- Email: [email protected]
ELEVATED CSF STREM2 IN AUTOSOMAL DOMINANTLY INHERITED ALZHEIMER’S DISEASE ASSOCIATED WITH REGIONAL FIBER TRACT INJURY: RESULTS FROM THE DIAN STUDY
P188
Podium Presentations: Sunday, July 16, 2017
deposition in ADAD may begin in the striatum, though this pattern has not been universally observed. Using data from the Dominantly Inherited Alzheimer Network (DIAN), we tested whether the balance of cortical vs. striatal amyloid (as measured by PiB PET) may vary by ADAD genotype. Methods:Striatal and cortical PiB binding was assessed in 168 individuals (111 mutation carriers, 57 non-carriers, 232 PET sessions). Forty unique genotypes (38 PSEN1, 1 PSEN2, 1 APP) were categorized into 9 gene category groups based on the topological location of the mutation within the folded protein. We used linear mixed-effects models to examine interactions between estimated years to symptom onset (EYO) and genotype to predict striatal and cortical PiB, as well as the difference between these regions. Results: Not accounting for genotypic variance, both striatal and cortical PiB increased in mutation carriers with similar slopes across EYO, but without difference between striatum and cortex (p>0.5; Figure 1). However, including genotype in models demonstrated differential striatal vs. cortical PiB binding among some genotype groups (Figure 2). Specifically, PSEN2 transmembrane domain (TM) 2 mutation carriers had higher PiB accumulation in cortex as compared to striatum (0.06SUVR/y, p¼0.001) whereas PSEN1 TM 2 and 8 mutation carriers (domains that include the previously described, striatal predominant C410Y and A426P genotypes) had greater and earlier accumulation in the striatum vs. cortex (0.05SUVR/y, p¼0.001). The difference between striatal and cortical PiB in PSEN1 TM2 and TM 8 carriers (n¼22) was greater in those that also carried the APOE ε4 AD risk allele (APOE ε4 carrier status by EYO interaction, p¼0.016), though greater striatal vs. cortical PiB was present in ε4 non-carriers as well. Conclusions: Significant inter-genotypic variance in ADAD may be responsible for differential fibrillar amyloid accumulation in the striatum and cortex. For genotypes that show greater striatal vs. cortical amyloid deposition, concurrent carriage of the APOE ε4 risk allele may exaggerate this striatal predominant pattern.
Figure 2. PiB SUVR Across Mutation Carriers, Categorized by Genotype: Mutations in PSEN1, PSEN2, and APP were categorized based on the location of the mutation within the protein (affected topological domain). Only gene categories with data from 5 mutation carriers across 10 PET sessions were retained in the analysis. Cortical mean PiB binding (solid black) and striatum (dashed red) are plotted against EYO in ADAD mutation carriers. (red asterisks denote significantly greater striatal vs. cortical amyloid; the black asterisk denotes significantly greater cortical vs. striatal amyloid).
O1-02-06
ELEVATED CSF STREM2 IN AUTOSOMAL DOMINANTLY INHERITED ALZHEIMER’S DISEASE ASSOCIATED WITH REGIONAL FIBER TRACT INJURY: RESULTS FROM THE DIAN STUDY
Miguel A. Araque Caballero1, Marc Suarez-Calvet2, Marco Duering3, Gernot Kleinberger4, Randall J. Bateman5, Tammie L. S. Benzinger6, Anne M. Fagan7, John C. Morris7, Johannes Levin8, Adrian Danek9, Christian Haass2, Michael Ewers10, 1Institute for Stroke and Dementia Research, Ludwig-Maximilians-Universit€at LMU, Munich, Germany; 2 German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; 3Institute for Stroke and Dementia Research, M€unchen, Germany; 4 Ludwig-Maximilians-University Munich, Munich, Germany; 5Hope Center for Neurological Disorders, St. Louis, MO, USA; 6Washington University in St. Louis, St. Louis, MO, USA; 7Washington University School of Medicine, St. Louis, MO, USA; 8University of Munich, Munich, Germany; 9Department of Neurology, LMU Munich, Munich, Germany; 10Institute for Stroke and Dementia Research (ISD), Klinikum der Universit€at M€unchen, Munich, Germany. Contact e-mail: [email protected] Background: The soluble fraction of the protein “triggering receptor
