UNIVERSITA’ DI PISA
DIPARTIMENTO DI MEDICINA CLINICA E
SPERIMENTALE
Scuola di Specializzazione in Medicina Interna
Direttore: Prof. Stefano Taddei
GDF-15 plasma levels are associated with the classical
cardiovascular risk factors but do not modulate their effect
on vascular damage
Relatore
Chiar.mo Prof. Andrea Natali
Candidato
Dott.ssa Elena Venturi
To my little dragon, or Achilles, or Helen or whoever you want to be
Index
Abstract pag.1
Abbreviations pag.2
Introduction pag.3
Rationale of the study pag.6
Methods pag.7
Results pag.14
Discussion pag.17
Tables and figures pag.20
1 Abstract
Objective: To understand why the protective effect of GDF-15, demonstrated in the in vitro studies, does not clearly emerge from the clinical studies.
Methods: We have analyzed the data of a cross sectional study in outpatients selected to have a large variability in the extent of micro and macro vascular disease, who have been extensively evaluated in terms of both cardiovascular risk factors and micro and macro vascular end-organ damage.
Results: Plasma GDF-15 is raised at the presence of all the major risk factors for atherosclerotic cardiovascular disease, cardiac diseases, microvascular organ damage (neuropathy and nephropathy) and large arteries disease. Ageing, diabetes and the degree of metabolic control are the only statistically independent predictors of plasma GDF-15. GDF-15 does not modify the impact of risk factors on carotid atherosclerosis (IMT), it seems to have a direct negative effect on peripheral arteriopathy (PWV and ABPI) and no effect on myocardial function (NT-proBNP). It seems to have a negative impact on ACR, to be protective for cardiac autonomic neuropathy, while it is neutral on endothelial function. Conclusions: These findings altogether confirm the role of GDF-15 as a marker of cardiovascular risk and end-organ damage, but do not support a major role for GDF-15 per se or in the modulation of the impact of risk factors on micro and macro CV complications.
Keywords: Growth differentiation factor-15, cardiovascular risk factors, atherosclerotic cardiovascular disease.
2 Abbreviations
ABPI, ankle-brachial pressure index; ACR, albumin to creatinine ratio; AF, auto fluorescence; AG-II, angiotensin II; AGE, advanced glycation end products; ANS, autonomic nervous system; AP, activator protein; BNP, brain natriuretic peptide; BMI, body mass index; CAN, cardiac autonomic neuropathy; CAVI, cardio-ankle vascular index; CC, calcium channel; CCA, common carotid artery; CRP, C-reactive protein; CVD, cardiovascular disease; eGFR, estimated glomerular filtration rate; FRHI, Framingham reactive hyperemia index; GDF-15, growth differentiation factor-15; HbA1c, hemoglobin A1c; HDL, high-density lipoprotein; HR, heart rate; IL-1β, interleukin-1β; IL-2, interleukin-2; IMT, intima-media thickness; LDL, low-density lipoprotein; LEAD, lower extremities arterial disease; Lp(a), lipoprotein(a); MIC-1, macrophage inhibiting cytokine 1; M-SCF, macrophage colony stimulating factor; NT-proBNP, N-terminal prohormone of brain natriuretic peptide; PAT, peripheral arterial tonometry; PMA, phorbol myristate acetate; PWV, pulse wave velocity; RHI, reactive hyperemia index; SP, specifity protein; TGF-β, transforming growth factor-β; TNF-α, tumor necrosis factor-α; T2D, type 2 diabetes.
3 Introduction
GDF-15 (growth
differentiation factor-15), also known as MIC-1 (macrophage inhibiting cytokine 1), is a member of the TGF-β (transforming growth factor-β) super family [1] whose gene is localized on chromosome 19p12–13.1 and is composed of two exons and one intron. GDF15 is produced as a 40-kDa pro peptide
that is cleaved in the endoplasmic reticulum to release a 25-kDa active circulating dimeric protein [2]. It is primarily expressed in the placenta [3] and prostate but also in intestinal mucosa, kidney, lung, secretory tubuli of exocrine glands, and brain [4] and it is induced by tissue injury in macrophages [5], cardiomyocytes [6], adipocytes [7], and endothelial cells [8]. Promoter analysis revealed multiple regulatory elements such as p53, AP-1 (activator protein 1), AP-4 (activator protein 4), SP-1 (specifity protein 1) and androgen receptors [9]. Under stress conditions GDF-15 is highly expressed in response to many cytokines like IL-1β (interleukin-1β), IL-2 (interleukin-2), TNF-α (tumor necrosis factor-α), AG-II (angiotensin II), M-SCF (macrophage colony stimulating factor), TGF-β, PMA (phorbol myristate acetate) and androgen hormones [1]. Its biologic effects depend on the activation of the PI3K/AKT/eNOS, ALK/Smad2/3 and ALK/Smad1/5/8 signaling, and the inhibition of the NF-kβ/JNK and EGFR/ERK pathways [1], thereby participating in the modulation of inflammation, atherosclerosis, myocardial ischemic injury and cardiac hypertrophy [9].
