Skip to main content
Download PDF
Download ePub

Triglyceride to high-density lipoprotein ratio as a predictor for 10-year cardiovascular disease in individuals with diabetes in Thailand

Journal of Health, Population and Nutrition volume 44, Article number: 147 (2025) Cite this article

Abstract

Background

Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide. The triglyceride to high-density lipoprotein cholesterol (TG/HDL) ratio has emerged as a potential marker for CVD risk. However, its predictive value for high 10-year predicted Cardiovascular (CV) risk remains unclear; This study evaluates the predictive value of the TG/HDL-C ratio for 10-year cardiovascular risk using the Framingham Heart Study (FHS) risk prediction model in individuals with Type 2 Diabetes Mellitus (T2DM).

Methods

A cross-sectional study was conducted on 61,004 adults from 2014,2015, and 2018 aged 30–74 years with T2DM, without a history of CVD. The FHS model was used to estimate 10-year predicted CV risk, and high CVD risk was defined as ≥ 20%. ROC curve analysis was used to determine the optimal TG/HDL cutoff for high 10-year predicted CV risk in the overall population and age-specific subgroups. Logistic regression was performed to find the association between TG/HDL and high 10-year predicted CV risk, adjusting for potential confounders.

Results

The optimal TG/HDL-C cutoff was 2.52 (AUC = 0.618, 95% CI: 0.612–0.624), with 67% sensitivity and 50% specificity. Higher TG/HDL were associated with increased odds of high predicted CVD risk in a dose-dependent manner, with an adjusted odds ratio (AOR) of 5.16 (95% CI: 4.86–5.49) in the highest TG/HDL quartile (> 4.91). Age-stratified analysis identified lower cutoffs for older adults (≥ 60 years: 2.42, AUC = 0.694) than younger individuals (< 60 years: 2.98, AUC = 0.636), indicating stronger predictive performance in older adults.

Conclusions

The TG/HDL ratio is significantly associated with 10-year predicted CVD risk in T2DM with age-specific differences in predictive value. The lower cutoff for older adults (2.42) suggests even modest elevations indicate increased risk. These findings support TG/HDL integration into routine CVD risk assessments and highlight the importance of age-specific cutoffs for improved risk stratification.

Introduction

Cardiovascular disease (CVD) is a group of disorders that affect the heart and blood vessels, including coronary artery disease (CAD), stroke, peripheral arterial disease (PAD), and heart failure. It remains a leading cause of global morbidity and mortality, with its impact steadily increasing. From 1990 to 2019, the prevalence of CVD doubled, contributing to a rise in cumulative deaths from 12.1 million to 18.6 million [1, 2]. This growing burden has placed significant strain on healthcare systems, leading to escalating costs and an increased caregiving load [3, 4]. Despite advancements in medical management, early identification and control of Cardiovascular (CV) risk factors remain crucial for reducing disease burden and improving patient outcomes [5].

In Thailand, although a Thai CV risk score has been developed for a particular professional demographic, additional external validation is still required [6]. Numerous tools for assessing cardiovascular risk exist, including the Framingham Heart Study (FHS) model, one of the most widely recognized risk-predicting tools. This model plays a pivotal role in care management, intervention evaluation, and risk stratification [7, 8]. Originally designed for the U.S. population, its application in Asian populations has been validated by two retrospective cohort studies demonstrating its utility in multiethnic settings [9, 10].

The triglyceride-to-high-density lipoprotein cholesterol (TG/HDL) ratio is a widely used clinical marker with applications in various metabolic and cardiovascular contexts. It is recognized as an early indicator of insulin sensitivity, with studies incorporating it into predictive models to enhance the accuracy of insulin resistance estimation [11, 12]. Additionally, the TG/HDL ratio has been identified as a reliable cardiovascular risk marker, correlating with early vascular damage even in younger individuals [13]. Abnormal lipid profiles, characterized by elevated TG and reduced HDL levels, are well-established modifiable risk factors for CVD [14]. While lowering low-density lipoprotein (LDL) cholesterol has proven effective in reducing CV risk, emerging evidence highlights that abnormal TG and HDL levels remain significant contributors to cardiovascular pathology [15,16,17]. Numerous studies demonstrate an association between higher TG/HDL ratios and cardiovascular disease [15, 18,19,20]. Both prospective and retrospective cohort studies link a high TG/HDL ratio to an increased risk of major adverse cardiovascular events (MACE) [16, 21]. Furthermore, the TG/HDL ratio has been employed to predict revascularization, coronary artery calcifications, stent stenosis, and future cardiovascular events [22,23,24,25].

