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Cerebral perfusion and cognitive decline in type 2 diabetes
Vera Novak , Rodrigo Quispe , Charles Saunders
Published: Jan. 20, 2021. Version: 1.0.0 <View latest version>
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Novak, V., Quispe, R., & Saunders, C. (2021). Cerebral perfusion and cognitive decline in type 2 diabetes (version 1.0.0). PhysioNet. https://doi.org/10.13026/rbeh-9r20.
Novak V1, Zhao P, Manor B, Sejdic E, Alsop D, Abduljalil A, Roberson PK, Munshi M, Novak PDiabetes Care. 2011 Nov;34(11):2438-41. doi: 10.2337/dc11-0969. Epub 2011 Sep 16.
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Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.
This dataset was collected as part of a study to explore vasoregulation and blood flow in patients with type 2 Diabetes mellitus. It comprises cerebral blood, autonomic functions, laboratory and gait variables in 70 patients with type 2 DM and 70 healthy controls (50-85 years old). Diabetes mellitus is a prevalent, costly condition with high morbidity and mortality that affects nearly 6.3% of the population in the United States. Diabetes mellitus alters the permeability of the blood-brain barrier, thus affecting regional metabolism and microcirculatory regulation. Predictors of cerebrovascular complications of diabetes that are evidence-based on cerebral blood flow, MRI imaging, and cognitive testing are lacking.
Blood flow velocities were measured in the anterior and middle cerebral arteries using transcranial Doppler ultrasound. Cerebral vasoregulation was evaluated by comparing blood flow velocity responses during hypocapnia and hypercapnia, Valsalva maneuver, head-up tilt and sit-to-stand test using simultaneous recordings of cardiovascular variables, blood flow velocity in the anterior and middle cerebral artery using transcranial Doppler and respiratory variables. We applied continuous arterial spin labeling at 3 Tesla MRI to evaluate regional distribution of cerebral blood flow and vasomotor reactivity to CO2. Cognitive and executive functions were assessed via a battery of psychometric measures. Gait and balance were characterized from dynamics of foot pressure distribution and center of pressure displacement.
There are 18.2 million people in the United States of all ages affected with diabetes mellitus (DM), nearly 6.3% of the population. More than 8.6 million older people (16.4%) over age 60 years had diabetes in 2004. The direct cost of diabetes epidemic was $92 billion, and an additional cost of $40 billion was for disability, work loss, and premature mortality. Diabetes increases the risk for stroke 2-4 times, doubles the mortality rate, and increases the risk for Alzheimer’s disease and vascular dementia .
Cardiovascular risk indices, e.g., the Framingham index, do not incorporate measures of brain function. Predictors of cerebrovascular complications of diabetes that are evidence-based on cerebral blood flow, MRI imaging, and cognitive testing are lacking. DM alters the permeability of the blood-brain barrier, thus affecting regional metabolism and microcirculatory regulation . Specifically, the fronto-temporal cortex  and periventricular white matter  are most likely damaged by DM. Neuroanatomical changes in these structures affect regional perfusion, cognitive and executive functions in the elderly [4,5].
DM may impair cerebral vasoregulation and contribute to frontal lobe dysfunction through the effects of chronic hyperglycemia on the capillary structure , consequent white matter changes on magnetic resonance imaging (MRI), and regional blood flow redistribution . DM management tasks are complex and difficult for elderly people. Executive dysfunction may lead to poor DM control, worsening cognitive decline and cerebrovascular morbidity.
This dataset was generated during the course of our study that sought to determine the mechanisms by which type 2 DM affects cerebral perfusion in older adults and by which uncontrolled DM contributes to cognitive impairment as assessed by a clinical assessment of each patient’s competence. The dataset comprises cerebral blood, autonomic function, laboratory and gait variables that are valuable resources for outcomes studies in the elderly diabetic and healthy controls.
This dataset was collected as part of a study to determine the mechanisms by which type 2 diabetes mellitus affects cerebral perfusion in older adults and by which uncontrolled DM contributes to cognitive impairment. The primary hypothesis was that type 2 DM is associated with cerebral microvascular disease, presented as impairments in vasoregulation and blood flow distribution, white matter changes on MRI, and altered cognitive and motor performance. The second hypothesis was that poor glycemic control increases the severity of microvascular disease and that disturbance of blood flow regulation in the fronto-temporal cortex and periventricular white matter contributes to executive dysfunction in adults with type 2 DM. The
GE75_protocol.pdf file contains a write-up of the full experimental protocol, which was approved by the Institutional Review Board of Beth Israel Deaconess Medical Center.
