54 research outputs found

    Meta-Analysis of Tumor Stem-Like Breast Cancer Cells Using Gene Set Analysis

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์•ฝํ•™๊ณผ, 2016. 8. ๋ฐ•์ •์ผ.์ผ๋ฐ˜์ ์œผ๋กœ ์ข…์–‘๊ฐœ์‹œ์„ธํฌ๋Š” epithelial-to-mesenchymal-transition ์„ฑ์งˆ์„ ๊ฐ€์ง€๋ฉฐ ์ฆ์‹์†๋„๊ฐ€ ๋นจ๋ผ ์•”์˜ ์ „์ด๋ฅผ ์ผ์œผํ‚ค๋Š”๋ฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์•„์ง๊นŒ์ง€ ์ข…์–‘๊ฐœ์‹œ์„ธํฌ์— ๊ด€ํ•˜์—ฌ ํ™•์‹คํ•œ ๋งˆ์ปค๋Š” ์•Œ๋ ค์ง€์ง€ ์•Š์•˜์œผ๋ฉฐ ์ข…์–‘๊ฐœ์‹œ์„ธํฌ ๊ด€๋ จ๊ธฐ์ „๊ณผ ๋งˆ์ปค์˜ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•ด ๊ฐ€์žฅ ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” ๋ฐฉ๋ฒ•์€ ์ข…์–‘๊ฐœ์‹œ์„ธํฌ์˜ ์„ฑ์งˆ์„ ๋‚˜ํƒ€๋‚ธ๋‹ค๊ณ  ์•Œ๋ ค์ง„ sphere cells๊ณผ ๊ทธ ๋Œ€์กฐ๊ตฐ์ธ adherent cells์˜ ๋น„๊ต๋ฐฉ๋ฒ•์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์„œ๋กœ ๋‹ค๋ฅธ ์œ ๋ฐฉ์•” ์ข…์–‘๊ฐœ์‹œ์„ธํฌ ์—ฐ๊ตฌ์—์„œ ์–ป์€ sphere cells๊ณผ adherent cells์˜ ์œ ์ „์ž ๋ฐœํ˜„ ๋ฐ์ดํ„ฐ๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ฉ”ํƒ€๋ถ„์„์„ ์ˆ˜ํ–‰ํ•จ์œผ๋กœ์จ ์œ ๋ฐฉ์•” ์ข…์–‘๊ฐœ์‹œ์„ธํฌ์˜ ๊ธฐ์ „์„ ๋ฐํžˆ๊ณ  ์ƒˆ๋กœ์šด ๋งˆ์ปค๋ฅผ ๊ฒ€์ƒ‰ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด Gene Expression Omnibus์—์„œ ์ถœ์ฒ˜๊ฐ€ ๋‹ค๋ฅธ ์œ ๋ฐฉ์•” ์ข…์–‘๊ฐœ์‹œ์„ธํฌ ์—ฐ๊ตฌ์—์„œ 3๊ฐœ์˜ ์œ ์ „์ž ๋ฐœํ˜„ ๋ฐ์ดํ„ฐ๋ฅผ ์–ป๊ณ  ์ด๋ฅผ ComBat ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ด์šฉํ•˜์—ฌ ํ•˜๋‚˜์˜ ๋ฐ์ดํ„ฐ๋กœ ํ†ตํ•ฉํ•˜์˜€์œผ๋ฉฐ, ์—ฌ๊ธฐ์— ๋ณธ ์—ฐ๊ตฌ์ง„๊ณผ ๊ณต๋™์œผ๋กœ ์—ฐ๊ตฌ๋ฅผ ์ˆ˜ํ–‰ํ•œ ์•„์ฃผ๋Œ€ํ•™๊ต์—์„œ ์œ ์ „์ž ๋ฐœํ˜„ ๋ฐ์ดํ„ฐ๋ฅผ ์ œ๊ณตํ•˜์—ฌ ๋ณธ ์—ฐ๊ตฌ์— ์ถ”๊ฐ€ํ•˜์˜€๋‹ค. ๋ฉ”ํƒ€๋ถ„์„์„ ์ง„ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด ์œ„์˜ ๊ฒฐ๊ณผ๋กœ ์–ป์€ ๋‘ ๊ฐœ์˜ ๋ฐ์ดํ„ฐ์— ๊ฐ๊ฐ gene set analysis๋ฅผ ์ ์šฉํ•˜์—ฌ ์œ ์˜์„ฑ ์žˆ๋Š” gene set์„ ์–ป์€ ํ›„ ๋‘ ๋ฐ์ดํ„ฐ ๋ชจ๋‘์—์„œ ๊ณตํ†ต์ ์œผ๋กœ ์œ ์˜์„ฑ์„ ๋ณด์ธ 4๊ฐœ์˜ gene set์„ ์–ป์—ˆ๋‹ค. ์œ ๋ฐฉ์•” ์ข…์–‘๊ฐœ์‹œ์„ธํฌ์— ๊ด€์—ฌํ•˜๋Š” ์œ ์ „์ž ๋งˆ์ปค๋Š” ์œ ์˜์„ฑ์„ ๋ณด์ธ 4๊ฐœ์˜ gene set์ด ํฌํ•จํ•˜๋Š” ์œ ์ „์ž ์ค‘์—์„œ, ๋‘ ๋ฐ์ดํ„ฐ ๋ชจ๋‘์—์„œ p-value < 0.05๋ฅผ ๋งŒ์กฑํ•˜๊ณ  ๋ฐœํ˜„์ด ์ฆ๊ฐ€ํ–ˆ๋˜ CXCR4์™€ CXCL1์„ ํฌํ•จํ•˜๋Š” 6๊ฐœ์˜ ์œ ์ „์ž๋กœ ์„ ํƒํ•˜์˜€๋‹ค. ์‹คํ—˜์  ๊ฒ€์ฆ์„ ์œ„ํ•˜์—ฌ ์•„์ฃผ๋Œ€ํ•™๊ต์—์„œ ์ตœ์ข…์ ์œผ๋กœ ์–ป์–ด์ง„ 6๊ฐœ์˜ ์œ ์ „์ž์— ๋Œ€ํ•ด quantitative reverse transcription-polymerase chain reaction์„ ์ˆ˜ํ–‰ํ•˜์˜€๊ณ  6๊ฐœ์˜ ์œ ์ „์ž ์ค‘์—์„œ CXCR4, CXCL1, HMGCS1์ด MCF-7์˜ sphere cells์—์„œ adherent cells๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ ๋ฐœํ˜„์ด ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” gene set analysis๋ฅผ ์ด์šฉํ•˜์—ฌ ๋ฉ”ํƒ€๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์˜€์œผ๋ฉฐ ์ด๋ฅผ ํ†ตํ•ด ์œ ๋ฐฉ์•” ์ข…์–‘๊ฐœ์‹œ์„ธํฌ ๊ธฐ์ „์— ๊ด€์—ฌํ•˜๋Š” 4๊ฐœ์˜ gene set๊ณผ ์œ ์ „์ž ๋งˆ์ปค์ธ CXCR4, CXCL1, HMGCS1์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์ตœ์ข…์ ์œผ๋กœ ๋ฉ”ํƒ€๋ถ„์„์— gene set analysis๋ฅผ ๋„์ž…ํ•จ์œผ๋กœ์จ ํ†ต๊ณ„์ ์ธ ์œ ์˜์„ฑ์„ ๊ฐ€์งˆ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ gene set ๊ฐœ๋…์„ ๋ฐ”ํƒ•์œผ๋กœ ์ƒ๋ฌผํ•™์  ๊ธฐ์ „ ์ •๋ณด๋ฅผ ๊ณ ๋ คํ•œ ์œ ์ „์ž ๋งˆ์ปค๋ฅผ ์ œ์‹œํ•˜์˜€๋‹ค.โ… . ์„œ๋ก  1 โ…ก. ์žฌ๋ฃŒ ๋ฐ ๋ฐฉ๋ฒ• 4 1. ๋ฐ์ดํ„ฐ ์ˆ˜์ง‘ 4 2. ์„ธํฌ๋ฐฐ์–‘ ๋ฐ ์œ ์ „์ž ๋ฐœํ˜„ profiling 5 3. ๋ฐ์ดํ„ฐ ์ „์ฒ˜๋ฆฌ 6 4. Gene set analysis 7 5. ํ†ต๊ณ„์  ๊ฒ€์ฆ๊ณผ ๊ฐœ๋ณ„ ์œ ์ „์ž ๋งˆ์ปค ์„ ํƒ 7 6. Reverse transcription-PCR 9 โ…ข. ๊ฒฐ๊ณผ 9 1. ์ˆ˜์ง‘๋œ ๋ฐ์ดํ„ฐ์˜ ์„ฑ๊ฒฉ 9 2. Gene set analysis์™€ ํ†ต๊ณ„์  ๊ฒ€์ฆ 11 3. ๊ฐœ๋ณ„ ์œ ์ „์ž ๋งˆ์ปค ์„ ํƒ 13 4. Reverse transcription-PCR 18 โ…ฃ. ๊ฒฐ๋ก  19 โ…ค. ์ฐธ๊ณ  ๋ฌธํ—Œ 21 โ…ฅ. ๋ถ€๋ก 26 1. The Gene Expression Omnibus (GEO) 26 2. ๋ฉ”ํƒ€๋ถ„์„ (Meta-analysis) 28 3. Affymetrix 31 4. ComBat method (R ์„ค์น˜ ๋ฐ R package ์„ค์น˜) 32 5. Illumina 38 6. Gene set analysis 42 7. Prediction analysis for microarrays (PAM) 44 8. Globaltest 46 9. ArrayExpress 47 10. affy 48 11. DAVID 49 12. Entrez ID 51 13. Leave-one-out cross validation 51 14. C-X-C chemokine receptor type 4 (CXCR4) 53 15. C-X-C motif chemokine 12 (CXCL12) 56 16. chemokine (C-X-C motif) ligand 1 (CXCL1) 56 17. Hydroxymethylglutaryl-CoA synthase (HMGCS1) 57 Abstract 58Docto

