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Vulnerable Populations in Health Research (228) Cardiovascular Disease Prevention (201) Hypertension Prevention and Treatment (294) Cardiac Risk and Cardiac Risk Calculators (97) Longitudinal and Cohort Studies in Public Health (405) Disabilities (156)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/78068, first published .
Association Between Triglyceride-Glucose Index, Blood Pressure Status, and Coronary Heart Disease Risk Among Chinese Adults With Disabilities: 10-Year Disability Health Survey Cohort Study

Association Between Triglyceride-Glucose Index, Blood Pressure Status, and Coronary Heart Disease Risk Among Chinese Adults With Disabilities: 10-Year Disability Health Survey Cohort Study

Association Between Triglyceride-Glucose Index, Blood Pressure Status, and Coronary Heart Disease Risk Among Chinese Adults With Disabilities: 10-Year Disability Health Survey Cohort Study

Authors of this article:

Hui Liu1 Author Orcid Image ;   Yao Li1, 2 Author Orcid Image ;   Yiyan Wang1 Author Orcid Image ;   Tao Wang1 Author Orcid Image ;   Chenghua Jiang1 Author Orcid Image ;   Hengjing Wu1 Author Orcid Image ;   Jing Wu1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    1Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Guangxing Road 2209, Shanghai, China

    2School of Nursing, Wannan Medical College, Wuhu, China

    *these authors contributed equally

    Corresponding Author:

    Jing Wu, PhD


    Background: The triglyceride-glucose (TyG) index and blood pressure (BP) status are key indicators associated with coronary heart disease (CHD). However, limited research has focused on individuals with disabilities.

    Objective: This study explores the potential combined effects of the TyG index and BP status on CHD risk in groups with varying disability characteristics.

    Methods: This study analyzed data from the Shanghai Disability Health Survey, conducted between January 2012 and December 2022. Participants were then categorized into 3 BP status groups: nonelevated BP, elevated BP, and hypertension. Cox proportional hazards regression models were used to assess the associations between BP status, the TyG index, and CHD incidence. Additionally, the mediating, interaction, and combined effects of these factors on CHD risk were examined. A stratified analysis was performed based on participants’ disability characteristics, including disability type and severity, to explore potential variations in the associations.

    Results: Among the 21,628 participants, the mean age was 53.30 (SD 10.57) years, and 50.89% (11007/21628) were male. In a follow-up of 77.45 months, CHD events occurred in 2312 participants (10.69%). The TyG index and BP status were independently associated with an increased risk of CHD. Mediation analysis showed that TyG explained 20.5% (95% CI 13.6%‐22.0%) of the BP and CHD association. Significant multiplicative interactions were identified (hazard ratio [HR] 1.41, 95% CI 1.02‐1.94), and joint analysis indicated the highest CHD risk in those with both hypertension and elevated TyG (HR 1.92, 95% CI 1.52‐2.42). Stratified analyses revealed stronger mediation in participants with physical disabilities (22.6%, 95% CI 9.0%‐60%) or visual disabilities (16.6%, 95% CI 4.8%‐51%), while this was not significant in those with hearing or speech (P=.07) or intellectual or mental disabilities (P=.13). By disability grading, the mediated proportion was 22.3% (95% CI 9.2%‐59.4%) in mild or moderate and 18.8% (95% CI 15.7%‐29%) in severe or very severe groups. Joint associations showed consistently higher CHD risk across most disability classifications, with particularly elevated risk in people with intellectual or mental disabilities (HR 3.51, 95% CI 1.89‐6.50).

    Conclusions: BP and the TyG index were significantly associated with CHD risk in individuals with disabilities, with TyG mediating a part of this association and showing stronger effects in physical and mild to moderate disabilities. Significant interactions between BP and TyG further highlight their combined impact, underscoring the need for integrated interventions targeting both factors.

    JMIR Public Health Surveill 2025;11:e78068

    doi:10.2196/78068

    Keywords

    disabilities; coronary heart disease; CHD; risk; triglyceride-glucose index; blood pressure 


    Background

    Coronary heart disease (CHD) is a leading cause of cardiovascular mortality worldwide, with particularly high prevalence in certain high-risk populations [1,2]. While there is extensive research on CHD in the general population [3,4], studies on CHD in individuals with disabilities remain scarce, despite the fact that this group constitutes a significant portion of the global population. According to the latest report by the World Health Organization, over 1 billion people (approximately 15% of the global population) live with some form of disability [5]. Individuals with disabilities are at an elevated risk for cardiovascular diseases (CVDs) due to functional limitations, metabolic disorders, and restricted physical activity [6,7]. Hypertension is a recognized risk factor for CHD, closely linked to atherosclerosis, left ventricular hypertrophy, and other detrimental cardiovascular outcomes. Among people with disabilities, the prevalence of hypertension is often higher, further exacerbating the cardiovascular burden in this population [8].

    Recently, the triglyceride-glucose (TyG) index has gained widespread application as a biomarker of insulin resistance in assessing CVD risk and adverse outcomes [9]. Derived from the product of fasting glucose and triglycerides, the TyG index provides an indirect measure of insulin sensitivity. Studies show that the TyG index independently and significantly predicts several CVDs and poor prognoses [10,11], with a higher TyG index being significantly associated with increased risks of stroke [12], heart failure [13], and all-cause mortality [14]. Moreover, interactions between the TyG index and other common cardiovascular risk factors may yield additional impacts on cardiovascular risk. For example, research has shown that the TyG index mediates a substantial portion of the association between BMI and stroke [12], and a co-exposure and mutual mediation effect has been observed between the TyG index and high-sensitivity C-reactive protein for CVD [15]. These findings further support the potential of the TyG index as a tool for predicting CVD risk.

