Browsing by Author "Kim, Jun Pyo"
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Item BMI1 is associated with CS8F amyloid-β and rates of cognitive decline in Alzheimer's disease(Springer Nature, 2021-10-05) Kim, Jun Pyo; Kim, Bo‑Hyun; Bice, Paula J.; Seo, Sang Won; Bennett, David A.; Saykin, Andrew J.; Nho, Kwangsik; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineBackground: Accumulating evidence suggests that BMI1 confers protective effects against Alzheimer's disease (AD). However, the mechanism remains elusive. Based on recent pathophysiological evidence, we sought for the first time to identify genetic variants in BMI1 as associated with AD biomarkers, including amyloid-β. Methods: We used genetic, longitudinal cognition, and cerebrospinal fluid (CSF) biomarker data from participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (N = 1565). First, we performed a gene-based association analysis of common single nucleotide polymorphisms (SNPs) (minor allele frequency (MAF) > 5%) located within ± 20 kb of the gene boundary of BMI1, an optimal width for including potential regulatory SNPs in the 5' and 3' untranslated regions (UTR) of BMI1, with CSF Aβ1-42 levels. Second, we performed cross-sectional and longitudinal association analyses of SNPs in BMI1 with cognitive performance using linear and mixed-effects models. We replicated association of SNPs in BMI1 with cognitive performance in an independent cohort (N=1084), Religious Orders Study and the Rush Memory and Aging Project (ROS/MAP). Results: Gene-based genetic association analysis showed that BMI1 was significantly associated with CSF Aβ1-42 levels after adjusting for multiple testing using permutation (permutation-corrected p value=0.005). rs17415557 in BMI1 showed the most significant association with CSF Aβ1-42 levels. Participants with minor alleles of rs17415557 have increased CSF Aβ1-42 levels compared to those with no minor alleles. Further analysis identified and replicated the minor allele of rs17415557 as being significantly associated with slower cognitive decline rates in AD. Conclusions: Our findings provide fundamental evidence that BMI1 rs17415557 may serve as a protective mechanism related to AD pathogenesis, which supports the results of previous studies linking BMI1 to protection against AD.Item Circulating lipid profiles are associated with cross-sectional and longitudinal changes of central biomarkers for Alzheimer's disease(medRxiv, 2023-06-21) Kim, Jun Pyo; Nho, Kwangsik; Wang, Tingting; Huynh, Kevin; Arnold, Matthias; Risacher, Shannon L.; Bice, Paula J.; Han, Xianlin; Kristal, Bruce S.; Blach, Colette; Baillie, Rebecca; Kastenmüller, Gabi; Meikle, Peter J.; Saykin, Andrew J.; Kaddurah-Daouk, Rima; Alzheimer’s Disease Neuroimaging Initiative; Alzheimer’s Disease Metabolomics Consortium; Radiology and Imaging Sciences, School of MedicineInvestigating the association of lipidome profiles with central Alzheimer's disease (AD) biomarkers, including amyloid/tau/neurodegeneration (A/T/N), can provide a holistic view between the lipidome and AD. We performed cross-sectional and longitudinal association analysis of serum lipidome profiles with AD biomarkers in the Alzheimer's Disease Neuroimaging Initiative cohort (N=1,395). We identified lipid species, classes, and network modules that were significantly associated with cross-sectional and longitudinal changes of A/T/N biomarkers for AD. Notably, we identified the lysoalkylphosphatidylcholine (LPC(O)) as associated with "A/N" biomarkers at baseline at lipid species, class, and module levels. Also, GM3 ganglioside showed significant association with baseline levels and longitudinal changes of the "N" biomarkers at species and class levels. Our study of circulating lipids and central AD biomarkers enabled identification of