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    ์ œ2ํ˜• ๋‹น๋‡จ๋ณ‘์„ ๊ฐ€์ง„ ํ•œ๊ตญ์ธ์—์„œ DNA ๋ฉ”ํ‹ธ๋ ˆ์ด์…˜ ์‹œ๊ณ„ ๋ถ„์„ ๋ฐ ๋น„๊ต

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋ณด๊ฑด๋Œ€ํ•™์› ๋ณด๊ฑดํ•™๊ณผ, 2023. 2. ์„ฑ์ฃผํ—Œ.Background: Diabetes mellitus, a chronic disease whose prevalence is increasing worldwide, is important to manage because it causes systemic and complex complications. It is also known that the prevalence of diabetes tends to increase with age, and age-related diseases appear earlier in diabetic patients. Among them, type 2 diabetes is an important type of diabetes, accounting for 90% of all diabetes patients. On the other hand, DNA methylation is an emerging tool for studying the epigenetic aging mechanism. It is known to play an essential role in developing various diseases because it reflects the effects of the individual's environment and lifestyle and causes changes in gene expression. In addition, it can function as a clinically and epidemiologically important biomarker because it is stable, has low invasiveness, and is reversible. Estimating age according to DNA methylated loci is called DNA methylation age (mAge), also called epigenetic biomarker of aging. The basic principle is to select hypermethylated or hypomethylated CpGs based on various health risks related to aging, such as cancer, obesity, and cardiovascular disease, and measure aging linearly through this. The modeling calculating the methylation age is called a DNA methylation clock, or a methylation clock. Meanwhile, DNA methylation clock tends to be underestimated in those aged 60-65 years and older. Furthermore, it lacks ethnic diversity, especially in Asians, so the tendency to underestimate the methylation age in the Korean diabetic cohort or the relationship between diabetes-related factors and methylation age was not confirmed yet. Therefore, in order to find out the relationship between Koreans with type 2 diabetes and aging through the correlation between methylation age and the Korean diabetic cohort, this study investigated a relationship among nine DNA methylation clocks (Horvath, Hannum, PhenoAge, GrimAge, AAHorvath, AAHannum, AAPheno, AAGrim, and DunedinPACE), and part of the Seoul National University Hospital T2D cohort (SNUH), and part of the Ansan/Anseong Community-based Cohort (AS) of the Korean Genome and Epidemiology Study (KoGES). Methods: The SNUH cohort consisted of 429 patients (232 patients with type 2 diabetes and 197 patients in the control group), and the AS cohort consisted of 400 patients (200 patients with type 2 diabetes and 200 patients in the control group). After receiving the raw DNA methylation data of the two groups, the methylation density was calculated as a beta value, from which the methylation age and acceleration were obtained. In addition, according to diabetes status, duration of diabetes, and stage of CKD, groups were divided as follows: diabetes group/control group, long duration group (more than 10 years)/short duration group (less than 10 years)/non-diabetic group, and CKD stage 5/CKD stage 4/CKD stage 3 or less with type 2 diabetes groups. Each groups methylation age and acceleration were plotted according to age, and the mean and variance were compared. By applying a multiple linear regression equation, it was confirmed that the clock significantly reflects fasting glucose, an index reflecting the results of diabetes and the primary criterion for diagnosing diabetes. In addition, the AICs of the multiple linear regression models for each clock in the entire cohort were compared. Through a Bland-Altman plot, it was confirmed that the Korean diabetic cohort also shows underestimation tendency of DNA methylation clocks. In addition, it was confirmed whether CpGs known to be related to type 2 diabetes were included in the clocks used in this study. Among those CpGs, CpGs that showed significant differences between groups in this study were