    Given that both blood pressure (BP) and the TyG index are closely associated with CHD risk, their potential combined effect on CVD has garnered growing attention. Multiple studies indicate that hypertension and insulin resistance may interact, and their joint evaluation and intervention may have important implications for CVD prevention [16,17]. A more recent cohort study of 57,192 participants using cross-lagged analysis found that elevated TyG exerted a stronger impact on subsequent BP changes than the reverse effect [18]. However, there is still a lack of research focused on systematically evaluating the combined effects of BP and the TyG index on CHD, particularly their impact among people with disabilities.

    Objectives

    Based on a 10-year longitudinal cohort of people with disabilities, we aim to evaluate the independent and combined effects of different BP levels and the TyG index on incident CHD in people with disabilities, as well as to further analyze the potential mediating role of the TyG index in the association between BP and CHD.


    Data Collection

    This study is a retrospective cohort data analysis based on information sourced from the Shanghai Disabled Persons Rehabilitation Comprehensive Information Platform, established by the Shanghai Disabled Persons’ Federation. Since 2012, the Shanghai Disabled Persons’ Federation has provided annual free health screening services for local individuals with disabilities. As of December 2022, the Shanghai Sunshine Rehabilitation Center (Shanghai Sunshine Rehabilitation Hospital) has facilitated screenings for more than 20,000 persons with disabilities, and the health data obtained have been uploaded to the Shanghai Disabled Persons Rehabilitation Comprehensive Information Platform for health monitoring and scientific research purposes. The recruitment process for this study was based on participants attending the annual health screenings, where individuals with various types of disabilities voluntarily participated. The recruitment aimed to include a diverse range of disability types and severities.

    Ethical Considerations

    This study was approved by the Ethics Committee of Shanghai Sunshine Rehabilitation Hospital (2019‐051). All participants were informed that their health screening data could be used for scientific research, and informed consent was obtained prior to data collection. To ensure privacy and confidentiality, all data used in this analysis were anonymized and deidentified before being accessed by the research team. Participants did not receive financial compensation for their participation. No individual participants are identifiable in any images or supplementary materials associated with this study.

    Study Population

    The study population included individuals with disabilities meeting the following criteria: (1) aged over 18 years and (2) participated in at least 2 health screenings during the 10-year follow-up period from 2012 to 2022, with complete electronic health records for each screening. Exclusion criteria were as follows: (1) a prior diagnosis of CHD at baseline, confirmed by a qualified physician in accordance with the World Health Organization diagnostic criteria; (2) presence of other severe conditions, such as malignancies, organ failure, or major trauma; (3) women who were pregnant, breastfeeding, or planning to become pregnant; and (4) individuals with multiple types of disabilities rather than a single disability type. The final eligible sample consisted of 21,628 individuals (Figure S1 in Multimedia Appendix 1).

    Assessment of BP and TyG

    After participants rested for approximately 10 minutes, trained nursing staff measured BP using a standardized procedure with an automated arm BP monitor (Omron Corp). BP was recorded twice. For participants with upper limb amputation or injuries that could lead to measurement errors, physicians conducted supine BP measurements on both ankles via the posterior tibial artery and recorded the reading from the limb with the higher value. To ensure consistency with standard arm BP readings, leg BP values were corrected following recommendations in references [18].

    BP status was classified based on the 2024 Chinese Hypertension Prevention and Treatment Guidelines [19]. Participants were categorized into 3 groups: nonelevated BP, elevated BP, and hypertension. Nonelevated BP was defined as systolic BP (SBP) <120 mm Hg and diastolic BP (DBP) <80 mm Hg, without a history of hypertension. Elevated BP was defined as SBP ranging from 120 mm Hg to 139 mm Hg or DBP from 80 mm Hg to 89 mm Hg. Hypertension was defined as SBP≥140 mm Hg, DBP≥90 mm Hg, or a self-reported history of hypertension.

    The TyG index, used as a marker of metabolic health, was calculated from venous blood samples. All blood samples were immediately frozen after collection and transported to a specialized laboratory, where they were stored at –80°C to maintain sample quality. Serum triglyceride (TG) and fasting plasma glucose (FPG) levels were measured using an enzymatic colorimetric method, and the TyG index was calculated according to the formula: ln [TG (mg/dL)×FPG (mg/dL)/2] [20,21]. Both the TyG index and BP were measured at baseline for the analysis.

    Outcome Ascertainment

    The primary outcome of this study was the incidence of CHD occurring from the time of health screening in 2012 until December 2022. CHD was defined as fatal ischemic heart disease (according to ICD-10 codes I20-I25) or nonfatal myocardial infarction (ICD-10 codes I21-I23). The CHD history was assessed through interviews with qualified physicians and verified by reviewing patient medical records. The timing of CHD onset was recorded based on case documentation to ensure the reliability and temporal accuracy of outcome event data.

    Covariates

    Participants’ medical and functional status was comprehensively evaluated by trained health care professionals at baseline. The health questionnaire collected data on general demographics (eg, gender, age, and marital status); medical history (eg, hypertension, diabetes, and fatty liver); and disability status (classification and severity). The classification and grading of disabilities were based on China’s National Standard “Classification and Grading Criteria of Disabilities” (GB/T 26341‐2010) [22]. Hospital health examinations included anthropometric measurements (eg, height and weight); biochemical assessments (eg, complete blood count, blood biochemistry, and urinalysis); and electrocardiogram and ultrasound examinations. Blood tests were conducted after an overnight fast of at least 12 hours.

    Statistical Analyses

    In the statistical analysis, continuous variables that follow a normal distribution were expressed as means and SDs, while nonnormally distributed data were reported as medians and IQR. Categorical variables were presented as frequencies and percentages. For comparisons of baseline characteristics across groups based on BP (divided into 3 categories), chi-square tests, ANOVA, or Kruskal-Wallis rank-sum tests were used according to the data type. Missing data were handled using multiple imputation with chained equations using the MICE package of R (version 4.3.0; R Foundation for Statistical Computing) to minimize bias. Detailed information on the missing data can be found in Table S1 in Multimedia Appendix 1.

