Association of the triglyceride-glucose index with cardiovascular outcomes across cardiovascular-kidney-metabolic syndrome stages
Article information
Abstract
Background/Aims
Cardiovascular-kidney-metabolic (CKM) syndrome reflects the interplay between metabolic dysfunction, chronic kidney disease, and cardiovascular disease. Insulin resistance (IR) is a key driver of CKM and is associated with adverse cardiovascular outcomes. The triglyceride-glucose (TyG) index is a cost-effective surrogate marker of IR; however, its prognostic value across CKM syndrome stages remains unclear.
Methods
We conducted a retrospective cohort study using data of 1,497,913 adults enrolled in the Korean National Health Insurance Database between 2009 and 2012. The participants were stratified into four CKM stages (0/1, 2, 3, and 4) and further categorized into three TyG index tertiles: Group 1 (< 8.27), Group 2 (8.27–8.81), and Group 3 (> 8.81). The primary composite outcomes were all-cause mortality, heart failure, stroke, and myocardial infarction.
Results
Over an average follow-up period of 12.6 ± 1.50 years, individuals in the highest TyG tertile demonstrated a significantly higher risk of the composite primary outcome compared to those in the lowest tertile (hazard ratio, 1.116; 95% confidence interval, 1.101–1.131; p < 0.001). This dose-dependent relationship was consistent across CKM stages, with the strongest associations observed in the early CKM stages (0/1 and 2). An elevated TyG index is also associated with an increased risk of secondary outcomes, including all-cause death, heart failure, stroke, and myocardial infarction.
Conclusions
The TyG index independently predicted cardiovascular risk across the CKM syndrome stages. Its integration into routine clinical assessments could enhance early risk stratification and guide preventive strategies, particularly for patients in the early stages of CKM syndrome.
INTRODUCTION
Cardiovascular-kidney-metabolic (CKM) syndrome is a clinically integrated framework that reflects the synergistic pathophysiological interplay between metabolic dysfunction (e.g., obesity, insulin resistance [IR], and dyslipidemia), chronic kidney disease (CKD), and cardiovascular disease (CVD) [1]. The American Heart Association has recently proposed a staging framework, ranging from 0–4, wherein stage 0 indicates no current cardiometabolic or renal risk factors, and extending to stage 4 denotes clinically manifested CVD [2]. This structured approach underscores the need to identify at-risk individuals in the early or subclinical stages before overt cardiovascular or renal complications occur. Advanced CKM syndrome stages (3 and 4) carry a notably heightened burden of morbidity and mortality, necessitating intensive lifestyle interventions and comprehensive guideline-directed pharmacotherapy to curb disease progression and improve clinical outcomes [1–4].
IR plays an integral role in the pathophysiology of CKM syndrome, driving the development of metabolic risk factors, accelerating kidney disease, and ultimately contributing to the development of CVD [2]. It is closely associated with increased cardiovascular risk [5]; however, a practical method for accurately evaluating IR in population-based settings remains elusive [6]. Although the euglycemic–hyperinsulinemic clamp is considered the gold standard, its high cost and complexity limit its widespread use. Similarly, the homeostasis model assessment of IR is commonly employed; however, it relies on measuring circulating insulin, which is not routinely quantified in clinical practice, thus limiting its clinical applicability [7]. The triglyceride-glucose (TyG) index, which is derived from fasting triglyceride and glucose levels, has emerged as a feasible and cost-effective marker of IR. Validation studies have indicated that it offers a performance comparable to or exceeding that of conventional markers and provides a robust prognostic value for type 2 diabetes, a range of CVDs, and mortality [8,9].
Considering the critical role of IR in CKM syndrome and its strong association with CVD, it is essential to understand whether the TyG index offers additional prognostic value beyond CKM staging. Despite its potential, evidence on the association between the TyG index and cardiovascular outcomes within CKM syndrome stages remains scarce. Therefore, this study aimed to assess the relationship between the TyG index and cardiovascular outcomes across all stages of CKM syndrome to enhance risk stratification and inform tailored management strategies.
METHODS
Study populations and data collection
This retrospective cohort study used data from the Korean National Health Insurance Database (NHID), a comprehensive resource that includes the medical claims, demographic data, and health examination records of nearly the entire Korean population. The NHID integrates data from biennial health-screening programs to promote the early detection and management of chronic diseases. These health checkups include measurements of dlifestyle behaviors, anthropometric parameters, and laboratory markers, providing a robust dataset for epidemiological research. This dataset has been described in prior publications [10–12].
