https://orcid.org/0009-0006-2303-0116
Figures
Abstract
Objective
This study aimed to investigate the relationship between cumulative average triglyceride glucose-body mass index (TyG-BMI) and the risk of developing hypertension among middle-aged and elderly population.
Methods
Data were sourced from the 2012 and 2015 China Health and Retirement Longitudinal Study. The population was stratified into four exposure levels based on the quartiles of average TyG-BMI. Logistic regression analyses were employed to assess the associations between varying levels of average TyG-BMI and the risk of hypertension. Additionally, restricted cubic spline regression models were utilized to delineate the dose-response relationship between average TyG-BMI and hypertension. The contributions of fasting plasma glucose, triglyceride, and body mass index were quantified using weighted quantile sum regression to comprehensively elucidate the role of each component of TyG-BMI.
Results
A total of 2,841 participants aged 45 years and older were included in this study, of whom 1,302 (45.83%) were male, with a mean age of 57.63 ± 8.39 years at baseline. Throughout the follow-up period, 704 (24.78%) participants developed new onset hypertension. After adjusting for confounding variables, higher average TyG-BMI levels (from two measurements) were associated with an increased risk of developing hypertension (Q2: OR = 1.37, 95% CI = 1.04–1.80; Q3: OR = 1.93, 95% CI = 1.46–2.56; Q4: OR = 2.71, 95% CI = 2.01–3.66). The results from the restricted cubic spline regression indicated a linear association between average TyG-BMI and the risk of developing hypertension (P for association < 0.001, P for nonlinear = 0.078). Weighted quantile sum regression revealed that BMI was a significant component of TyG-BMI, with weights of 0.666 and 0.769 in 2012 and 2015, respectively.
Citation: Zheng C, Liu Y, Zeng S, Xie S, Luo X, Wu Q (2025) Association between average triglyceride glucose-body mass index and risk of hypertension in middle-aged and elderly Chinese: A study based on Chinese CHARLS cohort data. PLoS One 20(12): e0337710. https://doi.org/10.1371/journal.pone.0337710
Editor: Neftali Eduardo Antonio-Villa, Instituto Nacional de Cardiologia Ignacio Chavez, MEXICO
Received: December 18, 2024; Accepted: November 11, 2025; Published: December 4, 2025
Copyright: © 2025 Zheng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The datasets generated and/or analysed during the current study are available in the China Health and Retirement Longitudinal Study repository [https://charls.pku.edu.cn/en/].
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: TyG, Triglyceride-glucose index; BMI, Body mass index; TyG-BMI, Triglyceride glucose-body mass index; CHARLS, China Health and Retirement Longitudinal Study; TG, Triglyceride; FPG, Fasting plasma glucose; TC, Total cholesterol; LDL-C, Low-density lipoprotein cholesterol; HDL-C, High-density lipoprotein cholesterol; HbA1c, Glycosylated hemoglobin; OR, Odds ratio; CI, Confidence interval; VIF, Variance inflation factor; BIC, Bayesian information criterion; AIC, Akaike information criterion; RCS, Restricted cubic spline; WQS, Weighted quantile sum; ROC, Receiver Operating Characteristic
Introduction
Hypertension is a significant risk factor for cardiovascular disease and premature death, imposing a substantial economic burden on various countries [1,2], particularly in low-income and middle-income nations [3]. Mei Zhang et al. [4] conducted an analysis of three national surveys spanning from 2004 to 2018. Their findings indicated that the standardized prevalence of hypertension among Chinese adults increased from 20.8% in 2004 to 29.6% in 2010, followed by a decrease to 24.7% in 2018. Despite this downward trend, the prevalence remains elevated compared to the figures recorded in 2004. The prevalence of hypertension will continue to rise with the increasing aging of Chinese society, leading to more prominent adverse effects caused by hypertension.