However, the prognostic capability of the TG/HDL ratio varies across ethnic groups. Studies indicate that TG/HDL is a strong predictor of cardiovascular risk in Caucasian populations [26, 27], while its predictive value appears less consistent in individuals of African ancestry [27]. These differences may arise from genetic variations in lipid metabolism, disparities in adipose tissue distribution affecting triglyceride and HDL levels, and lifestyle factors influencing lipid profiles and cardiovascular risk [28,29,30]. These ethnic discrepancies underscore the necessity for customized risk evaluation strategies when using TG/HDL as a CV risk marker in heterogeneous populations.

This cross-sectional study aims to evaluate the role of the TG/HDL ratio in predicting 10-year CV risk among individuals with T2DM, utilizing the FHS risk prediction model. By integrating TG/HDL into a validated risk assessment framework, this research seeks to determine its utility as a reliable indicator for cardiovascular risk stratification, particularly within Asian populations.

Methods

Study design and subjects

The data for this study were obtained from the database titled ‘Assessment of Quality of Care among Patients Diagnosed with Type 2 Diabetes and Hypertension Visiting the Ministry of Public Health (MoPH) and Bangkok Metropolitan Administration Hospital in Thailand (Thailand DM/HT) [31]. This serial cross-sectional study was conducted in 2014, 2015, and 2018. Before commencing the study, we obtained the necessary permissions from the National Health Security Office (NHSO) and the Medical Research Network of the Consortium of Thai Medical Schools (MedResNet). The study included Thai adult patients with T2DM or Hypertension (HT) from all MoPH Hospitals, Bangkok Metropolitan Hospitals, and public and private clinics participating in the nationwide NHSO program. A total of 33,288, 32,616, and 36,793 patients with T2DM were recruited in 2014, 2015, and 2018, respectively (Fig. 1). The study included T2DM patients aged 30 to 74. The FHS equation was employed to calculate the predicted 10-year CV risk [32]. Participants with a history of CVD (including coronary death, myocardial infarction, coronary insufficiency, angina, ischemic stroke, hemorrhagic stroke, transient ischemic attack, PAD, and heart failure) or those with unavailable laboratory data necessary for calculating FHS (TG, FBS, HDL, TC) were excluded. Thus, 61,004 participants were included in the study.

Fig. 1
figure 1

Flow of enrolled with Type 2 diabetes receiving care in Thailand in 2014, 2015, and 2018

Full size image

Data collection

We utilized a standardized case report form (CRF) for medical records. This CRF was completed by well-trained registered nurses following a standard protocol. After completion, the forms were sent to the MedResNet central data management unit in Nonthaburi, Thailand. The data collected from the patient’s medical records included baseline information, current medications, laboratory testing results, and the status of diabetes complications.

Variables and measurements

For the operationalization of demographic variables. BMI was categorized as ≥ 25 and < 25 kg/m², following the WHO classification for obesity tailored to the Asian population [33]. Age was stratified into two groups: <60 years and ≥ 60 years. These cutoffs were based on the average age of premature myocardial infarction occurrence (55 years for males and 65 years for females), reflecting age-specific cardiovascular risk patterns [34]. Fasting plasma glucose (FPG) was classified into ≤ 130 mg/dL and > 130 mg/dL, based on the target glycemic control recommended for diabetic patients by established clinical guidelines [35]. Hypertension, SBP, and smoking status were excluded from the model analysis to reduce redundancy, as these variables are already integral components of the Framingham 10-year CV risk score. Multicollinearity diagnostics confirmed that all included variables, including TG/HDL ratio and age, were free from multicollinearity with the Framingham score (Variance Inflation Factor, VIF < 5). TG/HDL ratio and age were retained in the model due to their clinical and research relevance. The predicted 10-year CV risk was calculated using the FHS equation. This calculation incorporated variables from laboratory-based data such as age, sex, systolic blood pressure (SBP), history of current smoking, history of diabetes, treatment for hypertension, total cholesterol (TC), and HDL cholesterol. The predicted 10-year CV risk of 10–20% was defined as low to intermediate risk, while a risk ≥ 20% was categorized as high [32]. The TG/HDL ratio was calculated using the formula TG divided by HDL.