    To estimate the association between different BP categories (nonelevated BP, elevated BP, and hypertension); TyG quartiles; and outcomes, we used Cox proportional hazards regression models to calculate hazard ratios (HRs) and 95% CIs. The models were adjusted in 3 stages: model 1 adjusted for age and gender; model 2 further adjusted for disability-specific factors—disability type and severity; and model 3 additionally controlled for marital status, education level, diabetes history, and serum creatinine level. Furthermore, restricted cubic spline (RCS) models with 3 knots were used to explore nonlinear associations between TyG and the outcome.

    To assess the mediating effect of TyG between BP and CHD, we used the mediation package in R. First, a logistic regression model (mediator model) was built with the independent and confounding variables, using the mediator variable as the outcome. A second Cox proportional hazards regression model (outcome model) included the independent variable and mediator, with CHD as the outcome. This approach allowed us to estimate both the indirect and direct effects, showing the proportion of the total effect mediated by TyG. The proportion mediated was calculated as the ratio of the natural indirect effect (NIE) to the sum of the natural direct effect and NIE. The bootstrap method was used to estimate the 95% CI of the indirect effect. TyG was dichotomized based on an RCS-determined threshold (8.56). In R, the mets package was used to calculate BP’s NIE and natural direct effect on CHD incidence through TyG as a mediator.

    To further explore the association between TyG and CHD, stratified analyses by BP level were conducted. Interaction terms between BP (nonelevated BP, elevated BP, and hypertension) and TyG quartiles were included, and a likelihood ratio test was performed to compare models with and without interaction terms, assessing whether interaction improved model fit [23]. For joint association analyses, participants were categorized into 12 groups based on their BP and TyG quartiles, with HRs for CHD calculated and compared against a reference group with nonelevated BP and TyG in the first quartile.

    Given the unique characteristics of individuals with disabilities, subgroup analyses were performed by disability type and severity. Participants were divided into those with limb disabilities and other disability types, as well as into mild and moderate versus severe and very severe categories. Primary analyses, including mediation analysis, stratified mediation, interaction, and joint association analyses, were repeated within these subgroups.


    Baseline Characteristics

    A total of 21,628 eligible participants were included in the study, with details on the sample selection process illustrated in Figure S1 in Multimedia Appendix 1. The mean age of the participants was 53.3 (SD 10.57) years, and 10,621 (49.11%) were female. More than half of the participants (11,901/21,628, 55.00%) had physical disabilities, with the majority classified as having either mild or severe disabilities (16,975/21,628, 78.49%). The incidence rate per year is provided in Table S2 in Multimedia Appendix 1.

    RCS regression was used to explore the potential nonlinear associations between TyG and CHD events. The results revealed a nonlinear association between TyG and the risk of CHD (P=.002; Figure S2A and S2B in Multimedia Appendix 1). Therefore, nonlinear modeling was adopted to better capture the association between TyG and CHD risk. BP categories were based on the 2024 “Chinese Guidelines for the Prevention and Treatment of Hypertension,” while TyG was classified according to the commonly used quartile method in the literature.

    Baseline characteristics of the participants, categorized by baseline BP into nonelevated BP (4964/21628, 22.95%), elevated BP (1321/21628, 54.28%), and hypertension (610/21628, 22.77%) groups, are compared in Table 1. The average TyG index at baseline was 8.62 (SD 0.60). Table S3 in Multimedia Appendix 1 provides the baseline characteristics of participants based on TyG quartiles.

    Table 1. Baseline characteristics of 21,628 participants with disabilities by blood pressure (BP) level (N=21,628).
    VariablesTotal sampleNonelevated BP (SBPa<120, DBPb<80; n=4964)Elevated BP (120≤SBP<140, 80≤DBP<90; n=11,739)Hypertension (SBP≥140, DBP≥90; n=4925)P valuec
    Age (y), mean (SD)53.30 (10.57)49.62 (11.03)53.75 (10.36)55.93 (9.54)<.001
    Sex, n (%)<.001
    Male11,007 (50.89)2325 (46.84)6064 (51.66)2618 (53.16)
    Female10,621 (49.11)2639 (53.16)5675 (48.34)2307 (46.84)
    BMI (kg/m2), mean (SD)24.09 (3.58)22.40 (3.09)24.27 (3.45)25.37 (3.69)<.001
    Marital status, n (%)<.001
    Married17,852 (82.54)3874 (78.04)9788 (83.38)4190 (85.08)
    Other3776 (14.46)1090 (21.96)1951 (16.62)735 (14.92)
    Education, n (%)<.001
    Primary school and illiterate4683 (21.70)1033 (20.81)2535 (21.59)1115 (22.64)
    Junior high school11,199 (51.80)2596 (52.30)6092 (51.90)2511 (50.98)
    Senior high school4896 (22.60)1129 (22.74)2671 (22.75)1096 (22.25)
    College and higher850 (3.93)206 (4.15)441 (3.76)203 (4.12)
    Classification of disabilities, n (%)<.001
    Intellectual and mental disability3799 (17.60)1047 (21.09)1989 (16.94)763 (15.49)
    Hearing and speech disability1775 (8.21)476 (9.59)908 (7.73)391 (7.94)
    Visual disability4153 (19.20)900 (18.13)2302 (19.61)951 (19.31)
    Physical disability11,901 (55.00)2541 (51.19)6540 (55.71)2820 (57.26)
    Grading of disabilities, n (%)<.001
    Very severe1924 (8.90)485 (9.77)996 (8.48)443 (8.99)
    Severe2729 (12.60)1689 (34.02)4095 (34.88)1674 (33.99)
    Moderate7458 (34.50)692 (13.94)1453 (12.38)584 (11.86)
    Mild9517 (44.00)2098 (42.26)5195 (44.25)2224 (45.16)
    Comorbidities, n (%)<.001
    Hypertension5961 (27.60)281 (5.66)3125 (26.62)2555 (51.88)
    Diabetes1317 (6.09)174 (3.35)459 (5.75)684 (8.08)
    Blood pressure, mean (SD)<.001
    SBP135.15 (20.87)109.39 (7.47)135.78 (12.28)159.61 (15.13)
    DBP79.31 (12.46)65.56 (7.05)79.70 (7.40)92.25 (11.94)
    Metabolic biomarkers
    FBGd (mmol/L), mean (SD)5.60 (1.53)5.20 (1.11)5.62 (1.52)5.94 (1.82)<.001
    TCe (mmol/L), mean (SD)4.58 (0.87)4.37 (0.77)4.79 (0.90)4.94 (0.93)<.001
    TGf (mmol/L), median (IQR)1.22 (0.8-1.75)1.02 (0.76-1.43)1.24 (0.89-1.77)1.41 (1.00-2.02)<.001
    TPg (g/L), mean (SD)72.38 (3.61)71.44 (4.12)72.40 (4.12)73.29 (4.25)<.001
    Albh (g/L), mean (SD)43.43 (2.33)43.16 (2.35)43.46 (2.31)43.62 (2.38)<.001
    Gloi (g/L), mean (SD)28.97 (3.61)28.37 (3.65)28.96 (3.52)29.60 (3.68)<.001
    Altj (U/L), median (IQR)20.00 (15.00-29.00)18.00 (13.00-25.00)21.00 (15.00-29.00)22.00 (16.00-32.00).115
    UAk (μmol/L), mean (SD)318.40 (87.54)299.65 (81.11)319.51 (86.62)334.67 (92.29)<.001
    SCrl (μmol/L), median (IQR)62.10 (50.70-73.32)61.20 (50.10-72.50)62.20 (50.70-73.40)62.80 (51.50-74.10)<.001
    SUm (mmol/L), median (IQR)5.00 (4.20-6.00)4.90 (4.10-5.80)5.10 (4.30-6.00)5.10 (4.30-6.00)<.001
    Hemoglobin (g/L), mean (SD)137.01 (15.27)133.80 (15.31)137.39 (15.08)139.38 (15.16)<.001
    Platelet count (10^9/L), mean (SD)200.88 (58.27)198.63 (57.69)200.63 (57.81)203.73 (59.82)<.001
    TyGn8.62±0.608.38±0.548.64±0.598.80±0.62<.001

