Evidence map›Paper›PMID 39184550›Full record

ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2024

Comparison of TyG and Newly TyG Related Indicators for Chronic Kidney Diseases Estimation in a Chinese Population.

Tong Chen, Yu Liu, Shiquan Wu, Siyu Long, Ling Feng, Wenqian Lu, Wenya Chen, Guoai Hong, Li Zhou, Fang Wang and 3 more

Abstract read
In one paragraph

Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

14 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Observational
  14. Article
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

13 authors.

Tong ChenDepartment of Nephrology, Chongqing Key Laboratory of Prevention and Treatment of Kidney Disease, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, 400037, People's Republic of China.
Yu LiuSouth China Hospital of Shenzhen University, Shenzhen, 518116, People's Republic of China.
Shiquan WuSouth China Hospital of Shenzhen University, Shenzhen, 518116, People's Republic of China.
Siyu LongSouth China Hospital of Shenzhen University, Shenzhen, 518116, People's Republic of China.
Ling FengDepartment of Nephrology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, People's Republic of China.
Wenqian LuSchool of Medicine, The Chinese University of Hong Kong, Shenzhen, People's Republic of China.
Wenya ChenSouth China Hospital of Shenzhen University, Shenzhen, 518116, People's Republic of China.
Guoai HongSouth China Hospital of Shenzhen University, Shenzhen, 518116, People's Republic of China.ORCID 0009-0006-2367-4819
Li ZhouSouth China Hospital of Shenzhen University, Shenzhen, 518116, People's Republic of China.
Fang WangSouth China Hospital of Shenzhen University, Shenzhen, 518116, People's Republic of China.
Yuechan LuoSouth China Hospital of Shenzhen University, Shenzhen, 518116, People's Republic of China.
Hequn ZouSouth China Hospital of Shenzhen University, Shenzhen, 518116, People's Republic of China.
Weihua LiuDepartment Nephrology of Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian, 350001, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Obesity and insulin resistance (IR) are positively associated with chronic kidney disease (CKD). Previous studies have identified triglyceride-glucose index (TyG) as a valuable surrogate of insulin resistance. Recently, new indicators combining TyG and simple anthropometric indices have emerged, The objective of this study was to assess the diagnostic accuracy of TyG and newly TyG related indicators in detecting CKD and explore which indices were superior in associating with CKD in Chinese population. Methods: Correlation test, logistic regression analysis, and receiver operating characteristic (ROC) analyses were used to evaluate the optimal cut-off and value of TyG, TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), TyG-waist to height ratio (TyG-WHtR) for predicting CKD. Results: TyG-WHtR, TyG-WC, and TyG-BMI correlated with several risk factors for CKD. After adjusting for confounders, TyG-WHtR and TyG-WC remained significantly associated with CKD, while TyG-BMI did not. The highest quartiles of TyG-WHtR and TyG-WC had 1.95- and 1.91-fold increased risk of CKD than the lowest quartiles (P<0.05). TyG-WHtR had the largest AUC (0.687) for CKD detection, followed by TyG-WC (0.669), TyG (0.652), and TyG-BMI (0.648). A united model that involved TyG-WHtR and other risk variables had higher predictive performance (AUC=0.791) than a single TyG related indicator. However, TyG had the highest OR (2.713, 95% CI, 1.446-5.090) for reduced eGFR in the fully adjusted model. A united model that involved TyG and WHtR separately had stronger predictive ability (AUC: 0.794) than the model that involved TyG-WHtR individually (AUC:0.791). Conclusion: This study found that TyG-WHtR had a better diagnostic value in the diagnosis of CKD, compared to other TyG related indicators, but none of the TyG related indicators showed a stronger association with CKD than TyG. Further research and more refined algorithms are needed to verify these new indicators.

Indexed as

chronic kidney diseaseinsulin resistanceobesityTyG related indicators

Identifiers

PMID39184550
PMCPMC11342947

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.