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Low CD34+ cell count and metabolic syndrome synergistically increase the risk of adverse outcomes
Atherosclerosis 207 (2009) 213–219
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Atherosclerosis journal homepage: www.elsevier.com/locate/atherosclerosis
Low CD34+ cell count and metabolic syndrome synergistically increase the risk of adverse outcomes Gian Paolo Fadini a,∗ , Saula de Kreutzenberg a , Carlo Agostini a , Elisa Boscaro a , Antonio Tiengo a , Stefanie Dimmeler b , Angelo Avogaro a a b
Department of Clinical and Experimental Medicine, University of Padova, Medical School, Padova, Italy Molecular Cardiology, Department of Internal Medicine IV, University of Frankfurt, Frankfurt, Germany
a r t i c l e
i n f o
Article history: Received 22 January 2009 Received in revised form 21 March 2009 Accepted 26 March 2009 Available online 5 April 2009 Keywords: Endothelium Metabolic syndrome Stem cells Cardiovascular risk
a b s t r a c t Objectives: Metabolic syndrome (MetS) associates with endothelial dysfunction and a high risk of cardiovascular events and death. Circulating progenitor cells have been shown to contribute to endothelial homeostasis and repair . We aimed to test whether progenitor cell count is an independent event predictor and modifies cardiovascular risk associated with MetS. Methods: On the basis of the expression of CD34, CD133 and KDR, 6 phenotypes of progenitor cells were counted using flow cytometry in 214 subjects with and without MetS. We recorded classical risk factors and MetS components, cumulative risk estimates, and high-sensitive C-reactive protein. Subjects were followed-up for a median of 34 months to collect total events, cardiovascular events and all-cause mortality. Results: In the Cox proportional hazards regression analyses, we found that, unlike other phenotypes, reduced CD34+ cells predicted cardiovascular and total events and death, independently of all potential confounders. Remarkably, a low CD34+ cell count significantly increased the risk associated with MetS, as shown by synergy indexes. Conclusion: The level of circulating CD34+ cells is a novel independent risk biomarker and modulates outcomes in the MetS, suggesting that generic progenitor cells have a role in disease development or progression over the long-term. © 2009 Elsevier Ireland Ltd. All rights reserved.
1. Introduction The metabolic syndrome (MetS) comprises a cluster of cardiovascular risk factors that are present together in the same individuals more often than would be expected by chance [1]. According to the ATP-III definition, diagnosis of MetS is allowed with at least 3 among abdominal obesity, hypertension, low HDL, high triglycerides, and hyperglycemia [2]. Although the clinical utility of this designation is controversial [3], patients with such a combination of risk factors have a markedly elevated risk of cardiovascular disease (CVD) and total mortality [4,5]. Inflammation and insulin resistance are thought to mediate the aggregation of risk factors and the subsequent increase in cardiovascular events [6] but the mechanisms are still poorly understood. Even among patients with the syndrome, the risk is highly variable as it depends on other parameters, such as smoking and LDL [3,7]. Understand-
∗ Corresponding author at: Department of Clinical and Experimental Medicine, Metabolic Division, University of Padova, Medical School, V. Giustiniani, 2, 35128 Padova, Italy. Tel.: +39 049 8212185; fax: +39 049 8212184. E-mail address: [email protected] (G.P. Fadini). 0021-9150/$ – see front matter © 2009 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.atherosclerosis.2009.03.040
ing what modulates the risk in patients with the MetS will likely identify novel pathophysiological mechanisms of CVD induction, beyond classical risk factors. Dysfunction of the endothelial layer is a common feature of MetS components and is generally regarded as the earliest step of the atherogenetic process [8]. Both inflammation and insulin resistance converge towards inducing endothelial dysfunction, and increasing the risk of atherosclerotic CVD [9]. Homeostasis of the intimal layer relies on the contribution of circulating cells, called endothelial progenitor cells (EPCs) [10]. These cells participate in the turnover of healthy and damaged endothelium, as well as in angiogenetic processes [11]. Therefore, a reduction in EPCs may compromise endothelial integrity and hamper collateralization, thus promoting CVD development and progression. Circulating functional EPCs are believed to derive from more immature, relatively undifferentiated stem or progenitor cells [12]. Despite controversies on EPC definition, staining for the stem cell antigen CD34 and the endothelial antigen KDR (type 2 VEGF receptor) would provide a good estimate of circulating EPC levels [13]. Some studies also suggest that the stem cell antigen CD133 may help in identifying EPCs [14]. We have shown that CD34 + KDR + EPC count is reduced in early and late atherosclerotic disease [15–17], while longitudinal studies suggest
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that CD34 + KDR + EPCs negatively predict cardiovascular events in the short-term [18,19]. Consistently, MetS and its components have been associated with EPC reduction and/or dysfunction [20,21]. In contrast, we have demonstrated that total CD34+ cells, a more immature and generic cell population, correlated with all cardiovascular parameters and risk estimates better than CD34 + KDR+ cells and CD133-based phenotypes in a cohort of patients with different degrees of cardiovascular risk [22]. Moreover, CD34+ cells were synergistically affected by clustering MetS components, suggesting an interaction between progenitor cells and MetS in the induction/progression of CVD. In the present study, we describe follow-up data of the same cohort with the pre-specified aims of assessing whether CD34+ cell count predicts future events in the entire population, and whether it modifies the risk associated with the MetS. 2. Methods 2.1. Patients The study was approved from the Local Insitutional and Ethical Committee and informed consent was obtained from all subjects. From February 2004 to July 2005, a total of 214 subjects were recruited at our outpatient clinic. These included 114 apparently healthy subjects from a middle-aged population of office employees, who responded to an advertisement for cardiometabolic screening, plus 100 patients presenting for metabolic and related disorders (diabetes, hypertension, dyslipidemia, and/or obesity). The baseline clinical characteristics of these patients are described elsewhere [22]. Briefly, we recorded age, gender, family history for cardiovascular disease, body mass index, waist circumference, fasting blood glucose, systolic and diastolic blood pressure, total cholesterol, high-density lipoprotein (HDL), triglyceride concentrations, and a complete pharmacological history. LDL cholesterol was calculated using Friedewald’s formula. CVD was defined as coronary, cerebral or peripheral arterial disease. MetS was defined according to the revised ATP-III definition. The Framingham risk score was calculated as previously described [23]. Additionally, we calculated the predicted 10-year cardiovascular risk according to the Italian risk assessment program (accessible at the URL http://www.cuore.iss.it/sopra/calc-rischio en.asp), which is considered more accurate than the Framingham algorithm, for the Italian population [24]. The equation of the Italian risk score includes age, sex, smoking habit, systolic blood pressure, total, LDL and HDL cholesterol, and the presence/absence of diabetes and known hypertension. C-reactive protein was measured using the high-sensitive technique and categorized in “high” and “low” according to the established cut-off of 3.0 mg/L [25–27]. The presence of chronic renal failure (CRF, defined as an estimated glomerular filtration rate <60 mL/min/1.73 m2 , according to the modification of diet in renal disease (MDRD) equation)[28] was also assessed and included in the analyses. 