Insulin Resistance Is Independently Associated With Enlarged Perivascular Space in the Basal Ganglia in Nondiabetic Healthy Elderly Population

Abstract

Objectives:

To investigate the association between insulin resistance (IR) and enlarged perivascular space (EPVS) in the basal gangliain nondiabetic healthy elderly population.

Methods:

A total of 235 nondiabetic healthy elderly population were recruited. A 3-level scale was used to evaluate the burden of EPVSs. The homeostasis model assessment–estimated insulin resistance index (HOMA-IR) was used for IR estimation. Correlation between IR and severity of EPVS was assessed using the regression model after adjusting demographics and cardiovascular risk factors.

Results:

The top quartile of HOMA-IR was 2.52, and 25.11% of patients showed IR. The proportion of patients with IR was higher in the moderate to severe EPVS group than in the mild group (36.51% vs 20.93%, P = .015). In multivariate logistic analysis, IR was positively correlated with the moderate to severe EPVS (adjusted odds ratio: 3.532, 95% confidence interval: 1.633-7.636, P = .001) after adjusting classical risk factors.

Conclusions:

Insulin resistance was independently correlated with EPVS in the basal ganglia in nondiabetic healthy elderly population.

Keywords

enlarged perivascular space cerebral small vessel disease insulin resistance nondiabetic healthy elderly

Introduction

Magnetic resonance imaging (MRI)–visible enlarged perivascular space (EPVS), a well-established neuroimaging hallmark of cerebral small vessel diseases (CSVD), is associated with other morphological features of CSVD, such as white matter hyperintensities¹ and lacunes.² EPVSs are fluid-filled cavities surrounding small penetrating cerebral arterioles, and they are important drainage conduit for cerebral interstitial fluid.^2
-4 The number of EPVSs increases with age, manifesting as visible round or tubular hyperintensities in the basal ganglia, centrum semiovale, and midbrain on T2-weighted brain MRI.^2,4,5 A small number of EPVSs might be normal findings,⁶ but accumulating evidence has shown that an increased number of EPVSs might reflect damage to cerebral microvessels.⁷ EPVS in the basal ganglia has been linked to cognitive impairment,^8

-11 depression,¹² and an increased risk of recurrent ischemic stroke.¹³ Although there are some known risk factors for EPVSs, such as old age, hypertension, and atherosclerosis,^14,15 there are other unknown risk factors remaining to be explored.¹⁶

Insulin resistance (IR) is a syndrome that correlates with a cluster of metabolic disorders, including diabetes mellitus, obesity, hypertension, and lipid abnormalities,^17,18 characterized by decreased sensitivity or responsiveness to metabolic actions of insulin. Previous studies suggest that IR is driving the increased incidence and recurrence of stroke.^19
-21 Furthermore, increasing evidence links IR to reduced cortical perfusion²² and cognitive impairment in neurologically healthy adults in CSVD studies.^23,24 A recent study suggests that IR is related to silent lacunar infarcts that are another hallmark of CSVD.^25,26 Another study has demonstrated that type 2 diabetes mellitus may be causally associated with lacunar stroke,¹⁶ but little is known about the unique effect of IR on EPVSs though both lacunar stroke and EPVSs are due to endothelial dysfunction as their underlying pathogenic mechanism. Therefore, the objective of this study was to investigate whether IR is associated with EPVSs independent of other clinical risk factors in nondiabetic healthy adult population.

Methods

Study Population

The present study was conducted according to the principles of the Declaration of Helsinki and was approved by the ethics committee of Shanghai Fifth People’s Hospital (approval number: 2017EC032), and written informed consent was obtained from all individual participants. We recruited nondiabetic elderly patients who were admitted to our hospital for health checkups from January 2013 to January 2017 consecutively. Those who met any of the following criteria were excluded: history of stroke, cognitive dysfunction, heart diseases, malignancies, autoimmune diseases, and hepatic or renal diseases.

