ORIGINAL PAPER
Magnetic resonance spectroscopy of the frontal region in patients with metabolic syndrome: correlation with anthropometric measurement
Publication date: 2018-05-15
Pol J Radiol, 2018; 83: 215-219
KEYWORDS
ABSTRACT
Purpose:
to demonstrate 1H-MR spectroscopy of the frontal region in patients with metabolic syndrome and to correlate the metabolic ratios with anthropometric measurement.
Material and methods:
A prospective study was conducted upon 20 patients with metabolic syndrome (10 male, 10 female; mean age 52 years) and 20 age- and sex-matched volunteers. Patients were mild-moderate (n = 14) and marked and morbid obesity (n = 6). Patients and volunteers underwent 1H-MR spectroscopy of the frontal region. The Ch/Cr and NAA/Cr ratio were calculated and correlated with anthropometric measurement.
Results:
The Cho/Cr and NAA/Cr of patients with Mets (1.03 ± 0.08 and 1.62 ± 0.08) were significantly different (p = 0.001) to those of volunteers (0.78 ± 0 and 1.71 ± 0.61, respectively). The Cho/Cr and NAA/Cr cutoffs used to differentiate patients from volunteers were 0.89 and 1.77 with areas under the curve of 0.992 and 0.867 and accuracy of 97% and 93%, respectively. There was a significant difference in Cho/Cr and NAA/Cr between patients with marked-morbid obesity and moderate-mild obesity (p = 0.001 respectively).
Conclusions:
We concluded that NAA/Cr and Cho/Cr ratios of the frontal region can differentiate patients with metabolic syndrome from volunteers and are well correlated with the anthropometric measurement.
to demonstrate 1H-MR spectroscopy of the frontal region in patients with metabolic syndrome and to correlate the metabolic ratios with anthropometric measurement.
Material and methods:
A prospective study was conducted upon 20 patients with metabolic syndrome (10 male, 10 female; mean age 52 years) and 20 age- and sex-matched volunteers. Patients were mild-moderate (n = 14) and marked and morbid obesity (n = 6). Patients and volunteers underwent 1H-MR spectroscopy of the frontal region. The Ch/Cr and NAA/Cr ratio were calculated and correlated with anthropometric measurement.
Results:
The Cho/Cr and NAA/Cr of patients with Mets (1.03 ± 0.08 and 1.62 ± 0.08) were significantly different (p = 0.001) to those of volunteers (0.78 ± 0 and 1.71 ± 0.61, respectively). The Cho/Cr and NAA/Cr cutoffs used to differentiate patients from volunteers were 0.89 and 1.77 with areas under the curve of 0.992 and 0.867 and accuracy of 97% and 93%, respectively. There was a significant difference in Cho/Cr and NAA/Cr between patients with marked-morbid obesity and moderate-mild obesity (p = 0.001 respectively).
Conclusions:
We concluded that NAA/Cr and Cho/Cr ratios of the frontal region can differentiate patients with metabolic syndrome from volunteers and are well correlated with the anthropometric measurement.
REFERENCES (31)
1.
Samson SL, Garber AJ. Metabolic syndrome. Endocrinol Metab Clin North Am 2014; 43: 1-23.
2.
Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet 2005; 365: 1415-1428.
3.
Oda E. Metabolic syndrome: its history, mechanisms, and limitations. Acta Diabet 2012; 49: 89-95.
4.
Moran C, Beare R, Phan TG, et al. Type 2 diabetes mellitus and biomarkers of neurodegeneration. Neurology 2015; 85: 1123-1130.
5.
Yu Y, Sun Q, Yan LF, et al. Multimodal MRI for early diabetic mild cognitive impairment: study protocol of a prospective diagnostic trial. BMC Med Imaging 2016; 16: 50.
6.
Tan X, Fang P, An J, et al. Micro-structural white matter abnormalities in type 2 diabetic patients: a DTI study using TBSS analysis. Neuroradiology 2016; 58: 1209-1216.
7.
deBresser J, Reijmer YD, van den Berg E, et al. Microvascular determinants of cognitive decline and brain volume change in elderly patients with type 2 diabetes. Dement Geriatr Cogn Disord 2010; 30: 381-386.
8.
Sims RC, Katzel LI, Lefkowitz DM, et al. Association of fasting glucose with subclinical cerebrovascular disease in older adults without Type 2 diabetes. Diabet Med 2014; 31: 691-698.
9.
Chen Y, Liu Z, Zhang J, et al. Selectively Disrupted Functional Connectivity Networks in Type 2 Diabetes Mellitus. Front Aging Neurosci 2015; 7: 233.
10.