4
More in details, GDF-15 acts as an autocrine regulator of macrophage activation in many inflammatory processes: it represses leukocyte recruitment into the infarcted heart lesioned tissue [10] and modulates chemotaxis and apoptosis of macrophages in the arteriosclerotic plaque [11-15]; while its anti-inflammatory effect is consistent, still unclear is its overall effect on the development of the plaque. In atherosclerosis prone animal models (ApoE--) in fact, both the lack of GDF15 (whole body knock out) and its overexpression results in a less severe atherosclerosis. In cardiomyocytes, in normal conditions, GDF-15 is expressed at very low levels, while in response to ischemic injury the expression is strongly upregulated and the polypeptide is released, especially within the infarct peripheral area, where it exerts an anti-apoptotic action, exerting a protective action versus the ischemia/reperfusion tissue injury [6, 16]. In cardiomyocytes GDF15 expression is induced also by pressure overload with an overall anti-hypertrophic effect [17, 18].
GDF-15 has also been proven to exert a protective role against high glucose-induced apoptosis in human umbilical vein endothelial cell [19] and to be expressed proportionally to adipokines in human adipose tissue in response to different stressors [7]. Despite the large number of studies already available, an unequivocal knowledge of the pathophysiological relevance of GDF-15 in cardiovascular and metabolic disorders is still lacking. In clinical studies GDF-15 has been found to be a reliable positive biomarker of cardiovascular risk [20, 21], it is associated with classic cardiovascular risk factors and it predicts cardiovascular events and overall mortality in large population cohorts [22-26]. Higher plasma GDF-15 levels predict mortality in patients with ST-elevation myocardial infarction [27] and non-ST-elevation acute coronary syndrome [28], identifying those, in the latter case, who may derive the greatest benefit from an invasive therapeutic strategy [29] or have the greatest incidence of recurrent events [30]. Higher levels of MIC-1 provide prognostic information in stable ischemic heart disease [31] and heart failure [32, 33], identifying those at greater risk of left ventricular remodeling after acute coronary syndrome, worsening of functional status and developing of pulmonary
5
hypertension [34]. With regard to metabolic syndrome, GDF-15 has been proven to be increased in obese and type 2 diabetic patients [35, 36], to predict worsening of microalbuminuria in type 2 diabetic patients [37] and to have a positive correlation with insulin resistance and impaired fasting glucose tolerance independently of age and body mass index [38]. Thus some researchers believe that it is not associated with the incidence of type 2 diabetes itself, but rather with the inflammatory response which follow the new onset of the disease [39].
6 Rationale of the study
We designed this study to understand why the protective effect of GDF-15 does not emerge from the clinical studies. We hypothesize that, similarly to hsCRP, the levels of GDF-15 circulating in the plasma reflects the overall tissues level of stress induced by a number of biologic disruptors/risk factors, but GDF-15, by virtue of its anti-inflammatory/ischemic/apoptotic effects, also protects from the development of end-organ tissue damage. To this purpose, we have analyzed the data of a cross sectional study in which subjects with an extreme variability in the extent of micro and macro vascular disease have been extensively evaluated in terms of cardiovascular risk factors and end-organ damage.
7 Methods
Study Population
The study cohort consists of 310 volunteers (232 men and 78 women, from 40 to 80 years old), recruited at department within the SUMMIT multicenter study (http://www.imi-summit.eu) according to the following criteria: (i) patients with T2D (type 2 diabetes) and clinically manifest CVD (cardiovascular disease) (n = 107), (ii) patients with T2D without clinical signs of CVD (n = 101), (iii) patients with CVD but no T2D (n = 43), and (iv) subjects with neither CVD nor T2D (n = 59). Diabetes was defined on the basis of evidence of hyperglycemia (fasting plasma glucose >7.0 mMol/L or two hour plasma glucose >11.1 mMol/L, or both) or by current medication with any anti-diabetic drug occurring after the age of 40. Classification of CVD included non-fatal acute myocardial infarction, resuscitated cardiac arrest, any coronary revascularization procedure, hospitalized unstable angina, non-fatal stroke, transient ischemic attack confirmed by a specialist, LEAD (lower extremities arterial disease) defined as diagnosis of intermittent claudication, ABPI (ankle-brachial pressure index) <0.9, angioplasty or vascular surgery. Exclusion criteria included atrial fibrillation, malignancy requiring active treatment, end-stage renal disease, any chronic inflammatory disease on therapy. Demographics, clinical characteristics including medication and laboratory examinations were obtained from medical interview. The subjects were invited to undergo three visits; one for blood and urine sample collection, one for carotid ultrasound, and one for the other vascular tests that were performed sequentially in the same order with intervals of 30 minutes for full recovery. The study was approved by the local ethical review boards and all study subjects provided written informed consent in accordance with the principles of the Declaration of Helsinki.
8 Analysis of GDF-15 in plasma
Plasma levels of GDF-15 were analyzed by the Proximity Extension Assay (PEA) technique using the Proseek Multiplex CVD 96x96 reagents kit (Olink Bioscience, Uppsala, Sweden) at the Clinical Biomarkers Facility, Science for Life Laboratory, Uppsala. Oligonucleotide-labeled antibody probe pairs were allowed to bind to their respective target present in the plasma sample and addition of a DNA polymerase led to an extension and joining of the two oligonucleotides and formation of a PCR template. Universal primers were used to pre-amplify the DNA templates in parallel. Finally, the individual DNA sequences were detected and quantified using specific primers by microfluidic real-time quantitative PCR chip (96.96, Dynamic Array IFC, Fluidigm Biomark). The chip was run with a Biomark HD instrument®. All samples were analyzed in the same run. Data analysis was performed by a preprocessing normalization procedure using Olink Wizard for GenEx (Multid Analyses, Sweden). All data are presented as arbitrary units. General calibrator curves to calculate the approximate concentrations are available on the Olink home page (http://www.olink.com).