    aSBP: systolic blood pressure.

    bDBP: diastolic blood pressure.

    cThe P value was determined using the chi-square test for categorical data, ANOVA for continuous data across categorical groups, and the Kruskal-Wallis test for continuous data that are ordinal or nonnormally distributed.

    dFBG: fasting plasma glucose.

    eTC: total cholesterol.

    fTG: total triglyceride.

    gTP: total protein.

    hAlb: albumin

    iGlo: globulin

    jAlt: alanine aminotransferase.

    kUA: uric acid.

    lSCr: serum creatinine.

    mSU: serum urea.

    nTyG: triglyceride-glucose. The TyG was calculated using the formula ln [TC (mg/dl)×FBG (mg/dl)/2].

    Association of TyG and BP With CHD

    During a median follow-up of 77.45 (IQR 44.81-108.17) months from 2012 to 2022, a total of 2312 participants developed CHD, resulting in an incidence rate of 10.69%. Figure 1 illustrates the association between BP and the TyG index with CHD events. After adjusting for potential confounders in model 3, compared to nonelevated BP, the adjusted HR (aHR) for CHD events in the hypertension group was 1.28 (95% CI 1.12-1.47). When comparing TyG quartile 4 to quartile 1, the aHR for CHD events was 1.47 (95% CI 1.29-1.67).

    ‎
    Figure 1. Associations of triglyceride-glucose (TyG) index and blood pressure (BP) with coronary heart disease (CHD). Model 1 was adjusted for age and gender. Model 2 was adjusted for age, gender, classification of disabilities, and grading of disabilities. Model 3 was adjusted for age, gender, classification of disabilities, grading of disabilities, marital status, education level, total cholesterol (TC), serum creatinine (SCr), total protein (TP), and hemoglobin. HR: hazard ratio.

    Mediation and Interaction Analyses of TyG Between BP and CHD

    The mediation analysis of BP and TyG in relation to the incidence of CHD is shown in Figure S3 in Multimedia Appendix 1 (model 3). The proportion of mediation of BP on CHD incidence through TyG was 20.50% (95% CI 13.60%-22%). Significant multiplicative interactions were observed between TyG and BP on incident CHD (Table 2; multiplicative, HR 1.41, 95% CI 1.02‐1.94). Figure 2 presents the results of the joint analysis of BP and TyG on the outcome. Compared to individuals with nonelevated BP and TyG in the first quartile, those with hypertension and TyG in the fourth quartile had a higher risk of CHD incidence, with an HR of 1.92 (95% CI 1.52‐2.42) after adjusting for confounding factors.

    Table 2. Interactive effects of triglyceride-glucose index and blood pressure (BP) on coronary heart disease (CHD).
    Interactive itemsInteractive effects (95% CI)
    Model 1aModel 2bModel 3c
    Additive effects
    Relative excess risk due to interaction1.22 (–0.31 to 2.74)1.18 (–0.32 to 2.67)1.01 (–0.36 to 2.39)
    Proportion attributable to interaction0.39 (0.15 to 0.62)0.38 (0.14 to 0.62)0.35 (0.10 to 0.60)
    Synergy index2.32 (1.48 to 3.63)2.29 (1.45 to 3.59)2.18 (1.36 to 3.50)
    Multiplicative effect1.47 (1.07 to 2.03)1.46 (1.06 to 2.01)1.41 (1.02 to 1.94)

    aModel 1 was adjusted for age and gender.

    bModel 2 was adjusted for age, gender, classification of disabilities, and grading of disabilities.

    cModel 3 was adjusted for age, gender, classification of disabilities, grading of disabilities, marital status, education level, total cholesterol (TC), serum creatinine (SCr), total protein (TP), and hemoglobin.