This study included 1,500,959 adults who participated in the National Health Screening Program between 2009 and 2012. A total of 3,046 individuals were excluded, including participants aged 90 years or older (n = 374) and participants with missing values for any of the following variables: blood pressure (n = 35), smoking status (n = 166), body mass index (BMI; n = 67), waist circumference (n = 68), fasting glucose (n = 47), lipid profile (n = 1,176), or estimated glomerular filtration rate (eGFR; n = 1,286). The final cohort comprised of 1,497,913 participants. The participants were categorized into four stages: stage 0 or 1 (n = 495,261); stage 2 (n = 862,009); stage 3 (n = 94,864); and stage 4 (n = 45,779). Within each CKM syndrome stage, the participants were divided into three groups based on the TyG index tertiles: Group 1 (TyG index < 8.27), Group 2 (TyG index 8.27–8.81), and Group 3 (TyG index > 8.81). Outcomes were analyzed according to these classifications (Fig. 1). This study was approved by the Institutional Review Board (GURI 2024-12-021), and the requirement for informed consent was waived due to the anonymized and de-identified nature of the NHID dataset. All analyses adhered to relevant ethical guidelines, and the study complied with the tenets of the Declaration of Helsinki.
Study flowchart. CKM, cardiovascular-kidney-metabolic; TyG, triglyceride-glucose.
The key variables collected were demographic factors (age and sex), lifestyle behaviors (smoking status, alcohol consumption, and physical activity), and socioeconomic status (household income categorized into quartiles). Anthropometric measurements included BMI and waist circumference. Laboratory markers included fasting glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein cholesterol, triglycerides, and eGFR. Blood pressure (systolic and diastolic) was measured. Clinical history variables included prior diagnoses of hypertension, diabetes mellitus (DM), and dyslipidemia. Medication use was documented for antihypertensive, glucose-lowering, lipid-lowering, and antiplatelet drugs.
TyG index calculation
The TyG index was calculated using the following formula: TyG index = ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2] [13]. The participants were classified into three tertiles based on their TyG indices, relative to the distribution of the entire study population: Group 1 (< 8.27), Group 2 (8.27–8.81), and Group 3 (> 8.81), with Group 1
serving as the reference for comparative analysis. In addition, the receiver operating characteristic (ROC) curve analysis for the composite primary outcome was performed in the total population and across the CKM syndrome stages to identify the optimal cutoff values for the TyG index using Youden’s index (Supplementary Fig. 1). These cutoffs were subsequently used to dichotomize the participants into high- and low-TyG groups in the supplementary analysis.
Staging of CKM syndrome
The participants were stratified into CKM syndrome stages (0–4) based on previously established criteria incorporating metabolic, cardiovascular, and renal parameters [1,2]. Stage 0 represented individuals without cardiometabolic risk factors or CKD. Stage 1 included those with overweight or dysfunctional adiposity, but no additional metabolic or renal dysfunction. Stage 2 included participants with metabolic risk factors, such as hypertriglyceridemia, hypertension, metabolic syndrome, or diabetes, as well as patients with CKD, who exhibited eGFRs ranging from 30–59 mL/min/1.73 m2. Stage 3 included individuals with subclinical CVD, defined by either very high-risk CKD (eGFR < 30 mL/min/1.73 m2) or an elevated 10-year cardiovascular risk, as indicated by a Predicting Risk of Cardiovascular Disease EVENTs (PREVENT) score ≥ 20% [14]. The PREVENT score—a cardiovascular risk model recently developed by the American Heart Association—is derived and validated using data from over six million individuals across 46 U.S.-based datasets. In this study, we used a base model, which incorporated variables, such as age, non-HDL cholesterol levels, HDL cholesterol levels, systolic blood pressure, diabetes, smoking status, BMI, eGFR, and use of antihypertensive or statin medications. The detailed formula is available at: https://professional.heart.org/prevent [14]. Stage 4 represented individuals clinically diagnosed with CVD. A detailed description of the CKM staging is provided in Supplementary Table 1.