Measurements of Endothelial Function
Endothelial function was measured using an EndoPAT® (Itamar Medical, Caesarea Ind. Park,
Israel). This method evaluates the time course of the blood flow increase in the fingertip (by plethysmography) in response to proximal (arm) ischemia by continuously measuring the amplitude of the digital pulse wave. The subjects were asked to refrain from coffee and tea for ≥2 hour, nicotine for ≥4 hour, and alcohol intake for ≥12 hours before the investigation. Only a light meal was allowed during the previous three hours (volunteers were asked to do not have snacks after a light breakfast consumed at home and the examinations were done in the morning between 10:00 am and 13:00 pm). The examination was performed in a quiet room, at 21–24°C. The subjects were in a supine position, with restrictive clothing as well as watches and jewelry on the hands removed. A cuff was placed on
9
the non-dominant upper arm. The index fingers or middle fingers were placed in pneumo-electric tubes. Arterial pulsatile volume changes from both hands were recorded continuously. After 10 minutes of rest, the cuff was inflated to 200 mmHg, with the opportunity to increase the pressure to a maximum of 300 mmHg if the digital pulse was not flat (occurred in six subjects). After five minutes of occlusion, the pressure of the cuff was released and the arterial dilation mediated by the occlusion assessed as an increase in the signal amplitude, was recorded for another eight minutes. For each 30-second interval, the response in the test finger was calculated as the ratio of the post deflation pulse amplitude to the baseline pulse amplitude (Xht/Xh0). We divided this result by the corresponding ratio from the contralateral finger (Xct/Xc0) to obtain the PAT (peripheral arterial tonometry) ratio (Xht/Xh0)/(Xct/Xc0), which was log transformed to generate the FRHI (Framingham reactive hyperemia index) or normalized according to baseline to generate RHI (reactive hyperemia index) [40].
Measurement of AGE (advanced glycation end products) in the Skin
AGEs accumulation in the skin was assessed in a non-invasive manner using the AGE Reader®
spectrophotometer (DiagnOptics Technologies B.V., Groningen, The Netherlands), which is able to quantify cutaneous AGEs by measuring the auto fluorescence (AF) elicited in the skin when illuminated by a light source of selected wavelength [41] (see www.diagnoptics.com for more details). The result of the measurement is defined as normal or pathologic referring to an age adjusted normal value.
Peripheral Sensitive Neuropathy Assessment
All patients underwent a 10 g monofilament test. The device was placed perpendicular to the skin, with pressure applied until the monofilament buckled, held in place for at least one second and then released. Six sites (all metatarsal heads and plantar surface of distal hallux) were tested on each
10
foot and patients were asked to respond “yes” or “no” whether they felt the monofilament applied to the particular site. Peripheral sensitive neuropathy was defined as three or more wrong answers.
CAN (cardiac autonomic neuropathy) Assessment
Subjects were screened for the presence of CAN using the Neurotester® device (Meteda s.r.l.,
San Benedetto del Tronto, Italy). These tests are based on the analysis of HR variation in response to maneuvers that stimulate mainly the parasympathetic system as proposed by Ewing and Clarke [42]: deep breathing, standing, and Valsalva. Pathologic or borderline results were defined as an HR (heart rate) mean increase during deep inspiration, a 30:15 ratio (i.e., the ratio of the shortest RR interval around the 15th beat to the longest RR interval around the 30th beat) after standing or a Valsalva ratio (i.e., the ratio of the longest RR interval after the maneuver to the shortest RR interval during the maneuver) inferior to the age-related normal ranges as established by Ziegler et al. [43]. Each test was performed three times and patients observed a five minutes period of rest after every test (see
www.meteda.it for details). The diagnosis was made according to Jermendy's score [44] as following: results of parasympathetic tests were scored 0 = normal, 1 = borderline, 2 = abnormal; those with a score of 0–1 = without CAN, score of 2-3 = early CAN, score of 4–6 = definitive CAN. To perform the statistical analysis we considered as affected by autonomic neuropathy those patients with early and definitive CAN.
Measurement of PWV (pulse wave velocity)
Arterial stiffness was assessed by calculating PWV (pulse wave velocity) using a Sphygmocor®
device (Atcor Medical, Australia). A blood pressure cuff was attached to the left arm, and three electrocardiographic electrodes (lead I) were attached. The carotid and femoral pulses were carefully located. The proximal distance was measured as from the carotid pulse to the fossa jugularis. The distal
11
distance was entered as the combined distances of fossa jugularis to umbilicus, and umbilicus to femoral pulse. PWV equaled the distal minus proximal distance. After five minutes of rest, the blood pressure was measured three times, with one minute between measurements, and the mean value of the two final measurements was entered. The carotid and femoral pulses were captured. PWV (m/sec) was automatically calculated as the differences in time between the R wave of the ECG to the foot of the carotid and femoral pulse curves divided by the calculated distance.