2.2. Flow cytometry Peripheral blood progenitor cells were analyzed for the expression of surface antigens with direct flow cytometry (FACS Calibur, Becton Dickinson) as previously described in detail [17,22]. Briefly, cells were labelled with FITC-conjugated anti-CD34 (Becton Dickinson), PE-conjugated anti-KDR (R&D Systems) and APC-conjugated anti-CD133 (clone AC133 from Miltenyi Biotec) monoclonal antibodies. First CD34+ or CD133+ cells were gated and then assayed for expression of KDR in the mononuclear cell fraction. In all analyses, 5 × 105 events were acquired, scored using a FACS Calibur analyzer (Becton Dickinson), and processed using the Macintosh
CELLQuest software program (Becton Dickinson). Cell count is always expressed per 106 events. Double CD34/KDR staining was done in all 214 subjects, allowing the determination of CD34+ and CD34 + KDR+ cells. Triple CD133/CD34/KDR staining was done in 136 subjects (64% of total), allowing the determination of 4 additional phenotypes: CD133+, CD34 + CD133+, CD133 + KDR+, and CD34 + CD133 + KDR+ cells. 2.3. Follow-up Recruitment took 17 months, and subjects were followed for up to 42 months (median 34 months, interquartile range 29–38 months). The primary endpoint was first cardiovascular event, defined as cardiovascular death, acute myocardial infarction, unstable angina requiring hospitalization, severe peripheral ischemia, and stroke. Secondary endpoints were death from any cause and all events (including death and emergency hospitalization for any cause). Follow-up was performed directly or by telephone contact. All information about potential events was validated by review of medical records, hospital discharge letters, and charts of hospital stay. Death causes were confirmed by review of autopsy reports, death certificates, or information from family members regarding the circumstances of death. Cardiovascular death was defined as death due to acute myocardial infarction, cardiac perforation or pericardial tamponade, arrhythmia, cerebrovascular accident, or any death in which a cardiovascular cause could not be ruled out. Myocardial infarction was defined as the presence of diagnostic electrocardiographic changes and at least two of the following findings: typical ischemic chest pain, elevation of the serum level of creatine kinase MB fraction, or cardiac specific troponin. Unstable angina was defined as resting or new-onset or exacerbating typical chest pain with either ≥0.1 mV ST-segment depression or T-wave inversion in ≥2 contiguous electrocardiographic leads, normal creatine kinase MB fraction and serum cardiac troponin, and angiographically confirmed coronary artery disease. Stroke was defined as a new focal neurologic deficit lasting more than 24 h, with sidecoherent ischemic lesion(s) on a cerebral computed tomography scan. Severe peripheral ischemia was defined as ischemia threatening the viability of the limb and leading to a revascularization procedure or amputation. 2.4. Statistical analysis Continuous data are expressed as mean ± SD. Comparisons between two or more groups were performed with two-sided Student’s t-test or ANOVA (with the LSD post hoc test), respectively. The chi-square test was used for categorical variables. Using the approach of Schoenfeld and Richter [29], we calculated that a sample size of 214 would allow detection of a significant 1.79 relative risk of an event if subjects were divided into two groups of equal size according to cell count. To test the hypothesis that progenitor cell counts are independent prognostic markers, Cox proportional hazards regression analyses were repeated using a reference model with and without one progenitor cell count. The reference model included age, sex, family history, MetS and all its components as defined by the revised ATP-III criteria, LDL cholesterol, smoking habit, baseline CVD and CRF, C-reactive protein, and the Italian 10year risk score. The model included the Italian risk score because, in a preliminary analysis, it showed better calibration and event discrimination than the Framingham score (not shown). In separate analyses, medications were included among explanatory variables to assess if they modified risk prediction. To assess whether CD34+ cell count modifies the risk associated with MetS, we evaluated interaction between below-median CD34+ cells and the presence of MetS, as dichotomous risk factors.
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Table 1 Ability of progenitor cell phenotypes to predict and discriminate outcomes. All events
CV events
All-cause mortality
CD34+ (n = 214)
HR p No event Event %
1.87 (1.17–2.98) 0.003 402.3 ± 165.3 254.1 ± 119.4* 36.8
1.90 (1.10–3.59) 0.019 387.0 ± 165.3 245.6 ± 126.7* 36.4
2.83 (1.14–7.02) 0.027 376.9 ± 166.8 233.7 ± 112.1* 37.9
CD133+ (n = 136)
HR p No event Event %
3.93 (0.92–16.7) 0.064 250.0 ± 178.9 161.4 ± 71.6* 35.6
11.5 (0.1–1.1 × 105 ) 0.600 244.9 ± 174.5 135.1 ± 75.4* 44.9
4.83 (0.30–78.9) 0.292 244.9 ± 175.2 160.9 ± 87.2* 34.3
CD34 + CD133+ (n = 136)
HR p No event Event %
2.19 (0.56–8.64) 0.258 179.1 ± 139.4 114.5 ± 58.6* 35.8
5.45 (0.28–104.2) 0.258 175.6 ± 135.4 95.1 ± 63.7* 46.0
5.01 (0.24–102.9) 0.245 176.0 ± 136.2 107.1 ± 69.1* 39.2
CD34 + KDR+ (n = 214)
HR p No event Event %
1.28 (0.93–1.76) 0.151 66.9 ± 40.1 52.2 ± 39.0* 22.4
1.23 (0.83–1.83) 0.379 65.2 ± 40.0 52.2 ± 41.1 20
1.12 (0.64–1.95) 0.743 63.9 ± 39.4 54.3 ± 47.8 15.6
CD133 + KDR+ (n = 136)
HR p No event Event %
2.01 (0.48–8.41) 0.268 22.6 ± 28.4 15.0 ± 11.9 34.8
7.71 (0.23–250.3) 0.746 22.2 ± 27.6 11.6 ± 12.9 45.5
1.47 (0.32–6.87) 0.534 22.2 ± 27.7 14.9 ± 13.8 31.8
CD34 + CD133 + KDR+ (n = 136)
HR p No event Event %
1.32 (0.43–4.12) 0.599 8.4 ± 13.2 6.4 ± 6.2 23.8
9.83 (0.01–8817) 0.509 8.4 ± 12.7 5.5 ± 7.4 34.5
1.59 (0.27–9.53) 0.602 8.3 ± 12.8 7.0 ± 7.1 15.7
In each distinct Cox hazard-proportional regression analyses, one of the progenitor cell phenotype count was added to the reference model. Hazard ratios (HR) with 95% C.I. for 1 SD decrease in cell count and p values are shown. Mean cell count in subjects with and without event occurrence (±SD) and respective percentage differences (%) are also reported (*p < 0.05 for t-test).