Study outcomes
The primary outcome of the study was a composite endpoint comprising all-cause death, heart failure, stroke (both ischemic and hemorrhagic), and myocardial infarction during the follow-up period, which concluded on December 31, 2022. The average follow-up duration was 12.60 ± 1.50 years. Secondary outcomes included the individual components of the primary composite outcomes. Heart failure was identified based on hospitalization records using the International Classification of Diseases, Tenth Revision (ICD-10) codes I50, I42.0, I11.0, or I13.0–I13.2. Myocardial infarction was defined as hospitalization with coronary revascularization and a discharge diagnosis coded I21 or I22. Stroke was confirmed in hospitalized individuals using brain imaging and discharge diagnoses of ICD-10 codes I63–I64 for ischemic stroke and I60–I62 for hemorrhagic stroke. Supplementary Table 2 provides a detailed list of diagnostic and procedural definitions, including the ICD-10 codes used to classify the comorbidities, CKM syndrome stages, and clinical outcomes.
Statistical analyses
Baseline characteristics according to the TyG index tertiles were compared using the chi-square test for categorical variables and one-way analysis of variance (ANOVA) for continuous variables. The incidence rates of cardiovascular outcomes were calculated as the total number of events divided by the cumulative person-years of follow-up and expressed per 1,000 person-years. Kaplan–Meier survival curves were constructed for each CKM syndrome stage to compare event-free survival across TyG tertiles. Statistical differences between survival curves were tested using the log-rank test. Cox proportional hazards regression models were used to assess the association between the TyG index tertiles and outcomes within each CKM syndrome stage. Hazard ratios (HRs) and 95% confidence intervals (CIs) were adjusted for confounders including age, sex, smoking status, alcohol consumption, physical activity, household income, and medication use (antihypertensive, glucose-lowering, lipid-lowering, and antiplatelet drugs). Model 1 included adjustments for age and sex, whereas Model 2 included additional lifestyle and socioeconomic variables. Model 3 was further adjusted for medication use (antihypertensive, glucose-lowering, lipid-lowering, and antiplatelet drugs); eGFR category (≥ 90, 60–89, 30–59, 15–30, < 15); and dipstick proteinuria. To assess the continuous relationship between the TyG index and cardiovascular outcomes, restricted cubic spline regression models were applied using Model 3 adjustments across all CKM syndrome stages. All analyses were performed using complete case data, excluding participants with missing data. Statistical significance was set at a two-tailed p value < 0.05. All the analyses were performed using SAS (version 9.4; SAS Institute, Cary, NC, USA) and R (version 4.2.1; R Foundation for Statistical Computing).
RESULTS
Baseline characteristics
Table 1 presents the baseline characteristics of the study population stratified by TyG index tertiles. Participants in the higher TyG tertiles were older, with Group 3 having the highest mean age (50.83 ± 13.17 years) and a greater proportion of men (66.9% vs. 53.8% vs. 38.9%, Group 3 vs. 2 vs. 1). A higher TyG index was associated with increased metabolic risk factors, including an elevated BMI; waist circumference; and fasting glucose, triglyceride, and total cholesterol levels. The prevalence of hypertension, DM, and dyslipidemia increased progressively across the TyG tertiles, with Group 3 exhibiting the highest prevalence of these conditions. Similarly, the use of antihypertensive, glucose-lowering, lipid-lowering, and antiplatelet medications was most frequent in Group 3 (p < 0.001). In addition, the mean PREVENT score increased progressively across TyG tertiles (3.10 ± 6.08 in Group 1, 5.42 ± 7.55 in Group 2, and 8.72 ± 9.59 in Group 3; p < 0.001). The distribution of CKM syndrome stages differed notably across TyG tertiles (p < 0.001). In Group 1, a higher proportion of participants were classified as stage 0 or 1, whereas Group 3 was predominantly composed of individuals with stage 2 or higher. The proportions of participants in stages 3 and 4 were significantly higher in Group 3 (10.1% and 3.8%, respectively) than in Group 1 (3.2% and 2.1%, respectively).
Baseline characteristics
Clinical outcomes according to TyG index across CKM syndrome stages
Figure 2 illustrates the Kaplan–Meier survival curves for the composite primary outcomes (all-cause death, heart failure, stroke, or myocardial infarction) and the secondary outcomes across the TyG index tertiles in the total population. Individuals in the highest TyG tertile (Group 3) exhibited a significantly higher cumulative incidence of the composite primary outcome than those in the lower tertiles, demonstrating a clear dose–response relationship (Fig. 2A, log-rank p < 0.001). A similar pattern was observed for the secondary outcomes: all-cause death (Fig. 2B), heart failure (Fig. 2C), stroke (Fig. 2D), and myocardial infarction (Fig. 2E). Overall, these trends were consistent with the primary outcome, showing a higher cumulative incidence in the highest TyG tertile with a dose–response relationship (all log-rank p < 0.001).