    ๆŽ่ฌๆ•ท์˜ ้“ๆฑ็ทจ ๊ณผ ๆŽ็€ท์˜ ้“ๆฑ้Œ„ ์„ ํ†ตํ•ด๋ณธ ๊ทผ๊ธฐ๋‚จ์ธ์˜ โ€˜้“ๆฑโ€™ ์˜์‹

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    ๋ณธ ์—ฐ๊ตฌ๋Š” ๆฏๅฑฑ ๆŽ่ฌๆ•ท(1664~1732)์˜ ้“ๆฑ็ทจ ๊ณผ ๆ˜Ÿๆน– ๆŽ็€ท(1681~1763)์˜ ้“ๆฑ้Œ„ ์˜ ์ €์ˆ ๋ฐฐ๊ฒฝ, ๋‚ด์šฉ ๋ฐ ๊ตฌ์กฐ, ํŠน์ง• ๋“ฑ์„ ์‚ดํŽด๋ด„์œผ๋กœ์จ ๊ทผ๊ธฐ๋‚จ์ธ๊ณ„์˜ โ€˜้“ๆฑโ€™ ์˜์‹์˜ ์‹ค์ฒด๋ฅผ ์‹œ๋ก ํ•œ ๊ฒƒ ์ด๋‹ค. ๋„๋™ํŽธ ์€ ้€€ๆบช ๆŽๆป‰(1501~1570)๊ณผ ๏งš่ฐท ๏งก็ฅ(1536~1584) ๋“ฑ ํ•œ๊ตญ ์„ฑ๋ฆฌํ•™์ž๋“ค๋งŒ์˜ ์–ธ์„ค์„ ๆ€ง็†ๅคงๅ…จ ์˜ ์ฒด์ œ๋กœ ๋ง๋ผํ•œ ๊ฒƒ์œผ๋กœ, ์‹์‚ฐ์€ ๋„ํ†ต์˜ ์ ์ „์„ ํ‡ด๊ณ„๋กœ ์ƒ์ •ํ•˜๋ฉด์„œ๋„ ์œจ๊ณก์˜ ์„ค๊นŒ์ง€ ์•„์šธ๋Ÿฌ ์ˆ˜๋กํ•จ์œผ๋กœ์จ ํ•™ํŒŒ์™€ ์ •ํŒŒ์— ๊ด€๊ณ„์—†์ด ํ•œ๊ตญ ์œ ํ•™์˜ ๋ฐœ์ „์ƒ์„ ๋ณด์—ฌ์ฃผ๊ณ ์ž ํ•˜์˜€๋‹ค. ๋„๋™๋ก ์€ ํ‡ด๊ณ„์˜ ์–ธ์„ค์„ ่ฟ‘ๆ€้Œ„ ์ฒด์ œ์— ์ค€ํ•˜์—ฌ ์ •๋ฆฌํ•œ ๊ฒƒ์œผ๋กœ, ์„ฑํ˜ธ๋Š” ์‚ฌ์„œ์˜ ์ž…๋ฌธ์„œ์ธ ๊ทผ์‚ฌ๋ก ์„ ์˜์‹ํ•œ ์ฑ„ ํ‡ด๊ณ„์„ค์„ ์ •๋ฆฌํ•จ์œผ๋กœ์จ ํ•œ๊ตญ์  ๋„ํ•™ ์ „ํ†ต์˜ ๊ธฐ์›์„ ๋ฐํžˆ๊ณ  ์ด๋ฅผ ํ•œ๊ตญ์  ๋„ํ•™ ์ž…๋ฌธ์„œ ๋กœ ์‚ผ๊ณ ์ž ํ•˜์˜€๋‹ค. ๋‘ ์ €์„œ๋Š” ๋ชจ๋ณธ์˜ ์ฐจ์ด๋กœ ์ธํ•ด ํŽธ์ฐจ ๊ตฌ์„ฑ, ์ธ์šฉ ๋ฌธํ—Œ์˜ ๋ฒ”์œ„, ๋ถ„๋Ÿ‰ ๋“ฑ์ด ์ผ์น˜ ํ•˜์ง€๋Š” ๋ชปํ•˜๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ฐ ์ €์„œ์˜ ์ €์ž๋“ค์€ ํ•œ๊ตญ ์œ ํ•™์ž์˜ ์–ธ์„ค๋งŒ์„ ๋Œ€์ƒ์œผ๋กœ ์‚ผ์•„ ์ˆ˜์ง‘ ๋ฐ ํŽธ์ง‘ ํ•˜๊ณ  ์ด๋ฅผ ๋™์ผํ•œ ํ‘œ์ œ๋กœ ๋ช…๋ช…ํ•จ์œผ๋กœ์จ โ€˜้“ๅญธโ€™ ๋˜๋Š” โ€˜้“็ตฑโ€™์˜ ๆฑๅ‚ณ์„ ํ˜•์šฉํ•˜๊ณ ์ž ํ•˜์˜€์œผ๋ฉฐ, ์ด ๊ณผ ์ •์—์„œ ๊ณตํ†ต์ ์œผ๋กœ ํ‡ด๊ณ„๋ฅผ ๋„ํ•™์˜ ์ •์ ์ด์ž ๋„ํ†ต์˜ ์ ์ „์œผ๋กœ ์„ค์ •ํ•˜๋Š” ๋ชจ์Šต์„ ๋ณด์ธ๋‹ค. ์‹์‚ฐ๊ณผ ์„ฑํ˜ธ๋Š” ๋‹น์Ÿ์˜ ์†Œ์šฉ๋Œ์ด ์†์—์„œ ์ถœ์‚ฌ๋ฅผ ํฌ๊ธฐํ•˜๊ณ  ํ•™๋ฌธ์— ๋งค์ง„ํ•˜์—ฌ ํ›„์ผ ๊ทผ๊ธฐ๋‚จ์ธ์„ ๋Œ€ ํ‘œํ•˜๋Š” ์„ํ•™์œผ๋กœ ์ธ์ •๋ฐ›์•˜๋‹ค. ๋‘ ์ €์„œ๊ฐ€ ์ €์ˆ ๋œ ์‹œ๊ธฐ๋Š” ๋‚จ์ธ์ด ์ •๊ณ„์—์„œ ์ถ•์ถœ๋˜๊ณ  ์„œ์ธ ๋…ธ๋ก  ์ • ๊ถŒ์ด ๋“ค์–ด์„œ๊ณ , ๅฐคๅบต ๅฎ‹ๆ™‚็ƒˆ(1607โˆผ1689)์˜ ์œ ์ง€๋ฅผ ์ด์€ ๋…ธ๋ก ์˜ ์กด๋ช…์˜๋ฆฌ์‚ฌ์ƒ์ด ่ฌๆฑๅปŸ์™€ ๅคงๅ ฑๅฃ‡ ์ด๋ผ๋Š” ์ค‘ํ™”๊ณ„์Šน ์ƒ์ง•๋ฌผ๋กœ ๊ฐ€์‹œํ™”๋˜๋˜ ๋•Œ์˜€๋‹ค. ๊ทผ๊ธฐ๋‚จ์ธ๊ณ„์ธ ๋‘ ํ•™์ž๋กœ์„œ๋Š” ๋…ธ๋ก ์ด ์„ ์ ํ•œ ์ค‘ํ™” ๊ณ„์Šน ์˜์‹์— ์ƒ์‘ํ•˜๋ฉด์„œ๋„ ์ €๋“ค์ด ๊ตฌ์ถ•ํ•œ ์ฃผ์žํ•™ ์ ˆ๋Œ€์ฃผ์˜ ๋ฐ ์ถ˜์ถ”์˜๋ฆฌ์™€๋Š” ๊ตฌ๋ณ„๋˜๋Š” ์‚ฌ์œ ๋ฅผ ์ œ ์‹œํ•  ํ•„์š”๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋‘ ํ•™์ž์˜ โ€˜๋„๋™โ€™ ์„œ์  ์ €์ˆ ์€ ๊ทผ๊ธฐ๋‚จ์ธ๊ณ„ ์ง€์‹์ธ์œผ๋กœ์จ์˜ ์ฑ…์ž„์˜์‹๊ณผ ์œ„๊ธฐ ์˜์‹์˜ ๋ฐœ๋กœ๋กœ, ํ‡ด๊ณ„๊ฐ€ ๋„ํ•™์˜ ์ •์ ์ด์ž ๋„ํ†ต์˜ ์ ์ „์œผ๋กœ ์„ค์ •๋˜์—ˆ๋‹ค๋Š” ์ ์€ ์ด๋Ÿฌํ•œ ์‚ฌ์ •๊ณผ ๋ฌด๊ด€ ํ•˜์ง€ ์•Š๋‹ค. ํ•œ๊ตญ ์œ ํ•™์˜ ๋ฐœ์ „์ƒ์„ ๋“œ๋Ÿฌ๋‚ด๊ณ  ๊ทธ ์ค‘์‹ฌ์„ ํ‡ด๊ณ„๋กœ ์„ค์ •ํ•จ์œผ๋กœ์จ ์‹์‚ฐ๊ณผ ์„ฑํ˜ธ๊ฐ€ ์ œ์‹œ ํ•˜๊ณ ์ž ํ•œ โ€˜๋„๋™โ€™ ์˜์‹์€ ์ฃผ์žํ•™ ๊ทธ ์ž์ฒด์— ๋Œ€ํ•œ ์ •์น˜ํ•œ ๋ถ„์„์— ๊ธฐ๋ฐ˜ํ•˜๊ณ  ์ผ๋ จ์˜ ์ƒ์ง•๋ฌผ๋กœ ๊ฐ€์‹œ ํ™”๋œ ๋…ธ๋ก ์‹์˜ โ€˜์ค‘ํ™”๊ณ„์Šนโ€™ ์˜์‹๊ณผ๋Š” ๋ถ„๋ช…ํžˆ ๊ตฌ๋ถ„๋˜๋Š” ์ง€์ ์ด ์žˆ๋‹ค.์ด ๋…ผ๋ฌธ์€ 2020๋…„๋„ ์„œ์šธ๋Œ€ํ•™๊ต ๊ทœ์žฅ๊ฐํ•œ๊ตญํ•™์—ฐ๊ตฌ์› ๊ตญ๋‚ด ์‹ ์ง„ํ•™์ž ์ดˆ์ฒญ ์—ฐ๊ตฌ๊ต๋ฅ˜ ์‚ฌ์—…์˜ ์ง€์›์„ ๋ฐ›์•„ ์ˆ˜ํ–‰๋œ ์—ฐ๊ตฌ์ž„

