| Item Type |
Article |
| ID |
|
| Preview |
| Image |
|
| Caption |
|
|
| Full text |
KAKEN_18K17394seika.pdf
| Type |
:application/pdf |
Download
|
| Size |
:246.1 KB
|
| Last updated |
:May 17, 2022 |
| Downloads |
: 298 |
Total downloads since May 17, 2022 : 298
|
|
| Release Date |
|
| Title |
| Title |
腎機能低下を早期に予測するメタボロミクス・バイオマーカーの解明と予防医療への展開
|
| Kana |
ジンキノウ テイカ オ ソウキ ニ ヨソクスル メタボロミクス・バイオマーカー ノ カイメイ ト ヨボウ イリョウ エノ テンカイ
|
| Romanization |
Jinkinō teika o sōki ni yosokusuru metaboromikusu baiomākā no kaimei to yobō iryō eno tenkai
|
|
| Other Title |
| Title |
Metabolomics biomarkers for predicting renal function decline
|
| Kana |
|
| Romanization |
|
|
| Creator |
| Name |
原田, 成
 |
| Kana |
ハラダ, セイ
|
| Romanization |
Harada, Sei
|
| Affiliation |
慶應義塾大学・医学部 (信濃町) ・講師
|
| Affiliation (Translated) |
|
| Role |
Research team head
|
| Link |
科研費研究者番号 : 10738090
|
|
| Edition |
|
| Place |
|
| Publisher |
|
| Date |
| Issued (from:yyyy) |
2021
|
| Issued (to:yyyy) |
|
| Created (yyyy-mm-dd) |
|
| Updated (yyyy-mm-dd) |
|
| Captured (yyyy-mm-dd) |
|
|
| Physical description |
|
| Source Title |
| Name |
科学研究費補助金研究成果報告書
|
| Name (Translated) |
|
| Volume |
|
| Issue |
|
| Year |
2020
|
| Month |
|
| Start page |
|
| End page |
|
|
| ISSN |
|
| ISBN |
|
| DOI |
|
| URI |
|
| JaLCDOI |
|
| NII Article ID |
|
| Ichushi ID |
|
| Other ID |
|
| Doctoral dissertation |
| Dissertation Number |
|
| Date of granted |
|
| Degree name |
|
| Degree grantor |
|
|
| Abstract |
60-74歳の一般市民1,672人に対し、調査開始時の腎機能測定（血清クレアチニン、血清シスタチンC、尿中アルブミン）と血漿・尿メタボローム測定を行った。また同じ参加者に対して、6年後も同様に評価した。
機械学習的手法(OPLS-DA)を用いて、6年間での腎機能低下を予測した結果、古典的な腎機能指標に加えて、血漿・尿メタボロームを用いることで、より正確な予測モデルが構築できた。さらに別の機械学習的手法(SVM)を用いてROCカーブを作成したところ、３種類の代謝物を含む上位5つの変数を選択した場合にもっとも精度が高く、AUC 0.904(95%CI 0.871-0.944)と好成績であった。
Renal function measurements (serum creatinine, serum cystatin C, and urine albumin) and plasma and urine metabolomics were performed on 1,672 participants aged 60-74 years at the beginning of the study. The same participants were also evaluated in the same way after 6 years.
A machine learning method (OPLS-DA) was used to predict the decline in renal function over 6 years, and a more accurate prediction model was constructed by using plasma and urine metabolome in addition to classical renal function indicators. Furthermore, another machine-learning approach (SVM) was used to create ROC curves, which were most accurate when the top five variables including three metabolites were selected, with a good performance of AUC 0.904 (95%CI 0.871-0.944).
|
|
| Table of contents |
|
| Keyword |
|
| NDC |
|
| Note |
研究種目 : 若手研究
研究期間 : 2018～2020
課題番号 : 18K17394
研究分野 : メタボロミクス疫学
|
|
| Language |
|
| Type of resource |
|
| Genre |
|
| Text version |
|
| Related DOI |
|
| Access conditions |
|
| Last modified date |
|
| Creation date |
|
| Registerd by |
|
| History |
|
| Index |
|
| Related to |
|
