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Relationship of obesity and insulin resistance with the cerebrovascular reactivity: a case control study



Obesity is associated with increased risk for stroke. The breath-holding index (BHI) is a measure of vasomotor reactivity of the brain which can be measured with the transcranial Doppler (TCD). We aim to evaluate obesity as an independent factor for altered cerebrovascular reactivity.


Cerebrovascular hemodynamics (mean flow velocities MFV, pulsatility index, PI, resistance index, RI, and BHI) was determined in 85 non-obese (Body Mass Index, BMI ≤27 kg/m2) and 85 obese subjects (BMI ≥35 kg/m2) without diabetes mellitus and hypertension. Anthropometric and metabolic variables, and scores to detect risk for obstructive sleep apnea (OSA) were analyzed for their association with the cerebrovascular reactivity.


The BHI was significantly lower in subjects with obesity according to BMI and in subjects with abdominal obesity, but the PI and RI were not different between groups. There was a linear association between the BMI, the HOMA-IR, the Matsuda index, the waist circumference, and the neck circumference, with the cerebrovascular reactivity. After adjusting for insulin resistance, neck circumference, and abdominal circumference, obesity according to BMI was negatively correlated with the cerebrovascular reactivity.


We found a diminished vasomotor reactivity in individuals with obesity which was not explained by the presence of insulin resistance.


Obesity is a condition which increases the risk of developing several diseases as a consequence of metabolic derangements and by its mechanical effects [1]. The promotion of insulin resistance and type 2 diabetes confers increased cardiovascular risk in obese subjects [2], which commonly coexist with hypertension and dyslipidemia contributing further to the development of vascular dysfunction and atherothrombotic process [3], Within the atherothrombotic events, cerebrovascular disease is one of the leading diseases for global disease burden [4] and a cause of enormous rates of disability [5]. Data from epidemiological studies has shown hypertension, dyslipidemia, obesity, smoking, and diabetes mellitus to be risk factors for the development of cerebrovascular disease [6].

Among the effects that obesity can have on the cardiovascular system, Obstructive sleep apnea (OSA) is commonly present as weight increases, being reported in over 90% of people with severe obesity [7]; it is associated with sleep fragmentation, somnolence and hypoxemia [8]. Several studies have found an increased risk of developing cerebrovascular disease and myocardial infarction in people with OSA [9, 10], as well as an association between persistent hypoxia with inflammation and altered production of adipokines [11].

In order to establish risk of having OSA, the combination of the Epworth somnolence scale (ESS), the sleep apnea clinical scale (SACS) and night oximetry are significantly correlated with polysomnography [12].

It is important to highlight that the reflection of obesity over health can be very variable, according with the studies of obese people without cardiometabolic risk factors, or metabolically healthy [13]. It was reported that body mass index (BMI), abdominal circumference and waist-to-hip ratio did not show to contribute to the prediction of cardiovascular disease better than classical risk factors by a study which calculated hazard ratios from 58 cohorts including individual records of 221,934 people in 17 countries [14]. In contrast, in the assessment of 92 patients (90% of them older than 50 years of age) with ischemic stroke, Singh et al. found BMI to be positively associated with carotid intima-media-thickness [15].

The transcranial Doppler (TCD) allows the non-invasive evaluation of several physiological parameters of the blood flow velocities (BFV) on the main basal intracranial arteries. Its main clinical applications are related with established cerebrovascular disease, malformations, and monitoring during vascular procedures [16]. It provides information on the systolic, diastolic and mean BFV (SBF, DBF, MBF). Measures related with the cerebrovascular resistance are the Goslin’s Pulsatility Index (PI), which is a measure of the variability of BFV in a blood vessel, and the Resistance Index (RI), or Pourcelot Index, which reflects the resistance against the arterial flow originated in the distal microvascular vessels. A retrospective study which performed TCD study in 1208 patients without cerebrovascular disease found that the main factors associated with intracerebral atherosclerosis were age >65 years, smoking, hypertension, and diabetes [17].

The cerebral BFV can also be modified by processes which provoke vasoconstriction or vasodilation, allowing for the evaluation of the cerebrovascular reactivity in response to the injection of acetazolamide, hyperventilation, or breath-holding [18]. Hypercapnia is an important stimulus for cerebral vasodilation, and this condition has allowed the development of a non-invasive test, known as the apnea test, to establish the status of the cerebrovascular reactivity in different clinical conditions [19].