Measurement of ABPI (ankle-brachial pressure index)
Blood pressure cuffs of the appropriate size for the subject were attached to the upper arms and the ankles. A sphygmomanometer was attached to the cuffs. The systolic blood pressure was measured using a 5–10 MHz Doppler probe. The blood pressure in the arms was measured over the brachial arteries. For the ankle, the posterior tibial artery and the dorsal artery of the foot was used. Blood pressures were measured in a horseshoe shape, beginning in the right arm, and continuing with right foot, left foot, and finally left arm. The ankle brachial index was calculated as the ratio between the highest systolic blood pressure values from each foot, respectively and the blood pressure from the arm giving the highest value. During the statistical analysis we considered the limb with the lowest value. Measurement of CAVI (cardio-ankle vascular index)
CAVI is used to measure arterial stiffness from the aortic valve to the ankle. CAVI was recorded using a VaSera VS-1500 vascular screening system® (Fukuda Denshi, Tokyo, Japan), with the patient resting in a supine position, while electrocardiogram, heart sounds and blood pressure at the brachial artery were monitored. All the measurements and calculations were made automatically by VaSera and CAVI was obtain through the following equation:
12
where a and b are constants, ρ is blood density, SBP and DBP are systolic and diastolic blood pressure and PWV is pulse wave velocity obtained by dividing vascular length from the aortic valve to the ankle by the time taken for the pulse wave to propagate from the aortic valve to the ankle [45]. The VaSera device provides two CAVI measurements, one for each inferior limb; to perform the statistical analysis we considered the limb with the highest value.
Ultrasound Imaging for IMT (intima-media thickness)
An ultrasound examination of the carotid arteries was performed to assess atherosclerotic status. The subject was in a supine position with the head turned approximately 45° away from the examined side. End-diastolic images of the artery, captured on the top of the R wave of an ECG (lead I) simultaneously shown on the screen, were saved for off-line measurement of IMT. IMT was measured both in CCA (common carotid artery) and in the bulb, the beginning of the bulb set to be where the far wall began to curve. If a plaque was present, it was included in the IMT measurement. The sonographer took the images striving to get the echoes representing the transitions between lumen and intima and media and adventitia in the far wall sharp over at least 10 mm of the CCA and bulb had to be sharp, to ensure that images were taken perpendicular to the artery. All images were taken in the projection showing the thickest IMT in the far wall of the artery at each site and measured according to the leading edge principle, using a semiautomatic analysis system, Artery Measurement Software [46]. The thickness of the intima–media complex was measured as the distance between the leading edges of the echoes representing the lumen–intima and media–adventitia transitions. The echoes were automatically outlined in the analysis system, with the possibility for the observer to make manual adjustments when needed. The computer system measured the distance between the lines at approximately 100 sites over each 10 mm section, and values for the mean, median, maximum, and minimum IMT were
13
automatically calculated. For statistical analysis only the bulb IMT was considered and results were expressed as the mean value of both right and left measurements of mean and maximum IMT.
Data and Statistical Analysis
Subjects were divided into four groups according to quartiles of plasma levels of GDF-15 expressed as natural logarithm of its serum concentrations. Mean and standard errors are reported for continuous variables, whereas percentage for nominal variables. Differences across quartiles of GDF-15 were investigated using either logistic or ANOVA for frequency or continuous values, respectively. p ≤0.05 was considered statistically significant. Multiple regression analysis was conducted according to standard least squares method and logistic analysis, for continuous or frequency dependent variables, respectively. Statistics was performed with JMP® 9.0.2 SAS software (SAS Campus Drive, Cary, NC,
14 Results
In terms of clinical phenotype higher plasma levels of GDF-15 were associated with male gender, older age, higher BMI and waist, smoke load and a higher prevalence of diabetes, hypertension and evidence of major atherosclerotic cardiovascular disease (Table 1).
Among the serum biochemical parameters, GDF-15 was associated with higher plasma levels of creatinine, uric acid, homocysteine, HbA1c and triglycerides and lower levels of HDL and ApoA. Furthermore, the analysis revealed an inverse correlation with total cholesterol, LDL and ApoB (Table 2), which was coupled to a higher rate of ongoing statin therapy.
As shown in Table 3, higher serum GDF-15 was associated with almost all our indicators of functional and anatomic cardiovascular organ damage with the exception of FRHI, with PWV, ABPI and CAVI resulting the strongest.
We performed multivariate analysis to find out which factors among the clinical phenotype characteristics and the biochemical variables, separately, were the most powerful predictors of plasma GDF-15. Among the clinical characteristics, diabetic disease (Stdß=0.38), vascular disease (Stdß=0.23) and age (Stdß=0.25) emerged as the strongest predictors (Table 4), while among the biochemical parameters serum creatinine (Stdß=0.16) and HbA1c (Stdß=0.50) showed the highest significance (Table 5). When the clinical phenotype and the biochemical profile were combined in the same model, only diabetes (Stdß=0.27), age (Stdß=0.28) and HbA1c (Stdß=0.23) remained statistically significant independent predictors of plasma GDF-15 (Table 6).
To understand whether, and how, GDF-15 participates to the emergence of micro and macrovascular disease, we performed for each index of organ damage two models of multivariate analysis: the first including the major classical risk factors (age, male gender, smoke, diabetes, hypertension, systolic blood pressure, statin therapy, LDL and HDL) and the second adding GDF-15 as
15
an independent variable. IMT, PWV and CAVI were used as indices of macrovascular disease; FRHI, ACR, pathologic skin AF and CAN as markers of micro-vascular disease and only for the latter triglycerides and HbA1c were included in the model. BNP was the only marker of macrovascular disease for whom prior ischemic heart disease was added among the classical risk factors.