lipids that play potential roles in the cascade of AD pathogenesis. Our results suggest dysregulation of lipid metabolic pathways as precursors to AD development and progression.Item Classification and prediction of cognitive trajectories of cognitively unimpaired individuals(Frontiers Media, 2023-03-13) Kim, Young Ju; Kim, Si Eun; Hahn, Alice; Jang, Hyemin; Kim, Jun Pyo; Kim, Hee Jin; Na, Duk L.; Chin, Juhee; Seo, Sang Won; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineObjectives: Efforts to prevent Alzheimer's disease (AD) would benefit from identifying cognitively unimpaired (CU) individuals who are liable to progress to cognitive impairment. Therefore, we aimed to develop a model to predict cognitive decline among CU individuals in two independent cohorts. Methods: A total of 407 CU individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 285 CU individuals from the Samsung Medical Center (SMC) were recruited in this study. We assessed cognitive outcomes by using neuropsychological composite scores in the ADNI and SMC cohorts. We performed latent growth mixture modeling and developed the predictive model. Results: Growth mixture modeling identified 13.8 and 13.0% of CU individuals in the ADNI and SMC cohorts, respectively, as the "declining group." In the ADNI cohort, multivariable logistic regression modeling showed that increased amyloid-β (Aβ) uptake (β [SE]: 4.852 [0.862], p < 0.001), low baseline cognitive composite scores (β [SE]: -0.274 [0.070], p < 0.001), and reduced hippocampal volume (β [SE]: -0.952 [0.302], p = 0.002) were predictive of cognitive decline. In the SMC cohort, increased Aβ uptake (β [SE]: 2.007 [0.549], p < 0.001) and low baseline cognitive composite scores (β [SE]: -4.464 [0.758], p < 0.001) predicted cognitive decline. Finally, predictive models of cognitive decline showed good to excellent discrimination and calibration capabilities (C-statistic = 0.85 for the ADNI model and 0.94 for the SMC model). Conclusion: Our study provides novel insights into the cognitive trajectories of CU individuals. Furthermore, the predictive model can facilitate the classification of CU individuals in future primary prevention trials.Item Clinical outcomes of increased focal amyloid uptake in individuals with subthreshold global amyloid levels(Frontiers Media, 2023-03-02) Kim, Jaeho; Choe, Yeong Sim; Park, Yuhyun; Kim, Yeshin; Kim, Jun Pyo; Jang, Hyemin; Kim, Hee Jin; Na, Duk L.; Cho, Soo-Jin; Moon, Seung Hwan; Seo, Sang Won; Radiology and Imaging Sciences, School of MedicineBackground: Although the standardized uptake value ratio (SUVR) method is objective and simple, cut-off optimization using global SUVR values may not reflect focal increased uptake in the cerebrum. The present study investigated clinical and neuroimaging characteristics according to focally increased β-amyloid (Aβ) uptake and global Aβ status. Methods: We recruited 968 participants with cognitive continuum. All participants underwent neuropsychological tests and 498 18F-florbetaben (FBB) amyloid positron emission tomography (PET) and 470 18F-flutemetamol (FMM) PET. Each PET scan was assessed in 10 regions (left and right frontal, lateral temporal, parietal, cingulate, and striatum) with focal-quantitative SUVR-based cutoff values for each region by using an iterative outlier approach. Results: A total of 62 (6.4%) subjects showed increased focal Aβ uptake with subthreshold global Aβ status [global (-) and focal (+) Aβ group, G(-)F(+) group]. The G(-)F(+) group showed worse performance in memory impairment (p < 0.001), global cognition (p = 0.009), greater hippocampal atrophy (p = 0.045), compared to those in the G(-)F(-). Participants with widespread Aβ involvement in the whole region [G(+)] showed worse neuropsychological (p < 0.001) and neuroimaging features (p < 0.001) than