Clinical and Laboratory Variables

Clinical information, including age, sex, smoking status, comorbid conditions, hypertension, dyslipidemia, and intake of antiplatelet or statin medications, was obtained within 8 hours after admission through face-to-face interviews by neurological residents. Hypertension was defined as a high systolic (≥140 mm Hg) or diastolic (≥90 mm Hg) blood pressure,²⁷ or patients were currently taking antihypertensive medications. Hypercholesterolemia was defined as total cholesterol level ≥240 mg/dL²⁸ or currently taking lipid-lowering agents. Intracranial arterial stenosis was defined as more than 50% stenosis in intracranial arteries as shown on magnetic resonance angiography.²⁹ Carotid artery stenosis was defined as more than 50% stenosis in the carotid artery as shown on ultrasound. Fasting venous blood samples were drawn to measure levels of glucose, insulin, creatinine, and total cholesterol. The body mass index (BMI) was calculated as weight (kg) divided by the height squared (m²). Overweight (BMI, 23.0-24.9) and obesity (BMI ≥25.0) were classified according to the World Health Organization criteria for the Asia-Pacific region.³⁰

Brain MRI and EPVS

All MRI scans were performed using a 3.0 T MRI system (Magnetom Skyra, Siemens, Germany). Axial T2-weighted sequences, fluid-attenuated inversion recovery (FLAIR), T1-weighted sequences, and diffusion-weighted imaging were used for this study. EPVS defined as round, oval, or linear-shaped lesions with a diameter smaller than 3 mm along the course of penetrating arteries, with signal intensity similar to that of cerebrospinal fluid on all sequence spaces and without a T2-hyperintense rim on FLAIR.⁵

To count the total burden of EPVSs, 2 neuroradiologists who were blind to clinical details of the patients rated EPVSs in the basal ganglia and stratified their scores of EPVSs as follows: 1 = 0 to 10 EPVSs, 2 = 11 to 25 EPVSs, and 3 = >25 EPVSs.³¹ If asymmetrical EPVSs were observed on both hemisphere, the hemisphere with more EPVSs was selected. Interobserver agreement values for the EPVS score was 0.888. Any disagreement about the number of EPVSs was resolved by consulting with a neuroimaging expert.

Insulin Resistance Evaluation

The homeostasis model assessment–estimated insulin resistance index (HOMA-IR)was used to identify patients with IR. HOMA-IR was calculated as fasting insulin (μU/mL) × fasting glucose (mmol/L)/22.5,³² and its top quartile was used to define IR as reported in a previous study.¹⁹

Statistical Analyses

Statistical analyses were performed using the SPSS 21.0 software (IBM Corp, Armonk, New York). We dichotomized basal ganglia EPVSs into degree 1 (score 1) and degrees 2 + 3 (score 2, score 3), reflecting mild versus moderate/severe EPVS, respectively, as previously reported.^1,31,33 For demographic and clinical features, continuous variables with a normal distribution were presented as mean ± standard deviation and compared using the independent sample t test, continuous variables with a non-normal distribution were presented as median (P25, P75) and compared using the nonparametric test. Categorical variables were expressed as frequency (percentage) and compared using the χ² test. Logistic regression was used to assess the relationship between IR and the severity of EPVSs. The following risk factors were included as reported previously²⁶: age, sex, hypertension, hypercholesterolemia, smoking, and BMI. Ordered logistic regression was used to assess the association between IR and the total EPVS scores. All probability values were 2-tailed, and P ≤ .05 were considered to be statistically significant.

Results

Characteristics of the Study Population

A total of 235 patients were included in the analysis. The mean age of the moderate to severe EPVS group and the mild group were 74.98 ± 8.74 years and 66.00 ± 8.60 years, respectively. No significant difference was observed in the proportion of males and females. The top quartile of HOMA-IR was 2.52, and 25.11% of patients showed IR (HOMA-IR ≥2.52). The proportion of patients with IR was larger in the moderate to severe EPVS group than that in the mild group (36.51% vs 20.93%, P = .015). The proportion of BMI ≥25 tended to be larger in the moderate to severe EPVS group than in the mild group (P = .061). No significant difference was observed in the comparison of smoking status, hypertension, and other cardiovascular risk factors between the 2 groups (Table 1).

Table 1.

Characteristics of Subgroups Based on severity of EPVS.