    Gender differences in hypertension control among older korean adults: Korean social life, health, and aging project

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    OBJECTIVES: Controlling blood pressure is a key step in reducing cardiovascular mortality in older adults. Gender differences in patients' attitudes after disease diagnosis and their management of the disease have been identified. However, it is unclear whether gender differences exist in hypertension management among older adults. We hypothesized that gender differences would exist among factors associated with hypertension diagnosis and control among community-dwelling, older adults. METHODS: This cross-sectional study analyzed data from 653 Koreans aged โ‰ฅ60 years who participated in the Korean Social Life, Health, and Aging Project. Multiple logistic regression was used to compare several variables between undiagnosed and diagnosed hypertension, and between uncontrolled and controlled hypertension. RESULTS: Diabetes was more prevalent in men and women who had uncontrolled hypertension than those with controlled hypertension or undiagnosed hypertension. High body mass index was significantly associated with uncontrolled hypertension only in men. Multiple logistic regression analysis indicated that in women, awareness of one's blood pressure level (odds ratio [OR], 2.86; p=0.003) and the number of blood pressure checkups over the previous year (OR, 1.06; p=0.011) might influence the likelihood of being diagnosed with hypertension. More highly educated women were more likely to have controlled hypertension than non-educated women (OR, 5.23; p=0.013). CONCLUSIONS: This study suggests that gender differences exist among factors associated with hypertension diagnosis and control in the study population of community-dwelling, older adults. Education-based health promotion strategies for hypertension control might be more effective in elderly women than in elderly men. Gender-specific approaches may be required to effectively control hypertension among older adults.ope

    Social Network Characteristics and Body Mass Index in an Elderly Korean Population

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    OBJECTIVES: Research has shown that obesity appears to spread through social ties. However, the association between other characteristics of social networks and obesity is unclear. This study aimed to identify the association between social network characteristics and body mass index (BMI, kg/m(2)) in an elderly Korean population. METHODS: This cross-sectional study analyzed data from 657 Koreans (273 men, 384 women) aged 60 years or older who participated in the Korean Social Life, Health, and Aging Project. Network size is a count of the number of friends. Density of communication network is the number of connections in the social network reported as a fraction of the total links possible in the personal (ego-centric) network. Average frequency of communication (or meeting) measures how often network members communicate (or meet) each other. The association of each social network measure with BMI was investigated by multiple linear regression analysis. RESULTS: After adjusting for potential confounders, the men with lower density (0.83) and lower size (1-2), but not in the women (p=0.393). The lowest tertile of communication frequency was associated with higher BMI in the women (ฮฒ=0.885, p=0.049), but not in the men (p=0.140). CONCLUSIONS: Our study suggests that social network structure (network size and density) and activation (communication frequency and meeting frequency) are associated with obesity among the elderly. There may also be gender differences in this associationope