Several studies have utilized the cerebrovascular reactivity to 1) assess the intracranial hemodynamic status in patients with carotid artery disease to predict for the development of cerebrovascular disease; 2) compare the intracranial hemodynamics before and after endarterectomy; 3) compare self-regulation and collateral flow in different regions of the Willis circle; and to 4) predict the development of dementia in patients with cerebrovascular disease [20, 21]. One study found a cutoff value of 0.69 to distinguish between normal and abnormal cerebrovascular reactivity in subjects with carotid stenosis [22].

A diminished cerebrovascular reactivity indicates that the reserve self-regulation in the brain is reduced, which has been observed in subjects with diabetes and hypertension [23]. In relation with metabolic syndrome (MetS), Giannopoulos et al. retrospectively analyzed the cerebral vasomotor reactivity in subjects aged 59.19 ± 15. They found an independent association of MetS with reduced cerebrovascular reactivity, even after adjusting for age, gender, race, cardiac disease, current statin therapy, and small vessel disease [24]. Another study evaluated the association of the components of the MetS separately and in combination in 15 volunteers 59 ± 15 years old without evidence of cerebrovascular disease by magnetic resonance. They found a non-significantly increased cerebrovascular reserve calculated after administration of acetazolamide, which was not associated with brain increased oxygen extraction fraction [25].

Taking into consideration the relationship that obesity has with cardiovascular risk factors, it is relevant to know the effect of obesity, in absence of established cardiovascular risk factors (age older than 60 years, hypertension, diabetes, smoking) on the intracranial hemodynamics (PI, RI, and BHI) in comparison with subjects without obesity, to determine if obesity is independently associated with asymptomatic cerebrovascular dysfunction. Secondary objectives are to determine the association of incipient metabolic disorders (insulin resistance, glucose intolerance, dyslipidemia) and high neck circumference with the vasomotor reactivity.

Research design and methods

We performed an observational, crossectional, case control and prolective study. We included 85 consecutive subjects with obesity Class II and III according to the World Health Organization (WHO) criteria who attended the Obesity and Eating Disorders Clinic at the Instituto Nacional de Ciencias Médicas y Nutrición “Salvador Zubirán” in Mexico City, and 85 subjects without obesity matched by age and gender who signed informed consent. The study was approved by the institution Ethics Committee of the Instituto Nacional de Ciencias Médicas y Nutrición. Inclusion criteria were age 18 to 59 years. Exclusion criteria were recent bariatric surgery (less than two years), weight loss ≥10% of body weight in the last six months, known vascular disease (coronary artery disease, CAD, arrhythmias, congestive heart or kidney failure, cerebrovascular disease, peripheral vascular disease, and carotid stenosis >50%); chronic obstructive pulmonary disease; acute infections; type 2 diabetes mellitus; hypertension; smoking; and inflammatory or autoimmune diseases with vascular effects (arteritis, systemic lupus erithematosus, rheumatoid arthritis).

The study protocol included measurement of weight, height, BMI, abdominal circumference, and neck circumference. Height was measured without shoes using a wall-mounted stadiometer and weight was recorded on a digital scale in a hospital gown. Clinical and metabolic variables included blood pressure, fasting glucose, insulin and lipids, and a 2 hour glucose tolerance test. Glucose intolerance was defined as a 2 hour glucose ≥ 140 mg/dL with a 75 g glucose tolerance test [26]. HOMA-IR and Matsuda indexes were calculated to assess insulin resistance in all subjects [27, 28]. Abdominal obesity was determined by the presence of waist circumference >90 cm in men and >80 cm in women, according to the International Diabetes Federation (IDF) [29]. MetS was defined by the presence of three or more of the following: waist circumference >90 cm in men and >80 cm in women, triglycerides ≥ 150 mg/dl; HDL cholesterol < 40 mg/dl in men and < 50 mg/dl in women; systolic blood pressure ≥ 135 mm Hg or diastolic blood pressure ≥ 85 mm Hg; and a fasting glucose ≥ 100 mg/dl, according to the worldwide harmonizing definition of MetS of the International Diabetes Federation (IDF), National Heart, Lung, and Blood Institute (NHLBI), American Heart Association (AHA), World Heart Federation (WHF), International Atherosclerosis Society (IAS), and International Association for the Study of Obesity (IASO) [30]. The Epworth Sleep Scale and the Sleep Apnea Clinical Scale were applied to every subject. Subjects with an ESS score ≥10 or a SACS score ≥15 were considered with risk for OSA.