Statistical analysis

Data analysis in this study was performed using IBM SPSS version 29. Categorical data are presented as numbers and percentages. Continuous variables are shown as means and standard deviations (SD) for normally distributed data and as medians and interquartile ranges (IQR) for non-normally distributed data. Logistic regression was conducted to assess whether TG/HDL-C was independently associated with a higher likelihood of high predicted CV risk (≥ 20%). Univariable logistic regression was performed to identify potential confounders. Multicollinearity diagnostics were conducted on all demographic and clinical variables included in the analysis. VIF values were calculated for all variables, and those with VIF < 5 were considered free of multicollinearity. The TG/HDL ratio identified confounders were incorporated into the final multivariable logistic regression model, with results expressed as adjusted odds ratios (AORs) and 95% confidence intervals (CIs). Receiver Operating Characteristic (ROC) curve analysis was conducted to assess the discriminative performance of the TG/HDL ratio for predicting high 10-year CV risk. The optimal cutoff value was determined using Youden’s J statistic, with sensitivity and specificity reported for the overall population and specific subgroups. Interaction tests were performed to identify potential effect modification by key demographic and clinical variables. Subgroup analyses were subsequently performed for variables demonstrating significant interaction effects. Sensitivity analyses were conducted to confirm the robustness of findings, particularly for subgroups with strong associations or significant interactions. All statistical tests were two-sided, with p-values < 0.05 considered statistically significant.

Results

Characteristics of study participants

Table 1 demonstrates the baseline demographic and laboratory characteristics of study participants. A total of 61,004 diabetic patients were enrolled in this study. There were 17,761, 18,551, and 24,692 from the database in 2014, 2015 and 2018, respectively. Over the years, there was a significant increase in the proportion of participants aged ≥ 60 years (from 48.1% in 2014 to 52.8% in 2018, p < 0.001). The majority of participants were female, with a notable upward trend in representation from 67.5% in 2014 to 70.7% in 2018 (p < 0.001).

Table 1 Baseline characteristics of enrolled diabetic patients from 2014, 2015 and 2018 (N = 61,004)
Full size table

In terms of clinical characteristics, the prevalence of hypertension and dyslipidemia remained consistently high across all three time points. The proportion of individuals with BMI ≥ 25 kg/m² increased from 52.3% in 2014 to 54.3% in 2018 (p < 0.001), while the percentage of participants with FPG ≥ 130 mg/dL also showed an increasing trend (p < 0.001). Similarly, the proportion of participants with FPG > 130 mg/dL increased over time (p < 0.001). Median TG/HDL-C ratios showed a declining trend from 3.3 (IQR: 2.2–5.1) in 2014 to 3.0 (IQR: 2.0–4.7) in 2018 (p < 0.001), suggesting potential changes in lipid profiles among participants over time. Baseline characteristics, including geographic location, hospital level, occupation, smoking status and SBP were shown in Supplementary Table.

Logistic regression analysis for the relationship between TG/HDL and high predicted 10-year CV risk

Table 2 displays a logistic regression analysis of the high predicted 10-year CV risk and TG/HDL ratio, adjusting for potential confounders, for every one-unit increase in the TG/HDL-C ratio, the odds of having a high predicted CVD risk increased by 22% (AOR: 1.22, 95% CI: 1.21–1.23, p < 0.001). After using ROC analysis to determine the cut-point for high predicted CV risk (TG/HDL ≥ 2.52). Participants with TG/HDL-C ≥ 2.52 had a nearly 2.83-fold higher risk of high predicted CVD risk compared to those with TG/HDL-C < 2.52 with an AOR of 2.83 (95% CI: 2.72–2.96, p < 0.001). When analyzed by quartiles, a dose-response relationship was observed (Fig. 2), with higher TG/HDL-C quartiles showing greater odds of high predicted CVD risk. Participants in the second quartile (TG/HDL 2.05–3.06) had 1.88 times higher odds (AOR: 1.88, 95% CI: 1.77–2.00, p < 0.001) compared to the reference group (TG/HDL < 2.05). The highest quartile (TG/HDL > 4.91) demonstrated the strongest association, with participants having 5.16 times higher odds of high predicted CV risk (AOR: 5.16, 95% CI: 4.86–5.49, p < 0.001).

