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      Significant Impact of Coffee Consumption on MR-Based Measures of Cardiac Function in a Population-Based Cohort Study without Manifest Cardiovascular Disease

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          Abstract

          Subclinical effects of coffee consumption (CC) with regard to metabolic, cardiac, and neurological complications were evaluated using a whole-body magnetic resonance imaging (MRI) protocol. A blended approach was used to estimate habitual CC in a population-based study cohort without a history of cardiovascular disease. Associations of CC with MRI markers of gray matter volume, white matter hyperintensities, cerebral microhemorrhages, total and visceral adipose tissue (VAT), hepatic proton density fat fraction, early/late diastolic filling rate, end-diastolic/-systolic and stroke volume, ejection fraction, peak ejection rate, and myocardial mass were evaluated by linear regression. In our analysis with 132 women and 168 men, CC was positively associated with MR-based cardiac function parameters including late diastolic filling rate, stroke volume ( p < 0.01 each), and ejection fraction ( p < 0.05) when adjusting for age, sex, smoking, hypertension, diabetes, Low-density lipoprotein (LDL), triglycerides, cholesterol, and alcohol consumption. CC was inversely associated with VAT independent of demographic variables and cardiovascular risk factors ( p < 0.05), but this association did not remain significant after additional adjustment for alcohol consumption. CC was not significantly associated with potential neurodegeneration. We found a significant positive and independent association between CC and MRI-based systolic and diastolic cardiac function. CC was also inversely associated with VAT but not independent of alcohol consumption.

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          Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

          An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.
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            A new rating scale for age-related white matter changes applicable to MRI and CT.

            MRI is more sensitive than CT for detection of age-related white matter changes (ARWMC). Most rating scales estimate the degree and distribution of ARWMC either on CT or on MRI, and they differ in many aspects. This makes it difficult to compare CT and MRI studies. To be able to study the evolution and possible effect of drug treatment on ARWMC in large patient samples, it is necessary to have a rating scale constructed for both MRI and CT. We have developed and evaluated a new scale and studied ARWMC in a large number of patients examined with both MRI and CT. Seventy-seven patients with ARWMC on either CT or MRI were recruited and a complementary examination (MRI or CT) performed. The patients came from 4 centers in Europe, and the scans were rated by 4 raters on 1 occasion with the new ARWMC rating scale. The interrater reliability was evaluated by using kappa statistics. The degree and distribution of ARWMC in CT and MRI scans were compared in different brain areas. Interrater reliability was good for MRI (kappa=0.67) and moderate for CT (kappa=0.48). MRI was superior in detection of small ARWMC, whereas larger lesions were detected equally well with both CT and MRI. In the parieto-occipital and infratentorial areas, MRI detected significantly more ARWMC than did CT. In the frontal area and basal ganglia, no differences between modalities were found. When a fluid-attenuated inversion recovery sequence was used, MRI detected significantly more lesions than CT in frontal and parieto-occipital areas. No differences were found in basal ganglia and infratentorial areas. We present a new ARWMC scale applicable to both CT and MRI that has almost equal sensitivity, except for certain regions. The interrater reliability was slightly better for MRI, as was the detectability of small lesions.
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              Subcutaneous and visceral adipose tissue: structural and functional differences.