Variable	EPVS Score = 1	EPVS Score >1	t/Z/χ² Value	P Value
Variable	N = 172	N = 63	t/Z/χ² Value	P Value
Male, n (%)	79 (45.93%)	30 (47.62%)	0.053	.818
Age (mean ± SD)	66.00 ± 8.60	74.98 ± 8.74	−7.064	<.001
HOMA-IR
IR >2.52, n (%)	36 (20.93%)	23 (36.51%)	5.951	.015
IR (P25, P75)	1.04 (0.40, 2.29)	2.15 (1.43, 3.86)
TC (mean ± SD)	4.72 ± 1.09	4.55 ± 1.18	0.997	.32
HDL-C (P25, P75)	1.18 (1.00, 1.37)	1.10 (0.95, 1.39)	−1.461	.144
LDL-C (P25, P75)	2.86 (2.17, 3.50)	2.74 (2.24, 3.57)	−0.119	.905
TT (P25, P75)	1.31 (0.93, 1.84)	1.44 (1.06, 2.00)	−1.224	.221
Current smoking, n (%)	47 (27.33%)	14 (22.22%)	0.625	.429
Current drinking, n (%)	23 (13.37%)	4 (6.35%)	2.236	.135
Hypertension, n (%)	112 (65.12%)	48 (76.19%)	2.602	.107
Hypercholesterolemia, n (%)	9 (5.23%)	2 (3.17%)	0.438	.508
Antiplatelet medication, n (%)	25 (14.53%)	6 (9.52%)	1.011	.315
Statin medication, n (%)	47 (27.33%)	9 (14.29%)	4.32	.038
Cerebral MRA, n (%)	14 (8.14%)	13 (20.63%)	7.08	.008
Carotid artery ultrasound, n (%)	14 (8.14%)	12 (19.05%)	5.576	.018
BMI, n (%)
<23	60 (34.88%)	15 (23.81%)	5.585	.061
≥23, <25	74 (43.02%)	25 (39.68%)
≥25	38 (22.09%)	23 (36.51%)

Abbreviations: BMI, body mass index; EPVS, enlarged perivascular space; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MRA, magnetic resonance angiography; SD, standard deviation.

Association Between IR and Severity of EPVS

Correlation analysis was performed between demographic as well as clinical variables and the severity of EPVSs. It was found that IR along with age, statin medication, intracranial arterial stenosis, and carotid artery stenosis was linked to moderate to severe EPVSs (P < .05, respectively; Table 1). After normalizing these classical risk factors, IR was found to be positively associated with the moderate to severe EPVS in the binary logistic regression model (adjusted odds ratio: 3.532, 95% confidence interval [CI]: 1.633-7.636, P = .001). Meanwhile, aging (adjusted odds ratio: 1.145, 95% CI: 1.095-1.198, P < .001) and intracranial arterial stenosis (adjusted odds ratio: 2.920, 95% CI: 1.074-7.942, P = .036) increased the likelihood of developing moderate to severe EPVS, whereas statin medication decreased the likelihood of developing moderate to severe EPVS (adjusted odds ratio: 0.361, 95% CI: 0.147-0.887, P = .026; Table 2).

Table 2.

Association of Insulin Resistance and Other Cardiovascular Risk Factors With Moderate to Severe EPVS.^a

Variable		Model 1
Variable	β Value	OR Value (95% CI)	P Value
Age	0.136	1.145 (1.095-1.198)	< .001
HOMA-IR	1.262	3.532 (1.633-7.636)	.001
statin medication	−1.018	0.361 (0.147-0.887)	.026
Cerebral MRA stenosis	1.072	2.920 (1.074-7.942)	.036

Abbreviations: CI, confidence interval; EPVS, enlarged perivascular space; OR, odds ratio; MRA, magnetic resonance angiography.

^a Model 1: binary logistic regression (LR) based on the severity of EPVS (EPVS score =1 vs EPVS score = 2 + 3); the enter and delete of variables were forward LR.

The association between IR and other cardiovascular risk factors with increasing severity of EPVS was assessed using the ordered logistic regression (model 2). In this statistical model, the odds ratio was presumed for each level (parallel line examination = 0.337), and the tendency still remained significant, only the odds ratio changed mildly, implying that IR, aging, and intracranial arterial stenosis increase the likelihood of increased numbers of EPVSs, whereas the statin medication decreased the tendency (Table 3).

Table 3.

Association of Insulin Resistance and Other Cardiovascular Risk Factors With Increasing Severity of EPVS Occurrence.^a

Variable		Model 2
Variable	β Value	OR Value (95% CI)	P Value
Age	0.119	1.13 (1.081-1.172)	<.01
HOMA-IR	0.887	2.43 (1.19-4.958)	.015
Statin medication	−1.069	0.34 (0.142-0.829)	.017
Cerebral MRA stenosis	0.900	2.46 (1.006-6.01)	.048

Abbreviations: CI, confidence interval; EPVS, enlarged perivascular space; OR, odds ratio; MRA, magnetic resonance angiography.

^a Model 2: ordered logistic regression based on the specific score of EPVS.