The strongest determinant of IMT was male gender (Stdß ranging from 0.20 to 0.16) and when GDF-15 was entered into the model the Stdß values rise by 30% (Table 7, Figure 2). Male gender (Stdß=0.31) and age (Stdß=-0.22) were both independent predictors of ABPI; when GDF-15 was added to the model GDF-15 itself (Stdß=-0.26) reached the statistical significance as a negative predictor, male gender was unaffected (Stdß=0.33) while the effect of age was reduced (Stdß=-0.16) (Table 8, Figure 3). LDL cholesterol (Stdß=-0.19) was the only independent predictor of CAVI and GDF-15 resulted neutral in this case (Table 8, Figure 3). Male gender (Stdß=0.14), diabetes (Stdß=0.19), hypertension (Stdß=0.17) and systolic blood pressure (Stdß=0.27) were identified as positive determinants of PWV; in the second model GDF-15, itself, reached the statistical significance as a positive predictor (Stdß=0.17), without affecting the impact of male gender (Stdß=0.14), hypertension (Stdß=0.19) or systolic blood pressure (Stdß=0.25), while diabetes lost its predictive role (Table 8, Figure 3). The strongest positive predictors of serum NT-proBNP were age (Stdß=0.19), ischemic heart disease (Stdß=0.28) and HDL (Stdß=0.16); in the model with GDF-15 only ischemic heart disease (Stdß=0.29) and HDL (Stdß=0.18) maintained intact their statistical significance (Table 9, Figure 4).
The multivariate analysis with or without GDF-15 didn’t identify any significant predictors of FRHI (Table 10, Figure 5). Smoke load was identified as a positive determinant of ACR; in the second model GDF-15, itself, reached the statistical significance as a positive predictor (Stdß=0.29), decreasing the impact of smoke load (Stdß=0.15) (Table 10, Figure 5). Pathologic skin AF was significantly affected by male gender (OR=3.36) and the presence of GDF-15 in the model didn’t make
16
any difference (Table 11, Figure 6). Smoke load (OR=0.69), hypertension (OR=2.29) and systolic blood pressure (OR=0.75) proved to be predictors of CAN in the model without 15, when GDF-15 was entered into the model smoke load (OR=0.75) lost its significance, hypertension was attenuated (OR=2.10) and lost significance, while age became statistically significant (OR=1.95) and systolic blood pressure remained unaltered (OR=0.78) with GDF-15 itself being protective and statistical significant (OR=0.41) (Table 11, Figure 6).
17 Discussion
Our data show that plasma GDF-15 is raised at the presence of each and all the major risk factors for atherosclerotic cardiovascular disease and this is true for both the clinical and the biochemical/metabolic risk factors. The only exception is total plasma cholesterol, LDL cholesterol and ApoB, that result lower in subjects of the highest quartiles of GDF-15, but this is due to the higher prevalence of current statin therapy occurring, as expected, in this classes of patients (Table 1 and 2). In keeping with this finding, and with previous studies [20], higher levels of GDF-15 are also found in presence of atherosclerotic and functional cardiac diseases. Our data show that, at least in a cohort enriched with T2D, GDF-15 also tracks the presence of microvascular organ damage (neuropathy, and nephropathy) and large arteries disease in terms of both atherosclerosis (IMT, ABPI) and stiffness (PVW, and CAVI) (Table 3). Interestingly, FRHI displayed no association with GDF-15 suggesting that the protein is not a marker of early vascular damage as assessed by our method to evaluate endothelial dysfunction (EndoPAT). GDF-15, therefore, appears to be produced in a non-specific manner whenever the micro or macrovascular systems are under the aggression of any noxae. It would be plausible to consider GDF-15 as a marker of inflammation, which represents the common response to vascular damage but we could not find an association between GDF-15 and CRP. Provided that the major stimulus for GDF-15 release, at least in the myocardial cell, is hypoxia, we suggest that plasma GDF-15 is a sensitive marker of tissue hypoxia, which is also the common denominator of both micro and macrovascular diseases.
The attempt to understand which of the above mentioned risk factors has the greatest power to induce the expression of GDF-15, rather surprisingly, in our cohort ageing, diabetes and the degree of metabolic control were the only statistically significant predictors of plasma GDF-15 (Table 6) suggesting a greater impact of micro- over macro-vascular disease in modulating its expression. Within the T2D subjects the significant predictors of GDF-15 were age (Stdß=0.32) and HbA1c (Stdß=0.33).
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Most of the available experimental data [1] suggest that GDF-15 might exert a protective action on the tissues with respect to the damages induced by ischemia. On the other hand epidemiologic studies have concluded that GDF-15 is and independent risk factor for cardiovascular event [25]. Given its strong association with both risk factors and the extent of end-organ damage it is difficult to understand whether it simply captures more efficiently the overall risk factor exposure or does really exert a protective action. We tried to address this question by comparing in multivariate analysis the impact of traditional and specific risk factors on the severity of micro and macrovascular complications without and with GDF-15 as an independent variable. With respect to carotid atherosclerosis GDF-15 does not appear to have any effect, it seems to have a negative impact on peripheral arteriopathy and no effect on myocardial function. With regard for microvascular complications (FRHI, skin auto fluorescence and ACR), GDF-15 has a negative impact on ACR, acts as a neutral variable for FRHI, and seems to be protective against the development of cardiac autonomic neuropathy without substantially interfering with the other risk factors (Table 10 and 11, Figure 5 and 6). These findings altogether do not support a major role for GDF-15 in modulating the impact of risk factors on end organ damage, except for ACR, this in contrast to in vitro studies that have described its anti-inflammatory/ischemic/apoptotic effects.