Table 2 Logistic regression analysis of high 10-year predicted CV risk and TG/HDL ratio
Full size table
Fig. 2
figure 2

Boxplot of Predicted 10-year CV risk and TG/HDL ratio quartiles

Full size image

Subgroup analysis

To assess potential effect modification, interaction testing was performed for sex, age, BMI, and FPG (Fig. 3). Significant effect modification was observed for age and FPG in the association between TG/HDL ratio and cardiovascular risk (p for interaction < 0.001 for both). Conversely, no evidence of effect modification was found for sex or BMI. The result led to further subgroup analyses as shown in Table 3. Stratified analysis by age demonstrated that the association between TG/HDL-C and high predicted CVD risk was more pronounced in individuals aged < 60 years (AOR = 3.09, 95% CI: 2.88–3.31, p < 0.001) compared to those aged ≥ 60 years (AOR = 2.73, 95% CI: 2.58–2.89, p < 0.001). Stratification by FPG levels (< 130 mg/dL vs. ≥130 mg/dL) showed a consistent association across both groups. Among those with FPG < 130 mg/dL, a TG/HDL-C ratio > 2.52 was associated with a 2.86-fold higher CVD risk (AOR: 2.86. 95% CI: 2.67–3.07, p < 0.001). Similarly, in participants with FPG ≥ 130 mg/dL, the risk was 2.82 times higher (AOR: 2.82, 95% CI: 2.67–2.98, p < 0.001).

Fig. 3
figure 3

Interaction test assessing effect modification of the association between TG/HDL ratio and high predicted 10-year CVD risk across selected variables

Full size image
Table 3 Age-specific, FPG-specific logistic regression analysis of high FHS-CV risk and TG/HDL ratio
Full size table

Receiver operational characteristic curve analysis of TG/HDL ratio for predicting high predicted 10-year CV risk

Figure 4A. illustrates an ROC curve with an optimal cut point of 2.52 (Sensitivity 67%, Specificity 50%) for the TG/HDL ratio to predict high predicted 10-year CV risk, and AUC of 0.618 (95% CI: 0.612–0.624). Given the observed interaction between TG/HDL ratio and age, an age-stratified ROC analysis was conducted to assess whether distinct cutoff values could improve predictive performance in different age groups. Figure 4B illustrates an ROC curve with an optimal cut point of 2.98, with an AUC of 0.636 (95% CI: 0.629–0.643). The sensitivity and specificity at this threshold were 61% and 42%, respectively for Participants aged < 60 years. Figure 4C exhibits a higher AUC of 0.694 (95% CI: 0.678–0.709) for Participants aged ≥ 60 years. Sensitivity was 66%, while specificity was 37%. The TG/HDL ratio cutoff was lower at 2.42.

Fig. 4
figure 4

ROC curve of TG/HDL ratio for predicting high predicted 10-year CV risk. (A) Overall participants (B) age < 60 and (C) age ≥ 60

Full size image

Sensitivity analysis

Figure 5 illustrates the prevalence of high predicted 10-year CV risk across TG/HDL ratio quartiles, stratified by age groups (< 60 and ≥ 60 years). The prevalence increases progressively across TG/HDL quartiles in both age groups, with a higher prevalence observed in participants aged ≥ 60 years compared to those < 60 years for each quartile (P for trend < 0.001).

Fig. 5
figure 5

Prevalence of high predicted 10-year CV risk and TG/HDL quartiles stratified by age

Full size image

Table 4 presents the results of the sensitivity analysis conducted to evaluate the association between TG/HDL ratio and high predicted CV risk stratified by age groups. Among participants aged < 60 years, a high TG/HDL ratio (≥ 2.98) was associated with a significantly higher likelihood of high predicted CV risk, with an adjusted odds ratio (AOR) of 2.94 (95% CI: 2.76–3.13, p < 0.001), compared to those with a low TG/HDL ratio (< 2.98). Similarly, in participants aged ≥ 60 years, a high TG/HDL ratio (≥ 2.42) was significantly associated with high predicted CV risk, with an AOR of 2.74 (95% CI: 2.59–2.91, p < 0.001), compared to the low TG/HDL group (< 2.42).

Table 4 Sensitivity analysis
Full size table

Discussion

This study investigated the association between the TG/HDL ratio and 10-year predicted CV risk in a large cohort of individuals with T2DM. The baseline characteristics highlight a significant burden of cardiovascular risk factors in this cohort, including high prevalence rates of overweight and obesity, hypertension, and dyslipidemia. The TG/HDL ratio serves as a novel lipid marker providing insights beyond its HDL component, while age remains a well-established predictor of cardiovascular outcomes.

Our findings demonstrate that a higher TG/HDL ratio is significantly associated with increased likelihood of high predicted CV risk in individuals with T2DM. This result is consistent with prior research showing that elevated TG/HDL ratios predict cardiovascular risk. For instance, an 8-year follow-up in the Copenhagen Male Study reported that a high TG-low HDL profile is as strong a predictor of ischemic heart disease as isolated high LDL levels [15]. Additionally, the dose-response relationship observed across TG/HDL quartiles in our study supports its utility in identifying individuals at elevated CVD risk, consistent with prior studies reporting a significant upward trend in cardiovascular risk across increasing TG/HDL quartiles [26].

