The association of triglyceride-glucose index and related indices with all-cause and cardiovascular mortality in biologically aging populations

Biological age refers to the gradual decline of physiological functions over time, and, compared to chronological age (CA), more accurately reflects an individual's health status and aging level.With the global aging population and the advancement of precision medicine, biological age has gained increasing attention as an important tool for predicting disease risk and guiding interventions.Studies have shown that individuals of the same CA can exhibit significant physiological differences, emphasizing the importance of identifying quantifiable and predictive biomarkers for personalized health assessments and aging interventions.Various methods for measuring biological age have been developed, including clinical indicators, molecular biomarkers, and composite scoring systems, such as homeostatic dysregulation,the Klemera–Doubal method (KDM),phenotypic age,and allostatic load,all of which have demonstrated good performance in predicting mortality risk, age-related diseases, and functional decline. [,] ^{1 2} [,] ^{3 4} [–] ^{5 7} [] ⁸ [] ⁹ [] ¹⁰ [] ¹¹

Cardiovascular diseases (CVDs) are the leading cause of death worldwide, causing over 17 million deaths annually. Aging, as a core risk factor, exacerbates cardiovascular damage by regulating key pathways. Insulin resistance (IR), a key metabolic disorder in aging, exacerbates mitochondrial dysfunction by inhibiting AMP-activated protein kinase (AMPK) and weakens the antioxidant effect of nuclear factor erythroid 2-related factor 2 (Nrf2). This forms a "Aging-IR-Pathway Dysfunction" vicious cycle, accelerating the progression of CVDs.IR induces chronic hyperinsulinemia, enhances oxidative stress, and promotes inflammation, thereby accelerating the aging process.In recent years, the triglyceride glucose index (TyG), a simple and stable alternative marker of IR based on fasting triglyceride (TG) and glucose levels, has received widespread attention.Compared to traditional methods, such as the glucose clamp test or homeostasis model assessment of IR, TyG offers greater convenience and clinical accessibility, particularly for large-scale epidemiological studies.Additionally, its derived indices, TyG-body mass index (TyG-BMI) and TyG-waist-to-height ratio (TyG-WHtR), which incorporate an individual's obesity status, have been shown to be closely associated with various metabolic diseases.However, the performance of the TyG and its derivatives in the context of biological aging (BA) – particularly their distribution across distinct BA subtypes (e.g., Klemera–Doubal biological age acceleration [KDM-BA] and phenotypic age [PhenoAge]) and their associations with mortality outcomes – has not been systematically explored. [–] ^{12 15} [] ¹⁶ [] ¹⁷ [] ¹⁸ [,] ^{19 20}

Given the potential central role of IR in the aging process and related mortality risks, there is an urgent need to identify quantifiable predictive markers reflecting metabolic status that are applicable to biologically aged populations. Therefore, utilizing large-scale representative data from the U.S. National Health and Nutrition Examination Survey(NHANES) database, this study aimed to comprehensively evaluate the distribution patterns of TyG and its derived indices in BA populations and explore their association with all-cause and cardiovascular mortality risks. This will deepen our understanding of the mechanisms of metabolic disturbances in the aging process and provide theoretical and practical tools for early identification and intervention in high-risk populations.

All analyses in this study incorporated sample weights, clustering, and stratification to estimate appropriate variance and ensure national. To assess the normality of continuous variables, we primarily used histograms to inspect the distribution of each variable visually. Variable. A normal distribution was considered when the histogram showed a bell shape. As shown in Figures S1 and S2, Supplemental Digital Content,, the continuous variables age, alanine aminotransferase (ALT), aspartate aminotransferase (AST), TG, UA, and FBG showed non-normal distributions. The study population was grouped according to quartiles (Q1–Q4) of TyG and its related indicators. Continuous variables were expressed as median (Q₁, Q₃) if skewed and using a Kruskal–Wallis test, or as mean (standard error) if normally distributed. Comparisons between groups were made using analysis of variance. Categorical variables were expressed as numerical values and weighted percentages (n [weighted %]) using the chi-square test or Fisher exact test, and differences were considered statistically significant at < .05. All analyses adhered to the STROCSS 2024 guidelines (see Table S1, Supplemental Digital Content,for detailed missing data). https://links.lww.com/MD/Q724↗ https://links.lww.com/MD/Q724↗ P [] ²³

