Edited by: Osama O. Zaidat, Northeast Ohio Medical University, United States
Reviewed by: Mohamed Osman, St. Vincent Mercy Medical Center, United States; Philipp Gruber, Aarau Cantonal Hospital, Switzerland
This article was submitted to Endovascular and Interventional Neurology, a section of the journal Frontiers in Neurology
†These authors have contributed equally to this work
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Metabolic syndrome (MetS) can worsen cerebral arterial atherosclerosis stenosis in patients with stroke; however, its effect on patients without stroke remains ambiguous. This study explored the association of MetS and its individual components with asymptomatic intracranial arterial stenosis (aICAS) and asymptomatic extracranial arterial stenosis (aECAS) among older Chinese adults. A total of 1988 participants from the Kongcun Town study aged ≥40 years and without a history of stroke were enrolled. The baseline data were obtained via face-to-face interviews. MetS was defined according to International Diabetes Federation criteria. Detection of aICAS was conducted using transcranial Doppler ultrasound, followed by diagnosis via magnetic resonance angiography. The evaluation of aECAS was performed using bilateral carotid ultrasonography. The aICAS and aECAS groups were 1:1 matched separately to the non-stenosis group by age and sex. The association between MetS and aICAS or aECAS was analyzed using multivariate logistic regression. Among the 1988 participants, 909 were diagnosed with MetS. The prevalence of MetS was higher in the aICAS group than in the non-stenosis group (
Ischemic stroke caused by cerebral arterial atherosclerotic stenosis is a serious health and social issue worldwide, often leading to disability and mortality (
Metabolic syndrome (MetS) is a constellation of several metabolic risk factors, including central obesity, hypertension, elevated fasting blood glucose, and hyperlipidemia. The prevalence of MetS is gradually increasing with the aging population and changing lifestyle (
MetS and its individual components are closely associated with ICAS or ECAS in patients with stroke, and this relationship is more significant with respect to ICAS than to ECAS (
This study aimed to explore whether there is a differential profile in the association of MetS and its individual components with aICAS and aECAS among middle-aged and older adults living in rural communities in China.
This study was based on the Kongcun Town study (
The study protocol was approved by the Ethical Standards Committee on Human Experimentation at Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University. This study was conducted in accordance with the principles of the Declaration of Helsinki. All participants provided a written informed consent.
Baseline data on demographics and risk factors were collected via interviews, clinical examinations, and laboratory tests in a similar manner as reported in our previous study (
MetS was defined using the criteria previously published by the International Diabetes Federation (
The protocol for the evaluation of aICAS and aECAS has been described in detail in our previous study (
All analyses were conducted using IBM Statistical Package for the Social Sciences Statistics V22.0 for Windows (IBM Corp., released 2013, Armonk, NY, USA). Baseline population statistics and continuous laboratory-based variables are expressed as terms of mean and standard deviation, and categorical variables are expressed as frequencies and percentages. Continuous variables were compared using the
The demographic and clinical characteristics of the study participants are shown in
Demographic and clinical characteristics of study participants.
Age (years), mean (SD) | 57.6 (10.3) | 57.2 (10.3) | 60.3 (10.8) |
66.6 (8.1) |
<0.001 |
Male, |
956 (48.0) | 880 (48.5) | 51 (38.6) | 25 (58.1) | 0.037 |
Hypertension, |
1148 (57.7) | 1004 (55.3) | 110 (83.3) |
34 (79.0) |
<0.001 |
Diabetes mellitus, |
304 (15.2) | 250 (13.7) | 30 (35.7) |
15 (34.8) |
<0.001 |
Total cholesterol (mmol/l), mean (SD) | 5.4 (1.0) | 5.4 (1.0) | 5.4 (1.0) | 5.6 (1.0) | 0.265 |
Triglycerides (mmol/l), mean (SD) | 1.4 (0.9) | 1.3 (0.9) | 1.7 (1.2) |
1.5 (1.1) | <0.001 |
HDL-C (mmol/l), mean (SD) | 1.6 (0.4) | 1.6 (0.4) | 1.5 (0.3) |
1.7 (0.4) | <0.001 |
LDL-C (mmol/l), mean (SD) | 3.0 (0.7) | 3.0 (0.7) | 3.2 (0.7) |
3.1 (0.7) | 0.013 |
Smoking habits, |
448 (22.5) | 420 (23.1) | 15(11.3) |
13 (30.2) | 0.004 |
Drinking habits, |
658 (33.0) | 608 (33.5) | 35 (26.5) | 15 (34.8) | 0.246 |
BMI (kg/m2), mean (SD) | 25.1 (3.3) | 25.1 (3.4) | 26.2 (3.0) |
24.6 (3.3) | 0.001 |
Waist circumference (cm), mean (SD) | 91 (9) | 91 (9) | 95 (8) |
91 (10) | <0.001 |
MetS, |
909 (45.7) | 788 (43.4) | 95 (71.9) |
26 (60.4) | <0.001 |
Central obesity, |
1547 (77.8) | 1389 (76.6) | 126 (95.4) |
32 (74.4) | <0.001 |
Raised triglycerides, |
448 (22.5) | 392(21.6) | 45 (34.0) |
11 (25.5) | 0.004 |
Reduced HDL-C, n (%) | 217 (10.9) | 188 (10.3) | 25 (18.9) |
4 (9.3) | 0.009 |
Raised BP, |
1578 (79.3) | 1419 (78.2) | 118 (89.3) |
41 (95.3) |
<0.001 |
Elevated fasting glucose, |
1062 (53.4) | 944 (52.0) | 86(65.1) |
32 (74.4) |
<0.001 |
Number of MetS component | 2 (1) | 2 (1) | 3 (1) |
3 (1) | <0.001 |
Demographic and clinical characteristics of the participants after matching for age and sex.
