The Triglyceride-Glucose Index: An Accessible Tool for Risk Stratification in Chronic Kidney Disease
Bibliographic record
Abstract
Background: Chronic kidney disease (CKD) presents a significant global burden, requiring accessible risk assessment tools. Insulin resistance (IR), pivotal in cardiometabolic pathologies, contributes significantly to kidney injury and progression. The triglyceride-glucose (TyG) index offers a simple, cost-effective, insulin-independent surrogate for IR. This review synthesizes evidence on TyG’s utility, limitations, and future directions concerning kidney disease outcomes. Methods: A literature search was conducted in PubMed, Scopus, and Google Scholar for publications from December 2008 to May 2025. Systematic reviews, meta-analyses, and observational studies (cohort, cross-sectional) were included, examining TyG’s relationship with kidney disease (incident CKD, progression, DKD), T2DM as a DKD risk factor, and cardiometabolic outcomes in CKD populations. Results: Evidence from diverse cohorts has consistently shows elevated TyG index consistently associated with increased risk of incident T2DM, incident CKD, established CKD progression (including ESRD), and prevalent/incident diabetic kidney disease (DKD). Higher TyG also independently predicted increased cardiovascular events and mortality in CKD. Mechanistically, TyG reflects IR-driven pathways (endothelial dysfunction, oxidative stress, inflammation) linked to kidney damage. Existing challenges include the lack of standardized formula/thresholds and complex associations in diverse CKD subgroups. Conclusion: The TyG index is a promising marker for increased risk of developing and progressing CKD, including DKD. Although evidence demonstrates its association with adverse renal/cardiorenal outcomes, this needs to be clarified and standardized through robust clinical validation in diverse CKD populations, while adjusting for potential confounders to promote its application in CKD risk assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".