expressed on myeloid cell 2” (sTREM2) is detectable in cerebrospinal fluid (CSF) and is a biomarker of microglial activation. In autosomal dominantly inherited Alzheimer’s disease (ADAD), we recently showed that CSF-TREM2 levels are elevated up to seven years before the onset of dementia symptoms (Suarez-Calvet et al., Science Transl. Med. 2016). However, it is unclear whether higher microglia activation is associated with increased neurodegeneration. Here we assessed the association between CSF-sTREM2 levels and fiber-tract changes in ADAD, as measured by diffusion-tensor imaging (DTI). Methods: We included 59 mutation carriers (MC) and 44 non-carriers (NC) from the Dominantly Inherited Alzheimer Network (DIAN). Concentration of sTREM2 in CSF was determined with ELISA. Based on DTI, we used tract-based spatial statistics (TBSS) to produce skeletonized maps of mean diffusivity (MD). As a global measure of white matter damage, histogram peak-width
Podium Presentations: Sunday, July 16, 2017
of skeletonized-MD (PSMD) was applied. Estimated years from symptom onset (EYO), based on parental age-of-onset, were used as a measure of disease progression. First, we tested in regression analyses whether regional increases in MD (TBSS) and global PSMD were stronger in MC than NC (interaction EYOxMutation group), controlled for gender and education. Next, using similar regression models, we tested CSF-sTREM2 levels as a predictor of regional MD and global PSMD. For TBSS, the voxel-wise significance threshold was p<0.01 (uncorrected) and the cluster-extent threshold p<0.05 (FWE-corrected). Results: The interaction EYOxMutation (t(73.6)¼4.14, p<0.001) on PSMD indicated faster global fiber-tract alterations in MC compared to NC (Fig. 1A). TBSS analysis showed the increase in MD to be predominantly present within posterior fiber-tracts (inferior fronto-occipital fasciculus and forceps major) in MC (Fig. 1B, red colored). For CSF-sTREM2, higher levels were associated with increased global PSMD in MC but not NC (interaction: t(63.8)¼2.34, p¼0.021; Fig. 1C), where the regional effects clustered in the vulnerable posterior regions of the fiber-tracts (Fig. 1D, red colored). Conclusions: Disease progression in ADAD is associated with a pathological increase in posterior fiber-tract degeneration, which is accompanied by increased microglia activation. The brain’s neuroimmune response may have an active role in modulating disease progression in ADAD. SUNDAY, JULY 16, 2017 ORAL SESSION O1-03 GENETICS: ALZHEIMER’S DISEASE GENETICS — ROLE OF COMMON VARIANTS IN DISEASE RISK O1-03-01
GENOME-WIDE RARE VARIANT IMPUTATION AND TISSUE-SPECIFIC TRANSCRIPTOMIC ANALYSIS IDENTIFY NOVEL RARE VARIANT CANDIDATE LOCI IN LATE-ONSET ALZHEIMER’S DISEASE: THE ALZHEIMER’S DISEASE GENETICS CONSORTIUM
Adam C. Naj1, Jennifer Below2, Yi Zhao1, Hung-Hsin Chen3, Sven J. van der Lee4, Kara L. Hamilton-Nelson5, Lauren Petty2, Brian W. Kunkle6, Amanda B. Kuzma1, Otto Valladares1, Christiane Reitz7, Gary W. Beecham5, Eden R. Martin5, Li-San Wang1, Jonathan L. Haines8, Richard Mayeux7, Lindsay A. Farrer9, Margaret A. Pericak-Vance5, Gerard D. Schellenberg1, Alzheimer’s Disease Genetics Consortium, 1 University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; 2University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA; 3University of Texas Health Science Center at Houston, Houston, TX, USA; 4Department of Epidemiology, Erasmus Medical Centre, Rotterdam, Netherlands; 5John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA; 6University of Miami Miller School of Medicine, Miami, FL, USA; 7Columbia University, New York, NY, USA; 8 Case Western Reserve University School of Medicine, Cleveland, OH, USA; 9Boston University School of Medicine, Boston, MA, USA. Contact e-mail: [email protected] Background: The International Genomics of Alzheimer’s Project