summarized. Results: As a result of applying nine epigenetic clocks in the Korean diabetic cohort and examining them in various ways, the clocks in which diabetes status and progression of CKD stage in diabetic patients were reflected in aging were Horvath mAge/EAA, Hannum mAge/EAA, and DunedinPACE. In addition, in the group with more patients with accelerated aging due to aggravated progression of T2D, the glucose variable in the multiple linear regression model appeared significantly, and the underestimation tendency was prominent. The above indicated an association between the Korean diabetic cohort and mAge/EAA. Therefore, this study provides a basis for using mAge/EAA to study aging and complications in Korean diabetic patients. On the other hand, it was found that T2D-related CpGs with significant differences between the subgroups in the Korean diabetic cohort were not included more than 10 in the clocks. Therefore, to study aging and complications in Korean with T2D, we propose to create a next-generation clock that includes them. However, since the two cohorts differ in the composition of diabetes severity and the whole sample size is small, follow-up studies are needed to support this result. If a large sample size is obtained, it is proposed to conduct a study to see the difference in absolute values as well as the difference in slope in the plot of mAges/EAAs by fixing the age of the diabetic group and the control group equally.์„ธ๊ณ„์ ์œผ๋กœ ์œ ๋ณ‘๋ฅ ์ด ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋Š” ๋งŒ์„ฑ์งˆํ™˜์ธ ๋‹น๋‡จ๋ณ‘์€ ์ „์‹ ์ ์ด๊ณ  ๋ณตํ•ฉ์ ์ธ ํ•ฉ๋ณ‘์ฆ์„ ์œ ๋ฐœํ•˜๋ฏ€๋กœ ๊ด€๋ฆฌ๊ฐ€ ์ค‘์š”ํ•œ ์งˆํ™˜์ด๋‹ค. ๋˜ ๋‹น๋‡จ๋ณ‘์€ ๋‚˜์ด๊ฐ€ ๋“ค์ˆ˜๋ก ์œ ๋ณ‘๋ฅ ์ด ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒฝํ–ฅ์„ฑ์ด ์žˆ๊ณ , ๋‹น๋‡จ๋ณ‘ ํ™˜์ž์—์„œ๋Š” ์—ฐ๋ น ๊ด€๋ จ ์งˆํ™˜๋“ค์ด ์ผ์ฐ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๊ทธ์ค‘์—์„œ๋„ ์ œ2ํ˜• ๋‹น๋‡จ๋ณ‘์€ ์ „์ฒด ๋‹น๋‡จ๋ณ‘ ํ™˜์ž์˜ 90%๋ฅผ ์ฐจ์ง€ํ•  ๋งŒํผ ์ค‘์š”ํ•œ ๋‹น๋‡จ๋ณ‘ ์œ ํ˜•์ด๋‹ค. ํ•œํŽธ ํ›„์ƒ์œ ์ „ํ•™์  ๋…ธํ™”์˜ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์—ฐ๊ตฌํ•˜๋Š” ๋ฐ์— ์žˆ์–ด DNA ๋ฉ”ํ‹ธ๋ ˆ์ด์…˜์€ ๋– ์˜ค๋ฅด๋Š” ๋„๊ตฌ๋กœ, ๊ฐœ์ฒด์˜ ํ™˜๊ฒฝ ๋ฐ ์ƒํ™œ์Šต๊ด€์˜ ์˜ํ–ฅ์ด ๋ฐ˜์˜๋˜๊ณ  ์œ ์ „์ž ๋ฐœํ˜„์— ๋ณ€ํ™”๋ฅผ ์ผ์œผํ‚ค๊ธฐ ๋•Œ๋ฌธ์— ๋‹ค์–‘ํ•œ ์งˆํ™˜์˜ ๋ฐœ๋ณ‘์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋˜ ์•ˆ์ •์ ์ด๊ณ  ์นจ์Šต์„ฑ์ด ๋‚ฎ์œผ๋ฉฐ ๊ฐ€์—ญ์ ์ด๋ผ๋Š” ํŠน์„ฑ์ด ์žˆ์–ด ์ž„์ƒ์ , ์—ญํ•™์ ์œผ๋กœ ์ค‘์š”ํ•œ ๋ฐ”์ด์˜ค๋งˆ์ปค๋กœ ๊ธฐ๋Šฅํ•  ์ˆ˜ ์žˆ๋‹ค. DNA ๋ฉ”ํ‹ธํ™”๊ฐ€ ๋œ CpG์˜ ์œ„์น˜์™€ ์ •๋„์— ๋”ฐ๋ผ ์—ฐ๋ น์„ ์ถ”์‚ฐํ•˜๋Š” ๊ฒƒ์„ DNA ๋ฉ”ํ‹ธ๋ ˆ์ด์…˜ ์—ฐ๋ น์ด๋ผ๊ณ  ๋ถ€๋ฅด๊ณ , ์ด๊ฒƒ์€ ํ›„์„ฑ์œ ์ „ํ•™์  ๋…ธํ™” ๋ฐ”์ด์˜ค๋งˆ์ปค๋ผ๊ณ ๋„ ๋ถ€๋ฅธ๋‹ค. ๊ธฐ๋ณธ ์›๋ฆฌ๋Š” ์•”, ๋น„๋งŒ, ์‹ฌํ˜ˆ๊ด€ ์งˆํ™˜ ๋“ฑ ๋…ธํ™”์™€ ๊ด€๋ จ๋œ ๋‹ค์–‘ํ•œ ๊ฑด๊ฐ• ์œ„ํ—˜๋“ค์„ ๊ธฐ์ค€์œผ๋กœ ๊ณผ๋ฉ”ํ‹ธํ™” ๋˜๋Š” ๊ณผ์†Œ๋ฉ”ํ‹ธํ™”๋œ CpG๋ฅผ ์„ ์ •ํ•˜๊ณ  ์ด๋ฅผ ํ†ตํ•ด ์„ ํ˜•์ ์œผ๋กœ ๋…ธํ™”๋ฅผ ์ธก์ •ํ•˜๋Š” ๊ฒƒ์ด๋ฉฐ, ์ด๋ ‡๊ฒŒ ๋ฉ”ํ‹ธ๋ ˆ์ด์…˜ ์—ฐ๋ น์„ ์‚ฐ์ถœํ•˜๋Š” ๋ชจ๋ธ๋ง์„ ๋ฉ”ํ‹ธ๋ ˆ์ด์…˜ ์‹œ๊ณ„๋ผ๊ณ  ๋ถ€๋ฅธ๋‹ค. ํ•œํŽธ ๋ฉ”ํ‹ธ๋ ˆ์ด์…˜ ์‹œ๊ณ„๋Š” 60-65์„ธ ์ด์ƒ์—์„œ ๊ณผ์†Œํ‰๊ฐ€๋˜๋Š” ๊ฒฝํ–ฅ์„ฑ์ด ์žˆ๊ณ , ํŠนํžˆ ์•„์‹œ์•„์ธ์—์„œ ๋ฏผ์กฑ ๋‹ค์–‘์„ฑ์ด ๋ถ€์กฑํ•˜์—ฌ, ํ•œ๊ตญ์ธ ๋‹น๋‡จ๋ณ‘ ์ฝ”ํ˜ธํŠธ์—์„œ ๋ฉ”ํ‹ธ๋ ˆ์ด์…˜ ์—ฐ๋ น์˜ ๊ณผ์†Œํ‰๊ฐ€ ๊ฒฝํ–ฅ์„ฑ์ด๋‚˜ ๋‹น๋‡จ ๊ด€๋ จ ์š”์ธ๋“ค๊ณผ ๋ฉ”ํ‹ธ๋ ˆ์ด์…˜ ์—ฐ๋ น์˜ ๊ด€๋ จ์„ฑ์€ ํ™•์ธ๋˜์ง€ ์•Š์•˜๋‹ค. ๋”ฐ๋ผ์„œ ๋ฉ”ํ‹ธ๋ ˆ์ด์…˜ ์—ฐ๋ น๊ณผ ํ•œ๊ตญ์ธ ๋‹น๋‡จ๋ณ‘ ์ฝ”ํ˜ธํŠธ์™€์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ํ†ตํ•ด ์ œ2ํ˜• ๋‹น๋‡จ๋ณ‘์„ ๊ฐ€์ง„ ํ•œ๊ตญ์ธ๊ณผ ๋…ธํ™”์˜ ๊ด€๊ณ„๋ฅผ ์•Œ์•„๋ณด๊ธฐ ์œ„ํ•ด, ๋ณธ ์—ฐ๊ตฌ๋Š” ๋ฉ”ํ‹ธ๋ ˆ์ด์…˜ ์—ฐ๋ น ๋ฐ ๊ฐ€์†๋„๋ฅผ ๊ณ„์‚ฐํ•˜๋Š” ๋ฉ”ํ‹ธ๋ ˆ์ด์…˜ ์‹œ๊ณ„ 9๊ฐ€์ง€(Horvath, Hannum, PhenoAge, GrimAge, AAHorvath, AAHannum, AAPheno, AAGrim, and DunedinPACE)์™€ ์„œ์šธ๋Œ€ํ•™๊ต๋ณ‘์› ์ธ์ฒด์ž์›์€ํ–‰ ๋‹น๋‡จ๋ณ‘ ํด๋ฆฌ๋‹‰ ์ฝ”ํ˜ธํŠธ์˜ ์ผ๋ถ€(SNUH), ์งˆ๋ณ‘๊ด€๋ฆฌ์ฒญ ํ•œ๊ตญ์ธ์œ ์ „์ฒด์—ญํ•™์กฐ์‚ฌ์‚ฌ์—… KoGES์˜ ์•ˆ์‚ฐ/์•ˆ์„ฑ ์ง€์—ญ์‚ฌํšŒ ๊ธฐ๋ฐ˜ ์ฝ”ํ˜ธํŠธ์˜ ์ผ๋ถ€(AS)์˜ ๊ด€๊ณ„๋ฅผ ์‚ดํŽด๋ณด์•˜๋‹ค. SNUH ์ฝ”ํ˜ธํŠธ๋Š” ์ œ2ํ˜• ๋‹น๋‡จ๋ณ‘ ํ™˜์ž 