    ‎
    Figure 2. Joint associations of triglyceride-glucose (TyG) index and blood pressure (BP) on coronary heart disease (CHD). Model 1 was adjusted for age and gender. Model 2 was adjusted for age, gender, classification of disabilities, and grading of disabilities. Model 3 was adjusted for age, gender, classification of disabilities, grading of disabilities, marital status, education level, total cholesterol (TC), serum creatinine (SCr), total protein (TP), and hemoglobin. HR: hazard ratio.

    Group Analysis by Disability Characteristics

    Global interaction tests between disability grading or classification and the combined effects of BP and TyG on CHD showed significant additive and multiplicative interactions after adjustment for covariates (Table 3). Stratified mediation analyses by disability classification (Figure 3A) revealed that the mediation effect of TyG was significant among individuals with physical disabilities (22.64%, 95% CI 9.03%‐60%) and those with visual disabilities (16.58%, 95% CI 4.75%‐51%), whereas the effect was not significant in participants with hearing and speech disabilities (P=.074) or intellectual and mental disabilities (P=.132). When stratified by disability grading (Figure 3B), the proportion mediated was 22.3% (95% CI 9.24%‐59.38%) in the mild or moderate group and 18.8% (95% CI 15.7%‐29%) in the severe or very severe group.

    The joint association analyses (Table S5 in Multimedia Appendix 1) further showed that, compared with individuals in the first quartile of both nonhypertension and TyG, those in the fourth quartile of both hypertension and TyG exhibited a higher risk of CHD across most disability classifications. Specifically, the aHRs were 1.81 (95% CI 1.34‐2.44) for physical disabilities, 1.31 (95% CI 0.77‐2.23) for visual disabilities, 2.81 (95% CI 1.11‐7.09) for hearing and speech disabilities, and 3.51 (95% CI 1.89‐6.50) for intellectual and mental disabilities. Similarly, by disability grading, the HRs were 2.45 (95% CI 1.49‐4.02) for the severe or very severe group and 1.84 (95% CI 1.41‐2.39) for the mild or moderate group.

    Table 3. Interactive effects of disability classification/grading on blood pressure (BP) and triglyceride-glucose index in relation to coronary heart disease (CHD).
    Interactive itemsInteractive effects (95% CI)
    Classification of disabilitiesGrading of disabilities
    Additive effects
    Relative excess risk due to interaction0.61 (0.42-1.64)0.86 (0.27-1.46)
    Proportion attributable to interaction0.41 (0.18-0.98)0.35 (0.23-0.47)
    Synergy index1.83 (0.88-3.83)2.45 (2.04-2.93)
    Multiplicative effect1.84 (0.83-1.99)1.46 (1.27-1.69)
    ‎
    Figure 3. Mediating role of triglyceride-glucose (TyG) index in the association between blood pressure (BP) and coronary heart disease (CHD) according to classification and grading of disability. (A) Mediating role of TyG index in the association between BP and CHD according to the classification of disability and (B) mediating role of the TyG index in the association between BP and CHD according to the grading of disability. All the models were adjusted for age, gender, classification of disabilities, grading of disabilities, marital status, education level, TC, SCr, total protein (TP), and hemoglobin. ACME: average casual mediation effect; ADE: average direct effect; βDE: direct effect; βIE: indirect effect.

    Subgroup and Sensitivity Analyses

    After converting SBP, DBP, and TyG into continuous variables, the mediation analysis was conducted with full adjustment for confounding factors. The results shown in Figure S4 in Multimedia Appendix 1 indicate mediation proportions of 34.5% (P<.001) and 34.1% (P<.001). The mediation effect of TyG on the association between BP and CHD is greater in participants aged <60 years than in those aged ≥60 years and is more pronounced in male individuals than in female individuals. In the joint analysis, participants in the highest quartile of both hypertension and TyG had an increased risk of CHD compared with those in the lowest quartile. The aHRs were 2.69 (95% CI 1.84‐3.93) in male individuals, 1.50 (95% CI 1.11‐2.02) in female individuals, 2.01 (95% CI 1.59‐2.76) in participants aged <60 years, and 1.58 (95% CI 1.02‐2.44) in those aged ≥60 years.


    Principal Findings

    This study expands the understanding of the association between BP, TyG, and CHD by including individuals with disabilities, a group that has often been overlooked in previous research. Our research fills a critical gap by elucidating the significant independent associations of BP and TyG with CHD risk in a cohort of 21,628 individuals with disabilities. Notably, TyG was found to mediate the effect of BP on CHD risk, with this mediation being especially pronounced in individuals with physical disabilities and those with mild or moderate disabilities. Furthermore, significant additive and multiplicative interactions between BP and TyG were observed, with their combination associated with an even higher risk of CHD. These findings suggest that the unique metabolic profiles of these populations may play an important role in their increased CHD risk.

    Our results are consistent with existing literature showing that both high BP [24,25] and elevated TyG [26,27] are independent risk factors for CHD. The association between BP and TyG in relation to CVD risk has been studied in normal populations before. One prospective study [17] demonstrated that SBP significantly altered the association between TyG and CVD, with SBP mediating about 10% of the TyG-CVD association, a finding similar to what we observed in our cohort of individuals with disabilities. This suggests that in the population with disabilities, TyG plays a significant role in the pathway linking BP to CHD, possibly reflecting enhanced insulin resistance and metabolic abnormalities in this group. Potential mechanisms for this could include limited physical activity, which often leads to lower metabolic rates and worsened vascular function [28], making TyG a stronger mediator of BP and CHD [29].