We acknowledge that our cohort has peculiar characteristics and therefore we cannot extend our conclusion to the general population. However, its enrichment with patients with cardiovascular diseases and risk factors offered the possibility to explore the full range of pathophysiologic variability of the different variables related to cardiovascular disease. Clearly our analysis is cross sectional and the subjects were treated according to the standard guidelines and this might have hampered our ability to detect the relevance of GDF-15 in the natural history of the disease. On the other hand we should also acknowledge that the cohort does represent the patients as they are now and what is relevant for their care is the residual risk, ie what we can detect on top of the traditional risk factors and the
19
standard treatments. In addition, the associations between GDF-15 and cardiovascular damage and risk factors found in our study group are similar to larger epidemiologic studies. We finally should acknowledge that our index of cardiac function (NT-pro BNP) is poor in term of sensitivity and specificity, so only studies with more direct measurement of either systolic or diastolic dysfunction can address this question.
20 Tables and figures
Table 1 – Clinical characteristics of the study population according to quartiles of plasma GDF-15
*Differences across quartiles of GDF-15 were investigated using either logistic (χ2)or ANOVA for
frequency or continuous values, respectively; p ≤0.05 was considered statistically significant.
I II III IV Statistics* n 76 78 79 77 GDF-15 (pg/ml) 7.62.0 15.81.9 29.41.9 72.21.9 <0.0001 Women (%) 39 26 18 18 0.0056 Diabetes (%) 38 50 81 99 <0.0001 Hypertension (%) 46 69 75 77 0.0001 Any CVD (%) 21 46 59 66 <0.0001 Age (years) 590.8 620.8 660.8 66±0.8 <0.0001 BMI (kg/m2) 270.5 28.20.5 29.10.5 29.10.5 0.0074 Waist (cm) 981.4 1021.4 1061.4 1071.4 <0.0001
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Table 2 – Biochemical risk factors of the study population according to quartiles of plasma GDF-15
*Differences across quartiles of GDF-15 were investigated using either logistic (χ2)or ANOVA for
frequency or continuous values, respectively; p ≤0.05 was considered statistically significant.
I II III IV Statistics*
Creatinine (µmol/L) 752.3 762.2 842.2 892.3 <0.0001
Uric Acid (mg/dl) 5.10.1 5.30.1 5.60.1 5.80.1 0.0033
Total Cholesterol (mmol/L) 5.20.1 4.80.1 4.50.1 4.40.1 <0.0001
LDL Cholesterol (mmol/L) 3.20.1 2.90.1 2.60.1 2.50.1 <0.0001 HDL Cholesterol (mmol/L) 1.40.4 1.30.4 1.30.4 1.20.4 0.0034 Triglycerides (mmol/L) 1.40.1 1.40.1 1.40.1 1.70.1 0.0233 Lp(a) (mg/dl) 233.4 283.3 283.3 263.3 0.6019 ApoA (mg/dl) 1523.0 1423.0 1453.0 1403.0 0.0342 ApoB (mg/dl) 952.9 882.8 832.8 842.8 0.0170 Statin therapy (%) 32 59 59 73 <0.0001 Homocysteine (µmol/L) 8.30.5 8.30.5 9.00.5 11.60.5 <0.0001 HbA1c (mmol/mol) 42.51.3 45.31.3 52.81.3 601.3 <0.0001 CRP (mg/dl) 0.20.1 0.30.1 0.30.1 0.50.1 0.1231
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Table 3 – Indices of cardiovascular end-organ damage of the study population according to quartiles of GDF-15
*Differences across quartiles of GDF-15 were investigated using either logistic (χ2)or ANOVA for
frequency or continuous values, respectively; p ≤0.05 was considered statistically significant.
I II III IV Statistics* Pathologic skin AF (%) 24.3 29.6 43.7 46.5 0.0139 Sensitive neuropathy (%) 5.5 5.3 9.5 18.7 0.0181 CAN (%) 27.4 35.7 48.6 57.1 0.0015 ACR (mg/mMol) 1.64.1 2.04.1 7.64.0 18.94.2 0.0122 FRHI 0.620.05 0.640.05 0.530.05 0.490.05 0.0622 PWV (m/s) 9.534 10.10.34 11.50.33 12.430.33 <0.0001 ABPI min 1.120.02 1.080.02 1.050.02 0.980.02 <0.0001 CAVI max 8.80.2 8.990.2 9.70.2 9.80.2 <0.0001
Mean IMT bulb (mm) 0.900.03 1.020.03 1.040.03 1.030.03 0.0006
Max IMT bulb (mm) 1.130.04 1.310.04 1.330.04 1.320.04 0.0016
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Table 4 - Multivariate predictors of plasma GDF-15 among the clinical characteristics
*Multiple regression analysis was conducted according to standard least squares method and logistic analysis, for continuous or frequency dependent variables, respectively; p ≤0.05 was considered statistically significant. std-β Statistics* Male Gender -0.04 0.4588 Diabetes 0.38 <0.0001 Hypertension -0.05 0.4866 Any VD 0.23 0.0006 Age 0.25 <0.0001
Systolic blood pressure -0.005 0.9424
BMI -0.06 0.5627
Waist 0.22 0.0444
24
Table 5 - Multivariate predictors of plasma GDF-15 among the biochemical variables
*Multiple regression analysis was conducted according to standard least squares method and logistic analysis, for continuous or frequency dependent variables, respectively; p ≤0.05 was considered statistically significant. std-β Statistics* Serum creatinine 0.16 0.0084 Uric Acid 0.09 0.0681 Total Cholesterol -0.11 0.6848 LDL Cholesterol -0.17 0.4972 HDL Cholesterol -0.01 0.9212 (Ln)Triglycerides -0.01 0.9165 Lp(a) 0.07 0.1769 ApoA 0.08 0.3301 ApoB 0.08 0.5470 Statin therapy 0.09 0.1128 Homocysteine 0.10 0.0637 HbA1c 0.50 <0.0001 CRP -0.03 0.6493
25
Table 6 - Multivariate predictors of plasma GDF-15 among the clinical characteristics and the biochemical variables
*Multiple regression analysis was conducted according to standard least squares method and logistic analysis, for continuous or frequency dependent variables, respectively; p ≤0.05 was considered statistically significant.