(IGAP) genome-wide association study (GWAS) identified 19 susceptibility LOAD loci in addition to APOE, however the majority of these were common (minor allele frequency (MAF)>0.05). The Haplotype Reference Consortium (HRC) released a dense reference panel (64,976 haplotypes/39,235,157 SNPs) allowing imputation of rare variants (MAF>0.00008) for discovery association testing. ADGC imputed 33 GWAS datasets to HRC to identify
P189
novel rare variant associations and genetically-regulated gene expression patterns contributing to LOAD. Methods: We imputed 14,743 cases and 15,871 controls to the HRC r1.1 reference panel using Minimac3 on the University of Michigan Imputation Server. Logistic regression on individual variants with MAF>0.01 was performed in PLINKv1.9 (generalized linear mixed model in R for family-based variants) using imputed genotype probabilities and meta-analyzed in METAL, while variants with MAF0.01 were analyzed using score-based tests and meta-analyzed in the SeqMeta/R package; both analyses adjusted for age, sex, and population substructure. Gene-based association was also performed using SKAT-O and gene-based testing of expression regulation in LOAD was performed using PrediXcan. Results: Preliminary analyses of w39.2 million genotyped or imputed SNVs identified single variant associations of P<5310-8 in 5 known IGAP LOAD candidate loci (APOE, BIN1, the MS4A region, PICALM, and CR1), and multiple suggestive associations (P<10-5) at each of an additional 13 loci (including known IGAP loci), rs13155750 in MEF2C (OR(95% CI): 1.13(1.08,1.20); P¼5.09310-6) and rs755951 in PTK2B (OR(95% CI): 1.11(1.06,1.16); P¼5.61310-6). Novel associations include signals at LILRA5 (19:54821819; OR(95% CI): 1.14(1.08,1.20); P¼4.84310-7), involved in innate immunity pathways; and at SMOX (rs1884732; OR(95% CI): 1.11(1.06,1.17); P¼5.17310-6), involved in catabolism of polyamines, levels of which are altered in AD brains. Rare variant and gene-based analyses demonstrated significant APOE and TREM2 associations, while gene-based testing identified strong but marginal association for SORL1 (P¼5.55310-6). PrediXcan analyses identified significant strong genetically-regulated gene expression in LOAD for MS4A4A (Q¼4.26310-26), BIN1 (Q¼1.70310-18), and FBXO46 (Q¼4.25310-8). Conclusions: Several novel candidate loci for LOAD have been identified using high-quality imputation of rare and low-frequency variants in the ADGC, reinforcing the utility of high-density imputation panels, and providing a resource to newly identify genes with perturbed expression in LOAD.
O1-03-02
FOUR NOVEL SUSCEPTIBILITY VARIANTS IDENTIFIED IN CHINESE ARE ASSOCIATED WITH SPORADIC ALZHEIMER’S DISEASE
Jianping Jia1,2,3,4, Xianbo Zuo5,6, Ling Wei5, Cuibai Wei2, Kai Wang5, Luxi Shen2, Fangyu Li2, Wei Qin2, Yi Tang2, Dantao Peng7, Lan Tan8, Benyan Luo9, Qihao Guo10, Yong Ji11, Muni Tang12, Yanjiang Wang13, Yifeng Du14, Jiewen Zhang15, Junjian Zhang16, Qiumin Qu17, Peng Xie18, Jiying Zhou18, Lu Shen19, Jihui Lv20, Lu Lu2, Aihong Zhou2, Fen Wang2, Changbiao Chu2, Haiqing Song2, Liyong Wu2, Ying Han21, Dongmei Guo2, Xiumei Zuo2, Yue Han2, Liyuan Huang2, Junhua Liang2, Qi Wang2, Lina Zhao2, Hongmei Jin2, Jing Dong2, Xueyan Feng2, Lu Shi2, Wei Wang2, Haitao Li2, Longfei Jia22, 1Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China; 2Xuan Wu Hospital, Capital Medical University, Beijing, China; 3 Neurodegenerative Laboratory of Ministry of Education of the People’s Republic of China, Beijing, China; 4Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China; 5First Affiliated Hospital of Anhui Medical University, Hefei, China; 6State Key Laboratory Incubation Base of Dermatology, Ministry of National Science and Technology, Hefei, China; 7China-Japan Friendship Hospital, Beijing, China; 8 Qingdao Municipal Hospital, School of Medicine, Qingdao University, Qingdao, China; 9First Affiliated Hospital, Zhejiang University, Hangzhou, China; 10Huashan Hospital, Fudan University, Shanghai, China; 11Tianjin Huanhu Hospital, Tianjin, China; 12Guangzhou Huiai
