Browsing by Author "Kim, Junpyo"

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    Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data
    (Elsevier, 2023) Jo, Taeho; Kim, Junpyo; Bice, Paula; Huynh, Kevin; Wang, Tingting; Arnold, Matthias; Meikle, Peter J.; Giles, Corey; Kaddurah-Daouk, Rima; Saykin, Andrew J.; Nho, Kwangsik; Alzheimer’s Disease Metabolomics Consortium (ADMC); Alzheimer’s Disease Neuroimaging Initiative (ADNI); Radiology and Imaging Sciences, School of Medicine
    Background: Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics. Methods: The c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification. Findings: The application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6). Interpretation: Our results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation.
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    Differential effects of risk factors on the cognitive trajectory of early- and late-onset Alzheimer’s disease
    (BMC, 2021-06-14) Kim, Jaeho; Woo, Sook-Young; Kim, Seonwoo; Jang, Hyemin; Kim, Junpyo; Kim, Jisun; Kang, Sung Hoon; Na, Duk L.; Chin, Juhee; Apostolova, Liana G.; Seo, Sang Won; Kim, Hee Jin; Neurology, School of Medicine
    Background: Although few studies have shown that risk factors for Alzheimer's disease (AD) are associated with cognitive decline in AD, not much is known whether the impact of risk factors differs between early-onset AD (EOAD, symptom onset < 65 years of age) versus late-onset AD (LOAD). Therefore, we evaluated whether the impact of Alzheimer's disease (AD) risk factors on cognitive trajectories differ in EOAD and LOAD. Methods: We followed-up 193 EOAD and 476 LOAD patients without known autosomal dominant AD mutation for 32.3 ± 23.2 months. Mixed-effects model analyses were performed to evaluate the effects of APOE ε4, low education, hypertension, diabetes, dyslipidemia, and obesity on cognitive trajectories. Results: APOE ε4 carriers showed slower cognitive decline in general cognitive function, language, and memory domains than APOE ε4 carriers in EOAD but not in LOAD. Although patients with low education showed slower cognitive decline than patients with high education in both EOAD and LOAD, the effect was stronger in EOAD, specifically in frontal-executive function. Patients with hypertension showed faster cognitive decline than did patients without hypertension in frontal-executive and general cognitive function in LOAD but not in EOAD. Patients with obesity showed slower decline in general cognitive function than non-obese patients in EOAD but not in LOAD. Conclusions: Known risk factors for AD were associated with slower cognitive decline in EOAD but rapid cognitive decline in LOAD.