Currently, hypertension is not curable. The early identification of high-risk groups and timely interventions to control their risk factors are crucial prevention and management strategies for hypertension. Research has demonstrated that insulin resistance is a significant predictive factor for the development of hypertension [5]. However, the process of detecting this indicator is complex, time-consuming, and costly, which limits its clinical application [6]. In recent years, studies have indicated that the triglyceride-glucose index (TyG) can serve as a novel surrogate marker for evaluating insulin resistance [7,8]. Given the high availability and low cost of the biomarkers involved, this approach is more conducive to clinical promotion and application [9]. Obesity remains the most critical acquired factor contributing to insulin resistance, yet the TyG has not accounted for the impact of obesity on this condition [10]. When combined with obesity indicators such as body mass index (BMI), waist circumference, and waist-to-height ratio, the efficacy of evaluating insulin resistance could be enhanced [11,12]. One study compared the associations of TyG, triglyceride glucose-body mass index (TyG-BMI), triglyceride glucose-waist circumference, and triglyceride glucose-waist-height ratio with insulin resistance. The results indicated that TyG-BMI outperformed the other parameters in predicting insulin resistance, further supporting its potential as a surrogate marker for assessing insulin resistance in clinical settings [13]. Additionally, other studies have shown that TyG-BMI demonstrates strong predictive ability for cardiovascular disease, heart failure, and metabolic syndrome [14–16].
Additionally, studies have indicated that the TyG-BMI is a more effective predictor of hypertension compared to the TyG [17,18]. Danying Deng et al [19] revealed that for each one standard deviation increase in TyG-BMI, the risk of developing hypertension escalated by a factor of 1.51. Nonetheless, the majority of existing studies rely on cross-sectional research designs, with limited longitudinal evidence examining the cumulative impact of TyG-BMI on hypertension incidence. Consequently, this study utilized data from the China Health and Retirement Longitudinal Study (CHARLS) conducted in 2012 and 2015 to assess the relationship between average TyG-BMI and hypertension risk among middle-aged and elderly individuals in China.
Methods
Study population
The data for this study were derived from the CHARLS conducted in 2011 and 2015 [20,21]. The CHARLS collected high-quality data on families and individuals aged 45 and older in China to analyze issues related to population aging and to promote interdisciplinary research in this field. The national baseline survey (Wave 1) was carried out from 2011 to 2012, encompassing 150 county-level units, 450 village-level units, and approximately 17,000 individuals across 10,000 households. These samples were tracked every two to three years, with subsequent waves occurring in 2013 (Wave 2), 2015 (Wave 3), 2018 (Wave 4), and 2020 (Wave 5). Only Wave 1 (n = 11,847) and Wave 3 (n = 13,420) collected blood indicators from participants [22]; thus, a total of 11,847 participants with collected blood indicators were included in this study at baseline. The inclusion criteria were: (1) aged = 45 or older; (2) willingness to participate in the study and informed consent provided; (3) no mental illness and ability to cooperate with the investigation. The exclusion criteria were: (1) missing data on relevant variables; (2) non-fasting blood collection indicators; (3) BMI > 45.0 kg/m² or < 13.9 kg/m²; (4) pre-existing hypertension at baseline; (5) loss to follow-up; (6) patients with severe heart, brain, kidney, or other serious diseases and pregnant women. Based on these criteria, a total of 2,841 individuals were ultimately included in the analysis.(Fig 1). The Biomedical Ethics Review Board of Peking University approved the collection of CHARLS data (IRB00001052–11015). Before the survey commenced, professionally trained interviewers provided all participants with comprehensive explanations of the study’s objectives, procedures, potential risks, anticipated benefits, and measures for data confidentiality. Each participant signed a written informed consent form that was approved by the ethics committee. For individuals unable to participate in person due to physical limitations, family members were allowed to respond on their behalf.
CHARLS, China Health and Retirement Longitudinal Study; BMI, body mass index.