    Appendicular skeletal muscle mass and insulin resistance in an elderly korean population: the korean social life, health and aging project-health examination cohort

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    BACKGROUND: Increasing evidence supports an association between age-related loss of muscle mass and insulin resistance. However, the association has not been fully investigated in the general population. Thus, we investigated the association between appendicular skeletal muscle mass (ASM) and insulin resistance in an elderly Korean population. METHODS: This cross-sectional study included 158 men (mean age, 71.8) and 241 women (mean age, 70.6) from the Korean Social Life, Health and Aging Project, which started in 2011. In this study, ASM was measured by bioelectrical impedance analysis and was analyzed in three forms: ASM (kg), ASM/height(2) (kg/m(2)), and ASM/weight (%). The homeostasis model assessment of insulin resistance (HOMA-IR) was used as a measure of insulin resistance. The relationships between the ASM values and the HOMA-IR were investigated by multiple linear regression models. RESULTS: The HOMA-IR was positively associated with ASM (ฮฒ=0.43, P<0.0001) and ASM/height(2) (ฮฒ=0.36, P<0.0001) when adjusted for sex and age. However, after additional adjustment for body weight, HOMA-IR was inversely associated with ASM (ฮฒ=-0.43, P<0.001) and ASM/height(2) (ฮฒ=-0.30, P=0.001). Adjustment for other potential confounders did not change these associations. Conversely, HOMA-IR was consistently and inversely associated with ASM/weight before and after adjustment for other potential confounders. CONCLUSION: Our results support the idea that lower skeletal muscle mass is independently associated with insulin resistance in older adults. When evaluating sarcopenia or muscle-related conditions in older adults, their whole body sizes also need to be considered.ope

    Medical Care Utilization During 1 Year Prior to Death in Suicides Motivated by Physical Illnesses

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    OBJECTIVES: Many epidemiological studies have suggested that a variety of medical illnesses are associated with suicide. Investigating the time-varying pattern of medical care utilization prior to death in suicides motivated by physical illnesses would be helpful for developing suicide prevention programs for patients with physical illnesses. METHODS: Suicides motivated by physical illnesses were identified by the investigator's note from the National Police Agency, which was linked to the data from the Health Insurance Review and Assessment. We investigated the time-varying patterns of medical care utilization during 1 year prior to suicide using repeated-measures data analysis after adjustment for age, gender, area of residence, and socioeconomic status. RESULTS: Among 1994 suicides for physical illness, 1893 (94.9%) suicides contacted any medical care services and 445 (22.3%) suicides contacted mental health care during 1 year prior to suicide. The number of medical care visits and individual medical expenditures increased as the date of suicide approached (p<0.001). The number of medical care visits for psychiatric disorders prior to suicide significantly increased only in 40- to 64-year-old men (p=0.002), women <40 years old (p=0.011) and women 40 to 64 years old (p=0.021) after adjustment for residence, socioeconomic status, and morbidity. CONCLUSIONS: Most of the suicides motivated by physical illnesses contacted medical care during 1 year prior to suicide, but many of them did not undergo psychiatric evaluation. This underscores the need for programs to provide psychosocial support to patients with physical illnesses.ope

    The Korean Social Life, Health and Aging Project-Health Examination Cohort

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    The Korean Social Life, Health, and Aging Project (KSHAP) is a population-based longitudinal study of health determinants among elderly Koreans. The target population of the KSHAP are people aged 60 years or older and their spouses living in a rural community of Korea. A complete enumeration survey was conducted in the first wave of the KSHAP on 94.7% (814 of 860) of the target population between December 2011 and July 2012. The KSHAP-Health Examination (KSHAP-HE) cohort consists of 698 people who completed additional health examinations at a public health center (n=533) or at their home (n=165). Face-to-face questionnaires were used to interview participants on their demographics, social network characteristics, medical history, health behaviors, cognitive function, and depression symptoms. Health center examinations included anthropometric measures, body impedance analysis, resting blood pressure measurement, radial artery tonometry, bone densitometry, the timed up-and-go test, and fasting blood analysis. However, only anthropometric measures, blood pressure measurement, and non-fasting blood analysis were available for home health examinations. Collaboration is encouraged and access to the KSHAP baseline data will be available via the website of the Korean Social Science Data Archive (http://www.kossda.or.kr).ope

