Serum Resistin, Cardiovascular Disease and All-Cause Mortality in Patients with Type 2 Diabetes
Cardiovascular Disease and All-Cause Mortality in Patients with
Type 2 Diabetes. PLoS ONE 8(6): e64729. doi:10.1371/journal.pone.0064729
Serum Resistin, Cardiovascular Disease and All-Cause Mortality in Patients with Type 2 Diabetes
Claudia Menzaghi 0
Simonetta Bacci 0
Lucia Salvemini 0
Christine Mendonca 0
Giuseppe Palladino 0
Andrea Fontana 0
Concetta De Bonis 0
Antonella Marucci 0
Elizabeth Goheen 0
Sabrina Prudente 0
Eleonora Morini 0
Stefano Rizza 0
Alyssa Kanagaki 0
Grazia Fini 0
Davide Mangiacotti 0
Massimo Federici 0
Salvatore De Cosmo 0
Fabio Pellegrini 0
Alessandro Doria 0
Vincenzo Trischitta 0
Francesco Dotta, University of Siena, Italy
0 1 Research Unit of Diabetes and Endocrine Diseases, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy, 2 Unit of Endocrinology, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy, 3 Research Division, Joslin Diabetes Center, Boston, Massachusetts, United States of America, 4 Unit of Biostatistics, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy, 5 IRCSS Casa Sollievo della Sofferenza-Mendel Laboratory , Rome , Italy , 6 Department of Internal Medicine, University of Rome Tor Vergata , Rome , Italy , 7 Unit of Biostatistics , Consorzio Mario Negri Sud, Santa Maria Imbaro , Italy , 8 Department of Medicine, Harvard Medical School , Boston , Massachusetts, United States of America, 9 Department of Experimental Medicine, Sapienza University of Rome , Italy
Background: High serum resistin has been associated with increased risk of cardiovascular disease in the general population, Only sparse and conflicting results, limited to Asian individuals, have been reported, so far, in type 2 diabetes. We studied the role of serum resistin on coronary artery disease, major cardiovascular events and all-cause mortality in type 2 diabetes. Methods: We tested the association of circulating resistin concentrations with coronary artery disease, major cardiovascular events (cardiovascular death, non-fatal myocardial infarction and non-fatal stroke) and all-cause mortality in 2,313 diabetic patients of European ancestry from two cross-sectional and two prospective studies. In addition, the expression of resistin gene (RETN) was measured in blood cells of 68 diabetic patients and correlated with their serum resistin levels. Results: In a model comprising age, sex, smoking habits, BMI, HbA1c, and insulin, antihypertensive and antidyslipidemic therapies, serum resistin was associated with coronary artery disease in both cross-sectional studies: OR (95%CI) per SD increment = 1.35 (1.10-1.64) and 1.99 (1.55-2.55). Additionally, serum resistin predicted incident major cardiovascular events (HR per SD increment = 1.31; 1.10-1.56) and all-cause mortality (HR per SD increment = 1.16; 1.06-1.26). Adjusting also for fibrinogen levels affected the association with coronary artery disease and incident cardiovascular events, but not that with all cause-mortality. Finally, serum resistin was positively correlated with RETN mRNA expression (rho = 0.343). Conclusions: This is the first study showing that high serum resistin (a likely consequence, at least partly, of increased RETN expression) is a risk factor for cardiovascular disease and all-cause mortality in diabetic patients of European ancestry.
Funding: This research was supported by Accordo Programma Quadro in Materia di Ricerca Scientifica nella Regione Puglia-PST 2006 and PO Puglia FESR 2007
2013, Italian Ministry of Health grants RC2011, RC2012, European Foundation for the Study of Diabetes/Pfizer grant (CM) and National Institutes of Health grant
HL073168 (AD). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: CM received funding from the EFSD/Pfizer. MF is a PLOS ONE Editorial Board member. There are no patents, products in development or
marketed products to declare. This does not alter the authors adherence to all the PLOS ONE policies on sharing data and materials.
Cardiovascular disease (CVD) is a major cause of morbidity and
mortality among patients with type 2 diabetes . Although
several components of the diabetic milieu contribute to the
increased risk of CVD associated with diabetes, insulin resistance
and inflammation have been recognized as particularly important
pathogenic factors . Both conditions have been linked to
cytokines released by the adipose tissue and collectively known as
adipokines . Among these is resistin, a 12.5 kDa cysteine-rich
protein, which, in humans, is primarily secreted by macrophages
[4,5]. Several cross-sectional studies based on resistin serum levels
and/or tissue expression have pointed to this molecule as a
proinflammatory adipokine contributing to atherosclerosis and the
clinical phenotypes resulting from it [5,6,7,8,9,10,11]. High serum
resistin levels have also been found, although with some
inconsistencies, to predict incident cardiovascular events in
prospective studies [12,13,14,15,16,17]. This evidence, however,
mostly concerns the general population since the few published
studies of resistin as a CVD marker in diabetic subjects are small,
limited to Asian individuals, and contradictory in their findings
[9,11,18]. Given that cardiovascular risk may be differently shaped
in non diabetic as compared to diabetic individuals answering the
question of whether or not resistin plays a role in the development
of CVD also among the latter group is definitely needed. To
address this question, we analyzed data from over 2,300 European
subjects with type 2 from four different studies: two case-control
collections of such patients with and without evidence of coronary
artery disease (CAD), a prospective cohort of patients with type 2
diabetes followed over time for incident major cardiovascular
events and another prospective cohort of patients with type 2
diabetes followed over time with regard to all-cause mortality.