Interaction, as departure from additivity, was assessed with Rothman’s synergy index (SI) [30], while its confidence interval was derived according to Zou [31]. In these analyses, we raised type 1 error to 0.10 according to the criteria suggested by Marshall [32]. To assess whether this interaction was modified by population- and outcome-specific cut-offs, in separate analyses, CD34 cell counts were categorized according to optimal cut-offs, defined as those which minimised the misclassification cost term, derived from ROC curves. A bootstrap procedure based on 5000 replicates was used to obtain 95% bias-corrected and accelerated confidence intervals for cut-offs and the other ROC curve data. SPSS ver 13.0 was used, and statistical significance was accepted at p < 0.05. 3. Results 3.1. Performance of progenitor cell phenotypes in event prediction The study sample was representative of an intermediate-tohigh risk population. As described elsewhere, patient baseline characteristics were used to evaluate the correlation of progenitor cell phenotypes with cardiovascular parameters and risk estimates [22]. Herein, we first analyzed the performance of 6 progenitor cell phenotypes as independent predictors of all-cause mortality, cardiovascular events, and total events. Distinct Cox hazard-proportional regression models were built as the reference model (see Section 2.4) plus each progenitor cell phenotype. We found that, as continuous variable, CD34+ cell count was an independent predictor of all-cause mortality, cardiovascular events, and total events, while none of the other phenotypes did predict any of the outcomes (Table 1). This allowed us to focus only on CD34+ cell count thereafter. When CD34+ cell count was categorized according
to the median value, it remained a significant predictor of all events and cardiovascular events (Fig. 1A). Continuous CD34+ cell count remained an independent predictor of all the three outcome measures even after correction for CD34 + KDR+ cell count (not shown). 3.2. Outcome predictors Follow-up of the 214 participants was complete. During a median 34 months period, we recorded 21 deaths (13 cardiovascular, 6 cancer, 1 respiratory failure, 1 unknown), 37 cardiovascular events (cardiovascular deaths plus 7 congestive heart failure, 4 non-fatal acute myocardial infarction, 3 unstable angina, 5 coronary/peripheral revascularizations, 4 amputations, 1 stroke), 57 total events (including cardiovascular events and cardiovascular deaths plus 7 emergency hospitalization for other causes). As shown in Table 2, together with baseline CVD, CD34+ cell counts were significantly associated with all-cause mortality, cardiovascular events, and all events, while MetS was an independent predictor of CV events and all events. Inclusion of data on medications (statin, aspirin, antihypertensive drugs, and anti-hyperglycemic drugs) in the model did not change these results. Chronic renal failure (defined as eGFR <60 mL/min/1.73 m2 ), a potential outcome modifier, was not an independent prognostic marker in our cohort. To assess whether CD34+ cell count modulated outcomes in relation to the MetS, we divided subjects into four groups according to the presence/absence of MetS and to their level of CD34+ cells (categorized as high and low according to the median value, that was 344/106 cytometric events). Table 3 shows clinical characteristics and progenitor cell counts, while Fig. 1B shows fully adjusted hazard ratios in these four groups. Patients with the MetS and a low CD34+ cell count had a higher rate of cardiovascular events and
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Table 2 Outcome predictors. Cox hazard-proportional regression analyses including the reference model plus CD34+ cell count as explanatory variables are shown. Parameter
Age Sex Family history Smoke Obesity High TG Low HDL Hypertension Hyperglycemia LDL-C CRF CVD MetS CRP Italian risk score CD34+ cells
All events
CV events
3.3. Use of outcome-specific optimized cut-offs ROC curves were used to obtain outcome-specific cut-offs, for the population of the present study. Optimal cut-offs for all events and CV events were similar (342/106 and 351/106 events, respectively) to that previously determined in the cross-sectional analysis (342/106 ) [22] and close to the median value, while cut-off for all-cause mortality was much lower (273/106 ) (Table 4). To assess whether optimal cut-offs improved the discrimination capacity of CD34+ cell count, we repeated interaction analyses dividing subjects into four groups according to the presence/absence of MetS and to CD34+ cell count above or below outcome-specific cut-off. Likely due to the high uncertainty of optimal cut-off estimation, improvement was limited to achieving statistical significance in the comparison between absence and presence of MetS among high CD34+ cell subjects (Fig. 1C), but synergy indexes did not improve significantly (not shown).
Mortality
B
p
B
p
B
p
0.041 0.677 −0.591 0.179 −0.596 −0.422 −0.579 −0.294 0.032 −0.005 0.084 10.482 10.239 0.546 −0.003 −0.004
0.005 0.05 0.080 0.639 0.135 0.252 0.136 0.430 0.937 0.352 0.818 <0.001 0.029 0.172 0.854 0.003
0.053 0.667 0.212 0.078 −0.444 10.069 −0.792 −0.568 0.485 −0.002 0.391 10.774 10.811 0.438 0.024 −0.004
0.011 0.129 0.599 0.879 0.374 0.015 0.113 0.260 0.396 0.805 0.360 0.002 0.013 0.383 0.178 0.027
0.059 2.426 −0.807 0.437 −0.276 −0.114 −0.319 0.429 −0.134 −0.003 −0.164 10.592 0.969 0.142 −0.006 −0.006
0.042 0.003 0.174 0.514 0.674 0.857 0.674 0.522 0.827 0.783 0.150 0.012 0.341 0.856 0.819 0.019
4. Discussion
Inclusion of medications among explanatory variables did not modify statistically significant associations (not shown).
Circulating progenitor cells are believed to take part in the homeostasis of the cardiovascular system, and their alterations are related to the presence of risk factors for and of established atherosclerosis [15]. We previously showed a strong inverse correlation between CD34+ cells and all cardiovascular parameters and risk estimates [22]. Herein, we report that circulating CD34+ cells independently predicted CV events, all events and death from any cause. Importantly, CD34+ cells significantly modulated the risk in the specific population of MetS patients. Major innovations in the present study include: (i) a longer follow-up in comparison with other studies assessing the prognostic performance of progenitor cells; (ii) a specific evaluation of MetS; (iii) the comparison between different progenitor cell phenotypes.
of all events as compared with MetS patients with a high CD34+ cell count. The interaction between low CD34+ cells and presence of MetS in increasing risk was confirmed by the synergy indexes for all events (5.25; 90% C.I. 2.65–10.4) and cardiovascular events (3.22; 90% C.I. 1.07–9.67). A quite similar trend was found for allcause mortality, but results were not statistically significant, likely due to the low number of deaths (SI 3.00; 90% C.I. 0.42–21.3). Power analysis restricted to the 81 patients with MetS revealed that the study design allowed detection of a significant 2.66 relative risk associated with low CD34+ cell count with 80% statistical power. Taken together, these data suggest that CD34+ cell count modulates outcomes of the MetS.