Age (years), mean (SD) | 58.4 (10.6) | 60.3 (10.8) | 0.157 | 64.7 (8.0) | 66.6 (8.1) | 0.276 |
Male, |
51 (38.6) | 51 (38.6) | 1.000 | 25 (58.1) | 25 (58.1) | 1.000 |
Hypertension, |
84 (63.6) | 110 (83.3) | <0.001 | 30 (69.8) | 34 (79.0) | 0.323 |
Diabetes mellitus, |
32 (24.2) | 39 (29.5) | 0.331 | 15 (34.9) | 15 (34.9) | 1.000 |
Total cholesterol (mmol/l), mean (SD) | 6.9 (1.0) | 5.4 (1.0) | <0.001 | 7.1 (0.9) | 5.6 (1.0) | <0.001 |
Triglycerides (mmol/l), mean (SD) | 1.8 (1.2) | 1.7 (1.2) | 0.419 | 1.8 (1.3) | 1.5 (1.1) | 0.312 |
HDL-C (mmol/l), mean (SD) | 1.7 (0.4) | 1.5 (0.3) | <0.001 | 1.9 (0.4) | 1.7 (0.4) | 0.003 |
LDL-C (mmol/l), mean (SD) | 3.9 (0.7) | 3.2 (0.7) | <0.001 | 3.9 (0.8) | 3.1 (0.7) | <0.001 |
Smoking habits, |
22 (16.7) | 15(11.3) | 0.215 | 17(39.5) | 13 (30.2) | 0.365 |
Drinking habits, |
34 (25.8) | 35 (26.5) | 0.889 | 18 (41.9) | 15 (34.8) | 0.506 |
BMI (kg/m2), mean (SD) | 24.9 (3.5) | 26.2 (3.0) | 0.002 | 23.2 (3.7) | 24.6 (3.3) | 0.059 |
Waist circumference (cm), mean (SD) | 92 (10) | 95 (8) | 0.021 | 87 (10) | 91 (10) | 0.058 |
MetS, |
78 (53.8) | 95 (71.9) | 0.002 | 19 (44.2) | 26 (60.4) | 0.131 |
Central obesity, |
108 (81.8) | 126 (95.4) | <0.001 | 24 (55.8) | 32 (74.4) | 0.070 |
Raised triglycerides, |
55(41.7) | 45 (34.0) | 0.205 | 17(39.5) | 11 (25.5) | 0.167 |
Reduced HDL-C, |
11 (8.3) | 25 (18.9) | 0.012 | 3(7.0) | 4 (9.3) | 0.693 |
Raised BP, |
109 (82.6) | 118 (89.3) | 0.111 | 35(81.4) | 41 (95.3) | 0.044 |
Elevated fasting glucose, |
89(67.4) | 86(65.1) | 0.696 | 32 (74.4) | 32 (74.4) | 1.000 |
Number of MetS component | 3 (1) | 3 (1) | 0.105 | 3 (1) | 3 (1) | 0.389 |
In the multivariate logistic regression analysis (
Multivariate logistic regression analysis of the association of MetS with aICAS or aECAS. aICAS, asymptomatic intracranial arterial stenosis; aECAS, asymptomatic extracranial arterial stenosis; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; BMI, body mass index; BP, blood pressure; MetS, metabolic syndrome; OR, odds ratio. ORs were calculated using logistic regression model after adjusting total cholesterol, BMI, and LDL-C.
Univariate logistic regression analysis of the association of MetS with aICAS or aECAS.