The study utilized both univariate and multivariate Cox proportional hazards regression models to assess the association between TyG and its related indicators and the risk of all-cause and cardiovascular mortality in the BA population. The results are presented as hazard ratios (HR) with 95% confidence intervals (CI). For easier comparison, the exposure variables were standardized into-scores (i.e., the effect size change per 1 standard deviation [SD] increase). Three regression models were constructed: Model 1, unadjusted; Model 2, adjusted for age, sex, and race; and Model 3, further adjusted for age, cancer, sex, race, education level, marital status, poverty-income ratio, smoking status, drinking status, CVD, CKD, ALT, AST, BUN, and UA levels. To account for multicollinearity, variance inflation factors were calculated to assess multicollinearity; variables with variance inflation factors < 5 were included (BMI was excluded because of its high correlation with TyG-BMI and TyG-WHtR). Detailed data of the multicollinearity tests are provided in Table S2, Supplemental Digital Content,. Kaplan (KM) survival curves were used to illustrate the censoring data and survival pattern differences across different TyG quartiles. To explore the dose–response relationship between TyG-related indicators and all-cause and cardiovascular mortality in the BA population (measured by PA/KDM-BA), restricted cubic spline regression was performed with 4 knots, using the median value of the variable as a reference. When a nonlinear relationship was detected, a recursive approach was used to identify potential inflection points, and two-stage fully adjusted Cox models were employed to characterize the risk associations on both sides of the inflection points. Subgroup analyses were conducted based on age, antihypertensive medication use, statin use, antihyperglycemic agents, and severity of BA. Stratification factors were introduced as potential effect modifiers and interaction terms were included to test for differences in effects between the subgroups. Given that this study was exploratory and aimed to generate hypotheses rather than validate them, no corrections for multiple comparisons were made. All data analyses were performed using R software (version 4.2.3,). Z https://links.lww.com/MD/Q724↗ https://www.r-project.org/↗

This study demonstrated that TyG and its related indices, TyG-BMI and TyG-WHtR, are significant predictors of all-cause and cardiovascular mortality in BA populations. These results highlight that higher levels of TyG and its derivatives were consistently associated with worse survival outcomes across different BA groups, particularly in the KDM-BA population. Notably, the associations between TyG-related indices and mortality risk were more pronounced in individuals under the age of 65 years and those exhibiting higher BA, further supporting the idea that metabolic dysregulation, as reflected by TyG, may play a crucial role in the aging process and its associated mortality risks. These findings underscore the potential utility of TyG and its derivatives as biomarkers for the early identification of individuals at a higher risk of mortality in aging populations.

