Evidence map›Paper›PMID 29999591›Full record

ArticleJournal of diabetes investigation2019

Plasma tyrosine and its interaction with low high-density lipoprotein cholesterol and the risk of type 2 diabetes mellitus in Chinese.

Jing Li, Yun-Feng Cao, Xiao-Yu Sun, Liang Han, Sai-Nan Li, Wen-Qing Gu, Min Song, Chang-Tao Jiang, Xilin Yang, Zhong-Ze Fang

Open access · goldAbstract read
In one paragraph

Article in Journal of diabetes investigation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
2.1field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed, 53 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 3 institutions in 2 countries.

Jing LiDepartment of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China.
Yun-Feng CaoKey Laboratory of Liaoning Tumor Clinical Metabolomics (KLLTCM), Jinzhou, Liaoning, China.
Xiao-Yu SunKey Laboratory of Liaoning Tumor Clinical Metabolomics (KLLTCM), Jinzhou, Liaoning, China.
Liang HanDepartment of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China.
Sai-Nan LiDepartment of Toxicology, School of Public Health, Tianjin Medical University, Tianjin, China.
Wen-Qing GuDepartment of Toxicology, School of Public Health, Tianjin Medical University, Tianjin, China.
Min SongDepartment of Toxicology, School of Public Health, Tianjin Medical University, Tianjin, China.
Chang-Tao JiangDepartment of Physiology and Pathophysiology, School of Basic Medical Sciences, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Peking University, Beijing, China.
Xilin YangDepartment of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China.
Zhong-Ze FangDepartment of Toxicology, School of Public Health, Tianjin Medical University, Tianjin, China.ORCID http://orcid.org/0000-0003-1113-9262
Tianjin Medical University · CNLiaoning University of Technology · CNPeking University · CN

Funding

13th five year plan and TMU talent project 11601501/2016KJ0313China Postdoctoral Science Foundation 2016M590210Foundation of Committee on Science and Technology of Tianjin 15JCYBJC54700individualized diagnosis and treatment of colorectal cancer LNCCC-B05-2015National Key Research and Development Program 2016YFC0903100National Key Research and Development Program 2016YFC0903102National Natural Science Foundation of China 81602826National Natural Science Foundation of China 81672961Natural Science Foundation of Liaoning Province L2015317Tianjin Health Bureau Science Foundation Key Project 16KG154Tianjin Project of Thousand Youth Talents Not applicable
6 · The paper itself

Abstract

AIMS/

introductionMetabolomic markers have the potential to improve the predicting accuracy of existing risk scores for type 2 diabetes mellitus. The present study aimed to test the associations between plasma tyrosine and type 2 diabetes mellitus with special attention to identifying possible cut-off points for type 2 diabetes mellitus, and its interactive effects with low high-density lipoprotein cholesterol (HDL-C) and/or high triglyceride for type 2 diabetes mellitus.

methodsFrom 27 May 2015 to 3 August 2016, we retrieved the medical notes of 1,898 inpatients with type 2 diabetes mellitus as the cases, and 1,522 individuals without diabetes as the controls who attended annual medical checkups from the same tertiary care center in Jinzhou, China. Logistic regression analyses were carried out to obtain odds ratios (ORs) and 95% confidence intervals (CIs). Restricted cubic spline analysis nested in the logistic regression analysis was used to identify possible cut-off points of tyrosine for type 2 diabetes mellitus. The additive interaction was used to estimate interactions between high tyrosine and low HDL-C in type 2 diabetes mellitus patients.

resultsThe OR of tyrosine for type 2 diabetes mellitus did not increase until 46 μmol/L and after that point, the OR rapidly rose with increasing tyrosine in a nearly linear manner. If 46 μmol/L was used to define high tyrosine, high tyrosine was associated with an increased OR of type 2 diabetes mellitus (adjusted OR 1.88, 95% CI 1.44-2.45). The presence of low HDL-C greatly enhanced the ORs of tyrosine for type 2 diabetes mellitus from 1.11 (95% CI 0.82-1.51) to 54.11 (95% CI 33.96-86.22) with significant additive interaction.

conclusionsIn Chinese adults, tyrosine >46 μmol/L was associated with increased odds of type 2 diabetes mellitus, which was contingent on low HDL-C.

Indexed as

Asian PeopleBiomarkersCase-Control StudiesCholesterol, HDLCross-Sectional StudiesDiabetes Mellitus, Type 2FemaleFollow-Up StudiesHumansMaleMiddle AgedPrognosisRisk FactorsROC CurveTyrosineBiomarkersCholesterol, HDLTyrosineAmino acidsLipoproteinType 2 diabetes

Identifiers

PMID29999591
PMCPMC6400201
OpenAlexW2855216278

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.