Smoking habits | 1.56 (0.77–3.16) | 0.217 | 1.51 (0.62–3.69) | 0.367 |
Drinking habits | 1.04 (0.60–1.80) | 0.889 | 1.34 (0.56–3.21) | 0.506 |
BMI | 1.13 (1.04–1.22) | 0.003 | 1.13 (0.99–1.29) | 0.064 |
Total cholesterol | 3.89 (2.82–5.58) | <0.001 | 6.90 (3.02–15.87) | <0.001 |
LDL-C | 4.13 (2.75–6.25) | <0.001 | 5.85 (2.53–13.51) | <0.001 |
MetS (+) | 2.22 (1.32–3.68) | 0.002 | 1.93 (0.81–4.56) | 0.132 |
≤ 1 | Reference | Reference | ||
2 | 1.11 (0.44–2.80) | 0.830 | 1.44 (0.34–6.05) | 0.618 |
3 | 1.21 (0.49–3.00) | 0.677 | 3.15 (0.86–11.60) | 0.084 |
≥4 | 2.62 (1.01–6.80) | 0.049 | 1.35 (0.34–5.44) | 0.673 |
As continuous variables | 1.21 (0.96–1.52) | 0.105 | 1.19 (0.81–1.74) | 0.385 |
Central obesity | 4.67 (1.84–11.84) | 0.001 | 2.30 (0.93–5.73) | 0.073 |
Raised triglycerides | 0.72 (0.44–1.19) | 0.205 | 1.90 (0.76–4.76) | 0.170 |
Reduced HDL-C | 2.57 (1.21–5.47) | 0.014 | 1.37 (0.29–6.51) | 0.694 |
Raised BP | 1.78 (0.87–3.63) | 0.114 | 4.69 (0.93–23.53) | 0.061 |
Elevated fasting glucose | 0.90 (0.54–1.51) | 0.696 | 1.00 (0.38–2.64) | 1.000 |
The prevalence of aICAS increased significantly in proportion to the number of MetS components from 3.4% in the ≤ 1 component group to 12.7% in the ≥4 components group (
Prevalence of aICAS and aECAS according to the number of MetS components. aICAS, asymptomatic intracranial arterial stenosis; aECAS, asymptomatic extracranial arterial stenosis; MetS, metabolic syndrome.
This study found that MetS was associated with aICAS, but not with aECAS, and different components play different roles in the pathological process leading to aICAS. Among MetS components, central obesity, elevated TG levels, and elevated blood pressure were significantly associated with aICAS. To the best of our knowledge, this is the first study to investigate the association between MetS and aICAS or aECAS among middle-aged and older adults living in rural communities in China.
MetS is a proinflammatory and hypercoagulable state, which is mainly mediated by insulin resistance. It has been suggested that accelerated atherosclerosis in MetS is associated with defective insulin signaling pathways (
In this study, central obesity was associated with aICAS, but not with aECAS. Central obesity can lead to an increase in the free fatty acids, which play an important role in the pathogenesis of insulin resistance (
In this study, elevated TG levels were significantly associated with aICAS. High TG levels can promote the formation of low-density lipoprotein particles (
The significant association between elevated blood pressure and aICAS detected in this study is consistent with previous studies (
Among MetS components, the association between reduced HDL-C and elevated fasting glucose levels with MetS was not found. However, previous hospital-based studies found that the components (reduced HDL-C and elevated fasting glucose levels) constituting MetS were related to aICAS (
The reasons for the aforementioned differential effects of MetS on the distribution of cerebral arterial stenosis are not well-understood. The potential reasons for this are as follows: first, the differential responses of intracranial and extracranial arteries to oxidative stress may explain our finding that most components constituting MetS were associated with aICAS, since oxidative stress has been reported to be associated with MetS (
Some potential limitations of our study are worth mentioning. First, a cross-sectional study cannot prove the existence of a causal relationship between MetS and aICAS/aECAS; further studies using a prospective study are needed to confirm this relationship. Second, owning to the relatively small sample size, this study was unable to evaluate the association between MetS and distribution of stenosis in various strata of severity of stenosis. Finally, the findings of this study may not be generalizable to other populations since it included only Chinese adults living in rural areas. Nevertheless, to the best of our knowledge, this is the first study to investigate the differences in the associations between certain MetS components and the distribution of cerebral arterial stenosis.
In conclusion, the study findings indicate that MetS is associated with aICAS, but not with aECAS, and different components play different roles in the pathological process of aICAS. These differences may prompt the employment of individualized preventive measures during the asymptomatic stage of cerebral arterial stenosis; thereby, reducing the incidence of stroke.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
The studies involving human participants were reviewed and approved by Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University. The patients/participants provided their written informed consent to participate in this study.
QS, YD, and FX conceived and designed the research. SL, YZ, XW, XJ, SSa, SSh, and YX acquired the data. SL, XS, YZ, XW, XJ, SSa, SSh, YX, and GW analyzed and interpreted the data. SL and XS draft the manuscript. XW, ML, FX, QS, and YD made critical revisions of the manuscript. All authors approved the final manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We would like to thank all the study participants, staff of the participating hospitals, and the Steering Committee Members of this study.