PMID- 29626587
OWN - NLM
STAT- MEDLINE
DCOM- 20180725
LR  - 20181202
IS  - 1872-8227 (Electronic)
IS  - 0168-8227 (Linking)
VI  - 140
DP  - 2018 Jun
TI  - Metabolite changes in risk of type 2 diabetes mellitus in cohort studies: A 
      systematic review and meta-analysis.
PG  - 216-227
LID - S0168-8227(17)31236-6 [pii]
LID - 10.1016/j.diabres.2018.03.045 [doi]
AB  - AIMS: Fasting plasma glucose, oral glucose tolerance test, and glycated 
      hemoglobin are diagnostic markers for type 2 diabetes mellitus (T2DM). However, 
      it is necessary to detect physiological changes in T2DM rapidly and stratify 
      diabetic stage using other biomarkers. We performed a systematic review and 
      meta-analysis to contribute to the development of objective and sensitive 
      diagnostic indicators by integrating metabolite biomarkers derived from 
      large-scale cohort studies. METHODS: We searched for metabolomics studies of T2DM 
      cohort in PubMed, Scopus, and Web of Science for studies published within the 
      last 10 years from January 2008 to February 2017. The concentrations of 
      metabolites and odds ratios (ORs) were integrated and risk ratio (RR) values were 
      estimated to distinguish subjects with T2DM and normal participants. RESULTS: 
      Fourteen cohort studies were investigated in this meta-analysis. There were 4592 
      patients in the case group and 11,492 participants in the control group. We noted 
      a 1.89-, 1.63-, and 1.87-fold higher risk of T2DM associated with leucine (RR 
      1.89 [95% CI 1.57-2.29]), alanine (RR 1.63 [95% CI 1.48-1.79]), and oleic acid 
      (RR 1.87 [95% CI 1.62-2.17]), respectively. Lysophosphatidylcholine C18:0 (RR 
      0.80 [95% CI 0.72-0.90]) and creatinine (RR 0.63 [95% CI 0.53-0.74]) were 
      associated with 20% and 37% decreased T2DM risks, respectively. CONCLUSIONS: Most 
      amino acids in patients were positively related to diabetes, while creatinine and 
      some lysophosphatidylcholines showed a negative relationship. This suggests that 
      diabetic risk prediction using metabolites that sensitively reflect changes in 
      the body will improve individual diagnosis and personalize medicine.
CI  - Copyright (c) 2018 Elsevier B.V. All rights reserved.
FAU - Park, Jeong-Eun
AU  - Park JE
AD  - College of Pharmacy, Research Institute of Pharmaceutical Sciences, Kyungpook 
      National University, Daegu, Republic of Korea.
FAU - Lim, Hye Rin
AU  - Lim HR
AD  - College of Pharmacy, Research Institute of Pharmaceutical Sciences, Kyungpook 
      National University, Daegu, Republic of Korea.
FAU - Kim, Jun Woo
AU  - Kim JW
AD  - Department of Family Medicine, Daegu Catholic University Medical Center, Daegu, 
      Republic of Korea.
FAU - Shin, Kwang-Hee
AU  - Shin KH
AD  - College of Pharmacy, Research Institute of Pharmaceutical Sciences, Kyungpook 
      National University, Daegu, Republic of Korea. Electronic address: 
      kshin@knu.ac.kr.
LA  - eng
PT  - Journal Article
PT  - Meta-Analysis
PT  - Review
PT  - Systematic Review
DEP - 20180404
PL  - Ireland
TA  - Diabetes Res Clin Pract
JT  - Diabetes research and clinical practice
JID - 8508335
SB  - IM
MH  - Aged
MH  - Case-Control Studies
MH  - Cohort Studies
MH  - Cross-Sectional Studies
MH  - Diabetes Mellitus, Type 2/*blood
MH  - Female
MH  - Humans
MH  - Male
MH  - Middle Aged
MH  - Prospective Studies
MH  - Risk Factors
OTO - NOTNLM
OT  - Cohort
OT  - Glucose intolerance
OT  - Metabolite
OT  - Type 2 diabetes mellitus
EDAT- 2018/04/08 06:00
MHDA- 2018/07/26 06:00
CRDT- 2018/04/08 06:00
PHST- 2017/08/04 00:00 [received]
PHST- 2018/02/21 00:00 [revised]
PHST- 2018/03/26 00:00 [accepted]
PHST- 2018/04/08 06:00 [pubmed]
PHST- 2018/07/26 06:00 [medline]
PHST- 2018/04/08 06:00 [entrez]
AID - S0168-8227(17)31236-6 [pii]
AID - 10.1016/j.diabres.2018.03.045 [doi]
PST - ppublish
SO  - Diabetes Res Clin Pract. 2018 Jun;140:216-227. doi: 
      10.1016/j.diabres.2018.03.045. Epub 2018 Apr 4.