Average TyG-BMI assessment
In this study, the exposure was defined as the average TyG-BMI, which was calculated as the arithmetic mean of the TyG-BMI values from 2012 and 2015. It is crucial to emphasize that this calculation represents a simple average across the two time points, rather than a time-weighted integral of cumulative exposure. TyG-BMI was calculated from triglyceride (TG), fasting plasma glucose (FPG) and BMI. The TyG-BMI was calculated by the formula [23]. We calculated the average TyG-BMI with reference to the cumulative average TyG change formula [23,24]:
, the BMI was calculated by the formula
.
Covariate measure
Trained interviewers utilized structured questionnaires to gather demographic and health-related information. The demographic data encompassed age, sex, residence, marital status, and education. Health-related information included self-rated health status, smoking status, drinking status, and self-reported medical history, specifically regarding dyslipidemia and diabetes. Laboratory tests conducted comprised total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and glycosylated hemoglobin (HbA1c).
Relevant indicators and diagnostic criteria
The primary outcome of this study was the development of hypertension. The diagnostic criteria for hypertension include a systolic blood pressure of ≥140 mmHg and/or a diastolic blood pressure of ≥90 mmHg, measured without the use of antihypertensive medications, or through self-reported hypertension [25]. Dyslipidemia was diagnosed when TC ≥ 240 mg/dL, TG ≥ 150 mg/dL, LDL-C ≥ 160 mg/dL, and HDL-C ≤ 40 mg/dL, without the use of lipid-lowering medications. Additionally, the presence of one or more of these four indicators was required for a diagnosis of abnormal or self-reported dyslipidemia [26]. Type 2 diabetes was defined as a fasting blood glucose level of ≥126 mg/dL or self-reported diabetes, again without the use of antidiabetic medications [27].
Statistical analyses
The study subjects were divided into four groups (Q1-Q4) based on the quartiles of average TyG-BMI. Categorical variables were represented by constituent ratios or rates, and χ2 test was employed to compare differences between groups. For continuous variables with a normal distribution, was used for description, and differences between groups were analyzed using ANOVA test. For continuous variables with a non-normal distribution, the median (P50) along with the interquartile range (P25, P75) was reported, and differences between groups were assessed using the Wilcoxon rank sum test. Linear regression was employed to assess multicollinearity among variables. Variance inflation factor (VIF) > 5 or tolerance < 0.1 indicated the presence of multicollinearity. After fully adjusting for covariates, the logistic regression model was used to analyze the association between average TyG-BMI and the risk of hypertension. The bayesian information criterion (BIC) and Akaike information criterion (AIC) of different nodes were comprehensively compared to determine the nodes of average TyG-BMI, and the restricted cubic spline(RCS) model was constructed to explore the dose-response relationship between accumulated TyG-BMI and the risk of hypertension.
To further investigate the association between average TyG-BMI and the risk of hypertension, a series of subgroup analyses were conducted based on potential risk factors, including sex, age groups, place of residence, education background, marital status, and other relevant factors. After splitting the data into a 40% training set and a 60% validation set, weighted quantile sum (WQS) regression model and Bootstrap resampling processes (1,000 iterations) were conducted to determine the specific contributions of TyG-BMI components to the overall impact [23,28]. A two-sided test was employed with a significance level of α = 0.05. All statistical analyses were conducted using SPSS version 14.0 and R version 4.3.2.
Results
Baseline characteristics of participants
A total of 2,841 eligible subjects were included in this study, comprising 1,302 males (45.83%) and 1,539 females (54.17%). The average age at baseline was 57.63 ± 8.39 years. The TyG-BMI2012 and TyG-BMI2015 were 197.69 ± 36.01 and 216.38 ± 39.66, respectively, with a average TyG-BMI of 621.11 ± 108.42. Statistical differences were observed among the four groups (Q1-Q4) regarding sex, age, residence, education, marital status, health status, smoking status, drinking status, history of dyslipidemia, history of diabetes, FPG, HbA1c, TC, LDL-C, HDL-C, TG, BMI, and TyG (P < 0.05). (Table 1).