TCD were performed using a Pioneer TC ultrasound 4040 (Nicolet Biomedical Inc.) Resting velocities in the mean cerebral arteries (MCA) were measured and after ruling out vascular stenosis, cerebrovascular reactivity was determined through the apnea test: During a 30-second apnea, the BFV of the MCA are determined through the temporal or ocular window. Systolic and diastolic values 10 seconds after the apnea phase are registered. For each patient, the pulsatility (PI), resistance (RI) and breath holding indexes (BHI) were calculated, the last for the determination of the cerebrovascular reactivity with the following formulas:



BHI = 100 × [(apneaMV – restingMV/(restingMV × apneaT)] [100] =% cm/seg19.

Where: PSV, peak systolic velocity; EDV, end-diastolic velocity; MV, mean velocity (equal to PSV – EDV/3 + EDV); T, time.

Statistical analysis

The analysis was made with a STATA Data Analysis and Statistical Software Package version 11.1. Sample size was calculated to detect a significant difference in median BHI between the comparison groups, with a power of 90% to detect a difference of 15%, which gives 83 cases in every group. Results are presented as means ± SEM when normally distributed and median (range) when not. Baseline comparisons of continuous and categorical variables in subjects with and without obesity were done by unpaired Student t test and chi2 test when data were normally distributed, or Mann Whitney U test and Fisher’s exact test for non-parametric, respectively. A simple linear regression analysis was made between the cerebrovascular reactivity and the independent variables (BMI, weight, abdominal circumference, neck circumference, glucose, insulin, insulin resistance indexes, blood lipids, and scores in the ESS and SACS); those with a linear association were included in a stepped multivariable linear regression to adjust for possible confounders associated with the cerebrovascular reactivity. The BHI values were logarithmically transformed before applying a linear regression. A logistic regression was performed to identify factors associated with decreased cerebrovascular reactivity. Decreased BHI was considered as the cut-off value below the 10th percentile of the population without obesity. P values <0.05 were considered statistically significant.


170 subjects were included in the study, 85 with obesity and 85 subjects without obesity. The anthropometric and metabolic variables within each group are shown in Table 1. There was a predominance of women in the sample (80%) given to the increased participation of women in obesity programs. There were significant differences in the metabolic variables between comparison groups, with higher levels of glucose, insulin, and triglycerides, higher values of HOMA-IR, and lower HDL levels and Matsuda Index values in subjects with obesity in comparison with subjects without obesity. There was increased prevalence of metabolic syndrome and risk for OSA according to questionnaires in the obese subjects group as well.

Table 1 Characteristics of subjects with and without obesity

There were no significant differences in the resting mean velocity (MV) in the median cerebral arteries (MCA) in subjects with obesity in comparison to subjects without obesity, respectively. The cerebrovascular reactivity was significantly lower in the group of subjects with obesity in comparison with subjects without obesity. There were no significant differences in the RI or in the PI in subjects without obesity versus subjects without obesity. The same results were found when analyzing according to abdominal obesity (Table 2).

Table 2 Doppler velocities and indexes in subjects with and without obesity according to BMI and according to waist circumference

The variables with a significant negative linear association with the cerebrovascular reactivity in a simple linear regression were weight, BMI, waist, neck circumference, and HOMA-IR. The Matsuda Index had a positive linear association with it (Table 3). A multiple linear regression was performed with the cerebrovascular reactivity as the dependent variable and obesity as exposure factor. After adjusting for Matsuda Index, neck circumference, and waist circumference, there was an independent correlation between the cerebrovascular reactivity and obesity according to BMI, with an r2 of 0.1 (Table 4). When considering a cut-off value for reduced cerebrovascular reactivity as below the 10th percentile of the BHI in subjects without obesity (0.708), obesity according to BMI was an independent risk factor for diminished cerebrovascular reactivity when adjusting for neck circumference and glucose intolerance (Table 5).

Table 3 Simple linear regression of variables associated with the cerebrovascular reactivity
Table 4 Multiple linear regression of variables associated with the cerebrovascular reactivity
Table 5 Logistic regression of variables associated with decreased cerebrovascular reactivity


The present study found a significantly reduced cerebrovascular reactivity in young adults with obesity and incipient metabolic disorders, which after adjustment for insulin resistance, glucose intolerance, waist circumference, and neck circumference showed and independent association with elevated BMI.