Table 3 Clinical characteristics, outcomes, and progenitor cell counts in the 4 groups of subjects. No metabolic syndrome
Number Age (years) Male sex, n (%) Family history, n (%) Obesity, n (%) High triglycerides, n (%) Low HDL, n (%) Hypertension, n (%) Hyperglycemia, n (%) LDL (mg/dL) Smoking habit, n (%) Baseline CVD, n (%) Baseline CRF, n (%) High CRP, n (%) Italian risk score (%) All events, n (%) CV events, n (%) Deaths, n (%) CD34+ CD133+ CD34 + CD133+ CD34 + KDR+ CD133 + KDR+ CD34 + CD133 + KDR+
Metabolic syndrome
High CD34+
Low CD34+
High CD34+
Low CD34+
84 48.2 (11.9) 37 (44) 41 (49) 18 (21) 3 (4) 20 (24) 19 (23) 9 (7) 112.4 (32.1) 18 (21) 11 (13) 2 (2.4) 7 (8) 3.0 (4.6) 7 (8.3) 2 (2.4) 2 (2.4) 496.9 (130.0) 273.7 (117.8) 198.1 (87.3) 78.7 (41.1) 27.8 (34.8) 10.2 (15.8)
49 54.9 (15.4) 30 (61) 17 (35) 11 (22) 3 (6) 13 (27) 20 (41) 11 (22) 124.6 (24.5) 8 (17) 18 (37) 7 (14.3)* 4 (8) 8.5 (11.2) 13 (26.5)† 8 (16.3)† 4 (8.2) 257.7 (63.1)† 230.6 (318.5)† 160.9 (251.0)† 52.6 (41.2) 16.6 (9.5) 6.9 (6.9)
21 58.3 (14.2) 14 (67)* 9 (43) 17 (81)* 8 (38)* 14 (67)* 19 (90)* 13 (62)* 125.4 (37.1) 2 (11) 8 (38) 2 (9.1) 1 (5) 11.0 (10.1)* , † 4 (19.0) 4 (19.0)* 2 (9.5) 492.1 (139.5) 249.3 (57.7) 186.7 (59.8) 47.9 (31.1)* 16.2 (5.8) 4.7 (3.2)
60 68.2 (10.1)* , † 37 (62) 28 (47) 47 (79)* 41 (69)* , † 48 (80)* 54 (90)* 46 (77)* , † 134.6 (31.8)* 17 (29) 37 (61)* , † 10 (16.7) 17 (28) 23.6 (15.5)* , † 33 (55.0)* , † 23 (38.3)* , † 13 (21.7) 216.5 (70.0)† 145.1 (65.4)† 99.4 (50.46)† 55.2 (35.0) 12.2 (10.3) 5.3 (7.0)
The 214 study subjects divided according to the presence/absence of MetS and a high/low CD34+ cell count (categorized according to the median value). Data presented as mean (SD) or number (%). * Significantly different versus no MetS, same cell count. † Significantly different versus same MetS group, high cell count; after type I error correction (LSD).
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Fig. 1. Outcome analysis. (A) Kaplan–Meyer survival curves for the three endpoints in patients divided according to the median value of CD34+ cells. (B) Fully adjusted hazard ratios in the four groups of subjects divided according to the presence/absence of MetS and the high/low CD34+ cell count. CD34+ cell count was categorized according to the median value (344/106 ). (C) Analyses repeated with outcome-specific optimal cut-offs. Adjustment was done for all the variables included in the reference model. *Statistically significant after correction for multiple testing (Hochberg procedure).
4.1. Biological and clinical meaning of circulating CD34+ cells Contrary to what shown in previous studies [18,19], neither CD34 + KDR+ cell count, nor the phenotypes based on CD133 expression, were independent predictors of events. Remarkably, this fits with our previous report that CD34+ is the phenotype best related to all cardiovascular parameters at baseline [22]. We acknowledge that we studied CD133 expression in a smaller subset of subjects, thus limiting statistical power in the survival analyses evaluating performance of CD133+ phenotypes. We can, however, speculate on the reasons why CD34 + KDR+ cells, the most accepted EPC phenotype [13], did not perform as well as CD34+ cells in predicting events. One technical issue is that CD34+ cells are more accurately measured than CD34 + KDR+ cells. However, there
may be other explanations for these results, because CD34+ and CD34 + KDR+ cell counts were poorly correlated each other at baseline [22], indicating that they have a different biological meaning. Most research thus far focused on EPCs, because of their supposed role in endothelial homeostasis: cross-sectional and short-term longitudinal studies support a role for reduced CD34 + KDR + EPCs in CVD development and progression [15], suggesting that a defective endothelial regenerative capacity promotes CVD. However, complexity of the cardiovascular system goes beyond the endothelium. CD34 is expressed on immature cells and involved in development of both the hematopoietic and vascular systems; thus, the circulating CD34+ population contains mainly hematopoietic stem/progenitor cells plus a small amount of progenitors for other lineages such as endothelium, smooth muscle, and cardiomyocytes
Table 4 Receiver operating characteristics (ROC) curve data for the discriminative capacity of CD34+ cell count. Confidence intervals obtained with bootstrapping.
Optimal cut-off Sensitivity Specificity AUC
All events
Cardiovascular events
Death from any cause
342.5 (261–391) 0.807 (0.491–0.947) 0.620 (0.421–0.816) 0.772 (0.710–0.853)
351 (228–394) 0.865 (0.594–1.000) 0.559 (0.344–0.638) 0.771 (0.697–0.867)
273 (176–392) 0.714 (0.476–0.952) 0.675 (0.392–0.953) 0.751 (0.663–0.867)
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[33]. The non-hematopoietic progenitors should reside in the small CD45-negative fraction, even if this issue is controversial [13]. Our preliminary results indicate that only about 6% of all circulating CD34+ cells are CD45+, while the vast majority is CD45dim , therefore it is unlikely that selective alterations of the very small CD45–CD34+ population account for the large changes in CD34+ cell counts. Regardless of CD45 expression, bone marrow is thought to be the reservoir of these CD34+ progenitors committed to tissue repair [34,35]. If total peripheral blood CD34+ cell count represents a mirror of bone marrow function or reserve, reduction of these cells may reflect bone marrow incompetence, which is likely to have profoundly detrimental effects, finally translating into CVD in subjects with predisposing risk factors. Thus, unlike a specific progenitor cell lineage (such as EPCs), total circulating CD34+ cells would provide a more comprehensive window on the global risk in the longer term. Indeed, the observation that CD34+ cells predicted not only cardiovascular events, but also all events and all-cause mortality, fits with this hypothesis.
4.2. CD34+ cell count modulates risk in MetS patients We also found an interaction between a low CD34+ cell count and the presence of MetS, suggesting that progenitor cells modulates the risk associated with the syndrome. Remarkably, the risk of patients without MetS but with a low cell count was similar to that of patients with MetS and a high cell count. Most importantly, the risk dramatically increased in MetS patients with a low CD34+ cell count (4.3×, 3.3× and 2.8× increase in all events, cardiovascular events and death, respectively). The large risk gradient between MetS patients with high and low circulating CD34+ cells deserves a special attention. Many studies have shown that patients diagnosed with MetS have a greater risk of developing CVD [4,36,37]. However, there are exceptions to this body of evidence [38], and the risk associated with the syndrome itself, beyond its individual components, is uncertain [3,7,39]. Actually, CV risk varies considerably among patients with the syndrome, because some relevant risk indexes are not included in its definition, such as LDL cholesterol and smoking. We suggest that CD34+ progenitor cell count further modulates risk in MetS [40], because in this study all analyses were fully adjusted for conventional risk factors. Since the categorical basis of MetS has been criticized [3], we also controlled for an ethnic-specific continuous risk estimate. A high (>3.0 mg/L) CRP level has been identified as a prognostic marker in patients with MetS [25,26], also in the Italian population [27]. Actually, in one study, stratification according to MetS and CRP reported similar results as ours [25], but here we show an independent effect of progenitor cells beyond chronic inflammation, because CRP was included in the multivariable analysis.
4.3. Conclusions Our results have two implications. First, we support indirectly the notion that reduction of circulating progenitor cells contribute to the pathophysiology of CVD development and progression in high-risk individuals. Second, the easy measure of CD34+ cells may be used clinically to stratify patients’ risk beyond the classical risk assessment. In conclusion, circulating CD34+ progenitor cell count represents an independent risk biomarker, and significantly modulates outcomes of the MetS. Although it is still not clear to what extent this result has a practical value, it strengthens the notion that progenitor cells have a role in the pathophysiology of CVD and MetS.
