Predictive models for type 2 diabetes onset in middle-aged subjects with the metabolic syndrome

Ozery-Flato et al. Diabetology & Metabolic Syndrome 2013, 5:36
http://www.dmsjournal.com/content/5/1/36
RESEARCH
DIABETOLOGY &
METABOLIC SYNDROME
Open Access
Predictive models for type 2 diabetes onset in
middle-aged subjects with the metabolic
syndrome
Michal Ozery-Flato1*, Naama Parush1, Tal El-Hay1, Žydrūnė Visockienė2,4, Ligita Ryliškytė3,4, Jolita Badarienė3,4,
Svetlana Solovjova3,4, Milda Kovaitė3,4, Rokas Navickas3,4 and Aleksandras Laucevičius3,4
Abstract
Objective: To investigate the predictive value of different biomarkers for the incidence of type 2 diabetes mellitus
(T2DM) in subjects with metabolic syndrome.
Methods: A prospective study of 525 non-diabetic, middle-aged Lithuanian men and women with metabolic
syndrome but without overt atherosclerotic diseases during a follow-up period of two to four years. We used
logistic regression to develop predictive models for incident cases and to investigate the association between
various markers and the onset of T2DM.
Results: Fasting plasma glucose (FPG), body mass index (BMI), and glycosylated haemoglobin can be used to
predict diabetes onset with a high level of accuracy and each was shown to have a cumulative predictive value.
The estimated area under the receiver-operating characteristic curve (AUC) for this combination was 0.92. The oral
glucose tolerance test (OGTT) did not show cumulative predictive value. Additionally, progression to diabetes was
associated with high values of aortic pulse-wave velocity (aPWV).
Conclusion: T2DM onset in middle-aged metabolic syndrome subjects can be predicted with remarkable accuracy
using the combination of FPG, BMI, and HbA1c, and is related to elevated aPWV measurements.
Keywords: Metabolic syndrome, Type 2 diabetes mellitus, Risk assessment, Biomarkers, Arterial markers, Predictive
models
Background
Metabolic syndrome (MetS) is a complex disorder defined by a cluster of interconnected factors that increase
the risk of cardiovascular (CV) atherosclerotic diseases
and type 2 diabetes mellitus (T2DM). The presence of
MetS as a risk factor for T2DM has been examined in
numerous population-based studies [1-5]. The metaanalysis of prospective studies shows MetS to be associated
with an approximately five times higher risk for incident
T2DM in many different populations, regardless of how
the MetS is defined [6]. Impaired fasting glucose (IFG) and
impaired glucose tolerance (IGT) are shown to be strong
predictors of T2DM in many studies [7-9]. Other
* Correspondence: [email protected]
1
Machine Learning and Data Mining group, IBM Research - Haifa, Mount
Carmel, Haifa 3498825, Israel
Full list of author information is available at the end of the article
components of MetS, particularly waist circumference
(WC), body mass index (BMI), and triglycerides were
shown to be associated with incidence of T2DM in cohorts
composed of subjects with high post-prandial glucose [10]
and in the general population [11]. A recent study in
patients with manifest atherosclerosis revealed that the
presence of ≥ 3 metabolic risk factors or the presence
of a high waist circumference alone are associated with
increased risk for developing T2DM [12]. The combined presence of ≥ 3 metabolic risk factors and high
waist circumference is associated with a 10-fold increased risk of future T2DM [12].
To date, only limited information is available on the
predictors of T2DM in the group of patients that are
already diagnosed with MetS but no overt atherosclerotic disease. While the majority of available studies report associations between the incidence of T2DM and
© 2013 Ozery-Flato et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the
Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work is properly cited.
Ozery-Flato et al. Diabetology & Metabolic Syndrome 2013, 5:36
http://www.dmsjournal.com/content/5/1/36
the presence of MetS or other risk factors, the analysis
of the predictive and cumulative value of these factors is
lacking. The aim of our study was to investigate the predictive value of different clinical markers, including the
ones described above, for T2DM onset in subjects with
MetS before the manifestation of atherosclerotic disease.