Several studies have found a poor prediction of cardiovascular outcomes when categorizing people according to BMI [14], so other anthropomethric measures have been evaluated for disease prediction. A significant association between neck circumference with several cardiovascular risk factors has been demonstrated [31], in relation with the respiratory obstruction that an elevated neck circumference produces [32]. The fact that the longitudinal associations between neck and waist circumference with the cerebrovascular reactivity were lost when including BMI and insulin resistance, suggests that although related with hemodynamic affection, an elevated BMI have additional effects on the vasomotor reactivity than the respiratory and metabolic impact that result from the range of neck and waist circumference of the participants.

Inclusion of subjects with moderate and severe obesity confers a risk to include patients with OSA, but excluding patients with hypertension greatly reduces the risk of OSA, since hypertension is one of the most often related conditions [33], and one of the mechanisms by which OSA promotes cardiovascular diseases, in addition to inflammatory status, smooth muscle cell activation, endothelial dysfunction, and increased markers of atherosclerosis [34]. Besides OSA, obesity can affect respiratory physiology in many other ways, including altered work of breathing, ventilatory drive and exercise capacity [35], with the possibility to alter vascular function. Excluding subjects with diabetes also is important for the study of incipient changes in the cerebrovascular function, since it is one of the main conditions that accelerate vascular damage [36]. Nevertheless, in the examination of the cerebrovascular reactivity in subjects with incipient metabolic disturbances, insulin resistance did not show to have an independent association with it. The fact that we found a linear association between different metabolic and anthropometric variables with the cerebrovascular reactivity, but with models reporting low coefficients of correlation for each variable, is consistent with the multiple different mechanisms that can influence the cerebrovascular hemodynamics.

Some studies have explored the development of vascular disorders in association with chronic diseases. One study evaluated the cerebrovascular reactivity in association with cognitive performance and brain volumes in subjects with type 2 diabetes mellitus 56–80 years old at baseline and after four years. Subjects with diabetes had worse cognitive performance and smaller brain volumes in comparison with the control group, but these characteristics did not correlate with the cerebrovascular reactivity, nor with the progressive decline of brain volume in the follow-up [37]. Several reports have shown the association between MetS with ischemic stroke, white matter alteration, and altered brain metabolism in nonelderly adults; and with cognitive dysfunction, in particular in executive function in adolescents [38]. As for obesity alone, one report failed to find statistically significant cognitive impairment associated with obesity [39]. The assessment of the carotid circulation through Doppler ultrasound in subjects free of cardiovascular disease, with and without metabolic syndrome (MS) showed that incidence of cardiovascular events (myocardial infarction, transient ischemic attack, abdominal aortic aneurysm, and thrombo-endo-arterectomy) in a 20-year follow-up was higher among subjects aged 62 ± 12 years old with metabolic syndrome (MetS), and the incidence increased whenever there was presence of preclinical carotid atherosclerosis [40].

The reduction of the cerebrovascular reactivity translates in an altered capacity for vasodilation of the cerebral arteries, which has been associated with the future development of cerebrovascular disease [41]. In this study, the assessment of subjects without established cardiovascular risk factors did not show an advanced disorder in the cerebrovascular hemodynamics, since the PI, which translates distal vascular resistance and is altered in clinical vascular damage [42], was not different in subjects with obesity in comparison with subjects without obesity. The breath holding index is an incipient vascular dysfunction marker, which was negatively related with factors accompanying an elevated BMI. Lack of measurement of all the mechanical and inflammatory mechanisms coexistent with obesity with a vascular repercussion represents a limitation to explain the altered cerebrovascular reactivity that we found.

This study highlights the importance of understanding the implicated mechanisms that an elevated BMI has on the cerebrovascular hemodynamics in young adults, in absence of other established risk factors for stroke. Additional studies are needed to discover the different processes that determine a clinical event in the presence of this alteration of the cerebrovascular reactivity, and to examine the interactions of multiple phenomena contributing with the development of cerebrovascular disease.

Authors’ information

CCB is Professor of the high specialty course on Cerebrovascular Disease for Neurologists at the Instituto Nacional de Ciencias Médicas y Nutrición.

MRF is currently Chair of the Latin American Affairs Section of the Obesity Society.