ROC curve analysis identified an optimal TG/HDL cutoff of 2.52 for predicting high 10-year CV risk, with an AUC of 0.618 (95% CI: 0.612–0.624), sensitivity of 67%, and specificity of 50%. We did not perform sex-specific cutoffs due to the absence of significant interaction between TG/HDL and sex. However, our cutoff differs from those reported in other populations. For example, Silva et al. identified cutoffs of 3.26 for men and 2.72 for women [18], while studies in Argentina reported cutoffs of 3.5 for men and 2.5 for women [19]. Similarly, research in Iran reported thresholds of 4.42 for men, 3.76 for women, and 3.68 for the overall population [20]. These variations likely reflect differences in geographic regions, race/ethnicity, age distribution, and cardiovascular risk assessment tools [29, 36]. Our study provides a population-specific cutoff tailored to Asian individuals with T2DM, reinforcing the importance of contextual factors in risk stratification.

Interaction testing and subgroup analyses revealed that the association between TG/HDL ratio and high predicted CV risk varied by age group. The association between the TG/HDL and high predicted CVD risk remained significant across all age groups, but a stronger association was observed in younger individuals. ROC analysis showed greater predictive value of TG/HDL in older adults, with a lower cutoff of 2.42 and an AUC of 0.694, compared to a higher cutoff of 2.98 and an AUC of 0.636 in younger adults. These findings suggest age-related differences in lipid metabolism and cumulative cardiovascular burden may influence TG/HDL’s predictive utility [37,38,39]. Younger individuals may require higher thresholds to identify those at elevated risk, reflecting differences in baseline cardiovascular risk and lipid physiology [40].

The clinical implications of this study are notable. The TG/HDL ratio may serve as an accessible, cost-effective marker for early risk detection in high-risk patients. Age-specific cutoffs could enhance predictive accuracy, particularly for older individuals, ensuring better identification of those at risk. Additionally, these findings highlight the importance of triglyceride-lowering strategies, especially in individuals with elevated TG/HDL ratios and other risk factors. Interventions such as lifestyle modifications and pharmacologic therapy targeting triglycerides could play a key role in reducing cardiovascular risk.

Limitations

This study provides robust evidence from a large, nationally representative dataset of T2DM patients and the use of ROC analysis to define optimal TG/HDL cutoffs and stratified analyses to account for effect modification by age strengthens the clinical applicability of these results. However, several limitations should be considered. Firstly, due to the cross-sectional nature of this study, it does not establish causality between TG/HDL and CV risk but rather identifies the predictive relationship. Future longitudinal studies are needed to determine whether TG/HDL changes over time influence actual CVD outcomes. Additionally, a total of 23,598 participants (28.9% of the initial sample) were excluded due to incomplete data, which may have implications for statistical power. Furthermore, the use of the FHS model for risk prediction introduces a degree of interdependence, as HDL is a component of both the TG/HDL ratio and the FHS score. Although multicollinearity diagnostics confirmed no significant issues, this overlap could potentially influence the results and requires the need of careful interpretation. Finally, geographic and ethnic differences in TG/HDL cutoffs highlight the need for population-specific validation.

Conclusion

This study highlights the TG/HDL ratio as a significant predictor of high 10-year predicted CV risk in individuals with T2DM, demonstrating a strong dose-response relationship and age-specific differences in predictive value. The ROC-defined cutoff of 2.52 provides a clinically relevant threshold for CVD risk stratification, with lower cutoffs (2.42) recommended for older adults to enhance predictive performance. These findings underscore the potential utility of the TG/HDL ratio as a simple, cost-effective tool for screening high-risk individuals, supporting age-adjusted risk assessment strategies in clinical practice.

Data availability

Data cannot be shared publicly because the dataset contains identifying information.Additionally, the data belong to the Thailand DM/HT study of the MedResNet. Thus, ethicsrestrictions exist on the data set. Data are available from the Thai NHSO, Bangkok, Thailand(contact Sirikorn Khunsri via sirikorn@nhso.go.th) for researchers meeting the criteria to access confidential data.

