The interactive effect of diabetes and central obesity on stroke: a prospective cohort study of inner Mongolians
Olofindayo et al. BMC Neurology
The interactive effect of diabetes and central obesity on stroke: a prospective cohort study of inner Mongolians
Jennifer Olofindayo 1 2
Hao Peng 1
Yan Liu 1
Hongmei Li 1
Mingzhi Zhang 1
Aili Wang 1
Yonghong Zhang 0 1
0 Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, School of Public Health, Soochow University , Suzhou , China
1 Department of Epidemiology, School of Public Health, Medical College of Soochow University , 199 Ren-ai Road Industrial Park District, Suzhou , China
2 Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine , New Orleans, LA , USA
Background: The relationship between central obesity and stroke is inconsistent in diabetic and non-diabetic populations. This indicates an interaction between diabetes and central obesity on stroke risk. The present study aimed to examine the interaction in a cohort of Inner Mongolians. Methods: In this prospective cohort study, we assessed the interaction between diabetes and central obesity on stroke incidence between June 2003 and July 2012. At baseline, 2,589 adults were recruited and examined from Inner Mongolia, China. Participants were categorized into four subgroups according to presence of diabetes and/or central obesity. Both additive and multiplicative interactions were evaluated using Cox proportional-hazard models. Results: 121 stroke events were recorded during the follow-up period. The cumulative incidence of stroke was highest for participants with both diabetes and central obesity (log-rank test, P = 0.042). The multivariable-adjusted risk for stroke was significantly higher in participants with both conditions (HR = 3.02, 95% CI 1.24-7.33, P = 0.015) compared to those with neither diabetes nor central obesity. Attributable proportion due to the interaction between diabetes and central obesity was 0.571 (95% CI 0.017-1.125). The multiplicative interactive effect between diabetes and central obesity on stroke was also statistically significant (HR = 2.67, 95% CI 1.14-6.26, P = 0.024). Conclusions: The participants who were both diabetic and centrally obese had significantly higher risk for incident stroke than the combination of individuals who individually had either condition among Mongolian population. This study suggests that central obesity and diabetes act synergistically to increase the risk of stroke.
Central obesity; Diabetes; Stroke; Interaction; Mongolians
Stroke is most commonly known as the second leading
cause of death worldwide and the first leading cause of
adult disability [1,2]. Diabetes is a clear risk factor for
stroke [1-3], although intensive management of diabetes
has not shown significant reduction of risk for stroke
. A high waist circumference, which is predictive of
central obesity, has been found to be a strong risk factor
for diabetes . Although recent studies identify central
obesity as one of the most powerful predictors of stroke
in patients with diabetes , numerous guideline
statements for stroke prevention categorize obesity as a less
documented or potentially modifiable risk factor for
stroke [7,8]. Limited studies have examined the
relationship between central obesity as defined by waist
circumference and stroke. It has been reported that
central obesity increases an individuals risk of
developing diabetes [9,10], and diabetes has been associated
with increased risk of stroke , central obesity may
have biological interactive effect with diabetes on
incident stroke. However, the interaction of central obesity
and diabetes on the risk of stroke has not been well
documented. The aim of this study was to examine the
interaction of diabetes and central obesity on incident
stroke among Mongolians, in Inner Mongolia, China.
All participants were at least 20 years of age and were
recruited from 32 villages in two neighboring townships
located in the counties of Kezuohou Banner and Naiman
Banner in Inner Mongolia. Most of the residents of these
townships are Mongolians who have lived there for
many generations and maintain a traditional diet and
lifestyle. At baseline, in 2003, this study included 2,589
individuals (1064 males and 1525 females) all without
previous cardiovascular disease (CVD) including stroke.
The subjects were selected from the 32 villages. The
selection criteria were to meet all of the followings: (1)
age: 20 years, (2) ethnicity: Mongolian. There were a
total of 3,475 eligible residents in the study fields. The
exclusion criteria were to meet one of the followings: (1)
self-reported history of CVD, stroke, or tumors, (2)
taking antihypertensive medications, (3) being pregnant.