Kaplan–Meier curves illustrating the cumulative incidence of the composite primary outcome (all-cause death, heart failure, stroke, and myocardial infarction) and secondary outcomes across the TyG index tertiles in the total population. Curves for (A) composite primary outcomes (all-cause death, heart failure, stroke, and myocardial infarction); (B) all-cause death; (C) heart failure; (D) stroke; and (E) myocardial infarction. TyG, triglyceride-glucose.
Table 2 shows the association between the TyG index tertiles and the composite primary outcome after adjusting for potential confounders in both the total population and across the CKM syndrome stages. In the fully adjusted model (Model 3), the individuals in Group 3 exhibited a significantly higher risk of the composite primary outcome than those in Group 1 (HR, 1.116; 95% CI, 1.101–1.131; p < 0.001), showing a dose-dependent relationship. Group 2 showed a modest increase in risk (HR, 1.035; 95% CI, 1.021–1.049; p < 0.001). When stratified by CKM syndrome stage (interaction p < 0.001), a stronger association was observed in the earlier stages, with the highest HRs observed in Group 3 at stage 0/1 (HR, 1.166; 95% CI, 1.036–1.312; p = 0.011) and stage 2 (HR, 1.090; 95% CI, 1.069–1.111, p < 0.001). The effect remained significant in stage 3, although it was attenuated compared to that of the earlier stages (HR, 1.055; 95% CI, 1.025–1.085; p < 0.001). However, in Stage 4, the association between Group 3 and the composite outcome was no longer statistically significant after adjustment (HR, 1.040; 95% CI, 0.999–1.083; p = 0.055). Group 2 followed a similar trend, with a moderate increase in risk across all CKM syndrome stages. However, only the increased risk for stage 2 was significant (HR, 1.023; 95% CI, 1.004–1.044; p = 0.019). In addition, when the participants were dichotomized using the optimal TyG cutoff values identified by the ROC curve analysis for the composite outcome in the total population and CKM stages (8.489 for the total population, 8.097 for Stage 0/1, 8.132 for Stage 2, 9.571 for Stage 3, and 8.509 for Stage 4), those with a high TyG index had a higher risk of the primary composite outcome than those with a low TyG index relative to the total population and all CKM stages. For secondary outcomes (Supplementary Tables 3–6), Group 3 showed significantly higher risks for all-cause death (HR, 1.061; 95% CI, 1.042–1.081; p < 0.001); heart failure (HR, 1.054; 95% CI, 1.032–1.078; p < 0.001); stroke (HR, 1.209; 95% CI, 1.179–1.239; p < 0.001); and myocardial infarction (HR, 1.736; 95% CI, 1.647–1.829; p < 0.001) in the total population. The relative risks of myocardial infarction and stroke were higher than those of all-cause death and heart failure. Across CKM stages, the association showed a similar pattern to that of the primary outcome, with stronger effects observed in the earlier stages than in the later stages.
Association between TyG index and composite primary outcome across CKM syndrome stages
Furthermore, Figure 3 presents the RCS curves illustrating the association between the TyG index and composite primary outcome based on the fully adjusted model across the total population (Fig. 3A) and CKM syndrome stages (Fig. 3B–E). The analysis demonstrated a nonlinear, dose-dependent relationship, with a notable increase in HRs at higher TyG index levels. Excluding Stage 0/1, in which the TyG index distribution was concentrated at lower values, earlier CKM syndrome stages exhibited a greater increase in HRs at higher TyG index levels than later stages.
Restricted cubic spline curves displaying the continuous relationship between the TyG index and the hazard of the composite primary outcome based on fully adjusted Cox regression models. Curves for (A) total population, (B) CKM syndrome stage 0/1, (C) CKM syndrome stage 2, (D) CKM syndrome stage 3, and (E) CKM syndrome stage 4. TyG, triglyceride-glucose; CKM, cardiovascular-kidney-metabolic; CI, confidence interval.
To assess the potential effect of lipid-lowering therapy, we conducted a subgroup analysis stratified by baseline statin use. Although the association between a higher TyG index and clinical outcomes remained generally consistent across the statin user and non-user groups, the magnitude of the association appeared slightly more pronounced among non-users. The interaction p values were statistically significant for heart failure and myocardial infarction (Supplementary Table 7).