This study revealed the prognostic value of TyG and its derived indices in BA subpopulations. The KDM-BA approach quantifies the molecular cumulative damage of BA by integrating multiple biological markers, such as inflammatory factors,metabolic enzymes,and epigenetic clockswhereas the PA approach reflects organ functional decline based on clinical phenotype parameters, such as albumin and ALP levels.As a key surrogate marker for IR, the TyG may accelerate the BA process through the metabolic aging-related mortality risk, involving 2 core pathways. First, mitochondrial-telomere axis damage: in IR states, the efficiency of oxidative phosphorylation in skeletal muscle and liver cells is reduced, leading to excessive production of reactive oxygen species (ROS). ROS activates the p53/p21 pathway, inducing cell cycle arrest and telomere attrition, a process that aligns with the acceleration of epigenetic age assessed by KDM-BA (e.g., DNA methylation clock shift).Notably, this ROS accumulation is amplified by impaired Nrf2-mediated antioxidant defenses: TyG elevation suppresses Nrf2 nuclear translocation by enhancing its binding to Keap1 (a cytoplasmic repressor), thereby reducing transcription of downstream antioxidants like HO-1 and NQO1. This blunted antioxidant response – consistent with Nrf2's role as a "master regulator of redox homeostasis"exacerbates mitochondrial DNA damage and telomere shortening, particularly in KDM-BA populations sensitive to molecular perturbations.Survival analysis results indicate that in the KDM-BA population, high TyG levels are significantly associated with both all-cause and cardiovascular mortality ( < .001), suggesting that elevated TyG may accelerate cellular aging through mitochondrial damage and telomere attrition, thereby increasing the risk of mortality, particularly in individuals with metabolic disorders. Second, inflammation-endothelial dysfunction axis dysregulation: elevated TyG levels promote M1 polarization of adipose tissue macrophages, release pro-inflammatory cytokines (such as IL-6 and TNF-α), and induce the senescence-associated secretory phenotypewhich disrupts endothelial function and accelerates atherosclerotic plaque formation.This process is exacerbated by suppressed AMPK activity: IR inhibits AMPK phosphorylation at Thr172, impairing its ability to restrain NF-κB nuclear translocation and pro-inflammatory gene expression In PA populations, where organ functional decline predominates, TyG-related inflammation still drives cardiovascular mortality, as evidenced by the association between TyG and cardiovascular death – likely via amplified endothelial dysfunction and plaque instability, similar to how inflammation promotes postinfarction remodeling. [] ²⁴ [,] ^{25 26} [,] ^{27 28} [,] ^{22 29} [,] ^{30 31} [] ¹³ [] ³² [] ³³ [,] ^{12 15} P

This study further revealed the differential prognostic value of TyG-BMI and TyG-WHtR in the BA subpopulations. TyG-derived indices, by integrating IR with body composition parameters (such as obesity levels and visceral fat distribution), provide a more sensitive reflection of the cumulative effects of metabolic aging. The underlying mechanism may involve an imbalance in the adiponectin/leptin ratio caused by visceral fat accumulation, which exacerbates insulin signal transduction disorders by inhibiting AMPK activity and activating mTOR pathway.This metabolic-inflammation interaction aligns closely with the multi-omics characteristics of the KDM-BA assessment system, such as inflammatory markersand metabolic enzyme profiles.TyG-derived indices exhibited stronger predictive power for mortality risk in the KDM-BA population (TyG-BMI: HR range = 1.19–1.23, < .05; TyG-WHtR: HR range = 1.28–1.34, < .001). In contrast, PA assessment, which focuses on organ decline markers such as liver and kidney function and nutritional status, is less sensitive to metabolic abnormalities, leading to the lack of a significant association between TyG-BMI and mortality in the PA population. Only TyG-WHtR retained its independent predictive value for cardiovascular mortality (HR = 1.26, 95% CI 1.03–1.51). This heterogeneity suggests that visceral fat distribution (TyG-WHtR) may serve as a central hub linking metabolic disorders to organ function decline. Even in PA populations, TyG-WHtR may drive cardiovascular events through its pro-inflammatory microenvironment and hemodynamic load, potentially via AMPK/Nrf2 crosstalk: visceral fat-derived free fatty acids suppress AMPK, which normally phosphorylates and stabilizes Nrf2, creating a feedforward loop of oxidative stress and organ damage. [–] ^{34 36} [] ³⁷ [] ³⁸ [,] ^{39 40} P P