those with focal Aβ involvement G(-)F(+). Conclusion: Our findings suggest that individuals show distinctive clinical outcomes according to focally increased Aβ uptake and global Aβ status. Thus, researchers and clinicians should pay more attention to focal increased Aβ uptake in addition to global Aβ status.Item Contribution of clinical information to the predictive performance of plasma β-amyloid levels for amyloid positron emission tomography positivity(Frontiers Media, 2023-03-14) Chun, Min Young; Jang, Hyemin; Kim, Hee Jin; Kim, Jun Pyo; Gallacher, John; Allué, José Antonio; Sarasa, Leticia; Castillo, Sergio; Pascual-Lucas, María; Na, Duk L.; Seo, Sang Won; DPUK; Radiology and Imaging Sciences, School of MedicineBackground: Early detection of β-amyloid (Aβ) accumulation, a major biomarker for Alzheimer's disease (AD), has become important. As fluid biomarkers, the accuracy of cerebrospinal fluid (CSF) Aβ for predicting Aβ deposition on positron emission tomography (PET) has been extensively studied, and the development of plasma Aβ is beginning to receive increased attention recently. In the present study, we aimed to determine whether APOE genotypes, age, and cognitive status increase the predictive performance of plasma Aβ and CSF Aβ levels for Aβ PET positivity. Methods: We recruited 488 participants who underwent both plasma Aβ and Aβ PET studies (Cohort 1) and 217 participants who underwent both cerebrospinal fluid (CSF) Aβ and Aβ PET studies (Cohort 2). Plasma and CSF samples were analyzed using ABtest-MS, an antibody-free liquid chromatography-differential mobility spectrometry-triple quadrupole mass spectrometry method and INNOTEST enzyme-linked immunosorbent assay kits, respectively. To evaluate the predictive performance of plasma Aβ and CSF Aβ, respectively, logistic regression and receiver operating characteristic analyses were performed. Results: When predicting Aβ PET status, both plasma Aβ42/40 ratio and CSF Aβ42 showed high accuracy (plasma Aβ area under the curve (AUC) 0.814; CSF Aβ AUC 0.848). In the plasma Aβ models, the AUC values were higher than plasma Aβ alone model, when the models were combined with either cognitive stage (p < 0.001) or APOE genotype (p = 0.011). On the other hand, there was no difference between the CSF Aβ models, when these variables were added. Conclusion: Plasma Aβ might be a useful predictor of Aβ deposition on PET status as much as CSF Aβ, particularly when considered with clinical information such as APOE genotype and cognitive stage.Item Correction to: BMI1 is associated with CSF amyloid-β and rates of cognitive decline in Alzheimer’s disease(BMC, 2022-01-20) Kim, Jun Pyo; Kim, Bo‑Hyun; Bice, Paula J.; Seo, Sang Won; Bennett, David A.; Saykin, Andrew J.; Nho, Kwangsik; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineErratum for: BMI1 is associated with CS8F amyloid-β and rates of cognitive decline in Alzheimer's disease. Kim JP, Kim BH, Bice PJ, Seo SW, Bennett DA, Saykin AJ, Nho K; Alzheimer’s Disease Neuroimaging Initiative. Alzheimers Res Ther. 2021 Oct 5;13(1):164. doi: 10.1186/s13195-021-00906-4. PMID: 34610832Item CYP1B1-RMDN2 Alzheimer's disease endophenotype locus identified for cerebral tau PET(Springer Nature, 2024-09-20) Nho, Kwangsik; Risacher, Shannon L.; Apostolova, Liana G.; Bice, Paula J.; Brosch, Jared R.; Deardorff, Rachael; Faber, Kelley; Farlow, Martin R.; Foroud, Tatiana; Gao, Sujuan; Rosewood, Thea; Kim, Jun Pyo; Nudelman, Kelly; Yu, Meichen; Aisen, Paul; Sperling, Reisa; Hooli, Basavaraj; Shcherbinin, Sergey; Svaldi, Diana; Jack, Clifford R., Jr.; Jagust, William J.; Landau, Susan; Vasanthakumar, Aparna; Waring, Jeffrey F.; Doré, Vincent; Laws, Simon M.; Masters, Colin L.; Porter, Tenielle; Rowe, Christopher C.; Villemagne, Victor L.; Dumitrescu, Logan; Hohman, Timothy