    A significant synergistic interaction between BP and TyG was identified in our analysis. This result is consistent with prior evidence. A recent Kailuan study [29] also reported a significant interaction between the TyG index and BP status in relation to CVD risk. In addition, a population-based analysis [30] from the National Health and Nutrition Examination Survey found that the combination of a low TyG index and low SBP (<120 mm Hg or <130 mm Hg) was associated with a further reduction in all-cause and cardiovascular mortality, with a particularly strong effect on cardiovascular mortality. Several mechanisms may underlie the interplay between the TyG index and BP in CVD development. Hypertension promotes target organ damage, while an elevated TyG index reflects insulin resistance [31], which drives hyperglycemia, inflammation, oxidative stress, and endothelial dysfunction [32,33]. Together, these factors can accelerate cardiovascular injury and substantially increase CVD risk. Clarifying their joint effects is therefore essential for improving risk assessment and tailoring prevention strategies.

    Our findings highlight the need to account for both BP and TyG in CHD risk assessment. The combined assessment of these 2 factors may help identify individuals with disabilities who are at higher risk for CHD. Our study shows that individuals with both elevated BP and TyG have a significantly higher CHD risk compared to those with either factor alone. A few other studies have also reached similar conclusions [17,31]. This suggests a complex, nonlinear association between the two, rather than a simple additive effect. Indeed, prior research has established a significant association between the TyG index and an increased risk of developing hypertension [34-38]. Given this, future risk assessment tools or clinical practices may benefit from integrating BP and TyG management, particularly in individuals with metabolic abnormalities and hypertension, to more effectively manage CHD risk.

    Stratified analyses revealed that the mediating role of TyG was particularly strong in physical disabilities, with a moderate effect in visual disabilities, while no significant mediation was detected in hearing and speech or intellectual and mental disabilities. Moreover, the mediation proportion was greater in mild to moderate disabilities than in severe or very severe disabilities. This finding suggests that different types and degrees of disability may amplify the impact of BP on CHD through metabolic pathways. In individuals with physical disabilities or visual disabilities, reduced physical activity and energy expenditure often lead to increased body fat and insulin resistance, which collectively elevate TyG levels [39]. Given the prevalence of metabolic abnormalities in this population, TyG may enhance the impact of BP on CHD risk by reflecting insulin resistance and lipid metabolism disturbances [40]. Furthermore, reduced physical activity may trigger systemic inflammation and endothelial dysfunction, further magnifying the mediating role of TyG. Future research is needed to explore these mechanisms in greater depth and validate the role of TyG in different disability groups. Among individuals with mild to moderate disabilities, the significant mediating effect of TyG may reflect the group’s heightened sensitivity to metabolic stress. These individuals may retain some physiological resilience, but it is limited, making them more vulnerable to the combined impact of elevated BP and higher TyG levels on CHD risk. Furthermore, the relatively preserved health reserves in this group may allow for more precise detection of metabolic effects, which might be overshadowed by other health factors in those with severe disabilities. This highlights the need for targeted interventions to address metabolic health in individuals with disabilities, particularly those with physical disabilities or mild to moderate severity, who may be more susceptible to the metabolic factors that contribute to CHD.

    Strengths and Limitations

    The findings of this study have important implications. First, the large sample size of 21,628 individuals with disabilities provides high statistical power and generalizability. Second, this study enhances our understanding of the contribution of metabolic factors to cardiovascular risk in populations with disabilities and addresses a critical gap in the existing literature. Third, by using a novel analytical approach that decomposes the total association between BP, TyG, and CHD into direct and indirect effects, we offer a deeper understanding of the critical role of metabolic factors in cardiovascular health [41]. The stratified analyses further enhance the precision of our findings and provide valuable insights for developing targeted interventions.

    However, there are several limitations to this study. First, although the study benefits from a large sample size, its retrospective cohort design inherently limits causal inference, restricting the ability to draw definitive conclusions about the temporal association between exposures and outcomes. In addition, we recognize that factors such as lifestyle behaviors, health care access, and socioeconomic status are important, but due to current data limitations, we have adjusted for other objective health indicators in an attempt to minimize their potential impact. Additionally, the uniform access to living support provided by the Shanghai Disability Federation for all participants may help reduce the influence of these unmeasured factors. Third, since all data were collected from Shanghai, the generalizability of the findings to other regions or countries may be limited, and future studies will aim to expand the data by incorporating additional variables and conducting multi-center prospective cohort studies to validate these results in a broader context.

    Conclusions

    In conclusion, we identified a significant association between BP and the TyG index with CHD risk in individuals with disabilities, with TyG acting as a prominent mediator in the association between BP and CHD. This mediation effect was particularly pronounced in individuals with physical disabilities and those classified with mild-to-moderate disability severity. Our findings underscore the importance of addressing the combined effects of insulin resistance and blood pressure in high-risk populations. Integrated intervention strategies targeting both factors may be crucial for reducing CHD risk.

    Acknowledgments

    The study was funded by the Health Management Outstanding Talents of Shanghai Association of Rehabilitation Medical (2023JGYQ08); the project supported by National Natural Science Foundation of China (72474136); National clinical key specialty construction project of China (Z155080000004); Shanghai Research Center of Rehabilitation Medicine (Top Priority Research Center of Shanghai; 2023ZZ02027); Shanghai Hospital Development Center Foundation—Shanghai Municipal Hospital Rehabilitation Medicine Specialty Alliance (SHDC22023304) and Shanghai Clinical Research Ward (SHDC2023CRW018B). The funder had no involvement in the study design, data collection, analysis, interpretation, or the writing of the manuscript.

    Data Availability

    The data that support the findings of this study are available from the corresponding author upon reasonable request.

    Authors' Contributions

    HL and YL analyzed the participants’ data and were major contributors in writing the manuscript. All authors participated in data collection and approved the final manuscript.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Multimedia appendix. Additional tables and figures supporting the main results.