std-β Statistics*
Diabetes 0.27 0.0016
Age 0.28 <0.0001
26
Table 7 - Multivariate predictors of carotid intima media thickness without and with GDF-15 as independent variable
*Multiple regression analysis was conducted according to standard least squares method and logistic analysis, for continuous or frequency dependent variables, respectively; p ≤0.05 was considered statistically significant.
Mean IMT Max IMT
std-β Stat* std-β Stat* std-β Stat* std-β Stat* Age 0.18 0.0581 0.18 0.0755 0.15 0.1012 0.16 0.1250 Male gender 0.20 0.0243 0.26 0.0070 0.16 0.0801 0.22 0.0223 (Ln)Smoke load -0.004 0.9541 -0.03 0.7097 0.01 0.9042 -0.01 0.9025 Diabetes -0.07 0.4686 -0.07 0.4583 -0.12 0.1725 -0.13 0.1932 Hypertension 0.03 0.7580 0.03 0.7822 -0.02 0.8682 -0.02 0.8109 Systolic BP -0.01 0.9392 -0.02 0.8019 0.06 0.5499 0.05 0.6074 Statin therapy 0.23 0.0378 0.20 0.0827 0.28 0.0095 0.26 0.0257 LDL 0.12 0.2524 0.09 0.4401 0.14 0.2036 0.10 0.3960 HDL -0.03 0.7534 -0.01 0.9013 -0.03 0.7303 -0.01 0.9276 GDF-15 - - -0.002 0.9925 - - -0.02 0.8651
27
Table 8 - Multivariate predictors of peripheral artery atherosclerosis without and with GDF-15 as independent variable
*Multiple regression analysis was conducted according to standard least squares method and logistic analysis, for continuous or frequency dependent variables, respectively; p ≤0.05 was considered statistically significant.
ABPI min CAVI max PWV
std-β Stat* std-β Stat* std-β Stat* std-β Stat* std-β Stat* std-β Stat* Age -0.22 0.0035 -0.16 0.0385 0.12 0.1253 0.09 0.2329 0.13 0.0731 0.08 0.2888 Male gender 0.31 <.0001 0.33 <.0001 0.11 0.1310 0.13 0.0880 0.14 0.0413 0.14 0.0491 (Ln)Smoke load -0.12 0.0953 -0.09 0.2030 0.03 0.6823 0.03 0.7058 0.04 0.5154 0.03 0.6814 Diabetes -0.03 0.6807 0.06 0.4444 0.05 0.5297 0.05 0.5609 0.19 0.0086 0.13 0.0955 Hypertension 0.02 0.7985 -0.004 0.9557 -0.13 0.0992 -0.12 0.1358 0.17 0.0197 0.19 0.0147 Systolic BP -0.14 0.0555 -0.12 0.0814 0.08 0.2711 0.08 0.3176 0.27 0.0002 0.25 0.0005 Statin therapy -0.11 0.1778 -0.05 0.5335 0.05 0.5664 0.05 0.5907 0.02 0.8424 -0.007 0.9309 LDL -0.01 0.9311 -0.02 0.8343 -0.19 0.0280 -0.19 0.0415 0.014 0.8664 0.04 0.6469 HDL 0.04 0.5645 0.03 0.7217 -0.06 0.4303 -0.04 0.6038 0.11 0.1333 0.13 0.0803 GDF-15 - - -0.26 0.0027 - - 0.006 0.9435 - - 0.17 0.0486
28
Table 9 - Multivariate predictors of BNP without and with GDF-15 as independent variable
*Multiple regression analysis was conducted according to standard least squares method and logistic analysis, for continuous or frequency dependent variables, respectively; p ≤0.05 was considered statistically significant. (Ln)NT-proBNP std-β Stat* std-β Stat* Age 0.19 0.0176 0.16 0.0587 Male gender -0.02 0.7699 -0.009 0.8987 (Ln)Smoke load -0.07 0.3363 -0.09 0.2296 Diabetes 0.01 0.8440 -0.04 0.6368 Hypertension 0.01 0.8942 0.02 0.7995
Ischemic heart disease 0.28 0.0017 0.29 0.0010
Systolic BP 0.11 0.1520 0.10 0.1838
Statin therapy 0.24 0.0168 0.21 0.0384
LDL 0.007 0.9355 0.02 0.8161
HDL 0.16 0.0490 0.18 0.0309
29
Table 10 - Multivariate predictors of endothelial function and ACR without and with GDF-15 as independent variable
*Multiple regression analysis was conducted according to standard least squares method and logistic analysis, for continuous or frequency dependent variables, respectively; p ≤0.05 was considered statistically significant.