Incidence of hypertension
A total of 704 study subjects (24.78%) developed new onset hypertension during the follow-up period. The prevalence of hypertension exhibited a gradual increase across the different quartiles of average TyG-BMI. The incidence of hypertension in the four groups was 131 (18.42%), 150 (21.13%), 185 (26.06%), and 238 (33.52%) (Fig 2).
Q1, Quartile 1; Q2, Quartile 2; Q3, Quartile 3; Q4, Quartile 4; TyG-BMI, triglyceride glucose-body mass index.
Logistic regression analysis of different average TyG-BMI quartiles and risk of hypertension
Collinearity diagnostic results indicated that the VIF for TC and LDL-C exceeded 5, leading to the exclusion of this factor when adjusting for covariates. The results of univariate logistic regression analysis revealed that, compared to Q1, both Q3 and Q4 exhibited a significantly higher risk of developing hypertension (Q3: OR=1.56, 95%CI = 1.21–2.01; Q4: OR=2.23, 95%CI = 1.75–2.85). No significant association was found between Q2 and the risk of hypertension (P > 0.05). After adjusting for covariates such as sex, age, residence, and education, logistic regression analysis demonstrated that higher levels of average TyG-BMI (Q2-Q4) were associated with an increased risk of hypertension (Q2: OR=1.37, 95%CI = 1.04–1.80; Q3: OR=1.93, 95%CI = 1.46–2.56; Q4: OR=2.71, 95%CI = 2.01–3.66) (Table 2 and S1 Table in S1 Appendix).
Dose-response relationship between average TyG-BMI and risk of hypertension
After comprehensively comparing the BIC and AIC across various nodes, the RCS model was ultimately constructed with three nodes at P10, P50, and P90 of the average TyG-BMI to investigate the relationship between average TyG-BMI and the incidence of hypertension. The dose-response analysis indicated a linear correlation between average TyG-BMI and the risk of hypertension (P for association < 0.001, P for nonlinearity = 0.078). Notably, when average TyG-BMI exceeded 613 (OR = 1.003, 95%CI = 1.002–1.003), the risk of hypertension significantly increased (Fig 3 and S2 Table in S1 Appendix).
The model was adjusted for sex, age, residence, education, marital status, health status, smoking status, drinking status, history of dyslipidemia, history of diabetes, HDL-C, and HbA1c. TyG-BMI, Triglyceride glucose-body mass index; CI, Confidence interval; OR, Odds ratio; HbA1c, Glycosylated hemoglobin; HDL-C, High-density lipoprotein cholesterol.
Subgroup analyses
To further investigate the association between average TyG-BMI and the risk of hypertension, a series of subgroup analyses were conducted based on potential risk factors. After adjusting for confounding variables, none of the subgroup factors altered the association between average TyG-BMI and the incidence of hypertension, and no interaction between average TyG-BMI and the subgroup variables was observed (P for interaction > 0.05) (Table 3).
WQS analyses
WQS regression model was conducted to assess the overall effect of TyG-BMI on the incidence of hypertension, as well as the contribution of each component (TG, FPG, and BMI) to this overall effect. After adjusting for potential confounding factors, the results indicated that BMI emerged as the primary contributor at both baseline and the conclusion of the follow-up, with weights of 0.666 and 0.769, respectively. The dominant role of BMI in the WQS model suggests that this composite index may offer limited incremental value. Consequently, this study conducted a comparative analysis of the predictive performance of BMI and TyG-BMI using Receiver Operating Characteristic (ROC) curves. The results indicated that TyG-BMI (AUC = 0.594) demonstrated superior predictive efficacy compared to BMI alone (AUC = 0.582). (Fig 4 and S2 Fig in S1 Appendix).
The model was adjusted for sex, age, residence, education, marital status, health status, smoking status, drinking status, history of dyslipidemia, history of diabetes, HDL-C, and HbA1c. TyG-BMI, Triglyceride glucose-body mass index; WQS, Weighted quantile sum FPG, Fasting plasma glucose; BMI, Body mass index; TG, Triglyceride; HbA1c, Glycosylated hemoglobin; HDL-C, High-density lipoprotein cholesterol.