DISCUSSION
This study investigated the association between TyG index and cardiovascular outcomes across CKM syndrome stages in a large nationwide cohort. Our findings suggested that the TyG index serves as an independent predictor of adverse outcomes beyond the CKM staging. This is the first large-scale study to evaluate the prognostic value of the TyG index across all stages of CKD. In this study, CKM Stages 0 and 1 were combined into a single reference category (Stage 0/1) because of their low event rates, and the distribution of the TyG index in Stage 0 was heavily skewed towards the lower values, such that no participants qualified for the highest TyG tertile (Group 3). CKM Stage 0 is defined as the absence of any metabolic risk factors, whereas stage 1 includes individuals with overweight or dysfunctional adiposity, but without metabolic or renal dysfunction [2]. Both groups represented populations at relatively low cardiometabolic risk, where the primary drivers of future disease are subclinical metabolic disturbances, such as IR. A separate analysis of Stages 0 and 1 for the primary composite outcomes is shown in Supplementary Table 8. The key findings were as follows: (1) the individuals in the highest TyG tertile (Group 3, TyG index > 8.81) had a significantly higher risk of the composite primary outcome than those in the lowest tertile (Group 1, TyG index < 8.27), with a dose-dependent relationship observed both in the total population and across most CKM syndrome stages, although the association was not statistically significant in Stage 4 after full adjustment; (2) this association was stronger in earlier CKM syndrome stages, particularly in Stages 0/1 and 2, and attenuated in advanced stages; and (3) similar trends were observed for secondary outcomes, including all-cause death, heart failure, stroke, and myocardial infarction. These findings indicated that the TyG index may serve as a valuable marker to enhance the prognostic utility of the CKM syndrome staging system, particularly for facilitating early risk stratification before the development of overt CVD.
CKM syndrome represents a complex, multidirectional interplay between metabolic risk factors, CKD, and the cardiovascular system, which contributes to adverse clinical outcomes [3]. IR is the fundamental driver of this syndrome and accelerates the progression of metabolic disturbances, CKD, and CVD [2,15–17]. The TyG index, a simple and cost-effective marker of IR, is associated with cardiovascular risk [8,18]. In previous cross-sectional studies, an elevated TyG index was associated with advanced CKM syndrome [19]. Moreover, a recent study in China demonstrated that a high TyG index was associated with kidney function deterioration in patients with CKM syndrome [20]. However, its prognostic significance in cardiovascular outcomes within the CKM staging framework remains largely unknown. While a modified version of the TyG index, the triglyceride glucose-BMI, previously correlated with cardiovascular risk in populations with CKM syndrome stages 0–3 [21], the study relied on survey-based self-reports for outcome assessment and had a relatively small sample size, limiting the consistency of findings across CKM subgroups. Our study extended these previous observations [19–21] by utilizing a large, comprehensive, and nationwide cohort data to accurately capture a range of outcomes, including all-cause death, across the entire CKM syndrome spectrum, thereby providing more robust evidence of the prognostic value of the TyG index for cardiovascular events across CKM syndrome stages.
Intriguingly, the cardiovascular risk in the highest TyG tertile (group 3, TyG > 8.81) was most pronounced in the earlier stages of CKM syndrome. Several mechanisms may explain these observations. In the early stages of CKM (particularly Stages 0/1 and 2), the predominant pathology involves IR, low-grade inflammation, and early vascular dysfunction, rather than irreversible organ damage [2]. Consequently, the TyG index may capture the subclinical metabolic disturbances that precede overt CVD, underscoring its role as an important early risk marker [22]. This suggests that, in populations with a relatively low baseline risk, the TyG index may serve as an early warning marker, identifying individuals who might benefit from timely lifestyle interventions to prevent progression to more advanced, high-risk stages. Furthermore, we observed that the impact of the TyG index on myocardial infarction and stroke was greater than its effect on heart failure or all-cause death, suggesting that IR may drive atherosclerotic processes more strongly [23]. These findings warrant further prospective studies to confirm and expand upon these observations.
