Evidence map›Paper›PMID 42570033›Full record

ArticleEndocrine2026

Association of the combined triglyceride-glucose and frailty index with chronic kidney disease: evidence from two national cohort studies.

Chao Cheng, Yaohui Jiang, Qiuping Luo, Feng Qin, Jiuhong Yuan

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Article in Endocrine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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5 authors.

Chao Cheng *Andrology Laboratory, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China.
Yaohui Jiang *Department of Urology, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China.
Qiuping Luo *Out-patient Department, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China.
Feng QinAndrology Laboratory, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China.
Jiuhong YuanAndrology Laboratory, West China Hospital, Sichuan University, Chengdu, 610041, People's Republic of China. jiuhongyuan@scu.edu.cn.

Funding

Sichuan Provincial Science and Technology Support Program 2025YFHZ0212
6 · The paper itself

Abstract

objectiveThis study aimed to explore the relationships between TyG-Frailty Index (TyGFI), a comprehensive indicator of the triglyceride-glucose (TyG) index and frailty index (FI), and the risk of chronic kidney disease (CKD).

methodsA total of 8721 participants from the China Health and Retirement Longitudinal Study (CHARLS) and 9670 participants from the U.S. National Health and Nutrition Examination Survey (NHANES) were included for cross-sectional study; while 4102participants from CHARLS were included for longitudinal study. For longitudinal study, cumulative TyGFI (CumTyGFI) is calculated, and the participants were categorized into four clusters on the basis of the dynamic changes in the TyGFI through K-means clustering. Multivariable logistic regression models and cox proportional hazards models were applied to estimate associations with CKD, with adjustment for demographic, clinical, and lifestyle covariates. Restricted cubic spline (RCS) and subgroup analyses were used to explore dose-response relationships and interaction effects, respectively.

resultHigher TyGFI, older age, hypertension, diabetes, and dyslipidemia were associated with CKD. In fully adjusted models, the highest TyGFI quartile (CHARLS: OR 3.98, 95% CI 3.03-5.22; NHANES: OR 4.49, 95% CI 3.55-5.69), highest CumTyGFI quartile (HR 4.18, 95% CI 3.09-5.65), and "increasing TyGFI alteration" cluster (HR 3.56, 95% CI 2.69-4.71) all showed significant associations, with dose‑response trends confirmed by RCS.

conclusionIn two national datasets, higher TyGFI was associated with prevalent CKD in the cross-sectional analyses and with incident CKD in the longitudinal CHARLS analysis. TyGFI may serve as a compact epidemiological marker combining metabolic and frailty-related burden, although its predictive and clinical utility requires further validation.

Indexed as

Blood GlucoseFrailtyRenal Insufficiency, ChronicTriglyceridesAgedChinaCohort StudiesCross-Sectional StudiesFemaleHumansLongitudinal StudiesMaleMiddle AgedNutrition SurveysRisk FactorsUnited StatesBlood GlucoseTriglyceridesChronic kidney diseaseFrailty indexInsulin resistancePopulation-based cohortTriglyceride-glucose index

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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.