Methods
Subject recruitment
All patients included in our study were recruited between
2007 and 2011 from the Lithuanian High Cardiovascular
Risk (LitHiR) primary prevention programme [13]. This
long-term programme has focused on employable-aged
women (aged 50–65) and men (aged 40–55) without overt
cardiovascular disease. Cardiovascular disease was defined
as angina pectoris, known coronary stenosis, myocardial
infarction, coronary artery bypass grafting, percutaneous
coronary intervention, transient ischemic attack or stroke,
and peripheral artery disease. As part of the programme, a
two-level approach involving primary healthcare institutions (PHCI) and specialized cardiovascular prevention
units (CVPU) was applied. Five secondary-level institutions having CVPU participated in the LitHiR programme
across Lithuania, including the Vilnius University Hospital
Santariškių Klinikos. Participants of the first level of the
programme were recruited in three ways. The first group
consisted of people registered in PHCI and invited by general practitioners to participate in the programme. The
second group consisted of people who visited PHCIs for
reasons other than cardiovascular problems. The third
group included people who found out about the
programme via local mass media. All participants had to
match the programme criteria. After cardiovascular risk
evaluation at the PHCI level, subjects for whom high cardiovascular risk was established were sent for additional
examination and treatment plans in the CVPUs (secondary level). High cardiovascular risk was defined as having
one or more of the following conditions: 1) a Systematic
Coronary Risk Evaluation (SCORE) [14] risk assessment
of over 11, 2) diabetes, 3) metabolic syndrome, 4) positive
family history of cardiovascular disease and/or 4) severe
dyslipidemia.
The number of PHCIs taking part in this program was
385/420, which comprise 91.6% of all PHCI in Lithuania.
From 2006 to 2010, 266,391 patients were examined
overall. Out of those patients, our cohort includes 2891
[1072 (37%) men and 1819 (63%) women] patients who
were diagnosed with MetS and referred to the CVPU at
the Vilnius University Hospital Santariškių Klinikos for
additional assessment, risk stratification, and setting up
of a prevention plan.
We carried out follow-up calls between January 2011
and August 2011 for 650 out of the 2891 subjects with
MetS initially referred to the CPVU in Vilnius University
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Hospital Santariškių Klinikos. These follow-up calls were
made with preference to subjects who were examined
earlier in the programme. After we excluded four subjects whose follow-up periods were less than two years,
the median of the follow-up period was 3.3 years. We
also excluded 117 participants who already had diabetes
at the baseline examination and 4 participants with
missing information on their diabetic status. As a result,
the final study cohort consisted of 525 individuals, with
187 (36%) men and 338 (64%) women.
The study was approved by the Local Ethics Committee
of the Vilnius University Hospital Santariškių Klinikos.
Diagnosis of MetS
We diagnosed patients as having MetS if they met three
or more of the revised National Cholesterol Education
Program Adult Treatment Panel III (NCEP ATPIII) criteria [15,16]:
– Waist circumference ≥ 102 cm in men, ≥ 88 cm in
women
– Triglycerides ≥ 1.7 mmol/L
– High-density lipoprotein cholesterol < 1.03 mmol/L
in men, < 1.29 mmol/L in women
– Blood pressure (BP) ≥ 130/85 mmHg
– Fasting plasma glucose (FPG) ≥ 5.6 mmol/L
We calculated the MetS score as the sum of MetS
components present.
Baseline examinations
All participants in our study underwent a baseline examination, which included gathering information on their
medical history, physical examination, risk profile and
lifestyle assessment, evaluation of cardiovascular (CV)
family history, 12-lead electrocardiogram (ECG), laboratory blood tests, and non-invasive assessment of arterial
markers of subclinical atherosclerosis. Weight, height,
and waist circumference were measured with the subject
wearing light clothing and without shoes. BMI was calculated as weight in kilograms divided by the square of
height in meters. Blood pressure was measured after the
patient rested at least five minutes, using an oscillometric
semiautomatic device (Schiller Argus VCM) with a standard bladder (12–13 cm long and 35 cm wide), validated
according to standardized mercury sphygmomanometer.
We took at least one measurement on each arm and additional measurements if the first two were significantly different. The higher value was taken as the reference one
and the average of the two highest values, if measured
more than twice. Assessment of arterial stiffness was carried out by applanation tonometry (Sphygmocor v.7.01,
AtCor Medical).
Ozery-Flato et al. Diabetology & Metabolic Syndrome 2013, 5:36
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Information about smoking and drug use was collected
by a questionnaire. Current smoking was recorded if the
subject smoked at least one cigarette a day. Positive CV
family history was recorded if first-degree relatives of the
patient had any CV events at a young age (men ≤ 45
years, women ≤ 55 years old).