Gargano heart study (GHS)-cross sectional design. This
study includes 798 European subjects from Italy with type 2
diabetes (ADA 2003 criteria) who were consecutively recruited at
the Endocrine Unit of IRCCS Casa Sollievo della Sofferenza in
San Giovanni Rotondo (Gargano, Center East Coast of Italy) from
2001 to 2008, as part of an ongoing investigation on the genetics of
CAD in type 2 diabetes [19,20] (Figure S1).
Cases are patients who underwent coronary angiography and
had a stenosis .50% in at least one coronary major vessel or with
previous myocardial infarction (MI). Controls include
asymptomatic patients without signs of myocardial ischemia at resting and
maximal symptom limited stress ECG. The latter was conducted
on a treadmill according to a Bruce protocol after cardiovascular
drugs as b-blockers and Ca-channel blockers were stopped for 48
hours. The test was defined as maximal if 85% of the predicted
heart rate for the participants age was reached. Ischemia was
defined as a horizontal or downsloping ST segment depression of
1 mm or more calculated at 0.08 s after the J point (i.e. the
junction between QRS complex and ST segment) or the
development of typical angina pectoris.
Serum resistin was measured in 776 (97%) participants.
Joslin Heart Study (JHS). This study consists of a series of
868 CAD cases and controls, all with type 2 diabetes (ADA 2003
criteria), who lived in the greater Boston area and received
treatment at the Joslin Clinic and/or the Beth Israel Deaconess
Medical Center (BIDMC) at the time of their recruitment .
All participants were self-reported non-Hispanic Whites. Case
participants with CAD were a random sample of patients with
type 2 diabetes who had a stenosis greater than 50% in a major
coronary artery or a main branch thereof that was documented by
cardiac catheterization at the BIDMC between 2001 and 2008.
Sixty percent of the case patients received diabetes management
care at the Joslin Clinic. Control participants without CAD were
randomly selected from among Joslin patients who were identified
between 2001 and 2008 as fulfilling the following criteria: (1)
current age between 55 and 74 years; (2) type 2 diabetes for 5 years
or more; (3) negative cardiovascular history (i.e., normal resting
electrocardiogram, absence of cardiac symptoms, and no
hospitalization for cardiovascular events); and (4) non inducible
ischemia to an exercise treadmill test performed for screening
Serum resistin was measured in 861 (99%) participants.
GHS-prospective design. This study comprises 368 patients
with type 2 diabetes and CAD (as previously defined), who were all
case participants of the GHS-cross sectional design (Figure S1).
Follow-up information on outcomes was collected yearly from
2002 to 2011. The only exclusion criterion was the presence of
poor life expectancy for non diabetes-related diseases. The
endpoint was a combination of major cardiovascular events including
cardiovascular death (i.e. according to the international
classification of diseases codes: 428.1- ninth edition - and I21.0I21.9,
I25.9, I46.9I50.9, I63.0, I63.9, I70,2 tenth edition), non-fatal
MI and non-fatal stroke . For all non-fatal MI and strokes,
confirmation of the events was obtained from the hospital medical
records. In the case of patients who did not show up at the
scheduled clinical control, information on the incident
cardiovascular events was obtained through telephone interviews with the
patients or their primary care physicians or from death certificates.
Serum resistin was measured in 359 (98%) participants.
Gargano Mortality Study (GMS). One thousand and
twenty-eight patients with type 2 diabetes (ADA 2003 criteria)
were consecutively recruited from November 1th 2000 to
September 30th 2005 at the Endocrine Unit of IRCCS Casa
Sollievo della Sofferenza in San Giovanni Rotondo, for a study
having all-cause mortality as the end-point (Figure S1). The only
exclusion criterion was the presence of poor life expectancy due to
non diabetes-related disorders. This cohort was followed until
2010 by obtaining information on the participants vital status by
direct contact with patients and/or their relatives or by queries to
the registry offices of the cities of residence. Such information was
available in 838 individuals whose data were therefore analyzed in
the present study. One hundred and three of the GMS
participants are also participants of the GHS-prospective design
Serum resistin was measured in 779 (93%) participants.
Data Collection and Definitions
Clinical data were obtained from a standardized interview and
examination. Body mass index (BMI) was calculated by dividing
the weight (in kilograms) by the square of height (in meters).
Smoking habits and history of hypertension (as indicated by the
presence of anti-hypertensive therapy), dyslipidemia (as indicated
by the presence of anti-dyslipidemic therapy), and MI as well as
glucose-lowering treatment were also recorded at time of
examination. Data regarding medications were confirmed by
review of medical records. Those who reported smoking cigarettes
regularly during the year before the examination were considered
In the GHS-cross sectional and-prospective designs and GMS,
blood samples were collected between 8:00 and 9:00 AM after an
overnight fast. In the JHS, blood samples were obtained between
7:00 AM and 6:00 PM without the requirement of fasting. Serum
aliquots were stored at 280uC. Peripheral whole blood cells
(PWBC) RNA was obtained from 68 fasting patients with type 2
diabetes, with no clinical evidence of CVD (38 males/30 females,
age 65.167.0 years, BMI 30.965.0 kg/m2, HbA1c 7.961.7%) by
PAXgene Blood RNA collection tubes (PreAnalytiX, GmbH,
Germany). These patients, not belonging to any of the previous
samples, were consecutively recruited at the Endocrine Unit of
IRCCS Casa Sollievo della Sofferenza in San Giovanni
Rotondo, with the specific purpose of correlating gene expression
levels on PWBC with clinical features and/or biomarkers levels.