FRHI (Ln)ACR
std-β Stat* std-β Stat* std-β Stat* std-β Stat* Age 0.05 0.5572 0.03 0.7105 -0.06 0.4646 -0.15 0.0734 Male gender -0.002 0.9814 0.03 0.7149 -0.14 0.0730 0.09 0.2576 (Ln)Smoke load -0.1 0.1925 -0.09 0.2229 0.17 0.0235 0.15 0.0446 Diabetes 0.006 0.9461 0.03 0.7542 0.13 0.1507 0.07 0.4465 Hypertension 0.08 0.3378 0.06 0.4844 0.11 0.1804 0.13 0.1119 Systolic BP 0.06 0.3927 0.07 0.3634 0.14 0.0587 0.14 0.0686 Statin therapy -0.06 0.5293 -0.02 0.8230 -0.04 0.6291 -0.09 0.3210 LDL 0.13 0.1860 0.14 0.1563 -0.12 0.1760 -0.08 0.4096 HDL -0.06 0.4862 -0.06 0.5368 0.02 0.8118 0.04 0.7042 (Ln)Triglycerides -0.13 0.1459 -0.12 0.1795 0.12 0.2043 0.07 0.4752 HbA1c -0.15 0.1095 -0.18 0.0735 0.09 0.3264 0.01 0.9129 GDF-15 - - -0.03 0.7887 - - 0.29 0.0036
30
Table 11 – Multivariate predictors of skin and autonomic nervous system damage without and with GDF-15 as independent variable
*Multiple regression analysis was conducted according to standard least squares method and logistic analysis, for continuous or frequency dependent variables, respectively; p ≤0.05 was considered statistically significant.
Pathologic skin AF CAN
OR Stat* OR Stat* OR Stat* OR Stat*
Age/10 1.13 0.6033 1.15 0.6018 1.51 0.1036 1.95 0.0172 Male gender 3.36 0.0138 3.50 0.0191 0.90 0.8389 0.87 0.8187 (Ln)Smoke load 0.79 0.1631 0.83 0.2828 0.69 0.0322 0.75 0.1086 Diabetes 0.49 0.1302 0.44 0.1071 0.96 0.9380 1.07 0.8953 Hypertension 1.74 0.1523 1.90 0.1206 2.29 0.0393 2.10 0.0707 Systolic BP/10 0.94 0.5801 0.98 0.8393 0.75 0.0087 0.78 0.0312 Statin therapy 1.42 0.4207 1.42 0.4260 0.56 0.1951 0.62 0.3034 LDL 1.59 0.0628 1.60 0.0679 1.32 0.2867 1.12 0.6782 HDL 1.27 0.7516 1.48 0.6044 0.85 0.8310 0.96 0.9626 (Ln)Triglycerides 0.71 0.4896 0.72 0.5189 0.57 0.2287 0.59 0.2739 HbA1c/10 1.15 0.4035 1.30 0.1707 0.88 0.4096 1.12 0.5413 GDF-15 - - 0.81 0.4607 - - 0.41 0.0033
31
Figure 1 – Distribution of GDF-15 plasma levels in the study population expressed in pg/ml (top chart) and ln-transformed (below chart)
32
Figure 2 - Multivariate predictors of carotid intima media thickness without and with GDF-15 as independent variable -0,4 -0,3 -0,2 -0,1 0 0,1 0,2 0,3 0,4 St d-β
IMT
Mean IMT without GDF-15 Mean IMT with GDF-15 Max IMT without GDF-15 Max IMT with GDF-15
33
Figure 3 - - Multivariate predictors of peripheral artery atherosclerosis without and with GDF-15 as independent variable -0,4 -0,3 -0,2 -0,1 0 0,1 0,2 0,3 0,4 St d-β
ABPI, CAVI and PWV
ABPI min without GDF-15 ABPI min with GDF-15 CAVI max without GDF-15 CAVI max with GDF-15 PWV without GDF-15 PWV with GDF-15
34
Figure 4 - Multivariate predictors of BNP without and with GDF-15 as independent variable
-0,4 -0,3 -0,2 -0,1 0 0,1 0,2 0,3 0,4 St d-β
NT-proBNP
NT-proBNP without GDF-15 NT-proBNP with GDF-1535
Figure 5 - Multivariate predictors of endothelial function and ACR without and with GDF-15 as independent variable -0,4 -0,3 -0,2 -0,1 0 0,1 0,2 0,3 0,4 St d-β
FRHI and ACR
FRHI without GDF-15 FRHI with GDF-15 ACR without GDF-15 ACR with GDF-15
36
Figure 6 – Multivariate predictors of skin and autonomic nervous system damage without and with GDF-15 as independent variable
0,1 1 10
O
R
Pathologic skin AF and CAN
Path skin AF without GDF-15 Path skin AF with GDF-15 CAN without GDF-15 CAN with GDF-15
37
Special thanks to my mum and to all my family for their support and endless love.
Thanks to Prof. Natali for his major human and professional contribution to the physician that I have become.
Thanks to Dr. Caputo for “the heart" and for her friendship. Thanks to Dr. Pinnola, my “SUMMIT-mate”!!
Thanks to all the medical colleagues, nurses, oss and patients that filled with valuable experience my way here.
A big thank to the staff of the Emergency Department of Pisa, and especially to Dr. Bardini, for this last awesome year.
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