Laboratory tests and assessment of glucose metabolism
Venous blood samples were collected after patients completed a 12-hour fast. Serum cholesterol [17,18], triglycerides [19,20], and plasma glucose concentrations were
determined enzymatically. High-density lipoprotein
cholesterol was analyzed by the Accelerator Selective
Detergent method (Architect ci8200; Abbott Laboratories,
Abbott Park, Illinois, USA). Low-density lipoprotein
cholesterol was calculated with the Friedewald formula
[21]. High-sensitivity serum C-reactive protein (hs-CRP)
was analyzed by a latex turbidimetric immunoassay kit
(Architect ci8200; Abbott Laboratories, Abbott Park,
Illinois, USA). Multigent HbA1c was determined by turbidimetric microparticle immunoinhibition assay (Architect
ci8200; Abbott Laboratories, Abbott Park, Illinois,
USA). Plasma fasting and oral glucose tolerance test
(OGTT) insulin were measured by chemiluminescent
microparticle immunoassay (CMIA) (Architect ci8200;
Abbott Laboratories, Abbott Park, Illinois, USA). A
standard 75-g OGTT was carried out after patients
completed a 12-hour overnight fast. Plasma glucose
and insulin concentrations were measured at 0 and
120 minutes. The examination protocol allowed the
omission of OGTT, HbA1c, and fasting insulin tests for
patients with FPG < 5.6.
We classified the subjects into various categories of glucose tolerance using the WHO criteria [22]. Normal glucose tolerance (NGT) was defined by fasting glucose <6.1
mmol/l and 2-h OGTT glucose <7.8 mmol/l. Impaired
fasting glucose was defined by fasting glucose ≥ 6.1 mmol/l
and <7.0 mmol/l and 2-h OGTT glucose <7.8 mmol/l.
Impaired glucose tolerance was defined by fasting glucose <7.0 mmol/l and 2-h OGTT glucose between 7.8 and
11.0 mmol/l inclusive. Diabetes was defined by fasting glucose ≥7.0 mmol/l and/or 2-h OGTT glucose ≥11.1 mmol/l.
Insulin resistance indices
In this study, we considered four surrogate indices for
the assessment of insulin resistance (IR) or insulin sensitivity. The Homeostasis Model Assessment insulin resistance (HOMA-IR) index [23] was calculated as fasting
insulin [μU/ml] × FPG [mmol/l] / 22.5. The quantitative
insulin-sensitivity check index (QUICKI) index [24] was
calculated as 1/[log(fasting insulin [μU/ml]) + log(FPG
[mg/dl])]. The Cederholm insulin sensitivity index (ISI),
which represents peripheral insulin sensitivity, was calculated as ISICederholm = 75000 + (G0-G120) × 1.15 ×
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180 × 0.19 × weight/120 × Gmean × log (Imean) [25],
where G0 and G120 are plasma glucose (mmol/l) concentrations at 0 and 120 minutes, and Gmean and Imean
are the mean glucose (mmol/l) and insulin (mU/l)
values calculated from values at 0 and 120 minutes. Finally, the Matsuda insulin sensitivity index, which reflects
a composite estimate of hepatic and muscle insulin sensitivity, was calculated as ISIMatsuda = 10,000 / sqrt (G0 x I0
x G120 x I120) [26,27], where G0, G120, and I0, I120 are the
plasma glucose (mg/dl) and the plasma insulin (μU/ml)
concentrations respectively at time 0 and 120 minutes.
Statistical analysis
We conducted descriptive statistics on the study cohort
at the baseline; we calculated the mean and standard deviation (SD) for the continuous variables and the frequency and proportion for the categorical variables. The
investigated set of variables included: age, gender, smoking status (never, former, current), BMI, waist circumference, weight, FPG, HbA1c, fasting plasma insulin, OGTT
glucose, OGTT insulin, serum triglycerides, total cholesterol, HDL cholesterol, LDL cholesterol, lipid treatment
(1=yes, 0=no), hs-CRP, aortic and radial pulse wave velocity (aPWV, rPWV), aortic augmentation index adjusted for heart rate 75 beats per minute (AIx@75),
mean arterial pressure (MAP), MetS score, HOMA-IR,
QUICKI, ISIMatsuda, and ISICederholm.
We measured the association between each variable
and the development of T2DM by calculating genderadjusted odds ratios (ORs). We initially included the
gender variable in any set of predictors tested. We investigated the dependency between variables and their cumulative contribution to the prediction based on their
combined logistic regression model. P values are based on
two-sided tests with a cutoff for statistical significance of
0.05. To address the inherent problem of multiple hypotheses testing, we applied the Bonferroni correction, multiplying the P value by the number of independent tests.
We performed all tests on complete data; that is, excluding those patients with data missing for the relevant
variables. We used Little’s [28] missing completely at
random (MCAR) test to identify systematic differences
between the missing values and the observed values. A
significant P value in Little’s MCAR test, indicating the
existence of such systematic differences, means that it is
plausible that data are missing at random (MAR), but
not completely at random (MCAR). In these cases, since
restricting analyses to complete cases can introduce bias,
we validated the results using multiple imputation
[29,30]. We used the fully conditional specification [31]
imputation method, as implemented in SPSS MULTIPLE
IMPUTATION command, to make 20 complete datasets.
We then combined (pooled) multiple analyses’ results
using Rubin’s Rules [30,32].
Ozery-Flato et al. Diabetology & Metabolic Syndrome 2013, 5:36
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In a separate analysis, we considered the tested variables using a stepwise algorithm that automatically selected variables for a multivariate logistic regression
model. This method used the Bayesian Information Criterion (BIC), which assesses model fit based on a loglikelihood function [33]. The model with the lowest
value of BIC is the one preferred. We took a “forward”
approach, starting with a model initialized with the gender variable, adding at each step one variable that maximally reduced the BIC statistic and terminated when
the BIC statistic stopped decreasing. We estimated the
accuracy of the predictive models using leave-one-out
cross-validation; that is, each subject in its turn was used
as a validation set, while the remaining subjects were
used to generate the model. We assessed the predictive discrimination of the model using the receiveroperating characteristic (ROC) curve of the scores of
all subjects by plotting the sensitivity against the corresponding false positive rate. We used the area under
the ROC curve, calculated by the trapezoidal rule, to
measure how well a model predicts the development
of T2DM. The model generation involved a preliminary step of data imputation for missing values using
mean values. We also used an alternative analyses
using K-nearest-neighbors data imputation, which
yielded similar results; only the mean imputation results are presented.
All statistical and modeling analysis was done using
MATLAB 7.13 (R2011b) and SPSS Statistics 19.0.0.
Results
We observed data from 187 men and 338 women with
mean ±SD ages at a baseline of 48±4 and 57±4 years for an
average of 3.2 and 3.3 years, respectively. During the
follow-up period of 2 to 4 years, a total of 32 subjects
progressed to diabetes: 16 (8.5%) of the 187 men and 16
(5%) of the 338 women. Table 1 shows the baseline characteristics of the two groups: progressors and nonprogressors.
Missing values
One hundred (19%) of the subjects had missing OGTT
glucose test values, and 120 (23%) of the subjects had
missing HbA1c values. Applying Little’s MCAR test on
the entire set of variables had a significant result (χ2
(636)=589.6, P<0.001). Repeating Little’s MCAR test after
the exclusion of these variables led to non-significant
results (χ2(180)=179.6, P>0.1). These results were
expected as the examination protocol recommended
OGTT, HbA1c and fasting insulin tests in patients with
higher FPG values. Since missing values were not missing completely at random (MCAR), we validated our
results in multiple imputation analysis (see “Statistical
Analysis” Section).
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Baseline classification of subjects
At the baseline, 237 (45%) had NGT, 99 (19%) had impaired fasting glucose (IFG), and 67 (13%) had impaired
glucose tolerance (IGT). Twenty two (4%) subjects had
FPG ≥7 mmol/l, but were diagnosed as non-diabetic by
an endocrinologist, based on additional test results (including former fasting glucose test). One hundred (19%)
of the subjects were not classified mainly due to missing
OGTT glucose test values. In the multiple imputation
analysis, most of the unclassified patients were in the
NGT group, which then increased to 60% (95% confidence interval [CI] 58-61%) of the patients. The IFG and
the IGT group contained 20% (CI 20-21%) and 16% (CI
14.5-17 %) of the patients, respectively.
Impaired fasting glucose and impaired glucose tolerance
In this section, we report the results of multiple imputation analysis. Complete data analysis had similar results
(not shown). The association of T2DM onset with the
IFG and IGT groups was significant: the odds-ratio in
the IFG group was 3.7 (CI 1.5-9.4 P= 0.006) and in the
IGT group was 3.3 (CI 1.2-8.7, P=0.01). The odds ratios
for T2DM onset were higher when the underlying criteria for FPG [≥6.1, <7 mmol/l] and OGTT glucose
[≥6.1, <11 mmol/l] were combined: 11 (CI 3.2-38, P =
0.0001) in subjects satisfying at least one criterion, and
7.9 (CI = 2.8-22.4, P = 0.0001) in subjects satisfying
both. The FPG criterion alone showed an even stronger
association with T2DM onset: odds-ratio 12.3 (CI 4.137.4, P<0.0001).
Identifying an effective set of predictors for T2DM
We found a combination of variables that effectively
predicts T2DM using the following iterative analysis. Iteratively, after adjusting for previously included variables, we added to the set of predictors the strongest
predictor for T2DM whose cumulative effect was shown
to be significant (Bonferonni corrected P < 0.05, oddsratio test). The iteration ended when no variable could
be added. We report the results of multiple imputation
analysis. Complete data analysis yielded similar results
(not shown). Table 2 presents the odds-ratio results for
all variables after they were adjusted for gender. In the
first iteration, we identified 11 significant predictors
(presented in decreasing order of their association):
FPG, BMI, Waist circumference, OGTT glucose, HbA1c,
Quicki, MetS score, Weight, ISIMatsuda, OGTT insuline,
HOMA-IR, and Fasting Insuline. In the second iteration,
after adjusting for FPG and gender, the BMI showed the
most significant association. After the selection of BMI
(third iteration), only HbA1c remained a significant
predictor. The final set included: gender, FPG, BMI
and HbA1c. The selected variables: FPG, BMI and
HbA1c, each showed a significant cumulative effect in
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Table 1 Baseline characteristics
Variable
Complete
case
FPG (mmol/L)
2
521 (99%)
Men
Women
Non-progressors
Progressors
Non-progressors
Progressors
5.9 (0.8)
7.1 (1.2)
5.7 (0.6)
6.7 (0.6)
BMI (kg/m )
524 (100%)
30.3 (3.8)
35.6 (5.4)
30.7 (4.6)
34.6 (5.4)
Waist circumference (cm)
520 (99%)
106.6 (9.4)
116.5 (9.7)
100.8 (9.6)
108.9 (7.3)
OGTT glucose (mmol/L)
425 (81%)
5.4 (1.6)
6.9 (1.8)
6.3 (1.7)
7.8 (1.8)
HbA1c (%)
405 (77%)
5.6 (0.2)
6.0 (0.5)
5.7 (0.3)
5.9 (0.2)
Quicki
326 (62%)
0.1 (0.0)
0.1 (0.0)
0.1 (0.0)
0.1 (0.0)
MetS score (0–5)
525 (100%)
3.3 (1.0)
3.8 (0.8)
3.4 (1.0)
4.3 (0.7)
Weight (kg)
524 (100%)
95.4 (13.9)
107.6 (16.2)
80.0 (12.9)
87.6 (12.9)
ISIMatsuda
299 (57%)
7.8 (5.4)
3.8 (2.4)
6.6 (4.8)
3.3 (2.2)
OGTT insuline (pmol/l)
301 (57%)
254.5 (199.3)
473.7 (209.2)
415.5 (345.8)
694.7 (492.8)
HOMA-IR
326 (62%)
3.2 (1.8)
5.7 (2.3)
3.4 (2.6)
4.9 (2.0)
Fasting insuline (pmol/l)
326 (62%)
84.3 (43.0)
131.9 (49.3)
88.0 (61.6)
115.5 (49.0)
HDL cholesterol (mmol//l)
523 (100%)
1.2 (0.3)
1.1 (0.2)
1.4 (0.3)
1.2 (0.2)
LDL cholesterol (mmol/L)
524 (100%)
4.3 (1.2)
3.7 (1.1)
4.8 (1.3)
4.4 (0.9)
Total cholesterol (mmol/l)
525 (100%)
6.7 (1.4)
6.1 (1.3)
7.1 (1.4)
6.7 (1.2)
hs-CRP (mg/L)
502 (96%)
4.0 (6.8)
3.9 (4.3)
2.9 (3.2)
7.2 (13.7)
ISICederholm
299 (57%)
75088.2 (1048.6)
75363.3 (1453.0)
74441.7 (1223.9)
73737.3 (1848.3)
Age (years)
525 (100%)
48.0 (4.0)
49.1 (4.4)
56.9 (4.1)
56.9 (3.6)
Smoking status
522 (99%)
Never
82 (49%)
8 (50%)
267 (83%)
15 (94%)
Former
13 (8%)
0 (0%)
7 (2%)
0 (0%)
Current
73 (43%)
8 (50%)
48 (15%)
1 (6%)
525 (100%)
2.7 (2.4)
3.0 (1.6)
2.1 (2.2)
2.2 (0.9)
Statin treatment
501 (95%)
163 (98%) 4 (2%)
13 (100%) 0 (0%)
303 (99%) 3 (1%)
15 (100%) 0 (0%)
aPWV (m/s)
480 (91%)
8.6 (1.4)
9.3 (1.8)
8.8 (1.4)
9.1 (1.6)
rPWV
496 (94%)
9.1 (1.2)
9.0 (1.5)
8.9 (1.3)
8.5 (1.3)
MAP (mmHg)
493 (94%)
107.5 (13.2)
103.9 (9.9)
106.7 (14.6)
106.4 (16.3)
Aix@75 (%)
499 (95%)
18.1 (8.8)
16.3 (9.1)
30.3 (7.8)
28.1 (14.1)
Triglycerides (mmol/l)
the final model (FPG: P=0.000001; BMI: 0.00001;
HbA1c: P=0.0004).
Model selection and accuracy estimation
In a separate analysis, we tested a model selection algorithm for building a predictive model for T2DM. This algorithm used a stepwise multivariate logistic regression
with the Bayesian Information Criterion (BIC) measurement as a goodness-of-fit. To account for gender differences, the initial model contained the gender variable.
Notably, the FPG-BMI-HbA1c combination was consistently selected for all training sets. The overall estimated
accuracy of the model was remarkably high (AUC=0.91).
Figure 1 exemplifies the predictive power of FPG, BMI,
and HbA1c, as well as the improvement in the prediction
for their combined, gender-adjusted score, by plotting
the ROC curves of the corresponding models.
Comparison of BMI, waist circumference and weight
We tested the cumulative value of the obesity measures:
BMI, waist circumference (WC), and weight, with respect to one another by combining them all into one
model and adjusting for gender and FPG. Complete
cases and multiple imputation analyses had similar results; only the latter is reported. Under this model, BMI
had the most significant cumulative effect (P = 0.003,
odds ratio test), compared to weight (P=0.03, odds ratio
test), and waist (P>0.1, odds ratio test).
Additionally, we compared the estimated accuracy of
three prediction models, each corresponding to one of
the three of obesity measures, together with gender,
FPG, and HbA1c. All three models had high estimated
accuracy (AUC: BMI: 0.91, Weight=0.9, Waist=0.92).
In summary, although BMI showed the strongest cumulative effect, all three obesity measures exhibited
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Table 2 ORs of the various investigated markers, adjusted for gender
Variables
FPG
BMI
Waist circumference
OGTT glucose
OR (95% CI) Bonferroni corrected P-value
All
Men
Women
4.3 (2.6 - 7.2) p<0.0001
3.9 (1.9 - 8.1) p=0.01
4.8 (2.4 - 9.5) p=0.0002
1.2 (1.1 - 1.3) p<0.0001
1.3 (1.2 - 1.5) p=0.0005
1.2 (1.1 - 1.3) p=0.04
1.09 (1.05 - 1.13) p<0.0001
1.1 (1.0 - 1.2) p=0.01
1.1 (1.0 - 1.1) p=0.1
1.6 (1.3 - 2.0) p=0.0002
1.6 (1.2 - 2.2) p=0.05
1.7 (1.2 - 2.3) p=0.04
HbA1c
13.0 (4.1 - 41.7) p=0.0003
33.1 (4.5 - 240.9) p=0.01
6.5 (1.5 - 27.6) NS
Quicki
0.00 (0.00 - 0.00) p=0.001
0.00 (0.00 - 0.00) p=0.1
0.00 (0.00 - 0.00) NS
MetS score
2.5 (1.6 - 3.8) p=0.001
1.8 (1.0 - 3.3) NS
3.4 (1.8 - 6.7) p=0.01
Weight
1.0 (1.0 - 1.1) p=0.003
1.1 (1.0 - 1.1) p=0.1
1.0 (1.0 - 1.1) NS
ISIMatsuda
0.6 (0.5 - 0.8) p=0.01
0.6 (0.4 - 0.9) NS
0.6 (0.4 - 0.9) NS
OGTT insuline
1.002 (1.001 - 1.003) p=0.02
1.00 (1.00 - 1.01)NS
1.002 (1.000 - 1.003) NS
HOMA-IR
1.3 (1.1 - 1.5) p=0.02
1.5 (1.1 - 2.0) NS
1.2 (1.0 - 1.3) NS
Fasting insuline
1.01 (1.00 - 1.01) NS
1.02 (1.00 - 1.03) NS
1.00 (1.00 - 1.01) NS
0.1 (0.0 - 0.6) NS
0.2 (0.0 - 2.5) NS
0.1 (0.0 - 0.7) NS
HDL_Ch
LDL cholesterol
0.7 (0.5 - 1.0) NS
0.6 (0.4 - 1.0) NS
0.8 (0.5 - 1.2) NS
Total cholesterol
0.7 (0.6 - 1.0) NS
0.7 (0.5 - 1.1) NS
0.8 (0.5 - 1.1) NS
hs-CRP
1.0 (1.0 - 1.1) NS
1.0 (0.9 - 1.1) NS
1.1 (1.0 - 1.2) NS
0.99 (0.99 - 1.00) NS
1.00 (1.00 - 1.00) NS
1.00 (0.99 - 1.00) NS
Age
1.0 (0.9 - 1.1) NS
1.1 (0.9 - 1.2) NS
1.0 (0.9 - 1.1) NS
Smoking (never, former, current)
0.9 (0.6 - 1.4) NS
1.1 (0.6 - 1.8) NS
0.6 (0.2 - 1.7) NS
Triglycerides
1.0 (0.9 - 1.2) NS
1.0 (0.9 - 1.3) NS
1.0 (0.8 - 1.2) NS
0.0 --
0.0 --
0.0 --
ISICederholm
Statin treatment (no, yes)
P-values are Bonferonni corrected. P>0.1 is marked insignificant (NS). For the binary statin treatment variable all incident cases had a negative value. Hence oddsratio for statin treatment is 0, and the confidence interval and p-value are not defined.
comparable discrimination under a model that contains FPG, HbA1c, and gender.
predicting T2DM, and that OGTT glucose shows no cumulative effect in a model that contains FPG.
OGTT glucose and FPG
Association of diabetes with arterial markers of
cardiovascular risk
We compared the cumulative values of OGTT glucose and
FPG with respect to each other by testing their combination.
Complete cases and multiple imputation analysis were in
agreement; we report results for the latter. We tested the cumulative effect of FPG and OGTT glucose in two settings:
after adjustment to gender and after adjustment to gender,
BMI, and HbA1c. In both cases, FPG exhibited a very significant cumulative effect (P< 0.00001, odds-ratio test). On the
other hand, OGTT glucose showed a milder cumulative effect in the gender-adjusted model (P=0.007, odds-ratio test),
and no significant effect when BMI and HbA1c were added
to the model (P> 0.1, odds-ratio test).
In an ROC analysis with cross validation, the FPG
model exhibited better performance than the OGTT glucose model (AUC: FPG=0.83, OGTT glucose = 0.71).
The combined model FPG-OGTT glucose did not show
any improvement (AUC=0.83). This confirmed that in
our cohort, FPG is superior to OGTT glucose in
We used the following measures of arterial stiffness as
surrogate markers for cardiovascular risk (CVR) at baseline examination: aortic and radial pulse wave velocity
(aPWV, rPWV) adjusted to the mean arterial pressure
(MAP), and aortic augmentation index adjusted for a
heart rate of 75 beats per minute (AIx@75). Complete
case analysis showed that Aix@75 and rPWV markers
have no significant association with either progression to
diabetes or IGT/IFG pre-diabetes conditions. On the
other hand, high aPWV values were significantly associated with the IGT condition at baseline (P=0.01; odds ratio test) and with progression to diabetes (P = 0.04; odds
ratio test). Repeating the tests with multiple imputations
yielded no significant results. As aPWV seemed to be
missing completely at random (Little’s MCAR test), we repeated the multiple imputation analysis after restricting
the data to patients with non-missing aPWV values,
Ozery-Flato et al. Diabetology & Metabolic Syndrome 2013, 5:36
http://www.dmsjournal.com/content/5/1/36
Figure 1 Comparison of prediction models. ROC curves of four
diabetes onset prediction models: FPG-model, BMI-model, HbA1cmodel, and a FPG-BMI-HbA1c-model. All models were adjusted for
the gender variable.
retaining 480 (91%) of the patients. This time, the results
of the multiple imputation analysis matched the complete
case analysis. Testing the association between aPWV and
CRP yielded no significant result (P>0.1, Pearson correlation test, complete case, and multiple imputation
analyses).
Discussion
In this study, the combination of FPG, BMI, and HbA1c
was shown to be a powerful predictor for the development of T2DM in subjects with MetS. FPG was shown
to be superior to OGTT glucose in predicting T2DM,
with OGTT glucose showing no cumulative value to
FPG. Our study is aligned with general population studies showing that both IGT and IFG are similarly associated with an increased risk of diabetes, and that risks are
higher when IGT and IFG coexist [34]. IFG was more
prevalent than IGT in our cohort, while the opposite
trend is usually observed in the general population [34].
The higher rate of IFG can be attributed to the fact that
our study cohort consisted of subjects with high metabolic risk, in whom higher values of FPG are expected.
Our findings of FPG being a stronger predictor than
OGTT glucose and that OGTT exhibited no cumulative
value to FPG, are different from the reports of other
studies [35,36]. This increased predication power of FPG
can be explained by the high prevalence of elevated FPG
in our group. Similar to [37-39], which studied longterm prediction of T2DM risk in the general population,
Page 7 of 9
our results do not support the need for performing a
2-h OGTT to pinpoint the possible candidates for future diabetes in MetS subjects.