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Atherosclerosis journal homepage: www.elsevier.com/locate/atherosclerosis
Low CD34+ cell count and metabolic syndrome synergistically increase the risk of adverse outcomes Gian Paolo Fadini a,∗ , Saula de Kreutzenberg a , Carlo Agostini a , Elisa Boscaro a , Antonio Tiengo a , Stefanie Dimmeler b , Angelo Avogaro a a b
Department of Clinical and Experimental Medicine, University of Padova, Medical School, Padova, Italy Molecular Cardiology, Department of Internal Medicine IV, University of Frankfurt, Frankfurt, Germany
a r t i c l e
i n f o
Article history: Received 22 January 2009 Received in revised form 21 March 2009 Accepted 26 March 2009 Available online 5 April 2009 Keywords: Endothelium Metabolic syndrome Stem cells Cardiovascular risk
a b s t r a c t Objectives: Metabolic syndrome (MetS) associates with endothelial dysfunction and a high risk of cardiovascular events and death. Circulating progenitor cells have been shown to contribute to endothelial homeostasis and repair . We aimed to test whether progenitor cell count is an independent event predictor and modifies cardiovascular risk associated with MetS. Methods: On the basis of the expression of CD34, CD133 and KDR, 6 phenotypes of progenitor cells were counted using flow cytometry in 214 subjects with and without MetS. We recorded classical risk factors and MetS components, cumulative risk estimates, and high-sensitive C-reactive protein. Subjects were followed-up for a median of 34 months to collect total events, cardiovascular events and all-cause mortality. Results: In the Cox proportional hazards regression analyses, we found that, unlike other phenotypes, reduced CD34+ cells predicted cardiovascular and total events and death, independently of all potential confounders. Remarkably, a low CD34+ cell count significantly increased the risk associated with MetS, as shown by synergy indexes. Conclusion: The level of circulating CD34+ cells is a novel independent risk biomarker and modulates outcomes in the MetS, suggesting that generic progenitor cells have a role in disease development or progression over the long-term. © 2009 Elsevier Ireland Ltd. All rights reserved.
1. Introduction The metabolic syndrome (MetS) comprises a cluster of cardiovascular risk factors that are present together in the same individuals more often than would be expected by chance [1]. According to the ATP-III definition, diagnosis of MetS is allowed with at least 3 among abdominal obesity, hypertension, low HDL, high triglycerides, and hyperglycemia [2]. Although the clinical utility of this designation is controversial [3], patients with such a combination of risk factors have a markedly elevated risk of cardiovascular disease (CVD) and total mortality [4,5]. Inflammation and insulin resistance are thought to mediate the aggregation of risk factors and the subsequent increase in cardiovascular events [6] but the mechanisms are still poorly understood. Even among patients with the syndrome, the risk is highly variable as it depends on other parameters, such as smoking and LDL [3,7]. Understand-
∗ Corresponding author at: Department of Clinical and Experimental Medicine, Metabolic Division, University of Padova, Medical School, V. Giustiniani, 2, 35128 Padova, Italy. Tel.: +39 049 8212185; fax: +39 049 8212184. E-mail address: [email protected] (G.P. Fadini). 0021-9150/$ – see front matter © 2009 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.atherosclerosis.2009.03.040
ing what modulates the risk in patients with the MetS will likely identify novel pathophysiological mechanisms of CVD induction, beyond classical risk factors. Dysfunction of the endothelial layer is a common feature of MetS components and is generally regarded as the earliest step of the atherogenetic process [8]. Both inflammation and insulin resistance converge towards inducing endothelial dysfunction, and increasing the risk of atherosclerotic CVD [9]. Homeostasis of the intimal layer relies on the contribution of circulating cells, called endothelial progenitor cells (EPCs) [10]. These cells participate in the turnover of healthy and damaged endothelium, as well as in angiogenetic processes [11]. Therefore, a reduction in EPCs may compromise endothelial integrity and hamper collateralization, thus promoting CVD development and progression. Circulating functional EPCs are believed to derive from more immature, relatively undifferentiated stem or progenitor cells [12]. Despite controversies on EPC definition, staining for the stem cell antigen CD34 and the endothelial antigen KDR (type 2 VEGF receptor) would provide a good estimate of circulating EPC levels [13]. Some studies also suggest that the stem cell antigen CD133 may help in identifying EPCs [14]. We have shown that CD34 + KDR + EPC count is reduced in early and late atherosclerotic disease [15–17], while longitudinal studies suggest
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that CD34 + KDR + EPCs negatively predict cardiovascular events in the short-term [18,19]. Consistently, MetS and its components have been associated with EPC reduction and/or dysfunction [20,21]. In contrast, we have demonstrated that total CD34+ cells, a more immature and generic cell population, correlated with all cardiovascular parameters and risk estimates better than CD34 + KDR+ cells and CD133-based phenotypes in a cohort of patients with different degrees of cardiovascular risk [22]. Moreover, CD34+ cells were synergistically affected by clustering MetS components, suggesting an interaction between progenitor cells and MetS in the induction/progression of CVD. In the present study, we describe follow-up data of the same cohort with the pre-specified aims of assessing whether CD34+ cell count predicts future events in the entire population, and whether it modifies the risk associated with the MetS. 2. Methods 2.1. Patients The study was approved from the Local Insitutional and Ethical Committee and informed consent was obtained from all subjects. From February 2004 to July 2005, a total of 214 subjects were recruited at our outpatient clinic. These included 114 apparently healthy subjects from a middle-aged population of office employees, who responded to an advertisement for cardiometabolic screening, plus 100 patients presenting for metabolic and related disorders (diabetes, hypertension, dyslipidemia, and/or obesity). The baseline clinical characteristics of these patients are described elsewhere [22]. Briefly, we recorded age, gender, family history for cardiovascular disease, body mass index, waist circumference, fasting blood glucose, systolic and diastolic blood pressure, total cholesterol, high-density lipoprotein (HDL), triglyceride concentrations, and a complete pharmacological history. LDL cholesterol was calculated using Friedewald’s formula. CVD was defined as coronary, cerebral or peripheral arterial disease. MetS was defined according to the revised ATP-III definition. The Framingham risk score was calculated as previously described [23]. Additionally, we calculated the predicted 10-year cardiovascular risk according to the Italian risk assessment program (accessible at the URL http://www.cuore.iss.it/sopra/calc-rischio en.asp), which is considered more accurate than the Framingham algorithm, for the Italian population [24]. The equation of the Italian risk score includes age, sex, smoking habit, systolic blood pressure, total, LDL and HDL cholesterol, and the presence/absence of diabetes and known hypertension. C-reactive protein was measured using the high-sensitive technique and categorized in “high” and “low” according to the established cut-off of 3.0 mg/L [25–27]. The presence of chronic renal failure (CRF, defined as an estimated glomerular filtration rate <60 mL/min/1.73 m2 , according to the modification of diet in renal disease (MDRD) equation)[28] was also assessed and included in the analyses. 