The 886 people who refused to participate or met the
exclusion criteria were excluded. In 2012, 2,583
individuals (99.8%) were successfully contacted to provide
comprehensive health information. All participants provided
written informed consent. The present analysis is based
on the baseline and follow-up examinations. The ethics
committee at Soochow University in China approved
Participants underwent a thorough physical examination
at baseline and the last year of follow-up where
anthropometric information, blood pressure measurements, and
blood samples were obtained. Data on demographic
information, lifestyle risk factors, family history of CVD, and
personal medical history were gathered from standard
questionnaires written in Chinese and administered by
trained staff. Smoking and drinking were two lifestyle risk
factors that were pertinent to this study. An individual was
classified as current cigarette smoker if they smoked at
least 1 cigarette per day for 1 year or more leading up to
the start of the study. An individual was classified as
current drinker if they consumed any type of alcoholic
beverage at least once per week during the last three years.
A physical examination was then administered. After
the participants had been resting for 5 minutes, they
remained seated and three consecutive blood pressure
measurements (3 minutes between each) were made with
a standard mercury sphygmomanometer and an
appropriately sized cuff . The first and fifth Korotkoff sounds
were recorded as systolic and diastolic blood pressure,
respectively. The mean of the 3 physician-obtained
easurements constituted the reported blood pressure used
in further analyses. In our study, hypertension was defined
as systolic blood pressure (SBP) of at least 140 mmHg
and/or diastolic blood pressure (DBP) of at least
90 mmHg. Body weight and height were measured using
standard methods, and body mass index (BMI) was
calculated as weight in kilograms divided by the square of the
height in meters (kg/m2). Waist circumference (WC) was
measured 1 cm above the umbilicus. Women with a WC
greater than 80 cm and men with a WC greater than
85 cm were classified as centrally obese .
Blood samples were obtained by venipuncture in the
morning after a requested overnight fast (at least 8 hours).
All plasma and serum samples were frozen at 80C until
laboratory testing. Fasting plasma glucose (FPG) was
measured using a modified hexokinase enzymatic method
. Diabetes was defined as one of the following: (1)
FPG 7 mmol/L (2) self-reported history of diabetes or
(3) current use of either insulin or oral diabetes
medication . Serum total cholesterol (TC), high-density
lipoprotein cholesterol (HDL-C), and triglycerides (TG) were
assessed enzymatically using commercial reagents .
Low-density lipoprotein cholesterol (LDL-C)
concentration was calculated by means of the Friedewald equation
for participants who had less than 400 mg/dL TG .
Participant follow-up was executed between June 2003
and July 2012. In this study, stroke as defined by both
ischemic and hemorrhagic stroke was the event of
interest. Stroke was defined as a sudden critical onset of
neurological symptoms lasting at least 24 hours .
Participants were also diagnosed by cranial computed
tomography or magnetic resonance imaging (MRI).
Trained staff interviewed either the participants or their
relatives, if participants were dead or unable to
communicate, every two years to find new stroke cases. When a
new case was found during follow-up, the staff reviewed
the hospital records and completed a standard event
form. An end point review committee made the final
decisions regarding a participants stroke diagnosis.
Baseline characteristics were analyzed for the total
population. In the results continuous variables that were not
normally distributed were expressed as median and
interquartile range, normally distributed continuous
variables were expressed as mean standard deviation, and
binary variables were expressed as frequency and percent.