BMI and HbA1c were evaluated as predictors of diabetes in numerous studies. BMI is known to be a major
predictor for T2DM in the general population [35], as
well as in the MetS population [11]. The T2DM risk was
shown to increase exponentially with HbA1c in both
genders [40]. In another large study, the model including
both FPG and HbA1c was shown to be more effective for
T2DM prediction than models including FPG alone or
HbA1c alone [41]. Recently, a study confirmed that
HbA1c of ≥5.6% had an increased risk for progression to
T2DM, independent of other confounding factors [42].
This supports our finding on the cumulative effect of
HbA1c, with respect to FPG and HbA1c.
Our investigation of four common insulin resistance/
sensitivity indices yielded that these are less predictive
for T2DM than FPG and OGTT glucose, as previously
indicated by other studies whose cohorts were characterized by a high rate of IFG [37]. The association of these
indices with progression to T2DM became insignificant
after adjusting for FPG. This is similar to another report
[43], which tested the association of the HOMA-IR
index with T2DM after adjustment for BMI and familial
history.
Our applanation tonometry results correspond with
previous studies concerning the association between
aPWV and diabetes, and the lack of association between
elevated augmentation index and the presence of diabetes [44]. Similar to previous reports [45], our study
demonstrated that the association between increased
aortic stiffness and glucose metabolism abnormalities
(IGT) is already found in pre-diabetic stages, and that IGT
is more strongly associated with cardiovascular risk than
IFG. The increased aPWV in our study cannot be
explained by the elevation of CRP, and is predominantly associated with elevated 2h-OGTT glucose measurements.
To the best of our knowledge, no previous study
established a predictive model for a new onset of diabetes in subjects with MetS. Since we focused on
middle-aged metabolic-syndrome subjects, a possible
limitation of our study is that its results cannot be generalized to subjects without MetS. Our study was also
limited by the size of our dataset (525 subjects) and by
the short duration of the follow-up period (2 to 4 years),
resulting in only 32 participants that developed diabetes
during the follow-up period. The subsequent unbalanced
ratio between progressors and non-progressors, together
with the relatively small size of the dataset, led to higher
uncertainty in assessing the level of the risk estimate for
considered variables. Another drawback of our study is
the lack of information on diabetes familial history,
which was shown to be a strong predictor for T2DM in
Ozery-Flato et al. Diabetology & Metabolic Syndrome 2013, 5:36
http://www.dmsjournal.com/content/5/1/36
the general population as well as in MetS subjects [11].
As 2h-OGTT glucose was found to be inferior to FPG in
predicting T2DM in MetS subjects, future studies should
also consider 1h-OGTT glucose, which was found to be
a stronger predictor than 2h-OGTT glucose in several
studies [37,46].
Conclusions
The main finding of our study suggests that simple measures, such as BMI, FPG, and HbA1c can accurately predict the development of T2DM in subjects with MetS.
Meta-analysis of data from many population-based studies has shown that MetS, regardless of how it is defined,
is a significant predictor of incident diabetes in many
different populations [6]. Our study added to the current
knowledge that for subjects who already have MetS, no
sophisticated tests are needed to accurately identify the
risk of incident diabetes: fasting plasma glucose is the
strongest predictor with BMI and glycosylated haemoglobin having cumulative value.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
MO researched data, wrote the manuscript, and is the guarantor of this
work. NP researched data and wrote the manuscript. TE researched the data
and reviewed/edited the manuscript. ZV wrote the manuscript. LR collected
data and wrote the manuscript. JB, SS, and MK collected data and reviewed
the manuscript. RN and AL reviewed/edited the manuscript. All authors read
and approved the final manuscript.
Page 8 of 9
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
Acknowledgements
The study was supported by the Vilnius University Hospital Santariškių
Klinikos and IBM Research.
The authors thank Saharon Rosset and Aya Vituri, Tel Aviv University, and
Dan Geiger, Technion, for helpful discussions.
16.
Author details
1
Machine Learning and Data Mining group, IBM Research - Haifa, Mount
Carmel, Haifa 3498825, Israel. 2Centre of Endocrinology, Vilnius University
Hospital Santariškių Klinikos, Santariskiu g. 2, Vilnius LT-08661, Lithuania.
3
Centre of Cardiology and Angiology, Vilnius University Hospital Santariškių
Klinikos, Santariskiu g. 2, Vilnius LT-08661, Lithuania. 4Vilnius University,
Medical Faculty, M. K. Ciurlionio g. 21, Vilnius LT-03101, Lithuania.
19.
Received: 19 March 2013 Accepted: 4 July 2013
Published: 15 July 2013
20.
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