Discussion

In the present study, the association between IR and EPVSs in a nondiabetic elderly population was investigated. It was found that IR along with aging and intracranial arterial stenosis was related to increased severity of EPVSs, independent of other clinical risk factors.

It proposed that endothelial dysfunction and atherosclerosis were the main pathologic mechanisms of CSVDs,^34,35 including EPVSs. Endothelial dysfunction contributed to the increased permeability of the blood–brain barrier, which led to deposition of plasma and leakage of inflammatory cells into the vascular wall and the adjacent tissue space, initiating or accelerating the formation of EPVSs. Our study demonstrated that IR, aging, and intracranial arterial stenosis increased the likelihood of developing EPVSs, whereas the statin medication decreased this tendency. The abovementioned factors were all related to the function of endothelial cells,^36

-39 suggesting that endothelial dysfunction might play a pivotal role in the pathogenesis of EPVS.

IR is not an exclusive feature of diabetes mellitus, it is also a fundamental mechanism of the metabolic syndrome and its components, namely, hypertension and hypercholesterolemia, which were also known as risk factors for EPVSs.^14,15 Previous studies suggested that IR was responsible for the increased incidence and recurrence of stroke.^19
-21 In CSVD regions, increasing evidence has linked IR to reduced cortical perfusion²² and cognitive impairment in neurologically healthy adults.²² Recently, a small number of studies demonstrated a possible causal relationship between IR and lacunar infarction,^26,25 but little is known about the unique effect of IR on EPVSs though they share the common pathogenic mechanisms. In our study, the correlation between IR and EPVSs in nondiabetic elderly population was analyzed and IR was found to be related to the increased severity of EPVSs, which suggests that IR is a risk factor of EPVSs, irrespective of diabetes mellitus and other cardiovascular risk factors. Previous studies have demonstrated that IR was definitely correlated with endothelial dysfunction in many cardiovascular diseases,^37,38 which might be confirmed by molecular and cellular mechanistic studies. For example, IR increased lipolysis and induced production of pro-inflammatory cytokines, which in turn contributed to endothelial dysfunction.^37,38 Therefore, we hypothesized that IR was involved in initiating or accelerating the formation of EPVSs through compromising the function of endothelial cells.

Atherosclerosis has been observed as a pathological feature of EPVSs,³⁴ many typical vascular risk factors that are known to be associated with atherosclerosis contribute to the development of EPVSs. Association between EPVSs in the basal ganglia and atherosclerosis has been explored, and EPVSs were related to not only extracranial atherosclerosis^40
-42 but also intracranial atherosclerosis.¹⁵ Our research also demonstrated that intracranial arterial stenosis was associated with increased numbers of EPVSs in the basal ganglia, demonstrating that a common pathogenic mechanism, such as endothelial dysfunction, might be responsible for the occurrence of both EPVSs and intra- and extra-atherosclerosis.

The present study had a couple of main strengths. Firstly, the association between IR and EPVSs was investigated. Secondly, the present study was conducted in a nondiabetic elderly population, which excluded the effect of confounding factors such as diabetes mellitus, coronary heart diseases, chronic kidney diseases, and stroke.

However, the present study also has a number of limitations. Firstly, the homeostasis model assessment–estimated insulin resistance index (HOMA-IR) method was used to measure IR rather than the euglycemic hyperinsulinemic clamp method which is the gold standard. The clamp method is unsuitable for clinical and epidemiological studies, so HOMA-IR is a commonly used alternative in many studies.^{19,26,22,43,44} Secondly, we did not collect white matter hyperintensities, lacunes, and microbleeds in cerebral MRI in the current study. These structural changes may be found simultaneously in one patient, so the relationship between IR and EPVS may be interfered. But in the previous study, the same research method was applied.²⁶ Thirdly, health-check patients are not completely healthy. An American study showed that of 348 individuals who were subject to in-person history and physical examinations, only 51.7% passed the screening procedures and were regarded as healthy.⁴⁵ We developed a set of exclusion criteria and performed comprehensive physical examination of the nervous system to ensure all patients were nondiabetic healthy elderly population as much as possible.

In conclusion, IR was positively related to increased severity of EPVSs, together with aging and intracranial arterial stenosis, independent of other clinical risk factors in a nondiabetic healthy adult population. Longitudinal studies are needed to confirm the causal relationship between IR and EPVSs.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by grants from Shanghai Municipal Committee of Science and Technology (17411967700 and 19401935700), Shanghai Minhang District Health and Family Planning Commission for constructing big disciplines (2017MWDXK02), and Shanghai Municipal Commission of Health and Family Planning (Number 20184Y0178).