232๋ช…, ๋Œ€์กฐ๊ตฐ 197๋ช… ์ด 429๋ช…์œผ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ์œผ๋ฉฐ, AS ์ฝ”ํ˜ธํŠธ๋Š” ์ œ2ํ˜• ๋‹น๋‡จ๋ณ‘ ํ™˜์ž 200๋ช…, ๋Œ€์กฐ๊ตฐ 200๋ช… ์ด 400๋ช…์œผ๋กœ ๊ตฌ์„ฑ๋˜์—ˆ๋‹ค. ๋‘ ๊ทธ๋ฃน์˜ DNA ๋ฉ”ํ‹ธ๋ ˆ์ด์…˜ ์›์‹œ ๋ฐ์ดํ„ฐ๋ฅผ ๋ฐ›์•„ ๋ฉ”ํ‹ธ๋ ˆ์ด์…˜ ๋ฐ€๋„๋ฅผ ๋ฒ ํƒ€ ๊ฐ’์œผ๋กœ ๊ณ„์‚ฐํ•˜๊ณ , ์ด๋กœ๋ถ€ํ„ฐ ๋ฉ”ํ‹ธ๋ ˆ์ด์…˜ ์—ฐ๋ น๊ณผ ๊ฐ€์†๋„๋ฅผ ๊ตฌํ–ˆ๋‹ค. ๋˜ ๋‘ ๊ทธ๋ฃน์˜ ๋‹น๋‡จ ์—ฌ๋ถ€, ๋‹น๋‡จ ์œ ๋ณ‘ ๊ธฐ๊ฐ„, CKD ๊ธฐ์ˆ˜์— ๋”ฐ๋ผ ๊ฐ๊ฐ ๋‹น๋‡จ๊ตฐ/๋Œ€์กฐ๊ตฐ, ์œ ๋ณ‘ ๊ธฐ๊ฐ„ ๊ธด ๊ตฐ(10๋…„ ์ดˆ๊ณผ)/์งง์€ ๊ตฐ(10๋…„ ์ดํ•˜)/๋น„๋‹น๋‡จ๊ตฐ, CKD 5๊ธฐ/CKD 4๊ธฐ/CKD 3๊ธฐ ์ดํ•˜์˜ ๋‹น๋‡จ๊ตฐ์œผ๋กœ ํ•˜์œ„๊ตฐ์„ ๋‚˜๋ˆ„์–ด ๊ฐ ๊ตฐ์˜ ๋ฉ”ํ‹ธ๋ ˆ์ด์…˜ ์—ฐ๋ น๊ณผ ๊ฐ€์†๋„๋ฅผ ์—ฐ๋ น์— ๋”ฐ๋ผ ๊ทธ๋ž˜ํ”„๋ฅผ ๊ทธ๋ฆฌ๊ณ , ํ‰๊ท ๊ณผ ๋ถ„์‚ฐ์„ ๋น„๊ตํ–ˆ๋‹ค. ๋˜ ๋‹ค์ค‘์„ ํ˜•ํšŒ๊ท€์‹์„ ์ ์šฉํ•ด ๋‹น๋‡จ๋ณ‘์˜ ๊ฒฐ๊ณผ๊ฐ€ ๋ฐ˜์˜๋˜๋Š” ์ง€ํ‘œ์ด์ž ๋‹น๋‡จ๋ณ‘ ์ง„๋‹จ์˜ 1์ฐจ์  ๊ธฐ์ค€์ด ๋˜๋Š” ๊ณต๋ณต ํ˜ˆ๋‹น์„ ์œ ์˜ํ•˜๊ฒŒ ๋ฐ˜์˜ํ•˜๋Š” ์‹œ๊ณ„๋ฅผ ํ™•์ธํ–ˆ๋‹ค. ๋˜ ์ „์ฒด ์ฝ”ํ˜ธํŠธ์—์„œ ๊ฐ ์‹œ๊ณ„๋ณ„ ๋‹ค์ค‘์„ ํ˜•ํšŒ๊ท€์‹ ๋ชจ๋ธ์˜ AIC๋ฅผ ๋น„๊ตํ•˜์˜€๋‹ค. ๋ฉ”ํ‹ธ๋ ˆ์ด์…˜ ์—ฐ๋ น์ด ๊ณ ๋ น์ž์—์„œ๋Š” ๊ณผ์†Œํ‰๊ฐ€๋˜๋Š” ๊ฒฝํ–ฅ์„ฑ์ด ์•Œ๋ ค์ ธ ์žˆ๋Š”๋ฐ, ์ด๊ฒƒ์ด ํ•œ๊ตญ์ธ ๋‹น๋‡จ๋ณ‘ ์ฝ”ํ˜ธํŠธ์—์„œ๋„ ๋‚˜ํƒ€๋‚˜๋Š”์ง€๋ฅผ ๋ธ”๋žœ๋“œ-์•ŒํŠธ๋งŒ ํ”Œ๋ž์„ ํ†ตํ•ด ํ™•์ธํ–ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ์ œ2ํ˜• ๋‹น๋‡จ๋ณ‘๊ณผ ์œ ๊ด€ํ•œ ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ง„ CpG๋“ค์ด ๋ณธ ์—ฐ๊ตฌ์— ์‚ฌ์šฉ๋œ ์‹œ๊ณ„๋“ค์—๋„ ํฌํ•จ๋˜์–ด ์žˆ๋Š”์ง€ ํ™•์ธํ•˜๊ณ , ๋˜ ํ•ด๋‹น CpG๋“ค ์ค‘ ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ทธ๋ฃน ๊ฐ„ ์ฐจ์ด๋ฅผ ์œ ์˜ํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚ธ CpG๋“ค์„ ์ •๋ฆฌํ–ˆ๋‹ค. ํ•œ๊ตญ์ธ ๋‹น๋‡จ๋ณ‘ ์ฝ”ํ˜ธํŠธ์—์„œ 9๊ฐ€์ง€ ํ›„์ƒ์œ ์ „ํ•™ ์‹œ๊ณ„๋“ค์„ ์ ์šฉ์‹œํ‚ค๊ณ  ์œ„์™€ ๊ฐ™์€ ๋‹ค์–‘ํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ์‚ดํŽด๋ณธ ๊ฒฐ๊ณผ ๋‹น๋‡จ๋ณ‘ ์—ฌ๋ถ€์™€ ๋‹น๋‡จ๋ณ‘ ํ™˜์ž์˜ CKD ๋‹จ๊ณ„์˜ ์‹ฌํ™”๊ฐ€ ๋…ธํ™”์— ๋ฐ˜์˜๋˜๋Š” ์‹œ๊ณ„๋Š” Horvath, Hannum, AAHorvath, AAHannum, DunedinPACE์˜€๋‹ค. ๋˜ ์ œ2ํ˜• ๋‹น๋‡จ๋ณ‘์˜ ์ง„ํ–‰ ์ƒํƒœ๊ฐ€ ์‹ฌํ™”๋˜์–ด ๋…ธํ™”๊ฐ€ ๊ฐ€์†ํ™”๋œ ํ™˜์ž๊ฐ€ ๋งŽ์€ ๊ตฐ์ผ์ˆ˜๋ก ๋‹ค์ค‘์„ ํ˜•ํšŒ๊ท€๋ชจ๋ธ ์ค‘ ๊ณต๋ณต ํ˜ˆ๋‹น ๋ณ€์ˆ˜๊ฐ€ ์œ ์˜๋ฏธํ•˜๊ฒŒ ๋‚˜ํƒ€๋‚˜๊ณ , ๊ณผ์†Œํ‰๊ฐ€ ๊ฒฝํ–ฅ์„ฑ์ด ๋‘๋“œ๋Ÿฌ์ง€๊ฒŒ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด์ƒ์—์„œ ์ œ2ํ˜• ๋‹น๋‡จ๋ณ‘์„ ๊ฐ€์ง„ ํ•œ๊ตญ์ธ ์ฝ”ํ˜ธํŠธ์™€ mAge/EAA๋Š” ์—ฐ๊ด€์„ฑ์ด ์žˆ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ํ•œ๊ตญ์ธ ๋‹น๋‡จ๋ณ‘ ํ™˜์ž์˜ ๋…ธํ™” ๋ฐ ํ•ฉ๋ณ‘์ฆ ์—ฐ๊ตฌ์— mAge/EAA๋ฅผ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๊ทผ๊ฑฐ๊ฐ€ ๋œ๋‹ค. ํ•œํŽธ ํ•œ๊ตญ์ธ ๋‹น๋‡จ๋ณ‘ ์ฝ”ํ˜ธํŠธ์—์„œ ํ•˜์œ„๊ตฐ ๊ฐ„ ์œ ์˜ํ•œ ์ฐจ์ด๋ฅผ ๊ฐ–๋Š” T2D ๊ด€๋ จ CpG๋“ค์€ ์‹œ๊ณ„์— ๋งŽ์ด ํฌํ•จ๋˜์ง€ ์•Š์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋”ฐ๋ผ์„œ T2D๋ฅผ ๊ฐ€์ง„ ํ•œ๊ตญ์ธ ์ฝ”ํ˜ธํŠธ์—์„œ ๋…ธํ™” ๋ฐ ํ•ฉ๋ณ‘์ฆ์„ ์—ฐ๊ตฌํ•˜๊ธฐ ์œ„ํ•ด ์ด CpG๋“ค์„ ํฌํ•จํ•˜๋Š” ์ฐจ์„ธ๋Œ€ ์‹œ๊ณ„๋ฅผ ๋งŒ๋“ค ๊ฒƒ์„ ์ œ์•ˆํ•œ๋‹ค. ๋‹ค๋งŒ ๋‘ ์ฝ”ํ˜ธํŠธ์—๋Š” ๋‹น๋‡จ๋ณ‘ ์ค‘์ฆ๋„์˜ ๊ตฌ์„ฑ์— ์žˆ์–ด ์ฐจ์ด๊ฐ€ ์žˆ๊ณ  ํ‘œ๋ณธ ํฌ๊ธฐ๊ฐ€ ์ž‘๊ธฐ ๋•Œ๋ฌธ์— ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋ฅผ ๋’ท๋ฐ›์นจํ•  ํ›„์† ์—ฐ๊ตฌ๊ฐ€ ํ•„์š”ํ•˜๋‹ค. ํฐ ์ƒ˜ํ”Œ ์‚ฌ์ด์ฆˆ๊ฐ€ ํ™•๋ณด๋œ๋‹ค๋ฉด, ๋‹น๋‡จ๊ตฐ๊ณผ ์ปจํŠธ๋กค๊ตฐ์˜ ๋‚˜์ด๋ฅผ ๋™์ผํ•˜๊ฒŒ ๊ณ ์ •ํ•˜์—ฌ mAge์˜ ํ”Œ๋ž์—์„œ์˜ ๊ธฐ์šธ๊ธฐ์˜ ์ฐจ์ด๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ ˆ๋Œ€์ ์ธ ๊ฐ’์˜ ์ฐจ์ด๋ฅผ ๋ณด๋Š” ์—ฐ๊ตฌ๋ฅผ ํ•ด๋ณผ ๊ฒƒ์„ ์ œ์•ˆํ•œ๋‹ค.I. Introduction 1 1. Background 1 1.1. Type 2 diabetes and aging 1 1.2. Epigenetics and DNA methylation 3 1.3. DNA Methylation age as the new biomarker in aging and age- related diseases 4 2. Objective 8 II. Methods 10 1. Study population 10 2. Data collection 10 3. DNA methylation clocks used in study 12 4. DNA methylation profiling and calculation of mAge 16 5. Statistical analysis 21 III. Results 23 1. Descriptive analysis 23 2. Comparison of the clocks by the subgroups 30 3. Multiple linear regression 46 4. Bland-Altman plot 54 5. T2D-related CpGs in the clocks and the cohorts 56 IV. Discussion 67 V. Conclusion 74 VI. Acknowledgement 75 VII. Reference 76 VIII. Abstract in Korean (๊ตญ๋ฌธ ์ดˆ๋ก) 84์„