Podium Presentations: Sunday, July 16, 2017
deposition in ADAD may begin in the striatum, though this pattern has not been universally observed. Using data from the Dominantly Inherited Alzheimer Network (DIAN), we tested whether the balance of cortical vs. striatal amyloid (as measured by PiB PET) may vary by ADAD genotype. Methods:Striatal and cortical PiB binding was assessed in 168 individuals (111 mutation carriers, 57 non-carriers, 232 PET sessions). Forty unique genotypes (38 PSEN1, 1 PSEN2, 1 APP) were categorized into 9 gene category groups based on the topological location of the mutation within the folded protein. We used linear mixed-effects models to examine interactions between estimated years to symptom onset (EYO) and genotype to predict striatal and cortical PiB, as well as the difference between these regions. Results: Not accounting for genotypic variance, both striatal and cortical PiB increased in mutation carriers with similar slopes across EYO, but without difference between striatum and cortex (p>0.5; Figure 1). However, including genotype in models demonstrated differential striatal vs. cortical PiB binding among some genotype groups (Figure 2). Specifically, PSEN2 transmembrane domain (TM) 2 mutation carriers had higher PiB accumulation in cortex as compared to striatum (0.06SUVR/y, p¼0.001) whereas PSEN1 TM 2 and 8 mutation carriers (domains that include the previously described, striatal predominant C410Y and A426P genotypes) had greater and earlier accumulation in the striatum vs. cortex (0.05SUVR/y, p¼0.001). The difference between striatal and cortical PiB in PSEN1 TM2 and TM 8 carriers (n¼22) was greater in those that also carried the APOE ε4 AD risk allele (APOE ε4 carrier status by EYO interaction, p¼0.016), though greater striatal vs. cortical PiB was present in ε4 non-carriers as well. Conclusions: Significant inter-genotypic variance in ADAD may be responsible for differential fibrillar amyloid accumulation in the striatum and cortex. For genotypes that show greater striatal vs. cortical amyloid deposition, concurrent carriage of the APOE ε4 risk allele may exaggerate this striatal predominant pattern.
Figure 2. PiB SUVR Across Mutation Carriers, Categorized by Genotype: Mutations in PSEN1, PSEN2, and APP were categorized based on the location of the mutation within the protein (affected topological domain). Only gene categories with data from 5 mutation carriers across 10 PET sessions were retained in the analysis. Cortical mean PiB binding (solid black) and striatum (dashed red) are plotted against EYO in ADAD mutation carriers. (red asterisks denote significantly greater striatal vs. cortical amyloid; the black asterisk denotes significantly greater cortical vs. striatal amyloid).
O1-02-06
ELEVATED CSF STREM2 IN AUTOSOMAL DOMINANTLY INHERITED ALZHEIMER’S DISEASE ASSOCIATED WITH REGIONAL FIBER TRACT INJURY: RESULTS FROM THE DIAN STUDY
Miguel A. Araque Caballero1, Marc Suarez-Calvet2, Marco Duering3, Gernot Kleinberger4, Randall J. Bateman5, Tammie L. S. Benzinger6, Anne M. Fagan7, John C. Morris7, Johannes Levin8, Adrian Danek9, Christian Haass2, Michael Ewers10, 1Institute for Stroke and Dementia Research, Ludwig-Maximilians-Universit€at LMU, Munich, Germany; 2 German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; 3Institute for Stroke and Dementia Research, M€unchen, Germany; 4 Ludwig-Maximilians-University Munich, Munich, Germany; 5Hope Center for Neurological Disorders, St. Louis, MO, USA; 6Washington University in St. Louis, St. Louis, MO, USA; 7Washington University School of Medicine, St. Louis, MO, USA; 8University of Munich, Munich, Germany; 9Department of Neurology, LMU Munich, Munich, Germany; 10Institute for Stroke and Dementia Research (ISD), Klinikum der Universit€at M€unchen, Munich, Germany. Contact e-mail: [email protected] Background: The soluble fraction of the protein “triggering receptor