    DOCX File, 427 KB
    1. Mensah GA, Fuster V, Murray CJL, Roth GA. Global burden of cardiovascular diseases and risks, 1990-2022. J Am Coll Cardiol. Dec 19, 2023;82(25):2350-2473. [CrossRef] [Medline]
    2. Bevan G, Pandey A, Griggs S, et al. Neighborhood-level social vulnerability and prevalence of cardiovascular risk factors and coronary heart disease. Curr Probl Cardiol. Aug 2023;48(8):101182. [CrossRef] [Medline]
    3. Rosoff DB, Davey Smith G, Mehta N, Clarke TK, Lohoff FW. Evaluating the relationship between alcohol consumption, tobacco use, and cardiovascular disease: a multivariable Mendelian randomization study. PLoS Med. Dec 4, 2020;17(12):e1003410. [CrossRef] [Medline]
    4. McEvoy JW, Jennings C, Kotseva K, et al. Variation in secondary prevention of coronary heart disease: the INTERASPIRE study. Eur Heart J. Oct 14, 2024;45(39):4184-4196. [CrossRef] [Medline]
    5. Global report on health equity for persons with disabilities. World Health Organization. 2022. URL: https://www.who.int/publications/i/item/9789240063600 [Accessed 2025-10-07]
    6. Fong JH. Disability incidence and functional decline among older adults with major chronic diseases. BMC Geriatr. Nov 21, 2019;19(1):323. [CrossRef] [Medline]
    7. GBD 2016 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. Sep 16, 2017;390(10100):1345-1422. [CrossRef] [Medline]
    8. Li Y, Wu J, Wang Y, et al. Association of blood pressure trajectories with coronary heart disease among the disabled population in Shanghai, China: a cohort study of 7 years following up. Eur J Med Res. Aug 9, 2023;28(1):275. [CrossRef] [Medline]
    9. Alizargar J, Bai CH, Hsieh NC, Wu SF. Use of the triglyceride-glucose index (TyG) in cardiovascular disease patients. Cardiovasc Diabetol. Jan 15, 2020;19(1):8. [CrossRef] [Medline]
    10. Mo D, Zhang P, Zhang M, Dai H, Guan J. Cholesterol, high-density lipoprotein, and glucose index versus triglyceride-glucose index in predicting cardiovascular disease risk: a cohort study. Cardiovasc Diabetol. Mar 10, 2025;24(1):116. [CrossRef] [Medline]
    11. Qiao Y, Wang Y, Chen C, Huang Y, Zhao C. Association between triglyceride-glucose (TyG) related indices and cardiovascular diseases and mortality among individuals with metabolic dysfunction-associated steatotic liver disease: a cohort study of UK Biobank. Cardiovasc Diabetol. Jan 13, 2025;24(1):12. [CrossRef] [Medline]
    12. Huo RR, Liao Q, Zhai L, You XM, Zuo YL. Interacting and joint effects of triglyceride-glucose index (TyG) and body mass index on stroke risk and the mediating role of TyG in middle-aged and older Chinese adults: a nationwide prospective cohort study. Cardiovasc Diabetol. Jan 13, 2024;23(1):30. [CrossRef] [Medline]
    13. Yang Z, Gong H, Kan F, Ji N. Association between the triglyceride glucose (TyG) index and the risk of acute kidney injury in critically ill patients with heart failure: analysis of the MIMIC-IV database. Cardiovasc Diabetol. Aug 31, 2023;22(1):232. [CrossRef] [Medline]
    14. Yao Y, Wang B, Geng T, Chen J, Chen W, Li L. The association between TyG and all-cause/non-cardiovascular mortality in general patients with type 2 diabetes mellitus is modified by age: results from the cohort study of NHANES 1999–2018. Cardiovasc Diabetol. 2024;23(1):43. [CrossRef]
    15. Cui C, Liu L, Qi Y, et al. Joint association of TyG index and high sensitivity C-reactive protein with cardiovascular disease: a national cohort study. Cardiovasc Diabetol. May 7, 2024;23(1):156. [CrossRef] [Medline]
    16. Wu K, Zheng H, Wu W, et al. Temporal relationship between triglyceride-glucose index and blood pressure and their joint cumulative effect on cardiovascular disease risk: a longitudinal cohort study. Cardiovasc Diabetol. Nov 28, 2023;22(1):332. [CrossRef] [Medline]
    17. Qiu W, Cai A, Li L, Feng Y. Systolic blood pressure status modifies the associations between the triglyceride-glucose index and incident cardiovascular disease: a national cohort study in China. Cardiovasc Diabetol. Apr 24, 2024;23(1):135. [CrossRef] [Medline]
    18. Sheppard JP, Albasri A, Franssen M, et al. Defining the relationship between arm and leg blood pressure readings: a systematic review and meta-analysis. J Hypertens. Apr 2019;37(4):660-670. [CrossRef] [Medline]
    19. Chinese Association of Rehabilitation Medicine, Hypertension Committee of Cross-Straits Medicine Exchange Association, Cardiovascular Disease Prevention and Rehabilitation Committee, Chinese Association of Rehabilitation Medicine. Clinical practice guidelines for the management of hypertension in China. Zhonghua Xin Xue Guan Bing Za Zhi. Sep 24, 2024;52(9):985-1032. [CrossRef] [Medline]
    20. Xu X, Huang R, Lin Y, et al. High triglyceride-glucose index in young adulthood is associated with incident cardiovascular disease and mortality in later life: insight from the CARDIA study. Cardiovasc Diabetol. Aug 12, 2022;21(1):155. [CrossRef] [Medline]
    21. Zhang R, Shi S, Chen W, et al. Independent effects of the triglyceride-glucose index on all-cause mortality in critically ill patients with coronary heart disease: analysis of the MIMIC-III database. Cardiovasc Diabetol. Jan 13, 2023;22(1):10. [CrossRef] [Medline]
