Determinants of iron accumulation in the normal aging brain

作者: Lukas Pirpamer , Edith Hofer , Benno Gesierich , François De Guio , Paul Freudenberger

DOI: 10.1016/J.NEUROBIOLAGING.2016.04.002

关键词:

摘要: In a recent postmortem study, R-2* relaxometry in gray matter (GM) of the brain has been validated as noninvasive measure for iron content tissue. Iron accumulation normal aging is common finding and relates to maturation degeneration. The goal this study was assess determinants during aging. cohort consisted 314 healthy community-dwelling participants Austrian Stroke Prevention Study. Their age ranged from 38-82 years. Quantitative magnetic resonance imaging performed on 3T included mapping, based 3D multi-echo gradient echo sequence. median values measured all GM regions, which were segmented automatically using FreeSurfer. We investigated 25 possible cerebral deposition. These demographics, volume, lifestyle factors, cerebrovascular risk serum levels iron, single nucleotide polymorphisms related regulating genes (rs1800562, rs3811647, rs1799945, rs1049296). body mass index (BMI) significantly 15/32 analyzed regions with strongest correlations found amygdala (p = 0.0091), medial temporal lobe 0.0002), hippocampus <= 0.0001). Further associations deep smoking. No significant gender, or iron-associated genetic polymorphisms. conclusion, besides age, BMI smoking are only normally subjects. Smoking deposition basal ganglia, whereas higher associated neocortex following an Alzheimer-like distribution. (C) 2016 ELSEVIER. All rights reserved.

参考文章(59)
Deborah Janowitz, Katharina Wittfeld, Jan Terock, Harald Jürgen Freyberger, Katrin Hegenscheid, Henry Völzke, Mohamad Habes, Norbert Hosten, Nele Friedrich, Matthias Nauck, Grazyna Domanska, Hans Jörgen Grabe, None, Association between waist circumference and gray matter volume in 2344 individuals from two adult community-based samples. NeuroImage. ,vol. 122, pp. 149- 157 ,(2015) , 10.1016/J.NEUROIMAGE.2015.07.086
N Cherbuin, K Sargent-Cox, M Fraser, P Sachdev, K J Anstey, Being overweight is associated with hippocampal atrophy: the PATH Through Life Study International Journal of Obesity. ,vol. 39, pp. 1509- 1514 ,(2015) , 10.1038/IJO.2015.106
Jennifer F. Bobb, Brian S. Schwartz, Christos Davatzikos, Brian Caffo, Cross‐sectional and longitudinal association of body mass index and brain volume Human Brain Mapping. ,vol. 35, pp. 75- 88 ,(2014) , 10.1002/HBM.22159
Erika P. Raven, Po H. Lu, Todd A. Tishler, Panthea Heydari, George Bartzokis, Increased iron levels and decreased tissue integrity in hippocampus of Alzheimer's disease detected in vivo with magnetic resonance imaging. Journal of Alzheimer's Disease. ,vol. 37, pp. 127- 136 ,(2013) , 10.3233/JAD-130209
Stefan Ropele, Mike P. Wattjes, Christian Langkammer, Iris D. Kilsdonk, Wolter L. de Graaf, Jette L Frederiksen, Dan Fuglø, Marios Yiannakas, Claudia A. M. Wheeler-Kingshott, Christian Enzinger, Maria A. Rocca, Till Sprenger, Michael Amman, Ludwig Kappos, Massimo Filippi, Alex Rovira, Olga Ciccarelli, Frederik Barkhof, Franz Fazekas, Multicenter R-2* Mapping in the Healthy Brain Magnetic Resonance in Medicine. ,vol. 71, pp. 1103- 1107 ,(2014) , 10.1002/MRM.24772
Daniel Yekutieli, Yoav Benjamini, THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY Annals of Statistics. ,vol. 29, pp. 1165- 1188 ,(2001) , 10.1214/AOS/1013699998
Tim L. Emmerzaal, Amanda J. Kiliaan, Deborah R. Gustafson, 2003-2013: A Decade of Body Mass Index, Alzheimer's Disease, and Dementia Journal of Alzheimer's Disease. ,vol. 43, pp. 739- 755 ,(2014) , 10.3233/JAD-141086
L. Zecca, D. Tampellini, E. Rizzio, G. Giaveri, M. Gallorini, The determination of iron and other metals by INAA in Cortex, Cerebellum and Putamen of human brain and in their neuromelanins Journal of Radioanalytical and Nuclear Chemistry. ,vol. 248, pp. 129- 131 ,(2001) , 10.1023/A:1010650729843
Saman Sargolzaei, Arman Sargolzaei, Mercedes Cabrerizo, Gang Chen, Mohammed Goryawala, Shirin Noei, Qi Zhou, Ranjan Duara, Warren Barker, Malek Adjouadi, A practical guideline for intracranial volume estimation in patients with Alzheimer's disease BMC Bioinformatics. ,vol. 16, pp. 1- 10 ,(2015) , 10.1186/1471-2105-16-S7-S8