    Serum 25-Hydroxyvitamin D and Insulin Resistance in Apparently Healthy Adolescents

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    PURPOSE: Vitamin D deficiency is a common condition that is associated with diabetes and insulin resistance. However, the association between vitamin D and insulin resistance has not been fully studied, especially in the general adolescent population. Therefore, we assessed the association between serum 25-hydroxyvitamin D [25(OH)D] level and insulin resistance among apparently healthy Korean adolescents. METHODS: A total of 260 (135 male and 125 female) adolescents in a rural high school were assessed for serum 25(OH)D, fasting plasma glucose, and insulin. All of the participants were aged 15 to 16 years old, and without known hypertension or diabetes. Serum 25(OH)D was analyzed both as a continuous and categorical variable in association with insulin resistance. Insulin resistance was estimated by homeostasis model assessment (HOMA-IR). Increased insulin resistance was operationally defined as a HOMA-IR value higher than the sex-specific 75th percentile. RESULTS: In male adolescents, every 10 ng/ml decrease in 25(OH)D level was associated with a 0.25 unit increase in HOMA-IR (pโ€Š=โ€Š0.003) after adjusting for age and BMI. Compared to those in the highest quartile, male adolescents in the lowest 25(OH)D quartile were at significantly higher risk for insulin resistance: unadjusted odds ratio 4.06 (95% CI, 1.26 to 13.07); age and BMI adjusted odds ratio 3.59 (95% CI, 1.03 to 12.57). However, 25(OH)D level, either in continuous or categorical measure, was not significantly associated with insulin resistance among female adolescents. CONCLUSIONS: This study suggests that serum 25(OH)D level may be inversely associated with insulin resistance in healthy male adolescents.ope

    Factors Associated with a Low-sodium Diet: The Fourth Korean National Health and Nutrition Examination Survey

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    OBJECTIVES: The low-sodium diet is a known preventive factor for hypertension and cardiovascular disease. Factors associated with low-sodium diets should be identified to reduce sodium intake effectively. This study was conducted to identify factors correlated with a low-sodium diet. METHODS: This cross-sectional study analyzed data from a total of 14,539 Koreans aged 20 years or older, who participated in the Fourth (2007-2009) Korean National Health and Nutrition Examination Survey. A low-sodium diet was defined as having โ‰ค2,000 mg/day based on 24-hour recalls. Multiple logistic regression models were used to assess sex, age, education, number of family members, household income, occupation, alcohol drinking, total energy intake, frequency of eating out, and hypertension management status for their associations with low-sodium diets. RESULTS: Among all participants, only 13.9% (n=2,016) had low-sodium diets. In the multivariate analysis, 40-49 years of age, clerical work jobs, higher total energy intake, and frequent eating out were inversely associated with low-sodium diets. And female sex and living-alone were associated with low-sodium diets. Lower frequency of eating out was significantly associated with low-sodium diets, even after adjusting for total energy intake and other potential confounders. Adjusted odds ratios (95% confidence interval) for a low-sodium diet were 1.97 (1.49-2.61), 1.47 (1.13-1.91), 1.24 (0.96-1.61), and 1.00 (reference) in people who eat out <1 time/month, 1-3 times/month, 1-6 times/week, and โ‰ฅ1 time/day, respectively. CONCLUSIONS: Our study suggests that sex, age, number of family members, occupation, total energy intake, and lower frequency of eating out were associated with a low-sodium diet in Korean adults.ope

    (The)Effects of Ubiquitous interactivity characteristic of new product on adoption and diffusion

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ฒฝ์˜ํ•™๊ณผ ๊ฒฝ์˜ํ•™์ „๊ณต,2005.Docto
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