Abbreviations

FHS:

Framingham heart study

TG:

Triglyceride

HDL:

High density lipoprotein

TG/HDL:

Triglyceride to high density lipoprotein

CVD:

Cardiovascular disease

T2DM:

Type 2 diabetes mellitus

FPG:

Fasting plasma glucose

BMI:

Body mass index

CI:

Confidence interval

SD:

Standard deviation

IQR:

Interquartile range

OR:

Odds ratio

AOR:

Adjusted odds ratio

References

  1. Qu C, Liao S, Zhang J, Cao H, Zhang H, Zhang N et al. Burden of cardiovascular disease among elderly: based on the global burden of disease study 2019. Eur Heart J Qual Care Clin Outcomes. 2023.

  2. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global burden of cardiovascular diseases and risk factors, 1990–2019. J Am Coll Cardiol. 2020;76:2982–3021.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Mensah GA, Roth GA, Fuster V. The Global Burden of Cardiovascular Diseases and Risk Factors 2020 and Beyond. [cited 2023 Nov 30]; Available from: https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jacc.2019.10.009

  4. Zhang D, Wang G, Fang J, Mercado C. Hyperlipidemia and medical expenditures by cardiovascular disease status in US adults. Med Care. 2017;55:4–11.

    Article  PubMed  Google Scholar 

  5. Oude Wolcherink MJ, Behr CM, Pouwels XGLV, Doggen CJM, Koffijberg H. Health economic research assessing the value of early detection of cardiovascular disease: A systematic review. PharmacoEconomics. 2023;41:1183–203.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Lertsakulbunlue S, Mungthin M, Rangsin R, Kantiwong A, Sakboonyarat B. Trends in predicted 10-year risk for cardiovascular diseases among patients with type 2 diabetes in Thailand, from 2014 to 2018. BMC Cardiovasc Disord. 2023;23:183.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Kwon HS, Song KH, Yu JM, Kim DS, Shon HS, Ahn KJ, et al. Framingham risk score assessment in subjects with Pre-diabetes and diabetes: A Cross-Sectional study in Korea. J Obes Metab Syndr. 2021;30:261–70.

    Article  PubMed  PubMed Central  Google Scholar 

  8. D’Agostino RB, Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, et al. General cardiovascular risk profile for use in primary care. Circulation. 2008;117:743–53.

    Article  PubMed  Google Scholar 

  9. Chia YC, Gray SYW, Ching SM, Lim HM, Chinna K. Validation of the Framingham general cardiovascular risk score in a multiethnic Asian population: a retrospective cohort study. BMJ Open. 2015;5:e007324–007324.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Borhanuddin B, Mohd Nawi A, Shah SA, Abdullah N, Syed Zakaria SZ, Kamaruddin MA, et al. 10-Year cardiovascular disease risk Estimation based on lipid Profile-Based and BMI-Based Framingham risk scores across multiple sociodemographic characteristics: the Malaysian cohort project. Sci World J. 2018;2018:1–8.

    Article  Google Scholar 

  11. Zhou M, Zhu L, Cui X, Feng L, Zhao X, He S, et al. The triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio as a predictor of insulin resistance but not of Î’ cell function in a Chinese population with different glucose tolerance status. Lipids Health Dis. 2016;15:104.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Paulmichl K, Hatunic M, Højlund K, Jotic A, Krebs M, Mitrakou A, et al. Modification and validation of the Triglyceride-to–HDL cholesterol ratio as a surrogate of insulin sensitivity in white juveniles and adults without diabetes mellitus: the single point insulin sensitivity estimator (SPISE). Clin Chem. 2016;62:1211–9.

    Article  CAS  PubMed  Google Scholar 

  13. de Giorgis T, Marcovecchio ML, Di Giovanni I, Giannini C, Chiavaroli V, Chiarelli F, et al. Triglycerides-to-HDL ratio as a new marker of endothelial dysfunction in obese prepubertal children. Eur J Endocrinol. 2014;170:173–80.

    Article  PubMed  Google Scholar 

  14. Chehade JM, Gladysz M, Mooradian AD. Dyslipidemia in type 2 diabetes: prevalence, pathophysiology, and management. Drugs. 2013;73:327–39.

    Article  CAS  PubMed  Google Scholar 

  15. Jeppesen J, Hein HO, Suadicani P, Gyntelberg F. Relation of high TG–Low HDL cholesterol and LDL cholesterol to the incidence of ischemic heart disease. Arterioscler Thromb Vasc Biol. 1997;17:1114–20.