J.; Libby, Julia B.; Mormino, Elizabeth; Buckley, Rachel F.; Johnson, Keith; Yang, Hyun-Sik; Petersen, Ronald C.; Ramanan, Vijay K.; Ertekin-Taner, Nilüfer; Vemuri, Prashanthi; Cohen, Ann D.; Fan, Kang-Hsien; Kamboh, M. Ilyas; Lopez, Oscar L.; Bennett, David A.; Ali, Muhammad; Benzinger, Tammie; Cruchaga, Carlos; Hobbs, Diana; De Jager, Philip L.; Fujita, Masashi; Jadhav, Vaishnavi; Lamb, Bruce T.; Tsai, Andy P.; Castanho, Isabel; Mill, Jonathan; Weiner, Michael W.; Alzheimer’s Disease Neuroimaging Initiative (ADNI); Department of Defense Alzheimer’s Disease Neuroimaging Initiative (DoD-ADNI); Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Study (A4 Study) and Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN); Australian Imaging, Biomarker & Lifestyle Study (AIBL); Saykin, Andrew J.; Radiology and Imaging Sciences, School of MedicineDetermining the genetic architecture of Alzheimer's disease pathologies can enhance mechanistic understanding and inform precision medicine strategies. Here, we perform a genome-wide association study of cortical tau quantified by positron emission tomography in 3046 participants from 12 independent studies. The CYP1B1-RMDN2 locus is associated with tau deposition. The most significant signal is at rs2113389, explaining 4.3% of the variation in cortical tau, while APOE4 rs429358 accounts for 3.6%. rs2113389 is associated with higher tau and faster cognitive decline. Additive effects, but no interactions, are observed between rs2113389 and diagnosis, APOE4, and amyloid beta positivity. CYP1B1 expression is upregulated in AD. rs2113389 is associated with higher CYP1B1 expression and methylation levels. Mouse model studies provide additional functional evidence for a relationship between CYP1B1 and tau deposition but not amyloid beta. These results provide insight into the genetic basis of cerebral tau deposition and support novel pathways for therapeutic development in AD.Item Distinctive Temporal Trajectories of Alzheimer's Disease Biomarkers According to Sex and APOE Genotype: Importance of Striatal Amyloid(Frontiers Media, 2022-02-07) Kim, Jun Pyo; Chun, Min Young; Kim, Soo-Jon; Jang, Hyemin; Kim, Hee Jin; Jeong, Jee Hyang; Na, Duk L.; Seo, Sang Won; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicinePurpose: Previously, sex and apolipoprotein E (APOE) genotype had distinct effects on the cognitive trajectory across the Alzheimer's disease (AD) continuum. We therefore aimed to investigate whether these trajectory curves including β-amyloid (Aβ) accumulation in the cortex and striatum, and tau accumulation would differ according to sex and APOE genotype. Methods: We obtained 534 subjects for 18F-florbetapir (AV45) PET analysis and 163 subjects for 18F-flortaucipir (AV1451) PET analysis from the Alzheimer's Disease Neuroimaging Initiative database. For cortical Aβ, striatal Aβ, and tau SUVR, we fitted penalized splines to model the slopes of SUVR value as a non-linear function of baseline SUVR value. By integrating the fitted splines, we obtained the predicted SUVR curves as a function of time. Results: The time from initial SUVR to the cutoff values were 14.9 years for cortical Aβ, 18.2 years for striatal Aβ, and 22.7 years for tau. Although there was no difference in cortical Aβ accumulation rate between women and men, striatal Aβ accumulation was found to be faster in women than in men, and this temporal difference according to sex was more pronounced in tau accumulation. However, APOE ε4 carriers showed faster progression than non-carriers regardless of kinds of AD biomarkers' trajectories. Conclusion: Our temporal trajectory models illustrate that there is a distinct progression pattern of AD biomarkers depending on sex and APOE genotype. In this regard, our models will be able to contribute to designing personalized treatment