Sensitivity analysis
This study examined the association between average TyG-BMI, treated as a continuous variable, and the risk of hypertension. Due to the minimal change in effect size per unit of average TyG-BMI, we standardized this variable to assess its effects based on changes in standard deviation. After adjusting for covariates, the results indicated that for each standard deviation increase in average TyG-BMI, the risk of hypertension increased by 43% (OR=1.43, 95% CI = 1.29–1.58). Furthermore, To avoid reverse causation (i.e., early onset of hypertension may lead to changes in lifestyle or treatment, thereby affecting TyG-BMI), we excluded cases of hypertension from 2013 through sensitivity analysis. A comparison with Q1 revealed that both Q3 and Q4 were associated with an elevated risk of hypertension (Q3: OR=1.79, 95% CI = 1.22–2.63; Q4: OR=2.41, 95% CI = 1.62–3.61). Notably, no association was found between Q2 and the risk of hypertension (P > 0.05). Next., adjusting for HDL-C and HbA1c, which are closely related to insulin resistance, may constitute over-adjustment and potentially attenuate the observed effects. Therefore, this study further provides the results of the minimally adjusted model for robustness comparison. The study results indicate that, compared to Q1, both Q2 and Q4 are associated with an increased risk of hypertension (Q2: OR=1.37, 95% CI = 1.04–1.80; Q3: OR=1.93, 95% CI = 1.46–2.56; Q4: OR=2.71, 95% CI = 2.01–3.67). Finally, Due to severe multicollinearity (VIF > 5) between TC, LDL-C, and other variables, these two variables were excluded from the primary analysis. However, this approach may overlook critical lipid information. Consequently, we retained these variables in the sensitivity analysis. The results indicated that, compared to the Q1 levels, the average TyG-BMI in the second to fourth quartiles (Q2-Q4) was associated with an increased risk of hypertension (Q2: OR=1.36, 95% CI = 1.03–1.79; Q3: OR=1.93, 95% CI = 1.45–2.56; Q4: OR=2.79, 95% CI = 2.05–3.80).These findings are consistent with the primary analysis (S3 – S6 Tables and S1 Fig in S1 Appendix).
Discussion
This study utilized CHARLS data from 2012 and 2015, employs the average TyG-BMI from two time points as the evaluation index and utilizes a cohort study design to investigate the association between this mid-term measure of TyG-BMI and the risk of hypertension. The results indicated that higher levels of average TyG-BMI (Q2-Q4) were associated with an increased risk of hypertension (Q2: OR=1.37, 95%CI = 1.04–1.80; Q3: OR=1.93, 95%CI = 1.46–2.56; Q4: OR=2.71, 95%CI = 2.01–3.66). Additionally, the results from the RCS model demonstrated a linear correlation between average TyG-BMI and the risk of hypertension, with a significant increase in hypertension risk observed when average TyG-BMI exceeded 613. Furthermore, subgroup analysis did not reveal any interactions between potential risk factors and the development of hypertension. Lastly, considering that TyG-BMI was jointly derived from TG, FPG, and BMI, this study, through WQS regression, identified BMI as the primary contributor to this association. Although the primary model adjusted for HDL-C and HbA1c to control for confounders, these factors may lie on the causal pathway between TyG-BMI and hypertension. Therefore, adjusting for such mediating variables might partially attenuate the true association. This attenuation is evident when comparing the effect sizes between the minimally adjusted (Model 1) and fully adjusted (Model 2) models. For example, the odds ratio for the highest quartile (Q4) of average TyG-BMI decreased from 2.93 (95% CI: 2.25–3.82) in Model 1 to 2.71 (95% CI: 2.01–3.66) in Model 2, following the inclusion of HDL-C and HbA1c. This consistent pattern of slight attenuation across quartiles (see S5 Table in S1 Appendix) indicates that a portion of the association between TyG-BMI and hypertension is likely mediated through pathways related to lipid and glucose metabolism. Furthermore, HDL-C and HbA1c reflect independent cardiovascular risk pathways that are not entirely captured by TyG-BMI. To balance these considerations, this study presents both fully adjusted and minimally adjusted models to ensure transparency Figs 5 and 6.