The threshold effects revealed by the nonlinear analysis (TyG-BMI > 215.145, TyG-WHtR > 5.034) had significant clinical implications. When TyG-BMI exceeds this threshold, adipose tissue transitions from a compensatory metabolic phase (where lipids are safely stored in subcutaneous fat) to a decompensated phase (where ectopic fat is deposited in the liver and heart), leading to systemic damage mediated by free fatty acid spillover and lipotoxicity.At this tipping point, AMPK activity is severely blunted (as AMP/ATP ratio homeostasis is disrupted), impairing its regulation of fatty acid oxidation and mitochondrial biogenesis.simultaneously, Nrf2 is sequestered in the cytoplasm by Keap1, losing its ability to induce antioxidant genes.The TyG-BMI threshold (215.145) identified in this study aligns with the pathological tipping point of metabolic obesity,suggesting that the metabolic buffering capacity of visceral fat becomes saturated, significantly increasing mortality risk (HR = 1.48, 95% CI 1.22–1.79). TyG-WHtR, which integrates the waist-to-height ratio, more directly reflects the abnormal visceral fat distribution. Even in the PA population, where organ function decline is the primary characteristic, TyG-WHtR independently predicted cardiovascular mortality (HR = 1.26, 95% CI 1.03–1.51). The threshold effect of TyG-WHtR (>5.034) further underscores the central role of abdominal obesity: WHtR is more sensitive than BMI in reflecting visceral fat load,thus allowing the identification of high-risk individuals even within the PA population. [,] ^{41 42} [,] ^{13 15} [] ⁴³ [,] ^{44 45}

Subgroup analysis revealed that individuals under 65 years of age exhibited a stronger association between TyG-related indices and mortality risk, possibly because of the more pronounced long-term cumulative effects of metabolic disorders in younger populations. For example, in patients with metabolic dysfunction-associated fatty liver disease, the predictive sensitivity of the TyG-WHtR index for mortality is more prominent in the younger subgroup.This difference may relate to preserved AMPK/Nrf2 responsiveness in younger individuals: aging reduces AMPK activation capacity (via increased ST loop phosphorylation) and Nrf2 transcriptional efficiency (via Neh7 domain-mediated repression by retinoid X receptor alpha), blunting the metabolic-aging interaction in older populations.Older individuals also often present with comorbidities and compensatory physiological changes, which may partially offset the prognostic power of TyG for mortality risk. [] ⁴⁶ [,] ^{13 14} [,] ^{47 48}

Importantly, in populations with higher levels of BA (e.g., KDM-BA ≥ 5 years), each 1-SD increase in the TyG was associated with a 32% increase in all-cause mortality risk (HR = 1.32, 95% CI 1.17–1.47) and a 43% increase in cardiovascular mortality risk (HR = 1.43, 95% CI 1.17–1.75). This gradient effect suggests that metabolic disorders (such as IR) and aging-related molecular damage (such as telomere attrition and protein homeostasis imbalance) may synergistically amplify mortality risk. The potential mechanism is that IR accelerates molecular damage through oxidative stress, whereas the decline in cellular repair capacity associated with aging exacerbates metabolic dysregulation, creating a vicious cycle. [,] ^{49 50}

Additionally, the use of antihypertensive drugs, statins, and antidiabetic medications did not significantly alter the relationship between TyG levels and mortality in most cases. Although statin use showed a slight increase in the association, the effect was small, indicating that the impact of medication on the relationship between TyG and mortality was limited. This further supports the inherent independent association between metabolic indicators (such as TyG) and mortality risk, which may not be significantly influenced by pharmacological treatments for hypertension, diabetes, or dyslipidemia.Future studies should explore whether AMPK activators (e.g., 5-aminoimidazole-4-carboxamide ribonucleotide) or Nrf2 inducers (e.g., sulforaphane) can mitigate the TyG-related mortality risk, particularly in high-risk subgroups such as KDM-BA ≥ 5 years.

The strengths of this study lie in utilizing a large, nationally representative sample to minimize sampling bias, conducting rigorous statistical adjustment for potential confounding factors (e.g., demographic characteristics and underlying diseases) to enhance the reliability of the results, and performing a systematic analysis of multiple TyG-derived indices – within the framework of 2 fully validated BA assessment methods – which enriches the dimensions of evaluation.