We studied 70 patients with type 2 DM and 70 healthy controls (50-85 years old). Blood flow velocities (BFV) were measured in the anterior and middle cerebral arteries using transcranial Doppler ultrasound. Cerebral vasoregulation was evaluated by comparing BFV responses during hypocapnia and hypercapnia, Valsalva maneuver, head-up tilt and sit-to-stand test using simultaneous recordings of cardiovascular variables, blood flow velocity in the anterior and middle cerebral artery using transcranial Doppler and respiratory variables. We applied continuous arterial spin labeling at 3 Tesla MRI to evaluate regional distribution of cerebral blood flow and vasomotor reactivity to CO2. Cognitive and executive functions were assessed via a battery of psychometric measures. Gait and balance were characterized from dynamics of foot pressure distribution and center of pressure displacement.
Screening and pre-tests
All subjects who meet inclusion/exclusion criteria (see protocol) were asked to sign the informed consent and fill out medical history and autonomic symptoms and activity questionnaires. Pre-tests: ECG, vital signs (sitting and standing heart rate and BP), height, weight, waist circumference, TCD window. Control participants were asked to perform a short psychological task (Mini-Mental State Exam described below) to assess memory and executive function. Laboratory blood tests (glucose and renal panels, HbA1c, lipid profile, hematocrit, CBC, WBC, C-reactive protein (CRP) and endothelium and pro-inflammation markers (intracellular adhesion molecule-1 (ICAM-1), vascular adhesion molecule (sVCAM-1), endothelin 1(ET-1) and interleukin 1-6 (IL1-6) and angiogenic growth factors (erythropoietin (EPO), vascular endothelial growth factor (VEGF)). Permission from primary physician was contacted for pertinent medical information and their approval for participation.
To establish BP baseline, hypertensive subjects were asked to monitor their BP at home, while on antihypertensive medications 4x per day and to call daily the investigators. The patients were asked to call or email the study coordinator with their blood pressure readings from the previous day.
Subjects were admitted to Beth Israel Deaconess Medical Center (BIDMC) General Clinical Research Center (GCRC) for an overnight stay. Day 1 procedures (vital signs, glucose measurements, physical exam, cognitive test, walking test, Holter monitor) were performed. Ophthalmologic examinations (level of diabetic retinopathy) were conducted at the Beetham Eye Institute (JDC).
Subjects checked into the SAFE laboratory at BIDMC GCRC. TCD ultrasonography (baseline, hyperventilation, CO2 rebreathing, Valsalva maneuver, head-up tilt, sit-to-stand) was performed to monitor blood flow velocity (BFV) in anterior cerebral artery (ACA) and middle cerebral artery (MCA), cardiovascular and respiratory variables that were simultaneously recorded. Twelve minute walk test measured foot pressure distribution and walking parameters during normal hallway walking (Pedar files). After one hour, MRI study (T1- and T2-weighted imaging) was done at 3T MRI at the Magnetic Resonance Imaging Center. Vital signs, CO2 and glucose were measured. Diffusion Tensor Imaging (FA, MD, ADC, fiber thickness and density) was also performed.
1. The following .csv files are located in
GE-75_files_and_channels.csvfile contains a detailed description of files and channels
GE-75_data_dictionary.csvfile enlists all the variables of the study (variable description and units) and provides actual values from two participants as examples.
GE-75_data_summary_table.csvfile contains the complete data summary table which includes the study variables and values for every subject (demographics, MRI, past medical history, medications, laboratory, eye examination, cognitive and walking test). Labview, ECG and Pedar data files are presented separately in the
GE-75_files_per_subject.csvfile enlists open-format data files from
datafolder available for each subject.
2. The following open-format data files are located in
data directory (~5GB of data files):
labview– From Day 2. TCD ultrasonography (blood flow velocity in ACA and MCA) during head-up tilt (DB files) and sit-to-stand (DC files) tests. "Head-up tilt files" (DB) include signals recorded during: baseline, hyperventilation, CO2 rebreathing, Valsalva maneuver, head-up tilt, sit-to-stand) was performed to monitor blood flow velocity (BFV) in anterior cerebral artery (ACA) and middle cerebral artery (MCA), cardiovascular and respiratory variables that were simultaneously recorded. Sit-to-stand files (DC) include signals recorded during sit to stand test with eyes open and closed that have all cerebrovascular, cardiovascular and respiratory signals described above as well as simultaneous recordings of balance. Each testing conditions is separated by the markers.
ecg- From Day 1. 24-hour ECG, EMG monitoring using the ME6000 device, sampled at 1000 Hz during sleep, walking and sit-to-stand test.
pedar- From Day 1. Measurements of foot pressure distribution placing 99 sensors on the foot insole (Maximum pressure, maximum force, mean pressure, mean force, relative load) calculated and then averaged over the whole walk.
This dataset may support a broad range of studies on cerebrovascular events and cognitive decline in elderly people with diabetes. Previous studies have explored topics such as cognitive decline and microstructural white matter abnormalities; impaired cerebral autoregulation and brain atrophy; cerebral vasoreactivity and cognition [8-15]. Several example papers have been included in the
The study was supported by NIH-NIA 1R01-AG0287601A2, American Diabetes Association, Clinical 1-03-CR-23 and 1-06-CR-25 to Dr. Vera Novak. The project described was supported by Grant Number UL1 RR025758- Harvard Clinical and Translational Science Center and M01-RR-01032, from the National Center for Research Resources.