expressed on myeloid cell 2” (sTREM2) is detectable in cerebrospinal fluid (CSF) and is a biomarker of microglial activation. In autosomal dominantly inherited Alzheimer’s disease (ADAD), we recently showed that CSF-TREM2 levels are elevated up to seven years before the onset of dementia symptoms (Suarez-Calvet et al., Science Transl. Med. 2016). However, it is unclear whether higher microglia activation is associated with increased neurodegeneration. Here we assessed the association between CSF-sTREM2 levels and fiber-tract changes in ADAD, as measured by diffusion-tensor imaging (DTI). Methods: We included 59 mutation carriers (MC) and 44 non-carriers (NC) from the Dominantly Inherited Alzheimer Network (DIAN). Concentration of sTREM2 in CSF was determined with ELISA. Based on DTI, we used tract-based spatial statistics (TBSS) to produce skeletonized maps of mean diffusivity (MD). As a global measure of white matter damage, histogram peak-width
Podium Presentations: Sunday, July 16, 2017
of skeletonized-MD (PSMD) was applied. Estimated years from symptom onset (EYO), based on parental age-of-onset, were used as a measure of disease progression. First, we tested in regression analyses whether regional increases in MD (TBSS) and global PSMD were stronger in MC than NC (interaction EYOxMutation group), controlled for gender and education. Next, using similar regression models, we tested CSF-sTREM2 levels as a predictor of regional MD and global PSMD. For TBSS, the voxel-wise significance threshold was p<0.01 (uncorrected) and the cluster-extent threshold p<0.05 (FWE-corrected). Results: The interaction EYOxMutation (t(73.6)¼4.14, p<0.001) on PSMD indicated faster global fiber-tract alterations in MC compared to NC (Fig. 1A). TBSS analysis showed the increase in MD to be predominantly present within posterior fiber-tracts (inferior fronto-occipital fasciculus and forceps major) in MC (Fig. 1B, red colored). For CSF-sTREM2, higher levels were associated with increased global PSMD in MC but not NC (interaction: t(63.8)¼2.34, p¼0.021; Fig. 1C), where the regional effects clustered in the vulnerable posterior regions of the fiber-tracts (Fig. 1D, red colored). Conclusions: Disease progression in ADAD is associated with a pathological increase in posterior fiber-tract degeneration, which is accompanied by increased microglia activation. The brain’s neuroimmune response may have an active role in modulating disease progression in ADAD. SUNDAY, JULY 16, 2017 ORAL SESSION O1-03 GENETICS: ALZHEIMER’S DISEASE GENETICS — ROLE OF COMMON VARIANTS IN DISEASE RISK O1-03-01
GENOME-WIDE RARE VARIANT IMPUTATION AND TISSUE-SPECIFIC TRANSCRIPTOMIC ANALYSIS IDENTIFY NOVEL RARE VARIANT CANDIDATE LOCI IN LATE-ONSET ALZHEIMER’S DISEASE: THE ALZHEIMER’S DISEASE GENETICS CONSORTIUM
Adam C. Naj1, Jennifer Below2, Yi Zhao1, Hung-Hsin Chen3, Sven J. van der Lee4, Kara L. Hamilton-Nelson5, Lauren Petty2, Brian W. Kunkle6, Amanda B. Kuzma1, Otto Valladares1, Christiane Reitz7, Gary W. Beecham5, Eden R. Martin5, Li-San Wang1, Jonathan L. Haines8, Richard Mayeux7, Lindsay A. Farrer9, Margaret A. Pericak-Vance5, Gerard D. Schellenberg1, Alzheimer’s Disease Genetics Consortium, 1 University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; 2University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA; 3University of Texas Health Science Center at Houston, Houston, TX, USA; 4Department of Epidemiology, Erasmus Medical Centre, Rotterdam, Netherlands; 5John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA; 6University of Miami Miller School of Medicine, Miami, FL, USA; 7Columbia University, New York, NY, USA; 8 Case Western Reserve University School of Medicine, Cleveland, OH, USA; 9Boston University School of Medicine, Boston, MA, USA. Contact e-mail: [email protected] Background: The International Genomics of Alzheimer’s Project