  1. Freedman DS, Khan LK, Serdula MK, Galuska DA, Dietz WH: Trends and correlates of class 3 obesity in the United States from 1990 through 2000. JAMA. 2002, 288: 1758-1761. 10.1001/jama.288.14.1758.

    Article  PubMed  Google Scholar 

  2. Kahn SE, Hull RL, Utzschneider KM: Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature. 2006, 444: 840-846. 10.1038/nature05482.

    Article  CAS  PubMed  Google Scholar 

  3. Ronti T, Lupattelli G, Mannarino E: The endocrine function of adipose tissue: an update. Clin Endocrinol (Oxf). 2006, 64: 355-365.

    CAS  Google Scholar 

  4. Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray CJ: Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data. Lancet. 2006, 367: 1747-57. 10.1016/S0140-6736(06)68770-9.

    Article  PubMed  Google Scholar 

  5. Norrving B, Kissela B: The global burden of stroke and need for a continuum of care. Neurology. 2013, 80: S5-12. 10.1212/WNL.0b013e3182762397.

    Article  PubMed  Google Scholar 

  6. Goldstein LB, Adams R, Alberts MJ, Appel LJ, Brass LM, Bushnell CD: American heart association; American stroke association stroke council: primary prevention of ischemic stroke: a guideline from the American heart association/American stroke association stroke council: cosponsored by the atherosclerotic peripheral vascular disease interdisciplinary working group; cardiovascular nursing council; clinical cardiology council; nutrition, physical activity, and metabolism council; and the quality of care and outcomes research interdisciplinary working group; American academy of neurology. Stroke. 2006, 37: 1583-1633. 10.1161/01.STR.0000223048.70103.F1.

    Article  PubMed  Google Scholar 

  7. Valencia-Flores M, Rebollar V, Santiago V, Orea A, Rodríguez C, Resendiz M: Prevalence of pulmonary hypertension and its association with respiratory disturbances in obese patients living at moderately high altitude. Int J Obes. 2004, 28: 1174-1180. 10.1038/sj.ijo.0802726.

    Article  CAS  Google Scholar 

  8. Young T, Peppard P, Gottlieb D: Epidemiology of obstructive sleep apnea: a population health perspective. Am J Respir Crit Care Med. 2002, 165: 1217-1239. 10.1164/rccm.2109080.

    Article  PubMed  Google Scholar 

  9. Arzt M, Young T, Finn L, Skatrud JB, Bradley TD: Association of sleep-disordered breathing and the occurrence of stroke. Am J Respir Crit Care Med. 2005, 172: 1447-1451. 10.1164/rccm.200505-702OC.

    Article  PubMed Central  PubMed  Google Scholar 

  10. Hung J, Whitford EG, Parsons RW, Hillman DR: Association of sleep apnoea with myocardial infarction in men. Lancet. 1990, 336: 261-264. 10.1016/0140-6736(90)91799-G.

    Article  CAS  PubMed  Google Scholar 

  11. Calvin AD, Albuquerque FN, Lopez-Jimenez F, Somers VK: Obstructive sleep apnea, inflammation, and the metabolic syndrome. Metab Synd Relat Disord. 2009, 7: 271-278. 10.1089/met.2008.0093.

    Article  Google Scholar 

  12. Mulgrew AT, Fox N, Ayas NT, Ryan F: Diagnosis and initial management of obstructive sleep apnea without polysomnography. Ann Intern Med. 2007, 146: 157-166. 10.7326/0003-4819-146-3-200702060-00004.

    Article  PubMed  Google Scholar 

  13. Wildman RP, Muntner P, Reynolds K, McGinn AP, Rajpathak S, Wylie-Rosett J: The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999–2004). Arch Intern Med. 2008, 168: 1617-1624. 10.1001/archinte.168.15.1617.

    Article  PubMed  Google Scholar 

  14. The Emerging Risk Factors Collaboration: Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies. Lancet. 2011, 377: 1085-95.

    Article  PubMed Central  Google Scholar 

  15. Singh AS, Atam V, Patel ML, Chaudhary SC, Sawlani KK, Das L: Carotid intima media thickness as a reflection of generalized atherosclerosis is related to body mass index in ischemic stroke patients. N Am J Med Sci. 2013, 5 (3): 228-2234.