Conflicts of Interest
The authors do not have any actual or potential conflicts of interest. Appropriate approval and procedures were used concerning human subjects. Vera Novak was funded by the National Institutes of Health (NIH-NIDDK 1R01DK103902 -03, NIH-NIA 1R01- AG0287601A2, NIH-NIDDK 5R21 DK084463), American Diabetes Association (Clinical 1-03-CR-23 and 1-06-CR-25).
- Xu WL, Qiu CX, Wahlin A, Winblad B, Fratiglioni L. Diabetes mellitus and risk of dementia in the Kungsholmen project: a 6-year follow-up study. Neurology 2004; 63:1181-1186
- Makimattila S, Malmberg-Ceder K, Hakkinen AM, Vuori K, Salonen O, Summanen P et al. Brain metabolic alterations in patients with type 1 diabetes-hyperglycemia-induced injury. J Cereb Blood Flow Metab 2004; 24:1393-1399.
- Keymeulen B, Jacobs A, de Metx K, de Sadeleer C, Bossuyt A, Somers G. Regional cerebral hypoperfusion in long-term type 1 (insulin-dependent) diabetic patients: relation to hypoglycaemic event. Nucl Med Commun 1995; 16:10-6.
- Kannel WB, Kannel C, Paffenbarger RSJ, Cupples LA. Heart rate and cardiovascular mortality: The Framingham study. American Heart Journal 1987; 113: 1489-1494.
- Gunning-Dixon FM, Raz N. The cognitive correlates of white matter abnormalities in normal aging: a quantitative review. Neuropsychology 2000; 14:224-232.
- Harik SI, La Manna JC. Vascular perfusion and blood-brain glucose transport in acute andchronic hyperglycemia. J Neurochem 1988;1924-1929.
- Wakisaka M, Nagamachi S, Inoue K, Morotomi Y, Nunoi K, Fujishima M. Reduced regionalcerebral blood flow in aged noninsulin-dependent diabetic patients with no history ofcerebrovascular disease: evaluation by N-isopropyl-123I-p-iodoamphetamine with single-photonemission computed tomography. J Diabetes Complications 1990; 4:170-174.
- Alfaro FJ, Lioutas VA, Pimentel DA, Chung CC, Bedoya F, Yoo WK, Novak V. Cognitive decline in metabolic syndrome is linked to microstructural white matter abnormalities. J Neurol. 2016 Dec;263(12):2505-2514. http://doi.org/10.1007/s00415-016-8292-z
- Aoi MC, Hu K, Lo MT, Selim M, Olufsen MS, Novak V. Impaired cerebral autoregulation is associated with brain atrophy and worse functional status in chronic ischemic stroke. PLoS One. 2012;7(10):e46794. http://doi.org/10.1371/journal.pone.0046794
- Chen IH, Novak V, Manor B. Infarct hemisphere and noninfarcted brain volumes affect locomotor performance following stroke. Neurology. 2014 Mar 11;82(10):828-34. http://doi.org/ 10.1212/WNL.0000000000000186
- Chung CC, Pimentel D, Jor'dan AJ, Hao Y, Milberg W, Novak V. Inflammation-associated declines in cerebral vasoreactivity and cognition in type 2 diabetes. Neurology. 2015;85(5):450-458. http://doi.org/10.1212/WNL.0000000000001820
- Cui X, Abduljalil A, Manor BD, Peng CK, Novak V. Multi-scale glycemic variability: a link to gray matter atrophy and cognitive decline in type 2 diabetes. PLoS One. 2014 Jan 24;9(1):e86284. http://doi.org/10.1371/journal.pone.0086284
- Dai W, Duan W, Alfaro FJ, Gavrieli A, Kourtelidis F, Novak V. The resting perfusion pattern associates with functional decline in type 2 diabetes. Neurobiol Aging. 2017 Dec;60:192-202. http://doi.org/10.1016/j.neurobiolaging.2017.09.004
- Devault K, Gremaud PA, Novak V, Olufsen MS, Vernières G, Zhao P. BLOOD FLOW IN THE CIRCLE OF WILLIS: MODELING AND CALIBRATION. Multiscale Model Simul. 2008;7(2):888-909. http://doi.org/10.1137/07070231X
- Drew DA, Bhadelia R, Tighiouart H, Novak V, Scott TM, Lou KV, Shaffi K, Weiner DE, Sarnak MJ. Anatomic brain disease in hemodialysis patients: a cross-sectional study. Am J Kidney Dis. 2013 Feb;61(2):271-8. http://doi.org/10.1053/j.ajkd.2012.08.035
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|GE-75_protocol.pdf (download)||262.4 KB||2021-01-19|
|LICENSE.txt (download)||14.5 KB||2021-01-20|
|RECORDS (download)||5.7 KB||2021-01-19|
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