| Title: | Associations of six insulin resistance-related indices with the risk and progression of cardio-renal-metabolic multimorbidity: evidence from the UK biobank |
| Journal: | Cardiovascular Diabetology |
| Published: | 30 Sep 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41029369/ |
| DOI: | https://doi.org/10.1186/s12933-025-02928-w |
| URL: | https://cardiab.biomedcentral.com/counter/pdf/10.1186/s12933-025-02928-w |
Publication 19373
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Abstract
BackgroundInsulin resistance (IR)-related indices are increasingly recognized as important contributors to cardio-renal-metabolic (CRM) diseases. However, most prior studies have focused on isolated outcomes or cross-sectional disease status, lacking dynamic insight into disease progression. This study aimed to evaluate the predictive value of six IR-related indices for CRM onset and multistage progression using multistate modeling, and to explore potential biological mechanisms through mediation analysis.MethodsWe included 327,692 CRM-free participants from the UK Biobank in this prospective cohort study. Six IR-related indices including triglyceride-glucose (TyG) index, TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC), TyG-waist-to-height ratio (TyG-WHtR), triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio, and metabolic score for insulin resistance (METS-IR), were calculated using established formulas. Cox proportional hazards and multistate models were used to assess associations with CRM incidence and progression. Predictive performance was evaluated using area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Mediation analysis was conducted for inflammatory, hepatic, and renal biomarkers.ResultsOver a mean follow-up of 13.6 years, 17.3% (n = 58,563) of participants developed at least one CRM disease. All six IR-related indices were significantly associated with CRM multimorbidity, both in terms of incidence and progression. In predicting the incidence of CRM diseases, TyG-WC, TyG-WHtR, and METS-IR demonstrated superior performance. For each 1-standard deviation (SD) increase in TyG-WC, the risks of developing first, double, and triple CRM diseases increased by 51.4%, 88.6%, and 128.7%, respectively (all P < 0.001), with similar gradients observed for TyG-WHtR and METS-IR. Multistate Cox models further confirmed consistent associations between IR-related indices and CRM progression, particularly for TyG-WHtR and TyG. Specifically, a 1-SD increase in TyG-WHtR was associated with a 65.3% increased risk of transitioning from healthy to first CRM disease, 34.6% from first to double, and 26.7% from double to triple CRM diseases (all P < 0.001). In predictive performance evaluation, TyG-WC, TyG-WHtR, and METS-IR achieved the highest AUC, NRI, and IDI values. Mediation analyses indicated that systemic inflammation, organ function, and especially kidney function partially mediated the observed associations.ConclusionIn summary, IR-related indices, particularly TyG-WC, TyG-WHtR, and METS-IR, were observed to be associated with the presence and progression of CRM diseases. Their potential incorporation into risk assessment and prevention strategies, together with consideration of inflammatory and organ function pathways, might help reduce the burden of CRM multimorbidity; however, further prospective studies are needed to confirm these findings and clarify their clinical relevance.</p>
26 Keywords
- Adult
- Aged
- Biological Specimen Banks
- Biomarkers
- Blood Glucose
- Cardiometabolic Risk Factors
- Comorbidity
- Disease Progression
- Female
- Humans
- Incidence
- Insulin Resistance
- Kidney Diseases
- Lipids
- Male
- Metabolic Syndrome
- Middle Aged
- Predictive Value of Tests
- Prognosis
- Prospective Studies
- Risk Assessment
- Risk Factors
- Time Factors
- Triglycerides
- UK Biobank
- United Kingdom
5 Authors
- Lulu Liu
- Guangzan Yu
- Xuhou Ji
- Yunlong Wang
- Hua He
1 Application
| Application ID | Title |
|---|---|
| 177024 | The influence of genotype, biomarkers, lifestyle, and psychosocial factors on the occurrence, development, and prognosis of cardiovascular diseases, to establish an early risk prediction model. |
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