    Article  PubMed Central  PubMed  Google Scholar 

  16. Markus HS: Transcranial doppler ultrasound. Br Med Bull. 2000, 56: 378-88. 10.1258/0007142001903021.

    Article  CAS  PubMed  Google Scholar 

  17. Bae HJ, Lee J, Park JM, Kwon O, Koo JS, Kim BK: Risk factors of intracranial cerebral atherosclerosis among asymptomatics. Cerebrovasc Dis. 2007, 24: 355-60. 10.1159/000106982.

    Article  PubMed  Google Scholar 

  18. Settakis G, Lengyel A, Molnar C, Bereczki D, Csiba L, Fulesdi B: Transcranial doppler study of cerebral hemodynamics changes during breath-holding and hyperventilation test. J Neuroimaging. 2002, 12: 252-258.

    Article  PubMed  Google Scholar 

  19. Markus HS, Harrison M: Estimation of cerebrovascular reactivity using transcranial doppler, including the use of breath holding as the vasodilatory stimulus. Stroke. 1992, 23: 669-673.

    Article  Google Scholar 

  20. Gur AY, Bornstein NM: TCD and de diamox test for testing vasomotor reactivity: clinical significance. Neurol Neurochir Pol. 2001, 35: 51-6.

    PubMed  Google Scholar 

  21. Vernieri F, Pasqualetti P, Passarelli F, Rossini PM, Silvestrini M: Outcome of carotid artery occlusion is predicted by cerebrovascular reactivity. Stroke. 1999, 30: 593-8. 10.1161/01.STR.30.3.593.

    Article  CAS  PubMed  Google Scholar 

  22. Silvestrini M, Vernieri F, Pasqualetti P, Matteis M, Passarelli F, Troisi E: Impaired cerebral vasoreactivity and risk of stroke in patients with asymptomatic carotid artery stenosis. JAMA. 2000, 283: 2122-7. 10.1001/jama.283.16.2122.

    Article  CAS  PubMed  Google Scholar 

  23. Sigimori H, Ibayashi S, Fujii K, Sadoshima S, Kuwabara Y, Fujishima M: Can transcranial doppler really detect reduced cerebral perfusion states?. Stroke. 1995, 26: 2053-2060. 10.1161/01.STR.26.11.2053.

    Article  Google Scholar 

  24. Giannopoulos S, Boden-Albala B, Choi JH, Carrera E, Doyle M, Perez T: Metabolic syndrome and cerebral vasomotor reactivity. Eur J Neurol. 2010, 17: 1457-1462. 10.1111/j.1468-1331.2010.03087.x.

    Article  CAS  PubMed  Google Scholar 

  25. Uchino K, Lin R, Zaidi SF, Kuwabara H, Sashin D, Bircher N: Increased cerebral oxygen metabolism and ischemic stress in subjects with metabolic syndrome-associated risk factors: preliminary observations. Transl Stroke Res. 2010, 1 (3): 178-183. 10.1007/s12975-010-0028-2.

    Article  PubMed Central  PubMed  Google Scholar 

  26. American Diabetes Association: Diagnosis and classification of diabetes mellitus. Diabetes Care. 2008, 31: S55-S60.

    Article  Google Scholar 

  27. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC: Homeostasis model assessment: insulin resistance and beta-cell function from casting plasma glucose and insulin concentrations in man. Diabetologia. 1985, 28: 412-419. 10.1007/BF00280883.

    Article  CAS  PubMed  Google Scholar 

  28. Matsuda M, DeFronzo RA: Insulin sensitivity indices obtained from oral glucose tolerance testing. Diabetes Care. 1999, 22: 1462-147. 10.2337/diacare.22.9.1462.

    Article  CAS  PubMed  Google Scholar 

  29. Alberti KG, Zimmet P, Shaw J: The metabolic syndrome: a new worldwide definition. Lancet. 2005, 366: 1059-1062. 10.1016/S0140-6736(05)67402-8.

    Article  PubMed  Google Scholar 

  30. Alberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA: Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; national heart, lung, and blood institute; americanheart association; world heart federation; International atherosclerosismsociety; and International association for the study of obesity. Circulation. 2009, 120: 1640-1645. 10.1161/CIRCULATIONAHA.109.192644.