2.2. Flow cytometry Peripheral blood progenitor cells were analyzed for the expression of surface antigens with direct flow cytometry (FACS Calibur, Becton Dickinson) as previously described in detail [17,22]. Briefly, cells were labelled with FITC-conjugated anti-CD34 (Becton Dickinson), PE-conjugated anti-KDR (R&D Systems) and APC-conjugated anti-CD133 (clone AC133 from Miltenyi Biotec) monoclonal antibodies. First CD34+ or CD133+ cells were gated and then assayed for expression of KDR in the mononuclear cell fraction. In all analyses, 5 × 105 events were acquired, scored using a FACS Calibur analyzer (Becton Dickinson), and processed using the Macintosh
CELLQuest software program (Becton Dickinson). Cell count is always expressed per 106 events. Double CD34/KDR staining was done in all 214 subjects, allowing the determination of CD34+ and CD34 + KDR+ cells. Triple CD133/CD34/KDR staining was done in 136 subjects (64% of total), allowing the determination of 4 additional phenotypes: CD133+, CD34 + CD133+, CD133 + KDR+, and CD34 + CD133 + KDR+ cells. 2.3. Follow-up Recruitment took 17 months, and subjects were followed for up to 42 months (median 34 months, interquartile range 29–38 months). The primary endpoint was first cardiovascular event, defined as cardiovascular death, acute myocardial infarction, unstable angina requiring hospitalization, severe peripheral ischemia, and stroke. Secondary endpoints were death from any cause and all events (including death and emergency hospitalization for any cause). Follow-up was performed directly or by telephone contact. All information about potential events was validated by review of medical records, hospital discharge letters, and charts of hospital stay. Death causes were confirmed by review of autopsy reports, death certificates, or information from family members regarding the circumstances of death. Cardiovascular death was defined as death due to acute myocardial infarction, cardiac perforation or pericardial tamponade, arrhythmia, cerebrovascular accident, or any death in which a cardiovascular cause could not be ruled out. Myocardial infarction was defined as the presence of diagnostic electrocardiographic changes and at least two of the following findings: typical ischemic chest pain, elevation of the serum level of creatine kinase MB fraction, or cardiac specific troponin. Unstable angina was defined as resting or new-onset or exacerbating typical chest pain with either ≥0.1 mV ST-segment depression or T-wave inversion in ≥2 contiguous electrocardiographic leads, normal creatine kinase MB fraction and serum cardiac troponin, and angiographically confirmed coronary artery disease. Stroke was defined as a new focal neurologic deficit lasting more than 24 h, with sidecoherent ischemic lesion(s) on a cerebral computed tomography scan. Severe peripheral ischemia was defined as ischemia threatening the viability of the limb and leading to a revascularization procedure or amputation. 2.4. Statistical analysis Continuous data are expressed as mean ± SD. Comparisons between two or more groups were performed with two-sided Student’s t-test or ANOVA (with the LSD post hoc test), respectively. The chi-square test was used for categorical variables. Using the approach of Schoenfeld and Richter [29], we calculated that a sample size of 214 would allow detection of a significant 1.79 relative risk of an event if subjects were divided into two groups of equal size according to cell count. To test the hypothesis that progenitor cell counts are independent prognostic markers, Cox proportional hazards regression analyses were repeated using a reference model with and without one progenitor cell count. The reference model included age, sex, family history, MetS and all its components as defined by the revised ATP-III criteria, LDL cholesterol, smoking habit, baseline CVD and CRF, C-reactive protein, and the Italian 10year risk score. The model included the Italian risk score because, in a preliminary analysis, it showed better calibration and event discrimination than the Framingham score (not shown). In separate analyses, medications were included among explanatory variables to assess if they modified risk prediction. To assess whether CD34+ cell count modifies the risk associated with MetS, we evaluated interaction between below-median CD34+ cells and the presence of MetS, as dichotomous risk factors.
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Table 1 Ability of progenitor cell phenotypes to predict and discriminate outcomes. All events
CV events
All-cause mortality
CD34+ (n = 214)
HR p No event Event %
1.87 (1.17–2.98) 0.003 402.3 ± 165.3 254.1 ± 119.4* 36.8
1.90 (1.10–3.59) 0.019 387.0 ± 165.3 245.6 ± 126.7* 36.4
2.83 (1.14–7.02) 0.027 376.9 ± 166.8 233.7 ± 112.1* 37.9
CD133+ (n = 136)
HR p No event Event %
3.93 (0.92–16.7) 0.064 250.0 ± 178.9 161.4 ± 71.6* 35.6
11.5 (0.1–1.1 × 105 ) 0.600 244.9 ± 174.5 135.1 ± 75.4* 44.9
4.83 (0.30–78.9) 0.292 244.9 ± 175.2 160.9 ± 87.2* 34.3
CD34 + CD133+ (n = 136)
HR p No event Event %
2.19 (0.56–8.64) 0.258 179.1 ± 139.4 114.5 ± 58.6* 35.8
5.45 (0.28–104.2) 0.258 175.6 ± 135.4 95.1 ± 63.7* 46.0
5.01 (0.24–102.9) 0.245 176.0 ± 136.2 107.1 ± 69.1* 39.2
CD34 + KDR+ (n = 214)
HR p No event Event %
1.28 (0.93–1.76) 0.151 66.9 ± 40.1 52.2 ± 39.0* 22.4
1.23 (0.83–1.83) 0.379 65.2 ± 40.0 52.2 ± 41.1 20
1.12 (0.64–1.95) 0.743 63.9 ± 39.4 54.3 ± 47.8 15.6
CD133 + KDR+ (n = 136)
HR p No event Event %
2.01 (0.48–8.41) 0.268 22.6 ± 28.4 15.0 ± 11.9 34.8
7.71 (0.23–250.3) 0.746 22.2 ± 27.6 11.6 ± 12.9 45.5
1.47 (0.32–6.87) 0.534 22.2 ± 27.7 14.9 ± 13.8 31.8
CD34 + CD133 + KDR+ (n = 136)
HR p No event Event %
1.32 (0.43–4.12) 0.599 8.4 ± 13.2 6.4 ± 6.2 23.8
9.83 (0.01–8817) 0.509 8.4 ± 12.7 5.5 ± 7.4 34.5
1.59 (0.27–9.53) 0.602 8.3 ± 12.8 7.0 ± 7.1 15.7
In each distinct Cox hazard-proportional regression analyses, one of the progenitor cell phenotype count was added to the reference model. Hazard ratios (HR) with 95% C.I. for 1 SD decrease in cell count and p values are shown. Mean cell count in subjects with and without event occurrence (±SD) and respective percentage differences (%) are also reported (*p < 0.05 for t-test).
Interaction, as departure from additivity, was assessed with Rothman’s synergy index (SI) [30], while its confidence interval was derived according to Zou [31]. In these analyses, we raised type 1 error to 0.10 according to the criteria suggested by Marshall [32]. To assess whether this interaction was modified by population- and outcome-specific cut-offs, in separate analyses, CD34 cell counts were categorized according to optimal cut-offs, defined as those which minimised the misclassification cost term, derived from ROC curves. A bootstrap procedure based on 5000 replicates was used to obtain 95% bias-corrected and accelerated confidence intervals for cut-offs and the other ROC curve data. SPSS ver 13.0 was used, and statistical significance was accepted at p < 0.05. 3. Results 3.1. Performance of progenitor cell phenotypes in event prediction The study sample was representative of an intermediate-tohigh risk population. As described elsewhere, patient baseline characteristics were used to evaluate the correlation of progenitor cell phenotypes with cardiovascular parameters and risk estimates [22]. Herein, we first analyzed the performance of 6 progenitor cell phenotypes as independent predictors of all-cause mortality, cardiovascular events, and total events. Distinct Cox hazard-proportional regression models were built as the reference model (see Section 2.4) plus each progenitor cell phenotype. We found that, as continuous variable, CD34+ cell count was an independent predictor of all-cause mortality, cardiovascular events, and total events, while none of the other phenotypes did predict any of the outcomes (Table 1). This allowed us to focus only on CD34+ cell count thereafter. When CD34+ cell count was categorized according
to the median value, it remained a significant predictor of all events and cardiovascular events (Fig. 1A). Continuous CD34+ cell count remained an independent predictor of all the three outcome measures even after correction for CD34 + KDR+ cell count (not shown). 3.2. Outcome predictors Follow-up of the 214 participants was complete. During a median 34 months period, we recorded 21 deaths (13 cardiovascular, 6 cancer, 1 respiratory failure, 1 unknown), 37 cardiovascular events (cardiovascular deaths plus 7 congestive heart failure, 4 non-fatal acute myocardial infarction, 3 unstable angina, 5 coronary/peripheral revascularizations, 4 amputations, 1 stroke), 57 total events (including cardiovascular events and cardiovascular deaths plus 7 emergency hospitalization for other causes). As shown in Table 2, together with baseline CVD, CD34+ cell counts were significantly associated with all-cause mortality, cardiovascular events, and all events, while MetS was an independent predictor of CV events and all events. Inclusion of data on medications (statin, aspirin, antihypertensive drugs, and anti-hyperglycemic drugs) in the model did not change these results. Chronic renal failure (defined as eGFR <60 mL/min/1.73 m2 ), a potential outcome modifier, was not an independent prognostic marker in our cohort. To assess whether CD34+ cell count modulated outcomes in relation to the MetS, we divided subjects into four groups according to the presence/absence of MetS and to their level of CD34+ cells (categorized as high and low according to the median value, that was 344/106 cytometric events). Table 3 shows clinical characteristics and progenitor cell counts, while Fig. 1B shows fully adjusted hazard ratios in these four groups. Patients with the MetS and a low CD34+ cell count had a higher rate of cardiovascular events and
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Table 2 Outcome predictors. Cox hazard-proportional regression analyses including the reference model plus CD34+ cell count as explanatory variables are shown. Parameter
Age Sex Family history Smoke Obesity High TG Low HDL Hypertension Hyperglycemia LDL-C CRF CVD MetS CRP Italian risk score CD34+ cells
All events
CV events
3.3. Use of outcome-specific optimized cut-offs ROC curves were used to obtain outcome-specific cut-offs, for the population of the present study. Optimal cut-offs for all events and CV events were similar (342/106 and 351/106 events, respectively) to that previously determined in the cross-sectional analysis (342/106 ) [22] and close to the median value, while cut-off for all-cause mortality was much lower (273/106 ) (Table 4). To assess whether optimal cut-offs improved the discrimination capacity of CD34+ cell count, we repeated interaction analyses dividing subjects into four groups according to the presence/absence of MetS and to CD34+ cell count above or below outcome-specific cut-off. Likely due to the high uncertainty of optimal cut-off estimation, improvement was limited to achieving statistical significance in the comparison between absence and presence of MetS among high CD34+ cell subjects (Fig. 1C), but synergy indexes did not improve significantly (not shown).
Mortality
B
p
B
p
B
p
0.041 0.677 −0.591 0.179 −0.596 −0.422 −0.579 −0.294 0.032 −0.005 0.084 10.482 10.239 0.546 −0.003 −0.004
0.005 0.05 0.080 0.639 0.135 0.252 0.136 0.430 0.937 0.352 0.818 <0.001 0.029 0.172 0.854 0.003
0.053 0.667 0.212 0.078 −0.444 10.069 −0.792 −0.568 0.485 −0.002 0.391 10.774 10.811 0.438 0.024 −0.004
0.011 0.129 0.599 0.879 0.374 0.015 0.113 0.260 0.396 0.805 0.360 0.002 0.013 0.383 0.178 0.027
0.059 2.426 −0.807 0.437 −0.276 −0.114 −0.319 0.429 −0.134 −0.003 −0.164 10.592 0.969 0.142 −0.006 −0.006
0.042 0.003 0.174 0.514 0.674 0.857 0.674 0.522 0.827 0.783 0.150 0.012 0.341 0.856 0.819 0.019
4. Discussion
Inclusion of medications among explanatory variables did not modify statistically significant associations (not shown).
Circulating progenitor cells are believed to take part in the homeostasis of the cardiovascular system, and their alterations are related to the presence of risk factors for and of established atherosclerosis [15]. We previously showed a strong inverse correlation between CD34+ cells and all cardiovascular parameters and risk estimates [22]. Herein, we report that circulating CD34+ cells independently predicted CV events, all events and death from any cause. Importantly, CD34+ cells significantly modulated the risk in the specific population of MetS patients. Major innovations in the present study include: (i) a longer follow-up in comparison with other studies assessing the prognostic performance of progenitor cells; (ii) a specific evaluation of MetS; (iii) the comparison between different progenitor cell phenotypes.
of all events as compared with MetS patients with a high CD34+ cell count. The interaction between low CD34+ cells and presence of MetS in increasing risk was confirmed by the synergy indexes for all events (5.25; 90% C.I. 2.65–10.4) and cardiovascular events (3.22; 90% C.I. 1.07–9.67). A quite similar trend was found for allcause mortality, but results were not statistically significant, likely due to the low number of deaths (SI 3.00; 90% C.I. 0.42–21.3). Power analysis restricted to the 81 patients with MetS revealed that the study design allowed detection of a significant 2.66 relative risk associated with low CD34+ cell count with 80% statistical power. Taken together, these data suggest that CD34+ cell count modulates outcomes of the MetS.
Table 3 Clinical characteristics, outcomes, and progenitor cell counts in the 4 groups of subjects. No metabolic syndrome
Number Age (years) Male sex, n (%) Family history, n (%) Obesity, n (%) High triglycerides, n (%) Low HDL, n (%) Hypertension, n (%) Hyperglycemia, n (%) LDL (mg/dL) Smoking habit, n (%) Baseline CVD, n (%) Baseline CRF, n (%) High CRP, n (%) Italian risk score (%) All events, n (%) CV events, n (%) Deaths, n (%) CD34+ CD133+ CD34 + CD133+ CD34 + KDR+ CD133 + KDR+ CD34 + CD133 + KDR+
Metabolic syndrome
High CD34+
Low CD34+
High CD34+
Low CD34+
84 48.2 (11.9) 37 (44) 41 (49) 18 (21) 3 (4) 20 (24) 19 (23) 9 (7) 112.4 (32.1) 18 (21) 11 (13) 2 (2.4) 7 (8) 3.0 (4.6) 7 (8.3) 2 (2.4) 2 (2.4) 496.9 (130.0) 273.7 (117.8) 198.1 (87.3) 78.7 (41.1) 27.8 (34.8) 10.2 (15.8)
49 54.9 (15.4) 30 (61) 17 (35) 11 (22) 3 (6) 13 (27) 20 (41) 11 (22) 124.6 (24.5) 8 (17) 18 (37) 7 (14.3)* 4 (8) 8.5 (11.2) 13 (26.5)† 8 (16.3)† 4 (8.2) 257.7 (63.1)† 230.6 (318.5)† 160.9 (251.0)† 52.6 (41.2) 16.6 (9.5) 6.9 (6.9)
21 58.3 (14.2) 14 (67)* 9 (43) 17 (81)* 8 (38)* 14 (67)* 19 (90)* 13 (62)* 125.4 (37.1) 2 (11) 8 (38) 2 (9.1) 1 (5) 11.0 (10.1)* , † 4 (19.0) 4 (19.0)* 2 (9.5) 492.1 (139.5) 249.3 (57.7) 186.7 (59.8) 47.9 (31.1)* 16.2 (5.8) 4.7 (3.2)
60 68.2 (10.1)* , † 37 (62) 28 (47) 47 (79)* 41 (69)* , † 48 (80)* 54 (90)* 46 (77)* , † 134.6 (31.8)* 17 (29) 37 (61)* , † 10 (16.7) 17 (28) 23.6 (15.5)* , † 33 (55.0)* , † 23 (38.3)* , † 13 (21.7) 216.5 (70.0)† 145.1 (65.4)† 99.4 (50.46)† 55.2 (35.0) 12.2 (10.3) 5.3 (7.0)
The 214 study subjects divided according to the presence/absence of MetS and a high/low CD34+ cell count (categorized according to the median value). Data presented as mean (SD) or number (%). * Significantly different versus no MetS, same cell count. † Significantly different versus same MetS group, high cell count; after type I error correction (LSD).
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Fig. 1. Outcome analysis. (A) Kaplan–Meyer survival curves for the three endpoints in patients divided according to the median value of CD34+ cells. (B) Fully adjusted hazard ratios in the four groups of subjects divided according to the presence/absence of MetS and the high/low CD34+ cell count. CD34+ cell count was categorized according to the median value (344/106 ). (C) Analyses repeated with outcome-specific optimal cut-offs. Adjustment was done for all the variables included in the reference model. *Statistically significant after correction for multiple testing (Hochberg procedure).
4.1. Biological and clinical meaning of circulating CD34+ cells Contrary to what shown in previous studies [18,19], neither CD34 + KDR+ cell count, nor the phenotypes based on CD133 expression, were independent predictors of events. Remarkably, this fits with our previous report that CD34+ is the phenotype best related to all cardiovascular parameters at baseline [22]. We acknowledge that we studied CD133 expression in a smaller subset of subjects, thus limiting statistical power in the survival analyses evaluating performance of CD133+ phenotypes. We can, however, speculate on the reasons why CD34 + KDR+ cells, the most accepted EPC phenotype [13], did not perform as well as CD34+ cells in predicting events. One technical issue is that CD34+ cells are more accurately measured than CD34 + KDR+ cells. However, there
may be other explanations for these results, because CD34+ and CD34 + KDR+ cell counts were poorly correlated each other at baseline [22], indicating that they have a different biological meaning. Most research thus far focused on EPCs, because of their supposed role in endothelial homeostasis: cross-sectional and short-term longitudinal studies support a role for reduced CD34 + KDR + EPCs in CVD development and progression [15], suggesting that a defective endothelial regenerative capacity promotes CVD. However, complexity of the cardiovascular system goes beyond the endothelium. CD34 is expressed on immature cells and involved in development of both the hematopoietic and vascular systems; thus, the circulating CD34+ population contains mainly hematopoietic stem/progenitor cells plus a small amount of progenitors for other lineages such as endothelium, smooth muscle, and cardiomyocytes
Table 4 Receiver operating characteristics (ROC) curve data for the discriminative capacity of CD34+ cell count. Confidence intervals obtained with bootstrapping.
Optimal cut-off Sensitivity Specificity AUC
All events
Cardiovascular events
Death from any cause
342.5 (261–391) 0.807 (0.491–0.947) 0.620 (0.421–0.816) 0.772 (0.710–0.853)
351 (228–394) 0.865 (0.594–1.000) 0.559 (0.344–0.638) 0.771 (0.697–0.867)
273 (176–392) 0.714 (0.476–0.952) 0.675 (0.392–0.953) 0.751 (0.663–0.867)
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[33]. The non-hematopoietic progenitors should reside in the small CD45-negative fraction, even if this issue is controversial [13]. Our preliminary results indicate that only about 6% of all circulating CD34+ cells are CD45+, while the vast majority is CD45dim , therefore it is unlikely that selective alterations of the very small CD45–CD34+ population account for the large changes in CD34+ cell counts. Regardless of CD45 expression, bone marrow is thought to be the reservoir of these CD34+ progenitors committed to tissue repair [34,35]. If total peripheral blood CD34+ cell count represents a mirror of bone marrow function or reserve, reduction of these cells may reflect bone marrow incompetence, which is likely to have profoundly detrimental effects, finally translating into CVD in subjects with predisposing risk factors. Thus, unlike a specific progenitor cell lineage (such as EPCs), total circulating CD34+ cells would provide a more comprehensive window on the global risk in the longer term. Indeed, the observation that CD34+ cells predicted not only cardiovascular events, but also all events and all-cause mortality, fits with this hypothesis.
4.2. CD34+ cell count modulates risk in MetS patients We also found an interaction between a low CD34+ cell count and the presence of MetS, suggesting that progenitor cells modulates the risk associated with the syndrome. Remarkably, the risk of patients without MetS but with a low cell count was similar to that of patients with MetS and a high cell count. Most importantly, the risk dramatically increased in MetS patients with a low CD34+ cell count (4.3×, 3.3× and 2.8× increase in all events, cardiovascular events and death, respectively). The large risk gradient between MetS patients with high and low circulating CD34+ cells deserves a special attention. Many studies have shown that patients diagnosed with MetS have a greater risk of developing CVD [4,36,37]. However, there are exceptions to this body of evidence [38], and the risk associated with the syndrome itself, beyond its individual components, is uncertain [3,7,39]. Actually, CV risk varies considerably among patients with the syndrome, because some relevant risk indexes are not included in its definition, such as LDL cholesterol and smoking. We suggest that CD34+ progenitor cell count further modulates risk in MetS [40], because in this study all analyses were fully adjusted for conventional risk factors. Since the categorical basis of MetS has been criticized [3], we also controlled for an ethnic-specific continuous risk estimate. A high (>3.0 mg/L) CRP level has been identified as a prognostic marker in patients with MetS [25,26], also in the Italian population [27]. Actually, in one study, stratification according to MetS and CRP reported similar results as ours [25], but here we show an independent effect of progenitor cells beyond chronic inflammation, because CRP was included in the multivariable analysis.
4.3. Conclusions Our results have two implications. First, we support indirectly the notion that reduction of circulating progenitor cells contribute to the pathophysiology of CVD development and progression in high-risk individuals. Second, the easy measure of CD34+ cells may be used clinically to stratify patients’ risk beyond the classical risk assessment. In conclusion, circulating CD34+ progenitor cell count represents an independent risk biomarker, and significantly modulates outcomes of the MetS. Although it is still not clear to what extent this result has a practical value, it strengthens the notion that progenitor cells have a role in the pathophysiology of CVD and MetS.
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