(IGAP) genome-wide association study (GWAS) identified 19 susceptibility LOAD loci in addition to APOE, however the majority of these were common (minor allele frequency (MAF)>0.05). The Haplotype Reference Consortium (HRC) released a dense reference panel (64,976 haplotypes/39,235,157 SNPs) allowing imputation of rare variants (MAF>0.00008) for discovery association testing. ADGC imputed 33 GWAS datasets to HRC to identify
P189
novel rare variant associations and genetically-regulated gene expression patterns contributing to LOAD. Methods: We imputed 14,743 cases and 15,871 controls to the HRC r1.1 reference panel using Minimac3 on the University of Michigan Imputation Server. Logistic regression on individual variants with MAF>0.01 was performed in PLINKv1.9 (generalized linear mixed model in R for family-based variants) using imputed genotype probabilities and meta-analyzed in METAL, while variants with MAF0.01 were analyzed using score-based tests and meta-analyzed in the SeqMeta/R package; both analyses adjusted for age, sex, and population substructure. Gene-based association was also performed using SKAT-O and gene-based testing of expression regulation in LOAD was performed using PrediXcan. Results: Preliminary analyses of w39.2 million genotyped or imputed SNVs identified single variant associations of P<5310-8 in 5 known IGAP LOAD candidate loci (APOE, BIN1, the MS4A region, PICALM, and CR1), and multiple suggestive associations (P<10-5) at each of an additional 13 loci (including known IGAP loci), rs13155750 in MEF2C (OR(95% CI): 1.13(1.08,1.20); P¼5.09310-6) and rs755951 in PTK2B (OR(95% CI): 1.11(1.06,1.16); P¼5.61310-6). Novel associations include signals at LILRA5 (19:54821819; OR(95% CI): 1.14(1.08,1.20); P¼4.84310-7), involved in innate immunity pathways; and at SMOX (rs1884732; OR(95% CI): 1.11(1.06,1.17); P¼5.17310-6), involved in catabolism of polyamines, levels of which are altered in AD brains. Rare variant and gene-based analyses demonstrated significant APOE and TREM2 associations, while gene-based testing identified strong but marginal association for SORL1 (P¼5.55310-6). PrediXcan analyses identified significant strong genetically-regulated gene expression in LOAD for MS4A4A (Q¼4.26310-26), BIN1 (Q¼1.70310-18), and FBXO46 (Q¼4.25310-8). Conclusions: Several novel candidate loci for LOAD have been identified using high-quality imputation of rare and low-frequency variants in the ADGC, reinforcing the utility of high-density imputation panels, and providing a resource to newly identify genes with perturbed expression in LOAD.
O1-03-02
FOUR NOVEL SUSCEPTIBILITY VARIANTS IDENTIFIED IN CHINESE ARE ASSOCIATED WITH SPORADIC ALZHEIMER’S DISEASE
Jianping Jia1,2,3,4, Xianbo Zuo5,6, Ling Wei5, Cuibai Wei2, Kai Wang5, Luxi Shen2, Fangyu Li2, Wei Qin2, Yi Tang2, Dantao Peng7, Lan Tan8, Benyan Luo9, Qihao Guo10, Yong Ji11, Muni Tang12, Yanjiang Wang13, Yifeng Du14, Jiewen Zhang15, Junjian Zhang16, Qiumin Qu17, Peng Xie18, Jiying Zhou18, Lu Shen19, Jihui Lv20, Lu Lu2, Aihong Zhou2, Fen Wang2, Changbiao Chu2, Haiqing Song2, Liyong Wu2, Ying Han21, Dongmei Guo2, Xiumei Zuo2, Yue Han2, Liyuan Huang2, Junhua Liang2, Qi Wang2, Lina Zhao2, Hongmei Jin2, Jing Dong2, Xueyan Feng2, Lu Shi2, Wei Wang2, Haitao Li2, Longfei Jia22, 1Center of Alzheimer’s Disease, Beijing Institute for Brain Disorders, Beijing, China; 2Xuan Wu Hospital, Capital Medical University, Beijing, China; 3 Neurodegenerative Laboratory of Ministry of Education of the People’s Republic of China, Beijing, China; 4Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China; 5First Affiliated Hospital of Anhui Medical University, Hefei, China; 6State Key Laboratory Incubation Base of Dermatology, Ministry of National Science and Technology, Hefei, China; 7China-Japan Friendship Hospital, Beijing, China; 8 Qingdao Municipal Hospital, School of Medicine, Qingdao University, Qingdao, China; 9First Affiliated Hospital, Zhejiang University, Hangzhou, China; 10Huashan Hospital, Fudan University, Shanghai, China; 11Tianjin Huanhu Hospital, Tianjin, China; 12Guangzhou Huiai