    Article  CAS  PubMed  Google Scholar 

  31. Preis SR, Massaro JM, Hoffman U, D’Agostino RBS, Levy D, Robins SJ: Neck circumference as a novel measure of cardiometabolic risk: the Framingham heart study. J Clin Endocrinol Metab. 2010, 95: 3701-3710. 10.1210/jc.2009-1779.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  32. Schwab RJ, Pasirstein M, Pierson R, Mackley A, Hachadoorian R, Arens R: Identification of upper airway anatomic risk factors for obstructive sleep apnea with volumetric magnetic resonance imaging. Am J Respir Crit Care Med. 2003, 168: 522-530. 10.1164/rccm.200208-866OC.

    Article  PubMed  Google Scholar 

  33. Williams AJ, Yu G, Santiago S, Stein M: Screening for sleep apnea using pulse oximetry and a clinical score. Chest. 1991, 100: 631-635. 10.1378/chest.100.3.631.

    Article  CAS  PubMed  Google Scholar 

  34. Drager LF, Polotsky VY, Lorenzi-Filho G: Obstructive sleep apnea: an emerging risk factor for atherosclerosis. Chest. 2011, 140: 534-42. 10.1378/chest.10-2223.

    Article  PubMed Central  PubMed  Google Scholar 

  35. Sood A: Altered resting and exercise respiratory physiology in obesity. Clin Chest Med. 2009, 30: 445-54. 10.1016/j.ccm.2009.05.003.

    Article  PubMed Central  PubMed  Google Scholar 

  36. Papa G, Degano C, Iurato MP, Licciardello C, Maiorana R, Finocchiaro C: Macrovascular complication phenotypes in type 2 diabetic patients. Cardiovasc Diabetol. 2013, 12: 20-10.1186/1475-2840-12-20.

    Article  PubMed Central  PubMed  Google Scholar 

  37. Brundel M, van den Verg E, Reijmer YD, de Bresser J, Kappelle DJ, Biessels GJ: Cerebral haemodynamics, cognition and brain volumes in patients with type 2 diabetes. J Diabetes Complications. 2012, 26: 205-209. 10.1016/j.jdiacomp.2012.03.021.

    Article  PubMed  Google Scholar 

  38. Yates KF, Sweat V, Yau PL, Turchiano MM, Convit A: Impact of metabolic syndrome on cognition and brain: a selected review of the literature. Arterioscler Thromb Vasc Biol. 2012, 32: 2060-2067. 10.1161/ATVBAHA.112.252759.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  39. Gunstad J, Spitznagel MB, Paul RH, Cohen RA, Kohn M, Luyster FS, Clark R, Williams LM, Gordon E: Body mass index and neuro-psychological function in healthy children and adolescents. Appetite. 2008, 50: 246-251. 10.1016/j.appet.2007.07.008.

    Article  PubMed  Google Scholar 

  40. Novo S, Peritore A, Trovato RL, Guarneri FP, Lisi DD, Muratori I: Preclinical atherosclerosis and metabolic syndrome increase cardio- and cerebrovascular events rate: a 20-year follow-up. Cardiovasc Diabetol. 2013, 12: 155-10.1186/1475-2840-12-155.

    Article  PubMed Central  PubMed  Google Scholar 

  41. Wijnhoud AD, Koudstaal PJ, Dippel DW: The prognostic value of pulsatility index, flow velocity, and their ratio, measured with TCD ultrasound, in patients with a recent TIA or ischemic stroke. Acta Neurol Scand. 2011, 124: 238-44. 10.1111/j.1600-0404.2010.01462.x.

    Article  CAS  PubMed  Google Scholar 

  42. Cupini LM, Diomedi M, Placidi F, Silvestrini M, Giacomini P: Cerebrovascular reactivity and subcortical infarctions. Arch Neurol. 2001, 58: 577-81.

    Article  CAS  PubMed  Google Scholar 

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The department of Neurology at the Instituto Nacional de Ciencias Médicas y Nutrición was the sole source of funding for this study.

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Correspondence to Marcela Rodríguez-Flores.

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None of the authors has any competing interest to declare.

Authors’ contributions

CCB, MRF and EGG worked on the study design, VCN and MRF carried out the clinical evaluations, MRF and CCB analysed the data. MRF wrote the article. All authors read and approved the final manuscript.

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Rodríguez-Flores, M., García-García, E., Cano-Nigenda, C.V. et al. Relationship of obesity and insulin resistance with the cerebrovascular reactivity: a case control study. Cardiovasc Diabetol 13, 2 (2014).

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