However, the study still has several limitations. First, as an observational study, it can only identify statistical associations of the TyG and its related indices with mortality, but cannot establish a causal relationship. Future validation via randomized controlled trials and investigation into causal mechanisms are therefore needed. Second, although adjustments have been made for key confounding factors, unmeasured variables (e.g., genetic susceptibility and long-term environmental exposures) may introduce residual confounding, which could restrict the generalizability of the findings. Third, lifestyle factors such as smoking and alcohol consumption were assessed via self-report, a method susceptible to recall bias or social desirability bias. Fourth, the BA model employs a cross-sectional approach, which fails to dynamically capture the aging process; incorporating longitudinal data would better enhance the accuracy of association assessments. Finally, while adjustments have been made for common medications, critical information (e.g., drug dosage, duration of use, and medication adherence) was unavailable, potentially compromising the accuracy of association estimates.

The clinical implications of our findings are as follows: First, it provides a convenient and subtype-adapted tool for stratifying mortality risk in biologically aging populations. The TyG and its related indices (TyG-BMI and TyG-WHtR) can be calculated solely based on routine physical examination data and can be fully applied in biologically aging populations.

Second, it clarifies quantifiable metabolic intervention thresholds: the TyG has a key threshold of 8.1 to 8.6 (mortality risk increases significantly when exceeding this range), and in the PA population, mortality risk surges when TyG-BMI > 215.145 or TyG-WHtR > 5.034, which provides specific basis for formulating personalized metabolic control targets in clinical practice. Third, it supports personalized management based on BA characteristics: for populations with high aging burden (KDM-BA/PA ≥ 5), the follow-up interval for TyG should be shortened and stricter control targets established; meanwhile, combined with the subgroup analysis result that medications have limited impact on the association between TyG and mortality, lifestyle interventions (e.g., low-GI diet, aerobic exercise) are prioritized as the core intervention when thresholds are exceeded, facilitating precise metabolic health management in aging populations.

This study underscores the significance of TyG and its related indices (TyG-BMI and TyG-WHtR) as key indicators of all-cause and cardiovascular mortality in biologically aging populations. Notably, strong associations were found in the KDM-BA cohort, particularly among individuals aged < 65 years and those with advanced BA. These findings suggest that TyG and its related indices may play a crucial role in the long-term management of biologically aging populations and serve as valuable biomarkers for identifying high-risk individuals. This further highlights the importance of metabolic dysregulation in the aging process and its contribution to an increased mortality risk.

Meng-Qun Cheng, Zhong-Ping Bai, Gao Song, Cai-Qiong Zhang, Xing Liu, Rong Li. Conceptualization:

Meng-Qun Cheng, Gao Song, Xing Liu, Rong Li. Data curation:

Meng-Qun Cheng, Zhong-Ping Bai, Gao Song, Cai-Qiong Zhang, Xing Liu, Rong Li. Formal analysis:

Gao Song, Rong Li. Funding acquisition:

Gao Song, Cai-Qiong Zhang, Xing Liu, Rong Li. Investigation:

Meng-Qun Cheng, Zhong-Ping Bai, Gao Song, Cai-Qiong Zhang, Rong Li. Methodology:

Meng-Qun Cheng, Zhong-Ping Bai, Gao Song, Rong Li. Project administration:

Zhong-Ping Bai, Gao Song, Rong Li. Resources:

Zhong-Ping Bai, Gao Song, Rong Li. Software:

Meng-Qun Cheng, Zhong-Ping Bai, Gao Song, Rong Li. Supervision:

Meng-Qun Cheng, Zhong-Ping Bai, Gao Song, Cai-Qiong Zhang, Xing Liu, Rong Li. Validation:

Zhong-Ping Bai, Gao Song, Cai-Qiong Zhang, Rong Li. Visualization:

Meng-Qun Cheng, Zhong-Ping Bai, Gao Song, Cai-Qiong Zhang, Rong Li. Writing – original draft:

Gao Song, Rong Li. Writing – review & editing: