Younghee Yim1,2, Jong Duck Choi1, Jun Heong Cho1, Yeonsil Moon3, Seol-Heui Han3, Won-Jin Moon4. 1. Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, 120-1, Neungdong-ro, Hwayang-dong, Gwangjin-gu, Seoul, 05030, Republic of Korea. 2. Department of Radiology, Chung-Ang University, College of Medicine, Chung-Ang University Hospital, Seoul, Republic of Korea. 3. Department of Neurology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Republic of Korea. 4. Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, 120-1, Neungdong-ro, Hwayang-dong, Gwangjin-gu, Seoul, 05030, Republic of Korea. mdmoonwj@naver.com.
Abstract
PURPOSE: To examine the relationship between apolipoprotein E gene (APOE) mutation status and iron accumulation in the deep gray matter of subjects with cognitive symptoms using quantitative susceptibility mapping (QSM). METHODS: A total of 105 patients with cognitive symptoms were enrolled. QSM data were generated from 3D gradient-echo data using an STI Suite algorithm. A region of interest-based analysis with QSM was performed in the deep gray matter. Differences between APOE4 carriers and non-carriers were assessed by analysis of covariance. Multiple regression analysis was performed to identify the factors associated with magnetic susceptibility. RESULTS: Clinical characters such as age, education, MMSE, vascular risk burden, and systolic blood pressure differ between APOE4 carrier and non-carrier groups. The APOE4 carrier group had higher magnetic susceptibility values than the non-carrier group, with significant differences in the caudate (p = 0.004), putamen (p < 0.0001), and globus pallidus (p < 0.0001) which imply higher iron accumulation. In a multiple regression analysis, APOE4 status was found to be a predictor of magnetic susceptibility value in the globus pallidus (p = 0.03); age for magnetic susceptibility value in the caudate nucleus (p = 0.0064); and age and hippocampal atrophy for magnetic susceptibility value in the putamen (p < 0.05). CONCLUSION: Our study demonstrates that magnetic susceptibility in globus pallidus is related to APOE4 status while those of caudate and putamen are related to other factors including age. It suggests that brain iron accumulation in the deep gray matter is modulated by APOE4 and age with differential regional predilection.
PURPOSE: To examine the relationship between apolipoprotein E gene (APOE) mutation status and iron accumulation in the deep gray matter of subjects with cognitive symptoms using quantitative susceptibility mapping (QSM). METHODS: A total of 105 patients with cognitive symptoms were enrolled. QSM data were generated from 3D gradient-echo data using an STI Suite algorithm. A region of interest-based analysis with QSM was performed in the deep gray matter. Differences between APOE4 carriers and non-carriers were assessed by analysis of covariance. Multiple regression analysis was performed to identify the factors associated with magnetic susceptibility. RESULTS: Clinical characters such as age, education, MMSE, vascular risk burden, and systolic blood pressure differ between APOE4 carrier and non-carrier groups. The APOE4 carrier group had higher magnetic susceptibility values than the non-carrier group, with significant differences in the caudate (p = 0.004), putamen (p < 0.0001), and globus pallidus (p < 0.0001) which imply higher iron accumulation. In a multiple regression analysis, APOE4 status was found to be a predictor of magnetic susceptibility value in the globus pallidus (p = 0.03); age for magnetic susceptibility value in the caudate nucleus (p = 0.0064); and age and hippocampal atrophy for magnetic susceptibility value in the putamen (p < 0.05). CONCLUSION: Our study demonstrates that magnetic susceptibility in globus pallidus is related to APOE4 status while those of caudate and putamen are related to other factors including age. It suggests that brain iron accumulation in the deep gray matter is modulated by APOE4 and age with differential regional predilection.
Authors: Christine Ghadery; Lukas Pirpamer; Edith Hofer; Christian Langkammer; Katja Petrovic; Marisa Loitfelder; Petra Schwingenschuh; Stephan Seiler; Marco Duering; Eric Jouvent; Helena Schmidt; Franz Fazekas; Jean-Francois Mangin; Hugues Chabriat; Martin Dichgans; Stefan Ropele; Reinhold Schmidt Journal: Neurobiol Aging Date: 2014-09-19 Impact factor: 4.673
Authors: Gerard Blasco; Josep Puig; Josep Daunis-I-Estadella; Xavier Molina; Gemma Xifra; Fernando Fernández-Aranda; Salvador Pedraza; Wifredo Ricart; Manuel Portero-Otín; José Manuel Fernández-Real Journal: Diabetes Care Date: 2014-08-14 Impact factor: 19.112
Authors: Kofi Deh; Keigo Kawaji; Marjolein Bulk; Louise Van Der Weerd; Emelie Lind; Pascal Spincemaille; Kelly McCabe Gillen; Johan Van Auderkerke; Yi Wang; Thanh D Nguyen Journal: Magn Reson Med Date: 2018-10-04 Impact factor: 4.668
Authors: George Bartzokis; Po H Lu; Daniel H Geschwind; Kathleen Tingus; Danny Huang; Mario F Mendez; Nancy Edwards; Jim Mintz Journal: Biol Psychiatry Date: 2007-07-20 Impact factor: 13.382
Authors: Weiwei Chen; Susan A Gauthier; Ajay Gupta; Joseph Comunale; Tian Liu; Shuai Wang; Mengchao Pei; David Pitt; Yi Wang Journal: Radiology Date: 2013-11-18 Impact factor: 11.105
Authors: Adonis Sfera; Karina G Thomas; Christina V Andronescu; Nyla Jafri; Dan O Sfera; Sarvin Sasannia; Carlos M Zapata-Martín Del Campo; Jose C Maldonado Journal: Front Neurosci Date: 2022-05-12 Impact factor: 5.152