    Article  CAS  PubMed  Google Scholar 

  16. Shao QY, Ma XT, Yang ZQ, LI QX, Wang YF, Liang J, et al. Prognostic significance of multiple triglycerides-derived metabolicindices in patients with acute coronary syndrome. J Geriatric Cardiol. 2022;19:456–68.

    CAS  Google Scholar 

  17. Tahir KS, Cavender MA. Diabetes and cardiovascular disease. Textbook of Catheter-Based cardiovascular interventions. Cham: Springer International Publishing; 2018. pp. 823–38.

    Book  Google Scholar 

  18. Aranha LN. TG/HDL-c ratio as a predictor of cardiovascular risk. Int J Cardiovasc Sci. 2021;34:66–7.

    Google Scholar 

  19. Salazar MR, Carbajal HA, Espeche WG, Leiva Sisnieguez CE, Balbín E, Dulbecco CA, et al. Relation among the plasma Triglyceride/High-Density lipoprotein cholesterol concentration ratio, insulin resistance, and associated Cardio-Metabolic risk factors in men and women. Am J Cardiol. 2012;109:1749–53.

    Article  CAS  PubMed  Google Scholar 

  20. Gharipour M, Sadeghi M, Dianatkhah M, Nezafati P, Talaie M, Oveisgharan S, et al. Comparison between European and Iranian cutoff points of triglyceride/high-density lipoprotein cholesterol concentrations in predicting cardiovascular disease outcomes. J Clin Lipidol. 2016;10:143–9.

    Article  PubMed  Google Scholar 

  21. Sultani R, Tong DC, Peverelle M, Lee YS, Baradi A, Wilson AM. Elevated triglycerides to High-Density lipoprotein cholesterol (TG/HDL-C) ratio predicts Long-Term mortality in High-Risk patients. Heart Lung Circ. 2020;29:414–21.

    Article  PubMed  Google Scholar 

  22. Wang B, Hua J, Ma L. Triglyceride to High-Density lipoprotein ratio can predict coronary artery calcification. Pak J Med Sci. 2022;38.

  23. Miki T, Miyoshi T, Suruga K, Ichikawa K, Otsuka H, Toda H et al. Triglyceride to HDL-cholesterol ratio is a predictor of future coronary events: a possible role of high-risk coronary plaques detected by coronary CT angiography. Eur Heart J. 2020;41.

  24. Su Y-M, Zhang R, Xu R-F, Wang H-L, Geng H-H, Pan M, et al. Triglyceride to high-density lipoprotein cholesterol ratio as a risk factor of repeat revascularization among patients with acute coronary syndrome after first-time percutaneous coronary intervention. J Thorac Dis. 2019;11:5087–95.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kundi H, Korkmaz A, Balun A, Cicekcioglu H, Kiziltunc E, Gursel K, et al. Is In-Stent restenosis after a successful coronary stent implantation due to stable angina associated with TG/HDL-C ratio?? Angiology. 2017;68:816–22.

    Article  CAS  PubMed  Google Scholar 

  26. Che B, Zhong C, Zhang R, Pu L, Zhao T, Zhang Y, et al. Triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio as potential cardiovascular disease risk factors: an analysis of UK biobank data. Cardiovasc Diabetol. 2023;22:34.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Moses A, Nguyen L, Awan GM, Omar B. Race-Related variations in lipid parameters in a cardiology clinic. J Clin Lipidol. 2016;10:689.

    Article  Google Scholar 

  28. Bentley AR, Chen G, Shriner D, Doumatey AP, Zhou J, Huang H, et al. Gene-Based sequencing identifies Lipid-Influencing variants with Ethnicity-Specific effects in African Americans. PLoS Genet. 2014;10:e1004190.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Zoratti R. A review on ethnic differences in plasma triglycerides and high-density-lipoprotein cholesterol: is the lipid pattern the key factor for the low coronary heart disease rate in people of African origin? Eur J Epidemiol. 1998;14:9–21.

    Article  CAS  PubMed  Google Scholar 

  30. Moore-Harrison T, Keane K, Jerome Brandon L. Cardiometabolic risk factors and cardiovascular disease predictions in older African and European Americans. Prev Med Rep. 2022;30:102019.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Sakboonyarat B, Pima W, Chokbumrungsuk C, Pimpak T, Khunsri S, Ukritchon S, et al. National trends in the prevalence of glycemic control among patients with type 2 diabetes receiving continuous care in Thailand from 2011 to 2018. Sci Rep. 2021;11:14260.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Wilson PWF, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837–47.