ORCID iDs

Xiaoli Yang

Xueyuan Liu

References

Doubal

MacLullich

Ferguson

Dennis

Wardlaw

. Enlarged perivascular spaces on MRI are a feature of cerebral small vessel disease. Stroke. 2010;41(3):450–454.

Kwee

. Virchow-Robin spaces at MR imaging. Radiographics. 2007;27(4):1071–1086.

Weller

Kida

Zhang

. Pathways of fluid drainage from the brain—morphological aspects and immunological significance in rat and man. Brain Pathol. 1992;2(4):277–284.

Abbott

. Evidence for bulk flow of brain interstitial fluid: significance for physiology and pathology. Neurochem Int. 2004;45(4):545–552.

Wardlaw

Smith

Biessels

, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013;12(8):822–838.

Groeschel

Chong

Surtees

Hanefeld

. Virchow-Robin spaces on magnetic resonance images: normative data, their dilatation, and a review of the literature. Neuroradiology. 2006;48(10):745–754.

Adachi

Hosoya

Haku

Yamaguchi

Dilated Virchow-Robin spaces: MRI pathological study. Neuroradiology. 1998;40(1):27–31.

Maclullich

Wardlaw

Ferguson

, et al. Enlarged perivascular spaces are associated with cognitive function in healthy elderly men. J Neurol Neurosurg Psychiatry. 2004;75(11):1519–1523.

Yakushiji

Charidimou

Hara

, et al. Topography and associations of perivascular spaces in healthy adults: the Kashima scan study. Neurology. 2014;83(23):2116–2123.

10.

Zhu

Tzourio

Soumare

, et al. Severity of dilated Virchow-Robin spaces is associated with age, blood pressure, and MRI markers of small vessel disease: a population-based study. Stroke. 2010;41(11):2483–2490.

11.

Huijts

Duits

Staals

, et al. Basal ganglia enlarged perivascular spaces are linked to cognitive function in patients with cerebral small vessel disease. Curr Neurovasc Res. 2014;11(2):136–141.

12.

Patankar

Baldwin

Mitra

, et al. Virchow-Robin space dilatation may predict resistance to antidepressant monotherapy in elderly patients with depression. J Affect Disord. 2007;97(1-3):265–270.

13.

Lau

Lovelock

, et al. Clinical correlates, ethnic differences, and prognostic implications of perivascular spaces in transient ischemic attack and ischemic stroke. Stroke. 2017;48(6):1470–1477.

14.

Akhtar

Salam

Kamran

, et al. Pre-existing small vessel disease in patients with acute stroke from the middle east, southeast Asia, and Philippines. Transl Stroke Res. 2018;9(3):274–282.

15.

Del

Mera

. Enlarged perivascular spaces in the basal ganglia are independently associated with intracranial atherosclerosis in the elderly. Atherosclerosis. 2017;267:34–38.

16.

Liu

Rutten-Jacobs

Liu

Markus

Traylor

. Causal impact of type 2 diabetes mellitus on cerebral small vessel disease: a Mendelian randomization analysis. Stroke. 2018;49(6):1325–1331.

17.

Kernan

Inzucchi

Viscoli

, et al. Insulin resistance and risk for stroke. Neurology. 2002;59(6):809–815.

18.

Ginsberg

. Insulin resistance and cardiovascular disease. J Clin Invest. 2000;106(4):453–458.

19.

Rundek

Gardener

, et al. Insulin resistance and risk of ischemic stroke among nondiabetic individuals from the northern Manhattan study. Arch Neurol. 2010;67(10):1195–1200.

20.

Garg

Biggs

Kaplan

, et al. Fasting and post-glucose load measures of insulin resistance and risk of incident atrial fibrillation: the cardiovascular health study. Nutr Metab Cardiovasc Dis. 2018;28(7):716–721.

21.

Thacker

Psaty

McKnight

, et al. Fasting and post-glucose load measures of insulin resistance and risk of ischemic stroke in older adults. Stroke. 2011;42(12):3347–3351.

22.

Hoscheidt

Kellawan

Berman

, et al. Insulin resistance is associated with lower arterial blood flow and reduced cortical perfusion in cognitively asymptomatic middle-aged adults. J Cereb Blood Flow Metab. 2017;37(6):2249–2261.

23.