    22. National standards information public service platform of China. National Public Service Platform for Standards Information. URL: https://std.samr.gov.cn/gb/search/gbDetailed?id=71F772D7E01ED3A7E05397BE0A0AB82A [Accessed 2024-11-15]
    23. Lim CC, Hayes RB, Ahn J, et al. Mediterranean diet and the association between air pollution and cardiovascular disease mortality risk. Circulation. Apr 9, 2019;139(15):1766-1775. [CrossRef] [Medline]
    24. Colafella KM, Denton KM. Sex-specific differences in hypertension and associated cardiovascular disease. Nat Rev Nephrol. Mar 2018;14(3):185-201. [CrossRef] [Medline]
    25. Brunström M, Carlberg B. Association of blood pressure lowering with mortality and cardiovascular disease across blood pressure levels: a systematic review and meta-analysis. JAMA Intern Med. Jan 1, 2018;178(1):28-36. [CrossRef] [Medline]
    26. Wan Y, Zhang Z, Ling Y, et al. Association of triglyceride-glucose index with cardiovascular disease among a general population: a prospective cohort study. Diabetol Metab Syndr. Oct 16, 2023;15(1):204. [CrossRef] [Medline]
    27. Hong S, Han K, Park CY. The triglyceride glucose index is a simple and low-cost marker associated with atherosclerotic cardiovascular disease: a population-based study. BMC Med. Nov 25, 2020;18(1):361. [CrossRef] [Medline]
    28. Wu J, Wang Y, Li Y, et al. Are physically disabled people at high risk of coronary heart disease among disabled population - evidence from 7.5-year retrospective cohort study. Ann Epidemiol. Feb 2024;90:42-48. [CrossRef] [Medline]
    29. Cui H, Liu Q, Zhao Z, Ma X. Interacting and joint effects of triglyceride-glucose index and blood pressure on cardiovascular diseases risk: a prospective cohort study. Diabetol Metab Syndr. Aug 2, 2024;16(1):188. [CrossRef] [Medline]
    30. Yu Y, Gu M, Huang H, et al. Combined association of triglyceride-glucose index and systolic blood pressure with all-cause and cardiovascular mortality among the general population. J Transl Med. Oct 20, 2022;20(1):478. [CrossRef] [Medline]
    31. Khan SH, Sobia F, Niazi NK, Manzoor SM, Fazal N, Ahmad F. Metabolic clustering of risk factors: evaluation of triglyceride-glucose index (TyG index) for evaluation of insulin resistance. Diabetol Metab Syndr. 2018;10:74. [CrossRef] [Medline]
    32. Molina MN, Ferder L, Manucha W. Emerging role of nitric oxide and heat shock proteins in insulin resistance. Curr Hypertens Rep. Jan 2016;18(1):1. [CrossRef] [Medline]
    33. Yang Q, Vijayakumar A, Kahn BB. Metabolites as regulators of insulin sensitivity and metabolism. Nat Rev Mol Cell Biol. Oct 2018;19(10):654-672. [CrossRef] [Medline]
    34. Gao Q, Lin Y, Xu R, et al. Positive association of triglyceride-glucose index with new-onset hypertension among adults: a national cohort study in China. Cardiovasc Diabetol. Mar 16, 2023;22(1):58. [CrossRef] [Medline]
    35. Lee DH, Park JE, Kim SY, Jeon HJ, Park JH. Association between the triglyceride-glucose (TyG) index and increased blood pressure in normotensive subjects: a population-based study. Diabetol Metab Syndr. Oct 29, 2022;14(1):161. [CrossRef] [Medline]
    36. Tsai KZ, Chu CC, Huang WC, Sui X, Lavie CJ, Lin GM. Prediction of various insulin resistance indices for the risk of hypertension among military young adults: the CHIEF cohort study, 2014-2020. Cardiovasc Diabetol. Apr 25, 2024;23(1):141. [CrossRef] [Medline]
    37. Miao H, Zhou Z, Yang S, Zhang Y. The association of triglyceride-glucose index and related parameters with hypertension and cardiovascular risk: a cross-sectional study. Hypertens Res. Apr 2024;47(4):877-886. [CrossRef] [Medline]
    38. Lee JH, Heo SJ, Kwon YJ. Sex-specific comparison between triglyceride glucose index and modified triglyceride glucose indices to predict new-onset hypertension in middle-aged and older adults. J Am Heart Assoc. Sep 19, 2023;12(18):e030022. [CrossRef] [Medline]
    39. Ahmadi MN, Hamer M, Gill JM, et al. Brief bouts of device-measured intermittent lifestyle physical activity and its association with major adverse cardiovascular events and mortality in people who do not exercise: a prospective cohort study. Lancet Public Health. Oct 2023;8(10):e800-e810. [CrossRef] [Medline]
    40. Dempsey PC, Rowlands AV, Strain T, et al. Physical activity volume, intensity, and incident cardiovascular disease. Eur Heart J. Dec 7, 2022;43(46):4789-4800. [CrossRef] [Medline]
    41. Fasanelli F, Giraudo MT, Ricceri F, Valeri L, Zugna D. Marginal time-dependent causal effects in mediation analysis with survival data. Am J Epidemiol. May 1, 2019;188(5):967-974. [CrossRef] [Medline]

    ‎
    aHR: adjusted hazard ratio
    BP: blood pressure
    CHD: coronary heart disease
    CVD: cardiovascular disease
    DBP: diastolic blood pressure
    HR: hazard ratio
    NIE: natural indirect effect
    RCS: restricted cubic spline
    SBP: systolic blood pressure
    TyG: triglyceride-glucose

    Edited by Amaryllis Mavragani, Travis Sanchez; submitted 26.May.2025; peer-reviewed by Longbing Ren, Shaojie Li; final revised version received 25.Sep.2025; accepted 28.Sep.2025; published 03.Nov.2025.

    Copyright

    © Hui Liu, Yao Li, Yiyan Wang, Tao Wang, Chenghua Jiang, Hengjing Wu, Jing Wu. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 3.Nov.2025.

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