These findings have important clinical implications. Incorporating the TyG index into routine clinical assessments can enhance early risk stratification in patients with CKM syndrome, particularly during the initial stages. This straightforward and cost-effective measure enables clinicians to identify individuals at an elevated risk of progressing to advanced CKM syndrome stages and developing CVD, before the overt disease manifests [19]. Early detection using the TyG index may help inform early lifestyle modification strategies, such as dietary changes, increased physical activity, and weight management, aimed at reducing IR and mitigating disease progression [24,25]. Specifically, individuals with elevated TyG levels in the early stages of CKM syndrome may benefit from structured lifestyle interventions combined with regular evaluations of IR and CKM stage progression, enabling the timely identification of candidates for pharmacological therapy. Ultimately, the integration of the TyG index into current screening protocols can refine patient management by providing a more nuanced understanding of cardiometabolic risk across the CKM spectrum.
The strengths of our study include its large, nationally representative sample size and long follow-up period of > 12 years, which allowed for a robust assessment of long-term outcomes. Furthermore, the comprehensive nature of the Korean NHID enabled detailed adjustment for potential confounders, including demographic, lifestyle, and pharmacological factors. By stratifying participants according to both the CKM syndrome stage and TyG index tertiles, we were able to delineate a clear, graded relationship between IR and adverse outcomes across different stages of cardiometabolic and renal health. Despite these strengths, this study had several limitations that warrant consideration. First, the retrospective observational design precluded definitive causal inferences regarding the relationship between TyG index and cardiovascular outcomes. Second, our reliance on claims data and health-screening records might have led to the misclassification or underestimation of some clinical events despite rigorous coding protocols. Third, although the homogeneity of the Korean population provided a consistent dataset, it may limit the generalizability of our findings to other ethnic groups and healthcare settings. Furthermore, the PREVENT risk score used in this study was developed based on U.S.-based cohorts. Therefore, its applicability to Koreans should be interpreted with caution. Fourth, unmeasured confounding factors, such as dietary habits, genetic predisposition, and socioeconomic status may have influenced both the TyG index and clinical outcomes. These factors were not fully accounted for in this study. Fifth, while the TyG index is a dynamic biomarker reflecting the ongoing metabolic status, our study was restricted to baseline measurements, which limited our ability to assess the impact of temporal changes on cardiovascular outcomes. Future longitudinal studies with more frequent measurements are essential for a better understanding of their prognostic roles. Finally, although the TyG index demonstrated an independent prognostic value across CKM stages, its applicability and method of integration into existing cardiovascular risk models remain unclear and requires further investigation. Future research should validate these findings in diverse populations and explore the mechanisms linking IR to CKM syndrome progression, including the potential role of longitudinal changes in the TyG index and the effects stratified by age and sex, which may inform personalized management strategies across the CKM stages. Prospective studies or randomized controlled trials evaluating whether aggressive lifestyle interventions and close monitoring in the early stages of CKM syndrome, specifically in individuals with a high TyG index, can effectively reduce the incidence of cardiovascular events would be particularly valuable.
In conclusion, our study demonstrated that the TyG index is a valuable prognostic marker for cardiovascular outcomes across the CKM syndrome stages. Individuals with a high TyG index, particularly those in the early stages of CKM syndrome, are at an increased risk of progressing to advanced CKM syndrome stages and developing adverse cardiovascular events. Integrating the TyG index into routine clinical assessments could enhance early risk stratification, enabling timely lifestyle interventions to mitigate the progression of cardiometabolic and renal dysfunctions. Future prospective studies in diverse populations are warranted to validate these findings and to elucidate the underlying mechanisms linking IR to CKM syndrome progression.
KEY MESSAGE
1. The TyG index is independently associated with dose-dependent adverse cardiovascular outcomes.
2. Its prognostic value is consistent across all CKM syndrome stages, with the highest risk in early stages.
3. Incorporating the TyG index into clinical assessments may enable early, targeted management of CKM syndrome and prevent its progression.
Notes
Acknowledgments
This study used data from the National Health Insurance Service database (NHIS-2024-1-258).
CRedit authorship contributions
Byung Sik Kim: conceptualization, data curation, formal analysis, investigation, writing - original draft, writing - review & editing; Hyun-Jin Kim: conceptualization, data curation, formal analysis, investigation, writing - original draft, writing - review & editing; Shinje Moon: writing - review & editing; Hasung Kim: investigation, methodology, validation, visualization; Jungkuk Lee: investigation, methodology, software, validation; Jeong-Hun Shin: conceptualization, data curation, formal analysis, supervision, writing - review & editing
Conflicts of interest
The authors disclose no conflicts.
Funding
This study did not receive any specific grants from funding agencies in public, commercial, or non-profit sectors.