Each study protocol and the informed consent procedures were
approved by the local Institutional Ethic Committee IRCCS
(Istituto di Ricovero e Cura a Carattere Scientifico) Casa Sollievo
della Sofferenza for GHS and GMS and by the Joslin Committee
on Human Studies and the Beth Israel Deaconess Medical Center
Committee on Clinical Investigations for JHS. All participants
gave written consent.
Measurement of Circulating Resistin Levels
Serum resistin concentrations were measured by a commercial
ELISA (Bio Vendor, Brno Czech Republic) at the Research Unit
of Diabetes and Endocrine Diseases in San Giovanni Rotondo, as
previously described . Inter- and intra-assay coefficients of
variation were 3.24% and 6.37.2% respectively.
Measurement of RETN mRNA Levels
Total RNA from PWBC was extracted using PAXgene Blood
RNA kit (PreAnalytiX, GmbH, Germany). RNA was eluted in
RNAse free-water and stored at 280uC until used. Total RNA
yield and purity were determined spectrophotometrically using the
NanoDrop ND-1000 (Wilmington, DE, USA). Integrity of
resuspended total RNA was determined by electrophoretic
separation and subsequent laser induced florescence detection
using the RNA 6000 Nano Assay Chip Kit on the Bioanalyzer
2100 (Agilent Technologies, Waldbronn, Germany).
Five hundred nanograms of RNA were reverse transcripted by
AMV Reverse Transcription System (Promega Corp., Wis, USA)
and used as template in subsequent analyses. RETN
(Hs00220767_m1) and GAPDH (Hs99999905_m1) gene
expression assays on demand kit reagents Applied Biosystems (Foster
City, CA) were used to quantify in triplicates relative gene
expression on ABI-PRISM 7500 Applied Biosystems (Foster City,
CA). RETN transcription levels were normalized using the GAPDH
housekeeping gene. RETN/GAPDH mRNA ratios were obtained
from the equation 22DCt, where DCt is the difference in threshold
cycles between RETN and GAPDH.
Patients baseline characteristics were reported as mean
6standard deviation (SD) and percentages for continuous and
categorical variables, respectively. Correlations between
continuous variables were assessed by Pearson coefficient.
In case-control studies, the association between resistin
circulating levels and CAD was assessed with univariate and
multivariate logistic regression models with CAD status as the
dependent and resistin as the independent variable. Separate
analyses were performed for continuous resistin values and tertiles
of its distribution. The strength of the associations was estimated
by means of odds ratios (ORs), along with their 95% Confidence
Intervals (95% CI), per SD increase in baseline resistin level and
for tertiles of its distribution. In addition, a test for linear trend in
OR estimates over resistin tertiles was performed by including
resistin tertiles (coded as 1, 2, and 3) as a continuous variable into
the logistic model.
In both prospective studies, the time variable was defined as the
time between the baseline examination and date of the event
(namely, major cardiovascular events for GHS-prospective, and
all-cause mortality for GMS), or, for subjects who did not
experience any event, the date of the last available clinical
follow-up. Incidence rates for the endpoint of interest were
expressed as the number of new events per total number of
personyears (py) and were compared between baseline serum resistin
levels tertiles using a Poisson regression model. In addition, a test
for linear trend in incidence rates over resistin tertiles was
performed by including resistin tertiles as a continuous variable
into the Poisson model.
Univariate and multivariate Cox proportional hazards
regressions analyses were performed to assess the association between
resistin values or tertiles of its distribution and the event
occurrence. Risks were reported as Hazard Ratios (HR) along
with their 95% CI per SD increase in resistin levels and for tertiles
of its distribution. Test for linear trend in HR estimates over
resistin tertiles was performed by including resistin tertiles as a
continuous variable into the Cox proportional hazard models.
Adjusted survival curves were derived from the Cox proportional
hazard models, using the direct approach .
Predicted risk probabilities were derived from the Framingham
Risk Score (FRS), which is an established risk model for
cardiovascular event in the general population  and from
the UKPDS risk engine, which is a model for the risk of coronary
heart disease in type 2 diabetes . Models calibration, i.e. the
agreement between observed outcomes and predictions, was
assessed using the survival-based Hosmer-Lemeshow (HL)
goodness-of-fit test , a chi-square test based on grouping
observations into deciles of predicted risk and testing associations
with observed outcomes. Models discrimination, i.e. the ability to
distinguish subjects who will develop an event from those who will
not, was assessed by computing the modified C statistic for
censored survival data [28,29]. Comparison between C-indices
was carried out following Pencina and DAgostinos approach
Reclassification improvement offered by resistin was quantified
using the survival-based net reclassification index (NRI) following
the Kaplan-Meier approach with one-sided bootstrapped p-values
based on 1000 re-samplings with replacement [30,31] and by
Integrated Discrimination Improvement (IDI) (28). Since no
established risk cut-offs were available for our high risk patients
as those affected by diabetes, we computed the categories-free
version of NRI (i.e. cNRI) . The main difference consists in the
definition of a reclassified subject: for the NRI a subject has to
move from one risk-category to another one; the cNRI requires
that the subjects risk probability changes, without any limit, to
define an upward or downward reclassification. cNRI is a more
objective measure of improvement in risk prediction while NRI
has a more attractive interpretation for clinicians. The time
horizon of risk prediction was set to 7 years. (i.e. upper cut-off of
the 3th quartile).
A p-value ,0.05 was considered as significant. All analyses were
performed using SAS Release 9.1.3 (SAS Institute, Cary, NC,
Clinical features of participants in the GHS-cross sectional
design and the JHS are summarized in Table 1. In both studies,
serum resistin concentrations were significantly higher in
CADpositive cases than in CAD-negative controls (Table 1). For each
SD increment in resistin levels, the odds of CAD increased by
,30% in the GHS cross-sectional design (OR = 1.29, 95% CI:
1.101.51, p = 0.002) and by ,80% in the JHS (OR = 1.83, 95%
CI: 1.492.24; p = 3.53610212) (Table 2). This association was
unaffected by adjustments for age, sex, smoking habits, BMI,
HbA1c, and insulin, antihypertensive and antidyslipidemic
therapies (Table 2). The linear relationship between resistin levels and
CAD risk was confirmed in an analysis by resistin tertiles (Figure 1).
In both studies, individuals in the second tertile had an OR of
CAD that was intermediate between the first and third tertiles,
with p-values for linear trend of 0.012 for the GHS-cross sectional
design and 3.76610211 for the JHS (Figure 1).
Given the role of resistin in low-grade inflammation, we also
tested plasma fibrinogen as a covariate. After adjustment for this
variable, the association between resistin and CAD in the
GHScross sectional design was no longer significant (Table 2). Data on
fibrinogen levels were not available for the JHS.
n = 418
Figure 1. Odds ratios (95% CI) of CAD in cross sectional studies, according to baseline tertile (T1T3, range in parentheses) of
resistin levels. ORs were estimated by logistic regression after adjusting for age, sex, smoking habits, BMI, HbA1c and insulin, antihypertensive and
GHS-cross sectional design
Insulin w/wo oral agents (%)
Antihypertensive therapy (%)
Antidyslipidemic therapy (%)
n = 416
n = 360
n = 443
Continuous variables were reported as mean6SD whereas categorical variables were reported as total frequency and percentages. GHS: Gargano Heart Study; JHS:
Joslin Heart Study; CAD: Coronary Artery Disease; BMI: Body Mass Index; HbA1c: glycated haemoglobin. N.A: Not Available.
GHS-cross sectional design
CAD Negative n = 416
CAD Positive n = 360
CAD Negative n = 443
CAD Positive n = 418
Continuous variables were reported as mean6SD whereas categorical variables
as total frequency and percentages. GHS: Gargano Heart Study; GMS: Gargano
Mortality Study; BMI: body mass index; HbA1c: glycated haemoglobin.
It is of note that serum resistin levels in CAD negative controls
from both GHS-prospective design and JHS were significantly
higher than those previously measured in our laboratory  in
non diabetic controls (p,0,001 and ,0,01, respectively; data not
The GHS-prospective design. The clinical features of study
participants are summarized in Table 3. During follow-up
(5.462.5 years), 58 cardiovascular deaths, 6 non-fatal MIs and 9
non-fatal strokes occurred, corresponding to an overall annual
incidence rate of 3.8% (73 events/1,934 py). Given that this cohort
comprises only very high risk individuals (i.e. diabetic patients who
already suffered by coronary stenosis and/or previous MI), it is not
surprising that most of major cardiovascular events are
representAntihypertensive therapy (%)
Antidyslipidemic therapy (%)
Insulin w/wo oral agents (%) 194 (54.0)
ed by death. Each SD increment of serum resistin levels was
associated with a 31% increase in the risk of major cardiovascular
events (HR = 1.31, 95% CI: 1.131.53; p = 3.38610 4). As in the
case-control studies, the association was not affected by adjustment
for age, sex, smoking habits, BMI, HbA1c, and insulin,
antihypertensive and antidyslipidemic therapies (HR = 1.31, 95%
CI: 1.101.56; p = 0.003), but was attenuated and lost significance
after adjusting for fibrinogen levels (HR = 1.18, 95% CI: 0.97
1.45; p = 0.099).
After stratification by tertiles of baseline resistin levels, the
incidence rate of major cardiovascular events was 2.4% (17
events/711 py) in the first, 3.5% (23 events/665py) in the second,
and 5.9% (33 events/558 py) in the third tertile (p for
trend = 0.001). Accordingly, the HR of major cardiovascular
events progressively increased across resistin tertiles, and persisted
after adjustment for age, sex, smoking habits, BMI, HbA1c and
insulin, antihypertensive and antidyslipidemic therapies
(HR = 1.68, 95% CI: 0.80 to 3.53 and HR = 2.79, 95% CI: 1.37
to 5.69 in the second and third tertile, respectively; p for
trend = 0.004) (Figure 2).
Survival C statistic, IDI and cNRI indices were used to evaluate
the incremental prognostic information of serum resistin for major
cardiovascular events as obtained by the FRS  and the
UKPDS risk engine . Time horizon prediction was set to 7
years, replacing the baseline survival probability accordingly.
Patients whose information of some clinical variable used in FRS
and/or UKPDS risk engine was not available (n = 61) were
FRS did not perform well in our sample with survival C statistic
being equal to 0.584 (95% CI: 0.5100.657). The addition of
serum resistin produced a significant (p = 0.028) improvement,
with survival C statistic becoming 0.640 (95% CI: 0.5680.713).
Both models resulted well calibrated (HL p-values being 0.532 and
0.256, respectively). Moreover, a significant improvement in
discrimination was also detected by IDI: 0.022, 95% CI: 0.004
0.048, p = 0.003. Finally, the addition of serum resistin to the FRS
allowed to reclassify correctly 95/298 patients (cNRI = 0.433,
p = 0.006): 92/244 (37.7%) and 3/54 (5.6%) in those without and
with incident events, respectively.
The UKPDS risk engine too performed poorly (survival C
statistic = 0.674; 95% CI: 0.6070.741). The addition of serum
resistin produced a significant (p = 0.025) improvement (survival C
statistic = 0.704; 95% CI: 0.6410.766). Both models resulted well
calibrated (HL p-values being 0.169 and 0.176, respectively). In
contrast, resistin addition did not result in a significant IDI (0.005;
Figure 2. Survival curves for major cardiovascular events in the GHS-prospective design, according to baseline tertile (T1T3, range
in parentheses) of resistin levels. Curves are estimated by Cox regression after adjusting for age, sex, smoking habits, BMI, HbA1c and insulin,
antihypertensive and antidyslipidemic therapies.
95% CI: 20.010.024, p = 0.286). Finally, the addition of serum
resistin to the UKPDS risk engine allowed to reclassify correctly
98/298 patients (cNRI = 0.459, p = 0.004): 94/244 (38.5%) and
4/54 (7.4%) in those without and with incident events,
The GMS. Given that CVD is the main cause of death among
patients with type 2 diabetes, we assessed the role of serum resistin
in predicting all-cause mortality in a cohort of such patients. The
clinical features of the GMS participants are summarized in
Table 3. During follow-up (7.662.1 years), 150 deaths occurred,
corresponding to an annual incidence rate of 2.7% (150 events/
Serum resistin predicted the risk for all-cause death with an HR
of 1.18 (95% CI: 1.121.27; p = 1.561026) per SD increment.
One hundred and three GMS participants overlapped with those
of GHS-prospective design. Exclusion of these subjects did not
substantially alter the results (HR = 1.17, 95% CI: 1.071.27;
p = 0.00047). Similar results were obtained in the whole cohort
after adjusting for age, sex, smoking habits, BMI, HbA1c and
insulin, antihypertensive and antidyslipidemic therapies
(HR = 1.16, 95% CI: 1.061.26; p = 0.001).
The all-cause mortality rates in the first, second, and third tertile
of resistin levels were 1.8% (36 events/2,022 py), 2.8% (52 events/
1,862 py), and 3.5% (62 events/1,775 py), respectively (p for
trend = 0.001). At variance with the linear trend observed in both
cross-sectional studies and the GHS-prospective design, the
adjusted HRs for all-cause mortality were similar in the second
and third tertile (HRs = 1.72, 95% CI: 1.11 to 2.68, and 1.81, 95%
CI: 1.16 to 2.82, respectively, p for trend = 0.019) (Figure 3). Also
at odds with the other studies, the association between resistin and
mortality remained significant, though less robustly, after adjusting
for fibrinogen levels (HR per resistin SD = 1.15, 95% CI: 1.05
1.27; p = 0.03).
Correlation between Serum and mRNA Resistin Levels
Since the data above indicate that serum resistin is a marker of
cardiovascular risk in patients with type 2 diabetes, we measured
RETN mRNA levels in circulating PWBC and serum resistin levels
in 68 diabetic patients in order to obtain mechanistic insights on
the biology of serum resistin variability. The two variables were
positively correlated (rho = 0.343, p = 0.006) (Figure S2).
Despite improvements in the treatment of CVD and in the
control of risk factors, individuals with type 2 diabetes remain at
increased cardiovascular risk as compared to the general
population  with cardiovascular events being the most
important cause of death in these patients . Discovering novel
biomarkers able to predict CVD in diabetic patients is therefore
urgently needed to decrease the burden of this devastating
Figure 3. Survival curves for all-cause mortality in the GMS, according to baseline tertile (T1T3, range in parentheses) of resistin
levels. Curves are estimated by Cox regression after adjusting for age, sex, smoking habits, BMI, HbA1c and insulin, antihypertensive and
To the best of our knowledge, this is the first study to show that
elevated serum resistin concentration is a risk factor for CVD in
patients with type 2 diabetes of European ancestry. While a rich
literature exists on serum resistin as a cardiovascular risk factor in
the general population [13,14,15,16,17], data concerning the type
2 diabetes population have been thus far sparse, contradictory,
and limited to Asian individuals [9,11,18]. Two small
crosssectional studies of patients with type 2 diabetes from Japan and
Korea described an association of serum resistin with CAD and
stroke, respectively, but a third study on 343 diabetic Korean
patients failed to confirm such findings in a prospective setting. By
contrast, we have obtained strong and consistent evidence of
association between serum resistin and CVD from both
crosssectional and prospective studies on patients with type 2 diabetes
of European ancestry. This effect of resistin is independent of the
most established cardiovascular risk factor including sex, smoking
habits, BMI, HbA1c and insulin, antihypertensive and
antidyslipidemic therapies. However, if the analysis is adjusted for fibrinogen,
the association is no longer significant, suggesting it is mediated at
least in part by low-grade inflammation an established
cardiovascular risk factor. Whether resistin is a true risk factor
which may causally contribute to CVD or, in contrast, a simple
biomarker of pro-inflammatory status, cannot be addressed by our
We wanted to investigate whether serum resistin provides
incremental information in predicting major cardiovascular events
as obtained by well established models such as the FRS and the
UKPDS risk engine [25,26] which, similarly to other models, are
known not to perform well in the subset of patients with type 2
diabetes [33,34]. Thus, it was not unexpected that both models
performed poorly also in our sample in predicting major
cardiovascular events. Of note, the addition of serum resistin
improved the two models in terms of both discriminatory and
reclassification performance. Such improvement was not only
statistically significant, but also of clinical relevance. Further larger
studies, are needed to deeper address the relative importance of
resistin as an additional marker of clinical utility. for predicting
CVD in patients with type 2 diabetes.
Our study also shows for the first time that serum resistin is an
independent predictor of all-cause mortality in a study comprising
779 patients with type 2 diabetes. In contrast to what was observed
with CVD risk, the association with mortality is only modestly
affected by adjustment for fibrinogen levels. Therefore, though
fibrinogen is not the best marker of low-grade inflammation, it
may be hypothesized that the effects of resistin on all-cause
mortality are mediated by mechanisms that are independent of
this pathway. The possibility that different mechanisms underlie
the effects of resistin on CVD and all-cause mortality is also
suggested by the fact that the relationship between resistin levels
and all-cause mortality does not appear to be linear as that
between resistin and CAD or major CVD events. Consistent with
such hypothesis, the association between resistin and all-cause
mortality observed in the general population [35,36,37] seems to
be independent from cardiovascular mortality [35,36].
Unfortunately, data on cause of death that could confirm this finding
among type 2 diabetes patients were not available in the GMS.
An additional finding of our study is that RETN mRNA in
PWBC is correlated to serum resistin. This result, which is
consistent with the observation that human resistin is mainly
produced by macrophages , strongly suggest that resistin
circulating levels are modulated by gene expression levels. The
mechanism(s) underlying such modulation are not yet known and
need further studies to be unraveled.
One strength of our study is the overall sample size, consisting of
a total of 2,313 diabetic patients from two cross-sectional and two
prospective investigations, and the completeness of clinical
information, including standardized clinical evaluations and hard
end-points validated by medical records or death certificates.
Another strength is the fact that the resistin measurements were
centralized and all the samples were handled identically. In this
context, the observed difference in serum resistin concentration
between the two cross-sectional studies, with JHS participants
having 2030% lower mean levels as compared to GHS
individuals, is somewhat surprising. One possibility is that such
difference was due to the different proportion of patients treated
with lipid lowering agents in the two studies (77% in the JHS as
compared to 48% in the GHS). Such agents are mainly statins,
which are known to decrease serum resistin levels . This
hypothesis is supported by the observation that participants in the
GHS-cross sectional design who were on statins had serum resistin
levels 1015% lower than patients who were not on statins (data
not shown). At variance, given that no substantial effects of fasting
on resistin levels have been described , we can exclude that the
observed difference in serum resistin levels between GHS and JHS
is due to the different fasting status of the two studies.
Despite these differences, the fact that the association between
serum resistin and CAD, that was found in the GHS-cross
sectional was fully replicated in the JHS makes our finding
Of note, resistin levels in CAD-negative controls from both
GHS-cross sectional and JHS were clearly higher than those from
non diabetic controls .
The major limitation of our study is represented by the lack of
C-reactive protein measurements which surely would have help
clarify the link between resistin and chronic inflammation state. In
fact, the role of fibrinogen on inflammation remains largely
speculative, thus making not possible to draw firm conclusions
about the biology underlying the association we observed in our
Finally, whether our finding can be generalized to other
populations of different ethnicity having different environmental
and/or genetic background remains to be established. This issue
deserves particular attention given that a different genetic
regulation in different ethnic groups has been hypothesized for
serum resistin . Therefore, additional studies are certainly
needed to confirm our present finding in a broader context.
In conclusion, our study is the first to show that high serum
resistin (a likely consequence, at least in part, of increased resistin
mRNA expression) is a risk factor for CVD and all-cause mortality
in patients with type 2 diabetes of European ancestry. Further
studies are warranted to determine whether this biomarker can be
used in a clinical setting to improve the stratification of diabetic
patients with regard to their risk for CVD and death.
Figure S1 Diagrams and participants of Gargano Heart Study
and Gargano Mortality Study in whom serum resistin levels were
available. Gargano Heart Study (GHS)-cross sectional design includes 776
European subjects with type 2 diabetes mellitus (T2DM), 360
CAD positive and 416 CAD negative as defined in methods.
GHSprospective design comprises 359 patients with T2D and CAD who
were all case participants of the GHS-cross sectional design as
described in methods. Gargano Mortality Study (GMS) comprises 779
patients with T2D as describes in methods. One hundred and
three of the GMS participants are also participants of the
GHSprospective design (gray box). Raw data on resistin levels and
association with related variables can be provided upon request for
Figure S2 Correlation between serum and mRNA resistin levels.
Correlation between serum and mRNA resistin levels in 68
patients with type 2 diabetes. RETN mRNA levels in PWBC are
expressed as arbitrary units (AU) of RETN/GAPDH ratios.
We are indebted to the staffs and participants of the GHS, JHS and GMS
for their dedication and contributions.
Conceived and designed the experiments: C. Menzaghi SB MF SDC AD
VT. Performed the experiments: LS C. Mendonca GP CDB AM SP EM
SR GF DM. Analyzed the data: C. Menzaghi SB GP AF FP AD.
Contributed reagents/materials/analysis tools: SB EG AK MF SDC FP
AD. Wrote the paper: C. Menzaghi AD VT.
1. Seshasai SR , Kaptoge S , Thompson A , Di Angelantonio E , Gao P , et al. ( 2011 ) Diabetes mellitus, fasting glucose, and risk of cause-specific death . N Engl J Med 364 : 829 - 841 .
2. Haffner SM ( 2006 ) The metabolic syndrome: inflammation, diabetes mellitus, and cardiovascular disease . Am J Cardiol 97 : 3A - 11A .
3. Scherer PE ( 2006 ) Adipose tissue: from lipid storage compartment to endocrine organ . Diabetes 55 : 1537 - 1545 .
4. Steppan CM , Bailey ST , Bhat S , Brown EJ , Banerjee RR , et al. ( 2001 ) The hormone resistin links obesity to diabetes . Nature 409 : 307 - 312 .
5. Reilly MP , Lehrke M , Wolfe ML , Rohatgi A , Lazar MA , et al. ( 2005 ) Resistin is an inflammatory marker of atherosclerosis in humans . Circulation 111 : 932 - 939 .
6. Burnett MS , Lee CW , Kinnaird TD , Stabile E , Durrani S , et al. ( 2005 ) The potential role of resistin in atherogenesis . Atherosclerosis 182 : 241 - 248 .
7. Langheim S , Dreas L , Veschini L , Maisano F , Foglieni C , et al. ( 2010 ) Increased expression and secretion of resistin in epicardial adipose tissue of patients with acute coronary syndrome . Am J Physiol Heart Circ Physiol 298 : H746 - 753 .
8. Pischon T , Bamberger CM , Kratzsch J , Zyriax BC , Algenstaedt P , et al. ( 2005 ) Association of plasma resistin levels with coronary heart disease in women . Obes Res 13 : 1764 - 1771 .
9. On YK , Park HK , Hyon MS , Jeon ES ( 2007 ) Serum resistin as a biological marker for coronary artery disease and restenosis in type 2 diabetic patients . Circ J 71 : 868 - 873 .
10. Ohmori R , Momiyama Y , Kato R , Taniguchi H , Ogura M , et al. ( 2005 ) Associations between serum resistin levels and insulin resistance, inflammation, and coronary artery disease . J Am Coll Cardiol 46 : 379 - 380 .
11. Tsukahara T , Nakashima E , Watarai A , Hamada Y , Naruse K , et al. ( 2009 ) Polymorphism in resistin promoter region at 2420 determines the serum resistin levels and may be a risk marker of stroke in Japanese type 2 diabetic patients . Diabetes Res Clin Pract 84 : 179 - 186 .
12. Lubos E , Messow CM , Schnabel R , Rupprecht HJ , Espinola-Klein C , et al. ( 2007 ) Resistin, acute coronary syndrome and prognosis results from the AtheroGene study . Atherosclerosis 193 : 121 - 128 .
13. Hoefle G , Saely CH , Risch L , Koch L , Schmid F , et al. ( 2007 ) Relationship between the adipose-tissue hormone resistin and coronary artery disease . Clin Chim Acta 386 : 1 - 6 .
14. Weikert C , Westphal S , Berger K , Dierkes J , Mohlig M , et al. ( 2008 ) Plasma resistin levels and risk of myocardial infarction and ischemic stroke . J Clin Endocrinol Metab 93 : 2647 - 2653 .
15. Frankel DS , Vasan RS , D'Agostino RB Sr, Benjamin EJ , Levy D , et al. ( 2009 ) Resistin, adiponectin, and risk of heart failure the Framingham offspring study . J Am Coll Cardiol 53 : 754 - 762 .
16. Butler J , Kalogeropoulos A , Georgiopoulou V , de Rekeneire N , Rodondi N , et al. ( 2009 ) Serum resistin concentrations and risk of new onset heart failure in older persons: the health, aging, and body composition (Health ABC) study . Arterioscler Thromb Vasc Biol 29 : 1144 - 1149 .
17. Luc G , Empana JP , Morange P , Juhan-Vague I , Arveiler D , et al. ( 2010 ) Adipocytokines and the risk of coronary heart disease in healthy middle aged men: the PRIME Study . Int J Obes (Lond) 34 : 118 - 126 .
18. Lim S , Koo BK , Cho SW , Kihara S , Funahashi T , et al. ( 2008 ) Association of adiponectin and resistin with cardiovascular events in Korean patients with type 2 diabetes: the Korean atherosclerosis study (KAS): a 42-month prospective study . Atherosclerosis 196 : 398 - 404 .
19. Bacci S , Menzaghi C , Ercolino T , Ma X , Rauseo A , et al. ( 2004 ) The +276 G/T single nucleotide polymorphism of the adiponectin gene is associated with coronary artery disease in type 2 diabetic patients . Diabetes Care 27 : 2015 - 2020 .
20. Prudente S , Morini E , Larmon J , Andreozzi F , Di Pietro N , et al. ( 2011 ) The SH2B1 obesity locus is associated with myocardial infarction in diabetic patients and with NO synthase activity in endothelial cells . Atherosclerosis 219 : 667 - 672 .
21. Qi L , Parast L , Cai T , Powers C , Gervino EV , et al. ( 2011 ) Genetic susceptibility to coronary heart disease in type 2 diabetes: 3 independent studies . J Am Coll Cardiol 58 : 2675 - 2682 .
22. Bacci S , Rizza S , Prudente S , Spoto B , Powers C , et al. ( 2011 ) The ENPP1 Q121 variant predicts major cardiovascular events in high-risk individuals: evidence for interaction with obesity in diabetic patients . Diabetes 60 : 1000 - 1007 .
23. Menzaghi C , Coco A , Salvemini L , Thompson R , De Cosmo S , et al. ( 2006 ) Heritability of serum resistin and its genetic correlation with insulin resistancerelated features in nondiabetic Caucasians . J Clin Endocrinol Metab 91 : 2792 - 2795 .
24. Ghali WA , Quan H , Brant R , van Melle G , Norris CM , et al. ( 2001 ) Comparison of 2 methods for calculating adjusted survival curves from proportional hazards models . JAMA 286 : 1494 - 1497 .
25. D'Agostino RB , Vasan RS , Pencina MJ , Wolf PA , Cobain M , et al. ( 2008 ) General cardiovascular risk profile for use in primary care: the Framingham Heart Study . Circulation 117 : 743 - 753 .
26. Stevens RJ , Kothari V , Adler AI , Stratton IM , Group UKPDS ( 2001 ) The UKPDS risk engine: a model for the risk of coronary heart disease in Type II diabetes (UKPDS 56) . Clin Sci (Lond) 101 : 671 - 679 .
27. D'Agostino R , Nam BH ( 2004 ) Evaluation of the performance of survival analysis models: discrimination and calibration measures . Handbook of Statistics , vol 23 Elsevier Science BV.
28. Uno H , Cai T , Pencina MJ , D'Agostino RB , Wei LJ ( 2011 ) On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data . Stat Med 30 : 1105 - 1117 .
29. Pencina MJ , D'Agostino RB ( 2004 ) Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation . Stat Med 23 : 2109 - 2123 .
30. Pencina MJ , D'Agostino RB Sr , D' Agostino RB Jr, Vasan RS ( 2008 ) Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond . Stat Med 27 : 157 - 172 ; discussion 207 - 112 .
31. Pencina MJ , D'Agostino RB , Steyerberg EW ( 2011 ) Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers . Stat Med 30 : 11 - 21 .
32. Preis SR , Pencina MJ , Hwang SJ , D'Agostino RB Sr, Savage PJ , et al. ( 2009 ) Trends in cardiovascular disease risk factors in individuals with and without diabetes mellitus in the Framingham Heart Study . Circulation 120 : 212 - 220 .
33. Coleman RL , Stevens RJ , Retnakaran R , Holman RR ( 2007 ) Framingham, SCORE, and DECODE risk equations do not provide reliable cardiovascular risk estimates in type 2 diabetes . Diabetes Care 30 : 1292 - 1293 .
34. Chamnan P , Simmons RK , Sharp SJ , Griffin SJ , Wareham NJ ( 2009 ) Cardiovascular risk assessment scores for people with diabetes: a systematic review . Diabetologia 52 : 2001 - 2014 .
35. Lee SH , Ha JW , Kim JS , Choi EY , Park S , et al. ( 2009 ) Plasma adiponectin and resistin levels as predictors of mortality in patients with acute myocardial infarction: data from infarction prognosis study registry . Coron Artery Dis 20 : 33 - 39 .
36. Pilz S , Weihrauch G , Seelhorst U , Wellnitz B , Winkelmann BR , et al. ( 2007 ) Implications of resistin plasma levels in subjects undergoing coronary angiography . Clin Endocrinol (Oxf) 66 : 380 - 386 .
37. Zhang MH , Na B , Schiller NB , Whooley MA ( 2011 ) Association of resistin with heart failure and mortality in patients with stable coronary heart disease: data from the heart and soul study . J Card Fail 17 : 24 - 30 .
38. von Eynatten M , Schneider JG , Hadziselimovic S , Hamann A , Bierhaus A , et al. ( 2005 ) Adipocytokines as a novel target for the anti-inflammatory effect of atorvastatin in patients with type 2 diabetes . Diabetes Care 28 : 754 - 755 .
39. Weikert C , Westphal S , Luley C , Willich SN , Boeing H , et al. ( 2007 ) Withinsubject variation of plasma resistin levels over a 1-year period . Clin Chem Lab Med 45 : 899 - 902 .
40. Menzaghi C , Trischitta V ( 2010 ) Genetics of serum resistin: a paradigm of population-specific regulation? Diabetologia 53 : 226 - 228 .