Willette

Johnson

, et al. Insulin resistance, brain atrophy, and cognitive performance in late middle-aged adults. Diabetes Care. 2013;36(2):443–449.

24.

Huijts

Duits

van Oostenbrugge

, et al. Accumulation of MRI markers of cerebral small vessel disease is associated with decreased cognitive function. A study in first-ever lacunar stroke and hypertensive patients. Front Aging Neurosci. 2013;5:72.

25.

Dearborn

Schneider

Sharrett

, et al. Obesity, insulin resistance, and incident small vessel disease on magnetic resonance imaging: atherosclerosis risk in communities study. Stroke. 2015;46(11):3131–3136.

26.

Lee

Shin

Yun

, et al. Insulin resistance is a risk factor for silent lacunar infarction. Stroke. 2016;47(12):2938–2944.

27.

Chobanian

Bakris

Black

, et al. The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003;289(19):2560–2572.

28.

National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). Third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III) final report. Circulation. 2002;106(25):3143–3421.

29.

Adams

Bendixen

Kappelle

, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in acute stroke treatment. Stroke. 1993;24(1):35–41.

30.

World Health Organization. The Asia-Pacific Perspective: Redefining Obesity and Its Treatment. 2000. http://www.wpro.who.int/nutrition/documents/docs/Redefiningobesity.pdf.

31.

Xiao

Lan

Sun

, et al. Chronic kidney disease in patients with lacunar stroke: association with enlarged perivascular spaces and total magnetic resonance imaging burden of cerebral small vessel disease. Stroke. 2015;46(8):2081–2086.

32.

Matthews

Hosker

Rudenski

, et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28(7):412–419.

33.

Potter

Doubal

Jackson

, et al. Enlarged perivascular spaces and cerebral small vessel disease. Int J Stroke. 2015;10(3):376–381.

34.

Wardlaw

Smith

Dichgans

. Mechanisms of sporadic cerebral small vessel disease: insights from neuroimaging. Lancet Neurol. 2013;12(5):483–497.

35.

Knottnerus

Ten

Lodder

Kessels

van Oostenbrugge

. Endothelial dysfunction in lacunar stroke: a systematic review. Cerebrovasc Dis. 2009;27(5):519–526.

36.

Karaca

Schram

Houben

Muris

Stehouwer

. Microvascular dysfunction as a link between obesity, insulin resistance and hypertension. Diabetes Res Clin Pract. 2014;103(3):382–387.

37.

Janus

Szahidewicz-Krupska

Mazur

Doroszko

. Insulin resistance and endothelial dysfunction constitute a common therapeutic target in cardiometabolic disorders. Mediators Inflamm. 2016;2016:3634948.

38.

Kim

Montagnani

Koh

Quon

. Reciprocal relationships between insulin resistance and endothelial dysfunction: molecular and pathophysiological mechanisms. Circulation. 2006;113(15):1888–1904.

39.

Oikonomou

Siasos

Zaromitidou

, et al. Atorvastatin treatment improves endothelial function through endothelial progenitor cells mobilization in ischemic heart failure patients. Atherosclerosis. 2015;238(2):159–164.

40.

Gutierrez

Rundek

Ekind

Sacco

Wright

. Perivascular spaces are associated with atherosclerosis: an insight from the Northern Manhattan Study. AJNR Am J Neuroradiol. 2013;34(9):1711–1716.

41.

Sahin

Solak

Genc

Akpinar

. Dilatation of the Virchow-Robin spaces as an indicator of unilateral carotid artery stenosis: correlation with white matter lesions. Acta Radiol. 2015;56(7):852–859.

42.

Song

Kim

Yoo

, et al. Association between aortic atheroma and cerebral small vessel disease in patients with ischemic stroke. J Stroke. 2016;18(3):312–320.

43.

Cartolano

Dias

de Freitas

Figueiredo

Damasceno

. Insulin resistance predicts atherogenic lipoprotein profile in nondiabetic subjects. J Diabetes Res. 2017;2017:1018796.

44.

Jing

Pan

Zhao

, et al. Insulin resistance and prognosis of nondiabetic patients with ischemic stroke: the ACROSS-China study (abnormal glucose regulation in patients with acute stroke across China). Stroke. 2017;48(4):887–893.

45.

Mazziotta

Woods

Iacoboni

, et al. The myth of the normal, average human brain—the ICBM experience: (1) subject screening and eligibility. Neuroimage. 2009;44(3):914–922.