Evidence map›Paper›PMID 37421131›Full record

ArticleMedical science monitor : international medical journal of experimental and clinical research2023

The Triglyceride Glucose (TyG) Index as a Sensible Marker for Identifying Insulin Resistance and Predicting Diabetic Kidney Disease.

Hui Fang Li, Xia Miao, Ying Li

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical science monitor : international medical journal of experimental and clinical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 3 pooled it
3.2field-weighted citation impact, top 8% of its field
1 · What the graph read from it

What it found

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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

22 citing papers in PubMed, 3 syntheses or guidelines pooled it, 16 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

3 authors at 2 institutions in 1 country.

Hui Fang LiDepartment of Nephrology, Hebei Yiling Hospital, Shijiazhuang, Hebei, China (mainland).
Xia MiaoDepartment of Nephrology, Hebei Yiling Hospital, Shijiazhuang, Hebei, China (mainland).
Ying LiDepartment of Nephrology, The Third Hospital of Hebei Medical University, Shijiazhuang, Hebei, China (mainland).
Hebei Yiling Hospital · CNThird Hospital of Hebei Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND Patients with insulin-resistant diabetes have the highest risk of kidney disease. The triglyceride glucose (TyG) index is considered a reliable and simple marker of insulin resistance. We studied the relationship between the TyG index, diabetic kidney disease (DKD), and related metabolic disorders in patients with type 2 diabetes. MATERIAL AND METHODS This retrospective study included a consecutive case series from January 2021 to October 2022 in the Department of Endocrinology at Hebei Yiling Hospital. In total, 673 patients with type 2 diabetes met the inclusion criteria. The TyG index was calculated by napierian logarithmic (ln) (fasting triglyceride×fasting glucose /2). Patient demographic and clinical indicators were obtained from medical records, and statistical analysis was conducted using SPSS version 23. RESULTS The TyG index was significantly related to metabolic indicators (low-density lipoprotein, high-density lipoprotein, alanine aminotransferase, plasma albumin, serum uric acid, triglyceride, and fasting glucose) and urine albumin (P<0.01) but not with serum creatinine and estimated glomerular filtration rate. In multiple regression analysis, an increase in the TyG index was revealed to be an independent risk factor for DKD (OR: 1.699, P<0.001). CONCLUSIONS The TyG index was independently related to DKD and related metabolic disorders; therefore, the TyG index can be used as an early sensitive target for clinical guidance in the treatment of DKD with insulin resistance.

Indexed as

Diabetes Mellitus, Type 2Diabetic NephropathiesInsulin ResistanceBiomarkersBlood GlucoseGlucoseHumansRetrospective StudiesRisk FactorsTriglyceridesUric AcidBiomarkersBlood GlucoseGlucoseTriglyceridesUric Acid

Identifiers

PMID37421131
PMCPMC10337482
OpenAlexW4379880514

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.