Razek AA, Poptani H. MR spectroscopy of head and neck cancer. Eur J Radiol 2013; 82: 982-989.
11.
Razek AA, Nada N. Correlation of Choline/Creatine and Apparent Diffusion Coefficient values with the prognostic parameters of Head and Neck Squamous Cell Carcinoma. NMR Biomed 2016; 29: 483-489.
12.
Abdel Razek A, Abdalla A, Abdel Gaber N, et al. Proton MR Spectroscopy of the brain in children with neuronopathic Gaucher’s disease. Eur Radiol 2013; 23: 3005-3011.
13.
Razek AA, Abdalla A, Ezzat A, et al. Minimal hepatic encephalopathy in children with liver cirrhosis: diffusion-weighted MR imaging and proton MR spectroscopy of the brain. Neuroradiology 2014; 56: 885-891.
14.
Sinha S, Ekka M, Sharma U, et al. Assessment of changes in brain metabolites in Indian patients with type-2 diabetes mellitus using proton magnetic resonance spectroscopy. BMC Res Notes 2014; 7: 41.
15.
Tiehuis A, van der Meer F, Mali W, et al. MR spectroscopy of cerebral white matter in type 2 diabetes; no association with clinical variables and cognitive performance. Neuroradiology 2010; 52: 155-1561.
16.
Modi S, Bhattacharya M, Sekhri T, et al. Assessment of the metabolic profile in Type 2 diabetes mellitus and hypothyroidism through proton MR spectroscopy. Magn Reson Imaging 2008; 26: 420-425.
17.
Sahin I, Alkan A, Keskin L, et al. Evaluation of in vivo cerebral metabolism on proton magnetic resonance spectroscopy in patients with impaired glucose tolerance and type 2 diabetes mellitus. J Diabet Comp 2008; 22: 254-260.
18.
Ajilore O, Haroon E, Kumaran S, et al. Measurement of brain metabolites in patients with type 2 diabetes and major depression using proton magnetic resonance spectroscopy. Neuropsychopharmacology 2007; 32: 1224-1231.
20.
Abdel Razek AA, Elkammary S, Elmorsy AS, et al. Characterization of mediastinal lymphadenopathy with diffusion-weighted imaging. Magn Reson Imaging 2011; 29: 167-172.
21.
Abdel Razek A, Samir S, El-Said A. Role of diffusion-weighted MR imaging in differentiation of Graves’ disease from painless thyroiditis. Polish J Radiol 2017; 30: 230-234.
22.
Abdel Razek A, Mazroa J, Baz H. Assessment of white matter integrity of autistic preschool children with diffusion weighted MR imaging. Brain Dev 2014; 36: 28-34.
23.
Abdel Razek A, Al-Adlany M, Alhadidy A, et al. Diffusion tensor imaging of the renal cortex in diabetic patients: correlation with urinary and serum biomarkers. Abdom Radiol 2017; 42: 1493-1500.
24.
El-Serougy L, Abdel Razek AA, Ezzat A, et al. Assessment of diffusion tensor imaging metrics in differentiating low-grade from high-grade gliomas. Neuroradiol J 2016; 29: 400-407.
25.
Abdel Razek AA, Mousa A, Farouk A, et al. Assessment of semiquantitative parameters of dynamic contrast-Enhanced Perfusion MR Imaging in Differentiation of Subtypes of Renal Cell Carcinoma. Polish J Radiol 2016; 81: 90-94.
26.
Abdel Razek AA, Gaballa G. Role of perfusion magnetic resonance imaging in cervical lymphadenopathy. J Comput Assist Tomogr 2011; 35: 21-25.
27.
Razek AA, Elsorogy LG, Soliman NY, et al. Dynamic susceptibility contrast perfusion MR imaging in distinguishing malignant from benign head and neck tumors: a pilot study. Eur J Radiol 2011; 77: 73-79.
28.
Abdel Razek AA, Elkhamary S, Al-Mesfer S, et al. Correlation of apparent diffusion coefficient at 3T with prognostic parameters of retinoblastoma. AJNR Am J Neuroradiol 2012; 33: 944-948.
29.
Abdel Razek AA, Gaballa G, Denewer A, et al. Diffusion weighted MR imaging of the breast. Acad Radiol 2010; 17: 382-386.
30.
Razek AA, Sieza S, Maha B. Assessment of nasal and paranasal sinus masses by diffusion-weighted MR imaging. J Neuroradiol 2009; 36: 206-211.
31.
Razek AA. Diffusion magnetic resonance imaging of chest tumors. Cancer Imaging 2012; 12: 452-463.
Share
RELATED ARTICLE
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