    Hospital Characteristics Associated with Patientsโ€™ Experience of Care in Korean Hospitals

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    Objectives: This cross-sectional correlational study aimed to examine the hospital characteristics associated with patientsโ€™ experiences of care reported by healthcare consumers in South Korea. Methods: This study used data from the second Korean Patient Experience Survey conducted by the Health Insurance Review and Assessment (HIRA). Additional hospital characteristics were obtained from the HIRA and Korea Institute for Healthcare Association websites. Results: In total, 149 hospitals were included. We found that high-technology hospitals had a significant relationship with the scores on all patient experience dimensions. Hospital nurse staffing levels, location, and high technology status were significantly associated with the nurse service dimension. Conclusions: Patient experience-of-care scores varied according to hospital characteristics. The findings of this study provide preliminary evidence for future research. Further research should examine patient and hospital characteristics that could affect patient experience of care scores in Korean hospitals.ope

    ์ธ๊ฐ„ ๋‡Œ ์˜ค๊ฐ€๋…ธ์ด๋“œ๋ฅผ ์ด์šฉํ•œ ์•Œ์ธ ํ•˜์ด๋จธ ๋ฐ ๋‹ˆ๋งŒ-ํ”ผํฌ๋ณ‘ ์งˆํ™˜ ๋ชจ๋ธ๋ง ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ˆ˜์˜๊ณผ๋Œ€ํ•™ ์ˆ˜์˜ํ•™๊ณผ, 2022. 8. ๊ฐ•๊ฒฝ์„ .์•Œ์ธ ํ•˜์ด๋จธ์™€ ๋‹ˆ๋งŒ-ํ”ผํฌ C1ํ˜• ์งˆํ™˜์€ ์‹ ๊ฒฝ ํ‡ดํ–‰์„ฑ ๋‡Œ ์งˆํ™˜์œผ๋กœ ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋จ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์ •ํ™•ํ•œ ๋ฉ”์ปค๋‹ˆ์ฆ˜๊ณผ ํšจ๊ณผ์ ์ธ ์น˜๋ฃŒ์ œ๊ฐ€ ๋ถˆ๋ช…ํ™•ํ•˜๋‹ค. ๋งŽ์€ ์ œ์•ฝ์‚ฌ๊ฐ€ ์น˜๋ฃŒ์ œ ๊ฐœ๋ฐœ์— ์•ž์žฅ์„œ๊ณ  ์žˆ์Œ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ํ˜„์žฌ๊นŒ์ง€ ์น˜๋ฃŒ์ œ๋“ค์€ ์ฆ์ƒ์„ ์™„ํ™”ํ•˜๊ณ  ์ง„ํ–‰ ์†๋„๋ฅผ ๋Šฆ์ถฐ์ฃผ๋Š” ํšจ๊ณผ๋งŒ ์žˆ์„ ๋ฟ ์ฆ์ƒ ์ž์ฒด์˜ ์น˜๋ฃŒ ํšจ๊ณผ๋Š” ์—†๋‹ค. ์ด์™€ ๊ฐ™์€ ์‹คํŒจ ์›์ธ ์ค‘ ํ•˜๋‚˜๋Š” ์ธ์ฒด ์ƒ๋ฆฌ ํ˜„์ƒ๊ณผ ์งˆ๋ณ‘์˜ ๋ณ‘๋ฆฌ ์ƒํƒœ๋ฅผ ๋ชจ๋ฐฉํ•œ ์‹คํ—˜์  ๋ชจ๋ธ์˜ ๋ถ€์žฌ ๋•Œ๋ฌธ์ด๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ํ˜„์žฌ ์งˆํ™˜ ์›์ธ ๋ถ„์„ ๋ฐ ์น˜๋ฃŒ์ œ ๊ฐœ๋ฐœ์ด ์ง๋ฉดํ•˜๊ณ  ์žˆ๋Š” ๋ฌธ์ œ์ ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด, ์‹ค์ œ ์ธ์ฒด ์ƒ๋ฆฌ ํ˜„์ƒ ๋ชจ๋ฐฉ์ด ๊ฐ€๋Šฅํ•œ ์ƒˆ๋กœ์šด ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•ด์•ผ ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์ตœ๊ทผ ํ™˜์ž ์ฒด์„ธํฌ๋กœ๋ถ€ํ„ฐ ์œ ๋ž˜๋œ ์ด์ฐจ์› ์œ ๋„ ์‹ ๊ฒฝ์ค„๊ธฐ์„ธํฌ ๋ชจ๋ธ์„ ์ด์šฉํ•ด ์—ฐ๊ตฌ์™€ ์‹ ์•ฝ ๊ฐœ๋ฐœ์„ ์œ„ํ•œ ์Šคํฌ๋ฆฌ๋‹ ํ”Œ๋žซํผ์œผ๋กœ ์ ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ ์ค„๊ธฐ์„ธํฌ์˜ ์ž๊ฐ€ ์กฐ์งํ™” ๋Šฅ๋ ฅ์„ ํ™œ์šฉํ•˜์—ฌ ์ธ๊ฐ„ ์‹ค์ œ ๋‡Œ๋ฅผ ๊ตฌ์กฐ์ , ๊ธฐ๋Šฅ์ ์œผ๋กœ ๋ชจ์‚ฌํ•  ์ˆ˜ ์žˆ๋Š” ์‚ผ์ฐจ์› ๊ตฌ์กฐ์˜ ๋ฏธ๋‹ˆ ๋‡Œ ์˜ค๊ฐ€๋…ธ์ด๋“œ๊ฐ€ ๊ฐœ๋ฐœ๋จ์— ๋”ฐ๋ผ ์งˆํ™˜ ๋ชจ๋ธ๋ง๊ณผ ์•ฝ๋ฌผ ์Šคํฌ๋ฆฌ๋‹์ด ํ™œ๋ฐœํ•˜๊ฒŒ ์ง„ํ–‰ ์ค‘์ด๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์งˆํ™˜์˜ ๋ณ‘๋ฆฌ์  ํŠน์ง•๊ณผ ์›์ธ ๋ฐ ์•ฝ๋ฌผ ํšจ๋Šฅ ๊ฒ€์ฆ์„ ์œ„ํ•ด ์ด์ฐจ์› ์œ ๋„ ์‹ ๊ฒฝ์ค„๊ธฐ์„ธํฌ๋ถ€ํ„ฐ ์‚ผ์ฐจ์› ๋‡Œ ์˜ค๊ฐ€๋…ธ์ด๋“œ๊นŒ์ง€ ์งˆํ™˜ ์—ฐ๊ตฌ ํ”Œ๋žซํผ์œผ๋กœ ์ œ์‹œํ•˜๊ณ ์ž ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ์ฒซ ๋ฒˆ์งธ ์žฅ์—์„œ๋Š” ๊ฐ€์กฑ์„ฑ ์•Œ์ธ ํ•˜์ด๋จธ ํ™˜์ž ์„ฌ์œ ์•„์„ธํฌ ์œ ๋ž˜ ์œ ๋„ ์‹ ๊ฒฝ์ค„๊ธฐ์„ธํฌ๋ฅผ ์ด์šฉํ•ด ์•Œ์ธ ํ•˜์ด๋จธ ์ดˆ๊ธฐ ๋ณ‘๋ฆฌ์— ๊ธฐ์—ฌํ•˜๋Š” ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ๊ธฐ๋Šฅ ์ €ํ•˜ ํ‘œํ˜„ํ˜• ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ–ˆ๋‹ค. ์•„๋ฐ€๋กœ์ด๋“œ ์ „๊ตฌ์ฒด ๋‹จ๋ฐฑ์งˆ(APP)์˜ ์•„๋ฐ€๋กœ์ด๋“œ ์ƒ์„ฑ๊ฒฝ๋กœ์—์„œ ์ƒ๊ธฐ๋Š” C-terminal fragments (APP-CTFs)์˜ ์ถ•์ ์ด ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ํ•ญ์ƒ์„ฑ ์ €ํ•ด ๋ฐ ๋ฏธํ† ํŒŒ์ง€ ์žฅ์• ์™€ ๊ด€๋ จ ์žˆ์Œ์„ ํ™•์ธํ–ˆ๋‹ค. ๋˜ํ•œ APP-CTFs์˜ ์ถ•์ ์€ ฮณ-secretase ์ฐจ๋‹จ์— ์˜ํ•œ ๊ฒƒ์ด๋ฉฐ PSEN ๊ฒฐํ• ์„ธํฌ์—์„œ ๋ฐœ์ƒํ•˜๊ธฐ ๋•Œ๋ฌธ์— ์ •์ƒ ์œ ๋„ ์‹ ๊ฒฝ์ค„๊ธฐ์„ธํฌ์—์„œ ์œ ์ „์ž ํŽธ์ง‘ ๊ธฐ์ˆ ์„ ํ†ตํ•ด PSEN ๋„‰์•„์›ƒ ์„ธํฌ๋ฅผ ์ œ์ž‘ํ–ˆ๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ PSEN1๊ณผ PSEN2๊ฐ€ ๋„‰์•„์›ƒ๋œ ์œ ๋„ ์‹ ๊ฒฝ์ค„๊ธฐ์„ธํฌ๋Š” ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ๊ธฐ๋Šฅ ์ €ํ•˜์™€ ๋ฏธํ† ํŒŒ์ง€ ์–ต์ œ๋ฅผ ์ดˆ๋ž˜ํ–ˆ์œผ๋ฉฐ ์ด์ฐจ์› ์‹ ๊ฒฝ์„ธํฌ์™€ ์‚ผ์ฐจ์› ์˜ค๊ฐ€๋…ธ์ด๋“œ์—์„œ ์‹ ๊ฒฝ์„ธํฌ์˜ ๋ถ„ํ™” ์ €ํ•˜๋ฅผ ๋ณด์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๋‘ ๋ฒˆ์งธ ์žฅ์—์„œ๋Š” ๋‹ˆ๋งŒ-ํ”ผํฌ C1ํ˜• ํ™˜์ž ์œ ๋ž˜ ์‚ผ์ฐจ์› ๋‡Œ ์˜ค๊ฐ€๋…ธ์ด๋“œ๋ฅผ ํ™•๋ฆฝํ•˜์—ฌ ์งˆํ™˜ ํŠน์ด์  ํ˜•์งˆ์„ ์žฌํ˜„ํ•˜๊ณ  ์•ฝ๋ฌผ ์Šคํฌ๋ฆฌ๋‹์— ์ ํ•ฉํ•œ ์งˆํ™˜ ๋ชจ๋ธ์ž„์„ ํ™•์ธํ–ˆ๋‹ค. ๋‹ˆ๋งŒ-ํ”ผํฌ C1ํ˜• ์˜ค๊ฐ€๋…ธ์ด๋“œ๋Š” ์ •์ƒ ์˜ค๊ฐ€๋…ธ์ด๋“œ์— ๋น„ํ•ด ํฌ๊ธฐ, ์„ธํฌ ์ฆ์‹, ์‹ ๊ฒฝ ๋ถ„ํ™”์—์„œ ๊ฐ์†Œํ•˜์ง€๋งŒ ์„ธํฌ ์‚ฌ๋ฉธ์€ ์ฆ๊ฐ€๋˜์–ด ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ ๋‹ˆ๋งŒ-ํ”ผํฌ C1ํ˜• ์˜ค๊ฐ€๋…ธ์ด๋“œ ๋‚ด๋ถ€์˜ ๋ฆฌ์†Œ์ข€์— ์ถ•์ ๋œ ์ฝœ๋ ˆ์Šคํ…Œ๋กค์€ ์ž๊ฐ€ํฌ์‹๊ณผ์ •์˜ ๊ธฐ๋Šฅ ์ €ํ•˜์™€ ๊ด€๋ จ์ด ๋†’์Œ์„ ํ™•์ธํ–ˆ๋‹ค. ์ œ์ž‘๋œ ๋‹ˆ๋งŒ-ํ”ผํฌ C1ํ˜• ์˜ค๊ฐ€๋…ธ์ด๋“œ๊ฐ€ ์•ฝ๋ฌผ ํ…Œ์ŠคํŠธ์— ์ ํ•ฉํ•œ ๋ชจ๋ธ์ž„์„ ์ฆ๋ช…ํ•˜๊ธฐ ์œ„ํ•ด ์ฝœ๋ ˆ์Šคํ…Œ๋กค ๊ฐ์†Œ์— ํšจ๊ณผ์ ์ธ ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ง„ ํ•˜์ด๋“œ๋ก์‹œํ”„๋กœํ•„ ๋ฒ ํƒ€ ์‚ฌ์ดํด๋กœ๋ฑ์‹  (HPฮฒCD)๊ณผ ๋”๋ถˆ์–ด ํžˆ์Šคํ†ค ํƒˆ์•„์„ธํ‹ธํ™” ์–ต์ œ์ œ ์ค‘ ํ•˜๋‚˜์ธ ๋ฐœํ”„๋กœ ์‚ฐ (valproic acid) ์ฒ˜๋ฆฌ๋กœ ์งˆํ™˜์˜ ํšŒ๋ณต์„ ํ™•์ธํ–ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‹ˆ๋งŒ-ํ”ผํฌ C1ํ˜• ์งˆํ™˜ ํ™˜์ž ์œ ๋ž˜ ์‚ผ์ฐจ์› ๋‡Œ ์˜ค๊ฐ€๋…ธ์ด๋“œ๋ฅผ ํ™•๋ฆฝํ•˜์—ฌ ์งˆํ™˜์˜ ์น˜๋ฃŒ ๊ธฐ์ˆ ์„ ๊ฐœ๋ฐœํ•˜๋Š”๋ฐ ์œ ์šฉ์„ฑ์„ ์ œ์‹œํ•œ๋‹ค. ๋‡Œ ์˜ค๊ฐ€๋…ธ์ด๋“œ๋Š” ์งˆํ™˜ ๋ชจ๋ธ๋ง๋ฟ๋งŒ์ด ์•„๋‹ˆ๋ผ ๋ฐ”์ด๋Ÿฌ์Šค ๊ฐ์—ผ์„ ํ†ตํ•ด ๋ณ‘์›์„ฑ ๊ธฐ์ „ ๋ฐ ๊ฐ์—ผ๊ฒฝ๋กœ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ์— ์ ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋ณธ ์—ฐ๊ตฌ์˜ ์„ธ ๋ฒˆ์งธ ์žฅ์—์„œ๋Š” ๋‡Œ ์˜ค๊ฐ€๋…ธ์ด๋“œ์— ์ง€์นด ๋ฐ”์ด๋Ÿฌ์Šค๋ฅผ ๊ฐ์—ผ์‹œ์ผœ ์•Œ์ธ ํ•˜์ด๋จธ ์งˆํ™˜ ํ‘œํ˜„ํ˜•์ด ์ฆ๊ฐ€ํ•จ์„ ํ™•์ธํ–ˆ๋‹ค. ์ด๋Š” ์ง€์นด ๋ฐ”์ด๋Ÿฌ์Šค๊ฐ€ ๊ฐ์—ผ๋œ ๋‡Œ ์˜ค๊ฐ€๋…ธ์ด๋“œ์—์„œ ์ง€์†์ ์ธ ER stress๊ฐ€ ๊ด€์ฐฐ๋˜์—ˆ๊ณ , ๊ฒฐ๊ณผ์ ์œผ๋กœ BACE์™€ GSK3ฮฒ๋ฅผ ์ด‰์ง„ํ•ด ์•„๋ฐ€๋กœ์ด๋“œ ๋ฒ ํƒ€์™€ ๊ณผ์ธ์‚ฐํ™”๋œ ํƒ€์šฐ ๋‹จ๋ฐฑ์งˆ์ด ์ฆ๊ฐ€ํ•จ์„ ํ™•์ธํ–ˆ๋‹ค. ๋ฐ”์ด๋Ÿฌ์Šค ๊ฐ์—ผ์€ ์•Œ์ธ ํ•˜์ด๋จธ์˜ ์œ„ํ—˜ ์š”์ธ ์ค‘ ํ•˜๋‚˜์ด๋ฉฐ, ์ง€์นด ๋ฐ”์ด๋Ÿฌ์Šค ๊ฐ์—ผ์ด ์•Œ์ธ ํ•˜์ด๋จธ ๋ณ‘๋ฆฌํ•™์  ํŠน์ง•์„ ์ด‰๋ฐœํ•  ์ˆ˜ ์žˆ์Œ์„ ๋‡Œ ์˜ค๊ฐ€๋…ธ์ด๋“œ ๋ชจ๋ธ์„ ํ†ตํ•ด ์ž…์ฆํ–ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์•Œ์ธ ํ•˜์ด๋จธ ๋ฐ ๋‹ˆ๋งŒ-ํ”ผํฌ C1ํ˜•์˜ ์—ฐ๊ตฌ ํ”Œ๋žซํผ์œผ๋กœ์„œ ํ™˜์ž ์œ ๋ž˜ ์œ ๋„ ์‹ ๊ฒฝ์ค„๊ธฐ์„ธํฌ ๋ฐ ๋‡Œ ์˜ค๊ฐ€๋…ธ์ด๋“œ๋ฅผ ์ œ์‹œํ–ˆ๋‹ค. ์ด ๋ชจ๋ธ์„ ํ†ตํ•ด ์•Œ์ธ ํ•˜์ด๋จธ์˜ ์ดˆ๊ธฐ๋ณ‘๋ฆฌ์™€ ๊ด€๋ จํ•œ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ๊ธฐ๋Šฅ ์ €ํ•˜๋ฅผ ํ™•์ธํ•˜๊ณ  ๋ฐ”์ด๋Ÿฌ์Šค ๊ฐ์—ผ์— ์˜ํ•ด ์•Œ์ธ ํ•˜์ด๋จธ ๋ณ‘๋ฆฌ์  ํŠน์ง•์ด ์ฆ๊ฐ€ํ•˜๋Š” ํ˜„์ƒ์„ ๊ตฌํ˜„ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋‹ˆ๋งŒ-ํ”ผํฌ C1ํ˜• ๋‡Œ ์˜ค๊ฐ€๋…ธ์ด๋“œ ์ œ์ž‘์„ ํ†ตํ•ด ๋ฆฌ์†Œ์ข€ ์ถ•์  ์งˆํ™˜ ๊ด€๋ จ ํŠน์ง•๊ณผ ์•ฝ๋ฌผ ํšจ๋Šฅ์„ ํ™•์ธํ–ˆ๋‹ค. ์ด๋กœ์จ ์•Œ์ธ ํ•˜์ด๋จธ ๋ฐ ๋‹ˆ๋งŒ-ํ”ผํฌ C1ํ˜•์˜ ์ด์ฐจ์› ์œ ๋„ ์‹ ๊ฒฝ์ค„๊ธฐ์„ธํฌ๋ถ€ํ„ฐ ์‚ผ์ฐจ์› ๋‡Œ ์˜ค๊ฐ€๋…ธ์ด๋“œ๊นŒ์ง€ ๋ชจ๋ธ ํ™•๋ฆฝ์„ ํ†ตํ•ด ๋ฉ”์ปค๋‹ˆ์ฆ˜ ์Šคํ„ฐ๋”” ๋ฐ ์‹ ์•ฝ ์Šคํฌ๋ฆฌ๋‹ ํ”Œ๋žซํผ์„ ์ œ์‹œํ•œ๋‹ค.Alzheimerโ€™s disease (AD) and Niemann-Pick type C disease (NPC) are progressive neurodegenerative diseases, and although many studies have been conducted, the mechanism or effective treatment for the cause of the diseases have not been elucidated. Despite multiple attempts on developing treatments by several pharmaceutical companies, current treatments do not target the disease directly but only show the effects of relieving symptoms. One of the reasons for this failure is the lack of experimental models that mimics human physiology and the biological changes that occur during the onset and progression of diseases in a human brain. Therefore, it is necessary to develop a new model capable of mimicking actual human brain physiology. Recently, two-dimensional (2D) induced neural stem cell models derived from patient somatic cells are used as a screening platform for disease research and drug development. In addition, as mini-brain organoids with a three-dimensional (3D) structure that can structurally and functionally mimic the human brain by utilizing the self-organizing ability of stem cells have been developed, disease modeling and drug screening are actively progressing. Therefore, these studies intend to present a therapeutic platform for AD and NPC disease ranging from 2D cell models to 3D organoids to verify pathological characteristics, causes, and drug effects of cells of pathological phenomena. In the first chapter of this study, familial AD patientโ€™s fibroblast-derived induced neural stem cells (iNSCs) were used to study mitochondrial dysfunction phenotypes contributing to the early pathology of AD. I represented that the accumulation of C-terminal fragments (APP-CTFs) generated in the amyloidogenic pathway of amyloid precursor protein (APP) was associated with mitochondrial homeostasis inhibition and mitophagy dysfunction. In addition, since the accumulation of APP-CTF is due to the ฮณ-secretase blockade and occurs in PSEN-deficient cells, PSEN knockout cells were generated from normal induced neural stem cells through gene editing technology. As a result, PSEN1 and PSEN2 knockout-induced neural stem cells showed not only decreased mitochondrial function and mitophagy inhibition, but also decreased neuronal differentiation in 2D neuron and 3D organoids. In the second chapter of this study, 3D brain organoids derived from the patient's iNSCs were established to represent disease-specific traits and apply for drug screening. I confirmed that size, cell proliferation, and neuronal differentiation were downregulated in NPC organoids compared to wild-type organoids, while the cell death increased. I also represented that the lysosomal cholesterol accumulation in NPC organoids was highly related to the impairment of autophagy. These pathological phenotypes observed in NPC organoids were rescued by treatment with 2-hydroxypropyl-ฮฒ-cyclodextrin (HPBCD) and Valproic acid (VPA), which are known to be effective candidates for NPC disease. Therefore, in this study, NPC brain organoids were established for the first time to verify various phenotypes and drug efficacy of the disease treatments. Organoids are applicable not only to disease modeling, but also to the study of pathogenic mechanisms and transmission routes of virus infection. In the third chapter of this study, I represented that zika virus infection increase AD phenotype. I confirmed that continuous ER stress was observed in brain organoids with zika virus, and as a result, the level of amyloid beta (Aฮฒ) and phosphorylated tau protein was increased by BACE and GSK3ฮฒ. Virus infection can be one of the risk factors for AD, and it has been proven that zika virus infection can trigger AD pathological features in brain organoids. Through the results of these studies, I generated AD and NPC patient-derived induced neural stem cells and brain organoids as the disease research platform. In these models, I confirmed the mitochondrial dysfunction related to the early pathology of the disease and the increased AD pathological characteristics by zika virus infection. In addition, lysosomal storage disease-related characteristics and drug efficacy were confirmed in NPC brain organoids. Taken together, I present the mechanism study and drug screening platform by establishing models from 2D induced neural stem cells to 3D brain organoids of AD and NPC diseases.CHAPTER I 1.1 INTRODUCTION 35 1.2 MATERIALS AND METHODS 39 1.2.1 iNSCs culture 39 1.2.2 2D neuron differentiation 39 1.2.3 Generation of WT and AD fibroblasts derived iNSCs 39 1.2.4 Generation of PSEN double knock-out iNSCs 39 1.2.5 Western blotting 39 1.2.6 Quantitative reverse transcription PCR (RT-qPCR) 41 1.2.7 Immunocytochemistry 41 1.2.8 MitoTracker & ER-Tracker 42 1.2.9 ATP production assay 42 1.2.10 Statistical analysis 42 1.3 RESULTS 44 1.3.1 Characterization of AD patient iNSCs 45 1.3.2 Altered localization and dysfunction of mitochondria in AD patient iNSCs 47 1.3.3 APP-CTFs accumulation in AD-patient iNSCs altered mitochondrial function 49 1.3.4 Mitophagy failure in AD-patient iNSCs is related to APP-CTFs accumulation 52 1.3.5 Generation of PSEN KO-iNSCs and characterization 57 1.3.6 APP-CTFs accumulation is associated with mitophagy failure in PSEN KO-iNSCs 61 1.3.7 Neuronal differentiation was downregulated in PSEN KO-iNSCs 66 1.4 DISCUSSION 68 CHAPTER II 2.1 INTRODUCTION 72 2.2 MATERIALS AND METHODS 75 2.2.1 iNSCs culture 75 2.2.2 Organoid culture 75 2.2.3 Quantitative reverse transcription PCR (RT-qPCR) 76 2.2.4 Western blot analysis 76 2.2.5 Histology and Immunofluorescence 76 2.2.6 Clearing for 3D fluorescence images 77 2.2.7 Cholesterol assay 78 2.2.8 Quantitative mRNA-sequencing analysis 78 2.2.9 Statistical analysis 79 2.3 RESULTS 80 2.3.1 Generation of brain organoids from iNSCs 80 2.3.2 Characterization of NPC brain organoids 82 2.3.3 Limited expansion ability of NPC brain organoids 87 2.3.4 Cholesterol accumulation in NPC brain organoids 89 2.3.5 Gene expression analysis of WT and NPC organoids 92 2.3.6 VPA treatment increases the number of neuronal-positive cells in NPC organoids 94 2.3.7 VPA rescues NPC organoids via activation of autophagic signaling 99 2.4 DISCUSSION 103 CHAPTER III 3.1 INTRODUCTION 107 3.2 MATERIALS AND METHODS 110 3.2.1 Zika virus production and titration 110 3.2.2 Maintenance of human iPSCs 110 3.2.3 Generation of brain organoids 110 3.2.4 Infection of the brain organoids 112 3.2.5 Inhibitor treatment 112 3.2.6 RNA extraction and quantitative reverse transcription PCR (RT-qPCR) 112 3.2.7 Immunostaining and imaging 113 3.2.8 Western blotting 114 3.2.9 ELISA 114 3.2.10 Statistical analysis 114 3.3 RESULTS 116 3.3.1 The characterization of brain organoids generated from AD patient-derived iPSCs 116 3.3.2 Brain organoids from AD patient-derived iPSCs recapitulate AD pathology 120 3.3.3 ZIKV infection induces Aฮฒ accumulation through increasing BACE abundance 122 3.3.4 ZIKV infection enhances p-TAU levels in AD organoids through GSK3ฮฑ/ฮฒ 128 3.3.5 PERK-eIF2ฮฑ activation is associated with AD phenotypes in ZIKV-infected organoids 130 3.3.6 PERK inhibition attenuated AD phenotypes including Aฮฒ and p-Tau 135 3.4 DISCUSSION 139๋ฐ•

