – ACS, n (%) – Previous PCI/CABG, n (%)
19 (20) 16 (17)
25 (31) 27 (34)
NA NA
0.033b 0.005b
Medications – Aspirin, n (%) – Ticlopidin/clopidogrel, n (%) – Low molecular weight heparin, n (%) – Glycoprotein IIb/IIIa inhibitors, n (%) – β-Blockers, n (%) – ACE-inhibitors/ARBs, n (%) – Statins, n (%) – Insulin, n (%) – Oral antidiabetic drugs, n (%)
95 (100) 84 (88) 95 (100) 52 (55) 62 (65) 74 (78) 43 (45) 10 (11) 15 (16)
74 (93) 34 (43) NA NA 53 (66) 59 (74) 54 (68) 8 (10) 18 (22)
27 (39) 2 (3) NA NA 28 (40) 44 (63) 11 (16) 1 (1) 3 (4)
b0.001a b0.001ab – – 0.001a 0.096 b0.001ab 0.064 0.007a
In-hospital management – cTnT N 0.01 ng/mL, n (%) – LVEF (mean ± SD) – Multi-vessel disease, n (%) – PCI/CABG for the index event, n (%)
69 (73) 50 ± 8 55 (58) 87 (92)
NA 55 ± 7 44 (55) 65 (81)
NA NA NA NA
– 0.89 0.11 0.037b
Follow-up events – Non-ST-elevation ACS (%) – ST-elevation acute MI (%) – Cardiac death (%) – Total events (%)
12 (13) 2 (2) 2 (2) 16 (17)
4 (5) 1 (1) 0 5 (6)
– – – –
– – – b0.001b
Laboratory assay (mean ± SD) – Total Cholesterol (mg/dL) – LDL (mg/dL) – HDL (mg/dL) – Triglycerides (mg/dL) – Lymphocyte count (109/L) – Total CD4+ T-cell frequency (%) – hs-CRP (mg/L), median (range)
188 ± 50 119 ± 40 43 ± 10 160 ± 105 2 ± 0.9 50.3 ± 20.2 5.5 (0.3–96.4)
180 ± 52 99 ± 49 50 ± 13 130 ± 70 2 ± 0.8 50.5 ± 19.7 2.0 (0.2–49.2)
183 ± 49 114 ± 38 49 ± 14 150 ± 101 2 ± 0.5 48.4 ± 24.3 1.1 (0.2–8.9)
0.68 0.10 0.001b 0.33 0.70 0.40 b0.001c
2.2 (0.2–35.0) 7.0 (0.9–12.0) 1.3 (0.1–4.5) 0.35 (0.02–8.7) 0.30 (0.03–1.73)
1.5 (0.1–8.0) 7.8 (4.1–12.3) 1.0 (0.2–2.3) 0.22 (0.02–1.3) 0.12 (0.02–0.50)
b0.001d b0.001e 0.003f b0.001g b0.001g
Frequency of different T-cell subsets Expressed as percentage of the entire CD4+ T-cell population, median (range) 7.0 (0.2–39.0) – CD4+CD28null T-cells (%) – CD4+Foxp3+ T-cells (Treg) (%) 3.1 (0.2–12.1) – CD4+IL17+ T-cells (Th17) (%) 1.9 (0.4–6.6) + null – CD4 CD28 /Treg ratio 2.9 (0.2–69.6) – Th17/Treg ratio 0.81 (0.08–31.5)
ACS = acute coronary syndromes; DM = diabetes mellitus; IHD = ischemic heart disease; PCI = percutaneous coronary intervention; CABG = coronary artery by-pass graft; cTnT = cardiac troponin T; LVEF = left ventricular ejection fraction; MI = myocardial infarction; hs-CRP = high-sensitivity C-reactive protein. a Controls were younger, had a better risk factor profile, and took less medications at the time of blood sampling as compared with both ACS and SA patients. b ACS and SA patients differed regarding risk factors, treatment and recurrence of new acute coronary events at 12-months follow-up. c hs-CRP levels were higher in ACS, than in SA and controls. d CD4+CD28null T-cells frequency was higher in ACS than in SA and in controls, and it was higher in SA than in controls. e Treg frequency was lower in ACS than in SA and in controls, and it was similar in SA and controls. f Th17 frequency was higher in ACS than in SA and in controls, and it was similar in SA and controls. g CD4+CD28null/Treg ratio and Th17/Treg ratio were higher in ACS than in SA and in controls, and they were higher in SA than in controls.
controls) was characterized by CD4+CD28nullT-cells ≥4%, Treg ≤5%, and CD4+CD28null/Treg ratio ≥1.1. Cluster-2 (15% of ACS, 62% of SA, 80% of controls) was characterized by CD4+CD28nullT-cells b4%, Treg N5%, and CD4+CD28null/Treg ratio b1.1 (an immune profile opposite to the one observed in Cluster-1). Cluster-3 included the remaining patients, heterogeneous according to their immune profile (Fig. 2, Panel A).