Participants were then divided into four categories:
individuals having neither risk factor, only diabetes, only
central obesity, or both risk factors. The distributions of
other conventional risk factors were compared across
the four subgroups mentioned. Normally distributed
continuous variables were assessed by ANOVA,
continuous variables with a skewed distribution were assessed by
Wilcoxon rank-sum test, and categorical variables were
assessed by a Chi-square test. Univariate and multivariate
Cox proportional hazard models were utilized to calculate
the hazard ratios (HRs) and 95% confidence intervals
(95% CI) for each category compared to individuals with
neither risk factor. A multiplicative interaction term of
diabetes and central obesity was set in the models to test
the presence of an effect. In the multivariate models, some
important confounders at baseline such as age, gender,
TC, TG, family history of CVD, smoking, drinking, and
hypertension were all included as covariates. The
biological additive interaction between diabetes and central
obesity on stroke was evaluated by three indexes: relative
excess risk because of interaction (RERI), attributable
proportion because of interaction (AP), and synergy index (S)
. If there was no biological interaction the 95% CI of
RERI and AP would include 0 and the 95% CI of S would
contain 1. Finally, the yearly cumulative incidence of
stroke among the four subgroups was estimated using
Kaplan-Meier survival curves and compared using the
log-rank test. A two-tailed P value < 0.05 was considered
statistically significant. All analyses were completed using
SAS 9.1 software.
Among the 2589 individuals included in the study at
baseline, 6 were lost to follow-up and 22 were excluded
from further analyses due to missing WC or FPG. A
total of 2561 participants were included in the final
analyses. The study sample was comprised of 1513 women
and 1048 men, with a combined mean age of 46.5 years.
After being followed for a mean of 9.2 years and
contributing approximately 23,292 person-years, 121 stroke
events (75 ischemic, 44 hemorrhagic, and 2 unknown
subtypes) were observed. The cumulative incidence of
stroke was 4.78%. With only 6 participants lost, the
follow-up rate for this study was 99.8%. Table 1 lists the
baseline characteristics for all the participants. The mean
FPG measurement was 4.99 mmol/L and 3.67% of the
participants were diabetic at baseline. Average WC and
BMI were 80.78 cm and 22.26 kg/m2, respectively.
37.29% of the participants were hypertensive at baseline
with the average blood pressure being 129.7/84.5 mmHg.
Average TC, TG, LDL-C, and HDL-C were 3.74, 1.26,
2.31, 1.17 mmol/L, respectively. There were 1137 (44.4%)
cigarette smokers, 855 (33.39%) alcohol consumers, and
334 (13.04%) participants who reported having family
history of CVD.
Table 2 presents the baseline characteristics of
participants by the 4 study subgroups: participants with neither
diabetes nor central obesity, participants with either
diabetes or central obesity, and participants with both diabetes
and central obesity. Conventional stroke risk factors such
as age, sex, BMI, blood pressure, blood lipids, FPG,
smoking, and family history of CVD were significantly different
Table 1 Baseline characteristics
Family history of CVD, n(%)
Current smoker, n(%)
Current drinker, n(%)
Central obesity, n(%)
TC, total cholesterol; TG, triglycerides; LDL-C, low density lipoprotein cholesterol;
HDL-C, high density lipoprotein cholesterol; FPG, fasting plasma glucose; BMI,
body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP,
diastolic blood pressure; CVD, cardiovascular disease. SD, standard deviation.
among the 4 subgroups. Central obese participants with or
without diabetes tended to be smokers and have higher
TC, TG, LDL-C, BMI, SBP, and DBP than those with
normal WC. Participants with both diabetes and central
obesity were more likely to be older, current smokers, and
have higher TC, TG, LDL-C, BMI, SBP, DBP, and family
history of CVD compared with those with neither diabetes
nor central obesity.
Interactive effect of diabetes and central obesity on stroke
Figure 1 depicts the cumulative incidence of stroke by the
4 risk categories for each year of follow-up. The
cumulative incidence of stroke for participants without diabetes
or central obesity (4.10%) was significantly lower than that
for those with both diabetes and central obesity (11.1%)
(log-rank test, P = 0.042). The participants with neither
diabetes nor central obesity were used as the reference
group to calculate subsequent measures of association.