              Obesity is a heterogeneous disorder. Obese individuals vary in their body fat distribution, their metabolic profile and degree of associated cardiovascular and metabolic risk. Abdominal obesity carries greater risk of developing diabetes and future cardiovascular events than peripheral or gluteofemoral obesity. There are differences between adipose tissue present in subcutaneous areas (SCAT) and visceral adipose tissue (VAT) present in the abdominal cavity. These include anatomical, cellular, molecular, physiological, clinical and prognostic differences. Anatomically, VAT is present mainly in the mesentery and omentum, and drains directly through the portal circulaion to the liver. VAT compared with SCAT is more cellular, vascular, innervated and contains a larger number of inflammatory and immune cells, lesser preadipocyte differentiating capacity and a greater percentage of large adipocytes. There are more glucocorticoid and androgen receptors in VAT than in SCAT. VAT adipocytes are more metabolically active, more sensitive to lipolysis and more insulin-resistant than SCAT adipocytes. VAT has a greater capacity to generate free fatty acids and to uptake glucose than SCAT and is more sensitive to adrenergic stimulation, while SCAT is more avid in absorption of circulating free fatty acids and triglycerides. VAT carries a greater prediction of mortality than SCAT.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Nutrients
                Nutrients
                nutrients
                Nutrients
                MDPI
                2072-6643
                13 April 2021
                April 2021
                : 13
                : 4
                : 1275
                Affiliations
                [1 ]Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, 18057 Rostock, Germany; felix.meinel@ 123456med.uni-rostock.de
                [2 ]Department of Radiology, University Hospital, LMU Munich, 81377 Munich, Germany; roberto.lorbeer@ 123456med.uni-muenchen.de (R.L.); daniel.keeser@ 123456med.uni-muenchen.de (D.K.); franziskamgalie@ 123456gmail.com (F.G.); Sergio.Grosu@ 123456med.uni-muenchen.de (S.G.); sophia.stoecklein@ 123456med.uni-muenchen.de (S.S.)
                [3 ]Department of Psychiatry and Psychotherapy, Ludwig-Maximilians University Hospital LMU, 80336 Munich, Germany
                [4 ]Munich Center for Neurosciences (MCN)–Brain & Mind, 82152 Planegg-Martinsried, Germany
                [5 ]Department of Diagnostic and Interventional Radiology, Medical Center–University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany; fabian.bamberg@ 123456uniklinik-freiburg.de (F.B.); christopher.schlett@ 123456uniklinik-freiburg.de (C.L.S.)
                [6 ]University Heart Center Freiburg-Bad Krozingen, 79189 Bad Krozingen, Germany
                [7 ]Department of Neuroradiology, Medical Center–University of Freiburg, Faculty of Medicine, University of Freiburg, 79098 Freiburg, Germany; corinna.storz@ 123456uniklinik-freiburg.de
                [8 ]Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health (GmbH), 85764 Neuherberg, Germany; peters@ 123456helmholtz-muenchen.de (A.P.); alexandra.schneider@ 123456helmholtz-muenchen.de (A.S.)
                [9 ]LMU Munich, IBE-Chair of Epidemiology, 85764 Neuherberg, Germany
                [10 ]German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, 80802 Munich, Germany
                [11 ]Independent Research Group Clinical Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, 85764 Neuherberg, Germany; j.linseisen@ 123456unika-t.de
                [12 ]Ludwig-Maximilians Universität München, UNIKA-T Augsburg, 86156 Augsburg, Germany; christa.meisinger@ 123456helmholtz-muenchen.de
                [13 ]German Diabetes Center, Institute of Biometrics and Epidemiology, Leibniz Institute at Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany; wolfgang.rathmann@ 123456ddz.de
                [14 ]Department of Medical Imaging, The Hospital for Sick Children, University of Toronto, Toronto, ON M5G 1X8, Canada; BirgitBetina.Ertl-Wagner@ 123456sickkids.ca
                Author notes
                [* ]Correspondence: Ebba.Beller@ 123456med.uni-rostock.de ; Tel.: +49-(0)381-494-9201; Fax: +49-(0)381-494-9202
                Author information
                https://orcid.org/0000-0002-0244-1024
                https://orcid.org/0000-0002-1084-2442
                https://orcid.org/0000-0002-9386-382X
                Article
                nutrients-13-01275
                10.3390/nu13041275
                8069927
                34444856
                687e7f25-fe99-4618-8d9a-7d3a84407bf0
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 17 February 2021
                : 06 April 2021
                Categories
                Article

                Nutrition & Dietetics
                coffee,magnetic resonance imaging,cardiac function,visceral adipose tissue

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