    Assessment of Patient Safety and Cultural Competencies among Senior Baccalaureate Nursing Students

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    This descriptive, correlational, cross-sectional study examined nursing students' educational experiences on self-reported perceptions of patient safety and cultural competence in terms of curriculum content and learning venues. We performed descriptive analyses and a one-way analysis of variance with a sample of senior-year nursing students (N = 249) attending three state universities in the United States. We used the Nurse of the Future Nursing Core Competency Model, the Patient Safety Competency Self-Evaluation Tool for Nursing Students, and The Cultural Competence Assessment Instrument. Overall, participants reported that patient safety and cultural competencies were addressed in their curricula primarily through classroom activities as opposed to laboratory/simulation or clinical settings. Among the required patient safety knowledge topics, elements of highly reliable organizations were covered the least. For patient safety competency, participants reported higher scores for attitude and lower scores for skill and knowledge. For cultural competency, participants scored much higher for cultural awareness and sensitivity than behavior. There was no statistically significant difference between scores for patient safety and cultural competencies by nursing school. The results support the need for curriculum development to include all important aspects of patient safety and cultural competencies in various teaching/learning venues.ope

    Mediating roles of patient safety knowledge and motivation in the relationship between safety climate and nurses' patient safety behaviors: a structural equation modeling analysis

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    Background: Few studies have examined the relationship between patient safety climate and two forms of patient safety behavior (i.e., safety compliance and safety participation) among nurses. Better understanding of factors contributing to nurses' safety behaviors could enhance patient safety. Therefore, this study aimed to examine the effect of patient safety climate on nurses' patient safety behavior and to explore whether patient safety knowledge and motivation mediate this relationship. Methods: This correlational, cross-sectional study used survey data from 1,053 staff nurses working at a general hospital located in a metropolitan area of South Korea. Structural equation modeling was employed to test a hypothesized multiple mediation model that was guided by Griffin and Neal's model of safety performance. Results: The results indicated that patient safety climate was directly related to both patient safety compliance behavior (ฮฒ = 0.27, p < 0.001) and patient safety participation behavior (ฮฒ = 0.25, p < 0.001). Concerning indirect effects, patient safety climate was associated with patient safety compliance behavior through both patient safety knowledge (ฮฒ = 0.26, p < 0.001) and patient safety motivation (ฮฒ = 0.04, p = 0.038), whereas patient safety climate was related to patient safety participation behavior only through patient safety knowledge (ฮฒ = 0.27, p < 0.001) and not through patient safety motivation (ฮฒ = 0.00, p = 0.985). Conclusion: Based on this study's findings, building an organizational climate focused on patient safety is vital for improving nurses' patient safety behavior. Improving an organization's patient safety climate could promote both safety knowledge and motivation in nurses and thereby potentially enhance their patient safety behavior. Hence, healthcare organizations should implement practical interventions to improve their patient safety climate. Also, nursing management interventions designed to transfer patient safety knowledge to nurses would be particularly effective in improving their safety behavior.ope