and prevention strategies for AD in clinical practice.Item The Effects of Longitudinal White Matter Hyperintensity Change on Cognitive Decline and Cortical Thinning over Three Years(MDPI, 2020-08-17) Kim, Seung Joo; Lee, Dong Kyun; Jang, Young Kyoung; Jang, Hyemin; Kim, Si Eun; Cho, Soo Hyun; Kim, Jun Pyo; Jung, Young Hee; Kim, Eun-Joo; Na, Duk L.; Lee, Jong-Min; Seo, Sang Won; Kim, Hee Jin; Radiology and Imaging Sciences, School of MedicineWhite matter hyperintensity (WMH) has been recognised as a surrogate marker of small vessel disease and is associated with cognitive impairment. We investigated the dynamic change in WMH in patients with severe WMH at baseline, and the effects of longitudinal change of WMH volume on cognitive decline and cortical thinning. Eighty-seven patients with subcortical vascular mild cognitive impairment were prospectively recruited from a single referral centre. All of the patients were followed up with annual neuropsychological tests and 3T brain magnetic resonance imaging. The WMH volume was quantified using an automated method and the cortical thickness was measured using surface-based methods. Participants were classified into WMH progression and WMH regression groups based on the delta WMH volume between the baseline and the last follow-up. To investigate the effects of longitudinal change in WMH volume on cognitive decline and cortical thinning, a linear mixed effects model was used. Seventy patients showed WMH progression and 17 showed WMH regression over a three-year period. The WMH progression group showed more rapid cortical thinning in widespread regions compared with the WMH regression group. However, the rate of cognitive decline in language, visuospatial function, memory and executive function, and general cognitive function was not different between the two groups. The results of this study indicated that WMH volume changes are dynamic and WMH progression is associated with more rapid cortical thinning.Item Emerging role of vascular burden in AT(N) classification in individuals with Alzheimer's and concomitant cerebrovascular burdens(BMJ, 2023-12-14) Chun, Min Young; Jang, Hyemin; Kim, Soo-Jong; Park, Yu Hyun; Yun, Jihwan; Lockhart, Samuel N.; Weiner, Michael; De Carli, Charles; Moon, Seung Hwan; Choi, Jae Yong; Nam, Kyung Rok; Byun, Byung-Hyun; Lim, Sang-Moo; Kim, Jun Pyo; Choe, Yeong Sim; Kim, Young Ju; Na, Duk L.; Kim, Hee Jin; Seo, Sang Won; Radiology and Imaging Sciences, School of MedicineObjectives: Alzheimer's disease (AD) is characterised by amyloid-beta accumulation (A), tau aggregation (T) and neurodegeneration (N). Vascular (V) burden has been found concomitantly with AD pathology and has synergistic effects on cognitive decline with AD biomarkers. We determined whether cognitive trajectories of AT(N) categories differed according to vascular (V) burden. Methods: We prospectively recruited 205 participants and classified them into groups based on the AT(N) system using neuroimaging markers. Abnormal V markers were identified based on the presence of severe white matter hyperintensities. Results: In A+ category, compared with the frequency of Alzheimer's pathological change category (A+T-), the frequency of AD category (A+T+) was significantly lower in V+ group (31.8%) than in V- group (64.4%) (p=0.004). Each AT(N) biomarker was predictive of cognitive decline in the V+ group as well as in the V- group (p<0.001). Additionally, the V+ group showed more severe cognitive trajectories than the V- group in the non-Alzheimer's pathological changes (A-T+, A-N+; p=0.002) and Alzheimer's pathological changes (p<0.001) categories. Conclusion: The distribution and longitudinal outcomes of AT(N) system differed according to vascular burdens, suggesting the importance of incorporating a V biomarker into the AT(N) system.