All ACS were followed-up for 12 months. The 17% of ACS patients developed new acute coronary events (Table 1). The 40 ACS patients in Cluster-1 had a significantly higher incidence of new acute coronary events as compared with the remaining patients (12/40, 30% versus 4/ 55, 7%; P =0.005); in particular, no events occurred in the 14 ACS patients in Cluster-2. Survival analysis based on Kaplan–Meier curves
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Fig. 1. Frequencies of T-cell subsets in different groups. Frequencies of CD4+CD28nullT-cells (Panel A), CD4+Foxp3+ T-cells (Treg) (Panel B) and CD4+IL17+T-cells (Th17) (Panel C) were determined by two-color flow-cytometry, and expressed as percentage of total CD4+T-cells. CD4+CD28null and Th17 frequencies were significantly higher, and Treg significantly lower, in ACS than in SA and in controls. Data are presented as single data points. Dotted lines indicate cut-off values: CD4+CD28nullT-cell frequency ≥4% (Panel A); Treg ≤5% (Panel B); Th17 cells ≥ 2% (Panel C).
and log-rank tests according to clusters (Fig. 2, Panels C and D) confirmed the association between altered immune balance and outcome. In Cox proportional hazards regression analysis Cluster-1 (HR = 3.82, 95% CI 1.13–12.76; P = 0.032) was the only variable independently associated with the outcome. These findings confirm and expand our previous observations that Tcell perturbation in ACS impacts on prognosis [6,7], providing a more comprehensive assessment of T-cell subsets. A plethora of previous studies have showed that ACS associated with raised levels of soluble markers of inflammation exhibit a worse outcome [1,2]. A change of focus from soluble markers of inflammation to markers of adaptive immunity activation might prove to be more rewarding, as T-cells are the main conductors of immune response and their assessment might allow the identification of new therapeutic targets in the subset of patients in whom an inflammatory outburst is the likely cause of coronary instability [15]. In our study, Treg were defined as Foxp3+ cells, and this might represent a limitation [14]; however, this transcription factor is crucial to Treg development and function [13]. Moreover, T-cell count and function in peripheral blood not necessarily reflect what happens in the microenvironment of the unstable atherosclerotic plaque; yet, we have previously shown that CD4+CD28nullT-cells infiltrate unstable coronary plaques where they undergo clonal expansion and Foxp3+Treg represent a small minority of T-cell population in vulnerable atherosclerotic plaques. Finally, the clustering of patients as
performed in the present study remains exploratory in nature; therefore, our findings suggest an association between an altered immune profile and a worse outcome of ACS patients, but do not resolve the question of causality and they need to be validated in a separate independent prospective study. In conclusion, our data show for the first time that about half of patients with ACS exhibit a unique immune profile, associated with a worse outcome at 1-year follow-up, which is very rarely found in SA and never in healthy controls. In this subset of ACS patients, the failure to mount a counter regulatory response to the activation of aggressive Tcells might play a key pathogenetic role and might represent an attractive therapeutic target [16]. The remaining half of ACS patients consisted of a small subset with an immune profile similar to that found in SA and in controls, and of a larger subset exhibiting a heterogeneous immune profile. In these subsets, coronary instability is unlikely to be caused by an inflammatory outburst.
References [1] Liuzzo G, Biasucci LM, Gallimore JR, et al. The prognostic value of C-reactive protein and serum amyloid a protein in severe unstable angina. N Engl J Med 1994;331:417–24. [2] Blake GJ, Ridker PM. C-reactive protein and other inflammatory risk markers in acute coronary syndromes. J Am Coll Cardiol 2003;41(4 Suppl S):37S–42S. [3] Caligiuri G, Paulsson G, Nicoletti A, Maseri A, Hansson GK. Evidence for antigen-driven T-cell response in unstable angina. Circulation 2000;102:1114–9.
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Fig. 2. T-cell cluster analysis and outcome in ACS patients. Panel A: in the overall population of 245 individuals, two clusters of homogeneous patients were identified according to frequencies of CD4+CD28nullT-cells, CD4+Foxp3+T-cells (Treg), and CD4+CD28null/Treg ratio. Cluster 1: characterized by a harmful immune profile, with CD4+CD28nullT-cells ≥ 4%, Treg ≤5%, and CD4+CD28null/Treg ratio ≥ 1.1; Cluster 2: characterized by a favorable immune profile, with CD4+CD28nullT-cells b 4%, Treg N5%, and CD4+CD28null/Treg ratio b1.1; Cluster 3: heterogeneous population including 43% of ACS, 34% of SA, and 20% of controls. Panel B: hs-CRP levels were similar in ACS patients belonging to different clusters. Data are presented as single data points. Dotted lines indicate cut-off values: hs-CRP ≥3 mg/L and ≥10 mg/L, respectively. Panel C: unadjusted Kaplan–Meier cumulative survival plot in ACS patients according to clusters. The 12-months event-free survival from cardiovascular events was significantly lower in ACS patients belonging to Cluster-1 (red line) than in ACS patients belonging to Cluster-2 (green line) or Cluster-3 (blue line), according to log-rank tests. In particular, no events occurred in the 14 ACS patients in Cluster-2. Clusters are defined as in Panel A. Panel D: Kaplan–Meier cumulative survival plot in ACS patients according to clusters (Cluster-1, red line versus Cluster-2 + 3, blue line), adjusted for age, sex, diabetes, previous ACS, cTnT (N 0.01 ng/mL) and CRP levels (≥3 mg/L). Cluster-1 (HR 3.82, 95% CI 1.13–12.76; P = 0.032) was the only variable independently associated with the outcome, by Cox proportional hazards regression analysis. Clusters are defined as in Panel A.
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