    Article  CAS  PubMed  Google Scholar 

  33. Appropriate body-mass. Index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157–63.

    Article  Google Scholar 

  34. De Sutter J. Screening of family members of patients with premature coronary heart disease results from the EUROASPIRE II family survey. Eur Heart J. 2003;24:249–57.

    Article  CAS  PubMed  Google Scholar 

  35. ElSayed NA, Aleppo G, Bannuru RR, Bruemmer D, Collins BS, Ekhlaspour L, et al. 6. Glycemic goals and hypoglycemia: Standards of care in Diabetes—2024. Diabetes Care. 2024;47:S111–25.

    Article  Google Scholar 

  36. Nilsen A, Hanssen TA, Lappegård KT, Eggen AE, Løchen M-L, Selmer RM, et al. Change in cardiovascular risk assessment tool and updated Norwegian guidelines for cardiovascular disease in primary prevention increase the population proportion at risk: the Tromsø study 2015–2016. Open Heart. 2021;8:e001777.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Wang JC, Bennett M. Aging and atherosclerosis. Circ Res. 2012;111:245–59.

    Article  CAS  PubMed  Google Scholar 

  38. Feng L, Nian S, Tong Z, Zhu Y, Li Y, Zhang C, et al. Age-related trends in lipid levels: a large-scale cross-sectional study of the general Chinese population. BMJ Open. 2020;10:e034226.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Park Y-MM, Sui X, Liu J, Zhou H, Kokkinos PF, Lavie CJ, et al. The effect of cardiorespiratory fitness on Age-Related lipids and lipoproteins. J Am Coll Cardiol. 2015;65:2091–100.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Koskinen JS, Kytö V, Juonala M, Viikari JSA, Nevalainen J, Kähönen M et al. Childhood dyslipidemia and carotid atherosclerotic plaque in adulthood: the cardiovascular risk in young Finns study. J Am Heart Assoc. 2023;12.

Download references

Acknowledgements

The authors would like to extend their gratitude to everyone involved in the successful execution of the study and for their invaluable assistance and insights throughout the research. Special thanks go to all staff members of the Research Unit for Military Medicine at Phramongkutklao College of Medicine in Bangkok, Thailand, for providing the essential facilities and support.

Funding

This research was supported by (1) the FETP-NCD, Division of Epidemiology, Department of Disease Control, Ministry of Public Health, Thailand and (2) the Research Unit for Military Medicine, Phramongkutklao College of Medicine, Bangkok, Thailand.

Author information

Author notes

  1. Methavee Poochanasri and Sethapong Lertsakulbunlue contributed equally to this work.

Authors and Affiliations

  1. Phramongkutklao College of Medicine, Bangkok, 10400, Thailand

    Methavee Poochanasri & Chutawat Kookanok

  2. Department of Pharmacology, Phramongkutklao College of Medicine, Bangkok, 10400, Thailand

    Sethapong Lertsakulbunlue

  3. Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok, 10400, Thailand

    Ram Rangsin & Wisit Kaewput

  4. Department of Parasitology, Phramongkutklao College of Medicine, Bangkok, 10400, Thailand

    Mathirut Mungthin

  5. Division of Endocrinology, Department of Medicine, Phramongkutklao Hospital and College of Medicine, Bangkok, 10400, Thailand

    Parinya Samakkarnthai

Authors
  1. Methavee Poochanasri
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Sethapong Lertsakulbunlue
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Chutawat Kookanok
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ram Rangsin
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Wisit Kaewput
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Mathirut Mungthin
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Parinya Samakkarnthai
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

MP, SL, CK, MM, RR, WK, and PS developed the study concept. RR collected the data, MP and SL analyzed the data, and wrote the first draft. PS oversee all the study protocols. All authors contributed to and approved the final version.

Corresponding author

Correspondence to Parinya Samakkarnthai.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Institutional Review Board, Royal Thai Army (Approved No. Q004h/67), in accordant with international guidelines including the Declaration of Helsinki, the Belmont Report, CIOMS Guidelines, and the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use - Good Clinical Practice (ICH-GCP). Due to using secondary data, a waiver of documentation of informed consent was utilized, and the waiver for informed consent was granted by the Institutional Review Board, RTA Medical Department.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Poochanasri, M., Lertsakulbunlue, S., Kookanok, C. et al. Triglyceride to high-density lipoprotein ratio as a predictor for 10-year cardiovascular disease in individuals with diabetes in Thailand. J Health Popul Nutr 44, 147 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41043-025-00835-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41043-025-00835-0

Keywords

Journal of Health, Population and Nutrition

ISSN: 2072-1315

Contact us