    Exploring Nursesโ€™ Experience and Grievance: Network Analysis and Topic Modeling using a Social Networking Service

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    Purpose To describe clinical nurses' experience and grievance in an online community using a co-occurrence network and topic modeling. Methods We analyzed posts from Nurscape, which is the largest online community for nurses in Korea. After extracting posts using web scrapping, text preprocessing was done to detect nouns. In a visualization phase, co-occurrence network analysis and latent dirichlet allocation-based topic modeling were applied. Results A total of 13,200 posts were analyzed. The co-occurrence network's core keywords were newly graduate nurse, general ward, career, turnover, and grievance. The topic modeling showed four topics: (1) โ€˜Clinical practice-related difficultiesโ€™ described clinical hardships, such as the heavy workload of nurses; (2) โ€˜Concerns about resignationโ€™ incorporated keywords asking for advice on resignation; (3) โ€˜Searching for information on employment/reemploymentโ€™ focused on the working conditions or working climate of a specific hospital; and (4) โ€˜Organizational action callโ€™ captured the voices urging organized actions to improve nurses' work environment. Conclusion Clinical nurses share experiences through the online community and seek advice or information and urge organizational action. Professional nursing organizations should identify and deal with problems that nurses are currently facing. The results of this study can contribute to establishing the policy direction of nursing organizations.ope

    Associations among leadership, resources, and nurses' work engagement: findings from the fifth korean Working Conditions Survey

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    Background: Nurses' work engagement has received extensive attention due to its positive impacts on individual and organizational outcomes, including patient safety and quality care in healthcare organizations. Although nurse managers' leadership and a variety of resources have been identified as important factors of nurses' work engagement, these relationships have not been well understood in Korean nursing contexts. The purpose of this study was to examine the associations among nurse managers' leadership, resources, and work engagement among Korean nurses after controlling for nurses' demographic and work-related characteristics. Methods: This is a cross-sectional study using data from the fifth Korean Working Conditions Survey. Using a sample of 477 registered nurses, we employed hierarchical linear regression analyses. Nurse managers' leadership, job resources (organizational justice and support from peers), professional resources (employee involvement), and personal resources (meaning of work) were examined as potential predictors of nurses' work engagement. Results: We found that nurse managers' leadership (ฮฒ = 0.26, 95% confidence interval [CI] = 0.17-0.41) was the strongest predictor of nurses' work engagement, followed by meaning of work (ฮฒ = 0.20, 95% CI = 0.07-0.18), organizational justice (ฮฒ = 0.19, 95% CI = 0.10-0.32), and support from peers (ฮฒ = 0.14, 95% CI = 0.04-0.23). Employee involvement was not a statistically significant predictor of nurses' work engagement (ฮฒ = -0.07, 95% CI = -0.11-0.01). Conclusions: Our findings suggest that comprehensive approaches are required to promote nurses' work engagement. Considering that nurse managers' leadership was the strongest predictor of nurses' work engagement, nurse managers should demonstrate supportive leadership behaviors such as acknowledging and praising their unit nurses' work performance. Furthermore, both individual- and organizational-level strategies are necessary for nurses to be engaged at work.ope

    Patient safety educational interventions: A systematic review with recommendations for nurse educators

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    Aim: This study identified and evaluated tested patient safety educational interventions. This study also described the content, curricular structures and teaching strategies of the educational interventions and determined the methods used for evaluating patient safety learning outcomes. Design: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines directed this review. Methods: Searches for articles describing and evaluating patient safety educational interventions were conducted using four scholarly databases. Study quality was assessed using the McMaster Critical Review Form. Results: Seven studies met the inclusion criteria. Educational interventions were either presented as stand-alone courses or as lessons embedded in an existing course. All studies employed a mixture of various teaching modalities and several evaluation methods and outcomes. Mixed results were observed in terms of the effects of educational interventions. Future researchers should continue to develop patient safety curricula and examine their effect on student competencies with stronger methodological rigour.ope

    ์‹ ๊ทœ ํ”„๋กœํ”ผ์˜ค๋‹ ํ”„๋ฝํ† ์˜ฌ๋ฆฌ๊ณ ๋‹น์ด ์žฅ๋‚ด ๊ท  ์„ฑ์žฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‹ํ’ˆ์˜์–‘ํ•™๊ณผ, 2017. 2. ์ง€๊ทผ์–ต.Numerous studies reported that ingestion of fructooligosaccharides (FOS) can promote the growth of Bifidobacterium in large intestine. Therefore, FOS is currently used as prebiotics. Propionic acid (PA) has an inhibitory effect on the growth of pathogenic molds and bacteria. Propionates such as sodium propionate and calcium propionate are used as preservatives for food. In this study, the effect of novel propionyl-fructooligosaccharides (P-FOS) on the growth of various intestinal bacteria was assessed. According to the structural analyses using FT-IR, MALDI-TOF MS, LC-ESI-MS, and LC-ESI-MS/MS, the major components of P-FOS used in this study contained FOS with 1-3 propionyl groups attached. P-FOS promoted the growth of the most experimental Bifidobacterium and some of the other lactic acid bacteria. In contrast to FOS, P-FOS showed no growth promotion or slight suppression against most of the non-probiotic bacteria. The novel P-FOS is expected to be useful for the improvement of human intestinal microflora.1. Introduction 1 2. Materials and methods 4 2.1. Materials 4 2.1.1. Source of P-FOS 4 2.1.2. The bacterial strains and culture condition 4 2.1.3. Chemicals and reagents 7 2.1.4. The media and carbohydrate sources for bacterial growth test 7 2.2. Purification and preparation of P-FOS 7 2.3. Structural analysis of P-FOS 8 2.3.1. Determination of the linkages in P-FOS by FT-IR 8 2.3.2. Mass analysis by MALDI-TOF MS and LC-ESI-MS 8 2.4. Effect of glucose, FOS, and P-FOS on growth of intestinal bacteria 9 3. Results and Discussion 11 3.1. Structural analysis of P-FOS 11 3.1.1. Investigation of linkages in P-FOS using FT-IR 11 3.1.2. Mass spectra analysis of FOS by MALDI-TOF MS and LC-ESI-MS 14 3.1.3. Mass spectra analysis of P-FOS by MALDI-TOF MS, LC-ESI-MS, and LC-ESI-MS/MS 16 3.2. Growth of various bacteria in the presence of P-FOS, FOS, and glucose 20 4. Conclusion 28 References 29 Abstract in Korean 34Maste

    The Yield Curve and Monetary Policy in a Small Open Economy

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ๊ฒฝ์ œํ•™๋ถ€, 2018. 8. ๋ฐ•์›…์šฉ.This paper analyzes co-movements among the yield curves of small open countries, and the efficiency of each countrys monetary policy. First, I estimate a dynamic factor model to find common movements among the yield curves of six small open countries: Australia, Canada, Denmark, Norway, Switzerland, and the United Kingdom. The empirical results show that nominal interest rates of the countries are well-accounted for by their US counterpart, rather than the small open countries policy rates at long maturities. This may imply that the long-term rates decouple from the short-term policy rates in the small open countries, resulting in limited effects of the countries monetary policy. Thus, to examine the effectiveness of monetary policy, I analyze dynamic responses of macro variables to monetary expansions in Canada and Norway. Estimating a vector auto-regression (VAR) model, I conclude that the high yield curve correlations with the United States reduce the impacts of expansionary monetary policy in small open countries. Norway, whose interest rates are the least explained by the US rates, succeeds in boosting its economy by decreasing the policy rate. On the other hand, Canada, which has the highest yield curve correlations with its US counterpart, fails to invigorate economic activities by monetary expansion.1. Introduction 2. Data 3. The Yield Curve Co-movements 3.1. Dynamic Factor Model 3.2. Estimation 4. Efficiency of Monetary Policy 5. Conclusion AppendixMaste
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