Although there was an observed increase in risk of incident
stroke among participants with either diabetes or central
obesity, the increase was not statistically significant when
compared to those with neither risk factors (Table 3). In
contrast, participants with both diabetes and central
obesity had a significantly increased risk for incident stroke
(HR = 2.94, P = 0.012). The multiplicative effect between
diabetes and central obesity on stroke was also statistically
Family history of CVD, n(%)
Current smoker, n(%)
significant (HR = 2.63, P = 0.021). After adjusting for
important variables such as age, gender, TG, TC, family
history of CVD, drinking, smoking, and hypertension, risk
for incident stroke remained significantly increased in
participants with both diabetes and central obesity, compared
to those with neither (HR = 3.02, P = 0.015). The HR of
stroke for individuals with both diabetes and central
obesity was higher than the sum of the HR for individuals with
only diabetes and individuals with only central obesity.
Also after adjustment, the multiplicative effect between
diabetes and central obesity on stroke remained significant
(HR = 2.67, P = 0.024).
Figure 1 Cumulative incidence of stroke by diabetes and/or central obesity risk categories. For comparison of cumulative incidence distribution
among categories, the log-rank test was used (log-rank test, X2 = 8.209, P = 0.042).
Table 3 Interactive effect analysis of diabetes and central obesity on stroke
Covariates in adjusted model - age, gender, TG, TC, family history of CVD, drinking, smoking, and hypertension. The interaction term diabetes*central obesity was
treated as a variable in the COX proportional hazard model.
Further examination of the additive interaction
between diabetes and central obesity on stroke was then
executed. Crude and multivariate adjusted measures
were calculated. None of the three crude measures of
additive interaction between diabetes and central obesity
indicated a significant biological interaction (Table 4).
After adjustment, significant biological additive interactive
effect between diabetes and central obesity on stroke was
indicated by the significant AP estimate and its confidence
interval, 0.571 (95% CI 0.017-1.125). Approximately 57%
of the stroke risk in this cohort can be attributed to the
co-effect of diabetes and central obesity.
Although central obesity has been recognized as a
predictor of diabetes [9,10] and diabetes subsequently
increases an individuals stroke risk [12,19], there are
very few studies that have examined the interaction of
the two risk factors on stroke among general population.
This study utilized a prospective cohort study to
examine the interactive effect of diabetes and central obesity
on incident stroke among Mongolians over the age of
20. We found that the individuals who were both
diabetic and centrally obese had significantly higher risk
for incident stroke than those who did not have either
condition. Furthermore, after adjusting for important
confounding factors, individuals with both diabetes and
central obesity had a significantly 73% higher risk for
stroke than the combination of individuals with either
diabetes or central obesity individually. Obviously, there
was a significant additive interaction between diabetes
and central obesity on stroke among the Mongolians.
Table 4 Indexes of additive biological interactive effect of
diabetes and central obesity on stroke
Estimate Lower Upper Estimate Lower Upper
RERI - the relative excess risk because of the interaction; AP - the attributable
proportion because of the interaction; S - the synergy index.
Approximately 57% of the incident strokes that occurred
during the study could be attributed to the interaction of
diabetes and centrally obesity. Additionally, the
multiplicative interaction between diabetes and central
obesity showed a statistically significance. These findings
suggest that the risk for stroke in diabetic patients can
be moderated and/or reduced by exercise, diet, and
other clinical measures if they are also at condition of
Diabetes is a well-known risk factor for stroke, a
common condition associated with cardiovascular morbidity
and mortality . The increased stroke risk present in
diabetic individuals can be attributed to a number of
factors. Most of which involve metabolic components
such as insulin resistance, central obesity, impaired
glucose tolerance, and hyperinsulinaemia . Central
obesity as defined by waist circumference has been found to be
a stronger predictor of diabetes and subsequent stroke risk
than overall obesity defined by BMI [5,20-22]. Central
obesity participates in the pathway that increases risk for
stroke . It leads to an imbalanced production of
several metabolic products that potentially affect almost all
organ and tissues of the body . Not only is diabetes a
major concern in stroke prevention, central obesity is as
well. It has been reported that more than 50% of type 2
diabetic patients are centrally obese and possess more risk
factors for stroke . The participants with both diabetes
and central obesity were also more likely to be current
smokers, be hypertensive, and have a family history of
CVD in our study. In light of our findings, multiple risk
factor managements are essential to prevent incident
stroke among the individuals with both diabetes and
Among our Mongolian population, the prevalence of
diabetes is only 3.67%, much lower than total Chinese
(11.6%) . We failed to obtain data on plasma
hemoglobin A1c which might lead to an underestimated
prevalence of diabetes. The number of individuals with
diabetes was limited. As a result, the 95% CI of point
estimate of diabetes risk for individuals with both
diabetes and central obesity was wide. As well, the
estimates of RERI, AP and S varied largely after multivariate
adjustment. Although we observed a significant estimate
of AP, the 95% CI of AP was very wide. This indicated
an unstable result. Even though the multiplicative
interaction of diabetes and central obesity on stroke was
significant after multivariate adjustment, the interaction
still needed further study in other ethnicity populations.
This study has several strengths that deserve mention.
To our knowledge, it is the first study to examine the
interactive effect of diabetes and central obesity on
incident stroke among the Mongolians in China. The study
participants were homogeneous regarding their genetic
background and environmental exposures, the study data
were collected with rigid quality control, and important
confounders were measured and controlled in the analysis.
In addition, our follow-up time is relatively long and the
follow-up rate was relatively high, which enabled us to get
a less biased association between exposure variables and
outcome events. Despite these strengths a number of
limitations should be considered. Because this study
specifically observed an Inner Mongolian population, the results
may not be readily generalizable to other populations.
Another issue influencing generalizability would be the
WC cutoffs for central obesity that were arbitrarily set
according to previous knowledge and recommendations
for Chinese populations. Lastly, although multivariate
analysis was conducted, unmeasured confounding
factors could still be possible. For instance, participants
were not asked to report use of various medications
throughout the study period. We also failed to obtain
data on dietary and physical activity which contributed
to diabetes and stroke risks.
In summary, this prospective cohort study showed that
diabetes and central obesity might synergistically increase
an individuals risk of stroke. We found a significant
interaction between diabetes and central obesity on incident
stroke in a population of Inner Mongolians. Further
research should be done to evaluate the interactive effect
of diabetes and central obesity on stroke in other
populations. Clinical measures can be taken to help reduce
central obesity in diabetic patients. Individuals with both
diabetes and central obesity should be intensively followed
and treated to prevent incident stroke.
TC: Total cholesterol; TG: Triglycerides; LDL-C: Low density lipoprotein
cholesterol; HDL-C: High density lipoprotein cholesterol; FPG: Fasting plasma
glucose; BMI: Body mass index; WC: Waist circumference; SBP: Systolic blood
pressure; DBP: Diastolic blood pressure; CVD: Cardiovascular disease;
RERI: The relative excess risk because of the interaction; AP: The attributable
proportion because of the interaction; S: The synergy index; HR: Hazard ratio;
CI: Confidence intervals.
The authors declare that they have no competing interests.
YZ conceived of the study, and participated in its design and coordination and
helped to draft the manuscript. HP, YL, HL, MZ and AW conducted the study
and participated in data collection. HP and JO performed data analysis. JO and
HP wrote the paper. All authors read and approved the final manuscript.
Jennifer Olofindayo, Hao Peng and Yan Liu are co-first authors.
We are deeply appreciative of the participants in the study, and thank
Kezuohouqi Banner Center for Disease Prevention and Control, and Naiman
Banner Center for Disease Prevention and Control for their support and
assistance. This study was supported by the National Natural Science
Foundation of China (Grant Nos. 81172761 and 30972531) and a Project of
the Priority Academic Program Development of Jiangsu Higher Education
Institutions of China.
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