The results of this study indicate that average TyG-BMI is an independent risk factor for new onset hypertension. This finding is consistent with previous studies that have explored the association between TyG-BMI and hypertension risk based on measurements taken at a specific time point. A cross-sectional study conducted in Japan demonstrated that TyG-BMI was strongly correlated with hypertension risk; specifically, for every 10-unit increase in TyG-BMI, the risk of developing hypertension increased by 31% (OR=1.31, 95%CI: 1.25–1.37) [29]. Additionally, Lu Chen et al [30] investigated the relationship between TyG-BMI and hypertension in East Asian populations, revealing that TyG-BMI was independently associated with both prehypertension and hypertension. The findings from the 2017–2020 NHANES study indicate that for every 10-unit increase in TyG-BMI, the risk of hypertension increases by 4.3% (95% CI: 1.007–1.08). When TyG-BMI was categorized into quartiles, the association between TyG-BMI and the heightened risk of hypertension remained significant, demonstrating statistical significance across all models [31]. However, relying on a single measurement of TyG-BMI failed to account for its dynamic changes over time. Researchers have noted that blood parameters assessed at a single time point may be influenced by various factors, including diet and medications, leading to significant variability that may not accurately represent the subject’s long-term exposure [32]. This study found that individuals with high levels of average TyG-BMI had a 171% increased risk of developing hypertension compared to those with low levels (OR=2.71, 95%CI = 2.01–3.66). Therefore, it can be inferred from this study that prolonged exposure to elevated TyG-BMI poses a greater risk of hypertension than a single measurement. Furthermore, a sensitivity analysis that excluded hypertension cases from 2013 strengthened the causal inference by reducing reverse causality bias. Individuals diagnosed early may adopt dietary modifications, exercise interventions, or initiate antihypertensive or lipid-lowering treatments [33], all of which could independently reduce TyG-BMI. The persistence of the association, even after excluding these cases, indicates that cumulative TyG-BMI elevation is a risk factor for the incidence of hypertension, and the observed effect is not merely an artifact of post-diagnosis metabolic changes. This finding aligns with longitudinal evidence suggesting that insulin resistance predicts hypertension [34]; however, future research should directly track biomarker trajectories following diagnosis.
The mechanisms underlying long-term changes in TyG-BMI and their association with hypertension remain unclear, although insulin resistance may play a significant role. Insulin resistance is recognized as a risk factor for elevated blood pressure. When the body experiences prolonged insulin resistance, elevated insulin levels can enhance insulin-mediated glucose metabolism in neurons, stimulate central nervous pathways in the brainstem, and lead to increased sympathetic activity. This process results in heightened secretion of epinephrine and norepinephrine, which contribute to vascular smooth muscle thickening and lumen narrowing, ultimately resulting in increased blood pressure [35,36]. Additionally, some researchers suggest that insulin resistance may affect sodium and calcium ion pumps, leading to increased intracellular concentrations of sodium and calcium ions. This increase heightens the responsiveness of vascular smooth muscle to insulin-like growth factors, promotes further thickening of the vascular smooth muscle, stimulates sympathetic nerve activity, and enhances demethylation of adrenaline levels, thereby causing vasoconstriction and contributing to elevated blood pressure in patients [37].
WQS regression was employed to enhance the interpretability of TyG-BMI. The results indicated that in both 2012 and 2015, BMI was the primary contributor to the outcomes, suggesting that appropriate management of BMI ranges can mitigate the risk of hypertension. A study analyzing the NHANES non-diabetic and non-prediabetic population from 1999 to 2014 revealed that, compared to individuals with normal BMI, those classified as obese had a risk of insulin resistance that was 3.62 times higher, while the risk for those categorized as overweight was 3.19 times greater [38]. The duration of obesity is a significant risk factor for insulin resistance. Early insulin resistance may be countered by compensatory activation of pancreatic β-cell function; however, long-term persistent obesity can lead to pancreatic β-cell failure, potentially resulting in glucose metabolism disorders [39]. Research conducted by Baoyu Feng et al [40] demonstrated that as BMI increased, the risk of hypertension among overweight and obese individuals was 1.16 to 1.28 times greater than those of the normal weight group. Furthermore, the findings of a cross-sectional study involving 1.7 million participants indicated a positive linear relationship between BMI and both systolic and diastolic blood pressure. Specifically, for individuals with a BMI ranging from 18.5 to 30.0 kg/m², each 1 unit increase in BMI corresponded to an increase of 1.15 mmHg in systolic blood pressure and 0.75 mmHg in diastolic blood pressure [41]. The mechanisms through which obesity induces hypertension are complex. It is widely accepted in academic circles that this process involves the excessive activation of the sympathetic nervous system, stimulation of the renin-angiotensin-aldosterone system, alterations in adipose-derived cytokines, and insulin resistance, all of which contribute to the development of hypertension [42]. Although BMI emerged as the primary driver of the TyG-BMI index in our WQS analysis, this should not be interpreted as diminishing the value of this composite index. The TyG-BMI integrates complementary pathophysiological pathways of lipid metabolism (TG), glucose homeostasis (FPG), and obesity (BMI), thereby providing a more comprehensive measure of metabolic dysfunction. Moreover, previous studies have demonstrated that TyG-BMI outperforms BMI alone in predicting insulin resistance, metabolic syndrome, and cardiovascular events [13–16].
Notably, the data collection period for this study (2011−2015) predates the global COVID-19 pandemic (2020 onward). The exclusion of data from the pandemic era mitigates potential confounding effects arising from pandemic-related disruptions, such as lifestyle changes, limitations in healthcare access, and altered metabolic profiles due to lockdowns. This approach enhances the internal validity of our findings regarding the longitudinal association between average TyG-BMI and hypertension risk. However,This study has several limitations. (1) While numerous studies have demonstrated that TyG-BMI exhibits superior predictive performance compared to TyG in assessing insulin resistance, establishing a direct association between insulin resistance and hypertension risk necessitates comparison with a gold standard. However, CHARLS has not yet conducted a hyperinsulinemic euglycemic clamp trial, which precludes such comparisons in this study. (2) All participants in this study were of Chinese ethnicity, which may limit the generalizability of our findings to other ethnic populations. Significant variations in body composition, lipid metabolism, and genetic susceptibility to insulin resistance exist among individuals of different ethnicities and nationalities. Consequently, the risk threshold of cumulative mean TyG-BMI identified in our study may be most relevant to populations sharing similar genetic backgrounds, lifestyles, and environmental contexts as our study cohort. Therefore, it is recommended that future research validate these findings across diverse ethnicities, nationalities, and geographical settings to ascertain the universal applicability and optimal cutoff points of TyG-BMI in predicting hypertension. (3) Although we have accounted for potential confounding factors such as general information, behavioral habits, and medical history, it is possible that there remain unconsidered confounding variables that could influence the research outcomes. (4) Although the average TyG-BMI is widely adopted in cohort studies with limited time points, it remains an arithmetic mean rather than a time-weighted integral. (5) This study conducted only two measurements during the three-year follow-up period, which limited our ability to accurately describe the dynamic changes in individual TyG-BMI trajectories. This constitutes a significant limitation of the research.
Conclusions
In summary, among middle-aged and elderly Chinese individuals, average TyG-BMI served as an independent risk factor for the development of new-onset hypertension, exhibiting a linear correlation between the two variables. Therefore, long-term monitoring of changes in TyG-BMI should have been a critical component of hypertension prevention strategies. Additionally, BMI emerged as the primary factor influencing the results, thereby elucidating the underlying mechanism associated with TyG-BMI.
Acknowledgments
The authors thank the CHARLS staff team for their efforts and all participants involved for contributing the data.
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